MIDSEASON NITROGEN APPLICATION
USING REMOTE SENSING
D.L. Wright, Jr. and R.D. Ramsey
NASA Affiliated Research Center
Utah State University
Logan, Utah
V.P. Rasmussen, Jr.
NASA Geospatial Extension Program
Utah State University
Logan, Utah
J.W. Ellsworth
Twin Falls R&E Center
University of Idaho
Twin Falls, Idaho
ABSTRACT
An application of nitrogen (N) late in the season has the potential to increase yield
and/or quality of wheat. The objective of this study was to use remote sensing
data to quantify N stress in wheat and to monitor growth through an entire
growing season. The circular, center-pivot study site was divided into four equal
quarters. Each quarter was subdivided into four plots; each having a different N
rate (0, 40, 100, and 130% of the farmer recommended N rate) applied to
Westbred 936 hard red spring wheat. Two quarters of the pivot containing eight
plots received no additional N, and the other two quarters received additional N
estimated with remote sensing. Five randomly selected points in each plot were
randomly selected for sampling. An empirical equation relating the Normalized
Difference Vegetation Index (NDVI) values to the midseason nitrogen content of
the wheat (flagleaf N) was used to quantify N deficiency for a midseason
application. Supplemental N was applied at heading. Water was limiting during
the growing season because of an Idaho power company buyback program.
Harvest results showed significant differences in the yield between plots with N
applied at midseason and those without any supplemental N, however, stress due
most likely to lack of water linked to topography was the greatest source of yield
variation. Yield was highly correlated with topography (R2 =0.92). Remote
sensing can be an effective tool to quantify in-season N applications in hard red
spring wheat.
Keywords: Precision agriculture, Remote sensing, GIS, Nutrient management,
Midseason nitrogen application, Protein augmentation
INTRODUCTION
Remote sensing has been used in agricultural research and management for
over three decades. It has not reached its full potential at the farm level because of
cost, spatial resolution, and advanced technical skill requirements. However,
factors including competition among satellite and aerial imagery providers;
improvements in resolution and accuracy of imagery; and Internet availability are
mitigating the limiting factors affecting the wide-spread use of remote sensing.
Nitrogen (N) is a critical component of crop quality and yield. The adequacy
of available N has been observed by both trained and casual observers as the
“greenness” of the crop. Long-term cropping system researchers have
documented this from the earliest recorded wheat research in the western United
States (Widtsoe and Merrill, 1902 and 1905; Widtsoe, 1919; Bracken et al., 1930;
and, Bennett et al., 1954). Large-scale chlorosis, or the obvious absence of
chlorophyll and carotenoids, is easily identifiable by a field scout or by anyone
with an adequate view of the crop (leaf) canopy. Chlorosis can be caused by
deficiencies in vital nutrients such as nitrogen, iron, sulfur, and magnesium or by
the competitive action of specific ions at the root-soil interface. Chlorosis can also
be caused by disease or other crop stresses. However, field-scale chlorosis is
usually attributed to N deficiencies, while spotty chlorosis is usually attributed to
disease or other crop stresses (Wescott, 1998).
Abundant plant N uptake in wheat will tends to increase grain protein. One
common practice used by farmers to increase grain protein is to apply N at or near
anthesis. Recent greenhouse research has shown an increase in wheat protein
content when ammonium (NH4+) is applied after crop heading (Hooten, 1998;
Muhlestein, 2001). These studies have shown an increase over 50% in protein
content above the usual maximum (8-15%). This finding has a couple of
ramifications. First, the addition of N as NH4+ may be a method of increasing
protein in wheat, and second, N content in the flag leaf is increased and may
produce a spectral signature characteristic of abundant N content in the crop
(Muhlestein, 2001).
Midseason N applications may help both yield quantity and quality in certain
environments. Wuest and Cassman (1992) found the amount of N applied at
anthesis had the greatest influence on postanthesis uptake of N; however, Boman
et al. (1995) found that midseason nitrogen applications resulted in tissue damage
and lower forage yields due to less early-season growth.
Many investigators have reported site-specific measurement methods for N
using ground-based sensors such as Minolta’s SPAD chlorophyll meter (Murdock
et al., 1999; Peterson et al., 1999, and Stevens and Hefner, 1999). These sensor
measurements enable site-specific, need-based applications of supplemental N
fertilizers. Remotely-sensed N stress indicators have also been reported with
increasing frequency (Beatty et al., 1999, Cassady et al., 1999, Herzog et al.,
1999, Perry et al., 1999, Read et al., 1999, Schadchina, T. M., 1999, Zur et al.,
1999, Johannsen et al., 1998, and Schepers et al., 1996).
