This project uses data from the Farm Business Survey to investigate whether investment in renewable energy technologies increases the resilience of farm businesses.
Department: Agri-Environment
Supervised by: Lindsay Todman
Income from the agricultural component of farm businesses varies because of variability in crop yield to weather and also volatile crop prices. Many farms have therefore sought to diversify their income streams and to do this some have invested in renewable technologies such as wind, solar and micro-hydro. However, possible income (or cost savings if the energy is used on-farm) are also variable as the energy generated depends on the weather and may therefore make incomes more variable. In this project the student will use data from the Farm Business Survey to investigate whether this is the case. Specifically, the student will look at how farms with renewable energy technologies perform in years with different weather patterns (wet and dry) and how this varies relative to other farms. Using simple models that represent the weather dependence of different types of energy production and agricultural production, the student will consider which technologies might best compliment different production types (i.e. which energy technologies are likely to do well in years when production is likely to do less well).
The student will: • Identify variables from the Farm Business Survey that are relevant for the project. • Use various statistical methods (potentially including hierarchical clustering and Bayes nets) to investigate the relationship between the presence of renewables on-farm and the farm profit in years representing different weather conditions. • Use simple models of energy and agricultural production in different weather conditions and analyses results to identify patterns in production.
Experience of programming (e.g. in R or MATLAB) is desirable and the student should be keen to learn more. They should also be interested in the topic and in using maths to understand it better.
• programming • statistical methods • energy and food production • critical thinking to frame questions about complex systems in a way that can improve understanding • communication
School of Agriculture, Policy and Development (SAPD, Agriculture Building), Whiteknights Campus
210
Friday 13 July 2018 - Thursday 23 August 2018
The post will be advertised centrally on the UROP website until Monday 30th April. Submit a CV and 1 page cover letter explaining your interest in the project to Dr Lindsay Todman (l.todman@reading.ac.uk). Applicants will be shortlisted for interview after the closing date.