Association between automatic milking system parameters and intramammary infections in dairy cows at dry-off - Ongom, Jonathan; Okella, Hedmon; Ferreira, Fernanda C.; Okello, Emmanuel (Frontiers in Animal Science, 2024)
The current global concern over increasing antimicrobial resistance among animal and human pathogens has motivated efforts to reduce antimicrobial drug use in food animals and its impact on antimicrobial resistance. One such strategy is to use selective dry cow therapy (SDCT) in dairy cows, which involves treating only cows with intramammary infection (IMI) at dry-off. However, efficient methods are needed to identify cows with IMI at dry-off to implement SDCT. Automatic Milking Systems (AMS) data may help farmers identify cows with IMI when individual Somatic Cell Count (SCC) is not routinely tested. This study assessed the correlation between cow-level and quarter-level AMS parameters and IMI at dry-off. A total of 733 udder quarters (comprising both Primiparous [PRIM] and Multiparous [MULT] cows) were sampled and categorized for IMI based on bacterial growth and SCC. Data were aggregated both daily and into 7-day and 15-day intervals preceding dryoff. The quarter-level prevalence of bacterial growth at dry-off was 24.28% overall. When stratified by parity, logistic regression analysis at 15 days to dry-off revealed that the average difference in mastitis detection index (MDi) in PRIM, MDi, and standard deviation milk flow rate in MULT were associated with increased odds of IMI at dry-off. Similarly, data from 7 days to dry-off revealed that average peak milk flow rate in PRIM, and MDi in MULT were associated with increased odds of IMI at dry-off. However, an increase in average milk yield was associated with decreased odds of IMI. Our findings underscore the significance of MDi, milk flow rate, peak milk flow rate, and milk yield in predicting IMI at dry-off. Notably, stronger associations were observed with data collected 7 days preceding dry-off. Further research is warranted to refine and validate algorithms amalgamating these variables for precise IMI prediction in cows at dry-off.