| Sumario: | Climate change poses a significant threat to livestock production in East Africa, with major implications for food security, rural livelihoods, and greenhouse gas emissions. Existing approaches to livestock carrying capacity often rely on either localized ground surveys, which are insufficient for capturing the spatial variability and dynamic responses of rangelands at regional scales, or on process-based models, which require extensive calibration and are often unsuitable for data-scarce regions such as East Africa. Here, we address this gap by developing a novel machine learning-based approach that integrates remote sensing-derived biomass data with climate projections to estimate future changes in livestock carrying capacity and to diagnose their primary drivers. Our results project substantial declines in carrying capacity, particularly across mixed crop-livestock rainfed temperate systems. For example, reductions of up to 37% in tropical livestock units (TLU) are projected in Ethiopia’s dominant mixed crop-livestock rainfed temperate system, while Kenya is expected to experience up to a 24% reduction in the same production system, alongside moderate declines in Uganda. Modest increases are projected for some production systems, especially in parts of Uganda and Kenya. The main climatic drivers underlying the projected declines include increased precipitation during the wettest quarter, decreased temperature seasonality, and increased temperature during the driest quarter. Our findings highlight the urgency of implementing tailored adaptation strategies in the mixed crop-livestock rainfed temperate systems, especially in Ethiopia, with a focus on strengthening monitoring systems. Simultaneously, Uganda, Tanzania, and Kenya should capitalize on projected increases in carrying capacity, promoting sustainable productivity growth while prioritizing low-emissions livestock development.
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