Environmental effects on quantitative traits such as seed oil fatty acid composition in soybean [Glycine max (L.) Merrill] can significantly affect the performance of a given genotype when exposed to varying growing conditions. High linoleic acid oils have the potential to be used as raw materials for the production of polyols and polyurethane that can be used in the automotive industry. The objectives of this study were (i) to determine the sources of variation affecting seed oil fatty acids profiles and (ii) to evaluate stability of soybean genotypes with different fatty acids across different environments. Fifty‐six soybean genotypes with altered fatty acid composition selected from two recombinant inbred line populations segregating for saturated and linoleic acids were used along with commercial high yielding cultivars. All genotypes were evaluated in southwestern Ontario, Canada, at three locations in 2008 and two locations in 2009, and data was collected for fatty acid composition as well as other seed and agronomic traits. Combined analysis of variances showed significant location and genotype × location effects for stearic and oleic acids. Genotype × environment effect was significant for unsaturated fatty acids plus seed oil and protein concentrations. The effect of genotype × year was significant for unsaturated fatty acids. Stability analyses using Francis and Kannenberg's mean coefficient of variation stability, Shukla's stability variance statistic (σ2), and Lin and Binns cultivar superiority measure identified genotypes E‐49 and E‐14 as being superior for high linoleic and low saturated fatty acids oil production for potential use in the automotive industry.

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Insoybean [Glycine max (L.) Merr.], seed oil concentration is a complex quantitative trait, and genomic selection (GS) has been shown to be a valuable tool for performing selection on such traits. The objectives of this study were to evaluate multiple GS models for seed oil concentration using a low-density marker panel in four biparental soybean populations and to assess predictive ability of the models using six unique training populations (TPs). Individuals were grown as BC 1 F 4 :F 5 progeny rows in 2014. Genomic estimated breeding values (GEBVs) were calculated for each genotype within a population using genomic best linear unbiased predictor (GBLUP), BayesA, and BayesB models in a biparental specific context. In 2015, 60 individuals from each population were randomly selected and grown at six locations with two replications each to generate a "true" phenotypic value for each genotype. Prediction accuracies for each estimation set were generated by correlating the GEBVs with the "true" phenotypic value. Across all populations, the GS prediction accuracy was greatest using GBLUP; however, no GS prediction model showed a significant advantage in accuracy over the phenotypic values. Generally, TPs consisting of more individuals had higher prediction accuracies; however, variations were observed across populations and models. The results show that GS in a biparental context and with low marker densities can be a valuable tool for breeders focused on making gains for oil concentration; however, consideration must be given as to how to apply these methods to each situation.