Association of uterine health in the first lactation with transition cow health and reproductive performance in the second lactation of Holstein dairy cows
Notice bibliographique
Résumé
The objective of this study was to evaluate the effects of puerperal metritis (PM) diagnosed and treated during the early postpartum period of the first lactation on transition cow health, milk production, reproduction, and culling of dairy cows in their second lactation. Diagnosis of PM was based on fetid watery red-brown uterine discharge and rectal temperature above 39.5°C. Two farms were enrolled in this retrospective observational cohort study (farms A and B). In both farms, the following diseases were recorded during the first 30 DIM in lactation 1 and 2: clinical hypocalcemia (CH), retained fetal membrane (RFM), PM, hyperketonemia (KET), left displaced abomasum (LDA), and clinical mastitis (MAST). Statistical analyses were performed using SPSS for Windows separately for each farm. Linear and logistic regression models were used for continuous (e.g., milk yield) and binary (e.g., disease, pregnancy per AI, pregnancy loss) outcomes, respectively. Cox proportional hazard regression models were calculated to model the time to event outcomes for culling or death during the first 60 DIM and for pregnancy within 250 d of the second lactation. The initial models contained the following variables: year of calving, month of calving, calving ease, stillbirth, twins, days open in lactation 1, 305-d milk yield in lactation 1, PM in lactation 1, and PM in lactation 2 as explanatory variables. A total of 4,834 cows (farm A) and 4,238 cows (farm B) in the second lactation were considered for statistical analyses. On farm A, the incidence of PM in lactations 1 and 2 were 20.1% and 11.2%, respectively. On farm B, the incidence of PM in lactations 1 and 2 were 14.4% and 8.5%, respectively. On both farms, cows with PM in their first lactation had greater odds for RFM and PM in their second lactation, whereas there was no association of PM in the first lactation with any other nonuterine diseases (i.e., CH, KET, LDA, and MAST) in the second lactation. Cows with PM in lactation 2 had reduced milk yield. The reduction in milk yield in second lactation was greater for cows that already experienced PM in lactation 1. On farm A, cows with PM in their first lactation had a greater hazard for culling within 60 DIM of the second lactation; however, the same association was not present on farm B. Cows with PM in lactation 1 had reduced pregnancy per AI at first service in the second lactation only on farm B. Cows with PM in lactation 2 had reduced pregnancy per AI at first service in the second lactation on both farms. Pregnancy loss in lactation 2 was only associated with PM in lactation 2 but not with PM in lactation 1. On both farms, cows had a reduced hazard for pregnancy in their second lactation within 250 DIM when they experienced PM in either lactation. In conclusion, PM in the first lactation had long-lasting negative consequences (i.e., risk of uterine disease and lower reproductive performance) for cows in their next lactation.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,001 |
| 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) | 0,000 | 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 ».