Analyzing Sunflower Trait Patterns Using MANOVA, PCA, and Correlation Across Seasons and Locations

Abstract

Sunflower is an important agricultural crop valued for its high oil yield, versatility in culinary and industrial applications and adaptability to diverse environments. Eight advanced sunflower genotypes were tested in a randomized complete block design (RCBD) with three replications at six locations over the 2018 and 2019 seasons. The study aimed to evaluate the effects of environmental and genotypic variations using MANOVA, PCA, and correlation analysis to discover trait patterns and relationships. The MANOVA results revealed highly significant effects of genotype, environment, and their interaction on the 11 dependent variables (p < 0.001). The four principal components account for 74.23% of the total variation, with key traits such as seed yield per hectare, oil yield per hectare, days to maturity, plant height, and grain filling period significantly contributing to the overall variability. Oil yield per hectare and seed yield per hectare exhibited a very strong association (0.974). Days to maturity (DM) and grain filling period (GFP) showed a strong correlation (0.666), suggesting that longer grain filling periods may enhance both maturity and yield. Additionally, plant height (PH) and seed yield per hectare (YELDK) had a moderate correlation (0.491). Breeding programs should target traits with strong correlations to boost sunflower productivity and adaptability. Future research should prioritize selecting genotypes that perform well across diverse environments, focusing on seed yield, oil yield, and traits such as maturity and grain filling period. Additionally, breeding should incorporate disease resistance and optimize days to flowering to develop more robust and productive sunflower varieties.