Volume 17, Issue 1 (3-2026)
J Crop Breed 2026, 17(1): 88-103 |
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Asadi A A, Amini A, Rezaei Murad Alaa M, Mahmoodi Pirahani A A, Babaei T, Ghadiri A. (2026). The Genotype × Environment Interaction Effect in Water Deficit in Promising Bread Wheat Genotypes Using the Selection Index Ideal Genotype (SIIG). J Crop Breed. 17(1), 88-103. doi:10.61186/jcb.17.1.88
URL: http://jcb.sanru.ac.ir/article-1-1544-en.html
URL: http://jcb.sanru.ac.ir/article-1-1544-en.html
Ali Akbar Asadi *1 
, Ashkboos Amini2, Mohammad Rezaei Murad Alaa3, Ali Akbar Mahmoodi Pirahani4, Taghi Babaei5, Adel Ghadiri5
, Ashkboos Amini2, Mohammad Rezaei Murad Alaa3, Ali Akbar Mahmoodi Pirahani4, Taghi Babaei5, Adel Ghadiri5
1- Department of Seed and Plant Improvement Research, Zanjan Agricultural and Natural Resources Research and Education Center, AREEO, Zanjan, Iran
2- Institute of Seed and Plant Improvement, Agricultural Research, Education and Extension Organization, AREEO, Karaj, Iran
3- Department of Seed and Plant Improvement Research, West Azerbaijan Agricultural and Natural Resources Research and Education Center, AREEO, Urmia, Iran
4- Department of Seed and Plant Improvement, Khorasan Razavi Agricultural and Natural Resources Research and Education Center, AREEO, Mashhad, Iran
5- Department of Seed and Plant Improvement, Markazi Agricultural and Natural Resources Research and Education Center, AREEO, Arak, Iran
2- Institute of Seed and Plant Improvement, Agricultural Research, Education and Extension Organization, AREEO, Karaj, Iran
3- Department of Seed and Plant Improvement Research, West Azerbaijan Agricultural and Natural Resources Research and Education Center, AREEO, Urmia, Iran
4- Department of Seed and Plant Improvement, Khorasan Razavi Agricultural and Natural Resources Research and Education Center, AREEO, Mashhad, Iran
5- Department of Seed and Plant Improvement, Markazi Agricultural and Natural Resources Research and Education Center, AREEO, Arak, Iran
Abstract: (348 Views)
Extended Abstract
Background: Considering that a major part of Iran is part of arid and semi-arid regions, obtaining stable genotypes with good yield stability is one of the ways to deal with drought stress. Due to the genotype × environment interaction effect, however, it is difficult to identify cultivars and genotypes that have good stability and acceptable yields in various environmental conditions. Many methods are known to determine the genotype × environment interaction effect to identify stable cultivars, which are divided into two univariate (parametric and non-parametric) and multivariate groups. Each of these methods shows different aspects of the stability of genotypes, and one method alone cannot investigate the yield of a genotype in different environments from different aspects of stability. This research aimed to select promising bread wheat genotypes with high yields and suitable stability in the water deficit conditions in the cold climate of Iran using various univariate and multivariate stability analysis methods.
Methods: Fourteen wheat genotypes along with Mihan, Heydari, Zarineh, and Zare cultivars (18 genotypes) were investigated under water deficit conditions in a randomized complete block design (RCBD) with three replications in the research stations of Karaj, Mashhad, Miandoab, Arak, and Zanjan in crop years 2020-2022. The stability of genotypes was examined using some parametric and non-parametric univariate methods, AMMI multivariate analysis, and AMMI analysis parameters. Moreover, parametric and non-parametric univariate methods and AMMI stability parameters were integrated using the selection index ideal genotype (SIIG).
Results: The location, genotype, year × place, and genotype × year × place interaction effect at 1% and the genotype × place interaction effect were significant at a 5% probability level. The main effect of the environment, the genotype × environment the interaction effect, and the main effect of the genotype explained 43.61%, 22.92%, and 8.03% of the sum of squares of the experiment, respectively. In parametric methods, G17, G5, G13, and G1 genotypes based on the regression coefficient of Finley and Wilkinson, G9, G7, and G1 genotypes based on the variance of deviation from the regression line, G9, G1, G7, G17, and G4 genotypes based on Wrick's equivalence indices and Shokla stability variance, G9, G1, G17, G4, and G7 genotypes based on Plaisted and Peterson's method, G9, G1, G17, and G4 genotypes based on Plaisted's method, and G9, G1, G7, and G4 genotypes based on Kang's total rank method were known as stable genotypes. In non-parametric methods, G9, G15, and G7 genotypes based on Si(1) and Si(2), G9 and G1 genotypes based on Si(3) nd Si(6), G9, G1, and G7genotypes based on NP(1), G3, G9, G17, and G1 genotypes based on NP(2) statistics, and G9 and G1 genotypes based on NP(3) and NP(4) statistics were regarded as stable genotypes. In AMMI analysis, the first and second components showed the largest contribution (57.8%) in explaining the genotype × environment interaction effect according to the significance of the six main components from the first to the sixth. Based on the AMMI1 biplot, G9 and G17 genotypes, and Zanjan1 and Arak2 environments were recognized as the most stable genotypes and environments due to higher than average grain yield and very low value of the first component. Based on the AMMI2 biplot, a specific genotype cannot be introduced as a genotype with high general compatibility due to its lack of proximity to the coordinate origin. However, G9 and G17 genotypes showed somewhat better general compatibility than the others, and they could be recommended because of their higher yields than the average. Genotypes G18, G17, G15, G9, and G16 based on ASV, G9, G1, G7, G4, and G17 genotypes based on WAAS, G9, 33, G1, and G17 genotypes based on SIPC, G9, G15, and G17 genotypes based on ZA, G9, G1, G3, and G7 genotypes based on EV, G9 and G1 genotypes based on ASTB, G17, G18, G15, G9, and G16 genotypes based on ASI, G9, G1, G17, and G7 genotypes based on FA, G9, G3, G1, and G7 genotypes based on DZ, G9, G1, and G7 genotypes based on DA, G9, G17, G18, and G16 genotypes based on MASI, G9, G1, and G4 genotypes based on MASV, and G9, G1, and G7 genotypes based on the AVAMGE index were selected as the most stable genotypes.
