The Effect of Synchronized Breeding on Genetic Evaluations of Fertility Traits in Dairy Cattle
Bibliographic record
Abstract
Estrus detection is labor-intensive and time-consuming, with decreased expression in many high-producing dairy cows. To overcome this issue, some producers use hormone protocols to synchronize ovulation and perform timed artificial insemination (timed AI). The objective of this study was to assess the potential bias that timed AI might add to the estimated genetic parameters of female reproductive traits. A Holstein population with 400 sires and 3 000 dams was simulated over 20 years, resulting in 30 000 cows randomly distributed in 200 herds. The simulated traits mimicked calving to first service (CTFS), first service to conception (FSTC) and days open (DO), assuming these to be the most affected traits by hormone synchronization. A total of 13 scenarios were tested, changing the percentage of herds and cows that were randomly selected to be under timed AI. To simulate the effect of timed AI, cows had their phenotypes masked by setting CTFS and DO to the mean of CTFS, and FSTC was set to zero. Four parameters were used to indirectly measure the presence of bias: 1) the correlation between true (TBV) and estimated (EBV) breeding values (accuracy); 2) the differences in the mean EBV of top 25, 50, 75 and 100 sires; 3) changes in correlation between TBV’s and EBV’s ranks; and 4) the changes in the genetic trend. The accuracy within each class of animals (bulls, dams, and cows) decreased proportionally with the increase of the use of timed AI. The average EBV of the top sires went toward zero when increasing the number of hormonal synchronized animals. The sires’ rank correlation of EBVs and TBVs followed similar behaviour, with smaller correlation for scenarios with more timed AI animals. The genetic trend was also more affected by scenarios that considered more intense use of hormonal synchronization. This simulation study indicated that genetic evaluations that included herds that used timed AI are likely biased, and the amount of bias is proportional to the number of animals on timed AI.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".