Remote Sensing for N Decision Support.

With the introduction of new types of small unmanned aerial vehicles that are capable of carrying a wide range of sensors there is renewed interest in the use of remote sensing to determine nutrient requirements for agricultural crops. Aerial remote sensing using color and color-infrared (CIR) photography or digital multi-band sensors has been used to detect N deficiencies in corn and determine N fertilizer requirements for site-specific application by utilizing green digital counts early in the corn-growing season. Sripada et al., 2005 and 2006, used a linear-plateau function of the green difference vegetation index (GDVI) measured relative to a well N-fertilized reference (an area that is never N deficient) to predict economic optimum N rates for corn at the both the V7 and VT stage with reasonable success. Flowers et al., 2001 and 2003, demonstrated that aerial remote sensing could be used to determine when an early application of N was needed on winter wheat. Unfortunately, several potential drawbacks have limited the commercial application of aerial remote sensing.  These drawbacks include the need to remove pixels showing soil, the requirement that no prior N be applied, the fact that the crop needs to be at a specific growth stage to achieve accurate results, the difficulties associated with receiving aerial images at a given time and the problems associated with quickly turning images into application recommendations. Despite these drawbacks there are a few examples of successful commercial applications of aerial remote sensing to determine N requirements for crops.  This presentation will examine commercial applications of aerial remote sensing with the focus on the factors that have contributed to the success of these enterprises along with the difficulties that have been encountered.