Impact of reproductive traits on productive life in Iranian Holstein dairy cows
Bibliographic record
Abstract
Enhancing reproductive performance is a key strategy to mitigate involuntary culling rates, thereby extending productive life (PL) and ultimately improving profitability in dairy cattle herds. A piecewise Weibull proportional hazards model was used to investigate the effect of several important reproductive traits on PL in Holstein dairy cows. Data comprised 200,747 lactation records from 82,505 cows sired by 1952 bulls across 36 dairy herds. PL was defined as the number of days from the first calving to the last milk record or censoring. The statistical model accounted for the time-dependent fixed effects of changes in herd size, year-season, milk production, fat and protein contents, and the time-independent fixed effect of age at first calving. Herd-year and sire effects were also included as random effects. Reproductive traits include calving traits such as calving ease (CE), calf size (CZ), and calf survival (CS), as well as female fertility traits such as number of inseminations per conception (NI), days from calving to first service (CFS), days from first service to conception (FSC), and days open (DO). All reproductive traits had a significant effect on PL (p < 0.001). Each reproductive trait was analysed separately. The relative risk (RR) of being culled increased as the severity of calving difficulties increased in both primiparous and multiparous cows. Cows that calved small or large calves showed a higher risk of being culled compared with those that calved medium size calves. The increased RR of culling was observed only for primiparous cows that gave birth to dead calves. In addition, cows that required more NI, a longer CFS, FSC, and DO had shorter longevity. These insights can deepen our comprehension of the factors affecting PL and provide information for refining management and breeding strategies, which could lead to increased profitability and sustainability in Iranian dairy farming.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".