Global Spatially-Disaggregated Crop Production Statistics Data for 2020 Version 2.0 Release 2, represented as a gridded crop production data cube. The record describes crop area, harvested area, production, and yield by crop and production technology at 5 arc-minute resolution.

#food security #crop production #spatial distribution #gridded agriculture

Important note

Derived from available subnational statistics; local national or subnational data providers may hold more precise source statistics for some locations.

Quick start

Python

import xarray as xr

url = "https://digital-atlas.s3.amazonaws.com/cdh/data/mapspam2020-v2r2/spam2020-v2r2.zarr"
ds = xr.open_zarr(url)

import rioxarray

url = "https://digital-atlas.s3.amazonaws.com/cdh/data/mapspam2020-v2r2/cog/spam2020-all-physical_area.tif"
da = rioxarray.open_rasterio(url, masked=True)

library(stars)

url <- 'ZARR:"/vsicurl/https://digital-atlas.s3.amazonaws.com/cdh/data/mapspam2020-v2r2/spam2020-v2r2.zarr"'
ds <- read_mdim(url, proxy = TRUE)

library(terra)

url <- "/vsicurl/https://digital-atlas.s3.amazonaws.com/cdh/data/mapspam2020-v2r2/cog/spam2020-all-physical_area.tif"
r <- rast(url)

Full access details and endpoints

Intended uses

global and regional analysis of crop area, production, and yield patterns
food security and agricultural systems assessment
spatial targeting at national and sub-national scale

Not recommended for

field-scale farm management

Why: the grid is too coarse for field-scale operational decisions.

Use instead: local survey or administrative production data.

Commodities

Variables

Variable	Type	Unit	Description
physical_area	float32	ha	Physical crop area allocated to each grid cell.
harvested_area	float32	ha	Harvested crop area allocated to each grid cell.
production	float32	t	Crop production allocated to each grid cell. Absolute quantity; regional summaries should sum values across cells.
yield	float32	t ha-1	Crop yield for each grid cell. Relative quantity; do not sum yield values across grid cells. Regional summaries should use a weighted mean with harvested_area as the weight.

Dimensions

Axes of the cube — spatial, temporal, and domain axes.

cropcrop

MAPSPAM crop code. Full labels are provided in the dimension codes asset.

whearicemaizbarlmillpmilsorgocerpotaswpoyamscassortsbeanchiccowppigelentopulsoybgroucnutoilpsunfrapesesaooilsugcsugbcottofibcoffrcofcocoteastobabanaplntcitrtroftemftomaoniovegerubbrest

technologytechnology

MAPSPAM production system code. Full labels are provided in the dimension codes asset.

allirrigatedrainfed

Access & use

Cloud-optimised distributions — read just the window you need directly from object storage.

Machine-readable file index: STAC Collection

Data

zarr500 MB

Zarr representation of the source MAPSPAM files.

Format: Zarr — best for cloud-native analysis, reading just the slices you need from the full cube

No Data: -9999

Access the data

https://digital-atlas.s3.amazonaws.com/cdh/data/mapspam2020-v2r2/spam2020-v2r2.zarr

Other access methods

S3:s3://digital-atlas/cdh/data/mapspam-2020-v2r2/spam2020-v2r2.zarr

zarr — usage examples

Python

import xarray as xr

url = "https://digital-atlas.s3.amazonaws.com/cdh/data/mapspam2020-v2r2/spam2020-v2r2.zarr"
ds = xr.open_zarr(url)

# Clip a window (East Africa) — subsets every variable in the store
window = ds.sel(x=slice(28, 52), y=slice(18, -12))

# ...or sample a single point
point = ds.sel(x=36.8, y=-1.3, method="nearest")

cogs

Cloud Optimized GeoTIFFs of the MAPSPAM files.

