Data/hora: 29/05/2024 02:40:49

Biblioteca(s): 	Embrapa Milho e Sorgo.	
Data corrente: 	20/08/2020	
Data da última atualização: 	04/11/2020	
Autoria: 	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.	
Afiliação: 	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.	
Título: 	Genotype-by-environment interaction and yield stability analysis of biomass sorghum hybrids using factor analytic models and environmental covariates.	
Ano de publicação: 	2020	
Fonte/Imprenta: 	Field Crops Research, v. 257, 107929, 2020.	
Idioma: 	Inglês	
Conteúdo: 	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.	
Thesagro: 	Bioenergia; Melhoramento Genético Vegetal; Sorghum Bicolor.	
Categoria do assunto: 	--	
URL: 	http://ainfo.cnptia.embrapa.br/digital/bitstream/item/215435/1/Genotype-environment.pdf	
Registro original: 	Embrapa Milho e Sorgo (CNPMS)	
Exemplares: 	CNPMS - (29331-1) - UPC - AP - DD -  -  -  - DIS