PSXI-10 Movement patterns and water source seeking in grazing lactating first-calf beef cows with different residual feed intake.
Notice bibliographique
Résumé
Abstract Feed-efficient animals positively influence both environmental and economic sustainability. Selecting cows for a lower residual feed intake (RFI) has guided feed efficiency and been thoroughly studied; however, its association with thermoregulatory behavior on grazing systems is not widely explored. This study evaluated the impact of weather conditions on the distance walked and daily water proximity of grazing first-calf beef cows with different RFI. Thirty-five crossbred heifers with 11 mo of age were classified as more efficient (LOW-RFI: n = 17, -0.8 ± 0.214 kg dry matter/d) or less efficient (HIGH-RFI: n = 18, 1.5 ± 0.220 kg dry matter/d). After calving, cows (432 ± 8.40 kg, 26 ± 1 mo) were grazed within a single pasture from June to August. Geolocation data were recorded every 7.5 min and used to compute walking distances and water proximity using Nofence© collars for 34 d. Locations were calculated with the “geosphere” Spherical Trigonometry R package v. 1.5-20 to calculate kilometers and meters walked by hour. Animal distribution was mapped in Tableau 2024.3 displaying the location relative to water sources. Environmental parameters were recorded from a meteorological station within one kilometer and included temperature, solar radiation (SR), humidity, and wind speed (WS), which were used to compute the Comprehensive Climate Index (CCI) and adjusted temperature-humidity index (THIadj). Simple linear regression models were used to assess the ability of environmental variables to predict walking distance and proximity to water sources (SAS 9.4). The daily CCI ranged from 10 to 40 (absent to extreme environmental stress; respectively) while THIadj ranged from 7 to 63 (absent stress). The LOW-RFI cows had lower walked activity at 3 AM compared with HIGH-RFI (10 vs. 14 m; P ≤ 0.005). Regardless of RFI, cows walked more when THIadj was higher (1.97 km with THIadj of 67, vs. 1.43 km with THIadj of 46; P < 0.001). Moreover, when THIadj was ≤ 63 and CCI ≤ 40 (extreme), significant correlations with water proximity were found (P = 0.0026 and P = 0.03, respectively). SR and WS were the most influential variables when modeling predictors that explained cows’ proximity to a water source, with SR increasing nearness to water source and WS reducing it (P < 0.01). However, no significant correlations were detected between RFI classification and water proximity (P > 0.085). In conclusion, these findings highlight the influence of environmental conditions on movement patterns and water-seeking behavior of grazing lactating beef cows. Solar radiation and wind speed were the strongest predictors of cow proximity to water sources. However, residual feed intake classification did not significantly impact daily water procurement but impacted walked activity at early morning.
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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 ».