Technical note: Evaluation of an ear-attached movement sensor to record cow feeding behavior and activity

Abstract

The ability to monitor dairy cow feeding behavior and activity could improve dairy herd management. A 3-dimensional accelerometer (SensOor; Agis Automatisering BV, Harmelen, the Netherlands) has been developed that can be attached to ear identification tags. Based on the principle that behavior can be identified by ear movements, a proprietary model classifies sensor data as "ruminating," "eating," "resting," or "active." The objective of the study was to evaluate this sensor on accuracy and precision. First, a pilot evaluation of agreement between 2 independent observers, recording behavior from 3 cows for a period of approximately 9h each, was performed. Second, to evaluate the sensor, the behavior of 15 cows was monitored both visually (VIS) and with the sensor (SENS), with approximately 20 h of registration per cow, evenly distributed over a 24-h period, excluding milking. Cows were chosen from groups of animals in different lactation stages and parities. Each minute of SENS and VIS data was classified into 1 of 9 categories (8 behaviors and 1 transition behavior) and summarized into 4 behavioral groups, namely ruminating, eating, resting, or active, which were analyzed by calculating kappa (κ) values. For the pilot evaluation, a high level of agreement between observers was obtained, with κ values of ≥ 0.96 for all behavioral categories, indicating that visual observation provides a good standard. For the second trial, relationships between SENS and VIS were studied by κ values on a minute basis and Pearson correlation and concordance correlation coefficient analysis on behavior expressed as percentage of total registration time. Times spent ruminating, eating, resting, and active were 42.6, 15.9, 31.6, and 9.9% (SENS) respectively, and 42.1, 13.0, 30.0, and 14.9% (VIS), respectively. Overall κ for the comparison of SENS and VIS was substantial (0.78), with κ values of 0.85, 0.77, 0.86, and 0.47 for "ruminating," "eating," "resting," and "active," respectively. Pearson correlation and concordance correlation coefficients between SENS and VIS for "ruminating," "eating," "resting," and "active" were 0.93, 0.88, 0.98, and 0.73, and 0.93, 0.75, 0.97, and 0.35, respectively. In conclusion, the results provide strong evidence that the present ear sensor technology can be used to monitor ruminating and resting behavior of freestall-housed dairy cattle. Our results also suggest that this technology shows promise for monitoring eating behavior, whereas more work is needed to determine its suitability to monitor activity of dairy cattle.

A validation of technologies monitoring dairy cow feeding, ruminating, and lying behaviors

“…CowManager SensOor data were more weakly correlated with visual observations (r = 0.69, P < 0.01; CCC = 0.59) than the Smartbow system (r = 0.97, P < 0.01; CCC = 0.96). In a previous evaluation of the CowManager SensOor, rumination was strongly correlated with visual observation (r = 0.93, P < 0.01; CCC = 0.93; Bikker et al, 2014). In the current study, rumination was the most subjective for observers to evaluate and showed slight variation among observers (r = 0.89, P < 0.01; interobserver variability).…”

“…All power tests were calculated to obtain a power (1 -β) of 0.90 and a type I error probability (α) of 0.05 (2-tailed). Effect size was determined by previous studies (equal to correlation coefficients; Bikker et al, 2014) and was 0.93 for rumination time (Bikker et al, 2014), 0.88 for feeding time (Bikker et al, 2014) and 0.90 for lying time (Ledgerwood et al, 2010). A minimum of 24 cows was needed to meet statistical power requirements and this number was doubled to account for potential instances of missing data (n = 48).…”

“…Observations were recorded using the aforementioned techniques and tested for agreement at each second during the observation period. The CORR procedure of SAS version 9.3 (SAS Institute, Cary, NC) generated Pearson correlation coefficients among all observers, and all correlations throughout the study were classified according to criteria used by Bikker et al (2014): negligible, 0.00 to 0.30; slight, 0.31 to 0.50; minor, 0.51 to 0.70; moderate, 0.71 to 0.90; and strong, 0.91 to 1.00. A chi-squared test was performed using R version 3.1.0 (R Foundation for Statistical Computing, Vienna, Austria) to evaluate observer agreement on behaviors performed at each second.…”

“…In the current study, rumination was the most subjective for observers to evaluate and showed slight variation among observers (r = 0.89, P < 0.01; interobserver variability). The evaluation of rumination behaviors has traditionally been completed in tiestalls, small pens, or a similar controlled setting (Schirmann et al, 2009;Bikker et al, 2014). The current study allowed cattle to express behaviors as they would in a general herd, potentially causing observers to miss or misidentify rumination events.…”

“…Chewing activity (pressure and strain) and feeding behavior monitors are primarily used in research settings, but commercially available rumination and feeding behavior quantification methods have recently been developed and evaluated. Bikker et al (2014) evaluated a technology (CowManager Sensoor, Agis, Harmelen, the Netherlands) monitoring rumination and feeding behavior through head movement and found these behaviors to be closely related to visual observations. Similarly, Schirmann et al (2009) evaluated a technology (HR Tag, SCR Engineers Ltd., Netanya, Israel) quantifying rumination sounds through a microphone and microprocessor and found a strong correlation between visual observations and technology.…”