Project Details
Description
Tail biting is a major animal welfare and economic issue in modern pig production. This abnormal
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, we will collaborate with artificial intelligence experts who
will develop 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. 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.
Status | Active |
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Effective start/end date | 15/09/21 → … |
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