Improve mastitis detection through better decisions

Despite all efforts to prevent, control and treat mastitis produced over the last 60 years, this disease is still one of the major sources of dairy farms costs worldwide. In addition to the direct and indirect costs, the need to improve the welfare cow and to reduce the use of antibiotics, increase the burden represented by mastitis. Prevention has been always defined as “the key” to control mastitis and to reduce the consequences of the disease. Therefore, several different approaches have been suggested, some of them were very successful (i.e. pre and post dipping), while others much less. Prevention should be based on identification of risk factors and on the reduction of these risks. However, prevention is also based on monitoring cow (and udder) status and on identifying alterations potentially leading to a disease status. Often, this latter approach is defined as early diagnosis, but is a very reductive way to define this important part of prevention. There are several systems to monitor cow and udder status, some very well-known and simple (i.e. individual SCC, udder hygiene score, teat score), other more sophisticated and based on the availability of new technologies (i.e. conductivity, NAGase, LDH, IR sensors). Regardless of the system used to monitor mastitis, the key issue is to decide when the cow or the udder are moving from a healthy status to a diseased one and, even more important, what to do when this deviation is observed or registered. Making the right decision is crucial for an effective mastitis control and it will be even more important as long as the use of antibiotics will be constrained. Monitor systems are based on one or few marker(s) and the deviation of the marker(s) suggest the presence of a diseases animal. However, the “deviation” depends on thresholds defined for an ideal situation, not easily reproducible in practice. Moreover, even if dairy herds often appear very similar, there are significant differences among cows’ responses among herds for many different physiological and pathological markers. Finally, dairy farms are also included in different social and economic environments and these imply that the decisions taken after the same "deviation" may be different as social and economic factors are different. Therefore, marker thresholds, benchmarks, and decision tools should consider economic and biological differences in order to identify the proper approach to solve herd or cow specific problems. The real challenge for researcher and practitioners is not the application of the new technologies, but to identify how to fit these old and new approaches and technologies to the specific dairy herd in order to improve cow health and welfare and to increase herd sustainability.