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Objectively measuring behaviour traits in an automated restraint-test for ungulates: towards making temperament measurable
Research Unit Behavioural Physiology, Leibniz Institute for Farm Animal Biology (FBN), Dummerstorf, Germany.
Research Unit Behavioural Physiology, Leibniz Institute for Farm Animal Biology (FBN), Dummerstorf, Germany.
Research Unit Genetics and Biometry, Leibniz Institute for Farm Animal Biology (FBN), Dummerstorf, Germany.ORCID iD: 0000-0002-7173-5579
Research Unit Behavioural Physiology, Leibniz Institute for Farm Animal Biology (FBN), Dummerstorf, Germany.
2012 (English)In: Journal of Agricultural Science, ISSN 0021-8596, Vol. 151, no 1, p. 141-149Article in journal (Refereed) Published
Abstract [en]

The personality of an animal is described by traits that cause consistent actions and reactions to environmental stimuli. An important part of personality is the reaction to unpleasant or uncontrollable situations. Methods described in the literature to measure personality in animals are often based on measuring or rating escape behaviour in these situations. In the methods described, human handlers are frequently part of the experiment or the animals’ personalities are scored by humans. Thus, these methods are at least partly subjective. In the current study, an appliance to measure objectively the escape behaviour of ungulates and their reluctance during an uncontrollable situation (restraint) with a rather simple and comprehensible methodology is presented using a force transducer with adequate peripheral equipment. While the animals were restrained, a tractive forcetime diagram describing escape behaviour was recorded and later analysed with software developed specifically. To evaluate this newly developed technical method, 24 three-month-old calves were restrained by being tethered for 30 min on a halter that was connected to the force transducer. From the tractive force-time diagram, tractive force, maximal tractive force and the number of pulls that the calves performed during 5-min intervals were calculated. The multivariate results were analysed with a k-means-algorithm (function ‘kcca’) and a hierarchical clustering (function ‘hclust’) included in R version 2.12.1. Both analyses revealed two clearly separated clusters including the same individuals in each analysis. The animals of cluster 1 showed a continuously higher reaction level than those of cluster 2 with a strong reaction in the beginning, a short decrease before increasing during the middle of the experiment and a final decrease at the end of the test. The animals of cluster 2 had a lower and quite steady reaction level throughout the experiment, although even here a slight increase during the middle of the experiment could be detected before a final decrease towards the end of the test was shown. There was no significant difference in weight between the two clusters. The results showed that this newly developed method was able to detect differences in the animals’ escape behaviour patterns and reluctance with the measured parameters.

Place, publisher, year, edition, pages
New York, USA: Cambridge University Press, 2012. Vol. 151, no 1, p. 141-149
National Category
Agricultural Sciences
Identifiers
URN: urn:nbn:se:oru:diva-40738DOI: 10.1017/S0021859612000408ISI: 000312920700011Scopus ID: 2-s2.0-84870890025OAI: oai:DiVA.org:oru-40738DiVA, id: diva2:778574
Available from: 2015-01-11 Created: 2015-01-11 Last updated: 2018-05-14Bibliographically approved

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Repsilber, Dirk

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