Identification of superior quality protein maize hybrids for different mega-environments using the biplot methodology

By:

Preciado-Ortiz, R.E

Contributor(s):

Material type: Article

ArticleLanguage: English Publication details: Bergamo, (Italy) : Consiglio per la Ricerca e la Sperimentazione in Agricoltura, 2006.ISSN:

0025-6153

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Open Access through DSpace

In: Maydica v. 51, no. 3-4, p. 451-460634602Summary: The utilization of site regression models (SREG) on multilocation testing allow the detection of significant differences in the genotype x environment interaction, even though these may not be detected by the analysis of variance (ANOVA). The results can be graphically displayed using the Biplot technique, revealing the additive effects on the genotypes and the genotype x environment interaction across years. Thus, the objectives of this work were to identify mega-environments, superior maize hybrids for each environment and mega-environment, stable maize hybrids with good performance across environments, and the most suitable environments for evaluation as well. A total of 66 field trials were grouped in five sets of experiments. An individual SREG analysis for each set of experiments and their combined analysis were conducted to assist in the graphic representation by the Biplot methodology. Results revealed that the constructed Biplots, graphically allowed the identification of superior maize hybrids, and the proper environments to conduct maize hybrid evaluation trials; however, it was not a reliable option for grouping test-sites in mega-environments.

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Item type	Current library	Collection	Call number	Copy number	Status	Date due	Barcode	Item holds
Article	CIMMYT Knowledge Center: John Woolston Library	CIMMYT Staff Publications Collection	CIS-4959 (Browse shelf(Opens below))	1	Available		634602

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The utilization of site regression models (SREG) on multilocation testing allow the detection of significant differences in the genotype x environment interaction, even though these may not be detected by the analysis of variance (ANOVA). The results can be graphically displayed using the Biplot technique, revealing the additive effects on the genotypes and the genotype x environment interaction across years. Thus, the objectives of this work were to identify mega-environments, superior maize hybrids for each environment and mega-environment, stable maize hybrids with good performance across environments, and the most suitable environments for evaluation as well. A total of 66 field trials were grouped in five sets of experiments. An individual SREG analysis for each set of experiments and their combined analysis were conducted to assist in the graphic representation by the Biplot methodology. Results revealed that the constructed Biplots, graphically allowed the identification of superior maize hybrids, and the proper environments to conduct maize hybrid evaluation trials; however, it was not a reliable option for grouping test-sites in mega-environments.

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Knowledge Center Catalog

Identification of superior quality protein maize hybrids for different mega-environments using the biplot methodology

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International Maize and Wheat Improvement Center (CIMMYT) © Copyright 2021.
Carretera México-Veracruz. Km. 45, El Batán, Texcoco, México, C.P. 56237.
If you have any question, please contact us at
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Knowledge Center Catalog

Identification of superior quality protein maize hybrids for different mega-environments using the biplot methodology

Browsing CIMMYT Knowledge Center: John Woolston Library shelves, Collection: CIMMYT Staff Publications Collection Close shelf browser (Hides shelf browser)

International Maize and Wheat Improvement Center (CIMMYT) © Copyright 2021. Carretera México-Veracruz. Km. 45, El Batán, Texcoco, México, C.P. 56237. If you have any question, please contact us at CIMMYT-Knowledge-Center@cgiar.org

International Maize and Wheat Improvement Center (CIMMYT) © Copyright 2021.
Carretera México-Veracruz. Km. 45, El Batán, Texcoco, México, C.P. 56237.
If you have any question, please contact us at
CIMMYT-Knowledge-Center@cgiar.org