A COMPARISON OF NITROGEN STRESS DETECTION METHODS IN SPRING WHEAT*
Dennis L. Wright Jr.
V. Philip Rasmussen Jr.
Christopher M. U. Neale
Utah State University
Logan, UT USA
Kurt Harman
Greg Searle
Land View Systems
Minidoka, ID USA
Duane Grant
National Association of
Wheat Growers/Idaho
Wheat Commission
Minidoka, ID USA
Cliff Holle
NASA Affiliated
Research Center
Utah State University
Logan, UT USA
ABSTRACT
This experiment was designed to compare ground-based methods of nitrogen (N) stress
detection with methods using remote sensing information. The study area, located in Minidoka,
Idaho, was a 64 ha (quarter section) center pivot with a crop of Penawawa spring white wheat.
Nitrogen was varied on four transects approximately 40 m wide and 805 m long. The N
application rates chosen for the research were 0%, 40%, 100%, and 130% of normal. Nitrogen
deficiency was quantified from tissue sampling and estimated at key stages in the wheat growth
cycle using visual observation, a chlorophyll meter, and remotely-sensed data. These methods
of nitrogen stress detection were compared for accuracy, timeliness, usefulness, and cost.
Remote sensing was comparable to the chlorophyll meter in accuracy. The chlorophyll meter
was the timeliest method for obtaining a quantitative measurement. Remote sensing is the most
economical method, assuming a provider preprocesses the imagery for the grower.
1.0 INTRODUCTION
If farmers are to remain economically viable with the present economic depression in agriculture, they must increase
revenues while reducing costs. Precision agriculture is one route presently being explored to help cut costs and
increase yield quality and quantity. Precision agriculture is farming based on spatially site-specific information,
and may include remote sensing, variable rate fertilizer application, variable seeding rates, and variable irrigation
rates. Precision agriculture utilizing remote sensing imagery shows promise in helping the farmer save money,
increase yield, and reduce the pollution from fertilizer overapplication.
There have been many applications for remote sensing in agriculture in the last 60 yr. Some have proven effective,
while others have only cost farmers money and time. Until recently the use of remotely-sensed data has proven
most economical in high value crops (Johannsen et al., 2000). Now with increasing image resolution and decreasing
imagery prices, many farmers may start using data provided by remote sensing.
With the advent of global positioning systems (GPS), farmers were able to accurately locate spots in a field and
come back to the exact positions later in the season. Farmers began integrating precision agriculture in their
production systems in the 1990s as GPS technology became affordable by varying fertilizer rates based on known
deficiencies and by installing yield-measuring devices on harvesting equipment. From this yield monitoring data,
farmers could return to positions with decreased production to determine the reasons for the low yield.
Through new GPS and remote sensing technologies, new applications are becoming available to farmers. Some new
products can be found at websites that provide preprocessed imagery. These websites allow farmers to pay a small
price and receive images of their fields, which can be used to measure and examine any field problems. The
websites eliminate the cost of expensive image processing software for the farmer, making the use of remote sensing
easier and affordable.
Although the most common use of remote sensing in agriculture is yield predictions (Aase et al., 1982; Anderson et
al., 2000; Bhatti et al., 1991; Heilmann et al., 1977), perhaps a more valuable tool for the farmer would be using
* Presented
at the Third International Conference on Geospatial Information in Agriculture and Forestry, Denver, Colorado, 5-7 November 2001.
remotely-sensed data for real-time scouting. Until recently there has been little application of remotely-sensed
scouting for several reasons. Limited imaging opportunities for a given field, poor image delivery turnaround, and
insufficient image resolution were all factors contributing to the low rates of usage. However, the recent launch of
the IKONOS and other high-resolution satellites should help foster agricultural scouting by providing more
acquisition opportunities, better resolution, and cheaper prices (Bullock et al., 2000).
This research was funded by NASA’s Ag20/20 Program to evaluate the latest remote sensing technology for
assessing crop stress. It is now possible for farmers to purchase imagery for observing crop stress from Internet sites
for a price starting out at $.15 ha-1 (Earthscan, 2001). Farmers are able to buy multiple images during the course of
the season at key growth stages of the crop to utilize for improving both yield and quality with a midseason nitrogen
application. This project was designed to assess the technical and economic feasibility of using remote sensing for
such an application.
