Can Models and Weather Databases Enhance the Use of Sensor-Based Nitrogen Management?.

Nitrogen management in corn production is often imprecise and inefficient in humid regions due to dynamic, complex and locally-specific interactions among weather, soil and management factors.   Reflectance measurements from leaves or canopies have shown to contain some diagnostic potential for determining optimum N rates for corn.  This technology is especially attractive if it is aimed at managing in-field variability in N availability, and associated needs for supplemental N fertilizer.  However, the use of equipment-mounted reflectance sensors poses several limitations from the producer perspective: They typically require a high-N reference strip in the same field, and the needs for supplemental N fertilizer is not a-priori known and may require unnecessary field passes.  Also, research results generally show that algorithms generated from specific fields and growing seasons often don’t perform well when applied to different sites and seasons.  There appears to be considerable potential to improve the performance of in-field sensors with the use of computer simulation models that use weather information to simulate soil C and N transformations, water transport and N uptake to calculate N fertilizer needs.  Adapt-N focuses on real-time optimization using high resolution weather data and relies on information related to the management of the corn crop, soil properties, rotations, tillage, irrigation, fertilizer management, and manuring.  It is a web-accessible server-based tool that is also mobile enabled.  Adapt-N has shown good predictability for estimating optimum N sidedress rates and has low cost to the producer. It can offer a good first estimate for a field’s N needs, which in turn would allow for initial tuning of the sensor-based algorithms and setting the range of N rates for fields, thereby potentially increasing the precision of N sensors.