A main part of vegetable oil in Iran is imported to the country, and so cultivation and breeding of oil seeds to increase yield is necessary. Due to the adaptation of sunflower to different environmental conditions, its cultivation area and production has been the center of attention in Iran. Sunflower is a relatively new crop in Iran and the improvement of oil yield is very important in breeding of this crop. The lack of genotypes which consistently perform well across diverse growing environments is as the main problem of oil yield improvement programs in sunflower. Therefore, the development of new high-yielding hybrids with a low G×E interaction effect is one of the most important breeding objectives of sunflower. The G×E interaction effect reduces the relation between phenotypic and genotypic values and confuses the plant breeders to the selection of new improved genotypes (Ebdon and Gauch, 2002; Dia et al., 2016). There are two strategies to reducing the G×E interaction effects: (1) the sub-division of different environments into smaller, relatively homogeneous environments to develop genotypes for specific environments, and (2) identifying genotypes with high stability across a diverse range of environments (Eberhart and Russell, 1966; Tai, 1971). Multi-environment trials (MET) play a main role in selecting the best genotypes for diverse environments and even specific genotypes for specific environmental conditions.
In this study, we evaluated the stability and oil yield of 12 new hybrids along with four cultivars under the different environmental conditions in Iran. The combined ANOVA indicated that oil yield was significantly affected by genotype, environment and G×E interaction. Based on the results, environment was the major source of variability (51.94% of TSS). The environment is the sum of all external conditions affecting genotype development and growth. The test locations cover a relatively wide range of land and soil types, pH, depth, fertility, organic matter, insects, diseases, growing seasons and climatic conditions. Hence, a large variation explained by environments is due to large differences among environmental means causing most of the variation in oil yield. Previous reports in Iran also showed that the environment was the major source of the total variation in sunflower genotypes under different environmental conditions (Ullah et al., 2007; Jockovic et al., 2019). The magnitude of sum of squares for G×E interaction was higher than genotypic effect suggesting considerable differences in genotypic response of sunflower genotypes across the test environments. In other words, the responses of the sunflower genotypes changed depending on the environmental conditions. Therefore, we can estimate the adaptation patterns and stability of sunflower genotypes. In the present study, the GGE biplot methodology was used to study the stability of sunflower genotypes. This method for selecting stable genotypes was used in different crops by Jamshidmoghaddam and Pourdad (2013) in safflower, Dehghani et al. (2016) in tall fescue and Vaezi et al. (2019) in barley.
When evaluating some genotypes across several locations and years, it is often difficult to identify the pattern of genotypic response across different environments without the help of graphical display of the data. The GGE biplot analysis is a very powerful multivariate analytical method and contains of a set of biplot interpretation models that graphically displays the relationships between genotypes, environments, and G×E interactions (Yan and Kang, 2003). The ‘which-won-where’ pattern polygon view is an important graphical pattern of GGE biplot for recognizing the different mega-environments and to identification of the best genotype in each mega-environment (Yan, 2001). The mega-environments have different high yielding genotypes and it shows crossover G×E interaction. The information on G×E interaction will be useful in grouping the target environment into different mega-environments and deploying different genotypes in different mega-environments (Gauch and Zobel, 1997; Yan et al., 2007). The polygon plot in this study identified two mega-environments with different winning genotypes, that shows there are specific adaptations of a genotype to a mega-environment and positive utilization of the G×E interaction. G8 and G5 were the best specific adapted genotypes for oil yield under Sa19, Sa20, Krj19, Krj20 and Krm19. The second mega-environment consisted Krm20, Dez19 and Dez20 that G3 and G14 were identified as the best specific adapted genotypes for oil yield. Generally, based on the GGE biplot mega-environment analysis, the test environments separated into two groups of different genotypic performances. The reasons for this may be related to the amount of variation accounted for by environments. In this study, the environment contributed 51.94% of the total variation in the data. A similar trend was reported by Jamshidmoghaddam and Pourdad (2013) in safflower (Carthamus tinctorius L.), Hassani et al. (2018) in sugar beet (Beta vulgaris L.) and Vaezi et al. (2019) in barley (Hordeum vulgare).
According to described recommendation by Yan (2001), an ideal environment should have the most discriminating and representative abilities. In other world, an ideal test environment should be most discriminating of the genotypes and representative of all environments (Yan and Kang, 2002). Environments that have the most discriminating and representative abilities are an important in breeding programs. These types of environments can assist breeders in assessing new varieties for their full yield potential as well as be a suitable place for growing seed increase plots in preparation for the release of a variety. In this study, the representativeness versus discriminating power view of the GGE biplot indicated that the location Dezful is a Type I environment. A Type I environment, or a redundant environment should be removed to reduce the costs of field testing. The location of Sari had a high discriminating power and representativeness. Therefore, this location can be used as suitable and ideal test location (Type II environment) for selecting superior genotypes of winter sunflower in Iran. It is possible that fewer but better test locations can provide equally or more informative data for cultivar evaluation (Yan and Kang, 2003). GGE biplot analysis was also applied to identifying the interrelationships between environments. The correlation between the environments was determined by the angle between the environmental vectors. In this study, the correlations among Sari and Karaj locations were high and positively significant, suggesting that these locations are very similar. Therefore, we suggest that one of the two locations be dropped to reduce the cost of testing increase breeding efficiency in multi-environment trials (MET) of sunflower.
In this study we used the Mean versus Stability view of the GGE biplot to identification of ideal genotype(s). Based on Yan and Kang’s theory, an ideal genotype should have both high mean yield and high stability across test environments (Yan and Kang, 2002). Genotypes G3 and G5 with high mean yield and high stability were identified as the ideal genotypes than other genotypes in this study. The GGE biplot method is preferred compared with the other methods to evaluate genotype, environment and their interactions. The GGE biplot method has some advantages compared with the other methods of data analysis in multi-environment trials (MET). The first advantage of this method is graphical display of data, which largely enhances our ability to understand the patterns of the data. The second is that it is the best method for recognizing the different mega-environments and to identification of the specific adaptations of a genotype to a mega-environment. The third advantage of this method is that it is an excellent tool for visual evaluation of ideal genotypes and environments, and also to studying relationship among environments. The GGE biplot method for evaluation of genotypes, environments and their interactions was used in different crops by Rakshit et al. (2012) in sorghum, Dehghani et al. (2016) in tall fescue, Omoigui et al. (2017) in cowpea, Hassani et al. (2018) in sugar beet, Sserumaga et al. (2018) in maize, Vaezi et al. (2019) in barley, Gerrish et al. (2019) in winter wheat and Dallo´ et al. (2019) in soybean.