PSVIII-A-3 Activity Behavior and Growth Performance During Summer of Grazing Beef Heifers with Divergent Residual Feed Intake
Notice bibliographique
Résumé
Abstract Concerns about sustainability of beef production systems generate an interest in improved feed efficiency; however, there is a lack of research evaluating cattle activity budgets in response to the interaction between residual feed intake (RFI) and environment while grazing. Lying behavior and activity can provide insight into how animals interact with the environment and serve as an indicator of animal comfort. Furthermore, exposure to severe environmental factors can change behavioral patterns and impair animal performance. Therefore, this study evaluated activity budgets and performance in grazing beef heifers with divergent residual feed intake (RFI) during the summer season. From July to August 2022, forty-four crossbred beef heifers [358 ± 4.78 kg of body weight (BW); approximately 14 months of age] previously tested for RFI in drylot and classified as more (n = 21; LOW-RFI = -0.9 ± 0.70) or less feed efficient (n = 23; HIGH-RFI = 1.3 ± 1.00) were grazed at 2.72 AUM/ha over 7 wk in Alberta, Canada. IceRoboticTM pedometers (IceQube+) were used to track 24-hr heifer activity budgets [n = 43; total steps and lying and standing time (min/d and min/h) for 36 d]. Full BW was obtained on d -1, 0, 14, 28, 42, and 43 while fat scan on rib and rump were measured by ultrasound (Aloka 500 V diagnostic real-time) on d 0 and 42. Air temperature, relative humidity, wind speed and solar radiation information were collected within 1 km of the grazed area to calculate the Comprehensive Climate Index (CCI). Based on CCI, weather conditions were considered to impose risk to cause mild, moderate, severe, and extreme stress for 5, 18, 7, and 1 days, respectively. For BW, average daily gain (ADG), rump (RF) and rib fat (RiF), the data were analyzed as a completely randomized design, while behavior activity included repeated measures. An RFI x day interaction was observed for lying and standing times (P = 0.02) and total steps (P = 0.001). Greater number of steps (P < 0.01) and an increased standing time (P < 0.01) were observed in HIGH-RFI heifers. RFI × hour interaction was observed for lying and standing times (P = 0.006), where LOW-RFI heifers spent more time lying at 10:00 am (P < 0.01). Furthermore, LOW-RFI heifers had decreased number of steps per hour throughout the study (P = 0.03; 178 vs 191 ± 4.1). No effects were observed for ADG, BW, RF, and RiF (P > 0.24). In summary, selected activity behaviors differed between beef heifers with divergent residual feed intake while summer grazing. Further studies are needed to investigate the effects of continuous changes in weather conditions to better understand the environmental impacts on animal behavior while selecting for more efficient beef cattle.
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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 | 0,000 | 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) | 0,001 | 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 ».