A Study on the Genotype × Environment Interaction in Promising Advanced Genotypes of Soybean using Graphical GGE-biplot Analysis - Bahram Masoudi; Amir Gholizadeh; Parastoo Majidian; Ebrahim Hezarjaribi; Nasrin Razmi; Farnaz Shariati (پژوهشنامه اصلاح گیاهان زراعی)
Extended Abstract Background: Oilseeds are among the most important sources of energy all over the world. Soybean (Glycine max L.) is an important crop and its oil has nutritional and high economic value. As an annual, self-pollinating, diploid plant belonging to the Leguminosae pea family, soybean falls into the most important oil plants in the world, containing 18-22% oil and 40-50% protein, depending on the genotype and environmental factors. Soybean has been the food of Asian people, especially China, for centuries, and Chinese people consume it along with rice as their main food. The United States of America is the largest producer of soybeans and produces almost two-thirds of the world's crop. Improving seed yield is always a major goal in soybean breeding programs. The economic performance of soybeans can be increased by using new and high-yield varieties. It is essential to evaluate promising advanced soybean genotypes under different environmental conditions for identifying and selecting superior genotypes with high and stable yield potential. Genotype × environment interaction effects are important limiting factors in the introduction of new cultivars. The genotype × environment interaction is a major challenge in the study of quantitative characters because it reduces yield stability in different environments and complicates the interpretation of genetic experiments, making predictions difficult. Therefore, it is crucial to know the type and nature of the interaction effect and reach the verities that have the least role in creating interaction effects. Various methods have been introduced to evaluate the interaction effect, each of which examines the nature of the interaction effect from a specific point of view. The GGE-biplot graphic method is a technique with suitable efficiency to investigate the genotype × environment interaction effect and provides good information about the studied genotypes and environments graphically. This study aimed to investigate the genotype × environment interaction effect using the GGE-biplot graphic method to evaluate genotypes, environments, and relationships between genotypes and environments. Finally, this research seeks to identify stable soybean genotypes with high grain yields under different environmental conditions. Methods: In total, 27 new soybean lines along with Saba and Amir cultivars were evaluated under different environmental conditions in a randomized complete block design with three replications in four experimental field stations (Karaj, Gorgan, Sari, and Moghan) during the 2022 cropping season. The plots consisted of four rows of 5 m in length with 50 cm spacing between the rows. The GGE biplot statistical method (the genotype effect + genotype × environment interaction) was used to study the stability of genotypes in the studied environments. Plants were harvested at maturity, and then the seed yield was recorded for each genotype at each test environment. Results: The results of the combined analysis of variance indicated that the effects of environments (E), genotypes (G), and genotype × environment (G×E) interaction were significant for seed yield, suggesting that the genotypes responded differently in the studied environmental conditions, making the stability analysis possible. The results of the genotype × environment interaction analysis using the GGE-biplot method indicated that the two first and second principal components of the GGE-biplot explained 84.8% of the total seed yield variation, indicating the high validity of the biplot in explaining the variations of genotypes and the genotype × environment interaction (G + GE). This study identified two mega-environments, the first of which included Gorgan and Mughan, and the second mega-environment included Sari and Karaj. Based on the polygon view of the biplot, the genotype G1 in Sari and Karaj environments, and the genotypes G21 and G22 in Gorgan and Moghan environments were superior genotypes with high specific adaptation. The results of the average environment coordinate biplot showed that the G1, G22, G5, and G9 genotypes produced the highest seed yield, respectively. On the other hand, the G28, G25, G16, and G19 genotypes produced the lowest seed yield, respectively. Based on the hypothetical ideal genotype biplot, the G22, G5, G16, G12, G14, and G9 genotypes were better than the other genotypes for seed yield and stability and showed high general adaptation to all environments. Moreover, the Karaj and Moghan environments were the nearest environments to the ideal environment with the highest discriminating ability and representativeness. Therefore, the Karaj and Moghan environments can be used as a suitable test location for selecting superior soybean genotypes. Conclusion: Based on the results of this study, the G22, G5, G16, G12, G14, and G9 genotypes are superior for seed yield and stability in this study. Therefore, these hybrids can be used for further testing, including adaptation tests. Besides, the results show that the Karaj and Moghan environments can be used as suitable test locations for selecting superior soybean genotypes. Generally, our results demonstrate the efficiency of the GGE-biplot graphical method to investigate the G × E interaction effect and provide good information about the studied genotypes and environments.