Overview
CROPGRIDS maps physical crop area and harvested area for 173 crops circa 2020 on a global 0.05 degree grid (about 5.6 km at the equator), fusing 27 gridded crop maps, subnational statistics from 52 countries, and FAOSTAT 2020 national totals. This is the FAO release, regenerated with FAO’s Python pipeline, holding 171 of the crops as one GeoTIFF per crop and mapset. Crop area counts land once a year; harvested area counts every harvest, so it can exceed crop area under multiple cropping.
Important note
Values are modelled allocations, not observations. No nodata is set: ocean and cells without the crop hold 0. Totals differ from the figshare v1.08 release (Tang et al. 2024), so do not mix the two; that release's quality and source layers are not published here.
Quick start
import rioxarray
url = "https://storage.googleapis.com/fao-gismgr-faostat-data/DATA/FAOSTAT/MAPSET/CG-CA/FAOSTAT.CG-CA.2020.ABACA.tif"
da = rioxarray.open_rasterio(url, masked=True)library(terra)
url <- "/vsicurl/https://storage.googleapis.com/fao-gismgr-faostat-data/DATA/FAOSTAT/MAPSET/CG-CA/FAOSTAT.CG-CA.2020.ABACA.tif"
r <- rast(url)Intended uses
- global and regional mapping of crop distribution and cropping intensity
- crop-specific exposure layers for hazard, land-use, and sustainability assessment
- spatial targeting at national and sub-national scale
Not recommended for
Why: the grid is too coarse for field-scale operational decisions.
Use instead: local survey or administrative crop area data.
Why: the dataset holds area only.
Use instead: MapSPAM 2020 for gridded production and yield.
Commodities
Access & use
Files, endpoints, and worked examples for each distribution.
Data
One COG per crop, about 540 MB in total. The folder also holds four byte-identical duplicates with truncated names (CASHEWAPPL, CAULIFLOWE, CUCUMBERET, SWEETPOTAT); ignore them.
Format: Cloud-Optimized GeoTIFF — best for single-file downloads, visualisation, and traditional GIS tools
List of files 171 filesDownload CSV
Individual file
https://storage.googleapis.com/fao-gismgr-faostat-data/DATA/FAOSTAT/MAPSET/CG-CA/FAOSTAT.CG-CA.2020.ABACA.tif
Usage examples
import rioxarray
url = "https://storage.googleapis.com/fao-gismgr-faostat-data/DATA/FAOSTAT/MAPSET/CG-CA/FAOSTAT.CG-CA.2020.ABACA.tif"
da = rioxarray.open_rasterio(url, masked=True)
# Clip a window (East Africa)
window = da.rio.clip_box(
minx=28, miny=-12, maxx=52, maxy=18
)
# ...or sample a single point
point = da.sel(x=36.8, y=-1.3, method="nearest")library(terra)
url <- "/vsicurl/https://storage.googleapis.com/fao-gismgr-faostat-data/DATA/FAOSTAT/MAPSET/CG-CA/FAOSTAT.CG-CA.2020.ABACA.tif"
r <- rast(url)
# Clip a window (East Africa)
window <- crop(r, ext(28, 52, -12, 18))
# ...or sample a single point
point <- extract(r, cbind(36.8, -1.3))Other access methods
File pattern: FAOSTAT.CG-CA.2020.{crop}.tif
One COG per crop, about 530 MB in total.
Format: Cloud-Optimized GeoTIFF — best for single-file downloads, visualisation, and traditional GIS tools
List of files 171 filesDownload CSV
Individual file
https://storage.googleapis.com/fao-gismgr-faostat-data/DATA/FAOSTAT/MAPSET/CG-HA/FAOSTAT.CG-HA.2020.ABACA.tif
Usage examples
import rioxarray
url = "https://storage.googleapis.com/fao-gismgr-faostat-data/DATA/FAOSTAT/MAPSET/CG-HA/FAOSTAT.CG-HA.2020.ABACA.tif"
da = rioxarray.open_rasterio(url, masked=True)
# Clip a window (East Africa)
window = da.rio.clip_box(
minx=28, miny=-12, maxx=52, maxy=18
)
# ...or sample a single point
point = da.sel(x=36.8, y=-1.3, method="nearest")library(terra)
url <- "/vsicurl/https://storage.googleapis.com/fao-gismgr-faostat-data/DATA/FAOSTAT/MAPSET/CG-HA/FAOSTAT.CG-HA.2020.ABACA.tif"
r <- rast(url)
# Clip a window (East Africa)
window <- crop(r, ext(28, 52, -12, 18))
# ...or sample a single point
point <- extract(r, cbind(36.8, -1.3))Other access methods
File pattern: FAOSTAT.CG-HA.2020.{crop}.tif
Data dictionary
What each file holds. A layout describes how the data is stored in a file: its variables, the dimensions they vary over, and any keys.