Several new technology developments suggest the need for further study of
wheat N management as assisted by remote sensing. New satellites will be
launched in the near future designed to carry improved sensors, provide better
revisit time, and produce higher spatial resolution. The impacts of both
environmental regulation and the public perception of agriculture’s role in
groundwater pollution also necessitate the need for studies that will aid
agricultural management. Khanna and Zilberman (1997) modeled the economic
and environmental impacts of precision fertilizer technology adoption, indicating
the likelihood of increased productivity and decreased input requirements and
pollution. These sustainable outcomes are requisite – especially as the growing
human population increases demand on finite resources. Technology, particularly
space-borne imaging sensors, can help with this task provided that reliable
methods can be used to quantify crop health and nutrient status.
A previous experiment in Minidoka, Idaho was designed to test remote sensing
as a tool for nutrient management. 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 from aerial and
satellite platforms. These methods of nitrogen stress detection were compared for
accuracy, timeliness, usefulness, and cost. Wright et al., (2001) concluded that
remote sensing is indeed a viable tool for nitrogen stress detection in soft white
spring wheat.
The experiment presented in this paper was designed to use remote sensing
information at early and midseason growth periods of wheat to determine nitrogen
stress and then to apply N to stressed areas for one half of the study area.
Concurrently, N application for the remaining half of the study site was based on
traditional visual estimates of stress. Our objectives were (i) to monitor the
growth of a wheat crop throughout an entire growing season, (ii) to observe wheat
growth under nitrogen stress compared with non-stressed vegetation, (iii) to apply
midseason N based on digital image analysis as compared to ground based visual
estimates, and (iv) to analyze additional benefits of digital imagery and GIS
technologies to evaluate current and perennial field characteristics.
The state of Idaho implemented an initiative to conserve energy in the 2001
growing season termed “the Power Buyback Program.” Wheat growers who
participated in the program were paid a certain amount of money for every
kilowatt hour of power saved. Many farmers in the Northwest took advantage of
the program and either didn’t irrigate, or greatly reduced the amount of water
applied to their crops. The Idaho Power Buyback Program had direct implications
on this 2001 growing season experiment.
METHODS AND MATERIALS
The study site is located in south-central Idaho, along the Snake River Plain on
60 Ha of land in Minidoka County, Idaho (42º46’ N, 113º27’ W). Average annual
precipitation is 203 to 279 mm. Average annual temperatures range from 7 to
11ºC. The soil at the Minidoka site is predominately Minidoka silt loam (Xerollic
Durorthid), with minor intrusions of Portneuf silt loam (Durixerollic Calciorthid).
These soils are generally shallow (150 cm or less) and overlay basalt uplands. The
normal crop rotations for wheat in southern Idaho are wheat/corn/alfalfa,
wheat/fallow/wheat,
alfalfa/wheat/barley,
potatoes/wheat/corn,
and
sugarbeets/wheat (Fuchs and Hirnyck, 2000).
The crop rotation for the study site was potatoes (1999), sugarbeets (2000),
and wheat (2001). The wheat was Westbred 936 variety of hard red spring wheat.
According to the Idaho Agriculture Statistics Service, Westbred 936 surpassed
Penewawa last year to become Idaho’s most popular wheat variety (IASS 2000).
Westbred 936 is a white-chaffed, awned, early season, semidwarf variety released
by Western Plant Breeders in 1993. Westbred 936 has stiff straw with a high-test
weight/yield potential and is tolerant to stripe rust and moderately tolerant to
stem/leaf rust, but is susceptible to powdery mildew (University of Idaho, 2000).
Westbred 936 has excellent yield potential, straw strength, and uniformity. This
wheat variety also has good stress tolerance, very good test weight, and high
protein percent (Western Plant Breeders, 2000).
The study site consisted of a standard center-pivot irrigated field. The circle
made by the pivot was divided into four quarters with four plots in each quarter.
Each plot was 20 m wide and the radius of the field (402 m). Four different rates
of nitrogen were applied in each quarter to represent rates both above and below
normal and to represent normally applied nitrogen. The first rate represented a
region of no applied nitrogen (rate of nitrogen application = 0). The second rate
represented a region of under-applied nitrogen (rate = 40% of normal). The third
rate represented a region of over-applied nitrogen (rate = 130% of normal). The
fourth rate represented the “normal” nitrogen application (rate of nitrogen
application = normal). Within each transect, five points were randomly selected to
collect soil samples, tissue samples, harvest samples, and pixel values of imagery.
Two of the quarters were managed by traditional techniques and two of the plots
were managed using remote sensing information.
