Trait Contributions to Yield in Rice Genotypes: Insights from PCA and Cluster Analysis with Emphasis on Pollen Fertility
Background: Rice (Oryza sativa L.) productivity is increasingly constrained by climate variability, biotic stresses and limited natural resources, necessitating the effective utilisation of genetically diverse germplasm. Traditional rice landraces possess valuable adaptive, stress-tolerant and yield-related traits; however, their potential remains largely underutilized in breeding programmes. Methods: The present investigation was conducted during the zaid season of 2025 under field conditions using 130 rice (Oryza sativa L.) landraces evaluated in an augmented experimental design. A total of 21 agronomic, physiological and reproductive traits were assessed. Genetic divergence and trait relationships were analysed using principal component analysis (PCA) and hierarchical cluster analysis to identify the major traits contributing to phenotypic variability and yield performance. Result: Eight principal components with eigenvalues greater than one explained a substantial proportion of the total phenotypic variation. The first two components contributed most of the variability and were primarily associated with plant vigour, reproductive efficiency, biomass accumulation and yield-related traits. Plant height (X3), flag leaf length (X4), internode length (X7), grains per panicle (X9), 1000-seed weight (X10) and pollen fertility (X19) were the major contributors to genotype differentiation. Cluster analysis grouped the 130 genotypes into four distinct clusters. Groups I, II and IV exhibited high pollen fertility and superior yield performance, whereas Group III showed lower pollen fertility and poor yield traits. PCA biplot analysis further revealed a close association of high-yielding genotypes with pollen fertility, biomass traits and physiological efficiency. pollen fertility emerged as a key trait influencing genetic divergence and yield performance. The genetically diverse and superior-performing genotypes identified in this study represent valuable resources for rice improvement programmes aimed at enhancing productivity and adaptability.