AN ABSTRACT OF THE THESIS OF
Michael C. Roberts for the degree of Doctor of Philosophy
in Crop and Soil Science
Title:
presented on
July 16, 1991
.
Field Sampling and Mapping Strategies for
Balancing Nitrogen to Variable Soil Water Across
Landscapes.
Redacted for Privacy
Abstract approved:_
F.E. Bolton
Farmers know that wheat yields vary across landscapes
in accordance with soil depth and water storage.
This
study was conducted to (i) quantify the correlation
between yield and water storage as affected by landscape
position, (ii) develop soil, water, and yield sampling
strategies acceptable to farmers, and (iii) map soil
management units.
Two study areas were selected within
the Columbia Plateau winter wheat region.
Soil nitrogen, available water, and grain yield
patterns were estimated by the use of reconnaissance
transects across landscapes.
Grain yields and crop
***
available water were positively correlated
(r2
= 0.96
).
Data suggest that gently rolling fields (slopes < 12%) can
be sampled on a square 158 meter grid (variance estimate
of 0.5 Mg ha-1).
Sharply rolling fields with more
variation down slope than across slope (anisotrophy) can
be sampled with a rectangular grid; transects should
traverse slopes at 70 to 158 meter spacing (variance est.
0.5 Mg ha-1), and be sampled at 40 meter intervals
(variance est. 0.25 Mg ha
1
).
This arrangement will
ensure that each landscape position on the transect is
sampled.
Landscape units mapped from aerial photographs and
digital terrain models (tlree dimensional surface models
of field topography) were analyzed with geographic
information systems (GIS) to generate soil management maps
that correlate yield with variable soil water storage
across landscape positions.
Using soil management maps to
vary nitrogen application adjusted to available water
levels across landscapes will optimize grain protein
content, protect surface and groundwater, and decrease
production costs.
Field Sampling and Mapping Strategies for Balancing
Nitrogen to Variable Soil Water Across Landscapes.
by
Michael C. Roberts
A THESIS
submitted to
Oregon State University
in partial fulfillment of
the requirements for the
degree of
Doctor of Philosophy
Completed
July 16, 1991
Commencement
June 1992
Approved:
Redacted for Privacy
Professor Emeritus of Crop Science in charge of major
Redacted for Privacy
/ f
C/
Head of Department of Crop and Soil Science
Redacted for Privacy
Dean of Gra
School
Date thesis is presented
July 16, 1991
Typed by Michael C. Roberts
Copyright by Michael C. Roberts
March 16, 1991
All Rights Reserved
Acknowledgements
I would like to express gratitude to my wife,
Darlene, and my children, Bryce, Brooke, Breahna, Brandon
and Brett for their patience and faith with me.
I would
like to thank my family, friends, and fellow graduate
students for their support and encouragement with my
thesis.
I would like to thank Dr. Floyd E. Bolton for
support and assistance with this thesis.
ii
TABLE OF CONTENTS
INTRODUCTION
1
LITERATURE REVIEW
4
Identifying Yield Variability
4
Soil Water and Fertility
Remote-sensing
4
4
Assessing Spatial Variability
12
Classical Statistics
Geostatistics
12
13
Variable Field Mapping
20
Geographic Information Systems
Digital Terrain Models
Basic Cartography
20
22
23
CHAPTER I.
FERTILIZING WHEAT WITH NITROGEN BALANCED TO
SOIL WATER VARIABILITY ACROSS LANDSCAPES
24
.
.
.
ABSTRACT
24
INTRODUCTION
25
MATERIAL AND METHODS
28
The Pendleton study area
The Wasco study area
Sampling methods
Analyses
29
31
32
34
RESULTS AND DISCUSSION
36
REFERENCES
43
iii
TABLE OF CONTENTS
CONT.
CHAPTER II.
FIELD SAMPLING STRATEGIES BALANCING COST
AND PRECISION
45
ABSTRACT
45
INTRODUCTION
46
MATERIALS AND METHODS
47
RESULTS AND DISCUSSION
48
REFERENCES
54
CHAPTER III.
MAPPING STRATEGIES TO REPRESENT AND
MANAGE YIELD VARIATIONS ACROSS LANDSCAPES
.
.
55
ABSTRACT
55
INTRODUCTION
56
MATERIALS AND METHODS
57
Aerial Photography
Digital Elevation Models
Soil Management Map Units
RESULTS AND DISCUSSION
Digital Elevation Model Accuracy
Reconnaissance Sampling of Landscape Positions
Creation of Soil Management Maps
REFERENCES
57
58
59
59
59
60
61
67
CONCLUSIONS
69
BIBLIOGRAPHY
71
APPENDIX
77
iv
LIST OF FIGURES
Figure I.1.
Schematic representation of landscape
positions (A), and grain yield (B) along
transect 2, Wasco study area.
40
Figure 1.2. Wheat grain yield correlated to crop
available water (A),ula.total nitrate (B) at
Wasco study area.
indicate significance
at the 0.05, 0.01, and 0.001 probability levels,
respectively.
41
Figure 1.3.
Crop available water correlated to soil
profile depth (A); wheat grain yield correlated
to soil profile depth (B), and organic matter
(C), Wasco study area, transect 2.
42
Figure II.1.
Location of grain yield sampling sites
along transects, Pendleton study area
51
Figure 11.2.
Grain yield semivariogram, four
transects, Pendleton study area
52
Figure 11.3. Grain yield semivariograms indicating
anisotrophy; across slope (A), and down
slope (B)
53
Figure III.1 Digital elevation model for the
Pendleton study area derived from the Barnhart
USGS 7.5 Minute Quadrangle. View direction is
310°, 30° above horizon, vertical exaggeration
1:1 (no vertical exaggeration).
63
Figure 111.2 Digital elevation model for the Wasco
study area derived from the Wasco USGS 7.5
Minute Quadrangle. View direction is 110°, 30°
above horizon, vertical exaggeration 0.7:1.
64
Figure 111.3 Soil management map units for the
Pendleton study area. Legend, table III.1.
65
'
Figure 111.4 Soil management map units for the
Wasco study area.
Legend, table III.1.
.
66
V
LIST OF TABLES
Precipitation for fallow-crop cycle near
Table I.1.
Wasco and Pendleton study areas.
39
Table III.1 Soil management map unit legend with
family or higher taxonomic class.
62
vi
LIST OF APPENDIX FIGURES
Figure A.1. Wasco (Moore) study area transects
with sample locations plotted.
78
Figure A.2. Pendleton (Timmerman) study area
transects with sample locations plotted.
79
Figure A.3. Wasco (Moore) study area transect
2, soil profile characteristics.
80
Figure A.4. Wasco (Moore) study area transect
2, core 1, soil water (A), and nitrate (B),
profile data.
81
Figure A.S. Wasco (Moore) study area transect
2, core 2, soil water (A), and nitrate (B),
profile data.
82
Figure A.6. Wasco (Moore) study area transect
2, core 3, soil water (A), and nitrate (B),
profile data.
83
Figure A.7.
Wasco (Moore) study area transect
2, core 4.5, soil water (A), and nitrate (B),
profile data.
84
Figure A.8.
Wasco (Moore) study area transect
2, core 6, soil water (A), and nitrate (B),
profile data.
85
Figure A.9.
Wasco (Moore) study area transect
2, core 7, soil water (A), and nitrate (B),
profile data.
86
Figure A.10. Wasco (Moore) study area transect
2, core 8, soil water (A), and nitrate (B),
profile data.
87
Figure A.11. Wasco (Moore) study area transect
2, core 9, soil water (A), and nitrate (B),
profile data.
88
Figure A.12.
Pendleton study area (Timmerman)
transect 1, soil profile characteristics.
89
Figure A.13.
Pendleton study area (Timmerman)
transect 1, core 1, soil water (A), and
nitrate (B), profile data.
90
vii
LIST OF APPENDIX FIGURES
CONT.
Pendleton study area (Timmerman)
Figure A.14.
transect 1, core 2, soil water (A), and
nitrate (B), profile data.
91
Pendleton study area (Timmerman)
Figure A.15.
transect 1, core 3, soil water (A), and
nitrate (B), profile data.
92
Pendleton study area (Timmerman)
Figure A.16.
transect 1, core 5, soil water (A), and
nitrate (B), profile data.
93
Pendleton study area (Timmerman)
Figure A.17.
transect 1, core 6, soil water (A), and
nitrate (B), profile data.
94
Pendleton study area (Timmerman)
Figure A.18.
transect 1, core 7, soil water (A), and
nitrate (B), profile data.
95
Pendleton study area (Timmerman)
Figure A.19.
transect 1, core 8, soil water (A), and
nitrate (B), profile data.
96
Pendleton study area (Timmerman)
Figure A.20.
transect 1, core 9, soil water (A), and
nitrate (B), profile data.
97
Pendleton study area (Timmerman)
Figure A.21.
transect 1, core 10, soil water (A), and
nitrate (B), profile data.
98
Pendleton study area (Timmerman)
Figure A.22.
transect 2, soil profile characteristics.
99
Figure A.23.
Pendleton study area (Timmerman)
transect 2, core 1, soil water (A), and
nitrate (B), profile data.
100
Pendleton study area (Timmerman)
Figure A.24.
transect 2, core 2, soil water (A), and
nitrate (B), profile data.
101
Figure A.25. Pendleton study area (Timmerman)
transect 2, core 3, soil water (A), and
nitrate (B), profile data.
102
viii
LIST OF APPENDIX FIGURES
CONT.
Figure A.26.
Pendleton study area (Timmerman)
transect 2, core 4, soil water (A), and
nitrate (B), profile data.
103
Pendleton study area (Timmerman)
Figure A.27.
transect 2, core 5, soil water (A), and
nitrate (B), profile data.
104
Figure A.28.
Pendleton study area (Timmerman)
transect 2, core 6, soil water (A), and
nitrate (B), profile data.
105
FIELD SAMPLING AND MAPPING STRATEGIES FOR BALANCING
NITROGEN TO VARIABLE SOIL WATER ACROSS LANDSCAPES
INTRODUCTION
Some areas of the world are currently faced with the
challenges of food overproduction, while others face
critical shortages.
While political and distribution
problems may be beyond the control of researchers, a
strong awareness of the need to increase food production
exists.
Increased food production will require optimal
management of the arable land resources of the world.
The
amount of land projected to be brought under cultivation
will probably only balance the land removed from
agricultural production because of urbanization, increases
in salinization of irrigated land, desertification, and
the depletion of groundwater aquifers by irrigated
agriculture.
The production of food and fiber will be
increasingly relegated to marginal lands.
These marginal -- as well as some prime -- lands, are
sometimes quite variable in production potential within a
field or farm.
As increases in efficiency of subsistence
agriculture involves optimal management of limited
resources, plantation agriculture as well could optimally
manage soil resources on a soil basis within a field as
opposed to treating fields as a uniform unit.
This
approach to within-field management would conserve
2
resources, reduce the potential of non-point source
pollution of streams by agricultural chemicals, and
increase production efficiency.
Within a few years all satellites comprising the
global positioning system (GPS) will be in orbit.
Ground
vehicles using real-time, differential GPS systems can now
be guided to within 1.5 meters of the intended target or
travel line.
Algorithms soon to be released will increase
the three-dimensional accuracy to 0.1 meter.
The Federal Aviation Administration (FAA) is nearing
approval of GPS for commercial aircraft navigation and
glide path navigation for rural airports without
instrument landing systems.
Recent aircraft accidents on
runways are also hastening FAA approval of GPS.
Conflicts
with the Department of Defense concerning selectiveavailability (purposely induced signal degradation for
security reasons) is delaying FAA response.
Differential-
GPS accuracy is not affected by selective availability.
Without selective availability, only one receiver is
required for one meter accuracy.
This accuracy would
economically allow FAA to manage aircraft in flight and on
the ground at any airport (Montgomery, 1991).
The benefit of the FAA approval to agriculture is
reduced cost.
As more receivers are sold, the development
cost is recaptured, and the receiver cost is reduced.
The GPS navigation system would allow precise location
3
tracking and guidance of farm machinery within the fields.
Integrating the yield production potential maps and the
precise tracking of the farm machinery with computer
controlled equipment to distribute chemicals, fertilizers,
or water would allow optimum crop production on variable
soils.
This document is divided into three chapters or
papers.
The first paper correlates yield variations to
stored soil water across landscape positions.
The yield-
water correlation will help farmers balance nitrogen
fertilizer with stored soil water so that
overfertilization, groundwater and grain protein problems
will be alleviated.
The second paper focuses on classical and
geostatistical soil sampling strategies acceptable to
farmers.
Farmers apply fertilizer based on estimates of
future rainfall or available water -- estimates that are
correct less than half the time.
The farmer must know
where to sample so that a few samples will provide
adequate, precise information upon which to base
fertilizer management decisions.
The third paper discusses using degital terrain
models (DTM), geographic information systems (GIS), and
aerial photographs to map landscape units to generate soil
management maps that correlate yield with variable soil
water storage across landscape positions.
