Post-weaning growth rate effects on body composition of Nellore bulls
Notice bibliographique
Résumé
Context Previously feed-restricted cattle may exhibit compensatory growth during the finishing phase. However, the efficiency in converting feed into carcass should be evaluated since cattle undergoing compensatory growth usually have high non-carcass weight gain. Aims The objective of this study was to evaluate the effect of growth rate throughout the post-weaning growing phase on subsequent feed efficiency, carcass gain, and gain composition. Methods Thirty-nine weaned young Nellore bulls averaging 230.4 ± 5.62 kg of bodyweight and 8.5 ± 0.25 months of age were used. Initially, five bulls were slaughtered as a reference initial group. The remaining bulls were randomly assigned to one of three nutritional plans to achieve Low (0 kg/day), Medium (0.6 kg/day) or High (1.2 kg/day) average daily gain (ADG) throughout the post-weaning growing phase, followed by high growth rate during the finishing phase. One-half of the bulls from each treatment were slaughtered at the end of the post-weaning growing phase, and the other one-half after the finishing phase. During both phases the feed intake, apparent digestibility, performance, and body composition were evaluated. Key results Throughout the post-weaning growing phase, High bulls had greater ADG and more efficiently converted feed into carcass, compared with other nutritional plans (P < 0.01). Throughout the finishing phase, Low bulls had greater ADG, carcass gain, and feed efficiency than High and Medium bulls (P < 0.01). Previous feed restriction did not affect (P > 0.05) apparent digestibility. During the finishing phase, previously restricted bulls fully compensated for the lost visceral organ weight, whereas the losses of bodyweight and carcass weight were only partially compensated. Throughout finishing, Low bulls had the greatest feed efficiency and profitability among nutritional plans. However, considering the overall experiment, Hight bulls converted feed into carcass more efficiently than Low bulls (P = 0.02), but did not differ from Medium (P > 0.05). Conclusions Although previously restricted bulls had greater performance and efficiency throughout finishing, the improvement was not enough to reach the same carcass weight at the same age of the unrestricted bulls. Implications Despite the greater profitability of previously restricted bulls throughout finishing, unrestricted bulls were more profitable considering both growing and finishing phases.
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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 | 0,000 | 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 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 ».