Multivariate analysis of agronomic traits in mid-season soybean varieties
Mladenović Drinić, Snežana
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Principal Component Analysis (PCA) is a useful tool for processing multiple data, which are often encountered in breeding practice. This method is suitable for the evaluation of genotypes on the basis of multiple traits and graphical presentation of relationships between traits. This study included 16 soybean mid-season genotypes (maturity group I), originated from different regions of the world, maintained in soybean collection of Maize Research Institute Zemun Polje. Field trials were carried out at two locations, during two years, according to a RCB design with three replications. The genotypes were evaluated in respect to eight major agronomic traits: PH – plant height, NN – node number, PN – pod number, SN – seed number, TSW – 1000 seed weight, SYP – seed yield per plant, PROT – protein content, OIL – oil content. First two PCA axes encompassed a large portion of the variance of standardized data (75,9%). Biplot distinguished genotypes of potential importance for various... breeding targets. Two genotypes stood out with the largest 1000 seed weight. One variety formed a larger number of pods and the seed number per plant as compared to the group average, achieving the highest grain yield per plant. Two genotypes were among the most productive ones, with a larger number of pods as well as a higher 1000 seed weight, compared to the average. The most promising variety was Laura, which had a high yield and higher protein content than the average, and could be used as a potential germplasm source for the simultaneous improvement of both traits. Correlations among traits determined by PC biplot were in accordance with Pearson’s correlation coefficients.
Keywords:soybean / quantitative traits / multivariate analysis / correlations
Source:13. International scientific agriculture symposium “AGROSYM 2022 - Book of proceedings, 2022
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