PRECISION AGRICULTURE INITIATIVE FOR
TEXAS HIGH PLAINS
2001 ANNUAL
COMPREHENSIVE REPORT
Texas
Agricultural Experiment Station and Texas Agricultural Extension Service
Texas
A&M University System
Principal Investigator: Robert
J. Lascano (r-lascano@tamu.edu)
Texas
A&M University – USDA-ARS
3810
4th Street
Lubbock,
TX 79415
Name Title/Agency Location
Cooperators: Jill Booker Res. Assoc., TAES Lubbock
Kevin Bronson Asst. Prof., TAES Lubbock
Ted Wilson Prof. and Res. Direc., Beaumont
Jim
Bordovsky Res. Eng. Halfway
Project Title: The Balance and Variability of Water/Nitrogen
within Large Agricultural Fields.
Primary Research Location: Lamesa and
Helms Farm, TX
Project Objectives:
Objective 1: Quantify the spatial and temporal variability of factors
that can be addressed by precision agriculture practices.
Objective 2: Develop and evaluate instrumentation and software to measure
and analyze variability in crop production and plant response to that
variability.
Objective 3: Evaluate the application of variable
rate irrigation to cotton.
Reporting
Period: 1
January 2001 — 31 December 2001
A. Summary
of Progress (Address all applicable
objectives; data must be included)
Objective 1: Quantify the spatial and temporal variability of factors
that can be addressed by precision agriculture practices.
Lint Yield as a Function of
Variability of Soil Factors in a Large Field. Field heterogeneity may affect yield
potential, irrigation responses, and N transport. A water and N balance study
was conducted in a large cotton field beginning in the spring 1998 to determine
lint yield and N uptake potential related to local water and N input. Two
irrigation levels, 50% ET and 75% ET, were applied from north to south across
the field. In 1998, lint yields increased significantly with increasing
irrigation (P > 0.0012). There was neither an effect of N inputs nor
interaction between water and N inputs (P > 0.6780). However, the covariance
of the model was significant on lint yield and N uptake (P > 0.0001).
Therefore, a state-space approach was used to identify the spatial soil
variability along transects. Lint yield, soil water, P2O5
content, and elevation were correlated in space. The state-space equations were
determined through multivariate autoregressive processes to quantify the
spatially correlated parameters that create spatial difference in a
heterogeneous soil. Results showed that the natural landscape variability could
affect lint yield and N uptake potential related to water and N use.
Furthermore, the variability of our field measurements can be described with
state-space models. These results are
summarized in the publications listed below.
Objective 2: Develop and evaluate instrumentation and software to measure
and analyze variability in crop production and plant response to that
variability.
Multispectral Remote Sensing
Related to Water and Nitrogen Use in Irrigated Cotton. Assessment of real–time
crop and soil conditions using remotely sensed data promises to realize
site-specific water and N application in large fields under semiarid
conditions. Plant/soil reflectance and spectral vegetation index have been used
in characterizing soil, water, nutrient, and plant development conditions, in
forecasting crop yield, and in making day-to-day farm management decisions in
irrigation and fertilization. The objectives of this study were (i) measurement
of cotton/soil reflectance related to specific irrigation and N fertilization,
(ii) determination of cotton/soil spectral and agronomic characteristics, (iii)
assessment of variability in N status and lint yield across large cotton field,
and (iv) to forecast irrigation and N fertilization using spectral vegetation index.
These results are summarized in the
publications listed below.
Objective 3:
Evaluate the
application of variable rate irrigation to cotton.
Summary. The performance of a variable rate
irrigation system on a center pivot was evaluated during the 2001-growing
season. This performance included two objectives: 1) evaluation of delivery
rates and positioning, and 2) phenological responses of cotton irrigated with
variable amounts of water. Both objectives were preliminary and the evaluation
was done at the Texas Agricultural Experiment Station at the Helm’s Farm, near
Halfway, TX. Our results only concentrate on objective 2, as the first
objective is part of an ongoing project by Jim Bordovsky.
