Deciphering Trait Interrelationships in Sunflower (Helianthus annuus L.): A Multivariate Approach to Optimise Seed Yield and Oil Quality

Abstract

To investigate the complex relationships between seed yield and oil content, with their related agronomic traits in sunflower, this correlation analysis study was done at Punjab Agricultural University (30.9010° N and 75.85.73° E) using 113 genotypes, which were grown in spring 2022 in a randomised block design with three replications. Eighteen quantitative traits were analysed, revealing key correlations, like seed yield per plant (economic yield) positively associated with structural traits like plant height, head diameter, stem girth, number of leaves per plant, days to maturity, biological yield per plant, number of seeds per head, hundred seed weight and seed volume weight, while oil content was positively correlated with seed volume weight and inversely correlated with hundred seed weight, indicating a trade-off between yield and oil content. Additionally, oleic acid content demonstrated strong positive correlations with palmitic and linoleic acid content. These insights provide direction for breeding programs aiming to optimise sunflower yield and quality. Furthermore, this research applied principal component analysis (PCA) to elucidate the genetic and phenotypic interrelations influencing oil content and fatty acid composition, the results of which identified five principal components, with the first three accounting for 93% of the total variation, out of which the first two components captured about 77% of the variation. The scatter plot and biplot analysis revealed that oleic acid and linoleic acids dominate the first component with a strong inverse correlation, while the second component is significantly influenced by the positive correlations of palmitic and stearic acids, thereby explaining 77% of variations in oil parameters, and offering actionable insights for enhancing sunflower breeding programs targeting oil quality optimisation.