Dr JORGE VAZQUEZ DIOSDADO JORGE.VAZQUEZDIOSDADO@NOTTINGHAM.AC.UK
ASSISTANT PROFESSOR IN PRECISION LIVE STOCK TECHNOLOGIES
Quantification of play behaviour in farmed calves using automated ultra-wide band location data and its association with age, weaning and health status
Vázquez-Diosdado, J.A.; Doidge, C.; Bushby, E.V.; Occhiuto, F.; Kaler, J.
Authors
Dr CHARLOTTE DOIDGE CHARLOTTE.DOIDGE@NOTTINGHAM.AC.UK
RESEARCH FELLOW
E.V. Bushby
F. Occhiuto
Professor JASMEET KALER JASMEET.KALER@NOTTINGHAM.AC.UK
PROFESSOR OF EPIDEMIOLOGY & PRECISION LIVESTOCK INFORMATICS
Abstract
Play behaviour can act as an indicator of positive animal welfare. Previous attempts to predict play behaviour in farmed calves are limited because of the classification methods used, which lead to overestimation, and the short time periods that calves are observed. The study aimed to automatically classify and quantify play behaviour in farmed calves using location data from ultra-wide band sensors and to investigate factors associated with play behaviour. Location data were collected from 46 calves in three cohorts for a period of 18 weeks. Behavioural observations from video footage were merged with location data to obtain a total of 101.36 h of labelled data. An AdaBoost ensemble learning algorithm was implemented to classify play behaviour. To account for overestimation, generally seen in low-prevalence behaviours, an adjusted count technique was applied to the outputs of the classifier. Two generalized linear mixed models were fitted to investigate factors (e.g. age, health) associated with duration of play and number of play instances per day. Our algorithm identified play behaviour with > 94% accuracy when evaluated on the test set with no animals used for training, and 16% overestimation, which was computed based on the predicted number of samples of play versus the number of samples labelled as play on the test set. The instances and duration of play behaviour per day significantly decreased with age and sickness, whilst play behaviour significantly increased during and after weaning. The instances of play also significantly decreased as mean temperature increased. We suggest that the quantification method that we used could be used to detect and monitor other low prevalence behaviours (e.g. social grooming) from location data, including indicators of positive welfare.
Citation
Vázquez-Diosdado, J., Doidge, C., Bushby, E., Occhiuto, F., & Kaler, J. (2024). Quantification of play behaviour in farmed calves using automated ultra-wide band location data and its association with age, weaning and health status. Scientific Reports, 14, Article 8872. https://doi.org/10.1038/s41598-024-59142-z
Journal Article Type | Article |
---|---|
Acceptance Date | Apr 8, 2024 |
Online Publication Date | Apr 17, 2024 |
Publication Date | Apr 17, 2024 |
Deposit Date | Apr 15, 2024 |
Publicly Available Date | Apr 18, 2024 |
Journal | Scientific Reports |
Electronic ISSN | 2045-2322 |
Publisher | Nature Publishing Group |
Peer Reviewed | Peer Reviewed |
Volume | 14 |
Article Number | 8872 |
DOI | https://doi.org/10.1038/s41598-024-59142-z |
Keywords | Play; Positive welfare; Calf; Machine learning; quantification; Precision livestock technology |
Public URL | https://nottingham-repository.worktribe.com/output/33825058 |
Publisher URL | https://www.nature.com/articles/s41598-024-59142-z |
Files
s41598-024-59142-z
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Publisher Licence URL
https://creativecommons.org/licenses/by/4.0/
Copyright Statement
© The Author(s) 2024.
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