Improving udder health management in dairy herds with automatic milking systems

Abstract

The overall goal of this thesis therefore was to explore the potential use and benefit of using frequently measured data to optimize on-farm decision making in udder health management in herds using an automatic milking system (AMS). In chapter 2, the risk factors for bovine mastitis in Dutch AMS herds were described. The results indicated that mastitis control measurements as advised in CMS herds generally are also applicable in AMS herds, while specifically in larger herds, extra attention should be given to hygiene of cows and of the AMS. In chapter 3, we compared the results of an online automated California Mastitis Test sensor (O-CMT) to estimate the SCC, with the SCC as measured in a milk quality laboratory (L-SCC). The overall concordance correlation coefficient between O-CMT and L-SCC was 0.53, with substantial variation between farms. The optimal time window for aggregating multiple O-CMT measurements was found to be 24h. We also found that the agreement between O-CMT and L-SCC was positively associated with herd SCC. In chapter 4, we described the SCC pattern based on O-CMT in 1,000 cows from 55 dairy herds using AMS in 6 different countries. We identified the rfSCC pattern in 4.7% (95% CI: 3.5%-6.2%) of these episodes. The rfSCC episodes had a median SCC of 701 (2.5%-97.5% quantile: 539-1,162) × 1,000 cells/mL, a median amplitude of 552 (2.5%-97.5% quantile: 409-886) × 1,000 cells/mL and a median cycle length of 4.1 (2.5%-97.5% quantile: 3.7-4.9) days. No clear association between pathogen species and the rfSCC pattern was found. In chapter 5, the transmission rates, duration of intramammary infections (IMI) and the basic reproduction number of Staphylococcus aureus and Streptococcus agalactiae in a Dutch AMS herd were estimated and compared with those of CMS farms. The transmission rate for Staph. aureus was estimated to be within the range of 0.002 (95% CI: 0-0.005) quarter-day-1 to 0.019 (95% CI: 0.010-0.032) quarter-day-1, and for Strep. agalactiae of 0.007 (95% CI: 0.005-0.010) quarter-day-1 to 0.019 (95% CI: 0.011-0.032) quarter-day-1. The median duration of chronic IMI was estimated at 95 (95% CI: 72-125) days for Staph. aureus and at 86 (95% CI: 67-111) days for Strep. agalactiae, and the R0 between 0.16 (95% CI: 0.05-0.27) and 0.34 (95% CI: 0.20-0.48) for Staph. aureus, and between 0.64 (95% CI: 0.41-0.87) and 0.68 (95% CI: 0.48-0.88) for Strep. agalactiae. In chapter 6, we compared the antimicrobial usage (AMU) and the distribution of bovine mastitis causing pathogens between AMS and CMS farms. The total antimicrobial usage and antimicrobial usage for dry cow therapy were comparable between AMS and CMS farms, whereas antimicrobial usage for intramammary infection tended to be lower and antimicrobial usage for injection was higher on AMS farms than on CMS farms. These results suggest AMU is comparable between AMS and CMS farms, but AMS farms tend to use more injectables and less intramammary treatments during lactation. Farmers’ attitudes toward udder health and toward mastitis treatment were associated with AMU in both AMS and CMS herds. Based on the findings in this thesis, we conclude that the frequently measured data in AMS herds holds great potential to support data-driven mastitis management decision making. This can be done for instance by monitoring individual cow udder health, identifying herd specific risk factors automatically, capturing patterns of IMI dynamics and quantifying the transmission process of infectious mastitis pathogens. Concerns that now limit the implementation of sensor technologies must be addressed. Further research to integrate the data from different sources, and algorithms to turn the data into interpretable information that can be used by the farmer and his advisor are needed.