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Kleanthous, N, Hussain, A, Sneddon, J, Khan, W, Khan, B, Aung, Z and Liatsis, P (2022) Towards a Virtual Fencing System: Training Domestic Sheep Using Audio Stimuli. Animals, 12 (21). ISSN 2076-2615
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Towards a Virtual Fencing System Training Domestic Sheep Using Audio Stimuli.pdf - Published Version Available under License Creative Commons Attribution. Download (1MB) | Preview |
Abstract
Fencing in livestock management is essential for location and movement control yet with conventional methods to require close labour supervision, leading to increased costs and reduced flexibility. Consequently, virtual fencing systems (VF) have recently gained noticeable attention as an effective method for the maintenance and control of restricted areas for animals. Existing systems to control animal movement use audio followed by controversial electric shocks which are prohibited in various countries. Accordingly, the present work has investigated the sole application of audio signals in training and managing animal behaviour. Audio cues in the range of 125–17 kHz were used to prohibit the entrance of seven Hebridean ewes from a restricted area with a feed bowl. Two trials were performed over the period of a year which were video recorded. Sound signals were activated when the animal approached a feed bowl and a restricted area with no feed bowl present. Results from both trials demonstrated that white noise and sounds in the frequency ranges of 125–440 Hz to 10–17 kHz successfully discouraged animals from entering a specific area with an overall success rate of 89.88% (white noise: 92.28%, 10–14 kHz: 89.13%, 15–17 kHz: 88.48%, 125–440 Hz: 88.44%). The study demonstrated that unaided audio stimuli were effective at managing virtual fencing for sheep.
| Item Type: | Article |
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| Uncontrolled Keywords: | animal behaviour; audio stimuli; sheep response; virtual fence; 0502 Environmental Science and Management; 0608 Zoology; 0702 Animal Production |
| Subjects: | S Agriculture > SF Animal culture T Technology > T Technology (General) |
| Divisions: | Biological & Environmental Sciences (from Sep 19) Computer Science & Mathematics |
| Publisher: | MDPI |
| SWORD Depositor: | A Symplectic |
| Date Deposited: | 11 Mar 2024 14:23 |
| Last Modified: | 11 Mar 2024 14:30 |
| DOI or ID number: | 10.3390/ani12212920 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/22770 |
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