Validation of automatic systems for monitoring the licking behaviour in Angus and Brahman cattle

Title
Validation of automatic systems for monitoring the licking behaviour in Angus and Brahman cattle
Publication Date
2022-02
Author(s)
Simanungkalit, Gamaliel
( author )
OrcID: https://orcid.org/0000-0002-9401-8388
Email: gsimanu2@une.edu.au
UNE Id une-id:gsimanu2
Clay, Jonathon
( author )
OrcID: https://orcid.org/0000-0002-3469-2012
Email: jclay4@une.edu.au
UNE Id une-id:jclay4
Barwick, Jamie
( author )
OrcID: https://orcid.org/0000-0003-0905-8527
Email: jbarwic2@une.edu.au
UNE Id une-id:jbarwic2
Cowley, Frances
( author )
OrcID: https://orcid.org/0000-0002-6475-1503
Email: fcowley@une.edu.au
UNE Id une-id:fcowley
Dawson, Bradley
( author )
OrcID: https://orcid.org/0000-0001-7290-9000
Email: bdawson@une.edu.au
UNE Id une-id:bdawson
Dobos, Robin
( author )
OrcID: https://orcid.org/0000-0002-9110-6729
Email: rdobos2@une.edu.au
UNE Id une-id:rdobos2
Hegarty, Roger
Type of document
Journal Article
Language
en
Entity Type
Publication
Publisher
Elsevier BV
Place of publication
Netherlands
DOI
10.1016/j.applanim.2022.105543
UNE publication id
une:1959.11/53170
Abstract

Monitoring feeding time in ruminants is one means to quantify feed intake. In grazing cattle offered feed supplement blocks, time spent licking can provide valuable information in estimating the level of blocks being ingested. This current study aimed to 1) assess an ear-tag accelerometer's capability to identify the licking behaviour at supplement blocks in grazing cattle and 2) evaluate the performance of the ear-tag accelerometer and radio-frequency identification (RFID) system to predict the individual time spent licking. Two breed groups of Angus (n = 7) and Brahman (n = 7) beef heifers were kept in two separate yards over 28 days. Each heifer was fitted with an ear-tag containing a tri-axial accelerometer set at 12.5 Hz frequency. Feed supplement blocks were provided through an RFID-equipped automatic supplement weighing unit within each yard, with access to the unit being given daily only from 16:00 h - 20:00 h. The accelerometer classification model developed using support vector machine (SVM) algorithm could distinguish between licking and non-licking behaviours, with an accuracy, sensitivity, F1 score, Cohen's kappa coefficient, and Matthew's correlation coefficient (MCC) of 86%, 93%, 0.88, 0.70, and 0.77 for Angus and 87%, 93%, 0.89, 0.73, and 0.79 for Brahman heifers. Time spent licking predicted by accelerometers were acceptable with a mean absolute error (MAE) of 22% and 11%, modelling efficiency (MEF) of 0.81 and 0.94, concordance correlation coefficient (CCC) of 0.88 and 0.96, and a ratio of root mean square prediction error (RSR) of 0.44 and 0.25, for Angus and Brahman heifers, respectively. However, the RFID system derived predictions of time spent licking in grazing heifers were unacceptable for both breeds. Overall, the ear-tag accelerometer offers the potential to predict individual time spent licking in grazing cattle to estimate block supplement intake.

Link
Citation
Applied Animal Behaviour Science, v.247, p. 1-9
ISSN
1872-9045
0168-1591
Start page
1
End page
9

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