Genotype-by-environment interaction and yield stability analysis of biomass sorghum hybrids using factor analytic models and environmental covariates

• Published in: Field crops research 2020-10, Vol.257, p.107929, Article 107929
• Main Authors: Oliveira, Isadora Cristina Martins, Guilhen, José Henrique Soler, Ribeiro, Pedro César de Oliveira, Gezan, Salvador Alejandro, Schaffert, Robert Eugene, Simeone, Maria Lucia Ferreira, Damasceno, Cynthia Maria Borges, Carneiro, José Eustáquio de Souza, Carneiro, Pedro Crescêncio Souza, Parrella, Rafael Augusto da Costa, Pastina, Maria Marta
• Format: Article
• Language: English
• Subjects: bioenergy; mixed models; multi-environment-trials; plant breeding; Sorghum bicolor (L.) Moench
• ISSN: 0378-4290; 1872-6852
• DOI: 10.1016/j.fcr.2020.107929

•FA models allow G × E studies even for highly unbalanced historical datasets.•G × E studies are valuable tools to optimize resources in sorghum breeding programs.•FA loadings can be successfully used to investigate the main factors affecting G × E. Biomass sorghum has emerged as an alternative crop for biofuel and bioelectricity production. Fresh biomass yield (FBY) is a quantitative trait highly correlated with the calorific power of energy sorghum cultivars, but also highly affected by the environment. The main goal of this study was to investigate the genotype-by-environment interaction (G × E) and the stability of sorghum hybrids evaluated for FBY across different locations and years, using factor analytic (FA) mixed models and environmental covariates. Pairwise genetic correlations between environments ranged from -0.21 to 0.99, indicating the existence of null to high G × E. The FA analysis unveiled that solely three factors explained more than 79% of the genetic variance, and that more than 60% of the environments were clustered in the first factor. Moderate correlations were found between some environmental covariates and the loadings of FA models for environments, suggesting the possible factors to explain the high G × E between environments clustered in a given factor. For example: precipitation, minimum temperature and speed wind were correlated to the environmental loadings of factor 1; minimum temperature, solar radiation and altitude to factor 2; and crop growth cycle to factor 3. The latent regression analysis was used to identify hybrids more responsive to a set of environments, as well as hybrids specifically adapted to a given environment. Finally, FA models can be successfully used to identify the main environmental factors affecting G × E, such as minimum temperature, precipitation, solar radiation, crop growth cycle and altitude.