Improving grain yield remains the central objective of soybean breeding programs. During early-stage yield trials, breeders often evaluate thousands of genotypes; however, limited seed availability constrains the number of tested environments and replications, reducing selection accuracy. Genomic prediction offers a promising approach to identify high-yielding and stable genotypes earlier in the breeding pipeline. The objective of this study was to develop a classification-based genomic prediction framework that directly targets advancement decisions by assigning genotypes to yield performance classes while estimating the probability of class membership to prioritize genotypes with higher confidence. A total of 1,789 soybean genotypes, ranging from maturity groups III to V, were evaluated for grain yield across 10 environments (year × location combinations) in Arkansas and Missouri during the 2023 and 2024 growing seasons. Genomic Best Linear Unbiased Predictors (GBLUPs) were obtained for each genotype in each environment, and a selection index (MSI) was calculated as the average yield deviation from the mean of the checks across the tested environments, centered at zero. This metric captures both yield and consistency across environments using a simple, check-referenced scale that is directly interpretable in breeding decisions. Genotypes were then classified as high-yielding (MSI ≥ –5), moderate (–5 MSI ≥ –15), or low-yielding (MSI –15). Two classification-based genomic prediction models, Generalized Linear Model via Elastic Net Regularization (GLMNet) and Random Forest (RF), were trained using the SoySNP3K BeadChip markers as predictors and the MSI-based yield classes as response categories. The MSI ranged from -32.4 to 7.2, with a small proportion of genotypes in the high-yielding class. GLMNet and RF achieved macro-averaged balanced accuracies of 0.84 and 0.83, respectively, with high specificity (0.89 for both) and sensitivity (0.78 and 0.76), and minimal extreme misclassification between low- and high-yielding classes. Compared to regression-based genomic prediction, this classification framework aligns with advancement decisions, is less sensitive to early-stage noise, and retains greater genetic diversity than GBLUP-based ranking, enabling more efficient resource allocation and more targeted advancement of promising genotypes.
Marmo et al. (Wed,) studied this question.