Remote Sensing to identify Spatial Patterns in Optimal
Rates of Nitrogen Fertilization
A. M. Blackmer
S. E. White
Department of Agronomy
Iowa State University
Ames, Iowa
Procedings of the 3rd International Conference
Mineapolis, Minnesota, ASA/CSSA/SSSA
June 23-26, 1996.
ASA – American Society of Agronomy
CSSA – Crop Science Society of America
SSSA - Soil Science Society of America
ABSTRACT
Aerial photography was used with yield mapping to identify spatial patterns in
nitrogen (N) sufficiency cornfields having various N rates applied in strips. These
spatial patterns were used to map crop response to the N. This should be helpful in
efforts to develop a new generation of N fertilizer recommendations needed for
variable-rate application technologies.
INTRODUCTION
Maps indicating spatial patterns in optimal rates of N fertilization are required
for effective use of variable-rate application technologies during corn production.
Although it commonly is assumed that such maps can be generated directly from maps
of yields from previous seasons, mounting evidence suggests that yield data alone do
not provide reliable assessments of N fertilizer needs. Extensive studies in Iowa, for
example, showed no correlation between current recommendations based on yields and
observed optimal rates of N fertilization (Blackmer et al., 1992).
One problem with recommendations based solely on yields is that they fail to
consider variability in amounts of plant-available N supplied by soils. Another problem
is that such recommendations fail to consider variability in percentages of fertilizer N
lost from the rooting zone before it is needed by the crop.
Recommended rates of N fertilization should be based on differences between amounts
of plant-available N supplied by specific soils and amounts of N needed by crops on
these soils.
Maps of observed yield responses to applied N should provide a more reliable
basis for developing maps showing the rates at which fertilizers should be applied.
Maps of yield response, however, are difficult to develop. One reason is that a single
measurement of yield response must involve yield measurements on a series of plots
having different rates of N added. Another reason is that soil-N availability and many
1
other yield-affecting factors (water availability, weeds, insects, etc.) tend to vary in
different spatial patterns, so meaningful assessments of yield response require that all
the treatments be replicated. This poses a dilemma on spatially variable soils because
increasing the size of the experimental area adds more yield variability not associated
with treatments. Spatial variability within an experimental area, therefore, adds
uncertainty to each individual assessment of yield response.
Combines equipped with on-the-go yield monitors and global positioning
systems may offer a practical way to make enough observations to construct maps of
yield response. It has not been demonstrated, however, that current systems have the
required precision when used on plots small enough to assess patterns of yield response
in spatially variable soils. A system that requires a 100 m swath to attain the necessary
level of precision, for example, could not be used to map yield response in a field where
important differences in fertilizer requirement were caused by undulating topography in
which crests were separated by an average distance of 100 m. Inability to resolve
differences in fertilizer requirements in highly variable soils could limit the value of
variable-rate fertilization in situations where this new technology is most needed.
Recent studies have shown that N deficiency in corn can be detected by
analyzing color photographs taken from airplanes (Blackcmer & Schepers, 1996;
Blackmer et al., 1995, 1994, 1996). Deficiencies of N result in leaves having less
chlorophyll, less photosynthesis, and increased reflectance of radiation normally
absorbed during photosynthesis. Persons with normal color vision see the deficiencies
as a lighter-green color. The perceived color of corn varies with many factors other than
N deficiencies, so assessments of N deficiency must be based on comparisons with
plants known to have adequate N under otherwise identical conditions. The assessments
of deficiency, therefore, are based on a measurement of crop-color response to N and
require experimental designs similar to those needed for measuring yield response.
The objective of this report is to describe an ongoing research project utilizing
aerial photography of canopy color, yield monitoring, and other new technologies to
develop a new generation of N fertilizer recommendations for use with variable-rate
application technologies. An underlying assumption is that better methods of
characterizing spatial patterns in response of corn to N fertilization is a critical step in
developing such recommendations. Results from a single field are presented to illustrate
the nature of these studies and offer some preliminary observations.
METHODS
Strop plots were established for 1995 in a 5.4-ha field within the LesterFluvaquents-Wadena soil association in Greene Country, Iowa. Soil survey maps
indicate 6 different soil series (Fig. 1a) and expected corn yields that ranged from 3.1
Mg/ha for the Salida-Storden complex to 7.6 Mg/ha for the Spiliville loam. Except for
applications of N, the field was managed by the producer using his normal practices.
These include harvesting with an eight-row combine (76 cm between rows) equipped
with a yield monitor and differential global positioning system. The planter sprayed 34
kg N/ha as urea-ammonium-nitrate solution in a 30-cm band over the row. The
preceding crop was soybean, and no fertilizer N was applied in the fall or before
planting.
