Comparison of Single, Fixed-Time Artificial Insemination in Gilts Using Two Different Protocols to Synchronize Ovulation
Bibliographic record
Abstract
In order to efficiently have a consistent supply of service-ready gilts available to incorporate into each batch of breeding sows, it is necessary to manipulate the timing of estrus and possibly the timing of ovulation of gilts. Estrus can be synchronized by the withdrawal of altrenogest after at least 14 days of treatment. It is possible that protocols developed to induce ovulation, and therefore allow fixed-time artificial insemination (FTAI), can improve the predictability of gilt breeding. This study investigated the effect of two FTAI protocols in gilts on reproductive performance and timing of farrowing and piglet weaning weight compared to gilts bred based on signs of estrus after cessation of altrenogest. Puberty was induced in gilts, followed by treatment with altrenogest. Following altrenogest withdrawal, 180 gilts were assigned to one of three treatment groups. Group 1 gilts (LUT, n = 62) were treated with 600 IU equine chorionic gonadotropin 24 h after altrenogest withdrawal and 5 mg porcine luteinizing hormone (pLH) 80 h later, followed by a single FTAI 36 h after pLH. Group 2 gilts (TRI, n= 61) received 2 mL of a gonadotropin-releasing hormone agonist, triptorelin acetate, intravaginally 6 d after altrenogest withdrawal and were bred by a single FTAI 24 h later. Group 3 gilts (CON, n = 57) were observed for estrus and bred twice by AI, 24 h apart. LUT and TRI gilts farrowed closer together (2.4 ± 1.6 and 2.9 ± 1.2 d(days), respectively) compared to CON gilts (4.5 ± 3.3 d). Piglets in LUT were 80 g (p < 0.001) heavier and piglets in TRI were 64 g (p < 0.05) heavier at weaning than CON piglets, when controlling for birth weight. Results indicate that FTAI might be useful as a means of minimizing the time from the first to the last gilt farrowing in a breeding batch of gilts. However, modifications of the protocols may be required to ensure optimum farrowing rates and litter size.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 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 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".