File layouts
| Files | Format | Variables | Dimensions |
|---|---|---|---|
| crop-area | Cloud-Optimized GeoTIFF | crop_area | cell × crop |
| harvested-area | Cloud-Optimized GeoTIFF | harvested_area | cell × crop |
In every file
Dimensions
0.05 degree cells (~5.6 km at the equator).
Grid spacing: 0.05 degree
FAO crop code, the {crop} token of the file names, labelled with FAOSTAT item names.
Type: string
Coded against: https://data.apps.fao.org/gismgr/api/v2/catalog/workspaces/FAOSTAT/dimensions/CROP
ABACAAbacaAGAVEAgave fibres, otherALFALFAAlfalfaALMONDAlmonds, with shellANISEETCAnise, badian, fennel, corianderAPPLEApplesAPRICOTApricotsARECAAreca nutsARTICHOKEArtichokesASPARAGUSAsparagusAVOCADOAvocadosBAMBARABambara beansBANANABananasBARLEYBarleyBEANBeans, dryBEETFORBeets for fodderBERRYNESBerries, otherBLUEBERRYBlueberriesBRAZILBrazil nuts, with shellBROADBEANBroad beans, horse beans, dryBUCKWHEATBuckwheatCABBAGECabbages and other brassicasCABBAGEFORCabbages for fodderCANARYSEEDCanary seedCAROBCarobsCARROTCarrots and turnipsCARROTFORCarrots for fodderCASHEWAPPLECashewappleCASSAVACassavaCASTORCastor oil seedCAULIFLOWERCauliflowers and broccoliCEREALNESCereals, otherCHERRYCherriesCHESTNUTChest nutsCHICKPEAChick peasCHICORYChicory rootsCHILLEETCChillies and peppers, greenCINNAMONCinnamon (cannella)CITRUSNESCitrus fruit, otherCLOVEClovesCLOVERCloverCOCOACocoa, beansCOCONUTCoconutsCOFFEECoffee, greenCOTTONCotton seedCOWPEACow peas, dryCRANBERRYCranberriesCUCUMBERETCCucumbers and gherkinsCURRANTCurrantsDATEDatesEGGPLANTEggplants (aubergines)FIGFigsFLAXFlax fibre and towFONIOFonioFORNESFornesFRUITNESFruit fresh, otherGARLICGarlicGINGERGingerGOOSEBERRYGooseberriesGRAPEGrapesGRAPEFRUITGrapefruit (inc. pomelos)GRASSNESGrasses, othersGREENBEANGreen beansGREENBROADGreen broad beansGREENCORNGreen corn (maize, green)GREENONIONGreen onion, shallotsGREENPEAGreen peasGROUNDNUTGroundnuts, with shellHAZELNUTHazelnuts, with shellHEMPHemp tow wasteHEMPSEEDHempseedHOPHopsJUTEJuteJUTELIKEFIBERJute-like fibersKAPOKFIBERKapokKAPOKSEEDKapok fruitKARITEKarite nuts (sheanuts)KIWIKiwi fruitKOLANUTKola nutsLEGUMENESLegumes, otherLEMONLIMELemons and limesLENTILLentilsLETTUCELettuce and chicoryLINSEEDLinseedLUPINLupinsMAIZEMaizeMAIZEFORMaize for forage and silageMANGOMangoes, mangosteens, guavasMATEMateMELONETCMelons, other (inc. cantaloupes)MELONSEEDMelonseedMILLETMilletMIXEDGRAINMixed grainMIXEDGRASSMixed grasses and legumesMUSHROOMMushrooms and trufflesMUSTARDMustard seedNUTMEGNutmeg, mace and cardamomsNUTNESNuts, otherOATSOatsOILPALMOil palm fruitOILSEEDFOROilseed for fodder, greenOILSEEDNESOilseed, otherOKRAOkraOLIVEOlivesONIONOnions, dryORANGEOrangesPAPAYAPapayasPEAPeas, dryPEACHETCPeaches and nectarinesPEARPearsPEPPERPepper (piper spp.)PEPPERMINTPeppermintPERSIMMONPersimmonsPIGEONPEAPigeon peasPIMENTOPimentoPINEAPPLEPineapplesPISTACHIOPistachiosPLANTAINPlantains and othersPLUMPlums and sloesPOPCORNPopcornPOPPYPoppy seedPOTATOPotatoesPULSENESPulses, otherPUMPKINETCPumpkins, squash and gourdsPYRETHRUMPyrethrum, dried