From 622cd3dfd2530fe2d5cf091db240a856ddd2272c Mon Sep 17 00:00:00 2001 From: Koen van Greevenbroek Date: Mon, 20 Jul 2026 17:21:00 -0700 Subject: [PATCH 1/5] feat: observed multi-cropping baseline from MIRCA-OS Anchor multiple cropping to the observed 2020 baseline derived from MIRCA-OS v2. A config-independent Snakemake checkpoint (derive_mirca_multicropping) attributes MIRCA's extra-cycle harvested area (annual harvested minus AEI-capped footprint) to crop-sequence combinations on the 5-arcmin grid, discovering the combination set itself; the static multiple_cropping config section now only supplements the derived set, and combinations referencing unmodeled crops are dropped at resolution time (workflow/scripts/multi_cropping_combinations.py). crop_production_multi links are anchored via baseline_area_mha with p_nom_max = max(GAEZ potential, anchor), and share the crop stability budget, the bounded cost-calibration mechanism (new per-(combination, country) multi_crop_cost.csv artefact extracted directly from the multi links' own band duals), the crop growth cap (cycle-multiplicity weighted) and validation-mode pinning with single-crop links. Harvested cycles carried by multi links are reconciled out of the single-crop FAOSTAT baselines (with country-level redistribution) so each harvested cycle is counted exactly once, and the land-correction generator absorbs only the residual. Multi-cropping is no longer disabled under the land deviation penalty or use_actual_production. The GAEZ sequence_feasible gate is removed: GAEZ attainable season lengths overshoot the farmed cycle and rejected nearly all observed double-cropping. Feasibility evidence is MIRCA observation plus the GAEZ multiple-cropping zone; GAEZ supplies suitability, yields and water requirements. MIRCA-OS grids are fetched from the HydroShare BagIt zip via HTTP-range partial extraction (32 MB / 284 MB / 1.5 GB members instead of the ~10 GB bag); the nested RAR5 archives are unpacked with bsdtar (new libarchive dependency). Extracted from the water-aware branch, without the intra-year water periods and MIRCA crop-calendar retiming that belong to the upcoming water supply restructure. --- CHANGELOG.md | 19 + config/default.yaml | 1 + config/schemas/config.schema.yaml | 9 +- .../calibration/default/multi_crop_cost.csv | 790 ++++++++++++++++++ data/curated/mirca_os_crop_mapping.csv | 44 + docs/calibration.rst | 8 +- docs/costs.rst | 11 +- docs/crop_production.rst | 76 +- docs/data_sources.rst | 23 + docs/land_use.rst | 19 +- docs/validation.rst | 8 + docs/workflow.rst | 16 +- pixi.toml | 1 + tests/test_crop_costs.py | 108 ++- tests/test_integration.py | 19 + tests/test_multi_cropping.py | 330 ++++++++ workflow/Snakefile | 1 + workflow/rules/crops.smk | 145 +++- workflow/rules/model.smk | 4 +- workflow/rules/retrieve.smk | 137 +++ .../aggregate_multicropping_baseline.py | 158 ++++ workflow/scripts/build_model.py | 73 +- workflow/scripts/build_model/crops.py | 476 ++++++++--- workflow/scripts/build_model/land.py | 15 +- workflow/scripts/build_model/utils.py | 36 +- workflow/scripts/build_multi_cropping.py | 284 ++++--- .../scripts/derive_mirca_multicropping.py | 390 +++++++++ .../scripts/download_mirca_os_bag_member.py | 69 ++ workflow/scripts/extract_cost_calibration.py | 44 +- .../scripts/multi_cropping_combinations.py | 37 + .../solve_model/production_stability.py | 186 ++++- workflow/validation/calibration.py | 1 + workflow/validation/multi_cropping.py | 140 +++- 33 files changed, 3287 insertions(+), 391 deletions(-) create mode 100644 data/curated/calibration/default/multi_crop_cost.csv create mode 100644 data/curated/mirca_os_crop_mapping.csv create mode 100644 tests/test_multi_cropping.py create mode 100644 workflow/scripts/aggregate_multicropping_baseline.py create mode 100644 workflow/scripts/derive_mirca_multicropping.py create mode 100644 workflow/scripts/download_mirca_os_bag_member.py create mode 100644 workflow/scripts/multi_cropping_combinations.py diff --git a/CHANGELOG.md b/CHANGELOG.md index 06ca60e0..396eecdc 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -17,6 +17,25 @@ introduce breaking changes to configuration and outputs. ### Added +- Multiple cropping is now anchored to an **observed 2020 baseline** derived + from MIRCA-OS v2 (new automated data source). A config-independent Snakemake + checkpoint (`derive_mirca_multicropping`) attributes MIRCA's extra-cycle + harvested area to crop-sequence combinations and discovers the combination + set itself (8+ combinations instead of the 3 previously hard-coded; the + static `multiple_cropping` config section now merely supplements the derived + set). `crop_production_multi` links are anchored at the observed baseline + (`p_nom_max = max(GAEZ potential, baseline)`), participate in the land + deviation penalty, the crop growth cap and cost calibration like single-crop + links (a new `multi_crop_cost.csv` calibration artefact carries + per-(combination, country) bundle corrections), and are pinned in + validation mode, with harvested cycles reconciled out of the single-crop + FAOSTAT baselines so each cycle is counted exactly once. Multi-cropping is + consequently no longer disabled when the land deviation penalty or + `use_actual_production` is active. The broken GAEZ `sequence_feasible` + gate (which rejected nearly all observed double-cropping) is removed; + feasibility evidence now comes from MIRCA observation 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/default.yaml b/config/default.yaml index 09a8b121..724bec8e 100644 --- a/config/default.yaml +++ b/config/default.yaml @@ -1798,6 +1798,7 @@ 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..f7ecd5b7 100644 --- a/config/schemas/config.schema.yaml +++ b/config/schemas/config.schema.yaml @@ -698,8 +698,8 @@ properties: type: string water_supplies: type: array - minItems: 2 - maxItems: 3 + minItems: 1 + maxItems: 2 items: type: string enum: [r, i] @@ -1658,7 +1658,7 @@ 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 +1673,9 @@ 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/multi_crop_cost.csv b/data/curated/calibration/default/multi_crop_cost.csv new file mode 100644 index 00000000..4032a7cc --- /dev/null +++ b/data/curated/calibration/default/multi_crop_cost.csv @@ -0,0 +1,790 @@ +combination,country,correction_bnusd_per_mha +cotton_wheat,AFG,-1.6099050045013428 +cotton_wheat,AGO,-1.814477801322937 +cotton_wheat,ALB,7.5 +cotton_wheat,ARG,7.5 +cotton_wheat,ARM,7.5 +cotton_wheat,AUS,-5.853282690048218 +cotton_wheat,AZE,-0.6669307351112366 +cotton_wheat,BDI,-0.9382950663566589 +cotton_wheat,BGD,-1.3390784859657288 +cotton_wheat,BGR,-1.3807790279388428 +cotton_wheat,BOL,7.5 +cotton_wheat,BRA,-2.3770229816436768 +cotton_wheat,BWA,3.1791040897369385 +cotton_wheat,CHN,-6.063660144805908 +cotton_wheat,COD,-1.8446723222732544 +cotton_wheat,DZA,7.5 +cotton_wheat,EGY,-2.120392918586731 +cotton_wheat,ESP,-0.9637662768363953 +cotton_wheat,ETH,-2.7257684469223022 +cotton_wheat,GEO,7.5 +cotton_wheat,GRC,-1.315471351146698 +cotton_wheat,HND,-1.938868522644043 +cotton_wheat,IND,-1.3647518157958984 +cotton_wheat,IRN,-3.0847361087799072 +cotton_wheat,IRQ,-2.056082606315613 +cotton_wheat,ISR,-0.5670958757400513 +cotton_wheat,JOR,-1.9030465483665466 +cotton_wheat,KAZ,-1.6555743217468262 +cotton_wheat,KEN,-0.6111141741275787 +cotton_wheat,KGZ,-1.31385737657547 +cotton_wheat,KOR,7.5 +cotton_wheat,LBN,7.5 +cotton_wheat,LSO,-1.1813910603523254 +cotton_wheat,MAR,-2.646002769470215 +cotton_wheat,MDG,-1.5335040092468262 +cotton_wheat,MEX,-3.4887030124664307 +cotton_wheat,MKD,-1.4253509044647217 +cotton_wheat,MMR,2.898737072944641 +cotton_wheat,MOZ,7.5 +cotton_wheat,MWI,7.5 +cotton_wheat,NAM,7.5 +cotton_wheat,NER,3.5284949243068695 +cotton_wheat,NGA,-1.7977930307388306 +cotton_wheat,NPL,7.5 +cotton_wheat,OMN,-0.0 +cotton_wheat,PAK,-1.2787038087844849 +cotton_wheat,PRT,7.5 +cotton_wheat,PRY,-0.5581440925598145 +cotton_wheat,PSE,-2.3085778951644897 +cotton_wheat,ROU,-0.0 +cotton_wheat,RUS,7.5 +cotton_wheat,RWA,-1.5222082138061523 +cotton_wheat,SDN,-1.6430537700653076 +cotton_wheat,SOM,-2.054495632648468 +cotton_wheat,SWZ,-1.6668144464492798 +cotton_wheat,SYR,-2.3456783294677734 +cotton_wheat,TJK,-2.956416606903076 +cotton_wheat,TKM,-1.5223835706710815 +cotton_wheat,TUN,7.5 +cotton_wheat,TUR,-1.2363390922546387 +cotton_wheat,TZA,-1.431849181652069 +cotton_wheat,UGA,-1.6493436098098755 +cotton_wheat,URY,7.5 +cotton_wheat,USA,-1.3119173049926758 +cotton_wheat,UZB,-2.144982099533081 +cotton_wheat,YEM,-1.9001551270484924 +cotton_wheat,ZAF,-2.24241304397583 +cotton_wheat,ZMB,7.5 +cotton_wheat,ZWE,3.960923194885254 +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,7.5 +double_rice,BEN,-2.0670456886291504 +double_rice,BFA,-1.9492504000663757 +double_rice,BGD,-0.9019208252429962 +double_rice,BLZ,2.32931649684906 +double_rice,BOL,7.5 +double_rice,BRA,7.5 +double_rice,BRN,-2.805904507637024 +double_rice,BTN,7.5 +double_rice,BWA,-0.0 +double_rice,CAF,7.5 +double_rice,CHL,7.5 +double_rice,CHN,-2.8561073541641235 +double_rice,CIV,7.5 +double_rice,CMR,2.668047308921814 +double_rice,COD,7.5 +double_rice,COG,7.5 +double_rice,COL,-2.3517508506774902 +double_rice,CRI,-1.9862532019615173 +double_rice,CUB,7.5 +double_rice,DOM,2.198575973510742 +double_rice,DZA,7.5 +double_rice,ECU,-1.0549455285072327 +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,-0.3800714612007141 +double_rice,GIN,-0.8479809165000916 +double_rice,GMB,0.18228022754192352 +double_rice,GNB,-2.3635621070861816 +double_rice,GNQ,7.5 +double_rice,GRC,7.5 +double_rice,GTM,7.5 +double_rice,GUF,7.5 +double_rice,GUY,7.5 +double_rice,HND,7.5 +double_rice,HTI,2.4892295598983765 +double_rice,IDN,-2.4515942335128784 +double_rice,IND,-1.1609660387039185 +double_rice,IRN,7.5 +double_rice,IRQ,7.5 +double_rice,ITA,7.5 +double_rice,JPN,-7.040684938430786 +double_rice,KEN,7.5 +double_rice,KHM,-1.8155657052993774 +double_rice,KOR,-7.5 +double_rice,LAO,-2.1919310092926025 +double_rice,LBR,7.5 +double_rice,LKA,3.428750306367874 +double_rice,LSO,-0.0 +double_rice,MAR,7.5 +double_rice,MDG,3.6049813479185104 +double_rice,MEX,7.5 +double_rice,MKD,7.5 +double_rice,MLI,0.24248556792736053 +double_rice,MMR,-2.551267623901367 +double_rice,MOZ,7.5 +double_rice,MRT,-1.4914619326591492 +double_rice,MUS,7.5 +double_rice,MWI,-1.7444035410881042 +double_rice,MYS,-0.6751475781202316 +double_rice,NAM,7.5 +double_rice,NER,7.5 +double_rice,NGA,-0.04781103506684303 +double_rice,NIC,2.312941074371338 +double_rice,NPL,3.7156719639897346 +double_rice,PAK,7.5 +double_rice,PAN,-1.7208068370819092 +double_rice,PER,7.5 +double_rice,PHL,-1.3351789712905884 +double_rice,PNG,7.5 +double_rice,PRI,7.5 +double_rice,PRT,7.5 +double_rice,PRY,7.5 +double_rice,RWA,-3.063134789466858 +double_rice,SAU,7.5 +double_rice,SDN,7.5 +double_rice,SEN,0.5294859409332275 +double_rice,SLB,7.5 +double_rice,SLE,3.7774145510047674 +double_rice,SLV,7.5 +double_rice,SOM,7.5 +double_rice,SSD,7.5 +double_rice,SUR,7.5 +double_rice,SWZ,7.5 +double_rice,SYR,7.5 +double_rice,TCD,-1.041583001613617 +double_rice,TGO,0.021735727787017822 +double_rice,THA,-0.6866416335105896 +double_rice,TJK,7.5 +double_rice,TKM,7.5 +double_rice,TLS,7.5 +double_rice,TTO,7.5 +double_rice,TUN,-0.0 +double_rice,TUR,7.5 +double_rice,TWN,-1.2726807594299316 +double_rice,TZA,7.5 +double_rice,UGA,7.5 +double_rice,URY,7.5 +double_rice,USA,7.5 +double_rice,UZB,7.5 +double_rice,VEN,7.5 +double_rice,VNM,-1.6253113746643066 +double_rice,YEM,-0.0 +double_rice,ZAF,7.5 +double_rice,ZMB,7.5 +double_rice,ZWE,7.5 +maize_soybean,AFG,-0.4736453890800476 +maize_soybean,AGO,-1.5113000273704529 +maize_soybean,ALB,7.5 +maize_soybean,ARG,-0.001545419916510582 +maize_soybean,ARM,7.5 +maize_soybean,AUS,1.390625536441803 +maize_soybean,AUT,-0.3401510715484619 +maize_soybean,AZE,4.152604818344116 +maize_soybean,BDI,-1.0835424065589905 +maize_soybean,BEL,7.5 +maize_soybean,BEN,-0.8253427743911743 +maize_soybean,BFA,-0.5887079238891602 +maize_soybean,BGD,0.632455587387085 +maize_soybean,BGR,7.5 +maize_soybean,BIH,7.5 +maize_soybean,BLZ,7.5 +maize_soybean,BOL,0.12550845742225647 +maize_soybean,BRA,-0.03387417644262314 +maize_soybean,BTN,4.1264379024505615 +maize_soybean,BWA,7.5 +maize_soybean,CAF,-1.607940912246704 +maize_soybean,CAN,-0.7665511965751648 +maize_soybean,CHE,-1.1859102249145508 +maize_soybean,CHN,-1.4345921874046326 +maize_soybean,CIV,-0.9916943311691284 +maize_soybean,CMR,-1.224189281463623 +maize_soybean,COD,-1.6027145385742188 +maize_soybean,COG,-1.5472362637519836 +maize_soybean,COL,-0.292894572019577 +maize_soybean,CRI,-0.9751254320144653 +maize_soybean,CUB,7.5 +maize_soybean,CZE,7.5 +maize_soybean,DEU,7.5 +maize_soybean,DZA,-0.0 +maize_soybean,ECU,-0.6386651396751404 +maize_soybean,EGY,-1.6338169574737549 +maize_soybean,ESP,-0.43243037164211273 +maize_soybean,ETH,-0.22050713002681732 +maize_soybean,FRA,7.5 +maize_soybean,GAB,-1.2179523706436157 +maize_soybean,GEO,0.07373932749032974 +maize_soybean,GHA,-0.8419823944568634 +maize_soybean,GIN,7.5 +maize_soybean,GRC,0.0970049574971199 +maize_soybean,GTM,-0.7145440578460693 +maize_soybean,HND,-0.4602302610874176 +maize_soybean,HRV,7.5 +maize_soybean,HUN,7.5 +maize_soybean,IDN,0.4130144417285919 +maize_soybean,IND,-0.6799671053886414 +maize_soybean,IRN,-1.0338594913482666 +maize_soybean,IRQ,7.5 +maize_soybean,ISR,7.5 +maize_soybean,ITA,7.5 +maize_soybean,JOR,-0.0 +maize_soybean,KAZ,7.5 +maize_soybean,KEN,-0.4090971052646637 +maize_soybean,KGZ,-0.2840305417776108 +maize_soybean,KHM,-0.5590203106403351 +maize_soybean,KOR,-0.30528104305267334 +maize_soybean,LAO,-0.03479786217212677 +maize_soybean,LBN,7.5 +maize_soybean,LKA,-0.9143259227275848 +maize_soybean,LSO,7.5 +maize_soybean,LUX,7.5 +maize_soybean,MAR,3.441688358783722 +maize_soybean,MDA,3.948325455188751 +maize_soybean,MEX,0.012745517306029797 +maize_soybean,MKD,7.5 +maize_soybean,MLI,-0.7469700872898102 +maize_soybean,MMR,-0.9117441177368164 +maize_soybean,MNE,7.5 +maize_soybean,MOZ,7.5 +maize_soybean,MWI,3.6040261685848236 +maize_soybean,NGA,-0.720582515001297 +maize_soybean,NIC,-0.3675630474463105 +maize_soybean,NLD,7.5 +maize_soybean,NPL,-0.5565337538719177 +maize_soybean,PAK,-1.0684007406234741 +maize_soybean,PER,7.5 +maize_soybean,POL,7.5 +maize_soybean,PRT,7.5 +maize_soybean,PRY,-0.21764160692691803 +maize_soybean,ROU,0.32373878359794617 +maize_soybean,RUS,7.5 +maize_soybean,RWA,-0.34852825105190277 +maize_soybean,SDN,-0.5342399477958679 +maize_soybean,SLV,-0.8975279331207275 +maize_soybean,SRB,7.5 +maize_soybean,SSD,-1.321803331375122 +maize_soybean,SVK,7.5 +maize_soybean,SVN,7.5 +maize_soybean,SWZ,-0.8048333525657654 +maize_soybean,SYR,7.5 +maize_soybean,TCD,7.5 +maize_soybean,TGO,-0.3580051511526108 +maize_soybean,THA,-0.3933115601539612 +maize_soybean,TJK,7.5 +maize_soybean,TKM,0.9870100617408752 +maize_soybean,TLS,4.11736986041069 +maize_soybean,TUR,7.5 +maize_soybean,TWN,0.8484704494476318 +maize_soybean,TZA,-0.3797968029975891 +maize_soybean,UGA,-1.3789831399917603 +maize_soybean,UKR,7.5 +maize_soybean,URY,-0.47526396811008453 +maize_soybean,USA,-0.5723414123058319 +maize_soybean,UZB,7.5 +maize_soybean,VEN,-0.8426925539970398 +maize_soybean,VNM,-0.27706438302993774 +maize_soybean,ZAF,-0.006137027870863676 +maize_soybean,ZMB,-0.5981044173240662 +maize_soybean,ZWE,0.9514963030815125 +rice_maize,AFG,-1.6404048204421997 +rice_maize,AGO,-2.3160706758499146 +rice_maize,ALB,-0.0 +rice_maize,ARG,7.5 +rice_maize,ARM,-0.0 +rice_maize,AUS,-1.590356171131134 +rice_maize,AZE,-2.83044695854187 +rice_maize,BDI,7.5 +rice_maize,BEN,-1.4021406173706055 +rice_maize,BGD,0.39136016368865967 +rice_maize,BGR,7.5 +rice_maize,BLZ,-2.3535351753234863 +rice_maize,BOL,7.5 +rice_maize,BRA,0.5185031741857529 +rice_maize,BTN,0.26831182837486267 +rice_maize,BWA,-0.0 +rice_maize,CAF,-2.315131902694702 +rice_maize,CAN,-0.0 +rice_maize,CHL,-0.9636813998222351 +rice_maize,CHN,-2.491113066673279 +rice_maize,CIV,-0.7100791335105896 +rice_maize,CMR,-1.6961027383804321 +rice_maize,COD,-2.435609221458435 +rice_maize,COL,-1.5935273170471191 +rice_maize,COM,-2.337308883666992 +rice_maize,CUB,-1.5886424779891968 +rice_maize,DOM,2.4203288555145264 +rice_maize,DZA,7.5 +rice_maize,ECU,-0.7153042256832123 +rice_maize,EGY,-2.13281512260437 +rice_maize,ERI,7.5 +rice_maize,ESP,-1.1463301181793213 +rice_maize,ETH,7.5 +rice_maize,FRA,7.5 +rice_maize,GEO,7.5 +rice_maize,GHA,-0.728587806224823 +rice_maize,GIN,-0.4560774564743042 +rice_maize,GNB,7.5 +rice_maize,GRC,-1.3117156028747559 +rice_maize,GTM,-2.1169002056121826 +rice_maize,HND,-1.633684515953064 +rice_maize,HTI,-2.1469744443893433 +rice_maize,HUN,7.5 +rice_maize,IDN,-0.3467683792114258 +rice_maize,IND,-1.1606595516204834 +rice_maize,IRN,-4.914326429367065 +rice_maize,IRQ,-3.0447577238082886 +rice_maize,ITA,-2.2469688057899475 +rice_maize,KAZ,0.25109851360321045 +rice_maize,KEN,-1.8319658041000366 +rice_maize,KGZ,-3.0051995515823364 +rice_maize,KOR,-6.520980596542358 +rice_maize,LAO,-1.216350793838501 +rice_maize,LKA,-1.0294817984104156 +rice_maize,LSO,-0.0 +rice_maize,MAR,-1.6206347942352295 +rice_maize,MDA,7.5 +rice_maize,MDG,-1.1849598288536072 +rice_maize,MEX,-0.2136644870042801 +rice_maize,MKD,7.5 +rice_maize,MLI,-0.3211269825696945 +rice_maize,MMR,-1.6517590284347534 +rice_maize,MOZ,-0.46825529634952545 +rice_maize,MUS,7.5 +rice_maize,MWI,-1.1541817784309387 +rice_maize,NAM,7.5 +rice_maize,NGA,-0.4805299639701843 +rice_maize,NIC,-0.0 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+wheat_maize,GEO,0.09364751726388931 +wheat_maize,GRC,3.5302646458148956 +wheat_maize,GTM,3.372650295495987 +wheat_maize,HND,-1.0760682821273804 +wheat_maize,HRV,7.5 +wheat_maize,HUN,7.5 +wheat_maize,IND,-0.3966492712497711 +wheat_maize,IRN,-1.7036104202270508 +wheat_maize,IRQ,-1.5998313426971436 +wheat_maize,ISR,-6.92481803894043 +wheat_maize,ITA,-0.7050333023071289 +wheat_maize,JOR,0.6081819571554661 +wheat_maize,KAZ,0.13862000405788422 +wheat_maize,KEN,-0.655785322189331 +wheat_maize,KGZ,-0.36566704511642456 +wheat_maize,KOR,0.012307032011449337 +wheat_maize,LAO,3.3776745796203613 +wheat_maize,LBN,-0.38848312199115753 +wheat_maize,LBY,7.5 +wheat_maize,LSO,-0.07805822603404522 +wheat_maize,LUX,7.5 +wheat_maize,MAR,-0.30309001356363297 +wheat_maize,MDA,3.8919558972120285 +wheat_maize,MDG,-1.0458958148956299 +wheat_maize,MEX,-0.8088407814502716 +wheat_maize,MKD,7.5 +wheat_maize,MMR,-0.9253009855747223 +wheat_maize,MNE,7.5 +wheat_maize,MOZ,7.5 +wheat_maize,MWI,7.5 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+wheat_maize,UGA,-1.1047067642211914 +wheat_maize,UKR,7.5 +wheat_maize,URY,-0.24283335357904434 +wheat_maize,USA,-0.24570778757333755 +wheat_maize,UZB,-2.2322025299072266 +wheat_maize,VEN,7.5 +wheat_maize,VNM,-7.5 +wheat_maize,YEM,-1.2792218923568726 +wheat_maize,ZAF,-0.047328826040029526 +wheat_maize,ZMB,0.6400972902774811 +wheat_maize,ZWE,7.117492198944092 +wheat_soybean,AFG,-0.21164609491825104 +wheat_soybean,AGO,-1.2595300078392029 +wheat_soybean,ALB,7.5 +wheat_soybean,ARG,-0.6065599322319031 +wheat_soybean,ARM,7.5 +wheat_soybean,AUS,0.6463535279035568 +wheat_soybean,AUT,-0.01924823597073555 +wheat_soybean,AZE,4.210933417081833 +wheat_soybean,BDI,-1.4682312607765198 +wheat_soybean,BEL,7.5 +wheat_soybean,BGD,-0.5767140686511993 +wheat_soybean,BGR,7.5 +wheat_soybean,BIH,7.5 +wheat_soybean,BOL,7.5 +wheat_soybean,BRA,-0.39443439245224 +wheat_soybean,BTN,7.5 +wheat_soybean,BWA,7.5 +wheat_soybean,CAN,7.5 +wheat_soybean,CHE,-0.8971370458602905 +wheat_soybean,CHN,-1.3253093957901 +wheat_soybean,CMR,-0.43673020601272583 +wheat_soybean,COD,-1.2637889981269836 +wheat_soybean,CZE,7.5 +wheat_soybean,DEU,7.5 +wheat_soybean,DZA,-0.0 +wheat_soybean,ECU,-0.6838064789772034 +wheat_soybean,EGY,-1.2459351420402527 +wheat_soybean,ESP,0.2736721336841583 +wheat_soybean,ETH,-0.8871381282806396 +wheat_soybean,FRA,7.5 +wheat_soybean,GEO,-0.05915307253599167 +wheat_soybean,GRC,-0.2598990797996521 +wheat_soybean,GTM,3.78983361646533 +wheat_soybean,HND,-0.959020584821701 +wheat_soybean,HRV,7.5 +wheat_soybean,HUN,7.5 +wheat_soybean,IND,-0.40162912011146545 +wheat_soybean,IRN,-0.473108172416687 +wheat_soybean,IRQ,7.5 +wheat_soybean,ISR,7.5 +wheat_soybean,ITA,7.5 +wheat_soybean,JOR,-0.0 +wheat_soybean,JPN,1.6746549606323242 +wheat_soybean,KAZ,7.5 +wheat_soybean,KEN,-0.6842177510261536 +wheat_soybean,KGZ,-0.27548111975193024 +wheat_soybean,KOR,3.5200543254613876 +wheat_soybean,LAO,7.5 +wheat_soybean,LBN,7.5 +wheat_soybean,LSO,7.5 +wheat_soybean,LUX,7.5 +wheat_soybean,MAR,-1.4421567916870117 +wheat_soybean,MDA,3.895781621336937 +wheat_soybean,MEX,-0.34212958812713623 +wheat_soybean,MKD,7.5 +wheat_soybean,MMR,-1.0974624752998352 +wheat_soybean,MNE,7.5 +wheat_soybean,MOZ,7.5 +wheat_soybean,MWI,7.5 +wheat_soybean,NGA,-0.6289743781089783 +wheat_soybean,NLD,7.5 +wheat_soybean,NPL,3.5407437086105347 +wheat_soybean,PAK,-0.4830322265625 +wheat_soybean,PER,-0.46757999062538147 +wheat_soybean,POL,7.5 +wheat_soybean,PRT,7.5 +wheat_soybean,PRY,-0.12475436180830002 +wheat_soybean,ROU,7.5 +wheat_soybean,RUS,7.5 +wheat_soybean,RWA,-0.45277969539165497 +wheat_soybean,SRB,7.5 +wheat_soybean,SVK,7.5 +wheat_soybean,SVN,3.762704192660749 +wheat_soybean,SWZ,-0.5005633234977722 +wheat_soybean,SYR,7.5 +wheat_soybean,THA,-0.7992278635501862 +wheat_soybean,TJK,7.5 +wheat_soybean,TKM,0.47631368041038513 +wheat_soybean,TUR,7.5 +wheat_soybean,TWN,-0.3602020740509033 +wheat_soybean,TZA,-0.46302691102027893 +wheat_soybean,UGA,-1.1337865591049194 +wheat_soybean,UKR,7.5 +wheat_soybean,URY,-0.31937259435653687 +wheat_soybean,USA,-0.3088880181312561 +wheat_soybean,UZB,7.5 +wheat_soybean,VNM,-7.5 +wheat_soybean,ZAF,-0.254607193171978 +wheat_soybean,ZMB,-0.18164435029029846 +wheat_soybean,ZWE,4.234316349029541 diff --git a/data/curated/mirca_os_crop_mapping.csv b/data/curated/mirca_os_crop_mapping.csv new file mode 100644 index 00000000..75ad5ce1 --- /dev/null +++ b/data/curated/mirca_os_crop_mapping.csv @@ -0,0 +1,44 @@ +# 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 GLADE crop-sequence combinations. +# +# Columns: +# mirca_crop - exact MIRCA-OS crop label as it appears in the annual +# harvested-area GeoTIFF filenames (MIRCA-OS_{mirca_crop}_2020_{ir,rf}_v2.tif). +# glade_crop - target crop in config["crops"], or blank to DROP the crop from +# combination discovery (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/mirca_crops.py). +mirca_crop,glade_crop,note +Barley,barley, +Cassava,cassava,long-duration root crop; retained but GAEZ season feasibility usually excludes it from sequences +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/docs/calibration.rst b/docs/calibration.rst index dc288b65..b4be2c5c 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- @@ -207,7 +208,7 @@ 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 +337,10 @@ 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/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..7a33eb30 100644 --- a/docs/crop_production.rst +++ b/docs/crop_production.rst @@ -473,23 +473,43 @@ 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 2020 baseline** derived from +MIRCA-OS v2 (see :doc:`data_sources`), so the model starts from today's +double-cropped area -- notably India's rice-wheat and rice-rice systems -- rather +than treating extra cropping cycles as unanchored potential. It is built in two +stages. + +**Stage 1 (config-independent derivation).** The Snakemake checkpoint +``derive_mirca_multicropping`` (``workflow/scripts/derive_mirca_multicropping.py``) +attributes MIRCA's extra-cycle harvested area -- harvested minus the AEI-capped +physical footprint (``ir_maxmonth + rf_maxmonth``) -- to crop-sequence +combinations on the global 5-arcmin grid, writing to +``processing/shared/multi_cropping/``: the discovered ``combinations.yaml``, +per-combination baseline rasters, and a residual raster for the bulk land +correction. The effective combination set the model builds with is the derived +set merged over the static ``multiple_cropping`` config section (derived entries +take precedence; combinations referencing unmodeled crops are dropped). + +A combination is a candidate in a cell where **both crops are 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; GAEZ enters only for suitability, yields and water +requirements. + +**Stage 2 (build-time aggregation).** 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 rule +``aggregate_multicropping_baseline`` aggregates the Stage-1 rasters to the +observed baseline on the active config's regions. The three tables land in +``processing/{name}/multi_cropping/``: ``eligible_area.csv`` (potential area + +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). The RES01 classes report the agro-climatic zone the pixel belongs to. We interpret the numeric codes as: @@ -510,16 +530,24 @@ 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 cycle. Because the single-crop ``crop_production`` +baselines are FAOSTAT *harvested* area (already counting every cycle), the +harvested cycles now carried by the multi links are subtracted from the +corresponding single-crop baselines (``m_k`` times the multi anchor for crop +``k``), with country-level redistribution to preserve national totals. The +crop-agnostic ``multi_cropping_land_correction`` generator absorbs only the +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..09d31253 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 uses the 2020 annual harvested-area grids (which count every harvested cycle) and the maximum-monthly-cropped-area grids (the AEI-capped physical field footprint) to derive the observed multiple-cropping baseline, and the 2020 subcrop monthly growing-area grids (``Rice1/2/3``) to detect 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 (model anchors to 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 with ``bsdtar`` (``extract_mirca_os_*`` rules). Note the v2 archive ships only ``ir``/``rf`` footprint layers despite its README mentioning a ``tot`` layer, so the combined footprint is taken as ``ir + rf`` (disjoint land, single-count). + +**Usage**: Source of the observed multiple-cropping baseline (see :doc:`crop_production`, Stage 1), derived by the ``derive_mirca_multicropping`` checkpoint. The MIRCA-OS-to-GLADE crop concordance is in ``data/curated/mirca_os_crop_mapping.csv``. + FAOSTAT Prices (PP) ~~~~~~~~~~~~~~~~~~~~ diff --git a/docs/land_use.rst b/docs/land_use.rst index ab1a92d9..82c9a1cc 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 (e.g. mixed +irrigated/rainfed rotations, 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..491ad28e 100644 --- a/docs/workflow.rst +++ b/docs/workflow.rst @@ -78,11 +78,23 @@ Data Preparation Rules * **Script**: ``workflow/scripts/build_crop_yields.py`` * **Purpose**: Aggregate yields by (region, class) for each crop +**derive_mirca_multicropping** (checkpoint) + * **Input**: MIRCA-OS 2020 annual harvested-area and footprint grids, the rice subcrop monthly grids, the GAEZ RES01 multiple-cropping zone rasters, and the crop concordance + * **Output**: ``processing/shared/multi_cropping/`` -- ``combinations.yaml`` (the discovered combination set), ``baseline/{combination}_{ws}.tif`` (observed physical link area per combination), ``residual_multicrop.tif`` (unattributed extra-cycle area, diagnostic) + * **Script**: ``workflow/scripts/derive_mirca_multicropping.py`` + * **Purpose**: Config-independent Stage-1 derivation of the observed multiple-cropping baseline from MIRCA-OS 2020. A checkpoint: the discovered combination set is merged over ``config.multiple_cropping`` to form the effective set, which determines downstream rule 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 derived 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 the effective combination 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 + +**aggregate_multicropping_baseline** + * **Input**: The Stage-1 baseline rasters for the effective combination set, resource classes, regions + * **Output**: ``processing/{name}/multi_cropping/baseline_area.csv`` + * **Script**: ``workflow/scripts/aggregate_multicropping_baseline.py`` + * **Purpose**: Aggregate the observed multi-cropping baseline to the active config's regions and resource classes; anchors ``crop_production_multi`` links **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/test_crop_costs.py b/tests/test_crop_costs.py index fddb08f9..a7dfe25c 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(): @@ -60,3 +63,106 @@ 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): + """Build a single rice-wheat multi-cropping link and return the network.""" + n = pypsa.Network() + n.buses.add( + [ + "land:cropland:regionA_c0_r", + "crop:wetland-rice:USA", + "crop:wheat:USA", + "fertilizer:USA", + ] + ) + eligible_area = pd.DataFrame( + { + "combination": ["rice_wheat"], + "region": ["regionA"], + "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", "rice_wheat"], + "region": ["regionA", "regionA"], + "resource_class": [0, 0], + "water_supply": ["r", "r"], + "cycle_index": [0, 1], + "crop": ["wetland-rice", "wheat"], + "yield_t_per_ha": [2.0, 3.0], + } + ) + baseline_area = pd.DataFrame( + { + "combination": ["rice_wheat"], + "region": ["regionA"], + "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({"regionA": "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}, + 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}, + baseline_area=baseline_area, + multi_crop_cost_calibration=multi_crop_cost_calibration, + ) + return n + + +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) diff --git a/tests/test_integration.py b/tests/test_integration.py index 566f0868..b7633076 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() + # Multi-cropping integration: the effective (MIRCA-derived) combination set + # 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): diff --git a/tests/test_multi_cropping.py b/tests/test_multi_cropping.py new file mode 100644 index 00000000..5136cc97 --- /dev/null +++ b/tests/test_multi_cropping.py @@ -0,0 +1,330 @@ +# SPDX-FileCopyrightText: 2026 Koen van Greevenbroek +# +# SPDX-License-Identifier: GPL-3.0-or-later + +"""Unit tests for the MIRCA-OS multi-cropping baseline. + +Covers Stage-1 attribution (magnitude allocation, repeated-crop supports, +residual conservation), the per-cycle seasonal water split (T-column magnitude +preservation, distinct-crop period placement, repeated-crop stagger), and the +single-crop baseline reconciliation (cycle-multiplicity reduction, no negatives, +land balance). +""" + +from collections import Counter + +import numpy as np +import pandas as pd +import pypsa +import pytest + +from workflow.scripts.build_model.crops import ( + fix_crop_production_to_baseline, + reconcile_single_crop_baselines, +) +from workflow.scripts.derive_mirca_multicropping import ( + allocate, + candidate_capacity, + run_derivation, +) +from workflow.scripts.solve_model.production_stability import _multi_cycle_long + + +def _network_with_links(rows): + """Minimal network whose links carry the columns reconciliation reads. + + Multi-cropping rows may carry an ``output_buses`` list; those become bus1, + bus2, ... so reconciliation can count the built crop-output cycles. + """ + 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", + "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 + + +# ─── Stage-1 attribution ─────────────────────────────────────────────────── + + +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]])] + cycles = [2, 3] + areas, residual = allocate(m_total, caps, cycles) + extra = sum((n - 1) * a for n, a in zip(cycles, areas)) + # 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 + cycles = [2, 2] + areas, residual = allocate(m_total, caps, cycles) + extra = sum((n - 1) * a for n, a in zip(cycles, 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_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] + # cap A_max = min(20, 15) = 15; extra cap 15 <= 25 -> attributed 15, residual 10 + assert attributed == pytest.approx(15.0) + assert residual[0, 0] == pytest.approx(m_total - attributed) + + +# ─── Reconciliation multiplicity ─────────────────────────────────────────── + + +def test_reconciliation_multiplicity_counts(): + """Cycle multiplicity: rice-wheat subtracts X from each; double rice 2X.""" + assert dict(Counter(["wetland-rice", "wheat"])) == {"wetland-rice": 1, "wheat": 1} + assert dict(Counter(["wetland-rice", "wetland-rice"])) == {"wetland-rice": 2} + assert dict(Counter(["wetland-rice"] * 3)) == {"wetland-rice": 