Volume 15, Issue 45 (5-2023)
J Crop Breed 2023, 15(45): 234-242 |
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ghorbanipour A, rabiei B, rahmanpour S, khodaparast S A. (2023). Evaluation of Soybean Genotypes Yield and Yield Stability Under Charcoal Rot Disease Conditions using GGE Biplot Method. J Crop Breed. 15(45), 234-242. doi:10.61186/jcb.15.45.234
URL: http://jcb.sanru.ac.ir/article-1-1386-en.html
URL: http://jcb.sanru.ac.ir/article-1-1386-en.html
1- Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran
2- Department of oilseed plants, Seed and Plant Improvement Institute (SPII), Karaj, Iran
2- Department of oilseed plants, Seed and Plant Improvement Institute (SPII), Karaj, Iran
Abstract: (1712 Views)
Extended Abstract
Introduction and Objective: The most important goal in all crop breeding programs is to increase yield, and yield improvement requires the use of efficient statistical methods to identify superior genotypes. In determining the superiority of genotype, in addition to high yield, yield stability in different environments must also be considered.Biplot analyses are good tools for selecting superior genotypes and to increase efficiency in selection.
Material and Methods: In the present study GGE biplot method was used for assessment yield and yield stability of 130 genotypes of soybean under two environmental conditions, natural conditions and disease stress (artificial induction of charcoal rot disease), were evaluated in a simple lattice design with two replications at Seed and Plant Improvement Research Institute (SPII), Karaj, Alborz province, Iran, during 2014 and 2015 (four environments).
Results: The results of combined analysis of grain yield/plant revealed that effects of location, genotype and interaction of genotype × location were significant. The results of stability analysis using GGE-biplot method revealed that the first (Genotype) and second (genotype × environment interaction) components explained 70% and 14%, respectively, and the both components 84% of the total variation, which indicates a good validity of the biplot in explaining the variations of genotypes and genotype × environment interaction (G + GE). Polygonal biplot showed that the genotype 66 had the highest grain yield in environment E2 (disease conditions in 2014) and E4 (disease conditions in 2015), however, the genotypes 1, 3, 5, 43, 63, 66, 75, 76, 77 and 89 had a good combination of stability and yield.
Conclusion: Some of these genotypes such as genotype 66 did not show any signs of charcoal rot in both experimental years, they also had a good grain yield.
Introduction and Objective: The most important goal in all crop breeding programs is to increase yield, and yield improvement requires the use of efficient statistical methods to identify superior genotypes. In determining the superiority of genotype, in addition to high yield, yield stability in different environments must also be considered.Biplot analyses are good tools for selecting superior genotypes and to increase efficiency in selection.
Material and Methods: In the present study GGE biplot method was used for assessment yield and yield stability of 130 genotypes of soybean under two environmental conditions, natural conditions and disease stress (artificial induction of charcoal rot disease), were evaluated in a simple lattice design with two replications at Seed and Plant Improvement Research Institute (SPII), Karaj, Alborz province, Iran, during 2014 and 2015 (four environments).
Results: The results of combined analysis of grain yield/plant revealed that effects of location, genotype and interaction of genotype × location were significant. The results of stability analysis using GGE-biplot method revealed that the first (Genotype) and second (genotype × environment interaction) components explained 70% and 14%, respectively, and the both components 84% of the total variation, which indicates a good validity of the biplot in explaining the variations of genotypes and genotype × environment interaction (G + GE). Polygonal biplot showed that the genotype 66 had the highest grain yield in environment E2 (disease conditions in 2014) and E4 (disease conditions in 2015), however, the genotypes 1, 3, 5, 43, 63, 66, 75, 76, 77 and 89 had a good combination of stability and yield.
Conclusion: Some of these genotypes such as genotype 66 did not show any signs of charcoal rot in both experimental years, they also had a good grain yield.
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