Environmental Factors Associated With Nitrogen Fixation Prediction in Soybean

André Froes de Borja Reis, Luiz Moro Rosso, Larry C. Purcell, Seth Naeve, Shaun N. Casteel, Péter Kovács, Sotirios Archontoulis, Dan Davidson, Ignacio A. Ciampitti

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


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.

Original languageEnglish (US)
Article number675410
JournalFrontiers in Plant Science
StatePublished - Jun 15 2021

Bibliographical note

Funding Information:
This research was funded by United no. 2020-152-0104.

Funding Information:
This is a contribution to no. 21-285-J from the Kansas Agricultural Experiment Station. The authors are thankful for the respective research staff from each university for conducting field experiments and laboratory analysis. Funding. This research was funded by United Soybean Board, project no. 2020-152-0104.

Publisher Copyright:
© Copyright © 2021 de Borja Reis, Moro Rosso, Purcell, Naeve, Casteel, Kovács, Archontoulis, Davidson and Ciampitti.


  • elastic net
  • relative abundance of ureides
  • ridge
  • symbiotic nitrogen fixation

PubMed: MeSH publication types

  • Journal Article


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