Abstract Cropping allocations have normally been studied using frameworks that assume the existence of a representative farmer who cares about maximising gross margin. Evidence has shown that results obtained from these studies to predict cropping allocations in response to policy reforms are not satisfactory. On the other hand, an alternative research using multivariate models (i.e. models that consider economic and social-psychological variables to explain farmers’ behaviour) has been developed with the purpose of identifying farmers’ motivations to adopt specific environmental policies. However, this research has not been extended to study strategic cropping decisions. This is surprising given the fact that policy reforms strongly affect the allocation of crops when they are accompanied with the elimination of domestic distorting policies. The objective of this thesis is to fill this gap by proposing a novel holistic multivariate model designed exclusively to study farmers’ strategic cropping decisions. The proposed model integrates a number of alternative and complementary approaches that can explain farmers’ strategic behaviour. The model was applied to a sample of ex-sugar beet farmers in the West Midlands region of the UK to investigate the way in which these individuals adjusted to the Sugar Regime reform introduced on 20th February 2006. As a consequence of this reform, the sugar beet factory located in Allscott in Shropshire was closed and the sugar beet growers in this area adjusted by replacing sugar beet with alternative crops. Evidence has revealed that these farmers replaced sugar beet with crops with low gross margin such as oilseed. This choice is puzzling because other crops with high levels of gross margin such as carrots and parsnips were also available when the reform was implemented. The proposed multivariate model not only was useful to explain this choice, but also identified heterogeneous behavioural responses that no related research has identified so far.
Rangwala, Norman (University of Wolverhampton, 2012-10)
This thesis presents the Ph.D. research from the initial stages of investigation, to design and development of an intelligent architecture for vehicles. It was identified that vehicles, intelligent transport systems (ITS) and infrastructure lack a shared platform that allows them to be integrated and work together. With a robust and intelligent framework, distributed ITS can work and improve traffic efficiency. If these gaps are addressed, then they can provide reductions in cost, space and integration opportunities for enhanced functionality as well as additional services. As a part of this research a novel framework was developed, and two ITS systems were integrated such that remote communication with the infrastructure was achieved. Evaluation of this framework indicated that information can be shared across vehicle systems and other ITS systems could be added to the network to improve performance, safety and enforcement. To support the framework design, a Traffic Improvement Algorithm (TIA) was developed that improves traffic efficiency. This was validated using micro simulation tool that showed improvement in traffic efficiency when the algorithm was used. When bringing new technology into the market, there are some fundamental influencing factors affecting the selection and development prior to entering the end-user market. These factors are often neglected, and the current market lacks the ability to analyse the time it would take the new technology to come into the market. As a part of this research, a toolkit was developed that helps in estimating the time the technology takes to penetrate the market.
Walton, Julie (University of Wolverhampton, 2012-07)
This study investigated the effect of detergent treatment on susceptibility of attached Escherichia coli and Listeria monocytogenes to subsequent disinfectant treatment, in relation to food industry cleaning procedures. E. coli attached to stainless steel surfaces became significantly more susceptible to benzalkonium chloride (BAC) after treatment with sodium alkyl sulphate (SAS) by 0.51 Log10 cfu ml-1 and fatty alcohol ethoxylate (FAE) by 0.96 Log10 cfu ml-1. No change in susceptibility was observed with sodium dodecyl sulphate (SDS), sodium lauryl ethyl sulphate (SLES) or polyethoxylated alcohol (PEA). L. monocytogenes became significantly less susceptible to BAC after treatment with anionic detergents SAS by 0.79 Log10 cfu ml-1, SDS by 0.33 Log10 cfu ml-1 and SLES by 0.22 Log10 cfu ml-1, yet no change in susceptibility was observed with FAE. Following treatment with all detergents both organisms became significantly more susceptible to sodium dichloroisocyanurate (NaDCC) demonstrating that the effect of the disinfectant was independent of detergent type. Flow cytometry using the fluorochrome propidium iodide (PI) revealed significant increases in cell membrane permeability of both organisms by all detergents except sodium dodecyl sulphate (SDS) and the effect was much greater in E. coli. Increasing above the in-use concentration of SAS and FAE had no further effect on cell membrane permeability, or susceptibility to BAC. Hydrophobic interaction chromatography (HIC) showed that E. coli became less hydrophobic following treatment with SAS, SDS, FAE and L. monocytogenes became less hydrophobic following treatment with SAS and SDS but no effect was seen with FAE. Investigations into carbon chain length of detergent revealed that SAS and the C18 standard increased susceptibility of E. coli to BAC which, with permeability results, suggests a link between increase in susceptibility to BAC and increase in membrane permeability. Efflux experiments with L. monocytogenes showed that efflux of ethidium bromide (EtBr) was greater from cells treated with SAS than with FAE suggesting that the anionic charge on the detergent molecule influences an efflux mechanism that reduces susceptibility to BAC. Overall the results demonstrate that detergent type can influence the sensitivity of persistent food borne microorganisms to BAC and NaDCC and the significance of the findings may impact on the choice of agents used in cleaning procedures in the food industry.
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