Options for incorporating feed intake into national selection indexes
Notice bibliographique
Résumé
Feed costs are a significant proportion of total costs in most dairy production systems and there is strong evidence for substantial genetic variation in total feed intake. However, a large component of this variation is unfavorably correlated with important maintenance and production functions of the animal. Ideally, selection indexes for improved feed efficiency would consider total feed intake that explicitly accounts for the feed required for valuable energy sinks such as milk production, high fertility and adequate body reserves. Residual feed intake (RFI), which is defined at the phenotypic level as the difference between actual feed intake and predicted feed intake, is a potential selection criterion to improve efficiency of feed utilization. However, there are other potential approaches that might have desirable attributes when considered in the context of well-established genetic evaluation systems with breeding objective definitions that are accepted by industry. In this study, we used simulations to unravel the complex inter-relationships among traits such as milk production, live weight and total feed intake. Feed intake phenotypes were simulated as a composite of simulated component phenotypes, so that the underlying genetic relationships between total feed intake and other traits of interest in dairy production systems could be specified precisely. Genetic variance components were then estimated on animals simulated from a simple pedigree structure and estimated breeding values (EBVs) were populated into several selection indexes with and without feed intake components included. The performance of each index was measured by comparing the index predictions against the true observed merit of simulated sires. Additionally, we examined how feed intake-based selection indexes would perform when only limited feed records are available because feed intake is not routinely recorded in dairy systems. Our results show that selection indexes that explicitly account for feed intake were more strongly correlated with the true observed merit than a selection criterion that is only parameterized with EBVs for milk production. All indexes that included feed intake parameters were more accurate than our base index (i.e. without feed intake) even under poor data conditions with limited feed intake recording (e.g. when only 10% of daughters were phenotyped for feed intake). Including wasted feed by adjusting total feed intake EBVs for other traits that represent known energy sinks while accounting for differences in EBV reliability would be very simple to deploy and we found that such an index was almost as efficient as our selection indexes for feed intake.
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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 | 0,002 |
| 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 | 0,001 | 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) | 0,003 | 0,001 |
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 ».