Maize (Zea mays L.) production accounts for the largest share of crop land area in the U.S. It is the largest consumer of nitrogen (N) fertilizers but has low N Recovery Efficiency (NRE, the proportion of applied N taken up by the crop). This has resulted in well-documented environmental problems and social costs associated with high reactive N losses associated with maize production. There is a potential to reduce these costs through precision management, i.e., better application timing, use of enhanced efficiency products, and more precise rate calculations. However, promoting management changes by means of environmental policies requires robust analysis of the possible environmental outcomes. This research gap is addressed using Adapt-N, a computational precision N management tool that combines soil, crop and management information with near-real-time weather data to estimate optimum N application rates for maize. Using results from a large synthetic dataset of 8100 simulations spanning 6 years (2010-2015), we have explored the total required N rates and environmental losses resulting from seven N management scenarios applied in the top 5 US maize production states – IL, IN, IA, MN and NE. To cover a wide range of weather and production environments, all scenarios were applied at five randomly selected locations in each state, using combinations of three soil texture classes and two organic matter contents. The results indicate that fall applications lead to the lowest NRE with substantial amounts of N losses and highest total amount of required N.  Nitrification inhibitors were found to have marginal benefits for fall applied N, but effective with spring applications. Spring pre-plant N applications where found to have higher NRE than fall applications, but could still lead to high N losses under wet spring conditions. These losses were significantly reduced when nitrification and urease inhibitors were applied. Out of all simulated N management scenarios, applying a split application of a modest starter followed by the majority of N applied at sidedress was found to have on average the lowest total N amount required, lowest N losses and overall, and highest NRE. These results demonstrate that computational precision management tools could be used to inform environmental policies and business models to reduce environmental costs associated with maize production in the U.S.