Biplot Analysis of Genotype by Environment Interaction for Barley Yield in Iran
Hamid Dehghani, A. Ebadi, Ali Reza Yousefi
Abstract
Hamid Dehghani, A. Ebadi, Ali Reza Yousefi
Abstract
Cultivar evaluation and mega‐environment identification are the most important objectives of multienvironment trials (MET). The objective of this study was to explore the effect of genotype and genotype × environment interaction on the grain yield of 19 barley ( Hordeum vulgare L.) genotypes via GGE (genotype plus genotype × environment) biplot methodology. Experiments were conducted using a randomized complete block design with four replications for 3 yr at 10 locations. The biplot analysis identified three barley mega‐environments in Iran. The first mega‐environment contained locations Khoy, Mashhad, Miandoab, Karaj, and Nyshabour, where genotype Bahtim7‐D1/79‐w40762 was the winner; the second mega‐environment contained locations Tabriz, Hamedan, Ardabil, and Arak, where genotype Walfajre/W1‐2291 was the winner. The location of Zanjan made up the other mega‐environment, with 73‐M4‐30 as the winner. Genotypes Bahtim7‐D1/79‐w40762 and Walfajre/W1‐2291 had the highest mean yield and genotype K‐201/3‐2 had the poorest mean yield. The estimated relative yield of genotypes at Karaj station shows that genotype Bahtim7‐D1/79‐w40762 had the highest yield and genotype Owb70173‐2H‐OH had the poorest. The performances of genotypes Star/Alger and K‐201/3‐2 were highly variable, whereas genotypes Cossak/Gerbel/Harmal and Toji“S”/Robur were highly stable. The results of this study indicate the possibility of improving progress from selections under diverse location conditions by applying the GGL (genotype plus genotype × location) biplot methodology.
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Cultivar evaluation and mega‐environment identification are the most important objectives of multienvironment trials (MET). The objective of this study was to explore the effect of genotype and genotype × environment interaction on the grain yield of 19 barley ( Hordeum vulgare L.) genotypes via GGE (genotype plus genotype × environment) biplot methodology. Experiments were conducted using a randomized complete block design with four replications for 3 yr at 10 locations. The biplot analysis identified three barley mega‐environments in Iran. The first mega‐environment contained locations Khoy, Mashhad, Miandoab, Karaj, and Nyshabour, where genotype Bahtim7‐D1/79‐w40762 was the winner; the second mega‐environment contained locations Tabriz, Hamedan, Ardabil, and Arak, where genotype Walfajre/W1‐2291 was the winner. The location of Zanjan made up the other mega‐environment, with 73‐M4‐30 as the winner. Genotypes Bahtim7‐D1/79‐w40762 and Walfajre/W1‐2291 had the highest mean yield and genotype K‐201/3‐2 had the poorest mean yield. The estimated relative yield of genotypes at Karaj station shows that genotype Bahtim7‐D1/79‐w40762 had the highest yield and genotype Owb70173‐2H‐OH had the poorest. The performances of genotypes Star/Alger and K‐201/3‐2 were highly variable, whereas genotypes Cossak/Gerbel/Harmal and Toji“S”/Robur were highly stable. The results of this study indicate the possibility of improving progress from selections under diverse location conditions by applying the GGL (genotype plus genotype × location) biplot methodology.
Key concepts: Biplot, Genotype, Randomized block design, Cultivar, Yield (engineering), Gene–environment interaction, Biology, Grain yield