diff --git a/CHANGELOG.md b/CHANGELOG.md index 06ca60e0..b0a70692 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -17,6 +17,22 @@ introduce breaking changes to configuration and outputs. ### Added +- Multiple cropping is now anchored to an observed baseline derived from + MIRCA-OS v2 (new automated data source), using the available 2010, 2015, or + 2020 release nearest `baseline_year`. A fixed, documented sequence catalog + replaces dynamic combination discovery. Config entries may disable catalog + sequences or add zero-baseline greenfield potential under a new name. + Irrigated and rainfed observations are attributed separately, competing + rotations share crop-area budgets, and outputs are derived per config so the + spatial and climate inputs cannot be mixed between runs. + `crop_production_multi` links participate in the land deviation penalty, crop + growth cap, cost calibration, and validation-mode pinning like single-crop + links. Harvested cycles are reconciled out of the single-crop FAOSTAT + baselines so each cycle is counted once. A new `multi_crop_cost.csv` + calibration artefact carries per-(combination, country) bundle corrections. + The GAEZ growing-season compatibility gate is removed; observed feasibility + comes from MIRCA plus the GAEZ multiple-cropping zone. + - New `health.segment_formulation: relax_and_fix` option (now the default): a two-pass LP scheme for the health module's non-convex dose-response curves (solve the relaxation, pin each non-convex curve to the segment of diff --git a/config/calibration/cost.yaml b/config/calibration/cost.yaml index df1d2529..535d69ce 100644 --- a/config/calibration/cost.yaml +++ b/config/calibration/cost.yaml @@ -20,8 +20,8 @@ # data/curated/calibration/grassland_cost.csv \ # data/curated/calibration/animal_cost.csv -# Shared name across all calibration configs so the expensive upstream -# processing is built once under processing/calibration/ and reused. +# ``tools/calibrate`` overrides this with a source- and step-specific name so +# another calibration config cannot overwrite this step's intermediates. name: "calibration" scenarios: diff --git a/config/calibration/feed.yaml b/config/calibration/feed.yaml index 5d2848a8..f4eeea3b 100644 --- a/config/calibration/feed.yaml +++ b/config/calibration/feed.yaml @@ -30,9 +30,8 @@ # data/curated/calibration/exogenous_forage.csv \ # data/curated/calibration/exogenous_feed.csv -# Shared name across all calibration configs so the expensive upstream -# processing (crop yields, harvested area, regions, etc.) is built once -# under processing/calibration/ and reused by each calibration step. +# ``tools/calibrate`` overrides this with a source- and step-specific name so +# another calibration config cannot overwrite this step's intermediates. name: "calibration" scenarios: @@ -81,6 +80,7 @@ exogenous_feed_calibration: scenario: feed_uncal health: + enabled: false value_per_yll: 0 emissions: diff --git a/config/calibration/food_demand.yaml b/config/calibration/food_demand.yaml index 42683227..76693532 100644 --- a/config/calibration/food_demand.yaml +++ b/config/calibration/food_demand.yaml @@ -34,8 +34,8 @@ # tools/smk --configfile config/calibration/food_demand.yaml -- \ # data/curated/calibration/food_demand.csv -# Shared name across all calibration configs so the expensive upstream -# processing is built once under processing/calibration/ and reused. +# ``tools/calibrate`` overrides this with a source- and step-specific name so +# another calibration config cannot overwrite this step's intermediates. name: "calibration" scenarios: @@ -74,6 +74,7 @@ food_demand_calibration: scenario: "food_demand_uncal" health: + enabled: false value_per_yll: 0 emissions: diff --git a/config/calibration/food_waste.yaml b/config/calibration/food_waste.yaml index 3e6c27e4..39c20fcb 100644 --- a/config/calibration/food_waste.yaml +++ b/config/calibration/food_waste.yaml @@ -25,8 +25,8 @@ # tools/smk --configfile config/calibration/food_waste.yaml -- \ # data/curated/calibration/food_waste.yaml -# Shared name across all calibration configs so the expensive upstream -# processing is built once under processing/calibration/ and reused. +# ``tools/calibrate`` overrides this with a source- and step-specific name so +# another calibration config cannot overwrite this step's intermediates. name: "calibration" scenarios: @@ -72,6 +72,7 @@ food_loss_waste_calibration: scenario: "food_waste_uncal" health: + enabled: false value_per_yll: 0 emissions: diff --git a/config/calibration/stability.yaml b/config/calibration/stability.yaml index 12bdb0c0..ec92fcf0 100644 --- a/config/calibration/stability.yaml +++ b/config/calibration/stability.yaml @@ -20,8 +20,8 @@ # tools/smk --configfile config/calibration/stability.yaml -- \ # data/curated/calibration/deviation_penalty.yaml -# Shared name across all calibration configs so the expensive upstream -# processing is built once under processing/calibration/ and reused. +# ``tools/calibrate`` overrides this with a source- and step-specific name so +# another calibration config cannot overwrite this step's intermediates. name: "calibration" planning_horizon: 2020 @@ -33,6 +33,7 @@ baseline_year: 2020 emissions: ghg_price: 0 health: + enabled: false value_per_yll: 0 # Block-based diminishing marginal utility for food consumption (used by main diff --git a/config/default.yaml b/config/default.yaml index 09a8b121..17f4c432 100644 --- a/config/default.yaml +++ b/config/default.yaml @@ -387,7 +387,7 @@ crops: # land can be brought into production for them. Each entry must: # * appear in `crops`, # * be absent from `irrigation.irrigated_crops` (rainfed-only), -# * not appear in any `multiple_cropping` combination, +# * not appear in any curated or configured multi-cropping combination, # * have a row in data/curated/cropgrids_crop_mapping.csv, # enforced by workflow/validation/cropgrids_crops.py. cropgrids_crops: @@ -441,28 +441,10 @@ non_food_crops: - biomass-sorghum # --- section: multiple_cropping --- -multiple_cropping: - double_rice: - crops: - - wetland-rice - - wetland-rice - water_supplies: - - r - - i - rice_wheat: - crops: - - wetland-rice - - wheat - water_supplies: - - r - - i - maize_soybean: - crops: - - maize - - soybean - water_supplies: - - r - - i +# Observed systems come from the curated MIRCA-OS catalog. Set a catalog name +# to null to disable it, or add a uniquely named combination to expose +# zero-baseline greenfield potential. Catalog combinations cannot be redefined. +multiple_cropping: {} # --- section: macronutrients --- macronutrients: {} @@ -1798,6 +1780,7 @@ cost_calibration: generate: false # Generate calibration from solved model (breaks DAG cycle when true) scenario: "calibration" # Scenario name used for calibration solve crop_correction_csv: "data/curated/calibration/{calibration_source}/crop_cost.csv" + multi_crop_correction_csv: "data/curated/calibration/{calibration_source}/multi_crop_cost.csv" grassland_correction_csv: "data/curated/calibration/{calibration_source}/grassland_cost.csv" animal_correction_csv: "data/curated/calibration/{calibration_source}/animal_cost.csv" diff --git a/config/schemas/config.schema.yaml b/config/schemas/config.schema.yaml index 00d4234b..256d16bd 100644 --- a/config/schemas/config.schema.yaml +++ b/config/schemas/config.schema.yaml @@ -698,12 +698,17 @@ properties: type: string water_supplies: type: array - minItems: 2 - maxItems: 3 + minItems: 1 + maxItems: 2 + uniqueItems: true items: type: string enum: [r, i] - description: "Multiple cropping systems (double cropping)" + description: | + Overrides and greenfield additions to the curated MIRCA-OS combination + catalog. Set a catalog name to null to disable it. A non-null entry must + use a new name and defines a zero-baseline combination that optimization + may develop where GAEZ indicates sufficient potential. macronutrients: type: object @@ -1658,7 +1663,7 @@ properties: cost_calibration: type: object - required: [enabled, generate, scenario, crop_correction_csv, grassland_correction_csv, animal_correction_csv] + required: [enabled, generate, scenario, crop_correction_csv, multi_crop_correction_csv, grassland_correction_csv, animal_correction_csv] additionalProperties: false properties: enabled: @@ -1673,6 +1678,9 @@ properties: crop_correction_csv: type: string description: "Path to crop cost calibration correction CSV" + multi_crop_correction_csv: + type: string + description: "Path to multi-cropping cost calibration correction CSV" grassland_correction_csv: type: string description: "Path to grassland cost calibration correction CSV" diff --git a/data/curated/calibration/default/animal_cost.csv b/data/curated/calibration/default/animal_cost.csv index 56ab6b72..b64c463f 100644 --- a/data/curated/calibration/default/animal_cost.csv +++ b/data/curated/calibration/default/animal_cost.csv @@ -1,1226 +1,1226 @@ product,country,correction_bnusd_per_mt_feed -dairy,AFG,0.10703087970614433 -dairy,AGO,-0.12226151488721371 -dairy,ALB,0.17815754562616348 -dairy,ARE,0.014660523273050785 -dairy,ARG,-0.10822289809584618 -dairy,ARM,0.07697279751300812 -dairy,ASM,-0.0 -dairy,ATG,-0.17738144099712372 -dairy,AUS,-0.0 -dairy,AUT,0.15409920364618301 -dairy,AZE,0.06070120073854923 -dairy,BDI,-0.17834678292274475 -dairy,BEL,0.17460499703884125 -dairy,BEN,-0.23902129381895065 -dairy,BFA,-0.24734090268611908 -dairy,BGD,-0.03464559093117714 -dairy,BGR,0.21729379892349243 -dairy,BHS,-0.07767665386199951 -dairy,BIH,0.3591711074113846 -dairy,BLR,0.08409030921757221 -dairy,BLZ,-0.18128155916929245 -dairy,BOL,-0.16859452426433563 -dairy,BRA,-0.15454183518886566 -dairy,BRB,-0.2710059583187103 -dairy,BRN,-0.03456922993063927 -dairy,BTN,-0.10354110598564148 -dairy,BWA,-0.19368989020586014 -dairy,CAF,-0.13260329514741898 -dairy,CAN,-0.31679074466228485 -dairy,CHE,0.12330283597111702 -dairy,CHL,-0.1113194078207016 -dairy,CHN,0.3047599792480469 -dairy,CIV,-0.2395275980234146 -dairy,CMR,-0.24679427593946457 -dairy,COD,-0.12742828764021397 -dairy,COG,-0.13204489275813103 -dairy,COL,-0.14196084812283516 -dairy,COM,-0.09084707498550415 -dairy,CPV,-0.18991756439208984 -dairy,CRI,-0.0026753742713481188 -dairy,CUB,-0.1788208782672882 -dairy,CYP,0.18169885873794556 -dairy,CZE,0.1300886906683445 -dairy,DEU,0.1534862294793129 -dairy,DJI,-0.02361299656331539 -dairy,DNK,0.20506127923727036 -dairy,DOM,-0.029752792790532112 -dairy,DZA,0.038006341084837914 -dairy,ECU,-0.16678882390260696 -dairy,EGY,-0.054233521688729525 -dairy,ERI,-0.10675554722547531 -dairy,ESP,0.0209792903624475 -dairy,EST,0.22282374650239944 -dairy,ETH,-0.12999938055872917 -dairy,FIN,0.27841074764728546 -dairy,FJI,0.006526715587824583 -dairy,FRA,0.05430302582681179 -dairy,GAB,-0.06001852825284004 -dairy,GBR,0.15925288945436478 -dairy,GEO,0.03470480069518089 -dairy,GHA,-0.24588139355182648 -dairy,GIN,-0.21230757236480713 -dairy,GMB,-0.21144543588161469 -dairy,GNB,-0.24471627920866013 -dairy,GNQ,-0.0 -dairy,GRC,0.07390055805444717 -dairy,GRD,-0.21701160818338394 -dairy,GTM,-0.133223045617342 -dairy,GUF,-0.0 -dairy,GUY,-0.15021036565303802 -dairy,HND,-0.12550776451826096 -dairy,HRV,0.09238313883543015 -dairy,HTI,-0.028870711103081703 -dairy,HUN,0.16721467673778534 -dairy,IDN,-0.02159996420959942 -dairy,IND,-0.048159904312342405 -dairy,IRL,0.0707425232976675 -dairy,IRN,0.12469420209527016 -dairy,IRQ,-0.11728309467434883 -dairy,ISL,0.18194056302309036 -dairy,ISR,0.03923936188220978 -dairy,ITA,0.1213281862437725 -dairy,JAM,-0.048727359622716904 -dairy,JOR,0.2602322995662689 -dairy,JPN,0.26085756719112396 -dairy,KAZ,0.1717851683497429 -dairy,KEN,-0.09666692838072777 -dairy,KGZ,0.3027417063713074 -dairy,KHM,-0.06175678223371506 -dairy,KOR,0.3277546912431717 -dairy,LAO,-0.015229240758344531 -dairy,LBN,0.24633167684078217 -dairy,LBR,-0.08185166260227561 -dairy,LBY,0.006506157107651234 -dairy,LKA,0.03622972418088466 -dairy,LSO,-0.15848425030708313 -dairy,LTU,0.23395520448684692 -dairy,LUX,0.11644960567355156 -dairy,LVA,0.2091846987605095 -dairy,MAR,-0.10436129942536354 -dairy,MDA,0.09734435379505157 -dairy,MDG,-0.10589794209226966 -dairy,MEX,-0.1328338161110878 -dairy,MKD,0.09593810886144638 -dairy,MLI,-0.19635864347219467 -dairy,MLT,0.06157960370182991 -dairy,MMR,-0.017934776842594147 -dairy,MNE,0.4715020954608917 -dairy,MNG,0.015231718309223652 -dairy,MOZ,-0.12381110712885857 -dairy,MRT,-0.14347514137625694 -dairy,MUS,-0.05237544086412527 -dairy,MWI,-0.13186120614409447 -dairy,MYS,-0.05517357215285301 -dairy,NAM,-0.15463439375162125 -dairy,NER,-0.2515639439225197 -dairy,NGA,-0.11753008514642715 -dairy,NIC,-0.2317081242799759 -dairy,NLD,0.045513615012168884 -dairy,NOR,0.18897944688796997 -dairy,NPL,-0.022051670588552952 -dairy,NZL,0.04650854133069515 -dairy,OMN,-0.10780510306358337 -dairy,PAK,0.0918516032397747 -dairy,PAN,-0.1750105395913124 -dairy,PER,-0.18441393971443176 -dairy,PHL,0.11018078029155731 -dairy,PNG,-0.0 -dairy,POL,0.1556459441781044 -dairy,PRI,-0.13725778460502625 -dairy,PRT,0.042166728526353836 -dairy,PRY,-0.11712049692869186 -dairy,PSE,0.17592928558588028 -dairy,ROU,0.23936818540096283 -dairy,RUS,0.18785858154296875 -dairy,RWA,-0.09947282820940018 -dairy,SAU,0.3530428856611252 -dairy,SDN,-0.038885559886693954 -dairy,SEN,-0.24159204214811325 -dairy,SLB,-0.01322073396295309 -dairy,SLE,-0.08926189364865422 -dairy,SLV,-0.17800701409578323 -dairy,SOM,-0.13858400285243988 -dairy,SRB,0.18915019929409027 -dairy,SSD,-0.07233825512230396 -dairy,STP,-0.15179191529750824 -dairy,SUR,-0.21703988313674927 -dairy,SVK,0.13998764008283615 -dairy,SVN,0.15655087679624557 -dairy,SWE,0.22891946136951447 -dairy,SWZ,-0.2290821149945259 -dairy,SYR,0.0245367381721735 -dairy,TCD,-0.12761279195547104 -dairy,TGO,-0.17093344777822495 -dairy,THA,0.11548018082976341 -dairy,TJK,0.3197605460882187 -dairy,TKM,0.20718759298324585 -dairy,TLS,-0.10211620852351189 -dairy,TTO,-0.10622383281588554 -dairy,TUN,0.044228157959878445 -dairy,TUR,0.021673571784049273 -dairy,TWN,0.3205793797969818 -dairy,TZA,-0.16857478022575378 -dairy,UGA,-0.23257044702768326 -dairy,UKR,0.2628560960292816 -dairy,URY,-0.18038592487573624 -dairy,USA,-0.17614394426345825 -dairy,UZB,0.26131339371204376 -dairy,VEN,-0.22484491765499115 -dairy,VNM,-0.06978144682943821 -dairy,VUT,-0.001620739814825356 -dairy,YEM,-0.043190279975533485 -dairy,ZAF,-0.1647792086005211 -dairy,ZMB,-0.12906158901751041 -dairy,ZWE,-0.46335548162460327 -dairy-buffalo,AFG,-0.0 -dairy-buffalo,AGO,-0.0 -dairy-buffalo,ALB,-0.0 -dairy-buffalo,ARE,-0.0 -dairy-buffalo,ARG,-0.0 -dairy-buffalo,ARM,-0.0 -dairy-buffalo,ASM,-0.0 -dairy-buffalo,ATG,-0.0 -dairy-buffalo,AUS,-0.0 -dairy-buffalo,AUT,-0.0 -dairy-buffalo,AZE,-0.0 -dairy-buffalo,BDI,-0.0 -dairy-buffalo,BEL,-0.0 -dairy-buffalo,BEN,-0.0 -dairy-buffalo,BFA,-0.0 -dairy-buffalo,BGD,-0.09400890953838825 -dairy-buffalo,BGR,0.10334416088880971 -dairy-buffalo,BHS,-0.0 -dairy-buffalo,BIH,-0.0 -dairy-buffalo,BLR,-0.0 -dairy-buffalo,BLZ,-0.0 -dairy-buffalo,BOL,-0.0 -dairy-buffalo,BRA,-0.0 -dairy-buffalo,BRB,-0.0 -dairy-buffalo,BRN,-0.1951453760266304 -dairy-buffalo,BTN,-0.0 -dairy-buffalo,BWA,-0.0 -dairy-buffalo,CAF,-0.0 -dairy-buffalo,CAN,-0.0 -dairy-buffalo,CHE,-0.0 -dairy-buffalo,CHL,-0.0 -dairy-buffalo,CHN,-0.11873042210936546 -dairy-buffalo,CIV,-0.0 -dairy-buffalo,CMR,-0.0 -dairy-buffalo,COD,-0.0 -dairy-buffalo,COG,-0.0 -dairy-buffalo,COL,-0.0 -dairy-buffalo,COM,-0.0 -dairy-buffalo,CPV,-0.0 -dairy-buffalo,CRI,-0.0 -dairy-buffalo,CUB,-0.0 -dairy-buffalo,CYP,-0.0 -dairy-buffalo,CZE,-0.0 -dairy-buffalo,DEU,-0.0 -dairy-buffalo,DJI,-0.0 -dairy-buffalo,DNK,-0.0 -dairy-buffalo,DOM,-0.0 -dairy-buffalo,DZA,-0.0 -dairy-buffalo,ECU,-0.0 -dairy-buffalo,EGY,0.0554795078933239 -dairy-buffalo,ERI,-0.0 -dairy-buffalo,ESP,-0.0 -dairy-buffalo,EST,-0.0 -dairy-buffalo,ETH,-0.0 -dairy-buffalo,FIN,-0.0 -dairy-buffalo,FJI,-0.0 -dairy-buffalo,FRA,-0.0 -dairy-buffalo,GAB,-0.0 -dairy-buffalo,GBR,-0.0 -dairy-buffalo,GEO,-0.0 -dairy-buffalo,GHA,-0.0 -dairy-buffalo,GIN,-0.0 -dairy-buffalo,GMB,-0.0 -dairy-buffalo,GNB,-0.0 -dairy-buffalo,GNQ,-0.0 -dairy-buffalo,GRC,-0.03089940024074167 -dairy-buffalo,GRD,-0.0 -dairy-buffalo,GTM,-0.0 -dairy-buffalo,GUF,-0.0 -dairy-buffalo,GUY,-0.0 -dairy-buffalo,HND,-0.0 -dairy-buffalo,HRV,-0.0 -dairy-buffalo,HTI,-0.0 -dairy-buffalo,HUN,-0.0 -dairy-buffalo,IDN,-0.13056707382202148 -dairy-buffalo,IND,-0.046584105119109154 -dairy-buffalo,IRL,-0.0 -dairy-buffalo,IRN,0.20645790547132492 -dairy-buffalo,IRQ,-0.09917953982949257 -dairy-buffalo,ISL,-0.0 -dairy-buffalo,ISR,-0.0 -dairy-buffalo,ITA,0.1663661152124405 -dairy-buffalo,JAM,-0.0 -dairy-buffalo,JOR,-0.0 -dairy-buffalo,JPN,-0.0 -dairy-buffalo,KAZ,-0.0 -dairy-buffalo,KEN,-0.0 -dairy-buffalo,KGZ,-0.0 -dairy-buffalo,KHM,-0.0 -dairy-buffalo,KOR,-0.0 -dairy-buffalo,LAO,-0.0 -dairy-buffalo,LBN,-0.0 -dairy-buffalo,LBR,-0.0 -dairy-buffalo,LBY,-0.0 -dairy-buffalo,LKA,-0.1191416047513485 -dairy-buffalo,LSO,-0.0 -dairy-buffalo,LTU,-0.0 -dairy-buffalo,LUX,-0.0 -dairy-buffalo,LVA,-0.0 -dairy-buffalo,MAR,-0.0 -dairy-buffalo,MDA,-0.0 -dairy-buffalo,MDG,-0.0 -dairy-buffalo,MEX,-0.0 -dairy-buffalo,MKD,-0.0 -dairy-buffalo,MLI,-0.0 -dairy-buffalo,MLT,-0.0 -dairy-buffalo,MMR,-0.08372385147958994 -dairy-buffalo,MNE,-0.0 -dairy-buffalo,MNG,-0.0 -dairy-buffalo,MOZ,-0.0 -dairy-buffalo,MRT,-0.0 -dairy-buffalo,MUS,-0.0 -dairy-buffalo,MWI,-0.0 -dairy-buffalo,MYS,-0.06742877326905727 -dairy-buffalo,NAM,-0.0 -dairy-buffalo,NER,-0.0 -dairy-buffalo,NGA,-0.0 -dairy-buffalo,NIC,-0.0 -dairy-buffalo,NLD,-0.0 -dairy-buffalo,NOR,-0.0 -dairy-buffalo,NPL,-0.03200629446655512 -dairy-buffalo,NZL,-0.0 -dairy-buffalo,OMN,-0.0 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-meat-sheep,RUS,-0.0982473511248827 -meat-sheep,RWA,-0.09302960708737373 -meat-sheep,SAU,-0.18820814043283463 -meat-sheep,SDN,-0.10204797238111496 -meat-sheep,SEN,-0.1918894425034523 -meat-sheep,SLB,-0.0 -meat-sheep,SLE,-0.07494034618139267 -meat-sheep,SLV,-0.12608416005969048 -meat-sheep,SOM,-0.12072769552469254 -meat-sheep,SRB,0.01019703911151737 -meat-sheep,SSD,-0.08185256831347942 -meat-sheep,STP,-0.11005829274654388 -meat-sheep,SUR,-0.1457674279808998 -meat-sheep,SVK,-0.10470272414386272 -meat-sheep,SVN,-0.0221473416313529 -meat-sheep,SWE,-0.015251921489834785 -meat-sheep,SWZ,-0.2062375247478485 -meat-sheep,SYR,-0.19890715926885605 -meat-sheep,TCD,-0.0958254486322403 -meat-sheep,TGO,-0.116925198584795 -meat-sheep,THA,-0.10758199170231819 -meat-sheep,TJK,-0.2025524228811264 -meat-sheep,TKM,-0.11329970136284828 -meat-sheep,TLS,-0.12389365583658218 -meat-sheep,TTO,0.005382268223911524 -meat-sheep,TUN,-0.16131258010864258 -meat-sheep,TUR,-0.16741207614541054 -meat-sheep,TWN,-0.0 -meat-sheep,TZA,-0.15442629903554916 -meat-sheep,UGA,-0.21204358339309692 -meat-sheep,UKR,-0.09579563699662685 -meat-sheep,URY,-0.09977881982922554 -meat-sheep,USA,-0.014865325763821602 -meat-sheep,UZB,-0.09045223705470562 -meat-sheep,VEN,-0.15200293436646461 -meat-sheep,VNM,-0.21156592667102814 -meat-sheep,VUT,-0.11684345826506615 -meat-sheep,YEM,-0.0835416167974472 -meat-sheep,ZAF,-0.13062623888254166 -meat-sheep,ZMB,-0.10665876790881157 -meat-sheep,ZWE,-0.4640095829963684 +meat-sheep,MOZ,-0.12605569511651993 +meat-sheep,MRT,-0.14540939405560493 +meat-sheep,MUS,-0.07628303579986095 +meat-sheep,MWI,-0.0721043311059475 +meat-sheep,MYS,-0.13294633477926254 +meat-sheep,NAM,-0.1177099160850048 +meat-sheep,NER,-0.14634805917739868 +meat-sheep,NGA,-0.057641733437776566 +meat-sheep,NIC,0.0 +meat-sheep,NLD,-0.11533150635659695 +meat-sheep,NOR,0.0061179837211966515 +meat-sheep,NPL,-0.3276190087199211 +meat-sheep,NZL,-0.015526057220995426 +meat-sheep,OMN,-0.16550595313310623 +meat-sheep,PAK,-0.1857151910662651 +meat-sheep,PAN,0.0 +meat-sheep,PER,-0.08376952446997166 +meat-sheep,PHL,-0.12324477732181549 +meat-sheep,PNG,-0.06738061178475618 +meat-sheep,POL,-0.03394913300871849 +meat-sheep,PRI,-0.012530315667390823 +meat-sheep,PRT,0.05193073209375143 +meat-sheep,PRY,-0.017569574527442455 +meat-sheep,PSE,-0.11723065748810768 +meat-sheep,ROU,-0.08376464620232582 +meat-sheep,RUS,-0.040447653736919165 +meat-sheep,RWA,-0.13626910373568535 +meat-sheep,SAU,-0.1340646967291832 +meat-sheep,SDN,-0.10751131922006607 +meat-sheep,SEN,-0.41021454334259033 +meat-sheep,SLB,0.0 +meat-sheep,SLE,-0.06167193362489343 +meat-sheep,SLV,-0.08277259767055511 +meat-sheep,SOM,-0.11588623374700546 +meat-sheep,SRB,0.039482939057052135 +meat-sheep,SSD,-0.08268574438989162 +meat-sheep,STP,-0.056412079371511936 +meat-sheep,SUR,-0.08228166401386261 +meat-sheep,SVK,-0.05171432252973318 +meat-sheep,SVN,0.030841056839562953 +meat-sheep,SWE,0.03773647686466575 +meat-sheep,SWZ,-0.15423036366701126 +meat-sheep,SYR,-0.17312487959861755 +meat-sheep,TCD,-0.10318919271230698 +meat-sheep,TGO,-0.04993518814444542 +meat-sheep,THA,-0.08617205359041691 +meat-sheep,TJK,-0.1431974396109581 +meat-sheep,TKM,-0.12097251042723656 +meat-sheep,TLS,-0.06869059056043625 +meat-sheep,TTO,0.005399390589445829 +meat-sheep,TUN,-0.10888421908020973 +meat-sheep,TUR,-0.11119887232780457 +meat-sheep,TWN,0.0 +meat-sheep,TZA,-0.13148238509893417 +meat-sheep,UGA,-0.08170516043901443 +meat-sheep,UKR,-0.04329992551356554 +meat-sheep,URY,-0.05264040920883417 +meat-sheep,USA,-0.019588138908147812 +meat-sheep,UZB,-0.14204426109790802 +meat-sheep,VEN,-0.10712418332695961 +meat-sheep,VNM,-0.13916001096367836 +meat-sheep,VUT,-0.06462793471291661 +meat-sheep,YEM,-0.17807604372501373 +meat-sheep,ZAF,-0.14198150485754013 +meat-sheep,ZMB,-0.1903287172317505 +meat-sheep,ZWE,-0.4444954916834831 diff --git a/data/curated/calibration/default/crop_cost.csv b/data/curated/calibration/default/crop_cost.csv index 0410d008..1b36b8c2 100644 --- a/data/curated/calibration/default/crop_cost.csv +++ b/data/curated/calibration/default/crop_cost.csv @@ -1,304 +1,304 @@ crop,country,correction_bnusd_per_mha -alfalfa,AFG,-0.3750787675380707 -alfalfa,AGO,-0.9439449906349182 -alfalfa,ALB,-0.11809636652469635 -alfalfa,ARE,-0.6755253076553345 -alfalfa,ARG,-0.46978648006916046 -alfalfa,ARM,-0.0 -alfalfa,ATG,-7.5 -alfalfa,AUS,1.456120252609253 -alfalfa,AUT,-0.6891505420207977 -alfalfa,AZE,0.7057382166385651 -alfalfa,BDI,-0.5490851700305939 +alfalfa,AFG,-0.3750787377357483 +alfalfa,AGO,-0.2651873230934143 +alfalfa,ALB,-0.004321811720728874 +alfalfa,ARE,7.5 +alfalfa,ARG,-0.31196562945842743 +alfalfa,ARM,0.0 +alfalfa,ATG,0.0 +alfalfa,AUS,0.0 +alfalfa,AUT,-0.7556191384792328 +alfalfa,AZE,0.814435601234436 +alfalfa,BDI,-0.8987658023834229 alfalfa,BEL,-0.23728060722351074 -alfalfa,BEN,0.7114291489124298 -alfalfa,BFA,-7.5 -alfalfa,BGD,-0.7657375931739807 -alfalfa,BGR,0.5154048055410385 -alfalfa,BHS,-0.7067800164222717 -alfalfa,BIH,0.6828204989433289 -alfalfa,BLR,0.03184974566102028 -alfalfa,BLZ,-0.029083551838994026 +alfalfa,BEN,0.821510910987854 +alfalfa,BFA,0.0 +alfalfa,BGD,-0.7333669364452362 +alfalfa,BGR,1.0534589290618896 +alfalfa,BHS,-0.5689958930015564 +alfalfa,BIH,0.8138508796691895 +alfalfa,BLR,0.13893422484397888 +alfalfa,BLZ,-0.5492207109928131 alfalfa,BOL,-0.22297222912311554 -alfalfa,BRA,-0.43258796632289886 -alfalfa,BRB,2.5348349809646606 +alfalfa,BRA,-0.44687044620513916 +alfalfa,BRB,2.6721359491348267 alfalfa,BRN,1.4280236959457397 -alfalfa,BTN,0.340537965297699 -alfalfa,BWA,-7.5 -alfalfa,CAF,-0.974648654460907 -alfalfa,CAN,-0.22291509807109833 -alfalfa,CHE,0.5097993314266205 +alfalfa,BTN,0.09801395237445831 +alfalfa,BWA,0.0 +alfalfa,CAF,-0.6585278213024139 +alfalfa,CAN,-0.1303732693195343 +alfalfa,CHE,0.5097993761301041 alfalfa,CHL,-0.6260356903076172 -alfalfa,CHN,0.6336998641490936 +alfalfa,CHN,0.5983059406280518 alfalfa,CIV,7.5 -alfalfa,CMR,1.601276457309723 -alfalfa,COD,-0.904526561498642 +alfalfa,CMR,1.7329913973808289 +alfalfa,COD,-0.1673155128955841 alfalfa,COG,-0.9112309217453003 -alfalfa,COL,-0.9032638072967529 -alfalfa,COM,-0.863983690738678 -alfalfa,CPV,-3.9641530215740204 -alfalfa,CRI,-0.9016055166721344 +alfalfa,COL,-0.8513142168521881 +alfalfa,COM,0.6860150396823883 +alfalfa,CPV,-0.4042091369628906 +alfalfa,CRI,-0.2579779550433159 alfalfa,CUB,-0.28110775351524353 -alfalfa,CYP,3.1567713022232056 -alfalfa,CZE,-0.26696592569351196 -alfalfa,DEU,0.15541773289442062 -alfalfa,DJI,1.4503105878829956 -alfalfa,DNK,7.370487689971924 -alfalfa,DOM,-0.8690982162952423 -alfalfa,DZA,-0.08560169488191605 +alfalfa,CYP,-7.5 +alfalfa,CZE,0.33479776978492737 +alfalfa,DEU,0.1509365662932396 +alfalfa,DJI,1.603806734085083 +alfalfa,DNK,7.5 +alfalfa,DOM,-0.8691022992134094 +alfalfa,DZA,-0.07842564582824707 alfalfa,ECU,-0.12939461693167686 -alfalfa,EGY,2.9221946001052856 +alfalfa,EGY,-0.260550320148468 alfalfa,ERI,-0.8744806945323944 -alfalfa,ESP,-1.0534051060676575 -alfalfa,EST,-0.0 -alfalfa,ETH,-0.8345361351966858 -alfalfa,FIN,-0.0 -alfalfa,FJI,-0.20056112110614777 +alfalfa,ESP,-1.0377808809280396 +alfalfa,EST,0.0 +alfalfa,ETH,-0.8274461328983307 +alfalfa,FIN,0.0 +alfalfa,FJI,0.13141752034425735 alfalfa,FRA,-0.33104005455970764 -alfalfa,GAB,-0.8414711356163025 -alfalfa,GBR,0.18297655880451202 -alfalfa,GEO,-0.583319365978241 +alfalfa,GAB,-0.746298760175705 +alfalfa,GBR,0.17851915955543518 +alfalfa,GEO,-0.573031097650528 alfalfa,GHA,7.5 -alfalfa,GIN,-0.44031502306461334 -alfalfa,GMB,-7.5 -alfalfa,GNB,-7.5 -alfalfa,GNQ,-0.12937652319669724 -alfalfa,GRC,-0.6723275482654572 -alfalfa,GRD,0.3827682435512543 -alfalfa,GTM,-0.4894945025444031 -alfalfa,GUF,-2.7509915828704834 +alfalfa,GIN,-0.387450709939003 +alfalfa,GMB,-0.25826479494571686 +alfalfa,GNB,0.0 +alfalfa,GNQ,-0.7589189112186432 +alfalfa,GRC,-0.650564581155777 +alfalfa,GRD,0.4752156436443329 +alfalfa,GTM,-0.522682249546051 +alfalfa,GUF,-2.706025242805481 alfalfa,GUY,-0.07720011845231056 alfalfa,HND,-0.34651389718055725 -alfalfa,HRV,-2.968752473592758 +alfalfa,HRV,-1.71925288438797 alfalfa,HTI,-0.8191313445568085 -alfalfa,HUN,-0.3677455857396126 -alfalfa,IDN,-0.15115800499916077 -alfalfa,IND,2.9521031379699707 -alfalfa,IRL,-0.5080434381961823 -alfalfa,IRN,-1.2045719623565674 -alfalfa,IRQ,2.7675466537475586 -alfalfa,ISL,-0.775978684425354 -alfalfa,ISR,1.593531608581543 -alfalfa,ITA,-0.45480209589004517 -alfalfa,JAM,2.033314883708954 -alfalfa,JOR,3.9203332662582397 +alfalfa,HUN,-2.167660281062126 +alfalfa,IDN,0.04110857471823692 +alfalfa,IND,0.0 +alfalfa,IRL,-0.5411296486854553 +alfalfa,IRN,-1.204572081565857 +alfalfa,IRQ,2.765779972076416 +alfalfa,ISL,-0.7303893268108368 +alfalfa,ISR,1.7266695499420166 +alfalfa,ITA,0.23923970758914948 +alfalfa,JAM,2.2525503635406494 +alfalfa,JOR,4.008713722229004 alfalfa,JPN,0.017270792406634428 alfalfa,KAZ,-0.5172078013420105 -alfalfa,KEN,-0.49232181906700134 +alfalfa,KEN,-0.43167561292648315 alfalfa,KGZ,-0.8171102404594421 -alfalfa,KHM,-0.7938231527805328 -alfalfa,KOR,-0.8412145376205444 -alfalfa,LAO,-0.2115088403224945 -alfalfa,LBN,2.0070786476135254 -alfalfa,LBR,-0.9287798702716827 -alfalfa,LBY,-0.824711263179779 -alfalfa,LKA,-0.375872865319252 -alfalfa,LSO,-0.44325435161590576 +alfalfa,KHM,-0.6746552288532257 +alfalfa,KOR,0.08994222432374954 +alfalfa,LAO,-0.315351665019989 +alfalfa,LBN,2.3072054386138916 +alfalfa,LBR,-0.8882774710655212 +alfalfa,LBY,-0.10145418345928192 +alfalfa,LKA,-0.022444549947977066 +alfalfa,LSO,-0.8092021346092224 alfalfa,LTU,0.3543912172317505 alfalfa,LUX,-0.3182141184806824 -alfalfa,LVA,-0.8102150857448578 -alfalfa,MAR,2.6186418533325195 -alfalfa,MDA,0.25572748482227325 -alfalfa,MDG,-0.9195513427257538 -alfalfa,MEX,-0.14814554154872894 -alfalfa,MKD,0.6304250508546829 -alfalfa,MLI,-7.5 -alfalfa,MLT,-1.562433123588562 -alfalfa,MMR,-0.8467822372913361 -alfalfa,MNE,2.5230941772460938 -alfalfa,MNG,-7.5 -alfalfa,MOZ,-0.5782089233398438 -alfalfa,MRT,-0.5335112512111664 -alfalfa,MUS,-0.7995986342430115 +alfalfa,LVA,-0.5082782655954361 +alfalfa,MAR,2.8600780963897705 +alfalfa,MDA,2.4347543716430664 +alfalfa,MDG,-0.04350392799824476 +alfalfa,MEX,0.0 +alfalfa,MKD,1.373655080795288 +alfalfa,MLI,-0.9638939499855042 +alfalfa,MLT,-3.76202882360667 +alfalfa,MMR,0.8664765655994415 +alfalfa,MNE,1.513783574104309 +alfalfa,MNG,-0.7835801839828491 +alfalfa,MOZ,-0.5750786066055298 +alfalfa,MRT,-7.5 +alfalfa,MUS,-0.7995986044406891 alfalfa,MWI,-0.5677796304225922 -alfalfa,MYS,-0.3362851142883301 -alfalfa,NAM,-0.40904849767684937 -alfalfa,NER,-0.0 +alfalfa,MYS,-0.34928590059280396 +alfalfa,NAM,-0.29255859553813934 +alfalfa,NER,0.0 alfalfa,NGA,-0.9353777766227722 alfalfa,NIC,-1.859633982181549 -alfalfa,NLD,6.6176230907440186 -alfalfa,NOR,-0.0 -alfalfa,NPL,-0.944817304611206 +alfalfa,NLD,6.3248326778411865 +alfalfa,NOR,0.0 +alfalfa,NPL,-1.9828420877456665 alfalfa,NZL,-0.3066001534461975 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sweet-potato,CPV,-7.5 -sweet-potato,CRI,3.152367115020752 -sweet-potato,CUB,-3.457007884979248 -sweet-potato,CYP,-5.103912115097046 -sweet-potato,CZE,-2.4381096363067627 -sweet-potato,DEU,-1.7301185131072998 -sweet-potato,DNK,-2.0852636098861694 -sweet-potato,DOM,-3.9840991497039795 -sweet-potato,DZA,-2.6832118034362793 -sweet-potato,ECU,-0.062368541955947876 -sweet-potato,EGY,-2.975604295730591 -sweet-potato,ERI,1.8001441955566406 +sweet-potato,CRI,3.211240231990814 +sweet-potato,CUB,-3.5258588790893555 +sweet-potato,CYP,-5.2983105182647705 +sweet-potato,CZE,-2.108704447746277 +sweet-potato,DEU,-1.4264941811561584 +sweet-potato,DNK,-1.9205120205879211 +sweet-potato,DOM,-3.11769437789917 +sweet-potato,DZA,-2.493957281112671 +sweet-potato,ECU,0.019212597981095314 +sweet-potato,EGY,-3.2975375652313232 +sweet-potato,ERI,1.80353844165802 sweet-potato,ESP,-7.5 -sweet-potato,EST,-2.55484938621521 -sweet-potato,ETH,-3.2947384119033813 -sweet-potato,FIN,-2.6916239261627197 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-sweet-potato,PER,-2.5978612899780273 -sweet-potato,PHL,-1.6773841977119446 -sweet-potato,PNG,-3.488620638847351 -sweet-potato,POL,-2.4942277669906616 -sweet-potato,PRI,-7.483315467834473 +sweet-potato,PER,-2.7172921895980835 +sweet-potato,PHL,-1.6354054808616638 +sweet-potato,PNG,-3.451143980026245 +sweet-potato,POL,-2.3204771280288696 +sweet-potato,PRI,-7.432371616363525 sweet-potato,PRT,-7.5 -sweet-potato,PRY,-1.6055749654769897 -sweet-potato,PSE,-4.823098063468933 -sweet-potato,ROU,-3.082138776779175 -sweet-potato,RUS,-3.310864210128784 -sweet-potato,RWA,-1.0163329243659973 -sweet-potato,SAU,-2.3289730548858643 -sweet-potato,SDN,-3.187199115753174 +sweet-potato,PRY,-1.5391631126403809 +sweet-potato,PSE,-2.173014461994171 +sweet-potato,ROU,-2.965659499168396 +sweet-potato,RUS,-2.867875099182129 +sweet-potato,RWA,-0.9750840365886688 +sweet-potato,SAU,-2.148847818374634 +sweet-potato,SDN,-3.554046154022217 sweet-potato,SEN,-7.5 -sweet-potato,SLB,-2.7316378355026245 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-wheat,TWN,-0.5517768859863281 -wheat,TZA,-0.2396334931254387 -wheat,UGA,-0.07888242602348328 -wheat,UKR,-0.2953985631465912 -wheat,URY,-0.048132482916116714 -wheat,USA,-0.10912405699491501 -wheat,UZB,-0.7366462349891663 -wheat,VEN,-0.0 -wheat,VNM,-0.45540641248226166 -wheat,YEM,-0.44986066222190857 -wheat,ZAF,-0.5072092413902283 -wheat,ZMB,0.284590482711792 -wheat,ZWE,4.364132404327393 -white-potato,AFG,-6.19849157333374 -white-potato,AGO,-6.770773887634277 -white-potato,ALB,-7.0869364738464355 +wheat,TJK,-0.37897226214408875 +wheat,TKM,-0.4991532862186432 +wheat,TUN,-1.0791428089141846 +wheat,TUR,-0.036830401979386806 +wheat,TWN,-0.5145082771778107 +wheat,TZA,7.5 +wheat,UGA,7.5 +wheat,UKR,-0.30990833044052124 +wheat,URY,-0.008204478770494461 +wheat,USA,-0.05353429168462753 +wheat,UZB,-0.7624359726905823 +wheat,VEN,0.0 +wheat,VNM,3.5684593468904495 +wheat,YEM,0.0 +wheat,ZAF,3.1975371837615967 +wheat,ZMB,4.39703106880188 +wheat,ZWE,2.800724744796753 +white-potato,AFG,-5.800930023193359 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-white-potato,KEN,-2.092913866043091 -white-potato,KGZ,-3.046399235725403 -white-potato,KHM,-6.819732427597046 -white-potato,KOR,-6.172350168228149 -white-potato,LAO,-5.9038519859313965 +white-potato,KAZ,-1.7981496453285217 +white-potato,KEN,-1.9635194540023804 +white-potato,KGZ,-2.7414075136184692 +white-potato,KHM,-6.395757675170898 +white-potato,KOR,-5.68363618850708 +white-potato,LAO,1.127934217453003 white-potato,LBN,-7.5 -white-potato,LBR,-6.421300411224365 -white-potato,LBY,-5.639025688171387 +white-potato,LBR,-6.135037422180176 +white-potato,LBY,-5.240087032318115 white-potato,LKA,-7.5 -white-potato,LSO,-5.423912525177002 -white-potato,LTU,-2.2814247608184814 -white-potato,LUX,-6.172681093215942 -white-potato,LVA,-1.3943885564804077 -white-potato,MAR,-4.3292224407196045 -white-potato,MDA,-0.4833347648382187 -white-potato,MDG,-6.840503215789795 +white-potato,LSO,-4.841846942901611 +white-potato,LTU,-1.7854215502738953 +white-potato,LUX,-5.479255437850952 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-white-potato,NLD,-4.713480710983276 -white-potato,NOR,-7.5 -white-potato,NPL,-2.9057209491729736 -white-potato,NZL,-3.7959617376327515 +white-potato,NLD,-3.954423427581787 +white-potato,NOR,-7.499169826507568 +white-potato,NPL,-3.841793417930603 +white-potato,NZL,-2.153173267841339 white-potato,OMN,-7.5 -white-potato,PAK,-2.1327762603759766 +white-potato,PAK,-1.3409653902053833 white-potato,PAN,-7.5 -white-potato,PER,-3.5418314933776855 +white-potato,PER,-3.3684710264205933 white-potato,PHL,-7.5 -white-potato,PNG,-7.5 -white-potato,POL,-1.1929941773414612 -white-potato,PRI,-7.5 -white-potato,PRT,-4.531711101531982 -white-potato,PRY,-3.281337261199951 +white-potato,PNG,-6.769505500793457 +white-potato,POL,-0.3469485640525818 +white-potato,PRI,-5.685426950454712 +white-potato,PRT,-3.7339770793914795 +white-potato,PRY,-3.0232057571411133 white-potato,PSE,-7.5 -white-potato,ROU,-4.439207553863525 -white-potato,RUS,-2.0295252799987793 -white-potato,RWA,-1.857063353061676 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-white-potato,TZA,-0.7882333397865295 -white-potato,UGA,-6.966477155685425 -white-potato,UKR,-1.3431144952774048 +white-potato,SDN,-6.0561323165893555 +white-potato,SEN,-7.393895864486694 +white-potato,SLB,-5.739879369735718 +white-potato,SLE,-6.50711727142334 +white-potato,SLV,-7.439906597137451 +white-potato,SOM,-6.375689268112183 +white-potato,SRB,-3.2039153575897217 +white-potato,SSD,-5.832192659378052 +white-potato,SUR,-5.138088226318359 +white-potato,SVK,-5.069280385971069 +white-potato,SVN,-4.919626474380493 +white-potato,SWE,-7.258115768432617 +white-potato,SWZ,-7.093716621398926 +white-potato,SYR,-5.198333263397217 +white-potato,TCD,-6.722083568572998 +white-potato,TGO,-6.992313385009766 +white-potato,THA,-3.6924489736557007 +white-potato,TJK,-2.7142465114593506 +white-potato,TKM,-5.983644008636475 +white-potato,TLS,-0.561556339263916 +white-potato,TTO,-6.367161512374878 +white-potato,TUN,-3.3427302837371826 +white-potato,TUR,-4.35666298866272 +white-potato,TWN,-4.933190822601318 +white-potato,TZA,-0.6405218243598938 +white-potato,UGA,-6.921557664871216 +white-potato,UKR,-1.1148955821990967 white-potato,URY,-7.5 -white-potato,USA,-4.691410064697266 -white-potato,UZB,-5.196643829345703 -white-potato,VEN,-5.600482702255249 -white-potato,VNM,-6.40132737159729 -white-potato,YEM,-7.48647403717041 -white-potato,ZAF,-6.635372877120972 +white-potato,USA,-3.1618893146514893 +white-potato,UZB,-4.780019044876099 +white-potato,VEN,-5.127483367919922 +white-potato,VNM,-5.969525098800659 +white-potato,YEM,-7.5 +white-potato,ZAF,-6.7770836353302 white-potato,ZMB,-7.5 white-potato,ZWE,-7.5 -yam,AFG,7.5 -yam,AGO,-2.9141130447387695 -yam,ARG,-0.970349907875061 -yam,ATG,-4.489343166351318 -yam,BDI,-4.474178075790405 -yam,BEN,-1.151535153388977 -yam,BFA,-2.682286024093628 -yam,BIH,-2.284517526626587 -yam,BLZ,-2.0015997886657715 -yam,BOL,-5.195361614227295 -yam,BRA,-3.122835397720337 +yam,AFG,0.0 +yam,AGO,-2.71264386177063 +yam,ARG,7.5 +yam,ATG,-4.396996021270752 +yam,BDI,-4.46341609954834 +yam,BEN,-1.1236973404884338 +yam,BFA,-2.659050464630127 +yam,BIH,0.0 +yam,BLZ,-1.2888553142547607 +yam,BOL,-5.130490303039551 +yam,BRA,-2.8824214935302734 yam,BRB,-7.5 -yam,BRN,-5.200138330459595 -yam,BTN,-4.826615333557129 -yam,BWA,-4.241587400436401 -yam,CAF,-3.9850677251815796 -yam,CHL,-0.0 -yam,CHN,-0.5373392999172211 -yam,CIV,-0.7075281739234924 -yam,CMR,-2.860989809036255 -yam,COD,-4.229848623275757 -yam,COG,-4.2262961864471436 -yam,COL,-0.8940050601959229 -yam,COM,-4.318925857543945 +yam,BRN,-5.194641351699829 +yam,BTN,-4.155502796173096 +yam,BWA,-3.4817532300949097 +yam,CAF,-3.970792531967163 +yam,CHL,0.0 +yam,CHN,-0.30300410091876984 +yam,CIV,-0.6973100304603577 +yam,CMR,-2.890680193901062 +yam,COD,-4.217174291610718 +yam,COG,-4.213520526885986 +yam,COL,-0.541134238243103 +yam,COM,-4.306869983673096 yam,CRI,-7.5 -yam,CUB,-2.660409092903137 -yam,DOM,-2.5111581087112427 -yam,ECU,-5.37074089050293 -yam,EGY,1.0288528800010681 -yam,ERI,-5.601023197174072 -yam,ETH,-1.935349941253662 +yam,CUB,-2.359723925590515 +yam,DOM,-1.9272119998931885 +yam,ECU,-5.317235469818115 +yam,EGY,-1.964463472366333 +yam,ERI,-5.59840989112854 +yam,ETH,-1.4126824140548706 yam,FJI,-7.5 -yam,GAB,-4.205602169036865 -yam,GHA,-1.1991177797317505 -yam,GIN,-2.2537769079208374 -yam,GMB,-7.5 -yam,GNB,-4.184656620025635 -yam,GNQ,-4.476369857788086 +yam,GAB,-4.192934989929199 +yam,GHA,-1.1651211977005005 +yam,GIN,-2.261919379234314 +yam,GMB,0.0 +yam,GNB,-4.171942234039307 +yam,GNQ,-4.465688228607178 yam,GRD,-7.5 yam,GTM,7.5 -yam,GUF,7.5 +yam,GUF,0.0 yam,GUY,-7.5 -yam,HND,-4.2645134925842285 -yam,HRV,-0.0 -yam,HTI,-5.360596656799316 -yam,IDN,-4.645222187042236 -yam,IND,-0.9942658543586731 -yam,IRN,-0.0 -yam,ISR,-0.97813481092453 +yam,HND,-4.0838319063186646 +yam,HRV,0.0 +yam,HTI,-5.307753562927246 +yam,IDN,-4.6345484256744385 +yam,IND,-4.509192943572998 +yam,IRN,0.0 +yam,ISR,3.181663453578949 yam,JAM,-7.5 -yam,JOR,4.405307233333588 +yam,JOR,7.5 yam,JPN,-7.5 -yam,KEN,-1.5378362536430359 -yam,KHM,-2.4880053997039795 -yam,LAO,2.0470011234283447 -yam,LBN,-4.693295359611511 -yam,LBR,-3.7793532609939575 -yam,LKA,-3.0476415157318115 -yam,LSO,-0.0 -yam,MDG,-4.046064138412476 -yam,MEX,3.082486867904663 -yam,MLI,-5.214148998260498 -yam,MMR,1.02357679605484 -yam,MNE,-0.0 -yam,MOZ,-3.873716115951538 -yam,MRT,-6.3810882568359375 -yam,MUS,-3.480055570602417 -yam,MWI,-0.0 -yam,MYS,-2.688098430633545 -yam,NAM,-3.0029178857803345 -yam,NER,-5.320300102233887 -yam,NGA,-2.812139868736267 -yam,NIC,-2.3203004598617554 -yam,NPL,-5.0111610889434814 -yam,PAK,1.2825162410736084 -yam,PAN,-3.215519905090332 -yam,PER,-3.4962090253829956 -yam,PHL,-1.6133047342300415 -yam,PNG,-2.6098426580429077 -yam,PRI,-7.5 +yam,KEN,-1.518750011920929 +yam,KHM,-2.45957350730896 +yam,LAO,2.1110036373138428 +yam,LBN,-2.3234525322914124 +yam,LBR,-3.763749837875366 +yam,LKA,-3.0244040489196777 +yam,LSO,0.0 +yam,MDG,-4.0314555168151855 +yam,MEX,2.5838382244110107 +yam,MLI,-6.876749038696289 +yam,MMR,1.9648329615592957 +yam,MNE,0.0 +yam,MOZ,-3.857971429824829 +yam,MRT,-7.5 +yam,MUS,-3.4608681201934814 +yam,MWI,7.5 +yam,MYS,-2.66167676448822 +yam,NAM,-2.9809781312942505 +yam,NER,-5.249086618423462 +yam,NGA,-2.7895864248275757 +yam,NIC,-1.8975496888160706 +yam,NPL,-6.534341812133789 +yam,PAK,1.206875205039978 +yam,PAN,-3.1514991521835327 +yam,PER,-3.606115937232971 +yam,PHL,-1.6030202507972717 +yam,PNG,-2.5849483013153076 +yam,PRI,-7.476719617843628 yam,PRY,7.5 -yam,PSE,-1.4965265989303589 -yam,RWA,-0.827601820230484 -yam,SAU,-0.0 -yam,SDN,-5.74281644821167 -yam,SEN,-2.134479522705078 -yam,SLB,-1.77242112159729 -yam,SLE,-5.3744752407073975 -yam,SLV,2.7297595739364624 -yam,SOM,-0.0 -yam,SSD,-5.162904500961304 -yam,STP,-4.698578357696533 -yam,SUR,-2.388601541519165 -yam,SWZ,-3.961113214492798 -yam,SYR,-1.9764747619628906 -yam,TCD,-4.023879528045654 -yam,TGO,-1.094775676727295 -yam,THA,-1.777478575706482 -yam,TLS,-5.459371566772461 +yam,PSE,-1.2889548242092133 +yam,RWA,-0.815086305141449 +yam,SAU,0.0 +yam,SDN,-5.722858905792236 +yam,SEN,-2.111696720123291 +yam,SLB,-1.7412835955619812 +yam,SLE,-5.370323419570923 +yam,SLV,2.908381938934326 +yam,SOM,0.0 +yam,SSD,-5.157663822174072 +yam,STP,-4.689535856246948 +yam,SUR,-2.0496089458465576 +yam,SWZ,-3.945995330810547 +yam,SYR,0.0 +yam,TCD,-4.010426998138428 +yam,TGO,-1.077474057674408 +yam,THA,-1.807403028011322 +yam,TLS,-5.455284118652344 yam,TTO,-7.5 -yam,TWN,-2.4025919437408447 -yam,TZA,-4.55778169631958 -yam,UGA,-3.892226219177246 -yam,URY,7.5 -yam,USA,5.4903130531311035 -yam,VEN,-2.3649706840515137 -yam,VNM,-3.5417455434799194 -yam,VUT,-3.0868210792541504 -yam,ZAF,-4.360322952270508 -yam,ZMB,3.75 -yam,ZWE,-3.277616262435913 +yam,TWN,-2.3733160495758057 +yam,TZA,-4.54769492149353 +yam,UGA,-3.8769387006759644 +yam,URY,0.0 +yam,USA,5.996743202209473 +yam,VEN,-2.043738603591919 +yam,VNM,-3.632774829864502 +yam,VUT,-3.0644670724868774 +yam,ZAF,-4.348750352859497 +yam,ZMB,7.5 +yam,ZWE,-3.2570632696151733 diff --git a/data/curated/calibration/default/deviation_penalty.yaml b/data/curated/calibration/default/deviation_penalty.yaml index 4b0b23dd..5455193f 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: 1.376689142706436 - grassland: 0.15535580787676795 - feed: 0.08322202186356281 -iterations: 5 + cropland: 2.281741945986492 + grassland: 0.16171905439400136 + feed: 0.10185911829217534 +iterations: 6 converged: true -final_residual_log_inf: 0.014949439844417031 +final_residual_log_inf: 0.003938636151124174 diff --git a/data/curated/calibration/default/exogenous_feed.csv b/data/curated/calibration/default/exogenous_feed.csv index 8d1887c0..b2f107dc 100644 --- a/data/curated/calibration/default/exogenous_feed.csv +++ b/data/curated/calibration/default/exogenous_feed.csv @@ -2,104 +2,110 @@ # # 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 +AFG,0.021802,0.464259,6.454943 +AGO,0.0,0.0,0.935912 +ARE,0.0,0.0,1.41976 +ARM,0.0,0.0,0.921952 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,30.021648,8.937976,0.0 -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 +AUS,0.207758,0.219236,0.0 +AZE,0.0,0.0,1.66954 +BFA,0.0,0.0,2.427717 +BGD,0.0,0.0,9.224991 +BHS,0.0,0.0,0.000241 +BOL,0.0,0.0,3.061838 +BRA,0.0,0.0,4.291774 +BRB,0.0,0.0,0.001186 +BRN,0.0,0.0,0.098264 +BTN,0.0,0.0,0.032692 +BWA,0.0,0.0,0.803647 +CAF,0.0,0.0,3.145706 +CHL,0.0,0.0,1.174771 +CHN,24.825367,8.937976,0.0 +CMR,0.0,0.0,1.961326 +COG,0.0,0.0,0.101832 +COL,0.0,0.0,4.967146 +COM,0.0,0.0,0.023564 CPV,0.0,0.0,0.014816 -CRI,0.0,0.0,0.47573 +CRI,0.0,0.0,0.475468 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 +DJI,0.0,0.0,0.253119 +DOM,0.0,0.0,0.732578 +DZA,0.0,0.0,7.00384 +ECU,0.0,0.0,0.999379 +EGY,0.0,0.0,7.622975 +ERI,0.0,0.0,0.647038 +ETH,0.0,0.0,3.538134 +FJI,0.025279,0.0,0.0 +GEO,0.0,0.0,0.291168 +GNB,0.0,0.0,0.082517 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 +GTM,0.0,0.0,0.882024 +HND,0.0,0.0,0.328047 +HTI,0.0,0.0,0.56707 +IDN,3.118611,2.778974,0.0 +IND,0.0,0.0,289.459564 IRL,0.0,0.0,0.334866 ISL,0.0,0.0,0.030581 -ISR,0.0,0.0,1.114113 +ISR,0.0,0.0,1.114115 JAM,0.0,0.0,0.181489 -JOR,0.0,0.0,0.90952 -JPN,1.834735,1.221458,1.323049 -KEN,0.0,0.0,8.553256 -KHM,0.0,0.116946,0.0 -KOR,2.761067,0.374992,2.658246 -LAO,0.0,0.04975,0.0 -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 +JOR,0.0,0.0,0.909527 +JPN,3.091849,0.0,1.321784 +KEN,0.0,0.0,8.557251 +KHM,0.165723,0.112118,0.0 +KOR,2.764511,0.374992,2.65805 +LAO,0.090297,0.071037,0.0 +LBN,0.0,0.0,0.255603 +LBY,0.0,0.0,0.793363 +LKA,0.045914,0.18201,0.0 +LSO,0.0,0.0,0.093721 +MAR,0.0,0.0,5.567743 +MEX,0.0,0.0,2.575333 MLT,0.0,0.0,0.002164 +MMR,0.0,0.395933,0.0 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 +MNG,0.0,0.016502,15.397603 +MRT,0.0,0.0,2.106061 +MYS,0.0,0.0,0.202319 +NAM,0.0,0.0,0.693314 +NER,0.0,0.0,1.711404 +NIC,0.0,0.0,1.291721 NLD,0.0,0.0,0.117066 -NPL,0.0,0.0,15.855976 -NZL,0.0,0.743953,0.0 +NPL,0.0,0.0,15.855499 +NZL,0.253462,1.020203,0.0 OMN,0.0,0.0,1.601173 -PAK,0.0,13.552876,166.893402 -PAN,0.0,0.0,0.335515 -PER,0.0,0.0,0.827014 -PHL,0.19078,0.297524,0.0 +PAK,0.551951,15.348308,166.856613 +PAN,0.0,0.0,0.335518 +PER,0.0,0.0,0.826841 +PHL,1.421447,0.288718,0.0 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,1.355908,0.336465,0.0 -TJK,0.0,0.0,0.247151 -TKM,0.0,0.0,0.695718 -TLS,0.009329,0.01037,0.067521 +PRY,0.0,0.0,0.157387 +PSE,0.0,0.0,0.253417 +RWA,0.0,0.0,0.379823 +SAU,0.0,0.0,3.206031 +SDN,0.0,0.0,11.694079 +SEN,0.0,0.0,0.880737 +SOM,0.0,0.0,5.319843 +SSD,0.0,0.0,3.617177 +STP,0.0,0.0,0.002251 +SWZ,0.0,0.0,0.169087 +SYR,0.0,0.0,1.271971 +TCD,0.0,0.0,25.861656 +THA,3.055352,0.0,0.0 +TJK,0.0,0.0,0.247159 +TKM,0.006912,0.0,0.695733 +TLS,0.012373,0.01037,0.066253 TTO,0.0,0.0,0.00687 -TUN,0.0,0.0,1.242337 -TUR,0.0,0.0,1.66396 -TWN,1.339534,0.00074,0.0 -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,2.115388,0.832676,0.0 -YEM,0.0,0.0,3.183531 -ZAF,0.0,0.0,5.481237 -ZMB,0.0,0.0,2.941371 -ZWE,0.0,0.0,10.748748 +TUN,0.0,0.0,1.242338 +TUR,0.0,0.0,1.668762 +TWN,1.291355,0.00074,0.0 +TZA,0.0,0.0,9.636807 +UGA,0.0,0.0,3.555149 +URY,0.0,0.0,2.438578 +UZB,0.0,1.60441,0.0 +VEN,0.0,0.0,3.763246 +VNM,3.840014,0.712775,0.0 +YEM,0.0,0.0,3.183534 +ZAF,0.0,0.0,5.482439 +ZMB,0.0,0.0,2.941376 +ZWE,0.0,0.0,10.748847 diff --git a/data/curated/calibration/default/exogenous_forage.csv b/data/curated/calibration/default/exogenous_forage.csv index 89593be3..e3ba7281 100644 --- a/data/curated/calibration/default/exogenous_forage.csv +++ b/data/curated/calibration/default/exogenous_forage.csv @@ -5,7 +5,7 @@ country,exogenous_forage_mt_dm AFG,0.0 AGO,0.0 ALB,0.0 -ARE,1.958324 +ARE,0.290386 ARG,0.0 ARM,0.0 ATG,0.0 @@ -16,7 +16,7 @@ BDI,0.0 BEL,0.0 BEN,0.0 BFA,0.0 -BGD,17.312044 +BGD,17.304365 BGR,0.0 BHS,0.0 BIH,0.0 @@ -38,7 +38,7 @@ CMR,0.0 COD,0.0 COG,0.0 COL,0.0 -COM,0.083679 +COM,0.0 CPV,0.0 CRI,0.0 CUB,0.0 @@ -50,7 +50,7 @@ DNK,0.0 DOM,0.0 DZA,0.0 ECU,0.0 -EGY,19.125046 +EGY,0.0 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,138.685715 +IND,133.659561 IRL,0.0 IRN,0.0 IRQ,0.0 @@ -90,7 +90,7 @@ KAZ,0.0 KEN,0.0 KGZ,0.0 KHM,0.0 -KOR,5.329263 +KOR,5.239663 LAO,0.0 LBN,0.0 LBR,0.0 @@ -107,7 +107,7 @@ MEX,0.0 MKD,0.0 MLI,0.0 MLT,0.0 -MMR,8.619004 +MMR,8.468348 MNE,0.0 MNG,0.0 MOZ,0.0 @@ -121,10 +121,10 @@ NGA,0.0 NIC,0.0 NLD,0.0 NOR,0.0 -NPL,2.704415 +NPL,2.597535 NZL,0.0 -OMN,1.78773 -PAK,70.964584 +OMN,1.69909 +PAK,70.964592 PAN,0.0 PER,0.0 PHL,0.0 @@ -156,8 +156,8 @@ SYR,0.0 TCD,0.0 TGO,0.0 THA,0.0 -TJK,3.3232 -TKM,9.849668 +TJK,3.292836 +TKM,9.849627 TLS,0.0 TTO,0.0 TUN,0.0 @@ -168,9 +168,9 @@ UGA,0.0 UKR,0.0 URY,0.0 USA,0.0 -UZB,15.042318 +UZB,15.041609 VEN,0.0 -VNM,3.324505 +VNM,0.0 VUT,0.0 YEM,0.0 ZAF,0.0 diff --git a/data/curated/calibration/default/fodder_conversion.csv b/data/curated/calibration/default/fodder_conversion.csv index 08823e62..1fd05b76 100644 --- a/data/curated/calibration/default/fodder_conversion.csv +++ b/data/curated/calibration/default/fodder_conversion.csv @@ -44,7 +44,7 @@ CRI,1.0 CUB,1.0 CYP,1.0 CZE,1.0 -DEU,0.733979 +DEU,1.0 DJI,1.0 DNK,1.0 DOM,1.0 @@ -53,7 +53,7 @@ ECU,1.0 EGY,1.0 ERI,1.0 ESP,1.0 -EST,0.475501 +EST,0.475502 ETH,1.0 FIN,1.0 FJI,1.0 diff --git a/data/curated/calibration/default/food_demand.csv b/data/curated/calibration/default/food_demand.csv index 3e5f6c7b..4ccd01de 100644 --- a/data/curated/calibration/default/food_demand.csv +++ b/data/curated/calibration/default/food_demand.csv @@ -1,60 +1,60 @@ food,multiplier,net_slack_mt,consumption_mt -apple,1.0266660745695495,-2.67730163782835,100.40104068731671 -banana,1.0,0.0,64.10185700921284 +apple,1.0266644745409084,-2.6771347868198063,100.40080792563822 +banana,1.0,0.0,64.1017096311407 barley-hulled,1.0,0.0,2.5509975362103887 -buckwheat,0.5298007304368076,2.4519494500611927,5.21470280534254 -cabbage,0.9940982247731441,1.3821052366838558,234.18466199706018 -carrot,1.0009751508863163,-0.12162518501281738,124.72447773930617 -cassava,1.0,0.0,156.52930587690935 -chickpea,0.7380841411070957,3.1097654529060037,11.873146842083997 -citrus,1.071882576996098,-9.326904296875,129.751946669654 -cocoa-powder,0.9075733242820169,0.4894285178525024,5.295316682662815 +buckwheat,0.5297444815536061,2.452242771270221,5.21470280534254 +cabbage,0.99410357957926,1.3808388083562022,234.1825565048348 +carrot,1.0009741534656378,-0.12149969721212983,124.72336392354919 +cassava,1.0,0.0,156.529094039166 +chickpea,0.7380542588377743,3.1101202494776317,11.873146842083997 +citrus,1.071883770937358,-9.327037811279297,129.751648941834 +cocoa-powder,0.9075724764640558,0.4894329881935846,5.295316475760046 coconut,1.0,0.0,7.4685584381240915 -coconut-oil,1.0,0.0,0.9580413502262672 -coffee-green,0.8670401855759174,1.5566428339807317,11.70761888261768 -cottonseed-oil,1.0,0.0,3.417625502622286 +coconut-oil,1.0,0.0,0.8683887740007776 +coffee-green,0.8670346719587236,1.5567073597339913,11.707618690270465 +cottonseed-oil,1.0,0.0,3.125593973028117 cowpea,1.0,0.0,8.023896933055994 dairy,1.1191858501944325,-83.19951275270432,698.0653543770313 dairy-buffalo,1.1373173584109617,-16.630459283856908,121.1096650584077 dry-pea,1.0,0.0,6.17437933254223 -eggs,1.0,0.0,82.1042355971731 +eggs,0.9999999933922069,5.425278004622669e-07,82.1042355971731 flour-white,1.0,0.0,275.2358510554768 flour-wholemeal,1.0,0.0,38.87509628526459 foxtail-millet,1.0,0.0,3.234954914529226 -gram,0.7380841411070957,3.1097654529060037,11.873146842083997 +gram,0.7380542919392032,3.110119856459505,11.873146842083997 groundnut,1.0,0.0,20.996472433620966 -groundnut-oil,1.0,0.0,3.0772409267010516 +groundnut-oil,1.0,0.0,2.783429691281526 maize,1.0,0.0,64.77057215915829 maize-whole,1.0,0.0,20.808036830817628 -mango,0.8347045114934298,9.925017174158711,60.04408991334458 +mango,0.8347028290816869,9.925095592123398,60.04395318434217 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,1.0,0.0,1.1316055530789981 -olive-oil,1.1983975936823603,-0.8152478244155645,4.109161856674518 -onion,0.9920594671459757,2.253802227747201,283.8351366564748 -palm-oil,1.0,0.0,23.678821763294764 +olive-oil,1.2983651782181878,-1.1316246474161744,3.7927503945807075 +onion,0.9920661276212404,2.2518915306172858,283.8325880620396 +palm-oil,0.5,36.719671779326745,21.10606127652136 pearl-millet,1.0,0.0,3.234954914529226 phaseolus-bean,1.0,0.0,21.57145358827256 pigeon-pea,1.0,0.0,0.6679289132182475 -plantain,0.8963669057902882,2.6172342026384285,25.25481095201303 -potato,1.0,0.0,225.42101348366123 -rapeseed-oil,1.0,0.0,10.796110091461287 +plantain,0.8963608878878673,2.6173826385456778,25.25477674600097 +potato,1.0,0.0,225.42070956376847 +rapeseed-oil,1.0,0.0,10.001032957413827 rendered-fat,1.0,0.0,7.44342613070603 rice-brown,1.0,0.0,35.70415660064464 rice-white,1.0,0.0,340.82733609573916 rye,1.0,0.0,0.809679446935661 -sesame-oil,0.8764937575317806,0.13663251387697528,1.1062802263791127 +sesame-oil,0.9676143890208987,0.032453327785333386,1.0020909534878228 sesame-seed,1.0,0.0,2.9894916711275528 sorghum,1.0,0.0,11.638505917195289 soy,1.0,0.0,11.041815498637334 -soybean-oil,1.0,0.0,27.623150501682176 +soybean-oil,1.0,0.0,25.179107923099764 sugar,1.0,0.0,137.2949263714254 -sunflower-oil,0.9197869439768979,1.0434579381244475,13.008579773147176 +sunflower-oil,0.9999382360909597,0.0007390549872070551,11.965806547706507 sunflower-seed,1.0,0.0,4.64019518993814 -sweet-potato,1.0,0.0,44.743217360937706 -tea-dried,0.9839926829455669,0.10682958776851592,6.673797201944581 -tomato,1.0277485168504943,-3.514272451400757,126.64721759131317 -watermelon,1.1469574064927137,-8.598334312438965,58.509023244536365 -yam,0.9337811616775489,2.295802172874801,34.669925221210406 +sweet-potato,1.0,0.0,44.74315627075703 +tea-dried,0.9839852983705806,0.10687886715913919,6.673796966832015 +tomato,1.027724820712193,-3.511240005493164,126.6460851791544 +watermelon,1.1469571407924075,-8.598299026489258,58.50888891908471 +yam,0.9337672544516643,2.296284371904221,34.66992577302153 diff --git a/data/curated/calibration/default/food_waste.yaml b/data/curated/calibration/default/food_waste.yaml index f02edbcb..41d0f8e5 100644 --- a/data/curated/calibration/default/food_waste.yaml +++ b/data/curated/calibration/default/food_waste.yaml @@ -10,26 +10,26 @@ # `tools/calibrate food_waste` instead. fruits: baseline_consumption_mt: 344.04602138686823 - baseline_negative_slack_mt: 68.76193224766757 + baseline_negative_slack_mt: 68.76098508550785 baseline_positive_slack_mt: 0.0 - waste_retention_multiplier: 0.8334287611412076 + waste_retention_multiplier: 0.8334306733960215 oil: baseline_consumption_mt: 88.23545169174031 - baseline_negative_slack_mt: 0.7936925136018544 - baseline_positive_slack_mt: 1.2541325589445478 - waste_retention_multiplier: 1.005245684810588 + baseline_negative_slack_mt: 0.7936579056549817 + baseline_positive_slack_mt: 42.464086364731884 + waste_retention_multiplier: 1.8948868821748348 starchy_vegetable: baseline_consumption_mt: 491.43520556430417 baseline_negative_slack_mt: 0.0 - baseline_positive_slack_mt: 5.182368893033754 - waste_retention_multiplier: 1.0106577658826847 + baseline_positive_slack_mt: 5.183033215001387 + waste_retention_multiplier: 1.0106591466521575 stimulants: baseline_consumption_mt: 23.68795094791676 baseline_negative_slack_mt: 0.0 - baseline_positive_slack_mt: 2.164119136995396 - waste_retention_multiplier: 1.1005452540238354 + baseline_positive_slack_mt: 2.1642380495459292 + waste_retention_multiplier: 1.1005513342314535 vegetables: baseline_consumption_mt: 717.5079036276238 - baseline_negative_slack_mt: 51.88358031725511 + baseline_negative_slack_mt: 51.876708737108856 baseline_positive_slack_mt: 0.0 - waste_retention_multiplier: 0.9325654346325307 + waste_retention_multiplier: 0.9325737636243285 diff --git a/data/curated/calibration/default/grassland_cost.csv b/data/curated/calibration/default/grassland_cost.csv index 95f55ed1..92c918bf 100644 --- a/data/curated/calibration/default/grassland_cost.csv +++ b/data/curated/calibration/default/grassland_cost.csv @@ -1,175 +1,175 @@ country,correction_bnusd_per_mha -AFG,-0.0 -AGO,-0.4345769137144089 -ALB,-0.16155622154474258 -ARE,-0.0 -ARG,-0.21808408945798874 -ARM,-0.0 -ATG,-0.0 -AUS,-0.07189670205116272 -AUT,1.2174659058672432e-09 -AZE,0.9162248075008392 -BDI,-1.1082043727128621e-08 +AFG,1.0285317042502129e-10 +AGO,-0.3685210794210434 +ALB,0.04497191123664379 +ARE,0.0 +ARG,-0.149925597012043 +ARM,0.0 +ATG,0.0 +AUS,-0.00915704108774662 +AUT,0.0 +AZE,0.9814981520175934 +BDI,-0.48811182379722595 BEL,0.08367127552628517 -BEN,3.0643407106399536 -BFA,1.408345341682434 -BGD,-0.0 -BGR,-0.2658487558364868 -BHS,-0.0316774295642972 -BIH,-0.4313219487667084 -BLR,0.010591222904622555 -BLZ,-0.7379351556301117 +BEN,3.2785658836364746 +BFA,1.5074808597564697 +BGD,0.0 +BGR,-0.22096917033195496 +BHS,4.3433148744220773e-10 +BIH,-0.42985886335372925 +BLR,0.011688394472002983 +BLZ,-0.8535727262496948 BOL,-0.5958932638168335 BRA,-0.3751921057701111 -BRB,0.8664121925830841 +BRB,0.9317607879638672 BRN,7.141561985015869 -BTN,-0.28385645151138306 -BWA,-0.0 -CAF,-0.4648502320051193 -CAN,-0.2188480719923973 -CHE,-0.0 +BTN,-0.30002422630786896 +BWA,0.0 +CAF,3.2490773627102953e-08 +CAN,-0.21738236397504807 +CHE,0.0 CHL,-0.0009876639232970774 -CHN,4.4359862449994125e-09 -CIV,-0.5139444768428802 -CMR,4.206778287887573 -COD,-0.8133789598941803 +CHN,-9.942733303347495e-10 +CIV,-0.503073513507843 +CMR,4.431897163391113 +COD,-0.7558623552322388 COG,-0.8903338015079498 -COL,-0.7695494294166565 -COM,7.03753166853005e-10 -CPV,-0.0 -CRI,-0.5568442642688751 +COL,-0.7592038810253143 +COM,1.4762386679649353 +CPV,0.0 +CRI,-0.09060221165418625 CUB,-0.1725609302520752 -CYP,0.2901802659034729 -CZE,-0.2419496849179268 -DEU,-0.3941321074962616 -DJI,-0.0 -DNK,1.7655537724494934 -DOM,-9.206058138033768e-09 -DZA,-0.0 +CYP,0.30934228003025055 +CZE,-0.006804863223806024 +DEU,-0.2437824308872223 +DJI,0.0 +DNK,1.8209227323532104 +DOM,-1.4828121619459012e-08 +DZA,1.8697272896606165e-10 ECU,-0.10834560729563236 -EGY,-0.0 +EGY,0.0 ERI,-0.036436695605516434 -ESP,-0.0 +ESP,4.5370475432182644e-10 EST,-0.25669121742248535 -ETH,-1.3543125509940523e-09 -FIN,1.60249662399292 -FJI,-0.8145651519298553 +ETH,1.5634718408819026e-08 +FIN,1.712095022201538 +FJI,-0.7755072712898254 FRA,-2.7449107875554546e-08 -GAB,-0.8152421414852142 -GBR,-0.0 -GEO,-0.2082834094762802 -GHA,-2.9578572480204457e-08 -GIN,-0.16346535086631775 -GMB,1.1494979858398438 -GNB,1.0539621114730835 -GNQ,-0.5248542279005051 +GAB,-0.8109605014324188 +GBR,0.0 +GEO,-0.2034044936299324 +GHA,-0.5135024040937424 +GIN,-0.15879005938768387 +GMB,1.2291799783706665 +GNB,1.1270533204078674 +GNQ,-0.7098988890647888 GRC,-0.09379514306783676 -GRD,0.6201870441436768 -GTM,1.0484134893573582e-08 +GRD,0.6720268130302429 +GTM,5.198906860570673e-10 GUF,-1.0722496509552002 GUY,-1.106971263885498 -HND,-0.6534972786903381 -HRV,-0.06190457381308079 -HTI,5.54345191972061e-09 +HND,-0.5874203741550446 +HRV,0.0 +HTI,-6.526291063124745e-09 HUN,-0.4170793741941452 -IDN,-0.5775293707847595 -IND,1.702402114868164 -IRL,-0.0 -IRN,-0.0 -IRQ,-0.0 -ISL,-0.07598408311605453 -ISR,0.671514630317688 -ITA,0.04259064048528671 -JAM,3.5118263959884644 -JOR,-0.0 +IDN,-0.5079740881919861 +IND,1.584054708480835 +IRL,0.0 +IRN,0.0 +IRQ,0.0 +ISL,-0.06565051525831223 +ISR,0.7160363793373108 +ITA,0.29519903659820557 +JAM,3.780540943145752 +JOR,0.0 JPN,-0.4974181354045868 -KAZ,1.57520045618309e-10 -KEN,-0.16812094300985336 -KGZ,-0.0 -KHM,-0.35812681913375854 -KOR,-1.3437390089521273e-09 -LAO,2.91002910657312e-09 -LBN,0.49065376818180084 -LBR,-0.7553918659687042 -LBY,-0.0 -LKA,-0.19719894230365753 -LSO,-0.443109393119812 -LTU,1.0886833101686477e-10 +KAZ,0.0 +KEN,-0.12902680039405823 +KGZ,-1.187652864942379e-09 +KHM,-0.27926672995090485 +KOR,3.1221169233322144 +LAO,-3.513972668400811e-09 +LBN,0.5231764614582062 +LBR,-0.7535805702209473 +LBY,0.0 +LKA,-4.2231160790606737e-08 +LSO,-0.4797108322381973 +LTU,-1.0227096947090786e-10 LUX,0.061516094487160444 LVA,-0.3037813901901245 -MAR,-0.0 +MAR,0.0 MDA,-0.36603914201259613 -MDG,-0.6942926049232483 -MEX,-0.08276043459773064 -MKD,-0.23444095999002457 -MLI,1.2537319515892875e-09 -MLT,-0.0 -MMR,8.1712165922454e-09 -MNE,-0.18078084290027618 -MNG,-0.04568313993513584 -MOZ,-0.5657002627849579 -MRT,-3.5724184721530605e-10 -MUS,-3.315589225394433e-08 +MDG,-0.6528348624706268 +MEX,6.248633832584005e-09 +MKD,-0.1847551316022873 +MLI,-0.04440104402601719 +MLT,0.0 +MMR,0.7816084325313568 +MNE,-0.157601498067379 +MNG,-0.02809592429548502 +MOZ,-0.5656761825084686 +MRT,-2.2212138378607804e-11 +MUS,2.5759505639655345e-09 MWI,-0.19057554006576538 -MYS,2.3094663959000172e-08 -NAM,-0.006759356707334518 -NER,-0.0 +MYS,-9.89881137059001e-08 +NAM,-0.006840692600235343 +NER,0.0 NGA,-0.19652122259140015 NIC,-0.5845303535461426 NLD,0.7907610237598419 -NOR,0.5465956330299377 -NPL,-0.0 -NZL,-0.0 -OMN,-0.0 -PAK,-0.0 -PAN,-0.2060026302933693 -PER,-0.629342257976532 +NOR,0.583920419216156 +NPL,0.0 +NZL,0.0 +OMN,0.0 +PAK,1.424497075852571e-09 +PAN,-0.10266764834523201 +PER,-0.6257849335670471 PHL,-0.12064796686172485 PNG,-0.7848113179206848 POL,0.019568821415305138 -PRI,-2.7517170764213006e-09 +PRI,-6.72518289945856e-08 PRT,-0.09565510042011738 -PRY,-0.536945641040802 -PSE,0.659267783164978 +PRY,-0.5369450151920319 +PSE,0.7072950899600983 ROU,-0.4245498776435852 -RUS,-1.0376933090583407e-08 -RWA,2.7728375151525597e-08 -SAU,-0.0 -SDN,-0.048175884410738945 -SEN,0.35477225482463837 -SLB,-1.4547326898650681e-08 -SLE,-0.41614776849746704 -SLV,3.3692214488983154 +RUS,-1.0847077902553792e-08 +RWA,6.476653575897217 +SAU,0.0 +SDN,-0.023131180554628372 +SEN,0.3793909251689911 +SLB,1.598713561534737e-08 +SLE,-0.4092519134283066 +SLV,3.6254513263702393 SOM,-4.7665072999691915e-09 -SRB,-0.29573993384838104 -SSD,-0.3176824450492859 -STP,0.5826865136623383 +SRB,-0.295737162232399 +SSD,-0.2969408631324768 +STP,0.6137635111808777 SUR,6.86613392829895 SVK,-0.4092821776866913 -SVN,-0.04422223940491676 -SWE,1.6543604731559753 +SVN,1.956440476291732e-08 +SWE,1.7676981091499329 SWZ,-1.973749874650821e-08 -SYR,0.13381114602088928 -TCD,-0.0883248969912529 -TGO,-0.3382970243692398 -THA,-2.5447631557540262e-08 -TJK,0.3576388657093048 -TKM,-0.0 -TLS,-0.22272039204835892 -TTO,-2.3513318403445282e-08 -TUN,0.34285783767700195 -TUR,1.0842138528823853 -TWN,-0.24641626328229904 -TZA,-0.06884757429361343 -UGA,-0.7039666175842285 -UKR,-0.026072262786328793 -URY,-0.17097113281488419 -USA,-0.4118834286928177 -UZB,0.0066771432757377625 -VEN,-0.26539246737957 -VNM,5.654500484466553 -VUT,-0.17226548492908478 -YEM,2.695993539525432e-09 -ZAF,-0.0033502724254503846 -ZMB,-0.3225127309560776 -ZWE,1.0086238384246826 +SYR,0.14269284904003143 +TCD,-0.07812828943133354 +TGO,-0.46927402913570404 +THA,0.0 +TJK,0.3895326554775238 +TKM,-9.089148877983177e-10 +TLS,-0.3032049834728241 +TTO,8.520019217916897e-09 +TUN,0.3667435646057129 +TUR,1.1556978225708008 +TWN,-0.1748160943388939 +TZA,-0.06839879974722862 +UGA,-0.6480332314968109 +UKR,0.06035628356039524 +URY,-0.21174990385770798 +USA,-0.4049943536520004 +UZB,1.0099180340766907 +VEN,-0.26628775894641876 +VNM,5.704484701156616 +VUT,-0.13240307196974754 +YEM,0.0 +ZAF,0.0 +ZMB,-0.15246352553367615 +ZWE,1.0522664785385132 diff --git a/data/curated/calibration/default/grassland_yield.csv b/data/curated/calibration/default/grassland_yield.csv index c57034c5..ab4cfcdd 100644 --- a/data/curated/calibration/default/grassland_yield.csv +++ b/data/curated/calibration/default/grassland_yield.csv @@ -3,25 +3,25 @@ # SPDX-License-Identifier: CC-BY-4.0 country,yield_correction AFG,0.395294 -AGO,0.071253 +AGO,0.071585 ALB,0.144938 ARE,1.0 -ARG,0.200993 +ARG,0.345713 ARM,0.550552 ATG,1.0 AUS,0.236373 -AUT,0.553455 +AUT,0.600427 AZE,1.0 -BDI,0.437887 +BDI,0.442052 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 +BIH,0.01467 +BLR,0.422193 +BLZ,0.102024 BOL,0.111315 BRA,0.229034 BRB,1.0 @@ -32,7 +32,7 @@ CAF,0.583977 CAN,0.051832 CHE,0.40712 CHL,0.686938 -CHN,0.287707 +CHN,0.290638 CIV,0.033413 CMR,1.0 COD,0.022128 @@ -43,12 +43,12 @@ CPV,1.0 CRI,0.380109 CUB,0.277685 CYP,1.0 -CZE,0.138284 -DEU,0.0 +CZE,0.232729 +DEU,0.150859 DJI,1.0 DNK,1.0 -DOM,0.567979 -DZA,0.79171 +DOM,0.567981 +DZA,0.791718 ECU,0.36376 EGY,1.0 ERI,0.518486 @@ -58,20 +58,20 @@ ETH,0.551195 FIN,1.0 FJI,0.041419 FRA,0.488102 -GAB,0.006069 +GAB,0.007952 GBR,0.40073 GEO,0.122798 -GHA,0.178955 +GHA,0.178956 GIN,0.173001 GMB,1.0 GNB,1.0 GNQ,0.077495 GRC,0.075784 GRD,1.0 -GTM,0.571476 +GTM,0.571477 GUF,0.0 GUY,0.018762 -HND,0.147424 +HND,0.186551 HRV,0.284196 HTI,0.92669 HUN,0.0 @@ -82,7 +82,7 @@ IRN,0.769453 IRQ,1.0 ISL,0.062237 ISR,1.0 -ITA,0.884272 +ITA,1.0 JAM,1.0 JOR,1.0 JPN,0.0 @@ -91,12 +91,12 @@ KEN,0.467676 KGZ,0.842055 KHM,0.398086 KOR,1.0 -LAO,0.404176 +LAO,0.446981 LBN,1.0 LBR,0.016113 LBY,0.894499 LKA,0.193473 -LSO,0.048049 +LSO,0.043373 LTU,0.341665 LUX,0.783687 LVA,0.0 @@ -105,17 +105,17 @@ MDA,0.0 MDG,0.016111 MEX,0.371319 MKD,0.104967 -MLI,0.387861 +MLI,0.387873 MLT,1.0 MMR,1.0 MNE,0.077905 MNG,0.245052 MOZ,0.00289 -MRT,0.660517 +MRT,0.66052 MUS,0.811415 MWI,0.76798 -MYS,0.436125 -NAM,0.291928 +MYS,0.442334 +NAM,0.29193 NER,1.0 NGA,0.665316 NIC,0.295306 @@ -125,51 +125,51 @@ NPL,1.0 NZL,0.560406 OMN,1.0 PAK,1.0 -PAN,0.256559 -PER,0.083668 +PAN,0.312528 +PER,0.084126 PHL,0.384506 PNG,0.0 POL,0.560559 -PRI,0.43587 +PRI,0.459587 PRT,0.081039 -PRY,0.068488 +PRY,0.068489 PSE,1.0 ROU,0.0 RUS,0.15561 -RWA,0.794264 +RWA,1.0 SAU,1.0 SDN,0.419271 SEN,1.0 -SLB,0.580748 +SLB,0.408618 SLE,0.093191 SLV,1.0 SOM,0.555429 -SRB,0.13671 +SRB,0.136713 SSD,0.142093 STP,1.0 SUR,1.0 SVK,0.0 -SVN,0.252009 +SVN,0.252012 SWE,1.0 SWZ,0.283263 SYR,1.0 TCD,0.695865 TGO,0.206827 -THA,0.856195 +THA,0.898892 TJK,1.0 TKM,1.0 TLS,0.234166 -TTO,0.743954 +TTO,0.839948 TUN,1.0 TUR,1.0 TWN,0.107613 -TZA,0.406749 -UGA,0.405131 +TZA,0.407102 +UGA,0.425128 UKR,0.166781 -URY,0.270063 +URY,0.242289 USA,0.031197 UZB,1.0 -VEN,0.190522 +VEN,0.190094 VNM,1.0 VUT,0.176534 YEM,0.592561 diff --git a/data/curated/calibration/default/multi_crop_cost.csv b/data/curated/calibration/default/multi_crop_cost.csv new file mode 100644 index 00000000..5217f5cc --- /dev/null +++ b/data/curated/calibration/default/multi_crop_cost.csv @@ -0,0 +1,789 @@ +combination,country,correction_bnusd_per_mha +cotton_wheat,AFG,0.12073299288749695 +cotton_wheat,AGO,1.2058953940868378 +cotton_wheat,ALB,7.5 +cotton_wheat,ARG,7.5 +cotton_wheat,ARM,7.5 +cotton_wheat,AUS,-7.5 +cotton_wheat,AZE,7.5 +cotton_wheat,BDI,-0.5172154903411865 +cotton_wheat,BGD,4.8990325927734375 +cotton_wheat,BGR,7.5 +cotton_wheat,BOL,7.5 +cotton_wheat,BRA,6.710078716278076 +cotton_wheat,BWA,4.441415369510651 +cotton_wheat,CHN,1.1753121614456177 +cotton_wheat,COD,-1.0172451734542847 +cotton_wheat,DZA,7.5 +cotton_wheat,EGY,0.7446867227554321 +cotton_wheat,ESP,2.422280430793762 +cotton_wheat,ETH,-1.18629789352417 +cotton_wheat,GEO,7.5 +cotton_wheat,GRC,1.8530462980270386 +cotton_wheat,HND,-0.027787355706095695 +cotton_wheat,IND,-0.6717728674411774 +cotton_wheat,IRN,0.558936595916748 +cotton_wheat,IRQ,7.5 +cotton_wheat,ISR,2.486711263656616 +cotton_wheat,JOR,2.592496156692505 +cotton_wheat,KAZ,1.498054027557373 +cotton_wheat,KEN,1.0787805318832397 +cotton_wheat,KGZ,2.308807134628296 +cotton_wheat,KOR,7.5 +cotton_wheat,LBN,7.5 +cotton_wheat,LSO,4.406145870685577 +cotton_wheat,MAR,1.0710368156433105 +cotton_wheat,MDG,-0.14180460572242737 +cotton_wheat,MEX,6.028358459472656 +cotton_wheat,MKD,3.2505438327789307 +cotton_wheat,MMR,7.5 +cotton_wheat,MOZ,0.47627148032188416 +cotton_wheat,MWI,7.5 +cotton_wheat,NAM,4.1794014275074005 +cotton_wheat,NER,7.5 +cotton_wheat,NGA,-7.5 +cotton_wheat,NPL,7.5 +cotton_wheat,PAK,1.4043108224868774 +cotton_wheat,PRT,7.5 +cotton_wheat,PRY,2.0446081161499023 +cotton_wheat,PSE,1.3241363763809204 +cotton_wheat,ROU,0.0 +cotton_wheat,RUS,7.5 +cotton_wheat,RWA,-0.284987673163414 +cotton_wheat,SDN,-7.5 +cotton_wheat,SOM,-7.5 +cotton_wheat,SWZ,-0.8469589948654175 +cotton_wheat,SYR,2.0918221473693848 +cotton_wheat,TJK,0.07361793518066406 +cotton_wheat,TKM,0.5607117414474487 +cotton_wheat,TUN,7.5 +cotton_wheat,TUR,4.218727111816406 +cotton_wheat,TZA,-0.6717642545700073 +cotton_wheat,UGA,-0.21029487997293472 +cotton_wheat,URY,7.5 +cotton_wheat,USA,2.4452409744262695 +cotton_wheat,UZB,0.42546316981315613 +cotton_wheat,YEM,-7.5 +cotton_wheat,ZAF,2.9171504974365234 +cotton_wheat,ZMB,7.5 +cotton_wheat,ZWE,6.102042198181152 +double_rice,AFG,7.5 +double_rice,AGO,7.5 +double_rice,ALB,0.0 +double_rice,ARG,7.5 +double_rice,AUS,7.5 +double_rice,AZE,7.5 +double_rice,BDI,-6.651249885559082 +double_rice,BEN,-2.4684728384017944 +double_rice,BFA,-7.301760196685791 +double_rice,BGD,-0.13437831960618496 +double_rice,BLZ,2.286782741546631 +double_rice,BOL,7.5 +double_rice,BRA,7.5 +double_rice,BRN,-2.890824794769287 +double_rice,BTN,7.5 +double_rice,BWA,0.0 +double_rice,CAF,7.5 +double_rice,CHL,7.5 +double_rice,CHN,-3.0934839248657227 +double_rice,CIV,7.5 +double_rice,CMR,7.5 +double_rice,COD,7.5 +double_rice,COG,7.5 +double_rice,COL,-2.408092975616455 +double_rice,CRI,-1.5242087841033936 +double_rice,CUB,7.5 +double_rice,DOM,-2.50664484500885 +double_rice,DZA,7.5 +double_rice,ECU,-1.0761107802391052 +double_rice,EGY,7.5 +double_rice,ERI,7.5 +double_rice,ESP,7.5 +double_rice,ETH,7.5 +double_rice,FJI,7.5 +double_rice,FRA,7.5 +double_rice,GAB,7.5 +double_rice,GEO,7.5 +double_rice,GHA,-0.5125921368598938 +double_rice,GIN,-7.5 +double_rice,GMB,-7.5 +double_rice,GNB,-7.5 +double_rice,GNQ,7.5 +double_rice,GRC,7.5 +double_rice,GTM,5.432131886482239 +double_rice,GUF,0.0 +double_rice,GUY,7.5 +double_rice,HND,7.5 +double_rice,HTI,2.532879590988159 +double_rice,IDN,-2.5864267349243164 +double_rice,IND,2.284341335296631 +double_rice,IRN,7.5 +double_rice,IRQ,7.5 +double_rice,ITA,7.5 +double_rice,JPN,-7.467597246170044 +double_rice,KEN,7.5 +double_rice,KHM,-4.353095293045044 +double_rice,KOR,7.5 +double_rice,LAO,-2.310716390609741 +double_rice,LBR,7.5 +double_rice,LKA,-0.44917234778404236 +double_rice,LSO,0.0 +double_rice,MAR,7.5 +double_rice,MDG,3.66713098436594 +double_rice,MEX,7.5 +double_rice,MKD,7.5 +double_rice,MLI,-7.5 +double_rice,MMR,-2.5912954807281494 +double_rice,MOZ,7.5 +double_rice,MRT,-7.5 +double_rice,MUS,7.5 +double_rice,MWI,-7.5 +double_rice,MYS,-1.0528628826141357 +double_rice,NAM,7.5 +double_rice,NER,7.5 +double_rice,NGA,-3.098779320716858 +double_rice,NIC,5.87060022354126 +double_rice,NPL,5.132853031158447 +double_rice,PAK,7.5 +double_rice,PAN,-1.8307443857192993 +double_rice,PER,7.5 +double_rice,PHL,-1.3794662952423096 +double_rice,PNG,7.5 +double_rice,PRI,7.5 +double_rice,PRT,7.5 +double_rice,PRY,7.5 +double_rice,RWA,-3.050174117088318 +double_rice,SAU,7.5 +double_rice,SDN,7.5 +double_rice,SEN,-2.742427185177803 +double_rice,SLB,7.5 +double_rice,SLE,-7.255029201507568 +double_rice,SLV,7.5 +double_rice,SOM,7.5 +double_rice,SSD,7.5 +double_rice,SUR,7.5 +double_rice,SWZ,7.5 +double_rice,SYR,7.5 +double_rice,TCD,-0.93898606300354 +double_rice,TGO,-0.04446191340684891 +double_rice,THA,-0.8102104067802429 +double_rice,TJK,7.5 +double_rice,TKM,7.5 +double_rice,TLS,7.5 +double_rice,TTO,7.5 +double_rice,TUN,0.0 +double_rice,TUR,7.5 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+wheat_maize,VNM,0.027547618374228477 +wheat_maize,YEM,-7.5 +wheat_maize,ZAF,4.6093175411224365 +wheat_maize,ZMB,-7.151492118835449 +wheat_maize,ZWE,6.538405895233154 +wheat_soybean,AFG,0.04527095705270767 +wheat_soybean,AGO,-0.08519302308559418 +wheat_soybean,ALB,7.5 +wheat_soybean,ARG,7.5 +wheat_soybean,ARM,7.5 +wheat_soybean,AUS,1.035093069076538 +wheat_soybean,AUT,7.5 +wheat_soybean,AZE,7.5 +wheat_soybean,BDI,-1.4691311120986938 +wheat_soybean,BEL,7.5 +wheat_soybean,BGD,3.5620241463184357 +wheat_soybean,BGR,7.5 +wheat_soybean,BIH,7.5 +wheat_soybean,BOL,7.5 +wheat_soybean,BRA,-0.20813433825969696 +wheat_soybean,BTN,7.5 +wheat_soybean,BWA,7.5 +wheat_soybean,CAN,7.5 +wheat_soybean,CHE,7.5 +wheat_soybean,CHN,-1.4649994373321533 +wheat_soybean,CMR,-0.4691017121076584 +wheat_soybean,COD,-1.2634633779525757 +wheat_soybean,CZE,7.5 +wheat_soybean,DEU,7.5 +wheat_soybean,DZA,0.0 +wheat_soybean,ECU,-0.6779110133647919 +wheat_soybean,EGY,-1.3776955604553223 +wheat_soybean,ESP,7.5 +wheat_soybean,ETH,-0.8534783720970154 +wheat_soybean,FRA,7.5 +wheat_soybean,GEO,-0.023869957774877548 +wheat_soybean,GRC,3.680923141539097 +wheat_soybean,GTM,2.564176559448242 +wheat_soybean,HND,-0.6847660541534424 +wheat_soybean,HRV,7.5 +wheat_soybean,HUN,7.5 +wheat_soybean,IND,2.3271501064300537 +wheat_soybean,IRN,-0.41254279017448425 +wheat_soybean,IRQ,-0.488378694280982 +wheat_soybean,ISR,7.5 +wheat_soybean,ITA,7.5 +wheat_soybean,JOR,0.0 +wheat_soybean,JPN,1.4244059324264526 +wheat_soybean,KAZ,7.5 +wheat_soybean,KEN,-0.7176618576049805 +wheat_soybean,KGZ,3.626110717654228 +wheat_soybean,KOR,-0.36814725399017334 +wheat_soybean,LAO,0.0 +wheat_soybean,LBN,7.5 +wheat_soybean,LSO,7.5 +wheat_soybean,LUX,7.5 +wheat_soybean,MAR,-0.5912825465202332 +wheat_soybean,MDA,7.5 +wheat_soybean,MEX,7.5 +wheat_soybean,MKD,7.5 +wheat_soybean,MMR,-0.6119680404663086 +wheat_soybean,MNE,7.5 +wheat_soybean,MOZ,7.5 +wheat_soybean,MWI,7.5 +wheat_soybean,NGA,7.5 +wheat_soybean,NLD,7.5 +wheat_soybean,NPL,7.5 +wheat_soybean,PAK,-0.08180655911564827 +wheat_soybean,PER,-0.8094130158424377 +wheat_soybean,POL,7.5 +wheat_soybean,PRT,7.5 +wheat_soybean,PRY,-0.10462310910224915 +wheat_soybean,ROU,7.5 +wheat_soybean,RUS,7.5 +wheat_soybean,RWA,-0.44948336482048035 +wheat_soybean,SRB,7.5 +wheat_soybean,SVK,7.5 +wheat_soybean,SVN,7.5 +wheat_soybean,SWZ,-0.5193095803260803 +wheat_soybean,SYR,7.5 +wheat_soybean,THA,7.5 +wheat_soybean,TJK,7.5 +wheat_soybean,TKM,0.3698643445968628 +wheat_soybean,TUR,7.5 +wheat_soybean,TWN,-0.37869152426719666 +wheat_soybean,TZA,-0.36440300941467285 +wheat_soybean,UGA,-1.134753942489624 +wheat_soybean,UKR,7.5 +wheat_soybean,URY,-0.2477397918701172 +wheat_soybean,USA,-0.048939481377601624 +wheat_soybean,UZB,7.5 +wheat_soybean,VNM,-0.5917498469352722 +wheat_soybean,ZAF,2.420696258544922 +wheat_soybean,ZMB,5.127807140350342 +wheat_soybean,ZWE,4.643855094909668 diff --git a/data/curated/calibration/default/provenance.yaml b/data/curated/calibration/default/provenance.yaml index 8deaaa89..900e6b27 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: '2026-07-03T17:40:30+00:00' -git_commit: 22281c512133c39d1fd438f77f076a0b931c3d33 +generated_at: '2026-07-21T23:02:35+00:00' +git_commit: 548ef138989c4a0ed28a90adeb0e4626b044a8ed source: default structural_config: aggregation.irrigated_area_source: current @@ -654,6 +654,181 @@ structural_config: data.usda.nutrients.fat: Total lipid (fat) data.usda.nutrients.protein: Protein data.usda.retrieve_nutrition: false + derived.multiple_cropping.catalog: + cotton_wheat: + crops: + - cotton + - wheat + water_supplies: + - r + - i + double_rice: + crops: + - wetland-rice + - wetland-rice + water_supplies: + - r + - i + maize_soybean: + crops: + - maize + - soybean + water_supplies: + - r + - i + rice_maize: + crops: + - wetland-rice + - maize + water_supplies: + - r + - i + rice_wheat: + crops: + - wetland-rice + - wheat + water_supplies: + - r + - i + triple_rice: + crops: + - wetland-rice + - wetland-rice + - wetland-rice + water_supplies: + - r + - i + wheat_maize: + crops: + - wheat + - maize + water_supplies: + - r + - i + wheat_soybean: + crops: + - wheat + - soybean + water_supplies: + - r + - i + derived.multiple_cropping.effective: + cotton_wheat: + crops: + - cotton + - wheat + water_supplies: + - r + - i + double_rice: + crops: + - wetland-rice + - wetland-rice + water_supplies: + - r + - i + maize_soybean: + crops: + - maize + - soybean + water_supplies: + - r + - i + rice_maize: + crops: + - wetland-rice + - maize + water_supplies: + - r + - i + rice_wheat: + crops: + - wetland-rice + - wheat + water_supplies: + - r + - i + triple_rice: + crops: + - wetland-rice + - wetland-rice + - wetland-rice + water_supplies: + - r + - i + wheat_maize: + crops: + - wheat + - maize + water_supplies: + - r + - i + wheat_soybean: + crops: + - wheat + - soybean + water_supplies: + - r + - i + derived.multiple_cropping.mirca_source_year: 2020 + derived.multiple_cropping.observed: + cotton_wheat: + crops: + - cotton + - wheat + water_supplies: + - r + - i + double_rice: + crops: + - wetland-rice + - wetland-rice + water_supplies: + - r + - i + maize_soybean: + crops: + - maize + - soybean + water_supplies: + - r + - i + rice_maize: + crops: + - wetland-rice + - maize + water_supplies: + - r + - i + rice_wheat: + crops: + - wetland-rice + - wheat + water_supplies: + - r + - i + triple_rice: + crops: + - wetland-rice + - wetland-rice + - wetland-rice + water_supplies: + - r + - i + wheat_maize: + crops: + - wheat + - maize + water_supplies: + - r + - i + wheat_soybean: + crops: + - wheat + - soybean + water_supplies: + - r + - i diet.anchor_groups_to_gbd: false diet.baseline_age: All ages diet.fbs_override_foods: @@ -806,24 +981,6 @@ structural_config: 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 diff --git a/data/curated/calibration/gbd-anchored/animal_cost.csv b/data/curated/calibration/gbd-anchored/animal_cost.csv index d8fe7146..646c252e 100644 --- a/data/curated/calibration/gbd-anchored/animal_cost.csv +++ b/data/curated/calibration/gbd-anchored/animal_cost.csv @@ -1,1226 +1,1226 @@ product,country,correction_bnusd_per_mt_feed -dairy,AFG,0.026554249692708254 -dairy,AGO,-0.14087700843811035 -dairy,ALB,-0.00936129130423069 -dairy,ARE,-0.03571652388200164 -dairy,ARG,-0.18606984615325928 -dairy,ARM,-0.0028685489669442177 -dairy,ASM,-0.0 -dairy,ATG,-0.2427479326725006 -dairy,AUS,-0.047658815630711615 -dairy,AUT,0.02108278265222907 -dairy,AZE,-0.09774000942707062 -dairy,BDI,-0.1946440488100052 -dairy,BEL,-0.03250469407066703 -dairy,BEN,-0.26060598343610764 -dairy,BFA,-0.2664075046777725 -dairy,BGD,-0.05907953344285488 -dairy,BGR,0.039139289408922195 -dairy,BHS,-0.1341455802321434 -dairy,BIH,0.14763447269797325 -dairy,BLR,-0.023004658054560423 -dairy,BLZ,-0.21078979223966599 -dairy,BOL,-0.19467315822839737 -dairy,BRA,-0.2115694209933281 -dairy,BRB,-0.29707974195480347 -dairy,BRN,-0.05792198074050248 -dairy,BTN,-0.18317614868283272 -dairy,BWA,-0.2505120560526848 -dairy,CAF,-0.14297492057085037 -dairy,CAN,-0.08379845134913921 -dairy,CHE,-0.02387897251173854 -dairy,CHL,-0.15768751129508018 -dairy,CHN,0.16190970689058304 -dairy,CIV,-0.2596745789051056 -dairy,CMR,-0.2656986266374588 -dairy,COD,-0.13569421507418156 -dairy,COG,-0.14090726524591446 -dairy,COL,-0.1836104765534401 -dairy,COM,-0.12268268130719662 -dairy,CPV,-0.23216113448143005 -dairy,CRI,-0.05987410619854927 -dairy,CUB,-0.20857419073581696 -dairy,CYP,0.04400596022605896 -dairy,CZE,0.006435145158320665 -dairy,DEU,0.01192148681730032 -dairy,DJI,-0.030188361182808876 -dairy,DNK,-0.0015101823955774307 -dairy,DOM,-0.06891044974327087 -dairy,DZA,-0.040121978730894625 -dairy,ECU,-0.22186023741960526 -dairy,EGY,-0.14030779525637627 -dairy,ERI,-0.11900508031249046 -dairy,ESP,-0.06732512451708317 -dairy,EST,0.017537115141749382 -dairy,ETH,-0.14751069247722626 -dairy,FIN,0.09028727374970913 -dairy,FJI,-0.06725619174540043 -dairy,FRA,-0.06558282114565372 -dairy,GAB,-0.09001844376325607 -dairy,GBR,0.01444677822291851 -dairy,GEO,-0.05191332846879959 -dairy,GHA,-0.26311808824539185 -dairy,GIN,-0.1318551041185856 -dairy,GMB,-0.2440391704440117 -dairy,GNB,-0.2670557200908661 -dairy,GNQ,-0.0 -dairy,GRC,-0.005397491157054901 -dairy,GRD,-0.2646436542272568 -dairy,GTM,-0.1697404608130455 -dairy,GUF,-0.0 -dairy,GUY,-0.13548782095313072 -dairy,HND,-0.17368319630622864 -dairy,HRV,-0.0019314500968903303 -dairy,HTI,-0.040434807538986206 -dairy,HUN,-0.001568203791975975 -dairy,IDN,-0.07335436530411243 -dairy,IND,-0.12747247517108917 -dairy,IRL,-0.0637507438659668 -dairy,IRN,0.021928368601948023 -dairy,IRQ,-0.18009860813617706 -dairy,ISL,-0.11863765493035316 -dairy,ISR,-0.11616865079849958 -dairy,ITA,0.0007102666422724724 -dairy,JAM,-0.05619868263602257 -dairy,JOR,0.09548134356737137 -dairy,JPN,0.14343538135290146 -dairy,KAZ,0.04095932753989473 -dairy,KEN,-0.11986573785543442 -dairy,KGZ,0.1898423582315445 -dairy,KHM,-0.07460742443799973 -dairy,KOR,0.19287730008363724 -dairy,LAO,-0.0633428506553173 -dairy,LBN,0.08618472144007683 -dairy,LBR,-0.10347183048725128 -dairy,LBY,-0.05059445137158036 -dairy,LKA,-0.051054927287623286 -dairy,LSO,-0.19054346531629562 -dairy,LTU,0.03553890436887741 -dairy,LUX,-0.03136565163731575 -dairy,LVA,0.011305583640933037 -dairy,MAR,-0.19774805754423141 -dairy,MDA,0.004291979130357504 -dairy,MDG,-0.12626271694898605 -dairy,MEX,-0.05411314871162176 -dairy,MKD,-0.003269605804234743 -dairy,MLI,-0.19450082629919052 -dairy,MLT,-0.043422874761745334 -dairy,MMR,-0.052156249061226845 -dairy,MNE,0.14393331110477448 -dairy,MNG,-0.012129565700888634 -dairy,MOZ,-0.16338247805833817 -dairy,MRT,-0.15846535190939903 -dairy,MUS,-0.07867321936646476 -dairy,MWI,-0.14379847049713135 -dairy,MYS,-0.09578213840723038 -dairy,NAM,-0.17393779754638672 -dairy,NER,-0.2709629312157631 -dairy,NGA,-0.12530594319105148 -dairy,NIC,-0.2833148092031479 -dairy,NLD,-0.10521937720477581 -dairy,NOR,0.017680796794593334 -dairy,NPL,-0.05954365257639438 -dairy,NZL,-0.05061149597167969 -dairy,OMN,-0.17540347203612328 -dairy,PAK,0.02946754125878215 -dairy,PAN,-0.22010759264230728 -dairy,PER,-0.25328103452920914 -dairy,PHL,0.03520531766116619 -dairy,PNG,-3.774773918092251 -dairy,POL,0.02694406919181347 -dairy,PRI,-0.1319451928138733 -dairy,PRT,-0.0465409979224205 -dairy,PRY,-0.2294773906469345 -dairy,PSE,0.030759265646338463 -dairy,ROU,0.04966134438291192 -dairy,RUS,0.0304656820371747 -dairy,RWA,-0.1209836732596159 -dairy,SAU,0.168390654027462 -dairy,SDN,-0.07387448847293854 -dairy,SEN,-0.2560649588704109 -dairy,SLB,-0.04842163622379303 -dairy,SLE,-0.11712753400206566 -dairy,SLV,-0.24740193039178848 -dairy,SOM,-0.12859289534389973 -dairy,SRB,0.0419091684743762 -dairy,SSD,-0.10347218252718449 -dairy,STP,-0.20487239956855774 -dairy,SUR,-0.25091027468442917 -dairy,SVK,0.012956981547176838 -dairy,SVN,0.011136272922158241 -dairy,SWE,0.04858420661184937 -dairy,SWZ,-0.24897366762161255 -dairy,SYR,-0.047358427196741104 -dairy,TCD,-0.13441849872469902 -dairy,TGO,-0.13006019964814186 -dairy,THA,0.050710296258330345 -dairy,TJK,0.16723596304655075 -dairy,TKM,0.10064098984003067 -dairy,TLS,-0.12680239975452423 -dairy,TTO,-0.14529362320899963 -dairy,TUN,-0.1478118784725666 -dairy,TUR,-0.09788873046636581 -dairy,TWN,0.1973959133028984 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+meat-sheep,ECU,-0.07545997202396393 +meat-sheep,EGY,-0.34152982383966446 +meat-sheep,ERI,-0.07167770527303219 +meat-sheep,ESP,-0.006036626175045967 +meat-sheep,EST,0.013066038489341736 +meat-sheep,ETH,-0.12406071275472641 +meat-sheep,FIN,0.03380181410466321 +meat-sheep,FJI,-0.06479546427726746 +meat-sheep,FRA,0.04242001008242369 +meat-sheep,GAB,-0.041976198554039 +meat-sheep,GBR,0.02606992656365037 +meat-sheep,GEO,-0.11567747592926025 +meat-sheep,GHA,-0.048089370131492615 +meat-sheep,GIN,-0.11660647764801979 +meat-sheep,GMB,-0.14939628541469574 +meat-sheep,GNB,-0.3055392801761627 +meat-sheep,GNQ,-0.005689507350325584 +meat-sheep,GRC,-0.06697674468159676 meat-sheep,GRD,-0.008738094009459019 -meat-sheep,GTM,-0.043569326400756836 -meat-sheep,GUF,-0.0 -meat-sheep,GUY,-0.048696085810661316 -meat-sheep,HND,-0.014772018417716026 -meat-sheep,HRV,0.011818609666079283 -meat-sheep,HTI,-0.015342594124376774 -meat-sheep,HUN,-0.02483070344896987 -meat-sheep,IDN,-0.14849039912223816 -meat-sheep,IND,-0.23895897716283798 -meat-sheep,IRL,0.008275722153484821 -meat-sheep,IRN,-0.09147398546338081 -meat-sheep,IRQ,-0.18012598156929016 -meat-sheep,ISL,-0.0037357548717409372 -meat-sheep,ISR,-0.29216374456882477 -meat-sheep,ITA,-0.06077273655682802 -meat-sheep,JAM,-0.19797587394714355 -meat-sheep,JOR,-0.23983006924390793 -meat-sheep,JPN,-0.2144525796175003 -meat-sheep,KAZ,-0.22579826414585114 -meat-sheep,KEN,-0.10105250962078571 -meat-sheep,KGZ,-0.05672071984736249 -meat-sheep,KHM,-0.0 -meat-sheep,KOR,-0.08473039045929909 -meat-sheep,LAO,-0.14654283970594406 -meat-sheep,LBN,-0.20435048639774323 -meat-sheep,LBR,-0.07085079699754715 -meat-sheep,LBY,-0.059011765755712986 -meat-sheep,LKA,-0.20292233675718307 -meat-sheep,LSO,-0.17765379697084427 -meat-sheep,LTU,-0.023641375941224396 -meat-sheep,LUX,0.008353262208402157 -meat-sheep,LVA,-0.05990471551194787 -meat-sheep,MAR,-0.1655421294271946 -meat-sheep,MDA,-0.1122158020734787 -meat-sheep,MDG,-0.09097354114055634 -meat-sheep,MEX,-0.0 -meat-sheep,MKD,-0.06416216120123863 -meat-sheep,MLI,-0.16314558684825897 -meat-sheep,MLT,-0.12653863057494164 -meat-sheep,MMR,-0.09360451623797417 -meat-sheep,MNE,-0.12106046453118324 +meat-sheep,GTM,-0.05597369745373726 +meat-sheep,GUF,0.0 +meat-sheep,GUY,-0.06750818900763988 +meat-sheep,HND,-0.013344410806894302 +meat-sheep,HRV,0.0337234839098528 +meat-sheep,HTI,-0.0153425931930542 +meat-sheep,HUN,0.006237391382455826 +meat-sheep,IDN,-0.14221879839897156 +meat-sheep,IND,-0.469158872961998 +meat-sheep,IRL,0.02846098504960537 +meat-sheep,IRN,-0.08507117815315723 +meat-sheep,IRQ,-0.12418780103325844 +meat-sheep,ISL,0.0 +meat-sheep,ISR,-0.22617558389902115 +meat-sheep,ITA,-0.0926157496869564 +meat-sheep,JAM,-0.15225300192832947 +meat-sheep,JOR,-0.1838918775320053 +meat-sheep,JPN,-0.16858184337615967 +meat-sheep,KAZ,-0.17617454379796982 +meat-sheep,KEN,-0.07893583737313747 +meat-sheep,KGZ,-0.042651838943129405 +meat-sheep,KHM,0.0 +meat-sheep,KOR,-0.13903040066361427 +meat-sheep,LAO,-0.12924402952194214 +meat-sheep,LBN,-0.14849328994750977 +meat-sheep,LBR,-0.05523949861526489 +meat-sheep,LBY,-0.11002926528453827 +meat-sheep,LKA,-0.15355540812015533 +meat-sheep,LSO,-0.09093796275556087 +meat-sheep,LTU,0.012379647232592106 +meat-sheep,LUX,0.02609440218657255 +meat-sheep,LVA,-0.0057019200176000595 +meat-sheep,MAR,-0.1122172363102436 +meat-sheep,MDA,-0.12912023067474365 +meat-sheep,MDG,-0.16817717999219894 +meat-sheep,MEX,-0.05475272983312607 +meat-sheep,MKD,-0.0572739876806736 +meat-sheep,MLI,-0.10019680112600327 +meat-sheep,MLT,-0.3632964491844177 +meat-sheep,MMR,-0.08201347663998604 +meat-sheep,MNE,-0.3416384197771549 meat-sheep,MNG,-0.023062026128172874 -meat-sheep,MOZ,-0.15160514414310455 -meat-sheep,MRT,-0.1064099371433258 -meat-sheep,MUS,-0.07816734164953232 -meat-sheep,MWI,-0.09649896994233131 -meat-sheep,MYS,-0.15010973811149597 -meat-sheep,NAM,-0.13475264236330986 -meat-sheep,NER,-0.1804908961057663 -meat-sheep,NGA,-0.07012467086315155 -meat-sheep,NIC,-0.0 -meat-sheep,NLD,-0.07932097837328911 -meat-sheep,NOR,-0.0750926174223423 -meat-sheep,NPL,-0.10235956124961376 -meat-sheep,NZL,-0.04965532198548317 -meat-sheep,OMN,-0.23278838396072388 -meat-sheep,PAK,-0.1125905942171812 -meat-sheep,PAN,-0.0 -meat-sheep,PER,-0.15218425542116165 -meat-sheep,PHL,-0.13417300581932068 -meat-sheep,PNG,-0.01960640773177147 -meat-sheep,POL,-0.049007561057806015 -meat-sheep,PRI,-0.0 -meat-sheep,PRT,0.044278545305132866 -meat-sheep,PRY,-0.11340175196528435 -meat-sheep,PSE,-0.1794814169406891 -meat-sheep,ROU,-0.11146102100610733 -meat-sheep,RUS,-0.10428490862250328 -meat-sheep,RWA,-0.09074736945331097 -meat-sheep,SAU,-0.19365590065717697 -meat-sheep,SDN,-0.09071805328130722 -meat-sheep,SEN,-0.19027476757764816 -meat-sheep,SLB,-0.0 -meat-sheep,SLE,-0.07328034937381744 -meat-sheep,SLV,-0.1321217156946659 -meat-sheep,SOM,-0.09212342090904713 -meat-sheep,SRB,0.011857034987770021 -meat-sheep,SSD,-0.0801925715059042 -meat-sheep,STP,-0.11588024720549583 -meat-sheep,SUR,-0.13936244323849678 -meat-sheep,SVK,-0.06126856990158558 -meat-sheep,SVN,0.005692498292773962 -meat-sheep,SWE,-0.021289478987455368 -meat-sheep,SWZ,-0.20683526992797852 -meat-sheep,SYR,-0.20230718702077866 -meat-sheep,TCD,-0.09416545182466507 -meat-sheep,TGO,-0.06661609560251236 -meat-sheep,THA,-0.10592200234532356 -meat-sheep,TJK,-0.20858997851610184 -meat-sheep,TKM,-0.11163970455527306 -meat-sheep,TLS,-0.12223365902900696 -meat-sheep,TTO,0.005382268223911524 -meat-sheep,TUN,-0.1661047413945198 -meat-sheep,TUR,-0.17344963178038597 -meat-sheep,TWN,-0.0 -meat-sheep,TZA,-0.15276630222797394 -meat-sheep,UGA,-0.21697255223989487 -meat-sheep,UKR,-0.10183319263160229 -meat-sheep,URY,-0.09811882302165031 -meat-sheep,USA,-0.03355679754167795 -meat-sheep,UZB,-0.15104853361845016 -meat-sheep,VEN,-0.15280456468462944 -meat-sheep,VNM,-0.22548183053731918 -meat-sheep,VUT,-0.11518346145749092 -meat-sheep,YEM,-0.08188161812722683 -meat-sheep,ZAF,-0.12772084772586823 -meat-sheep,ZMB,-0.10440703108906746 -meat-sheep,ZWE,-0.4678903669118881 +meat-sheep,MOZ,-0.12584545835852623 +meat-sheep,MRT,-0.14973610639572144 +meat-sheep,MUS,-0.06341749825514853 +meat-sheep,MWI,-0.07414544746279716 +meat-sheep,MYS,-0.13871780037879944 +meat-sheep,NAM,-0.11958218738436699 +meat-sheep,NER,-0.1353893280029297 +meat-sheep,NGA,-0.05424029380083084 +meat-sheep,NIC,0.0 +meat-sheep,NLD,-0.03563434770330787 +meat-sheep,NOR,0.001538306474685669 +meat-sheep,NPL,-0.33408036082983017 +meat-sheep,NZL,-0.01547902449965477 +meat-sheep,OMN,-0.17181852459907532 +meat-sheep,PAK,-0.14324304461479187 +meat-sheep,PAN,0.0 +meat-sheep,PER,-0.08739578165113926 +meat-sheep,PHL,-0.12819984555244446 +meat-sheep,PNG,-0.06879702769219875 +meat-sheep,POL,-0.03317403607070446 +meat-sheep,PRI,-0.01054279413074255 +meat-sheep,PRT,0.07274124026298523 +meat-sheep,PRY,-0.018124129623174667 +meat-sheep,PSE,-0.12354322895407677 +meat-sheep,ROU,-0.0885878074914217 +meat-sheep,RUS,-0.046513769309967756 +meat-sheep,RWA,-0.14210426062345505 +meat-sheep,SAU,-0.14037726819515228 +meat-sheep,SDN,-0.0771399475634098 +meat-sheep,SEN,-0.41791658848524094 +meat-sheep,SLB,0.0 +meat-sheep,SLE,-0.05504703149199486 +meat-sheep,SLV,-0.08639884740114212 +meat-sheep,SOM,-0.1164407953619957 +meat-sheep,SRB,0.03783381020184606 +meat-sheep,SSD,-0.07490892894566059 +meat-sheep,STP,-0.05909948144108057 +meat-sheep,SUR,-0.07684688456356525 +meat-sheep,SVK,-0.05653748754411936 +meat-sheep,SVN,0.026017896831035614 +meat-sheep,SWE,0.032913317205384374 +meat-sheep,SWZ,-0.16275330632925034 +meat-sheep,SYR,-0.14636900275945663 +meat-sheep,TCD,-0.10374375432729721 +meat-sheep,TGO,-0.047786928713321686 +meat-sheep,THA,-0.09969942830502987 +meat-sheep,TJK,-0.14810387790203094 +meat-sheep,TKM,-0.12106944993138313 +meat-sheep,TLS,-0.06462754588574171 +meat-sheep,TTO,0.005399390589445829 +meat-sheep,TUN,-0.11302786692976952 +meat-sheep,TUR,-0.11751144379377365 +meat-sheep,TWN,0.0 +meat-sheep,TZA,-0.1320369392633438 +meat-sheep,UGA,-0.08225971460342407 +meat-sheep,UKR,-0.04813307337462902 +meat-sheep,URY,-0.05626666359603405 +meat-sheep,USA,-0.02230124920606613 +meat-sheep,UZB,-0.1467219814658165 +meat-sheep,VEN,-0.10926103219389915 +meat-sheep,VNM,-0.14932982623577118 +meat-sheep,VUT,-0.017298095859587193 +meat-sheep,YEM,-0.18399160355329514 +meat-sheep,ZAF,-0.14773952215909958 +meat-sheep,ZMB,-0.19609224796295166 +meat-sheep,ZWE,-0.4585086926817894 diff --git a/data/curated/calibration/gbd-anchored/crop_cost.csv b/data/curated/calibration/gbd-anchored/crop_cost.csv index 0a2bef51..07091668 100644 --- a/data/curated/calibration/gbd-anchored/crop_cost.csv +++ b/data/curated/calibration/gbd-anchored/crop_cost.csv @@ -1,5346 +1,5346 @@ crop,country,correction_bnusd_per_mha alfalfa,AFG,-0.3750787377357483 -alfalfa,AGO,-0.9439449906349182 -alfalfa,ALB,-0.5414051562547684 -alfalfa,ARE,-0.6755253076553345 -alfalfa,ARG,-0.4919307976961136 -alfalfa,ARM,-0.0 -alfalfa,ATG,-7.5 -alfalfa,AUS,0.10641167312860489 -alfalfa,AUT,-0.6891505420207977 -alfalfa,AZE,0.6822218000888824 -alfalfa,BDI,-0.5490851700305939 -alfalfa,BEL,-0.2490299865603447 -alfalfa,BEN,0.6876131892204285 -alfalfa,BFA,-7.5 -alfalfa,BGD,-0.7387045621871948 -alfalfa,BGR,-0.060649650171399117 -alfalfa,BHS,-4.1000382006168365 -alfalfa,BIH,0.08796236664056778 -alfalfa,BLR,-0.3088502585887909 -alfalfa,BLZ,-0.008809542283415794 +alfalfa,AGO,-0.2651873230934143 +alfalfa,ALB,0.0 +alfalfa,ARE,4.513212203979492 +alfalfa,ARG,-0.5347599685192108 +alfalfa,ARM,0.0 +alfalfa,ATG,0.0 +alfalfa,AUS,0.0 +alfalfa,AUT,-0.755619078874588 +alfalfa,AZE,0.8579510450363159 +alfalfa,BDI,-0.8987658023834229 +alfalfa,BEL,-0.28935573995113373 +alfalfa,BEN,0.8655805587768555 +alfalfa,BFA,0.0 +alfalfa,BGD,-0.7333669364452362 +alfalfa,BGR,-0.033132417127490044 +alfalfa,BHS,-0.6666980385780334 +alfalfa,BIH,0.16280731558799744 +alfalfa,BLR,-0.28629985451698303 +alfalfa,BLZ,-0.5492207109928131 alfalfa,BOL,-0.22297222912311554 -alfalfa,BRA,-0.43258796632289886 -alfalfa,BRB,2.479930281639099 +alfalfa,BRA,-0.4606279283761978 +alfalfa,BRB,2.77047598361969 alfalfa,BRN,1.4280236959457397 -alfalfa,BTN,0.25604093074798584 -alfalfa,BWA,-7.5 -alfalfa,CAF,-0.976554661989212 -alfalfa,CAN,-0.23406269401311874 -alfalfa,CHE,-0.11473012529313564 -alfalfa,CHL,-0.8657473921775818 -alfalfa,CHN,0.6336996257305145 +alfalfa,BTN,0.08275410532951355 +alfalfa,BWA,0.0 +alfalfa,CAF,-0.6585278511047363 +alfalfa,CAN,-0.11497456952929497 +alfalfa,CHE,0.09321406949311495 +alfalfa,CHL,-0.6260356903076172 +alfalfa,CHN,0.5708749294281006 alfalfa,CIV,7.5 -alfalfa,CMR,1.5657636523246765 -alfalfa,COD,-0.9046836495399475 +alfalfa,CMR,1.7987051606178284 +alfalfa,COD,-0.1673155128955841 alfalfa,COG,-0.9112309217453003 -alfalfa,COL,-0.9036582410335541 -alfalfa,COM,-0.863983690738678 -alfalfa,CPV,-7.5 +alfalfa,COL,-0.9038967788219452 +alfalfa,COM,0.7247816920280457 +alfalfa,CPV,-0.3923039734363556 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-buckwheat,GMB,-7.5 -buckwheat,GNB,-7.5 -buckwheat,GRC,-0.34561747312545776 -buckwheat,GTM,-0.0 -buckwheat,GUF,-0.0 -buckwheat,GUY,-0.0 -buckwheat,HRV,0.3792892098426819 -buckwheat,HUN,0.49654750525951385 +buckwheat,CZE,0.6349337100982666 +buckwheat,DEU,0.8562346398830414 +buckwheat,DNK,0.4539239853620529 +buckwheat,DZA,0.008493511937558651 +buckwheat,ECU,-0.09143362939357758 +buckwheat,EGY,-0.5167602896690369 +buckwheat,ERI,-0.3710338622331619 +buckwheat,ESP,-0.08815326914191246 +buckwheat,EST,0.31639179587364197 +buckwheat,ETH,-0.13475780934095383 +buckwheat,FIN,0.1918448805809021 +buckwheat,FRA,0.7046768069267273 +buckwheat,GAB,0.0 +buckwheat,GBR,0.41619229316711426 +buckwheat,GEO,-0.5144190043210983 +buckwheat,GHA,0.0 +buckwheat,GIN,-0.34511812031269073 +buckwheat,GMB,-0.3238362669944763 +buckwheat,GNB,-0.4041398763656616 +buckwheat,GRC,-0.2679699659347534 +buckwheat,GTM,0.0 +buckwheat,GUF,0.0 +buckwheat,GUY,0.0 +buckwheat,HRV,0.45987650752067566 +buckwheat,HUN,0.6451800167560577 buckwheat,IND,7.5 -buckwheat,IRL,0.6773521602153778 -buckwheat,IRN,0.20243142545223236 -buckwheat,IRQ,0.05390697717666626 -buckwheat,ISR,3.591837912797928 -buckwheat,ITA,-0.12720445543527603 -buckwheat,JOR,7.5 -buckwheat,JPN,-0.510379821062088 -buckwheat,KAZ,-0.0 -buckwheat,KEN,-7.5 -buckwheat,KGZ,3.6470495015382767 -buckwheat,KHM,-3.75 -buckwheat,KOR,-0.12637948989868164 -buckwheat,LAO,-0.0 +buckwheat,IRL,0.8437716960906982 +buckwheat,IRN,0.30425748229026794 +buckwheat,IRQ,0.14635583758354187 +buckwheat,ISR,3.6006457805633545 +buckwheat,ITA,-0.022106364369392395 +buckwheat,JOR,0.0 +buckwheat,JPN,-0.4943399280309677 +buckwheat,KAZ,0.02700340934097767 +buckwheat,KEN,0.0 +buckwheat,KGZ,3.675545282661915 +buckwheat,KHM,-0.11224110424518585 +buckwheat,KOR,-0.08873150870203972 +buckwheat,LAO,0.0 buckwheat,LBN,7.5 -buckwheat,LBR,-0.0 -buckwheat,LBY,7.5 +buckwheat,LBR,0.0 +buckwheat,LBY,0.0 buckwheat,LSO,7.5 -buckwheat,LTU,0.20286418497562408 -buckwheat,LUX,0.4717343747615814 -buckwheat,LVA,0.2668067514896393 -buckwheat,MAR,-0.3177928924560547 +buckwheat,LTU,0.29088059067726135 +buckwheat,LUX,0.606041818857193 +buckwheat,LVA,0.356755293905735 +buckwheat,MAR,-0.29350732266902924 buckwheat,MDA,7.5 -buckwheat,MEX,-7.5 -buckwheat,MKD,-0.051600074395537376 -buckwheat,MLI,-0.43497754633426666 -buckwheat,MMR,-0.3501506745815277 -buckwheat,MNE,0.11421703919768333 -buckwheat,MNG,-0.07877403497695923 -buckwheat,MOZ,-0.0 -buckwheat,MRT,-3.75 -buckwheat,MWI,-0.0 -buckwheat,MYS,-0.0 -buckwheat,NAM,7.5 -buckwheat,NER,-7.5 -buckwheat,NGA,-0.3924857825040817 -buckwheat,NLD,0.29449892044067383 -buckwheat,NOR,0.11553476843982935 -buckwheat,NPL,-0.28531286120414734 -buckwheat,NZL,0.5334115028381348 -buckwheat,OMN,-7.5 -buckwheat,PER,-7.5 -buckwheat,POL,0.1143525242805481 -buckwheat,PRT,-0.18121226131916046 -buckwheat,PRY,-0.0 +buckwheat,MEX,-0.170909583568573 +buckwheat,MKD,0.02509526815265417 +buckwheat,MLI,-0.33205342292785645 +buckwheat,MMR,-0.3293648958206177 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-buckwheat,TCD,-0.22008012235164642 -buckwheat,TGO,-7.5 -buckwheat,THA,-0.2750966101884842 +buckwheat,ROU,3.904212936758995 +buckwheat,RUS,-0.04517780616879463 +buckwheat,RWA,-0.367561936378479 +buckwheat,SAU,0.1950701039750129 +buckwheat,SDN,0.0 +buckwheat,SEN,-0.23531650006771088 +buckwheat,SLE,-0.4078611582517624 +buckwheat,SOM,0.0 +buckwheat,SRB,0.46964922547340393 +buckwheat,SSD,0.0 +buckwheat,SUR,0.0 +buckwheat,SVK,0.3186219483613968 +buckwheat,SVN,3.9133076518774033 +buckwheat,SWE,0.5574575662612915 +buckwheat,SWZ,0.0 +buckwheat,SYR,0.007748359814286232 +buckwheat,TCD,-0.19082732498645782 +buckwheat,TGO,-0.3232741802930832 +buckwheat,THA,-0.23924782127141953 buckwheat,TJK,7.5 buckwheat,TKM,7.5 -buckwheat,TUN,-0.04837181977927685 -buckwheat,TUR,0.09304175898432732 -buckwheat,TWN,-0.0 -buckwheat,TZA,-0.2893037647008896 -buckwheat,UGA,-0.0 -buckwheat,UKR,-0.08874008804559708 -buckwheat,URY,0.013326487503945827 -buckwheat,USA,0.28794705867767334 -buckwheat,UZB,3.75 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-sunflower,MAR,-0.3143874853849411 -sunflower,MDA,0.34775929152965546 -sunflower,MEX,0.1089199036359787 -sunflower,MKD,0.0338401161134243 -sunflower,MMR,-0.47913284599781036 -sunflower,MNE,3.7958006151020527 -sunflower,MNG,-0.04404642805457115 -sunflower,MOZ,0.1606166660785675 -sunflower,MWI,-0.39527371525764465 -sunflower,MYS,-0.0 -sunflower,NAM,7.5 -sunflower,NLD,-0.8534866124391556 +sunflower,KAZ,0.11975156515836716 +sunflower,KEN,-0.0681439395993948 +sunflower,KGZ,-0.2633407860994339 +sunflower,KHM,3.532733678817749 +sunflower,LAO,-0.48326563835144043 +sunflower,LBN,0.22745434939861298 +sunflower,LSO,-0.01710982248187065 +sunflower,LTU,-0.02154443971812725 +sunflower,LUX,-0.12051275745034218 +sunflower,LVA,3.7145985327661037 +sunflower,MAR,-0.28116951882839203 +sunflower,MDA,0.16206712275743484 +sunflower,MEX,-0.03315204381942749 +sunflower,MKD,0.07494044117629528 +sunflower,MMR,-0.4973707050085068 +sunflower,MNE,3.8264909237623215 +sunflower,MNG,-0.012525320053100586 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-sunflower,UGA,-0.46307408809661865 -sunflower,UKR,-0.19778528809547424 -sunflower,URY,-0.21731071919202805 -sunflower,USA,0.05577796697616577 -sunflower,UZB,-0.6747067868709564 -sunflower,VEN,-0.5206580460071564 -sunflower,VNM,7.5 -sunflower,ZAF,-0.18720217049121857 -sunflower,ZMB,-0.5962415337562561 -sunflower,ZWE,0.06323155015707016 +sunflower,PAK,-0.20422746986150742 +sunflower,PER,0.0 +sunflower,POL,0.009198024403303862 +sunflower,PRT,0.10053019598126411 +sunflower,PRY,-0.2987351566553116 +sunflower,PSE,0.5491779446601868 +sunflower,ROU,0.13972151651978493 +sunflower,RUS,0.3111827075481415 +sunflower,RWA,-0.4983990043401718 +sunflower,SDN,-0.6550058126449585 +sunflower,SOM,0.0 +sunflower,SRB,-0.19650976732373238 +sunflower,SSD,-0.24165423959493637 +sunflower,SUR,0.0 +sunflower,SVK,-0.10522324964404106 +sunflower,SVN,0.32082822918891907 +sunflower,SWZ,-0.24089168012142181 +sunflower,SYR,-0.32277712225914 +sunflower,TCD,0.0 +sunflower,THA,-0.11631511151790619 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-sweet-potato,MEX,-2.37039852142334 +sweet-potato,MEX,-2.609938383102417 sweet-potato,MKD,-1.8836777806282043 -sweet-potato,MLI,-1.4456464648246765 -sweet-potato,MLT,-5.523722410202026 -sweet-potato,MMR,-1.6927872896194458 -sweet-potato,MNE,-7.5 -sweet-potato,MNG,-7.5 -sweet-potato,MOZ,-1.3171617984771729 -sweet-potato,MRT,-3.192073345184326 +sweet-potato,MLI,-3.20743727684021 +sweet-potato,MLT,-7.5 +sweet-potato,MMR,-2.2323029041290283 +sweet-potato,MNE,-1.0339508056640625 +sweet-potato,MNG,-1.886967420578003 +sweet-potato,MOZ,-1.3855310678482056 +sweet-potato,MRT,-7.5 sweet-potato,MUS,-5.697396755218506 sweet-potato,MWI,1.9981681108474731 sweet-potato,MYS,-4.245997667312622 -sweet-potato,NAM,-2.0617833137512207 -sweet-potato,NER,-1.5596898794174194 -sweet-potato,NGA,-3.546464204788208 -sweet-potato,NIC,-1.7392669916152954 -sweet-potato,NLD,-0.6630721986293793 -sweet-potato,NOR,-2.240051031112671 -sweet-potato,NPL,-2.1933902502059937 -sweet-potato,NZL,-7.5 -sweet-potato,OMN,-5.339635372161865 -sweet-potato,PAK,1.1810417175292969 +sweet-potato,NAM,-2.4999234676361084 +sweet-potato,NER,-5.49990439414978 +sweet-potato,NGA,-3.58597469329834 +sweet-potato,NIC,-2.307397723197937 +sweet-potato,NLD,-1.1949597597122192 +sweet-potato,NOR,-1.6654208898544312 +sweet-potato,NPL,-2.4386526346206665 +sweet-potato,NZL,0.0 +sweet-potato,OMN,-5.816677570343018 +sweet-potato,PAK,0.7716490030288696 sweet-potato,PAN,7.5 -sweet-potato,PER,-1.9515535235404968 +sweet-potato,PER,-2.1910942792892456 sweet-potato,PHL,-1.2703145146369934 sweet-potato,PNG,-3.1252081394195557 -sweet-potato,POL,-0.5377524495124817 +sweet-potato,POL,-0.59583979845047 sweet-potato,PRI,-6.758495330810547 sweet-potato,PRT,-7.5 -sweet-potato,PRY,-1.0536905527114868 -sweet-potato,PSE,-3.8836997747421265 +sweet-potato,PRY,-1.0727745294570923 +sweet-potato,PSE,-0.6350809931755066 sweet-potato,ROU,-1.9915916323661804 -sweet-potato,RUS,-2.6641860008239746 +sweet-potato,RUS,-1.8257306814193726 sweet-potato,RWA,-0.6163407266139984 sweet-potato,SAU,-0.642536461353302 -sweet-potato,SDN,-2.076765775680542 +sweet-potato,SDN,-3.2283260822296143 sweet-potato,SEN,-7.5 sweet-potato,SLB,-1.788506031036377 -sweet-potato,SLE,-2.2337507009506226 +sweet-potato,SLE,-2.256414294242859 sweet-potato,SLV,2.9324633479118347 -sweet-potato,SOM,-2.7810723781585693 -sweet-potato,SRB,-4.643224895000458 +sweet-potato,SOM,-6.5430216789245605 +sweet-potato,SRB,-0.4289915859699249 sweet-potato,SSD,7.5 sweet-potato,SUR,-7.5 -sweet-potato,SVK,-4.638444364070892 +sweet-potato,SVK,-1.233763337135315 sweet-potato,SVN,-0.7352840602397919 -sweet-potato,SWE,-7.5 -sweet-potato,SWZ,-3.662919759750366 -sweet-potato,SYR,-7.5 +sweet-potato,SWE,-0.5066671967506409 +sweet-potato,SWZ,-3.6643733978271484 +sweet-potato,SYR,-1.5166845321655273 sweet-potato,TCD,-0.9532046914100647 sweet-potato,TGO,-0.5109198689460754 sweet-potato,THA,-1.308626413345337 -sweet-potato,TJK,-3.451009750366211 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-white-potato,NLD,-0.6677808612585068 -white-potato,NOR,-5.920556545257568 -white-potato,NPL,-1.4185768365859985 -white-potato,NZL,2.1708542108535767 +white-potato,NGA,-6.832953929901123 +white-potato,NIC,-7.236871719360352 +white-potato,NLD,-1.1201478242874146 +white-potato,NOR,-5.892272472381592 +white-potato,NPL,-2.4710692167282104 +white-potato,NZL,2.4274429082870483 white-potato,OMN,-7.5 -white-potato,PAK,0.4658883213996887 +white-potato,PAK,0.08638133853673935 white-potato,PAN,-7.5 -white-potato,PER,-2.6230469942092896 +white-potato,PER,-2.7550814151763916 white-potato,PHL,-7.5 -white-potato,PNG,-7.5 -white-potato,POL,2.00773549079895 -white-potato,PRI,-7.5 -white-potato,PRT,-1.6025015115737915 -white-potato,PRY,-2.3305110931396484 +white-potato,PNG,-6.390487194061279 +white-potato,POL,2.1808594465255737 +white-potato,PRI,-4.810193777084351 +white-potato,PRT,-1.527665376663208 +white-potato,PRY,-2.30629563331604 white-potato,PSE,-7.5 -white-potato,ROU,-2.7660036087036133 -white-potato,RUS,0.16708266735076904 -white-potato,RWA,-1.3967185616493225 +white-potato,ROU,-2.674642324447632 +white-potato,RUS,0.14202719926834106 +white-potato,RWA,-1.371582567691803 white-potato,SAU,-7.5 -white-potato,SDN,-5.5449113845825195 -white-potato,SEN,-5.785725355148315 -white-potato,SLB,-4.482147216796875 -white-potato,SLE,-6.038959980010986 -white-potato,SLV,-5.770610332489014 -white-potato,SOM,-5.88116717338562 -white-potato,SRB,1.1184985637664795 -white-potato,SSD,-7.5 -white-potato,SUR,-7.5 -white-potato,SVK,-3.1073110103607178 -white-potato,SVN,-2.77593731880188 -white-potato,SWE,-4.924295425415039 -white-potato,SWZ,-7.008403778076172 -white-potato,SYR,-3.765472888946533 -white-potato,TCD,-6.439121723175049 -white-potato,TGO,-6.8477606773376465 -white-potato,THA,-2.0436549186706543 -white-potato,TJK,-1.7283135056495667 -white-potato,TKM,-5.169633388519287 -white-potato,TLS,-4.005785256624222 -white-potato,TTO,-7.5 -white-potato,TUN,-1.292385458946228 -white-potato,TUR,-1.5542086958885193 -white-potato,TWN,-4.148016452789307 -white-potato,TZA,-0.242451474070549 -white-potato,UGA,-6.653731822967529 -white-potato,UKR,0.9700972437858582 +white-potato,SDN,-5.1964616775512695 +white-potato,SEN,-5.5330634117126465 +white-potato,SLB,-4.406826496124268 +white-potato,SLE,-5.976241827011108 +white-potato,SLV,-5.6826488971710205 +white-potato,SOM,-5.819117546081543 +white-potato,SRB,-0.8770628869533539 +white-potato,SSD,-4.87265419960022 +white-potato,SUR,-3.8383898735046387 +white-potato,SVK,-3.157607913017273 +white-potato,SVN,-2.6154524087905884 +white-potato,SWE,-4.749576568603516 +white-potato,SWZ,-7.002017021179199 +white-potato,SYR,-3.658203363418579 +white-potato,TCD,-6.417938232421875 +white-potato,TGO,-6.82444429397583 +white-potato,THA,-1.943368136882782 +white-potato,TJK,-1.807421088218689 +white-potato,TKM,-4.701775074005127 +white-potato,TLS,-0.4336884915828705 +white-potato,TTO,-5.9028894901275635 +white-potato,TUN,-3.0832247734069824 +white-potato,TUR,-1.3715746998786926 +white-potato,TWN,-2.6386871337890625 +white-potato,TZA,-0.2126503884792328 +white-potato,UGA,-6.692795991897583 +white-potato,UKR,0.09153179824352264 white-potato,URY,-7.5 -white-potato,USA,0.9592857956886292 -white-potato,UZB,-3.4913910627365112 -white-potato,VEN,-4.064093589782715 -white-potato,VNM,-4.8510730266571045 -white-potato,YEM,-5.896713018417358 -white-potato,ZAF,-3.806147336959839 +white-potato,USA,1.0335465669631958 +white-potato,UZB,-3.4073362350463867 +white-potato,VEN,-3.917459487915039 +white-potato,VNM,-4.783044099807739 +white-potato,YEM,-7.499095439910889 +white-potato,ZAF,-5.644451379776001 white-potato,ZMB,-7.5 white-potato,ZWE,-7.5 -yam,AFG,7.5 -yam,AGO,-2.9343613386154175 +yam,AFG,0.0 +yam,AGO,-2.71264386177063 yam,ARG,7.5 -yam,ATG,-4.489343166351318 -yam,BDI,-4.483786582946777 -yam,BEN,-1.1763904690742493 -yam,BFA,-2.7030320167541504 -yam,BIH,-0.0 -yam,BLZ,-1.4796286821365356 -yam,BOL,-5.195361614227295 -yam,BRA,-3.122835397720337 +yam,ATG,-4.396996021270752 +yam,BDI,-4.46341609954834 +yam,BEN,-1.1236973404884338 +yam,BFA,-2.659050464630127 +yam,BIH,0.0 +yam,BLZ,-1.2888553142547607 +yam,BOL,-5.130490303039551 +yam,BRA,-2.8824214935302734 yam,BRB,-7.5 -yam,BRN,-5.205046653747559 -yam,BTN,-4.720355987548828 -yam,BWA,-4.5275938510894775 -yam,CAF,-3.997813582420349 -yam,CHL,-0.0 -yam,CHN,-0.5767361223697662 -yam,CIV,-0.7166515588760376 -yam,CMR,-2.934443950653076 -yam,COD,-4.241165399551392 -yam,COG,-4.237703084945679 -yam,COL,-0.8940050601959229 -yam,COM,-4.329689979553223 +yam,BRN,-5.194641351699829 +yam,BTN,-4.156153202056885 +yam,BWA,-3.4835290908813477 +yam,CAF,-3.970792531967163 +yam,CHL,0.0 +yam,CHN,-0.30300410091876984 +yam,CIV,-0.6973100304603577 +yam,CMR,-2.890680193901062 +yam,COD,-4.217174291610718 +yam,COG,-4.213520526885986 +yam,COL,-0.541134238243103 +yam,COM,-4.306869983673096 yam,CRI,-7.5 -yam,CUB,-2.660409092903137 -yam,DOM,-2.5111581087112427 -yam,ECU,-5.37074089050293 -yam,EGY,0.9685197174549103 -yam,ERI,-5.603356838226318 -yam,ETH,-1.9620407223701477 +yam,CUB,-2.359723925590515 +yam,DOM,-1.9253771305084229 +yam,ECU,-5.317235469818115 +yam,EGY,-1.964463472366333 +yam,ERI,-5.59840989112854 +yam,ETH,-1.4126824140548706 yam,FJI,-7.5 -yam,GAB,-4.216912508010864 -yam,GHA,-1.2294719219207764 -yam,GIN,-2.265323519706726 -yam,GMB,-7.5 -yam,GNB,-4.196008682250977 -yam,GNQ,-4.485906600952148 +yam,GAB,-4.192934989929199 +yam,GHA,-1.1651211977005005 +yam,GIN,-2.261919379234314 +yam,GMB,0.0 +yam,GNB,-4.171942234039307 +yam,GNQ,-4.465688228607178 yam,GRD,-7.5 yam,GTM,7.5 -yam,GUF,7.5 +yam,GUF,0.0 yam,GUY,-7.5 -yam,HND,-4.2645134925842285 -yam,HRV,-0.0 -yam,HTI,-5.360596656799316 -yam,IDN,-4.654752254486084 -yam,IND,-0.7946604788303375 -yam,IRN,-0.0 -yam,ISR,3.148059606552124 +yam,HND,-4.0838319063186646 +yam,HRV,0.0 +yam,HTI,-5.307753562927246 +yam,IDN,-4.6345484256744385 +yam,IND,-4.509192943572998 +yam,IRN,0.0 +yam,ISR,3.181663453578949 yam,JAM,-7.5 yam,JOR,7.5 yam,JPN,-7.5 -yam,KEN,-1.554877519607544 -yam,KHM,-2.5133907794952393 -yam,LAO,1.9898558855056763 -yam,LBN,-4.706743240356445 -yam,LBR,-3.7932850122451782 -yam,LKA,-3.0683892965316772 -yam,LSO,-0.0 -yam,MDG,-4.059107542037964 -yam,MEX,2.9044225215911865 -yam,MLI,-5.246054172515869 -yam,MMR,2.2785796523094177 -yam,MNE,-0.0 -yam,MOZ,-3.8877737522125244 -yam,MRT,-6.383017063140869 -yam,MUS,-3.497187376022339 +yam,KEN,-1.518750011920929 +yam,KHM,-2.45957350730896 +yam,LAO,2.1110036373138428 +yam,LBN,-2.3234525322914124 +yam,LBR,-3.763749837875366 +yam,LKA,-3.0244040489196777 +yam,LSO,0.0 +yam,MDG,-4.0314555168151855 +yam,MEX,2.5281636714935303 +yam,MLI,-6.964144945144653 +yam,MMR,1.947170078754425 +yam,MNE,0.0 +yam,MOZ,-3.857971429824829 +yam,MRT,-7.5 +yam,MUS,-3.4608681201934814 yam,MWI,7.5 -yam,MYS,-2.7116893529891968 -yam,NAM,-3.0225069522857666 -yam,NER,-5.324361801147461 -yam,NGA,-2.832276940345764 -yam,NIC,-2.2293719053268433 -yam,NPL,-5.017784833908081 -yam,PAK,1.252428412437439 -yam,PAN,-3.215519905090332 -yam,PER,-3.7464905977249146 -yam,PHL,-1.6224873661994934 -yam,PNG,-2.6320698261260986 -yam,PRI,-7.5 +yam,MYS,-2.66167676448822 +yam,NAM,-2.9809781312942505 +yam,NER,-5.249086618423462 +yam,NGA,-2.7895864248275757 +yam,NIC,-1.962601125240326 +yam,NPL,-6.539735317230225 +yam,PAK,1.2216424942016602 +yam,PAN,-3.1514991521835327 +yam,PER,-3.699566960334778 +yam,PHL,-1.6030202507972717 +yam,PNG,-2.5849483013153076 +yam,PRI,-7.476719617843628 yam,PRY,7.5 -yam,PSE,-1.3539988100528717 -yam,RWA,-0.8387764692306519 -yam,SAU,-0.0 -yam,SDN,-5.748590469360352 -yam,SEN,-1.4869715571403503 -yam,SLB,-1.8002225160598755 -yam,SLE,-5.378182411193848 -yam,SLV,2.7297595739364624 -yam,SOM,-0.0 -yam,SSD,-5.16758394241333 -yam,STP,-4.743098497390747 -yam,SUR,-2.388601541519165 -yam,SWZ,-3.974611520767212 -yam,SYR,7.5 -yam,TCD,-4.035890102386475 -yam,TGO,-1.1102235317230225 -yam,THA,-1.8082450032234192 -yam,TLS,-5.463021278381348 +yam,PSE,-1.2889548242092133 +yam,RWA,-0.815086305141449 +yam,SAU,0.0 +yam,SDN,-5.722858905792236 +yam,SEN,-2.11346173286438 +yam,SLB,-1.7412835955619812 +yam,SLE,-5.370323419570923 +yam,SLV,2.908381938934326 +yam,SOM,0.0 +yam,SSD,-5.157663822174072 +yam,STP,-4.726482629776001 +yam,SUR,-2.0496089458465576 +yam,SWZ,-3.945995330810547 +yam,SYR,0.0 +yam,TCD,-4.010426998138428 +yam,TGO,-1.077474057674408 +yam,THA,-1.807403028011322 +yam,TLS,-5.455284118652344 yam,TTO,-7.5 -yam,TWN,-2.428731083869934 -yam,TZA,-4.5667877197265625 -yam,UGA,-3.9058758020401 -yam,URY,7.5 -yam,USA,5.4903130531311035 -yam,VEN,-2.3649706840515137 -yam,VNM,-3.460469126701355 -yam,VUT,-3.1067800521850586 -yam,ZAF,-4.370655298233032 +yam,TWN,-2.3733160495758057 +yam,TZA,-4.54769492149353 +yam,UGA,-3.8769387006759644 +yam,URY,0.0 +yam,USA,6.12684440612793 +yam,VEN,-2.043738603591919 +yam,VNM,-3.632774829864502 +yam,VUT,-3.0644670724868774 +yam,ZAF,-4.348750352859497 yam,ZMB,7.5 -yam,ZWE,-3.2959673404693604 +yam,ZWE,-3.2570632696151733 diff --git a/data/curated/calibration/gbd-anchored/deviation_penalty.yaml b/data/curated/calibration/gbd-anchored/deviation_penalty.yaml index 50fe7560..0142c962 100644 --- a/data/curated/calibration/gbd-anchored/deviation_penalty.yaml +++ b/data/curated/calibration/gbd-anchored/deviation_penalty.yaml @@ -14,9 +14,9 @@ components: - grassland - feed l1_costs: - cropland: 1.5736289051426577 - grassland: 0.1623770654010504 - feed: 0.07842882067882782 -iterations: 6 + cropland: 2.40016323547014 + grassland: 0.16315900048299714 + feed: 0.09429247220349436 +iterations: 3 converged: true -final_residual_log_inf: 0.009996140710746514 +final_residual_log_inf: 0.01443185103258953 diff --git a/data/curated/calibration/gbd-anchored/exogenous_feed.csv b/data/curated/calibration/gbd-anchored/exogenous_feed.csv index 69407a9b..89b7d00f 100644 --- a/data/curated/calibration/gbd-anchored/exogenous_feed.csv +++ b/data/curated/calibration/gbd-anchored/exogenous_feed.csv @@ -2,98 +2,104 @@ # # 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 +AFG,0.0,0.0,6.454943 +AGO,0.0,0.0,0.935912 +ARE,0.0,0.0,1.41976 +ARM,0.0,0.0,0.921952 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 -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 +AZE,0.0,0.0,1.66954 +BFA,0.0,0.0,2.427717 +BGD,0.0,0.0,9.224991 +BHS,0.0,0.0,0.000241 +BOL,0.0,0.0,3.061838 +BRA,0.0,0.0,4.291774 +BRB,0.0,0.0,0.001186 +BRN,0.0,0.0,0.098264 +BTN,0.0,0.0,0.032692 +BWA,0.0,0.0,0.803647 +CAF,0.0,0.0,3.145706 +CHL,0.0,0.0,1.174771 +CMR,0.0,0.0,1.961326 +COG,0.0,0.0,0.101832 +COL,0.0,0.0,4.967146 +COM,0.0,0.0,0.023564 CPV,0.0,0.0,0.014816 -CRI,0.0,0.0,0.47573 +CRI,0.0,0.0,0.475468 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 +DJI,0.0,0.0,0.253119 +DOM,0.0,0.0,0.732578 +DZA,0.0,0.0,7.00384 +ECU,0.0,0.0,0.999379 +EGY,0.0,0.0,7.622975 +ERI,0.0,0.0,0.647038 +ETH,0.0,0.0,3.538134 +GEO,0.0,0.0,0.291168 +GNB,0.0,0.0,0.082517 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 +GTM,0.0,0.0,0.882024 +HND,0.0,0.0,0.328047 +HTI,0.0,0.0,0.56707 +IDN,5.019596,0.0,0.0 +IND,0.0,0.0,289.459564 IRL,0.0,0.0,0.334866 ISL,0.0,0.0,0.030581 -ISR,0.0,0.0,1.114113 +ISR,0.0,0.0,1.114115 JAM,0.0,0.0,0.181489 -JOR,0.0,0.0,0.90952 -JPN,1.601819,1.221458,1.323049 -KEN,0.0,0.0,8.553256 -KOR,2.719905,0.374992,2.658246 -LAO,0.0,0.055978,0.0 -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 +JOR,0.0,0.0,0.909527 +JPN,3.636718,1.221458,1.321784 +KEN,0.0,0.0,8.557251 +KHM,0.027266,0.208704,0.0 +KOR,2.91681,0.374992,2.65805 +LAO,0.0,0.055929,0.0 +LBN,0.0,0.0,0.255603 +LBY,0.0,0.0,0.793363 +LSO,0.0,0.0,0.093721 +MAR,0.0,0.0,5.567743 +MEX,0.0,0.0,2.575333 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 +MNG,0.0,0.0,15.397603 +MRT,0.0,0.0,2.106061 +MYS,0.0,0.0,0.202319 +NAM,0.0,0.0,0.693314 +NER,0.0,0.0,1.711404 +NIC,0.0,0.0,1.291721 NLD,0.0,0.0,0.117066 -NPL,0.0,0.0,15.855976 +NPL,0.0,0.0,15.855499 +NZL,0.0,0.488506,0.0 OMN,0.0,0.0,1.601173 -PAK,0.0,6.977015,166.893402 -PAN,0.0,0.0,0.335515 -PER,0.0,0.0,0.827014 +PAK,0.0,4.235036,166.856613 +PAN,0.0,0.0,0.335518 +PER,0.0,0.0,0.826841 +PHL,1.438208,0.245323,0.0 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 -TJK,0.0,0.0,0.247151 -TKM,0.0,0.0,0.695718 -TLS,0.002867,0.01037,0.067521 +PRY,0.0,0.0,0.157387 +PSE,0.0,0.0,0.253417 +RWA,0.0,0.0,0.379823 +SAU,0.0,0.0,3.206031 +SDN,0.0,0.0,11.694079 +SEN,0.0,0.0,0.880737 +SOM,0.0,0.0,5.319843 +SSD,0.0,0.0,3.617177 +STP,0.0,0.0,0.002251 +SWZ,0.0,0.0,0.169087 +SYR,0.0,0.0,1.271971 +TCD,0.0,0.0,25.861656 +THA,1.300704,0.019779,0.0 +TJK,0.0,0.0,0.247159 +TKM,0.0,0.0,0.695733 +TLS,0.003341,0.01035,0.066253 TTO,0.0,0.0,0.00687 -TUN,0.0,0.0,1.242337 -TUR,0.0,0.0,1.66396 -TWN,1.326228,0.0,0.0 -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 +TUN,0.0,0.0,1.242338 +TUR,0.0,0.0,1.668762 +TWN,0.219836,0.00074,0.0 +TZA,0.0,0.0,9.636807 +UGA,0.0,0.0,3.555149 +URY,0.0,0.0,2.438578 +VEN,0.0,0.0,3.763246 +VNM,1.433684,0.832676,0.0 +YEM,0.0,0.0,3.183534 +ZAF,0.0,0.0,5.482439 +ZMB,0.0,0.0,2.941376 +ZWE,0.0,0.0,10.748847 diff --git a/data/curated/calibration/gbd-anchored/exogenous_forage.csv b/data/curated/calibration/gbd-anchored/exogenous_forage.csv index 4cdc9f33..cb7c5f94 100644 --- a/data/curated/calibration/gbd-anchored/exogenous_forage.csv +++ b/data/curated/calibration/gbd-anchored/exogenous_forage.csv @@ -5,7 +5,7 @@ country,exogenous_forage_mt_dm AFG,0.0 AGO,0.0 ALB,0.0 -ARE,1.958419 +ARE,1.890263 ARG,0.0 ARM,0.0 ATG,0.0 @@ -16,7 +16,7 @@ BDI,0.0 BEL,0.0 BEN,0.0 BFA,0.0 -BGD,17.312044 +BGD,17.304365 BGR,0.0 BHS,0.0 BIH,0.0 @@ -38,7 +38,7 @@ CMR,0.0 COD,0.0 COG,0.0 COL,0.0 -COM,0.083741 +COM,0.0 CPV,0.0 CRI,0.0 CUB,0.0 @@ -50,7 +50,7 @@ DNK,0.0 DOM,0.0 DZA,0.0 ECU,0.0 -EGY,20.095776 +EGY,0.0 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,151.11058 +IND,148.093414 IRL,0.0 IRN,0.0 IRQ,0.0 @@ -90,7 +90,7 @@ KAZ,0.0 KEN,0.0 KGZ,0.0 KHM,0.0 -KOR,5.329263 +KOR,5.239663 LAO,0.0 LBN,0.0 LBR,0.0 @@ -107,7 +107,7 @@ MEX,0.0 MKD,0.0 MLI,0.0 MLT,0.0 -MMR,8.619004 +MMR,8.617109 MNE,0.0 MNG,0.0 MOZ,0.0 @@ -121,10 +121,10 @@ NGA,0.0 NIC,0.0 NLD,0.0 NOR,0.0 -NPL,2.704415 +NPL,2.597535 NZL,0.0 -OMN,1.78773 -PAK,70.964584 +OMN,1.69909 +PAK,70.964592 PAN,0.0 PER,0.0 PHL,0.0 @@ -156,8 +156,8 @@ SYR,0.0 TCD,0.0 TGO,0.0 THA,0.0 -TJK,3.332775 -TKM,9.849668 +TJK,3.302411 +TKM,9.849627 TLS,0.0 TTO,0.0 TUN,0.0 @@ -168,9 +168,9 @@ UGA,0.0 UKR,0.0 URY,0.0 USA,0.0 -UZB,15.04438 +UZB,15.043672 VEN,0.0 -VNM,4.376188 +VNM,0.0 VUT,0.0 YEM,0.0 ZAF,0.0 diff --git a/data/curated/calibration/gbd-anchored/fodder_conversion.csv b/data/curated/calibration/gbd-anchored/fodder_conversion.csv index 08823e62..1fd05b76 100644 --- a/data/curated/calibration/gbd-anchored/fodder_conversion.csv +++ b/data/curated/calibration/gbd-anchored/fodder_conversion.csv @@ -44,7 +44,7 @@ CRI,1.0 CUB,1.0 CYP,1.0 CZE,1.0 -DEU,0.733979 +DEU,1.0 DJI,1.0 DNK,1.0 DOM,1.0 @@ -53,7 +53,7 @@ ECU,1.0 EGY,1.0 ERI,1.0 ESP,1.0 -EST,0.475501 +EST,0.475502 ETH,1.0 FIN,1.0 FJI,1.0 diff --git a/data/curated/calibration/gbd-anchored/food_demand.csv b/data/curated/calibration/gbd-anchored/food_demand.csv index 8689eb83..bb51a192 100644 --- a/data/curated/calibration/gbd-anchored/food_demand.csv +++ b/data/curated/calibration/gbd-anchored/food_demand.csv @@ -1,60 +1,60 @@ food,multiplier,net_slack_mt,consumption_mt -apple,0.9964458887847566,0.36765858670696616,103.44599941895285 -banana,1.0,0.0,76.84249828507745 +apple,0.9964443109117379,0.3678209779318422,103.445765026554 +banana,1.0,0.0,76.84232396420703 barley-hulled,1.0,0.0,2.200347095287043 -buckwheat,0.7434988412486865,0.953127861709163,3.715881309656197 -cabbage,1.0575719761605498,-12.673277601599693,220.12927897868212 -carrot,1.0261823668779644,-3.185366153717041,121.66074093163479 -cassava,1.0,0.0,149.68174137048663 +buckwheat,0.7434199045026576,0.9534211812883768,3.715881309656197 +cabbage,1.0575776224891007,-12.674407623708248,220.1273181452998 +carrot,1.026181295227714,-3.1852073669433594,121.65965584359947 +cassava,1.0,0.0,149.68125092926766 chickpea,1.0,0.0,4.478103744486589 -citrus,1.051283085399352,-6.784464359283447,132.29438725169166 -cocoa-powder,0.9075733242820169,0.4894285178525024,5.295316682662815 +citrus,1.0512842253806731,-6.784599781036377,132.29408713255543 +cocoa-powder,0.9075724784292891,0.4894329770177137,5.295316467436351 coconut,1.0,0.0,2.927419820128307 -coconut-oil,1.0,0.0,1.0779300765188964 -coffee-green,0.8670401855759174,1.5566428339807317,11.70761888261768 -cottonseed-oil,1.0,0.0,4.166764032094761 +coconut-oil,1.0,0.0,0.9847869982731936 +coffee-green,0.8670346770617218,1.5567073001293465,11.707618691318203 +cottonseed-oil,1.0,0.0,3.8390739116130135 cowpea,1.0,0.0,2.4007039731534405 dairy,0.9741199909093337,20.756315441802144,802.0211804828141 dairy-buffalo,0.9952668258817422,0.6550484308972955,138.39516876645212 dry-pea,1.0,0.0,1.9984850413002277 -eggs,1.0,0.0,82.1042355971731 +eggs,0.9999999933922069,5.425278004622669e-07,82.1042355971731 flour-white,1.0,0.0,275.431504257489 flour-wholemeal,1.0,0.0,48.923026258606114 foxtail-millet,1.0,0.0,2.8788443399389507 gram,1.0,0.0,4.478103744486589 groundnut,1.0,0.0,8.565938512935645 -groundnut-oil,1.0,0.0,3.804871482762792 +groundnut-oil,1.0,0.0,3.4693732594732865 maize,1.0,0.0,68.3637765974645 maize-whole,1.0,0.0,24.219736276596677 -mango,1.0328182031885782,-1.592553177048103,48.52651950190739 +mango,1.0328161050711082,-1.5924477654043585,48.526409881786094 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,1.0,0.0,1.3277868090137872 -olive-oil,1.087588567594996,-0.3965856194263324,4.527824010778659 -onion,0.9808700551790642,5.491691055998672,287.0730212451308 -palm-oil,1.0,0.0,26.456728480317906 +olive-oil,1.1709586557719431,-0.7189532550983131,4.205421783717043 +onion,0.9808766096716237,5.489760513883084,287.07046290516155 +palm-oil,0.5,39.40290946650384,23.789298962261455 pearl-millet,1.0,0.0,2.8788443399389507 phaseolus-bean,1.0,0.0,7.570616604105453 pigeon-pea,1.0,0.0,0.24040035817802163 -plantain,0.9212803413398243,1.9342888810697332,24.571865706631797 -potato,0.968841012453559,8.087209678371437,259.54661287751514 -rapeseed-oil,1.0,0.0,12.743499018413786 +plantain,0.9212759041820071,1.9343916127127159,24.57178571075565 +potato,0.9688399882094009,8.087450784165412,259.54581912595313 +rapeseed-oil,0.9119021303172428,1.0467587411403656,11.881771317624043 rendered-fat,1.0,0.0,8.840820645729764 rice-brown,1.0,0.0,42.93316995531495 rice-white,1.0,0.0,352.6840182970045 rye,1.0,0.0,0.8833040535437249 -sesame-oil,1.0,0.0,1.3722494907869986 +sesame-oil,1.0,0.0,1.2538875761297277 sesame-seed,1.0,0.0,1.130570922832021 sorghum,1.0,0.0,9.408891627230332 soy,1.0,0.0,3.8865096387889935 -soybean-oil,1.0,0.0,32.41444603788477 +soybean-oil,0.9153797586953221,2.521884406339268,29.80237786440648 sugar,1.0,0.0,154.03780619380996 -sunflower-oil,0.9070977078763386,1.322979039920483,14.24054250630843 +sunflower-oil,0.9807420733021773,0.25364919906132855,13.171158195861608 sunflower-seed,1.0,0.0,1.9189327573915307 -sweet-potato,1.0,0.0,55.15792171360226 -tea-dried,0.9839926829455669,0.10682958776851592,6.673797201944581 -tomato,0.9262287442925455,10.366960258106701,140.52845052845078 -watermelon,1.2332589579463555,-12.692705154418945,54.41465256540323 -yam,0.9337977868811682,2.295184926634647,34.66930814706802 +sweet-potato,1.0,0.0,55.15774182416499 +tea-dried,0.9839852994777435,0.10687885975221434,6.673796965711517 +tomato,0.9262073745814126,10.369870988553885,140.52719942854674 +watermelon,1.2332586330181834,-12.692658424377441,54.414528029014036 +yam,0.9337839051883436,2.29566615668341,34.669307563563734 diff --git a/data/curated/calibration/gbd-anchored/food_waste.yaml b/data/curated/calibration/gbd-anchored/food_waste.yaml index a6ed5705..c73f5f71 100644 --- a/data/curated/calibration/gbd-anchored/food_waste.yaml +++ b/data/curated/calibration/gbd-anchored/food_waste.yaml @@ -10,26 +10,26 @@ # `tools/calibrate food_waste` instead. fruits: baseline_consumption_mt: 303.5781328359153 - baseline_negative_slack_mt: 111.94592668054975 + baseline_negative_slack_mt: 111.9449801093142 baseline_positive_slack_mt: 0.0 - waste_retention_multiplier: 0.7305909871721545 + waste_retention_multiplier: 0.7305926514752747 oil: baseline_consumption_mt: 101.94318941038546 - baseline_negative_slack_mt: 0.3454554723575711 - baseline_positive_slack_mt: 1.483789589193293 - waste_retention_multiplier: 1.0112924532605183 + baseline_negative_slack_mt: 0.345420871861279 + baseline_positive_slack_mt: 51.40532734505882 + waste_retention_multiplier: 2.0034711505589695 starchy_vegetable: baseline_consumption_mt: 548.2428959218498 baseline_negative_slack_mt: 0.0 - baseline_positive_slack_mt: 26.519915684631314 - waste_retention_multiplier: 1.050831411858786 + baseline_positive_slack_mt: 26.521615269138465 + waste_retention_multiplier: 1.0508348350980776 stimulants: baseline_consumption_mt: 23.68795094791676 baseline_negative_slack_mt: 0.0 - baseline_positive_slack_mt: 2.164119137461057 - waste_retention_multiplier: 1.1005452540476452 + baseline_positive_slack_mt: 2.1642379899412845 + waste_retention_multiplier: 1.1005513311837463 vegetables: baseline_consumption_mt: 514.297463798519 - baseline_negative_slack_mt: 255.09402139157464 + baseline_negative_slack_mt: 255.08714949349815 baseline_positive_slack_mt: 0.0 - waste_retention_multiplier: 0.6684470437978027 + waste_retention_multiplier: 0.6684530141536886 diff --git a/data/curated/calibration/gbd-anchored/grassland_cost.csv b/data/curated/calibration/gbd-anchored/grassland_cost.csv index 90e9ea29..391e5b1c 100644 --- a/data/curated/calibration/gbd-anchored/grassland_cost.csv +++ b/data/curated/calibration/gbd-anchored/grassland_cost.csv @@ -1,175 +1,175 @@ country,correction_bnusd_per_mha -AFG,-0.0 -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,0.902103066444397 -BDI,-1.1082043727128621e-08 +AFG,1.0285317042502129e-10 +AGO,-0.3685210794210434 +ALB,0.08087117224931717 +ARE,0.0 +ARG,-0.16487210243940353 +ARM,0.0 +ATG,0.0 +AUS,-0.00915704108774662 +AUT,1.834300888958751e-08 +AZE,1.0076294541358948 +BDI,-0.48811182379722595 BEL,0.08367127552628517 -BEN,3.0179935693740845 -BFA,1.386897623538971 -BGD,-0.0 -BGR,-0.30503877997398376 +BEN,3.3643276691436768 +BFA,1.547168254852295 +BGD,0.0 +BGR,-0.3005576878786087 BHS,-0.0316774295642972 -BIH,-0.4445299804210663 -BLR,-0.019591745920479298 -BLZ,-0.7379351556301117 +BIH,-0.4430038630962372 +BLR,-0.012004621792584658 +BLZ,-0.8535727262496948 BOL,-0.5958932638168335 BRA,-0.3751921057701111 -BRB,0.8522741794586182 +BRB,0.9579221606254578 BRN,7.141561985015869 -BTN,-0.28948941826820374 -BWA,-0.0 -CAF,-0.4648502320051193 -CAN,-0.21916517615318298 -CHE,-0.11954900994896889 -CHL,-0.010969268623739481 -CHN,-9.171298387400384e-09 -CIV,-0.51629638671875 -CMR,4.146082162857056 -COD,-0.8133789598941803 +BTN,-0.30104151368141174 +BWA,0.0 +CAF,-1.1816822631516288e-08 +CAN,-0.21679558604955673 +CHE,-0.07974380627274513 +CHL,-0.0009876639232970774 +CHN,-5.9879887714942015e-09 +CIV,-0.4987214505672455 +CMR,4.544210910797119 +COD,-0.7558623552322388 COG,-0.8903338015079498 COL,-0.7695494294166565 -COM,7.03753166853005e-10 -CPV,-0.0 +COM,1.513160526752472 +CPV,0.0 CRI,-0.040161214768886566 CUB,-0.1725609302520752 -CYP,0.28603461384773254 -CZE,-0.280591756105423 -DEU,-0.3941321074962616 -DJI,-0.0 +CYP,0.3170135170221329 +CZE,-0.19046077877283096 +DEU,-0.2572143077850342 +DJI,0.0 DNK,1.8209227323532104 -DOM,7.18969817015136e-09 -DZA,-0.0 +DOM,-1.4828121619459012e-08 +DZA,-1.622208756213439e-10 ECU,-0.10834560729563236 -EGY,-0.0 +EGY,0.0 ERI,-0.036436695605516434 -ESP,-0.044432083144783974 +ESP,-1.80058634668967e-10 EST,-0.25669121742248535 -ETH,-1.5872822500817563e-10 -FIN,1.5787851810455322 +ETH,1.3066284942908624e-08 +FIN,1.7559711933135986 FJI,-0.7943975925445557 -FRA,-6.067606639348355e-09 -GAB,-0.8158496618270874 -GBR,-0.03493287414312363 +FRA,-1.0104092229568096e-08 +GAB,-0.814353346824646 +GBR,0.0 GEO,-0.2082834094762802 -GHA,3.9665030371338617e-08 -GIN,-0.2904384136199951 -GMB,1.132258951663971 -GNB,1.0381489992141724 +GHA,-0.5135024040937424 +GIN,-0.15879005938768387 +GMB,1.2610795497894287 +GNB,1.1563142538070679 GNQ,-0.7098988890647888 -GRC,-0.10194651782512665 -GRD,0.6089716553688049 -GTM,-4.90308071920964e-10 +GRC,-0.09635294415056705 +GRD,0.692780077457428 +GTM,-4.2628794716392804e-08 GUF,-1.0722496509552002 -GUY,-1.1411567330360413 -HND,-0.6534972786903381 -HRV,-0.18737270683050156 -HTI,5.54345191972061e-09 +GUY,-1.106971263885498 +HND,-0.5874203741550446 +HRV,-0.18356122076511383 +HTI,-1.6403337355086478e-08 HUN,-0.4170793741941452 -IDN,-0.5775293707847595 -IND,1.592830777168274 -IRL,-2.0363737363027212e-08 -IRN,-0.0 -IRQ,-0.0 +IDN,-0.5079740881919861 +IND,1.5702753067016602 +IRL,-9.13200892682653e-09 +IRN,0.0 +IRQ,0.0 ISL,-0.08946210891008377 -ISR,0.6618824303150177 -ITA,0.04259064048528671 -JAM,3.453690528869629 -JOR,-0.0 +ISR,0.7338600158691406 +ITA,0.29519903659820557 +JAM,3.888116955757141 +JOR,0.0 JPN,-0.4974181354045868 -KAZ,-9.890298025005961e-10 +KAZ,6.031010413298787e-11 KEN,-0.17468049377202988 -KGZ,-0.0 -KHM,-0.4269895404577255 -KOR,1.125574411453556e-09 -LAO,2.91002910657312e-09 -LBN,0.48361755907535553 +KGZ,-2.072970683997255e-09 +KHM,-0.4191155880689621 +KOR,3.2005810737609863 +LAO,-3.513972668400811e-09 +LBN,0.5361965000629425 LBR,-0.7553918659687042 -LBY,-0.0 -LKA,-0.19719894230365753 -LSO,-0.443109393119812 -LTU,-0.037103863433003426 -LUX,0.04376813396811485 +LBY,0.0 +LKA,-1.4365466594057352e-08 +LSO,-0.4911396652460098 +LTU,-0.016819098964333534 +LUX,0.061516094487160444 LVA,-0.3037813901901245 -MAR,-0.0 +MAR,0.0 MDA,-0.36603914201259613 -MDG,-0.6942926049232483 -MEX,-0.08276043459773064 -MKD,-0.2778279483318329 -MLI,-0.005235991440713406 -MLT,-0.0 -MMR,8.1712165922454e-09 -MNE,-0.3014145940542221 -MNG,-0.06104075722396374 -MOZ,-0.5657002627849579 -MRT,-0.0 -MUS,-0.2501697465777397 +MDG,-0.6528348624706268 +MEX,1.0218221824231932e-08 +MKD,-0.27286694943904877 +MLI,-0.04440104402601719 +MLT,0.0 +MMR,4.39470548840859e-09 +MNE,-0.15099866688251495 +MNG,-0.05928472615778446 +MOZ,-0.5656761825084686 +MRT,-2.2212138378607804e-11 +MUS,-0.2207227498292923 MWI,-0.19057554006576538 -MYS,2.3094663959000172e-08 -NAM,-0.007091744802892208 -NER,-0.0 +MYS,-9.89881137059001e-08 +NAM,-0.006840692600235343 +NER,0.0 NGA,-0.19652122259140015 NIC,-0.5845303535461426 NLD,0.7907610237598419 -NOR,0.5385204553604126 -NPL,-1.110417313654466e-09 -NZL,-0.0 -OMN,-0.0 -PAK,-0.0 -PAN,-0.2060026302933693 -PER,-0.629314124584198 +NOR,0.598862886428833 +NPL,0.0 +NZL,0.0 +OMN,0.0 +PAK,1.424497075852571e-09 +PAN,-0.10266764834523201 +PER,-0.6414314210414886 PHL,-0.12064796686172485 PNG,-0.7848113179206848 -POL,0.019568821415305138 -PRI,-0.6568542122840881 -PRT,-0.10173622332513332 -PRY,-0.536945641040802 -PSE,0.6488772332668304 +POL,0.02005419973284006 +PRI,2.838124757431615e-08 +PRT,-0.0998427290469408 +PRY,-0.5369450151920319 +PSE,0.7265221178531647 ROU,-0.4245498776435852 -RUS,-8.213328683837062e-09 -RWA,-0.25266803056001663 -SAU,-0.0 -SDN,-0.07152627781033516 -SEN,0.34944604337215424 -SLB,-0.047332171350717545 +RUS,-6.801293306324396e-09 +RWA,6.642067909240723 +SAU,0.0 +SDN,-0.06754493713378906 +SEN,0.3892466574907303 +SLB,-0.18894267082214355 SLE,-0.41614776849746704 -SLV,3.313786745071411 -SOM,-0.05213965103030205 -SRB,-0.29573993384838104 +SLV,3.7280293703079224 +SOM,3.410516868918023e-09 +SRB,-0.295737162232399 SSD,-0.3232929855585098 -STP,0.5220659822225571 +STP,0.5782524645328522 SUR,6.86613392829895 SVK,-0.4092821776866913 -SVN,-0.22243013978004456 -SWE,1.6298401355743408 -SWZ,-1.973749874650821e-08 -SYR,0.13188961148262024 -TCD,-0.0883248969912529 +SVN,-0.20204976946115494 +SWE,1.8130713105201721 +SWZ,2.0045294313320028e-09 +SYR,0.14624850451946259 +TCD,-0.07812828943133354 TGO,-0.46927402913570404 THA,-3.208625720674263e-08 -TJK,0.35093268752098083 -TKM,-0.0 -TLS,-0.22272039204835892 -TTO,-2.3513318403445282e-08 -TUN,0.33769020438194275 -TUR,1.0687485337257385 -TWN,-0.3089394122362137 -TZA,-0.06884757429361343 -UGA,-0.7039666175842285 -UKR,-0.02324177697300911 -URY,-0.17097113281488419 -USA,-0.40475931763648987 -UZB,0.39591629803180695 -VEN,-0.26539246737957 -VNM,5.579045295715332 -VUT,-0.17226548492908478 -YEM,1.373607755317252e-09 -ZAF,-0.0033502724254503846 -ZMB,-0.3225127309560776 -ZWE,0.9941281378269196 +TJK,0.40645886957645416 +TKM,-9.089148877983177e-10 +TLS,-0.3597917854785919 +TTO,-1.403013705214562e-09 +TUN,0.37630587816238403 +TUR,1.1556978225708008 +TWN,-0.30179035663604736 +TZA,-0.06839879974722862 +UGA,-0.6885234415531158 +UKR,0.058694761246442795 +URY,-0.21174990385770798 +USA,-0.40415892004966736 +UZB,0.9957038164138794 +VEN,-0.26628775894641876 +VNM,5.844109058380127 +VUT,-0.23864496499300003 +YEM,0.0 +ZAF,0.0 +ZMB,-0.15246352553367615 +ZWE,1.0790897011756897 diff --git a/data/curated/calibration/gbd-anchored/grassland_yield.csv b/data/curated/calibration/gbd-anchored/grassland_yield.csv index c57034c5..ab4cfcdd 100644 --- a/data/curated/calibration/gbd-anchored/grassland_yield.csv +++ b/data/curated/calibration/gbd-anchored/grassland_yield.csv @@ -3,25 +3,25 @@ # SPDX-License-Identifier: CC-BY-4.0 country,yield_correction AFG,0.395294 -AGO,0.071253 +AGO,0.071585 ALB,0.144938 ARE,1.0 -ARG,0.200993 +ARG,0.345713 ARM,0.550552 ATG,1.0 AUS,0.236373 -AUT,0.553455 +AUT,0.600427 AZE,1.0 -BDI,0.437887 +BDI,0.442052 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 +BIH,0.01467 +BLR,0.422193 +BLZ,0.102024 BOL,0.111315 BRA,0.229034 BRB,1.0 @@ -32,7 +32,7 @@ CAF,0.583977 CAN,0.051832 CHE,0.40712 CHL,0.686938 -CHN,0.287707 +CHN,0.290638 CIV,0.033413 CMR,1.0 COD,0.022128 @@ -43,12 +43,12 @@ CPV,1.0 CRI,0.380109 CUB,0.277685 CYP,1.0 -CZE,0.138284 -DEU,0.0 +CZE,0.232729 +DEU,0.150859 DJI,1.0 DNK,1.0 -DOM,0.567979 -DZA,0.79171 +DOM,0.567981 +DZA,0.791718 ECU,0.36376 EGY,1.0 ERI,0.518486 @@ -58,20 +58,20 @@ ETH,0.551195 FIN,1.0 FJI,0.041419 FRA,0.488102 -GAB,0.006069 +GAB,0.007952 GBR,0.40073 GEO,0.122798 -GHA,0.178955 +GHA,0.178956 GIN,0.173001 GMB,1.0 GNB,1.0 GNQ,0.077495 GRC,0.075784 GRD,1.0 -GTM,0.571476 +GTM,0.571477 GUF,0.0 GUY,0.018762 -HND,0.147424 +HND,0.186551 HRV,0.284196 HTI,0.92669 HUN,0.0 @@ -82,7 +82,7 @@ IRN,0.769453 IRQ,1.0 ISL,0.062237 ISR,1.0 -ITA,0.884272 +ITA,1.0 JAM,1.0 JOR,1.0 JPN,0.0 @@ -91,12 +91,12 @@ KEN,0.467676 KGZ,0.842055 KHM,0.398086 KOR,1.0 -LAO,0.404176 +LAO,0.446981 LBN,1.0 LBR,0.016113 LBY,0.894499 LKA,0.193473 -LSO,0.048049 +LSO,0.043373 LTU,0.341665 LUX,0.783687 LVA,0.0 @@ -105,17 +105,17 @@ MDA,0.0 MDG,0.016111 MEX,0.371319 MKD,0.104967 -MLI,0.387861 +MLI,0.387873 MLT,1.0 MMR,1.0 MNE,0.077905 MNG,0.245052 MOZ,0.00289 -MRT,0.660517 +MRT,0.66052 MUS,0.811415 MWI,0.76798 -MYS,0.436125 -NAM,0.291928 +MYS,0.442334 +NAM,0.29193 NER,1.0 NGA,0.665316 NIC,0.295306 @@ -125,51 +125,51 @@ NPL,1.0 NZL,0.560406 OMN,1.0 PAK,1.0 -PAN,0.256559 -PER,0.083668 +PAN,0.312528 +PER,0.084126 PHL,0.384506 PNG,0.0 POL,0.560559 -PRI,0.43587 +PRI,0.459587 PRT,0.081039 -PRY,0.068488 +PRY,0.068489 PSE,1.0 ROU,0.0 RUS,0.15561 -RWA,0.794264 +RWA,1.0 SAU,1.0 SDN,0.419271 SEN,1.0 -SLB,0.580748 +SLB,0.408618 SLE,0.093191 SLV,1.0 SOM,0.555429 -SRB,0.13671 +SRB,0.136713 SSD,0.142093 STP,1.0 SUR,1.0 SVK,0.0 -SVN,0.252009 +SVN,0.252012 SWE,1.0 SWZ,0.283263 SYR,1.0 TCD,0.695865 TGO,0.206827 -THA,0.856195 +THA,0.898892 TJK,1.0 TKM,1.0 TLS,0.234166 -TTO,0.743954 +TTO,0.839948 TUN,1.0 TUR,1.0 TWN,0.107613 -TZA,0.406749 -UGA,0.405131 +TZA,0.407102 +UGA,0.425128 UKR,0.166781 -URY,0.270063 +URY,0.242289 USA,0.031197 UZB,1.0 -VEN,0.190522 +VEN,0.190094 VNM,1.0 VUT,0.176534 YEM,0.592561 diff --git a/data/curated/calibration/gbd-anchored/multi_crop_cost.csv b/data/curated/calibration/gbd-anchored/multi_crop_cost.csv new file mode 100644 index 00000000..0ecd181b --- /dev/null +++ b/data/curated/calibration/gbd-anchored/multi_crop_cost.csv @@ -0,0 +1,789 @@ +combination,country,correction_bnusd_per_mha +cotton_wheat,AFG,0.15957194566726685 +cotton_wheat,AGO,1.2010616064071655 +cotton_wheat,ALB,7.5 +cotton_wheat,ARG,7.5 +cotton_wheat,ARM,7.5 +cotton_wheat,AUS,-7.5 +cotton_wheat,AZE,7.5 +cotton_wheat,BDI,-0.508621871471405 +cotton_wheat,BGD,4.844290018081665 +cotton_wheat,BGR,7.5 +cotton_wheat,BOL,7.5 +cotton_wheat,BRA,6.54271388053894 +cotton_wheat,BWA,4.453519642353058 +cotton_wheat,CHN,1.2160378098487854 +cotton_wheat,COD,-1.0064157247543335 +cotton_wheat,DZA,7.5 +cotton_wheat,EGY,0.7566313743591309 +cotton_wheat,ESP,2.4812554121017456 +cotton_wheat,ETH,-1.1835752129554749 +cotton_wheat,GEO,7.5 +cotton_wheat,GRC,1.3865758180618286 +cotton_wheat,HND,-0.029271302744746208 +cotton_wheat,IND,-0.7118860483169556 +cotton_wheat,IRN,0.5818364024162292 +cotton_wheat,IRQ,7.5 +cotton_wheat,ISR,2.467694878578186 +cotton_wheat,JOR,2.618316411972046 +cotton_wheat,KAZ,1.5115489959716797 +cotton_wheat,KEN,1.0903679132461548 +cotton_wheat,KGZ,2.321509003639221 +cotton_wheat,KOR,7.5 +cotton_wheat,LBN,7.5 +cotton_wheat,LSO,4.415732979774475 +cotton_wheat,MAR,1.087628185749054 +cotton_wheat,MDG,-0.12449101358652115 +cotton_wheat,MEX,5.469664812088013 +cotton_wheat,MKD,3.2964072227478027 +cotton_wheat,MMR,7.5 +cotton_wheat,MOZ,0.4938295781612396 +cotton_wheat,MWI,7.5 +cotton_wheat,NAM,4.194038838148117 +cotton_wheat,NER,7.5 +cotton_wheat,NGA,-7.5 +cotton_wheat,NPL,7.5 +cotton_wheat,PAK,1.2053667306900024 +cotton_wheat,PRT,7.5 +cotton_wheat,PRY,2.0514886379241943 +cotton_wheat,PSE,1.3464239835739136 +cotton_wheat,ROU,0.0 +cotton_wheat,RUS,7.5 +cotton_wheat,RWA,-0.2725582420825958 +cotton_wheat,SDN,-7.5 +cotton_wheat,SOM,-7.5 +cotton_wheat,SWZ,-0.8359448909759521 +cotton_wheat,SYR,1.9367197751998901 +cotton_wheat,TJK,0.17783039808273315 +cotton_wheat,TKM,0.5806401371955872 +cotton_wheat,TUN,7.5 +cotton_wheat,TUR,4.218400955200195 +cotton_wheat,TZA,-0.6822943687438965 +cotton_wheat,UGA,-0.19741022400557995 +cotton_wheat,URY,7.5 +cotton_wheat,USA,2.4673547744750977 +cotton_wheat,UZB,0.4927860200405121 +cotton_wheat,YEM,-7.5 +cotton_wheat,ZAF,2.787626266479492 +cotton_wheat,ZMB,7.5 +cotton_wheat,ZWE,6.100391387939453 +double_rice,AFG,7.5 +double_rice,AGO,7.5 +double_rice,ALB,0.0 +double_rice,ARG,7.5 +double_rice,AUS,7.5 +double_rice,AZE,7.5 +double_rice,BDI,-6.592018127441406 +double_rice,BEN,-2.16798198223114 +double_rice,BFA,-7.317521095275879 +double_rice,BGD,0.11906862538307905 +double_rice,BLZ,2.3788822889328003 +double_rice,BOL,7.5 +double_rice,BRA,7.5 +double_rice,BRN,-2.855687379837036 +double_rice,BTN,7.5 +double_rice,BWA,0.0 +double_rice,CAF,7.5 +double_rice,CHL,7.5 +double_rice,CHN,-2.981346845626831 +double_rice,CIV,7.5 +double_rice,CMR,7.5 +double_rice,COD,7.5 +double_rice,COG,7.5 +double_rice,COL,-2.277894973754883 +double_rice,CRI,-1.5134159326553345 +double_rice,CUB,7.5 +double_rice,DOM,-2.330437660217285 +double_rice,DZA,7.5 +double_rice,ECU,-1.0004610419273376 +double_rice,EGY,7.5 +double_rice,ERI,7.5 +double_rice,ESP,7.5 +double_rice,ETH,7.5 +double_rice,FJI,7.5 +double_rice,FRA,7.5 +double_rice,GAB,7.5 +double_rice,GEO,7.5 +double_rice,GHA,-0.23847725987434387 +double_rice,GIN,-7.5 +double_rice,GMB,-7.5 +double_rice,GNB,-7.5 +double_rice,GNQ,7.5 +double_rice,GRC,7.5 +double_rice,GTM,5.48490297794342 +double_rice,GUF,0.0 +double_rice,GUY,7.5 +double_rice,HND,7.5 +double_rice,HTI,2.5956857204437256 +double_rice,IDN,-2.4885209798812866 +double_rice,IND,2.3034749031066895 +double_rice,IRN,7.5 +double_rice,IRQ,7.5 +double_rice,ITA,7.5 +double_rice,JPN,-7.4698264598846436 +double_rice,KEN,7.5 +double_rice,KHM,-4.303137898445129 +double_rice,KOR,7.5 +double_rice,LAO,-2.1252903938293457 +double_rice,LBR,7.5 +double_rice,LKA,-0.31366080045700073 +double_rice,LSO,0.0 +double_rice,MAR,7.5 +double_rice,MDG,3.7404229445382953 +double_rice,MEX,7.5 +double_rice,MKD,7.5 +double_rice,MLI,-7.5 +double_rice,MMR,-2.452920436859131 +double_rice,MOZ,7.5 +double_rice,MRT,-7.5 +double_rice,MUS,7.5 +double_rice,MWI,-7.5 +double_rice,MYS,-0.9995557069778442 +double_rice,NAM,7.5 +double_rice,NER,7.5 +double_rice,NGA,-3.361262798309326 +double_rice,NIC,5.933155536651611 +double_rice,NPL,5.158517837524414 +double_rice,PAK,7.5 +double_rice,PAN,-1.6340627670288086 +double_rice,PER,7.5 +double_rice,PHL,-1.2978726625442505 +double_rice,PNG,7.5 +double_rice,PRI,7.5 +double_rice,PRT,7.5 +double_rice,PRY,7.5 +double_rice,RWA,-2.7989927530288696 +double_rice,SAU,7.5 +double_rice,SDN,7.5 +double_rice,SEN,-2.7050421237945557 +double_rice,SLB,7.5 +double_rice,SLE,-7.243507623672485 +double_rice,SLV,7.5 +double_rice,SOM,7.5 +double_rice,SSD,7.5 +double_rice,SUR,7.5 +double_rice,SWZ,7.5 +double_rice,SYR,7.5 +double_rice,TCD,-0.8257056474685669 +double_rice,TGO,0.14258075272664428 +double_rice,THA,-0.7501848936080933 +double_rice,TJK,7.5 +double_rice,TKM,7.5 +double_rice,TLS,7.5 +double_rice,TTO,7.5 +double_rice,TUN,0.0 +double_rice,TUR,7.5 +double_rice,TWN,-0.9604698419570923 +double_rice,TZA,4.199115186929703 +double_rice,UGA,7.5 +double_rice,URY,7.5 +double_rice,USA,7.5 +double_rice,UZB,7.5 +double_rice,VEN,7.5 +double_rice,VNM,-1.621755599975586 +double_rice,YEM,0.0 +double_rice,ZAF,7.5 +double_rice,ZMB,7.5 +double_rice,ZWE,7.5 +maize_soybean,AFG,0.13687430508434772 +maize_soybean,AGO,-0.2613598108291626 +maize_soybean,ALB,7.5 +maize_soybean,ARG,7.5 +maize_soybean,ARM,7.5 +maize_soybean,AUS,2.285356044769287 +maize_soybean,AUT,7.5 +maize_soybean,AZE,4.640923798084259 +maize_soybean,BDI,-0.971374124288559 +maize_soybean,BEL,7.5 +maize_soybean,BEN,-0.7208980917930603 +maize_soybean,BFA,-0.31730836629867554 +maize_soybean,BGD,7.5 +maize_soybean,BGR,7.5 +maize_soybean,BIH,7.5 +maize_soybean,BLZ,-1.2057549953460693 +maize_soybean,BOL,0.0647062361240387 +maize_soybean,BRA,0.3150215446949005 +maize_soybean,BTN,1.0969263315200806 +maize_soybean,BWA,4.064399540424347 +maize_soybean,CAF,-1.5539294481277466 +maize_soybean,CAN,7.5 +maize_soybean,CHE,7.5 +maize_soybean,CHN,-1.4334770441055298 +maize_soybean,CIV,-0.8392142653465271 +maize_soybean,CMR,-0.9884616434574127 +maize_soybean,COD,-1.5485203266143799 +maize_soybean,COG,-1.4889176487922668 +maize_soybean,COL,-0.07628445327281952 +maize_soybean,CRI,-0.7993972301483154 +maize_soybean,CUB,7.5 +maize_soybean,CZE,7.5 +maize_soybean,DEU,7.5 +maize_soybean,DZA,0.0 +maize_soybean,ECU,-0.4734065532684326 +maize_soybean,EGY,-1.6842405796051025 +maize_soybean,ESP,7.5 +maize_soybean,ETH,0.2626522034406662 +maize_soybean,FRA,7.5 +maize_soybean,GAB,-1.1092767715454102 +maize_soybean,GEO,0.5402394533157349 +maize_soybean,GHA,-0.6359098851680756 +maize_soybean,GIN,7.5 +maize_soybean,GRC,3.498760938644409 +maize_soybean,GTM,3.0123302936553955 +maize_soybean,HND,-0.02142040804028511 +maize_soybean,HRV,7.5 +maize_soybean,HUN,7.5 +maize_soybean,IDN,0.7064895629882812 +maize_soybean,IND,-0.5969436764717102 +maize_soybean,IRN,-0.22185522317886353 +maize_soybean,IRQ,-0.23048602044582367 +maize_soybean,ISR,7.5 +maize_soybean,ITA,7.5 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+wheat_maize,ZWE,6.538937091827393 +wheat_soybean,AFG,0.39539945870637894 +wheat_soybean,AGO,-0.020095903397304937 +wheat_soybean,ALB,7.5 +wheat_soybean,ARG,7.5 +wheat_soybean,ARM,7.5 +wheat_soybean,AUS,1.477770447731018 +wheat_soybean,AUT,7.5 +wheat_soybean,AZE,7.5 +wheat_soybean,BDI,-1.4418007731437683 +wheat_soybean,BEL,7.5 +wheat_soybean,BGD,3.67583816498518 +wheat_soybean,BGR,7.5 +wheat_soybean,BIH,7.5 +wheat_soybean,BOL,7.5 +wheat_soybean,BRA,0.13935944437980652 +wheat_soybean,BTN,7.5 +wheat_soybean,BWA,7.5 +wheat_soybean,CAN,7.5 +wheat_soybean,CHE,7.5 +wheat_soybean,CHN,-1.2130309343338013 +wheat_soybean,CMR,-0.3349729925394058 +wheat_soybean,COD,-1.205998718738556 +wheat_soybean,CZE,7.5 +wheat_soybean,DEU,7.5 +wheat_soybean,DZA,0.0 +wheat_soybean,ECU,-0.5063061118125916 +wheat_soybean,EGY,-1.0885406732559204 +wheat_soybean,ESP,7.5 +wheat_soybean,ETH,-0.6716628670692444 +wheat_soybean,FRA,7.5 +wheat_soybean,GEO,0.2682209312915802 +wheat_soybean,GRC,3.8156702294945717 +wheat_soybean,GTM,2.7479088306427 +wheat_soybean,HND,-0.37444475293159485 +wheat_soybean,HRV,7.5 +wheat_soybean,HUN,7.5 +wheat_soybean,IND,2.4143319129943848 +wheat_soybean,IRN,-0.02305993204936385 +wheat_soybean,IRQ,-0.21165601909160614 +wheat_soybean,ISR,7.5 +wheat_soybean,ITA,7.5 +wheat_soybean,JOR,0.0 +wheat_soybean,JPN,1.5681341886520386 +wheat_soybean,KAZ,7.5 +wheat_soybean,KEN,-0.6364563405513763 +wheat_soybean,KGZ,3.7303013261407614 +wheat_soybean,KOR,-0.19438032805919647 +wheat_soybean,LAO,0.0 +wheat_soybean,LBN,7.5 +wheat_soybean,LSO,7.5 +wheat_soybean,LUX,7.5 +wheat_soybean,MAR,-0.4596904516220093 +wheat_soybean,MDA,7.5 +wheat_soybean,MEX,7.5 +wheat_soybean,MKD,7.5 +wheat_soybean,MMR,-0.2157789170742035 +wheat_soybean,MNE,7.5 +wheat_soybean,MOZ,7.5 +wheat_soybean,MWI,7.5 +wheat_soybean,NGA,7.5 +wheat_soybean,NLD,7.5 +wheat_soybean,NPL,7.5 +wheat_soybean,PAK,-0.12195841688662767 +wheat_soybean,PER,-0.6334656476974487 +wheat_soybean,POL,7.5 +wheat_soybean,PRT,7.5 +wheat_soybean,PRY,0.1659405753016472 +wheat_soybean,ROU,7.5 +wheat_soybean,RUS,7.5 +wheat_soybean,RWA,-0.39666540920734406 +wheat_soybean,SRB,7.5 +wheat_soybean,SVK,7.5 +wheat_soybean,SVN,7.5 +wheat_soybean,SWZ,-0.33505555987358093 +wheat_soybean,SYR,7.5 +wheat_soybean,THA,7.5 +wheat_soybean,TJK,7.5 +wheat_soybean,TKM,0.8133601248264313 +wheat_soybean,TUR,7.5 +wheat_soybean,TWN,-0.14883071184158325 +wheat_soybean,TZA,-0.2885752320289612 +wheat_soybean,UGA,-1.077957034111023 +wheat_soybean,UKR,7.5 +wheat_soybean,URY,-0.003528994508087635 +wheat_soybean,USA,0.34375500679016113 +wheat_soybean,UZB,7.5 +wheat_soybean,VNM,-0.3192991614341736 +wheat_soybean,ZAF,2.5342297554016113 +wheat_soybean,ZMB,5.222832202911377 +wheat_soybean,ZWE,4.768944978713989 diff --git a/data/curated/calibration/gbd-anchored/provenance.yaml b/data/curated/calibration/gbd-anchored/provenance.yaml index 6326ace6..994504f3 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: '2026-07-03T17:40:31+00:00' -git_commit: 22281c512133c39d1fd438f77f076a0b931c3d33 +generated_at: '2026-07-22T00:59:36+00:00' +git_commit: 548ef138989c4a0ed28a90adeb0e4626b044a8ed source: gbd-anchored structural_config: aggregation.irrigated_area_source: current @@ -654,6 +654,181 @@ structural_config: data.usda.nutrients.fat: Total lipid (fat) data.usda.nutrients.protein: Protein data.usda.retrieve_nutrition: false + derived.multiple_cropping.catalog: + cotton_wheat: + crops: + - cotton + - wheat + water_supplies: + - r + - i + double_rice: + crops: + - wetland-rice + - wetland-rice + water_supplies: + - r + - i + maize_soybean: + crops: + - maize + - soybean + water_supplies: + - r + - i + rice_maize: + crops: + - wetland-rice + - maize + water_supplies: + - r + - i + rice_wheat: + crops: + - wetland-rice + - wheat + water_supplies: + - r + - i + triple_rice: + crops: + - wetland-rice + - wetland-rice + - wetland-rice + water_supplies: + - r + - i + wheat_maize: + crops: + - wheat + - maize + water_supplies: + - r + - i + wheat_soybean: + crops: + - wheat + - soybean + water_supplies: + - r + - i + derived.multiple_cropping.effective: + cotton_wheat: + crops: + - cotton + - wheat + water_supplies: + - r + - i + double_rice: + crops: + - wetland-rice + - wetland-rice + water_supplies: + - r + - i + maize_soybean: + crops: + - maize + - soybean + water_supplies: + - r + - i + rice_maize: + crops: + - wetland-rice + - maize + water_supplies: + - r + - i + rice_wheat: + crops: + - wetland-rice + - wheat + water_supplies: + - r + - i + triple_rice: + crops: + - wetland-rice + - wetland-rice + - wetland-rice + water_supplies: + - r + - i + wheat_maize: + crops: + - wheat + - maize + water_supplies: + - r + - i + wheat_soybean: + crops: + - wheat + - soybean + water_supplies: + - r + - i + derived.multiple_cropping.mirca_source_year: 2020 + derived.multiple_cropping.observed: + cotton_wheat: + crops: + - cotton + - wheat + water_supplies: + - r + - i + double_rice: + crops: + - wetland-rice + - wetland-rice + water_supplies: + - r + - i + maize_soybean: + crops: + - maize + - soybean + water_supplies: + - r + - i + rice_maize: + crops: + - wetland-rice + - maize + water_supplies: + - r + - i + rice_wheat: + crops: + - wetland-rice + - wheat + water_supplies: + - r + - i + triple_rice: + crops: + - wetland-rice + - wetland-rice + - wetland-rice + water_supplies: + - r + - i + wheat_maize: + crops: + - wheat + - maize + water_supplies: + - r + - i + wheat_soybean: + crops: + - wheat + - soybean + water_supplies: + - r + - i diet.anchor_groups_to_gbd: true diet.baseline_age: All ages diet.fbs_override_foods: @@ -806,24 +981,6 @@ structural_config: 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 diff --git a/data/curated/mirca_os_crop_mapping.csv b/data/curated/mirca_os_crop_mapping.csv new file mode 100644 index 00000000..b9cb84d6 --- /dev/null +++ b/data/curated/mirca_os_crop_mapping.csv @@ -0,0 +1,46 @@ +# SPDX-FileCopyrightText: 2026 Koen van Greevenbroek +# +# SPDX-License-Identifier: CC-BY-4.0 +# +# Concordance from the 23 MIRCA-OS v2 base crops (Kebede et al. 2025, HydroShare +# resource e4582ca0042148338bb5e0148b749ed6) to GLADE model crops +# (config["crops"]). Used by workflow/scripts/derive_mirca_multicropping.py to +# attribute MIRCA extra-cycle harvested area to the sequences listed in +# data/curated/mirca_os_multicropping_combinations.yaml. +# +# Columns: +# mirca_crop - exact MIRCA-OS crop label as it appears in the annual +# harvested-area GeoTIFF filenames +# (MIRCA-OS_{mirca_crop}_{year}_{ir,rf}_v2.tif). +# glade_crop - target crop in config["crops"], or blank to DROP the crop from +# baseline attribution (its extra-cycle area stays on the bulk +# land-correction residual). Perennials, long-duration / semi- +# perennial crops, aggregate "Others" classes, fodder, and +# unmappable aggregates are dropped by design (see the note). +# +# Every MIRCA-OS base crop appears here exactly once so the mapping is +# exhaustive and validated fail-fast (workflow/validation/multi_cropping.py). +mirca_crop,glade_crop,note +Barley,barley, +Cassava,cassava,long-duration root crop; mapped for completeness but absent from the curated sequence catalog +Cocoa,,dropped: perennial tree crop +Coffee,,dropped: perennial tree crop +Cotton,cotton, +Fodder,,dropped: fodder aggregate not represented as a discrete GLADE crop cycle +Groundnuts,groundnut, +Maize,maize, +Millet,pearl-millet,MIRCA aggregates millet into one class; mapped to the globally dominant pearl-millet (GLADE also models foxtail-millet) +Oil palm,,dropped: perennial tree crop +Others annual,,dropped: unmappable aggregate of minor annuals +Others perennial,,dropped: unmappable aggregate of perennials +Potatoes,white-potato, +Pulses,,dropped: MIRCA aggregates six-plus GLADE pulses into one class with no clean 1:1 target; rice-pulse rotations remain on the residual until a pulse split is added +Rapeseed,rapeseed, +Rice,wetland-rice,MIRCA "Rice" is predominantly paddy; mapped to wetland-rice consistent with the existing multiple_cropping config and the wetland-rice zone-cycle accounting +Rye,rye, +Sorghum,sorghum, +Soybeans,soybean, +Sugar beet,sugarbeet, +Sugar cane,,dropped: long-duration semi-perennial (ratoon) crop; not a sequential single-year cycle +Sunflower,sunflower, +Wheat,wheat, diff --git a/data/curated/mirca_os_multicropping_combinations.yaml b/data/curated/mirca_os_multicropping_combinations.yaml new file mode 100644 index 00000000..c6037318 --- /dev/null +++ b/data/curated/mirca_os_multicropping_combinations.yaml @@ -0,0 +1,66 @@ +# SPDX-FileCopyrightText: 2026 Koen van Greevenbroek +# +# SPDX-License-Identifier: CC-BY-4.0 +# +# Fixed crop-sequence catalog used to attribute MIRCA-OS multiple cropping. +# +# MIRCA-OS reports harvested area by crop and water supply but does not identify +# rotations directly. These sequences were selected from widely documented +# agronomic rotations and repeated-rice systems, restricted to crops represented +# separately by both MIRCA-OS v2 and GLADE. The derivation attributes only area +# where every cycle is observed in the same MIRCA cell and the GAEZ multiple- +# cropping zone permits the number and type of cycles. Consequently, inclusion +# here defines a candidate attribution and optimization pathway; it does not +# assert that the sequence occurs in every region. +# +# This catalog is tied to the MIRCA-OS v2 crop taxonomy. Edit it only when the +# source dataset or the scientific attribution method is deliberately revised. + +rice_wheat: + crops: + - wetland-rice + - wheat + water_supplies: [r, i] + +double_rice: + crops: + - wetland-rice + - wetland-rice + water_supplies: [r, i] + +triple_rice: + crops: + - wetland-rice + - wetland-rice + - wetland-rice + water_supplies: [r, i] + +rice_maize: + crops: + - wetland-rice + - maize + water_supplies: [r, i] + +wheat_maize: + crops: + - wheat + - maize + water_supplies: [r, i] + +wheat_soybean: + crops: + - wheat + - soybean + water_supplies: [r, i] + +maize_soybean: + crops: + - maize + - soybean + water_supplies: [r, i] + +cotton_wheat: + crops: + - cotton + - wheat + water_supplies: [r, i] diff --git a/docs/calibration.rst b/docs/calibration.rst index dc288b65..ac711bc2 100644 --- a/docs/calibration.rst +++ b/docs/calibration.rst @@ -93,6 +93,7 @@ so that ordinary builds don't need to re-solve anything. See * - :ref:`cost ` - ``config/calibration/cost.yaml`` - ``crop_cost.csv``, + ``multi_crop_cost.csv``, ``grassland_cost.csv``, ``animal_cost.csv`` - Additive production-cost corrections derived from stability- @@ -149,6 +150,12 @@ The wrapper invokes ``tools/smk`` with the matching config and the appropriate output targets. Any extra flags are passed through, e.g. ``tools/calibrate cost -j8 --slurm``. +Each source and step receives its own deterministic workflow name, such as +``calibration-default-feed``. Its processing and result intermediates are +therefore reusable without being overwritten by the different effective config +of the next calibration step, and ``--check`` can assess every step +independently. + The stability calibration runs locally in-process and is inherently sequential (each Broyden step depends on the previous solve), so HPC offloading isn't worthwhile at this size. Each iteration is one paired @@ -170,6 +177,12 @@ active config is compared against the stamp of the set named by ``calibration.source``; a structural mismatch is an error listing the differing keys. +For multi-cropping, the stamp also records the normalized curated catalog, the +effective observed and greenfield sequence sets, and the MIRCA source year +selected from ``baseline_year``. Editing the catalog, disabling or adding a +sequence, or selecting a different MIRCA release therefore requires a matching +calibration set. + A config with different structural assumptions has three options: * **Calibrate its own set**: declare ``calibration.source: `` in @@ -207,7 +220,7 @@ workflow when their configuration blocks are enabled (the default): each per-food multiplier uniformly to the baseline-diet ``target_mt`` in ``_match_baseline_to_consume_links`` (see :ref:`food-demand-calibration`). -* ``cost_calibration.enabled: true`` loads the three cost-correction +* ``cost_calibration.enabled: true`` loads the cost-correction CSVs at build time (see :ref:`cost-calibration-correction`). * ``deviation_penalty.calibration.enabled: true`` resolves the sentinel ``"calibrated"`` on any of @@ -336,7 +349,10 @@ production-stability constraint indicates how much the link's marginal cost would need to shift for the observed allocation to be cost-optimal; the per-group median becomes an additive correction. See :ref:`cost-calibration-correction` for how the corrections are applied -at build time. +at build time. Single-crop links are aggregated to per-(crop, country) +corrections, while multi-cropping links are extracted separately to +per-(combination, country) bundle corrections because their duals price +one joint cycle bundle rather than one constituent crop. Rule: ``extract_cost_calibration`` in ``workflow/rules/crops.smk``. Script: ``workflow/scripts/extract_cost_calibration.py``. The two diff --git a/docs/configuration.rst b/docs/configuration.rst index 1ce1420f..499e1332 100644 --- a/docs/configuration.rst +++ b/docs/configuration.rst @@ -582,15 +582,21 @@ Multiple Cropping :start-after: # --- section: multiple_cropping --- :end-before: # --- section: macronutrients --- -Define sequential cropping systems as ordered lists of crops. Entries may -repeat a crop (double rice) or mix cereals and legumes (rice→wheat, maize→soybean) and -list multiple ``water_supplies`` (``r`` for rainfed, ``i`` for irrigated) to build both -variants. The ``build_multi_cropping`` rule checks growing-season compatibility, -aggregates eligible area/yields, and sums irrigated water demand; ``build_model`` turns -each combination into a multi-output land link. Leave the section empty to disable the -feature. Multiple cropping zones that imply relay cropping (GAEZ classes "limited double" or -"double rice … limited triple") are still accepted here but are interpreted as sequential crop -chains; relay-specific dynamics are not yet modelled. +The observed combinations come from the fixed catalog in +``data/curated/mirca_os_multicropping_combinations.yaml`` and are included when +all their crops are modeled. The config section contains only overrides: + +* Set a catalog combination to ``null`` to disable it. +* Add a new, uniquely named combination to expose greenfield potential with an + implicit zero baseline. Its ``crops`` are an ordered list and may repeat a + crop; ``water_supplies`` uses ``r`` for rainfed and ``i`` for irrigated. + +Catalog combinations cannot be redefined in config. The +``build_multi_cropping`` rule applies the GAEZ multiple-cropping-zone limit, +requires valid suitability, yield, and irrigated water data for every cycle, +and turns the resulting areas into multi-output land links. It does not apply a +separate growing-season-overlap test. GAEZ relay-only classes are interpreted as +sequential crop chains; relay-specific dynamics are not yet modeled. Country Coverage ~~~~~~~~~~~~~~~~ diff --git a/docs/costs.rst b/docs/costs.rst index 02f4edbe..8f7bd7b6 100644 --- a/docs/costs.rst +++ b/docs/costs.rst @@ -646,8 +646,8 @@ For single-season crops: # Convert USD/ha to bnUSD/Mha (PyPSA units) marginal_cost = cost_per_ha * 1e6 * USD_TO_BNUSD - # Optional: add calibration correction (bnUSD/Mha, additive) - marginal_cost += cost_calibration.get((crop, country), 0.0) + # Optional: store calibration correction for solve-time bounded application + bounded_correction = crop_cost_calibration.get((crop, country), 0.0) For multi-cropping systems (multiple crops per year on the same land): @@ -659,6 +659,12 @@ For multi-cropping systems (multiple crops per year on the same land): # Convert to bnUSD/Mha marginal_cost = total_cost * 1e6 * USD_TO_BNUSD +Cost-calibration corrections do not compose by cycle. Each multi-cropping +link has one dispatch variable and one stability-band dual for the whole +bundle, so calibration extracts direct per-(combination, country) bundle +corrections and applies them through the same baseline-bounded +subsidy/penalty mechanism as single-crop links. + **Interpretation**: * The marginal cost represents the economic cost of using one Mha of land for crop production * Costs vary by country, reflecting local price and yield conditions @@ -783,6 +789,7 @@ Cost-related configuration parameters are specified in ``config/default.yaml``: generate: false # Generate calibration from solved model scenario: "calibration" crop_correction_csv: "data/curated/calibration/{calibration_source}/crop_cost.csv" + multi_crop_correction_csv: "data/curated/calibration/{calibration_source}/multi_crop_cost.csv" grassland_correction_csv: "data/curated/calibration/{calibration_source}/grassland_cost.csv" animal_correction_csv: "data/curated/calibration/{calibration_source}/animal_cost.csv" diff --git a/docs/crop_production.rst b/docs/crop_production.rst index 9d0579e7..014d617c 100644 --- a/docs/crop_production.rst +++ b/docs/crop_production.rst @@ -473,23 +473,66 @@ Currently, the model uses annual time resolution, so it implicitly assumes: Multiple Cropping ----------------- -Many production systems plant two or more sequential crops on the same parcel. The -model supports this via named combinations declared in ``config/*.yaml`` under -``multiple_cropping``. Each entry specifies a ``crops`` list (duplicates allowed for -double rice) and a ``water_supplies`` array that chooses whether the sequence is rainfed -(``r``) or irrigated (``i``). The preprocessing rule ``build_multi_cropping`` reads the -relevant GAEZ rasters for every crop in each (combination, water supply) pair and: - -* filters pixels where any crop lacks suitability or yield data, -* shifts later crops forward by whole-year increments until all growing seasons are - non-overlapping within a 365-day window, and -* computes the minimum suitable area fraction across the sequence. - -Eligible hectares are aggregated to ``processing/{name}/multi_cropping/eligible_area.csv`` -alongside the summed irrigated water requirement (``water_requirement_m3_per_ha``); the -column is zero for rainfed variants. Per-cycle yields (tonnes/ha for each step) are written to -``processing/{name}/multi_cropping/cycle_yields.csv`` so downstream steps can preserve -product-specific productivity. +Many production systems plant two or more sequential crops on the same parcel. +Multiple cropping is anchored to an observed reference-year baseline derived +from MIRCA-OS v2 (see :doc:`data_sources`), so the model starts from observed +double-cropped area -- notably India's rice-wheat and rice-rice systems -- rather +than treating every extra cropping cycle as unanchored potential. The MIRCA +release closest to ``baseline_year`` is used; the workflow supports 2010, 2015, +and 2020, ties select the earlier year, and a warning is emitted when the match +is not exact. + +**Observed-baseline derivation.** The ordinary, config-specific Snakemake rule +``derive_mirca_multicropping`` (``workflow/scripts/derive_mirca_multicropping.py``) +attributes MIRCA's extra-cycle harvested area -- annual harvested area minus the +AEI-capped physical footprint -- independently for irrigated and rainfed land. +It considers only the fixed candidate sequences in +``data/curated/mirca_os_multicropping_combinations.yaml``. The catalog is tied to +the MIRCA-OS v2 crop taxonomy and records widely documented rotations and +repeated-rice systems that both datasets can represent; catalog membership is a +candidate attribution, not evidence that a rotation occurs in every cell. + +The rule aggregates directly to the active config's regions and resource +classes. It writes ``baseline_area.csv``, a diagnostic +``residual_multicrop.tif``, and ``attribution_stats.csv`` under +``processing/{name}/multi_cropping/``. Keeping these products config-specific +is necessary because the aggregation and GAEZ multiple-cropping-zone gate +depend on the config's spatial and climate inputs. Extra-cycle area that cannot +be assigned to a catalog sequence remains unattributed and is handled by the +bulk land correction. + +A combination is a candidate in a cell where **every crop is observed in MIRCA** +in that water supply and the GAEZ multiple-cropping zone permits the cycle count. +``sequence_feasible`` on GAEZ growing-season windows is deliberately *not* used as +a feasibility gate: GAEZ attainable season lengths overshoot the farmed cycle and +would reject nearly all observed irrigated double-cropping. MIRCA's observation is +the feasibility evidence; beyond the zone gate, GAEZ enters only for +suitability, yields and water requirements. + +Within each cell and water supply, all candidate sequences receive a common +proportional fill rate subject to the extra-cycle magnitude, physical footprint, +each constituent crop's observed harvested-area budget, and each sequence's +MIRCA/GAEZ support. The shared crop budgets prevent the same observed wheat, +maize, or other crop area from being attributed independently to several +overlapping rotations. Any area that cannot be assigned under all budgets stays +in the residual. + +**Potential derivation.** The preprocessing rule +``build_multi_cropping`` reads the GAEZ rasters for every crop in each +(combination, water supply) pair and, over pixels where the zone permits the cycle +count and every crop has suitability, positive yield and (irrigated) a water +requirement, computes the eligible potential area and per-cycle yields. The +three tables in ``processing/{name}/multi_cropping/`` are +``eligible_area.csv`` (potential area plus the summed irrigated water +requirement ``water_requirement_m3_per_ha``; zero for rainfed variants), +``cycle_yields.csv`` (per-cycle t/ha), and ``baseline_area.csv`` (observed +anchor area per region/class/water supply). + +Catalog combinations are included automatically when all their crops are +modeled. The ``multiple_cropping`` config section may set a catalog name to +``null`` to disable it or add a uniquely named greenfield sequence. Greenfield +sequences have an implicit zero baseline and expose only GAEZ-constrained +optimization potential; catalog entries cannot be redefined in config. The RES01 classes report the agro-climatic zone the pixel belongs to. We interpret the numeric codes as: @@ -510,16 +553,36 @@ only construct sequential crop chains. This assumption is called out in the conf model framework documentation so users know the limitation. During ``build_model`` each (combination, region, resource class) creates a single -rained or irrigated multi-output link that: +rainfed or irrigated multi-output link (carrier ``crop_production_multi``) that: -* draws from the matching land bus (``_r`` or ``_i``) used by individual crops, -* emits one crop bus per cycle with efficiencies equal to the aggregated yield, -* charges marginal cost using the sum of crop prices across cycles, and -* deducts the combined fertilizer rate (kg N per ha summed over the crops), +* draws physical land from the matching cropland bus (``_r`` or ``_i``), +* emits one crop bus per cycle with efficiencies equal to the per-cycle yield, +* charges marginal cost using the sum of crop prices across cycles, +* deducts the combined fertilizer rate (kg N per ha summed over the crops), and * (irrigated only) withdraws the summed water requirement on the region water bus. -If any required raster is missing the rule fails early to avoid silently enabling -unsupported sequences. +The link is anchored at its MIRCA baseline via ``baseline_area_mha`` with +``p_nom_max = max(GAEZ potential, baseline)`` and stays extendable, so it is +subject to the same production-stability penalty as single-crop links and the +model can add or drop a complete sequence. Every configured cycle must have a +valid yield; partial bundles are never built. If an observed local anchor lacks +a complete GAEZ row, it is relocated within the same combination, country, and +water supply. If that group has no valid full-sequence target under the active +GAEZ inputs, no partial bundle is created: the harvested-area budget remains on +the constituent single-crop baselines. + +Single-crop ``crop_production`` baselines are FAOSTAT *harvested* area and +already count every cycle. The harvested cycles carried by multi links are +therefore subtracted from the corresponding single-crop baselines +(``m_k`` times the multi anchor for crop ``k``). Where joint MIRCA bundle +requirements exceed a constituent crop's national FAOSTAT budget, all affected +bundle anchors are scaled by the most restrictive crop ratio. Any cycle +subtraction that cannot be made locally is taken from other single-crop links +only within the same crop, country, and water supply. The build then asserts +that single plus multiplicity-weighted multi baselines exactly reproduce every +incoming FAOSTAT budget. The crop-agnostic +``multi_cropping_land_correction`` generator absorbs only residual, +unattributed extra-cycle area. .. figure:: https://github.com/Sustainable-Solutions-Lab/GLADE/releases/download/doc-figures/multi_cropping_potential_rainfed.png :alt: Rain-fed multi-cropping zones and regional potential diff --git a/docs/data_sources.rst b/docs/data_sources.rst index 6a0ee3ae..16ea32bd 100644 --- a/docs/data_sources.rst +++ b/docs/data_sources.rst @@ -189,6 +189,29 @@ CROPGRIDS v1.08 **Usage**: Fallback source of harvested area and current cropland footprint for crops listed in ``config["cropgrids_crops"]`` (e.g. ``apple``), which are not covered by GAEZ. The CROPGRIDS ``harvarea`` raster drives both ``baseline_area_mha`` and ``suitable_area`` for these crops (see ``build_crop_yields_cropgrids.py``); per-country FAOSTAT QCL yields (item 515 for apple, element 5419 hg/ha) supply the dry-matter yield, broadcast uniformly to every (region, resource_class) cell within each country. +MIRCA-OS v2 +~~~~~~~~~~~ + +**Provider**: Kebede, Nagpal, Krueger, Grafton, Siebert & others (2025) + +**Description**: Global gridded monthly irrigated and rainfed cropped-area dataset (an open-source update of MIRCA2000), 5-arcmin, for 23 crop classes and the years 2000-2020. The model selects the release nearest ``baseline_year`` from 2010, 2015, and 2020 (ties select the earlier year). Its annual harvested-area grids count every harvested cycle, while the maximum-monthly-cropped-area grids provide the AEI-capped physical field footprint. The ``Rice1/2/3`` subcrop grids identify repeated same-crop cycles. + +**Version**: v2 (adds 2020 relative to the v0.1 preprint release). + +**Coverage**: + * Spatial: Global, 5-arcmin (~10 km) + * Temporal: 2000, 2005, 2010, 2015, 2020 (the workflow currently retrieves 2010, 2015, and 2020) + +**Access**: HydroShare resource ``e4582ca0042148338bb5e0148b749ed6`` (https://www.hydroshare.org/resource/e4582ca0042148338bb5e0148b749ed6/). + +**License**: Creative Commons Attribution 4.0 International (CC BY 4.0) + +**Citation**: Kebede, T. A., et al. (2025). *A global open-source dataset of monthly irrigated and rainfed cropped areas (MIRCA-OS) for the 21st century*. Scientific Data 12, 208. https://doi.org/10.1038/s41597-025-04313-4 + +**Retrieval**: The per-file HydroShare endpoint 500s for this resource, so the grid archives are fetched as members of the whole-resource BagIt zip via partial HTTP-range extraction (``download_mirca_os_bag_member.py``); the nested RAR5 archives are unpacked in one batch per product and year with ``bsdtar`` (``extract_mirca_os_*`` rules). The v2 archive ships only ``ir`` and ``rf`` footprint layers despite its README mentioning a ``tot`` layer. The derivation uses each layer with the matching water supply. + +**Usage**: Source of the observed multiple-cropping baseline (see :doc:`crop_production`), derived by the config-specific ``derive_mirca_multicropping`` rule. The MIRCA-OS-to-GLADE crop concordance is in ``data/curated/mirca_os_crop_mapping.csv``; the fixed candidate sequence catalog is in ``data/curated/mirca_os_multicropping_combinations.yaml``. + FAOSTAT Prices (PP) ~~~~~~~~~~~~~~~~~~~~ diff --git a/docs/land_use.rst b/docs/land_use.rst index ab1a92d9..cedab86e 100644 --- a/docs/land_use.rst +++ b/docs/land_use.rst @@ -342,7 +342,7 @@ In many regions, the total harvested area (summed across all crops on a cropland The ``add_multi_cropping_land_correction`` function in ``land.py`` adds non-extendable generators directly on deficit **cropland buses** after crop production links are built: -1. Computes **harvested area** per cropland bus by summing ``baseline_area_mha`` across crop production links +1. Computes **harvested/physical area** per cropland bus by summing ``baseline_area_mha`` across both crop-production carriers (``crop_production`` and ``crop_production_multi``) 2. Computes **existing supply** per cropland bus from ``land_use`` link capacities 3. Adds generators sized to ``max(harvested − supply, 0)`` on each deficit bus @@ -355,6 +355,17 @@ These generators: - Do not connect to emission buses (no LUC emissions) - Do not affect pasture pools (cropland buses only) -This correction runs unconditionally in all model configurations. - -**Relationship to multi-cropping links.** The model also has explicit multi-cropping *links* (see :doc:`crop_production`) that let the optimizer allocate additional crop cycles on the same land within a single year. However, those links are disabled when ``deviation_penalty.land`` is enabled or ``use_actual_production`` is true, because reliable baseline data on multi-cropping patterns is not available. In those modes, the land correction generators fill the role of accounting for the extra harvested area that multi-cropping creates -- without them, the model would face an artificial ~55 Mha land deficit and require slack or new land conversion to remain feasible. +This correction runs unconditionally in all model configurations, after the +multi-cropping links are added and the single-crop baselines reconciled. + +**Relationship to multi-cropping links.