This experiment was designed to test whether imagery taken in midseason without any calibration corrections for
atmospheric effects can detect nitrogen (N) stress as well as the current ground-based methods of N stress detection.
This experiment compared accuracy, time, and cost of each of the methods.
2.0 METHODS AND MATERIALS
2.1
EXPERIMENT DESIGN
Four transects, each 36.5 m wide, were established across the entire diameter of a standard 64.75 ha (160 acre)
center-pivot field (Figure 1). Urea-sulfuric acid (USA), 26-0-0-06, was used as a nitrogen fertilizer. Transect 1
represented a region of no applied nitrogen (rate of USA application = 0 L ha -1) and served as the control. Transect
2 represented a region of underapplied nitrogen (rate = 198 L ha -1). Transect 3 represented a region of overapplied
nitrogen (rate = 655 L ha-1). Transect 4 represented the “normal” USA application (rate of nitrogen application =
496 L ha-1), or the rate the farmer was using. Translated into mass of nitrogen, the normal amount was 168 kg ha -1
(150 lb acre-1).
In each of these transects, seven points were randomly selected using a grid of the transects and a random number
generator on a calculator. A Trimble TSC1 GPS unit with a Pro XRS receiver was used to navigate to each point
during the sampling. Using the DGPS capabilities of the GPS unit provided by Omnistar, the horizontal accuracy is
better than 50 cm + 1 ppm times the distance between the base and the rover.
Figure 1. Experiment Design for the 2000 Nitrogen Study in Minidoka, ID
2.2
MATERIALS
The test plots were located on a Kimama silt loam (0-2% slopes), a fine-silty, superactive, mesic Calciargidic
Argixeroll, with minor intrusions of Portneuf silt loam, a coarse-silty, mixed, superactive, mesic Duridnodic Xeric
Haplocalcid. These soils are brown (10YR 5/3) when dry, very dark grayish brown (10YR 3/2) when moist,
alluvium derived from loess. Soils are generally shallow (150 cm or less) and overlay basalt uplands (USDA et al.,
1975).
The wheat variety chosen for the experiment was Penawawa, a soft, white, semi-dwarf spring wheat. The
Agricultural Research Service, USDA, and Washington Agricultural Experiment Station developed Penawawa from
the joint cross of Potam 70/Fielder. Penawawa was released in 1985. The spikes are erect, semilax, and oblong in
shape with medium long awns. The kernels are mid-sized, soft, white, mid-long, and ovate with a large bush and
ovate germ. Penawawa is resistant to stripe rust, leaf rust, and stem rust. It is moderately susceptible to mildew,
common bunt, and hessian fly. Industry has accepted Penawawa as having satisfactory quality for milling and pastry
(Bowman et al., 1997).
The plot was irrigated with a Valley low-pressure center pivot irrigation system with drip nozzles. During the
course of the season, 58 cm (23 in) of water was applied to the wheat. In addition to the urea-sulfuric acid fertilizer,
50 units of phosphorous were applied as 11-52.
2.3
DATA ACQUISITION
On 3 separate occasions, ground samples were taken simultaneously with aerial and satellite imagery. These dates
were May 20-22, 2000, when the wheat was at the Feekes 6th growth stage (Large, 1954); June 6-7, 2000, at Feekes
10th growth stage; and June 28-29, 2000, at the 10.5.2 growth stage. The weather on these days was sunny and
clear with a light west wind.
2.3.1
Visual Method
On each visit to the field, pictures of each transect were taken. Visual observations of color, height of plants,
vegetation thickness, and number of tillers were noted. This measurement technique is, admittedly, very subjective.
2.3.2
Chlorophyll Meter Method
The meter used was a SPAD 502 chlorophyll meter made by Minolta. The meter was clamped on a leaf and pressed
to give a reading of the amount of chlorophyll in the leaf on a scale of 1-100 based on transmission of two
wavelengths through the leaf. The top leaf was chosen to sample because it reflects the current nitrogen condition,
and the sample was taken from the middle of the leaf based on prior studies of chlorophyll meter sampling (Hoel,
1998). Thirty samples were taken and averaged at each of the seven points per transect.
