The use of precision dairy technologies to detect illness in group housed automatically fed pre-weaned dairy calves

Abstract

Precision dairy management is the use of sensor derived parameters to measure physiological, behavioral, and production indicators on individual animals to improve management strategies and farm performance. Automated milk feeding systems for group housed pre-weaned calves may offer some benefits including reallocation of labor, earlier socialization of calves, and an easy way to deliver more milk. However, there are some important disadvantages including increased risk for morbidity and mortality as well as delays in disease detection. Software programs aim to assist in the detection of sick calves through such methods as flagging calves when there has been a large reduction in milk intake or large changes in drinking speed over the last 24 hr period as compared to the previous 72 hr period. However, early research suggests that these simplistic algorithms may not be any more sensitive or timely than a human observer, and in some cases may miss detecting sick calves altogether. The greater aim of our research program is to determine if we can use different algorithms or approaches to examine feeding behavior that may improve the sensitivity and timeliness of detecting sick calves. As a first step towards this aim, the objective of the current preliminary study was to identify which feeding behaviors are most different between healthy and sick calves.