Ground truth analysis was performed during the months of May and June to
correspond with imagery and included (i) collecting tissue samples for laboratory
analysis, (ii) acquiring digital photographs to calculate percent cover, and (iii)
utilizing a spectroradiometer to collect ground-based spectral reflectance
measurements. All leaf samples were processed at Stuckenholz Labs in Twin
Falls, Idaho, using a specific ion electrode test for nitrate-N and dry combustion
(equivalent to the Kjeldahl method) for total N. The tissue samples were taken at
different locations close to the exact sample point to minimize skewed results in
the imagery.
Images were collected during May and June with a Real-time Digital Airborne
Camera System (RDACS) (Pearson et al., 1994). Images from this sensor were
geometrically corrected and converted to a Normalized Difference Vegetation
Index (NDVI).
Wright et al. (2001) established midseason NDVI values collected from
imagery depicting nitrogen stressed and non-stressed plots. From these data, a
simple linear equation relating NDVI stressed and non-stressed values and
nitrogen deficiency was established:
N = NDVI*2000
Where N is the amount of nitrogen needed and NDVI is the difference between
NDVI pixel averages for areas of stressed and non-stressed vegetation. This
formula was adapted from an equation for the SPAD chlorophyll meter (Murdock
et al., 1997) and calibrated using ground-truth data from the year 2000 growing
season. The multiplier value of 2000 is rounded from the calculated slope to
simplify the calculation of N. No intercept was included under the assumption
that when there is no difference between stressed and non-stressed wheat, no
additional N is needed. The equation is limited to this study. However, additional
data from other study sites under different water and nutrient conditions could be
used to generate a more universal estimate. Based on this formula, a midseason
application of N was applied to stressed areas (0 applied N plots) in the two crop
quarters managed by remote sensing information.
Following the midseason application of N, one square meter of wheat was
harvested at each sampling point with hand sickles to collect sample-point yield
data. A combine equipped with an Ag Leader PF3000 yield monitor and a
differentially corrected GPS was used to collect yield data on a field scale. Yield
data were used to evaluate the influence of elevation by segmenting yield and
elevation using a 5-class quartile break. Averages from each break were regressed
to determine the influence of elevation on yield. Analysis of variance was
performed on the N content of plant tissue, and NDVI values for the imagery
within each sampling date and means were separated using Fisher’s least
significant difference.
RESULTS AND DISCUSSION
Due to the Power Buyback Program, water application was restricted to 80%
of normal. The grower who manages the study site reported a 20% overall
reduction from average yields due to lack of water. The concomitant reduction in
plant growth affected N uptake, thus reducing our ability to detect variations in N
applications.
Tissue samples taken on May 11, 2001, at Feekes 1 (4-leaf) growth stage
showed no significant difference in nitrogen content between the plots with 40,
100, and 130 percent of normally applied nitrogen (Table 1). The plots with 0
applied nitrogen (N), however, showed significant differences at the .01 alpha
level. Tissue collection on June 2, 2001 showed a similar trend to that of the first
acquisition with regard to total nitrogen but a more significant difference in nitrate
content of the plant. These results suggest the 40% of normal nitrogen rate was
sufficient for wheat crops in years of comparable reduced water application.
All images used for in-season decisions were captured from an aerial platform.
The first image taken on May 11, 2001, at the 4-leaf stage, showed no sign of
stress due to nitrogen deficiency. The next image taken on June 6, 2001, showed
the same stress pattern as the initial image. Significant difference was observed in
the 0 applied nitrogen plot when compared to the other plots, showing a positive
relationship between remote sensing and tissue sampling.
Table 1. Comparison of wheat plant tissue samples for three different dates.
N Rate
Total N
mean
%
P>F
Nitrate N
mean
P>F
ppm
% normal
15-May
130
4.7a*
<.0001
3085.5a
<.0001
100
4.93a
2540.7a
40
4.64a
2225.5ab
0
3.9b
1433.9b
5-Jun
130
4.24a
<.0001
1341.5ab
<.0001
100
4.32a
1612a
40
4.16a
949bc
0
3.18b
616.5c
*Within a column, means followed by a different letter showed significant
differences (P < .01)
NDVI values from May 24, 2001 were applied to the NDVI stress/no-stress
equation created from a previous year’s data (Table 2). The results of this
equation were based on normal water conditions and therefore gave values higher
than needed. The values of nitrogen were averaged and the amount of nitrogen
was given to the grower’s fertilizer company. On June 14, 2001, 151 kg of N was
applied to the plots with stressed wheat.
Imagery examined during the season revealed irrigation malfunctions from a
few sprinklers on the center pivot (Figure 1). The ring close to the outer edge was
the most obvious, but an inner ring 20 m from the center was also noted. The
lateral strips are the plots with no applied N.
