Volume 16, Issue 3 (9-2024)
J Crop Breed 2024, 16(3): 13-24 |
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Esmaeilzadeh Moghadam M, Dastfal M, Tabib Ghaffary S M, Anderzian S B A, Sayyahfar M, Miri K, et al . (2024). Stability Analysis of Bread Wheat (Triticum aestivum L.) Genotypes by the Genotype × Genotype-Environment Biplot. J Crop Breed. 16(3), 13-24. doi:10.61186/jcb.16.3.13
URL: http://jcb.sanru.ac.ir/article-1-1475-en.html
URL: http://jcb.sanru.ac.ir/article-1-1475-en.html
Mohsen Esmaeilzadeh Moghadam1, Manoochehr Dastfal2, Seyed Mahmoud Tabib Ghaffary3, Seyyed Bahram Anderzian4 Anderzian4, Manoochehr Sayyahfar5, Khaled Miri6, Shirali Koohkan7, Alireza Askari Kalestani * 
2
2
1- Seed and Plant Improvement Department, Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran
2- Crop and Horticultural Science Research Department, Fars Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Darab, Iran
3- Crop and Horticultural Science Research Department, Safiabad Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Dezful, Iran
4- Agricultural Research, Education and Extension Organization (AREEO)
5- Crop and Horticultural Science Research Department, Lorestan Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Khorramabad, Iran
6- Baluchistan Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Iranshahr, Iran
7- Crop and Horticultural Science Research Department, Sistan Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Zabol, Iran
2- Crop and Horticultural Science Research Department, Fars Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Darab, Iran
3- Crop and Horticultural Science Research Department, Safiabad Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Dezful, Iran
4- Agricultural Research, Education and Extension Organization (AREEO)
5- Crop and Horticultural Science Research Department, Lorestan Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Khorramabad, Iran
6- Baluchistan Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Iranshahr, Iran
7- Crop and Horticultural Science Research Department, Sistan Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Zabol, Iran
Abstract: (426 Views)
Extended Abstract
Background: The Production of high-yielding and stable cultivars is the most important objective of crop breeding programs, including wheat. Wheat is one of the key crops cultivated in Iran. The final yield of each plant is determined by the genotype potential, the environmental effect, and the interaction effect of genotype × environment. Studies on genotype × environment interactions can help determine whether a genotype is stable in performance across a wide range of environments. Various methods (univariate and multivariate 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 results of different methods may not be the same, but the best result is obtained when a genotype with different evaluation methods shows similar results in terms of stability. Univariate methods do not provide a complete view of the complex and multidimensional nature of genotype × environment interaction, therefore, the use of multivariate methods is suggested to solve this problem. Among the multivariate methods, genotype × genotype-environment (GGE) biplot methods are more important. Therefore, this study aimed to identify promising and stable top-performing lines of bread wheat for warm and dry climates using the GGE biplot method.
Methods: The adaptability and stability of 37 promising bread wheat lines were evaluated in 10 environments, along with three checks (Chamran2, Sarang, and Mehregan). The experiment was conducted using a randomized complete block design with three replications in two cropping seasons (2020-2021 and 2021-2022) at five research stations (Darab, Ahvaz, Dezful, Khorramabad, and Zabol). In the field, each plot was planted with a density of 450 seeds/m2. Each line was planted in plots with six four-meter lines with 20 cm line spacing. At the end of the growing season, six rows of five-meter spikes from each plot were harvested and threshed by a Wintersteiger combine. The weight of the obtained grains was measured by a digital scale and reported in hectares.
Grain yield was determined using combined analyses of variance. The GGE biplot statistical method (genotype effect + genotype × environment interaction) was used to study the stability of genotypes in the studied environments. SPSSv22 software was used to analyze the experimental data using the analysis of the combined experiment. The data were analyzed with GGE-Biplot software using the GGE biplot graphic method.
Results: The Smirnov-Kolmogorov test was conducted to examine residual errors in each environment. The results for each environment separately showed that the residual data were normal in all environments. Bartlett's test results for the environments indicated the homogeneity of error variances, allowing for a combined analysis of variance, which showed the significant main effects of the environment, genotype, and genotype × environment interaction for grain yield. The significance of the interaction effects of genotypes in this study showed that the genotypes responded differently in different environments; in other words, the difference between genotypes is not the same from one environment to another, and the stability of grain yield can be evaluated in these conditions. The environment, genotype, and genotype × environment interaction effects accounted for 70.12%, 1.24% and 9.57% of the total variation, respectively. The results showed that the three PCAs explained 54% of the total agronomical variability residing in the tested wheat genotypes. The first two PCAs accounted for 29% and 25% of the total variation, respectively. The GGE biplot analysis revealed four mega-environments and five superior genotypes. The polygonal diagram obtained from the analysis showed that the genotypes GT biplot arising G31،G21 ،G29, G27, and G32, which were located at the vertices of the polygon, were the superior genotypes. The average environmental coordinate of the GGE biplot analysis showed that genotypes G29, G28, and G16 had high grain yield and stability. The biplot of the correlation among environments revealed that the environmental vectors of Ahwaz and Zabol were near 90◦, thus these locations were different environments. Based on the results, the environment of Zabol can be introduced as a favorable environment for selecting the best bread wheat genotypes.
