GGE Biplot and AMMI Analysis of Yield Performance of Barley Genotypes across Different Environments in Iran

Authors
S. M. M. Mortazavian 1
H. R. Nikkhah 2
F. A. Hassani 3
M. Sharif-al-Hosseini 4
M. Taheri 5
M. Mahlooji 6
1 Department of Agronomy and Plant Breeding Sciences, College of Abouraihan, University of Tehran, P. O. Box: 4117, Islamic Republic of Iran.
2 Seed and Plant Improvement Institute (SPII) of Iran
3 Agricultural and natural resources research center of Fars, Shiraz, Iran
4 Agricultural and natural resources of Khorasan Razavi, Mashhad, Iran
5 Agricultural and natural resources research of Tehran, Varamin, Iran
6 Agricultural and natural resources research of Isfahan, Isfahan, Iran
Abstract
Twenty promising barley lines were evaluated at seven research stations in Iran, during two cropping seasons. The analysis of variance on grain yield data showed mean squares of environments, genotypes and Genotype×Environment Interaction (GEI) as significant, respectively accounting accounted for 60.38, 4.52 and 35.09% of treatment combination sum of squares. To find out the effects of GEI on grain yield, the data were subjected to Additive Main effects and Multiplicative Interaction (AMMI) and Sites Regression (SREG) GGE biplot analysis. Mega-environmental investigation is the most suitable way to utilize GEI. "Which-won-where" pattern was followed with three distinct mega-environments found in the barley assessment. Entries G5 and G6 showed general adaptability while G7 and G13 exhibited specific adaptation to Neishabour and Esfehan, respectively. Considering both techniques, genotype G1 revealed high grain yield along with yield stability. With regard to barley assessment, Esfehan was identified as a location with larger main effects interaction, making it a less predictable location for barley variety evaluation. The results finally indicated that AMMI and GGE biplot are informative methods to explore stability and adaptation pattern of genotypes in practical plant breeding and in subsequent variety recommendations. In addition, finding mega-environments help to identify the must suitable barley cultivars that can be recommended for areas within the mega-environment in either one or more test locations.

Keywords

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Journal of Agricultural Science and Technology
Volume 16, Issue 3
May and June 2014
Pages 609-622