Evaluation of a method estimating real-time individual lysine requirements in two lines of growing–finishing pigs
Notice bibliographique
Résumé
The implementation of precision feeding in growing-finishing facilities requires accurate estimates of the animals' nutrient requirements. The objectives of the current study was to validate a method for estimating the real-time individual standardized ileal digestible (SID) lysine (Lys) requirements of growing-finishing pigs and the ability of this method to estimate the Lys requirements of pigs with different feed intake and growth patterns. Seventy-five pigs from a terminal cross and 72 pigs from a maternal cross were used in two 28-day experimental phases beginning at 25.8 (±2.5) and 73.3 (±5.2) kg BW, respectively. Treatments were randomly assigned to pigs within each experimental phase according to a 2×4 factorial design in which the two genetic lines and four dietary SID Lys levels (70%, 85%, 100% and 115% of the requirements estimated by the factorial method developed for precision feeding) were the main factors. Individual pigs' Lys requirements were estimated daily using a factorial approach based on their feed intake, BW and weight gain patterns. From 25 to 50 kg BW, this method slightly underestimated the pigs' SID Lys requirements, given that maximum protein deposition and weight gain were achieved at 115% of SID Lys requirements. However, the best gain-to-feed ratio (G : F) was obtained at a level of 85% or more of the estimated Lys requirement. From 70 to 100 kg, the method adequately estimated the pigs' individual requirements, given that maximum performance was achieved at 100% of Lys requirements. Terminal line pigs ate more (P=0.04) during the first experimental phase and tended to eat more (P=0.10) during the second phase than the maternal line pigs but both genetic lines had similar ADG and protein deposition rates during the two phases. The factorial method used in this study to estimate individual daily SID Lys requirements was able to accommodate the small genetic differences in feed intake, and it was concluded that this method can be used in precision feeding systems without adjustments. However, the method's ability to accommodate large genetic differences in feed intake and protein deposition patterns needs to be studied further.
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 | 0,002 | 0,000 |
| 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 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 ».