enhancing irrigation scheduling in the mississippi … · 2015-10-21 · evaluation tool (phaucet)...
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MISSISSIPPI SOYBEAN PROMOTION BOARD 1
ENHANCING IRRIGATION SCHEDULING IN THE MISSISSIPPI DELTA THROUGH SOIL
MOISTURE MONITORING AND IMPROVED MODELING CAPABILITIES
Submitted By
John Caleb Rawson
A Research Paper
Submitted to the Faculty of Mississippi State University
in Fulfillment of the Requirements
for the Degree of Master of Science
in Engineering Technology
in the Department of Agricultural and Biological Engineering
Mississippi State, Mississippi
May 2015
MISSISSIPPI SOYBEAN PROMOTION BOARD 1
BACKGROUND AND OBJECTIVES
Increasing reliance of crop producers on water for irrigation coupled with expansion of
irrigated acreage has resulted in the overdraft of the Mississippi River Valley alluvial
aquifer (MRVA). As water resources continue to decline, there is an immediate need for
more efficient water management and greater implementation of water conservation
practices. Mississippi’s Natural Resources Conservation Service (NRCS) has been
working with farmers to increase voluntary implementation of water conservation
practices, but these systems often require financial input from the grower and take time to
install and manage. The Mississippi Irrigation Scheduling Tool (MIST) uses a
“checkbook” water balance calculation and is being developed to offer producers a free
online irrigation management tool that indicates a need for irrigation when the soil water
available to the plant falls below the level needed for crop growth.
The overall objective of this study was to evaluate and test the MIST model on corn and
soybean fields with differing irrigation methods and soil types. Soil moisture sensors and
data loggers were used to continually measure and record soil moisture in increments of 6
inches to a depth of 3 feet in various research and production fields throughout the
growing season for several years. Soil water retention curves were generated for each
field at each depth increment and used to convert soil water tension data to actual soil
water balance. This was then compared to the MIST-calculated soil water balance. In
addition, comparisons were done between sets of soil moisture readings within the same
field to characterize the precision of the measurements. Next Generation Radar’s
(Nexrad) 4-kilometer precipitation data were used to apply and test the model for a
soybean field under pivot irrigation and a corn field under furrow irrigation.
ACKNOWLEDGEMENTS
Next Generation Radar data used in this study was provided by NCAR/EOL
(http://data.eol.ucar.edu/) under sponsorship of the National Science Foundation and
accessed with the assistance of Dr. Jamie Dyer. We are grateful to the producers who
collaborated in this project. This work would not have been possible without their
collaboration and continued support. We would also like to thank Mr. Jason Corbitt for
his assistance with the Watermark sensors and data collection. This project was supported
by funding from the Mississippi Soybean Promotion Board and the Mississippi Corn
Promotion Board, for which we are especially grateful.
GENERAL INTRODUCTION
Decreasing water availability, higher costs associated with pumping, and heightened
environmental concerns about agricultural water diversions are issues causing growing
concern among producers. Improvements in irrigation application uniformity and
scheduling management have occurred steadily over the last decade or two, resulting in
higher water productivity, especially for horticultural crops (Fereres, 2003).
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Expanding reliance of crop producers on water for irrigation has resulted in an overdraft
of the alluvial aquifer in the Lower Mississippi River Valley. Despite an annual average
rainfall above 50 inches, periodic summertime drought and lack of timely rainfall make
irrigation necessary to avoid crop failure (Evett et al., 2003; NOAA, 2011). The
increasing use of water resources and expansion of irrigated acreage has resulted in an
average decline in the alluvial aquifer of 300,000 acre-feet of water per year for the last
10 years (Powers, 2007). Over time, the increased pumping depths require more energy
to bring the water to the surface and resulted in a lower return on investment (Ferguson et
al., 1998). Growers are now implementing water conservation measures such as tailwater
recovery ditches and holding ponds and using surface water to supplement irrigation with
groundwater. These conservation management practices have ameliorated the declining
aquifer levels in some areas and helped to maintain profitability of the agricultural
system.
Declines in groundwater levels are much greater in the central Delta region. To address
these declines, farmers in this area are implementing detainment ponds and tailwater
recovery ditches to capture surface water runoff and excess water from furrow irrigation
to help reduce their dependence on groundwater as the sole source of water to meet
irrigation needs. Groundwater declines have not been as severe outside central Delta
counties because perimeter Delta counties have had aquifer recharge from the Mississippi
River in western counties and rainfall runoff from the hills in east Delta counties (YMD,
2014a). However, if pumpage by producers continues to exceed the recharge rates of the
alluvial aquifer, water levels in the aquifer will continue to decline (Powers, 2007).