2.3.3
Tissue Samples
Tissues samples were taken from each of the field sampling points in order to correlate total nitrogen against other
methods. In the first acquisition (Feekes sixth growth stage), 30 tillers were taken from each point. In the second
and third acquisitions, 50 flag leaves were pulled from each point. The same leaves used for tissue sampling were
used for chlorophyll meter sampling. The samples were pulled 1 m north of the point during the first acquisition and
then from the east and south, respectively, during the second and third acquisitions in order to minimize damage to
the wheat. All samples were processed at Stuckenholz Labs in Twin Falls, Idaho, using the Specific Ion Electrode
test for nitrate-N and the Dry Combustion test (equivalent to the Kjeldahl method) for total N.
2.3.4
CAMS and ATLAS Sensors
The CAMS and ATLAS airborne sensors were mounted on a NASA Learjet 23 and flown at 7500 m above sea level
to get 2.5 m pixel resolution. The sensors were flown on May 21, June 6, and June 28, 2000 between 11:00 a.m. and
1:00 p.m. The CAMS sensor has nine channels from .45-12.5 m, and the ATLAS sensor has 14 channels. Both
sensors have bandwidths for blue (.45-.52 m), green (.52-.60 m), red (.63-.69 m), and NIR (.76-.90 m). All
flight lines were processed through the CAMS/ATLAS “known corrections” algorithm, to provide an initial
georeferencing. The algorithm involved the inertial mapping unit, GPS data, and calibration data from the sensor.
The flight lines were reformatted to ERDAS Imagine format, and the CD-ROMs of the georeferenced data were sent
to Utah State University once preprocessing was complete.
2.3.5
IKONOS
Five IKONOS images were ordered, and four were actually received as of October 10, 2000. The dates of the
IKONOS image acquisitions corresponded within 24 hours of tissue and chlorophyll meter samples. These image
and ground collections occurred May 20, 2000, at 10:00 a.m.; June 7, 2000, at 11:00 a.m.; and June 29, 2000, at
10:00 a.m.
2.4
IMAGE PROCESSING
All of the images, both aerial and satellite, were initially examined and processed using ERDAS Imagine. Points
were collected at eight different intersections in a 2-km radius around the study area using the Satloc GPS unit with
DGPS provided by Omnistar. These points were converted from a Shapefile to an Arc coverage in Arc/Info and
brought into ERDAS Imagine as a vector layer. This layer was used to georectify one IKONOS image using a first
order transform. This image was then used to rectify the other IKONOS images. The IKONOS image was also
used to correct the aerial imagery. The digital numbers of images were all enhanced using RVI, NDVI, and SAVI
with the digital numbers to give a value for each point in the transects. RVI was used in statistics because the
vegetation cover reached 70% or more for all of the images. Vegetation cover was estimated for the May 20, 2000,
acquisition by selecting 100 random points in two digital photos and counting the points on vegetation.
2.5
HARVESTING
Wheat was harvested on August 3, 2000. Harvesting was done using hand sickles, bags, twine, and a 1 m piece of
PVC pipe. The PVC was placed between two rows, and the two rows were harvested with hand sickles from the
bottom of the plant. The wheat from the 1 m by .3 m plot was tied at the bottom, and a paper bag was tied around
the top to prevent loss of kernels. These samples were then taken to Utah State University and dried in the drying
ovens at the Greenville Experiment Farm at 105 degrees Fahrenheit. The samples were weighed with a Sartorius
3862 scale to calculate total dry mass after two days in the oven. They were then threshed using a Vogel small plot
thresher designed for research use. The threshed grain was put in smaller lunch bags and weighed to calculate grain
yield. The samples were then ground in a Udy cyclone grinder with a 1 mm screen and analyzed on a NIRSytems
6500 spectrometer for protein.
3.0 RESULTS AND DISCUSSION
3.1
CORRELATION
The accuracy of each method was one of the most important points of this study. Accuracy is expressed through
correlation. In this experiment, the methods of using a chlorophyll meter and imagery from the IKONOS platform
and CAMS/ATLAS aerial sensors were correlated to the values of total-N and nitrate-N. The methods of N stress
detection were also correlated to yield results because (1) this is the actual result of the wheat plant, (2) this is the
most valuable information to the farmer, and (3) the yield may be more accurate information on the plant than the
lab tests for total-N and nitrate-N.
A correlation matrix is an easy way to show correlation between multiple variables. The values are Pearson r
values, which can be squared to give the more common r2 value. It is important to remember that each stage is not
equal. Although the methods correlate well with total-N in the June 6, 2000 acquisition (Feekes 10th growth stage),
it is already too late to improve yield, and therefore the only benefit of supplemental N is for a protein increase.