4
LITERATURE REVIEW
Identifying Yield Variability
Soil Moisture and Fertility
Farmers and researchers first observed yield
variations associated with landscape position.
et. al.
Miller,
(1988) observed wheat yield differences on
landscape positions in California.
Roberts (1987)
reported yield variation associated with changes in
landscape position and loess depth. Differences in soil
fertility across landscape positions have been reported by
Mulla and Hammond (1988),
and Veseth (1989).
Veseth (1989) and Wilson, et. al. (1991) described
soil water redistribution on landscape positions in
dryland wheat production areas.
More soil moisture is
available in the foot and toe slope positions.
The amount
of water available to the crop influences yield.
Aerial photographs are valuable for identifying low
yields associated with shallow soil (Roberts 1987).
Remote-sensing
By definition, remote-sensing involves detection,
identification, and analysis of objects or features using
sensors at remote positions from the objects studied.
distance may range from a few meters to many kilometers
(Avery and Berlin, 1985).
Remote-sensing applications to agriculture involve
The
5
measuring the electromagnetic spectrum (EMS) as it
interacts with crop canopies and soil.
The most
frequently sensed portion of the spectrum is visible light
(0.4 to 0.7 um), near or reflected- infrared (0.7 to 1.1
um) and thermal or emitted-infrared (8 to 14 um).
Sometimes active or passive microwave (0.1 to 1.0 meter
wavelength) radiation is used for soil moisture studies
(Myers, 1983).
Some sensors are photographic, producing a
picture (the visible light and reflected infrared), others
are analog devices which produce an image of the sensed
object or features.
Advanced sensors record the
electromagnetic spectrum in numerical or digital format,
for later conversion to image form.
Many factors influence the reflectance of light or
other portions of the spectrum from leaves.
Some of the
more important factors are chlorophyll density, water
content, and internal leaf structure.
One of the leading
factors contributing to reflectance is the air-cell wall
interface.
As energy (EMS) impinges on the leaf, it is
reflected, absorbed, or transmitted by the epidermis.
The
transmitted energy is then scattered primarily by the aircell wall interfaces of the palisade parenchyma of the
mesophyll.
At this point much of the visible light is
absorbed by chlorophyll and other plant pigments in the
chloroplasts (Knipling, 1970).
The plant leaf has low
reflection in the visible portion of the spectrum.
In the
6
near-infrared portion of the spectrum plants exhibit high
reflectance and transmission, with little absorption
because of internal leaf scattering (Knipling, 1970).
Opposed to reflected-infrared or near-infrared energy,
plants emit energy in the thermal-infrared portion of the
spectrum as heat.
The heat emitted is a function of plant
temperature and is regulated by convection, reflection of
incident energy, and transpiration (Myers, 1983).
Remote-sensors primarily record reflected energy
from crop canopies instead of individual leaves, although,
individual leaves make up the canopy.
The canopy
variables that influence the reflected energy are leaf
angle and geometry, leaf area index (LAI), total biomass,
percent ground cover, and background reflection from the
soil (Knipling, 1970; Gates, et al., 1965; Myers and
Allen, 1968; Myers, 1983; Daughtry, et al., 1980).
Most
important are leaf area index (LAI), total above-ground
biomass, and soil background reflection-percent soil
cover.
Many environmental interactions influence crop
spectral reflectance, but recording data in conditions as
similar as possible helps keep the interactions constant.
Leaf area index (LAI), total biomass, and percent
ground cover influence reflectance primarily because more
leaves reflect more energy.
This is particularly the case
in the reflected- or near-infrared portion of the
7
spectrum.
As near-infrared energy enters the canopy
approximately half of the energy is reflected by the upper
leaves and the remaining near-infrared energy is
transmitted to lower leaves (Knipling, 1970).
The lower
leaves, likewise, reflect about half of the near-infrared
energy toward the upper leaves.
Depending on leaf area
index (LAI), biomass, and percent ground cover, the lower
leaves transmit half the near-infrared energy to still
lower leaves or to the ground.
As the infrared energy reaches the ground, soil
conditions determine what percentage is reflected up
through the canopy.
As the infrared energy is reflected
up through the canopy, internal leaf scattering again
reflects some energy back to the lower leaves and ground,
and transmits some energy back up through the canopy to
the sensors.
Thus, the higher the total biomass, leaf
area index (LAI), and percent soil cover the greater the
sum of all possible increments of infrared reflection from
a crop canopy.
This is known as the infrared-enhancement
effect (Knipling, 1970).
Relatively more near-infrared energy than visible
light is reflected by canopies with high leaf area indices
(LAI's); and relatively less near-infrared energy than
visible light is reflected by canopies with low leaf area
indices.
Near-infrared reflectance, then, results in more
contrast between canopies of high and low biomass or leaf
8
area index (LAI) than visible light reflectance.
(Knipling, 1970)
Soil properties that influence reflection of the
electromagnetic spectrum (EMS) are primarily related to
soil color.
The visible and reflected portions of the
spectrum are most useful for distinguishing differences in
color and, therefore, soil properties.
Remote-sensing is
useful for distinguishing soil properties that influence
the color of the soil surface;
and the soil surface color
influences the background, or soil, reflection component
of crop canopies (Myers, 1983; Myers and Allen, 1968).
The Munsell color system is used in soil
classification and soil surveys as a descriptive color
system of hue, value, and chroma.
Hue is the dominant
spectral color (ie. green, red, or yellow,) and
corresponds to the wavelength of the spectrum measured by
sensors.
Value is the relative brightness and refers to
the total amount of light reflected.
Chroma is the
relative purity of the color, ie., the relative shades of
a color. (Myers, 1983).
Soil factors that influence color and reflection
include water content, organic matter content, color of
parent material, mineralogical composition, particle size,
and texture (Myers, 1983).
Past research estimating leaf area index (LAI)
(Ahlrichs and Bauer, 1983; Wiegand, Richardson, and
9
Kanemasu, 1979), above-ground phytomass (biomass)
(Asrar,
et al., 1985), and dry matter production (Aase and
Siddoway, 1981) of wheat by remote-sensing vegetation
index methods (digital ratios of reflected red to nearinfrared increments of the electromagnetic spectrum)
report accuracies (R2 values) of 0.70 to 0.95.
These
reports show almost a 1:1 relation for estimated to
measured biomass, although some methods (vegetation
indices) had higher standard errors than others.
Daughtry, et al.
(1980) report that crop cultural
practices influencing biomass, such as planting date,
moisture stress, crop development, and, in some instances,
fertility rate, can be identified by remote-sensing
methods.
The agronomic factors influencing canopy biomass
the most, therefore, causing a greater spectral response,
are crop development stage and moisture status.
High
grain yield was also correlated with high canopy biomass.
Asrar, Kanemasu, and Yoshida (1985) estimated leaf
area index (LAI) from spectral reflectance measurements of
red and near-infrared reflectance for wheat at three
geographical locations.
The actual measured LAI was not
significantly different (p = 0.01) from an indirect LAI
estimate or LAI estimated by a regression method (both
estimates for LAI were made from digital vegetation index
formulas:
the indirect from an absorbed
photosynthetically active radiation index, and the
10
regression from a red to infrared ratio).
Spectral
reflectance measurements were recorded for different
combinations of planting dates and irrigation frequencies.
Standard errors ranged from 0.2 to 0.78 (units of LAI).
The above results were from Phoenix, Az. and Manhattan,
Ks.
At Obregon, Mexico excessive standard error estimates
attributed to experimental variance and high within field
variation caused estimated and measured LAI to be
significantly different.
Measurements were corrected for
background reflection in some cases.
Dozier and Strahler (1983) reviewed ground
investigations supporting remote-sensing data collection.
The purpose of the ground investigation, sometimes
referred to as ground-verification or ground-truthing, was
to correlate the remotely-sensed data with actual
conditions found on the ground.
Ground investigations
usually fall into four categories:
calibration/correction, interpretation of properties,
training, and verification.
Calibration/correction is
essential to ensure that correct data are obtained and
account for variations caused by uncontrollable
interactions.
For correct interpretation of properties,
ground investigations ensure that the relation between the
reflected data and the property to be estimated are
correct.
For digital analysis of remotely-sensed data, a
training area is selected where the ground features are
11
correlated with the digital data.
Any interpretation
procedure from remotely-sensed data must be verified by
actual ground observations because many anomalous
interactions may lead to erroneous interpretations.
The
ground-verification should include examples of the entire
range of the interpretation.
Aerial infrared photography was selected to quantify
canopy biomass variation in this study.
Daughtry, et al.
(1980) reported that high grain yield was also correlated
with high canopy biomass.
Subjective interpretation of
infrared photography used in this study may not be as
accurate as the digital vegetation index used by Daughtry,
et al. (1980).
Color infrared photography measures both the amount
of infrared energy (0.7 - 1.1 um) and the green and red
visible light (0.5
0.7) reflected by a crop canopy (for
photographic infrared review, see Lillisand and Kiefer,
1979).
In general, areas of a crop canopy reflecting
higher amounts of infrared energy have larger leaf area
indices (LAI's), the canopy architecture is more erect,
and the canopy has greater biomass.
Of course, many
interactions may contribute to anomalies.
Reviews of
canopy infrared reflectance are provided by Knipling
(1970); Gates, et al., (1965); Myers and Allen (1968); and
Myers (1983).
Infrared sensitive color photographic film records
12
reflected infrared energy at the red sensitive dye layer.
In final prints (there are many uncontrollable processing
and printing variables) bright red areas are associated
with large biomass areas in the field.
Light or white
areas on the print are associated with very low biomass,
Interpretation of color
or bare spots in the field.
infrared photographs is subjective; the photo interpreter
uses knowledge of all areas of crop culture, growing
conditions, and soil characteristics to identify crop
variations on the basis of biomass.
Ground verification
of photo interpretation is essential to the
interpretation.
Assessing Spatial Variability
Classical Statistics
For some time research has addressed soil variability
influences on crop yields.
Difficulty has been
encountered in assessing the actual magnitude and field
area influenced by soil heterogeneity.
Field plot design
attempts to remove soil effects from treatment effects by
blocking (Petersen, 1985).
Sampling plans are designed to
increase precision by stratifying homogeneous areas within
heterogeneous fields to ensure adequate sampling coverage
of all portions of the field (Petersen and Calvin, 1965).
Soil survey personnel involved in mapping soil variability
over regions must resolve the objectives of the survey
13
(land use) with the accuracy and cost of the soil map.
Soil map accuracy depends on how well the description of a
sampled site (pit or borehole) estimates the soil
characteristics of the site neighborhood (Beckett and
Webster, 1971).
Classical statistical methods assume that the
population from which the samples are drawn is normally
distributed and that each sample is independent of all
other samples.
Because soil characteristics are often
related to landscape position, close samples as well as
distant samples may be correlated to each other depending
on the characteristic.
The population from which each
sample is drawn is not normally distributed and samples
are spatially dependent.
Geostatistics
A statistical analytical method, one that takes into
account the interrelation of soil samples over landscape
position, has been adapted to soil variability studies
from geology.
geostatistics.
This statistical approach is known as
Geostatistics was developed by Matheron in
a theory called the "Theory of Regionalized Variables"
(Vieira, et al., 1982).
The first step in geostatistical analysis of spatial
variation involves assessing the variability over the
region or area to be studied.
Samples are obtained at a
14
defined, regular interval along the length of a transect.
Both very closely spaced samples (approximately one
meter), and widely spaced samples are obtained.
If
geomorphic landscape relations to soil genesis are known,
such as fluvial, loessal, or erosional features, transects
are established and sampled both along and across the
geomorphic pattern.
Multiple sample transects provide an
estimate of the variability of the soil characteristic as
a function of distance and direction.
Isotropic variation
(variation independent of the direction of measurement)
and anisotropic variation (variation dependent on the
direction of measurement) can be assessed by sampling
transects established in different directions across the
landscape.
The first step, or reconnaissance stage, of a
geostatistical survey allows for the graphical analysis of
soil variability derived from the sampled transects.
This
graph is known as a semivariogram (Burgess and Webster,
1980 a) and is a measure of variance among sample sites
(dependent variable, or y-axis) as a function of distance
(independent variable, or x-axis).
Semivariograms are
constructed for each transect direction.
The variance
between very close samples is called the "nugget"
variance, it is the point on the graph where the relation
crosses the y-axis (Burgess and Webster, 1980 b).
The
variance increases with distance (sometimes linearly or
15
spherically) eventually reaching a maximum variance known
as the "sill".
Nugget and sill are terms adapted from
geologic mining referring to mining related variance (ie.
nugget variance is the ore variance associated with the
size of a gold nugget within a sample, and sill variance
is the ore variance associated with a block or area of the
mine).
The "range" is distance (actual measured distance
on the transect) relating the distance between samples
necessary to obtain an estimated variance, or spatial
dependence of sampled points.
The range is the distance
along the transect, from the nugget variance to the sill
variance.
Once the spatial dependence, or the variance increase
with distance, has been accurately developed from
intensive sampling to produce the semi-variogram, a
sampling strategy is developed.