Despite decreasing water levels, irrigated acreage is expanding as farmers try to combat
the humid Southeast’s unpredictable rainfall distribution (Powers, 2007; Vories, 2005).
Over the past few years, different water conservation practices have been implemented to
assist growers with water use management solutions. For example, tailwater recovery
ditches capture and hold surface water runoff, and the Pipe Hole And Universal Crown
Evaluation Tool (PHAUCET) program, developed by the Missouri Natural Resources
Conservation Service (NRCS), improves irrigation application efficiencies for furrow
irrigators (YMD, 2014c). However, tailwater recovery ditches can require financial input
from the grower and remove valuable cropland from production, while the furrow
irrigation planning tool PHAUCET offers no assistance to growers irrigating under more
efficient pivot irrigation systems. While water use conservation practices such as these
are helpful and are being adopted, the applicability of PHAUCET is limited to furrow
irrigators, and not all farmers have the time and resources to implement surface water
holding systems. Addressing water use management in the Delta will need the
cooperation and participation of all growers who are irrigating or have an interest in
irrigating.
The Mississippi Irrigation Scheduling Tool (MIST) is a water management practice that
could benefit all producers, regardless of their irrigation method or source of water.
Designed as a web-based irrigation scheduling model, the tool is easily accessible to
growers from a variety of access points. MIST calculates soil water balance using
reliable precipitation estimates, accounting for soil storage and runoff, and applying the
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Penman Montieth equation to calculate daily losses. MIST is an easy-to-use tool that can
address water use efficiency for a large range of potential users.
OBJECTIVES
The overall goal of this study was to improve the Mississippi Irrigation Scheduling Tool.
Listed below are the specific objectives.
Objective One: Collect 2014 growing season data for the purpose of testing and
applying the model.
Soil moisture measurements were collected using Watermark 200SS brand sensors and
data loggers for five site locations consisting of furrow- and pivot-irrigated corn and
soybeans. Farm study sites were located throughout the Mississippi Delta and were
selected based on field accessibility and Watermark and soil sample data collected in
previous years. Fieldwork and data collection began in May on a bi-weekly/weekly basis
and were continued until just prior to each location’s individual harvest time.
Objective Two: Collect, evaluate, and convert data needed to test and apply the MIST
model.
Task One: Soil Moisture Data
First, soil moisture retention curves (SMRC) were developed to convert the collected soil
moisture data from the Watermark sensors in centibars of pressure to inches of water.
This allowed for a more direct comparison of the observed Watermark soil moisture data
to the MIST water balance for model application and testing.
Task Two: Precipitation Data
The MIST model currently uses weather data collected from both Natural Resources
Conservation Service (NRCS) Soil Climate Analysis Network (SCAN) sites and other
sites maintained by the Delta Research and Experiment Center (DREC). Given the
shortage of quality controlled rain data, Nexrad is the preferred precipitation source for
the MIST model (Sassenrath et al., 2013). The second task incorporated the use of
Nexrad precipitation data into the application of the model for the test sites. This task
also provided a test run of MIST with Nexrad data prior to incorporation of Nexrad data
into the MIST online user interface.
Objective Three: The MIST model was applied and tested using the soil and
precipitation data collected from previous years and objectives one and two. The
application and testing of the MIST model was done using 2012 data from the Jonestown
corn furrow and Redgum bean pivot study sites. Model application and testing helped
determine the most appropriate Kc and CN values that best fit the observed soil moisture
data.
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LITERATURE REVIEW
Water Use and Conservation
For years, the Mississippi Delta’s ground water levels have been dropping, with only a
few localized areas where levels have remained constant and indicated recharge (Scott et
al., 1998). The greatest depletion of the Mississippi River Valley Alluvial Aquifer
(MRVA) is primarily due to irrigation, particularly of rice, soybeans, cotton, and corn
(Ferguson et al., 1998). Currently, the Yazoo Mississippi Delta Joint Water Management
District (YMD) requires farmers to acquire a water use permit before drilling a well or
constructing a surface water diversion (YMD, 2014b). Roughly 80% of Mississippi’s
water use permits are in the Mississippi Delta region (YMD, 2011), where the shallow
MRVA serves as the primary source of water for irrigation.
