Climate change impacts livestock carrying capacity in East Africa
Bibliographic record
Abstract
Abstract 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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".