Combining corn N recommendation tools for an improved economical optimal nitrogen rate estimation

Authors

Curtis J. Ransom, USDA-ARS
Newell R. Kitchen, USDA-ARS
James J. Camberato, Purdue University
Paul R. Carter, Retired Corteva Agriscience
Richard B. Ferguson, University of Nebraska - LincolnFollow
Fabián G. Fernández, University of Minnesota
David W. Franzen, North Dakota State University
Carrie A. M. Laboski, USDA-ARS
David Brenton Myers, Retired Corteva Agriscience
Emerson D. Nafziger, University of Illinois
John E. Sawyer, Retired Iowa State University
John F. Shanahan, Agoro Carbon Alliance

Date of this Version

2-20-2023

Citation

Ransom, C. J., Kitchen, N. R., Camberato, J. J., Carter, P. R., Ferguson, R. B., Fernández, F. G., Franzen, D. W., Laboski, C. A. M., Myers, D. B., Nafziger, E. D., Sawyer, J. E., & Shanahan, J. F. (2023). Combining corn N recommendation tools for an improved economical optimal nitrogen rate estimation. Soil Science Society of America Journal, 1–16. https://doi.org/10.1002/saj2.20539

Comments

U.S. government works are not subject to copyright.

Abstract

Improving corn (Zea mays L.) nitrogen (N) rate fertilizer recommendation tools can improve farmers’ profits and mitigate N pollution. Numerous approaches have been tested to improve these tools, but to date improvements for predicting economically optimum N rate (EONR) have been modest. This work’s objective was to use ensemble learning to improve our estimation of EONR (for a single at-planting and split N application timing) by combining multiple corn N recommendation tools. The evaluation was conducted using 49 corn N response trials from eight states in the US Corn Belt and three growing seasons (2014–2016). Elastic net and decision tree approaches regressed EONR against three unique tools for each N application timing. Tools used in various combinations included a yield goal method, two soil nitrate tests (pre-plant and late season), a computer simulation crop model (Maize-N), and canopy reflectance sensing. Any combination of two or three N recommendation tools improved or maintained performance metrics (R², root-mean square error , and number of sites close to EONR). The best results for a single at-planting recommendation occurred when combining the three at-planting N recommendation tools (including interactions) with an elastic net regression model. This combined recommendation tool had a significant linear relationship with EONR (R² = 0.46), an increase of 0.27 over the best tool evaluated alone. Combining multiple tools increased the implementation cost, but it did not reduce profitability and, sometimes, improved profitability. These results show tools can be combined to better match EONR, and thus could aid farmers in improving N management.