Literature DB >> 32089298

Validation of an ear tag-based accelerometer system for detecting grazing behavior of dairy cows.

G M Pereira1, B J Heins2, B O'Brien3, A McDonagh3, L Lidauer4, F Kickinger4.   

Abstract

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. The Authors. Published by FASS Inc. and Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Entities:  

Keywords:  accelerometer; ear tag; grazing; validation

Mesh:

Year:  2020        PMID: 32089298     DOI: 10.3168/jds.2019-17269

Source DB:  PubMed          Journal:  J Dairy Sci        ISSN: 0022-0302            Impact factor:   4.034


  4 in total

1.  A Pilot Study Using Accelerometers to Characterise the Licking Behaviour of Penned Cattle at a Mineral Block Supplement.

Authors:  Gamaliel Simanungkalit; Jamie Barwick; Frances Cowley; Robin Dobos; Roger Hegarty
Journal:  Animals (Basel)       Date:  2021-04-17       Impact factor: 2.752

2.  Modern livestock farming under tropical conditions using sensors in grazing systems.

Authors:  Eliéder Prates Romanzini; Rafael Nakamura Watanabe; Natália Vilas Boas Fonseca; Andressa Scholz Berça; Thaís Ribeiro Brito; Priscila Arrigucci Bernardes; Danísio Prado Munari; Ricardo Andrade Reis
Journal:  Sci Rep       Date:  2022-02-16       Impact factor: 4.379

3.  Assessment of feeding, ruminating and locomotion behaviors in dairy cows around calving - a retrospective clinical study to early detect spontaneous disease appearance.

Authors:  Mahmoud Fadul; Luigi D'Andrea; Maher Alsaaod; Giuliano Borriello; Antonio Di Lori; Dimitri Stucki; Paolo Ciaramella; Adrian Steiner; Jacopo Guccione
Journal:  PLoS One       Date:  2022-03-04       Impact factor: 3.240

Review 4.  Affective State Recognition in Livestock-Artificial Intelligence Approaches.

Authors:  Suresh Neethirajan
Journal:  Animals (Basel)       Date:  2022-03-17       Impact factor: 3.231

  4 in total

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