BETTER UNDERSTANDING OF DAIRY COW MASTITIS: INCIDENCE OF CLINICAL MASTITIS AND PATHOGEN-SPECIFIC MILK LOSSES, BEDDING BACTERIAL COUNTS, AND E-LEARNING TRAINING METHODS

186 pagesBovine clinical mastitis is a frequent, complex, and costly disease in dairy cattle, where environmental mastitis represents major a challenge through production losses, hence understanding the environment is critical to improving udder health programs. On the other hand, achieving or maintaining high standards still relies heavily on dairy employees andremains a constant challenge for many farms. The objectives herein were to 1) understand clinical mastitis incidence and the associated pathogen-specific milk losses, 2) describe bacterial counts in bedding material and their associations with milk quality, and 3) test the use of e-learning training systems as an approach for dairy farm workers on milking equipment. Clear reporting about the methods used to report mastitis incidence can improve our ability to discuss and learn about the differences and similarities across regions and worldwide. In terms of applicability, incidence risk could be the easiest to calculate method to measure the frequency at the farm level. However, for research describing the mastitis dynamics, the incidence rate at the quarter level may be a more complete and granular evaluation. Although farms may have similar mastitis incidence, the pathogen distribution was different. Regarding milk yield losses, the most important pathogen causing losses both in primiparous and multiparous was E. coli, with losses that lasted longer than any other pathogen in the analysis. Cases with a no-growth result caused fewer milk losses and more rapid recovery compared to other pathogens. No associations between bulk tank bacteria counts and bedding bacterial counts were observed. No association between bulk tank somatic cell counts based on bedding type was observed. Despite using a standardized procedure to consistently collect samples, we still observed a large amount of variability, both within and among bedding samples. This variability may have obscured any potential association between BT milk quality and bedding type. Finally, milking equipment malfunctions, which milkers could detect, are common on dairy farms and reinforces the need for additional milker training in this area. Practical logistics of on-farm training are a limiting factor, so the use of online training is a reasonable alternative for dairy farms