For the purpose of variable rate
irrigation a land area under the pivot was divided into 3 crop management zones
(CMZ), each receiving different quantities, i.e., variable, of water during the
growing season. Irrigation applications were determined according to the
potential moisture holding capacity of the soil, which varied spatially due to
variability in soil texture and topography. These two parameters were
determined by taking soil samples to measure texture and elevation measurements
to determine slope. Texture and slope were selected based on past research showing
the importance of these two factors in determining cotton lint yield across a
field. Crop water requirements were based on the evapotranspiration (ET),
calculated from a Penman-Monteith equation. Our criterion for irrigation was to
apply less water to areas with soils with a higher water holding capacity,
which received a lower application rate, i.e., 60% ET, than to areas with soils
with a lower water holding capacity, which received a higher application rate,
i.e., 100% ET. In our research, each CMZ was divided in 2 parts, one receiving
a base rate of 80% ET, or a variable rate according to the pre-determined soil
water holding capacity.
To evaluate the cotton phenological
response to variable rate irrigation, soil and plant measurements were done during
the growing season (July – September). Soil measurement consisted of measuring
soil moisture profiles every two weeks using neutron attenuation at 64
locations. Plant samples were collected twice a month at the same 64 locations,
measuring plant height, number of leaves, leaf area index (LAI), number of
bolls/squares, plant fresh biomass and plant dry weight. Additionally, plant
reflectance was measured using a portable multi-radiometer. The crop was
hand-harvested (24 October 2001) at the 64 sampling locations.
We evaluated our preliminary results by
comparing for each CMZ the base and variable rate. Soil water content showed an
increase for areas with higher application rates compared to the same area with
lower rates. Results from LAI, plant height and crop reflectance measurements
showed that plants under similar conditions showed better development, i.e.,
higher measurements of LAI, height and plant biomass, for higher application
rates than for lower. Results showed that VR leveled out differences in the
field. Measured differences in plant height and crop reflectance for cotton in
different sections under the base application rate were similar to those under
variable rates. Fresh plant biomass and number of bolls per plant showed that
cotton under variable rate application with 60% ET on clay soils was water
stressed. Lint yield showed a gain for variable rate on the slope with 100% ET
and a loss on clay with 60% ET when compared to lint with the 80% base
application rate. Lint yields irrigated with the 80% ET base application rate
were fairly constant throughout the field. Results are preliminary due to
equipment installation delays the cotton crop was irrigated late into the
growing season. These experiments will be repeated during the 2002-growing season.
Introduction.
Lack of water during the growing season is the limiting factor in the
production of agricultural crops on the Texas High Plains (THP). The semi-arid
climate in combination with persistent high winds, results in a high water
demand. As a result of the climatic conditions the THP heavily rely on the use
of groundwater for the production of crops. Gardner et al. (1996) expressed it,
“…irrigation is the basis for an economy and a way of life”. Because of good
soils and irrigation, the THP region, alone, would rank very high in
agricultural production as a separate state in the U.S. or as another country
in the world (Howell and Musick, 1997). Water for irrigation is obtained from
the non-replenishable underground Ogallala aquifer. Water has been withdrawn
from the aquifer for many years and as a result of overdraft, the future of
Texas agriculture is uncertain. The effects of overdraft are likely to be the
most severe in the southern Great Plains, where significant irrigated area may
be removed from production within the next 20 years (Vaux et al., 1996). Since
approximately 95% of the water pumped from the Ogallala is for irrigation
(McReynolds, 2001), the severe decline of the water table prompted for changes
in water management strategies. One of the
changes is the use of deficit irrigation. Crop water demand is determined from
the evapotranspiration (ET), consisting of the evaporation of soil water and
the transpiration of a crop, as calculated with a Penman-Monteith equation.
