Do Dairy Cattle Need Protection against Weather in a Temperate Climate? A Review
Bibliographic record
Abstract
Information on effects of weather conditions on milk production of dairy cows is rather scarce. Legislation exists in some countries saying when and how protection should be available for cows on pasture. Producers refer to the extra costs, and are not always convinced of the return of investment. Therefore, (re)production variables of high producing dairy cows were reviewed in relation to weather conditions in a mild climate. The objective was to understand mechanisms cows are using for acclimatization, which might affect (re)production, and to propose managing tools. An overall critical dry air temperature seems to be about 16 °C, with cumulative interactions from relative humidity, wind speed, radiation and rain fall. The explanation is related to the cow’s thermoregulatory physiology associated with her heat and energy balance, as a primary need. Modulating factors, such as breed, individual capacity, feed composition and farm management have to be taken into account. The effects have to be considered as important at herd level, especially in a system with year round calving, since production might be below peak production up to six months of the year. Planning of day of calving should avoid peak production during summer, since mild heat stress might counteract the expression of genetic progress for (re)production. However, since the most important factor seems to be the level of dry air temperature, the effects will be independent of protection or not. Hence, there will be no direct return of investment moneywise, but indirectly as appreciation from society for animal welfare.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".