Early warnings from automatic milk yield monitoring with online synergistic control

Abstract

Sensors play a crucial role in the future of dairy farming. Modern dairy farms today are equipped with many different sensors for milk yield, body weight, activity, and even milk composition. The challenge, however, is to translate signals from these sensors into relevant information for the farmer. Because the measured values for an individual cow show nonstationary behavior, the concepts of statistical process control, which are commonly used in industry, cannot be used directly. The synergistic control concept overcomes this problem by on-line (real-time) modeling of the process and application of statistical process control to the residuals between the measured and modeled values. In this study, the synergistic control concept was developed and tested for early detection of anomalies in dairy cows based on detection of shifts in milk yield. Compared with the combination of visual observation and milk conductivity measurements, the developed strategy had a sensitivity of 63% for detecting clinical mastitis. Consequently, this technique could have added value on many farms, as it extracts practical information out of inexpensive data that are already available. As it can be easily extended to other measured parameters, the technique shows potential for early detection of other nutrition and health problems. © 2014 American Dairy Science Association.