Evaluation of Mean Performance and Genetic Variability in the M₂ Generation of Sunflower (Helianthus annuus L.) Developed through Induced Mutagenesis

Sunflower (Helianthus annuus L.) is an important oilseed crop, but a narrow genetic base in cultivated material can constrain the improvement of agronomic and yield-related traits. Induced mutagenesis can broaden genetic variability and generate material for selection. This study evaluated gamma irradiation and ethyl methane sulphonate (EMS) to induce variation in two sunflower genotypes, TAS-82 and MORDEN. Seeds were treated with gamma rays at 200, 300, and 400 Gy or EMS at 0.1, 0.2, and 0.3%, alongside untreated controls. Progenies selected in the M₁ generation were evaluated in the M₂ generation using a randomised block design with three replications. We assessed days to 50% flowering, days to maturity, plant height, head diameter, 100-seed weight, volume weight, seed filling, hull content, and seed yield per plant. An analysis of variance was performed to assess treatment differences, and genotypic and phenotypic coefficients of variation (GCV and PCV) were estimated to characterise variability. In both genotypes, 200 Gy gamma irradiation produced the highest mean seed yield per plant: 41.433 g in TAS-82 and 43.067 g in MORDEN. This treatment also recorded the highest 100-seed weight (5.167 and 5.367 g, respectively) and volume weight (35.233 and 35.967 g/100 ml, respectively). The highest observed head diameter differed between genotypes, occurring at 200 Gy in TAS-82 (14.100 cm) and 0.1% EMS in MORDEN (13.200 cm). GCV was moderate for plant height, head diameter, 100-seed weight, and seed yield per plant, whereas it was low for days to 50% flowering, days to maturity, volume weight, seed filling, and hull content. These findings demonstrate trait- and genotype-dependent responses to mutagen treatment. The evaluated M₂ progenies provide a source of variability for further evaluation and selection in sunflower improvement programmes; subsequent generations and cross-environment confirmation are needed to identify stable, useful material.