Abstract
The objective of this study was to investigate the distribution of mastitis pathogens in quarter milk samples (QMSs) submitted to the laboratory of the Bavarian Animal Health Service (TGD) between 2014 and 2023 in general, in relation to the clinical status of the quarters, and to analyze seasonal differences in the detection risk. Each QMS sent to the TGD during this period was analyzed and tested using the California Mastitis Test (CMT). Depending on the result, QMSs were classified as CMT-negative, subclinical, or clinical if the milk character showed abnormalities. Mastitis pathogens were detected in 19% of the QMSs. Non-aureus staphylococci (NAS) were the most common species isolated from the culture positive samples (30%), followed by Staphylococcus (S.) aureus (19%), Streptococcus (Sc.) uberis (19%), and Sc. dysgalactiae (9%). In culture-positive QMSs from CMT-negative and subclinically affected quarters, the most frequently isolated pathogens were NAS (44% and 27%, respectively), followed by S. aureus (25% and 17%, respectively) and Sc. uberis (8% and 22%, respectively). In QMSs from clinically affected quarters, the most frequently isolated pathogens were Sc. uberis (32%), S. aureus (13%), Sc. dysgalactiae (11%), and Escherichia (E.) coli (11%). The distribution of NAS and Sc. uberis increased throughout the study period, while that of S. aureus decreased. From June to October, QMSs from subclinically affected quarters increased and environmental pathogens, such as Sc. uberis, were detected more frequently. In conclusion, this study highlights the dynamic nature of the distribution of mastitis pathogens, influenced by mastitis status and seasonal factors. Environmental pathogens still play an important role, especially in clinical mastitis and seasonal dependency, with the number of positive samples continuing to increase. It is therefore essential to continue mastitis control measures and to regularly monitor the spread of mastitis pathogens in order to track trends and adapt targeted prevention measures.