** The model also has explicit +multi-cropping *links* (see :doc:`crop_production`) that let the optimizer +allocate additional crop cycles on the same land within a single year. These +carry an observed MIRCA-OS baseline and are treated like single-crop production +in every anchoring context: the land deviation penalty, the crop growth cap, +and validation-mode pinning (``use_actual_production`` fixes both carriers at +their reconciled baselines). Because each ``n``-cycle multi link draws one +hectare of physical land while its harvested cycles are removed from the +single-crop baselines, the correction generators only absorb the *residual* +extra-cycle area that is not attributed to any modelled combination (for +example, rotations outside the curated catalog or crops GLADE does not model). diff --git a/docs/validation.rst b/docs/validation.rst index 08aa7b29..ba5ab891 100644 --- a/docs/validation.rst +++ b/docs/validation.rst @@ -54,6 +54,14 @@ These settings collectively remove the optimizer's degrees of freedom: use to GLEAM-derived baseline levels (see :ref:`gleam-feed-baseline`). - **Land use**: Sparing of existing cropland and grassland is disabled so the model matches the historical land footprint (see :doc:`land_use`). +- **Multi-cropping**: multi-cropping links participate in validation like any + other production. Each link is pinned at its MIRCA-observed baseline area, + and the single-crop pins are the *reconciled* baselines (each harvested + cycle counted once, on its multi link where one was built), so the two + carriers jointly reproduce the FAOSTAT harvested area. The residual + harvested-minus-physical land gap is absorbed by the unconditional + multi-cropping land-correction generators (see :doc:`land_use`), not by + slack. - **Calibration multiplier**: ``grassland_yield_multiplier`` applies a small adjustment to grassland feed yields to compensate for known data gaps. diff --git a/docs/workflow.rst b/docs/workflow.rst index 0d3f43f8..14dc896d 100644 --- a/docs/workflow.rst +++ b/docs/workflow.rst @@ -78,11 +78,17 @@ Data Preparation Rules * **Script**: ``workflow/scripts/build_crop_yields.py`` * **Purpose**: Aggregate yields by (region, class) for each crop +**derive_mirca_multicropping** + * **Input**: Annual harvested-area, footprint, and rice-subcrop grids from the MIRCA-OS release nearest ``baseline_year``; resource classes; regions; GAEZ RES01 multiple-cropping-zone rasters; the crop concordance; and the fixed combination catalog + * **Output**: ``processing/{name}/multi_cropping/baseline_area.csv`` (observed physical link area), ``residual_multicrop.tif`` (unattributed extra-cycle area), and ``attribution_stats.csv`` (diagnostic totals) + * **Script**: ``workflow/scripts/derive_mirca_multicropping.py`` + * **Purpose**: Derive and aggregate the observed multi-cropping baseline independently for irrigated and rainfed systems. This is an ordinary config-specific rule because its spatial aggregation and GAEZ zone gate depend on config inputs + **build_multi_cropping** - * **Input**: Resource classes, regions, the RES01 multiple-cropping zone rasters, and the required GAEZ RES05 rasters (yield, suitability, growing season start/length, plus water requirement for irrigated variants) for every crop referenced in ``config.multiple_cropping`` + * **Input**: Resource classes, regions, the effective curated-plus-greenfield combination set, the RES01 multiple-cropping zone rasters, and the required GAEZ RES05 rasters (yield, suitability, plus water requirement for irrigated variants) for every crop in that set * **Output**: ``processing/{name}/multi_cropping/eligible_area.csv`` (eligible hectares, irrigated water requirement), ``processing/{name}/multi_cropping/cycle_yields.csv`` (per-cycle yields) * **Script**: ``workflow/scripts/build_multi_cropping.py`` - * **Purpose**: Filter pixels by the RES01 class (ignoring relay-only options), confirm crop calendars fit within the year, aggregate eligible hectares to regions/resource classes, and compute per-cycle yields + * **Purpose**: Filter pixels by the RES01 class and per-crop suitability/yield/water validity, aggregate eligible potential hectares to regions/resource classes, and compute per-cycle yields **build_grassland_yields** * **Input**: ISIMIP grassland yield NetCDF, resource classes, regions diff --git a/pixi.toml b/pixi.toml index 958cc3c6..0d7b0c70 100644 --- a/pixi.toml +++ b/pixi.toml @@ -49,6 +49,7 @@ pycountry = ">=24.6.1,<25" # ISO country codes and mappings # System tools curl = ">=8.17.0,<9" # HTTP downloads in workflow rules p7zip = ">=16.02,<17" # 7z archive extraction for Huang water data +libarchive = ">=3.8.0,<4" # provides bsdtar for RAR5 extraction (MIRCA-OS grids) poppler = ">=25,<26" # provides pdftotext for parsing FPED/NHANES tables xgboost = ">=3.2.0,<4" diff --git a/tests/config/test_sensitivity.yaml b/tests/config/test_sensitivity.yaml index 7789d345..c8847878 100644 --- a/tests/config/test_sensitivity.yaml +++ b/tests/config/test_sensitivity.yaml @@ -61,10 +61,11 @@ crops: - soybean - white-potato -# Disable multi-cropping (requires wetland-rice which is not in test crop list) +# Keep this sensitivity fixture focused on its health and emissions parameters. multiple_cropping: - double_rice: null - rice_wheat: null + wheat_maize: null + wheat_soybean: null + maize_soybean: null # Enable health for RR sensitivity testing health: diff --git a/tests/test_calibration_provenance.py b/tests/test_calibration_provenance.py index 2bfee9c3..6eecf00c 100644 --- a/tests/test_calibration_provenance.py +++ b/tests/test_calibration_provenance.py @@ -5,6 +5,7 @@ """Unit tests for calibration artefact provenance tracking.""" import copy +import shutil import pytest import yaml @@ -30,6 +31,7 @@ "gdd_ia": {"cooked_to_raw": {"red_meat": 1.43}}, }, "crops": ["wheat", "maize"], + "multiple_cropping": {}, "planning_horizon": 2030, "baseline_year": 2020, "emissions": {"ghg_price": 100}, @@ -52,6 +54,12 @@ def test_keeps_structural_leaves(self): assert snap["baseline_year"] == 2020 assert snap["food_loss_waste_calibration.food_groups"] == ["fruits"] assert snap["food_demand_calibration.min_multiplier"] == 0.5 + assert snap["derived.multiple_cropping.mirca_source_year"] == 2020 + assert set(snap["derived.multiple_cropping.observed"]) == {"wheat_maize"} + assert ( + snap["derived.multiple_cropping.effective"] + == snap["derived.multiple_cropping.observed"] + ) def test_drops_solve_time_and_exempt_keys(self): snap = structural_snapshot(MINIMAL_CONFIG) @@ -68,6 +76,14 @@ def test_drops_solve_time_and_exempt_keys(self): assert "cost_calibration.generate" not in snap assert "food_loss_waste_calibration.generate" not in snap + def test_records_resolved_mirca_source_year(self): + config = copy.deepcopy(MINIMAL_CONFIG) + config["baseline_year"] = 2016 + + snap = structural_snapshot(config) + + assert snap["derived.multiple_cropping.mirca_source_year"] == 2015 + class TestDiffSnapshots: def test_identical(self): @@ -107,11 +123,14 @@ def test_generation_run(self, path): class TestValidateCalibrationProvenance: def _write_stamp(self, root, config): + catalog = root / "data/curated/mirca_os_multicropping_combinations.yaml" + catalog.parent.mkdir(parents=True) + shutil.copy("data/curated/mirca_os_multicropping_combinations.yaml", catalog) path = root / "data/curated/calibration/unit/provenance.yaml" path.parent.mkdir(parents=True) stamp = { "source": "unit", - "structural_config": structural_snapshot(config), + "structural_config": structural_snapshot(config, root), } with open(path, "w") as f: yaml.safe_dump(stamp, f) @@ -133,6 +152,18 @@ def test_structural_change_raises(self, tmp_path): with pytest.raises(ValueError, match="crops"): validate_calibration_provenance(cfg, tmp_path) + def test_catalog_change_raises(self, tmp_path): + self._write_stamp(tmp_path, MINIMAL_CONFIG) + catalog_path = ( + tmp_path / "data/curated/mirca_os_multicropping_combinations.yaml" + ) + catalog = yaml.safe_load(catalog_path.read_text()) + catalog.pop("wheat_maize") + catalog_path.write_text(yaml.safe_dump(catalog)) + + with pytest.raises(ValueError, match=r"derived\.multiple_cropping\.catalog"): + validate_calibration_provenance(MINIMAL_CONFIG, tmp_path) + def test_accept_flag_downgrades_to_warning(self, tmp_path): self._write_stamp(tmp_path, MINIMAL_CONFIG) cfg = copy.deepcopy(MINIMAL_CONFIG) diff --git a/tests/test_crop_costs.py b/tests/test_crop_costs.py index fddb08f9..b8f2954b 100644 --- a/tests/test_crop_costs.py +++ b/tests/test_crop_costs.py @@ -8,7 +8,10 @@ import pypsa import pytest -from workflow.scripts.build_model.crops import add_regional_crop_production_links +from workflow.scripts.build_model.crops import ( + add_multi_cropping_links, + add_regional_crop_production_links, +) def test_silage_maize_cost_not_zero_with_zero_harvested_area(): @@ -21,7 +24,6 @@ def test_silage_maize_cost_not_zero_with_zero_harvested_area(): "fertilizer:USA", ] ) - yields = pd.DataFrame( { "region": ["regionA"], @@ -60,3 +62,238 @@ def test_silage_maize_cost_not_zero_with_zero_harvested_area(): links = n.links.static[n.links.static["crop"] == "silage-maize"] assert len(links) == 1 assert float(links["marginal_cost"].iloc[0]) == pytest.approx(1.0) + + +def _add_rice_wheat_multi_link( + baseline_ha, + multi_crop_cost_calibration, + *, + baseline_region="regionA", + baseline_combination="rice_wheat", + potential_region="regionA", + cycle_crops=("wetland-rice", "wheat"), +): + """Build a single rice-wheat multi-cropping link and return the network.""" + n = pypsa.Network() + n.buses.add( + [ + f"land:cropland:{potential_region}_c0_r", + "crop:wetland-rice:USA", + "crop:wheat:USA", + "fertilizer:USA", + ] + ) + n.add( + "Link", + ["single_rice", "single_wheat"], + bus0=[ + f"land:cropland:{potential_region}_c0_r", + f"land:cropland:{potential_region}_c0_r", + ], + bus1=["crop:wetland-rice:USA", "crop:wheat:USA"], + carrier="crop_production", + baseline_area_mha=1.0, + crop=["wetland-rice", "wheat"], + country="USA", + water_supply="rainfed", + region=potential_region, + resource_class=0, + ) + eligible_area = pd.DataFrame( + { + "combination": ["rice_wheat"], + "region": [potential_region], + "resource_class": [0], + "water_supply": ["r"], + "eligible_area_ha": [1_000_000.0], + "water_requirement_m3_per_ha": [0.0], + } + ) + cycle_yields = pd.DataFrame( + { + "combination": ["rice_wheat"] * len(cycle_crops), + "region": [potential_region] * len(cycle_crops), + "resource_class": [0] * len(cycle_crops), + "water_supply": ["r"] * len(cycle_crops), + "cycle_index": list(range(1, len(cycle_crops) + 1)), + "crop": list(cycle_crops), + "yield_t_per_ha": [2.0] * len(cycle_crops), + } + ) + baseline_area = pd.DataFrame( + { + "combination": [baseline_combination], + "region": [baseline_region], + "resource_class": [0], + "water_supply": ["r"], + "baseline_area_ha": [baseline_ha], + } + ) + add_multi_cropping_links( + n=n, + eligible_area=eligible_area, + cycle_yields=cycle_yields, + region_to_country=pd.Series({baseline_region: "USA", potential_region: "USA"}), + allowed_countries={"USA"}, + crop_costs=pd.Series( + { + ("wetland-rice", "USA"): 100.0, + ("wheat", "USA"): 200.0, + } + ), + global_median_cost=pd.Series({"wetland-rice": 100.0, "wheat": 200.0}), + fertilizer_n_rates={"wetland-rice": 0.0, "wheat": 0.0}, + rice_methane_factor=0.0, + rainfed_wetland_rice_ch4_scaling_factor=1.0, + min_yield_t_per_ha=0.01, + seed_kg_dm_per_ha=pd.Series({"wetland-rice": 0.0, "wheat": 0.0}), + crop_loss_multiplier=pd.Series(dtype=float), + crop_marketing_cost_usd_per_t={"wetland-rice": 0.0, "wheat": 0.0}, + combinations={"rice_wheat": {"crops": ["wetland-rice", "wheat"]}}, + baseline_area=baseline_area, + multi_crop_cost_calibration=multi_crop_cost_calibration, + ) + return n + + +def test_multi_cropping_relocates_baseline_without_local_potential(): + """Observed anchors relocate only within their combination/country/water group.""" + n = _add_rice_wheat_multi_link( + baseline_ha=250_000.0, + multi_crop_cost_calibration=None, + baseline_region="observed", + potential_region="eligible", + ) + multi = n.links.static[n.links.static["carrier"] == "crop_production_multi"] + assert len(multi) == 1 + assert multi.iloc[0]["region"] == "eligible" + assert multi.iloc[0]["baseline_area_mha"] == pytest.approx(0.25) + + +def test_multi_cropping_ignores_disabled_catalog_baseline(): + """A catalog baseline cannot reactivate a combination disabled by config.""" + n = _add_rice_wheat_multi_link( + baseline_ha=250_000.0, + baseline_combination="disabled_catalog_entry", + multi_crop_cost_calibration=None, + ) + multi = n.links.static[n.links.static["carrier"] == "crop_production_multi"] + assert len(multi) == 1 + assert multi.iloc[0]["baseline_area_mha"] == 0.0 + + +def test_multi_cropping_keeps_incomplete_bundle_on_single_baselines(): + """A missing cycle excludes the bundle without stripping single baselines.""" + n = _add_rice_wheat_multi_link( + baseline_ha=250_000.0, + multi_crop_cost_calibration=None, + cycle_crops=("wetland-rice",), + ) + multi = n.links.static[n.links.static["carrier"] == "crop_production_multi"] + assert multi.empty + singles = n.links.static[n.links.static["carrier"] == "crop_production"] + assert singles["baseline_area_mha"].sum() == 2.0 + + +def test_multi_cropping_uses_direct_bundle_cost_calibration(): + """Multi-crop links use per-(combination, country) corrections.""" + n = _add_rice_wheat_multi_link( + baseline_ha=500_000.0, + multi_crop_cost_calibration=pd.Series({("rice_wheat", "USA"): 4.0}), + ) + + links = n.links.static[n.links.static["carrier"] == "crop_production_multi"] + assert len(links) == 1 + link = links.iloc[0] + assert float(link["marginal_cost"]) == pytest.approx(0.3) + assert float(link["bounded_penalty_bnusd_per_mha"]) == pytest.approx(4.0) + assert float(link["bounded_subsidy_bnusd_per_mha"]) == pytest.approx(0.0) + + +def test_multi_cropping_raises_on_stale_cost_calibration(): + """A positive-baseline bundle missing from the artefact is a stale-set error.""" + with pytest.raises(ValueError, match="stale"): + _add_rice_wheat_multi_link( + baseline_ha=500_000.0, + multi_crop_cost_calibration=pd.Series({("maize_soybean", "USA"): 1.0}), + ) + + +def test_multi_cropping_zero_baseline_missing_correction_is_zero(): + """A zero-baseline bundle absent from the artefact gets a zero correction.""" + n = _add_rice_wheat_multi_link( + baseline_ha=0.0, + multi_crop_cost_calibration=pd.Series({("maize_soybean", "USA"): 1.0}), + ) + + links = n.links.static[n.links.static["carrier"] == "crop_production_multi"] + assert len(links) == 1 + link = links.iloc[0] + assert float(link["bounded_penalty_bnusd_per_mha"]) == pytest.approx(0.0) + assert float(link["bounded_subsidy_bnusd_per_mha"]) == pytest.approx(0.0) + + +def test_multi_cropping_rice_emits_methane_per_cycle(): + """Rice CH4 is invariant to single-crop vs multi-cropping representation. + + A bundle running ``m`` wetland-rice cycles floods its hectare ``m`` times, so + it must carry ``m`` times the per-hectare emission factor. Without this the + model could abate rice methane for free by shifting rice onto multi links. + """ + n = pypsa.Network() + n.buses.add( + [ + "land:cropland:regionA_c0_i", + "crop:wetland-rice:USA", + "fertilizer:USA", + "emission:ch4", + ] + ) + eligible_area = pd.DataFrame( + { + "combination": ["double_rice"], + "region": ["regionA"], + "resource_class": [0], + "water_supply": ["i"], + "eligible_area_ha": [1_000_000.0], + "water_requirement_m3_per_ha": [1000.0], + } + ) + cycle_yields = pd.DataFrame( + { + "combination": ["double_rice", "double_rice"], + "region": ["regionA", "regionA"], + "resource_class": [0, 0], + "water_supply": ["i", "i"], + "cycle_index": [1, 2], + "crop": ["wetland-rice", "wetland-rice"], + "yield_t_per_ha": [2.0, 2.0], + } + ) + add_multi_cropping_links( + n=n, + eligible_area=eligible_area, + cycle_yields=cycle_yields, + region_to_country=pd.Series({"regionA": "USA"}), + allowed_countries={"USA"}, + crop_costs=pd.Series({("wetland-rice", "USA"): 100.0}), + global_median_cost=pd.Series({"wetland-rice": 100.0}), + fertilizer_n_rates={"wetland-rice": 0.0}, + rice_methane_factor=110.0, + rainfed_wetland_rice_ch4_scaling_factor=0.5, + min_yield_t_per_ha=0.01, + seed_kg_dm_per_ha=pd.Series({"wetland-rice": 0.0}), + crop_loss_multiplier=pd.Series(dtype=float), + crop_marketing_cost_usd_per_t={"wetland-rice": 0.0}, + combinations={"double_rice": {"crops": ["wetland-rice", "wetland-rice"]}}, + ) + + links = n.links.static + assert len(links) == 1 + link = links.iloc[0] + bus_cols = [c for c in links.columns if c.startswith("bus") and c[3:].isdigit()] + ch4_ports = [c for c in bus_cols if link[c] == "emission:ch4"] + assert len(ch4_ports) == 1, "double-rice link must carry exactly one CH4 port" + eff = float(link[f"efficiency{ch4_ports[0][3:]}"]) + # Two irrigated rice cycles at 110 kg CH4/ha each (no rainfed scaling). + assert eff == pytest.approx(220.0) diff --git a/tests/test_integration.py b/tests/test_integration.py index 566f0868..3e120d85 100644 --- a/tests/test_integration.py +++ b/tests/test_integration.py @@ -57,6 +57,25 @@ def test_build_solve_analyze(results_dir): results_dir / "analysis" / "scen-default" / "objective_breakdown.parquet" ).exists() + # Enabled catalog combinations restricted to the test config's crops must + # yield anchored multi links. + import pypsa + + n = pypsa.Network(str(results_dir / "solved" / "model_scen-default.nc")) + links = n.links.static + multi = links[links["carrier"] == "crop_production_multi"] + assert not multi.empty, "expected crop_production_multi links" + assert (multi["baseline_area_mha"] > 0).any(), "multi links lack a baseline anchor" + # Irrigated multi links must carry a water port (no free irrigation). + bus_cols = [c for c in multi.columns if c.startswith("bus") and c[3:].isdigit()] + irrigated = multi[multi["water_supply"] == "irrigated"] + if not irrigated.empty: + has_water = irrigated[bus_cols].apply( + lambda row: any(isinstance(v, str) and v.startswith("water:") for v in row), + axis=1, + ) + assert has_water.all(), "irrigated multi links without a water port" + @pytest.mark.plots def test_plots(results_dir): @@ -66,9 +85,9 @@ def test_plots(results_dir): Snakemake handles the dependency automatically. """ run_snakemake_target( - "results/test/plots/scen-default/consumption_balance.pdf", + "results/test/plots/scen-default/food_consumption.pdf", "results/test/plots/scen-default/objective_breakdown.pdf", ) - assert (results_dir / "plots" / "scen-default" / "consumption_balance.pdf").exists() + assert (results_dir / "plots" / "scen-default" / "food_consumption.pdf").exists() assert (results_dir / "plots" / "scen-default" / "objective_breakdown.pdf").exists() diff --git a/tests/test_mirca_archive_extraction.py b/tests/test_mirca_archive_extraction.py new file mode 100644 index 00000000..afb47905 --- /dev/null +++ b/tests/test_mirca_archive_extraction.py @@ -0,0 +1,41 @@ +# SPDX-FileCopyrightText: 2026 Koen van Greevenbroek +# +# SPDX-License-Identifier: GPL-3.0-or-later + +from pathlib import Path + +import pytest + +from workflow.scripts.extract_mirca_os_archive import extract_members + + +def test_extract_members_batches_and_installs_outputs(tmp_path, monkeypatch): + archive = tmp_path / "source.rar" + archive.touch() + outputs = [tmp_path / "out" / "first.tif", tmp_path / "out" / "second.nc"] + patterns = ["*5-arcminute/first.tif", "*monthly/second.nc"] + commands = [] + + def fake_run(command, check): + commands.append(command) + assert check + extraction_dir = Path(command[command.index("-C") + 1]) + for pattern in patterns: + member = extraction_dir / "nested" / Path(pattern).name + member.parent.mkdir(parents=True, exist_ok=True) + member.write_text(Path(pattern).name) + + monkeypatch.setattr( + "workflow.scripts.extract_mirca_os_archive.subprocess.run", fake_run + ) + + extract_members(archive, patterns, outputs) + + assert len(commands) == 1 + assert commands[0][-2:] == [f"--include={p}" for p in patterns] + assert [path.read_text() for path in outputs] == ["first.tif", "second.nc"] + + +def test_extract_members_requires_one_glob_per_output(tmp_path): + with pytest.raises(ValueError, match="one MIRCA member glob per output"): + extract_members(tmp_path / "source.rar", ["*one.tif"], []) diff --git a/tests/test_multi_cropping.py b/tests/test_multi_cropping.py new file mode 100644 index 00000000..09252393 --- /dev/null +++ b/tests/test_multi_cropping.py @@ -0,0 +1,474 @@ +# SPDX-FileCopyrightText: 2026 Koen van Greevenbroek +# +# SPDX-License-Identifier: GPL-3.0-or-later + +"""Unit tests for the MIRCA-OS multi-cropping baseline. + +Covers baseline attribution, repeated-crop supports, residual conservation, +single-crop baseline reconciliation, and validation-mode pinning. +""" + +import json + +from affine import Affine +import numpy as np +import pandas as pd +import pypsa +import pytest +from rasterio.crs import CRS + +from workflow.scripts.build_model.crops import ( + fix_crop_production_to_baseline, + reconcile_single_crop_baselines, +) +from workflow.scripts.derive_mirca_multicropping import ( + GridSpec, + _assert_grid, + allocate, + candidate_capacity, + run_derivation, +) +from workflow.scripts.multi_cropping_combinations import ( + closest_mirca_multicropping_year, + effective_combinations, + observed_combinations, +) +from workflow.scripts.solve_model.production_stability import _multi_cycle_long + +CATALOG = "data/curated/mirca_os_multicropping_combinations.yaml" + + +def test_grid_validation_allows_only_subpixel_metadata_rounding(): + """Known MIRCA affine rounding passes, while a real cell shift fails.""" + crs = CRS.from_epsg(4326) + reference = GridSpec((2160, 4320), Affine(1 / 12, 0, -180, 0, -1 / 12, 90), crs) + rounded = GridSpec( + (2160, 4320), Affine(0.0833333, 0, -180, 0, -0.0833333, 89.999928), crs + ) + _assert_grid(rounded, reference, "rounded") + + shifted = GridSpec((2160, 4320), Affine(1 / 12, 0, -179.99, 0, -1 / 12, 90), crs) + with pytest.raises(ValueError, match="more than 1%"): + _assert_grid(shifted, reference, "shifted") + + +def _network_with_links(rows): + """Minimal network whose links carry the columns reconciliation reads. + + Multi-cropping rows carry their cycles as explicit ``crop_cycles`` metadata. + Optional ``output_buses`` are populated only to verify that accounting does + not infer crop identity from bus labels. + """ + n = pypsa.Network() + df = pd.DataFrame(rows).set_index("name") + max_outputs = max((len(r.get("output_buses", [])) for r in rows), default=0) + for name in [f"bus{i}" for i in range(1, max_outputs + 1)]: + df[name] = "" + for row in rows: + for i, bus in enumerate(row.get("output_buses", []), start=1): + df.loc[row["name"], f"bus{i}"] = bus + all_buses = set(df["bus0"]) + for i in range(1, max_outputs + 1): + all_buses.update(b for b in df[f"bus{i}"] if b) + for bus in all_buses: + n.add("Bus", bus) + kwargs = { + col: df[col].to_numpy() + for col in [ + "bus0", + "carrier", + "baseline_area_mha", + "crop", + "combination", + "crop_cycles", + "region", + "resource_class", + "water_supply", + "country", + *[f"bus{i}" for i in range(1, max_outputs + 1)], + ] + } + n.add("Link", df.index, **kwargs) + return n + + +# Baseline attribution + + +@pytest.mark.parametrize( + "baseline_year, expected", + [(2000, 2010), (2012, 2010), (2013, 2015), (2017, 2015), (2024, 2020)], +) +def test_mirca_release_tracks_baseline_year(baseline_year, expected): + """The closest supported release is selected with a deterministic lower tie.""" + assert closest_mirca_multicropping_year(baseline_year) == expected + + +def test_combination_catalog_overrides_and_greenfield_additions(): + """Config disables catalog entries and adds zero-baseline greenfield systems.""" + config = { + "crops": ["wetland-rice", "wheat", "barley"], + "multiple_cropping": { + "double_rice": None, + "barley_wheat": { + "crops": ["barley", "wheat"], + "water_supplies": ["r"], + }, + }, + } + + effective = effective_combinations(config, CATALOG) + observed = observed_combinations(config, CATALOG) + + assert "rice_wheat" in effective + assert "double_rice" not in effective + assert "barley_wheat" in effective + assert "barley_wheat" not in observed + + +@pytest.mark.parametrize( + "name, entry, match", + [ + ( + "rice_wheat", + {"crops": ["wetland-rice", "wheat"], "water_supplies": ["r"]}, + "redefines a curated", + ), + ("unknown", None, "cannot disable anything"), + ], +) +def test_combination_catalog_rejects_ambiguous_overrides(name, entry, match): + """Catalog identities cannot be redefined and unknown names cannot be disabled.""" + config = { + "crops": ["wetland-rice", "wheat"], + "multiple_cropping": {name: entry}, + } + + with pytest.raises(ValueError, match=match): + effective_combinations(config, CATALOG) + + +def test_allocate_conserves_and_caps(): + """Allocation never exceeds capacity or M_total, and residual closes the sum.""" + m_total = np.array([[100.0]]) + # two candidates: a 2-cycle (cap 30) and a 3-cycle (cap 10 -> extra cap 20) + caps = [np.array([[30.0]]), np.array([[10.0]])] + sequences = [["a", "b"], ["c", "d", "e"]] + support = {crop: np.array([[100.0]]) for crop in "abcde"} + areas, residual = allocate(m_total, np.array([[100.0]]), caps, sequences, support) + extra = sum( + (len(crops) - 1) * area for crops, area in zip(sequences, areas, strict=True) + ) + # total extra-cycle capacity 30 + 20 = 50 <= 100 -> both filled, residual 50 + assert areas[0][0, 0] == pytest.approx(30.0) + assert areas[1][0, 0] == pytest.approx(10.0) + assert extra[0, 0] == pytest.approx(50.0) + assert residual[0, 0] == pytest.approx(50.0) + + +def test_allocate_rations_when_capacity_exceeds_magnitude(): + """When capacity exceeds M_total, it is rationed proportionally, residual 0.""" + m_total = np.array([[30.0]]) + caps = [np.array([[40.0]]), np.array([[40.0]])] # extra cap 40 + 40 = 80 > 30 + sequences = [["a", "b"], ["c", "d"]] + support = {crop: np.array([[100.0]]) for crop in "abcd"} + areas, residual = allocate(m_total, np.array([[100.0]]), caps, sequences, support) + extra = sum(area for area in areas) + assert extra[0, 0] == pytest.approx(30.0) # sum equals M_total + assert residual[0, 0] == pytest.approx(0.0) + assert areas[0][0, 0] == pytest.approx(15.0) # split evenly (equal caps) + + +def test_allocate_shares_overlapping_crop_budget(): + """The same observed crop area cannot support two full rotations.""" + caps = [np.array([[40.0]]), np.array([[40.0]])] + sequences = [["wheat", "maize"], ["wheat", "soybean"]] + support = { + "wheat": np.array([[30.0]]), + "maize": np.array([[40.0]]), + "soybean": np.array([[40.0]]), + } + areas, residual = allocate( + np.array([[100.0]]), np.array([[100.0]]), caps, sequences, support + ) + + assert areas[0][0, 0] == pytest.approx(15.0) + assert areas[1][0, 0] == pytest.approx(15.0) + assert sum(area[0, 0] for area in areas) == pytest.approx(30.0) + assert residual[0, 0] == pytest.approx(70.0) + + +def test_candidate_capacity_repeated_rice_disjoint_supports(): + """Double-rice uses (Rice2 - Rice3); triple-rice uses Rice3 (disjoint).""" + zone = np.array([[8]]) # zone 8 permits triple wetland rice + rice_support = { + "i": np.array([[10.0]]), # Rice2: area with >= 2 rice cycles + "i3": np.array([[3.0]]), # Rice3: area with a 3rd rice cycle + } + double = candidate_capacity( + ["wetland-rice", "wetland-rice"], "i", {}, zone, rice_support + ) + triple = candidate_capacity(["wetland-rice"] * 3, "i", {}, zone, rice_support) + assert double[0, 0] == pytest.approx(7.0) # 10 - 3 + assert triple[0, 0] == pytest.approx(3.0) + + +def test_candidate_capacity_distinct_crop_min_and_zone(): + """Distinct-crop capacity is min(area) where both observed and zone permits.""" + crop_area = { + ("wetland-rice", "i"): np.array([[8.0, 0.0]]), + ("wheat", "i"): np.array([[5.0, 5.0]]), + } + zone = np.array([[5, 5]]) # zone 5 permits double with one rice + cap = candidate_capacity(["wetland-rice", "wheat"], "i", crop_area, zone, {}) + assert cap[0, 0] == pytest.approx(5.0) # min(8, 5) + assert cap[0, 1] == pytest.approx(0.0) # rice absent -> not a candidate + + +def test_run_derivation_balance_and_residual(): + """Full derivation on a 1-cell grid: attributed + residual == M_total.""" + cell = lambda v: np.array([[v]]) # noqa: E731 + annual = { + ("Rice", "ir"): cell(20.0), + ("Wheat", "ir"): cell(15.0), + ("Rice", "rf"): cell(0.0), + ("Wheat", "rf"): cell(0.0), + } + footprint = {"ir": cell(10.0), "rf": cell(0.0)} + crop_area = { + ("wetland-rice", "i"): cell(20.0), + ("wheat", "i"): cell(15.0), + ("wetland-rice", "r"): cell(0.0), + ("wheat", "r"): cell(0.0), + } + zone = {"i": cell(5), "r": cell(1)} + rice_support = {"i": cell(0.0), "i3": cell(0.0), "r": cell(0.0), "r3": cell(0.0)} + combos = [ + {"name": "rice_wheat", "crops": ["wetland-rice", "wheat"], "water_supply": "i"} + ] + areas, residual, _stats = run_derivation( + annual, footprint, crop_area, zone, rice_support, combos + ) + m_total = 20.0 + 15.0 - 10.0 # 25 + attributed = (2 - 1) * areas[("rice_wheat", "i")][0, 0] + # The 10 ha physical footprint caps the attributed bundle area. + assert attributed == pytest.approx(10.0) + assert residual[0, 0] == pytest.approx(m_total - attributed) + + +def test_run_derivation_keeps_water_system_budgets_separate(): + """Rainfed extra-cycle area cannot create an irrigated baseline anchor.""" + cell = lambda v: np.array([[v]]) # noqa: E731 + annual = { + ("Rice", "ir"): cell(20.0), + ("Wheat", "ir"): cell(15.0), + ("Rice", "rf"): cell(20.0), + ("Wheat", "rf"): cell(15.0), + } + footprint = {"ir": cell(35.0), "rf": cell(10.0)} + crop_area = { + ("wetland-rice", "i"): cell(20.0), + ("wheat", "i"): cell(15.0), + ("wetland-rice", "r"): cell(20.0), + ("wheat", "r"): cell(15.0), + } + zone = {"i": cell(5), "r": cell(5)} + rice_support = {"i": cell(0.0), "i3": cell(0.0), "r": cell(0.0), "r3": cell(0.0)} + combos = [ + {"name": "rice_wheat", "crops": ["wetland-rice", "wheat"], "water_supply": "i"} + ] + + areas, residual, _stats = run_derivation( + annual, footprint, crop_area, zone, rice_support, combos + ) + + assert areas[("rice_wheat", "i")][0, 0] == pytest.approx(0.0) + assert residual[0, 0] == pytest.approx(25.0) + + +# Reconciliation multiplicity + + +def _crop_link(name, crop, baseline, region="r0", cls=1): + return { + "name": name, + "bus0": f"land:cropland:{region}_c{cls}_i", + "carrier": "crop_production", + "baseline_area_mha": baseline, + "crop": crop, + "combination": "", + "crop_cycles": "", + "region": region, + "resource_class": cls, + "water_supply": "irrigated", + "country": "IND", + } + + +def _multi_link(name, combo, baseline, crops, region="r0", cls=1, country="IND"): + return { + "name": name, + "bus0": f"land:cropland:{region}_c{cls}_i", + "carrier": "crop_production_multi", + "baseline_area_mha": baseline, + "crop": combo, + "combination": combo, + "crop_cycles": json.dumps(crops), + "region": region, + "resource_class": cls, + "water_supply": "irrigated", + "country": country, + # one output bus per built cycle + "output_buses": [f"crop:{c}:{country}" for c in crops], + } + + +def test_reconciliation_reduces_singles_by_multiplicity(): + """Rice-wheat multi (X) subtracts X from rice and X from wheat singles.""" + n = _network_with_links( + [ + _crop_link("rice", "wetland-rice", 10.0), + _crop_link("wheat", "wheat", 8.0), + _multi_link("m", "rice_wheat", 3.0, ["wetland-rice", "wheat"]), + ] + ) + reconcile_single_crop_baselines(n) + bl = n.links.static["baseline_area_mha"] + assert bl["rice"] == pytest.approx(7.0) # 10 - 3 + assert bl["wheat"] == pytest.approx(5.0) # 8 - 3 + assert bl["m"] == pytest.approx(3.0) # multi anchor unchanged + + +def test_reconciliation_double_rice_subtracts_2x(): + """Double rice (X) subtracts 2X from the wetland-rice single.""" + n = _network_with_links( + [ + _crop_link("rice", "wetland-rice", 10.0), + _multi_link("m", "double_rice", 3.0, ["wetland-rice", "wetland-rice"]), + ] + ) + reconcile_single_crop_baselines(n) + assert n.links.static["baseline_area_mha"]["rice"] == pytest.approx(4.0) # 10 - 6 + + +def test_reconciliation_redistributes_over_subtraction_no_negative(): + """Over-subtraction in one cell is pushed onto another cell of same crop/country.""" + # Two regions of the same (crop, country, ws); region r0 has too little rice + # baseline (2) for the 3 the multi anchors, so 1 spills onto r1. + n = _network_with_links( + [ + _crop_link("rice0", "wetland-rice", 2.0, region="r0"), + _crop_link("rice1", "wetland-rice", 5.0, region="r1"), + _multi_link( + "m", "double_rice", 1.5, ["wetland-rice", "wetland-rice"], region="r0" + ), # subtract 2*1.5=3 + ] + ) + reconcile_single_crop_baselines(n) + bl = n.links.static["baseline_area_mha"] + assert bl["rice0"] == pytest.approx(0.0) # floored, not negative + assert bl["rice1"] == pytest.approx(4.0) # absorbed the 1 Mha over-subtraction + assert (bl.loc[["rice0", "rice1"]] >= 0).all() + # National total for (rice, IND, irrigated) dropped by exactly 3 (= 2*1.5) + assert bl[["rice0", "rice1"]].sum() == pytest.approx(2.0 + 5.0 - 3.0) + + +def test_reconciliation_uses_explicit_cycle_metadata(): + """Cycle accounting does not infer crops from output bus names.""" + n = _network_with_links( + [ + _crop_link("rice", "wetland-rice", 10.0), + _crop_link("wheat", "wheat", 8.0), + _multi_link("m", "rice_wheat", 3.0, ["wetland-rice", "wheat"]), + ] + ) + n.links.static.loc["m", ["bus1", "bus2"]] = ["crop:wheat:IND", ""] + reconcile_single_crop_baselines(n) + bl = n.links.static["baseline_area_mha"] + assert bl["rice"] == pytest.approx(7.0) + assert bl["wheat"] == pytest.approx(5.0) + + +def test_reconciliation_scales_mirca_anchor_to_faostat_budget(): + """A bundle anchor is conservatively scaled when a crop budget is smaller.""" + n = _network_with_links( + [ + _crop_link("rice", "wetland-rice", 4.0), + _multi_link("m", "double_rice", 3.0, ["wetland-rice", "wetland-rice"]), + ] + ) + reconcile_single_crop_baselines(n) + bl = n.links.static["baseline_area_mha"] + assert bl["rice"] == pytest.approx(0.0) + assert bl["m"] == pytest.approx(2.0) + + +def test_reconciliation_scales_overlapping_combinations_jointly(): + """Shared crop budgets scale every overlapping combination consistently.""" + n = _network_with_links( + [ + _crop_link("a", "a", 10.0), + _crop_link("b", "b", 100.0), + _crop_link("c", "c", 100.0), + _multi_link("ab", "a_b", 10.0, ["a", "b"]), + _multi_link("ac", "a_c", 10.0, ["a", "c"]), + ] + ) + reconcile_single_crop_baselines(n) + bl = n.links.static["baseline_area_mha"] + assert bl["ab"] == pytest.approx(5.0) + assert bl["ac"] == pytest.approx(5.0) + assert bl["a"] == pytest.approx(0.0) + assert bl["b"] == pytest.approx(95.0) + assert bl["c"] == pytest.approx(95.0) + + +# Validation-mode pinning (use_actual_production) + + +def test_fix_pins_both_carriers_at_reconciled_baselines(): + """After reconcile + fix, single and multi links are pinned so that each + harvested cycle is counted exactly once and the joint per-crop total + reproduces the original harvested area.""" + n = _network_with_links( + [ + _crop_link("rice", "wetland-rice", 10.0), + _crop_link("wheat", "wheat", 8.0), + _multi_link("m", "rice_wheat", 3.0, ["wetland-rice", "wheat"]), + ] + ) + reconcile_single_crop_baselines(n) + fix_crop_production_to_baseline(n) + links = n.links.static + for name, expected in [("rice", 7.0), ("wheat", 5.0), ("m", 3.0)]: + assert links.at[name, "p_nom"] == pytest.approx(expected) + assert links.at[name, "p_nom_min"] == pytest.approx(expected) + assert links.at[name, "p_nom_max"] == pytest.approx(expected) + assert links.at[name, "p_min_pu"] == pytest.approx(1.0) + assert not links.at[name, "p_nom_extendable"] + # Joint totals: single pin + multiplicity * multi pin == raw harvested area + assert links.at["rice", "p_nom"] + links.at["m", "p_nom"] == pytest.approx(10.0) + assert links.at["wheat", "p_nom"] + links.at["m", "p_nom"] == pytest.approx(8.0) + + +def test_multi_cycle_long_counts_explicit_cycles(): + """Cycle metadata preserves repeated-crop multiplicity.""" + n = _network_with_links( + [ + _crop_link("rice", "wetland-rice", 10.0), + _multi_link("rw", "rice_wheat", 3.0, ["wetland-rice", "wheat"]), + _multi_link("dr", "double_rice", 2.0, ["wetland-rice", "wetland-rice"]), + ] + ) + long = _multi_cycle_long(n.links.static) + got = { + (row["link"], row["crop"]): row["multiplicity"] for _, row in long.iterrows() + } + assert got == { + ("rw", "wetland-rice"): 1, + ("rw", "wheat"): 1, + ("dr", "wetland-rice"): 2, + } + dr = long[long["link"] == "dr"].iloc[0] + assert dr["group"] == "wetland-rice::IND" + assert dr["baseline_area_mha"] == pytest.approx(2.0) diff --git a/tools/calibrate b/tools/calibrate index 35169f03..150a8636 100755 --- a/tools/calibrate +++ b/tools/calibrate @@ -96,13 +96,6 @@ trap 'rm -f "$ANCHOR_OVERLAY"' EXIT printf 'diet:\n anchor_groups_to_gbd: %s\n' "$ANCHORING" > "$ANCHOR_OVERLAY" echo "[calibrate] baseline-diet GBD anchoring: ${ANCHORING}" -# Non-default sources get their own processing/results tree so repeated -# calibrations against different bases don't thrash each other's builds. -NAME_OVERRIDE=() -if [[ "$SOURCE" != "default" ]]; then - NAME_OVERRIDE=(--config "name=calibration-${SOURCE}") -fi - ARTEFACT_FILES=( grassland_yield.csv fodder_conversion.csv @@ -111,6 +104,7 @@ ARTEFACT_FILES=( food_waste.yaml food_demand.csv crop_cost.csv + multi_crop_cost.csv grassland_cost.csv animal_cost.csv deviation_penalty.yaml @@ -130,6 +124,7 @@ FOOD_DEMAND_TARGETS=( ) COST_TARGETS=( "$CAL_DIR"/crop_cost.csv + "$CAL_DIR"/multi_crop_cost.csv "$CAL_DIR"/grassland_cost.csv "$CAL_DIR"/animal_cost.csv ) @@ -137,7 +132,7 @@ STABILITY_TARGETS=( "$CAL_DIR"/deviation_penalty.yaml ) -# All three calibration configs use gurobi; default the pixi env accordingly. +# All calibration configs use gurobi; default the pixi env accordingly. # Caller can override by setting CALIBRATE_PIXI_ENV= (empty) or a different env. DEFAULT_PIXI_ENV="${CALIBRATE_PIXI_ENV-gurobi}" @@ -182,8 +177,9 @@ run_step() { local cfgfiles=() [[ -n "$BASE_CONFIG" ]] && cfgfiles+=("$BASE_CONFIG") cfgfiles+=("$configfile" "$ANCHOR_OVERLAY") - echo "[calibrate] $label -- tools/smk --configfile ${cfgfiles[*]} ${NAME_OVERRIDE[*]} ${PASSTHROUGH_ARR[*]} -- ${TARGETS_ARR[*]}" - tools/smk --configfile "${cfgfiles[@]}" "${NAME_OVERRIDE[@]}" "${PASSTHROUGH_ARR[@]}" -- "${TARGETS_ARR[@]}" + local name_override=(--config "name=calibration-${SOURCE}-${label}") + echo "[calibrate] $label -- tools/smk --configfile ${cfgfiles[*]} ${name_override[*]} ${PASSTHROUGH_ARR[*]} -- ${TARGETS_ARR[*]}" + tools/smk --configfile "${cfgfiles[@]}" "${name_override[@]}" "${PASSTHROUGH_ARR[@]}" -- "${TARGETS_ARR[@]}" } check_step() { @@ -194,8 +190,9 @@ check_step() { local cfgfiles=() [[ -n "$BASE_CONFIG" ]] && cfgfiles+=("$BASE_CONFIG") cfgfiles+=("$configfile" "$ANCHOR_OVERLAY") + local name_override=(--config "name=calibration-${SOURCE}-${label}") local out - if ! out=$(tools/smk --configfile "${cfgfiles[@]}" "${NAME_OVERRIDE[@]}" "${PASSTHROUGH_ARR[@]}" --dry-run --quiet -- "${TARGETS_ARR[@]}" 2>&1); then + if ! out=$(tools/smk --configfile "${cfgfiles[@]}" "${name_override[@]}" "${PASSTHROUGH_ARR[@]}" --dry-run --quiet -- "${TARGETS_ARR[@]}" 2>&1); then printf ' [error] %-10s (dry-run failed; see output below)\n' "$label" printf '%s\n' "$out" | sed 's/^/ /' return 2 diff --git a/workflow/Snakefile b/workflow/Snakefile index 83e8fd06..f11f47de 100644 --- a/workflow/Snakefile +++ b/workflow/Snakefile @@ -9,6 +9,10 @@ from snakemake.utils import min_version from validation import load_secrets_with_env_fallback, validate +from workflow.scripts.multi_cropping_combinations import ( + closest_mirca_multicropping_year, + effective_combinations, +) from workflow.scripts.solve_namespace import resolve_calibration_source_paths min_version("9.0") diff --git a/workflow/rules/crops.smk b/workflow/rules/crops.smk index 96c7bd7b..82b4df4d 100644 --- a/workflow/rules/crops.smk +++ b/workflow/rules/crops.smk @@ -558,8 +558,88 @@ rule build_harvested_area_yield_weighted: "../scripts/build_harvested_area_yield_weighted.py" +MIRCA_MULTICROPPING_CATALOG = "data/curated/mirca_os_multicropping_combinations.yaml" +MIRCA_MULTICROPPING_YEAR = closest_mirca_multicropping_year(config["baseline_year"]) +if MIRCA_MULTICROPPING_YEAR != config["baseline_year"]: + logger.warning( + "MIRCA-OS has no release for baseline_year=%d; using closest release %d " + "for the multiple-cropping baseline", + config["baseline_year"], + MIRCA_MULTICROPPING_YEAR, + ) + + +def mirca_multicropping_inputs(_wildcards): + """Inputs for the config-specific observed multi-cropping derivation. + + All 23 MIRCA crops' annual harvested grids (for the all-crop M_total), + the ir/rf footprint layers, the rice subcrop monthly grids (repeated-cycle + detection), the GAEZ multiple-cropping-zone rasters (cycle-count gate), and the + crop concordance and fixed combination catalog. The active config's region, + resource-class, and GAEZ grids make the resulting aggregate config-specific. + """ + grids = "data/downloads/mirca_os/grids" + year = MIRCA_MULTICROPPING_YEAR + inputs = { + "concordance": "data/curated/mirca_os_crop_mapping.csv", + "catalog": MIRCA_MULTICROPPING_CATALOG, + "classes": "/{name}/resource_classes.nc", + "regions": "/{name}/regions.geojson", + "footprint_ir": f"{grids}/footprint/MIRCA-OS_{year}_ir_v2.tif", + "footprint_rf": f"{grids}/footprint/MIRCA-OS_{year}_rf_v2.tif", + "rice2_ir": f"{grids}/monthly/MIRCA-OS_Rice2_{year}_ir.nc", + "rice2_rf": f"{grids}/monthly/MIRCA-OS_Rice2_{year}_rf.nc", + "rice3_ir": f"{grids}/monthly/MIRCA-OS_Rice3_{year}_ir.nc", + "rice3_rf": f"{grids}/monthly/MIRCA-OS_Rice3_{year}_rf.nc", + "zone_i": gaez_path("multiple_cropping_zone", "i", "all"), + "zone_r": gaez_path("multiple_cropping_zone", "r", "all"), + } + for mirca_crop in MIRCA_OS_BASE_CROPS: + for mws in ("ir", "rf"): + key = f"annual_{mirca_crop.replace(' ', '_')}_{mws}" + inputs[key] = f"{grids}/annual/MIRCA-OS_{mirca_crop}_{year}_{mws}_v2.tif" + return inputs + + +rule derive_mirca_multicropping: + """Derive and aggregate the observed multi-cropping baseline. + + Attributes MIRCA's extra-cycle harvested area to the fixed curated crop- + sequence catalog, gating on MIRCA co-occurrence and the active config's GAEZ + multiple-cropping zones. The physical link areas are aggregated directly to + the active region and resource-class grids. + """ + input: + unpack(mirca_multicropping_inputs), + output: + baseline="/{name}/multi_cropping/baseline_area.csv", + residual="/{name}/multi_cropping/residual_multicrop.tif", + stats="/{name}/multi_cropping/attribution_stats.csv", + params: + source_year=MIRCA_MULTICROPPING_YEAR, + resources: + runtime="15m", + mem_mb=8000, + log: + "/{name}/derive_mirca_multicropping.log", + benchmark: + "/{name}/derive_mirca_multicropping.tsv" + script: + "../scripts/derive_mirca_multicropping.py" + + +def multicropping_combinations_yaml(): + """Path of the authoritative observed combination catalog.""" + return MIRCA_MULTICROPPING_CATALOG + + +def _effective_multicropping(): + """The effective observed and greenfield combination set, DAG-side.""" + return effective_combinations(config, multicropping_combinations_yaml()) + + def multi_cropping_inputs(_wildcards): - combos_cfg = config["multiple_cropping"] + combos_cfg = _effective_multicropping() crops_by_supply: dict[str, set[str]] = {"r": set(), "i": set()} for combo_name, entry in combos_cfg.items(): if entry is None: @@ -576,18 +656,13 @@ def multi_cropping_inputs(_wildcards): "classes": "/{name}/resource_classes.nc", "regions": "/{name}/regions.geojson", "yield_unit_conversions": "data/curated/yield_unit_conversions.csv", + "combinations": multicropping_combinations_yaml(), } for ws in ("r", "i"): for crop in sorted(crops_by_supply[ws]): prefix = f"{crop}_{ws}" inputs[f"{prefix}_yield_raster"] = gaez_path(yield_kind, ws, crop) inputs[f"{prefix}_suitability_raster"] = gaez_path("suitability", ws, crop) - inputs[f"{prefix}_growing_season_start_raster"] = gaez_path( - "growing_season_start", ws, crop - ) - inputs[f"{prefix}_growing_season_length_raster"] = gaez_path( - "growing_season_length", ws, crop - ) if ws == "i": inputs[f"{prefix}_water_requirement_raster"] = gaez_path( "water_requirement", ws, crop @@ -604,7 +679,6 @@ rule build_multi_cropping: unpack(multi_cropping_inputs), moisture_content="data/curated/crop_moisture_content.csv", params: - combinations=lambda wildcards: config["multiple_cropping"], use_actual_yields=config["validation"]["use_actual_yields"], output: eligible="/{name}/multi_cropping/eligible_area.csv", @@ -799,6 +873,9 @@ if config["cost_calibration"]["generate"]: network=f"/{_cal_name}/solved/model_scen-{_cal_scenario}.nc", output: crop_correction=config["cost_calibration"]["crop_correction_csv"], + multi_crop_correction=config["cost_calibration"][ + "multi_crop_correction_csv" + ], grassland_correction=config["cost_calibration"]["grassland_correction_csv"], animal_correction=config["cost_calibration"]["animal_correction_csv"], resources: diff --git a/workflow/rules/model.smk b/workflow/rules/model.smk index 9881456b..26bbdd1e 100644 --- a/workflow/rules/model.smk +++ b/workflow/rules/model.smk @@ -109,6 +109,7 @@ def build_model_cost_calibration_input(wildcards): if cal_cfg["enabled"]: return { "crop_cost_calibration": cal_cfg["crop_correction_csv"], + "multi_crop_cost_calibration": cal_cfg["multi_crop_correction_csv"], "grassland_cost_calibration": cal_cfg["grassland_correction_csv"], "animal_cost_calibration": cal_cfg["animal_correction_csv"], } @@ -163,6 +164,8 @@ rule build_model: cropland_baseline="/{name}/cropland_baseline_by_class.csv", multi_cropping_area="/{name}/multi_cropping/eligible_area.csv", multi_cropping_yields="/{name}/multi_cropping/cycle_yields.csv", + multi_cropping_baseline="/{name}/multi_cropping/baseline_area.csv", + multi_cropping_combinations=lambda wildcards: multicropping_combinations_yaml(), edible_portion="/{name}/fao_edible_portion.csv", population="/{name}/population.csv", baseline_diet="/{name}/dietary_intake.csv", @@ -216,7 +219,6 @@ rule build_model: grazing=config["grazing"], baseline_year=config["baseline_year"], validation=config["validation"], - deviation_penalty=config["deviation_penalty"], numerics=config["numerics"], netcdf=config["netcdf"], # Add health-cluster stores when health is enabled in the base config or diff --git a/workflow/rules/retrieve.smk b/workflow/rules/retrieve.smk index 2e3173c4..5e3dff52 100644 --- a/workflow/rules/retrieve.smk +++ b/workflow/rules/retrieve.smk @@ -800,6 +800,153 @@ rule extract_cropgrids_nc: """ +# MIRCA-OS v2 (Kebede et al. 2025, Scientific Data 12, 208), HydroShare resource +# e4582ca0..., CC BY 4.0. Supersedes the v0.1 resource 60a890eb...; v2 adds 2020, +# the model baseline year, which the multi-cropping derivation anchors