Qualitative Behaviour Assessment: quantifying the emotionally expressive qualities of animal behaviour
Qualitative Behaviour Assessment (QBA) is a method that asks human observers to describe and score the emotionally expressive qualities that animals show when they engage with their environment1. Animals can perform any given behaviour (e.g. walking or resting) in different expressive styles, presenting us with a dynamic body language that can, for example, be described as tense and anxious, or relaxed and joyful. Because it includes positive emotions QBA is a valuable method for assessing quality of life. It is, for example, the main indicator for ‘positive emotional state’ in various EU Welfare Quality® assessment protocols2.
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Background
Most scientific methods for animal welfare assessment focus on measuring detailed physical aspects of an animal’s health and behaviour, however QBA is designed to assess the dynamic whole animal as it engages with its environment. At this level of integration animals are primarily viewed as sentient, psychologically expressive beings, whose quality of movement reflects their quality of experience. By describing and quantifying such qualities QBA provides useful information, that, in combination with other health and welfare measures, can help us to more confidently assess an animal’s state of well-being3. One of QBA’s strengths is that it captures subtle shifts in demeanour that are difficult to measure otherwise. A transition from a relaxed/content to a more tense/lethargic expression, for example, often indicates the onset of illness4. By contrast a shift towards greater attentiveness and boisterousness in response to environmental enrichment signifies the animals’ enjoyment and a higher level of positive welfare5.
Box 1: The QBA mobile application
SRUC (Scotland’s Rural College) is in the process of building a new QBA mobile application for Android and iOS. Development of the app is taking place in collaboration with Waitrose and Partners, and despite being at an early stage has won several awards.
Once publicly available, the app will be usable with any animal species in any context, including farm, laboratory, kenneled, zoo and wild animals. Users can create QBA descriptors that are specifically meaningful to their organisation and culture. The app will analyse scores on these descriptors and show the outcome in an easy-to-read chart for emotional wellbeing that compares an assessment to previous assessments of animals in a project.
The app offers enhanced statistical reliability thanks to the contribution of a built-in algorithm for data normalisation by statistical experts at Biomathematics and Statistics Scotland (BioSS).
QBA can be performed in different ways. A good way to involve stakeholders in a project is to invite them to select 15-20 QBA descriptors particularly suited to that project. Another, faster way is to provide participants with a ready-made ‘fixed list’ of descriptors based on existing scientific literature for a species. QBA term lists should balance positive and negative emotions (valence), and higher and lower energy levels (arousal). Scores based on these terms are analysed using multi-variate statistics, generating dimensional patterns of emotional expressivity. An extensive body of scientific publications shows that with appropriate training, QBA can be performed reliably and make a valuable contribution to scientific animal welfare assessment5.
Project application
There is growing interest in the practical implementation of QBA as a tool for assessing emotional well-being in animals5. For example, the UK retailer Waitrose recently invited SRUC to collaborate in the roll-out of QBA across 12 Waitrose own-brand supply chains (see also Box 1).
The Project Team:
Other links:
References:
- Wemelsfelder, F. 2007. How animals communicate quality of life: the qualitative assessment of animal behaviour. Animal Welfare 16(S) 25-31. https://doi.org/10.1017/S0962728600031699
- Keeling, L, Evans, A, Forkman, B & Kjaernes, U. 2013. Welfare Quality® principles and criteria. in H Blokhuis, M Miele, I Veissier & B Jones (eds), Improving farm animal welfare: science and society working together: the Welfare Quality approach. Wageningen Academic Publishers, Holland, pp. 91-114. https://doi.org/10.3920/978-90-8686-770-7_5
- Wemelsfelder, F., Hunter, E.A., Mendl, M.T. and Lawrence, A.B. 2001. Assessing the ‘whole animal’: a Free-Choice-Profiling approach. Animal Behaviour 62, 209-220. https://doi.org/10.1006/anbe.2001.1741
- De Boyer des Roches, A., Lussert, A., Faurea, M., Herry, V., Rainard, P., Duranda, D., Wemelsfelder, F., Foucras, G. 2018. Dairy cows under experimentally-induced Escherichia coli mastitis show negative emotional states assessed through Qualitative Behaviour Assessment. Applied Animal Behaviour Science 206, 1-11. https://doi.org/10.1016/j.applanim.2018.06.004
- Shaw, N., Wemelsfelder, F., Riley, L. 2022. Bark to the future: The welfare of domestic dogs during interaction with a positively reinforcing artificial agent. Applied Animal Behaviour Science 249, 105595. https://doi.org/10.1016/j.applanim.2022.105595
- Fleming, P.A., Clarke, T., Wickham, S.L., Stockman, C.A., Barnes, L., Collins, T., and Miller, D.W. 2016. The contribution of qualitative behavioural assessment to appraisal of livestock welfare. Animal Production Science 56(10), 1569-1578. https://doi.org/10.1071/AN15101
- Cooper, R., Wemelsfelder, F. 2020. Qualitative Behaviour Assessment as an indicator of animal emotional welfare in farm assurance. Livestock 25 (4), 2-5. https://doi.org/10.12968/live.2020.25.4.180
