Global Pasture Watch - Annual goat density layers at 1-km for 2000–2022 (including 95% prediction interval)

Parente, L., Ehrmann, S., Hengl, T., Fritz, S. ORCID: https://orcid.org/0000-0003-0420-8549, Bonannella, C., Malek, Z. ORCID: https://orcid.org/0000-0002-6981-6708, Gonzalez Fischer, C., Pérez Guzmán, K. ORCID: https://orcid.org/0000-0001-5189-6570, Stanimirova, R., Meyer, C., Wisser, D., Cinardi, G., & Sloat, L. (2025). Global Pasture Watch - Annual goat density layers at 1-km for 2000–2022 (including 95% prediction interval). 10.5281/zenodo.17490112.

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Abstract

Global annual layers of goat density 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 172,536 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).

Raster cell values represent heads km2 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 Layers 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.

Item Type:	Data
Additional Information:	Creative Commons Attribution 4.0 International
Research Programs:	Advancing Systems Analysis (ASA) Advancing Systems Analysis (ASA) > Novel Data Ecosystems for Sustainability (NODES) Strategic Initiatives (SI) Advancing Systems Analysis (ASA) > Cooperation and Transformative Governance (CAT) Advancing Systems Analysis (ASA) > Exploratory Modeling of Human-natural Systems (EM)
Depositing User:	Luke Kirwan
Date Deposited:	08 Jan 2026 14:40
Last Modified:	09 Jan 2026 09:17
URI:	https://pure.iiasa.ac.at/21154

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