Soybean response to nitrogen application across the United States: A synthesis-analysis

Spyridon Mourtzinis, Gurpreet Kaur, John M. Orlowski, Charles A. Shapiro, Chad D. Lee, Charles Wortmann, David Holshouser, Emerson D. Nafziger, Hans Kandel, Jason Niekamp, William J. Ross, Josh Lofton, Joshua Vonk, Kraig L. Roozeboom, Kurt D. Thelen, Laura E. Lindsey, Michael Staton, Seth L. Naeve, Shaun N. Casteel, William J. WieboldShawn P. Conley

Research output: Contribution to journalArticlepeer-review

86 Scopus citations


The effects of supplemental nitrogen (N) on soybean [Glycine max (L.) Merr.] seed yield have been the focus of much research over the past four decades. However, most experiments were region-specific and focused on the effect of a single N-related management choice, thus resulting in a limited inference space. Here, we composited data from individual experiments conducted across the US that examined the effect of N fertilization on soybean yield. The combined database included 207 environments (experiment × year combinations) for a total of 5991 N-treated soybean yields. We used hierarchical modeling and conditional inference tree analysis on the combined dataset to establish the relationship and contribution of several N management choices on soybean yield. The N treatment variables were: N-application (single or split), N-method (soil incorporated, foliar, etc.), N-timing (pre-plant, at a reproductive stage, etc.), and N-rate (from a 0 N control to as much as 560 kg ha−1). Of the total yield variability, 68% was associated with the effect of environment, whereas only a small fraction of that variability (< 1%) was attributable to each N variable. Averaged over all experiments, a single N application and the split N application were 60 and 110 kg ha−1 greater yielding than the zero N control treatment, respectively. A split N application with more than one method (e.g., soil incorporated and foliar) resulted in 120 kg ha−1 greater yield than zero N plots. Split N application between planting and reproductive stages (Rn) resulted in greater yield than zero N and single application during a Rn; however, the effect was not significantly different than N application at other growth stages. Increasing the N rate increased the environment average soybean yield; however, 93% of the environment-specific N-rate responses were not significant which suggested a minimal effect of N across the examined region. A large yield variability was observed among environments within the same N rates, which was attributed to growing environment differences (e.g., in-season weather conditions, soil type etc.) and non-N related management (e.g., irrigation). Conditional inference tree analysis identified N-timing and N-rate to be conditional to irrigation, and to seeding rates >420,000 seeds ha−1, indicating that N management decisions should take into account major, non-N related management practices. Overall, the analysis revealed that N management decisions had a measurable, but small, effect on soybean yield. Given the growing pressure for increasing food production, it is imperative to further examine all soybean N decisions (application method, timing, and rate) in environment- and cropping system-specific randomized trials in important agricultural regions.

Original languageEnglish
Pages (from-to)74-82
Number of pages9
JournalField Crops Research
StatePublished - Jan 2018

Bibliographical note

Publisher Copyright:
© 2017 Elsevier B.V.


  • Hierarchical model
  • Nitrogen
  • Regression tree
  • Soybean

ASJC Scopus subject areas

  • Agronomy and Crop Science
  • Soil Science


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