Impact of heat stress on dairy cattle and selection strategies for thermotolerance: a review
Bibliographic record
Abstract
Climate change is a problem that causes many environmental issues that impact the productivity of livestock species. One of the major issues associated with climate change is an increase of the frequency of hot days and heat waves, which increases the risk of heat stress for livestock species. Dairy cattle have been identified as being susceptible to heat stress due to their high metabolic heat load. Studies have shown heat stress impacts several biological processes that can result in large economic consequences. When heat stress occurs, dairy cattle employ several physiological and cellular mechanisms in order to dissipate heat and protect cells from damage. These mechanisms require an increase and diversion in energy toward protection and away from other biological processes. Therefore, in turn heat stress in dairy cattle can lead numerous issues including reductions in milk production and reproduction as well as increased risk for disease and mortality. This indicates a need to select dairy cattle that would be thermotolerant. Various selection strategies to confer thermotolerance have been discussed in the literature, including selecting for reduced milk production, crossbreeding with thermotolerant breeds, selecting based on physiological traits and most recently selecting for enhanced immune response. This review discusses the various issues associated with heat stress in dairy cattle and the pros and cons to the various selection strategies that have been proposed to select for thermotolerance in dairy cattle.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".