Intermediary Metabolism in the Dairy Cow
Notice bibliographique
Résumé
Current feed evaluation systems for ruminants include very simple models of intermediary metabolism – either single, static coefficients or simple equations to describe the efficiency with which the energy and nitrogen available for absorption are converted into milk and meat. For example, the revised UK system ‘Feed into milk’ defines a fixed efficiency of utilization of metabolizable protein (MP) for milk production of 0·68 (Thomas, 2004). Such a simple ‘efficiency’ coefficient is the tip of the tip of a rather large iceberg, representing the metabolism of the end-products of digestion between the point of their absorption into the animal and their use for the synthesis of milk and meat. The complex biochemistry of metabolism in the tissues of the gut (the portal-drained viscera, PDV), the liver and other internal organs, the adipose tissue, muscle and mammary gland: all reduced (a term used nonpejoratively) to single coefficients or simple equations. Simple and very useful – up to a point. A Working Party established by the Agricultural and Food Research Council (AFRC) Technical Committee on Responses to Nutrients wrote, in 1998, ‘ The present state of knowledge on the nutritional and hormonal interactions “in intermediary metabolism” is considerably inferior to the knowledge which exists with respect to the processes of ruminal and intestinal digestion, and thus to develop a satisfactory model of metabolism requires research not only in model construction but also in the physiology and biochemistry of animals .’ (AFRC, 1998). Perhaps contrary to the common perception of a contracting research base in farm animal science, the required research in physiology and biochemistry is continuing, research which is summarized and put in context by two review articles in this issue of Animal Science . Kristensen (2005) focuses on a key aspect of the ‘energy economy’ of the cow: the metabolism of volatile fatty acids (VFA) in the tissues of the digestive tract and the liver. Using novel techniques, Kristensen and colleagues have re-examined (and overturned?) one of the key paradigms of ruminant physiology, that a large proportion of the VFA generated by fermentation in the rumen is metabolised during absorption. Researchers from Canada and the UK are untangling factors affecting the partitioning of absorbed amino acids between use for protein synthesis and catabolism and this topic is reviewed by Lapierre et al. (2005). This paper emphasizes the important concept that ‘efficiency of utilization’ of absorbed amino acids is as much a function of demand (in the lactating cow, principally from the mammary gland) as it is of supply. The statement from AFRC (1998), quoted above, demands two activities, physiological research and model construction. These authors continue: ‘ It is likely, however, that the best progress will be achieved if both types of research are undertaken in parallel .’ In a third review article in this volume, Hanigan (2005) summarizes the ‘state of the art’ in the simulation of PDV and hepatic metabolism, demonstrating how a computer model is a quantitative library in which to store new knowledge as well as a driver of future research. All three papers are based on presentations given to the 2004 annual meeting of the British Society of Animal Science. They demonstrate ‘the current state of the art’ for these important topics in ruminant physiology, offering a below the water-line look at these rather large and often forbidding icebergs. The reviews should serve as a valuable summary for those already working in this field of research, a concise and convenient introduction for those entering the field, and a window onto the future of practical systems for predicting the responses of dairy cows to the feedingstuffs they are offered.
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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| 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 ».