196 Effects of thermal efficiency index on growth performance and carcass quality in grow-finish pigs.
Notice bibliographique
Résumé
Abstract The thermal efficiency Index (TEI) generated through infrared thermography is associated with the growth efficiency in pigs. It is hypothesized that pigs with high TEI are energetically less efficient and produce more radiant heat to the environment, whereas those with low TEI are more efficient with greater ability to conserve energy for growth and production while minimizing energy loss to the environment. This study evaluated the growth performance and carcass quality of grow-finish pigs selected based on low and high TEI categories. A total of 176 eight-week-old pigs were enrolled in a 16-week experiment, with 88 pigs per batch (replicated once over time) in a randomized complete block design. At nursery exit, pigs were scanned using an infrared camera to determine TEI (mean dorsal temperature/body weight0.75). In each replicate, 44 pigs with high TEI (“HIGH”: 4.29 ±0.39; body weight 15.62 ±1.43kg) and 44 with low TEI (“LOW”: 3.48 ±035; body weight 20.28 ±1.82kg) were selected and housed at 11 pigs/pen. The study was partitioned into the grower (8-12 weeks of age), Finisher1 (12-16 weeks) and Finisher2 (16-20 weeks) phases. TEI and body weight were recorded at the end of each phase to determine average daily gain (ADG). Individual feed intake was recorded daily using automated feeders (IVOG pro) to determine average daily feed intake (ADFI) and Gain: Feed (G: F). Pigs were marketed at 20 weeks of age and carcass grading data were obtained. Mixed model was used for data analysis with TEI category as main effect and replication as random effect. Spearman’s Rho correlation was used to explore the relationship between TEI and feed efficiency. In the grower phase, LOW pigs tended to gain more weight (p=0.06), but there was no difference in ADFI or G:F. During Finisher1, LOW pigs consumed more feed (p=0.02) and had greater ADG (p=0.01) with no difference in G:F. During Finisher2, there was no difference in ADFI, ADG or G:F between TEI categories. The overall ADG throughout grower-finisher stage was greater for LOW than HIGH pigs (p=0.01). LOW pigs had greater net weight at slaughter (p< 0.01), and fat content (p=0.03). There was no effect of TEI on loin depth. The lean yield percentage tended to be lower in LOW than HIGH pigs (p=0.05). The results of this study showed a decreasing correlation between TEI and G:F overtime from 12-20 weeks of age. At week 12, there was a moderate positive correlation (r= 0.38, p< 0.01). By week 16, the correlation weakened (r=0.25, p< 0.01). At week 20, the correlation was further reduced and not significant (r=0.15, p=0.06). In conclusion, this study reveals that TEI can be used to identify pigs with better performance in terms of weight gain and carcass yield.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| 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) | 0,001 | 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 source (Gemma direct ou Codex distillé), 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 ».