flowersQUINCEQuincesQUINOAQuinoaRAMIERamieRAPESEEDRapeseedRASBERRYRasberriesRICERice, paddyROOTNESRoots and tubers, otherRUBBERRubber, naturalRYERyeRYEFORRye grass for forage and silageSAFFLOWERSafflower seedSESAMESesame seedSISALSisalSORGHUMSorghumSORGHUMFORSorghum for forage and silageSOURCHERRYSour cherriesSOYBEANSoybeansSPICENESSpices, otherSPINACHSpinachSTONEFRUITStone fruit, otherSTRAWBERRYStrawberriesSTRINGBEANString beansSUGARBEETSugar beetSUGARCANESugar caneSUGARNESSugar crops, otherSUNFLOWERSunflower seedSWEDEFORSwedes for fodderSWEETPOTATOSweet potatoesTANGETCTangerines, mandarins, clementines, satsumasTAROTaro (cocoyam)TEATeaTOBACCOTobacco leavesTOMATOTomatoesTRITICALETriticaleTROPICALNESFruit tropical fresh, otherTUNGTung nutsTURNIPFORTurnips for fodderVANILLAVanillaVEGETABLENESVegetablenesVEGFORVegetables and roots for fodderVETCHVetchesWALNUTWalnuts, with shellWATERMELONWatermelonsWHEATWheatYAMYamsYAUTIAYautia (cocoyam)
Show all 171 valuesShow fewer
Only in crop-area
Variables
| Variable | Type | Unit | Description |
|---|---|---|---|
| crop_area | float32 | km2 | Physical area of the crop in the cell, counted once per year regardless of harvests. Absolute quantity; sum across cells for regional totals. |
Only in harvested-area
Variables
| Variable | Type | Unit | Description |
|---|---|---|---|
| harvested_area | float32 | km2 | Harvested area of the crop in the cell, counting each harvest in the year. Absolute quantity; sum across cells for regional totals. |
Technical metadata
The full spatial, temporal, and modeling detail behind the summaries elsewhere on this page.
Spatial
- CRS
- EPSG:4326
- Extent (W S E N)
- [-180, -90, 180, 90]
- Coverage
- World
- Grid spacing
- 3 arc-minutes (~5.6 km at the equator)
Temporal
- Reference date
- 2020
Provenance
Ordered processing steps, from source to publication.
- source2025-04-14
FAO Python re-implementation of the CROPGRIDS pipeline (Bottini et al. 2025): inputs harmonized to the grid, ranked by quality, mosaicked, gap-filled with the FAO cropland agreement map, and scaled to FAOSTAT 2020 national totals. Code not public as of this record.
How to cite
Please cite the dataset and any associated peer-reviewed publications.
Food and Agriculture Organization of the United Nations (2025) CROPGRIDS Global Geo-referenced Dataset of 173 Crops. Version 2.0. FAO Agro-Informatic Data Catalog. https://data.apps.fao.org/catalog/dataset/cropgrids. Accessed through the CGIAR Climate Action Data Hub, https://cgiar-climate-data-hub.github.io/catalog/cropgrids_2.0/.
BibTeX
@misc{cropgrids_2025,
title = {CROPGRIDS Global Geo-referenced Dataset of 173 Crops},
author = {{Food and Agriculture Organization of the United Nations}},
year = {2025},
version = {2.0},
publisher = {FAO Agro-Informatic Data Catalog},
url = {https://data.apps.fao.org/catalog/dataset/cropgrids},
note = {Accessed through the CGIAR Climate Action Data Hub, https://cgiar-climate-data-hub.github.io/catalog/cropgrids_2.0/}
}Contacts
Who produced this dataset, and who to reach about it.