Professor Rachel Warren, UEA, ENV
Climate change is now widely understood to be impacting food supplies. Ensuring future food security will require understanding how climate change will affect crop yields in different agricultural systems and the design of appropriate measures for adaptation. This may require breeding new traits, new agronomic practises or switching to crops better adapted to future weather.
Oilseed rape is an important and profitable crop in the United Kingdom, but its yield is highly variable depending on the weather in each growing season. Because of the large effects of weather on yield, oilseed rape is likely to be vulnerable to climate change, but there is currently no basis for predicting the impact of climate change on yields.
This project aims to bring together crop and climate models to generate new tools for predicting how oilseed rape crops will be affected in future climate scenarios. Using these tools you will explore the potential of proposed adaptation measures to inform agricultural policy and planning.
The specific steps will be:
- Test and improve crop models to effectively simulate known effects of past weather and climate on UK oilseed rape yields.
- Generate climate change predictions for UK and Europe and feed these into crop models to understand how yield will be affected in different scenarios.
- Design and test adaptation strategies by modelling and on the experimental farm, in collaboration with the field trials team.
The successful applicant will receive an exciting opportunity to be trained in computational and statistical approaches to understanding climate change impacts, and in relevant agricultural physiology. Farm experiments will be undertaken by a dedicated field trials team, under the direction of the student. The student will be based at the John Innes Centre and at the Tyndall Centre for Climate Change Research at the UEA.
Applicants will possess or expect to obtain a minimum 2:1 bachelor degree in a numerical subject, such as mathematics, statistics, computer science, or physics. Applicants with science degrees are welcome if a strong numerical background can be evidenced. Experience in at least one programming language (e.g. R) is preferred.
The successful candidate for this project will be hosted at the John Innes Centre