While regular irrigation applies water according to the ET, deficit irrigation
exposes plants to a certain level of water stress by applying less than the
water demand. Optimum crop yield can be
obtained through allowing a certain level of yield reduction of a given crop
while higher returns can be obtained with saved water, which can be diverted to
irrigation of other areas (Kirda and Kander, 1998). Deficit irrigation is now
widely practiced on the THP for cotton and literature regarding crop
performance with various water treatments and crops is available (Lyle and
Bordovsky, 1995; Bordovsky et al., 1992). Additionally, reducing irrigation
water losses and increasing water use efficiencies requires a new approach
towards irrigation technology: precision irrigation. Precision irrigation is a
term given to irrigation methods that recognize and manage spatial and temporal
variations in the soil-plant-atmosphere system. The objective of precision
irrigation is to improve the control of water input, together with fertilizer
and chemicals, to increase application efficiency and to reduce water use (Cook
and Bramley, 1998).
The
THP landscape is characterized by the presence of center pivot (CP) systems,
which are considered an efficient means of irrigation. A center pivot is a
moving irrigation system that rotates around a fixed point, a pivot (Broner,
1998). The application rate varies laterally because the CP lateral covers more
area per unit length towards the outer end. Center pivots are classified
according to pressure or nozzle type. High-pressure systems have a pressure of
>3.5 kPa at the pivot; medium pressure systems work with pressures between
2.5 to 3.5 kPa, and low-pressure systems have < 2.5 kPa.
Originally,
CP’s sprayed water under high pressures through the air. However, losses due to
water evaporation and low uniformity resulted in low irrigation and application
efficiencies. With the introduction of LEPA (Low Energy Precision Application),
an irrigation system designed for maximizing irrigation efficiency and
increasing rain utilization, a forward step towards significant savings in both
water and energy requirements was made. LEPA is a self-moving circular or
linear irrigation system that applies water for the production of crops and
forage in an energy efficient manner. Water is distributed directly to the
furrow at a very low pressure through drop tubes and orifice controlled
emitters (Lyle and Bordovsky, 1980). Since water is not sprayed through the air
by high pressures, water losses due to evaporation are significantly reduced.
Additionally, the water is applied directly into the furrow, which leads to
very high application efficiencies. Since the introduction of LEPA much
research has been conducted on their efficiency. High application efficiencies
from 95-98% for LEPA when compared to spray sprinkler methods were found, with
negligible runoff or deep percolation (Schneider, 1999).
Present
technology applies water uniformly throughout the field, even though the field
may not be uniform. Although efficiency has improved due to LEPA, the spatial
variability of the soil is not considered in the application of water
(Kaspersma and Sonnemans, 2000).
A
technology that is still in development and could potentially save more water
is Variable Rate irrigation (VR). According to Bordovsky et al., 2000, areas
within a field may require different quantities of irrigation due to variation
in soil texture, depth of soil, and the effect of topography on rainfall runoff
(lower elevations benefiting, or suffering, from runoff from higher
elevations). Instead of treating entire fields uniformly, precision VR allows
on-the-go adjustments in the rate of water delivery to specific portions of the
field (Leidner, 2000). Non-uniform distribution of irrigation based on
topography and water holding capacity of the soil profile could better utilize
both rain and irrigation water and improve water use efficiencies. Although
this does not necessarily mean water savings, re-allocation of the water may
result in higher water use efficiencies and thus crop yield. Another advantage is that crops could also
benefit by changing irrigation amounts at points (along the pivot lateral) as
the relationships among soil chemical properties, pest infestations, production
inputs, and crop yields are determined (Bordovsky, 2000).
Research reports on variable rate application of
irrigation water are available. Kincaid (1997) developed a sprinkler head with
a pin that moves smoothly in and out of the nozzle that reduced the flow to
about 35 % of a nozzle at full capacity. Another type of variable flow
sprinkler head consists of a digitally controlled metering device (Camp et al.,
2000). Other types of variable rate systems use multiple manifolds to vary the
flow-rate, in which several individually controlled manifolds each delivering
discrete but different flow rates, and each designed at a certain water volume
that could be combined into a range of flow rates at the drop (Bordovsky et al., 1992; Duke et al., 1992;
Fraisse et al., 1992; Camp et al., 1998). Currently, most of this technology is
not yet commercially available and their implementation is mainly restricted to
research applications.
