Spatio-temporal analysis of milk safety under climate change

Bio-industrial processes can be optimized considering climate change using climate change impact assessments. These are based on models that describe the effect of climate on the affected sector. In this case, we focus on milk safety, thus we study Somatic Cell Counts (SCC), which is an indicator of mastitis, and Total Bacterial Counts (TBC), an indicator of the microbiological state of raw milk. The objective is to identify SSC and TBC patterns by looking into their spatial variability. For this purpose, traditional data mining methods are adopted to cover both space and time using data from 53 farms (2014-2019). According to the results, both SCC and TBC indicate spatial correlation. This means that apart from the already established temporal dependency, there is also a spatial dependency. In conclusion, collecting temporal data only from one farm may be misleading. Climate change-proof process design is essential for the dairy sector.