Soybean (Glycine max L.) is a major global crop due to its diverse uses, high nutritional value, and strong production potential. This study quantified selection gains for grain protein content across segregating generations and proposed breeding strategies adjusted to heterozygosity levels. The experiment was conducted in Ijuí, Rio Grande do Sul, using an augmented block design with common controls, including 170 progenies and 44 cultivars. Genetic effects, heritability, and selection gains were estimated through Bayesian inference with Markov Chain Monte Carlo. Broad-sense heritability (H2 = 0.451) indicated moderate genetic control of protein content. Greater selection intensity was suitable for the F10 generation due to low heterozygosity (0.19%), whereas earlier generations (F5, F7, and F8) required milder selection to preserve genetic variability. Adjusting selection intensity across generations ensures reliable parameter estimation and consistent genetic gain, highlighting the usefulness of Bayesian approaches in protein-oriented soybean breeding.
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