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Published online 2 June 2009
Published in Agron J 101:789-796 (2009)
DOI: 10.2134/agronj2008.0203x
© 2009 American Society of Agronomy
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BARLEY

Biplot Analysis of Rainfed Barley Multienvironment Trials in Iran

Reza Mohammadia,*, Ahmed Amrib and Yousef Ansaric

a Dryland Agricultural Research Institute (DARI), P.O. Box 67145-1164, Kermanshah, Iran
b International Center for Agricultural Research in the Dry Areas (ICARDA), P.O. Box 5466, Aleppo, Syria
c Dryland Agricultural Research Institute (DARI), P.O. Box 119, Maragheh, Iran

* Corresponding author (rmohammadi95{at}yahoo.com).

The main objective of this work was the analysis of barley (Hordeum vulgare L.) multienvironment trials (MET) first to identify superior genotypes for the barley crop area in Iran, and second to investigate if different megaenvironments exist. The analyses were performed via GGE (genotype + genotype x environment) biplot methodology on the grain yield of 13 barley genotypes, selected from the joint project of Iran/ICARDA, grown for three consecutive seasons (2003–2005) at six locations, representative of major barley growing areas in Iran. The by-year and combined analyses showed that the variation between locations was always the most important source of yield variability. Collective analyses of yearly and combined GGE biplots were able to identify high-yielding genotypes and their areas of adaptation, and suggested the existence of four barley megaenvironments. The first megaenvironment consisted of the location of Ardabil (ARDA), where genotype G11 was the highest yielder; another megaenvironment is represented by the location of Kermanshah (KERM), with G2 as the best genotype. The megaenvironment was comprised of the locations Maragheh (MARA) and Zanjan (ZANJ), with G13 as the highest yielder, whereas the Ghamlo (GHAM) and Shirvan (SHIR) locations made up the fourth megaenvironment, with G4 and G10 as the recommended genotypes. The SHIR, ZANJ, and GHAM were more representative and are considered desirable for selecting genotypes adapted to the whole target region. In contrast, ARDA was best at discriminating high- and low-yielding genotypes. The highest-yielding genotypes (G4, G3, and G13) were more unstable over locations and years, while high-yielding genotypes (G5 and G8) were more stable.

Abbreviations: AMMI, additive main effects and multiplicative interaction • ARDA, Ardabil • b, regression coefficient • E, environment main effect • G, genotype main effect • GE, genotype x environment • GGE, genotype + genotype x environment interaction • GHAM, Ghamlo • KERM, Kermanshah • L, location • MARA, Maragheh • MET, multienvironment trials • PC, principal component • SHIR, Shirvan • Y, year • ZANJ, Zanjan

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Received for publication November 19, 2008.





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