Climate change is increasingly threatening livestock production in East Africa, with serious implications for food security, rural livelihoods, and greenhouse gas emissions. Traditional methods of estimating livestock carrying capacity—either through localized ground surveys or process-based models—have significant limitations. Ground surveys cannot capture spatial variability or the dynamic nature of rangelands at regional scales, while process-based models require extensive calibration and are often unsuitable for data-scarce regions such as East Africa.
A recent study published in Regional Environmental Change (August 2025) introduced a novel machine learning approach that combines remote sensing-derived biomass data with climate projections to estimate future changes in livestock carrying capacity and identify the main drivers behind these changes. The study’s findings indicate substantial declines in carrying capacity, particularly across mixed crop-livestock rainfed temperate systems. In Ethiopia, the dominant production system in this category could see reductions of up to 37% in tropical livestock units, while Kenya may face declines of up to 24%, with moderate reductions expected in Uganda. Some production systems in Kenya and Uganda, however, are projected to experience modest increases in carrying capacity.
Key climatic drivers behind these declines include increased precipitation during the wettest quarter, reduced temperature seasonality, and higher temperatures during the driest quarter. The study underscores the urgent need for tailored adaptation strategies, especially in Ethiopia’s mixed crop-livestock rainfed temperate systems, with an emphasis on strengthening monitoring systems. Concurrently, Kenya, Tanzania, and Uganda have opportunities to leverage projected increases in carrying capacity to promote sustainable productivity growth while prioritizing low-emissions livestock development.
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