Utilizing hydrologic soil grouping to estimate corn nitrogen rate recommendations

Abstract

Nitrogen fertilizer recommendations in corn (Zea mays L.) that match crop N need are imperative to increasing profitability and preventing environmental contamination. However, processes influencing corn production (i.e. leaching and denitrification) are affected by soil and weather characteristics making it difficult to know when and how much N fertilizer to apply to match crop N need. In an effort to improve N fertilizer management, this dissertation explored the ability of soil, weather, and microbial respiration measurements to inform N rate recommendations based on hydrologic soil grouping (HG) and drainage classifications. Using 49 sites over three growing seasons (2014-2016), five HG-based groups were delineated. Following linear regression, soil and weather measurements were found related to the economical optimum N fertilizer rate (EONR) unique to each of the respective groups resulting in five separate N recommendation models. Furthermore, a total of 182 site-years of corn N response trial data were gathered across six U.S. Midwest Corn Belt states to validate these models. Two analyses were performed, one for comparing the HG-based model recommendations to EONR and another comparing HG-based model recommendations to state-specific corn N rate tools. Across all site-years and N timings the HG-based N recommendations were within 34 kg N ha-1 of EONR 38% of the time. State-specific N recommendation tools were within 34 kg N ha-1 of EONR 32% of the time. Over all site-years the HG-based models were more sensitive to site-specific crop N demand. In addition, when analyzed within each individual state, significant differences between approaches were observed. While showing some promise, these results suggest more research is required to determine the applicability of the HG-based model approach for corn N fertilizer management.