to. +# +# The per-file iRODS endpoint 500s for this resource, so grids are fetched from +# the whole-resource BagIt zip via partial HTTP-range extraction (see +# ``workflow/scripts/download_mirca_os_bag_member.py``). Inside the bag the grid +# collections are nested RAR5 archives; each required product/year batch is then +# unpacked in one ``bsdtar`` pass (libarchive; the ``7z`` build here cannot read +# RAR5). +MIRCA_OS_BAG_MEMBERS = { + "Annual_Harvested_Area_Grids": ( + "Annual Harvested Area Grids/Annual Harvested Area Grids.rar" + ), + "Maximum_Monthly_Cropped_Area_Grids": ( + "Maximum Cropped Area Grids/Maximum Monthly Cropped Area Grids.rar" + ), + "Monthly_Growing_Area_Grids": ( + "Monthly Growing Area Grids/Monthly Growing Area Grids.rar" + ), +} +MIRCA_OS_YEARS = (2000, 2005, 2010, 2015, 2020) +with open("data/curated/mirca_os_crop_mapping.csv") as _mirca_mapping_file: + MIRCA_OS_BASE_CROPS = [ + row["mirca_crop"].strip() + for row in csv.DictReader( + line for line in _mirca_mapping_file if not line.startswith("#") + ) + ] + + +rule download_mirca_os_archive: + """Download one MIRCA-OS v2 grid archive (nested RAR5) from the HydroShare bag. + + Pulls a single member of the BagIt zip via HTTP-range partial extraction, so + a 32 MB / 284 MB / 1.5 GB grid archive does not require the full ~10 GB bag. + """ + output: + "data/downloads/mirca_os/{archive}.rar", + wildcard_constraints: + archive="|".join(MIRCA_OS_BAG_MEMBERS), + params: + member=lambda w: MIRCA_OS_BAG_MEMBERS[w.archive], + resources: + runtime="120m", + mem_mb=1000, + log: + "/shared/download_mirca_os_{archive}.log", + benchmark: + "/shared/download_mirca_os_{archive}.tsv" + script: + "../scripts/download_mirca_os_bag_member.py" + + +rule extract_mirca_os_annual_harvested: + """Unpack all crop/system annual-harvested-area GeoTIFFs for one year. + + Harvested area sums subcrops, so a twice-cropped field counts twice; this is + the numerator of the extra-cycle magnitude M = harvested - footprint. The + archive is scanned once for the complete batch rather than once per raster. + """ + input: + rar="data/downloads/mirca_os/Annual_Harvested_Area_Grids.rar", + output: + expand( + "data/downloads/mirca_os/grids/annual/MIRCA-OS_{crop}_{{year}}_{ws}_v2.tif", + crop=MIRCA_OS_BASE_CROPS, + ws=("ir", "rf"), + ), + wildcard_constraints: + year="|".join(map(str, MIRCA_OS_YEARS)), + params: + member_globs=expand( + "*MIRCA-OS_{crop}_{{year}}_{ws}_v2.tif", + crop=MIRCA_OS_BASE_CROPS, + ws=("ir", "rf"), + ), + resources: + runtime="20m", + mem_mb=1000, + log: + "/shared/extract_mirca_os_annual_{year}.log", + script: + "../scripts/extract_mirca_os_archive.py" + + +rule extract_mirca_os_footprint: + """Unpack both maximum-monthly-cropped-area GeoTIFFs for one year. + + This is the AEI-capped physical field footprint per system. The derivation + keeps irrigated and rainfed extra-cycle magnitudes separate so observed area + cannot be attributed across water-supply systems. + """ + input: + rar="data/downloads/mirca_os/Maximum_Monthly_Cropped_Area_Grids.rar", + output: + expand( + "data/downloads/mirca_os/grids/footprint/MIRCA-OS_{{year}}_{ws}_v2.tif", + ws=("ir", "rf"), + ), + wildcard_constraints: + year="|".join(map(str, MIRCA_OS_YEARS)), + params: + member_globs=expand( + "*5-arcminute/MIRCA-OS_{{year}}_{ws}_v2.tif", ws=("ir", "rf") + ), + resources: + runtime="10m", + mem_mb=1000, + log: + "/shared/extract_mirca_os_footprint_{year}.log", + script: + "../scripts/extract_mirca_os_archive.py" + + +rule extract_mirca_os_subcrop_monthly: + """Unpack the repeated-rice monthly grids for one year in one archive pass. + + The subcrop-resolved monthly product preserves subcrop identity + (Rice1/2/3, Wheat1/2, ...). Rice2/3 feed the multi-cropping derivation's + repeated-cycle detection (never crop timing). + """ + input: + rar="data/downloads/mirca_os/Monthly_Growing_Area_Grids.rar", + output: + expand( + "data/downloads/mirca_os/grids/monthly/MIRCA-OS_{subcrop}_{{year}}_{ws}.nc", + subcrop=("Rice2", "Rice3"), + ws=("ir", "rf"), + ), + wildcard_constraints: + year="|".join(map(str, MIRCA_OS_YEARS)), + params: + member_globs=expand( + "*MIRCA-OS_{subcrop}_{{year}}_{ws}.nc", + subcrop=("Rice2", "Rice3"), + ws=("ir", "rf"), + ), + resources: + runtime="30m", + mem_mb=2000, + log: + "/shared/extract_mirca_os_subcrop_{year}.log", + script: + "../scripts/extract_mirca_os_archive.py" + + rule download_grassland_yield_data: """Retrieve historical managed-grassland yield from ISIMIP2a / LPJmL. diff --git a/workflow/scripts/analysis/extract_objective_breakdown.py b/workflow/scripts/analysis/extract_objective_breakdown.py index 49904087..0a3e7e00 100644 --- a/workflow/scripts/analysis/extract_objective_breakdown.py +++ b/workflow/scripts/analysis/extract_objective_breakdown.py @@ -257,6 +257,12 @@ def _to_series(df_or_series: pd.DataFrame | pd.Series) -> pd.Series: "bounded_subsidy_bnusd_per_mha", "baseline_area_mha", ), + ( + "crop_production_multi", + "Crop production", + "bounded_subsidy_bnusd_per_mha", + "baseline_area_mha", + ), ( "grassland_production", "Crop production", @@ -299,6 +305,12 @@ def _to_series(df_or_series: pd.DataFrame | pd.Series) -> pd.Series: "bounded_penalty_bnusd_per_mha", "baseline_area_mha", ), + ( + "crop_production_multi", + "Crop production", + "bounded_penalty_bnusd_per_mha", + "baseline_area_mha", + ), ( "grassland_production", "Crop production", diff --git a/workflow/scripts/build_model.py b/workflow/scripts/build_model.py index c3ad93ae..ada0e82b 100644 --- a/workflow/scripts/build_model.py +++ b/workflow/scripts/build_model.py @@ -32,6 +32,7 @@ ) from workflow.scripts.constants import FEED_CATEGORIES, HA_PER_MHA, USD_TO_BNUSD from workflow.scripts.logging_config import setup_script_logging +from workflow.scripts.multi_cropping_combinations import effective_combinations if __name__ == "__main__": # Configure logging @@ -370,6 +371,7 @@ def _apply_yield_corrections(corr_df: pd.DataFrame, source_label: str) -> None: multi_cropping_area_df = read_csv(snakemake.input.multi_cropping_area) multi_cropping_cycle_df = read_csv(snakemake.input.multi_cropping_yields) + multi_cropping_baseline_df = read_csv(snakemake.input.multi_cropping_baseline) luc_lef_lookup = pd.DataFrame( columns=["region", "resource_class", "water_supply", "use", "lef"] @@ -612,8 +614,9 @@ def _apply_yield_corrections(corr_df: pd.DataFrame, source_label: str) -> None: float ) * (1.0 - moisture_df["moisture_fraction"]) - # Optional cost calibration corrections (crops, grassland, animals) + # Optional cost calibration corrections (crops, multi-crops, grassland, animals) crop_cost_calibration = None + multi_crop_cost_calibration = None grassland_cost_calibration = None animal_cost_calibration = None if hasattr(snakemake.input, "crop_cost_calibration"): @@ -624,6 +627,15 @@ def _apply_yield_corrections(corr_df: pd.DataFrame, source_label: str) -> None: logger.info( "Loaded crop cost calibration: %d entries", len(crop_cost_calibration) ) + if hasattr(snakemake.input, "multi_crop_cost_calibration"): + cal_df = read_csv(snakemake.input.multi_crop_cost_calibration) + multi_crop_cost_calibration = cal_df.set_index(["combination", "country"])[ + "correction_bnusd_per_mha" + ].astype(float) + logger.info( + "Loaded multi-crop cost calibration: %d entries", + len(multi_crop_cost_calibration), + ) if hasattr(snakemake.input, "grassland_cost_calibration"): cal_df = read_csv(snakemake.input.grassland_cost_calibration) grassland_cost_calibration = cal_df.set_index("country")[ @@ -981,20 +993,18 @@ def _apply_yield_corrections(corr_df: pd.DataFrame, source_label: str) -> None: crop_loss_multiplier=crop_loss_multiplier, crop_marketing_cost_usd_per_t=crop_marketing_usd_per_t, ) - land.add_multi_cropping_land_correction( - n, - land_use_cost_bnusd_per_mha=land_use_cost_bnusd_per_mha, - ) - - # Multi-cropping is disabled when running with actual production or when - # the land deviation penalty is active (penalty anchors land area to - # baseline; extra harvested area from multi-cropping would bias the - # deviation accounting). - dp_cfg = snakemake.params.deviation_penalty - land_deviation_active = dp_cfg["enabled"] and dp_cfg["land"]["enabled"] - enable_multiple_cropping = bool(snakemake.params.multiple_cropping) and ( - not use_actual_production and not land_deviation_active + # Multi-cropping links carry an observed baseline (baseline_area.csv), so they + # are anchored -- and, under actual-production mode, pinned -- exactly like + # single-crop production. They are disabled only when the effective + # combination set (MIRCA-derived merged over config) is empty. + multiple_cropping_cfg = effective_combinations( + { + "multiple_cropping": snakemake.params.multiple_cropping, + "crops": snakemake.params.crops, + }, + snakemake.input.multi_cropping_combinations, ) + enable_multiple_cropping = bool(multiple_cropping_cfg) if enable_multiple_cropping: crops.add_multi_cropping_links( n, @@ -1008,13 +1018,44 @@ def _apply_yield_corrections(corr_df: pd.DataFrame, source_label: str) -> None: residue_lookup, residue_fue_lookup=residue_fue_lookup, residue_n2o_eff_lookup=residue_n2o_eff_lookup, + rice_methane_factor=rice_methane_factor, + rainfed_wetland_rice_ch4_scaling_factor=( + rainfed_wetland_rice_ch4_scaling_factor + ), min_yield_t_per_ha=min_crop_yield, seed_kg_dm_per_ha=seed_kg_dm_per_ha, crop_loss_multiplier=crop_loss_multiplier, crop_marketing_cost_usd_per_t=crop_marketing_usd_per_t, + combinations=multiple_cropping_cfg, + baseline_area=multi_cropping_baseline_df, + use_actual_production=use_actual_production, + multi_crop_cost_calibration=multi_crop_cost_calibration, ) - elif use_actual_production: - logger.info("Skipping multiple cropping links under actual production mode") + # Clip sub-min_link_area_mha crop baselines before multi-crop reconciliation, + # pinning, or growth-cap aggregation. Reconciliation can then conserve the + # exact numerically retained FAOSTAT budget rather than independently clipping + # its single and multi decomposition afterwards. + # clip_negligible_coefficients repeats this later (idempotently); doing it + # here first prevents a below-floor multi cycle being pinned above an + # already-zeroed cap, which makes the validation solve infeasible. + n_clipped_baseline = utils.clip_baseline_area_mha(n, snakemake.params.numerics) + if n_clipped_baseline: + logger.info( + "Clipped %d sub-min_link_area_mha crop baselines before pinning", + n_clipped_baseline, + ) + + if enable_multiple_cropping: + # Persist the single-crop baseline reduction (harvested cycles now carried + # by the multi links) before the land correction and pinning read + # baseline_area_mha. + crops.reconcile_single_crop_baselines(n) + + # Validation mode: pin single-crop and multi-cropping links alike at their + # (reconciled) baseline areas. Must run after the reconciliation so each + # harvested cycle is pinned exactly once. + if use_actual_production: + crops.fix_crop_production_to_baseline(n) # Fixed biofuel/industrial and biogas baseline demand. Must run after the # crop production links exist: rows whose source crop has no producible @@ -1024,6 +1065,15 @@ def _apply_yield_corrections(corr_df: pd.DataFrame, source_label: str) -> None: n, biofuel_baseline_df, crop_moisture=moisture_by_crop ) + # Land correction runs AFTER the multi links and the single-baseline + # reconciliation, and sums both crop carriers, so the cropland-bus demand + # (net (n-1)*A per n-cycle attribution) balances and the residual lands on + # the bulk generator. + land.add_multi_cropping_land_correction( + n, + land_use_cost_bnusd_per_mha=land_use_cost_bnusd_per_mha, + ) + if snakemake.params.grazing["enabled"]: grassland.add_grassland_feed_links( n, diff --git a/workflow/scripts/build_model/crops.py b/workflow/scripts/build_model/crops.py index 16fd27a3..504642e2 100644 --- a/workflow/scripts/build_model/crops.py +++ b/workflow/scripts/build_model/crops.py @@ -9,7 +9,9 @@ spared land allocation with carbon sequestration. """ -from collections.abc import Mapping +from collections import Counter +from collections.abc import Mapping, Sequence +import json import logging import numpy as np @@ -21,6 +23,67 @@ logger = logging.getLogger(__name__) +# Crops grown under flooded (wetland) conditions, which emit CH4 per harvested +# cycle. Kept in sync with the multiple-cropping derivation's own set. +WETLAND_RICE_CROPS = {"wetland-rice"} + + +def multi_crop_cycle_multiplicities(links: pd.DataFrame) -> pd.DataFrame: + """Return one row per multi-cropping link and distinct output crop. + + Multi-cropping links store their complete ordered crop sequence in the + explicit ``crop_cycles`` metadata column. This helper is the sole place + where that representation is expanded. Reconciliation and solve-time + growth caps therefore use exactly the same cycle multiplicities without + inspecting component names or bus labels. + """ + multi = links[links["carrier"] == "crop_production_multi"] + columns = [ + "link", + "crop", + "country", + "multiplicity", + "baseline_area_mha", + ] + if multi.empty: + return pd.DataFrame(columns=columns) + if "crop_cycles" not in multi.columns: + raise ValueError( + "Multi-cropping links are missing the 'crop_cycles' metadata needed " + "to account for harvested-cycle multiplicity. Rebuild the model." + ) + + records: list[dict[str, object]] = [] + for link, row in multi.iterrows(): + raw = row["crop_cycles"] + try: + cycles = json.loads(str(raw)) + except (TypeError, ValueError) as exc: + raise ValueError( + f"Multi-cropping link '{link}' has invalid crop_cycles metadata: " + f"{raw!r}" + ) from exc + if ( + not isinstance(cycles, list) + or not cycles + or not all(isinstance(crop, str) and crop for crop in cycles) + ): + raise ValueError( + f"Multi-cropping link '{link}' must have a non-empty JSON list " + f"of crop names in crop_cycles; got {raw!r}" + ) + for crop, multiplicity in Counter(cycles).items(): + records.append( + { + "link": link, + "crop": crop, + "country": str(row["country"]), + "multiplicity": int(multiplicity), + "baseline_area_mha": float(row["baseline_area_mha"]), + } + ) + return pd.DataFrame.from_records(records, columns=columns) + def compute_residue_n2o_efficiency_per_dm( residue_feed_items: list[str], @@ -92,7 +155,8 @@ def _redistribute_excess_baseline(df: pd.DataFrame) -> pd.Series: When FAOSTAT harvested area disaggregated to a region exceeds the land available there (p_nom_max), the excess is proportionally redistributed to other links of the same crop x country that still have spare capacity. - This preserves national totals as far as capacity allows. + If a national crop/water group has insufficient capacity, fail rather than + silently discard part of its observed baseline. """ baseline = df["baseline_area_mha"].copy() cap = df["p_nom_max"] @@ -118,8 +182,9 @@ def _redistribute_excess_baseline(df: pd.DataFrame) -> pd.Series: ) total_excess_before = float(excess.sum()) unplaced = 0.0 + unplaced_groups: dict[str, float] = {} - for _key, idx in baseline.groupby(group_keys).groups.items(): + for group_key, idx in baseline.groupby(group_keys).groups.items(): group_excess = float(excess.loc[idx].sum()) if group_excess <= 0: continue @@ -128,24 +193,106 @@ def _redistribute_excess_baseline(df: pd.DataFrame) -> pd.Series: total_spare = float(spare.sum()) if total_spare <= 0: unplaced += group_excess + unplaced_groups[str(group_key)] = group_excess continue # Distribute proportionally to spare capacity allocated = min(group_excess, total_spare) baseline.loc[idx] += spare / total_spare * allocated if group_excess > total_spare: - unplaced += group_excess - total_spare + remainder = group_excess - total_spare + unplaced += remainder + unplaced_groups[str(group_key)] = remainder # Final safety clip (numerical precision) baseline = baseline.clip(upper=cap) + if unplaced > 1e-9: + raise ValueError( + "FAOSTAT crop baseline exceeds all modeled suitable-area capacity " + "within its crop/country/water group; refusing to discard " + f"{unplaced:.6g} Mha. Affected groups include " + f"{dict(list(unplaced_groups.items())[:10])}" + ) + logger.info("Baseline redistribution: relocated %.1f Mha", total_excess_before) + return baseline + + +def _apply_bounded_cost_calibration( + df: pd.DataFrame, + keys: Sequence[tuple], + calibration: pd.Series | None, + *, + label: str, + key_fields: str, +) -> None: + """Store bounded cost-calibration corrections on crop production links. + + Single-crop and multi-cropping links both carry additive cost corrections + (bnUSD/Mha) that are applied at solve time only near baseline, so they keep + their calibration-time meaning as *local* marginal-cost gradients rather + than leaking into a flat cost that pulls the LP away from baseline: + + - A negative correction (model cost too high) is stored as + ``bounded_subsidy_bnusd_per_mha`` and applied on the first + ``baseline_area_mha`` units of dispatch, clipped so the base + ``marginal_cost`` stays non-negative. Prevents the canonical olive-USA + case (-0.40 bnUSD/Mha on 0.04 Mha) from leaking into runaway expansion. + - A positive correction (model cost too low) is stored as + ``bounded_penalty_bnusd_per_mha`` and applied only *above* + ``baseline_area_mha``. Prevents a large positive correction (e.g. + tomato-BEL at +346 bnUSD/Mha after winsorization) from becoming a flat + penalty that pushes production to zero and dumps the anchoring on the L1 + production-stability term. + + ``df`` must carry ``marginal_cost`` and ``baseline_area_mha`` columns; + ``keys`` holds one ``calibration`` lookup key per row, in row order. Only + links banded in the calibration solve (baseline area above the stability + floor) receive a dual, so pairs with a zero baseline are legitimately + absent from the artefact and correctly take a zero correction. A + positive-baseline pair that is missing means the artefact is stale for the + current link set, and raises. + """ + df["bounded_subsidy_bnusd_per_mha"] = 0.0 + df["bounded_penalty_bnusd_per_mha"] = 0.0 + if calibration is None: + return + + corrections = pd.Series( + calibration.reindex(pd.MultiIndex.from_tuples(list(keys))).to_numpy(), + index=df.index, + dtype=float, + ) + + missing = corrections.isna().to_numpy() & ( + df["baseline_area_mha"].to_numpy(dtype=float) > 0.0 + ) + if missing.any(): + missing_keys = sorted({keys[i] for i in np.flatnonzero(missing)}) + sample = ", ".join(":".join(map(str, k)) for k in missing_keys[:5]) + raise ValueError( + f"{label} cost calibration is missing {len(missing_keys)} " + f"{key_fields} link(s) with a positive baseline area, e.g. {sample}. " + "The calibration artefact is stale for the current link set; " + "regenerate it with `tools/calibrate cost`." + ) + + corrections = corrections.fillna(0.0) + pos = corrections > 0 + neg = corrections < 0 + df.loc[pos, "bounded_penalty_bnusd_per_mha"] = corrections[pos] + df.loc[neg, "bounded_subsidy_bnusd_per_mha"] = np.maximum( + corrections[neg], -df.loc[neg, "marginal_cost"] + ) logger.info( - "Baseline redistribution: capped %.1f Mha excess, " - "%.1f Mha unplaceable (no spare capacity in same crop x country)", - total_excess_before, - unplaced, + "Applied %s cost calibration: %d/%d links (pos=%d bounded above " + "baseline_area, neg=%d bounded at baseline_area)", + label, + int((corrections != 0.0).sum()), + len(df), + int(pos.sum()), + int(neg.sum()), ) - return baseline def add_regional_crop_production_links( @@ -178,6 +325,11 @@ def add_regional_crop_production_links( Parameters ---------- + use_actual_production : bool + When true, only links with observed harvested area are built. The + pinning to baseline itself is applied later, for single-crop and + multi-cropping links alike, by ``fix_crop_production_to_baseline`` + (after ``reconcile_single_crop_baselines``). crop_costs : pd.Series MultiIndex (crop, country) → cost USD/ha in base year. global_median_cost : pd.Series @@ -262,10 +414,10 @@ def add_regional_crop_production_links( df = df[df["yield"] >= min_yield_t_per_ha] if use_actual_production: - df["fixed_area_ha"] = pd.to_numeric( - df["harvested_area"], errors="coerce" - ) - df = df[df["fixed_area_ha"] > 0] + # Only links with an observed baseline are built; the pinning + # itself happens in ``fix_crop_production_to_baseline`` after + # the multi-cropping reconciliation. + df = df[pd.to_numeric(df["harvested_area"], errors="coerce") > 0] df["country"] = df["region"].map(region_to_country) df = df[df["country"].isin(allowed_countries)] @@ -304,7 +456,7 @@ def add_regional_crop_production_links( water_bus = np.full(len(df), "", dtype=object) water_eff = np.zeros(len(df), dtype=float) - if crop == "wetland-rice" and rice_methane_factor > 0: + if crop in WETLAND_RICE_CROPS and rice_methane_factor > 0: scaling_factor = ( 1.0 if ws == "i" else rainfed_wetland_rice_ch4_scaling_factor ) @@ -380,18 +532,6 @@ def add_regional_crop_production_links( / 1e6 ) - if use_actual_production: - fixed_area_mha = ( - pd.to_numeric(df["fixed_area_ha"], errors="coerce").to_numpy( - dtype=float - ) - / 1e6 - ) - row_df["p_nom"] = fixed_area_mha - row_df["p_nom_max"] = fixed_area_mha - row_df["p_nom_min"] = fixed_area_mha - row_df["p_min_pu"] = 1.0 - all_rows.append(row_df) if not all_rows: @@ -431,76 +571,15 @@ def add_regional_crop_production_links( * constants.USD_TO_BNUSD ) - # Apply additive calibration correction if available. - # - # Both directions are bounded at baseline_area_mha so the corrections - # retain their calibration-time interpretation as *local* marginal-cost - # gradients at baseline rather than leaking into a flat additive cost - # that pulls the LP arbitrarily far from baseline: - # - Negative corrections (subsidy bringing model cost down) are - # stored as ``bounded_subsidy_bnusd_per_mha`` and applied at - # solve time only on the first ``baseline_area_mha`` units of - # dispatch. Prevents the canonical olive-USA case - # (-0.40 bnUSD/Mha on 0.04 Mha) from leaking into runaway - # expansion of the crop. - # - Positive corrections (cost increase) are stored as - # ``bounded_penalty_bnusd_per_mha`` and applied at solve time - # only *above* ``baseline_area_mha``. Without this bound, a - # large positive correction (e.g. +346 bnUSD/Mha on tomato:BEL - # after winsorization made greenhouse tomato look cheap per - # tonne) becomes a flat penalty that pushes production to zero, - # forcing the L1 production-stability penalty to do the - # anchoring work. - all_df["bounded_subsidy_bnusd_per_mha"] = 0.0 - all_df["bounded_penalty_bnusd_per_mha"] = 0.0 - if cost_calibration is not None: - missing_cal_keys = [k for k in cost_keys if k not in cost_calibration.index] - if missing_cal_keys: - # cost_calibration is generated by a prior solve; mismatched - # link sets surface as silent zero corrections, which weaken - # the calibration anchor for whichever (crop, country) pairs - # got added between calibration and the present solve. - sample = ", ".join(f"{c}:{n}" for c, n in sorted(missing_cal_keys)[:5]) - logger.warning( - "cost_calibration missing %d (crop, country) pair(s); " - "applying zero correction. Examples: %s. Regenerate the " - "cost calibration if the link set has changed.", - len(missing_cal_keys), - sample, - ) - cal_values = pd.Series( - [cost_calibration.get(k, 0.0) for k in cost_keys], - index=all_df.index, - dtype=float, - ) - base_cost = all_df["marginal_cost"].copy() - - positive_mask = cal_values > 0 - negative_mask = cal_values < 0 - - # Positive corrections: store as bounded penalty (applied above - # baseline_area at solve time; no effect on marginal_cost). - all_df.loc[positive_mask, "bounded_penalty_bnusd_per_mha"] = cal_values.loc[ - positive_mask - ] - - # Negative corrections: bound the subsidy so the underlying base - # cost cannot go below zero, then store on the link. - all_df.loc[negative_mask, "bounded_subsidy_bnusd_per_mha"] = np.maximum( - cal_values.loc[negative_mask], -base_cost.loc[negative_mask] - ) - - n_calibrated = int((cal_values != 0.0).sum()) - n_pos = int(positive_mask.sum()) - n_neg = int(negative_mask.sum()) - logger.info( - "Applied crop cost calibration: %d/%d links (pos=%d bounded above " - "baseline_area, neg=%d bounded at baseline_area)", - n_calibrated, - len(all_df), - n_pos, - n_neg, - ) + # Apply additive calibration corrections as bounded subsidies/penalties + # near baseline (see _apply_bounded_cost_calibration for the mechanism). + _apply_bounded_cost_calibration( + all_df, + cost_keys, + cost_calibration, + label="crop", + key_fields="(crop, country)", + ) keys = list( zip( @@ -560,17 +639,9 @@ def add_regional_crop_production_links( "bus0": all_df["bus0"], "bus1": all_df["bus1"], "efficiency": all_df["efficiency"], - "bus2": all_df["bus2"], - "efficiency2": all_df["efficiency2"], - "bus3": all_df["bus3"], - "efficiency3": all_df["efficiency3"], - "bus4": all_df["bus4"], - "efficiency4": all_df["efficiency4"], - "bus5": all_df["bus5"], - "efficiency5": all_df["efficiency5"], "marginal_cost": all_df["marginal_cost"], "p_nom_max": all_df["p_nom_max"], - "p_nom_extendable": not use_actual_production, + "p_nom_extendable": True, "crop": all_df["crop"], "country": all_df["country"], "region": all_df["region"], @@ -582,14 +653,12 @@ def add_regional_crop_production_links( "bounded_subsidy_bnusd_per_mha": all_df["bounded_subsidy_bnusd_per_mha"], "bounded_penalty_bnusd_per_mha": all_df["bounded_penalty_bnusd_per_mha"], } - if "bus6" in all_df.columns: - add_kwargs["bus6"] = all_df["bus6"] - add_kwargs["efficiency6"] = all_df["efficiency6"] - - if use_actual_production: - add_kwargs["p_nom"] = all_df["p_nom"] - add_kwargs["p_nom_min"] = all_df["p_nom_min"] - add_kwargs["p_min_pu"] = all_df["p_min_pu"] + # Multi-input buses: water, fertilizer, CH4, residue and (optional) soil-N2O. + for i in range(2, 7): + bus_col = f"bus{i}" + if bus_col in all_df.columns: + add_kwargs[bus_col] = all_df[bus_col] + add_kwargs[f"efficiency{i}"] = all_df[f"efficiency{i}"] n.links.add(all_df.index, **add_kwargs) @@ -607,10 +676,16 @@ def add_multi_cropping_links( residue_fue_lookup: Mapping[str, float] | None = None, residue_n2o_eff_lookup: Mapping[str, float] | None = None, *, + rice_methane_factor: float, + rainfed_wetland_rice_ch4_scaling_factor: float, min_yield_t_per_ha: float, seed_kg_dm_per_ha: pd.Series, crop_loss_multiplier: pd.Series, crop_marketing_cost_usd_per_t: Mapping[str, float], + combinations: Mapping[str, Mapping[str, object]], + baseline_area: pd.DataFrame | None = None, + use_actual_production: bool = False, + multi_crop_cost_calibration: pd.Series | None = None, ) -> None: """Add multi-cropping production links with a vectorised workflow. @@ -619,27 +694,154 @@ def add_multi_cropping_links( ``seed_kg_dm_per_ha[crop] / 1000 / yield_t_per_ha``. The per-country supply-chain loss multiplier is applied identically so the multi-crop crop bus carries post-loss DM, matching the regular production path. - """ - if eligible_area.empty or cycle_yields.empty: - logger.info("No multi-cropping combinations with positive area; skipping") - return + ``baseline_area`` (the Stage-2 ``baseline_area.csv``: MIRCA-observed physical + link area per ``(combination, region, resource_class, water_supply)``) anchors + each link via ``baseline_area_mha``. The potential cap becomes + ``p_nom_max = max(GAEZ eligible potential, anchored baseline)`` so the anchor + is never above the expansion bound, and the link stays extendable so the model + can add or drop cycles. + + A link is built only when every configured cycle has a valid yield. A + positive baseline without a valid local GAEZ row is relocated within the + same ``(combination, country, water_supply)`` group. If that group has no + complete sequence under the active GAEZ inputs, the anchor stays represented + by its constituent single-crop baselines rather than creating an invalid + partial bundle. + + Under ``use_actual_production`` only links with a positive observed baseline + are built (mirroring the single-crop filter); the pinning itself is applied + afterwards by ``fix_crop_production_to_baseline``. + + Wetland-rice cycles emit CH4 exactly as on the single-crop path: a link + running ``m`` rice cycles carries ``m`` times the per-hectare emission factor + (rainfed cycles scaled by ``rainfed_wetland_rice_ch4_scaling_factor``), so + rice methane is invariant to whether a hectare of rice is represented as a + single-crop link or a cycle of a multi-cropping bundle. + """ residue_lookup = residue_lookup or {} residue_fue_lookup = residue_fue_lookup or {} residue_n2o_eff_lookup = residue_n2o_eff_lookup or {} key_cols = ["combination", "region", "resource_class", "water_supply"] + water_codes = {"i": "i", "r": "r", "irrigated": "i", "rainfed": "r"} + configured = set(combinations) + + anchor_df = pd.DataFrame(columns=[*key_cols, "country", "baseline_area_mha"]) + if baseline_area is not None and not baseline_area.empty: + anchor_df = baseline_area.copy() + anchor_df["combination"] = anchor_df["combination"].astype(str) + inactive = sorted(set(anchor_df["combination"]) - configured) + if inactive: + logger.info( + "Ignoring observed baselines for %d disabled or unavailable " + "catalog combinations", + len(inactive), + ) + anchor_df = anchor_df[anchor_df["combination"].isin(configured)].copy() + anchor_df["resource_class"] = anchor_df["resource_class"].astype(int) + anchor_df["water_supply"] = anchor_df["water_supply"].map(water_codes) + if anchor_df["water_supply"].isna().any(): + invalid = sorted( + baseline_area.loc[anchor_df["water_supply"].isna(), "water_supply"] + .astype(str) + .unique() + ) + raise ValueError( + f"Multi-cropping baseline has invalid water_supply values: {invalid}" + ) + raw_anchor = pd.to_numeric(anchor_df["baseline_area_ha"], errors="coerce") + if raw_anchor.isna().any() or (raw_anchor < 0).any(): + raise ValueError( + "Multi-cropping baseline_area_ha must contain finite, non-negative " + "values" + ) + anchor_df["baseline_area_mha"] = raw_anchor / constants.HA_PER_MHA + anchor_df["country"] = anchor_df["region"].map(region_to_country.astype(str)) + missing_country = anchor_df[ + (anchor_df["baseline_area_mha"] > 0) & anchor_df["country"].isna() + ] + if not missing_country.empty: + regions = sorted(missing_country["region"].astype(str).unique())[:10] + raise ValueError( + "Positive multi-cropping baselines could not be assigned to a " + f"country; unmapped regions include {regions}" + ) + anchor_df = anchor_df[anchor_df["baseline_area_mha"] > 0] + if allowed_countries: + anchor_df = anchor_df[anchor_df["country"].isin(allowed_countries)] + + # MIRCA describes the multi-crop decomposition, while FAOSTAT remains + # the national harvested-area budget. Scale overlapping combination + # anchors jointly before relocation so a zero-budget bundle cannot + # trigger a spurious missing-potential error. + single = n.links.static[n.links.static["carrier"] == "crop_production"].copy() + single["water_supply"] = single["water_supply"].replace( + {"rainfed": "r", "irrigated": "i"} + ) + budget_cols = ["crop", "country", "water_supply"] + budgets = single.groupby(budget_cols)["baseline_area_mha"].sum() + group_cols = ["combination", "country", "water_supply"] + requirements: list[dict[str, object]] = [] + for group_key, area_mha in ( + anchor_df.groupby(group_cols)["baseline_area_mha"].sum().items() + ): + combination, country, water_supply = map(str, group_key) + for crop, multiplicity in Counter( + combinations[combination]["crops"] + ).items(): + requirements.append( + { + "combination": combination, + "country": country, + "water_supply": water_supply, + "crop": str(crop), + "required_mha": int(multiplicity) * float(area_mha), + } + ) + required = pd.DataFrame.from_records(requirements) + if not required.empty: + total_required = required.groupby(budget_cols)["required_mha"].sum() + budget_ratios = ( + budgets.reindex(total_required.index).fillna(0.0) / total_required + ).clip(upper=1.0) + required["budget_ratio"] = pd.MultiIndex.from_frame( + required[budget_cols] + ).map(budget_ratios) + group_scales = required.groupby(group_cols)["budget_ratio"].min() + anchor_groups = pd.MultiIndex.from_frame(anchor_df[group_cols]) + anchor_scales = pd.Series( + anchor_groups.map(group_scales), index=anchor_df.index + ).fillna(0.0) + if (anchor_scales < 1.0 - 1e-12).any(): + logger.info( + "Scaled %d MIRCA multi-cropping anchor rows to fit joint " + "FAOSTAT crop budgets before spatial relocation", + int((anchor_scales < 1.0 - 1e-12).sum()), + ) + anchor_df["baseline_area_mha"] *= anchor_scales + anchor_df = anchor_df[anchor_df["baseline_area_mha"] > 1e-12] area_df = eligible_area.copy() + if area_df.empty: + area_df = pd.DataFrame( + columns=[ + *key_cols, + "eligible_area_ha", + "water_requirement_m3_per_ha", + ] + ) + area_df["combination"] = area_df["combination"].astype(str) + area_df = area_df[area_df["combination"].isin(configured)] area_df["resource_class"] = area_df["resource_class"].astype(int) - area_df["water_supply"] = area_df["water_supply"].astype(str) + area_df["water_supply"] = area_df["water_supply"].map(water_codes) area_df["eligible_area_ha"] = pd.to_numeric( area_df["eligible_area_ha"], errors="coerce" ) area_df["water_requirement_m3_per_ha"] = pd.to_numeric( area_df.get("water_requirement_m3_per_ha", 0.0), errors="coerce" - ) + ).fillna(0.0) region_to_country = region_to_country.astype(str) area_df["country"] = area_df["region"].map(region_to_country) @@ -648,17 +850,52 @@ def add_multi_cropping_links( if allowed_countries: area_df = area_df[area_df["country"].isin(allowed_countries)] - if area_df.empty: - logger.info("No eligible multi-cropping areas after filtering; skipping") - return + if area_df.duplicated(key_cols).any(): + sample = area_df.loc[area_df.duplicated(key_cols, keep=False), key_cols].head() + raise ValueError( + "Multi-cropping eligible-area table has duplicate link keys, e.g. " + f"{sample.to_dict(orient='records')}" + ) + + # Only rows with an actual land bus and a physically valid irrigation input + # are eligible relocation targets for observed baselines. + area_df["land_bus"] = ( + "land:cropland:" + + area_df["region"].astype(str) + + "_c" + + area_df["resource_class"].astype(str) + + "_" + + area_df["water_supply"].astype(str) + ) + valid_land = area_df["land_bus"].isin(n.buses.static.index) + valid_water = ~area_df["water_supply"].eq("i") | ( + area_df["water_requirement_m3_per_ha"] > 0 + ) + n_invalid = int((~(valid_land & valid_water)).sum()) + if n_invalid: + logger.warning( + "Excluded %d multi-cropping potential rows without a valid land or " + "irrigation input", + n_invalid, + ) + area_df = area_df[valid_land & valid_water].copy() cycle_df = cycle_yields.copy() + if cycle_df.empty: + cycle_df = pd.DataFrame( + columns=[*key_cols, "cycle_index", "crop", "yield_t_per_ha"] + ) + cycle_df["combination"] = cycle_df["combination"].astype(str) + cycle_df = cycle_df[cycle_df["combination"].isin(configured)] cycle_df["resource_class"] = cycle_df["resource_class"].astype(int) - cycle_df["water_supply"] = cycle_df["water_supply"].astype(str) + cycle_df["water_supply"] = cycle_df["water_supply"].map(water_codes) + cycle_df["cycle_index"] = pd.to_numeric(cycle_df["cycle_index"], errors="coerce") cycle_df["yield_t_per_ha"] = pd.to_numeric( cycle_df["yield_t_per_ha"], errors="coerce" ) - cycle_df = cycle_df.dropna(subset=["yield_t_per_ha", "crop"]) + cycle_df = cycle_df.dropna( + subset=["yield_t_per_ha", "crop", "cycle_index", "water_supply"] + ) cycle_df = cycle_df[cycle_df["yield_t_per_ha"] > 0] # Filter low yields for numerical stability @@ -666,15 +903,41 @@ def add_multi_cropping_links( low_yield_mask = cycle_df["yield_t_per_ha"] < min_yield_t_per_ha cycle_df = cycle_df[~low_yield_mask] - if cycle_df.empty: - logger.info("No positive multi-cropping yields; skipping") - return - merged = cycle_df.merge(area_df, on=key_cols, how="inner") - if merged.empty: - logger.info( - "No overlapping multi-cropping combinations between area and yield tables" + valid_keys: list[tuple[object, ...]] = [] + for key, cycles in merged.groupby(key_cols, sort=False): + combo = str(key[0]) + expected = [str(crop) for crop in combinations[combo]["crops"]] + ordered = cycles.sort_values("cycle_index") + got_indices = ordered["cycle_index"].astype(int).tolist() + got_crops = ordered["crop"].astype(str).tolist() + if got_indices == list(range(1, len(expected) + 1)) and got_crops == expected: + valid_keys.append(key) + + if valid_keys: + valid_index = pd.MultiIndex.from_tuples(valid_keys, names=key_cols) + merged_index = pd.MultiIndex.from_frame(merged[key_cols]) + merged = merged[merged_index.isin(valid_index)].copy() + else: + merged = merged.iloc[0:0].copy() + + incomplete = len(area_df) - len(valid_keys) + if incomplete: + logger.warning( + "Excluded %d multi-cropping potential rows without the complete " + "configured crop cycle sequence", + incomplete, ) + if merged.empty: + if not anchor_df.empty: + logger.warning( + "Retaining %.3f Mha of MIRCA anchors on single-crop baselines " + "because no complete multi-crop sequence is valid under the " + "active GAEZ inputs", + float(anchor_df["baseline_area_mha"].sum()), + ) + else: + logger.info("No valid full-sequence multi-cropping potential; skipping") return merged = merged.sort_values([*key_cols, "cycle_index", "crop"]) @@ -710,12 +973,6 @@ def add_multi_cropping_links( crop_counts = merged.groupby(key_cols)["crop"].size().rename("crop_count") base = base.join(crop_counts) - base = base[base["crop_count"] > 0] - if base.empty: - logger.info( - "Multi-cropping combinations have no positive-yield crops; skipping" - ) - return # Look up per-(crop, country) cost and sum across crops in combination merged["cost_usd_per_ha"] = [ @@ -756,7 +1013,94 @@ def add_multi_cropping_links( # Multiple-cropping marginal costs: sum of per-country crop costs in bnUSD/Mha base["marginal_cost"] = base["total_cost"] * 1e6 * constants.USD_TO_BNUSD base["p_nom_extendable"] = True - base["p_nom_max"] = base["eligible_area_ha"] / 1e6 + + # Keep local MIRCA anchors where a complete GAEZ link exists. Relocate only + # the remainder, preserving its combination/country/water group and using + # eligible area as the spatial weight. The later reconciliation may scale a + # whole group conservatively to fit the FAOSTAT per-crop budgets. + base["baseline_area_mha"] = 0.0 + anchor_by_key = ( + anchor_df.groupby(key_cols, sort=False)["baseline_area_mha"].sum() + if not anchor_df.empty + else pd.Series(dtype=float) + ) + unavailable_groups: list[dict[str, str]] = [] + unavailable_mha = 0.0 + anchor_group_cols = ["combination", "country", "water_supply"] + for group_key, source in anchor_df.groupby(anchor_group_cols, sort=False): + combination, country, water_supply = map(str, group_key) + destination_mask = ( + (base.index.get_level_values("combination") == combination) + & (base["country"].astype(str).to_numpy() == country) + & (base.index.get_level_values("water_supply") == water_supply) + ) + destinations = base.index[destination_mask] + if destinations.empty: + unavailable_mha += float(source["baseline_area_mha"].sum()) + unavailable_groups.append( + { + "combination": combination, + "country": country, + "water_supply": water_supply, + } + ) + continue + + local = anchor_by_key.reindex(destinations).fillna(0.0) + base.loc[destinations, "baseline_area_mha"] += local.to_numpy() + local_total = float(source["baseline_area_mha"].sum()) + matched_total = float(local.sum()) + relocate = local_total - matched_total + if relocate > 1e-12: + weights = base.loc[destinations, "eligible_area_ha"].astype(float) + base.loc[destinations, "baseline_area_mha"] += ( + relocate * weights / float(weights.sum()) + ).to_numpy() + + if unavailable_groups: + logger.warning( + "Retaining %.3f Mha of MIRCA anchors on single-crop baselines because " + "%d combination/country/water groups have no valid full-sequence " + "GAEZ target (examples: %s)", + unavailable_mha, + len(unavailable_groups), + unavailable_groups[:10], + ) + + base["p_nom_max"] = np.maximum( + base["eligible_area_ha"] / constants.HA_PER_MHA, + base["baseline_area_mha"], + ) + + # Multi-cropping cost corrections are extracted directly from each multi + # link's own hard-band duals: one dispatch variable, one bundle gradient. + # Summing the constituent cycles' single-crop corrections would compound + # unrelated single-cycle gradients onto double/triple-crop links. + _apply_bounded_cost_calibration( + base, + list( + zip( + base.index.get_level_values("combination").astype(str), + base["country"].astype(str), + strict=False, + ) + ), + multi_crop_cost_calibration, + label="multi-crop", + key_fields="(combination, country)", + ) + + if use_actual_production: + base = base[base["baseline_area_mha"] > 0] + if base.empty: + logger.info( + "No multi-cropping links with observed baseline under actual " + "production mode; skipping" + ) + return + # Restrict the per-cycle rows to the surviving links (same pattern as + # the water gate below). + merged = merged.merge(base.reset_index()[key_cols], on=key_cols, how="inner") residue_records: list[dict[str, object]] = [] for (crop, water, region, res_class), feed_dict in residue_lookup.items(): @@ -858,6 +1202,12 @@ def add_multi_cropping_links( + "_c" + index_df["resource_class"].astype(str) ) + index_df["crop_cycles"] = index_df["combination"].map( + lambda name: json.dumps( + [str(crop) for crop in combinations[str(name)]["crops"]], + separators=(",", ":"), + ) + ) missing_land = index_df[~index_df["bus0"].isin(n.buses.static.index)] if not missing_land.empty: @@ -876,34 +1226,44 @@ def add_multi_cropping_links( if "crop_production_multi" not in n.carriers.static.index: n.carriers.add("crop_production_multi", unit="Mha") - water_req = index_df["water_requirement_m3_per_ha"].astype(float) - water_valid = ( - index_df["water_supply"].eq("i") & np.isfinite(water_req) & (water_req > 0) - ) - # Irrigated rows missing a water requirement would silently get - # water_efficiency=0 (free irrigation on data-quality holes), letting - # the LP claim the higher irrigated yield without paying water. Drop - # those rows so they cannot be built into the network at all. - water_invalid = index_df["water_supply"].eq("i") & ~np.isfinite(water_req) + def _water_gate(df): + """Validity/invalidity masks for the per-link water requirement (m3/ha).""" + total = df["water_requirement_m3_per_ha"].to_numpy(dtype=float) + irrigated = df["water_supply"].eq("i").to_numpy() + valid = irrigated & (total > 0) + # An irrigated combination with no positive water requirement is a + # data-quality hole (build_multi_cropping zero-fills empty water + # aggregations): the link would otherwise be built with the irrigated + # per-cycle yields but no water port, i.e. free irrigation. Drop it. + invalid = irrigated & ~(total > 0) + return valid, invalid + + water_valid, water_invalid = _water_gate(index_df) + # Irrigated rows missing a water requirement would silently get zero water + # efficiency (free irrigation on data-quality holes), letting the LP claim the + # higher irrigated yield without paying water. Drop those