Genetic diversity based on multivariate techniques in elephant grass genotypes for bioenergy.

The use of biomass for energy production constitutes a promising strategy that warrants the search for new sources of biomass. Elephant grass has been gaining notoriety due to its high dry matter yield and rapid growth. The present study was carried out to quantify the genetic divergence of nine elephant grass half-sib families in order to identify genotypes with greater genetic divergence and productive potential for hybridization, using the hierarchical clustering methodology based on principal components. Half-sib families were generated using genotypes from the Active Germplasm Bank of Elephant Grass. The experiment was laid out in a randomized-block design with nine half-sib families, three replicates, and eight plants per plot. A total of 216 genotypes of elephant grass were evaluated. Principal component (PC), biplot, and hierarchical clustering analyses for diversity estimation were conducted using R software. The first two PCs of biplot analysis accounted for 64% of the cumulative variation. Dry matter yield was the most important trait for genotype discrimination (0.89), followed by plant height (0.67) and stem diameter (0.61) in PC1. In this analysis, the distances between accessions were considered and there were no family links, which indicates the existence of wide variability within the evaluated families, since genotypes belonging to the same family were not grouped together, but rather distributed into different groups. Crosses between genotypes of group three and genotypes of groups one and two are recommended for the development of high-yielding genotypes when aiming at energy production.