Journal of Animal Environmental Science 축산시설환경학회지 ISSN:1226-0274(Print)

This study aimed to contribute to the early diagnosis of mastitis by conducting a time-series analysis using lactose levels. Mastitis is a disease that affects the health and productivity of dairy cows, making early detection and appropriate management essential. Therefore, we investigated the lactose levels during the mastitis occurrence process and found that they decreased in individuals with mastitis compared to healthy ones. The difference in lactose levels between normal individuals and those with mastitis was not affected by parity. Moreover, we observed a gradual decrease in lactose levels in mastitis-affected individuals from six months before the onset of the condition, providing potential clues for early detection. Considering the outcomes of this study, lactose levels could be regarded as a potential factor in second-generation smart farm research, utilizing artificial intelligence and big data technologies. However, further studies are needed to understand the complex relationship between various factors, including lactose indicators, intra-ruminal temperature, activity levels, ambient temperature, humidity, and heat stress index, using machine learning or deep learning approaches.