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ARS Home » Midwest Area » Columbia, Missouri » Cropping Systems and Water Quality Research » Research » Publications at this Location » Publication #353410
Title: Corn nitrogen fertilizer recommendation models based on soil hydrologic groups aid in predicting economically optimal nitrogen rates

Author
item BEAN, G - University Of Missouri
item Kitchen, Newell
item CAMBERATO, J - Purdue University
item FERGUSON, R - University Of Nebraska
item FERNANDEZ, F - University Of Minnesota
item FRANZEN, D - North Dakota State University
item LABOSKI, C - University Of Wisconsin
item NAFZIGER, E - University Of Illinois
item SAWYER, J - Iowa State University
item SCHARF, P - University Of Missouri

Submitted to: International Conference on Precision Agriculture Abstracts & Proceedings
Publication Type: Proceedings
Publication Acceptance Date: 6/5/2018
Publication Date: 6/24/2018
Citation: Bean, G.M., Kitchen, N.R., Camberato, J.J., Ferguson, R.B., Fernandez, F.G., Franzen, D.W., Laboski, C.A., Nafziger, E.D., Sawyer, J.E., Scharf, P.C. 2018. Corn nitrogen fertilizer recommendation models based on soil hydrologic groups aid in predicting economically optimal nitrogen rates. 14th International Conference on Precision Agriculture, June 24-27, 2018, Montreal, Quebec, Canada. Paper No.4962.

Interpretive Summary: Nitrogen (N) fertilizer recommendations that match corn N needs for improved grower profits and reduce water quality consequences. However, variability within fields and from one year to the next make determining future N requirements difficult. The purpose of this research was to assess if examining corn response to N fertilizer after accounting for hydrological properties could be used to help explain the impact of specific soil and weather variability. Research sites were delineated into five groups based on USDA-NRCS hydrologic designation and drainage class. This research conducted across eight Midwestern states (49 sites) found unique soil and weather information could be used to develop N fertilizer recommendation models for each of the five hydrologic groups. Examples of soil and weather information found to be important included rainfall uniformity early in the growing season, organic carbon, clay content, and soil plant available water content. The developed models resulted in predictions that explain between 48 to 85% of corn N response. Average error using these models was reduced 30-50%. This approach for delineating unique N response regions for N fertilizer recommendations resulted in 79% of the research sites being within 30 lbs/ac of economic optimal N rate. These results suggest that soil hydrological groups can assist in using site-specific soil and weather information for making localized N fertilizer recommendations. The outcomes of this research have the potential to improve N fertilizer recommendations for improved grower profit, and reducing over-applications that could result in off-field losses into rivers and streams.

Technical Abstract: Nitrogen (N) fertilizer recommendations that match corn N needs for improved grower profits and reduce water quality consequences. However, spatial and temporal variability makes determining future N requirements difficult. Studies have shown no single soil or weather measurement consistently increases accuracy, especially when applied over a regional scale, in predicting economically optimal N rate (EONR). Basing site N response on soil hydrological group could help account for soil and weather variability and better match in-season corn N fertilization need. Research was conducted across eight Midwestern states totaling 49 different site locations. Sites were categorized into five groups based on USDA-NRCS hydrologic designation and drainage class. Economic optimal N rate from each group were regressed against measured soil and weather variables. Measured soil variables were analyzed by 0 to 0.30 and 0 to 0.60 m depths and included clay content, organic matter, plant available water, and total organic carbon. Measured weather variables, from the time of planting to the time of in-season canopy sensing, included site growing degree days, total precipitation, evenness of rainfall (using the Shannon Diversity Index(SDI)), and the abundant and well-distributed rainfall (SDI x accumulative precipitation). The most significant soil and weather variables for improving EONR estimation were selected to develop an N fertilizer recommendation model for each of the five groups. Model R^2 values ranged from 0.48 to 0.85 while root-mean-square errors ranged from 16 to 43 kg N/ha. When compared to EONR, and considering all five models, 79% of the sites fell within 34 kg N/ha of EONR with an R^2 of 0.72 and a root-mean-square-error of 34.5 kg N/ha. Overall, these results suggest that soil hydrological groups can be very helpful in determining which soil and weather interaction will most affect site-specific EONR.