Depending on the
objectives of the study and the desired level of precision
required, the density of sampling for the survey can be
estimated from the semi-variogram by selecting the
distance between samples needed to obtain the desired
precision or variance estimate (Burgess, Webster, and
McBratney, 1981). The most efficient sampling design will
be one with the fewest samples, or maximum distance
between samples, necessary to obtain the desired
precision.
Usually a square grid at a defined density is
easiest to operate in the field.
16
After the survey is conducted, an interpolation
procedure known as kriging is used to connect inferred
similar values together with isarithms, similar to
topographic contour lines connecting points of equal
elevation (Burgess and Webster, 1980a).
The kriging
procedure is named for D.G. Krige who did much to develop
the method for South African gold fields.
Kriging is a
form of weighted local averaging (Burgess and Webster,
Kriging uses the spatial dependence from the semi-
1980).
variogram to weight values at unrecorded or unsampled
locations as a function of distance from recorded
locations.
Kriging provides an interpolated estimate at
an unrecorded point that is unbiased, with known variance,
and minimum variance, (Burgess and Webster, 1980a)
therefore, it is a statistically sound and optimal method.
If soil properties have large nugget variances (in
other words, if the soil is very heterogeneous, if samples
near each other have large variance) the punctual kriging
isarithms (the contour-like lines on the map) are very
erratic.
In this case block kriging is described by
Burgess and Webster (1980b) as a means of grouping areas
together and averaging properties to produce a smoother
map.
The precision may necessarily be lower; however, the
map may reveal a regional pattern.
Sometimes a soil property is very difficult to
measure or may require large outlays of resources (time,
17
labor, or money) to sample at an intensity required for
the desired precision for interpolation methods.
Another
variable may be found to be highly correlated to the
property of interest.
If both variables are found to be
spatially interdependent and exhibit the same anisotrophy
or isotrophy, McBratney and Webster (1983) provide a
method of cross semivariograms and co-kriging to
interpolate one variable from measuring another.
Actually, both variables are measured, the easiest to
measure variable at a higher density or frequency than the
more difficult to measure variable.
Semivariograms are
made from intensive reconnaissance surveys for both
variables, compared for anisotrophy or isotrophy, and
spatial dependence.
From the semi-variograms, survey
method and sample density are determined for both
variables.
Spatially correlated and co-regionalized
variables, one sampled intensively (the easiest and most
economical to measure), the other sampled much less
intensively, can produce a map of the spatial variability
of the variable of interest much more economically than
intensive sampling of the difficult to measure variable.
Remotely-sensed subsidiary variables are in many
instances more economically measured than the primary
variable of interest.
The spatial variability of the
remotely-sensed variable is often highly co-regionalized
and has potential for co-kriging (McBratney and Webster,
18
1983).
Examples provided by
and Vieira, et al.,
McBratney and Webster (1983)
(1982) include, temperature measured
with remote-sensing techniques correlated to soil water,
or organic matter content.
One assumption of the geostatistical interpolation
method is that the boundaries of the soil parameter to be
measured cannot be recognizable by distinct geomorphic
breaks and must have very gradual gradients across the
isarithm.
In other words the interpolation techniques do
not estimate soil properties across a cliff (Burgess and
Webster, 1980a).
Bouma (1985) points out the value of using
traditional soil survey methods combined with
reconnaissance geostatistical methods.
First, the aerial
photography and geomorphological associations used to
identify preliminary soil boundaries could be used to
identify subpopulations or homogeneous areas based on
vegetation, landform position, or topography.
Next, the
subpopulations could be used for initial high intensity
reconnaissance sampling transects to assess the
variability within the subpopulation.
A group of similar
subpopulations could be used to assess the variability
within the region using the semi-variograms of the
transects.
Then, the estimate of the variance from the
semi-variograms could provide the sampling density and
method needed for both local and regional surveys at a
19
desired precision.
Combining the ideas of Bouma (1985),
Burgess and Webster (1980), McBratney and Webster (1983),
and Vieira, et al. (1982), subpopulations, locally and
regionally, could be identified based on remotly-sensed
subsidiary variables associated with grain yield.
The
variance of the subpopulation (estimated from the
previously obtained reconnaissance semi-variogram for the
region) could provide the grower with a sampling plan and
density for assessing the variability of grain yield
within his farm.
GIS systems could overlay several
remotely-sensed subsidiary variables accelerating the
identification of regional subpopulations and providing
maps of the spatial variability of grain yield.
It should be pointed out that with the regionalized
estimate of the variance of the soil property to be
measured, geostatistical methods may increase precision
over classical sampling designs, at a given cost, by
reducing the total number of samples, even though the
reconnaissance survey is sampled intensively.
By integrating the classical soil survey, remotesensing, and geostatistical methods, an economical
approach may be formulated for assessing the regional and
local variation in grain yield for the Columbia Plateau in
eastern Oregon.
Warrick and Gardner (1983) and Russo (1984) provide
papers dealing with assessing the spatial variability of
20
yield (a parameter with many interactions, therefore, hard
to measure) and improved crop management by spatially
varying irrigation.
Bresler, et al.
(1981) correlated
crop yield of peas (total dry matter, pod, and hay) with
soil water content.
Excellent reviews of geostatistical procedures of
soils are provided in a five part series of papers by
Burgess and Webster (1980, 1980b), Webster and Burgess
(1980) Burgess, et al.
(1981) and McBratney and Webster
(1983), including examples of analyses.
Vieira, et al.
(1982) include a review of geostatistical procedures, with
examples and computer code.
Neilsen and Bouma (1985)
edited a workshop proceedings on various aspects of soil
spatial variability.
Variable Field Mapping
Geographic Information Systems
Remote-sensing systems characteristically collect
volumes of data relating to large geographical areas.
Extracting the relatively small amount of relevant data
from the total data set presents a formidable task.
The
development of computer systems to handle large volumes of
geographic or spatial (referring to geographic arrangement
in space) data is essential for optimum use of remotesensing systems (Marble and Peuquet, 1983).
Geographic information computer analysis systems
21
(GIS) are designed to handle large volumes of spatial
data, obtained from remote-sensing systems as well as
ground derived data such as soil surveys, streams, or
geological surveys, in either map or attribute (ie. stream
flow volume) data format.
The GIS provides an efficient
means of storage, retrieval, manipulation, analysis, and
presentation of spatial data, usually in map form (Marble
and Peuquet, 1983).
Unlike other data analysis systems, GIS systems
handle and analyze spatial data as well as non-spatial
attribute data associated with the spatial data.
To
preserve the data's spatial dependence, raster (a gridlike network of x-y coordinates) or vector (a series of
points, line segments, line nodes, and polygons) formats
(or a combination of both, raster and vector) are used for
data management and analysis (Marble and Peuquet, 1983;
Eastman, 1990).
Future combinations of remote-sensing and GIS systems
may provide efficient means where ground investigation
derived GIS data can be imported into remote-sensing
classification systems, thereby improving the accuracy of
interpretation and classification.
Presently, the remote-
sensing information is transferred to the GIS, where, in
combination with ground data, it is analyzed.
The GIS analysis is similar to overlaying one map
onto another map, finding where areas of interest from
22
both maps intersect, or where areas are spatially
dependent, then producing a map of the areas of
intersection, or the spatial dependence.
In a sense, GIS
analysis is a form of visual association of spatially
dependent geographic areas, that allows for the analysis
of non-spatial attribute data associated with the areal
(referring to geographic area) data.
For example, when
combining a map of soil associations in which the soil
types differ in sewage disposal attributes with a map of
utility services,
the combination map may identify future
sites for rural housing subdivisions.
Marble, et al.
(1984), Marble and Peuquet (1983), and Starr and Estes
(1990) present information, examples, and technical
details of geographic information systems.
Digital Terrain Models
Contour surface models of fields can aid farmers in
identifying spatial patterns in fields.
Slope and aspect
are correlated to yield potential.
Digital elevation data can be obtained from aerial
photographs by photogrammetric processes, from USGS 7.5
minute quadrangles, or by "field digitizing" with global
positioning system receivers.
The photogrammetricly
produced digital terrain model (DTM) from 70 mm,
photography though useful, does not provide the detail of
the quadrangle data.
23
Basic Cartography
Aerial photos can be updated in the field to indicate
low yield areas.
Aerial photos are not maps, the
delineated areas on the photos can be transferred to an
accurate base map in the office with a zoom transfer
scope. (Pain, 1981)
The USGS quadrangles provide an economical and
accurate base map (scale 1:24 000).
function of scale.
Map accuracy is a
Variable fertilizer application with
vehicular guidance systems may require increased base map
accuracy or precision, requiring a scale of 1:5 000 or
1:10 000.
Field boundaries and map units can be "field
digitized" with GPS receivers.
Marking the map units
with flags may be necessary to enable allowing the farmer
to drive along the map unit and field boundaries to field
digitize the field base map at the required scale for
precision.
24
CHAPTER I.
FERTILIZING WHEAT WITH NITROGEN BALANCED TO SOIL WATER
VARIABILITY ACROSS LANDSCAPES
ABSTRACT
Potential grain yield varies within fields -- even
within delineations of soil map units.
The variation is
often associated with landscape position.
This study was
conducted to evaluate yield variations across landscape
positions correlated with changes in soil water.
Rainfall is often redistributed, with more stored
soil water on concave landscapes, and less on steeper,
convex landscapes.
Two fields were selected to represent
the regional wheat yield variability on the Columbia
Plateau.
One field was situated in a high (30-50 cm)
precipitation area and no noticeable shallow soils.
The
second was located in a low (25-30 cm) precipitation area
and areas with shallow (<50 cm) soils.
Soil moisture,
nitrogen, and grain yield were sampled along
reconnaissance transects traversing landscapes.
yield was correlated (r
= 0.96
)
Grain
to crop available water
(CAW) and to total soil nitrate nitrogen (r2 = 0.67*).
Using these estimates farmers can establish a transect
crossing landscape positions, measure crop available water
25
and nitrate nitrogen, then, balance nitrate fertilizers to
crop available water.
INTRODUCTION
Farmers and researchers know that yield differences
exist within fields.
Dryland winter wheat farmers in the
Columbia Plateau of Eastern Oregon apply nitrogen
fertilizer in the spring of the summer fallow year or
just prior to planting in mid-September.
Hoping for the
highest yield possible -- and more rain than usual during
the coming crop year -- farmers often over-fertilize with
nitrogen.
Farmers know that the soft white wheat produced in
the region is higher in protein than the consumer prefers.
They also understand the potential for excessive nitrogen
to foul surface and groundwater resources.
Some growers
soil profile depth, then apply more nitrogen on deep soils
and less on shallow soils.
Others revert shallow soil
areas to rangeland.
Researchers across the United States have identified
agronomic causes of yield variation.
In glacial till
soils of the northern midwest, drainage differences within
fields have been documented with aerial color infrared
photography (Fairchild and Knutson, 1989).
Photographic
maps of the fields enable farmers to test and vary
26
fertilizer according to drainage class. In the rolling
hills of the central plains, surface nitrate differences
are detected with real-time sensors attached to fertilizer
spreaders (Colburn, 1986).
The rate of nitrogen
fertilizer is varied according to soil nitrate levels.
In
the high organic matter soils of Texas, real-time
photocells detect changes in soil chroma correlated to
organic matter content (Van Scoyoc, et al., 1989).
Organic matter content is correlated to the amount of
mineralized nitrogen.
Both agricultural chemical and
fertilizer rates are adjusted with soil organic matter.
Differences in subsurface fertility were identified using
soil sampling and geostatistical techniques (Mulla and
Hammond, 1988;
Hammond, Mulla, and Fairchild, 1988) in
pivot-irrigated fields in eastern Washington state.
The source of yield variation determines the field
methodology for identification, measurement, statistical
analysis, and application of chemicals and fertilizers.
In the past, field plot type statistical analysis of
variance methods were used to evaluate yield variations.
On landscapes, yield often changes gradually as sample
position progresses across the landscape.
Two questions
could be asked about yield variation across landscapes.
First, how many locations should be individually sampled
for yield?
Second, are landscape positions considered as
treatments or varieties, in the analysis of variance?
27
Presuming landscape positions are considered treatments,
paired observations are collected on many landscape
positions.
Careful use of mean separation procedures
(Hatfield, 1990) suggest comparisons among landscape
positions be made in progressive order (as are treatments)
across the landscape.
This procedure may indicate
statistically nonsignificant differences among landscape
positions.
Conversely, if landscape positions were
considered to be varieties, random means could be
compared.
Comparing random means, a south shoulder
position could be significantly different from a north
back slope, yet not different from the summit.
Both
interpretations could be correct for identical data
collected from one landscape transect.
The assignment of
landscape position in the analysis of variance depends on
the objectives of the study.
With the use of geostatistical procedures and
transect experimental designs (Nelson and Buol, 1990)
yield variation studies across landscapes are easier to
interpret.
When using geostatistical procedures, the researcher
must use care not to violate geostatistical hypotheses.
When collecting data along transects that cross
landscapes, a landscape may not have characteristics known
to the researcher (a priori knowledge) to be radically
different from other landscapes. (Burgess and Webster,
28
1980a)
Nelson and Buol (1990) point out that the transect
designs are inherently low in precision because
replication is constrained by inability to randomize
individual landscape positions.