Conclusion: Given the climate change in Iran, particularly in the hot and dry regions of the south, there is always a pressing need for using sustainable varieties with high performance. This study has clearly and easily aided in the identification of stable and superior genotypes graphically. Wheat breeders worldwide consider breeding varieties specifically adapted to different geographical and climatic agricultural regions. The general adaptability of varieties to several regions was identified in this study, indicating that the Zabol environment could be introduced as a suitable environment for selecting superior genotypes of bread wheat. Finally, it is recommended to select genotypes G29, G28, and G16 for further testing and promotion after seed multiplication and selection under farm conditions, eventually introducing them as new wheat varieties. The results obtained in this study demonstrate the efficiency of the GGE biplot technique for selecting high-yielding and stable varieties.
Background: The Production of high-yielding and stable cultivars is the most important objective of crop breeding programs, including wheat. Wheat is one of the key crops cultivated in Iran. The final yield of each plant is determined by the genotype potential, the environmental effect, and the interaction effect of genotype × environment. Studies on genotype × environment interactions can help determine whether a genotype is stable in performance across a wide range of environments. Various methods (univariate and multivariate 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 results of different methods may not be the same, but the best result is obtained when a genotype with different evaluation methods shows similar results in terms of stability. Univariate methods do not provide a complete view of the complex and multidimensional nature of genotype × environment interaction, therefore, the use of multivariate methods is suggested to solve this problem. Among the multivariate methods, genotype × genotype-environment (GGE) biplot methods are more important. Therefore, this study aimed to identify promising and stable top-performing lines of bread wheat for warm and dry climates using the GGE biplot method.
Methods: The adaptability and stability of 37 promising bread wheat lines were evaluated in 10 environments, along with three checks (Chamran2, Sarang, and Mehregan). The experiment was conducted using a randomized complete block design with three replications in two cropping seasons (2020-2021 and 2021-2022) at five research stations (Darab, Ahvaz, Dezful, Khorramabad, and Zabol). In the field, each plot was planted with a density of 450 seeds/m2. Each line was planted in plots with six four-meter lines with 20 cm line spacing. At the end of the growing season, six rows of five-meter spikes from each plot were harvested and threshed by a Wintersteiger combine. The weight of the obtained grains was measured by a digital scale and reported in hectares.
Grain yield was determined using combined analyses of variance. The GGE biplot statistical method (genotype effect + genotype × environment interaction) was used to study the stability of genotypes in the studied environments. SPSSv22 software was used to analyze the experimental data using the analysis of the combined experiment. The data were analyzed with GGE-Biplot software using the GGE biplot graphic method.
Results: The Smirnov-Kolmogorov test was conducted to examine residual errors in each environment. The results for each environment separately showed that the residual data were normal in all environments. Bartlett's test results for the environments indicated the homogeneity of error variances, allowing for a combined analysis of variance, which showed the significant main effects of the environment, genotype, and genotype × environment interaction for grain yield. The significance of the interaction effects of genotypes in this study showed that the genotypes responded differently in different environments; in other words, the difference between genotypes is not the same from one environment to another, and the stability of grain yield can be evaluated in these conditions. The environment, genotype, and genotype × environment interaction effects accounted for 70.12%, 1.24% and 9.57% of the total variation, respectively. The results showed that the three PCAs explained 54% of the total agronomical variability residing in the tested wheat genotypes. The first two PCAs accounted for 29% and 25% of the total variation, respectively. The GGE biplot analysis revealed four mega-environments and five superior genotypes. The polygonal diagram obtained from the analysis showed that the genotypes GT biplot arising G31،G21 ،G29, G27, and G32, which were located at the vertices of the polygon, were the superior genotypes. The average environmental coordinate of the GGE biplot analysis showed that genotypes G29, G28, and G16 had high grain yield and stability. The biplot of the correlation among environments revealed that the environmental vectors of Ahwaz and Zabol were near 90◦, thus these locations were different environments. Based on the results, the environment of Zabol can be introduced as a favorable environment for selecting the best bread wheat genotypes.
Conclusion: Given the climate change in Iran, particularly in the hot and dry regions of the south, there is always a pressing need for using sustainable varieties with high performance. This study has clearly and easily aided in the identification of stable and superior genotypes graphically. Wheat breeders worldwide consider breeding varieties specifically adapted to different geographical and climatic agricultural regions. The general adaptability of varieties to several regions was identified in this study, indicating that the Zabol environment could be introduced as a suitable environment for selecting superior genotypes of bread wheat. Finally, it is recommended to select genotypes G29, G28, and G16 for further testing and promotion after seed multiplication and selection under farm conditions, eventually introducing them as new wheat varieties. The results obtained in this study demonstrate the efficiency of the GGE biplot technique for selecting high-yielding and stable varieties.
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