Climate change forecasts for the UK suggest changes to the intensity and frequency of flooding, drought, heat and strong winds, as already evidenced by the record-breaking years observed in the UK over the past decade. At present, we do not have reliable data on the impacts of climate stresses – including extreme events – on key crops (e.g., cereals and grass). This means recommendations for cereal and livestock farm management strategies to help maximise resilience to future climate change cannot be made with confidence. As a result, 47% of farmers feel ‘not at all positive’ about their future in farming. Agroecosystems deliver benefits, food, but also disbenefits for the environment, including water and air quality. This trade-off increases the complexity of needed tools to on the one hand maximise agroecosystems resilience and on the other reduce their environmental footprint.
This PhD will use remotely sensed data from: i) unique plot experiments simulating 2050 extreme events; ii) the North Wyke Farm Platform (NWFP) – containing over 80 million measurements on all major inputs and outputs for four different farming systems; iii) yield monitor data from commercial farms in the UK, and iv) satellite imagery across Europe. Across these datasets we will work to detect and understand localised ‘tipping points’ – where crop yield suddenly falls because of declining resilience (e.g., caused by climate change).
For the successful student, considerable training in a range of data and digital science skillsets will be provided. This includes those in high performance computation, advanced statistics and machine learning, together with specialisms in remote sensing and the analysis of spatial and/or temporal processes. This PhD will work with two large project – the NFWP (above) and the Resilient Farming Futures project; both are world-leading and should lead to several high-impact publications, which makes a formidable opportunity to start a research career.
Candidates shall be good honours graduates in appropriate subject areas, of a recognised university or comparable university, or persons holding equivalent qualifications who show evidence of exceptional ability, or who have demonstrated their ability in graduate studies. Example subject areas include 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 Gonzalo Irisarri (firstname.lastname@example.org).