Assessing the uncertainty of maize yield without nitrogen fertilization

Adrian A. Correndo, Jose L. Rotundo, Nicolas Tremblay, Sotirios Archontoulis, Jeffrey A. Coulter, Dorivar Ruiz-Diaz, Dave Franzen, Alan J. Franzluebbers, Emerson Nafziger, Rai Schwalbert, Kurt Steinke, Jared Williams, Charlie D. Messina, Ignacio A. Ciampitti

Research output: Contribution to journalArticlepeer-review


Maize (Zea Mays L.) yield responsiveness to nitrogen (N) fertilization depends on the yield under non-limiting N supply as well as on the inherent productivity under zero N fertilizer (Y0). Understanding the driving factors and developing predictive algorithms for Y0 will enhance the optimization of N fertilization in maize. Using a random forest algorithm, we analyzed data from 679 maize N fertilization studies (1031 Y0 observations) conducted between 1999–2019 in the United States and Canada. Predictability of Y0 was assessed while identifying determinant factors such as soil, crop management, and weather. The inclusion of weather variables as predictors improved the model efficiency (ME) from 51 up to 64 %, and reduced the root mean square error (RMSE) from 2.5 to 2.0 Mg ha−1, 34 to 27 % in relative terms (RRMSE). The most relevant predictors of Y0 were previous crop, irrigation, and soil organic matter (SOM), while the most influential weather data was linked to the radiation per unit of thermal time (Q quotient) around flowering and spring precipitations. The crop rotation effect resulted in Alfalfa (Medicago sativa L.) as the previous crop with the highest Y0 level (IQR = 11.5–15.0 Mg ha−1) as compared to annual legumes (IQR = 5.6–10.0 Mg ha−1) and other previous crops (IQR = 3.6–7.8 Mg ha−1). The Q quotient around flowering positively affected Y0, while spring precipitations and extreme temperature events during grain filling showed a negative association to Y0. Overall, these results reinforce the concept that yields are controlled not only by soil N supply but also by factors modifying plant demand and ability to capture N. Lastly, we foresee a promising future for the use of machine learning to address both prediction and interpretation of maize yield to obtain more reliable N guidelines.

Original languageEnglish (US)
Article number107985
JournalField Crops Research
StatePublished - Jan 1 2021

Bibliographical note

Funding Information:
The authors gratefully acknowledge the financial support provided by Fulbright Program, Kansas Corn Commission, Corteva Agriscience, and Kansas State University for sponsoring A. Correndo’s doctoral studies and Dr. I.A. Ciampitti’s research program. This is contribution no. 21-092-J from the Kansas Agricultural Experiment Station.


  • Maize
  • Soil nitrogen supply
  • Yield forecast

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