Management of cull dairy cows: Culling decisions, duration of transport, and effect on cow condition
Notice bibliographique
Résumé
Many cows culled from dairy farms arrive at livestock markets or abattoirs in compromised condition, but it is unknown whether cows are compromised at the time of culling or deteriorate during the marketing process. This study followed cull cows from farm to abattoir, monitored any deterioration, and tested the effect of providing such information on subsequent culling. For 11 mo, data were collected from 20 dairy farms, livestock markets, and abattoirs. Cows' body condition, lameness, and udder condition (as binary outcomes) were assessed at 4 points: when placed on the culling list, before leaving the farm, at auction, and at the abattoir. Based on the first 5 mo of data (baseline period), information materials were provided to 10 randomly selected farms (Informed treatment), and the other 10 served as a Control. Logistic mixed models were used to test differences in cows' condition at the 4 points of assessment, using the assessment before leaving the farm as a reference value and cow-within-farm as a random effect. The effects of providing information on outcome measures (proportion thin, severely lame, with udder edema, poor fitness for transport, euthanized, and died) were tested with a general linear model. As demand for milk increased during the baseline period, additional linear mixed models were fitted to test whether the same outcome measures were affected by time (baseline or treatment period) and treatment (Informed or Control) using farm as a random effect. While cows were on the culling list (before shipping) the odds of being thin or having poor fitness for transport increased. After leaving the farms, cows spent (mean ± standard deviation) 82 ± 46 h in the marketing system before being slaughtered. Shipping from farm to abattoir increased the odds of being thin, having udder edema, or showing poor fitness for transport [odds ratio (OR) 5.8, 95% confidence interval 4.2 to 8.1; OR 14.7, 10.7 to 20.2; and OR 7.3, 5.7 to 9.5, respectively]. Providing this information to farmers (Informed treatment) did not affect the outcome measures. Compared with the baseline, during the treatment period fewer cows were shipped when thin (4.9 versus 9.0%) and with poor fitness for transport (15.7 versus 25.8%), and fewer cows died (5.4 versus 11.9%) or were euthanized (13.3 versus 20.5%). Increased demand for milk appeared to lead to delayed culling, such that more compromised cows were sent to slaughter. The results of this study show a need for clear and specific guidelines to assess fitness for transport of cull dairy cows.
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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 | 0,001 | 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) | 0,000 | 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 ».