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ARS Home » Midwest Area » Columbia, Missouri » Cropping Systems and Water Quality Research » Research » Publications at this Location » Publication #383334

Research Project: Sustainable Intensification of Cropping Systems on Spatially Variable Landscapes and Soils

Location: Cropping Systems and Water Quality Research

Title: Soil hydrologic grouping guide which soil and weather properties best estimate corn nitrogen need

item BEAN, GREGORY - McCain Foods, Inc
item Ransom, Curtis
item Kitchen, Newell
item SCHARF, PETER - University Of Missouri
item Veum, Kristen
item CAMBERATO, JAMES - Purdue University
item FERGUSON, RICHARD - University Of Nebraska
item FERNANDEZ, FABIAN - University Of Minnesota
item FRANZEN, DAVID - North Dakota State University
item LABOSKI, CARRIE - University Of Wisconsin
item NAFZIGER, EMERSON - University Of Illinois
item SAWYER, JOHN - Iowa State University
item NIELSEN, ROBERT - Purdue University

Submitted to: Agronomy Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/7/2021
Publication Date: 10/27/2021
Citation: Bean, G.M., Ransom, C.J., Kitchen, N.R., Scharf, P.C., Veum, K.S., Camberato, J.J., Ferguson, R.B., Fernandez, F.G., Franzen, D.W., Laboski, C.A., Nafziger, E.D., Sawyer, J.E., Nielsen, R.L. 2021. Soil hydrologic grouping guide which soil and weather properties best estimate corn nitrogen need. Agronomy Journal. 113(6):5541-5555.

Interpretive Summary: Applying nitrogen (N) fertilizer at a rate sufficient for crop N needs, but not more, can improve farmers’ profits and help reduce loss of N off agricultural fields. However, it is difficult to know the right N rate for any year or field because it depends on how precipitation interacts with the soil. This research used USDA’s soil hydrologic classifications to help predict economically optimal N rates (EONR) for corn fields in the U.S. Midwest. Sites from 49 locations around the Midwest were classified by soil hydrologic and drainage characteristics into five groups. Within each of these groups, different soil and weather properties were tested for predicting EONR. The most important properties for most groups were soil organic matter, clay content, and growing-season rainfall evenness. Next, we tested these prediction models on an additional 181 other Midwest locations. We found improvements with these models when compared to currently used N recommendation tools. Current methods generally overestimated EONR while recommendations using the hydrologic-based models were closer to EONR. This was especially true for locations that needed less than 90 lbs N fertilizer/acre. When EONR was over 140 lbs N fertilizer/acre, the hydrologic-based models underestimated what the crop needed. Thus, improvements are needed before implementing the methods developed from this research. However, the results were very encouraging, and using this approach could help farmers optimize their yields while reducing the amount of N that ends up in lakes, streams, and oceans — which will reduce eutrophication.

Technical Abstract: Nitrogen fertilizer recommendations in corn (Zea mays L.) that match the economically optimal N fertilizer rate (EONR) are imperative for profitability and minimizing environmental losses. However, the amount of soil N available for the crop depends on soil and weather factors, making it difficult to know the EONR from year-to-year and from field-to-field. Our objective was to explore, within the framework of hydrologic soil groups and drainage classifications (HGDC), which site-specific soil and weather properties best estimated corn N needs. Included in this investigation was a validation step using an independent dataset. Forty-nine N response trials conducted across the U.S., Midwest Corn Belt over three growing seasons (2014 – 2016) were used for recommendation model development, and 181 independent site-years were used for validation. For HGDC models, soil organic matter, clay content, and evenness of rainfall distribution before side-dress N application were the properties generally most helpful in predicting EONR. Using the validation data, model recommendations were within 34 kg N/ha of EONR for 37% and 42% of the sites with a RMSE of 70 and 68 kg N/ha for at-planting and side-dress applications, respectively. Compared to state-specific recommendations, sites needing <100 kg N/ha or no N were better estimated with HGDC models. In contrast, for sites where EONR was >150 kg N/ha, HGDC models underestimated N needs compared to state-specific recommendation. These results show HGDC groupings could aid in developing tools for N fertilizer recommendations.