Soybean [Glycine max (L.) Merr.] seed composition and yield are a function of genetics (G), environment (E), and management (M) practices, but contribution of each factor to seed composition and yield are not well understood. The goal of this synthesis-analysis was to identify the main effects of G, E, and M factors on seed composition (protein and oil concentration) and yield. The entire dataset (13,574 data points) consisted of 21 studies conducted across the United States (US) between 2002 and 2017 with varying treatments and all reporting seed yield and composition. Environment (E), defined as site-year, was the dominant factor accounting for more than 70% of the variation for both seed composition and yield. Of the crop management factors: (i) delayed planting date decreased oil concentration by 0.007 to 0.06% per delayed week (R2∼0.70) and a 0.01 to 0.04 Mg ha-1 decline in seed yield per week, mainly in northern latitudes (40–45 N); (ii) crop rotation (corn-soybean) resulted in an overall positive impact for both seed composition and yield (1.60 Mg ha-1 positive yield difference relative to continuous soybean); and (iii) other management practices such as no-till, seed treatment, foliar nutrient application, and fungicide showed mixed results. Fertilizer N application in lower quantities (10–50 kg N ha-1) increased both oil and protein concentration, but seed yield was improved with rates above 100 kg N ha-1. At southern latitudes (30–35 N), trends of reduction in oil and increases in protein concentrations with later maturity groups (MG, from 3 to 7) was found. Continuing coordinated research is critical to advance our understanding of G × E × M interactions.

Continued economic relevancy of soybean is a function of seed quality. The objectives of this study were to: (i) assess the spatial association between soybean yield and quality across major US soybean producing regions, (ii) investigate the relationship between protein, oil, and yield with amino acids (AAs) composition, and (iii) study interrelationship among essential AAs in soybean seed. Data from soybean testing programs conducted across 14 US states from 2012 to 2016 period (n = 35,101 data points) were analyzed. Results indicate that for each Mg ha−1 yield increase, protein yield increased by 0.35 Mg protein ha−1 and oil yield improved by 0.20 Mg oil ha−1. Essential AA concentrations exhibit a spatial autocorrelation and there was a negative relationship between concentration of AA, protein, and oil, with latitude. There was a positive interrelationship with different degree of strength among all AAs, and the correlation between Isoleucine and Valine was the strongest (r = 0.93) followed by the correlation among Arginine, Leucine, Lysine, and Threonine (0.71 < r < 0.88). We concluded that the variability in genotype (G) x management (M) x environment (E) across latitudes influencing yield also affected soybean quality; AA, protein, and oil content in a similar manner.

No abstract

It is unclear if additional inoculation with Bradyrhizobia at varying soybean [Glycine max (L.) Merr.] growth stages can impact biological nitrogen fixation (BNF), increase yield and improve seed composition [protein, oil, and amino acid (AA) concentrations]. The objectives of this study were to evaluate the effect of different soybean inoculation strategies (seed coating and additional soil inoculation at V4 or R1) on: (i) seed yield, (ii) seed composition, and (iii) BNF traits [nodule number and relative abundance of ureides (RAU)]. Soybean field trials were conducted in 11 environments (four states of the US) to evaluate four treatments: (i) control without inoculation, (ii) seed inoculation, (iii) seed inoculation + soil inoculation at V4, and (iv) seed inoculation + soil inoculation at R1. Results demonstrated no effect of seed or additional soil inoculation at V4 or R1 on either soybean seed yield or composition. Also, inoculation strategies produced similar values to the non-inoculated control in terms of nodule number and RAU, a reflection of BNF. Therefore, we conclude that in soils with previous history of soybean and under non-severe stress conditions (e.g. high early-season temperature and/or saturated soils), there is no benefit to implementing additional inoculation on soybean yield and seed composition.

Biological nitrogen (N)-fixation is the most important source of N for soybean [Glycine max (L.) Merr.], with considerable implications for sustainable intensification. Therefore, this study aimed to investigate the relevance of environmental factors driving N-fixation and to develop predictive models defining the role of N-fixation for improved productivity and increased seed protein concentration. Using the elastic net regularization of multiple linear regression, we analyzed 40 environmental factors related to weather, soil, and crop management. We selected the most important factors associated with the relative abundance of ureides (RAU) as an indicator of the fraction of N derived from N-fixation. The most relevant RAU predictors were N fertilization, atmospheric vapor pressure deficit (VPD) and precipitation during early reproductive growth (R1–R4 stages), sowing date, drought stress during seed filling (R5–R6), soil cation exchange capacity (CEC), and soil sulfate concentration before sowing. Soybean N-fixation ranged from 60 to 98% across locations and years (n = 95). The predictive model for RAU showed relative mean square error (RRMSE) of 4.5% and an R2 value of 0.69, estimated via cross-validation. In addition, we built similar predictive models of yield and seed protein to assess the association of RAU and these plant traits. The variable RAU was selected as a covariable for the models predicting yield and seed protein, but with a small magnitude relative to the sowing date for yield or soil sulfate for protein. The early-reproductive period VPD affected all independent variables, namely RAU, yield, and seed protein. The elastic net algorithm successfully depicted some otherwise challenging empirical relationships to assess with bivariate associations in observational data. This approach provides inference about environmental variables while predicting N-fixation. The outcomes of this study will provide a foundation for improving the understanding of N-fixation within the context of sustainable intensification of soybean production.