3} + + +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": "", + "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, + "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, {"rice_wheat": {"crops": ["wetland-rice", "wheat"]}} + ) + 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, {"double_rice": {"crops": ["wetland-rice", "wetland-rice"]}} + ) + 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, {"double_rice": {"crops": ["wetland-rice", "wetland-rice"]}} + ) + 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_built_cycles_not_config(): + """A combo built with fewer cycles reduces singles only for produced crops. + + rice_wheat configured, but the built link produces only rice (wheat cycle + dropped, e.g. below min_yield). Only the rice single is reduced; wheat is not. + """ + n = _network_with_links( + [ + _crop_link("rice", "wetland-rice", 10.0), + _crop_link("wheat", "wheat", 8.0), + # built with only the rice output bus + _multi_link("m", "rice_wheat", 3.0, ["wetland-rice"]), + ] + ) + reconcile_single_crop_baselines( + n, {"rice_wheat": {"crops": ["wetland-rice", "wheat"]}} + ) + bl = n.links.static["baseline_area_mha"] + assert bl["rice"] == pytest.approx(7.0) # 10 - 3 (rice produced) + assert bl["wheat"] == pytest.approx(8.0) # unchanged: no wheat cycle built + + +# ─── 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, {"rice_wheat": {"crops": ["wetland-rice", "wheat"]}} + ) + 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_ports_per_cycle(): + """_multi_cycle_long counts one cycle per crop-output port, so repeated + crops carry their multiplicity and distinct crops one row each.""" + 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/workflow/Snakefile b/workflow/Snakefile index 83e8fd06..1d088174 100644 --- a/workflow/Snakefile +++ b/workflow/Snakefile @@ -9,6 +9,7 @@ from snakemake.utils import min_version from validation import load_secrets_with_env_fallback, validate +from workflow.scripts.multi_cropping_combinations import 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..1a9137b0 100644 --- a/workflow/rules/crops.smk +++ b/workflow/rules/crops.smk @@ -558,8 +558,101 @@ rule build_harvested_area_yield_weighted: "../scripts/build_harvested_area_yield_weighted.py" +def _mirca_base_crops(): + """The 23 MIRCA-OS base crop labels, read from the concordance at parse time.""" + import pandas as pd + + mapping = pd.read_csv("data/curated/mirca_os_crop_mapping.csv", comment="#") + return mapping["mirca_crop"].astype(str).str.strip().tolist() + + +def mirca_multicropping_inputs(_wildcards): + """Inputs for the config-independent Stage-1 multi-cropping derivation. + + All 23 MIRCA crops' 2020 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. Input keys must be identifier-safe, so spaces in MIRCA crop + labels are mapped to underscores (see ``_annual_key`` in the script). + """ + grids = "data/downloads/mirca_os/grids" + inputs = { + "concordance": "data/curated/mirca_os_crop_mapping.csv", + "footprint_ir": f"{grids}/footprint/MIRCA-OS_2020_ir_v2.tif", + "footprint_rf": f"{grids}/footprint/MIRCA-OS_2020_rf_v2.tif", + "rice2_ir": f"{grids}/monthly/MIRCA-OS_Rice2_2020_ir.nc", + "rice2_rf": f"{grids}/monthly/MIRCA-OS_Rice2_2020_rf.nc", + "rice3_ir": f"{grids}/monthly/MIRCA-OS_Rice3_2020_ir.nc", + "rice3_rf": f"{grids}/monthly/MIRCA-OS_Rice3_2020_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_base_crops(): + for mws in ("ir", "rf"): + key = f"annual_{mirca_crop.replace(' ', '_')}_{mws}" + inputs[key] = f"{grids}/annual/MIRCA-OS_{mirca_crop}_2020_{mws}_v2.tif" + return inputs + + +# Agronomic seed set; mirrors COMBO_CROPS in the derivation script. Any +# additional sequence clearing the coverage floor is added to the discovered +# set by the script itself. +MIRCA_SEED_COMBINATIONS = [ + "rice_wheat", + "double_rice", + "triple_rice", + "rice_maize", + "wheat_maize", + "wheat_soybean", + "maize_soybean", + "cotton_wheat", +] + + +checkpoint derive_mirca_multicropping: + """Stage 1: derive the observed multi-cropping baseline from MIRCA-OS 2020. + + Config-independent (shared across configs). Attributes MIRCA's extra-cycle + harvested area (harvested minus AEI-capped footprint) to crop-sequence + combinations, gating on MIRCA co-occurrence + GAEZ multiple-cropping zone. + Writes ``combinations.yaml`` (the discovered combination set, resolved into + the effective config via ``effective_combinations``), the per-combination + baseline rasters consumed by the Stage-2 aggregation, and the residual + extra-cycle raster (diagnostic). A checkpoint, because the combination set + determines downstream rule inputs. + """ + input: + unpack(mirca_multicropping_inputs), + output: + out_dir=directory("/shared/multi_cropping"), + params: + seed_combinations=MIRCA_SEED_COMBINATIONS, + coverage_floor_mha=0.5, + max_combinations=20, + resources: + runtime="15m", + mem_mb=8000, + log: + "/shared/derive_mirca_multicropping.log", + benchmark: + "/shared/derive_mirca_multicropping.tsv" + script: + "../scripts/derive_mirca_multicropping.py" + + +def derived_combinations_yaml(): + """Path of the Stage-1 ``combinations.yaml`` (defers on the checkpoint).""" + out_dir = checkpoints.derive_mirca_multicropping.get().output.out_dir + return f"{out_dir}/combinations.yaml" + + +def _effective_multicropping(): + """The effective combination set (derived merged over config), DAG-side.""" + return effective_combinations(config, derived_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 +669,13 @@ def multi_cropping_inputs(_wildcards): "classes": "/{name}/resource_classes.nc", "regions": "/{name}/regions.geojson", "yield_unit_conversions": "data/curated/yield_unit_conversions.csv", + "combinations": derived_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 +692,6 @@ 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", @@ -622,6 +709,45 @@ rule build_multi_cropping: "../scripts/build_multi_cropping.py" +def multicropping_baseline_inputs(_wildcards): + """Inputs for the Stage-2 baseline aggregation: the Stage-1 rasters for the + effective combination set, plus the region/class grids to aggregate to. + """ + out_dir = checkpoints.derive_mirca_multicropping.get().output.out_dir + inputs = { + "classes": "/{name}/resource_classes.nc", + "regions": "/{name}/regions.geojson", + "combinations": derived_combinations_yaml(), + } + for combo_name, entry in _effective_multicropping().items(): + for ws in entry["water_supplies"]: + inputs[f"baseline_{combo_name}_{ws}"] = ( + f"{out_dir}/baseline/{combo_name}_{ws}.tif" + ) + return inputs + + +rule aggregate_multicropping_baseline: + """Stage 2: aggregate the Stage-1 multi-cropping baseline rasters to the + active config's regions/classes, the anchor for crop_production_multi links. + """ + input: + unpack(multicropping_baseline_inputs), + output: + "/{name}/multi_cropping/baseline_area.csv", + group: + "prep" + resources: + runtime="3m", + mem_mb=5500, + log: + "/{name}/aggregate_multicropping_baseline.log", + benchmark: + "/{name}/aggregate_multicropping_baseline.tsv" + script: + "../scripts/aggregate_multicropping_baseline.py" + + rule build_grassland_yields: input: grassland="data/downloads/grassland_yield_historical.nc4", @@ -799,6 +925,9 @@ 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..cc486e54 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: derived_combinations_yaml(), edible_portion="/{name}/fao_edible_portion.csv", population="/{name}/population.csv", baseline_diet="/{name}/dietary_intake.csv", @@ -198,7 +201,6 @@ rule build_model: constants_script="workflow/scripts/constants.py", params: crops=config["crops"], - multiple_cropping=config["multiple_cropping"], countries=config["countries"], land=config["land"], fertilizer=config["fertilizer"], diff --git a/workflow/rules/retrieve.smk b/workflow/rules/retrieve.smk index 2e3173c4..feb65da1 100644 --- a/workflow/rules/retrieve.smk +++ b/workflow/rules/retrieve.smk @@ -800,6 +800,143 @@ 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; individual GeoTIFF/NetCDF layers are then +# unpacked with ``bsdtar`` (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" + ), +} + + +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 one crop/system 2020 annual-harvested-area GeoTIFF (5-arcmin). + + Harvested area sums subcrops, so a twice-cropped field counts twice; this is + the numerator of the extra-cycle magnitude M = harvested - footprint. Keyed by + the MIRCA crop label (see data/curated/mirca_os_crop_mapping.csv). + """ + input: + rar="data/downloads/mirca_os/Annual_Harvested_Area_Grids.rar", + output: + "data/downloads/mirca_os/grids/annual/MIRCA-OS_{crop}_2020_{ws}_v2.tif", + wildcard_constraints: + # MIRCA crop labels include spaces (e.g. "Oil palm", "Others annual"), + # which the all-crop M_total sum needs (~21% of global harvested area). + crop="[A-Za-z][A-Za-z ]*", + ws="ir|rf", + resources: + runtime="10m", + mem_mb=1000, + log: + "/shared/extract_mirca_os_annual_{crop}_{ws}.log", + shell: + r""" + mkdir -p "$(dirname '{output}')" + bsdtar -xOf {input.rar} \ + --include="*MIRCA-OS_{wildcards.crop}_2020_{wildcards.ws}_v2.tif" \ + > '{output}' 2> '{log}' + test -s '{output}' + """ + + +rule extract_mirca_os_footprint: + """Unpack the 2020 maximum-monthly-cropped-area GeoTIFF for one system. + + This is the AEI-capped physical field footprint per system; the combined + footprint is the ir + rf sum (disjoint land, single-count). MIRCA-OS v2 ships + no `tot` layer despite its README, so the derivation sums the two systems. + """ + input: + rar="data/downloads/mirca_os/Maximum_Monthly_Cropped_Area_Grids.rar", + output: + "data/downloads/mirca_os/grids/footprint/MIRCA-OS_2020_{ws}_v2.tif", + wildcard_constraints: + ws="ir|rf", + resources: + runtime="10m", + mem_mb=1000, + log: + "/shared/extract_mirca_os_footprint_{ws}.log", + shell: + r""" + mkdir -p "$(dirname {output})" + bsdtar -xOf {input.rar} \ + --include='*5-arcminute/MIRCA-OS_2020_{wildcards.ws}_v2.tif' \ + > {output} 2> {log} + test -s {output} + """ + + +rule extract_mirca_os_subcrop_monthly: + """Unpack one subcrop's monthly growing-area NetCDF (5-arcmin) for a year. + + The subcrop-resolved monthly product preserves subcrop identity + (Rice1/2/3, Wheat1/2, ...); the 2020 vintage feeds the multi-cropping + derivation's repeated-cycle detection (never crop timing). + """ + input: + rar="data/downloads/mirca_os/Monthly_Growing_Area_Grids.rar", + output: + "data/downloads/mirca_os/grids/monthly/MIRCA-OS_{subcrop}_{year}_{ws}.nc", + wildcard_constraints: + # Base crops ("Barley", "Sugar beet") and digit-suffixed sub-crops + # ("Rice1", "Wheat2"); the multi-cropping derivation needs the + # digit-suffixed rice cycles. + subcrop="[A-Za-z][A-Za-z0-9 ]*", + year="2000|2005|2010|2015|2020", + ws="ir|rf", + resources: + runtime="30m", + mem_mb=2000, + log: + "/shared/extract_mirca_os_subcrop_{subcrop}_{year}_{ws}.log", + shell: + r""" + mkdir -p "$(dirname '{output}')" + bsdtar -xOf {input.rar} \ + --include='*MIRCA-OS_{wildcards.subcrop}_{wildcards.year}_{wildcards.ws}.nc' \ + > '{output}' 2> '{log}' + test -s '{output}' + """ + + rule download_grassland_yield_data: """Retrieve historical managed-grassland yield from ISIMIP2a / LPJmL. diff --git a/workflow/scripts/aggregate_multicropping_baseline.py b/workflow/scripts/aggregate_multicropping_baseline.py new file mode 100644 index 00000000..d1ca03fd --- /dev/null +++ b/workflow/scripts/aggregate_multicropping_baseline.py @@ -0,0 +1,158 @@ +""" +SPDX-FileCopyrightText: 2026 Koen van Greevenbroek + +SPDX-License-Identifier: GPL-3.0-or-later + +Stage 2 (aggregation): reduce the config-independent Stage-1 multi-cropping +baseline rasters to a per-region, per-resource-class, per-water-supply table for +the active config's regions. + +Each Stage-1 raster (``baseline/{combination}_{ws}.tif`` under the +``derive_mirca_multicropping`` checkpoint's output directory) +holds the *physical link area* ``A`` (ha) -- the field area that runs the whole +sequence once. This aggregates ``A`` to ``(combination, region, resource_class, +water_supply)`` via ``exact_extract`` against ``resource_classes.nc`` + +``regions.geojson``, reusing the machinery in ``build_multi_cropping``. The result +anchors ``crop_production_multi`` links in production stability. +""" + +from pathlib import Path + +import geopandas as gpd +import numpy as np +import pandas as pd +import xarray as xr + +from workflow.scripts.build_multi_cropping import region_coverage_entries +from workflow.scripts.multi_cropping_combinations import effective_combinations +from workflow.scripts.raster_utils import raster_bounds, read_raster_float + +BASELINE_COLUMNS = [ + "combination", + "region", + "resource_class", + "water_supply", + "baseline_area_ha", +] + + +def main() -> None: + inp = dict(snakemake.input.items()) # type: ignore[name-defined] + output = Path(snakemake.output[0]) # type: ignore[name-defined] + + classes_nc = inp.pop("classes") + regions_path = inp.pop("regions") + combinations = effective_combinations( + snakemake.config, # type: ignore[name-defined] + inp.pop("combinations"), + ) + + # Remaining inputs are the per-(combination, ws) Stage-1 baseline rasters, + # keyed "baseline_{combination}_{ws}". + raster_paths = { + key[len("baseline_") :]: path + for key, path in inp.items() + if key.startswith("baseline_") + } + + ds = xr.load_dataset(classes_nc) + if "resource_class" not in ds: + raise ValueError("resource_classes.nc is missing 'resource_class' data") + class_labels = ds["resource_class"].values.astype(np.int16) + valid_classes = [ + int(cls) + for cls in np.unique(class_labels[np.isfinite(class_labels)]) + if int(cls) >= 0 + ] + + regions_gdf = gpd.read_file(regions_path) + regions_for_extract = regions_gdf.reset_index() + + # All Stage-1 rasters share one grid, so the region coverage fractions are + # extracted once (on the first raster's grid) and every aggregation is a + # gather + bincount against them; a raster on a different grid fails fast. + coverage_grid = None + records: list[pd.DataFrame] = [] + for combo_name, entry in combinations.items(): + if entry is None: + continue + for ws in entry["water_supplies"]: + raster_key = f"{combo_name}_{ws}" + path = raster_paths.get(raster_key) + if path is None: + raise KeyError( + f"Missing Stage-1 baseline raster for '{raster_key}'; the " + "derive_mirca_multicropping checkpoint output is stale." + ) + area_arr, src = read_raster_float(path) + try: + if area_arr.shape != class_labels.shape: + raise ValueError( + f"Baseline raster '{raster_key}' shape {area_arr.shape} does " + f"not match resource class grid {class_labels.shape}" + ) + transform = src.transform + crs = src.crs + crs_wkt = crs.to_wkt() if crs else None + height, width = area_arr.shape + xmin, ymin, xmax, ymax = raster_bounds(transform, width, height) + finally: + src.close() + + if coverage_grid is None: + if regions_gdf.crs and crs and regions_gdf.crs != crs: + regions_for_extract = regions_gdf.to_crs(crs).reset_index() + coverage_grid = (transform, crs_wkt) + region_names, cov_rows, cov_cells, cov_fracs = region_coverage_entries( + regions_for_extract, + xmin, + ymin, + xmax, + ymax, + crs_wkt, + area_arr.shape, + ) + n_regions = len(region_names) + class_at_entry = class_labels.ravel()[cov_cells] + elif coverage_grid != (transform, crs_wkt): + raise ValueError( + f"Baseline raster '{raster_key}' is not on the shared " + "Stage-1 grid" + ) + + area_flat = np.where(np.isfinite(area_arr), area_arr, 0.0).ravel() + area_entries = area_flat[cov_cells] * cov_fracs + for cls in valid_classes: + sel = class_at_entry == cls + sums = np.bincount( + cov_rows[sel], weights=area_entries[sel], minlength=n_regions + ) + pos = sums > 0 + if not pos.any(): + continue + stats = pd.DataFrame( + { + "combination": combo_name, + "region": region_names[pos], + "resource_class": cls, + "water_supply": ws, + "baseline_area_ha": sums[pos], + } + ) + records.append(stats[BASELINE_COLUMNS]) + + if records: + result = pd.concat(records, ignore_index=True) + result["resource_class"] = result["resource_class"].astype(int) + result = result.sort_values( + ["combination", "water_supply", "region", "resource_class"], + ignore_index=True, + ) + else: + result = pd.DataFrame(columns=BASELINE_COLUMNS) + + output.parent.mkdir(parents=True, exist_ok=True) + result.to_csv(output, index=False) + + +main() diff --git a/workflow/scripts/build_model.py b/workflow/scripts/build_model.py index c3ad93ae..ccae4e7b 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 +993,14 @@ 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( + snakemake.config, snakemake.input.multi_cropping_combinations ) + enable_multiple_cropping = bool(multiple_cropping_cfg) if enable_multiple_cropping: crops.add_multi_cropping_links( n, @@ -1012,9 +1018,35 @@ def _apply_yield_corrections(corr_df: pd.DataFrame, source_label: str) -> None: 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, + baseline_area=multi_cropping_baseline_df, + use_actual_production=use_actual_production, + multi_crop_cost_calibration=multi_crop_cost_calibration, + ) + # Persist the single-crop baseline reduction (harvested cycles now carried + # by the multi links) BEFORE the land correction and the pin read + # baseline_area_mha. + crops.reconcile_single_crop_baselines( + n, + multiple_cropping_cfg, ) - elif use_actual_production: - logger.info("Skipping multiple cropping links under actual production mode") + + # Clip sub-min_link_area_mha crop baselines BEFORE they are pinned or + # summed into the solve-time growth cap, so the pin and the cap agree. + # 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, + ) + + # 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 +1056,15 @@ 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..e313fa65 100644 --- a/workflow/scripts/build_model/crops.py +++ b/workflow/scripts/build_model/crops.py @@ -9,7 +9,7 @@ spared land allocation with carbon sequestration. """ -from collections.abc import Mapping +from collections.abc import Mapping, Sequence import logging import numpy as np @@ -148,6 +148,83 @@ def _redistribute_excess_baseline(df: pd.DataFrame) -> pd.Series: 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( + "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()), + ) + + def add_regional_crop_production_links( n: pypsa.Network, crop_list: list, @@ -178,6 +255,11 @@ 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 +344,10 @@ 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)] @@ -380,18 +462,6 @@ 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 +501,15 @@ 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 +569,9 @@ 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 +583,12 @@ 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) @@ -611,6 +610,9 @@ def add_multi_cropping_links( seed_kg_dm_per_ha: pd.Series, crop_loss_multiplier: pd.Series, crop_marketing_cost_usd_per_t: Mapping[str, float], + 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. @@ -619,6 +621,17 @@ 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. + + ``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. + + 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``. """ if eligible_area.empty or cycle_yields.empty: @@ -639,7 +652,7 @@ def add_multi_cropping_links( ) 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) @@ -756,7 +769,56 @@ 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 + + # Anchor each link at its MIRCA-observed baseline area (Mha), keyed by + # (combination, region, resource_class, water_supply). Missing anchors are 0 + # (guarded so a missing baseline can never inject a NaN into the stability + # term). Cap = max(GAEZ potential, anchor) so the anchor is never + # above the expansion bound. + if baseline_area is not None and not baseline_area.empty: + anchor = baseline_area.copy() + anchor["resource_class"] = anchor["resource_class"].astype(int) + anchor["water_supply"] = anchor["water_supply"].astype(str) + anchor["baseline_area_mha"] = ( + pd.to_numeric(anchor["baseline_area_ha"], errors="coerce").fillna(0.0) / 1e6 + ) + anchor = anchor.set_index(key_cols)["baseline_area_mha"] + base["baseline_area_mha"] = anchor.reindex(base.index).fillna(0.0) + else: + base["baseline_area_mha"] = 0.0 + base["p_nom_max"] = np.maximum( + base["eligible_area_ha"] / 1e6, 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(): @@ -876,34 +938,44 @@ 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) @@ -1124,6 +1196,9 @@ 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 = [ @@ -1197,6 +1272,159 @@ def add_multi_cropping_links( n.links.add(link_df.index, **kwargs) +def reconcile_single_crop_baselines( + n: pypsa.Network, + combinations: Mapping[str, Mapping[str, object]], +) -> 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. For each *built* multi link with anchored + baseline area ``X`` in ``(region, class, water_supply)``, crop ``k`` appearing + ``m_k`` times in its sequence, this subtracts ``m_k * X`` from that crop's + single-crop ``baseline_area_mha`` in the same cell, so each harvested cycle is + counted once (on the multi link). Local MIRCA-vs-FAOSTAT disagreement can drive + a cell negative, so any over-subtraction is redistributed onto other + ``(crop, country, water_supply)`` rows that still have baseline to give up + (preserving the FAOSTAT national total per water supply); only when a whole + group runs out does it spill to the residual bulk correction. + + The reduction is derived from the links actually added (not the raw + ``baseline_area.csv``), so baselines whose multi link could not be built (e.g. + a missing land bus) do not strip single-crop area with nothing replacing it. + It is persisted on the ``crop_production`` ``baseline_area_mha`` column, which + the L1 stability penalty and irrigation calibration re-read, so it must run + before those steps. + """ + 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 + + # Cycle multiplicity is counted from the crop-output buses the link was + # actually built with, not from the config sequence: a combination can build + # with fewer cycles than configured (a cycle whose yield fell below + # min_yield_t_per_ha is dropped), and reconciling against the config count + # would strip single-crop area for a cycle that no multi link produces. The + # candidate crops per combination come from the config; the count comes from + # matching each crop's constructed output bus among the link's buses. + output_bus_cols = [ + col + for col in multi.columns + if col.startswith("bus") and col != "bus0" and col[3:].isdigit() + ] + bus_arrays = {col: multi[col].to_numpy() for col in output_bus_cols} + combo_arr = multi["combination"].astype(str).to_numpy() + region_arr = multi["region"].astype(str).to_numpy() + class_arr = multi["resource_class"].astype(int).to_numpy() + ws_arr = multi["water_supply"].astype(str).to_numpy() + country_arr = multi["country"].astype(str).to_numpy() + baseline_arr = multi["baseline_area_mha"].astype(float).to_numpy() + + # Required reduction (Mha) per (crop, region, class, water_supply). water_supply + # is already "irrigated"/"rainfed" on both carriers. + reductions: dict[tuple[str, str, int, str], float] = {} + for i in range(len(multi)): + area_mha = float(baseline_arr[i]) + if area_mha <= 0: + continue + entry = combinations.get(combo_arr[i]) + if entry is None: + continue + country = country_arr[i] + link_buses = [bus_arrays[col][i] for col in output_bus_cols] + for crop in set(entry["crops"]): + expected_bus = f"crop:{crop}:{country}" + built_cycles = sum(1 for bus in link_buses if bus == expected_bus) + if built_cycles == 0: + continue + key = (crop, region_arr[i], class_arr[i], ws_arr[i]) + reductions[key] = reductions.get(key, 0.0) + built_cycles * area_mha + if not reductions: + return + + df = links[is_crop] + link_keys = list( + zip( + df["crop"].astype(str), + df["region"].astype(str), + df["resource_class"].astype(int), + df["water_supply"].astype(str), + ) + ) + required = pd.Series( + [reductions.get(key, 0.0) for key in link_keys], index=df.index, dtype=float + ) + baseline = df["baseline_area_mha"].astype(float) + new_baseline = baseline - required + # Amount over-subtracted per link (local anchor too small for the reduction). + over = (-new_baseline).clip(lower=0.0) + new_baseline = new_baseline.clip(lower=0.0) + + group = ( + df["crop"].astype(str) + + ":" + + df["country"].astype(str) + + ":" + + df["water_supply"].astype(str) + ) + unplaced = 0.0 + for _key, idx in new_baseline.groupby(group).groups.items(): + group_over = float(over.loc[idx].sum()) + if group_over <= 0: + continue + spare = new_baseline.loc[idx] + total_spare = float(spare.sum()) + if total_spare <= 0: + unplaced += group_over + continue + take = min(group_over, total_spare) + new_baseline.loc[idx] -= spare / total_spare * take + if group_over > total_spare: + unplaced += group_over - total_spare + new_baseline = new_baseline.clip(lower=0.0) + + n.links.static.loc[new_baseline.index, "baseline_area_mha"] = new_baseline.values + logger.info( + "Reconciled single-crop baselines against multi-cropping: reduced %.1f Mha " + "of harvested-cycle anchor (%.1f Mha unplaceable, left to residual)", + float((baseline - new_baseline).sum()), + unplaced, + ) + + +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/derive_mirca_multicropping.py b/workflow/scripts/derive_mirca_multicropping.py new file mode 100644 index 00000000..23e1a3e8 --- /dev/null +++ b/workflow/scripts/derive_mirca_multicropping.py @@ -0,0 +1,390 @@ +""" +SPDX-FileCopyrightText: 2026 Koen van Greevenbroek + +SPDX-License-Identifier: GPL-3.0-or-later + +Stage 1 of the multi-cropping baseline: derive an observed baseline area for +crop-sequence combinations from MIRCA-OS 2020, on the global 5-arcmin grid. + +This is a config-independent Snakemake checkpoint (shared across configs). It +writes to its output directory: + + * ``combinations.yaml`` -- the discovered combination set (crop sequences and + water supplies in GLADE ``i``/``r`` codes), merged over + ``config["multiple_cropping"]`` to form the effective combination set. + * ``baseline/{combination}_{ws}.tif`` -- per-combination, per-water-supply + sparse 5-arcmin GeoTIFF of the *physical link area* ``A`` (ha): the field area + that runs the whole sequence once (an ``n``-cycle combination on ``A`` ha + harvests ``n * A`` ha). + * ``residual_multicrop.tif`` -- extra-cycle harvested area not attributed to any + combination, left for the bulk land-correction generator. + +Method, per cell: + +1. Magnitude ``M_total = sum_crops (harvested_ir + harvested_rf) - footprint``, + clipped at 0, where ``footprint = footprint_ir + footprint_rf`` are MIRCA's + AEI-capped maximum-monthly-cropped-area layers (no ``tot`` layer ships, and the + two systems occupy disjoint land, so the sum is single-count). ``M_total`` is + the harvested area above the physical field footprint -- the extra-cycle area. +2. Candidate combinations are gated on **MIRCA observation** (both crops harvested + in the given system) plus the GAEZ multiple-cropping-zone cycle-count limit. + ``sequence_feasible`` on GAEZ windows is deliberately NOT used as the gate: GAEZ + attainable season lengths overshoot the farmed cycle, so it rejects ~all + observed irrigated double-cropping. GAEZ timing enters only later, at the + Stage-2 water split. +3. Repeated same-crop cycles (double/triple rice) use the MIRCA subcrop stack + (``Rice1/2/3``) for mutually consistent physical supports, not ``min(area, + area)``. +4. Each candidate has physical cap ``A_max`` and extra-cycle capacity + ``(n-1) * A_max``. The cell's ``M_total`` is allocated across candidates + proportionally to capacity, never exceeding it; any unallocated remainder is + residual. The physical link area written out is ``A = E / (n-1)``. +""" + +from pathlib import Path + +import numpy as np +import pandas as pd +import rasterio +import xarray as xr +import yaml + +from workflow.scripts.build_multi_cropping import WETLAND_RICE_CROPS, ZONE_CAPABILITIES + + +def load_tif(path: str) -> np.ndarray: + """Load a GeoTIFF band as float64 with negatives (nodata sentinels) zeroed.""" + with rasterio.open(path) as src: + arr = src.read(1).astype(np.float64) + arr[~np.isfinite(arr)] = 0.0 + arr[arr < 0] = 0.0 + return arr + + +def load_subcrop_maxmonth(path: str) -> np.ndarray: + """Collapse a MIRCA monthly growing-area NetCDF to its max over months (ha).""" + da = xr.open_dataset(path)["harvested_area"] + arr = np.nan_to_num(da.values, nan=0.0) # (month, lat, lon) + arr[arr < 0] = 0.0 + return arr.max(axis=0) + + +def zone_mask(zone_arr: np.ndarray, n_cycles: int, n_rice: int) -> np.ndarray: + """Cells whose GAEZ multiple-cropping zone permits ``n_cycles`` (``n_rice`` rice).""" + 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: + """Physical cap ``A_max`` (ha) for a crop sequence in one water supply, per cell. + + Distinct-crop sequences: ``A_max = min_k area_{crop_k, ws}`` where both crops + are observed, under the zone mask. Repeated wetland-rice sequences: disjoint + supports from the MIRCA subcrop stack (double = cells with a 2nd but not 3rd + rice cycle; triple = cells with a 3rd cycle). + """ + n = len(crops) + n_rice = sum(1 for c in crops if c in WETLAND_RICE_CROPS) + zmask = zone_mask(zone_arr, n, n_rice) + + repeated_rice = n_rice == n and n >= 2 # all cycles are wetland rice + if repeated_rice: + rice2 = rice_support[ws] # area with a 2nd rice cycle (>= double) + rice3 = rice_support[ws + "3"] # area with a 3rd rice cycle (triple) + if n == 2: + a_max = np.clip(rice2 - rice3, 0.0, None) # exactly-double fields + elif n == 3: + a_max = rice3.copy() + else: + raise ValueError(f"Unsupported repeated-rice cycle count: {n}") + return np.where(zmask, a_max, 0.0) + + # Distinct-crop sequence: min over the (possibly repeated) crop areas. + stack = np.stack([crop_area[(c, ws)] for c in crops], axis=0) + both_observed = np.all(stack > 0, axis=0) + a_max = np.min(stack, axis=0) + return np.where(zmask & both_observed, a_max, 0.0) + + +def allocate( + m_total: np.ndarray, + capacities: list[np.ndarray], + cycle_counts: list[int], +) -> tuple[list[np.ndarray], np.ndarray]: + """Allocate the extra-cycle magnitude across candidates, capped by capacity. + + Each candidate ``i`` has extra-cycle capacity ``(n_i - 1) * A_max_i``. Per + cell: if total capacity fits within ``m_total`` every candidate is filled and + the remainder is residual; otherwise ``m_total`` is rationed proportionally to + capacity (no candidate exceeds its cap, and the sum never exceeds ``m_total``, + so no co-located rotation is invented). Returns per-candidate *physical link + area* ``A = E / (n-1)`` and the residual extra-cycle area. + """ + extra_caps = [ + (n - 1) * cap for n, cap in zip(cycle_counts, capacities) + ] # capacity in extra-cycle units + total_cap = np.sum(extra_caps, axis=0) + # scale in [0,1]: 1 where capacity fits, else m_total/total_cap + with np.errstate(divide="ignore", invalid="ignore"): + scale = np.where(total_cap > m_total, m_total / total_cap, 1.0) + scale = np.clip(np.nan_to_num(scale, nan=0.0, posinf=0.0), 0.0, 1.0) + + areas: list[np.ndarray] = [] + allocated_extra = np.zeros_like(m_total) + for n, cap in zip(cycle_counts, capacities): + e = (n - 1) * cap * scale # extra-cycle area for this candidate + allocated_extra += e + areas.append(e / (n - 1)) # physical link area + residual = np.clip(m_total - 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], +) -> tuple[dict[tuple[str, str], np.ndarray], np.ndarray, pd.DataFrame]: + """Run the full per-cell attribution over the global grid. + + Parameters mirror the loaded rasters (all same shape). ``combos`` is a list of + dicts ``{"name", "crops": [...], "water_supply": "i"|"r"}``. Returns: + * ``area_rasters`` -- physical link area ``A`` (ha) per (combo name, ws). + * ``residual`` -- unattributed extra-cycle area (ha). + * ``stats`` -- per-(combo, ws) global attributed physical and extra-cycle area. + """ + # Magnitude: total harvested (all crops, both systems) minus combined footprint. + h_total = np.sum(list(annual_harvested.values()), axis=0) + foot = np.clip(footprint["ir"], 0.0, None) + np.clip(footprint["rf"], 0.0, None) + m_total = np.clip(h_total - foot, 0.0, None) + + capacities = [ + candidate_capacity( + c["crops"], + c["water_supply"], + crop_area, + zone[c["water_supply"]], + rice_support, + ) + for c in combos + ] + cycle_counts = [len(c["crops"]) for c in combos] + + areas, residual = allocate(m_total, capacities, cycle_counts) + + area_rasters: dict[tuple[str, str], np.ndarray] = {} + records: list[dict] = [] + for c, area in zip(combos, areas): + key = (c["name"], c["water_supply"]) + area_rasters[key] = area + n = len(c["crops"]) + records.append( + { + "combination": c["name"], + "water_supply": c["water_supply"], + "cycles": n, + "physical_area_mha": area.sum() / 1e6, + "extra_cycle_area_mha": (n - 1) * area.sum() / 1e6, + } + ) + stats = pd.DataFrame.from_records(records) + return area_rasters, residual, stats + + +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, ir/rf) harvested arrays to GLADE (crop, i/r) supports.""" + ws_map = {"i": "ir", "r": "rf"} + return { + (glade_crop, gws): annual_harvested[(mirca_crop, mws)] + for glade_crop, mirca_crop in glade_to_mirca.items() + for gws, mws in ws_map.items() + } + + +def discover_combinations( + stats: pd.DataFrame, + seed_names: set[str], + floor_mha: float, + max_combos: int, +) -> dict[str, dict]: + """Select the combination set and emit a ``config["multiple_cropping"]`` block. + + A combination is kept if it is in the agronomic seed set or its global + attributed extra-cycle area clears ``floor_mha``; the kept set is capped at + ``max_combos`` (largest first). Within a kept combination, only water supplies + with positive attributed area are listed. + """ + per_combo = stats.groupby("combination")["extra_cycle_area_mha"].sum() + kept = [ + name + for name in per_combo.index + if name in seed_names or per_combo[name] >= floor_mha + ] + kept = sorted(kept, key=lambda n: -per_combo[n])[:max_combos] + + combos_cfg: dict[str, dict] = {} + for name in kept: + rows = stats[stats["combination"] == name] + supplies = sorted( + rows.loc[rows["physical_area_mha"] > 0, "water_supply"].unique() + ) + if not supplies: + continue + crops = COMBO_CROPS[name] + combos_cfg[name] = {"crops": list(crops), "water_supplies": supplies} + return combos_cfg + + +# Agronomic seed set, in GLADE crop names. +COMBO_CROPS: dict[str, list[str]] = { + "rice_wheat": ["wetland-rice", "wheat"], + "double_rice": ["wetland-rice", "wetland-rice"], + "triple_rice": ["wetland-rice", "wetland-rice", "wetland-rice"], + "rice_maize": ["wetland-rice", "maize"], + "wheat_maize": ["wheat", "maize"], + "wheat_soybean": ["wheat", "soybean"], + "maize_soybean": ["maize", "soybean"], + "cotton_wheat": ["cotton", "wheat"], +} + + +def write_outputs( + area_rasters: dict[tuple[str, str], np.ndarray], + residual: np.ndarray, + combos_cfg: dict[str, dict], + profile: dict, + out_dir: Path, +) -> None: + """Write combinations.yaml, per-(combo, ws) baseline rasters, and the residual.""" + baseline_dir = out_dir / "baseline" + baseline_dir.mkdir(parents=True, exist_ok=True) + + raster_profile = { + **profile, + "count": 1, + "dtype": "float32", + "nodata": 0.0, + "compress": "deflate", + } + + for name, entry in combos_cfg.items(): + for ws in entry["water_supplies"]: + arr = area_rasters[(name, ws)].astype(np.float32) + with rasterio.open( + baseline_dir / f"{name}_{ws}.tif", "w", **raster_profile + ) as dst: + dst.write(arr, 1) + + with rasterio.open( + out_dir / "residual_multicrop.tif", "w", **raster_profile + ) as dst: + dst.write(residual.astype(np.float32), 1) + + header = ( + "# SPDX-FileCopyrightText: 2026 Koen van Greevenbroek\n" + "#\n" + "# SPDX-License-Identifier: CC-BY-4.0\n" + "#\n" + "# Multi-cropping combination set discovered from MIRCA-OS 2020 by\n" + "# workflow/scripts/derive_mirca_multicropping.py (a Snakemake\n" + "# checkpoint). Merged over config['multiple_cropping'] to form the\n" + "# effective combination set. Do not hand-edit.