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.