Remote sensing of canopy reflectance on a field scale has been proposed as a useful tool for diagnosing nitrogen (N) deficiency of corn plants. Differences in leaf color among plants can be quantified by analyzing the canopy reflectance. The approach to making fertilizer recommendation through remote sensing deserves attention because factors other than N deficiencies can also influence canopy reflectance.

When extra N is applied to reference strips of cornfields and differences in canopy reflectance between non-fertilized strips and the reference strips are compared to estimate N rates for in-season correction, the temporal patterns in canopy reflectance could have shown confounding effects of N fertilization and other factors. We report field-scale studies in which remote sensing of corn canopy reflectance produced unexpected evidence for fertilizer-induced advancement of growth stage early in the season.

Materials and Methods

Results and Discussion

Canopy reflectance values measured early in the season were mainly affected by the application time and that measured late in the season were mainly affected by the application rate (Fig. 1).

The canopy reflectance values measured late in the season were better correlated to yield responses to N (Fig. 2). The difference in yields between the early and late times of fertilizer applications was not statistically different (data not shown). Therefore, temporary shortages of N may produce symptoms of N deficiency in situations where subsequent additions of N should not be expected to increase yields.

Three no-till fields under the corn-soybean rotation were chosen in 2002 within the Clarion-Nicollet-Webster soil association in Green County, Iowa. Treatments were applied in strips going the length of the fields and consisted of various application dates and rates of N fertilizer in a split-plot design with 4 replications. The early (June 7, 6, and 5) and late (June 17, 14, 13) applications of N fertilizer were as main plots at Sites 1, 2 and 3, respectively. Three N rates (56, 112, and 168 kg N ha-1) applied as UAN were as sub-plots.

Aerial images taken for each site on three dates are shown in Fig. 1. Reflectance values were derived from six test areas within each replication in each site. Relative reflectance was expressed as a percentage of the mean reflectance for lower N rates over that for the highest N rate (168 kg N ha-1). Corn yields in the 6-row strips were measured by using combine equipped with a yield monitor at one-second intervals.

Introduction

Conclusions

Remote sensing late in the season has greater ability to detect yield-limiting deficiencies of N because supplies of N become exhausted by growth under such a condition.

The appropriate use of remote sensing requires distinction between the short term effects of a deficiency (often only temporarily) and the net effects as expressed in grain yields at the end of the season. The key to recognizing the power of remote sensing is to make these distinctions and recognize that the growth of corn is a dynamic process that occurs over several months and it is divided into clearly different growth phases.

Figure 1. Aerial images taken on three dates at the three sites. Strips that received early N applications are marked by solid lines and strips that received late N applications are marked by dotted lines.

Figure 2. Relationships between canopy reflectance and yield responses to N.

Quantifying Temporal Patterns in Symptoms of Nitrogen Deficiencies in Corn by Remote Sensing

Jun Zhang a, Alfred M. Blackmer a, Peter M. Kyveryga a, Mark J. Glady b, and Tracy M. Blackmer b

a Agronomy Department, Iowa State University, Ames, IA 50011; b Iowa Soybean Association, 4554 114th Street, Urbandale, IA 50322

Site 1, Sept 6Y=-8.43+0.09Xr2=0.41, P<0.001

Site 1, July 20Y=-0.55+0.07Xr2=0.01, P=0.590

Site 1, July 2Y=-2.08+0.04Xr2<0.01, P=0.786

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Site 2, Sept 6Y=-3.97+0.04Xr2=0.40, P<0.001

Site 2, July 20Y=-9.44+0.10Xr2=0.23, P=0.018

Site 2, July 2Y=8.79+0.10Xr2=0.29, P=0.006

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Site 3, Sept 6Y=-6.49+0.06Xr2=0.52, P=0.001

Relative reflectance (%)

90 100 110 120 130 140 150

Site 3, July 20Y=1.87-0.01Xr2=0.01, P=0.700

90 100 110 120 130 140

Site 3, July 2Y=3.14-0.03Xr2=0.02, P=0.517

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Site 1 Site 2 Site 3