\n" + ) + with open(out_dir / "combinations.yaml", "w") as fh: + fh.write(header) + yaml.safe_dump(combos_cfg, fh, sort_keys=True, default_flow_style=False) + + +def _annual_key(mirca_crop: str, mws: str) -> str: + """Snakemake input key for an annual raster (spaces are not valid in keys).""" + return f"annual_{mirca_crop.replace(' ', '_')}_{mws}" + + +def _load_inputs(inp, mirca_crops, glade_to_mirca): + """Load all rasters from a snakemake-style input mapping.""" + annual = { + (mc, mws): load_tif(inp[_annual_key(mc, mws)]) + for mc in mirca_crops + for mws in ("ir", "rf") + } + footprint = {ws: load_tif(inp[f"footprint_{ws}"]) for ws in ("ir", "rf")} + zone = {ws: load_tif(inp[f"zone_{ws}"]) 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}"]) + rice_support[gws + "3"] = load_subcrop_maxmonth(inp[f"rice3_{mws}"]) + return annual, footprint, zone, rice_support + + +def main() -> None: + inp = dict(snakemake.input.items()) # type: ignore[name-defined] + params = snakemake.params # type: ignore[name-defined] + out_dir = Path(snakemake.output.out_dir) # type: ignore[name-defined] + + 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() + mirca_crops = mapping["mirca_crop"].tolist() + glade_to_mirca = { + row.glade_crop: row.mirca_crop for row in mapping.itertuples() if row.glade_crop + } + + annual, footprint, zone, rice_support = _load_inputs( + inp, mirca_crops, glade_to_mirca + ) + crop_area = build_crop_area(annual, glade_to_mirca) + + seed_names = set(params.seed_combinations) + combos = [ + {"name": name, "crops": COMBO_CROPS[name], "water_supply": ws} + for name in seed_names + for ws in ("i", "r") + ] + + area_rasters, residual, stats = run_derivation( + annual, footprint, crop_area, zone, rice_support, combos + ) + + combos_cfg = discover_combinations( + stats, + seed_names, + float(params.coverage_floor_mha), + int(params.max_combinations), + ) + + with rasterio.open(inp["footprint_ir"]) as src: + profile = src.profile + + out_dir.mkdir(parents=True, exist_ok=True) + write_outputs(area_rasters, residual, combos_cfg, profile, out_dir) + + h_total = np.sum(list(annual.values()), axis=0) + foot = footprint["ir"] + footprint["rf"] + m_total = float(np.clip(h_total - foot, 0.0, None).sum()) / 1e6 + resid = float(residual.sum()) / 1e6 + print( + f"Multi-cropping derivation: M_total={m_total:.1f} Mha, " + f"attributed={m_total - resid:.1f} Mha, residual={resid:.1f} Mha " + f"({resid / m_total * 100:.0f}% of M); {len(combos_cfg)} combinations kept" + ) + + +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..6c723d3e --- /dev/null +++ b/workflow/scripts/download_mirca_os_bag_member.py @@ -0,0 +1,69 @@ +""" +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 remotezip +import requests + +MIRCA_OS_V2_RESOURCE = "e4582ca0042148338bb5e0148b749ed6" +BAG_URL = ( + "https://www.hydroshare.org/django_irods/download/bags/" + f"{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}" + + 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)" + ) + data = bag.read(member_path) + + tmp = output.with_suffix(output.suffix + ".part") + tmp.write_bytes(data) + tmp.replace(output) + print(f"Downloaded {len(data):,} 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/multi_cropping_combinations.py b/workflow/scripts/multi_cropping_combinations.py new file mode 100644 index 00000000..e1537ee2 --- /dev/null +++ b/workflow/scripts/multi_cropping_combinations.py @@ -0,0 +1,37 @@ +# SPDX-FileCopyrightText: 2026 Koen van Greevenbroek +# +# SPDX-License-Identifier: GPL-3.0-or-later + +"""Effective multi-cropping combination set: MIRCA-derived merged over config. + +The Stage-1 derivation (``derive_mirca_multicropping``, a Snakemake checkpoint) +discovers the observed crop-sequence combinations and writes them to +``combinations.yaml``. The effective set the model builds with is that derived +set merged over the static ``multiple_cropping`` config section (derived entries +take precedence; config entries supplement combos not derived), with any +combination referencing a crop the config does not model dropped. + +Shared by the Snakemake input functions (via the checkpoint) and the build +scripts, so both resolve the identical combination set. +""" + +from pathlib import Path + +import yaml + + +def effective_combinations(config: dict, combinations_yaml: str | Path) -> dict: + """The effective multi-cropping combination set for a config. + + ``combinations_yaml`` is the Stage-1 checkpoint's derived set; entries merge + over ``config["multiple_cropping"]`` and combos with unmodeled crops drop. + """ + with open(combinations_yaml) as f: + derived = yaml.safe_load(f) or {} + merged = {**config["multiple_cropping"], **derived} + model_crops = set(config["crops"]) + return { + name: entry + for name, entry in merged.items() + if entry and set(entry["crops"]) <= model_crops + } diff --git a/workflow/scripts/solve_model/production_stability.py b/workflow/scripts/solve_model/production_stability.py index 6f5bb2e8..cac5f04c 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 @@ -202,14 +203,19 @@ 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 +225,7 @@ def add_production_stability_constraints( n, link_p, links_df, - "crop_production", + crop_carriers, "crop", deviation_type, crops_cfg["l1_cost"], @@ -230,7 +236,7 @@ def add_production_stability_constraints( n, link_p, links_df, - "crop_production", + crop_carriers, "crop", deviation_type, quadratic_cost, @@ -299,7 +305,7 @@ 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 +402,46 @@ 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 +530,7 @@ 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 +629,7 @@ 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 +693,7 @@ 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 +1075,7 @@ def add_bounded_subsidy_constraints( n: pypsa.Network, carriers: list[str] = ( "crop_production", + "crop_production_multi", "grassland_production", "animal_production", ), @@ -1201,15 +1221,49 @@ 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 is counted from the crop-output ports (``crop:{crop}:*`` + buses); a repeated crop occupies one port per cycle, so the port 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``. + """ + multi = links_df[links_df["carrier"] == "crop_production_multi"] + if multi.empty: + return pd.DataFrame( + columns=["link", "crop", "group", "multiplicity", "baseline_area_mha"] + ) + bus_cols = [ + c + for c in multi.columns + if c.startswith("bus") and c != "bus0" and c[3:].isdigit() + ] + stacked = multi[bus_cols].astype(str).stack() + stacked = stacked[stacked.str.startswith("crop:")] + long = pd.DataFrame( + { + "link": stacked.index.get_level_values(0), + "crop": stacked.str.split(":").str[1].to_numpy(), + } + ) + long = long.groupby(["link", "crop"]).size().rename("multiplicity").reset_index() + long["group"] = long["crop"] + "::" + long["link"].map(multi["country"]).astype(str) + long["baseline_area_mha"] = long["link"].map(multi["baseline_area_mha"]).fillna(0.0) + 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 +1309,23 @@ 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 are counted from the crop-output ports (a repeated crop + # occupies one port 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 +1333,66 @@ 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 +1405,11 @@ 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), ) 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/multi_cropping.py b/workflow/validation/multi_cropping.py index 8e47f64c..b08d91fe 100644 --- a/workflow/validation/multi_cropping.py +++ b/workflow/validation/multi_cropping.py @@ -2,36 +2,136 @@ # # 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 + +# 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", + } +) + +# Crops in the agronomic seed set of the Stage-1 derivation. These must be +# mapped (non-blank) for the seeded combinations to be derivable at all. +REQUIRED_MAPPED_MIRCA_CROPS = frozenset( + {"Rice", "Wheat", "Maize", "Soybeans", "Cotton"} +) + 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. + Two invariants: + + 1. Every crop referenced by a ``config["multiple_cropping"]`` combination is + in ``config["crops"]`` (so its crop buses exist at build time). + 2. ``data/curated/mirca_os_crop_mapping.csv`` is exhaustive over the 23 + MIRCA-OS base crops, every non-blank ``glade_crop`` is in + ``config["crops"]``, and the agronomic seed-set crops are mapped. This + mirrors ``validate_cropgrids_crops`` for the CROPGRIDS concordance. """ + config_crops = set(config["crops"]) + combinations = config.get("multiple_cropping") - if not combinations: - return + if combinations: + 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}") + if missing: + detail = ", ".join(missing) + raise ValueError( + "Multi-cropping combinations reference crops not in " + f"config['crops']: {detail}" + ) - config_crops = set(config["crops"]) - missing: list[str] = [] + _validate_mirca_concordance(config_crops, project_root) + + +def _validate_mirca_concordance(config_crops: set[str], project_root: Path) -> 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}") - 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}") + 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)}" + ) - if missing: - detail = ", ".join(missing) + unmapped_required = sorted( + crop for crop in REQUIRED_MAPPED_MIRCA_CROPS if glade.get(crop, "") == "" + ) + if unmapped_required: raise ValueError( - f"Multi-cropping combinations reference crops not in config['crops']: " - f"{detail}" + "mirca_os_crop_mapping.csv leaves seed-set crops unmapped: " + f"{unmapped_required}" ) From 9bc091415a8ba356e51d9b13cc3c5e635513f808 Mon Sep 17 00:00:00 2001 From: Koen van Greevenbroek Date: Mon, 20 Jul 2026 17:26:25 -0700 Subject: [PATCH 2/5] fix: account for multi-cropping bounded cost corrections in objective breakdown crop_production_multi links carry bounded_subsidy/bounded_penalty rates like single-crop links, but the objective-breakdown extractor only recovered those linopy-level auxiliary terms for crop_production, grassland_production and animal_production. The unaccounted subsidy made extracted costs exceed the model objective by ~5.5%, tripping the identity check in analyze_model. --- .../scripts/analysis/extract_objective_breakdown.py | 12 ++++++++++++ 1 file changed, 12 insertions(+) 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", From 5b135c32e8ef7e0fa77aebe1f8ab5cfe46e63e2f Mon Sep 17 00:00:00 2001 From: Koen van Greevenbroek Date: Mon, 20 Jul 2026 17:40:19 -0700 Subject: [PATCH 3/5] fix: emit rice methane from multi-cropping cycles add_multi_cropping_links wired crop outputs, water, fertilizer, residues and soil-N2O but never emission:ch4, so every wetland-rice cycle carried by a multi-cropping bundle emitted no methane. The gap predates this branch but was dormant: multi links had no baseline anchor, were left out of the crop growth cap, and were disabled entirely under actual-production mode and under an active land deviation penalty. Anchoring them removes all three protections, and the single-crop baseline reconciliation moves the same harvested cycles off the links that do carry CH4 -- on the test config, 89 Mha of rice cycles, about 11.5 Mt CH4/yr, silently vanished. It also opened a free-abatement channel: under a GHG price, shifting rice onto a double-rice link cost nothing and the growth cap credits the cycles either way. A bundle running m rice cycles now carries m times the per-hectare emission factor (rainfed cycles scaled as on the single-crop path), so rice methane is invariant to whether a hectare is represented as a single-crop link or a cycle of a bundle. Verified on the built test model: 134.47 kt CH4/Mha per irrigated rice cycle on both carriers. Also: ship multi_crop_cost.csv in the gbd-anchored calibration set (its absence broke DAG resolution for every health-enabled config), raise instead of silently skipping when a built link's combination is absent from the effective set (its cycles would be double-counted), and pass the static multiple_cropping section as a build_model param again so editing it retriggers the build (dropping the unused solve-time deviation_penalty param at the same time). --- .../gbd-anchored/multi_crop_cost.csv | 790 ++++++++++++++++++ tests/test_crop_costs.py | 67 ++ workflow/rules/model.smk | 2 +- workflow/scripts/build_model.py | 10 +- workflow/scripts/build_model/crops.py | 100 ++- 5 files changed, 956 insertions(+), 13 deletions(-) create mode 100644 data/curated/calibration/gbd-anchored/multi_crop_cost.csv 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..4032a7cc --- /dev/null +++ b/data/curated/calibration/gbd-anchored/multi_crop_cost.csv @@ -0,0 +1,790 @@ +combination,country,correction_bnusd_per_mha +cotton_wheat,AFG,-1.6099050045013428 +cotton_wheat,AGO,-1.814477801322937 +cotton_wheat,ALB,7.5 +cotton_wheat,ARG,7.5 +cotton_wheat,ARM,7.5 +cotton_wheat,AUS,-5.853282690048218 +cotton_wheat,AZE,-0.6669307351112366 +cotton_wheat,BDI,-0.9382950663566589 +cotton_wheat,BGD,-1.3390784859657288 +cotton_wheat,BGR,-1.3807790279388428 +cotton_wheat,BOL,7.5 +cotton_wheat,BRA,-2.3770229816436768 +cotton_wheat,BWA,3.1791040897369385 +cotton_wheat,CHN,-6.063660144805908 +cotton_wheat,COD,-1.8446723222732544 +cotton_wheat,DZA,7.5 +cotton_wheat,EGY,-2.120392918586731 +cotton_wheat,ESP,-0.9637662768363953 +cotton_wheat,ETH,-2.7257684469223022 +cotton_wheat,GEO,7.5 +cotton_wheat,GRC,-1.315471351146698 +cotton_wheat,HND,-1.938868522644043 +cotton_wheat,IND,-1.3647518157958984 +cotton_wheat,IRN,-3.0847361087799072 +cotton_wheat,IRQ,-2.056082606315613 +cotton_wheat,ISR,-0.5670958757400513 +cotton_wheat,JOR,-1.9030465483665466 +cotton_wheat,KAZ,-1.6555743217468262 +cotton_wheat,KEN,-0.6111141741275787 +cotton_wheat,KGZ,-1.31385737657547 +cotton_wheat,KOR,7.5 +cotton_wheat,LBN,7.5 +cotton_wheat,LSO,-1.1813910603523254 +cotton_wheat,MAR,-2.646002769470215 +cotton_wheat,MDG,-1.5335040092468262 +cotton_wheat,MEX,-3.4887030124664307 +cotton_wheat,MKD,-1.4253509044647217 +cotton_wheat,MMR,2.898737072944641 +cotton_wheat,MOZ,7.5 +cotton_wheat,MWI,7.5 +cotton_wheat,NAM,7.5 +cotton_wheat,NER,3.5284949243068695 +cotton_wheat,NGA,-1.7977930307388306 +cotton_wheat,NPL,7.5 +cotton_wheat,OMN,-0.0 +cotton_wheat,PAK,-1.2787038087844849 +cotton_wheat,PRT,7.5 +cotton_wheat,PRY,-0.5581440925598145 +cotton_wheat,PSE,-2.3085778951644897 +cotton_wheat,ROU,-0.0 +cotton_wheat,RUS,7.5 +cotton_wheat,RWA,-1.5222082138061523 +cotton_wheat,SDN,-1.6430537700653076 +cotton_wheat,SOM,-2.054495632648468 +cotton_wheat,SWZ,-1.6668144464492798 +cotton_wheat,SYR,-2.3456783294677734 +cotton_wheat,TJK,-2.956416606903076 +cotton_wheat,TKM,-1.5223835706710815 +cotton_wheat,TUN,7.5 +cotton_wheat,TUR,-1.2363390922546387 +cotton_wheat,TZA,-1.431849181652069 +cotton_wheat,UGA,-1.6493436098098755 +cotton_wheat,URY,7.5 +cotton_wheat,USA,-1.3119173049926758 +cotton_wheat,UZB,-2.144982099533081 +cotton_wheat,YEM,-1.9001551270484924 +cotton_wheat,ZAF,-2.24241304397583 +cotton_wheat,ZMB,7.5 +cotton_wheat,ZWE,3.960923194885254 +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,7.5 +double_rice,BEN,-2.0670456886291504 +double_rice,BFA,-1.9492504000663757 +double_rice,BGD,-0.9019208252429962 +double_rice,BLZ,2.32931649684906 +double_rice,BOL,7.5 +double_rice,BRA,7.5 +double_rice,BRN,-2.805904507637024 +double_rice,BTN,7.5 +double_rice,BWA,-0.0 +double_rice,CAF,7.5 +double_rice,CHL,7.5 +double_rice,CHN,-2.8561073541641235 +double_rice,CIV,7.5 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+triple_rice,GNQ,7.5 +triple_rice,GRC,7.5 +triple_rice,GTM,7.5 +triple_rice,GUF,7.5 +triple_rice,GUY,7.5 +triple_rice,HND,7.5 +triple_rice,HTI,7.5 +triple_rice,IDN,2.271872878074646 +triple_rice,IND,7.5 +triple_rice,IRN,7.5 +triple_rice,IRQ,7.5 +triple_rice,ITA,7.5 +triple_rice,KEN,7.5 +triple_rice,KHM,-3.3303961753845215 +triple_rice,LAO,7.5 +triple_rice,LBR,7.5 +triple_rice,LKA,7.5 +triple_rice,MAR,7.5 +triple_rice,MDG,7.5 +triple_rice,MEX,7.5 +triple_rice,MLI,7.5 +triple_rice,MMR,7.5 +triple_rice,MOZ,7.5 +triple_rice,MRT,7.5 +triple_rice,MUS,7.5 +triple_rice,MWI,7.5 +triple_rice,MYS,7.5 +triple_rice,NAM,7.5 +triple_rice,NER,7.5 +triple_rice,NGA,7.5 +triple_rice,NIC,7.5 +triple_rice,NPL,7.5 +triple_rice,PAK,7.5 +triple_rice,PAN,7.5 +triple_rice,PER,7.5 +triple_rice,PHL,7.5 +triple_rice,PNG,7.5 +triple_rice,PRI,7.5 +triple_rice,PRY,7.5 +triple_rice,RWA,7.5 +triple_rice,SAU,7.5 +triple_rice,SDN,7.5 +triple_rice,SEN,7.5 +triple_rice,SLB,-4.246044874191284 +triple_rice,SLE,7.5 +triple_rice,SLV,7.5 +triple_rice,SOM,7.5 +triple_rice,SSD,7.5 +triple_rice,SUR,7.5 +triple_rice,SWZ,7.5 +triple_rice,TCD,7.5 +triple_rice,TGO,7.5 +triple_rice,THA,7.5 +triple_rice,TLS,1.3693647384643555 +triple_rice,TTO,7.5 +triple_rice,TUR,7.5 +triple_rice,TWN,7.5 +triple_rice,TZA,7.5 +triple_rice,UGA,7.5 +triple_rice,URY,7.5 +triple_rice,USA,7.5 +triple_rice,VEN,7.5 +triple_rice,VNM,7.5 +triple_rice,YEM,-0.0 +triple_rice,ZAF,7.5 +triple_rice,ZMB,7.5 +triple_rice,ZWE,7.5 +wheat_maize,AFG,-1.059177041053772 +wheat_maize,AGO,-1.4748095273971558 +wheat_maize,ALB,7.5 +wheat_maize,ARE,7.5 +wheat_maize,ARG,7.5 +wheat_maize,ARM,0.19232968986034393 +wheat_maize,AUS,-0.15932096540927887 +wheat_maize,AUT,-0.005322609096765518 +wheat_maize,AZE,-0.29749178886413574 +wheat_maize,BDI,-1.041087031364441 +wheat_maize,BEL,7.5 +wheat_maize,BGD,0.027580395340919495 +wheat_maize,BGR,0.03009234555065632 +wheat_maize,BIH,7.5 +wheat_maize,BOL,7.5 +wheat_maize,BRA,0.10624170303344727 +wheat_maize,BTN,1.5968497395515442 +wheat_maize,BWA,3.844961404800415 +wheat_maize,CAN,7.5 +wheat_maize,CHE,2.516054391860962 +wheat_maize,CHL,-0.93019700050354 +wheat_maize,CHN,-2.075305938720703 +wheat_maize,CMR,-0.5750364661216736 +wheat_maize,COD,-1.4551146030426025 +wheat_maize,COL,-0.6131998300552368 +wheat_maize,CZE,3.7804106660187244 +wheat_maize,DEU,7.5 +wheat_maize,DZA,7.5 +wheat_maize,ECU,-1.0126038789749146 +wheat_maize,EGY,-1.5646820068359375 +wheat_maize,ERI,-1.3822872042655945 +wheat_maize,ESP,-0.7554497122764587 +wheat_maize,ETH,-0.32751739025115967 +wheat_maize,FRA,7.5 +wheat_maize,GEO,0.09364751726388931 +wheat_maize,GRC,3.5302646458148956 +wheat_maize,GTM,3.372650295495987 +wheat_maize,HND,-1.0760682821273804 +wheat_maize,HRV,7.5 +wheat_maize,HUN,7.5 +wheat_maize,IND,-0.3966492712497711 +wheat_maize,IRN,-1.7036104202270508 +wheat_maize,IRQ,-1.5998313426971436 +wheat_maize,ISR,-6.92481803894043 +wheat_maize,ITA,-0.7050333023071289 +wheat_maize,JOR,0.6081819571554661 +wheat_maize,KAZ,0.13862000405788422 +wheat_maize,KEN,-0.655785322189331 +wheat_maize,KGZ,-0.36566704511642456 +wheat_maize,KOR,0.012307032011449337 +wheat_maize,LAO,3.3776745796203613 +wheat_maize,LBN,-0.38848312199115753 +wheat_maize,LBY,7.5 +wheat_maize,LSO,-0.07805822603404522 +wheat_maize,LUX,7.5 +wheat_maize,MAR,-0.30309001356363297 +wheat_maize,MDA,3.8919558972120285 +wheat_maize,MDG,-1.0458958148956299 +wheat_maize,MEX,-0.8088407814502716 +wheat_maize,MKD,7.5 +wheat_maize,MMR,-0.9253009855747223 +wheat_maize,MNE,7.5 +wheat_maize,MOZ,7.5 +wheat_maize,MWI,7.5 +wheat_maize,NAM,-2.086304545402527 +wheat_maize,NIC,7.5 +wheat_maize,NLD,3.8913015574216843 +wheat_maize,NPL,-0.3817053288221359 +wheat_maize,NZL,-0.8886459320783615 +wheat_maize,OMN,-4.1484681367874146 +wheat_maize,PAK,-0.43155471980571747 +wheat_maize,PER,-0.6361838579177856 +wheat_maize,POL,7.5 +wheat_maize,PRT,0.12055753171443939 +wheat_maize,PRY,-0.04922150820493698 +wheat_maize,PSE,1.644733488559723 +wheat_maize,ROU,0.03139835596084595 +wheat_maize,RUS,7.5 +wheat_maize,RWA,-0.43893852829933167 +wheat_maize,SAU,-5.622318267822266 +wheat_maize,SLV,0.0 +wheat_maize,SOM,0.2279478758573532 +wheat_maize,SRB,7.5 +wheat_maize,SSD,7.5 +wheat_maize,SVK,7.5 +wheat_maize,SVN,7.5 +wheat_maize,SWZ,-1.0872985124588013 +wheat_maize,SYR,-0.9373708069324493 +wheat_maize,THA,3.39390966296196 +wheat_maize,TJK,-1.2326552867889404 +wheat_maize,TKM,-0.6474591195583344 +wheat_maize,TUN,-0.0 +wheat_maize,TUR,0.3453899919986725 +wheat_maize,TWN,0.3946537971496582 +wheat_maize,TZA,-0.24894577264785767 +wheat_maize,UGA,-1.1047067642211914 +wheat_maize,UKR,7.5 +wheat_maize,URY,-0.24283335357904434 +wheat_maize,USA,-0.24570778757333755 +wheat_maize,UZB,-2.2322025299072266 +wheat_maize,VEN,7.5 +wheat_maize,VNM,-7.5 +wheat_maize,YEM,-1.2792218923568726 +wheat_maize,ZAF,-0.047328826040029526 +wheat_maize,ZMB,0.6400972902774811 +wheat_maize,ZWE,7.117492198944092 +wheat_soybean,AFG,-0.21164609491825104 +wheat_soybean,AGO,-1.2595300078392029 +wheat_soybean,ALB,7.5 +wheat_soybean,ARG,-0.6065599322319031 +wheat_soybean,ARM,7.5 +wheat_soybean,AUS,0.6463535279035568 +wheat_soybean,AUT,-0.01924823597073555 +wheat_soybean,AZE,4.210933417081833 +wheat_soybean,BDI,-1.4682312607765198 +wheat_soybean,BEL,7.5 +wheat_soybean,BGD,-0.5767140686511993 +wheat_soybean,BGR,7.5 +wheat_soybean,BIH,7.5 +wheat_soybean,BOL,7.5 +wheat_soybean,BRA,-0.39443439245224 +wheat_soybean,BTN,7.5 +wheat_soybean,BWA,7.5 +wheat_soybean,CAN,7.5 +wheat_soybean,CHE,-0.8971370458602905 +wheat_soybean,CHN,-1.3253093957901 +wheat_soybean,CMR,-0.43673020601272583 +wheat_soybean,COD,-1.2637889981269836 +wheat_soybean,CZE,7.5 +wheat_soybean,DEU,7.5 +wheat_soybean,DZA,-0.0 +wheat_soybean,ECU,-0.6838064789772034 +wheat_soybean,EGY,-1.2459351420402527 +wheat_soybean,ESP,0.2736721336841583 +wheat_soybean,ETH,-0.8871381282806396 +wheat_soybean,FRA,7.5 +wheat_soybean,GEO,-0.05915307253599167 +wheat_soybean,GRC,-0.2598990797996521 +wheat_soybean,GTM,3.78983361646533 +wheat_soybean,HND,-0.959020584821701 +wheat_soybean,HRV,7.5 +wheat_soybean,HUN,7.5 +wheat_soybean,IND,-0.40162912011146545 +wheat_soybean,IRN,-0.473108172416687 +wheat_soybean,IRQ,7.5 +wheat_soybean,ISR,7.5 +wheat_soybean,ITA,7.5 +wheat_soybean,JOR,-0.0 +wheat_soybean,JPN,1.6746549606323242 +wheat_soybean,KAZ,7.5 +wheat_soybean,KEN,-0.6842177510261536 +wheat_soybean,KGZ,-0.27548111975193024 +wheat_soybean,KOR,3.5200543254613876 +wheat_soybean,LAO,7.5 +wheat_soybean,LBN,7.5 +wheat_soybean,LSO,7.5 +wheat_soybean,LUX,7.5 +wheat_soybean,MAR,-1.4421567916870117 +wheat_soybean,MDA,3.895781621336937 +wheat_soybean,MEX,-0.34212958812713623 +wheat_soybean,MKD,7.5 +wheat_soybean,MMR,-1.0974624752998352 +wheat_soybean,MNE,7.5 +wheat_soybean,MOZ,7.5 +wheat_soybean,MWI,7.5 +wheat_soybean,NGA,-0.6289743781089783 +wheat_soybean,NLD,7.5 +wheat_soybean,NPL,3.5407437086105347 +wheat_soybean,PAK,-0.4830322265625 +wheat_soybean,PER,-0.46757999062538147 +wheat_soybean,POL,7.5 +wheat_soybean,PRT,7.5 +wheat_soybean,PRY,-0.12475436180830002 +wheat_soybean,ROU,7.5 +wheat_soybean,RUS,7.5 +wheat_soybean,RWA,-0.45277969539165497 +wheat_soybean,SRB,7.5 +wheat_soybean,SVK,7.5 +wheat_soybean,SVN,3.762704192660749 +wheat_soybean,SWZ,-0.5005633234977722 +wheat_soybean,SYR,7.5 +wheat_soybean,THA,-0.7992278635501862 +wheat_soybean,TJK,7.5 +wheat_soybean,TKM,0.47631368041038513 +wheat_soybean,TUR,7.5 +wheat_soybean,TWN,-0.3602020740509033 +wheat_soybean,TZA,-0.46302691102027893 +wheat_soybean,UGA,-1.1337865591049194 +wheat_soybean,UKR,7.5 +wheat_soybean,URY,-0.31937259435653687 +wheat_soybean,USA,-0.3088880181312561 +wheat_soybean,UZB,7.5 +wheat_soybean,VNM,-7.5 +wheat_soybean,ZAF,-0.254607193171978 +wheat_soybean,ZMB,-0.18164435029029846 +wheat_soybean,ZWE,4.234316349029541 diff --git a/tests/test_crop_costs.py b/tests/test_crop_costs.py index a7dfe25c..57d164cb 100644 --- a/tests/test_crop_costs.py +++ b/tests/test_crop_costs.py @@ -120,6 +120,8 @@ def _add_rice_wheat_multi_link(baseline_ha, multi_crop_cost_calibration): ), 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), @@ -166,3 +168,68 @@ def test_multi_cropping_zero_baseline_missing_correction_is_zero(): 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": [0, 1], + "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}, + ) + + 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/workflow/rules/model.smk b/workflow/rules/model.smk index cc486e54..9729d52f 100644 --- a/workflow/rules/model.smk +++ b/workflow/rules/model.smk @@ -201,6 +201,7 @@ rule build_model: constants_script="workflow/scripts/constants.py", params: crops=config["crops"], + multiple_cropping=config["multiple_cropping"], countries=config["countries"], land=config["land"], fertilizer=config["fertilizer"], @@ -218,7 +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/scripts/build_model.py b/workflow/scripts/build_model.py index ccae4e7b..ae21243c 100644 --- a/workflow/scripts/build_model.py +++ b/workflow/scripts/build_model.py @@ -998,7 +998,11 @@ def _apply_yield_corrections(corr_df: pd.DataFrame, source_label: str) -> None: # single-crop production. They are disabled only when the effective # combination set (MIRCA-derived merged over config) is empty. multiple_cropping_cfg = effective_combinations( - snakemake.config, snakemake.input.multi_cropping_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: @@ -1014,6 +1018,10 @@ 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, diff --git a/workflow/scripts/build_model/crops.py b/workflow/scripts/build_model/crops.py index e313fa65..8aac1336 100644 --- a/workflow/scripts/build_model/crops.py +++ b/workflow/scripts/build_model/crops.py @@ -21,6 +21,10 @@ 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 compute_residue_n2o_efficiency_per_dm( residue_feed_items: list[str], @@ -386,7 +390,7 @@ 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 ) @@ -606,6 +610,8 @@ 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, @@ -632,6 +638,12 @@ def add_multi_cropping_links( 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. """ if eligible_area.empty or cycle_yields.empty: @@ -985,6 +997,30 @@ def _water_gate(df): ) 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) @@ -1069,6 +1105,37 @@ def _water_gate(df): ].