From 140a383b6e3ac2b1094a93dcdcaac2f18ac87d69 Mon Sep 17 00:00:00 2001 From: Koen van Greevenbroek Date: Thu, 2 Jul 2026 10:58:58 -0700 Subject: [PATCH] feat: derive the baseline diet from FAOSTAT FBS by default, make GDD-IA optional The GDD-IA dietary dataset is not yet public, so a default GLADE setup could not run without requesting data from its author. Add an FBS-derived baseline-diet source and make it the default (diet.source: fbs); the GDD-IA pipeline remains available via diet.source: gdd_ia, with its input CSVs required only in that mode (checked early with an actionable error). The FBS source derives per-(country, food-group) intake from the FBS 'Food supply (kcal/capita/day)' element at model-basis energy densities, corrected for consumer waste. Deriving mass from energy sidesteps per-item mass-basis bookkeeping (flour extraction, refuse fractions, carcass-to-retail, milk equivalents): FAO's nutritive factors already net these out, mirroring the GDD-IA dairy convention. Wheat and rice are the only FBS items spanning grain and whole_grains; diet.fbs.whole_grain_shares splits them, with defaults fit to GDD-IA's global whole-grain intake by population-weighted least squares. The kcal-normalisation step is GDD-IA-only: the FBS diet is FAO-energy-consistent by construction, and the cereal residual fix reconstructs its cereal budget from the group totals when whole_grains is GBD-anchored. Calibration artefact sets: the default set is refit against the FBS diet (L1 costs land at the previous centre; food_demand multipliers tighten toward 1, e.g. dairy 1.22-1.31 -> ~1.0); the previous GDD-fit default set is preserved as gdd-ia for GDD-IA configs without health; gbd-anchored is unchanged. Provenance snapshots now record diet.source and drop the inactive source's config block. The GDD-IA path is verified byte-identical on unchanged inputs. Against the GDD-IA diet, the FBS-derived diet correlates at r = 0.7-0.96 for most food groups with a ~+10% energy bias (the known supply-vs-survey gap); FBS-override foods are identical by construction. The largest definitional gap is whole grains, where GDD-IA counts decorticated millet/sorghum as processed grain while the food taxonomy classes them as whole_grains; aligning the GDD-IA ingestion to the taxonomy is left as a follow-up. --- .claude/skills/model-calibration/SKILL.md | 12 +- .github/workflows/test.yml | 14 +- AGENTS.md | 9 +- config/default.yaml | 30 +- config/example.yaml | 7 +- config/gsa.yaml | 5 + config/gsa_fixed_diet.yaml | 3 + config/schemas/config.schema.yaml | 24 +- config/validation.yaml | 5 + .../calibration/default/animal_cost.csv | 1996 ++-- .../curated/calibration/default/crop_cost.csv | 8554 ++++++++--------- .../default/deviation_penalty.yaml | 12 +- .../calibration/default/exogenous_feed.csv | 23 +- .../calibration/default/exogenous_forage.csv | 22 +- .../calibration/default/food_demand.csv | 100 +- .../calibration/default/food_waste.yaml | 30 +- .../calibration/default/grassland_cost.csv | 148 +- .../calibration/default/provenance.yaml | 9 +- .../calibration/gbd-anchored/provenance.yaml | 6 +- .../calibration/gdd-ia/animal_cost.csv | 1226 +++ data/curated/calibration/gdd-ia/crop_cost.csv | 6153 ++++++++++++ .../calibration/gdd-ia/deviation_penalty.yaml | 22 + .../calibration/gdd-ia/exogenous_feed.csv | 103 + .../calibration/gdd-ia/exogenous_forage.csv | 178 + .../calibration/gdd-ia/fodder_conversion.csv | 178 + .../calibration/gdd-ia/food_demand.csv | 59 + .../calibration/gdd-ia/food_waste.yaml | 35 + .../calibration/gdd-ia/grassland_cost.csv | 175 + .../calibration/gdd-ia/grassland_yield.csv | 178 + .../calibration/gdd-ia/provenance.yaml | 856 ++ data/manually_downloaded/README.md | 49 +- docs/calibration.rst | 37 +- docs/config/doc_figures.yaml | 5 + docs/config/doc_validation.yaml | 5 + docs/current_diets.rst | 173 +- docs/data_sources.rst | 8 +- tests/test_calibration_provenance.py | 5 + tests/test_fbs_dietary_intake.py | 380 + workflow/rules/common.smk | 33 + workflow/rules/diet.smk | 83 +- workflow/scripts/diet/fbs_intake.py | 352 + workflow/scripts/estimate_baseline_diet.py | 104 +- workflow/scripts/merge_dietary_sources.py | 61 +- workflow/scripts/prepare_faostat_fbs_items.py | 66 +- .../scripts/prepare_fbs_dietary_intake.py | 90 + .../scripts/prepare_gdd_ia_dietary_intake.py | 12 +- workflow/validation/calibration_provenance.py | 9 + 47 files changed, 15997 insertions(+), 5647 deletions(-) create mode 100644 data/curated/calibration/gdd-ia/animal_cost.csv create mode 100644 data/curated/calibration/gdd-ia/crop_cost.csv create mode 100644 data/curated/calibration/gdd-ia/deviation_penalty.yaml create mode 100644 data/curated/calibration/gdd-ia/exogenous_feed.csv create mode 100644 data/curated/calibration/gdd-ia/exogenous_forage.csv create mode 100644 data/curated/calibration/gdd-ia/fodder_conversion.csv create mode 100644 data/curated/calibration/gdd-ia/food_demand.csv create mode 100644 data/curated/calibration/gdd-ia/food_waste.yaml create mode 100644 data/curated/calibration/gdd-ia/grassland_cost.csv create mode 100644 data/curated/calibration/gdd-ia/grassland_yield.csv create mode 100644 data/curated/calibration/gdd-ia/provenance.yaml create mode 100644 tests/test_fbs_dietary_intake.py create mode 100644 workflow/scripts/diet/fbs_intake.py create mode 100644 workflow/scripts/prepare_fbs_dietary_intake.py diff --git a/.claude/skills/model-calibration/SKILL.md b/.claude/skills/model-calibration/SKILL.md index e4830215..a21ea15f 100644 --- a/.claude/skills/model-calibration/SKILL.md +++ b/.claude/skills/model-calibration/SKILL.md @@ -13,11 +13,13 @@ SPDX-License-Identifier: CC-BY-4.0 The default workflow consumes five calibration artefact groups organized in per-config *sets* under `data/curated/calibration//`, selected -by the `calibration.source` config key. Two sets are git-tracked: -`default` (fit against the anchoring-off baseline diet of the health-off -default config) and `gbd-anchored` (fit against the GBD-anchored diet; -consumed by the health-enabled configs gsa, gsa_fixed_diet, validation -and the doc configs). `tools/calibrate` resolves the base config's +by the `calibration.source` config key. Three sets are git-tracked: +`default` (fit against the FBS-derived baseline diet of the health-off +default config), `gdd-ia` (fit against the anchoring-off GDD-IA diet; +consumed by central, ghg_sensitivity_* and region_resolution configs) +and `gbd-anchored` (fit against the GBD-anchored GDD-IA diet; consumed +by the health-enabled configs gsa, gsa_fixed_diet, validation and the +doc configs). `tools/calibrate` resolves the base config's diet.anchor_groups_to_gbd sentinel once and pins it across all five steps, and provenance stamps record the *resolved* anchoring. Each artefact group is produced by a dedicated validation-mode solve and diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index 0a1599b3..6711ee01 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -47,17 +47,17 @@ jobs: cache: true environments: dev - # The manually-downloaded, license-restricted source datasets (GDD, IHME) - # have no producing rule, so the DAG can't resolve unless the paths exist. - # A dryrun never reads their contents, so empty stubs suffice. Paths track - # the rules in workflow/rules/{diet,health}.smk (baseline_year: 2020). + # The manually-downloaded, license-restricted IHME source datasets have + # no producing rule, so the DAG can't resolve unless the paths exist. + # A dryrun never reads their contents, so empty stubs suffice. Paths + # track the rules in workflow/rules/health.smk (baseline_year: 2020). + # The GDD-IA dietary data needs no stubs: the test config uses the + # default FBS-derived baseline diet (diet.source: fbs). - name: Stub manually-downloaded inputs run: | d=data/manually_downloaded mkdir -p "$d" "$d/IHME_GBD_2023_RISK_EXPOSURE_DIET_1" "$d/IHME_GBD_2023_RISK_EXPOSURE_DIET_2" - touch "$d/GDD-IA-intake_grams_2020.csv" \ - "$d/GDD-IA-intake_kcals_2020.csv" \ - "$d/IHME-GBD_2023-death-rates-2020.csv" \ + touch "$d/IHME-GBD_2023-death-rates-2020.csv" \ "$d/IHME_GBD_2019_RELATIVE_RISKS_Y2020M10D15.XLSX" # Validates the full Snakemake DAG without executing any rule (see diff --git a/AGENTS.md b/AGENTS.md index fcbed709..ece4b6f4 100644 --- a/AGENTS.md +++ b/AGENTS.md @@ -328,10 +328,11 @@ pixi run -e dev pytest -v # verbose output Five calibrations feed the default workflow. Their outputs are organized in per-config artefact *sets* under `data/curated/calibration//` -(selected by the `calibration.source` config key; the `default` set -- -fit against the anchoring-off baseline diet -- and the `gbd-anchored` -set -- consumed by the health-enabled configs -- are git-tracked) and -builds depend on them. When upstream data or build logic changes +(selected by the `calibration.source` config key; git-tracked sets: +`default` -- fit against the FBS-derived baseline diet -- `gdd-ia` -- +fit against the anchoring-off GDD-IA diet, consumed by the health-off +GDD-IA configs -- and `gbd-anchored` -- consumed by the health-enabled +configs) and builds depend on them. When upstream data or build logic changes materially, regenerate in this order: 1. **feed** — `config/calibration/feed.yaml` → `grassland_yield.csv`, diff --git a/config/default.yaml b/config/default.yaml index 50b93601..a5f3f3fc 100644 --- a/config/default.yaml +++ b/config/default.yaml @@ -838,6 +838,19 @@ weight_conversion: # --- section: diet --- diet: + # Source of the per-(country, food-group) baseline dietary intake totals: + # - "fbs": derived from FAOSTAT Food Balance Sheet energy supply + # (auto-retrieved; no manual downloads). Group masses are derived + # from FBS kcal supply at model-basis energy densities and + # waste-corrected to consumer-eaten intake; see the fbs block below + # and docs/current_diets.rst. + # - "gdd_ia": Global Dietary Database intake aggregation (survey-based; + # manual download of a not-yet-public dataset). Used for the + # published results; see docs/data_sources.rst. + # The baseline diet is structural: calibration artefact sets are fit + # against it, so changing the source requires a matching + # calibration.source (see docs/calibration.rst). + source: fbs baseline_age: "All ages" # Whether to anchor the per-country baseline-diet totals of the health # risk-factor food groups (health.risk_factors, e.g. fruits, vegetables, @@ -960,7 +973,21 @@ diet: # high-volume per-country exceptions in the CSV (it scales better # than a YAML block) and reserve this YAML for the cross-cutting # global defaults. - # GDD-IA pipeline configuration. + # FBS diet-source configuration (used when source: fbs). + fbs: + # FBS reports wheat (item 2511) and rice (item 2807) supply without a + # whole-grain vs refined split, while the model carries them as + # separate foods (flour-wholemeal/rice-brown vs flour-white/ + # rice-white) in separate food groups. These fractions allocate each + # item's energy supply to the whole-grain food; the remainder goes to + # the refined counterpart. Defaults from a population-weighted + # least-squares fit of per-country FBS-derived whole-grain intake to + # the survey-based (GDD-IA) estimate; they reproduce GDD-IA's global + # population-weighted whole-grain intake (~42 g/day) within ~1 g/day. + whole_grain_shares: + flour-wholemeal: 0.11 + rice-brown: 0.04 + # GDD-IA pipeline configuration (used when source: gdd_ia). gdd_ia: # GDD-IA reports meat in cooked weight (implied kcal/g for beef # ≈ 2.4, between raw 2.15 and cooked 2.50). Apply cooked-to-raw @@ -1618,6 +1645,7 @@ data: faostat: qcl_production_element_code: 5510 # "Production" in tonnes (QCL dataset, covers crops and livestock) fbs_food_supply_element_code: 645 # "Food supply quantity (kg/capita/yr)" in FBS dataset + fbs_food_kcal_element_code: 664 # "Food supply (kcal/capita/day)" in FBS dataset fbs_other_uses_element_code: 5154 # "Other uses (non-food)" in 1000 tonnes (FBS dataset) fbs_production_element_code: 5511 # "Production" in 1000 tonnes (FBS dataset) soilgrids: diff --git a/config/example.yaml b/config/example.yaml index 1c24ece7..5d2cc143 100644 --- a/config/example.yaml +++ b/config/example.yaml @@ -32,13 +32,14 @@ name: "example" # default) none of that data is needed. Enabling health also anchors the # baseline diet to GBD intake exposure (diet.anchor_groups_to_gbd defaults to # "match_health"); set that key explicitly to decouple the two. -# With anchoring on, also consume the calibration artefacts fit against the -# anchored diet (the default set is fit against the anchoring-off diet). +# The gbd-anchored calibration artefact set was fit against the anchored +# GDD-IA diet, so it matches exactly only with diet.source: gdd_ia (which +# needs the manually-obtained GDD-IA data, see docs/current_diets.rst). # health: # enabled: true # value_per_yll: 50000 # USD per year of life lost (0 = evaluate post-hoc only) # diet: -# anchor_groups_to_gbd: true # or false to keep the GDD/FAOSTAT-only diet +# anchor_groups_to_gbd: true # or false to keep the source-only diet # calibration: # source: "gbd-anchored" diff --git a/config/gsa.yaml b/config/gsa.yaml index 6f86b7f0..8035423c 100644 --- a/config/gsa.yaml +++ b/config/gsa.yaml @@ -31,6 +31,11 @@ name: "gsa" +# The published results use the GDD-IA survey-based baseline diet (the +# calibration artefact set consumed here was fit against it). +diet: + source: gdd_ia + health: enabled: true diff --git a/config/gsa_fixed_diet.yaml b/config/gsa_fixed_diet.yaml index 7c6cbcee..4199d8a5 100644 --- a/config/gsa_fixed_diet.yaml +++ b/config/gsa_fixed_diet.yaml @@ -123,6 +123,9 @@ validation: enforce_baseline_diet: true diet: + # The published results use the GDD-IA survey-based baseline diet (the + # gbd-anchored calibration artefact set was fit against it). + source: gdd_ia # Anchor to GBD so the fixed baseline diet matches the health-enabled # gsa.yaml diet (with health off, the sentinel would resolve to false). anchor_groups_to_gbd: true diff --git a/config/schemas/config.schema.yaml b/config/schemas/config.schema.yaml index 990ac11b..41595b6c 100644 --- a/config/schemas/config.schema.yaml +++ b/config/schemas/config.schema.yaml @@ -987,12 +987,18 @@ properties: diet: type: object required: + - source - baseline_age - anchor_groups_to_gbd - source_basis + - fbs - gdd_ia additionalProperties: false properties: + source: + type: string + enum: ["fbs", "gdd_ia"] + description: "Source of the per-(country, food-group) baseline dietary intake totals: 'fbs' (FAOSTAT Food Balance Sheets, auto-retrieved) or 'gdd_ia' (Global Dietary Database intake aggregation, manual download)" baseline_age: type: string description: "Age group for baseline diet (e.g., 'All ages')" @@ -1033,6 +1039,19 @@ properties: additionalProperties: type: string enum: ["dry", "fresh", "cooked", "carcass", "brewed"] + fbs: + type: object + required: [whole_grain_shares] + additionalProperties: false + properties: + whole_grain_shares: + type: object + description: "Per-whole-grain-food fraction of the shared FBS item's supply (wheat 2511, rice 2807) allocated to the whole-grain food; the remainder goes to the refined counterpart in the same FBS item" + additionalProperties: + type: number + minimum: 0.0 + maximum: 1.0 + description: "FBS diet-source configuration (used when diet.source is 'fbs')." gdd_ia: type: object required: [cooked_to_raw] @@ -1394,7 +1413,7 @@ properties: description: "Zenodo record id hosting the mirrored lccs_class file (refresh with tools/mirror_land_cover.py)" faostat: type: object - required: [qcl_production_element_code, fbs_food_supply_element_code, fbs_other_uses_element_code, fbs_production_element_code] + required: [qcl_production_element_code, fbs_food_supply_element_code, fbs_food_kcal_element_code, fbs_other_uses_element_code, fbs_production_element_code] additionalProperties: false properties: qcl_production_element_code: @@ -1403,6 +1422,9 @@ properties: fbs_food_supply_element_code: type: integer description: "FAOSTAT element code for 'Food supply quantity (kg/capita/yr)' in FBS dataset" + fbs_food_kcal_element_code: + type: integer + description: "FAOSTAT element code for 'Food supply (kcal/capita/day)' in FBS dataset" fbs_other_uses_element_code: type: integer description: "FAOSTAT element code for 'Other uses (non-food)' in 1000 tonnes (FBS dataset)" diff --git a/config/validation.yaml b/config/validation.yaml index 9574d3bb..6fb57654 100644 --- a/config/validation.yaml +++ b/config/validation.yaml @@ -8,6 +8,11 @@ # and feed) to compare model behaviour against observed 2020 outcomes. name: "validation" + +# The published results use the GDD-IA survey-based baseline diet (the +# calibration artefact set consumed here was fit against it). +diet: + source: gdd_ia scenarios: default: {} diff --git a/data/curated/calibration/default/animal_cost.csv b/data/curated/calibration/default/animal_cost.csv index b0302633..5eb10449 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,0.009775234386324883 -dairy,AGO,-0.1468677781522274 -dairy,ALB,0.028023586375638843 -dairy,ARE,-0.04836616409011185 -dairy,ARG,-0.19965669512748718 -dairy,ARM,-0.021885029273107648 +dairy,AFG,0.03480381052941084 +dairy,AGO,-0.1715051867067814 +dairy,ALB,0.10780100524425507 +dairy,ARE,-0.05395280942320824 +dairy,ARG,-0.17908449470996857 +dairy,ARM,0.0006774384528398514 dairy,ASM,-0.0 -dairy,ATG,-0.20854145288467407 -dairy,AUS,-0.07088138302788138 -dairy,AUT,-0.0029114224016666412 -dairy,AZE,-0.0643788380548358 -dairy,BDI,-0.208308644592762 -dairy,BEL,0.019830304197967052 -dairy,BEN,-0.2670818790793419 -dairy,BFA,-0.27394019067287445 -dairy,BGD,-0.07141656382009387 -dairy,BGR,0.02044672518968582 -dairy,BHS,-0.10683811269700527 -dairy,BIH,0.11757176741957664 -dairy,BLR,-0.045248162001371384 -dairy,BLZ,-0.2015547826886177 -dairy,BOL,-0.21501590311527252 -dairy,BRA,-0.22465115040540695 -dairy,BRB,-0.30794185400009155 -dairy,BRN,-0.0752631826326251 -dairy,BTN,-0.2185128517448902 -dairy,BWA,-0.24830980598926544 -dairy,CAF,-0.15660355985164642 -dairy,CAN,-0.034870133735239506 -dairy,CHE,-0.0393193899653852 -dairy,CHL,-0.1893482506275177 -dairy,CHN,0.1607140377163887 -dairy,CIV,-0.26611560583114624 -dairy,CMR,-0.2728709429502487 -dairy,COD,-0.14927423745393753 -dairy,COG,-0.1545010581612587 -dairy,COL,-0.1667589470744133 -dairy,COM,-0.13520731776952744 -dairy,CPV,-0.23950542509555817 -dairy,CRI,-0.014960226602852345 -dairy,CUB,-0.1991748958826065 -dairy,CYP,0.021383875980973244 -dairy,CZE,0.005600878037512302 -dairy,DEU,0.02896498329937458 -dairy,DJI,-0.03034788928925991 -dairy,DNK,-0.024929175153374672 -dairy,DOM,-0.03341811150312424 -dairy,DZA,-0.05347512639127672 -dairy,ECU,-0.1954895555973053 -dairy,EGY,-0.04868289828300476 -dairy,ERI,-0.16784296929836273 -dairy,ESP,-0.055624060332775116 -dairy,EST,-0.005761606618762016 -dairy,ETH,-0.16132669895887375 -dairy,FIN,0.06725708581507206 -dairy,FJI,-0.04846467077732086 -dairy,FRA,-0.07994907535612583 -dairy,GAB,-0.1042232746258378 -dairy,GBR,-0.009183213114738464 -dairy,GEO,-0.06488664634525776 -dairy,GHA,-0.26966172456741333 -dairy,GIN,-0.23668600618839264 -dairy,GMB,-0.2511493191123009 -dairy,GNB,-0.27105822414159775 +dairy,ATG,-0.2529653310775757 +dairy,AUS,-0.03154987841844559 +dairy,AUT,0.07019750587642193 +dairy,AZE,-0.028826472349464893 +dairy,BDI,-0.23235119134187698 +dairy,BEL,0.08315861690789461 +dairy,BEN,-0.289348840713501 +dairy,BFA,-0.29859188199043274 +dairy,BGD,-0.08461874444037676 +dairy,BGR,0.10074347630143166 +dairy,BHS,-0.14818473160266876 +dairy,BIH,0.24740789085626602 +dairy,BLR,-0.008713938295841217 +dairy,BLZ,-0.23693380504846573 +dairy,BOL,-0.2153988927602768 +dairy,BRA,-0.1791379749774933 +dairy,BRB,-0.3912408947944641 +dairy,BRN,0.007051797583699226 +dairy,BTN,-0.17378254141658545 +dairy,BWA,-0.14496407844126225 +dairy,CAF,-0.186598539352417 +dairy,CAN,-0.47215335071086884 +dairy,CHE,0.004667982924729586 +dairy,CHL,-0.17685041949152946 +dairy,CHN,0.20091811567544937 +dairy,CIV,-0.291747510433197 +dairy,CMR,-0.30095502734184265 +dairy,COD,-0.17557429522275925 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-dairy,GTM,-0.1558070182800293 +dairy,GRC,-0.004284021910279989 +dairy,GRD,-0.2823149710893631 +dairy,GTM,-0.19203030690550804 dairy,GUF,-0.0 -dairy,GUY,-0.21677342057228088 -dairy,HND,-0.15193508192896843 -dairy,HRV,-0.023165365681052208 -dairy,HTI,-0.03268309310078621 -dairy,HUN,-0.006732109002768993 -dairy,IDN,-0.09213399142026901 -dairy,IND,-0.133881613612175 -dairy,IRL,-0.07851577922701836 -dairy,IRN,0.008007684722542763 -dairy,IRQ,-0.18612314015626907 -dairy,ISL,0.02330385148525238 -dairy,ISR,-0.059871623292565346 -dairy,ITA,0.02880171127617359 -dairy,JAM,-0.051712632179260254 -dairy,JOR,0.0862271599471569 -dairy,JPN,0.13801487907767296 -dairy,KAZ,0.026172621408477426 -dairy,KEN,-0.1337904967367649 -dairy,KGZ,0.16921938210725784 -dairy,KHM,-0.08911564201116562 -dairy,KOR,0.18644990026950836 -dairy,LAO,-0.07593757659196854 -dairy,LBN,0.07638140395283699 -dairy,LBR,-0.11606167070567608 -dairy,LBY,-0.0634374306537211 -dairy,LKA,-0.0587678924202919 -dairy,LSO,-0.1971079632639885 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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,-0.05737338587641716 +dairy-buffalo,GRC,-0.11626055091619492 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,-0.19634506851434708 -dairy-buffalo,IND,-0.132305808365345 +dairy-buffalo,IDN,-0.20950321853160858 +dairy-buffalo,IND,-0.09237702190876007 dairy-buffalo,IRL,-0.0 -dairy-buffalo,IRN,0.11357088759541512 -dairy-buffalo,IRQ,-0.15010995417833328 +dairy-buffalo,IRN,0.04075390473008156 +dairy-buffalo,IRQ,-0.21532338857650757 dairy-buffalo,ISL,-0.0 dairy-buffalo,ISR,-0.0 -dairy-buffalo,ITA,0.11909181997179985 +dairy-buffalo,ITA,0.02848787885159254 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,SOM,-0.13241857290267944 -meat-sheep,SRB,0.0004699041019193828 -meat-sheep,SSD,-0.09354344569146633 -meat-sheep,STP,-0.122706588357687 -meat-sheep,SUR,-0.1477217674255371 -meat-sheep,SVK,-0.06757794320583344 -meat-sheep,SVN,4.106154665350914e-05 -meat-sheep,SWE,-0.025936471298336983 -meat-sheep,SWZ,-0.21770837903022766 -meat-sheep,SYR,-0.207680806517601 -meat-sheep,TCD,-0.1075163260102272 -meat-sheep,TGO,-0.12707118317484856 -meat-sheep,THA,-0.11838966980576515 -meat-sheep,TJK,-0.2132369726896286 -meat-sheep,TKM,-0.12302683666348457 -meat-sheep,TLS,-0.1467193365097046 +meat-sheep,SLE,-0.1177222728729248 +meat-sheep,SLV,-0.16846820712089539 +meat-sheep,SOM,-0.16557646542787552 +meat-sheep,SRB,-0.027668321155942976 +meat-sheep,SSD,-0.1267013382166624 +meat-sheep,STP,-0.15807212889194489 +meat-sheep,SUR,-0.18435800075531006 +meat-sheep,SVK,-0.14087767899036407 +meat-sheep,SVN,-0.07090217247605324 +meat-sheep,SWE,-0.0640067532658577 +meat-sheep,SWZ,-0.24143613874912262 +meat-sheep,SYR,-0.2451178878545761 +meat-sheep,TCD,-0.14067421853542328 +meat-sheep,TGO,-0.15948807820677757 +meat-sheep,THA,-0.16117023304104805 +meat-sheep,TJK,-0.12537630088627338 +meat-sheep,TKM,-0.15814847126603127 +meat-sheep,TLS,-0.17695389688014984 meat-sheep,TTO,0.005382265895605087 -meat-sheep,TUN,-0.16555969417095184 -meat-sheep,TUR,-0.17809662222862244 +meat-sheep,TUN,-0.20513779670000076 +meat-sheep,TUR,-0.11104271560907364 meat-sheep,TWN,-0.0 -meat-sheep,TZA,-0.16611717641353607 -meat-sheep,UGA,-0.22475197166204453 -meat-sheep,UKR,-0.1064801849424839 -meat-sheep,URY,-0.10977669432759285 -meat-sheep,USA,-0.04353543370962143 -meat-sheep,UZB,-0.09816614352166653 -meat-sheep,VEN,-0.16307301074266434 -meat-sheep,VNM,-0.23012882471084595 -meat-sheep,VUT,-0.12690399587154388 -meat-sheep,YEM,-0.09523249790072441 -meat-sheep,ZAF,-0.13910797983407974 -meat-sheep,ZMB,-0.11528013274073601 -meat-sheep,ZWE,-0.4924807548522949 +meat-sheep,TZA,-0.1982869729399681 +meat-sheep,UGA,-0.258425772190094 +meat-sheep,UKR,-0.14455045759677887 +meat-sheep,URY,-0.13825680315494537 +meat-sheep,USA,-0.034966714680194855 +meat-sheep,UZB,-0.12532063387334347 +meat-sheep,VEN,-0.1894868090748787 +meat-sheep,VNM,-0.1354546993970871 +meat-sheep,VUT,-0.12513652443885803 +meat-sheep,YEM,-0.1283903829753399 +meat-sheep,ZAF,-0.1704377755522728 +meat-sheep,ZMB,-0.14318739250302315 +meat-sheep,ZWE,-0.571391373872757 diff --git a/data/curated/calibration/default/crop_cost.csv b/data/curated/calibration/default/crop_cost.csv index d5950475..0764eb54 100644 --- a/data/curated/calibration/default/crop_cost.csv +++ b/data/curated/calibration/default/crop_cost.csv @@ -1,178 +1,178 @@ crop,country,correction_bnusd_per_mha -alfalfa,AFG,-0.38436196744441986 +alfalfa,AFG,-0.4258258491754532 alfalfa,AGO,-0.9439449906349182 -alfalfa,ALB,-0.5414051562547684 +alfalfa,ALB,-0.19879434816539288 alfalfa,ARE,-0.6755253076553345 -alfalfa,ARG,-0.5018861889839172 -alfalfa,ARM,-0.0 +alfalfa,ARG,-0.5323620736598969 +alfalfa,ARM,-0.000549434102140367 alfalfa,ATG,-7.5 -alfalfa,AUS,0.10641167312860489 +alfalfa,AUS,0.871025800704956 alfalfa,AUT,-0.6891505420207977 -alfalfa,AZE,0.8190054893493652 +alfalfa,AZE,1.2508390545845032 alfalfa,BDI,-0.5490851700305939 -alfalfa,BEL,-0.22992710024118423 -alfalfa,BEN,0.8548685610294342 +alfalfa,BEL,-0.20986243337392807 +alfalfa,BEN,1.263472318649292 alfalfa,BFA,-7.5 -alfalfa,BGD,-0.7961883842945099 -alfalfa,BGR,-0.050924789160490036 -alfalfa,BHS,-0.7067800164222717 -alfalfa,BIH,-0.005093845073133707 -alfalfa,BLR,-0.3088502585887909 +alfalfa,BGD,-0.8596000373363495 +alfalfa,BGR,0.23349159210920334 +alfalfa,BHS,-0.6657916605472565 +alfalfa,BIH,0.43763408064842224 +alfalfa,BLR,-0.19639672338962555 alfalfa,BLZ,-0.029083551838994026 alfalfa,BOL,-0.22297222912311554 alfalfa,BRA,-0.43258796632289886 -alfalfa,BRB,2.8655171394348145 -alfalfa,BRN,1.4280236959457397 -alfalfa,BTN,0.30772432684898376 +alfalfa,BRB,3.807503581047058 +alfalfa,BRN,-0.8346821069717407 +alfalfa,BTN,0.392868310213089 alfalfa,BWA,-7.5 alfalfa,CAF,-0.974648654460907 -alfalfa,CAN,-0.17618552595376968 -alfalfa,CHE,-0.11473012529313564 +alfalfa,CAN,-0.04307100735604763 +alfalfa,CHE,0.45106977224349976 alfalfa,CHL,-0.6260356903076172 alfalfa,CHN,0.6336996257305145 alfalfa,CIV,7.5 -alfalfa,CMR,1.8151640892028809 +alfalfa,CMR,2.3113012313842773 alfalfa,COD,-0.904526561498642 alfalfa,COG,-0.9112309217453003 alfalfa,COL,-0.9032638072967529 alfalfa,COM,-0.863983690738678 -alfalfa,CPV,-3.944527491927147 -alfalfa,CRI,-0.9016055166721344 +alfalfa,CPV,-3.8886219263076782 +alfalfa,CRI,-0.8749768435955048 alfalfa,CUB,-0.28110775351524353 -alfalfa,CYP,2.8504176139831543 -alfalfa,CZE,-0.4846976697444916 -alfalfa,DEU,-0.31395669281482697 -alfalfa,DJI,1.668068766593933 +alfalfa,CYP,-0.32458072900772095 +alfalfa,CZE,-0.4530545473098755 +alfalfa,DEU,-0.19063856452703476 +alfalfa,DJI,2.237818956375122 alfalfa,DNK,7.370487689971924 -alfalfa,DOM,-0.8690982162952423 -alfalfa,DZA,-0.043717604130506516 -alfalfa,ECU,-0.12939461693167686 -alfalfa,EGY,-1.314817488193512 +alfalfa,DOM,-0.8690981864929199 +alfalfa,DZA,-0.08560169488191605 +alfalfa,ECU,-0.08738121017813683 +alfalfa,EGY,-1.5447824597358704 alfalfa,ERI,-0.8243190050125122 -alfalfa,ESP,-1.2043046355247498 -alfalfa,EST,-0.10771290212869644 -alfalfa,ETH,-0.8311939537525177 +alfalfa,ESP,-1.0982974171638489 +alfalfa,EST,-0.039650071412324905 +alfalfa,ETH,-0.8383620381355286 alfalfa,FIN,-0.0 -alfalfa,FJI,-0.5175904631614685 -alfalfa,FRA,-0.33104005455970764 +alfalfa,FJI,-0.322286993265152 +alfalfa,FRA,-0.3310399800539017 alfalfa,GAB,-0.870643675327301 -alfalfa,GBR,-0.04500511288642883 -alfalfa,GEO,-0.583319365978241 +alfalfa,GBR,0.17996426671743393 +alfalfa,GEO,-0.5883668661117554 alfalfa,GHA,7.5 alfalfa,GIN,-0.44031502306461334 alfalfa,GMB,-7.5 alfalfa,GNB,-7.5 alfalfa,GNQ,-0.12937652319669724 -alfalfa,GRC,-0.7368557453155518 -alfalfa,GRD,0.5032296180725098 -alfalfa,GTM,-0.4894945025444031 +alfalfa,GRC,-0.7172459065914154 +alfalfa,GRD,0.8463779091835022 +alfalfa,GTM,-0.48949453234672546 alfalfa,GUF,-2.7509915828704834 alfalfa,GUY,-0.07720011845231056 alfalfa,HND,-0.34651389718055725 -alfalfa,HRV,-3.854497104883194 +alfalfa,HRV,-3.6160955727100372 alfalfa,HTI,-0.8191313445568085 -alfalfa,HUN,-0.7193954288959503 +alfalfa,HUN,-0.5625778287649155 alfalfa,IDN,-0.15115800499916077 -alfalfa,IND,3.203026294708252 -alfalfa,IRL,-0.5453056693077087 -alfalfa,IRN,-1.2045719623565674 +alfalfa,IND,-0.6358039379119873 +alfalfa,IRL,-0.5510099232196808 +alfalfa,IRN,-1.2105963230133057 alfalfa,IRQ,2.765779972076416 -alfalfa,ISL,-0.8195151090621948 -alfalfa,ISR,1.7322670221328735 +alfalfa,ISL,-0.7953901886940002 +alfalfa,ISR,2.2611982822418213 alfalfa,ITA,-0.45480209589004517 -alfalfa,JAM,2.3189845085144043 -alfalfa,JOR,4.258453369140625 +alfalfa,JAM,2.9409842491149902 +alfalfa,JOR,5.5240089893341064 alfalfa,JPN,0.017270792406634428 alfalfa,KAZ,-0.5172078013420105 alfalfa,KEN,-0.5022623538970947 -alfalfa,KGZ,-0.8171102404594421 -alfalfa,KHM,-0.9076248407363892 +alfalfa,KGZ,-0.8296313583850861 +alfalfa,KHM,-0.837518185377121 alfalfa,KOR,-0.8412145376205444 alfalfa,LAO,-0.2115088403224945 -alfalfa,LBN,1.6291322708129883 +alfalfa,LBN,0.18820440769195557 alfalfa,LBR,-0.9287798702716827 alfalfa,LBY,-0.824711263179779 -alfalfa,LKA,-0.375872865319252 +alfalfa,LKA,-0.7325403690338135 alfalfa,LSO,-0.44325435161590576 -alfalfa,LTU,0.04975191503763199 -alfalfa,LUX,-0.4098461866378784 -alfalfa,LVA,-0.9586898982524872 -alfalfa,MAR,3.024399518966675 -alfalfa,MDA,-0.06984036602079868 +alfalfa,LTU,0.3543912172317505 +alfalfa,LUX,-0.3182141184806824 +alfalfa,LVA,-0.9249886870384216 +alfalfa,MAR,4.396327495574951 +alfalfa,MDA,0.25572748482227325 alfalfa,MDG,-0.9195513427257538 -alfalfa,MEX,-0.1405026614665985 -alfalfa,MKD,-0.1161428913474083 +alfalfa,MEX,-0.13977743685245514 +alfalfa,MKD,0.3703063055872917 alfalfa,MLI,-7.5 -alfalfa,MLT,-1.7023547291755676 +alfalfa,MLT,-0.8582369685173035 alfalfa,MMR,-0.8467822372913361 -alfalfa,MNE,1.0304622948169708 +alfalfa,MNE,1.9910903573036194 alfalfa,MNG,-7.5 alfalfa,MOZ,-0.5782089233398438 alfalfa,MRT,-0.5335112512111664 -alfalfa,MUS,-0.7995986342430115 +alfalfa,MUS,-0.7995986044406891 alfalfa,MWI,-0.5677796304225922 alfalfa,MYS,-0.3362851142883301 alfalfa,NAM,-0.40904849767684937 alfalfa,NER,-0.0 alfalfa,NGA,-0.9353777766227722 alfalfa,NIC,-1.859633982181549 -alfalfa,NLD,6.6176230907440186 +alfalfa,NLD,6.689833879470825 alfalfa,NOR,-0.0 -alfalfa,NPL,-0.945355236530304 -alfalfa,NZL,-0.31611891090869904 -alfalfa,OMN,0.2603931128978729 -alfalfa,PAK,-0.8296497762203217 -alfalfa,PAN,-0.10728327557444572 -alfalfa,PER,-0.1228768602013588 +alfalfa,NPL,-0.945535808801651 +alfalfa,NZL,-0.3066001534461975 +alfalfa,OMN,0.40435174107551575 +alfalfa,PAK,-0.8302765190601349 +alfalfa,PAN,-1.0854015946388245 +alfalfa,PER,-0.12115039676427841 alfalfa,PHL,-0.08774245902895927 -alfalfa,PNG,-0.5814321935176849 -alfalfa,POL,-0.46259021759033203 +alfalfa,PNG,-0.40741904079914093 +alfalfa,POL,-0.44983451068401337 alfalfa,PRI,-0.1749081201851368 -alfalfa,PRT,1.5364129543304443 +alfalfa,PRT,2.1297249794006348 alfalfa,PRY,-0.329582542181015 -alfalfa,PSE,1.237277626991272 -alfalfa,ROU,-0.02981695532798767 -alfalfa,RUS,-0.3100622743368149 +alfalfa,PSE,1.644728660583496 +alfalfa,ROU,0.048095038160681725 +alfalfa,RUS,-0.33958540856838226 alfalfa,RWA,-0.22619490325450897 -alfalfa,SAU,0.7380248010158539 -alfalfa,SDN,-0.866632342338562 -alfalfa,SEN,2.543222188949585 -alfalfa,SLB,-0.6907225847244263 +alfalfa,SAU,1.126312255859375 +alfalfa,SDN,-0.7998061180114746 +alfalfa,SEN,3.2440857887268066 +alfalfa,SLB,-0.6252312958240509 alfalfa,SLE,-0.910034567117691 -alfalfa,SLV,1.316790223121643 +alfalfa,SLV,1.7648570537567139 alfalfa,SOM,-0.5522036254405975 -alfalfa,SRB,0.026569997891783714 +alfalfa,SRB,0.004816184751689434 alfalfa,SSD,-0.8872385025024414 -alfalfa,STP,1.477776288986206 +alfalfa,STP,2.008856475353241 alfalfa,SUR,1.9646342992782593 -alfalfa,SVK,-0.5123840868473053 -alfalfa,SVN,-0.44250205159187317 +alfalfa,SVK,-0.26271412521600723 +alfalfa,SVN,0.015565186738967896 alfalfa,SWE,-0.0 -alfalfa,SWZ,-0.23009125888347626 +alfalfa,SWZ,-0.23009134829044342 alfalfa,SYR,-0.0 alfalfa,TCD,-0.9240545034408569 alfalfa,TGO,-0.11689066886901855 alfalfa,THA,-0.6453790664672852 -alfalfa,TJK,-0.8308174312114716 +alfalfa,TJK,-2.000891774892807 alfalfa,TKM,-0.8394652009010315 alfalfa,TLS,-0.03928087651729584 alfalfa,TTO,-0.7204273641109467 -alfalfa,TUN,0.7002188563346863 -alfalfa,TUR,4.086003065109253 -alfalfa,TWN,0.43456339836120605 +alfalfa,TUN,1.104285717010498 +alfalfa,TUR,-1.506419837474823 +alfalfa,TWN,0.631376326084137 alfalfa,TZA,-0.3090435713529587 alfalfa,UGA,-0.955134242773056 -alfalfa,UKR,-0.2991945445537567 +alfalfa,UKR,-0.3029917776584625 alfalfa,URY,-0.005964932031929493 -alfalfa,USA,0.14364345371723175 -alfalfa,UZB,-1.884817361831665 +alfalfa,USA,-0.4892602264881134 +alfalfa,UZB,-1.86110520362854 alfalfa,VEN,-0.27461254596710205 -alfalfa,VNM,0.6191823780536652 +alfalfa,VNM,-1.1179290413856506 alfalfa,VUT,-0.10433314368128777 alfalfa,YEM,-0.49167701601982117 -alfalfa,ZAF,-0.3983919471502304 +alfalfa,ZAF,-0.4025203138589859 alfalfa,ZMB,-0.9244880676269531 -alfalfa,ZWE,-0.0 +alfalfa,ZWE,0.14634424820542336 apple,AFG,-0.0 apple,ALB,-0.0 apple,ARG,-7.5 @@ -279,29 +279,29 @@ apple,ZAF,-0.0 apple,ZMB,-0.0 apple,ZWE,-0.0 banana,AFG,-0.0 -banana,AGO,-7.5 +banana,AGO,-5.979532957077026 banana,ALB,7.5 banana,ARE,-7.5 -banana,ARG,-5.8250792026519775 +banana,ARG,-2.4249993562698364 banana,ATG,-0.0 banana,AUS,-7.5 -banana,BDI,-1.2550127506256104 +banana,BDI,-0.8035980761051178 banana,BEN,-7.5 -banana,BFA,-6.676374912261963 -banana,BGD,-7.187574148178101 -banana,BHS,-7.359156370162964 -banana,BLZ,-7.23839259147644 -banana,BOL,-1.8833130598068237 -banana,BRA,-3.567880630493164 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-dryland-rice,BOL,-0.4153996407985687 -dryland-rice,BRA,-0.624791830778122 -dryland-rice,BRN,-1.3424453735351562 -dryland-rice,BTN,-0.8014591336250305 +dryland-rice,BLZ,-0.9823723137378693 +dryland-rice,BOL,-0.23364242911338806 +dryland-rice,BRA,-0.2584369480609894 +dryland-rice,BRN,-1.227376103401184 +dryland-rice,BTN,-0.7275798618793488 dryland-rice,BWA,7.5 -dryland-rice,CAF,-1.444136381149292 +dryland-rice,CAF,-1.3513840436935425 dryland-rice,CAN,3.75 dryland-rice,CHE,-0.0 -dryland-rice,CHL,-0.48353883624076843 -dryland-rice,CHN,-1.547296166419983 -dryland-rice,CIV,-0.04111891705542803 -dryland-rice,CMR,-1.3140379786491394 -dryland-rice,COD,-1.5691264867782593 -dryland-rice,COG,-1.5833926796913147 -dryland-rice,COL,-1.2395041584968567 +dryland-rice,CHL,-0.0 +dryland-rice,CHN,-0.8246506452560425 +dryland-rice,CIV,0.23470860719680786 +dryland-rice,CMR,-1.2006254196166992 +dryland-rice,COD,-1.489477276802063 +dryland-rice,COG,-1.542385220527649 +dryland-rice,COL,-0.9553550481796265 dryland-rice,COM,7.5 -dryland-rice,CRI,-1.101885437965393 -dryland-rice,CUB,-1.0410478115081787 -dryland-rice,DOM,-2.5794440507888794 +dryland-rice,CRI,-0.8588040471076965 +dryland-rice,CUB,-0.7648134827613831 +dryland-rice,DOM,-2.411929130554199 dryland-rice,DZA,-0.0 -dryland-rice,ECU,-0.6212940216064453 -dryland-rice,EGY,-1.3617092967033386 +dryland-rice,ECU,-0.4832213222980499 +dryland-rice,EGY,-0.7125310897827148 dryland-rice,ERI,7.5 -dryland-rice,ESP,-1.2683366537094116 -dryland-rice,ETH,-0.3887041211128235 -dryland-rice,FJI,-0.5894087553024292 -dryland-rice,FRA,-0.6140397787094116 +dryland-rice,ESP,-0.7834238708019257 +dryland-rice,ETH,-0.17929938435554504 +dryland-rice,FJI,-0.33597058057785034 +dryland-rice,FRA,-0.14469369128346443 dryland-rice,GAB,-0.0 dryland-rice,GEO,-0.0 -dryland-rice,GHA,-0.32774362713098526 -dryland-rice,GIN,-0.565986156463623 -dryland-rice,GMB,-0.35436099767684937 -dryland-rice,GNB,-1.3103870749473572 +dryland-rice,GHA,-0.04805594542995095 +dryland-rice,GIN,-0.4769357740879059 +dryland-rice,GMB,-0.312553346157074 +dryland-rice,GNB,-1.1761356592178345 dryland-rice,GNQ,7.5 -dryland-rice,GRC,-0.537587508559227 -dryland-rice,GTM,-1.4263858795166016 +dryland-rice,GRC,-0.0 +dryland-rice,GTM,-1.2784941792488098 dryland-rice,GUF,0.0 -dryland-rice,GUY,-1.004263162612915 -dryland-rice,HND,-1.1594120860099792 +dryland-rice,GUY,-0.6391945481300354 +dryland-rice,HND,-0.8922071158885956 dryland-rice,HRV,7.5 -dryland-rice,HTI,-1.3150500655174255 -dryland-rice,HUN,-0.0850883275270462 -dryland-rice,IDN,-1.2118024826049805 -dryland-rice,IND,-1.1165772676467896 -dryland-rice,IRN,-3.082484722137451 -dryland-rice,IRQ,-1.5275567770004272 +dryland-rice,HTI,-1.1769548058509827 +dryland-rice,HUN,-0.0 +dryland-rice,IDN,-0.9257065951824188 +dryland-rice,IND,-0.9625709652900696 +dryland-rice,IRN,-2.66560959815979 +dryland-rice,IRQ,-1.2104865908622742 dryland-rice,ISR,-0.0 -dryland-rice,ITA,-3.087238907814026 -dryland-rice,JPN,-3.0823535919189453 -dryland-rice,KAZ,-0.12560350447893143 -dryland-rice,KEN,-0.9278556406497955 -dryland-rice,KGZ,-5.229932188987732 -dryland-rice,KHM,-1.1244550347328186 -dryland-rice,KOR,-6.239935874938965 -dryland-rice,LAO,-1.0737311840057373 -dryland-rice,LBR,-1.4794554710388184 +dryland-rice,ITA,-2.6424337029457092 +dryland-rice,JPN,-2.938998579978943 +dryland-rice,KAZ,-0.0 +dryland-rice,KEN,-0.6069097518920898 +dryland-rice,KGZ,-5.151049256324768 +dryland-rice,KHM,-0.8705109059810638 +dryland-rice,KOR,-5.822206020355225 +dryland-rice,LAO,-0.7062052488327026 +dryland-rice,LBR,-1.3710217475891113 dryland-rice,LBY,-0.0 -dryland-rice,LKA,-0.29075081646442413 +dryland-rice,LKA,-0.008507391437888145 dryland-rice,LSO,-0.0 dryland-rice,MAR,-7.5 dryland-rice,MDA,-0.0 -dryland-rice,MDG,-0.31136587262153625 -dryland-rice,MEX,-0.23755130171775818 -dryland-rice,MKD,-4.15339669585228 -dryland-rice,MLI,-0.2491341084241867 -dryland-rice,MMR,-1.1796547174453735 -dryland-rice,MOZ,-0.2917872369289398 -dryland-rice,MRT,-0.8436525464057922 +dryland-rice,MDG,-0.13584440387785435 +dryland-rice,MEX,0.09927597641944885 +dryland-rice,MKD,-3.9048002511262894 +dryland-rice,MLI,-0.00927770510315895 +dryland-rice,MMR,-0.8760976791381836 +dryland-rice,MOZ,-0.22449111938476562 +dryland-rice,MRT,-0.5680365562438965 dryland-rice,MUS,7.5 -dryland-rice,MWI,-0.9853283166885376 -dryland-rice,MYS,-0.34802602231502533 +dryland-rice,MWI,-0.9656501710414886 +dryland-rice,MYS,-0.14604141749441624 dryland-rice,NAM,-0.0 -dryland-rice,NER,-1.2328954339027405 -dryland-rice,NGA,-0.18268626555800438 -dryland-rice,NIC,1.6373028755187988 -dryland-rice,NPL,-0.231791153550148 -dryland-rice,PAK,-1.083001732826233 -dryland-rice,PAN,-0.9030466675758362 -dryland-rice,PER,1.101096272468567 -dryland-rice,PHL,-0.6054533123970032 +dryland-rice,NER,-0.9428642094135284 +dryland-rice,NGA,-0.00904546957463026 +dryland-rice,NIC,1.583376705646515 +dryland-rice,NPL,-0.03929765522480011 +dryland-rice,PAK,-0.8914159536361694 +dryland-rice,PAN,-0.5597976446151733 +dryland-rice,PER,1.251444935798645 +dryland-rice,PHL,-0.36061668395996094 dryland-rice,PNG,-0.0 -dryland-rice,PRI,-2.44837486743927 -dryland-rice,PRT,-0.867307722568512 -dryland-rice,PRY,-0.9091903567314148 +dryland-rice,PRI,-2.1950498819351196 +dryland-rice,PRT,-0.40465280413627625 +dryland-rice,PRY,-0.7768224477767944 dryland-rice,PSE,-7.5 dryland-rice,ROU,-7.5 -dryland-rice,RUS,-3.9223434925079346 -dryland-rice,RWA,-1.817745327949524 +dryland-rice,RUS,-3.5230748653411865 +dryland-rice,RWA,-1.469328224658966 dryland-rice,SAU,-0.0 dryland-rice,SDN,-0.0 -dryland-rice,SEN,-0.17419010400772095 -dryland-rice,SLB,-1.3815945982933044 -dryland-rice,SLE,-0.2055397629737854 -dryland-rice,SLV,2.7511189579963684 -dryland-rice,SOM,-1.2224610447883606 +dryland-rice,SEN,-0.10451180394738913 +dryland-rice,SLB,-1.2587385177612305 +dryland-rice,SLE,-0.09619397995993495 +dryland-rice,SLV,3.066270172595978 +dryland-rice,SOM,-1.066550076007843 dryland-rice,SRB,-0.0 -dryland-rice,SSD,-1.4394530057907104 -dryland-rice,SUR,0.006390124559402466 +dryland-rice,SSD,-1.3663427829742432 +dryland-rice,SUR,0.3716561049222946 dryland-rice,SVK,3.75 dryland-rice,SVN,7.5 dryland-rice,SWZ,7.5 dryland-rice,SYR,-7.5 -dryland-rice,TCD,-0.6570872664451599 -dryland-rice,TGO,-0.15136796236038208 -dryland-rice,THA,-0.4256027936935425 -dryland-rice,TJK,-1.708930253982544 +dryland-rice,TCD,-0.5287216305732727 +dryland-rice,TGO,0.010156067088246346 +dryland-rice,THA,-0.27963167428970337 +dryland-rice,TJK,-1.1746107339859009 dryland-rice,TKM,-7.5 -dryland-rice,TLS,-1.830076515674591 -dryland-rice,TTO,-0.1399339735507965 +dryland-rice,TLS,-1.7070834636688232 +dryland-rice,TTO,-0.06781266257166862 dryland-rice,TUN,7.5 -dryland-rice,TUR,-2.4590591192245483 -dryland-rice,TWN,-0.606400191783905 -dryland-rice,TZA,0.014682264998555183 -dryland-rice,UGA,-0.8995059728622437 -dryland-rice,UKR,-3.1382293701171875 -dryland-rice,URY,-0.2534005716443062 +dryland-rice,TUR,-1.9274609684944153 +dryland-rice,TWN,-0.04058335395529866 +dryland-rice,TZA,0.2753441631793976 +dryland-rice,UGA,-0.7990955114364624 +dryland-rice,UKR,-2.6927809715270996 +dryland-rice,URY,-0.0 dryland-rice,USA,-0.0 dryland-rice,UZB,-7.5 -dryland-rice,VEN,-0.9345723986625671 -dryland-rice,VNM,-0.7967083752155304 +dryland-rice,VEN,-0.6182149350643158 +dryland-rice,VNM,-0.479926273226738 dryland-rice,YEM,7.5 dryland-rice,ZAF,-0.0 -dryland-rice,ZMB,-1.0462843179702759 -dryland-rice,ZWE,2.8345950841903687 +dryland-rice,ZMB,-0.922489196062088 +dryland-rice,ZWE,2.8170409202575684 foxtail-millet,AFG,-0.6007698476314545 foxtail-millet,AGO,-0.4080769270658493 foxtail-millet,ARG,-0.23928023874759674 foxtail-millet,ARM,7.5 -foxtail-millet,AUS,-0.6373620629310608 +foxtail-millet,AUS,-0.6403901875019073 foxtail-millet,AZE,-4.388915061950684 foxtail-millet,BDI,-0.48629334568977356 foxtail-millet,BEN,-0.539930909872055 foxtail-millet,BFA,-0.3755684345960617 foxtail-millet,BGD,-7.5 foxtail-millet,BGR,7.5 -foxtail-millet,BLR,-0.41845284402370453 +foxtail-millet,BLR,-0.5246927589178085 foxtail-millet,BOL,-0.0 foxtail-millet,BRA,7.5 foxtail-millet,BTN,-7.5 -foxtail-millet,BWA,-0.47896677255630493 +foxtail-millet,BWA,-0.3865332454442978 foxtail-millet,CAF,-0.46454034745693207 foxtail-millet,CAN,-0.0 -foxtail-millet,CHE,-2.6450395584106445 +foxtail-millet,CHE,-2.644416570663452 foxtail-millet,CHN,-0.7386873662471771 foxtail-millet,CIV,-0.5795082151889801 -foxtail-millet,CMR,-0.4618046432733536 +foxtail-millet,CMR,-0.47215035557746887 foxtail-millet,COD,-0.459261953830719 foxtail-millet,COG,-0.44761890172958374 -foxtail-millet,DEU,-0.8485472500324249 +foxtail-millet,DEU,-1.0389275252819061 foxtail-millet,DZA,-0.0 foxtail-millet,ERI,-0.4152943789958954 foxtail-millet,ETH,-0.7575334310531616 -foxtail-millet,FRA,-0.47617650032043457 +foxtail-millet,FRA,-0.47555333375930786 foxtail-millet,GAB,-0.0 foxtail-millet,GEO,-0.0 foxtail-millet,GHA,-0.6628236174583435 @@ -2377,10 +2377,10 @@ foxtail-millet,GMB,-0.46135395765304565 foxtail-millet,GNB,-0.44429850578308105 foxtail-millet,GRC,7.5 foxtail-millet,HUN,-0.6123685538768768 -foxtail-millet,IND,-0.47607550024986267 +foxtail-millet,IND,-0.49342063069343567 foxtail-millet,IRN,-1.0534720420837402 foxtail-millet,IRQ,-0.837611585855484 -foxtail-millet,KAZ,-0.27249687910079956 +foxtail-millet,KAZ,-0.27429893612861633 foxtail-millet,KEN,-0.49672308564186096 foxtail-millet,KGZ,-0.7578834295272827 foxtail-millet,KHM,-0.0 @@ -2389,15 +2389,15 @@ foxtail-millet,LAO,-0.0 foxtail-millet,LBY,-0.505948543548584 foxtail-millet,LKA,-1.0432093739509583 foxtail-millet,LSO,7.5 -foxtail-millet,MAR,-0.9959200918674469 -foxtail-millet,MDA,-0.6630499362945557 +foxtail-millet,MAR,-1.1078962087631226 +foxtail-millet,MDA,-0.7160716652870178 foxtail-millet,MLI,-0.3269065320491791 foxtail-millet,MMR,-0.4658123552799225 foxtail-millet,MNG,7.5 -foxtail-millet,MOZ,-0.4439863860607147 +foxtail-millet,MOZ,-0.448646605014801 foxtail-millet,MRT,-0.3641741871833801 -foxtail-millet,MWI,-0.4691985696554184 -foxtail-millet,NAM,-0.07696927338838577 +foxtail-millet,MWI,-0.48196548223495483 +foxtail-millet,NAM,-0.07871648669242859 foxtail-millet,NER,-0.14092829823493958 foxtail-millet,NGA,-0.46023768186569214 foxtail-millet,NPL,-0.46213237941265106 @@ -2405,808 +2405,808 @@ foxtail-millet,PAK,-0.2994333803653717 foxtail-millet,POL,3.4531050622463226 foxtail-millet,PRY,-0.0 foxtail-millet,ROU,-0.32146039605140686 -foxtail-millet,RUS,-0.3323908597230911 +foxtail-millet,RUS,-0.3345818519592285 foxtail-millet,RWA,-0.42522843182086945 -foxtail-millet,SAU,-1.415258765220642 +foxtail-millet,SAU,-1.6018290519714355 foxtail-millet,SDN,-0.43429309129714966 -foxtail-millet,SEN,-0.517208531498909 +foxtail-millet,SEN,-0.5188337415456772 foxtail-millet,SLE,-0.4744160324335098 foxtail-millet,SOM,-0.0 -foxtail-millet,SSD,-0.41217321157455444 +foxtail-millet,SSD,-0.4053361862897873 foxtail-millet,SVK,7.5 foxtail-millet,SWZ,7.5 foxtail-millet,SYR,-0.0 foxtail-millet,TCD,-0.21936063468456268 foxtail-millet,TGO,-0.3318389505147934 foxtail-millet,THA,-3.9916176944971085 -foxtail-millet,TJK,3.5118793100118637 +foxtail-millet,TJK,3.5353973507881165 foxtail-millet,TKM,-0.0 foxtail-millet,TUN,-0.0 -foxtail-millet,TUR,-1.334710955619812 +foxtail-millet,TUR,-1.317931056022644 foxtail-millet,TWN,-0.0 foxtail-millet,TZA,-0.5092430412769318 foxtail-millet,UGA,-0.20531106740236282 -foxtail-millet,UKR,-0.7608203291893005 +foxtail-millet,UKR,-0.7762373685836792 foxtail-millet,URY,7.5 foxtail-millet,USA,-0.42737188935279846 -foxtail-millet,UZB,-0.8081822693347931 +foxtail-millet,UZB,-0.7896413803100586 foxtail-millet,VNM,-1.5837616920471191 foxtail-millet,YEM,-0.43398135900497437 -foxtail-millet,ZAF,-0.38650934398174286 -foxtail-millet,ZMB,-0.4005720466375351 -foxtail-millet,ZWE,0.18884319812059402 -gram,AFG,-0.7848499119281769 -gram,AGO,-0.9248799681663513 -gram,ALB,-0.13625607825815678 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+sweet-potato,BOL,-0.7356737554073334 +sweet-potato,BRA,-2.5935747623443604 sweet-potato,BRB,-7.5 sweet-potato,BRN,-7.5 sweet-potato,BTN,7.5 sweet-potato,BWA,-0.0 -sweet-potato,CAF,-3.8992300033569336 -sweet-potato,CAN,-3.2703652381896973 -sweet-potato,CHE,-2.8814823627471924 -sweet-potato,CHL,-1.954980492591858 +sweet-potato,CAF,-3.870824456214905 +sweet-potato,CAN,-2.924433946609497 +sweet-potato,CHE,-2.197257399559021 +sweet-potato,CHL,-1.9586389064788818 sweet-potato,CHN,-7.5 -sweet-potato,CIV,-0.5358869433403015 -sweet-potato,CMR,-3.6167469024658203 -sweet-potato,COD,-3.754379153251648 -sweet-potato,COG,-3.6232420206069946 -sweet-potato,COL,-3.257773756980896 -sweet-potato,COM,-3.8211779594421387 +sweet-potato,CIV,-0.48770684003829956 +sweet-potato,CMR,-3.4301960468292236 +sweet-potato,COD,-3.6522908210754395 +sweet-potato,COG,-3.470755100250244 +sweet-potato,COL,-2.9651559591293335 +sweet-potato,COM,-3.7514426708221436 sweet-potato,CPV,-7.5 -sweet-potato,CRI,3.1882646083831787 -sweet-potato,CUB,-3.401297450065613 -sweet-potato,CYP,-5.572687029838562 -sweet-potato,CZE,-2.7953556776046753 -sweet-potato,DEU,-2.2669942378997803 -sweet-potato,DNK,-2.6334924697875977 -sweet-potato,DOM,-3.758204460144043 -sweet-potato,DZA,-3.0178561210632324 -sweet-potato,ECU,-0.004254129249602556 -sweet-potato,EGY,-3.22887647151947 -sweet-potato,ERI,1.7941423654556274 +sweet-potato,CRI,3.2859817445278168 +sweet-potato,CUB,-3.192458391189575 +sweet-potato,CYP,-4.8332730531692505 +sweet-potato,CZE,-2.3518240451812744 +sweet-potato,DEU,-1.3784958720207214 +sweet-potato,DNK,-1.566912829875946 +sweet-potato,DOM,-3.743538737297058 +sweet-potato,DZA,-2.389132499694824 +sweet-potato,ECU,0.20075105130672455 +sweet-potato,EGY,-2.773144006729126 +sweet-potato,ERI,1.8053042888641357 sweet-potato,ESP,-7.5 -sweet-potato,EST,-2.8890633583068848 -sweet-potato,ETH,-3.4546852111816406 -sweet-potato,FIN,-2.9987776279449463 -sweet-potato,FJI,-5.528375148773193 -sweet-potato,FRA,-2.566088080406189 -sweet-potato,GAB,-3.8769218921661377 -sweet-potato,GBR,-2.951210379600525 -sweet-potato,GEO,-3.3808960914611816 -sweet-potato,GHA,-0.5988484025001526 -sweet-potato,GIN,-0.032059043645858765 -sweet-potato,GMB,2.161909580230713 -sweet-potato,GNB,-3.770545244216919 -sweet-potato,GNQ,-3.788648009300232 +sweet-potato,EST,-2.3912513256073 +sweet-potato,ETH,-3.2045083045959473 +sweet-potato,FIN,-2.4217031002044678 +sweet-potato,FJI,-5.368408918380737 +sweet-potato,FRA,-1.6360112428665161 +sweet-potato,GAB,-3.836724638938904 +sweet-potato,GBR,-2.353866219520569 +sweet-potato,GEO,-3.1331419944763184 +sweet-potato,GHA,-0.5540166199207306 +sweet-potato,GIN,0.13996688649058342 +sweet-potato,GMB,2.4159231185913086 +sweet-potato,GNB,-3.6199960708618164 +sweet-potato,GNQ,-3.694688558578491 sweet-potato,GRC,-7.5 sweet-potato,GRD,-7.5 -sweet-potato,GTM,-3.3679639101028442 -sweet-potato,GUF,-1.5889720916748047 +sweet-potato,GTM,-3.064687967300415 +sweet-potato,GUF,-1.4737539291381836 sweet-potato,GUY,3.75 -sweet-potato,HND,-2.0446035861968994 -sweet-potato,HRV,-5.602762579917908 -sweet-potato,HTI,-3.534436345100403 -sweet-potato,HUN,-3.06414258480072 -sweet-potato,IDN,-3.260046362876892 -sweet-potato,IND,-2.653688669204712 -sweet-potato,IRL,-2.5817734003067017 -sweet-potato,IRN,-2.643378496170044 -sweet-potato,IRQ,-2.63101863861084 +sweet-potato,HND,-1.9272927641868591 +sweet-potato,HRV,-5.546827673912048 +sweet-potato,HTI,-3.3784064054489136 +sweet-potato,HUN,-2.5335429906845093 +sweet-potato,IDN,-2.175451934337616 +sweet-potato,IND,-2.1696889400482178 +sweet-potato,IRL,-1.7951508164405823 +sweet-potato,IRN,-1.9163601398468018 +sweet-potato,IRQ,-1.9029540419578552 sweet-potato,ISR,-7.5 -sweet-potato,ITA,-3.1978600025177 +sweet-potato,ITA,-2.6832648515701294 sweet-potato,JAM,-7.5 -sweet-potato,JOR,-2.090037226676941 +sweet-potato,JOR,-1.1963051557540894 sweet-potato,JPN,-7.5 -sweet-potato,KAZ,-3.3957751989364624 -sweet-potato,KEN,-3.2448930740356445 -sweet-potato,KGZ,-3.3189765214920044 -sweet-potato,KHM,-3.4648683071136475 -sweet-potato,KOR,-2.8328808546066284 -sweet-potato,LAO,-3.016925811767578 -sweet-potato,LBN,-2.7440521717071533 -sweet-potato,LBR,-3.4996118545532227 -sweet-potato,LBY,-3.138014554977417 -sweet-potato,LKA,-2.138522505760193 -sweet-potato,LSO,-3.28141713142395 -sweet-potato,LTU,-3.369910717010498 +sweet-potato,KAZ,-3.153166174888611 +sweet-potato,KEN,-3.1092779636383057 +sweet-potato,KGZ,-3.04319167137146 +sweet-potato,KHM,-3.2483290433883667 +sweet-potato,KOR,-2.4257622957229614 +sweet-potato,LAO,-2.6437822580337524 +sweet-potato,LBN,-2.193209648132324 +sweet-potato,LBR,-3.313787579536438 +sweet-potato,LBY,-2.6807072162628174 +sweet-potato,LKA,-1.6644306778907776 +sweet-potato,LSO,-2.8778929710388184 +sweet-potato,LTU,-3.0298101902008057 sweet-potato,LUX,-7.5 -sweet-potato,LVA,-5.47293484210968 -sweet-potato,MAR,-3.0093458890914917 -sweet-potato,MDA,-3.6332337856292725 -sweet-potato,MDG,-3.7496888637542725 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-sweet-potato,NOR,-3.3438236713409424 -sweet-potato,NPL,-3.1954652070999146 +sweet-potato,MOZ,-1.8960859775543213 +sweet-potato,MRT,-3.503438949584961 +sweet-potato,MUS,-6.735344648361206 +sweet-potato,MWI,1.415738582611084 +sweet-potato,MYS,-5.253255128860474 +sweet-potato,NAM,-3.028991222381592 +sweet-potato,NER,-3.8455570936203003 +sweet-potato,NGA,-3.7472870349884033 +sweet-potato,NIC,-2.5928202867507935 +sweet-potato,NLD,-2.3725627660751343 +sweet-potato,NOR,-3.9320766925811768 +sweet-potato,NPL,-2.896385431289673 sweet-potato,NZL,-7.5 -sweet-potato,OMN,-5.8763813972473145 -sweet-potato,PAK,-1.8062944412231445 +sweet-potato,OMN,-5.6633195877075195 +sweet-potato,PAK,-1.3899307250976562 sweet-potato,PAN,7.5 -sweet-potato,PER,-2.5971872806549072 -sweet-potato,PHL,-1.7809141874313354 -sweet-potato,PNG,-3.5541218519210815 -sweet-potato,POL,-2.8525530099868774 -sweet-potato,PRI,-7.4526097774505615 +sweet-potato,PER,-2.498339891433716 +sweet-potato,PHL,-1.6412044763565063 +sweet-potato,PNG,-3.43398916721344 +sweet-potato,POL,-2.183580756187439 +sweet-potato,PRI,-7.336395977297798 sweet-potato,PRT,-7.5 -sweet-potato,PRY,-1.6539114713668823 -sweet-potato,PSE,-5.000513434410095 -sweet-potato,ROU,-3.288100242614746 -sweet-potato,RUS,-3.4596469402313232 -sweet-potato,RWA,-1.0884273052215576 -sweet-potato,SAU,-2.6474746465682983 -sweet-potato,SDN,-3.3355014324188232 +sweet-potato,PRY,-1.654632806777954 +sweet-potato,PSE,-4.66718864440918 +sweet-potato,ROU,-2.9413540363311768 +sweet-potato,RUS,-3.194880962371826 +sweet-potato,RWA,-0.9562024474143982 +sweet-potato,SAU,-2.049079656600952 +sweet-potato,SDN,-3.0596923828125 sweet-potato,SEN,-7.5 -sweet-potato,SLB,-2.9016273021698 -sweet-potato,SLE,-2.850681185722351 -sweet-potato,SLV,2.223991274833679 -sweet-potato,SOM,-3.4272947311401367 -sweet-potato,SRB,-5.358563661575317 +sweet-potato,SLB,-2.589857578277588 +sweet-potato,SLE,-2.680174469947815 +sweet-potato,SLV,2.3964531421661377 +sweet-potato,SOM,-3.236374020576477 +sweet-potato,SRB,-5.145063281059265 sweet-potato,SSD,3.75 sweet-potato,SUR,-7.5 -sweet-potato,SVK,-3.150352120399475 -sweet-potato,SVN,-2.866389036178589 -sweet-potato,SWE,-3.1616050004959106 -sweet-potato,SWZ,-3.8567726612091064 +sweet-potato,SVK,-2.695002317428589 +sweet-potato,SVN,-2.230338215827942 +sweet-potato,SWE,-2.6863261461257935 +sweet-potato,SWZ,-3.8024771213531494 sweet-potato,SYR,-7.5 -sweet-potato,TCD,-1.364348292350769 -sweet-potato,TGO,-0.8037306666374207 -sweet-potato,THA,-2.8495818376541138 -sweet-potato,TJK,-4.57469367980957 -sweet-potato,TKM,-3.2451012134552 -sweet-potato,TLS,-1.39150869846344 -sweet-potato,TTO,-4.86424708366394 -sweet-potato,TUN,-3.631168842315674 -sweet-potato,TUR,-2.540877342224121 -sweet-potato,TWN,-2.3723249435424805 -sweet-potato,TZA,-0.47835156321525574 -sweet-potato,UGA,-3.658787727355957 -sweet-potato,UKR,-3.5635502338409424 -sweet-potato,URY,-4.494444847106934 -sweet-potato,USA,-7.5 -sweet-potato,UZB,-6.900585889816284 -sweet-potato,VEN,-3.171257257461548 -sweet-potato,VNM,-3.6089500188827515 -sweet-potato,YEM,-2.908735990524292 -sweet-potato,ZAF,-0.16265007853507996 -sweet-potato,ZMB,-0.6554797887802124 -sweet-potato,ZWE,-1.5015268325805664 +sweet-potato,TCD,-1.2428795099258423 +sweet-potato,TGO,-0.7031485438346863 +sweet-potato,THA,-2.4338234663009644 +sweet-potato,TJK,-3.908847212791443 +sweet-potato,TKM,-2.9678845405578613 +sweet-potato,TLS,-1.335360050201416 +sweet-potato,TTO,-4.749549150466919 +sweet-potato,TUN,-3.35207462310791 +sweet-potato,TUR,-1.8488444089889526 +sweet-potato,TWN,-2.0574740767478943 +sweet-potato,TZA,-0.3789993226528168 +sweet-potato,UGA,-3.5547441244125366 +sweet-potato,UKR,-3.268665075302124 +sweet-potato,URY,-4.58931040763855 +sweet-potato,USA,-7.495031833648682 +sweet-potato,UZB,-6.071527004241943 +sweet-potato,VEN,-2.8997915983200073 +sweet-potato,VNM,-3.2728283405303955 +sweet-potato,YEM,-2.5011892318725586 +sweet-potato,ZAF,0.12290084548294544 +sweet-potato,ZMB,-0.5687954425811768 +sweet-potato,ZWE,-1.2651100158691406 tea,AGO,2.2871118783950806 tea,ALB,-7.5 tea,ARG,-0.0 tea,AZE,-0.17108497023582458 tea,BDI,2.0637975931167603 -tea,BGD,3.2354726791381836 +tea,BGD,3.1444193720817566 tea,BLZ,2.5659022331237793 tea,BOL,0.29397615045309067 tea,BRA,-0.0 -tea,BRN,2.2204816341400146 +tea,BRN,2.2388362884521484 tea,BTN,5.042233467102051 tea,CAF,7.5 tea,CHL,-0.0 tea,CHN,-7.5 -tea,CMR,5.953675746917725 +tea,CMR,6.104929447174072 tea,COD,-2.9547393321990967 tea,COG,7.5 tea,COL,-0.28785134851932526 @@ -5258,7 +5258,7 @@ tea,ETH,1.183477520942688 tea,GAB,1.9945328831672668 tea,GEO,-7.5 tea,GNQ,7.5 -tea,GRC,-2.475684881210327 +tea,GRC,-2.606701135635376 tea,GTM,-2.6566847562789917 tea,GUF,-0.0 tea,GUY,7.5 @@ -5266,7 +5266,7 @@ tea,HND,-1.622299611568451 tea,HRV,-0.0 tea,IDN,1.9280319809913635 tea,IND,4.118756294250488 -tea,IRN,3.3347302675247192 +tea,IRN,3.284339189529419 tea,JPN,-7.5 tea,KEN,-7.5 tea,KHM,-0.5314709097146988 @@ -5284,10 +5284,10 @@ tea,MYS,0.9520937204360962 tea,NGA,7.5 tea,NPL,2.3863542079925537 tea,PAN,7.5 -tea,PER,1.2528804540634155 +tea,PER,0.8766741454601288 tea,PNG,2.540162682533264 tea,PRY,0.09399520605802536 -tea,RUS,-4.867967128753662 +tea,RUS,-4.887292861938477 tea,RWA,2.2659374475479126 tea,SLV,-1.2915285229682922 tea,SSD,-7.5 @@ -5297,7 +5297,7 @@ tea,THA,-0.8766462504863739 tea,TLS,3.2436299920082092 tea,TUR,-4.172898769378662 tea,TWN,0.12510744109749794 -tea,TZA,1.5808573961257935 +tea,TZA,1.3460158109664917 tea,UGA,5.701246738433838 tea,URY,2.479839324951172 tea,VEN,7.5 @@ -5308,35 +5308,35 @@ tea,ZWE,1.0640369653701782 tomato,AFG,-7.5 tomato,AGO,-7.5 tomato,ALB,-7.5 -tomato,ARE,-6.009433269500732 +tomato,ARE,-5.928670883178711 tomato,ARG,-7.5 -tomato,ARM,-2.2367351055145264 +tomato,ARM,-2.2727038860321045 tomato,ATG,-0.0 tomato,AUS,-7.5 tomato,AUT,-3.75 tomato,AZE,-7.5 tomato,BDI,-7.5 tomato,BEL,-3.75 -tomato,BEN,-1.9216867089271545 +tomato,BEN,-1.909310758113861 tomato,BFA,-7.5 -tomato,BGD,-0.35448526591062546 +tomato,BGD,-0.375543974339962 tomato,BGR,-7.5 -tomato,BHS,-2.787252426147461 -tomato,BIH,-5.555819511413574 +tomato,BHS,-2.9444785118103027 +tomato,BIH,-5.532538414001465 tomato,BLR,-7.5 -tomato,BLZ,-7.218199253082275 -tomato,BOL,-0.6573793888092041 +tomato,BLZ,-7.246235370635986 +tomato,BOL,-0.6255947947502136 tomato,BRA,-7.5 tomato,BRB,-7.5 tomato,BRN,-7.5 -tomato,BTN,-1.0860890746116638 +tomato,BTN,-1.078811228275299 tomato,BWA,-7.5 tomato,CAF,7.5 -tomato,CAN,-3.670215606689453 +tomato,CAN,-2.6451125144958496 tomato,CHE,-7.5 tomato,CHL,-7.5 tomato,CHN,-7.5 -tomato,CIV,-3.850853681564331 +tomato,CIV,-3.8386088609695435 tomato,CMR,-7.5 tomato,COD,-7.5 tomato,COG,-7.5 @@ -5353,18 +5353,18 @@ tomato,DNK,7.5 tomato,DOM,-7.5 tomato,DZA,-7.5 tomato,ECU,-7.5 -tomato,EGY,-1.5385516285896301 +tomato,EGY,-1.505592942237854 tomato,ERI,-3.75 -tomato,ESP,-0.8938909769058228 +tomato,ESP,-0.14651678502559662 tomato,EST,-0.0 -tomato,ETH,-1.2529821991920471 +tomato,ETH,-1.2477747201919556 tomato,FIN,-7.5 -tomato,FJI,-7.382971286773682 +tomato,FJI,-7.372045278549194 tomato,FRA,-7.5 tomato,GAB,-7.5 tomato,GBR,-7.5 -tomato,GEO,-1.955545723438263 -tomato,GHA,-4.864142894744873 +tomato,GEO,-1.9398113489151 +tomato,GHA,-5.005348443984985 tomato,GIN,-3.75 tomato,GMB,-7.5 tomato,GNB,7.5 @@ -5378,56 +5378,56 @@ tomato,HND,-7.5 tomato,HRV,-7.5 tomato,HTI,-7.5 tomato,HUN,-7.5 -tomato,IDN,-6.240472793579102 +tomato,IDN,-6.229624271392822 tomato,IND,-7.5 -tomato,IRN,-0.2685989439487457 -tomato,IRQ,-1.428577184677124 +tomato,IRN,-0.23197424411773682 +tomato,IRQ,-1.396009385585785 tomato,ISR,-7.5 tomato,ITA,-7.5 tomato,JAM,-7.5 -tomato,JOR,4.613020300865173 +tomato,JOR,4.650860071182251 tomato,JPN,-7.5 tomato,KAZ,-7.5 -tomato,KEN,-3.9817135334014893 -tomato,KGZ,-7.208740711212158 +tomato,KEN,-4.047902345657349 +tomato,KGZ,-7.197674751281738 tomato,KHM,-3.75 -tomato,KOR,-6.035221338272095 +tomato,KOR,-5.992538928985596 tomato,LAO,-7.5 tomato,LBN,-7.5 tomato,LBR,-7.5 tomato,LBY,-7.5 -tomato,LKA,-3.2934858798980713 -tomato,LSO,-5.526358127593994 +tomato,LKA,-3.27603542804718 +tomato,LSO,-5.444462299346924 tomato,LTU,-7.5 tomato,LUX,-3.75 -tomato,LVA,-7.378035068511963 -tomato,MAR,-2.9082216024398804 -tomato,MDA,1.723491132259369 +tomato,LVA,-7.421118259429932 +tomato,MAR,-2.8563719987869263 +tomato,MDA,1.740979015827179 tomato,MDG,-7.5 tomato,MEX,-7.5 -tomato,MKD,-5.520841717720032 -tomato,MLI,-7.157349109649658 +tomato,MKD,-5.486315965652466 +tomato,MLI,-7.155513763427734 tomato,MLT,-7.5 tomato,MMR,-7.5 tomato,MNE,-7.5 tomato,MNG,-0.0 tomato,MOZ,-7.5 tomato,MUS,-7.5 -tomato,MWI,-5.070799350738525 +tomato,MWI,-5.615180730819702 tomato,MYS,-3.75 tomato,NAM,-7.5 -tomato,NER,-0.6928260922431946 +tomato,NER,-0.6562777161598206 tomato,NGA,-7.5 tomato,NIC,-7.5 tomato,NLD,-7.5 tomato,NOR,-0.0 tomato,NPL,-7.5 -tomato,NZL,-4.867916703224182 +tomato,NZL,-4.893571257591248 tomato,OMN,-7.5 -tomato,PAK,-1.0334705114364624 +tomato,PAK,-1.0231562852859497 tomato,PAN,-7.5 tomato,PER,-0.0 -tomato,PHL,-3.116124391555786 +tomato,PHL,-3.101489782333374 tomato,PNG,-7.5 tomato,POL,-7.5 tomato,PRI,-7.5 @@ -5436,14 +5436,14 @@ tomato,PRY,-7.5 tomato,PSE,-7.5 tomato,ROU,-7.5 tomato,RUS,-7.5 -tomato,RWA,-3.1285563707351685 +tomato,RWA,-3.1195030212402344 tomato,SAU,-7.5 tomato,SDN,-7.5 -tomato,SEN,-4.383784651756287 +tomato,SEN,-4.37800657749176 tomato,SLE,-7.5 -tomato,SLV,-7.094966650009155 +tomato,SLV,-7.166978120803833 tomato,SOM,-7.5 -tomato,SRB,-2.240092620253563 +tomato,SRB,-2.302497923374176 tomato,SSD,-7.5 tomato,SUR,-7.5 tomato,SVK,-7.5 @@ -5452,632 +5452,632 @@ tomato,SWE,-7.5 tomato,SWZ,-7.5 tomato,SYR,-7.5 tomato,TCD,-7.5 -tomato,TGO,-1.3027095794677734 -tomato,THA,-4.745960712432861 +tomato,TGO,-1.2967983484268188 +tomato,THA,-4.726319313049316 tomato,TJK,-7.5 tomato,TKM,-7.5 -tomato,TLS,-2.0356668829917908 +tomato,TLS,-2.026857614517212 tomato,TTO,-7.5 -tomato,TUN,4.2335450649261475 -tomato,TUR,-5.40532922744751 +tomato,TUN,4.164569020271301 +tomato,TUR,-5.318279266357422 tomato,TWN,-7.5 -tomato,TZA,-0.7187851667404175 +tomato,TZA,-0.7131596803665161 tomato,UGA,-7.5 -tomato,UKR,7.2868757247924805 +tomato,UKR,7.284955024719238 tomato,URY,-7.5 tomato,USA,-7.5 tomato,UZB,-7.5 tomato,VEN,-7.5 tomato,VNM,-7.5 -tomato,YEM,-5.8541765213012695 +tomato,YEM,-5.834274053573608 tomato,ZAF,-7.5 -tomato,ZMB,-2.366493344306946 -tomato,ZWE,-3.962544798851013 -watermelon,AFG,-2.5404698848724365 +tomato,ZMB,-2.3540090322494507 +tomato,ZWE,-3.9345903396606445 +watermelon,AFG,-2.574960231781006 watermelon,AGO,-0.0 -watermelon,ALB,2.1228625774383545 -watermelon,ARG,3.792503595352173 -watermelon,ATG,-4.487538814544678 +watermelon,ALB,2.075734853744507 +watermelon,ARG,3.5657224655151367 +watermelon,ATG,-4.523897409439087 watermelon,AUS,-7.5 -watermelon,AZE,5.405060291290283 +watermelon,AZE,5.432509660720825 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,-0.525654137134552 -watermelon,BLZ,-2.3436901569366455 -watermelon,BOL,0.8949553370475769 -watermelon,BRA,3.7893426418304443 +watermelon,BIH,-0.4913305640220642 +watermelon,BLZ,-2.3443217277526855 +watermelon,BOL,0.8736466765403748 +watermelon,BRA,3.7866098880767822 watermelon,BRB,-0.0 watermelon,BTN,7.5 -watermelon,BWA,0.5943982824683189 +watermelon,BWA,0.6326888371258974 watermelon,CAF,-0.0 -watermelon,CHL,0.33044493198394775 +watermelon,CHL,0.331685870885849 watermelon,CHN,-7.3488969802856445 watermelon,CIV,-0.0 -watermelon,CMR,-4.6977269649505615 +watermelon,CMR,-4.8426032066345215 watermelon,COD,-0.0 watermelon,COG,-0.0 -watermelon,COL,2.342010259628296 +watermelon,COL,2.3393189907073975 watermelon,COM,-0.0 watermelon,CPV,-7.5 -watermelon,CRI,0.7461357712745667 -watermelon,CUB,-2.294926643371582 -watermelon,CYP,-4.909425735473633 +watermelon,CRI,0.6470394730567932 +watermelon,CUB,-2.296638011932373 +watermelon,CYP,-3.8598817586898804 watermelon,DOM,-7.5 -watermelon,DZA,0.5467897057533264 -watermelon,ECU,-2.7963982820510864 -watermelon,EGY,-1.806895136833191 +watermelon,DZA,0.5141026973724365 +watermelon,ECU,-2.8360843658447266 +watermelon,EGY,-1.7246053814888 watermelon,ERI,-0.0 watermelon,ESP,-7.5 watermelon,ETH,-0.0 watermelon,FJI,-7.5 -watermelon,FRA,0.317782998085022 +watermelon,FRA,0.3750508725643158 watermelon,GAB,-0.0 -watermelon,GEO,-0.6953272223472595 +watermelon,GEO,-0.6862759292125702 watermelon,GHA,-0.0 -watermelon,GIN,-5.165235280990601 +watermelon,GIN,-5.155868291854858 watermelon,GMB,-0.0 watermelon,GNB,-0.0 -watermelon,GRC,2.3914804458618164 -watermelon,GTM,0.8414393663406372 +watermelon,GRC,2.501931667327881 +watermelon,GTM,0.8385482430458069 watermelon,GUF,-0.0 watermelon,GUY,-7.5 watermelon,HND,-7.5 -watermelon,HRV,-2.692413091659546 +watermelon,HRV,-2.668400287628174 watermelon,HTI,-0.0 watermelon,IDN,1.9798773527145386 -watermelon,IND,-2.0248392820358276 -watermelon,IRN,4.4493584632873535 -watermelon,IRQ,-1.0727357864379883 -watermelon,ISR,-0.4883614256978035 -watermelon,ITA,2.2241111993789673 +watermelon,IND,-2.1139591932296753 +watermelon,IRN,4.505133390426636 +watermelon,IRQ,-1.04517662525177 +watermelon,ISR,-0.3505546972155571 +watermelon,ITA,2.2972904443740845 watermelon,JAM,-7.5 -watermelon,JOR,-3.694989562034607 +watermelon,JOR,-3.621855854988098 watermelon,JPN,-7.5 -watermelon,KEN,3.4178760051727295 +watermelon,KEN,3.356675148010254 watermelon,KGZ,-3.4708921909332275 watermelon,KHM,-0.0 -watermelon,LAO,-1.5314116477966309 +watermelon,LAO,-1.4717159867286682 watermelon,LBN,-7.5 watermelon,LBR,-0.0 -watermelon,LBY,3.541520118713379 +watermelon,LBY,3.623983383178711 watermelon,LKA,-0.0 watermelon,LSO,-0.0 -watermelon,MAR,-0.6773760914802551 +watermelon,MAR,-0.5849184393882751 watermelon,MDG,-0.0 -watermelon,MEX,0.23679153621196747 -watermelon,MKD,4.596242904663086 -watermelon,MLI,2.5631765127182007 -watermelon,MLT,-1.6350895166397095 +watermelon,MEX,0.237123042345047 +watermelon,MKD,4.657097578048706 +watermelon,MLI,2.564253091812134 +watermelon,MLT,-1.2255296111106873 watermelon,MMR,-0.0 -watermelon,MNE,0.00744473934173584 +watermelon,MNE,0.03648453950881958 watermelon,MOZ,-0.0 -watermelon,MRT,-3.5862910747528076 +watermelon,MRT,-3.585927963256836 watermelon,MUS,-0.0 -watermelon,MWI,0.1385079026222229 -watermelon,MYS,3.1642797142267227 -watermelon,NAM,-1.4109147191047668 +watermelon,MWI,-0.16907623410224915 +watermelon,MYS,3.0852835327386856 +watermelon,NAM,-1.383013367652893 watermelon,NER,-7.5 watermelon,NGA,-0.0 watermelon,NIC,-0.0 watermelon,NPL,-0.0 watermelon,OMN,7.5 -watermelon,PAK,0.30105262994766235 -watermelon,PER,7.159733295440674 +watermelon,PAK,0.1287597194314003 +watermelon,PER,7.159071445465088 watermelon,PHL,0.6292181499302387 watermelon,PNG,-0.0 -watermelon,PRI,-4.507873296737671 -watermelon,PRT,-5.646059513092041 -watermelon,PRY,3.3263490200042725 +watermelon,PRI,-4.510475158691406 +watermelon,PRT,-5.601900100708008 +watermelon,PRY,3.3017184734344482 watermelon,PSE,-7.5 -watermelon,RUS,-1.1100904941558838 +watermelon,RUS,-1.1990001201629639 watermelon,RWA,-0.0 -watermelon,SAU,1.7145582288503647 -watermelon,SDN,-0.9286142587661743 -watermelon,SEN,2.716188430786133 +watermelon,SAU,1.8481697738170624 +watermelon,SDN,-0.8980551958084106 +watermelon,SEN,2.2787766456604004 watermelon,SLE,-0.0 -watermelon,SLV,4.322170555591583 -watermelon,SOM,-2.506080746650696 +watermelon,SLV,4.3804771900177 +watermelon,SOM,-2.451156735420227 watermelon,SSD,-0.0 -watermelon,SUR,-5.571103096008301 -watermelon,SVN,-1.8389701843261719 +watermelon,SUR,-5.573114395141602 +watermelon,SVN,-1.8186774253845215 watermelon,SWZ,-0.0 -watermelon,SYR,1.2022953033447266 +watermelon,SYR,1.2373881340026855 watermelon,TCD,-0.0 watermelon,TGO,-0.0 -watermelon,THA,-5.306277513504028 -watermelon,TJK,-3.12197744846344 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-watermelon,ZWE,3.1718236207962036 -wetland-rice,AFG,-0.8275950849056244 -wetland-rice,AGO,-1.4929708242416382 -wetland-rice,ALB,3.465828597545624 -wetland-rice,ARG,-0.9034924507141113 -wetland-rice,ARM,-0.0 -wetland-rice,AUS,-0.9897509813308716 -wetland-rice,AZE,-2.6064926385879517 -wetland-rice,BDI,-0.2853267639875412 -wetland-rice,BEN,-1.0398336946964264 -wetland-rice,BFA,-0.9958655834197998 -wetland-rice,BGD,-0.15939340740442276 -wetland-rice,BGR,-0.9581283330917358 -wetland-rice,BLZ,-1.4124342799186707 -wetland-rice,BOL,-0.42352741956710815 -wetland-rice,BRA,-0.6442113220691681 -wetland-rice,BRN,-1.337172031402588 -wetland-rice,BTN,-0.7956008911132812 +watermelon,ZWE,3.049709439277649 +wetland-rice,AFG,-0.5307767689228058 +wetland-rice,AGO,-1.419264316558838 +wetland-rice,ALB,3.822022683918476 +wetland-rice,ARG,-0.5103040337562561 +wetland-rice,ARM,0.40610799193382263 +wetland-rice,AUS,-0.3991473317146301 +wetland-rice,AZE,-2.390504240989685 +wetland-rice,BDI,-0.11139058321714401 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-white-potato,AFG,-6.68327260017395 -white-potato,AGO,-6.939966201782227 -white-potato,ALB,-7.5 +wheat,TJK,-0.2072129249572754 +wheat,TKM,-0.4069984257221222 +wheat,TUN,-0.2059165984392166 +wheat,TUR,0.19364532083272934 +wheat,TWN,-0.3833581507205963 +wheat,TZA,-0.17928141355514526 +wheat,UGA,0.03254390321671963 +wheat,UKR,-0.012617677450180054 +wheat,URY,0.08313551917672157 +wheat,USA,0.17730778455734253 +wheat,UZB,-0.5695970952510834 +wheat,VEN,0.04561551660299301 +wheat,VNM,-0.2147604376077652 +wheat,YEM,-0.34837374091148376 +wheat,ZAF,-0.39297154545783997 +wheat,ZMB,0.5700001418590546 +wheat,ZWE,4.354022741317749 +white-potato,AFG,-6.393143653869629 +white-potato,AGO,-6.841919422149658 +white-potato,ALB,-7.264606952667236 white-potato,ARE,-7.5 white-potato,ARG,-7.5 -white-potato,ARM,-4.12828803062439 +white-potato,ARM,-3.767865300178528 white-potato,ATG,-7.5 white-potato,AUS,-7.5 -white-potato,AUT,-5.894105911254883 -white-potato,AZE,-4.3284759521484375 -white-potato,BDI,-2.677011489868164 -white-potato,BEL,-4.930318832397461 +white-potato,AUT,-5.482745409011841 +white-potato,AZE,-4.065100908279419 +white-potato,BDI,-2.5292998552322388 +white-potato,BEL,-4.0044883489608765 white-potato,BEN,-0.0 -white-potato,BFA,-7.0709228515625 -white-potato,BGD,-2.870118260383606 -white-potato,BGR,-3.086044430732727 -white-potato,BHS,-7.307168483734131 -white-potato,BIH,-3.035238742828369 -white-potato,BLR,-3.1253622174263 -white-potato,BLZ,-7.401734828948975 -white-potato,BOL,-1.4882731437683105 +white-potato,BFA,-7.010142803192139 +white-potato,BGD,-2.4241175651550293 +white-potato,BGR,-2.686634659767151 +white-potato,BHS,-7.29744553565979 +white-potato,BIH,-2.897777795791626 +white-potato,BLR,-2.54943311214447 +white-potato,BLZ,-7.497157335281372 +white-potato,BOL,-1.4480978846549988 white-potato,BRA,-7.5 -white-potato,BRB,-6.377061367034912 +white-potato,BRB,-6.219797849655151 white-potato,BRN,-7.5 -white-potato,BTN,-4.19608736038208 +white-potato,BTN,-4.044549942016602 white-potato,BWA,-7.5 -white-potato,CAF,-7.197818040847778 -white-potato,CAN,-6.646045207977295 +white-potato,CAF,-7.186646223068237 +white-potato,CAN,-6.197249412536621 white-potato,CHE,-7.5 -white-potato,CHL,-7.5 -white-potato,CHN,-4.7370240688323975 -white-potato,CIV,-7.1886491775512695 -white-potato,CMR,-6.860898733139038 -white-potato,COD,-7.077121019363403 -white-potato,COG,-6.934155464172363 -white-potato,COL,-5.034844160079956 -white-potato,COM,-7.312669992446899 +white-potato,CHL,-7.245083332061768 +white-potato,CHN,-3.8146637678146362 +white-potato,CIV,-7.168732404708862 +white-potato,CMR,-6.78924560546875 +white-potato,COD,-7.022324562072754 +white-potato,COG,-6.830710411071777 +white-potato,COL,-4.8368024826049805 +white-potato,COM,-7.292238235473633 white-potato,CPV,-7.5 white-potato,CRI,-7.5 -white-potato,CUB,-6.494585752487183 -white-potato,CYP,-7.5 -white-potato,CZE,-4.996541500091553 -white-potato,DEU,-6.530590772628784 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white-potato,ITA,-7.5 white-potato,JAM,-7.5 white-potato,JOR,-7.5 white-potato,JPN,-7.5 -white-potato,KAZ,-3.023425579071045 -white-potato,KEN,-2.24357533454895 -white-potato,KGZ,-3.4183019399642944 -white-potato,KHM,-6.998397588729858 -white-potato,KOR,-6.683784008026123 -white-potato,LAO,-6.496181488037109 +white-potato,KAZ,-2.620534658432007 +white-potato,KEN,-2.1562671661376953 +white-potato,KGZ,-3.195727825164795 +white-potato,KHM,-6.840086936950684 +white-potato,KOR,-6.094832181930542 +white-potato,LAO,-6.083521842956543 white-potato,LBN,-7.5 -white-potato,LBR,-6.754613161087036 -white-potato,LBY,-6.370383262634277 +white-potato,LBR,-6.561459064483643 +white-potato,LBY,-5.946003437042236 white-potato,LKA,-7.5 -white-potato,LSO,-6.013373374938965 -white-potato,LTU,-2.858950614929199 -white-potato,LUX,-6.961222887039185 -white-potato,LVA,-2.1272966861724854 -white-potato,MAR,-5.30958104133606 -white-potato,MDA,-0.8479516506195068 -white-potato,MDG,-6.979810476303101 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-white-potato,NGA,-7.132198333740234 +white-potato,NGA,-7.096549987792969 white-potato,NIC,-7.5 -white-potato,NLD,-5.6572630405426025 +white-potato,NLD,-5.109620809555054 white-potato,NOR,-7.5 -white-potato,NPL,-3.3017061948776245 -white-potato,NZL,-5.133574724197388 +white-potato,NPL,-2.9867249727249146 +white-potato,NZL,-3.8746185302734375 white-potato,OMN,-7.5 -white-potato,PAK,-3.2088358402252197 +white-potato,PAK,-2.7640929222106934 white-potato,PAN,-7.5 -white-potato,PER,-3.809300184249878 +white-potato,PER,-3.6559057235717773 white-potato,PHL,-7.5 white-potato,PNG,-7.5 -white-potato,POL,-2.197211742401123 +white-potato,POL,-1.6355042457580566 white-potato,PRI,-7.5 -white-potato,PRT,-5.459334850311279 -white-potato,PRY,-3.581894636154175 +white-potato,PRT,-4.921069145202637 +white-potato,PRY,-3.407722234725952 white-potato,PSE,-7.5 -white-potato,ROU,-4.966475009918213 -white-potato,RUS,-2.517961263656616 -white-potato,RWA,-2.0021292567253113 +white-potato,ROU,-4.7422215938568115 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-white-potato,TJK,-3.2776272296905518 -white-potato,TKM,-6.899640083312988 -white-potato,TLS,-0.6534139811992645 -white-potato,TTO,-7.120465993881226 -white-potato,TUN,-3.238673686981201 -white-potato,TUR,-6.2570483684539795 -white-potato,TWN,-6.581104040145874 -white-potato,TZA,-0.9602226614952087 -white-potato,UGA,-7.065031051635742 -white-potato,UKR,-1.8508853912353516 +white-potato,SWZ,-7.140873432159424 +white-potato,SYR,-5.990346908569336 +white-potato,TCD,-6.878490924835205 +white-potato,TGO,-7.087501049041748 +white-potato,THA,-4.388147354125977 +white-potato,TJK,-3.0217273235321045 +white-potato,TKM,-6.739927768707275 +white-potato,TLS,-0.6238982975482941 +white-potato,TTO,-7.086574077606201 +white-potato,TUN,-2.897592782974243 +white-potato,TUR,-5.6321704387664795 +white-potato,TWN,-6.001934766769409 +white-potato,TZA,-0.8605552315711975 +white-potato,UGA,-7.007919073104858 +white-potato,UKR,-1.6265573501586914 white-potato,URY,-7.5 -white-potato,USA,-6.48323917388916 -white-potato,UZB,-5.730321407318115 -white-potato,VEN,-6.108365058898926 -white-potato,VNM,-6.79145359992981 +white-potato,USA,-5.864063739776611 +white-potato,UZB,-5.398216247558594 +white-potato,VEN,-5.93286395072937 +white-potato,VNM,-6.441448926925659 white-potato,YEM,-7.5 -white-potato,ZAF,-7.122694730758667 +white-potato,ZAF,-6.789102554321289 white-potato,ZMB,-7.5 white-potato,ZWE,-7.5 yam,AFG,7.5 -yam,AGO,-2.915623903274536 +yam,AGO,-2.891434907913208 yam,ARG,-0.970349907875061 yam,ATG,-4.489343166351318 -yam,BDI,-4.474895000457764 -yam,BEN,-1.1533899307250977 -yam,BFA,-2.6838340759277344 -yam,BIH,-2.2863107323646545 -yam,BLZ,-1.9457488059997559 +yam,BDI,-4.46341609954834 +yam,BEN,-1.1236973404884338 +yam,BFA,-2.659050464630127 +yam,BIH,-2.257602244615555 +yam,BLZ,-1.4796286821365356 yam,BOL,-5.195361614227295 yam,BRA,-3.122835397720337 yam,BRB,-7.5 -yam,BRN,-5.200504541397095 -yam,BTN,-4.776039123535156 -yam,BWA,-4.242413759231567 -yam,CAF,-3.9860188961029053 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-yam,GNB,-4.185503721237183 -yam,GNQ,-4.477081298828125 +yam,GNB,-4.171942234039307 +yam,GNQ,-4.465688228607178 yam,GRD,-7.5 yam,GTM,7.5 yam,GUF,7.5 @@ -6085,69 +6085,69 @@ yam,GUY,-7.5 yam,HND,-4.2645134925842285 yam,HRV,-0.0 yam,HTI,-5.360596656799316 -yam,IDN,-4.645933389663696 -yam,IND,-0.9961451292037964 +yam,IDN,-4.6345484256744385 +yam,IND,-0.9660604596138 yam,IRN,-0.0 -yam,ISR,-0.980597972869873 +yam,ISR,-0.9411650002002716 yam,JAM,-7.5 -yam,JOR,4.4035040736198425 +yam,JOR,4.40989077091217 yam,JPN,-7.5 -yam,KEN,-1.539107859134674 -yam,KHM,-2.4898996353149414 -yam,LAO,2.042736828327179 -yam,LBN,-4.69429886341095 -yam,LBR,-3.78039288520813 -yam,LKA,-3.049189805984497 +yam,KEN,-1.518750011920929 +yam,KHM,-2.45957350730896 +yam,LAO,2.1110036373138428 +yam,LBN,-4.678233802318573 +yam,LBR,-3.763749837875366 +yam,LKA,-3.0244040489196777 yam,LSO,-0.0 -yam,MDG,-4.047037363052368 -yam,MEX,3.244262456893921 -yam,MLI,-5.216529846191406 -yam,MMR,1.0198920369148254 +yam,MDG,-4.0314555168151855 +yam,MEX,3.215477466583252 +yam,MLI,-5.178415060043335 +yam,MMR,1.078879714012146 yam,MNE,-0.0 -yam,MOZ,-3.8747650384902954 -yam,MRT,-6.381232023239136 -yam,MUS,-3.481334090232849 +yam,MOZ,-3.857971429824829 +yam,MRT,-6.378927946090698 +yam,MUS,-3.4608681201934814 yam,MWI,-0.0 -yam,MYS,-2.689858913421631 -yam,NAM,-3.0043797492980957 -yam,NER,-5.320603370666504 -yam,NGA,-2.8136426210403442 +yam,MYS,-2.704085350036621 +yam,NAM,-2.9809781312942505 +yam,NER,-5.315751314163208 +yam,NGA,-2.7895864248275757 yam,NIC,-2.3203004598617554 -yam,NPL,-5.011655330657959 -yam,PAK,1.2802709341049194 +yam,NPL,-5.003742456436157 +yam,PAK,1.0892291069030762 yam,PAN,-3.215519905090332 -yam,PER,-3.4855637550354004 -yam,PHL,-1.6139899492263794 -yam,PNG,-2.6115013360977173 +yam,PER,-3.7270339727401733 +yam,PHL,-1.6030202507972717 +yam,PNG,-2.5849483013153076 yam,PRI,-7.5 yam,PRY,7.5 -yam,PSE,-1.4987291097640991 -yam,RWA,-0.8284357190132141 +yam,PSE,-1.4634703397750854 +yam,RWA,-0.815086305141449 yam,SAU,-0.0 -yam,SDN,-5.742918968200684 -yam,SEN,-2.142036259174347 -yam,SLB,-1.774495780467987 -yam,SLE,-5.374751806259155 +yam,SDN,-5.74127721786499 +yam,SEN,-2.1293981075286865 +yam,SLB,-1.7412835955619812 +yam,SLE,-5.370323419570923 yam,SLV,2.7297595739364624 yam,SOM,-0.0 -yam,SSD,-5.1632537841796875 -yam,STP,-4.699181079864502 +yam,SSD,-5.157663822174072 +yam,STP,-4.689535856246948 yam,SUR,-2.388601541519165 -yam,SWZ,-3.962120532989502 -yam,SYR,-1.978471279144287 -yam,TCD,-4.024775505065918 -yam,TGO,-1.095928430557251 -yam,THA,-1.7797744274139404 -yam,TLS,-5.459644079208374 +yam,SWZ,-3.945995330810547 +yam,SYR,-1.946508526802063 +yam,TCD,-4.010426998138428 +yam,TGO,-1.077474057674408 +yam,THA,-1.770674705505371 +yam,TLS,-5.455284118652344 yam,TTO,-7.5 -yam,TWN,-2.4045424461364746 -yam,TZA,-4.558453798294067 -yam,UGA,-3.893244743347168 +yam,TWN,-2.3733160495758057 +yam,TZA,-4.54769492149353 +yam,UGA,-3.8769387006759644 yam,URY,7.5 yam,USA,5.4903130531311035 yam,VEN,-2.3649706840515137 -yam,VNM,-3.535680413246155 -yam,VUT,-3.088310480117798 -yam,ZAF,-4.361093759536743 +yam,VNM,-3.632774829864502 +yam,VUT,-3.0644670724868774 +yam,ZAF,-4.348750352859497 yam,ZMB,3.75 -yam,ZWE,-3.2789857387542725 +yam,ZWE,-3.2570632696151733 diff --git a/data/curated/calibration/default/deviation_penalty.yaml b/data/curated/calibration/default/deviation_penalty.yaml index 3331dd52..aa86ee9f 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: 1.468666316419478 - grassland: 0.16337061661222912 - feed: 0.07852586344897906 -iterations: 3 -converged: true -final_residual_log_inf: 0.01697416334606783 + cropland: 1.6000131138497027 + grassland: 0.15162800698265483 + feed: 0.07350089094630664 +iterations: 8 +converged: false +final_residual_log_inf: 0.02362323567556563 diff --git a/data/curated/calibration/default/exogenous_feed.csv b/data/curated/calibration/default/exogenous_feed.csv index 539ab70d..a71d0a94 100644 --- a/data/curated/calibration/default/exogenous_feed.csv +++ b/data/curated/calibration/default/exogenous_feed.csv @@ -19,7 +19,7 @@ BTN,0.0,0.0,0.032702 BWA,0.0,0.0,0.803633 CAF,0.0,0.0,3.145721 CHL,0.0,0.0,1.174709 -CHN,22.060617,8.937976,0.0 +CHN,26.864496,8.937976,0.0 CMR,0.0,0.0,1.961351 COG,0.0,0.0,0.10183 COL,0.0,0.0,4.952398 @@ -47,13 +47,14 @@ ISL,0.0,0.0,0.030581 ISR,0.0,0.0,1.114113 JAM,0.0,0.0,0.181489 JOR,0.0,0.0,0.90952 -JPN,1.910623,1.221458,1.323049 +JPN,2.136678,1.221458,1.323049 KEN,0.0,0.0,8.553256 -KHM,0.0,0.135121,0.0 -KOR,2.714647,0.374992,2.658246 -LAO,0.0,0.053306,0.0 +KHM,0.0,0.062052,0.0 +KOR,2.700713,0.374992,2.658246 +LAO,0.0,0.045429,0.0 LBN,0.0,0.0,0.255602 LBY,0.0,0.0,0.793355 +LKA,0.0,0.031254,0.0 LSO,0.0,0.0,0.093215 MAR,0.0,0.0,5.567759 MEX,0.0,0.0,2.591533 @@ -67,11 +68,12 @@ NER,0.0,0.0,1.711398 NIC,0.0,0.0,1.291705 NLD,0.0,0.0,0.117066 NPL,0.0,0.0,15.855976 -NZL,0.0,0.72566,0.0 +NZL,0.0,0.844478,0.0 OMN,0.0,0.0,1.601173 -PAK,0.0,13.613902,166.893402 +PAK,0.0,11.805126,166.893402 PAN,0.0,0.0,0.335515 PER,0.0,0.0,0.827014 +PHL,0.443097,0.297524,0.0 PRI,0.0,0.0,0.093587 PRY,0.0,0.0,0.155431 PSE,0.0,0.0,0.253434 @@ -85,18 +87,19 @@ STP,0.0,0.0,0.00225 SWZ,0.0,0.0,0.169067 SYR,0.0,0.0,1.271963 TCD,0.0,0.0,25.86199 -THA,1.626361,0.336465,0.0 +THA,1.408307,0.336465,0.0 TJK,0.0,0.0,0.247151 TKM,0.0,0.0,0.695718 -TLS,0.009463,0.01037,0.067521 +TLS,0.009341,0.01037,0.067521 TTO,0.0,0.0,0.00687 TUN,0.0,0.0,1.242337 TUR,0.0,0.0,1.66396 -TWN,1.330431,0.0,0.0 +TWN,1.303451,0.00074,0.0 TZA,0.0,0.0,9.637837 UGA,0.0,0.0,3.557199 URY,0.0,0.0,2.438584 VEN,0.0,0.0,3.763237 +VNM,2.271382,0.832676,0.0 YEM,0.0,0.0,3.183531 ZAF,0.0,0.0,5.481237 ZMB,0.0,0.0,2.941371 diff --git a/data/curated/calibration/default/exogenous_forage.csv b/data/curated/calibration/default/exogenous_forage.csv index 785b5cbe..5d8a41de 100644 --- a/data/curated/calibration/default/exogenous_forage.csv +++ b/data/curated/calibration/default/exogenous_forage.csv @@ -25,9 +25,9 @@ BLZ,0.0 BOL,0.0 BRA,0.0 BRB,0.0 -BRN,0.0 +BRN,0.07307 BTN,0.0 -BWA,0.0 +BWA,1.075567 CAF,0.0 CAN,0.0 CHE,0.0 @@ -42,7 +42,7 @@ COM,0.083786 CPV,0.0 CRI,0.0 CUB,0.0 -CYP,0.0 +CYP,0.279522 CZE,0.0 DEU,0.0 DJI,0.0 @@ -50,7 +50,7 @@ DNK,0.0 DOM,0.0 DZA,0.0 ECU,0.0 -EGY,23.06752 +EGY,23.173895 ERI,0.0 ESP,0.0 EST,0.0 @@ -76,7 +76,7 @@ HRV,0.0 HTI,0.0 HUN,0.0 IDN,0.0 -IND,158.148087 +IND,169.214523 IRL,0.0 IRN,0.0 IRQ,0.0 @@ -137,7 +137,7 @@ PSE,0.0 ROU,0.0 RUS,0.0 RWA,0.0 -SAU,0.0 +SAU,0.981909 SDN,0.0 SEN,0.0 SLB,0.0 @@ -156,21 +156,21 @@ SYR,0.0 TCD,0.0 TGO,0.0 THA,0.0 -TJK,3.370195 -TKM,9.84958 +TJK,8.252677 +TKM,9.854296 TLS,0.0 TTO,0.0 TUN,0.0 -TUR,5.091218 +TUR,17.094727 TWN,0.0 TZA,0.0 UGA,0.0 UKR,0.0 URY,0.0 USA,0.0 -UZB,15.074042 +UZB,15.15907 VEN,0.0 -VNM,7.549819 +VNM,9.029598 VUT,0.0 YEM,0.0 ZAF,0.0 diff --git a/data/curated/calibration/default/food_demand.csv b/data/curated/calibration/default/food_demand.csv index 415df6fb..838aeb76 100644 --- a/data/curated/calibration/default/food_demand.csv +++ b/data/curated/calibration/default/food_demand.csv @@ -1,59 +1,59 @@ food,multiplier,net_slack_mt,consumption_mt -apple,1.0176942965794498,-1.7921872404403985,101.28615355763031 -banana,1.0,0.0,61.92605545501283 -barley-hulled,1.0,0.0,2.363381609684666 -buckwheat,0.6021654848612251,1.8252767490432689,4.58803014717455 -cabbage,1.0029202519677038,-0.6778625786682824,232.12468861078378 -carrot,0.9980453528955658,0.24450801113562193,125.09061639870924 -cassava,1.0,0.0,141.4352919480798 -chickpea,0.851845683838795,1.524140822612253,10.287522241025044 -citrus,1.0790555938505626,-10.189429759979248,128.8894215283508 +apple,0.8654283599431496,16.028387761252816,119.10672824141511 +banana,1.0,0.0,83.86400781446719 +barley-hulled,1.0,0.0,3.394025166120855 +buckwheat,0.5,3.2981354128501152,6.060888907615208 +cabbage,0.7855264912178118,63.56243884723517,296.3649879565637 +carrot,0.7865465876700821,33.880798760816106,158.72690153319854 +cassava,1.0,0.0,146.6446998956235 +chickpea,0.7969112725607213,2.233302548617303,10.996683945863197 +citrus,0.9028745481366645,14.961210516426945,154.0400608635391 cocoa-powder,0.9075733242820169,0.4894285178525024,5.295316682662815 -coconut,1.0,0.0,6.34032663140988 -coconut-oil,1.0,0.0,0.8418856649004738 +coconut,1.0,0.0,9.481804876784736 +coconut-oil,1.0,0.0,1.034274928166269 coffee-green,0.8670401855759174,1.5566428339807317,11.70761888261768 -cottonseed-oil,1.0,0.0,3.0515467647910555 -cowpea,1.0,0.0,7.114182244494486 -dairy,1.2246117331091,-143.29542238643626,637.9694435501006 -dairy-buffalo,1.3121931617236198,-32.77072726066217,104.96939484431641 -dry-pea,1.0,0.0,5.485299755293454 +cottonseed-oil,1.0,0.0,3.677182665908731 +cowpea,1.0,0.0,7.6320487640243755 +dairy,0.9938007550182093,4.873464077711105,786.1383268488571 +dairy-buffalo,1.0009651107745667,-0.13280630613993882,137.60731891069736 +dry-pea,1.0,0.0,5.687653609949848 eggs,1.0,0.0,82.1042355971731 -flour-white,1.0,0.0,286.239123725798 -flour-wholemeal,1.0,0.0,54.90201137149779 -foxtail-millet,1.0,0.0,3.3274160848886822 -gram,0.851845683838795,1.524140822612253,10.287522241025044 -groundnut,1.0,0.0,18.481263347034655 -groundnut-oil,1.0,0.0,2.7442013276422585 -maize,1.0,0.0,135.35636450930542 -mango,0.8877094698642342,6.339796370230033,56.458869350468376 +flour-white,1.0,0.0,325.9317221327219 +flour-wholemeal,1.0,0.0,51.86023051282973 +foxtail-millet,1.0,0.0,3.9923812641500263 +gram,0.7969112725607213,2.233302548617303,10.996683945863197 +groundnut,1.0,0.0,19.555445860052714 +groundnut-oil,1.0,0.0,2.9765150743314734 +maize,1.0,0.0,145.23005755046324 +mango,0.6362446439942644,28.654200031810433,78.77327318682455 meat-cattle,0.952400557178112,2.308293342590332,48.49412526166998 meat-chicken,0.9895527158773688,0.8225271608680487,78.7311947500566 meat-pig,1.0184832063942477,-1.3977279365062714,75.62150779971125 meat-sheep,1.0872487686362589,-0.8913660735124722,10.21637425312656 -oat,1.0,0.0,1.3459041314893057 -olive-oil,1.2668818455267778,-1.0373781486414373,3.8870315311025934 -onion,0.9942682966895774,1.6232445702771656,283.2045698048314 -palm-oil,1.0,0.0,21.015993093062207 -pearl-millet,1.0,0.0,3.3274160848886822 -phaseolus-bean,1.0,0.0,19.514856620977298 -pigeon-pea,1.0,0.0,0.6056507342029818 -plantain,0.9925006851893258,0.17104906383610796,22.808625608387956 -potato,1.0,0.0,201.368981817679 -rapeseed-oil,1.0,0.0,9.829376123423572 -rendered-fat,1.0,0.0,6.7047482385273725 -rice-brown,1.0,0.0,50.360311507614824 -rice-white,1.0,0.0,368.47423265205 -rye,1.0,0.0,1.409586055518048 -sesame-oil,1.0,0.0,0.9990107708404139 -sesame-seed,1.0,0.0,2.652062442012948 -sorghum,1.0,0.0,7.308949817877874 -soy,1.0,0.0,9.501944424790508 -soybean-oil,1.0,0.0,25.274998503079587 -sugar,1.0,0.0,122.5639517629752 -sunflower-oil,1.0,0.0,11.998033070957717 -sunflower-seed,1.0,0.0,4.278090002906765 -sweet-potato,1.0,0.0,39.67487038175386 +oat,1.0,0.0,1.2681771293066504 +olive-oil,1.1170134510333023,-0.515859653824009,4.40855003650131 +onion,0.7933623085725866,73.34015701734461,354.92148847930366 +palm-oil,1.0,0.0,27.267514950561235 +pearl-millet,1.0,0.0,3.9923812641500263 +phaseolus-bean,1.0,0.0,19.357075008662832 +pigeon-pea,1.0,0.0,0.5646468485849709 +plantain,0.7299339839906631,8.375606859240179,31.013183306079554 +potato,1.0,0.0,221.53952335636131 +rapeseed-oil,1.0,0.0,11.607539789480597 +rendered-fat,1.0,0.0,7.868158038405454 +rice-brown,1.0,0.0,48.25430652747309 +rice-white,1.0,0.0,396.68971105018863 +rye,1.0,0.0,1.3160487005452524 +sesame-oil,1.0,0.0,1.0742609339886258 +sesame-seed,1.0,0.0,2.324006459434031 +sorghum,1.0,0.0,13.855257002680446 +soy,1.0,0.0,12.341748054756863 +soybean-oil,1.0,0.0,28.59252351838495 +sugar,1.0,0.0,142.7356754541397 +sunflower-oil,0.9034769699946354,1.302207993938282,13.491163651471652 +sunflower-seed,1.0,0.0,4.0006701732518195 +sweet-potato,1.0,0.0,45.579975634080256 tea-dried,0.9839926829455669,0.10682958776851592,6.673797201944581 -tomato,1.0092259783163016,-1.1898891925811768,128.97160081969196 -watermelon,1.172003167677489,-9.848674774169922,57.25868254145462 -yam,0.9337791237755573,2.2958778842339598,34.67000159364443 +tomato,0.8109165321283277,30.350085220447,160.51157492544553 +watermelon,0.9357314237804649,4.609115598845165,71.71647280781326 +yam,0.9337863014739,2.2956113516147525,34.66973455213156 diff --git a/data/curated/calibration/default/food_waste.yaml b/data/curated/calibration/default/food_waste.yaml index b86e2117..9f990412 100644 --- a/data/curated/calibration/default/food_waste.yaml +++ b/data/curated/calibration/default/food_waste.yaml @@ -9,27 +9,27 @@ # from a solved validation scenario. Do not hand-edit; rerun # `tools/calibrate food_waste` instead. fruits: - baseline_consumption_mt: 304.3055541798858 - baseline_negative_slack_mt: 101.51363178525935 - baseline_positive_slack_mt: 0.0 - waste_retention_multiplier: 0.7498550209156989 + baseline_consumption_mt: 509.1456383276636 + baseline_negative_slack_mt: 0.0 + baseline_positive_slack_mt: 65.78844199162813 + waste_retention_multiplier: 1.1483869948098573 oil: - baseline_consumption_mt: 78.5514709682393 - baseline_negative_slack_mt: 1.0906066512688994 - baseline_positive_slack_mt: 0.0 - waste_retention_multiplier: 0.986306150167512 + baseline_consumption_mt: 94.16507102176183 + baseline_negative_slack_mt: 0.5121270318049937 + baseline_positive_slack_mt: 1.3026912717632513 + waste_retention_multiplier: 1.008466596153548 starchy_vegetable: - baseline_consumption_mt: 442.3559145834927 + baseline_consumption_mt: 479.6430849393066 baseline_negative_slack_mt: 0.0 - baseline_positive_slack_mt: 2.6019802293478733 - waste_retention_multiplier: 1.0059169003983315 + baseline_positive_slack_mt: 10.674629568871978 + waste_retention_multiplier: 1.0227619351507131 stimulants: baseline_consumption_mt: 23.68795094791676 baseline_negative_slack_mt: 0.0 baseline_positive_slack_mt: 2.164119136995396 waste_retention_multiplier: 1.1005452540238354 vegetables: - baseline_consumption_mt: 624.8539440262484 - baseline_negative_slack_mt: 144.53753955755383 - baseline_positive_slack_mt: 0.0 - waste_retention_multiplier: 0.8121404478194867 + baseline_consumption_mt: 971.5815352395748 + baseline_negative_slack_mt: 0.0 + baseline_positive_slack_mt: 202.19004818416033 + waste_retention_multiplier: 1.2627921566405347 diff --git a/data/curated/calibration/default/grassland_cost.csv b/data/curated/calibration/default/grassland_cost.csv index c0082fab..b40ec530 100644 --- a/data/curated/calibration/default/grassland_cost.csv +++ b/data/curated/calibration/default/grassland_cost.csv @@ -1,115 +1,115 @@ country,correction_bnusd_per_mha -AFG,-0.0002518995097489096 +AFG,-0.0 AGO,-0.4345769137144089 -ALB,-0.20895707607269287 +ALB,-0.1705925464630127 ARE,-0.0 ARG,-0.21808408945798874 ARM,-0.0 -ATG,-0.0 -AUS,-0.11690390110015869 +ATG,0.04392857477068901 +AUS,-0.09140718728303909 AUT,1.2174659058672432e-09 -AZE,0.9842424392700195 +AZE,1.243560791015625 BDI,-1.1082043727128621e-08 BEL,0.08367127552628517 -BEN,3.3434815406799316 -BFA,1.5375214219093323 -BGD,-0.0 -BGR,-0.3087073564529419 +BEN,4.138646721839905 +BFA,1.9054945707321167 +BGD,1.418456463397888e-08 +BGR,-0.28112442791461945 BHS,-0.0316774295642972 -BIH,-0.4457664042711258 -BLR,-0.019591745920479298 +BIH,-0.43647024035453796 +BLR,-0.0 BLZ,-0.7379351556301117 BOL,-0.5958932638168335 BRA,-0.3751921057701111 -BRB,0.9515631198883057 -BRN,7.141561985015869 -BTN,-0.2860439717769623 +BRB,1.1941253542900085 +BRN,-2.138270405183107e-08 +BTN,-0.2803678661584854 BWA,-0.0 CAF,-0.4648502320051193 -CAN,-0.21693821251392365 -CHE,-0.11954900994896889 +CAN,-0.21149776130914688 +CHE,-0.0 CHL,-0.0009876639232970774 -CHN,-1.4773446466165296e-08 -CIV,-0.4997792989015579 -CMR,4.572341203689575 +CHN,-8.477159418873725e-09 +CIV,-0.4562175124883652 +CMR,5.61368989944458 COD,-0.8133789598941803 COG,-0.8903338015079498 COL,-0.7695494294166565 COM,7.03753166853005e-10 CPV,-0.0 -CRI,-0.5568442642688751 +CRI,-0.5208668857812881 CUB,-0.1725609302520752 -CYP,0.31014789640903473 -CZE,-0.2703308165073395 +CYP,0.032756803557276726 +CZE,-0.2419496849179268 DEU,-0.3941321074962616 DJI,-0.0 DNK,1.7655537724494934 -DOM,-9.206058138033768e-09 +DOM,7.18969817015136e-09 DZA,-0.0 -ECU,-0.10834560729563236 +ECU,-0.09306279849261045 EGY,-0.0 ERI,-5.40463007681069e-09 -ESP,-0.044432083144783974 +ESP,-0.013218460138887167 EST,-0.25669121742248535 ETH,-1.3543125509940523e-09 -FIN,1.7167028188705444 -FJI,-0.8518642485141754 -FRA,-2.7449107875554546e-08 +FIN,2.1196298599243164 +FJI,-0.8288864195346832 +FRA,-3.172531681805424e-11 GAB,-0.8158496618270874 -GBR,-0.04809712804853916 +GBR,-0.0 GEO,-0.2082834094762802 -GHA,-2.9578572480204457e-08 +GHA,3.9665030371338617e-08 GIN,-0.16346535086631775 -GMB,1.2533257603645325 -GNB,1.1492018103599548 +GMB,1.5490911602973938 +GNB,1.4205031394958496 GNQ,-0.5248542279005051 -GRC,-0.0983348861336708 -GRD,0.6877356171607971 -GTM,1.0484134893573582e-08 +GRC,-0.0965074934065342 +GRD,0.8801554441452026 +GTM,-9.517153731763983e-10 GUF,-1.0722496509552002 GUY,-1.106971263885498 HND,-0.6534972786903381 -HRV,-0.18737270683050156 +HRV,-0.11544658988714218 HTI,5.54345191972061e-09 HUN,-0.4170793741941452 IDN,-0.5775293707847595 -IND,1.91958349943161 -IRL,-2.0363737363027212e-08 +IND,0.11168644204735756 +IRL,-0.0 IRN,-0.0 IRQ,-0.0 -ISL,-0.08585232496261597 -ISR,0.7179081439971924 +ISL,-0.0803840197622776 +ISR,0.8947843909263611 ITA,0.04259064048528671 -JAM,3.8619683980941772 +JAM,4.624346971511841 JOR,-0.0 JPN,-0.4974181354045868 -KAZ,-1.7263098728292903e-09 +KAZ,-1.0923817961305815e-09 KEN,-0.17468049377202988 KGZ,-0.0 -KHM,-0.4334357678890228 +KHM,-0.3870422840118408 KOR,-1.3437390089521273e-09 LAO,2.91002910657312e-09 -LBN,0.524543821811676 +LBN,0.6537501811981201 LBR,-0.7553918659687042 LBY,-0.0 -LKA,-0.19719894230365753 +LKA,-0.39620520174503326 LSO,-0.443109393119812 -LTU,-0.05371060222387314 -LUX,0.037571508437395096 +LTU,1.0886833101686477e-10 +LUX,0.061516094487160444 LVA,-0.3037813901901245 MAR,-0.0 MDA,-0.36603914201259613 MDG,-0.6942926049232483 MEX,-0.08276043459773064 -MKD,-0.2818893939256668 +MKD,-0.25135253369808197 MLI,1.2537319515892875e-09 MLT,-0.0 MMR,8.1712165922454e-09 -MNE,-0.2836945503950119 -MNG,-0.06247838772833347 +MNE,-0.21746134757995605 +MNG,-0.05213180370628834 MOZ,-0.5657002627849579 MRT,-3.3330131710140876e-10 -MUS,-3.315589225394433e-08 +MUS,2.5759505639655345e-09 MWI,-0.19057554006576538 MYS,2.3094663959000172e-08 NAM,-0.006759356707334518 @@ -117,59 +117,59 @@ NER,-0.0 NGA,-0.19652122259140015 NIC,-0.5845303535461426 NLD,0.7907610237598419 -NOR,0.5854896306991577 +NOR,0.7337738275527954 NPL,-0.0 NZL,-0.0 OMN,-0.0 PAK,-0.0 -PAN,-0.2060026302933693 -PER,-0.629342257976532 +PAN,-0.609392911195755 +PER,-0.629314124584198 PHL,-0.12064796686172485 PNG,-0.7848113179206848 -POL,0.019568821415305138 +POL,0.012749426998198032 PRI,-2.7517170764213006e-09 -PRT,-0.09743807837367058 +PRT,-0.09565510042011738 PRY,-0.536945641040802 -PSE,0.7093142569065094 +PSE,0.9001172184944153 ROU,-0.4245498776435852 -RUS,-1.064132693073816e-08 +RUS,-1.0402462891079267e-08 RWA,2.7728375151525597e-08 SAU,-0.0 -SDN,-0.07209280133247375 -SEN,0.3868510276079178 -SLB,-0.06834621354937553 +SDN,-0.05768662877380848 +SEN,0.47823119163513184 +SLB,-1.4547326898650681e-08 SLE,-0.41614776849746704 -SLV,3.70309579372406 +SLV,4.654175281524658 SOM,-4.7665072999691915e-09 SRB,-0.29573993384838104 SSD,-0.3232929855585098 -STP,0.63315150141716 +STP,0.7769068777561188 SUR,6.86613392829895 SVK,-0.4092821776866913 -SVN,-0.23911219835281372 -SWE,1.7724632024765015 -SWZ,2.2704852220556404e-08 -SYR,0.14306625723838806 +SVN,-0.11368491873145103 +SWE,2.222731590270996 +SWZ,-1.973749874650821e-08 +SYR,0.1783515065908432 TCD,-0.0883248969912529 TGO,-0.3382970243692398 THA,-2.5447631557540262e-08 -TJK,0.39508867263793945 +TJK,-0.0 TKM,-0.0 TLS,-0.22272039204835892 TTO,-1.403013705214562e-09 -TUN,0.3739815652370453 -TUR,1.1239208579063416 -TWN,-0.3147922307252884 +TUN,0.462641179561615 +TUR,1.4188752395227766e-09 +TWN,-0.2726697325706482 TZA,-0.06884757429361343 UGA,-0.7039666175842285 UKR,-0.02324177697300911 URY,-0.17097113281488419 -USA,-0.4052763879299164 +USA,-0.4118834286928177 UZB,-0.0 VEN,-0.26539246737957 -VNM,6.096648454666138 +VNM,-1.9626262393046545e-08 VUT,-0.17226548492908478 YEM,2.695993539525432e-09 ZAF,-0.0033502724254503846 ZMB,-0.3225127309560776 -ZWE,1.0959289073944092 +ZWE,1.3446277379989624 diff --git a/data/curated/calibration/default/provenance.yaml b/data/curated/calibration/default/provenance.yaml index 3a136fe4..fd76361f 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: '2026-07-01T22:29:28+00:00' -git_commit: d3f07986e72ad291e10e5a11844c18752cddf7cc +generated_at: '2026-07-02T17:52:12+00:00' +git_commit: 4779577ccbbee7ef1d77fad71c517dd53faa8d01 source: default structural_config: aggregation.irrigated_area_source: current @@ -632,6 +632,7 @@ structural_config: - 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 @@ -654,6 +655,8 @@ structural_config: data.usda.retrieve_nutrition: false diet.anchor_groups_to_gbd: false diet.baseline_age: All ages + diet.fbs.whole_grain_shares.flour-wholemeal: 0.11 + diet.fbs.whole_grain_shares.rice-brown: 0.04 diet.fbs_override_foods: - yam - cocoa-powder @@ -664,10 +667,10 @@ structural_config: - 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: fbs 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 diff --git a/data/curated/calibration/gbd-anchored/provenance.yaml b/data/curated/calibration/gbd-anchored/provenance.yaml index ecc10cbe..03c27df9 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/gsa.yaml -generated_at: '2026-07-01T21:08:01+00:00' -git_commit: d3f07986e72ad291e10e5a11844c18752cddf7cc +generated_at: '2026-07-02T17:16:36+00:00' +git_commit: 4779577ccbbee7ef1d77fad71c517dd53faa8d01 source: gbd-anchored structural_config: aggregation.irrigated_area_source: current @@ -632,6 +632,7 @@ structural_config: - 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 @@ -668,6 +669,7 @@ structural_config: 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 diff --git a/data/curated/calibration/gdd-ia/animal_cost.csv b/data/curated/calibration/gdd-ia/animal_cost.csv new file mode 100644 index 00000000..b0302633 --- /dev/null +++ b/data/curated/calibration/gdd-ia/animal_cost.csv @@ -0,0 +1,1226 @@ +product,country,correction_bnusd_per_mt_feed +dairy,AFG,0.009775234386324883 +dairy,AGO,-0.1468677781522274 +dairy,ALB,0.028023586375638843 +dairy,ARE,-0.04836616409011185 +dairy,ARG,-0.19965669512748718 +dairy,ARM,-0.021885029273107648 +dairy,ASM,-0.0 +dairy,ATG,-0.20854145288467407 +dairy,AUS,-0.07088138302788138 +dairy,AUT,-0.0029114224016666412 +dairy,AZE,-0.0643788380548358 +dairy,BDI,-0.208308644592762 +dairy,BEL,0.019830304197967052 +dairy,BEN,-0.2670818790793419 +dairy,BFA,-0.27394019067287445 +dairy,BGD,-0.07141656382009387 +dairy,BGR,0.02044672518968582 +dairy,BHS,-0.10683811269700527 +dairy,BIH,0.11757176741957664 +dairy,BLR,-0.045248162001371384 +dairy,BLZ,-0.2015547826886177 +dairy,BOL,-0.21501590311527252 +dairy,BRA,-0.22465115040540695 +dairy,BRB,-0.30794185400009155 +dairy,BRN,-0.0752631826326251 +dairy,BTN,-0.2185128517448902 +dairy,BWA,-0.24830980598926544 +dairy,CAF,-0.15660355985164642 +dairy,CAN,-0.034870133735239506 +dairy,CHE,-0.0393193899653852 +dairy,CHL,-0.1893482506275177 +dairy,CHN,0.1607140377163887 +dairy,CIV,-0.26611560583114624 +dairy,CMR,-0.2728709429502487 +dairy,COD,-0.14927423745393753 +dairy,COG,-0.1545010581612587 +dairy,COL,-0.1667589470744133 +dairy,COM,-0.13520731776952744 +dairy,CPV,-0.23950542509555817 +dairy,CRI,-0.014960226602852345 +dairy,CUB,-0.1991748958826065 +dairy,CYP,0.021383875980973244 +dairy,CZE,0.005600878037512302 +dairy,DEU,0.02896498329937458 +dairy,DJI,-0.03034788928925991 +dairy,DNK,-0.024929175153374672 +dairy,DOM,-0.03341811150312424 +dairy,DZA,-0.05347512639127672 +dairy,ECU,-0.1954895555973053 +dairy,EGY,-0.04868289828300476 +dairy,ERI,-0.16784296929836273 +dairy,ESP,-0.055624060332775116 +dairy,EST,-0.005761606618762016 +dairy,ETH,-0.16132669895887375 +dairy,FIN,0.06725708581507206 +dairy,FJI,-0.04846467077732086 +dairy,FRA,-0.07994907535612583 +dairy,GAB,-0.1042232746258378 +dairy,GBR,-0.009183213114738464 +dairy,GEO,-0.06488664634525776 +dairy,GHA,-0.26966172456741333 +dairy,GIN,-0.23668600618839264 +dairy,GMB,-0.2511493191123009 +dairy,GNB,-0.27105822414159775 +dairy,GNQ,-0.0 +dairy,GRC,-0.04338577575981617 +dairy,GRD,-0.24167951941490173 +dairy,GTM,-0.1558070182800293 +dairy,GUF,-0.0 +dairy,GUY,-0.21677342057228088 +dairy,HND,-0.15193508192896843 +dairy,HRV,-0.023165365681052208 +dairy,HTI,-0.03268309310078621 +dairy,HUN,-0.006732109002768993 +dairy,IDN,-0.09213399142026901 +dairy,IND,-0.133881613612175 +dairy,IRL,-0.07851577922701836 +dairy,IRN,0.008007684722542763 +dairy,IRQ,-0.18612314015626907 +dairy,ISL,0.02330385148525238 +dairy,ISR,-0.059871623292565346 +dairy,ITA,0.02880171127617359 +dairy,JAM,-0.051712632179260254 +dairy,JOR,0.0862271599471569 +dairy,JPN,0.13801487907767296 +dairy,KAZ,0.026172621408477426 +dairy,KEN,-0.1337904967367649 +dairy,KGZ,0.16921938210725784 +dairy,KHM,-0.08911564201116562 +dairy,KOR,0.18644990026950836 +dairy,LAO,-0.07593757659196854 +dairy,LBN,0.07638140395283699 +dairy,LBR,-0.11606167070567608 +dairy,LBY,-0.0634374306537211 +dairy,LKA,-0.0587678924202919 +dairy,LSO,-0.1971079632639885 +dairy,LTU,0.015747329220175743 +dairy,LUX,-0.044849056750535965 +dairy,LVA,0.007993112318217754 +dairy,MAR,-0.2045445218682289 +dairy,MDA,-0.012624678201973438 +dairy,MDG,-0.13429574482142925 +dairy,MEX,-0.049311187118291855 +dairy,MKD,-0.020900454372167587 +dairy,MLI,-0.21731411665678024 +dairy,MLT,-0.05349260941147804 +dairy,MMR,-0.08132225857116282 +dairy,MNE,0.1987200789153576 +dairy,MNG,-0.026233380660414696 +dairy,MOZ,-0.16900363564491272 +dairy,MRT,-0.1708943396806717 +dairy,MUS,-0.10606717132031918 +dairy,MWI,-0.15280770137906075 +dairy,MYS,-0.11351840198040009 +dairy,NAM,-0.18502292782068253 +dairy,NER,-0.28497033566236496 +dairy,NGA,-0.1371927484869957 +dairy,NIC,-0.2576863616704941 +dairy,NLD,-0.11492612212896347 +dairy,NOR,-0.0024363044649362564 +dairy,NPL,-0.07188067934475839 +dairy,NZL,-0.07812317321076989 +dairy,OMN,-0.18154404312372208 +dairy,PAK,0.016526592895388603 +dairy,PAN,-0.2004229873418808 +dairy,PER,-0.2609187066555023 +dairy,PHL,0.014251965912990272 +dairy,PNG,-0.09601053968071938 +dairy,POL,0.0033537475392222404 +dairy,PRI,-0.164003387093544 +dairy,PRT,-0.03184168413281441 +dairy,PRY,-0.18788132444024086 +dairy,PSE,0.022239917423576117 +dairy,ROU,0.02755645615980029 +dairy,RUS,0.013616328593343496 +dairy,RWA,-0.1398402638733387 +dairy,SAU,0.17004648596048355 +dairy,SDN,-0.08820837642997503 +dairy,SEN,-0.2693217545747757 +dairy,SLB,-0.06565168499946594 +dairy,SLE,-0.1298689041286707 +dairy,SLV,-0.20984957367181778 +dairy,SOM,-0.1736203469336033 +dairy,SRB,0.017962328158318996 +dairy,SSD,-0.11761870235204697 +dairy,STP,-0.21278973668813705 +dairy,SUR,-0.26024675369262695 +dairy,SVK,-0.004923256579786539 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+white-potato,PER,-3.809300184249878 +white-potato,PHL,-7.5 +white-potato,PNG,-7.5 +white-potato,POL,-2.197211742401123 +white-potato,PRI,-7.5 +white-potato,PRT,-5.459334850311279 +white-potato,PRY,-3.581894636154175 +white-potato,PSE,-7.5 +white-potato,ROU,-4.966475009918213 +white-potato,RUS,-2.517961263656616 +white-potato,RWA,-2.0021292567253113 +white-potato,SAU,-7.5 +white-potato,SDN,-6.781405925750732 +white-potato,SEN,-7.5 +white-potato,SLB,-6.6078855991363525 +white-potato,SLE,-6.9266862869262695 +white-potato,SLV,-7.5 +white-potato,SOM,-6.76738715171814 +white-potato,SRB,-4.046455144882202 +white-potato,SSD,-6.979392766952515 +white-potato,SUR,-7.5 +white-potato,SVK,-6.268165111541748 +white-potato,SVN,-6.641280174255371 +white-potato,SWE,-7.5 +white-potato,SWZ,-7.166557312011719 +white-potato,SYR,-6.378358840942383 +white-potato,TCD,-6.9514923095703125 +white-potato,TGO,-7.126158714294434 +white-potato,THA,-4.8481669425964355 +white-potato,TJK,-3.2776272296905518 +white-potato,TKM,-6.899640083312988 +white-potato,TLS,-0.6534139811992645 +white-potato,TTO,-7.120465993881226 +white-potato,TUN,-3.238673686981201 +white-potato,TUR,-6.2570483684539795 +white-potato,TWN,-6.581104040145874 +white-potato,TZA,-0.9602226614952087 +white-potato,UGA,-7.065031051635742 +white-potato,UKR,-1.8508853912353516 +white-potato,URY,-7.5 +white-potato,USA,-6.48323917388916 +white-potato,UZB,-5.730321407318115 +white-potato,VEN,-6.108365058898926 +white-potato,VNM,-6.79145359992981 +white-potato,YEM,-7.5 +white-potato,ZAF,-7.122694730758667 +white-potato,ZMB,-7.5 +white-potato,ZWE,-7.5 +yam,AFG,7.5 +yam,AGO,-2.915623903274536 +yam,ARG,-0.970349907875061 +yam,ATG,-4.489343166351318 +yam,BDI,-4.474895000457764 +yam,BEN,-1.1533899307250977 +yam,BFA,-2.6838340759277344 +yam,BIH,-2.2863107323646545 +yam,BLZ,-1.9457488059997559 +yam,BOL,-5.195361614227295 +yam,BRA,-3.122835397720337 +yam,BRB,-7.5 +yam,BRN,-5.200504541397095 +yam,BTN,-4.776039123535156 +yam,BWA,-4.242413759231567 +yam,CAF,-3.9860188961029053 +yam,CHL,-0.0 +yam,CHN,-0.5402792096138 +yam,CIV,-0.7082089781761169 +yam,CMR,-2.8625242710113525 +yam,COD,-4.230693340301514 +yam,COG,-4.227147579193115 +yam,COL,-0.8940050601959229 +yam,COM,-4.3197290897369385 +yam,CRI,-7.5 +yam,CUB,-2.660409092903137 +yam,DOM,-2.483414888381958 +yam,ECU,-5.37074089050293 +yam,EGY,1.0253205299377441 +yam,ERI,-5.601197242736816 +yam,ETH,-1.9373416304588318 +yam,FJI,-7.5 +yam,GAB,-4.206446409225464 +yam,GHA,-1.2013828754425049 +yam,GIN,-2.2546385526657104 +yam,GMB,-7.5 +yam,GNB,-4.185503721237183 +yam,GNQ,-4.477081298828125 +yam,GRD,-7.5 +yam,GTM,7.5 +yam,GUF,7.5 +yam,GUY,-7.5 +yam,HND,-4.2645134925842285 +yam,HRV,-0.0 +yam,HTI,-5.360596656799316 +yam,IDN,-4.645933389663696 +yam,IND,-0.9961451292037964 +yam,IRN,-0.0 +yam,ISR,-0.980597972869873 +yam,JAM,-7.5 +yam,JOR,4.4035040736198425 +yam,JPN,-7.5 +yam,KEN,-1.539107859134674 +yam,KHM,-2.4898996353149414 +yam,LAO,2.042736828327179 +yam,LBN,-4.69429886341095 +yam,LBR,-3.78039288520813 +yam,LKA,-3.049189805984497 +yam,LSO,-0.0 +yam,MDG,-4.047037363052368 +yam,MEX,3.244262456893921 +yam,MLI,-5.216529846191406 +yam,MMR,1.0198920369148254 +yam,MNE,-0.0 +yam,MOZ,-3.8747650384902954 +yam,MRT,-6.381232023239136 +yam,MUS,-3.481334090232849 +yam,MWI,-0.0 +yam,MYS,-2.689858913421631 +yam,NAM,-3.0043797492980957 +yam,NER,-5.320603370666504 +yam,NGA,-2.8136426210403442 +yam,NIC,-2.3203004598617554 +yam,NPL,-5.011655330657959 +yam,PAK,1.2802709341049194 +yam,PAN,-3.215519905090332 +yam,PER,-3.4855637550354004 +yam,PHL,-1.6139899492263794 +yam,PNG,-2.6115013360977173 +yam,PRI,-7.5 +yam,PRY,7.5 +yam,PSE,-1.4987291097640991 +yam,RWA,-0.8284357190132141 +yam,SAU,-0.0 +yam,SDN,-5.742918968200684 +yam,SEN,-2.142036259174347 +yam,SLB,-1.774495780467987 +yam,SLE,-5.374751806259155 +yam,SLV,2.7297595739364624 +yam,SOM,-0.0 +yam,SSD,-5.1632537841796875 +yam,STP,-4.699181079864502 +yam,SUR,-2.388601541519165 +yam,SWZ,-3.962120532989502 +yam,SYR,-1.978471279144287 +yam,TCD,-4.024775505065918 +yam,TGO,-1.095928430557251 +yam,THA,-1.7797744274139404 +yam,TLS,-5.459644079208374 +yam,TTO,-7.5 +yam,TWN,-2.4045424461364746 +yam,TZA,-4.558453798294067 +yam,UGA,-3.893244743347168 +yam,URY,7.5 +yam,USA,5.4903130531311035 +yam,VEN,-2.3649706840515137 +yam,VNM,-3.535680413246155 +yam,VUT,-3.088310480117798 +yam,ZAF,-4.361093759536743 +yam,ZMB,3.75 +yam,ZWE,-3.2789857387542725 diff --git a/data/curated/calibration/gdd-ia/deviation_penalty.yaml b/data/curated/calibration/gdd-ia/deviation_penalty.yaml new file mode 100644 index 00000000..3331dd52 --- /dev/null +++ b/data/curated/calibration/gdd-ia/deviation_penalty.yaml @@ -0,0 +1,22 @@ +# SPDX-FileCopyrightText: 2026 Koen van Greevenbroek +# +# SPDX-License-Identifier: CC-BY-4.0 +# +# Auto-generated by workflow/scripts/calibrate_deviation_penalty.py. +# Consumed at solve time when deviation_penalty..l1_cost +# is the sentinel string "calibrated". +# Do not edit by hand -- run ``tools/calibrate stability`` to regenerate. +target_deviation_pct: 5.0 +penalty_mode: l1 +deviation_type: absolute +components: +- cropland +- grassland +- feed +l1_costs: + cropland: 1.468666316419478 + grassland: 0.16337061661222912 + feed: 0.07852586344897906 +iterations: 3 +converged: true +final_residual_log_inf: 0.01697416334606783 diff --git a/data/curated/calibration/gdd-ia/exogenous_feed.csv b/data/curated/calibration/gdd-ia/exogenous_feed.csv new file mode 100644 index 00000000..539ab70d --- /dev/null +++ b/data/curated/calibration/gdd-ia/exogenous_feed.csv @@ -0,0 +1,103 @@ +# 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,0.0,0.0,6.454991 +AGO,0.0,0.0,0.935713 +ARE,0.0,0.0,1.419746 +ARM,0.0,0.0,0.921945 +ATG,0.0,0.0,0.001203 +AZE,0.0,0.0,1.66953 +BFA,0.0,0.0,2.427716 +BGD,0.0,0.0,9.275321 +BHS,0.0,0.0,0.00024 +BOL,0.0,0.0,3.061832 +BRA,0.0,0.0,4.273265 +BRB,0.0,0.0,0.001185 +BRN,0.0,0.0,0.098262 +BTN,0.0,0.0,0.032702 +BWA,0.0,0.0,0.803633 +CAF,0.0,0.0,3.145721 +CHL,0.0,0.0,1.174709 +CHN,22.060617,8.937976,0.0 +CMR,0.0,0.0,1.961351 +COG,0.0,0.0,0.10183 +COL,0.0,0.0,4.952398 +COM,0.0,0.0,0.023562 +CPV,0.0,0.0,0.014816 +CRI,0.0,0.0,0.47573 +CUB,0.0,0.0,0.474311 +CYP,0.0,0.0,0.026106 +DJI,0.0,0.0,0.253116 +DOM,0.0,0.0,0.732631 +DZA,0.0,0.0,7.00381 +ECU,0.0,0.0,0.99931 +EGY,0.0,0.0,7.632216 +ERI,0.0,0.0,0.647034 +ETH,0.0,0.0,3.538889 +GEO,0.0,0.0,0.291165 +GNB,0.0,0.0,0.082521 +GRD,0.0,0.0,0.003329 +GTM,0.0,0.0,0.88201 +HND,0.0,0.0,0.32824 +HTI,0.0,0.0,0.567069 +IND,0.0,0.0,289.219116 +IRL,0.0,0.0,0.334866 +ISL,0.0,0.0,0.030581 +ISR,0.0,0.0,1.114113 +JAM,0.0,0.0,0.181489 +JOR,0.0,0.0,0.90952 +JPN,1.910623,1.221458,1.323049 +KEN,0.0,0.0,8.553256 +KHM,0.0,0.135121,0.0 +KOR,2.714647,0.374992,2.658246 +LAO,0.0,0.053306,0.0 +LBN,0.0,0.0,0.255602 +LBY,0.0,0.0,0.793355 +LSO,0.0,0.0,0.093215 +MAR,0.0,0.0,5.567759 +MEX,0.0,0.0,2.591533 +MLT,0.0,0.0,0.002164 +MNE,0.0,0.0,0.002303 +MNG,0.0,0.0,15.397595 +MRT,0.0,0.0,2.105997 +MYS,0.0,0.0,0.217634 +NAM,0.0,0.0,0.693243 +NER,0.0,0.0,1.711398 +NIC,0.0,0.0,1.291705 +NLD,0.0,0.0,0.117066 +NPL,0.0,0.0,15.855976 +NZL,0.0,0.72566,0.0 +OMN,0.0,0.0,1.601173 +PAK,0.0,13.613902,166.893402 +PAN,0.0,0.0,0.335515 +PER,0.0,0.0,0.827014 +PRI,0.0,0.0,0.093587 +PRY,0.0,0.0,0.155431 +PSE,0.0,0.0,0.253434 +RWA,0.0,0.0,0.380831 +SAU,0.0,0.0,3.207553 +SDN,0.0,0.0,11.693514 +SEN,0.0,0.0,0.880735 +SOM,0.0,0.0,5.319848 +SSD,0.0,0.0,3.616102 +STP,0.0,0.0,0.00225 +SWZ,0.0,0.0,0.169067 +SYR,0.0,0.0,1.271963 +TCD,0.0,0.0,25.86199 +THA,1.626361,0.336465,0.0 +TJK,0.0,0.0,0.247151 +TKM,0.0,0.0,0.695718 +TLS,0.009463,0.01037,0.067521 +TTO,0.0,0.0,0.00687 +TUN,0.0,0.0,1.242337 +TUR,0.0,0.0,1.66396 +TWN,1.330431,0.0,0.0 +TZA,0.0,0.0,9.637837 +UGA,0.0,0.0,3.557199 +URY,0.0,0.0,2.438584 +VEN,0.0,0.0,3.763237 +YEM,0.0,0.0,3.183531 +ZAF,0.0,0.0,5.481237 +ZMB,0.0,0.0,2.941371 +ZWE,0.0,0.0,10.748748 diff --git a/data/curated/calibration/gdd-ia/exogenous_forage.csv b/data/curated/calibration/gdd-ia/exogenous_forage.csv new file mode 100644 index 00000000..785b5cbe --- /dev/null +++ b/data/curated/calibration/gdd-ia/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,1.960736 +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,17.312044 +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,0.0 +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,0.083786 +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,23.06752 +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,158.148087 +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,5.329263 +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,8.619004 +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,2.704415 +NZL,0.0 +OMN,1.78773 +PAK,70.964584 +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,0.0 +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,3.370195 +TKM,9.84958 +TLS,0.0 +TTO,0.0 +TUN,0.0 +TUR,5.091218 +TWN,0.0 +TZA,0.0 +UGA,0.0 +UKR,0.0 +URY,0.0 +USA,0.0 +UZB,15.074042 +VEN,0.0 +VNM,7.549819 +VUT,0.0 +YEM,0.0 +ZAF,0.0 +ZMB,0.0 +ZWE,0.0 diff --git a/data/curated/calibration/gdd-ia/fodder_conversion.csv b/data/curated/calibration/gdd-ia/fodder_conversion.csv new file mode 100644 index 00000000..08823e62 --- /dev/null +++ b/data/curated/calibration/gdd-ia/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,0.733979 +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,0.475501 +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,0.592926 +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,0.763731 +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,0.294322 +MAR,1.0 +MDA,0.857278 +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,0.986016 +POL,1.0 +PRI,1.0 +PRT,1.0 +PRY,1.0 +PSE,1.0 +ROU,0.753901 +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,0.954113 +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/gdd-ia/food_demand.csv b/data/curated/calibration/gdd-ia/food_demand.csv new file mode 100644 index 00000000..415df6fb --- /dev/null +++ b/data/curated/calibration/gdd-ia/food_demand.csv @@ -0,0 +1,59 @@ +food,multiplier,net_slack_mt,consumption_mt +apple,1.0176942965794498,-1.7921872404403985,101.28615355763031 +banana,1.0,0.0,61.92605545501283 +barley-hulled,1.0,0.0,2.363381609684666 +buckwheat,0.6021654848612251,1.8252767490432689,4.58803014717455 +cabbage,1.0029202519677038,-0.6778625786682824,232.12468861078378 +carrot,0.9980453528955658,0.24450801113562193,125.09061639870924 +cassava,1.0,0.0,141.4352919480798 +chickpea,0.851845683838795,1.524140822612253,10.287522241025044 +citrus,1.0790555938505626,-10.189429759979248,128.8894215283508 +cocoa-powder,0.9075733242820169,0.4894285178525024,5.295316682662815 +coconut,1.0,0.0,6.34032663140988 +coconut-oil,1.0,0.0,0.8418856649004738 +coffee-green,0.8670401855759174,1.5566428339807317,11.70761888261768 +cottonseed-oil,1.0,0.0,3.0515467647910555 +cowpea,1.0,0.0,7.114182244494486 +dairy,1.2246117331091,-143.29542238643626,637.9694435501006 +dairy-buffalo,1.3121931617236198,-32.77072726066217,104.96939484431641 +dry-pea,1.0,0.0,5.485299755293454 +eggs,1.0,0.0,82.1042355971731 +flour-white,1.0,0.0,286.239123725798 +flour-wholemeal,1.0,0.0,54.90201137149779 +foxtail-millet,1.0,0.0,3.3274160848886822 +gram,0.851845683838795,1.524140822612253,10.287522241025044 +groundnut,1.0,0.0,18.481263347034655 +groundnut-oil,1.0,0.0,2.7442013276422585 +maize,1.0,0.0,135.35636450930542 +mango,0.8877094698642342,6.339796370230033,56.458869350468376 +meat-cattle,0.952400557178112,2.308293342590332,48.49412526166998 +meat-chicken,0.9895527158773688,0.8225271608680487,78.7311947500566 +meat-pig,1.0184832063942477,-1.3977279365062714,75.62150779971125 +meat-sheep,1.0872487686362589,-0.8913660735124722,10.21637425312656 +oat,1.0,0.0,1.3459041314893057 +olive-oil,1.2668818455267778,-1.0373781486414373,3.8870315311025934 +onion,0.9942682966895774,1.6232445702771656,283.2045698048314 +palm-oil,1.0,0.0,21.015993093062207 +pearl-millet,1.0,0.0,3.3274160848886822 +phaseolus-bean,1.0,0.0,19.514856620977298 +pigeon-pea,1.0,0.0,0.6056507342029818 +plantain,0.9925006851893258,0.17104906383610796,22.808625608387956 +potato,1.0,0.0,201.368981817679 +rapeseed-oil,1.0,0.0,9.829376123423572 +rendered-fat,1.0,0.0,6.7047482385273725 +rice-brown,1.0,0.0,50.360311507614824 +rice-white,1.0,0.0,368.47423265205 +rye,1.0,0.0,1.409586055518048 +sesame-oil,1.0,0.0,0.9990107708404139 +sesame-seed,1.0,0.0,2.652062442012948 +sorghum,1.0,0.0,7.308949817877874 +soy,1.0,0.0,9.501944424790508 +soybean-oil,1.0,0.0,25.274998503079587 +sugar,1.0,0.0,122.5639517629752 +sunflower-oil,1.0,0.0,11.998033070957717 +sunflower-seed,1.0,0.0,4.278090002906765 +sweet-potato,1.0,0.0,39.67487038175386 +tea-dried,0.9839926829455669,0.10682958776851592,6.673797201944581 +tomato,1.0092259783163016,-1.1898891925811768,128.97160081969196 +watermelon,1.172003167677489,-9.848674774169922,57.25868254145462 +yam,0.9337791237755573,2.2958778842339598,34.67000159364443 diff --git a/data/curated/calibration/gdd-ia/food_waste.yaml b/data/curated/calibration/gdd-ia/food_waste.yaml new file mode 100644 index 00000000..b86e2117 --- /dev/null +++ b/data/curated/calibration/gdd-ia/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: 304.3055541798858 + baseline_negative_slack_mt: 101.51363178525935 + baseline_positive_slack_mt: 0.0 + waste_retention_multiplier: 0.7498550209156989 +oil: + baseline_consumption_mt: 78.5514709682393 + baseline_negative_slack_mt: 1.0906066512688994 + baseline_positive_slack_mt: 0.0 + waste_retention_multiplier: 0.986306150167512 +starchy_vegetable: + baseline_consumption_mt: 442.3559145834927 + baseline_negative_slack_mt: 0.0 + baseline_positive_slack_mt: 2.6019802293478733 + waste_retention_multiplier: 1.0059169003983315 +stimulants: + baseline_consumption_mt: 23.68795094791676 + baseline_negative_slack_mt: 0.0 + baseline_positive_slack_mt: 2.164119136995396 + waste_retention_multiplier: 1.1005452540238354 +vegetables: + baseline_consumption_mt: 624.8539440262484 + baseline_negative_slack_mt: 144.53753955755383 + baseline_positive_slack_mt: 0.0 + waste_retention_multiplier: 0.8121404478194867 diff --git a/data/curated/calibration/gdd-ia/grassland_cost.csv b/data/curated/calibration/gdd-ia/grassland_cost.csv new file mode 100644 index 00000000..c0082fab --- /dev/null +++ b/data/curated/calibration/gdd-ia/grassland_cost.csv @@ -0,0 +1,175 @@ +country,correction_bnusd_per_mha +AFG,-0.0002518995097489096 +AGO,-0.4345769137144089 +ALB,-0.20895707607269287 +ARE,-0.0 +ARG,-0.21808408945798874 +ARM,-0.0 +ATG,-0.0 +AUS,-0.11690390110015869 +AUT,1.2174659058672432e-09 +AZE,0.9842424392700195 +BDI,-1.1082043727128621e-08 +BEL,0.08367127552628517 +BEN,3.3434815406799316 +BFA,1.5375214219093323 +BGD,-0.0 +BGR,-0.3087073564529419 +BHS,-0.0316774295642972 +BIH,-0.4457664042711258 +BLR,-0.019591745920479298 +BLZ,-0.7379351556301117 +BOL,-0.5958932638168335 +BRA,-0.3751921057701111 +BRB,0.9515631198883057 +BRN,7.141561985015869 +BTN,-0.2860439717769623 +BWA,-0.0 +CAF,-0.4648502320051193 +CAN,-0.21693821251392365 +CHE,-0.11954900994896889 +CHL,-0.0009876639232970774 +CHN,-1.4773446466165296e-08 +CIV,-0.4997792989015579 +CMR,4.572341203689575 +COD,-0.8133789598941803 +COG,-0.8903338015079498 +COL,-0.7695494294166565 +COM,7.03753166853005e-10 +CPV,-0.0 +CRI,-0.5568442642688751 +CUB,-0.1725609302520752 +CYP,0.31014789640903473 +CZE,-0.2703308165073395 +DEU,-0.3941321074962616 +DJI,-0.0 +DNK,1.7655537724494934 +DOM,-9.206058138033768e-09 +DZA,-0.0 +ECU,-0.10834560729563236 +EGY,-0.0 +ERI,-5.40463007681069e-09 +ESP,-0.044432083144783974 +EST,-0.25669121742248535 +ETH,-1.3543125509940523e-09 +FIN,1.7167028188705444 +FJI,-0.8518642485141754 +FRA,-2.7449107875554546e-08 +GAB,-0.8158496618270874 +GBR,-0.04809712804853916 +GEO,-0.2082834094762802 +GHA,-2.9578572480204457e-08 +GIN,-0.16346535086631775 +GMB,1.2533257603645325 +GNB,1.1492018103599548 +GNQ,-0.5248542279005051 +GRC,-0.0983348861336708 +GRD,0.6877356171607971 +GTM,1.0484134893573582e-08 +GUF,-1.0722496509552002 +GUY,-1.106971263885498 +HND,-0.6534972786903381 +HRV,-0.18737270683050156 +HTI,5.54345191972061e-09 +HUN,-0.4170793741941452 +IDN,-0.5775293707847595 +IND,1.91958349943161 +IRL,-2.0363737363027212e-08 +IRN,-0.0 +IRQ,-0.0 +ISL,-0.08585232496261597 +ISR,0.7179081439971924 +ITA,0.04259064048528671 +JAM,3.8619683980941772 +JOR,-0.0 +JPN,-0.4974181354045868 +KAZ,-1.7263098728292903e-09 +KEN,-0.17468049377202988 +KGZ,-0.0 +KHM,-0.4334357678890228 +KOR,-1.3437390089521273e-09 +LAO,2.91002910657312e-09 +LBN,0.524543821811676 +LBR,-0.7553918659687042 +LBY,-0.0 +LKA,-0.19719894230365753 +LSO,-0.443109393119812 +LTU,-0.05371060222387314 +LUX,0.037571508437395096 +LVA,-0.3037813901901245 +MAR,-0.0 +MDA,-0.36603914201259613 +MDG,-0.6942926049232483 +MEX,-0.08276043459773064 +MKD,-0.2818893939256668 +MLI,1.2537319515892875e-09 +MLT,-0.0 +MMR,8.1712165922454e-09 +MNE,-0.2836945503950119 +MNG,-0.06247838772833347 +MOZ,-0.5657002627849579 +MRT,-3.3330131710140876e-10 +MUS,-3.315589225394433e-08 +MWI,-0.19057554006576538 +MYS,2.3094663959000172e-08 +NAM,-0.006759356707334518 +NER,-0.0 +NGA,-0.19652122259140015 +NIC,-0.5845303535461426 +NLD,0.7907610237598419 +NOR,0.5854896306991577 +NPL,-0.0 +NZL,-0.0 +OMN,-0.0 +PAK,-0.0 +PAN,-0.2060026302933693 +PER,-0.629342257976532 +PHL,-0.12064796686172485 +PNG,-0.7848113179206848 +POL,0.019568821415305138 +PRI,-2.7517170764213006e-09 +PRT,-0.09743807837367058 +PRY,-0.536945641040802 +PSE,0.7093142569065094 +ROU,-0.4245498776435852 +RUS,-1.064132693073816e-08 +RWA,2.7728375151525597e-08 +SAU,-0.0 +SDN,-0.07209280133247375 +SEN,0.3868510276079178 +SLB,-0.06834621354937553 +SLE,-0.41614776849746704 +SLV,3.70309579372406 +SOM,-4.7665072999691915e-09 +SRB,-0.29573993384838104 +SSD,-0.3232929855585098 +STP,0.63315150141716 +SUR,6.86613392829895 +SVK,-0.4092821776866913 +SVN,-0.23911219835281372 +SWE,1.7724632024765015 +SWZ,2.2704852220556404e-08 +SYR,0.14306625723838806 +TCD,-0.0883248969912529 +TGO,-0.3382970243692398 +THA,-2.5447631557540262e-08 +TJK,0.39508867263793945 +TKM,-0.0 +TLS,-0.22272039204835892 +TTO,-1.403013705214562e-09 +TUN,0.3739815652370453 +TUR,1.1239208579063416 +TWN,-0.3147922307252884 +TZA,-0.06884757429361343 +UGA,-0.7039666175842285 +UKR,-0.02324177697300911 +URY,-0.17097113281488419 +USA,-0.4052763879299164 +UZB,-0.0 +VEN,-0.26539246737957 +VNM,6.096648454666138 +VUT,-0.17226548492908478 +YEM,2.695993539525432e-09 +ZAF,-0.0033502724254503846 +ZMB,-0.3225127309560776 +ZWE,1.0959289073944092 diff --git a/data/curated/calibration/gdd-ia/grassland_yield.csv b/data/curated/calibration/gdd-ia/grassland_yield.csv new file mode 100644 index 00000000..c57034c5 --- /dev/null +++ b/data/curated/calibration/gdd-ia/grassland_yield.csv @@ -0,0 +1,178 @@ +# SPDX-FileCopyrightText: 2026 Koen van Greevenbroek +# +# SPDX-License-Identifier: CC-BY-4.0 +country,yield_correction +AFG,0.395294 +AGO,0.071253 +ALB,0.144938 +ARE,1.0 +ARG,0.200993 +ARM,0.550552 +ATG,1.0 +AUS,0.236373 +AUT,0.553455 +AZE,1.0 +BDI,0.437887 +BEL,0.928966 +BEN,1.0 +BFA,1.0 +BGD,1.0 +BGR,0.074007 +BHS,0.669108 +BIH,0.014661 +BLR,0.422191 +BLZ,0.101975 +BOL,0.111315 +BRA,0.229034 +BRB,1.0 +BRN,1.0 +BTN,0.03586 +BWA,1.0 +CAF,0.583977 +CAN,0.051832 +CHE,0.40712 +CHL,0.686938 +CHN,0.287707 +CIV,0.033413 +CMR,1.0 +COD,0.022128 +COG,0.00797 +COL,0.103539 +COM,1.0 +CPV,1.0 +CRI,0.380109 +CUB,0.277685 +CYP,1.0 +CZE,0.138284 +DEU,0.0 +DJI,1.0 +DNK,1.0 +DOM,0.567979 +DZA,0.79171 +ECU,0.36376 +EGY,1.0 +ERI,0.518486 +ESP,0.429751 +EST,0.0 +ETH,0.551195 +FIN,1.0 +FJI,0.041419 +FRA,0.488102 +GAB,0.006069 +GBR,0.40073 +GEO,0.122798 +GHA,0.178955 +GIN,0.173001 +GMB,1.0 +GNB,1.0 +GNQ,0.077495 +GRC,0.075784 +GRD,1.0 +GTM,0.571476 +GUF,0.0 +GUY,0.018762 +HND,0.147424 +HRV,0.284196 +HTI,0.92669 +HUN,0.0 +IDN,0.144268 +IND,1.0 +IRL,0.633733 +IRN,0.769453 +IRQ,1.0 +ISL,0.062237 +ISR,1.0 +ITA,0.884272 +JAM,1.0 +JOR,1.0 +JPN,0.0 +KAZ,0.256835 +KEN,0.467676 +KGZ,0.842055 +KHM,0.398086 +KOR,1.0 +LAO,0.404176 +LBN,1.0 +LBR,0.016113 +LBY,0.894499 +LKA,0.193473 +LSO,0.048049 +LTU,0.341665 +LUX,0.783687 +LVA,0.0 +MAR,1.0 +MDA,0.0 +MDG,0.016111 +MEX,0.371319 +MKD,0.104967 +MLI,0.387861 +MLT,1.0 +MMR,1.0 +MNE,0.077905 +MNG,0.245052 +MOZ,0.00289 +MRT,0.660517 +MUS,0.811415 +MWI,0.76798 +MYS,0.436125 +NAM,0.291928 +NER,1.0 +NGA,0.665316 +NIC,0.295306 +NLD,1.0 +NOR,1.0 +NPL,1.0 +NZL,0.560406 +OMN,1.0 +PAK,1.0 +PAN,0.256559 +PER,0.083668 +PHL,0.384506 +PNG,0.0 +POL,0.560559 +PRI,0.43587 +PRT,0.081039 +PRY,0.068488 +PSE,1.0 +ROU,0.0 +RUS,0.15561 +RWA,0.794264 +SAU,1.0 +SDN,0.419271 +SEN,1.0 +SLB,0.580748 +SLE,0.093191 +SLV,1.0 +SOM,0.555429 +SRB,0.13671 +SSD,0.142093 +STP,1.0 +SUR,1.0 +SVK,0.0 +SVN,0.252009 +SWE,1.0 +SWZ,0.283263 +SYR,1.0 +TCD,0.695865 +TGO,0.206827 +THA,0.856195 +TJK,1.0 +TKM,1.0 +TLS,0.234166 +TTO,0.743954 +TUN,1.0 +TUR,1.0 +TWN,0.107613 +TZA,0.406749 +UGA,0.405131 +UKR,0.166781 +URY,0.270063 +USA,0.031197 +UZB,1.0 +VEN,0.190522 +VNM,1.0 +VUT,0.176534 +YEM,0.592561 +ZAF,0.346808 +ZMB,0.203235 +ZWE,1.0 diff --git a/data/curated/calibration/gdd-ia/provenance.yaml b/data/curated/calibration/gdd-ia/provenance.yaml new file mode 100644 index 00000000..7cdfbbae --- /dev/null +++ b/data/curated/calibration/gdd-ia/provenance.yaml @@ -0,0 +1,856 @@ +# 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/default.yaml +generated_at: '2026-07-02T17:34:56+00:00' +git_commit: 4779577ccbbee7ef1d77fad71c517dd53faa8d01 +source: gdd-ia +structural_config: + aggregation.irrigated_area_source: current + aggregation.regions.allow_cross_border: false + aggregation.regions.method: kmeans + aggregation.regions.target_count: 750 + 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 + 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 + - 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_model_ensemble: ENSEMBLE + 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.land_cover.zenodo_record: '21085632' + 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 + 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_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 + health.ssb_sugar_g_per_100g: 5.7 + 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.filtering.min_area_ha: 100 + land.filtering.min_crop_yield_t_per_ha: 0.01 + land.filtering.min_grassland_yield_t_per_ha: 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 + multiple_cropping.double_rice.crops: + - wetland-rice + - wetland-rice + multiple_cropping.double_rice.water_supplies: + - r + - i + multiple_cropping.maize_soybean.crops: + - maize + - soybean + multiple_cropping.maize_soybean.water_supplies: + - r + - i + multiple_cropping.rice_wheat.crops: + - wetland-rice + - wheat + multiple_cropping.rice_wheat.water_supplies: + - r + - i + non_food_crops: + - alfalfa + - silage-maize + - biomass-sorghum + 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 + residues.max_feed_fraction_by_region.Sub-Saharan Africa: 0.7 + water.huang_reference_year: 2010 + water.supply_scenario: current_use + 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/data/manually_downloaded/README.md b/data/manually_downloaded/README.md index 13fc3866..aee959fb 100644 --- a/data/manually_downloaded/README.md +++ b/data/manually_downloaded/README.md @@ -52,34 +52,31 @@ The following permalink reproduces this query for year 2020: https://vizhub.heal --- -### GDD-dietary-intake (directory) +### GDD-IA-intake_grams_{year}.csv / GDD-IA-intake_kcals_{year}.csv -**Source:** Global Dietary Database (Tufts University) -**Download:** https://globaldietarydatabase.org/data-download +**Source:** Global Dietary Database — Integrated Assessment (GDD-IA), Marco Springmann (University of Oxford / UCL) +**Access:** Pending publication; available on personal request from Marco Springmann. -Downloading requires free user registration and acceptance of terms of use. +**Only needed when `diet.source: gdd_ia` is set.