326 Relationship between activity and social interactions of beef bulls during the pre-breeding period and siring capacity in multi-sire breeding groups
Notice bibliographique
Résumé
Abstract It is currently unknown whether the behavior of bulls during pre-breeding is related to their siring capacity. This information could help producers anticipate the reproductive potential of their bulls and include it in their decision-making. This study aimed to investigate the relationship between the activity and characteristics of social interaction of beef bulls expressed in drylot pens during the pre-breeding period and their siring capacity. Collars with 3-axis accelerometers and proximity sensors were placed on bulls for 12 d prior to breeding season in 2020 (n = 11 bulls) and in 2021 (n = 10 bulls). Bulls were satisfactory at the breeding soundness evaluation, and ages ranged from 1.5 to 5.4 yr. Afterward, bulls were exposed to cows in multi-sire groups at a 1:30 ratio for 2 mo, however, some bulls were removed early due to injuries (range = 15 to 62 d). DNA samples were collected from bulls and calves to calculate the number of calves sired per bull (SRC), adjusted to days exposed to cows. Daily proportion of time active (AC) was measured through a machine learning algorithm created in Python for beef bulls to differentiate activity vs. non-activity from accelerometer data every 20 sec (Precision = 95%). Daily non-directional social networks were created from proximity perceived by the sensors (Precision = 91%, <3m) in Python, where nodes represented bulls and a connection represented one second in proximity. Eigencentrality (how well connected a bull is to others, EC) and strength (proportion of connections among overall connections, ST) were calculated for each bull daily, where greater EC and ST values indicated more interactions. The statistical analyses were performed in RStudio. Negative binomial mixed effect models were fitted to AC (mean ± S.E., 26.05 ± 0.31 %), EC (26.50 ± 0.89), and ST (8.90 ± 0.21 %) as repeated measures, age (3.3 ± 0.3 yr), days exposed to cows (54 ± 3.1 d), and year as covariates, identification of bull as a random effect, and SRC as the outcome variable (21 ± 2, 2 to 45 calves). Estimates were transformed into percentage change. Days exposed to cows, year, and interactions were not significant (P > 0.05). Separate models with EC or ST (model-EC and model-ST) were built due to collinearity (r = 0.89, P < 0.001). In model-EC (marginal-R2 = 0.26, conditional-R2 = 0.70) bulls with increased AC (estimate = 2.16±0.67, P = 0.001), increased EC (0.56±0.24, P = 0.019), and younger (-20.75 ± 5.51, P < 0.001) had greater SRC. In model-ST (marginal-R2 = 0.25, conditional-R2 = 0.72) bulls with increased AC (2.29 ± 0.65, P < 0.001), increased ST (3.57 ± 0.98, P < 0.001), and younger (-19.90 ± 5.49, P < 0.001) had greater SRC. These preliminary results suggest that activity and social interactions during pre-breeding of beef bulls are related to SRC when accounted for age. Therefore, the behavior of beef bulls during the pre-breeding period might have the potential to predict their SRC, and future studies could investigate using this information to select bulls and form breeding groups aiming at optimization of the use of the bulls.
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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 | 0,001 |
| 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) | 0,002 | 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 ».