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[ @@ -1078,6 +1145,7 @@ def _water_gate(df): "crop_count", "has_water", "has_fertilizer", + "has_ch4", ] ], on=key_cols, @@ -1086,15 +1154,15 @@ def _water_gate(df): 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 ) @@ -1130,6 +1198,7 @@ def _water_gate(df): "crop_count", "has_water", "has_fertilizer", + "has_ch4", "residue_count", ] ], @@ -1138,17 +1207,21 @@ def _water_gate(df): ) 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 ) @@ -1331,7 +1404,12 @@ def reconcile_single_crop_baselines( continue entry = combinations.get(combo_arr[i]) if entry is None: - continue + raise ValueError( + f"Multi-cropping link carries combination '{combo_arr[i]}', which " + "is not in the effective combination set. The built link set and " + "the combination set have diverged; its harvested cycles would be " + "double-counted against the single-crop baselines." + ) country = country_arr[i] link_buses = [bus_arrays[col][i] for col in output_bus_cols] for crop in set(entry["crops"]): From 548ef138989c4a0ed28a90adeb0e4626b044a8ed Mon Sep 17 00:00:00 2001 From: Koen van Greevenbroek Date: Mon, 20 Jul 2026 18:04:51 -0700 Subject: [PATCH 4/5] fix: track the multi-crop cost artefact in tools/calibrate The cost calibration emits per-(combination, country) corrections to multi_crop_cost.csv, but the wrapper's artefact and cost-target lists never learned about it, so a 'tools/calibrate cost' run left the file untouched and the staleness check ignored it. --- tools/calibrate | 2 ++ 1 file changed, 2 insertions(+) diff --git a/tools/calibrate b/tools/calibrate index 35169f03..de74b4c8 100755 --- a/tools/calibrate +++ b/tools/calibrate @@ -111,6 +111,7 @@ 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 +131,7 @@ 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 ) From 43f670fe1d231d856ef0be14491336950479ace1 Mon Sep 17 00:00:00 2001 From: Koen van Greevenbroek Date: Tue, 21 Jul 2026 18:49:22 -0700 Subject: [PATCH 5/5] fix: harden multi-cropping baseline workflow --- CHANGELOG.md | 33 +- config/calibration/cost.yaml | 4 +- config/calibration/feed.yaml | 6 +- config/calibration/food_demand.yaml | 5 +- config/calibration/food_waste.yaml | 5 +- config/calibration/stability.yaml | 5 +- config/default.yaml | 28 +- config/schemas/config.schema.yaml | 7 +- .../calibration/default/animal_cost.csv | 2434 ++-- .../curated/calibration/default/crop_cost.csv | 11242 ++++++++-------- .../default/deviation_penalty.yaml | 10 +- .../calibration/default/exogenous_feed.csv | 180 +- .../calibration/default/exogenous_forage.csv | 28 +- .../calibration/default/fodder_conversion.csv | 4 +- .../calibration/default/food_demand.csv | 60 +- .../calibration/default/food_waste.yaml | 22 +- .../calibration/default/grassland_cost.csv | 284 +- .../calibration/default/grassland_yield.csv | 74 +- .../calibration/default/multi_crop_cost.csv | 953 +- .../calibration/default/provenance.yaml | 197 +- .../calibration/gbd-anchored/animal_cost.csv | 2440 ++-- .../calibration/gbd-anchored/crop_cost.csv | 11074 +++++++-------- .../gbd-anchored/deviation_penalty.yaml | 10 +- .../gbd-anchored/exogenous_feed.csv | 168 +- .../gbd-anchored/exogenous_forage.csv | 28 +- .../gbd-anchored/fodder_conversion.csv | 4 +- .../calibration/gbd-anchored/food_demand.csv | 56 +- .../calibration/gbd-anchored/food_waste.yaml | 22 +- .../gbd-anchored/grassland_cost.csv | 274 +- .../gbd-anchored/grassland_yield.csv | 74 +- .../gbd-anchored/multi_crop_cost.csv | 953 +- .../calibration/gbd-anchored/provenance.yaml | 197 +- data/curated/mirca_os_crop_mapping.csv | 12 +- .../mirca_os_multicropping_combinations.yaml | 66 + docs/calibration.rst | 12 + docs/configuration.rst | 24 +- docs/crop_production.rst | 101 +- docs/data_sources.rst | 8 +- docs/land_use.rst | 4 +- docs/workflow.rst | 16 +- tests/config/test_sensitivity.yaml | 7 +- tests/test_calibration_provenance.py | 33 +- tests/test_crop_costs.py | 94 +- tests/test_integration.py | 8 +- tests/test_mirca_archive_extraction.py | 41 + tests/test_multi_cropping.py | 246 +- tools/calibrate | 17 +- workflow/Snakefile | 5 +- workflow/rules/crops.smk | 140 +- workflow/rules/model.smk | 2 +- workflow/rules/retrieve.smk | 118 +- .../aggregate_multicropping_baseline.py | 158 - workflow/scripts/build_model.py | 21 +- workflow/scripts/build_model/crops.py | 577 +- .../scripts/calibrate_deviation_penalty.py | 3 +- .../scripts/compute_food_waste_calibration.py | 3 +- .../scripts/derive_mirca_multicropping.py | 721 +- .../scripts/download_mirca_os_bag_member.py | 31 +- workflow/scripts/extract_mirca_os_archive.py | 78 + .../scripts/multi_cropping_combinations.py | 123 +- .../solve_model/production_stability.py | 37 +- .../scripts/write_calibration_provenance.py | 2 +- workflow/validation/calibration_provenance.py | 41 +- workflow/validation/cropgrids_crops.py | 6 +- workflow/validation/multi_cropping.py | 72 +- 65 files changed, 17417 insertions(+), 16291 deletions(-) create mode 100644 data/curated/mirca_os_multicropping_combinations.yaml create mode 100644 tests/test_mirca_archive_extraction.py delete mode 100644 workflow/scripts/aggregate_multicropping_baseline.py create mode 100644 workflow/scripts/extract_mirca_os_archive.py diff --git a/CHANGELOG.md b/CHANGELOG.md index 396eecdc..b0a70692 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -17,24 +17,21 @@ introduce breaking changes to configuration and outputs. ### Added -- Multiple cropping is now anchored to an **observed 2020 baseline** derived - from MIRCA-OS v2 (new automated data source). A config-independent Snakemake - checkpoint (`derive_mirca_multicropping`) attributes MIRCA's extra-cycle - harvested area to crop-sequence combinations and discovers the combination - set itself (8+ combinations instead of the 3 previously hard-coded; the - static `multiple_cropping` config section now merely supplements the derived - set). `crop_production_multi` links are anchored at the observed baseline - (`p_nom_max = max(GAEZ potential, baseline)`), participate in the land - deviation penalty, the crop growth cap and cost calibration like single-crop - links (a new `multi_crop_cost.csv` calibration artefact carries - per-(combination, country) bundle corrections), and are pinned in - validation mode, with harvested cycles reconciled out of the single-crop - FAOSTAT baselines so each cycle is counted exactly once. Multi-cropping is - consequently no longer disabled when the land deviation penalty or - `use_actual_production` is active. The broken GAEZ `sequence_feasible` - gate (which rejected nearly all observed double-cropping) is removed; - feasibility evidence now comes from MIRCA observation plus the GAEZ - multiple-cropping zone. +- 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 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 724bec8e..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: {} diff --git a/config/schemas/config.schema.yaml b/config/schemas/config.schema.yaml index f7ecd5b7..256d16bd 100644 --- a/config/schemas/config.schema.yaml +++ b/config/schemas/config.schema.yaml @@ -700,10 +700,15 @@ properties: type: array 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 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 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+meat-sheep,BRN,-0.17224597185850143 +meat-sheep,BTN,-0.2813273072242737 +meat-sheep,BWA,-0.20595574378967285 +meat-sheep,CAF,-0.11926142871379852 +meat-sheep,CAN,-0.00657991785556078 +meat-sheep,CHE,0.01898271171376109 +meat-sheep,CHL,-0.05021362192928791 +meat-sheep,CHN,-0.08622147142887115 +meat-sheep,CIV,-0.13294005021452904 +meat-sheep,CMR,-0.12869767844676971 +meat-sheep,COD,-0.13845789805054665 +meat-sheep,COG,-0.07707135379314423 +meat-sheep,COL,-0.07684163004159927 +meat-sheep,COM,-0.15948818624019623 +meat-sheep,CPV,-0.38646674901247025 +meat-sheep,CRI,-0.03200763463973999 +meat-sheep,CUB,-0.06449948251247406 +meat-sheep,CYP,-0.2808142751455307 +meat-sheep,CZE,0.01747049391269684 +meat-sheep,DEU,0.028647599217947572 meat-sheep,DJI,-0.022527582943439484 -meat-sheep,DNK,0.010926133021712303 -meat-sheep,DOM,-0.08600067719817162 -meat-sheep,DZA,-0.07173862028867006 -meat-sheep,ECU,-0.10427603125572205 -meat-sheep,EGY,-0.16656970605254173 -meat-sheep,ERI,-0.08788355998694897 -meat-sheep,ESP,-0.03903145715594292 -meat-sheep,EST,-0.032323179417289793 -meat-sheep,ETH,-0.13311580941081047 -meat-sheep,FIN,-0.014363422989845276 -meat-sheep,FJI,-0.12183855101466179 -meat-sheep,FRA,0.010253536514937878 -meat-sheep,GAB,-0.07315213326364756 -meat-sheep,GBR,-0.01610680646263063 -meat-sheep,GEO,-0.1657050922513008 -meat-sheep,GHA,-0.1789134368300438 -meat-sheep,GIN,-0.15460022166371346 -meat-sheep,GMB,-0.19915278255939484 -meat-sheep,GNB,-0.16172293573617935 -meat-sheep,GNQ,-0.10738009959459305 -meat-sheep,GRC,-0.10272587835788727 +meat-sheep,DNK,0.06391452997922897 +meat-sheep,DOM,-0.06206746771931648 +meat-sheep,DZA,-0.04665589053183794 +meat-sheep,ECU,-0.07490541785955429 +meat-sheep,EGY,-0.3484552875161171 +meat-sheep,ERI,-0.07112314738333225 +meat-sheep,ESP,-0.00697148684412241 +meat-sheep,EST,0.017889200244098902 +meat-sheep,ETH,-0.12350615859031677 +meat-sheep,FIN,0.038624973967671394 +meat-sheep,FJI,-0.07951982133090496 +meat-sheep,FRA,0.04148515686392784 +meat-sheep,GAB,-0.06729981675744057 +meat-sheep,GBR,0.015769253484904766 +meat-sheep,GEO,-0.11077103391289711 +meat-sheep,GHA,-0.04753481224179268 +meat-sheep,GIN,-0.11467761546373367 +meat-sheep,GMB,-0.14488698542118073 +meat-sheep,GNB,-0.3104589283466339 +meat-sheep,GNQ,-0.008697163313627243 +meat-sheep,GRC,-0.06340852193534374 meat-sheep,GRD,-0.008738094009459019 -meat-sheep,GTM,-0.045229324139654636 -meat-sheep,GUF,-0.0 -meat-sheep,GUY,-0.11864395439624786 +meat-sheep,GTM,-0.052902002818882465 +meat-sheep,GUF,0.0 +meat-sheep,GUY,-0.06388193555176258 meat-sheep,HND,-0.01635592244565487 -meat-sheep,HRV,-0.01924530090764165 -meat-sheep,HTI,-0.015342594124376774 -meat-sheep,HUN,-0.04192784754559398 -meat-sheep,IDN,-0.15015039592981339 -meat-sheep,IND,-0.23227694630622864 -meat-sheep,IRL,0.005370326340198517 -meat-sheep,IRN,-0.09313397482037544 -meat-sheep,IRQ,-0.17408842593431473 -meat-sheep,ISL,-0.03557713609188795 -meat-sheep,ISR,-0.28230249881744385 -meat-sheep,ITA,-0.06243273336440325 -meat-sheep,JAM,-0.19193831086158752 -meat-sheep,JOR,-0.2337924987077713 -meat-sheep,JPN,-0.19900433719158173 -meat-sheep,KAZ,-0.22745827585458755 -meat-sheep,KEN,-0.10271250642836094 -meat-sheep,KGZ,-0.058380716654937714 -meat-sheep,KHM,-0.0 -meat-sheep,KOR,-0.08639038726687431 -meat-sheep,LAO,-0.1482028365135193 -meat-sheep,LBN,-0.19903955608606339 -meat-sheep,LBR,-0.07251079380512238 -meat-sheep,LBY,-0.06191715691238642 -meat-sheep,LKA,-0.2045823335647583 -meat-sheep,LSO,-0.17990553379058838 -meat-sheep,LTU,-0.03578558827575762 -meat-sheep,LUX,0.0021972721442580223 -meat-sheep,LVA,-0.053867158479988575 -meat-sheep,MAR,-0.16074996814131737 -meat-sheep,MDA,-0.13656171411275864 -meat-sheep,MDG,-0.09322527796030045 -meat-sheep,MEX,-0.0 -meat-sheep,MKD,-0.11798243969678879 -meat-sheep,MLI,-0.17534299194812775 -meat-sheep,MLT,-0.15064366161823273 -meat-sheep,MMR,-0.09526451304554939 -meat-sheep,MNE,-0.11336180567741394 +meat-sheep,HRV,0.031616091220712406 +meat-sheep,HTI,-0.0153425931930542 +meat-sheep,HUN,0.011060552671551704 +meat-sheep,IDN,-0.12527593970298767 +meat-sheep,IND,-0.4551456719636917 +meat-sheep,IRL,0.027719820849597454 +meat-sheep,IRN,-0.0864000003784895 +meat-sheep,IRQ,-0.11793847009539604 +meat-sheep,ISL,-0.014576388522982597 +meat-sheep,ISR,-0.23037447780370712 +meat-sheep,ITA,-0.08779258653521538 +meat-sheep,JAM,-0.14862675219774246 +meat-sheep,JOR,-0.1784602776169777 +meat-sheep,JPN,-0.16282382607460022 +meat-sheep,KAZ,-0.17212583124637604 +meat-sheep,KEN,-0.07838128320872784 +meat-sheep,KGZ,-0.04350341422832571 +meat-sheep,KHM,0.0 +meat-sheep,KOR,-0.13893577829003334 +meat-sheep,LAO,-0.11579862982034683 +meat-sheep,LBN,-0.14306169003248215 +meat-sheep,LBR,-0.06547847203910351 +meat-sheep,LBY,-0.10583574324846268 +meat-sheep,LKA,-0.13631759583950043 +meat-sheep,LSO,-0.08921365439891815 +meat-sheep,LTU,0.017202808521687984 +meat-sheep,LUX,0.025159545242786407 +meat-sheep,LVA,-0.0008787577971816063 +meat-sheep,MAR,-0.10739407688379288 +meat-sheep,MDA,-0.12429706752300262 +meat-sheep,MDG,-0.16019195690751076 +meat-sheep,MEX,-0.05475272983312607 +meat-sheep,MKD,-0.0731512438505888 +meat-sheep,MLI,-0.09964224696159363 +meat-sheep,MLT,-0.34928324818611145 +meat-sheep,MMR,-0.16586805135011673 +meat-sheep,MNE,-0.32762518897652626 meat-sheep,MNG,-0.023062026128172874 -meat-sheep,MOZ,-0.15385688096284866 -meat-sheep,MRT,-0.10806993395090103 -meat-sheep,MUS,-0.09608052112162113 -meat-sheep,MWI,-0.09940436109900475 -meat-sheep,MYS,-0.1517697349190712 -meat-sheep,NAM,-0.140342365950346 -meat-sheep,NER,-0.17531684041023254 -meat-sheep,NGA,-0.07178466022014618 -meat-sheep,NIC,-0.0 -meat-sheep,NLD,-0.10425715148448944 -meat-sheep,NOR,-0.069055057130754 -meat-sheep,NPL,-0.10401955805718899 -meat-sheep,NZL,-0.051315318793058395 -meat-sheep,OMN,-0.22675082087516785 -meat-sheep,PAK,-0.11425058357417583 -meat-sheep,PAN,-0.0 -meat-sheep,PER,-0.1472552865743637 -meat-sheep,PHL,-0.1358330026268959 -meat-sheep,PNG,-0.0524916835129261 -meat-sheep,POL,-0.05066755600273609 -meat-sheep,PRI,-0.010542795062065125 -meat-sheep,PRT,0.012510450673289597 -meat-sheep,PRY,-0.05599368270486593 -meat-sheep,PSE,-0.17354008927941322 -meat-sheep,ROU,-0.11835058778524399 -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 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-white-potato,BLR,-2.40826016664505 -white-potato,BLZ,-6.975500822067261 -white-potato,BOL,-1.3165290355682373 -white-potato,BRA,-7.5 -white-potato,BRB,-5.921956777572632 -white-potato,BRN,-7.5 -white-potato,BTN,-3.978571653366089 -white-potato,BWA,-7.5 -white-potato,CAF,-7.178539514541626 -white-potato,CAN,-5.34727668762207 +white-potato,AUT,-4.3990867137908936 +white-potato,AZE,-3.721527099609375 +white-potato,BDI,-2.2032004594802856 +white-potato,BEL,-2.27272891998291 +white-potato,BEN,0.0 +white-potato,BFA,-6.743122577667236 +white-potato,BGD,-1.5443406105041504 +white-potato,BGR,-1.7481514811515808 +white-potato,BHS,-6.989185571670532 +white-potato,BIH,-2.301161527633667 +white-potato,BLR,-2.0556673407554626 +white-potato,BLZ,-5.6266326904296875 +white-potato,BOL,-1.21503484249115 +white-potato,BRA,-7.35870885848999 +white-potato,BRB,-3.5080467462539673 +white-potato,BRN,-6.459218740463257 +white-potato,BTN,-3.739386796951294 +white-potato,BWA,-6.167452096939087 +white-potato,CAF,-7.157416105270386 +white-potato,CAN,-4.231839656829834 white-potato,CHE,-7.5 -white-potato,CHL,-7.026157855987549 -white-potato,CHN,-3.8956282138824463 -white-potato,CIV,-7.156691789627075 -white-potato,CMR,-6.643963098526001 -white-potato,COD,-6.982562303543091 -white-potato,COG,-6.755647659301758 -white-potato,COL,-4.461732864379883 -white-potato,COM,-7.283514738082886 +white-potato,CHL,-6.579020023345947 +white-potato,CHN,-2.868831992149353 +white-potato,CIV,-7.118312120437622 +white-potato,CMR,-6.4576497077941895 +white-potato,COD,-6.8986804485321045 +white-potato,COG,-6.60233736038208 +white-potato,COL,-3.8808412551879883 +white-potato,COM,-7.013578653335571 white-potato,CPV,-7.5 white-potato,CRI,-7.5 -white-potato,CUB,-6.133505821228027 -white-potato,CYP,-6.968465566635132 -white-potato,CZE,-3.9378150701522827 -white-potato,DEU,-5.011577129364014 -white-potato,DNK,-7.5 +white-potato,CUB,-5.719202995300293 +white-potato,CYP,-7.5 +white-potato,CZE,-3.0285369157791138 +white-potato,DEU,-3.7069848775863647 +white-potato,DNK,-6.850000381469727 white-potato,DOM,-7.5 -white-potato,DZA,-7.5 -white-potato,ECU,-2.7543752193450928 -white-potato,EGY,-4.930022954940796 -white-potato,ERI,-7.190220594406128 +white-potato,DZA,-6.895639419555664 +white-potato,ECU,-2.365859031677246 +white-potato,EGY,-5.034525156021118 +white-potato,ERI,-7.1761579513549805 white-potato,ESP,-7.5 -white-potato,EST,-2.5702712535858154 -white-potato,ETH,-1.1093079447746277 -white-potato,FIN,-3.4985347986221313 +white-potato,EST,-1.682485818862915 +white-potato,ETH,-0.7621832489967346 +white-potato,FIN,-2.781749963760376 white-potato,FRA,-7.5 -white-potato,GAB,-7.141166925430298 -white-potato,GBR,-6.928457260131836 -white-potato,GEO,-1.7411337494850159 -white-potato,GHA,-7.1507790088653564 -white-potato,GIN,-6.96183180809021 -white-potato,GMB,-3.75 -white-potato,GNB,-6.9923415184021 -white-potato,GNQ,-7.024986505508423 +white-potato,GAB,-7.112677335739136 +white-potato,GBR,-6.1423609256744385 +white-potato,GEO,-1.441060721874237 +white-potato,GHA,-7.124129772186279 +white-potato,GIN,-6.644963502883911 +white-potato,GMB,-2.326287269592285 +white-potato,GNB,-6.860036373138428 +white-potato,GNQ,-6.954638481140137 white-potato,GRC,-7.5 -white-potato,GRD,-6.992668151855469 -white-potato,GTM,-5.070298910140991 -white-potato,GUF,-7.5 -white-potato,GUY,-0.0 +white-potato,GRD,-6.949544429779053 +white-potato,GTM,-4.927204847335815 +white-potato,GUF,-4.891628265380859 +white-potato,GUY,0.0 white-potato,HND,-7.5 -white-potato,HRV,-5.2063987255096436 -white-potato,HTI,-6.96152138710022 -white-potato,HUN,-5.442982912063599 -white-potato,IDN,-7.5 -white-potato,IND,-5.864567756652832 -white-potato,IRL,-4.323651671409607 -white-potato,IRN,-3.3191661834716797 -white-potato,IRQ,-7.5 +white-potato,HRV,-3.6453075408935547 +white-potato,HTI,-5.919316291809082 +white-potato,HUN,-6.12166428565979 +white-potato,IDN,-6.803520202636719 +white-potato,IND,-5.963769912719727 +white-potato,IRL,-3.221374750137329 +white-potato,IRN,-2.458118200302124 +white-potato,IRQ,-6.84987211227417 white-potato,ISR,-7.5 white-potato,ITA,-7.5 -white-potato,JAM,-7.5 +white-potato,JAM,-6.927983522415161 white-potato,JOR,-7.5 white-potato,JPN,-7.5 -white-potato,KAZ,-2.350228428840637 -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 +white-potato,LVA,-0.7649366557598114 +white-potato,MAR,-3.6025643348693848 +white-potato,MDA,-0.6232761144638062 +white-potato,MDG,-6.702943801879883 white-potato,MEX,-7.5 -white-potato,MKD,-3.202763080596924 +white-potato,MKD,-2.71600604057312 white-potato,MLI,-7.5 -white-potato,MLT,-6.536999940872192 -white-potato,MMR,-6.299448490142822 -white-potato,MNE,-5.965052843093872 -white-potato,MNG,-2.5573318004608154 +white-potato,MLT,-7.5 +white-potato,MMR,-5.703470706939697 +white-potato,MNE,-6.1300578117370605 +white-potato,MNG,-2.187525153160095 white-potato,MOZ,-7.5 -white-potato,MRT,-7.5 +white-potato,MRT,-6.6742963790893555 white-potato,MUS,-7.5 -white-potato,MWI,1.2430293560028076 -white-potato,MYS,-6.0908286571502686 -white-potato,NAM,-6.399621486663818 +white-potato,MWI,1.5743064880371094 +white-potato,MYS,-5.515684127807617 +white-potato,NAM,-5.880058765411377 white-potato,NER,-7.5 -white-potato,NGA,-7.070682525634766 +white-potato,NGA,-6.996363639831543 white-potato,NIC,-7.5 -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 +white-potato,ROU,-3.9863680601119995 +white-potato,RUS,-1.607715368270874 +white-potato,RWA,-1.732474684715271 white-potato,SAU,-7.5 -white-potato,SDN,-6.468541145324707 -white-potato,SEN,-7.5 -white-potato,SLB,-6.2179646492004395 -white-potato,SLE,-6.7139732837677 -white-potato,SLV,-7.5 -white-potato,SOM,-6.526790380477905 -white-potato,SRB,-2.792048931121826 -white-potato,SSD,-6.8707311153411865 -white-potato,SUR,-7.5 -white-potato,SVK,-5.510774374008179 -white-potato,SVN,-5.715082883834839 -white-potato,SWE,-7.5 -white-potato,SWZ,-7.129907131195068 -white-potato,SYR,-5.730023384094238 -white-potato,TCD,-6.829763889312744 -white-potato,TGO,-7.059450149536133 -white-potato,THA,-4.329001426696777 -white-potato,TJK,-2.9643325805664062 -white-potato,TKM,-6.632774353027344 -white-potato,TLS,-0.6201033592224121 -white-potato,TTO,-7.02238655090332 -white-potato,TUN,-2.6432697772979736 -white-potato,TUR,-5.212929010391235 -white-potato,TWN,-6.052992820739746 -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 index 4032a7cc..5217f5cc 100644 --- a/data/curated/calibration/default/multi_crop_cost.csv +++ b/data/curated/calibration/default/multi_crop_cost.csv @@ -1,102 +1,101 @@ combination,country,correction_bnusd_per_mha -cotton_wheat,AFG,-1.6099050045013428 -cotton_wheat,AGO,-1.814477801322937 +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,-5.853282690048218 -cotton_wheat,AZE,-0.6669307351112366 -cotton_wheat,BDI,-0.9382950663566589 -cotton_wheat,BGD,-1.3390784859657288 -cotton_wheat,BGR,-1.3807790279388428 +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,-2.3770229816436768 -cotton_wheat,BWA,3.1791040897369385 -cotton_wheat,CHN,-6.063660144805908 -cotton_wheat,COD,-1.8446723222732544 +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,-2.120392918586731 -cotton_wheat,ESP,-0.9637662768363953 -cotton_wheat,ETH,-2.7257684469223022 +cotton_wheat,EGY,0.7446867227554321 +cotton_wheat,ESP,2.422280430793762 +cotton_wheat,ETH,-1.18629789352417 cotton_wheat,GEO,7.5 -cotton_wheat,GRC,-1.315471351146698 -cotton_wheat,HND,-1.938868522644043 -cotton_wheat,IND,-1.3647518157958984 -cotton_wheat,IRN,-3.0847361087799072 -cotton_wheat,IRQ,-2.056082606315613 -cotton_wheat,ISR,-0.5670958757400513 -cotton_wheat,JOR,-1.9030465483665466 -cotton_wheat,KAZ,-1.6555743217468262 -cotton_wheat,KEN,-0.6111141741275787 -cotton_wheat,KGZ,-1.31385737657547 +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,-1.1813910603523254 -cotton_wheat,MAR,-2.646002769470215 -cotton_wheat,MDG,-1.5335040092468262 -cotton_wheat,MEX,-3.4887030124664307 -cotton_wheat,MKD,-1.4253509044647217 -cotton_wheat,MMR,2.898737072944641 -cotton_wheat,MOZ,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,7.5 -cotton_wheat,NER,3.5284949243068695 -cotton_wheat,NGA,-1.7977930307388306 +cotton_wheat,NAM,4.1794014275074005 +cotton_wheat,NER,7.5 +cotton_wheat,NGA,-7.5 cotton_wheat,NPL,7.5 -cotton_wheat,OMN,-0.0 -cotton_wheat,PAK,-1.2787038087844849 +cotton_wheat,PAK,1.4043108224868774 cotton_wheat,PRT,7.5 -cotton_wheat,PRY,-0.5581440925598145 -cotton_wheat,PSE,-2.3085778951644897 -cotton_wheat,ROU,-0.0 +cotton_wheat,PRY,2.0446081161499023 +cotton_wheat,PSE,1.3241363763809204 +cotton_wheat,ROU,0.0 cotton_wheat,RUS,7.5 -cotton_wheat,RWA,-1.5222082138061523 -cotton_wheat,SDN,-1.6430537700653076 -cotton_wheat,SOM,-2.054495632648468 -cotton_wheat,SWZ,-1.6668144464492798 -cotton_wheat,SYR,-2.3456783294677734 -cotton_wheat,TJK,-2.956416606903076 -cotton_wheat,TKM,-1.5223835706710815 +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,-1.2363390922546387 -cotton_wheat,TZA,-1.431849181652069 -cotton_wheat,UGA,-1.6493436098098755 +cotton_wheat,TUR,4.218727111816406 +cotton_wheat,TZA,-0.6717642545700073 +cotton_wheat,UGA,-0.21029487997293472 cotton_wheat,URY,7.5 -cotton_wheat,USA,-1.3119173049926758 -cotton_wheat,UZB,-2.144982099533081 -cotton_wheat,YEM,-1.9001551270484924 -cotton_wheat,ZAF,-2.24241304397583 +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,3.960923194885254 +cotton_wheat,ZWE,6.102042198181152 double_rice,AFG,7.5 double_rice,AGO,7.5 -double_rice,ALB,-0.0 +double_rice,ALB,0.0 double_rice,ARG,7.5 double_rice,AUS,7.5 double_rice,AZE,7.5 -double_rice,BDI,7.5 -double_rice,BEN,-2.0670456886291504 -double_rice,BFA,-1.9492504000663757 -double_rice,BGD,-0.9019208252429962 -double_rice,BLZ,2.32931649684906 +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,-2.805904507637024 +double_rice,BRN,-2.890824794769287 double_rice,BTN,7.5 -double_rice,BWA,-0.0 +double_rice,BWA,0.0 double_rice,CAF,7.5 double_rice,CHL,7.5 -double_rice,CHN,-2.8561073541641235 +double_rice,CHN,-3.0934839248657227 double_rice,CIV,7.5 -double_rice,CMR,2.668047308921814 +double_rice,CMR,7.5 double_rice,COD,7.5 double_rice,COG,7.5 -double_rice,COL,-2.3517508506774902 -double_rice,CRI,-1.9862532019615173 +double_rice,COL,-2.408092975616455 +double_rice,CRI,-1.5242087841033936 double_rice,CUB,7.5 -double_rice,DOM,2.198575973510742 +double_rice,DOM,-2.50664484500885 double_rice,DZA,7.5 -double_rice,ECU,-1.0549455285072327 +double_rice,ECU,-1.0761107802391052 double_rice,EGY,7.5 double_rice,ERI,7.5 double_rice,ESP,7.5 @@ -105,390 +104,390 @@ double_rice,FJI,7.5 double_rice,FRA,7.5 double_rice,GAB,7.5 double_rice,GEO,7.5 -double_rice,GHA,-0.3800714612007141 -double_rice,GIN,-0.8479809165000916 -double_rice,GMB,0.18228022754192352 -double_rice,GNB,-2.3635621070861816 +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,7.5 -double_rice,GUF,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,2.4892295598983765 -double_rice,IDN,-2.4515942335128784 -double_rice,IND,-1.1609660387039185 +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,-7.040684938430786 +double_rice,JPN,-7.467597246170044 double_rice,KEN,7.5 -double_rice,KHM,-1.8155657052993774 -double_rice,KOR,-7.5 -double_rice,LAO,-2.1919310092926025 +double_rice,KHM,-4.353095293045044 +double_rice,KOR,7.5 +double_rice,LAO,-2.310716390609741 double_rice,LBR,7.5 -double_rice,LKA,3.428750306367874 -double_rice,LSO,-0.0 +double_rice,LKA,-0.44917234778404236 +double_rice,LSO,0.0 double_rice,MAR,7.5 -double_rice,MDG,3.6049813479185104 +double_rice,MDG,3.66713098436594 double_rice,MEX,7.5 double_rice,MKD,7.5 -double_rice,MLI,0.24248556792736053 -double_rice,MMR,-2.551267623901367 +double_rice,MLI,-7.5 +double_rice,MMR,-2.5912954807281494 double_rice,MOZ,7.5 -double_rice,MRT,-1.4914619326591492 +double_rice,MRT,-7.5 double_rice,MUS,7.5 -double_rice,MWI,-1.7444035410881042 -double_rice,MYS,-0.6751475781202316 +double_rice,MWI,-7.5 +double_rice,MYS,-1.0528628826141357 double_rice,NAM,7.5 double_rice,NER,7.5 -double_rice,NGA,-0.04781103506684303 -double_rice,NIC,2.312941074371338 -double_rice,NPL,3.7156719639897346 +double_rice,NGA,-3.098779320716858 +double_rice,NIC,5.87060022354126 +double_rice,NPL,5.132853031158447 double_rice,PAK,7.5 -double_rice,PAN,-1.7208068370819092 +double_rice,PAN,-1.8307443857192993 double_rice,PER,7.5 -double_rice,PHL,-1.3351789712905884 +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,-3.063134789466858 +double_rice,RWA,-3.050174117088318 double_rice,SAU,7.5 double_rice,SDN,7.5 -double_rice,SEN,0.5294859409332275 +double_rice,SEN,-2.742427185177803 double_rice,SLB,7.5 -double_rice,SLE,3.7774145510047674 +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,-1.041583001613617 -double_rice,TGO,0.021735727787017822 -double_rice,THA,-0.6866416335105896 +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,TUN,0.0 double_rice,TUR,7.5 -double_rice,TWN,-1.2726807594299316 -double_rice,TZA,7.5 +double_rice,TWN,-1.1616821885108948 +double_rice,TZA,4.307738244533539 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,-1.6253113746643066 -double_rice,YEM,-0.0 +double_rice,VNM,-1.7286503314971924 +double_rice,YEM,0.0 double_rice,ZAF,7.5 double_rice,ZMB,7.5 double_rice,ZWE,7.5 -maize_soybean,AFG,-0.4736453890800476 -maize_soybean,AGO,-1.5113000273704529 +maize_soybean,AFG,-0.18903027474880219 +maize_soybean,AGO,-0.3301544785499573 maize_soybean,ALB,7.5 -maize_soybean,ARG,-0.001545419916510582 +maize_soybean,ARG,7.5 maize_soybean,ARM,7.5 -maize_soybean,AUS,1.390625536441803 -maize_soybean,AUT,-0.3401510715484619 -maize_soybean,AZE,4.152604818344116 -maize_soybean,BDI,-1.0835424065589905 +maize_soybean,AUS,1.764742136001587 +maize_soybean,AUT,7.5 +maize_soybean,AZE,4.357408821582794 +maize_soybean,BDI,-1.053770124912262 maize_soybean,BEL,7.5 -maize_soybean,BEN,-0.8253427743911743 -maize_soybean,BFA,-0.5887079238891602 -maize_soybean,BGD,0.632455587387085 +maize_soybean,BEN,-0.8169119656085968 +maize_soybean,BFA,-0.4642575979232788 +maize_soybean,BGD,7.5 maize_soybean,BGR,7.5 maize_soybean,BIH,7.5 -maize_soybean,BLZ,7.5 -maize_soybean,BOL,0.12550845742225647 -maize_soybean,BRA,-0.03387417644262314 -maize_soybean,BTN,4.1264379024505615 -maize_soybean,BWA,7.5 -maize_soybean,CAF,-1.607940912246704 -maize_soybean,CAN,-0.7665511965751648 -maize_soybean,CHE,-1.1859102249145508 -maize_soybean,CHN,-1.4345921874046326 -maize_soybean,CIV,-0.9916943311691284 -maize_soybean,CMR,-1.224189281463623 -maize_soybean,COD,-1.6027145385742188 -maize_soybean,COG,-1.5472362637519836 -maize_soybean,COL,-0.292894572019577 -maize_soybean,CRI,-0.9751254320144653 +maize_soybean,BLZ,-1.3509411811828613 +maize_soybean,BOL,-0.17573681473731995 +maize_soybean,BRA,0.027264581993222237 +maize_soybean,BTN,1.0892248153686523 +maize_soybean,BWA,3.902852773666382 +maize_soybean,CAF,-1.5961941480636597 +maize_soybean,CAN,7.5 +maize_soybean,CHE,7.5 +maize_soybean,CHN,-1.6655081510543823 +maize_soybean,CIV,-0.9765808284282684 +maize_soybean,CMR,-1.1323017477989197 +maize_soybean,COD,-1.6054496765136719 +maize_soybean,COG,-1.5415217876434326 +maize_soybean,COL,-0.4117032289505005 +maize_soybean,CRI,-0.8962375521659851 maize_soybean,CUB,7.5 maize_soybean,CZE,7.5 maize_soybean,DEU,7.5 -maize_soybean,DZA,-0.0 -maize_soybean,ECU,-0.6386651396751404 -maize_soybean,EGY,-1.6338169574737549 -maize_soybean,ESP,-0.43243037164211273 -maize_soybean,ETH,-0.22050713002681732 +maize_soybean,DZA,0.0 +maize_soybean,ECU,-0.6328637003898621 +maize_soybean,EGY,-1.9893345832824707 +maize_soybean,ESP,7.5 +maize_soybean,ETH,-0.001739787869155407 maize_soybean,FRA,7.5 -maize_soybean,GAB,-1.2179523706436157 -maize_soybean,GEO,0.07373932749032974 -maize_soybean,GHA,-0.8419823944568634 +maize_soybean,GAB,-1.2152018547058105 +maize_soybean,GEO,0.1554230898618698 +maize_soybean,GHA,-0.7944550216197968 maize_soybean,GIN,7.5 -maize_soybean,GRC,0.0970049574971199 -maize_soybean,GTM,-0.7145440578460693 -maize_soybean,HND,-0.4602302610874176 +maize_soybean,GRC,3.332735449075699 +maize_soybean,GTM,2.6926229000091553 +maize_soybean,HND,-0.32529017329216003 maize_soybean,HRV,7.5 maize_soybean,HUN,7.5 -maize_soybean,IDN,0.4130144417285919 -maize_soybean,IND,-0.6799671053886414 -maize_soybean,IRN,-1.0338594913482666 -maize_soybean,IRQ,7.5 +maize_soybean,IDN,0.41661420464515686 +maize_soybean,IND,-0.6923241168260574 +maize_soybean,IRN,-0.6999404430389404 +maize_soybean,IRQ,-0.48491914197802544 maize_soybean,ISR,7.5 maize_soybean,ITA,7.5 -maize_soybean,JOR,-0.0 +maize_soybean,JOR,0.0 maize_soybean,KAZ,7.5 -maize_soybean,KEN,-0.4090971052646637 -maize_soybean,KGZ,-0.2840305417776108 -maize_soybean,KHM,-0.5590203106403351 -maize_soybean,KOR,-0.30528104305267334 -maize_soybean,LAO,-0.03479786217212677 +maize_soybean,KEN,-0.4269101023674011 +maize_soybean,KGZ,3.6940583027899265 +maize_soybean,KHM,-0.5335918366909027 +maize_soybean,KOR,-0.2164778858423233 +maize_soybean,LAO,0.043448932468891144 maize_soybean,LBN,7.5 -maize_soybean,LKA,-0.9143259227275848 +maize_soybean,LKA,-0.8782811760902405 maize_soybean,LSO,7.5 maize_soybean,LUX,7.5 -maize_soybean,MAR,3.441688358783722 -maize_soybean,MDA,3.948325455188751 -maize_soybean,MEX,0.012745517306029797 +maize_soybean,MAR,7.5 +maize_soybean,MDA,7.5 +maize_soybean,MEX,2.179497241973877 maize_soybean,MKD,7.5 -maize_soybean,MLI,-0.7469700872898102 -maize_soybean,MMR,-0.9117441177368164 +maize_soybean,MLI,-0.6292112469673157 +maize_soybean,MMR,-0.9021268486976624 maize_soybean,MNE,7.5 maize_soybean,MOZ,7.5 -maize_soybean,MWI,3.6040261685848236 -maize_soybean,NGA,-0.720582515001297 -maize_soybean,NIC,-0.3675630474463105 +maize_soybean,MWI,-0.2680986821651459 +maize_soybean,NGA,-0.7314013838768005 +maize_soybean,NIC,1.3897043466567993 maize_soybean,NLD,7.5 -maize_soybean,NPL,-0.5565337538719177 -maize_soybean,PAK,-1.0684007406234741 +maize_soybean,NPL,7.5 +maize_soybean,PAK,-0.3264614902436733 maize_soybean,PER,7.5 maize_soybean,POL,7.5 maize_soybean,PRT,7.5 -maize_soybean,PRY,-0.21764160692691803 -maize_soybean,ROU,0.32373878359794617 +maize_soybean,PRY,-0.22252461314201355 +maize_soybean,ROU,4.383970379829407 maize_soybean,RUS,7.5 -maize_soybean,RWA,-0.34852825105190277 -maize_soybean,SDN,-0.5342399477958679 -maize_soybean,SLV,-0.8975279331207275 +maize_soybean,RWA,-0.3237958699464798 +maize_soybean,SDN,-0.1569017469882965 +maize_soybean,SLV,-0.8325892686843872 maize_soybean,SRB,7.5 -maize_soybean,SSD,-1.321803331375122 +maize_soybean,SSD,7.5 maize_soybean,SVK,7.5 maize_soybean,SVN,7.5 -maize_soybean,SWZ,-0.8048333525657654 +maize_soybean,SWZ,-0.7677897810935974 maize_soybean,SYR,7.5 -maize_soybean,TCD,7.5 -maize_soybean,TGO,-0.3580051511526108 -maize_soybean,THA,-0.3933115601539612 +maize_soybean,TCD,-0.3061584234237671 +maize_soybean,TGO,-0.365085169672966 +maize_soybean,THA,-0.3549405187368393 maize_soybean,TJK,7.5 -maize_soybean,TKM,0.9870100617408752 -maize_soybean,TLS,4.11736986041069 +maize_soybean,TKM,4.443183600902557 +maize_soybean,TLS,7.5 maize_soybean,TUR,7.5 -maize_soybean,TWN,0.8484704494476318 -maize_soybean,TZA,-0.3797968029975891 -maize_soybean,UGA,-1.3789831399917603 +maize_soybean,TWN,0.0 +maize_soybean,TZA,-0.26504120230674744 +maize_soybean,UGA,-1.386080265045166 maize_soybean,UKR,7.5 -maize_soybean,URY,-0.47526396811008453 -maize_soybean,USA,-0.5723414123058319 +maize_soybean,URY,-0.33979201316833496 +maize_soybean,USA,-0.45227915048599243 maize_soybean,UZB,7.5 -maize_soybean,VEN,-0.8426925539970398 -maize_soybean,VNM,-0.27706438302993774 -maize_soybean,ZAF,-0.006137027870863676 -maize_soybean,ZMB,-0.5981044173240662 -maize_soybean,ZWE,0.9514963030815125 -rice_maize,AFG,-1.6404048204421997 -rice_maize,AGO,-2.3160706758499146 -rice_maize,ALB,-0.0 +maize_soybean,VEN,-0.7985731959342957 +maize_soybean,VNM,-0.16958361864089966 +maize_soybean,ZAF,2.642836332321167 +maize_soybean,ZMB,2.1038013696670532 +maize_soybean,ZWE,1.4439507126808167 +rice_maize,AFG,-1.4417428970336914 +rice_maize,AGO,-0.08673781901597977 +rice_maize,ALB,0.0 rice_maize,ARG,7.5 -rice_maize,ARM,-0.0 -rice_maize,AUS,-1.590356171131134 -rice_maize,AZE,-2.83044695854187 -rice_maize,BDI,7.5 -rice_maize,BEN,-1.4021406173706055 -rice_maize,BGD,0.39136016368865967 +rice_maize,ARM,0.0 +rice_maize,AUS,7.5 +rice_maize,AZE,7.5 +rice_maize,BDI,-7.5 +rice_maize,BEN,-1.5544970035552979 +rice_maize,BGD,0.5139995068311691 rice_maize,BGR,7.5 -rice_maize,BLZ,-2.3535351753234863 +rice_maize,BLZ,-2.317697763442993 rice_maize,BOL,7.5 -rice_maize,BRA,0.5185031741857529 -rice_maize,BTN,0.26831182837486267 -rice_maize,BWA,-0.0 -rice_maize,CAF,-2.315131902694702 -rice_maize,CAN,-0.0 -rice_maize,CHL,-0.9636813998222351 -rice_maize,CHN,-2.491113066673279 -rice_maize,CIV,-0.7100791335105896 -rice_maize,CMR,-1.6961027383804321 -rice_maize,COD,-2.435609221458435 -rice_maize,COL,-1.5935273170471191 -rice_maize,COM,-2.337308883666992 -rice_maize,CUB,-1.5886424779891968 -rice_maize,DOM,2.4203288555145264 +rice_maize,BRA,0.13870170712471008 +rice_maize,BTN,0.8440825194120407 +rice_maize,BWA,0.0 +rice_maize,CAF,-2.3169145584106445 +rice_maize,CAN,0.0 +rice_maize,CHL,-1.1006411910057068 +rice_maize,CHN,-2.5344632863998413 +rice_maize,CIV,-0.7863163352012634 +rice_maize,CMR,-1.5311205387115479 +rice_maize,COD,-2.4407894611358643 +rice_maize,COL,-1.5295484066009521 +rice_maize,COM,-2.315112590789795 +rice_maize,CUB,7.5 +rice_maize,DOM,-1.5913703441619873 rice_maize,DZA,7.5 -rice_maize,ECU,-0.7153042256832123 -rice_maize,EGY,-2.13281512260437 +rice_maize,ECU,-0.24355557560920715 +rice_maize,EGY,-1.5326589345932007 rice_maize,ERI,7.5 -rice_maize,ESP,-1.1463301181793213 +rice_maize,ESP,-0.9732767343521118 rice_maize,ETH,7.5 rice_maize,FRA,7.5 rice_maize,GEO,7.5 -rice_maize,GHA,-0.728587806224823 -rice_maize,GIN,-0.4560774564743042 -rice_maize,GNB,7.5 -rice_maize,GRC,-1.3117156028747559 -rice_maize,GTM,-2.1169002056121826 -rice_maize,HND,-1.633684515953064 -rice_maize,HTI,-2.1469744443893433 +rice_maize,GHA,-0.8649216294288635 +rice_maize,GIN,-0.7394095063209534 +rice_maize,GNB,-7.5 +rice_maize,GRC,-1.3736534714698792 +rice_maize,GTM,2.3227310180664062 +rice_maize,HND,-1.6148372888565063 +rice_maize,HTI,-2.080762505531311 rice_maize,HUN,7.5 -rice_maize,IDN,-0.3467683792114258 -rice_maize,IND,-1.1606595516204834 -rice_maize,IRN,-4.914326429367065 -rice_maize,IRQ,-3.0447577238082886 -rice_maize,ITA,-2.2469688057899475 -rice_maize,KAZ,0.25109851360321045 -rice_maize,KEN,-1.8319658041000366 -rice_maize,KGZ,-3.0051995515823364 -rice_maize,KOR,-6.520980596542358 -rice_maize,LAO,-1.216350793838501 -rice_maize,LKA,-1.0294817984104156 -rice_maize,LSO,-0.0 -rice_maize,MAR,-1.6206347942352295 +rice_maize,IDN,0.0 +rice_maize,IND,0.07284808903932571 +rice_maize,IRN,-3.7375322580337524 +rice_maize,IRQ,-2.032711148262024 +rice_maize,ITA,-2.783283829689026 +rice_maize,KAZ,0.6285150051116943 +rice_maize,KEN,-2.693563222885132 +rice_maize,KGZ,-2.9307796955108643 +rice_maize,KOR,-6.474852085113525 +rice_maize,LAO,-1.099143147468567 +rice_maize,LKA,-0.9970139861106873 +rice_maize,LSO,0.0 +rice_maize,MAR,-0.18347854539752007 rice_maize,MDA,7.5 -rice_maize,MDG,-1.1849598288536072 -rice_maize,MEX,-0.2136644870042801 +rice_maize,MDG,-1.121085524559021 +rice_maize,MEX,5.394611835479736 rice_maize,MKD,7.5 -rice_maize,MLI,-0.3211269825696945 -rice_maize,MMR,-1.6517590284347534 -rice_maize,MOZ,-0.46825529634952545 +rice_maize,MLI,-3.8538168370723724 +rice_maize,MMR,-1.7492262125015259 +rice_maize,MOZ,-0.5586673021316528 rice_maize,MUS,7.5 -rice_maize,MWI,-1.1541817784309387 +rice_maize,MWI,-4.375118672847748 rice_maize,NAM,7.5 -rice_maize,NGA,-0.4805299639701843 -rice_maize,NIC,-0.0 -rice_maize,NPL,-0.3239421471953392 -rice_maize,PAK,-1.193999707698822 -rice_maize,PER,-3.7227985858917236 -rice_maize,PHL,-0.5532599687576294 +rice_maize,NGA,-0.5389896035194397 +rice_maize,NIC,-0.5032784044742584 +rice_maize,NPL,-0.8382139205932617 +rice_maize,PAK,0.2885686755180359 +rice_maize,PER,-3.8790206909179688 +rice_maize,PHL,-0.7126205563545227 rice_maize,PRI,7.5 -rice_maize,PRT,3.333097815513611 -rice_maize,PRY,-0.42590242624282837 -rice_maize,ROU,7.5 +rice_maize,PRT,3.2787038683891296 +rice_maize,PRY,-0.45439404249191284 +rice_maize,ROU,3.2799732387065887 rice_maize,RUS,7.5 -rice_maize,RWA,-1.792549729347229 +rice_maize,RWA,-1.786763310432434 rice_maize,SAU,7.5 -rice_maize,SDN,2.7841517329216003 -rice_maize,SEN,-0.42498359084129333 -rice_maize,SLE,7.5 -rice_maize,SLV,-2.752364754676819 -rice_maize,SOM,-0.06363929528743029 -rice_maize,SSD,-2.3028550148010254 -rice_maize,SVK,-0.0 -rice_maize,SVN,-0.0 -rice_maize,SWZ,-1.83369779586792 -rice_maize,SYR,7.5 -rice_maize,TCD,-0.514793187379837 -rice_maize,TGO,-0.19198282808065414 -rice_maize,THA,-0.6313942074775696 -rice_maize,TJK,-4.319259166717529 -rice_maize,TKM,-0.3873455673456192 -rice_maize,TLS,-1.235890656709671 -rice_maize,TUN,-0.0 -rice_maize,TUR,-3.054778575897217 -rice_maize,TZA,-0.0 -rice_maize,UGA,-2.1543128490448 +rice_maize,SDN,-2.4222562313079834 +rice_maize,SEN,7.5 +rice_maize,SLE,3.625309444963932 +rice_maize,SLV,-2.999232769012451 +rice_maize,SOM,-7.0224387645721436 +rice_maize,SSD,-2.288255214691162 +rice_maize,SVK,0.0 +rice_maize,SVN,0.0 +rice_maize,SWZ,7.5 +rice_maize,SYR,0.48557618260383606 +rice_maize,TCD,-0.3451007902622223 +rice_maize,TGO,-0.27118003368377686 +rice_maize,THA,-0.6752943396568298 +rice_maize,TJK,-2.6985824704170227 +rice_maize,TKM,-1.0258724689483643 +rice_maize,TLS,-1.3125015497207642 +rice_maize,TUN,0.0 +rice_maize,TUR,-2.069835662841797 +rice_maize,TZA,0.0 +rice_maize,UGA,-2.1919249296188354 rice_maize,UKR,7.5 -rice_maize,URY,0.002652596915140748 +rice_maize,URY,-0.10472245514392853 rice_maize,USA,7.5 -rice_maize,UZB,-6.202516078948975 -rice_maize,VEN,-1.1330994963645935 -rice_maize,VNM,-0.779794454574585 -rice_maize,YEM,-0.0 -rice_maize,ZAF,-0.7060503959655762 -rice_maize,ZMB,-1.0463234186172485 +rice_maize,UZB,-5.992863893508911 +rice_maize,VEN,-0.8770258128643036 +rice_maize,VNM,-0.8021101355552673 +rice_maize,YEM,0.0 +rice_maize,ZAF,5.006124019622803 +rice_maize,ZMB,-7.5 rice_maize,ZWE,7.5 -rice_wheat,AFG,-1.304873526096344 -rice_wheat,AGO,7.5 -rice_wheat,ALB,-0.0 +rice_wheat,AFG,-1.1142328977584839 +rice_wheat,AGO,4.096597284078598 +rice_wheat,ALB,0.0 rice_wheat,ARG,7.5 -rice_wheat,ARM,-0.0 -rice_wheat,AUS,-1.6019264459609985 -rice_wheat,AZE,-2.757871150970459 -rice_wheat,BGD,-0.8078254461288452 -rice_wheat,BGR,3.10767525434494 +rice_wheat,ARM,0.0 +rice_wheat,AUS,-7.5 +rice_wheat,AZE,7.5 +rice_wheat,BGD,-0.3995904177427292 +rice_wheat,BGR,7.5 rice_wheat,BOL,7.5 -rice_wheat,BRA,-0.05252578854560852 -rice_wheat,BTN,0.11618829518556595 -rice_wheat,BWA,-0.0 -rice_wheat,CAN,-0.0 -rice_wheat,CHL,-0.6050255596637726 -rice_wheat,CHN,-2.6085113286972046 -rice_wheat,CMR,7.5 -rice_wheat,COD,-2.166265606880188 +rice_wheat,BRA,7.5 +rice_wheat,BTN,7.5 +rice_wheat,BWA,0.0 +rice_wheat,CAN,0.0 +rice_wheat,CHL,-0.7711638808250427 +rice_wheat,CHN,-2.575352430343628 +rice_wheat,CMR,-1.5374877452850342 +rice_wheat,COD,-2.1612181663513184 rice_wheat,DZA,7.5 -rice_wheat,ECU,-1.2411775588989258 -rice_wheat,EGY,-1.6681876182556152 -rice_wheat,ESP,-0.6500231623649597 -rice_wheat,ETH,-2.4989004135131836 +rice_wheat,ECU,-1.2879406213760376 +rice_wheat,EGY,-0.9247386455535889 +rice_wheat,ESP,-0.6620495915412903 +rice_wheat,ETH,7.5 rice_wheat,FRA,7.5 rice_wheat,GEO,7.5 -rice_wheat,GRC,-0.660244032740593 -rice_wheat,GTM,7.5 -rice_wheat,HND,-2.5053634643554688 +rice_wheat,GRC,-0.43876534700393677 +rice_wheat,GTM,0.0 +rice_wheat,HND,-1.9596569538116455 rice_wheat,HUN,7.5 -rice_wheat,IND,-0.8473137617111206 -rice_wheat,IRN,-3.968905210494995 -rice_wheat,IRQ,-2.386203169822693 -rice_wheat,ITA,-1.9720418453216553 -rice_wheat,JPN,-0.11923962086439133 -rice_wheat,KAZ,-0.22488220036029816 -rice_wheat,KGZ,-3.0229909420013428 -rice_wheat,KOR,-6.342390537261963 -rice_wheat,LAO,7.5 -rice_wheat,LSO,-0.0 -rice_wheat,MAR,-1.8250325322151184 +rice_wheat,IND,1.573748528957367 +rice_wheat,IRN,-3.188071608543396 +rice_wheat,IRQ,-1.8810048699378967 +rice_wheat,ITA,-2.728669047355652 +rice_wheat,JPN,-1.294859528541565 +rice_wheat,KAZ,-0.10879222303628922 +rice_wheat,KGZ,-3.064410090446472 +rice_wheat,KOR,7.5 +rice_wheat,LAO,0.0 +rice_wheat,LSO,0.0 +rice_wheat,MAR,-0.622918963432312 rice_wheat,MDA,7.5 -rice_wheat,MDG,-0.8599275946617126 +rice_wheat,MDG,-0.8242219090461731 rice_wheat,MEX,7.5 rice_wheat,MKD,7.5 -rice_wheat,MLI,-0.18869413435459137 -rice_wheat,MMR,-1.868966281414032 +rice_wheat,MLI,0.05432529002428055 +rice_wheat,MMR,-1.964646577835083 rice_wheat,MOZ,7.5 rice_wheat,MRT,7.5 rice_wheat,MWI,7.5 rice_wheat,NAM,7.5 -rice_wheat,NER,7.5 -rice_wheat,NGA,-0.6789183020591736 -rice_wheat,NPL,-0.46460652351379395 -rice_wheat,PAK,-1.3554804921150208 -rice_wheat,PRT,-0.8906027674674988 -rice_wheat,PRY,-0.4173676371574402 +rice_wheat,NER,-7.5 +rice_wheat,NGA,-7.5 +rice_wheat,NPL,3.393115282058716 +rice_wheat,PAK,-0.13051317632198334 +rice_wheat,PRT,-0.9750400185585022 +rice_wheat,PRY,-0.5190354287624359 rice_wheat,ROU,7.5 rice_wheat,RUS,7.5 -rice_wheat,RWA,-1.9549353122711182 -rice_wheat,SDN,-1.3346004486083984 -rice_wheat,SOM,2.7530112862586975 -rice_wheat,SVK,-0.0 -rice_wheat,SVN,-0.0 +rice_wheat,RWA,-1.969196081161499 +rice_wheat,SDN,-7.5 +rice_wheat,SOM,-7.5 +rice_wheat,SVK,0.0 +rice_wheat,SVN,0.0 rice_wheat,SWZ,7.5 -rice_wheat,SYR,7.5 -rice_wheat,TCD,-0.8246639966964722 +rice_wheat,SYR,0.09349346905946732 +rice_wheat,TCD,-6.476876258850098 rice_wheat,THA,7.5 -rice_wheat,TJK,-3.2520639896392822 -rice_wheat,TKM,-0.7869112193584442 -rice_wheat,TUN,-0.0 -rice_wheat,TUR,-2.744300127029419 -rice_wheat,TZA,-0.3317982703447342 -rice_wheat,UGA,-1.9048722982406616 +rice_wheat,TJK,-2.811855733394623 +rice_wheat,TKM,-1.210829734802246 +rice_wheat,TUN,0.0 +rice_wheat,TUR,-2.0662403106689453 +rice_wheat,TZA,-0.1595538780093193 +rice_wheat,UGA,-1.9200326204299927 rice_wheat,UKR,7.5 -rice_wheat,URY,7.5 +rice_wheat,URY,-0.02746044471859932 rice_wheat,USA,7.5 -rice_wheat,UZB,-5.182010650634766 +rice_wheat,UZB,-5.090646266937256 rice_wheat,VEN,7.5 -rice_wheat,VNM,-7.5 -rice_wheat,ZAF,-1.2216403484344482 -rice_wheat,ZMB,-0.500923752784729 +rice_wheat,VNM,-1.170015573501587 +rice_wheat,ZAF,4.613178253173828 +rice_wheat,ZMB,-7.269059658050537 rice_wheat,ZWE,7.5 triple_rice,AFG,7.5 triple_rice,AGO,7.5 @@ -497,13 +496,13 @@ triple_rice,AUS,7.5 triple_rice,BDI,7.5 triple_rice,BEN,7.5 triple_rice,BFA,7.5 -triple_rice,BGD,-1.1383903622627258 +triple_rice,BGD,0.046352850273251534 triple_rice,BLZ,7.5 triple_rice,BOL,7.5 triple_rice,BRA,7.5 triple_rice,BRN,7.5 -triple_rice,BTN,-2.4064918756484985 -triple_rice,BWA,-0.0 +triple_rice,BTN,-2.8238296508789062 +triple_rice,BWA,0.0 triple_rice,CHN,7.5 triple_rice,CIV,7.5 triple_rice,CMR,7.5 @@ -528,17 +527,17 @@ triple_rice,GNB,7.5 triple_rice,GNQ,7.5 triple_rice,GRC,7.5 triple_rice,GTM,7.5 -triple_rice,GUF,7.5 +triple_rice,GUF,0.0 triple_rice,GUY,7.5 triple_rice,HND,7.5 triple_rice,HTI,7.5 -triple_rice,IDN,2.271872878074646 +triple_rice,IDN,7.5 triple_rice,IND,7.5 triple_rice,IRN,7.5 triple_rice,IRQ,7.5 triple_rice,ITA,7.5 triple_rice,KEN,7.5 -triple_rice,KHM,-3.3303961753845215 +triple_rice,KHM,-7.5 triple_rice,LAO,7.5 triple_rice,LBR,7.5 triple_rice,LKA,7.5 @@ -568,7 +567,7 @@ triple_rice,RWA,7.5 triple_rice,SAU,7.5 triple_rice,SDN,7.5 triple_rice,SEN,7.5 -triple_rice,SLB,-4.246044874191284 +triple_rice,SLB,-4.267874717712402 triple_rice,SLE,7.5 triple_rice,SLV,7.5 triple_rice,SOM,7.5 @@ -578,7 +577,7 @@ triple_rice,SWZ,7.5 triple_rice,TCD,7.5 triple_rice,TGO,7.5 triple_rice,THA,7.5 -triple_rice,TLS,1.3693647384643555 +triple_rice,TLS,7.5 triple_rice,TTO,7.5 triple_rice,TUR,7.5 triple_rice,TWN,7.5 @@ -588,203 +587,203 @@ triple_rice,URY,7.5 triple_rice,USA,7.5 triple_rice,VEN,7.5 triple_rice,VNM,7.5 -triple_rice,YEM,-0.0 +triple_rice,YEM,0.0 triple_rice,ZAF,7.5 triple_rice,ZMB,7.5 triple_rice,ZWE,7.5 -wheat_maize,AFG,-1.059177041053772 -wheat_maize,AGO,-1.4748095273971558 +wheat_maize,AFG,-0.9223511517047882 +wheat_maize,AGO,0.06085094437003136 wheat_maize,ALB,7.5 wheat_maize,ARE,7.5 wheat_maize,ARG,7.5 -wheat_maize,ARM,0.19232968986034393 -wheat_maize,AUS,-0.15932096540927887 -wheat_maize,AUT,-0.005322609096765518 -wheat_maize,AZE,-0.29749178886413574 -wheat_maize,BDI,-1.041087031364441 +wheat_maize,ARM,0.13379235565662384 +wheat_maize,AUS,0.7673986256122589 +wheat_maize,AUT,7.5 +wheat_maize,AZE,0.21032091975212097 +wheat_maize,BDI,-1.0480952262878418 wheat_maize,BEL,7.5 -wheat_maize,BGD,0.027580395340919495 -wheat_maize,BGR,0.03009234555065632 +wheat_maize,BGD,-0.06975655257701874 +wheat_maize,BGR,7.5 wheat_maize,BIH,7.5 wheat_maize,BOL,7.5 -wheat_maize,BRA,0.10624170303344727 -wheat_maize,BTN,1.5968497395515442 -wheat_maize,BWA,3.844961404800415 +wheat_maize,BRA,0.05257541500031948 +wheat_maize,BTN,1.7523919343948364 +wheat_maize,BWA,3.8771475106477737 wheat_maize,CAN,7.5 -wheat_maize,CHE,2.516054391860962 -wheat_maize,CHL,-0.93019700050354 -wheat_maize,CHN,-2.075305938720703 -wheat_maize,CMR,-0.5750364661216736 -wheat_maize,COD,-1.4551146030426025 -wheat_maize,COL,-0.6131998300552368 -wheat_maize,CZE,3.7804106660187244 +wheat_maize,CHE,7.5 +wheat_maize,CHL,-0.6160206198692322 +wheat_maize,CHN,-2.0744786262512207 +wheat_maize,CMR,-0.6157926917076111 +wheat_maize,COD,-1.461105227470398 +wheat_maize,COL,-0.6463930606842041 +wheat_maize,CZE,7.5 wheat_maize,DEU,7.5 wheat_maize,DZA,7.5 -wheat_maize,ECU,-1.0126038789749146 -wheat_maize,EGY,-1.5646820068359375 -wheat_maize,ERI,-1.3822872042655945 -wheat_maize,ESP,-0.7554497122764587 -wheat_maize,ETH,-0.32751739025115967 +wheat_maize,ECU,-1.0442391633987427 +wheat_maize,EGY,-0.6321408748626709 +wheat_maize,ERI,-1.3943330645561218 +wheat_maize,ESP,0.06611741334199905 +wheat_maize,ETH,-0.2691202014684677 wheat_maize,FRA,7.5 -wheat_maize,GEO,0.09364751726388931 -wheat_maize,GRC,3.5302646458148956 -wheat_maize,GTM,3.372650295495987 -wheat_maize,HND,-1.0760682821273804 +wheat_maize,GEO,-0.1255870759487152 +wheat_maize,GRC,-0.7035238444805145 +wheat_maize,GTM,6.486738681793213 +wheat_maize,HND,-1.047223448753357 wheat_maize,HRV,7.5 wheat_maize,HUN,7.5 -wheat_maize,IND,-0.3966492712497711 -wheat_maize,IRN,-1.7036104202270508 -wheat_maize,IRQ,-1.5998313426971436 -wheat_maize,ISR,-6.92481803894043 -wheat_maize,ITA,-0.7050333023071289 -wheat_maize,JOR,0.6081819571554661 -wheat_maize,KAZ,0.13862000405788422 -wheat_maize,KEN,-0.655785322189331 -wheat_maize,KGZ,-0.36566704511642456 -wheat_maize,KOR,0.012307032011449337 -wheat_maize,LAO,3.3776745796203613 -wheat_maize,LBN,-0.38848312199115753 +wheat_maize,IND,-0.23743987828493118 +wheat_maize,IRN,-0.9351812601089478 +wheat_maize,IRQ,-1.0056968927383423 +wheat_maize,ISR,-6.639710426330566 +wheat_maize,ITA,0.14105527848005295 +wheat_maize,JOR,1.4564753770828247 +wheat_maize,KAZ,0.41205526888370514 +wheat_maize,KEN,-0.6920293867588043 +wheat_maize,KGZ,0.0 +wheat_maize,KOR,0.08538535237312317 +wheat_maize,LAO,0.0 +wheat_maize,LBN,-0.3937086760997772 wheat_maize,LBY,7.5 -wheat_maize,LSO,-0.07805822603404522 +wheat_maize,LSO,0.12134746462106705 wheat_maize,LUX,7.5 -wheat_maize,MAR,-0.30309001356363297 -wheat_maize,MDA,3.8919558972120285 -wheat_maize,MDG,-1.0458958148956299 -wheat_maize,MEX,-0.8088407814502716 +wheat_maize,MAR,-0.1560538113117218 +wheat_maize,MDA,7.5 +wheat_maize,MDG,-0.9783177375793457 +wheat_maize,MEX,3.709369421005249 wheat_maize,MKD,7.5 -wheat_maize,MMR,-0.9253009855747223 +wheat_maize,MMR,-1.0011625289916992 wheat_maize,MNE,7.5 -wheat_maize,MOZ,7.5 +wheat_maize,MOZ,-0.17766039073467255 wheat_maize,MWI,7.5 -wheat_maize,NAM,-2.086304545402527 +wheat_maize,NAM,-5.040892124176025 wheat_maize,NIC,7.5 -wheat_maize,NLD,3.8913015574216843 -wheat_maize,NPL,-0.3817053288221359 -wheat_maize,NZL,-0.8886459320783615 -wheat_maize,OMN,-4.1484681367874146 -wheat_maize,PAK,-0.43155471980571747 -wheat_maize,PER,-0.6361838579177856 +wheat_maize,NLD,7.5 +wheat_maize,NPL,0.5430206060409546 +wheat_maize,NZL,-1.0715875327587128 +wheat_maize,OMN,-5.012444019317627 +wheat_maize,PAK,0.6206960678100586 +wheat_maize,PER,-1.0850019454956055 wheat_maize,POL,7.5 -wheat_maize,PRT,0.12055753171443939 -wheat_maize,PRY,-0.04922150820493698 -wheat_maize,PSE,1.644733488559723 -wheat_maize,ROU,0.03139835596084595 +wheat_maize,PRT,7.5 +wheat_maize,PRY,-0.13775226473808289 +wheat_maize,PSE,2.3259319067001343 +wheat_maize,ROU,7.5 wheat_maize,RUS,7.5 -wheat_maize,RWA,-0.43893852829933167 -wheat_maize,SAU,-5.622318267822266 +wheat_maize,RWA,-0.4318433403968811 +wheat_maize,SAU,-4.549129247665405 wheat_maize,SLV,0.0 -wheat_maize,SOM,0.2279478758573532 +wheat_maize,SOM,-7.5 wheat_maize,SRB,7.5 wheat_maize,SSD,7.5 wheat_maize,SVK,7.5 wheat_maize,SVN,7.5 -wheat_maize,SWZ,-1.0872985124588013 -wheat_maize,SYR,-0.9373708069324493 -wheat_maize,THA,3.39390966296196 -wheat_maize,TJK,-1.2326552867889404 -wheat_maize,TKM,-0.6474591195583344 -wheat_maize,TUN,-0.0 -wheat_maize,TUR,0.3453899919986725 -wheat_maize,TWN,0.3946537971496582 -wheat_maize,TZA,-0.24894577264785767 -wheat_maize,UGA,-1.1047067642211914 +wheat_maize,SWZ,-1.1316282749176025 +wheat_maize,SYR,0.12519118189811707 +wheat_maize,THA,7.5 +wheat_maize,TJK,-0.10244544222950935 +wheat_maize,TKM,-0.7906519174575806 +wheat_maize,TUN,0.0 +wheat_maize,TUR,0.6864449679851532 +wheat_maize,TWN,0.46693259477615356 +wheat_maize,TZA,-0.13013962656259537 +wheat_maize,UGA,-1.1253196001052856 wheat_maize,UKR,7.5 -wheat_maize,URY,-0.24283335357904434 -wheat_maize,USA,-0.24570778757333755 -wheat_maize,UZB,-2.2322025299072266 +wheat_maize,URY,-0.0017564594745635986 +wheat_maize,USA,-0.067537322640419 +wheat_maize,UZB,-1.6876318454742432 wheat_maize,VEN,7.5 -wheat_maize,VNM,-7.5 -wheat_maize,YEM,-1.2792218923568726 -wheat_maize,ZAF,-0.047328826040029526 -wheat_maize,ZMB,0.6400972902774811 -wheat_maize,ZWE,7.117492198944092 -wheat_soybean,AFG,-0.21164609491825104 -wheat_soybean,AGO,-1.2595300078392029 +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,-0.6065599322319031 +wheat_soybean,ARG,7.5 wheat_soybean,ARM,7.5 -wheat_soybean,AUS,0.6463535279035568 -wheat_soybean,AUT,-0.01924823597073555 -wheat_soybean,AZE,4.210933417081833 -wheat_soybean,BDI,-1.4682312607765198 +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,-0.5767140686511993 +wheat_soybean,BGD,3.5620241463184357 wheat_soybean,BGR,7.5 wheat_soybean,BIH,7.5 wheat_soybean,BOL,7.5 -wheat_soybean,BRA,-0.39443439245224 +wheat_soybean,BRA,-0.20813433825969696 wheat_soybean,BTN,7.5 wheat_soybean,BWA,7.5 wheat_soybean,CAN,7.5 -wheat_soybean,CHE,-0.8971370458602905 -wheat_soybean,CHN,-1.3253093957901 -wheat_soybean,CMR,-0.43673020601272583 -wheat_soybean,COD,-1.2637889981269836 +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,-0.6838064789772034 -wheat_soybean,EGY,-1.2459351420402527 -wheat_soybean,ESP,0.2736721336841583 -wheat_soybean,ETH,-0.8871381282806396 +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,-0.05915307253599167 -wheat_soybean,GRC,-0.2598990797996521 -wheat_soybean,GTM,3.78983361646533 -wheat_soybean,HND,-0.959020584821701 +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,-0.40162912011146545 -wheat_soybean,IRN,-0.473108172416687 -wheat_soybean,IRQ,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,1.6746549606323242 +wheat_soybean,JOR,0.0 +wheat_soybean,JPN,1.4244059324264526 wheat_soybean,KAZ,7.5 -wheat_soybean,KEN,-0.6842177510261536 -wheat_soybean,KGZ,-0.27548111975193024 -wheat_soybean,KOR,3.5200543254613876 -wheat_soybean,LAO,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,-1.4421567916870117 -wheat_soybean,MDA,3.895781621336937 -wheat_soybean,MEX,-0.34212958812713623 +wheat_soybean,MAR,-0.5912825465202332 +wheat_soybean,MDA,7.5 +wheat_soybean,MEX,7.5 wheat_soybean,MKD,7.5 -wheat_soybean,MMR,-1.0974624752998352 +wheat_soybean,MMR,-0.6119680404663086 wheat_soybean,MNE,7.5 wheat_soybean,MOZ,7.5 wheat_soybean,MWI,7.5 -wheat_soybean,NGA,-0.6289743781089783 +wheat_soybean,NGA,7.5 wheat_soybean,NLD,7.5 -wheat_soybean,NPL,3.5407437086105347 -wheat_soybean,PAK,-0.4830322265625 -wheat_soybean,PER,-0.46757999062538147 +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,-0.12475436180830002 +wheat_soybean,PRY,-0.10462310910224915 wheat_soybean,ROU,7.5 wheat_soybean,RUS,7.5 -wheat_soybean,RWA,-0.45277969539165497 +wheat_soybean,RWA,-0.44948336482048035 wheat_soybean,SRB,7.5 wheat_soybean,SVK,7.5 -wheat_soybean,SVN,3.762704192660749 -wheat_soybean,SWZ,-0.5005633234977722 +wheat_soybean,SVN,7.5 +wheat_soybean,SWZ,-0.5193095803260803 wheat_soybean,SYR,7.5 -wheat_soybean,THA,-0.7992278635501862 +wheat_soybean,THA,7.5 wheat_soybean,TJK,7.5 -wheat_soybean,TKM,0.47631368041038513 +wheat_soybean,TKM,0.3698643445968628 wheat_soybean,TUR,7.5 -wheat_soybean,TWN,-0.3602020740509033 -wheat_soybean,TZA,-0.46302691102027893 -wheat_soybean,UGA,-1.1337865591049194 +wheat_soybean,TWN,-0.37869152426719666 +wheat_soybean,TZA,-0.36440300941467285 +wheat_soybean,UGA,-1.134753942489624 wheat_soybean,UKR,7.5 -wheat_soybean,URY,-0.31937259435653687 -wheat_soybean,USA,-0.3088880181312561 +wheat_soybean,URY,-0.2477397918701172 +wheat_soybean,USA,-0.048939481377601624 wheat_soybean,UZB,7.5 -wheat_soybean,VNM,-7.5 -wheat_soybean,ZAF,-0.254607193171978 -wheat_soybean,ZMB,-0.18164435029029846 -wheat_soybean,ZWE,4.234316349029541 +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 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+meat-sheep,ATG,-0.12201206386089325 +meat-sheep,AUS,-0.056488606380298734 +meat-sheep,AUT,0.07041175290942192 +meat-sheep,AZE,-0.12003717198967934 +meat-sheep,BDI,-0.05513974651694298 +meat-sheep,BEL,0.04353190027177334 +meat-sheep,BEN,-0.11972034350037575 +meat-sheep,BFA,-0.15383009612560272 +meat-sheep,BGD,-0.091155169531703 +meat-sheep,BGR,-0.09192641451954842 +meat-sheep,BHS,-0.008302724338136613 +meat-sheep,BIH,0.03218399919569492 +meat-sheep,BLR,-0.03988480009138584 +meat-sheep,BLZ,0.015569881536066532 +meat-sheep,BOL,-0.07442529127001762 +meat-sheep,BRA,-0.060573678463697433 +meat-sheep,BRB,-0.3205314204096794 +meat-sheep,BRN,-0.18255271017551422 +meat-sheep,BTN,-0.28371892869472504 +meat-sheep,BWA,-0.21375487744808197 +meat-sheep,CAF,-0.11622504889965057 +meat-sheep,CAN,-0.009651617147028446 +meat-sheep,CHE,0.03420906187966466 +meat-sheep,CHL,-0.0507681779563427 +meat-sheep,CHN,-0.10613422095775604 +meat-sheep,CIV,-0.1329195238649845 +meat-sheep,CMR,-0.12654469907283783 +meat-sheep,COD,-0.14429305121302605 +meat-sheep,COG,-0.07489274814724922 +meat-sheep,COL,-0.0743846744298935 +meat-sheep,COM,-0.16729283332824707 +meat-sheep,CPV,-0.39867671579122543 +meat-sheep,CRI,-0.014930696226656437 +meat-sheep,CUB,-0.06449948251247406 +meat-sheep,CYP,-0.3210277557373047 +meat-sheep,CZE,0.016448525711894035 +meat-sheep,DEU,0.023824437521398067 meat-sheep,DJI,-0.022527582943439484 -meat-sheep,DNK,0.004888575524091721 -meat-sheep,DOM,-0.14180059358477592 -meat-sheep,DZA,-0.06883323285728693 -meat-sheep,ECU,-0.10261603817343712 -meat-sheep,EGY,-0.17260726541280746 -meat-sheep,ERI,-0.08320167474448681 -meat-sheep,ESP,-0.015139233320951462 -meat-sheep,EST,-0.03836073633283377 -meat-sheep,ETH,-0.13145582005381584 -meat-sheep,FIN,-0.020400981418788433 -meat-sheep,FJI,-0.12888086959719658 -meat-sheep,FRA,0.013158921618014574 -meat-sheep,GAB,-0.06616145372390747 -meat-sheep,GBR,0.006212768144905567 -meat-sheep,GEO,-0.17174265161156654 -meat-sheep,GHA,-0.18316997587680817 -meat-sheep,GIN,-0.06046628579497337 -meat-sheep,GMB,-0.2043268382549286 -meat-sheep,GNB,-0.16939865052700043 -meat-sheep,GNQ,-0.03611837327480316 -meat-sheep,GRC,-0.06173376180231571 +meat-sheep,DNK,0.059091370552778244 +meat-sheep,DOM,-0.06113261170685291 +meat-sheep,DZA,-0.04572103638201952 +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 alfalfa,CRI,-0.21360260248184204 alfalfa,CUB,-0.28110775351524353 -alfalfa,CYP,3.2153319120407104 -alfalfa,CZE,-0.5634166598320007 -alfalfa,DEU,-0.613816648721695 -alfalfa,DJI,1.4348506927490234 +alfalfa,CYP,-7.5 +alfalfa,CZE,-0.5772684216499329 +alfalfa,DEU,-0.0678469343110919 +alfalfa,DJI,1.6652567386627197 alfalfa,DNK,7.5 -alfalfa,DOM,-1.0432733595371246 -alfalfa,DZA,-0.08560183644294739 +alfalfa,DOM,-0.8691022992134094 +alfalfa,DZA,-0.07842585444450378 alfalfa,ECU,-0.12939461693167686 -alfalfa,EGY,2.858594298362732 -alfalfa,ERI,-0.8760250210762024 -alfalfa,ESP,-1.2043046355247498 -alfalfa,EST,-0.14292681217193604 -alfalfa,ETH,-0.8099172413349152 -alfalfa,FIN,-0.0 +alfalfa,EGY,-0.24773885309696198 +alfalfa,ERI,-0.8744806945323944 +alfalfa,ESP,-1.0377808809280396 +alfalfa,EST,-0.4961422383785248 +alfalfa,ETH,-0.8274461328983307 +alfalfa,FIN,0.0 alfalfa,FJI,-0.0291437734849751 alfalfa,FRA,-0.3310399800539017 alfalfa,GAB,-0.870643675327301 -alfalfa,GBR,-0.0076062113512307405 +alfalfa,GBR,0.15797654539346695 alfalfa,GEO,-0.583319365978241 alfalfa,GHA,7.5 -alfalfa,GIN,-4.188888192176819 -alfalfa,GMB,-7.5 -alfalfa,GNB,-7.5 +alfalfa,GIN,-0.387450709939003 +alfalfa,GMB,-0.242458738386631 +alfalfa,GNB,0.0 alfalfa,GNQ,-0.7589189112186432 -alfalfa,GRC,-0.7949534356594086 -alfalfa,GRD,0.36276742815971375 -alfalfa,GTM,-0.48949453234672546 -alfalfa,GUF,-2.7509915828704834 -alfalfa,GUY,-0.7936551868915558 +alfalfa,GRC,-0.6836957335472107 +alfalfa,GRD,0.5122256278991699 +alfalfa,GTM,-0.5226822793483734 +alfalfa,GUF,-2.706025242805481 +alfalfa,GUY,-0.07720011845231056 alfalfa,HND,-0.34651389718055725 -alfalfa,HRV,-3.9364892542362213 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-alfalfa,LBR,-0.9302260279655457 -alfalfa,LBY,-0.824711263179779 -alfalfa,LKA,-0.375872865319252 -alfalfa,LSO,-0.44325435161590576 -alfalfa,LTU,0.14394311606884003 -alfalfa,LUX,-0.37912678718566895 -alfalfa,LVA,-0.5082782655954361 -alfalfa,MAR,2.5664076805114746 -alfalfa,MDA,-0.01771822606679052 -alfalfa,MDG,-0.9195513427257538 -alfalfa,MEX,-0.1496129333972931 -alfalfa,MKD,-0.05144424282480031 -alfalfa,MLI,-7.5 -alfalfa,MLT,-4.870208501815796 -alfalfa,MMR,-0.8467822372913361 -alfalfa,MNE,0.773455411195755 -alfalfa,MNG,-7.5 -alfalfa,MOZ,-0.5782089233398438 -alfalfa,MRT,-0.5335112512111664 -alfalfa,MUS,-0.9187363982200623 +alfalfa,KHM,-0.8859851360321045 +alfalfa,KOR,0.11334379017353058 +alfalfa,LAO,-0.315351665019989 +alfalfa,LBN,2.2714288234710693 +alfalfa,LBR,-0.9222033023834229 +alfalfa,LBY,-0.10145418345928192 +alfalfa,LKA,-0.02244449034333229 +alfalfa,LSO,-0.9464530348777771 +alfalfa,LTU,0.25899556279182434 +alfalfa,LUX,-0.3182141184806824 +alfalfa,LVA,-0.49128568172454834 +alfalfa,MAR,2.956733465194702 +alfalfa,MDA,2.51277756690979 +alfalfa,MDG,-0.04350392799824476 +alfalfa,MEX,0.0 +alfalfa,MKD,0.02758372575044632 +alfalfa,MLI,-0.9638939499855042 +alfalfa,MLT,-3.751420696033165 +alfalfa,MMR,-0.8475324213504791 +alfalfa,MNE,1.588646650314331 +alfalfa,MNG,-0.8200780153274536 +alfalfa,MOZ,-0.5750786066055298 +alfalfa,MRT,-7.5 +alfalfa,MUS,-0.8998470306396484 alfalfa,MWI,-0.5677796304225922 -alfalfa,MYS,-0.3362851142883301 -alfalfa,NAM,-1.4871166348457336 -alfalfa,NER,-0.0 +alfalfa,MYS,-0.34928590059280396 +alfalfa,NAM,-0.29255859553813934 +alfalfa,NER,0.0 alfalfa,NGA,-0.9353777766227722 -alfalfa,NIC,-1.863908052444458 -alfalfa,NLD,6.568096399307251 -alfalfa,NOR,-0.0 -alfalfa,NPL,-0.944817304611206 -alfalfa,NZL,-0.31611888110637665 -alfalfa,OMN,-0.005131949204951525 -alfalfa,PAK,-0.8294757902622223 +alfalfa,NIC,-1.8693143725395203 +alfalfa,NLD,5.937969446182251 +alfalfa,NOR,0.0 +alfalfa,NPL,-1.9854430556297302 +alfalfa,NZL,-0.3066001534461975 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-apple,BLZ,-7.5 -apple,BOL,-7.5 -apple,BRA,-0.0 -apple,BTN,-0.0 -apple,BWA,-0.0 -apple,CAN,-0.0 -apple,CHE,-0.0 -apple,CHL,-0.0 -apple,CHN,-0.0 -apple,COL,-7.5 -apple,CZE,-0.0 -apple,DEU,-0.0 -apple,DNK,-0.0 -apple,DZA,-0.0 -apple,ECU,-0.0 -apple,EGY,-0.0 -apple,ESP,-0.0 -apple,EST,-0.0 -apple,ETH,-0.0 -apple,FIN,-0.0 -apple,FRA,-0.0 -apple,GBR,-0.0 -apple,GEO,-0.0 -apple,GRC,-0.0 -apple,GTM,-0.0 -apple,HND,-7.5 -apple,HRV,-0.0 -apple,HUN,-0.0 -apple,IND,-0.0 -apple,IRL,-0.0 -apple,IRN,-0.0 -apple,IRQ,-0.0 -apple,ISR,-0.0 -apple,ITA,-0.0 -apple,JOR,-0.0 -apple,JPN,-0.0 -apple,KAZ,-0.0 -apple,KEN,-7.5 -apple,KGZ,-0.0 -apple,KOR,-0.0 -apple,LAO,-7.5 -apple,LBN,-0.0 -apple,LBY,-7.5 -apple,LSO,-7.5 -apple,LTU,-0.0 -apple,LUX,-0.0 -apple,LVA,-0.0 -apple,MAR,-0.0 -apple,MDA,-0.0 -apple,MDG,-1.9327445030212402 -apple,MEX,-0.0 -apple,MKD,-0.0 -apple,MMR,-7.5 -apple,MNE,-0.0 -apple,MNG,-0.0 -apple,MOZ,-0.0 -apple,NIC,-0.0 -apple,NLD,-0.0 -apple,NOR,-0.0 -apple,NPL,-0.0 -apple,NZL,-0.0 -apple,PAK,-0.0 -apple,PER,-7.5 -apple,POL,-0.0 -apple,PRT,-0.0 -apple,PRY,-7.5 -apple,PSE,-0.0 -apple,ROU,-0.0 -apple,RUS,-0.0 -apple,SAU,-7.5 -apple,SLV,-0.0 -apple,SRB,-0.0 -apple,SVK,-0.0 -apple,SVN,-0.0 -apple,SWE,-0.0 -apple,SWZ,-0.0 -apple,SYR,-0.0 -apple,TJK,-0.0 -apple,TKM,-0.0 -apple,TUN,-0.0 -apple,TUR,-0.0 -apple,TWN,-3.75 -apple,TZA,-0.0 -apple,UGA,-0.0 -apple,UKR,-0.0 -apple,URY,-0.0 -apple,USA,-7.5 -apple,UZB,-0.0 -apple,VEN,-0.0 -apple,VNM,-7.5 -apple,YEM,-0.0 -apple,ZAF,-0.0 -apple,ZMB,-0.0 -apple,ZWE,-7.5 -banana,AFG,-0.0 -banana,AGO,-5.899302244186401 -banana,ALB,7.5 -banana,ARE,-7.5 -banana,ARG,-2.187955617904663 -banana,ATG,-0.0 +alfalfa,VNM,0.21912385523319244 +alfalfa,VUT,-0.48392657935619354 +alfalfa,YEM,-0.6435136795043945 +alfalfa,ZAF,-0.45210833847522736 +alfalfa,ZMB,-0.3911762535572052 +alfalfa,ZWE,0.0 +apple,AFG,0.0 +apple,ALB,0.0 +apple,ARG,0.0 +apple,ARM,0.0 +apple,AUS,0.0 +apple,AUT,0.0 +apple,AZE,0.0 +apple,BEL,0.0 +apple,BGR,0.0 +apple,BIH,0.0 +apple,BLR,0.0 +apple,BLZ,0.0 +apple,BOL,0.0 +apple,BRA,0.0 +apple,BTN,0.0 +apple,BWA,0.0 +apple,CAN,0.0 +apple,CHE,0.0 +apple,CHL,0.0 +apple,CHN,0.0 +apple,COL,0.0 +apple,CZE,0.0 +apple,DEU,0.0 +apple,DNK,0.0 +apple,DZA,0.0 +apple,ECU,0.0 +apple,EGY,0.0 +apple,ESP,0.0 +apple,EST,0.0 +apple,ETH,0.0 +apple,FIN,0.0 +apple,FRA,0.0 +apple,GBR,0.0 +apple,GEO,0.0 +apple,GRC,0.0 +apple,GTM,0.0 +apple,HND,0.0 +apple,HRV,0.0 +apple,HUN,0.0 +apple,IND,0.0 +apple,IRL,0.0 +apple,IRN,0.0 +apple,IRQ,0.0 +apple,ISR,0.0 +apple,ITA,0.0 +apple,JOR,0.0 +apple,JPN,0.0 +apple,KAZ,0.0 +apple,KEN,0.0 +apple,KGZ,0.0 +apple,KOR,0.0 +apple,LAO,0.0 +apple,LBN,0.0 +apple,LBY,0.0 +apple,LSO,0.0 +apple,LTU,0.0 +apple,LUX,0.0 +apple,LVA,0.0 +apple,MAR,0.0 +apple,MDA,0.0 +apple,MDG,-1.9327446222305298 +apple,MEX,0.0 +apple,MKD,0.0 +apple,MMR,0.0 +apple,MNE,0.0 +apple,MNG,0.0 +apple,MOZ,0.0 +apple,NIC,0.0 +apple,NLD,0.0 +apple,NOR,0.0 +apple,NPL,0.0 +apple,NZL,0.0 +apple,PAK,0.0 +apple,PER,0.0 +apple,POL,0.0 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foxtail-millet,GMB,-0.46135395765304565 -foxtail-millet,GNB,-0.44429850578308105 -foxtail-millet,GRC,7.5 +foxtail-millet,GNB,-0.1982787400484085 +foxtail-millet,GRC,0.0 foxtail-millet,HUN,-0.6123685538768768 -foxtail-millet,IND,-0.4750804603099823 -foxtail-millet,IRN,-1.0526432394981384 -foxtail-millet,IRQ,-0.837611585855484 -foxtail-millet,KAZ,-0.27249687910079956 +foxtail-millet,IND,0.0 +foxtail-millet,IRN,-0.9743119180202484 +foxtail-millet,IRQ,-0.8080403506755829 +foxtail-millet,KAZ,-0.2661903202533722 foxtail-millet,KEN,-0.49672308564186096 foxtail-millet,KGZ,-0.7578834295272827 -foxtail-millet,KHM,-0.0 +foxtail-millet,KHM,0.0 foxtail-millet,KOR,-0.3979724496603012 -foxtail-millet,LAO,-0.0 -foxtail-millet,LBY,-0.505948543548584 -foxtail-millet,LKA,-1.0432093739509583 -foxtail-millet,LSO,7.5 -foxtail-millet,MAR,-0.9500845074653625 +foxtail-millet,LAO,0.0 +foxtail-millet,LBY,-0.4712028205394745 +foxtail-millet,LKA,-1.0404086709022522 +foxtail-millet,LSO,0.0 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sweet-potato,CAF,-3.7978127002716064 -sweet-potato,CAN,-2.033702850341797 -sweet-potato,CHE,-7.5 -sweet-potato,CHL,-0.0 -sweet-potato,CHN,-7.5 -sweet-potato,CIV,-0.39846716821193695 +sweet-potato,CAN,-0.9752460718154907 +sweet-potato,CHE,0.0 +sweet-potato,CHL,0.0 +sweet-potato,CHN,-7.430725336074829 +sweet-potato,CIV,-0.4168329983949661 sweet-potato,CMR,-2.989189624786377 sweet-potato,COD,-3.38988995552063 sweet-potato,COG,-3.0788127183914185 -sweet-potato,COL,-2.060367465019226 +sweet-potato,COL,-2.112469792366028 sweet-potato,COM,-3.572199583053589 sweet-potato,CPV,-7.5 -sweet-potato,CRI,3.704215444624424 -sweet-potato,CUB,-2.543387770652771 -sweet-potato,CYP,-4.132151812314987 -sweet-potato,CZE,-0.7031247913837433 -sweet-potato,DEU,-0.0 -sweet-potato,DNK,-0.0 -sweet-potato,DOM,-3.6504751443862915 +sweet-potato,CRI,3.6996853910386562 +sweet-potato,CUB,-2.851918578147888 +sweet-potato,CYP,-5.073360562324524 +sweet-potato,CZE,-0.29459044337272644 +sweet-potato,DEU,0.0 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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,LAO,1.9178624153137207 -white-potato,LBN,-7.144816160202026 -white-potato,LBR,-5.363583087921143 -white-potato,LBY,-3.329577922821045 -white-potato,LKA,-7.3072614669799805 -white-potato,LSO,-3.4697256088256836 -white-potato,LTU,-0.44873427599668503 -white-potato,LUX,-3.755157709121704 -white-potato,LVA,0.9313837885856628 -white-potato,MAR,-1.6215576529502869 -white-potato,MDA,0.6737214624881744 -white-potato,MDG,-6.37866997718811 -white-potato,MEX,-6.026825904846191 -white-potato,MKD,-1.4042362570762634 +white-potato,KAZ,0.020822050515562296 +white-potato,KEN,-1.5887069702148438 +white-potato,KGZ,-1.7365307211875916 +white-potato,KHM,-5.59790301322937 +white-potato,KOR,-3.604013681411743 +white-potato,LAO,1.9693878889083862 +white-potato,LBN,-7.006861448287964 +white-potato,LBR,-5.330983638763428 +white-potato,LBY,-3.6808035373687744 +white-potato,LKA,-7.231513738632202 +white-potato,LSO,-3.3670032024383545 +white-potato,LTU,-0.34866466745734215 +white-potato,LUX,-3.7001019716262817 +white-potato,LVA,1.048218011856079 +white-potato,MAR,-1.7070000767707825 +white-potato,MDA,-0.08225812343880534 +white-potato,MDG,-6.354393720626831 +white-potato,MEX,-6.915231704711914 +white-potato,MKD,-1.3060320615768433 white-potato,MLI,-7.5 -white-potato,MLT,-5.732154369354248 -white-potato,MMR,-4.697368621826172 -white-potato,MNE,-4.207022190093994 -white-potato,MNG,-0.8164067566394806 +white-potato,MLT,-7.5 +white-potato,MMR,-4.387173652648926 +white-potato,MNE,-5.13407039642334 +white-potato,MNG,-0.9619754552841187 white-potato,MOZ,-7.5 -white-potato,MRT,-7.5 +white-potato,MRT,-6.344697952270508 white-potato,MUS,-7.5 -white-potato,MWI,2.3552368879318237 -white-potato,MYS,-4.025948524475098 -white-potato,NAM,-5.3291215896606445 +white-potato,MWI,2.4137003421783447 +white-potato,MYS,-3.935336470603943 +white-potato,NAM,-5.036626815795898 white-potato,NER,-7.5 -white-potato,NGA,-6.8754730224609375 -white-potato,NIC,-7.5 -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 index 4032a7cc..0ecd181b 100644 --- a/data/curated/calibration/gbd-anchored/multi_crop_cost.csv +++ b/data/curated/calibration/gbd-anchored/multi_crop_cost.csv @@ -1,102 +1,101 @@ combination,country,correction_bnusd_per_mha -cotton_wheat,AFG,-1.6099050045013428 -cotton_wheat,AGO,-1.814477801322937 +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,-5.853282690048218 -cotton_wheat,AZE,-0.6669307351112366 -cotton_wheat,BDI,-0.9382950663566589 -cotton_wheat,BGD,-1.3390784859657288 -cotton_wheat,BGR,-1.3807790279388428 +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,-2.3770229816436768 -cotton_wheat,BWA,3.1791040897369385 -cotton_wheat,CHN,-6.063660144805908 -cotton_wheat,COD,-1.8446723222732544 +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,-2.120392918586731 -cotton_wheat,ESP,-0.9637662768363953 -cotton_wheat,ETH,-2.7257684469223022 +cotton_wheat,EGY,0.7566313743591309 +cotton_wheat,ESP,2.4812554121017456 +cotton_wheat,ETH,-1.1835752129554749 cotton_wheat,GEO,7.5 -cotton_wheat,GRC,-1.315471351146698 -cotton_wheat,HND,-1.938868522644043 -cotton_wheat,IND,-1.3647518157958984 -cotton_wheat,IRN,-3.0847361087799072 -cotton_wheat,IRQ,-2.056082606315613 -cotton_wheat,ISR,-0.5670958757400513 -cotton_wheat,JOR,-1.9030465483665466 -cotton_wheat,KAZ,-1.6555743217468262 -cotton_wheat,KEN,-0.6111141741275787 -cotton_wheat,KGZ,-1.31385737657547 +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,-1.1813910603523254 -cotton_wheat,MAR,-2.646002769470215 -cotton_wheat,MDG,-1.5335040092468262 -cotton_wheat,MEX,-3.4887030124664307 -cotton_wheat,MKD,-1.4253509044647217 -cotton_wheat,MMR,2.898737072944641 -cotton_wheat,MOZ,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,7.5 -cotton_wheat,NER,3.5284949243068695 -cotton_wheat,NGA,-1.7977930307388306 +cotton_wheat,NAM,4.194038838148117 +cotton_wheat,NER,7.5 +cotton_wheat,NGA,-7.5 cotton_wheat,NPL,7.5 -cotton_wheat,OMN,-0.0 -cotton_wheat,PAK,-1.2787038087844849 +cotton_wheat,PAK,1.2053667306900024 cotton_wheat,PRT,7.5 -cotton_wheat,PRY,-0.5581440925598145 -cotton_wheat,PSE,-2.3085778951644897 -cotton_wheat,ROU,-0.0 +cotton_wheat,PRY,2.0514886379241943 +cotton_wheat,PSE,1.3464239835739136 +cotton_wheat,ROU,0.0 cotton_wheat,RUS,7.5 -cotton_wheat,RWA,-1.5222082138061523 -cotton_wheat,SDN,-1.6430537700653076 -cotton_wheat,SOM,-2.054495632648468 -cotton_wheat,SWZ,-1.6668144464492798 -cotton_wheat,SYR,-2.3456783294677734 -cotton_wheat,TJK,-2.956416606903076 -cotton_wheat,TKM,-1.5223835706710815 +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,-1.2363390922546387 -cotton_wheat,TZA,-1.431849181652069 -cotton_wheat,UGA,-1.6493436098098755 +cotton_wheat,TUR,4.218400955200195 +cotton_wheat,TZA,-0.6822943687438965 +cotton_wheat,UGA,-0.19741022400557995 cotton_wheat,URY,7.5 -cotton_wheat,USA,-1.3119173049926758 -cotton_wheat,UZB,-2.144982099533081 -cotton_wheat,YEM,-1.9001551270484924 -cotton_wheat,ZAF,-2.24241304397583 +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,3.960923194885254 +cotton_wheat,ZWE,6.100391387939453 double_rice,AFG,7.5 double_rice,AGO,7.5 -double_rice,ALB,-0.0 +double_rice,ALB,0.0 double_rice,ARG,7.5 double_rice,AUS,7.5 double_rice,AZE,7.5 -double_rice,BDI,7.5 -double_rice,BEN,-2.0670456886291504 -double_rice,BFA,-1.9492504000663757 -double_rice,BGD,-0.9019208252429962 -double_rice,BLZ,2.32931649684906 +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,-2.805904507637024 +double_rice,BRN,-2.855687379837036 double_rice,BTN,7.5 -double_rice,BWA,-0.0 +double_rice,BWA,0.0 double_rice,CAF,7.5 double_rice,CHL,7.5 -double_rice,CHN,-2.8561073541641235 +double_rice,CHN,-2.981346845626831 double_rice,CIV,7.5 -double_rice,CMR,2.668047308921814 +double_rice,CMR,7.5 double_rice,COD,7.5 double_rice,COG,7.5 -double_rice,COL,-2.3517508506774902 -double_rice,CRI,-1.9862532019615173 +double_rice,COL,-2.277894973754883 +double_rice,CRI,-1.5134159326553345 double_rice,CUB,7.5 -double_rice,DOM,2.198575973510742 +double_rice,DOM,-2.330437660217285 double_rice,DZA,7.5 -double_rice,ECU,-1.0549455285072327 +double_rice,ECU,-1.0004610419273376 double_rice,EGY,7.5 double_rice,ERI,7.5 double_rice,ESP,7.5 @@ -105,390 +104,390 @@ double_rice,FJI,7.5 double_rice,FRA,7.5 double_rice,GAB,7.5 double_rice,GEO,7.5 -double_rice,GHA,-0.3800714612007141 -double_rice,GIN,-0.8479809165000916 -double_rice,GMB,0.18228022754192352 -double_rice,GNB,-2.3635621070861816 +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,7.5 -double_rice,GUF,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,2.4892295598983765 -double_rice,IDN,-2.4515942335128784 -double_rice,IND,-1.1609660387039185 +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,-7.040684938430786 +double_rice,JPN,-7.4698264598846436 double_rice,KEN,7.5 -double_rice,KHM,-1.8155657052993774 -double_rice,KOR,-7.5 -double_rice,LAO,-2.1919310092926025 +double_rice,KHM,-4.303137898445129 +double_rice,KOR,7.5 +double_rice,LAO,-2.1252903938293457 double_rice,LBR,7.5 -double_rice,LKA,3.428750306367874 -double_rice,LSO,-0.0 +double_rice,LKA,-0.31366080045700073 +double_rice,LSO,0.0 double_rice,MAR,7.5 -double_rice,MDG,3.6049813479185104 +double_rice,MDG,3.7404229445382953 double_rice,MEX,7.5 double_rice,MKD,7.5 -double_rice,MLI,0.24248556792736053 -double_rice,MMR,-2.551267623901367 +double_rice,MLI,-7.5 +double_rice,MMR,-2.452920436859131 double_rice,MOZ,7.5 -double_rice,MRT,-1.4914619326591492 +double_rice,MRT,-7.5 double_rice,MUS,7.5 -double_rice,MWI,-1.7444035410881042 -double_rice,MYS,-0.6751475781202316 +double_rice,MWI,-7.5 +double_rice,MYS,-0.9995557069778442 double_rice,NAM,7.5 double_rice,NER,7.5 -double_rice,NGA,-0.04781103506684303 -double_rice,NIC,2.312941074371338 -double_rice,NPL,3.7156719639897346 +double_rice,NGA,-3.361262798309326 +double_rice,NIC,5.933155536651611 +double_rice,NPL,5.158517837524414 double_rice,PAK,7.5 -double_rice,PAN,-1.7208068370819092 +double_rice,PAN,-1.6340627670288086 double_rice,PER,7.5 -double_rice,PHL,-1.3351789712905884 +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,-3.063134789466858 +double_rice,RWA,-2.7989927530288696 double_rice,SAU,7.5 double_rice,SDN,7.5 -double_rice,SEN,0.5294859409332275 +double_rice,SEN,-2.7050421237945557 double_rice,SLB,7.5 -double_rice,SLE,3.7774145510047674 +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,-1.041583001613617 -double_rice,TGO,0.021735727787017822 -double_rice,THA,-0.6866416335105896 +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,TUN,0.0 double_rice,TUR,7.5 -double_rice,TWN,-1.2726807594299316 -double_rice,TZA,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,-1.6253113746643066 -double_rice,YEM,-0.0 +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,-0.4736453890800476 -maize_soybean,AGO,-1.5113000273704529 +maize_soybean,AFG,0.13687430508434772 +maize_soybean,AGO,-0.2613598108291626 maize_soybean,ALB,7.5 -maize_soybean,ARG,-0.001545419916510582 +maize_soybean,ARG,7.5 maize_soybean,ARM,7.5 -maize_soybean,AUS,1.390625536441803 -maize_soybean,AUT,-0.3401510715484619 -maize_soybean,AZE,4.152604818344116 -maize_soybean,BDI,-1.0835424065589905 +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,-0.8253427743911743 -maize_soybean,BFA,-0.5887079238891602 -maize_soybean,BGD,0.632455587387085 +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,7.5 -maize_soybean,BOL,0.12550845742225647 -maize_soybean,BRA,-0.03387417644262314 -maize_soybean,BTN,4.1264379024505615 -maize_soybean,BWA,7.5 -maize_soybean,CAF,-1.607940912246704 -maize_soybean,CAN,-0.7665511965751648 -maize_soybean,CHE,-1.1859102249145508 -maize_soybean,CHN,-1.4345921874046326 -maize_soybean,CIV,-0.9916943311691284 -maize_soybean,CMR,-1.224189281463623 -maize_soybean,COD,-1.6027145385742188 -maize_soybean,COG,-1.5472362637519836 -maize_soybean,COL,-0.292894572019577 -maize_soybean,CRI,-0.9751254320144653 +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,-0.6386651396751404 -maize_soybean,EGY,-1.6338169574737549 -maize_soybean,ESP,-0.43243037164211273 -maize_soybean,ETH,-0.22050713002681732 +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,-1.2179523706436157 -maize_soybean,GEO,0.07373932749032974 -maize_soybean,GHA,-0.8419823944568634 +maize_soybean,GAB,-1.1092767715454102 +maize_soybean,GEO,0.5402394533157349 +maize_soybean,GHA,-0.6359098851680756 maize_soybean,GIN,7.5 -maize_soybean,GRC,0.0970049574971199 -maize_soybean,GTM,-0.7145440578460693 -maize_soybean,HND,-0.4602302610874176 +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,0.4130144417285919 -maize_soybean,IND,-0.6799671053886414 -maize_soybean,IRN,-1.0338594913482666 -maize_soybean,IRQ,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 -maize_soybean,JOR,-0.0 +maize_soybean,JOR,0.0 maize_soybean,KAZ,7.5 -maize_soybean,KEN,-0.4090971052646637 -maize_soybean,KGZ,-0.2840305417776108 -maize_soybean,KHM,-0.5590203106403351 -maize_soybean,KOR,-0.30528104305267334 -maize_soybean,LAO,-0.03479786217212677 +maize_soybean,KEN,-0.3480319082736969 +maize_soybean,KGZ,3.8359000459313393 +maize_soybean,KHM,-0.37067945301532745 +maize_soybean,KOR,-0.044323911890387535 +maize_soybean,LAO,0.32661522924900055 maize_soybean,LBN,7.5 -maize_soybean,LKA,-0.9143259227275848 +maize_soybean,LKA,-0.6501298546791077 maize_soybean,LSO,7.5 maize_soybean,LUX,7.5 -maize_soybean,MAR,3.441688358783722 -maize_soybean,MDA,3.948325455188751 -maize_soybean,MEX,0.012745517306029797 +maize_soybean,MAR,7.5 +maize_soybean,MDA,7.5 +maize_soybean,MEX,2.211527109146118 maize_soybean,MKD,7.5 -maize_soybean,MLI,-0.7469700872898102 -maize_soybean,MMR,-0.9117441177368164 +maize_soybean,MLI,-0.5398649573326111 +maize_soybean,MMR,-0.7718805074691772 maize_soybean,MNE,7.5 maize_soybean,MOZ,7.5 -maize_soybean,MWI,3.6040261685848236 -maize_soybean,NGA,-0.720582515001297 -maize_soybean,NIC,-0.3675630474463105 +maize_soybean,MWI,-0.16863681375980377 +maize_soybean,NGA,-0.6313824951648712 +maize_soybean,NIC,1.6180334091186523 maize_soybean,NLD,7.5 -maize_soybean,NPL,-0.5565337538719177 -maize_soybean,PAK,-1.0684007406234741 +maize_soybean,NPL,7.5 +maize_soybean,PAK,-0.4839424192905426 maize_soybean,PER,7.5 maize_soybean,POL,7.5 maize_soybean,PRT,7.5 -maize_soybean,PRY,-0.21764160692691803 -maize_soybean,ROU,0.32373878359794617 +maize_soybean,PRY,0.0 +maize_soybean,ROU,4.643324792385101 maize_soybean,RUS,7.5 -maize_soybean,RWA,-0.34852825105190277 -maize_soybean,SDN,-0.5342399477958679 -maize_soybean,SLV,-0.8975279331207275 +maize_soybean,RWA,-0.2505238503217697 +maize_soybean,SDN,0.17269662022590637 +maize_soybean,SLV,-0.637964516878128 maize_soybean,SRB,7.5 -maize_soybean,SSD,-1.321803331375122 +maize_soybean,SSD,7.5 maize_soybean,SVK,7.5 maize_soybean,SVN,7.5 -maize_soybean,SWZ,-0.8048333525657654 +maize_soybean,SWZ,-0.5656325221061707 maize_soybean,SYR,7.5 -maize_soybean,TCD,7.5 -maize_soybean,TGO,-0.3580051511526108 -maize_soybean,THA,-0.3933115601539612 +maize_soybean,TCD,-0.10343484580516815 +maize_soybean,TGO,-0.2898097485303879 +maize_soybean,THA,-0.1728121116757393 maize_soybean,TJK,7.5 -maize_soybean,TKM,0.9870100617408752 -maize_soybean,TLS,4.11736986041069 +maize_soybean,TKM,4.70890748500824 +maize_soybean,TLS,7.5 maize_soybean,TUR,7.5 -maize_soybean,TWN,0.8484704494476318 -maize_soybean,TZA,-0.3797968029975891 -maize_soybean,UGA,-1.3789831399917603 +maize_soybean,TWN,0.0 +maize_soybean,TZA,-0.18530809879302979 +maize_soybean,UGA,-1.319997251033783 maize_soybean,UKR,7.5 -maize_soybean,URY,-0.47526396811008453 -maize_soybean,USA,-0.5723414123058319 +maize_soybean,URY,-0.08187820017337799 +maize_soybean,USA,-0.049517277628183365 maize_soybean,UZB,7.5 -maize_soybean,VEN,-0.8426925539970398 -maize_soybean,VNM,-0.27706438302993774 -maize_soybean,ZAF,-0.006137027870863676 -maize_soybean,ZMB,-0.5981044173240662 -maize_soybean,ZWE,0.9514963030815125 -rice_maize,AFG,-1.6404048204421997 -rice_maize,AGO,-2.3160706758499146 -rice_maize,ALB,-0.0 +maize_soybean,VEN,-0.6624778509140015 +maize_soybean,VNM,0.09539355337619781 +maize_soybean,ZAF,2.829430341720581 +maize_soybean,ZMB,2.2089239358901978 +maize_soybean,ZWE,1.625107765197754 +rice_maize,AFG,-1.3318929076194763 +rice_maize,AGO,-0.08585424534976482 +rice_maize,ALB,0.0 rice_maize,ARG,7.5 -rice_maize,ARM,-0.0 -rice_maize,AUS,-1.590356171131134 -rice_maize,AZE,-2.83044695854187 -rice_maize,BDI,7.5 -rice_maize,BEN,-1.4021406173706055 -rice_maize,BGD,0.39136016368865967 +rice_maize,ARM,0.0 +rice_maize,AUS,7.5 +rice_maize,AZE,7.5 +rice_maize,BDI,-7.5 +rice_maize,BEN,-1.4199044704437256 +rice_maize,BGD,0.740846574306488 rice_maize,BGR,7.5 -rice_maize,BLZ,-2.3535351753234863 +rice_maize,BLZ,-2.2126665115356445 rice_maize,BOL,7.5 -rice_maize,BRA,0.5185031741857529 -rice_maize,BTN,0.26831182837486267 -rice_maize,BWA,-0.0 -rice_maize,CAF,-2.315131902694702 -rice_maize,CAN,-0.0 -rice_maize,CHL,-0.9636813998222351 -rice_maize,CHN,-2.491113066673279 -rice_maize,CIV,-0.7100791335105896 -rice_maize,CMR,-1.6961027383804321 -rice_maize,COD,-2.435609221458435 -rice_maize,COL,-1.5935273170471191 -rice_maize,COM,-2.337308883666992 -rice_maize,CUB,-1.5886424779891968 -rice_maize,DOM,2.4203288555145264 +rice_maize,BRA,0.3613024950027466 +rice_maize,BTN,0.7664981931447983 +rice_maize,BWA,0.0 +rice_maize,CAF,-2.2831876277923584 +rice_maize,CAN,0.0 +rice_maize,CHL,-0.8489557802677155 +rice_maize,CHN,-2.4107013940811157 +rice_maize,CIV,-0.6918438673019409 +rice_maize,CMR,-1.4365019798278809 +rice_maize,COD,-2.40900456905365 +rice_maize,COL,-1.4636902809143066 +rice_maize,COM,-2.2929179668426514 +rice_maize,CUB,7.5 +rice_maize,DOM,-1.4875823259353638 rice_maize,DZA,7.5 -rice_maize,ECU,-0.7153042256832123 -rice_maize,EGY,-2.13281512260437 +rice_maize,ECU,-0.15525095164775848 +rice_maize,EGY,-1.4274488687515259 rice_maize,ERI,7.5 -rice_maize,ESP,-1.1463301181793213 +rice_maize,ESP,-0.6584328413009644 rice_maize,ETH,7.5 rice_maize,FRA,7.5 rice_maize,GEO,7.5 -rice_maize,GHA,-0.728587806224823 -rice_maize,GIN,-0.4560774564743042 -rice_maize,GNB,7.5 -rice_maize,GRC,-1.3117156028747559 -rice_maize,GTM,-2.1169002056121826 -rice_maize,HND,-1.633684515953064 -rice_maize,HTI,-2.1469744443893433 +rice_maize,GHA,-0.7420739531517029 +rice_maize,GIN,-0.6635527610778809 +rice_maize,GNB,-7.5 +rice_maize,GRC,-1.0973598062992096 +rice_maize,GTM,2.292964458465576 +rice_maize,HND,-1.5444250106811523 +rice_maize,HTI,-2.014344811439514 rice_maize,HUN,7.5 -rice_maize,IDN,-0.3467683792114258 -rice_maize,IND,-1.1606595516204834 -rice_maize,IRN,-4.914326429367065 -rice_maize,IRQ,-3.0447577238082886 -rice_maize,ITA,-2.2469688057899475 -rice_maize,KAZ,0.25109851360321045 -rice_maize,KEN,-1.8319658041000366 -rice_maize,KGZ,-3.0051995515823364 -rice_maize,KOR,-6.520980596542358 -rice_maize,LAO,-1.216350793838501 -rice_maize,LKA,-1.0294817984104156 -rice_maize,LSO,-0.0 -rice_maize,MAR,-1.6206347942352295 +rice_maize,IDN,0.0 +rice_maize,IND,0.05001901835203171 +rice_maize,IRN,-3.5843350887298584 +rice_maize,IRQ,-1.9353299736976624 +rice_maize,ITA,-2.7837042808532715 +rice_maize,KAZ,0.8348073363304138 +rice_maize,KEN,-3.0181379318237305 +rice_maize,KGZ,-2.7912529706954956 +rice_maize,KOR,-6.391165018081665 +rice_maize,LAO,-0.9558684229850769 +rice_maize,LKA,-0.9154897928237915 +rice_maize,LSO,0.0 +rice_maize,MAR,-0.029556401073932648 rice_maize,MDA,7.5 -rice_maize,MDG,-1.1849598288536072 -rice_maize,MEX,-0.2136644870042801 +rice_maize,MDG,-1.0697451829910278 +rice_maize,MEX,4.226221084594727 rice_maize,MKD,7.5 -rice_maize,MLI,-0.3211269825696945 -rice_maize,MMR,-1.6517590284347534 -rice_maize,MOZ,-0.46825529634952545 +rice_maize,MLI,-3.798837773501873 +rice_maize,MMR,-1.661135196685791 +rice_maize,MOZ,-0.5322675704956055 rice_maize,MUS,7.5 -rice_maize,MWI,-1.1541817784309387 +rice_maize,MWI,-4.355122685432434 rice_maize,NAM,7.5 -rice_maize,NGA,-0.4805299639701843 -rice_maize,NIC,-0.0 -rice_maize,NPL,-0.3239421471953392 -rice_maize,PAK,-1.193999707698822 -rice_maize,PER,-3.7227985858917236 -rice_maize,PHL,-0.5532599687576294 +rice_maize,NGA,-0.42868663370609283 +rice_maize,NIC,-0.6727326214313507 +rice_maize,NPL,-0.8327913284301758 +rice_maize,PAK,-0.17984464764595032 +rice_maize,PER,-4.246907711029053 +rice_maize,PHL,-0.6145907342433929 rice_maize,PRI,7.5 -rice_maize,PRT,3.333097815513611 -rice_maize,PRY,-0.42590242624282837 -rice_maize,ROU,7.5 +rice_maize,PRT,3.393346428871155 +rice_maize,PRY,-0.28299304842948914 +rice_maize,ROU,3.283622592687607 rice_maize,RUS,7.5 -rice_maize,RWA,-1.792549729347229 +rice_maize,RWA,-1.6551365852355957 rice_maize,SAU,7.5 -rice_maize,SDN,2.7841517329216003 -rice_maize,SEN,-0.42498359084129333 -rice_maize,SLE,7.5 -rice_maize,SLV,-2.752364754676819 -rice_maize,SOM,-0.06363929528743029 -rice_maize,SSD,-2.3028550148010254 -rice_maize,SVK,-0.0 -rice_maize,SVN,-0.0 -rice_maize,SWZ,-1.83369779586792 -rice_maize,SYR,7.5 -rice_maize,TCD,-0.514793187379837 -rice_maize,TGO,-0.19198282808065414 -rice_maize,THA,-0.6313942074775696 -rice_maize,TJK,-4.319259166717529 -rice_maize,TKM,-0.3873455673456192 -rice_maize,TLS,-1.235890656709671 -rice_maize,TUN,-0.0 -rice_maize,TUR,-3.054778575897217 -rice_maize,TZA,-0.0 -rice_maize,UGA,-2.1543128490448 +rice_maize,SDN,-2.432253122329712 +rice_maize,SEN,7.5 +rice_maize,SLE,3.665683776140213 +rice_maize,SLV,-3.3491971492767334 +rice_maize,SOM,-7.033926963806152 +rice_maize,SSD,-2.2848644256591797 +rice_maize,SVK,0.0 +rice_maize,SVN,0.0 +rice_maize,SWZ,7.5 +rice_maize,SYR,0.0741366297006607 +rice_maize,TCD,-0.23063230514526367 +rice_maize,TGO,-0.17723476886749268 +rice_maize,THA,-0.6269423365592957 +rice_maize,TJK,-2.3703906536102295 +rice_maize,TKM,-0.8995541632175446 +rice_maize,TLS,-1.247755527496338 +rice_maize,TUN,0.0 +rice_maize,TUR,-2.077794075012207 +rice_maize,TZA,0.0 +rice_maize,UGA,-2.147894024848938 rice_maize,UKR,7.5 -rice_maize,URY,0.002652596915140748 +rice_maize,URY,0.1967543587088585 rice_maize,USA,7.5 -rice_maize,UZB,-6.202516078948975 -rice_maize,VEN,-1.1330994963645935 -rice_maize,VNM,-0.779794454574585 -rice_maize,YEM,-0.0 -rice_maize,ZAF,-0.7060503959655762 -rice_maize,ZMB,-1.0463234186172485 +rice_maize,UZB,-5.961887836456299 +rice_maize,VEN,-0.756818950176239 +rice_maize,VNM,-0.7148265242576599 +rice_maize,YEM,0.0 +rice_maize,ZAF,4.899500370025635 +rice_maize,ZMB,-7.5 rice_maize,ZWE,7.5 -rice_wheat,AFG,-1.304873526096344 -rice_wheat,AGO,7.5 -rice_wheat,ALB,-0.0 +rice_wheat,AFG,-0.9810315072536469 +rice_wheat,AGO,4.118537247180939 +rice_wheat,ALB,0.0 rice_wheat,ARG,7.5 -rice_wheat,ARM,-0.0 -rice_wheat,AUS,-1.6019264459609985 -rice_wheat,AZE,-2.757871150970459 -rice_wheat,BGD,-0.8078254461288452 -rice_wheat,BGR,3.10767525434494 +rice_wheat,ARM,0.0 +rice_wheat,AUS,-7.5 +rice_wheat,AZE,7.5 +rice_wheat,BGD,-0.22736575454473495 +rice_wheat,BGR,7.5 rice_wheat,BOL,7.5 -rice_wheat,BRA,-0.05252578854560852 -rice_wheat,BTN,0.11618829518556595 -rice_wheat,BWA,-0.0 -rice_wheat,CAN,-0.0 -rice_wheat,CHL,-0.6050255596637726 -rice_wheat,CHN,-2.6085113286972046 -rice_wheat,CMR,7.5 -rice_wheat,COD,-2.166265606880188 +rice_wheat,BRA,7.5 +rice_wheat,BTN,7.5 +rice_wheat,BWA,0.0 +rice_wheat,CAN,0.0 +rice_wheat,CHL,-0.5391653776168823 +rice_wheat,CHN,-2.510178327560425 +rice_wheat,CMR,-1.4577618837356567 +rice_wheat,COD,-2.1262660026550293 rice_wheat,DZA,7.5 -rice_wheat,ECU,-1.2411775588989258 -rice_wheat,EGY,-1.6681876182556152 -rice_wheat,ESP,-0.6500231623649597 -rice_wheat,ETH,-2.4989004135131836 +rice_wheat,ECU,-1.2690553665161133 +rice_wheat,EGY,-0.8356800675392151 +rice_wheat,ESP,-0.4351092576980591 +rice_wheat,ETH,7.5 rice_wheat,FRA,7.5 rice_wheat,GEO,7.5 -rice_wheat,GRC,-0.660244032740593 -rice_wheat,GTM,7.5 -rice_wheat,HND,-2.5053634643554688 +rice_wheat,GRC,-0.21108395606279373 +rice_wheat,GTM,0.0 +rice_wheat,HND,-1.8997968435287476 rice_wheat,HUN,7.5 -rice_wheat,IND,-0.8473137617111206 -rice_wheat,IRN,-3.968905210494995 -rice_wheat,IRQ,-2.386203169822693 -rice_wheat,ITA,-1.9720418453216553 -rice_wheat,JPN,-0.11923962086439133 -rice_wheat,KAZ,-0.22488220036029816 -rice_wheat,KGZ,-3.0229909420013428 -rice_wheat,KOR,-6.342390537261963 -rice_wheat,LAO,7.5 -rice_wheat,LSO,-0.0 -rice_wheat,MAR,-1.8250325322151184 +rice_wheat,IND,1.5363070368766785 +rice_wheat,IRN,-3.057201385498047 +rice_wheat,IRQ,-1.7608040571212769 +rice_wheat,ITA,-2.7929333448410034 +rice_wheat,JPN,-1.3070594668388367 +rice_wheat,KAZ,0.025521814823150635 +rice_wheat,KGZ,-2.949662446975708 +rice_wheat,KOR,7.5 +rice_wheat,LAO,0.0 +rice_wheat,LSO,0.0 +rice_wheat,MAR,-0.44990822672843933 rice_wheat,MDA,7.5 -rice_wheat,MDG,-0.8599275946617126 +rice_wheat,MDG,-0.778467059135437 rice_wheat,MEX,7.5 rice_wheat,MKD,7.5 -rice_wheat,MLI,-0.18869413435459137 -rice_wheat,MMR,-1.868966281414032 +rice_wheat,MLI,0.17528533935546875 +rice_wheat,MMR,-1.8721712827682495 rice_wheat,MOZ,7.5 rice_wheat,MRT,7.5 rice_wheat,MWI,7.5 rice_wheat,NAM,7.5 -rice_wheat,NER,7.5 -rice_wheat,NGA,-0.6789183020591736 -rice_wheat,NPL,-0.46460652351379395 -rice_wheat,PAK,-1.3554804921150208 -rice_wheat,PRT,-0.8906027674674988 -rice_wheat,PRY,-0.4173676371574402 +rice_wheat,NER,-7.5 +rice_wheat,NGA,-7.5 +rice_wheat,NPL,3.39884352684021 +rice_wheat,PAK,-0.5648182034492493 +rice_wheat,PRT,-0.8216572403907776 +rice_wheat,PRY,-0.3582548648118973 rice_wheat,ROU,7.5 rice_wheat,RUS,7.5 -rice_wheat,RWA,-1.9549353122711182 -rice_wheat,SDN,-1.3346004486083984 -rice_wheat,SOM,2.7530112862586975 -rice_wheat,SVK,-0.0 -rice_wheat,SVN,-0.0 +rice_wheat,RWA,-1.8426926136016846 +rice_wheat,SDN,-7.5 +rice_wheat,SOM,-7.5 +rice_wheat,SVK,0.0 +rice_wheat,SVN,0.0 rice_wheat,SWZ,7.5 -rice_wheat,SYR,7.5 -rice_wheat,TCD,-0.8246639966964722 +rice_wheat,SYR,-0.2721511125564575 +rice_wheat,TCD,-6.479332208633423 rice_wheat,THA,7.5 -rice_wheat,TJK,-3.2520639896392822 -rice_wheat,TKM,-0.7869112193584442 -rice_wheat,TUN,-0.0 -rice_wheat,TUR,-2.744300127029419 -rice_wheat,TZA,-0.3317982703447342 -rice_wheat,UGA,-1.9048722982406616 +rice_wheat,TJK,-2.529783070087433 +rice_wheat,TKM,-1.085111916065216 +rice_wheat,TUN,0.0 +rice_wheat,TUR,-2.02057147026062 +rice_wheat,TZA,-0.14180435985326767 +rice_wheat,UGA,-1.8810851573944092 rice_wheat,UKR,7.5 -rice_wheat,URY,7.5 +rice_wheat,URY,0.2689153738319874 rice_wheat,USA,7.5 -rice_wheat,UZB,-5.182010650634766 +rice_wheat,UZB,-5.035796880722046 rice_wheat,VEN,7.5 -rice_wheat,VNM,-7.5 -rice_wheat,ZAF,-1.2216403484344482 -rice_wheat,ZMB,-0.500923752784729 +rice_wheat,VNM,-1.0420973896980286 +rice_wheat,ZAF,4.236782073974609 +rice_wheat,ZMB,-7.5 rice_wheat,ZWE,7.5 triple_rice,AFG,7.5 triple_rice,AGO,7.5 @@ -497,13 +496,13 @@ triple_rice,AUS,7.5 triple_rice,BDI,7.5 triple_rice,BEN,7.5 triple_rice,BFA,7.5 -triple_rice,BGD,-1.1383903622627258 +triple_rice,BGD,0.42879411578178406 triple_rice,BLZ,7.5 triple_rice,BOL,7.5 triple_rice,BRA,7.5 triple_rice,BRN,7.5 -triple_rice,BTN,-2.4064918756484985 -triple_rice,BWA,-0.0 +triple_rice,BTN,-2.9541730880737305 +triple_rice,BWA,0.0 triple_rice,CHN,7.5 triple_rice,CIV,7.5 triple_rice,CMR,7.5 @@ -528,17 +527,17 @@ triple_rice,GNB,7.5 triple_rice,GNQ,7.5 triple_rice,GRC,7.5 triple_rice,GTM,7.5 -triple_rice,GUF,7.5 +triple_rice,GUF,0.0 triple_rice,GUY,7.5 triple_rice,HND,7.5 triple_rice,HTI,7.5 -triple_rice,IDN,2.271872878074646 +triple_rice,IDN,7.5 triple_rice,IND,7.5 triple_rice,IRN,7.5 triple_rice,IRQ,7.5 triple_rice,ITA,7.5 triple_rice,KEN,7.5 -triple_rice,KHM,-3.3303961753845215 +triple_rice,KHM,-7.5 triple_rice,LAO,7.5 triple_rice,LBR,7.5 triple_rice,LKA,7.5 @@ -568,7 +567,7 @@ triple_rice,RWA,7.5 triple_rice,SAU,7.5 triple_rice,SDN,7.5 triple_rice,SEN,7.5 -triple_rice,SLB,-4.246044874191284 +triple_rice,SLB,-4.206660032272339 triple_rice,SLE,7.5 triple_rice,SLV,7.5 triple_rice,SOM,7.5 @@ -578,7 +577,7 @@ triple_rice,SWZ,7.5 triple_rice,TCD,7.5 triple_rice,TGO,7.5 triple_rice,THA,7.5 -triple_rice,TLS,1.3693647384643555 +triple_rice,TLS,7.5 triple_rice,TTO,7.5 triple_rice,TUR,7.5 triple_rice,TWN,7.5 @@ -588,203 +587,203 @@ triple_rice,URY,7.5 triple_rice,USA,7.5 triple_rice,VEN,7.5 triple_rice,VNM,7.5 -triple_rice,YEM,-0.0 +triple_rice,YEM,0.0 triple_rice,ZAF,7.5 triple_rice,ZMB,7.5 triple_rice,ZWE,7.5 -wheat_maize,AFG,-1.059177041053772 -wheat_maize,AGO,-1.4748095273971558 +wheat_maize,AFG,-0.8690876364707947 +wheat_maize,AGO,0.059513647109270096 wheat_maize,ALB,7.5 wheat_maize,ARE,7.5 wheat_maize,ARG,7.5 -wheat_maize,ARM,0.19232968986034393 -wheat_maize,AUS,-0.15932096540927887 -wheat_maize,AUT,-0.005322609096765518 -wheat_maize,AZE,-0.29749178886413574 -wheat_maize,BDI,-1.041087031364441 +wheat_maize,ARM,0.2058352828025818 +wheat_maize,AUS,0.9481626749038696 +wheat_maize,AUT,7.5 +wheat_maize,AZE,0.2792627140879631 +wheat_maize,BDI,-1.0171025395393372 wheat_maize,BEL,7.5 -wheat_maize,BGD,0.027580395340919495 -wheat_maize,BGR,0.03009234555065632 +wheat_maize,BGD,0.06772636622190475 +wheat_maize,BGR,7.5 wheat_maize,BIH,7.5 wheat_maize,BOL,7.5 -wheat_maize,BRA,0.10624170303344727 -wheat_maize,BTN,1.5968497395515442 -wheat_maize,BWA,3.844961404800415 +wheat_maize,BRA,0.03994758613407612 +wheat_maize,BTN,1.6954857110977173 +wheat_maize,BWA,3.9140300303697586 wheat_maize,CAN,7.5 -wheat_maize,CHE,2.516054391860962 -wheat_maize,CHL,-0.93019700050354 -wheat_maize,CHN,-2.075305938720703 -wheat_maize,CMR,-0.5750364661216736 -wheat_maize,COD,-1.4551146030426025 -wheat_maize,COL,-0.6131998300552368 -wheat_maize,CZE,3.7804106660187244 +wheat_maize,CHE,7.5 +wheat_maize,CHL,-0.5218610167503357 +wheat_maize,CHN,-1.9780700206756592 +wheat_maize,CMR,-0.5570536255836487 +wheat_maize,COD,-1.4385384321212769 +wheat_maize,COL,-0.6037926077842712 +wheat_maize,CZE,7.5 wheat_maize,DEU,7.5 wheat_maize,DZA,7.5 -wheat_maize,ECU,-1.0126038789749146 -wheat_maize,EGY,-1.5646820068359375 -wheat_maize,ERI,-1.3822872042655945 -wheat_maize,ESP,-0.7554497122764587 -wheat_maize,ETH,-0.32751739025115967 +wheat_maize,ECU,-1.0156046152114868 +wheat_maize,EGY,-0.5802799463272095 +wheat_maize,ERI,-1.3752935528755188 +wheat_maize,ESP,0.25326231122016907 +wheat_maize,ETH,-0.21749646216630936 wheat_maize,FRA,7.5 -wheat_maize,GEO,0.09364751726388931 -wheat_maize,GRC,3.5302646458148956 -wheat_maize,GTM,3.372650295495987 -wheat_maize,HND,-1.0760682821273804 +wheat_maize,GEO,-0.05994178354740143 +wheat_maize,GRC,-0.5499691963195801 +wheat_maize,GTM,6.511103868484497 +wheat_maize,HND,-1.0363565683364868 wheat_maize,HRV,7.5 wheat_maize,HUN,7.5 -wheat_maize,IND,-0.3966492712497711 -wheat_maize,IRN,-1.7036104202270508 -wheat_maize,IRQ,-1.5998313426971436 -wheat_maize,ISR,-6.92481803894043 -wheat_maize,ITA,-0.7050333023071289 -wheat_maize,JOR,0.6081819571554661 -wheat_maize,KAZ,0.13862000405788422 -wheat_maize,KEN,-0.655785322189331 -wheat_maize,KGZ,-0.36566704511642456 -wheat_maize,KOR,0.012307032011449337 -wheat_maize,LAO,3.3776745796203613 -wheat_maize,LBN,-0.38848312199115753 +wheat_maize,IND,-0.4139341861009598 +wheat_maize,IRN,-0.8348695039749146 +wheat_maize,IRQ,-0.9702644348144531 +wheat_maize,ISR,-6.798701763153076 +wheat_maize,ITA,0.3427973538637161 +wheat_maize,JOR,0.9669528603553772 +wheat_maize,KAZ,0.5049141943454742 +wheat_maize,KEN,-0.658938467502594 +wheat_maize,KGZ,0.002808114979416132 +wheat_maize,KOR,0.18171603977680206 +wheat_maize,LAO,0.0 +wheat_maize,LBN,-0.3543650656938553 wheat_maize,LBY,7.5 -wheat_maize,LSO,-0.07805822603404522 +wheat_maize,LSO,0.15696971118450165 wheat_maize,LUX,7.5 -wheat_maize,MAR,-0.30309001356363297 -wheat_maize,MDA,3.8919558972120285 -wheat_maize,MDG,-1.0458958148956299 -wheat_maize,MEX,-0.8088407814502716 +wheat_maize,MAR,-0.12357106804847717 +wheat_maize,MDA,7.5 +wheat_maize,MDG,-0.9451302886009216 +wheat_maize,MEX,3.6584975719451904 wheat_maize,MKD,7.5 -wheat_maize,MMR,-0.9253009855747223 +wheat_maize,MMR,-0.9334716796875 wheat_maize,MNE,7.5 -wheat_maize,MOZ,7.5 +wheat_maize,MOZ,-0.14417719841003418 wheat_maize,MWI,7.5 -wheat_maize,NAM,-2.086304545402527 +wheat_maize,NAM,-5.4132819175720215 wheat_maize,NIC,7.5 -wheat_maize,NLD,3.8913015574216843 -wheat_maize,NPL,-0.3817053288221359 -wheat_maize,NZL,-0.8886459320783615 -wheat_maize,OMN,-4.1484681367874146 -wheat_maize,PAK,-0.43155471980571747 -wheat_maize,PER,-0.6361838579177856 +wheat_maize,NLD,7.5 +wheat_maize,NPL,0.5423147678375244 +wheat_maize,NZL,-0.7800803184509277 +wheat_maize,OMN,-5.889230728149414 +wheat_maize,PAK,0.21099938452243805 +wheat_maize,PER,-1.0934141874313354 wheat_maize,POL,7.5 -wheat_maize,PRT,0.12055753171443939 -wheat_maize,PRY,-0.04922150820493698 -wheat_maize,PSE,1.644733488559723 -wheat_maize,ROU,0.03139835596084595 +wheat_maize,PRT,7.5 +wheat_maize,PRY,-0.10249026119709015 +wheat_maize,PSE,2.3718225955963135 +wheat_maize,ROU,7.5 wheat_maize,RUS,7.5 -wheat_maize,RWA,-0.43893852829933167 -wheat_maize,SAU,-5.622318267822266 +wheat_maize,RWA,-0.4008308947086334 +wheat_maize,SAU,-4.490570783615112 wheat_maize,SLV,0.0 -wheat_maize,SOM,0.2279478758573532 +wheat_maize,SOM,-7.5 wheat_maize,SRB,7.5 wheat_maize,SSD,7.5 wheat_maize,SVK,7.5 wheat_maize,SVN,7.5 -wheat_maize,SWZ,-1.0872985124588013 -wheat_maize,SYR,-0.9373708069324493 -wheat_maize,THA,3.39390966296196 -wheat_maize,TJK,-1.2326552867889404 -wheat_maize,TKM,-0.6474591195583344 -wheat_maize,TUN,-0.0 -wheat_maize,TUR,0.3453899919986725 -wheat_maize,TWN,0.3946537971496582 -wheat_maize,TZA,-0.24894577264785767 -wheat_maize,UGA,-1.1047067642211914 +wheat_maize,SWZ,-1.1068166494369507 +wheat_maize,SYR,-0.2270970493555069 +wheat_maize,THA,7.5 +wheat_maize,TJK,0.031677063554525375 +wheat_maize,TKM,-0.7436801791191101 +wheat_maize,TUN,0.0 +wheat_maize,TUR,0.6552011966705322 +wheat_maize,TWN,0.6043800711631775 +wheat_maize,TZA,-0.1421385556459427 +wheat_maize,UGA,-1.0860267877578735 wheat_maize,UKR,7.5 -wheat_maize,URY,-0.24283335357904434 -wheat_maize,USA,-0.24570778757333755 -wheat_maize,UZB,-2.2322025299072266 +wheat_maize,URY,0.02791692316532135 +wheat_maize,USA,0.012668413110077381 +wheat_maize,UZB,-1.54970121383667 wheat_maize,VEN,7.5 -wheat_maize,VNM,-7.5 -wheat_maize,YEM,-1.2792218923568726 -wheat_maize,ZAF,-0.047328826040029526 -wheat_maize,ZMB,0.6400972902774811 -wheat_maize,ZWE,7.117492198944092 -wheat_soybean,AFG,-0.21164609491825104 -wheat_soybean,AGO,-1.2595300078392029 +wheat_maize,VNM,0.15827590227127075 +wheat_maize,YEM,-7.5 +wheat_maize,ZAF,4.614251613616943 +wheat_maize,ZMB,-7.169391393661499 +wheat_maize,ZWE,6.538937091827393 +wheat_soybean,AFG,0.39539945870637894 +wheat_soybean,AGO,-0.020095903397304937 wheat_soybean,ALB,7.5 -wheat_soybean,ARG,-0.6065599322319031 +wheat_soybean,ARG,7.5 wheat_soybean,ARM,7.5 -wheat_soybean,AUS,0.6463535279035568 -wheat_soybean,AUT,-0.01924823597073555 -wheat_soybean,AZE,4.210933417081833 -wheat_soybean,BDI,-1.4682312607765198 +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,-0.5767140686511993 +wheat_soybean,BGD,3.67583816498518 wheat_soybean,BGR,7.5 wheat_soybean,BIH,7.5 wheat_soybean,BOL,7.5 -wheat_soybean,BRA,-0.39443439245224 +wheat_soybean,BRA,0.13935944437980652 wheat_soybean,BTN,7.5 wheat_soybean,BWA,7.5 wheat_soybean,CAN,7.5 -wheat_soybean,CHE,-0.8971370458602905 -wheat_soybean,CHN,-1.3253093957901 -wheat_soybean,CMR,-0.43673020601272583 -wheat_soybean,COD,-1.2637889981269836 +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,-0.6838064789772034 -wheat_soybean,EGY,-1.2459351420402527 -wheat_soybean,ESP,0.2736721336841583 -wheat_soybean,ETH,-0.8871381282806396 +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,-0.05915307253599167 -wheat_soybean,GRC,-0.2598990797996521 -wheat_soybean,GTM,3.78983361646533 -wheat_soybean,HND,-0.959020584821701 +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,-0.40162912011146545 -wheat_soybean,IRN,-0.473108172416687 -wheat_soybean,IRQ,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,1.6746549606323242 +wheat_soybean,JOR,0.0 +wheat_soybean,JPN,1.5681341886520386 wheat_soybean,KAZ,7.5 -wheat_soybean,KEN,-0.6842177510261536 -wheat_soybean,KGZ,-0.27548111975193024 -wheat_soybean,KOR,3.5200543254613876 -wheat_soybean,LAO,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,-1.4421567916870117 -wheat_soybean,MDA,3.895781621336937 -wheat_soybean,MEX,-0.34212958812713623 +wheat_soybean,MAR,-0.4596904516220093 +wheat_soybean,MDA,7.5 +wheat_soybean,MEX,7.5 wheat_soybean,MKD,7.5 -wheat_soybean,MMR,-1.0974624752998352 +wheat_soybean,MMR,-0.2157789170742035 wheat_soybean,MNE,7.5 wheat_soybean,MOZ,7.5 wheat_soybean,MWI,7.5 -wheat_soybean,NGA,-0.6289743781089783 +wheat_soybean,NGA,7.5 wheat_soybean,NLD,7.5 -wheat_soybean,NPL,3.5407437086105347 -wheat_soybean,PAK,-0.4830322265625 -wheat_soybean,PER,-0.46757999062538147 +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,-0.12475436180830002 +wheat_soybean,PRY,0.1659405753016472 wheat_soybean,ROU,7.5 wheat_soybean,RUS,7.5 -wheat_soybean,RWA,-0.45277969539165497 +wheat_soybean,RWA,-0.39666540920734406 wheat_soybean,SRB,7.5 wheat_soybean,SVK,7.5 -wheat_soybean,SVN,3.762704192660749 -wheat_soybean,SWZ,-0.5005633234977722 +wheat_soybean,SVN,7.5 +wheat_soybean,SWZ,-0.33505555987358093 wheat_soybean,SYR,7.5 -wheat_soybean,THA,-0.7992278635501862 +wheat_soybean,THA,7.5 wheat_soybean,TJK,7.5 -wheat_soybean,TKM,0.47631368041038513 +wheat_soybean,TKM,0.8133601248264313 wheat_soybean,TUR,7.5 -wheat_soybean,TWN,-0.3602020740509033 -wheat_soybean,TZA,-0.46302691102027893 -wheat_soybean,UGA,-1.1337865591049194 +wheat_soybean,TWN,-0.14883071184158325 +wheat_soybean,TZA,-0.2885752320289612 +wheat_soybean,UGA,-1.077957034111023 wheat_soybean,UKR,7.5 -wheat_soybean,URY,-0.31937259435653687 -wheat_soybean,USA,-0.3088880181312561 +wheat_soybean,URY,-0.003528994508087635 +wheat_soybean,USA,0.34375500679016113 wheat_soybean,UZB,7.5 -wheat_soybean,VNM,-7.5 -wheat_soybean,ZAF,-0.254607193171978 -wheat_soybean,ZMB,-0.18164435029029846 -wheat_soybean,ZWE,4.234316349029541 +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 index 75ad5ce1..b9cb84d6 100644 --- a/data/curated/mirca_os_crop_mapping.csv +++ b/data/curated/mirca_os_crop_mapping.csv @@ -5,22 +5,24 @@ # 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 GLADE crop-sequence combinations. +# 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}_2020_{ir,rf}_v2.tif). +# 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 -# combination discovery (its extra-cycle area stays on the bulk +# 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/mirca_crops.py). +# exhaustive and validated fail-fast (workflow/validation/multi_cropping.py). mirca_crop,glade_crop,note Barley,barley, -Cassava,cassava,long-duration root crop; retained but GAEZ season feasibility usually excludes it from sequences +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, 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 b4be2c5c..ac711bc2 100644 --- a/docs/calibration.rst +++ b/docs/calibration.rst @@ -150,6 +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 @@ -171,6 +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 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/crop_production.rst b/docs/crop_production.rst index 7a33eb30..014d617c 100644 --- a/docs/crop_production.rst +++ b/docs/crop_production.rst @@ -474,42 +474,65 @@ Multiple Cropping ----------------- Many production systems plant two or more sequential crops on the same parcel. -Multiple cropping is anchored to an **observed 2020 baseline** derived from -MIRCA-OS v2 (see :doc:`data_sources`), so the model starts from today's +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 extra cropping cycles as unanchored potential. It is built in two -stages. +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. -**Stage 1 (config-independent derivation).