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Selected Publication:

Type of publication: Journal Article
Type of document: Note

Year: 2018

Authors: Roland, L; Schweinzer, V; Kanz, P; Sattlecker, G; Kickinger, F; Lidauer, L; Berger, A; Auer, W; Mayer, J; Sturm, V; Efrosinin, D; Breitenberger, S; Drillich, M; Iwersen, M

Title: Technical note: Evaluation of a triaxial accelerometer for monitoring selected behaviors in dairy calves.

Source: J Dairy Sci. 2018; 101(11):10421-10427

Authors Vetmeduni Vienna:

Drillich Marc
Iwersen Michael
Kanz Peter
Schweinzer Vanessa

Vetmed Research Units
University Clinic for Ruminants, Clinical Unit of Herd Management in ruminants

The objectives of this study were (1) to develop an algorithm for the acceleration sensor of the Smartbow Eartag (Smartbow GmbH, Weibern, Austria) to distinguish between postures (lying and standing or locomotion) and to detect 6 kinds of activities (milk intake, water intake, solid feed intake, ruminating, licking or sucking without milk intake, and other activities) in dairy calves and (2) to evaluate this sensor for identifying these behaviors in dairy calves compared with observations from video. Accelerometers were applied to the left ears of 15 preweaned Holstein dairy calves. Calves were kept in a group pen and received milk replacer from an automatic calf feeder. Based on 38 h of acceleration data and video observation, an algorithm was established to detect the predefined behaviors. Using cross-validation, video recordings were used to analyze whether a behavior was detected correctly by the developed algorithm. For posture, sensitivity (94.4%), specificity (94.3%), precision (95.8%), and accuracy (94.3%) were high. Cohen's kappa was calculated as 0.88. For the 6 defined activities, overall (i.e., aggregated for all activities) accuracy was 70.8% and kappa was calculated as 0.58. Some activities (e.g., ruminating, feed intake, other activities) were identified better than others. In conclusion, the developed algorithm based on the acceleration data of the Smartbow Eartag was successful in detecting lying behavior, rumination, feed intake, and other activities in calves, but further development of the underlying algorithm will be necessary to produce reliable results for milk and water intake.Copyright © 2018 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Keywords Pubmed: Accelerometryveterinary
Animal Feed
Behavior, Animal
Video Recording

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