Remote Sensing To Monitor Nitrogen Deficiencies in No-Till Cornfields

Remote sensing of canopy reflectance has great potentials to help develop a newgeneration of nitrogen (N) recommendations in the Corn Belt. Much of theinformation can be gathered through trials conducted at many different sites andyears. A more immediate problem with the acceptance of this technique is that thereis no established methodology for quantitatively describing the accuracy andprecision of remote sensing for assessing N stresses, transforming these assessmentsto fertilizer recommendations, and evaluating the benefits of using theserecommendations. Although qualitative methods are adequate to show the potentialof remote sensing and in-season applications of N, quantitative methods are neededto assess the benefits obtained in production agriculture. Much of our effort wasdirected toward conducting field experiments in 1998-1999 with an appropriatedesign, gathering and analyzing the data. The data analyses address the complexproblems caused by having different scales of measurement in the spatially variableno-till cornfields. The unique ability of this approach to making many observationswith reasonable low cost helps reduce errors of measurement and make estimates offertilizer needs more accurate and practical. Results obtained from this study clearlysupport the conclusion that remote sensing offers more flexibility for improving Nmanagement during corn production in the Corn Belt. Remarkable agreement was obtained between assessments of N status based on canopy reflectance and actualgrain yield responses. The data collected can be used to quantitatively estimate Nfertilizer needs in spatially variable fields and to convincingly document the benefitsof using remote sensing to guide in-season fertilization in no-till cornfields under thesimilar condition.