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PeerJ2026; 14; e21494; doi: 10.7717/peerj.21494

Global distribution of cattle, horses, goats, sheep and buffaloes at 1 km resolution for 2000-2022 based on subnational census data and spatiotemporal machine learning.

Abstract: The article describes the production and evaluation of annual livestock densities and headcounts of cattle, horses, sheep, goats and buffaloes (including 95% probability prediction intervals) at 1 km spatial resolution for the 2000-2022 period using spatiotemporal machine learning. A compilation of subnational livestock census data has been imported, harmonized and used as reference data (55,336 census polygons and 939,257 individual data entries; covering 147 countries) to build predictive models. A large stack of multi-source harmonized raster data sets (128 individual layers) were used as features. Models were fitted using scikit-map and scikit-learn libraries with recursive feature elimination and Poisson criteria to represent the distribution of the target variable. Intermediate rasters estimating potential land for livestock production based on grassland and cropland extent, along with biophysical features, were used to estimate the spatial domain of livestock. The final predictions at 1 km were further adjusted to annual headcounts based on Food and Agriculture Organization Corporate Statistical (FAOSTAT) national database to ensure consistency. Model benchmarking based on 10% test samples (with spatial blocking) shows that Random Forest outperforms Gradient Boosting Tree for predicting livestock densities, with concordance correlation coefficient (CCC) values of 0.603, 0.547, 0.622, 0.598, 0.689, and Root Mean Squared Error (RMSE) values of 104.59, 6.06, 67.57, 64.09, 30.37 (heads per km) for cattle, horses, sheep, goats and buffaloes. Feature importance analysis shows that the key variables include climate and socio-economic layers, such as water vapor, aridity index, land surface temperature, travel time to the nearest cities, and the spatial distribution of religious groups. Further evaluation of the output layers shows similar distributions to existing global livestock products (FAO Gridded Livestock of The World-GLW, and Annual Gridded Livestock of the World-AGLW). The spatial domain of livestock (active grazing/forage areas) is often difficult to validate, with many countries having very specific management cultures that can not be seamlessly represented using existing global raster layers, hence modeling distribution of livestock per country using local country-specific features (instead of using global models) could help increase accuracy, specially for regional/local applications. The modeling pipeline is open source and available on GitHub (https://github.com/wri/global-pasture-watch) with output layers (both original ML predictions and FAOSTAT-adjusted values) publicly available under Creative Commons Attribution (CC-BY) license on Zenodo (https://doi.org/10.5281/zenodo.17491242).
© 2026 Parente et al.
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Publication Date: 2026-07-17 PubMed ID: 42519147PubMed Central: PMC13383963DOI: 10.7717/peerj.21494Google Scholar: Lookup
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Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

Overview

  • This research presents high-resolution, annually updated maps of livestock densities and populations (cattle, horses, goats, sheep, and buffaloes) from 2000 to 2022 using advanced machine learning techniques and extensive subnational census data.
  • The study combines census data with environmental and socio-economic factors to predict livestock distribution at a fine 1 km resolution globally, adjusting the results to official national statistics for accuracy.

Data Collection and Preparation

  • Compiled a vast dataset of livestock census information:
    • 55,336 census polygons and 939,257 individual data entries.
    • Coverage spanning 147 countries worldwide.
  • Harmonized this census data to serve as reference input for the predictive models.
  • Collected 128 harmonized raster data layers representing diverse environmental and socio-economic features to use as model covariates.

Modeling Approach

  • Used spatiotemporal machine learning frameworks implemented via scikit-map and scikit-learn libraries.
  • Employed:
    • Recursive feature elimination to select important predictors.
    • Poisson criteria suitable for count data to model livestock headcounts at fine spatial scale.
  • Included intermediate raster layers estimating potential livestock land based on:
    • Grassland and cropland extents.
    • Relevant biophysical features to define livestock spatial domain.
  • Model output predictions at 1 km resolution were then adjusted to align with official national livestock counts from the FAOSTAT database, ensuring consistency with reported data.

Model Validation and Performance

  • Model benchmarking used 10% spatially blocked test samples to evaluate predictive accuracy.
  • Compared two machine learning algorithms:
    • Random Forest demonstrated better performance than Gradient Boosted Trees.
  • Quality metrics for the Random Forest model include:
    • Concordance correlation coefficients (CCC) between 0.547 and 0.689 across species, reflecting strong agreement between predictions and observations.
    • Root Mean Squared Errors (RMSE) showing typical deviations in heads per square kilometer, varying by species (e.g., 104.59 for cattle, 30.37 for buffaloes).

