Cost of lameness in dairy herds: An integrated bioeconomic modeling approach
Notice bibliographique
Résumé
Foot disorders are costly health disorders in dairy farms, and their prevalence is related to several factors such as breed, nutrition, and farmer's management strategy. Very few modeling approaches have considered the dynamics of foot disorders and their interaction with farm management strategies within a holistic farm simulation model. The aim of this study was to estimate the cost of foot disorders in dairy herds by simulating strategies for managing lameness. A dynamic and stochastic simulation model (DairyHealthSim) was used to simulate the herd dynamics, reproduction management, and health events. A specific module was built for lameness and related herd-level management strategies. Foot disorder occurrences were simulated with a base risk for each etiology [digital dermatitis (DD), interdigital dermatitis, interdigital phlegmon, sole ulcer (SU), white line disease (WLD)]. Two state machines were implemented in the model: the first was related to the disease-induced lameness score (from 1 to 5), and the second concerned DD-state transitions. A total of 880 simulations were run to represent the combination of the following 5 scenarios: (1) housing (concrete vs. textured), (2) hygiene (2 different scraping frequencies), (3) the existence of preventive trimming, (4) different thresholds of DD prevalence detected and from which a collective footbath is applied to treat DD, and (5) farmer's ability to detect lameness (detection rate). Housing, hygiene, and trimming scenarios were associated with risk factors applied for each foot disorder etiologies. The footbath and lameness detection scenarios both determined the treatment setup and the policy of herd observance. The economic evaluation outcome was the gross margin per year. A linear regression model was run to estimate the cost per lame cow (lameness score ≥3), per case of DD and per week of a cow's medium lameness duration. The bioeconomic model reproduced a lameness prevalence varying from 26 to 98% depending on the management scenario, demonstrating a high capacity of the model to represent the diversity of the field situations. Digital dermatitis represented half of the total lameness cases, followed by interdigital dermatitis (28%), SU (19%), WLD (13%), and interdigital phlegmon (4%). The housing scenarios dramatically influenced the prevalence of SU and WLD, whereas scraping frequency and threshold for footbath application mainly determined the presence of DD. Interestingly, the results showed that preventive trimming allowed a better reduction in lameness prevalence than spending time on early detection. Scraping frequency was highly associated with DD occurrence, especially with a textured floor. The regression showed that costs were homogeneous (i.e., did not change with lameness prevalence; marginal cost equals average cost). A lame cow and a DD-affected cow cost €307.50 ± 8.40 (SD) and €391.80 ± 10.0 per year on average, respectively. The results also showed a cost of €12.10 ± 0.36 per week-cow lameness. The present estimation is the first to account for interactions between etiologies and for the complex DD dynamics with all the M-stage transitions, bringing a high level of accuracy to the results.
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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 | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 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 ».