Variability, Correlation Patterns and Principal Component Analysis (PCA) for Seed Yield and Contributing Traits in Castor (Ricinus communis L.)
Kadam Abhishek Deepak, T. Manjunatha, V. Hemalatha, D. Srinivasa Chary
Journal of Advances in Biology & Biotechnology · pp. 1217–1227 · Published 9 Aug 2024
10.9734/jabb/2024/v27i81246Abstract
Castor (Ricinus communis L.) is a vital crop for industrial applications in more than 250 products including lubricants, paints, cosmetics, pharmaceuticals etc. This study is an attempt to understand the genetic diversity in 15 male (monoecious) and 15 female (pistillate) advanced breeding lines of castor. 11 quantitative traits were subjected to analysis of variance, correlation analysis, principal component analysis (PCA) and K-means clustering. Significant genetic variability and trait correlations were noticed, revealing opportunities for targeted improvement in castor. Clustering identified six distinct genetic groups, facilitating the identification of diverse parental lines. Principal component analysis elucidated key contributors of variation, enabling informed breeding decisions. This comprehensive study provides a foundation for further improvement in seed yield, oil content and environmental resilience in castor.
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Cited by 6
Diego Santos, Diego Fernando Marmolejo Cortes, Mylena Almeida dos Santos · Euphytica · 2026
K. Chandana, V. S. Durga Prasad · Journal of oilseeds research · 2026
César Cueva-Carhuatanta, Ester Choque-Incaluque, Ronald Pio Carrera-Rojo · Plants · 2025
Showing 3 of 6 known citations — external sources report more than can currently be individually listed.
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