Dairy cow behaviour and estrous expression : effects of disease and management
Bibliographic record
Abstract
Management of modern dairy farms spend a considerable amount of time ensuring that every cow produces a calf every year, that all cows stay healthy. Despite these laudable goals, fertility and disease continue to be two of the major challenges facing the dairy industry. In this thesis I address both of these areas in an attempt to improve the health and welfare of dairy cattle and to aid farmers in the early identification of sick cows and identifying ways of improving fertility on their farms. My first aim was to assess changes in rumination and feeding behaviour associated with diseases common after calving (Chapter 2). The results indicate that monitoring rumination and feeding behaviour during the time around calving is helpful in the detection of cows with metabolic problems. The second aim focused on understanding how stocking density affects estrous expression and biomarkers of stress (Chapter 3). The results from Chapter 3 showed only mild effects of short-term exposure of lactating dairy cows to housing conditions where there is insufficient lying space for all cows to lie down at the same time, suggesting that many dairy cows are relatively resilient to short-term sub-optimal housing conditions. Lastly, I examined how standing and lying time, in general and around estrus, are affected by stocking density (Chapter 4). This chapter showed that even a short-term increase in stocking density to 133% (cow to stall ratio of 4:3) can have detrimental effects on the standing behaviour of healthy lactating dairy cattle and that individual standing times can be an indicator for the onset of estrus, particularly in understocked cows. In summary, this body of work shows how an improved understanding of behaviour can identify cows at risk for disease, that the choices made by farmers regarding how much lying space cows are given can affect standing and lying behaviour, and that changes in standing behaviour can be used detect estrus in dairy cows.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".