Invited review: Mastitis in dairy heifers: Nature of the disease, potential impact, prevention, and control
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Notice bibliographique
Résumé
Heifer mastitis is a disease that potentially threatens production and udder health in the first and subsequent lactations. In general, coagulase-negative staphylococci (CNS) are the predominant cause of intramammary infection and subclinical mastitis in heifers around parturition, whereas Staphylococcus aureus and environmental pathogens cause a minority of the cases. Clinical heifer mastitis is typically caused by the major pathogens. The variation in proportions of causative pathogens between studies, herds, and countries is considerable. The magnitude of the effect of heifer mastitis on an individual animal is influenced by the form of mastitis (clinical versus subclinical), the virulence of the causative pathogen(s) (major versus minor pathogens), the time of onset of infection relative to calving, cure or persistence of the infection when milk production has started, and the host's immunity. Intramammary infection in early lactation caused by CNS does not generally have a negative effect on subsequent productivity. At the herd level, the impact will depend on the prevalence and incidence of the disease, the nature of the problem (clinical, subclinical, nonfunctional quarters), the causative pathogens involved (major versus minor pathogens), the ability of the animals to cope with the disease, and the response of the dairy manager to control the disease through management changes. Specific recommendations to prevent and control mastitis in late gestation in periparturient heifers are not part of the current National Mastitis Council mastitis and prevention program. Control and prevention is currently based on avoidance of inter-sucking among young stock, fly control, optimal nutrition, and implementation of hygiene control and comfort measures, especially around calving. More risk factors for subclinical and clinical heifer mastitis have been identified (e.g., season, location of herd, stage of pregnancy) although they do not lend themselves to the development of specific intervention strategies designed to prevent the disease. Pathogen-specific risk factors and associated control measures need to be identified due to the pathogen-related variation in epidemiology and effect on future performance. Prepartum intramammary treatment with antibiotics has been proposed as a simple and effective way of controlling heifer mastitis but positive long-lasting effects on somatic cell count and milk yield do not always occur, ruling out universal recommendation of this practice. Moreover, use of antibiotics in this manner is off-label and results in an increased risk of antibiotic residues in milk. Prepartum treatment can be implemented only as a short-term measure to assist in the control of a significant heifer mastitis problem under supervision of the herd veterinarian. When CNS are the major cause of intramammary infection in heifers, productivity is not affected, making prepartum treatment redundant and even unwanted. In conclusion, heifer mastitis can affect the profitability of dairy farming because of a potential long-term negative effect on udder health and milk production and an associated culling risk, specifically when major pathogens are involved. Prevention and control is not easy but is possible through changes in young stock and heifer management. However, the pathogenesis and epidemiology of the disease remain largely unknown and more pathogen-specific risk factors should be identified to optimize current prevention programs.
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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 | 0,003 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 découle