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Principal component analysis of yield and yield related traits in rice (Oryza sativa L.) landraces

T. Thirumurugan G. Raiza Christina, T. Thirumurugan, P. Jeyaprakash, V. Rajanbabu

2021Electronic Journal of Plant Breeding20 citationsDOIOpen Access PDF

Abstract

A total of 49 rice landraces were investigated for eight traits using principal component analysis (PCA) for the determination of variation pattern, the relationship among genotypes and its traits. Out of eight principal components (PC), three PC’s exhibited Eigenvalue more than one with 72.9 per cent of total variability among the characters. The highest positive Eigenvalue observed for the number of productive tillers per plant (0.148) and flag leaf length (0.148) in PC1 indicated their pronounced effect in the overall variation of the genotypes. The study revealed the traits that are contributing maximum for the variation. Hence, selective rice landraces can be utilized for improving these traits in high yielding cultivars through suitable breeding programmes.

Topics & Concepts

Principal component analysisOryza sativaBiologyForensic scienceCultivarYield (engineering)AgronomyGenetic diversityNon-invasive ventilationVeterinary medicineGenetic variationBiotechnologyHorticultureMathematicsStatisticsGeneticsMedicineMetallurgyEnvironmental healthPopulationMaterials scienceGeneRice Cultivation and Yield ImprovementGenetics and Plant BreedingGABA and Rice Research