ABSTRACT The mixed-model methodology is an alternative to select genotypes for traits highly influenced by the environment. In addition, this method allows FOR estimating the repeatability coefficient and predicting the number of assessments needed for a selection process to increase reliability. This study aimed to determine the minimum number of evaluations necessary for a reliable selection process and to estimate the variance components used for predicting genetic gains between and within half-sib families of elephant grass ( Cenchrus purpureus (Schumach.) Morrone ) using the mixed-model methodology. Half-sib families were generated using genotypes from the Active Germplasm Bank of Elephant Grass. The experiment was performed in a randomized block design with nine half-sib families, three replicates, and eight plants per plot. We evaluated 216 genotypes (individual plants) of elephant grass. The deviance analysis was carried out, genetic parameters were estimated, gains between and within families were predicted, and repeatability coefficients were obtained using Selegen software. There was genetic variability for selection within the families evaluated. The reliability values found above 60 % for plant height and number of tillers and above 80 % for dry matter yield suggest that only two evaluations are required to select superior genotypes with outstanding reliability. Sixteen genotypes were identified and selected for their productive potential, which can be used as parents in elephant grass breeding programs for bioenergy production.

ABSTRACT One of the challenges of the energy sector is the identification of renewable resources with less impact on the environment and that are economically viable. This study aimed to estimate the genetic diversity of eleven full-sib families of elephant grass, using quantitative traits associated with bioenergy production. The trial followed a randomized blocks design, with four replications and each plot (family) consisting of five plants, totaling 220 genotypes. Five quantitative traits were measured: dry matter yield, dry matter percentage, plant height, stem diameter and number of tillers. The genetic diversity was estimated using multivariate methods (principal component analysis and hierarchical clustering). The analysis revealed a significant genetic diversity among the full-sib of elephant grass, with a greater variability observed for dry matter yield and number of tillers. The families 1, 2, 5, 7 and 8 exhibited superior genotypes for bioenergy production-related traits. The grouping analysis generated twenty clusters, enabling the differentiation of genotypes. Eight clusters comprised genotypes with a high dry matter yield and plant height, simultaneously, surpassing the overall mean for number of tillers.

RESUMO Um dos desafios do setor energético é a identificação de recursos renováveis com menor impacto ao meio ambiente e que sejam economicamente viáveis. Objetivou-se estimar a diversidade genética de onze famílias de irmãos completos de capim-elefante, utilizando-se características quantitativas associadas à produção de bioenergia. O ensaio foi conduzido em delineamento de blocos ao acaso, com quatro repetições, sendo cada parcela (família) composta por cinco plantas, totalizando 220 genótipos. Cinco características quantitativas foram mensuradas: produção de matéria seca, porcentagem de matéria seca, altura de planta, diâmetro de colmo e número de perfilhos. A estimativa da diversidade genética foi realizada por meio de métodos multivariados (componentes principais e agrupamento hierárquico). Observou-se que há diversidade genética em irmãos completos de capim-elefante, com maior amplitude de variabilidade para produção de matéria seca e número de perfilhos. As famílias 1, 2, 5, 7 e 8 apresentam genótipos superiores para características relacionadas à produção de bioenergia. Vinte grupos obtidos no agrupamento maximizaram a distinção dos genótipos. Oito grupos continham genótipos com alta produção de matéria seca e altura de planta, simultaneamente, além de valores acima da média geral para número de perfilhos.

ABSTRACT Elephant grass stands out among lignocellulosic biomass plants utilized for second-generation biofuel production due to several advantageous characteristics compared to other raw materials. Its short production cycle and ability to thrive in adverse soil and climate conditions contribute to its appeal. Additionally, breeders seek genotypes with high productivity potential and adaptability to various favorable cultivation environments. This study aimed to estimate genetic parameters, predict genetic values using mixed models (REML/BLUP), and evaluate stability and adaptability for energy biomass production in elephant grass genotypes. The experiment was conducted in Campos dos Goytacazes, RJ, Brazil, utilizing a two-replicate experimental block design that included 40 elephant grass genotypes. Four harvest assessments were performed between 2016 and 2019. Genetic parameter estimation and selection of superior genotypes based on genetic value using the REML/BLUP procedure were performed using Selegen software. Stability and adaptability analyses were obtained through the harmonic mean of genotypic values (HMGV), enabling the identification of stable and highly productive genotypes. Genotypes 17, 18, 32, 16, 36, 6, 15, 31, and 34 exhibited outstanding performance in terms of HMGV, indicating enhanced stability, adaptability, and simultaneous productivity, thus ensuring robustness in cultivation. These selected genotypes hold potential for future breeding programs aimed at improving elephant grass yield for biomass production.

Abstract The study of adaptability and yield stability is essential for the selection and recommendation of new cultivars. Thus, this study determined the adaptability and yield stability of black bean genotypes for cultivar recommendation using a GGE biplot. To this end, nine trials were carried out at different locations in the state of Rio de Janeiro. Eleven genotypes were evaluated in three growing seasons, in a randomized block design, with three replications. Adaptability and stability of grain yield were analyzed by the GGE biplot method. The effects of genotypes, environments and of the genotype - environment interaction were significant, which explained the different responses of the genotypes to the tested environments. The GGE biplot analysis revealed Macaé as an ideal district for testing and high yield, adaptability, and stability of performance were observed for genotype CNFP 15290, which can be recommended for grain production in the state of Rio de Janeiro.