The Use of Explainable Machine Learning for the Prediction of the Quality of Bulk-Tank Milk in Sheep and Goat Farms.

The specific objective of the present study was to develop computational models, by means of which predictions could be performed regarding the quality of the bulk-tank milk in dairy sheep and goat farms. Our hypothesis was that use of specific variables related to the health management applied in the farm can facilitate the development of predictions regarding values related to milk quality, specifically for fat content, protein content, fat and protein content combined, somatic cell counts, and total bacterial counts. Bulk-tank milk from 325 sheep and 119 goat farms was collected and evaluated by established techniques for analysis of fat and protein content, for somatic cell counting, and for total bacterial counting.