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ABSTRACT. Brazil has great potential to produce bioenergy since it is located in a tropical region that receives high incidence of solar energy and presents favorable climatic conditions for such purpose. However, the use of bioenergy in the country is below its productivity potential. The aim of the current study was to select full-sib progenies and families of elephant grass (Pennisetum purpureum S.) to optimize phenotypes relevant to bioenergy production through mixed models

The objective of this study was to evaluate the agronomic traits of 80 accessions of elephant grass under the soil and weather conditions of Campos dos Goytacazes/RJ, Brazil. The experimental design was set as randomized blocks with 2 replicates. The experiment continued from March 2012 to May 2013, with 5 harvests made in the dry and rainy seasons. The following traits were assessed: percentage of dry matter (%DM), dry matter yield (DMY), number of tillers per meter (NT), plant height (HGT), stem diameter (SD), leaf blade width (LBW) and leaf blade length (LBL). Data from each harvest were subjected to analysis of variance and to the Scott-Knott test (P < 0.05). Tocher’s optimization method, Mahalanobis distance, and canonical variables were utilized for the multiple traits, and the importance of the characters in the canonical variables. Genotypes with high yield were Elefante da Colômbia, Taiwan A-25, Albano, Hib. Gigante da Colômbia, Elefante de Pinda, Taiwan A-121, P241 Piracicaba, Guaçu/I.Z.2, CPAC, EMPASC 309, EMPASC 307, Australiano, and Pasto Panamá. Stem diameter (rainy season) and LBW (dry season) were the most important variables to differentiate between genotypes. There was wide phenotypic variation between genotypes, which could be divided into 15 groups by Tocher’s optimization method.

Management of variability in germplasm banks is essential for genetic improvement, so that the breeder can estimate the genetic similarity between cultivars, as well as maintain genetic diversity in breeding programs. Elephant grass is a forage crop plant native to Africa of great socio-economic and environmental importance; it can be used for animal feed and for bioenergy production. Understanding the genetic variability of elephant grass is essential for breeding programs. In this context, we examined the genetic divergence of elephant grass accessions using the Gower algorithm. The experiment was conducted under field conditions in the municipality of Campos dos Goytacazes-RJ, Brazil. All 85 elephant grass accessions belonging to the Active Elephant Grass Germplasm Bank of Embrapa Gado de Leite were included. These genotypes are commercial varieties from various countries. They were evaluated for morphoagronomic, morphological and phenological characteristics. The experimental design was ©FUNPEC-RP www.funpecrp.com.br Genetics and Molecular Research 20 (4): gmr18944 A.K.F. Vidal et al. 2randomized blocks with two repetitions. The plots were composed of 5.5-meter rows, with 2.0-meter spacing between the planting rows, totaling 11.0 m 2 . The useful area was a sample in the center of the plot. We generated an illustrative dendrogram, obtained by the UPGMA method and the Tocher clustering, based on the Gower algorithm. Data were examined by means of the GENES statistical programs and the R program. According to the dissimilarity matrices based on the Gower algorithm, the genetic distances varied between 0.08 and 0.56, and the mean distance of the 85 evaluated accessions was 0.25, suggesting, consequently, that there is wide genetic variability between the accessions. Of the 85 genotypes, seven presented genetic distances smaller than 0.1, being indicative of duplicates in the germplasm bank, which could be eliminated without risk of loss of genetic variability.