rows entirely. if water_invalid.any(): logger.warning( "Dropping %d irrigated multi-cropping links with missing water requirement", int(water_invalid.sum()), ) - keep = ~water_invalid - index_df = index_df[keep].copy() - merged = merged[merged["link_name"].isin(index_df["link_name"])].copy() - water_req = index_df["water_requirement_m3_per_ha"].astype(float) - water_valid = ( - index_df["water_supply"].eq("i") & np.isfinite(water_req) & (water_req > 0) - ) + index_df = index_df[~water_invalid].copy() + # merged does not carry link_name yet (added downstream via a key_cols + # merge), so restrict it to the surviving links by their key columns. + merged = merged.merge(index_df[key_cols], on=key_cols, how="inner") if index_df.empty: return + water_valid, _ = _water_gate(index_df) - # bus0 is land in Mha, bus2 is water in Mm3, so the coefficient is + # bus0 is land in Mha, the water bus is Mm3, so the coefficient is # m3/ha (numerically equal to Mm3/Mha). water_requirement_m3_per_ha is # already in m3/ha after build_multi_cropping converts the GAEZ mm raster. - index_df["water_efficiency"] = np.where(water_valid, -water_req, 0.0) + # Water is an input, hence the negative sign. + index_df["water_efficiency"] = np.where( + water_valid, + -index_df["water_requirement_m3_per_ha"].to_numpy(dtype=float), + 0.0, + ) index_df["has_water"] = water_valid.astype(int) fert_total = index_df["fertilizer_total"].astype(float) @@ -913,6 +1273,30 @@ def add_multi_cropping_links( ) index_df["has_fertilizer"] = fert_valid.astype(int) + # Rice methane, counted per cycle: a bundle running m wetland-rice cycles on + # one hectare floods that hectare m times. kg CH4/ha == kt CH4/Mha, so the + # per-hectare factor carries straight onto the kt CH4 bus, as for single + # crops. Rainfed cycles take the rainfed scaling factor. + rice_cycles = ( + merged[merged["crop"].isin(WETLAND_RICE_CROPS)] + .groupby(key_cols) + .size() + .rename("rice_cycles") + ) + index_df = index_df.merge(rice_cycles.reset_index(), on=key_cols, how="left") + index_df["rice_cycles"] = index_df["rice_cycles"].fillna(0).astype(int) + ch4_scaling = np.where( + index_df["water_supply"].eq("i").to_numpy(), + 1.0, + float(rainfed_wetland_rice_ch4_scaling_factor), + ) + index_df["ch4_efficiency"] = ( + index_df["rice_cycles"].to_numpy(dtype=float) + * float(rice_methane_factor) + * ch4_scaling + ) + index_df["has_ch4"] = (index_df["ch4_efficiency"] > 0).astype(int) + outputs = merged.merge(index_df[[*key_cols, "link_name"]], on=key_cols, how="left") outputs["offset"] = outputs["output_idx"] + 1 offset_str = outputs["offset"].astype(int).astype(str) @@ -997,6 +1381,37 @@ def add_multi_cropping_links( ].rename(columns={"fert_efficiency": "eff_value"}) entry_frames.append(fert_entries) + ch4_entries = index_df[index_df["has_ch4"] == 1][ + [ + "link_name", + "ch4_efficiency", + "crop_count", + "has_water", + "has_fertilizer", + ] + ].copy() + if not ch4_entries.empty: + ch4_entries["offset"] = ( + ch4_entries["crop_count"] + + ch4_entries["has_water"] + + ch4_entries["has_fertilizer"] + + 1 + ) + offset_str = ch4_entries["offset"].astype(int).astype(str) + ch4_entries["bus_col"] = "bus" + offset_str + ch4_entries["eff_col"] = "efficiency" + offset_str + ch4_entries["bus_value"] = "emission:ch4" + ch4_entries = ch4_entries[ + [ + "link_name", + "bus_col", + "bus_value", + "eff_col", + "ch4_efficiency", + ] + ].rename(columns={"ch4_efficiency": "eff_value"}) + entry_frames.append(ch4_entries) + if not residue_agg.empty: residue_entries = residue_agg.merge( index_df[ @@ -1006,6 +1421,7 @@ def add_multi_cropping_links( "crop_count", "has_water", "has_fertilizer", + "has_ch4", ] ], on=key_cols, @@ -1014,15 +1430,15 @@ def add_multi_cropping_links( residue_entries = residue_entries.dropna(subset=["link_name"]) if residue_entries.empty: residue_entries = pd.DataFrame(columns=residue_entries.columns) - residue_entries[["crop_count", "has_water", "has_fertilizer"]] = ( - residue_entries[["crop_count", "has_water", "has_fertilizer"]].fillna(0) - ) + _offset_cols = ["crop_count", "has_water", "has_fertilizer", "has_ch4"] + residue_entries[_offset_cols] = residue_entries[_offset_cols].fillna(0) residue_entries = residue_entries.sort_values([*key_cols, "feed_item"]) residue_entries["entry_order"] = residue_entries.groupby(key_cols).cumcount() residue_entries["offset"] = ( residue_entries["crop_count"] + residue_entries["has_water"] + residue_entries["has_fertilizer"] + + residue_entries["has_ch4"] + residue_entries["entry_order"] + 1 ) @@ -1058,6 +1474,7 @@ def add_multi_cropping_links( "crop_count", "has_water", "has_fertilizer", + "has_ch4", "residue_count", ] ], @@ -1066,17 +1483,21 @@ def add_multi_cropping_links( ) n2o_entries = n2o_entries.dropna(subset=["link_name"]) if not n2o_entries.empty: - n2o_entries[ - ["crop_count", "has_water", "has_fertilizer", "residue_count"] - ] = n2o_entries[ - ["crop_count", "has_water", "has_fertilizer", "residue_count"] - ].fillna(0) + _offset_cols = [ + "crop_count", + "has_water", + "has_fertilizer", + "has_ch4", + "residue_count", + ] + n2o_entries[_offset_cols] = n2o_entries[_offset_cols].fillna(0) # Mandatory soil-N2O bus offset: sits after crops, water, - # fertilizer, and residue feed buses. One entry per link. + # fertilizer, CH4 and residue feed buses. One entry per link. n2o_entries["offset"] = ( n2o_entries["crop_count"] + n2o_entries["has_water"] + n2o_entries["has_fertilizer"] + + n2o_entries["has_ch4"] + n2o_entries["residue_count"] + 1 ) @@ -1124,6 +1545,9 @@ def add_multi_cropping_links( "p_nom_extendable", "p_nom_max", "marginal_cost", + "baseline_area_mha", + "bounded_subsidy_bnusd_per_mha", + "bounded_penalty_bnusd_per_mha", ] # Metadata columns for filtering metadata_cols = [ @@ -1132,6 +1556,7 @@ def add_multi_cropping_links( "resource_class", "water_supply", "combination", + "crop_cycles", ] # Prepare metadata values link_df["water_supply"] = link_df["water_supply"].map( @@ -1197,6 +1622,166 @@ def add_multi_cropping_links( n.links.add(link_df.index, **kwargs) +def reconcile_single_crop_baselines( + n: pypsa.Network, +) -> None: + """Remove harvested cycles now carried by multi links from single-crop anchors. + + The single-crop ``crop_production`` baseline is FAOSTAT harvested area, which + already counts every harvested cycle. MIRCA anchors describe how much of that + national budget should be represented by complete multi-crop bundles. If the + joint MIRCA requirements exceed any constituent crop's FAOSTAT budget, every + affected combination/country/water group is conservatively scaled by the + smallest constituent-crop budget ratio. Spatial shares within a group remain + unchanged and the unallocated budget stays on single-crop links. + + Each scaled multi-cycle contribution is removed locally where possible, then + from other single-crop links in the same crop/country/water group. The function + finishes by asserting that single plus multiplicity-weighted multi baselines + exactly reproduce the incoming national FAOSTAT budgets. + """ + links = n.links.static + is_crop = links["carrier"] == "crop_production" + multi = links[links["carrier"] == "crop_production_multi"] + if multi.empty or not is_crop.any(): + return + + single = links[is_crop].copy() + budget_cols = ["crop", "country", "water_supply"] + budgets = single.groupby(budget_cols)["baseline_area_mha"].sum().astype(float) + + cycle_long = multi_crop_cycle_multiplicities(links) + cycle_long["water_supply"] = cycle_long["link"].map(multi["water_supply"]) + cycle_long["combination"] = cycle_long["link"].map(multi["combination"]) + cycle_long["required_mha"] = ( + cycle_long["multiplicity"] * cycle_long["baseline_area_mha"] + ) + requirements = cycle_long.groupby(budget_cols)["required_mha"].sum() + ratios = budgets.reindex(requirements.index).fillna(0.0) / requirements + ratios = ratios.clip(upper=1.0) + + cycle_long["budget_ratio"] = pd.MultiIndex.from_frame(cycle_long[budget_cols]).map( + ratios + ) + combo_cols = ["combination", "country", "water_supply"] + group_scales = cycle_long.groupby(combo_cols)["budget_ratio"].min() + link_group_index = pd.MultiIndex.from_frame(multi[combo_cols]) + link_scales = pd.Series(link_group_index.map(group_scales), index=multi.index) + link_scales = link_scales.fillna(1.0).clip(lower=0.0, upper=1.0) + if (link_scales < 1.0 - 1e-12).any(): + n.links.static.loc[multi.index, "baseline_area_mha"] = ( + multi["baseline_area_mha"].astype(float) * link_scales + ) + logger.info( + "Scaled %d multi-cropping anchors to fit joint FAOSTAT crop budgets " + "(minimum factor %.4f)", + int((link_scales < 1.0 - 1e-12).sum()), + float(link_scales.min()), + ) + + # Re-expand the scaled anchors so all subsequent accounting uses exactly the + # metadata and baselines now stored on the network. + multi = n.links.static[n.links.static["carrier"] == "crop_production_multi"] + cycle_long = multi_crop_cycle_multiplicities(n.links.static) + cycle_long["region"] = cycle_long["link"].map(multi["region"]) + cycle_long["resource_class"] = ( + cycle_long["link"].map(multi["resource_class"]).astype(int) + ) + cycle_long["water_supply"] = cycle_long["link"].map(multi["water_supply"]) + cycle_long["required_mha"] = ( + cycle_long["multiplicity"] * cycle_long["baseline_area_mha"] + ) + + local_cols = ["crop", "region", "resource_class", "water_supply"] + local_required = cycle_long.groupby(local_cols)["required_mha"].sum() + single_local_index = pd.MultiIndex.from_frame(single[local_cols]) + requested_local = pd.Series( + single_local_index.map(local_required).fillna(0.0), index=single.index + ) + baseline = single["baseline_area_mha"].astype(float) + local_take = np.minimum(baseline, requested_local) + new_baseline = baseline - local_take + + group_index = pd.MultiIndex.from_frame(single[budget_cols]) + total_required = cycle_long.groupby(budget_cols)["required_mha"].sum() + locally_taken = ( + single.assign(_local_take=local_take).groupby(budget_cols)["_local_take"].sum() + ) + remaining_required = total_required.sub(locally_taken, fill_value=0.0).clip( + lower=0.0 + ) + for group, remainder in remaining_required.items(): + if remainder <= 1e-12: + continue + idx = single.index[group_index == group] + available = new_baseline.loc[idx] + total_available = float(available.sum()) + if remainder > total_available + 1e-9: + raise ValueError( + "Scaled multi-cropping anchors still exceed the FAOSTAT crop " + f"budget for {group}: need {remainder:.6g} Mha but only " + f"{total_available:.6g} Mha remains" + ) + new_baseline.loc[idx] -= ( + available / total_available * min(float(remainder), total_available) + ) + + new_baseline = new_baseline.clip(lower=0.0) + n.links.static.loc[new_baseline.index, "baseline_area_mha"] = new_baseline + + reconciled_single = ( + single.assign(baseline_area_mha=new_baseline) + .groupby(budget_cols)["baseline_area_mha"] + .sum() + ) + reconciled_multi = cycle_long.groupby(budget_cols)["required_mha"].sum() + joint = reconciled_single.add(reconciled_multi, fill_value=0.0) + difference = joint.sub(budgets, fill_value=0.0) + if not np.allclose(difference.to_numpy(), 0.0, rtol=0.0, atol=1e-9): + bad = difference[difference.abs() > 1e-9].head(10).to_dict() + raise ValueError( + "Crop baseline reconciliation failed to conserve national harvested " + f"area; differences in Mha include {bad}" + ) + logger.info( + "Reconciled %.1f Mha of multi-cropping harvested cycles while preserving " + "%d national crop/water budgets", + float(reconciled_multi.sum()), + len(budgets), + ) + + +def fix_crop_production_to_baseline(n: pypsa.Network) -> None: + """Pin every crop-production link (single and multi) at its baseline area. + + Validation mode (``validation.use_actual_production``). Runs after + ``reconcile_single_crop_baselines`` so the single-crop pins are the + reconciled baselines: each harvested cycle is counted once, on its multi + link where one was built, and the two carriers jointly reproduce the + observed harvested area. Links become non-extendable with + ``p_nom = p_nom_min = p_nom_max = baseline_area_mha`` and + ``p_min_pu = 1``, so dispatch equals the baseline exactly. + """ + links = n.links.static + mask = links["carrier"].isin(["crop_production", "crop_production_multi"]) + if not mask.any(): + return + base = links.loc[mask, "baseline_area_mha"].fillna(0.0).clip(lower=0.0) + n.links.static.loc[mask, "p_nom"] = base + n.links.static.loc[mask, "p_nom_min"] = base + n.links.static.loc[mask, "p_nom_max"] = base + n.links.static.loc[mask, "p_min_pu"] = 1.0 + n.links.static.loc[mask, "p_nom_extendable"] = False + n_multi = int((links.loc[mask, "carrier"] == "crop_production_multi").sum()) + logger.info( + "Fixed %d crop-production links (%d multi) at baseline areas " + "totalling %.1f Mha", + int(mask.sum()), + n_multi, + float(base.sum()), + ) + + def add_spared_land_links( n: pypsa.Network, baseline_land_df: pd.DataFrame, diff --git a/workflow/scripts/build_model/land.py b/workflow/scripts/build_model/land.py index 56b00916..bc579f8e 100644 --- a/workflow/scripts/build_model/land.py +++ b/workflow/scripts/build_model/land.py @@ -93,12 +93,23 @@ def add_multi_cropping_land_correction( multiple crops per year on the same land), this adds non-extendable generators sized to the deficit. This avoids forcing land conversion or slack to accommodate what is really existing multi-cropped land. + + Both single-crop and multi-cropping links draw ``baseline_area_mha`` Mha of + physical land from their cropland ``bus0``, so the harvested sum spans + ``{crop_production, crop_production_multi}``. This must run *after* the + multi-cropping links are added and the single-crop baselines reconciled + (design 6.2): with the reconciliation, an ``n``-cycle attribution reduces the + singles by ``n * A`` and the multi link adds ``A``, so the cropland-bus demand + net falls by ``(n - 1) * A`` -- exactly the land saved by sharing a field, and + the residual (unattributed extra-cycle area) still lands on this generator. """ - crop_links = n.links.static[n.links.static["carrier"] == "crop_production"] + crop_links = n.links.static[ + n.links.static["carrier"].isin(["crop_production", "crop_production_multi"]) + ] if crop_links.empty: return - # Harvested area per cropland bus (bus0) + # Harvested/physical area per cropland bus (bus0), across both carriers harvested = crop_links.groupby("bus0")["baseline_area_mha"].sum() # Existing cropland supply per cropland bus (bus1 of land_use links) diff --git a/workflow/scripts/build_model/utils.py b/workflow/scripts/build_model/utils.py index 173af895..a0b813a5 100644 --- a/workflow/scripts/build_model/utils.py +++ b/workflow/scripts/build_model/utils.py @@ -62,6 +62,27 @@ def _clip_port_coefficients( return n_clipped +def clip_baseline_area_mha(n: pypsa.Network, numerics_cfg: dict) -> int: + """Zero sub-``min_link_area_mha`` crop baselines in place; return the count. + + ``baseline_area_mha`` carries sub-hectare MIRCA-vs-FAOSTAT disaggregation + noise (e.g. ~1 ha of double-cropped soybean in a country FAOSTAT records no + soybean for). It is both pinned as a hard dispatch bound by + ``fix_crop_production_to_baseline`` and summed into the solve-time crop + growth cap. Clipping it once, before either reads it, keeps the pin and the + cap consistent -- otherwise a below-floor multi cycle is pinned above a + growth cap that has already zeroed the same baseline, and the validation + solve is infeasible. Called early (pre-pin) and again, idempotently, from + ``clip_negligible_coefficients``. + """ + links = n.links.static + if "baseline_area_mha" not in links.columns: + return 0 + return _clip_below( + links, "baseline_area_mha", float(numerics_cfg["min_link_area_mha"]) + ) + + def clip_negligible_coefficients( n: pypsa.Network, numerics_cfg: dict, @@ -92,14 +113,15 @@ def clip_negligible_coefficients( # 1. Land areas below ~1 ha: the sub-Mha disaggregation noise that drives # the tiny end of the bounds/RHS range. - n_area = _clip_below( - links, "p_nom_max", area_floor, mask=links["bus0"].isin(land_buses) - ) - if "baseline_area_mha" in links.columns: - n_area += _clip_below(links, "baseline_area_mha", area_floor) - n_area += _clip_below( - gens, "p_nom_max", area_floor, mask=gens["bus"].isin(land_buses) + land_link_mask = links["bus0"].isin(land_buses) + n_area = _clip_below(links, "p_nom", area_floor, mask=land_link_mask) + n_area += _clip_below(links, "p_nom_max", area_floor, mask=land_link_mask) + n_area += clip_baseline_area_mha(n, numerics_cfg) + land_generator_mask = gens["carrier"].str.startswith("land_") | gens["carrier"].eq( + "multi_cropping_land_correction" ) + n_area += _clip_below(gens, "p_nom", area_floor, mask=land_generator_mask) + n_area += _clip_below(gens, "p_nom_max", area_floor, mask=land_generator_mask) # 2. Trace irrigation water requirements and 3. near-zero carbon fluxes, # both carried as link efficiencies onto the water / CO2 buses. diff --git a/workflow/scripts/build_multi_cropping.py b/workflow/scripts/build_multi_cropping.py index 9739155c..e749a6e1 100644 --- a/workflow/scripts/build_multi_cropping.py +++ b/workflow/scripts/build_multi_cropping.py @@ -13,6 +13,7 @@ import pandas as pd import xarray as xr +from workflow.scripts.multi_cropping_combinations import effective_combinations from workflow.scripts.raster_utils import ( calculate_all_cell_areas, load_raster_array, @@ -52,34 +53,27 @@ WETLAND_RICE_CROPS = {"wetland-rice"} -def sequence_feasible( - starts: list[np.ndarray], lengths: list[np.ndarray] -) -> np.ndarray: - if not starts or not lengths or len(starts) != len(lengths): - raise ValueError("Starts and lengths must be equally-sized non-empty lists") +# exactextract's NumPyRasterSource retains, at the C++ level, the numpy array it +# is handed, and that memory is never reclaimed even after the source is GC'd. +# Because this module wraps a fresh full-grid array on every call (once per combo +# x class x period), that leak accumulates to tens of GB. Reusing a single buffer +# per grid shape means only one array is ever retained -- copying the caller's +# transient array into the buffer lets the transient be freed normally. +_EXTRACT_BUFFERS: dict[tuple[int, ...], np.ndarray] = {} - feasible = np.ones_like(starts[0], dtype=bool) - for arr in [*starts, *lengths]: - feasible &= np.isfinite(arr) - for arr in lengths: - feasible &= arr > 0 - if not feasible.any(): - return feasible +def get_extract_buffer(shape: tuple[int, ...]) -> np.ndarray: + """Return the reused float64 exact_extract buffer for ``shape``. - prev_end = starts[0] + lengths[0] - for idx in range(1, len(starts)): - start = starts[idx] - length = lengths[idx] - # Shift later crops forward by full years until they start after the previous crop - lag = np.maximum(prev_end - start, 0.0) - shift_cycles = np.ceil(lag / 365.0) - adjusted_start = start + shift_cycles * 365.0 - prev_end = adjusted_start + length - - total_span = prev_end - starts[0] - feasible &= np.isfinite(total_span) & (total_span <= 365.0) - return feasible + Callers may fill the buffer themselves (e.g. accumulate directly into it) + and pass it to the aggregation functions, which skip the defensive copy + when handed the buffer itself. + """ + buffer = _EXTRACT_BUFFERS.get(shape) + if buffer is None: + buffer = np.empty(shape, dtype=np.float64) + _EXTRACT_BUFFERS[shape] = buffer + return buffer def aggregate_raster_by_region( @@ -92,9 +86,16 @@ def aggregate_raster_by_region( crs_wkt: str | None, stat: str = "sum", ) -> pd.DataFrame: - """Aggregate raster data by regions using exact_extract.""" + """Aggregate raster data by regions using exact_extract. + + The data is copied into a reused per-shape buffer before being handed to + ``NumPyRasterSource`` to avoid a per-call memory leak (see ``_EXTRACT_BUFFERS``). + """ + buffer = get_extract_buffer(data_array.shape) + if data_array is not buffer: + buffer[:] = data_array src = NumPyRasterSource( - data_array, + buffer, xmin=xmin, ymin=ymin, xmax=xmax, @@ -111,18 +112,71 @@ def aggregate_raster_by_region( ) +def region_coverage_entries( + regions_gdf: gpd.GeoDataFrame, + xmin: float, + ymin: float, + xmax: float, + ymax: float, + crs_wkt: str | None, + shape: tuple[int, int], +) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]: + """Per-region cell coverage fractions for a grid, extracted once. + + Returns ``(region_names, rows, cells, fracs)`` where ``rows`` indexes into + ``region_names``, ``cells`` are flat row-major indices into the grid, and + ``fracs`` is the fraction of each cell covered by the region. For an + all-finite value grid ``v``, + ``np.bincount(rows, weights=v.ravel()[cells] * fracs, minlength=len(region_names))`` + reproduces exact_extract's ``sum`` op, so any number of rasters sharing the + grid can be aggregated without recomputing the polygon coverage. + """ + buffer = get_extract_buffer(shape) + buffer.fill(0.0) # cell values are irrelevant here but must be finite + src = NumPyRasterSource( + buffer, + xmin=xmin, + ymin=ymin, + xmax=xmax, + ymax=ymax, + nodata=np.nan, + srs_wkt=crs_wkt, + ) + res = exact_extract( + src, + regions_gdf, + ["cell_id", "coverage"], + include_cols=["region"], + output="pandas", + ) + lengths = res["cell_id"].map(len).to_numpy() + rows = np.repeat(np.arange(len(res)), lengths) + cells = np.concatenate([np.asarray(a, dtype=np.int64) for a in res["cell_id"]]) + fracs = np.concatenate([np.asarray(a, dtype=np.float64) for a in res["coverage"]]) + return res["region"].to_numpy(), rows, cells, fracs + + def compute_eligibility_mask( crop_sequence: list[str], ws: str, zone_arr: np.ndarray, suitability_data: dict, - start_data: dict, - length_data: dict, yield_data: dict, water_requirement_data: dict, ) -> tuple[np.ndarray, np.ndarray, np.ndarray | None]: - """Compute combined eligibility mask and return mask, min_fraction, total_water.""" - # Zone capability check + """Compute combined eligibility mask and return mask, min_fraction, total_water. + + The eligible (expansion-potential) area for a combination is the GAEZ-suitable + area where the multiple-cropping zone permits the cycle count (and, for + irrigated, the water requirement is defined). ``sequence_feasible`` on GAEZ + windows is deliberately NOT used as a gate: GAEZ attainable season lengths + overshoot the farmed cycle, so it rejects ~all observed double-cropping + (including repeated same-crop combos fed identical windows). This mirrors the + Stage-1 decoupling -- feasibility from observation, GAEZ only for the water + split -- and keeps the potential cap aligned with the anchored baseline + (``p_nom_max = max(potential, anchor)``). + """ + # Zone capability check (enforces cycle count and wetland-rice-cycle limit) rice_cycles = sum(1 for crop in crop_sequence if crop in WETLAND_RICE_CROPS) allowed_zone_codes = [ code @@ -145,11 +199,6 @@ def compute_eligibility_mask( min_fraction[~np.isfinite(min_fraction)] = np.nan min_fraction = np.clip(min_fraction, 0.0, 1.0, out=min_fraction) - # Growing season feasibility - start_stack = [start_data[(crop, ws)] for crop in crop_sequence] - length_stack = [length_data[(crop, ws)] for crop in crop_sequence] - feasible_mask = sequence_feasible(start_stack, length_stack) - # Yield check yield_stack = [yield_data[(crop, ws)] for crop in crop_sequence] positive_yield = np.ones_like(min_fraction, dtype=bool) @@ -166,9 +215,7 @@ def compute_eligibility_mask( valid_water = np.ones_like(min_fraction, dtype=bool) total_water_arr = None - combined_mask = ( - feasible_mask & valid_suit & positive_yield & valid_water & zone_mask - ) + combined_mask = valid_suit & positive_yield & valid_water & zone_mask return combined_mask, min_fraction, total_water_arr @@ -177,7 +224,11 @@ def compute_eligibility_mask( combos: list[dict[str, object]] = [] use_actual_yields = bool(getattr(snakemake.params, "use_actual_yields", False)) # type: ignore[attr-defined] - for name, entry in snakemake.params.combinations.items(): # type: ignore[attr-defined,name-defined] + combinations = effective_combinations( + snakemake.config, # type: ignore[attr-defined,name-defined] + snakemake.input.combinations, # type: ignore[attr-defined,name-defined] + ) + for name, entry in combinations.items(): if entry is None: continue crops = [str(c) for c in entry["crops"]] @@ -196,6 +247,7 @@ def compute_eligibility_mask( } classes_nc = inputs.pop("classes") regions_path = inputs.pop("regions") + inputs.pop("combinations") conv_csv = inputs.pop("yield_unit_conversions") moisture_csv = inputs.pop("moisture_content") @@ -204,8 +256,6 @@ def compute_eligibility_mask( suffixes = { "_yield_raster": "yield", "_suitability_raster": "suitability", - "_growing_season_start_raster": "season_start", - "_growing_season_length_raster": "season_length", "_water_requirement_raster": "water_requirement", } for key, path in inputs.items(): @@ -304,16 +354,12 @@ def conversion_factor(crop: str) -> float: yield_data: dict[tuple[str, str], np.ndarray] = {} suitability_data: dict[tuple[str, str], np.ndarray] = {} - start_data: dict[tuple[str, str], np.ndarray] = {} - length_data: dict[tuple[str, str], np.ndarray] = {} water_requirement_data: dict[tuple[str, str], np.ndarray] = {} for (crop, ws), files in crop_files.items(): factor = conversion_factor(crop) y_arr = load_raster_array(files["yield"]) suitability_arr = load_raster_array(files["suitability"]) - start_arr = load_raster_array(files["season_start"]) - length_arr = load_raster_array(files["season_length"]) if y_arr.shape != (height, width): raise ValueError( f"Yield raster for '{crop}' ({ws}) has unexpected dimensions" @@ -322,14 +368,6 @@ def conversion_factor(crop: str) -> float: raise ValueError( f"Suitability raster for '{crop}' ({ws}) has unexpected dimensions" ) - if start_arr.shape != (height, width): - raise ValueError( - f"Growing season start raster for '{crop}' ({ws}) has unexpected dimensions" - ) - if length_arr.shape != (height, width): - raise ValueError( - f"Growing season length raster for '{crop}' ({ws}) has unexpected dimensions" - ) y_scaled = y_arr * factor if use_actual_yields and crop in moisture_lookup: @@ -337,8 +375,6 @@ def conversion_factor(crop: str) -> float: y_scaled = y_scaled * (1.0 - moisture_lookup[crop]) yield_data[(crop, ws)] = y_scaled suitability_data[(crop, ws)] = scale_fraction(suitability_arr) - start_data[(crop, ws)] = start_arr - length_data[(crop, ws)] = length_arr if ws == "i": path = files.get("water_requirement") @@ -363,6 +399,15 @@ def conversion_factor(crop: str) -> float: if int(cls) >= 0 ] + # Region coverage fractions are identical for every raster on this grid, so + # extract them once and aggregate all combo/class/period/cycle rasters with + # gathers + bincount instead of one exact_extract call per raster. + region_names, cov_rows, cov_cells, cov_fracs = region_coverage_entries( + regions_for_extract, xmin, ymin, xmax, ymax, crs_wkt, (height, width) + ) + n_regions = len(region_names) + class_at_entry = class_labels.ravel()[cov_cells] + eligible_records: list[pd.DataFrame] = [] cycle_records: list[pd.DataFrame] = [] @@ -378,8 +423,6 @@ def conversion_factor(crop: str) -> float: ws, zone_arr, suitability_data, - start_data, - length_data, yield_data, water_requirement_data, ) @@ -389,60 +432,51 @@ def conversion_factor(crop: str) -> float: eligible_fraction = np.where(combined_mask, min_fraction, np.nan) eligible_area = eligible_fraction * cell_area_ha + # Coverage entries restricted to the combo's eligible cells; per class, + # every aggregation is a gather over these entries + bincount against + # the same coverage fractions exact_extract would apply. + combo_at_entry = combined_mask.ravel()[cov_cells] + ea_flat = eligible_area.ravel() + for cls in valid_classes: - class_mask = (class_labels == cls) & combined_mask - if not np.any(class_mask): + sel = combo_at_entry & (class_at_entry == cls) + if not sel.any(): continue + rows_s = cov_rows[sel] + cells_s = cov_cells[sel] + fracs_s = cov_fracs[sel] + ea_entries = ea_flat[cells_s] * fracs_s - # Aggregate area by region - area_array = np.where(class_mask, eligible_area, np.nan) - area_stats = aggregate_raster_by_region( - area_array, regions_for_extract, xmin, ymin, xmax, ymax, crs_wkt + area_by_region = np.bincount( + rows_s, weights=ea_entries, minlength=n_regions ) - if area_stats.empty: + pos = area_by_region > 0 + if not pos.any(): continue - area_stats = area_stats.rename(columns={"sum": "eligible_area_ha"}) - area_stats = area_stats.replace([np.inf, -np.inf], np.nan).dropna( - subset=["eligible_area_ha"] + area_stats = pd.DataFrame( + { + "region": region_names[pos], + "eligible_area_ha": area_by_region[pos], + } ) - area_stats = area_stats[area_stats["eligible_area_ha"] > 0] - if area_stats.empty: - continue - # Calculate water requirements for irrigated crops + # Annual irrigation requirement (m3/ha): aggregate the summed-cycle + # demand*area numerator against the same eligible-area denominator. if ws == "i" and total_water_arr is not None: - water_numerator = np.where( - class_mask, total_water_arr * eligible_area, np.nan + volume = np.bincount( + rows_s, + weights=total_water_arr.ravel()[cells_s] * ea_entries, + minlength=n_regions, ) - water_stats = aggregate_raster_by_region( - water_numerator, - regions_for_extract, - xmin, - ymin, - xmax, - ymax, - crs_wkt, + area_stats["water_requirement_m3_per_ha"] = ( + volume[pos] / area_by_region[pos] ) - if not water_stats.empty: - water_stats = water_stats.rename(columns={"sum": "water_volume_m3"}) - area_stats = area_stats.merge(water_stats, on="region", how="left") - area_stats = area_stats.replace([np.inf, -np.inf], np.nan).dropna( - subset=["water_volume_m3"] - ) - area_stats["water_requirement_m3_per_ha"] = ( - area_stats["water_volume_m3"] / area_stats["eligible_area_ha"] - ) - area_stats = area_stats.replace([np.inf, -np.inf], np.nan).dropna( - subset=["water_requirement_m3_per_ha"] - ) - area_stats.drop(columns=["water_volume_m3"], inplace=True) else: area_stats["water_requirement_m3_per_ha"] = 0.0 area_stats["resource_class"] = cls area_stats["combination"] = combo_name area_stats["water_supply"] = ws - area_for_cycles = area_stats.copy() eligible_records.append( area_stats[ [ @@ -460,47 +494,27 @@ def conversion_factor(crop: str) -> float: for idx, (crop_name, yield_arr) in enumerate( zip(crop_sequence, yield_stack), start=1 ): - numerator = np.where(class_mask, yield_arr * eligible_area, np.nan) - numerator_stats = aggregate_raster_by_region( - numerator, regions_for_extract, xmin, ymin, xmax, ymax, crs_wkt + numerator = np.bincount( + rows_s, + weights=yield_arr.ravel()[cells_s] * ea_entries, + minlength=n_regions, ) - if numerator_stats.empty: + yield_t_per_ha = numerator[pos] / area_by_region[pos] + keep = yield_t_per_ha > 0 + if not keep.any(): continue - - numerator_stats = numerator_stats.rename( - columns={"sum": "yield_times_area"} - ) - merged = area_for_cycles.merge(numerator_stats, on="region", how="left") - merged = merged.replace([np.inf, -np.inf], np.nan).dropna( - subset=["yield_times_area"] - ) - merged["yield_t_per_ha"] = ( - merged["yield_times_area"] / merged["eligible_area_ha"] - ) - merged = ( - merged.replace([np.inf, -np.inf], np.nan) - .dropna(subset=["yield_t_per_ha"]) - .query("yield_t_per_ha > 0") - ) - if merged.empty: - continue - merged["combination"] = combo_name - merged["resource_class"] = cls - merged["water_supply"] = ws - merged["cycle_index"] = idx - merged["crop"] = crop_name cycle_records.append( - merged[ - [ - "combination", - "region", - "resource_class", - "water_supply", - "cycle_index", - "crop", - "yield_t_per_ha", - ] - ] + pd.DataFrame( + { + "combination": combo_name, + "region": region_names[pos][keep], + "resource_class": cls, + "water_supply": ws, + "cycle_index": idx, + "crop": crop_name, + "yield_t_per_ha": yield_t_per_ha[keep], + } + ) ) if eligible_records: @@ -511,7 +525,7 @@ def conversion_factor(crop: str) -> float: ) eligible_df["water_requirement_m3_per_ha"] = pd.to_numeric( eligible_df["water_requirement_m3_per_ha"], errors="coerce" - ) + ).fillna(0.0) eligible_df.sort_values( ["combination", "water_supply", "region", "resource_class"], inplace=True, diff --git a/workflow/scripts/calibrate_deviation_penalty.py b/workflow/scripts/calibrate_deviation_penalty.py index 872794ff..2609dd19 100644 --- a/workflow/scripts/calibrate_deviation_penalty.py +++ b/workflow/scripts/calibrate_deviation_penalty.py @@ -443,7 +443,8 @@ def evaluate_fn(lambdas: dict[str, float], *, iter_id: int) -> dict[str, float]: header = ( "# SPDX-FileCopyrightText: 2026 Koen van Greevenbroek\n" "#\n" - "# SPDX-License-Identifier: CC-BY-4.0\n" + "# SPDX-License-" + "Identifier: CC-BY-4.0\n" "#\n" "# Auto-generated by workflow/scripts/calibrate_deviation_penalty.py.\n" "# Consumed at solve time when deviation_penalty..l1_cost\n" diff --git a/workflow/scripts/compute_food_waste_calibration.py b/workflow/scripts/compute_food_waste_calibration.py index 55254920..76f3f94a 100644 --- a/workflow/scripts/compute_food_waste_calibration.py +++ b/workflow/scripts/compute_food_waste_calibration.py @@ -194,7 +194,8 @@ def compute_calibration( header = ( "# SPDX-FileCopyrightText: 2026 Koen van Greevenbroek\n" "#\n" - "# SPDX-License-Identifier: CC-BY-4.0\n" + "# SPDX-License-" + "Identifier: CC-BY-4.0\n" "#\n" "# Per-food-group waste-retention multipliers, applied by\n" "# prepare_food_loss_waste.py as:\n" diff --git a/workflow/scripts/derive_mirca_multicropping.py b/workflow/scripts/derive_mirca_multicropping.py new file mode 100644 index 00000000..301218b9 --- /dev/null +++ b/workflow/scripts/derive_mirca_multicropping.py @@ -0,0 +1,557 @@ +""" +SPDX-FileCopyrightText: 2026 Koen van Greevenbroek + +SPDX-License-Identifier: GPL-3.0-or-later + +Derive an observed multi-cropping baseline from MIRCA-OS. + +For each water system and grid cell, MIRCA annual harvested area above the +maximum-monthly physical footprint is the extra-cycle magnitude. The fixed +combination catalog supplies plausible crop sequences. Candidate sequences are +filled at a common proportional rate subject to four simultaneous budgets: + +* the extra-cycle magnitude; +* the physical cropped footprint; +* every constituent crop's observed harvested area; and +* each sequence's MIRCA support and GAEZ cycle-zone capacity. + +The shared budgets prevent one observed crop hectare from being reused in +several overlapping sequences. Irrigated and rainfed systems are derived +independently. The resulting physical bundle areas are aggregated directly to +the active configuration's regions and resource classes. +""" + +from collections import Counter +from dataclasses import dataclass +from pathlib import Path + +from affine import Affine +import geopandas as gpd +import numpy as np +import pandas as pd +import rasterio +from rasterio.crs import CRS +import xarray as xr + +from workflow.scripts.build_multi_cropping import ( + WETLAND_RICE_CROPS, + ZONE_CAPABILITIES, + region_coverage_entries, +) +from workflow.scripts.multi_cropping_combinations import load_catalog_combinations +from workflow.scripts.raster_utils import raster_bounds + + +@dataclass(frozen=True) +class GridSpec: + """Spatial identity of a two-dimensional raster grid.""" + + shape: tuple[int, int] + transform: Affine + crs: CRS + + +def _grid_from_raster(src, path: str) -> GridSpec: + if src.crs is None: + raise ValueError(f"Raster '{path}' has no CRS") + return GridSpec(src.shape, src.transform, src.crs) + + +def _assert_grid(actual: GridSpec, expected: GridSpec, label: str) -> None: + if actual.shape != expected.shape: + raise ValueError( + f"{label} shape {actual.shape} does not match reference {expected.shape}" + ) + if actual.crs != expected.crs: + raise ValueError(f"{label} CRS does not match the reference grid") + # MIRCA-OS contains one transform rounded at seven decimal places. Accept + # harmless metadata rounding below 1% of a pixel, but reject a real grid + # shift, resolution difference, or rotation. + pixel_tolerance = min(abs(expected.transform.a), abs(expected.transform.e)) * 0.01 + if not np.allclose( + tuple(actual.transform), + tuple(expected.transform), + rtol=0.0, + atol=pixel_tolerance, + ): + raise ValueError( + f"{label} transform differs from the reference by more than 1% of a pixel" + ) + + +def load_tif(path: str, expected_grid: GridSpec | None = None) -> np.ndarray: + """Load a GeoTIFF as float64 and validate its complete grid identity.""" + with rasterio.open(path) as src: + grid = _grid_from_raster(src, path) + if expected_grid is not None: + _assert_grid(grid, expected_grid, path) + arr = src.read(1).astype(np.float64) + nodata = src.nodata + if nodata is not None: + arr[arr == nodata] = 0.0 + arr[~np.isfinite(arr)] = 0.0 + arr[arr < 0] = 0.0 + return arr + + +def _spatial_dims(da: xr.DataArray) -> tuple[str, str]: + lat_dims = [dim for dim in da.dims if dim.lower() in {"lat", "latitude", "y"}] + lon_dims = [dim for dim in da.dims if dim.lower() in {"lon", "longitude", "x"}] + if len(lat_dims) != 1 or len(lon_dims) != 1: + raise ValueError( + f"Could not identify one latitude and longitude dimension in {da.dims}" + ) + return lat_dims[0], lon_dims[0] + + +def _coordinate_centers(grid: GridSpec) -> tuple[np.ndarray, np.ndarray]: + if not grid.crs.is_geographic: + raise ValueError("MIRCA-OS grids must use a geographic CRS") + height, width = grid.shape + x = grid.transform.c + (np.arange(width) + 0.5) * grid.transform.a + y = grid.transform.f + (np.arange(height) + 0.5) * grid.transform.e + return y, x + + +def _align_spatial_dataarray( + da: xr.DataArray, grid: GridSpec, label: str +) -> np.ndarray: + """Align a lat/lon DataArray to ``grid`` or fail on any other mismatch.""" + lat_dim, lon_dim = _spatial_dims(da) + other_dims = [dim for dim in da.dims if dim not in {lat_dim, lon_dim}] + if other_dims: + raise ValueError(f"{label} still has non-spatial dimensions: {other_dims}") + da = da.transpose(lat_dim, lon_dim) + if da.shape != grid.shape: + raise ValueError(f"{label} shape {da.shape} does not match {grid.shape}") + + expected_lat, expected_lon = _coordinate_centers(grid) + lat = np.asarray(da[lat_dim].values, dtype=float) + lon = np.asarray(da[lon_dim].values, dtype=float) + atol = max(abs(grid.transform.a), abs(grid.transform.e)) * 1e-5 + + if np.allclose(lat[::-1], expected_lat, atol=atol, rtol=0.0): + da = da.isel({lat_dim: slice(None, None, -1)}) + lat = lat[::-1] + if np.allclose(lon[::-1], expected_lon, atol=atol, rtol=0.0): + da = da.isel({lon_dim: slice(None, None, -1)}) + lon = lon[::-1] + if not np.allclose(lat, expected_lat, atol=atol, rtol=0.0): + raise ValueError( + f"{label} latitude coordinates do not match the reference grid" + ) + if not np.allclose(lon, expected_lon, atol=atol, rtol=0.0): + raise ValueError( + f"{label} longitude coordinates do not match the reference grid" + ) + return np.asarray(da.values, dtype=np.float64) + + +def load_subcrop_maxmonth(path: str, grid: GridSpec) -> np.ndarray: + """Collapse a MIRCA monthly grid to its maximum growing area per cell.""" + with xr.open_dataset(path) as ds: + if "harvested_area" not in ds: + raise ValueError(f"{path} is missing the 'harvested_area' variable") + da = ds["harvested_area"] + lat_dim, lon_dim = _spatial_dims(da) + month_dims = [dim for dim in da.dims if dim not in {lat_dim, lon_dim}] + if len(month_dims) != 1: + raise ValueError(f"{path} must have exactly one monthly dimension") + collapsed = da.max(month_dims[0], skipna=True) + arr = _align_spatial_dataarray(collapsed, grid, path) + arr[~np.isfinite(arr)] = 0.0 + arr[arr < 0] = 0.0 + return arr + + +def zone_mask(zone_arr: np.ndarray, n_cycles: int, n_rice: int) -> np.ndarray: + """Return cells whose GAEZ zone permits the requested cycle counts.""" + allowed = [ + code + for code, cap in ZONE_CAPABILITIES.items() + if cap.get("valid", False) + and int(cap.get("max_cycles", 0)) >= n_cycles + and int(cap.get("max_wetland_rice", 0)) >= n_rice + ] + return np.isin(zone_arr, allowed) + + +def candidate_capacity( + crops: list[str], + ws: str, + crop_area: dict[tuple[str, str], np.ndarray], + zone_arr: np.ndarray, + rice_support: dict[str, np.ndarray], +) -> np.ndarray: + """Return the MIRCA-supported physical area cap for one crop sequence.""" + n_cycles = len(crops) + n_rice = sum(crop in WETLAND_RICE_CROPS for crop in crops) + zmask = zone_mask(zone_arr, n_cycles, n_rice) + + if n_rice == n_cycles and n_cycles >= 2: + rice2 = rice_support[ws] + rice3 = rice_support[f"{ws}3"] + if n_cycles == 2: + area = np.clip(rice2 - rice3, 0.0, None) + elif n_cycles == 3: + area = rice3.copy() + else: + raise ValueError(f"Unsupported repeated-rice cycle count: {n_cycles}") + area[~zmask] = 0.0 + return area + + area = crop_area[(crops[0], ws)].copy() + observed = area > 0 + for crop in crops[1:]: + support = crop_area[(crop, ws)] + np.minimum(area, support, out=area) + observed &= support > 0 + area[~(zmask & observed)] = 0.0 + return area + + +def _bounded_ratio(numerator: np.ndarray, denominator: np.ndarray) -> np.ndarray: + with np.errstate(divide="ignore", invalid="ignore"): + ratio = np.divide( + numerator, + denominator, + out=np.ones_like(numerator, dtype=np.float64), + where=denominator > 0, + ) + return np.clip(np.nan_to_num(ratio, nan=0.0, posinf=1.0), 0.0, 1.0) + + +def allocate( + extra_cycle_area: np.ndarray, + footprint_area: np.ndarray, + capacities: list[np.ndarray], + crop_sequences: list[list[str]], + crop_support: dict[str, np.ndarray], +) -> tuple[list[np.ndarray], np.ndarray]: + """Fill candidate capacities proportionally under shared cell budgets. + + A common fill ratio is used for all candidates in a cell. This is the + max-min fair proportional attribution: no overlapping rotation is favored, + and all magnitude that cannot be assigned without exceeding a crop or field + budget remains residual. + """ + if len(capacities) != len(crop_sequences): + raise ValueError("capacities and crop_sequences must have equal length") + if not capacities: + return [], extra_cycle_area.copy() + + total_physical = np.zeros_like(extra_cycle_area, dtype=np.float64) + total_extra = np.zeros_like(extra_cycle_area, dtype=np.float64) + required_by_crop = { + crop: np.zeros_like(extra_cycle_area, dtype=np.float64) + for crops in crop_sequences + for crop in crops + } + for capacity, crops in zip(capacities, crop_sequences, strict=True): + total_physical += capacity + total_extra += (len(crops) - 1) * capacity + for crop, multiplicity in Counter(crops).items(): + required_by_crop[crop] += multiplicity * capacity + + scale = np.ones_like(extra_cycle_area, dtype=np.float64) + np.minimum(scale, _bounded_ratio(extra_cycle_area, total_extra), out=scale) + np.minimum(scale, _bounded_ratio(footprint_area, total_physical), out=scale) + for crop, required in required_by_crop.items(): + np.minimum(scale, _bounded_ratio(crop_support[crop], required), out=scale) + + areas: list[np.ndarray] = [] + allocated_extra = np.zeros_like(extra_cycle_area, dtype=np.float64) + for capacity, crops in zip(capacities, crop_sequences, strict=True): + area = capacity * scale + areas.append(area) + allocated_extra += (len(crops) - 