Evaluation of an ear-tag-based accelerometer for monitoring rumination in dairy cows

Abstract

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.

Detection of the time of foaling by accelerometer technique in horses (Equus caballus)—a pilot study

“…In horses, owner acceptance of alert systems which are attached directly to the animal is low. Recently, extremely small-size accelerometers have become available which can be included into ear tags for oestrous detection or detection of rumination activity in cattle (Borchers et al, 2016;Reiter et al, 2018). In swine, accelerometers attached to a neck collar have been used for prediction of the time of farrowing with 90% of animals showing increased activity before onset of parturition (Oczak, Maschat, Berckmans, Vranken, & Baumgartner, 2016;Pastell, Hietaoja, Yun, Tiusanen, & Valros, 2013).…”

Combination of Sensor Data and Health Monitoring for Early Detection of Subclinical Ketosis in Dairy Cows

“…In this study, we used the accelerometer system SMARTBOW (Smartbow GmbH, Weibern, Austria), which was recently evaluated for monitoring of rumination [17,18] and detecting estrus events [19]. The system includes ear-tags (size and weight of 52 × 36 × 17 mm and 34 g) equipped with a three-dimensional accelerometer, receivers (SMARTBOW WallPoint) that are installed in the barn, and a local server (SMARTBOW Farmserver).…”

Internet of Things for evaluating foraging and feeding behavior of cattle on grassland-based farming systems: concepts and review of sensor technologies

“…In many studies, rumination is assessed by using accelerometers with acoustic sensors included. In [53], accelerometer systems have been validated for detecting rumination time, chewing cycles, and rumination bouts. Accelerometer data on cow activity and rumination have been used for improving prediction of the start of calving in dairy cows [54] as well as monitoring feeding behavior in feedlot cattle [55].…”

“…Other authors have assessed rumination and activity patterns by using accelerometers. For example, rumination, feeding, activity, and animal temperature [54], feeding, ruminating, active, and resting [55], rumination time, chewing cycles, and rumination bouts [53], lying time, neck activity, reticulorumen temperature, and rumination time [58], grazing, searching, ruminating, resting, and scratching [56], level of activity and rumination [60], grazing, ruminating, walking, resting behaviors to develop algorithms for pasture intake by individual grazing cattle [61], rumination and its relationship to feeding and lying behavior in Holstein dairy cows] [62]. In [63], GPS locations were recorded to calculate mean slope, elevation, distance from water, distance traveled per day, and elevation for each cow.…”