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Title: Developing robust arable farming systems for multiple benefits : mathematical programming approach
Author: Ahodo, Kwadjo
ISNI:       0000 0004 6349 0836
Awarding Body: University of Sheffield
Current Institution: University of Sheffield
Date of Award: 2017
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To be able to meet the growing demand for food and ensure food security, arable farming systems need to be made more sustainable. However, making arable farming systems more sustainable could sometimes mean reductions in the use of productivity improving inputs such as fertiliser and pesticide in order to reduce their impacts on the environment. This presents conflicting environmental and economic goals, which increase management complexities in sustainable arable farming systems. An arable farm level model, consisting of four modules, which combines mixed-integer, risk and goal-programming approaches, has been developed to capture many of the complexities in arable farming and optimise farm profit, risk and nitrate leaching. Statistical validation of the model using data from the Farm Business Survey (FBS) showed a good association between model-predicted results and observed farm data. Results of the application of the mixed-integer weighted goal-programming module to estimate aggregate cost of non-chemical (spring cropping) control of black-grass showed that in the short run the strategy could cost the UK arable farming sector, however there could be a long term benefit of reductions in black-grass infestation. On per hectare basis, cost estimates provide indication of possible farm payment to incentivise adoption of the strategy. On individual farm basis, spring cropping could be beneficial dependent on the soil type, rainfall and hectares of land available to the farm. The application of the MOTAD module and randomly generated risk-aversion parameter method showed that arable farmers in England are risk-averse and that farmers in different regions would react to change in policy differently depending on their levels of risk-aversion. The results also showed the need for regional policies and relevance of the model in policy analysis. The model, which has been developed as part of this research adds to the few arable farm level models identified in the UK and bridges model capability gaps identified in arable farm modelling. Given available data for calibration and validation, results generated by the model can be applied to better inform arable farming and policy decisions to enhance the development of robust and sustainable arable farming systems to ensure food security.
Supervisor: Freckleton, Robert ; Oglethorpe, David Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available