The
two objectives of this research were to test and evaluate the functioning of a
CP VR system designed by J.P. Bordovsky (a similar VR system on a linear move
irrigation system was described in Bordovsky et al., 1992), and to find
differences in crop phenological response between VR application, taking
spatial variability in the field into account, and uniform water application,
thus neglecting spatial variability in the field. However, in this report we
will only discuss results from our second objective.
Material
and Methods
Description
of the Research Area
The
research was conducted at the Texas Agricultural Experiment Station in Halfway
at the Helms farm (long. 101° 57’, lat. 34° 11’), on the
Texas High Plains (THP). Fig. 1 is a map of Texas showing annual precipitation
and location of the experiment site in Halfway. The altitude of the research
site is 1045 m above sea level. The semi-arid climate in this area is characterized
by an average annual rain of 450 mm and an annual water evaporation of 2300
mm. For the production of crops, ground water is pumped from the Ogallala
aquifer, and used for irrigation in almost 50% of the area under cultivation.
The soil of the research area consists
of an Olton Loam (fine, mixed, thermic Aridic Paleustolls), characterized
by the presence of a caliche layer that on average is 1 m below the surface.
Caliche is a layer of indurated material near the surface, more or less cemented
by secondary carbonates of calcium and magnesium, precipitated from the soil
solution. The presence of such a layer affects penetration of roots into the
soil and the movement of soil water.
For this research, a cotton crop variety Paymaster 2200, planted on 24 May 2001, was irrigated with a standard ¼ mile Zimmatic™ center pivot equipped with a VR system. The CP consisted of 8 spans each 48.8 m in length, covering a field with a radius of almost 400 m and 47.8 ha. On the outer 3 spans, a VR system was mounted. Except for the research area, the field was irrigated with a deficit, receiving 80% ET of the crop, which was uniformly applied with regular LEPA. The center pivot received water from 3 wells, which at top capacity pumped 2.3 m3/min, sufficient to irrigate the crop at 80% ET.
The research area,
4.8 ha, covered a 60º angle over the outer 3 spans at the south side of the
field (Fig. 2) and received water with the VR system. The south side of the field
had a lower elevation than the north side and there existed some variation in
soil texture. According to spatial variability in soil texture and topography,
the field –as described in Determination of the Crop Management Zones- for our
purpose was divided into 3 different crop management zones (CMZ), each
representing areas of similar characteristics. Application rates in this area
varied between 60-100% of the ET.
The main advantage of the VR system compared to a conventional one is that it allows irrigation according to local requirements in the field by changing flow rates over part of a CP span (Fig. 2), instead of uniform application of irrigation water over the field. Different application rates for the CMZ’s were programmed into the control system for the control program of the center pivot allowed flow-rate changes in the field over 3º increments. The minimum area allowing variation in the irrigation rate, consisted of the length of 1 manifold over a 3º angle of the circle, equaling about 250 m2, which is 0.025 ha.
The
design of the VR system required water to be supplied from the pivot lateral
through pressure regulators and solenoid valves to each of three manifolds
comprising the manifold unit. Three manifold units were present per span.
Hoses were used to direct water from the manifolds to the modified LEPA applicator.
Initial nozzle sizes for each applicator provided rates of 1x, 2x, and 3x,
which, in various combinations, which provided 6 discrete irrigation amounts
ranging -with 20% increments- from 20% to 120% of a base irrigation rate.
In total, the three spans were equipped with ninety-six specially constructed
LEPA variable rate applicators (Bordovsky et al., 2000). To avoid large pressures
variations in the main system, a pump at one of 3 wells is equipped with a
pressure regulator to adjust and maintain the pressure at certain level at
any delivered flow rate. In case of high flow rates, a booster pump is connected
at the pivot to maintain system pressure. Before the water enters the manifolds
and drops with pressure-designed nozzles, it passes pressure regulators to
ensure the right pressure for the nozzles.
