ArticlePeerJ2026
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.
Article in PeerJ, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Potential distribution, range dynamics, and livestock exposure risk ofFrontiers in plant science · 2026Article
- Projecting the impacts of climate and land-use change on avian influenza suitability in Bangladesh.One health (Amsterdam, Netherlands) · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
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
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.