diff --git a/CHANGELOG.md b/CHANGELOG.md index 7dd360df..f9500533 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -67,9 +67,8 @@ introduce breaking changes to configuration and outputs. (`market_response.elasticities.demand`, central values from the Green et al. 2013 meta-regression). Ordinary solves previously had no demand-side valuation at all unless a config enabled the consumer-values / - piecewise-utility mechanism; the demand component replaces that pattern (the - two are mutually exclusive, and `food_utility_piecewise` remains available - for configs that keep it, with `market_response.components.demand: false`). + piecewise-utility mechanism; the demand component replaces that mechanism + (removed below). The demand curves' cost appears as its own `demand_response` category in the objective breakdown. Demand groups whose marginal utility is still positive at the outer end of the sampled range have their unbounded expansion tail @@ -345,6 +344,19 @@ introduce breaking changes to configuration and outputs. ### Removed +- The legacy demand-side mechanisms superseded by the `market_response` demand + component: the consumer-values workflow (baseline dual extraction and its + `results/{name}/consumer_values/` outputs), `food_utility_piecewise` + (piecewise utility blocks calibrated from consumer values), + `food_incentives` (flat per-food objective adjustments), and the + `optimal_taxes` workflow. Configs using these keys must migrate to + `market_response.components.demand` (with a calibrated artefact set for + their structure) or, for fixed-diet runs, `validation.enforce_baseline_diet`. + The GSA and documentation-figure configs now use the demand component, and + GSA runs no longer need a locally solved `baseline` scenario. The cost + calibration's tight-band solve now keeps the diet pinned to baseline instead + of using utility blocks, and the legacy `stability` calibration runs under + the base config's own demand regime. - The MARS surrogate method; supported surrogates are now `pce`, `rf`, `xgb` and `mlp`. - Unused configuration keys `health.ssb_sugar_g_per_100g`, diff --git a/config/calibration/cost.yaml b/config/calibration/cost.yaml index 7c24bc65..b173d010 100644 --- a/config/calibration/cost.yaml +++ b/config/calibration/cost.yaml @@ -6,10 +6,10 @@ # ------------------------------- # Generates production cost calibration corrections for crops, grassland, # and animals by: -# 1. Solving a baseline scenario (enforce_baseline_diet, no stability) -# → extracts consumer values for piecewise food utility -# 2. Solving a calibration scenario (piecewise utility + hard production -# stability at ±1%) → extracts duals as cost corrections +# 1. Solving a baseline scenario (enforce_baseline_diet, hard production +# stability at +/-5%) to absorb residual supply/demand mismatch +# 2. Solving a calibration scenario (enforce_baseline_diet, hard production +# stability at +/-1%) → extracts duals as cost corrections # # Usage: # tools/calibrate cost @@ -25,7 +25,7 @@ name: "calibration" scenarios: - # Step 1: Baseline solve to extract consumer values / utility blocks. + # Step 1: Baseline solve. # # Production stability is enabled with a +/-5 % band so that the food # p_set duals reflect the marginal cost of supply under realistic @@ -59,11 +59,13 @@ scenarios: enable_slack: true diet: enabled: false - food_utility_piecewise: - enabled: false - # Step 2: Calibration solve with tight production stability + # Step 2: Calibration solve with tight production stability. The diet stays + # pinned to baseline (matching step 1), so the tight-band duals measure the + # marginal cost of holding observed production under the observed diet. cost_calibration: + validation: + enforce_baseline_diet: true deviation_penalty: enabled: true penalty_mode: "hard" @@ -85,12 +87,6 @@ scenarios: enable_slack: true diet: enabled: false - food_utility_piecewise: - enabled: true - -# Enable piecewise utility at base level (blocks get built from baseline) -food_utility_piecewise: - enabled: true # Later-step calibration (lower-triangle rule): the market-response curves # and their intercept artefact must not influence the cost duals. @@ -102,11 +98,6 @@ cost_calibration: generate: true # Generate calibration from solved model scenario: "cost_calibration" -# Override the default consumer_values.baseline_scenario ("baseline") to -# point at the renamed scenario above. -consumer_values: - baseline_scenario: "cost_baseline" - validation: use_actual_yields: true # Cap slack-driven duals at the upper end of realistic food wholesale diff --git a/config/calibration/feed.yaml b/config/calibration/feed.yaml index 5f736262..76f9e732 100644 --- a/config/calibration/feed.yaml +++ b/config/calibration/feed.yaml @@ -50,11 +50,6 @@ validation: disable_spared_grassland: true slack_marginal_cost: 10 -# The enforced-baseline validation solve is incompatible with piecewise -# food utility; disable it regardless of the base config's setting. -food_utility_piecewise: - enabled: false - cost_calibration: enabled: false diff --git a/config/calibration/food_demand.yaml b/config/calibration/food_demand.yaml index a2005bf1..40ae7d11 100644 --- a/config/calibration/food_demand.yaml +++ b/config/calibration/food_demand.yaml @@ -57,11 +57,6 @@ validation: disable_spared_grassland: true slack_marginal_cost: 10 -# The enforced-baseline validation solve is incompatible with piecewise -# food utility; disable it regardless of the base config's setting. -food_utility_piecewise: - enabled: false - # Skip downstream calibrations so this step is order-independent. cost_calibration: enabled: false diff --git a/config/calibration/food_waste.yaml b/config/calibration/food_waste.yaml index d7ed7ba5..1d909eec 100644 --- a/config/calibration/food_waste.yaml +++ b/config/calibration/food_waste.yaml @@ -48,11 +48,6 @@ validation: disable_spared_grassland: true slack_marginal_cost: 10 -# The enforced-baseline validation solve is incompatible with piecewise -# food utility; disable it regardless of the base config's setting. -food_utility_piecewise: - enabled: false - # Skip downstream calibrations so this step is order-independent. cost_calibration: enabled: false diff --git a/config/calibration/market_response.yaml b/config/calibration/market_response.yaml index 2bc352c1..67db2a4b 100644 --- a/config/calibration/market_response.yaml +++ b/config/calibration/market_response.yaml @@ -42,9 +42,6 @@ health: deviation_penalty: enabled: false -food_utility_piecewise: - enabled: false - market_response: enabled: true pin_baseline: true diff --git a/config/calibration/stability.yaml b/config/calibration/stability.yaml index e161524b..5fc606f5 100644 --- a/config/calibration/stability.yaml +++ b/config/calibration/stability.yaml @@ -7,8 +7,8 @@ # Drives the land and animal-feed deviation simultaneously to # `target_deviation_pct` (default 5% of observed 2020 totals) by running # Broyden's quasi-Newton iteration in log-log coordinates. Each iteration is -# one paired solve (baseline with `enforce_baseline_diet=true` to derive -# consumer values, then main with piecewise utility active). +# one paired solve (baseline with `enforce_baseline_diet=true`, then main +# under the base config's own demand regime). # # Warm-starts from an existing calibrated YAML when one exists on disk; # otherwise uses the seed values in `deviation_penalty.calibration.seeds`. @@ -25,24 +25,14 @@ name: "calibration" # Pin the calibration regime explicitly: the main solve inherits these -# values (the baseline solve is always forced to 0/0 in the script so that -# consumer values reflect raw revealed preferences). +# values (the baseline solve is always forced to 0/0 in the script, +# independent of any policy regime under investigation). emissions: ghg_pricing_enabled: false health: enabled: false value_per_yll: 0 -# Block-based diminishing marginal utility for food consumption (used by main -# solves; baseline solves disable this inside the calibration script). -food_utility_piecewise: - enabled: true - -# Fallback consumer_values baseline; the script overrides this per iteration -# to point at that iteration's baseline scenario. -consumer_values: - baseline_scenario: "_cal_baseline_iter00" - validation: use_actual_yields: true diff --git a/config/default.yaml b/config/default.yaml index e23d3119..63d5d53f 100644 --- a/config/default.yaml +++ b/config/default.yaml @@ -312,8 +312,7 @@ market_response: # consumption groups are fixed to zero, like their supply counterparts. It # is fitted sequentially against the calibrated production curves, because a # joint pin cannot identify how a commodity-chain wedge is split between - # supply and demand. It replaces - # food_utility_piecewise and food_incentives (mutually exclusive with both). + # supply and demand. components: crops: true multi_crops: true @@ -321,27 +320,6 @@ market_response: animals: true demand: true -# --- section: food_incentives --- -food_incentives: - enabled: false # When true, food-level incentives are applied to the objective - sources: [] - -# --- section: consumer_values --- -consumer_values: - baseline_scenario: "baseline" # Scenario name for consumer values extraction (must have enforce_baseline_diet=true) - -# --- section: food_utility_piecewise --- -food_utility_piecewise: - enabled: false # When true, use piecewise diminishing marginal utility for food consumption - n_blocks: 4 - decline_factor: 0.7 # Multiplicative utility decay by block (0 < factor <= 1) - total_width_multiplier: 2.0 # Total incentivized quantity as multiple of baseline consumption - min_block_width_mt: 0.00001 # Minimum width floor (Mt/year) for each utility block to avoid tiny upper bounds - -# --- section: optimal_taxes --- -optimal_taxes: - enabled: false # When true, enables the optimal taxes/subsidies workflow - # --- section: land --- land: regional_limit: 1.0 # fraction of each region's potential cropland that is made available. diff --git a/config/schemas/config.schema.yaml b/config/schemas/config.schema.yaml index 5aed750e..e520fb01 100644 --- a/config/schemas/config.schema.yaml +++ b/config/schemas/config.schema.yaml @@ -20,10 +20,6 @@ required: - netcdf - numerics - validation - - consumer_values - - food_incentives - - food_utility_piecewise - - optimal_taxes - land - water - water_scarcity @@ -301,64 +297,6 @@ properties: minimum: 0 description: "Maximum relative increase from baseline area (e.g., 0.5 = +50%)" - food_incentives: - type: object - required: [enabled, sources] - additionalProperties: false - properties: - enabled: - type: boolean - description: "When true, food-level incentives are applied to the objective" - sources: - type: array - items: - type: string - description: "Paths to food incentives CSVs (may include {name} placeholder)" - - consumer_values: - type: object - required: [baseline_scenario] - additionalProperties: false - properties: - baseline_scenario: - type: string - description: "Scenario name used for consumer values extraction (must have enforce_baseline_diet=true)" - - food_utility_piecewise: - type: object - required: [enabled, n_blocks, decline_factor, total_width_multiplier, min_block_width_mt] - additionalProperties: false - properties: - enabled: - type: boolean - description: "When true, apply piecewise diminishing marginal utility for food consumption" - n_blocks: - type: integer - minimum: 1 - description: "Number of utility blocks per (food, country)" - decline_factor: - type: number - exclusiveMinimum: 0 - maximum: 1 - description: "Multiplicative marginal-utility decay by block index" - total_width_multiplier: - type: number - exclusiveMinimum: 0 - description: "Total incentivized quantity as multiple of baseline consumption" - min_block_width_mt: - type: number - minimum: 0 - description: "Minimum per-block width floor (Mt/year) applied at solve time for numerical robustness" - - optimal_taxes: - type: object - required: [enabled] - additionalProperties: false - properties: - enabled: - type: boolean - description: "When true, enables the optimal taxes/subsidies workflow" - land: type: object required: [regional_limit, reforestation_cap, land_use_cost_usd_per_ha, conversion_cost_forest_usd_per_ha, conversion_cost_nonforest_usd_per_ha, investment_horizon, discount_rate] @@ -2568,17 +2506,6 @@ properties: rr_low (q=0) and rr_high (q=1) for each dose-response curve. allOf: - - not: - required: [food_incentives, food_utility_piecewise] - properties: - food_incentives: - properties: - enabled: - const: true - food_utility_piecewise: - properties: - enabled: - const: true - not: required: [cost_calibration, market_response] properties: diff --git a/config/tutorial/02_consumer_values.yaml b/config/tutorial/02_consumer_values.yaml deleted file mode 100644 index 66dbe6f6..00000000 --- a/config/tutorial/02_consumer_values.yaml +++ /dev/null @@ -1,96 +0,0 @@ -# SPDX-FileCopyrightText: 2026 Koen van Greevenbroek -# -# SPDX-License-Identifier: CC-BY-4.0 - -# Tutorial 2 — Letting diet respond via consumer values -# ----------------------------------------------------- -# Extends Tutorial 1 by allowing the diet itself to adapt to rising GHG prices, -# rather than being held fixed. The approach: -# -# 1. A `baseline` scenario with `validation.enforce_baseline_diet=true` -# solves the model with today's observed diet. The shadow prices of the -# food-group equality constraints are the "consumer values" — each food -# group's marginal utility in bn USD / Mt. -# 2. Subsequent scenarios drop `enforce_baseline_diet` and instead enable a -# piecewise diminishing-marginal-utility curve calibrated from those -# consumer values. The optimizer then trades off "move away from today's -# diet" against GHG costs. -# -# Note: `food_utility_piecewise.enabled=true` is mutually exclusive with -# `validation.enforce_baseline_diet=true`. The baseline scenario disables the -# piecewise curve; non-baseline scenarios disable baseline-diet enforcement. -# -# See docs/tutorial.rst (Part 2) for a walkthrough. - -name: "tutorial_02" - -# The health module stays off (the default), so this tutorial needs no -# manually-downloaded IHME GBD data; see docs/health.rst to switch it on. - -# Tutorials knowingly reuse the default calibration artefacts at a coarser -# regional resolution. -calibration: - accept_provenance_mismatch: true - -# See config/tutorial/01_ghg_prices.yaml for the rationale on target_count. -aggregation: - regions: - target_count: 200 - -# Enable the piecewise food-utility curve globally. It is switched off for the -# baseline scenario below so that scenario can fix consumption instead. -food_utility_piecewise: - enabled: true - n_blocks: 4 # Number of utility steps per (food, country) - decline_factor: 0.7 # Each successive block is worth 70% of the previous - total_width_multiplier: 2.0 # Curve spans 2x baseline consumption - -# Use the `baseline` scenario defined below as the source for consumer values. -consumer_values: - baseline_scenario: "baseline" - -scenarios: - # Fixes consumption at the observed 2020 diet; the solver's dual variables on - # the per-food-group equality constraints are extracted as consumer values. - baseline: - validation: - enforce_baseline_diet: true - food_utility_piecewise: - enabled: false - emissions: - ghg_price: 0 - - # GHG price + flexible diet: consumption can deviate from baseline, paying - # the piecewise utility cost to do so. We also hold total caloric intake - # per country equal to its baseline level so the optimiser can reshape the - # diet but not shrink it away. Without this floor, rising GHG prices drive - # total consumption toward zero, which doesn't represent a plausible - # policy response. - ghg_mid: - emissions: - ghg_price: 50 - macronutrients: - cal: - equal_to_baseline: true - - ghg_high: - emissions: - ghg_price: 200 - macronutrients: - cal: - equal_to_baseline: true - -plotting: - comparison_scenarios: "all" - -# This config's build differs structurally from the default, so the default -# set's link-keyed market-response intercepts do not apply; production -# anchoring stays with the L1 deviation penalty. -market_response: - enabled: false -# Keep the legacy calibrated corrections with the L1 anchor for this reduced -# structural config; PMP and cost calibration are mutually exclusive. -cost_calibration: - enabled: true -deviation_penalty: - enabled: true diff --git a/config/tutorial/02_demand_response.yaml b/config/tutorial/02_demand_response.yaml new file mode 100644 index 00000000..5753959c --- /dev/null +++ b/config/tutorial/02_demand_response.yaml @@ -0,0 +1,73 @@ +# SPDX-FileCopyrightText: 2026 Koen van Greevenbroek +# +# SPDX-License-Identifier: CC-BY-4.0 + +# Tutorial 2 — Demand response: letting the diet adjust to policy +# --------------------------------------------------------------- +# Extends Tutorial 1 by allowing consumption itself to respond to rising GHG +# prices, rather than being held fixed. Demand is priced by the calibrated +# market-response curves: every (food, country) consumption link carries a +# concave marginal-utility curve through its observed intake, so today's diet +# is the model's unconstrained optimum and deviations from it are governed by +# per-food-group demand elasticities. +# +# The curves are keyed to this config's exact model structure (200 regions), +# so the tutorial ships its own calibration artefact set, regenerated with: +# +# tools/calibrate --base config/tutorial/02_demand_response.yaml +# +# See docs/tutorial.rst (Part 2) for a walkthrough. + +name: "tutorial_02" + +# The health module stays off (the default), so this tutorial needs no +# manually-downloaded IHME GBD data; see docs/health.rst to switch it on. + +calibration: + source: "tutorial-02" + +# See config/tutorial/01_ghg_prices.yaml for the rationale on target_count. +aggregation: + regions: + target_count: 200 + +scenarios: + # No GHG pricing: with the calibrated curves and no policy pressure, the + # solve should land on (very nearly) the observed 2020 food system -- + # without a single constraint pinning it there. + reference: + emissions: + ghg_pricing_enabled: false + + # Rising GHG prices with elastic demand: consumption shifts away from + # emission-intensive foods, paying the marginal-utility cost to do so. + ghg_mid: + emissions: + ghg_price: 50 + + ghg_high: + emissions: + ghg_price: 200 + + # The same carbon price with the diet frozen at the observed baseline: + # mitigation must come from the production side alone. The gap to ghg_high + # measures what dietary flexibility is worth. + ghg_high_fixed: + emissions: + ghg_price: 200 + market_response: + components: + demand: false + validation: + enforce_baseline_diet: true + + # Same price, half the demand elasticities: consumers respond more stiffly. + # Elasticities enter only at solve time, so no recalibration is needed. + ghg_high_stiff: + emissions: + ghg_price: 200 + market_response: + elasticity_factor: 0.5 + +plotting: + comparison_scenarios: "all" diff --git a/data/curated/calibration/default/animal_cost.csv b/data/curated/calibration/default/animal_cost.csv index 1bade2e1..2250722e 100644 --- a/data/curated/calibration/default/animal_cost.csv +++ b/data/curated/calibration/default/animal_cost.csv @@ -1,179 +1,179 @@ product,country,correction_bnusd_per_mt_feed -dairy,AFG,0.15704311430454254 -dairy,AGO,-0.10720374993979931 -dairy,ALB,0.2039732038974762 -dairy,ARE,0.02107481099665165 -dairy,ARG,-0.0830312967300415 -dairy,ARM,0.1000119112432003 +dairy,AFG,0.17321490496397018 +dairy,AGO,-0.11171812191605568 +dairy,ALB,0.28432923555374146 +dairy,ARE,0.021534573286771774 +dairy,ARG,-0.0593632897362113 +dairy,ARM,0.12713494896888733 dairy,ASM,-0.0 -dairy,ATG,-0.10122861713171005 -dairy,AUS,0.01685505546629429 -dairy,AUT,0.17007198184728622 -dairy,AZE,0.10581577196717262 -dairy,BDI,-0.15968918055295944 -dairy,BEL,0.19801177084445953 -dairy,BEN,-0.15707719326019287 -dairy,BFA,-0.1656585931777954 -dairy,BGD,-0.03687192779034376 -dairy,BGR,0.2208825871348381 -dairy,BHS,-0.054491519927978516 -dairy,BIH,0.3211663067340851 -dairy,BLR,0.11171425879001617 -dairy,BLZ,-0.12958690524101257 -dairy,BOL,-0.14613374322652817 -dairy,BRA,-0.09746488928794861 -dairy,BRB,-0.23594264686107635 -dairy,BRN,0.014300180599093437 -dairy,BTN,0.05847013369202614 -dairy,BWA,-0.12191631644964218 -dairy,CAF,-0.110183609649539 -dairy,CAN,-0.10675651952624321 -dairy,CHE,0.19595547765493393 -dairy,CHL,-0.09876278042793274 -dairy,CHN,0.31595245003700256 -dairy,CIV,-0.16207504272460938 -dairy,CMR,-0.17060378193855286 -dairy,COD,-0.17692848294973373 -dairy,COG,-0.1707666888833046 -dairy,COL,-0.1525387018918991 -dairy,COM,-0.12215038761496544 -dairy,CPV,-0.11238348484039307 -dairy,CRI,-0.03940237686038017 -dairy,CUB,-0.13583030179142952 -dairy,CYP,0.21193087100982666 -dairy,CZE,0.16133342683315277 -dairy,DEU,0.17980960756540298 -dairy,DJI,-0.015711184591054916 -dairy,DNK,0.21237867325544357 -dairy,DOM,-0.03312259167432785 -dairy,DZA,0.06647635996341705 -dairy,ECU,-0.14603416621685028 -dairy,EGY,-0.0010244521545246243 -dairy,ERI,-0.09022246487438679 -dairy,ESP,0.061828985810279846 -dairy,EST,0.19788579642772675 -dairy,ETH,-0.12361417338252068 -dairy,FIN,0.27306415140628815 -dairy,FJI,0.03348633646965027 -dairy,FRA,0.10673965141177177 -dairy,GAB,-0.030947238206863403 -dairy,GBR,0.2269890457391739 -dairy,GEO,0.07478091958910227 -dairy,GHA,-0.10408715717494488 -dairy,GIN,-0.10693381540477276 -dairy,GMB,-0.13216522336006165 -dairy,GNB,-0.16765744239091873 +dairy,ATG,-0.142782561480999 +dairy,AUS,0.04594320338219404 +dairy,AUT,0.21544521301984787 +dairy,AZE,0.10905029997229576 +dairy,BDI,-0.15887708216905594 +dairy,BEL,0.2569368928670883 +dairy,BEN,-0.22299622744321823 +dairy,BFA,-0.23291583359241486 +dairy,BGD,-0.0825637262314558 +dairy,BGR,0.26906917989254 +dairy,BHS,-0.036951775662600994 +dairy,BIH,0.4127001017332077 +dairy,BLR,0.11931942030787468 +dairy,BLZ,-0.1477774828672409 +dairy,BOL,-0.13757402449846268 +dairy,BRA,-0.08392645418643951 +dairy,BRB,-0.2197188436985016 +dairy,BRN,0.022386107593774796 +dairy,BTN,0.03130359761416912 +dairy,BWA,-0.1739141158759594 +dairy,CAF,-0.13905518501996994 +dairy,CAN,-0.04789787903428078 +dairy,CHE,0.24565979093313217 +dairy,CHL,-0.06755233742296696 +dairy,CHN,0.36489062011241913 +dairy,CIV,-0.22875813394784927 +dairy,CMR,-0.23744354397058487 +dairy,COD,-0.24497907608747482 +dairy,COG,-0.2376207709312439 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dairy-buffalo,ESP,-0.0 dairy-buffalo,EST,-0.0 @@ -239,7 +239,7 @@ dairy-buffalo,GIN,-0.0 dairy-buffalo,GMB,-0.0 dairy-buffalo,GNB,-0.0 dairy-buffalo,GNQ,-0.0 -dairy-buffalo,GRC,0.035413739271461964 +dairy-buffalo,GRC,-0.006836198270320892 dairy-buffalo,GRD,-0.0 dairy-buffalo,GTM,-0.0 dairy-buffalo,GUF,-0.0 @@ -248,14 +248,14 @@ dairy-buffalo,HND,-0.0 dairy-buffalo,HRV,-0.0 dairy-buffalo,HTI,-0.0 dairy-buffalo,HUN,-0.0 -dairy-buffalo,IDN,-0.06900065205991268 -dairy-buffalo,IND,-0.0011650319211184978 +dairy-buffalo,IDN,-0.09840728715062141 +dairy-buffalo,IND,-0.020745113492012024 dairy-buffalo,IRL,-0.0 -dairy-buffalo,IRN,0.17490185797214508 -dairy-buffalo,IRQ,-0.015229092008667067 +dairy-buffalo,IRN,0.21555812656879425 +dairy-buffalo,IRQ,-0.06904379045590758 dairy-buffalo,ISL,-0.0 dairy-buffalo,ISR,-0.0 -dairy-buffalo,ITA,0.17062978446483612 +dairy-buffalo,ITA,0.20540259033441544 dairy-buffalo,JAM,-0.0 dairy-buffalo,JOR,-0.0 dairy-buffalo,JPN,-0.0 @@ -268,7 +268,7 @@ 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-meat-sheep,SLV,-0.053304096683859825 -meat-sheep,SOM,-0.08115079626441002 -meat-sheep,SRB,0.07432524114847183 -meat-sheep,SSD,-0.06250460539013147 -meat-sheep,STP,-0.012918364722281694 -meat-sheep,SUR,-0.012233213521540165 -meat-sheep,SVK,-0.01716447778744623 -meat-sheep,SVN,0.06638031639158726 -meat-sheep,SWE,0.07157809473574162 -meat-sheep,SWZ,-0.07748000510036945 -meat-sheep,SYR,-0.13206765800714493 -meat-sheep,TCD,-0.08943183161318302 -meat-sheep,TGO,-0.09361173585057259 -meat-sheep,THA,-0.08318294957280159 -meat-sheep,TJK,-0.10897107049822807 -meat-sheep,TKM,-0.15593166649341583 -meat-sheep,TLS,-0.037802004255354404 +meat-sheep,SLE,-0.14041998982429504 +meat-sheep,SLV,-0.12794439494609833 +meat-sheep,SOM,-0.0901408102363348 +meat-sheep,SRB,0.022976247710175812 +meat-sheep,SSD,-0.075896923430264 +meat-sheep,STP,-0.0840152045711875 +meat-sheep,SUR,-0.04126464203000069 +meat-sheep,SVK,-0.09186081402003765 +meat-sheep,SVN,-0.008316023275256157 +meat-sheep,SWE,-0.0031182430684566498 +meat-sheep,SWZ,-0.10091527737677097 +meat-sheep,SYR,-0.21605509519577026 +meat-sheep,TCD,-0.0905593428760767 +meat-sheep,TGO,-0.11313560605049133 +meat-sheep,THA,-0.08811904862523079 +meat-sheep,TJK,-0.18875626847147942 +meat-sheep,TKM,-0.23062801361083984 +meat-sheep,TLS,-0.12238025665283203 meat-sheep,TTO,0.005440737586468458 -meat-sheep,TUN,-0.07886662892997265 -meat-sheep,TUR,-0.08159522712230682 +meat-sheep,TUN,-0.1535629667341709 +meat-sheep,TUR,-0.15629155933856964 meat-sheep,TWN,-0.0 -meat-sheep,TZA,-0.1277274712920189 -meat-sheep,UGA,-0.125447116792202 -meat-sheep,UKR,-0.00801339722238481 -meat-sheep,URY,-0.025299393571913242 -meat-sheep,USA,-0.0 -meat-sheep,UZB,-0.10539974644780159 -meat-sheep,VEN,-0.040142289362847805 -meat-sheep,VNM,-0.11625975370407104 -meat-sheep,VUT,-0.08387019112706184 -meat-sheep,YEM,-0.07212707214057446 -meat-sheep,ZAF,-0.0349787981249392 -meat-sheep,ZMB,-0.0713496245443821 -meat-sheep,ZWE,-0.3634076565504074 +meat-sheep,TZA,-0.14525528997182846 +meat-sheep,UGA,-0.20008742064237595 +meat-sheep,UKR,-0.08270973805338144 +meat-sheep,URY,-0.08350206352770329 +meat-sheep,USA,-0.0030635655857622623 +meat-sheep,UZB,-0.1800960898399353 +meat-sheep,VEN,-0.05680697504431009 +meat-sheep,VNM,-0.19129285588860512 +meat-sheep,VUT,-0.10494021698832512 +meat-sheep,YEM,-0.07331060990691185 +meat-sheep,ZAF,-0.054669673088938 +meat-sheep,ZMB,-0.09304003044962883 +meat-sheep,ZWE,-0.428226962685585 diff --git a/data/curated/calibration/default/crop_cost.csv b/data/curated/calibration/default/crop_cost.csv index e24069fc..0a2d3ffb 100644 --- a/data/curated/calibration/default/crop_cost.csv +++ b/data/curated/calibration/default/crop_cost.csv @@ -1,61 +1,61 @@ crop,country,correction_bnusd_per_mha alfalfa,AFG,-0.3613618314266205 alfalfa,AGO,-0.9381504952907562 -alfalfa,ALB,-0.21057342505082488 -alfalfa,ARE,-0.5358802676200867 -alfalfa,ARG,-0.7116400003433228 +alfalfa,ALB,0.19918883964419365 +alfalfa,ARE,0.09598714113235474 +alfalfa,ARG,-0.7139765024185181 alfalfa,ARM,-0.0 alfalfa,ATG,-7.5 -alfalfa,AUS,1.3287680745124817 +alfalfa,AUS,1.9390984773635864 alfalfa,AUT,-0.8089405298233032 -alfalfa,AZE,0.7338229417800903 +alfalfa,AZE,0.7522021532058716 alfalfa,BDI,-0.5545685291290283 -alfalfa,BEL,0.045041752979159355 -alfalfa,BEN,0.49464063346385956 +alfalfa,BEL,0.03831017090124078 +alfalfa,BEN,0.5106204897165298 alfalfa,BFA,-0.0 -alfalfa,BGD,1.3184418678283691 -alfalfa,BGR,0.32402290403842926 -alfalfa,BHS,-4.105992168188095 -alfalfa,BIH,4.001559287309647 -alfalfa,BLR,-2.44552518427372 +alfalfa,BGD,1.3433443903923035 +alfalfa,BGR,0.6580539345741272 +alfalfa,BHS,-4.066162675619125 +alfalfa,BIH,4.126855105161667 +alfalfa,BLR,-2.06520511303097 alfalfa,BLZ,-0.010105629451572895 alfalfa,BOL,-0.37848326563835144 alfalfa,BRA,-0.6503801345825195 -alfalfa,BRB,2.0719677209854126 +alfalfa,BRB,2.1092604398727417 alfalfa,BRN,-0.8346583247184753 -alfalfa,BTN,-0.24752895534038544 +alfalfa,BTN,-0.03300642967224121 alfalfa,BWA,-7.5 -alfalfa,CAF,-0.9728804230690002 -alfalfa,CAN,-0.5860996246337891 +alfalfa,CAF,-0.6662335693836212 +alfalfa,CAN,-0.5417565703392029 alfalfa,CHE,0.49974900484085083 -alfalfa,CHL,-0.5726382732391357 -alfalfa,CHN,0.07321733981370926 +alfalfa,CHL,-0.868007481098175 +alfalfa,CHN,0.0634298138320446 alfalfa,CIV,7.5 -alfalfa,CMR,0.5377728343009949 +alfalfa,CMR,0.5486071705818176 alfalfa,COD,-0.10757382959127426 alfalfa,COG,-0.09945236518979073 alfalfa,COL,-0.1985142081975937 -alfalfa,COM,0.4107678234577179 +alfalfa,COM,0.4249976873397827 alfalfa,CPV,-7.5 alfalfa,CRI,-0.22407843172550201 alfalfa,CUB,-0.2862354665994644 -alfalfa,CYP,3.0854803323745728 -alfalfa,CZE,0.03545874357223511 +alfalfa,CYP,3.1287132501602173 +alfalfa,CZE,0.39317023754119873 alfalfa,DEU,0.06634383089840412 alfalfa,DJI,-0.835690438747406 -alfalfa,DNK,6.501215934753418 +alfalfa,DNK,6.589122772216797 alfalfa,DOM,-0.616400271654129 alfalfa,DZA,-0.0 alfalfa,ECU,-0.07392867282032967 -alfalfa,EGY,2.2388031482696533 +alfalfa,EGY,2.2808501720428467 alfalfa,ERI,-0.8745495080947876 -alfalfa,ESP,-1.05612051486969 +alfalfa,ESP,-1.0390456914901733 alfalfa,EST,-0.0 -alfalfa,ETH,-0.8379373550415039 +alfalfa,ETH,-0.8379373252391815 alfalfa,FIN,-0.0 -alfalfa,FJI,-0.17977124452590942 +alfalfa,FJI,-0.015432659070938826 alfalfa,FRA,-0.31077052652835846 -alfalfa,GAB,-0.835041344165802 +alfalfa,GAB,-0.786928653717041 alfalfa,GBR,0.17221324890851974 alfalfa,GEO,-0.5318324565887451 alfalfa,GHA,-0.9061825275421143 @@ -63,116 +63,116 @@ alfalfa,GIN,-0.8761205673217773 alfalfa,GMB,-7.5 alfalfa,GNB,-0.0 alfalfa,GNQ,-0.7592925727367401 -alfalfa,GRC,-0.5641406774520874 -alfalfa,GRD,0.21419155597686768 -alfalfa,GTM,-0.4861665815114975 +alfalfa,GRC,-0.5294201672077179 +alfalfa,GRD,0.22777661681175232 +alfalfa,GTM,-0.5239778757095337 alfalfa,GUF,-2.7332428693771362 alfalfa,GUY,-0.07167529128491879 -alfalfa,HND,-0.8811778128147125 -alfalfa,HRV,-3.9414610415697098 -alfalfa,HTI,-0.9849196076393127 -alfalfa,HUN,-0.4732874929904938 +alfalfa,HND,-0.3479782044887543 +alfalfa,HRV,-3.6974340388551354 +alfalfa,HTI,-0.8184686303138733 +alfalfa,HUN,-0.2495489977300167 alfalfa,IDN,-0.14206960052251816 -alfalfa,IND,2.8002794981002808 +alfalfa,IND,2.8403193950653076 alfalfa,IRL,-0.5063982009887695 -alfalfa,IRN,-1.4752857685089111 -alfalfa,IRQ,1.8413341045379639 -alfalfa,ISL,-0.7656219005584717 -alfalfa,ISR,1.3454011678695679 -alfalfa,ITA,0.28998690843582153 -alfalfa,JAM,1.6410280466079712 -alfalfa,JOR,0.9218956232070923 +alfalfa,IRN,-1.4941312074661255 +alfalfa,IRQ,1.8664162158966064 +alfalfa,ISL,-0.7419991791248322 +alfalfa,ISR,1.3649656772613525 +alfalfa,ITA,0.29763343930244446 +alfalfa,JAM,1.6732444763183594 +alfalfa,JOR,0.8013899326324463 alfalfa,JPN,-0.03383052349090576 -alfalfa,KAZ,-0.5461084842681885 -alfalfa,KEN,-0.4971180856227875 +alfalfa,KAZ,-0.5352659225463867 +alfalfa,KEN,-0.47085392475128174 alfalfa,KGZ,-0.8171840310096741 -alfalfa,KHM,-0.8327589333057404 -alfalfa,KOR,-0.041700076311826706 -alfalfa,LAO,3.51857428252697 -alfalfa,LBN,2.5593464374542236 +alfalfa,KHM,-0.2528349310159683 +alfalfa,KOR,-0.0329343609046191 +alfalfa,LAO,3.607166960835457 +alfalfa,LBN,2.5462141036987305 alfalfa,LBR,-0.9216444790363312 alfalfa,LBY,-1.7812474370002747 -alfalfa,LKA,-0.6845715045928955 +alfalfa,LKA,-0.346634104847908 alfalfa,LSO,-0.38055141270160675 alfalfa,LTU,0.40700384974479675 alfalfa,LUX,-0.31373557448387146 -alfalfa,LVA,-0.5559296607971191 -alfalfa,MAR,1.4986063838005066 +alfalfa,LVA,-0.7810502350330353 +alfalfa,MAR,1.5329021215438843 alfalfa,MDA,0.2783278673887253 alfalfa,MDG,-0.26456888020038605 alfalfa,MEX,-0.33953896164894104 -alfalfa,MKD,0.29456257820129395 +alfalfa,MKD,0.6686723530292511 alfalfa,MLI,-7.5 alfalfa,MLT,-7.5 -alfalfa,MMR,4.061458110809326 -alfalfa,MNE,2.0865092873573303 -alfalfa,MNG,-0.8187125325202942 +alfalfa,MMR,4.115209102630615 +alfalfa,MNE,2.1216850876808167 +alfalfa,MNG,-0.8132220208644867 alfalfa,MOZ,-0.6108028888702393 alfalfa,MRT,-1.277125060558319 -alfalfa,MUS,-0.8183156549930573 +alfalfa,MUS,-0.7995986342430115 alfalfa,MWI,-0.453051894903183 -alfalfa,MYS,-0.29772913455963135 +alfalfa,MYS,-0.2977290749549866 alfalfa,NAM,-7.5 -alfalfa,NER,-0.11455793399363756 +alfalfa,NER,-0.10588193219155073 alfalfa,NGA,-0.9605015814304352 alfalfa,NIC,-1.8537434339523315 -alfalfa,NLD,7.197600841522217 +alfalfa,NLD,7.178840398788452 alfalfa,NOR,-0.0 -alfalfa,NPL,-0.8966032564640045 +alfalfa,NPL,-0.8760634660720825 alfalfa,NZL,-0.2654487043619156 alfalfa,OMN,-0.0 alfalfa,PAK,-0.8345114588737488 -alfalfa,PAN,-1.012258231639862 +alfalfa,PAN,-0.09573017060756683 alfalfa,PER,-0.16441432386636734 alfalfa,PHL,-0.07465366646647453 alfalfa,PNG,-0.18700392544269562 alfalfa,POL,-0.4525585323572159 alfalfa,PRI,-0.2348995879292488 -alfalfa,PRT,2.5423593521118164 +alfalfa,PRT,2.542359232902527 alfalfa,PRY,-0.2994462549686432 -alfalfa,PSE,0.6010168194770813 +alfalfa,PSE,0.6163609027862549 alfalfa,ROU,0.07659449614584446 -alfalfa,RUS,-0.3809717297554016 -alfalfa,RWA,0.5868625640869141 -alfalfa,SAU,-0.6671143770217896 -alfalfa,SDN,-0.73411625623703 +alfalfa,RUS,-0.3799716979265213 +alfalfa,RWA,0.5951080918312073 +alfalfa,SAU,-0.66097491979599 +alfalfa,SDN,-0.6735749840736389 alfalfa,SEN,-0.0 -alfalfa,SLB,-0.4885735958814621 +alfalfa,SLB,-0.3817773461341858 alfalfa,SLE,-0.49844561517238617 -alfalfa,SLV,0.9478060603141785 +alfalfa,SLV,0.9655389785766602 alfalfa,SOM,-1.204360544681549 alfalfa,SRB,0.17493992671370506 -alfalfa,SSD,-0.8797294795513153 -alfalfa,STP,0.95741206407547 -alfalfa,SUR,-0.7841159105300903 -alfalfa,SVK,0.0976475179195404 -alfalfa,SVN,0.12827468197792768 +alfalfa,SSD,-0.7946434319019318 +alfalfa,STP,0.9784372746944427 +alfalfa,SUR,-0.7841159403324127 +alfalfa,SVK,0.25657545775175095 +alfalfa,SVN,0.43265416473150253 alfalfa,SWE,-0.0 alfalfa,SWZ,-0.9169159531593323 alfalfa,SYR,-0.0 alfalfa,TCD,-0.9309031963348389 alfalfa,TGO,-0.03396269865334034 alfalfa,THA,-0.67161625623703 -alfalfa,TJK,-0.24844133853912354 +alfalfa,TJK,-0.24068886041641235 alfalfa,TKM,-0.0 -alfalfa,TLS,-0.5261611938476562 +alfalfa,TLS,-0.038952913135290146 alfalfa,TTO,-0.7713225185871124 -alfalfa,TUN,0.17586714029312134 -alfalfa,TUR,2.9100624322891235 -alfalfa,TWN,1.3609405159950256 +alfalfa,TUN,0.18293552100658417 +alfalfa,TUR,2.9586375951766968 +alfalfa,TWN,1.7562566995620728 alfalfa,TZA,-0.31079547107219696 -alfalfa,UGA,-0.2817348837852478 -alfalfa,UKR,-0.30763496458530426 +alfalfa,UGA,-0.281734861433506 +alfalfa,UKR,-0.3082531690597534 alfalfa,URY,-0.005927370395511389 alfalfa,USA,-0.5345850884914398 -alfalfa,UZB,-1.2347599267959595 +alfalfa,UZB,-1.2268394231796265 alfalfa,VEN,-0.8396434485912323 -alfalfa,VNM,1.6334990710020065 -alfalfa,VUT,-0.24049730598926544 -alfalfa,YEM,-0.843728244304657 +alfalfa,VNM,1.5681107342243195 +alfalfa,VUT,-0.06947773694992065 +alfalfa,YEM,-0.8437283337116241 alfalfa,ZAF,-0.6283970773220062 alfalfa,ZMB,-7.5 -alfalfa,ZWE,-0.7625682950019836 +alfalfa,ZWE,-0.7531022429466248 apple,AFG,-0.0 apple,ALB,-0.0 apple,ARG,-7.5 @@ -233,7 +233,7 @@ apple,LUX,-0.0 apple,LVA,-0.0 apple,MAR,-0.0 apple,MDA,-0.0 -apple,MDG,-1.9526464939117432 +apple,MDG,-1.952646255493164 apple,MEX,-0.0 apple,MKD,-0.0 apple,MMR,-7.5 @@ -282,19 +282,19 @@ banana,AFG,-0.0 banana,AGO,-7.5 banana,ALB,-0.0 banana,ARE,-7.5 -banana,ARG,-4.398356676101685 +banana,ARG,-4.380629777908325 banana,ATG,7.5 banana,AUS,-7.5 -banana,BDI,-1.2015145421028137 +banana,BDI,-1.200043499469757 banana,BEN,-7.5 -banana,BFA,-6.449549198150635 -banana,BGD,-6.866764783859253 -banana,BHS,-7.164050579071045 -banana,BLZ,-6.84993052482605 -banana,BOL,-1.7917412519454956 -banana,BRA,-3.39028799533844 +banana,BFA,-6.441171169281006 +banana,BGD,-6.857705593109131 +banana,BHS,-7.157028675079346 +banana,BLZ,-6.836521148681641 +banana,BOL,-1.7865676879882812 +banana,BRA,-3.3914679288864136 banana,BRB,-7.5 -banana,BRN,-2.36167049407959 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cabbage,ARM,-1.1778666526079178 cabbage,ATG,-7.5 cabbage,AUS,-7.5 @@ -810,16 +810,16 @@ cabbage,BEN,-5.923673152923584 cabbage,BFA,-7.5 cabbage,BGD,0.9815003499388695 cabbage,BGR,-0.9267120361328125 -cabbage,BHS,-6.426521301269531 -cabbage,BIH,2.4797356724739075 -cabbage,BLR,3.1227803230285645 +cabbage,BHS,-7.395512580871582 +cabbage,BIH,2.4577139019966125 +cabbage,BLR,3.103569269180298 cabbage,BLZ,-7.5 cabbage,BOL,-2.459995985031128 cabbage,BRA,-5.329397916793823 cabbage,BRB,-7.5 cabbage,BRN,-7.5 -cabbage,BTN,-0.8268683850765228 -cabbage,BWA,-4.352359294891357 +cabbage,BTN,-0.9633532166481018 +cabbage,BWA,-4.658273696899414 cabbage,CAF,-7.5 cabbage,CAN,-4.488381862640381 cabbage,CHE,-7.5 @@ -846,7 +846,7 @@ cabbage,EGY,-0.0 cabbage,ERI,-7.5 cabbage,ESP,-3.0665398836135864 cabbage,EST,-2.6214559674263 -cabbage,ETH,-0.028657998889684677 +cabbage,ETH,-0.09511537477374077 cabbage,FIN,-7.5 cabbage,FJI,-7.5 cabbage,FRA,-7.5 @@ -864,7 +864,7 @@ cabbage,GTM,-6.043471813201904 cabbage,GUF,-7.5 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-cabbage,TCD,-7.372105121612549 +cabbage,TCD,-7.5 cabbage,TGO,-3.8998230695724487 cabbage,THA,-5.462543964385986 cabbage,TJK,-2.2506799697875977 @@ -952,11 +952,11 @@ cabbage,TKM,-4.65995979309082 cabbage,TLS,-6.185270547866821 cabbage,TTO,-7.5 cabbage,TUN,2.7783093452453613 -cabbage,TUR,0.4627869203686714 +cabbage,TUR,0.4612012580037117 cabbage,TWN,-4.957915544509888 -cabbage,TZA,-2.064137578010559 +cabbage,TZA,-2.076874852180481 cabbage,UGA,0.0 -cabbage,UKR,1.89513498544693 +cabbage,UKR,1.8950119018554688 cabbage,URY,-3.3633846044540405 cabbage,USA,-7.5 cabbage,UZB,-1.8313884735107422 @@ -964,278 +964,278 @@ cabbage,VEN,-7.5 cabbage,VNM,-4.729036331176758 cabbage,VUT,-7.5 cabbage,YEM,-6.173388481140137 -cabbage,ZAF,-3.545754909515381 +cabbage,ZAF,-3.556178331375122 cabbage,ZMB,-7.1423020362854 cabbage,ZWE,-7.5 -carrot,AFG,-5.585590600967407 -carrot,AGO,-6.817250728607178 -carrot,ALB,-6.3789918422698975 -carrot,ARE,-1.2642307877540588 +carrot,AFG,-5.577932834625244 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chickpea,BOL,7.5 chickpea,BRA,-0.6405691504478455 chickpea,BRB,-7.5 chickpea,BRN,0.0 -chickpea,BTN,-0.34706197679042816 -chickpea,BWA,-0.9824286103248596 +chickpea,BTN,-0.4894617646932602 +chickpea,BWA,-0.9827342629432678 chickpea,CAF,-0.5668359100818634 chickpea,CAN,0.39990419149398804 chickpea,CHE,2.323651671409607 @@ -1281,7 +1281,7 @@ chickpea,DEU,2.066950559616089 chickpea,DJI,-7.5 chickpea,DNK,-0.0 chickpea,DOM,-0.0 -chickpea,DZA,0.022225311025977135 +chickpea,DZA,0.01703718677163124 chickpea,ECU,-0.5532186478376389 chickpea,EGY,1.0593062341213226 chickpea,ERI,-0.8356009721755981 @@ -1304,11 +1304,11 @@ chickpea,GRD,-7.5 chickpea,GTM,-0.38124576210975647 chickpea,GUF,7.5 chickpea,GUY,-7.5 -chickpea,HND,-0.8345318138599396 +chickpea,HND,-0.9132257997989655 chickpea,HRV,3.914081409573555 chickpea,HTI,-0.8373245894908905 chickpea,HUN,0.8685125112533569 -chickpea,IDN,-0.8900873363018036 +chickpea,IDN,-0.8832660913467407 chickpea,IND,-0.6349109709262848 chickpea,IRL,-0.0 chickpea,IRN,0.0022426832001656294 @@ -1332,17 +1332,17 @@ chickpea,LSO,-0.8371503353118896 chickpea,LTU,-0.0 chickpea,LUX,0.5116190016269684 chickpea,LVA,-0.0 -chickpea,MAR,0.06144399102777243 +chickpea,MAR,0.050283696851693094 chickpea,MDA,0.6079802140593529 chickpea,MDG,-0.26950400322675705 chickpea,MEX,-0.6907674670219421 -chickpea,MKD,0.8121582567691803 +chickpea,MKD,0.8112282454967499 chickpea,MLI,-0.6266210675239563 chickpea,MMR,-0.5259549021720886 chickpea,MNE,1.1454066038131714 chickpea,MNG,0.0 chickpea,MOZ,-0.8375687599182129 -chickpea,MRT,-1.1196767687797546 +chickpea,MRT,-1.0755565762519836 chickpea,MWI,3.5217777490615845 chickpea,MYS,3.749336086155381 chickpea,NAM,-0.22205625474452972 @@ -1364,10 +1364,10 @@ chickpea,PRI,7.5 chickpea,PRT,-0.37909167259931564 chickpea,PRY,-0.6062327027320862 chickpea,PSE,-1.7580406665802002 -chickpea,ROU,0.4513508826494217 +chickpea,ROU,0.4425079971551895 chickpea,RUS,-0.025009913370013237 chickpea,RWA,-0.7512563467025757 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-dry-pea,ZWE,-0.7070204019546509 -dryland-rice,AFG,-1.0930031538009644 -dryland-rice,AGO,-1.5250561237335205 +dry-pea,SVK,0.031748310662806034 +dry-pea,SVN,0.04901581071317196 +dry-pea,SWE,-0.13627387583255768 +dry-pea,SWZ,-0.5145768225193024 +dry-pea,SYR,-0.3649042844772339 +dry-pea,TCD,-0.5715228617191315 +dry-pea,TGO,-0.5550335347652435 +dry-pea,THA,-0.4526021182537079 +dry-pea,TJK,-0.4159001260995865 +dry-pea,TKM,-0.3482961058616638 +dry-pea,TLS,3.6092213690280914 +dry-pea,TTO,-3.822768911719322 +dry-pea,TUN,-0.4316784739494324 +dry-pea,TUR,-0.7664478719234467 +dry-pea,TWN,-0.03532921336591244 +dry-pea,TZA,-0.3221610486507416 +dry-pea,UGA,-0.48590952157974243 +dry-pea,UKR,-0.18275870382785797 +dry-pea,URY,-0.2692426107823849 +dry-pea,USA,-0.11652266979217529 +dry-pea,UZB,-2.1371965408325195 +dry-pea,VEN,3.620812088251114 +dry-pea,VNM,-0.2855430841445923 +dry-pea,YEM,-1.4556112885475159 +dry-pea,ZAF,-0.5377421379089355 +dry-pea,ZMB,-0.5200809836387634 +dry-pea,ZWE,-0.682245671749115 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+dryland-rice,DOM,-1.9923142194747925 dryland-rice,DZA,7.5 -dryland-rice,ECU,-0.7025416791439056 -dryland-rice,EGY,-1.9776173830032349 +dryland-rice,ECU,-0.6981906592845917 +dryland-rice,EGY,-1.9363343119621277 dryland-rice,ERI,-0.0 -dryland-rice,ESP,-1.7297003269195557 -dryland-rice,ETH,-0.7632920742034912 -dryland-rice,FJI,-0.8150743246078491 -dryland-rice,FRA,-1.0230319499969482 +dryland-rice,ESP,-1.7202720642089844 +dryland-rice,ETH,-0.7407501637935638 +dryland-rice,FJI,-0.8084312677383423 +dryland-rice,FRA,-1.013867735862732 dryland-rice,GAB,7.5 dryland-rice,GEO,7.5 -dryland-rice,GHA,-0.5298930704593658 -dryland-rice,GIN,-0.6517085433006287 -dryland-rice,GMB,-0.39132118225097656 -dryland-rice,GNB,-1.4618096947669983 +dryland-rice,GHA,-0.522957444190979 +dryland-rice,GIN,-0.6490567922592163 +dryland-rice,GMB,-0.3904073238372803 +dryland-rice,GNB,-1.4592395424842834 dryland-rice,GNQ,7.5 -dryland-rice,GRC,-1.235911339521408 -dryland-rice,GTM,-1.5114827156066895 +dryland-rice,GRC,-1.2267370223999023 +dryland-rice,GTM,-1.5313764810562134 dryland-rice,GUF,7.5 -dryland-rice,GUY,-1.3247894644737244 -dryland-rice,HND,-1.4366456270217896 +dryland-rice,GUY,-1.3168500065803528 +dryland-rice,HND,-1.4327635169029236 dryland-rice,HRV,-0.0 -dryland-rice,HTI,-1.448270320892334 -dryland-rice,HUN,-3.9223957657814026 -dryland-rice,IDN,-1.477501928806305 -dryland-rice,IND,-1.0962949991226196 -dryland-rice,IRN,-3.3405325412750244 -dryland-rice,IRQ,-1.598702073097229 +dryland-rice,HTI,-1.4445627927780151 +dryland-rice,HUN,-7.5 +dryland-rice,IDN,-1.469868242740631 +dryland-rice,IND,-1.012530118227005 +dryland-rice,IRN,-3.3325096368789673 +dryland-rice,IRQ,-1.590801179409027 dryland-rice,ISR,0.0 -dryland-rice,ITA,-2.014738619327545 -dryland-rice,JPN,-3.617790102958679 -dryland-rice,KAZ,-0.29414980113506317 -dryland-rice,KEN,-1.2246223092079163 -dryland-rice,KGZ,-3.1309397220611572 -dryland-rice,KHM,-1.379055917263031 -dryland-rice,KOR,-6.389946937561035 -dryland-rice,LAO,-1.3268045783042908 -dryland-rice,LBR,-1.5703086853027344 +dryland-rice,ITA,-2.0560960173606873 +dryland-rice,JPN,-3.5644900798797607 +dryland-rice,KAZ,-0.28441551327705383 +dryland-rice,KEN,-1.214832365512848 +dryland-rice,KGZ,-3.124980926513672 +dryland-rice,KHM,-1.372390627861023 +dryland-rice,KOR,-6.395137548446655 +dryland-rice,LAO,-1.3190978169441223 +dryland-rice,LBR,-1.5678764581680298 dryland-rice,LBY,-0.0 -dryland-rice,LKA,-0.46656161546707153 +dryland-rice,LKA,-0.45803074538707733 dryland-rice,LSO,-0.0 -dryland-rice,MAR,-0.7614849209785461 +dryland-rice,MAR,-0.7479532063007355 dryland-rice,MDA,-0.0 -dryland-rice,MDG,-0.4398861527442932 -dryland-rice,MEX,-0.620079755783081 +dryland-rice,MDG,-0.4362912029027939 +dryland-rice,MEX,-0.6068223416805267 dryland-rice,MKD,-7.5 -dryland-rice,MLI,-0.5428740382194519 -dryland-rice,MMR,-1.423747479915619 -dryland-rice,MOZ,-0.3881491720676422 -dryland-rice,MRT,-1.1760107278823853 +dryland-rice,MLI,-0.5385791063308716 +dryland-rice,MMR,-1.417638897895813 +dryland-rice,MOZ,-0.38672301173210144 +dryland-rice,MRT,-1.1693511009216309 dryland-rice,MUS,-0.0 -dryland-rice,MWI,-1.1942031979560852 -dryland-rice,MYS,-0.5297153145074844 +dryland-rice,MWI,-1.1918519735336304 +dryland-rice,MYS,-0.5242082178592682 dryland-rice,NAM,7.5 -dryland-rice,NER,-1.3870487809181213 -dryland-rice,NGA,-0.44037358462810516 -dryland-rice,NIC,0.18319819122552872 -dryland-rice,NPL,-0.40171898901462555 -dryland-rice,PAK,-1.316495418548584 -dryland-rice,PAN,-1.1122459173202515 -dryland-rice,PER,-0.24929870665073395 -dryland-rice,PHL,-0.8647339344024658 +dryland-rice,NER,-1.3799799680709839 +dryland-rice,NGA,-0.43449175357818604 +dryland-rice,NIC,0.37484851479530334 +dryland-rice,NPL,-0.39608730375766754 +dryland-rice,PAK,-1.3106311559677124 +dryland-rice,PAN,-1.1043299436569214 +dryland-rice,PER,-0.11108823120594025 +dryland-rice,PHL,-0.8583106100559235 dryland-rice,PNG,7.5 -dryland-rice,PRI,-2.6236497163772583 -dryland-rice,PRT,-1.2852699160575867 -dryland-rice,PRY,-0.9915218949317932 +dryland-rice,PRI,-2.6181124448776245 +dryland-rice,PRT,-1.2753233313560486 +dryland-rice,PRY,-0.9882286787033081 dryland-rice,PSE,-7.5 -dryland-rice,ROU,-4.561271607875824 -dryland-rice,RUS,-3.0613452196121216 -dryland-rice,RWA,-2.0632824897766113 +dryland-rice,ROU,-4.565342307090759 +dryland-rice,RUS,-3.080842971801758 +dryland-rice,RWA,-2.055779814720154 dryland-rice,SAU,7.5 dryland-rice,SDN,7.5 -dryland-rice,SEN,-0.25237707793712616 -dryland-rice,SLB,-1.498716950416565 -dryland-rice,SLE,-0.3982353210449219 -dryland-rice,SLV,2.978124737739563 -dryland-rice,SOM,-1.4103583097457886 +dryland-rice,SEN,-0.2835896499454975 +dryland-rice,SLB,-1.495481550693512 +dryland-rice,SLE,-0.39394618570804596 +dryland-rice,SLV,2.928979814052582 +dryland-rice,SOM,-1.406767725944519 dryland-rice,SRB,-0.0 -dryland-rice,SSD,-1.5237433910369873 -dryland-rice,SUR,-0.2976420968770981 +dryland-rice,SSD,-1.5225961804389954 +dryland-rice,SUR,-0.28898824751377106 dryland-rice,SVK,-0.0 dryland-rice,SVN,-0.0 dryland-rice,SWZ,7.5 dryland-rice,SYR,-7.5 -dryland-rice,TCD,-0.8544504642486572 -dryland-rice,TGO,-0.28513742983341217 -dryland-rice,THA,-0.5343890488147736 -dryland-rice,TJK,-3.6293163299560547 -dryland-rice,TKM,-1.0818672180175781 -dryland-rice,TLS,-1.958392322063446 -dryland-rice,TTO,-0.18983447551727295 +dryland-rice,TCD,-0.9014450907707214 +dryland-rice,TGO,-0.28134559094905853 +dryland-rice,THA,-0.529471829533577 +dryland-rice,TJK,-3.6409339904785156 +dryland-rice,TKM,-1.0749194622039795 +dryland-rice,TLS,-1.9554120302200317 +dryland-rice,TTO,-0.18825803697109222 dryland-rice,TUN,-0.0 -dryland-rice,TUR,-2.5626981258392334 -dryland-rice,TWN,-1.0094498693943024 -dryland-rice,TZA,-0.1177944615483284 -dryland-rice,UGA,3.0044767260551453 -dryland-rice,UKR,-1.892574667930603 -dryland-rice,URY,-0.7203420400619507 -dryland-rice,USA,-1.404603362083435 -dryland-rice,UZB,-4.526167869567871 -dryland-rice,VEN,-1.1281927824020386 -dryland-rice,VNM,-1.0916321873664856 +dryland-rice,TUR,-2.560770630836487 +dryland-rice,TWN,-0.9974303543567657 +dryland-rice,TZA,-0.15952866524457932 +dryland-rice,UGA,3.3061901330947876 +dryland-rice,UKR,-1.8974433541297913 +dryland-rice,URY,-0.7075426578521729 +dryland-rice,USA,-1.3890995979309082 +dryland-rice,UZB,-4.518707275390625 +dryland-rice,VEN,-1.120782196521759 +dryland-rice,VNM,-1.0838068127632141 dryland-rice,YEM,-0.0 dryland-rice,ZAF,7.5 -dryland-rice,ZMB,-1.1651034355163574 +dryland-rice,ZMB,-1.1619796752929688 dryland-rice,ZWE,7.5 foxtail-millet,AFG,-0.5469042658805847 foxtail-millet,AGO,-0.40684472024440765 @@ -2353,13 +2353,13 @@ foxtail-millet,AZE,-4.347458600997925 foxtail-millet,BDI,-0.48558299243450165 foxtail-millet,BEN,-0.5482176244258881 foxtail-millet,BFA,-0.3707796037197113 -foxtail-millet,BGD,-0.4668000191450119 +foxtail-millet,BGD,-0.4440851956605911 foxtail-millet,BGR,-0.0 foxtail-millet,BLR,-0.3229052722454071 foxtail-millet,BOL,-0.0 foxtail-millet,BRA,-0.0 -foxtail-millet,BTN,-3.8268341347575188 -foxtail-millet,BWA,-0.4341735243797302 +foxtail-millet,BTN,-4.508623838424683 +foxtail-millet,BWA,-0.4348180592060089 foxtail-millet,CAF,-7.5 foxtail-millet,CAN,-0.0 foxtail-millet,CHE,-2.6688425540924072 @@ -2381,9 +2381,9 @@ foxtail-millet,GMB,-0.46135395765304565 foxtail-millet,GNB,-0.4469609558582306 foxtail-millet,GRC,-0.0 foxtail-millet,HUN,7.5 -foxtail-millet,IND,-0.4285693019628525 -foxtail-millet,IRN,-1.2599350810050964 -foxtail-millet,IRQ,-0.8328883051872253 +foxtail-millet,IND,-0.3955204039812088 +foxtail-millet,IRN,-1.2693577408790588 +foxtail-millet,IRQ,-0.834003359079361 foxtail-millet,KAZ,-0.2774594873189926 foxtail-millet,KEN,-0.49875615537166595 foxtail-millet,KGZ,-0.7159605026245117 @@ -2393,7 +2393,7 @@ foxtail-millet,LAO,0.0 foxtail-millet,LBY,-0.8158671259880066 foxtail-millet,LKA,-1.034119188785553 foxtail-millet,LSO,7.5 -foxtail-millet,MAR,-0.7610122263431549 +foxtail-millet,MAR,-0.7631213366985321 foxtail-millet,MDA,3.4062991440296173 foxtail-millet,MLI,-0.3181726336479187 foxtail-millet,MMR,-0.4503839612007141 @@ -2414,7 +2414,7 @@ foxtail-millet,RUS,-0.2878616452217102 foxtail-millet,RWA,-0.4246528446674347 foxtail-millet,SAU,-7.5 foxtail-millet,SDN,-0.4389075040817261 -foxtail-millet,SEN,-0.49853432178497314 +foxtail-millet,SEN,-0.5168838500976562 foxtail-millet,SLE,-0.4648708999156952 foxtail-millet,SOM,7.5 foxtail-millet,SSD,-0.40603186190128326 @@ -2427,10 +2427,10 @@ foxtail-millet,THA,-0.4839485436677933 foxtail-millet,TJK,-0.4736505150794983 foxtail-millet,TKM,7.5 foxtail-millet,TUN,-0.0 -foxtail-millet,TUR,-1.2996327877044678 +foxtail-millet,TUR,-1.3076333403587341 foxtail-millet,TWN,-7.5 foxtail-millet,TZA,-0.5091361999511719 -foxtail-millet,UGA,-0.4788183271884918 +foxtail-millet,UGA,-0.15505176782608032 foxtail-millet,UKR,-0.5722740292549133 foxtail-millet,URY,-0.0 foxtail-millet,USA,-0.43647652864456177 @@ -2439,7 +2439,7 @@ foxtail-millet,VNM,2.965010344982147 foxtail-millet,YEM,-0.48086604475975037 foxtail-millet,ZAF,-0.3926069587469101 foxtail-millet,ZMB,-0.40837621688842773 -foxtail-millet,ZWE,0.035087288822978735 +foxtail-millet,ZWE,0.03417539410293102 gram,AFG,-0.7224279344081879 gram,AGO,-0.899444043636322 gram,ALB,-0.1283540464937687 @@ -2460,16 +2460,16 @@ gram,BIH,4.012606054544449 gram,BLR,3.75 gram,BLZ,-0.43359893560409546 gram,BOL,7.5 -gram,BRA,-0.3412286341190338 +gram,BRA,-0.3512887954711914 gram,BRB,0.6225399971008301 gram,BRN,0.0 -gram,BTN,-0.3150663673877716 -gram,BWA,-0.976485550403595 +gram,BTN,-0.47050003707408905 +gram,BWA,-0.9767912030220032 gram,CAF,-0.5218968391418457 gram,CAN,-7.5 gram,CHE,2.4137197136878967 gram,CHL,0.1703679859638214 -gram,CHN,0.29879921674728394 +gram,CHN,0.2811867594718933 gram,CIV,-0.6592907011508942 gram,CMR,-0.5745219588279724 gram,COD,-0.86709925532341 @@ -2540,7 +2540,7 @@ gram,MAR,-0.08030140213668346 gram,MDA,-3.9867861568927765 gram,MDG,-0.24424779415130615 gram,MEX,-0.6752154231071472 -gram,MKD,0.9194293916225433 +gram,MKD,0.9193709790706635 gram,MLI,-0.6104013919830322 gram,MMR,-0.44743940234184265 gram,MNE,1.206932783126831 @@ -2569,18 +2569,18 @@ gram,PRT,-0.3568218797445297 gram,PRY,-0.5880894064903259 gram,PSE,-1.8850900530815125 gram,ROU,-3.9044624865055084 -gram,RUS,-0.8063134551048279 +gram,RUS,-0.7740770280361176 gram,RWA,-0.737376481294632 gram,SAU,1.1136308908462524 gram,SDN,-0.5654671788215637 -gram,SEN,-0.006521798670291901 +gram,SEN,-0.09580892324447632 gram,SLB,-0.24319685250520706 gram,SLE,-0.8298742473125458 gram,SLV,3.617624819278717 gram,SOM,-1.075678288936615 gram,SRB,-0.0 gram,SSD,-0.47389450669288635 -gram,SUR,3.4570164382457733 +gram,SUR,3.4631773829460144 gram,SVK,-0.0 gram,SVN,4.394844591617584 gram,SWE,-7.5 @@ -2589,7 +2589,7 @@ gram,SYR,-0.0 gram,TCD,-0.8710505664348602 gram,TGO,-0.816396176815033 gram,THA,-0.4644381105899811 -gram,TJK,0.026327146217226982 +gram,TJK,0.012611432000994682 gram,TKM,-0.3070851266384125 gram,TLS,3.7304899655282497 gram,TTO,0.4026298224925995 @@ -2601,7 +2601,7 @@ gram,UGA,-0.6179071962833405 gram,UKR,-0.0 gram,URY,0.2623838782310486 gram,USA,0.09906517714262009 -gram,UZB,-0.2229640632867813 +gram,UZB,-0.2439085990190506 gram,VEN,-0.4345783591270447 gram,VNM,3.5786690711975098 gram,YEM,-1.1357987523078918 @@ -2609,602 +2609,602 @@ gram,ZAF,-0.8973642587661743 gram,ZMB,-0.7733388543128967 gram,ZWE,-1.0259044170379639 groundnut,AFG,7.5 -groundnut,AGO,-1.3118253946304321 -groundnut,ARG,-0.9777169227600098 -groundnut,ARM,3.4300957918167114 -groundnut,AUS,-1.5619280934333801 -groundnut,AZE,-0.6265506148338318 -groundnut,BDI,-1.3380441069602966 -groundnut,BEN,-1.2856730818748474 -groundnut,BFA,-1.2800033688545227 -groundnut,BGD,-0.9501891434192657 +groundnut,AGO,-1.2546062469482422 +groundnut,ARG,-0.49102118611335754 +groundnut,ARM,3.6370922923088074 +groundnut,AUS,-0.8986455798149109 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-groundnut,DOM,-1.4399995803833008 -groundnut,DZA,0.18224218487739563 -groundnut,ECU,-2.2381346225738525 -groundnut,EGY,-5.7515387535095215 -groundnut,ERI,-1.3108656406402588 -groundnut,ETH,-1.155702829360962 -groundnut,FJI,-1.2711898684501648 -groundnut,GAB,-1.277253270149231 -groundnut,GEO,-1.1632561683654785 -groundnut,GHA,-1.022307813167572 -groundnut,GIN,-0.06206162087619305 -groundnut,GMB,-0.5780824720859528 -groundnut,GNB,-1.2237040996551514 +groundnut,CHN,-3.0942859649658203 +groundnut,CIV,-0.28410736471414566 +groundnut,CMR,-1.0939753651618958 +groundnut,COD,-1.2267266511917114 +groundnut,COG,-1.2964542508125305 +groundnut,COL,-0.8781954050064087 +groundnut,COM,-1.2006711959838867 +groundnut,CRI,-1.0581260323524475 +groundnut,CUB,-1.1086804270744324 +groundnut,DOM,-1.2372700572013855 +groundnut,DZA,1.4030950665473938 +groundnut,ECU,-2.1265037059783936 +groundnut,EGY,-5.444257497787476 +groundnut,ERI,-1.2596694231033325 +groundnut,ETH,-1.0147515535354614 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-groundnut,MLI,-0.35894064605236053 -groundnut,MMR,-1.198872685432434 -groundnut,MOZ,-0.28301140666007996 -groundnut,MRT,-1.3183653950691223 -groundnut,MUS,-4.083080768585205 -groundnut,MWI,3.5150306820869446 -groundnut,MYS,-1.1427658200263977 -groundnut,NAM,-1.329983115196228 -groundnut,NER,-0.14394929260015488 -groundnut,NGA,-1.2414829730987549 -groundnut,NIC,-0.9285194575786591 -groundnut,NPL,-0.7956202030181885 +groundnut,MAR,-2.3703352212905884 +groundnut,MDG,-1.2229573130607605 +groundnut,MEX,-0.5169493556022644 +groundnut,MLI,-0.2749307230114937 +groundnut,MMR,-1.0245792865753174 +groundnut,MOZ,-0.2542504072189331 +groundnut,MRT,-1.2440681457519531 +groundnut,MUS,-3.8467148542404175 +groundnut,MWI,3.558933064341545 +groundnut,MYS,-0.9851301610469818 +groundnut,NAM,-1.3065476417541504 +groundnut,NER,-0.11693862080574036 +groundnut,NGA,-1.1090847849845886 +groundnut,NIC,-0.44470934569835663 +groundnut,NPL,-0.4080306738615036 groundnut,OMN,-0.0 -groundnut,PAK,-1.156627893447876 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-groundnut,TJK,-0.9637318253517151 -groundnut,TKM,-0.7340461015701294 -groundnut,TLS,3.7331939693540335 +groundnut,SOM,-1.1717879176139832 +groundnut,SSD,-1.1719512939453125 +groundnut,SUR,-2.747868537902832 +groundnut,SWZ,-1.2955129146575928 +groundnut,SYR,3.515926107764244 +groundnut,TCD,-0.40539154410362244 +groundnut,TGO,-0.20910603553056717 +groundnut,THA,-1.7532261610031128 +groundnut,TJK,-0.6193121075630188 +groundnut,TKM,-0.3704144060611725 +groundnut,TLS,3.7909704260528088 groundnut,TUN,-0.0 -groundnut,TUR,-3.6016619205474854 -groundnut,TWN,-0.9593173265457153 -groundnut,TZA,-0.19913724064826965 -groundnut,UGA,-1.3395758271217346 -groundnut,URY,-1.3170964121818542 -groundnut,USA,-1.3820906281471252 +groundnut,TUR,-3.1476969718933105 +groundnut,TWN,-0.6188009977340698 +groundnut,TZA,-0.12301760911941528 +groundnut,UGA,-1.1749117374420166 +groundnut,URY,-1.2668952941894531 +groundnut,USA,-0.8239550888538361 groundnut,UZB,-7.5 -groundnut,VEN,-0.815345287322998 -groundnut,VNM,-2.047034978866577 -groundnut,VUT,-1.198502242565155 -groundnut,YEM,-1.2835575342178345 -groundnut,ZAF,-0.44035883247852325 -groundnut,ZMB,-0.5764057636260986 -groundnut,ZWE,-0.453232079744339 -maize,AFG,3.4550502002239227 -maize,AGO,-0.889428436756134 -maize,ALB,-1.374246597290039 -maize,ARE,0.8439314067363739 -maize,ARG,-0.3476416766643524 -maize,ARM,-0.43620625138282776 -maize,ATG,-1.1002861261367798 +groundnut,VEN,-0.3287461996078491 +groundnut,VNM,-1.7518609166145325 +groundnut,VUT,-1.0426597595214844 +groundnut,YEM,-1.15145742893219 +groundnut,ZAF,-0.37142592668533325 +groundnut,ZMB,-0.5148239731788635 +groundnut,ZWE,-0.4151510000228882 +maize,AFG,3.457675486803055 +maize,AGO,-0.8875872492790222 +maize,ALB,-1.3637967705726624 +maize,ARE,0.8804170191287994 +maize,ARG,-0.3368646502494812 +maize,ARM,-0.430043563246727 +maize,ATG,-1.0978877544403076 maize,AUS,7.5 -maize,AUT,-0.6805731654167175 -maize,AZE,-0.459731787443161 -maize,BDI,-0.42534708976745605 -maize,BEL,-0.08503523841500282 -maize,BEN,-0.25645336508750916 -maize,BFA,-0.2878548353910446 -maize,BGD,-0.06742815114557743 -maize,BGR,-0.21474787592887878 -maize,BHS,-0.07496079429984093 -maize,BIH,-3.359537422657013 -maize,BLR,-0.8240810632705688 -maize,BLZ,-1.1014838218688965 -maize,BOL,-0.34895962476730347 -maize,BRA,-0.3968585133552551 -maize,BRB,5.427735090255737 -maize,BTN,0.20006755739450455 -maize,BWA,-0.8977821469306946 -maize,CAF,-0.9545150697231293 -maize,CAN,-1.0441977977752686 -maize,CHE,-1.747071623802185 -maize,CHL,-1.565345287322998 -maize,CHN,-1.5007679462432861 -maize,CIV,-0.6667470335960388 -maize,CMR,-0.8047137260437012 -maize,COD,-0.9722076654434204 -maize,COG,-0.8982167541980743 -maize,COL,-0.9010091125965118 -maize,COM,-0.7787017822265625 -maize,CPV,-0.0035533702466636896 -maize,CRI,-0.4931536912918091 -maize,CUB,-0.6826362311840057 -maize,CZE,-0.30941399931907654 -maize,DEU,-0.642281174659729 +maize,AUT,-0.665150910615921 +maize,AZE,-0.4465123564004898 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-maize,DNK,-0.3474145829677582 -maize,DOM,3.5041882544755936 -maize,DZA,-0.2921432852745056 -maize,ECU,-0.8306211829185486 -maize,EGY,-1.2262573838233948 -maize,ERI,-0.8350259959697723 -maize,ESP,-1.7876605987548828 -maize,EST,-0.5034712553024292 -maize,ETH,-0.0015929798828437924 +maize,DNK,-0.336529016494751 +maize,DOM,3.5058157444000244 +maize,DZA,-0.2820625901222229 +maize,ECU,-0.8247424364089966 +maize,EGY,-1.1905767321586609 +maize,ERI,-0.8326709568500519 +maize,ESP,-1.7788046598434448 +maize,EST,-0.49514317512512207 +maize,ETH,-0.054648835211992264 maize,FIN,7.5 -maize,FJI,-1.937498688697815 -maize,FRA,-0.5816808342933655 -maize,GAB,-0.8681106269359589 -maize,GEO,-0.226317398250103 -maize,GHA,-0.4717613458633423 -maize,GIN,-0.26459185779094696 -maize,GMB,-0.3019232600927353 -maize,GNB,-0.9245427548885345 -maize,GNQ,-0.9817771911621094 -maize,GRC,-1.9587759971618652 -maize,GRD,-0.5235036611557007 -maize,GTM,-0.8345959186553955 +maize,FJI,-1.932082176208496 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-sugarbeet,TKM,-1.7706559896469116 -sugarbeet,TUN,-1.0342015326023102 -sugarbeet,TUR,-2.0574265718460083 -sugarbeet,UKR,-1.283802330493927 -sugarbeet,USA,-2.025824785232544 -sugarbeet,UZB,-0.36482204496860504 +sugarbeet,TKM,-1.7681677341461182 +sugarbeet,TUN,-1.013607144355774 +sugarbeet,TUR,-2.037208080291748 +sugarbeet,UKR,-1.2732674479484558 +sugarbeet,USA,-1.9947584867477417 +sugarbeet,UZB,-0.35600341856479645 sugarbeet,VNM,7.5 sugarcane,AFG,-2.3949992656707764 sugarcane,AGO,-2.2606115341186523 @@ -4863,13 +4863,13 @@ sugarcane,ARE,-0.0 sugarcane,ARG,-2.927295446395874 sugarcane,AUS,-3.911032199859619 sugarcane,BDI,-2.1910054683685303 -sugarcane,BEN,-2.5455479621887207 +sugarcane,BEN,-2.5502634048461914 sugarcane,BFA,-2.63287353515625 -sugarcane,BGD,-2.5678900480270386 -sugarcane,BHS,-2.3660157918930054 +sugarcane,BGD,-2.53499972820282 +sugarcane,BHS,-2.366015672683716 sugarcane,BLZ,-1.9845242500305176 -sugarcane,BOL,-4.803053379058838 -sugarcane,BRA,-2.0936713218688965 +sugarcane,BOL,-4.669949054718018 +sugarcane,BRA,-2.0856943130493164 sugarcane,BRB,-0.0 sugarcane,BRN,-0.0 sugarcane,BTN,-7.5 @@ -4882,15 +4882,15 @@ sugarcane,COD,-2.3136093616485596 sugarcane,COG,-2.3536309003829956 sugarcane,COL,-2.7655266523361206 sugarcane,CPV,-2.3767861127853394 -sugarcane,CRI,-2.2228267192840576 -sugarcane,CUB,-2.7152363061904907 +sugarcane,CRI,-2.222826600074768 +sugarcane,CUB,-2.715236186981201 sugarcane,DJI,-0.0 -sugarcane,DOM,-3.6279057264328003 -sugarcane,DZA,2.6295454502105713 +sugarcane,DOM,-3.634234309196472 +sugarcane,DZA,2.5956567525863647 sugarcane,ECU,-2.44791316986084 -sugarcane,EGY,-4.561208963394165 +sugarcane,EGY,-4.524753570556641 sugarcane,ERI,-2.2367587089538574 -sugarcane,ETH,-4.721463918685913 +sugarcane,ETH,-4.718851327896118 sugarcane,FJI,-2.1166298389434814 sugarcane,GAB,-2.2554919719696045 sugarcane,GHA,-2.5612746477127075 @@ -4903,39 +4903,39 @@ sugarcane,GTM,-3.1844719648361206 sugarcane,GUF,7.5 sugarcane,GUY,-3.093653440475464 sugarcane,HND,-2.8159632682800293 -sugarcane,HTI,-2.6820844411849976 +sugarcane,HTI,-2.6820842027664185 sugarcane,IDN,-2.49464213848114 -sugarcane,IND,-2.887693762779236 +sugarcane,IND,-2.80614697933197 sugarcane,IRN,-3.3052284717559814 sugarcane,IRQ,-2.2859115600585938 sugarcane,ISR,-0.0 -sugarcane,JAM,-2.0053582787513733 -sugarcane,JPN,-7.3378143310546875 -sugarcane,KEN,-2.182090401649475 +sugarcane,JAM,-2.0053582191467285 +sugarcane,JPN,-7.315605640411377 +sugarcane,KEN,-2.202704906463623 sugarcane,KHM,-2.46723735332489 sugarcane,LAO,-2.553240656852722 sugarcane,LBR,-2.3199148178100586 -sugarcane,LKA,-1.4503036141395569 +sugarcane,LKA,-1.450303554534912 sugarcane,LSO,-2.2442567348480225 sugarcane,MAR,-3.7259137630462646 sugarcane,MDG,-2.365809679031372 -sugarcane,MEX,-2.781857490539551 +sugarcane,MEX,-2.7818572521209717 sugarcane,MLI,-2.4535062313079834 sugarcane,MMR,-2.787962794303894 sugarcane,MOZ,-2.242034435272217 sugarcane,MRT,-2.556851625442505 -sugarcane,MUS,-2.893990159034729 +sugarcane,MUS,-2.8939902782440186 sugarcane,MWI,-2.379712224006653 -sugarcane,MYS,-3.6528178453445435 +sugarcane,MYS,-3.652817726135254 sugarcane,NAM,-2.474418520927429 sugarcane,NER,-7.149641036987305 sugarcane,NGA,-2.4635720252990723 -sugarcane,NIC,-1.7803146839141846 +sugarcane,NIC,-1.7050963044166565 sugarcane,NPL,-2.345479369163513 -sugarcane,OMN,-2.621695637702942 +sugarcane,OMN,-2.6956467628479004 sugarcane,PAK,-2.9893369674682617 sugarcane,PAN,-2.7891409397125244 -sugarcane,PER,-2.440039277076721 +sugarcane,PER,-2.403666138648987 sugarcane,PHL,-2.7199974060058594 sugarcane,PNG,-2.514993667602539 sugarcane,PRY,-2.0175960063934326 @@ -4944,7 +4944,7 @@ sugarcane,RWA,-4.039017677307129 sugarcane,SDN,-2.3614293336868286 sugarcane,SEN,-2.057752847671509 sugarcane,SLE,-4.981103301048279 -sugarcane,SLV,-2.9942466020584106 +sugarcane,SLV,-3.0492128133773804 sugarcane,SOM,-2.4060962200164795 sugarcane,SSD,-2.1481828689575195 sugarcane,SUR,-2.7716208696365356 @@ -4954,396 +4954,396 @@ sugarcane,TGO,2.5518370866775513 sugarcane,THA,-1.7326825261116028 sugarcane,TKM,7.5 sugarcane,TLS,-3.5254807472229004 -sugarcane,TWN,-2.515976309776306 -sugarcane,TZA,-2.220639705657959 -sugarcane,UGA,-3.9726401567459106 +sugarcane,TWN,-2.5159761905670166 +sugarcane,TZA,-2.2224148511886597 +sugarcane,UGA,-4.07844078540802 sugarcane,URY,-2.985336422920227 -sugarcane,USA,-3.3077828884124756 -sugarcane,VEN,-2.620065927505493 +sugarcane,USA,-3.3077826499938965 +sugarcane,VEN,-2.620065689086914 sugarcane,VNM,-2.607033133506775 -sugarcane,ZAF,-2.427682638168335 +sugarcane,ZAF,-2.2507143020629883 sugarcane,ZMB,-2.4530463218688965 sugarcane,ZWE,-7.5 -sunflower,AFG,-0.33802174031734467 -sunflower,AGO,-0.555449366569519 -sunflower,ALB,-1.2673794031143188 -sunflower,ARG,-0.15546204894781113 -sunflower,ARM,0.32200489938259125 +sunflower,AFG,-0.3361276090145111 +sunflower,AGO,-0.5543612241744995 +sunflower,ALB,-1.2633401155471802 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-sunflower,CHL,-0.4039814621210098 -sunflower,CHN,-1.2157331705093384 -sunflower,COD,-0.3423815369606018 -sunflower,COG,-0.5994851589202881 +sunflower,CAN,0.022066850680857897 +sunflower,CHE,-0.9567216634750366 +sunflower,CHL,-0.40237857401371 +sunflower,CHN,-1.2016000747680664 +sunflower,COD,-0.34045282006263733 +sunflower,COG,-0.5987298488616943 sunflower,COL,7.5 -sunflower,CZE,0.060032472014427185 -sunflower,DEU,0.07882095873355865 +sunflower,CZE,0.06487629562616348 +sunflower,DEU,0.08286943286657333 sunflower,DNK,-0.0 -sunflower,DZA,0.1976080685853958 -sunflower,ECU,-0.29396333545446396 -sunflower,EGY,-0.13049006462097168 +sunflower,DZA,0.20220068097114563 +sunflower,ECU,-0.2918355390429497 +sunflower,EGY,-0.12503710389137268 sunflower,ERI,-0.0 -sunflower,ESP,-0.07941759005188942 -sunflower,EST,-0.1390195293352008 -sunflower,ETH,-0.1748467981815338 +sunflower,ESP,-0.07714540138840675 +sunflower,EST,-0.1359615083783865 +sunflower,ETH,-0.17331622540950775 sunflower,FIN,-0.0 -sunflower,FRA,-0.10552354902029037 -sunflower,GEO,-0.10254861786961555 -sunflower,GRC,-0.36639304459095 +sunflower,FRA,-0.10094765573740005 +sunflower,GEO,-0.10139216855168343 +sunflower,GRC,-0.36358480155467987 sunflower,GUF,-0.0 sunflower,GUY,-0.0 -sunflower,HRV,-3.9556023329496384 -sunflower,HUN,0.44667816162109375 -sunflower,IND,-0.39436565339565277 -sunflower,IRN,-0.670644611120224 -sunflower,IRQ,0.10907627269625664 -sunflower,ISR,-3.4178109169006348 -sunflower,ITA,-0.0046901763416826725 +sunflower,HRV,-3.952893912792206 +sunflower,HUN,0.4539814665913582 +sunflower,IND,-0.40110088884830475 +sunflower,IRN,-0.6688131988048553 +sunflower,IRQ,0.10329950228333473 +sunflower,ISR,-3.412010669708252 +sunflower,ITA,-0.0027071447111666203 sunflower,JOR,7.5 -sunflower,KAZ,0.07249315455555916 -sunflower,KEN,-0.03714205231517553 -sunflower,KGZ,-0.3473319262266159 -sunflower,KHM,3.514693096280098 -sunflower,LAO,3.5314412713050842 +sunflower,KAZ,0.07460348680615425 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+sweet-potato,ARG,-3.6343629360198975 +sweet-potato,ARM,-2.780849575996399 +sweet-potato,ATG,-4.392462730407715 sweet-potato,AUS,7.5 -sweet-potato,AUT,-2.7696714401245117 -sweet-potato,AZE,-3.205769181251526 -sweet-potato,BDI,-1.154886245727539 -sweet-potato,BEL,-1.8922550082206726 -sweet-potato,BEN,-1.6711445450782776 -sweet-potato,BFA,-3.577608346939087 -sweet-potato,BGD,-0.49009793996810913 -sweet-potato,BGR,-2.9617868661880493 -sweet-potato,BHS,-3.834680438041687 -sweet-potato,BIH,2.076244831085205 -sweet-potato,BLR,-5.288215160369873 -sweet-potato,BLZ,-3.588991403579712 -sweet-potato,BOL,-0.7833565473556519 -sweet-potato,BRA,-2.6574018001556396 +sweet-potato,AUT,-2.759915590286255 +sweet-potato,AZE,-3.200307011604309 +sweet-potato,BDI,-1.1513694524765015 +sweet-potato,BEL,-1.875126302242279 +sweet-potato,BEN,-1.6688194870948792 +sweet-potato,BFA,-3.5748777389526367 +sweet-potato,BGD,-0.4669499695301056 +sweet-potato,BGR,-2.9531651735305786 +sweet-potato,BHS,-3.8333626985549927 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sweet-potato,CPV,-7.5 -sweet-potato,CRI,3.1104167103767395 -sweet-potato,CUB,-3.439860701560974 -sweet-potato,CYP,-5.020488262176514 -sweet-potato,CZE,-2.366992712020874 -sweet-potato,DEU,-1.8211749792099 -sweet-potato,DNK,-2.005644142627716 -sweet-potato,DOM,-2.865404725074768 -sweet-potato,DZA,-3.411538243293762 -sweet-potato,ECU,-0.09098181501030922 -sweet-potato,EGY,-3.0873857736587524 -sweet-potato,ERI,1.7972681522369385 +sweet-potato,CRI,3.1132550835609436 +sweet-potato,CUB,-3.434414863586426 +sweet-potato,CYP,-5.016991138458252 +sweet-potato,CZE,-2.3541070222854614 +sweet-potato,DEU,-1.8033985495567322 +sweet-potato,DNK,-1.9892099499702454 +sweet-potato,DOM,-2.8649075031280518 +sweet-potato,DZA,-3.4050660133361816 +sweet-potato,ECU,-0.08588851429522038 +sweet-potato,EGY,-3.071489691734314 +sweet-potato,ERI,1.7974598407745361 sweet-potato,ESP,-7.5 -sweet-potato,EST,-2.649698257446289 -sweet-potato,ETH,-3.2210209369659424 -sweet-potato,FIN,-2.8535757064819336 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tomato,MEX,-7.5 -tomato,MKD,-5.207885980606079 -tomato,MLI,-6.908870220184326 +tomato,MKD,-5.1101861000061035 +tomato,MLI,-6.858166217803955 tomato,MLT,-7.5 -tomato,MMR,-5.846468925476074 +tomato,MMR,-5.723777413368225 tomato,MNE,-7.5 tomato,MNG,-7.5 tomato,MOZ,-7.5 tomato,MUS,-7.5 -tomato,MWI,-4.894988059997559 +tomato,MWI,-5.644064664840698 tomato,MYS,0.0 tomato,NAM,-7.5 -tomato,NER,-2.0605620741844177 +tomato,NER,-1.9810211956501007 tomato,NGA,-7.5 tomato,NIC,-7.5 tomato,NLD,0.0 tomato,NOR,7.5 tomato,NPL,-7.5 -tomato,NZL,-5.071157693862915 -tomato,OMN,-7.5 -tomato,PAK,-1.3330402374267578 +tomato,NZL,-5.144897103309631 +tomato,OMN,-7.491223096847534 +tomato,PAK,-1.3094182014465332 tomato,PAN,-7.5 tomato,PER,7.5 -tomato,PHL,-2.9522377252578735 +tomato,PHL,-2.9095849990844727 tomato,PNG,-7.5 tomato,POL,-7.5 tomato,PRI,-7.5 tomato,PRT,-7.5 tomato,PRY,-7.5 tomato,PSE,-7.5 -tomato,ROU,-6.712526559829712 +tomato,ROU,-6.636677980422974 tomato,RUS,-7.5 -tomato,RWA,-3.0303962230682373 +tomato,RWA,-3.004433512687683 tomato,SAU,-7.5 tomato,SDN,-7.5 -tomato,SEN,-2.970670223236084 +tomato,SEN,-2.908738851547241 tomato,SLE,-7.5 -tomato,SLV,-7.5 +tomato,SLV,-7.391984701156616 tomato,SOM,-7.5 -tomato,SRB,-1.7163639962673187 +tomato,SRB,-1.6151087582111359 tomato,SSD,-7.5 tomato,SUR,-7.5 tomato,SVK,-7.5 @@ -5459,16 +5459,16 @@ tomato,SWE,-7.5 tomato,SWZ,-7.5 tomato,SYR,-7.5 tomato,TCD,-7.5 -tomato,TGO,-0.1059923768043518 -tomato,THA,-4.043104529380798 +tomato,TGO,-0.07241648435592651 +tomato,THA,-3.9785505533218384 tomato,TJK,-7.5 tomato,TKM,-7.5 -tomato,TLS,-1.901757001876831 +tomato,TLS,-1.875635027885437 tomato,TTO,-7.5 -tomato,TUN,4.287184000015259 -tomato,TUR,-3.426007032394409 +tomato,TUN,4.449695587158203 +tomato,TUR,-3.164095640182495 tomato,TWN,-7.5 -tomato,TZA,-0.5132878720760345 +tomato,TZA,-0.5086344480514526 tomato,UGA,0.0 tomato,UKR,7.5 tomato,URY,-7.5 @@ -5476,575 +5476,575 @@ tomato,USA,-7.5 tomato,UZB,-7.5 tomato,VEN,-7.5 tomato,VNM,-7.5 -tomato,YEM,-5.586902856826782 +tomato,YEM,-5.525478363037109 tomato,ZAF,-7.5 -tomato,ZMB,-2.5164899826049805 -tomato,ZWE,-4.045441150665283 -watermelon,AFG,-2.236729621887207 +tomato,ZMB,-2.484849452972412 +tomato,ZWE,-3.9791858196258545 +watermelon,AFG,-2.204031467437744 watermelon,AGO,-0.0 -watermelon,ALB,2.384539842605591 -watermelon,ARG,4.206809043884277 -watermelon,ATG,-4.527795314788818 +watermelon,ALB,2.343723773956299 +watermelon,ARG,4.284459114074707 +watermelon,ATG,-4.510143518447876 watermelon,AUS,-7.5 -watermelon,AZE,5.3073410987854 +watermelon,AZE,5.362346172332764 watermelon,BDI,-0.0 watermelon,BEN,-0.0 watermelon,BFA,-0.0 watermelon,BGD,-0.0 watermelon,BGR,-0.0 watermelon,BHS,-0.0 -watermelon,BIH,-2.6389297246932983 -watermelon,BLZ,-2.3515089862048626 -watermelon,BOL,0.7415351271629333 -watermelon,BRA,3.3008424043655396 +watermelon,BIH,-2.616724669933319 +watermelon,BLZ,-2.3029398918151855 +watermelon,BOL,0.7738784551620483 +watermelon,BRA,3.3622846603393555 watermelon,BRB,-0.0 watermelon,BTN,-0.0 -watermelon,BWA,-0.615912415087223 +watermelon,BWA,-0.5714491195976734 watermelon,CAF,-0.0 -watermelon,CHL,-0.8589187860488892 -watermelon,CHN,-7.44974160194397 +watermelon,CHL,-0.7887177467346191 +watermelon,CHN,-7.3791022300720215 watermelon,CIV,-0.0 -watermelon,CMR,-4.982077598571777 +watermelon,CMR,-4.96832799911499 watermelon,COD,-0.0 watermelon,COG,-0.0 -watermelon,COL,2.2098357677459717 +watermelon,COL,2.2673182487487793 watermelon,COM,-0.0 watermelon,CPV,-7.5 -watermelon,CRI,0.2794278636574745 -watermelon,CUB,-2.3378288745880127 -watermelon,CYP,-4.076464414596558 +watermelon,CRI,0.37614670395851135 +watermelon,CUB,-2.3009045124053955 +watermelon,CYP,-4.040016174316406 watermelon,DOM,-7.5 -watermelon,DZA,-0.6453738212585449 -watermelon,ECU,-2.0786582827568054 -watermelon,EGY,-1.561140537261963 +watermelon,DZA,-0.5642794631421566 +watermelon,ECU,-2.0417306423187256 +watermelon,EGY,-1.4853214621543884 watermelon,ERI,-0.0 watermelon,ESP,-7.5 watermelon,ETH,-0.0 watermelon,FJI,-7.5 -watermelon,FRA,0.37828168272972107 +watermelon,FRA,0.43030107021331787 watermelon,GAB,-0.0 -watermelon,GEO,-1.0497099459171295 +watermelon,GEO,-1.0352270007133484 watermelon,GHA,-0.0 -watermelon,GIN,-5.200958728790283 +watermelon,GIN,-5.188660383224487 watermelon,GMB,-0.0 watermelon,GNB,-0.0 -watermelon,GRC,-0.9907316565513611 -watermelon,GTM,0.05109880864620209 +watermelon,GRC,-0.9158758521080017 +watermelon,GTM,0.10763424634933472 watermelon,GUF,-0.0 watermelon,GUY,-7.5 watermelon,HND,-7.5 -watermelon,HRV,-2.994636058807373 +watermelon,HRV,-2.9731006622314453 watermelon,HTI,-0.0 -watermelon,IDN,1.7590585350990295 -watermelon,IND,-1.798515796661377 -watermelon,IRN,4.41841983795166 +watermelon,IDN,1.8012058734893799 +watermelon,IND,-1.762557864189148 +watermelon,IRN,4.468055248260498 watermelon,IRQ,-0.0 -watermelon,ISR,-0.8636201024055481 -watermelon,ITA,2.4583151638507843 +watermelon,ISR,-0.7847718000411987 +watermelon,ITA,2.5260443091392517 watermelon,JAM,-7.5 -watermelon,JOR,-4.532098293304443 +watermelon,JOR,-4.611759424209595 watermelon,JPN,-7.5 -watermelon,KEN,3.1792982816696167 -watermelon,KGZ,-4.042240619659424 +watermelon,KEN,3.2484041452407837 +watermelon,KGZ,-4.021951675415039 watermelon,KHM,-0.0 -watermelon,LAO,-2.5215246081352234 +watermelon,LAO,-2.4907222390174866 watermelon,LBN,-7.5 watermelon,LBR,-0.0 -watermelon,LBY,0.7576630115509033 +watermelon,LBY,0.8140200972557068 watermelon,LKA,-0.0 watermelon,LSO,-0.0 -watermelon,MAR,-1.6146932244300842 +watermelon,MAR,-1.5707295536994934 watermelon,MDG,-0.0 -watermelon,MEX,0.015563325956463814 -watermelon,MKD,4.3390793800354 -watermelon,MLI,2.9262337684631348 +watermelon,MEX,0.05470643937587738 +watermelon,MKD,4.391755104064941 +watermelon,MLI,2.9970343112945557 watermelon,MLT,-7.5 watermelon,MMR,-0.0 -watermelon,MNE,-0.27781975269317627 +watermelon,MNE,-0.23039132356643677 watermelon,MOZ,-0.0 -watermelon,MRT,-4.701624870300293 +watermelon,MRT,-4.686065673828125 watermelon,MUS,-0.0 -watermelon,MWI,-0.10165491700172424 -watermelon,MYS,2.9730932414531708 -watermelon,NAM,-2.3994698524475098 +watermelon,MWI,-0.8761657178401947 +watermelon,MYS,3.026997834444046 +watermelon,NAM,-2.366490125656128 watermelon,NER,-7.5 watermelon,NGA,-0.0 watermelon,NIC,-0.0 watermelon,NPL,-0.0 watermelon,OMN,7.5 -watermelon,PAK,1.0031843185424805 -watermelon,PER,7.31973123550415 -watermelon,PHL,0.35256873071193695 +watermelon,PAK,1.0457053184509277 +watermelon,PER,7.291035175323486 +watermelon,PHL,0.38703905045986176 watermelon,PNG,-0.0 -watermelon,PRI,-4.750716209411621 -watermelon,PRT,-5.802447080612183 -watermelon,PRY,2.6748673915863037 +watermelon,PRI,-4.696043252944946 +watermelon,PRT,-5.764101028442383 +watermelon,PRY,2.7316455841064453 watermelon,PSE,-7.5 watermelon,RUS,-0.0 watermelon,RWA,-0.0 -watermelon,SAU,-1.1366316080093384 -watermelon,SDN,-2.684457778930664 -watermelon,SEN,2.8443455696105957 +watermelon,SAU,-1.0339206457138062 +watermelon,SDN,-2.6544870138168335 +watermelon,SEN,2.864959239959717 watermelon,SLE,-0.0 -watermelon,SLV,4.158967554569244 -watermelon,SOM,-2.646620512008667 +watermelon,SLV,4.2466477155685425 +watermelon,SOM,-2.616337537765503 watermelon,SSD,-0.0 -watermelon,SUR,-6.3321332931518555 -watermelon,SVN,-2.0189093351364136 +watermelon,SUR,-6.294924974441528 +watermelon,SVN,-1.9838616251945496 watermelon,SWZ,-0.0 -watermelon,SYR,0.6365787982940674 +watermelon,SYR,0.692438006401062 watermelon,TCD,-0.0 watermelon,TGO,-0.0 -watermelon,THA,-5.432672739028931 -watermelon,TJK,-3.1726510524749756 -watermelon,TKM,0.8908295631408691 +watermelon,THA,-5.401907205581665 +watermelon,TJK,-3.156886339187622 +watermelon,TKM,0.9455832242965698 watermelon,TLS,-0.0 -watermelon,TTO,-0.32425180822610855 -watermelon,TUN,-5.477811813354492 -watermelon,TUR,1.4131667613983154 -watermelon,TZA,-5.03788685798645 +watermelon,TTO,-0.30343370139598846 +watermelon,TUN,-5.537434339523315 +watermelon,TUR,1.469543695449829 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-wetland-rice,BFA,-1.3988558053970337 -wetland-rice,BGD,-0.3768765926361084 -wetland-rice,BGR,-1.5536565780639648 -wetland-rice,BLZ,-1.6328113675117493 -wetland-rice,BOL,-0.5732261538505554 -wetland-rice,BRA,-0.9615084528923035 -wetland-rice,BRN,-1.4507603645324707 -wetland-rice,BTN,-1.541964828968048 +wetland-rice,AZE,-2.686246156692505 +wetland-rice,BDI,-0.40030887722969055 +wetland-rice,BEN,-1.2800905108451843 +wetland-rice,BFA,-1.3953356742858887 +wetland-rice,BGD,-0.3048108071088791 +wetland-rice,BGR,-1.5456146001815796 +wetland-rice,BLZ,-1.6258651614189148 +wetland-rice,BOL,-0.5693243145942688 +wetland-rice,BRA,-0.9526200592517853 +wetland-rice,BRN,-1.4476772546768188 +wetland-rice,BTN,-1.3104565143585205 wetland-rice,BWA,-0.0 -wetland-rice,CAF,-1.5079582929611206 +wetland-rice,CAF,-1.5059316158294678 wetland-rice,CAN,-0.0 wetland-rice,CHE,-0.0 -wetland-rice,CHL,-0.8539003431797028 -wetland-rice,CHN,-1.8526922464370728 -wetland-rice,CIV,-0.24676543474197388 -wetland-rice,CMR,-1.4124432802200317 -wetland-rice,COD,-1.6159614324569702 -wetland-rice,COG,-1.6048216819763184 -wetland-rice,COL,-1.3683701157569885 -wetland-rice,COM,-1.6488174796104431 -wetland-rice,CRI,-1.3397555351257324 -wetland-rice,CUB,-1.2474965453147888 -wetland-rice,DOM,-1.5725502967834473 -wetland-rice,DZA,-1.477386236190796 -wetland-rice,ECU,3.522797644138336 -wetland-rice,EGY,-1.6011502146720886 -wetland-rice,ERI,3.1218422055244446 -wetland-rice,ESP,-1.9275996685028076 -wetland-rice,ETH,-0.8258471786975861 -wetland-rice,FJI,-0.8098074197769165 -wetland-rice,FRA,-1.0175399780273438 -wetland-rice,GAB,-1.4850825071334839 -wetland-rice,GEO,-1.1893012523651123 -wetland-rice,GHA,-0.516352042555809 -wetland-rice,GIN,-0.6447137892246246 -wetland-rice,GMB,-0.38659101724624634 -wetland-rice,GNB,-1.456748902797699 +wetland-rice,CHL,-0.8420618176460266 +wetland-rice,CHN,-1.840096354484558 +wetland-rice,CIV,-0.24026069045066833 +wetland-rice,CMR,-1.4089547395706177 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-white-potato,BTN,-3.937493920326233 +white-potato,BTN,-3.928954601287842 white-potato,BWA,-7.5 white-potato,CAF,-7.5 -white-potato,CAN,-6.0769712924957275 +white-potato,CAN,-6.040417671203613 white-potato,CHE,-7.5 -white-potato,CHL,-7.020837068557739 -white-potato,CHN,-4.184109210968018 -white-potato,CIV,-7.1635026931762695 -white-potato,CMR,-6.722692012786865 -white-potato,COD,-6.997422695159912 -white-potato,COG,-6.7811572551727295 -white-potato,COL,-4.740231275558472 -white-potato,COM,-7.287262439727783 +white-potato,CHL,-7.001836061477661 +white-potato,CHN,-4.160447597503662 +white-potato,CIV,-7.162545204162598 +white-potato,CMR,-6.715784072875977 +white-potato,COD,-6.994198560714722 +white-potato,COG,-6.775019407272339 +white-potato,COL,-4.722493410110474 +white-potato,COM,-7.286263942718506 white-potato,CPV,-7.5 white-potato,CRI,-7.5 -white-potato,CUB,-6.0313544273376465 -white-potato,CYP,-6.9254724979400635 -white-potato,CZE,-4.058832883834839 -white-potato,DEU,-5.119875431060791 +white-potato,CUB,-6.016811370849609 +white-potato,CYP,-6.902883529663086 +white-potato,CZE,-4.020892381668091 +white-potato,DEU,-5.066328525543213 white-potato,DNK,-7.5 white-potato,DOM,-7.5 white-potato,DZA,-7.5 -white-potato,ECU,-3.0222052335739136 -white-potato,EGY,-4.9807212352752686 -white-potato,ERI,-7.193163633346558 +white-potato,ECU,-3.0087833404541016 +white-potato,EGY,-4.958549976348877 +white-potato,ERI,-7.1926209926605225 white-potato,ESP,-7.5 -white-potato,EST,-2.5839550495147705 -white-potato,ETH,-0.9443575143814087 -white-potato,FIN,-3.6873782873153687 +white-potato,EST,-2.5508134365081787 +white-potato,ETH,-0.9296299815177917 +white-potato,FIN,-3.659327507019043 white-potato,FRA,-7.5 -white-potato,GAB,-7.146055459976196 -white-potato,GBR,-7.0010085105896 -white-potato,GEO,-1.8045406341552734 -white-potato,GHA,-7.156730651855469 -white-potato,GIN,-6.865027666091919 +white-potato,GAB,-7.144915580749512 +white-potato,GBR,-6.965727806091309 +white-potato,GEO,-1.7928955554962158 +white-potato,GHA,-7.155675888061523 +white-potato,GIN,-6.857684850692749 white-potato,GMB,0.0 -white-potato,GNB,-7.038236379623413 -white-potato,GNQ,-7.045498847961426 +white-potato,GNB,-7.035315036773682 +white-potato,GNQ,-7.042709112167358 white-potato,GRC,-7.5 -white-potato,GRD,-7.0049567222595215 -white-potato,GTM,-5.268960475921631 +white-potato,GRD,-7.003247261047363 +white-potato,GTM,-5.245393514633179 white-potato,GUF,-7.5 white-potato,GUY,3.75 white-potato,HND,-7.5 -white-potato,HRV,-5.259632110595703 -white-potato,HTI,-6.9830217361450195 -white-potato,HUN,-5.461965799331665 +white-potato,HRV,-5.2488086223602295 +white-potato,HTI,-6.979995250701904 +white-potato,HUN,-5.42437744140625 white-potato,IDN,-7.5 -white-potato,IND,-5.737733602523804 -white-potato,IRL,-4.733020544052124 -white-potato,IRN,-3.812199592590332 +white-potato,IND,-5.712126731872559 +white-potato,IRL,-4.687557220458984 +white-potato,IRN,-3.779602885246277 white-potato,IRQ,-7.5 white-potato,ISR,-7.5 white-potato,ITA,-7.5 white-potato,JAM,-7.5 white-potato,JOR,-7.5 white-potato,JPN,-7.5 -white-potato,KAZ,-2.5382083654403687 -white-potato,KEN,-1.9925309419631958 -white-potato,KGZ,-3.1703871488571167 -white-potato,KHM,-6.849397897720337 -white-potato,KOR,-6.124982833862305 -white-potato,LAO,0.7065777778625488 +white-potato,KAZ,-2.517109513282776 +white-potato,KEN,-1.985495924949646 +white-potato,KGZ,-3.1584112644195557 +white-potato,KHM,-6.842567682266235 +white-potato,KOR,-6.119489431381226 +white-potato,LAO,0.7164418697357178 white-potato,LBN,-7.5 -white-potato,LBR,-6.492815017700195 -white-potato,LBY,-6.399089813232422 +white-potato,LBR,-6.481634616851807 +white-potato,LBY,-6.380927562713623 white-potato,LKA,-7.5 -white-potato,LSO,-5.208927869796753 -white-potato,LTU,-2.358489513397217 -white-potato,LUX,-6.519000768661499 -white-potato,LVA,-1.4858868718147278 -white-potato,MAR,-4.797761917114258 -white-potato,MDA,-0.5823559463024139 -white-potato,MDG,-6.836494445800781 +white-potato,LSO,-5.184953451156616 +white-potato,LTU,-2.3380517959594727 +white-potato,LUX,-6.49144434928894 +white-potato,LVA,-1.460618793964386 +white-potato,MAR,-4.781239986419678 +white-potato,MDA,-0.5706814825534821 +white-potato,MDG,-6.831455945968628 white-potato,MEX,-7.5 -white-potato,MKD,-3.3781875371932983 +white-potato,MKD,-3.3599528074264526 white-potato,MLI,-7.5 white-potato,MLT,-7.5 -white-potato,MMR,-6.371878385543823 -white-potato,MNE,-6.062822580337524 -white-potato,MNG,-2.75472354888916 +white-potato,MMR,-6.357487440109253 +white-potato,MNE,-6.043331623077393 +white-potato,MNG,-2.7427220344543457 white-potato,MOZ,-7.5 white-potato,MRT,-7.5 white-potato,MUS,-7.5 -white-potato,MWI,1.1859216690063477 -white-potato,MYS,0.7290282249450684 -white-potato,NAM,-6.320168972015381 +white-potato,MWI,1.197267770767212 +white-potato,MYS,0.7389030456542969 +white-potato,NAM,-6.309365272521973 white-potato,NER,-7.5 -white-potato,NGA,-7.082876205444336 +white-potato,NGA,-7.0807976722717285 white-potato,NIC,-7.5 -white-potato,NLD,-5.387480974197388 +white-potato,NLD,-5.357752799987793 white-potato,NOR,-7.5 -white-potato,NPL,-2.957761764526367 -white-potato,NZL,-4.951113224029541 +white-potato,NPL,-2.9443137645721436 +white-potato,NZL,-4.913975238800049 white-potato,OMN,-7.5 -white-potato,PAK,-2.503129482269287 +white-potato,PAK,-2.4748913049697876 white-potato,PAN,-7.5 -white-potato,PER,-3.7263704538345337 +white-potato,PER,-3.780031442642212 white-potato,PHL,-7.5 white-potato,PNG,-7.5 -white-potato,POL,-1.3211389780044556 +white-potato,POL,-1.2876835465431213 white-potato,PRI,-7.5 -white-potato,PRT,-4.874550104141235 -white-potato,PRY,-3.6027733087539673 +white-potato,PRT,-4.8467700481414795 +white-potato,PRY,-3.5955880880355835 white-potato,PSE,-7.5 -white-potato,ROU,-4.425055742263794 -white-potato,RUS,-2.248544216156006 -white-potato,RWA,-1.8876572251319885 +white-potato,ROU,-4.408791542053223 +white-potato,RUS,-2.2352378368377686 +white-potato,RWA,-1.8827179074287415 white-potato,SAU,-7.5 -white-potato,SDN,-6.3385021686553955 +white-potato,SDN,-6.325501203536987 white-potato,SEN,-7.5 -white-potato,SLB,-6.314012050628662 -white-potato,SLE,0.3943898677825928 +white-potato,SLB,-6.2992260456085205 +white-potato,SLE,0.39832067489624023 white-potato,SLV,0.0 -white-potato,SOM,-6.556298732757568 -white-potato,SRB,-2.987592935562134 +white-potato,SOM,-6.5482258796691895 +white-potato,SRB,-2.945641279220581 white-potato,SSD,-7.5 white-potato,SUR,-7.5 -white-potato,SVK,-5.130753993988037 -white-potato,SVN,-5.908241033554077 +white-potato,SVK,-5.096436262130737 +white-potato,SVN,-5.876758575439453 white-potato,SWE,-7.5 -white-potato,SWZ,-7.126000642776489 -white-potato,SYR,-5.971303939819336 -white-potato,TCD,-6.944597005844116 -white-potato,TGO,-7.07819676399231 -white-potato,THA,-4.560039043426514 -white-potato,TJK,-3.1162619590759277 -white-potato,TKM,-6.4295172691345215 -white-potato,TLS,-0.6145033240318298 -white-potato,TTO,-7.046309471130371 -white-potato,TUN,-2.9065123796463013 -white-potato,TUR,-5.4903669357299805 -white-potato,TWN,-5.729758024215698 -white-potato,TZA,-0.7964752912521362 -white-potato,UGA,-6.985170841217041 -white-potato,UKR,-1.6022288799285889 +white-potato,SWZ,-7.124634504318237 +white-potato,SYR,-5.952817440032959 +white-potato,TCD,-6.941866397857666 +white-potato,TGO,-7.076017379760742 +white-potato,THA,-4.541188716888428 +white-potato,TJK,-3.1071523427963257 +white-potato,TKM,-6.417346954345703 +white-potato,TLS,-0.61323681473732 +white-potato,TTO,-7.042969703674316 +white-potato,TUN,-2.8935259580612183 +white-potato,TUR,-5.438063621520996 +white-potato,TWN,-5.704899787902832 +white-potato,TZA,-0.7902447581291199 +white-potato,UGA,-6.981731653213501 +white-potato,UKR,-1.5888681411743164 white-potato,URY,-7.5 -white-potato,USA,-4.937712669372559 -white-potato,UZB,-5.057949542999268 +white-potato,USA,-4.879367351531982 +white-potato,UZB,-5.039907455444336 white-potato,VEN,-7.5 -white-potato,VNM,-6.541966438293457 +white-potato,VNM,-6.528693675994873 white-potato,YEM,-7.5 -white-potato,ZAF,-6.664819955825806 +white-potato,ZAF,-6.664993762969971 white-potato,ZMB,-7.5 white-potato,ZWE,-7.5 yam,AFG,-0.0 @@ -6055,13 +6055,13 @@ yam,BDI,-4.517582178115845 yam,BEN,-1.3836317658424377 yam,BFA,-2.303346633911133 yam,BIH,3.75 -yam,BLZ,-1.8298237919807434 +yam,BLZ,-1.8104787468910217 yam,BOL,-5.207590579986572 yam,BRA,-2.857956290245056 yam,BRB,-7.5 yam,BRN,-5.238788366317749 -yam,BTN,-2.0726643800735474 -yam,BWA,-3.601670265197754 +yam,BTN,-3.52375590801239 +yam,BWA,-3.602139949798584 yam,CAF,-4.066412687301636 yam,CHN,-0.7409265637397766 yam,CIV,-0.7242934703826904 @@ -6106,7 +6106,7 @@ yam,LBR,-3.7778022289276123 yam,LKA,-3.0773404836654663 yam,LSO,-0.0 yam,MDG,-4.15418004989624 -yam,MEX,2.997802972793579 +yam,MEX,2.9787617921829224 yam,MLI,-5.247462272644043 yam,MMR,-0.903235524892807 yam,MNE,-0.0 @@ -6122,7 +6122,7 @@ yam,NIC,2.760577142238617 yam,NPL,-5.062485456466675 yam,PAK,-0.0 yam,PAN,-3.2238476276397705 -yam,PER,-3.545705556869507 +yam,PER,-3.549304485321045 yam,PHL,-1.6432258486747742 yam,PNG,-3.0153849124908447 yam,PRI,-7.5 @@ -6151,7 +6151,7 @@ yam,TZA,-4.683187007904053 yam,UGA,-4.008604407310486 yam,URY,-0.0 yam,USA,7.5 -yam,VEN,-2.4179983139038086 +yam,VEN,-2.419607996940613 yam,VNM,-2.4278478622436523 yam,VUT,-3.4420140981674194 yam,ZAF,-4.377967357635498 diff --git a/data/curated/calibration/default/deviation_penalty.yaml b/data/curated/calibration/default/deviation_penalty.yaml index 348bdadb..26baf358 100644 --- a/data/curated/calibration/default/deviation_penalty.yaml +++ b/data/curated/calibration/default/deviation_penalty.yaml @@ -14,9 +14,9 @@ components: - grassland - feed l1_costs: - cropland: 1.3806531957603645 - grassland: 0.23273990727700028 - feed: 0.06260083969106763 + cropland: 6.5416093438351055 + grassland: 0.3386281276841899 + feed: 0.49772318007627314 iterations: 4 converged: true -final_residual_log_inf: 0.010466412346931233 +final_residual_log_inf: 0.01568176405589944 diff --git a/data/curated/calibration/default/fingerprint.yaml b/data/curated/calibration/default/fingerprint.yaml index 6e874e94..c1ad8e50 100644 --- a/data/curated/calibration/default/fingerprint.yaml +++ b/data/curated/calibration/default/fingerprint.yaml @@ -5,18 +5,17 @@ steps: cost: artefacts: - data/curated/calibration/default/animal_cost.csv: f8cc89b61307dd2fd5971ea6622a85b83ed13fe0e5e4c1e1826cec92d83de5c6 - data/curated/calibration/default/crop_cost.csv: 9f14731bad3c0350b8ff41e6a19661c99ba26a681b1e1e09fd76aaf4956fec38 - data/curated/calibration/default/grassland_cost.csv: dc3e9d6480874206b2b72dee47acfd6f7f081992c6ec0be70670b4daa7bb3d4f - data/curated/calibration/default/multi_crop_cost.csv: 9c5d32c2ce8d29c29b16f3d1e509d342fc6cd0a01f301f3e3bea81001c963756 + data/curated/calibration/default/animal_cost.csv: cc7c1c07cc12e2464f99a691f2cbae28ac7233b79bc9e8bac924925acfe905fc + data/curated/calibration/default/crop_cost.csv: 72c9c7b10f53ee5ae1abe131c750b27c094d2fbb1d8a869b5aceb1da0f679795 + data/curated/calibration/default/grassland_cost.csv: fe822326a6ca8c5325423cc6c5c8442d759ad15ddbe24fa27defc16afa38732b + data/curated/calibration/default/multi_crop_cost.csv: befe3ee28a0fb32c43095d3b0e71f11e3cb98af49c0cdf4ad6e5f71584a69335 code: workflow/rules/animals.smk: fa07f649a53263c151e1799f63b40c66fba42ee7858a97d51ff988c406ba0024 - workflow/rules/consumer_values.smk: 4f3fb2650b690a41b45443b322745b698910abae3cb013f70e0eea8e54ba6290 workflow/rules/crops.smk: 8d7d87b61959819ae9a753126767a71a10d7463af7963bffd423fa4560f470d1 workflow/rules/diet.smk: 448908aecc357fd119c1e5cd43acdec2075f2d5a4d9ebbefefa1299c1afb15f4 workflow/rules/geography.smk: 54b113f5647787bacea8d2a2f45298a24c605a84ced48630abece8b2dc887943 workflow/rules/luc.smk: e64fb669c0d6c40299d47c995dbb9f04a71f4d99d8edcd1fe9ce3ed700639257 - workflow/rules/model.smk: 00791268796d8467b88ad755fb1bedf8babe2a12f2e0a78b657b01279387f942 + workflow/rules/model.smk: d2d2e71ecce83dad9728184b7a7903c5f7ab6ba8030126307818d05bff1ae71c workflow/rules/retrieve.smk: d440ebccd4e6f2d6aa5e86066e865ec5c54dbca2a4b5149228a2ef20ad776748 workflow/rules/water.smk: 9fe3fc69051df02bab7dde53d2057b2b64142c7212741f8a45bc0171447c951d workflow/scripts/aggregate_class_areas.py: 93e9a52eec4dac86eeff03fa7e574ad4bdd91bb4049f715a321850f327aba92e @@ -47,7 +46,6 @@ steps: workflow/scripts/build_regions.py: 08a1f3c0247bdbe4600f2b1596396a352ff6c459496566e7e24da5b2f83558a7 workflow/scripts/build_yield_calibration.py: 1225a0d55add24ce44acc86cde5dd3335ed9d0a233ba6025bfc8c0e5afc8375d workflow/scripts/calculate_manure_emissions.py: 4f39f5370dcf931d4648bf7c0727a807ec7bc8fbb80561dae4ff5afa08834591 - workflow/scripts/calibrate_food_utility_blocks.py: e20676cd1b44c4504df3c9856131fa234088b1fd77c9f242144483f6f58ad330 workflow/scripts/categorize_feeds.py: 575b37f430e4fbf54b8477be96f5069ce6522fa1882c2f024b9b9a25a8540140 workflow/scripts/compose_water_supply.py: c1fc61844079219f2381664b44b6a917f51ecbc07bb08e1318702985deaf9231 workflow/scripts/compute_gleam3_feed_fractions.py: 83cc4268fe7f1762932ac63fc7f05ecedda9b03bcc1b502a69d85ec6779e6289 @@ -61,7 +59,6 @@ steps: workflow/scripts/download_mirca_os_bag_member.py: e869f4ec2f87a03b65d7c6f875d6ad393dcce647789ba7fc0f1c2d2fa2e48049 workflow/scripts/download_soilgrids_ocs.py: bcb361c0ada72cd6264685eb36b2efabc1a03bc25c93918c37da8da7fe049d58 workflow/scripts/estimate_baseline_diet.py: d9df39a6cc46b41da12059f5c38929a62e4b53301149488f89ce704cb5440336 - workflow/scripts/extract_consumer_values.py: eba7d4bd3d10632fc5fbcbc46f65acd5775f2bb74ca3aa3f5d6ff15113c3dd97 workflow/scripts/extract_cost_calibration.py: f7475beca73108a5f6ef63f1c284625e996abc9dadf9efc981c92e2f259d3f23 workflow/scripts/extract_mirca_os_archive.py: 687fe1d413ea063d585cc526ff53ce7d91c1df90e1d1ec898bfc62d19890647c workflow/scripts/merge_animal_costs.py: db83828da2fad888f0bae49e3a4d19ebc8315e27d2edcd0c6fde579b736de57c @@ -73,7 +70,7 @@ steps: workflow/scripts/prepare_faostat_animal_production.py: 38980d9bf9324ae01664c63104026aa4b3589f71f5bc592ae8f3bbe96cde9c2a workflow/scripts/prepare_faostat_crop_costs.py: be66f2aad20af7c88085b3c24e59ce2e26cfcee25b202fd96f3a47666d65ad1b workflow/scripts/prepare_faostat_crop_production.py: f446a0f22d865d6dfb7aaab643ea245932169769070b7a72ab16c98b09bf7435 - workflow/scripts/prepare_faostat_fbs_items.py: 87446f863e0e26e06b863cabcaafda362fb033f86b522b519922f86b5f80bb05 + workflow/scripts/prepare_faostat_fbs_items.py: b06a2e8c1188b488086c7ea804cf1d822c83bc5e410457137d4f027063b88429 workflow/scripts/prepare_faostat_food_group_supply.py: e1702c7c67e7295c70b4cd1fdcf871f3d02c5a90dcb2c0c5b0c6b891e8e81809 workflow/scripts/prepare_faostat_pasture_area.py: 3f0d6212cf0c54edaf6f47145e75334c532e6e7a2d243a4996d4f96bda5096fe workflow/scripts/prepare_faostat_yields.py: 178264f3eaa3e386a7ea6813fe607af7183663624cc0388a9df724562a5dd9ab @@ -92,16 +89,16 @@ steps: workflow/scripts/resample_land_cover.py: 3a953b4c2a0a4a226688e282af9a00c778a077bfbf3d8504829a637a6281a1ce workflow/scripts/resample_luicube_grassland.py: 8efad93db0c694e2b9152acc2373b356614d65c8170cf4effa08f6df9b4cc8fb workflow/scripts/retrieve_cpi_data.py: ee11749f80d1ab2dd0ade2909b40ef001f7bd24c6a56fa381a251662b51f54b8 - workflow/scripts/retrieve_eurostat_fodder.py: c90bbfbbb2c08b83144c84f2199da9ec01d17a8ecea99d2ead77a1baf6de22f9 + workflow/scripts/retrieve_eurostat_fodder.py: 888097d8d0220842558b842b40e04958df6a6fc136fd8f9b2042a913a4bb7069 workflow/scripts/retrieve_fadn_animal_costs.py: 7791580c7c04f99a474909e4e53bdaa7af0a23056c2036b36753c43076ac25f7 workflow/scripts/retrieve_hicp_data.py: 8375097c1fb9f18d096116dd133268e36a332373b56720790a4abb35a2b6290b - workflow/scripts/retrieve_ppp_rates.py: 13ff1ed85f2153d4a5a8b4b9031432c59b23e0a85eb4fc62bfd4806c32aee444 + workflow/scripts/retrieve_ppp_rates.py: dc90953fe87c3dc0990c386dac1171e64cdbd0ac2085db804d97277e4f280f88 workflow/scripts/retrieve_usda_animal_costs.py: 5849db38933f9323fb581e5b81a17223e3e5a12b5c8471727c4b9cef4c2211cc workflow/scripts/simplify_gadm.py: 9b206abc2f561840b5a964e9fc06a6f4ee6b71cb2673277d4e2c27ebf532d1af workflow/scripts/solve_model.py: 8b13b5a820ba69e4385dcc0b4f576744d9759485a642ca43282c7b65861fe5cb workflow/scripts/validate_baseline_diet.py: 6d54d506a2d8382321c849d77698098429019e0f089f969c43ee2848becc05f4 config_files: - config/calibration/cost.yaml: 1409ef4367b99829be254e641d82475d82d4cb1ca4469d781f46243b5c7123ae + config/calibration/cost.yaml: cab2a3387d34e633b423ec40b6ea9183d060439b94d577634091d9ca3058a1cc effective_config: {} inputs: data/bundled/doi_10_5061_dryad_2rbnzs7qh__v20250311/FUBC_1_to_9_data.csv: 3b5dc29b6e2f6844807f2983c1596c28a127f48a67f7deec7e36f6a49c0b17f3 @@ -165,7 +162,7 @@ steps: workflow/scripts/build_model/commodity_costs.py: 97a4c3aa148355adac88d7a45462158f55a7322a0c351babd0a243bd941eb0e2 workflow/scripts/build_model/crops.py: 4b8638d8902b0a688d0ab55df3e3625d0953f3b4ac63886ddae50b3bf47b4610 workflow/scripts/build_model/food.py: 9e53b6c847202a2192ea0e014746e62a9a5f88eedffc5a48833fc4186ef65ae9 - workflow/scripts/build_model/grassland.py: bf39536ad916a0c7a47d72fe2de4171fd256e483b81ac552d4403fe0123036da + workflow/scripts/build_model/grassland.py: dcbe90ec563dbe3c3ce9431085dd59e91dfb418a1fb30d5ebfe8b10c154f45b4 workflow/scripts/build_model/health.py: 5ddc8cbae98992f67140db787946a0f9fd9ae15ad683592b4f81b4bf6a2df702 workflow/scripts/build_model/infrastructure.py: b792d568d0f199699f5d53bc6f175138a899e41a71f86b5d72a037de1130785c workflow/scripts/build_model/irrigation.py: 3d1d6216069eb7854046266a68b202b1f4c981395c499f8d05ab04c22f5645c2 @@ -179,14 +176,13 @@ steps: workflow/scripts/population.py: b45821fb719171d516083bf3ff8a4186b4424b22dd8a24216d199bf5ff70a13d workflow/scripts/snakemake_utils.py: 52c6bb89c4efe655aa5f41d0cd850b0c2894c9dd72c644f9c0633c04a503989e workflow/scripts/solve_model/__init__.py: 23fb1fde8c5d964c8fda94020ff41ce9fdeefd04f6334a1ab3e7e755c93af04c - workflow/scripts/solve_model/core.py: 83307fcdffd50a1e6f83ad3fcbfcfff3abdf51ebfd91c02f8352502dfe7eba8b + workflow/scripts/solve_model/core.py: 769d02b66edcc0ff55b2ab154b7e2c527a5e7bb0eb4907d1f63bc7cdacec98d6 workflow/scripts/solve_model/diet_stability.py: 38186fe2e56474736d9da727cd6ebb3420dd24a432391cc5da477015be0b54f4 - workflow/scripts/solve_model/food_utility.py: c12ac00a859e76d7dffa77c62fc5cb58525d92f6b886373ab49c9790692ac6a4 workflow/scripts/solve_model/health.py: 4e8b34555c4a7aaf94b59f081c0ba5e89a3369eaaf7c8e918ef5e660eb9d37a5 workflow/scripts/solve_model/market_response.py: 11ba687519e9e697559e9000f4dad1583c794d3b31d067d570138735e4c38fb1 workflow/scripts/solve_model/production_stability.py: 4f238ab0f219335272a4146b118066124bf9d107dae5cc3dfe7ce89d5feabf3a workflow/scripts/solve_model/sensitivity.py: 3ba13e457cfe34ce5ecb04c28d19d873ea6a9d4cc3c2118c3a2c4384a5bbee2a - workflow/scripts/solve_namespace.py: e81dc3fbd294f42706bfdf0f08fc43d006c17376c51234e73c87bb5fc8fe3f02 + workflow/scripts/solve_namespace.py: 1f54bdc13daac2b0f239fa706cbb59b8707b45d55e23d0251e008b0d3bc2b421 workflow/scripts/water_periods.py: 25e9e7e9a6fe41717ca4c61017641f59e3a22c83598ec73716b7d070f9a9962d missing_inputs: [] feed: @@ -201,7 +197,7 @@ steps: workflow/rules/diet.smk: 448908aecc357fd119c1e5cd43acdec2075f2d5a4d9ebbefefa1299c1afb15f4 workflow/rules/geography.smk: 54b113f5647787bacea8d2a2f45298a24c605a84ced48630abece8b2dc887943 workflow/rules/luc.smk: e64fb669c0d6c40299d47c995dbb9f04a71f4d99d8edcd1fe9ce3ed700639257 - workflow/rules/model.smk: 00791268796d8467b88ad755fb1bedf8babe2a12f2e0a78b657b01279387f942 + workflow/rules/model.smk: d2d2e71ecce83dad9728184b7a7903c5f7ab6ba8030126307818d05bff1ae71c workflow/rules/retrieve.smk: d440ebccd4e6f2d6aa5e86066e865ec5c54dbca2a4b5149228a2ef20ad776748 workflow/rules/water.smk: 9fe3fc69051df02bab7dde53d2057b2b64142c7212741f8a45bc0171447c951d workflow/scripts/aggregate_class_areas.py: 93e9a52eec4dac86eeff03fa7e574ad4bdd91bb4049f715a321850f327aba92e @@ -257,7 +253,7 @@ steps: workflow/scripts/prepare_faostat_animal_production.py: 38980d9bf9324ae01664c63104026aa4b3589f71f5bc592ae8f3bbe96cde9c2a workflow/scripts/prepare_faostat_crop_costs.py: be66f2aad20af7c88085b3c24e59ce2e26cfcee25b202fd96f3a47666d65ad1b workflow/scripts/prepare_faostat_crop_production.py: f446a0f22d865d6dfb7aaab643ea245932169769070b7a72ab16c98b09bf7435 - workflow/scripts/prepare_faostat_fbs_items.py: 87446f863e0e26e06b863cabcaafda362fb033f86b522b519922f86b5f80bb05 + workflow/scripts/prepare_faostat_fbs_items.py: b06a2e8c1188b488086c7ea804cf1d822c83bc5e410457137d4f027063b88429 workflow/scripts/prepare_faostat_food_group_supply.py: e1702c7c67e7295c70b4cd1fdcf871f3d02c5a90dcb2c0c5b0c6b891e8e81809 workflow/scripts/prepare_faostat_pasture_area.py: 3f0d6212cf0c54edaf6f47145e75334c532e6e7a2d243a4996d4f96bda5096fe workflow/scripts/prepare_faostat_yields.py: 178264f3eaa3e386a7ea6813fe607af7183663624cc0388a9df724562a5dd9ab @@ -276,16 +272,16 @@ steps: workflow/scripts/resample_land_cover.py: 3a953b4c2a0a4a226688e282af9a00c778a077bfbf3d8504829a637a6281a1ce workflow/scripts/resample_luicube_grassland.py: 8efad93db0c694e2b9152acc2373b356614d65c8170cf4effa08f6df9b4cc8fb workflow/scripts/retrieve_cpi_data.py: ee11749f80d1ab2dd0ade2909b40ef001f7bd24c6a56fa381a251662b51f54b8 - workflow/scripts/retrieve_eurostat_fodder.py: c90bbfbbb2c08b83144c84f2199da9ec01d17a8ecea99d2ead77a1baf6de22f9 + workflow/scripts/retrieve_eurostat_fodder.py: 888097d8d0220842558b842b40e04958df6a6fc136fd8f9b2042a913a4bb7069 workflow/scripts/retrieve_fadn_animal_costs.py: 7791580c7c04f99a474909e4e53bdaa7af0a23056c2036b36753c43076ac25f7 workflow/scripts/retrieve_hicp_data.py: 8375097c1fb9f18d096116dd133268e36a332373b56720790a4abb35a2b6290b - workflow/scripts/retrieve_ppp_rates.py: 13ff1ed85f2153d4a5a8b4b9031432c59b23e0a85eb4fc62bfd4806c32aee444 + workflow/scripts/retrieve_ppp_rates.py: dc90953fe87c3dc0990c386dac1171e64cdbd0ac2085db804d97277e4f280f88 workflow/scripts/retrieve_usda_animal_costs.py: 5849db38933f9323fb581e5b81a17223e3e5a12b5c8471727c4b9cef4c2211cc workflow/scripts/simplify_gadm.py: 9b206abc2f561840b5a964e9fc06a6f4ee6b71cb2673277d4e2c27ebf532d1af workflow/scripts/solve_model.py: 8b13b5a820ba69e4385dcc0b4f576744d9759485a642ca43282c7b65861fe5cb workflow/scripts/validate_baseline_diet.py: 6d54d506a2d8382321c849d77698098429019e0f089f969c43ee2848becc05f4 config_files: - config/calibration/feed.yaml: a301d4c644f0022a5135bf7882c87f4f0889f0963753260249c378007c18c83a + config/calibration/feed.yaml: 0d9bf9aa62c79047b92ea2c25ef1ffa1cf9401f206cd7bc8eed6aa3a4ed5e72d effective_config: {} inputs: data/bundled/doi_10_5061_dryad_2rbnzs7qh__v20250311/FUBC_1_to_9_data.csv: 3b5dc29b6e2f6844807f2983c1596c28a127f48a67f7deec7e36f6a49c0b17f3 @@ -343,7 +339,7 @@ steps: workflow/scripts/build_model/commodity_costs.py: 97a4c3aa148355adac88d7a45462158f55a7322a0c351babd0a243bd941eb0e2 workflow/scripts/build_model/crops.py: 4b8638d8902b0a688d0ab55df3e3625d0953f3b4ac63886ddae50b3bf47b4610 workflow/scripts/build_model/food.py: 9e53b6c847202a2192ea0e014746e62a9a5f88eedffc5a48833fc4186ef65ae9 - workflow/scripts/build_model/grassland.py: bf39536ad916a0c7a47d72fe2de4171fd256e483b81ac552d4403fe0123036da + workflow/scripts/build_model/grassland.py: dcbe90ec563dbe3c3ce9431085dd59e91dfb418a1fb30d5ebfe8b10c154f45b4 workflow/scripts/build_model/health.py: 5ddc8cbae98992f67140db787946a0f9fd9ae15ad683592b4f81b4bf6a2df702 workflow/scripts/build_model/infrastructure.py: b792d568d0f199699f5d53bc6f175138a899e41a71f86b5d72a037de1130785c workflow/scripts/build_model/irrigation.py: 3d1d6216069eb7854046266a68b202b1f4c981395c499f8d05ab04c22f5645c2 @@ -357,14 +353,13 @@ steps: workflow/scripts/population.py: b45821fb719171d516083bf3ff8a4186b4424b22dd8a24216d199bf5ff70a13d workflow/scripts/snakemake_utils.py: 52c6bb89c4efe655aa5f41d0cd850b0c2894c9dd72c644f9c0633c04a503989e workflow/scripts/solve_model/__init__.py: 23fb1fde8c5d964c8fda94020ff41ce9fdeefd04f6334a1ab3e7e755c93af04c - workflow/scripts/solve_model/core.py: 83307fcdffd50a1e6f83ad3fcbfcfff3abdf51ebfd91c02f8352502dfe7eba8b + workflow/scripts/solve_model/core.py: 769d02b66edcc0ff55b2ab154b7e2c527a5e7bb0eb4907d1f63bc7cdacec98d6 workflow/scripts/solve_model/diet_stability.py: 38186fe2e56474736d9da727cd6ebb3420dd24a432391cc5da477015be0b54f4 - workflow/scripts/solve_model/food_utility.py: c12ac00a859e76d7dffa77c62fc5cb58525d92f6b886373ab49c9790692ac6a4 workflow/scripts/solve_model/health.py: 4e8b34555c4a7aaf94b59f081c0ba5e89a3369eaaf7c8e918ef5e660eb9d37a5 workflow/scripts/solve_model/market_response.py: 11ba687519e9e697559e9000f4dad1583c794d3b31d067d570138735e4c38fb1 workflow/scripts/solve_model/production_stability.py: 4f238ab0f219335272a4146b118066124bf9d107dae5cc3dfe7ce89d5feabf3a workflow/scripts/solve_model/sensitivity.py: 3ba13e457cfe34ce5ecb04c28d19d873ea6a9d4cc3c2118c3a2c4384a5bbee2a - workflow/scripts/solve_namespace.py: e81dc3fbd294f42706bfdf0f08fc43d006c17376c51234e73c87bb5fc8fe3f02 + workflow/scripts/solve_namespace.py: 1f54bdc13daac2b0f239fa706cbb59b8707b45d55e23d0251e008b0d3bc2b421 workflow/scripts/water_periods.py: 25e9e7e9a6fe41717ca4c61017641f59e3a22c83598ec73716b7d070f9a9962d missing_inputs: [] food_demand: @@ -376,7 +371,7 @@ steps: workflow/rules/diet.smk: 448908aecc357fd119c1e5cd43acdec2075f2d5a4d9ebbefefa1299c1afb15f4 workflow/rules/geography.smk: 54b113f5647787bacea8d2a2f45298a24c605a84ced48630abece8b2dc887943 workflow/rules/luc.smk: e64fb669c0d6c40299d47c995dbb9f04a71f4d99d8edcd1fe9ce3ed700639257 - workflow/rules/model.smk: 00791268796d8467b88ad755fb1bedf8babe2a12f2e0a78b657b01279387f942 + workflow/rules/model.smk: d2d2e71ecce83dad9728184b7a7903c5f7ab6ba8030126307818d05bff1ae71c workflow/rules/retrieve.smk: d440ebccd4e6f2d6aa5e86066e865ec5c54dbca2a4b5149228a2ef20ad776748 workflow/rules/water.smk: 9fe3fc69051df02bab7dde53d2057b2b64142c7212741f8a45bc0171447c951d workflow/scripts/aggregate_class_areas.py: 93e9a52eec4dac86eeff03fa7e574ad4bdd91bb4049f715a321850f327aba92e @@ -431,7 +426,7 @@ steps: workflow/scripts/prepare_faostat_animal_production.py: 38980d9bf9324ae01664c63104026aa4b3589f71f5bc592ae8f3bbe96cde9c2a workflow/scripts/prepare_faostat_crop_costs.py: be66f2aad20af7c88085b3c24e59ce2e26cfcee25b202fd96f3a47666d65ad1b workflow/scripts/prepare_faostat_crop_production.py: f446a0f22d865d6dfb7aaab643ea245932169769070b7a72ab16c98b09bf7435 - workflow/scripts/prepare_faostat_fbs_items.py: 87446f863e0e26e06b863cabcaafda362fb033f86b522b519922f86b5f80bb05 + workflow/scripts/prepare_faostat_fbs_items.py: b06a2e8c1188b488086c7ea804cf1d822c83bc5e410457137d4f027063b88429 workflow/scripts/prepare_faostat_food_group_supply.py: e1702c7c67e7295c70b4cd1fdcf871f3d02c5a90dcb2c0c5b0c6b891e8e81809 workflow/scripts/prepare_faostat_pasture_area.py: 3f0d6212cf0c54edaf6f47145e75334c532e6e7a2d243a4996d4f96bda5096fe workflow/scripts/prepare_faostat_yields.py: 178264f3eaa3e386a7ea6813fe607af7183663624cc0388a9df724562a5dd9ab @@ -450,16 +445,16 @@ steps: workflow/scripts/resample_land_cover.py: 3a953b4c2a0a4a226688e282af9a00c778a077bfbf3d8504829a637a6281a1ce workflow/scripts/resample_luicube_grassland.py: 8efad93db0c694e2b9152acc2373b356614d65c8170cf4effa08f6df9b4cc8fb workflow/scripts/retrieve_cpi_data.py: ee11749f80d1ab2dd0ade2909b40ef001f7bd24c6a56fa381a251662b51f54b8 - workflow/scripts/retrieve_eurostat_fodder.py: c90bbfbbb2c08b83144c84f2199da9ec01d17a8ecea99d2ead77a1baf6de22f9 + workflow/scripts/retrieve_eurostat_fodder.py: 888097d8d0220842558b842b40e04958df6a6fc136fd8f9b2042a913a4bb7069 workflow/scripts/retrieve_fadn_animal_costs.py: 7791580c7c04f99a474909e4e53bdaa7af0a23056c2036b36753c43076ac25f7 workflow/scripts/retrieve_hicp_data.py: 8375097c1fb9f18d096116dd133268e36a332373b56720790a4abb35a2b6290b - workflow/scripts/retrieve_ppp_rates.py: 13ff1ed85f2153d4a5a8b4b9031432c59b23e0a85eb4fc62bfd4806c32aee444 + workflow/scripts/retrieve_ppp_rates.py: dc90953fe87c3dc0990c386dac1171e64cdbd0ac2085db804d97277e4f280f88 workflow/scripts/retrieve_usda_animal_costs.py: 5849db38933f9323fb581e5b81a17223e3e5a12b5c8471727c4b9cef4c2211cc workflow/scripts/simplify_gadm.py: 9b206abc2f561840b5a964e9fc06a6f4ee6b71cb2673277d4e2c27ebf532d1af workflow/scripts/solve_model.py: 8b13b5a820ba69e4385dcc0b4f576744d9759485a642ca43282c7b65861fe5cb workflow/scripts/validate_baseline_diet.py: 6d54d506a2d8382321c849d77698098429019e0f089f969c43ee2848becc05f4 config_files: - config/calibration/food_demand.yaml: f2ca2acac7caf21713332eb1fa63308b9a3c15f6d7323e95fc72e6f855440e48 + config/calibration/food_demand.yaml: b5690d099a84f753acc456b4d6865d2dbc357fe86cc8281080fb1bc740fce06b effective_config: {} inputs: data/bundled/doi_10_5061_dryad_2rbnzs7qh__v20250311/FUBC_1_to_9_data.csv: 3b5dc29b6e2f6844807f2983c1596c28a127f48a67f7deec7e36f6a49c0b17f3 @@ -522,7 +517,7 @@ steps: workflow/scripts/build_model/commodity_costs.py: 97a4c3aa148355adac88d7a45462158f55a7322a0c351babd0a243bd941eb0e2 workflow/scripts/build_model/crops.py: 4b8638d8902b0a688d0ab55df3e3625d0953f3b4ac63886ddae50b3bf47b4610 workflow/scripts/build_model/food.py: 9e53b6c847202a2192ea0e014746e62a9a5f88eedffc5a48833fc4186ef65ae9 - workflow/scripts/build_model/grassland.py: bf39536ad916a0c7a47d72fe2de4171fd256e483b81ac552d4403fe0123036da + workflow/scripts/build_model/grassland.py: dcbe90ec563dbe3c3ce9431085dd59e91dfb418a1fb30d5ebfe8b10c154f45b4 workflow/scripts/build_model/health.py: 5ddc8cbae98992f67140db787946a0f9fd9ae15ad683592b4f81b4bf6a2df702 workflow/scripts/build_model/infrastructure.py: b792d568d0f199699f5d53bc6f175138a899e41a71f86b5d72a037de1130785c workflow/scripts/build_model/irrigation.py: 3d1d6216069eb7854046266a68b202b1f4c981395c499f8d05ab04c22f5645c2 @@ -536,14 +531,13 @@ steps: workflow/scripts/population.py: b45821fb719171d516083bf3ff8a4186b4424b22dd8a24216d199bf5ff70a13d workflow/scripts/snakemake_utils.py: 52c6bb89c4efe655aa5f41d0cd850b0c2894c9dd72c644f9c0633c04a503989e workflow/scripts/solve_model/__init__.py: 23fb1fde8c5d964c8fda94020ff41ce9fdeefd04f6334a1ab3e7e755c93af04c - workflow/scripts/solve_model/core.py: 83307fcdffd50a1e6f83ad3fcbfcfff3abdf51ebfd91c02f8352502dfe7eba8b + workflow/scripts/solve_model/core.py: 769d02b66edcc0ff55b2ab154b7e2c527a5e7bb0eb4907d1f63bc7cdacec98d6 workflow/scripts/solve_model/diet_stability.py: 38186fe2e56474736d9da727cd6ebb3420dd24a432391cc5da477015be0b54f4 - workflow/scripts/solve_model/food_utility.py: c12ac00a859e76d7dffa77c62fc5cb58525d92f6b886373ab49c9790692ac6a4 workflow/scripts/solve_model/health.py: 4e8b34555c4a7aaf94b59f081c0ba5e89a3369eaaf7c8e918ef5e660eb9d37a5 workflow/scripts/solve_model/market_response.py: 11ba687519e9e697559e9000f4dad1583c794d3b31d067d570138735e4c38fb1 workflow/scripts/solve_model/production_stability.py: 4f238ab0f219335272a4146b118066124bf9d107dae5cc3dfe7ce89d5feabf3a workflow/scripts/solve_model/sensitivity.py: 3ba13e457cfe34ce5ecb04c28d19d873ea6a9d4cc3c2118c3a2c4384a5bbee2a - workflow/scripts/solve_namespace.py: e81dc3fbd294f42706bfdf0f08fc43d006c17376c51234e73c87bb5fc8fe3f02 + workflow/scripts/solve_namespace.py: 1f54bdc13daac2b0f239fa706cbb59b8707b45d55e23d0251e008b0d3bc2b421 workflow/scripts/water_periods.py: 25e9e7e9a6fe41717ca4c61017641f59e3a22c83598ec73716b7d070f9a9962d missing_inputs: [] food_waste: @@ -555,7 +549,7 @@ steps: workflow/rules/diet.smk: 448908aecc357fd119c1e5cd43acdec2075f2d5a4d9ebbefefa1299c1afb15f4 workflow/rules/geography.smk: 54b113f5647787bacea8d2a2f45298a24c605a84ced48630abece8b2dc887943 workflow/rules/luc.smk: e64fb669c0d6c40299d47c995dbb9f04a71f4d99d8edcd1fe9ce3ed700639257 - workflow/rules/model.smk: 00791268796d8467b88ad755fb1bedf8babe2a12f2e0a78b657b01279387f942 + workflow/rules/model.smk: d2d2e71ecce83dad9728184b7a7903c5f7ab6ba8030126307818d05bff1ae71c workflow/rules/retrieve.smk: d440ebccd4e6f2d6aa5e86066e865ec5c54dbca2a4b5149228a2ef20ad776748 workflow/rules/water.smk: 9fe3fc69051df02bab7dde53d2057b2b64142c7212741f8a45bc0171447c951d workflow/scripts/aggregate_class_areas.py: 93e9a52eec4dac86eeff03fa7e574ad4bdd91bb4049f715a321850f327aba92e @@ -610,7 +604,7 @@ steps: workflow/scripts/prepare_faostat_animal_production.py: 38980d9bf9324ae01664c63104026aa4b3589f71f5bc592ae8f3bbe96cde9c2a workflow/scripts/prepare_faostat_crop_costs.py: be66f2aad20af7c88085b3c24e59ce2e26cfcee25b202fd96f3a47666d65ad1b workflow/scripts/prepare_faostat_crop_production.py: f446a0f22d865d6dfb7aaab643ea245932169769070b7a72ab16c98b09bf7435 - workflow/scripts/prepare_faostat_fbs_items.py: 87446f863e0e26e06b863cabcaafda362fb033f86b522b519922f86b5f80bb05 + workflow/scripts/prepare_faostat_fbs_items.py: b06a2e8c1188b488086c7ea804cf1d822c83bc5e410457137d4f027063b88429 workflow/scripts/prepare_faostat_food_group_supply.py: e1702c7c67e7295c70b4cd1fdcf871f3d02c5a90dcb2c0c5b0c6b891e8e81809 workflow/scripts/prepare_faostat_pasture_area.py: 3f0d6212cf0c54edaf6f47145e75334c532e6e7a2d243a4996d4f96bda5096fe workflow/scripts/prepare_faostat_yields.py: 178264f3eaa3e386a7ea6813fe607af7183663624cc0388a9df724562a5dd9ab @@ -629,16 +623,16 @@ steps: workflow/scripts/resample_land_cover.py: 3a953b4c2a0a4a226688e282af9a00c778a077bfbf3d8504829a637a6281a1ce workflow/scripts/resample_luicube_grassland.py: 8efad93db0c694e2b9152acc2373b356614d65c8170cf4effa08f6df9b4cc8fb workflow/scripts/retrieve_cpi_data.py: ee11749f80d1ab2dd0ade2909b40ef001f7bd24c6a56fa381a251662b51f54b8 - workflow/scripts/retrieve_eurostat_fodder.py: c90bbfbbb2c08b83144c84f2199da9ec01d17a8ecea99d2ead77a1baf6de22f9 + workflow/scripts/retrieve_eurostat_fodder.py: 888097d8d0220842558b842b40e04958df6a6fc136fd8f9b2042a913a4bb7069 workflow/scripts/retrieve_fadn_animal_costs.py: 7791580c7c04f99a474909e4e53bdaa7af0a23056c2036b36753c43076ac25f7 workflow/scripts/retrieve_hicp_data.py: 8375097c1fb9f18d096116dd133268e36a332373b56720790a4abb35a2b6290b - workflow/scripts/retrieve_ppp_rates.py: 13ff1ed85f2153d4a5a8b4b9031432c59b23e0a85eb4fc62bfd4806c32aee444 + workflow/scripts/retrieve_ppp_rates.py: dc90953fe87c3dc0990c386dac1171e64cdbd0ac2085db804d97277e4f280f88 workflow/scripts/retrieve_usda_animal_costs.py: 5849db38933f9323fb581e5b81a17223e3e5a12b5c8471727c4b9cef4c2211cc workflow/scripts/simplify_gadm.py: 9b206abc2f561840b5a964e9fc06a6f4ee6b71cb2673277d4e2c27ebf532d1af workflow/scripts/solve_model.py: 8b13b5a820ba69e4385dcc0b4f576744d9759485a642ca43282c7b65861fe5cb workflow/scripts/validate_baseline_diet.py: 6d54d506a2d8382321c849d77698098429019e0f089f969c43ee2848becc05f4 config_files: - config/calibration/food_waste.yaml: b43c3152299ff87ab7637f41b666a74a45ae1d99e2e9ff8fb96f6230d19cd677 + config/calibration/food_waste.yaml: 2d44f60257d42a739982926cd0df61c358dc516b0b29a82e5214ae2ba4031b4c effective_config: {} inputs: data/bundled/doi_10_5061_dryad_2rbnzs7qh__v20250311/FUBC_1_to_9_data.csv: 3b5dc29b6e2f6844807f2983c1596c28a127f48a67f7deec7e36f6a49c0b17f3 @@ -700,7 +694,7 @@ steps: workflow/scripts/build_model/commodity_costs.py: 97a4c3aa148355adac88d7a45462158f55a7322a0c351babd0a243bd941eb0e2 workflow/scripts/build_model/crops.py: 4b8638d8902b0a688d0ab55df3e3625d0953f3b4ac63886ddae50b3bf47b4610 workflow/scripts/build_model/food.py: 9e53b6c847202a2192ea0e014746e62a9a5f88eedffc5a48833fc4186ef65ae9 - workflow/scripts/build_model/grassland.py: bf39536ad916a0c7a47d72fe2de4171fd256e483b81ac552d4403fe0123036da + workflow/scripts/build_model/grassland.py: dcbe90ec563dbe3c3ce9431085dd59e91dfb418a1fb30d5ebfe8b10c154f45b4 workflow/scripts/build_model/health.py: 5ddc8cbae98992f67140db787946a0f9fd9ae15ad683592b4f81b4bf6a2df702 workflow/scripts/build_model/infrastructure.py: b792d568d0f199699f5d53bc6f175138a899e41a71f86b5d72a037de1130785c workflow/scripts/build_model/irrigation.py: 3d1d6216069eb7854046266a68b202b1f4c981395c499f8d05ab04c22f5645c2 @@ -714,14 +708,13 @@ steps: workflow/scripts/population.py: b45821fb719171d516083bf3ff8a4186b4424b22dd8a24216d199bf5ff70a13d workflow/scripts/snakemake_utils.py: 52c6bb89c4efe655aa5f41d0cd850b0c2894c9dd72c644f9c0633c04a503989e workflow/scripts/solve_model/__init__.py: 23fb1fde8c5d964c8fda94020ff41ce9fdeefd04f6334a1ab3e7e755c93af04c - workflow/scripts/solve_model/core.py: 83307fcdffd50a1e6f83ad3fcbfcfff3abdf51ebfd91c02f8352502dfe7eba8b + workflow/scripts/solve_model/core.py: 769d02b66edcc0ff55b2ab154b7e2c527a5e7bb0eb4907d1f63bc7cdacec98d6 workflow/scripts/solve_model/diet_stability.py: 38186fe2e56474736d9da727cd6ebb3420dd24a432391cc5da477015be0b54f4 - workflow/scripts/solve_model/food_utility.py: c12ac00a859e76d7dffa77c62fc5cb58525d92f6b886373ab49c9790692ac6a4 workflow/scripts/solve_model/health.py: 4e8b34555c4a7aaf94b59f081c0ba5e89a3369eaaf7c8e918ef5e660eb9d37a5 workflow/scripts/solve_model/market_response.py: 11ba687519e9e697559e9000f4dad1583c794d3b31d067d570138735e4c38fb1 workflow/scripts/solve_model/production_stability.py: 4f238ab0f219335272a4146b118066124bf9d107dae5cc3dfe7ce89d5feabf3a workflow/scripts/solve_model/sensitivity.py: 3ba13e457cfe34ce5ecb04c28d19d873ea6a9d4cc3c2118c3a2c4384a5bbee2a - workflow/scripts/solve_namespace.py: e81dc3fbd294f42706bfdf0f08fc43d006c17376c51234e73c87bb5fc8fe3f02 + workflow/scripts/solve_namespace.py: 1f54bdc13daac2b0f239fa706cbb59b8707b45d55e23d0251e008b0d3bc2b421 workflow/scripts/water_periods.py: 25e9e7e9a6fe41717ca4c61017641f59e3a22c83598ec73716b7d070f9a9962d missing_inputs: [] market_response: @@ -734,7 +727,7 @@ steps: workflow/rules/geography.smk: 54b113f5647787bacea8d2a2f45298a24c605a84ced48630abece8b2dc887943 workflow/rules/luc.smk: e64fb669c0d6c40299d47c995dbb9f04a71f4d99d8edcd1fe9ce3ed700639257 workflow/rules/market_response.smk: 1c8e729d8009d60535dc8beb3c6446103e55b6b0cf4fa27d89dec793235a9a9a - workflow/rules/model.smk: 00791268796d8467b88ad755fb1bedf8babe2a12f2e0a78b657b01279387f942 + workflow/rules/model.smk: d2d2e71ecce83dad9728184b7a7903c5f7ab6ba8030126307818d05bff1ae71c workflow/rules/retrieve.smk: d440ebccd4e6f2d6aa5e86066e865ec5c54dbca2a4b5149228a2ef20ad776748 workflow/rules/water.smk: 9fe3fc69051df02bab7dde53d2057b2b64142c7212741f8a45bc0171447c951d workflow/scripts/aggregate_class_areas.py: 93e9a52eec4dac86eeff03fa7e574ad4bdd91bb4049f715a321850f327aba92e @@ -789,7 +782,7 @@ steps: workflow/scripts/prepare_faostat_animal_production.py: 38980d9bf9324ae01664c63104026aa4b3589f71f5bc592ae8f3bbe96cde9c2a workflow/scripts/prepare_faostat_crop_costs.py: be66f2aad20af7c88085b3c24e59ce2e26cfcee25b202fd96f3a47666d65ad1b workflow/scripts/prepare_faostat_crop_production.py: f446a0f22d865d6dfb7aaab643ea245932169769070b7a72ab16c98b09bf7435 - workflow/scripts/prepare_faostat_fbs_items.py: 87446f863e0e26e06b863cabcaafda362fb033f86b522b519922f86b5f80bb05 + workflow/scripts/prepare_faostat_fbs_items.py: b06a2e8c1188b488086c7ea804cf1d822c83bc5e410457137d4f027063b88429 workflow/scripts/prepare_faostat_food_group_supply.py: e1702c7c67e7295c70b4cd1fdcf871f3d02c5a90dcb2c0c5b0c6b891e8e81809 workflow/scripts/prepare_faostat_pasture_area.py: 3f0d6212cf0c54edaf6f47145e75334c532e6e7a2d243a4996d4f96bda5096fe workflow/scripts/prepare_faostat_yields.py: 178264f3eaa3e386a7ea6813fe607af7183663624cc0388a9df724562a5dd9ab @@ -808,15 +801,15 @@ steps: workflow/scripts/resample_land_cover.py: 3a953b4c2a0a4a226688e282af9a00c778a077bfbf3d8504829a637a6281a1ce workflow/scripts/resample_luicube_grassland.py: 8efad93db0c694e2b9152acc2373b356614d65c8170cf4effa08f6df9b4cc8fb workflow/scripts/retrieve_cpi_data.py: ee11749f80d1ab2dd0ade2909b40ef001f7bd24c6a56fa381a251662b51f54b8 - workflow/scripts/retrieve_eurostat_fodder.py: c90bbfbbb2c08b83144c84f2199da9ec01d17a8ecea99d2ead77a1baf6de22f9 + workflow/scripts/retrieve_eurostat_fodder.py: 888097d8d0220842558b842b40e04958df6a6fc136fd8f9b2042a913a4bb7069 workflow/scripts/retrieve_fadn_animal_costs.py: 7791580c7c04f99a474909e4e53bdaa7af0a23056c2036b36753c43076ac25f7 workflow/scripts/retrieve_hicp_data.py: 8375097c1fb9f18d096116dd133268e36a332373b56720790a4abb35a2b6290b - workflow/scripts/retrieve_ppp_rates.py: 13ff1ed85f2153d4a5a8b4b9031432c59b23e0a85eb4fc62bfd4806c32aee444 + workflow/scripts/retrieve_ppp_rates.py: dc90953fe87c3dc0990c386dac1171e64cdbd0ac2085db804d97277e4f280f88 workflow/scripts/retrieve_usda_animal_costs.py: 5849db38933f9323fb581e5b81a17223e3e5a12b5c8471727c4b9cef4c2211cc workflow/scripts/simplify_gadm.py: 9b206abc2f561840b5a964e9fc06a6f4ee6b71cb2673277d4e2c27ebf532d1af workflow/scripts/validate_baseline_diet.py: 6d54d506a2d8382321c849d77698098429019e0f089f969c43ee2848becc05f4 config_files: - config/calibration/market_response.yaml: 44cfacb32415b36612d60cbb8a553c0647e7603281c3278888912f471c3c727c + config/calibration/market_response.yaml: 26139cbf06038a0b2ea724a42755c7ac241b14a2bf68bbb697fb9668caf41bed effective_config: market_response.calibration.sweeps: 3 market_response.components: @@ -915,7 +908,7 @@ steps: workflow/scripts/build_model/commodity_costs.py: 97a4c3aa148355adac88d7a45462158f55a7322a0c351babd0a243bd941eb0e2 workflow/scripts/build_model/crops.py: 4b8638d8902b0a688d0ab55df3e3625d0953f3b4ac63886ddae50b3bf47b4610 workflow/scripts/build_model/food.py: 9e53b6c847202a2192ea0e014746e62a9a5f88eedffc5a48833fc4186ef65ae9 - workflow/scripts/build_model/grassland.py: bf39536ad916a0c7a47d72fe2de4171fd256e483b81ac552d4403fe0123036da + workflow/scripts/build_model/grassland.py: dcbe90ec563dbe3c3ce9431085dd59e91dfb418a1fb30d5ebfe8b10c154f45b4 workflow/scripts/build_model/health.py: 5ddc8cbae98992f67140db787946a0f9fd9ae15ad683592b4f81b4bf6a2df702 workflow/scripts/build_model/infrastructure.py: b792d568d0f199699f5d53bc6f175138a899e41a71f86b5d72a037de1130785c workflow/scripts/build_model/irrigation.py: 3d1d6216069eb7854046266a68b202b1f4c981395c499f8d05ab04c22f5645c2 @@ -929,19 +922,18 @@ steps: workflow/scripts/population.py: b45821fb719171d516083bf3ff8a4186b4424b22dd8a24216d199bf5ff70a13d workflow/scripts/snakemake_utils.py: 52c6bb89c4efe655aa5f41d0cd850b0c2894c9dd72c644f9c0633c04a503989e workflow/scripts/solve_model/__init__.py: 23fb1fde8c5d964c8fda94020ff41ce9fdeefd04f6334a1ab3e7e755c93af04c - workflow/scripts/solve_model/core.py: 83307fcdffd50a1e6f83ad3fcbfcfff3abdf51ebfd91c02f8352502dfe7eba8b + workflow/scripts/solve_model/core.py: 769d02b66edcc0ff55b2ab154b7e2c527a5e7bb0eb4907d1f63bc7cdacec98d6 workflow/scripts/solve_model/diet_stability.py: 38186fe2e56474736d9da727cd6ebb3420dd24a432391cc5da477015be0b54f4 - workflow/scripts/solve_model/food_utility.py: c12ac00a859e76d7dffa77c62fc5cb58525d92f6b886373ab49c9790692ac6a4 workflow/scripts/solve_model/health.py: 4e8b34555c4a7aaf94b59f081c0ba5e89a3369eaaf7c8e918ef5e660eb9d37a5 workflow/scripts/solve_model/market_response.py: 11ba687519e9e697559e9000f4dad1583c794d3b31d067d570138735e4c38fb1 workflow/scripts/solve_model/production_stability.py: 4f238ab0f219335272a4146b118066124bf9d107dae5cc3dfe7ce89d5feabf3a workflow/scripts/solve_model/sensitivity.py: 3ba13e457cfe34ce5ecb04c28d19d873ea6a9d4cc3c2118c3a2c4384a5bbee2a - workflow/scripts/solve_namespace.py: e81dc3fbd294f42706bfdf0f08fc43d006c17376c51234e73c87bb5fc8fe3f02 + workflow/scripts/solve_namespace.py: 1f54bdc13daac2b0f239fa706cbb59b8707b45d55e23d0251e008b0d3bc2b421 workflow/scripts/water_periods.py: 25e9e7e9a6fe41717ca4c61017641f59e3a22c83598ec73716b7d070f9a9962d missing_inputs: [] stability: artefacts: - data/curated/calibration/default/deviation_penalty.yaml: f8e5b345f8f75cbab165d1ae3cbed16df97487e6a0f41890e765851c929cd1af + data/curated/calibration/default/deviation_penalty.yaml: 4d3dbb95b7d46fb1b4df6b54edf956e376d2e1e516ed06994b804319760d0ad1 code: workflow/rules/animals.smk: fa07f649a53263c151e1799f63b40c66fba42ee7858a97d51ff988c406ba0024 workflow/rules/crops.smk: 8d7d87b61959819ae9a753126767a71a10d7463af7963bffd423fa4560f470d1 @@ -949,7 +941,7 @@ steps: workflow/rules/diet.smk: 448908aecc357fd119c1e5cd43acdec2075f2d5a4d9ebbefefa1299c1afb15f4 workflow/rules/geography.smk: 54b113f5647787bacea8d2a2f45298a24c605a84ced48630abece8b2dc887943 workflow/rules/luc.smk: e64fb669c0d6c40299d47c995dbb9f04a71f4d99d8edcd1fe9ce3ed700639257 - workflow/rules/model.smk: 00791268796d8467b88ad755fb1bedf8babe2a12f2e0a78b657b01279387f942 + workflow/rules/model.smk: d2d2e71ecce83dad9728184b7a7903c5f7ab6ba8030126307818d05bff1ae71c workflow/rules/retrieve.smk: d440ebccd4e6f2d6aa5e86066e865ec5c54dbca2a4b5149228a2ef20ad776748 workflow/rules/water.smk: 9fe3fc69051df02bab7dde53d2057b2b64142c7212741f8a45bc0171447c951d workflow/scripts/aggregate_class_areas.py: 93e9a52eec4dac86eeff03fa7e574ad4bdd91bb4049f715a321850f327aba92e @@ -980,7 +972,7 @@ steps: workflow/scripts/build_regions.py: 08a1f3c0247bdbe4600f2b1596396a352ff6c459496566e7e24da5b2f83558a7 workflow/scripts/build_yield_calibration.py: 1225a0d55add24ce44acc86cde5dd3335ed9d0a233ba6025bfc8c0e5afc8375d workflow/scripts/calculate_manure_emissions.py: 4f39f5370dcf931d4648bf7c0727a807ec7bc8fbb80561dae4ff5afa08834591 - workflow/scripts/calibrate_deviation_penalty.py: fe674bb0ada6ca8676d202693b05cc7e6410380dd0c40634799d1a24852db02b + workflow/scripts/calibrate_deviation_penalty.py: 83ecac8f9d46b5045642bcf49ec3f6e16a6973de13dbca7b5f8892a59a1c35e7 workflow/scripts/categorize_feeds.py: 575b37f430e4fbf54b8477be96f5069ce6522fa1882c2f024b9b9a25a8540140 workflow/scripts/compose_water_supply.py: c1fc61844079219f2381664b44b6a917f51ecbc07bb08e1318702985deaf9231 workflow/scripts/compute_gleam3_feed_fractions.py: 83cc4268fe7f1762932ac63fc7f05ecedda9b03bcc1b502a69d85ec6779e6289 @@ -1004,7 +996,7 @@ steps: workflow/scripts/prepare_faostat_animal_production.py: 38980d9bf9324ae01664c63104026aa4b3589f71f5bc592ae8f3bbe96cde9c2a workflow/scripts/prepare_faostat_crop_costs.py: be66f2aad20af7c88085b3c24e59ce2e26cfcee25b202fd96f3a47666d65ad1b workflow/scripts/prepare_faostat_crop_production.py: f446a0f22d865d6dfb7aaab643ea245932169769070b7a72ab16c98b09bf7435 - workflow/scripts/prepare_faostat_fbs_items.py: 87446f863e0e26e06b863cabcaafda362fb033f86b522b519922f86b5f80bb05 + workflow/scripts/prepare_faostat_fbs_items.py: b06a2e8c1188b488086c7ea804cf1d822c83bc5e410457137d4f027063b88429 workflow/scripts/prepare_faostat_food_group_supply.py: e1702c7c67e7295c70b4cd1fdcf871f3d02c5a90dcb2c0c5b0c6b891e8e81809 workflow/scripts/prepare_faostat_pasture_area.py: 3f0d6212cf0c54edaf6f47145e75334c532e6e7a2d243a4996d4f96bda5096fe workflow/scripts/prepare_faostat_yields.py: 178264f3eaa3e386a7ea6813fe607af7183663624cc0388a9df724562a5dd9ab @@ -1023,15 +1015,15 @@ steps: workflow/scripts/resample_land_cover.py: 3a953b4c2a0a4a226688e282af9a00c778a077bfbf3d8504829a637a6281a1ce workflow/scripts/resample_luicube_grassland.py: 8efad93db0c694e2b9152acc2373b356614d65c8170cf4effa08f6df9b4cc8fb workflow/scripts/retrieve_cpi_data.py: ee11749f80d1ab2dd0ade2909b40ef001f7bd24c6a56fa381a251662b51f54b8 - workflow/scripts/retrieve_eurostat_fodder.py: c90bbfbbb2c08b83144c84f2199da9ec01d17a8ecea99d2ead77a1baf6de22f9 + workflow/scripts/retrieve_eurostat_fodder.py: 888097d8d0220842558b842b40e04958df6a6fc136fd8f9b2042a913a4bb7069 workflow/scripts/retrieve_fadn_animal_costs.py: 7791580c7c04f99a474909e4e53bdaa7af0a23056c2036b36753c43076ac25f7 workflow/scripts/retrieve_hicp_data.py: 8375097c1fb9f18d096116dd133268e36a332373b56720790a4abb35a2b6290b - workflow/scripts/retrieve_ppp_rates.py: 13ff1ed85f2153d4a5a8b4b9031432c59b23e0a85eb4fc62bfd4806c32aee444 + workflow/scripts/retrieve_ppp_rates.py: dc90953fe87c3dc0990c386dac1171e64cdbd0ac2085db804d97277e4f280f88 workflow/scripts/retrieve_usda_animal_costs.py: 5849db38933f9323fb581e5b81a17223e3e5a12b5c8471727c4b9cef4c2211cc workflow/scripts/simplify_gadm.py: 9b206abc2f561840b5a964e9fc06a6f4ee6b71cb2673277d4e2c27ebf532d1af workflow/scripts/validate_baseline_diet.py: 6d54d506a2d8382321c849d77698098429019e0f089f969c43ee2848becc05f4 config_files: - config/calibration/stability.yaml: f761fe7a2e76e5a558e39e5e9982738eede2eae3939f82805d5c204bbd985d92 + config/calibration/stability.yaml: 935bff56c220e526377283ce5c3c0caf0b675f47acece5c1e9f2d5c3e49b7829 effective_config: {} inputs: data/bundled/doi_10_5061_dryad_2rbnzs7qh__v20250311/FUBC_1_to_9_data.csv: 3b5dc29b6e2f6844807f2983c1596c28a127f48a67f7deec7e36f6a49c0b17f3 @@ -1041,16 +1033,16 @@ steps: data/bundled/gleam3/production.csv: eb85e82ba9a61c71c6cd33ab36c00b5d844f2550e1caa45b6b250410adc6f2b5 data/curated/M49-codes.csv: 10ae52e639e62f07ce992ead08536167d0c8aa9f467870dbfcfbf465ef3aa9be data/curated/biogas_crop_demand.csv: 34ef148f810b8153ba469f96fa4a32a9f1076adc2d1bcd76df66a0aa63573bfa - data/curated/calibration/default/animal_cost.csv: f8cc89b61307dd2fd5971ea6622a85b83ed13fe0e5e4c1e1826cec92d83de5c6 - data/curated/calibration/default/crop_cost.csv: 9f14731bad3c0350b8ff41e6a19661c99ba26a681b1e1e09fd76aaf4956fec38 + data/curated/calibration/default/animal_cost.csv: cc7c1c07cc12e2464f99a691f2cbae28ac7233b79bc9e8bac924925acfe905fc + data/curated/calibration/default/crop_cost.csv: 72c9c7b10f53ee5ae1abe131c750b27c094d2fbb1d8a869b5aceb1da0f679795 data/curated/calibration/default/exogenous_feed.csv: cf88c6a673e03e2ef65daadd2e31972dd93efb6f0c2da8313782c849527f183c data/curated/calibration/default/exogenous_forage.csv: 6fb01795f1354ae6b2c54aebceae1cab63c30abe7bd1c24dcfee688ba8f40b25 data/curated/calibration/default/fodder_conversion.csv: f46d89574830cefba376f9fdb86c0ae4462bdaf9889dcc25d8c15b0da1524458 data/curated/calibration/default/food_demand.csv: dbfe6f3a203dd777adc183397a19c456fa863dcec66e8446052346efd809cb7a data/curated/calibration/default/food_waste.yaml: d506d816b8e71e26e76fe694278580d9ca4bd03e4aac4e02c9544109f3bb260b - data/curated/calibration/default/grassland_cost.csv: dc3e9d6480874206b2b72dee47acfd6f7f081992c6ec0be70670b4daa7bb3d4f + data/curated/calibration/default/grassland_cost.csv: fe822326a6ca8c5325423cc6c5c8442d759ad15ddbe24fa27defc16afa38732b data/curated/calibration/default/grassland_yield.csv: 7b25a3f7ce6f6e56643ae8bbeb890ec5ff95a61741906417f698fb38c853dcf4 - data/curated/calibration/default/multi_crop_cost.csv: 9c5d32c2ce8d29c29b16f3d1e509d342fc6cd0a01f301f3e3bea81001c963756 + data/curated/calibration/default/multi_crop_cost.csv: befe3ee28a0fb32c43095d3b0e71f11e3cb98af49c0cdf4ad6e5f71584a69335 data/curated/country_mottet_region.csv: 8c41e8d0a8ad7ec8cd89b9c9cdbb72b6870fb8e9b59dd6ba6e49be468c5651a3 data/curated/country_wirsenius_region.csv: 73ae37e68c61286b0312b04b0d1dbc661322d8be5c5e5e66754fff1c9dcfac6e data/curated/crop_moisture_content.csv: b9e6ac4455fdbb6c28f211a291d2cde59439b7e2215d987e104544cc9eeebfe7 @@ -1096,7 +1088,7 @@ steps: workflow/scripts/build_model/commodity_costs.py: 97a4c3aa148355adac88d7a45462158f55a7322a0c351babd0a243bd941eb0e2 workflow/scripts/build_model/crops.py: 4b8638d8902b0a688d0ab55df3e3625d0953f3b4ac63886ddae50b3bf47b4610 workflow/scripts/build_model/food.py: 9e53b6c847202a2192ea0e014746e62a9a5f88eedffc5a48833fc4186ef65ae9 - workflow/scripts/build_model/grassland.py: bf39536ad916a0c7a47d72fe2de4171fd256e483b81ac552d4403fe0123036da + workflow/scripts/build_model/grassland.py: dcbe90ec563dbe3c3ce9431085dd59e91dfb418a1fb30d5ebfe8b10c154f45b4 workflow/scripts/build_model/health.py: 5ddc8cbae98992f67140db787946a0f9fd9ae15ad683592b4f81b4bf6a2df702 workflow/scripts/build_model/infrastructure.py: b792d568d0f199699f5d53bc6f175138a899e41a71f86b5d72a037de1130785c workflow/scripts/build_model/irrigation.py: 3d1d6216069eb7854046266a68b202b1f4c981395c499f8d05ab04c22f5645c2 diff --git a/data/curated/calibration/default/grassland_cost.csv b/data/curated/calibration/default/grassland_cost.csv index 2ac9d36f..db59a662 100644 --- a/data/curated/calibration/default/grassland_cost.csv +++ b/data/curated/calibration/default/grassland_cost.csv @@ -1,98 +1,98 @@ country,correction_bnusd_per_mha AFG,-4.0427408296017653e-10 AGO,-0.38166816532611847 -ALB,-0.17037621140480042 +ALB,-0.124451894313097 ARE,-0.0 ARG,-0.1545550674200058 ARM,-0.0 ATG,-0.0 -AUS,-0.02819783240556717 +AUS,-0.02019733376801014 AUT,9.24111176470177e-10 -AZE,0.7983635067939758 -BDI,-2.8265783935665922e-08 -BEL,0.08877222612500191 -BEN,2.4493881464004517 -BFA,1.1333651542663574 -BGD,4.87229061126709 -BGR,-0.26634590327739716 -BHS,-0.03126266412436962 -BIH,-0.4433588683605194 -BLR,0.0088651436381042 +AZE,0.8079160451889038 +BDI,-1.8208973884270563e-08 +BEL,0.08780838921666145 +BEN,2.4782121181488037 +BFA,1.1468080282211304 +BGD,4.923708438873291 +BGR,-0.24517704546451569 +BHS,1.5290118060562463e-08 +BIH,-0.4380365461111069 +BLR,0.09296828880906105 BLZ,-0.7399452030658722 BOL,-0.6221835613250732 BRA,-0.5204504728317261 -BRB,0.7472234666347504 +BRB,0.7568263709545135 BRN,-3.437714735099462e-08 -BTN,-0.3196943402290344 +BTN,-0.3074518144130707 BWA,-0.0 -CAF,-0.44557350873947144 -CAN,-0.21678771078586578 +CAF,1.5021680344062815e-08 +CAN,-0.21498434245586395 CHE,-0.0 -CHL,1.629338955422277e-10 -CHN,-3.2507327940578534e-09 -CIV,-0.5337249934673309 -CMR,2.3173455595970154 +CHL,-0.03965021949261427 +CHN,-3.932268732853572e-09 +CIV,-0.5321653187274933 +CMR,2.3432746529579163 COD,-0.7531084418296814 COG,-0.8099571466445923 COL,-0.6204277873039246 -COM,1.2140896320343018 +COM,1.2276423573493958 CPV,-0.0 CRI,-0.04483989253640175 CUB,-0.17085150629281998 -CYP,0.25486013293266296 -CZE,-0.10818929225206375 +CYP,0.25767597556114197 +CZE,-0.04161635786294937 DEU,-0.24095454812049866 DJI,-0.0 -DNK,1.6174811124801636 +DNK,1.6363895535469055 DOM,-1.3908177720622916e-08 -DZA,-1.8267637402757941e-09 +DZA,-2.098951901174928e-09 ECU,-0.19687405228614807 EGY,-0.0 ERI,-0.04815981723368168 -ESP,-0.005286545027047396 +ESP,4.217466376132961e-09 EST,-0.26680906116962433 -ETH,-1.9732169675990008e-08 -FIN,1.3762693405151367 -FJI,-0.8415849506855011 +ETH,-1.9151058516797548e-09 +FIN,1.3921850323677063 +FJI,-0.8222250640392303 FRA,-0.0 -GAB,-0.8197974264621735 +GAB,-0.8185282647609711 GBR,-8.699288400748628e-09 GEO,-0.20096751302480698 GHA,-0.5431598126888275 GIN,-0.37204913794994354 -GMB,1.000960797071457 -GNB,0.929963618516922 +GMB,1.0126700401306152 +GNB,0.9408482909202576 GNQ,-0.7101413011550903 -GRC,-0.14909013360738754 -GRD,0.5256579518318176 +GRC,-0.14644146710634232 +GRD,0.5332757234573364 GTM,-2.610952076764761e-08 GUF,-0.9834447503089905 GUY,-1.1032667756080627 -HND,-0.729015976190567 -HRV,-0.11155558750033379 -HTI,-0.30410297214984894 +HND,-0.5581271648406982 +HRV,-0.03193093277513981 +HTI,1.2459450360324809e-08 HUN,-0.4085644632577896 IDN,-0.562181144952774 -IND,1.366616427898407 +IND,1.3811563849449158 IRL,-0.0 IRN,-0.0 IRQ,-0.0 -ISL,-0.07663380727171898 -ISR,0.5885988771915436 -ITA,0.42469048500061035 -JAM,3.0310044288635254 +ISL,-0.07117843255400658 +ISR,0.5951419472694397 +ITA,0.4292159676551819 +JAM,3.0704917907714844 JOR,-0.0 JPN,-0.4728243798017502 -KAZ,-0.0042623227927833796 -KEN,-0.21574831753969193 +KAZ,-0.0 +KEN,-0.19165847450494766 KGZ,-1.2954760597594372e-09 -KHM,-0.3838875740766525 -KOR,2.6751197576522827 -LAO,-0.1339438185095787 -LBN,0.4314165264368057 +KHM,2.1605234268307072e-10 +KOR,2.7044516801834106 +LAO,4.666452224633133e-10 +LBN,0.4361957162618637 LBR,-0.7618840336799622 -LBY,-0.0 -LKA,-0.3844701498746872 +LBY,2.2779291473928875e-12 +LKA,-0.1928924173116684 LSO,-0.4435950964689255 LTU,-0.0 LUX,0.01513665821403265 @@ -101,75 +101,75 @@ MAR,-0.0 MDA,-0.3453979641199112 MDG,-0.6868364214897156 MEX,-0.0679156482219696 -MKD,-0.24906019866466522 +MKD,-0.2244415134191513 MLI,-0.037232644855976105 MLT,-0.0 -MMR,2.3069229125976562 -MNE,-0.21098285913467407 -MNG,-0.028478331863880157 +MMR,2.332178235054016 +MNE,-0.20855934917926788 +MNG,-0.023557634092867374 MOZ,-0.5160333812236786 -MRT,-0.033999696373939514 -MUS,-0.0412104157730937 +MRT,-0.03399969823658466 +MUS,-3.315589225394433e-08 MWI,-3.3964567824895653e-09 -MYS,-6.708681432598951e-08 +MYS,1.4313578766689261e-08 NAM,-0.0047507211565971375 NER,-0.0 NGA,-0.14038769900798798 NIC,-0.5779949724674225 NLD,0.8321638405323029 -NOR,1.140479564666748 -NPL,0.1342678926885128 +NOR,1.1537559628486633 +NPL,0.1910489946603775 NZL,-0.0 OMN,-0.0 PAK,-0.0 -PAN,-0.5686025023460388 +PAN,-0.10512995347380638 PER,-0.6808821260929108 PHL,-0.11315194144845009 PNG,-0.8056034743785858 POL,0.005302567966282368 -PRI,2.9440236914979323e-09 +PRI,-2.7517170764213006e-09 PRT,-0.15870346128940582 PRY,-0.6874429285526276 -PSE,0.5552514493465424 +PSE,0.5614200234413147 ROU,-0.4107300937175751 RUS,-0.0 -RWA,5.363595008850098 +RWA,5.424291372299194 SAU,-0.0 -SDN,-0.04855332709848881 -SEN,0.31002364307641983 -SLB,-0.0787055566906929 +SDN,-0.03526219353079796 +SEN,0.31365448981523514 +SLB,-1.6454283713329687e-08 SLE,-0.3628302663564682 -SLV,2.9333982467651367 +SLV,2.9712127447128296 SOM,-0.05690718814730644 SRB,-0.2870781123638153 -SSD,-0.2865627706050873 -STP,0.4922967851161957 -SUR,-0.13022971898317337 +SSD,-0.2790371775627136 +STP,0.4979879856109619 +SUR,-0.13022972643375397 SVK,-0.4101209193468094 -SVN,-0.08304522931575775 -SWE,1.2091391682624817 +SVN,1.952455142006926e-09 +SWE,1.2232388257980347 SWZ,-0.3180921822786331 -SYR,0.11797674745321274 +SYR,0.11928173899650574 TCD,-0.08657535165548325 TGO,-0.30542606115341187 THA,-3.214273469609452e-08 -TJK,0.338009774684906 +TJK,0.34327860176563263 TKM,-0.0 -TLS,-0.3610769063234329 +TLS,-0.22256753593683243 TTO,-3.4331349985095017e-09 -TUN,0.7419246435165405 -TUR,0.8447172045707703 -TWN,-0.3257727175951004 +TUN,0.7505517303943634 +TUR,0.854434072971344 +TWN,-0.2866273373365402 TZA,-0.06110341474413872 -UGA,-3.941181603295263e-08 +UGA,-2.739427173992226e-08 UKR,-0.01624846225604415 URY,-0.21180780231952667 USA,-0.2134283185005188 UZB,-0.0 VEN,-0.6751556694507599 -VNM,4.726685047149658 -VUT,-0.21887072920799255 -YEM,2.167515278372889e-09 +VNM,4.779263734817505 +VUT,-0.190080426633358 +YEM,-6.187674372881702e-09 ZAF,-0.04584670439362526 ZMB,-0.39802390336990356 -ZWE,0.40796668641269207 +ZWE,0.4141126535832882 diff --git a/data/curated/calibration/default/multi_crop_cost.csv b/data/curated/calibration/default/multi_crop_cost.csv index 364fc689..78d5ece3 100644 --- a/data/curated/calibration/default/multi_crop_cost.csv +++ b/data/curated/calibration/default/multi_crop_cost.csv @@ -1,102 +1,102 @@ combination,country,correction_bnusd_per_mha -cotton_wheat,AFG,-1.5952080488204956 -cotton_wheat,AGO,-1.9206392765045166 +cotton_wheat,AFG,-1.5393315553665161 +cotton_wheat,AGO,-1.828579306602478 cotton_wheat,ALB,7.5 cotton_wheat,ARG,7.5 cotton_wheat,ARM,7.5 -cotton_wheat,AUS,-6.046213626861572 +cotton_wheat,AUS,-5.821630477905273 cotton_wheat,AZE,7.5 -cotton_wheat,BDI,-0.986487865447998 -cotton_wheat,BGD,-1.7510145902633667 +cotton_wheat,BDI,-0.9709151387214661 +cotton_wheat,BGD,-1.4354977011680603 cotton_wheat,BGR,7.5 cotton_wheat,BOL,7.5 -cotton_wheat,BRA,-2.717313766479492 -cotton_wheat,BWA,-1.3096258640289307 -cotton_wheat,CHN,-6.671764373779297 -cotton_wheat,COD,-1.9201487302780151 +cotton_wheat,BRA,-2.414412021636963 +cotton_wheat,BWA,-1.2212975025177002 +cotton_wheat,CHN,-6.380103588104248 +cotton_wheat,COD,-1.8903422355651855 cotton_wheat,DZA,7.5 -cotton_wheat,EGY,-2.4428935050964355 -cotton_wheat,ESP,-1.7609281539916992 -cotton_wheat,ETH,-2.8228458166122437 +cotton_wheat,EGY,-2.2849302291870117 +cotton_wheat,ESP,-1.6384779214859009 +cotton_wheat,ETH,-2.763818383216858 cotton_wheat,GEO,7.5 -cotton_wheat,GRC,-1.7193666696548462 -cotton_wheat,HND,-1.9979214668273926 -cotton_wheat,IND,-1.6127487421035767 -cotton_wheat,IRN,-3.4113048315048218 -cotton_wheat,IRQ,-1.821287751197815 -cotton_wheat,ISR,-0.5829929411411285 -cotton_wheat,JOR,-2.197067618370056 -cotton_wheat,KAZ,-1.1165745258331299 -cotton_wheat,KEN,-0.7473786175251007 -cotton_wheat,KGZ,-1.4271141290664673 +cotton_wheat,GRC,-1.5952184796333313 +cotton_wheat,HND,-1.9269369840621948 +cotton_wheat,IND,-1.502088189125061 +cotton_wheat,IRN,-3.319577693939209 +cotton_wheat,IRQ,-1.7154425382614136 +cotton_wheat,ISR,-0.48658348619937897 +cotton_wheat,JOR,-2.074430465698242 +cotton_wheat,KAZ,-1.0356212258338928 +cotton_wheat,KEN,-0.6847791075706482 +cotton_wheat,KGZ,-1.3036302328109741 cotton_wheat,KOR,7.5 cotton_wheat,LBN,7.5 -cotton_wheat,LSO,3.31293323636055 -cotton_wheat,MAR,-2.9538320302963257 -cotton_wheat,MDG,-1.6943604946136475 -cotton_wheat,MEX,-3.8441927433013916 -cotton_wheat,MKD,-1.7522202730178833 +cotton_wheat,LSO,3.3486852645874023 +cotton_wheat,MAR,-2.8828492164611816 +cotton_wheat,MDG,-1.6440579891204834 +cotton_wheat,MEX,-3.6862032413482666 +cotton_wheat,MKD,-1.6068271398544312 cotton_wheat,MMR,7.5 -cotton_wheat,MOZ,-1.3748512864112854 -cotton_wheat,MWI,2.7887847423553467 -cotton_wheat,NAM,-2.4965704679489136 -cotton_wheat,NER,-0.7983721792697906 -cotton_wheat,NGA,-2.009230375289917 +cotton_wheat,MOZ,-1.3153573274612427 +cotton_wheat,MWI,2.797903537750244 +cotton_wheat,NAM,-2.4259612560272217 +cotton_wheat,NER,-0.7186118960380554 +cotton_wheat,NGA,-1.9649395942687988 cotton_wheat,NPL,7.5 cotton_wheat,OMN,-0.0 -cotton_wheat,PAK,-1.5079033374786377 +cotton_wheat,PAK,-1.444903016090393 cotton_wheat,PRT,7.5 -cotton_wheat,PRY,-0.7615677118301392 -cotton_wheat,PSE,-2.5268391370773315 +cotton_wheat,PRY,-0.6654393076896667 +cotton_wheat,PSE,-2.4168334007263184 cotton_wheat,ROU,-0.0 cotton_wheat,RUS,7.5 -cotton_wheat,RWA,-1.5867229104042053 -cotton_wheat,SDN,-1.730271339416504 -cotton_wheat,SOM,2.729339122772217 -cotton_wheat,SWZ,-1.7671488523483276 -cotton_wheat,SYR,-2.547218084335327 -cotton_wheat,TJK,-4.083103179931641 -cotton_wheat,TKM,-1.658170223236084 +cotton_wheat,RWA,-1.544191300868988 +cotton_wheat,SDN,-1.6667670607566833 +cotton_wheat,SOM,2.736971139907837 +cotton_wheat,SWZ,-1.7392154932022095 +cotton_wheat,SYR,-2.3691604137420654 +cotton_wheat,TJK,-4.012031078338623 +cotton_wheat,TKM,-1.5757298469543457 cotton_wheat,TUN,7.5 -cotton_wheat,TUR,-1.3289867639541626 -cotton_wheat,TZA,-1.4957273602485657 -cotton_wheat,UGA,-1.7584633231163025 +cotton_wheat,TUR,-1.164076805114746 +cotton_wheat,TZA,-1.4643433094024658 +cotton_wheat,UGA,-1.2309663891792297 cotton_wheat,URY,7.5 -cotton_wheat,USA,-1.547662377357483 -cotton_wheat,UZB,-2.7247159481048584 -cotton_wheat,YEM,-2.346269130706787 -cotton_wheat,ZAF,-2.8606035709381104 +cotton_wheat,USA,-1.4203462600708008 +cotton_wheat,UZB,-2.6358386278152466 +cotton_wheat,YEM,-2.2954611778259277 +cotton_wheat,ZAF,-2.8457608222961426 cotton_wheat,ZMB,7.5 -cotton_wheat,ZWE,3.7780861854553223 +cotton_wheat,ZWE,3.845416307449341 double_rice,AFG,7.5 double_rice,AGO,7.5 double_rice,ALB,-0.0 double_rice,ARG,7.5 double_rice,AUS,7.5 double_rice,AZE,7.5 -double_rice,BDI,3.459120362997055 -double_rice,BEN,-2.27530038356781 -double_rice,BFA,-2.307965397834778 -double_rice,BGD,-0.8014280200004578 -double_rice,BLZ,2.15325129032135 +double_rice,BDI,3.4640720188617706 +double_rice,BEN,-2.252288818359375 +double_rice,BFA,-2.2946739196777344 +double_rice,BGD,-0.6609027683734894 +double_rice,BLZ,2.160777449607849 double_rice,BOL,7.5 double_rice,BRA,7.5 -double_rice,BRN,-2.940953493118286 +double_rice,BRN,-2.9351606369018555 double_rice,BTN,7.5 double_rice,BWA,-0.0 double_rice,CAF,7.5 double_rice,CHL,7.5 -double_rice,CHN,-3.638684868812561 +double_rice,CHN,-3.61395001411438 double_rice,CIV,7.5 -double_rice,CMR,-2.4245405197143555 +double_rice,CMR,-2.4112186431884766 double_rice,COD,7.5 double_rice,COG,7.5 -double_rice,COL,-2.8125067949295044 -double_rice,CRI,-1.8621540665626526 +double_rice,COL,-2.7953860759735107 +double_rice,CRI,-1.8377521634101868 double_rice,CUB,7.5 -double_rice,DOM,2.074710249900818 +double_rice,DOM,2.077667713165283 double_rice,DZA,7.5 -double_rice,ECU,-1.2538227438926697 +double_rice,ECU,-1.242812693119049 double_rice,EGY,7.5 double_rice,ERI,7.5 double_rice,ESP,7.5 @@ -105,390 +105,390 @@ double_rice,FJI,7.5 double_rice,FRA,7.5 double_rice,GAB,7.5 double_rice,GEO,7.5 -double_rice,GHA,-0.86676886677742 -double_rice,GIN,-1.135214924812317 -double_rice,GMB,-0.14571018517017365 -double_rice,GNB,-2.637994170188904 +double_rice,GHA,-0.8505060970783234 +double_rice,GIN,-1.1266756057739258 +double_rice,GMB,-0.1345061957836151 +double_rice,GNB,-2.628569722175598 double_rice,GNQ,7.5 double_rice,GRC,7.5 -double_rice,GTM,2.298312783241272 +double_rice,GTM,2.3020763397216797 double_rice,GUF,7.5 double_rice,GUY,7.5 double_rice,HND,7.5 -double_rice,HTI,2.451133131980896 -double_rice,IDN,-2.7724738121032715 -double_rice,IND,-1.7454763650894165 +double_rice,HTI,2.4569265842437744 +double_rice,IDN,-2.754401206970215 +double_rice,IND,-1.5427192449569702 double_rice,IRN,7.5 double_rice,IRQ,7.5 double_rice,ITA,7.5 -double_rice,JPN,-7.0643720626831055 +double_rice,JPN,-6.955480098724365 double_rice,KEN,7.5 -double_rice,KHM,-2.599738359451294 +double_rice,KHM,-2.5836461782455444 double_rice,KOR,0.0 -double_rice,LAO,-2.6285818815231323 +double_rice,LAO,-2.611363172531128 double_rice,LBR,7.5 -double_rice,LKA,3.3538200557231903 +double_rice,LKA,3.363393157720566 double_rice,LSO,-0.0 double_rice,MAR,7.5 -double_rice,MDG,3.566383972764015 +double_rice,MDG,3.572959065437317 double_rice,MEX,7.5 double_rice,MKD,7.5 -double_rice,MLI,-0.3003535568714142 -double_rice,MMR,-2.776987075805664 +double_rice,MLI,-0.28100598603487015 +double_rice,MMR,-2.762181878089905 double_rice,MOZ,7.5 -double_rice,MRT,-2.035184144973755 +double_rice,MRT,-2.018742799758911 double_rice,MUS,7.5 -double_rice,MWI,-1.9879395365715027 -double_rice,MYS,-0.9627632796764374 +double_rice,MWI,-1.9781286716461182 +double_rice,MYS,-0.9503328204154968 double_rice,NAM,7.5 double_rice,NER,7.5 -double_rice,NGA,-0.5721426010131836 -double_rice,NIC,0.46788736432790756 -double_rice,NPL,3.4711324274539948 +double_rice,NGA,-0.5552420616149902 +double_rice,NIC,0.8526699244976044 +double_rice,NPL,3.480261266231537 double_rice,PAK,7.5 -double_rice,PAN,-2.177530288696289 +double_rice,PAN,-2.160972833633423 double_rice,PER,7.5 -double_rice,PHL,-1.6920741200447083 +double_rice,PHL,-1.678591251373291 double_rice,PNG,7.5 double_rice,PRI,7.5 double_rice,PRT,7.5 double_rice,PRY,7.5 -double_rice,RWA,-3.4936609268188477 +double_rice,RWA,-3.471513032913208 double_rice,SAU,7.5 double_rice,SDN,7.5 -double_rice,SEN,0.0678448062390089 +double_rice,SEN,0.08604421094059944 double_rice,SLB,7.5 -double_rice,SLE,3.564606726169586 +double_rice,SLE,3.5717902183532715 double_rice,SLV,7.5 double_rice,SOM,7.5 double_rice,SSD,7.5 double_rice,SUR,7.5 double_rice,SWZ,7.5 double_rice,SYR,7.5 -double_rice,TCD,-1.4169777631759644 -double_rice,TGO,-0.3228706866502762 -double_rice,THA,-0.9933395385742188 +double_rice,TCD,-1.4070334434509277 +double_rice,TGO,-0.31172609329223633 +double_rice,THA,-0.9801812171936035 double_rice,TJK,7.5 double_rice,TKM,7.5 double_rice,TLS,7.5 double_rice,TTO,7.5 double_rice,TUN,-0.0 double_rice,TUR,7.5 -double_rice,TWN,-1.8986664414405823 +double_rice,TWN,-1.8727219104766846 double_rice,TZA,7.5 double_rice,UGA,7.5 double_rice,URY,7.5 double_rice,USA,7.5 double_rice,UZB,7.5 double_rice,VEN,7.5 -double_rice,VNM,-2.0829989910125732 +double_rice,VNM,-2.064521551132202 double_rice,YEM,-0.0 double_rice,ZAF,7.5 double_rice,ZMB,7.5 double_rice,ZWE,7.5 -maize_soybean,AFG,-0.36834096908569336 -maize_soybean,AGO,-1.5214587450027466 +maize_soybean,AFG,-0.24639928340911865 +maize_soybean,AGO,-1.495425283908844 maize_soybean,ALB,7.5 -maize_soybean,ARG,-0.14088768512010574 +maize_soybean,ARG,0.005930550396442413 maize_soybean,ARM,7.5 -maize_soybean,AUS,1.358855128288269 -maize_soybean,AUT,4.534917175769806 -maize_soybean,AZE,0.8574774861335754 -maize_soybean,BDI,-1.1291301846504211 +maize_soybean,AUS,1.5263348817825317 +maize_soybean,AUT,4.658754765987396 +maize_soybean,AZE,1.064544439315796 +maize_soybean,BDI,-1.1078417897224426 maize_soybean,BEL,7.5 -maize_soybean,BEN,-0.864732563495636 -maize_soybean,BFA,-0.6915163397789001 +maize_soybean,BEN,-0.8307377994060516 +maize_soybean,BFA,-0.6477399170398712 maize_soybean,BGD,7.5 maize_soybean,BGR,7.5 maize_soybean,BIH,7.5 -maize_soybean,BLZ,-1.533581018447876 -maize_soybean,BOL,3.5874201208353043 -maize_soybean,BRA,-0.3474487364292145 -maize_soybean,BTN,3.947512686252594 -maize_soybean,BWA,-0.024261871352791786 +maize_soybean,BLZ,-1.4829245805740356 +maize_soybean,BOL,3.6306029930710793 +maize_soybean,BRA,-0.23076701164245605 +maize_soybean,BTN,4.1895085871219635 +maize_soybean,BWA,0.07603363692760468 maize_soybean,CAF,-7.5 -maize_soybean,CAN,-0.6935787200927734 +maize_soybean,CAN,-0.5440887808799744 maize_soybean,CHE,7.5 -maize_soybean,CHN,-1.875643253326416 -maize_soybean,CIV,-1.0517835021018982 -maize_soybean,CMR,-1.1753299832344055 -maize_soybean,COD,-1.6665139198303223 -maize_soybean,COG,-1.5524505376815796 -maize_soybean,COL,-0.5641129612922668 -maize_soybean,CRI,-1.0713813304901123 +maize_soybean,CHN,-1.7595432996749878 +maize_soybean,CIV,-1.0037175416946411 +maize_soybean,CMR,-1.1228896379470825 +maize_soybean,COD,-1.6491206884384155 +maize_soybean,COG,-1.5342630743980408 +maize_soybean,COL,-0.45324236154556274 +maize_soybean,CRI,-0.9920011162757874 maize_soybean,CUB,7.5 maize_soybean,CZE,7.5 maize_soybean,DEU,7.5 maize_soybean,DZA,-0.0 -maize_soybean,ECU,-0.8420949578285217 -maize_soybean,EGY,-1.7173116207122803 -maize_soybean,ESP,-1.1982262134552002 -maize_soybean,ETH,-0.26363644003868103 +maize_soybean,ECU,-0.7863299250602722 +maize_soybean,EGY,-1.5779436826705933 +maize_soybean,ESP,-1.0426932573318481 +maize_soybean,ETH,-0.17589585483074188 maize_soybean,FRA,7.5 -maize_soybean,GAB,-1.311855673789978 -maize_soybean,GEO,-0.014212247915565968 -maize_soybean,GHA,-0.9053362309932709 +maize_soybean,GAB,-1.277942180633545 +maize_soybean,GEO,0.0985608771443367 +maize_soybean,GHA,-0.8515743017196655 maize_soybean,GIN,7.5 -maize_soybean,GRC,3.186633288860321 -maize_soybean,GTM,-0.7608882188796997 -maize_soybean,HND,-0.5387400984764099 +maize_soybean,GRC,3.2253053188323975 +maize_soybean,GTM,-0.6474599242210388 +maize_soybean,HND,-0.4367038309574127 maize_soybean,HRV,7.5 -maize_soybean,HUN,0.23578396439552307 -maize_soybean,IDN,0.002664275001734495 -maize_soybean,IND,-0.8897814154624939 -maize_soybean,IRN,-1.318925142288208 -maize_soybean,IRQ,-0.7942403256893158 +maize_soybean,HUN,0.3925742208957672 +maize_soybean,IDN,0.09553293883800507 +maize_soybean,IND,-0.7820799946784973 +maize_soybean,IRN,-1.1933389902114868 +maize_soybean,IRQ,-0.7225880324840546 maize_soybean,ISR,7.5 maize_soybean,ITA,7.5 maize_soybean,JOR,-0.0 maize_soybean,KAZ,7.5 -maize_soybean,KEN,-0.4876989722251892 -maize_soybean,KGZ,-0.4609765112400055 -maize_soybean,KHM,-0.7535949647426605 -maize_soybean,KOR,-0.5010929703712463 -maize_soybean,LAO,-0.24195507913827896 +maize_soybean,KEN,-0.4605250954627991 +maize_soybean,KGZ,-0.372392863035202 +maize_soybean,KHM,-0.6993884742259979 +maize_soybean,KOR,-0.4483306109905243 +maize_soybean,LAO,-0.14955738559365273 maize_soybean,LBN,7.5 -maize_soybean,LKA,-1.0605616569519043 +maize_soybean,LKA,-0.9821675717830658 maize_soybean,LSO,7.5 maize_soybean,LUX,7.5 maize_soybean,MAR,7.5 maize_soybean,MDA,7.5 -maize_soybean,MEX,-0.27350837737321854 +maize_soybean,MEX,-0.16023508459329605 maize_soybean,MKD,7.5 -maize_soybean,MLI,-0.8302015066146851 -maize_soybean,MMR,-1.0919128060340881 +maize_soybean,MLI,-0.8005207777023315 +maize_soybean,MMR,-1.052363932132721 maize_soybean,MNE,7.5 -maize_soybean,MOZ,3.7639795625582337 -maize_soybean,MWI,3.5732914805412292 -maize_soybean,NGA,-0.8235953450202942 -maize_soybean,NIC,-0.35855451226234436 +maize_soybean,MOZ,3.82740980386734 +maize_soybean,MWI,3.5895184725522995 +maize_soybean,NGA,-0.7847930192947388 +maize_soybean,NIC,-0.15438295155763626 maize_soybean,NLD,7.5 -maize_soybean,NPL,3.480672299861908 -maize_soybean,PAK,-0.875553697347641 +maize_soybean,NPL,3.5199737548828125 +maize_soybean,PAK,-0.8209753930568695 maize_soybean,PER,7.5 maize_soybean,POL,7.5 maize_soybean,PRT,7.5 -maize_soybean,PRY,-0.5678771436214447 -maize_soybean,ROU,3.957804352045059 +maize_soybean,PRY,-0.49139875173568726 +maize_soybean,ROU,4.046223938465118 maize_soybean,RUS,7.5 -maize_soybean,RWA,-0.3930656760931015 -maize_soybean,SDN,-0.6107593774795532 -maize_soybean,SLV,-0.982840359210968 +maize_soybean,RWA,-0.37034963071346283 +maize_soybean,SDN,-0.5079055428504944 +maize_soybean,SLV,-0.9140610694885254 maize_soybean,SRB,7.5 maize_soybean,SSD,7.5 maize_soybean,SVK,7.5 maize_soybean,SVN,7.5 -maize_soybean,SWZ,-0.9142074584960938 +maize_soybean,SWZ,-0.8523779511451721 maize_soybean,SYR,7.5 -maize_soybean,TCD,3.5093756914138794 -maize_soybean,TGO,-0.4277437925338745 -maize_soybean,THA,-0.5364570021629333 +maize_soybean,TCD,3.536100909113884 +maize_soybean,TGO,-0.4024434983730316 +maize_soybean,THA,-0.47078707814216614 maize_soybean,TJK,7.5 maize_soybean,TKM,7.5 -maize_soybean,TLS,4.06027552485466 +maize_soybean,TLS,4.094823390245438 maize_soybean,TUR,7.5 maize_soybean,TWN,-0.0 -maize_soybean,TZA,-0.4137718230485916 -maize_soybean,UGA,-1.431039571762085 +maize_soybean,TZA,-0.37977515161037445 +maize_soybean,UGA,-0.8976666927337646 maize_soybean,UKR,7.5 -maize_soybean,URY,-0.25373270362615585 -maize_soybean,USA,-0.9644927978515625 +maize_soybean,URY,-0.1253272145986557 +maize_soybean,USA,-0.8225187659263611 maize_soybean,UZB,7.5 -maize_soybean,VEN,-0.9532034397125244 -maize_soybean,VNM,-0.4869907647371292 -maize_soybean,ZAF,-0.3208414912223816 -maize_soybean,ZMB,-0.6546103656291962 -maize_soybean,ZWE,0.8771634101867676 -rice_maize,AFG,-1.7281314134597778 -rice_maize,AGO,-2.376017928123474 +maize_soybean,VEN,-0.9020123183727264 +maize_soybean,VNM,-0.39292284846305847 +maize_soybean,ZAF,-0.2782892882823944 +maize_soybean,ZMB,-0.6135813593864441 +maize_soybean,ZWE,0.9308375120162964 +rice_maize,AFG,-1.7156256437301636 +rice_maize,AGO,-2.371963620185852 rice_maize,ALB,-0.0 rice_maize,ARG,7.5 rice_maize,ARM,-0.0 -rice_maize,AUS,-1.7170004844665527 -rice_maize,AZE,-2.7783961296081543 -rice_maize,BDI,-0.7229403853416443 -rice_maize,BEN,-1.6560019850730896 -rice_maize,BGD,-0.2345089465379715 +rice_maize,AUS,-1.6824020147323608 +rice_maize,AZE,-2.755751132965088 +rice_maize,BDI,-0.715380072593689 +rice_maize,BEN,-1.6448749899864197 +rice_maize,BGD,-0.007895642891526222 rice_maize,BGR,7.5 -rice_maize,BLZ,-2.591189384460449 +rice_maize,BLZ,-2.576688051223755 rice_maize,BOL,7.5 -rice_maize,BRA,-0.28851568698883057 -rice_maize,BTN,-0.7812106907367706 +rice_maize,BRA,-0.25463810563087463 +rice_maize,BTN,-0.2728106379508972 rice_maize,BWA,-0.0 -rice_maize,CAF,-2.3874566555023193 +rice_maize,CAF,-2.383610248565674 rice_maize,CAN,-0.0 -rice_maize,CHL,-1.7879591584205627 -rice_maize,CHN,-3.1381964683532715 -rice_maize,CIV,-0.9518814086914062 +rice_maize,CHL,-1.755562961101532 +rice_maize,CHN,-3.1142866611480713 +rice_maize,CIV,-0.9420885443687439 rice_maize,CMR,7.5 -rice_maize,COD,-2.5160088539123535 -rice_maize,COL,-1.9876278638839722 -rice_maize,COM,-2.392709732055664 +rice_maize,COD,-2.5120245218276978 +rice_maize,COL,-1.9699820280075073 +rice_maize,COM,-2.38859224319458 rice_maize,CUB,7.5 -rice_maize,DOM,2.334658145904541 +rice_maize,DOM,2.3344167470932007 rice_maize,DZA,7.5 -rice_maize,ECU,-0.6110467314720154 -rice_maize,EGY,-2.5272467136383057 +rice_maize,ECU,-0.587449312210083 +rice_maize,EGY,-2.432095766067505 rice_maize,ERI,7.5 -rice_maize,ESP,-3.4207522869110107 +rice_maize,ESP,-3.3779726028442383 rice_maize,ETH,7.5 rice_maize,FRA,7.5 rice_maize,GEO,7.5 -rice_maize,GHA,-1.0176984071731567 -rice_maize,GIN,-0.6864410638809204 -rice_maize,GNB,-1.703679084777832 -rice_maize,GRC,-2.030602812767029 -rice_maize,GTM,-2.182709813117981 -rice_maize,HND,-1.7741804122924805 -rice_maize,HTI,-2.2090805768966675 +rice_maize,GHA,-1.006989449262619 +rice_maize,GIN,-0.6754734814167023 +rice_maize,GNB,-1.6904276609420776 +rice_maize,GRC,-1.9942151308059692 +rice_maize,GTM,-2.209757924079895 +rice_maize,HND,-1.7683686017990112 +rice_maize,HTI,-2.2013126611709595 rice_maize,HUN,7.5 rice_maize,IDN,-0.0 -rice_maize,IND,-1.5077426433563232 -rice_maize,IRN,-5.5161731243133545 -rice_maize,IRQ,-2.3709990978240967 -rice_maize,ITA,-3.0599868297576904 -rice_maize,KAZ,0.07683989033102989 -rice_maize,KEN,-1.3079563975334167 -rice_maize,KGZ,-3.3971409797668457 -rice_maize,KOR,-6.8399059772491455 -rice_maize,LAO,-1.6010198593139648 -rice_maize,LKA,-1.2200570702552795 +rice_maize,IND,-1.3796111345291138 +rice_maize,IRN,-5.495550870895386 +rice_maize,IRQ,-2.3703798055648804 +rice_maize,ITA,-3.072711706161499 +rice_maize,KAZ,0.1040768101811409 +rice_maize,KEN,-1.3002654910087585 +rice_maize,KGZ,-3.3748340606689453 +rice_maize,KOR,-6.82611083984375 +rice_maize,LAO,-1.5817742347717285 +rice_maize,LKA,-1.2066054940223694 rice_maize,LSO,-0.0 -rice_maize,MAR,2.6312246322631836 +rice_maize,MAR,2.641839861869812 rice_maize,MDA,7.5 -rice_maize,MDG,-1.2577229738235474 -rice_maize,MEX,-0.6994309723377228 +rice_maize,MDG,-1.2508602142333984 +rice_maize,MEX,-0.6694565117359161 rice_maize,MKD,7.5 -rice_maize,MLI,-0.5936621427536011 -rice_maize,MMR,-1.8536213636398315 -rice_maize,MOZ,-0.5327273607254028 +rice_maize,MLI,-0.58109050989151 +rice_maize,MMR,-1.8382737636566162 +rice_maize,MOZ,-0.5283017158508301 rice_maize,MUS,7.5 -rice_maize,MWI,-1.2785919308662415 +rice_maize,MWI,-1.271949827671051 rice_maize,NAM,7.5 -rice_maize,NGA,-0.7131123244762421 +rice_maize,NGA,-0.7033146619796753 rice_maize,NIC,-0.0 -rice_maize,NPL,-0.6358287334442139 -rice_maize,PAK,-1.4368464946746826 -rice_maize,PER,-2.0332024097442627 -rice_maize,PHL,-1.1073416471481323 +rice_maize,NPL,-0.6228554397821426 +rice_maize,PAK,-1.41712486743927 +rice_maize,PER,-1.903525471687317 +rice_maize,PHL,-1.0916112065315247 rice_maize,PRI,7.5 -rice_maize,PRT,2.98510479927063 -rice_maize,PRY,-1.0809534788131714 -rice_maize,ROU,-1.5118907690048218 +rice_maize,PRT,3.0016173124313354 +rice_maize,PRY,-1.0660040378570557 +rice_maize,ROU,-1.4883065819740295 rice_maize,RUS,7.5 -rice_maize,RWA,-2.059702157974243 +rice_maize,RWA,-2.0454370975494385 rice_maize,SAU,7.5 -rice_maize,SDN,-2.523366689682007 +rice_maize,SDN,-2.5199800729751587 rice_maize,SEN,7.5 rice_maize,SLE,7.5 -rice_maize,SLV,-2.9107296466827393 -rice_maize,SOM,-0.6507601141929626 -rice_maize,SSD,-2.357653021812439 +rice_maize,SLV,-2.8959721326828003 +rice_maize,SOM,-0.6264951229095459 +rice_maize,SSD,-2.3550257682800293 rice_maize,SVK,-0.0 rice_maize,SVN,-0.0 rice_maize,SWZ,7.5 -rice_maize,SYR,-1.494040846824646 -rice_maize,TCD,-0.8244449496269226 -rice_maize,TGO,-0.45745545625686646 -rice_maize,THA,-0.9042835235595703 -rice_maize,TJK,-5.1068339347839355 -rice_maize,TKM,-1.2737932205200195 -rice_maize,TLS,-1.5115097761154175 +rice_maize,SYR,-1.2665398120880127 +rice_maize,TCD,-0.8180773258209229 +rice_maize,TGO,-0.45031896233558655 +rice_maize,THA,-0.8905860185623169 +rice_maize,TJK,-5.088619709014893 +rice_maize,TKM,-1.2574118971824646 +rice_maize,TLS,-1.493562936782837 rice_maize,TUN,-0.0 -rice_maize,TUR,-2.9757723808288574 -rice_maize,TZA,-0.11803673207759857 -rice_maize,UGA,-2.2781561613082886 +rice_maize,TUR,-2.9647523164749146 +rice_maize,TZA,-0.25860366225242615 +rice_maize,UGA,-1.3163431286811829 rice_maize,UKR,7.5 -rice_maize,URY,-1.0172240436077118 -rice_maize,USA,-1.8339455127716064 -rice_maize,UZB,-5.909193515777588 -rice_maize,VEN,-1.3209790587425232 -rice_maize,VNM,-1.2580879926681519 +rice_maize,URY,-0.9857281148433685 +rice_maize,USA,-1.7936946153640747 +rice_maize,UZB,-5.886659622192383 +rice_maize,VEN,-1.3058283925056458 +rice_maize,VNM,-1.2342076301574707 rice_maize,YEM,-0.0 -rice_maize,ZAF,-1.2925183176994324 -rice_maize,ZMB,-1.247011423110962 +rice_maize,ZAF,-1.2491300404071808 +rice_maize,ZMB,-1.2398909330368042 rice_maize,ZWE,7.5 -rice_wheat,AFG,-1.3623466491699219 +rice_wheat,AFG,-1.3488233089447021 rice_wheat,AGO,7.5 rice_wheat,ALB,-0.0 rice_wheat,ARG,7.5 rice_wheat,ARM,-0.0 -rice_wheat,AUS,-1.657541036605835 -rice_wheat,AZE,-2.706134557723999 -rice_wheat,BGD,-0.7609938383102417 -rice_wheat,BGR,2.8189637660980225 +rice_wheat,AUS,-1.6337718963623047 +rice_wheat,AZE,-2.69415545463562 +rice_wheat,BGD,-0.6395861506462097 +rice_wheat,BGR,2.8327901363372803 rice_wheat,BOL,7.5 rice_wheat,BRA,7.5 -rice_wheat,BTN,-0.4836849570274353 +rice_wheat,BTN,0.18891866505146027 rice_wheat,BWA,-0.0 rice_wheat,CAN,-0.0 -rice_wheat,CHL,-1.2868680953979492 -rice_wheat,CHN,-3.0675368309020996 -rice_wheat,CMR,-1.7619750499725342 -rice_wheat,COD,-2.2423064708709717 +rice_wheat,CHL,-1.2614909410476685 +rice_wheat,CHN,-3.0564918518066406 +rice_wheat,CMR,-1.7530806064605713 +rice_wheat,COD,-2.237744092941284 rice_wheat,DZA,7.5 -rice_wheat,ECU,-1.3073182106018066 -rice_wheat,EGY,-2.175827980041504 -rice_wheat,ESP,-2.198929786682129 +rice_wheat,ECU,-1.3023431301116943 +rice_wheat,EGY,-2.0646121501922607 +rice_wheat,ESP,-2.1721324920654297 rice_wheat,ETH,7.5 rice_wheat,FRA,7.5 rice_wheat,GEO,7.5 -rice_wheat,GRC,-0.9091585576534271 +rice_wheat,GRC,-0.9038871228694916 rice_wheat,GTM,7.5 -rice_wheat,HND,-2.0543227195739746 +rice_wheat,HND,-2.0475199222564697 rice_wheat,HUN,7.5 -rice_wheat,IND,-1.2919622659683228 -rice_wheat,IRN,-4.440040111541748 -rice_wheat,IRQ,-2.0962564945220947 -rice_wheat,ITA,-2.425924062728882 -rice_wheat,JPN,-0.553981602191925 -rice_wheat,KAZ,-0.38919803500175476 -rice_wheat,KGZ,-3.278252124786377 -rice_wheat,KOR,-6.691973686218262 +rice_wheat,IND,-1.1506867408752441 +rice_wheat,IRN,-4.425135374069214 +rice_wheat,IRQ,-2.090679407119751 +rice_wheat,ITA,-2.450207233428955 +rice_wheat,JPN,-0.5424911379814148 +rice_wheat,KAZ,-0.37714827060699463 +rice_wheat,KGZ,-3.2667434215545654 +rice_wheat,KOR,-6.678932189941406 rice_wheat,LAO,-0.0 rice_wheat,LSO,-0.0 -rice_wheat,MAR,-2.4127984046936035 +rice_wheat,MAR,-2.3947017192840576 rice_wheat,MDA,7.5 -rice_wheat,MDG,-0.9586451649665833 +rice_wheat,MDG,-0.9512857794761658 rice_wheat,MEX,7.5 rice_wheat,MKD,7.5 -rice_wheat,MLI,-0.5922143459320068 -rice_wheat,MMR,-1.9737898111343384 +rice_wheat,MLI,-0.57689368724823 +rice_wheat,MMR,-1.9620091915130615 rice_wheat,MOZ,7.5 rice_wheat,MRT,7.5 -rice_wheat,MWI,2.859695315361023 -rice_wheat,NAM,2.4526288509368896 -rice_wheat,NER,-1.3006837964057922 -rice_wheat,NGA,-0.9350881576538086 -rice_wheat,NPL,-0.7741658389568329 -rice_wheat,PAK,-1.6653406620025635 -rice_wheat,PRT,-1.3311695456504822 -rice_wheat,PRY,-0.9251327514648438 +rice_wheat,MWI,2.8620771765708923 +rice_wheat,NAM,2.4589394330978394 +rice_wheat,NER,-1.2836182713508606 +rice_wheat,NGA,-0.9234455227851868 +rice_wheat,NPL,-0.7631363272666931 +rice_wheat,PAK,-1.654567003250122 +rice_wheat,PRT,-1.3136804103851318 +rice_wheat,PRY,-0.9142377078533173 rice_wheat,ROU,7.5 rice_wheat,RUS,7.5 -rice_wheat,RWA,-2.2173590660095215 -rice_wheat,SDN,-1.6103779077529907 +rice_wheat,RWA,-2.2048232555389404 +rice_wheat,SDN,-1.598426103591919 rice_wheat,SOM,7.5 rice_wheat,SVK,-0.0 rice_wheat,SVN,-0.0 rice_wheat,SWZ,7.5 -rice_wheat,SYR,-1.1393061876296997 -rice_wheat,TCD,-1.0778582096099854 +rice_wheat,SYR,-0.933681845664978 +rice_wheat,TCD,-1.0673243403434753 rice_wheat,THA,7.5 -rice_wheat,TJK,-4.4916205406188965 -rice_wheat,TKM,-1.363931655883789 +rice_wheat,TJK,-4.49611234664917 +rice_wheat,TKM,-1.3513121604919434 rice_wheat,TUN,-0.0 -rice_wheat,TUR,-2.5900498628616333 -rice_wheat,TZA,-0.4893316626548767 -rice_wheat,UGA,-2.034787178039551 +rice_wheat,TUR,-2.587208390235901 +rice_wheat,TZA,-0.48051702976226807 +rice_wheat,UGA,-0.9530906081199646 rice_wheat,UKR,7.5 -rice_wheat,URY,-0.7634176015853882 -rice_wheat,USA,-1.4629401564598083 -rice_wheat,UZB,-4.858599662780762 +rice_wheat,URY,-0.7386437356472015 +rice_wheat,USA,-1.435759961605072 +rice_wheat,UZB,-4.843373775482178 rice_wheat,VEN,7.5 -rice_wheat,VNM,-1.4786078929901123 -rice_wheat,ZAF,-1.4668359756469727 -rice_wheat,ZMB,-0.9197326302528381 +rice_wheat,VNM,-1.4645306468009949 +rice_wheat,ZAF,-1.4215174913406372 +rice_wheat,ZMB,-0.9056271314620972 rice_wheat,ZWE,7.5 triple_rice,AFG,7.5 triple_rice,AGO,7.5 @@ -497,12 +497,12 @@ triple_rice,AUS,7.5 triple_rice,BDI,7.5 triple_rice,BEN,7.5 triple_rice,BFA,7.5 -triple_rice,BGD,-1.2986932396888733 +triple_rice,BGD,-1.083229899406433 triple_rice,BLZ,7.5 triple_rice,BOL,7.5 triple_rice,BRA,7.5 triple_rice,BRN,7.5 -triple_rice,BTN,-3.1063289642333984 +triple_rice,BTN,-2.1642297506332397 triple_rice,BWA,-0.0 triple_rice,CHN,7.5 triple_rice,CIV,7.5 @@ -527,7 +527,7 @@ triple_rice,GMB,7.5 triple_rice,GNB,7.5 triple_rice,GNQ,7.5 triple_rice,GRC,7.5 -triple_rice,GTM,1.5672242641448975 +triple_rice,GTM,1.5727946758270264 triple_rice,GUF,7.5 triple_rice,GUY,7.5 triple_rice,HND,7.5 @@ -538,7 +538,7 @@ triple_rice,IRN,7.5 triple_rice,IRQ,7.5 triple_rice,ITA,7.5 triple_rice,KEN,7.5 -triple_rice,KHM,-3.8996076583862305 +triple_rice,KHM,-3.8754693269729614 triple_rice,LAO,7.5 triple_rice,LBR,7.5 triple_rice,LKA,7.5 @@ -568,7 +568,7 @@ triple_rice,RWA,7.5 triple_rice,SAU,7.5 triple_rice,SDN,7.5 triple_rice,SEN,7.5 -triple_rice,SLB,-4.463399887084961 +triple_rice,SLB,-4.453315019607544 triple_rice,SLE,7.5 triple_rice,SLV,7.5 triple_rice,SOM,7.5 @@ -578,7 +578,7 @@ triple_rice,SWZ,7.5 triple_rice,TCD,7.5 triple_rice,TGO,7.5 triple_rice,THA,7.5 -triple_rice,TLS,1.2070257663726807 +triple_rice,TLS,1.216883897781372 triple_rice,TTO,7.5 triple_rice,TUR,7.5 triple_rice,TWN,7.5 @@ -587,204 +587,204 @@ triple_rice,UGA,7.5 triple_rice,URY,7.5 triple_rice,USA,7.5 triple_rice,VEN,7.5 -triple_rice,VNM,2.3774657249450684 +triple_rice,VNM,2.3972527980804443 triple_rice,YEM,-0.0 triple_rice,ZAF,7.5 triple_rice,ZMB,7.5 triple_rice,ZWE,7.5 -wheat_maize,AFG,-0.987142950296402 -wheat_maize,AGO,-1.5639793276786804 +wheat_maize,AFG,-0.9771143794059753 +wheat_maize,AGO,-1.5606600046157837 wheat_maize,ALB,7.5 wheat_maize,ARE,7.5 wheat_maize,ARG,7.5 -wheat_maize,ARM,-0.20531372725963593 -wheat_maize,AUS,0.21831773221492767 +wheat_maize,ARM,-0.18824926018714905 +wheat_maize,AUS,0.2493405044078827 wheat_maize,AUT,7.5 -wheat_maize,AZE,-0.16577379405498505 -wheat_maize,BDI,-1.1285237669944763 +wheat_maize,AZE,-0.14304496347904205 +wheat_maize,BDI,-1.1241341829299927 wheat_maize,BEL,7.5 -wheat_maize,BGD,-0.2805894762277603 +wheat_maize,BGD,-0.11300360597670078 wheat_maize,BGR,7.5 wheat_maize,BIH,7.5 -wheat_maize,BOL,-0.5379511713981628 -wheat_maize,BRA,-0.2551809549331665 -wheat_maize,BTN,0.9775815010070801 -wheat_maize,BWA,-0.12806838750839233 +wheat_maize,BOL,-0.5302393436431885 +wheat_maize,BRA,-0.2379850596189499 +wheat_maize,BTN,1.4397804737091064 +wheat_maize,BWA,-0.1105387881398201 wheat_maize,CAN,7.5 wheat_maize,CHE,7.5 -wheat_maize,CHL,-1.4204902052879333 -wheat_maize,CHN,-2.534622550010681 -wheat_maize,CMR,-0.8777397871017456 -wheat_maize,COD,-1.5509594678878784 -wheat_maize,COL,-1.017543077468872 +wheat_maize,CHL,-1.3878008723258972 +wheat_maize,CHN,-2.517098546028137 +wheat_maize,CMR,-0.8661477565765381 +wheat_maize,COD,-1.5462779998779297 +wheat_maize,COL,-0.9976252615451813 wheat_maize,CZE,7.5 wheat_maize,DEU,7.5 wheat_maize,DZA,7.5 -wheat_maize,ECU,-1.174363374710083 -wheat_maize,EGY,-1.9403982162475586 -wheat_maize,ERI,-1.483628749847412 -wheat_maize,ESP,-0.37183816730976105 -wheat_maize,ETH,-0.48095831274986267 +wheat_maize,ECU,-1.1636704206466675 +wheat_maize,EGY,-1.8505535125732422 +wheat_maize,ERI,-1.479116439819336 +wheat_maize,ESP,-0.3309044763445854 +wheat_maize,ETH,-0.46882209181785583 wheat_maize,FRA,7.5 -wheat_maize,GEO,0.03364504501223564 -wheat_maize,GRC,3.1327497363090515 -wheat_maize,GTM,3.283692806959152 -wheat_maize,HND,-1.1598638892173767 +wheat_maize,GEO,0.041972968727350235 +wheat_maize,GRC,3.129971146583557 +wheat_maize,GTM,3.290408134460449 +wheat_maize,HND,-1.1538816094398499 wheat_maize,HRV,7.5 -wheat_maize,HUN,-0.1889820173382759 -wheat_maize,IND,-0.7155007719993591 -wheat_maize,IRN,-2.1927868127822876 -wheat_maize,IRQ,-1.309715747833252 -wheat_maize,ISR,-6.799214601516724 -wheat_maize,ITA,3.2047759294509888 -wheat_maize,JOR,-0.4517395347356796 -wheat_maize,KAZ,0.05836658552289009 -wheat_maize,KEN,-0.8034234642982483 -wheat_maize,KGZ,-0.4759717583656311 -wheat_maize,KOR,-0.32594048976898193 +wheat_maize,HUN,-0.15658293664455414 +wheat_maize,IND,-0.6105437278747559 +wheat_maize,IRN,-2.175376057624817 +wheat_maize,IRQ,-1.3184446096420288 +wheat_maize,ISR,-6.801563262939453 +wheat_maize,ITA,3.2249295711517334 +wheat_maize,JOR,-0.5672706365585327 +wheat_maize,KAZ,0.07492727786302567 +wheat_maize,KEN,-0.7965687215328217 +wheat_maize,KGZ,-0.45488810539245605 +wheat_maize,KOR,-0.31005895137786865 wheat_maize,LAO,-0.0 -wheat_maize,LBN,-0.6256889700889587 +wheat_maize,LBN,-0.6128232777118683 wheat_maize,LBY,7.5 -wheat_maize,LSO,-0.02948465198278427 +wheat_maize,LSO,-0.020995542407035828 wheat_maize,LUX,7.5 -wheat_maize,MAR,-0.5155181288719177 +wheat_maize,MAR,-0.49854278564453125 wheat_maize,MDA,7.5 -wheat_maize,MDG,-1.1989635229110718 -wheat_maize,MEX,-1.1473992466926575 +wheat_maize,MDG,-1.1903154850006104 +wheat_maize,MEX,-1.1308531761169434 wheat_maize,MKD,7.5 -wheat_maize,MMR,-0.7055597007274628 +wheat_maize,MMR,-0.6871878206729889 wheat_maize,MNE,7.5 -wheat_maize,MOZ,-0.27358514070510864 -wheat_maize,MWI,3.3749821186065674 -wheat_maize,NAM,-1.4859395027160645 +wheat_maize,MOZ,-0.2654828205704689 +wheat_maize,MWI,3.377698630094528 +wheat_maize,NAM,-1.4721729159355164 wheat_maize,NIC,7.5 wheat_maize,NLD,7.5 -wheat_maize,NPL,-0.640615701675415 -wheat_maize,NZL,-1.7160097360610962 -wheat_maize,OMN,-4.420738577842712 -wheat_maize,PAK,-0.7934242486953735 -wheat_maize,PER,-0.9803022146224976 +wheat_maize,NPL,-0.6297461986541748 +wheat_maize,NZL,-1.676266610622406 +wheat_maize,OMN,-4.358198404312134 +wheat_maize,PAK,-0.7793705761432648 +wheat_maize,PER,-0.8892223834991455 wheat_maize,POL,7.5 -wheat_maize,PRT,-0.3829764425754547 -wheat_maize,PRY,-0.4256771057844162 -wheat_maize,PSE,0.8025093078613281 +wheat_maize,PRT,-0.3535839915275574 +wheat_maize,PRY,-0.41323547065258026 +wheat_maize,PSE,0.8442691266536713 wheat_maize,ROU,7.5 wheat_maize,RUS,7.5 -wheat_maize,RWA,-0.5496123433113098 -wheat_maize,SAU,-6.034632444381714 +wheat_maize,RWA,-0.543095588684082 +wheat_maize,SAU,-6.014848947525024 wheat_maize,SLV,-0.0 -wheat_maize,SOM,-0.16130700707435608 +wheat_maize,SOM,-0.14136090874671936 wheat_maize,SRB,7.5 wheat_maize,SSD,7.5 wheat_maize,SVK,7.5 wheat_maize,SVN,7.5 -wheat_maize,SWZ,-1.169561505317688 -wheat_maize,SYR,-1.1873837113380432 +wheat_maize,SWZ,-1.162410855293274 +wheat_maize,SYR,-1.029508799314499 wheat_maize,THA,7.5 -wheat_maize,TJK,-1.7446982860565186 -wheat_maize,TKM,-0.8227759003639221 +wheat_maize,TJK,-1.7142441272735596 +wheat_maize,TKM,-0.8120319843292236 wheat_maize,TUN,-0.0 -wheat_maize,TUR,0.18247121572494507 -wheat_maize,TWN,-0.015094302594661713 -wheat_maize,TZA,-0.32702822983264923 -wheat_maize,UGA,-1.261471152305603 +wheat_maize,TUR,0.17583587393164635 +wheat_maize,TWN,0.011174635961651802 +wheat_maize,TZA,-0.32217082381248474 +wheat_maize,UGA,-0.3979317545890808 wheat_maize,UKR,7.5 -wheat_maize,URY,-0.4494054391980171 -wheat_maize,USA,-0.7829434871673584 -wheat_maize,UZB,-2.4144288301467896 +wheat_maize,URY,-0.4275017976760864 +wheat_maize,USA,-0.7527242004871368 +wheat_maize,UZB,-2.3942429423332214 wheat_maize,VEN,7.5 -wheat_maize,VNM,-0.46575742959976196 -wheat_maize,YEM,-1.6690679788589478 -wheat_maize,ZAF,-0.4967718869447708 -wheat_maize,ZMB,0.23622439801692963 +wheat_maize,VNM,-0.4419398009777069 +wheat_maize,YEM,-1.6598316431045532 +wheat_maize,ZAF,-0.41288168728351593 +wheat_maize,ZMB,0.2423555850982666 wheat_maize,ZWE,7.5 -wheat_soybean,AFG,0.018615351989865303 -wheat_soybean,AGO,-1.2983956933021545 +wheat_soybean,AFG,0.1416904777288437 +wheat_soybean,AGO,-1.268706202507019 wheat_soybean,ALB,7.5 wheat_soybean,ARG,7.5 wheat_soybean,ARM,7.5 -wheat_soybean,AUS,0.7388911843299866 -wheat_soybean,AUT,4.02235934138298 -wheat_soybean,AZE,0.9244644641876221 -wheat_soybean,BDI,-1.507067620754242 +wheat_soybean,AUS,0.8940979838371277 +wheat_soybean,AUT,4.131305754184723 +wheat_soybean,AZE,1.1209875345230103 +wheat_soybean,BDI,-1.4990118145942688 wheat_soybean,BEL,7.5 -wheat_soybean,BGD,3.5125124603509903 +wheat_soybean,BGD,3.568268746137619 wheat_soybean,BGR,7.5 wheat_soybean,BIH,7.5 wheat_soybean,BOL,7.5 -wheat_soybean,BRA,-0.3770797550678253 +wheat_soybean,BRA,-0.26030583679676056 wheat_soybean,BTN,7.5 wheat_soybean,BWA,7.5 wheat_soybean,CAN,7.5 wheat_soybean,CHE,7.5 -wheat_soybean,CHN,-1.71414315700531 -wheat_soybean,CMR,-0.623664140701294 -wheat_soybean,COD,-1.329619824886322 +wheat_soybean,CHN,-1.5884044170379639 +wheat_soybean,CMR,-0.5788586139678955 +wheat_soybean,COD,-1.3126713633537292 wheat_soybean,CZE,7.5 wheat_soybean,DEU,7.5 wheat_soybean,DZA,-0.0 -wheat_soybean,ECU,-0.7199182212352753 -wheat_soybean,EGY,-1.5721540451049805 -wheat_soybean,ESP,-0.011029114946722984 -wheat_soybean,ETH,-0.9927521347999573 +wheat_soybean,ECU,-0.6604350507259369 +wheat_soybean,EGY,-1.4199702739715576 +wheat_soybean,ESP,0.13766144216060638 +wheat_soybean,ETH,-0.9223664999008179 wheat_soybean,FRA,7.5 -wheat_soybean,GEO,-0.015507880598306656 -wheat_soybean,GRC,3.6241913586854935 -wheat_soybean,GTM,3.729996809735894 -wheat_soybean,HND,-0.9084713160991669 +wheat_soybean,GEO,0.09215723723173141 +wheat_soybean,GRC,3.6550807505846024 +wheat_soybean,GTM,3.8035224974155426 +wheat_soybean,HND,-0.8701233267784119 wheat_soybean,HRV,7.5 -wheat_soybean,HUN,0.06872709095478058 -wheat_soybean,IND,-0.721184492111206 -wheat_soybean,IRN,-0.6055783033370972 -wheat_soybean,IRQ,-0.6384706273674965 +wheat_soybean,HUN,0.22135350853204727 +wheat_soybean,IND,-0.5626658201217651 +wheat_soybean,IRN,-0.44643235206604004 +wheat_soybean,IRQ,-0.561536580324173 wheat_soybean,ISR,7.5 wheat_soybean,ITA,7.5 wheat_soybean,JOR,-0.0 -wheat_soybean,JPN,1.809073805809021 +wheat_soybean,JPN,1.9242297410964966 wheat_soybean,KAZ,7.5 -wheat_soybean,KEN,-0.7868411540985107 -wheat_soybean,KGZ,-0.37445786595344543 -wheat_soybean,KOR,3.447060853242874 +wheat_soybean,KEN,-0.7631901800632477 +wheat_soybean,KGZ,-0.29753217101097107 +wheat_soybean,KOR,3.471531331539154 wheat_soybean,LAO,-0.0 wheat_soybean,LBN,7.5 wheat_soybean,LSO,7.5 wheat_soybean,LUX,7.5 -wheat_soybean,MAR,-1.7621086835861206 +wheat_soybean,MAR,-1.7197248339653015 wheat_soybean,MDA,7.5 -wheat_soybean,MEX,-0.617741733789444 +wheat_soybean,MEX,-0.49481111764907837 wheat_soybean,MKD,7.5 -wheat_soybean,MMR,3.705183546990156 +wheat_soybean,MMR,3.77154241502285 wheat_soybean,MNE,7.5 wheat_soybean,MOZ,7.5 -wheat_soybean,MWI,3.2974755465984344 +wheat_soybean,MWI,3.3128238916397095 wheat_soybean,NGA,7.5 wheat_soybean,NLD,7.5 wheat_soybean,NPL,7.5 -wheat_soybean,PAK,-0.6344019174575806 -wheat_soybean,PER,-0.4700112044811249 +wheat_soybean,PAK,-0.5844757556915283 +wheat_soybean,PER,-0.32537347078323364 wheat_soybean,POL,7.5 wheat_soybean,PRT,7.5 -wheat_soybean,PRY,-0.41118374466896057 +wheat_soybean,PRY,-0.3288303017616272 wheat_soybean,ROU,7.5 wheat_soybean,RUS,7.5 -wheat_soybean,RWA,-0.501826599240303 +wheat_soybean,RWA,-0.4854601174592972 wheat_soybean,SRB,7.5 wheat_soybean,SVK,7.5 wheat_soybean,SVN,7.5 -wheat_soybean,SWZ,-0.6659440994262695 +wheat_soybean,SWZ,-0.6040565967559814 wheat_soybean,SYR,7.5 wheat_soybean,THA,7.5 wheat_soybean,TJK,7.5 -wheat_soybean,TKM,4.051137536764145 +wheat_soybean,TKM,4.1461657881736755 wheat_soybean,TUR,7.5 -wheat_soybean,TWN,-0.5661178827285767 -wheat_soybean,TZA,-0.5074585676193237 -wheat_soybean,UGA,-1.2226170301437378 +wheat_soybean,TWN,-0.4966292679309845 +wheat_soybean,TZA,-0.4755927324295044 +wheat_soybean,UGA,-0.5922997891902924 wheat_soybean,UKR,7.5 -wheat_soybean,URY,-0.15360423922538757 -wheat_soybean,USA,-0.42715510725975037 +wheat_soybean,URY,-0.031324923038482666 +wheat_soybean,USA,-0.27806317806243896 wheat_soybean,UZB,7.5 -wheat_soybean,VNM,-0.8331124782562256 -wheat_soybean,ZAF,-0.5704772472381592 -wheat_soybean,ZMB,-0.41744768619537354 -wheat_soybean,ZWE,4.304408550262451 +wheat_soybean,VNM,-0.7635192275047302 +wheat_soybean,ZAF,-0.4697279632091522 +wheat_soybean,ZMB,-0.37300992012023926 +wheat_soybean,ZWE,4.352203845977783 diff --git a/data/curated/calibration/default/provenance.yaml b/data/curated/calibration/default/provenance.yaml index 4fd0376a..becd2ade 100644 --- a/data/curated/calibration/default/provenance.yaml +++ b/data/curated/calibration/default/provenance.yaml @@ -4,8 +4,8 @@ # tools/calibrate; do not edit by hand. # Licensing: see the annotation in REUSE.toml. base_config: config/default.yaml -generated_at: '2026-07-31T21:23:26+00:00' -git_commit: 5496ce53b867cc2b979ba4db94628e9bc36842d5 +generated_at: '2026-07-31T22:51:46+00:00' +git_commit: 4492a036cad165a3624d950ac1aebd8df1170d17 source: default structural_config: aggregation.irrigated_area_source: current @@ -1007,7 +1007,6 @@ structural_config: numerics.min_link_area_mha: 1.0e-06 numerics.min_water_capacity_mm3: 1.0e-06 numerics.min_water_requirement_m3_per_ha: 0.1 - optimal_taxes.enabled: false residues.max_feed_fraction: 0.3 residues.max_feed_fraction_by_region.Asia: 0.7 residues.max_feed_fraction_by_region.Southern Asia: 0.85 diff --git a/data/curated/calibration/gbd-anchored/animal_cost.csv b/data/curated/calibration/gbd-anchored/animal_cost.csv index 865d8fa2..1c529d22 100644 --- a/data/curated/calibration/gbd-anchored/animal_cost.csv +++ b/data/curated/calibration/gbd-anchored/animal_cost.csv @@ -1,179 +1,179 @@ product,country,correction_bnusd_per_mt_feed -dairy,AFG,0.08779405429959297 -dairy,AGO,-0.11807353422045708 -dairy,ALB,0.030521942302584648 -dairy,ARE,-0.02422388643026352 -dairy,ARG,-0.15797340124845505 -dairy,ARM,0.02506777737289667 +dairy,AFG,0.11665473133325577 +dairy,AGO,-0.10958565771579742 +dairy,ALB,0.09681235626339912 +dairy,ARE,0.002835091669112444 +dairy,ARG,-0.12173284590244293 +dairy,ARM,0.05384315364062786 dairy,ASM,-0.0 -dairy,ATG,-0.1343848705291748 -dairy,AUS,-0.010158785618841648 -dairy,AUT,0.039724791422486305 -dairy,AZE,0.010201757773756981 -dairy,BDI,-0.16113214194774628 -dairy,BEL,0.01118060969747603 -dairy,BEN,-0.15002331882715225 -dairy,BFA,-0.1561267450451851 -dairy,BGD,-0.03128131711855531 -dairy,BGR,0.09701193124055862 -dairy,BHS,-0.09497595950961113 -dairy,BIH,0.1474909782409668 -dairy,BLR,-0.01906001102179289 -dairy,BLZ,-0.13580263406038284 -dairy,BOL,-0.16305941343307495 -dairy,BRA,-0.14912422001361847 -dairy,BRB,-0.23805740475654602 -dairy,BRN,-0.000833330035675317 -dairy,BTN,0.015676639042794704 -dairy,BWA,-0.15388945490121841 -dairy,CAF,-0.10843019187450409 -dairy,CAN,-0.12017573043704033 -dairy,CHE,0.040070025250315666 -dairy,CHL,-0.1389896757900715 -dairy,CHN,0.226161889731884 -dairy,CIV,-0.15333694964647293 -dairy,CMR,-0.15779545903205872 -dairy,COD,-0.16078463941812515 -dairy,COG,-0.15586724132299423 -dairy,COL,-0.18215588480234146 -dairy,COM,-0.14449886232614517 -dairy,CPV,-0.12624528259038925 -dairy,CRI,-0.06190120056271553 -dairy,CUB,-0.14545568823814392 -dairy,CYP,0.0896730087697506 -dairy,CZE,0.037587711587548256 -dairy,DEU,0.04229319468140602 -dairy,DJI,-0.024252383038401604 -dairy,DNK,0.06210261024534702 -dairy,DOM,-0.040988773107528687 -dairy,DZA,0.004441245808266103 -dairy,ECU,-0.18561379611492157 -dairy,EGY,-0.035196490585803986 -dairy,ERI,-0.09568149782717228 -dairy,ESP,-0.03327555023133755 -dairy,EST,0.04387612361460924 -dairy,ETH,-0.13133932650089264 -dairy,FIN,0.13209064304828644 -dairy,FJI,0.013514197431504726 -dairy,FRA,-0.022746789269149303 -dairy,GAB,-0.04812097828835249 -dairy,GBR,0.11036580428481102 -dairy,GEO,0.01809266209602356 -dairy,GHA,-0.09991642646491528 -dairy,GIN,-0.10654427483677864 -dairy,GMB,-0.13684069365262985 -dairy,GNB,-0.1589794084429741 +dairy,ATG,-0.07967411354184151 +dairy,AUS,-0.0 +dairy,AUT,0.09793809801340103 +dairy,AZE,0.039731714874506 +dairy,BDI,-0.15231163799762726 +dairy,BEL,0.07386421225965023 +dairy,BEN,-0.14079925045371056 +dairy,BFA,-0.1478307694196701 +dairy,BGD,-0.01161117188166827 +dairy,BGR,0.14611319452524185 +dairy,BHS,-0.0363323800265789 +dairy,BIH,0.21504786610603333 +dairy,BLR,0.041194526478648186 +dairy,BLZ,-0.09966141358017921 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-dairy,UZB,0.08995126560330391 -dairy,VEN,-0.12907173857092857 -dairy,VNM,-0.04298138804733753 -dairy,VUT,-0.0 -dairy,YEM,-0.06630112044513226 -dairy,ZAF,-0.11691146343946457 -dairy,ZMB,-0.11438443884253502 -dairy,ZWE,-0.3665000647306442 +dairy,SLE,-0.1290481835603714 +dairy,SLV,-0.07854030467569828 +dairy,SOM,-0.10920494049787521 +dairy,SRB,0.1253434382379055 +dairy,SSD,-0.0724406149238348 +dairy,STP,-0.08015126734972 +dairy,SUR,-0.08754929900169373 +dairy,SVK,0.09875765815377235 +dairy,SVN,0.1025872454047203 +dairy,SWE,0.14308694005012512 +dairy,SWZ,-0.10412796586751938 +dairy,SYR,0.039660222828388214 +dairy,TCD,-0.1197659820318222 +dairy,TGO,-0.1458600088953972 +dairy,THA,0.09464703872799873 +dairy,TJK,0.3000872880220413 +dairy,TKM,0.10936862975358963 +dairy,TLS,-0.12579500675201416 +dairy,TTO,-0.07214759849011898 +dairy,TUN,0.024136618711054325 +dairy,TUR,0.008856874890625477 +dairy,TWN,0.20665578544139862 +dairy,TZA,-0.15376924723386765 +dairy,UGA,-0.1616581380367279 +dairy,UKR,0.09019911661744118 +dairy,URY,-0.17051266133785248 +dairy,USA,0.15388260036706924 +dairy,UZB,0.1388033740222454 +dairy,VEN,-0.0945858284831047 +dairy,VNM,-0.01719812015653588 +dairy,VUT,-0.009442624635994434 +dairy,YEM,-0.04489763267338276 +dairy,ZAF,-0.098368339240551 +dairy,ZMB,-0.1046583279967308 +dairy,ZWE,-0.3578476533293724 dairy-buffalo,AFG,-0.0 dairy-buffalo,AGO,-0.0 dairy-buffalo,ALB,-0.0 @@ -189,8 +189,8 @@ dairy-buffalo,BDI,-0.0 dairy-buffalo,BEL,-0.0 dairy-buffalo,BEN,-0.0 dairy-buffalo,BFA,-0.0 -dairy-buffalo,BGD,-0.07310926541686058 -dairy-buffalo,BGR,0.036647021770477295 +dairy-buffalo,BGD,-0.060135023668408394 +dairy-buffalo,BGR,0.100676778703928 dairy-buffalo,BHS,-0.0 dairy-buffalo,BIH,-0.0 dairy-buffalo,BLR,-0.0 @@ -198,14 +198,14 @@ dairy-buffalo,BLZ,-0.0 dairy-buffalo,BOL,-0.0 dairy-buffalo,BRA,-0.0 dairy-buffalo,BRB,-0.0 -dairy-buffalo,BRN,-0.06423420831561089 +dairy-buffalo,BRN,-0.05233748350292444 dairy-buffalo,BTN,-0.0 dairy-buffalo,BWA,-0.0 dairy-buffalo,CAF,-0.0 dairy-buffalo,CAN,-0.0 dairy-buffalo,CHE,-0.0 dairy-buffalo,CHL,-0.0 -dairy-buffalo,CHN,-0.05201144888997078 +dairy-buffalo,CHN,-0.0348187992349267 dairy-buffalo,CIV,-0.0 dairy-buffalo,CMR,-0.0 dairy-buffalo,COD,-0.0 @@ -223,7 +223,7 @@ dairy-buffalo,DNK,-0.0 dairy-buffalo,DOM,-0.0 dairy-buffalo,DZA,-0.0 dairy-buffalo,ECU,-0.0 -dairy-buffalo,EGY,-0.005252341739833355 +dairy-buffalo,EGY,0.055593742057681084 dairy-buffalo,ERI,-0.0 dairy-buffalo,ESP,-0.0 dairy-buffalo,EST,-0.0 @@ -239,7 +239,7 @@ dairy-buffalo,GIN,-0.0 dairy-buffalo,GMB,-0.0 dairy-buffalo,GNB,-0.0 dairy-buffalo,GNQ,-0.0 -dairy-buffalo,GRC,-0.022178315732162446 +dairy-buffalo,GRC,0.03399278217693791 dairy-buffalo,GRD,-0.0 dairy-buffalo,GTM,-0.0 dairy-buffalo,GUF,-0.0 @@ -248,14 +248,14 @@ dairy-buffalo,HND,-0.0 dairy-buffalo,HRV,-0.0 dairy-buffalo,HTI,-0.0 dairy-buffalo,HUN,-0.0 -dairy-buffalo,IDN,-0.10516064614057541 -dairy-buffalo,IND,-0.024943988770246506 +dairy-buffalo,IDN,-0.09161068499088287 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dairy-buffalo,MWI,-0.0 -dairy-buffalo,MYS,-0.04106001649051905 +dairy-buffalo,MYS,-0.02279369463212788 dairy-buffalo,NAM,-0.0 dairy-buffalo,NER,-0.0 dairy-buffalo,NGA,-0.0 dairy-buffalo,NIC,-0.0 dairy-buffalo,NLD,-0.0 dairy-buffalo,NOR,-0.0 -dairy-buffalo,NPL,-0.03376719821244478 +dairy-buffalo,NPL,-0.01524300966411829 dairy-buffalo,NZL,-0.0 dairy-buffalo,OMN,-0.0 -dairy-buffalo,PAK,0.038014208897948265 +dairy-buffalo,PAK,0.06585447303950787 dairy-buffalo,PAN,-0.0 dairy-buffalo,PER,-0.0 dairy-buffalo,PHL,-0.0 @@ -325,7 +325,7 @@ dairy-buffalo,SVK,-0.0 dairy-buffalo,SVN,-0.0 dairy-buffalo,SWE,-0.0 dairy-buffalo,SWZ,-0.0 -dairy-buffalo,SYR,-0.02798618283122778 +dairy-buffalo,SYR,0.014146866276860237 dairy-buffalo,TCD,-0.0 dairy-buffalo,TGO,-0.0 dairy-buffalo,THA,-0.0 @@ -334,7 +334,7 @@ dairy-buffalo,TKM,-0.0 dairy-buffalo,TLS,-0.0 dairy-buffalo,TTO,-0.0 dairy-buffalo,TUN,-0.0 -dairy-buffalo,TUR,-0.027922209817916155 +dairy-buffalo,TUR,0.02017058851197362 dairy-buffalo,TWN,-0.0 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-meat-sheep,YEM,-0.0634921956807375 -meat-sheep,ZAF,-0.021188186947256327 -meat-sheep,ZMB,-0.05993673950433731 +meat-sheep,USA,0.006823573261499405 +meat-sheep,UZB,-0.08521183952689171 +meat-sheep,VEN,-0.022341537289321423 +meat-sheep,VNM,-0.09065870568156242 +meat-sheep,VUT,-0.09149482473731041 +meat-sheep,YEM,-0.060509148985147476 +meat-sheep,ZAF,-0.018012625630944967 +meat-sheep,ZMB,-0.056761179119348526 meat-sheep,ZWE,-0.3338071405887604 diff --git a/data/curated/calibration/gbd-anchored/crop_cost.csv b/data/curated/calibration/gbd-anchored/crop_cost.csv index f9c7ea33..b04ba86e 100644 --- a/data/curated/calibration/gbd-anchored/crop_cost.csv +++ b/data/curated/calibration/gbd-anchored/crop_cost.csv @@ -3,31 +3,31 @@ alfalfa,AFG,-0.3613618314266205 alfalfa,AGO,-0.9381504952907562 alfalfa,ALB,-0.5532752722501755 alfalfa,ARE,-0.8472728133201599 -alfalfa,ARG,-0.7233680784702301 +alfalfa,ARG,-0.724528580904007 alfalfa,ARM,-0.0 alfalfa,ATG,-7.5 -alfalfa,AUS,0.14821802079677582 +alfalfa,AUS,0.37977923452854156 alfalfa,AUT,-0.8089405298233032 alfalfa,AZE,0.5910350233316422 alfalfa,BDI,-0.5545685291290283 -alfalfa,BEL,0.045041752979159355 +alfalfa,BEL,0.03831017090124078 alfalfa,BEN,0.3704932779073715 alfalfa,BFA,-0.0 alfalfa,BGD,-0.7667536735534668 -alfalfa,BGR,-0.20587100088596344 -alfalfa,BHS,-4.099594205617905 -alfalfa,BIH,3.66515351831913 -alfalfa,BLR,-2.8165052980184555 +alfalfa,BGR,0.008758635260164738 +alfalfa,BHS,-0.6647240519523621 +alfalfa,BIH,3.789812069386244 +alfalfa,BLR,-2.806079253554344 alfalfa,BLZ,-0.010105629451572895 alfalfa,BOL,-0.37848326563835144 alfalfa,BRA,-0.6524094343185425 alfalfa,BRB,1.7822409272193909 alfalfa,BRN,-0.8346583247184753 -alfalfa,BTN,-0.3915858715772629 +alfalfa,BTN,-0.001871597021818161 alfalfa,BWA,-7.5 -alfalfa,CAF,-0.9728804230690002 -alfalfa,CAN,-0.4953949749469757 -alfalfa,CHE,-0.05504773184657097 +alfalfa,CAF,-0.6662335693836212 +alfalfa,CAN,-0.4924847185611725 +alfalfa,CHE,0.49974900484085083 alfalfa,CHL,-0.868007481098175 alfalfa,CHN,0.07321733981370926 alfalfa,CIV,7.5 @@ -40,8 +40,8 @@ alfalfa,CPV,-7.5 alfalfa,CRI,-0.22407843172550201 alfalfa,CUB,-0.2862354665994644 alfalfa,CYP,2.749604105949402 -alfalfa,CZE,-0.5457680821418762 -alfalfa,DEU,-0.19640730321407318 +alfalfa,CZE,-0.3859436810016632 +alfalfa,DEU,-0.022476794430986047 alfalfa,DJI,-0.835690438747406 alfalfa,DNK,6.004988431930542 alfalfa,DOM,-0.616400271654129 @@ -49,29 +49,29 @@ alfalfa,DZA,-0.25785040855407715 alfalfa,ECU,-0.07392867282032967 alfalfa,EGY,1.912139892578125 alfalfa,ERI,-0.8745495080947876 -alfalfa,ESP,-1.2047779560089111 +alfalfa,ESP,-1.1559807658195496 alfalfa,EST,-0.119085893034935 -alfalfa,ETH,-0.8379373550415039 +alfalfa,ETH,-0.8379373252391815 alfalfa,FIN,-0.0 -alfalfa,FJI,-0.4873450696468353 +alfalfa,FJI,-0.373370498418808 alfalfa,FRA,-0.31077052652835846 alfalfa,GAB,-0.8707610964775085 -alfalfa,GBR,0.07716290466487408 +alfalfa,GBR,0.1568083092570305 alfalfa,GEO,-0.5318324565887451 alfalfa,GHA,-0.9061825275421143 alfalfa,GIN,-0.8761205673217773 alfalfa,GMB,-7.5 alfalfa,GNB,-0.0 alfalfa,GNQ,-0.7592925727367401 -alfalfa,GRC,-0.6801228523254395 +alfalfa,GRC,-0.6527281105518341 alfalfa,GRD,0.10864944756031036 alfalfa,GTM,-0.5668327510356903 alfalfa,GUF,-2.7332428693771362 alfalfa,GUY,-0.07167529128491879 -alfalfa,HND,-0.8811778128147125 -alfalfa,HRV,-4.087149053812027 -alfalfa,HTI,-0.9849196076393127 -alfalfa,HUN,-0.8540847301483154 +alfalfa,HND,-0.3479782044887543 +alfalfa,HRV,-4.087068527936935 +alfalfa,HTI,-0.8184686303138733 +alfalfa,HUN,-0.6875935196876526 alfalfa,IDN,-0.14206960052251816 alfalfa,IND,2.2658207416534424 alfalfa,IRL,-0.5063982009887695 @@ -79,74 +79,74 @@ alfalfa,IRN,-1.4752857685089111 alfalfa,IRQ,1.6464712619781494 alfalfa,ISL,-0.8342201411724091 alfalfa,ISR,1.1934051513671875 -alfalfa,ITA,0.31106430292129517 +alfalfa,ITA,0.3182814121246338 alfalfa,JAM,1.3907389640808105 -alfalfa,JOR,0.4178725481033325 +alfalfa,JOR,0.4094598889350891 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+alfalfa,SVN,-0.30759669095277786 alfalfa,SWE,-0.0 alfalfa,SWZ,-0.9169159531593323 alfalfa,SYR,-0.0 @@ -155,24 +155,24 @@ alfalfa,TGO,-0.03396269865334034 alfalfa,THA,-0.67161625623703 alfalfa,TJK,-0.3050016388297081 alfalfa,TKM,-0.0 -alfalfa,TLS,-0.5261611938476562 +alfalfa,TLS,-0.038952913135290146 alfalfa,TTO,-0.7713225185871124 alfalfa,TUN,0.12095299363136292 -alfalfa,TUR,2.5061681270599365 -alfalfa,TWN,0.6210721731185913 +alfalfa,TUR,2.4885298013687134 +alfalfa,TWN,0.8952378630638123 alfalfa,TZA,-0.31079547107219696 -alfalfa,UGA,-0.2817348837852478 -alfalfa,UKR,-0.2906511127948761 +alfalfa,UGA,-0.281734861433506 +alfalfa,UKR,-0.28483082354068756 alfalfa,URY,-0.005927370395511389 -alfalfa,USA,0.016562855802476406 +alfalfa,USA,0.049377115443348885 alfalfa,UZB,-1.2962945699691772 alfalfa,VEN,-0.8396434485912323 alfalfa,VNM,2.0635113641619682 -alfalfa,VUT,-0.5605751574039459 -alfalfa,YEM,-0.843728244304657 +alfalfa,VUT,-0.10066892206668854 +alfalfa,YEM,-0.8437283337116241 alfalfa,ZAF,-0.608656644821167 alfalfa,ZMB,-7.5 -alfalfa,ZWE,-0.9153462052345276 +alfalfa,ZWE,-0.926518976688385 apple,AFG,-0.0 apple,ALB,-0.0 apple,ARG,-7.5 @@ -233,7 +233,7 @@ apple,LUX,-0.0 apple,LVA,-0.0 apple,MAR,-0.0 apple,MDA,-0.0 -apple,MDG,-1.9526464939117432 +apple,MDG,-1.952646255493164 apple,MEX,-0.0 apple,MKD,-0.0 apple,MMR,-7.5 @@ -279,126 +279,126 @@ apple,ZAF,-0.0 apple,ZMB,-0.0 apple,ZWE,-3.75 banana,AFG,-0.0 -banana,AGO,-6.958126544952393 +banana,AGO,-6.9355995655059814 banana,ALB,-0.0 banana,ARE,-7.5 -banana,ARG,-0.2507493123412132 +banana,ARG,-0.14173108711838722 banana,ATG,7.5 banana,AUS,-7.5 -banana,BDI,-0.7890473902225494 +banana,BDI,-0.7801598906517029 banana,BEN,-7.5 -banana,BFA,-4.5478644371032715 -banana,BGD,-4.864613771438599 -banana,BHS,-5.580332517623901 -banana,BLZ,-3.5261470079421997 -banana,BOL,-0.4299512207508087 -banana,BRA,-2.035299062728882 +banana,BFA,-4.497248888015747 +banana,BGD,-4.8153839111328125 +banana,BHS,-5.537912130355835 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barley,KEN,-0.7242023050785065 barley,KGZ,-0.2471529245376587 @@ -461,32 +461,32 @@ barley,LBN,-0.4228936582803726 barley,LBY,-0.8255002498626709 barley,LSO,-0.5060571283102036 barley,LTU,-0.2725280523300171 -barley,LUX,-0.25364426523447037 +barley,LUX,-0.25911958143115044 barley,LVA,-0.21785104274749756 barley,MAR,-0.5466785430908203 barley,MDA,-0.06059621088206768 -barley,MEX,-0.5090495944023132 +barley,MEX,-0.5044541209936142 barley,MKD,-0.03423933684825897 -barley,MLT,-2.1880600452423096 +barley,MLT,-2.1910479068756104 barley,MMR,-0.2215878665447235 -barley,MNE,-0.22380105406045914 +barley,MNE,-0.17735646665096283 barley,MNG,-0.15878962725400925 barley,MOZ,7.5 barley,MRT,-0.19137148559093475 barley,MWI,-0.0 barley,NAM,-0.0 -barley,NLD,-1.6565624475479126 +barley,NLD,-1.5992348194122314 barley,NOR,-0.9029717147350311 barley,NPL,-0.0024124756455421448 barley,NZL,-0.8330382108688354 -barley,OMN,-1.3771550059318542 +barley,OMN,-1.5959952026605606 barley,PAK,-0.5070545673370361 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-dry-pea,PSE,-1.8412739634513855 -dry-pea,ROU,-1.9241222739219666 -dry-pea,RUS,-0.19581736624240875 -dry-pea,RWA,-0.7504282891750336 -dry-pea,SAU,-0.44989416003227234 -dry-pea,SDN,-0.5861651003360748 -dry-pea,SEN,-0.5925172567367554 -dry-pea,SLB,-0.5386015176773071 -dry-pea,SLE,-0.5921421051025391 -dry-pea,SLV,3.4794811606407166 -dry-pea,SOM,-0.6069486439228058 -dry-pea,SRB,-0.346237376332283 -dry-pea,SSD,-0.5640181303024292 +dry-pea,PRT,-0.5241944789886475 +dry-pea,PRY,-0.6052982211112976 +dry-pea,PSE,-1.8448768258094788 +dry-pea,ROU,-1.9320248365402222 +dry-pea,RUS,-0.20649497210979462 +dry-pea,RWA,-0.7529760003089905 +dry-pea,SAU,-0.4644748568534851 +dry-pea,SDN,-0.5890860557556152 +dry-pea,SEN,-0.5942176580429077 +dry-pea,SLB,-0.5439764261245728 +dry-pea,SLE,-0.5938681960105896 +dry-pea,SLV,3.4767514765262604 +dry-pea,SOM,-0.6075789034366608 +dry-pea,SRB,-0.35762879252433777 +dry-pea,SSD,-0.5679865479469299 dry-pea,SUR,0.0 -dry-pea,SVK,-0.299690306186676 -dry-pea,SVN,-0.3057422637939453 -dry-pea,SWE,-0.38953590393066406 -dry-pea,SWZ,-0.5678097307682037 -dry-pea,SYR,-0.5855399370193481 -dry-pea,TCD,-0.6213465631008148 -dry-pea,TGO,-0.6105064451694489 -dry-pea,THA,-0.5460451245307922 -dry-pea,TJK,-0.5949536263942719 -dry-pea,TKM,-0.47641411423683167 -dry-pea,TLS,3.5246971547603607 -dry-pea,TTO,-3.962533116340637 -dry-pea,TUN,-0.6023371517658234 -dry-pea,TUR,-1.169978141784668 -dry-pea,TWN,-0.30261053144931793 -dry-pea,TZA,-0.4995932877063751 -dry-pea,UGA,-0.5833180844783783 -dry-pea,UKR,-0.35087335109710693 -dry-pea,URY,-0.5175571739673615 -dry-pea,USA,-0.3295847177505493 -dry-pea,UZB,-2.300550937652588 -dry-pea,VEN,3.51328544318676 -dry-pea,VNM,-0.45602816343307495 -dry-pea,YEM,-1.5868552327156067 -dry-pea,ZAF,-0.5753569602966309 -dry-pea,ZMB,-0.575914740562439 -dry-pea,ZWE,-0.7098487019538879 +dry-pea,SVK,-0.31163185834884644 +dry-pea,SVN,-0.31852400302886963 +dry-pea,SWE,-0.398660808801651 +dry-pea,SWZ,-0.5697385668754578 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dryland-rice,AZE,-2.6981661319732666 dryland-rice,BDI,-0.40925656259059906 -dryland-rice,BEN,-1.838856279850006 +dryland-rice,BEN,-1.8482831120491028 dryland-rice,BFA,-1.3975671529769897 -dryland-rice,BGD,-0.4306889921426773 +dryland-rice,BGD,-0.4436982721090317 dryland-rice,BGR,-1.274803102016449 dryland-rice,BLZ,-1.6705456972122192 -dryland-rice,BOL,-0.5394448637962341 -dryland-rice,BRA,-0.9496661424636841 +dryland-rice,BOL,-0.5432115793228149 +dryland-rice,BRA,-0.9480697214603424 dryland-rice,BRN,-1.4630968570709229 -dryland-rice,BTN,-1.5592828392982483 +dryland-rice,BTN,-1.2915107011795044 dryland-rice,BWA,-0.0 dryland-rice,CAF,-1.5133150815963745 dryland-rice,CAN,-0.0 dryland-rice,CHE,-0.0 dryland-rice,CHL,-4.2121778428554535 -dryland-rice,CHN,-1.959573209285736 +dryland-rice,CHN,-1.962005078792572 dryland-rice,CIV,-0.2833888381719589 dryland-rice,CMR,-1.4252973198890686 dryland-rice,COD,-1.6212639808654785 @@ -2238,13 +2238,13 @@ dryland-rice,COL,-1.452290654182434 dryland-rice,COM,-0.0 dryland-rice,CRI,-1.3775368332862854 dryland-rice,CUB,-1.2810020446777344 -dryland-rice,DOM,-2.0593067407608032 +dryland-rice,DOM,-2.0771167278289795 dryland-rice,DZA,7.5 dryland-rice,ECU,-0.7868479490280151 dryland-rice,EGY,-2.032635807991028 dryland-rice,ERI,-0.0 dryland-rice,ESP,-1.7250423431396484 -dryland-rice,ETH,-1.0975691974163055 +dryland-rice,ETH,-1.0962245762348175 dryland-rice,FJI,-0.8348589837551117 dryland-rice,FRA,-1.025679349899292 dryland-rice,GAB,7.5 @@ -2254,7 +2254,7 @@ dryland-rice,GIN,-0.6473399102687836 dryland-rice,GMB,-0.38981568813323975 dryland-rice,GNB,-1.457575500011444 dryland-rice,GNQ,7.5 -dryland-rice,GRC,-1.2547135651111603 +dryland-rice,GRC,-1.491008460521698 dryland-rice,GTM,-1.5701602697372437 dryland-rice,GUF,7.5 dryland-rice,GUY,-1.3117097616195679 @@ -2263,26 +2263,26 @@ dryland-rice,HRV,-0.0 dryland-rice,HTI,-1.488037884235382 dryland-rice,HUN,-7.5 dryland-rice,IDN,-1.5002368688583374 -dryland-rice,IND,-1.2640531063079834 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dryland-rice,MYS,-0.546116828918457 dryland-rice,NAM,7.5 dryland-rice,NER,-1.3835564851760864 dryland-rice,NGA,-0.4306328147649765 -dryland-rice,NIC,-0.23938850313425064 +dryland-rice,NIC,1.2431981563568115 dryland-rice,NPL,-0.40334589779376984 dryland-rice,PAK,-1.3181896209716797 dryland-rice,PAN,-1.1545853018760681 -dryland-rice,PER,-1.1555179357528687 +dryland-rice,PER,-0.18516041338443756 dryland-rice,PHL,-0.8838640451431274 dryland-rice,PNG,7.5 dryland-rice,PRI,-2.6505953073501587 dryland-rice,PRT,-1.2803558111190796 dryland-rice,PRY,-0.9990241229534149 dryland-rice,PSE,-7.5 -dryland-rice,ROU,-4.358515381813049 -dryland-rice,RUS,-2.987667679786682 +dryland-rice,ROU,-4.628606498241425 +dryland-rice,RUS,-2.9697678089141846 dryland-rice,RWA,-2.0654500126838684 dryland-rice,SAU,7.5 dryland-rice,SDN,7.5 dryland-rice,SEN,-0.2576884850859642 dryland-rice,SLB,-1.5083528757095337 dryland-rice,SLE,-0.3911692500114441 -dryland-rice,SLV,2.8035911321640015 +dryland-rice,SLV,2.7369037866592407 dryland-rice,SOM,-1.41139554977417 dryland-rice,SRB,-0.0 dryland-rice,SSD,-1.5240747928619385 @@ -2325,16 +2325,16 @@ dryland-rice,SYR,-7.5 dryland-rice,TCD,-0.9026926159858704 dryland-rice,TGO,-0.27889062464237213 dryland-rice,THA,-0.5490337312221527 -dryland-rice,TJK,-3.5690500736236572 +dryland-rice,TJK,-3.5525572299957275 dryland-rice,TKM,-1.0838743448257446 dryland-rice,TLS,-1.9672682881355286 dryland-rice,TTO,-0.1975058764219284 dryland-rice,TUN,-0.0 -dryland-rice,TUR,-2.865366220474243 +dryland-rice,TUR,-2.7801766395568848 dryland-rice,TWN,-1.0452470183372498 -dryland-rice,TZA,-0.1515534147620201 -dryland-rice,UGA,3.004153847694397 -dryland-rice,UKR,-1.923391580581665 +dryland-rice,TZA,-0.16903217881917953 +dryland-rice,UGA,3.3149598836898804 +dryland-rice,UKR,-1.924157202243805 dryland-rice,URY,-0.7495004236698151 dryland-rice,USA,-1.4800493717193604 dryland-rice,UZB,-4.528323173522949 @@ -2353,17 +2353,17 @@ foxtail-millet,AZE,-4.347458600997925 foxtail-millet,BDI,-0.48601867258548737 foxtail-millet,BEN,-0.5482176244258881 foxtail-millet,BFA,-0.3707796037197113 -foxtail-millet,BGD,-0.488968625664711 +foxtail-millet,BGD,-0.4858352988958359 foxtail-millet,BGR,-0.0 foxtail-millet,BLR,-0.3229052722454071 foxtail-millet,BOL,-0.0 foxtail-millet,BRA,-0.0 -foxtail-millet,BTN,-4.289632320404053 +foxtail-millet,BTN,-4.4991448521614075 foxtail-millet,BWA,-0.4291660338640213 foxtail-millet,CAF,-7.5 foxtail-millet,CAN,-0.0 foxtail-millet,CHE,-2.6688425540924072 -foxtail-millet,CHN,-0.719974935054779 +foxtail-millet,CHN,-0.7207225561141968 foxtail-millet,CIV,-0.577817440032959 foxtail-millet,CMR,-0.4593006670475006 foxtail-millet,COD,-0.4491131752729416 @@ -2381,7 +2381,7 @@ foxtail-millet,GMB,-0.46135395765304565 foxtail-millet,GNB,-0.4469609558582306 foxtail-millet,GRC,-0.0 foxtail-millet,HUN,7.5 -foxtail-millet,IND,-0.4914517402648926 +foxtail-millet,IND,-0.46838973462581635 foxtail-millet,IRN,-1.2599350810050964 foxtail-millet,IRQ,-0.8328883051872253 foxtail-millet,KAZ,-0.2774594873189926 @@ -2404,7 +2404,7 @@ foxtail-millet,MWI,-0.4817148894071579 foxtail-millet,NAM,-0.07812097668647766 foxtail-millet,NER,-0.13796497881412506 foxtail-millet,NGA,-0.46686674654483795 -foxtail-millet,NPL,-0.47128425538539886 +foxtail-millet,NPL,-0.49501223862171173 foxtail-millet,OMN,-0.0 foxtail-millet,PAK,-0.3077806532382965 foxtail-millet,POL,7.5 @@ -2427,10 +2427,10 @@ foxtail-millet,THA,-0.4839485436677933 foxtail-millet,TJK,-0.4736505150794983 foxtail-millet,TKM,7.5 foxtail-millet,TUN,-0.0 -foxtail-millet,TUR,-1.3685643672943115 +foxtail-millet,TUR,-1.3409274220466614 foxtail-millet,TWN,-7.5 foxtail-millet,TZA,-0.5091361999511719 -foxtail-millet,UGA,-0.4820815324783325 +foxtail-millet,UGA,-0.14731775969266891 foxtail-millet,UKR,-0.5722740292549133 foxtail-millet,URY,-0.0 foxtail-millet,USA,-0.43647652864456177 @@ -2439,298 +2439,298 @@ foxtail-millet,VNM,2.965010344982147 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-maize,IND,-0.5567933917045593 +maize,IND,-0.5288410186767578 maize,IRN,-1.5734480619430542 maize,IRQ,3.3758135735988617 maize,ISR,-7.469736814498901 -maize,ITA,-1.5075510144233704 +maize,ITA,-1.5573293566703796 maize,JAM,-0.5601756423711777 maize,JOR,0.1837824434041977 maize,KAZ,-0.3655751645565033 @@ -2821,16 +2821,16 @@ maize,LBY,-0.7348927557468414 maize,LKA,-0.6854110956192017 maize,LSO,-0.33112430572509766 maize,LTU,-0.41597379744052887 -maize,LUX,-0.5776959508657455 +maize,LUX,-0.5831712633371353 maize,LVA,-0.44954001903533936 maize,MAR,-0.16542376577854156 maize,MDA,0.11440286040306091 maize,MDG,-0.7905453443527222 -maize,MEX,-0.5801578760147095 +maize,MEX,-0.5721585154533386 maize,MKD,-0.31789055466651917 maize,MLI,-0.30224817991256714 maize,MMR,-0.6571893692016602 -maize,MNE,-0.42061182856559753 +maize,MNE,-0.3529267907142639 maize,MNG,-0.4865938425064087 maize,MOZ,-0.21048052608966827 maize,MRT,-0.8863611817359924 @@ -2840,15 +2840,15 @@ maize,MYS,-7.5 maize,NAM,-0.35467538237571716 maize,NER,-0.2390461266040802 maize,NGA,-0.31639693677425385 -maize,NIC,-0.046499100513756275 -maize,NLD,-1.4075786918401718 +maize,NIC,0.15312248468399048 +maize,NLD,-1.3496343940496445 maize,NOR,-0.0 maize,NPL,-0.3402838706970215 maize,NZL,-1.563895583152771 -maize,OMN,-4.095101833343506 +maize,OMN,-4.312058210372925 maize,PAK,-0.7465835213661194 maize,PAN,-0.8652222752571106 -maize,PER,-1.3317623734474182 +maize,PER,-0.9920499920845032 maize,PHL,-0.5062786340713501 maize,PNG,-0.39486274123191833 maize,POL,-0.37615562975406647 @@ -2856,8 +2856,8 @@ maize,PRI,-1.62974613904953 maize,PRT,-0.5833457708358765 maize,PRY,-0.36698880791664124 maize,PSE,4.357960522174835 -maize,ROU,-0.32242631912231445 -maize,RUS,-0.3057945966720581 +maize,ROU,-0.35763442516326904 +maize,RUS,-0.30557769536972046 maize,RWA,-0.3364266902208328 maize,SAU,7.5 maize,SDN,-0.8885903358459473 @@ -2873,7 +2873,7 @@ maize,SVK,-0.3433014899492264 maize,SVN,-0.5801605582237244 maize,SWE,-0.5741465091705322 maize,SWZ,-0.8188715577125549 -maize,SYR,-0.8604607582092285 +maize,SYR,-0.8042577505111694 maize,TCD,-0.20251883566379547 maize,TGO,-0.2905884385108948 maize,THA,-0.5171343088150024 @@ -2882,10 +2882,10 @@ maize,TKM,-0.7101861834526062 maize,TLS,3.6146592497825623 maize,TTO,-0.8000484108924866 maize,TUN,-0.0 -maize,TUR,-0.9434753060340881 +maize,TUR,-0.878672868013382 maize,TWN,-0.2747230678796768 maize,TZA,-0.1148873008787632 -maize,UGA,-0.8007032573223114 +maize,UGA,-0.4109315276145935 maize,UKR,-0.5962257385253906 maize,URY,-0.5233606100082397 maize,USA,-0.9802699387073517 @@ -2896,115 +2896,115 @@ maize,VUT,-0.9315140247344971 maize,YEM,-0.9088909327983856 maize,ZAF,-0.41581904888153076 maize,ZMB,-0.1295097917318344 -maize,ZWE,0.7749455571174622 -mango,AGO,-1.8043760061264038 -mango,ARG,4.85997200012207 +maize,ZWE,0.776217520236969 +mango,AGO,-1.8043758869171143 +mango,ARG,4.859972238540649 mango,ATG,-5.9299726486206055 -mango,AUS,3.6783087253570557 +mango,AUS,3.678309202194214 mango,BDI,-0.0 -mango,BEN,-5.070006608963013 -mango,BFA,2.2009966373443604 -mango,BGD,-1.9765393733978271 -mango,BHS,1.3321688175201416 +mango,BEN,-5.08003830909729 +mango,BFA,2.2009971141815186 +mango,BGD,-1.976539134979248 +mango,BHS,1.3321691155433655 mango,BLZ,-0.0 -mango,BOL,1.1487901210784912 -mango,BRA,0.1323447823524475 +mango,BOL,1.1487902402877808 +mango,BRA,0.13060247898101807 mango,BRB,-0.0 mango,BRN,-0.0 -mango,BTN,-2.912118673324585 +mango,BTN,-3.2665116786956787 mango,BWA,-0.0 -mango,CAF,-4.412701368331909 -mango,CHN,-6.412930011749268 -mango,CIV,0.7973107099533081 +mango,CAF,-4.41270112991333 +mango,CHN,-6.412929534912109 +mango,CIV,0.7973107695579529 mango,CMR,-4.12673020362854 -mango,COD,-1.2005916833877563 +mango,COD,-1.200591504573822 mango,COG,-4.3320839405059814 -mango,COL,-2.1216830015182495 +mango,COL,-2.1216827630996704 mango,COM,-0.0 -mango,CPV,-3.4983479976654053 -mango,CRI,-4.449241399765015 -mango,CUB,-2.098416030406952 -mango,DOM,4.551478147506714 -mango,ECU,1.3338513374328613 -mango,EGY,5.677823781967163 +mango,CPV,-3.498347759246826 +mango,CRI,-4.4492411613464355 +mango,CUB,-2.0984159111976624 +mango,DOM,4.493650913238525 +mango,ECU,1.3338515162467957 +mango,EGY,5.6778244972229 mango,ERI,-0.0 -mango,ETH,0.5745884776115417 +mango,ETH,0.5745885670185089 mango,FJI,-7.5 mango,GAB,-0.0 -mango,GHA,-0.7652482986450195 -mango,GIN,1.2452408075332642 -mango,GMB,1.6892479062080383 -mango,GNB,-1.562175989151001 +mango,GHA,-0.76524817943573 +mango,GIN,1.245240867137909 +mango,GMB,1.6892480850219727 +mango,GNB,-1.5621758699417114 mango,GNQ,-0.0 -mango,GRD,0.0930548906326294 -mango,GTM,-0.12153744697570801 +mango,GRD,0.09305500984191895 +mango,GTM,-0.12153719365596771 mango,GUF,-0.0 mango,GUY,-7.5 -mango,HND,6.655632495880127 -mango,HTI,-1.8978594541549683 -mango,IDN,-3.417733669281006 -mango,IND,-1.4526039361953735 +mango,HND,6.6524412631988525 +mango,HTI,-1.8978592157363892 +mango,IDN,-3.4177335500717163 +mango,IND,-1.4526036977767944 mango,ISR,-7.5 -mango,JAM,-0.8278069198131561 -mango,JOR,1.325854331254959 -mango,JPN,-3.6686209440231323 -mango,KEN,5.085688591003418 -mango,KHM,-2.933666467666626 -mango,LAO,-2.5387405157089233 +mango,JAM,-0.8278067111968994 +mango,JOR,1.3258545994758606 +mango,JPN,-3.6686207056045532 +mango,KEN,4.815747976303101 +mango,KHM,-2.9336663484573364 +mango,LAO,-2.538740396499634 mango,LBR,-0.0 -mango,LKA,1.8383594155311584 -mango,MAR,-1.163823425769806 -mango,MDG,-4.55850076675415 -mango,MEX,2.230954885482788 -mango,MLI,2.559091329574585 -mango,MMR,-3.84570050239563 +mango,LKA,1.8383595943450928 +mango,MAR,-1.1638231873512268 +mango,MDG,-4.558500289916992 +mango,MEX,2.230955123901367 +mango,MLI,2.559091567993164 +mango,MMR,-3.8457003831863403 mango,MOZ,-2.7925570011138916 mango,MRT,-0.0 mango,MUS,-0.0 -mango,MWI,-0.8181677758693695 -mango,MYS,-2.909432053565979 -mango,NAM,-2.412468910217285 -mango,NER,0.35264745354652405 +mango,MWI,-1.1103677153587341 +mango,MYS,-2.9094318747520447 +mango,NAM,-2.412468671798706 +mango,NER,0.3526475429534912 mango,NGA,-3.8400527238845825 mango,NIC,-0.0 -mango,NPL,0.6037481818348169 -mango,OMN,-5.826828956604004 -mango,PAK,0.0812443196773529 -mango,PAN,-1.823959231376648 -mango,PER,5.944425821304321 -mango,PHL,1.5975165963172913 +mango,NPL,0.6037483867257833 +mango,OMN,-6.267499923706055 +mango,PAK,0.08124453574419022 +mango,PAN,-1.8239590525627136 +mango,PER,5.652411937713623 +mango,PHL,1.5975167155265808 mango,PNG,-0.0 mango,PRI,-7.5 -mango,PRY,-2.333696722984314 -mango,PSE,-5.772782325744629 +mango,PRY,-2.333696484565735 +mango,PSE,-5.772781848907471 mango,RWA,-3.8084962368011475 -mango,SAU,-6.554208993911743 -mango,SDN,0.03677179291844368 -mango,SEN,1.2846832871437073 +mango,SAU,-6.554208755493164 +mango,SDN,0.036772046238183975 +mango,SEN,1.284683495759964 mango,SLB,-0.0 mango,SLE,-2.8811758756637573 -mango,SLV,2.3984335511922836 -mango,SOM,-2.572509288787842 +mango,SLV,2.4045009464025497 +mango,SOM,-2.5725090503692627 mango,SSD,-0.0 mango,STP,-0.0 mango,SUR,-5.867498159408569 -mango,SWZ,4.127260357141495 +mango,SWZ,4.127260506153107 mango,TCD,-0.0 mango,TGO,-0.0 -mango,THA,-1.7679228782653809 -mango,TLS,3.4559943675994873 -mango,TTO,-3.679188847541809 -mango,TWN,0.4314611107110977 -mango,TZA,-4.454237461090088 +mango,THA,-1.7679227590560913 +mango,TLS,3.4559946060180664 +mango,TTO,-3.67918860912323 +mango,TWN,0.4314614087343216 +mango,TZA,-4.45423698425293 mango,UGA,-0.0 mango,USA,7.5 -mango,VEN,-0.4351174831390381 -mango,VNM,-2.8116146326065063 +mango,VEN,-0.4351172596216202 +mango,VNM,-2.811614513397217 mango,VUT,-0.0 -mango,YEM,-3.2953884601593018 -mango,ZAF,-1.647788166999817 +mango,YEM,-3.2953882217407227 +mango,ZAF,-1.7390047311782837 mango,ZMB,-0.0 -mango,ZWE,-1.207976758480072 +mango,ZWE,-1.2097299695014954 oat,AFG,-0.13327856361865997 oat,AGO,-0.0 oat,ALB,-0.6619435250759125 @@ -3021,17 +3021,17 @@ oat,BFA,-0.3938731849193573 oat,BGD,-0.0 oat,BGR,-0.2797299176454544 oat,BIH,-0.27431556582450867 -oat,BLR,-2.822245478630066 +oat,BLR,-2.8118194341659546 oat,BLZ,7.5 oat,BOL,-0.3910616636276245 oat,BRA,-0.18151214718818665 -oat,BTN,0.1768800914287567 +oat,BTN,0.2161884307861328 oat,BWA,-0.2933477610349655 oat,CAF,0.0 oat,CAN,-0.44360698759555817 oat,CHE,-0.9501529335975647 oat,CHL,-0.5374706983566284 -oat,CHN,-0.35358890891075134 +oat,CHN,-0.35485267639160156 oat,CIV,-0.3874320834875107 oat,CMR,-0.38605116307735443 oat,COD,-7.5 @@ -3041,7 +3041,7 @@ oat,CYP,-0.04404866322875023 oat,CZE,-0.33972054719924927 oat,DEU,-0.209464430809021 oat,DNK,-0.29091256111860275 -oat,DZA,-0.1790148764848709 +oat,DZA,-0.19812801480293274 oat,ECU,-0.19905215501785278 oat,EGY,-0.2714719921350479 oat,ERI,-0.41490109264850616 @@ -3051,8 +3051,8 @@ oat,ETH,-0.48230527341365814 oat,FIN,-0.40378448367118835 oat,FRA,-0.3987346440553665 oat,GAB,7.5 -oat,GBR,-0.6116591989994049 -oat,GEO,-0.12696179375052452 +oat,GBR,-0.5924690663814545 +oat,GEO,-0.10004109516739845 oat,GHA,-7.5 oat,GIN,-0.4360026568174362 oat,GMB,-0.43191398680210114 @@ -3063,14 +3063,14 @@ oat,GUF,-0.0 oat,GUY,7.5 oat,HRV,3.696773659437895 oat,HUN,-0.04002060182392597 -oat,IND,-0.30636677145957947 +oat,IND,-0.29376836121082306 oat,IRL,-0.304133802652359 oat,IRN,-0.19707639515399933 oat,IRQ,-0.0 oat,ISR,0.1386416181921959 oat,ITA,-0.40245918929576874 oat,JOR,7.5 -oat,JPN,-0.034983305260539055 +oat,JPN,-0.03446677327156067 oat,KAZ,-0.054961878806352615 oat,KEN,-0.2700447738170624 oat,KGZ,-0.2479068860411644 @@ -3086,11 +3086,11 @@ oat,LUX,-0.30302347242832184 oat,LVA,-0.06330564431846142 oat,MAR,-0.23011146485805511 oat,MDA,-0.00723792240023613 -oat,MEX,-0.40349188446998596 +oat,MEX,-0.3997030556201935 oat,MKD,-0.24016372114419937 oat,MLI,-0.42763856053352356 oat,MMR,-0.4442124366760254 -oat,MNE,-0.11039287224411964 +oat,MNE,-0.06715377047657967 oat,MNG,-0.09740832448005676 oat,MOZ,0.0 oat,MRT,-7.5 @@ -3099,18 +3099,18 @@ oat,MYS,-0.0 oat,NAM,-0.0 oat,NER,-3.9557620733976364 oat,NGA,-0.4454670697450638 -oat,NLD,-1.2676411271095276 +oat,NLD,-1.2347463071346283 oat,NOR,-0.8242159187793732 oat,NPL,-0.3028006851673126 oat,NZL,-0.7654881179332733 -oat,OMN,-0.8046389222145081 +oat,OMN,-1.023850440979004 oat,PER,-0.0 oat,POL,-0.18640068918466568 oat,PRT,-0.19330399483442307 oat,PRY,-7.5 oat,PSE,-0.3199342042207718 -oat,ROU,-0.24269773066043854 -oat,RUS,-0.11369966343045235 +oat,ROU,-0.24826303124427795 +oat,RUS,-0.1122869960963726 oat,RWA,-7.5 oat,SAU,-0.14163187891244888 oat,SDN,-0.4041660726070404 @@ -3124,18 +3124,18 @@ oat,SVK,-0.30803045630455017 oat,SVN,-0.25656498223543167 oat,SWE,-0.3509441316127777 oat,SWZ,-7.5 -oat,SYR,-0.23197783529758453 +oat,SYR,-0.20985688269138336 oat,TCD,-0.30479809641838074 oat,TGO,-0.4264407455921173 oat,THA,-0.3944617211818695 oat,TJK,-0.1327425353229046 oat,TKM,-0.1498054563999176 oat,TUN,-0.3738185167312622 -oat,TUR,-0.25539612770080566 +oat,TUR,-0.22277065366506577 oat,TWN,-0.37210462987422943 oat,TZA,-0.3526216894388199 oat,UGA,7.5 -oat,UKR,-0.2623877227306366 +oat,UKR,-0.26242803782224655 oat,URY,0.0037017464637756348 oat,USA,-0.2942340224981308 oat,UZB,-0.42627573013305664 @@ -3145,66 +3145,66 @@ oat,YEM,-7.5 oat,ZAF,-0.26045089960098267 oat,ZMB,7.5 oat,ZWE,-7.5 -oil-palm,AGO,-1.6501270532608032 -oil-palm,BDI,-4.650212526321411 -oil-palm,BEN,-1.5152283310890198 -oil-palm,BLZ,-0.8906666338443756 +oil-palm,AGO,-1.651216983795166 +oil-palm,BDI,-4.651302337646484 +oil-palm,BEN,-1.5174720287322998 +oil-palm,BLZ,-0.8946569263935089 oil-palm,BOL,7.5 -oil-palm,BRA,0.0133635513484478 -oil-palm,BRN,-2.075209140777588 -oil-palm,CAF,-1.7019859552383423 -oil-palm,CHN,-1.728135347366333 -oil-palm,CIV,-0.5366305410861969 -oil-palm,CMR,-1.6982844471931458 -oil-palm,COD,-1.6954774856567383 -oil-palm,COG,-1.5845192074775696 -oil-palm,COL,-7.196508169174194 -oil-palm,CRI,-1.5934602618217468 -oil-palm,DOM,-1.5267077088356018 -oil-palm,ECU,-1.4508421421051025 -oil-palm,GAB,-1.722981333732605 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@@ olive,EGY,0.07486620545387268 olive,ESP,-0.24604055285453796 olive,FRA,0.6897424906492233 olive,GAB,-7.5 -olive,GEO,-0.3426142781972885 +olive,GEO,-0.3570984788239002 olive,GIN,0.0 olive,GMB,-7.5 olive,GNB,-0.0 @@ -3255,7 +3255,7 @@ olive,MAR,0.2928430587053299 olive,MDA,-7.5 olive,MEX,-0.658452033996582 olive,MKD,-0.15051641687750816 -olive,MLT,-2.1883184909820557 +olive,MLT,-2.1844990253448486 olive,MNE,-0.43609488010406494 olive,MNG,-7.5 olive,NAM,0.0 @@ -3263,7 +3263,7 @@ olive,NIC,-0.0 olive,NOR,-0.0 olive,NZL,-0.0 olive,PAK,-0.0 -olive,PER,4.329333662986755 +olive,PER,4.329071581363678 olive,PRT,0.5581578016281128 olive,PSE,-0.3389883041381836 olive,ROU,-0.0 @@ -3279,7 +3279,7 @@ olive,SYR,-0.8499757051467896 olive,TJK,-0.0 olive,TKM,7.5 olive,TUN,-0.03372928127646446 -olive,TUR,-0.7081306278705597 +olive,TUR,-0.7082476913928986 olive,TWN,-7.5 olive,URY,-0.6887072324752808 olive,USA,7.5 @@ -3287,177 +3287,177 @@ olive,UZB,-1.1979424357414246 olive,YEM,-7.5 olive,ZAF,-0.0 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-3479,7 +3479,7 @@ pearl-millet,BWA,-0.3286331593990326 pearl-millet,CAF,-7.5 pearl-millet,CAN,-0.0 pearl-millet,CHE,-1.8898600339889526 -pearl-millet,CHN,-0.1823417842388153 +pearl-millet,CHN,-0.18338361382484436 pearl-millet,CIV,-0.44734491407871246 pearl-millet,CMR,-0.2659517079591751 pearl-millet,COD,-0.359405055642128 @@ -3497,7 +3497,7 @@ pearl-millet,GMB,-0.3531493991613388 pearl-millet,GNB,-0.29049496352672577 pearl-millet,GRC,-0.0 pearl-millet,HUN,7.5 -pearl-millet,IND,-0.12922101467847824 +pearl-millet,IND,-0.11110261455178261 pearl-millet,IRN,-0.712905764579773 pearl-millet,IRQ,-0.5056772530078888 pearl-millet,KAZ,-0.11909784004092216 @@ -3507,7 +3507,7 @@ pearl-millet,KHM,-0.0 pearl-millet,KOR,-7.5 pearl-millet,LAO,0.0 pearl-millet,LBY,-0.44693028926849365 -pearl-millet,LKA,-0.776326596736908 +pearl-millet,LKA,-0.7805797457695007 pearl-millet,LSO,7.5 pearl-millet,MAR,-0.3854451850056648 pearl-millet,MDA,0.0 @@ -3543,10 +3543,10 @@ pearl-millet,THA,-0.3619624376296997 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+sunflower,ISR,-3.384186267852783 sunflower,ITA,-0.0 sunflower,JOR,7.5 -sunflower,KAZ,0.07907525449991226 -sunflower,KEN,-0.03318547643721104 -sunflower,KGZ,-0.3417547941207886 -sunflower,KHM,3.516571208834648 -sunflower,LAO,3.533530592918396 +sunflower,KAZ,0.08389965817332268 +sunflower,KEN,-0.03016811516135931 +sunflower,KGZ,-0.3375275135040283 +sunflower,KHM,3.51799476146698 +sunflower,LAO,3.5351142287254333 sunflower,LBN,7.5 -sunflower,LSO,0.12017571553587914 -sunflower,LTU,-0.25305742770433426 -sunflower,LUX,-0.22418257035315037 -sunflower,LVA,3.7147355154156685 -sunflower,MAR,-0.9045921862125397 -sunflower,MDA,0.32911431789398193 -sunflower,MEX,0.07194328308105469 -sunflower,MKD,0.07674377970397472 -sunflower,MMR,-0.4196018576622009 -sunflower,MNE,3.807077329605818 -sunflower,MNG,-0.14567600190639496 -sunflower,MOZ,0.16816741228103638 -sunflower,MWI,3.554990276694298 +sunflower,LSO,0.13050762563943863 +sunflower,LTU,-0.2464323416352272 +sunflower,LUX,-0.22155694477260113 +sunflower,LVA,3.7192168794572353 +sunflower,MAR,-0.8991047143936157 +sunflower,MDA,0.3417966663837433 +sunflower,MEX,0.07905950769782066 +sunflower,MKD,0.08387590199708939 +sunflower,MMR,-0.4163387417793274 +sunflower,MNE,3.812772162258625 +sunflower,MNG,-0.14002054557204247 +sunflower,MOZ,0.17174912989139557 +sunflower,MWI,3.5571961104869843 sunflower,MYS,-0.0 sunflower,NAM,7.5 -sunflower,NLD,-1.3241107165813446 -sunflower,NPL,3.5269596874713898 -sunflower,PAK,-0.3594595193862915 +sunflower,NLD,-1.3153252452611923 +sunflower,NPL,3.528859406709671 +sunflower,PAK,-0.35267147421836853 sunflower,PER,-0.0 -sunflower,POL,3.751647054683417 -sunflower,PRT,-0.041858808137476444 -sunflower,PRY,-0.3506888449192047 -sunflower,PSE,0.15652285516262054 -sunflower,ROU,0.3665842339396477 -sunflower,RUS,0.19937261939048767 -sunflower,RWA,-0.508271187543869 -sunflower,SDN,-0.5516582727432251 +sunflower,POL,3.756648302078247 +sunflower,PRT,-0.03398194536566734 +sunflower,PRY,-0.34519442915916443 +sunflower,PSE,0.1733616292476654 +sunflower,ROU,0.1674722209572792 +sunflower,RUS,0.20681889355182648 +sunflower,RWA,-0.5051471292972565 +sunflower,SDN,-0.5490227937698364 sunflower,SOM,-0.0 -sunflower,SRB,0.7818926423788071 -sunflower,SSD,-0.27194279432296753 +sunflower,SRB,0.8033342063426971 +sunflower,SSD,-0.2663742005825043 sunflower,SUR,-0.0 -sunflower,SVK,0.3664610395208001 -sunflower,SVN,0.2705685570836067 -sunflower,SWZ,-0.2539714053273201 -sunflower,SYR,-0.5464197993278503 -sunflower,TCD,3.485738068819046 -sunflower,THA,-0.13218184188008308 -sunflower,TJK,-0.298012912273407 -sunflower,TKM,0.07396804541349411 -sunflower,TUN,-0.40021976828575134 -sunflower,TUR,-0.5322834253311157 -sunflower,TWN,0.9306599497795105 -sunflower,TZA,0.10516033321619034 -sunflower,UGA,-0.44273070991039276 -sunflower,UKR,-0.2593200355768204 -sunflower,URY,-0.19466568529605865 -sunflower,USA,0.017087969463318586 -sunflower,UZB,-0.5804853439331055 -sunflower,VEN,-0.511641263961792 +sunflower,SVK,0.38335967250168324 +sunflower,SVN,0.2832423150539398 +sunflower,SWZ,-0.24797315150499344 +sunflower,SYR,-0.5365603566169739 +sunflower,TCD,3.486934572458267 +sunflower,THA,-0.12978080287575722 +sunflower,TJK,-0.29290036857128143 +sunflower,TKM,0.08384233713150024 +sunflower,TUN,-0.39563706517219543 +sunflower,TUR,-0.5073097944259644 +sunflower,TWN,0.9465600848197937 +sunflower,TZA,0.10924512892961502 +sunflower,UGA,-0.4387935698032379 +sunflower,UKR,-0.25254666805267334 +sunflower,URY,-0.18729369342327118 +sunflower,USA,0.027588097378611565 +sunflower,UZB,-0.5697300434112549 +sunflower,VEN,-0.5089805722236633 sunflower,VNM,-0.0 -sunflower,ZAF,-0.24306177347898483 -sunflower,ZMB,-0.5919135808944702 -sunflower,ZWE,-0.07368859648704529 +sunflower,ZAF,-0.2503822445869446 +sunflower,ZMB,-0.5898570418357849 +sunflower,ZWE,-0.07149853557348251 sweet-potato,AFG,-1.9192432761192322 sweet-potato,AGO,-3.198032855987549 sweet-potato,ALB,-1.8855436444282532 @@ -5081,20 +5081,20 @@ sweet-potato,ARG,-0.13471268489956856 sweet-potato,ARM,-1.5438308119773865 sweet-potato,ATG,-4.1917619705200195 sweet-potato,AUS,7.5 -sweet-potato,AUT,-0.9499566853046417 +sweet-potato,AUT,-0.9551993906497955 sweet-potato,AZE,-2.2849515676498413 sweet-potato,BDI,-0.5438300371170044 sweet-potato,BEL,-0.0 -sweet-potato,BEN,-1.360405683517456 +sweet-potato,BEN,-1.3705680966377258 sweet-potato,BFA,-3.103135347366333 sweet-potato,BGD,0.9769735038280487 sweet-potato,BGR,-1.508312165737152 sweet-potato,BHS,-3.5734505653381348 sweet-potato,BIH,2.5080337524414062 -sweet-potato,BLR,-3.869260787963867 +sweet-potato,BLR,-3.858834981918335 sweet-potato,BLZ,-2.7601243257522583 -sweet-potato,BOL,-0.27304965257644653 -sweet-potato,BRA,-0.9793644547462463 +sweet-potato,BOL,-0.3270563781261444 +sweet-potato,BRA,-0.9810467958450317 sweet-potato,BRB,-7.5 sweet-potato,BRN,-7.5 sweet-potato,BTN,7.5 @@ -5117,10 +5117,10 @@ sweet-potato,CYP,-4.172020584344864 sweet-potato,CZE,-0.27531448006629944 sweet-potato,DEU,-0.0 sweet-potato,DNK,-0.0 -sweet-potato,DOM,-1.7689489126205444 +sweet-potato,DOM,-1.7867589592933655 sweet-potato,DZA,-1.8986735939979553 sweet-potato,ECU,0.7807817161083221 -sweet-potato,EGY,-1.6685402393341064 +sweet-potato,EGY,-1.661179006099701 sweet-potato,ERI,1.8305708169937134 sweet-potato,ESP,-7.5 sweet-potato,EST,-0.7400773763656616 @@ -5129,8 +5129,8 @@ sweet-potato,FIN,-1.2138773202896118 sweet-potato,FJI,-4.901826858520508 sweet-potato,FRA,0.6889048218727112 sweet-potato,GAB,-3.7182594537734985 -sweet-potato,GBR,-0.6050923578441143 -sweet-potato,GEO,-2.499752402305603 +sweet-potato,GBR,-0.5768061280250549 +sweet-potato,GEO,-2.488084554672241 sweet-potato,GHA,-0.46097269654273987 sweet-potato,GIN,0.6378686428070068 sweet-potato,GMB,3.0768012404441833 @@ -5146,14 +5146,14 @@ sweet-potato,HRV,-4.954775929450989 sweet-potato,HTI,-2.832951545715332 sweet-potato,HUN,-1.2691103219985962 sweet-potato,IDN,-1.1633429527282715 -sweet-potato,IND,-0.906154602766037 +sweet-potato,IND,-0.8886237740516663 sweet-potato,IRL,-0.0 sweet-potato,IRN,-0.7688232660293579 sweet-potato,IRQ,-0.3500778526067734 sweet-potato,ISR,-7.5 sweet-potato,ITA,-1.546323299407959 sweet-potato,JAM,-7.5 -sweet-potato,JOR,-0.548787459731102 +sweet-potato,JOR,-0.5572001188993454 sweet-potato,JPN,-7.5 sweet-potato,KAZ,-1.984000027179718 sweet-potato,KEN,-2.5283477306365967 @@ -5172,12 +5172,12 @@ sweet-potato,LVA,-5.157782196998596 sweet-potato,MAR,-1.679243564605713 sweet-potato,MDA,-2.651017904281616 sweet-potato,MDG,-3.2593929767608643 -sweet-potato,MEX,-2.5217217206954956 +sweet-potato,MEX,-2.516261577606201 sweet-potato,MKD,-1.9618785977363586 sweet-potato,MLI,-1.404753565788269 sweet-potato,MLT,-7.5 sweet-potato,MMR,-1.9281203746795654 -sweet-potato,MNE,-1.8759564757347107 +sweet-potato,MNE,-1.8530572652816772 sweet-potato,MNG,-7.5 sweet-potato,MOZ,-1.2957948446273804 sweet-potato,MRT,-3.388947367668152 @@ -5185,17 +5185,17 @@ sweet-potato,MUS,-5.612195730209351 sweet-potato,MWI,2.0278756618499756 sweet-potato,MYS,1.7713966369628906 sweet-potato,NAM,-2.5317625999450684 -sweet-potato,NER,-1.4954947233200073 +sweet-potato,NER,-1.4738686084747314 sweet-potato,NGA,-3.5510075092315674 -sweet-potato,NIC,-2.5864782333374023 -sweet-potato,NLD,-0.8233820497989655 +sweet-potato,NIC,-2.3338298201560974 +sweet-potato,NLD,-0.7952304482460022 sweet-potato,NOR,-1.6521366238594055 sweet-potato,NPL,-2.2190775871276855 sweet-potato,NZL,-7.5 -sweet-potato,OMN,-4.124903202056885 +sweet-potato,OMN,-4.344872713088989 sweet-potato,PAK,0.039837926626205444 sweet-potato,PAN,7.5 -sweet-potato,PER,-2.0079994201660156 +sweet-potato,PER,-1.8811441659927368 sweet-potato,PHL,-1.2197287678718567 sweet-potato,PNG,-3.114119052886963 sweet-potato,POL,-0.6363043189048767 @@ -5229,121 +5229,121 @@ sweet-potato,TKM,-2.3853678703308105 sweet-potato,TLS,-1.1886039972305298 sweet-potato,TTO,-4.361142158508301 sweet-potato,TUN,-2.72883403301239 -sweet-potato,TUR,-0.6072741150856018 +sweet-potato,TUR,-0.5956711173057556 sweet-potato,TWN,-0.7262731194496155 sweet-potato,TZA,0.13157109916210175 -sweet-potato,UGA,-3.3281540870666504 -sweet-potato,UKR,-2.6421035528182983 +sweet-potato,UGA,-3.245410919189453 +sweet-potato,UKR,-2.6356165409088135 sweet-potato,URY,-3.501429319381714 sweet-potato,USA,-5.022455215454102 sweet-potato,UZB,-1.8442890644073486 sweet-potato,VEN,-1.4541926383972168 sweet-potato,VNM,-2.439815640449524 sweet-potato,YEM,-1.9331015944480896 -sweet-potato,ZAF,1.4740299582481384 +sweet-potato,ZAF,1.3927119374275208 sweet-potato,ZMB,-0.3304411768913269 -sweet-potato,ZWE,-1.2365542650222778 +sweet-potato,ZWE,-1.2359645664691925 tea,AGO,7.5 tea,ALB,-7.5 tea,ARG,-0.0 tea,AZE,-3.75 -tea,BDI,1.8739714622497559 -tea,BGD,3.2497454285621643 -tea,BLZ,2.5428900718688965 +tea,BDI,2.0089532136917114 +tea,BGD,2.9058756828308105 +tea,BLZ,2.5569612979888916 tea,BOL,7.5 -tea,BRA,1.159130573272705 +tea,BRA,1.3149378299713135 tea,BRN,-0.0 -tea,BTN,5.884720087051392 +tea,BTN,6.058766603469849 tea,CAF,-0.0 tea,CHN,-7.5 -tea,CMR,5.91594123840332 -tea,COD,-2.955920457839966 +tea,CMR,6.3386147022247314 +tea,COD,-2.9527783393859863 tea,COG,7.5 -tea,COL,3.6429773345589638 +tea,COL,3.7054744213819504 tea,ECU,-7.5 -tea,ETH,2.402393102645874 -tea,GAB,4.583633720874786 +tea,ETH,2.658815622329712 +tea,GAB,4.693883776664734 tea,GEO,-7.5 tea,GNQ,7.5 -tea,GRC,-4.95764946937561 -tea,GTM,-2.643732190132141 +tea,GRC,-5.173074126243591 +tea,GTM,-2.6262084245681763 tea,GUF,-0.0 tea,GUY,-0.0 -tea,HND,-1.269011676311493 +tea,HND,-1.1878583431243896 tea,HRV,-0.0 -tea,IDN,1.508267343044281 -tea,IND,5.511372089385986 +tea,IDN,1.7250143885612488 +tea,IND,5.9182939529418945 tea,IRN,-0.0 tea,JPN,-7.5 tea,KEN,-7.5 -tea,KHM,-0.6927017569541931 -tea,KOR,4.45109748840332 -tea,LAO,-0.20283034443855286 -tea,LKA,0.48506924510002136 -tea,MDG,-2.7660884857177734 -tea,MEX,-2.2383440732955933 -tea,MMR,-1.9867051839828491 -tea,MNE,-4.865100622177124 -tea,MOZ,-2.065869092941284 -tea,MUS,-0.4771202504634857 -tea,MWI,5.585178375244141 -tea,MYS,0.6794877648353577 +tea,KHM,-0.5818332433700562 +tea,KOR,4.833953857421875 +tea,LAO,-0.06802260875701904 +tea,LKA,0.6467579305171967 +tea,MDG,-2.754048705101013 +tea,MEX,-2.2022173404693604 +tea,MMR,-1.9374827146530151 +tea,MNE,-4.846425414085388 +tea,MOZ,-2.020885467529297 +tea,MUS,-0.39420345425605774 +tea,MWI,6.025756359100342 +tea,MYS,0.8553168773651123 tea,NGA,-0.0 -tea,NPL,4.834060788154602 +tea,NPL,4.944964170455933 tea,PAN,-0.0 -tea,PER,0.735787034034729 -tea,PNG,2.1929014921188354 -tea,PRY,0.04696913808584213 -tea,RUS,-4.947874546051025 -tea,RWA,2.0498228073120117 -tea,SLV,-1.3953935503959656 +tea,PER,0.45598268508911133 +tea,PNG,2.443913698196411 +tea,PRY,0.1894693709909916 +tea,RUS,-4.814428806304932 +tea,RWA,2.201931357383728 +tea,SLV,-1.3201144337654114 tea,SSD,-7.5 tea,SUR,-0.0 tea,SWZ,-0.0 -tea,THA,-1.4225171208381653 -tea,TLS,3.177404999732971 -tea,TUR,-4.409297943115234 -tea,TWN,-0.33041758090257645 -tea,TZA,1.5612176656723022 -tea,UGA,6.97077488899231 -tea,URY,2.33587384223938 +tea,THA,-1.3462185859680176 +tea,TLS,3.2222511172294617 +tea,TUR,-4.266822338104248 +tea,TWN,-0.2018272951245308 +tea,TZA,1.7786271572113037 +tea,UGA,7.194136381149292 +tea,URY,2.578874349594116 tea,VEN,-0.0 -tea,VNM,4.141996264457703 -tea,ZAF,-1.581347107887268 -tea,ZMB,-1.400840401649475 -tea,ZWE,1.954085648059845 +tea,VNM,4.515710830688477 +tea,ZAF,-1.5186957120895386 +tea,ZMB,-1.324572205543518 +tea,ZWE,2.210686147212982 tomato,AFG,-7.5 tomato,AGO,-7.5 tomato,ALB,-7.5 -tomato,ARE,-4.055720925331116 +tomato,ARE,-4.047128260135651 tomato,ARG,-7.5 -tomato,ARM,-1.5713253021240234 +tomato,ARM,-1.5630316138267517 tomato,ATG,7.5 tomato,AUS,-7.5 tomato,AUT,-7.5 -tomato,AZE,-7.034759044647217 +tomato,AZE,-7.03026556968689 tomato,BDI,-7.5 tomato,BEL,0.0 -tomato,BEN,-1.672958105802536 +tomato,BEN,-1.6712896227836609 tomato,BFA,-7.5 -tomato,BGD,-0.12310740537941456 -tomato,BGR,-7.2074134349823 -tomato,BHS,-1.2021023035049438 -tomato,BIH,-2.6767368998844177 +tomato,BGD,-0.11912421509623528 +tomato,BGR,-7.202856540679932 +tomato,BHS,-1.1862614154815674 +tomato,BIH,-2.6570974066853523 tomato,BLR,-7.5 -tomato,BLZ,-6.936527252197266 -tomato,BOL,-0.6714333295822144 +tomato,BLZ,-6.933705568313599 +tomato,BOL,-0.579502522945404 tomato,BRA,-7.5 tomato,BRB,-7.5 tomato,BRN,-7.5 -tomato,BTN,0.2813168168067932 +tomato,BTN,0.004502475261688232 tomato,BWA,-7.5 tomato,CAF,-7.5 -tomato,CAN,-4.301813125610352 +tomato,CAN,-4.311575412750244 tomato,CHE,-7.5 tomato,CHL,-7.5 tomato,CHN,-7.5 -tomato,CIV,-3.7628172636032104 +tomato,CIV,-3.760883927345276 tomato,CMR,-7.5 tomato,COD,-7.5 tomato,COG,-7.5 @@ -5357,21 +5357,21 @@ tomato,CZE,-7.5 tomato,DEU,-7.5 tomato,DJI,-7.5 tomato,DNK,0.0 -tomato,DOM,0.7143454551696777 +tomato,DOM,0.7011146545410156 tomato,DZA,-7.5 -tomato,ECU,-7.090726375579834 -tomato,EGY,-0.8630908131599426 +tomato,ECU,-7.0886006355285645 +tomato,EGY,-0.8560433685779572 tomato,ERI,7.5 -tomato,ESP,2.7078895568847656 +tomato,ESP,2.7338006496429443 tomato,EST,7.5 -tomato,ETH,-1.159163475036621 +tomato,ETH,-1.1583015322685242 tomato,FIN,7.5 -tomato,FJI,-7.276002645492554 +tomato,FJI,-7.27418851852417 tomato,FRA,-7.5 tomato,GAB,-7.5 tomato,GBR,0.0 -tomato,GEO,-1.9785673022270203 -tomato,GHA,-4.94463324546814 +tomato,GEO,-1.9761812090873718 +tomato,GHA,-4.943235158920288 tomato,GIN,-7.5 tomato,GMB,-7.5 tomato,GNB,-0.0 @@ -5385,72 +5385,72 @@ tomato,HND,-7.5 tomato,HRV,-7.5 tomato,HTI,-7.5 tomato,HUN,-7.5 -tomato,IDN,-5.746147871017456 +tomato,IDN,-5.744016170501709 tomato,IND,-7.5 tomato,IRN,-0.0 -tomato,IRQ,-0.12743985652923584 +tomato,IRQ,-0.12171522527933121 tomato,ISR,-7.5 tomato,ITA,0.0 tomato,JAM,-7.5 -tomato,JOR,5.009065628051758 +tomato,JOR,5.022494673728943 tomato,JPN,-7.5 tomato,KAZ,-7.5 -tomato,KEN,-3.5256428718566895 -tomato,KGZ,-7.060367107391357 +tomato,KEN,-3.802867293357849 +tomato,KGZ,-7.05851936340332 tomato,KHM,7.5 -tomato,KOR,-5.589771151542664 +tomato,KOR,-5.582479476928711 tomato,LAO,-7.5 tomato,LBN,-7.5 tomato,LBR,-7.5 tomato,LBY,-7.5 -tomato,LKA,-3.265217661857605 -tomato,LSO,1.4075794219970703 +tomato,LKA,-3.262461543083191 +tomato,LSO,1.4141747951507568 tomato,LTU,-7.5 tomato,LUX,7.5 -tomato,LVA,-6.905583620071411 -tomato,MAR,0.16655799746513367 -tomato,MDA,1.9516196846961975 +tomato,LVA,-6.90186333656311 +tomato,MAR,0.18175485730171204 +tomato,MDA,1.9544562101364136 tomato,MDG,-7.5 tomato,MEX,-7.5 -tomato,MKD,-4.9198750257492065 -tomato,MLI,-6.799897193908691 +tomato,MKD,-4.914142847061157 +tomato,MLI,-6.796959400177002 tomato,MLT,-7.5 -tomato,MMR,-5.846469163894653 +tomato,MMR,-5.839361190795898 tomato,MNE,-7.5 tomato,MNG,-7.5 tomato,MOZ,-7.5 tomato,MUS,-7.5 -tomato,MWI,-5.287248373031616 +tomato,MWI,-5.574982166290283 tomato,MYS,0.0 tomato,NAM,-7.5 -tomato,NER,-1.8896127045154572 +tomato,NER,-1.8850045800209045 tomato,NGA,-7.5 tomato,NIC,-7.5 tomato,NLD,0.0 tomato,NOR,7.5 tomato,NPL,-7.5 -tomato,NZL,-5.071157455444336 -tomato,OMN,-7.372703790664673 -tomato,PAK,-1.373893141746521 +tomato,NZL,-5.075429558753967 +tomato,OMN,-7.366729021072388 +tomato,PAK,-1.3725589513778687 tomato,PAN,-7.5 tomato,PER,7.5 -tomato,PHL,-2.952237844467163 +tomato,PHL,-2.949766755104065 tomato,PNG,-7.5 tomato,POL,-7.5 tomato,PRI,-7.5 tomato,PRT,-7.5 tomato,PRY,-7.5 tomato,PSE,-7.5 -tomato,ROU,-6.5495123863220215 +tomato,ROU,-6.54511833190918 tomato,RUS,-7.5 -tomato,RWA,-2.9748942852020264 +tomato,RWA,-2.973390221595764 tomato,SAU,-7.5 tomato,SDN,-7.5 -tomato,SEN,-2.876231551170349 -tomato,SLE,-7.49881386756897 -tomato,SLV,-7.262470245361328 +tomato,SEN,-2.8726435899734497 +tomato,SLE,-7.497976779937744 +tomato,SLV,-7.255941152572632 tomato,SOM,-7.5 -tomato,SRB,-1.4880952835083008 +tomato,SRB,-1.4822291433811188 tomato,SSD,-7.5 tomato,SUR,-7.5 tomato,SVK,-7.5 @@ -5459,159 +5459,159 @@ tomato,SWE,-7.5 tomato,SWZ,-7.5 tomato,SYR,-7.5 tomato,TCD,-7.5 -tomato,TGO,-0.03383094072341919 -tomato,THA,-4.043104887008667 +tomato,TGO,-0.031885743141174316 +tomato,THA,-4.03936493396759 tomato,TJK,-7.5 tomato,TKM,-7.5 -tomato,TLS,-1.9017571210861206 +tomato,TLS,-1.9002437591552734 tomato,TTO,-7.5 -tomato,TUN,4.6364545822143555 -tomato,TUR,-2.59389066696167 +tomato,TUN,4.645869255065918 +tomato,TUR,-2.5980515480041504 tomato,TWN,-7.5 -tomato,TZA,-0.4218834787607193 +tomato,TZA,-0.4583733081817627 tomato,UGA,0.0 -tomato,UKR,7.493260622024536 +tomato,UKR,7.5 tomato,URY,-7.5 tomato,USA,-7.5 tomato,UZB,-7.5 tomato,VEN,-7.5 tomato,VNM,-7.5 -tomato,YEM,-5.4548890590667725 +tomato,YEM,-5.4513304233551025 tomato,ZAF,-7.5 -tomato,ZMB,-2.4583306312561035 -tomato,ZWE,-3.9236555099487305 -watermelon,AFG,-2.245093822479248 +tomato,ZMB,-2.4564976692199707 +tomato,ZWE,-3.9198169708251953 +watermelon,AFG,-2.2142319679260254 watermelon,AGO,-0.0 -watermelon,ALB,2.272031784057617 -watermelon,ARG,4.429373741149902 -watermelon,ATG,-4.4580397605896 +watermelon,ALB,2.2335076332092285 +watermelon,ARG,4.538205146789551 +watermelon,ATG,-4.433868408203125 watermelon,AUS,-7.5 -watermelon,AZE,5.442526340484619 +watermelon,AZE,5.494441986083984 watermelon,BDI,-0.0 watermelon,BEN,-0.0 watermelon,BFA,-0.0 watermelon,BGD,-0.0 watermelon,BGR,-0.0 watermelon,BHS,-0.0 -watermelon,BIH,-2.7320520281791687 -watermelon,BLZ,-2.308080665767193 -watermelon,BOL,0.8568992018699646 -watermelon,BRA,3.3800708055496216 +watermelon,BIH,-2.6857842206954956 +watermelon,BLZ,-2.2420415952801704 +watermelon,BOL,0.8961392045021057 +watermelon,BRA,3.464172601699829 watermelon,BRB,-0.0 watermelon,BTN,-0.0 -watermelon,BWA,-0.5193916112184525 +watermelon,BWA,-0.45707350969314575 watermelon,CAF,-0.0 -watermelon,CHL,-0.6577045917510986 -watermelon,CHN,-7.484024524688721 +watermelon,CHL,-0.5593133568763733 +watermelon,CHN,-7.4503538608551025 watermelon,CIV,-0.0 -watermelon,CMR,-4.952229976654053 +watermelon,CMR,-4.93295955657959 watermelon,COD,-0.0 watermelon,COG,-0.0 -watermelon,COL,2.3601794242858887 +watermelon,COL,2.4299185276031494 watermelon,COM,-0.0 watermelon,CPV,-7.5 -watermelon,CRI,0.532394103705883 -watermelon,CUB,-2.2412538528442383 -watermelon,CYP,-3.965402126312256 +watermelon,CRI,0.6497363746166229 +watermelon,CUB,-2.1964561343193054 +watermelon,CYP,-3.9310009479522705 watermelon,DOM,-7.5 -watermelon,DZA,-0.44858303666114807 -watermelon,ECU,-1.9820749163627625 -watermelon,EGY,-1.3301100134849548 +watermelon,DZA,-0.41035155998542905 +watermelon,ECU,-1.9372732043266296 +watermelon,EGY,-1.2585487961769104 watermelon,ERI,-0.0 watermelon,ESP,-7.5 watermelon,ETH,-0.0 watermelon,FJI,-7.5 -watermelon,FRA,0.5216711014509201 +watermelon,FRA,0.5707691758871078 watermelon,GAB,-0.0 -watermelon,GEO,-1.0231907069683075 +watermelon,GEO,-1.009521096944809 watermelon,GHA,-0.0 -watermelon,GIN,-5.15928840637207 +watermelon,GIN,-5.1420512199401855 watermelon,GMB,-0.0 watermelon,GNB,-0.0 -watermelon,GRC,-0.784394383430481 -watermelon,GTM,0.1989661604166031 +watermelon,GRC,-0.7137422561645508 +watermelon,GTM,0.26755671203136444 watermelon,GUF,-0.0 watermelon,GUY,-7.5 watermelon,HND,-7.5 -watermelon,HRV,-2.924773931503296 +watermelon,HRV,-2.904731273651123 watermelon,HTI,-0.0 -watermelon,IDN,1.7185547351837158 -watermelon,IND,-2.276355743408203 -watermelon,IRN,4.509305477142334 +watermelon,IDN,1.7583351731300354 +watermelon,IND,-2.2455122470855713 +watermelon,IRN,4.556153297424316 watermelon,IRQ,-0.0 -watermelon,ISR,-0.5726282000541687 -watermelon,ITA,2.64500792324543 +watermelon,ISR,-0.4982078820466995 +watermelon,ITA,2.7089335173368454 watermelon,JAM,-7.5 -watermelon,JOR,-4.644447326660156 +watermelon,JOR,-4.590991258621216 watermelon,JPN,-7.5 -watermelon,KEN,2.7071703672409058 -watermelon,KGZ,-4.034734725952148 +watermelon,KEN,2.5340858697891235 +watermelon,KGZ,-4.015584945678711 watermelon,KHM,-0.0 -watermelon,LAO,-2.537648558616638 +watermelon,LAO,-2.508575916290283 watermelon,LBN,-7.5 watermelon,LBR,-0.0 -watermelon,LBY,0.9130091667175293 +watermelon,LBY,0.9662013053894043 watermelon,LKA,-0.0 watermelon,LSO,-0.0 -watermelon,MAR,-1.443446159362793 +watermelon,MAR,-1.4019514322280884 watermelon,MDG,-0.0 -watermelon,MEX,-0.08883213251829147 -watermelon,MKD,4.5114219188690186 -watermelon,MLI,3.1213927268981934 +watermelon,MEX,-0.05278988927602768 +watermelon,MKD,4.560974359512329 +watermelon,MLI,3.2206242084503174 watermelon,MLT,-7.5 watermelon,MMR,-0.0 -watermelon,MNE,-0.1470850706100464 +watermelon,MNE,-0.10232019424438477 watermelon,MOZ,-0.0 -watermelon,MRT,-4.658735752105713 +watermelon,MRT,-4.636927604675293 watermelon,MUS,-0.0 -watermelon,MWI,-0.523480759235099 -watermelon,MYS,2.9212904274463654 -watermelon,NAM,-2.3278777599334717 +watermelon,MWI,-0.7432744354009628 +watermelon,MYS,2.972167894244194 +watermelon,NAM,-2.2816545963287354 watermelon,NER,-7.5 watermelon,NGA,-0.0 watermelon,NIC,-0.0 watermelon,NPL,-0.0 watermelon,OMN,7.5 -watermelon,PAK,0.948249340057373 -watermelon,PER,7.174327850341797 -watermelon,PHL,0.319442555308342 +watermelon,PAK,0.9883823990821838 +watermelon,PER,6.916561603546143 +watermelon,PHL,0.3519771099090576 watermelon,PNG,-0.0 -watermelon,PRI,-4.607720136642456 -watermelon,PRT,-5.696748495101929 -watermelon,PRY,2.7609466314315796 +watermelon,PRI,-4.541389346122742 +watermelon,PRT,-5.660556316375732 +watermelon,PRY,2.829785108566284 watermelon,PSE,-7.5 watermelon,RUS,-0.0 watermelon,RWA,-0.0 -watermelon,SAU,-0.8236573934555054 -watermelon,SDN,-2.4636390209198 -watermelon,SEN,2.922377586364746 +watermelon,SAU,-0.7267142534255981 +watermelon,SDN,-2.4175244569778442 +watermelon,SEN,2.9780750274658203 watermelon,SLE,-0.0 -watermelon,SLV,4.181053459644318 -watermelon,SOM,-2.536484479904175 +watermelon,SLV,4.293497622013092 +watermelon,SOM,-2.4940409660339355 watermelon,SSD,-0.0 -watermelon,SUR,-6.2348151206970215 -watermelon,SVN,-1.9223019480705261 +watermelon,SUR,-6.1896727085113525 +watermelon,SVN,-1.889222502708435 watermelon,SWZ,-0.0 -watermelon,SYR,0.8067891001701355 +watermelon,SYR,0.8595113754272461 watermelon,TCD,-0.0 watermelon,TGO,-0.0 -watermelon,THA,-5.462238311767578 -watermelon,TJK,-3.182804822921753 -watermelon,TKM,1.0307010412216187 +watermelon,THA,-5.4332005977630615 +watermelon,TJK,-3.167925477027893 +watermelon,TKM,1.082379937171936 watermelon,TLS,-0.0 -watermelon,TTO,-0.2698024809360504 -watermelon,TUN,-5.537251710891724 -watermelon,TUR,1.657686710357666 -watermelon,TZA,-5.008765697479248 +watermelon,TTO,-0.2445453181862831 +watermelon,TUN,-5.4918153285980225 +watermelon,TUR,1.7108975648880005 +watermelon,TZA,-4.989964008331299 watermelon,UGA,-0.0 watermelon,UKR,-0.0 -watermelon,URY,1.4525058269500732 -watermelon,USA,0.22569002956151962 -watermelon,UZB,-0.07877466082572937 -watermelon,VEN,0.33532315492630005 -watermelon,VNM,-0.3652151823043823 +watermelon,URY,1.5217979550361633 +watermelon,USA,0.30481110513210297 +watermelon,UZB,-0.02097168192267418 +watermelon,VEN,0.4041577875614166 +watermelon,VNM,-0.3293232321739197 watermelon,VUT,-0.0 -watermelon,YEM,1.2473609447479248 +watermelon,YEM,1.3101935982704163 watermelon,ZAF,7.5 watermelon,ZMB,-0.0 watermelon,ZWE,7.5 @@ -5623,21 +5623,21 @@ wetland-rice,ARM,-0.0 wetland-rice,AUS,7.5 wetland-rice,AZE,-2.6935129165649414 wetland-rice,BDI,-0.4047177881002426 -wetland-rice,BEN,-1.7779616117477417 +wetland-rice,BEN,-1.7860558032989502 wetland-rice,BFA,-1.3930566310882568 wetland-rice,BGD,-0.4267389178276062 wetland-rice,BGR,-1.4718642234802246 wetland-rice,BLZ,-1.6666133403778076 -wetland-rice,BOL,-0.5397570133209229 -wetland-rice,BRA,-0.9525447189807892 +wetland-rice,BOL,-0.5433197617530823 +wetland-rice,BRA,-0.951948881149292 wetland-rice,BRN,-1.45994234085083 -wetland-rice,BTN,-1.5550525784492493 +wetland-rice,BTN,-1.2872804403305054 wetland-rice,BWA,-0.0 wetland-rice,CAF,-1.5085437297821045 wetland-rice,CAN,-0.0 wetland-rice,CHE,-0.0 wetland-rice,CHL,-0.8808698654174805 -wetland-rice,CHN,-1.9491270184516907 +wetland-rice,CHN,-1.9506058096885681 wetland-rice,CIV,-0.23604928702116013 wetland-rice,CMR,-1.4134510159492493 wetland-rice,COD,-1.6164772510528564 @@ -5647,22 +5647,22 @@ wetland-rice,COM,-1.649828553199768 wetland-rice,CRI,-1.3741123676300049 wetland-rice,CUB,-1.2808098196983337 wetland-rice,DOM,-1.6238070726394653 -wetland-rice,DZA,-1.9268113374710083 +wetland-rice,DZA,-1.932600975036621 wetland-rice,ECU,3.459453821182251 wetland-rice,EGY,-1.672710120677948 wetland-rice,ERI,3.1211240887641907 wetland-rice,ESP,-1.922120451927185 -wetland-rice,ETH,-1.135880708694458 +wetland-rice,ETH,-1.13453608751297 wetland-rice,FJI,-0.8297505378723145 wetland-rice,FRA,-1.020203709602356 wetland-rice,GAB,-1.4860550165176392 -wetland-rice,GEO,-1.2105573415756226 +wetland-rice,GEO,-1.1891896724700928 wetland-rice,GHA,-0.6429682075977325 wetland-rice,GIN,-0.6402623057365417 wetland-rice,GMB,-0.38500267267227173 wetland-rice,GNB,-1.452115535736084 wetland-rice,GNQ,7.5 -wetland-rice,GRC,-1.2496370077133179 +wetland-rice,GRC,-1.485931932926178 wetland-rice,GTM,-1.52126944065094 wetland-rice,GUF,-1.2947791814804077 wetland-rice,GUY,-1.3078415989875793 @@ -5671,26 +5671,26 @@ wetland-rice,HRV,-0.0 wetland-rice,HTI,-1.491448700428009 wetland-rice,HUN,-0.3124268651008606 wetland-rice,IDN,-1.4961115717887878 -wetland-rice,IND,-1.2529677748680115 +wetland-rice,IND,-1.2067438960075378 wetland-rice,IRN,-3.8611674308776855 wetland-rice,IRQ,-1.5961377024650574 wetland-rice,ISR,4.204609662294388 wetland-rice,ITA,-2.3482693433761597 -wetland-rice,JPN,-3.7190444469451904 +wetland-rice,JPN,-3.7152687311172485 wetland-rice,KAZ,-0.39256639778614044 -wetland-rice,KEN,-1.6334169507026672 +wetland-rice,KEN,-1.648304045200348 wetland-rice,KGZ,-3.10903263092041 wetland-rice,KHM,-1.395193636417389 wetland-rice,KOR,-6.413891792297363 wetland-rice,LAO,-1.3473759889602661 wetland-rice,LBR,-1.5615077018737793 wetland-rice,LBY,-0.0 -wetland-rice,LKA,-0.4937780946493149 +wetland-rice,LKA,-0.5103325396776199 wetland-rice,LSO,-0.0 wetland-rice,MAR,-1.6885667443275452 wetland-rice,MDA,3.2253977060317993 wetland-rice,MDG,-0.44303373992443085 -wetland-rice,MEX,-0.8995972573757172 +wetland-rice,MEX,-0.8840499520301819 wetland-rice,MKD,-1.123809039592743 wetland-rice,MLI,-0.5188568234443665 wetland-rice,MMR,-1.4329081177711487 @@ -5702,26 +5702,26 @@ wetland-rice,MYS,-0.5421236753463745 wetland-rice,NAM,-1.457148551940918 wetland-rice,NER,-1.3785419464111328 wetland-rice,NGA,-0.4231751710176468 -wetland-rice,NIC,-0.23596098273992538 +wetland-rice,NIC,1.2466256618499756 wetland-rice,NPL,-0.39979974925518036 wetland-rice,PAK,-1.5440089702606201 wetland-rice,PAN,-1.1511292457580566 -wetland-rice,PER,-1.4421217441558838 +wetland-rice,PER,-0.3680562674999237 wetland-rice,PHL,-0.863765299320221 wetland-rice,PNG,-1.520492136478424 wetland-rice,PRI,-2.6471487283706665 wetland-rice,PRT,-1.2753294110298157 wetland-rice,PRY,-0.7168880105018616 wetland-rice,PSE,-1.0338850021362305 -wetland-rice,ROU,-1.3000193238258362 -wetland-rice,RUS,-1.1121360063552856 +wetland-rice,ROU,-1.520885169506073 +wetland-rice,RUS,-1.1044092774391174 wetland-rice,RWA,-2.060689091682434 wetland-rice,SAU,7.5 wetland-rice,SDN,-1.077760934829712 wetland-rice,SEN,-0.24637958407402039 wetland-rice,SLB,2.99850732088089 wetland-rice,SLE,-0.3850844204425812 -wetland-rice,SLV,2.807352900505066 +wetland-rice,SLV,2.740665555000305 wetland-rice,SOM,-1.4259328842163086 wetland-rice,SRB,3.1836307644844055 wetland-rice,SSD,-1.48295259475708 @@ -5729,20 +5729,20 @@ wetland-rice,SUR,-0.29805608093738556 wetland-rice,SVK,-0.0 wetland-rice,SVN,-0.0 wetland-rice,SWZ,7.5 -wetland-rice,SYR,-1.2197078466415405 +wetland-rice,SYR,-1.1299304962158203 wetland-rice,TCD,-0.8468590378761292 wetland-rice,TGO,-0.27850475907325745 wetland-rice,THA,3.461722820997238 -wetland-rice,TJK,-4.147400856018066 +wetland-rice,TJK,-4.130908012390137 wetland-rice,TKM,-1.0802079439163208 wetland-rice,TLS,-1.9572893381118774 wetland-rice,TTO,-0.19401836395263672 wetland-rice,TUN,-0.0 -wetland-rice,TUR,-3.0866461992263794 +wetland-rice,TUR,-3.001083016395569 wetland-rice,TWN,7.5 wetland-rice,TZA,3.6727085635066032 -wetland-rice,UGA,-1.4930682182312012 -wetland-rice,UKR,-2.2693220376968384 +wetland-rice,UGA,-0.877206563949585 +wetland-rice,UKR,-2.2704272270202637 wetland-rice,URY,-0.8143447637557983 wetland-rice,USA,-1.6331813335418701 wetland-rice,UZB,-5.117491722106934 @@ -5751,7 +5751,7 @@ wetland-rice,VNM,-1.1111834645271301 wetland-rice,YEM,-0.0 wetland-rice,ZAF,7.5 wetland-rice,ZMB,-1.1754523515701294 -wetland-rice,ZWE,1.7023093700408936 +wetland-rice,ZWE,1.7018123865127563 wheat,AFG,-0.594604879617691 wheat,AGO,-0.7371805906295776 wheat,ALB,-0.8102755844593048 @@ -5765,11 +5765,11 @@ wheat,BDI,3.38121035695076 wheat,BEL,-0.5466453433036804 wheat,BGD,3.5663401782512665 wheat,BGR,3.594714507460594 -wheat,BIH,-3.495462045073509 -wheat,BLR,-2.9289645105600357 +wheat,BIH,-3.4844993203878403 +wheat,BLR,-2.9185384660959244 wheat,BOL,-0.2611357569694519 -wheat,BRA,-0.2975470870733261 -wheat,BTN,0.6839059293270111 +wheat,BRA,-0.2988650053739548 +wheat,BTN,0.8350795209407806 wheat,BWA,-0.6082069277763367 wheat,CAN,-0.49947166442871094 wheat,CHE,-1.730389654636383 @@ -5791,41 +5791,41 @@ wheat,EST,-0.2960323393344879 wheat,ETH,-0.5326666831970215 wheat,FIN,3.5585394501686096 wheat,FRA,-0.6279883682727814 -wheat,GBR,-1.0840263664722443 -wheat,GEO,-0.2836731821298599 +wheat,GBR,-1.0584267675876617 +wheat,GEO,-0.2604386955499649 wheat,GRC,-0.47753196954727173 wheat,GTM,7.5 wheat,HND,-0.7804300785064697 wheat,HRV,-4.15170294046402 wheat,HUN,-0.24718762934207916 -wheat,IND,-0.39058321714401245 +wheat,IND,-0.3556597828865051 wheat,IRL,-0.6626280546188354 wheat,IRN,-0.5889518857002258 wheat,IRQ,-0.661315381526947 wheat,ISR,0.5362032353878021 wheat,ITA,-0.6831164062023163 wheat,JOR,-0.7589737176895142 -wheat,JPN,2.6089839935302734 +wheat,JPN,2.6135120391845703 wheat,KAZ,-0.14942628145217896 wheat,KEN,-0.6309512555599213 wheat,KGZ,-0.37015578150749207 -wheat,KOR,0.06051094830036163 +wheat,KOR,0.0707123875617981 wheat,LAO,-0.0 wheat,LBN,-0.6281641721725464 wheat,LBY,-1.0016203224658966 wheat,LSO,-0.0037115374580025673 wheat,LTU,-0.4477772116661072 -wheat,LUX,-0.4366423487663269 +wheat,LUX,-0.44211769104003906 wheat,LVA,-0.37215811014175415 wheat,MAR,-0.6512438356876373 wheat,MDA,-0.11672109365463257 wheat,MDG,-0.5818796455860138 -wheat,MEX,-0.9628743529319763 +wheat,MEX,-0.9494690895080566 wheat,MKD,-0.16816334426403046 wheat,MLI,-0.43514300882816315 -wheat,MLT,0.1503661870956421 +wheat,MLT,0.14188706874847412 wheat,MMR,-0.6571457982063293 -wheat,MNE,-0.31177082657814026 +wheat,MNE,-0.23729397356510162 wheat,MNG,-0.11704491823911667 wheat,MOZ,-0.43872374296188354 wheat,MRT,-0.3839586079120636 @@ -5834,19 +5834,19 @@ wheat,NAM,-1.2708697319030762 wheat,NER,-0.2938033938407898 wheat,NGA,-0.6598149538040161 wheat,NIC,7.5 -wheat,NLD,-1.884195238351822 +wheat,NLD,-1.7940762639045715 wheat,NOR,-1.2662968635559082 wheat,NPL,-0.38834312558174133 wheat,NZL,-1.385552167892456 -wheat,OMN,-2.3489630818367004 +wheat,OMN,-2.567718744277954 wheat,PAK,-0.46277880668640137 -wheat,PER,-0.345427542924881 +wheat,PER,0.06111774401506409 wheat,POL,-0.498719185590744 wheat,PRT,3.6228275895118713 wheat,PRY,-0.16008544713258743 wheat,PSE,-0.8061827421188354 -wheat,ROU,-0.4136349707841873 -wheat,RUS,-0.2825326919555664 +wheat,ROU,-0.42945802211761475 +wheat,RUS,-0.28254225850105286 wheat,RWA,-0.41729216277599335 wheat,SAU,-2.4718408584594727 wheat,SDN,-0.6424590051174164 @@ -5858,17 +5858,17 @@ wheat,SVK,-0.38357892632484436 wheat,SVN,-0.36969655752182007 wheat,SWE,-0.9206871092319489 wheat,SWZ,-0.48412415385246277 -wheat,SYR,-0.6654495000839233 +wheat,SYR,-0.6389700174331665 wheat,TCD,-0.6328994631767273 wheat,THA,7.5 wheat,TJK,-0.45415376126766205 wheat,TKM,-0.5101821422576904 wheat,TUN,-0.34296010434627533 -wheat,TUR,-0.17179903388023376 +wheat,TUR,-0.10952656343579292 wheat,TWN,-0.41619129478931427 wheat,TZA,7.5 wheat,UGA,7.5 -wheat,UKR,-0.42287300527095795 +wheat,UKR,-0.42139849066734314 wheat,URY,-0.2196616530418396 wheat,USA,-0.35528913140296936 wheat,UZB,-0.80463045835495 @@ -5877,174 +5877,174 @@ wheat,VNM,7.5 wheat,YEM,-0.6164727210998535 wheat,ZAF,-0.38746193051338196 wheat,ZMB,0.17059378325939178 -wheat,ZWE,2.115170955657959 -white-potato,AFG,-4.2323386669158936 +wheat,ZWE,2.118193507194519 +white-potato,AFG,-4.2323384284973145 white-potato,AGO,-6.248565196990967 -white-potato,ALB,-4.838316917419434 -white-potato,ARE,-5.429963946342468 -white-potato,ARG,-4.8037660121917725 -white-potato,ARM,-1.324949562549591 +white-potato,ALB,-4.848561763763428 +white-potato,ARE,-5.429963827133179 +white-potato,ARG,-4.80927848815918 +white-potato,ARM,-1.3249493837356567 white-potato,ATG,-7.5 white-potato,AUS,7.5 -white-potato,AUT,-0.780416913330555 -white-potato,AZE,-2.26721727848053 +white-potato,AUT,-0.7804166451096535 +white-potato,AZE,-2.2672170996665955 white-potato,BDI,-1.583346426486969 -white-potato,BEL,1.345667839050293 +white-potato,BEL,1.3456681966781616 white-potato,BEN,0.0 white-potato,BFA,-6.657420635223389 -white-potato,BGD,0.6034288331866264 -white-potato,BGR,0.01282954216003418 +white-potato,BGD,0.6034289672970772 +white-potato,BGR,0.012829720973968506 white-potato,BHS,-7.187550783157349 -white-potato,BIH,-1.4009221196174622 -white-potato,BLR,-0.5546273589134216 +white-potato,BIH,-1.4009220600128174 +white-potato,BLR,-0.544201135635376 white-potato,BLZ,-6.015321969985962 -white-potato,BOL,-0.8154157996177673 -white-potato,BRA,-6.683359622955322 +white-potato,BOL,-0.8154157400131226 +white-potato,BRA,-6.683359146118164 white-potato,BRB,-7.5 white-potato,BRN,-7.5 -white-potato,BTN,-2.8564728498458862 +white-potato,BTN,-2.928027033805847 white-potato,BWA,-7.5 white-potato,CAF,-7.5 -white-potato,CAN,-2.336838722229004 +white-potato,CAN,-2.336838483810425 white-potato,CHE,-7.5 -white-potato,CHL,-5.076667785644531 -white-potato,CHN,-0.2575211226940155 +white-potato,CHL,-5.076667547225952 +white-potato,CHN,-0.10479666292667389 white-potato,CIV,-7.068480491638184 white-potato,CMR,-6.015861749649048 white-potato,COD,-6.667510986328125 white-potato,COG,-6.153130292892456 -white-potato,COL,-2.7065480947494507 -white-potato,COM,-7.182870626449585 -white-potato,CPV,-6.651533603668213 +white-potato,COL,-2.706547975540161 +white-potato,COM,-7.182870388031006 +white-potato,CPV,-6.651533365249634 white-potato,CRI,-7.5 white-potato,CUB,-4.543330192565918 -white-potato,CYP,-4.733559012413025 -white-potato,CZE,-0.1767502874135971 -white-potato,DEU,0.3590337112545967 -white-potato,DNK,-2.760642886161804 +white-potato,CYP,-4.733558893203735 +white-potato,CZE,-0.17675001919269562 +white-potato,DEU,0.35903410613536835 +white-potato,DNK,-2.7606425285339355 white-potato,DOM,-7.5 -white-potato,DZA,-5.563530445098877 -white-potato,ECU,-1.6488671898841858 -white-potato,EGY,-2.428444266319275 +white-potato,DZA,-5.563530206680298 +white-potato,ECU,-1.6488670706748962 +white-potato,EGY,-2.428444027900696 white-potato,ERI,-7.137639999389648 -white-potato,ESP,-5.260821104049683 -white-potato,EST,0.8071079850196838 -white-potato,ETH,0.5625659227371216 -white-potato,FIN,-0.6591880619525909 +white-potato,ESP,-5.2608208656311035 +white-potato,EST,0.8071082234382629 +white-potato,ETH,0.5625660419464111 +white-potato,FIN,-0.6394990086555481 white-potato,FRA,-7.5 white-potato,GAB,-7.02944278717041 -white-potato,GBR,-3.4748202562332153 -white-potato,GEO,-0.5480959415435791 +white-potato,GBR,-3.4426900148391724 +white-potato,GEO,-0.5480958521366119 white-potato,GHA,-7.048776149749756 white-potato,GIN,-6.113731145858765 white-potato,GMB,0.0 white-potato,GNB,-6.739335060119629 white-potato,GNQ,-6.760073184967041 white-potato,GRC,-7.5 -white-potato,GRD,-6.830006122589111 -white-potato,GTM,-3.0130324363708496 +white-potato,GRD,-6.830005645751953 +white-potato,GTM,-3.0130321979522705 white-potato,GUF,-7.5 white-potato,GUY,3.75 white-potato,HND,-7.19405198097229 -white-potato,HRV,-3.5440914034843445 +white-potato,HRV,-3.5390104055404663 white-potato,HTI,-6.67333722114563 -white-potato,HUN,-1.6039954326115549 -white-potato,IDN,-5.755913972854614 -white-potato,IND,-2.8240537643432617 -white-potato,IRL,-0.08122962340712547 -white-potato,IRN,-0.21224414557218552 -white-potato,IRQ,-3.7558659315109253 +white-potato,HUN,-1.603995144367218 +white-potato,IDN,-5.676455974578857 +white-potato,IND,-2.824053645133972 +white-potato,IRL,-0.08122931141406298 +white-potato,IRN,-0.21224390715360641 +white-potato,IRQ,-3.755865693092346 white-potato,ISR,-7.5 white-potato,ITA,-7.5 white-potato,JAM,-6.277691841125488 white-potato,JOR,-7.282428741455078 white-potato,JPN,-7.5 -white-potato,KAZ,-0.3035024255514145 +white-potato,KAZ,-0.30350229144096375 white-potato,KEN,-1.2727055549621582 -white-potato,KGZ,-1.7841445803642273 -white-potato,KHM,-6.010222434997559 -white-potato,KOR,-3.7122304439544678 -white-potato,LAO,1.9184892177581787 -white-potato,LBN,-7.129741430282593 +white-potato,KGZ,-1.754969835281372 +white-potato,KHM,-5.9935829639434814 +white-potato,KOR,-3.6637219190597534 +white-potato,LAO,1.942519187927246 +white-potato,LBN,-7.129741191864014 white-potato,LBR,-5.348794460296631 -white-potato,LBY,-3.6757400035858154 +white-potato,LBY,-3.6757397651672363 white-potato,LKA,-7.252150774002075 -white-potato,LSO,-2.7024563550949097 -white-potato,LTU,-0.26730214804410934 -white-potato,LUX,-3.8080108165740967 -white-potato,LVA,1.099543571472168 -white-potato,MAR,-2.1044013500213623 -white-potato,MDA,0.612177312374115 +white-potato,LSO,-2.7024561166763306 +white-potato,LTU,-0.267302006483078 +white-potato,LUX,-3.813485860824585 +white-potato,LVA,1.0995437502861023 +white-potato,MAR,-2.104401111602783 +white-potato,MDA,0.6121773719787598 white-potato,MDG,-6.309719562530518 -white-potato,MEX,-6.034812688827515 -white-potato,MKD,-1.3388609141111374 +white-potato,MEX,-5.977227687835693 +white-potato,MKD,-1.3388608247041702 white-potato,MLI,-7.5 white-potato,MLT,-7.5 -white-potato,MMR,-4.48793363571167 -white-potato,MNE,-4.132070183753967 -white-potato,MNG,-1.2369403839111328 +white-potato,MMR,-4.4513092041015625 +white-potato,MNE,-4.132069826126099 +white-potato,MNG,-1.206423282623291 white-potato,MOZ,-7.5 white-potato,MRT,-7.5 white-potato,MUS,-7.5 white-potato,MWI,2.372127890586853 -white-potato,MYS,1.9422821998596191 +white-potato,MYS,1.9663387537002563 white-potato,NAM,-5.190667152404785 white-potato,NER,-7.5 white-potato,NGA,-6.870185852050781 white-potato,NIC,-7.5 -white-potato,NLD,-0.6496542692184448 +white-potato,NLD,-0.6496539413928986 white-potato,NOR,-5.8460187911987305 -white-potato,NPL,-1.3680104613304138 -white-potato,NZL,-0.7484760284423828 +white-potato,NPL,-1.3680103421211243 +white-potato,NZL,-0.7484763637185097 white-potato,OMN,-7.5 white-potato,PAK,-0.0 white-potato,PAN,-7.5 -white-potato,PER,-2.4836816787719727 +white-potato,PER,-2.553200364112854 white-potato,PHL,-7.5 white-potato,PNG,-7.5 -white-potato,POL,2.1020238399505615 +white-potato,POL,2.1020240783691406 white-potato,PRI,-7.5 -white-potato,PRT,-2.033027172088623 +white-potato,PRT,-2.0330270528793335 white-potato,PRY,-2.867590069770813 white-potato,PSE,-7.5 -white-potato,ROU,-2.192002058029175 -white-potato,RUS,-0.2862212657928467 -white-potato,RWA,-1.3822731375694275 +white-potato,ROU,-2.1920018792152405 +white-potato,RUS,-0.2476472407579422 +white-potato,RWA,-1.3822730779647827 white-potato,SAU,-7.5 white-potato,SDN,-5.008236646652222 -white-potato,SEN,-5.556590557098389 -white-potato,SLB,-4.641535997390747 +white-potato,SEN,-5.55659031867981 +white-potato,SLB,-4.641536235809326 white-potato,SLE,0.7965755462646484 white-potato,SLV,0.9726858139038086 white-potato,SOM,-5.73030424118042 -white-potato,SRB,1.4517293274402618 +white-potato,SRB,1.4517296850681305 white-potato,SSD,-7.5 white-potato,SUR,-7.5 -white-potato,SVK,-1.6193366423249245 -white-potato,SVN,-2.6869579553604126 +white-potato,SVK,-1.6193363666534424 +white-potato,SVN,-2.6869577169418335 white-potato,SWE,-4.817352771759033 white-potato,SWZ,-6.983173847198486 white-potato,SYR,-3.8503787517547607 white-potato,TCD,-6.66522479057312 white-potato,TGO,-6.855196714401245 -white-potato,THA,-2.2440481185913086 -white-potato,TJK,-1.5787546634674072 +white-potato,THA,-2.1981265544891357 +white-potato,TJK,-1.5272667407989502 white-potato,TKM,-5.116387844085693 -white-potato,TLS,-0.47124533355236053 +white-potato,TLS,-0.47124534845352173 white-potato,TTO,-6.704572677612305 -white-potato,TUN,-1.3139909505844116 -white-potato,TUR,-1.440903663635254 -white-potato,TWN,-2.6755971908569336 -white-potato,TZA,-0.15896368026733398 +white-potato,TUN,-1.3206420540809631 +white-potato,TUR,-1.4409034252166748 +white-potato,TWN,-2.6150388717651367 +white-potato,TZA,-0.1589636355638504 white-potato,UGA,-6.633254766464233 -white-potato,UKR,0.5144858360290527 +white-potato,UKR,0.5139655470848083 white-potato,URY,-7.5 -white-potato,USA,0.7726277112960815 -white-potato,UZB,-3.1113035678863525 +white-potato,USA,0.772628128528595 +white-potato,UZB,-3.1113033294677734 white-potato,VEN,-7.5 -white-potato,VNM,-4.91122579574585 -white-potato,YEM,-5.8191752433776855 -white-potato,ZAF,-3.9112796783447266 +white-potato,VNM,-4.878891229629517 +white-potato,YEM,-5.8191750049591064 +white-potato,ZAF,-3.9656487703323364 white-potato,ZMB,-7.5 white-potato,ZWE,-7.5 yam,AFG,-0.0 @@ -6060,7 +6060,7 @@ yam,BOL,-5.207590579986572 yam,BRA,-2.857956290245056 yam,BRB,-7.5 yam,BRN,-5.238788366317749 -yam,BTN,-2.948604702949524 +yam,BTN,-3.52375590801239 yam,BWA,-3.5980206727981567 yam,CAF,-4.066412687301636 yam,CHN,-0.7409265637397766 @@ -6122,7 +6122,7 @@ yam,NIC,2.695525646209717 yam,NPL,-5.062485456466675 yam,PAK,-0.0 yam,PAN,-3.2238476276397705 -yam,PER,-3.7902177572250366 +yam,PER,-3.8684191703796387 yam,PHL,-1.6432258486747742 yam,PNG,-3.0153849124908447 yam,PRI,-7.5 diff --git a/data/curated/calibration/gbd-anchored/deviation_penalty.yaml b/data/curated/calibration/gbd-anchored/deviation_penalty.yaml index 83c3515a..c7c493a9 100644 --- a/data/curated/calibration/gbd-anchored/deviation_penalty.yaml +++ b/data/curated/calibration/gbd-anchored/deviation_penalty.yaml @@ -14,9 +14,9 @@ components: - grassland - feed l1_costs: - cropland: 1.5193569555690574 - grassland: 0.2382076672748479 - feed: 0.05642577463133512 -iterations: 2 + cropland: 6.639568979511352 + grassland: 0.3337459279524151 + feed: 0.5177289814572067 +iterations: 8 converged: true -final_residual_log_inf: 0.019008125150330513 +final_residual_log_inf: 0.012827410420280535 diff --git a/data/curated/calibration/gbd-anchored/fingerprint.yaml b/data/curated/calibration/gbd-anchored/fingerprint.yaml index 484edbce..973aeb5c 100644 --- a/data/curated/calibration/gbd-anchored/fingerprint.yaml +++ b/data/curated/calibration/gbd-anchored/fingerprint.yaml @@ -5,18 +5,17 @@ steps: cost: artefacts: - data/curated/calibration/gbd-anchored/animal_cost.csv: 7474ab68d873a39423d8712bcef116970396f1fc65dd099fdb6d98bf6d330e4e - data/curated/calibration/gbd-anchored/crop_cost.csv: 8cff30e9d6ba695506b8cdad41d482001184437e3fbe33e1faf453de9e495891 - data/curated/calibration/gbd-anchored/grassland_cost.csv: 22db5ab154fe85645cc1f9504bdf295e4d2f8d58ead458daecf9c0c026cd0c30 - data/curated/calibration/gbd-anchored/multi_crop_cost.csv: 182f603308a1a497bb6d4a7ab45d9771897176b03adf816992a63a56708b6a2a + data/curated/calibration/gbd-anchored/animal_cost.csv: ce8615f47ccb3dcb9a6c19a930b107bfd80f79a0cc76da8ccde2e8a25fb7ff56 + data/curated/calibration/gbd-anchored/crop_cost.csv: e9d2741655a952839aa83ead8a1f8537f658c49145f97f4e33e9f35cb9269704 + data/curated/calibration/gbd-anchored/grassland_cost.csv: 5f61085017134586af97684f36713289152bc4508585fd5a610886ba08c3c5e2 + data/curated/calibration/gbd-anchored/multi_crop_cost.csv: 18cfd65fce266322ee0573be81a7f5a62103369687af47e2f18629a46a8d6002 code: workflow/rules/animals.smk: fa07f649a53263c151e1799f63b40c66fba42ee7858a97d51ff988c406ba0024 - workflow/rules/consumer_values.smk: 4f3fb2650b690a41b45443b322745b698910abae3cb013f70e0eea8e54ba6290 workflow/rules/crops.smk: 8d7d87b61959819ae9a753126767a71a10d7463af7963bffd423fa4560f470d1 workflow/rules/diet.smk: 448908aecc357fd119c1e5cd43acdec2075f2d5a4d9ebbefefa1299c1afb15f4 workflow/rules/geography.smk: 54b113f5647787bacea8d2a2f45298a24c605a84ced48630abece8b2dc887943 workflow/rules/luc.smk: e64fb669c0d6c40299d47c995dbb9f04a71f4d99d8edcd1fe9ce3ed700639257 - workflow/rules/model.smk: 00791268796d8467b88ad755fb1bedf8babe2a12f2e0a78b657b01279387f942 + workflow/rules/model.smk: d2d2e71ecce83dad9728184b7a7903c5f7ab6ba8030126307818d05bff1ae71c workflow/rules/retrieve.smk: d440ebccd4e6f2d6aa5e86066e865ec5c54dbca2a4b5149228a2ef20ad776748 workflow/rules/water.smk: 9fe3fc69051df02bab7dde53d2057b2b64142c7212741f8a45bc0171447c951d workflow/scripts/aggregate_class_areas.py: 93e9a52eec4dac86eeff03fa7e574ad4bdd91bb4049f715a321850f327aba92e @@ -47,7 +46,6 @@ steps: workflow/scripts/build_regions.py: 08a1f3c0247bdbe4600f2b1596396a352ff6c459496566e7e24da5b2f83558a7 workflow/scripts/build_yield_calibration.py: 1225a0d55add24ce44acc86cde5dd3335ed9d0a233ba6025bfc8c0e5afc8375d workflow/scripts/calculate_manure_emissions.py: 4f39f5370dcf931d4648bf7c0727a807ec7bc8fbb80561dae4ff5afa08834591 - workflow/scripts/calibrate_food_utility_blocks.py: e20676cd1b44c4504df3c9856131fa234088b1fd77c9f242144483f6f58ad330 workflow/scripts/categorize_feeds.py: 575b37f430e4fbf54b8477be96f5069ce6522fa1882c2f024b9b9a25a8540140 workflow/scripts/compose_water_supply.py: c1fc61844079219f2381664b44b6a917f51ecbc07bb08e1318702985deaf9231 workflow/scripts/compute_gleam3_feed_fractions.py: 83cc4268fe7f1762932ac63fc7f05ecedda9b03bcc1b502a69d85ec6779e6289 @@ -61,7 +59,6 @@ steps: workflow/scripts/download_mirca_os_bag_member.py: e869f4ec2f87a03b65d7c6f875d6ad393dcce647789ba7fc0f1c2d2fa2e48049 workflow/scripts/download_soilgrids_ocs.py: bcb361c0ada72cd6264685eb36b2efabc1a03bc25c93918c37da8da7fe049d58 workflow/scripts/estimate_baseline_diet.py: d9df39a6cc46b41da12059f5c38929a62e4b53301149488f89ce704cb5440336 - workflow/scripts/extract_consumer_values.py: eba7d4bd3d10632fc5fbcbc46f65acd5775f2bb74ca3aa3f5d6ff15113c3dd97 workflow/scripts/extract_cost_calibration.py: f7475beca73108a5f6ef63f1c284625e996abc9dadf9efc981c92e2f259d3f23 workflow/scripts/extract_mirca_os_archive.py: 687fe1d413ea063d585cc526ff53ce7d91c1df90e1d1ec898bfc62d19890647c workflow/scripts/merge_animal_costs.py: db83828da2fad888f0bae49e3a4d19ebc8315e27d2edcd0c6fde579b736de57c @@ -73,7 +70,7 @@ steps: workflow/scripts/prepare_faostat_animal_production.py: 38980d9bf9324ae01664c63104026aa4b3589f71f5bc592ae8f3bbe96cde9c2a workflow/scripts/prepare_faostat_crop_costs.py: be66f2aad20af7c88085b3c24e59ce2e26cfcee25b202fd96f3a47666d65ad1b workflow/scripts/prepare_faostat_crop_production.py: f446a0f22d865d6dfb7aaab643ea245932169769070b7a72ab16c98b09bf7435 - workflow/scripts/prepare_faostat_fbs_items.py: 87446f863e0e26e06b863cabcaafda362fb033f86b522b519922f86b5f80bb05 + workflow/scripts/prepare_faostat_fbs_items.py: b06a2e8c1188b488086c7ea804cf1d822c83bc5e410457137d4f027063b88429 workflow/scripts/prepare_faostat_food_group_supply.py: e1702c7c67e7295c70b4cd1fdcf871f3d02c5a90dcb2c0c5b0c6b891e8e81809 workflow/scripts/prepare_faostat_pasture_area.py: 3f0d6212cf0c54edaf6f47145e75334c532e6e7a2d243a4996d4f96bda5096fe workflow/scripts/prepare_faostat_yields.py: 178264f3eaa3e386a7ea6813fe607af7183663624cc0388a9df724562a5dd9ab @@ -93,16 +90,16 @@ steps: workflow/scripts/resample_land_cover.py: 3a953b4c2a0a4a226688e282af9a00c778a077bfbf3d8504829a637a6281a1ce workflow/scripts/resample_luicube_grassland.py: 8efad93db0c694e2b9152acc2373b356614d65c8170cf4effa08f6df9b4cc8fb workflow/scripts/retrieve_cpi_data.py: ee11749f80d1ab2dd0ade2909b40ef001f7bd24c6a56fa381a251662b51f54b8 - workflow/scripts/retrieve_eurostat_fodder.py: c90bbfbbb2c08b83144c84f2199da9ec01d17a8ecea99d2ead77a1baf6de22f9 + workflow/scripts/retrieve_eurostat_fodder.py: 888097d8d0220842558b842b40e04958df6a6fc136fd8f9b2042a913a4bb7069 workflow/scripts/retrieve_fadn_animal_costs.py: 7791580c7c04f99a474909e4e53bdaa7af0a23056c2036b36753c43076ac25f7 workflow/scripts/retrieve_hicp_data.py: 8375097c1fb9f18d096116dd133268e36a332373b56720790a4abb35a2b6290b - workflow/scripts/retrieve_ppp_rates.py: 13ff1ed85f2153d4a5a8b4b9031432c59b23e0a85eb4fc62bfd4806c32aee444 + workflow/scripts/retrieve_ppp_rates.py: dc90953fe87c3dc0990c386dac1171e64cdbd0ac2085db804d97277e4f280f88 workflow/scripts/retrieve_usda_animal_costs.py: 5849db38933f9323fb581e5b81a17223e3e5a12b5c8471727c4b9cef4c2211cc workflow/scripts/simplify_gadm.py: 9b206abc2f561840b5a964e9fc06a6f4ee6b71cb2673277d4e2c27ebf532d1af workflow/scripts/solve_model.py: 8b13b5a820ba69e4385dcc0b4f576744d9759485a642ca43282c7b65861fe5cb workflow/scripts/validate_baseline_diet.py: 6d54d506a2d8382321c849d77698098429019e0f089f969c43ee2848becc05f4 config_files: - config/calibration/cost.yaml: 1409ef4367b99829be254e641d82475d82d4cb1ca4469d781f46243b5c7123ae + config/calibration/cost.yaml: cab2a3387d34e633b423ec40b6ea9183d060439b94d577634091d9ca3058a1cc effective_config: {} inputs: data/bundled/doi_10_5061_dryad_2rbnzs7qh__v20250311/FUBC_1_to_9_data.csv: 3b5dc29b6e2f6844807f2983c1596c28a127f48a67f7deec7e36f6a49c0b17f3 @@ -169,7 +166,7 @@ steps: workflow/scripts/build_model/commodity_costs.py: 97a4c3aa148355adac88d7a45462158f55a7322a0c351babd0a243bd941eb0e2 workflow/scripts/build_model/crops.py: 4b8638d8902b0a688d0ab55df3e3625d0953f3b4ac63886ddae50b3bf47b4610 workflow/scripts/build_model/food.py: 9e53b6c847202a2192ea0e014746e62a9a5f88eedffc5a48833fc4186ef65ae9 - workflow/scripts/build_model/grassland.py: bf39536ad916a0c7a47d72fe2de4171fd256e483b81ac552d4403fe0123036da + workflow/scripts/build_model/grassland.py: dcbe90ec563dbe3c3ce9431085dd59e91dfb418a1fb30d5ebfe8b10c154f45b4 workflow/scripts/build_model/health.py: 5ddc8cbae98992f67140db787946a0f9fd9ae15ad683592b4f81b4bf6a2df702 workflow/scripts/build_model/infrastructure.py: b792d568d0f199699f5d53bc6f175138a899e41a71f86b5d72a037de1130785c workflow/scripts/build_model/irrigation.py: 3d1d6216069eb7854046266a68b202b1f4c981395c499f8d05ab04c22f5645c2 @@ -183,14 +180,13 @@ steps: workflow/scripts/population.py: b45821fb719171d516083bf3ff8a4186b4424b22dd8a24216d199bf5ff70a13d workflow/scripts/snakemake_utils.py: 52c6bb89c4efe655aa5f41d0cd850b0c2894c9dd72c644f9c0633c04a503989e workflow/scripts/solve_model/__init__.py: 23fb1fde8c5d964c8fda94020ff41ce9fdeefd04f6334a1ab3e7e755c93af04c - workflow/scripts/solve_model/core.py: 83307fcdffd50a1e6f83ad3fcbfcfff3abdf51ebfd91c02f8352502dfe7eba8b + workflow/scripts/solve_model/core.py: 769d02b66edcc0ff55b2ab154b7e2c527a5e7bb0eb4907d1f63bc7cdacec98d6 workflow/scripts/solve_model/diet_stability.py: 38186fe2e56474736d9da727cd6ebb3420dd24a432391cc5da477015be0b54f4 - workflow/scripts/solve_model/food_utility.py: c12ac00a859e76d7dffa77c62fc5cb58525d92f6b886373ab49c9790692ac6a4 workflow/scripts/solve_model/health.py: 4e8b34555c4a7aaf94b59f081c0ba5e89a3369eaaf7c8e918ef5e660eb9d37a5 workflow/scripts/solve_model/market_response.py: 11ba687519e9e697559e9000f4dad1583c794d3b31d067d570138735e4c38fb1 workflow/scripts/solve_model/production_stability.py: 4f238ab0f219335272a4146b118066124bf9d107dae5cc3dfe7ce89d5feabf3a workflow/scripts/solve_model/sensitivity.py: 3ba13e457cfe34ce5ecb04c28d19d873ea6a9d4cc3c2118c3a2c4384a5bbee2a - workflow/scripts/solve_namespace.py: e81dc3fbd294f42706bfdf0f08fc43d006c17376c51234e73c87bb5fc8fe3f02 + workflow/scripts/solve_namespace.py: 1f54bdc13daac2b0f239fa706cbb59b8707b45d55e23d0251e008b0d3bc2b421 workflow/scripts/water_periods.py: 25e9e7e9a6fe41717ca4c61017641f59e3a22c83598ec73716b7d070f9a9962d missing_inputs: [] feed: @@ -205,7 +201,7 @@ steps: workflow/rules/diet.smk: 448908aecc357fd119c1e5cd43acdec2075f2d5a4d9ebbefefa1299c1afb15f4 workflow/rules/geography.smk: 54b113f5647787bacea8d2a2f45298a24c605a84ced48630abece8b2dc887943 workflow/rules/luc.smk: e64fb669c0d6c40299d47c995dbb9f04a71f4d99d8edcd1fe9ce3ed700639257 - workflow/rules/model.smk: 00791268796d8467b88ad755fb1bedf8babe2a12f2e0a78b657b01279387f942 + workflow/rules/model.smk: d2d2e71ecce83dad9728184b7a7903c5f7ab6ba8030126307818d05bff1ae71c workflow/rules/retrieve.smk: d440ebccd4e6f2d6aa5e86066e865ec5c54dbca2a4b5149228a2ef20ad776748 workflow/rules/water.smk: 9fe3fc69051df02bab7dde53d2057b2b64142c7212741f8a45bc0171447c951d workflow/scripts/aggregate_class_areas.py: 93e9a52eec4dac86eeff03fa7e574ad4bdd91bb4049f715a321850f327aba92e @@ -261,7 +257,7 @@ steps: workflow/scripts/prepare_faostat_animal_production.py: 38980d9bf9324ae01664c63104026aa4b3589f71f5bc592ae8f3bbe96cde9c2a workflow/scripts/prepare_faostat_crop_costs.py: be66f2aad20af7c88085b3c24e59ce2e26cfcee25b202fd96f3a47666d65ad1b workflow/scripts/prepare_faostat_crop_production.py: f446a0f22d865d6dfb7aaab643ea245932169769070b7a72ab16c98b09bf7435 - workflow/scripts/prepare_faostat_fbs_items.py: 87446f863e0e26e06b863cabcaafda362fb033f86b522b519922f86b5f80bb05 + workflow/scripts/prepare_faostat_fbs_items.py: b06a2e8c1188b488086c7ea804cf1d822c83bc5e410457137d4f027063b88429 workflow/scripts/prepare_faostat_food_group_supply.py: e1702c7c67e7295c70b4cd1fdcf871f3d02c5a90dcb2c0c5b0c6b891e8e81809 workflow/scripts/prepare_faostat_pasture_area.py: 3f0d6212cf0c54edaf6f47145e75334c532e6e7a2d243a4996d4f96bda5096fe workflow/scripts/prepare_faostat_yields.py: 178264f3eaa3e386a7ea6813fe607af7183663624cc0388a9df724562a5dd9ab @@ -281,16 +277,16 @@ steps: workflow/scripts/resample_land_cover.py: 3a953b4c2a0a4a226688e282af9a00c778a077bfbf3d8504829a637a6281a1ce workflow/scripts/resample_luicube_grassland.py: 8efad93db0c694e2b9152acc2373b356614d65c8170cf4effa08f6df9b4cc8fb workflow/scripts/retrieve_cpi_data.py: ee11749f80d1ab2dd0ade2909b40ef001f7bd24c6a56fa381a251662b51f54b8 - workflow/scripts/retrieve_eurostat_fodder.py: c90bbfbbb2c08b83144c84f2199da9ec01d17a8ecea99d2ead77a1baf6de22f9 + workflow/scripts/retrieve_eurostat_fodder.py: 888097d8d0220842558b842b40e04958df6a6fc136fd8f9b2042a913a4bb7069 workflow/scripts/retrieve_fadn_animal_costs.py: 7791580c7c04f99a474909e4e53bdaa7af0a23056c2036b36753c43076ac25f7 workflow/scripts/retrieve_hicp_data.py: 8375097c1fb9f18d096116dd133268e36a332373b56720790a4abb35a2b6290b - workflow/scripts/retrieve_ppp_rates.py: 13ff1ed85f2153d4a5a8b4b9031432c59b23e0a85eb4fc62bfd4806c32aee444 + workflow/scripts/retrieve_ppp_rates.py: dc90953fe87c3dc0990c386dac1171e64cdbd0ac2085db804d97277e4f280f88 workflow/scripts/retrieve_usda_animal_costs.py: 5849db38933f9323fb581e5b81a17223e3e5a12b5c8471727c4b9cef4c2211cc workflow/scripts/simplify_gadm.py: 9b206abc2f561840b5a964e9fc06a6f4ee6b71cb2673277d4e2c27ebf532d1af workflow/scripts/solve_model.py: 8b13b5a820ba69e4385dcc0b4f576744d9759485a642ca43282c7b65861fe5cb workflow/scripts/validate_baseline_diet.py: 6d54d506a2d8382321c849d77698098429019e0f089f969c43ee2848becc05f4 config_files: - config/calibration/feed.yaml: a301d4c644f0022a5135bf7882c87f4f0889f0963753260249c378007c18c83a + config/calibration/feed.yaml: 0d9bf9aa62c79047b92ea2c25ef1ffa1cf9401f206cd7bc8eed6aa3a4ed5e72d effective_config: {} inputs: data/bundled/doi_10_5061_dryad_2rbnzs7qh__v20250311/FUBC_1_to_9_data.csv: 3b5dc29b6e2f6844807f2983c1596c28a127f48a67f7deec7e36f6a49c0b17f3 @@ -351,7 +347,7 @@ steps: workflow/scripts/build_model/commodity_costs.py: 97a4c3aa148355adac88d7a45462158f55a7322a0c351babd0a243bd941eb0e2 workflow/scripts/build_model/crops.py: 4b8638d8902b0a688d0ab55df3e3625d0953f3b4ac63886ddae50b3bf47b4610 workflow/scripts/build_model/food.py: 9e53b6c847202a2192ea0e014746e62a9a5f88eedffc5a48833fc4186ef65ae9 - workflow/scripts/build_model/grassland.py: bf39536ad916a0c7a47d72fe2de4171fd256e483b81ac552d4403fe0123036da + workflow/scripts/build_model/grassland.py: dcbe90ec563dbe3c3ce9431085dd59e91dfb418a1fb30d5ebfe8b10c154f45b4 workflow/scripts/build_model/health.py: 5ddc8cbae98992f67140db787946a0f9fd9ae15ad683592b4f81b4bf6a2df702 workflow/scripts/build_model/infrastructure.py: b792d568d0f199699f5d53bc6f175138a899e41a71f86b5d72a037de1130785c workflow/scripts/build_model/irrigation.py: 3d1d6216069eb7854046266a68b202b1f4c981395c499f8d05ab04c22f5645c2 @@ -365,14 +361,13 @@ steps: workflow/scripts/population.py: b45821fb719171d516083bf3ff8a4186b4424b22dd8a24216d199bf5ff70a13d workflow/scripts/snakemake_utils.py: 52c6bb89c4efe655aa5f41d0cd850b0c2894c9dd72c644f9c0633c04a503989e workflow/scripts/solve_model/__init__.py: 23fb1fde8c5d964c8fda94020ff41ce9fdeefd04f6334a1ab3e7e755c93af04c - workflow/scripts/solve_model/core.py: 83307fcdffd50a1e6f83ad3fcbfcfff3abdf51ebfd91c02f8352502dfe7eba8b + workflow/scripts/solve_model/core.py: 769d02b66edcc0ff55b2ab154b7e2c527a5e7bb0eb4907d1f63bc7cdacec98d6 workflow/scripts/solve_model/diet_stability.py: 38186fe2e56474736d9da727cd6ebb3420dd24a432391cc5da477015be0b54f4 - workflow/scripts/solve_model/food_utility.py: c12ac00a859e76d7dffa77c62fc5cb58525d92f6b886373ab49c9790692ac6a4 workflow/scripts/solve_model/health.py: 4e8b34555c4a7aaf94b59f081c0ba5e89a3369eaaf7c8e918ef5e660eb9d37a5 workflow/scripts/solve_model/market_response.py: 11ba687519e9e697559e9000f4dad1583c794d3b31d067d570138735e4c38fb1 workflow/scripts/solve_model/production_stability.py: 4f238ab0f219335272a4146b118066124bf9d107dae5cc3dfe7ce89d5feabf3a workflow/scripts/solve_model/sensitivity.py: 3ba13e457cfe34ce5ecb04c28d19d873ea6a9d4cc3c2118c3a2c4384a5bbee2a - workflow/scripts/solve_namespace.py: e81dc3fbd294f42706bfdf0f08fc43d006c17376c51234e73c87bb5fc8fe3f02 + workflow/scripts/solve_namespace.py: 1f54bdc13daac2b0f239fa706cbb59b8707b45d55e23d0251e008b0d3bc2b421 workflow/scripts/water_periods.py: 25e9e7e9a6fe41717ca4c61017641f59e3a22c83598ec73716b7d070f9a9962d missing_inputs: [] food_demand: @@ -384,7 +379,7 @@ steps: workflow/rules/diet.smk: 448908aecc357fd119c1e5cd43acdec2075f2d5a4d9ebbefefa1299c1afb15f4 workflow/rules/geography.smk: 54b113f5647787bacea8d2a2f45298a24c605a84ced48630abece8b2dc887943 workflow/rules/luc.smk: e64fb669c0d6c40299d47c995dbb9f04a71f4d99d8edcd1fe9ce3ed700639257 - workflow/rules/model.smk: 00791268796d8467b88ad755fb1bedf8babe2a12f2e0a78b657b01279387f942 + workflow/rules/model.smk: d2d2e71ecce83dad9728184b7a7903c5f7ab6ba8030126307818d05bff1ae71c workflow/rules/retrieve.smk: d440ebccd4e6f2d6aa5e86066e865ec5c54dbca2a4b5149228a2ef20ad776748 workflow/rules/water.smk: 9fe3fc69051df02bab7dde53d2057b2b64142c7212741f8a45bc0171447c951d workflow/scripts/aggregate_class_areas.py: 93e9a52eec4dac86eeff03fa7e574ad4bdd91bb4049f715a321850f327aba92e @@ -439,7 +434,7 @@ steps: workflow/scripts/prepare_faostat_animal_production.py: 38980d9bf9324ae01664c63104026aa4b3589f71f5bc592ae8f3bbe96cde9c2a workflow/scripts/prepare_faostat_crop_costs.py: be66f2aad20af7c88085b3c24e59ce2e26cfcee25b202fd96f3a47666d65ad1b workflow/scripts/prepare_faostat_crop_production.py: f446a0f22d865d6dfb7aaab643ea245932169769070b7a72ab16c98b09bf7435 - workflow/scripts/prepare_faostat_fbs_items.py: 87446f863e0e26e06b863cabcaafda362fb033f86b522b519922f86b5f80bb05 + workflow/scripts/prepare_faostat_fbs_items.py: b06a2e8c1188b488086c7ea804cf1d822c83bc5e410457137d4f027063b88429 workflow/scripts/prepare_faostat_food_group_supply.py: e1702c7c67e7295c70b4cd1fdcf871f3d02c5a90dcb2c0c5b0c6b891e8e81809 workflow/scripts/prepare_faostat_pasture_area.py: 3f0d6212cf0c54edaf6f47145e75334c532e6e7a2d243a4996d4f96bda5096fe workflow/scripts/prepare_faostat_yields.py: 178264f3eaa3e386a7ea6813fe607af7183663624cc0388a9df724562a5dd9ab @@ -459,16 +454,16 @@ steps: workflow/scripts/resample_land_cover.py: 3a953b4c2a0a4a226688e282af9a00c778a077bfbf3d8504829a637a6281a1ce workflow/scripts/resample_luicube_grassland.py: 8efad93db0c694e2b9152acc2373b356614d65c8170cf4effa08f6df9b4cc8fb workflow/scripts/retrieve_cpi_data.py: ee11749f80d1ab2dd0ade2909b40ef001f7bd24c6a56fa381a251662b51f54b8 - workflow/scripts/retrieve_eurostat_fodder.py: c90bbfbbb2c08b83144c84f2199da9ec01d17a8ecea99d2ead77a1baf6de22f9 + workflow/scripts/retrieve_eurostat_fodder.py: 888097d8d0220842558b842b40e04958df6a6fc136fd8f9b2042a913a4bb7069 workflow/scripts/retrieve_fadn_animal_costs.py: 7791580c7c04f99a474909e4e53bdaa7af0a23056c2036b36753c43076ac25f7 workflow/scripts/retrieve_hicp_data.py: 8375097c1fb9f18d096116dd133268e36a332373b56720790a4abb35a2b6290b - workflow/scripts/retrieve_ppp_rates.py: 13ff1ed85f2153d4a5a8b4b9031432c59b23e0a85eb4fc62bfd4806c32aee444 + workflow/scripts/retrieve_ppp_rates.py: dc90953fe87c3dc0990c386dac1171e64cdbd0ac2085db804d97277e4f280f88 workflow/scripts/retrieve_usda_animal_costs.py: 5849db38933f9323fb581e5b81a17223e3e5a12b5c8471727c4b9cef4c2211cc workflow/scripts/simplify_gadm.py: 9b206abc2f561840b5a964e9fc06a6f4ee6b71cb2673277d4e2c27ebf532d1af workflow/scripts/solve_model.py: 8b13b5a820ba69e4385dcc0b4f576744d9759485a642ca43282c7b65861fe5cb workflow/scripts/validate_baseline_diet.py: 6d54d506a2d8382321c849d77698098429019e0f089f969c43ee2848becc05f4 config_files: - config/calibration/food_demand.yaml: f2ca2acac7caf21713332eb1fa63308b9a3c15f6d7323e95fc72e6f855440e48 + config/calibration/food_demand.yaml: b5690d099a84f753acc456b4d6865d2dbc357fe86cc8281080fb1bc740fce06b effective_config: {} inputs: data/bundled/doi_10_5061_dryad_2rbnzs7qh__v20250311/FUBC_1_to_9_data.csv: 3b5dc29b6e2f6844807f2983c1596c28a127f48a67f7deec7e36f6a49c0b17f3 @@ -534,7 +529,7 @@ steps: workflow/scripts/build_model/commodity_costs.py: 97a4c3aa148355adac88d7a45462158f55a7322a0c351babd0a243bd941eb0e2 workflow/scripts/build_model/crops.py: 4b8638d8902b0a688d0ab55df3e3625d0953f3b4ac63886ddae50b3bf47b4610 workflow/scripts/build_model/food.py: 9e53b6c847202a2192ea0e014746e62a9a5f88eedffc5a48833fc4186ef65ae9 - workflow/scripts/build_model/grassland.py: bf39536ad916a0c7a47d72fe2de4171fd256e483b81ac552d4403fe0123036da + workflow/scripts/build_model/grassland.py: dcbe90ec563dbe3c3ce9431085dd59e91dfb418a1fb30d5ebfe8b10c154f45b4 workflow/scripts/build_model/health.py: 5ddc8cbae98992f67140db787946a0f9fd9ae15ad683592b4f81b4bf6a2df702 workflow/scripts/build_model/infrastructure.py: b792d568d0f199699f5d53bc6f175138a899e41a71f86b5d72a037de1130785c workflow/scripts/build_model/irrigation.py: 3d1d6216069eb7854046266a68b202b1f4c981395c499f8d05ab04c22f5645c2 @@ -548,14 +543,13 @@ steps: workflow/scripts/population.py: b45821fb719171d516083bf3ff8a4186b4424b22dd8a24216d199bf5ff70a13d workflow/scripts/snakemake_utils.py: 52c6bb89c4efe655aa5f41d0cd850b0c2894c9dd72c644f9c0633c04a503989e workflow/scripts/solve_model/__init__.py: 23fb1fde8c5d964c8fda94020ff41ce9fdeefd04f6334a1ab3e7e755c93af04c - workflow/scripts/solve_model/core.py: 83307fcdffd50a1e6f83ad3fcbfcfff3abdf51ebfd91c02f8352502dfe7eba8b + workflow/scripts/solve_model/core.py: 769d02b66edcc0ff55b2ab154b7e2c527a5e7bb0eb4907d1f63bc7cdacec98d6 workflow/scripts/solve_model/diet_stability.py: 38186fe2e56474736d9da727cd6ebb3420dd24a432391cc5da477015be0b54f4 - workflow/scripts/solve_model/food_utility.py: c12ac00a859e76d7dffa77c62fc5cb58525d92f6b886373ab49c9790692ac6a4 workflow/scripts/solve_model/health.py: 4e8b34555c4a7aaf94b59f081c0ba5e89a3369eaaf7c8e918ef5e660eb9d37a5 workflow/scripts/solve_model/market_response.py: 11ba687519e9e697559e9000f4dad1583c794d3b31d067d570138735e4c38fb1 workflow/scripts/solve_model/production_stability.py: 4f238ab0f219335272a4146b118066124bf9d107dae5cc3dfe7ce89d5feabf3a workflow/scripts/solve_model/sensitivity.py: 3ba13e457cfe34ce5ecb04c28d19d873ea6a9d4cc3c2118c3a2c4384a5bbee2a - workflow/scripts/solve_namespace.py: e81dc3fbd294f42706bfdf0f08fc43d006c17376c51234e73c87bb5fc8fe3f02 + workflow/scripts/solve_namespace.py: 1f54bdc13daac2b0f239fa706cbb59b8707b45d55e23d0251e008b0d3bc2b421 workflow/scripts/water_periods.py: 25e9e7e9a6fe41717ca4c61017641f59e3a22c83598ec73716b7d070f9a9962d missing_inputs: [] food_waste: @@ -567,7 +561,7 @@ steps: workflow/rules/diet.smk: 448908aecc357fd119c1e5cd43acdec2075f2d5a4d9ebbefefa1299c1afb15f4 workflow/rules/geography.smk: 54b113f5647787bacea8d2a2f45298a24c605a84ced48630abece8b2dc887943 workflow/rules/luc.smk: e64fb669c0d6c40299d47c995dbb9f04a71f4d99d8edcd1fe9ce3ed700639257 - workflow/rules/model.smk: 00791268796d8467b88ad755fb1bedf8babe2a12f2e0a78b657b01279387f942 + workflow/rules/model.smk: d2d2e71ecce83dad9728184b7a7903c5f7ab6ba8030126307818d05bff1ae71c workflow/rules/retrieve.smk: d440ebccd4e6f2d6aa5e86066e865ec5c54dbca2a4b5149228a2ef20ad776748 workflow/rules/water.smk: 9fe3fc69051df02bab7dde53d2057b2b64142c7212741f8a45bc0171447c951d workflow/scripts/aggregate_class_areas.py: 93e9a52eec4dac86eeff03fa7e574ad4bdd91bb4049f715a321850f327aba92e @@ -622,7 +616,7 @@ steps: workflow/scripts/prepare_faostat_animal_production.py: 38980d9bf9324ae01664c63104026aa4b3589f71f5bc592ae8f3bbe96cde9c2a workflow/scripts/prepare_faostat_crop_costs.py: be66f2aad20af7c88085b3c24e59ce2e26cfcee25b202fd96f3a47666d65ad1b workflow/scripts/prepare_faostat_crop_production.py: f446a0f22d865d6dfb7aaab643ea245932169769070b7a72ab16c98b09bf7435 - workflow/scripts/prepare_faostat_fbs_items.py: 87446f863e0e26e06b863cabcaafda362fb033f86b522b519922f86b5f80bb05 + workflow/scripts/prepare_faostat_fbs_items.py: b06a2e8c1188b488086c7ea804cf1d822c83bc5e410457137d4f027063b88429 workflow/scripts/prepare_faostat_food_group_supply.py: e1702c7c67e7295c70b4cd1fdcf871f3d02c5a90dcb2c0c5b0c6b891e8e81809 workflow/scripts/prepare_faostat_pasture_area.py: 3f0d6212cf0c54edaf6f47145e75334c532e6e7a2d243a4996d4f96bda5096fe workflow/scripts/prepare_faostat_yields.py: 178264f3eaa3e386a7ea6813fe607af7183663624cc0388a9df724562a5dd9ab @@ -642,16 +636,16 @@ steps: workflow/scripts/resample_land_cover.py: 3a953b4c2a0a4a226688e282af9a00c778a077bfbf3d8504829a637a6281a1ce workflow/scripts/resample_luicube_grassland.py: 8efad93db0c694e2b9152acc2373b356614d65c8170cf4effa08f6df9b4cc8fb workflow/scripts/retrieve_cpi_data.py: ee11749f80d1ab2dd0ade2909b40ef001f7bd24c6a56fa381a251662b51f54b8 - workflow/scripts/retrieve_eurostat_fodder.py: c90bbfbbb2c08b83144c84f2199da9ec01d17a8ecea99d2ead77a1baf6de22f9 + workflow/scripts/retrieve_eurostat_fodder.py: 888097d8d0220842558b842b40e04958df6a6fc136fd8f9b2042a913a4bb7069 workflow/scripts/retrieve_fadn_animal_costs.py: 7791580c7c04f99a474909e4e53bdaa7af0a23056c2036b36753c43076ac25f7 workflow/scripts/retrieve_hicp_data.py: 8375097c1fb9f18d096116dd133268e36a332373b56720790a4abb35a2b6290b - workflow/scripts/retrieve_ppp_rates.py: 13ff1ed85f2153d4a5a8b4b9031432c59b23e0a85eb4fc62bfd4806c32aee444 + workflow/scripts/retrieve_ppp_rates.py: dc90953fe87c3dc0990c386dac1171e64cdbd0ac2085db804d97277e4f280f88 workflow/scripts/retrieve_usda_animal_costs.py: 5849db38933f9323fb581e5b81a17223e3e5a12b5c8471727c4b9cef4c2211cc workflow/scripts/simplify_gadm.py: 9b206abc2f561840b5a964e9fc06a6f4ee6b71cb2673277d4e2c27ebf532d1af workflow/scripts/solve_model.py: 8b13b5a820ba69e4385dcc0b4f576744d9759485a642ca43282c7b65861fe5cb workflow/scripts/validate_baseline_diet.py: 6d54d506a2d8382321c849d77698098429019e0f089f969c43ee2848becc05f4 config_files: - config/calibration/food_waste.yaml: b43c3152299ff87ab7637f41b666a74a45ae1d99e2e9ff8fb96f6230d19cd677 + config/calibration/food_waste.yaml: 2d44f60257d42a739982926cd0df61c358dc516b0b29a82e5214ae2ba4031b4c effective_config: {} inputs: data/bundled/doi_10_5061_dryad_2rbnzs7qh__v20250311/FUBC_1_to_9_data.csv: 3b5dc29b6e2f6844807f2983c1596c28a127f48a67f7deec7e36f6a49c0b17f3 @@ -716,7 +710,7 @@ steps: workflow/scripts/build_model/commodity_costs.py: 97a4c3aa148355adac88d7a45462158f55a7322a0c351babd0a243bd941eb0e2 workflow/scripts/build_model/crops.py: 4b8638d8902b0a688d0ab55df3e3625d0953f3b4ac63886ddae50b3bf47b4610 workflow/scripts/build_model/food.py: 9e53b6c847202a2192ea0e014746e62a9a5f88eedffc5a48833fc4186ef65ae9 - workflow/scripts/build_model/grassland.py: bf39536ad916a0c7a47d72fe2de4171fd256e483b81ac552d4403fe0123036da + workflow/scripts/build_model/grassland.py: dcbe90ec563dbe3c3ce9431085dd59e91dfb418a1fb30d5ebfe8b10c154f45b4 workflow/scripts/build_model/health.py: 5ddc8cbae98992f67140db787946a0f9fd9ae15ad683592b4f81b4bf6a2df702 workflow/scripts/build_model/infrastructure.py: b792d568d0f199699f5d53bc6f175138a899e41a71f86b5d72a037de1130785c workflow/scripts/build_model/irrigation.py: 3d1d6216069eb7854046266a68b202b1f4c981395c499f8d05ab04c22f5645c2 @@ -730,14 +724,13 @@ steps: workflow/scripts/population.py: b45821fb719171d516083bf3ff8a4186b4424b22dd8a24216d199bf5ff70a13d workflow/scripts/snakemake_utils.py: 52c6bb89c4efe655aa5f41d0cd850b0c2894c9dd72c644f9c0633c04a503989e workflow/scripts/solve_model/__init__.py: 23fb1fde8c5d964c8fda94020ff41ce9fdeefd04f6334a1ab3e7e755c93af04c - workflow/scripts/solve_model/core.py: 83307fcdffd50a1e6f83ad3fcbfcfff3abdf51ebfd91c02f8352502dfe7eba8b + workflow/scripts/solve_model/core.py: 769d02b66edcc0ff55b2ab154b7e2c527a5e7bb0eb4907d1f63bc7cdacec98d6 workflow/scripts/solve_model/diet_stability.py: 38186fe2e56474736d9da727cd6ebb3420dd24a432391cc5da477015be0b54f4 - workflow/scripts/solve_model/food_utility.py: c12ac00a859e76d7dffa77c62fc5cb58525d92f6b886373ab49c9790692ac6a4 workflow/scripts/solve_model/health.py: 4e8b34555c4a7aaf94b59f081c0ba5e89a3369eaaf7c8e918ef5e660eb9d37a5 workflow/scripts/solve_model/market_response.py: 11ba687519e9e697559e9000f4dad1583c794d3b31d067d570138735e4c38fb1 workflow/scripts/solve_model/production_stability.py: 4f238ab0f219335272a4146b118066124bf9d107dae5cc3dfe7ce89d5feabf3a workflow/scripts/solve_model/sensitivity.py: 3ba13e457cfe34ce5ecb04c28d19d873ea6a9d4cc3c2118c3a2c4384a5bbee2a - workflow/scripts/solve_namespace.py: e81dc3fbd294f42706bfdf0f08fc43d006c17376c51234e73c87bb5fc8fe3f02 + workflow/scripts/solve_namespace.py: 1f54bdc13daac2b0f239fa706cbb59b8707b45d55e23d0251e008b0d3bc2b421 workflow/scripts/water_periods.py: 25e9e7e9a6fe41717ca4c61017641f59e3a22c83598ec73716b7d070f9a9962d missing_inputs: [] market_response: @@ -750,7 +743,7 @@ steps: workflow/rules/geography.smk: 54b113f5647787bacea8d2a2f45298a24c605a84ced48630abece8b2dc887943 workflow/rules/luc.smk: e64fb669c0d6c40299d47c995dbb9f04a71f4d99d8edcd1fe9ce3ed700639257 workflow/rules/market_response.smk: 1c8e729d8009d60535dc8beb3c6446103e55b6b0cf4fa27d89dec793235a9a9a - workflow/rules/model.smk: 00791268796d8467b88ad755fb1bedf8babe2a12f2e0a78b657b01279387f942 + workflow/rules/model.smk: d2d2e71ecce83dad9728184b7a7903c5f7ab6ba8030126307818d05bff1ae71c workflow/rules/retrieve.smk: d440ebccd4e6f2d6aa5e86066e865ec5c54dbca2a4b5149228a2ef20ad776748 workflow/rules/water.smk: 9fe3fc69051df02bab7dde53d2057b2b64142c7212741f8a45bc0171447c951d workflow/scripts/aggregate_class_areas.py: 93e9a52eec4dac86eeff03fa7e574ad4bdd91bb4049f715a321850f327aba92e @@ -805,7 +798,7 @@ steps: workflow/scripts/prepare_faostat_animal_production.py: 38980d9bf9324ae01664c63104026aa4b3589f71f5bc592ae8f3bbe96cde9c2a workflow/scripts/prepare_faostat_crop_costs.py: be66f2aad20af7c88085b3c24e59ce2e26cfcee25b202fd96f3a47666d65ad1b workflow/scripts/prepare_faostat_crop_production.py: f446a0f22d865d6dfb7aaab643ea245932169769070b7a72ab16c98b09bf7435 - workflow/scripts/prepare_faostat_fbs_items.py: 87446f863e0e26e06b863cabcaafda362fb033f86b522b519922f86b5f80bb05 + workflow/scripts/prepare_faostat_fbs_items.py: b06a2e8c1188b488086c7ea804cf1d822c83bc5e410457137d4f027063b88429 workflow/scripts/prepare_faostat_food_group_supply.py: e1702c7c67e7295c70b4cd1fdcf871f3d02c5a90dcb2c0c5b0c6b891e8e81809 workflow/scripts/prepare_faostat_pasture_area.py: 3f0d6212cf0c54edaf6f47145e75334c532e6e7a2d243a4996d4f96bda5096fe workflow/scripts/prepare_faostat_yields.py: 178264f3eaa3e386a7ea6813fe607af7183663624cc0388a9df724562a5dd9ab @@ -825,15 +818,15 @@ steps: workflow/scripts/resample_land_cover.py: 3a953b4c2a0a4a226688e282af9a00c778a077bfbf3d8504829a637a6281a1ce workflow/scripts/resample_luicube_grassland.py: 8efad93db0c694e2b9152acc2373b356614d65c8170cf4effa08f6df9b4cc8fb workflow/scripts/retrieve_cpi_data.py: ee11749f80d1ab2dd0ade2909b40ef001f7bd24c6a56fa381a251662b51f54b8 - workflow/scripts/retrieve_eurostat_fodder.py: c90bbfbbb2c08b83144c84f2199da9ec01d17a8ecea99d2ead77a1baf6de22f9 + workflow/scripts/retrieve_eurostat_fodder.py: 888097d8d0220842558b842b40e04958df6a6fc136fd8f9b2042a913a4bb7069 workflow/scripts/retrieve_fadn_animal_costs.py: 7791580c7c04f99a474909e4e53bdaa7af0a23056c2036b36753c43076ac25f7 workflow/scripts/retrieve_hicp_data.py: 8375097c1fb9f18d096116dd133268e36a332373b56720790a4abb35a2b6290b - workflow/scripts/retrieve_ppp_rates.py: 13ff1ed85f2153d4a5a8b4b9031432c59b23e0a85eb4fc62bfd4806c32aee444 + workflow/scripts/retrieve_ppp_rates.py: dc90953fe87c3dc0990c386dac1171e64cdbd0ac2085db804d97277e4f280f88 workflow/scripts/retrieve_usda_animal_costs.py: 5849db38933f9323fb581e5b81a17223e3e5a12b5c8471727c4b9cef4c2211cc workflow/scripts/simplify_gadm.py: 9b206abc2f561840b5a964e9fc06a6f4ee6b71cb2673277d4e2c27ebf532d1af workflow/scripts/validate_baseline_diet.py: 6d54d506a2d8382321c849d77698098429019e0f089f969c43ee2848becc05f4 config_files: - config/calibration/market_response.yaml: 44cfacb32415b36612d60cbb8a553c0647e7603281c3278888912f471c3c727c + config/calibration/market_response.yaml: 26139cbf06038a0b2ea724a42755c7ac241b14a2bf68bbb697fb9668caf41bed effective_config: market_response.calibration.sweeps: 3 market_response.components: @@ -935,7 +928,7 @@ steps: workflow/scripts/build_model/commodity_costs.py: 97a4c3aa148355adac88d7a45462158f55a7322a0c351babd0a243bd941eb0e2 workflow/scripts/build_model/crops.py: 4b8638d8902b0a688d0ab55df3e3625d0953f3b4ac63886ddae50b3bf47b4610 workflow/scripts/build_model/food.py: 9e53b6c847202a2192ea0e014746e62a9a5f88eedffc5a48833fc4186ef65ae9 - workflow/scripts/build_model/grassland.py: bf39536ad916a0c7a47d72fe2de4171fd256e483b81ac552d4403fe0123036da + workflow/scripts/build_model/grassland.py: dcbe90ec563dbe3c3ce9431085dd59e91dfb418a1fb30d5ebfe8b10c154f45b4 workflow/scripts/build_model/health.py: 5ddc8cbae98992f67140db787946a0f9fd9ae15ad683592b4f81b4bf6a2df702 workflow/scripts/build_model/infrastructure.py: b792d568d0f199699f5d53bc6f175138a899e41a71f86b5d72a037de1130785c workflow/scripts/build_model/irrigation.py: 3d1d6216069eb7854046266a68b202b1f4c981395c499f8d05ab04c22f5645c2 @@ -949,19 +942,18 @@ steps: workflow/scripts/population.py: b45821fb719171d516083bf3ff8a4186b4424b22dd8a24216d199bf5ff70a13d workflow/scripts/snakemake_utils.py: 52c6bb89c4efe655aa5f41d0cd850b0c2894c9dd72c644f9c0633c04a503989e workflow/scripts/solve_model/__init__.py: 23fb1fde8c5d964c8fda94020ff41ce9fdeefd04f6334a1ab3e7e755c93af04c - workflow/scripts/solve_model/core.py: 83307fcdffd50a1e6f83ad3fcbfcfff3abdf51ebfd91c02f8352502dfe7eba8b + workflow/scripts/solve_model/core.py: 769d02b66edcc0ff55b2ab154b7e2c527a5e7bb0eb4907d1f63bc7cdacec98d6 workflow/scripts/solve_model/diet_stability.py: 38186fe2e56474736d9da727cd6ebb3420dd24a432391cc5da477015be0b54f4 - workflow/scripts/solve_model/food_utility.py: c12ac00a859e76d7dffa77c62fc5cb58525d92f6b886373ab49c9790692ac6a4 workflow/scripts/solve_model/health.py: 4e8b34555c4a7aaf94b59f081c0ba5e89a3369eaaf7c8e918ef5e660eb9d37a5 workflow/scripts/solve_model/market_response.py: 11ba687519e9e697559e9000f4dad1583c794d3b31d067d570138735e4c38fb1 workflow/scripts/solve_model/production_stability.py: 4f238ab0f219335272a4146b118066124bf9d107dae5cc3dfe7ce89d5feabf3a workflow/scripts/solve_model/sensitivity.py: 3ba13e457cfe34ce5ecb04c28d19d873ea6a9d4cc3c2118c3a2c4384a5bbee2a - workflow/scripts/solve_namespace.py: e81dc3fbd294f42706bfdf0f08fc43d006c17376c51234e73c87bb5fc8fe3f02 + workflow/scripts/solve_namespace.py: 1f54bdc13daac2b0f239fa706cbb59b8707b45d55e23d0251e008b0d3bc2b421 workflow/scripts/water_periods.py: 25e9e7e9a6fe41717ca4c61017641f59e3a22c83598ec73716b7d070f9a9962d missing_inputs: [] stability: artefacts: - data/curated/calibration/gbd-anchored/deviation_penalty.yaml: 00c15400faf468ea30e8690c1920215bace1d07fa66e3d185df965de323ab45a + data/curated/calibration/gbd-anchored/deviation_penalty.yaml: eb86f9cb6c3ac49526a0ea9ba5170faf4eb99b860a41fb144a0b3eef54a991e5 code: workflow/rules/animals.smk: fa07f649a53263c151e1799f63b40c66fba42ee7858a97d51ff988c406ba0024 workflow/rules/crops.smk: 8d7d87b61959819ae9a753126767a71a10d7463af7963bffd423fa4560f470d1 @@ -969,7 +961,7 @@ steps: workflow/rules/diet.smk: 448908aecc357fd119c1e5cd43acdec2075f2d5a4d9ebbefefa1299c1afb15f4 workflow/rules/geography.smk: 54b113f5647787bacea8d2a2f45298a24c605a84ced48630abece8b2dc887943 workflow/rules/luc.smk: e64fb669c0d6c40299d47c995dbb9f04a71f4d99d8edcd1fe9ce3ed700639257 - workflow/rules/model.smk: 00791268796d8467b88ad755fb1bedf8babe2a12f2e0a78b657b01279387f942 + workflow/rules/model.smk: d2d2e71ecce83dad9728184b7a7903c5f7ab6ba8030126307818d05bff1ae71c workflow/rules/retrieve.smk: d440ebccd4e6f2d6aa5e86066e865ec5c54dbca2a4b5149228a2ef20ad776748 workflow/rules/water.smk: 9fe3fc69051df02bab7dde53d2057b2b64142c7212741f8a45bc0171447c951d workflow/scripts/aggregate_class_areas.py: 93e9a52eec4dac86eeff03fa7e574ad4bdd91bb4049f715a321850f327aba92e @@ -1000,7 +992,7 @@ steps: workflow/scripts/build_regions.py: 08a1f3c0247bdbe4600f2b1596396a352ff6c459496566e7e24da5b2f83558a7 workflow/scripts/build_yield_calibration.py: 1225a0d55add24ce44acc86cde5dd3335ed9d0a233ba6025bfc8c0e5afc8375d workflow/scripts/calculate_manure_emissions.py: 4f39f5370dcf931d4648bf7c0727a807ec7bc8fbb80561dae4ff5afa08834591 - workflow/scripts/calibrate_deviation_penalty.py: fe674bb0ada6ca8676d202693b05cc7e6410380dd0c40634799d1a24852db02b + workflow/scripts/calibrate_deviation_penalty.py: 83ecac8f9d46b5045642bcf49ec3f6e16a6973de13dbca7b5f8892a59a1c35e7 workflow/scripts/categorize_feeds.py: 575b37f430e4fbf54b8477be96f5069ce6522fa1882c2f024b9b9a25a8540140 workflow/scripts/compose_water_supply.py: c1fc61844079219f2381664b44b6a917f51ecbc07bb08e1318702985deaf9231 workflow/scripts/compute_gleam3_feed_fractions.py: 83cc4268fe7f1762932ac63fc7f05ecedda9b03bcc1b502a69d85ec6779e6289 @@ -1024,7 +1016,7 @@ steps: workflow/scripts/prepare_faostat_animal_production.py: 38980d9bf9324ae01664c63104026aa4b3589f71f5bc592ae8f3bbe96cde9c2a workflow/scripts/prepare_faostat_crop_costs.py: be66f2aad20af7c88085b3c24e59ce2e26cfcee25b202fd96f3a47666d65ad1b workflow/scripts/prepare_faostat_crop_production.py: f446a0f22d865d6dfb7aaab643ea245932169769070b7a72ab16c98b09bf7435 - workflow/scripts/prepare_faostat_fbs_items.py: 87446f863e0e26e06b863cabcaafda362fb033f86b522b519922f86b5f80bb05 + workflow/scripts/prepare_faostat_fbs_items.py: b06a2e8c1188b488086c7ea804cf1d822c83bc5e410457137d4f027063b88429 workflow/scripts/prepare_faostat_food_group_supply.py: e1702c7c67e7295c70b4cd1fdcf871f3d02c5a90dcb2c0c5b0c6b891e8e81809 workflow/scripts/prepare_faostat_pasture_area.py: 3f0d6212cf0c54edaf6f47145e75334c532e6e7a2d243a4996d4f96bda5096fe workflow/scripts/prepare_faostat_yields.py: 178264f3eaa3e386a7ea6813fe607af7183663624cc0388a9df724562a5dd9ab @@ -1044,15 +1036,15 @@ steps: workflow/scripts/resample_land_cover.py: 3a953b4c2a0a4a226688e282af9a00c778a077bfbf3d8504829a637a6281a1ce workflow/scripts/resample_luicube_grassland.py: 8efad93db0c694e2b9152acc2373b356614d65c8170cf4effa08f6df9b4cc8fb workflow/scripts/retrieve_cpi_data.py: ee11749f80d1ab2dd0ade2909b40ef001f7bd24c6a56fa381a251662b51f54b8 - workflow/scripts/retrieve_eurostat_fodder.py: c90bbfbbb2c08b83144c84f2199da9ec01d17a8ecea99d2ead77a1baf6de22f9 + workflow/scripts/retrieve_eurostat_fodder.py: 888097d8d0220842558b842b40e04958df6a6fc136fd8f9b2042a913a4bb7069 workflow/scripts/retrieve_fadn_animal_costs.py: 7791580c7c04f99a474909e4e53bdaa7af0a23056c2036b36753c43076ac25f7 workflow/scripts/retrieve_hicp_data.py: 8375097c1fb9f18d096116dd133268e36a332373b56720790a4abb35a2b6290b - workflow/scripts/retrieve_ppp_rates.py: 13ff1ed85f2153d4a5a8b4b9031432c59b23e0a85eb4fc62bfd4806c32aee444 + workflow/scripts/retrieve_ppp_rates.py: dc90953fe87c3dc0990c386dac1171e64cdbd0ac2085db804d97277e4f280f88 workflow/scripts/retrieve_usda_animal_costs.py: 5849db38933f9323fb581e5b81a17223e3e5a12b5c8471727c4b9cef4c2211cc workflow/scripts/simplify_gadm.py: 9b206abc2f561840b5a964e9fc06a6f4ee6b71cb2673277d4e2c27ebf532d1af workflow/scripts/validate_baseline_diet.py: 6d54d506a2d8382321c849d77698098429019e0f089f969c43ee2848becc05f4 config_files: - config/calibration/stability.yaml: f761fe7a2e76e5a558e39e5e9982738eede2eae3939f82805d5c204bbd985d92 + config/calibration/stability.yaml: 935bff56c220e526377283ce5c3c0caf0b675f47acece5c1e9f2d5c3e49b7829 effective_config: {} inputs: data/bundled/doi_10_5061_dryad_2rbnzs7qh__v20250311/FUBC_1_to_9_data.csv: 3b5dc29b6e2f6844807f2983c1596c28a127f48a67f7deec7e36f6a49c0b17f3 @@ -1062,16 +1054,16 @@ steps: data/bundled/gleam3/production.csv: eb85e82ba9a61c71c6cd33ab36c00b5d844f2550e1caa45b6b250410adc6f2b5 data/curated/M49-codes.csv: 10ae52e639e62f07ce992ead08536167d0c8aa9f467870dbfcfbf465ef3aa9be data/curated/biogas_crop_demand.csv: 34ef148f810b8153ba469f96fa4a32a9f1076adc2d1bcd76df66a0aa63573bfa - data/curated/calibration/gbd-anchored/animal_cost.csv: 7474ab68d873a39423d8712bcef116970396f1fc65dd099fdb6d98bf6d330e4e - data/curated/calibration/gbd-anchored/crop_cost.csv: 8cff30e9d6ba695506b8cdad41d482001184437e3fbe33e1faf453de9e495891 + data/curated/calibration/gbd-anchored/animal_cost.csv: ce8615f47ccb3dcb9a6c19a930b107bfd80f79a0cc76da8ccde2e8a25fb7ff56 + data/curated/calibration/gbd-anchored/crop_cost.csv: e9d2741655a952839aa83ead8a1f8537f658c49145f97f4e33e9f35cb9269704 data/curated/calibration/gbd-anchored/exogenous_feed.csv: f928de92def4d9f9c5d903654f019b0c0a73d7bdd3b2742b3d79e08de34017f0 data/curated/calibration/gbd-anchored/exogenous_forage.csv: cc00ba5199ed7cab534947695257cb7017b5caf75ad8c120e2f314f93a18d0c8 data/curated/calibration/gbd-anchored/fodder_conversion.csv: f46d89574830cefba376f9fdb86c0ae4462bdaf9889dcc25d8c15b0da1524458 data/curated/calibration/gbd-anchored/food_demand.csv: c228d6fc35e13e6ac80553d94aa45a1888cb884533799d65c036dad828ec0777 data/curated/calibration/gbd-anchored/food_waste.yaml: 84b1e7cea92949c326f74675a6f8c6bd07e631e702adabb06f104062db7a7ee6 - data/curated/calibration/gbd-anchored/grassland_cost.csv: 22db5ab154fe85645cc1f9504bdf295e4d2f8d58ead458daecf9c0c026cd0c30 + data/curated/calibration/gbd-anchored/grassland_cost.csv: 5f61085017134586af97684f36713289152bc4508585fd5a610886ba08c3c5e2 data/curated/calibration/gbd-anchored/grassland_yield.csv: 7b25a3f7ce6f6e56643ae8bbeb890ec5ff95a61741906417f698fb38c853dcf4 - data/curated/calibration/gbd-anchored/multi_crop_cost.csv: 182f603308a1a497bb6d4a7ab45d9771897176b03adf816992a63a56708b6a2a + data/curated/calibration/gbd-anchored/multi_crop_cost.csv: 18cfd65fce266322ee0573be81a7f5a62103369687af47e2f18629a46a8d6002 data/curated/country_mottet_region.csv: 8c41e8d0a8ad7ec8cd89b9c9cdbb72b6870fb8e9b59dd6ba6e49be468c5651a3 data/curated/country_wirsenius_region.csv: 73ae37e68c61286b0312b04b0d1dbc661322d8be5c5e5e66754fff1c9dcfac6e data/curated/crop_moisture_content.csv: b9e6ac4455fdbb6c28f211a291d2cde59439b7e2215d987e104544cc9eeebfe7 @@ -1120,7 +1112,7 @@ steps: workflow/scripts/build_model/commodity_costs.py: 97a4c3aa148355adac88d7a45462158f55a7322a0c351babd0a243bd941eb0e2 workflow/scripts/build_model/crops.py: 4b8638d8902b0a688d0ab55df3e3625d0953f3b4ac63886ddae50b3bf47b4610 workflow/scripts/build_model/food.py: 9e53b6c847202a2192ea0e014746e62a9a5f88eedffc5a48833fc4186ef65ae9 - workflow/scripts/build_model/grassland.py: bf39536ad916a0c7a47d72fe2de4171fd256e483b81ac552d4403fe0123036da + workflow/scripts/build_model/grassland.py: dcbe90ec563dbe3c3ce9431085dd59e91dfb418a1fb30d5ebfe8b10c154f45b4 workflow/scripts/build_model/health.py: 