This study was conducted to evaluate yield variations
associated with landscape position correlated to available
soil water.
Low precision reconnaissance transect
sampling and experimental design methods were used.
The
preliminary yield-soil water correlations can be reevaluated later with additional high precision
experimental designs.
MATERIAL AND METHODS
In the summer of 1988, two fields were selected to
represent the variability within the Columbia Plateau
winter wheat producing region.
The Pendleton study area
is in a higher (40-50 cm) rainfall and potential (4-6 Mg
ha 1) yield part of the region.
The Wasco study area is
in a lower (25-30 cm) rainfall area and has shallow (<50
cm) soil areas.
Transects were established across landscapes for
sampling and plot experiments.
by use of landmarks,
The transects were sited,
so sample and plot sites could be
repeatedly visited without unnecessary physical markers in
the fields.
29
The Pendleton study area
The field in the Pendleton study area is about 4 km
west by northwest of the Pendleton airport.
The
approximately 18 ha field is located in the SW1/4 of the
NW1/4 of section 15, Township 3 North, Range 31 East
Willamette Principle Meridian.
The field is bordered on
the south by the county road, west by a field road, east
by a driveway, and north by a gulch.
Transect 1,
(Figure A.2) starts at the southwest
corner of the field and angles northeast (40) to a
utility pole on the horizon, intersecting the driveway
about 100 meters south of the house.
Transect 1 traverses
a gradual (3-7%) north slope, crosses an erosion control
terrace, traverses a steeper (7-12%) north slope,
intersects a second erosion control terrace and the
drainage way, proceeds up a steep (12-20%) southwest slope
to a level-to-north slope ridge to the driveway.
Transect
1 crosses two soil mapping units, 114B and 114C (Johnson
and Makinson, 1988).
Transect 2,
(Figure A.2) starts approximately 40
meters north of the second (lower, or north-most) erosion
control terrace. Transect 2 parallels the west boundary
field road, traversing northward, offset 15 meters east
of, the west boundary field road, on the west border of
the field.
Transect two is in mapping unit 114C (Johnson
and Makinson, 1988).
30
Topography can be obtained from the Barnhart, Oregon
USGS 7.5 minute quadrangle (USGS staff, 1979).
Mapping unit descriptions, soil survey area
Mapping unit 114B (Johnson and Makinson, 1988), is
Walla Walla silt loam, 1 to 7 percent slopes, classified
as coarse-silty, mixed, mesic Typic Haploxerolls.
These
soils are deep well drained soils on broad summits.
Included in this unit are small areas of Anderly and
Hermiston soils, Walla Walla soils that have a hardpan at
a depth of 100 to 150 cm or are eroded, and Vitrandepts
(now classified Vitraxerands).
Also included are small
areas of Walla Walla soils that have slopes of 7 to 12
percent.
Included areas make up about 15 percent of the
total area. (Johnson and Makinson, 1988).
Map unit 114C, is Walla Walla silt loam, 7 to 12
percent slopes, again classified as coarse-silty, mixed,
mesic Typic Haploxerolls.
Included in this unit are small
areas of Anderly and Hermiston soils, Walla Walla soils
that have a hardpan at a depth of 100 to 150 cm or are
eroded, and Vitrandepts (Vitraxerands).
Also included are
small areas of Walla Walla soils with slopes of 1 to 7
percent, and 12 to 20 percent.
Included soils make up
about 25 percent of the total area.
Anderly soils are
coarse-silty, mixed, mesic Typic Haploxerolls.
Hermiston
soils are coarse-silty, mixed, mesic Cumulic Haploxerolls.
31
(Johnson and Makinson, 1988).
The Wasco study area
The study area, approximately 40 ha, near Wasco,
Sherman County, Oregon, is located about 8 km southeast of
Wasco, Oregon, and about 8-10 km northwest of Moro,
Oregon.
The field location is the SE1/4 section, of
section 24, Township 1 North, Range 17 East Willamette
Principle Meridian.
The triangular shaped field is
bordered on the north by a county road, then an unimproved
road.
The east boundary is by the same unimproved road
bending south east, then south to highway 206.
The
highway (206) is the south west boundary.
Transect 2 starts 390 meters east, and 30 meters
south, of the north west corner, of the SE1/4, of section
24.
Transect 2 proceeds directly south approximately 300
meters to within 50 meters of highway 206.
traverses soil map units:
Transect 2
WaBN, WaA, and WmBS. (Mayers,
1959).
Topography of the field can be obtained from the
Wasco, Oregon USGS 7.5 Minute Quadrangle (USGS staff,
1973) .
The first mapping unit (Mayers, 1959) traversed by
transect 2 is WaBN, Walla Walla silt loam, very deep,
to 20 percent north slopes.
7
The Walla Walla soils are
coarse-silty, mixed, mesic, Typic Haploxerolls.
About 10
32
percent of this map unit is other Walla Walla soils.
After proceeding south up the north slope, transect 2
traverses a delineation of the WaA mapping unit. The
WaA
mapping unit is Walla Walla silt loam, very deep, 3 to 7
percent slopes.
About 10 percent of this mapping unit
(Mayers, 1959) consists of the more shallow or more
sloping Walla Walla soils.
These soils occur on broad
flat ridge tops.
After crossing the ridge top, transect 2 proceeds
across a delineation of the WmBS mapping unit.
mapping unit is a
The WmBS
Walla Walla silt loam, low rainfall,
moderately deep, 7 to 20 percent south slopes.
This soil
occurs on outer edges of broad ridge crests, ranges from
20 to 90 cm in depth to basalt or caliche.
This mapping
unit contains rock outcrops and field drainage-ways.
About 10 percent of this map unit consists of Walla Walla
soils that have low rainfall and Starbuck soils.
Starbuck
soils are shallow, less than 20 cm deep, stony to very
stony (basalt fragments) loamy, mixed, mesic, Lithic
Xerollic Camborthids.
In the study, Transect 2 traversed
all the soils described above by Mayers, 1959).
Sampling methods
In the fall of 1988, soil profile cores were obtained
with a Giddings soil probe mounted on a four-wheel drive
truck.
Intact cores (or as nearly as possible with a
33
Giddings probe) were sampled along the transects at
intervals of about 1.5 meter of vertical relief.
Cores
were sampled to lithic contact or to a depth of 4 meters.
Some indurated layers were disturbed by augering.
The
cores were described with soil survey field methods (Soil
Survey Staff, 1975).
After cores were obtained, the farmers planted winter
wheat with normal cultural and fertility practices.
In early April, soil cores were obtained within one
meter of the original core position.
content was measured.
Gravimetric water
Nitrate nitrogen (30 cm depth
intervals to 1.8 meter) and percentage of organic matter
(surface 30 cm) were measured at the OSU Soil Test Lab.
Soil fertility soil-test-calibration plots were
established at original core sites, normal to the
transect.
These plots were 2.5 meter2 in size.
At the
Pendleton study area, the farmer applied in the fall about
100 kg ha-1 nitrogen, therefore, three treatments were
applied, a control - no nitrogen, 25 kg ha-1, and 50 kg ha
of solution 32 in water.
At the Wasco study area, the
farmer applied about 60 kg ha-1 of nitrogen on north slope
positions and 45 kg ha-1 of nitrogen on the remainder of
the field.
An additional 3.5 kg ha-1 of nitrogen was
applied by air in the spring along with a herbicide at the
Wasco study area.
At the Wasco study area three
treatments were applied, a control (no nitrogen), 100 kg
34
hat, and 150 kg ha
of nitrogen as solution 32 in water;
however, the last two core sample sites on the transect
with noticeably shallow soils received treatments of
control (no nitrogen), 25 kg ha-1, and 50 kg ha
of
nitrogen as solution 32 in water.
At harvest one m 2 samples were hand harvested from
the center of the fertility plots.
Samples were cleaned
and yield, test weight, and 1000 kernel weight were
obtained.
Percentage of grain protein was measured with a
Technicon 400 Infra-Analyzer.
The Technicon was
calibrated for percentage of grain protein by the microKjeldahl procedure described by Nelson and Summers, 1973.
The simple correlation coefficient of the percentage of
grain protein measured by the Technicon as a function of
micro-Kjeldahl measured percentage of grain protein was
0.97, and standard error of the estimate was 0.45
percentage point of grain protein.
After harvest soil core samples were obtained within
1-2 meters of core sites, and gravimetric water content
(30 cm intervals) was measured to 1.8 meters.
Volume bulk
density measurements were made along the transect at foot
slope, back slope, and summit positions on transects, at
approximately 20 and 60 cm depths.
Analyses
Change in soil water content from April to August was
35
measured and expressed as cm depth of water in 30 cm
increments of soil profile depth (equivalent depth;
Hillel, 1982) and summed to 1.5 and 1.8 meters of soil
depth.
Crop available water (CAW) was calculated from
equivalent depth water content (April measurement), minus
the published wilting point (-1.5 Mpa) water content of
Walla Walla soil (Huddleston, 1982).
Necessary
conversions used measured volume bulk density and
equations of Hillel (1982).
Rainfall received after April
in most years does not wet the soil to a depth sufficient
for root uptake.
Yield measurements from the three nitrogen level
plots were plotted and regressed on total nitrate content
summed to 1.8 meters.
fitted.
A linear or quadratic function was
In the linear model the highest yield was
identified as the maximum fertilized yield.
The first
derivative of the quadratic model was interpreted to be
the maximum fertilized yield.
Maximum fertilized yield
was regressed on total nitrate nitrogen, and compared to
similar linear models obtained from the fertilizer guide
(Gardner and Goetze, 1980).
Soil residual nitrogen and change in soil water
content were plotted against soil profile depth and
studied for anomalous conditions.
Grain yield as a function of soil profile depth, crop
available water, total nitrogen, soil profile depth, and
36
organic matter were analyzed with simple (linear)
regression procedures.
Crop available water (CAW) as a
function of soil profile depth, was similarly analyzed.
RESULTS AND DISCUSSION
Grain yield correlation to crop available water (CAW)
was highly significant (Figure 1.2) at the Wasco study
area and non significant at the Pendleton study area.
Pendleton received considerably less precipitation than
Wasco in December and June of the summer fallow year
(Table I.1).
Dry soil conditions at the Pendleton study
area during planting resulted in delayed crop emergence
and development, and reduced yield.
Grain yield correlation to total nitrogen was very
similar (Figure 1.3) to the correlations obtained from the
fertilizer guide (Gardner and Goetze, 1984).
The
objective of the nitrogen fertility calibration trial on
landscape positions was to answer questions concerning
extrapolating fertility recommendations obtained on
uniform, representative portions of fields to
heterogeneous, previously unsampled areas.
This research
supports the fertilizer guide research conducted across
regional potential yield and environment variability.
The nitrogen required for maximum fertilized yield
may result in excessive grain protein content.
The three
control treatments with highest CAW had protein contents
37
between 9.2 and 9.8 percent.
Cerrato and Blackmer (1990)
indicate that quadratic models may overstate nitrate
levels for optimum corn yield.
Additional research
investigating the interaction of CAW and nitrate on wheat
yield and protein is needed.
Crop available water and grain yield
(Figure 1.3)
are not correlated well with soil profile depth probably
because of moisture redistribution (run-off on up-slope
and run-on at lower slope positions, Figure 1.1A).
Water
redistribution on landscapes is another area of additional
research, both to predict yield and protect groundwater.
Grain yield was not correlated well to surface
organic matter content (Figure 1.3).
The lowest organic
matter points on the plot (Figure 1.3) correspond to
samples taken in the shallow lithic xerollic camborthid
soils.
The nitrate nitrogen concentration of the surface
30 cm was uniform along the transect.
Real-time organic
matter or nitrate sensors would not be feasible for
variable fertilizer application in this region.
The soil core descriptions obtained on the transects
contained no discrepancies of map unit integrity
concerning descriptions of included areas.
Also, particle
size classifications of described cores indicated no
departure from soil water data provided by Huddleston
(1982) .
Farmers can establish transects crossing landscape
38
positions, measure crop available water and nitrate
nitrogen, then, balance nitrate fertilizers to crop
available water using Figure 1.2.
Applying a starter fertilizer rate at planting,
then, applying the balanced nitrogen rate early in the
spring when soil moisture flow has slowed may prevent
contaminating surface and groundwater with nitrates.
Balancing nitrate to water may lower percentage of grain
protein.
It is important to note the standard error of the
estimate associated with the grain yield prediction models
in Figure 1.2.
The prediction of grain yield from CAW is
plus or minus about 0.7 Mg ha-1;
and grain yield from
total nitrogen, about 1.7 Mg ha-1.
In this region a map based system of spatiallyvariable application seems appropriate given the causes of
grain yield variation.
The Wasco study area could
conceivably have five soil management map units:
toeslope, footslope, backslope, shoulder-summit-south
shoulder-south backslope, and south backslope (Figure
I.1) .
As noted previously, these data and results are
reconnaissance in nature and are of low precision.
correlations may be re-evaluated with more precise
experimental designs.
