Relationship among performance, carcass, and feed efficiency characteristics, and their ability to predict economic value in the feedlot1
Notice bibliographique
Résumé
A 4-yr study was conducted using 736 steers of known Angus, Simmental, or Simmental × Angus genetics to determine performance, carcass, and feed efficiency factors that explained variation in economic performance. Steers were pen fed and individual DMI was recorded using a GrowSafe automated feeding system (GrowSafe Systems Ltd., Airdrie, Alberta, Canada). Steers consumed a similar diet and received similar management each year. The objectives of this study were to: 1) determine current economic value of feed efficiency and 2) identify performance, carcass, and feed efficiency characteristics that predict: carcass value, profit, cost of gain, and feed costs. Economic data used were from 2011 values. Feed efficiency values investigated were: feed conversion ratio (FCR; feed to gain), residual feed intake (RFI), residual BW gain (RG), and residual intake and BW gain (RIG). Dependent variables were carcass value ($/steer), profit ($/steer), feed costs ($/steer • d(-1)), and cost of gain ($/kg). Independent variables were year, DMI, ADG, HCW, LM area, marbling, yield grade, dam breed, and sire breed. A 10% improvement in RG (P < 0.05) yielded the lowest cost of gain at $0.09/kg and highest carcass value at $17.92/steer. Carcass value increased (P < 0.05) as feed efficiency improved for FCR, RG, and RIG. Profit increased with a 10% improvement in feed efficiency (P < 0.05) with FCR at $34.65/steer, RG at $31.21/steer, RIG at $21.66/steer, and RFI at $11.47/steer. The carcass value prediction model explained 96% of the variation among carcasses and included HCW, marbling score, and yield grade. Average daily gain, marbling score, yield grade, DMI, HCW, and year born constituted 81% of the variation for prediction of profit. Eighty-five percent of the variation in cost of gain was explained by ADG, DMI, HCW, and year. Prediction equations were developed that excluded ADG and DMI, and included feed efficiency values. Using these equations, cost of gain was explained primarily by FCR (R(2) = 0.71). Seventy-three percent of profitability was explained, with 55% being accounted for by RG and marbling. These prediction equations represent the relative importance of factors contributing to economic success in feedlot cattle based on current prices.
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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 | 0,003 | 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 | 0,001 |
| 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 ».