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.

Gastrointestinal Helminth Infections in Dogs in Sheep and Goat Farms in Greece: Prevalence, Involvement of Wild Canid Predators and Use of Anthelmintics.

The objectives of the present work were the investigation of gastrointestinal parasitic infections in dogs in small ruminant farms in Greece, the elucidation of potential predictors for these infections and the description of practices related to administration of anthelmintics to dogs. This study was carried out in 444 small ruminant farms in Greece. Faecal samples were collected directly from the rectum of the dogs in the farms.

Lameness in Adult Sheep and Goats in Greece: Prevalence, Predictors, Treatment, Importance for Farmers.

The present study refers to an extensive investigation of lameness performed countrywide in Greece, on 325 sheep and 119 goat farms. The specific objectives of this work were to present data on the occurrence of lameness on sheep and goat farms and to identify variables (including variables related to climatic factors) associated with the disorder on the farms. Farms were visited and animals on the farm were assessed for the presence of lameness; further, an interview was carried out with the farmer to obtain information regarding practices applied on the farm.

The Use of Machine Learning to Predict Prevalence of Subclinical Mastitis in Dairy Sheep Farms.

The objective of the study was to develop a computational model with which predictions regarding the level of prevalence of mastitis in dairy sheep farms could be performed. Data for the construction of the model were obtained from a large Greece-wide field study with 111 farms. Unsupervised learning methodology was applied for clustering data into two clusters based on 18 variables (17 independent variables related to health management practices applied in farms, climatological data at the locations of the farms, and the level of prevalence of subclinical mastitis as the target value).