Adapting East and Southern Africa’s livestock to climate change: a decision making under deep uncertainty-based approach for effective actions
Bibliographic record
Abstract
Livestock farmers are increasingly challenged to adapt to the impacts of climate change, necessitating the selection of adaptation strategies to effectively mitigate risks and protect livelihoods. This paper introduces a framework designed specifically for guiding the selection of context-specific adaptation options in the Eastern and Southern Africa region. The framework builds on a decision tree that incorporates changes within a management system or switching to another one, enabling a nuanced evaluation of adaptation options. Driven repetitively under different scenarios of climate changes and/or climate models, the frequencies of selecting different adaptation measures vary across livestock value chains, climate zones, and systems. Responding to the evolution of the climate system, these frequencies evolve over time, affecting the selection. For instance, agroforestry emerges as an increasingly suitable option for cattle and, to a lesser extent, for goats due to the projected rise in moderate heat stress periods, particularly in tropical climates. Conversely, this frequency decreases for sheep, more susceptible to heat stress, beyond the effect of agroforestry. This framework resolves the need for more context – and time-specific decisions on adaptation. This decision tree-based framework serves as a robust decision-making tool to steer the livestock sector toward effective climate change adaptation.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| 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".