Over the years, pumpage from the MRVA has caused a decrease in outflow to rivers, an
increase in recharge from rivers, and an increase in recharge through the confining unit
(Ferguson et al., 1998). The confining unit is defined as the water-bearing layer of rock
that confines the aquifer but transmits smaller water quantities. Unrestricted water use
has led to annual groundwater recharge rates that are unable to keep up with pumpage
totals for the growing season. Producers annually use 1.5 million acre-feet of water,
while the aquifer is only replenished at a rate of 1.2 million acre-feet (Bennett, 2009).
Mississippi Delta Water Level Summary Reports indicate some ground water recharge in
the outer areas and the southern tip of the Delta, while aquifer levels under interior
counties have dropped by an average of 1 foot per year for the past 20 years (YMD,
2014a). In surveys of the Lower Mississippi Alluvial Aquiver conducted in the Arkansas
Delta region, 74% of the aquifer’s recharge is through the confining unit at an average
rate of 2.0 cm/year (Ferguson et al., 1998). In areas near the Mississippi and Arkansas
Rivers that are hydrologically connected, the level of the aquifer changes with the water
stage of the river (Ferguson et al., 1998). This could indicate that interior farms are
experiencing water shortages sooner than perimeter areas of the aquifer, highlighting an
immediate need for sustainable water management solutions.
Irrigation development in the United States accelerated as population growth triggered a
need for increased food production, and this pattern has been repeated on a worldwide
basis for most of the 20th
century (Howell, 2001). Our ever-growing society also creates
increased water demand for other uses. When increased demand is coupled with water
scarcity, such as during a drought, there is unprecedented pressure on fresh water
resources needed for irrigated agriculture (Fereres et al., 2003).
The dependency on water has become a critical constraint for agricultural growth. In
1996, irrigation was responsible for 65% of the world’s diverted water, with 49% of the
world’s irrigation occurring in India, China and the United States (Howell, 2001). As the
primary user of diverted water, agriculture is therefore under close scrutiny as high water
demands and perceived wasteful practices make it potentially vulnerable to criticism. In
fact, redistribution of water from agriculture to other sectors has already begun in many
areas and is expected to increase in the future (Fereres et al., 2003). At the same time
MISSISSIPPI SOYBEAN PROMOTION BOARD 5
agriculture is being asked to give up water, the world’s increasing population demands
that farmers increase food production (Fereres et al., 2003).
In an effort to manage future food demands and growing competition for clean water,
increased water use efficiency in both rain-fed and irrigated agriculture will be essential.
Increased water use efficiency includes, but is not limited to, the conservation and reuse
of rainfall and field runoff, the reduction of water losses through excessive irrigation, and
the adoption of methods or tools that increase production per unit of water. Mississippi’s
NRCS has been working with farmers to implement on-farm water storage systems in an
effort to capture surface runoff and minimize ground water pumpage, but these systems
are costly to install and can sometimes take valuable cropland out of production.
Irrigation Schedulers
An irrigation scheduler is a tool that can be used by growers to implement irrigation
water management programs, and can also be used in tandem with most existing
irrigation methods. They provide producers and conservationists a scientific
determination of when to irrigate and how much water to apply, based on collected
climate data for the area, to meet specific management objectives. These management
objectives may include outcomes such as: maximum yield, maximum economic benefit,
maintenance of favorable salt balance, maximization of allotted water use, and perhaps
others.
Use of regional hydrometeorological data ensures schedulers are able to accurately
account for evapotranspiration and water losses to indicate the need for scheduled
irrigations. Increased availability of local and regional weather data has encouraged
growers to adopt regional schedulers utilizing techniques such as visual crop stress, soil
moisture by the NRCS feel method, checkbook scheduling, scheduling via pan
evaporation, atmometer, or meteorology data in combination with soil moisture
measurements and crop-based scheduling (Farahani et al., 2008).
For example, irrigation schedulers in the Unites States commonly use the Penman
Montieth equation because weather data are often readily available (Allen et al., 1998).
However, due to the lack of standard Agro-climatic Weather Stations (AWS) in Saudi
Arabia, the Hargreaves formula is used in place of the Penman Montieth formula,
because the Hargreaves formula only requires temperature for its evapotranspiration
equations (EINesr et al., 2011).