Format: Cloud-Optimized GeoTIFF — best for single-file downloads, visualisation, and traditional GIS tools

No Data: -9999

Access a specific fileDownload

TechnologyVariable

https://digital-atlas.s3.amazonaws.com/cdh/data/mapspam2020-v2r2/cog/spam2020-all-physical_area.tif

Other access methods

File pattern: spam2020-{technology}-{variable}.tif

HTTPS directory:https://digital-atlas.s3.amazonaws.com/cdh/data/mapspam2020-v2r2/cog/

S3 directory:s3://digital-atlas/cdh/data/mapspam-2020-v2r2/cog/

cogs — usage examples

Python

import rioxarray

url = "https://digital-atlas.s3.amazonaws.com/cdh/data/mapspam2020-v2r2/cog/spam2020-all-physical_area.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://digital-atlas.s3.amazonaws.com/cdh/data/mapspam2020-v2r2/cog/spam2020-all-physical_area.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))

Additional assets

crop codes5 KBDownload

Dimension code list for crop values.

Format: application/json

Access the data

https://digital-atlas.s3.amazonaws.com/cdh/data/mapspam-2020-v2r2/crop-codes.json

Technical metadata

The full spatial, temporal, and modelling detail behind the summaries elsewhere on this page.

Spatial

CRS: EPSG:4326
Extent (W S E N): [-180, -90, 180, 90]
Coverage: World
Resolution (xy): 5 arc-minutes (~10km at equator)

Temporal

Reference date: 2020

Record

Resource type: dataset
Created: 2026-07-06
Updated: 2026-07-06

Provenance

Ordered processing steps, from source to publication.

source2026-04-22
Original MAPSPAM 2020 v2 release.
Derived from: Source MAPSPAM dataset
code: https://github.com/michielvandijk/mapspamc @ v0.0.2
cloud-optimize2026-07-07
Conversion of source MAPSPAM files to a consolidated Zarr data cube.
code: https://github.com/CGIAR-Climate-Data-Hub/cdh-data-pipeline/blob/main/recipes/mapspam2020.py @ 47f9830

How to cite

Format

Please cite the dataset and any associated peer-reviewed publications.

International Food Policy Research Institute (IFPRI) (2026). Global Spatially-Disaggregated Crop Production Statistics Data for 2020 Version 2.0 Release 2 (Version v2r2) [Data set]. Harvard Dataverse. https://doi.org/10.7910/DVN/SWPENT Accessed through the CGIAR Climate Action Data Hub, https://cgiar-climate-data-hub.github.io/catalog/spam2020/.

BibTeX

@misc{spam2020_2026,
  title     = {Global Spatially-Disaggregated Crop Production Statistics Data for 2020 Version 2.0 Release 2},
  author    = {International Food Policy Research Institute (IFPRI)},
  year      = {2026},
  version   = {v2r2},
  publisher = {Harvard Dataverse},
  doi       = {10.7910/DVN/SWPENT},
  note      = {Accessed through the CGIAR Climate Action Data Hub, https://cgiar-climate-data-hub.github.io/catalog/spam2020/}
}

Related publications

You, L., Wood, S., Wood-Sichra, U., Wu, W. (2014). Generating global crop distribution maps: From census to grid. Agricultural Systems. doi:10.1016/j.agsy.2014.01.002

Contacts

Who produced this dataset, and who to reach about it.

International Food Policy Research Institute (IFPRI)Licensor

www.ifpri.org/ ↗

Zhe Guo

International Food Policy Research Institute (IFPRI)

Producer

z.guo@cgiar.org www.ifpri.org/ ↗

Shuang Zhou

International Food Policy Research Institute (IFPRI)

Producer

s.zhou@cgiar.org www.ifpri.org/ ↗

Liangzhi You

International Food Policy Research Institute (IFPRI)

Producer

MAPSPAM 2020

Important note

Quick start

Intended uses

Not recommended for

Commodities

Variables

Dimensions

Access & use

Data

Additional assets

Technical metadata

Spatial

Temporal

Record

Provenance

How to cite

Related publications

Contacts