PCA-driven Insights into Hybrid-Parent Performance in Okra [Abelmoschus esculentus (L.) Moench.]
A. I. Abdalla
*
Department of Horticulture, Faculty of Agriculture, Al Zaeim Al Azhari University, Khartoum North 13311, P.O. Box 1432, Sudan.
M. M. Mahdi
Agricultural Research Center, Abu Arish, Jazan 84427, Saudi Arabia.
Y. A. ElKamil
Department of Horticulture, Faculty of Agriculture, Al Zaeim Al Azhari University, Khartoum North 13311, P.O. Box 1432, Sudan.
N. T. Khiery
Hudaiba Research Station, Agriculture Research Corporation, PO. Box 126, Wad Madani, Sudan.
*Author to whom correspondence should be addressed.
Abstract
Aims: The study aimed to evaluate genetic diversity and identify trait relationships among okra (Abelmoschus esculentus L. Moench) F1 hybrids and their parental lines using principal component analysis (PCA). It sought to uncover key traits contributing to yield variability and to support breeding efforts by identifying superior hybrids and combining parents suited to Sudanese agro-ecologies.
Study Design: The study used a line × tester mating design involving 10 parental lines and their 21 resulting F1 hybrids. Multivariate statistical tools, including PCA and hierarchical clustering, were employed to assess trait variability, relationships, and genotype grouping based on multiple morphological and yield-related parameters.
Place of Study: The research was conducted at the demonstration farm of the Faculty of Agriculture, University of Al Zaeim Al Azhari, located in Khartoum North, Sudan.
Methodology: Eleven agronomic traits were recorded, including plant height, number of fruits per plant, yield per plant, and others. Data were analyzed using analysis of variance and principal component analysis. The first four principal components were extracted, and their eigenvalues, trait loadings, and correlations were studied. Standardized Euclidean Distance and Ward’s method were used for hierarchical clustering to group genotypes based on similarity.
Results & Discussion: Substantial phenotypic variation was observed, with yield per plant (CV = 21.2%), fruit fresh weight (18.9%), and number of lateral branches (16.3%) showing the highest variability. PCA revealed four principal components explaining 71.85% of the total variance. PC1 (27.48%) was highly associated with yield traits, while PC2 (22.14%) captured architectural and earliness traits. PC3 and PC4 explained structural variation. Cluster analysis grouped genotypes into four major clusters. Sinnar-derived hybrids formed a compact, high-yielding group, indicating superior combining ability. Clemson crosses exhibited wide variability and heterotic potential, while Hjerat lines showed genetic divergence useful for broadening the gene pool.
Conclusion: Principal Component Analysis (PCA) effectively revealed genetic diversity and key trait associations among okra hybrids and their parental lines. Sinnar was identified as a superior parent for yield improvement, while other testers contributed distinct and complementary traits. These findings support the use of PCA for trait prioritization and the selection of promising hybrid-parent combinations, ultimately facilitating the development of improved okra cultivars adaptable to Sudanese and similar agro-ecological environments.
Keywords: Abelmoschus esculentus, principal component analysis, hybrid performance, yield traits, parental line selection, okra breeding