Often when stress symptoms are visible, damage has already occurred or will have
occurred by the time the field can be irrigated. In contrast, excessive water applications
also invariably reduce yields of many crops unless accompanied by larger nitrogen
fertilizer applications to compensate for nitrogen loss through leaching (Jensen et al.,
1970). Irrigation scheduling tools, implemented in concert with good weather
predictions, can eliminate the potential for over-irrigation of crops if rain is anticipated.
Irrigation scheduling tools also offer a simple alternative to many, often time-consuming
and costly, water monitoring methods. Schedulers use climate data from local weather
MISSISSIPPI SOYBEAN PROMOTION BOARD 6
servers to provide producers with information on when and how much to irrigate their
fields to meet their specific crop management objectives with minimal water excess.
Differences in Irrigation Schedulers by State or Region
Irrigation scheduling models are growing in popularity and have been developed in
various forms for multiple areas throughout the United States (AgEBB, 2014a).
Although similar in their objective outputs, schedulers can vary depending on their water
balance calculation method and regional data input limitations, by which they are
sometimes restricted. Evapotranspiration calculations are a common challenge in the
creation of an irrigation scheduling tool, and can depend on regional characteristics such
as soils, crop, weather, and precipitation data that are required to effectively operate and
forecast irrigation (Kingston et al., 2009). Most irrigation models focus on surface
irrigation in large-scale agricultural situations where large amounts of water are applied
through methods such as furrow, pivot, border and basin irrigation. Irrigation-scheduling
tools are not, however, limited to large-scale agriculture.
In areas where horticultural crops are growing in popularity, the use of drip and micro-
sprinkler irrigation is widespread. The water budget approach for irrigation scheduling in
these regions has been established with decades of research (Fereres et al., 2003; Broner,
1989). Water use efficiency through the use of irrigation schedulers to monitor
application rates in well designed, maintained, and managed systems is already high. But
as growing populations put higher demands on farmers, the focus is for producers to
increase the ratio of output produced to input (Fereres et al., 2003). Irrigation schedulers
are not only becoming more numerous, but varied by type to address particular needs or
management objectives. Irrigation scheduling tools are available in paper-and-pencil
versions, spreadsheet versions, compiled program versions, and online versions (Wright,
2002; Clark et al., 2001; Rogers et al., 2009; Hillyer and English, 2011).
The Arkansas Scheduler was developed in the 1970s and is intended primarily for use in
humid climates (Vories, 2005). Like many others, it also employs the checkbook style
water balance equation (Broner, 1989). But while developed for Arkansas producers, it
still relies on a grower to input evapotranspiration (ET) or select a program-estimated ET
from one of six sites located across a three-state area (Vories, 2005).
IRRIGATE is the University of Nebraska’s irrigation scheduling model designed for use
with the Agricultural Computer Network System (AGNET). This network serves the
University of Nebraska and the State of Nebraska, as well as several other states (Gilley,
2014). Anyone with a computer, tablet, or smartphone can access the system, providing
growers with a wide range of accessibility to IRRIGATE (Rice, 2009). Nebraska’s
IRRIGATE model tracks a field’s daily soil moisture status from the time of planting,
and like other models, answers the important questions of when and how much water
should be applied for future irrigations. The basic scheduling theory followed in
IRRIGATE employs the water balance equation and the use of the modified Penman-
Monteith equation when sufficient data are available to predict a crop’s ET using the
following climatic variables: maximum and minimum air temperature, average dew point
MISSISSIPPI SOYBEAN PROMOTION BOARD 7
temperature, solar radiation, and wind run (Gilley, 2014). When growers do not have
access to an on-farm weather station needed to collect the climate data, IRRIGATE
provides the option to use the Blaney-Criddle method, which only requires daily
temperatures (Kingston et al., 2009).
Availability of climate data is often a decisive factor in the development of an irrigation
scheduler. Missouri’s Woodruff Irrigation Model uses the Blaney-Criddle method
(AgEBB, 2014b), while a model in the Kingdom of Saudi Arabia employs the Modified
Hargreaves’ method. Both of these rely heavily on consideration of the temperature in
the surrounding region. These methods work well for both of these locations based on
regional needs and, in the case of Saudi Arabia, data availability. However, a
temperature-based ET calculation for the Mississippi Delta region would not address the
unique climate situation in the region and may not be the best-suited method in regions
across the Midwest where climate data availability is not an issue.
