Published June 30, 2026 | Version v1

Global Pasture Watch - Annual livestock headcount layers for cattle, goats, sheep, horses, and buffaloes at 1-km 2000–2022 (FAOSTAT-adjusted) (Part-1)

  • 1. OpenGeoHub Foundation
  • 2. ROR icon German Centre for Integrative Biodiversity Research
  • 3. OpenGeoHub foundation
  • 4. Envirometrix Ltd
  • 5. IIASA
  • 6. ROR icon International Institute for Applied Systems Analysis
  • 7. ROR icon Vrije Universiteit Amsterdam
  • 8. ROR icon University of Ljubljana
  • 9. ROR icon Cornell University
  • 10. German Centre For Integrtaive Biodiversity Research (iDiv) Halle-Jena-Leipzig
  • 11. ROR icon Food and Agriculture Organization of the United Nations
  • 12. ROR icon World Resources Institute

Description

Global annual layers of livestock distribution of cattle, goats, sheep, horses and buffaloes at 1-km spatial resolution convering the period of 2000–2022. The layers were produced using harmonized and used as reference data (55,336 census polygons and 939,257 individual data entries), Random Forest predictive models and a large stack of multi-source harmonized gridded/raster spatial layers (128 individual raster spatial layers harmonized at 1 km spatial resolution). Modeling areas were restricted to annual layer of potential land for livestock production, derived from Landsat-based cropland and grassland extents from 2000 to 2022.

Raster cell values represent number of livestock animals (headcounts) calibrated according to FAOSTAT database. The adjustment was applied individually to each country, constrainting national totals to FAOSTAT but without validate where within a country livestock are placed; the subnational allocation is modeled and should be treated as such. For subnational applications requiring high precision, users are encouraged to apply local calibration factors from national agencies rather than relying entirely on the FAOSTAT-adjusted estimates.

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Additional details

Funding

European Commission
OEMC - Open-Earth-Monitor Cyberinfrastructure 101059548
World Resources Institute
Land & Carbon Lab land-carbon-lab

Dates

Created
2025-03-03

Software

Repository URL
https://github.com/wri/global-pasture-watch
Programming language
Python
Development Status
Active