Space-Time Adaptive Nitrogen Management in Maize: Combining NIR Spectroscopy and Simulation Modeling.

Through complex chemical, physical and biological interaction, the rate at which N cycles through the soil profile varies both spatially and temporally at a very fine scale. Consequently, crop yields across a field fluctuate in response to such variations. In light of such interaction, conventional, single-rate N application may not be the most efficient means of supplying nitrogen to fields. Adaptive (variable-rate) nitrogen application has emerged as a response to the dynamic processes of soil N in order to reduce over-application of N fertilizers through time and location-specific recommendations. By simulating N dynamics using the Precision Nitrogen Management or PNM model, composed of a dynamic simulation model of soil and crop N transformations, we have developed a series of adjustments to the recommended in-season N application rates based on early season field conditions and climate data. In this study, we demonstrate how spatio-temporal processes can be incorporated into adaptive N management using the PNM model, newly-developed high resolution climate data, and inputs of spatially-explicit soil information based on NIR reflectance spectroscopy. By loosely coupling the PNM with GIS, we simulated carbon and nitrogen dynamics with roughly 30 years of data from four fields in Iowa. Data from the simulations were aggregated to a 30 x 30m grid, which allows for precise management of nutrients with respect to current field conditions. Furthermore, GIS provided a graphic time-step of N processes for the data record, which allows for further analysis of N dynamics while also identifying management zones for N allocation. The final product is a gridded map containing plant-available N at any give time during the growing season. This grid can be aggregated or disaggregated as necessary for management purposes to produce accurate, site-specific sidedress recommendations and to increase N efficiency.