Abstract
AbstractIn conjunction with improvements to processing technology, selective breeding can be used to increase bioenergy yield potential either through an increase in total harvestable biomass or by modifying biomass conversion efficiency. In this study, the measurement of biomass composition (lignin, cellulose and hemicellulose) and proximate (fixed carbon, volatile matter, ash and moisture contents) components was conducted on leaf and tissue samples from diverse Sorghum bicolor genotypes across two environments. Multiple regression analysis was used to create models that predict two biomass conversion traits: hydrolysis yield potential (HYP) and crystallinity index (CI). CI model variables included tissue type, ash and lignin. HYP model variables included tissue type, ash, cellulose and volatile matter. The presence of ash content in all models was a salient finding of our study. Given that several models were able to predict HYP and CI in alternate grow‐out years, these models may be useful in selective breeding programmes aimed at sorghum bioenergy feedstock improvement through increased conversion efficiency.
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