Integrating Model-Based Adaptive Management of Nitrogen with Site-Specific NIR-Based Carbon Estimates in Maize Production.

Nitrogen management for corn in humid regions is generally imprecise and inefficient.  Current approaches for estimating optimum nitrogen fertilizer rates are based on mass balances, average expected economic return based on field experiments, soil N tests, and crop leaf/canopy sensing.  However, N losses occur from dynamic and complex interactions among spatial and temporal processes including weather, soil organic matter mineralization, hydrology, and management practices, resulting in variable annual crop N needs in corn production.  Adaptive N management accounts for the processes that cause variability in N dynamics among farms, soils, and weather.  We used the PNM model, which simulates soil N transformations, soil N and water transport, and maize growth and N uptake to develop in-season N fertilizer rates for corn resulting in more precise N management. Site specific estimates of soil carbon using NIR reflectance spectroscopy and climate data were used to provide locally-optimized N fertilizer recommendations.   Such adaptive N management allows for more efficient N use and improved response to the effects of climate change.