Abstract
Persistence may be defined as high sustained yield over multi-harvest. Genetic insights about persistence are essential to ensure the success of breeding programs and any biomass-based project. This paper focuses on assessing the biomass yield persistence for bioenergy purpose of 100 elephantgrass clones measured in six growth seasons in Brazil. To assess the clones' persistence, an index based on random regression models and genotype-ideotype distance was proposed. Results suggested the existence of wide genetic variability between elephantgrass clones, and that the yield trajectories along the harvests generate genetic insights into elephantgrass clones’ persistence and G x E interaction. A gene pool that acts over the biomass yield (regardless of the harvest) was detected, as well as other gene pools, which show differences on genes expression (these genes are the major responsible for clones’ persistence). The lower and higher clones’ persistence was discussed based on genome dosage effect and natural biological nitrogen fixation ability applied to bioenergy industry. The huge potential of energy crops necessarily is associated with genetic insights into persistence, so just this way, breeding programs could breed a new cultivar that fulfills the bioenergy industries.
Highlights
Understanding the yield trajectory along the growth seasons/harvests may determine the success of any biomass-project and even generate genetic insights into elephantgrass clones’ behavior, which is useful for breeding programs
According to the Schwarz’s Bayesian information criterion (BIC), the best model is denoted by Leg4.1.D with diagonal residual variance and was adopted to describe the changes in the variance and covariance components for elephantgrass biomass yield over multi-harvest
Different models fitted with orthogonal Legendre polynomials, number of parameters (p), Schwarz Bayesian information criteria (BIC), and likelihood ratio test (LRT) for genetic and permanent environmental effect
Summary
Understanding the yield trajectory along the growth seasons/harvests may determine the success of any biomass-project and even generate genetic insights into elephantgrass clones’ behavior, which is useful for breeding programs. According to the Schwarz’s Bayesian information criterion (BIC), the best model is denoted by Leg4.1.D with diagonal residual variance and was adopted to describe the changes in the variance and covariance components for elephantgrass biomass yield over multi-harvest.
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