Behavioural studies on the emergence and spread of (harmful) behaviours in pigs

Tail biting is a major animal welfare and economic issue in modern pig production. This damaging behaviour may occur in isolated cases or drastically escalate and spread within groups. The underlying drivers of this behaviour remain unclear: an increased incidence has been associated with external factors (such as high stocking densities, imbalanced diet, poor environment), but internal motivators may also be involved.

Preventing tail biting without resorting to tail docking is a considerable challenge, and therefore the objective of this project is to get a better understanding of the mechanisms by which individuals affect and respond to each other’s behaviours and how this subsequently affects the emergence and spread of tail biting. Hereto, in the IMAGEN project, we collaborate with artificial intelligence experts who are developing an algorithm based on computer vision to track individuals and automatically detect tail biting.

First, we will investigate whether the structure of the social network is consistent across time and influenced by individual characteristics or group composition. Social network analysis (SNA) will be performed both on the spatial proximity between individuals (extracted from the individual tracking) and on manual annotations of social behaviours (which will also contribute to developing the algorithm).

Second, we will study if tail biting can be predicted by an early change of individual behaviour by investigating individual behaviours prior to an outbreak and using agent-based modelling (ABM) to understand the internal motivations to trigger tail biting. Third, we will study the spread of tail biting outbreaks across time and whether the role of individuals (victim, biter) is consistent and related to individual characteristics by integrating complementary approaches of SNA, ABM and epidemiologic models.

Lastly, we will investigate if the spread of an outbreak can be stopped by curative or preventive treatments, for this purpose, interventions will be tested both in real life and in simulations on the models developed.