Study on grain yield stability of soybean genotypes [Glycine max (L.) Merril ] through GGE biplot analysis

The main goal of the mostly soybean breeding programs is selection of desirable genotypes with high yield and stability. Genotype × environment interactions for quantitative traits such as grain yield cause genotypes to have not similar relative yields in different environments. In many statistical methods that have been used to determine yield stability and adaptability of cultivars, some basic assumptions of stability analysis such as nonlinear response of genotype and environment and dependence of environmental index on mean of genotypes are not true (Basford and cooper, 1998). Using multivariate methods such as principal components and GGE biplot method, genotype x environment interactions can be analyzed and their component values estimated ( Pacheco et al., 2009). Many breeders believe that selection of genotypes based on G or GE alone is not sufficient, and it is advisable to study these two effects together done by method such as GGE biplot . The aim of this study was to evaluate the grain yield and adaptability stability of 20 soybean pure lines in three soybean cultivation region and selection the best pureline as new crop cultivar in the territory.

In order to evaluate the grain yield and stability of 20 soybean pure lines along with Williams control (20 genotypes) during two year (2014-2015) in regions : Karaj (1321m altitude, 35.5 N & 51.1 E), Moghan ((45 m altitude, 39.3 N & 47.2 E), Khorramabad (1155 m altitude, 33.3 N & 48.2 E), farming operations and necessary fertilization were carried out uniformly in all three regions. A randomized complete block design with 3 replications was used in the locations. Each block consisted of 21 plots and each plot consisted of 4 rows of 4 m with intervals of 60 cm. Simple and composite analysis of variance on grain yield was performed to estimate main effects of genotype, environment and genotype × environment interaction and to determine adaptability and stability of grain yield of the genotypes used GGE Biplot analysis methods by Genstate Ver.12 Software .

Combined analysis of variance showed significant effects of environment, genotype, genotype x environment interaction, at 1% level probeblity (p ≤ 0.01). The first and second major components (PCA1 & PCA2) of GGE biplot analysis accounted for 47% and 28% of the variance of genotype × environment and genotype (G X E + G) interaction, respectively.. Based on GGE biplot criterion G6 (L12/Williams x Katool) genotype with yield of 3514 kg/ha had shortest distance from ideal genotype and was selected as the most desirable genotype and after that G4 (L12/Williams x Katool) with 3522 kg/ha in terms of desirability was ranked second. In this study one mega environment consisting of two environments E1 (Karaj 1394) and E3 (Moghan 2015) was identified. Also E4 environment (Moghan 2016) was the nearest to ideal environment and was recognized as the most effective environment in terms of discriminating ability and representativeness and then E2 (Karaj 1395), E1 (Karaj 1394), E3 (Moghan 1394) and E5 (Khorramabad 1394) environments were favorably ranked next.

The first and second major components (PCA1 & PCA2) of GGE biplot analysis accounted for 47% and 28% of the variance of genotype × environment and genotype (G x E + G) interaction, respectively. G6 genotype (L12/Williams x Katool) with yield of 3514 kg/ha was identified as the most desirable genotype and then G4 (L12/Williams x Katool) with 3522 kg/ha in terms of desirability was ranked second. In this study one mega environment consisting of two environments E1(Karaj 1394) and E3 (Moghan 2015) was identified. Also E4 (Moghan 2016) was recognized as the most desirable environment in terms of discriminating ability and representativeness.