Large temporal and spatial variability in soil N availability leads many farmers across the US to over apply N fertilizers in maize (Zea Mays L.) production environments, often resulting in large environmental N losses.  Static N recommendation tools are typically promoted in the US, but new dynamic model-based tools allow for more precise and adaptive N recommendations that account for specific production environments and conditions. This study compares two static N recommendation tools, one based on the Stanford equation (Cornell University Corn N Calculator) and another based empirical response curves (MRTN), to a dynamic simulation tool that combines weather, soil, crop and management information to estimate optimum N application rates for maize, Adapt-N. The efficiency of the tools in predicting the economically optimum N rate (EONR) is compared using field data from multiple N rate strip trials conducted in New York, Indiana, and Ohio. By accounting for weather and site-specific conditions the precision Adapt-N tool was found to improve the prediction of the EONR. Furthermore, using a dynamic instead of a static approach leads to reduced N application rates, increased profits and resulted in reduced simulated environmental N losses. This study shows that application of precision N management through a dynamic tool such as Adapt-N can help reduce environmental impacts while sustaining farm economic viability.