Simplified climate change adaptation strategies for livestock development in low-and middle-income countries
Bibliographic record
Abstract
Climate change, characterized by the increased frequency and intensity of extreme weather events, is the greatest environmental challenge threatening global food systems. Its impacts are particularly severe for livestock production systems in developing countries. In low-and middle-income countries (LMICs), livestock production provides critical livelihoods for millions of vulnerable people and plays a significant role in food security. However, the sector is highly susceptible to the adverse effects of climate change. Climate change in LMICs is associated with erratic rainfall, rising temperatures, flooding, drought, desertification, and a higher frequency of extreme weather events. In particular, when temperatures exceed the thresholds projected by the Intergovernmental Panel on Climate Change (IPCC), livestock are subjected to heat stress, which reduces productivity, lowers conception rates, and can be life-threatening for many species. In response, various climate adaptation strategies have been implemented to enhance resilience in livestock production systems. This review evaluates existing adaptation strategies including their effectiveness in LMICs and proposes simplified and targeted adaptation strategies to build resilience in livestock production systems. Key adaptation measures include genetic improvement and diversification of livestock species, early warning systems, precision livestock farming technologies, climate-smart strategies, institutional and policy frameworks and capacity-building initiatives. Further, key factors influencing adaptation strategies outcomes such as governance, financial investment, community engagement, and technological infrastructure were highlighted. While some strategies such as breeding programs for heat-tolerant livestock and early warning systems have yielded positive results, challenges including limited financial resources, weak institutional frameworks, and resistance to change hinder their widespread adoption. The review also provides recommendations for improving adaptation strategies, including enhanced investment in data-enabled innovations, integration of climate adaptation policies into national development plans, and increased participatory approaches involving local livestock farmers. In conclusion, this study provides a roadmap for building climate-resilient livestock production systems in LMICs to ensure sustainable food production and improved livelihoods under changing climate.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".