Principal component and correlation analyses study on fruit yield in cucumber (Cucumis sativus L.) genotypes

Abstract

The degree of association between yield and its components can be identified using correlation and Principal Component Analyses (PCA). PCA also reveals key characteristics that explain most of the differences between genotypes. A study was formulated to evaluate the relationship between yield and its contributing traits in cucumber. The experiment was conducted with 16 cucumber genotypes in a Randomized Complete Block Design, with three replications. The correlation analysis revealed a strong and statistically significant relationship in number of pistillate flowers (r = 0.58**), number of branches (r = 0.43**), vine length (r = 0.69**), number of leaves (r = 0.73**), leaf area (r = 0.70**), number of fruits (r = 0.91**), fruit length (r = 0.40**), fruit girth (r = 0.39**), and fruit weight (r = 0.74**) with fruit yield. PCA revealed that PC1 accounted for 51.53% of the total variation, while PC2 explained 13.91% of the total variability. This study demonstrated that choosing traits such as number of pistillate flowers, number of branches, vine length, number of leaves, leaf area, number of fruits, fruit length, fruit girth, and fruit weight that have a strong positive correlation with fruit yield could be given priority in selection for yield improvement.