The objective of the study was to develop a grazing algorithm for an ear tag-based accelerometer system (Smartbow GmbH, Weibern, Austria) and to validate the grazing algorithm with data from a noseband sensor. The ear tag has an acceleration sensor, a radio chip, and temperature sensor for calibration and it can monitor rumination and detect estrus and localization. To validate the ear tag, a noseband sensor (RumiWatch, Itin and Hoch GmbH, Liestal, Switzerland) was used. The noseband sensor detects pressure and acceleration patterns, and, with a software program specific to the noseband, pressure and acceleration patterns are used to classify data into eating, ruminating, drinking, and other activities. The study was conducted at the University of Minnesota West Central Research and Outreach Center (Morris, MN) and at Teagasc Animal and Grassland Research and Innovation Centre (Moorepark, Fermoy, Co. Cork, Ireland). During May and June 2017, observational data from Minnesota and Ireland were used to develop the grazing algorithm. During September 2018, data were collected by the ear tag and noseband sensor from 12 crossbred cows in Minnesota for a total of 248 h and from 9 Holstein-Friesian cows in Ireland for a total of 248 h. A 2-sided t-test was used to compare the percentage of grazing and nongrazing time recorded by the ear tag and the noseband sensor. Pearson correlations and concordance correlation coefficients (CCC) were used to evaluate associations between the ear tag and noseband sensor. The percentage of total grazing time recorded by the ear tag and by the noseband sensor was 37.0% [95% confidence interval (CI): 32.1 to 42.0] and 40.5% (95% CI: 35.5 to 45.6), respectively, in Minnesota, and 35.4% (95% CI: 30.6 to 40.2) and 36.9% (95% CI: 32.1 to 41.8), respectively, in Ireland. The ear tag and noseband sensor agreed strongly for monitoring grazing in Minnesota (r = 0.96; 95% CI: 0.94 to 0.97, CCC = 0.95) and in Ireland (r = 0.92; 95% CI: 0.90 to 0.94, CCC = 0.92). The results suggest that there is potential for the ear tag to be used on pasture-based dairy farms to support management decision-making.
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http://dx.doi.org/10.3168/jds.2019-17269 | DOI Listing |
Sensors (Basel)
December 2024
Center for Veterinary Systems Transformation and Sustainability, Clinical Department for Farm Animals and Food System Science, University of Veterinary Medicine, 1210 Vienna, Austria.
Monitoring animal behavior using sensor technologies requires prior testing under varying conditions because behaviors can differ significantly, such as between grazing and confined cows. This study aimed to validate several sensor systems for classifying rumination and lying behaviors in cows on pasture under different environmental conditions, compare the sensors' performance at different time resolutions, and evaluate a correction algorithm for rumination data. Ten Simmental dairy cows were monitored on pasture, each simultaneously equipped with an ear-tag accelerometer (ET), two different leg-mounted accelerometers (LMs), and a noseband sensor (NB).
View Article and Find Full Text PDFPLoS One
October 2024
IFCE (French Horse and Riding Institute), Saumur Technical Platform, Saumur, France.
Health and performance of vaulting horses cantering with reins might be affected by rein tensions. The primary aim of this present study was to measure rein and lunge line tensions in international-level vaulting horses with several types of reins adjusted in accordance with the requirements of the FEI Vaulting Rules and study the effect of reins types on it. The secondary aim was to evaluate behavioural signs of discomfort under the same conditions and study the effect of reins types on it.
View Article and Find Full Text PDFJDS Commun
July 2024
United States Department of Agriculture, Agricultural Research Service, Forage-Animal Production Research Unit, Lexington, KY 40506.
Precision monitoring of feeding behaviors can aid in dairy herd management. Noseband sensors (RumiWatch System [RW]; Itin + Hoch GmbH) have been established as an automated gold standard for evaluating precision technologies in grazing cows, but more advanced algorithms have not been validated in confinement settings. Additionally, little is known regarding effects of environmental conditions on sensor performance.
View Article and Find Full Text PDFJ Equine Vet Sci
September 2024
Department of Animal and Dairy Science, College of Agriculture and Environmental Sciences, University of Georgia, 425 River Road, Athens, Georgia, USA, 30602.
Mastication is the initial phase of digestion and is crucial to equine health due to its role in saliva production and food particle reduction. Hay nets have been promoted to slow the rate of hay consumption, with many styles of slow feeders available. Limited research has shown that nets may slow consumption, but no research has examined their effect on the horse's chewing frequency and patterns.
View Article and Find Full Text PDFAnimals (Basel)
March 2024
University Clinic for Ruminants, University of Veterinary Medicine, Veterinaerplatz 1, A-1210 Vienna, Austria.
This study hypothesizes that higher in-line milk lactose concentrations are indicative of enhanced dairy cow behaviors-including increased rumination, feeding, and locomotion activities-reflecting superior overall health and well-being. It posits that fluctuations in milk lactose levels have a substantial impact on the physiological and behavioral responses of dairy cows, thereby affecting their milk yields and compositions. Each cow's milk lactose, fat, protein, and fat-to-protein ratio were continuously monitored using the BROLIS HerdLine in-line milk analyzer (Brolis Sensor Technology, Vilnius, Lithuania).
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