3.1.1
May 20-22, 2000 Acquisition
The correlation matrix for the first acquisition (Table 1) shows the highest correlation of the methods of N detection
between IKONOS and nitrate (r2 = .59). IKONOS correlated even better with the end yield Total Dry Matter (r 2 =
.736) and well with grain yield (r2 = .63). The chlorophyll meter correlated with total-N and nitrate N almost equal
(r2 = .37, r2 = .36). CAMS correlated with nitrate the lowest of all (r 2 = .23) and also correlated much lower than
expected with IKONOS (r2 = .651), probably due to bad weather or sensor or data processing problems.
Table 1. Correlation Matrix of r Values for the May 20-22 Acquisition
1st Acquisition (May 20-22, 2000)
Total-N NO3-N SPAD IKONOS
Total-N
1.000
NO3-N
0.866
1.000
SPAD
0.606
0.602 1.000
IKONOS
0.628
0.780 0.660
1.000
CAMS
0.386
0.488 0.517
0.807
Dry matter
0.660
0.772 0.601
0.858
Grain
0.634
0.725 0.506
0.794
Protein
0.705
0.740 0.444
0.591
3.1.2
CAMS Dry matter Grain
1.000
0.647
0.594
0.410
1.000
0.971
0.625
1.000
0.591
Protein
1.000
June 6-7, 2000 Acquisition
The correlation matrix for the second acquisition (Table 2) shows a change in high correlation. Total nitrogen had
the highest correlation with IKONOS (r2 = .66), then SPAD (r2 = .71), and last ATLAS (r2 = .492). IKONOS
correlated much better with the ATLAS sensor (r2 = .802) than the CAMS sensor. Dry matter, however, correlated
best with IKONOS (r2 = .755), then ATLAS, (r2 = .593), then the chlorophyll meter (r2 = .43).
Table 2. Correlation Matrix of r Values for the June 6-7 Acquisition
2nd Acquisition (June 6-7, 2000)
Total-N NO3-N SPAD IKONOS ATLAS Dry matter
Total-N
1.000
NO3-N
0.615
1.000
SPAD
0.843
0.547 1.000
IKONOS
0.814
0.554 0.635
1.000
ATLAS
0.705
0.523 0.635
0.896
1.000
Dry matter
0.785
0.542 0.657
0.869
0.770
1.000
Grain
0.740
0.530 0.608
0.836
0.734
0.971
Protein
0.791
0.808 0.709
0.657
0.622
0.625
Grain
Protein
1.000
0.591
1.000
3.1.3
June 28-29, 2000 Acquisition
The correlation matrix for the June 28-29, 2000, acquisition (Table 3) had the lowest correlation to nitrogen of all
the acquisitions because the wheat at this point was at growth stage 10.5.2 and was starting to transfer the nutrients
up to the head. The chlorophyll meter correlated the best with total-N (r2 = .26), then IKONOS (r2 = .24), and then
ATLAS (r2 = .13). The highest correlations, as expected, are with the yield values. The highest correlation with
yield was between the total dry matter at harvest and the ATLAS sensor (r 2 = .76), then IKONOS (r2 = .74), and last
of all SPAD (r2 = .52).
These data were acquired at a time when an additional application of nitrogen could not have any effect. The wheat
was starting to senesce, and therefore, the methods detecting the stress correlated low.
Table 3. Correlation Matrix of r Values for the June 28-29 Acquisition
3rd Acquisition (June 28-29, 2000)
10.5.2 Feekes Growth Stage (Flowering)
Total N NO3-N
SPAD
Total N
1.000
NO3-N
0.488
1.000
SPAD
0.506
0.457
1.000
IKONOS
0.494
0.276
0.825
ATLAS
0.359
0.225
0.780
Dry Matter
0.398
0.142
0.724
Grain
0.341
0.176
0.682
Protein
0.455
0.553
0.707
3.2
IKONOS ATLAS Dry Matter Grain Protein
1.000
0.910
0.860
0.829
0.718
1.000
0.875
0.860
0.683
1.000
0.971
0.625
1.000
0.591
1.000
REMOTELY-SENSED IMAGERY
Imagery taken throughout the season showed the progress of the wheat growth. Stressed vegetation will not reflect
NIR as much as healthy vegetation and therefore shows up darker. The first image in Figures 2 and 3 (May 23,
2000) shows only the 0 applied N strip as having any stress. The second image in the time series (June 6, 2000)
shows both the 0 applied N and 40% of normal applied N as having some stress, and the third image (June 28, 2000)
clearly shows stress in the transects and on the edges of the field as well.
