Selection of Advanced Soybean Lines for Grain Yield and Seed Composition Using Genetic Parameters, a Z Selection Index, and Random Forest Classification

This study aimed to identify promising soybean lines by combining genetic parameter estimates, simultaneous selection for grain yield and oil content, and a complementary multivariate classification of already phenotyped materials. Four soybean populations were evaluated in Jaboticabal, São Paulo, Brazil, across three agricultural years corresponding to the F5:6, F5:7, and F5:8 generations. The breeding populations comprised 142 advanced lines, whereas the augmented-block field experiment contained 281 plot-level experimental records in each agricultural year because the complete layout also included the repeated check plots required by the design. Population-based analyses were conducted using REML/BLUP, likelihood-ratio tests, and a standardized Z selection index for grain yield and oil content. The Z index identified the highest-ranked lines within each population, including genotypes 19, 42, 38, and 27 in populations A, B, C, and D, respectively. A complementary Random Forest analysis was used as a decision-support classification procedure applied to observed phenotypic information, rather than as a model for forecasting future performance or estimating unknown oil or protein values. The procedure organized the phenotypic profiles into oil-based, protein-based, and combined oil-plus-protein groups. For the combined scenario, the classification criterion rewarded high standardized values for both seed-composition traits while penalizing imbalance between them. Out-of-fold classification results showed high agreement with the predefined operational groups, with overall accuracy rates of 0.99, 1.00, and 0.98 for the oil, protein, and combined scenarios, respectively. Principal component analysis described the multivariate structure of the same phenotypic dataset. Because generation and crop year coincide, changes in genetic parameter estimates across successive evaluations cannot be attributed exclusively to selection. Overall, the results support the use of mixed models and the Z index for line selection, while Random Forest provides a complementary framework for organizing already measured materials according to contrasting and combined seed-composition objectives.