This paper discusses the potential for using observations of behaviour to recognise distress in sheep. The term distress is used to describe situations in which an animal is likely to be suffering, and is indicating this by overt behavioural signs. Literature on the behavioural responses of sheep to procedures that induce a physiological stress response is reviewed. This approach is based on human analogy and the assumption that physiological changes can be used to differentiate between stimuli that induce an emotional response in sheep and those that do not. The degree to which the behaviour of sheep in certain situations represents, at least in part, an expression of emotional behaviour, or whether it can be fully explained as a functional response to a specific situation, is a fundamental and unresolved question in ethological and psychological studies. Therefore, the validity of compiling a list of objective common behavioural indicators of distress in sheep will be contentious. However, it is important to be able to recognise and deal with suffering, and the use of behavioural methods for the identification of distress in sheep is a practical welfare issue. There is a need for further research to identify indicators of distress in sheep, but in the meantime it would be reasonable to make the judgement that, in some circumstances, sheep that are found to be vocalising, panting, and/or showing markedly increased locomotory activity could be experiencing distress.

On-farm scoring of behavioural indicators of animal welfare is challenging but the increasing availability of low cost technology now makes automated monitoring of animal behaviour feasible. We discuss some of the issues with using automated methods to measure animal behaviour within the context of assessing animal welfare. Automated feeders (eg for dairy calves) can help measure the degree that animals are hungry and have potential to identify sick animals even in group housing. Such equipment is best used for longitudinal studies of individual animals rather than making comparisons between farms. Devices attached to animals (eg accelerometers or GPS devices) can help measure the activity levels of animals with a high degree of accuracy and can easily be transported between farms, making them best suited for welfare assessment at the group level. Automated image analysis has great potential to assess movement within groups of animals, but following individual animals can be difficult. The techniques have been validated against traditional methods (eg direct observation). The accuracy of measures taken automatically varies between methods but can be increased by combining measures. Technological developments have provided us with a variety of tools that can be used to monitor behaviour automatically, and these have great potential to improve our ability to monitor animal welfare indicators on-farm. However, it is important that methods be developed to measure a wider range of behaviour patterns. Animal welfare assessment schemes should not place undue emphasis on behavioural indicators solely on the basis that they can be monitored automatically.