Acknowledgements

(PC2) represents and accounted for 23.89% with eigenvalue of 2.87 had dominantly influenced by the mineral elements such as Ca, Na, Fe, Al, and B with the highest loading vector of Fe followed by Al with positive loading. The nutritional trait that contributed great variability among the genotypes showing 11.45% of variation were protein with the highest positive loading. The mineral elements Ca, Na, B, and Mn also contributed differences in this PC.

Figure 4. Biplot generated using the concentration of mineral elements and protein content data set of okra genotypes.

92 Rediscovery of Landraces as a Resource for the Future

The existence of wider nutritional variability among okra genotypes studied was further described by the PCA biplot (Figure 2) using multivariate technique. The PCA biplot provided important information regarding the similarities as well as the pattern of differences among the nutritional traits of the different okra genotypes and of the interrelationships between the quantified nutritional traits. The PCA clustered the okra genotypes into different groups over the four quadrants based on the nutritional traits determined (Figure 2). The okra genotypes scattered in all four quadrants on the axes, indicating that there were a wide genetic variability for the traits studied. Accessions that overlapped and closer to each other in the principal component axes had similar genetic relationships in the nutritional traits. However, genotypes which are far from each other could be considered as genetically

Agricultural Research Council (ARC) and National Research Foundation (NRF) for research and funding opportunity, South Africa. The author would also like to acknowledge the Genetic Resources and Seed Unit, World Vegetable Center (AVRDC), Taiwan for providing the okra germplasm for the study.
