The objective of this study was to evaluate the ear-tag-based accelerometer system Smartbow (Smartbow GmbH, Weibern, Austria) for detecting rumination time, chewing cycles, and rumination bouts in indoor-housed dairy cows. For this, the parameters were determined by analyses of video recordings as reference and compared with the results of the accelerometer system. Additionally, we tested the intra- and inter-observer reliability as well as the agreement of direct cow observations and video recordings. Ten Simmental dairy cows in early lactation were equipped with 10-Hz accelerometer ear tags and kept in a pen separated from herd mates. A total mixed ration was fed twice a day via a roughage intake control system. During the study, cows' rumination and other activities were directly observed for 20 h by 2 trained observers. Additionally, cows were video recorded for 19 d, 24 h a day. After exclusion of unsuitable videos, 2,490 h of cow individual 1-h video sequences were eligible for further analyses. Out of this, one hundred 1-h video sequences were randomly selected and visually and manually classified by a trained observer using professional video analyses software. Based on these analyses, half of the data was used for development (based on data of 50-h video analyses) and testing (based on data of additional 50-h video analyses) of the Smartbow algorithms, respectively. Inter- and intra-observer reliability as well as the comparison of direct against video observations revealed in high agreements for rumination time and chewing cycles with Pearson correlation coefficients >0.99. The rumination time, chewing cycles, as well as rumination bouts detected by Smartbow were highly associated (r > 0.99) with the analyses of video recordings. Algorithm testing revealed in an underestimation of the average ± standard deviation rumination time per 1-h period by the Smartbow system of 17.0 ± 35.3 s (i.e., -1.2%), compared with visual observations. The average number ± standard deviation of chewing cycles and rumination bouts was overestimated by Smartbow by 59.8 ± 79.6 (i.e., 3.7%) and by 0.5 ± 0.9 (i.e., 1.6%), respectively, compared with the video analyses. In summary, the agreement between the Smartbow system with video analyses was excellent. From a practical and clinical point of view, the detected differences were negligible. However, further research is necessary to test the system under various field conditions and to evaluate the benefit of incorporating rumination data into herd management decisions.

The objectives of this study were (1) to develop an algorithm for the acceleration sensor of the Smartbow Eartag (Smartbow GmbH, Weibern, Austria) to distinguish between postures (lying and standing or locomotion) and to detect 6 kinds of activities (milk intake, water intake, solid feed intake, ruminating, licking or sucking without milk intake, and other activities) in dairy calves and (2) to evaluate this sensor for identifying these behaviors in dairy calves compared with observations from video. Accelerometers were applied to the left ears of 15 preweaned Holstein dairy calves. Calves were kept in a group pen and received milk replacer from an automatic calf feeder. Based on 38 h of acceleration data and video observation, an algorithm was established to detect the predefined behaviors. Using cross-validation, video recordings were used to analyze whether a behavior was detected correctly by the developed algorithm. For posture, sensitivity (94.4%), specificity (94.3%), precision (95.8%), and accuracy (94.3%) were high. Cohen's kappa was calculated as 0.88. For the 6 defined activities, overall (i.e., aggregated for all activities) accuracy was 70.8% and kappa was calculated as 0.58. Some activities (e.g., ruminating, feed intake, other activities) were identified better than others. In conclusion, the developed algorithm based on the acceleration data of the Smartbow Eartag was successful in detecting lying behavior, rumination, feed intake, and other activities in calves, but further development of the underlying algorithm will be necessary to produce reliable results for milk and water intake.

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