The
Table 1.1.
Precipitation for fallow-crop cycle near Wasco and Pendleton study areas.
Fallow
Crop
1987
mo.
Wasco
1988
Pend.
mo.
Wasco
1988
Pend.
1989
Wasco
Pend.
0
14.22
0.5
N
D
63.75
5.58
7.87
2.54
54.86
9.39
mo.
Wasco
Pend.
F
33.78
19.55
M
48.51
47.24
34.54
43.68
39.87
37.33
14.47
2.28
mo.
MM
A
S
0
1.77
0
0.25
N
D
16.76
82.04
1.27
J
0.76
0
19.3
31.24
F
M
A
M
J
J
A
40.64
5.33
31.75
56.13
13.97
25.9
1.01
0
47.27
3.04
24.13
62.73
39.62
7.87
0.25
0
S
J
A
21.33
M
J
J
23.11
0
0
Figure 1.1.
Schematic representation of landscape positions (A), and grain yield (B)
along transect 2, Wasco study area.
A
0
0
DO
F
O
0
0
B
T
F
B
North
00
So
Su
oo
-
So
T
toeslope
footslope
backslope
shoulder
summit
Su
cr 0.00
0
0
Su
So
0
O
B
-00
F
B
0
_ _ lithic (basalt) contact
o
So
O
soil surface
=
D
o
4
So
cc3
Q)
0
O
100.00
200.00
""
Transect Distance (m)
O
CD
300.00
0
0
coo
100.00
200.00
Transect Distance (m)
300.00
Figure 1.2. Wheat grain yield correlated to crop available water (A) and total nitrate
(B) at Wasco study area.
*'**,*** indicate significance at the 0.05, 0.01, and 0.001
probability levels, respectively.
A
Y, = 1.43 + 0.206 X
OCCx:X) Spring of Cropping Year
Fertilizer Guide Data
r` = 0.96 ."
Y = 1.007 + 0.03 x
Stnd. Error of Est. = 0.351
o
01._-= csi
0
0
-
0
Y, = 1.81 + 0.02 x
r2 0.67
(Prob. Level = 0.012
Stand. Error of Est. = 1.12
0-
0-
0
0
cioo
takkeirte April of Cropping Year
5 00
10.00
15.00
20.00
25.00
Crop Available Water (cm)
30.00
0.00
1
50.00
1
100.00
Total NO3 (kg ha
I
1150.00
)
200.00
Figure 1.3. Crop available water correlated to soil profile depth (A);
wheat grain yield correlated to
soil profile depth (B), and organic matter (C), Wasco study area, transect 2.
A
E8
owl
Y
2.65 + 0.106X
(prob. level - 0.044)
.e 0.51
6.46
Stnd. Error of Eat.
11E8
O
3
N
8
o.).;
0
73- 8
>6
aoo
06
8
50.00
100.00
150.00
Soil Profile Depth (cm
200.00
C
B
3.2 + 5.71X
- 0.45 (prob. level
0.065)
Sind. Error of Eel.
1.35
Y. = 1.09 + 0.016X
0.40 (prob. level
Stand. Error of Eat.
40.00
80.00
120.00
0.08)
1.08
160.00
Soil Profile Depth (cm)
200.00
0. 0
1.00
1.20
Organic Matter (se)
1.40
43
REFERENCES
Burgess, T.M., and R. Webster. 1980. Optimal
interpolation and isarithmic mapping of soil
properties:
I.
The semi-variogram and punctual
kriging. J. of Soil Sci. 31:315-331.
Cerrato, M.E. and A.M. Blackmer.
1990.
Comparison of
models for describing corn yield response to nitrogen
fertilizer. Agron. J. 82:138-143.
Colburn, J. 1986.
(Aguila Corp.)
R&D on a fertilizer
sensor and control system.
U.S. Department of
Energy, Washington, D.C.
DOE/ID/12581-1
(DE87014929).
Fairchild, D.S., and R.D. Knutson. 1989.
Innovative
equipment for application of agricultural inputs by
soil variations.
p.315 in Agronomy abstracts. ASA,
Madison, WI.
Gardner, H. and N.R. Goetze.
1980.
O.S.U. Cooperative
Extension Service.
Fertilizer guide for winter wheat
(non-irrigated -- Columbia Plateau)
FG 54.
Oregon
State University, Corvallis, Oregon.
Hammond, M.W., D.J. Mulla, and D.S. Fairchild. 1988.
Development of management maps for spatially variable
soil fertility.
Proc. 39th Annual Far West Regional
Fertilizer Conference, Bozeman, Montana. July 11-13,
1988.
Hatfield, J.L. (ed.) 1990. Instructions to Authors
(statistical methods). Agron. J. 82:171-172.
Hillel, D.
1982.
Introduction to soil physics.
Press, Orlando, Fl.
Academic
Huddleston, J.H.
1982.
Soils of Oregon:
summaries of
physical and chemical data. OSU Extension Service,
special report 662.
Oregon State University,
Corvallis, OR.
Johnson, D.R., and A.J. Makinson. 1988.
Soil Survey of
Umatilla County Area, Oregon. USDA-SCS.
U.S. Govt.
Print. Office, Washington DC.
Mayers, L.R.
1959.
Soil Survey of Sherman County,
Oregon.
USDA-SCS. U.S. Govt. Print. Office,
Washington DC.
44
Mulla, D.J., and M.W. Hammond.
1988. Mapping of soil
test results from large irrigation circles. Proc.
39th Annual Far West Regional Fertilizer Conference,
Bozeman, Montana. July 11-13, 1988.
Nelson, L.A., and S.W. Buol.
1990.
Experimental
designs to evaluate crop response on adjacent soil
mapping units. Soil Sci. Soc. Am. J. 54:841-849.
Nelson, D.W., and L.E. Sommers.
1973.
Determinations of
total nitrogen in plant materials. Agron. J. 65:109112.
Soil Survey Staff. 1975. Soil Taxonomy. U.S. Dept. Agr.
Handbook No. 436. USDA-SCS. U.S. Government Printing
Office, Washington D.C. 754 p.
U.S. Geological Survey.
1973.
Wasco, Oregon Quadrangle - Sherman Co. 7.5 Minute Series.
(Topographic).
N4530--W12037.5/7.5 U.S. Govt. Print. Office,
Washington DC.
U.S. Geological Survey.
1973.
Barnhart Oregon Quadrangle
- - Umatilla Co. 7.5 Minute Series. (Topographic)
N4537.5--W11852.5/7.7 U.S. Govt. Print. Office,
Washington DC.
Van Scoyoc, G.E., D.G. Schulze, L.D. Gaultney, J.L. Nagel,
and J. Schonk.
1989.
Variable rate application of
chemicals using a soil organic matter sensor. p. 319
in Agronomy abstracts. ASA, Madison, WI.
45
CHAPTER II.
FIELD SAMPLING STRATEGIES FOR BALANCING COST AND PRECISION
ABSTRACT
Farmers are aware that wheat yields on the Columbia
Plateau loess region vary across the landscape.
Some have
noticed changes associated with loess mantle depth.
Previous research has indicated that yield increases with
increasing stored soil water, and optimal yields are
obtained by balancing nitrogen fertilizer to stored soil
water.
The farmer, faced with the expense of field
sampling, asks, "how many sites must be sampled?"
Four
reconnaissance yield transects were sampled in a 25 ha
field.
Grain yield was evaluated by geostatistical
semivariance techniques.
Results from one crop year
indicate that the field be sampled with a 158 meter square
grid.
Anisotropic analysis indicate a rectangular grid,
68 by 43 meter would increase precision.
For field
orientation the grid should consist of transects
traversing slopes (spaced 68 meters apart), sampled every
43 meters.
Many farmers may consider this sample number
to be excessive and are willing to sacrifice precision for
cost.
If sample number must be reduced, precision-sample
number may be optimized by eliminating alternate transects
while sampling each landscape position.
46
INTRODUCTION
Farmers are aware that wheat yields vary across the
landscapes.
Most farmers are aware of the interaction
effect of stored soil water and fertility on grain yield.
Some farmers have noticed more moisture storage with
increasing loess mantle depth.
The loess mantle depth
changes across landscape positions.
Previous research has
indicated that increased yields are associated with
increased soil water storage on foot and toe slope
position (Figure I.1).
Because of the high cost of field sampling, the
farmer asks, "where and how often must I sample"?
Some
farmers have chosen representative landscape positions,
sampled depth of loess mantle, and managed fields by
shallow, moderate, and deep soil depths.
The traditional
approach recommended for soil test sampling was to obtain
a number of subsamples for each strata to be subsequently
bulked (Peterson and Calvin, 1965).
as one unit.
The field was managed
With increased knowledge of the fields,
stratified sampling methods have been proposed (Roberts,
1987) .
Independence of samples is considered with all
statistical procedures.
Recently, the theory of
Regionalized variables (Matheron, 1971) has been
introduced to soil science.
The basic difference of
47
Regionalized variable theory and classical statistics is
that samples obtained h distance apart, if the distance is
small, samples may not be independent.
A graphical
analysis known as the semivariance is performed.
The
semivariogram indicates the range in distance h, that the
samples are dependent and at what distance h, the samples
are independent.
(Nash and Daughtery, 1990).
Once a semivariogram has been analyzed for a
reconnaissance transect, a sampling scheme can be devised
with known precision given sampling spacing (Burgess,
Webster and McBratney, 1981).
The objectives of this
study were to (i) evaluate geostatistical procedures to
estimate the number of grain yield samples required for
various precision estimates, and (ii) to compare the
variance estimate with that of an intensively sampled
uniform strata.
MATERIALS AND METHODS
This study site was located about 5 km northwest of
the Pendleton, Oregon airport.
The grain yield data were
collected by hand sampling one meter2 plots within the
control plots of fertilizer calibration trials on
transects one and two.
Grain yield data were obtained
with a commercial field combine between the plots along
the transect.
Approximately 50 meter2 samples were
48
harvested with the combine.
Transects three and four
were harvested with the combine.
Harvested sites were measured along the transects and
plotted on an aerial photo of the field.
digitized and plotted (Figure I.1).
The sites were
The x and y
coordinate of each plot with the associated yield were
analyzed using GEOPACK (Yates, 1989).
Yield data were
plotted and found to be normally distributed.
semivariogram was determined.
The
The automatic model fit was
used and modified with the manual model fit function to
obtain a model representing the semivariogram.
Resulting
semivariograms were analyzed by geostatistical procedures
for anisotropy.
The yield data in transect 3 was analyzed
by the descriptive statistics package of GEOPACK for the
mean and sample variance and compared to the semivariogram
of all four transects.
RESULTS AND DISCUSSION
The wheat stand was thought to be delayed because of
dry planting conditions which also resulted in poor
yields.
This year the stored soil water was poorly
correlated to yield.
This field is fairly uniform with low slopes and deep
soils.
The degree of anisotrophy is lower than in many
fields but should still be recognized.
49
The semivariogram for all data points with a width
angle of 90° is shown in figure II. 2.
Semivariograms
with a width angle of 45° and a direction angle of 90°,
across slope, and 0°, down slope are indicated in figure
II. 3.
The sampling plan using the non-isotropic
semivariogram would be to sample on a 158 meter grid.
This would be acceptable for this field.
There would be
two across slope positions sampled, and two or three down
slope sample positions.
The frequency of slope changes is
about that distance (158 meter), see figure III.1.
Care should be taken to assess the anisotropy of all
fields, particularly those with complex slopes.
There is
usually not as much variability within a given landscape
or landscape position (the across slope direction) as
among landscape positions (down slope direction).
As figure 11.3 indicates, the sampling plan for
anisotrophy would be a rectangular grid 68 by 43 meters.
For field orientation the grid should consist of transects
spaced 68 meters apart normal to the landscape, sampled
every 43 meters.
This arrangement would ensure adequate
sampling of most landscape positions.
The precision for these sampling plans is at least
one half a megagram per hectare.
This is far more precise
than many of the decisions farmers make such as decisions
based on future rainfall.
The farmer may conclude that
50
the sample number can be decreased, saving money, but
optimizing management of fields.
If the decision is made to decrease sample number,
the anisotropic semivariograms indicate that alternate
transects could be eliminated and all landscape positions
on remaining transects be retained.
This may equate with stratifying all landscape
positions in a stratified random sampling plan.
The
standard error of transect 3 is 0.27 Mg ha-1, and variance
is 0.07 (n = 22).
This error data can be used to compute
sample number - costs estimates for stratified sampling
schemes (Cochran, 1977).
The semivariance estimates for the across slope for
the entire field is 0.54 Mg ha-1;
this is greater than
the standard error (0.27 Mg ha-1) of stratifying the
landscape of transect 3, but precise for most management
decisions.
With geostatistics 80% of the total sampling
costs occur in the reconnaissance phase (sampling
transects to construct the initial semivariogram).
The
farmer could conceivably select some well sited
reconnaissance transects, then extrapolate the sampling
density of the initial semivariograms to the entire farm
or group of fields.
Figure 11.1.
