부산대학교 도서관

To keep pace with the increasing demand for new and improved barley varieties, commercial interest in novel breeding techniques which improve efficiency is high. With a growing population and a range of evolving demands placed on crop production by a more variable climate, breeders need new methods to breed barley faster, more accurately and where possible with fewer resources. Genomic selection (GS) is a breeding tool that uses genome-wide marker information to make selections/predictions in a breeding program. GS uses both genotypic data and phenotypic data to assign breeding values (BVs) for a specific trait to cultivars or breeding lines for use in crossing or selection. Progeny can then be selected and taken forward in a breeding program based on genotype alone, saving the cost, time and effort of phenotypic selection. This project aims to test a multi-environment (MET) GS model to breed elite 2-row spring barley for high straw saccharification (digestibility to yield simple sugars). Aligning with future climate policy, an increase in biofuel production will be useful as we reduce our reliance on fossil-based fuels. Second generation biofuels (produced from non-food sources) are promising for meeting sustainability targets as they need not result in land/resource competition with food crops. In comparison with first generation biofuels (produced from food sources), second generation biofuels should have much less impact on food security and food prices. Second generation biofuels currently have a higher production cost associated with them in comparison to first generation biofuels. To date there has not been a focus on breeding barley to develop varieties with high straw saccharification yield which would increase the amount of biofuel produced, reducing costs. The main objective of this thesis was to test a previously developed MET GS model to assess the potential of using GS to breed barley more efficiently for improved straw saccharification. In addition to saccharification, other agronomically important traits were also assessed to test the GS model predictions. Alongside the GS model, other methods to breed barley more rapidly were implemented.