Effect of milk production on reproductive performance in dairy herds
Bibliographic record
Abstract
The objective of the present study was to assess the relationship between individual cow milk yield and fertility, accounting for the contextual effect of the herd. A data set including 657,968 lactations from 677 dairy herds in Argentina from 2001 to 2012 was used. The odds of pregnancy by 100 d in milk (DIM) were assessed by a multilevel logistic model (with cow as the first and herd as the second hierarchical level), and time to pregnancy was assessed by a proportional hazards regression model. Multilevel logistic models included the fixed effects of milk yield by 80 DIM, parity, year, and calving season at cow level and quartiles of herd milk yield by 80 DIM as a contextual effect. The proportional hazards model included the effect of daily cow-level milk yield as time-dependent variable, with milk yield at herd level as the stratification variable. Cows producing 1 standard deviation over the mean milk yield of their herd had 1.3 percentage point lower pregnancy by 100 DIM (from 31.4 to 30.1%; odds ratio = 0.942) when in herds in the top quartile of milk yield, whereas they increased 0.5 percentage points (from 27.9 to 28.4%) when in herds in the lowest quartile of milk yield. Only 4% of the observed variation in pregnancy by 100 DIM was explained by the random effect of the herd. Similarly, cows producing 1 standard deviation (8 kg/d) greater than the herd mean daily milk had 1.3% lower hazard of pregnancy (hazard ratio = 0.987) at 63 DIM in herds in the top quartile of milk yield, whereas they had 14.8% higher hazard (hazard ratio = 1.148) in herds in the lowest quartile of milk yield. The magnitude of the negative association between the cow's daily milk yield and the hazard of pregnancy increased with DIM. In conclusion, the relationship between milk yield and reproductive performance is statistically significant, but the effect size is practically small and is modulated by herd production level.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".