| چکیده انگلیسی مقاله |
Extended Abstract
Introduction and Objective:
The sustainable development of rapeseed cultivation area, especially in Iran, requires the introduction of new cultivars with higher grain and oil yields and compatible for different regions through breeding programs. Evaluation of the genetic diversity of rapeseed genotypes should be done 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 identifying traits that affect performance. Identifying traits that affect performance gives the breeder the ability to focus on specific traits that have caused variation. accordingly, in order to study the agronomic characteristics and quantitative and qualitative traits of seeds in different canola lines and finally select the superior genotypes from the point of view of high seed and oil yield along with the highest amount of essential fatty acids, the ideal genotype selection index and factor analysis approaches were applied.
Material and Methods: 21 genotypes which were obtained via breeding programs were evaluated in a randomized complete block design with three replications in Gorgan Agricultural Research Station. During the growth season, various 23 quantitative and qualitative traits including phenological traits [number of days to beginning of flowering, the number of days to physiological maturity]; agronomical traits [plant height (cm), number of lateral branches, branching height (cm), main stem length (cm), pod length (cm)]; yield and its components [number of pods per main stem, the number of pod per lateral branches, the number of pod per plant, the number of grain per pod, thousand grain weight (g), grain yield (kg ha-1)] and qualitative traits [oil content (%), oil yield (kg ha-1), the amount of glucosinolatein the grain (micromol/g of grain), the percentage of fatty acids composition (orosic acid, linolenic acid, linoleic acid, oleic acid, stearic acid, palmitoleic acid, palmitic acid) were determined. The analysis of variance 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 the analysis of variance showed that the genotypes had statistically significant differences (P<0.01) in all the studied traits except the number of lateral branches and the number of grain 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 haveing the ideal genotype selection index of 0.621, 0.584, 0.673, 0.633, 0.591, 0.728 and 0.673 and grain yield 3258.67, 3140.67, 2941.33, 2763.33, 2712.67, 2575.33 and 2548 kg ha-1 respectively they were identified as genotypes with high grain yield potential and other desirable agronomical traits. Furthermore, the genotypes G20, G12, G16, G2, G1, G10 and G11 with haveing the ideal genotype selection index of 0.622, 0.584, 0.673, 0.589, 0.633, 0.727 and 0.672 and oil yield 1218.28, 1201.42, 1109.54, 1102.27, 1056.45, 987.40 and 961.27 kgha-1 respectively they were identified as genotypes with high oil yield potential and other desirable agronomical traits in which these genotypes can be used in compatibility tests trials. In this study, the 23 measured traits were applied for factor analysis. The obtained KMO values as well as 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, based on factor analysis the seven factors were identified. These factors explained 82.13% of the total data variation. The value of the first to seventh factors was estimated as 20.86, 15.99, 13.99, 10.65, 8.80, 6.27 and 5.57 percent, respectively. The first to seventh factors are recognized as factors affecting on oil quality, morphology and appearance, vegetative attributes and physiological sinks, economic grain yield, oil quantity and quality as well as phenology and ripening characteristics. In addition, the results of factor analysis showed that the traits of the number of pods per main stem, the number of per lateral branches and the number of pods per plant had 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 identtified as an extremely powerful tools for selecting the superior rapeseed genotypes based on 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 yield with higher ideal genotype selection index. In addition, the traits of the number of pods per main stem, the number of pods per lateral branches and the number of pods per plant can be used as a 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.
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