Sensor-based mastitis management in automatic milking system farms: Mastitis management from a data-centric and economic perspective

Sammanfattning: Mastitis, or udder inflammation, is one of the most prevalent and costliest diseases in dairy farming. Automatic milking systems, equipped with sensors measuring mastitis indicators, have been used commercially since the 1990s. The overall objective for this PhD project was to explore the potential applications for a decision support system in automatic milking systems supporting chronic mastitis decisionmaking. Paper I described that mastitis cases usually recover in somatic cell count within three to four weeks. Paper II found strong non-linearities between milk production and lactate dehydrogenase, somatic cell count, and electrical conductivity, combined with possible actionable thresholds based on the size of milk yield loss. Paper III showed that it was possible to forecast the progression of mastitis. Finally, Paper IV estimated the economic impact of different sensor-based mastitis management strategies to show which strategy tends to decrease the cost of mastitis and chronic mastitis the most. More specifically, it estimated the economic consequences of chronic mastitis cases to show the direct impact of management failure on the economic situation of a dairy farm. This thesis shows that it is possible to support management regarding chronic mastitis with sensors, and it provides the basis for a decision support system. This decision support system would be a system that could tell the farmer which cases of mastitis are chronic, are likely to become chronic, are associated with large milk production loss, and could tell the economic consequences of chronic mastitis cases.