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

• Autor(a) principal: OLIVEIRA, I. C. M.
• Data de Publicação: 2020
• Outros Autores: GUILHEN, J. H. S., RIBEIRO, P. C. de O., GEZAN, S. A., SCHAFFERT, R. E., SIMEONE, M. L. F., DAMASCENO, C. M. B., CARNEIRO, J. E. de S., CARNEIRO, P. C. S., PARRELLA, R. A. da C., PASTINA, M. M.
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)
• Texto Completo: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1124452
• topic: Sorghum Bicolor; Bioenergia; Melhoramento Genético Vegetal

Biomass sorghum has emerged as an alternative crop for biofuel and bioelectricity production. Fresh biomassyield (FBY) is a quantitative trait highly correlated with the calorific power of energy sorghum cultivars, but alsohighly affected by the environment. The main goal of this study was to investigate the genotype-by-environmentinteraction (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 betweenenvironments ranged from -0.21 to 0.99, indicating the existence of null to high G × E. The FA analysis unveiledthat solely three factors explained more than 79% of the genetic variance, and that more than 60% of theenvironments were clustered in thefirst factor. Moderate correlations were found between some environmentalcovariates 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 andspeed wind were correlated to the environmental loadings of factor 1; minimum temperature, solar radiation andaltitude to factor 2; and crop growth cycle to factor 3. The latent regression analysis was used to identify hybridsmore 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 minimumtemperature, precipitation, solar radiation, crop growth cycle and altitude.