<p> Despite the continuous breeding efforts towards improving yield, seed quality, and yield-related traits, there is still little understanding of several aspects of soybean breeding; however, crop breeding is ever-evolving, and plant breeding technologies offer immense potential for accelerating genetic improvement in soybeans. This thesis explores different frameworks to further characterize tradeoffs among seed quality traits, soybean maturity's genetic architecture, and selections for yield. We explored the interactions of carbohydrate traits with other seed traits, flowering, and maturity using data from a large panel of <em>G. max </em>accessions from the USDA soybean germplasm collection. We found a negative correlation between sucrose and protein and a negative correlation between protein and oil, representing a significant challenge for improving seed quality. In contrast to other well-documented correlations, such as protein and oil, correlations between raffinose and oil content seem more specific to populations and environments and are unlikely to generalize to the whole specie; however, the correlations of sucrose with protein and seed size appears to be more stable. In addition, we performed a genome-wide association analysis (GWA) to detect novel QTLs for flowering (R1) time, maturity (R8) time, and reproductive length (RL) using a soybean panel with the same genotype for major <em>E </em>genes (<em>e1-as/E2/E3). </em>While major maturity <em>E</em> genes are known to have pleiotropic effects on R1 and R8, we found two QTLs associated with R8 and RL that do not control R1, suggesting minor-effect, trait-specific loci are also involved in controlling R1 and R8. In addition, w<em>e identified six genes that may play essential roles in regulating R1, R8, and RL; however, further validation of the QTLs and f</em>ine mapping and map-based cloning studies of the candidate genes are necessary before they can be used in breeding programs. Lastly, we conducted a selection experiment in progeny row (PR) populations of four breeding programs to compare the agronomic performance of lines selected by breeders using their usual selection methods to lines selected through prediction of yield performance using new sources of data and information. Our results suggest that aerial average canopy coverage (ACC) used as a secondary trait in combination with field spatial variation adjustment is an efficient high throughput methodology to effectively select high-yielding lines from non-replicated experiments at the PR stage. </p>