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Biplot Analysis of Genotype by Environment Interaction for Barley Yield in Iran
Author(s) -
Dehghani H.,
Ebadi A.,
Yousefi A.
Publication year - 2006
Publication title -
agronomy journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.752
H-Index - 131
eISSN - 1435-0645
pISSN - 0002-1962
DOI - 10.2134/agronj2004.0310
Subject(s) - biplot , genotype , randomized block design , cultivar , yield (engineering) , gene–environment interaction , biology , grain yield , agronomy , hordeum vulgare , microbiology and biotechnology , horticulture , poaceae , genetics , gene , materials science , metallurgy
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.