Interpretive Summary: Integrating diverse data sources to predict disease risk in dairy cattle—a machine learning approach

By Anne Zinn

Livestock farming is currently undergoing a digital revolution. Specifically, precision livestock farming, the practice of taking information from many sources to form a holistic picture, holds great promise to steer livestock farming into a more environmentally sustainable direction by enabling preventive interventions and reducing animal losses. In livestock farming, the animals and their surroundings are closely monitored and observed, but such data often reside in disconnected silos, making it impossible to leverage their full potential to improve animal well-being.

A paper recently published in the Journal of Animal Science introduced a precision livestock farming approach, which brings together information streams from a variety of life domains of dairy cattle to study whether including more and diverse data sources improves the health and herd management of dairy farms. The main objective was to demonstrate the value of data reuse in the context of disease prediction for dairy cattle and to illustrate which diseases can be predicted well, given different classifiers and datasets.

Findings indicate a move towards data-driven point-of-care interventions is worthwhile and demonstrate the added value of integrating all available data in the dairy industry to improve animal well-being and reduce disease risk. Specifically, research demonstrated encouraging results in the prediction of lameness, acute and chronic mastitis, anestrus, ovarian cysts, metritis, ketosis, and milk fever from available data. Analysis of the importance of individual variables to prediction performance showed that disease in dairy cattle is a product of the complex interplay between a multitude of life domains, that including more and diverse data sources increases prediction performance, and that the reuse of existing data can create actionable information for preventive interventions. Overall, these results provide evidence that the integration of data from multiple sources can create added value in precision livestock farming and can be used to improve animal well-being.

The full paper can be found on the Journal of Animal Science webpage.