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The increasing changes in climate are threatening livestock productivity in parts of Africa, particularly in East Africa. This in turn, is having a ripple effect on food security, rural livelihoods, and greenhouse gas emissions. The traditional methods for estimating livestock carrying capacity face significant limitations through localised ground surveys or process-based models. Ground surveys cannot capture spatial variability or the dynamic nature of farms at regional scales, while process-based models require extensive calibration and are often unsuitable for data-scarce regions such as East Africa.
However, a recent study published in Regional Environmental Change (August 2025) introduced a novel machine learning approach that integrates remote sensing-derived biomass data with climate projections to estimate future changes in livestock carrying capacity and identify the key drivers behind these changes. The study’s findings reveal substantial declines in carrying capacity, particularly across mixed crop-livestock rainfed temperate systems, and emphasise the need to strengthen monitoring frameworks.
At the same time, Kenya, Tanzania, and Uganda have opportunities to leverage projected increases in carrying capacity to promote sustainable productivity growth while prioritising low-emission livestock development.
August 26, 2025/East Africa/
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