Abstract #145
Section: Animal Behavior and Well-Being (orals)
Session: Animal Behavior and Well-Being I
Format: Oral
Day/Time: Monday 4:00 PM–4:15 PM
Location: Room 300 AB
Session: Animal Behavior and Well-Being I
Format: Oral
Day/Time: Monday 4:00 PM–4:15 PM
Location: Room 300 AB
# 145
Validation of an ear-tag accelerometer to identify feeding and activity behaviors of tie-stall housed dairy cattle.
A. Zambelis1, T. Wolfe1, E. Vasseur*1, 1Department of Animal Science, McGill University,.
Key Words: precision dairy technology, activity monitoring, feeding behavior
Validation of an ear-tag accelerometer to identify feeding and activity behaviors of tie-stall housed dairy cattle.
A. Zambelis1, T. Wolfe1, E. Vasseur*1, 1Department of Animal Science, McGill University,.
The objective of this study was to validate the CowManager SensOor ear-tag accelerometer against visual observations of feeding, rumination, resting, and active behaviors in a tie-stall dairy facility. Prior validation of the sensor has been published for free-stall and grazing dairy herds, however the vast array of behavioral differences that exist among these and a tie-stall system necessitate additional validation. Lactating Holstein cows (n = 10) in different lactation stages and parities were included in the study at the McGill University Macdonald Campus Dairy Complex. Cows were monitored both visually and with the sensor for 10 h/d for 4 consecutive days (10 cows × 10 h × 4 d = 400 h of observation total). A single trained observer classified each minute of visual observation into 1 of 13 behaviors, and then summarized them into the 4 categories of eating, rumination, not active, or active. The sensor registered ear movements continuously and based on a proprietary model, converted them into the 5 behavioral categories of eating, rumination, not active, active, and high active. Multivariate mixed models were run to obtain covariance estimates, from which correlation coefficients were computed to assess agreement between observer and sensor data. The models included the percentage of each behavioral category per day as the dependent variable, and technology (observer versus sensor) and day as fixed effects. The models also included the random effects of technology, and the repeated effects of technology and day. The correlation strength between visual observation and sensor data varied from poor to almost perfect by behavior category (eating: r = 0.27; rumination: r = 0.69; total nutrition: r = 0.83; not active: r = 0.95; and active: r = 0.89). The results suggest that the sensor can be used to accurately monitor active and not active behaviors of tie-stall housed dairy cows. The results also suggest that while the sensor shows promise for identifying feeding behaviors in general, the independent classification of rumination and eating requires additional sensitivity.
Key Words: precision dairy technology, activity monitoring, feeding behavior