** The default configuration +(`diet.source: fbs`) derives the baseline diet from auto-retrieved FAOSTAT +Food Balance Sheets and needs no manually-obtained dietary data. **Dataset details:** -- **Content:** Country-level mean daily dietary intake (g/day per capita) for major food groups and dietary risk factors -- **Food groups:** Vegetables, fruits (temperate/tropical/starchy), whole grains, legumes, nuts & seeds, red meat (beef/lamb/pork), processed meat, seafood (fish types + shellfish), grains, dairy, eggs, oils, and others -- **Coverage:** 185+ countries with data circa 2015-2020 -- **Format:** CSV (~1.6 GB) with columns for country, food item, mean intake, standard error, and uncertainty bounds -- **Use case:** Baseline dietary patterns for health risk assessment +- **Content:** Harmonised country-level mean dietary intake, reported in parallel grams/day and kcal/day per food category +- **Coverage:** ~185 countries at the configured `baseline_year` +- **Use case:** Survey-based per-country food-group totals for the baseline diet (used for the published results) -**Processing:** The Snakemake workflow processes this file via `workflow/scripts/prepare_gdd_dietary_intake.py` to: -1. Filter to baseline (BMK) scenario equivalent -2. Map country names to ISO3 codes -3. Map GDD food items to model dietary risk factors -4. Aggregate multiple food items to risk factor categories -5. Output to `processing/{name}/dietary_intake_baseline.csv` +**Placement:** Save the two CSVs as `GDD-IA-intake_grams_{year}.csv` and +`GDD-IA-intake_kcals_{year}.csv` where `{year}` matches `baseline_year` +in the config (default: 2020). -**License:** Free for non-commercial research, teaching, and private study with attribution. May not be redistributed or used commercially without Tufts permission. +**Processing:** Processed via `workflow/scripts/prepare_gdd_ia_dietary_intake.py` +(rule `prepare_gdd_ia_dietary_intake`); see `docs/current_diets.rst`. -**Citation:** -> Global Dietary Database. Dietary intake data by country. https://www.globaldietarydatabase.org/ [Accessed YYYY-MM-DD]. +**License:** Pending publication; will be released under CC-BY-NC. Used here with permission. -**Attribution format (when publishing results):** -> Data provided by Global Dietary Database. https://www.globaldietarydatabase.org/ [Date accessed]. +**Citation:** +> Springmann M, *Global Dietary Database — Integrated Assessment dataset (GDD-IA)*. Pending publication. --- @@ -166,16 +163,12 @@ When new GBD data is released: 4. Save as `IHME-GBD_2023-death-rates-{year}.csv` (the year in the filename must match `baseline_year`) 6. Rerun workflow: `tools/smk processing/{name}/gbd_mortality_rates.csv` -### GDD Dietary Data +### GDD-IA Dietary Data -When updating GDD data: +When a new GDD-IA release arrives: -1. Visit https://globaldietarydatabase.org/data-download -2. Log in with user account -3. Download the complete dataset CSV -4. Replace `GDD-dietary-intake.csv` in this directory -5. Update access date in citations and documentation -6. Rerun workflow: `tools/smk processing/{name}/dietary_intake_baseline.csv` +1. Save the two CSVs as `GDD-IA-intake_grams_{year}.csv` and `GDD-IA-intake_kcals_{year}.csv` (the year must match `baseline_year`) +2. Rerun workflow: `tools/smk processing/{name}/gdd_ia_dietary_intake.csv` ### IHME GBD Dietary Risk Exposure Estimates diff --git a/docs/calibration.rst b/docs/calibration.rst index 7753dde5..4b12b8b2 100644 --- a/docs/calibration.rst +++ b/docs/calibration.rst @@ -16,29 +16,34 @@ directly. Calibration artefacts are organised in *sets*: one directory ``data/curated/calibration//`` per base configuration they were calibrated against, selected by the ``calibration.source`` config key. -The shipped ``default`` and ``gbd-anchored`` sets are version-controlled +The shipped ``default``, ``gdd-ia`` and ``gbd-anchored`` sets are version-controlled so that ordinary builds don't need to re-solve anything. See :ref:`calibration-provenance` for how sets are tied to configs. -.. admonition:: Calibration is tied to the baseline diet (and thus to health/anchoring) +.. admonition:: Calibration is tied to the baseline diet (and thus to diet source, health and anchoring) :class: warning Several artefacts (notably ``food_demand.csv``, ``food_waste.yaml`` and ``deviation_penalty.yaml``) are fit against a *specific* baseline - diet. The baseline diet depends on whether the GBD risk-factor groups - are anchored to GBD intake, which is controlled by - ``diet.anchor_groups_to_gbd`` (default: follow ``health.enabled``); see - :ref:`current-diets-gbd-anchoring`. - - Two artefact sets are therefore committed: ``default``, fit against - the anchoring-off baseline diet of the default config, and - ``gbd-anchored``, fit against the GBD-anchored diet and consumed by - the health-enabled configs (``validation``, ``gsa``, the doc-figure - configs) via ``calibration.source: gbd-anchored``. ``tools/calibrate`` - resolves anchoring from its base config and pins it across all five - steps, so each set is regenerated against the right diet - automatically. The provenance stamps record the *resolved* anchoring, - so consuming a set fit against the other diet fails at startup. + diet. The baseline diet depends on the diet source (``diet.source``: + FBS-derived or GDD-IA; see :ref:`current-diets-fbs-source`) and on + whether the GBD risk-factor groups are anchored to GBD intake, which + is controlled by ``diet.anchor_groups_to_gbd`` (default: follow + ``health.enabled``); see :ref:`current-diets-gbd-anchoring`. + + Three artefact sets are therefore committed: ``default``, fit against + the FBS-derived baseline diet of the default config; ``gdd-ia``, fit + against the anchoring-off GDD-IA diet and consumed by the GDD-IA + configs without health (``central``, the GHG-sensitivity and + region-resolution configs) via ``calibration.source: gdd-ia``; and + ``gbd-anchored``, fit against the GBD-anchored GDD-IA diet and + consumed by the health-enabled configs (``validation``, ``gsa``, the + doc-figure configs) via ``calibration.source: gbd-anchored``. + ``tools/calibrate`` resolves anchoring from its base config and pins + it across all five steps, so each set is regenerated against the + right diet automatically. The provenance stamps record the diet + source and the *resolved* anchoring, so consuming a set fit against + a different diet fails at startup. .. _calibration-enabled-generate-pattern: diff --git a/docs/config/doc_figures.yaml b/docs/config/doc_figures.yaml index 98f350f4..2ff365fc 100644 --- a/docs/config/doc_figures.yaml +++ b/docs/config/doc_figures.yaml @@ -8,6 +8,11 @@ name: "doc_figures" +# The published results use the GDD-IA survey-based baseline diet (the +# calibration artefact set consumed here was fit against it). +diet: + source: gdd_ia + scenarios: # Scenario definitions for documentation figures # ----------------------------------------------- diff --git a/docs/config/doc_validation.yaml b/docs/config/doc_validation.yaml index e8475a35..9290de88 100644 --- a/docs/config/doc_validation.yaml +++ b/docs/config/doc_validation.yaml @@ -13,6 +13,11 @@ # them directly and there's no need to regenerate. name: "doc_validation" + +# The published results use the GDD-IA survey-based baseline diet (the +# calibration artefact set consumed here was fit against it). +diet: + source: gdd_ia scenarios: default: {} diff --git a/docs/current_diets.rst b/docs/current_diets.rst index ff540c49..0f8fc7f2 100644 --- a/docs/current_diets.rst +++ b/docs/current_diets.rst @@ -8,13 +8,15 @@ Current Diets Overview -------- -The model represents current consumption patterns by combining three -intake datasets — **GDD-IA**, **GBD**, and **NHANES** (for the USA) — -with item-level food supply data from FAOSTAT for within-group -disaggregation. The pipeline produces a single per-country, per-food -baseline diet whose mass basis is aligned with what the model's food bus -delivers after applying food loss and waste. The baseline diet serves -several roles: +The model represents current consumption patterns by combining a +configurable primary intake source (``diet.source``) — the FAOSTAT +**FBS**-derived estimate (the default) or the survey-based **GDD-IA** +dataset — with the optional **GBD** risk-factor anchor and the +**NHANES** USA override, plus item-level food supply data from FAOSTAT +for within-group disaggregation. The pipeline produces a single +per-country, per-food baseline diet whose mass basis is aligned with +what the model's food bus delivers after applying food loss and waste. +The baseline diet serves several roles: * **Health impact assessment**: dietary risk exposure for the burden of disease attributable to current diets. @@ -53,14 +55,16 @@ Data Sources consistent per-country food and energy intake estimates. * **Status**: Pending publication; available upon personal request from Marco Springmann. Will be re-licensed under CC-BY-NC on - release. The public release will then become GLADE's default - input; until then, a temporary mode that lets GLADE run without - GDD-IA is under development. + release, and will then become GLADE's default input. Until then the + default baseline diet is derived from FAOSTAT FBS (see below), and + GDD-IA is only needed when ``diet.source: gdd_ia`` is set. * **Coverage**: ~185 countries, per-country mean dietary intake at the reference year, reported in parallel grams/day and kcal/day for every food category. - * **Role**: Primary source of per-country food-group totals for all - food groups except the GBD-anchored risk groups (see below). + * **Role**: With ``diet.source: gdd_ia``, primary source of + per-country food-group totals for all food groups except the + GBD-anchored risk groups (see below). Used for the published + results. **Global Burden of Disease (GBD) 2019 dietary risk exposure** * **Provider**: Institute for Health Metrics and Evaluation (IHME) @@ -84,17 +88,25 @@ Data Sources **FAOSTAT FBS + QCL** * **Provider**: FAO Statistics Division - * **Role**: Item-level supply (FBS) drives **within-group** - disaggregation of food-group totals into per-food consumption. - Production statistics (QCL) resolve shared FBS items (e.g. several - millet species under one FBS code) and weight module-pool - projections (see :ref:`current-diets-step2`). FBS supply also serves - as the **anchor source** for the foods in - ``diet.fbs_override_foods`` (meats, eggs, yam, coffee, cocoa). + * **Role**: With ``diet.source: fbs`` (the default), FBS energy supply + is also the source of the **food-group totals** themselves (see + :ref:`current-diets-fbs-source`). Independently of the source, + item-level supply (FBS) drives **within-group** disaggregation of + food-group totals into per-food consumption; production statistics + (QCL) resolve shared FBS items (e.g. several millet species under + one FBS code) and weight module-pool projections (see + :ref:`current-diets-step2`); and FBS supply serves as the **anchor + source** for the foods in ``diet.fbs_override_foods`` (meats, eggs, + yam, coffee, cocoa). Weight Conventions ~~~~~~~~~~~~~~~~~~ +The FBS source needs no mass-basis conversions: its group masses are +derived from FBS energy at model-basis densities (see +:ref:`current-diets-fbs-source`). The conventions below apply to the +GDD-IA source. + GDD-IA reports intake "as consumed" (cooked weight for cereals and meats, fresh weight for fruits and vegetables). The pipeline derives the food-group **mass** values that downstream rules consume in the model's @@ -123,6 +135,69 @@ Units in the merged ``dietary_intake.csv`` distinguish ``g/day (fresh wt)`` from ``g/day (milk equiv)`` for dairy and ``g/day (refined sugar eq)`` for sugar. +.. _current-diets-fbs-source: + +FBS-Derived Baseline Diet (default) +----------------------------------- + +With ``diet.source: fbs`` (the default), the per-(country, food-group) +intake totals are derived from FAOSTAT Food Balance Sheets instead of +GDD-IA, so GLADE runs without any manually-obtained dietary data. The +rule ``prepare_fbs_dietary_intake`` computes, per country and group, + +.. math:: + + \text{intake}_{g}\ [\mathrm{g/day}] = \sum_{i \in \text{items}(g)} + \frac{\text{kcal}_i \cdot (1 - w_g)}{k_i} + +where :math:`\text{kcal}_i` is the FBS "Food supply (kcal/capita/day)" +element for FBS item :math:`i`, :math:`w_g` the group's consumer waste +fraction, and :math:`k_i` the model-basis energy density (kcal/g, +nutrition.csv) of the item's mapped food(s). Deriving mass from energy +rather than from the FBS mass element sidesteps per-item mass-basis +bookkeeping: FAO's nutritive factors already net out flour extraction, +refuse fractions, and carcass-to-retail conversion, so dividing by the +model-basis density lands directly in model basis — the same convention +the GDD-IA pipeline uses for dairy. The FBS "Food supply" element is +net of supply-chain losses, so only consumer waste is deducted. + +Item-to-group attribution follows the food-to-FBS-item mapping used by +the within-group shares (each item counted once per group), with a few +special cases (see ``workflow/scripts/diet/fbs_intake.py`` for the full +rules): + +* **Wheat and rice** (FBS 2511, 2807) are the only items whose foods + span two groups: FBS does not distinguish whole-grain from refined + milling, so ``diet.fbs.whole_grain_shares`` sets the fraction of each + item's energy consumed as the whole-grain food (``flour-wholemeal``, + ``rice-brown``); the remainder goes to the refined counterpart. The + defaults are chosen so the FBS-derived global whole-grain intake + matches the survey-based (GDD-IA) estimate. +* **Dairy** folds butter and cream energy into the milk item at + cow-milk density (strict milk-equivalent mass, matching GDD-IA). +* **Oil** uses the FBS "Vegetable Oils" aggregate (2914) so unmodelled + oils are included in the group total. +* **Unmapped pool items** (pineapples, dates, "other" residuals) count + toward their projection pool's group at the mean density of the + pool's recipient foods. +* **Stimulants** are not emitted: all three stimulants foods are + ``diet.fbs_override_foods`` and get their per-food intake directly + from FBS mass supply in ``estimate_baseline_diet``. + +Because the masses are derived from FBS energy at the same densities +used by the kcal accounting, the FBS diet is FAO-energy-consistent by +construction and the kcal-normalisation step (Step 1c below) is +skipped. GBD anchoring, the NHANES USA override, the FBS per-food +overrides, and the within-group disaggregation apply unchanged. + +The FBS-derived diet is supply-based: it inherits FBS's known biases +relative to intake surveys beyond what the waste fractions correct. +It is the pragmatic default while GDD-IA is unpublished; results +intended to be comparable with the published GLADE results should set +``diet.source: gdd_ia``. The baseline diet is structural, so the two +sources need different calibration artefact sets (see +:doc:`calibration`). + .. _current-diets-gbd-anchoring: GBD Anchoring of Risk-Factor Groups (optional) @@ -310,19 +385,24 @@ Data Processing The diet pipeline runs in three preparation stages followed by the baseline-diet estimation: -1. **Prepare GDD-IA** (``prepare_gdd_ia_dietary_intake``): reads the - parallel grams and kcal CSVs, maps GDD-IA's food categories to the - model's food groups, derives the per-food-group mass in model basis - (pooling all dairy subcategories by energy, applying the cooked-to- - raw meat inflation), and emits two files: - - * ``gdd_ia_dietary_intake.csv`` — per-(country, food group) intake - (g/day) at age = ``All ages``. - * ``gdd_ia_kcal_target.csv`` — per-country kcal accounting: the - total dietary energy, the out-of-scope subtotal, the in-scope - target (total minus out-of-scope), and the refined / whole-grain - cereal energy split. Consumed by the cereal residual fix and the - kcal-normalisation step in ``estimate_baseline_diet``. +1. **Prepare the group-intake source** (``diet.source``): + + * ``fbs`` (default) — ``prepare_fbs_dietary_intake`` derives + per-(country, food group) intake from FBS energy supply (see + :ref:`current-diets-fbs-source`) into ``fbs_dietary_intake.csv``. + * ``gdd_ia`` — ``prepare_gdd_ia_dietary_intake`` reads the parallel + grams and kcal CSVs, maps GDD-IA's food categories to the model's + food groups, derives the per-food-group mass in model basis + (pooling all dairy subcategories by energy, applying the + cooked-to-raw meat inflation), and emits two files: + + * ``gdd_ia_dietary_intake.csv`` — per-(country, food group) intake + (g/day) at age = ``All ages``. + * ``gdd_ia_kcal_target.csv`` — per-country kcal accounting: the + total dietary energy, the out-of-scope subtotal, the in-scope + target (total minus out-of-scope), and the refined / whole-grain + cereal energy split. Consumed by the cereal residual fix and the + kcal-normalisation step in ``estimate_baseline_diet``. 2. **Prepare NHANES** (``prepare_nhanes_dietary_intake``): parses the USDA FPED demographic-table PDF for the configured cycle and emits @@ -331,8 +411,9 @@ baseline-diet estimation: for the FPED specifics). 3. **Merge sources** (``merge_dietary_sources``): NHANES overrides - GDD-IA for the (country, item) pairs it covers; the merged file - ``dietary_intake.csv`` is the input to ``estimate_baseline_diet``. + the group-intake source for the (country, item) pairs it covers; the + merged file ``dietary_intake.csv`` is the input to + ``estimate_baseline_diet``. The GBD risk-exposure data is processed independently by ``prepare_gbd_food_group_intake`` into @@ -355,7 +436,7 @@ Output Format ``g/day (refined sugar eq)`` (sugar). * ``item``: food group name. * ``country``: ISO 3166-1 alpha-3 code. -* ``age``: ``All ages`` for GDD-IA rows; NHANES uses the configured +* ``age``: ``All ages`` for GDD-IA and FBS rows; NHANES uses the configured ``diet.baseline_age`` literal (the FPED single population-mean row). * ``year``: reference year. * ``value``: mean daily intake in grams per person, in model basis. @@ -377,16 +458,17 @@ Step 1: Food group totals For groups in ``health.risk_factors`` (currently ``fruits``, ``vegetables``, ``whole_grains``, ``legumes``, ``nuts_seeds``, ``red_meat``) the per-country total is taken **from GBD when GBD reports -a value** and falls back to the merged GDD-IA/NHANES value otherwise. +a value** and falls back to the merged source/NHANES value otherwise. GBD strictly takes precedence on these groups — no averaging — so the baseline is on the same intake basis the GBD relative-risk functions are -calibrated against. All other groups use GDD-IA (or NHANES for the USA). +calibrated against. All other groups use the configured group-intake +source (or NHANES for the USA). GBD exposure is converted to the model's basis at load time, per food-group, using ``diet.source_basis`` plus per-(source, country, food_group) overrides from ``data/curated/diet_source_basis_overrides.csv`` and the conversion tables in ``diet.weight_conversion``. The script also -logs cross-validation metrics: median and range of the GDD-IA/GBD ratio +logs cross-validation metrics: median and range of the source/GBD ratio across countries for every risk group, and GBD's milk exposure as a cross-check on the dairy total. @@ -408,11 +490,18 @@ cereal budget. To preserve the cereal energy budget, the deficit is \text{new}\ g_{\text{grain}} = \max(0, \text{deficit\_kcal}) / k_{\text{grain, model}} -The IA cereal kcal pool comes from ``gdd_ia_kcal_target.csv`` (basis- -aware), not from nutrition.csv per-group averages. +For the GDD-IA source the cereal kcal pool comes from +``gdd_ia_kcal_target.csv`` (basis-aware), not from nutrition.csv +per-group averages; for the FBS source it is reconstructed from the +pre-anchoring group totals at model-basis densities, which reproduces +the FBS cereal energy exactly. + +Step 1c: Anchor-aware kcal normalisation (GDD-IA source only) +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -Step 1c: Anchor-aware kcal normalisation -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +This step runs only for ``diet.source: gdd_ia``; the FBS-derived diet +is FAO-energy-consistent by construction (see +:ref:`current-diets-fbs-source`). For each country, the **unanchored** groups are scaled by a single multiplicative factor so that total kcal across all groups lands on diff --git a/docs/data_sources.rst b/docs/data_sources.rst index f24915af..2f5870b0 100644 --- a/docs/data_sources.rst +++ b/docs/data_sources.rst @@ -17,9 +17,9 @@ Manual Download Checklist Several licensed datasets cannot be fetched automatically. While their use is free for non-commercial research purposes, these have to be downloaded manually or require API key registration. -**Always required:** +**Required only for the GDD-IA baseline diet (diet.source: gdd_ia):** -1. Obtain the **GDD-IA** intake CSVs by personal request to Marco Springmann and place them as ``data/manually_downloaded/GDD-IA-intake_grams_{year}.csv`` and ``data/manually_downloaded/GDD-IA-intake_kcals_{year}.csv`` (:ref:`gdd-ia-dietary-intake`). A public GDD-IA release is upcoming and will then be the default input; until then, a temporary mode that lets GLADE run without GDD-IA is under development. +1. Obtain the **GDD-IA** intake CSVs by personal request to Marco Springmann and place them as ``data/manually_downloaded/GDD-IA-intake_grams_{year}.csv`` and ``data/manually_downloaded/GDD-IA-intake_kcals_{year}.csv`` (:ref:`gdd-ia-dietary-intake`). With the default ``diet.source: fbs``, the baseline diet is derived from auto-retrieved FAOSTAT Food Balance Sheets instead (see :ref:`current-diets-fbs-source`) and no manual dietary data is needed. A public GDD-IA release is upcoming and will then become the default input. **Required only for the health module / GBD diet anchoring:** @@ -891,7 +891,9 @@ configured ``baseline_year``. **Access**: Pending publication. Currently available on personal request from Marco Springmann; an automatic retrieval rule will be added once -the public release is live. +the public release is live. Only needed when ``diet.source: gdd_ia`` is +set; the default configuration derives the baseline diet from FAOSTAT +FBS instead (see :ref:`current-diets-fbs-source`). **License**: Pending publication; will be released under Creative Commons Attribution-NonCommercial (CC-BY-NC). diff --git a/tests/test_calibration_provenance.py b/tests/test_calibration_provenance.py index 9f5a0e3c..2bfee9c3 100644 --- a/tests/test_calibration_provenance.py +++ b/tests/test_calibration_provenance.py @@ -24,6 +24,11 @@ MINIMAL_CONFIG = { "name": "unit", "calibration": {"source": "unit", "accept_provenance_mismatch": False}, + "diet": { + "source": "fbs", + "fbs": {"whole_grain_shares": {"flour-wholemeal": 0.11}}, + "gdd_ia": {"cooked_to_raw": {"red_meat": 1.43}}, + }, "crops": ["wheat", "maize"], "planning_horizon": 2030, "baseline_year": 2020, diff --git a/tests/test_fbs_dietary_intake.py b/tests/test_fbs_dietary_intake.py new file mode 100644 index 00000000..6b544050 --- /dev/null +++ b/tests/test_fbs_dietary_intake.py @@ -0,0 +1,380 @@ +# SPDX-FileCopyrightText: 2026 Koen van Greevenbroek +# +# SPDX-License-Identifier: GPL-3.0-or-later + +"""Unit tests for the FBS baseline-diet source (diet.source: fbs).""" + +import pandas as pd +import pytest + +from workflow.scripts.diet.fbs_intake import ( + DAIRY_ITEM_CODES, + OIL_GROUP_ITEM_CODE, + build_fbs_group_intake, + build_item_attribution, +) + +# --------------------------------------------------------------------------- +# Fixtures +# --------------------------------------------------------------------------- + + +@pytest.fixture +def food_groups_df(): + return pd.DataFrame( + { + "food": [ + "flour-white", + "flour-wholemeal", + "rice-white", + "rice-brown", + "foxtail-millet", + "pearl-millet", + "dairy", + "dairy-buffalo", + "palm-oil", + "olive-oil", + "tomato", + "onion", + "cabbage", + "carrot", + "plantain", + "banana", + "coffee-green", + ], + "group": [ + "grain", + "whole_grains", + "grain", + "whole_grains", + "whole_grains", + "whole_grains", + "dairy", + "dairy", + "oil", + "oil", + "vegetables", + "vegetables", + "vegetables", + "vegetables", + "starchy_vegetable", + "fruits", + "stimulants", + ], + } + ) + + +@pytest.fixture +def food_item_map_df(): + return pd.DataFrame( + { + "food": [ + "flour-white", + "flour-wholemeal", + "rice-white", + "rice-brown", + "foxtail-millet", + "pearl-millet", + "dairy", + "dairy-buffalo", + "palm-oil", + "olive-oil", + "tomato", + "onion", + "plantain", + "banana", + "coffee-green", + ], + "item_code": [ + 2511, + 2511, + 2807, + 2807, + 2517, + 2517, + 2848, + 2848, + 2577, + 2580, + 2601, + 2602, + 2616, + 2615, + 2630, + ], + } + ) + + +@pytest.fixture +def nutrition_df(food_groups_df): + # Includes pool-recipient foods (citrus, potato, ...) that are not in + # the fixture food_groups: pool densities only need nutrition entries. + kcal_per_100g = { + "flour-white": 364.0, + "flour-wholemeal": 332.5, + "rice-white": 365.0, + "rice-brown": 366.6, + "foxtail-millet": 378.1, + "pearl-millet": 378.1, + "dairy": 60.7, + "dairy-buffalo": 96.6, + "palm-oil": 884.1, + "olive-oil": 884.1, + "tomato": 17.7, + "onion": 39.7, + "cabbage": 24.6, + "carrot": 41.4, + "plantain": 60.0, + "banana": 88.7, + "coffee-green": 0.5, + "citrus": 47.1, + "mango": 