Estimations of prepartum feed intake and its effects on transition metabolism and subsequent milk production
Notice bibliographique
Résumé
The objectives of this study were to identify factors associated with prepartum DMI, evaluate the performance of linear models to estimate prepartum DMI using different classes of predictors, and investigate the consequences of different levels of prepartum DMI on transition metabolism and lactation performance. Individual feed intake of nulliparous (n = 100) and parous cows (n = 173) was measured by automatic feeding bins from d −35 to 98 relative to calving. Rumination and physical activities were monitored by wearable sensors. Blood metabolites were measured on d −21, −10, −3, 0, 3, 7, 10, 14, and 21. Body weight ( BW ) and body condition score ( BCS ) were assessed throughout the study. The average prepartum DMI as percentage of BW ( DMIpBW ) was calculated for each cow and used as dependent variable of linear models. Parity, prepartum BCS and BW, milk production in the previous lactation ( M305 ) and at dry-off ( MYDO ), and length of the dry period were associated with DMIpBW and explained 41% of the variation in all cows, and 49% in parous cows. Estimations of DMIpBW were improved when data on prepartum rumination and blood metabolites were added in the predictive models. In the latter, the adjusted R-Sq increased to values between 47 and 61%, and selected models performed consistently in a 5-fold cross-validation analysis. To evaluate the implications of DMIpBW to transition metabolism and performance, cows were ranked within parity and classified into terciles as low ( LFI ), moderate ( MFI ), or high feed intake ( HFI ). The mean DMI was 1.44, 1.70, and 1.91 ± 0.01% of BW, respectively. No differences in BW were observed in nulliparous cows, but all 3 groups of parous cows differed (LFI = 892, MFI=849, HFI=798 ± 8 kg). The proportion of cows with BCS > 3.5 at enrollment differed among all groups, and averaged 67.4, 55.1, and 36.5 ± 6%, respectively. For parous cows, M305 and MYDO differed among all groups and averaged 9,808, 10,457, and 11,182 ± 233 kg, and 18.1, 23.1, and 26.2 ± 1 kg/d, respectively. After calving, DMI (LFI = 20.9, MFI=21.9, and HFI=22.1 ± 0.2 kg/d) and milk yield (LFI = 36.7, MFI=38.2, and HFI=38.3 ± 0.4 kg/d) was lower in LFI cows compared with the other 2 groups. Postpartum EBAL differed among all groups and averaged −2.79, −1.63, and −0.66 ± 0.3 Mcal/d for LFI, MFI, and HFI, respectively. During the transition period, LFI cows had higher serum concentrations of NEFA, BHB, Cl (prepartum only), and AST (postpartum only), and lower serum concentrations of cholesterol, P, GLDH, GGT (prepartum only), AST (prepartum only), urea (parous only), and SOD activity (parous only). In conclusion, a low level of prepartum DMI was associated with fatter and heavier cows, lower milk production in previous lactation, important adjustments in energy metabolism, and moderate losses in DMI and milk yield in the subsequent lactation. Moreover, the inclusion of prepartum rumination activity and target blood metabolites into predictive models improved the estimations of prepartum DMI.
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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 | 0,002 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,001 | 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 | 0,001 | 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 ».