Data Collection
Data available for this research were collected
during the summer of 2001, from May to October. In the first week of June, 64
aluminum neutron access tubes were installed pre-determined locations over 9
east west transects in the research area over the CP spans 6,7 and 8. Holes
with a depth of 1.5 m were drilled using a Giddings Coring Drill. Additionally, soil samples were collected at
33 locations. The location of each access tube was determined by using a GPS
Sattloc SLX.
Data on field characteristics such as elevation and slope were collected for 64 locations by using a field level (Model 8114, David White Instruments, Menomonee Falls, WI, USA). The elevation data were plotted in a GIS using Arcview 3.2, and an overview of the elevation and slope of the research area was created. Soil samples were collected over 4 depths, 0.2 m each, at 33 locations throughout the field for determination of the soil texture in the research area. Samples were air-dried and sieved to 2 mm. Soil textures were determined in the laboratory using the hydrometer method (Gee and Bauder, 1986). Soil texture together with the elevation was used to determine the crop management zones.
From July to September, crop samples were
collected every other week at the 64 neutron access tube locations in the
research area. From the collected plants, plant height, number of leaves, leaf
area index (LAI) were measured. Number of squares and bolls were counted, and
fresh and dry weight of the plants and bolls were measured using a scale.
During July and September, a crop scan (MSR5 Multispectral Radiometer, Cropscan
Inc.) was used to measure crop reflectance, which is an indicator for green
mass of the plants. In October, lint yield samples were collected by hand as an
indicator of lint yield.
Soil
moisture content was measured at the 64 neutron access tube locations every
10-14 days during July to September. Using a neutron probe (CPN Corporation,
Model 503 Hydroprobe, Martinez, CA), soil moisture measurements were taken in
0.3 m increments to 1.5 m depth.
During the irrigation season, VR flow rates,
pressures and positioning system were collected. Flow rates were measured using
a hang scale, stopwatch and bucket. Pressures were measured with a pressure
gauge. For this, small Schreader valves (comparable to valve-stem in car tubes
that can be fixed into almost any pipe conduit) were located throughout the
system to which the pressure gauge was connected. For the positioning system,
stakes were placed at known positions in the field, which were determined using
a tape measure (every 3º in the research area in a transect from west to east).
Weather data of the season 2001 were obtained from the Texas Agricultural
Experiment Station in Halfway, located 1 km south of our research plot.
A.
Determination
of the Crop Management Zones (CMZ)
The CMZ were assigned using soil texture
and slope.
Soil Texture
Soil samples were collected at the neutron access tube locations, each 30 m apart, of the middle (east west) transect of the CP spans 6, 7 and 8. In total, 33 samples of each 4 layers with 0.2 m increments (0-0.2 m, 0.2-0.4 m etc.) were used for the determination of the soil texture. Because of time constraints before the irrigation with VR started, only the two top depths, i.e., 0-0.2 m and 0.2-0.4 m, were analyzed for soil texture. At a later time during the research, also of the 2 other depths, i.e., 0.4-0.6 and 0.6-0.8 m, soil textures were determined. Results of this soil analysis for clay content were plotted in a GIS map.
Slope
The
slope of a field may affect the application efficiency of irrigation and
rainfall due to potential runoff by which the water is lost for a specific
location. With the development of LEPA, a technology to reduce runoff in the
furrow by creating small dikes was introduced (Lyle and Bordovsky, 1981), which
was also implemented in the research area. Despite diked furrows, runoff can occur
due to erosion or overtopping of the dikes. Additionally, alternate furrows
application was practiced ((Bordovsky et al., 1984) where runoff may take place
in the intermediate ‘dry not-diked furrows’ in case of rain.
Elevation measurements were done using a level
and rod and then used to determine the field’s slope. Elevation was measured at
all 64 locations where neutron access tubes were situated at the top of the
ridge and data were plotted in a GIS. Using the inverse distance weighing
method (IDW), a map was created with elevation of the research area in
reference to the center pivot (Fig. 3). Because runoff is not caused by the
slope alone but also by the slope of the furrow, the slope between every two
neighboring points in a row was calculated individually. Dividing the measured
difference in elevation between points by the distance of the points in the
row, furrow slope data were obtained. Finally, points with the same slope were
connected with a line, creating a map with furrow slope contour lines in 5
categories, i.e., 1%, 0.8%, 0.6%, 0.4% and 0%. Division into these categories
was done for the practical reason that these were the numbers retrieved from
the calculations that could be connected easily.
