Developing an Ethically Acceptable Virtual Fencing System for Sheep

Animals : an Open Access Journal From MDPI
Danila MariniCaroline Lee

Abstract

To ensure animal welfare isn't compromised when using virtual fencing, animals must be able to associate a benign conditioned stimulus with an aversive stimulus. This study used an associative learning test to train 30, four-year-old, Merino x Suffolk ewes, to associate an audio cue with an electric stimulus. Collars manually controlled by a GPS hand-held unit were used to deliver the audio and electric stimuli cues. For the associative learning, when sheep approached an attractant at a distance of three m from the trough, an audio cue was applied for one s. If the sheep stopped or changed direction, the audio cue ceased immediately and no electrical stimulus was applied. If the sheep did not respond to the audio cue it was followed by a low-level electrical stimulus. Approaches to the attractant significantly decreased from day one to day two. It took a mean of three pairings of the audio cue and electrical stimulus for a change in behaviour to occur, after which sheep that approached the attractant had a 52% probability of avoiding the electrical stimulus and responding to the audio cue alone. Further research is required to determine whether sheep can be trained to associate an audio cue with a negative stimulus for use in g...Continue Reading

References

May 12, 2017·Animal : an International Journal of Animal Bioscience·E I BrunbergK M Sørheim

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Citations

Mar 3, 2018·Animals : an Open Access Journal From MDPI·Danila MariniCaroline Lee
Jan 24, 2019·Animals : an Open Access Journal From MDPI·Tellisa KeartonCaroline Lee
Dec 26, 2018·Animals : an Open Access Journal From MDPI·Dana L M CampbellCaroline Lee
Nov 21, 2020·The Journal of Dairy Research·Gerardo CajaChristopher H Knight
Mar 13, 2021·Frontiers in Veterinary Science·Caroline Lee, Dana L M Campbell
Apr 4, 2021·Animals : an Open Access Journal From MDPI·Anders HerlinAnna Skarin

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Software Mentioned

R Foundation for Statistical Computing
R Development Core Team
R
pgirmess

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