Regional Corn Nitrogen Recommendation Models for Predicting EONR Are Improved Using Hyrdrologic Classification.

In-season nitrogen (N) fertilizer applications that match corn (Zea mays L.) N demand help maximize grower profit and minimize environmental contamination. However, determining future corn N requirements is difficult and can vary substantially within a single field or from year-to-year based on spatial and temporal variability. Additionally, studies show that no one N recommendation tool, soil, weather, or active-optical reflectance characteristic is consistently accurate, especially on a regional scale, in predicting the economic optimal N rate (EONR). The objective for this research was to determine if reflectance (collected at ~ V9 development stage), soil, weather, and management information gathered across the US Midwest could be utilized to derive a regional corn N recommendation model. Nitrogen response trials were conducted across eight states over three growing seasons, totaling 49 site-years with soils ranging in productivity. Sites were separated into five groups based on USDA-NRCS hydrologic classification and drainage class. Soil and weather variables were regressed against EONR with single and two-way interactions. Regional corn N recommendation models were related to the end-of-season calculated EONR. Collectively, the models did well at predicting EONR with an R² value of 0.76, an RMSE of 31 kg N ha^-1, and 82% of the sites within 34 kg N ha^-1 of EONR. Therefore, using soil hydrological properties can help match the N fertilizer rate with corn N need.