Layout Crop Management Zones
The
actual CMZ’s were determined according to their water holding capacity. Soils
containing more clay have a higher potential water holding capacity than those
with sand. The same applies for soils without a slope, which have a higher
potential to use the applied irrigation water and rain, than sites with a slope
that are susceptible to runoff.
Roughly, the research area could -according to soil texture- be divided into two zones, a clayey zone with a high water holding capacity, containing > 40% of clay and a sandy zone with a lower water holding capacity, containing < 40% clay. In the case of slope of the furrows, it was assumed that runoff occurs mainly when the slope >0.5%. Considering the map with furrow slope contour lines, the area within the 0.6% or more contour line was defined as sufficient slope for runoff. The area with <0.6% slope was considered less susceptible to runoff, having a higher potential to utilize irrigation water and rain. Reason for using the 0.6% contour line was instead of a 0.5% was that it was easily derived from slope calculations, while the 0.5% contour line was not.
The
different CMZ’s were assigned to receive an application rate according to their
potential water holding capacity, based on their potential ET as determined
with the Penman-Monteith equation. One of the objectives of VR was to vary flow
rates without an increase of the total water demand of the crop by reallocation
of application rates. The base application rate in this field consists of 80%
ET. Areas with high potential water holding capacity received less than this
80% ET base rate and areas with low potential water holding capacity received
more than the base rate (BR).
Finally, three CMZ’s
were distinguished:
1. A
clayey zone without slope, which was assigned to receive the lowest flow-rate
of 60% ET,
2. A
sandy zone without slope, which was assigned to receive an intermediate
flow-rate of 80% ET,
3. A
sandy zone with slope, which was assigned to receive the highest flow-rate of
100% ET.
To
assure good monitoring of differences in crop phenological response and soil
water content (SWC) between different water treatments, 32 out of 64 sample
locations (neutron access tube locations) received the BR application and 32
received VR application. Of the 9 manifold units on 3 spans, 5 applied VR at
60%, 80% or 100% of the ET, depending on the location of the CP in the field.
The 4 manifold units left, which was every other manifold, applied BR.
Crop Phenological Response
Methodology
B.
Plant
sampling
During
July-September 2001, two weekly crop samples were taken. Six plants were cut at
random at every of the 64 sample locations, shown in Fig. 3. Plant samples were
processed in a laboratory. We measured plant height, number of leaves per
plant, number of squares and bolls, plant fresh weight and dry weight, leaf
area of the plants from which leaf area index (LAI) was calculated.
Plant Density
To
determine the number of plants per m row for every sampling location, a plant
count was done at the beginning of the growing season. At every sample
location, 5 random plant counts were done by placing a 1 m rod along the row
and counting of the plants within that m. Plant counts were average and this
value was used for the calculations of LAI, fresh/dry weight of the crop and
hand harvested cotton yield.
Leaf Area Index
The
leaf area (LA) was measured using a LI-3100 Area Meter. The LAI was calculated
as follows:
LAI = (plants
per m2/ plants in sample) * (LA / area)
Where, LAI, is the leaf
area index in m2/ m2; plants per m is the
number of plants per m obtained from the plant count; plants in sample is
the number of plants in sample (6 plants); LA is the leaf area
measured with LI-3100; and Area is the area of 1 m row in m2. The rows were 0.76
m apart.
Fresh/Dry Weight
Fresh
and dry weights of the 64 samples were measured every sampling date. Before the
dry weight was measured, all samples were dried in an oven at a temperature of
75º C. Depending on the size of the bolls in the sample, the samples were dried
for 2-3 days. Fresh and dry weights are
expressed in kg/ha, using the following formula:
Weight
= (plants per meter/ plants in sample) * weight sample * 43.75
Where Weight is the
fresh/dry crop weight in kg/ha; Plants per m, is the number of plants per m obtained from stem
count; Plants in sample is the number of plants
in sample (6 plants); Weight sample, is the fresh/dry
weight of sample in g; and 43.75 is a conversion factor from weight in kg/m row
to weight in kg/ha in m/m2.
