Monitoring selected behaviors of calves by use of an ear-attached accelerometer for detecting early indicators of diarrhea.

ABSTRACT One of the most important diseases in calves worldwide is neonatal calf diarrhea (NCD), which impairs calf welfare and leads to economic losses. The aim of this study was to test whether the activity patterns of calves can be used as early indicators to identify animals at risk for suffering from NCD, compared with physical examination. We monitored 310 healthy female Holstein-Friesian calves on a commercial dairy farm immediately after birth, equipped them with an ear tag–based accelerometer (Smartbow, Smartbow GmbH), and conducted daily physical examinations during the first 28 d of life. The Smartbow system captured acceleration data indicative of standing and lying periods and activity levels (active and inactive), shown as minutes per hour. We categorized calves as diarrheic if they showed fecal scores of ≥3 on a 4-point scale on at least 2 consecutive days. Incidence of diarrhea was 50.7% (n = 148). A mixed logistic regression model showed that lying [odds ratio (OR) = 1.19], inactive (OR = 1.14), and active (OR = 0.92) times, 1 d before clinical identification of diarrhea (d −1), were associated with the odds of diarrhea occurring on the subsequent day. Receiver operating characteristics curve showed that lying time at d −1 was a fair predictor for diarrhea on the subsequent day (area under curve = 0.69). Average lying time on d −1 was 64.8 min longer in diarrheic calves compared with their controls. Median lying and inactive times decreased, and active time increased with age over the study period. The 24-h pattern of behavior indices based on the output of the Smartbow system followed periods of resting and active times, and showed that between 2200 h and 0600 h, calves spent the greatest percentage of time lying and inactive. These results showed that the accelerometer system has the potential to detect early indicators associated with NCD. In future studies, additional data for the development and testing of calf- and event-specific algorithms (e.g., for detecting milk intake, playing behavior) should be collected, which might further improve the early detection of diarrhea in calves.