Meta-analysis of apparent ruminal synthesis and postruminal flow of B vitamins in dairy cows
Notice bibliographique
Résumé
As milk production has significantly increased over the past decade(s), existing estimates of the B-vitamin needs of the modern dairy cow are currently being reconsidered, as suboptimal B-vitamin supply may affect metabolic efficiency. At the same time, however, "true" (i.e., biologically active forms, excluding nonfunctional analogs) B-vitamin supply also cannot be adequately estimated by dietary intake, as the rumen microbiota has been shown to play a significant role in synthesis and utilization of B vitamins. Given their complex impact on the metabolism of dairy cows, incorporating these key nutrients into the next generation of mathematical models could help to better predict animal production and performance. Therefore, the purpose of this study was to generate hypotheses of regulation in the absence of supplemental B vitamins by creating empirical models, through a meta-analysis, to describe true B-vitamin supply to the cow (postruminal flow, PRF) and apparent ruminal synthesis (ARS). The database used for this meta-analysis consisted of 340 individual cow observations from 15 studies with 16 experiments, where diet and postruminal digesta samples were (post hoc) analyzed for content of B vitamins (B 1 , B 2 , B 3 , B 6 , B 9 , B 12 ). Equations of univariate and multivariate linear form were considered. Models describing ARS considered dry matter intake (DMI, kg/d), B-vitamin dietary concentration [mg/kg of dry matter (DM)] and rumen-level variables such as rumen digestible neutral detergent fiber (NDF) and starch (g/kg of DM), total volatile fatty acids (VFA, m M ), acetate, propionate, butyrate, and valerate molar proportions (% of VFA), mean pH, and fractional rates of degradation of NDF and starch (%/h). Models describing PRF considered dietary-level driving variables such as DMI, B-vitamin dietary concentration (mg/kg of DM), starch and crude protein (g/kg of DM) and forage NDF (g/kg of DM). Equations developed were required to contain all significant slope parameters and contained no significant collinearity between driving variables. Concordance correlation coefficient was used to evaluate the models on the developmental data set due to data scarcity. Overall, modeling ARS yielded better-performing models compared with modeling PRF, and DMI was included in all prediction equations as a scalar variable. The B-vitamin dietary concentration had a negative effect on the ARS of B 1 , B 2 , B 3 , and B 6 but increased the PRF of B 2 and B 9 . The rumen digestible NDF concentration had a negative effect on the ARS of B 2 , B 3 , and B 6 , whereas rumen digestible starch concentration had a negative effect on the ARS of B 1 and a positive effect on the ARS of B 9 . In the best prediction models, the dietary starch increased PRF of B 1 , B 2 , and B 9 but decreased PRF of B 12 . The equations developed may be used to better understand the effect of diet and ruminal environment on the true supply of B vitamins to the dairy cow and stimulate the development of better-defined requirements in the future.
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 | 0,018 | 0,022 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,009 | 0,035 |
| Bibliométrie | 0,003 | 0,004 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,003 | 0,001 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».