5ddc8cbae98992f67140db787946a0f9fd9ae15ad683592b4f81b4bf6a2df702 workflow/scripts/build_model/infrastructure.py: b792d568d0f199699f5d53bc6f175138a899e41a71f86b5d72a037de1130785c workflow/scripts/build_model/irrigation.py: 3d1d6216069eb7854046266a68b202b1f4c981395c499f8d05ab04c22f5645c2 diff --git a/data/curated/calibration/gbd-anchored/grassland_cost.csv b/data/curated/calibration/gbd-anchored/grassland_cost.csv index 8c02c0ce..be9b7823 100644 --- a/data/curated/calibration/gbd-anchored/grassland_cost.csv +++ b/data/curated/calibration/gbd-anchored/grassland_cost.csv @@ -1,35 +1,35 @@ country,correction_bnusd_per_mha -AFG,-4.0427408296017653e-10 +AFG,-2.6429036389941096e-10 AGO,-0.38166816532611847 ALB,-0.20878468453884125 ARE,-0.0 ARG,-0.1545550674200058 ARM,-0.0 ATG,-0.0 -AUS,-0.04442281275987625 +AUS,-0.0412256233394146 AUT,9.24111176470177e-10 AZE,0.7241496741771698 -BDI,-2.8265783935665922e-08 -BEL,0.08877222612500191 +BDI,-1.8208973884270563e-08 +BEL,0.08780838921666145 BEN,2.2254557609558105 BFA,1.0289277732372284 BGD,-0.0 -BGR,-0.30596522986888885 -BHS,-0.03126266412436962 -BIH,-0.45764872431755066 +BGR,-0.29128387570381165 +BHS,-0.031262665055692196 +BIH,-0.45235347747802734 BLR,-0.01833034586161375 BLZ,-0.7399452030658722 BOL,-0.6221835613250732 BRA,-0.5204504728317261 BRB,0.6726185977458954 BRN,-3.437714735099462e-08 -BTN,-0.3279154747724533 +BTN,-0.3056749999523163 BWA,-0.0 -CAF,-0.44557350873947144 -CAN,-0.2128412425518036 -CHE,-0.10497415065765381 +CAF,1.5021680344062815e-08 +CAN,-0.21270669996738434 +CHE,-0.0 CHL,-0.03965021949261427 -CHN,-2.221346218078679e-09 +CHN,-4.407169296172242e-09 CIV,-0.5458422899246216 CMR,2.1159034371376038 COD,-0.7531084418296814 @@ -40,113 +40,113 @@ CPV,-0.0 CRI,-0.04483989253640175 CUB,-0.17085150629281998 CYP,0.23298390954732895 -CZE,-0.2163601964712143 -DEU,-0.2619142234325409 +CZE,-0.18661560863256454 +DEU,-0.24803977459669113 DJI,-0.0 DNK,1.4705818891525269 DOM,-1.3908177720622916e-08 -DZA,-1.675111160892584e-09 +DZA,-2.098951901174928e-09 ECU,-0.19687405228614807 EGY,-0.0 ERI,-0.04815981723368168 -ESP,-0.04996956139802933 +ESP,-0.03535165823996067 EST,-0.26680906116962433 -ETH,-1.9732169675990008e-08 +ETH,-1.9151058516797548e-09 FIN,1.2526208758354187 -FJI,-0.8778187334537506 +FJI,-0.864391952753067 FRA,-2.9409451540729492e-09 GAB,-0.8207397162914276 -GBR,-0.01494628144428134 +GBR,-8.699288400748628e-09 GEO,-0.20096751302480698 GHA,-0.5431598126888275 GIN,-0.37204913794994354 GMB,0.9099922478199005 GNB,0.8454008102416992 GNQ,-0.7101413011550903 -GRC,-0.1545606106519699 +GRC,-0.15156953781843185 GRD,0.46647536754608154 GTM,-2.610952076764761e-08 GUF,-0.9834447503089905 GUY,-1.1032667756080627 -HND,-0.729015976190567 -HRV,-0.1710522323846817 -HTI,-0.30410297214984894 +HND,-0.5581271648406982 +HRV,-0.17101933807134628 +HTI,1.2459450360324809e-08 HUN,-0.4085644632577896 IDN,-0.562181144952774 IND,1.2002182602882385 -IRL,-3.817743010614549e-09 +IRL,-0.0 IRN,-0.0 IRQ,-0.0 ISL,-0.09247573837637901 ISR,0.5377657413482666 -ITA,0.437164843082428 +ITA,0.44143620133399963 JAM,2.724227786064148 JOR,-0.0 JPN,-0.4728243798017502 -KAZ,-0.017455806024372578 +KAZ,-0.012584597337991 KEN,-0.2179667055606842 -KGZ,-7.723922612612455e-10 -KHM,-0.4570121169090271 +KGZ,-1.2954760597594372e-09 +KHM,2.1605234268307072e-10 KOR,2.447239637374878 -LAO,-0.3048495054244995 +LAO,4.666452224633133e-10 LBN,0.3942871540784836 LBR,-0.7618840336799622 -LBY,-0.0 -LKA,-0.488057941198349 +LBY,2.2779291473928875e-12 +LKA,-0.1928924173116684 LSO,-0.4435950964689255 -LTU,-0.1002877913415432 -LUX,0.01585041731595993 +LTU,-0.04581737518310547 +LUX,0.01513665821403265 LVA,-0.2936539202928543 MAR,-0.0 MDA,-0.3453979641199112 MDG,-0.6868364214897156 -MEX,-0.0679156482219696 -MKD,-0.28447867929935455 +MEX,-0.06383734568953514 +MKD,-0.27806226909160614 MLI,-0.037232644855976105 MLT,-0.0 MMR,2.056019604206085 -MNE,-0.33641423285007477 -MNG,-0.037687841802835464 +MNE,-0.29938387870788574 +MNG,-0.03427516482770443 MOZ,-0.5160333812236786 -MRT,-0.033999696373939514 -MUS,-0.2491012066602707 +MRT,-0.03399969823658466 +MUS,-3.315589225394433e-08 MWI,-3.3964567824895653e-09 -MYS,-6.708681432598951e-08 +MYS,1.4313578766689261e-08 NAM,-0.0047507211565971375 NER,-0.0 NGA,-0.14038769900798798 NIC,-0.5779949724674225 NLD,0.8321638405323029 NOR,1.037335753440857 -NPL,0.02799716591835022 +NPL,0.06737685762345791 NZL,-0.0 OMN,-0.0 PAK,-0.0 -PAN,-0.6068116426467896 +PAN,-0.10512995347380638 PER,-0.6808821260929108 PHL,-0.11315194144845009 PNG,-0.8056034743785858 -POL,-0.018983316607773304 -PRI,-6.72518289945856e-08 -PRT,-0.18069716542959213 +POL,0.005302567966282368 +PRI,-2.7517170764213006e-09 +PRT,-0.17007751017808914 PRY,-0.6874429285526276 PSE,0.507328063249588 ROU,-0.4107300937175751 -RUS,-3.11136705288817e-09 +RUS,-1.2980387320560283e-09 RWA,4.892043590545654 SAU,-0.0 -SDN,-0.07342882826924324 +SDN,-0.06421096250414848 SEN,0.28181566298007965 -SLB,-0.24702571332454681 +SLB,-0.18465298414230347 SLE,-0.3628302663564682 SLV,2.639617085456848 SOM,-0.05690718814730644 -SRB,-0.2917206585407257 +SRB,-0.2870781123638153 SSD,-0.2865627706050873 STP,0.39302489161491394 -SUR,-0.13022971898317337 +SUR,-0.13022972643375397 SVK,-0.4101209193468094 -SVN,-0.2719762772321701 +SVN,-0.20196598023176193 SWE,1.0995991826057434 SWZ,-0.3180921822786331 SYR,0.10783830285072327 @@ -155,21 +155,21 @@ TGO,-0.30542606115341187 THA,-3.214273469609452e-08 TJK,0.29956962168216705 TKM,-0.0 -TLS,-0.3610769063234329 +TLS,-0.22256753593683243 TTO,-3.4331349985095017e-09 TUN,0.674901008605957 TUR,0.7692269086837769 -TWN,-0.3990366756916046 +TWN,-0.3718879818916321 TZA,-0.06110341474413872 -UGA,-3.941181603295263e-08 -UKR,-0.008017591899260879 +UGA,-2.739427173992226e-08 +UKR,-0.007491964614018798 URY,-0.21180780231952667 -USA,-0.21023427695035934 +USA,-0.2100314497947693 UZB,-0.0 VEN,-0.6751556694507599 VNM,4.380912780761719 -VUT,-0.2727542817592621 -YEM,2.167515278372889e-09 +VUT,-0.19533131271600723 +YEM,-6.187674372881702e-09 ZAF,-0.04584670439362526 ZMB,-0.39802390336990356 ZWE,0.3602187801152468 diff --git a/data/curated/calibration/gbd-anchored/multi_crop_cost.csv b/data/curated/calibration/gbd-anchored/multi_crop_cost.csv index 29edc795..c19e8795 100644 --- a/data/curated/calibration/gbd-anchored/multi_crop_cost.csv +++ b/data/curated/calibration/gbd-anchored/multi_crop_cost.csv @@ -1,73 +1,73 @@ combination,country,correction_bnusd_per_mha -cotton_wheat,AFG,-1.6047418117523193 -cotton_wheat,AGO,-1.9458357095718384 +cotton_wheat,AFG,-1.6005584001541138 +cotton_wheat,AGO,-1.9504379034042358 cotton_wheat,ALB,7.5 cotton_wheat,ARG,7.5 cotton_wheat,ARM,7.5 -cotton_wheat,AUS,-6.099520444869995 +cotton_wheat,AUS,-6.084402799606323 cotton_wheat,AZE,7.5 -cotton_wheat,BDI,-1.0002751350402832 -cotton_wheat,BGD,-1.8326017260551453 +cotton_wheat,BDI,-0.9992214441299438 +cotton_wheat,BGD,-1.812194049358368 cotton_wheat,BGR,7.5 cotton_wheat,BOL,7.5 -cotton_wheat,BRA,-2.7955737113952637 -cotton_wheat,BWA,-1.3724567890167236 -cotton_wheat,CHN,-6.761477947235107 -cotton_wheat,COD,-1.943487286567688 +cotton_wheat,BRA,-2.773737668991089 +cotton_wheat,BWA,-1.3783190250396729 +cotton_wheat,CHN,-6.743114471435547 +cotton_wheat,COD,-1.941415548324585 cotton_wheat,DZA,7.5 -cotton_wheat,EGY,-2.601527214050293 -cotton_wheat,ESP,-1.855180561542511 -cotton_wheat,ETH,-3.03496253490448 +cotton_wheat,EGY,-2.593240976333618 +cotton_wheat,ESP,-1.8542932271957397 +cotton_wheat,ETH,-3.0305421352386475 cotton_wheat,GEO,7.5 -cotton_wheat,GRC,-1.94819176197052 -cotton_wheat,HND,-2.015960693359375 -cotton_wheat,IND,-1.7251863479614258 -cotton_wheat,IRN,-3.440570116043091 -cotton_wheat,IRQ,-1.8723878860473633 -cotton_wheat,ISR,-0.5716027021408081 -cotton_wheat,JOR,-2.253195881843567 -cotton_wheat,KAZ,-1.1375041007995605 -cotton_wheat,KEN,-0.7796232402324677 -cotton_wheat,KGZ,-1.4607731103897095 +cotton_wheat,GRC,-2.174339711666107 +cotton_wheat,HND,-2.007324457168579 +cotton_wheat,IND,-1.692454218864441 +cotton_wheat,IRN,-3.433492064476013 +cotton_wheat,IRQ,-1.8649859428405762 +cotton_wheat,ISR,-0.5640974640846252 +cotton_wheat,JOR,-2.2443660497665405 +cotton_wheat,KAZ,-1.1319613456726074 +cotton_wheat,KEN,-0.775142639875412 +cotton_wheat,KGZ,-1.4534763097763062 cotton_wheat,KOR,7.5 cotton_wheat,LBN,7.5 -cotton_wheat,LSO,3.280999034643173 -cotton_wheat,MAR,-3.0014926195144653 -cotton_wheat,MDG,-1.7391724586486816 -cotton_wheat,MEX,-3.9567677974700928 -cotton_wheat,MKD,-1.3378469944000244 +cotton_wheat,LSO,3.2786869406700134 +cotton_wheat,MAR,-2.9958654642105103 +cotton_wheat,MDG,-1.7424260377883911 +cotton_wheat,MEX,-3.9346866607666016 +cotton_wheat,MKD,-1.327242136001587 cotton_wheat,MMR,7.5 -cotton_wheat,MOZ,-1.429490566253662 -cotton_wheat,MWI,2.780623197555542 -cotton_wheat,NAM,-2.5815569162368774 -cotton_wheat,NER,-0.8563618361949921 -cotton_wheat,NGA,-2.0326125621795654 +cotton_wheat,MOZ,-1.4333232045173645 +cotton_wheat,MWI,2.780034840106964 +cotton_wheat,NAM,-2.58591628074646 +cotton_wheat,NER,-0.8509052097797394 +cotton_wheat,NGA,-2.0296952724456787 cotton_wheat,NPL,7.5 cotton_wheat,OMN,-0.0 -cotton_wheat,PAK,-1.6866000890731812 +cotton_wheat,PAK,-1.6816506385803223 cotton_wheat,PRT,7.5 -cotton_wheat,PRY,-0.8066742420196533 -cotton_wheat,PSE,-2.578450560569763 +cotton_wheat,PRY,-0.7992074191570282 +cotton_wheat,PSE,-2.5705435276031494 cotton_wheat,ROU,-0.0 cotton_wheat,RUS,7.5 -cotton_wheat,RWA,-1.6123987436294556 -cotton_wheat,SDN,-1.7723118662834167 -cotton_wheat,SOM,2.7263638973236084 -cotton_wheat,SWZ,-1.7943490743637085 -cotton_wheat,SYR,-2.7098371982574463 -cotton_wheat,TJK,-4.032142162322998 -cotton_wheat,TKM,-1.675410270690918 +cotton_wheat,RWA,-1.6093989610671997 +cotton_wheat,SDN,-1.7677278518676758 +cotton_wheat,SOM,2.7269207239151 +cotton_wheat,SWZ,-1.7961340546607971 +cotton_wheat,SYR,-2.6562423706054688 +cotton_wheat,TJK,-4.010316848754883 +cotton_wheat,TKM,-1.6694691181182861 cotton_wheat,TUN,7.5 -cotton_wheat,TUR,-1.709210753440857 -cotton_wheat,TZA,-1.5142258405685425 -cotton_wheat,UGA,-1.790787935256958 +cotton_wheat,TUR,-1.6307328939437866 +cotton_wheat,TZA,-1.511807918548584 +cotton_wheat,UGA,-1.293732225894928 cotton_wheat,URY,7.5 -cotton_wheat,USA,-1.620875358581543 -cotton_wheat,UZB,-2.746370315551758 -cotton_wheat,YEM,-2.3810231685638428 -cotton_wheat,ZAF,-3.10784649848938 +cotton_wheat,USA,-1.6124464273452759 +cotton_wheat,UZB,-2.740176200866699 +cotton_wheat,YEM,-2.3774771690368652 +cotton_wheat,ZAF,-3.2222933769226074 cotton_wheat,ZMB,7.5 -cotton_wheat,ZWE,3.7470669746398926 +cotton_wheat,ZWE,3.743788957595825 double_rice,AFG,7.5 double_rice,AGO,7.5 double_rice,ALB,-0.0 @@ -86,7 +86,7 @@ double_rice,BTN,7.5 double_rice,BWA,-0.0 double_rice,CAF,7.5 double_rice,CHL,7.5 -double_rice,CHN,-3.838492512702942 +double_rice,CHN,-3.8518054485321045 double_rice,CIV,7.5 double_rice,CMR,-2.428388833999634 double_rice,COD,7.5 @@ -94,7 +94,7 @@ double_rice,COG,7.5 double_rice,COL,-2.8861072063446045 double_rice,CRI,-1.988244354724884 double_rice,CUB,7.5 -double_rice,DOM,2.005537509918213 +double_rice,DOM,1.9877275228500366 double_rice,DZA,7.5 double_rice,ECU,-1.4403660893440247 double_rice,EGY,7.5 @@ -117,17 +117,17 @@ double_rice,GUY,7.5 double_rice,HND,7.5 double_rice,HTI,2.365760564804077 double_rice,IDN,-2.8262991905212402 -double_rice,IND,-2.0979055166244507 +double_rice,IND,-1.9765093922615051 double_rice,IRN,7.5 double_rice,IRQ,7.5 double_rice,ITA,7.5 -double_rice,JPN,-7.281943321228027 +double_rice,JPN,-7.274365425109863 double_rice,KEN,7.5 double_rice,KHM,-2.6476649045944214 double_rice,KOR,0.0 double_rice,LAO,-2.679863214492798 double_rice,LBR,7.5 -double_rice,LKA,3.318230152130127 +double_rice,LKA,3.299778699874878 double_rice,LSO,-0.0 double_rice,MAR,7.5 double_rice,MDG,3.551405146718025 @@ -143,7 +143,7 @@ double_rice,MYS,-0.9997842311859131 double_rice,NAM,7.5 double_rice,NER,7.5 double_rice,NGA,-0.5443001091480255 -double_rice,NIC,-0.3844975382089615 +double_rice,NIC,2.580675721168518 double_rice,NPL,3.46849524974823 double_rice,PAK,7.5 double_rice,PAN,-2.2657395601272583 @@ -186,119 +186,119 @@ double_rice,YEM,-0.0 double_rice,ZAF,7.5 double_rice,ZMB,7.5 double_rice,ZWE,7.5 -maize_soybean,AFG,0.011530648916959763 -maize_soybean,AGO,-1.4472877979278564 +maize_soybean,AFG,0.04237610846757889 +maize_soybean,AGO,-1.440967082977295 maize_soybean,ALB,7.5 -maize_soybean,ARG,0.1555891502648592 +maize_soybean,ARG,0.18920697271823883 maize_soybean,ARM,7.5 -maize_soybean,AUS,1.8262438774108887 -maize_soybean,AUT,4.731407880783081 -maize_soybean,AZE,1.3761106729507446 -maize_soybean,BDI,-1.0768476724624634 +maize_soybean,AUS,1.8638125658035278 +maize_soybean,AUT,4.758497595787048 +maize_soybean,AZE,1.4257961511611938 +maize_soybean,BDI,-1.0719791650772095 maize_soybean,BEL,7.5 -maize_soybean,BEN,-0.7698748707771301 -maize_soybean,BFA,-0.5834342837333679 +maize_soybean,BEN,-0.761521190404892 +maize_soybean,BFA,-0.5730064809322357 maize_soybean,BGD,7.5 maize_soybean,BGR,7.5 maize_soybean,BIH,7.5 -maize_soybean,BLZ,-1.445022463798523 -maize_soybean,BOL,3.703203611075878 -maize_soybean,BRA,-0.0783945769071579 -maize_soybean,BTN,4.079217821359634 -maize_soybean,BWA,0.22230756282806396 +maize_soybean,BLZ,-1.4337624311447144 +maize_soybean,BOL,3.713705435395241 +maize_soybean,BRA,-0.04940510541200638 +maize_soybean,BTN,4.209444612264633 +maize_soybean,BWA,0.24546489119529724 maize_soybean,CAF,-7.5 -maize_soybean,CAN,-0.3834390938282013 +maize_soybean,CAN,-0.33801811933517456 maize_soybean,CHE,7.5 -maize_soybean,CHN,-1.6682442426681519 -maize_soybean,CIV,-0.9313527047634125 -maize_soybean,CMR,-1.0283037722110748 -maize_soybean,COD,-1.6195263862609863 -maize_soybean,COG,-1.5142959356307983 -maize_soybean,COL,-0.346640020608902 -maize_soybean,CRI,-0.870154619216919 +maize_soybean,CHN,-1.7164018154144287 +maize_soybean,CIV,-0.9198598861694336 +maize_soybean,CMR,-1.015499472618103 +maize_soybean,COD,-1.6154201030731201 +maize_soybean,COG,-1.5101972222328186 +maize_soybean,COL,-0.3221440762281418 +maize_soybean,CRI,-0.8509926199913025 maize_soybean,CUB,7.5 maize_soybean,CZE,7.5 maize_soybean,DEU,7.5 maize_soybean,DZA,-0.0 -maize_soybean,ECU,-0.8143638372421265 -maize_soybean,EGY,-1.5350552797317505 -maize_soybean,ESP,-0.9074086546897888 -maize_soybean,ETH,-0.021606355905532837 +maize_soybean,ECU,-0.8025993704795837 +maize_soybean,EGY,-1.5102565288543701 +maize_soybean,ESP,-0.8726418018341064 +maize_soybean,ETH,-0.0008028236916288733 maize_soybean,FRA,7.5 -maize_soybean,GAB,-1.2568727731704712 -maize_soybean,GEO,0.32013842463493347 -maize_soybean,GHA,-0.7804605066776276 +maize_soybean,GAB,-1.2493868470191956 +maize_soybean,GEO,0.38293859362602234 +maize_soybean,GHA,-0.767649233341217 maize_soybean,GIN,7.5 -maize_soybean,GRC,3.130979001522064 -maize_soybean,GTM,-0.4258793890476227 -maize_soybean,HND,-0.2472366988658905 +maize_soybean,GRC,3.141064763069153 +maize_soybean,GTM,-0.39747339487075806 +maize_soybean,HND,-0.22185418009757996 maize_soybean,HRV,7.5 -maize_soybean,HUN,0.5785587672144175 -maize_soybean,IDN,0.14274552464485168 -maize_soybean,IND,-0.9100357294082642 -maize_soybean,IRN,-0.876893937587738 -maize_soybean,IRQ,-0.5967424958944321 +maize_soybean,HUN,0.6146606765687466 +maize_soybean,IDN,0.15967151522636414 +maize_soybean,IND,-0.8637899458408356 +maize_soybean,IRN,-0.8291622996330261 +maize_soybean,IRQ,-0.5749030113220215 maize_soybean,ISR,7.5 maize_soybean,ITA,7.5 maize_soybean,JOR,-0.0 maize_soybean,KAZ,7.5 -maize_soybean,KEN,-0.43404343724250793 -maize_soybean,KGZ,-0.3314945697784424 -maize_soybean,KHM,-0.688298761844635 -maize_soybean,KOR,-0.43032127618789673 -maize_soybean,LAO,-0.07111307134618983 +maize_soybean,KEN,-0.4277620315551758 +maize_soybean,KGZ,-0.3163607716560364 +maize_soybean,KHM,-0.6771034300327301 +maize_soybean,KOR,-0.41967588663101196 +maize_soybean,LAO,-0.050583419390022755 maize_soybean,LBN,7.5 -maize_soybean,LKA,-0.8701399266719818 +maize_soybean,LKA,-0.8516020476818085 maize_soybean,LSO,7.5 maize_soybean,LUX,7.5 maize_soybean,MAR,7.5 maize_soybean,MDA,7.5 -maize_soybean,MEX,0.016067216638475657 +maize_soybean,MEX,0.05320619512349367 maize_soybean,MKD,7.5 -maize_soybean,MLI,-0.800273597240448 -maize_soybean,MMR,-1.0466876029968262 +maize_soybean,MLI,-0.7942398190498352 +maize_soybean,MMR,-1.0379907488822937 maize_soybean,MNE,7.5 -maize_soybean,MOZ,3.972331792116165 -maize_soybean,MWI,3.6033296436071396 -maize_soybean,NGA,-0.750243604183197 -maize_soybean,NIC,-0.38373610377311707 +maize_soybean,MOZ,3.988574281334877 +maize_soybean,MWI,3.607054829597473 +maize_soybean,NGA,-0.7412781119346619 +maize_soybean,NIC,0.5268668383359909 maize_soybean,NLD,7.5 -maize_soybean,NPL,3.573500394821167 -maize_soybean,PAK,-0.8483560681343079 +maize_soybean,NPL,3.582722544670105 +maize_soybean,PAK,-0.8364247977733612 maize_soybean,PER,7.5 maize_soybean,POL,7.5 maize_soybean,PRT,7.5 -maize_soybean,PRY,-0.401455894112587 -maize_soybean,ROU,4.181589424610138 +maize_soybean,PRY,-0.3837558925151825 +maize_soybean,ROU,4.1935000121593475 maize_soybean,RUS,7.5 -maize_soybean,RWA,-0.3523823022842407 -maize_soybean,SDN,-0.3060949146747589 -maize_soybean,SLV,-0.81214240193367 +maize_soybean,RWA,-0.3489294797182083 +maize_soybean,SDN,-0.2798515260219574 +maize_soybean,SLV,-0.7956073880195618 maize_soybean,SRB,7.5 maize_soybean,SSD,7.5 maize_soybean,SVK,7.5 maize_soybean,SVN,7.5 -maize_soybean,SWZ,-0.7508664131164551 +maize_soybean,SWZ,-0.7362450361251831 maize_soybean,SYR,7.5 -maize_soybean,TCD,3.5983566343784332 -maize_soybean,TGO,-0.36733002960681915 -maize_soybean,THA,-0.4094362258911133 +maize_soybean,TCD,3.6049290597438812 +maize_soybean,TGO,-0.3613187074661255 +maize_soybean,THA,-0.39470916986465454 maize_soybean,TJK,7.5 maize_soybean,TKM,7.5 -maize_soybean,TLS,4.128298372030258 +maize_soybean,TLS,4.1354206800460815 maize_soybean,TUR,7.5 maize_soybean,TWN,-0.0 -maize_soybean,TZA,-0.3315706104040146 -maize_soybean,UGA,-1.4039616584777832 +maize_soybean,TZA,-0.32316721975803375 +maize_soybean,UGA,-0.8696460425853729 maize_soybean,UKR,7.5 -maize_soybean,URY,0.027483180165290833 -maize_soybean,USA,-0.7005386352539062 +maize_soybean,URY,0.05724291503429413 +maize_soybean,USA,-0.6705518364906311 maize_soybean,UZB,7.5 -maize_soybean,VEN,-0.8652253448963165 -maize_soybean,VNM,-0.34182392060756683 -maize_soybean,ZAF,-0.20614733546972275 -maize_soybean,ZMB,-0.5492773652076721 -maize_soybean,ZWE,1.0711103677749634 +maize_soybean,VEN,-0.8543592393398285 +maize_soybean,VNM,-0.3216220289468765 +maize_soybean,ZAF,-0.1926969364285469 +maize_soybean,ZMB,-0.539668083190918 +maize_soybean,ZWE,1.0840790271759033 rice_maize,AFG,-1.7612311840057373 rice_maize,AGO,-2.391422986984253 rice_maize,ALB,-0.0 @@ -313,19 +313,19 @@ rice_maize,BGR,7.5 rice_maize,BLZ,-2.683790922164917 rice_maize,BOL,7.5 rice_maize,BRA,-0.42088496685028076 -rice_maize,BTN,-0.6739095151424408 +rice_maize,BTN,-0.27735983580350876 rice_maize,BWA,-0.0 rice_maize,CAF,-2.4026224613189697 rice_maize,CAN,-0.0 rice_maize,CHL,-1.9755549430847168 -rice_maize,CHN,-3.3502438068389893 +rice_maize,CHN,-3.373154878616333 rice_maize,CIV,-0.9736503958702087 rice_maize,CMR,7.5 rice_maize,COD,-2.5215160846710205 rice_maize,COL,-2.1165847778320312 rice_maize,COM,-2.4128477573394775 rice_maize,CUB,7.5 -rice_maize,DOM,2.27661395072937 +rice_maize,DOM,2.258803963661194 rice_maize,DZA,7.5 rice_maize,ECU,-0.912381649017334 rice_maize,EGY,-2.7287893295288086 @@ -337,27 +337,27 @@ rice_maize,GEO,7.5 rice_maize,GHA,-1.0406630039215088 rice_maize,GIN,-0.7359979748725891 rice_maize,GNB,-1.7585076093673706 -rice_maize,GRC,-2.2312287092208862 +rice_maize,GRC,-2.3588995933532715 rice_maize,GTM,-2.281901240348816 rice_maize,HND,-1.8415104150772095 rice_maize,HTI,-2.315851092338562 rice_maize,HUN,7.5 rice_maize,IDN,-0.0 -rice_maize,IND,-1.7849705219268799 +rice_maize,IND,-1.7118761539459229 rice_maize,IRN,-5.533003091812134 rice_maize,IRQ,-2.4512051343917847 -rice_maize,ITA,-3.4095007181167603 +rice_maize,ITA,-3.4592790603637695 rice_maize,KAZ,-0.0460036676377058 -rice_maize,KEN,-1.3616081476211548 +rice_maize,KEN,-1.5780656933784485 rice_maize,KGZ,-3.5197880268096924 rice_maize,KOR,-6.925231218338013 rice_maize,LAO,-1.7182810306549072 -rice_maize,LKA,-1.289830505847931 +rice_maize,LKA,-1.3051108717918396 rice_maize,LSO,-0.0 rice_maize,MAR,2.587197422981262 rice_maize,MDA,7.5 rice_maize,MDG,-1.2854368686676025 -rice_maize,MEX,-0.8802247643470764 +rice_maize,MEX,-0.8455647528171539 rice_maize,MKD,7.5 rice_maize,MLI,-0.6381320357322693 rice_maize,MMR,-1.8376070261001587 @@ -366,38 +366,38 @@ rice_maize,MUS,7.5 rice_maize,MWI,-1.3250725269317627 rice_maize,NAM,7.5 rice_maize,NGA,-0.7492590546607971 -rice_maize,NIC,-0.0 -rice_maize,NPL,-0.6812265813350677 +rice_maize,NIC,0.08604352921247482 +rice_maize,NPL,-0.6822277903556824 rice_maize,PAK,-2.0292766094207764 -rice_maize,PER,-3.7629215717315674 +rice_maize,PER,-1.8823392391204834 rice_maize,PHL,-1.185758650302887 rice_maize,PRI,7.5 rice_maize,PRT,2.9059571027755737 rice_maize,PRY,-1.1645232439041138 -rice_maize,ROU,-1.4931132197380066 +rice_maize,ROU,-1.7139790654182434 rice_maize,RUS,7.5 rice_maize,RWA,-2.1034271717071533 rice_maize,SAU,7.5 rice_maize,SDN,-2.53398597240448 rice_maize,SEN,7.5 rice_maize,SLE,7.5 -rice_maize,SLV,-3.215489149093628 +rice_maize,SLV,-3.2118743658065796 rice_maize,SOM,-0.7943862080574036 rice_maize,SSD,-2.373322010040283 rice_maize,SVK,-0.0 rice_maize,SVN,-0.0 rice_maize,SWZ,7.5 -rice_maize,SYR,-1.9613561630249023 +rice_maize,SYR,-1.8183244466781616 rice_maize,TCD,-0.8211153745651245 rice_maize,TGO,-0.4678821265697479 rice_maize,THA,-0.9868681728839874 -rice_maize,TJK,-5.260088920593262 +rice_maize,TJK,-5.243596076965332 rice_maize,TKM,-1.3183168172836304 rice_maize,TLS,-1.619733214378357 rice_maize,TUN,-0.0 -rice_maize,TUR,-3.7046124935150146 -rice_maize,TZA,-0.2410535216331482 -rice_maize,UGA,-2.310612678527832 +rice_maize,TUR,-3.5280336141586304 +rice_maize,TZA,-0.2998362183570862 +rice_maize,UGA,-1.3208303451538086 rice_maize,UKR,7.5 rice_maize,URY,-1.1678839921951294 rice_maize,USA,-2.102957010269165 @@ -405,7 +405,7 @@ rice_maize,UZB,-5.983398914337158 rice_maize,VEN,-1.4906173348426819 rice_maize,VNM,-1.4008294343948364 rice_maize,YEM,-0.0 -rice_maize,ZAF,-1.392294466495514 +rice_maize,ZAF,-1.498084008693695 rice_maize,ZMB,-1.2757176160812378 rice_maize,ZWE,7.5 rice_wheat,AFG,-1.36625337600708 @@ -415,15 +415,15 @@ rice_wheat,ARG,7.5 rice_wheat,ARM,-0.0 rice_wheat,AUS,-1.7297658920288086 rice_wheat,AZE,-2.7401392459869385 -rice_wheat,BGD,-0.8487881422042847 +rice_wheat,BGD,-0.8407561779022217 rice_wheat,BGR,2.9040696024894714 rice_wheat,BOL,7.5 rice_wheat,BRA,7.5 -rice_wheat,BTN,-0.23333557322621346 +rice_wheat,BTN,0.22375415824353695 rice_wheat,BWA,-0.0 rice_wheat,CAN,-0.0 rice_wheat,CHL,-1.42368882894516 -rice_wheat,CHN,-3.2957088947296143 +rice_wheat,CHN,-3.311853289604187 rice_wheat,CMR,-1.7874256372451782 rice_wheat,COD,-2.261791229248047 rice_wheat,DZA,7.5 @@ -433,15 +433,15 @@ rice_wheat,ESP,-2.280158758163452 rice_wheat,ETH,7.5 rice_wheat,FRA,7.5 rice_wheat,GEO,7.5 -rice_wheat,GRC,-1.1202884316444397 +rice_wheat,GRC,-1.2055567502975464 rice_wheat,GTM,7.5 rice_wheat,HND,-2.0931620597839355 rice_wheat,HUN,7.5 -rice_wheat,IND,-1.56135094165802 +rice_wheat,IND,-1.481703519821167 rice_wheat,IRN,-4.449605464935303 rice_wheat,IRQ,-2.142758369445801 -rice_wheat,ITA,-2.680411696434021 -rice_wheat,JPN,-0.573891282081604 +rice_wheat,ITA,-2.7301900386810303 +rice_wheat,JPN,-0.5729159116744995 rice_wheat,KAZ,-0.3956315815448761 rice_wheat,KGZ,-3.3019497394561768 rice_wheat,KOR,-6.736924171447754 @@ -472,22 +472,22 @@ rice_wheat,SOM,7.5 rice_wheat,SVK,-0.0 rice_wheat,SVN,-0.0 rice_wheat,SWZ,7.5 -rice_wheat,SYR,-1.541824460029602 +rice_wheat,SYR,-1.4075180292129517 rice_wheat,TCD,-1.1280630230903625 rice_wheat,THA,7.5 -rice_wheat,TJK,-4.452836036682129 +rice_wheat,TJK,-4.436343193054199 rice_wheat,TKM,-1.3714885115623474 rice_wheat,TUN,-0.0 -rice_wheat,TUR,-3.1631553173065186 +rice_wheat,TUR,-2.9973443746566772 rice_wheat,TZA,-0.5094838738441467 -rice_wheat,UGA,-2.058772087097168 +rice_wheat,UGA,-0.9457163214683533 rice_wheat,UKR,7.5 rice_wheat,URY,-0.8698312044143677 rice_wheat,USA,-1.6291799545288086 rice_wheat,UZB,-4.869426250457764 rice_wheat,VEN,7.5 rice_wheat,VNM,-1.5219956636428833 -rice_wheat,ZAF,-1.5276175737380981 +rice_wheat,ZAF,-1.6325544118881226 rice_wheat,ZMB,-0.9899722933769226 rice_wheat,ZWE,7.5 triple_rice,AFG,7.5 @@ -497,12 +497,12 @@ triple_rice,AUS,7.5 triple_rice,BDI,7.5 triple_rice,BEN,7.5 triple_rice,BFA,7.5 -triple_rice,BGD,-1.4878147840499878 +triple_rice,BGD,-1.5379945039749146 triple_rice,BLZ,7.5 triple_rice,BOL,7.5 triple_rice,BRA,7.5 triple_rice,BRN,7.5 -triple_rice,BTN,-2.8252837657928467 +triple_rice,BTN,-2.0796040296554565 triple_rice,BWA,-0.0 triple_rice,CHN,7.5 triple_rice,CIV,7.5 @@ -608,12 +608,12 @@ wheat_maize,BGR,7.5 wheat_maize,BIH,7.5 wheat_maize,BOL,-0.5978642106056213 wheat_maize,BRA,-0.39374731481075287 -wheat_maize,BTN,1.1004817485809326 +wheat_maize,BTN,1.4300478100776672 wheat_maize,BWA,-0.2336793690919876 wheat_maize,CAN,7.5 wheat_maize,CHE,7.5 wheat_maize,CHL,-1.6744529604911804 -wheat_maize,CHN,-2.7185128927230835 +wheat_maize,CHN,-2.757931351661682 wheat_maize,CMR,-0.9545796513557434 wheat_maize,COD,-1.5740829706192017 wheat_maize,COL,-1.1784802079200745 @@ -626,13 +626,13 @@ wheat_maize,ERI,-1.5175652503967285 wheat_maize,ESP,-0.6704811155796051 wheat_maize,ETH,-0.5842554569244385 wheat_maize,FRA,7.5 -wheat_maize,GEO,-0.06189274415373802 +wheat_maize,GEO,-0.014626766555011272 wheat_maize,GRC,2.977361559867859 wheat_maize,GTM,3.2315215468406677 wheat_maize,HND,-1.2084702253341675 wheat_maize,HRV,7.5 wheat_maize,HUN,-0.3870267905294895 -wheat_maize,IND,-0.9701172113418579 +wheat_maize,IND,-0.9039748907089233 wheat_maize,IRN,-2.214697241783142 wheat_maize,IRQ,-1.432152509689331 wheat_maize,ISR,-6.778619289398193 @@ -650,7 +650,7 @@ wheat_maize,LUX,7.5 wheat_maize,MAR,-0.6427487134933472 wheat_maize,MDA,7.5 wheat_maize,MDG,-1.2553812265396118 -wheat_maize,MEX,-1.3077812194824219 +wheat_maize,MEX,-1.2968561053276062 wheat_maize,MKD,7.5 wheat_maize,MMR,-0.8249058127403259 wheat_maize,MNE,7.5 @@ -661,9 +661,9 @@ wheat_maize,NIC,7.5 wheat_maize,NLD,7.5 wheat_maize,NPL,-0.6877874135971069 wheat_maize,NZL,-1.935607671737671 -wheat_maize,OMN,-5.0451414585113525 +wheat_maize,OMN,-5.260104179382324 wheat_maize,PAK,-0.9708907902240753 -wheat_maize,PER,-1.4284441471099854 +wheat_maize,PER,-0.693425327539444 wheat_maize,POL,7.5 wheat_maize,PRT,-0.5974171757698059 wheat_maize,PRY,-0.5223359763622284 @@ -679,15 +679,15 @@ wheat_maize,SSD,7.5 wheat_maize,SVK,7.5 wheat_maize,SVN,7.5 wheat_maize,SWZ,-1.2159836292266846 -wheat_maize,SYR,-1.5571393966674805 +wheat_maize,SYR,-1.45664781332016 wheat_maize,THA,7.5 wheat_maize,TJK,-1.944539189338684 wheat_maize,TKM,-0.8660793304443359 wheat_maize,TUN,-0.0 -wheat_maize,TUR,-0.4474106431007385 +wheat_maize,TUR,-0.3309807926416397 wheat_maize,TWN,-0.18248724937438965 wheat_maize,TZA,-0.3687501549720764 -wheat_maize,UGA,-1.3176888227462769 +wheat_maize,UGA,-0.43182340264320374 wheat_maize,UKR,7.5 wheat_maize,URY,-0.6195745170116425 wheat_maize,USA,-1.0190239548683167 @@ -698,93 +698,93 @@ wheat_maize,YEM,-1.7387548089027405 wheat_maize,ZAF,-0.4899768978357315 wheat_maize,ZMB,0.10961665958166122 wheat_maize,ZWE,7.5 -wheat_soybean,AFG,0.42101508378982544 -wheat_soybean,AGO,-1.2080053687095642 +wheat_soybean,AFG,0.4516116678714752 +wheat_soybean,AGO,-1.2005339860916138 wheat_soybean,ALB,7.5 wheat_soybean,ARG,7.5 wheat_soybean,ARM,7.5 -wheat_soybean,AUS,1.4152477979660034 -wheat_soybean,AUT,4.317115306854248 -wheat_soybean,AZE,1.5414074659347534 -wheat_soybean,BDI,-1.49884831905365 +wheat_soybean,AUS,1.4544084072113037 +wheat_soybean,AUT,4.343719899654388 +wheat_soybean,AZE,1.5911108255386353 +wheat_soybean,BDI,-1.4972206354141235 wheat_soybean,BEL,7.5 -wheat_soybean,BGD,3.580982968211174 +wheat_soybean,BGD,3.588158681988716 wheat_soybean,BGR,7.5 wheat_soybean,BIH,7.5 wheat_soybean,BOL,7.5 -wheat_soybean,BRA,-0.009148151380941272 +wheat_soybean,BRA,0.02180484775453806 wheat_soybean,BTN,7.5 wheat_soybean,BWA,7.5 wheat_soybean,CAN,7.5 wheat_soybean,CHE,7.5 -wheat_soybean,CHN,-1.5258830785751343 -wheat_soybean,CMR,-0.560064435005188 -wheat_soybean,COD,-1.3103182911872864 +wheat_soybean,CHN,-1.5014302730560303 +wheat_soybean,CMR,-0.5504315495491028 +wheat_soybean,COD,-1.306778609752655 wheat_soybean,CZE,7.5 wheat_soybean,DEU,7.5 wheat_soybean,DZA,-0.0 -wheat_soybean,ECU,-0.5827843546867371 -wheat_soybean,EGY,-1.3969708681106567 -wheat_soybean,ESP,0.3297085464000702 -wheat_soybean,ETH,-0.7914422750473022 +wheat_soybean,ECU,-0.5678057670593262 +wheat_soybean,EGY,-1.3718816041946411 +wheat_soybean,ESP,0.36447542905807495 +wheat_soybean,ETH,-0.7737743854522705 wheat_soybean,FRA,7.5 -wheat_soybean,GEO,0.3007889986038208 -wheat_soybean,GRC,3.660923033952713 -wheat_soybean,GTM,3.933987408876419 -wheat_soybean,HND,-0.6024420857429504 +wheat_soybean,GEO,0.36991259455680847 +wheat_soybean,GRC,3.6710087656974792 +wheat_soybean,GTM,3.95209302008152 +wheat_soybean,HND,-0.5721775144338608 wheat_soybean,HRV,7.5 -wheat_soybean,HUN,0.4543370921164751 -wheat_soybean,IND,-0.7040027379989624 -wheat_soybean,IRN,-0.13888409361243248 -wheat_soybean,IRQ,-0.3938130885362625 +wheat_soybean,HUN,0.4904110748320818 +wheat_soybean,IND,-0.6403534412384033 +wheat_soybean,IRN,-0.10655276663601398 +wheat_soybean,IRQ,-0.3719736337661743 wheat_soybean,ISR,7.5 wheat_soybean,ITA,7.5 wheat_soybean,JOR,-0.0 -wheat_soybean,JPN,1.895431399345398 +wheat_soybean,JPN,1.9145756959915161 wheat_soybean,KAZ,7.5 -wheat_soybean,KEN,-0.7460104525089264 -wheat_soybean,KGZ,-0.14488598704338074 -wheat_soybean,KOR,3.511589601635933 +wheat_soybean,KEN,-0.7405359447002411 +wheat_soybean,KGZ,-0.12983660399913788 +wheat_soybean,KOR,3.5172261744737625 wheat_soybean,LAO,-0.0 wheat_soybean,LBN,7.5 wheat_soybean,LSO,7.5 wheat_soybean,LUX,7.5 -wheat_soybean,MAR,-1.6643573641777039 +wheat_soybean,MAR,-1.654431939125061 wheat_soybean,MDA,7.5 -wheat_soybean,MEX,-0.2823370099067688 +wheat_soybean,MEX,-0.2334054857492447 wheat_soybean,MKD,7.5 -wheat_soybean,MMR,3.9234370440244675 +wheat_soybean,MMR,3.9401015490293503 wheat_soybean,MNE,7.5 wheat_soybean,MOZ,7.5 -wheat_soybean,MWI,3.3407569527626038 +wheat_soybean,MWI,3.3445391058921814 wheat_soybean,NGA,7.5 wheat_soybean,NLD,7.5 wheat_soybean,NPL,7.5 -wheat_soybean,PAK,-0.5933698117733002 -wheat_soybean,PER,-0.6185394525527954 +wheat_soybean,PAK,-0.5832934975624084 +wheat_soybean,PER,0.11377611011266708 wheat_soybean,POL,7.5 wheat_soybean,PRT,7.5 -wheat_soybean,PRY,-0.1657344251871109 +wheat_soybean,PRY,-0.1451331228017807 wheat_soybean,ROU,7.5 wheat_soybean,RUS,7.5 -wheat_soybean,RWA,-0.46984025835990906 +wheat_soybean,RWA,-0.46616190671920776 wheat_soybean,SRB,7.5 wheat_soybean,SVK,7.5 wheat_soybean,SVN,7.5 -wheat_soybean,SWZ,-0.5079210996627808 +wheat_soybean,SWZ,-0.4930545687675476 wheat_soybean,SYR,7.5 wheat_soybean,THA,7.5 wheat_soybean,TJK,7.5 -wheat_soybean,TKM,4.363159656524658 +wheat_soybean,TKM,4.387307524681091 wheat_soybean,TUR,7.5 -wheat_soybean,TWN,-0.37154558300971985 -wheat_soybean,TZA,-0.4255504310131073 -wheat_soybean,UGA,-1.1973188519477844 +wheat_soybean,TWN,-0.35517722368240356 +wheat_soybean,TZA,-0.41791167855262756 +wheat_soybean,UGA,-0.5605721175670624 wheat_soybean,UKR,7.5 -wheat_soybean,URY,0.18255292624235153 -wheat_soybean,USA,-0.017747722566127777 +wheat_soybean,URY,0.21173527091741562 +wheat_soybean,USA,0.017880627885460854 wheat_soybean,UZB,7.5 -wheat_soybean,VNM,-0.641160249710083 -wheat_soybean,ZAF,-0.4227421283721924 -wheat_soybean,ZMB,-0.37150371074676514 -wheat_soybean,ZWE,4.450417995452881 +wheat_soybean,VNM,-0.6248818635940552 +wheat_soybean,ZAF,-0.41188329458236694 +wheat_soybean,ZMB,-0.3624002933502197 +wheat_soybean,ZWE,4.464335918426514 diff --git a/data/curated/calibration/gbd-anchored/provenance.yaml b/data/curated/calibration/gbd-anchored/provenance.yaml index e5ea749c..7bfc6af1 100644 --- a/data/curated/calibration/gbd-anchored/provenance.yaml +++ b/data/curated/calibration/gbd-anchored/provenance.yaml @@ -4,8 +4,8 @@ # tools/calibrate; do not edit by hand. # Licensing: see the annotation in REUSE.toml. base_config: config/gbd_anchored.yaml -generated_at: '2026-07-31T21:28:15+00:00' -git_commit: 5496ce53b867cc2b979ba4db94628e9bc36842d5 +generated_at: '2026-07-31T22:28:16+00:00' +git_commit: 4492a036cad165a3624d950ac1aebd8df1170d17 source: gbd-anchored structural_config: aggregation.irrigated_area_source: current @@ -1007,7 +1007,6 @@ structural_config: numerics.min_link_area_mha: 1.0e-06 numerics.min_water_capacity_mm3: 1.0e-06 numerics.min_water_requirement_m3_per_ha: 0.1 - optimal_taxes.enabled: false residues.max_feed_fraction: 0.3 residues.max_feed_fraction_by_region.Asia: 0.7 residues.max_feed_fraction_by_region.Southern Asia: 0.85 diff --git a/data/curated/calibration/tutorial-02/exogenous_feed.csv b/data/curated/calibration/tutorial-02/exogenous_feed.csv new file mode 100644 index 00000000..c518c271 --- /dev/null +++ b/data/curated/calibration/tutorial-02/exogenous_feed.csv @@ -0,0 +1,110 @@ +# SPDX-FileCopyrightText: 2026 Koen van Greevenbroek +# +# SPDX-License-Identifier: CC-BY-4.0 +country,monogastric_protein_mt_dm,ruminant_protein_mt_dm,ruminant_roughage_mt_dm +AFG,0.0,0.0,6.400424 +AGO,0.0,0.0,0.935985 +ARE,0.0,0.0,1.420038 +ARM,0.0,0.0,0.921979 +ATG,0.0,0.0,0.001203 +AUS,0.179767,0.358525,0.0 +AZE,0.0,0.0,1.662367 +BFA,0.0,0.0,2.424631 +BGD,0.0,0.0,9.329955 +BHS,0.0,0.0,0.000241 +BOL,0.0,0.0,3.081205 +BRA,0.0,0.0,4.951871 +BRB,0.0,0.0,0.001186 +BRN,0.030088,0.002684,0.098271 +BTN,0.0,0.0,0.031303 +BWA,0.0,0.0,0.801495 +CAF,0.0,0.0,3.132843 +CHL,0.0,0.0,1.179778 +CHN,38.363338,8.937976,0.0 +CMR,0.0,0.0,2.125884 +COG,0.0,0.0,0.101986 +COL,0.0,0.0,5.010691 +COM,0.0,0.0,0.023564 +CPV,0.0,0.0,0.014816 +CRI,0.0,0.0,0.477426 +CUB,0.0,0.0,0.474619 +CYP,0.0,0.0,0.026106 +DJI,0.0,0.0,0.253112 +DOM,0.0,0.0,0.733529 +DZA,0.0,0.0,6.97226 +ECU,0.0,0.0,0.998855 +EGY,0.0,0.0,7.580072 +ERI,0.0,0.0,0.646949 +ETH,0.0,0.0,3.717979 +GEO,0.0,0.0,0.295351 +GNB,0.0,0.0,0.082731 +GRD,0.0,0.0,0.003329 +GTM,0.0,0.0,0.901118 +HND,0.0,0.0,0.341193 +HTI,0.0,0.0,0.566936 +IDN,5.012152,2.778974,0.0 +IND,0.0,0.0,294.066833 +IRL,0.0,0.0,0.339501 +IRN,0.0,0.035029,0.0 +ISL,0.0,0.0,0.030581 +ISR,0.0,0.0,1.114146 +JAM,0.0,0.0,0.181489 +JOR,0.0,0.0,0.909003 +JPN,1.832012,1.221458,1.343619 +KEN,0.0,0.0,8.626934 +KHM,0.076918,0.208704,0.0 +KOR,2.71836,0.374992,2.659726 +LAO,0.1049,0.071492,0.0 +LBN,0.0,0.0,0.255603 +LBY,0.0,0.0,0.79385 +LKA,0.038487,0.18201,0.0 +LSO,0.0,0.0,0.093317 +MAR,0.0,0.0,5.57414 +MEX,0.0,0.0,2.844587 +MLT,0.0,0.0,0.002164 +MMR,0.0,0.277599,0.0 +MNE,0.0,0.0,0.002312 +MNG,0.0,0.001171,15.392928 +MRT,0.0,0.0,2.123055 +MYS,0.788346,0.24535,0.234641 +NAM,0.0,0.0,0.689191 +NER,0.0,0.0,1.688753 +NIC,0.0,0.0,1.29114 +NLD,0.0,0.0,0.127492 +NPL,0.0,0.62717,15.80045 +NZL,0.113223,1.103938,0.0 +OMN,0.0,0.0,1.601247 +PAK,0.0,9.06523,166.422043 +PAN,0.0,0.0,0.335523 +PER,0.0,0.0,0.90663 +PHL,1.604549,0.297524,0.0 +PRI,0.0,0.0,0.093587 +PRY,0.0,0.0,0.124571 +PSE,0.0,0.0,0.253437 +RWA,0.0,0.0,0.379605 +SAU,0.0,0.0,3.202792 +SDN,0.0,0.0,11.667519 +SEN,0.0,0.0,0.880413 +SOM,0.0,0.0,5.312761 +SSD,0.0,0.0,3.61814 +STP,0.0,0.0,0.002251 +SWZ,0.0,0.0,0.169016 +SYR,0.0,0.0,1.271458 +TCD,0.0,0.0,25.921246 +THA,2.212016,0.336465,0.0 +TJK,0.0,0.0,0.245539 +TKM,0.0,0.0,0.704252 +TLS,0.012384,0.01037,0.066588 +TTO,0.0,0.0,0.00687 +TUN,0.0,0.0,1.232322 +TUR,0.0,0.0,1.70279 +TWN,1.287019,0.00074,0.0 +TZA,0.0,0.0,9.756378 +UGA,0.0,0.0,3.548836 +URY,0.0,0.0,2.438097 +VEN,0.0,0.0,3.76209 +VNM,2.279286,0.832676,0.0 +YEM,0.0,0.0,3.184532 +ZAF,0.0,0.0,5.651819 +ZMB,0.0,0.0,2.971979 +ZWE,0.0,0.0,10.745337 diff --git a/data/curated/calibration/tutorial-02/exogenous_forage.csv b/data/curated/calibration/tutorial-02/exogenous_forage.csv new file mode 100644 index 00000000..f1b36945 --- /dev/null +++ b/data/curated/calibration/tutorial-02/exogenous_forage.csv @@ -0,0 +1,178 @@ +# SPDX-FileCopyrightText: 2026 Koen van Greevenbroek +# +# SPDX-License-Identifier: CC-BY-4.0 +country,exogenous_forage_mt_dm +AFG,0.0 +AGO,0.0 +ALB,0.0 +ARE,1.955591 +ARG,0.0 +ARM,0.0 +ATG,0.0 +AUS,0.0 +AUT,0.0 +AZE,0.0 +BDI,0.0 +BEL,0.0 +BEN,0.0 +BFA,0.0 +BGD,0.0 +BGR,0.0 +BHS,0.0 +BIH,0.0 +BLR,0.0 +BLZ,0.0 +BOL,0.0 +BRA,0.0 +BRB,0.0 +BRN,0.073643 +BTN,0.0 +BWA,0.0 +CAF,0.0 +CAN,0.0 +CHE,0.0 +CHL,0.0 +CHN,0.0 +CIV,0.0 +CMR,0.0 +COD,0.0 +COG,0.0 +COL,0.0 +COM,0.0 +CPV,0.0 +CRI,0.0 +CUB,0.0 +CYP,0.0 +CZE,0.0 +DEU,0.0 +DJI,0.0 +DNK,0.0 +DOM,0.0 +DZA,0.0 +ECU,0.0 +EGY,1.05823 +ERI,0.0 +ESP,0.0 +EST,0.0 +ETH,0.0 +FIN,0.0 +FJI,0.0 +FRA,0.0 +GAB,0.0 +GBR,0.0 +GEO,0.0 +GHA,0.0 +GIN,0.0 +GMB,0.0 +GNB,0.0 +GNQ,0.0 +GRC,0.0 +GRD,0.0 +GTM,0.0 +GUF,0.0 +GUY,0.0 +HND,0.0 +HRV,0.0 +HTI,0.0 +HUN,0.0 +IDN,0.0 +IND,160.410477 +IRL,0.0 +IRN,0.0 +IRQ,0.0 +ISL,0.0 +ISR,0.0 +ITA,0.0 +JAM,0.0 +JOR,0.0 +JPN,0.0 +KAZ,0.0 +KEN,0.0 +KGZ,0.0 +KHM,0.0 +KOR,0.0 +LAO,0.0 +LBN,0.0 +LBR,0.0 +LBY,0.0 +LKA,0.0 +LSO,0.0 +LTU,0.0 +LUX,0.0 +LVA,0.0 +MAR,0.0 +MDA,0.0 +MDG,0.0 +MEX,0.0 +MKD,0.0 +MLI,0.0 +MLT,0.0 +MMR,8.542 +MNE,0.0 +MNG,0.0 +MOZ,0.0 +MRT,0.0 +MUS,0.0 +MWI,0.0 +MYS,0.0 +NAM,0.0 +NER,0.0 +NGA,0.0 +NIC,0.0 +NLD,0.0 +NOR,0.0 +NPL,2.530804 +NZL,0.0 +OMN,1.702735 +PAK,70.872116 +PAN,0.0 +PER,0.0 +PHL,0.0 +PNG,0.0 +POL,0.0 +PRI,0.0 +PRT,0.0 +PRY,0.0 +PSE,0.0 +ROU,0.0 +RUS,0.0 +RWA,0.0 +SAU,2.720182 +SDN,0.0 +SEN,0.0 +SLB,0.0 +SLE,0.0 +SLV,0.0 +SOM,0.0 +SRB,0.0 +SSD,0.0 +STP,0.0 +SUR,0.0 +SVK,0.0 +SVN,0.0 +SWE,0.0 +SWZ,0.0 +SYR,0.0 +TCD,0.0 +TGO,0.0 +THA,0.0 +TJK,8.196243 +TKM,9.817931 +TLS,0.0 +TTO,0.0 +TUN,0.0 +TUR,0.0 +TWN,0.0 +TZA,0.0 +UGA,0.0 +UKR,0.0 +URY,0.0 +USA,0.0 +UZB,14.487753 +VEN,0.0 +VNM,0.0 +VUT,0.0 +YEM,0.0 +ZAF,0.0 +ZMB,0.0 +ZWE,0.0 diff --git a/data/curated/calibration/tutorial-02/fingerprint.yaml b/data/curated/calibration/tutorial-02/fingerprint.yaml new file mode 100644 index 00000000..8c392767 --- /dev/null +++ b/data/curated/calibration/tutorial-02/fingerprint.yaml @@ -0,0 +1,751 @@ +# Content fingerprint of each calibration step's external inputs, written by +# workflow/scripts/calibration_fingerprint.py via tools/calibrate. Compared by +# `tools/calibrate --check`; do not edit by hand. +# Licensing: see the annotation in REUSE.toml. +steps: + feed: + artefacts: + data/curated/calibration/tutorial-02/exogenous_feed.csv: d33d7b202163bd1a30598b50a30bb0692917cb7aa3b031ea27bdfd7aea316e66 + data/curated/calibration/tutorial-02/exogenous_forage.csv: 7b7d1085f1025f8f732d3179fa79d5f924b8165c10fabfa264feff404dc50af4 + data/curated/calibration/tutorial-02/fodder_conversion.csv: 0fcfedb0662c452fcdbc9ebc125ff507ed79b60f7127d85d8bd43784476f8968 + data/curated/calibration/tutorial-02/grassland_yield.csv: 506a009134a67b9b15cc880ea9d64d28e5d5f99665778ba590945974788b3e8d + code: + workflow/rules/animals.smk: fa07f649a53263c151e1799f63b40c66fba42ee7858a97d51ff988c406ba0024 + workflow/rules/crops.smk: 8d7d87b61959819ae9a753126767a71a10d7463af7963bffd423fa4560f470d1 + workflow/rules/diet.smk: 448908aecc357fd119c1e5cd43acdec2075f2d5a4d9ebbefefa1299c1afb15f4 + workflow/rules/geography.smk: 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file mode 100644 index 00000000..fa45c320 --- /dev/null +++ b/data/curated/calibration/tutorial-02/fodder_conversion.csv @@ -0,0 +1,178 @@ +# SPDX-FileCopyrightText: 2026 Koen van Greevenbroek +# +# SPDX-License-Identifier: CC-BY-4.0 +country,fodder_conversion_correction +AFG,1.0 +AGO,1.0 +ALB,1.0 +ARE,1.0 +ARG,1.0 +ARM,1.0 +ATG,1.0 +AUS,1.0 +AUT,1.0 +AZE,1.0 +BDI,1.0 +BEL,1.0 +BEN,1.0 +BFA,1.0 +BGD,1.0 +BGR,1.0 +BHS,1.0 +BIH,1.0 +BLR,1.0 +BLZ,1.0 +BOL,1.0 +BRA,1.0 +BRB,1.0 +BRN,1.0 +BTN,1.0 +BWA,1.0 +CAF,1.0 +CAN,1.0 +CHE,1.0 +CHL,1.0 +CHN,1.0 +CIV,1.0 +CMR,1.0 +COD,1.0 +COG,1.0 +COL,1.0 +COM,1.0 +CPV,1.0 +CRI,1.0 +CUB,1.0 +CYP,1.0 +CZE,1.0 +DEU,1.0 +DJI,1.0 +DNK,1.0 +DOM,1.0 +DZA,1.0 +ECU,1.0 +EGY,1.0 +ERI,1.0 +ESP,1.0 +EST,0.475539 +ETH,1.0 +FIN,1.0 +FJI,1.0 +FRA,1.0 +GAB,1.0 +GBR,1.0 +GEO,1.0 +GHA,1.0 +GIN,1.0 +GMB,1.0 +GNB,1.0 +GNQ,1.0 +GRC,1.0 +GRD,1.0 +GTM,1.0 +GUF,0.0 +GUY,1.0 +HND,1.0 +HRV,1.0 +HTI,1.0 +HUN,0.593952 +IDN,1.0 +IND,1.0 +IRL,1.0 +IRN,1.0 +IRQ,1.0 +ISL,1.0 +ISR,1.0 +ITA,1.0 +JAM,1.0 +JOR,1.0 +JPN,0.757445 +KAZ,1.0 +KEN,1.0 +KGZ,1.0 +KHM,1.0 +KOR,1.0 +LAO,1.0 +LBN,1.0 +LBR,1.0 +LBY,1.0 +LKA,1.0 +LSO,1.0 +LTU,1.0 +LUX,1.0 +LVA,0.295377 +MAR,1.0 +MDA,0.855621 +MDG,1.0 +MEX,1.0 +MKD,1.0 +MLI,1.0 +MLT,1.0 +MMR,1.0 +MNE,1.0 +MNG,1.0 +MOZ,1.0 +MRT,1.0 +MUS,1.0 +MWI,1.0 +MYS,1.0 +NAM,1.0 +NER,1.0 +NGA,1.0 +NIC,1.0 +NLD,1.0 +NOR,1.0 +NPL,1.0 +NZL,1.0 +OMN,1.0 +PAK,1.0 +PAN,1.0 +PER,1.0 +PHL,1.0 +PNG,0.977939 +POL,1.0 +PRI,1.0 +PRT,1.0 +PRY,1.0 +PSE,1.0 +ROU,0.754508 +RUS,1.0 +RWA,1.0 +SAU,1.0 +SDN,1.0 +SEN,1.0 +SLB,1.0 +SLE,1.0 +SLV,1.0 +SOM,1.0 +SRB,1.0 +SSD,1.0 +STP,1.0 +SUR,1.0 +SVK,0.953835 +SVN,1.0 +SWE,1.0 +SWZ,1.0 +SYR,1.0 +TCD,1.0 +TGO,1.0 +THA,1.0 +TJK,1.0 +TKM,1.0 +TLS,1.0 +TTO,1.0 +TUN,1.0 +TUR,1.0 +TWN,1.0 +TZA,1.0 +UGA,1.0 +UKR,1.0 +URY,1.0 +USA,1.0 +UZB,1.0 +VEN,1.0 +VNM,1.0 +VUT,1.0 +YEM,1.0 +ZAF,1.0 +ZMB,1.0 +ZWE,1.0 diff --git a/data/curated/calibration/tutorial-02/food_demand.csv b/data/curated/calibration/tutorial-02/food_demand.csv new file mode 100644 index 00000000..4e5986d4 --- /dev/null +++ b/data/curated/calibration/tutorial-02/food_demand.csv @@ -0,0 +1,60 @@ +food,multiplier,net_slack_mt,consumption_mt +apple,1.0263250118126332,-2.6487680513237137,100.6179245113417 +banana,1.0,0.0,64.24033005723322 +barley-hulled,1.0,0.0,2.5509975362103887 +buckwheat,0.5161261748131606,2.5232581936336373,5.21470280534254 +cabbage,0.9964736419747056,0.8096190254727844,229.59070510294987 +carrot,1.0044122336364383,-0.5395181532949209,122.27778439458052 +cassava,1.0,0.0,157.42911003598238 +chickpea,0.7334505332274995,3.1647809596690877,11.873146842083997 +citrus,1.073033200667495,-9.496670365333557,130.0322357302939 +cocoa-powder,0.9014653617552157,0.5217663333751261,5.295258019610628 +coconut,1.0,0.0,7.4685584381240915 +coconut-oil,1.0,0.0,0.959177754493794 +coffee-green,0.8670796007508681,1.5561736629824736,11.707560854265466 +cottonseed-oil,1.0,0.0,3.4216795019938218 +cowpea,1.0,0.0,8.023896933055994 +dairy,1.1191858493138949,-83.19951213803142,698.0653543770313 +dairy-buffalo,1.1373173697611507,-16.630460658474476,121.1096650584077 +dry-pea,1.0,0.0,6.17437933254223 +eggs,1.0,0.0,82.1042355971731 +flour-white,1.0,0.0,275.2358510554768 +flour-wholemeal,1.0,0.0,38.87509628526459 +foxtail-millet,1.0,0.0,3.234954914529226 +gram,0.7334505332274995,3.1647809596690877,11.873146842083997 +groundnut,1.0,0.0,20.996472433620966 +groundnut-oil,1.0,0.0,3.0808910877995004 +maize,1.0,0.0,64.77057215915829 +maize-whole,1.0,0.0,20.808036830817628 +mango,0.8356771966491361,9.887926888011862,60.17379624968453 +meat-cattle,0.952400559021778,2.308293253183365,48.49412526166998 +meat-chicken,0.9895527158773688,0.8225271608680487,78.7311947500566 +meat-pig,1.0184832063942477,-1.3977279365062714,75.62150779971125 +meat-sheep,1.0872487700948157,-0.8913660884136334,10.21637425312656 +oat,1.0,0.0,1.1316055530789981 +olive-oil,1.1941383326246193,-0.7986921111587435,4.114036112090616 +onion,0.9923317363251919,2.1338261844357476,278.2671899306006 +palm-oil,1.0,0.0,23.706909050333707 +pearl-millet,1.0,0.0,3.234954914529226 +phaseolus-bean,1.0,0.0,21.57145358827256 +pigeon-pea,1.0,0.0,0.6679289132182475 +plantain,0.8922109768490607,2.7378398588425625,25.39998766858389 +potato,1.0,0.0,226.71684613649268 +rapeseed-oil,1.0,0.0,10.808916135382105 +rendered-fat,1.0,0.0,7.44342613070603 +rice-brown,1.0,0.0,35.70415660064464 +rice-white,1.0,0.0,340.82733609573916 +rye,1.0,0.0,0.809679446935661 +sesame-oil,0.8268496976483677,0.19177997224687715,1.1075924768379082 +sesame-seed,1.0,0.0,2.9894916711275528 +sorghum,1.0,0.0,11.638505917195289 +soy,1.0,0.0,11.041815498637334 +soybean-oil,1.0,0.0,27.65591717805819 +sugar,1.0,0.0,137.2949263714254 +sunflower-oil,0.9317219685796627,0.8892538040236104,13.02401058620353 +sunflower-seed,1.0,0.0,4.64019518993814 +sweet-potato,1.0,0.0,45.0004243637195 +tea-dried,0.982554400446164,0.11642721491807606,6.673729645048297 +tomato,1.0193611262739315,-2.403931736946106,124.16280452562569 +watermelon,1.145961136697393,-8.558491706848145,58.6354141965312 +yam,1.0136933459237951,-0.47474855883046985,34.67001866983378 diff --git a/data/curated/calibration/tutorial-02/food_waste.yaml b/data/curated/calibration/tutorial-02/food_waste.yaml new file mode 100644 index 00000000..c239d0bc --- /dev/null +++ b/data/curated/calibration/tutorial-02/food_waste.yaml @@ -0,0 +1,35 @@ +# SPDX-FileCopyrightText: 2026 Koen van Greevenbroek +# +# SPDX-License-Identifier: CC-BY-4.0 +# +# Per-food-group waste-retention multipliers, applied by +# prepare_food_loss_waste.py as: +# new_waste = 1 - (1 - sdg_waste) * waste_retention_multiplier +# Generated by workflow/scripts/compute_food_waste_calibration.py +# from a solved validation scenario. Do not hand-edit; rerun +# `tools/calibrate food_waste` instead. +fruits: + baseline_consumption_mt: 344.04602138686823 + baseline_negative_slack_mt: 69.65368572960142 + baseline_positive_slack_mt: 0.0 + waste_retention_multiplier: 0.8316322575737486 +oil: + baseline_consumption_mt: 88.23545169174031 + baseline_negative_slack_mt: 0.7820110784377903 + baseline_positive_slack_mt: 1.1383328779193107 + waste_retention_multiplier: 1.004054680558608 +starchy_vegetable: + baseline_consumption_mt: 491.43520556430417 + baseline_negative_slack_mt: 0.47466863691806793 + baseline_positive_slack_mt: 2.8618225902173435 + waste_retention_multiplier: 1.0048812257720598 +stimulants: + baseline_consumption_mt: 23.68795094791676 + baseline_negative_slack_mt: 0.0 + baseline_positive_slack_mt: 2.2057696664633113 + waste_retention_multiplier: 1.1026790360608145 +vegetables: + baseline_consumption_mt: 717.5079036276238 + baseline_negative_slack_mt: 36.790590174961835 + baseline_positive_slack_mt: 0.0 + waste_retention_multiplier: 0.9512254227242424 diff --git a/data/curated/calibration/tutorial-02/grassland_yield.csv b/data/curated/calibration/tutorial-02/grassland_yield.csv new file mode 100644 index 00000000..e3fe61da --- /dev/null +++ b/data/curated/calibration/tutorial-02/grassland_yield.csv @@ -0,0 +1,178 @@ +# SPDX-FileCopyrightText: 2026 Koen van Greevenbroek +# +# SPDX-License-Identifier: CC-BY-4.0 +country,yield_correction +AFG,0.388259 +AGO,0.067688 +ALB,0.144069 +ARE,1.0 +ARG,0.339432 +ARM,0.550528 +ATG,1.0 +AUS,0.233412 +AUT,0.589618 +AZE,1.0 +BDI,0.442213 +BEL,0.933014 +BEN,1.0 +BFA,1.0 +BGD,1.0 +BGR,0.072918 +BHS,0.669108 +BIH,0.014664 +BLR,0.421103 +BLZ,0.10203 +BOL,0.096857 +BRA,0.224921 +BRB,1.0 +BRN,1.0 +BTN,0.035824 +BWA,1.0 +CAF,0.580631 +CAN,0.056898 +CHE,0.409762 +CHL,0.531183 +CHN,0.274187 +CIV,0.032799 +CMR,1.0 +COD,0.021198 +COG,0.007568 +COL,0.103359 +COM,1.0 +CPV,1.0 +CRI,0.376304 +CUB,0.276615 +CYP,1.0 +CZE,0.232681 +DEU,0.157382 +DJI,1.0 +DNK,1.0 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+multi_crops,produce:multi_cotton_wheat_r:region0010_c1,-6.44092,0, +multi_crops,produce:multi_cotton_wheat_r:region0010_c2,-6.41636,0, +multi_crops,produce:multi_cotton_wheat_r:region0010_c3,-6.42822,0, +multi_crops,produce:multi_cotton_wheat_r:region0013_c1,-79.4123,0, +multi_crops,produce:multi_cotton_wheat_r:region0013_c2,-79.3351,0, +multi_crops,produce:multi_cotton_wheat_r:region0013_c3,-79.2369,0, +multi_crops,produce:multi_cotton_wheat_r:region0017_c0,-2.03153,0, +multi_crops,produce:multi_cotton_wheat_r:region0017_c1,-90.8394,0, +multi_crops,produce:multi_cotton_wheat_r:region0017_c2,-90.7463,0, +multi_crops,produce:multi_cotton_wheat_r:region0017_c3,-90.6251,0, +multi_crops,produce:multi_cotton_wheat_r:region0026_c1,-3.08816,0, +multi_crops,produce:multi_cotton_wheat_r:region0026_c2,-3.08786,0, +multi_crops,produce:multi_cotton_wheat_r:region0026_c3,-3.08332,0, +multi_crops,produce:multi_cotton_wheat_r:region0036_c0,-7.18247,0, +multi_crops,produce:multi_cotton_wheat_r:region0036_c1,-7.17988,0, +multi_crops,produce:multi_cotton_wheat_r:region0036_c2,-7.16615,0, +multi_crops,produce:multi_cotton_wheat_r:region0036_c3,-7.1563,0, +multi_crops,produce:multi_cotton_wheat_r:region0037_c2,-7.17047,0, +multi_crops,produce:multi_cotton_wheat_r:region0037_c3,-7.1316,0, +multi_crops,produce:multi_cotton_wheat_r:region0041_c0,-2.0508,0, +multi_crops,produce:multi_cotton_wheat_r:region0041_c1,-1.84504,0, +multi_crops,produce:multi_cotton_wheat_r:region0041_c2,-2.05248,0, +multi_crops,produce:multi_cotton_wheat_r:region0041_c3,-2.05284,0, +multi_crops,produce:multi_cotton_wheat_r:region0042_c3,-1.98834,0, +multi_crops,produce:multi_cotton_wheat_r:region0080_c2,-2.08303,0, +multi_crops,produce:multi_cotton_wheat_r:region0086_c0,-1.81549,0, +multi_crops,produce:multi_cotton_wheat_r:region0086_c1,-1.7915,0, +multi_crops,produce:multi_cotton_wheat_r:region0086_c2,-1.74686,0, +multi_crops,produce:multi_cotton_wheat_r:region0086_c3,-1.73202,0, +multi_crops,produce:multi_cotton_wheat_r:region0087_c1,-1.67043,0, +multi_crops,produce:multi_cotton_wheat_r:region0087_c2,-1.54049,0, +multi_crops,produce:multi_cotton_wheat_r:region0087_c3,-1.59755,0, +multi_crops,produce:multi_cotton_wheat_r:region0088_c0,-1.86977,0, +multi_crops,produce:multi_cotton_wheat_r:region0088_c1,-1.82242,0, +multi_crops,produce:multi_cotton_wheat_r:region0088_c2,-1.86899,0, +multi_crops,produce:multi_cotton_wheat_r:region0088_c3,-1.65915,0, +multi_crops,produce:multi_cotton_wheat_r:region0102_c1,-0.958704,0, +multi_crops,produce:multi_cotton_wheat_r:region0102_c2,-0.961711,0, +multi_crops,produce:multi_cotton_wheat_r:region0102_c3,-0.923464,0, +multi_crops,produce:multi_cotton_wheat_r:region0118_c0,-1.92026,0, +multi_crops,produce:multi_cotton_wheat_r:region0118_c1,-1.96209,0, +multi_crops,produce:multi_cotton_wheat_r:region0118_c2,-1.93934,0, +multi_crops,produce:multi_cotton_wheat_r:region0118_c3,-1.94014,0, +multi_crops,produce:multi_cotton_wheat_r:region0128_c0,-1.78797,0, +multi_crops,produce:multi_cotton_wheat_r:region0128_c1,-1.77924,0, +multi_crops,produce:multi_cotton_wheat_r:region0128_c2,-1.79577,0, +multi_crops,produce:multi_cotton_wheat_r:region0128_c3,-1.79419,0, +multi_crops,produce:multi_cotton_wheat_r:region0131_c0,-2.01081,0, +multi_crops,produce:multi_cotton_wheat_r:region0131_c1,-2.01663,0, +multi_crops,produce:multi_cotton_wheat_r:region0131_c2,-2.00901,0, +multi_crops,produce:multi_cotton_wheat_r:region0131_c3,-2.01729,0, +multi_crops,produce:multi_cotton_wheat_r:region0142_c1,-1.7516,0, +multi_crops,produce:multi_cotton_wheat_r:region0142_c2,-1.76937,0, +multi_crops,produce:multi_cotton_wheat_r:region0142_c3,-1.67362,0, +multi_crops,produce:multi_cotton_wheat_r:region0150_c0,25.8198,0, +multi_crops,produce:multi_cotton_wheat_r:region0150_c1,29.1634,0, +multi_crops,produce:multi_cotton_wheat_r:region0150_c2,33.0871,0, +multi_crops,produce:multi_cotton_wheat_r:region0150_c3,37.4095,0, +multi_crops,produce:multi_cotton_wheat_r:region0156_c1,-94.9736,0, +multi_crops,produce:multi_cotton_wheat_r:region0156_c3,-94.9623,0, +multi_crops,produce:multi_cotton_wheat_r:region0173_c0,-1.9719,0, +multi_crops,produce:multi_cotton_wheat_r:region0173_c1,-1.93696,0, +multi_crops,produce:multi_cotton_wheat_r:region0173_c2,-1.92542,0, +multi_crops,produce:multi_cotton_wheat_r:region0173_c3,-1.93397,0, +multi_crops,produce:multi_cotton_wheat_r:region0185_c0,-1.58235,0, +multi_crops,produce:multi_cotton_wheat_r:region0185_c1,-1.60483,0, +multi_crops,produce:multi_cotton_wheat_r:region0185_c2,-1.60621,0, +multi_crops,produce:multi_cotton_wheat_r:region0185_c3,-1.60196,0, +multi_crops,produce:multi_cotton_wheat_r:region0186_c0,13.1476,0, +multi_crops,produce:multi_cotton_wheat_r:region0186_c1,16.5363,0, +multi_crops,produce:multi_cotton_wheat_r:region0186_c2,17.3059,0, +multi_crops,produce:multi_cotton_wheat_r:region0186_c3,17.3338,0, +multi_crops,produce:multi_cotton_wheat_r:region0189_c1,-1.89631,0, +multi_crops,produce:multi_cotton_wheat_r:region0189_c2,-1.89487,0, +multi_crops,produce:multi_cotton_wheat_r:region0189_c3,-1.88541,0, +multi_crops,produce:multi_cotton_wheat_r:region0197_c3,52.5077,0, +multi_crops,produce:multi_cotton_wheat_r:region0198_c0,-1.5816,0, +multi_crops,produce:multi_cotton_wheat_r:region0198_c1,-1.57762,0, +multi_crops,produce:multi_cotton_wheat_r:region0198_c2,-1.62624,0, +multi_crops,produce:multi_cotton_wheat_r:region0198_c3,-1.66494,0, +multi_crops,produce:multi_double_rice_i:region0013_c0,-1.20567,0, +multi_crops,produce:multi_double_rice_i:region0013_c1,-1.15715,0, +multi_crops,produce:multi_double_rice_i:region0013_c2,-1.19888,0, +multi_crops,produce:multi_double_rice_i:region0015_c0,-16.4381,0, +multi_crops,produce:multi_double_rice_i:region0015_c1,-61.3009,0, +multi_crops,produce:multi_double_rice_i:region0015_c2,-30.3576,0, +multi_crops,produce:multi_double_rice_i:region0015_c3,-52.7858,0, +multi_crops,produce:multi_double_rice_i:region0016_c0,-80.0419,0, +multi_crops,produce:multi_double_rice_i:region0016_c1,-79.8141,0, +multi_crops,produce:multi_double_rice_i:region0016_c2,-79.778,0, +multi_crops,produce:multi_double_rice_i:region0016_c3,-79.6474,0, +multi_crops,produce:multi_double_rice_i:region0017_c0,-2.04166,0, +multi_crops,produce:multi_double_rice_i:region0017_c1,-1.86184,0, +multi_crops,produce:multi_double_rice_i:region0017_c2,-1.95688,0, +multi_crops,produce:multi_double_rice_i:region0017_c3,-2.14011,0, +multi_crops,produce:multi_double_rice_i:region0024_c2,-2.56805,0, +multi_crops,produce:multi_double_rice_i:region0029_c1,58.9307,0, +multi_crops,produce:multi_double_rice_i:region0029_c2,64.7297,0, +multi_crops,produce:multi_double_rice_i:region0029_c3,66.6657,0, +multi_crops,produce:multi_double_rice_i:region0036_c0,-4.73947,0, +multi_crops,produce:multi_double_rice_i:region0036_c1,-4.70827,0, +multi_crops,produce:multi_double_rice_i:region0036_c2,-4.65104,0, +multi_crops,produce:multi_double_rice_i:region0036_c3,-4.57141,0, +multi_crops,produce:multi_double_rice_i:region0038_c3,-1.00146,0, +multi_crops,produce:multi_double_rice_i:region0039_c0,-3.41266,0, +multi_crops,produce:multi_double_rice_i:region0039_c1,-3.32978,0, +multi_crops,produce:multi_double_rice_i:region0039_c2,-3.24504,0, +multi_crops,produce:multi_double_rice_i:region0039_c3,-3.23181,0, +multi_crops,produce:multi_double_rice_i:region0040_c0,-3.24261,0, +multi_crops,produce:multi_double_rice_i:region0040_c1,-3.28768,0, +multi_crops,produce:multi_double_rice_i:region0040_c2,-3.27423,0, +multi_crops,produce:multi_double_rice_i:region0041_c0,-3.41869,0, +multi_crops,produce:multi_double_rice_i:region0041_c3,-3.42584,0, +multi_crops,produce:multi_double_rice_i:region0044_c0,-3.72111,0, +multi_crops,produce:multi_double_rice_i:region0044_c1,-3.56271,0, +multi_crops,produce:multi_double_rice_i:region0044_c2,-3.43166,0, +multi_crops,produce:multi_double_rice_i:region0044_c3,-3.37588,0, +multi_crops,produce:multi_double_rice_i:region0047_c0,55.7385,0, +multi_crops,produce:multi_double_rice_i:region0047_c1,62.1494,0, +multi_crops,produce:multi_double_rice_i:region0047_c2,73.1768,0, +multi_crops,produce:multi_double_rice_i:region0047_c3,97.6307,0, +multi_crops,produce:multi_double_rice_i:region0054_c0,-3.18035,0, +multi_crops,produce:multi_double_rice_i:region0054_c1,-3.04067,0, +multi_crops,produce:multi_double_rice_i:region0054_c2,-2.97086,0, +multi_crops,produce:multi_double_rice_i:region0054_c3,-45.2526,0, +multi_crops,produce:multi_double_rice_i:region0058_c0,-1.8992,0, +multi_crops,produce:multi_double_rice_i:region0058_c1,-1.93712,0, +multi_crops,produce:multi_double_rice_i:region0058_c2,-2.01933,0, +multi_crops,produce:multi_double_rice_i:region0058_c3,-1.95341,0, +multi_crops,produce:multi_double_rice_i:region0070_c0,-1.53494,0, +multi_crops,produce:multi_double_rice_i:region0070_c1,-84.361,0, +multi_crops,produce:multi_double_rice_i:region0070_c2,-84.381,0, +multi_crops,produce:multi_double_rice_i:region0070_c3,-1.56916,0, +multi_crops,produce:multi_double_rice_i:region0071_c0,-1.53446,0, +multi_crops,produce:multi_double_rice_i:region0071_c1,-1.52638,0, +multi_crops,produce:multi_double_rice_i:region0071_c2,-79.4361,0, +multi_crops,produce:multi_double_rice_i:region0071_c3,-1.49831,0, +multi_crops,produce:multi_double_rice_i:region0072_c0,-0.632257,0, +multi_crops,produce:multi_double_rice_i:region0072_c1,-0.625688,0, +multi_crops,produce:multi_double_rice_i:region0072_c3,-0.668054,0, +multi_crops,produce:multi_double_rice_i:region0073_c0,-3.18383,0, +multi_crops,produce:multi_double_rice_i:region0073_c1,-3.07697,0, +multi_crops,produce:multi_double_rice_i:region0073_c2,-94.1849,0, +multi_crops,produce:multi_double_rice_i:region0073_c3,-3.0152,0, +multi_crops,produce:multi_double_rice_i:region0082_c0,-3.3011,0, +multi_crops,produce:multi_double_rice_i:region0082_c1,-3.24324,0, +multi_crops,produce:multi_double_rice_i:region0082_c2,-3.22609,0, +multi_crops,produce:multi_double_rice_i:region0084_c0,-3.60353,0, +multi_crops,produce:multi_double_rice_i:region0084_c1,-3.62703,0, +multi_crops,produce:multi_double_rice_i:region0084_c2,-3.56715,0, +multi_crops,produce:multi_double_rice_i:region0084_c3,-3.5718,0, +multi_crops,produce:multi_double_rice_i:region0085_c0,-3.50802,0, +multi_crops,produce:multi_double_rice_i:region0085_c1,-3.53454,0, +multi_crops,produce:multi_double_rice_i:region0085_c2,-3.54747,0, +multi_crops,produce:multi_double_rice_i:region0085_c3,-3.6469,0, +multi_crops,produce:multi_double_rice_i:region0086_c0,-2.54804,0, +multi_crops,produce:multi_double_rice_i:region0086_c1,-2.42054,0, +multi_crops,produce:multi_double_rice_i:region0086_c2,-2.34244,0, +multi_crops,produce:multi_double_rice_i:region0086_c3,-2.26798,0, +multi_crops,produce:multi_double_rice_i:region0087_c0,-2.75357,0, +multi_crops,produce:multi_double_rice_i:region0087_c1,-2.73824,0, +multi_crops,produce:multi_double_rice_i:region0087_c2,-2.72138,0, +multi_crops,produce:multi_double_rice_i:region0087_c3,-76.5226,0, +multi_crops,produce:multi_double_rice_i:region0088_c0,-2.71471,0, +multi_crops,produce:multi_double_rice_i:region0088_c1,-2.53992,0, +multi_crops,produce:multi_double_rice_i:region0088_c2,-2.48651,0, +multi_crops,produce:multi_double_rice_i:region0088_c3,-2.41847,0, +multi_crops,produce:multi_double_rice_i:region0098_c0,68.3581,0, +multi_crops,produce:multi_double_rice_i:region0098_c3,83.1997,0, +multi_crops,produce:multi_double_rice_i:region0102_c3,-3.42602,0, +multi_crops,produce:multi_double_rice_i:region0104_c0,-3.54535,0, +multi_crops,produce:multi_double_rice_i:region0104_c1,-3.53876,0, +multi_crops,produce:multi_double_rice_i:region0104_c2,-3.53641,0, +multi_crops,produce:multi_double_rice_i:region0104_c3,-3.46726,0, +multi_crops,produce:multi_double_rice_i:region0106_c0,-3.52103,0, +multi_crops,produce:multi_double_rice_i:region0106_c1,-3.55729,0, +multi_crops,produce:multi_double_rice_i:region0106_c2,-3.58295,0, +multi_crops,produce:multi_double_rice_i:region0106_c3,-3.54996,0, +multi_crops,produce:multi_double_rice_i:region0108_c1,-3.24877,0, +multi_crops,produce:multi_double_rice_i:region0108_c2,-80.5759,0, +multi_crops,produce:multi_double_rice_i:region0108_c3,-3.21706,0, +multi_crops,produce:multi_double_rice_i:region0111_c0,-2.38173,0, +multi_crops,produce:multi_double_rice_i:region0111_c1,-2.34703,0, +multi_crops,produce:multi_double_rice_i:region0111_c2,-2.20359,0, +multi_crops,produce:multi_double_rice_i:region0111_c3,-2.09135,0, +multi_crops,produce:multi_double_rice_i:region0118_c0,-1.42638,0, +multi_crops,produce:multi_double_rice_i:region0118_c1,-1.42158,0, +multi_crops,produce:multi_double_rice_i:region0118_c2,-1.34789,0, +multi_crops,produce:multi_double_rice_i:region0118_c3,-1.2932,0, +multi_crops,produce:multi_double_rice_i:region0122_c0,-1.03724,0, +multi_crops,produce:multi_double_rice_i:region0122_c1,-1.08324,0, +multi_crops,produce:multi_double_rice_i:region0122_c2,-1.00711,0, +multi_crops,produce:multi_double_rice_i:region0122_c3,-83.8266,0, +multi_crops,produce:multi_double_rice_i:region0124_c0,-3.64053,0, +multi_crops,produce:multi_double_rice_i:region0124_c1,-3.57219,0, +multi_crops,produce:multi_double_rice_i:region0124_c2,-3.68833,0, +multi_crops,produce:multi_double_rice_i:region0124_c3,-3.67955,0, +multi_crops,produce:multi_double_rice_i:region0128_c1,-0.914614,0, +multi_crops,produce:multi_double_rice_i:region0128_c3,-0.874536,0, +multi_crops,produce:multi_double_rice_i:region0129_c0,-2.8228,0, +multi_crops,produce:multi_double_rice_i:region0129_c1,-2.77612,0, +multi_crops,produce:multi_double_rice_i:region0129_c3,-2.75305,0, +multi_crops,produce:multi_double_rice_i:region0131_c0,-2.53642,0, +multi_crops,produce:multi_double_rice_i:region0131_c1,-2.50934,0, +multi_crops,produce:multi_double_rice_i:region0131_c2,-2.50979,0, +multi_crops,produce:multi_double_rice_i:region0131_c3,-2.50123,0, +multi_crops,produce:multi_double_rice_i:region0132_c0,-1.52616,0, +multi_crops,produce:multi_double_rice_i:region0132_c1,-1.51758,0, +multi_crops,produce:multi_double_rice_i:region0132_c2,-1.45231,0, +multi_crops,produce:multi_double_rice_i:region0132_c3,-1.40045,0, +multi_crops,produce:multi_double_rice_i:region0134_c0,-3.63717,0, +multi_crops,produce:multi_double_rice_i:region0134_c1,-3.57325,0, +multi_crops,produce:multi_double_rice_i:region0134_c3,-3.45591,0, +multi_crops,produce:multi_double_rice_i:region0135_c0,-1.4198,0, +multi_crops,produce:multi_double_rice_i:region0135_c1,-1.376,0, +multi_crops,produce:multi_double_rice_i:region0135_c2,-1.36364,0, +multi_crops,produce:multi_double_rice_i:region0135_c3,-1.3185,0, +multi_crops,produce:multi_double_rice_i:region0136_c0,-3.55837,0, +multi_crops,produce:multi_double_rice_i:region0136_c1,-78.3979,0, +multi_crops,produce:multi_double_rice_i:region0136_c2,-77.5166,0, +multi_crops,produce:multi_double_rice_i:region0136_c3,-76.3858,0, +multi_crops,produce:multi_double_rice_i:region0139_c0,54.331,0, +multi_crops,produce:multi_double_rice_i:region0139_c1,60.7849,0, +multi_crops,produce:multi_double_rice_i:region0139_c2,64.8285,0, +multi_crops,produce:multi_double_rice_i:region0139_c3,66.668,0, +multi_crops,produce:multi_double_rice_i:region0142_c2,-3.49096,0, +multi_crops,produce:multi_double_rice_i:region0143_c0,-66.2195,0, +multi_crops,produce:multi_double_rice_i:region0143_c1,-2.93465,0, 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+multi_crops,produce:multi_double_rice_i:region0161_c3,100,7.22562e-09, +multi_crops,produce:multi_double_rice_i:region0163_c0,-1.07603,0, +multi_crops,produce:multi_double_rice_i:region0163_c1,-90.7946,0, +multi_crops,produce:multi_double_rice_i:region0163_c2,-90.7065,0, +multi_crops,produce:multi_double_rice_i:region0163_c3,-0.994135,0, +multi_crops,produce:multi_double_rice_i:region0175_c0,-1.93342,0, +multi_crops,produce:multi_double_rice_i:region0175_c1,-1.86598,0, +multi_crops,produce:multi_double_rice_i:region0175_c2,-1.80662,0, +multi_crops,produce:multi_double_rice_i:region0175_c3,-1.76529,0, +multi_crops,produce:multi_double_rice_i:region0176_c0,-0.777148,0, +multi_crops,produce:multi_double_rice_i:region0176_c1,-0.792951,0, +multi_crops,produce:multi_double_rice_i:region0176_c2,-0.820329,0, +multi_crops,produce:multi_double_rice_i:region0176_c3,-90.3026,0, +multi_crops,produce:multi_double_rice_i:region0177_c0,-1.62099,0, +multi_crops,produce:multi_double_rice_i:region0177_c1,-1.65384,0, +multi_crops,produce:multi_double_rice_i:region0177_c2,-1.64305,0, +multi_crops,produce:multi_double_rice_i:region0177_c3,-1.52438,0, +multi_crops,produce:multi_double_rice_i:region0180_c1,-4.36502,0, +multi_crops,produce:multi_double_rice_i:region0180_c3,-4.24517,0, +multi_crops,produce:multi_double_rice_i:region0184_c2,-2.94693,0, +multi_crops,produce:multi_double_rice_i:region0184_c3,-2.90494,0, +multi_crops,produce:multi_double_rice_i:region0185_c0,-0.956635,0, +multi_crops,produce:multi_double_rice_i:region0185_c2,-0.907181,0, +multi_crops,produce:multi_double_rice_i:region0186_c3,100,-1.24998e-08, +multi_crops,produce:multi_double_rice_i:region0193_c0,-3.06698,0, +multi_crops,produce:multi_double_rice_i:region0193_c1,-79.8172,0, +multi_crops,produce:multi_double_rice_i:region0193_c2,-3.00261,0, +multi_crops,produce:multi_double_rice_i:region0193_c3,-79.1974,0, 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+multi_crops,produce:multi_double_rice_r:region0024_c0,-2.57358,0, +multi_crops,produce:multi_double_rice_r:region0024_c1,-2.58213,0, +multi_crops,produce:multi_double_rice_r:region0024_c2,-2.57207,0, +multi_crops,produce:multi_double_rice_r:region0024_c3,-2.57278,0, +multi_crops,produce:multi_double_rice_r:region0028_c0,-3.24591,0, +multi_crops,produce:multi_double_rice_r:region0028_c1,-3.23215,0, +multi_crops,produce:multi_double_rice_r:region0028_c2,-3.23058,0, +multi_crops,produce:multi_double_rice_r:region0028_c3,-3.19363,0, +multi_crops,produce:multi_double_rice_r:region0036_c0,-4.77987,0, +multi_crops,produce:multi_double_rice_r:region0036_c1,-4.78928,0, +multi_crops,produce:multi_double_rice_r:region0036_c2,-4.74746,0, +multi_crops,produce:multi_double_rice_r:region0036_c3,-4.72969,0, +multi_crops,produce:multi_double_rice_r:region0044_c0,-3.80908,0, +multi_crops,produce:multi_double_rice_r:region0044_c1,-3.62502,0, 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+multi_crops,produce:multi_double_rice_r:region0144_c0,-4.20887,0, +multi_crops,produce:multi_double_rice_r:region0144_c1,-4.11278,0, +multi_crops,produce:multi_double_rice_r:region0144_c2,-4.19764,0, +multi_crops,produce:multi_double_rice_r:region0144_c3,-4.14028,0, +multi_crops,produce:multi_double_rice_r:region0145_c0,-2.40498,0, +multi_crops,produce:multi_double_rice_r:region0145_c1,-2.39219,0, +multi_crops,produce:multi_double_rice_r:region0145_c2,-2.3685,0, +multi_crops,produce:multi_double_rice_r:region0145_c3,-2.35394,0, +multi_crops,produce:multi_double_rice_r:region0176_c0,-90.5363,0, +multi_crops,produce:multi_double_rice_r:region0176_c1,-90.6381,0, +multi_crops,produce:multi_double_rice_r:region0176_c2,-0.813669,0, +multi_crops,produce:multi_double_rice_r:region0176_c3,-0.818172,0, +multi_crops,produce:multi_double_rice_r:region0177_c0,-1.53032,0, +multi_crops,produce:multi_double_rice_r:region0177_c1,-1.52515,0, +multi_crops,produce:multi_double_rice_r:region0177_c2,-1.51653,0, +multi_crops,produce:multi_double_rice_r:region0177_c3,-1.51228,0, +multi_crops,produce:multi_double_rice_r:region0185_c0,-1.21588,0, +multi_crops,produce:multi_double_rice_r:region0185_c1,-1.11841,0, +multi_crops,produce:multi_double_rice_r:region0185_c2,-1.13476,0, +multi_crops,produce:multi_double_rice_r:region0185_c3,-1.11112,0, +multi_crops,produce:multi_double_rice_r:region0186_c1,41.7196,0, +multi_crops,produce:multi_double_rice_r:region0193_c1,-2.9781,0, +multi_crops,produce:multi_double_rice_r:region0193_c2,-2.92711,0, +multi_crops,produce:multi_double_rice_r:region0193_c3,-3.04234,0, +multi_crops,produce:multi_double_rice_r:region0194_c0,-2.98595,0, +multi_crops,produce:multi_double_rice_r:region0194_c1,-2.97953,0, +multi_crops,produce:multi_double_rice_r:region0194_c2,-2.95138,0, +multi_crops,produce:multi_double_rice_r:region0194_c3,-2.95209,0, +multi_crops,produce:multi_maize_soybean_i:region0000_c1,-2.79316,0, +multi_crops,produce:multi_maize_soybean_i:region0000_c2,-2.72681,0, +multi_crops,produce:multi_maize_soybean_i:region0000_c3,-80.377,0, +multi_crops,produce:multi_maize_soybean_i:region0005_c1,-1.60228,0, +multi_crops,produce:multi_maize_soybean_i:region0005_c2,-1.5953,0, +multi_crops,produce:multi_maize_soybean_i:region0005_c3,-1.61676,0, +multi_crops,produce:multi_maize_soybean_i:region0011_c1,-1.51744,0, +multi_crops,produce:multi_maize_soybean_i:region0011_c2,-1.21537,0, +multi_crops,produce:multi_maize_soybean_i:region0011_c3,-1.26031,0, +multi_crops,produce:multi_maize_soybean_i:region0012_c2,-1.98948,0, +multi_crops,produce:multi_maize_soybean_i:region0012_c3,-2.17949,0, +multi_crops,produce:multi_maize_soybean_i:region0018_c1,-0.778662,0, +multi_crops,produce:multi_maize_soybean_i:region0024_c2,-1.56637,0, +multi_crops,produce:multi_maize_soybean_i:region0025_c2,-1.69947,0, +multi_crops,produce:multi_maize_soybean_i:region0026_c0,-1.69973,0, +multi_crops,produce:multi_maize_soybean_i:region0026_c1,-1.56147,0, +multi_crops,produce:multi_maize_soybean_i:region0026_c2,-1.49846,0, +multi_crops,produce:multi_maize_soybean_i:region0026_c3,-1.42105,0, +multi_crops,produce:multi_maize_soybean_i:region0030_c3,-2.80636,0, +multi_crops,produce:multi_maize_soybean_i:region0033_c2,-2.2998,0, +multi_crops,produce:multi_maize_soybean_i:region0036_c0,-2.60996,0, +multi_crops,produce:multi_maize_soybean_i:region0036_c1,-2.53789,0, +multi_crops,produce:multi_maize_soybean_i:region0036_c2,-2.5891,0, +multi_crops,produce:multi_maize_soybean_i:region0036_c3,-2.51301,0, +multi_crops,produce:multi_maize_soybean_i:region0037_c0,-3.21666,0, +multi_crops,produce:multi_maize_soybean_i:region0037_c1,-3.08866,0, +multi_crops,produce:multi_maize_soybean_i:region0037_c2,-3.22595,0, +multi_crops,produce:multi_maize_soybean_i:region0037_c3,-3.11753,0, +multi_crops,produce:multi_maize_soybean_i:region0059_c1,-3.87389,0, +multi_crops,produce:multi_maize_soybean_i:region0059_c2,-3.65151,0, +multi_crops,produce:multi_maize_soybean_i:region0059_c3,-3.55994,0, +multi_crops,produce:multi_maize_soybean_i:region0061_c1,-3.10835,0, +multi_crops,produce:multi_maize_soybean_i:region0061_c2,-2.95022,0, +multi_crops,produce:multi_maize_soybean_i:region0061_c3,-2.9395,0, +multi_crops,produce:multi_maize_soybean_i:region0075_c1,-3.43152,0, +multi_crops,produce:multi_maize_soybean_i:region0075_c2,-3.38388,0, +multi_crops,produce:multi_maize_soybean_i:region0075_c3,-3.33873,0, +multi_crops,produce:multi_maize_soybean_i:region0083_c2,-1.12822,0, +multi_crops,produce:multi_maize_soybean_i:region0084_c0,-1.92758,0, +multi_crops,produce:multi_maize_soybean_i:region0084_c1,-2.25647,0, +multi_crops,produce:multi_maize_soybean_i:region0084_c2,-2.08228,0, +multi_crops,produce:multi_maize_soybean_i:region0084_c3,-2.07447,0, 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+demand,consume:watermelon:GTM,-0.0855897,0,0.876604 +demand,consume:watermelon:GUF,-0.0868358,0,0.877851 +demand,consume:watermelon:GUY,-0.0855188,0,0.876534 +demand,consume:watermelon:HND,-0.0884219,0,0.879437 +demand,consume:watermelon:HRV,-0.0618688,0,0.89427 +demand,consume:watermelon:HUN,-0.0608821,0,0.893283 +demand,consume:watermelon:IDN,-0.0541468,0,0.918294 +demand,consume:watermelon:IND,-0.0451393,0,0.912834 +demand,consume:watermelon:IRN,-0.0514903,0,0.90245 +demand,consume:watermelon:IRQ,-0.0560917,0,0.900319 +demand,consume:watermelon:ISL,-0.0738559,0,0.899827 +demand,consume:watermelon:ISR,-0.0528586,0,0.900905 +demand,consume:watermelon:ITA,-0.058213,0,0.890614 +demand,consume:watermelon:JAM,-0.0895634,0,0.880578 +demand,consume:watermelon:JOR,-0.0563707,0,0.900598 +demand,consume:watermelon:JPN,-0.0392123,0,0.927886 +demand,consume:watermelon:KAZ,-0.0492622,0,0.908659 +demand,consume:watermelon:KEN,-0.0674297,0,0.89534 +demand,consume:watermelon:KGZ,-0.0445619,0,0.903959 +demand,consume:watermelon:KOR,-0.0363652,0,0.925039 +demand,consume:watermelon:LAO,-0.0395019,0,0.911443 +demand,consume:watermelon:LBN,-0.0534922,0,0.900271 +demand,consume:watermelon:LBY,-0.0669909,0,0.899392 +demand,consume:watermelon:LSO,-0.0584025,0,0.892533 +demand,consume:watermelon:MAR,-0.062644,0,0.896712 +demand,consume:watermelon:MDA,-0.0633176,0,0.895719 +demand,consume:watermelon:MEX,-0.0935298,0,0.884545 +demand,consume:watermelon:MKD,-0.0591604,0,0.891561 +demand,consume:watermelon:MLI,-0.0645155,0,0.882537 +demand,consume:watermelon:MLT,-0.0643398,0,0.896741 +demand,consume:watermelon:MNE,-0.0619084,0,0.894309 +demand,consume:watermelon:MNG,-0.0366532,0,0.925327 +demand,consume:watermelon:MRT,-0.0694727,0,0.887495 +demand,consume:watermelon:MWI,-0.057112,0,0.891242 +demand,consume:watermelon:MYS,-0.0455412,0,0.917483 +demand,consume:watermelon:NAM,-0.0591775,0,0.893308 +demand,consume:watermelon:NER,-0.0711211,0,0.889143 +demand,consume:watermelon:NZL,-0.0661437,0,0.934785 +demand,consume:watermelon:OMN,-0.0597101,0,0.89444 +demand,consume:watermelon:PAK,-0.0511785,0,0.910575 +demand,consume:watermelon:PAN,-0.0906507,0,0.881666 +demand,consume:watermelon:PER,-0.0717362,0,0.862751 +demand,consume:watermelon:PHL,-0.0465308,0,0.918472 +demand,consume:watermelon:PNG,-0.0646105,0,0.933252 +demand,consume:watermelon:POL,-0.0620752,0,0.894476 +demand,consume:watermelon:PRI,-0.0863302,0,0.877345 +demand,consume:watermelon:PRT,-0.0692889,0,0.89526 +demand,consume:watermelon:PRY,-0.0717024,0,0.8595 +demand,consume:watermelon:PSE,-0.0565369,0,0.900764 +demand,consume:watermelon:ROU,-0.0623177,0,0.894719 +demand,consume:watermelon:RUS,-0.0402454,0,0.928919 +demand,consume:watermelon:SAU,-0.0585493,0,0.901859 +demand,consume:watermelon:SDN,-0.0592384,0,0.902549 +demand,consume:watermelon:SEN,-0.0636869,0,0.881709 +demand,consume:watermelon:SLB,-0.0618602,0,0.930501 +demand,consume:watermelon:SLV,-0.0892016,0,0.880216 +demand,consume:watermelon:SOM,-0.051689,0,0.894999 +demand,consume:watermelon:SRB,-0.0615506,0,0.893951 +demand,consume:watermelon:STP,-0.0678472,0,0.895758 +demand,consume:watermelon:SUR,-0.0862275,0,0.877242 +demand,consume:watermelon:SVK,-0.0612376,0,0.893638 +demand,consume:watermelon:SVN,-0.0622268,0,0.894628 +demand,consume:watermelon:SYR,-0.0552092,0,0.899436 +demand,consume:watermelon:THA,-0.0397836,0,0.917074 +demand,consume:watermelon:TJK,-0.045422,0,0.904819 +demand,consume:watermelon:TKM,-0.0454052,0,0.904802 +demand,consume:watermelon:TTO,-0.0835355,0,0.87455 +demand,consume:watermelon:TUN,-0.0624881,0,0.896868 +demand,consume:watermelon:TUR,-0.0529099,0,0.897137 +demand,consume:watermelon:TWN,-0.0380993,0,0.926773 +demand,consume:watermelon:TZA,-0.0675735,0,0.895484 +demand,consume:watermelon:UGA,-0.0658173,0,0.893728 +demand,consume:watermelon:UKR,-0.057275,0,0.889676 +demand,consume:watermelon:URY,-0.0662961,0,0.864906 +demand,consume:watermelon:USA,-0.0981795,0,0.890548 +demand,consume:watermelon:UZB,-0.046635,0,0.906032 +demand,consume:watermelon:VEN,-0.0847496,0,0.875764 +demand,consume:watermelon:VNM,-0.0444097,0,0.916351 +demand,consume:watermelon:VUT,-0.0602943,0,0.928935 +demand,consume:watermelon:YEM,-0.0567906,0,0.900101 +demand,consume:watermelon:ZAF,-0.0507399,0,0.88487 +demand,consume:watermelon:ZWE,-0.0539269,0,0.888057 +demand,consume:yam:ARE,0.00157525,0,0.664087 +demand,consume:yam:ASM,-0.0659214,0,0.704734 +demand,consume:yam:ATG,-0.0424362,0,0.681249 +demand,consume:yam:AUS,0.0380282,0,0.647033 +demand,consume:yam:BDI,-0.00346602,0,0.671532 +demand,consume:yam:BEL,-0.0129463,0,0.678609 +demand,consume:yam:BEN,-0.0194925,0,0.685155 +demand,consume:yam:BFA,-0.013063,0,0.682679 +demand,consume:yam:BHS,-0.0327051,0,0.671518 +demand,consume:yam:BLZ,-0.0241909,0,0.667831 +demand,consume:yam:BRA,-0.0293267,0,0.668139 +demand,consume:yam:BRB,-0.0403264,0,0.679139 +demand,consume:yam:BRN,0.0366511,0,0.629011 +demand,consume:yam:CAF,-0.00264897,0,0.670715 +demand,consume:yam:CAN,-0.0507903,0,0.689603 +demand,consume:yam:CHE,-0.0135072,0,0.67917 +demand,consume:yam:CIV,-0.018324,0,0.683986 +demand,consume:yam:CMR,0.00700218,0,0.661064 +demand,consume:yam:COD,0.00378593,0,0.66428 +demand,consume:yam:COG,0.00518226,0,0.662884 +demand,consume:yam:COL,-0.0446453,0,0.683458 +demand,consume:yam:COM,0.00568397,0,0.66025 +demand,consume:yam:CPV,-0.0243895,0,0.690052 +demand,consume:yam:CRI,-0.0225504,0,0.669472 +demand,consume:yam:CUB,-0.0224507,0,0.661263 +demand,consume:yam:DEU,-0.00384783,0,0.66951 +demand,consume:yam:DOM,-0.0446842,0,0.683497 +demand,consume:yam:ERI,0.0113088,0,0.654354 +demand,consume:yam:ETH,0.0140376,0,0.651625 +demand,consume:yam:FJI,0.0371317,0,0.64793 +demand,consume:yam:FRA,-0.0109009,0,0.676563 +demand,consume:yam:GAB,-0.00460532,0,0.672671 +demand,consume:yam:GBR,-0.0134614,0,0.679124 +demand,consume:yam:GHA,-0.0188257,0,0.684488 +demand,consume:yam:GIN,-0.018031,0,0.677711 +demand,consume:yam:GNQ,-0.00521302,0,0.673279 +demand,consume:yam:GRD,-0.0366871,0,0.6755 +demand,consume:yam:GUF,-0.0442066,0,0.683019 +demand,consume:yam:GUY,-0.0339062,0,0.672719 +demand,consume:yam:HTI,-0.0324904,0,0.671303 +demand,consume:yam:IRL,-0.0144564,0,0.680119 +demand,consume:yam:JAM,-0.0301421,0,0.668955 +demand,consume:yam:JPN,0.0360549,0,0.629608 +demand,consume:yam:KEN,0.00852145,0,0.659545 +demand,consume:yam:LBR,-0.0194753,0,0.685138 +demand,consume:yam:LVA,-0.00366024,0,0.669323 +demand,consume:yam:MLI,-0.0181478,0,0.68381 +demand,consume:yam:MLT,-0.0049108,0,0.670573 +demand,consume:yam:MRT,-0.0203251,0,0.685988 +demand,consume:yam:MYS,0.0473797,0,0.618283 +demand,consume:yam:NER,-0.0069638,0,0.672626 +demand,consume:yam:NGA,-0.00788643,0,0.687856 +demand,consume:yam:NIC,-0.0243422,0,0.663155 +demand,consume:yam:NLD,-0.0137482,0,0.679411 +demand,consume:yam:NOR,-0.00595268,0,0.671615 +demand,consume:yam:NZL,0.0150356,0,0.670026 +demand,consume:yam:OMN,0.00200508,0,0.663657 +demand,consume:yam:PAK,0.0306563,0,0.635006 +demand,consume:yam:PAN,-0.031983,0,0.670795 +demand,consume:yam:PHL,0.0490553,0,0.616607 +demand,consume:yam:PNG,0.0176315,0,0.66743 +demand,consume:yam:PRI,-0.0325324,0,0.671345 +demand,consume:yam:RWA,0.00515026,0,0.671013 +demand,consume:yam:SDN,0.00280371,0,0.662859 +demand,consume:yam:SEN,-0.0195507,0,0.685213 +demand,consume:yam:SLB,0.022288,0,0.662773 +demand,consume:yam:SOM,0.00295117,0,0.662711 +demand,consume:yam:SSD,-0.00431902,0,0.672385 +demand,consume:yam:STP,-0.00702487,0,0.675091 +demand,consume:yam:TCD,-0.00830912,0,0.676375 +demand,consume:yam:TGO,-0.0193125,0,0.684975 +demand,consume:yam:TTO,-0.0372641,0,0.676077 +demand,consume:yam:TWN,0.056748,0,0.608914 +demand,consume:yam:TZA,-0.00656137,0,0.674627 +demand,consume:yam:USA,-0.0470452,0,0.685858 +demand,consume:yam:VEN,-0.0352085,0,0.674021 +demand,consume:yam:YEM,0.00694812,0,0.658714 diff --git a/data/curated/calibration/tutorial-02/provenance.yaml b/data/curated/calibration/tutorial-02/provenance.yaml new file mode 100644 index 00000000..3d78f934 --- /dev/null +++ b/data/curated/calibration/tutorial-02/provenance.yaml @@ -0,0 +1,1043 @@ +# Provenance stamp of this calibration artefact set: the +# structural config it was fit against. Generated by +# workflow/scripts/write_calibration_provenance.py via +# tools/calibrate; do not edit by hand. +# Licensing: see the annotation in REUSE.toml. +base_config: config/tutorial/02_demand_response.yaml +generated_at: '2026-07-31T22:33:29+00:00' +git_commit: 4492a036cad165a3624d950ac1aebd8df1170d17 +source: tutorial-02 +structural_config: + aggregation.irrigated_area_source: current + aggregation.regions.basin_scarcity_weight: 2.0 + aggregation.regions.method: kmeans + aggregation.regions.target_count: 200 + aggregation.resource_class_quantiles: + - 0.25 + - 0.5 + - 0.75 + aggregation.resource_class_score: regional_crop_mix_actual_yield + aggregation.simplify_min_area_km: 25 + aggregation.simplify_tolerance_km: 5 + animal_costs.fadn.exclude_costs.SE320: Feed for pigs & poultry + animal_costs.fadn.exclude_costs.SE325: Feed for pigs & poultry home-grown + animal_costs.fadn.exclude_costs.SE375: Rent paid + animal_costs.fadn.grazing_cost_items.SE310: Feed for grazing livestock + animal_costs.fadn.grazing_cost_items.SE315: Feed for grazing livestock home-grown + animal_costs.fadn.high_cost_threshold_usd_per_t: 50000 + animal_costs.fadn.livestock_specific_costs.SE330: Other livestock specific costs + animal_costs.fadn.shared_farm_costs.SE340: Machinery & building current costs + animal_costs.fadn.shared_farm_costs.SE345: Energy + animal_costs.fadn.shared_farm_costs.SE350: Contract work + animal_costs.fadn.shared_farm_costs.SE356: Other direct inputs + animal_costs.fadn.shared_farm_costs.SE360: Depreciation + animal_costs.fadn.shared_farm_costs.SE370: Wages paid + animal_costs.fadn.shared_farm_costs.SE380: Interest paid + animal_costs.fadn.shared_farm_costs.SE390: Taxes + animal_costs.fallback_aliases.dairy-buffalo: dairy + animal_costs.fallback_values_usd_per_t.meat-chicken.grazing: 0 + animal_costs.fallback_values_usd_per_t.meat-chicken.production: 1300 + animal_costs.fallback_values_usd_per_t.meat-sheep.grazing: 2300 + animal_costs.fallback_values_usd_per_t.meat-sheep.production: 1200 + animal_costs.faostat.aggregate_area_code_limit: 5000 + animal_costs.faostat.element_codes.producing_animals: + - '2313' + - '5318' + - '5313' + animal_costs.faostat.element_codes.production: + - '2510' + - '5510' + animal_costs.faostat.element_codes.stocks: + - '2111' + - '5111' + animal_costs.usda.dressed_weight_kg_per_head.meat-cattle: 350.0 + animal_costs.usda.dressed_weight_kg_per_head.meat-pig: 90.0 + animal_costs.usda.exclude_items: + - Homegrown harvested feed + - Purchased feed + - Total, feed costs + - Opportunity cost of land + - Total, operating costs + - Costs listed + animal_costs.usda.grazing_cost_items: + - Grazed feed + animal_costs.usda.include_items: + - Hired labor + - Opportunity cost of unpaid labor + - Bedding and litter + - Custom services + - Fuel, lube, and electricity + - Repairs + - Interest on operating capital + - Marketing + - Veterinary and medicine + - Capital recovery of machinery and equipment + - General farm overhead + - Taxes and insurance + animal_costs.usda.request_timeout_seconds: 120 + animal_products.co_products.rendered-fat.yield_per_retail.meat-cattle: 0.09 + animal_products.co_products.rendered-fat.yield_per_retail.meat-pig: 0.137 + animal_products.faostat_items.dairy: + - Raw milk of cattle + - Raw milk of goats + - Raw milk of sheep + - Raw milk of camel + animal_products.faostat_items.dairy-buffalo: + - Raw milk of buffalo + animal_products.faostat_items.eggs: + - Hen eggs in shell, fresh + animal_products.faostat_items.meat-cattle: + - Meat of cattle with the bone, fresh or chilled + - Meat of buffalo, fresh or chilled + animal_products.faostat_items.meat-chicken: + - Meat of chickens, fresh or chilled + - Meat of ducks, fresh or chilled + - Meat of turkeys, fresh or chilled + - Meat of pigeons and other birds n.e.c., fresh, chilled or frozen + animal_products.faostat_items.meat-pig: + - Meat of pig with the bone, fresh or chilled + animal_products.faostat_items.meat-sheep: + - Meat of sheep, fresh or chilled + - Meat of goat, fresh or chilled + animal_products.gleam3_system_product_map.Buffalo.Grassland: + - dairy-buffalo + - meat-cattle + animal_products.gleam3_system_product_map.Buffalo.Mixed: + - dairy-buffalo + - meat-cattle + animal_products.gleam3_system_product_map.Cattle.Feedlots: + - meat-cattle + animal_products.gleam3_system_product_map.Cattle.Grassland: + - dairy + - meat-cattle + animal_products.gleam3_system_product_map.Cattle.Mixed: + - dairy + - meat-cattle + animal_products.gleam3_system_product_map.Chicken.Backyard: + - eggs + - meat-chicken + animal_products.gleam3_system_product_map.Chicken.Broiler: + - meat-chicken + animal_products.gleam3_system_product_map.Chicken.Layer: + - eggs + animal_products.gleam3_system_product_map.Goats.Grassland: + - dairy + - meat-sheep + animal_products.gleam3_system_product_map.Goats.Mixed: + - dairy + - meat-sheep + animal_products.gleam3_system_product_map.Pigs.Backyard: + - meat-pig + animal_products.gleam3_system_product_map.Pigs.Industrial: + - meat-pig + animal_products.gleam3_system_product_map.Pigs.Intermediate: + - meat-pig + animal_products.gleam3_system_product_map.Sheep.Grassland: + - dairy + - meat-sheep + animal_products.gleam3_system_product_map.Sheep.Mixed: + - dairy + - meat-sheep + animal_products.include: + - meat-cattle + - meat-pig + - meat-chicken + - dairy + - eggs + - dairy-buffalo + - meat-sheep + animal_products.me_scaling_clamp_factor: 2.0 + animal_products.net_to_metabolizable_energy_conversion.k_g: 0.43 + animal_products.net_to_metabolizable_energy_conversion.k_l: 0.64 + animal_products.net_to_metabolizable_energy_conversion.k_m: 0.65 + animal_products.residue_crops: + - banana + - barley + - cassava + - chickpea + - cowpea + - dry-pea + - dryland-rice + - foxtail-millet + - gram + - groundnut + - maize + - oat + - pearl-millet + - phaseolus-bean + - pigeonpea + - rapeseed + - rye + - sorghum + - soybean + - sugarbeet + - sugarcane + - sweet-potato + - wetland-rice + - wheat + baseline_year: 2020 + biomass.biogas_crop_demand: data/curated/biogas_crop_demand.csv + biomass.crops: + - maize + - oil-palm + - sugarcane + - biomass-sorghum + biomass.disposal_foods: + - cottonseed-oil + - sesame-oil + - sesame-seed + - groundnut-oil + - groundnut + - coconut-oil + - coconut + - foxtail-millet + - soybean-oil + - rapeseed-oil + - sunflower-oil + - chickpea + - gram + - rendered-fat + biomass.enforce_baseline_demand: true + biomass.enforce_fiber_demand: true + biomass.marginal_values_usd_per_tonne: 0 + byproducts: + - beet-pulp + - wheat-bran + - wheat-germ + - rice-bran + - barley-bran + - oat-bran + - buckwheat-hulls + - oilseed-meal + - palm-kernel-meal + - rapeseed-meal + - ddgs + - molasses + - maize-ethanol + - maize-gluten-feed + - maize-gluten-meal + - maize-starch + - sugarcane-ethanol + - cotton-lint + commodities.crops.classes.bulk_dry_goods.items: + - wheat + - dryland-rice + - wetland-rice + - maize + - barley + - oat + - rye + - sorghum + - buckwheat + - foxtail-millet + - pearl-millet + - soybean + - dry-pea + - chickpea + - cowpea + - gram + - phaseolus-bean + - pigeonpea + - cocoa + - coffee + - tea + - sunflower + - rapeseed + - groundnut + - sesame + - cotton + commodities.crops.classes.bulk_dry_goods.marketing_cost_per_t: 30 + commodities.crops.classes.bulk_dry_goods.trade_cost_per_t_km: 0.006 + commodities.crops.classes.bulky_fresh.items: + - white-potato + - sweet-potato + - cassava + - yam + - plantain + - sugarbeet + - sugarcane + - oil-palm + - alfalfa + - silage-maize + - biomass-sorghum + commodities.crops.classes.bulky_fresh.marketing_cost_per_t: 60 + commodities.crops.classes.bulky_fresh.trade_cost_per_t_km: 0.014 + commodities.crops.classes.perishable_high_value.items: + - tomato + - carrot + - onion + - cabbage + - banana + - watermelon + - mango + - citrus + - coconut + - apple + - olive + commodities.crops.classes.perishable_high_value.marketing_cost_per_t: 200 + commodities.crops.classes.perishable_high_value.trade_cost_per_t_km: 0.022 + commodities.crops.non_tradable: + - alfalfa + - biomass-sorghum + - silage-maize + commodities.feeds.classes.bulky_low_quality.items: + - ruminant_roughage + - monogastric_low_quality + commodities.feeds.classes.bulky_low_quality.marketing_cost_per_t: 35 + commodities.feeds.classes.bulky_low_quality.trade_cost_per_t_km: 0.016 + commodities.feeds.classes.forage.items: + - ruminant_forage + commodities.feeds.classes.forage.marketing_cost_per_t: 25 + commodities.feeds.classes.forage.trade_cost_per_t_km: 0.012 + commodities.feeds.classes.grain_protein.items: + - ruminant_grain + - ruminant_protein + - monogastric_grain + - monogastric_protein + commodities.feeds.classes.grain_protein.marketing_cost_per_t: 30 + commodities.feeds.classes.grain_protein.trade_cost_per_t_km: 0.006 + commodities.feeds.non_tradable: + - ruminant_forage + - ruminant_roughage + commodities.foods.classes.chilled_meat.items: + - meat-cattle + - meat-pig + - meat-chicken + - meat-sheep + commodities.foods.classes.chilled_meat.marketing_cost_per_t: 800 + commodities.foods.classes.chilled_meat.trade_cost_per_t_km: 0.028 + commodities.foods.classes.dairy_and_eggs.items: + - dairy + - dairy-buffalo + - eggs + commodities.foods.classes.dairy_and_eggs.marketing_cost_per_t: 300 + commodities.foods.classes.dairy_and_eggs.trade_cost_per_t_km: 0.024 + commodities.foods.classes.feed_byproduct.items: + - barley-bran + - beet-pulp + - buckwheat-hulls + - oat-bran + - rice-bran + - wheat-bran + - wheat-germ + - ddgs + - maize-gluten-feed + - maize-gluten-meal + - oilseed-meal + - palm-kernel-meal + - rapeseed-meal + - molasses + commodities.foods.classes.feed_byproduct.marketing_cost_per_t: 30 + commodities.foods.classes.feed_byproduct.trade_cost_per_t_km: 0.008 + commodities.foods.classes.fresh_produce.items: + - apple + - banana + - citrus + - coconut + - mango + - plantain + - watermelon + - cabbage + - carrot + - onion + - tomato + - cassava + - potato + - sweet-potato + - yam + commodities.foods.classes.fresh_produce.marketing_cost_per_t: 200 + commodities.foods.classes.fresh_produce.trade_cost_per_t_km: 0.022 + commodities.foods.classes.industrial_byproduct.items: + - cotton-lint + - maize-ethanol + - maize-starch + - sugarcane-ethanol + - rendered-fat + commodities.foods.classes.industrial_byproduct.marketing_cost_per_t: 40 + commodities.foods.classes.industrial_byproduct.trade_cost_per_t_km: 0.008 + commodities.foods.classes.processed_dry_goods.items: + - flour-white + - flour-wholemeal + - rice-brown + - rice-white + - barley-hulled + - buckwheat + - foxtail-millet + - maize + - maize-whole + - oat + - pearl-millet + - rye + - sorghum + - chickpea + - cowpea + - dry-pea + - gram + - phaseolus-bean + - pigeon-pea + - soy + - groundnut + - sesame-seed + - sunflower-seed + - sugar + - cocoa-powder + - coffee-green + - tea-dried + commodities.foods.classes.processed_dry_goods.marketing_cost_per_t: 80 + commodities.foods.classes.processed_dry_goods.trade_cost_per_t_km: 0.012 + commodities.foods.classes.processed_oils.items: + - coconut-oil + - cottonseed-oil + - groundnut-oil + - olive-oil + - palm-oil + - rapeseed-oil + - sesame-oil + - soybean-oil + - sunflower-oil + commodities.foods.classes.processed_oils.marketing_cost_per_t: 120 + commodities.foods.classes.processed_oils.trade_cost_per_t_km: 0.016 + commodities.foods.non_tradable: [] + commodities.hubs: 20 + costs.averaging_period.end_year: 2024 + costs.averaging_period.start_year: 2015 + countries: + - AFG + - AGO + - ALB + - ARE + - ARG + - ARM + - ASM + - ATG + - AUS + - AUT + - AZE + - BDI + - BEL + - BEN + - BFA + - BGD + - BGR + - BHS + - BIH + - BLR + - BLZ + - BOL + - BRA + - BRB + - BRN + - BTN + - BWA + - CAF + - CAN + - CHE + - CHL + - CHN + - CIV + - CMR + - COD + - COG + - COL + - COM + - CPV + - CRI + - CUB + - CYP + - CZE + - DEU + - DJI + - DNK + - DOM + - DZA + - ECU + - EGY + - ERI + - ESP + - EST + - ETH + - FIN + - FJI + - FRA + - GAB + - GBR + - GEO + - GHA + - GIN + - GMB + - GNB + - GNQ + - GRC + - GRD + - GTM + - GUF + - GUY + - HND + - HRV + - HTI + - HUN + - IDN + - IND + - IRL + - IRN + - IRQ + - ISL + - ISR + - ITA + - JAM + - JOR + - JPN + - KAZ + - KEN + - KGZ + - KHM + - KOR + - LAO + - LBN + - LBR + - LBY + - LKA + - LSO + - LTU + - LUX + - LVA + - MAR + - MDA + - MDG + - MEX + - MKD + - MLI + - MLT + - MMR + - MNE + - MNG + - MOZ + - MRT + - MUS + - MWI + - MYS + - NAM + - NER + - NGA + - NIC + - NLD + - NOR + - NPL + - NZL + - OMN + - PAK + - PAN + - PER + - PHL + - PNG + - POL + - PRI + - PRT + - PRY + - PSE + - ROU + - RUS + - RWA + - SAU + - SDN + - SEN + - SLB + - SLE + - SLV + - SOM + - SRB + - SSD + - STP + - SUR + - SVK + - SVN + - SWE + - SWZ + - SYR + - TCD + - TGO + - THA + - TJK + - TKM + - TLS + - TTO + - TUN + - TUR + - TWN + - TZA + - UGA + - UKR + - URY + - USA + - UZB + - VEN + - VNM + - VUT + - YEM + - ZAF + - ZMB + - ZWE + crop_costs.faostat.price_element_code: 5532 + crop_costs.faostat.yield_element_code: 5412 + crop_costs.non_endogenous_cost_share: 0.7 + crop_costs.outlier_cap_quantile: 0.9 + cropgrids.suitable_area_expansion: 1.0 + cropgrids_crops: + - apple + crops: + - wheat + - dryland-rice + - wetland-rice + - maize + - barley + - oat + - rye + - sorghum + - buckwheat + - foxtail-millet + - pearl-millet + - soybean + - dry-pea + - chickpea + - cowpea + - gram + - phaseolus-bean + - pigeonpea + - white-potato + - sweet-potato + - cassava + - yam + - plantain + - tomato + - carrot + - onion + - cabbage + - banana + - watermelon + - mango + - citrus + - coconut + - apple + - cocoa + - coffee + - tea + - sunflower + - rapeseed + - groundnut + - sesame + - oil-palm + - olive + - sugarcane + - sugarbeet + - cotton + - alfalfa + - silage-maize + - biomass-sorghum + currency_base_year: 2024 + data.faostat.fbs_food_kcal_element_code: 664 + data.faostat.fbs_food_supply_element_code: 645 + data.faostat.fbs_other_uses_element_code: 5154 + data.faostat.fbs_production_element_code: 5511 + data.faostat.qcl_production_element_code: 5510 + data.gaez.climate_model: GFDL-ESM4 + data.gaez.climate_scenario: SSP126 + data.gaez.input_level: H + data.gaez.period: FP2140 + data.gaez.suitability_var: RES05-SX1 + data.gaez.water_requirement_var: RES05-WDC + data.gaez.yield_var: RES05-YCX + data.land_cover.version: v2_1_1 + data.mirror.zenodo_record: '21694233' + data.soilgrids.target_resolution_m: 10000 + data.usda.nutrients.cal: Energy + data.usda.nutrients.carb: Carbohydrate, by difference + data.usda.nutrients.fat: Total lipid (fat) + data.usda.nutrients.protein: Protein + data.usda.retrieve_nutrition: false + derived.multiple_cropping.catalog: + cotton_wheat: + crops: + - cotton + - wheat + water_supplies: + - r + - i + double_rice: + crops: + - wetland-rice + - wetland-rice + water_supplies: + - r + - i + maize_soybean: + crops: + - maize + - soybean + water_supplies: + - r + - i + rice_maize: + crops: + - wetland-rice + - maize + water_supplies: + - r + - i + rice_wheat: + crops: + - wetland-rice + - wheat + water_supplies: + - r + - i + triple_rice: + crops: + - wetland-rice + - wetland-rice + - wetland-rice + water_supplies: + - r + - i + wheat_maize: + crops: + - wheat + - maize + water_supplies: + - r + - i + wheat_soybean: + crops: + - wheat + - soybean + water_supplies: + - r + - i + derived.multiple_cropping.effective: + cotton_wheat: + crops: + - cotton + - wheat + water_supplies: + - r + - i + double_rice: + crops: + - wetland-rice + - wetland-rice + water_supplies: + - r + - i + maize_soybean: + crops: + - maize + - soybean + water_supplies: + - r + - i + rice_maize: + crops: + - wetland-rice + - maize + water_supplies: + - r + - i + rice_wheat: + crops: + - wetland-rice + - wheat + water_supplies: + - r + - i + triple_rice: + crops: + - wetland-rice + - wetland-rice + - wetland-rice + water_supplies: + - r + - i + wheat_maize: + crops: + - wheat + - maize + water_supplies: + - r + - i + wheat_soybean: + crops: + - wheat + - soybean + water_supplies: + - r + - i + derived.multiple_cropping.mirca_source_year: 2020 + derived.multiple_cropping.observed: + cotton_wheat: + crops: + - cotton + - wheat + water_supplies: + - r + - i + double_rice: + crops: + - wetland-rice + - wetland-rice + water_supplies: + - r + - i + maize_soybean: + crops: + - maize + - soybean + water_supplies: + - r + - i + rice_maize: + crops: + - wetland-rice + - maize + water_supplies: + - r + - i + rice_wheat: + crops: + - wetland-rice + - wheat + water_supplies: + - r + - i + triple_rice: + crops: + - wetland-rice + - wetland-rice + - wetland-rice + water_supplies: + - r + - i + wheat_maize: + crops: + - wheat + - maize + water_supplies: + - r + - i + wheat_soybean: + crops: + - wheat + - soybean + water_supplies: + - r + - i + diet.anchor_groups_to_gbd: false + diet.baseline_age: All ages + diet.fbs_override_foods: + - yam + - cocoa-powder + - coffee-green + - tea-dried + - meat-cattle + - meat-pig + - meat-sheep + - meat-chicken + - eggs + diet.gdd_ia.cooked_to_raw.red_meat: 1.43 + diet.nhanes.cycle: '1720' + diet.nhanes.reference_year: 2018 + diet.nhanes.url: https://www.ars.usda.gov/ARSUserFiles/80400530/pdf/fped/Table_1_FPED_MaleFemale_{cycle}.pdf + diet.source: gdd_ia + diet.source_basis.faostat_fbs_supply.meat-cattle: carcass + diet.source_basis.faostat_fbs_supply.meat-chicken: carcass + diet.source_basis.faostat_fbs_supply.meat-pig: carcass + diet.source_basis.faostat_fbs_supply.meat-sheep: carcass + diet.source_basis.faostat_fbs_supply.tea-dried: fresh + diet.source_basis.gbd.fruits: fresh + diet.source_basis.gbd.legumes: cooked + diet.source_basis.gbd.milk: fresh + diet.source_basis.gbd.nuts_seeds: dry + diet.source_basis.gbd.red_meat: cooked + diet.source_basis.gbd.vegetables: fresh + diet.source_basis.gbd.whole_grains: dry + emissions.ch4_to_co2_factor: 27.0 + emissions.fertilizer.frac_gasf: 0.11 + emissions.fertilizer.frac_gasm: 0.21 + emissions.fertilizer.frac_leach: 0.24 + emissions.fertilizer.indirect_ef4: 0.01 + emissions.fertilizer.indirect_ef5: 0.011 + emissions.fertilizer.organic_n2o_factor: 0.006 + emissions.fertilizer.synthetic_n2o_factor: 0.01 + emissions.n2o_to_co2_factor: 273.0 + emissions.residues.incorporation_n2o_factor: 0.01 + emissions.rice.methane_emission_factor_kg_per_ha: 134.47 + emissions.rice.rainfed_wetland_rice_ch4_scaling_factor: 0.54 + fertilizer.limit: 200000000 + fertilizer.manure_n_to_fertilizer: 0.75 + fertilizer.marginal_cost_usd_per_tonne: 500 + fertilizer.n_percentile: 80 + fertilizer.proxy_rates.plantain: banana + fertilizer.proxy_rates.silage-maize: maize + fodder_decomposition.eurostat.averaging_years: 5 + fodder_decomposition.fdd_crops: + - alfalfa + - silage-maize + fodder_decomposition.suitability_blend_weight: 0.7 + fodder_decomposition.yield_corrections.ceiling: 2.0 + fodder_decomposition.yield_corrections.enabled: true + fodder_decomposition.yield_corrections.eurostat_moisture: 0.65 + fodder_decomposition.yield_corrections.floor: 0.2 + food_demand_calibration.max_multiplier: 2.0 + food_demand_calibration.min_multiplier: 0.5 + food_groups.included: + - whole_grains + - grain + - fruits + - vegetables + - legumes + - nuts_seeds + - starchy_vegetable + - oil + - red_meat + - poultry + - dairy + - eggs + - sugar + - stimulants + - animal_fat + food_groups.max_per_capita.animal_fat: 50 + food_groups.max_per_capita.dairy: 2865 + food_groups.max_per_capita.eggs: 213 + food_groups.max_per_capita.fruits: 658 + food_groups.max_per_capita.grain: 1403 + food_groups.max_per_capita.legumes: 300 + food_groups.max_per_capita.nuts_seeds: 79 + food_groups.max_per_capita.oil: 155 + food_groups.max_per_capita.poultry: 241 + food_groups.max_per_capita.red_meat: 285 + food_groups.max_per_capita.starchy_vegetable: 1221 + food_groups.max_per_capita.stimulants: 50 + food_groups.max_per_capita.sugar: 133 + food_groups.max_per_capita.vegetables: 785 + food_groups.max_per_capita.whole_grains: 300 + food_loss_waste_calibration.food_groups: + - vegetables + - fruits + - starchy_vegetable + - oil + - stimulants + gleam3_feed_attribution.pathway_dispatch_shares.maize_ethanol: 0.12 + gleam3_feed_attribution.pathway_dispatch_shares.maize_wetmill: 0.07 + gleam3_feed_attribution.pathway_dispatch_shares.sugarcane_ethanol: 0.25 + grazing.enabled: true + grazing.forage_overlap_crops: + - alfalfa + - silage-maize + - biomass-sorghum + grazing.isimip_utilization_rate: 0.6 + health.alternative_rr.red_meat: data/curated/red_meat_rr_log_linear.csv + health.breakpoint_rel_tol: 0.05 + health.causes: + - CHD + - Stroke + - T2DM + - CRC + health.clustering.weights.gdp: 0.5 + health.clustering.weights.geography: 1.0 + health.clustering.weights.population: 0.3 + health.gbd_cause_id.CHD: 493 + health.gbd_cause_id.CRC: 441 + health.gbd_cause_id.Stroke: 495 + health.gbd_cause_id.T2DM: 976 + health.gbd_rei_id.fruits: 111 + health.gbd_rei_id.legumes: 333 + health.gbd_rei_id.nuts_seeds: 114 + health.gbd_rei_id.red_meat: 116 + health.gbd_rei_id.vegetables: 112 + health.gbd_rei_id.whole_grains: 113 + health.intake_age_min: 11 + health.log_rr_points: 15 + health.region_clusters: 30 + health.risk_cause_map.fruits: + - CHD + - Stroke + - T2DM + health.risk_cause_map.legumes: + - CHD + health.risk_cause_map.nuts_seeds: + - CHD + health.risk_cause_map.red_meat: + - CHD + - Stroke + - T2DM + - CRC + health.risk_cause_map.vegetables: + - CHD + - Stroke + health.risk_cause_map.whole_grains: + - CHD + - Stroke + - T2DM + - CRC + health.risk_factors: + - fruits + - vegetables + - nuts_seeds + - legumes + - red_meat + - whole_grains + irrigation.irrigated_crops: all + land.conversion_cost_forest_usd_per_ha: 8000 + land.conversion_cost_nonforest_usd_per_ha: 2000 + land.discount_rate: 0.05 + land.investment_horizon: 30 + land.land_use_cost_usd_per_ha: 0.0 + luc.cropland_source: gaez + luc.forest_fraction_threshold: 0.2 + luc.horizon_years: 30 + luc.managed_flux_mode: zero + luc.savanna_pvc_threshold: 75 + non_food_crops: + - alfalfa + - silage-maize + - biomass-sorghum + numerics.min_co2_coefficient_tco2_per_ha: 0.001 + numerics.min_cost_correction_bnusd: 1.0e-06 + numerics.min_crop_yield_t_per_ha: 0.01 + numerics.min_grassland_yield_t_per_ha: 0.05 + numerics.min_land_area_ha: 100 + numerics.min_link_area_mha: 1.0e-06 + numerics.min_water_capacity_mm3: 1.0e-06 + numerics.min_water_requirement_m3_per_ha: 0.1 + residues.max_feed_fraction: 0.3 + residues.max_feed_fraction_by_region.Asia: 0.7 + residues.max_feed_fraction_by_region.Southern Asia: 0.85 + residues.max_feed_fraction_by_region.Sub-Saharan Africa: 0.7 + water.data.availability: aware + water.data.groundwater_trend_end: 2019 + water.data.groundwater_trend_start: 2000 + water.data.huang_reference_year: 2010 + water.data.surface_reference_end: 2019 + water.data.surface_reference_start: 1990 + water.irrigation.consumed_fraction: 0.58 + water.irrigation.eta_max: 5.0 + water.irrigation.eta_min: 0.2 + water.supply.groundwater_ceiling_factor: 3.0 + water.supply.pumping_cost_usd_per_m3: 0.04 + water.supply.scarcity_tiers: false + water.temporal_resolution: 1 + weight_conversion.carcass_to_fresh.meat-cattle: 0.67 + weight_conversion.carcass_to_fresh.meat-chicken: 0.6 + weight_conversion.carcass_to_fresh.meat-pig: 0.73 + weight_conversion.carcass_to_fresh.meat-sheep: 0.66 + weight_conversion.cooked_to_dry.grain: 0.45 + weight_conversion.cooked_to_dry.legumes: 0.4 + weight_conversion.cooked_to_dry.whole_grains: 0.45 + weight_conversion.cooked_to_fresh.poultry: 1.0 + weight_conversion.cooked_to_fresh.red_meat: 1.43 + weight_conversion.fresh_to_dry.tea-dried: 0.22 + yield_calibration.crops: + - plantain + - coffee + - tea + yield_calibration.enabled: true + yield_calibration.multiplier_max: 3.0 + yield_calibration.multiplier_min: 0.5 diff --git a/docs/analysis.rst b/docs/analysis.rst index 89f792e2..5e30eb6f 100644 --- a/docs/analysis.rst +++ b/docs/analysis.rst @@ -402,7 +402,6 @@ Output file: ``consumption``, float, bn USD, "Consumption-related costs" ``animal_production``, float, bn USD, "Livestock production costs" ``feed_conversion``, float, bn USD, "Feed processing costs" - ``consumer_values``, float, bn USD, "Utility from food consumption (negative)" ``biomass_exports``, float, bn USD, "Revenue from biomass exports (negative)" ``biomass_routing``, float, bn USD, "Internal biomass flow costs" ``health_burden``, float, bn USD, "Health costs from YLL" diff --git a/docs/calibration.rst b/docs/calibration.rst index 53c939a3..110aabcb 100644 --- a/docs/calibration.rst +++ b/docs/calibration.rst @@ -363,25 +363,21 @@ Cost calibration The cost calibration is a two-step paired solve: -**Step 1 (consumer-value extraction).** A baseline solve with -``enforce_baseline_diet: true`` extracts food-bus duals to build the -piecewise consumer-utility blocks used downstream. Step 1 enables hard -production-stability bounds at **+/-20 %** for crops, grassland, and -animals; this prevents the LP from idealising supply patterns and -pushing the consumer-value duals below realistic supply cost, which in -earlier versions forced step 2 to absorb a large negative correction. -The +/-20 % band is loose enough to accommodate the structural -FAOSTAT-vs-FBS mismatch carried by most foods. For the small set of -foods whose mismatch still exceeds the band (buckwheat, plantain, -coffee, tea, olive-oil), a file-level -``validation.slack_marginal_cost: 5.0`` ($5 000/t) override caps the +**Step 1 (mismatch absorption).** A baseline solve with +``enforce_baseline_diet: true`` and hard production-stability bounds at +**+/-5 %** for crops, grassland, and animals absorbs the residual +FAOSTAT-vs-FBS mismatch the food-demand calibration could not close. +For the small set of foods whose mismatch exceeds the band (buckwheat, +plantain, coffee, tea, olive-oil), a file-level +``validation.slack_marginal_cost: 7.5`` ($7 500/t) override caps the slack-driven duals at the upper end of realistic wholesale prices -- instead of the default ~$50 000/t slack ceiling -- while leaving enough headroom for legitimately high-value foods. -**Step 2 (cost-correction extraction).** A second solve activates the -piecewise utility built in step 1 and tightens production stability to -**+/-1 %**. The dual :math:`\mu^+_\ell - \mu^-_\ell` on each tight +**Step 2 (cost-correction extraction).** A second solve keeps the diet +pinned to baseline and tightens production stability to **+/-1 %**, so +the duals measure the marginal cost of holding observed production +under the observed diet. The dual :math:`\mu^+_\ell - \mu^-_\ell` on each tight production-stability constraint indicates how much the link's marginal cost would need to shift for the observed allocation to be cost-optimal; the per-group median becomes an additive correction. See @@ -626,8 +622,8 @@ A trust-region cap of :math:`\lvert \Delta x \rvert_\infty \le \log 2` prevents single-step overshoot near the zero-baseline growth caps. Each iteration is one paired solve (baseline with -``enforce_baseline_diet=true`` to derive consumer values, then main -with piecewise utility active). Convergence is typically reached in +``enforce_baseline_diet=true``, then main under the base config's own +demand regime). Convergence is typically reached in 3–5 iterations from a cold start and 1–2 from a warm start (the existing calibrated YAML is auto-detected and used as the seed). The initial Jacobian is :math:`\mathrm{diag}(-1, -1)`, which is the exact diff --git a/docs/cluster_execution.rst b/docs/cluster_execution.rst index aebc8a44..1e8e425e 100644 --- a/docs/cluster_execution.rst +++ b/docs/cluster_execution.rst @@ -22,8 +22,7 @@ Overview The workflow has five phases: 1. **Build & calibrate** (local): Run Snakemake to build the model and solve - any prerequisite scenarios (e.g., baselines that generate consumer values - for downstream scenarios). + any scenarios that must run locally. 2. **Export manifest** (local): Generate a JSON manifest describing all remaining scenarios to solve on the cluster. @@ -77,7 +76,7 @@ Four tools implement the cluster workflow: ``tools/sync-solve-inputs`` Syncs all files needed on the cluster: built model, processing outputs, - static data, consumer values, the manifest, workflow scripts, config + static data, the manifest, workflow scripts, config files, and the cluster tools. Supports ``rsync`` (default) or ``tar+ssh`` (``--tar``, faster for many small files). @@ -111,10 +110,9 @@ Step-by-Step Example The example below uses a global sensitivity analysis config (``gsa.yaml``) with ~12,000 scenarios (3 stability regimes x 4096 Sobol samples). -**1. Build and solve baselines locally** +**1. Build locally** -Baseline scenarios generate consumer values (dual variables) that downstream -GSA scenarios depend on. Solve them with Snakemake:: +Build the model (and solve any locally pinned scenarios) with Snakemake:: # Locally: build model + solve baselines + first GSA scenarios SMK_MEM_MAX=40G tools/smk -e gurobi -j5 \ diff --git a/docs/config/doc_figures.yaml b/docs/config/doc_figures.yaml index c45728b7..64978afa 100644 --- a/docs/config/doc_figures.yaml +++ b/docs/config/doc_figures.yaml @@ -17,29 +17,12 @@ scenarios: # The 'default' scenario mirrors config/scenarios.yaml so existing doc figure # rules (which hardcode scen-default) keep working unchanged. # - # The 'baseline' scenario fixes consumption to current-day levels and is used - # to extract consumer values (dual variables) for the consumer-values - # documentation figure. - # # The GHG price scenarios sweep carbon prices from $0 to $500/tCO2-eq for the # landing-page production pattern GIF, showing how land use shifts under # increasing carbon pricing, matching the sensitivity analysis setup. default: {} - baseline: - # Fixed consumption to extract consumer values (dual variables). With the - # diet enforced, the market-response demand component must be off. - validation: - enforce_baseline_diet: true - market_response: - components: - demand: false - emissions: - ghg_price: 0 - health: - value_per_yll: 0 - # --- GHG price scenarios for landing-page GIF --- # Round numbers on a roughly log scale from $0 to $500/tCO2-eq. ghg_0: &ghg_base diff --git a/docs/configuration.rst b/docs/configuration.rst index a03378d6..312b5d82 100644 --- a/docs/configuration.rst +++ b/docs/configuration.rst @@ -346,33 +346,6 @@ GLEAM-derived baseline levels (see :ref:`gleam-feed-baseline`). See :doc:`validation` for a detailed walkthrough of the validation workflow and diagnostic figures. -Consumer Utility Options -^^^^^^^^^^^^^^^^^^^^^^^^ - -Two mutually exclusive options can be used to represent consumer preference in -the objective: - -* ``food_incentives`` applies a single linear marginal-cost adjustment per - ``(food, country)`` pair. -* ``food_utility_piecewise`` applies a piecewise diminishing marginal utility - curve per ``(food, country)`` pair. - -When ``food_utility_piecewise.enabled`` is ``true``, the workflow always reads -utility blocks from -``results/{name}/consumer_values/{baseline_scenario}/utility_blocks.csv``. -These blocks are generated by ``calibrate_food_utility_blocks`` from: - -* baseline dual values extracted by ``extract_consumer_values``; and -* baseline per-food consumption from the baseline scenario solve. - -The current calibration anchors marginal utility at the baseline quantity: -the utility block containing baseline consumption uses the extracted dual -value, with higher utility below baseline and lower utility above baseline -according to ``food_utility_piecewise.decline_factor``. - -``food_utility_piecewise`` cannot be combined with -``validation.enforce_baseline_diet`` in the same scenario. - .. _production-stability-bounds: Deviation Penalty @@ -554,9 +527,7 @@ reference price :math:`P_g` (the artefact's ``slope_basis`` column): food demand elasticity in magnitude. The demand fit is sequential -- pinned against the calibrated production curves rather than jointly with them, which would leave the producer/consumer split of each wedge undetermined; see the -calibration documentation. The demand component supersedes the -consumer-values / ``food_utility_piecewise`` mechanism and ``food_incentives`` -and is mutually exclusive with both, as well as with +calibration documentation. The demand component is mutually exclusive with ``validation.enforce_baseline_diet``. **Configuration options**: @@ -965,7 +936,7 @@ the surplus other than food-balance slack. Two patterns are common: Without a disposal route the consumption equality on these foods would be satisfied by food slack at ``validation.slack_marginal_cost``, which inflates the objective and, more importantly, drives the dual variables of the consumption equality strongly -negative — which biases consumer-value calibration (see :doc:`consumer_values`). +negative — which biases the calibrations that read them. The amount actually routed to biomass in a baseline solve is itself a useful diagnostic of the gap between baseline production and modelled outlets; it can be inspected via the ``biomass_disposal`` carrier on links in the solved network. diff --git a/docs/consumer_values.rst b/docs/consumer_values.rst deleted file mode 100644 index e248ba37..00000000 --- a/docs/consumer_values.rst +++ /dev/null @@ -1,248 +0,0 @@ -.. SPDX-FileCopyrightText: 2026 Koen van Greevenbroek -.. -.. SPDX-License-Identifier: CC-BY-4.0 - -.. _consumer-values: - -Consumer Values -=============== - -Overview --------- - -Many of the most interesting questions for GLADE ask how diets *respond* -to environmental or health pricing — for example, "what would people eat under -a $50/tCO\ :sub:`2`-eq price?". Naively letting the optimizer choose the -cheapest macronutrient-and-food-group-feasible diet gives implausible answers: -the unconstrained model will gladly replace half of today's food consumption -with whatever is cheapest to grow. We need a way to bake **revealed consumer -preferences** into the objective. - -This page documents how the model derives those preferences from a baseline -solve and feeds them back as a piecewise utility curve in subsequent solves. -The full workflow has three steps: - -1. Solve a **baseline scenario** with consumption fixed to the observed diet - via ``validation.enforce_baseline_diet: true`` (see :doc:`current_diets`). -2. **Extract consumer values** as the dual variables of the per-(food, country) - consumption equality constraints in that solve. -3. **Calibrate piecewise utility blocks** centred on baseline consumption, - using the extracted duals as marginal utilities at the baseline quantity. - -In subsequent scenarios the diet is freed (``enforce_baseline_diet: false``) -and the calibrated blocks are applied via ``food_utility_piecewise.enabled: -true``. Consumption then deviates from baseline only when the GHG/health -savings outweigh the consumer-value cost of the deviation. - -Tutorial Part 2 (:doc:`tutorial`) walks through this workflow end-to-end with -a small config; the present page focuses on the *interpretation* of the -extracted values and on the model preconditions that make them meaningful. - -What the duals encode ---------------------- - -When ``enforce_baseline_diet`` is on, every food consumption link gets an -equality constraint :math:`p = p_{\mathrm{set}}` pinning consumption to the -processed baseline diet (see :ref:`baseline-diet-estimation`). The dual -variable :math:`\mu_{\text{p\_set}}` of this equality is the marginal change -in objective per unit of relaxed consumption: - -.. math:: - - \mu_{\text{p\_set}}(f, c) - = \frac{\partial (\text{total cost})}{\partial p_{\text{set}}(f, c)}. - -Sign convention in ``extract_consumer_values.py``: -``value_bnusd_per_mt = mu_p_set`` (and ``adjustment_bnusd_per_mt`` its -negation) so that positive values mean *consumption is valuable to the -consumer* (the model would pay this much per Mt to be allowed to consume -more). - -Read carefully, a positive dual is **not** a "preference" in the everyday -sense — it is whatever marginal cost the supply chain has to incur to deliver -the next Mt of that food. It bundles together land, water, fertilizer, -processing, trade, and emissions costs net of any byproduct value. The -calibration relies on the assumption that *at the baseline quantity*, this -marginal supply cost is a reasonable proxy for the consumer's willingness -to pay — i.e. that observed consumption is approximately at the equilibrium -of supply cost and demand value. That is a common revealed-preference -assumption in food-system modelling. - -**Negative duals are kept as-is.** A negative ``mu_p_set`` means the system -saves cost when required to consume more of the food — typically a -supply-side artifact (e.g. forced co-product disposal, or production -anchoring dragging output toward baseline through binding caps elsewhere). -The *Preconditions* section below catalogs the structural issues that produce -them. - -Visualisation -------------- - -The figure below shows the per-(food, country) consumer values from the -documentation baseline solve, ordered by food group. Each row is one food; -the boxen shows the spread across countries. Colours follow the food-group -palette used elsewhere in the documentation. - -.. figure:: https://github.com/Sustainable-Solutions-Lab/GLADE/releases/download/doc-figures/consumer_values_distribution.png - :width: 100% - :alt: Letter-value plot of consumer values per food, coloured by food group - - Distribution of consumer values (USD\ :sub:`2024` per kg) across 175 - countries for each modelled food, derived from the dual variables of the - per-(food, country) consumption equalities in the documentation baseline - solve. Foods are ordered by food group (group label on the right margin) - and within each group by within-group median value. The x-axis is symlog - with a linear region near zero; the vertical line at zero separates foods - the consumer would pay to consume more of (right) from foods that cost - nothing or less to consume more of (left). - -A few patterns are worth flagging. - -- **Animal products** (red meat, processed meat, dairy, eggs, poultry) sit - at the high end. This is consistent with their high supply cost — they - consume large amounts of crop and grassland feed, land, and emit substantial - GHGs. -- **Cereals and starchy vegetables** sit in the low-positive range. They are - cheap calorie sources at baseline. -- **A few oils and seeds** cluster near or below zero. These come from co-products - of larger commodity flows (e.g. coconut oil and meal from copra-based - coconut production), and their marginal cost is dominated by the - byproduct-value side of the balance sheet. Negative raw duals remain - negative in both the extracted data and the figure. - -Preconditions for sensible duals --------------------------------- - -Three model details are easy to overlook and each can pollute the extracted -duals if it goes wrong. They are the reason the documentation baseline -configuration looks the way it does (see ``docs/config/doc_figures.yaml``'s -``baseline`` scenario). - -1. **The fixed diet must be supplyable.** If the model cannot deliver the - baseline consumption of food *f* in country *c* through real production - pathways, the food consumption equality is closed by **food slack** at - ``validation.slack_marginal_cost`` (default 50 bn USD/Mt). The dual then - saturates at exactly that price with the wrong sign — it reflects the - slack penalty rather than any consumer preference. - - This is most likely to happen for foods that are forced co-products of - commodity demands the model represents only partially. Cottonseed oil is - the textbook case: cotton is grown for fiber demand (``enforce_fiber_demand``), - the ginning pathway has fixed coefficients (cotton-lint 0.38, cottonseed - oil 0.083, oilseed meal 0.275), and at the global fiber-demand level the - joint cottonseed oil output exceeds baseline-diet absorption. Without an - outlet the surplus exits via food slack and the cottonseed-oil dual - saturates at −50 USD/kg in every country. - - The mitigation is to give surplus a route to the energy sector via - ``biomass.disposal_foods`` (see :ref:`disposal foods `). Foods - currently on this list — cottonseed oil, the sesame and groundnut oils - and seeds, coconut and coconut oil, foxtail millet — were each identified - from a baseline solve where their dual sat at the slack price or had a - strongly negative median across countries. - -2. **The L1 deviation penalty pulls in the same direction.** When - ``deviation_penalty`` is enabled with ``penalty_mode: "l1"`` - (legacy configs and the tutorials; the default and GSA configs now - anchor production with market-response curves), the objective gains a - term :math:`l_1 \cdot \sum |a - a_{\mathrm{baseline}}|` on harvested area - per crop. If the modelled outlets for some crop's production cannot - absorb its baseline area, the L1 term drags the corresponding food - consumption duals **negative** — relaxing the consumption equality lets - the model grow more of the upstream crop and reduces L1 deviation, - so the marginal value of consumption is *negative* (the consumer would - "save" the L1 penalty per extra Mt consumed). - - Empirically this affected sesame, groundnut, coconut, foxtail-millet, - chickpea and gram in earlier baseline solves: each was under-produced - relative to its baseline area by 1–6 Mha. The fix is the same as in - point 1 — provide a missing real-world outlet (biomass disposal, - feed routing, or both). - -3. **Redundant constraints can leak into duals.** If the diet is enforced - per-food via ``enforce_baseline_diet`` *and* within-group ratios are - simultaneously enforced via ``food_groups.fix_within_group_ratios``, the - second set of constraints is mathematically redundant (the per-food - p_set already implies the within-group shares) but can split the - marginal value across the two constraint families in unpredictable ways. - Keep ``fix_within_group_ratios.enabled: false`` whenever - ``enforce_baseline_diet`` is on. The same goes for any additional - constraint that further pins what is already pinned. - -Interpreting disposal flows as a residual diagnostic ----------------------------------------------------- - -A useful side benefit of the disposal-route mechanism is that the *amount* -routed to biomass in a baseline solve is the gap between baseline production -and what the modelled diet absorbs. Reading these flows answers "what real -demand am I missing for this food?": - -- Cottonseed oil: ~1.8 Mt globally, all in cotton-fiber-producing countries — - the forced co-product story. -- Foxtail-millet: ~2.8 Mt — birdseed and forage demand outside of the East - and South Asian food markets. -- Coconut oil: ~3 Mt — coir/charcoal/husk uses are the missing demand; - the L1 baseline is calibrated against total coconut area but the modelled - outlets are only food and oil. -- Sesame oil and groundnut oil: ~0.8 and ~0–4 Mt respectively — partly post- - harvest losses beyond food-group waste factors, partly under-attribution - of these oils in the FBS-derived diet. - -Where these flows are large or geographically concentrated, they point to -specific model improvements: an explicit non-food demand term (analogous to -``fiber_demand`` for cotton), a finer split between competing pathways, or -revised loss/waste factors. Until those are in place the disposal route is -the pragmatic choice — it lets the L1 baseline reflect total observed area -without poisoning the consumer-value duals. - -How the calibrated blocks use the duals ---------------------------------------- - -The ``calibrate_food_utility_blocks`` rule reads ``values.csv`` together with -the per-(food, country) baseline consumption levels and emits a piecewise -diminishing-marginal-utility curve per (food, country). The block containing -baseline consumption uses the extracted dual as its marginal utility; blocks -below baseline are more valuable (decline_factor < 1) and blocks above -baseline are less valuable, all parameterised by: - -- ``food_utility_piecewise.n_blocks`` — number of steps per side - (default: 4). -- ``food_utility_piecewise.decline_factor`` — geometric ratio between - successive block values (default: 0.7, i.e. each step is worth 70% of the - previous one). -- ``food_utility_piecewise.total_width_multiplier`` — total width of the - curve relative to baseline (default: 2.0, so the curve spans 0 to 2× - baseline). - -See :doc:`configuration` and :doc:`tutorial` for the full configuration -reference and a worked example. - -Workflow Integration --------------------- - -**Snakemake rules**: - * ``extract_consumer_values`` — produces - ``/{name}/consumer_values/{baseline}/values.csv`` - * ``calibrate_food_utility_blocks`` — produces - ``/{name}/consumer_values/{baseline}/utility_blocks.csv`` - * ``plot_consumer_values_comparison`` — produces consumption, - objective and consumer-value comparison figures - -**Inputs**: - * Solved baseline network with ``mu_p_set`` duals on food consumption links. - -**Configuration parameters**: - * ``consumer_values.baseline_scenario`` — name of the scenario whose - duals are extracted (default: ``"baseline"``). - * ``food_utility_piecewise.enabled`` — set ``true`` for scenarios that - should respond to consumer values; **must** be ``false`` in any - scenario that also sets ``enforce_baseline_diet`` (the validation - layer rejects the combination). - * ``food_utility_piecewise.n_blocks``, ``decline_factor``, - ``total_width_multiplier`` — block geometry described above. - -**Output schema** (``values.csv``): - ``food, food_group, country, value_bnusd_per_mt, adjustment_bnusd_per_mt``. - ``value_bnusd_per_mt`` and ``adjustment_bnusd_per_mt`` differ only in sign - (the latter is what gets added to the marginal cost of the consumption - link in subsequent solves). diff --git a/docs/costs.rst b/docs/costs.rst index 236fef95..78545aed 100644 --- a/docs/costs.rst +++ b/docs/costs.rst @@ -221,13 +221,12 @@ for crops, grassland, and animals based on shadow prices from a two-step paired solve: * **Step 1** pins consumption to the baseline diet, enables hard - production-stability bounds at +/-20 %, and applies the file-level - ``validation.slack_marginal_cost: 5.0`` override (5 000 USD/t) to + production-stability bounds at +/-5 %, and applies the file-level + ``validation.slack_marginal_cost: 7.5`` override (7 500 USD/t) to cap the duals of the small set of foods whose FAOSTAT-vs-FBS - mismatch still exceeds the +/-20 % band (buckwheat, plantain, - coffee, tea, olive-oil). The food-bus duals feed the piecewise - consumer-utility blocks used by step 2. -* **Step 2** activates the step-1 piecewise utility and tightens + mismatch still exceeds the band (buckwheat, plantain, coffee, tea, + olive-oil). +* **Step 2** keeps consumption pinned to the baseline diet and tightens production stability to +/-1 %. The duals on these tight constraints become the per-group additive cost corrections. diff --git a/docs/current_diets.rst b/docs/current_diets.rst index 2c55ee58..bfdf5293 100644 --- a/docs/current_diets.rst +++ b/docs/current_diets.rst @@ -23,9 +23,8 @@ The baseline diet serves several roles: * **Optimization reference**: comparison point for optimized diets and, optionally, an equality constraint when ``enforce_baseline_diet`` is enabled. -* **Calibration**: anchors the market-response demand curves - (:doc:`calibration`) and the legacy consumer-utility piecewise blocks - (:doc:`consumer_values`). +* **Calibration**: anchors the market-response demand curves and the + production-side calibrations (:doc:`calibration`). .. figure:: https://github.com/Sustainable-Solutions-Lab/GLADE/releases/download/doc-figures/baseline_diet_by_region.png :alt: Baseline diet composition by world region @@ -717,8 +716,8 @@ multipliers, so the diet mass-balances against the food bus. (production net of feed/non-food/exports, after post-loss and consumer waste) can deliver — physically impossible — and inflates the calibrated ``feed`` L1 cost because the deviation-penalty - calibration must otherwise accommodate intake-derived consumer values - that exceed supply. + calibration must otherwise accommodate intake-derived demand + that exceeds supply. 2. **Trade is handled implicitly.** FBS supply per country already encodes ``production + imports − exports − feed − seed − non-food @@ -780,10 +779,8 @@ Downstream Uses ``config.food_groups.fix_within_group_ratios.enabled`` is true, foods within each group are constrained to keep their baseline proportions while group totals may vary. -* **Piecewise consumer utility calibration**: baseline per-food - consumption and baseline food-equality duals together calibrate - ``results/{name}/consumer_values/{baseline_scenario}/utility_blocks.csv`` - (:doc:`consumer_values`). +* **Demand-curve calibration**: baseline per-food consumption anchors the + market-response demand curves (:doc:`calibration`). * **Health impact assessment**: baseline consumption feeds the population-attributable fraction calculation (:doc:`health`). diff --git a/docs/index.rst b/docs/index.rst index 4b59aa1e..ecaf66c2 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -99,7 +99,6 @@ Dietary constraints ensure that each country's population meets nutritional requ food_processing nutrition current_diets - consumer_values health environment costs diff --git a/docs/tutorial.rst b/docs/tutorial.rst index 07beb69d..6588fcbd 100644 --- a/docs/tutorial.rst +++ b/docs/tutorial.rst @@ -150,144 +150,130 @@ reduce emissions more cheaply — the model had no way to weigh "change what people eat" against "change how food is produced". Part 2 adds that missing piece. -Part 2 — Letting diet respond via consumer values -------------------------------------------------- +Part 2 — Letting the diet respond to policy +-------------------------------------------- In Part 1, we fixed consumption with ``enforce_baseline_diet: true``. That guarantees realism (nobody is forced to eat something unusual), but it also -rules out dietary shift as a mitigation option. A more interesting model -lets the optimiser decide when giving up some of today's diet is worth the -GHG savings — which requires pricing the cost of deviating from today's -diet. - -GLADE does that by **deriving consumer values from a baseline -solve**: - -1. Solve a baseline scenario with ``enforce_baseline_diet: true``. The - per-(food, country) equality constraints on the ``food_consumption`` - links are binding, and their **dual variables** (shadow prices) represent - each food's marginal utility under today's diet — expressed as bn USD - per Mt. -2. Feed those consumer values into a **piecewise diminishing-marginal-utility - curve** centred at baseline consumption. Each block represents an - additional increment of consumption beyond (or below) baseline, with - decreasing utility. -3. In subsequent scenarios, drop ``enforce_baseline_diet`` and enable the - piecewise curve. Consumption is now free to move, but the optimiser - "pays" for deviations — so small dietary shifts are cheap while large - ones become expensive. - -The workflow automates steps 1–2: the ``extract_consumer_values`` and -``calibrate_food_utility_blocks`` rules run automatically whenever a -scenario needs the calibrated blocks. - -Step 1 — Look at the config -~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Open ``config/tutorial/02_consumer_values.yaml``: - -.. literalinclude:: ../config/tutorial/02_consumer_values.yaml +rules out dietary shift as a mitigation option. A more interesting model lets +the optimiser decide when giving up some of today's diet is worth the GHG +savings — which requires pricing how much today's diet is *worth*. + +GLADE prices it with **calibrated demand curves**, using the same +positive-mathematical-programming machinery that anchors production (the +``market_response`` mechanism, :doc:`calibration`). The idea rests on revealed +preference: today's diet is what consumers actually chose at today's prices, +so treat it as an economic equilibrium rather than a constraint. +Operationally: + +1. **Measure** (calibration): solve the model once with every (food, country) + consumption pinned at its observed intake. The shadow price of each pin is + the gap between what supplying that food costs and what consumers evidently + value it at — a per-food willingness to pay. +2. **Reproduce**: attach to each consumption link a marginal-utility curve + through the observed point, with the fitted willingness to pay as its + level. Today's diet is now the model's *unconstrained* optimum: solve with + no policy pressure and you get the observed food system back, with nothing + pinning it there. +3. **Respond**: away from the observed point the curve falls off at a rate + set by literature demand elasticities (per food group, e.g. staples ~0.55, + meat and dairy ~0.72). Under a carbon price, emission-intensive foods + become dearer and consumption slides down each curve — by a lot where + demand is elastic, barely at all where it is not. + +Step 1 — Calibrate the curves +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Open ``config/tutorial/02_demand_response.yaml``: + +.. literalinclude:: ../config/tutorial/02_demand_response.yaml :language: yaml -The key differences from Part 1: - -* ``food_utility_piecewise.enabled: true`` at the top level turns on the - piecewise utility curve globally. -* ``consumer_values.baseline_scenario: "baseline"`` tells the calibration - step which scenario's dual variables to extract. The name must match one - of the scenarios below. -* The ``baseline`` scenario keeps ``enforce_baseline_diet: true`` and - **explicitly disables** the piecewise curve - (``food_utility_piecewise.enabled: false``). These two settings are - mutually exclusive — attempting to combine them raises a validation - error. -* The ``ghg_mid`` and ``ghg_high`` scenarios inherit the top-level - ``food_utility_piecewise`` settings and do not set - ``enforce_baseline_diet``, so consumption is free. +The config inherits the market-response defaults; what it declares is a +dedicated artefact set (``calibration.source: "tutorial-02"``). The curves are +keyed to the exact model structure — at the default ``link`` granularity every +production link carries its own curve — so a 200-region tutorial model cannot +reuse the artefacts fit for the full-resolution default config. Fit the +tutorial's own set (one command, roughly 10–20 minutes; add +``CALIBRATE_PIXI_ENV=default`` to use the open-source solver): -The piecewise-utility parameters themselves are worth a brief look: +.. code-block:: bash -* ``n_blocks: 4`` — the curve has four steps above and below baseline. -* ``decline_factor: 0.7`` — each successive block is worth 70% of the - previous one, giving diminishing returns. -* ``total_width_multiplier: 2.0`` — the curve spans from 0 up to twice - baseline consumption. + tools/calibrate --base config/tutorial/02_demand_response.yaml -See :doc:`configuration` for the full description. +The result lands in ``data/curated/calibration/tutorial-02/``. The file to +look at is ``market_response.csv``: one row per curve group, whose +``intercept`` column holds the fitted wedges — for ``demand`` rows, minus the +willingness to pay (bn USD/Mt, numerically USD/kg). Skim the beef and tomato +rows for a feel of what the calibration inferred about consumer valuations. -Step 2 — Solve the baseline first -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +Step 2 — The reference solve +~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -Part 2 involves two sequential steps: the baseline must be solved before -consumer values can be extracted and the other scenarios can build their -utility blocks. Snakemake handles the dependency automatically, but it is -instructive to do the baseline on its own first: +The ``reference`` scenario turns GHG pricing off and nothing else: .. code-block:: bash - tools/smk -j4 --configfile config/tutorial/02_consumer_values.yaml -- \ - results/tutorial_02/solved/model_scen-baseline.nc - -After this finishes you will have: + tools/smk -j4 --configfile config/tutorial/02_demand_response.yaml -- \ + results/tutorial_02/solved/model_scen-reference.nc -* ``results/tutorial_02/solved/model_scen-baseline.nc`` — the baseline - solution. -* ``results/tutorial_02/consumer_values/baseline/values.csv`` — the - extracted dual variables. -* ``results/tutorial_02/consumer_values/baseline/utility_blocks.csv`` — the - calibrated piecewise utility curve. +This solve has **no baseline enforcement, no deviation penalty, and no hard +anchoring constraints** — yet it lands within a fraction of a percent of +observed 2020 production and consumption. That is the exact-calibration +property doing its work, and it is worth pausing on: the observed food system +is now the model's own economic optimum, so every later scenario measures a +*departure caused by policy*, not an artefact of loose calibration. -The companion notebook :doc:`tutorials/tutorial_02_analysis` begins with a -quick look at the extracted values — the ``value_bnusd_per_mt`` column of -``values.csv`` ranks each (food, country) pair by the marginal utility the -baseline implies. +Step 3 — Price the carbon +~~~~~~~~~~~~~~~~~~~~~~~~~ -Step 3 — Solve the remaining scenarios -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +Solve the remaining scenarios: .. code-block:: bash - tools/smk -j4 --configfile config/tutorial/02_consumer_values.yaml + tools/smk -j4 --configfile config/tutorial/02_demand_response.yaml -Now both the mid- and high-GHG scenarios solve, using the same calibrated -utility blocks. On a laptop, each solve takes a few minutes longer than -Part 1 because the LP has extra variables for the piecewise blocks. +Alongside the ``ghg_mid``/``ghg_high`` ladder, two variants make the demand +side itself the experiment: -Step 4 — Compare against Tutorial 1 in a notebook -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +* ``ghg_high_fixed`` freezes the diet at the observed baseline + (``components.demand: false`` + ``enforce_baseline_diet``) under the same + $200 carbon price — production-side mitigation only. +* ``ghg_high_stiff`` halves every demand elasticity via + ``elasticity_factor: 0.5``. Elasticities enter only at solve time, so + scanning them needs no recalibration. -The companion notebook :doc:`tutorials/tutorial_02_analysis` covers three -comparisons: +Step 4 — Compare in the notebook +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -* Global food-group consumption across the three scenarios, to see whether — - and which — food groups actually shift once the diet is free. -* The objective breakdown with the ``consumer_values`` column visible - alongside ``ghg_cost`` (the two forces trading off against each other). -* A side-by-side comparison of net GHG emissions between Tutorial 1 - (fixed diet) and Tutorial 2 (flexible diet) at identical GHG prices. The - gap between the two is a rough measure of the demand-side mitigation - potential. +The companion notebook :doc:`tutorials/tutorial_02_analysis` builds three +comparisons: -Also have a look at the auto-generated comparison plot at -``results/tutorial_02/plots/consumer_values/consumption_comparison.pdf``, -which shows the same pattern per food group. +* **What shifts**: global food-group consumption across the price ladder — + which foods absorb the carbon price and which barely move. +* **The two sides of the market**: the objective breakdown now carries + ``supply_response`` and ``demand_response`` columns — the cost of moving + production and consumption off their observed patterns — next to + ``ghg_cost``, the force pushing them. +* **The value of flexibility**: net emissions and total cost of ``ghg_high`` + vs ``ghg_high_fixed`` vs ``ghg_high_stiff``. The fixed-vs-flexible gap is a + direct measure of demand-side mitigation potential; the stiff variant shows + how much of it hinges on the elasticity assumption. Gotchas ~~~~~~~ -A few things that commonly trip people up: - -* ``food_utility_piecewise.enabled: true`` and - ``validation.enforce_baseline_diet: true`` cannot be active for the same - scenario. The baseline scenario enables the latter and disables the - former; all other scenarios do the opposite. -* ``consumer_values.baseline_scenario`` must name a scenario that exists and - that has ``enforce_baseline_diet: true``. If it doesn't, the calibration - rule fails with a validation error. -* The calibrated utility blocks are **specific to the baseline scenario** - that produced them. If you change the baseline (e.g. different - ``planning_horizon`` or ``baseline_year``), rerun the baseline solve so - the values and blocks are regenerated. +* The intercept artefact is **specific to the model structure it was fit + against**. Change the region count, crop set, or diet source and the solve + fails loudly on missing curve groups — rerun + ``tools/calibrate --base `` for the new structure. +* ``market_response.components.demand`` and + ``validation.enforce_baseline_diet`` are mutually exclusive: a scenario + pinning the diet must switch the demand component off (see + ``ghg_high_fixed``). +* Demand curves value consumption; they do not guarantee adequacy. The + nutrition constraints (:doc:`nutrition`) still put floors under nutrients, + so extreme carbon prices reshape diets rather than shrinking them away. Where to go from here --------------------- diff --git a/docs/tutorials/tutorial_02_analysis.ipynb b/docs/tutorials/tutorial_02_analysis.ipynb index 73501111..59c4059e 100644 --- a/docs/tutorials/tutorial_02_analysis.ipynb +++ b/docs/tutorials/tutorial_02_analysis.ipynb @@ -2,24 +2,17 @@ "cells": [ { "cell_type": "markdown", - "id": "intro", "metadata": {}, "source": [ - "# Tutorial 2 — Analysis\n", + "# Tutorial 2 \u2014 Analysis\n", "\n", - "Companion notebook to [Tutorial Part 2](../tutorial.rst). It loads the three solved `tutorial_02` scenarios, inspects the extracted consumer values, compares food-group consumption and the objective breakdown across scenarios, and finally compares total GHG emissions against the fixed-diet run from Tutorial 1.\n", - "\n", - "Prerequisites: both tutorial workflows must have been solved locally.\n", - "\n", - "```bash\n", - "tools/smk -j4 --configfile config/tutorial/01_ghg_prices.yaml\n", - "tools/smk -j4 --configfile config/tutorial/02_consumer_values.yaml\n", - "```" + "Companion notebook for Part 2 of the tutorial. It assumes all five\n", + "`tutorial_02` scenarios have been solved and analysed\n", + "(`tools/smk --configfile config/tutorial/02_demand_response.yaml -- analyze_all_scenarios`)." ] }, { "cell_type": "markdown", - "id": "setup-md", "metadata": {}, "source": [ "## Setup" @@ -27,16 +20,8 @@ }, { "cell_type": "code", - "execution_count": 1, - "id": "setup", - "metadata": { - "execution": { - "iopub.execute_input": "2026-07-29T19:05:11.735125Z", - "iopub.status.busy": "2026-07-29T19:05:11.734854Z", - "iopub.status.idle": "2026-07-29T19:05:12.134118Z", - "shell.execute_reply": "2026-07-29T19:05:12.133592Z" - } - }, + "metadata": {}, + "execution_count": null, "outputs": [], "source": [ "from pathlib import Path\n", @@ -45,216 +30,65 @@ "import pandas as pd\n", "\n", "project_root = Path(\"..\", \"..\").resolve()\n", - "scenarios = [\"baseline\", \"ghg_mid\", \"ghg_high\"]\n", + "scenarios = [\"reference\", \"ghg_mid\", \"ghg_high\", \"ghg_high_stiff\", \"ghg_high_fixed\"]\n", "\n", "\n", - "def load_analysis(config_name: str, filename: str) -> pd.DataFrame:\n", - " \"\"\"Concatenate a per-scenario parquet file for a given tutorial config.\"\"\"\n", - " results = project_root / \"results\" / config_name / \"analysis\"\n", + "def load_analysis(filename: str, which=None) -> pd.DataFrame:\n", + " \"\"\"Concatenate a per-scenario analysis parquet for tutorial_02.\"\"\"\n", + " results = project_root / \"results\" / \"tutorial_02\" / \"analysis\"\n", " return pd.concat(\n", - " pd.read_parquet(results / f\"scen-{s}\" / filename).assign(\n", - " scenario=s, config=config_name\n", - " )\n", - " for s in scenarios\n", + " pd.read_parquet(results / f\"scen-{s}\" / filename).assign(scenario=s)\n", + " for s in (which or scenarios)\n", " )" ] }, { "cell_type": "markdown", - "id": "values-md", "metadata": {}, "source": [ - "## Inspect the extracted consumer values\n", + "## What the calibration inferred about consumers\n", "\n", - "The `baseline` solve fixes consumption at observed 2020 levels; the dual variables of the binding per-(food, country) equality constraints on the `food_consumption` links are the consumer values. Each value is expressed in bn USD per Mt of that food in that country — the marginal utility the non-baseline scenarios use when letting diet shift." + "The fitted demand wedges live in the tutorial's artefact set. For `demand`\n", + "rows the intercept is *minus* the willingness to pay, in bn USD/Mt --\n", + "numerically the same as USD/kg. The ranking should look economically\n", + "sensible: high-value animal products at the top, staples near the bottom." ] }, { "cell_type": "code", - "execution_count": 2, - "id": "values", - "metadata": { - "execution": { - "iopub.execute_input": "2026-07-29T19:05:12.135866Z", - "iopub.status.busy": "2026-07-29T19:05:12.135714Z", - "iopub.status.idle": "2026-07-29T19:05:12.162280Z", - "shell.execute_reply": "2026-07-29T19:05:12.161784Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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foodfood_groupcountryvalue_bnusd_per_mt
0meat-sheepred_meatASM8.347789
1meat-sheepred_meatCAN8.317953
2meat-sheepred_meatUSA8.297657
3meat-sheepred_meatMEX8.281869
4meat-sheepred_meatBHS8.259365
5meat-sheepred_meatHTI8.258202
6meat-sheepred_meatPAN8.255452
7meat-sheepred_meatCRI8.248279
8meat-sheepred_meatJAM8.245475
9meat-sheepred_meatGTM8.243597
\n", - "