Yield monitor data revealed three main causes of variability at the study site;
(i) nitrogen treatments, (ii) irrigation problems, and (iii) water and N stresses due
to topography. Although this experiment was designed to compare N treatments,
the effects of topography, due most likely to sub-irrigation, exceeded variations
due to nitrogen stress. However, analysis showed results satisfactory to the
expectations of the experiment design.
Table 2. Results of two tests for nitrogen deficiency on the May 24, 2001
image.
Sample NDVI Stressed NDVI Non-stressed
1
2
0.4238
0.4248
0.504
0.49
N = D*2000
lb acre-1
130
160
Equivalent
kg ha-1
145.6
179.2
Figure 1. A midseason NDVI image from May 25th showing water stress
(dark ring) and N-stressed plots (horizontal lines in the middle of the pivot).
Nitrogen Stress Analysis
Yield from the plots in quarters one and two ranged from 2621 to 4314 kg ha-1
(Table 3). The yield from the plots in quarter two was lower than plots in quarter
one due most likely to topography effects (water variation, slope, aspect, soil type,
texture, etc). Quarters three and four exhibited much less yield variance compared
with the variances of plots one and two, most likely due to the midseason N
application. The 0 applied N on plot four was an exception because the plot lies at
a higher elevation and was directly affected by stress due to topographic effects.
These results suggest that a midseason N application based on remote sensing can
improve yield by increasing uniformity at harvest.
Table 3. Harvest data from the yield monitor.
Plots Managed Traditionally
N rate
plot
mean yield
%
kg ha-1
0
1
3279
0
2
2621
40
1
3252
40
2
2822
100
1
4314
100
2
4234
130
1
3595
130
2
3226
* No additional N
** Midseason N application
Plots Managed by Remote Sensing
N rate
plot
mean yield
%
kg ha-1
0
3
4193
0
4
2977
40
3
3965
40
4
3810
100
3
4301
100
4
3790
130
3
3884
130
4
3259
Water Stress Analysis
The water stress outer ring discussed above and seen in Figure 1 was caused
by an undersized sprinkler head. The nozzle was replaced at heading of the wheat
crop. Geographic Information System (GIS) analysis from the yield monitor
revealed the area of the field affected by this undersized sprinkler head was 1.7
ha, and the yield decreased by 670 kg ha-1. The stress caused by the smaller ring
was not corrected during the season. The area affected by the smaller ring was
283 m2, and the yield decreased by 2010 kg ha-1.
Effects Due to Topography
An elevation map of the study site was created by using the elevation column
in the data table from the yield monitor. The yield data was compared with the
elevation data with surprisingly high degree of correlation (R2 = 0.92) and is
shown in Figure 2. The study site was 805 m in diameter and varied in elevation
by only 12 m, yet this had significant effect on the yield (Table 4). Soil effects
due to topography affected the growth of wheat, by restricting water flow that in
turn affected the nutrient content of the soil.
CONCLUSIONS
The purpose of this experiment was to examine midseason N management
using remote sensing. The results suggest this is very realistic and attainable for
growers with irrigated wheat. Remote sensing is also extremely valuable in
helping identify irrigation problems during the early stages of crop development,
and can be used with yield data in a GIS to analyze crops in the field. However,
image processing and timeliness of data must be addressed before farm managers
can use the data effectively.
Working with active growers can present situations that are not anticipated.
Irrigation applied at rates less than normal most likely compounded stress due to
topography in our study area during the 2001 growing season. Stresses observed
by remote sensing were a combination of limited N, irrigation malfunctions, and
topographic fluctuations. Understanding the variables limiting or enhancing
production in agricultural fields can be enhanced through remote sensing
information. Information from remote sensing and GIS will allow farmers to
properly mitigate variables that cause loss of production and quality.
Table 4. Comparing elevation with yield.
Elevation
m
1297-1299.9
1300-1301.9
1302-1303.9
1304-1305.9
1306-1309
Samples
N
4073
1267
1982
783
1405
Mean
kg ha-1
4970
3974
3786
1789
1203
Std Dev.
1493
1447
1560
538
592
Elevation
Yield
High Elevation and Low Production
Low Elevation and High Production
Figure 2. Elevation map versus a yield map of the Minidoka, ID study site.
ACKNOWLEDGMENTS
The funding for this research was made possible by NASA and supported by the
staff at Stennis Space Center. Special thanks are expressed to Duane Grant for
providing the study site and Mike Larsen, our contact for Duane Grant’s field.
Thanks also to ITD Spectral Visions, who provided the imagery and a quick turn
around when it mattered most.
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