Location of grain yield sampling sites along transects, Pendleton study area.
N 5
N 5066814
E
E
350081
+ + ++
+ + ++ + ++
++ + + +
"C
34972T
4-
+
+
,++
Transect 3
+ +'
++
++
Transect 1
TAT
+
Transect 4
1
Transect 2
N 5067207
E
50
350013
meters
100
Figure 11.2.
Grain yield semivariogram, four transects, Pendleton study area.
0
0
0
Eo
C0
O
cn
l 11111111111111111111111111111111111111111)
Oo
N0
0
O
O
Spherical Model
L_
0
o
E0
Range = 158.8 m
Sill = 0.48; Sill
Nugget = 0.18
Nugget = 0.30
Width of Angle Class: 90°
Angle for Direction: 0
(f)
0
0
C.00
100.00
200.00
Lag Distance
300.00
Meters
400.00
Figure 11.3. Grain yield semivariograms indicating anisotrophy; across slope (A), and
down slope (B).
A
B
0
oo
0
co
d
Spherical Model
0
Ed
%%% 111111111111 119 11111111111111111111 II
a
CJ)
0
o
11)
EcO
0)
Co
C.=
Range = 68.3
Sill = 0.54; Sill
Nugget = 0.32
Nugget = 0.22
Width of Angle Class: 45°
Angle for Direction: 90°
100.00
200.00
Lag Distance
0
0
0
0
ci.00
Width of Angle Class: 45°
Angle for Direction: 0°
0)0
Gaussian Model
.20
Nugget = 0.11
CD
0
D0
0
C0
0
0
Range = 43 m
Sill = 0.26; Sill
Nugget = 0.15
300.00
Meters
400.00
o
0
llllllllllllllllllllllllllllllllllllllllllllllllllllll 1111111111111 9
:!
E6
0
0
Cf)
0
0
doo
50.00
100.00
150.00
Lag Distance
200.00
Meters
250.00
54
REFERENCES
Burgess, T.M.,
R. Webster, and A.B. McBratney. 1981.
Optimal interpolation and isarithmic mapping of soil
properties:
IV.
Sampling strategy. J. of Soil Sci.
32:643-659.
Cochran, W.G. 1977.
Sampling Techniques. John Wiley &
Sons, New York. 428 p.
Matheron, G.
1971.
The theory of regionalized variables
and its applications. Ecoles des Mines,
Fontainebleau, France. 211 p.
Nash, M.H. and L.A. Daughety. 1990.
Statistical
comparison of soil map-unit boundaries.
Soil Sci.
Soc. Am. J. 54:1677-1681.
Petersen, R.G., and L.D. Calvin. 1965. Sampling. pp. 5472. in C.A. Black (ed.) Methods of Soil Analysis,
Part 1.
Agronomy Monograph No. 9, American Society
of Agronomy, Madison, Wi.
Roberts, M,C. 1987. Remote-sensing and geographic
information system techniques to map spatial
variation of wheat grain yield. M.S. Thesis, Oregon
State University, Corvallis, Or.
Yates, S.R. and M.V. Yates. 1989. A Users Manual for the
GEOPACK Geostatistical Software System.
USDA/ARS
U.S. Salinity Laboratory, Riverside, CA.
55
CHAPTER III.
MAPPING STRATEGIES TO REPRESENT AND MANAGE YIELD
VARIATIONS ACROSS LANDSCAPES
ABSTRACT
Varying fertilizer rates according to yield potential
variation within fields can be a formidable task for
farmers.
Real-time surface organic matter or nitrate
sensors are not useful in controlling variable fertilizer
application in arid regions because in dryland cropping it
is the water stored in the soil profile that determines
yield.
Map based systems must be used for soil management
in this region.
In the present study, digitized 7.5
minute quadrangle data were found to provide more accurate
digital terrain models (DTM) than medium format (70 mm)
aerial photography data.
However, landscape position
units delineated from aerial photographs (70 mm) were used
with a geographic information systems (GIS) to generate
soil management maps that correlate yield with soil water
storage and landscape position.
The GIS can be used to
provide an initial DTM (three dimensional surface model of
field topography) map for soil sampling each slope,
updating the field map during data collection, and
producing the final soil management unit map.
56
INTRODUCTION
Farmers are aware of yield potential differences
within fields.
Management strategies for variable
fertilizer application fall into two categories:
time or map based.
real-
Real-time systems employ a soil
surface organic matter or a soil nitrate sensor attached
to the fertilizing machinery (Schueller, 1991;
1988;
Gaultney et al., 1988; Grifis, 1985).
Gaultney,
Changes in
soil nitrate or organic matter are relayed to a
microprocessor that adjusts fertilizer application rate to
a preset total soil concentration (Schueller, 1991).
If there is no correlation between crop yield and
soil surface characteristics such as surface nitrate or
organic matter, then map based systems are required.
In
dryland areas crop yield is often correlated with
characteristics of the entire soil profile, not just the
surface characteristic.
Water redistribution on the
landscape and subsequent storage in the soil profile was
correlated to winter wheat yield (Roberts, 1991; Veseth,
1986; and Wilson et al., 1991).
Map based systems require digital maps and a vehicle
guidance system in addition to the microprocessor variable
control unit.
Methods for creating digital field maps are
common and relatively inexpensive.
Accurate guidance
systems using the global satellite positioning system are
57
becoming available.
Attempts to apply technology already developed in
cartographic, geographic, and remote sensing fields to
large (1:10 000) scale soil management mapping are limited
to a few examples.
This technology was developed to study
large geographic areas (small map scale, 1:250 000), such
as the Great Basin of the western United States.
However,
Roberts (1987) and Borgelt (1988) have used GIS to analyze
within field variability of yield potential and soil
acidity.
The objectives of the present study were:
(i) to
compare relative accuracies of digital terrain models
(DTM) created with low level medium format (70 mm)
photography data and with digitized 7.5 minute quadrangle
data,
(ii) to use a geographic information system (GIS) in
the analysis of DTM's and landscape units derived from
aerial photographs for the purpose of optimizing the
sampling of landscape positions and developing variable
soil management maps.
MATERIALS AND METHODS
Aerial Photography
Aerial color and color infrared photography (scale
1:5 000 and 1:10 000) with 35 and 70 mm formats were
obtained before seeding and during late vegetative growth
for the Wasco and Pendleton study areas.
58
Areas with low vegetation and light colored soils
were classed as low potential yield, delineated on
transparent overlays using photo interpretation methods
(Pain, 1981) and entered into a GIS computer system in
digital format (digitized).
Landscape positions were
delineated on other transparent overlays and digitized.
Elevation data were obtained from the photography on
a twenty meter grid using the above mentioned methods
(Avery and Berlin, 1985; Pain, 1981).
The northing (y)
and easting (x) control coordinates (state plane
coordinate system - meters, USGS, 1973, 1979) were
calculated by triangulation (Avery and Berlin, 1985; Pain,
1981) and digitized.
The northing, easting, and elevation
data were used to create digital elevation models (DEM's)
using the IDRISI (Eastman, 1990) GIS system.
Digital Elevation Models
Field digital elevation models were obtained by
digitizing elevation contours from USGS 7.5 minute
quadrangles (USGS, 1973, 1979) and by the air photo method
mentioned above.
The northing and easting state plane
(meter) coordinates were digitized.
The IDRISI (Eastman,
1990) system was used to create a DEM.
used to create slope and aspect maps.
The data were
Actual commands and
procedures are similar but will vary with each field
mapped.
The IDRISI tutorials contain examples and
59
procedures for analysis of DEM's (Eastman, 1990).
Soil Management Map Units
Soil management map units were defined at a larger
scale (1:5 000 for Pendleton and 1:10 000 for Wasco) using
soil water (Roberts, 1991), the landscape position
overlay, and the existing soil survey.
The soil
management map units reflect detailed phases of slope and
aspect, landscape positions, and small soils areas not
similar in yield potential to the soil mapped, known as
inclusions, (table III.1), at a larger scale than the 1:20
000 scale of the soil survey.
RESULTS AND DISCUSSION
Digital Terrain Model Accuracy
The DTM derived from low altitude (1000 meters)
medium format (70 mm) photography was not accurate due to
turbulence encountered during aerial photography.
Turbulence causes aircraft and camera tilting that result
in photographs not accurately representing the ground
surface.
Accurate large scale DTM's require special
camera and photo analysis equipment capable of correcting
errors caused by tilting of aircraft.
The DTM obtained by digitizing elevation contours
from the USGS 7.5 minute quadrangles provide an accurate
maps (Figure III.1 and 111.2).
The 1:24 000 scale
60
quadrangle is essentially enlarged to 1:5 000 or 1:10
000 -- with the inherent accuracy limitations of a 1:24
000 map.
The DTM produced from 70 mm aerial photography was
not acceptable because of elevation data errors near the
photo edge.
The DTM derived from quadrangle data also has
some edge irregularities.
Figure II1.1 indicates obvious
regions of southwest aspect, > 12% slopes (white) along
the western edge that do not exist when field verified.
All maps may have some edge irregularities.
These areas
can be marked in the field, and the map updated.
Questions of map resolution, scale, and accuracy must
be addressed.
Large scale (1:10 000) maps are required to
represent detailed (resolution) soil units occuring in
fields -- and therefore, require equally detailed (same
scale) topographic and soil characteristic data to create
the map.
In the future, DTM's could be produced by tracking
northing, easting, and elevation with the satellite
navigation methods.
Reconnaissance Sampling of Landscape Positions
To save time, soil and yield sampling transects can
be delineated across hills on the surface plot (DTM) of
the field (Figure III.1, and 111.2), before visiting the
field.
This will ensure that all necessary landscape
61
positions representing the variability in the field can be
sampled.
Once in the field, the location of the transects
and sampling plan can be verified quickly, even in large - 300-400 ha -- fields with many hills.
Creation of Soil Management Maps
Soil management maps were created from landscape
positions delineated on aerial stereo-pair (Pain, 1981)
photographs and digitized with the IDRISI (Eastman,1990)
GIS (Figure 111.3 and 111.4).
Lower than normal rainfall
depressed yield at the Pendleton study area (Figure
111.3), therefore, soil management units represent phases
of slope and aspect where yield differences likely will
occur in normal rainfall years.
At the Wasco study area,
soil management map units (Figure 111.4 and Table III.1)
indicate north and east foot (WaEF, WbN F) and north back
slopes (WdNB) requiring additional fertilizer, and shallow
soil (CaS) areas requiring no fertilizer.
be fertilized with current management rate.
Other areas can
(Roberts,
1991)
The value of this system is that once a map is made,
soil water can be measured on every landscape position or
soil management unit each year and fertilizer can be
adjusted to the soil water (Roberts, 1991).
Every year a
soil management map can be updated with current soil data
economically using the GIS.
62
Table II1.1 Soil management map unit legend with family
or higher taxonomic class.
Pendleton Study Area
Symbol
Name
Coarse-silty, mixed, mesic Typic Haploxerolls
WaA
WaBN
WaCN
WaDN
WaFTN
WaCW
WaDW
WaDRN
Walla Walla silt loam, 0 to 3 percent slopes
Walla Walla silt loam, 3 to 7 percent north slopes
Walla Walla silt loam, 7 to 12 percent north slopes
Walla Walla sift loam, 12 to 20 percent north slopes
Walla Walla silt loam, 0 to 6 percent foot and toe north slopes
Walla Walla silt loam, 7 to 12 percent west slopes
Walla Walla silt loam, 12 to 20 percent west slopes
Walla Walla silt loam, 0 to 10 percent drainage way north slopes
Wasco Study Area
Symbol
Name
Coarse-silty, mixed, mesic Typic Haploxerolls
WaA
WaEF
WaNT
WbE
WbEB
WbN F
WcS
WcNSO
WdNB
Walla Walla silt loam, 0 to 3 percent slopes
Walla Walla silt loam, 0 to 3 percent east foot slopes
Walla Walla silt loam, 0 to 3 percent north toe slopes
Walla Walla silt loam, 3 to 9 percent east slopes
Walla Walla silt loam, 3 to 9 percent east back slopes
Walla Walla silt loam, 3 to 7 percent north foot slopes
Walla Walla sift loam, 7 to 12 percent south slopes
Walla Walla silt loam, 7 to 12 percent north shoulders
Walla Walla silt loam, 12 to 20 percent north back slopes
Loamy, mixed, mesic Lithollic Xerollic Camborthids
CaS
Starbuck silt loam, 3 to 12 percent south slopes
63
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0
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_ Z
c rocti
-,...1
cd P
..1 °I
la)
N
.
4 -, W
v.-. 4
(U -F.4
0
A
Z
Figure 111.2 Digital terrain model for the Wasco study area derived from the Wasco USGS
View direction is 110°, 30° above horizon, vertical exaggeration
7.5 Minute Quadrangle.
0.7:1.
0
100
meters
N 5047142
E
684633
N 504715U
N 50J
E
94
68528
5-3.01
Figure 111.3
Soil management map units for the Pendleton study area.
111.1.
