Methodology for quantifying the behavioral activity of dairy cows in freestall barns1

Abstract

The objectives of this study were to 1) evaluate the validity of automated monitoring systems as assessment method for the behavioral activity of dairy cows compared with video recording, and 2) determine the sampling intervals required to obtain reliable estimates of the daily behavior. To determine lying, standing, and walking, 12 cows were equipped with automatic recording devices (IceTag = 12 cows, HOBO Pendant G = 5 cows), and their behavior was simultaneously recorded using a video recording system. The correspondence between the IceTag, HOBO logger, and video recording data was analyzed using 2 × 2 contingency tables, and we determined the sensitivity, specificity, and predictive value (positive and negative). Both types of loggers demonstrated high sensitivity (Sen ≥ 0.961) and specificity (Sp ≥ 0.951) for lying and standing behaviors with predictive values near 1.00. The HOBO logger can accurately describe the laterality of lying behavior, whereas the IceTag device inadequately recorded walking, with probability predictive values ≤ 0.303. Daily behaviors of the dairy cows were compared for 10 different sampling intervals (1 s, and 1, 2, 3, 4, 5, 10, 15, 30, and 60 min) collected by the IceTag, using linear regression. A strong relationship (R(2) ≥ 0.978) was found between the total lying times from data on a per-second basis and estimates obtained by 1, 2, 3, 4, 5, 10, and 15 min sampling intervals. The sampling intervals of 1 and 2 min were comparable for all aspects of lying behavior (R(2) ≥ 0.813; P > 0.05 for slope = 1, intercept = 0). Long sampling intervals (30 and 60 min) showed positive relationship for estimating time spent lying and standing (R(2) ≥ 0.774), but were inappropriate for predicting these behaviors, because they lacked accuracy and precision. Both the IceTag and HOBO logger accurately measured all aspects of lying and standing behavior. Reliable estimates of lying and standing time can be generated using relatively short interval lengths (e.g., 3, 4, 5, 10, or 15 min). Shorter sampling intervals (≤ 2 min) are required to accurately measure aspects of lying behavior such as number of lying bouts per day. The automated monitoring systems are time- and labor-saving tools that can be used by research or on farm to assess cow comfort related to lying behavior.

Development and validation of a novel pedometer algorithm to quantify extended characteristics of the locomotor behavior of dairy cows

“…Mattachini et al (2013) reported that just 30% of the walking events were correctly detected. This is mainly because the transition from standing to walking and vice versa is physically less distinctive than from the lying to the upright position (Trenel et al, 2009).…”

“…Mattachini et al (2013) concluded that sampling intervals ≤2 min are required to accurately measure aspects of lying behavior such as number of lying bouts per day. From the results of the current study, it remains unclear whether reduction of the sampling rate to 1 Hz might be possible without loss of important information mainly concerning the walking behavior.…”

A Survey of Dairy Cattle Behavior in Different Barns in Northern Italy

“…HOBO U12 Temp/RH/Light/External Data Logger (Onset Computer Corporation, Bourne, MA, USA) sensors were installed in every farm in two different positions in the barn and at the height of 2 m in accordance with Mattachini et al (2013) [29]. They were installed inside the barn to collect data about the environmental living conditions of dairy cows.…”

“…The HOBO Pendant sensor was mounted on the leg by means of tape and a plastic tough leg band in order to have the sensor positioned with the x-axis of the data logger perpendicular to the floor. The degree of vertical tilt of the x-and z-axis was used to quantify the behavior of the animal and, in particular, the standing or lying behavior [29].…”

IoT-based measurement system for classifying cow behavior from tri-axial accelerometer

“…Behavior is an indicator of the well-being and health of dairy cows (BORCHERS et al, 2016). Cows exhibit different behavior when health problems or physiological conditions (e.g., estrus) develop (MATTACHINI et al, 2013). For instance, comparing with non-lamed cows, lamed cows spend more time lying and less time standing, perform fewer aggressive interactions and are less active.…”