Project description
There is a clear need to evaluate and enhance sustainability in agricultural systems, to balance food production with its environmental impacts. In the UK, extensive livestock farming practice is moving increasingly from traditional pastoralism towards the delivery of ecosystem services. However, the relationship between extensive livestock management and environmental impacts versus services is still poorly understood.
By combining advances in animal-mounted, environmental and ‘next gen’ satellite sensor technologies, this PhD position will bring the extensive upland farm into the precision livestock framework by addressing the following objectives:
O1) Validate bespoke animal-mounted and environmental sensor technologies for monitoring animal-environment interactions. Machine learning models will be trained using footage collected from a range of livestock (e.g. different breeds, welfare states) subject to different ecological experiments that modify their behaviour.
O2) Determine the efficacy of satellite sensor technologies (e.g. MAXAR Worldview) as a tool to: a) map livestock movement, and b) measure livestock impacts on biodiversity, soil health, water retention. This will be validated at the experimental scale and through stakeholder knowledge.
O3) Apply the best combination of technologies to determine the impact of livestock under different management pertinent to Environmental Land Management Schemes and Sustainable Farming Incentives at a field and landscape scale.
The successful candidate will benefit from the interdisciplinarity of this project, applying Big Data approaches to high-throughput movement, satellite and environmental data to address challenges within production, sustainability, and ecology. The candidate will be supported to develop a network of both academic collaborators and stakeholder contacts including the Central Dartmoor Farm Cluster and their Landscape Recovery Project.
Eligibility
We are looking for a candidate with a first or upper second-class honours degree in biological sciences, computer science or data science (or similar). The candidate should have experience in statistical analysis or computer modelling, and a keen interest in further developing and applying these skills to agricultural or ecological challenges. Practical experience or enthusiasm for fieldwork is essential. A current full driving licence and willingness to travel is essential/desirable as you will be required to travel to remote areas not easily accessible by public transport’. Enthusiasm to learn and develop a diverse set of skills and engage with different audiences is important.
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 four years and includes Home or International tuition fees plus a stipend of £20,780 per annum 2025/26 rate (UKRI).
NB: The studentship is supported for four years including a six-month writing-up period. There is no further funding beyond the four-year period.
The closing date for applications is 28 March 2025.
Shortlisted candidates will be invited for interview on either 8 or 9 May 2025 followed by a formal interview on a date to be confirmed. 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 unsuccessful on this occasion.
Research England