Association between genomic daughter pregnancy rates and reproductive parameters in Holstein dairy cattle
Notice bibliographique
Résumé
The objective of this observational study was to evaluate the relationship between genomic daughter pregnancy rate (GDPR) with reproduction parameters such as pregnancy at first artificial insemination (AI), pregnancy per AI, and pregnancy losses (PL). A total of 12,949 events from 3,499 Holstein cows were included. Cows were enrolled as nulliparous (n = 1,220), primiparous (n = 1,314), or multiparous (n = 965). Cows were bred either after a timed AI protocol, timed embryo transfer (ET), or spontaneous estrus. Most lactating cows were bred following a timed AI protocol based on estradiol and progesterone, and most nulliparous were artificially inseminated following estrus detection. Hair samples were collected from the tail switch and cows were genotyped using a SNP platform (Clarifide, Zoetis). Cows that were bred by timed AI were evaluated for estrous behavior using tail chalk. Tail chalk was applied on the head of the tail 2 d before timed AI and the chalk was evaluated at AI (no estrus: 100% of chalk remaining or ≥50% of chalk remaining; Estrus: <50% of chalk remaining). Pregnancy diagnosis was performed at d 32 and 60 after AI using ultrasonography, and the presence of a heartbeat was considered a positive diagnosis. Pregnancy loss was defined as a pregnant cow on d 32 that was nonpregnant on d 60. As GDPR increased, the odds of pregnancy at first AI increased [odds ratio (OR) = 1.28, 95% CI = 1.20-1.35], the odds of pregnancy per AI increased (OR = 1.31, 95% CI = 1.25-1.36), and the odds of PL decreased (OR = 0.66, 95% CI = 0.60-0.72). Most cows that were bred on the day of the timed AI demonstrated estrus (n = 6,075; 92.9%). The odds of demonstrating estrus on the day of timed AI increased as GDPR increased (OR = 1.31, 95% CI = 1.17-1.48). There was no interaction between GDPR and parity or breeding management for pregnancy at first AI, pregnancy per AI, and PL. In conclusion, the odds of pregnancy at first AI and pregnancy per AI increased as GDPR increased. Moreover, the odds of PL increased as GDPR decreased. Greater GDPR was also associated with greater occurrence of estrus on the day of timed AI. These results suggest that selecting for higher GDPR could result in better reproductive performance, but this would need to be assessed with additional research.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».