Variability, Mean Performance Evaluation, Trait Relationship and Principal Component Analysis of Sunflower Genotypes

Forty nine sunflower genotypes evaluated for mean performance and Variability parameters of yield contributing traits at Kulumsa in simple lattice design. The aim is to identify desired characters of the crop, information of nature and genetic variability for seed yield improvement. The traits revealed presence of highly significant genotypic differences at P≤0.01 for yield contrbuting traits: head diameter, number of seed head<sup>-1</sup>, thousand seed weight and seed yield ton ha<sup>-1</sup>. Among the studied genotypes mean performance evaluation indicates that the highest seed yield ton ha<sup>-1 </sup>recorded for genotypes SHRS-2020#18 (3.06ton ha<sup>-1</sup>), followed by SHRS-2020#4 (2.95tonha<sup>-1</sup>) and SHRS-2020#16 (2.84t ha<sup>-1</sup>) and the lowest average seed yield ton ha<sup>-1 </sup>recorded for genotype SHRS-2020#13 (1.15tonha<sup>-1</sup>). Genotypes SHRS-2020#46 (83.5) and SHRS-2020#38 (84.5) the early flowered whereas, the late flowered recorded for the genotype SHRS-2020#43 (107.5) after the date of sowing. Seed yield ton ha<sup>-1</sup> (YTPH), is the most economic trait, was positively and significantly associated with number seed head<sup>-1</sup> and plant height. The characters indicating significantly positively correlation among seed yield and important traits would be highly effective and efficient improving respective traits. Higher estimates of heritability coupled with higher genetic advance were observed for seed yieldtonha<sup>-1</sup> (46.49) and number of seed head<sup>-1</sup> (42.46). This indicated that heritability of the trait is mainly due to additive gene effect and selection is effective for such traits. Principle component analysis (PCA) is usually used to identify the most significant variables in the data. In this study the principle component analysis result showed that accumulative variability original data accounted about 100% for the traits. The first Principal component which accounted for 38.5% total variation were observed through agronomic traits such as: SD, DFF, HD, days to maturity, number of seed head<sup>-1</sup>. Similarily the second principal components which accounted for 17.4% of the total variations among the genoypes were attributed to differently from traits such as: yield ton ha<sup>-1</sup>, number of seed head<sup>-1 </sup>and head diameter were the most important of seed yield positive contributors in the second Principal component. Whereas the third and fourth PCA accounted 14.4% and 14% of variations for agronomic traits such as: TSW, HD and SD in PCA 3 and for PCA 4 TSW, seed yield ton ha<sup>-1</sup>, PH and DNM were the most important positive contributors traits for seed yield. Thus, these variation of traits observed in this experiment can help further as a selection index in genetic improvement of sunflower seed yield and its components.