N 5066814
E
350081
N 5066833
E
349729
IV
N 5067207
E
350013
0
50
meters
100
WaDN
WaFTN
Legend, table
Figure 111.4
Soil management map units for the Wasco study area.
N 5047142
E
604633
Legend, table 111.1.
N 5047159
Reference Power Pole
E
WaNT
605301
WdNB
We S
Hwy
206
-)
CaS
N
WcS
0
50
100
meters
CaS
_----
WhEB-C-1--WaNT
N 5046394 \
E
685289
67
REFERENCES
Avery, T.E., and G.L. Berlin. 1985. Interpretation of
Aerial Photographs, Fourth ed. Burgess Publishing Co.
Minneapolis, Mn. 554 p.
Borgelt, S.C. 1988. Geographic information systems for
on-farm use. ASAE paper 88-1606
Colburn, J. 1986. R&D on a fertilizer sensor and
control system. US Dept. of Energy DOE/ID/12518-1.
Eastman, J.R. 1990. IDRISI a grid based geographic
information system.
Graduate School of Geography,
Clark Univ., Worcester, MA.
Gaultney, L.D.
1988.
Soil physical properties sensing.
Research Activities. Purdue Univ. Dept. Agric.
Eng. 45.
J.K. Schueller, J.L. Shonk, and Z. YU.
Automatic soil organic matter mapping. ASAE
paper 88-1607.
,
1988.
Grifis, C.L.
1985.
Electronic sensing of soil organic
mater. Trans. ASAE 28(3):703-705.
Pain, D.P. 1981. Aerial photography and image
interpretation for resource management. John Wiley &
Sons, New York. 571 p.
Roberts, M.C. 1991. Fertilizing wheat with nitrogen
balanced to soil water variability across
landscapes. in press. Ph.D. Thesis, Oregon State
University, Corvallis, Oregon.
1987.
Remote-sensing and Geographic
Information System Techniques to Map Spatial
Variation of Wheat Grain Yield. M.S. Thesis, Oregon
State University, Corvallis, Or.
Schueller, J.K. 1991. A review and integrating analysis of
spatially-variable control of crop production.
Fertilizer Review, in press.
U.S. Geological Survey. 1973. Wasco, Oregon Quadrangle - Sherman Co. 7.5 Minute Series.
(Topographic).
N4530--W12037.5/7.5 U.S. Govt. Print. Office,
Washington DC.
68
U.S. Geological Survey. 1973. Barnhart Oregon Quadrangle
-- Umatilla Co. 7.5 Minute Series. (Topographic)
N4537.5--W11852.5/7.7 U.S. Govt. Print. Office,
Washington DC.
Veseth, R. 1989. Variable fertilizer application improves
profits and conservation. in Veseth, R., and D.
Wysocki (eds)
Steep Extension Conservation Farming
Update.
Steep extension program, Univ. of Idaho,
College of Agriculture, Moscow, ID.
Wilson, J.P., S.P. Sandor, and G.A. Nielsen.
1991.
Productivity index model modified to estimate
variability of Montana small grain yields. Soil Sci.
Soc. Am. J. 5:228-234.
69
CONCLUSIONS
Realizing that variable stored soil water across
landscape positions is the first constraint to potential
yield, farmers can sample soil water at each landscape
position along a few well placed transects.
Fertilizer
nitrogen can be adjusted to stored soil water.
Residual
nitrogen, soil water, and landscape position data can be
managed with a geographic information system, and soil
management maps produced. With some simple maps farmers may
be able to fertilize each landscape position with nitrogen
adjusted to stored soil water.
Economical vehicle guidance
systems may soon be available to automate variable
fertilization.
Spatially-variable management may be applied to other
crop production practices.
Plowing gently sloping convex
landscape positions may suppress downy brome
infestations.
No-till planting on steep back slopes and
shoulders may help control erosion.
Planting rate may be
increased in high yielding areas of fields and decreased in
low yielding areas.
Varieties may be chosen for specific
yield potential areas.
Barley could be planted on shallow soil south slopes.
Maturing earlier than wheat, barley may use limited soil
moisture more efficiently.
Weed populations, such as downy
70
brome, often are more severe on high potential yield areas.
Applying herbicides only to portions of fields with
threshold weed populations may reduce pesticide use.
Farmers may be able to store low protein (higher
quality) soft white wheat harvested on high yielding north
slopes separately.
After a few years of variable fertilizer
management, excessive residual soil nitrogen may be lowered
to acceptable levels and grain protein will be acceptable
for wheat grown on all landscape positions within the field.
Farmers using this information can save money on
fertilizer, chemicals, and fuel, while protecting surface
and groundwater resources.
71
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.
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76
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APPENDIX
77
Location of Study Areas
The Pendleton study area
The field in the Pendleton study area is about 4 km
west by northwest of the Pendleton airport. The
approximately 18 ha field is located in the SW1/4 of the
NW1/4 of section 15, Township 3 North, Range 31 East
Willamette Principle Meridian. The field is bordered on the
south by the county road, west by a field road, east by a
driveway, and north by a gulch.
The Wasco study area
The study area, approximately 40 ha, near Wasco,
Sherman County, Oregon, is located about 8 km southeast of
Wasco, Oregon, and about 8-10 km northwest of Moro, Oregon.
The field location is the SE1/4 section, of section 24,
Township 1 North, Range 17 East Willamette Principle
Meridian. The triangular shaped field is bordered on the
north by a county road, then an unimproved road. The east
boundary is by the same unimproved road bending south east,
then south to highway 206. The highway (206) is the south
west boundary.
Wasco (Moore) study area transects with sample locations plotted.
Figure A.1.
N 5047142
E
684633
N 5047159
Reference Power Pole
685381
E
ransect 2
1
+
Traect. 1
4
++ +++ +
4-i++++4
iNT
Hwy
206
-
0 50 100
Meters
N
E
504639N4
605209
.1N
Figure A.2.
Pendleton (Timmerman) study area transects with sample locations plotted.
N5
N 5066814
E
349729-
E
350081
++
r
++ +++ ++ +++
Transect 3
+++
+++
+
+
Transect 1
++
+
++
TAT
41-
++ +
+
Transect 4
I
+
Transect 2
N 5067207
E
50
350013
meters
100
+
+
+
+
+
Figure A.3.
Wasco (Moore) study area transects 2, soil profile characteristics.
Core Number
50.00
8.00
6.00
4.00
2.00
0.00
0.00
10.00
O
0
0
12.00
00
16.00
14.00
0
A
0
0
A
100.00
O
0
na)
A
150.00
0
L_
IL
A
200.00
0
A
A
A
O
0 0
C)
°COCO Lithic Contact
6.AA6v1 Effervescence
Indurated
MM.
U)
250.00
2nd Indurated
Figure A.4. Wasco (Moore) study area transects 2, core 1, soil water (A), and nitrate
(B), profile data.
A
Equivalent Depth H2O
0.00
0.00
1.00
3.00
2.00
4.00
5.00
5.00
3.00
4.00
1.00
2.00
0.00
0.00 ..1111,111,11iii..ili,.111111111,111
6.00
0.
Do_41es!
E
Residual NO3 (kg ha-1)
(cm)
E
50.00 -
50.00
6
cI.
_c 100.00
100.00
CL
a
ia)
CD
150.00 -
150.00
:7=
4-
0
L_
O
0- 200.00
0
CL 200.00
-1
()COCO Initial H2O
MOOD Final H2O
H2O
0
Available Water
(17
444446-1.5 Moo
U)
00000 Crop
250.00
trtdotra Residual NO3
250.00
co
Figure A.5. Wasco (Moore) study area transects 2, core 2, soil water (A), and nitrate
(B), profile data.
B
A
Equivalent Depth H2O
0.00
0.00
E
2.00
4.00
6.00
8.00
Residual NO3 (kg ha-1)
(cm)
10.00
0.00
0.00
12.00
E
50.00
50.00
_c 100.00
_C 100.00
CL
CL
0)
0)
U
12.00
8.00
I
11t 1 it111111111iim11ii
ii iii16.00
4.00
150.00
150.00
0)
0
0
200.00
a_ 200.00
0Q0Q0 Initial H2O
000:11:/ Final H2O
0
44Ls44 1.5 Mpa
(/)
00000 Crop
250.00
H2O
Available Water
0
*frtotrtr Residual NO3
to
250.00
Figure A.6. Wasco (Moore) study area transects 2, core 3, soil water (A), and nitrate
(B), profile data.
A
Equivalent Depth H2O
0.00
0.00
E
U
c
3.00
4.00
2.00
1.00
1111111111111111111111111111111
5.00
Residual NO3 (kg ha-1)
(cm)
6.00
0.00
0.00
7.00
lllll 111111 lllll 11111111111111
50.00 -
E
10.00
6.00
8.00
4.00
11,111111111ii1111111111iimitu,1111111
2.00
50.00
c 100.00
100.00
4-,
-
CIL
a)
a)
0
150.00
150.00
a)
a)
O
O
L_
CL 200.00
200.00 -_
OCOCX) Initial H2O
CIDCLI Final H2O
0
44444 -1.5 Mpa
U)
00000 Crop
250.00
H2O
0
Itntraitati Residual NO3
Available Water
250.00 -
Figure A.7. Wasco (Moore) study area transects 2, core 4.5, soil water (A), and nitrate
(B), profile data.
A
Equivalent Depth H2O
0.00
0.00
E
0
0)
1.00
4.00
3.00
2.00
Residual NO3 (kg ha-1)
(cm)
0.50
0.00
5.00
E
50.00
1.00
1.50
2.00
2.50
3.00
50.00 :
0
150.00
150.00
0
/11
0
0
200.00
-.=
CI- 200.00
000C10 Initial H2O
7..=
001:11:113 Final H2O
0
4444? -1.5 Mpa
V)
06000 Crop
250.00
H2O
Available Water
0
Ltot*tdr Residual NO3
V)
250.00
03
Figure A.8. Wasco (Moore) study area transects 2, core 6, soil water (A), and nitrate
(B), profile data.
A
Equivalent Depth H2O
0.00
0.00
E
1.00
2.00
3.00
4.00
Residual NO3 (kg ha-1)
(cm)
5.00
0.50
0.00
6.00
E
50.00
1.00
1.50
2.00
2.50
3.00
50.00
0
0
_c 100.00
100.00
b
a
a)
150.00
150.00
a)
O
U- 200.00
200.00
00000 Initial H2O
EICICI0 Final H2O
0
44Atit, -1.5 Mpa
(f)
00000 Crop
250.00
triatrtitr Residual NO2
H2O
Available Water
250.00
Figure A.9. Wasco (Moore) study area transects 2, core 7, soil water (A), and nitrate
(B), profile data.
A
Equivalent Depth H2O
0.00
0.00
E
1.00
2.00
3.00
B
Residual NO3 (kg ha-1)
(cm)
4.00
0.00
0.00
5.00
E
50.00
2.00
4.00
6.00
8.00
50.00
0
0
100.00
0_
a)
150.00
150.00
a)
4-
0
0
200.00
Q_
OCOQD Initial H2O
=
OO Final H2O
:L17
0
44LA,4, 1.5 MDa
(J)
00000 Crop
250.00
200.00
H20
Available Water
0
tatitcAntr Residual
U.)
250.00
1%,10a
Figure A.10. Wasco (Moore) study area transects 2, core 8, soil water (A), and nitrate
(B), profile data.
A
Equivalent Depth H2O
0.00
1.00
E
iiiiii
2.00
0.00
3.00
Residual NO3 (kg ha-1)
(cm)
4.00
0.00
5.00
6 A
50.00
--
0
2.00
4.00
6.00
8.00
10.00
0.00
E
50.00
0
_c 100.00
100.00
CL
CL
CD
(1)
150.00
(i)
a)
150.00
4=
0
0
200.00 7
-
0
_
_
(f)
-
250.00
0_ 200.00
0:1000 initial H2O
130000 Final H2O
44444 -1.5 Mpa H2O
00000 Crop Available Water
tatotatntr Residual NO3
In
250.00
Figure A.11. Wasco (Moore) study area transects 2, core 9, soil water (A), and nitrate
(B), profile data.
A
Equivalent Depth H2O
0.00
0.00
1.00
2.00
3.00
--ID
Residual NO3 (kg ha-1)
(cm)
0.00
0.00
5.00
4.00
1.00
1.50
2.00
2.50
3.00
-0
11
E
0.50
50.00
E
100.00
_c
-
50.00
0
100.00 =
I.
CL
0)
(1)
0
(i)
150.00
150.00
Z4-7-
0
L.
O
200.00
CI-
ooh Final H2O
0
kzerere. -1.5 Mpa H2O
Crop Available Water
(/)
WOO
250.00
200.00 =
00030 Initial H2O
0
*Jr*** Residual NO3
CT)
250.00 :-
Figure A.12.
Pendleton study area (Timmerman) transects 1, soil profile characteristics.
Core Number
E:-
0
100.00
6.00
4.00
2.00
0.00
0.00
A
A
A
A
A
A
A
8.00
10.00
A
A
A
0
0
12.00
0
I=1
0
o
o
0
O
0
O
Cl)
0
00000 Lithic Contact
,6A6A6, Effervescence
LI I Indurated
I
400.00
0
I
_I
00000 2nd Indurated
03
V)
Pendleton study area (Timmerman) transects 1, core 1, soil water (A), and
Figure A.13.
nitrate (B), profile data.