Conclusion: Based on the SIIG index in both univariate and multivariate methods, genotypes G9, G1, and G17 have the closest value to one, and these genotypes produced yields above the average; therefore, they were selected as the most stable genotypes. Furthermore, the use of the SIIG index in both univariate and multivariate methods showed somewhat the same results; therefore, it is better to use this general index to summarize all the information obtained from different methods.
Background: Considering that a major part of Iran is part of arid and semi-arid regions, obtaining stable genotypes with good yield stability is one of the ways to deal with drought stress. Due to the genotype × environment interaction effect, however, it is difficult to identify cultivars and genotypes that have good stability and acceptable yields in various environmental conditions. Many methods are known to determine the genotype × environment interaction effect to identify stable cultivars, which are divided into two univariate (parametric and non-parametric) and multivariate groups. Each of these methods shows different aspects of the stability of genotypes, and one method alone cannot investigate the yield of a genotype in different environments from different aspects of stability. This research aimed to select promising bread wheat genotypes with high yields and suitable stability in the water deficit conditions in the cold climate of Iran using various univariate and multivariate stability analysis methods.
Methods: Fourteen wheat genotypes along with Mihan, Heydari, Zarineh, and Zare cultivars (18 genotypes) were investigated under water deficit conditions in a randomized complete block design (RCBD) with three replications in the research stations of Karaj, Mashhad, Miandoab, Arak, and Zanjan in crop years 2020-2022. The stability of genotypes was examined using some parametric and non-parametric univariate methods, AMMI multivariate analysis, and AMMI analysis parameters. Moreover, parametric and non-parametric univariate methods and AMMI stability parameters were integrated using the selection index ideal genotype (SIIG).
Results: The location, genotype, year × place, and genotype × year × place interaction effect at 1% and the genotype × place interaction effect were significant at a 5% probability level. The main effect of the environment, the genotype × environment the interaction effect, and the main effect of the genotype explained 43.61%, 22.92%, and 8.03% of the sum of squares of the experiment, respectively. In parametric methods, G17, G5, G13, and G1 genotypes based on the regression coefficient of Finley and Wilkinson, G9, G7, and G1 genotypes based on the variance of deviation from the regression line, G9, G1, G7, G17, and G4 genotypes based on Wrick's equivalence indices and Shokla stability variance, G9, G1, G17, G4, and G7 genotypes based on Plaisted and Peterson's method, G9, G1, G17, and G4 genotypes based on Plaisted's method, and G9, G1, G7, and G4 genotypes based on Kang's total rank method were known as stable genotypes. In non-parametric methods, G9, G15, and G7 genotypes based on Si(1) and Si(2), G9 and G1 genotypes based on Si(3) nd Si(6), G9, G1, and G7genotypes based on NP(1), G3, G9, G17, and G1 genotypes based on NP(2) statistics, and G9 and G1 genotypes based on NP(3) and NP(4) statistics were regarded as stable genotypes. In AMMI analysis, the first and second components showed the largest contribution (57.8%) in explaining the genotype × environment interaction effect according to the significance of the six main components from the first to the sixth. Based on the AMMI1 biplot, G9 and G17 genotypes, and Zanjan1 and Arak2 environments were recognized as the most stable genotypes and environments due to higher than average grain yield and very low value of the first component. Based on the AMMI2 biplot, a specific genotype cannot be introduced as a genotype with high general compatibility due to its lack of proximity to the coordinate origin. However, G9 and G17 genotypes showed somewhat better general compatibility than the others, and they could be recommended because of their higher yields than the average. Genotypes G18, G17, G15, G9, and G16 based on ASV, G9, G1, G7, G4, and G17 genotypes based on WAAS, G9, 33, G1, and G17 genotypes based on SIPC, G9, G15, and G17 genotypes based on ZA, G9, G1, G3, and G7 genotypes based on EV, G9 and G1 genotypes based on ASTB, G17, G18, G15, G9, and G16 genotypes based on ASI, G9, G1, G17, and G7 genotypes based on FA, G9, G3, G1, and G7 genotypes based on DZ, G9, G1, and G7 genotypes based on DA, G9, G17, G18, and G16 genotypes based on MASI, G9, G1, and G4 genotypes based on MASV, and G9, G1, and G7 genotypes based on the AVAMGE index were selected as the most stable genotypes.
Conclusion: Based on the SIIG index in both univariate and multivariate methods, genotypes G9, G1, and G17 have the closest value to one, and these genotypes produced yields above the average; therefore, they were selected as the most stable genotypes. Furthermore, the use of the SIIG index in both univariate and multivariate methods showed somewhat the same results; therefore, it is better to use this general index to summarize all the information obtained from different methods.
Type of Study: Research |
Subject:
اصلاح نباتات، بیومتری
Received: 2024/04/11 | Accepted: 2024/09/30
Received: 2024/04/11 | Accepted: 2024/09/30
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