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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
Leandro Parente, Steffen Ehrmann, Tomislav Hengl, Steffen Fritz, Carmelo Bonannella, Žiga Malek, Dominik Wisser, Giuseppina Cinardi, Lindsey Sloat
Izvirni znanstveni članek
Oznake: census data;livestock;machine learning;areal regression;agriculture;random forest;gradient boosting tree;spatiotemporal modeling;global mapping;open data;
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 ...
Leto: 2026 Vir: Biotehniška fakulteta (UL BF)
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