A
Equivalent Depth H2O
0.00
0.00_
iiiii
E
1.00
2.00
3.00
Residual NO3 (kg ha-1)
(cm)
5.00
4.00
0.00
0.00
6.00
Il111111111
E
50.00
20.00
40.00
60.00
80.00
100.00 120.00
50.00
C)
r 100.00
_c 100.00
CL
a)
0
150.00
150.00
(1)
0
0
4-
0
L
O
L_
200.00
OCOOD Initial H2O
00000 Final H2O
0
44A1^. 1.5 Mpa
(I)
00000 Crop
250.00
(Eq. Depth)
(Eq. Depth)
H2O viEq. Depth)
Available ater (Eq. Depth)
CI- 200.00
O
tritrtctitr Residual NO3
250.00
Figure A.14. Pendleton study area (Timmerman) transects 1, core 2, soil water (A), and
nitrate (B), profile data.
A
Equivalent Depth H2O
Residual NO3 (kg ha-1)
(cm)
0.00
0.00
6.00
5.00
4.00
2.00
3.00
1.00
0.00
0.00 ttttuutluttuutltutttut luutttttlutuuuluuutttl
E
E
50.00 -
20.00
40.00
60.00
80.00
100.00 120.00
50.00
0
0
4)
c
1
o o
.00
8
150.00
150.00
a)
0
a)
4=
14.=
U- 200.00
(1- 200.00
0
O
L._
0
021000 Initial H2O
00OLIO Final H2O
444,40, -1.5 Mpa H2O
00000 Crop Available Water
(!)
250.00
Ittto&rtr Residual NO3
O
cn
250.00
Pendleton study area (Timmerman) transects 1, core 3, soil water (A), and
Figure A.15.
nitrate (B), profile data.
A
0.00
0.00
E
1.00
2.00
3.00
q
0.00
0.00
6.00
E
20.00
40.00
60.00
80.00
50.00
0
0
s
5.00
4.00
50.00 -
Residual NO3 (kg ha-1)
(cm)
Equivalent Depth H2O
_C 100.00
100.00
-F,
CL
-4-
0
150.00
N
150.00
4_
O
0
a- 200.00
=
0
200.00
0000D Initial H2O
00000 Final H2O
4eiLIA4, -1.5 Mpa H2O
Crop Available Water
0
***tett Residual NO2
000
(f)
250.00
250.00
Figure A.16. Pendleton study area (Timmerman) transects 1, core 5, soil water (A), and
nitrate (B), profile data.
A
0.00
0.00
E
Residual NO3 (kg ha-1)
(cm)
Equivalent Depth H2O
4.00
5.00
0.00
0.00
6.00
2.00
3.00
1.00
111111111111111 lllll i111111111111111t11111111J11111
b
50.00
U
10.00
20.00
30.00
lllll
40.00
50.00
60.00
50.00,
6
_c 100.00
100.00
4,
0
CL
a)
(1)
9
150.00
150.00
El
0
(1)
0
200.00 =
Q_
CXX)C0 Initial H2O
01:1000 Final H2O
4444,0, 1.5 Mpa
00000 Crop
250.00
200.00 =
=
H2O
Available Water
0
totdatio:r Residual NO3
to
250.00
to
L.)
Pendleton study area (Timmerman) transects 1, core 6, soil water (A), and
Figure A.17.
nitrate (B), profile data.
B
A
Equivalent Depth H2O
0.00
0.00
E
4.00
2.00
Residual NO3 (kg ha-1)
(cm)
0.00
0.00
6.00
E
50.00
10.00
20.00
30.00
40.00
iiiii50.00
50.00
0
0
4A
150.00
150.00
0)
0)
A
0
L_
0
L._
IZ 200.00
O
0_ 200.00
OCXXX) Initial H2O
011:100 Final H2O
444.44,
Cf)
1.5 Mpa
00000 Crop
250.00
H2O
Available Water
0
Litot7Idt Residual NO3
(r)
250.00 :1
Pendleton study area (Timmerman) transects 1, core 7, soil water (A), and
Figure A.18.
nitrate (B), profile data.
A
Equivalent Depth H2O
0.00
0.00
1.00
2.00
3.00
4.00
Residual NO3 (kg ha-1)
(cm)
5.00
0.00
0.00
6.00
E
50.00
20.00
40.00
60.00
80.00
50.00 -
U
I.Q
.c
4,
100.00
0
0
150.00
150.00
a)
0
0
L
O
0
100.00
200.00
CI- 200.00
00000 Initial H2O
COCCID Final H2O
44444 -1.5 Mpa
O
00000 Crop
250.00 -
1120
Available Water
0
**tat& Residual NO3
(f)
250.00
Pendleton study area (Timmerman) transects 1, core 8, soil water (A), and
Figure A.19.
nitrate (B), profile data.
A
'"'"'"I
N
..E
0
A
50.00
20.00
40.00
60.00
80.00
-1)
100.00
120.00
0.
EC-
E
0.00
0.00
8.00
6.00
4.00
2.00
0.00
0.00
Residual NO3 (kg ha
(cm)
Equivalent Depth H2O
50.00
0
\
A
_C
0
/2
150.00
0
\
\
A
100.00
N
0
a)
\
150.00
\
0
O
O
a_ 200.00
0
200.00
00000 Initial H2O
00000 Final H2O
44/10.Li, -1.5 Mpa H2O
004500 Crop Available Water
250.00
0
1VdcArtr Residual NO3
01
250.00
Pendleton study area (Timmerman) transects 1, core 9, soil water (A), and
Figure A.20.
nitrate (B), profile data.
B
A
1.00
E
50.00
o
..
5.00
\
d
%
A
,/
L. - -
di
E
I
....-
---
I
-..
100.00
-4---,
o
a
50.00
i1111111111
t
/
.
A
/
.0
4---
50.00 -
CL
b
150.00
40.00
_c 100.00
'0 N
CL
a)
30.00
0.00
10.00
20.00
111111111111111111111111111111
0.00
6.00
9
A
.
.,---",
4.00
3.00
2.00
Residual NO3 (kg ha-1)
(cm)
Equivalent Depth H2O
0.00
0.00
0
o
a)
150.00
a)
O
O
eL 200.00
0
200.00
00000 Initial H2O
00000 Final H2O
44644 1.5 Mpa
U9
00000 Crop
250.00
1-120
Available Water
0
litc*Cr Residual NO3
00
250.00
Pendleton study area (Timmerman) transects 1, core 10, soil water (A), and
Figure A.21.
nitrate (B), profile data.
A
omo
1[11 ttttt
5.00
4.00
3.00
2.00
Residual NO3 (kg ha-1)
(cm)
Equivalent Depth H2O
0.00
tttttt
40.00
30.00
10.00
20.00
0.00
1111111111111111111111111111111111111111
0.00
50.00
/
E
A
50.00
50.00 =
Q.
0
0
I
c
.....
100.00
A
4
CL
_c
...._
--.
a)
0
U)
13,
Q
d
a)
0
150.00
I
b
100.00 -
CL
.
ci
\
/
150.00
*,;:=
d)
7...=
0
0
0
+,
.... ....
200.00
0
0
000Q0 Initial H2O
00000 Final H2O
40444 -1.5 Mpa
060() Crop
250.00
200.00 =
totteokft Residual NO3
1120
Available Water
250.00
Figure A.22.
Pendleton study area (Timmerman) transects 2, soil profile characteristics.
Core Number
0.00
0.00
1.00
2.00
F
0 100.00
A
3.00
A
4.00
5.00
A
0
0
O
300.00
O
0
7.00
A
A
a)
6.00
O
0
O
0_
alto Lithic Contact
O
(r)
O
400.00
O
AAAAA, Effervescence
I
Indurated
I
I
I
I
I
00000 2nd Indurated
to
Figure A.23. Pendleton study area (Timmerman) transects 2, core 1, soil water (A), and
nitrate (B), profile data.
A
2.00
1.00
0.00
0.00 11111 liallitiltiattlittillitilmitlittl
R
E
g
i
`
1
\
\
R II
50.00
E
\
10.00 20.00 30.00
40.00 50.00 60.00 70.00
lit1uuuluuuutt
uuuutluu111111uu11i
50.00
0
.........,
\
_c
0.00
,------,
Q
U
0.00
6.00
5.00
Ittili11111
4.00
3.00
Residual NO3 (kg ha-1)
(cm)
Equivalent Depth H2O
100.00
\
_c
0
1
CL
a)
a
150.00
\
d
4=
o
/
0
100.00
-
CIL
Q)
?
-.7-._
150.00 -(1.)
.....
0
t_
0
Cl- 200.00
CI-
0:XXXX) Initial H2O
00000 Final H2O
0
444.44 -1.5 Mpa
Ui
0000
250.00
Crop
200.00
;7=
H2O
Available Water
0
tuktritintr Residual NO3
(1)
250.00
Pendleton study area (Timmerman) transects 2, core 2, soil water (A), and
Figure A.24.
nitrate (B), profile data.
A
Residual NO3 (kg ha-1)
(cm)
Equivalent Depth H2O
5.00
4.00
3.00
0.00...I.1.00
iiiii 2.00
II iiiii 1.1111.111.1.1
0.00
0.00
50.00
0
1
b
A
/
i
b
b.\
/
b
0
a)
150.00
0
I
I
I
V
,f?
60.00
50.00
0
..,,
_c
100.00
4-,
CL
I
a)
. --0
a
(!?
\
0
0
150.00
14=
0k
0
21-
50.00
/
/
I
CL
E
...
P14
_c 100.00
40.00
9
4
.
E
30.00
20.00
10.00
iiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiii
1111111111111111111111
0.00
200.00
0
00000 Initial H2O
00000 Final H2O
40.NO4 1.5 Mpa H2O
00000 Crop Available Water
250.00
a-
200.00
:7=
0
Residual NO3
(/)
250.00
Figure A.25. Pendleton study area (Timmerman) transects 2, core 3,
nitrate (B), profile data.
soil water (A), and
A
Equivalent Depth H2O
4.00
2.00
Residual NO3 (kg ha-1)
(cm)
6.00
0.00
8.00
0.00
aaaaa
10.00
20.00
30.00
40.00
50.00
111111111111111111111111111(111ft
0.00
E
50.00
E
50.00
I.
a
100.00
c
100.00
(i)
CL
a)
150.00
150.00
-o
4-
O
200.00
O
200.00
000C0 Initial H2O
0C10013 Final H2O
44444 -1.5 Mpa
00000 Crop
250.00 -7
ItA*Antr Residual NO3
1-120
Available Water
250.00 -7
Pendleton study area (Timmerman) transects 2, core 4, soil water (A), and
Figure A.26.
nitrate (B), profile data.
A
Residual NO3 (kg ha-1)
(cm)
Equivalent Depth H2O
40.00
20.00
0.00
0.00 IIIIII11111111111111
6.00
5.00
4.00
3.00
2.00
1.00
.fls.iss.lui.misi.I.11.11,11.1
0.00
0.00
E
50.00
iiiii60.00
IIIIIIII iiiii80.00
100.00
50.00
0
0
100.00
b
a.
a)
150.00
150.00
a)
El
0
a)
4-E
o
*4-=
0
s_
CI- 200.00
200.00
°woo Initial H2O
DOME/ Final H2O
O
444.40, -1.5 Mpa H2O
0000 Crop Available Water
(f)
250.00
ittstrtatra Residual NO3
O
(J)
250.00
Pendleton study area (Timmerman) transects 2, core 5, soil water (A), and
Figure A.27.
nitrate (B), profile data.
A
0.00
0.00
E
1.00
2.00
3.00
Residual NO3 (kg ha-1)
(cm)
Equivalent Depth H2O
4.00
5.00
50.00
40.00
30.00
20.00
10.00
0.00
0.00 1111111(11IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII
6.00
E
50.00
50.00
0
0
iP
_c 100.00
_c 100.00
CL
0
150.00
150.00
A
4-
0
200.00
200.00
0Q000 Initial H2O
00000 Final H2O
44444 -1.5 Mpa
O
(I)
60000 Crop
250.00
H2O
Available Water
0
to:dotria Residual NO3
Cf)
250.00
Pendleton study area (Timmerman) transects 2, core 6, soil water (A), and
Figure A.28.
nitrate (B), profile data.
A
Equivalent Depth H2O
0.00
E
4.00
2.00
Residual NO3 (kg ha-1)
(cm)
6.00
0.00
0.00
8.00
E
50.00
50.00
100.00
150.00
200.00
50.00
0
s
_c 1 00 .00 -
100.00
Cl
a)
150.00
150.00
a)
4=
O
0
CI-
200.00
Cl- 200.00
030Q0 Initial
00000 Final
H2O
H2O
440,4wok 1.5 Mpa 1120
40000 Crop Available Water
O
C/)
250.00
Itctrtak Residual NO3
O
(../)
250.00