C.
Crop
Reflectance
Additionally to plant
samples, crop reflectance measurements were used as an indicator of crop
development. Due to memory lost we were only able to collect data for 2
sampling dates. A crop scan measures the crop reflectance at a height of 2 m
above the crop canopy, viewing a ground area of 1 m diameter. Spectral
radiometer readings in millivolt were converted into spectral reflectance as
percentage of reflectance. Center wavelengths measured were 460, 485, 500, 560,
600, 660, 700, 750, 800, 830, 880, 940, 1100, 1260, 1480 and 1650 nm.
Different wavebands correlate with specific plant
components. Li et al., 2000, found for example that near infrared reflectance
(NIR) increased significantly with increasing irrigation, soil water content
(SWC), plant fresh biomass (PFB) and plant N-content. One of the most
significant relations between spectral and agronomic characteristics found by
Li et al., 2000, was that NIR reflectance varied as a function of PFB and
cotton lint yield was found to be related to the reflectance based normalized
difference vegetation index (NDVI). The NDVI was determined as follows:
NDVI = (NIR –
RED) / (NIR + RED)
Where NIR is the near infrared reflectance band between 797
and 829 nm, and RED is the red band between 648 and 674 nm
The NDIV and reflection of
several wavelengths were plotted into a GIS to make differences in various
CMZ’s the field visible. Additionally, reflections and NDVI were presented over
a transect through the research area.
D.
Soil
Moisture Content
For the measurement of the soil moisture content
of the soil, a neutron probe was used. Since a neutron probe is not very
sensitive to small differences in soil moisture, measurements were done every
10-14 d. The neutron probe measures moisture in the soil by use of a
radioactive source and neutron detector. The probe is lowered into an access
tube and neutron counts were obtained for five depths of each 30 cm (layer 1 =
0-30 cm, layer 2 = 30-60 cm etc.). In 1997, the neutron meter was calibrated,
resulting in the following linear relation (Bordovsky, personal communication):
SW
= ((CR503 * 7.10) + 0.43)/0.3
Where SW is the soil
moisture content in m3/ m3; CR503
is the count in specific soil layer/standard count. The standard count consists
of 10 readings on a neutral (not lowered into the soil) position.
E.
Harvest
Data
On
24 October 2001, cotton lint was hand harvested at all 64 sampling locations.
At each location, two areas, each 0.76 x 5.33 m, equaling 1/2500 ha (30 inch
rows x 17.5 ft = 1/1000 acre) were harvested. While harvesting, the number of
plants in a sample and the number of bolls of the first 6 plants in the sample
were counted and recorded to be able to combine these data with data previously
collected in the research. The samples were ginned at the Texas Agricultural
Experiment Station, where the total, seed and lint weight were measured. These
data were then plotted into a GIS.
Additionally,
lint yield was harvested with a cotton stripper. The yield monitor records the
weight of harvested cotton (including burr and seed) every second together with
the GPS position in the field. From the hand harvesting, a lint yield factor
was derived indicating the ratio between total weight and lint weight. Data
recorded with the yield monitor were multiplied with this factor and plotted
into a GIS.
F.
Mapping
in Arcview
Soil properties and plant
development were mapped with Arcview, a geographical information system (GIS).
In addition, information on elevation, soil (chemical) properties, soil water
content, fresh and dry weight of plant matter, lint yield and NDVI’s, were also
mapped using Arcview (v. 3.2), showing spatial variability of properties and
characteristics in the research plots. Using the inverse distance weighted
method (IDW), data collected at the 64 sample locations were interpolated,
creating overview maps for the whole research area. The IDW option in Arcview
interpolates data from the nearest neighbors and adds value to data from
surrounding points by multiplying these with the inverse of the distance. The
further a point, the smaller its weight in the interpolation. The number of
neighbors was set at 6. The inverse power on the distance was set at 2.
