2013European Journal of Experimental BiologyRequires access

GGE Biplot analysis of genotype à environment interaction in chickpea genotypes

Ezatollah Farshadfar, Mahnaz Rashidi, Mohammad Mahdi Jowkar, Hassan Zali

Open publisher page 65 citations

Abstract

The objective of this study was to explore the effect of genotype (G) and genotype × environment interaction (GEI) on grain yield of 20 chickpea genotypes under two different rainfed and irrigated environments for 4 consecutive growing seasons (2008-2011). Yield data were analyzed using the GGE biplot method. According to the results of combined analysis of variance, genotype × environment interaction was highly significant at 1% probability level, where G and GEI captured 68% of total variability. The first two principal components (PC1 and PC2) explained 68% of the total GGE variation, with PC1 and PC2 explaining 40.5 and 27.5 respectively. The first megaenvironment contains environments E1, E3, E4 and E6, with genotype G17 (X96TH41K4) being the winner; the second mega environment contains environments E5, E7 and E8, with genotype G12 (X96TH46) being the winner. The environment of E2 makes up another mega-environment, with G19 (FLIP-82-115) the winner. Mean performance and stability of genotypes indicated that genotypes G4, G16 and G20 were highly stable with high grain yield.

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What this paper is about

The objective of this study was to explore the effect of genotype (G) and genotype × environment interaction (GEI) on grain yield of 20 chickpea genotypes under two different rainfed and irrigated environments for 4 consecutive growing seasons (2008-2011). Yield data were analyzed using the GGE biplot method. According to the results of combined analysis of variance, genotype × environment interaction was highly significant at 1% probability level, where G and GEI captured 68% of total variability. The first two principal components (PC1 and PC2) explained 68% of the total GGE variation, with PC1 and PC2 explaining 40.5 and 27.5 respectively. The first megaenvironment contains environments E1, E3, E4 and E6, with genotype G17 (X96TH41K4) being the winner; the second mega environment contains environments E5, E7 and E8, with genotype G12 (X96TH46) being the winner. The environment of E2 makes up another mega-environment, with G19 (FLIP-82-115) the winner. Mean performance and stability of genotypes indicated that genotypes G4, G16 and G20 were highly stable with high grain yield.

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Available abstract

The objective of this study was to explore the effect of genotype (G) and genotype × environment interaction (GEI) on grain yield of 20 chickpea genotypes under two different rainfed and irrigated environments for 4 consecutive growing seasons (2008-2011). Yield data were analyzed using the GGE biplot method. According to the results of combined analysis of variance, genotype × environment interaction was highly significant at 1% probability level, where G and GEI captured 68% of total variability. The first two principal components (PC1 and PC2) explained 68% of the total GGE variation, with PC1 and PC2 explaining 40.5 and 27.5 respectively. The first megaenvironment contains environments E1, E3, E4 and E6, with genotype G17 (X96TH41K4) being the winner; the second mega environment contains environments E5, E7 and E8, with genotype G12 (X96TH46) being the winner. The environment of E2 makes up another mega-environment, with G19 (FLIP-82-115) the winner. Mean performance and stability of genotypes indicated that genotypes G4, G16 and G20 were highly stable with high grain yield.

Key concepts: Biplot, Genotype, Gene–environment interaction, Grain yield, Principal component analysis, Ammi, Yield (engineering), Open access publishing

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