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

Borchers, M.R.; Chang, Yu‐Mei; Tsai, I-Ni; Wadsworth, Barbara; Bewley, J.M.

doi:10.3168/jds.2015-10843

Cited by 199 publications

(167 citation statements)

References 25 publications

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“…Near-continuous observation of preparturient mares is extremely labour-intensive, and time requirements would be markedly reduced with automated monitoring systems. In cattle, automated monitoring and alert systems are used for oestrous detection (Chanvallon et al, 2014;Pennington, Albright, & Callahan, 1986;Roelofs, van Eerdenburg, Soede, & Kemp, 2005) and health management (Borchers, Chang, Tsai, Wadsworth, & Bewley, 2016;Müller & Schrader, 2003) and are also increasingly advocated for monitoring of cows before calving (Krieger et al, 2017;Marchesi et al, 2013;Ouellet et al, 2016). Monitoring devices in cattle are often made for long-term recordings and are fixed permanently on the animal whereas for birth alert, pedometers (Nishimura et al, 2017;Titler et al, 2015) or intravaginal sensors (Palombi et al, 2013) are used.…”

Section: Introductionmentioning

confidence: 99%

“…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).…”

Section: Introductionmentioning

confidence: 99%

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

Hartmann

Lidauer²,

Aurich

et al. 2018

Reprod Domestic Animals

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Observation of preparturient mares is labour-intensive and time requirements would be reduced with automated monitoring systems. Recently, small-size accelerometers have become available. We followed the hypothesis that increased restlessness in mares shortly before the expulsive stage of labour can be detected with such accelerometers. Accelerometers were attached medioventrally to the halter of eight late pregnant mares. To evaluate an influence of accelerometer position, in one mare two additional accelerometers were attached close to the mandibular and atlanto-occipital joints. Accelerometers were programmed to send 600 signals/min (10 Hz). Signals were recorded continuously and, for evaluation, four intervals of 30-min duration on day 4 before foaling and the last 2 hr before rupture of the allantochorion at foaling were selected. Signal detection was influenced by position of the accelerometer on the mares' halter. The highest signal detection rate was reached with the accelerometer in the lateral and dorsal position but differences of accelerations measured by the system differed neither between sensor positions nor between time intervals 4 days before foaling. Differences of accelerations increased from 120 min before foaling until birth of the foal (p < 0.001) and this increase was most pronounced during the last 30-20 min before birth of the foal (p < 0.001). Technical improvements and a foaling specific algorithm are required to improve the accuracy to predict foaling. Even if this preliminary study included only a small number of mares, results demonstrate that accelerometers could be an important component of birth alert systems in horses in the future.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Hartmann

Lidauer²,

Aurich

et al. 2018

Reprod Domestic Animals

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“…Although this was the only video period where the system obviously confused a different activity for rumination activity, single measuring errors due to related activities may occur and should be evaluated in further studies. Borchers et al (2016) revealed a slightly lower correlation (r = 0.97, P < 0.01) between visual observed rumination time with the outcomes of the Smartbow system. However, studies are not directly comparable, as different cows in a divergent environment (e.g., housing conditions, ration composition) were used.…”

Section: Discussionmentioning

confidence: 81%

“…To our knowledge, this is the first study evaluating the Smartbow system with regard to rumination time, number of chewing cycles, as well as the number of rumination bouts. A previous study by Borchers et al (2016) evaluated rumination time with this system but did not investigate the agreement on chewing cycles or rumination bouts.…”

Section: Discussionmentioning

confidence: 99%

“…Some devices for monitoring rumination, for instance the Hi-Tag rumination moni-toring system (SCR Engineers Ltd.) and Qwes-HR (Lely Ltd., St. Neots, UK), record mastication sounds (Beauchemin et al, 1989;Schirmann et al, 2009;Burfeind et al, 2011;Goldhawk et al, 2013;Ambriz-Vilchis et al, 2015), whereas others, for instance the IGER Behavior Recorder systems (Ultra Sound Advice, London, UK), measure jaw movements (Kononoff et al, 2002;Umemura et al, 2009). Another system, the CowManager SensOor (Agis Automatisering BV, Harmelen, the Netherlands) consists of an ear-tagged device (Bikker et al, 2014;Borchers et al, 2016). Similar to this system, the Smartbow ear tag (Smartbow GmbH, Weibern, Austria) used in this study consists of an acceleration sensor to recognize rumination activity, among others.…”

Section: Introductionmentioning

confidence: 99%

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

Reiter

Sattlecker²,

Lidauer³

et al. 2018

Journal of Dairy Science

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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.

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IoT-Based Cow Health Monitoring System

Unold

Nikodem

Piasecki

et al. 2020

Lecture Notes in Computer Science

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

Cited by 199 publications

References 25 publications

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

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

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

IoT-Based Cow Health Monitoring System

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