Although Arcview automatically determines map classifications, the number of
classes was decreased to in most cases 5, and classifications were
pragmatically reclassified into more equal and logic classes to assure a clear
overview the maps created.
G. Data Analysis over a Transect
A transect from the SE to NW corner of the research area
was delineated and used for spatial analysis of the research data. The transect
was defined from low to high elevation, crossing all 3 CMZ’s, by connecting the
mentioned corners with a line between them, crossing the middle point of the
research area. Every point (sample location) in the research area was connected
perpendicular to the transect. Cross-sections were given a number in
chronological order from southeast to northwest, starting with 1 and ending
with 64.
Our defined transect combined points and data from different locations, and
offered the possibility to take a different look at the dataset. Data analysis
along this transect proved reliable. Besides the transect, regression curves
between various variables were analyzed. To find relations between different
soil, water and lint yield properties, several variables were analyzed:
Results and Discussion
Crop Phenological
Response
G.
Plant Sampling
Fresh Weight
Graphs of VR and BR plant fresh
biomass (PFB) are presented in Fig. 4. Our results show large variations in PFB
for VR and rather constant and higher values for BR. Fresh weight in the clay
zone is for VR almost overall the lowest and for BR overall the highest,
indicating that crop growth in the clay zone is affected due to the reduced 60%
flow rate. Variation in PFB for cotton between VR and BR on sand and slope are
small.
Dry Weight
Graphs of the plant dry weight
(PDW), show the same trend as PFB, except for the fact that where curves of PFB
decrease, the PDW increases. This is because bolls, which are about half of the
total PDW, have a bulk density than leaves and
stems. Overall, the dry biomass is 10-20% lower for VR than for BR cotton
during the last sampling dates for which no direct explanation can be given.
Plant Height
Results show that most of the plant growth was established before implementation of variation of flow rates (day 213). Existing differences in plant height from before implementation of VR do not seem to in- or decrease later in the growing season. Plants on clay, sand and slope measure an average height over the last 3 measurements of in average 0.64, 0.61 and 0.59 m (Table 1). For VR, plants in the slope area (100%) are the only ones to show growth after implementation of varied flow rates, approaching the height of plants on sand and clay closely with an average over the last 3 measurements of 0.61 m and 0.62 m for both clay and sand.
LAI
The leaf areas of plants were
measured to determine the LAI. The general dip that is recognized in all 3
charts is due to the change to a more random methodology of cutting plants.
Decreasing LAI towards the end of the growing season is due to dying off of the
cotton plant. Deviations of VR LAI from BR LAI are shown in Table 2.
The
LAI’s showed clearly the pattern of water delivery over the field. For clay, a
difference was measured in LAI from DOY 236 to 261. Cotton plants watered with
the 60% VR application measured 7-13% lower LAI than those with 80% BR
application. Additionally, LAI’s in the sand zone showed any difference, except
for the first measurement at DOY 193. The measured difference, which varied
between –5%-4%, can be considered negligible given the variation in
measurements. In the slope section of the research area, the LAI from the start
of the growing season had higher values for 100% VR than for 80% BR. With a highest
measured difference in LAI of 25% halfway the growing season, slightly over a
week after the actual variation in flow rates was started, VR LAI showed a
steady deviation from BR of 10-16% over the last 3 samples.
Measurements regarding elevation of the field were done in relation to the
center pivot apron. The elevation varied over a height of 2.34 m from –1.65
m in the southeast corner of the field, to 0.69 m in the Midwest part. Fig.
8 (A) shows an increase for elevation along the transect in the slope zone
from –1.65 – 0.1 m. Sand zone is characterized by an elevation of 0.1 – 0.5
m. Points in the clay zone were all located higher than 0.5 m. Slope data,
Fig 8 (B), show steeper slopes for locations with low elevations. In the clay
CMZ (transect No. 45 and up), hardly any slope existed.