" - ], - "text/plain": [ - " food food_group country value_bnusd_per_mt\n", - "0 meat-sheep red_meat ASM 8.347789\n", - "1 meat-sheep red_meat CAN 8.317953\n", - "2 meat-sheep red_meat USA 8.297657\n", - "3 meat-sheep red_meat MEX 8.281869\n", - "4 meat-sheep red_meat BHS 8.259365\n", - "5 meat-sheep red_meat HTI 8.258202\n", - "6 meat-sheep red_meat PAN 8.255452\n", - "7 meat-sheep red_meat CRI 8.248279\n", - "8 meat-sheep red_meat JAM 8.245475\n", - "9 meat-sheep red_meat GTM 8.243597" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "execution_count": null, + "outputs": [], "source": [ - "values = pd.read_csv(\n", - " project_root / \"results/tutorial_02/consumer_values/baseline/values.csv\"\n", + "curves = pd.read_csv(\n", + " project_root / \"data/curated/calibration/tutorial-02/market_response.csv\"\n", ")\n", - "\n", - "# Top 10 (food, country) pairs by absolute consumer value.\n", - "top = (\n", - " values.reindex(\n", - " values[\"value_bnusd_per_mt\"].abs().sort_values(ascending=False).index\n", - " )\n", - " .head(10)[[\"food\", \"food_group\", \"country\", \"value_bnusd_per_mt\"]]\n", - " .reset_index(drop=True)\n", + "demand = curves[curves[\"component\"] == \"demand\"].assign(\n", + " wtp_usd_per_kg=lambda df: -df[\"intercept\"]\n", ")\n", - "top" + "demand.sort_values(\"wtp_usd_per_kg\", ascending=False).head(10)[\n", + " [\"mr_group\", \"wtp_usd_per_kg\", \"slope_basis\"]\n", + "].reset_index(drop=True)" ] }, { "cell_type": "markdown", - "id": "consumption-md", "metadata": {}, "source": [ "## Does the diet actually move?\n", "\n", - "Tutorial 1 held consumption fixed; Tutorial 2 lets it respond. The stacked bars below show global food-group consumption (Mt) for each scenario — we expect animal-product categories to contract and plant-based categories to expand as the GHG price rises." + "Global food-group consumption across the carbon-price ladder. The `reference`\n", + "column doubles as a calibration check: it should match observed 2020\n", + "consumption almost exactly." ] }, { "cell_type": "code", - "execution_count": 3, - "id": "consumption", - "metadata": { - "execution": { - "iopub.execute_input": "2026-07-29T19:05:12.164130Z", - "iopub.status.busy": "2026-07-29T19:05:12.164044Z", - "iopub.status.idle": "2026-07-29T19:05:12.324937Z", - "shell.execute_reply": "2026-07-29T19:05:12.324408Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "execution_count": null, + "outputs": [], "source": [ - "consumption = load_analysis(\"tutorial_02\", \"food_group_consumption.parquet\")\n", + "consumption = load_analysis(\"food_group_consumption.parquet\")\n", "by_group = (\n", " consumption.groupby([\"scenario\", \"food_group\"])[\"consumption_mt\"]\n", " .sum()\n", @@ -262,7 +96,7 @@ " .reindex(scenarios)\n", ")\n", "\n", - "ax = by_group.plot.bar(stacked=True, ylabel=\"Global consumption (Mt)\", rot=0)\n", + "ax = by_group.plot.bar(stacked=True, ylabel=\"Global consumption (Mt)\", rot=20)\n", "ax.set_title(\"Food-group consumption\")\n", "ax.legend(bbox_to_anchor=(1.02, 1), loc=\"upper left\", fontsize=8)\n", "plt.tight_layout()" @@ -270,171 +104,66 @@ }, { "cell_type": "markdown", - "id": "breakdown-md", "metadata": {}, "source": [ - "## Objective breakdown with the utility term visible\n", + "## The two sides of the market\n", "\n", - "The `consumer_values` column represents utility gained from (or disutility of deviating from) baseline consumption, entering the minimisation as a subtraction. It is negative when the flexible diet still sits close enough to baseline that the piecewise utility is net positive; it can turn positive at high GHG prices, when the optimiser pushes consumption far enough away from baseline that the cumulative utility loss exceeds the gain. Meanwhile `ghg_cost` is negative because the model achieves net-negative emissions at these prices — the gap between `ghg_cost` columns in Tutorial 1 vs Tutorial 2 measures how much extra mitigation the flexible diet unlocks." + "`supply_response` and `demand_response` are the deviation costs of moving\n", + "production and consumption off their observed patterns; `ghg_cost` is the\n", + "force pushing them. Note how both response terms grow with the carbon price,\n", + "and how `demand_response` vanishes in the fixed-diet scenario." ] }, { "cell_type": "code", - "execution_count": 4, - "id": "breakdown", - "metadata": { - "execution": { - "iopub.execute_input": "2026-07-29T19:05:12.326419Z", - "iopub.status.busy": "2026-07-29T19:05:12.326330Z", - "iopub.status.idle": "2026-07-29T19:05:12.337332Z", - "shell.execute_reply": "2026-07-29T19:05:12.336949Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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categoryghg_costconsumer_valuescrop_productionanimal_production
scenario
baselineNaNNaN1593.821008.01
ghg_mid-127.9-3752.721314.54942.26
ghg_high-1792.5-2900.50947.02551.76
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" - ], - "text/plain": [ - "category ghg_cost consumer_values crop_production animal_production\n", - "scenario \n", - "baseline NaN NaN 1593.82 1008.01\n", - "ghg_mid -127.9 -3752.72 1314.54 942.26\n", - "ghg_high -1792.5 -2900.50 947.02 551.76" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "execution_count": null, + "outputs": [], "source": [ "breakdown = (\n", - " load_analysis(\"tutorial_02\", \"objective_breakdown.parquet\")\n", + " load_analysis(\"objective_breakdown.parquet\")\n", " .set_index(\"scenario\")\n", " .reindex(scenarios)\n", ")\n", - "columns = [\"ghg_cost\", \"consumer_values\", \"crop_production\", \"animal_production\"]\n", - "breakdown[columns].round(2)" + "columns = [\n", + " \"ghg_cost\",\n", + " \"supply_response\",\n", + " \"demand_response\",\n", + " \"crop_production\",\n", + " \"animal_production\",\n", + "]\n", + "breakdown[[c for c in columns if c in breakdown.columns]].round(2)" ] }, { "cell_type": "markdown", - "id": "compare-md", "metadata": {}, "source": [ - "## Fixed-diet vs flexible-diet at the same GHG price\n", + "## The value of dietary flexibility\n", "\n", - "Tutorial 1 and Tutorial 2 share the same GHG prices but differ in whether diet is allowed to respond. The gap between the two bars at each GHG price is a rough measure of the additional, demand-side abatement available when consumption is free to move." + "Net emissions at the same $200/tCO2-eq price under three demand regimes:\n", + "fully elastic (`ghg_high`), half-elasticity (`ghg_high_stiff`), and frozen at\n", + "the observed diet (`ghg_high_fixed`). The elastic-vs-fixed gap is the\n", + "demand-side mitigation potential; the stiff case shows how much of it hinges\n", + "on the elasticity assumption." ] }, { "cell_type": "code", - "execution_count": 5, - "id": "compare", - "metadata": { - "execution": { - "iopub.execute_input": "2026-07-29T19:05:12.338669Z", - "iopub.status.busy": "2026-07-29T19:05:12.338584Z", - "iopub.status.idle": "2026-07-29T19:05:12.416195Z", - "shell.execute_reply": "2026-07-29T19:05:12.415761Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "scenario baseline ghg_mid ghg_high\n", - "config \n", - "tutorial_01 5425.6 -299.2 -4648.1\n", - "tutorial_02 5455.3 -2558.0 -8962.5\n" - ] - }, - { - "data": { - "image/png": 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Ace1LX2oKFkKpKSZy0s/49qEBEgow9KEeSk2M2o836jWpvC+T0C8vvW6nGm2aWGpa0xdC6LQmev/1PJSR8rIXyaHRzTpWlJU9FQVxynxpEJACNr2e8XVO12ui7IeOe31Rqjk0JST23BENWNBgGQ0Q0N/KKGp6Fw2KSYz4zruEJiRObP30nsV1LmlaDgVEkY+tqW68QSDxUYZZ57u6BajjflyP730uJHSexcV7n+M7Z+M7XyXynI0cqZzQPnROqkk/8vhR9xgdY5GDORJL9438YaqBLinRxKlzUsFv5GAjDcLRZ1XkXHYaFKKATsepfogkRAGtzsHQgPFMRsYOaeKdd95xX7hqslITkC5Kq6vfkUaOqq/IHXfc4T641TyiiSm1Tc0lCjDUf0ej6vRlriZg9W/TyELdrtFgGlGlEV+JaSIUjWjV9B4aGac0v77A9dgKFE5F9VVmQV/oS5YscdN5qClLv6Dnzp3rvozUvCX6W4GspkRQdkRTTSTUZK0vR2VyvLnhPN4XnJo0NaJRz1tfhsp0KBOh5hNlXjTSVx/4r776qvvCDuVlsDTXk+6nL0plKvSlpvvrF7F+reuXs76E9WtZZU71oRof9Z9U05v2p6lp9MGr1yGxI5cT06yvZl1Nl6BRfOrn5E1UGjmRr778veyt9yWsLxTvizbyPVGTul7LxGQ69OWuYE7BkqZiiJxLS/3LVC81bykrpuBWx4QyfKF9TE9HYs8dHaM6d/Tc9L7qeFR/Ox3Deo8SM5m26q7mV9VdP4507ChYVV+7061fQlk3jfjUuapMv95zvdb64k9M9wmdN5oSSV0DdG7qPdeIZE3voYDK65Ompl/9yFFd9TmjKTYUbMb3I0HBisrreFE/tlA6Z9V3UPtXQKNzTuesXgM1O2rEs851vZYKduKaxsPL9Oo90rGj81HbHn30URfE6fNL+1BGXAGYjjM9P7WKJIcCcNVFx7SCYM33qFHs55xzjp0uZU017Yo+rzQqXp8L6run407Hkqae8mjksj7PtE3PMbIPZOT8e97tyQ1ofYcRJEgqb/SWRpDFJXLE1PLly92oy8gRYocPHw7UrFkzULZsWTfqTTRqctCgQYELLrjAjVY766yzApUrV3ajH3///ffgfY8cORLo3r17oEiRIq7cZZdd5karRo4ETciqVavcKFjd/+yzzw60b98+8MUXX5xyVKxHI0MvvfRSN/pNz08jz+6+++6wUVsabXfbbbcF8ufP70ZtneqUW7dunSv3ySefxLqtT58+gRIlSrjRmqF11GuhkbilSpVy9dAoOo32jeu10IjkcuXKuVGpkaPa3nnnncD5558fyJYtWyBfvnyBm266KbBy5cqw+2t/er5xiet10shQ770sWbJk4LHHHgt89dVXiX6NT2X79u3uNdf75x0HM2bMSNJI7sjXSO9BXCM4E/LWW2+5++j1jxx5uWbNmsCdd97pjg/drtdWI1LffffdFBsVm5hzRyMcdZ+iRYvGGpnojbqeNGnSKUfFLl26NNCsWTO3/zx58rjntmPHjtOu36mcPHkyMHDgQHec6xjVsarjK/Kx4huxqe333nuvOw41orpw4cKByy+/PPDcc8+FldMIXtVLZeIbcR6qTZs2gapVq8barnOwbt26btRx5MjdRYsWucfWuaT66DF0/kWOit24caMbMarXWbeFniN//vmnG7FesGBBV9dKlSq59zF0hLb3Wmh7pLheJ30O63NQn6uq9xVXXBH44YcfEv0aJ4ZGq3ufM8WKFQt07do1sH///rAyeqz4zte4PkP1HtSoUSPJdfGrTPonvYNLAP+fN3IwrslXkfqUgVI2RJm9hEZ5A6KsvebIU9ZIGTykrX379rmMqro3aF5DmBHYARmMmpU1Kan6D3kz4SNtqHlUTV4aQRk5tQYQH/VR1EAhNbsjbanLiJqq1WeRH2L/w+AJIINRXzj1RUzMCF0/ipzLLK5LatmwYYP16dMnUfMZAqF9wvQj7Exd0upU52voMmcpTf2q1S+QoO7/I2MHIEPRJKvefIPxoQcJkDFozdlTTRejgTqnWnMXKYfADkCG+6I41ZxeiZkMG0Dq0zQ5kVO3REpoZDFSHoEdAACAT9DHDgAAwCcYy58K1FFUM2FrQsrTWYgcAAAgEAi4wTlq0tbE4gkKRJEtW7YEWrdu7SYj1USfmugydDJYTWCpiR6LFy/uJsDUJIe//vprrElxu3Tp4iZ11ASMmmhz8+bNYWU0qexdd90VyJs3r7vob28C3cTQ/hKaXJELrwHHAMcAxwDHAMcAx4Al8TWIjFfiEjUZOy1DpKWctGSIJm7V8kGaQDR0YWItVzJkyBA39FlLqmj5GS1VoiWGvEWmtZSUlnjRMi1aSql79+5uGRUtnaIlT0RTHWipG2/Bai0fpWVQ4lrLLy7eY2mJmMQucQUAABDfRMylSpUKxhe+GDzRu3dvmzdvnv3www9x3q6noRSlAjetPypHjhxxa4C++OKLbm26vXv3WuHChe399993E0qKmkz1Yk2bNs0aNWpkq1evdgsJh84irr+1FqTWEQ1dTDuhN0DrdOrxCOwAAMDpSEpcETWDJyZPnuymONDiz8rWaWb+t99+O2xiUU3o2rBhw+C27Nmzu8WuNYO/KCt37NixsDIKBjUhrFdmwYIF7sULXRpGCw5rm1cGAAAgI4qawO6PP/6wkSNHWsWKFe2bb76xjh07WteuXd2ajuLN0q8MXShd927T/9myZbMCBQokWEaBYyRti28lAGUGFU2HXgAAANJa1mgaaaqM3YABA9x1ZexWrlzpgr277747WC5yFKqaaE81MjWyTFzlE9rPwIED3Xp1AAAA6SlqArvixYu7vm+hqlSpYhMmTHB/FytWzP2vrJrKenbu3BnM4qmMZsnWQIzQrJ3KXH755cEyO3bsiPX4u3btipUN9GhtyW7dusXq5AgAwOk6ceKE60YE/4qJiQkO4DxjAjuNiNXo1lC//fablSlTxv2tteoUlM2YMcNl80RB3Jw5c9zgCalZs6Z78VSmRYsWbtu2bdvs119/dSNqRYMk1Dlx0aJFdskll7htCxcudNu84C+S+vLpAgBASlFLkZIV//77Ly/qGSB//vwujjnd+W+jJrB79NFHXWClplgFZQq83nrrLXcRvRAaEavb1Q9PF/2dK1cuN32JaABE+/bt3RQnmurk7LPPth49eliNGjXsuuuuC2YBGzdubB06dLA333wzON2JpkRJzIhYAABSghfUqY+3vsuY8N6fFMD/999/rvVQQlsdfR3Y1a5d2yZNmuSaPZ955hmXoRs2bJi1bt06WKZnz5526NAh69Spk2tu1cjW6dOnh837MnToUMuaNasLDlX22muvdfPehaZAx40b5wZmeKNnb7zxRnv11VfT+BkDAM7k5lcvqFMiAv6WM2dO97+CO73np9MsGzXz2EUT5rEDAJyOw4cPu2m8ypYtG/zSh78dOnTINm7c6BJXOXLk8P88dgAAnGlofj1zZEqhteUJ7AAAAHyCwA4AAGRoGlxw6623umZIZbbU/1DN1OprjygdPAEAAM5M7733nlsrXkt7FipUyPU3W7x4seXOnTu9q5bhENgBAIAMbf369W46Mq3t7ilcuHC61imjoikWAACc9rKfWgzg3HPPdRP2ly5d2p5//nl324oVK+yaa65xo3s1dYvmhj1w4EDwvu3atbObb77ZBg0a5OZwU5nOnTsHV9uoX7++DR482L7//nvXDKvrEtkUu2bNGrviiivciFKtVDVz5kxX/vPPPz+j3l0ydgAA4LRojtm3337bzRWr4EqrOinQUt84Tfp/2WWXuaZTzdN23333WZcuXdwcsp5Zs2a5oE7/r1u3zlq2bGkXXnihWyxg4sSJ1rt3b7dKlP7Oli1bnIGlgsPSpUu71aL279/vFiM4ExHYIXr1y2dRp9/e9K4BAKQoBVGvvPKKm8i/bdu2bluFChVcgKdgT/OzjR07NtgfTuWaNWvmMnzeGuxav13bNTFv5cqVrWnTpvbtt9+6wE6rRGnlDQV03rrwkbQYgZprZ8+eHSyjjGGDBg3OuHebwA5BZXtPjapXY2P4/I0AgHSwevVqO3LkiFvJKa7bLrjggrBBDlr7XRk2rf/uBXbVqlULW21B2Ts14SaW9lWqVKmwwM9b7/1MQx87AACQbAmtjKHFreKbeDd0e0xMTKzbFPwlVkKPc6YhsAMAAMlWsWJFF9yp6TSSBjH8/PPPdvDgweC2efPmWebMme28885LsVddzbebNm2yHTt2BLepT9+ZiMAOAAAkm0ah9urVy3r27On60qmv248//mijRo2y1q1bu9vV906DHzQ44qGHHrI2bdoEm2FTgvrSqV9f27Ztbfny5S547Nu3r7vtTMvkEdgBAIDT8uSTT7pRqE899ZSbb06jWjUCVoMevvnmG/vnn3+sdu3adtttt7m+eBookZLUP0/Tmhw4cMA9jkbePvHEE+42BZZnkkwBNUwjRe3bt8/Nir137163/Em0iL7BE60s6jAqFkAiHD582DZs2GDlypU74wKTlDJv3jw3MlfTpyibF83veVLiCkbFAgCAqDdp0iQ766yzXJ8/BXMPP/ywG4EbDUFdSiKwAwAAvphPr2fPnrZ582a3nux1113nVqw40xDYAQCAqHf33Xe7y5mOwRMAAAA+QWAHAADgEwR2AAAAPkFgBwAA4BMEdgAAAD5BYAcAAOATBHYAAMBX6tevb4888kiiy8+ePdutKfvvv/9atGMeOwAAokRaL/248YWmyQqqLrzwQhs2bFii79OvXz+31uvPP/9sKWHixIkWExNjqWXFihXWpUsXW7RokZ199tn2wAMPuPVyFRzKtm3b3Nq5S5cutd9//926du2apNfjdJCxAwAAvnDs2DH3v4KtPHnypMpjaN3WBg0aWIkSJWzx4sU2YsQIGzRokA0ZMiRY5siRI1a4cGHr27evXXDBBZaWCOwAAECKaNeunc2ZM8deeeUVl73S5d1337X8+fOHlVN2zstu6fb+/fvbL7/8EnYf2bRpk910001uDdi8efNaixYtbMeOHWGZPmUHR48ebeXLl7fs2bNbIBCI1RT7wQcfWK1atVywV6xYMWvVqpXt3LkzWc9x3LhxdvjwYVfH6tWrW/Pmze3xxx93gZ0eW8qWLeteA62EkS9fPktLBHYAACBFKJipU6eOdejQwTVH6nLixIkE79OyZUvXbFmtWrXgfbRNQdLNN99s//zzjwsWZ8yYYevXr3e3hVq3bp198sknNmHChHibco8ePWrPPvusCx4VVG7YsMEFocmxYMECq1evngsiPY0aNbKtW7faxo0bLb3Rxw4AAKQIZaeyZctmuXLlcpkxyZIlS4L3yZkzp8vIZc2aNXgfUSC3fPlyF4SVKlXKbXv//fddAKgm0Nq1aweDNm1X02d87r333uDfyuwNHz7cLrnkEjtw4IB77KTYvn27y8iFKlq0aPC2cuXKWXoiYwcAADKc1atXu4DOC+qkatWqrllXt3nKlCmTYFAny5Ytc026KqvmWDXVek29yeE1I3u8JtjI7emBwA4AAKReoJE5czDwiRzkkBDdJ65AKXJ77ty5E9zPwYMHrWHDhi4zp752yvZNmjQpmO1LKmUVlZkL5fXX8zJ36YnADgAApBg1xYb2q1M2bf/+/S7A8kT2hYu8j5edU0Zt8+bNwW2rVq2yvXv3WpUqVRJdnzVr1tju3bvthRdesCuvvNIqV66c7IEToj6E33//fVhQOH36dDdKNrKJNj0Q2AEAgBSj4GbhwoVuIIECqksvvdT1udPIUQ10+PDDD4OjXkPvo750Cvh0H00Xct1119n5559vrVu3tp9++snNGadRphq4oBGuiVW6dGkXOGpakj/++MMmT57sBlIkl0bUauCEBl/8+uuvLvs3YMAA69atW1gmUc9FF/Xj27Vrl/tbgWlqI7ADAAAppkePHm7AhDJuytZp3jc1gU6bNs1q1KhhH330kZumJNStt95qjRs3tquvvtrdR2UUJGkEa4ECBeyqq65ygZ4GPnz88cdJqk/hwoVdIPnpp5+6Oilzp3nnTmeAiAZ2bNmyxQWYnTp1ckGdLqEuuugid9EkxQpm9XeTJk0stWUKRDZ847TpINYbr3Sx5t2JFmk9o/np2pijlUWdfnvTuwYAooDmSVMGSyMsc+TIkd7VQTq/50mJK8jYAQAA+ASBHQAAwP+5/vrr3QjauC7qS5fRMUExAADA/3nnnXfs0KFDFhetQZvRRW3GbuDAga5jZehacOouqA6ZGnKsmaw1AeHKlSvD7qeRNg899JAVKlTIzX1z4403ug6Qofbs2WNt2rRx7dm66O9///03zZ4bAABIHyVLlrRzzz03zguBXSrR5IJvvfWWGwYd6qWXXnKL8L766quujCYRbNCggZs/x6NAUEOTx48fb3PnznXDkG+44Yaw+XM0lFnDkr/++mt30d8K7gAAADKyqMvYKRDTnDZvv/22GwIdmq0bNmyY9e3b15o3b27Vq1e39957z/777z83zFg0mmTUqFE2ePBgN2xaQ481BHvFihU2c+ZMV0bLlCiYUypWkxDqosf68ssvbe3aten2vAEAAHwX2HXu3NmaNm3qArNQGiKsJT60bIhHEwhqIsP58+e765pLRsuYhJZRs62CQK/MggULXPOrJlT0XHbZZW6bVyaSmnc1FDn0AgAAkNaiavCEmk81+7SaWSN567ZFrtOm63/++WewjGafDs30eWW8++v/IkWKxNq/tkWuDRfa369///6n8cwAAADOoIyd1op7+OGHXdNpQpM1Ri4YHN8iwgmVScyiw6H69Onjmnm9S+i6dgAAAGklagI7NaNq0d6aNWta1qxZ3WXOnDk2fPhw97eXqYvMquk+3m0aTKFFezXqNaEyO3bsiPX4WuctMhsY2uSrmaBDLwAAIH3Ur18/bNaMU5k9e7ZL3vhhBoyoaYq99tpr3SCHUPfcc49VrlzZevXq5daPU1Cm9ds0KEIUxCn4e/HFF911BYUxMTGuTIsWLdy2bdu2uUV8NaJWNFhCWTctNnzJJZe4bVrMWNsuv/zyNH7WAACE6Jcvwy+DqKDqwgsvdAMaE/0w/fq5dWE1C0VKmDhxovu+Ty2KR7p06eJiBU2B8sADD9iTTz4ZbNnT448cOdI9H/XDr1atmnuOjRo1stQWNYFdnjx53CCHUJqHrmDBgsHtis41K3TFihXdRX/nypXLTV8iGgDRvn176969u7uf3gwtVqxFib3BGFWqVHELEXfo0MHefPNNt+3+++93U6JUqlQpzZ83AABInGPHjrmALjXnm9MASU2ldvXVV7s+/7/99pu1a9fOxSSKL+T77793ZRSH5M+f38aMGWPNmjVziSIv+WRnelNsYvTs2dMFd506dbJatWrZX3/9ZdOnT3dBoWfo0KF28803u4xd3bp1XeA3ZcoUy5IlS7DMuHHjXLCn0bO6aL68999/P52eFQAA0UEBjlrKXnnlFZe90uXdd991wU0oZee87JZu1wDEX375Jew+smnTJrvpppvccl7q5qTv7tDuUsqCKTs4evRo13KXPXt21yc+silW/fMVFygeUOueEj7qhpUcihEOHz7s6qjEkqZYe/zxx908unpsUbZSMUnt2rWDiSb9r3gjtUVNxi6+NvFQOhj0JusSHw28GDFihLvER5G+DgIAAJB4CuiUwVLA88wzz7htU6dOTfA+LVu2dF2iNIesN6esWtgUJCkRo0yYgsXjx4+7xI3Kh37/r1u3zj755BObMGFCWJImlLpmPfvss67lTQHdo48+6oLQadOmJfnt1bRomkpNQaRHTawaSLlx40YrV65crPucPHnSLZaQFitXRHVgBwAAMg4FZJpWTK1hyoxJfMGWR0uAKiOngZDefUT94ZcvX+7mqS1VqpTbptYz9VdTE6iyYV7Qpu2FCxeO9zHuvffe4N/K7GngpfrRa9EDPXZSaJBm2bJlw7aFDuCMK7DTwggHDx4M9u9PTb5qigUAAP6glaAU0HlBnVStWtU16+o2T5kyZRIM6mTZsmWuSVdl1RyrplqvqTc54ppaLa7t8tFHH7mWxI8//jjOeXJTGoEdAABIvUAjc+Zg4BM6yOFU4ps/NnK7mmoTcvDgQddfXpk5dbNStk9rxnvZvqRSVjGuqdUkclo0BXMatKmm4sgVs1ILgR0AAEgxaoo9ceJE8LqyaepfpgDLEzmtSeR9vOycMmqhk/6vWrXKTT+mGSwSa82aNbZ792574YUX7Morr3TTpCV34IQ3LZpGvYYGhRqoqSVKQ5tolalTPz6tV6+lUNMKgR0AAEgxCm40rYcGEiig0trr6nOnkaMa6KBAxxv1Gnof9aVTwKf7aO43Zbg0K0Xr1q3dcqKaM+7uu+92Axc0wjWxSpcu7QJHDZr8448/bPLkyW4gRXJpRK0GTiho06APZf806rVbt27BTKKCOtVVfeu03rwyfLooKE1tBHYAACDFaH5YDZhQxk3ZOs37piZQjUDVVGJen7NQt956q5tDVnPD6T4qoyBJ06JofferrrrKBXoa+KDmzaQoXLiwCyQ//fRTVydl7gYNGnRaA0Q0sGPLli0uwNRIXQV1ung0D65G8Xbu3NmKFy8evGhp1NSWKRDZ8I3TpoNYb7wi82haXqxs74SHpGc0G3P8b+LpqJKMWdwBnHk0T5oyWBphmdD66Dgz3vN9SYgryNgBAAD4BIEdAADA/7n++uvdCNq4LupLl9ExQTEAAMD/eeedd+zQoUMWl7RYOeJ0EdgBAAD8n5IlS1o0oykWAADAJwjsAADIoJi44swRSKFJSgjsAADIYGJiYtz///33X3pXBWnEe6+99z656GMHAEAGowl+tdi9t/SVVm6Ia91U+CNT999//7n3Wu+53vvTQWAHAEAGpMXm5XTWNUX0UFDnveeng8AOAIAMSBk6LUNVpEgRO3bsWHpXB6lIza+nm6nzENgBAJCB6Qs/pb704X8MngAAAPAJAjsAAACfILADAADwCQI7AAAAnyCwAwAA8AkCOwAAAJ8gsAMAAPAJAjsAAACfILADAADwCQI7AAAAnyCwAwAA8AkCOwAAAJ8gsAMAAPAJAjsAAACfILADAADwCQI7AAAAnyCwAwAA8AkCOwAAAJ8gsAMAAPAJAjsAAACfiJrAbuDAgVa7dm3LkyePFSlSxG6++WZbu3ZtWJlAIGD9+vWzEiVKWM6cOa1+/fq2cuXKsDJHjhyxhx56yAoVKmS5c+e2G2+80bZs2RJWZs+ePdamTRvLly+fu+jvf//9N02eJwAAgO8Duzlz5ljnzp3txx9/tBkzZtjx48etYcOGdvDgwWCZl156yYYMGWKvvvqqLV682IoVK2YNGjSw/fv3B8s88sgjNmnSJBs/frzNnTvXDhw4YDfccIOdOHEiWKZVq1b2888/29dff+0u+lvBHQAAQEaWKaA0VxTatWuXy9wp4Lvqqqtctk6ZOgVuvXr1CmbnihYtai+++KI98MADtnfvXitcuLC9//771rJlS1dm69atVqpUKZs2bZo1atTIVq9ebVWrVnUB5KWXXurK6O86derYmjVrrFKlSqes2759+1ymT4+XN29eixZle0+1aLIxRyuLOv32pncNAABRJilxRdRk7CLpycnZZ5/t/t+wYYNt377dZfE82bNnt3r16tn8+fPd9aVLl9qxY8fCyigYrF69erDMggUL3IvnBXVy2WWXuW1emUgKIPWih14AAADSWlQGdsrOdevWza644goXlImCOlGGLpSue7fp/2zZslmBAgUSLKNMYCRt88rE1f/P64+nizKAAAAAaS0qA7suXbrY8uXL7aOPPop1W6ZMmWIFgZHbIkWWiat8Qvvp06ePyyB6l82bNyfh2QAAAJyhgZ1GtE6ePNlmzZpl55xzTnC7BkpIZFZt586dwSyeyhw9etSNek2ozI4dO+Ls0xeZDQxt8lWbd+gFAAAgrUVNYKeMmTJ1EydOtO+++87KlSsXdruuKyjTiFmPgjgNrrj88svd9Zo1a1pMTExYmW3bttmvv/4aLKNBEsq6LVq0KFhm4cKFbptXBgAAICPKalFCU518+OGH9sUXX7i57LzMnPq0ac46NZNqROyAAQOsYsWK7qK/c+XK5aYv8cq2b9/eunfvbgULFnQDL3r06GE1atSw6667zpWpUqWKNW7c2Dp06GBvvvmm23b//fe7KVESMyIWAAAgvURNYDdy5Ej3vyYdDjVmzBhr166d+7tnz5526NAh69Spk2tu1cjW6dOnu0DQM3ToUMuaNau1aNHClb322mvt3XfftSxZsgTLjBs3zrp27RocPatJjDU3HgAAQEYWtfPYZWTMY5c2mMcOAHAm2HcmzGMHAACAcAR2AAAAPkFgBwAA4BMEdgAAAD5BYAcAAOATBHYAAAA+QWAHAADgEwR2AAAAPkFgBwAA4BMEdgAAAD5BYAcAAOATBHYAAAA+QWAHAADgEwR2AAAAPkFgBwAA4BMEdgAAAD5xWoHdkSNHUq4mAAAASLvA7ptvvrF27dpZhQoVLCYmxnLlymV58uSxevXq2fPPP29bt249vdoAAAAgdQO7zz//3CpVqmRt27a1zJkz22OPPWYTJ050gd6oUaNcYDdz5kwrX768dezY0Xbt2pX8GgEAACBZsiam0IABA2zQoEHWtGlTF9hFatGihfv/r7/+sldeecXGjh1r3bt3T16NAAAAkHqB3aJFixK1s5IlS9pLL72UvJoAAADgtDAqFgAA4EzK2IXq1q1bossOGTIkqbsHAABAWgV2y5Yts59++smOHz/uBlTIb7/9ZlmyZLGLL744WC5TpkzJrRMAAADSIrBr1qyZm+LkvffeswIFCrhte/bssXvuuceuvPJKBk0AAABESx+7wYMH28CBA4NBnejv5557zt0GAACAKAns9u3bZzt27Ii1fefOnbZ///6UqhcAAABSO7C75ZZbXLPrZ599Zlu2bHEX/d2+fXtr3rx5UncHAACA9Opj98Ybb1iPHj3srrvusmPHjv1vJ1mzusDu5ZdfTql6AQAAILUDO60P+/rrr7sgbv369RYIBOzcc8+13LlzJ3VXAAAAyAgTFG/bts1dzjvvPBfUKcADAABAFAV2f//9t1177bUuoGvSpIkL7uS+++5jqhMAAIBoCuweffRRi4mJsU2bNrlmWU/Lli3t66+/Tun6AQAAILX62E2fPt2++eYbO+ecc8K2V6xY0f7888+k7g4AAADplbE7ePBgWKbOs3v3bsuePXtK1QsAAACpHdhdddVVNnbs2LA1YU+ePOlGyV599dVJ3R0AAADSqylWAVz9+vVtyZIldvToUevZs6etXLnS/vnnH5s3b15K1QsAAACpnbGrWrWqLV++3C655BJr0KCBa5rVihPLli2zChUqmJ9ovr5y5cpZjhw5rGbNmvbDDz+kd5UAAABSLmMnxYoVs/79+5ufffzxx/bII4+44K5u3br25ptv2vXXX2+rVq2y0qVLp3f1AAAAUm6CYr8bMmSIWyZN8/NVqVLFhg0bZqVKlbKRI0emd9UAAABOL7DTurDqT6flw9QMO2bMmLDbd+zYYVmyZDE/UN/BpUuXWsOGDcO26/r8+fPTrV4AAAAp0hT7/PPPu9GwPXr0sH///ddNVPzjjz+6JkqPX5YV09QtJ06csKJFi4Zt1/Xt27fHKn/kyBF38ezbty9N6gkAAJCswG7cuHH2zjvv2A033OCu33PPPa7Pmf4fPXp0cOoTP4l8Pgpc43qOAwcO9EWfw40vNLXosje9K4AMqGzvqRZNNuZoZVGnH+deauM4TgP99p7ZTbF//fWXVa9ePXhdI2Bnz55tCxYssDZt2rgMl18UKlTINStHZud27twZK4snffr0sb179wYvmzdvTsPaAgAAJDGw00jY9evXh20rUaKEfffdd7Z48WJr27at+UW2bNnc9CYzZswI267rl19+eazyWnEjb968YRcAAIAMG9hdc8019uGHH8ba7gV3GzduND/p1q2ba3pWM/Pq1atdn8JNmzZZx44d07tqAAAAp9fH7sknn7Q1a9bEeVvJkiXt+++/t+nTp5tftGzZ0v7++2975plnbNu2ba4Zetq0aVamTJn0rhoAAMDpBXYKaBIKaooXL+6r5ljp1KmTuwAAAPhyguJPP/3ULSGmDFaNGjXc35999lnq1A4AAAApn7E7efKk3XnnnS6wO++886xy5cpu+o+VK1e6Zsvbb7/dPvroI99NeQIgukTdtD390rsCAM7IwE5Las2cOdMmT54cnMvOo22az+6VV15x66sCAAAgAzfFvvvuu/byyy/HCurkxhtvtJdeeslGjRqV0vUDAABASgd2v//+u1133XXx3q7b1q1bl9jdAQAAIL0Cu5w5c7o1YuOj9VFVBgAAABk8sKtTp46NHDky3ttfe+01VwYAAAAZfPBE3759rX79+m7S3h49egRHxWpVhsGDB9sXX3xhs2bNSt3aAgAA4PQDO62R+vHHH9v9999vEyZMCLutQIECbqqTunXrJnZ3AAAASK/ATm655RZr1KiRffPNN24whWhOu4YNG1quXLlSum4AAABIrcBOFMApwAMAAECUDp747rvvrGrVqm70a6S9e/datWrV7Icffkjp+gEAACClAzutPNGhQwfLmzdvrNvy5ctnDzzwgA0ZMiSxuwMAAEB6BXa//PKLNW7cON7b1c9u6dKlKVUvAAAApFZgt2PHDouJiYn39qxZs9quXbuS+vgAAABI68CuZMmStmLFinhvX758uRUvXjyl6gUAAIDUCuyaNGliTz31lB0+fDjWbYcOHbKnn37abrjhhqQ+PgAAANJ6upMnnnjCJk6c6Oat69Kli1WqVMkyZcrkVp7QcmInTpxwq1MAAAAggwd2RYsWtfnz59uDDz5offr0ccuJiYI7TVr8+uuvuzIAAACIggmKy5QpY9OmTbM9e/bYunXrXHBXsWJFt6QYAAAAoqSPnefee+91I2Br165tl1xySTCoO3jwoLsNAAAAURLYvffee26wRCRtGzt2bErVCwAAAKnVFKulxNT0qsv+/fstR44cwds0cEJNtEWKFEnq4wMAACCtA7v8+fO7gRK6aGRsJG3v379/StULAAAAqRXYzZo1y2XrrrnmGpswYYKdffbZwduyZcvmBlaUKFEiqY8PAACAtA7s6tWr5/7fsGGDlSpVyjJnTnL3PAAAAGSEwG7Tpk3BJtctW7bEW6506dIpUzMAAACkTmBXrly54N+hkxOHbtN1DaQAAABABg7sFLSdc8451q5dO2vWrJmbyw4AAAAZR6KjMzW/ag67d99919544w276667rH379lalSpXUrSEAAAASJdEjIIoVK2a9evWy1atX22effeaWFbv00kvtsssus7fffttOnjyZ2F0BAAAgFSRraOsVV1xho0aNst9//91y5cplHTt2tH///TflawcAAIDUDezmz59v9913n5uo+MCBA/baa6+5CYwBAAAQBX3stm3b5taCHTNmjGuGbd26tQvwqlWrlro1BAAAQMoGdt7KEm3btrUbb7zRYmJi3NQmy5cvDyt3/vnnJ3aXAAAASI/A7vjx426S4meffdaee+65sPnsPMxjBwAAEAWBnZYSAwAAgE+aYgEAABDlo2K9dWIT66+//rKUtHHjRjcZspY1y5kzp1WoUMGefvppO3r0aKx6alWM3LlzW6FChaxr166xyqxYscLq1avn9lOyZEl75plnYjUpz5kzx2rWrGk5cuSw8uXLuwmZAQAAfBHY1a5d2zp06GCLFi2Kt8zevXvdRMXVq1e3iRMnpmQdbc2aNW4C5DfffNNWrlxpQ4cOdcHW448/HiyjgRxNmza1gwcP2ty5c238+PE2YcIE6969e7DMvn37rEGDBm4QyOLFi23EiBE2aNAgGzJkSFiTc5MmTezKK6+0ZcuWucdQgKh9AQAARH1TrFabGDBggDVu3NiNhq1Vq5YLjpTR0tQnq1atcgGXtr/88st2/fXXp2gl9bi6eJRFW7t2rY0cOdIFZjJ9+nRXj82bN7u6yeDBg93ats8//7zlzZvXxo0bZ4cPH3bLomXPnt0Fob/99psL7Lp16+YGfyhgLF26tA0bNsztQ0umLVmyxD3OrbfemqLPCwAAIM0zdmeffbYLbLZu3eqCKU1MvHv3brfyhGhOu6VLl9q8efNSPKhLKEOoenkWLFjgAjUvqJNGjRrZkSNHXN28MmqGVVAXWkbPS829XpmGDRuGPZbKKLg7duxYnHXRYygbGHoBAADIsIMnRBm65s2bu0t6Wr9+vWtGVUbOs337ditatGhYuQIFCli2bNncbV6ZsmXLhpXx7qPb1Icvrv3ouqZ7UTBbvHjxWPUZOHCg9e/fP0WfIwAAQJosKZZS+vXr55o/E7ooUxZK2TU1y95+++1uWbNQKh9JAyNCt0eW8QZOJLVMqD59+rgMondRczAAAECGztiltC5dutgdd9yRYJnQDJuCuquvvtrq1Kljb731Vli5YsWK2cKFC8O2qf+fmk+9DJzKeNk7z86dO93/pyqTNWtWK1iwYJx1VNNuaPMuAADAGRfYaUoSXRI7hYqCOk1DovVqM2cOTzYq2NMgCa1p6zWXakCFAi7dxyujUa6aAkVNtF4Z9cvzAkiVmTJlSti+VUYDQzRwBAAAIKNK16bYxFKmrn79+laqVCk3iGPXrl0uqxaaWdOAh6pVq1qbNm3cNCXffvut9ejRw03TohGx0qpVKxfoaaTsr7/+apMmTXKjfb0RsdKxY0f7888/3TaNBh49erSNGjXK7QsAACAjS9eMXWIpY7Zu3Tp3Oeecc+Ls/5YlSxabOnWqderUyerWresmIFYg502HIvny5bMZM2ZY586dXQZOgysUwOni0QCKadOm2aOPPmqvvfaay+YNHz6cqU4AAECGlykQuexCPDRBsOaqq1Gjhruu+d5CV3VQYPXggw/GaiI9E2m6EwWRGkjhZQsBIE798kXfC9Nvb3rXwPfK9p5q0WRjjlYWdfrt9WVckeiMnVZy0MoPWm5LHnvsMcufP78bVCCaCkTToWjpLwAAAKS9RKfXNGBB/c9CKcjTEly6aMWJDz74IDXqCAAAgJQM7DSQQIMT4qMVHX755ZfE7g4AAAApLNFNsWpqPeuss4LX//jjj7B53TQVyMGDB1O6fgAAAEjpjJ0m8F27dm3weuHChcMGSiijp8l9AQAAkMEDu2uvvdZNABwXDazVeqkqAwAAgAzeFNu3b1+7+OKL7dJLL3WT9Z533nluUt81a9a4ueKUzRs7dmzq1hYAAACnH9hVqFDBTe6rVRtatmwZXKlB2brKlSu7SYTPPffcxO4OABBlc2kB8NnKE5dccomtWrXKLdn1+++/u20VK1a0iy66KLXqBwAAgNRcUkyBHMEcAABAlAZ2oeupJmTIkCGnUx8AAACkdmCn5tdQc+fOtZo1a1rOnDmD27x+dwAAAMjAgd2sWbPCrufJk8c+/PBDK1++fGrUCwAAAKk1jx0AAAAyNgI7AAAAnyCwAwAAONP62C1fvjzsuiYm1qoTBw4cCNt+/vnnp1ztAAAAkPKB3YUXXuhGvSqg89xwww3uf2+7/j9x4kTiHx0AAABpH9ht2LAh5R4VAAAA6RfYlSlTJuUfHQAAAOmzpNi+ffssb9687u9p06bZ8ePHg7dlyZLFmjZtmnI1AwAAQOoEdl9++aU9+eSTwRUoWrZsaQcPHgzerv51H3/8sd12221JqwEAAADSdrqTt956y7p06RK2bd26dXby5El3GThwoI0ePTplagUAAIDUC+w03ckFF1wQ7+3XX3+9LVmyJOk1AAAAQNoGdtu3b7eCBQuGrR1bqlSp4PWzzjrL9u7dmzK1AgAAQOoFdmeffbatX78+eL1WrVoWExMTvP7777+7MgAAAMjggd1VV11lw4cPj/d23aYyAAAAyOCBXa9evWz69Ol2++232+LFi12zqy6LFi2yW2+91WbOnOnKAAAAIINPd3LRRRe56Uzuu+8+mzhxYthtBQoUsPHjx9vFF1+cGnUEAABASk9QfNNNN1mDBg3sm2++cX3qpGLFitawYUPLnTt3UnYFAACA9AzsJFeuXHbLLbekdD0AAACQVn3sAAAAkLER2AEAAPgEgR0AAIBPENgBAACcqYFdlixZbOfOnbG2//333+42AAAARElgFwgE4tx+5MgRy5YtW0rUCQAAAKk53Ym3nFimTJnsnXfesbPOOit424kTJ+z777+3ypUrJ6cOAAAASMuM3dChQ91FGbs33ngjeF0XXf/vv//c/6lNmcELL7zQBZg///xz2G2bNm2yZs2aucmSCxUqZF27drWjR4+GlVmxYoXVq1fPcubMaSVLlrRnnnkmVhZyzpw5VrNmTcuRI4eVL18+TZ4XAABAmmXsNmzY4P6/+uqr3ZJiWkYsPfTs2dNKlChhv/zyS9h2ZQ2bNm1qhQsXtrlz57o+f23btnVB24gRI1yZffv2uZUz9By03u1vv/1m7dq1c4Fg9+7dg8+zSZMm1qFDB/vggw9s3rx51qlTJ7dfrYkLAADgm5UnZs2a5f5XJkxBUIUKFSxr1iTvJlm++uormz59uk2YMMH9HUrbV61aZZs3b3aBnwwePNgFbs8//7zlzZvXxo0bZ4cPH7Z3333XsmfPbtWrV3fB3ZAhQ6xbt24uC6jsXOnSpW3YsGFuH1WqVLElS5bYoEGDCOwAAIC/Bk8cOnTI2rdv75YWq1atmmv+FDV7vvDCC5ZaduzY4bJo77//vnvsSAsWLHCBmhfUSaNGjVzT7dKlS4Nl1AyroC60zNatW23jxo3BMlr7NpTKKLg7duxYnHXTYygbGHoBAADI8IFd7969XTPo7NmzXR80z3XXXWcff/yxpQY1pyrz1rFjR6tVq1acZbZv325FixYN26bmYo3U1W3xlfGun6rM8ePHbffu3XE+9sCBAy1fvnzBS6lSpU7j2QIAAKRRYPf555/bq6++aldccYVruvRUrVrV1q9fn6R99evXz+0joYsyZeojpyxYnz59EtxfaH1Cg8LQ7ZFlvIETSS0TSvXau3dv8KLmYAAAgLSW5M5xu3btsiJFisTafvDgwXgDn/h06dLF7rjjjgTLlC1b1p577jn78ccfw5pQRdm71q1b23vvvWfFihWzhQsXht2+Z88e13zqZeBUxsvMebzJlk9VRv0ICxYsGGcdVa/IugEAAGT4wK527do2depUe+ihh9x1L5h7++23rU6dOknal6Yk0SUxc+gpuPOoT5z6vanp99JLL3Xb9NgaJLFt2zYrXrx4cECFAi5NXeKVefzxx93AD28yZZVRvzwFkF6ZKVOmhD2+yiiIjImJSdLzAwAAyNCBnfqTNW7c2I1AVb+zV155xVauXOkGHWj+t9SgUaqhvMmRNSL3nHPOcX9rwIOag9u0aWMvv/yy/fPPP9ajRw834EIjYqVVq1bWv39/119PAd7vv/9uAwYMsKeeeioYoKofn5qaNUpW99XzGjVqlH300Uep8twAAADSrY/d5Zdf7uZ204TECqyUzVIzpgIgLzOWHrROrTKJGtBRt25da9Gihd18881umhKPBjbMmDHDtmzZ4jJwmp9OAZwunnLlytm0adPc4BBNhPzss8+6jCFz2AEAgIwuUyC+xV+RbBrooSBSAym8bCEAAIlVtvfUqHqxNuZoZVGn317zY1yR5IwdAAAAoryPXebMmU856lW3q98dAAAAMnBgN2nSpHhvmz9/vptrjlZdAACAKAjsbrrppljb1qxZ4ybn1fQgmk9OAw0AAACQPpLVx07zyGkqkPPPP981vf78889ukuDIaUkAAACQQQM7jcbo1auXnXvuuW7uum+//dZl66pXr556NQQAAEDKNsW+9NJL9uKLL7oltzRZb1xNswAAAIiCwK53796WM2dOl61Ts6sucZk4cWJK1g8AAAApHdjdfffdp5zuBAAAAOmHlSdSAStPAADOKP3yWdTpx8oTAAAAyMBYUgwAAMAnCOwAAAB8gsAOAADAJwjsAAAAfILADgAAwCcI7AAAAHyCwA4AAMAnCOwAAAB8gsAOAADAJwjsAAAAfILADgAAwCcI7AAAAHyCwA4AAMAnCOwAAAB8gsAOAADAJwjsAAAAfILADgAAwCcI7AAAAHyCwA4AAMAnCOwAAAB8gsAOAADAJwjsAAAAfILADgAAwCcI7AAAAHyCwA4AAMAnCOwAAAB8IqoCu6lTp9qll15qOXPmtEKFClnz5s3Dbt+0aZM1a9bMcufO7W7v2rWrHT16NKzMihUrrF69em4fJUuWtGeeecYCgUBYmTlz5ljNmjUtR44cVr58eXvjjTfS5PkBAACcjqwWJSZMmGAdOnSwAQMG2DXXXOOCMQVpnhMnTljTpk2tcOHCNnfuXPv777+tbdu2rtyIESNcmX379lmDBg3s6quvtsWLF9tvv/1m7dq1c4Fg9+7dXZkNGzZYkyZN3GN98MEHNm/ePOvUqZPb76233ppuzx8AAOBUMgUi01UZ0PHjx61s2bLWv39/a9++fZxlvvrqK7vhhhts8+bNVqJECbdt/PjxLnDbuXOn5c2b10aOHGl9+vSxHTt2WPbs2V2ZF154wQV+W7ZssUyZMlmvXr1s8uTJtnr16uC+O3bsaL/88ostWLAgUfVVAJkvXz7bu3eve1wAAHytX770rkHS9dtr0SIpcUVUNMX+9NNP9tdff1nmzJntoosusuLFi9v1119vK1euDJZR0FW9evVgUCeNGjWyI0eO2NKlS4Nl1AzrBXVema1bt9rGjRuDZRo2bBj2+CqzZMkSO3bsWBo8WwAAgOSJisDujz/+cP/369fPnnjiCfvyyy+tQIECLkj7559/3G3bt2+3okWLht1PZbJly+Zui6+Md/1UZZQ13L17d5z1U/CoaDr0AgAAcEYFdgrU1PyZ0EWZspMnT7ryffv2df3cNLBhzJgx7vZPP/00uD9dj6SW5tDtkWW8luiklgk1cOBAlyL1LqVKlUrmKwIAABClgye6dOlid9xxR4Jl1Ldu//797u+qVasGt6s5VSNWNRJWihUrZgsXLgy77549e1zzqZeBUxkvM+dR/zs5VZmsWbNawYIF46yj+u1169YteF0ZO4I7AABwRgV2mpJEl1NRhk6B3Nq1a+2KK65w2xSwqV9cmTJl3PU6derY888/b9u2bXN98GT69Onufrq/V+bxxx93U6CoidYro355CiC9MlOmTAl7fJWpVauWxcTExFk/PUZovz0AAID0EBV97DQCRCNTn376aRdkKcB78MEH3W233367+18DHpTRa9OmjS1btsy+/fZb69Gjh5u2xBtB0qpVKxeAaaTsr7/+apMmTXLTpyjb5jWz6nH+/PNPt00jY0ePHm2jRo1y+wIAAMjIomYeu5dfftk1hypwO3TokJuo+LvvvnMDJCRLlixuAmPNOVe3bl03AbECuUGDBgX3of5vM2bMsM6dO7sMnO6rAC60GbVcuXI2bdo0e/TRR+21115z2bzhw4czhx0AAMjwomIeu2jDPHYAgDMK89ilKt/NYwcAAIBTI7ADAADwCQI7AAAAnyCwAwAA8AkCOwAAAJ8gsAMAAPAJAjsAAACfILADAADwCQI7AAAAnyCwAwAA8AkCOwAAAJ8gsAMAAPAJAjsAAACfILADAADwCQI7AAAAnyCwAwAA8AkCOwAAAJ8gsAMAAPAJAjsAAACfILADAADwCQI7AAAAnyCwAwAA8AkCOwAAAJ8gsAMAAPAJAjsAAACfILADAADwCQI7AAAAnyCwAwAA8AkCOwAAAJ8gsAMAAPAJAjsAAACfILADAADwCQI7AAAAnyCwAwAA8AkCOwAAAJ8gsAMAAPAJAjsAAACfILADAADwiagJ7H777Te76aabrFChQpY3b16rW7euzZo1K6zMpk2brFmzZpY7d25XrmvXrnb06NGwMitWrLB69epZzpw5rWTJkvbMM89YIBAIKzNnzhyrWbOm5ciRw8qXL29vvPFGmjxHAACAMyKwa9q0qR0/fty+++47W7p0qV144YV2ww032Pbt293tJ06ccGUOHjxoc+fOtfHjx9uECROse/fuwX3s27fPGjRoYCVKlLDFixfbiBEjbNCgQTZkyJBgmQ0bNliTJk3syiuvtGXLltnjjz/uAkTtCwAAICPLFIhMV2VAu3fvtsKFC9v333/vAi7Zv3+/y9zNnDnTrr32Wvvqq69coLd582YXuImCu3bt2tnOnTtd2ZEjR1qfPn1sx44dlj17dlfmhRdecAHeli1bLFOmTNarVy+bPHmyrV69Ovj4HTt2tF9++cUWLFiQqPoqgMyXL5/t3bvXPS4AAL7WL59FnX57LVokJa6IioxdwYIFrUqVKjZ27FiXkVPm7s0337SiRYu6JlNR0FW9evVgUCeNGjWyI0eOuAyfV0bNsF5Q55XZunWrbdy4MVimYcOGYY+vMkuWLLFjx46l0TMGAABIuqwWBZRJmzFjhutjlydPHsucObML6r7++mvLnz+/K6MmWW0LVaBAAcuWLVuwuVb/ly1bNqyMdx/dVq5cuTj3o+sKJpU5LF68eKz6KXjUJTSyBgAASGvpmrHr16+fC9oSuihTptbiTp06WZEiReyHH36wRYsWuSBPTa/btm0L7k/lI+m+odsjy3gt0UktE2rgwIEuRepdSpUqlezXBAAAICozdl26dLE77rgjwTLKsGnAxJdffml79uwJti2//vrrLov33nvvWe/eva1YsWK2cOHCsPuqvJpPvQycynjZO4/638mpymTNmtU1CcdF/fa6desWlrEjuAMAAGdUYKcpSXQ5lf/++8/9rybYULp+8uRJ93edOnXs+eefdxk8r7l0+vTprj+d1w9PZTTKVVOgqInWK6N+eV4TrcpMmTIl7HFUplatWhYTExNn/fQYof32AAAA0kNUDJ5QsKX+cm3btnWjUzWn3WOPPeamJtEUJ6IBD1WrVrU2bdq4aUq+/fZb69Gjh3Xo0CGY5WvVqpULwDRS9tdff7VJkybZgAEDXLbNa2bVCNg///zTbdPI2NGjR9uoUaPcvgAAADKyqAjslNXTQIkDBw7YNddc47Jnmqvuiy++sAsuuMCVyZIli02dOtVNKqzJi1u0aGE333yzm6fOo/5var7V1Cbah/rtKYALbUbVAIpp06bZ7Nmz3Vx5zz77rA0fPtxuvfXWdHnuAAAAvprHLtowjx0A4IzCPHapynfz2AEAAODUCOwAAAB8gsAOAADAJwjsAAAAfILADgAAwCcI7AAAAHyCwA4AAMAnCOwAAAB8gsAOAADAJwjsAAAAfILADgAAwCcI7AAAAHyCwA4AAMAnCOwAAAB8gsAOAADAJwjsAAAAfILADgAAwCeypncFAABAlOu3N71rgP9Dxg4AAMAnCOwAAAB8gsAOAADAJwjsAAAAfILADgAAwCcI7AAAAHyCwA4AAMAnCOwAAAB8gsAOAADAJwjsAAAAfILADgAAwCcI7AAAAHyCwA4AAMAnCOwAAAB8gsAOAADAJwjsAAAAfCJrelfAjwKBgPt/37596V0VAAAQ5bx4wosvEkJglwr279/v/i9VqlRq7B4AAJyh8UW+fPkSLJMpkJjwD0ly8uRJ27p1q+XJk8cyZcrEq5dKv14UOG/evNny5s3La4yoxHEMP+A4Tn0K1RTUlShRwjJnTrgXHRm7VKAX/ZxzzkmNXSOCgjoCO0Q7jmP4Acdx6jpVps7D4AkAAACfILADAADwCQI7RKXs2bPb008/7f4HohXHMfyA4zhjYfAEAACAT5CxAwAA8AkCOwAAAJ8gsEOKql+/vj3yyCPp9qq2a9fObr755gxTH/hP2bJlbdiwYZbRJOZYz6h1R9pJ7WNg9uzZbv7Wf//9N94y/fr1swsvvDBJ++WzPPGYxw6+NnHiRIuJiUnvagCpjmMd0aJHjx720EMPpXc1fIvADr529tlnp3cVgDTBsY5ocdZZZ7kLUgdNsUhxx48fty5dulj+/PmtYMGC9sQTTwQXLv7ggw+sVq1abrm1YsWKWatWrWznzp3B++7Zs8dat25thQsXtpw5c1rFihVtzJgxwdv/+usva9mypRUoUMDt+6abbrKNGzcmOn2vZogBAwbYvffe6+pQunRpe+utt8Luk9THgL9o2R4dg7lz57bixYvb0KFDYx1H//33X4LH0Pz5811TU44cOdzx/vnnn7vmqZ9//jnRTVnffPONXXTRRe48uOaaa9x58tVXX1mVKlXcDP933nmnq4cnso4q36xZM3f/cuXK2bhx41LsNULGld7Hr2fp0qXuvrly5bLLL7/c1q5dG29TrL4zunbtGvzO6NWrl7Vt2zasW423XGfPnj3djxh9f2g/iI3ADinuvffes6xZs9rChQtt+PDh7oPlnXfecbcdPXrUnn32Wfvll1/ch8WGDRtcvzjPk08+aatWrXJfYKtXr7aRI0daoUKFgh9GV199tful9/3339vcuXPd340bN3b7TazBgwe7D5xly5ZZp06d7MEHH7Q1a9ak6GMgenXr1s3mzZtnkydPthkzZtgPP/xgP/30U6KPIX2xKqCqUaOGu5+Od31RJZW+tF599VX3Jas1kVu0aOH6Rn344Yc2depUV7cRI0bEe3+dV/pB8t1339lnn31mr7/+etiPKPhTRjl++/bt6x5nyZIl7vtAgWR8XnzxRffDQz/iVXetPavvh7i+WxSw6rvlpZdesmeeecY9R0QIACmoXr16gSpVqgROnjwZ3NarVy+3LS6LFi1SKi+wf/9+d71Zs2aBe+65J86yo0aNClSqVCls30eOHAnkzJkz8M0337jrbdu2Ddx0001h9Xn44YeD18uUKRO46667gte1ryJFigRGjhyZ6MeAf+3bty8QExMT+PTTT4Pb/v3330CuXLmCx9GpjiH9X7BgwcChQ4eCZd5++213nC9btuyUdZg1a5YrO3PmzOC2gQMHum3r168PbnvggQcCjRo1ivNYX7t2rSv/448/Bm9fvXq12zZ06NBkvTbI+DLq8Tt16lS3zdvn008/HbjggguCtxctWjTw8ssvB68fP348ULp06Vif5VdccUXYY9WuXdt9vyAcGTukuMsuu8yl7T116tSx33//3U6cOOF+Iapps0yZMq4ZQE0EsmnTJve/fjmOHz/epemVcle2IjS1v27dOnc/r4+GUvKHDx+29evXJ7p+559/fvBv1VMpfS+TkVKPgej0xx9/2LFjx+ySSy4JW3i7UqVKiT6G1OSk29WM5QndX2KFPkbRokVdk1b58uXDtsWXgVO2W1kSZWU8lStXdk1d8K+MevyqSVjiOl737t1rO3bsCHuMLFmyWM2aNRPcp7dfstCxMXgCaUbBUcOGDd1Ffe3Uj04BXaNGjYLNnNdff739+eefrqlp5syZdu2111rnzp1t0KBBrn+FTva4+gppX4kVOUpWH2zat6TUYyA6eX1BQ3+YhG5PzDGksqe6f2KEPob2l9BjJvZ5wN8y8vEr8R2vianzqeqN/4+MHVLcjz/+GOu6BkGoD8fu3bvthRdesCuvvNJlEOL6taUASv2DFPypT5HXsffiiy92mb8iRYrYueeeG3bRr9KUkBaPgYyrQoUK7stj0aJFwW3q76NjIrF0XC9fvtyOHDkS3KZ+RmlJAyzUIT30cZWJSWhuMUS/aDx+9bmq7HNonb3WHSQPgR1SnDp6qwOvvkg++ugj18H74YcfdqOvsmXL5q6ryUCde9UxN9RTTz1lX3zxhWsOXblypX355ZfuS0o00ksDKdSUqw7BGngxZ84ct+8tW7akSN3T4jGQcakJXqPxHnvsMZs1a5Y7BtXpO3PmzInOfmmkt7II999/v2sS1ehWZZzTMoOmpjcN+OnQoYPraK4uBvfdd58bIQv/itbjV3PaDRw40H3263tDn7eaIYGMc/IQ2CHF3X333Xbo0CHXZ0LNqDpp9SGhTNy7775rn376qVWtWtVl7rwPDI8Cvz59+ri+FFdddZXra6E+d6I+RhqpqgCxefPmLuDTh5YeS9M/pIS0eAxkbEOGDHH9Qm+44Qa77rrrrG7duu44CO1zlBAdJ1OmTHFTQ6ivqEYH6geLJHYfKUEjDEuVKmX16tVzx7LOQWWi4W/RePxq1K2m79F3h+quvs3qopOW54ufZNIIivSuBABkVAcPHrSSJUu6qRvat2+frH2oz+Y999zjOoqTNUNaisbjVxlDBaOa4ieyVQenxuAJAAihvj3qD6qMs77INFeWqHk+scaOHetGsOoLVXM2KiOhLymCOqS2aDx+NWBu+vTpLrusvn2av1HdYNQsjKSjKRYAIqiLwAUXXOCaspTxUH9Lb6LsxNi+fbvdddddLuvw6KOP2u233x4cBNSxY8fgVDqRF90GnGnHr/oAqptO7dq1XdPxihUr3KwIXv9qJA1NsQCQhjQSXCMV4+vfRD84ZGQcvxkfgR0AAIBP0BQLAADgEwR2AAAAPkFgBwAA4BMEdgAAAD5BYAcAPqDpIvLnz5/e1QCQzhgVCwA+oGXv9u/fz3QpwBmOwA4AotyxY8csJiYmvasBIAOgKRYAInz22WdWo0YNt4RSwYIFgzP4y+jRo61atWqWPXt2K168uHXp0iV4Py3hdP/997usmSYbvuaaa9ySTJ5+/fq5hdXff/99K1u2rOXLl8/uuOMOl2nzfP3113bFFVe4ZlU9thZzX79+ffD2jRs3WqZMmeyTTz6x+vXru4XSP/jggzibYkeOHGkVKlSwbNmyWaVKldzjAvA3AjsACLFt2za788477d5777XVq1fb7NmzrXnz5hYIBFyg1LlzZxe8admjyZMn27nnnuvup9ubNm3qlmOaNm2aLV261C6++GK79tpr7Z9//gnuX0Ha559/bl9++aW7zJkzx1544YXg7Qogu3XrZosXL7Zvv/3WLbd0yy23uIXRQ2n9zq5du7o6NmrUKNZ7OGnSJHv44Yete/fu9uuvv9oDDzzgFnKfNWsW7zfgZwEAQNDSpUsD+mjcuHFjrFelRIkSgb59+8b5an377beBvHnzBg4fPhy2vUKFCoE333zT/f30008HcuXKFdi3b1/w9sceeyxw6aWXxvsO7Ny509VnxYoV7vqGDRvc9WHDhoWVGzNmTCBfvnzB65dffnmgQ4cOYWVuv/32QJMmTXi3AR8jYwcAIbR4urJsaorV4udvv/227dmzx62RuXXrVndbXJShO3DggGs+DV0YfcOGDWFNqWqCzZMnT/C6mnO1b4/KtmrVysqXL++ac8uVK+e2b9q0KezxatWqleD7pkyeFlQPpevaDsC/sqZ3BQAgI8mSJYvNmDHD5s+fb9OnT7cRI0ZY3759XbNoQtRUqiBNTbeRQvu+RQ5yUH+50GbWZs2aWalSpVxAWaJECXdb9erV7ejRo2H3y5079ymfi/YdSs3FkdsA+AsZOwCIoOBH2a3+/fvbsmXL3OADBXvKtsUX4Kk/nfrXZc2a1fW7C70UKlQoUa/x33//7TJqTzzxhMsMVqlSxWULk0P3nTt3btg2BavaDsC/yNgBQIiFCxe64K1hw4ZudKuu79q1ywVEGtXasWNHt/366693o1nnzZtnDz30kBs5W6dOHbv55pvtxRdfdKNQ1XSrgRTadqqmUylQoIBryn3rrbdc9k/Nr717907W+/PYY49ZixYtggM4pkyZYhMnTrSZM2fyfgM+RmAHACHUr+3777+3YcOG2b59+6xMmTI2ePBgF8jJ4cOHbejQodajRw+XibvtttuCWT4FcWq21YhaBYPFihWzq666yooWLZqo11gjYMePH+9Gu6r5VcHh8OHD3bQmSaVg8pVXXrGXX37Z7U999caMGZOsfQGIHkxQDAAA4BP0sQMAAPAJAjsAAACfILADAADwCQI7AAAAnyCwAwAA8AkCOwAAAJ8gsAMAAPAJAjsAAACfILADAADwCQI7AAAAnyCwAwAA8AkCOwAAAPOH/wfawl5RFwr9FgAAAABJRU5ErkJggg==", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "execution_count": null, + "outputs": [], "source": [ - "emissions = pd.concat(\n", - " [\n", - " load_analysis(\"tutorial_01\", \"net_emissions.parquet\"),\n", - " load_analysis(\"tutorial_02\", \"net_emissions.parquet\"),\n", - " ]\n", - ")\n", + "emissions = load_analysis(\"net_emissions.parquet\")\n", "totals = (\n", - " emissions.groupby([\"config\", \"scenario\"])[\"mtco2eq\"]\n", + " emissions.groupby(\"scenario\")[\"mtco2eq\"]\n", " .sum()\n", - " .unstack(\"scenario\")\n", - " .reindex(columns=scenarios)\n", + " .reindex([\"reference\", \"ghg_high\", \"ghg_high_stiff\", \"ghg_high_fixed\"])\n", ")\n", "print(totals.round(1))\n", "\n", - "ax = totals.T.plot.bar(ylabel=\"Net GHG (MtCO2eq)\", rot=0)\n", - "ax.set_title(\"Fixed-diet (tutorial_01) vs flexible-diet (tutorial_02)\")\n", + "ax = totals.plot.bar(ylabel=\"Net GHG (MtCO2eq)\", rot=20)\n", + "ax.set_title(\"Same carbon price, three demand regimes\")\n", "plt.tight_layout()" ] } @@ -460,4 +189,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} +} \ No newline at end of file diff --git a/tests/test_baseline_enforcement.py b/tests/test_baseline_enforcement.py index c207980b..3e831fcc 100644 --- a/tests/test_baseline_enforcement.py +++ b/tests/test_baseline_enforcement.py @@ -2,20 +2,15 @@ # # SPDX-License-Identifier: GPL-3.0-or-later -"""Unit tests for food-level baseline enforcement and consumer values.""" +"""Unit tests for food-level baseline enforcement.""" import pandas as pd import pypsa import pytest -from workflow.scripts.extract_consumer_values import extract_consumer_values from workflow.scripts.solve_model.core import ( _build_ratios_from_baseline, - _match_baseline_to_consume_links, _prepare_baseline_diet_for_food_constraints, - add_food_incentives_to_objective, - add_food_slack_generators, - fix_food_consumption_to_baseline, ) # --------------------------------------------------------------------------- @@ -237,303 +232,3 @@ def test_tiny_positive_lifted_to_floor(self): baseline, consume_links, min_consumption_g_per_day=0.1 ) assert result["consumption_g_per_day"].iloc[0] == pytest.approx(0.1) - - -# --------------------------------------------------------------------------- -# Test add_food_incentives_to_objective -# --------------------------------------------------------------------------- - - -class TestAddFoodIncentivesToObjective: - def test_applies_adjustment_to_marginal_cost(self, food_network, tmp_path): - csv_path = tmp_path / "incentives.csv" - pd.DataFrame( - { - "food": ["wheat", "rice"], - "country": ["USA", "USA"], - "adjustment_bnusd_per_mt": [0.5, -0.3], - } - ).to_csv(csv_path, index=False) - - original_wheat = food_network.links.static.at[ - "consume:wheat:USA", "marginal_cost" - ] - original_rice = food_network.links.static.at[ - "consume:rice:USA", "marginal_cost" - ] - - add_food_incentives_to_objective(food_network, [str(csv_path)]) - - assert food_network.links.static.at[ - "consume:wheat:USA", "marginal_cost" - ] == pytest.approx(original_wheat + 0.5) - assert food_network.links.static.at[ - "consume:rice:USA", "marginal_cost" - ] == pytest.approx(original_rice - 0.3) - - def test_does_not_affect_other_links(self, food_network, tmp_path): - csv_path = tmp_path / "incentives.csv" - pd.DataFrame( - { - "food": ["wheat"], - "country": ["USA"], - "adjustment_bnusd_per_mt": [1.0], - } - ).to_csv(csv_path, index=False) - - original_beef = food_network.links.static.at[ - "consume:beef:USA", "marginal_cost" - ] - - add_food_incentives_to_objective(food_network, [str(csv_path)]) - - assert food_network.links.static.at[ - "consume:beef:USA", "marginal_cost" - ] == pytest.approx(original_beef) - - def test_sums_across_multiple_sources(self, food_network, tmp_path): - csv1 = tmp_path / "inc1.csv" - csv2 = tmp_path / "inc2.csv" - - pd.DataFrame( - { - "food": ["wheat"], - "country": ["USA"], - "adjustment_bnusd_per_mt": [0.3], - } - ).to_csv(csv1, index=False) - - pd.DataFrame( - { - "food": ["wheat"], - "country": ["USA"], - "adjustment_bnusd_per_mt": [0.7], - } - ).to_csv(csv2, index=False) - - original = food_network.links.static.at["consume:wheat:USA", "marginal_cost"] - - add_food_incentives_to_objective(food_network, [str(csv1), str(csv2)]) - - assert food_network.links.static.at[ - "consume:wheat:USA", "marginal_cost" - ] == pytest.approx(original + 1.0) - - def test_raises_on_empty_paths(self, food_network): - with pytest.raises(ValueError, match="no sources"): - add_food_incentives_to_objective(food_network, []) - - def test_raises_on_missing_columns(self, food_network, tmp_path): - csv_path = tmp_path / "bad.csv" - pd.DataFrame({"food": ["wheat"], "country": ["USA"]}).to_csv( - csv_path, index=False - ) - - with pytest.raises(ValueError, match="Missing required columns"): - add_food_incentives_to_objective(food_network, [str(csv_path)]) - - def test_country_case_insensitive(self, food_network, tmp_path): - csv_path = tmp_path / "incentives.csv" - pd.DataFrame( - { - "food": ["wheat"], - "country": ["usa"], # lowercase - "adjustment_bnusd_per_mt": [0.5], - } - ).to_csv(csv_path, index=False) - - original = food_network.links.static.at["consume:wheat:USA", "marginal_cost"] - - add_food_incentives_to_objective(food_network, [str(csv_path)]) - - assert food_network.links.static.at[ - "consume:wheat:USA", "marginal_cost" - ] == pytest.approx(original + 0.5) - - -# --------------------------------------------------------------------------- -# Test add_food_slack_generators and fix_food_consumption_to_baseline -# --------------------------------------------------------------------------- - - -class TestFoodSlackGeneratorsAndFixation: - def test_generators_added_to_food_buses(self, food_network): - food_network.set_snapshots(["now"]) - - baseline_df = pd.DataFrame( - { - "food": ["wheat", "rice"], - "country": ["USA", "USA"], - "food_group": ["grain", "grain"], - "consumption_g_per_day": [100.0, 50.0], - } - ) - population = {"USA": 1_000_000.0, "IND": 1_000_000.0} - consume_links = food_network.links.static[ - food_network.links.static["carrier"] == "food_consumption" - ] - matched = _match_baseline_to_consume_links( - baseline_df, consume_links, population - ) - assert matched is not None - - add_food_slack_generators(food_network, matched, slack_cost=50.0) - - gens = food_network.generators.static - pos = gens[gens["carrier"] == "slack_positive_food"] - neg = gens[gens["carrier"] == "slack_negative_food"] - assert len(pos) == 2 - assert len(neg) == 2 - assert "food" in pos.columns - assert "food_group" in pos.columns - - def test_p_set_fixed_on_consume_links(self, food_network): - food_network.set_snapshots(["now"]) - - baseline_df = pd.DataFrame( - { - "food": ["wheat", "rice"], - "country": ["USA", "USA"], - "food_group": ["grain", "grain"], - "consumption_g_per_day": [100.0, 50.0], - } - ) - population = {"USA": 1_000_000.0, "IND": 1_000_000.0} - consume_links = food_network.links.static[ - food_network.links.static["carrier"] == "food_consumption" - ] - matched = _match_baseline_to_consume_links( - baseline_df, consume_links, population - ) - assert matched is not None - - fix_food_consumption_to_baseline(food_network, matched) - - p_set = food_network.links.dynamic.p_set - assert p_set.loc["now", "consume:wheat:USA"] > 0 - assert p_set.loc["now", "consume:rice:USA"] > 0 - # Beef not in baseline, should not have p_set - assert "consume:beef:USA" not in p_set.columns or pd.isna( - p_set.loc["now", "consume:beef:USA"] - ) - - -# --------------------------------------------------------------------------- -# Test extract_consumer_values -# --------------------------------------------------------------------------- - - -class TestExtractConsumerValues: - @staticmethod - def _make_network_with_duals( - foods: list[str], - food_groups: list[str], - countries: list[str], - mu_values: list[float], - ) -> pypsa.Network: - """Build a mock solved network with p_set duals on consume links.""" - - n = pypsa.Network() - n.set_snapshots(["now"]) - - link_names = [f"consume:{f}:{c}" for f, c in zip(foods, countries)] - bus0_names = [f"food:{f}:{c}" for f, c in zip(foods, countries)] - bus1_names = [f"group:{fg}:{c}" for fg, c in zip(food_groups, countries)] - - for bus in set(bus0_names + bus1_names): - n.add("Bus", bus) - - n.links.add( - link_names, - bus0=bus0_names, - bus1=bus1_names, - carrier="food_consumption", - food=foods, - food_group=food_groups, - country=countries, - ) - - # Simulate p_set (targets) - p_set_df = pd.DataFrame({name: [1.0] for name in link_names}, index=n.snapshots) - n.links.dynamic["p_set"] = p_set_df - - # Simulate mu_p_set (duals) - mu_df = pd.DataFrame( - {name: [mu] for name, mu in zip(link_names, mu_values)}, - index=n.snapshots, - ) - n.links.dynamic["mu_p_set"] = mu_df - - return n - - def test_extracts_correct_values(self): - n = self._make_network_with_duals( - foods=["wheat", "beef"], - food_groups=["grain", "red_meat"], - countries=["USA", "USA"], - mu_values=[1.5, -2.0], - ) - - result = extract_consumer_values(n) - - assert len(result) == 2 - wheat = result[result["food"] == "wheat"].iloc[0] - assert wheat["value_bnusd_per_mt"] == pytest.approx(1.5) - assert wheat["adjustment_bnusd_per_mt"] == pytest.approx(-1.5) - - beef = result[result["food"] == "beef"].iloc[0] - assert beef["value_bnusd_per_mt"] == pytest.approx(-2.0) - assert beef["adjustment_bnusd_per_mt"] == pytest.approx(2.0) - - def test_output_columns(self): - n = self._make_network_with_duals( - foods=["wheat"], - food_groups=["grain"], - countries=["USA"], - mu_values=[1.0], - ) - - result = extract_consumer_values(n) - - expected_cols = { - "food", - "food_group", - "country", - "value_bnusd_per_mt", - "adjustment_bnusd_per_mt", - } - assert set(result.columns) == expected_cols - - def test_country_uppercased(self): - n = self._make_network_with_duals( - foods=["wheat"], - food_groups=["grain"], - countries=["usa"], - mu_values=[1.0], - ) - - result = extract_consumer_values(n) - - assert result["country"].iloc[0] == "USA" - - def test_raises_when_no_p_set_duals(self): - n = pypsa.Network() - - with pytest.raises(ValueError, match="enforce_baseline_diet"): - extract_consumer_values(n) - - def test_multi_country(self): - n = self._make_network_with_duals( - foods=["wheat", "wheat"], - food_groups=["grain", "grain"], - countries=["USA", "IND"], - mu_values=[1.0, 3.0], - ) - - result = extract_consumer_values(n) - - assert len(result) == 2 - usa = result[result["country"] == "USA"].iloc[0] - ind = result[result["country"] == "IND"].iloc[0] - assert usa["value_bnusd_per_mt"] == pytest.approx(1.0) - assert ind["value_bnusd_per_mt"] == pytest.approx(3.0) diff --git a/tests/test_config_validation.py b/tests/test_config_validation.py index f9688d62..2e353992 100644 --- a/tests/test_config_validation.py +++ b/tests/test_config_validation.py @@ -2,12 +2,11 @@ # # SPDX-License-Identifier: GPL-3.0-or-later -from copy import deepcopy import pytest import yaml -from workflow.validation.consumer_values import validate_consumer_values +from workflow.validation.calibration import validate_calibration @pytest.fixture @@ -26,42 +25,43 @@ def test_schema_requires_fixed_default_sections(default_config) -> None: assert set(default_config[section]) <= set(section_schema["required"]) -def test_disabled_piecewise_utility_needs_no_baseline(default_config) -> None: - validate_consumer_values(default_config) - - -def test_piecewise_utility_requires_configured_baseline(default_config) -> None: - config = deepcopy(default_config) - config["food_utility_piecewise"]["enabled"] = True - - with pytest.raises(ValueError, match="baseline scenario 'baseline' is not defined"): - validate_consumer_values(config) - - -def test_piecewise_utility_requires_enforced_baseline(default_config) -> None: - config = deepcopy(default_config) - config["scenarios"]["baseline"] = {} - config["food_utility_piecewise"]["enabled"] = True - - with pytest.raises(ValueError, match="enforce_baseline_diet=true"): - validate_consumer_values(config) - - -def test_deviation_penalty_generation_skips_baseline_check(default_config) -> None: - """The Broyden iteration names a scenario it synthesizes per iteration.""" - config = deepcopy(default_config) - config["food_utility_piecewise"]["enabled"] = True - config["consumer_values"]["baseline_scenario"] = "_cal_baseline_iter00" - config["deviation_penalty"]["calibration"]["generate"] = True - - validate_consumer_values(config) - - -def test_scenario_piecewise_utility_accepts_enforced_baseline(default_config) -> None: - config = deepcopy(default_config) - config["scenarios"] = { - "baseline": {"validation": {"enforce_baseline_diet": True}}, - "utility": {"food_utility_piecewise": {"enabled": True}}, - } - - validate_consumer_values(config) +class TestStabilityYamlExistenceGate: + """The legacy stability yaml is required only when a solve would open it.""" + + @staticmethod + def _config(dp_enabled: bool, l1_cost) -> dict: + disabled = {"enabled": False, "generate": False, "scenario": "calibration"} + return { + "scenarios": {"default": {}}, + "grazing": {"grassland_forage_calibration": dict(disabled)}, + "exogenous_feed_calibration": dict(disabled), + "food_loss_waste_calibration": dict(disabled), + "food_demand_calibration": dict(disabled), + "cost_calibration": dict(disabled), + "deviation_penalty": { + "enabled": dp_enabled, + "penalty_mode": "l1", + "deviation_type": "absolute", + "land": { + "crops": {"l1_cost": l1_cost}, + "grassland": {"l1_cost": l1_cost}, + }, + "feed": {"l1_cost": l1_cost}, + "diet": {"l1_cost": 0.0}, + "calibration": { + "enabled": True, + "generate": False, + "calibrated_yaml": "missing/deviation_penalty.yaml", + }, + }, + } + + def test_missing_yaml_accepted_when_penalty_is_off(self, tmp_path): + validate_calibration(self._config(False, "calibrated"), tmp_path) + + def test_missing_yaml_accepted_without_calibrated_sentinel(self, tmp_path): + validate_calibration(self._config(True, 0.1), tmp_path) + + def test_missing_yaml_rejected_when_sentinel_resolves(self, tmp_path): + with pytest.raises(ValueError, match="calibrated_yaml"): + validate_calibration(self._config(True, "calibrated"), tmp_path) diff --git a/tests/test_food_utility.py b/tests/test_food_utility.py deleted file mode 100644 index 61c1cdff..00000000 --- a/tests/test_food_utility.py +++ /dev/null @@ -1,371 +0,0 @@ -# SPDX-FileCopyrightText: 2026 Koen van Greevenbroek -# -# SPDX-License-Identifier: GPL-3.0-or-later - -"""Tests for the piecewise food-utility helper. - -These tests are intentionally formulation-agnostic: they probe the -behaviour of the LP after a real (small) HiGHS solve rather than the -internal variable/constraint structure. This way the same suite locks -the contract before and after refactoring the formulation. -""" - -import pandas as pd -import pypsa -import pytest - -from workflow.scripts.solve_model.food_utility import ( - _merge_small_width_blocks, - add_piecewise_food_utility, - pop_piecewise_food_utility_value, -) - -# --------------------------------------------------------------------------- -# Helpers -# --------------------------------------------------------------------------- - - -def _make_network( - foods: tuple[str, ...] = ("x",), - supply_costs: dict[str, float] | None = None, - p_nom_consume: dict[str, float] | None = None, -) -> pypsa.Network: - """Build a minimal solvable network with one or more food_consumption links. - - Each food gets a dedicated food bus with a generator (the supply, at - ``supply_costs[food]`` per Mt) and a consume link routing into a shared - nutrient bus backed by a high-capacity store sink. The solver's only - incentive to consume is the piecewise utility added later. - """ - supply_costs = supply_costs or dict.fromkeys(foods, 1.0) - p_nom_consume = p_nom_consume or dict.fromkeys(foods, 1000.0) - - n = pypsa.Network() - n.set_snapshots(["now"]) - - food_carriers = [f"food_{f}" for f in foods] - n.carriers.add([*food_carriers, "nutrient_protein", "food_consumption"], unit="Mt") - - food_buses = [f"food:{f}:USA" for f in foods] - n.buses.add(food_buses, carrier=food_carriers) - n.buses.add(["nutrient:protein:USA"], carrier="nutrient_protein") - - n.generators.add( - [f"supply:{f}:USA" for f in foods], - bus=food_buses, - carrier=food_carriers, - p_nom=1e6, - marginal_cost=[supply_costs[f] for f in foods], - ) - n.stores.add( - ["sink:protein:USA"], - bus="nutrient:protein:USA", - e_nom=1e9, - e_initial=0.0, - e_cyclic=False, - marginal_cost=0.0, - ) - n.links.add( - [f"consume:{f}:USA" for f in foods], - bus0=food_buses, - bus1=["nutrient:protein:USA"] * len(foods), - carrier="food_consumption", - efficiency=1.0, - p_nom=[p_nom_consume[f] for f in foods], - marginal_cost=0.0, - food=list(foods), - country=["USA"] * len(foods), - ) - return n - - -def _write_blocks( - tmp_path, - spec: dict[str, list[tuple[float, float]]], -) -> str: - """Write a blocks CSV from ``{food: [(width, mu), ...]}``.""" - rows = [] - for food, blocks in spec.items(): - for i, (width, mu) in enumerate(blocks): - rows.append( - { - "food": food, - "country": "USA", - "block_id": i + 1, - "width_mt_per_year": width, - "marginal_utility_bnusd_per_mt": mu, - } - ) - path = tmp_path / "blocks.csv" - pd.DataFrame(rows).to_csv(path, index=False) - return str(path) - - -def _solve_with_utility( - n: pypsa.Network, blocks_path: str, min_block_width_mt: float = 0.0 -) -> None: - """Create the linopy model, add piecewise utility, solve with HiGHS.""" - n.optimize.create_model(include_objective_constant=False) - add_piecewise_food_utility(n, blocks_path, min_block_width_mt=min_block_width_mt) - status, condition = n.model.solve(solver_name="highs") - assert (status, condition) == ("ok", "optimal"), (status, condition) - - -def _link_p(n: pypsa.Network, food: str) -> float: - return float( - n.model.variables["Link-p"].solution.sel(name=f"consume:{food}:USA").item() - ) - - -# --------------------------------------------------------------------------- -# Validation / prep-time tests (no solve required) -# --------------------------------------------------------------------------- - - -def test_missing_required_columns_raises(tmp_path): - path = tmp_path / "bad.csv" - pd.DataFrame( - [{"food": "x", "country": "USA", "block_id": 1, "width_mt_per_year": 1.0}] - ).to_csv(path, index=False) - n = _make_network() - n.optimize.create_model(include_objective_constant=False) - with pytest.raises(ValueError, match="Missing required utility block columns"): - add_piecewise_food_utility(n, str(path), min_block_width_mt=0.0) - - -def test_non_monotonic_mu_raises(tmp_path): - # Block 2's MU > block 1's MU -> rising marginal utility, invalid. - blocks_path = _write_blocks(tmp_path, {"x": [(1.0, 1.0), (1.0, 5.0)]}) - n = _make_network() - n.optimize.create_model(include_objective_constant=False) - with pytest.raises(ValueError, match="non-increasing"): - add_piecewise_food_utility(n, blocks_path, min_block_width_mt=0.0) - - -def test_non_contiguous_block_ids_raises(tmp_path): - df = pd.DataFrame( - [ - { - "food": "x", - "country": "USA", - "block_id": 1, - "width_mt_per_year": 1.0, - "marginal_utility_bnusd_per_mt": 5.0, - }, - { - "food": "x", - "country": "USA", - "block_id": 3, - "width_mt_per_year": 1.0, - "marginal_utility_bnusd_per_mt": 3.0, - }, - ] - ) - path = tmp_path / "blocks.csv" - df.to_csv(path, index=False) - n = _make_network() - n.optimize.create_model(include_objective_constant=False) - with pytest.raises(ValueError, match="contiguous"): - add_piecewise_food_utility(n, str(path), min_block_width_mt=0.0) - - -def test_missing_link_coverage_logs(tmp_path, caplog): - # Network has two consume links; blocks file only covers one. The - # uncovered link earns zero utility (no piecewise variable), which - # is the documented semantic for foods filtered out at calibration - # time for negligible baseline consumption. - blocks_path = _write_blocks(tmp_path, {"x": [(1.0, 5.0)]}) - n = _make_network(foods=("x", "y")) - n.optimize.create_model(include_objective_constant=False) - with caplog.at_level("INFO"): - add_piecewise_food_utility(n, blocks_path, min_block_width_mt=0.0) - assert any( - "omits 1 of 2 food_consumption links" in rec.message for rec in caplog.records - ) - # `food_utility_value` should only carry the covered link. - names = n.model.variables["food_utility_value"].coords["name"].values.tolist() - assert any("consume:x:" in n_ for n_ in names) - assert not any("consume:y:" in n_ for n_ in names) - - -# --------------------------------------------------------------------------- -# Behavioural solve tests -# --------------------------------------------------------------------------- - - -def test_diminishing_mu_stops_at_break_even_block(tmp_path): - """Optimal consumption equals the cumulative width of blocks whose - marginal utility strictly exceeds the marginal supply cost.""" - # MU: 5, 3, 1; widths 2 each; supply cost 2. - # Blocks 1 (5>2) and 2 (3>2) fill; block 3 (1<2) and overflow (mu=1<2) skipped. - blocks_path = _write_blocks(tmp_path, {"x": [(2.0, 5.0), (2.0, 3.0), (2.0, 1.0)]}) - n = _make_network(supply_costs={"x": 2.0}) - _solve_with_utility(n, blocks_path) - - assert _link_p(n, "x") == pytest.approx(4.0, abs=1e-6) - - -def test_overflow_continues_at_last_block_mu(tmp_path): - """When supply cost < last-block MU, all blocks fill AND overflow - grows to p_nom; the overflow region must carry the last-block MU.""" - blocks_path = _write_blocks(tmp_path, {"x": [(1.0, 5.0), (1.0, 3.0)]}) - # Last MU = 3, supply = 2 -> overflow rewarded at +1 per Mt up to p_nom. - n = _make_network(supply_costs={"x": 2.0}, p_nom_consume={"x": 10.0}) - _solve_with_utility(n, blocks_path) - - # All 2 Mt of blocks + 8 Mt of overflow. - assert _link_p(n, "x") == pytest.approx(10.0, abs=1e-6) - - -def test_overflow_skipped_when_last_mu_below_cost(tmp_path): - """Sanity counterpart: overflow stays at zero when its marginal - reward does not cover supply cost.""" - blocks_path = _write_blocks(tmp_path, {"x": [(1.0, 5.0), (1.0, 1.0)]}) - n = _make_network(supply_costs={"x": 2.0}, p_nom_consume={"x": 10.0}) - _solve_with_utility(n, blocks_path) - - # Only block 1 (5>2) fills; block 2 and overflow (mu=1<2) skipped. - assert _link_p(n, "x") == pytest.approx(1.0, abs=1e-6) - - -def test_per_link_overflow_uses_link_specific_last_mu(tmp_path): - """Two links with different block counts must each carry their own - last-block MU on overflow. - - Behavioural guard against the historical "padding leak" bug where the - overflow MU of a short link inherited the zero padding from a longer - link's wider block_id range, removing any penalty on overflow. - """ - # x: 2 blocks (last MU = 3); y: 4 blocks (last MU = 2). Supply = 2.5. - # x's overflow MU = 3 > 2.5 -> overflows to p_nom. - # y's overflow MU = 2 < 2.5 -> stops at the last positive-margin block. - blocks_path = _write_blocks( - tmp_path, - { - "x": [(1.0, 5.0), (1.0, 3.0)], - "y": [(1.0, 5.0), (1.0, 4.0), (1.0, 3.0), (1.0, 2.0)], - }, - ) - n = _make_network( - foods=("x", "y"), - supply_costs={"x": 2.5, "y": 2.5}, - p_nom_consume={"x": 10.0, "y": 10.0}, - ) - _solve_with_utility(n, blocks_path) - - # x: full 10 Mt (blocks + overflow). If the bug were present, x.p - # would cap at 2.0 (overflow MU would be 0 < 2.5). - assert _link_p(n, "x") == pytest.approx(10.0, abs=1e-6) - # y: blocks 1..3 fill (5,4,3 > 2.5); block 4 (2 < 2.5) and overflow - # skipped. - assert _link_p(n, "y") == pytest.approx(3.0, abs=1e-6) - - -def test_realized_utility_matches_expected_credit(tmp_path): - """``pop_piecewise_food_utility_value`` must return the realized - utility credit (sum_k mu_k * x_k including overflow) consistent with - the optimal solution.""" - blocks_path = _write_blocks(tmp_path, {"x": [(2.0, 5.0), (2.0, 3.0), (2.0, 1.0)]}) - n = _make_network(supply_costs={"x": 2.0}) - _solve_with_utility(n, blocks_path) - - p = _link_p(n, "x") - assert p == pytest.approx(4.0, abs=1e-6) - - # Expected realized utility = 2 Mt at mu=5 + 2 Mt at mu=3 = 16. - util = pop_piecewise_food_utility_value(n) - assert util == pytest.approx(16.0, rel=1e-6) - - # Objective value cross-check: supply_cost*p - utility = 2*4 - 16 = -8. - obj = float(n.model.objective.value) - assert obj == pytest.approx(2.0 * p - util, abs=1e-6) - - -def test_realized_utility_includes_overflow(tmp_path): - """The overflow contribution must show up in the realized utility.""" - blocks_path = _write_blocks(tmp_path, {"x": [(1.0, 5.0), (1.0, 3.0)]}) - n = _make_network(supply_costs={"x": 2.0}, p_nom_consume={"x": 10.0}) - _solve_with_utility(n, blocks_path) - - p = _link_p(n, "x") - assert p == pytest.approx(10.0, abs=1e-6) - # Realized utility = 1*5 (block 1) + 1*3 (block 2) + 8*3 (overflow at last MU) = 32. - util = pop_piecewise_food_utility_value(n) - assert util == pytest.approx(32.0, rel=1e-6) - - -def test_block_merging_preserves_total_utility_when_all_blocks_fill(tmp_path): - """When supply cost is below the last MU (so every block fills), - realized utility = sum(mu_k * w_k), which is mass-conserved by the - width-weighted merge in ``_merge_small_width_blocks``.""" - # Mixed thin and wide blocks. Use a low-but-nonzero supply cost (PyPSA - # refuses to build the linopy model if no component carries any cost - # at create_model time) and cap p_nom to the sum of widths so every - # block fills exactly and no overflow region is exercised. - spec = {"x": [(0.5, 10.0), (0.5, 8.0), (1.0, 4.0)]} - expected_util = 0.5 * 10.0 + 0.5 * 8.0 + 1.0 * 4.0 # = 13.0 - total_width = sum(w for w, _ in spec["x"]) # = 2.0 - - # Solve without merging. - blocks_path = _write_blocks(tmp_path, spec) - n = _make_network(supply_costs={"x": 0.5}, p_nom_consume={"x": total_width}) - _solve_with_utility(n, blocks_path, min_block_width_mt=0.0) - assert _link_p(n, "x") == pytest.approx(total_width, abs=1e-6) - util_no_merge = pop_piecewise_food_utility_value(n) - assert util_no_merge == pytest.approx(expected_util, rel=1e-6) - - # Solve with a width floor that forces blocks 1 and 2 to merge. - n2 = _make_network(supply_costs={"x": 0.5}, p_nom_consume={"x": total_width}) - _solve_with_utility(n2, blocks_path, min_block_width_mt=0.7) - assert _link_p(n2, "x") == pytest.approx(total_width, abs=1e-6) - util_merged = pop_piecewise_food_utility_value(n2) - assert util_merged == pytest.approx(expected_util, rel=1e-6) - - -# --------------------------------------------------------------------------- -# Unit tests for the merge preprocessing (independent of the LP formulation) -# --------------------------------------------------------------------------- - - -def test_merge_small_width_blocks_passthrough_when_all_above_floor(): - """No floor or all blocks already wide enough -> unchanged.""" - rows = pd.DataFrame( - { - "name": ["L"] * 3, - "block_id": [1, 2, 3], - "width_mt_per_year": [1.0, 1.0, 1.0], - "marginal_utility_bnusd_per_mt": [5.0, 3.0, 1.0], - } - ) - out, merged, affected = _merge_small_width_blocks(rows, 0.5) - assert merged == 0 and affected == 0 - pd.testing.assert_frame_equal(out.reset_index(drop=True), rows) - - -def test_merge_small_width_blocks_width_weighted_average(): - """Merging blocks 1 and 2 must conserve total width and total utility - mass (mu * w).""" - rows = pd.DataFrame( - { - "name": ["L", "L", "L"], - "block_id": [1, 2, 3], - "width_mt_per_year": [0.2, 0.8, 1.0], - "marginal_utility_bnusd_per_mt": [10.0, 5.0, 2.0], - } - ) - out, merged, affected = _merge_small_width_blocks(rows, 0.5) - assert merged == 1 and affected == 1 - # Donor (block 1, width 0.2) merges into block 2 (the right neighbour - # since donor is at the left edge). Merged width = 1.0, merged mu = - # (10*0.2 + 5*0.8) / 1.0 = 6.0. Block 3 stays. - assert list(out["block_id"]) == [1, 2] - assert out.loc[0, "width_mt_per_year"] == pytest.approx(1.0) - assert out.loc[0, "marginal_utility_bnusd_per_mt"] == pytest.approx(6.0) - assert out.loc[1, "width_mt_per_year"] == pytest.approx(1.0) - assert out.loc[1, "marginal_utility_bnusd_per_mt"] == pytest.approx(2.0) - - # Total width and total mu*w preserved. - assert out["width_mt_per_year"].sum() == pytest.approx(2.0) - assert (out["width_mt_per_year"] * out["marginal_utility_bnusd_per_mt"]).sum() == ( - pytest.approx(10.0 * 0.2 + 5.0 * 0.8 + 2.0 * 1.0) - ) diff --git a/tests/test_objective_breakdown.py b/tests/test_objective_breakdown.py index 267f21c0..98eee1b2 100644 --- a/tests/test_objective_breakdown.py +++ b/tests/test_objective_breakdown.py @@ -372,16 +372,6 @@ def test_market_response_costs(self, solved_network): assert result["supply_response"].iloc[0] == pytest.approx(2.5, abs=1e-6) assert result["demand_response"].iloc[0] == pytest.approx(-0.75, abs=1e-6) - def test_food_utility_cost(self, solved_network): - """food_utility_cost should add to Consumer values.""" - n = solved_network - utility_cost = -1.5 - n._meta["food_utility_cost"] = utility_cost - n._objective += utility_cost - result = extract_objective_breakdown(n) - expected = EXPECTED_COSTS["consumer_values"] + utility_cost - assert result["consumer_values"].iloc[0] == pytest.approx(expected, abs=1e-6) - @pytest.mark.parametrize( ("rate_column", "rate", "baseline", "correction_cost"), [ diff --git a/tools/build-docs b/tools/build-docs index 9e214d27..fa798032 100755 --- a/tools/build-docs +++ b/tools/build-docs @@ -64,9 +64,8 @@ echo "=== Step 2/3: Solving tutorial scenarios and executing notebooks ===" # scenarios whose inputs changed, so repeated runs are cheap. tools/smk "${SMK_ARGS[@]}" --configfile config/tutorial/01_ghg_prices.yaml -- \ analyze_all_scenarios -tools/smk "${SMK_ARGS[@]}" --configfile config/tutorial/02_consumer_values.yaml -- \ - analyze_all_scenarios \ - results/tutorial_02/consumer_values/baseline/values.csv +tools/smk "${SMK_ARGS[@]}" --configfile config/tutorial/02_demand_response.yaml -- \ + analyze_all_scenarios # Bake the current solve outputs into the committed notebooks so MyST-NB can # render them. Executed in place; nbstripout is disabled for this subtree diff --git a/tools/sync-solve-inputs b/tools/sync-solve-inputs index 819c9d4b..78143fd1 100755 --- a/tools/sync-solve-inputs +++ b/tools/sync-solve-inputs @@ -71,11 +71,6 @@ if [[ -d "data/curated/calibration" ]]; then SOURCES+=("data/curated/calibration/") fi -# Consumer value files (if they exist). -if [[ -d "results/${CONFIG_NAME}/consumer_values" ]]; then - SOURCES+=("results/${CONFIG_NAME}/consumer_values/") -fi - # Manifest file for cluster-solve (if it exists). if [[ -f ".batch/manifest_${CONFIG_NAME}.json" ]]; then SOURCES+=(".batch/manifest_${CONFIG_NAME}.json") diff --git a/workflow/Snakefile b/workflow/Snakefile index d6ca4b5d..a5546122 100644 --- a/workflow/Snakefile +++ b/workflow/Snakefile @@ -87,10 +87,8 @@ include: "rules/model.smk" include: "rules/remote_solve.smk" include: "rules/plotting.smk" include: "rules/analysis.smk" -include: "rules/consumer_values.smk" include: "rules/deviation_penalty.smk" include: "rules/market_response.smk" -include: "rules/optimal_taxes.smk" include: "rules/luc.smk" include: "rules/documentation.smk" diff --git a/workflow/rules/analysis.smk b/workflow/rules/analysis.smk index d8268e34..7a8856fa 100644 --- a/workflow/rules/analysis.smk +++ b/workflow/rules/analysis.smk @@ -107,9 +107,6 @@ if config["solving"]["inline_analysis"]: crop_growth_cap=lambda w: get_effective_config(w.scenario)["validation"][ "crop_growth_cap" ], - food_utility_piecewise=lambda w: get_effective_config(w.scenario)[ - "food_utility_piecewise" - ], fix_within_group_ratios=lambda w: get_effective_config(w.scenario)[ "food_groups" ]["fix_within_group_ratios"], @@ -157,9 +154,6 @@ if config["solving"]["inline_analysis"]: groundwater_cap=lambda w: get_effective_config(w.scenario)[ "groundwater_depletion" ]["cap_mm3"], - food_incentives_enabled=lambda w: get_effective_config(w.scenario)[ - "food_incentives" - ]["enabled"], equal_by_country_source=lambda w: get_effective_config(w.scenario)[ "food_groups" ]["equal_by_country_source"], diff --git a/workflow/rules/consumer_values.smk b/workflow/rules/consumer_values.smk deleted file mode 100644 index b8d9fc8d..00000000 --- a/workflow/rules/consumer_values.smk +++ /dev/null @@ -1,113 +0,0 @@ -# SPDX-FileCopyrightText: 2026 Koen van Greevenbroek -# -# SPDX-License-Identifier: GPL-3.0-or-later - -"""Rules for consumer values workflow. - -This workflow: -1. Extracts "consumer values" (dual variables) from a baseline model with fixed - consumption (enforce_baseline_diet=True) -2. Calibrates piecewise utility blocks from extracted values and baseline food - consumption levels -3. Uses these values in subsequent solves to explore how health/environmental - pricing affects consumption while accounting for revealed consumer preferences - -The baseline scenario used for extraction is configurable per scenario via -``consumer_values.baseline_scenario`` (default: ``"baseline"``). This allows -different scenario groups (e.g. with different L1 production-stability costs) -to each have their own correctly-calibrated utility blocks. -""" - - -rule extract_consumer_values: - """Extract consumer values from baseline solve with fixed consumption. - - Consumer values are the dual variables (shadow prices) of the food group - equality constraints, representing the marginal value of consumption. - """ - input: - network="/{name}/solved/model_scen-{baseline}.nc", - output: - consumer_values="/{name}/consumer_values/{baseline}/values.csv", - group: - "prep" - resources: - runtime="5m", - mem_mb=2000, - log: - "/{name}/extract_consumer_values_{baseline}.log", - benchmark: - "/{name}/extract_consumer_values_{baseline}.tsv" - script: - "../scripts/extract_consumer_values.py" - - -rule calibrate_food_utility_blocks: - """Calibrate piecewise food utility blocks from baseline dual values.""" - input: - network="/{name}/solved/model_scen-{baseline}.nc", - consumer_values="/{name}/consumer_values/{baseline}/values.csv", - output: - utility_blocks="/{name}/consumer_values/{baseline}/utility_blocks.csv", - params: - n_blocks=config["food_utility_piecewise"]["n_blocks"], - decline_factor=config["food_utility_piecewise"]["decline_factor"], - total_width_multiplier=config["food_utility_piecewise"][ - "total_width_multiplier" - ], - group: - "prep" - log: - "/{name}/calibrate_food_utility_blocks_{baseline}.log", - script: - "../scripts/calibrate_food_utility_blocks.py" - - -# Consumer values comparison scenarios (from scenario definitions) -CV_SCENARIOS = list_scenarios() -if not CV_SCENARIOS: - raise ValueError("Missing scenarios in config for consumer values workflow") - - -def consumer_values_comparison_inputs(wildcards): - """Get analysis outputs for consumer values comparison.""" - inputs = {} - for scen in CV_SCENARIOS: - inputs[f"consumption_{scen}"] = ( - f"/{wildcards.name}/analysis/scen-{scen}/food_group_consumption.parquet" - ) - inputs[f"breakdown_{scen}"] = ( - f"/{wildcards.name}/analysis/scen-{scen}/objective_breakdown.parquet" - ) - return inputs - - -rule plot_consumer_values_comparison: - """Compare consumption and objective breakdown across consumer values scenarios.""" - input: - unpack(consumer_values_comparison_inputs), - consumer_values=( - f"/{{name}}/consumer_values" - f"/{config['consumer_values']['baseline_scenario']}/values.csv" - ), - output: - consumption_pdf="/{name}/plots/consumer_values/consumption_comparison.pdf", - consumption_csv="/{name}/plots/consumer_values/consumption_comparison.csv", - objective_pdf="/{name}/plots/consumer_values/objective_comparison.pdf", - objective_csv="/{name}/plots/consumer_values/objective_comparison.csv", - cv_pdf="/{name}/plots/consumer_values/consumer_values.pdf", - cv_csv="/{name}/plots/consumer_values/consumer_values.csv", - params: - scenarios=CV_SCENARIOS, - group_colors=plotting_cfg.get("colors", {}).get("food_groups", {}), - group: - "analysis_plot" - resources: - runtime="5m", - mem_mb=2000, - log: - "/{name}/plot_consumer_values_comparison.log", - benchmark: - "/{name}/plot_consumer_values_comparison.tsv" - script: - "../scripts/plotting/plot_consumer_values_comparison.py" diff --git a/workflow/rules/documentation.smk b/workflow/rules/documentation.smk index e179c4f3..4d3b094d 100644 --- a/workflow/rules/documentation.smk +++ b/workflow/rules/documentation.smk @@ -62,7 +62,6 @@ DOC_FIGURES = [ "baseline_diet_by_region", "baseline_diet_by_food", # Consumer values figure - "consumer_values_distribution", # Cost figures "costs_crop_cost_map", "costs_crop_cost_distribution", @@ -591,30 +590,6 @@ rule doc_fig_baseline_diet_by_food: "../scripts/doc_figures/baseline_diet_by_food.py" -rule doc_fig_consumer_values_distribution: - """Generate boxen plot of per-food consumer values across countries.""" - input: - values=f"/{DOC_FIG_NAME}/consumer_values/baseline/values.csv", - style=DOC_FIG_STYLE, - params: - group_colors=food_group_colors, - slack_marginal_cost=config["validation"]["slack_marginal_cost"], - output: - svg="docs/_static/figures/consumer_values_distribution.svg", - png="docs/_static/figures/consumer_values_distribution.png", - group: - "analysis_plot" - resources: - runtime="10m", - mem_mb=2000, - log: - "/shared/doc_fig_consumer_values_distribution.log", - benchmark: - "/shared/doc_fig_consumer_values_distribution.tsv" - script: - "../scripts/doc_figures/consumer_values_distribution.py" - - # --- Validation figures --- diff --git a/workflow/rules/model.smk b/workflow/rules/model.smk index c8f291b3..c98749ca 100644 --- a/workflow/rules/model.smk +++ b/workflow/rules/model.smk @@ -238,19 +238,6 @@ def solve_model_inputs(w): inputs.update(health_input_paths(w.name)) # Add food incentives input if enabled for this scenario - if eff_cfg["food_incentives"]["enabled"]: - sources = eff_cfg["food_incentives"]["sources"] - if not sources: - raise ValueError("food_incentives enabled but sources is empty") - inputs["food_incentives"] = [ - source.format(name=w.name, scenario=w.scenario) for source in sources - ] - utility_cfg = eff_cfg["food_utility_piecewise"] - if utility_cfg["enabled"]: - baseline_name = eff_cfg["consumer_values"]["baseline_scenario"] - inputs["food_utility_piecewise"] = ( - f"/{w.name}/consumer_values/{baseline_name}/utility_blocks.csv" - ) equal_source = eff_cfg["food_groups"]["equal_by_country_source"] if equal_source: inputs["food_group_equal"] = equal_source.format( @@ -374,9 +361,6 @@ rule solve_model: crop_growth_cap=lambda w: get_effective_config(w.scenario)["validation"][ "crop_growth_cap" ], - food_utility_piecewise=lambda w: get_effective_config(w.scenario)[ - "food_utility_piecewise" - ], fix_within_group_ratios=lambda w: get_effective_config(w.scenario)[ "food_groups" ]["fix_within_group_ratios"], @@ -424,9 +408,6 @@ rule solve_model: groundwater_cap=lambda w: get_effective_config(w.scenario)[ "groundwater_depletion" ]["cap_mm3"], - food_incentives_enabled=lambda w: get_effective_config(w.scenario)[ - "food_incentives" - ]["enabled"], equal_by_country_source=lambda w: get_effective_config(w.scenario)[ "food_groups" ]["equal_by_country_source"], diff --git a/workflow/rules/optimal_taxes.smk b/workflow/rules/optimal_taxes.smk deleted file mode 100644 index 217ad2e4..00000000 --- a/workflow/rules/optimal_taxes.smk +++ /dev/null @@ -1,117 +0,0 @@ -# SPDX-FileCopyrightText: 2026 Koen van Greevenbroek -# -# SPDX-License-Identifier: GPL-3.0-or-later - -"""Rules for optimal taxes/subsidies workflow. - -This workflow: -1. Runs optimization with health/GHG objectives to derive optimal consumption -2. Extracts consumption patterns from the optimized model -3. Fixes consumption to optimal values and solves with production costs only -4. Extracts dual variables as optimal Pigouvian taxes/subsidies -5. Resolves the model with taxes/subsidies applied in the objective -""" - -plotting_cfg = config["plotting"] - - -rule extract_optimal_consumption: - """Extract consumption by food group from optimized model (Stage 1 output). - - Consumption is extracted from food group stores in the solved network, - converted to per-capita g/day format for use as constraints in Stage 2. - Population data is read from the network metadata. - """ - input: - network="/{name}/solved/model_scen-optimize.nc", - food_groups="data/curated/food_groups.csv", - output: - consumption="/{name}/optimal_taxes/optimal_consumption.csv", - group: - "analysis_plot" - resources: - runtime="5m", - mem_mb=2000, - log: - "/{name}/extract_optimal_consumption.log", - benchmark: - "/{name}/extract_optimal_consumption.tsv" - script: - "../scripts/extract_optimal_consumption.py" - - -rule extract_optimal_taxes: - """Extract optimal taxes/subsidies from consumption constraint duals. - - Taxes are the dual variables (shadow prices) of the food group - equality constraints from Stage 2, representing the Pigouvian - tax/subsidy needed to incentivize optimal consumption. - """ - input: - network="/{name}/solved/model_scen-extract_taxes.nc", - output: - taxes="/{name}/optimal_taxes/taxes.csv", - group: - "analysis_plot" - resources: - runtime="5m", - mem_mb=2000, - log: - "/{name}/extract_optimal_taxes.log", - benchmark: - "/{name}/extract_optimal_taxes.tsv" - script: - "../scripts/extract_optimal_taxes.py" - - -rule plot_optimal_taxes: - """Visualize optimal taxes/subsidies by food group.""" - input: - taxes="/{name}/optimal_taxes/taxes.csv", - output: - taxes_pdf="/{name}/plots/optimal_taxes/taxes_by_food_group.pdf", - taxes_csv="/{name}/plots/optimal_taxes/taxes_by_food_group.csv", - params: - group_colors=plotting_cfg.get("colors", {}).get("food_groups", {}), - group: - "analysis_plot" - resources: - runtime="5m", - mem_mb=2000, - log: - "/{name}/plot_optimal_taxes.log", - benchmark: - "/{name}/plot_optimal_taxes.tsv" - script: - "../scripts/plotting/plot_optimal_taxes.py" - - -rule plot_optimal_taxes_diet_comparison: - """Compare global average diet across the optimal taxes optimizations.""" - input: - food_group_consumption=[ - "/{name}/analysis/scen-optimize/food_group_consumption.parquet", - "/{name}/analysis/scen-extract_taxes/food_group_consumption.parquet", - "/{name}/analysis/scen-apply_taxes/food_group_consumption.parquet", - ], - output: - pdf="/{name}/plots/optimal_taxes/diet_comparison.pdf", - csv="/{name}/plots/optimal_taxes/diet_comparison.csv", - params: - wildcards=[ - "Health/GHG optimized", - "Fixed consumption (costs only)", - "Taxes in objective", - ], - group_colors=plotting_cfg.get("colors", {}).get("food_groups", {}), - group: - "analysis_plot" - resources: - runtime="5m", - mem_mb=2000, - log: - "/{name}/plot_optimal_taxes_diet_comparison.log", - benchmark: - "/{name}/plot_optimal_taxes_diet_comparison.tsv" - script: - "../scripts/plotting/plot_food_consumption_comparison.py" diff --git a/workflow/scripts/analysis/extract_objective_breakdown.py b/workflow/scripts/analysis/extract_objective_breakdown.py index 156a16a6..2a8cc1f8 100644 --- a/workflow/scripts/analysis/extract_objective_breakdown.py +++ b/workflow/scripts/analysis/extract_objective_breakdown.py @@ -268,11 +268,6 @@ def _to_series(df_or_series: pd.DataFrame | pd.Series) -> pd.Series: if diet_stability_cost: total["Diet stability"] = total.get("Diet stability", 0.0) + diet_stability_cost - # Piecewise food utility is also a linopy-level objective term. - food_utility_cost = n.meta.get("food_utility_cost", 0.0) - if food_utility_cost: - total["Consumer values"] = total.get("Consumer values", 0.0) + food_utility_cost - # Bounded negative cost-calibration corrections (subsidies up to baseline) # are linopy-level objective terms on auxiliary variables not visible to # PyPSA statistics. Recover them exactly from solved dispatch: diff --git a/workflow/scripts/build_model/grassland.py b/workflow/scripts/build_model/grassland.py index c4dacc9f..0efa990b 100644 --- a/workflow/scripts/build_model/grassland.py +++ b/workflow/scripts/build_model/grassland.py @@ -225,8 +225,7 @@ def add_grassland_feed_links( missing_pool = ~work["bus0"].isin(n.buses.static.index) if missing_pool.any(): logger.warning( - "Skipping %d grassland_production links with no pasture-pool bus " - "(e.g. %s)", + "Skipping %d grassland_production links with no pasture-pool bus (e.g. %s)", int(missing_pool.sum()), work.loc[missing_pool, "bus0"].iloc[0], ) diff --git a/workflow/scripts/calibrate_deviation_penalty.py b/workflow/scripts/calibrate_deviation_penalty.py index 969b4d63..c06ddb5e 100644 --- a/workflow/scripts/calibrate_deviation_penalty.py +++ b/workflow/scripts/calibrate_deviation_penalty.py @@ -14,10 +14,10 @@ converges in a handful of evaluations. Each evaluation is a paired solve: - 1) baseline solve with ``enforce_baseline_diet=true`` to extract consumer - values (piecewise utility blocks) under the matching L1 regime; - 2) main solve with the calibrated utility blocks active and the listed - components' penalties enabled. + 1) baseline solve with ``enforce_baseline_diet=true`` under the matching L1 + regime; + 2) main solve with the listed components' penalties enabled, under the base + config's own demand regime. Convergence target: ``|log(d / target)|_inf < tolerance`` across all components in the calibration subset. @@ -34,12 +34,6 @@ from workflow.scripts.analysis.extract_baseline_deviation import ( extract_baseline_deviation, ) -from workflow.scripts.calibrate_food_utility_blocks import ( - _calibrate_blocks, - _load_baseline_food_consumption, - drop_zero_baseline_pairs, -) -from workflow.scripts.extract_consumer_values import extract_consumer_values from workflow.scripts.logging_config import setup_script_logging from workflow.scripts.solve_model.core import _ShadowPriceLogFilter, run_solve from workflow.scripts.solve_namespace import ( @@ -67,9 +61,8 @@ def _make_overrides( cost iterate. Components not in ``components`` are forced off so the calibration measures the deviations driven only by the listed knobs. - The **baseline** solve always runs at ghg_price=0 / value_per_yll=0 - so consumer values are derived from raw revealed preferences - (independent of any policy regime under investigation). The **main** + The **baseline** solve always runs at ghg_price=0 / value_per_yll=0, + independent of any policy regime under investigation. The **main** solve inherits emissions / health pricing from ``base_config``; set them at the top level of the calibration config to pin the regime. """ @@ -102,7 +95,6 @@ def _make_overrides( "feed": feed_block, "diet": diet_block, }, - "food_utility_piecewise": {"enabled": not enforce_baseline}, } if enforce_baseline: overrides["emissions"] = {"ghg_price": 0} @@ -110,35 +102,6 @@ def _make_overrides( return overrides -def _write_utility_blocks(n_baseline, base_config: dict, blocks_path: Path) -> None: - """Recreate the workflow/rules/consumer_values.smk outputs in-memory.""" - values_df = extract_consumer_values(n_baseline) - baseline_df = _load_baseline_food_consumption(n_baseline) - merged = baseline_df.merge( - values_df[["food", "country", "value_bnusd_per_mt"]], - on=["food", "country"], - how="inner", - ) - if merged.empty: - raise ValueError( - "No overlapping (food, country) pairs between baseline and values" - ) - merged = drop_zero_baseline_pairs(merged) - if merged.empty: - raise ValueError( - "All (food, country) pairs have negligible baseline consumption" - ) - utility_cfg = base_config["food_utility_piecewise"] - blocks_df = _calibrate_blocks( - merged, - n_blocks=int(utility_cfg["n_blocks"]), - decline_factor=float(utility_cfg["decline_factor"]), - total_width_multiplier=float(utility_cfg["total_width_multiplier"]), - ) - blocks_path.parent.mkdir(parents=True, exist_ok=True) - blocks_df.to_csv(blocks_path, index=False) - - def _deviation_pcts(n_main, components: list[str]) -> dict[str, float]: """Return per-component absolute deviation as a percentage of baseline.""" df = extract_baseline_deviation(n_main).set_index("component") @@ -195,7 +158,6 @@ def _evaluate( overrides_base = _make_overrides(lambdas, components, enforce_baseline=True) overrides_main = { **_make_overrides(lambdas, components, enforce_baseline=False), - "consumer_values": {"baseline_scenario": baseline_scen}, "macronutrients": {"cal": {"equal_to_baseline": True}}, } scenario_defs = {baseline_scen: overrides_base, main_scen: overrides_main} @@ -222,12 +184,9 @@ def _evaluate( if n_baseline is None: raise RuntimeError(f"[iter {iter_id}] baseline solve failed at {lambda_str}") - # --- Calibrate utility blocks (consumer_values workflow inline) --- entry_m_preview = build_scenario_entry( base_config, main_scen, name, path_roots, False, scenario_defs ) - blocks_path = Path(entry_m_preview["inputs"]["food_utility_piecewise"]) - _write_utility_blocks(n_baseline, base_config, blocks_path) del n_baseline # --- Main solve --- diff --git a/workflow/scripts/calibrate_food_utility_blocks.py b/workflow/scripts/calibrate_food_utility_blocks.py deleted file mode 100644 index 918d0528..00000000 --- a/workflow/scripts/calibrate_food_utility_blocks.py +++ /dev/null @@ -1,201 +0,0 @@ -# SPDX-FileCopyrightText: 2026 Koen van Greevenbroek -# -# SPDX-License-Identifier: GPL-3.0-or-later - -"""Calibrate piecewise food-utility blocks from baseline consumer values. - -Uses baseline dual-derived consumer values and baseline food consumption levels -to construct a piecewise diminishing marginal utility schedule per -``(food, country)`` pair. -""" - -import logging -import math - -import pandas as pd -import pypsa - -from workflow.scripts.logging_config import setup_script_logging - -logger = logging.getLogger(__name__) - -# Below this threshold the per-(food, country) baseline contributes no -# meaningful utility, and _calibrate_blocks would otherwise produce a -# sequence of microscopic blocks with sub-cent total mass that the LP -# treats as ill-conditioned piecewise terms. -ZERO_BASELINE_TOL_MT_PER_YEAR = 1e-9 # ~1 kg / year nationally - - -def drop_zero_baseline_pairs(merged: pd.DataFrame) -> pd.DataFrame: - """Filter merged (food, country) baseline/value rows to non-empty pairs. - - Shared between the standalone calibrate_food_utility_blocks rule and - the inline calibration in calibrate_deviation_penalty so both paths - produce identical utility blocks for identical inputs. - """ - return merged[merged["baseline_mt_per_year"] > ZERO_BASELINE_TOL_MT_PER_YEAR] - - -def _load_baseline_food_consumption(n: pypsa.Network) -> pd.DataFrame: - """Load per-food baseline consumption (Mt/year) from p_set on consume links.""" - links = n.links.static - consume = links[links["carrier"] == "food_consumption"] - - if "p_set" not in n.links.dynamic or n.links.dynamic.p_set.empty: - raise ValueError( - "No p_set values found on links. " - "Ensure baseline scenario uses validation.enforce_baseline_diet=true." - ) - - p_set = n.links.dynamic.p_set - snapshot = n.snapshots[-1] - targets = p_set.loc[snapshot].reindex(consume.index) - - # Only include links that had p_set (non-NaN) - has_target = targets.notna() - consume = consume[has_target] - targets = targets[has_target] - - if consume.empty: - raise ValueError( - "No fixed food consumption links found. " - "Ensure baseline scenario uses validation.enforce_baseline_diet=true." - ) - - return pd.DataFrame( - { - "food": consume["food"].astype(str).values, - "country": consume["country"].astype(str).str.upper().values, - "baseline_mt_per_year": targets.fillna(0.0).values, - } - ) - - -def _calibrate_blocks( - merged: pd.DataFrame, - n_blocks: int, - decline_factor: float, - total_width_multiplier: float, -) -> pd.DataFrame: - """Build per-block widths and marginal utilities.""" - if n_blocks < 1: - raise ValueError(f"n_blocks must be >= 1, got {n_blocks}") - if decline_factor <= 0 or decline_factor > 1: - raise ValueError( - f"decline_factor must satisfy 0 < decline_factor <= 1, got {decline_factor}" - ) - if total_width_multiplier <= 0: - raise ValueError( - f"total_width_multiplier must be > 0, got {total_width_multiplier}" - ) - - rows = [] - # Baseline quantity sits at fraction 1 / total_width_multiplier of total width. - # With equal-width blocks, this corresponds to a (possibly fractional) block - # position p = n_blocks / total_width_multiplier. We anchor the marginal - # utility at the block containing this position. - baseline_block_position = n_blocks / total_width_multiplier - anchor_block_id = min(max(math.ceil(baseline_block_position), 1), n_blocks) - - for row in merged.itertuples(index=False): - baseline_mt = float(row.baseline_mt_per_year) - base_utility = float(row.value_bnusd_per_mt) - # Zero-baseline rows should have been filtered out by the caller - # so the 1e-12 floor here only catches numerical edge cases, not - # the bulk "no consumption in this country" pattern that would - # otherwise produce ill-conditioned piecewise terms. - width_mt = max(baseline_mt * total_width_multiplier / n_blocks, 1e-12) - - for block_id in range(1, n_blocks + 1): - if base_utility >= 0: - exponent = block_id - anchor_block_id - else: - exponent = anchor_block_id - block_id - utility = base_utility * (decline_factor**exponent) - - rows.append( - { - "food": row.food, - "country": row.country, - "block_id": block_id, - "width_mt_per_year": width_mt, - "marginal_utility_bnusd_per_mt": utility, - "baseline_mt_per_year": baseline_mt, - "base_value_bnusd_per_mt": base_utility, - "anchor_block_id": anchor_block_id, - } - ) - - blocks_df = pd.DataFrame.from_records(rows) - if blocks_df.empty: - raise ValueError("No utility blocks generated from merged baseline/value data") - - return blocks_df.sort_values(["food", "country", "block_id"]).reset_index(drop=True) - - -if __name__ == "__main__": - logger = setup_script_logging(log_file=snakemake.log[0] if snakemake.log else None) # type: ignore[name-defined] - - logger.info("Loading baseline solved network from %s", snakemake.input.network) - n = pypsa.Network(snakemake.input.network) - - values_df = pd.read_csv(snakemake.input.consumer_values) - required = {"food", "country", "value_bnusd_per_mt"} - missing = required - set(values_df.columns) - if missing: - missing_text = ", ".join(sorted(missing)) - raise ValueError( - f"Missing required columns in consumer values file: {missing_text}" - ) - - values_df = values_df[["food", "country", "value_bnusd_per_mt"]].copy() - values_df["food"] = values_df["food"].astype(str) - values_df["country"] = values_df["country"].astype(str).str.upper() - values_df["value_bnusd_per_mt"] = pd.to_numeric( - values_df["value_bnusd_per_mt"], errors="coerce" - ).fillna(0.0) - - baseline_df = _load_baseline_food_consumption(n) - merged = baseline_df.merge( - values_df, - on=["food", "country"], - how="inner", - ) - if merged.empty: - raise ValueError( - "No overlapping (food, country) pairs between baseline and values" - ) - - n_before = len(merged) - merged = drop_zero_baseline_pairs(merged) - n_dropped = n_before - len(merged) - if n_dropped: - logger.info( - "Dropped %d (food, country) pairs with baseline <= %.1e Mt/year " - "from utility-block calibration", - n_dropped, - ZERO_BASELINE_TOL_MT_PER_YEAR, - ) - - logger.info( - "Calibrating piecewise utility for %d (food, country) pairs", - len(merged), - ) - - n_blocks = int(snakemake.params.n_blocks) - decline_factor = float(snakemake.params.decline_factor) - total_width_multiplier = float(snakemake.params.total_width_multiplier) - - blocks_df = _calibrate_blocks( - merged, - n_blocks=n_blocks, - decline_factor=decline_factor, - total_width_multiplier=total_width_multiplier, - ) - blocks_df.to_csv(snakemake.output.utility_blocks, index=False) - - logger.info( - "Wrote %d utility block rows to %s", - len(blocks_df), - snakemake.output.utility_blocks, - ) diff --git a/workflow/scripts/doc_figures/consumer_values_distribution.py b/workflow/scripts/doc_figures/consumer_values_distribution.py deleted file mode 100644 index ecddc408..00000000 --- a/workflow/scripts/doc_figures/consumer_values_distribution.py +++ /dev/null @@ -1,148 +0,0 @@ -#!/usr/bin/env python3 -# SPDX-FileCopyrightText: 2026 Koen van Greevenbroek -# -# SPDX-License-Identifier: GPL-3.0-or-later - -"""Generate boxen plot of consumer-value distributions across countries. - -Shows per-(food, country) consumer values (USD/kg) extracted from the -baseline solve as duals of the fixed food-consumption equality -constraints. Each row is one food; bars span the country distribution, -colored by food group. -""" - -import logging - -import matplotlib - -matplotlib.use("Agg") - -import matplotlib.pyplot as plt -import pandas as pd -import seaborn as sns - -from workflow.scripts.doc_figures_config import ( - FIGURE_WIDTH, - FONT_SIZES, - apply_doc_style, - save_doc_figure, -) -from workflow.scripts.logging_config import setup_script_logging - -logger = logging.getLogger(__name__) - -# bnUSD/Mt = (1e9 USD) / (1e9 kg) = USD/kg -BNUSD_PER_MT_TO_USD_PER_KG = 1.0 - - -def main() -> None: - setup_script_logging(snakemake.log[0]) # type: ignore[name-defined] - - df = pd.read_csv(snakemake.input.values) # type: ignore[name-defined] - group_colors: dict[str, str] = dict(snakemake.params.group_colors) # type: ignore[name-defined] - slack_marginal_cost = float(snakemake.params.slack_marginal_cost) # type: ignore[name-defined] - - # Drop rows whose dual is bounded by the food-slack price; these reflect - # baseline-supply gaps (foods the model cannot deliver at baseline-diet - # consumption) rather than consumer preference, and saturate at the slack - # cap regardless of the underlying economics. - threshold = 0.99 * slack_marginal_cost - n_total = len(df) - df = df[df["value_bnusd_per_mt"].abs() < threshold].copy() - n_dropped = n_total - len(df) - if n_dropped: - logger.info( - "Dropped %d/%d rows whose dual saturated at the slack price (|value| >= %.1f)", - n_dropped, - n_total, - threshold, - ) - - df["value_usd_per_kg"] = df["value_bnusd_per_mt"] * BNUSD_PER_MT_TO_USD_PER_KG - - # Order food groups by median value (desc), foods within each group by - # within-group median (desc). - group_median = df.groupby("food_group")["value_usd_per_kg"].median() - group_order = group_median.sort_values(ascending=False).index.tolist() - food_median = df.groupby(["food_group", "food"])["value_usd_per_kg"].median() - - ordered_foods: list[str] = [] - food_to_group: dict[str, str] = {} - for g in group_order: - foods = food_median.loc[g].sort_values(ascending=False).index.tolist() - ordered_foods.extend(foods) - for f in foods: - food_to_group[f] = g - - palette = {f: group_colors.get(food_to_group[f], "#888888") for f in ordered_foods} - - apply_doc_style() - - n_foods = len(ordered_foods) - fig_height = max(5, n_foods * 0.28) - fig, ax = plt.subplots(figsize=(FIGURE_WIDTH, fig_height)) - - sns.boxenplot( - data=df, - x="value_usd_per_kg", - y="food", - hue="food", - order=ordered_foods, - hue_order=ordered_foods, - palette=palette, - legend=False, - orient="h", - linewidth=0.5, - width=0.7, - ax=ax, - saturation=0.85, - ) - - # Symlog spans the ~3 orders of magnitude across foods while still - # showing the few foods with negative values. - ax.set_xscale("symlog", linthresh=0.05) - ax.set_xlabel("Consumer value (USD / kg)", fontsize=FONT_SIZES["label"]) - ax.set_ylabel("") - ax.tick_params(axis="y", labelsize=FONT_SIZES["tick"]) - ax.tick_params(axis="x", labelsize=FONT_SIZES["tick"]) - ax.axvline(0, color="#888888", linewidth=0.5) - - # Group separators - prev_group = None - for i, food in enumerate(ordered_foods): - g = food_to_group[food] - if prev_group is not None and g != prev_group: - ax.axhline(i - 0.5, color="#cccccc", linewidth=0.5) - prev_group = g - - # Right-margin group labels - positions: dict[str, list[int]] = {} - for i, food in enumerate(ordered_foods): - positions.setdefault(food_to_group[food], []).append(i) - for g, idxs in positions.items(): - mid = (idxs[0] + idxs[-1]) / 2 - ax.text( - 1.01, - mid, - g, - transform=ax.get_yaxis_transform(), - fontsize=FONT_SIZES["annotation"], - color=group_colors.get(g, "#444444"), - va="center", - ha="left", - fontweight="bold", - ) - - ax.grid(axis="x", alpha=0.3, linewidth=0.5) - ax.grid(axis="y", visible=False) - - fig.subplots_adjust(right=0.84) - - save_doc_figure(fig, snakemake.output.svg, format="svg") # type: ignore[name-defined] - save_doc_figure(fig, snakemake.output.png, format="png", dpi=300) # type: ignore[name-defined] - plt.close(fig) - logger.info("Saved consumer-values distribution plot") - - -if __name__ == "__main__": - main() diff --git a/workflow/scripts/extract_consumer_values.py b/workflow/scripts/extract_consumer_values.py deleted file mode 100644 index 80f35427..00000000 --- a/workflow/scripts/extract_consumer_values.py +++ /dev/null @@ -1,99 +0,0 @@ -# SPDX-FileCopyrightText: 2026 Koen van Greevenbroek -# -# SPDX-License-Identifier: GPL-3.0-or-later - -"""Extract consumer values (dual variables) from fixed food consumption links. - -Consumer values represent the marginal value of consuming one additional unit -of each food, as revealed by the dual variables of p_set constraints on -food_consumption links. These values can be used to construct an objective -function that replicates consumer preferences. - -Expects a solved network with: -- validation.enforce_baseline_diet=True (fixed per-food consumption via p_set) -- mu_p_set duals extracted to n.links.dynamic -""" - -import logging - -import pandas as pd -import pypsa - -from workflow.scripts.logging_config import setup_script_logging - -logger = logging.getLogger(__name__) - - -def extract_consumer_values(n: pypsa.Network) -> pd.DataFrame: - """Extract consumer values from p_set duals on food consumption links. - - Parameters - ---------- - n : pypsa.Network - Solved network with fixed food consumption links (p_set). - - Returns - ------- - pd.DataFrame - DataFrame with columns: food, food_group, country, value_bnusd_per_mt, - adjustment_bnusd_per_mt. The adjustment column is the value with - sign flipped for direct use as a marginal cost incentive. - """ - links = n.links.static - consume = links[links["carrier"] == "food_consumption"] - - if "mu_p_set" not in n.links.dynamic: - raise ValueError( - "No p_set duals found in the network. " - "Ensure the model was solved with validation.enforce_baseline_diet=true" - ) - - mu_p_set = n.links.dynamic.mu_p_set - snapshot = n.snapshots[-1] - duals = mu_p_set.loc[snapshot].reindex(consume.index).fillna(0.0) - - # Only include links that actually had p_set (non-NaN duals) - has_dual = duals != 0.0 - if "p_set" in n.links.dynamic: - p_set = n.links.dynamic.p_set - has_p_set = p_set.loc[snapshot].reindex(consume.index).notna() - has_dual = has_dual | has_p_set - - consume = consume[has_dual] - duals = duals[has_dual] - - if consume.empty: - raise ValueError( - "No fixed food consumption links found. " - "Ensure the model was solved with validation.enforce_baseline_diet=true" - ) - - df = pd.DataFrame( - { - "food": consume["food"].astype(str).values, - "food_group": consume["food_group"].astype(str).values, - "country": consume["country"].astype(str).str.upper().values, - "value_bnusd_per_mt": duals.values, - "adjustment_bnusd_per_mt": -duals.values, - } - ) - - logger.info( - "Extracted consumer values for %d (food, country) pairs (%d unique foods)", - len(df), - df["food"].nunique(), - ) - return df - - -if __name__ == "__main__": - logger = setup_script_logging(log_file=snakemake.log[0] if snakemake.log else None) # type: ignore[name-defined] - - logger.info("Loading solved network from %s", snakemake.input.network) - n = pypsa.Network(snakemake.input.network) - - df = extract_consumer_values(n) - - output_path = snakemake.output.consumer_values - df.to_csv(output_path, index=False) - logger.info("Saved consumer values to %s", output_path) diff --git a/workflow/scripts/extract_optimal_taxes.py b/workflow/scripts/extract_optimal_taxes.py deleted file mode 100644 index 74a5ac76..00000000 --- a/workflow/scripts/extract_optimal_taxes.py +++ /dev/null @@ -1,102 +0,0 @@ -# SPDX-FileCopyrightText: 2026 Koen van Greevenbroek -# -# SPDX-License-Identifier: GPL-3.0-or-later - -"""Extract optimal taxes from food group equality constraint duals. - -Taxes represent the Pigouvian tax (positive) or subsidy (negative) needed -to incentivize the optimal consumption pattern derived in Stage 1. - -The dual variable (shadow price) of each consumption constraint indicates -the marginal cost of forcing that consumption level when only production -costs are considered. This is the tax/subsidy that would make the -production-cost-only equilibrium match the health/GHG-optimal outcome. - -Interpretation: -- Positive dual: Increasing consumption at this constraint increases production - cost. The health/GHG-optimal consumption is higher than what pure production - costs would yield, so a SUBSIDY is needed to encourage more consumption. -- Negative dual: Increasing consumption would decrease production cost (e.g., - due to economies of scale or efficient utilization). The optimal consumption - is lower, so a TAX is needed to discourage consumption. - -Therefore: tax = -dual (positive tax discourages, negative tax = subsidy encourages) -""" - -import logging - -import pandas as pd -import pypsa - -from workflow.scripts.logging_config import setup_script_logging - -logger = logging.getLogger(__name__) - - -def extract_optimal_taxes(n: pypsa.Network) -> pd.DataFrame: - """Extract optimal taxes from food group equality constraint duals. - - Parameters - ---------- - n : pypsa.Network - Solved network with food group equality constraints. - - Returns - ------- - pd.DataFrame - Columns: group, country, tax_bnusd_per_mt, tax_usd_per_kg, - adjustment_bnusd_per_mt - Positive values indicate taxes (discourage consumption), - negative values indicate subsidies (encourage consumption). - """ - gc_df = n.global_constraints.static - - # Filter to food group equality constraints using the food_group column - food_group_constraints = gc_df[ - gc_df["food_group"].notna() & gc_df.index.str.startswith("food_group_equal_") - ] - - if food_group_constraints.empty: - raise ValueError( - "No food group equality constraints found in the network. " - "Ensure the model was solved with fixed consumption constraints." - ) - - # Use columns to get group and country - no name parsing needed - groups = food_group_constraints["food_group"].astype(str) - countries = food_group_constraints["country"].astype(str).str.upper() - duals = food_group_constraints["mu"].fillna(0.0).astype(float) - - # Tax = -dual - # - If dual > 0: consumption is costly to production → need subsidy (tax < 0) - # - If dual < 0: consumption saves production cost → need tax (tax > 0) - taxes_bnusd_per_mt = -duals - - # Unit conversion: bnUSD/Mt = 1e9 USD / 1e9 kg = USD/kg - df = pd.DataFrame( - { - "group": groups.values, - "country": countries.values, - "tax_bnusd_per_mt": taxes_bnusd_per_mt.values, - "tax_usd_per_kg": taxes_bnusd_per_mt.values, - "adjustment_bnusd_per_mt": taxes_bnusd_per_mt.values, - } - ) - - logger.info("Extracted taxes for %d (group, country) pairs", len(df)) - return df - - -if __name__ == "__main__": - setup_script_logging( - log_file=snakemake.log[0] if snakemake.log else None # type: ignore[name-defined] - ) - - logger.info("Loading solved network from %s", snakemake.input.network) - n = pypsa.Network(snakemake.input.network) - - df = extract_optimal_taxes(n) - - output_path = snakemake.output.taxes - df.to_csv(output_path, index=False) - logger.info("Saved optimal taxes to %s", output_path) diff --git a/workflow/scripts/plotting/plot_consumer_values_comparison.py b/workflow/scripts/plotting/plot_consumer_values_comparison.py deleted file mode 100644 index 1e2b58a6..00000000 --- a/workflow/scripts/plotting/plot_consumer_values_comparison.py +++ /dev/null @@ -1,451 +0,0 @@ -# SPDX-FileCopyrightText: 2026 Koen van Greevenbroek -# -# SPDX-License-Identifier: GPL-3.0-or-later - -"""Compare consumption and objective breakdown across consumer values scenarios. - -Generates: -1. Food consumption comparison (stacked horizontal bars, g/person/day) -2. Objective composition comparison (stacked bars by cost component) -""" - -from pathlib import Path - -import matplotlib - -matplotlib.use("pdf") -import matplotlib.pyplot as plt -import numpy as np -import pandas as pd -import seaborn as sns - -from workflow.scripts.logging_config import setup_script_logging -from workflow.scripts.plotting.color_utils import categorical_colors - - -def _assign_colors( - groups: list[str], overrides: dict[str, str] | None = None -) -> dict[str, str]: - """Assign colors to food groups.""" - return categorical_colors(groups, overrides) - - -def _load_consumption(path: str) -> pd.Series: - """Load per-capita consumption by food group from analysis output. - - Returns Series indexed by food_group with values in g/person/day. - """ - df = pd.read_parquet(path) - if df.empty: - return pd.Series(dtype=float) - - # Aggregate across countries to get global per-capita average - # The CSV has per-country consumption, so we need to weight by country population - # For simplicity, sum total consumption and divide by total population - # Actually, the values are already per-capita, so we can just average - global_avg = df.groupby("food_group")["consumption_g_per_person_day"].mean() - return global_avg - - -def _load_objective(path: str) -> pd.DataFrame: - """Load objective breakdown from analysis output. - - Returns DataFrame with category index and total column. - """ - df = pd.read_parquet(path) - if df.empty: - return pd.DataFrame(columns=["total", "capex", "opex"]) - - # Convert single-row format to DataFrame with category index - row = df.iloc[0] - costs_df = pd.DataFrame({"total": row}) - costs_df["capex"] = 0.0 - costs_df["opex"] = row - return costs_df - - -SCENARIO_LABELS = { - "baseline": "Baseline (fixed)", - "cv": "CV only", - "cv_H": "CV + Health", - "cv_G": "CV + GHG", - "cv_HG": "CV + Health + GHG", - "no_cv": "No CV", - "no_cv_H": "No CV + Health", - "no_cv_G": "No CV + GHG", - "no_cv_HG": "No CV + Health + GHG", -} - -SCENARIO_PAIR_ORDER = [ - ("cv", "no_cv"), - ("cv_H", "no_cv_H"), - ("cv_G", "no_cv_G"), - ("cv_HG", "no_cv_HG"), -] - - -def _plot_consumption_comparison( - consumption_data: dict[str, pd.Series], - colors: dict[str, str], - output_path: Path, -) -> None: - """Plot horizontal stacked bar chart of food group consumption.""" - if not consumption_data: - fig, ax = plt.subplots(figsize=(8, 4)) - ax.text(0.5, 0.5, "No consumption data", ha="center", va="center") - ax.axis("off") - fig.savefig(output_path, bbox_inches="tight", dpi=300) - plt.close(fig) - return - - df = pd.DataFrame(consumption_data).T.fillna(0.0) - ordered_groups = df.sum(axis=0).sort_values(ascending=False).index.tolist() - - bar_height = 0.7 - fig_height = max(4.0, 1.0 + bar_height * len(consumption_data)) - fig, ax = plt.subplots(figsize=(10, fig_height)) - - labels = list(consumption_data.keys()) - for idx, label in enumerate(labels): - series = df.loc[label] - left = 0.0 - for group in ordered_groups: - value = float(series.get(group, 0.0)) - if value <= 0.0: - continue - ax.barh( - idx, - value, - left=left, - height=bar_height, - color=colors.get(group, "#888888"), - ) - left += value - - ax.set_yticks(np.arange(len(labels))) - ax.set_yticklabels(labels) - ax.set_ylim(-0.5, len(labels) - 0.5) - ax.set_xlabel("g/person/day") - ax.set_title("Food Consumption by Scenario") - ax.grid(axis="x", alpha=0.3) - - legend_handles = [ - matplotlib.patches.Patch(color=colors.get(group, "#888888"), label=group) - for group in ordered_groups - ] - if legend_handles: - fig.legend( - legend_handles[::-1], - [patch.get_label() for patch in legend_handles][::-1], - loc="center left", - bbox_to_anchor=(1.0, 0.5), - ) - fig.tight_layout(rect=(0, 0, 0.85, 1)) - else: - fig.tight_layout() - - fig.savefig(output_path, bbox_inches="tight", dpi=300) - plt.close(fig) - - -def _plot_objective_comparison( - objective_data: dict[str, pd.DataFrame], - output_path: Path, -) -> None: - """Plot stacked bar chart of objective components by scenario.""" - if not objective_data: - fig, ax = plt.subplots(figsize=(8, 4)) - ax.text(0.5, 0.5, "No objective data", ha="center", va="center") - ax.axis("off") - fig.savefig(output_path, bbox_inches="tight", dpi=300) - plt.close(fig) - return - - # Collect all categories across scenarios - all_categories = set() - for costs_df in objective_data.values(): - all_categories.update(costs_df.index.tolist()) - - # Build matrix - labels = list(objective_data.keys()) - categories = sorted(all_categories) - data = np.zeros((len(labels), len(categories))) - - for i, label in enumerate(labels): - costs_df = objective_data[label] - for j, cat in enumerate(categories): - if cat in costs_df.index: - data[i, j] = float(costs_df.loc[cat, "total"]) - - # Plot - fig, ax = plt.subplots(figsize=(12, 6)) - x = np.arange(len(labels)) - bar_width = 0.6 - - # Use a colormap for categories - cmap = plt.get_cmap("tab20") - bottom_pos = np.zeros(len(labels)) - bottom_neg = np.zeros(len(labels)) - - for j, cat in enumerate(categories): - values = data[:, j] - color = cmap(j % 20) - # Handle positive and negative values separately for proper stacking - pos_vals = np.maximum(values, 0) - neg_vals = np.minimum(values, 0) - if np.any(pos_vals > 0): - ax.bar(x, pos_vals, bar_width, bottom=bottom_pos, label=cat, color=color) - bottom_pos += pos_vals - if np.any(neg_vals < 0): - ax.bar(x, neg_vals, bar_width, bottom=bottom_neg, color=color) - bottom_neg += neg_vals - - ax.set_xticks(x) - ax.set_xticklabels(labels, rotation=45, ha="right") - ax.set_ylabel("bnUSD") - ax.set_title("Objective Composition by Scenario") - ax.axhline(0, color="black", linewidth=0.5) - ax.grid(axis="y", alpha=0.3) - - # Legend outside - ax.legend(loc="center left", bbox_to_anchor=(1.0, 0.5), fontsize="small") - fig.tight_layout() - - fig.savefig(output_path, bbox_inches="tight", dpi=300) - plt.close(fig) - - -# Unit conversion: bnUSD/Mt -> USD/kg -# 1 bnUSD = 1e9 USD, 1 Mt = 1e9 kg -# bnUSD/Mt = 1e9/1e9 USD/kg = 1 -BNUSD_PER_MT_TO_USD_PER_KG = 1.0 - - -def _prepare_consumer_values_distribution(cv_df: pd.DataFrame) -> pd.DataFrame: - """Prepare per-country consumer values in USD/kg for plotting. - - Aggregates food-level values to food group level for comparison plots - by taking the mean per (country, food_group). - """ - # Support both food-level (food, food_group) and legacy group-level (group) formats - if "food_group" in cv_df.columns and "food" in cv_df.columns: - # Aggregate food-level values to group level - grouped = ( - cv_df.groupby(["country", "food_group"])["value_bnusd_per_mt"] - .mean() - .reset_index() - ) - grouped = grouped.rename(columns={"food_group": "group"}) - elif "group" in cv_df.columns: - grouped = cv_df[["country", "group", "value_bnusd_per_mt"]].copy() - else: - raise ValueError( - "Consumer values CSV must have either (food, food_group) or (group) columns" - ) - grouped["value_usd_per_kg"] = ( - grouped["value_bnusd_per_mt"] * BNUSD_PER_MT_TO_USD_PER_KG - ) - return grouped - - -def _plot_consumer_values_distribution( - cv_df: pd.DataFrame, - colors: dict[str, str], - output_path: Path, -) -> None: - """Plot letter-value distribution of consumer values by food group.""" - if cv_df.empty: - fig, ax = plt.subplots(figsize=(8, 4)) - ax.text(0.5, 0.5, "No consumer values data", ha="center", va="center") - ax.axis("off") - fig.savefig(output_path, bbox_inches="tight", dpi=300) - plt.close(fig) - return - - grouped = cv_df.groupby("group")["value_usd_per_kg"] - group_order = ( - grouped.median().sort_values(ascending=False).index.astype(str).tolist() - ) - - fig_height = max(6.0, 0.4 * len(group_order) + 3.0) - fig, ax = plt.subplots(figsize=(12, fig_height)) - - sns.boxenplot( - data=cv_df, - x="group", - y="value_usd_per_kg", - order=group_order, - hue="group", - palette=colors, - legend=False, - linewidth=0.8, - ax=ax, - ) - - ax.set_xticks(np.arange(len(group_order))) - ax.set_xticklabels(group_order, rotation=45, ha="right") - ax.set_ylabel("USD / kg") - ax.set_title("Consumer Values by Food Group (Across Countries)") - ax.grid(axis="y", alpha=0.3) - ax.set_ylim( - bottom=cv_df["value_usd_per_kg"].min() * 1.1, - top=cv_df["value_usd_per_kg"].max() * 1.1, - ) - - fig.tight_layout() - fig.savefig(output_path, bbox_inches="tight", dpi=300) - plt.close(fig) - - -def _write_consumer_values_csv(cv_df: pd.DataFrame, output_path: Path) -> None: - """Write per-country consumer values data to CSV.""" - out = cv_df.copy() - out["value_usd_per_kg"] = out["value_bnusd_per_mt"] * BNUSD_PER_MT_TO_USD_PER_KG - out.to_csv(output_path, index=False) - - -def _write_consumption_csv( - consumption_data: dict[str, pd.Series], output_path: Path -) -> None: - """Write consumption data to CSV.""" - records = [] - for label, series in consumption_data.items(): - for group, value in series.items(): - records.append( - { - "scenario": label, - "group": group, - "g_per_person_per_day": float(value), - } - ) - pd.DataFrame.from_records(records).to_csv(output_path, index=False) - - -def _write_objective_csv( - objective_data: dict[str, pd.DataFrame], output_path: Path -) -> None: - """Write objective breakdown data to CSV.""" - records = [] - for label, costs_df in objective_data.items(): - for cat in costs_df.index: - records.append( - { - "scenario": label, - "category": cat, - "capex_bnusd": float(costs_df.loc[cat, "capex"]), - "opex_bnusd": float(costs_df.loc[cat, "opex"]), - "total_bnusd": float(costs_df.loc[cat, "total"]), - } - ) - pd.DataFrame.from_records(records).to_csv(output_path, index=False) - - -def main() -> None: - try: - snakemake # type: ignore[name-defined] - except NameError as exc: - raise RuntimeError("This script must be run from Snakemake") from exc - - logger = setup_script_logging(snakemake.log[0]) - - scenarios = list(snakemake.params.scenarios) - - # Create output directories - Path(snakemake.output.consumption_pdf).parent.mkdir(parents=True, exist_ok=True) - - # Load analysis outputs for each scenario - consumption_data = {} - objective_data = {} - - ordered_scenarios: list[str] = [] - if "baseline" in scenarios: - ordered_scenarios.append("baseline") - for cv_scen, no_cv_scen in SCENARIO_PAIR_ORDER: - if cv_scen in scenarios: - ordered_scenarios.append(cv_scen) - if no_cv_scen in scenarios: - ordered_scenarios.append(no_cv_scen) - for scen in scenarios: - if scen not in ordered_scenarios: - ordered_scenarios.append(scen) - - for scen in ordered_scenarios: - consumption_key = f"consumption_{scen}" - breakdown_key = f"breakdown_{scen}" - consumption_path = getattr(snakemake.input, consumption_key, None) - breakdown_path = getattr(snakemake.input, breakdown_key, None) - - if consumption_path is None: - logger.warning("Consumption input not found for scenario: %s", scen) - continue - if breakdown_path is None: - logger.warning("Breakdown input not found for scenario: %s", scen) - continue - - label = SCENARIO_LABELS.get(scen, scen) - - # Load consumption from pre-computed analysis - logger.info("Loading consumption from: %s", consumption_path) - consumption = _load_consumption(consumption_path) - consumption_data[label] = consumption - - # Load objective breakdown from pre-computed analysis - logger.info("Loading objective breakdown from: %s", breakdown_path) - objective_data[label] = _load_objective(breakdown_path) - - # Assign colors to food groups - all_groups = set() - for series in consumption_data.values(): - all_groups.update(series.index.tolist()) - group_colors = _assign_colors( - sorted(all_groups), - overrides=snakemake.params.group_colors or None, - ) - - # Generate scenario comparison plots - _plot_consumption_comparison( - consumption_data, - group_colors, - Path(snakemake.output.consumption_pdf), - ) - _plot_objective_comparison( - objective_data, - Path(snakemake.output.objective_pdf), - ) - - # Write scenario comparison CSVs - _write_consumption_csv( - consumption_data, - Path(snakemake.output.consumption_csv), - ) - _write_objective_csv( - objective_data, - Path(snakemake.output.objective_csv), - ) - - # Load and visualize consumer values - cv_df = pd.read_csv(snakemake.input.consumer_values) - cv_df["country"] = cv_df["country"].astype(str).str.upper() - cv_prepared = _prepare_consumer_values_distribution(cv_df) - logger.info( - "Prepared consumer values distribution for %d food groups", - cv_prepared["group"].nunique(), - ) - - # Include food groups from consumer values in colors - for group in cv_prepared["group"].unique(): - if group not in group_colors: - group_colors[group] = "#888888" - - _plot_consumer_values_distribution( - cv_prepared, - group_colors, - Path(snakemake.output.cv_pdf), - ) - _write_consumer_values_csv(cv_df, Path(snakemake.output.cv_csv)) - - logger.info("Consumer values comparison plots saved") - - -if __name__ == "__main__": - main() diff --git a/workflow/scripts/prepare_faostat_fbs_items.py b/workflow/scripts/prepare_faostat_fbs_items.py index 1c2603e6..b7360faf 100644 --- a/workflow/scripts/prepare_faostat_fbs_items.py +++ b/workflow/scripts/prepare_faostat_fbs_items.py @@ -193,7 +193,7 @@ def layered(elem_code: int) -> pd.DataFrame: needs_fallback = non_fbs_cells[non_fbs_cells > 0].sort_values(ascending=False) if len(needs_fallback) > 0: logger.info( - "%d countries used a fallback for at least one item " "(top: %s)", + "%d countries used a fallback for at least one item (top: %s)", len(needs_fallback), ", ".join(needs_fallback.head(10).index), ) diff --git a/workflow/scripts/retrieve_eurostat_fodder.py b/workflow/scripts/retrieve_eurostat_fodder.py index e473f9ee..5116e524 100644 --- a/workflow/scripts/retrieve_eurostat_fodder.py +++ b/workflow/scripts/retrieve_eurostat_fodder.py @@ -97,8 +97,7 @@ def _fetch_eurostat_apro_cpsh1( ) base_url = ( - "https://ec.europa.eu/eurostat/api/dissemination/statistics/1.0/data/" - "apro_cpsh1" + "https://ec.europa.eu/eurostat/api/dissemination/statistics/1.0/data/apro_cpsh1" ) params = { "crops": list(EUROSTAT_CROP_CODES.keys()), diff --git a/workflow/scripts/retrieve_gdp_per_capita.py b/workflow/scripts/retrieve_gdp_per_capita.py index c67619bf..01e62084 100644 --- a/workflow/scripts/retrieve_gdp_per_capita.py +++ b/workflow/scripts/retrieve_gdp_per_capita.py @@ -169,8 +169,7 @@ def fetch_gdp_per_capita( # Log summary statistics n_imputed = df["imputed"].sum() logger.info( - f"Final dataset: {len(df)} countries " - f"({n_imputed} imputed from regional means)" + f"Final dataset: {len(df)} countries ({n_imputed} imputed from regional means)" ) logger.info( f"GDP per capita range: ${df['gdp_per_capita'].min():,.0f} - " diff --git a/workflow/scripts/retrieve_ppp_rates.py b/workflow/scripts/retrieve_ppp_rates.py index 6d7ba99a..c047a4af 100644 --- a/workflow/scripts/retrieve_ppp_rates.py +++ b/workflow/scripts/retrieve_ppp_rates.py @@ -120,7 +120,7 @@ def fetch_eurozone_ppp(start_year: int, end_year: int) -> float: logger.info( f"Overall average PPP rate {start_year}-{end_year}: {avg_ppp:.4f} EUR per international $" ) - logger.info(f"This implies: 1 EUR = {1/avg_ppp:.4f} international $") + logger.info(f"This implies: 1 EUR = {1 / avg_ppp:.4f} international $") return avg_ppp diff --git a/workflow/scripts/solve_model/core.py b/workflow/scripts/solve_model/core.py index 9c7f9a3f..85cbba52 100644 --- a/workflow/scripts/solve_model/core.py +++ b/workflow/scripts/solve_model/core.py @@ -25,10 +25,6 @@ add_diet_stability_constraints, evaluate_diet_stability_cost, ) -from workflow.scripts.solve_model.food_utility import ( - add_piecewise_food_utility, - pop_piecewise_food_utility_value, -) from workflow.scripts.solve_model.health import ( HEALTH_AUX_MAP, add_health_objective, @@ -484,8 +480,6 @@ def fix_food_consumption_to_baseline( the baseline diet may slightly exceed per-capita caps that are only meaningful when the optimizer freely chooses the diet. - Consumer value duals are available post-solve via ``n.links.dynamic.mu_p_set`` - (after calling ``_extract_p_set_duals``). """ link_names = matched["name"].values targets_mt = matched["target_mt"].values @@ -519,19 +513,6 @@ def fix_food_consumption_to_baseline( ) -def _extract_p_set_duals(n: pypsa.Network) -> None: - """Write Link p_set duals to n.links.dynamic.mu_p_set. - - Must be called after solving and ``assign_duals`` to populate the - consumer value shadow prices used by ``extract_consumer_values``. - """ - constraints = dict(n.model.constraints.items()) - if "Link-p_set" not in constraints: - return - dual_df = constraints["Link-p_set"].dual.to_pandas() - n.links.dynamic["mu_p_set"] = dual_df - - def _prepare_baseline_diet_for_food_constraints( baseline_df: pd.DataFrame, consume_links: pd.DataFrame, @@ -783,76 +764,6 @@ def add_groundwater_depletion_cap(n: pypsa.Network, cap_mm3: float) -> None: n.stores.static.at["store:impact:groundwater_depletion", "e_nom_max"] = cap_mm3 -def add_food_incentives_to_objective( - n: pypsa.Network, incentives_paths: list[str] -) -> None: - """Add food-level incentives/penalties to the objective function. - - Incentives are applied as adjustments to marginal costs of food - consumption links. Positive values penalize consumption; negative - values subsidize consumption. - - Parameters - ---------- - n : pypsa.Network - The network containing the model. - incentives_paths : list[str] - Paths to CSVs with columns: food, country, adjustment_bnusd_per_mt - """ - if not incentives_paths: - raise ValueError("food_incentives enabled but no sources are configured") - - combined = [] - for path in incentives_paths: - incentives_df = pd.read_csv(path) - required = {"food", "country", "adjustment_bnusd_per_mt"} - missing = required - set(incentives_df.columns) - if missing: - missing_text = ", ".join(sorted(missing)) - raise ValueError( - f"Missing required columns in incentives file {path}: {missing_text}" - ) - - incentives_df["country"] = incentives_df["country"].astype(str).str.upper() - combined.append( - incentives_df[["food", "country", "adjustment_bnusd_per_mt"]].copy() - ) - - all_incentives = pd.concat(combined, ignore_index=True) - summed = ( - all_incentives.groupby(["food", "country"])["adjustment_bnusd_per_mt"] - .sum() - .reset_index() - ) - - consume_links = n.links.static[n.links.static["carrier"] == "food_consumption"] - - applied = 0 - for _, row in summed.iterrows(): - mask = (consume_links["food"] == row["food"]) & ( - consume_links["country"] == row["country"] - ) - link_names = consume_links[mask].index - if not link_names.empty: - n.links.static.loc[link_names, "marginal_cost"] += row[ - "adjustment_bnusd_per_mt" - ] - applied += len(link_names) - - if applied == 0: - logger.info( - "No applicable food incentives found in %d sources", - len(incentives_paths), - ) - return - - logger.info( - "Applied food incentives to %d consumption links from %d sources", - applied, - len(incentives_paths), - ) - - def build_residue_feed_fraction_by_country( max_feed_fraction_by_region: dict, countries: list[str], @@ -1463,8 +1374,7 @@ def run_solve( skip_assign_duals If True, do not call ``n.optimize.assign_duals(...)``. This is safe whenever the caller does not need the generic dual assignment on - non-Link components. ``_extract_p_set_duals`` still runs and reads - Link ``p_set`` duals directly from ``n.model.constraints``. + non-Link components. accept_time_limit If True, treat ``time_limit`` termination with a feasible incumbent as a successful solve and proceed with solution assignment. Use for @@ -1591,15 +1501,6 @@ def run_solve( # The build uses the base config value; scenarios may override it. _apply_health_pricing(n, float(smk.params.health_value_per_yll)) - incentives_enabled = bool(smk.params.food_incentives_enabled) - piecewise_utility_cfg = smk.params.food_utility_piecewise - piecewise_utility_enabled = bool(piecewise_utility_cfg["enabled"]) - - if incentives_enabled and piecewise_utility_enabled: - raise ValueError( - "food_incentives and food_utility_piecewise cannot both be enabled" - ) - # Resolved here (rather than at the add_market_response_curves call) because # the demand component needs the baseline diet matched onto the consume # links below, and its guards against the other demand-side mechanisms @@ -1625,23 +1526,6 @@ def run_solve( demand_response_active = bool( market_response_cfg["enabled"] and market_response_cfg["components"]["demand"] ) - if demand_response_active and piecewise_utility_enabled: - raise ValueError( - "market_response.components.demand and food_utility_piecewise " - "cannot both be enabled: both add a marginal-utility curve per " - "(food, country) consumption link" - ) - if demand_response_active and incentives_enabled: - raise ValueError( - "market_response.components.demand and food_incentives cannot both " - "be enabled: the fitted demand intercepts already carry the " - "consumption-side value" - ) - - # Add food-level linear incentives to marginal costs if enabled - if incentives_enabled: - incentives_paths = list(smk.input.food_incentives) - add_food_incentives_to_objective(n, incentives_paths) # Get population from network metadata population_map = get_country_population(n) @@ -1763,19 +1647,6 @@ def run_solve( ) logger.info("Linopy model created.") - if piecewise_utility_enabled: - if enforce_baseline: - raise ValueError( - "food_utility_piecewise cannot be combined with " - "validation.enforce_baseline_diet=true" - ) - with _phase("add_piecewise_food_utility"): - add_piecewise_food_utility( - n, - smk.input.food_utility_piecewise, - float(piecewise_utility_cfg["min_block_width_mt"]), - ) - solver_name = smk.params.solver solver_options = dict(smk.params.solver_options) io_api = smk.params.io_api @@ -2106,8 +1977,6 @@ def solve_fallback(): if not skip_assign_duals: with _phase("assign_duals"): n.optimize.assign_duals(False) - with _phase("_extract_p_set_duals"): - _extract_p_set_duals(n) if not skip_post_processing: with _phase("post_processing"): n.optimize.post_processing() @@ -2115,10 +1984,6 @@ def solve_fallback(): if removed: variables_container.data.update(removed) - piecewise_utility_value = pop_piecewise_food_utility_value(n) - if abs(piecewise_utility_value) > 1e-12: - n.meta["food_utility_cost"] = -piecewise_utility_value - # Extract production stability slack values if present. Both lower # ("_slack") and upper ("_slack_upper") variables are recorded; # they are added symmetrically when hard mode enables slack. diff --git a/workflow/scripts/solve_model/food_utility.py b/workflow/scripts/solve_model/food_utility.py deleted file mode 100644 index 05fdcb4a..00000000 --- a/workflow/scripts/solve_model/food_utility.py +++ /dev/null @@ -1,381 +0,0 @@ -# SPDX-FileCopyrightText: 2026 Koen van Greevenbroek -# -# SPDX-License-Identifier: GPL-3.0-or-later - -"""Piecewise food utility helpers for solve-time objective augmentation.""" - -import logging - -from linopy.constants import BREAKPOINT_DIM -import numpy as np -import pandas as pd -import pypsa -import xarray as xr - -logger = logging.getLogger(__name__) - -# Sentinel x-coordinate used to extend the last utility segment past the -# right-most real breakpoint. Linopy's LP piecewise formulation imposes -# a `link_p <= max(x)` domain bound; placing the sentinel far beyond any -# plausible consumption makes this bound effectively inert while keeping -# the last-segment slope equal to the last-block marginal utility (the -# "overflow continuation" behaviour the model relies on). -# -# Note: this introduces an asymmetric implicit upper bound -- links -# covered by the piecewise utility carry an implicit `link_p <= 1e6 Mt` -# cap; links absent from the utility table (zero-baseline pairs filtered -# out at calibration time) have no such cap. 1e6 Mt is two orders of -# magnitude above the largest plausible single (food, country) flow -# (global wheat is ~770 Mt), so the cap is inert in practice. If a -# scenario ever generates an aggregate flow approaching this magnitude, -# raise the sentinel. -OVERFLOW_SENTINEL_MT: float = 1.0e6 - - -def _prepare_piecewise_utility_rows( - utility_df: pd.DataFrame, - consume_links: pd.DataFrame, -) -> pd.DataFrame: - """Validate and align utility rows to existing food consumption link names.""" - required = { - "food", - "country", - "block_id", - "width_mt_per_year", - "marginal_utility_bnusd_per_mt", - } - missing = required - set(utility_df.columns) - if missing: - missing_text = ", ".join(sorted(missing)) - raise ValueError(f"Missing required utility block columns: {missing_text}") - - df = utility_df.copy() - df["food"] = df["food"].astype(str) - df["country"] = df["country"].astype(str).str.upper() - df["block_id"] = pd.to_numeric(df["block_id"], errors="raise").astype(int) - df["width_mt_per_year"] = pd.to_numeric(df["width_mt_per_year"], errors="coerce") - df["marginal_utility_bnusd_per_mt"] = pd.to_numeric( - df["marginal_utility_bnusd_per_mt"], errors="coerce" - ) - - df = df.dropna(subset=["width_mt_per_year", "marginal_utility_bnusd_per_mt"]) - df = df[df["width_mt_per_year"] > 0].copy() - if df.empty: - logger.info("No valid piecewise utility rows after filtering") - return df - - consume_map = consume_links.reset_index()[["name", "food", "country"]].copy() - merged = df.merge(consume_map, on=["food", "country"], how="inner") - if merged.empty: - logger.info("No utility rows match food_consumption links") - return merged - - duplicate_mask = merged.duplicated(subset=["name", "block_id"], keep=False) - if duplicate_mask.any(): - sample = merged.loc[duplicate_mask, ["name", "block_id"]].head(5) - raise ValueError( - "Duplicate piecewise utility rows for (link, block_id). " - f"Examples:\n{sample.to_string(index=False)}" - ) - - for name, group in merged.groupby("name"): - ordered = group.sort_values("block_id") - expected = list(range(1, len(ordered) + 1)) - observed = ordered["block_id"].tolist() - if observed != expected: - raise ValueError( - "Block ids must be contiguous starting at 1 for each link. " - f"Link '{name}' has blocks {observed}" - ) - - utilities = ordered["marginal_utility_bnusd_per_mt"].to_numpy() - if len(utilities) > 1 and (utilities[1:] > utilities[:-1]).any(): - raise ValueError( - "Piecewise utility must be non-increasing by block_id for each link. " - f"Link '{name}' violates this rule." - ) - - return merged[ - ["name", "block_id", "width_mt_per_year", "marginal_utility_bnusd_per_mt"] - ] - - -def _merge_small_width_blocks( - rows: pd.DataFrame, min_width_mt: float -) -> tuple[pd.DataFrame, int, int]: - """Merge small-width utility blocks into adjacent blocks per link. - - The merged block keeps total utility mass using a width-weighted average - marginal utility and block ids are renumbered contiguously starting at 1. - """ - if rows.empty or min_width_mt <= 0: - return rows.copy(), 0, 0 - - # Fast path: short-circuit when no block falls below the threshold. - if (rows["width_mt_per_year"] >= min_width_mt).all(): - return rows.copy(), 0, 0 - - # Sort once globally; then walk groups via index slicing rather than groupby - # DataFrame iteration to avoid thousands of per-link DataFrame allocations. - ordered = rows.sort_values(["name", "block_id"], kind="stable").reset_index( - drop=True - ) - names_arr = ordered["name"].to_numpy() - widths_arr = ordered["width_mt_per_year"].to_numpy(dtype=float) - utils_arr = ordered["marginal_utility_bnusd_per_mt"].to_numpy(dtype=float) - - # Group boundaries via change-points on the sorted name column. - n_rows = len(names_arr) - group_change = np.empty(n_rows, dtype=bool) - group_change[0] = True - group_change[1:] = names_arr[1:] != names_arr[:-1] - group_starts = np.flatnonzero(group_change) - group_ends = np.empty_like(group_starts) - group_ends[:-1] = group_starts[1:] - group_ends[-1] = n_rows - - out_names_chunks: list[np.ndarray] = [] - out_blocks_chunks: list[np.ndarray] = [] - out_widths_chunks: list[np.ndarray] = [] - out_utils_chunks: list[np.ndarray] = [] - - merged_blocks = 0 - affected_links = 0 - - for s, e in zip(group_starts, group_ends): - n_blocks = e - s - w_slice = widths_arr[s:e] - u_slice = utils_arr[s:e] - - # Pass-through fast path: nothing to merge for this link. - if n_blocks <= 1 or (w_slice >= min_width_mt).all(): - out_names_chunks.append(np.full(n_blocks, names_arr[s], dtype=object)) - out_blocks_chunks.append(np.arange(1, n_blocks + 1)) - out_widths_chunks.append(w_slice) - out_utils_chunks.append(u_slice) - continue - - # Slow path: iterative merge of small blocks (n is small per link). - widths = w_slice.tolist() - utilities = u_slice.tolist() - link_merged = 0 - while len(widths) > 1: - donor = next( - (i for i, w in enumerate(widths) if w < min_width_mt), - -1, - ) - if donor < 0: - break - recv = donor - 1 if donor > 0 else 1 - total_w = widths[recv] + widths[donor] - utilities[recv] = ( - utilities[recv] * widths[recv] + utilities[donor] * widths[donor] - ) / total_w - widths[recv] = total_w - del widths[donor] - del utilities[donor] - link_merged += 1 - - if link_merged > 0: - merged_blocks += link_merged - affected_links += 1 - - n_out = len(widths) - out_names_chunks.append(np.full(n_out, names_arr[s], dtype=object)) - out_blocks_chunks.append(np.arange(1, n_out + 1)) - out_widths_chunks.append(np.asarray(widths, dtype=float)) - out_utils_chunks.append(np.asarray(utilities, dtype=float)) - - result = pd.DataFrame( - { - "name": np.concatenate(out_names_chunks), - "block_id": np.concatenate(out_blocks_chunks), - "width_mt_per_year": np.concatenate(out_widths_chunks), - "marginal_utility_bnusd_per_mt": np.concatenate(out_utils_chunks), - } - ) - return result, merged_blocks, affected_links - - -def _build_cumulative_breakpoints( - rows: pd.DataFrame, link_names: list[str] -) -> tuple[xr.DataArray, xr.DataArray, list[int]]: - """Build per-link (x, y) breakpoints for the cumulative utility curve. - - For each link with non-increasing marginal utilities ``mu_1 .. mu_K`` - and block widths ``w_1 .. w_K``: - - x_0 = 0, x_k = sum_{i=1..k} w_i - y_0 = 0, y_k = sum_{i=1..k} mu_i * w_i - - A final sentinel breakpoint at x = OVERFLOW_SENTINEL_MT extends the - last segment with slope mu_K, so consumption past the last block - earns the last-block marginal utility. - - Returns DataArrays with dims ``(name, BREAKPOINT_DIM)`` and NaN - padding so per-link breakpoint counts can differ (linopy masks NaN - pieces in the LP method). - """ - n_links = len(link_names) - # Pre-sort once and split via boundaries to avoid per-link groupby cost. - ordered = rows.sort_values(["name", "block_id"], kind="stable") - name_to_idx = {name: i for i, name in enumerate(link_names)} - - # Collect per-link arrays. - x_arrays: list[np.ndarray] = [np.empty(0)] * n_links - y_arrays: list[np.ndarray] = [np.empty(0)] * n_links - block_counts: list[int] = [0] * n_links - - for name, sub in ordered.groupby("name", sort=False): - i = name_to_idx[name] - w = sub["width_mt_per_year"].to_numpy(dtype=float) - mu = sub["marginal_utility_bnusd_per_mt"].to_numpy(dtype=float) - cum_x = np.concatenate(([0.0], np.cumsum(w))) - cum_y = np.concatenate(([0.0], np.cumsum(mu * w))) - last_mu = float(mu[-1]) - sentinel_y = float(cum_y[-1] + last_mu * (OVERFLOW_SENTINEL_MT - cum_x[-1])) - x_arrays[i] = np.concatenate((cum_x, [OVERFLOW_SENTINEL_MT])) - y_arrays[i] = np.concatenate((cum_y, [sentinel_y])) - block_counts[i] = len(w) - - # Pad to a common breakpoint dimension with NaN; trailing-NaN pieces are - # masked out by linopy's LP method. - max_len = max(arr.size for arr in x_arrays) - x_padded = np.full((n_links, max_len), np.nan, dtype=float) - y_padded = np.full((n_links, max_len), np.nan, dtype=float) - for i, (xs, ys) in enumerate(zip(x_arrays, y_arrays)): - x_padded[i, : xs.size] = xs - y_padded[i, : ys.size] = ys - - coords = {"name": link_names, BREAKPOINT_DIM: np.arange(max_len)} - x_pts = xr.DataArray(x_padded, coords=coords, dims=("name", BREAKPOINT_DIM)) - y_pts = xr.DataArray(y_padded, coords=coords, dims=("name", BREAKPOINT_DIM)) - return x_pts, y_pts, block_counts - - -def add_piecewise_food_utility( - n: pypsa.Network, utility_blocks_path: str, min_block_width_mt: float -) -> None: - """Add piecewise diminishing marginal utility for food consumption links. - - Adds one auxiliary ``food_utility_value`` variable per link, bounded - above by the per-link concave cumulative-utility curve via linopy's - LP tangent-line piecewise formulation (one chord inequality per - segment). The objective receives ``-sum(utility)``, so the LP - pushes each link's utility up to the curve value at the optimal - consumption level. - - The last segment is extended with a sentinel breakpoint at - ``OVERFLOW_SENTINEL_MT`` so consumption past the last real block - earns the last-block marginal utility (the "overflow continuation" - behaviour the model relies on for negative-MU foods). - """ - m = n.model - if m is None: - raise ValueError("Linopy model is not initialized; call after create_model()") - - utility_df = pd.read_csv(utility_blocks_path) - if utility_df.empty: - logger.info("Utility blocks file is empty: %s", utility_blocks_path) - return - - consume_links = n.links.static[ - n.links.static["carrier"] == "food_consumption" - ].copy() - if consume_links.empty: - logger.info("No food_consumption links found; skipping piecewise utility") - return - - all_rows = _prepare_piecewise_utility_rows(utility_df, consume_links) - if all_rows.empty: - return - - covered_links = set(all_rows["name"].astype(str)) - missing_links = [ - name for name in consume_links.index if str(name) not in covered_links - ] - if missing_links: - logger.info( - "Piecewise utility omits %d of %d food_consumption links " - "(no baseline consumption); these earn zero utility", - len(missing_links), - len(consume_links), - ) - - min_width = float(min_block_width_mt) - rows, merged_rows, affected_links = _merge_small_width_blocks(all_rows, min_width) - if merged_rows > 0: - logger.info( - "Piecewise utility width floor %.6g Mt/year merged %d small blocks across %d links", - min_width, - merged_rows, - affected_links, - ) - - remaining_small = int((rows["width_mt_per_year"] < min_width).sum()) - if remaining_small > 0: - logger.info( - "Piecewise utility width floor %.6g Mt/year left %d unmergeable blocks", - min_width, - remaining_small, - ) - - link_names = sorted(rows["name"].unique().tolist()) - x_pts, y_pts, block_counts = _build_cumulative_breakpoints(rows, link_names) - - # One utility variable per link. No bounds: chord constraints pin it - # from above and the objective pulls it up; a negative lower bound is - # required because some calibrated marginal utilities are negative - # (in which case the integrated utility goes negative beyond the - # break-even point). - utility_var = m.add_variables( - lower=-np.inf, - coords=[link_names], - dims=["name"], - name="food_utility_value", - ) - - link_p = m.variables["Link-p"].sel(snapshot="now").sel(name=link_names) - - # Concave curve bounded above: utility <= f(link_p), where f is the - # integral of the non-increasing marginal-utility schedule. linopy - # encodes this as the chord (secant) PWL through the breakpoints -- - # one inequality per piece -- which is itself a concave PWL upper - # bound. Between breakpoints the chord lies strictly below the true - # concave curve, so the LP under-credits utility within a piece by - # at most piece_width * (mu_k - mu_{k+1}) / 2; the gap goes to zero - # at every breakpoint and shrinks with finer block widths. Picking - # a tangent (over-estimator) instead would over-credit utility and - # make the LP unbounded above the true curve at interior points. - m.add_piecewise_formulation( - (utility_var, y_pts, "<="), - (link_p, x_pts), - name="food_utility_piecewise", - ) - - m.objective += -utility_var.sum() - - n.meta["food_utility_piecewise"] = { - "links": len(link_names), - "blocks_per_link_max": int(max(block_counts)), - "blocks_per_link_min": int(min(block_counts)), - "total_width_mt_per_year": float(rows["width_mt_per_year"].sum()), - } - - logger.info( - "Applied piecewise utility to %d food consumption links (%d-%d blocks each)", - len(link_names), - min(block_counts), - max(block_counts), - ) - - -def pop_piecewise_food_utility_value(n: pypsa.Network) -> float: - """Return the realized piecewise food-utility value from the solved model.""" - m = n.model - if m is None or "food_utility_value" not in m.variables: - return 0.0 - sol = m.variables["food_utility_value"].solution - if sol is None: - return 0.0 - return float(sol.sum().item()) diff --git a/workflow/scripts/solve_namespace.py b/workflow/scripts/solve_namespace.py index 3c01f973..211fdcac 100644 --- a/workflow/scripts/solve_namespace.py +++ b/workflow/scripts/solve_namespace.py @@ -42,8 +42,6 @@ "deviation_penalty", "market_response", "macronutrients", - "food_utility_piecewise", - "food_incentives", "food_groups.constraints", "food_groups.fix_within_group_ratios", "food_groups.equal_by_country_source", @@ -52,7 +50,6 @@ "land.reforestation_cap", "grazing.grassland_forage_calibration.enabled", "exogenous_feed_calibration.enabled", - "consumer_values", "sensitivity", "solving.solver", "solving.io_api", @@ -413,23 +410,6 @@ def rp(path: str) -> str: {key: rp(path) for key, path in health_input_paths("{name}").items()} ) - if eff["food_incentives"]["enabled"]: - sources = eff["food_incentives"]["sources"] - if not sources: - raise ValueError( - f"Scenario {scenario}: food_incentives enabled but sources is empty" - ) - inputs["food_incentives"] = [ - source.format(name=name, scenario=scenario) for source in sources - ] - - utility_cfg = eff["food_utility_piecewise"] - if utility_cfg["enabled"]: - baseline_name = eff["consumer_values"]["baseline_scenario"] - inputs["food_utility_piecewise"] = rp( - f"/{{name}}/consumer_values/{baseline_name}/utility_blocks.csv" - ) - equal_source = eff["food_groups"]["equal_by_country_source"] if equal_source: inputs["food_group_equal"] = equal_source.format(name=name, scenario=scenario) @@ -491,7 +471,6 @@ def rp(path: str) -> str: "market_response": eff["market_response"], "animal_growth_cap": eff["validation"]["animal_growth_cap"], "crop_growth_cap": eff["validation"]["crop_growth_cap"], - "food_utility_piecewise": utility_cfg, "fix_within_group_ratios": eff["food_groups"]["fix_within_group_ratios"], "sensitivity": eff["sensitivity"], "reforestation_cap": eff["land"]["reforestation_cap"], @@ -511,7 +490,6 @@ def rp(path: str) -> str: "groundwater_pricing_enabled": eff["groundwater_depletion"]["pricing_enabled"], "groundwater_price": eff["groundwater_depletion"]["price"], "groundwater_cap": eff["groundwater_depletion"]["cap_mm3"], - "food_incentives_enabled": eff["food_incentives"]["enabled"], "equal_by_country_source": equal_source, "slack_marginal_cost": eff["validation"]["slack_marginal_cost"], "residue_max_feed_fraction": eff["residues"]["max_feed_fraction"], diff --git a/workflow/validation/__init__.py b/workflow/validation/__init__.py index 8f6fe22b..d6b0d45a 100644 --- a/workflow/validation/__init__.py +++ b/workflow/validation/__init__.py @@ -13,7 +13,6 @@ from .calibration_provenance import validate_calibration_provenance from .commodities import validate_commodities from .config_schema import validate_config_schema -from .consumer_values import validate_consumer_values from .country_regions import validate_country_regions from .crop_food_pathways import validate_crop_food_pathways from .crop_groups import validate_crop_groups @@ -28,7 +27,6 @@ from .m49_codes import validate_m49_codes from .multi_cropping import validate_multi_cropping from .nutrition import validate_nutrition -from .optimal_taxes import validate_optimal_taxes from .secrets import load_secrets_with_env_fallback from .seed_rates import validate_seed_rates from .sensitivity_generator import validate_sensitivity_generator @@ -40,8 +38,6 @@ ("calibration", validate_calibration), ("calibration_provenance", validate_calibration_provenance), ("commodities", validate_commodities), - ("consumer_values", validate_consumer_values), - ("optimal_taxes", validate_optimal_taxes), ("country_regions", validate_country_regions), ("food_groups", validate_food_groups), ("food_basis", validate_food_basis), diff --git a/workflow/validation/calibration.py b/workflow/validation/calibration.py index 5472c35b..eacaee2f 100644 --- a/workflow/validation/calibration.py +++ b/workflow/validation/calibration.py @@ -70,6 +70,15 @@ "path": ("deviation_penalty", "calibration"), "files": ["calibrated_yaml"], "has_scenario": False, + # The calibrated yaml is only opened when the L1 penalty actually + # resolves the "calibrated" sentinel (mirrors the solve-input gate in + # workflow/rules/common.smk), so a set without the legacy stability + # artefact is valid for configs that never enable the penalty. + "consumed": lambda config: ( + bool(config["deviation_penalty"]["enabled"]) + and config["deviation_penalty"]["penalty_mode"] == "l1" + and deviation_penalty_uses_calibrated(config["deviation_penalty"]) + ), }, ] @@ -108,7 +117,8 @@ def validate_calibration(config: dict, project_root: Path | None = None) -> None f"but it is not defined under config['scenarios']." ) - if enabled and not generate: + consumed = section["consumed"](config) if "consumed" in section else True + if enabled and consumed and not generate: for key in section["files"]: path = root / cfg[key] if not path.exists(): diff --git a/workflow/validation/consumer_values.py b/workflow/validation/consumer_values.py deleted file mode 100644 index d70a4c54..00000000 --- a/workflow/validation/consumer_values.py +++ /dev/null @@ -1,40 +0,0 @@ -# SPDX-FileCopyrightText: 2026 Koen van Greevenbroek -# -# SPDX-License-Identifier: GPL-3.0-or-later - -"""Validation checks for consumer values configuration.""" - -from workflow.scenario_generators import expand_scenario_defs -from workflow.scripts.solve_namespace import get_effective_config - - -def validate_consumer_values(config: dict, _project_root=None) -> None: - """Validate piecewise food utility against its configured baseline.""" - if config["deviation_penalty"]["calibration"]["generate"]: - # The Broyden iteration synthesizes a baseline and a main scenario per - # iteration in-process, so the configured baseline names one of those - # rather than a scenario the workflow declares. - return - - scenario_defs = expand_scenario_defs(config["scenarios"]) - effective_configs = [("base config", config)] - effective_configs.extend( - (f"scenario '{name}'", get_effective_config(config, name, scenario_defs)) - for name in scenario_defs - ) - - for label, effective in effective_configs: - if not effective["food_utility_piecewise"]["enabled"]: - continue - baseline = effective["consumer_values"]["baseline_scenario"] - if baseline not in scenario_defs: - raise ValueError( - f"{label} enables food_utility_piecewise but configured consumer " - f"values baseline scenario '{baseline}' is not defined" - ) - baseline_config = get_effective_config(config, baseline, scenario_defs) - if not baseline_config["validation"]["enforce_baseline_diet"]: - raise ValueError( - f"consumer values baseline scenario '{baseline}' must set " - "validation.enforce_baseline_diet=true" - ) diff --git a/workflow/validation/crop_food_pathways.py b/workflow/validation/crop_food_pathways.py index 54ed309a..50b7dfed 100644 --- a/workflow/validation/crop_food_pathways.py +++ b/workflow/validation/crop_food_pathways.py @@ -59,8 +59,7 @@ def validate_crop_food_pathways(config: dict, project_root: Path) -> None: for (pathway, crop), total in violations.items() ) raise ValueError( - f"foods.csv pathway mass-balance violations (sum of factors > 1): " - f"{details}" + f"foods.csv pathway mass-balance violations (sum of factors > 1): {details}" ) # Ensure all food crops have a pathway entry in foods.csv. @@ -81,7 +80,7 @@ def validate_crop_food_pathways(config: dict, project_root: Path) -> None: if unused: unused_text = ", ".join(unused) logger.warning( - "foods.csv contains crops not in config (ignored): " f"{unused_text}" + f"foods.csv contains crops not in config (ignored): {unused_text}" ) # Check that byproducts listed in config appear as foods @@ -111,8 +110,7 @@ def validate_crop_food_pathways(config: dict, project_root: Path) -> None: if unused_crops: unused_text = ", ".join(unused_crops) logger.warning( - f"{csv_name} references crops not in config (ignored): " - f"{unused_text}" + f"{csv_name} references crops not in config (ignored): {unused_text}" ) bad_foods = sorted(set(map_df["source_item"]) - foods_in_csv) if bad_foods: diff --git a/workflow/validation/crop_moisture_content.py b/workflow/validation/crop_moisture_content.py index f4ea630c..9be05802 100644 --- a/workflow/validation/crop_moisture_content.py +++ b/workflow/validation/crop_moisture_content.py @@ -58,7 +58,7 @@ def validate_crop_moisture_content(config: dict, project_root: Path) -> None: if missing: missing_text = ", ".join(missing) raise ValueError( - "Config crops missing rows in crop_moisture_content.csv: " f"{missing_text}" + f"Config crops missing rows in crop_moisture_content.csv: {missing_text}" ) unused = sorted(mapped_crops - config_crops) diff --git a/workflow/validation/diet_basis.py b/workflow/validation/diet_basis.py index 66fa0692..5a22050f 100644 --- a/workflow/validation/diet_basis.py +++ b/workflow/validation/diet_basis.py @@ -49,8 +49,7 @@ def validate_diet_basis(config: dict, project_root: Path) -> None: m = TABLE_NAME_RE.match(table_name) if not m: errors.append( - f"weight_conversion.{table_name}: name does not match " - "'_to_'" + f"weight_conversion.{table_name}: name does not match '_to_'" ) continue src, dst = m.group(1), m.group(2) diff --git a/workflow/validation/optimal_taxes.py b/workflow/validation/optimal_taxes.py deleted file mode 100644 index 8f321c64..00000000 --- a/workflow/validation/optimal_taxes.py +++ /dev/null @@ -1,35 +0,0 @@ -# SPDX-FileCopyrightText: 2026 Koen van Greevenbroek -# -# SPDX-License-Identifier: GPL-3.0-or-later - -"""Validation checks for optimal taxes configuration.""" - - -def _optimal_taxes_enabled(config: dict, scenario_defs: dict) -> bool: - if bool(config["optimal_taxes"]["enabled"]): - return True - - for overrides in scenario_defs.values(): - if not isinstance(overrides, dict): - continue - ot_cfg = overrides.get("optimal_taxes", {}) - if isinstance(ot_cfg, dict) and bool(ot_cfg.get("enabled", False)): - return True - - return False - - -def validate_optimal_taxes(config: dict, _project_root=None) -> None: - """Ensure optimal taxes runs have required scenarios defined.""" - scenario_defs = config["scenarios"] - - if not _optimal_taxes_enabled(config, scenario_defs): - return - - required_scenarios = {"baseline", "optimize", "extract_taxes", "apply_taxes"} - missing = required_scenarios - set(scenario_defs.keys()) - if missing: - raise ValueError( - "optimal_taxes enabled but scenarios is missing required scenarios: " - f"{missing}" - ) diff --git a/workflow/validation/seed_rates.py b/workflow/validation/seed_rates.py index c190f5dc..e536870a 100644 --- a/workflow/validation/seed_rates.py +++ b/workflow/validation/seed_rates.py @@ -47,7 +47,7 @@ def validate_seed_rates(config: dict, project_root: Path) -> None: if missing: missing_text = ", ".join(missing) raise ValueError( - "Config crops missing sowing rates in seed_rates.csv: " f"{missing_text}" + f"Config crops missing sowing rates in seed_rates.csv: {missing_text}" ) unused = sorted(mapped_crops - config_crops)