|Funding for:||UK Students, EU Students|
|Placed On:||21st May 2019|
|Closes:||23rd June 2019|
Supervisors: Dr. Lindsay Todman, Dr. Martin Lukac
Management of land for multiple benefits that include food, drinking water and flood risk mitigation is crucial to support human health and wellbeing. Delivering these multiple benefits on farm depends upon a sound business model that enables farmers to make a living. At present, research on natural flood management focuses on how land use and management interventions affect environmental process; less is known about their implications for the wider farm business.
In this PhD project, the student will quantify the likely effect of different natural flood management strategies on agricultural production by developing a Bayesian Belief Network as a meta-model. This model will integrate understanding from existing, but disconnected models and data on natural processes, as well as expert understanding of production systems. This will show how and where natural flood management might also benefit food production. This will improve the evidence base for farmers, farm advisors, policy makers and decision makers within the water industry, as the effect on production can be considered alongside flood risk in planning for where and how natural flood management should be implemented.
The model will include the direct effects of changes in practices and land use (e.g. crop choice, tillage practice, tree planting) and the indirect effects due to changes in flood risk. Key interactions represented in model structure will be developed by interviewing local farmers.
This PhD studentship is closely associated with the LANDWISE project (landwise-nfm.org/about) which aims to assess the potential for natural flood management in the Upper Thames catchment, UK. The studentship offers the opportunity to join a wider network of PhD students and project partners from a range of disciplines and institutions.
The ideal candidate would have strong quantitative skills, be able to program (e.g. in MATLAB, C++ or an alternative programming language) or be keen to learn and have an interest in applying their skills to agriculture or environmental science. Training in Bayesian Belief Network will be provided if necessary.
How to apply:
We strongly encourage you to make informal contact before submitting a formal application. For further details please contact Dr Lindsay Todman, firstname.lastname@example.org.
To apply for this studentship please submit an application for a PhD in Ecology and Agri-environmental Research to the University and select Dr Lindsay Todman as the PhD supervisor – see How to apply – University of Reading
Please quote the reference ‘GS19-037’ in the ‘Scholarships applied for’ box which appears within the Funding Section of your on-line application.
Please note that, where a candidate is successful in being awarded funding, this will be confirmed via a formal studentship award letter; this will be provided separately from any Offer of Admission and will be subject to standard checks for eligibility and other criteria.
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