Important Predictors

  • Key variables influencing livestock distribution were identified as:
    • Climate-related factors such as water vapor, aridity index, and land surface temperature.
    • Socio-economic variables like travel time to nearest cities and spatial distribution of religious groups, indicating anthropogenic influences on livestock patterns.

Comparison and Limitations

  • The predicted livestock patterns were compared with existing global datasets such as FAO’s Gridded Livestock of the World (GLW) and Annual Gridded Livestock of the World (AGLW), showing similar distribution patterns.
  • Challenges noted in accurately capturing livestock spatial domains due to:
    • Country-specific livestock management cultures and practices.
    • Limitations of global raster data in representing local grazing and forage areas.
  • Authors suggest that improving regional or local modeling by incorporating country-specific features could enhance accuracy for certain applications.

Data Availability and Tools

  • The entire modeling pipeline is open source and available on GitHub, allowing reproducibility and further development: https://github.com/wri/global-pasture-watch.
  • Output livestock density and headcount maps (both raw machine learning predictions and FAOSTAT-adjusted data) are publicly accessible:

Significance of the Study

  • This work provides the most detailed spatial and temporal view to date on global livestock distribution, crucial for:
    • Supporting agricultural planning and food security assessments.
    • Understanding environmental impacts of livestock production.
    • Enabling targeted interventions and sustainable livestock management worldwide.
  • The fusion of extensive census data with advanced modeling and the provision of uncertainty estimates (95% prediction intervals) enhances confidence and utility of the data products.

Cite This Article

APA
Parente L, Ehrmann S, Hengl T, Fritz S, Bonannella C, Malek Ž, Gonzalez Fischer C, Perez K, Stanimirova R, Meyer C, Wisser D, Cinardi G, Sloat L. (2026). Global distribution of cattle, horses, goats, sheep and buffaloes at 1 km resolution for 2000-2022 based on subnational census data and spatiotemporal machine learning. PeerJ, 14, e21494. https://doi.org/10.7717/peerj.21494

Publication

PeerJ
ISSN: 2167-8359
NlmUniqueID: 101603425
Country: United States
Language: English
Volume: 14
Pages: e21494
PII: e21494

Researcher Affiliations

Parente, Leandro
  • OpenGeoHub Foundation, Doorwerth, Gelderland, Netherlands.
Ehrmann, Steffen
  • German Centre for Integrative Biodiversity Research (iDiv), Leipzig, Germany.
  • Institute of Biology, Universität Leipzig, Leipzig, Germany.
Hengl, Tomislav
  • OpenGeoHub Foundation, Doorwerth, Gelderland, Netherlands.
Fritz, Steffen
  • International Institute for Applied Systems Analysis (IIASA), Vienna, Austria.
Bonannella, Carmelo
  • OpenGeoHub Foundation, Doorwerth, Gelderland, Netherlands.
Malek, Žiga
  • Biotechnical Faculty, University of Ljubljana, Ljubljana, Slovenia.
Gonzalez Fischer, Carlos
  • Department of Global Development, Cornell University, Ithaca, United States.
Perez, Katya
  • International Institute for Applied Systems Analysis (IIASA), Vienna, Austria.
Stanimirova, Radost
  • World Resources Institute, Washington, D.C., United States.
Meyer, Carsten
  • German Centre for Integrative Biodiversity Research (iDiv), Leipzig, Germany.
  • Institute of Biology, Universität Leipzig, Leipzig, Germany.
Wisser, Dominik
  • Animal Production and Health Division, Food and Agriculture Organization of the United Nations|FAO, Rome, Italy.
Cinardi, Giuseppina
  • Animal Production and Health Division, Food and Agriculture Organization of the United Nations|FAO, Rome, Italy.
Sloat, Lindsey
  • World Resources Institute, Washington, D.C., United States.

MeSH Terms

  • Animals
  • Machine Learning
  • Sheep
  • Cattle
  • Goats
  • Buffaloes
  • Horses
  • Predictive Learning Models
  • Livestock
  • Random Forest
  • Spatio-Temporal Analysis

Conflict of Interest Statement

The authors declare that they have no competing interests.

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