Environmental Factors Associated With Nitrogen Fixation Prediction in Soybean - de Borja Reis, André Froes; Moro Rosso, Luiz; Purcell, Larry C.; Naeve, Seth; Casteel, Shaun N.; Kovács, Péter; Archontoulis, Sotirios; Davidson, Dan; Ciampitti, Ignacio A. (Frontiers in Plant Science, 2021)
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 N-fixation’s role 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 a 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 sowing date for yield, or soil sulfate for protein. The early-reproductive period VPD affected all independent variables, RAU, yield, and seed protein. The elastic net algorithm successfully depicted some empirical relationships otherwise challenging 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.