Assessing the uncertainty of maize yield without nitrogen fertilization

Authors: CORRENDO, ADRIAN - Kansas State University; ROTUNDO, JOSE - Corteva Agriscience; TREMBLAY, NICOLAS - Agriculture And Agri-Food Canada; ARCHONTOULIS, SOTIRIOS - Iowa State University; COULTER, JEFFREY - University Of Minnesota; RUIZ-DIAZ, DORIVAR - Kansas State University; FRANZEN, DAVE - North Dakota State University; Franzluebbers, Alan; NAFZIGER, EMERSON - University Of Illinois; SCHWALBERT, RAI - Kansas State University; STEINKE, KURT - Michigan State University; WILLIAMS, JARED - Brigham Young University; MESSINA, CHARLES - Corteva Agriscience; CIAMPITTI, IGNACIO - Kansas State University

Submitted to: Field Crops Research
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/13/2020
Publication Date: 11/3/2020
Citation: Correndo, A.A., Rotundo, J.L., Tremblay, N., Archontoulis, S., Coulter, J.A., Ruiz-Diaz, D., Franzen, D., Franzluebbers, A.J., Nafziger, E., Schwalbert, R., Steinke, K., Williams, J., Messina, C., Ciampitti, I.A. 2020. Assessing the uncertainty of maize yield without nitrogen fertilization. Field Crops Research. 260, Article 107985.

Interpretive Summary: Predicting how cereal grains are impacted by soil and weather variables would be valuable to better manage nitrogen fertilizer for optimized production. Grain yield without nitrogen fertilizer application accounts for available nitrogen in the soil system, which can be used to reduce the total quantity of nitrogen fertilizer needed to optimize production and prevent loss to the environment. A scientist with USDA Agricultural Research Service in Raleigh NC collaborated with lead investigators from Kansas State University and other collaborators from Corteva Agriscience, Agriculture and Agri-Food Canada, Iowa State University, University of Minnesota, North Dakota State University, University of Illinois, Michigan State University, and Brigham Young University to assess published data from 1104 field studies conducted between 1999 and 2019. Management factors such as previous crop and irrigation in combination with surface-soil organic matter accounted for the largest portion of variation in unfertilized yield level, while weather features helped refine predictions. A simple framework that included early spring weather variables was just as effective as full-season weather information. Refined prediction of yield without nitrogen fertilizer could help provide key insights for better nitrogen management of corn. This information will be valuable to a network of scientists to work collaboratively, as well as to agronomic advisors in making more effective nitrogen fertilizer recommendations for corn.

Technical Abstract: 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 1104 maize N fertilization studies 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 50 up to 66%, and reduced the root mean square error (RMSE) from 2.5 to 2.0 Mg/ha, 34 to 28% in relative terms (RRMSE). The most relevant predictors of Y0 were previous crop, irrigation and soil organic matter (SOM), while the most influential weather information was related to spring precipitation. 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) as compared to annual legumes (IQR = 5.6-10.0 Mg/ha) and other previous crops (IQR = 3.6-7.8 Mg/ha). Spring precipitation and extreme temperature events during grain filling were the most relevant weather factors, with 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.