2
Fig. 1. Soil survey map (a), aerial photograph of soil immediately after planting (b)),
aerial photograph of the crop canopy in mid August (c), and grain yield map (d)
for a 5.4-ha field planted to corn. Plots from which soil and tissue samples were
collected are marked on the photograph of the soil (b). The eight-row strips
receiving the lowest rate of N fertilization can be identified by their lighter color
in the canopy photograph (c). Yield levels are proportional to blackness in yield
map (d).
The field contained 17 eight-row strips to be harvested as individual swaths of
the combine. Fifteen of these were divided into five blocks of three strips. Fifteen small
(12-rn length, width of the block) plots were positioned (see Fig. lb) so as to have
maximal uniformity of soil characteristics within each plot and to capture the widest
possible range of soil characteristics among the plots. When corn plants were 30 cm tall,
soil samples (composite of 48 cores 3.2 cm in diameter) were collected to a depth of 30
cm for determination of nitrate and other relevant soil characteristics.
Soon after the soil samples were collected urea-ammonium-nitrate solution was
injected midway between rows to a depth of 15 cm to give N treatments (including N
applied at planting) of 56, 112, and 168 kg N/ha. The N treatments were randomly
assigned to the eight-row strips within each block. Each knife of the applicator had
3
independent flow control, and rate of fertilizer flow through the knives was adjusted for
rate of applicator travel by using ground-sensing radar.
The field was photographed using color film and a LMK-1000 aerial mapping
camera with a 15-cm distortion-free lens from a height of 730 m shortly after the corn
was planted and in mid August. The pictures were digitized on a grid having about
94,800 cells/ha. Samples for the end-of-season cornstalk test (Binford et al., 1992b)
were collected to spot check N status of the corn within each N treatment of each small
plot where soil samples had been collected in late spring.
Grain yields as measured by the combine were corrected to 15.5% moisture and
mapped on a grid size of 5,400 cells/ha. A Unix ArcInfo geographic information system
was used to study relationships among digitized soil survey maps, digitized photographs
of the soil soon after planting, digitized photographs of the crop canopy in mid August,
and maps of yields. Maps of color response and yield response were generated by
creating a geographically correct dataset consisting of the difference between measured
values resulting from an incremental increase in N application. This dataset was kriged
and smoothed.
RESULTS
The spring of 1995 had unusually large amounts of rainfall that delayed planting
by about three weeks. An intense rainfall event shortly after planting resulted in a
surface flow of water across the strips (320-340 m north in Fig. lb) and along the rows
on the east side of the field. This flow fanned out and covered much of the southeastern
corner of the field. The soil photograph showed an area of light-colored soil marking a
portion of the cornfield previously occupied by a barn (360 m north, 5 m east in Fig. lb).
A band of relatively dark soil includes the area mapped as Cylinder loam. Diffuse
streaks in soil color going lengthwise the field were caused by uneven distribution of
residue following tillage.
The mean concentration of nitrate in the surface 30-cm layer of soil across the
15 plots was 10 mg N/kg soil. The nitrate concentrations were within the range of 7 to
13 mg N/kg in all plots except one, which was 18 mg N/kg. The highest nitrate
concentration occurred on a plot near the site of the old barn and probably should be
attributed to the residual effects of an old barnyard. The observed concentrations of
nitrate were substantially below the 25 mg N/kg normally considered optimal for corn
production (Magdoff et al., 1990; Binford et al., 1992a). The observed concentrations,
however, are consistent with those expected following above-average spring rainfall.
The aerial photograph of the crop canopy (Fig. 1c) revealed light-colored strips
that correctly identify the 8-row strips receiving only 56 kg N/ha. Differences in color
between strips receiving 112 and 168 kg N/ha were not readily detectable; an
observation suggesting that the 112 kg N treatment avoided N deficiencies at essentially
all locations within the field. At some sites lack of color contrast between the 56-kg rate
and higher rates indicated that this rate corrected all deficiencies. This rate appeared to
be adequate north of the 320 m mark in Fig. 1c; the sharpness of the boundary suggests
that the northern end of the field probably had a different cropping history than did the
remainder of the field, even though cropping history had been similar for several years.
The aerial photograph of the crop canopy (Fig. 1c) showed spatial patterns in
color clearly unrelated to N treatments. An area of relatively light color seems to
approximately coincide with the Salida-Storden complex, which is rated as the poorest
soil for corn production. An area of relatively dark color within the area mapped as
4
Biscay seems to indicate that the plants detected a difference in soil type that was not
indicated by the soil survey map. At this site, as at many sites studied by this technique,
crop color often varied in spatial pattern somewhat differently than would be expected
from soil survey maps.
