Invited review: Effects of heat stress on dairy cattle welfare
Bibliographic record
Abstract
The effects of high ambient temperatures on production animals, once thought to be limited to tropical areas, has extended into northern latitudes in response to the increasing global temperature. The number of days where the temperature-humidity index (THI) exceeds the comfort threshold (>72) is increasing in the northern United States, Canada, and Europe. Compounded by the increasing number of dairy animals and the intensification of production, heat stress has become one of the most important challenges facing the dairy industry today. The objectives of this review were to present an overview of the effects of heat stress on dairy cattle welfare and highlight important research gaps in the literature. We will also briefly discuss current heat abatement strategies, as well as the sustainability of future heat stress management. Heat stress has negative effects on the health and biological functioning of dairy cows through depressed milk production and reduced reproductive performance. Heat stress can also compromise the affective state of dairy cows by inducing feelings of hunger and thirst, and we have highlighted the need for research efforts to examine the potential relationship between heat stress, frustration, aggression, and pain. Little work has examined how heat stress affects an animal's natural coping behaviors, as well as how the animal's evolutionary adaptations for thermoregulation are managed in modern dairy systems. More research is needed to identify improved comprehensive cow-side measurements that can indicate real-time responses to elevated ambient temperatures and that could be incorporated into heat abatement management 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".