Selection of Superior Rapeseed Genotypes based on Fatty Acids and Grain and Oil Yield Components with the Ideal Genotype Selection Index (SIIG) Method - Hassan Amiri Oghan; Kamal Payghamzadeh; Farnaz Shariati; Amir Gholizadeh (پژوهشنامه اصلاح گیاهان زراعی)
Extended Abstract Background: The sustainable development of rapeseed cultivation areas, especially in Iran, requires the introduction of new cultivars with higher grain and oil yields and compatibility with different regions through breeding programs. The genetic diversity of rapeseed genotypes should be evaluated based on a set of quantitative and qualitative traits. Evaluation of genotypes using a set of traits increases the probability of finding ideal genotypes. The ideal genotype selection index is one of the multivariate statistical methods that identifies the desired genotypes based on a set of different traits or indices. Besides, factor analysis is another multivariate statistical method that is used to categorize traits, determine the importance and relevance of each of them in creating changes in the total data, and identify traits that affect yield. Identifying traits that affect yield enables the breeder to focus on specific traits that have caused variation. Accordingly, the ideal genotype selection index and factor analysis approaches were applied to study the agronomic characteristics and quantitative and qualitative traits of seeds in different canola lines and finally select the superior genotypes from the viewpoint of high seed and oil yield along with the highest amount of essential fatty acids. Methods: In this study, 21 genotypes obtained via breeding programs were evaluated in a randomized complete block design with three replications in the Gorgan Agricultural Research Station. Various 23 quantitative and qualitative traits, including phenological traits [the number of days to the beginning of flowering, the number of days to physiological maturity], agronomical traits [plant height (cm), the number of lateral branches, branching height (cm), main stem length (cm), pod length (cm)], and yield and its components [the number of pods per main stem, the number of pods per lateral branches, the number of pods per plant, the number of grain per pod, thousand-grain weight (g), grain yield (kg ha-1)], as well as qualitative traits [oil content (%), oil yield (kg ha-1), the amount of glucosinolate in the grain (micromol/g of grain), and the percentage of fatty acid composition (orosic acid, linolenic acid, linoleic acid, oleic acid, stearic acid, palmitoleic acid, and palmitic acid) were determined during the growth season. The analysis of variance (ANOVA) was applied to examine differences between genotypes, the factor analysis was exploited for indirect selection for grain yield through other dependent traits as well as the ideal genotype selection index was used for the two important traits including grain yield and oil yield based on abovementioned 22 traits. Results: The results of ANOVA showed that the genotypes were statistically different (P < 0.01) in all the studied traits, except for the number of lateral branches and the number of grains per pod, which indicates the existence of genetic diversity between the studied genotypes. The results of the ideal genotype selection index depicted that the genotypes G20, G12, G16, G1, G7, G10, and G11 with the ideal genotype selection indexes of 0.621, 0.584, 0.673, 0.633, 0.591, 0.728, and 0.673 and grain yields of 3258.67, 3140.67, 2941.33, 2763.33, 2712.67, 2575.33, and 2548 kg ha-1, respectively, were identified as genotypes with high grain yield potential and other desirable agronomic traits. Furthermore, the genotypes G20, G12, G16, G2, G1, G10, and G11 with the ideal genotype selection indexes of 0.622, 0.584, 0.673, 0.589, 0.633, 0.727, and 0.672 and oil yields of 1218.28, 1201.42, 1109.54, 1102.27, 1056.45, 987.40, and 961.27 kg ha-1, respectively, were identified as genotypes with high oil yield potential and other desirable agronomical traits. Hence, these genotypes can be used in compatibility test trials. In this study, the 23 measured traits were applied for factor analysis. The obtained Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy values and the significance of Bartlett's sphericity test indicated the adequacy of the correlation values of the primary variables for factor analysis and the adequacy of the factor analysis model. In this research, seven factors were identified based on factor analysis. These factors explained 82.13% of the total data variation. The values of the first to seventh factors were estimated at 20.86, 15.99, 13.99, 10.65, 8.80, 6.27, and 5.57%, respectively. The first to seventh factors are recognized as factors affecting oil quality, morphology and appearance, vegetative attributes, physiological sinks, economic grain yield, and oil quantity and quality as well as phenology and ripening characteristics. In addition, the results of factor analysis showed that the number of pods per main stem, the number of pods per lateral branch, and the number of pods per plant were the traits with a positive relationship with grain yield and grain yield with oil yield. Conclusion: In general, the results of this experiment showed that the ideal genotype selection index and factor analysis approaches were identified as an extremely powerful tool for selecting superior rapeseed genotypes based on the aforementioned quantitative and qualitative traits. Based on the ideal genotype selection index, G20 and G12 genotypes were among the excellent genotypes in terms of grain and oil yields with higher ideal genotype selection indexes. In addition, the number of pods per main stem, the number of pods per lateral branch, and the number of pods per plant are the traits that can be used as an ideal selection index for the selection of grain yield and grain yield for the selection of oil yield to select high-potential genotypes in breeding programs.