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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 3529-3544 |
| Number of pages | 16 |
| Journal | Journal of Dairy Science |
| Volume | 103 |
| Issue number | 4 |
| DOIs | |
| State | Published - Apr 2020 |
Bibliographical note
Funding Information:The authors in Minnesota thank Darin Huot and coworkers at University of Minnesota West Central Research and Outreach Center (Morris, MN) for their assistance and care of the animals. This work was supported by Organic Agriculture Research and Extension Initiative (grant no. 2012-51300-20015/project accession no. 0230589) from the USDA National Institute of Food and Agriculture (Washington, DC). The authors in Ireland gratefully acknowledge Science Foundation Ireland for funding the project “Precision Dairy.” Laura Lidauer and Florian Kickinger had no financial relationships with the other authors because the Smartbow system was purchased and is owned by researchers at the individual locations. Lidauer and Kickinger participated in the algorithm development of the Smartbow system but did not participate in data collection methods or the data analyses. The content and results reported in this manuscript were chosen by Glenda Pereira, Brad Heins, and Bernadette O'Brien.
Publisher Copyright:
© 2020 American Dairy Science Association
Keywords
- accelerometer
- ear tag
- grazing
- validation