The Mississippi Irrigation Scheduling Tool
Different irrigation scheduling tools are needed for different geographic areas because no
two regions have the same input characteristics. Mississippi’s Irrigation Scheduling Tool
(MIST) was designed for Mississippi farmers because there are limited tools available for
irrigation scheduling in humid, high rainfall areas like Mississippi. Similar to Nebraska’s
IRRIGATE model, MIST is accessible via the web from any Internet-enabled device but
will stand alone as its own program unlike programs designed for use with AGNET.
MIST also incorporates climate data from 19 weather stations within the Delta to
calculate and provide ET for growers. With such a large selection of stations the model
is able to select the station closest to a grower’s farm and provide a calculated ET that is
updated daily within the model for the grower. Irrigation inputs are then the only
manually input components needed to improve the adaptability of MIST.
When designing MIST, developers relied on the guidelines set forth in Food and
Agriculture Organization (FAO) Irrigation and Drainage Paper No. 56, providing the
Penman-Monteith equation as the best physically based approach for computing
reference (ET0) and crop (ETC) evapotranspiration (Allen et al., 1998). The FAO
Penman-Monteith equation requires standard climatological data, including air
temperature, relative humidity, solar radiation, and wind speed. However, weather
stations that provide reliable data for these parameters are limited in some regions,
restricting the widespread use of the FAO Penman-Monteith equation (Pereira and Pruitt,
2004).
It is often substituted with approaches that have lower input requirements such as the
Hargreaves, Makkink, or Priestley-Taylor equations (Gavilan et al., 2006; DeBruin et al.,
2010; Espadafor et al., 2011). In studies on the guidelines for estimation of potential ET
in absence of sufficient climate data, researchers proposed the Hargreaves method in
place of the Penman-Monteith equation (Droogers et al., 2002; Kingston et al., 2009).
Further studies indicate similarities between the Makkink, Hargreaves, and Priestly-
Taylor equations to the Penman-Monteith method, and suggest they can be used to
MISSISSIPPI SOYBEAN PROMOTION BOARD 8
generate similar irrigation schedules and estimated yields (Cruz et al., 2013; Kingston et
al., 2009). However, the Hargreaves method generally overestimates ET in humid
locations, and the Makkink method produces large variations in ET, leaving the Priestly-
Taylor method as the next best method in humid areas where there is insufficient data for
the Penman-Monteith equation (Allen et al., 1998; Trajkovic, 2007; Droogers et al.,
2002,). The Priestly-Taylor method is a widely used simplification of the Penman-
Monteith equation based on net radiation and temperature (Kingston et al., 2009; Priestly
et al., 1972).
Irrigation scheduling in arid or remote areas with little climate data can be much less
challenging than scheduling irrigation in the humid southeast (EINesr et al., 2001;
Farahani et al., 2008; Vories et al., 2005). However, not all programs work equally well
in both arid and humid climates. With the local climate data available in the Mississippi
Delta, the Penman-Monteith method provides potential for the most accurate results over
other irrigation scheduling methods. Additionally, irrigation scheduling is more
complicated in humid regions due to factors such as weather, unpredictable rainfall, and
temperature swings caused by weather fronts (Vories, 2005). Although annual
precipitation in the southeast normally exceeds crop ET, it is often poorly distributed
throughout the season, potentially causing reduced productivity and profits (Farahani et
al., 2008). MIST addresses the unpredictable seasonal rainfall distribution and high
precipitation issue by incorporating the use of National Oceanic and Atmospheric
Administration’s (NOAA) Next-Generation Radar (Nexrad) and the calculation of runoff
(Q).
MATERIALS AND METHODS
Collection and Evaluation of Soil Moisture with Sensors
The first objective of the research project involved collecting soil moisture sensor data
and evaluating data collected from the project’s inception in 2011 to the present. Soil
moisture data was collected using the Irrometer Watermark monitor model-900 data
loggers with Watermark 200SS sensors (Irrometer, Inc., Riverside, CA), and the data
were used to apply and test the MIST. The model-900 data logger is equipped with eight
sensor connection points and can collect soil moisture and soil temperature readings at
intervals of one minute to once a day. Watermark 200SS soil moisture sensors consist of
a pair of electrodes that are imbedded within a granular matrix. As a current is applied to
the sensor, it records a resistance value that correlates to centibars or kilopascals of
pressure or soil water tension. A saturated soil would have a reading of zero, and a dry
soil would have a reading of 100 or higher. Results will vary depending on the soil type
that is being measured and will require specific soil moisture release curves to understand
exactly how much water can be held at a particular location in a specific soil type. Clay
and silt soils hold more water, but due to smaller pore space also release a smaller
percentage of that water to plants. This texture variability can be seen on Mississippi
fields and often varies throughout the soil profile as well (USDA, 2013b).
