2013Unpublished venueRequires access

Site regression analysis for interpreting genotype by environment interaction of grain yield in some chickpea (Cicer arietinum L.) genotypes

Sayyed Hossain Sabaghpour, Ebadi, Asghar, Seyydi, Faramarz, Poursiabaedi, Mohamma Mahdi, Pazashpour, Peyam, Sefikhani, Mansor

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Abstract

Multi-environment trials including 3 years and 5 locations for 17 genotypes of chickpea (Cicer arietinum L.) were carried out in Iran to explore the effect of genotype (G) and genotype × environment interaction (GE). The combined analysis of variance indicated that environment (E), G and GE were highly significant (P < 0.01), suggesting differential responses the genotypes. The analysis of variance of grain yield showed that 68.36% of the total sum of squares was attributable to environmental effects, only 9.23% to genotypic effects, and 22.41% to GL interaction effects. Site regression analysis was used to understand the GL interaction pattern. The first 2 principal components were used to create a 2-dimensional GGL biplot and explained 64% and 20% of GGL sum of squares, respectively. The mega-environment analysis identified three chickpea mega-environments in Iran. The first mega-environment contained locations Ghachsaran and Lorestan, where genotype Arman was the winner; the second mega-environment contained locations Ilam and Kermanshah, where genotype FLIP 98-82C and FLIP 98-201C were the winner. The tertiary mega-environment contained locations Gorgan, where genotype FLIP 98-126C was the winner.

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What this paper is about

Multi-environment trials including 3 years and 5 locations for 17 genotypes of chickpea (Cicer arietinum L.) were carried out in Iran to explore the effect of genotype (G) and genotype × environment interaction (GE). The combined analysis of variance indicated that environment (E), G and GE were highly significant (P < 0.01), suggesting differential responses the genotypes. The analysis of variance of grain yield showed that 68.36% of the total sum of squares was attributable to environmental effects, only 9.23% to genotypic effects, and 22.41% to GL interaction effects. Site regression analysis was used to understand the GL interaction pattern. The first 2 principal components were used to create a 2-dimensional GGL biplot and explained 64% and 20% of GGL sum of squares, respectively. The mega-environment analysis identified three chickpea mega-environments in Iran. The first mega-environment contained locations Ghachsaran and Lorestan, where genotype Arman was the winner; the second mega-environment contained locations Ilam and Kermanshah, where genotype FLIP 98-82C and FLIP 98-201C were the winner. The tertiary mega-environment contained locations Gorgan, where genotype FLIP 98-126C was the winner.

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Available abstract

Multi-environment trials including 3 years and 5 locations for 17 genotypes of chickpea (Cicer arietinum L.) were carried out in Iran to explore the effect of genotype (G) and genotype × environment interaction (GE). The combined analysis of variance indicated that environment (E), G and GE were highly significant (P < 0.01), suggesting differential responses the genotypes. The analysis of variance of grain yield showed that 68.36% of the total sum of squares was attributable to environmental effects, only 9.23% to genotypic effects, and 22.41% to GL interaction effects. Site regression analysis was used to understand the GL interaction pattern. The first 2 principal components were used to create a 2-dimensional GGL biplot and explained 64% and 20% of GGL sum of squares, respectively. The mega-environment analysis identified three chickpea mega-environments in Iran. The first mega-environment contained locations Ghachsaran and Lorestan, where genotype Arman was the winner; the second mega-environment contained locations Ilam and Kermanshah, where genotype FLIP 98-82C and FLIP 98-201C were the winner. The tertiary mega-environment contained locations Gorgan, where genotype FLIP 98-126C was the winner.

Key concepts: Biplot, Genotype, Gene–environment interaction, Interaction, Grain yield, Yield (engineering), Biology, Mega-

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