59.8, + "watermelon": 30.3, + "apple": 52.1, + "potato": 77.0, + "sweet-potato": 85.8, + "yam": 118.1, + "cassava": 159.4, + } + return pd.DataFrame( + { + "food": list(kcal_per_100g), + "nutrient": "cal", + "value": list(kcal_per_100g.values()), + } + ) + + +FOOD_GROUPS_INCLUDED = [ + "grain", + "whole_grains", + "dairy", + "oil", + "vegetables", + "starchy_vegetable", + "fruits", + "stimulants", +] + +WHOLE_GRAIN_SHARES = {"flour-wholemeal": 0.2, "rice-brown": 0.1} + + +def _kcal_supply(cells: dict[int, float], country: str = "AAA") -> pd.DataFrame: + return pd.DataFrame( + { + "country": country, + "item_code": list(cells), + "kcal_per_capita_day": list(cells.values()), + } + ) + + +def _flat_waste( + countries: list[str], groups: list[str], waste: float = 0.0 +) -> pd.DataFrame: + return pd.DataFrame( + [ + {"country": c, "food_group": g, "waste_fraction": waste} + for c in countries + for g in groups + ] + ) + + +def _intake( + kcal_cells: dict[int, float], + food_item_map_df, + food_groups_df, + nutrition_df, + waste: float = 0.0, + whole_grain_shares: dict[str, float] = WHOLE_GRAIN_SHARES, +) -> pd.Series: + result = build_fbs_group_intake( + kcal_supply=_kcal_supply(kcal_cells), + food_item_map=food_item_map_df, + food_groups=food_groups_df, + nutrition=nutrition_df, + food_loss_waste=_flat_waste(["AAA"], FOOD_GROUPS_INCLUDED, waste), + countries=["AAA"], + food_groups_included=FOOD_GROUPS_INCLUDED, + byproducts=[], + whole_grain_shares=whole_grain_shares, + ) + return result.set_index("food_group")["value"] + + +# --------------------------------------------------------------------------- +# Tests +# --------------------------------------------------------------------------- + + +class TestKcalDerivation: + def test_single_group_item(self, food_item_map_df, food_groups_df, nutrition_df): + # Tomato: 35.4 kcal/d at 0.177 kcal/g -> 200 g/d. + totals = _intake({2601: 35.4}, food_item_map_df, food_groups_df, nutrition_df) + assert totals["vegetables"] == pytest.approx(200.0) + + def test_waste_correction(self, food_item_map_df, food_groups_df, nutrition_df): + totals = _intake( + {2601: 35.4}, food_item_map_df, food_groups_df, nutrition_df, waste=0.25 + ) + assert totals["vegetables"] == pytest.approx(150.0) + + def test_shared_item_counted_once( + self, food_item_map_df, food_groups_df, nutrition_df + ): + # Millet 2517 maps to two whole_grains foods with equal density; + # the item's energy must enter the group total exactly once. + totals = _intake({2517: 378.1}, food_item_map_df, food_groups_df, nutrition_df) + assert totals["whole_grains"] == pytest.approx(100.0) + + def test_missing_cells_are_zero_supply( + self, food_item_map_df, food_groups_df, nutrition_df + ): + totals = _intake({2601: 35.4}, food_item_map_df, food_groups_df, nutrition_df) + assert "grain" not in totals.index + + +class TestWholeGrainSplit: + def test_wheat_split_between_groups( + self, food_item_map_df, food_groups_df, nutrition_df + ): + # Wheat 2511: 364 kcal/d; 20% to flour-wholemeal (3.325 kcal/g), + # 80% to flour-white (3.64 kcal/g). + totals = _intake({2511: 364.0}, food_item_map_df, food_groups_df, nutrition_df) + assert totals["whole_grains"] == pytest.approx(0.2 * 364.0 / 3.325) + assert totals["grain"] == pytest.approx(0.8 * 364.0 / 3.64) + + def test_missing_share_raises(self, food_item_map_df, food_groups_df, nutrition_df): + with pytest.raises(ValueError, match="whole_grain_shares"): + _intake( + {2511: 364.0}, + food_item_map_df, + food_groups_df, + nutrition_df, + whole_grain_shares={"rice-brown": 0.1}, + ) + + def test_unexpected_cross_group_item_raises( + self, food_item_map_df, food_groups_df, nutrition_df + ): + item_map = pd.concat( + [ + food_item_map_df, + pd.DataFrame({"food": ["tomato"], "item_code": [2511]}), + ], + ignore_index=True, + ) + with pytest.raises(ValueError, match="across groups"): + _intake({2511: 364.0}, item_map, food_groups_df, nutrition_df) + + +class TestSpecialGroups: + def test_dairy_folds_butter_and_cream_at_cow_density( + self, food_item_map_df, food_groups_df, nutrition_df + ): + # Milk 60.7 + butter 30 + cream 9.3 = 100 kcal/d at 0.607 kcal/g. + cells = dict(zip(DAIRY_ITEM_CODES, [60.7, 30.0, 9.3])) + totals = _intake(cells, food_item_map_df, food_groups_df, nutrition_df) + assert totals["dairy"] == pytest.approx(100.0 / 0.607) + + def test_oil_uses_aggregate_item_only( + self, food_item_map_df, food_groups_df, nutrition_df + ): + # Individual oil items are subsumed by the 2914 aggregate and + # must not double-count. + cells = {OIL_GROUP_ITEM_CODE: 88.41, 2577: 50.0, 2580: 30.0} + totals = _intake(cells, food_item_map_df, food_groups_df, nutrition_df) + assert totals["oil"] == pytest.approx(10.0) + + def test_stimulants_not_emitted( + self, food_item_map_df, food_groups_df, nutrition_df + ): + totals = _intake( + {2630: 2.0, 2601: 35.4}, food_item_map_df, food_groups_df, nutrition_df + ) + assert "stimulants" not in totals.index + + +class TestPoolItems: + def test_unmapped_pool_item_at_mean_recipient_density( + self, food_item_map_df, food_groups_df, nutrition_df + ): + # Vegetables-other 2605 is not mapped to a food; it lands on the + # vegetables group at the mean OVG recipient density + # ((0.397 + 0.246 + 0.414) / 3 kcal/g). + totals = _intake({2605: 105.7}, food_item_map_df, food_groups_df, nutrition_df) + mean_density = (0.397 + 0.246 + 0.414) / 3 + assert totals["vegetables"] == pytest.approx(105.7 / mean_density) + + def test_mapped_pool_item_attributed_via_mapping( + self, food_item_map_df, food_groups_df, nutrition_df + ): + # Plantains 2616 is in the fruits BAN pool but mapped to the + # plantain food (starchy_vegetable); the mapping wins. + totals = _intake({2616: 60.0}, food_item_map_df, food_groups_df, nutrition_df) + assert totals["starchy_vegetable"] == pytest.approx(100.0) + assert "fruits" not in totals.index + + +class TestValidation: + def test_missing_waste_fraction_raises( + self, food_item_map_df, food_groups_df, nutrition_df + ): + with pytest.raises(ValueError, match="waste fraction"): + build_fbs_group_intake( + kcal_supply=_kcal_supply({2601: 35.4}), + food_item_map=food_item_map_df, + food_groups=food_groups_df, + nutrition=nutrition_df, + food_loss_waste=_flat_waste(["BBB"], FOOD_GROUPS_INCLUDED), + countries=["AAA"], + food_groups_included=FOOD_GROUPS_INCLUDED, + byproducts=[], + whole_grain_shares=WHOLE_GRAIN_SHARES, + ) + + def test_food_missing_from_food_groups_raises( + self, food_item_map_df, food_groups_df, nutrition_df + ): + item_map = pd.concat( + [ + food_item_map_df, + pd.DataFrame({"food": ["mystery-food"], "item_code": [2999]}), + ], + ignore_index=True, + ) + with pytest.raises(ValueError, match="missing from"): + build_item_attribution( + item_map, + food_groups_df, + {"tomato": 0.177}, + FOOD_GROUPS_INCLUDED, + byproducts=[], + whole_grain_shares=WHOLE_GRAIN_SHARES, + ) + + def test_uncovered_group_raises(self, food_item_map_df, food_groups_df): + nutrition = pd.DataFrame( + { + "food": ["tomato", "onion", "cabbage", "carrot"], + "nutrient": "cal", + "value": [17.7, 39.7, 24.6, 41.4], + } + ) + with pytest.raises(ValueError, match="No FBS items attributed"): + build_fbs_group_intake( + kcal_supply=_kcal_supply({2601: 35.4}), + food_item_map=food_item_map_df[food_item_map_df["food"] == "tomato"], + food_groups=food_groups_df[ + food_groups_df["group"].isin(["vegetables", "grain"]) + ], + nutrition=nutrition, + food_loss_waste=_flat_waste(["AAA"], ["vegetables", "grain"]), + countries=["AAA"], + food_groups_included=["vegetables", "grain"], + byproducts=[], + whole_grain_shares={}, + ) diff --git a/workflow/rules/common.smk b/workflow/rules/common.smk index c7765a89..2caa911c 100644 --- a/workflow/rules/common.smk +++ b/workflow/rules/common.smk @@ -150,9 +150,42 @@ def assert_gbd_data_available(): # Snakemake-time guard share a single source of truth. +def assert_gdd_ia_data_available(): + """Fail early with actionable guidance if GDD-IA data is needed but absent. + + Only enforced when diet.source is "gdd_ia"; the default FBS-derived + baseline diet needs no manually-downloaded dietary data. + """ + if config["diet"]["source"] != "gdd_ia": + return + year = config["baseline_year"] + missing = [ + p + for p in ( + f"data/manually_downloaded/GDD-IA-intake_grams_{year}.csv", + f"data/manually_downloaded/GDD-IA-intake_kcals_{year}.csv", + ) + if not Path(p).exists() + ] + if not missing: + return + listing = "\n".join(f" - {p}" for p in missing) + raise FileNotFoundError( + "This run needs the manually-downloaded GDD-IA dietary intake data " + "because diet.source is 'gdd_ia', but the following are missing:\n" + + listing + + "\n\nEither place the files (see data/manually_downloaded/README.md)" + " or set diet.source: fbs to use the FAOSTAT-FBS-derived baseline " + "diet instead. Note that changing the diet source changes the " + "baseline diet, which calibration artefact sets are fit against " + "(see docs/calibration.rst)." + ) + + validate_scenario_overrides(load_scenario_defs()) validate_scenario_config_schemas(config, load_scenario_defs(), Path.cwd()) assert_gbd_data_available() +assert_gdd_ia_data_available() def scenario_override_hash(scenario_name): diff --git a/workflow/rules/diet.smk b/workflow/rules/diet.smk index 2d9b1464..0fe2550c 100644 --- a/workflow/rules/diet.smk +++ b/workflow/rules/diet.smk @@ -55,11 +55,11 @@ rule prepare_gdd_ia_dietary_intake: rule prepare_faostat_food_group_supply: """Prepare FAOSTAT supply data for downstream waste accounting. - Previously also supplemented GDD dietary intake; now retained only - as an input to ``prepare_food_loss_waste`` (dairy/oil/sugar/eggs/ - poultry supply totals used for FLW fractions). The diet pipeline - is fully GDD-IA-driven and no longer uses this file. Uses the same - layered FBS/FBSH/proxy fallback as ``prepare_faostat_fbs_items``. + Consumed by ``prepare_food_loss_waste`` (dairy/oil/sugar supply + totals used for FLW fractions) and by ``merge_dietary_sources`` as + the animal_fat fallback for countries the group-intake source does + not cover. Uses the same layered FBS/FBSH/proxy fallback as + ``prepare_faostat_fbs_items``. """ input: fbs_csv="data/downloads/faostat/FBS.parquet", @@ -88,8 +88,9 @@ rule prepare_faostat_food_group_supply: rule prepare_faostat_fbs_items: """Prepare raw item-level supply data from FAOSTAT Food Balance Sheets. - Reads supply data (kg/capita/year) for all items in the food item mapping, - used for calculating within-group food consumption ratios. Uses a layered + Reads mass supply (kg/capita/year; within-group consumption ratios and + FBS overrides) and energy supply (kcal/capita/day; the FBS baseline-diet + source) for all items in the food item mapping. Uses a layered fallback: new FBS at the reference year -> latest available year in new FBS -> latest available year in historic FBSH (covers Japan, Chad, Mali, Benin, Togo, Burundi, etc., which are not in new FBS) -> country proxy. @@ -103,8 +104,10 @@ rule prepare_faostat_fbs_items: countries=config["countries"], reference_year=config["baseline_year"], fbs_element_code=config["data"]["faostat"]["fbs_food_supply_element_code"], + fbs_kcal_element_code=config["data"]["faostat"]["fbs_food_kcal_element_code"], output: fbs_items="/{name}/faostat_fbs_items.csv", + fbs_items_kcal="/{name}/faostat_fbs_items_kcal.csv", fbs_provenance="/{name}/faostat_fbs_items_provenance.csv", group: "prep" @@ -160,20 +163,66 @@ rule prepare_nhanes_dietary_intake: "../scripts/prepare_nhanes_dietary_intake.py" +rule prepare_fbs_dietary_intake: + """Derive per-(country, group) dietary intake from FAOSTAT FBS energy supply. + + The FBS baseline-diet source (``diet.source: fbs``): group intake mass + is derived from the FBS "Food supply (kcal/capita/day)" element at + model-basis nutrition.csv densities and corrected for consumer waste. + Wheat and rice are split between grain and whole_grains via + ``diet.fbs.whole_grain_shares``; see workflow/scripts/diet/fbs_intake.py + for the full attribution rules. Output schema matches + ``gdd_ia_dietary_intake.csv``. + """ + input: + fbs_items_kcal="/{name}/faostat_fbs_items_kcal.csv", + food_item_map="data/curated/faostat_food_item_map.csv", + food_groups="data/curated/food_groups.csv", + nutrition="data/curated/nutrition.csv", + food_loss_waste="/{name}/food_loss_waste.csv", + params: + countries=config["countries"], + food_groups=config["food_groups"]["included"], + byproducts=config["byproducts"], + whole_grain_shares=config["diet"]["fbs"]["whole_grain_shares"], + baseline_age=config["diet"]["baseline_age"], + reference_year=config["baseline_year"], + output: + diet="/{name}/fbs_dietary_intake.csv", + group: + "prep" + resources: + runtime="1m", + mem_mb=500, + log: + "/{name}/prepare_fbs_dietary_intake.log", + benchmark: + "/{name}/prepare_fbs_dietary_intake.tsv" + script: + "../scripts/prepare_fbs_dietary_intake.py" + + rule merge_dietary_sources: - """Merge GDD-IA dietary intake with NHANES USA override. + """Merge the configured group-intake source with the NHANES USA override. - GDD-IA mass is already in model basis (kcal-derived). NHANES is + The group-intake source (``diet.source``) is either GDD-IA or the + FBS-derived estimate; both are already in model basis. NHANES is already in model basis. The FAOSTAT supply file is used only as - a fallback for the ``animal_fat`` group on countries that GDD-IA - does not cover. Output is the merged ``dietary_intake.csv``; + a fallback for the ``animal_fat`` group on countries that the + source does not cover. Output is the merged ``dietary_intake.csv``; GBD anchoring and kcal normalisation happen in ``estimate_baseline_diet``. """ input: - gdd_ia="/{name}/gdd_ia_dietary_intake.csv", + group_intake=( + "/{name}/gdd_ia_dietary_intake.csv" + if config["diet"]["source"] == "gdd_ia" + else "/{name}/fbs_dietary_intake.csv" + ), nhanes="/{name}/nhanes_dietary_intake.csv", faostat_supply="/{name}/faostat_food_group_supply.csv", + params: + diet_source=config["diet"]["source"], output: diet="/{name}/dietary_intake.csv", group: @@ -325,7 +374,14 @@ rule estimate_baseline_diet: if gbd_anchoring_enabled() else [] ), - kcal_target="/{name}/gdd_ia_kcal_target.csv", + # Survey kcal accounting: only the GDD-IA source provides one; the + # FBS source is FAO-energy-consistent by construction and skips + # the kcal normalisation (see estimate_baseline_diet.py). + kcal_target=( + "/{name}/gdd_ia_kcal_target.csv" + if config["diet"]["source"] == "gdd_ia" + else [] + ), nutrition="data/curated/nutrition.csv", fbs_items="/{name}/faostat_fbs_items.csv", crop_production="/{name}/faostat_crop_production.csv", @@ -348,6 +404,7 @@ rule estimate_baseline_diet: params: reference_year=config["baseline_year"], baseline_age=config["diet"]["baseline_age"], + diet_source=config["diet"]["source"], food_groups_included=config["food_groups"]["included"], byproducts=config["byproducts"], fbs_override_foods=config["diet"]["fbs_override_foods"], diff --git a/workflow/scripts/diet/fbs_intake.py b/workflow/scripts/diet/fbs_intake.py new file mode 100644 index 00000000..afac55b1 --- /dev/null +++ b/workflow/scripts/diet/fbs_intake.py @@ -0,0 +1,352 @@ +"""Derive per-(country, food-group) baseline dietary intake from FAOSTAT FBS. + +Group intake mass is derived from the FBS "Food supply (kcal/capita/day)" +element at model-basis energy densities (nutrition.csv): + + intake_g_day(country, group) = sum over the group's FBS items of + kcal_supply * (1 - waste_fraction) / kcal_per_g(item) + +Working from energy rather than the FBS mass element sidesteps the +per-item mass-basis bookkeeping that would otherwise be needed (flour +extraction for cereals, refuse fractions for fresh produce, carcass-to- +retail for meat, milk equivalents for dairy): FAO's nutritive factors +already net these out, so dividing FBS energy by the model-basis density +lands the mass directly in model basis. This is the same convention +``prepare_gdd_ia_dietary_intake`` uses for dairy. The FBS "Food supply" +element is net of supply-chain losses, so only consumer-level waste is +deducted to arrive at consumer-eaten intake. + +Item-to-group attribution: + +* Every FBS item mapped to a modelled food (faostat_food_item_map.csv, + byproducts excluded) counts toward that food's group exactly once; + several foods sharing an item (e.g. pearl- and foxtail-millet) do not + double-count it. The item's density is the mean density of its mapped + foods. +* Pool items from the within-group projections + (:mod:`workflow.scripts.diet.food_group_projection`) that are not + themselves mapped to a food (pineapples, dates, nuts-other, + roots-other, vegetables-other) count toward the pool's group at the + mean density of the pool's recipient foods -- matching how + ``estimate_baseline_diet`` redistributes their supply across those + recipients. Pool items that are mapped to a food (e.g. plantains 2616, + a starchy_vegetable) are attributed via their mapping instead. +* Items mapped to foods in two groups (wheat 2511 and rice 2807: refined + vs whole-grain milling of the same commodity, which FBS does not + distinguish) are split by the configured + ``diet.fbs.whole_grain_shares`` fraction. +* dairy folds butter (2740) and cream (2743) energy into the milk item + (2848) and derives mass at cow-milk density, mirroring the GDD-IA + butter/cream fold; the group total is strict milk-equivalent mass. +* oil uses the FBS "Vegetable Oils" aggregate (2914) instead of the + individual per-oil items so that unmodelled oils (maize germ, rice + bran, ...) are included in the group total, matching the survey + sources' coverage of all vegetable oils. +* stimulants are not emitted: the model densities for coffee and tea are + on a brewed basis, so kcal-derivation is meaningless there, and all + three stimulants foods are ``diet.fbs_override_foods`` whose per-food + intake is set directly from FBS mass supply in + ``estimate_baseline_diet``. + +SPDX-FileCopyrightText: 2026 Koen van Greevenbroek + +SPDX-License-Identifier: GPL-3.0-or-later +""" + +import logging + +import pandas as pd + +from workflow.scripts.diet.food_group_projection import ( + FRUITS_BAN_POOL_ITEM_CODES, + FRUITS_BAN_PROJECTION_FOODS, + FRUITS_FRT_POOL_ITEM_CODES, + FRUITS_FRT_PROJECTION_FOODS, + NUTS_POOL_ITEM_CODES, + NUTS_PROJECTION_FOODS, + OVG_CROPS, + OVG_POOL_ITEM_CODES, + STARCHY_POOL_ITEM_CODES, + STARCHY_PROJECTION_FOODS, +) + +logger = logging.getLogger(__name__) + +# Dairy items folded into one milk-equivalent energy pool at cow-milk +# density: Milk - Excluding Butter, Butter/Ghee, Cream. +DAIRY_ITEM_CODES: tuple[int, ...] = (2848, 2740, 2743) +DAIRY_FOOD = "dairy" + +# FBS aggregate covering all vegetable oils, modelled and unmodelled. +OIL_GROUP_ITEM_CODE = 2914 + +# Items needed by this module beyond the mapped + pool codes fetched for +# the within-group shares; imported by prepare_faostat_fbs_items so the +# fetch list stays in sync. +EXTRA_FETCH_ITEM_CODES: tuple[int, ...] = (2740, 2743, OIL_GROUP_ITEM_CODE) + +# Groups this source does not emit (see module docstring). +SKIPPED_GROUPS: tuple[str, ...] = ("stimulants",) + +# Per-group projection pools: (pool item codes, recipient foods). Pool +# items not mapped to any modelled food are attributed to the group at +# the mean density of the recipient foods. +POOL_SPECS_BY_GROUP: dict[str, list[tuple[tuple[int, ...], tuple[str, ...]]]] = { + "vegetables": [(OVG_POOL_ITEM_CODES, OVG_CROPS)], + "starchy_vegetable": [(STARCHY_POOL_ITEM_CODES, STARCHY_PROJECTION_FOODS)], + "nuts_seeds": [(NUTS_POOL_ITEM_CODES, NUTS_PROJECTION_FOODS)], + "fruits": [ + (FRUITS_BAN_POOL_ITEM_CODES, FRUITS_BAN_PROJECTION_FOODS), + (FRUITS_FRT_POOL_ITEM_CODES, FRUITS_FRT_PROJECTION_FOODS), + ], +} + +# Unit labels per group, consistent with gdd_ia_dietary_intake.csv. +UNIT_BY_GROUP = { + "dairy": "g/day (milk equiv)", + "sugar": "g/day (refined sugar eq)", + "animal_fat": "g/day (fresh wt)", + "oil": "g/day (fresh wt)", + "grain": "g/day (fresh wt)", + "whole_grains": "g/day (fresh wt)", + "legumes": "g/day (fresh wt)", + "fruits": "g/day (fresh wt)", + "vegetables": "g/day (fresh wt)", + "nuts_seeds": "g/day (fresh wt)", + "starchy_vegetable": "g/day (fresh wt)", + "red_meat": "g/day (fresh wt)", + "poultry": "g/day (fresh wt)", + "eggs": "g/day (fresh wt)", +} + +WHOLE_GRAINS_GROUP = "whole_grains" +GRAIN_GROUP = "grain" + + +def _mean_density(foods: list[str], kcal_per_g_food: dict[str, float]) -> float: + """Mean model-basis kcal/g over *foods*; fails on missing entries.""" + missing = [f for f in foods if f not in kcal_per_g_food] + if missing: + raise ValueError(f"kcal/g missing from nutrition.csv for foods: {missing}") + densities = [float(kcal_per_g_food[f]) for f in foods] + return sum(densities) / len(densities) + + +def build_item_attribution( + food_item_map: pd.DataFrame, + food_groups: pd.DataFrame, + kcal_per_g_food: dict[str, float], + food_groups_included: list[str], + byproducts: list[str], + whole_grain_shares: dict[str, float], +) -> list[tuple[int, str, float, float]]: + """Attribute FBS items to food groups with energy shares and densities. + + Returns a list of ``(item_code, food_group, kcal_share, kcal_per_g)`` + tuples. An item appears once per group it contributes to; the + ``kcal_share`` values of one item sum to 1 (wheat and rice appear + twice via the whole-grain split, every other item once). + """ + fg_map = food_groups.set_index("food")["group"].to_dict() + emitted_groups = [g for g in food_groups_included if g not in SKIPPED_GROUPS] + byproduct_set = set(byproducts) + + unmapped = sorted( + {str(r["food"]) for _, r in food_item_map.iterrows()} + - set(fg_map) + - byproduct_set + ) + if unmapped: + raise ValueError( + f"Foods in faostat_food_item_map.csv missing from " + f"food_groups.csv: {unmapped}" + ) + + # Foods participating in item attribution. Dairy and oil items are + # handled explicitly below, so their foods are left out here. + def participates(food: str) -> bool: + if food in byproduct_set: + return False + group = fg_map[food] + return group in emitted_groups and group not in ("dairy", "oil") + + foods_by_code: dict[int, list[str]] = {} + for _, row in food_item_map.iterrows(): + food = str(row["food"]) + if not participates(food): + continue + foods_by_code.setdefault(int(row["item_code"]), []).append(food) + + attribution: list[tuple[int, str, float, float]] = [] + + for code, foods in sorted(foods_by_code.items()): + groups = sorted({fg_map[f] for f in foods}) + if len(groups) == 1: + attribution.append( + (code, groups[0], 1.0, _mean_density(foods, kcal_per_g_food)) + ) + continue + if set(groups) != {GRAIN_GROUP, WHOLE_GRAINS_GROUP}: + raise ValueError( + f"FBS item {code} maps to foods across groups {groups}; only " + "the grain/whole_grains split (diet.fbs.whole_grain_shares) " + "is supported for cross-group items." + ) + whole_foods = [f for f in foods if fg_map[f] == WHOLE_GRAINS_GROUP] + refined_foods = [f for f in foods if fg_map[f] == GRAIN_GROUP] + if len(whole_foods) != 1: + raise ValueError( + f"FBS item {code} has {len(whole_foods)} whole-grain foods " + f"({whole_foods}); expected exactly one to apply " + "diet.fbs.whole_grain_shares." + ) + whole_food = whole_foods[0] + if whole_food not in whole_grain_shares: + raise ValueError( + f"diet.fbs.whole_grain_shares has no entry for '{whole_food}' " + f"(FBS item {code}); required to split the item between " + "grain and whole_grains." + ) + share = float(whole_grain_shares[whole_food]) + attribution.append( + ( + code, + WHOLE_GRAINS_GROUP, + share, + _mean_density([whole_food], kcal_per_g_food), + ) + ) + attribution.append( + ( + code, + GRAIN_GROUP, + 1.0 - share, + _mean_density(refined_foods, kcal_per_g_food), + ) + ) + + # Pool items not mapped to any modelled food, at the mean density of + # the pool's recipient foods. + mapped_codes = set(foods_by_code) + for group, pools in POOL_SPECS_BY_GROUP.items(): + if group not in emitted_groups: + continue + for pool_codes, recipients in pools: + density = _mean_density(list(recipients), kcal_per_g_food) + for code in pool_codes: + if int(code) in mapped_codes: + continue + attribution.append((int(code), group, 1.0, density)) + + # Dairy: milk + butter + cream energy at cow-milk density. + if "dairy" in emitted_groups: + dairy_density = _mean_density([DAIRY_FOOD], kcal_per_g_food) + for code in DAIRY_ITEM_CODES: + attribution.append((code, "dairy", 1.0, dairy_density)) + + # Oil: the vegetable-oils aggregate at the mean modelled-oil density. + if "oil" in emitted_groups: + oil_foods = sorted( + f for f, g in fg_map.items() if g == "oil" and f not in byproduct_set + ) + if not oil_foods: + raise ValueError("No oil foods found in food_groups.csv") + attribution.append( + ( + OIL_GROUP_ITEM_CODE, + "oil", + 1.0, + _mean_density(oil_foods, kcal_per_g_food), + ) + ) + + covered = {group for _, group, _, _ in attribution} + uncovered = [g for g in emitted_groups if g not in covered] + if uncovered: + raise ValueError( + f"No FBS items attributed to food groups {uncovered}; check " + "faostat_food_item_map.csv and POOL_SPECS_BY_GROUP." + ) + return attribution + + +def build_fbs_group_intake( + kcal_supply: pd.DataFrame, + food_item_map: pd.DataFrame, + food_groups: pd.DataFrame, + nutrition: pd.DataFrame, + food_loss_waste: pd.DataFrame, + countries: list[str], + food_groups_included: list[str], + byproducts: list[str], + whole_grain_shares: dict[str, float], +) -> pd.DataFrame: + """Per-(country, food-group) intake (g/day, model basis) from FBS energy. + + ``kcal_supply`` carries per-(country, item_code) FBS food energy + supply (``kcal_per_capita_day``, layered FBS/FBSH fallback); missing + cells are treated as zero supply. + + Returns a DataFrame with columns ``country``, ``food_group``, + ``value`` (g/day per capita, consumer-eaten intake in model basis). + """ + kcal_per_g_food = ( + nutrition[nutrition["nutrient"] == "cal"].set_index("food")["value"] / 100.0 + ).to_dict() + attribution = build_item_attribution( + food_item_map, + food_groups, + kcal_per_g_food, + food_groups_included, + byproducts, + whole_grain_shares, + ) + + kcal_lookup = kcal_supply.set_index(["country", "item_code"])[ + "kcal_per_capita_day" + ].to_dict() + waste_lookup = food_loss_waste.set_index(["country", "food_group"])[ + "waste_fraction" + ].to_dict() + + rows = [] + missing_waste: set[tuple[str, str]] = set() + for country in countries: + totals: dict[str, float] = {} + for code, group, share, density in attribution: + # Missing (country, item) cells are genuine zero supply (the + # country does not consume the item), matching how the + # within-group share machinery treats them. + kcal = float(kcal_lookup.get((country, code), 0.0)) + if kcal <= 0.0: + continue + totals[group] = totals.get(group, 0.0) + kcal * share / density + for group, g_day_pre_waste in sorted(totals.items()): + if (country, group) not in waste_lookup: + missing_waste.add((country, group)) + continue + waste = float(waste_lookup[(country, group)]) + rows.append( + { + "country": country, + "food_group": group, + "value": g_day_pre_waste * (1.0 - waste), + } + ) + + if missing_waste: + raise ValueError( + f"food_loss_waste.csv has no waste fraction for " + f"{len(missing_waste)} (country, group) pairs needed by the FBS " + f"diet source (e.g. {sorted(missing_waste)[:5]})" + ) + + result = pd.DataFrame(rows) + if result.empty: + raise ValueError("FBS kcal supply produced no dietary intake rows") + + means = result.groupby("food_group")["value"].mean().round(1) + logger.info("FBS-derived per-group mean intake (g/day) across countries:") + for group, value in means.sort_index().items(): + logger.info(" %s: %.1f", group, value) + return result diff --git a/workflow/scripts/estimate_baseline_diet.py b/workflow/scripts/estimate_baseline_diet.py index dcdd5876..1019ee9c 100644 --- a/workflow/scripts/estimate_baseline_diet.py +++ b/workflow/scripts/estimate_baseline_diet.py @@ -11,7 +11,8 @@ per-food consumption estimates (g/person/day). Algorithm: - 1. Load food group totals from dietary_intake.csv (GDD + FAOSTAT supplements) + 1. Load food group totals from dietary_intake.csv (the configured + diet.source -- GDD-IA or FBS-derived -- plus the NHANES USA override) 2. For groups in ``health.risk_factors`` (GBD-anchored), the per-country group total is taken from GBD when GBD reports a value, else the GDD/FAOSTAT value. No averaging; GBD strictly takes precedence on @@ -294,18 +295,21 @@ def apply_cereal_residual_fix( (India typical). To preserve the country's cereal energy budget, we reassign the deficit to refined ``grain``. - The deficit is computed against IA's *actual* cereal kcal pool - (whole_grains kcal + prc_grains kcal) carried via - ``gdd_ia_kcal_target.csv``, not via the broader nutrition.csv per- - group density — because IA's whole_grain mass is in a different - basis (~2.5-3 kcal/g) than the model's dry-flour basis (3.3 kcal/g). + The deficit is computed against the source's *actual* cereal kcal + pool (whole_grains kcal + refined-grain kcal) carried via + *kcal_target_df* -- GDD-IA's own kcal accounting, or the budget + synthesized from the FBS group totals (see + ``synthesize_cereal_kcal_budget``) -- not via the broader + nutrition.csv per-group density, because e.g. IA's whole_grain mass + is in a different basis (~2.5-3 kcal/g) than the model's dry-flour + basis (3.3 kcal/g). - deficit_kcal = (kcal_whole_grains_ia + kcal_grain_ia) + deficit_kcal = (kcal_whole_grains + kcal_grain) - g_whole_anchored x k_whole_model new_g_grain = max(0, deficit_kcal) / k_grain_model """ - k_whole = float(kcal_per_g_group.get(WHOLE_GRAINS_GROUP, 3.3)) - k_grain = float(kcal_per_g_group.get(GRAIN_GROUP, 3.6)) + k_whole = float(kcal_per_g_group[WHOLE_GRAINS_GROUP]) + k_grain = float(kcal_per_g_group[GRAIN_GROUP]) targets = kcal_target_df.set_index("country") totals = group_totals.set_index(["country", "food_group"])[ @@ -320,10 +324,10 @@ def apply_cereal_residual_fix( n_skipped += 1 continue whole_anchored = float(totals.get((country, WHOLE_GRAINS_GROUP), 0.0) or 0.0) - ia_cereal_kcal = float(targets.at[country, "kcal_whole_grains_ia"]) + float( - targets.at[country, "kcal_grain_ia"] + source_cereal_kcal = float(targets.at[country, "kcal_whole_grains"]) + float( + targets.at[country, "kcal_grain"] ) - deficit_kcal = ia_cereal_kcal - whole_anchored * k_whole + deficit_kcal = source_cereal_kcal - whole_anchored * k_whole if deficit_kcal <= 0: n_skipped += 1 continue @@ -333,7 +337,7 @@ def apply_cereal_residual_fix( total_deficit_kcal += deficit_kcal logger.info( - "Cereal residual fix: set refined-grain to absorb IA cereal kcal " + "Cereal residual fix: set refined-grain to absorb source cereal kcal " "minus anchored whole-grain kcal in %d countries (skipped %d). " "Mean refined-grain kcal per fixed country: %.0f kcal/d.", n_fixed, @@ -347,6 +351,43 @@ def apply_cereal_residual_fix( ) +def synthesize_cereal_kcal_budget( + dietary_intake_path: str, + baseline_age: str, + kcal_per_g_group: dict[str, float], +) -> pd.DataFrame: + """Cereal energy budget per country from the source group totals. + + Used by the cereal residual fix when the diet source carries no + survey kcal accounting (``diet.source: fbs``): the source's cereal + energy is reconstructed from the pre-anchoring ``grain`` / + ``whole_grains`` group totals at model-basis group densities. For + the FBS source the masses were themselves derived from FBS energy + at these densities, so this reproduces the FBS cereal energy + exactly. + + Returns a DataFrame with columns ``country``, ``kcal_whole_grains``, + ``kcal_grain`` (the subset of the kcal-target schema the cereal + residual fix consumes). + """ + intake = pd.read_csv(dietary_intake_path) + intake = intake[intake["age"] == baseline_age] + cereal = ( + intake[intake["item"].isin([WHOLE_GRAINS_GROUP, GRAIN_GROUP])] + .pivot_table(index="country", columns="item", values="value", fill_value=0.0) + .reindex(columns=[WHOLE_GRAINS_GROUP, GRAIN_GROUP], fill_value=0.0) + ) + k_whole = float(kcal_per_g_group[WHOLE_GRAINS_GROUP]) + k_grain = float(kcal_per_g_group[GRAIN_GROUP]) + return pd.DataFrame( + { + "country": cereal.index, + "kcal_whole_grains": cereal[WHOLE_GRAINS_GROUP].to_numpy() * k_whole, + "kcal_grain": cereal[GRAIN_GROUP].to_numpy() * k_grain, + } + ).reset_index(drop=True) + + def apply_kcal_normalisation( baseline_diet: pd.DataFrame, kcal_target_df: pd.DataFrame, @@ -1465,6 +1506,7 @@ def main(): reference_year = int(snakemake.params.reference_year) baseline_age = str(snakemake.params.baseline_age) + diet_source = str(snakemake.params.diet_source) food_groups_included = list(snakemake.params.food_groups_included) byproducts = list(snakemake.params.byproducts) fbs_override_foods = list(snakemake.params.fbs_override_foods) @@ -1559,21 +1601,29 @@ def main(): ) # Step 1b: Cereal residual fix — when GBD's narrow whole_grain anchor - # discards cereal kcal from the GDD-IA source, reassign that deficit - # to refined grain. Uses IA's actual cereal kcal pool carried in - # gdd_ia_kcal_target.csv. - kcal_target_df = pd.read_csv(kcal_target_path) + # discards cereal kcal from the source, reassign that deficit to + # refined grain. GDD-IA ships its own survey kcal accounting + # (gdd_ia_kcal_target.csv); the FBS source has none, so the cereal + # budget is reconstructed from the source group totals instead. nutrition_df = pd.read_csv(nutrition_path) kcal_per_g_food = build_kcal_per_g_food(nutrition_df) + kcal_target_df = pd.read_csv(kcal_target_path) if diet_source == "gdd_ia" else None # The cereal residual fix only compensates for energy lost to GBD's narrow # whole_grain anchor, so it applies only when whole_grains is anchored. - # With anchoring off the GDD/FAOSTAT whole_grains total already carries the + # With anchoring off the source whole_grains total already carries the # broad cereal energy and no reallocation to refined grain is needed. if WHOLE_GRAINS_GROUP in gbd_anchored_groups: kcal_per_g_group = build_kcal_per_g_group(food_groups_df, nutrition_df) + cereal_budget = ( + kcal_target_df + if kcal_target_df is not None + else synthesize_cereal_kcal_budget( + dietary_intake_path, baseline_age, kcal_per_g_group + ) + ) group_totals = apply_cereal_residual_fix( group_totals, - kcal_target_df, + cereal_budget, kcal_per_g_group, ) @@ -1711,12 +1761,16 @@ def main(): # unanchored foods so total kcal/day hits ``kcal_target_modelled``; # GBD-anchored foods and the refined-grain residual are preserved. # See ``apply_kcal_normalisation`` for the basis-mismatch motivation. - result = apply_kcal_normalisation( - result, - kcal_target_df, - gbd_anchored_groups=gbd_anchored_groups, - kcal_per_g_food=kcal_per_g_food, - ) + # GDD-IA only: the FBS source derives its masses from FBS energy at + # the same densities used here, so its diet is FAO-energy-consistent + # by construction and rescaling would only distort the masses. + if diet_source == "gdd_ia": + result = apply_kcal_normalisation( + result, + kcal_target_df, + gbd_anchored_groups=gbd_anchored_groups, + kcal_per_g_food=kcal_per_g_food, + ) # Step 4: Override specific foods with FBS-supply-anchored intake if fbs_override_foods: diff --git a/workflow/scripts/merge_dietary_sources.py b/workflow/scripts/merge_dietary_sources.py index ca9fe3cc..a2d39df4 100644 --- a/workflow/scripts/merge_dietary_sources.py +++ b/workflow/scripts/merge_dietary_sources.py @@ -3,29 +3,30 @@ # # SPDX-License-Identifier: GPL-3.0-or-later -"""Merge GDD-IA dietary intake with NHANES (USA override). +"""Merge the configured group-intake source with NHANES (USA override). Inputs: -- ``gdd_ia``: GDD-IA-derived per-(country, group) intake in model basis - (g/day), already kcal-derived and proxy-filled. +- ``group_intake``: per-(country, group) intake in model basis (g/day) + from the configured ``diet.source`` -- either GDD-IA (kcal-derived and + proxy-filled) or the FBS-derived estimate (kcal-derived and + waste-corrected). - ``nhanes``: NHANES (FPED-derived) USA intake, per food group. NHANES values are intake-based and already in model basis; they - override the GDD-IA values for the country/items NHANES covers. + override the source values for the country/items NHANES covers. - ``faostat_supply``: FAOSTAT FBS-derived per-(country, group) supply - (g/day). Used only to fill the ``animal_fat`` group where GDD-IA's - ``fat_ani`` is not reported (a known coverage gap on ~37 countries), - scaled by ``ANIMAL_FAT_SUPPLY_TO_INTAKE`` to approximate post-FLW - intake. Other groups in the supply file are ignored here; they enter - the pipeline through ``prepare_food_loss_waste``. - -This script's job is just the source merge. The country-level -kcal-normalisation step (against GDD-IA's `all-fg` minus out-of-scope -categories) happens in ``estimate_baseline_diet``, after GBD anchoring -and the cereal residual fix. - -No basis conversion is performed here. GDD-IA mass is derived via -``kcal_ia / kcal_per_g_model_basis``, so the output is already in -model basis by construction. NHANES values are intake-based and in + (g/day). Used only to fill the ``animal_fat`` group on countries the + source does not cover (a known GDD-IA coverage gap on ~37 countries; + a no-op for the FBS source, which covers animal_fat natively), scaled + by ``ANIMAL_FAT_SUPPLY_TO_INTAKE`` to approximate post-FLW intake. + Other groups in the supply file are ignored here; they enter the + pipeline through ``prepare_food_loss_waste``. + +This script's job is just the source merge. GBD anchoring, the cereal +residual fix, and (for GDD-IA) the country-level kcal normalisation +happen in ``estimate_baseline_diet``. + +No basis conversion is performed here: both sources emit model-basis +values by construction, and NHANES values are intake-based and in model basis already (the FPED extraction handles that upstream). Output: @@ -98,18 +99,20 @@ def _faostat_animal_fat_fallback( def main() -> None: - gdd_ia_path = snakemake.input["gdd_ia"] + group_intake_path = snakemake.input["group_intake"] nhanes_path = snakemake.input["nhanes"] faostat_path = snakemake.input["faostat_supply"] + diet_source = str(snakemake.params["diet_source"]) output_path = snakemake.output["diet"] - logger.info("Reading GDD-IA dietary intake from %s", gdd_ia_path) - gdd_ia = pd.read_csv(gdd_ia_path) + logger.info("Reading %s dietary intake from %s", diet_source, group_intake_path) + group_intake = pd.read_csv(group_intake_path) logger.info( - "GDD-IA: %d rows, %d countries, items %s", - len(gdd_ia), - gdd_ia["country"].nunique(), - sorted(gdd_ia["item"].unique()), + "%s: %d rows, %d countries, items %s", + diet_source, + len(group_intake), + group_intake["country"].nunique(), + sorted(group_intake["item"].unique()), ) logger.info("Reading NHANES dietary intake from %s", nhanes_path) @@ -121,13 +124,13 @@ def main() -> None: logger.info("Reading FAOSTAT food group supply from %s", faostat_path) faostat_supply = pd.read_csv(faostat_path) - # NHANES overrides GDD-IA for the (country, item) pairs it covers. - gdd_ia = _drop_overlap(gdd_ia, nhanes, "NHANES") + # NHANES overrides the source for the (country, item) pairs it covers. + group_intake = _drop_overlap(group_intake, nhanes, "NHANES") - combined = pd.concat([gdd_ia, nhanes], ignore_index=True) + combined = pd.concat([group_intake, nhanes], ignore_index=True) # Supplement animal_fat from FAOSTAT FBS supply for countries that - # neither GDD-IA nor NHANES covers. + # neither the group-intake source nor NHANES covers. animal_fat_supplement = _faostat_animal_fat_fallback(faostat_supply, combined) if not animal_fat_supplement.empty: logger.info( diff --git a/workflow/scripts/prepare_faostat_fbs_items.py b/workflow/scripts/prepare_faostat_fbs_items.py index ce6cf949..1c2603e6 100644 --- a/workflow/scripts/prepare_faostat_fbs_items.py +++ b/workflow/scripts/prepare_faostat_fbs_items.py @@ -5,8 +5,11 @@ """Prepare raw food supply data from FAOSTAT Food Balance Sheets. -Reads item-level supply data (kg/capita/year) for all items mapped in the -food item mapping file. Uses a layered fallback (see +Reads item-level food supply for all items mapped in the food item +mapping file, in two elements: mass supply (kg/capita/year, consumed by +the within-group share machinery and the FBS overrides) and energy +supply (kcal/capita/day, consumed by the FBS baseline-diet source in +``prepare_fbs_dietary_intake``). Both use a layered fallback (see :func:`build_layered_fbs_supply` in ``faostat_bulk.py``): 1. New FBS at the reference year @@ -18,14 +21,16 @@ ``FBS_COUNTRY_FALLBACKS`` 5. Skipped (zero supply downstream) -Outputs ``faostat_fbs_items.csv`` plus a provenance CSV summarising -which fallback tier each country lands on. +Outputs ``faostat_fbs_items.csv`` (mass) and +``faostat_fbs_items_kcal.csv`` (energy), plus a provenance CSV +summarising which fallback tier each country lands on (mass element). """ import logging import pandas as pd +from workflow.scripts.diet.fbs_intake import EXTRA_FETCH_ITEM_CODES from workflow.scripts.diet.food_group_projection import ( FRUITS_BAN_POOL_ITEM_CODES, FRUITS_FRT_POOL_ITEM_CODES, @@ -71,10 +76,15 @@ def main(): output_file = snakemake.output.fbs_items provenance_file = snakemake.output.fbs_provenance - # Load food-to-FBS-item mapping + # Load food-to-FBS-item mapping. EXTRA_FETCH_ITEM_CODES covers items + # only the FBS diet source needs (butter, cream, vegetable-oils + # aggregate); fetching them unconditionally keeps this rule + # source-independent. food_map_df = pd.read_csv(food_item_map_path, comment="#") explicit_codes = food_map_df["item_code"].dropna().astype(int).unique().tolist() - unique_item_codes = sorted(set(explicit_codes) | set(POOL_FETCH_CODES)) + unique_item_codes = sorted( + set(explicit_codes) | set(POOL_FETCH_CODES) | set(EXTRA_FETCH_ITEM_CODES) + ) if not unique_item_codes: raise ValueError("No item codes found in food item mapping file") @@ -90,7 +100,8 @@ def main(): logger.info("Found %d unique FBS item codes to fetch", len(unique_item_codes)) - elem_code = int(snakemake.params.fbs_element_code) + mass_elem_code = int(snakemake.params.fbs_element_code) + kcal_elem_code = int(snakemake.params.fbs_kcal_element_code) m49_to_iso3 = load_m49_to_iso3(m49_codes) # Include proxy countries in the filter so we can use them as fallbacks @@ -99,40 +110,59 @@ def main(): all_proxies.update(proxies) filter_countries = list(set(countries) | all_proxies) - # Load and filter new FBS bulk (2010-) + # Load and filter both bulks once for both elements, then resolve the + # layered fallback per element. logger.info("Loading FAOSTAT new FBS bulk") fbs_bulk = add_iso3_column(load_bulk(fbs_csv), m49_to_iso3) fbs_df = filter_bulk( fbs_bulk, - element_codes=[elem_code], + element_codes=[mass_elem_code, kcal_elem_code], item_codes=unique_item_codes, iso3_codes=filter_countries, ) - # Load and filter historic FBSH bulk (1961-2013) logger.info("Loading FAOSTAT historic FBSH bulk") fbsh_bulk = add_iso3_column(load_bulk(fbsh_csv), m49_to_iso3) fbsh_df = filter_bulk( fbsh_bulk, - element_codes=[elem_code], + element_codes=[mass_elem_code, kcal_elem_code], item_codes=unique_item_codes, iso3_codes=filter_countries, ) - result = build_layered_fbs_supply( - fbs_df=fbs_df, - fbsh_df=fbsh_df, - countries=countries, - item_codes=unique_item_codes, - reference_year=reference_year, - ) + def layered(elem_code: int) -> pd.DataFrame: + return build_layered_fbs_supply( + fbs_df=fbs_df[fbs_df["Element Code"] == elem_code], + fbsh_df=fbsh_df[fbsh_df["Element Code"] == elem_code], + countries=countries, + item_codes=unique_item_codes, + reference_year=reference_year, + ) + result = layered(mass_elem_code) if result.empty: raise ValueError( "Layered FBS/FBSH fallback produced zero rows for all " f"{len(countries)} target countries" ) + kcal_result = layered(kcal_elem_code).rename( + columns={"supply_kg_per_capita_year": "kcal_per_capita_day"} + ) + if kcal_result.empty: + raise ValueError( + "Layered FBS/FBSH fallback produced zero kcal-supply rows for " + f"all {len(countries)} target countries" + ) + kcal_result.to_csv(snakemake.output.fbs_items_kcal, index=False) + logger.info( + "Wrote %d kcal-supply rows (%d countries, %d items) to %s", + len(kcal_result), + kcal_result["country"].nunique(), + kcal_result["item_code"].nunique(), + snakemake.output.fbs_items_kcal, + ) + # Downstream consumers expect at least country, item_code, item_name, # supply_kg_per_capita_year. The added source/year columns are the # provenance trail; ignored by readers that don't know about them. diff --git a/workflow/scripts/prepare_fbs_dietary_intake.py b/workflow/scripts/prepare_fbs_dietary_intake.py new file mode 100644 index 00000000..a024d400 --- /dev/null +++ b/workflow/scripts/prepare_fbs_dietary_intake.py @@ -0,0 +1,90 @@ +#!/usr/bin/env python3 +# SPDX-FileCopyrightText: 2026 Koen van Greevenbroek +# +# SPDX-License-Identifier: GPL-3.0-or-later + +"""Derive baseline per-(country, food-group) dietary intake from FAOSTAT FBS. + +Thin wrapper around :mod:`workflow.scripts.diet.fbs_intake` (see its +module docstring for the method): group intake mass is derived from the +FBS "Food supply (kcal/capita/day)" element at model-basis energy +densities and corrected for consumer waste. The output mirrors the +schema of ``gdd_ia_dietary_intake.csv`` (unit, item, country, age, +year, value) so ``merge_dietary_sources`` can consume either source. + +Input: + - faostat_fbs_items_kcal.csv (per-(country, item) FBS energy supply) + - faostat_food_item_map.csv (food -> FBS item codes) + - food_groups.csv (food -> group) + - nutrition.csv (model-basis energy densities) + - food_loss_waste.csv (consumer waste fractions) + +Output: + - fbs_dietary_intake.csv: unit,item,country,age,year,value +""" + +import logging +from pathlib import Path + +import pandas as pd + +from workflow.scripts.diet.fbs_intake import UNIT_BY_GROUP, build_fbs_group_intake +from workflow.scripts.logging_config import setup_script_logging + +logger = logging.getLogger("prepare_fbs_dietary_intake") + + +def main() -> None: + kcal_supply = pd.read_csv(snakemake.input["fbs_items_kcal"]) + food_item_map = pd.read_csv(snakemake.input["food_item_map"], comment="#") + food_groups = pd.read_csv(snakemake.input["food_groups"]) + nutrition = pd.read_csv(snakemake.input["nutrition"]) + food_loss_waste = pd.read_csv(snakemake.input["food_loss_waste"]) + + countries = [str(c).upper() for c in snakemake.params["countries"]] + food_groups_included = list(snakemake.params["food_groups"]) + byproducts = list(snakemake.params["byproducts"]) + whole_grain_shares = { + str(k): float(v) + for k, v in dict(snakemake.params["whole_grain_shares"]).items() + } + baseline_age = str(snakemake.params["baseline_age"]) + reference_year = int(snakemake.params["reference_year"]) + + intake = build_fbs_group_intake( + kcal_supply=kcal_supply, + food_item_map=food_item_map, + food_groups=food_groups, + nutrition=nutrition, + food_loss_waste=food_loss_waste, + countries=countries, + food_groups_included=food_groups_included, + byproducts=byproducts, + whole_grain_shares=whole_grain_shares, + ) + + out = intake.rename(columns={"food_group": "item"}) + out["unit"] = out["item"].map(UNIT_BY_GROUP) + out["age"] = baseline_age + out["year"] = reference_year + out = ( + out[["unit", "item", "country", "age", "year", "value"]] + .sort_values(["country", "item"]) + .reset_index(drop=True) + ) + + output_path = Path(snakemake.output["diet"]) + output_path.parent.mkdir(parents=True, exist_ok=True) + out.to_csv(output_path, index=False) + logger.info( + "Wrote %d rows (%d countries, %d groups) to %s", + len(out), + out["country"].nunique(), + out["item"].nunique(), + output_path, + ) + + +if __name__ == "__main__": + logger = setup_script_logging(log_file=snakemake.log[0] if snakemake.log else None) + main() diff --git a/workflow/scripts/prepare_gdd_ia_dietary_intake.py b/workflow/scripts/prepare_gdd_ia_dietary_intake.py index 174ad213..097cde3c 100644 --- a/workflow/scripts/prepare_gdd_ia_dietary_intake.py +++ b/workflow/scripts/prepare_gdd_ia_dietary_intake.py @@ -399,15 +399,15 @@ def main() -> None: prcd_whole = kcal[ (kcal["type"] == "prcd") & (kcal["food_group"] == "whole_grains") ][["region", "value"]].rename( - columns={"region": "country", "value": "kcal_whole_grains_ia"} + columns={"region": "country", "value": "kcal_whole_grains"} ) prcd_grain = kcal[(kcal["type"] == "prcd") & (kcal["food_group"] == "prc_grains")][ ["region", "value"] - ].rename(columns={"region": "country", "value": "kcal_grain_ia"}) + ].rename(columns={"region": "country", "value": "kcal_grain"}) targets = targets.merge(prcd_whole, on="country", how="left") targets = targets.merge(prcd_grain, on="country", how="left") - targets["kcal_whole_grains_ia"] = targets["kcal_whole_grains_ia"].fillna(0.0) - targets["kcal_grain_ia"] = targets["kcal_grain_ia"].fillna(0.0) + targets["kcal_whole_grains"] = targets["kcal_whole_grains"].fillna(0.0) + targets["kcal_grain"] = targets["kcal_grain"].fillna(0.0) # Apply proxy filling to kcal targets too. have = set(targets["country"].unique()) @@ -450,8 +450,8 @@ def main() -> None: "kcal_all_fg", "kcal_oos", "kcal_target_modelled", - "kcal_whole_grains_ia", - "kcal_grain_ia", + "kcal_whole_grains", + "kcal_grain", ] ] .sort_values("country") diff --git a/workflow/validation/calibration_provenance.py b/workflow/validation/calibration_provenance.py index b6ca7287..4afa9ea7 100644 --- a/workflow/validation/calibration_provenance.py +++ b/workflow/validation/calibration_provenance.py @@ -102,6 +102,15 @@ def walk(node: dict, prefix: str) -> None: # identically. if "diet.anchor_groups_to_gbd" in snapshot: snapshot["diet.anchor_groups_to_gbd"] = resolve_gbd_anchoring(config) + # The diet-source blocks (diet.fbs, diet.gdd_ia) only shape the + # baseline diet when their source is active; drop the inactive one so + # its knobs cannot spuriously (in)validate a stamp. + inactive = "diet.gdd_ia" if config["diet"]["source"] == "fbs" else "diet.fbs" + snapshot = { + k: v + for k, v in snapshot.items() + if k != inactive and not k.startswith(inactive + ".") + } return snapshot