Yield maps (Fig. 1d) generated by using data from the combine showed patterns
generally similar to those observed in crop color; the areas of darkest green color tended
to have the highest yields. Strips receiving only 56 kg N/ha tended to have lower yields
than did strips receiving 112 kg N/ha, but these differences usually disappeared in
portions of the field where the higher N rate did not induce a response in canopy color.
Although strips having 112 and 168 kg N/ha seemed to be different in many small parts
of the field, the yield differences between these treatments were not consistent enough
to correctly identify these treatments from the yield map.
Data from the combine indicated mean yields of 6.4 Mg/ha for the 56 kg/ha
treatment, 7.9 Mg/ha for the 112 kg/ha treatment, and 7.4 Mg/ha for the 168 kg/ha
treatment. Analysis of mean yields for individual strips showed a significant (alpha
=0.05) yield difference between the first and second levels of N application and no
significant difference in yields between the first and third or second and third levels of
N. The yield decrease between the second and third N levels was significant at alpha =
0.20. Yield decreases from excesses of N could be expected because a period of lowmoisture stress occurred near the time of silking and because high rates of N application
frequently depress yields under such situations.
Mean stalk nitrate concentrations at the end of the season were 0.08 g N/kg for
the 56 kg N/ha treatment, 1.04 g N/kg for the 112 kg N/ha treatment, arid 2.60 g N/kg
for the 168 kg N/ha treatment if the one plot near the site of the old barn is excluded.
These findings indicate that the lowest rate was not adequate to maximize yields, the
middle rate was near optimal, and the highest rate was excessive (Binford et al., 1992b).
Stalk nitrate concentration for the plot near the site of the old barn was 3.12 g N/kg with
only 56 kg N/ha applied and clearly was different from the remainder of the field. Point
estimates of N sufficiency that were provided by the cornstalk test showed considerable
variability and were poorly related to soil mapping units. Such variability should be
expected after examining the canopy photograph, which revealed complex spatial
patterns in canopy color that were unrelated to fertilizer treatments applied in strips.
Spatial patterns were more clearly defined in the color photograph (Fig. 1c) than
in the yield map (Fig. ld). This should be expected because the canopy color is
illustrated by using many more cells/ha. Moreover, the yield maps have errors caused
by nonuniform or “bunchy" flow of corn through various parts of the combine. This
nonuniform flow may be caused in part by small areas of uneven soil that jar the
combine or alter its rate of travel. This problem results in measured yield being assigned
to a slightly incorrect location. Although the photographs can reveal spatial patterns
within plots of the size normally needed to measure yields, they do not indicate yields.
The aerial photographs, therefore, aid in interpretation of yield maps but they do not
provide a substitute for yield maps. Information concerning actual yield response (i.e.,
Mg/ha) is essential for selecting economically optimal rates of fertilization.
5
Fig. 2. Spatial patterns in intensity of crop responses (color and yield) to increasing rate
of N fertilization from 56 to 112 kg/ha. Magnitude of the response is
proportional to the intensity of shading in this figure. A positive color response
indicates that the canopy had a darker green color with a higher N rate.
Maps showing spatial patterns of crop response to increasing rate of N
fertilization from 56 kg N/ha to 112 kg N/ha are presented in Fig. 2. Positive and
negative responses are mapped independently to illustrate spatial patterns and intensity
of both positive and negative effects. The patterns of crop color and crop yields tend to
agree that the first increase in rate of fertilization gave a positive response in a large area
in the middle of the west half of the field (Fig. 2a and b) and a negative response in
areas at the north end and along the east edge of the field (Fig. 2c and d). Some areas
showed lack of agreement. The color response, for example, was much greater in the
area of the old barn (360 m north 5 m east in Fig. 2).
Disagreements between color response and yield response should he expected
under some conditions. Under conditions of extreme deficiencies of N, for example, an
increment of added N can increase yields in situations where the higher yielding corn is
still deficient and shows strong symptoms of N deficiency. Color response and yield
response would show opposite trends if additions of N intensify drought-stress at silking
time, reduce the number of kernels formed, and decrease the amount of N translocated
from leaves as the grain develops. Under these conditions, the net result of added N is
more green leaves and less grain at the end of the season. Such observations underscore
the need for ground-truthing aerial photographs with yield data or tissue-test data. They
also illustrate that studies of contradictions between color photographs and yield maps
may provide significant information that cannot be obtained from only the photographs
or yield maps.
6
Fig. 3.
Spatial patterns in intensity of crop response (color and yield) to increasing
rate of N fertilization from 112 to 168 kg/ha. Magnitude of the response is
proportional to the intensity of shading.
