Dynamic Model Improves Agronomic and Environmental Outcomes for Maize Nitrogen Management over Static Approach

Large temporal and spatial variability in soil nitrogen (N) availability leads many farmers across the United States to over‐apply N fertilizers in maize (Zea Mays L.) production environments, often resulting in large environmental N losses. Static Stanford‐type N recommendation tools are typically promoted in the United States, but new dynamic model‐based decision tools allow for highly adaptive N recommendations that account for specific production environments and conditions. This study compares the Corn N Calculator (CNC), a static N recommendation tool for New York, to Adapt‐N, a dynamic simulation tool that combines soil, crop, and management information with real‐time weather data to estimate optimum N application rates for maize. The efficiency of the two tools in predicting the Economically Optimum N Rate (EONR) is compared using field data from 14 multiple N‐rate trials conducted in New York during the years 2011 through 2015. The CNC tool was used with both realistic grower‐estimated potential yields and those extracted from the CNC default database, which were found to be unrealistically low when compared with field data. By accounting for weather and site‐specific conditions, the Adapt‐N tool was found to increase the farmer profits and significantly improve the prediction of the EONR (RMSE = 34 kg ha−1). Furthermore, using a dynamic instead of a static approach led to reduced N application rates, which in turn resulted in substantially lower simulated environmental N losses. This study shows that better N management through a dynamic decision tool such as Adapt‐N can help reduce environmental impacts while sustaining farm economic viability. Core Ideas Dynamic N recommendation tool reduces environmental impacts over static approach. Dynamic N recommendation tool accounts for different production environments. Dynamic N recommendation tool is successful in estimating field‐measured EONR.