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

Autor: 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.
Přispěvatelé: 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.
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Zdroj: Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA-Alice)
Empresa Brasileira de Pesquisa Agropecuária (Embrapa)
instacron:EMBRAPA
Popis: 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. Made available in DSpace on 2020-08-21T04:11:13Z (GMT). No. of bitstreams: 1 Genotype-environment.pdf: 2248923 bytes, checksum: 41b14ad96b520bcde6a15e8b214e773a (MD5) Previous issue date: 2020
Databáze: OpenAIRE
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