| چکیده انگلیسی مقاله |
Extended Abstract
Introduction and objective: Oilseeds are one of the most important sources of energy all over the world. Soybean is an important crop that its oil has nutritional and high economic value. Soybean (Glycine max L.) is an annual, self-pollinating, diploid plant belonging to the Leguminosae pea family and is one of the most important oil plants in the world. Soy 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. In Iran, soybeans are known as (oily beans), (Chinese beans), soja, and soybeans. By using new and high-yield varieties, the economic performance of this product can be increased. Evaluating of promising advanced lines of soybean under different environmental conditions is essential in identifying and selecting superior lines with high and stable yield potential. Genotype × environment interaction effect 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 also it complicates the interpretation of genetic experiments and makes predictions difficult. Therefore, it is very important 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 method with suitable efficiency to investigate the interaction effect of genotype × environment and provides good information about the studied genotypes and environments graphically. The purpose of this study was to investigate the interaction effect of genotype × environment using the GGE-biplot graphic method in order to evaluate genotypes, environments, relationships between genotypes and environments and finally to identify stable genotypes with high grain yield under different environmental conditions in soybean.
Material and Methods: 27 new soybean line 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 2022 cropping season. GGE biplot statistical method (genotype effect + genotype × environment interaction) was used to study 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: Results of combined analysis of variance indicated that the effects of environments (E), genotypes (G) and genotype × environment (G×E) interaction were significant for seed yield. Results of genotype × environment interaction analysis using 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, which indicates a high validity of the biplot in explaining the variations of genotypes and genotype × environment interaction (G+GE). In this study, two mega-environments were identified, the first mega-environment included Gorgan and Mughan. The second mega-environment also included the Mazandaran and Karaj. Based on the polygon view of biplot, the line G1 in Mazandaran and Karaj environments, and the lines G21 and G22 in Gorgan and Moghan environments were superior genotypes with the high specific adaptation. The results of the average environment coordinate biplot showed that the genotypes G1, G22, G5, G9, G16, G12, G14, G21, G7, G3, G17, G2, G11, G15, G26, G20 and G13 had the highest seed yield, respectively. On the other hand, the genotypes G28, G25, G16, G19, G4, G18, G27, G24, G8, G6, G23 and G29 had the lowest seed yield, respectively. Based on the hypothetical ideal genotype biplot, the lines G22, G5, G16, G12, G14 and G9 were better than the other lines for seed yield and stability and had the high general adaptation to all environments. Too, the results showed that the Karaj and Moghan environments were the nearest environments to ideal environment that had the highest discriminating ability and representativeness. Therefore, the Karaj and Moghan environments can be used as suitable test location for selecting superior lines of soybean.
Conclusion: Based on the results of the GGE-biplot graphical method, the lines G22, G5, G16, G12, G14 and G9 were better than the other lines for seed yield and stability and had the high general adaptation to all environments. Therefore, these hybrids can be used for further testing, including adaptation tests. Too, the results showed that the Karaj and Moghan environments can be used as suitable test location for selecting superior lines of soybean. Generally, our results showed the efficiency of the graphical method of the GGE-biplot to investigate the G×E interaction effect and provides good information about the studied genotypes and environments.
Keywords: Stability, Ideal genotype, Soybean, Seed yield.
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