Global Pasture Watch - Annual cattle density maps at 1-km for 2000–2022 including prediction interval

Global annual maps of cattle density at 1-km spatial resolution convering the period of 2000–2022. The maps were produced using harmonized and used as reference data (52,883 census polygons and 678,266 individual data points), random forest predictive models using and a large stack of multi-source harmonized gridded/raster spatial layers (307 individual raster spatial layers harmonized at 1 km spatial resolution). 

Pixels values represent heads km-square including:

• Mean predicted values (_m_)
• Upper prediction interval based on 97.5th percentiles (_p.975_)
• Lower prediction interval based on 2.5th percentiles (_p.025_)

 Based on 95% probability quantiles, prediction intervals are relatively wide; therefore, for a more effective use, we recommend converting them to standard deviation by dividing the range (p.975 - p.025) by four.

Raw/Uncalibrated headcounts are also provided and were computed by multiplying the density values by the actual area of potential land for livestock production.

In line with a request from our funders, livestock maps will remain under embargo in Zenodo until the final acceptance of peer-reviewed publication. They can be accessed during the reviewing process by filling-in a form via Global Pasture Watch Early Access data program (https://survey.alchemer.com/S3/7859804/Pasture-Early-Adopters). All modeling framework presented in this work is publicly available at: https://github.com/wri/global-pasture-watch. We are currently preparing the data to be ingested in STAC and Google Earth Engine.

Related resources

• Maps of livestock headcount for 2000—2022 (FAOSTAT-adjusted):
  All animals
• Maps of livestock density for 2000—2022 (including prediction interval):
  Cattle  Goat   Sheep  Horse
• Livestock reference census data (2000–2022):
  CSV and GeoPackage files
• Maps potential land for livestock production for 2000—2022 (including production systems)
  Raster files
• Data catalogues:
  OpenLandMap STAC Google Earth Engine