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|Research center of Embrapa/Collection:||Embrapa Milho e Sorgo - Artigo em periódico indexado (ALICE)|
|Type of Material:||Artigo em periódico indexado (ALICE)|
|Authors:||OLIVEIRA, I. C. M.|
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.
|Additional Information:||Isadora Cristina Martins Oliveira; José Henrique Soler Guilhen; Pedro César de Oliveira Ribeiro, Universidade Federal de Viçosa; Salvador Alejandro Gezan, VSN International; ROBERT EUGENE SCHAFFERT, CNPMS; MARIA LUCIA FERREIRA SIMEONE, CNPMS; CYNTHIA MARIA BORGES DAMASCENO, CNPMS; José Eustáquio de Souza Carneiro, Universidade Federal de Viçosa; Pedro Crescêncio Souza Carneiro, Universidade Federal de Viçosa; RAFAEL AUGUSTO DA COSTA PARRELLA, CNPMS; MARIA MARTA PASTINA, CNPMS.|
|Title:||Genotype-by-environment interaction and yield stability analysis of biomass sorghum hybrids using factor analytic models and environmental covariates.|
|Publisher:||Field Crops Research, v. 257, 107929, 2020.|
|Description:||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.|
Melhoramento Genético Vegetal
|Appears in Collections:||Artigo em periódico indexado (CNPMS)|