Identification of mega-environments for grain sorghum in Brazil using GGE biplot methodology.

The performance of genotypes in a wide range of environments can be affected by extensive genotype × environment (G × E) interactions, making the subdivision of the testing environments into relatively more homogeneous groups of locations (mega-environments) a necessary strategy. The genotype main effects + genotype × environment interaction biplot method (GGE) allows identification of megaenvironments and selection of stable genotypes adapted to specific environments and mega-environments. The objectives of this study were to identify mega-environments regarding sorghum [Sorghum bicolor (L.) Moench] grain yield and demonstrate that the GGE biplot method can identify essential locations for conducting tests in different mega-environments. A total of 22 competition trials of grain sorghum genotypes were conducted over three crop seasons across several production locations in Brazil. A total of 25, 22, and 30 genotypes were evaluated during the first, second, and third crop seasons, respectively. After identifying the presence of G × E interactions, the data were subjected to adaptability and stability analyses using the GGE biplot method. A phenotypic correlation network was used to express functional relationships between environments. The GGE biplot was found to be an efficient approach for identifying three mega-environments in grain sorghum in Brazil, selecting representative and discriminative environments, and recommending more adaptive and stable grain sorghum genotypes

Bibliographic Details

Main Authors:	SILVA, K. J. da, TEODORO, P. E., SILVA, M. J. da, TEODORO, L. P. R., CARDOSO, M. J., GODINHO, V. de P. C., MOTA, J. H., SIMON, G. A., TARDIN, F. D., SILVA, A. R. da, GUEDES, F. L., MENEZES, C. B. de
Other Authors:	KARLA JORGE DA SILVA; PAULO EDUARDO TEODORO, Universidade Federal de Mato Grosso do Sul; MICHELE JORGE DA SILVA, Universidade Federal de Viçosa; LARISSA PEREIRA RIBEIRO TEODORO, Universidade Federal de Mato Grosso do Sul; MILTON JOSE CARDOSO, CPAMN; VICENTE DE PAULO CAMPOS GODINHO, CPAF-RO; JOSÉ HORTÊNCIO MOTA, Universidade Federal de Jataí; GUSTAVO ANDRÉ SIMON, Universidade de Rio Verde; FLAVIO DESSAUNE TARDIN, CNPMS; ADELMO RESENDE DA SILVA, CNPMS; FERNANDO LISBOA GUEDES, CNPC; CICERO BESERRA DE MENEZES, CNPMS.
Format:	Artigo de periódico biblioteca
Language:	Ingles English
Published:	2021-12-15
Subjects:	Método biplot, Sorgo, Genótipo, Rendimento, Grão,
Online Access:	http://www.alice.cnptia.embrapa.br/alice/handle/doc/1137820 https://doi.org/10.1002/agj2.20707
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