Evaluating the role of Big Data in understanding livestock impacts on the upland landscape

Director of Studies: Dr Mark Whiteside
2nd Supervisor: Dr Katherine Herborn
3rd Supervisor: Dr Lauren Ansell
4th Supervisor: Dr James Buckley 

Applications are invited for a 3.5 years PhD studentship within the Environmental Intelligence doctoral training programme at the University of Plymouth. The studentship will start on 01 October 2024. 

Upland farming plays a pivotal role in UK upland ecology through existing negative and positive impacts of traditional livestock management but increasingly through incentives to provide ecosystem services (such as improved soil health, biodiversity, carbon sequestration and natural flood management). To evaluate these impacts, fine-scale understanding of livestock movement and behaviour in this environment is essential. In more intensive farm systems, ‘Precision Livestock Farming’ (PLF), the application of Big Data and remote sensing in animal management, is revolutionising the way we understand animals, their interaction with the environment and, crucially, how we farm sustainably.  

In this multidisciplinary project we will explore the potential of cutting-edge sensor technology, data retrieval and computational approaches for understanding landscape-level interactions between livestock and the environment. The aim of the project is to bring the upland farm into the PLF framework by addressing the following objectives:

1) Validating and automating an array of animal-mounted and environmental sensors under controlled field conditions. 

2) Ground-truthing these new methods for remotely monitoring animal-environment interactions in the heterogeneous upland landscape.

3) Determining how these data could inform current and emerging agricultural practice to better manage the complex interplay between livestock production and ecosystem services. 

Bespoke sensors (GPS and accelerometers) will be attached to livestock on Dartmoor. For validation, data will be collected from animals in controlled environments where we can manipulate behaviour (e.g. foraging heights, transitory paths), monitor welfare (e.g. limping) and determine fine-scale ecological impacts (e.g. biodiversity, soil health). Machine learning approaches will be used to automate behavioural classification, allowing for remote monitoring of habitat use and behaviour in animals ranging in larger areas (e.g. newtakes, common land), to explore ecological impacts. These impacts will be compared across livestock demographic (breeds, ages) and management practices (e.g. open flocks, worming protocol).

The successful candidate will benefit from this interdisciplinary project, applying Big Data approaches to high-throughput movement and behaviour data to address challenges within production, sustainability, and ecology. The candidate will develop key skills in using relevant programming languages (R and/or Python), sensor technologies, ecological sampling, experimental design, welfare/production assessment, and animal handling and husbandry. They will be supported to develop a network of both academic collaborators and stakeholder contacts. On completion they will be well-placed to seek academic positions in e.g. animal data science, behavioural and welfare science, and industry positions in the growing field of PLF.

We are looking for a candidate with a degree in biological sciences, computer science or data science (or similar) and an interest in agriculture and ecology. Enthusiasm to learn and develop a diverse set of skills and engage with different audiences is therefore important.

Nathan, R., Monk, C.T… Whiteside, M.A., & Jaric, I. (2022). Big-data approaches lead to an increased understanding of the ecology of animal movement. Science 375, eabg1780.

Wolfert, L., Ge L., & Verdouw, M.j. (2017). Big data in smart farming – a review. Agricultural Systems 153, 69-80.

Gallagher, Austin J., et al. "Energy landscapes and the landscape of fear." Trends in Ecology & Evolution 32.2 (2017): 88-96. 

Applicants should have a first or upper second class honours degree in a cognate discipline or a relevant masters qualification. 

If your first language is not English, you will need to meet the minimum English requirements for the programme, IELTS Academic score of 6.5 (with no less than 5.5 in each component test area) or equivalent. 

The studentship is supported for 3.5 years and includes full home tuition fees plus a stipend of £19,088 2024/25 rate (TBC). The studentship will only fully fund those applicants who are eligible for home fees with relevant qualifications. Applicants normally required to cover international fees will have to cover the difference between the home and the international tuition fee rates approximately £12,697 per annum 2023/24 rate (2024/25 rate TBC).

NB: The studentship is supported for 3.5 years of the four-year registration period. The subsequent 6 months of registration is a self-funded ‘writing-up’ period.

If you wish to discuss this project further informally, please contact Dr Mark Whiteside.

Please see our how to apply for a research degree page for a list of supporting documents to upload with your application.

For more information on the admissions process generally, please visit our how to apply for a research degree webpage or contact The Doctoral College at research.degree.admissions@plymouth.ac.uk.

Shortlisted candidates will be invited for interview after the deadline. We regret that we may not be able to respond to all applications. Applicants who have not received a response within six weeks of the closing date should consider their application has been unsuccessful on this occasion.