Principal Component Analysis for Yield and Yield Related Traits in Sesame (Sesamum indicum L.)

Author: Mukhthambica K., Bisen R. and Ramya K.T.

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Abstract

Sesame in India have been reported to have a wide variability for the characters but still no systemic efforts have been made to characterize and identify the genotypes having variable characters for the selection. The research was conducted using seventy genotypes of sesame based on yield and its contributing traits and also using Principal Component Analysis. The observations were recorded by selecting five random plants for fourteen quantitative characters. On the basis of Principal component analysis, out of fourteen components, only 4 principal components (PCs) exhibited more than 1.00 Eigen value and showed about 68.6 % variability among the traits studied. The PC1 had the highest variability (38.3%) followed by PC2 (12.2%), PC3 (10.2%) and PC4 (7.9%) for traits under study. Rotated component matrix revealed that the first principal component (PC1) was mostly related with traits such as days to 50% flowering, days to maturity, length to first capsule, plant height, secondary branches per plant and seed yield per plant. The second principal component (PC2) was related to the traits viz., capsule number per plant, number of leaf axils in main stem, capsule length, test weight and oil content while PC3 was consisting of traits viz., days to emergence, days to flower session. Fourth principal component was related to primary branches per plant. The genotypes like, Paiyur, VRI-3, TMV-6, TMV-3, DS-5, PKDS-8, Rajeshwari and JLT-408 were identified as putative genotypes and length to first capsule, plant height, primary branches per plant, secondary branches per plant, seed yield per plant are identified as main yield trait attributes. Thus, it can be utilized to select the more diverse germplasms for these traits and could be used as parents in heterosis breeding programmes.

Keywords

Sesame, Principal Component Analysis, variability, eigen values

Conclusion

PCA is an important technique for enhancing the breeding programme as it extracts all the key components and highlights their contribution to total variability. PCA biplot revealed the high performing genotypes viz., Paiyur, VRI-3, TMV-6, TMV-3, DS-5, PKDS-8, Rajeshwari and JLT-408 can be effectively utilized for crop improvement programmes.

References

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How to cite this article

Mukhthambica K., Bisen R. and Ramya K.T. (2023). Principal Component Analysis for Yield and Yield Related Traits in Sesame (Sesamum indicum L.). Biological Forum – An International Journal, 15(3): 227-232.