Invited review: Mastitis in dairy heifers: Nature of the disease, potential impact, prevention, and control
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.010 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".