Digital technologies in livestock farming: from measuring to assessing individual animal welfare

Abstract

The welfare of farm animals is a complex concept that is intrinsically linked to the animal's perception of its environment. Although welfare cannot be measured directly, it can be assessed by identifying and quantifying specific indicators according to the context of the assessment. Animal behaviour, widely recognized as a key welfare indicator, responds dynamically to changes in the rearing environment, such as access to pasture, which affect both the routine and spatial dynamics of the animals. The analysis of these behavioural changes allows the identification of new indicators and the negative or positive impact of these environmental changes on animal welfare. The integration of sensor technologies, mathematical models and artificial intelligence opens new avenues for longitudinal monitoring of activities, spatial dynamics and other parameters of interest throughout an animal's life cycle. For example, supervised classification algorithms have enabled the association of raw sensor data with specific behaviours, while unsupervised algorithms are expected to reveal novel indicators. This article explores the potential opportunities offered by digital technologies. We highlight the role of behavioural assessment in welfare assessment, illustrated by three case studies: (1) discriminating pathological, reproductive or stress conditions in cows, (2) lameness prediction in dairy cows, and (3) the study of emotions in pigs. Finally, we highlight the importance of close interdisciplinary collaboration between ethologists, physiologists, mathematicians and computer scientists to advance this emerging field, which we term 'digital ethology'.