Investigating the within-herd prevalence and risk factors for ketosis in dairy cattle in Ontario as diagnosed by the test-day concentration of β-hydroxybutyrate in milk
Notice bibliographique
Résumé
An observational study of 790 to over 3,000 herds was conducted to estimate the within-herd prevalence and cow-level risk factors for ketosis in dairy cattle in herds that participate in a Dairy Herd Improvement Association (DHIA) program. Ketosis or hyperketolactia (KET) was diagnosed as milk β-hydroxybutyrate ≥0.15 mmol/L at first DHIA test when tested within the first 30 d in milk. Seven hundred ninety-five herds providing at least 61 first milk tests from June 2014 to December 2015 were used to estimate the provincial within-herd prevalence of KET. All herds on DHIA in Ontario (n = 3,042) were used to construct cow-level multilevel logistic regression models to investigate the association of DHIA collected variables with the odds of KET at first DHIA milk test. Primiparous and multiparous animals were modeled independently. The cow-level KET prevalence in Ontario was 21%, with an average within-herd prevalence of 21% (standard deviation = 10.6) for dairy herds enrolled in a DHIA program. The prevalence of KET had a distinct seasonality with the lowest prevalence occurring from July to November. Automatic milking systems (AMS) were associated with increased within-herd prevalence, as well as increased odds of KET in multiparous animals at first test (odds ratio: 1.45; 95% confidence interval: 1.30 to 1.63). Jersey cattle had over 1.46 times higher odds of KET than Holstein cattle. Milk fat yield ≥1.12 kg/d at the last test of the previous lactation was associated with decreased odds of KET in the current lactation (odds ratio: 0.56; 95% confidence interval: 0.53 to 0.59). Increased days dry and longer calving intervals, for multiparous animals, and older age at first calving for primiparous animals increased the odds of KET at first test. This study confirms previous findings that increased days dry, longer calving intervals, and increased age at first calving are associated with increased odds of KET and is the first report of increased KET in herds with AMS and in relation to milk fat yield at the final test of the previous lactation. Feeding management on AMS herds likely contributes to the increased prevalence of KET and further work is required to investigate modifications to current management to minimize risk. Milk fat yield during the previous lactation may be representative of energy partitioning.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,005 |
| 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 | 0,001 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 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 tête enseignante, 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 ».