Estimation of genetic diversity is vital in sunflower breeding. Principal component analysis (PCA) is one of the best statistical approaches to study diversity among genotypes by using eigenvalue and PC-1/PC-2 based-biplots. An experiment commenced at the College of Agriculture, University of Sargodha, Pakistan to find out the diversity in 28 sunflower hybrids and select stable hybrids for normal and terminal heat-stress conditions. Growing sunflower hybrids in randomized complete block design had three replications. Under normal condition, sunflower hybrid sowing ensued in February 2019, while to check the effect of terminal stress, growing these hybrids transpired at the end of March 2019. The collected data of seven morpho-phenological traits underwent the principal component analysis, found greater than one for the first three and four PCs under normal conditions. In terminal stress environments, these respectively exposed the presence of ample genetic variation among sunflower hybrids in both environments. Bi-plot analysis signified that SF-18100, SF-18045, and SF-19025 were stable hybrids for most studied traits under normal sowing environment. Meanwhile, the performance of SF-18035 and SF-19010 proved better under the heat-stress environment for studied traits. Hence, these hybrids showed better adaptability under the current scenario of climate change.
Sunflower (H. annuus L.), diversity, PCA, eigenvalue, genetic variability, phonological traits, biplot
Heat stress negatively affects sunflower (H. annuus L.) crop growth, leading to lower achene yield and oil content. However, the sunflower hybrids SF-18100, SF-18045, SF-19012, and SF-19025 performed better than the rest for achene yield and yield-contributing traits under normal and heat stress environments, making them suitable for sunflower breeding programs and general cultivation.