** The Snakemake checkpoint +**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 -- harvested minus the AEI-capped -physical footprint (``ir_maxmonth + rf_maxmonth``) -- to crop-sequence -combinations on the global 5-arcmin grid, writing to -``processing/shared/multi_cropping/``: the discovered ``combinations.yaml``, -per-combination baseline rasters, and a residual raster for the bulk land -correction. The effective combination set the model builds with is the derived -set merged over the static ``multiple_cropping`` config section (derived entries -take precedence; combinations referencing unmodeled crops are dropped). - -A combination is a candidate in a cell where **both crops are observed in MIRCA** +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; GAEZ enters only for suitability, yields and water -requirements. - -**Stage 2 (build-time aggregation).** The preprocessing rule +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 rule -``aggregate_multicropping_baseline`` aggregates the Stage-1 rasters to the -observed baseline on the active config's regions. The three tables land in -``processing/{name}/multi_cropping/``: ``eligible_area.csv`` (potential area + -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). +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: @@ -541,13 +564,25 @@ rainfed or irrigated multi-output link (carrier ``crop_production_multi``) that: 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 cycle. Because the single-crop ``crop_production`` -baselines are FAOSTAT *harvested* area (already counting every cycle), the -harvested cycles now carried by the multi links are subtracted from the -corresponding single-crop baselines (``m_k`` times the multi anchor for crop -``k``), with country-level redistribution to preserve national totals. The -crop-agnostic ``multi_cropping_land_correction`` generator absorbs only the -residual, unattributed extra-cycle area. +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 09d31253..16ea32bd 100644 --- a/docs/data_sources.rst +++ b/docs/data_sources.rst @@ -194,13 +194,13 @@ 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 uses the 2020 annual harvested-area grids (which count every harvested cycle) and the maximum-monthly-cropped-area grids (the AEI-capped physical field footprint) to derive the observed multiple-cropping baseline, and the 2020 subcrop monthly growing-area grids (``Rice1/2/3``) to detect repeated same-crop cycles. +**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 (model anchors to 2020) + * Temporal: 2000, 2005, 2010, 2015, 2020 (the workflow currently retrieves 2010, 2015, and 2020) **Access**: HydroShare resource ``e4582ca0042148338bb5e0148b749ed6`` (https://www.hydroshare.org/resource/e4582ca0042148338bb5e0148b749ed6/). @@ -208,9 +208,9 @@ MIRCA-OS v2 **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 with ``bsdtar`` (``extract_mirca_os_*`` rules). Note the v2 archive ships only ``ir``/``rf`` footprint layers despite its README mentioning a ``tot`` layer, so the combined footprint is taken as ``ir + rf`` (disjoint land, single-count). +**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`, Stage 1), derived by the ``derive_mirca_multicropping`` checkpoint. The MIRCA-OS-to-GLADE crop concordance is in ``data/curated/mirca_os_crop_mapping.csv``. +**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 82c9a1cc..cedab86e 100644 --- a/docs/land_use.rst +++ b/docs/land_use.rst @@ -367,5 +367,5 @@ 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 (e.g. mixed -irrigated/rainfed rotations, or crops GLADE does not model). +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/workflow.rst b/docs/workflow.rst index 491ad28e..14dc896d 100644 --- a/docs/workflow.rst +++ b/docs/workflow.rst @@ -78,24 +78,18 @@ Data Preparation Rules * **Script**: ``workflow/scripts/build_crop_yields.py`` * **Purpose**: Aggregate yields by (region, class) for each crop -**derive_mirca_multicropping** (checkpoint) - * **Input**: MIRCA-OS 2020 annual harvested-area and footprint grids, the rice subcrop monthly grids, the GAEZ RES01 multiple-cropping zone rasters, and the crop concordance - * **Output**: ``processing/shared/multi_cropping/`` -- ``combinations.yaml`` (the discovered combination set), ``baseline/{combination}_{ws}.tif`` (observed physical link area per combination), ``residual_multicrop.tif`` (unattributed extra-cycle area, diagnostic) +**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**: Config-independent Stage-1 derivation of the observed multiple-cropping baseline from MIRCA-OS 2020. A checkpoint: the discovered combination set is merged over ``config.multiple_cropping`` to form the effective set, which determines downstream rule inputs + * **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 derived 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 the effective combination set + * **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 and per-crop suitability/yield/water validity, aggregate eligible potential hectares to regions/resource classes, and compute per-cycle yields -**aggregate_multicropping_baseline** - * **Input**: The Stage-1 baseline rasters for the effective combination set, resource classes, regions - * **Output**: ``processing/{name}/multi_cropping/baseline_area.csv`` - * **Script**: ``workflow/scripts/aggregate_multicropping_baseline.py`` - * **Purpose**: Aggregate the observed multi-cropping baseline to the active config's regions and resource classes; anchors ``crop_production_multi`` links - **build_grassland_yields** * **Input**: ISIMIP grassland yield NetCDF, resource classes, regions * **Output**: ``processing/{name}/grassland_yields.csv`` 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 57d164cb..b8f2954b 100644 --- a/tests/test_crop_costs.py +++ b/tests/test_crop_costs.py @@ -24,7 +24,6 @@ def test_silage_maize_cost_not_zero_with_zero_harvested_area(): "fertilizer:USA", ] ) - yields = pd.DataFrame( { "region": ["regionA"], @@ -65,21 +64,45 @@ def test_silage_maize_cost_not_zero_with_zero_harvested_area(): assert float(links["marginal_cost"].iloc[0]) == pytest.approx(1.0) -def _add_rice_wheat_multi_link(baseline_ha, multi_crop_cost_calibration): +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( [ - "land:cropland:regionA_c0_r", + 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": ["regionA"], + "region": [potential_region], "resource_class": [0], "water_supply": ["r"], "eligible_area_ha": [1_000_000.0], @@ -88,19 +111,19 @@ def _add_rice_wheat_multi_link(baseline_ha, multi_crop_cost_calibration): ) cycle_yields = pd.DataFrame( { - "combination": ["rice_wheat", "rice_wheat"], - "region": ["regionA", "regionA"], - "resource_class": [0, 0], - "water_supply": ["r", "r"], - "cycle_index": [0, 1], - "crop": ["wetland-rice", "wheat"], - "yield_t_per_ha": [2.0, 3.0], + "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": ["rice_wheat"], - "region": ["regionA"], + "combination": [baseline_combination], + "region": [baseline_region], "resource_class": [0], "water_supply": ["r"], "baseline_area_ha": [baseline_ha], @@ -110,7 +133,7 @@ def _add_rice_wheat_multi_link(baseline_ha, multi_crop_cost_calibration): n=n, eligible_area=eligible_area, cycle_yields=cycle_yields, - region_to_country=pd.Series({"regionA": "USA"}), + region_to_country=pd.Series({baseline_region: "USA", potential_region: "USA"}), allowed_countries={"USA"}, crop_costs=pd.Series( { @@ -126,12 +149,52 @@ def _add_rice_wheat_multi_link(baseline_ha, multi_crop_cost_calibration): 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( @@ -202,7 +265,7 @@ def test_multi_cropping_rice_emits_methane_per_cycle(): "region": ["regionA", "regionA"], "resource_class": [0, 0], "water_supply": ["i", "i"], - "cycle_index": [0, 1], + "cycle_index": [1, 2], "crop": ["wetland-rice", "wetland-rice"], "yield_t_per_ha": [2.0, 2.0], } @@ -222,6 +285,7 @@ def test_multi_cropping_rice_emits_methane_per_cycle(): 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 diff --git a/tests/test_integration.py b/tests/test_integration.py index b7633076..3e120d85 100644 --- a/tests/test_integration.py +++ b/tests/test_integration.py @@ -57,8 +57,8 @@ def test_build_solve_analyze(results_dir): results_dir / "analysis" / "scen-default" / "objective_breakdown.parquet" ).exists() - # Multi-cropping integration: the effective (MIRCA-derived) combination set - # restricted to the test config's crops must yield anchored multi links. + # 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")) @@ -85,9 +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 index 5136cc97..09252393 100644 --- a/tests/test_multi_cropping.py +++ b/tests/test_multi_cropping.py @@ -4,37 +4,60 @@ """Unit tests for the MIRCA-OS multi-cropping baseline. -Covers Stage-1 attribution (magnitude allocation, repeated-crop supports, -residual conservation), the per-cycle seasonal water split (T-column magnitude -preservation, distinct-crop period placement, repeated-crop stagger), and the -single-crop baseline reconciliation (cycle-multiplicity reduction, no negatives, -land balance). +Covers baseline attribution, repeated-crop supports, residual conservation, +single-crop baseline reconciliation, and validation-mode pinning. """ -from collections import Counter +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 may carry an ``output_buses`` list; those become bus1, - bus2, ... so reconciliation can count the built crop-output cycles. + 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") @@ -57,6 +80,7 @@ def _network_with_links(rows): "baseline_area_mha", "crop", "combination", + "crop_cycles", "region", "resource_class", "water_supply", @@ -68,7 +92,60 @@ def _network_with_links(rows): return n -# ─── Stage-1 attribution ─────────────────────────────────────────────────── +# 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(): @@ -76,9 +153,12 @@ def test_allocate_conserves_and_caps(): 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]])] - cycles = [2, 3] - areas, residual = allocate(m_total, caps, cycles) - extra = sum((n - 1) * a for n, a in zip(cycles, areas)) + 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) @@ -90,14 +170,34 @@ 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 - cycles = [2, 2] - areas, residual = allocate(m_total, caps, cycles) - extra = sum((n - 1) * a for n, a in zip(cycles, areas)) + 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 @@ -151,19 +251,42 @@ def test_run_derivation_balance_and_residual(): ) m_total = 20.0 + 15.0 - 10.0 # 25 attributed = (2 - 1) * areas[("rice_wheat", "i")][0, 0] - # cap A_max = min(20, 15) = 15; extra cap 15 <= 25 -> attributed 15, residual 10 - assert attributed == pytest.approx(15.0) + # 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) -# ─── Reconciliation multiplicity ─────────────────────────────────────────── +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) -def test_reconciliation_multiplicity_counts(): - """Cycle multiplicity: rice-wheat subtracts X from each; double rice 2X.""" - assert dict(Counter(["wetland-rice", "wheat"])) == {"wetland-rice": 1, "wheat": 1} - assert dict(Counter(["wetland-rice", "wetland-rice"])) == {"wetland-rice": 2} - assert dict(Counter(["wetland-rice"] * 3)) == {"wetland-rice": 3} +# Reconciliation multiplicity def _crop_link(name, crop, baseline, region="r0", cls=1): @@ -174,6 +297,7 @@ def _crop_link(name, crop, baseline, region="r0", cls=1): "baseline_area_mha": baseline, "crop": crop, "combination": "", + "crop_cycles": "", "region": region, "resource_class": cls, "water_supply": "irrigated", @@ -189,6 +313,7 @@ def _multi_link(name, combo, baseline, crops, region="r0", cls=1, country="IND") "baseline_area_mha": baseline, "crop": combo, "combination": combo, + "crop_cycles": json.dumps(crops), "region": region, "resource_class": cls, "water_supply": "irrigated", @@ -207,9 +332,7 @@ def test_reconciliation_reduces_singles_by_multiplicity(): _multi_link("m", "rice_wheat", 3.0, ["wetland-rice", "wheat"]), ] ) - reconcile_single_crop_baselines( - n, {"rice_wheat": {"crops": ["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 @@ -224,9 +347,7 @@ def test_reconciliation_double_rice_subtracts_2x(): _multi_link("m", "double_rice", 3.0, ["wetland-rice", "wetland-rice"]), ] ) - reconcile_single_crop_baselines( - n, {"double_rice": {"crops": ["wetland-rice", "wetland-rice"]}} - ) + reconcile_single_crop_baselines(n) assert n.links.static["baseline_area_mha"]["rice"] == pytest.approx(4.0) # 10 - 6 @@ -243,9 +364,7 @@ def test_reconciliation_redistributes_over_subtraction_no_negative(): ), # subtract 2*1.5=3 ] ) - reconcile_single_crop_baselines( - n, {"double_rice": {"crops": ["wetland-rice", "wetland-rice"]}} - ) + 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 @@ -254,29 +373,57 @@ def test_reconciliation_redistributes_over_subtraction_no_negative(): assert bl[["rice0", "rice1"]].sum() == pytest.approx(2.0 + 5.0 - 3.0) -def test_reconciliation_uses_built_cycles_not_config(): - """A combo built with fewer cycles reduces singles only for produced crops. - - rice_wheat configured, but the built link produces only rice (wheat cycle - dropped, e.g. below min_yield). Only the rice single is reduced; wheat is not. - """ +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), - # built with only the rice output bus - _multi_link("m", "rice_wheat", 3.0, ["wetland-rice"]), + _multi_link("m", "rice_wheat", 3.0, ["wetland-rice", "wheat"]), ] ) - reconcile_single_crop_baselines( - n, {"rice_wheat": {"crops": ["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["rice"] == pytest.approx(7.0) # 10 - 3 (rice produced) - assert bl["wheat"] == pytest.approx(8.0) # unchanged: no wheat cycle built + 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) ─────────────────────── +# Validation-mode pinning (use_actual_production) def test_fix_pins_both_carriers_at_reconciled_baselines(): @@ -290,9 +437,7 @@ def test_fix_pins_both_carriers_at_reconciled_baselines(): _multi_link("m", "rice_wheat", 3.0, ["wetland-rice", "wheat"]), ] ) - reconcile_single_crop_baselines( - n, {"rice_wheat": {"crops": ["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)]: @@ -306,9 +451,8 @@ def test_fix_pins_both_carriers_at_reconciled_baselines(): assert links.at["wheat", "p_nom"] + links.at["m", "p_nom"] == pytest.approx(8.0) -def test_multi_cycle_long_counts_ports_per_cycle(): - """_multi_cycle_long counts one cycle per crop-output port, so repeated - crops carry their multiplicity and distinct crops one row each.""" +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), diff --git a/tools/calibrate b/tools/calibrate index de74b4c8..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-${SOURCE}") -fi - ARTEFACT_FILES=( grassland_yield.csv fodder_conversion.csv @@ -139,7 +132,7 @@ STABILITY_TARGETS=( "$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="${CALIBRATE_PIXI_ENV-gurobi}" @@ -184,8 +177,9 @@ run_step() { local cfgfiles=() [[ -n "$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[@]}" -- "${TARGETS_ARR[@]}" } check_step() { @@ -196,8 +190,9 @@ check_step() { local cfgfiles=() [[ -n "$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 1d088174..f11f47de 100644 --- a/workflow/Snakefile +++ b/workflow/Snakefile @@ -9,7 +9,10 @@ from snakemake.utils import min_version from validation import load_secrets_with_env_fallback, validate -from workflow.scripts.multi_cropping_combinations import effective_combinations +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 1a9137b0..82b4df4d 100644 --- a/workflow/rules/crops.smk +++ b/workflow/rules/crops.smk @@ -558,97 +558,84 @@ rule build_harvested_area_yield_weighted: "../scripts/build_harvested_area_yield_weighted.py" -def _mirca_base_crops(): - """The 23 MIRCA-OS base crop labels, read from the concordance at parse time.""" - import pandas as pd - - mapping = pd.read_csv("data/curated/mirca_os_crop_mapping.csv", comment="#") - return mapping["mirca_crop"].astype(str).str.strip().tolist() +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-independent Stage-1 multi-cropping derivation. + """Inputs for the config-specific observed multi-cropping derivation. - All 23 MIRCA crops' 2020 annual harvested grids (for the all-crop M_total), + 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. Input keys must be identifier-safe, so spaces in MIRCA crop - labels are mapped to underscores (see ``_annual_key`` in the script). + 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", - "footprint_ir": f"{grids}/footprint/MIRCA-OS_2020_ir_v2.tif", - "footprint_rf": f"{grids}/footprint/MIRCA-OS_2020_rf_v2.tif", - "rice2_ir": f"{grids}/monthly/MIRCA-OS_Rice2_2020_ir.nc", - "rice2_rf": f"{grids}/monthly/MIRCA-OS_Rice2_2020_rf.nc", - "rice3_ir": f"{grids}/monthly/MIRCA-OS_Rice3_2020_ir.nc", - "rice3_rf": f"{grids}/monthly/MIRCA-OS_Rice3_2020_rf.nc", + "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_base_crops(): + 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}_2020_{mws}_v2.tif" + inputs[key] = f"{grids}/annual/MIRCA-OS_{mirca_crop}_{year}_{mws}_v2.tif" return inputs -# Agronomic seed set; mirrors COMBO_CROPS in the derivation script. Any -# additional sequence clearing the coverage floor is added to the discovered -# set by the script itself. -MIRCA_SEED_COMBINATIONS = [ - "rice_wheat", - "double_rice", - "triple_rice", - "rice_maize", - "wheat_maize", - "wheat_soybean", - "maize_soybean", - "cotton_wheat", -] - - -checkpoint derive_mirca_multicropping: - """Stage 1: derive the observed multi-cropping baseline from MIRCA-OS 2020. - - Config-independent (shared across configs). Attributes MIRCA's extra-cycle - harvested area (harvested minus AEI-capped footprint) to crop-sequence - combinations, gating on MIRCA co-occurrence + GAEZ multiple-cropping zone. - Writes ``combinations.yaml`` (the discovered combination set, resolved into - the effective config via ``effective_combinations``), the per-combination - baseline rasters consumed by the Stage-2 aggregation, and the residual - extra-cycle raster (diagnostic). A checkpoint, because the combination set - determines downstream rule 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: - out_dir=directory("/shared/multi_cropping"), + baseline="/{name}/multi_cropping/baseline_area.csv", + residual="/{name}/multi_cropping/residual_multicrop.tif", + stats="/{name}/multi_cropping/attribution_stats.csv", params: - seed_combinations=MIRCA_SEED_COMBINATIONS, - coverage_floor_mha=0.5, - max_combinations=20, + source_year=MIRCA_MULTICROPPING_YEAR, resources: runtime="15m", mem_mb=8000, log: - "/shared/derive_mirca_multicropping.log", + "/{name}/derive_mirca_multicropping.log", benchmark: - "/shared/derive_mirca_multicropping.tsv" + "/{name}/derive_mirca_multicropping.tsv" script: "../scripts/derive_mirca_multicropping.py" -def derived_combinations_yaml(): - """Path of the Stage-1 ``combinations.yaml`` (defers on the checkpoint).""" - out_dir = checkpoints.derive_mirca_multicropping.get().output.out_dir - return f"{out_dir}/combinations.yaml" +def multicropping_combinations_yaml(): + """Path of the authoritative observed combination catalog.""" + return MIRCA_MULTICROPPING_CATALOG def _effective_multicropping(): - """The effective combination set (derived merged over config), DAG-side.""" - return effective_combinations(config, derived_combinations_yaml()) + """The effective observed and greenfield combination set, DAG-side.""" + return effective_combinations(config, multicropping_combinations_yaml()) def multi_cropping_inputs(_wildcards): @@ -669,7 +656,7 @@ def multi_cropping_inputs(_wildcards): "classes": "/{name}/resource_classes.nc", "regions": "/{name}/regions.geojson", "yield_unit_conversions": "data/curated/yield_unit_conversions.csv", - "combinations": derived_combinations_yaml(), + "combinations": multicropping_combinations_yaml(), } for ws in ("r", "i"): for crop in sorted(crops_by_supply[ws]): @@ -709,45 +696,6 @@ rule build_multi_cropping: "../scripts/build_multi_cropping.py" -def multicropping_baseline_inputs(_wildcards): - """Inputs for the Stage-2 baseline aggregation: the Stage-1 rasters for the - effective combination set, plus the region/class grids to aggregate to. - """ - out_dir = checkpoints.derive_mirca_multicropping.get().output.out_dir - inputs = { - "classes": "/{name}/resource_classes.nc", - "regions": "/{name}/regions.geojson", - "combinations": derived_combinations_yaml(), - } - for combo_name, entry in _effective_multicropping().items(): - for ws in entry["water_supplies"]: - inputs[f"baseline_{combo_name}_{ws}"] = ( - f"{out_dir}/baseline/{combo_name}_{ws}.tif" - ) - return inputs - - -rule aggregate_multicropping_baseline: - """Stage 2: aggregate the Stage-1 multi-cropping baseline rasters to the - active config's regions/classes, the anchor for crop_production_multi links. - """ - input: - unpack(multicropping_baseline_inputs), - output: - "/{name}/multi_cropping/baseline_area.csv", - group: - "prep" - resources: - runtime="3m", - mem_mb=5500, - log: - "/{name}/aggregate_multicropping_baseline.log", - benchmark: - "/{name}/aggregate_multicropping_baseline.tsv" - script: - "../scripts/aggregate_multicropping_baseline.py" - - rule build_grassland_yields: input: grassland="data/downloads/grassland_yield_historical.nc4", diff --git a/workflow/rules/model.smk b/workflow/rules/model.smk index 9729d52f..26bbdd1e 100644 --- a/workflow/rules/model.smk +++ b/workflow/rules/model.smk @@ -165,7 +165,7 @@ rule build_model: 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: derived_combinations_yaml(), + 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", diff --git a/workflow/rules/retrieve.smk b/workflow/rules/retrieve.smk index feb65da1..5e3dff52 100644 --- a/workflow/rules/retrieve.smk +++ b/workflow/rules/retrieve.smk @@ -807,8 +807,9 @@ rule extract_cropgrids_nc: # 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; individual GeoTIFF/NetCDF layers are then -# unpacked with ``bsdtar`` (libarchive; the ``7z`` build here cannot read RAR5). +# 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" @@ -820,6 +821,14 @@ MIRCA_OS_BAG_MEMBERS = { "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: @@ -846,95 +855,96 @@ rule download_mirca_os_archive: rule extract_mirca_os_annual_harvested: - """Unpack one crop/system 2020 annual-harvested-area GeoTIFF (5-arcmin). + """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. Keyed by - the MIRCA crop label (see data/curated/mirca_os_crop_mapping.csv). + 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: - "data/downloads/mirca_os/grids/annual/MIRCA-OS_{crop}_2020_{ws}_v2.tif", + expand( + "data/downloads/mirca_os/grids/annual/MIRCA-OS_{crop}_{{year}}_{ws}_v2.tif", + crop=MIRCA_OS_BASE_CROPS, + ws=("ir", "rf"), + ), wildcard_constraints: - # MIRCA crop labels include spaces (e.g. "Oil palm", "Others annual"), - # which the all-crop M_total sum needs (~21% of global harvested area). - crop="[A-Za-z][A-Za-z ]*", - ws="ir|rf", + 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="10m", + runtime="20m", mem_mb=1000, log: - "/shared/extract_mirca_os_annual_{crop}_{ws}.log", - shell: - r""" - mkdir -p "$(dirname '{output}')" - bsdtar -xOf {input.rar} \ - --include="*MIRCA-OS_{wildcards.crop}_2020_{wildcards.ws}_v2.tif" \ - > '{output}' 2> '{log}' - test -s '{output}' - """ + "/shared/extract_mirca_os_annual_{year}.log", + script: + "../scripts/extract_mirca_os_archive.py" rule extract_mirca_os_footprint: - """Unpack the 2020 maximum-monthly-cropped-area GeoTIFF for one system. + """Unpack both maximum-monthly-cropped-area GeoTIFFs for one year. - This is the AEI-capped physical field footprint per system; the combined - footprint is the ir + rf sum (disjoint land, single-count). MIRCA-OS v2 ships - no `tot` layer despite its README, so the derivation sums the two systems. + 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: - "data/downloads/mirca_os/grids/footprint/MIRCA-OS_2020_{ws}_v2.tif", + expand( + "data/downloads/mirca_os/grids/footprint/MIRCA-OS_{{year}}_{ws}_v2.tif", + ws=("ir", "rf"), + ), wildcard_constraints: - ws="ir|rf", + 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_{ws}.log", - shell: - r""" - mkdir -p "$(dirname {output})" - bsdtar -xOf {input.rar} \ - --include='*5-arcminute/MIRCA-OS_2020_{wildcards.ws}_v2.tif' \ - > {output} 2> {log} - test -s {output} - """ + "/shared/extract_mirca_os_footprint_{year}.log", + script: + "../scripts/extract_mirca_os_archive.py" rule extract_mirca_os_subcrop_monthly: - """Unpack one subcrop's monthly growing-area NetCDF (5-arcmin) for a year. + """Unpack the repeated-rice monthly grids for one year in one archive pass. The subcrop-resolved monthly product preserves subcrop identity - (Rice1/2/3, Wheat1/2, ...); the 2020 vintage feeds the multi-cropping - derivation's repeated-cycle detection (never crop timing). + (Rice1/2/3, Wheat1/2, ...). Rice2/3 feed the multi-cropping derivation's + repeated-cycle detection (never crop timing). """ input: rar="data/downloads/mirca_os/Monthly_Growing_Area_Grids.rar", output: - "data/downloads/mirca_os/grids/monthly/MIRCA-OS_{subcrop}_{year}_{ws}.nc", + expand( + "data/downloads/mirca_os/grids/monthly/MIRCA-OS_{subcrop}_{{year}}_{ws}.nc", + subcrop=("Rice2", "Rice3"), + ws=("ir", "rf"), + ), wildcard_constraints: - # Base crops ("Barley", "Sugar beet") and digit-suffixed sub-crops - # ("Rice1", "Wheat2"); the multi-cropping derivation needs the - # digit-suffixed rice cycles. - subcrop="[A-Za-z][A-Za-z0-9 ]*", - year="2000|2005|2010|2015|2020", - ws="ir|rf", + year="|".join(map(str, MIRCA_OS_YEARS)), + params: + member_globs=expand( + "*MIRCA-OS_{subcrop}_{{year}}_{ws}.nc", + subcrop=("Rice2", "Rice3"), + ws=("ir", "rf"), + ), resources: runtime="30m", mem_mb=2000, log: - "/shared/extract_mirca_os_subcrop_{subcrop}_{year}_{ws}.log", - shell: - r""" - mkdir -p "$(dirname '{output}')" - bsdtar -xOf {input.rar} \ - --include='*MIRCA-OS_{wildcards.subcrop}_{wildcards.year}_{wildcards.ws}.nc' \ - > '{output}' 2> '{log}' - test -s '{output}' - """ + "/shared/extract_mirca_os_subcrop_{year}.log", + script: + "../scripts/extract_mirca_os_archive.py" rule download_grassland_yield_data: diff --git a/workflow/scripts/aggregate_multicropping_baseline.py b/workflow/scripts/aggregate_multicropping_baseline.py deleted file mode 100644 index d1ca03fd..00000000 --- a/workflow/scripts/aggregate_multicropping_baseline.py +++ /dev/null @@ -1,158 +0,0 @@ -""" -SPDX-FileCopyrightText: 2026 Koen van Greevenbroek - -SPDX-License-Identifier: GPL-3.0-or-later - -Stage 2 (aggregation): reduce the config-independent Stage-1 multi-cropping -baseline rasters to a per-region, per-resource-class, per-water-supply table for -the active config's regions. - -Each Stage-1 raster (``baseline/{combination}_{ws}.tif`` under the -``derive_mirca_multicropping`` checkpoint's output directory) -holds the *physical link area* ``A`` (ha) -- the field area that runs the whole -sequence once. This aggregates ``A`` to ``(combination, region, resource_class, -water_supply)`` via ``exact_extract`` against ``resource_classes.nc`` + -``regions.geojson``, reusing the machinery in ``build_multi_cropping``. The result -anchors ``crop_production_multi`` links in production stability. -""" - -from pathlib import Path - -import geopandas as gpd -import numpy as np -import pandas as pd -import xarray as xr - -from workflow.scripts.build_multi_cropping import region_coverage_entries -from workflow.scripts.multi_cropping_combinations import effective_combinations -from workflow.scripts.raster_utils import raster_bounds, read_raster_float - -BASELINE_COLUMNS = [ - "combination", - "region", - "resource_class", - "water_supply", - "baseline_area_ha", -] - - -def main() -> None: - inp = dict(snakemake.input.items()) # type: ignore[name-defined] - output = Path(snakemake.output[0]) # type: ignore[name-defined] - - classes_nc = inp.pop("classes") - regions_path = inp.pop("regions") - combinations = effective_combinations( - snakemake.config, # type: ignore[name-defined] - inp.pop("combinations"), - ) - - # Remaining inputs are the per-(combination, ws) Stage-1 baseline rasters, - # keyed "baseline_{combination}_{ws}". - raster_paths = { - key[len("baseline_") :]: path - for key, path in inp.items() - if key.startswith("baseline_") - } - - ds = xr.load_dataset(classes_nc) - if "resource_class" not in ds: - raise ValueError("resource_classes.nc is missing 'resource_class' data") - class_labels = ds["resource_class"].values.astype(np.int16) - valid_classes = [ - int(cls) - for cls in np.unique(class_labels[np.isfinite(class_labels)]) - if int(cls) >= 0 - ] - - regions_gdf = gpd.read_file(regions_path) - regions_for_extract = regions_gdf.reset_index() - - # All Stage-1 rasters share one grid, so the region coverage fractions are - # extracted once (on the first raster's grid) and every aggregation is a - # gather + bincount against them; a raster on a different grid fails fast. - coverage_grid = None - records: list[pd.DataFrame] = [] - for combo_name, entry in combinations.items(): - if entry is None: - continue - for ws in entry["water_supplies"]: - raster_key = f"{combo_name}_{ws}" - path = raster_paths.get(raster_key) - if path is None: - raise KeyError( - f"Missing Stage-1 baseline raster for '{raster_key}'; the " - "derive_mirca_multicropping checkpoint output is stale." - ) - area_arr, src = read_raster_float(path) - try: - if area_arr.shape != class_labels.shape: - raise ValueError( - f"Baseline raster '{raster_key}' shape {area_arr.shape} does " - f"not match resource class grid {class_labels.shape}" - ) - transform = src.transform - crs = src.crs - crs_wkt = crs.to_wkt() if crs else None - height, width = area_arr.shape - xmin, ymin, xmax, ymax = raster_bounds(transform, width, height) - finally: - src.close() - - if coverage_grid is None: - if regions_gdf.crs and crs and regions_gdf.crs != crs: - regions_for_extract = regions_gdf.to_crs(crs).reset_index() - coverage_grid = (transform, crs_wkt) - region_names, cov_rows, cov_cells, cov_fracs = region_coverage_entries( - regions_for_extract, - xmin, - ymin, - xmax, - ymax, - crs_wkt, - area_arr.shape, - ) - n_regions = len(region_names) - class_at_entry = class_labels.ravel()[cov_cells] - elif coverage_grid != (transform, crs_wkt): - raise ValueError( - f"Baseline raster '{raster_key}' is not on the shared " - "Stage-1 grid" - ) - - area_flat = np.where(np.isfinite(area_arr), area_arr, 0.0).ravel() - area_entries = area_flat[cov_cells] * cov_fracs - for cls in valid_classes: - sel = class_at_entry == cls - sums = np.bincount( - cov_rows[sel], weights=area_entries[sel], minlength=n_regions - ) - pos = sums > 0 - if not pos.any(): - continue - stats = pd.DataFrame( - { - "combination": combo_name, - "region": region_names[pos], - "resource_class": cls, - "water_supply": ws, - "baseline_area_ha": sums[pos], - } - ) - records.append(stats[BASELINE_COLUMNS]) - - if records: - result = pd.concat(records, ignore_index=True) - result["resource_class"] = result["resource_class"].astype(int) - result = result.sort_values( - ["combination", "water_supply", "region", "resource_class"], - ignore_index=True, - ) - else: - result = pd.DataFrame(columns=BASELINE_COLUMNS) - - output.parent.mkdir(parents=True, exist_ok=True) - result.to_csv(output, index=False) - - -main() diff --git a/workflow/scripts/build_model.py b/workflow/scripts/build_model.py index ae21243c..ada0e82b 100644 --- a/workflow/scripts/build_model.py +++ b/workflow/scripts/build_model.py @@ -1026,20 +1026,15 @@ def _apply_yield_corrections(corr_df: pd.DataFrame, source_label: str) -> None: 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, ) - # Persist the single-crop baseline reduction (harvested cycles now carried - # by the multi links) BEFORE the land correction and the pin read - # baseline_area_mha. - crops.reconcile_single_crop_baselines( - n, - multiple_cropping_cfg, - ) - - # Clip sub-min_link_area_mha crop baselines BEFORE they are pinned or - # summed into the solve-time growth cap, so the pin and the cap agree. + # 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. @@ -1050,6 +1045,12 @@ def _apply_yield_corrections(corr_df: pd.DataFrame, source_label: str) -> None: 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. diff --git a/workflow/scripts/build_model/crops.py b/workflow/scripts/build_model/crops.py index 8aac1336..