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Over the course of the 2014 growing season, data was recorded at five field locations
spread throughout the Mississippi Delta region. Farm study sites were located near
Satartia, Redgum, Sunnyside, Jonestown, and Dublin and include fields planted in corn
and soybeans both under furrow and pivot irrigation. At each study field, two complete
sets of Watermark sensors and data loggers were placed in case of an equipment failure
between visits, and to get duplication for the comparison of results. A complete set
included one data logger and six soil moisture sensors, installed at 6-inch depth
increments ranging from 6 to 36 inches. Soil temperature sensors were not used because
temperature accounts for only 1% of the raw resistance between the sensor electrodes and
has an S-curve relationship. Thus, a significant impact from temperature is only seen in
very dry situations and with large shifts in temperature (200 ohm/cbar) or wet with
similar shifts (50 ohms/cbar). This means a possible difference of a few centibars, which
is within the conditioned three-centibar error of the sensor itself. It is impractical to
calibrate the 200SS sensors, but they do require conditioning. New sensors were soaked
to saturation and dried fully, twice, then soaked again prior to installation. After initial
conditioning, if the sensor read 0-3 centibars when saturated, it was considered accurate.
Each sensor was glued to the bottom of a half-inch diameter, thin wall polyvinyl chloride
(PVC) pipe with a cap on the top of the pipe to protect the wires and limit the amount of
moisture received through the top end of the pipe protruding from the soil surface. The
sensors were installed with wires exiting the top of the PVC pipe and connecting to the
data loggers, which were attached to a stake next to the 36-inch sensor. Data loggers are
placed close to the ground to avoid solar radiation blockage to the plant canopy. Sensors
were then installed between each corn or soybean plant in the row to ensure proximity to
the root zone. The only exception was in fields with dual row soybeans, where sensors
were placed between the plant rows and spaced approximately four inches apart.
Soil Sampling and Generated Soil Moisture Release Curves
Soil water content and water release change by soil texture, depth, and type, which is why
irrigation models can get so complicated (Davidson et al., 1969; Sanden et al., 2003).
MIST was designed with the assumption that the soil moisture release curves at different
depths contributed negligible error to the irrigation application decision. It was assumed
that soil texture at the different fields (e.g. Bosket Very Fine Sandy Loam (VFSL) at
Jonestown versus Forestdale Silty Clay Loam (SiCL) at Redgum) would have more of an
influence on the soil moisture release curves rather than the changes in soil texture by
depth (e.g. 0 - 12 in. soil layer from Jonestown versus the 12 - 24 in. soil layer from
Jonestown).
In 2011, MIST team members took soil samples from a mixture of soil collected with a
bucket and shovel from the first 0-12 in. of the soil profile at several field study sites.
The composite samples were mixed and then sent to Decagon Devices Inc. for soil
analysis and generation of soil moisture release curves (SMRC) through the Van
Genuchten function (Van Genuchten, 1980). The SMRCs were needed to convert the
soil sensor pressure readings in (cbar) to inches of water for comparison with the
irrigation recommendations of the MIST model.
MISSISSIPPI SOYBEAN PROMOTION BOARD 10
Because original soil samples only accounted for soil physical properties in the first
twelve inches of soil, additional depth-specific soil samples were collected to more
accurately convert pressure readings to soil water content at each sensor depth. Soil
characteristics throughout a profile can be vaguely different from one horizon to another,
but potential error associated with the use of a single depth-specific SMRC could not be
assessed until a SMRC was developed for soil properties at the depth of each sensor. Soil
moisture data converted using SMRCs generated from both a composite 12-inch sample
and depth-specific samples were compared to determine the effect on the measured soil
water content.