Canopy color showed positive responses to increasing N rate from 112 to 168 kg
N/ha (Fig. 3c) in areas of the field having negative yield responses (Fig. 3d). This
probably is explained by the extra N intensifying drought stress during the silking
period. The maps showed that yield responses to this increment of N were negative over
much of the field. Data in Fig. 3 illustrate that when comparing high rates of N spatial
patterns in color response provide little useful information unless they are compared
with spatial patterns in yield. Comparisons of spatial patterns in color response and
yield response, however, help to identify probable reasons for differences in yields.
These comparisons also give information as to how land area in the field should be
divided for effective studies of the most important factors affecting N fertilizer
requirement.
Data from this field in 1995 clearly support previous observations that yields are
not reliable indicators of N fertilizer needs. Some of the best yield responses to the first
incremental increase in N rate occurred in an area of the field having the lowest yield
potential. Negative yield responses were produced by this increment in areas having
some of the highest yields. The finding that yields were decreased by increasing N rates
from 112 to 168 kg N/ha underscores the need to base N fertilizer recommendations on
observed responses to N fertilization.
Spatial patterns of N response usually showed no obvious relationship to soil mapping
units in the field studied. Additional studies on this soil association are necessary to
determine if patterns of yield response can be predicted from measurable soil
characteristics. Studies in other fields have shown patterns of yield response that vary
with measurable differences in soil characteristics, and additional studies are needed to
7
determine whether these patterns are reasonably stable across a normal range of weather
conditions. Although it will never be possible to adjust N rates for weather conditions
after fertilization, it should he possible to identify the rates most likely to maximize
profits for the producer within the range of weather conditions normally encountered.
CONCLUSIONS
Aerial photographs of cornfields taken in August show spatial patterns in color
that reveal crop-sensed differences in soil properties. These patterns can he compared
with soil survey maps and with aerial photographs of soils to determine if the spatial
patterns correspond to obvious differences in soil characteristics or soil mapping units.
Some of these patterns may be caused by earlier management practices and explained
by considering management history.
Application of N fertilizer treatments in strips enables characterization of spatial
patterns in response of the crop to N. Information provided by photographs of canopy
color can aid in interpretation of yield maps generated from data collected by combines
because the photographs have much grater power to resolve spatial patterns of crop
response that occur within small areas. The aerial photographs cannot replace yield
maps, however, because measurements of actual yield increases are necessary to
determine economically optimal rates of fertilization. Use of remote sensing with yield
monitoring, soil testing, and tissue analysis should aid efforts to map spatial patterns in
optimal rates of N fertilization within fields. Use of this method on selected fields
within a region should help develop a new generation of N fertilizer recommendations
that can be used to guide variable-rate fertilizer applicators in all fields within the
region.
ACKNOWLEDGMENTS
Funding for this study was provided in part by the Iowa Corn Promotion Board
and Agri Industries.
REFERENCES
BINFORD, G.D., A.M. BLACKINER, and B.G. MEESE. 1992b. Optimal
concentrations of nitrate in cornstalks at maturity. Agron. J. 84:881-887.
BINFORD, G.D., A.M. BLACKMER, and M.E. CERRATO. 1992. Relationships
between corn yields and soil nitrate in late spring. Agron. J. 84:53-59.
BLACKMER, T.M., AND J.S. SCHEPERS. 1996. Aerial photography to detect
nitrogen stress in corn. J. Plant Physiol. 148:440-44.
BLACKMER, T.M., J.S. SCHEPERS, and G.E. MEYER. 1995. Remote sensing to
detect nitrogen deficiency in corn. p.505-512. In P.C. Robert et. al., (ed.) Proceedings
of site-specific management for agricultural systems. Second international
conference. ASA, CSSA, SSSA, Madison, WI.
8
BLACKMER, T.M., J.S. SCHEPERS, and G.E. VARVEL. 1994. Light reflectance
compared to other N stress measurements in corn leaves. Agron. J. 86:934-938.
BLACKMER, T.M., J.S. SCHEPERS, G.E. VARVEL, and E.A. WALTER-SHEA
1996. Nitrogen deficiency detection using reflected shortwave radiation from
irrigated crop canopies. Agron. J.88:1-5.
BLADIMER, A.M., T.E MONRIS, and G.D. BINFORD. 1992. Predicting N fertilizer
needs for corn in humid regions: Advances in Iowa. p.57-72. In B.R. Bock and K.R.
Kelley (ed.) predicting N fertilizer needs for con in humid regions. Bull. Y226.
National Fertilizer and Environmental Research Center, Tennessee Valley Authority,
Muscle Shoals, Alabama 35660.
MAGDOFF, F.R., W.E. JOKELA, R.H. FOX, and G.F. GRIFFIN. 1990. A soil test for
nitrogen availability in the Northeastern United States. Commun. Soil Sci. Plant
Anal. 21:1103-1115.
9