Aarhus Universitets segl

Exploring behavioural and cognitive measures to examine positive and negative affective states in finisher pigs

Main subject area: Pig behaviour and welfare, applied ethology

Short project description

Good animal welfare is about the absence of negative affective states and the presence of positive affective states. Accordingly, there is a need to develop and validate measures that can meaningfully investigate the presence of positive and negative affective states as well as be non-invasive and quick.

For instance, the presence of positive and negative affective states in animals may be assessed by use of behavioural measures linked to underlying cognitive mechanisms, based on the empirical and theoretical links between affective states and objectively measurable cognitive processes such as engagement with a stimulus (i.e., lateralised movement) (Franchi et al., 2020; Mendl and Paul, 2019).

Hence, this project aims to explore the use of tail posture, tail lateralisation and tail motion, and compare these measures against more established behaviourbased welfare measures, such as agonistic behaviour and play, in finisher pigs subjected to pen enrichment.

Department and supervisor

Lene Juul Pedersen

Professor - Sektionsleder Institut for Husdyr- og Veterinærvidenskab - ANIVET Adfærd, Stress og Dyrevelfærd (BSW)

Project start

To be decided in agreement with the supervisor.


Physical location of project and students work

Department of Animal- and Veterinary Sciences, AU Viborg - Forskningscenter Foulum, DK-8830 Tjele. An office space will be available, so the student can become part of an active research environment. Naturally, part of the master's thesis work (e.g., video observation, data analysis, literature review, and writing) can be done remotely.

Extent and type of project

45 ECTS: Experimental theses in which the student is responsible for collection and analysis of his/her own original data.

Additional information

Prerequisite is the MSc course Animal Behaviour. Some prior data management and statistical analysis skills (e.g., R software) are advantageous. Please contact for more information.