Soil Sampling and Bulk Density Tests for Generation of Depth-Specific Soil
Moisture Release Curves
Depth-specific soil samples account for the changes in water holding capacity over the
depth of the soil profile. For instance, the sample collected at the 6-inch depth would
likely have a different percent of sand-silt-clay than that of the soil at the 36-inch depth,
resulting in differences of available water content. Using a curve generated at a depth of
6 inches to convert pressure readings of a 36-inch sensor could indicate a different
volumetric water content than actually exists, depending on the soil characteristics at the
36-inch depth.
Depth-specific soil samples were collected in the fall of 2014 to generate SMRCs at the
depth of each soil moisture sensor. Lab work required a minimum sample size of 250
cm³, along with a bulk density measurement at each depth. Care was taken to collect an
approximate volume of 400 cm³ for generation of SMRCs, and samples were placed in
plastic containers for transport back to the lab. Soil samples were left to dry at room
temperature for 48 hours and then ground in a soil grinder. The plastic containers were
then sealed with duct tape and labeled with the field and depth at which each sample was
taken, in preparation for shipment to the Decagon Devices Company for analysis and
generation of the SMRC.
Bulk density for each depth-specific soil sample was collected at the same depth and time
that each soil sample was taken. Bulk density samples were collected using 2 x 1.5-inch
stainless steel soil sampling rings, which were fabricated in the Agricultural and
Biological Engineering shop using a lathe. For the bulk density calculations, each ring
was stamped with a number from one to twelve, and each ring’s individual height and
weight was precisely recorded using a dial caliper and a Mettler Toledo precision scale.
Depth-specific soil samples for calculating bulk density and generating SMRCs were
collected at the 2014 Jonestown corn field under furrow irrigation and the Redgum
soybean field under pivot irrigation. Sampling was completed on separate days when
each field had a slightly moist soil profile. At each site, a soil pit was dug manually to a
depth of approximately 40 inches at the same location where soil sensors were placed
over the growing season. A spade was then used to clean and face one side of the soil pit.
Using a tape measure, the soil pit was marked starting at a depth of 6 inches from the soil
surface, continuing every 6 inches to a depth of 36 inches. At each 6-inch increment, a
MISSISSIPPI SOYBEAN PROMOTION BOARD 11
soil ring was hammered horizontally into the soil profile using a rubber mallet and wood
block to reduce compaction and soil loss. A trowel and flat bladed knife were used to
carefully remove and trim each sample. Samples were then labeled, sealed in a Ziploc
bag and placed in a cooler to reduce moisture loss. A moist soil weight (WM) in grams
was recorded immediately upon return from the field, by weighing the moist soil and ring
and then subtracting the predetermined weight of the ring. Next, samples were placed in
a Grieve laboratory oven at 105 degrees Centigrade until each sample reached its dry
weight (WD). It took approximately 26 and 50 hours, respectively, in the oven for the
Jonestown and Redgum soil samples to reach their WD, which was then recorded.
Bulk density (BD) was computed by taking the WD of the sample divided by the volume
of the soil core (VSC), or BD = WD/ VSC. Each sample’s WM and WD were determined by
subtracting the previously recorded weight of the ring and the drying tray used for each
sample. The VSC was computed by measuring the radius (r) and height (h) of the soil ring
in centimeters and applying the formula VSC = πr2*h.
In addition to the depth-specific soil sampling for individual SMRCs, a 36-inch
composite profile sample was also taken at both the Jonestown and Redgum study fields.
Approximately 400 cm³ of additional soil was collected for these profile samples and
placed in a plastic container. To reach a depth of 36 inches, each sample was collected
using a 1-inch diameter by 36-inch length soil probe. To verify that the correct depth had
been reached, a tape measure was inserted into the hole and read to a depth of 36 inches.
The online program Web Soil Survey (WSS) provided the “representative” bulk density
for each of these samples, where the representative value indicates the expected value of
the bulk density for the selected soil based on soil survey reports (USDA, 2013b). The
36-inch composite SMRC was then used to calculate total volumetric water content for
the 36-inch profile. This value was then compared to the total sum of the volumetric
water content determined from the depth-specific SMRCs for the corresponding soil
profile.
Watermark Data Conversion Calculations
MIST’s checkbook style water-balance calculation provides output in inches of water.
