Understanding differences between static and dynamic nitrogen fertilizer tools using simulation modeling

[Display omitted] •Estimating nitrogen rate is critical to balance economic and environmental goals in Illinois corn cropping systems.•We used a simulated data set to explain the economic and environmental incentives of using complex dynamic tools over simpler static ones.•We found that complex dynamic tools do not consistently increase profits over simpler static tools.•Both approaches can reduce N leaching by 15%; dynamic tools by its higher accuracy, static tools by recommending on the low end of the MRTN range.•Results help re-examine N recommendation goals because aiming for higher accuracy does not necessarily improve profits or reduce N leaching. Improving nitrogen (N) fertilizer recommendations for maize (Zea mays L.) in the US Midwest has been the focus of much research, yet there is no agreement for which methodology is the best to balance trade-offs between production and environmental outcomes. This study investigated the strengths and limitations of two broad approaches: dynamic and static recommendation tools. Dynamic tools use advanced technology to predict the Economically Optimum N Rate (EONR) using year-specific soil, weather, and crop growth characteristics to detect conditions that need lower or higher N rates. Static tools provide regional N recommendations that are static over time, maximizing long-term profits rather than predicting the best EONR for each field and season. The objective of this work was to explain the interactions between the accuracy, profitability, and environmental losses for different N recommendation tools under a wide range of production scenarios. For this, we used a calibrated synthetic dataset of 4200 fields over 30 years. In the first part, we compared multiple N recommendations tools belonging to the static and dynamic groups. In the second part, we selected each group's best tools and compared them in detail. From an economic view, results indicate that increasing profitability by increasing the accuracy in EONR predictions with dynamic tools is challenging. The reason is that these more accurate tools are not perfect, and around half of the time, they under predict. In that situation, the yield penalty is higher, and the economic loss is usually not compensated by savings in N fertilizer costs associated with other more accurate recommendations. The static recommendations avoid this penalty by recommending slightly higher N rates, providing similar profits. From an environmental view, both tools can redu