May 20, 2000
June 7, 2000
June 29, 2001
T1 T2 T3 T4
0 40 130 100
May 23, 2000
June 6, 2000
Figure 2. IKONOS Imagery Using Bands 4,3,2 in a Time Series
June 28, 2000
3.3
COST
The cost of the visual method was estimated at $60.00 for 81 ha (200 acre). However, the visual method must be
combined with another method of N stress detection to quantify the amount of N needed to ameliorate the
deficiency. The cost of using a chlorophyll meter to test for N stress was estimated at $360 per year, assuming that
this technology would be viable for a period of five years and normal depreciation. If this meter was used to test
over 243 ha, the cost per acre of the equipment only would be $0.24/ha, assuming that each acre was only tested one
time. When a value of time employed in testing is considered, at least $0.12/ha more would be required. Hence, a
total cost for testing for nutrient deficiency would be a minimum of $0.36/ha. For an 81 ha field, this would result in
a cost of $180. The cost of satellite and aerial imagery changes rapidly, but the estimated cost during the fall of
2000 was $100.00 for an 81 ha field. The cost is based on Internet sites that preprocess the imagery and provide
growers with an image and tools to use in conjunction with the imagery (Wright et al., 2000).
3.4
TIMELINESS
A chlorophyll meter is the timeliest method for generating a quantitative amount of N stress in a field. With a
simple equation, a farmer could actually have an answer to the amount of supplemental N needed in a matter of
minutes. The visual method requires a minimal amount of time for results depending on how thoroughly the field is
checked, but tissue sampling needed to quantify the N deficiency in a field in conjunction with visual interpretation
will take from 24 to 48 hours depending on the laboratory schedule. Aerial imagery took a few days to receive,
process, and quantify N stress. IKONOS data was not useful because the imagery was delayed until well after any
additional fertilizer input would help the wheat. If the imagery could be received for processing within 4-7 days,
this method would be viable from a time perspective.
3.5
ADDITIONAL BENEFITS OF REMOTE SENSING
There were advantages to the IKONOS method that have not been previously discussed but that are important to
note. Although the chlorophyll meter has similar accuracy to the IKONOS imagery, the chlorophyll meter and
tissue sampling were only sampling a small area. To diagnose a whole field of 64.7 ha is feasible, but to find all the
problem areas in the field would be next to impossible. This is the beauty of the IKONOS imagery, which allows a
different value for every 4 m pixel in the whole field (40468 pixels in the plot used for our experiment). It is also
well to note that although the field was nitrogen stressed for our study, the imagery, chlorophyll meter, and visual
interpretation could not easily differentiate the type of stress affecting the field. Nitrogen and sulfur stresses, for
example, have similar effects on plants. With hyperspectral imagery, this problem may be solved.
4.0 CONCLUSIONS
The objective of this study was to compare ground-based methods of nitrogen stress detection in wheat against
remote sensing (IKONOS) nitrogen stress detection for accuracy, cost, timeliness, and additional benefits.
IKONOS remote sensing was the most accurate of all the methods in the Feekes 6th growth stage (May 23, 2000)
and had similar results to the chlorophyll meter in the Feekes 10th growth stage (June 6, 2000) and Feekes 10.5.2
growth stage (June 28, 2000). The chlorophyll meter was as accurate as the IKONOS method after the Feekes 10th
growth stage. The CAMS aerial imagery had some problems and was not useful in the first acquisition, but the
ATLAS sensor used in the second two acquisitions was comparable to the IKONOS imagery and the chlorophyll
meter. The visual method is very subjective and must be combined with tissue sampling for any accuracy.
The visual method was the least expensive, but another form of sampling must be done to quantify the problem,
which increases the cost. The SPAD chlorophyll meter made by Minolta is around $1,500 for a one-time
investment. The satellite and aerial imagery would be the most economical, provided that data goes through a
carrier and is preprocessed for the grower.
The visual method was the timeliest method of N stress detection, but is very subjective and provides no quantitative
results. The chlorophyll meter was the timeliest method of N stress detection for a quantitative N deficiency value.
Aerial imagery took a few days to receive, process, and quantify N stress. Satellite imagery was received too late in
the season to be of value for the 2000 wheat crop.
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