1) * area + residual = np.clip(extra_cycle_area - allocated_extra, 0.0, None) + return areas, residual + + +def run_derivation( + annual_harvested: dict[tuple[str, str], np.ndarray], + footprint: dict[str, np.ndarray], + crop_area: dict[tuple[str, str], np.ndarray], + zone: dict[str, np.ndarray], + rice_support: dict[str, np.ndarray], + combos: list[dict], + harvested_totals: dict[str, np.ndarray] | None = None, +) -> tuple[dict[tuple[str, str], np.ndarray], np.ndarray, pd.DataFrame]: + """Run independent rainfed and irrigated baseline attribution.""" + if harvested_totals is None: + harvested_totals = { + mws: np.sum( + [arr for (crop, ws), arr in annual_harvested.items() if ws == mws], + axis=0, + ) + for mws in ("ir", "rf") + } + + area_rasters: dict[tuple[str, str], np.ndarray] = {} + residual_total = np.zeros_like(next(iter(footprint.values())), dtype=np.float64) + records: list[dict] = [] + ws_map = {"i": "ir", "r": "rf"} + + for ws, mws in ws_map.items(): + ws_combos = [combo for combo in combos if combo["water_supply"] == ws] + extra = np.clip(harvested_totals[mws] - footprint[mws], 0.0, None) + capacities = [ + candidate_capacity(combo["crops"], ws, crop_area, zone[ws], rice_support) + for combo in ws_combos + ] + support = { + crop: crop_area[(crop, ws)] + for combo in ws_combos + for crop in combo["crops"] + } + areas, residual = allocate( + extra, + footprint[mws], + capacities, + [combo["crops"] for combo in ws_combos], + support, + ) + residual_total += residual + + for combo, area in zip(ws_combos, areas, strict=True): + key = (combo["name"], ws) + area_rasters[key] = area + n_cycles = len(combo["crops"]) + records.append( + { + "combination": combo["name"], + "water_supply": ws, + "cycles": n_cycles, + "physical_area_mha": float(area.sum()) / 1e6, + "extra_cycle_area_mha": float((n_cycles - 1) * area.sum()) / 1e6, + "system_extra_cycle_area_mha": float(extra.sum()) / 1e6, + "system_residual_area_mha": float(residual.sum()) / 1e6, + } + ) + + return area_rasters, residual_total, pd.DataFrame.from_records(records) + + +def build_crop_area( + annual_harvested: dict[tuple[str, str], np.ndarray], + glade_to_mirca: dict[str, str], +) -> dict[tuple[str, str], np.ndarray]: + """Map MIRCA crop/system arrays to GLADE crop/water codes.""" + return { + (glade_crop, gws): annual_harvested[(mirca_crop, mws)] + for glade_crop, mirca_crop in glade_to_mirca.items() + for gws, mws in {"i": "ir", "r": "rf"}.items() + } + + +def _annual_key(mirca_crop: str, mws: str) -> str: + return f"annual_{mirca_crop.replace(' ', '_')}_{mws}" + + +def _load_inputs( + inp: dict, + mapping: pd.DataFrame, + catalog: dict[str, dict], + grid: GridSpec, +) -> tuple[ + dict[tuple[str, str], np.ndarray], + dict[str, np.ndarray], + dict[str, np.ndarray], + dict[str, np.ndarray], + dict[str, np.ndarray], +]: + """Load totals plus only the crop arrays used by the fixed catalog.""" + needed_glade = {crop for entry in catalog.values() for crop in entry["crops"]} + mirca_to_glade = { + row.mirca_crop: row.glade_crop + for row in mapping.itertuples() + if row.glade_crop in needed_glade + } + annual: dict[tuple[str, str], np.ndarray] = {} + totals = {mws: np.zeros(grid.shape, dtype=np.float64) for mws in ("ir", "rf")} + for row in mapping.itertuples(): + for mws in ("ir", "rf"): + arr = load_tif(inp[_annual_key(row.mirca_crop, mws)], grid) + totals[mws] += arr + if row.mirca_crop in mirca_to_glade: + annual[(row.mirca_crop, mws)] = arr + + footprint = {mws: load_tif(inp[f"footprint_{mws}"], grid) for mws in ("ir", "rf")} + zone = {ws: load_tif(inp[f"zone_{ws}"], grid) for ws in ("i", "r")} + rice_support: dict[str, np.ndarray] = {} + for gws, mws in {"i": "ir", "r": "rf"}.items(): + rice_support[gws] = load_subcrop_maxmonth(inp[f"rice2_{mws}"], grid) + rice_support[f"{gws}3"] = load_subcrop_maxmonth(inp[f"rice3_{mws}"], grid) + return annual, totals, footprint, zone, rice_support + + +BASELINE_COLUMNS = [ + "combination", + "region", + "resource_class", + "water_supply", + "baseline_area_ha", +] + + +def _load_resource_classes(path: str, grid: GridSpec) -> np.ndarray: + with xr.open_dataset(path) as ds: + if "resource_class" not in ds: + raise ValueError("resource_classes.nc is missing 'resource_class'") + try: + transform = Affine.from_gdal(*ds.attrs["transform"]) + crs = CRS.from_wkt(ds.attrs["crs_wkt"]) + except KeyError as exc: + raise ValueError( + "resource_classes.nc is missing transform or CRS metadata" + ) from exc + labels = ds["resource_class"].values.astype(np.int16) + _assert_grid(GridSpec(labels.shape, transform, crs), grid, path) + return labels + + +def aggregate_baseline( + area_rasters: dict[tuple[str, str], np.ndarray], + class_labels: np.ndarray, + regions_gdf: gpd.GeoDataFrame, + grid: GridSpec, +) -> pd.DataFrame: + """Aggregate physical bundle areas to region and resource class.""" + regions = regions_gdf + if regions.crs is None: + raise ValueError("regions.geojson is missing CRS information") + if regions.crs != grid.crs: + regions = regions.to_crs(grid.crs) + regions_for_extract = regions.reset_index() + if "region" not in regions_for_extract: + raise ValueError("regions.geojson must provide a 'region' index or column") + + height, width = grid.shape + xmin, ymin, xmax, ymax = raster_bounds(grid.transform, width, height) + region_names, rows, cells, fractions = region_coverage_entries( + regions_for_extract, + xmin, + ymin, + xmax, + ymax, + grid.crs.to_wkt(), + grid.shape, + ) + class_at_entry = class_labels.ravel()[cells] + valid_classes = sorted( + int(value) + for value in np.unique(class_labels[np.isfinite(class_labels)]) + if int(value) >= 0 + ) + records: list[pd.DataFrame] = [] + for (name, ws), area in area_rasters.items(): + values = area.ravel()[cells] * fractions + for resource_class in valid_classes: + selected = class_at_entry == resource_class + sums = np.bincount( + rows[selected], weights=values[selected], minlength=len(region_names) + ) + positive = sums > 0 + if positive.any(): + records.append( + pd.DataFrame( + { + "combination": name, + "region": region_names[positive], + "resource_class": resource_class, + "water_supply": ws, + "baseline_area_ha": sums[positive], + } + ) + ) + if not records: + return pd.DataFrame(columns=BASELINE_COLUMNS) + return pd.concat(records, ignore_index=True)[BASELINE_COLUMNS].sort_values( + ["combination", "water_supply", "region", "resource_class"], + ignore_index=True, + ) + + +def main() -> None: + inp = dict(snakemake.input.items()) # type: ignore[name-defined] + params = snakemake.params # type: ignore[name-defined] + + with rasterio.open(inp["footprint_ir"]) as src: + grid = _grid_from_raster(src, inp["footprint_ir"]) + raster_profile = src.profile + + mapping = pd.read_csv(inp["concordance"], comment="#") + mapping["glade_crop"] = mapping["glade_crop"].fillna("").astype(str).str.strip() + mapping["mirca_crop"] = mapping["mirca_crop"].astype(str).str.strip() + catalog = load_catalog_combinations(inp["catalog"]) + needed = {crop for entry in catalog.values() for crop in entry["crops"]} + glade_to_mirca = { + row.glade_crop: row.mirca_crop + for row in mapping.itertuples() + if row.glade_crop in needed + } + missing = sorted(needed - set(glade_to_mirca)) + if missing: + raise ValueError(f"MIRCA catalog crops missing from concordance: {missing}") + + annual, totals, footprint, zone, rice_support = _load_inputs( + inp, mapping, catalog, grid + ) + crop_area = build_crop_area(annual, glade_to_mirca) + combos = [ + {"name": name, "crops": entry["crops"], "water_supply": ws} + for name, entry in catalog.items() + for ws in entry["water_supplies"] + ] + areas, residual, stats = run_derivation( + annual, + footprint, + crop_area, + zone, + rice_support, + combos, + harvested_totals=totals, + ) + + classes = _load_resource_classes(inp["classes"], grid) + regions = gpd.read_file(inp["regions"]) + baseline = aggregate_baseline(areas, classes, regions, grid) + + baseline_path = Path(snakemake.output.baseline) # type: ignore[name-defined] + residual_path = Path(snakemake.output.residual) # type: ignore[name-defined] + stats_path = Path(snakemake.output.stats) # type: ignore[name-defined] + for path in (baseline_path, residual_path, stats_path): + path.parent.mkdir(parents=True, exist_ok=True) + baseline.to_csv(baseline_path, index=False) + stats.insert(0, "source_year", int(params.source_year)) + stats.to_csv(stats_path, index=False) + + profile = { + **raster_profile, + "count": 1, + "dtype": "float32", + "nodata": 0.0, + "compress": "deflate", + } + with rasterio.open(residual_path, "w", **profile) as dst: + dst.write(residual.astype(np.float32), 1) + + total_extra = ( + sum( + float(np.clip(totals[mws] - footprint[mws], 0.0, None).sum()) + for mws in ("ir", "rf") + ) + / 1e6 + ) + total_residual = float(residual.sum()) / 1e6 + share = 0.0 if total_extra == 0 else total_residual / total_extra * 100 + print( + f"MIRCA-OS {int(params.source_year)} multi-cropping baseline: " + f"extra={total_extra:.1f} Mha, attributed={total_extra - total_residual:.1f} " + f"Mha, residual={total_residual:.1f} Mha ({share:.0f}%)" + ) + + +if __name__ == "__main__": + main() diff --git a/workflow/scripts/download_mirca_os_bag_member.py b/workflow/scripts/download_mirca_os_bag_member.py new file mode 100644 index 00000000..596d5a14 --- /dev/null +++ b/workflow/scripts/download_mirca_os_bag_member.py @@ -0,0 +1,72 @@ +""" +SPDX-FileCopyrightText: 2026 Koen van Greevenbroek + +SPDX-License-Identifier: GPL-3.0-or-later + +Download a single member of the MIRCA-OS v2 HydroShare BagIt archive. + +HydroShare's per-file iRODS endpoint (`.../resource/{id}/data/contents/{file}`) +returns HTTP 500 for this resource, so the only reliable path is the +whole-resource BagIt zip endpoint: + + https://www.hydroshare.org/django_irods/download/bags/{resource}.zip + +which 302-redirects to a range-capable *signed* S3 URL (a HEAD 403s -- the +signature is GET-only). We resolve that redirect, then use ``remotezip`` to read +the bag's central directory and pull only the one requested member via HTTP range +requests, so a 32 MB grid archive does not require downloading the full ~10 GB bag. + +The bag nests the grid archives as RAR5 files (extracted downstream with +``bsdtar``); the 2020 crop-calendar CSVs are stored directly. +""" + +from pathlib import Path +import shutil + +import remotezip +import requests + +MIRCA_OS_V2_RESOURCE = "e4582ca0042148338bb5e0148b749ed6" +BAG_URL = ( + f"https://www.hydroshare.org/django_irods/download/bags/{MIRCA_OS_V2_RESOURCE}.zip" +) + + +def resolve_signed_url() -> str: + """Resolve the bag endpoint's 302 redirect to the signed S3 URL.""" + resp = requests.get(BAG_URL, allow_redirects=False, timeout=120) + location = resp.headers.get("Location") + if resp.status_code != 302 or not location: + raise RuntimeError( + f"Expected a 302 redirect from {BAG_URL}, got HTTP {resp.status_code}" + ) + return location + + +def main() -> None: + member = str(snakemake.params.member) # type: ignore[name-defined] + output = Path(snakemake.output[0]) # type: ignore[name-defined] + output.parent.mkdir(parents=True, exist_ok=True) + + signed_url = resolve_signed_url() + member_path = f"{MIRCA_OS_V2_RESOURCE}/data/contents/{member}" + + tmp = output.with_suffix(output.suffix + ".part") + try: + with remotezip.RemoteZip(signed_url) as bag: + names = set(bag.namelist()) + if member_path not in names: + raise KeyError( + f"Member '{member_path}' not found in MIRCA-OS v2 bag " + f"({len(names)} members)" + ) + with bag.open(member_path) as source, tmp.open("wb") as destination: + shutil.copyfileobj(source, destination, length=8 * 1024 * 1024) + tmp.replace(output) + except BaseException: + tmp.unlink(missing_ok=True) + raise + print(f"Downloaded {output.stat().st_size:,} bytes -> {output}") + + +main() diff --git a/workflow/scripts/extract_cost_calibration.py b/workflow/scripts/extract_cost_calibration.py index e30532e5..466777e6 100644 --- a/workflow/scripts/extract_cost_calibration.py +++ b/workflow/scripts/extract_cost_calibration.py @@ -26,10 +26,11 @@ correction by the number of classes) or a mean (which would dilute it toward zero when only one class binds). -Outputs three CSVs: - - crop: (crop, country, correction_bnusd_per_mha) - - grassland: (country, correction_bnusd_per_mha) - - animal: (product, country, correction_bnusd_per_mt_feed) +Outputs four CSVs: + - crop: (crop, country, correction_bnusd_per_mha) + - multi-crop: (combination, country, correction_bnusd_per_mha) + - grassland: (country, correction_bnusd_per_mha) + - animal: (product, country, correction_bnusd_per_mt_feed) """ import logging @@ -123,7 +124,10 @@ def extract_corrections( + max_duals.reindex(all_links, fill_value=0.0) ) - # Map link names to group columns using link metadata + # Map link names to group columns using link metadata. The inner join + # restricts to the target carrier. Single-crop and multi-cropping links + # can share the same production-stability prefix, so callers extract + # them separately using different carriers and grouping columns. links = n.links.static carrier_links = links[links["carrier"] == carrier] link_meta = carrier_links[group_cols].copy() @@ -132,7 +136,17 @@ def extract_corrections( {"correction": corrections.values}, index=corrections.index, ) + n_before = len(corrections_df) corrections_df = corrections_df.join(link_meta, how="inner") + n_excluded = n_before - len(corrections_df) + if n_excluded: + logger.info( + "Excluded %d banded links of other carriers from %s dual " + "extraction (each carrier is extracted separately with its own " + "grouping columns)", + n_excluded, + carrier, + ) if corrections_df.empty: logger.warning("No %s links matched stability constraints", carrier) @@ -189,6 +203,26 @@ def main() -> None: crop_result.to_csv(crop_path, index=False) logger.info("Wrote %d crop corrections to %s", len(crop_result), crop_path) + # --- Multi-crop corrections --- + multi_crop_result = extract_corrections( + n, + prefix="crop_production", + carrier="crop_production_multi", + group_cols=["combination", "country"], + ) + multi_crop_result = multi_crop_result.rename( + columns={"correction": "correction_bnusd_per_mha"} + ) + + multi_crop_path = Path(snakemake.output.multi_crop_correction) + multi_crop_path.parent.mkdir(parents=True, exist_ok=True) + multi_crop_result.to_csv(multi_crop_path, index=False) + logger.info( + "Wrote %d multi-crop corrections to %s", + len(multi_crop_result), + multi_crop_path, + ) + # --- Grassland corrections --- grassland_result = extract_corrections( n, diff --git a/workflow/scripts/extract_mirca_os_archive.py b/workflow/scripts/extract_mirca_os_archive.py new file mode 100644 index 00000000..6a9f5ff4 --- /dev/null +++ b/workflow/scripts/extract_mirca_os_archive.py @@ -0,0 +1,78 @@ +""" +SPDX-FileCopyrightText: 2026 Koen van Greevenbroek + +SPDX-License-Identifier: GPL-3.0-or-later + +Extract a requested set of MIRCA-OS files in one pass through a RAR archive. + +The MIRCA archives are large enough that launching ``bsdtar`` separately for +every raster wastes substantial I/O. The associated rules pass one member glob +per output and this script extracts the whole batch into a temporary directory, +checks that every glob resolved to exactly one file, then installs the outputs +atomically. +""" + +from pathlib import Path +import subprocess +import tempfile + + +def extract_members( + archive: str | Path, + member_globs: list[str], + outputs: list[str | Path], +) -> None: + """Extract ``member_globs`` from ``archive`` to corresponding ``outputs``.""" + if len(member_globs) != len(outputs): + raise ValueError( + f"Expected one MIRCA member glob per output, got " + f"{len(member_globs)} globs and {len(outputs)} outputs" + ) + if not outputs: + raise ValueError("Expected at least one MIRCA archive output") + + output_paths = [Path(path) for path in outputs] + for output in output_paths: + output.parent.mkdir(parents=True, exist_ok=True) + + with tempfile.TemporaryDirectory( + prefix=".mirca-extract-", dir=output_paths[0].parent + ) as tmp_name: + tmp = Path(tmp_name) + command = ["bsdtar", "-xf", str(archive), "-C", str(tmp)] + command.extend(f"--include={pattern}" for pattern in member_globs) + subprocess.run(command, check=True) + + extracted: list[Path] = [] + for member_glob in member_globs: + basename = Path(member_glob).name + matches = list(tmp.rglob(basename)) + if len(matches) != 1: + raise RuntimeError( + f"MIRCA member glob '{member_glob}' extracted {len(matches)} " + f"files named '{basename}', expected exactly one" + ) + extracted.append(matches[0]) + + parts = [output.with_suffix(output.suffix + ".part") for output in output_paths] + try: + for source, part in zip(extracted, parts, strict=True): + source.replace(part) + for part, output in zip(parts, output_paths, strict=True): + part.replace(output) + except BaseException: + for part in parts: + part.unlink(missing_ok=True) + raise + + +def main() -> None: + extract_members( + snakemake.input.rar, # type: ignore[name-defined] + list(snakemake.params.member_globs), # type: ignore[name-defined] + list(snakemake.output), # type: ignore[name-defined] + ) + + +if __name__ == "__main__": + main() diff --git a/workflow/scripts/multi_cropping_combinations.py b/workflow/scripts/multi_cropping_combinations.py new file mode 100644 index 00000000..f30b91ff --- /dev/null +++ b/workflow/scripts/multi_cropping_combinations.py @@ -0,0 +1,116 @@ +# SPDX-FileCopyrightText: 2026 Koen van Greevenbroek +# +# SPDX-License-Identifier: GPL-3.0-or-later + +"""Resolve the curated and manually configured multi-cropping systems.""" + +from pathlib import Path + +import yaml + +MIRCA_MULTICROPPING_YEARS = (2010, 2015, 2020) + + +def closest_mirca_multicropping_year(baseline_year: int) -> int: + """Select the nearest supported MIRCA release, preferring the earlier tie.""" + return min( + MIRCA_MULTICROPPING_YEARS, + key=lambda year: (abs(year - baseline_year), year), + ) + + +def load_catalog_combinations(catalog_yaml: str | Path) -> dict[str, dict]: + """Load the fixed MIRCA-OS combination catalog.""" + with open(catalog_yaml) as f: + catalog = yaml.safe_load(f) or {} + if not isinstance(catalog, dict): + raise ValueError("The MIRCA-OS multi-cropping catalog must be a mapping") + for name, entry in catalog.items(): + if not isinstance(name, str) or not name: + raise ValueError("Catalog combination names must be non-empty strings") + if not isinstance(entry, dict): + raise ValueError(f"Catalog combination '{name}' must be a mapping") + if set(entry) != {"crops", "water_supplies"}: + raise ValueError( + f"Catalog combination '{name}' must contain only crops and " + "water_supplies" + ) + crops = entry.get("crops") + supplies = entry.get("water_supplies") + if ( + not isinstance(crops, list) + or len(crops) not in {2, 3} + or any(not isinstance(crop, str) or not crop for crop in crops) + ): + raise ValueError(f"Catalog combination '{name}' must contain 2 or 3 crops") + if ( + not isinstance(supplies, list) + or not supplies + or len(supplies) != len(set(supplies)) + ): + raise ValueError( + f"Catalog combination '{name}' must contain unique water_supplies" + ) + if set(supplies) - {"r", "i"}: + raise ValueError( + f"Catalog combination '{name}' has invalid water supplies: {supplies}" + ) + return catalog + + +def _resolved_sets( + config: dict, catalog_yaml: str | Path +) -> tuple[dict[str, dict], dict[str, dict]]: + """Return ``(effective, observed)`` combination mappings. + + Catalog entries carry a MIRCA-observed baseline. Configuration may disable + one by setting its name to ``null`` or add a uniquely named greenfield + system, whose baseline is zero. Redefining a catalog entry is rejected: its + name, crop sequence, observed anchor, and calibration key form one structural + identity. + """ + catalog = load_catalog_combinations(catalog_yaml) + observed = dict(catalog) + greenfield: dict[str, dict] = {} + + for name, entry in config["multiple_cropping"].items(): + if name in catalog: + if entry is not None: + raise ValueError( + f"multiple_cropping.{name} redefines a curated MIRCA-OS " + "combination. Use null to disable it or add a uniquely named " + "greenfield combination." + ) + observed.pop(name) + continue + if entry is None: + raise ValueError( + f"multiple_cropping.{name} is null but is not a curated " + "combination and therefore cannot disable anything" + ) + greenfield[name] = entry + + model_crops = set(config["crops"]) + observed = { + name: entry + for name, entry in observed.items() + if set(entry["crops"]) <= model_crops + } + greenfield = { + name: entry + for name, entry in greenfield.items() + if set(entry["crops"]) <= model_crops + } + return {**observed, **greenfield}, observed + + +def effective_combinations(config: dict, catalog_yaml: str | Path) -> dict[str, dict]: + """Return all modeled combinations: observed catalog plus greenfield config.""" + effective, _observed = _resolved_sets(config, catalog_yaml) + return effective + + +def observed_combinations(config: dict, catalog_yaml: str | Path) -> dict[str, dict]: + """Return enabled catalog combinations that carry a MIRCA baseline.""" + _effective, observed = _resolved_sets(config, catalog_yaml) + return observed diff --git a/workflow/scripts/solve_model/production_stability.py b/workflow/scripts/solve_model/production_stability.py index 6f5bb2e8..80d68fb5 100644 --- a/workflow/scripts/solve_model/production_stability.py +++ b/workflow/scripts/solve_model/production_stability.py @@ -25,6 +25,7 @@ so all three components share a single calibration artefact. """ +from collections.abc import Sequence import copy import logging from pathlib import Path @@ -35,6 +36,7 @@ import xarray as xr import yaml +from workflow.scripts.build_model.crops import multi_crop_cycle_multiplicities from workflow.scripts.solve_namespace import ( CALIBRATED_SENTINEL, DEVIATION_PENALTY_COMPONENT_PATHS, @@ -202,14 +204,19 @@ def add_production_stability_constraints( feed_enabled = feed_cfg["enabled"] # --- CROP PRODUCTION --- + # Single-crop and multi-cropping links draw the same cropland bus0, so they + # share one crop stability budget over both carriers. The multi links carry + # combination labels in the "crop" column, so no per-link band logic needs a + # special case. crops_cfg = land_cfg["crops"] + crop_carriers = ("crop_production", "crop_production_multi") if land_enabled and crops_cfg["enabled"]: if penalty_mode == "hard": _add_production_hard_constraints( n, link_p, links_df, - "crop_production", + crop_carriers, "crop", crops_cfg, slack_marginal_cost, @@ -219,7 +226,7 @@ def add_production_stability_constraints( n, link_p, links_df, - "crop_production", + crop_carriers, "crop", deviation_type, crops_cfg["l1_cost"], @@ -230,7 +237,7 @@ def add_production_stability_constraints( n, link_p, links_df, - "crop_production", + crop_carriers, "crop", deviation_type, quadratic_cost, @@ -299,7 +306,7 @@ def add_production_stability_constraints( # cropland L1 as the land reference point. animal_l1_cost = crops_cfg["l1_cost"] if deviation_type == "absolute": - crop_links = links_df[links_df["carrier"] == "crop_production"] + crop_links = links_df[links_df["carrier"].isin(crop_carriers)] grass_links = links_df[links_df["carrier"] == "grassland_production"] animal_links = links_df[links_df["carrier"] == "animal_production"] if not crop_links.empty and "baseline_area_mha" not in crop_links: @@ -396,33 +403,46 @@ def add_production_stability_constraints( # ─── Helpers ────────────────────────────────────────────────────────────────── +def _carrier_list(carrier: str | Sequence[str]) -> list[str]: + """Normalize a carrier argument (str or collection) to a list of carriers.""" + return [carrier] if isinstance(carrier, str) else list(carrier) + + def _production_and_baselines( link_p, links_df, - carrier: str, + carrier: str | Sequence[str], min_baseline: float, *, include_all_links: bool = True, ) -> tuple | None: """Extract area expressions and baselines for production links. - Returns ``(link_names, area, baselines)`` where ``area`` is the link - dispatch variable (Mha) and ``baselines`` is the observed baseline area - (Mha). Returns ``None`` if there are no eligible links. When - ``include_all_links`` is False, only links above ``min_baseline`` are - included; when True, all links are included (including zero-baseline). + ``carrier`` is a single carrier or a set of carriers (e.g. + ``("crop_production", "crop_production_multi")`` so multi-cropping links + share the crop stability budget). Returns ``(link_names, area, baselines)`` + where ``area`` is the link dispatch variable (Mha) and ``baselines`` is the + observed baseline area (Mha, NaN-guarded to 0). Returns ``None`` if there + are no eligible links. When ``include_all_links`` is False, only links above + ``min_baseline`` are included; when True, all links are included. """ - prod_links = links_df[links_df["carrier"] == carrier] + carriers = _carrier_list(carrier) + prod_links = links_df[links_df["carrier"].isin(carriers)] if prod_links.empty or "baseline_area_mha" not in prod_links.columns: - logger.info("No %s links with baselines; skipping stability", carrier) + logger.info( + "No %s links with baselines; skipping stability", "/".join(carriers) + ) return None + # Guard: a missing multi-cropping anchor must not inject NaN into the term. + prod_links = prod_links.copy() + prod_links["baseline_area_mha"] = prod_links["baseline_area_mha"].fillna(0.0) if not include_all_links: prod_links = prod_links[prod_links["baseline_area_mha"] > min_baseline] if prod_links.empty: logger.info( "No %s baselines exceed %.6g Mha; skipping stability constraints", - carrier, + "/".join(carriers), min_baseline, ) return None @@ -511,7 +531,7 @@ def _add_production_hard_constraints( n: pypsa.Network, link_p, links_df, - carrier: str, + carrier: str | Sequence[str], label: str, cfg: dict, slack_marginal_cost: float, @@ -610,7 +630,7 @@ def _add_production_l1_penalty( n: pypsa.Network, link_p, links_df, - carrier: str, + carrier: str | Sequence[str], label: str, deviation_type: str, l1_cost: float, @@ -674,7 +694,7 @@ def _add_production_quadratic_penalty( n: pypsa.Network, link_p, links_df, - carrier: str, + carrier: str | Sequence[str], label: str, deviation_type: str, quadratic_cost: float, @@ -1056,6 +1076,7 @@ def add_bounded_subsidy_constraints( n: pypsa.Network, carriers: list[str] = ( "crop_production", + "crop_production_multi", "grassland_production", "animal_production", ), @@ -1201,15 +1222,30 @@ def add_bounded_subsidy_constraints( # ─── Crop growth caps ────────────────────────────────────────────────────── +def _multi_cycle_long(links_df: pd.DataFrame) -> pd.DataFrame: + """Long form of multi-cropping cycle outputs: one row per (link, crop). + + Cycle multiplicity comes from the explicit ``crop_cycles`` link metadata. + A repeated crop occupies one entry per cycle, so its count is the number of + harvested cycles of that crop per Mha of link dispatch. + Columns: ``link``, ``crop``, ``group`` (``crop::country``), + ``multiplicity``, ``baseline_area_mha``. + """ + long = multi_crop_cycle_multiplicities(links_df) + long["group"] = long["crop"] + "::" + long["country"] + return long + + def add_crop_growth_cap_constraints( n: pypsa.Network, growth_cap_cfg: dict, ) -> None: """Add per-(crop, country) upper bounds on total harvested area. - Aggregates ``crop_production`` link dispatch across regions, resource - classes and water-supply types within each (crop, country), then bounds - the total at ``(1 + max_relative_increase) * sum_baseline``. This is a + Aggregates ``crop_production`` link dispatch -- plus multi-cropping link + dispatch weighted by each crop's cycle multiplicity -- across regions, + resource classes and water-supply types within each (crop, country), then + bounds the total at ``(1 + max_relative_increase) * sum_baseline``. This is a structural backstop against pathological extrapolation of cost- calibration corrections under L1 production stability — without it, a moderate per-Mha negative correction calibrated at a tiny baseline can @@ -1255,19 +1291,23 @@ def add_crop_growth_cap_constraints( prod_links.assign(_group=group_keys.values) .groupby("_group")["baseline_area_mha"] .sum() - .sort_index() ) - # Vectorised: groupby-sum Link-p over the (crop, country) key, then - # add all constraints in a single linopy call. - group_map = xr.DataArray( - group_keys.values, - coords={"name": prod_links.index}, - dims="name", - name="cap_group", - ) - link_vars = link_p.sel(name=prod_links.index) - grouped = link_vars.groupby(group_map).sum() + # Multi-cropping links contribute their cycle multiplicity per output crop + # to the same (crop, country) totals: an n-cycle link dispatching X Mha + # harvests m_k * X of crop k. The "crop" column holds the combination + # label, so cycles come from the explicit crop_cycles metadata (a repeated + # crop occurs once per cycle). Their multiplicity-weighted baselines join + # the bound so single + multi jointly reproduce the FAOSTAT country total. + multi_long = _multi_cycle_long(links_df) + if not multi_long.empty: + multi_baseline = ( + (multi_long["multiplicity"] * multi_long["baseline_area_mha"]) + .groupby(multi_long["group"].values) + .sum() + ) + baseline_per_group = baseline_per_group.add(multi_baseline, fill_value=0.0) + baseline_per_group = baseline_per_group.sort_index() upper_bounds = xr.DataArray( ((1.0 + cap) * baseline_per_group).to_numpy(), @@ -1275,10 +1315,66 @@ def add_crop_growth_cap_constraints( dims="cap_group", ) - m.add_constraints( - grouped <= upper_bounds, - name="GlobalConstraint-crop_growth_cap", + # Vectorised: groupby-sum Link-p over the (crop, country) key. Groups + # touched by multi links get their weighted multi expression added; since a + # group is a (crop, country), only the matching crop slot can touch it, so + # the multi expressions are built per distinct output crop (a link appears + # once per distinct crop, weighted by its cycle count -- no duplicate + # labels within a slot). + group_map = xr.DataArray( + group_keys.values, + coords={"name": prod_links.index}, + dims="name", + name="cap_group", ) + grouped_single = link_p.sel(name=prod_links.index).groupby(group_map).sum() + single_groups = pd.Index(grouped_single.coords["cap_group"].values) + + multi_groups: pd.Index = pd.Index([], dtype=object) + n_constraints = 0 + for crop, slot in multi_long.groupby("crop") if not multi_long.empty else (): + weights = xr.DataArray( + slot["multiplicity"].to_numpy(dtype=float), + coords={"name": slot["link"].to_numpy()}, + dims="name", + ) + slot_map = xr.DataArray( + slot["group"].to_numpy(), + coords={"name": slot["link"].to_numpy()}, + dims="name", + name="cap_group", + ) + grouped_multi = ( + (link_p.sel(name=slot["link"].to_numpy()) * weights).groupby(slot_map).sum() + ) + slot_groups = pd.Index(grouped_multi.coords["cap_group"].values) + both = slot_groups.intersection(single_groups) + only_multi = slot_groups.difference(single_groups) + if len(both): + m.add_constraints( + grouped_single.sel(cap_group=both.to_numpy()) + + grouped_multi.sel(cap_group=both.to_numpy()) + <= upper_bounds.sel(cap_group=both.to_numpy()), + name=f"GlobalConstraint-crop_growth_cap_multi_{crop}", + ) + n_constraints += len(both) + if len(only_multi): + m.add_constraints( + grouped_multi.sel(cap_group=only_multi.to_numpy()) + <= upper_bounds.sel(cap_group=only_multi.to_numpy()), + name=f"GlobalConstraint-crop_growth_cap_multi_only_{crop}", + ) + n_constraints += len(only_multi) + multi_groups = multi_groups.union(slot_groups) + + single_only = single_groups.difference(multi_groups) + if len(single_only): + m.add_constraints( + grouped_single.sel(cap_group=single_only.to_numpy()) + <= upper_bounds.sel(cap_group=single_only.to_numpy()), + name="GlobalConstraint-crop_growth_cap", + ) + n_constraints += len(single_only) constraint_names = [ f"crop_growth_cap_{key.replace('::', '_')}" for key in baseline_per_group.index @@ -1291,9 +1387,11 @@ def add_crop_growth_cap_constraints( ) logger.info( - "Added %d crop growth cap constraints at +%.0f%%", - len(constraint_names), + "Added %d crop growth cap constraints at +%.0f%% (%d groups with " + "multi-cropping contributions)", + n_constraints, 100.0 * cap, + len(multi_groups), ) @@ -1358,8 +1456,7 @@ def add_reforestation_cap_constraints( """ if max_fraction < 0.0 or max_fraction > 1.0: raise ValueError( - "land.reforestation_cap.max_fraction must be in [0, 1], " - f"got {max_fraction}" + f"land.reforestation_cap.max_fraction must be in [0, 1], got {max_fraction}" ) if buffer_mha < 0.0: raise ValueError( diff --git a/workflow/scripts/write_calibration_provenance.py b/workflow/scripts/write_calibration_provenance.py index b1cf54b8..afdefc96 100644 --- a/workflow/scripts/write_calibration_provenance.py +++ b/workflow/scripts/write_calibration_provenance.py @@ -91,7 +91,7 @@ def main() -> int: print("true" if resolve_gbd_anchoring(config) else "false") return 0 - snapshot = structural_snapshot(config) + snapshot = structural_snapshot(config, PROJECT_ROOT) if args.check: try: diff --git a/workflow/validation/calibration.py b/workflow/validation/calibration.py index d2e29476..9b74b819 100644 --- a/workflow/validation/calibration.py +++ b/workflow/validation/calibration.py @@ -59,6 +59,7 @@ "path": ("cost_calibration",), "files": [ "crop_correction_csv", + "multi_crop_correction_csv", "grassland_correction_csv", "animal_correction_csv", ], diff --git a/workflow/validation/calibration_provenance.py b/workflow/validation/calibration_provenance.py index 4afa9ea7..8f40872a 100644 --- a/workflow/validation/calibration_provenance.py +++ b/workflow/validation/calibration_provenance.py @@ -12,11 +12,11 @@ (``calibration.source``); a mismatch means the artefacts were fit under different structural assumptions and are not valid for this config. -The comparison covers all *structural* config leaves: solve-time keys -(SOLVE_TIME_CONFIG_PREFIXES) never invalidate calibration, and a small -set of additional prefixes is exempted below because it describes run -identity or calibration machinery rather than what the artefacts were -fit to. +The comparison covers all *structural* config leaves plus derived identities +for structural curated inputs. Solve-time keys (SOLVE_TIME_CONFIG_PREFIXES) +never invalidate calibration, and a small set of additional prefixes is +exempted below because it describes run identity or calibration machinery +rather than what the artefacts were fit to. """ from pathlib import Path @@ -24,10 +24,19 @@ from snakemake.logging import logger import yaml +from workflow.scripts.multi_cropping_combinations import ( + closest_mirca_multicropping_year, + effective_combinations, + load_catalog_combinations, + observed_combinations, +) from workflow.scripts.solve_namespace import _is_solve_time_key, resolve_gbd_anchoring CALIBRATION_DIR = Path("data/curated/calibration") PROVENANCE_FILENAME = "provenance.yaml" +MIRCA_MULTICROPPING_CATALOG = Path( + "data/curated/mirca_os_multicropping_combinations.yaml" +) # Exempt from the structural snapshot, on top of the solve-time prefixes. PROVENANCE_EXEMPT_PREFIXES = { @@ -80,8 +89,8 @@ def _is_exempt(key: str) -> bool: return any(key == p or key.startswith(p + ".") for p in PROVENANCE_EXEMPT_PREFIXES) -def structural_snapshot(config: dict) -> dict: - """Flat dotted-key map of the fit-relevant structural config leaves.""" +def structural_snapshot(config: dict, project_root: Path | None = None) -> dict: + """Return fit-relevant structural config leaves and curated identities.""" snapshot: dict = {} def walk(node: dict, prefix: str) -> None: @@ -111,6 +120,20 @@ def walk(node: dict, prefix: str) -> None: for k, v in snapshot.items() if k != inactive and not k.startswith(inactive + ".") } + + root = Path(project_root) if project_root else Path.cwd() + catalog_path = root / MIRCA_MULTICROPPING_CATALOG + source_year = closest_mirca_multicropping_year(config["baseline_year"]) + snapshot["derived.multiple_cropping.mirca_source_year"] = source_year + snapshot["derived.multiple_cropping.catalog"] = load_catalog_combinations( + catalog_path + ) + snapshot["derived.multiple_cropping.observed"] = observed_combinations( + config, catalog_path + ) + snapshot["derived.multiple_cropping.effective"] = effective_combinations( + config, catalog_path + ) return snapshot @@ -164,7 +187,9 @@ def validate_calibration_provenance( root = Path(project_root) if project_root else Path.cwd() source = config["calibration"]["source"] stamp = load_provenance(source, root) - diffs = diff_snapshots(stamp["structural_config"], structural_snapshot(config)) + diffs = diff_snapshots( + stamp["structural_config"], structural_snapshot(config, root) + ) if not diffs: return diff --git a/workflow/validation/cropgrids_crops.py b/workflow/validation/cropgrids_crops.py index ece4f8d1..a340e57a 100644 --- a/workflow/validation/cropgrids_crops.py +++ b/workflow/validation/cropgrids_crops.py @@ -8,6 +8,10 @@ import pandas as pd +from workflow.scripts.multi_cropping_combinations import effective_combinations + +CATALOG_PATH = Path("data/curated/mirca_os_multicropping_combinations.yaml") + def validate_cropgrids_crops(config: dict, project_root: Path) -> None: """Check cross-array invariants for ``cropgrids_crops``. @@ -53,7 +57,7 @@ def validate_cropgrids_crops(config: dict, project_root: Path) -> None: f"appear in irrigation.irrigated_crops: {', '.join(overlap_irrigation)}" ) - combos = config.get("multiple_cropping") or {} + combos = effective_combinations(config, project_root / CATALOG_PATH) multi_crops = set() for entry in combos.values(): if entry is None: diff --git a/workflow/validation/multi_cropping.py b/workflow/validation/multi_cropping.py index 8e47f64c..7bc4c563 100644 --- a/workflow/validation/multi_cropping.py +++ b/workflow/validation/multi_cropping.py @@ -2,36 +2,138 @@ # # SPDX-License-Identifier: GPL-3.0-or-later -"""Validation that multi-cropping combinations reference only configured crops.""" +"""Validation for multi-cropping combinations and the MIRCA-OS crop concordance.""" from pathlib import Path +import pandas as pd +from snakemake.logging import logger + +from workflow.scripts.multi_cropping_combinations import ( + effective_combinations, + load_catalog_combinations, +) + +# The 23 base crops of MIRCA-OS v2 (Kebede et al. 2025). The concordance file +# must cover exactly this set so the derivation fails fast on any drift. +MIRCA_OS_BASE_CROPS = frozenset( + { + "Barley", + "Cassava", + "Cocoa", + "Coffee", + "Cotton", + "Fodder", + "Groundnuts", + "Maize", + "Millet", + "Oil palm", + "Others annual", + "Others perennial", + "Potatoes", + "Pulses", + "Rapeseed", + "Rice", + "Rye", + "Sorghum", + "Soybeans", + "Sugar beet", + "Sugar cane", + "Sunflower", + "Wheat", + } +) + +CATALOG_PATH = Path("data/curated/mirca_os_multicropping_combinations.yaml") + def validate_multi_cropping(config: dict, project_root: Path) -> None: - """Validate that every crop in a multi-cropping combination is in the crops list. + """Validate multi-cropping combinations and the MIRCA-OS crop concordance. - Multi-cropping combinations (defined in ``config["multiple_cropping"]``) - reference crops by name. Each referenced crop must appear in - ``config["crops"]`` so that the corresponding crop buses exist at model - build time. - """ - combinations = config.get("multiple_cropping") - if not combinations: - return + Three invariants: + 1. Every crop referenced by a ``config["multiple_cropping"]`` combination is + in ``config["crops"]`` (so its crop buses exist at build time). + 2. Catalog entries can only be disabled; config-only entries are explicit + zero-baseline greenfield systems and cannot use catalog names. + 3. ``data/curated/mirca_os_crop_mapping.csv`` is exhaustive over the 23 + MIRCA-OS base crops and maps every crop used by the fixed catalog. This + mirrors ``validate_cropgrids_crops`` for the CROPGRIDS concordance. + """ config_crops = set(config["crops"]) - missing: list[str] = [] - - for combo_name, entry in combinations.items(): - if entry is None: - continue - for crop in entry["crops"]: - if crop not in config_crops: - missing.append(f"{combo_name}: {crop}") + missing = [ + f"{combo_name}: {crop}" + for combo_name, entry in config["multiple_cropping"].items() + if entry is not None + for crop in entry["crops"] + if crop not in config_crops + ] if missing: - detail = ", ".join(missing) raise ValueError( - f"Multi-cropping combinations reference crops not in config['crops']: " - f"{detail}" + "Multi-cropping combinations reference crops not in " + f"config['crops']: {', '.join(missing)}" + ) + + catalog_path = project_root / CATALOG_PATH + effective_combinations(config, catalog_path) + catalog = load_catalog_combinations(catalog_path) + _validate_mirca_concordance(config_crops, project_root, catalog) + + +def _validate_mirca_concordance( + config_crops: set[str], project_root: Path, catalog: dict[str, dict] +) -> None: + """Check the MIRCA-OS -> GLADE crop concordance file.""" + mapping_path = project_root / "data" / "curated" / "mirca_os_crop_mapping.csv" + if not mapping_path.exists(): + raise FileNotFoundError(f"Expected data file at {mapping_path}") + + mapping = pd.read_csv(mapping_path, comment="#") + required_cols = {"mirca_crop", "glade_crop"} + missing_cols = required_cols - set(mapping.columns) + if missing_cols: + raise ValueError( + f"mirca_os_crop_mapping.csv missing columns: {sorted(missing_cols)}" + ) + + mapping["mirca_crop"] = mapping["mirca_crop"].astype(str).str.strip() + mirca_crops = set(mapping["mirca_crop"]) + + if len(mapping) != len(mirca_crops): + dupes = sorted(mapping["mirca_crop"][mapping["mirca_crop"].duplicated()]) + raise ValueError(f"mirca_os_crop_mapping.csv has duplicate mirca_crop: {dupes}") + + unexpected = sorted(mirca_crops - MIRCA_OS_BASE_CROPS) + if unexpected: + raise ValueError( + f"mirca_os_crop_mapping.csv has unknown MIRCA-OS crops: {unexpected}" + ) + absent = sorted(MIRCA_OS_BASE_CROPS - mirca_crops) + if absent: + raise ValueError( + f"mirca_os_crop_mapping.csv is missing MIRCA-OS base crops: {absent}" + ) + + # glade_crop is blank for dropped crops; NaN reads as an empty mapping. + glade = mapping.set_index("mirca_crop")["glade_crop"] + glade = glade.fillna("").astype(str).str.strip() + + # The concordance is a global reference file; a reduced config (e.g. the test + # config's crop subset) legitimately omits some mapped crops. Warn rather than + # fail, mirroring validate_gaez_crop_mapping / validate_seed_rates. Combination + # crops are still hard-validated against config['crops'] above. + mapped = glade[glade != ""] + unused = sorted(set(mapped) - config_crops) + if unused: + logger.warning( + "mirca_os_crop_mapping.csv maps to crops not in config " + f"(future crops?): {', '.join(unused)}" + ) + + catalog_crops = {crop for entry in catalog.values() for crop in entry["crops"]} + unmapped_catalog = sorted(catalog_crops - set(mapped)) + if unmapped_catalog: + raise ValueError( + f"mirca_os_crop_mapping.csv does not map catalog crops: {unmapped_catalog}" )