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 import Counter from collections.abc import Mapping, Sequence +import json import logging import numpy as np @@ -26,6 +28,63 @@ 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], ruminant_feed_mapping: pd.DataFrame, @@ -96,7 +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"] @@ -122,8 +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 @@ -132,23 +193,28 @@ 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) - logger.info( - "Baseline redistribution: capped %.1f Mha excess, " - "%.1f Mha unplaceable (no spare capacity in same crop x country)", - total_excess_before, - unplaced, - ) + 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 @@ -616,6 +682,7 @@ def add_multi_cropping_links( 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, @@ -635,6 +702,13 @@ def add_multi_cropping_links( 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``. @@ -646,19 +720,122 @@ def add_multi_cropping_links( single-crop link or a cycle of a multi-cropping bundle. """ - if eligible_area.empty or cycle_yields.empty: - logger.info("No multi-cropping combinations with positive area; skipping") - return - 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" ) @@ -673,17 +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 @@ -691,15 +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"]) @@ -735,12 +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"] = [ @@ -782,24 +1014,62 @@ def add_multi_cropping_links( base["marginal_cost"] = base["total_cost"] * 1e6 * constants.USD_TO_BNUSD base["p_nom_extendable"] = True - # Anchor each link at its MIRCA-observed baseline area (Mha), keyed by - # (combination, region, resource_class, water_supply). Missing anchors are 0 - # (guarded so a missing baseline can never inject a NaN into the stability - # term). Cap = max(GAEZ potential, anchor) so the anchor is never - # above the expansion bound. - if baseline_area is not None and not baseline_area.empty: - anchor = baseline_area.copy() - anchor["resource_class"] = anchor["resource_class"].astype(int) - anchor["water_supply"] = anchor["water_supply"].astype(str) - anchor["baseline_area_mha"] = ( - pd.to_numeric(anchor["baseline_area_ha"], errors="coerce").fillna(0.0) / 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) ) - anchor = anchor.set_index(key_cols)["baseline_area_mha"] - base["baseline_area_mha"] = anchor.reindex(base.index).fillna(0.0) - else: - base["baseline_area_mha"] = 0.0 + 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"] / 1e6, base["baseline_area_mha"] + base["eligible_area_ha"] / constants.HA_PER_MHA, + base["baseline_area_mha"], ) # Multi-cropping cost corrections are extracted directly from each multi @@ -932,6 +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: @@ -1280,6 +1556,7 @@ def _water_gate(df): "resource_class", "water_supply", "combination", + "crop_cycles", ] # Prepare metadata values link_df["water_supply"] = link_df["water_supply"].map( @@ -1347,27 +1624,21 @@ def _water_gate(df): def reconcile_single_crop_baselines( n: pypsa.Network, - combinations: Mapping[str, Mapping[str, object]], ) -> 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. For each *built* multi link with anchored - baseline area ``X`` in ``(region, class, water_supply)``, crop ``k`` appearing - ``m_k`` times in its sequence, this subtracts ``m_k * X`` from that crop's - single-crop ``baseline_area_mha`` in the same cell, so each harvested cycle is - counted once (on the multi link). Local MIRCA-vs-FAOSTAT disagreement can drive - a cell negative, so any over-subtraction is redistributed onto other - ``(crop, country, water_supply)`` rows that still have baseline to give up - (preserving the FAOSTAT national total per water supply); only when a whole - group runs out does it spill to the residual bulk correction. - - The reduction is derived from the links actually added (not the raw - ``baseline_area.csv``), so baselines whose multi link could not be built (e.g. - a missing land bus) do not strip single-crop area with nothing replacing it. - It is persisted on the ``crop_production`` ``baseline_area_mha`` column, which - the L1 stability penalty and irrigation calibration re-read, so it must run - before those steps. + 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" @@ -1375,100 +1646,108 @@ def reconcile_single_crop_baselines( if multi.empty or not is_crop.any(): return - # Cycle multiplicity is counted from the crop-output buses the link was - # actually built with, not from the config sequence: a combination can build - # with fewer cycles than configured (a cycle whose yield fell below - # min_yield_t_per_ha is dropped), and reconciling against the config count - # would strip single-crop area for a cycle that no multi link produces. The - # candidate crops per combination come from the config; the count comes from - # matching each crop's constructed output bus among the link's buses. - output_bus_cols = [ - col - for col in multi.columns - if col.startswith("bus") and col != "bus0" and col[3:].isdigit() - ] - bus_arrays = {col: multi[col].to_numpy() for col in output_bus_cols} - combo_arr = multi["combination"].astype(str).to_numpy() - region_arr = multi["region"].astype(str).to_numpy() - class_arr = multi["resource_class"].astype(int).to_numpy() - ws_arr = multi["water_supply"].astype(str).to_numpy() - country_arr = multi["country"].astype(str).to_numpy() - baseline_arr = multi["baseline_area_mha"].astype(float).to_numpy() - - # Required reduction (Mha) per (crop, region, class, water_supply). water_supply - # is already "irrigated"/"rainfed" on both carriers. - reductions: dict[tuple[str, str, int, str], float] = {} - for i in range(len(multi)): - area_mha = float(baseline_arr[i]) - if area_mha <= 0: - continue - entry = combinations.get(combo_arr[i]) - if entry is None: - raise ValueError( - f"Multi-cropping link carries combination '{combo_arr[i]}', which " - "is not in the effective combination set. The built link set and " - "the combination set have diverged; its harvested cycles would be " - "double-counted against the single-crop baselines." - ) - country = country_arr[i] - link_buses = [bus_arrays[col][i] for col in output_bus_cols] - for crop in set(entry["crops"]): - expected_bus = f"crop:{crop}:{country}" - built_cycles = sum(1 for bus in link_buses if bus == expected_bus) - if built_cycles == 0: - continue - key = (crop, region_arr[i], class_arr[i], ws_arr[i]) - reductions[key] = reductions.get(key, 0.0) + built_cycles * area_mha - if not reductions: - return + single = links[is_crop].copy() + budget_cols = ["crop", "country", "water_supply"] + budgets = single.groupby(budget_cols)["baseline_area_mha"].sum().astype(float) - df = links[is_crop] - link_keys = list( - zip( - df["crop"].astype(str), - df["region"].astype(str), - df["resource_class"].astype(int), - df["water_supply"].astype(str), + 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) ) - required = pd.Series( - [reductions.get(key, 0.0) for key in link_keys], index=df.index, dtype=float + cycle_long["water_supply"] = cycle_long["link"].map(multi["water_supply"]) + cycle_long["required_mha"] = ( + cycle_long["multiplicity"] * cycle_long["baseline_area_mha"] ) - baseline = df["baseline_area_mha"].astype(float) - new_baseline = baseline - required - # Amount over-subtracted per link (local anchor too small for the reduction). - over = (-new_baseline).clip(lower=0.0) - new_baseline = new_baseline.clip(lower=0.0) - group = ( - df["crop"].astype(str) - + ":" - + df["country"].astype(str) - + ":" - + df["water_supply"].astype(str) + 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 ) - unplaced = 0.0 - for _key, idx in new_baseline.groupby(group).groups.items(): - group_over = float(over.loc[idx].sum()) - if group_over <= 0: - continue - spare = new_baseline.loc[idx] - total_spare = float(spare.sum()) - if total_spare <= 0: - unplaced += group_over + 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 - take = min(group_over, total_spare) - new_baseline.loc[idx] -= spare / total_spare * take - if group_over > total_spare: - unplaced += group_over - total_spare + 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 - n.links.static.loc[new_baseline.index, "baseline_area_mha"] = new_baseline.values + 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 single-crop baselines against multi-cropping: reduced %.1f Mha " - "of harvested-cycle anchor (%.1f Mha unplaceable, left to residual)", - float((baseline - new_baseline).sum()), - unplaced, + "Reconciled %.1f Mha of multi-cropping harvested cycles while preserving " + "%d national crop/water budgets", + float(reconciled_multi.sum()), + len(budgets), ) 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 index 23e1a3e8..301218b9 100644 --- a/workflow/scripts/derive_mirca_multicropping.py +++ b/workflow/scripts/derive_mirca_multicropping.py @@ -3,74 +3,169 @@ SPDX-License-Identifier: GPL-3.0-or-later -Stage 1 of the multi-cropping baseline: derive an observed baseline area for -crop-sequence combinations from MIRCA-OS 2020, on the global 5-arcmin grid. - -This is a config-independent Snakemake checkpoint (shared across configs). It -writes to its output directory: - - * ``combinations.yaml`` -- the discovered combination set (crop sequences and - water supplies in GLADE ``i``/``r`` codes), merged over - ``config["multiple_cropping"]`` to form the effective combination set. - * ``baseline/{combination}_{ws}.tif`` -- per-combination, per-water-supply - sparse 5-arcmin GeoTIFF of the *physical link area* ``A`` (ha): the field area - that runs the whole sequence once (an ``n``-cycle combination on ``A`` ha - harvests ``n * A`` ha). - * ``residual_multicrop.tif`` -- extra-cycle harvested area not attributed to any - combination, left for the bulk land-correction generator. - -Method, per cell: - -1. Magnitude ``M_total = sum_crops (harvested_ir + harvested_rf) - footprint``, - clipped at 0, where ``footprint = footprint_ir + footprint_rf`` are MIRCA's - AEI-capped maximum-monthly-cropped-area layers (no ``tot`` layer ships, and the - two systems occupy disjoint land, so the sum is single-count). ``M_total`` is - the harvested area above the physical field footprint -- the extra-cycle area. -2. Candidate combinations are gated on **MIRCA observation** (both crops harvested - in the given system) plus the GAEZ multiple-cropping-zone cycle-count limit. - ``sequence_feasible`` on GAEZ windows is deliberately NOT used as the gate: GAEZ - attainable season lengths overshoot the farmed cycle, so it rejects ~all - observed irrigated double-cropping. GAEZ timing enters only later, at the - Stage-2 water split. -3. Repeated same-crop cycles (double/triple rice) use the MIRCA subcrop stack - (``Rice1/2/3``) for mutually consistent physical supports, not ``min(area, - area)``. -4. Each candidate has physical cap ``A_max`` and extra-cycle capacity - ``(n-1) * A_max``. The cell's ``M_total`` is allocated across candidates - proportionally to capacity, never exceeding it; any unallocated remainder is - residual. The physical link area written out is ``A = E / (n-1)``. +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 -import yaml -from workflow.scripts.build_multi_cropping import WETLAND_RICE_CROPS, ZONE_CAPABILITIES +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 -def load_tif(path: str) -> np.ndarray: - """Load a GeoTIFF band as float64 with negatives (nodata sentinels) zeroed.""" +@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 load_subcrop_maxmonth(path: str) -> np.ndarray: - """Collapse a MIRCA monthly growing-area NetCDF to its max over months (ha).""" - da = xr.open_dataset(path)["harvested_area"] - arr = np.nan_to_num(da.values, nan=0.0) # (month, lat, lon) +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.max(axis=0) + return arr def zone_mask(zone_arr: np.ndarray, n_cycles: int, n_rice: int) -> np.ndarray: - """Cells whose GAEZ multiple-cropping zone permits ``n_cycles`` (``n_rice`` rice).""" + """Return cells whose GAEZ zone permits the requested cycle counts.""" allowed = [ code for code, cap in ZONE_CAPABILITIES.items() @@ -88,66 +183,89 @@ def candidate_capacity( zone_arr: np.ndarray, rice_support: dict[str, np.ndarray], ) -> np.ndarray: - """Physical cap ``A_max`` (ha) for a crop sequence in one water supply, per cell. - - Distinct-crop sequences: ``A_max = min_k area_{crop_k, ws}`` where both crops - are observed, under the zone mask. Repeated wetland-rice sequences: disjoint - supports from the MIRCA subcrop stack (double = cells with a 2nd but not 3rd - rice cycle; triple = cells with a 3rd cycle). - """ - n = len(crops) - n_rice = sum(1 for c in crops if c in WETLAND_RICE_CROPS) - zmask = zone_mask(zone_arr, n, n_rice) - - repeated_rice = n_rice == n and n >= 2 # all cycles are wetland rice - if repeated_rice: - rice2 = rice_support[ws] # area with a 2nd rice cycle (>= double) - rice3 = rice_support[ws + "3"] # area with a 3rd rice cycle (triple) - if n == 2: - a_max = np.clip(rice2 - rice3, 0.0, None) # exactly-double fields - elif n == 3: - a_max = rice3.copy() + """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}") - return np.where(zmask, a_max, 0.0) + 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 - # Distinct-crop sequence: min over the (possibly repeated) crop areas. - stack = np.stack([crop_area[(c, ws)] for c in crops], axis=0) - both_observed = np.all(stack > 0, axis=0) - a_max = np.min(stack, axis=0) - return np.where(zmask & both_observed, a_max, 0.0) + +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( - m_total: np.ndarray, + extra_cycle_area: np.ndarray, + footprint_area: np.ndarray, capacities: list[np.ndarray], - cycle_counts: list[int], + crop_sequences: list[list[str]], + crop_support: dict[str, np.ndarray], ) -> tuple[list[np.ndarray], np.ndarray]: - """Allocate the extra-cycle magnitude across candidates, capped by capacity. - - Each candidate ``i`` has extra-cycle capacity ``(n_i - 1) * A_max_i``. Per - cell: if total capacity fits within ``m_total`` every candidate is filled and - the remainder is residual; otherwise ``m_total`` is rationed proportionally to - capacity (no candidate exceeds its cap, and the sum never exceeds ``m_total``, - so no co-located rotation is invented). Returns per-candidate *physical link - area* ``A = E / (n-1)`` and the residual extra-cycle area. + """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. """ - extra_caps = [ - (n - 1) * cap for n, cap in zip(cycle_counts, capacities) - ] # capacity in extra-cycle units - total_cap = np.sum(extra_caps, axis=0) - # scale in [0,1]: 1 where capacity fits, else m_total/total_cap - with np.errstate(divide="ignore", invalid="ignore"): - scale = np.where(total_cap > m_total, m_total / total_cap, 1.0) - scale = np.clip(np.nan_to_num(scale, nan=0.0, posinf=0.0), 0.0, 1.0) + 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(m_total) - for n, cap in zip(cycle_counts, capacities): - e = (n - 1) * cap * scale # extra-cycle area for this candidate - allocated_extra += e - areas.append(e / (n - 1)) # physical link area - residual = np.clip(m_total - allocated_extra, 0.0, None) + 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 @@ -158,231 +276,280 @@ def run_derivation( 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 the full per-cell attribution over the global grid. - - Parameters mirror the loaded rasters (all same shape). ``combos`` is a list of - dicts ``{"name", "crops": [...], "water_supply": "i"|"r"}``. Returns: - * ``area_rasters`` -- physical link area ``A`` (ha) per (combo name, ws). - * ``residual`` -- unattributed extra-cycle area (ha). - * ``stats`` -- per-(combo, ws) global attributed physical and extra-cycle area. - """ - # Magnitude: total harvested (all crops, both systems) minus combined footprint. - h_total = np.sum(list(annual_harvested.values()), axis=0) - foot = np.clip(footprint["ir"], 0.0, None) + np.clip(footprint["rf"], 0.0, None) - m_total = np.clip(h_total - foot, 0.0, None) - - capacities = [ - candidate_capacity( - c["crops"], - c["water_supply"], - crop_area, - zone[c["water_supply"]], - rice_support, - ) - for c in combos - ] - cycle_counts = [len(c["crops"]) for c in combos] - - areas, residual = allocate(m_total, capacities, cycle_counts) + """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] = [] - for c, area in zip(combos, areas): - key = (c["name"], c["water_supply"]) - area_rasters[key] = area - n = len(c["crops"]) - records.append( - { - "combination": c["name"], - "water_supply": c["water_supply"], - "cycles": n, - "physical_area_mha": area.sum() / 1e6, - "extra_cycle_area_mha": (n - 1) * area.sum() / 1e6, - } + 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, ) - stats = pd.DataFrame.from_records(records) - return area_rasters, residual, stats + 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, ir/rf) harvested arrays to GLADE (crop, i/r) supports.""" - ws_map = {"i": "ir", "r": "rf"} + """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 ws_map.items() + for gws, mws in {"i": "ir", "r": "rf"}.items() } -def discover_combinations( - stats: pd.DataFrame, - seed_names: set[str], - floor_mha: float, - max_combos: int, -) -> dict[str, dict]: - """Select the combination set and emit a ``config["multiple_cropping"]`` block. - - A combination is kept if it is in the agronomic seed set or its global - attributed extra-cycle area clears ``floor_mha``; the kept set is capped at - ``max_combos`` (largest first). Within a kept combination, only water supplies - with positive attributed area are listed. - """ - per_combo = stats.groupby("combination")["extra_cycle_area_mha"].sum() - kept = [ - name - for name in per_combo.index - if name in seed_names or per_combo[name] >= floor_mha - ] - kept = sorted(kept, key=lambda n: -per_combo[n])[:max_combos] - - combos_cfg: dict[str, dict] = {} - for name in kept: - rows = stats[stats["combination"] == name] - supplies = sorted( - rows.loc[rows["physical_area_mha"] > 0, "water_supply"].unique() - ) - if not supplies: - continue - crops = COMBO_CROPS[name] - combos_cfg[name] = {"crops": list(crops), "water_supplies": supplies} - return combos_cfg - - -# Agronomic seed set, in GLADE crop names. -COMBO_CROPS: dict[str, list[str]] = { - "rice_wheat": ["wetland-rice", "wheat"], - "double_rice": ["wetland-rice", "wetland-rice"], - "triple_rice": ["wetland-rice", "wetland-rice", "wetland-rice"], - "rice_maize": ["wetland-rice", "maize"], - "wheat_maize": ["wheat", "maize"], - "wheat_soybean": ["wheat", "soybean"], - "maize_soybean": ["maize", "soybean"], - "cotton_wheat": ["cotton", "wheat"], -} - - -def write_outputs( - area_rasters: dict[tuple[str, str], np.ndarray], - residual: np.ndarray, - combos_cfg: dict[str, dict], - profile: dict, - out_dir: Path, -) -> None: - """Write combinations.yaml, per-(combo, ws) baseline rasters, and the residual.""" - baseline_dir = out_dir / "baseline" - baseline_dir.mkdir(parents=True, exist_ok=True) - - raster_profile = { - **profile, - "count": 1, - "dtype": "float32", - "nodata": 0.0, - "compress": "deflate", - } - - for name, entry in combos_cfg.items(): - for ws in entry["water_supplies"]: - arr = area_rasters[(name, ws)].astype(np.float32) - with rasterio.open( - baseline_dir / f"{name}_{ws}.tif", "w", **raster_profile - ) as dst: - dst.write(arr, 1) - - with rasterio.open( - out_dir / "residual_multicrop.tif", "w", **raster_profile - ) as dst: - dst.write(residual.astype(np.float32), 1) - - header = ( - "# SPDX-FileCopyrightText: 2026 Koen van Greevenbroek\n" - "#\n" - "# SPDX-License-Identifier: CC-BY-4.0\n" - "#\n" - "# Multi-cropping combination set discovered from MIRCA-OS 2020 by\n" - "# workflow/scripts/derive_mirca_multicropping.py (a Snakemake\n" - "# checkpoint). Merged over config['multiple_cropping'] to form the\n" - "# effective combination set. Do not hand-edit.\n" - ) - with open(out_dir / "combinations.yaml", "w") as fh: - fh.write(header) - yaml.safe_dump(combos_cfg, fh, sort_keys=True, default_flow_style=False) - - def _annual_key(mirca_crop: str, mws: str) -> str: - """Snakemake input key for an annual raster (spaces are not valid in keys).""" return f"annual_{mirca_crop.replace(' ', '_')}_{mws}" -def _load_inputs(inp, mirca_crops, glade_to_mirca): - """Load all rasters from a snakemake-style input mapping.""" - annual = { - (mc, mws): load_tif(inp[_annual_key(mc, mws)]) - for mc in mirca_crops - for mws in ("ir", "rf") +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 } - footprint = {ws: load_tif(inp[f"footprint_{ws}"]) for ws in ("ir", "rf")} - zone = {ws: load_tif(inp[f"zone_{ws}"]) for ws in ("i", "r")} + 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}"]) - rice_support[gws + "3"] = load_subcrop_maxmonth(inp[f"rice3_{mws}"]) - return annual, footprint, zone, rice_support + 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] - out_dir = Path(snakemake.output.out_dir) # 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() - mirca_crops = mapping["mirca_crop"].tolist() + 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 + 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, footprint, zone, rice_support = _load_inputs( - inp, mirca_crops, glade_to_mirca + annual, totals, footprint, zone, rice_support = _load_inputs( + inp, mapping, catalog, grid ) crop_area = build_crop_area(annual, glade_to_mirca) - - seed_names = set(params.seed_combinations) combos = [ - {"name": name, "crops": COMBO_CROPS[name], "water_supply": ws} - for name in seed_names - for ws in ("i", "r") + {"name": name, "crops": entry["crops"], "water_supply": ws} + for name, entry in catalog.items() + for ws in entry["water_supplies"] ] - - area_rasters, residual, stats = run_derivation( - annual, footprint, crop_area, zone, rice_support, combos - ) - - combos_cfg = discover_combinations( - stats, - seed_names, - float(params.coverage_floor_mha), - int(params.max_combinations), + areas, residual, stats = run_derivation( + annual, + footprint, + crop_area, + zone, + rice_support, + combos, + harvested_totals=totals, ) - with rasterio.open(inp["footprint_ir"]) as src: - profile = src.profile - - out_dir.mkdir(parents=True, exist_ok=True) - write_outputs(area_rasters, residual, combos_cfg, profile, out_dir) + 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) - h_total = np.sum(list(annual.values()), axis=0) - foot = footprint["ir"] + footprint["rf"] - m_total = float(np.clip(h_total - foot, 0.0, None).sum()) / 1e6 - resid = float(residual.sum()) / 1e6 + 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"Multi-cropping derivation: M_total={m_total:.1f} Mha, " - f"attributed={m_total - resid:.1f} Mha, residual={resid:.1f} Mha " - f"({resid / m_total * 100:.0f}% of M); {len(combos_cfg)} combinations kept" + 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}%)" ) diff --git a/workflow/scripts/download_mirca_os_bag_member.py b/workflow/scripts/download_mirca_os_bag_member.py index 6c723d3e..596d5a14 100644 --- a/workflow/scripts/download_mirca_os_bag_member.py +++ b/workflow/scripts/download_mirca_os_bag_member.py @@ -21,14 +21,14 @@ """ from pathlib import Path +import shutil import remotezip import requests MIRCA_OS_V2_RESOURCE = "e4582ca0042148338bb5e0148b749ed6" BAG_URL = ( - "https://www.hydroshare.org/django_irods/download/bags/" - f"{MIRCA_OS_V2_RESOURCE}.zip" + f"https://www.hydroshare.org/django_irods/download/bags/{MIRCA_OS_V2_RESOURCE}.zip" ) @@ -51,19 +51,22 @@ def main() -> None: signed_url = resolve_signed_url() member_path = f"{MIRCA_OS_V2_RESOURCE}/data/contents/{member}" - 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)" - ) - data = bag.read(member_path) - tmp = output.with_suffix(output.suffix + ".part") - tmp.write_bytes(data) - tmp.replace(output) - print(f"Downloaded {len(data):,} bytes -> {output}") + 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_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 index e1537ee2..f30b91ff 100644 --- a/workflow/scripts/multi_cropping_combinations.py +++ b/workflow/scripts/multi_cropping_combinations.py @@ -2,36 +2,115 @@ # # SPDX-License-Identifier: GPL-3.0-or-later -"""Effective multi-cropping combination set: MIRCA-derived merged over config. - -The Stage-1 derivation (``derive_mirca_multicropping``, a Snakemake checkpoint) -discovers the observed crop-sequence combinations and writes them to -``combinations.yaml``. The effective set the model builds with is that derived -set merged over the static ``multiple_cropping`` config section (derived entries -take precedence; config entries supplement combos not derived), with any -combination referencing a crop the config does not model dropped. - -Shared by the Snakemake input functions (via the checkpoint) and the build -scripts, so both resolve the identical combination set. -""" +"""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 effective_combinations(config: dict, combinations_yaml: str | Path) -> dict: - """The effective multi-cropping combination set for a config. - ``combinations_yaml`` is the Stage-1 checkpoint's derived set; entries merge - over ``config["multiple_cropping"]`` and combos with unmodeled crops drop. +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. """ - with open(combinations_yaml) as f: - derived = yaml.safe_load(f) or {} - merged = {**config["multiple_cropping"], **derived} + 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"]) - return { + observed = { + name: entry + for name, entry in observed.items() + if set(entry["crops"]) <= model_crops + } + greenfield = { name: entry - for name, entry in merged.items() - if entry and set(entry["crops"]) <= model_crops + 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 cac5f04c..80d68fb5 100644 --- a/workflow/scripts/solve_model/production_stability.py +++ b/workflow/scripts/solve_model/production_stability.py @@ -36,6 +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, @@ -1224,33 +1225,14 @@ def add_bounded_subsidy_constraints( def _multi_cycle_long(links_df: pd.DataFrame) -> pd.DataFrame: """Long form of multi-cropping cycle outputs: one row per (link, crop). - Cycle multiplicity is counted from the crop-output ports (``crop:{crop}:*`` - buses); a repeated crop occupies one port per cycle, so the port count is - the number of harvested cycles of that crop per Mha of link dispatch. + 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``. """ - multi = links_df[links_df["carrier"] == "crop_production_multi"] - if multi.empty: - return pd.DataFrame( - columns=["link", "crop", "group", "multiplicity", "baseline_area_mha"] - ) - bus_cols = [ - c - for c in multi.columns - if c.startswith("bus") and c != "bus0" and c[3:].isdigit() - ] - stacked = multi[bus_cols].astype(str).stack() - stacked = stacked[stacked.str.startswith("crop:")] - long = pd.DataFrame( - { - "link": stacked.index.get_level_values(0), - "crop": stacked.str.split(":").str[1].to_numpy(), - } - ) - long = long.groupby(["link", "crop"]).size().rename("multiplicity").reset_index() - long["group"] = long["crop"] + "::" + long["link"].map(multi["country"]).astype(str) - long["baseline_area_mha"] = long["link"].map(multi["baseline_area_mha"]).fillna(0.0) + long = multi_crop_cycle_multiplicities(links_df) + long["group"] = long["crop"] + "::" + long["country"] return long @@ -1314,8 +1296,8 @@ def add_crop_growth_cap_constraints( # 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 are counted from the crop-output ports (a repeated crop - # occupies one port per cycle). Their multiplicity-weighted baselines join + # 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: @@ -1474,8 +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_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 b08d91fe..7bc4c563 100644 --- a/workflow/validation/multi_cropping.py +++ b/workflow/validation/multi_cropping.py @@ -9,6 +9,11 @@ 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( @@ -39,47 +44,46 @@ } ) -# Crops in the agronomic seed set of the Stage-1 derivation. These must be -# mapped (non-blank) for the seeded combinations to be derivable at all. -REQUIRED_MAPPED_MIRCA_CROPS = frozenset( - {"Rice", "Wheat", "Maize", "Soybeans", "Cotton"} -) +CATALOG_PATH = Path("data/curated/mirca_os_multicropping_combinations.yaml") def validate_multi_cropping(config: dict, project_root: Path) -> None: """Validate multi-cropping combinations and the MIRCA-OS crop concordance. - Two invariants: + 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. ``data/curated/mirca_os_crop_mapping.csv`` is exhaustive over the 23 - MIRCA-OS base crops, every non-blank ``glade_crop`` is in - ``config["crops"]``, and the agronomic seed-set crops are mapped. This + 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"]) - combinations = config.get("multiple_cropping") - if combinations: - 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}") - if missing: - detail = ", ".join(missing) - raise ValueError( - "Multi-cropping combinations reference crops not in " - f"config['crops']: {detail}" - ) - - _validate_mirca_concordance(config_crops, project_root) - - -def _validate_mirca_concordance(config_crops: set[str], project_root: Path) -> None: + 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: + raise ValueError( + "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(): @@ -127,11 +131,9 @@ def _validate_mirca_concordance(config_crops: set[str], project_root: Path) -> N f"(future crops?): {', '.join(unused)}" ) - unmapped_required = sorted( - crop for crop in REQUIRED_MAPPED_MIRCA_CROPS if glade.get(crop, "") == "" - ) - if unmapped_required: + 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( - "mirca_os_crop_mapping.csv leaves seed-set crops unmapped: " - f"{unmapped_required}" + f"mirca_os_crop_mapping.csv does not map catalog crops: {unmapped_catalog}" )