Review: Adaptation of ruminant livestock production systems to climate changes
Bibliographic record
Abstract
There is growing evidence on the extent to which projected changes in climate, including increases in atmospheric levels of carbon dioxide, higher temperatures, changes in amount, seasonality and variability of precipitation and increases in extreme weather events, may affect future availability of ruminant animal products. Elements of climate change affect livestock systems through direct impacts on animal physiology, behaviour, production and welfare and indirectly through feed availability, composition and quality. These impacts may be positive or negative and will vary across geographical regions, animal species and with adaptive capacity. However, adverse impacts are likely to be greatest in tropical and sub-tropical regions including countries where both current need and future growth in demand for nutrition is greatest. The complexity of effects means that effective adaptation strategies to mitigate negative impacts on ruminant production systems to climate changes will need to be multi-dimensional. Although predictions of future climate, particularly on regional and local scales, have a degree of uncertainty, adaptation planning is starting to be informed by changes already being observed and adjustments in management being made by farmers to maintain productivity and profitability. Regional case studies illustrate the benefits and limitations of adaptive management: potential mitigation through heightened awareness of heat stress-related mortality in French cattle; evidence of a drop in milk production in south-eastern Australian dairies during a January 2014 heat wave, from the theoretical potential of 53% to only 10% across the state; and limitations in response options to climate-induced thermal, nutritional and water stress for sheep and goat farmers in northern Ethiopia. Review of research on climate change impacts on ruminant livestock and effective adaptation together with evidence of practical adaptive management provide insights into potential strategies and gaps in knowledge to address challenges and improve future decisions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".