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

Simanungkalit, Gamaliel; Clay, Jonathon; Barwick, Jamie; Cowley, Frances; Dawson, Bradley; Dobos, Robin; Hegarty, Roger

doi:10.1016/j.applanim.2022.105543

Cited by 4 publications

(1 citation statement)

References 33 publications

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“…However, estimating supplement block intake by cattle at an individual animal level in commercial grazing environments is challenging. Hence, the effectiveness of block supplementation is typically evaluated on a herd basis (Simanungkalit et al 2022). This experiment defined the association between daily time spent licking, as predicted by either an ear-tag accelerometer algorithm or a radio-frequency identification (RFID) system, and the intake of a block supplement by grazing cattle at the herd level.…”

Section: Special Thanks Meat and Livestock Australia And The Universi...mentioning

confidence: 99%

Abstracts from the Proceedings of the 34th Biennial Conference of the Australian Association of Animal Sciences

2022

Anim. Prod. Sci.

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Section: Special Thanks Meat and Livestock Australia And The Universi...mentioning

confidence: 99%

Abstracts from the Proceedings of the 34th Biennial Conference of the Australian Association of Animal Sciences

2022

Anim. Prod. Sci.

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A hybrid deep learning based robust framework for cattle identification

Gunda¹,

Gulla

Kosana

et al. 2022

2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)

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Genetic parameters of behavior traits of beef cattle classified using wearable devices

Onogi,

Fujii,

Watanabe

et al. 2024

Animal Science Journal

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With the development of wearable devices, it is now possible to monitor livestock behavior 24 h a day. In this study, we estimated the genetic parameters of the daily duration of six behaviors (feeding, moving, lying, standing, ruminating while lying, and ruminating while standing) in beef cattle, automatically classified using wearable devices. The devices were attached to 332 Japanese beef cattle at two stations for approximately 5 months. We compared repeatability, Poisson regression, and random regression models using the deviance information criterion. Poisson regression models were selected for all traits at each station, probably because of the non‐normal distribution of the phenotypes. The heritability estimates by the Poisson regression models were moderate at each station: 0.67 and 0.68 for feeding, 0.68 and 0.53 for moving, 0.47 and 0.55 for lying, 0.45 and 0.40 for standing, 0.51 and 0.59 for ruminating while lying, and 0.37 and 0.45 for ruminating while standing. The genetic correlations between these traits were all negative at both stations, whereas the residual correlations showed different directions depending on the station. Although validation studies with larger populations are needed to confirm these findings, this study provides fundamental knowledge of the genetic basis of daily behavior in beef cattle.

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

Cited by 4 publications

References 33 publications

Abstracts from the Proceedings of the 34th Biennial Conference of the Australian Association of Animal Sciences

Abstracts from the Proceedings of the 34th Biennial Conference of the Australian Association of Animal Sciences

A hybrid deep learning based robust framework for cattle identification

Genetic parameters of behavior traits of beef cattle classified using wearable devices

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