Background:

The welfare of animals used in the food system is attracting growing attention, but our ability to assess animal welfare is still limited. It is increasingly recognized that animal welfare goes beyond assuring that animals are physically healthy and productive, and now strongly focuses on animals’ affective experiences (emotions and moods). The subjective nature of these states and lack of verbal report makes assessing animal emotion scientifically challenging.

Methods:

New methods in behavioural and cognitive science now allow better insight into what animals may feel. For instance, conditioned place preference tests can reveal whether a past experience was associated with a more positive or negative affective state and hence help, for example, to determine which pain-control drugs are most effective ( https://doi.org/10.1098/rsbl.2019.0642 ). Judgment bias tests can be used to assess animals’ mood states and thus to make inferences about the effect of a given practice on the welfare of the animals ( https://doi.org/10.3389/fnbeh.2019.00054 ). Although these tests provide very useful information, they are too complex to be carried out routinely on farms and hence their potential remains unrealized in ‘real-life’ contexts. A possible solution is offered by new computer vision technologies that use deep learning AI to track free-living individuals ( https://doi.org/10.1016/j.compag.2021.106133 ) and assess their behaviours ( https://arxiv.org/abs/2011.10759 ). However, it is currently unknown whether day-to-day behaviours can accurately relate to animal subjective experiences.

Aims and objectives:

The aim of this PhD project is to combine both approaches to determine whether spontaneous behaviours of dairy cattle (quantifiable via computer vision) reflect animal affective states (assessed using behavioural assays), and hence can be used as proxy markers of affect and welfare. Behaviours to be measured include resting and sleeping, positive and negative social interactions, and feeding behaviours. This data will then be matched with recurrent behavioural/cognitive tests of animal affect (e.g., judgment bias).

The student will receive training in experimental design, behavioural testing, machine vision, and data science, and should have a degree in biology, agriculture or psychology and ideally some experience with dairy cows and/or animal welfare. The successful applicant will be based at Bristol Vet School and join the Animal Welfare and Behaviour Research Community, led by co-supervisor Prof. Mike Mendl and including co-supervisor Dr Benjamin Lecorps who is an expert on dairy cow welfare and the School’s Data Science Research Community led by co- supervisor Professor Andrew Dowsey. The work will take place at the newly instrumented John Oldacre Centre (JOC) for Dairy Welfare and Sustainability Research ( https://bristol.ac.uk/vet-school/research/john-oldacre-centre/ ).

Key references:

https://doi.org/10.1098/rsbl.2019.0642

https://doi.org/10.3389/fnbeh.2019.00054

https://doi.org/10.1016/j.compag.2021.106133

https://arxiv.org/abs/2011.10759

Supervisors:

Dr Benjamin Lecorps

Prof Andrew Dowsey

Prof Mike Mendl

https://www.bristol.ac.uk/vet-school/research/welfare-behaviour/

Start date: Sept 2025

How to apply: See How to apply – SWBiosciences Doctoral Training Partnership

Candidate requirements:

See Eligibility – SWBiosciences Doctoral Training Partnership .

Standard University of Bristol eligibility rules for PhD admissions also apply. Please visit PhD Veterinary Sciences

Contacts: Contact the lead supervisor [email protected] if you have queries about the project. For queries about the SWBio DTP scheme contact [email protected]