Volume 13, Issue 40 (12-2021)
jcb 2021, 13(40): 74-82 |
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Masoudi B, Hezarjaribi E, andarkhor S A, Faraji A, Kia S. (2021). Yield Evaluation of Soybean Genotypes across Diverse Environments by Statistical Parameters and GGE biplot. jcb. 13(40), 74-82. doi:10.52547/jcb.13.40.74
URL: http://jcb.sanru.ac.ir/article-1-1278-en.html
URL: http://jcb.sanru.ac.ir/article-1-1278-en.html
Abstract: (1618 Views)
Extended Abstract
Introduction and Objective: The compatibility of soybean genotypes in different climates depends on environmental factors, especially the photoperiod. Genotype and genotype by environment (G × E) interaction refers to the difference in the response of genotypes to different environment. Assessing the interaction of genotype × environment improve breeding efficiency programs for wide adaptation and for high seed yield stability.
Materials and Methods: 23 soybean genotypes with two checks (Saba and Amir) were evaluated in a completely randomized block design with three replications in three regions (Gorgan, Sari and Moghan( during 2018-2019. Analysis of variance of seed yield was performed separately for each region and combined analysis for all regions. Univariate linear regression, deviation of regression coefficient, Shukla’s stability variance and Wricke’s ecovalence were used to evaluate the seed yield stability of these genotypes. GGE biplot analysis was used to understand the interaction between genotype and the environment.
Results: The partitioning of (environment + genotype × environment) mean squares showed that environments differed significantly and were quite diverse with regards to their effects on the performance of genotypes. Genotype × environment interaction was also analyzed by GGE biplot. Based on stability parameters and GGE biplot analysis, G18 had higher seed yield, lower Wricke’s ecovalence and Shukla’s stability variance values. Also, the regression coefficient was equal to one and deviation from regression was minimal in G18. Genotypes G16 and G23 were subsequent stable and high yielding. These results were also confirmed by GGE biplot analysis. The simultaneous studying of the effects of genotype and genotype × environment interactions by average tester coordinate (AEC) view of biplot illustrated that G22, G23, G17 are more stable to the tested environments than the other genotypes. G18 and G16 and Amir cultivar are considered as moderate stability. The vector view of GGE biplot indicated Environments were separated into two group groups and genotypes into three groups. G2, G15 and G18 genotypes had the highest seed yield in the first environmental group (Moghan and Gorgan) and G1 genotype had the highest seed yield in Sari region. G9, G13, G10, and G8 genotypes had the lowest average yield in all environments
Conclusion: Genotype G18 (Charleston×Sari) with 3421 Kg.ha-1 seed yield was desirable and stable across the environments and could be suggested in future breeding programs.
Introduction and Objective: The compatibility of soybean genotypes in different climates depends on environmental factors, especially the photoperiod. Genotype and genotype by environment (G × E) interaction refers to the difference in the response of genotypes to different environment. Assessing the interaction of genotype × environment improve breeding efficiency programs for wide adaptation and for high seed yield stability.
Materials and Methods: 23 soybean genotypes with two checks (Saba and Amir) were evaluated in a completely randomized block design with three replications in three regions (Gorgan, Sari and Moghan( during 2018-2019. Analysis of variance of seed yield was performed separately for each region and combined analysis for all regions. Univariate linear regression, deviation of regression coefficient, Shukla’s stability variance and Wricke’s ecovalence were used to evaluate the seed yield stability of these genotypes. GGE biplot analysis was used to understand the interaction between genotype and the environment.
Results: The partitioning of (environment + genotype × environment) mean squares showed that environments differed significantly and were quite diverse with regards to their effects on the performance of genotypes. Genotype × environment interaction was also analyzed by GGE biplot. Based on stability parameters and GGE biplot analysis, G18 had higher seed yield, lower Wricke’s ecovalence and Shukla’s stability variance values. Also, the regression coefficient was equal to one and deviation from regression was minimal in G18. Genotypes G16 and G23 were subsequent stable and high yielding. These results were also confirmed by GGE biplot analysis. The simultaneous studying of the effects of genotype and genotype × environment interactions by average tester coordinate (AEC) view of biplot illustrated that G22, G23, G17 are more stable to the tested environments than the other genotypes. G18 and G16 and Amir cultivar are considered as moderate stability. The vector view of GGE biplot indicated Environments were separated into two group groups and genotypes into three groups. G2, G15 and G18 genotypes had the highest seed yield in the first environmental group (Moghan and Gorgan) and G1 genotype had the highest seed yield in Sari region. G9, G13, G10, and G8 genotypes had the lowest average yield in all environments
Conclusion: Genotype G18 (Charleston×Sari) with 3421 Kg.ha-1 seed yield was desirable and stable across the environments and could be suggested in future breeding programs.
Type of Study: Research |
Subject:
General
Received: 2021/07/17 | Revised: 2022/01/15 | Accepted: 2021/08/23 | Published: 2022/01/16
Received: 2021/07/17 | Revised: 2022/01/15 | Accepted: 2021/08/23 | Published: 2022/01/16
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