The need for data-informed farmer decision-making has never been more critical. Climate change, floods, droughts, pests, diseases, and political pressures all pose significant threats to our ability to grow food. Agriculture itself sometimes makes these problems worse – being responsible for many negative environmental impacts including greenhouse gas emissions, water pollution, and biodiversity loss. But, because food security is so important, farmers and policy makers are constantly looking for new ways to face these challenges and grow more food in a way that is less damaging to the planet. It is hard to perform large-scale experiments to try to find solutions to these problems – if the experiment fails, the farm could go bankrupt!
In this project, you will have the unique opportunity to contribute to designing optimal sampling strategies for a virtual realisation (known as a Digital Twin) of a well-studied, real-world farm (the North Wyke Farm Platform; NWFP; currently with 80M measurements and counting!). This Digital Twin will allow us to safely run ‘what if’ experiments – trialing novel solutions in a virtual world. It will provide cutting-edge decision support tools for farmers and policy-makers alike, providing valuable insights into various aspects of agriculture, including soils, crops, livestock, biodiversity, emissions, and more.
This PhD will focus on the design element of farm-scale monitoring. Through research and analysis, you will develop and refine sampling strategies that capture the complexities of agricultural systems while considering spatial and temporal resolution, cost-benefit trade-offs, and the challenges posed by climate change and other stressors.
You will receive extensive training in a wide range of data science skills, as well as the opportunity to learn from experts in relevant fields. The project should lead to several high impact publications and will provide you with a fantastic opportunity to start a research career.
Applicants should hold a minimum of a UK Honours Degree at 2:1 level or equivalent in subjects such as Environmental Science, Applied Statistics, Geography, Data Science, Biology, Natural Sciences, Computer Sciences or related disciplines. Experience of working with big data, spatial databases (e.g., GIS) and writing computer code (e.g., in R, python) would be advantageous, although training will be provided.
Email address for enquiries.
For further information or informal discussion about the position, please contact Dr Helen Metcalfe (firstname.lastname@example.org).