Daily grazing time of dairy cows is recorded accurately using the Lifecorder Plus device

Delagarde, Rémy; Lamberton, Philippe

doi:10.1016/j.applanim.2015.01.014

Cited by 33 publications

(18 citation statements)

References 20 publications

(26 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Estimations of time spent eating and ruminating generally exhibit a lower MADP when compared with counts of rumination and eating chews. Delagarde and Lamberton (2015) obtained with a uniaxial accelerometer (Lifecorder Plus) a lower RPE for the estimation of grazing time compared with our study (12 vs. 16%), even if C11 was used. Blomberg (2011) reported a higher deviation for eating time measured by the HOBO logger (biaxial accelerometer) compared with our study [16 vs. 11% (C2) and 7% (C11)].…”

Section: Validation Of Rumiwatch Halter Data Evaluated Using C2 and C11contrasting

confidence: 72%

Evaluation and validation of an automatic jaw movement recorder (RumiWatch) for ingestive and rumination behaviors of dairy cows during grazing and supplementation

Rombach

Münger

Niederhauser

et al. 2018

Journal of Dairy Science

View full text Add to dashboard Cite

Observation of ingestive and rumination behaviors of dairy cows may assist in detecting diseases, controlling reproductive status, and estimating intake. However, direct observation of cows on pasture is time consuming and can be difficult to realize. Consequently, different systems have been developed to automatically record behavioral characteristics; among them is the RumiWatch System (RWS; Itin and Hoch GmbH, Liestal, Switzerland). Until now, the RWS has not been thoroughly validated under grazing conditions. The aim of the current study was to validate the RWS, against direct observation, in measuring ingestive and rumination behaviors of dairy cows during grazing and supplementation in the barn. A further objective was to examine whether it is possible to refine the algorithm used by the evaluation software RumiWatch Converter 0.7.3.2 to improve the accuracy of the RWS. The data were collected from an experiment carried out with 18 lactating Holstein cows in a crossover block design including 3 treatments and 3 measuring periods. All cows grazed night and day, 19 h/d, and were either unsupplemented or supplemented, with chopped whole-plant corn silage, or chopped whole-plant corn silage mixed with a protein concentrate. During the measuring periods, cows were equipped with the RumiWatch Halter, and their ingestive and rumination behaviors were recorded concurrently by the RumiWatch Halter and by direct observation (690 × 10 min). Comparison of concurrently measured data shows that the RWS detected jaw movements reliably, but classification errors occurred. A low relative prediction error of ≤0.10 for the number of rumination boluses, rumination chews, and total eating chews was found. A high relative prediction error of >0.10 was found for the number of prehension bites and time spent in prehension and eating. Both converter versions performed equally well in differentiating ingestive and rumination behaviors when cows were supplemented in the barn or when grazing and supplementation activities were combined. For grazing cows, with no supplementation, more reliable results for the total number of eating chews, rumination chews, prehension bites, and time spent in these activities were obtained, by using the RumiWatch Converter 0.7.3.11. In light of these findings, further research is warranted to improve the accuracy of the RWS and to allow a differentiation between mastication chews and prehension bites while eating.

show abstract

Section: Validation Of Rumiwatch Halter Data Evaluated Using C2 and C11contrasting

confidence: 72%

Evaluation and validation of an automatic jaw movement recorder (RumiWatch) for ingestive and rumination behaviors of dairy cows during grazing and supplementation

Rombach

Münger

Niederhauser

et al. 2018

Journal of Dairy Science

View full text Add to dashboard Cite

show abstract

“…Sensors attached around the animal’s legs can also provide information on the number of steps, lying and standing times and the number of lying bouts. There are a number of techniques for measuring rumination and feeding behaviours, of which the most common are 3D accelerometers, pressure gauges and microphones [ 24 , 25 , 26 , 27 , 28 ]. Commercially available systems mainly use 3D accelerometers and microphones.…”

Section: Sensor Technologiesmentioning

confidence: 99%

Animal Welfare Implications of Digital Tools for Monitoring and Management of Cattle and Sheep on Pasture

