Identification of adaptable sunflower ( Helianthus annuus L.) genotypes using yield performance and multiple-traits index.

Sunflower is the most important oil crop ranked as fourth edible oil in the world. The study was conducted in Northern Ethiopia during 2017-2019 cropping seasons using randomized completely block design with three replications. The objective was to decipher the genotype by environment interaction (GEI) in multi-environment trials (MET) and identify adaptable sunflower genotypes. Combined ANOVA, AMMI ANOVA and Eberhart and Rusell regression were analyzed, and GGE bi-plots, AMMI1 and AMMI2 bi-plots, Principal component Analysis (PCA), multi-trait genotype-ideotype distance index (MGIDI), correlation network plot for sunflower traits were sketched. AMMI stability measures, Best Linear Unbiased Prediction (BLUP) based indexes; parametric and non-parametric statistics were computed using R-statistical software. In the AMMI ANOVA the main effects of the environment (E) (54.18 % SS), genotype (G) (16.9 % SS) and GEI (23.50 % SS) were significant (p < 0.001). The genotypic Likely-hood Ratio Test revealed significant for all traits. The AMMI bi-plot and the GGE bi-plots selected G10 and G2 as the most adaptable genotypes. CV, HMGV, RPGV, HMRPGV, P _i , GAI, KRS, S ⁽³⁾ and S ⁽⁶⁾ also identified G10 as the most stable genotype. Based on the MGIDI, G10 (MGIDI = 1.45) and G5 (MGIDI = 2.19) are selected and these genotypes are recommended for further cultivation in Tigray.
Competing Interests: The authors are composition of researchers and instructor in different disciplines like crop production and protection. These authors contributed in different parts of the research planning, implementation, data collection, data analysis, manuscript drafting and editing. Every member contributed in the research is included in this manuscript. Hence, the authors declare that there are no conflicts of interest. Furthermore, this investigation received no specific fund from any funding agency, commercial or not-for-profit sectors.
(© 2024 The Authors. Published by Elsevier Ltd.)