Understanding the relationship between wheat grain traits and ethanol yield and predicting grain ethanol yield
Awole, K.D. (2014) Understanding the relationship between wheat grain traits and ethanol yield and predicting grain ethanol yield. Doctoral thesis, Harper Adams University.
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Abstract
Environmental, political and security issues are pushing the world towards a search for alternative fuel. Suitability of wheat varieties varies for different end uses. To increase process efficiency and to reduce cost of production, the right quality wheat has to be used. An economic analysis was conducted in order to quantify the significance of feedstock quality in the economics of bioethanol production. Ethanol yield (EY) variation within 84 samples was used to determine its effect on the cost of production by using cost of production data obtained from the literature and leaving all other variables constant. This analysis indicated that the best quality wheat among these samples could save up to three million pounds per year for a company with a capacity of 100000 tonnes of wheat per year. The other part of the study was to identify quality criteria which can be used at the refinery intake. Two independent experiments were conducted. i) Based on Recommended List (RL) samples comprising 14 varieties grown for two years at 11 sites. ii) Based on a field experiment conducted for two years using different agronomic practices in order to get a range of grain quality. EY, starch, nitrogen and non-starch polysaccharide (NSP) concentrations, thousand grain weight (TGW), specific weight, grain density, packing efficiency and the grain size and shape were measured in both experiments. Regression analysis was used to establish the relationship between the grain traits and EY. Both experiments revealed that nitrogen is the single best indicator of grain EY. TGW and specific weight are the second and third best indicators of EY respectively. Grain density and length are the poorest indicators of EY. Multiple linear regression result indicated that a model built with the combination of nitrogen and TGW can give the best prediction of EY. Adding variety and site to the model will increase the prediction potential.
| Item Type: | Thesis (Doctoral) |
|---|---|
| Depositing User: | Ms Kath Osborn |
| Date Deposited: | 02 Aug 2018 13:02 |
| Last Modified: | 31 Aug 2018 11:53 |
| URI: | https://hau.repository.guildhe.ac.uk/id/eprint/17297 |
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