Herlin

Brunberg

Hultgren

et al. 2021

Animals

View full text Add to dashboard Cite

The opportunities for natural animal behaviours in pastures imply animal welfare benefits. Nevertheless, monitoring the animals can be challenging. The use of sensors, cameras, positioning equipment and unmanned aerial vehicles in large pastures has the potential to improve animal welfare surveillance. Directly or indirectly, sensors measure environmental factors together with the behaviour and physiological state of the animal, and deviations can trigger alarms for, e.g., disease, heat stress and imminent calving. Electronic positioning includes Radio Frequency Identification (RFID) for the recording of animals at fixed points. Positioning units (GPS) mounted on collars can determine animal movements over large areas, determine their habitat and, somewhat, health and welfare. In combination with other sensors, such units can give information that helps to evaluate the welfare of free-ranging animals. Drones equipped with cameras can also locate and count the animals, as well as herd them. Digitally defined virtual fences can keep animals within a predefined area without the use of physical barriers, relying on acoustic signals and weak electric shocks. Due to individual variations in learning ability, some individuals may be exposed to numerous electric shocks, which might compromise their welfare. More research and development are required, especially regarding the use of drones and virtual fences.

show abstract

“…*P < 0.05, **P < 0.01, ***P < 0.001. also contribute effectively to the herd management efficiency in pasture-based systems. Observing the movement, lameness, lying, and feeding behavior is also of great importance for the herd management of grazing cows (Delagarde and Lamberton, 2015). However, obstacles in use due to connectivity problems could arise in using sensor technologies (Shalloo et al, 2018), which can be connected to smartphones.…”

Section: Descriptive Statisticsmentioning

confidence: 99%

Understanding the adoption of smartphone apps in dairy herd management

Michels

Bonke

Mußhoff

2019

Journal of Dairy Science

View full text Add to dashboard Cite

The number of decision support tools available to farmers, including dairy herd management smartphone apps, has been steadily increasing. The existing literature does not cover topics concerning the adoption and use of herd management smartphone apps or which specific functions of such apps are perceived as most useful by dairy farmers. It is unclear whether technology adoption can only be explained by economic reasoning, because the beliefs about a technology also play a role in decision-making. Therefore, this study seeks to determine whether an extended technology acceptance model can explain adoption and use of herd management smartphone apps. Results about the adoption and use of dairy herd management smartphone apps are derived from an online survey conducted in 2018 with 280 German dairy farmers. To model farmers' frequency of use of herd management smartphone apps, we applied partial least squares structural equation modeling and an ordered logit model. Our results show that 93% of the dairy farmers in our sample use a smartphone and 61% already use a herd management smartphone app. Daily use is reported by 38% of the adopters. Dairy farmers rated functions related to the observation of animal health, reproduction management, and data gathering as most useful, which should be in focus by developers and providers for future development. The key attitudinal components of the technology acceptance model, namely perceived ease of use and perceived usefulness, both positively influence the intention to use such apps. This ultimately has a positive effect on the actual usage behavior. Besides other factors, dairy farmers' education and knowledge of herd management smartphone apps have a positive effect on perceived ease of use. Our model explains 33% of the variance in the actual usage behavior related to herd management smartphone apps. Because perceived ease of use and perceived usefulness positively influence the intention to use such apps and ultimately the actual usage behavior, developers and providers should highlight the benefits of using herd management smartphone apps and also keep the interface of the apps as simple as possible.

show abstract

Daily grazing time of dairy cows is recorded accurately using the Lifecorder Plus device

Cited by 33 publications

References 20 publications

Evaluation and validation of an automatic jaw movement recorder (RumiWatch) for ingestive and rumination behaviors of dairy cows during grazing and supplementation

Evaluation and validation of an automatic jaw movement recorder (RumiWatch) for ingestive and rumination behaviors of dairy cows during grazing and supplementation

Animal Welfare Implications of Digital Tools for Monitoring and Management of Cattle and Sheep on Pasture

Understanding the adoption of smartphone apps in dairy herd management

Contact Info

Product

Resources

About