Association of rumination time and health status with milk yield and composition in early-lactation dairy cows
Notice bibliographique
Résumé
The objective of this study was to determine the associations of rumination time (RT) and health status with milk yield and milk composition. This study used 339 dairy cows from 4 commercial dairy farms in Ontario, Canada (first lactation, n = 107; second lactation, n = 112; ≥third lactation, n = 120). Rumination time was monitored (24 h/d) using an automated system from 1 to 28 d in milk (DIM). Cows were milked 3×/d on each farm, and 2 farms recorded milk weights at each milking to determine daily milk yield (n = 170). Cows were also monitored for milk composition (fat and protein content) 1×/wk. Last, subclinical ketosis (SCK) was diagnosed 1×/wk; cows with at least one blood sample with β-hydroxybutyrate ≥1.2 mmol/L postcalving were diagnosed with SCK. Cases of retained placenta, metritis, milk fever, or mastitis during the study period were also recorded. Cows were categorized into 1 of 4 groups: healthy cows that had no SCK or any other health issue (HLT; n = 139); cows that were treated for at least one health issue other than SCK (HLT+; n = 50); SCK cows with no other health problems during transition (HYK; n = 97); or cows that had SCK and one or more other health problems (HYK+; n = 53). All data were summarized by week across cows, and the associations between rumination time and milk yield (n = 170) and milk composition (n = 339) were modeled. Across all lactations, and including all health categories, milk yield increased by week, whereas fat and protein content both decreased by week. A positive association was found between summarized RT and milk yield in first-lactation (+0.006 ± 0.003 kg/min of RT) and second-lactation (+0.015 ± 0.004 kg/min of RT) cows from 4 to 28 DIM, as well as in ≥third-lactation cows; however, the relationship between RT and milk yield differed across weeks in those cows. A negative association between RT and milk fat content was found in ≥third-lactation cows (-0.002 ± 0.00059 percentage points/min of RT). From 4 to 28 DIM, ≥third-lactation HYK and HYK+ cows produced less protein (0.11 ± 0.051 and 0.13 ± 0.056 percentage points, respectively) than HLT cows. Over the 4-wk observation period, first-lactation HYK+ cows tended to deposit 0.11 ± 0.056 percentage points less protein in their milk compared with HLT cows. Second-lactation HYK+ cows produced less milk than HLT cows each week during early lactation. In summary, RT was positively associated with milk yield in early-lactation dairy cows, across all lactations, and negatively associated with milk fat content in ≥third-lactation cows. Further, the results showed that early-lactation cows that experience SCK, particularly with one or more other health problems, might have decreased milk yield and milk protein content.
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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 | 0,000 | 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 ».