To apply and test the MIST model, original soil moisture sensor data needed to be
converted from centibars of pressure (cbar) to inches of water (in. H2O). First, water
potential (WP) was computed using the conversion 0.0980665 cbar = 1 cm H2O, where
cm H2O is the WP in units of pressure. Next, WP was converted to a Soil Moisture
Tension (pF) value that could be used with the modeled soil moisture release curves from
Decagon to provide percent water content (%WC) at the depth of the sensor. The pF
value was computed as pF = Log10WP, where WP represents water potential in cm H2O.
A Vlookup command was used in Excel to take the pF value and find the percent water
content for the soil type from the modeled curve for each field site. Inches of H2O per
foot of soil were then computed by taking the H2O/ft.Soil = %WC*(12 in./ft.), which was
then divided by two to determine each sensor’s six-inch range (Werner, 1992).
MISSISSIPPI SOYBEAN PROMOTION BOARD 12
Model Application and Testing
The application and testing of the MIST model was the overall goal of this research. The
MIST model used for this research was developed in an Excel spreadsheet by Dr.
Gretchen Sassenrath. The model calculated daily evapotranspiration from daily weather
information and crop water use based on daily crop growth. Background information on
crop management (tillage) and date of planting was used with the ET and crop water use
in a water balance equation (Sassenrath et al., 2013). MIST incorporates specific soil
type, tillage depth, and crop type, and utilizes weather and evaporation data from weather
stations throughout the Mississippi Delta.
To calculate the need for irrigation, MIST uses a water balance equation, summing
incoming water from rainfall and irrigation minus water lost through ET and runoff. The
model calculates crop water loss as the product of the calculated ET times the crop
coefficient (KC), to provide daily crop water use. Prior to this study in 2014, the model
was initially run using weather and evaporation data obtained from MSU Cares weather
website. Climate conditions recorded by surrounding NRCS SCAN weather stations
initially provided much of the needed metrological and precipitation data for ET and
water loss calculations. For the results presented in this study, MIST was tested using
precipitation data obtained from NOAA’s Next Generation Radar (Nexrad) (Lin et al.,
2005). There are twelve NRCS SCAN sites sparsely scattered over the Mississippi Delta,
resulting in spatial data gaps. The lack of quality controlled rain gauge data from these
Delta weather stations also often results in temporal data gaps, sometimes at critical times
during the growing season. NOAA’s Nexrad is operated by the National Weather
Service (NWS) and generates an hourly precipitation estimate for a 4x4 kilometer grid,
which makes it the preferable alternative for use in MIST (Sassenrath et al., 2013).
MIST uses the NRCS curve numbers, and the Soil Conservation Service (SCS) Runoff
(Q) equation to calculate water storage and runoff (Schwab et al., 1993). These methods
require specific soil information associated with each individual soil type, which is
needed for the “Input_Info” portion of the latest spreadsheet version of the MIST model.
In addition to columns for field usage data are columns for soil name, type, runoff
potential, and average available water capacity illustrated in the “Input_Info” tab shown
in Figure 1. Of these inputs, the hydrologic soil group and average available water
capacity are used in the calculations to determine initial abstraction or infiltration rates
(Ia) and soil moisture status (USDA, 2013b). In the on-line version of the MIST model,
the user is currently asked to define their soil type as light, medium-light, medium-heavy,
or heavy, and an infiltration rate is assigned to each classification. There is a pop-up box
that guides the user in making this selection. The infiltration rate is the numerical value
representing the rate at which water moves and is absorbed within the profile.
For each day, MIST categorizes soil moisture as dry (<1.4 in.), average (1.4-2 in.), or wet
(>2 in.), depending on the previous 5 days’ precipitation. Potential Maximum Retention
(S) is calculated using a predetermined curve number based on the soil runoff potential
designation. Curve numbers are set in the model for poor and good hydrologic soil
conditions and for each of the four hydrologic soil groups (A, B, C, and D), as well as for
MISSISSIPPI SOYBEAN PROMOTION BOARD 13
all three soil moisture categories (average, dry, and wet). Hydrologic condition is based
on a combination of factors that affect infiltration and runoff, including density and
canopy of vegetative areas, amount of year-round cover, amount of grass or close-seeded
legumes, percent of residue cover on the land surface (good ≥20%), and degree of surface
roughness (Schwab et al., 1993). Upon determination of the curve number, the model
then computes runoff as Q = (P – 0.2S)2/P + 0.8S, where the runoff (Q) is determined by
the amount of precipitation (P) and S, the potential maximum retention after runoff
begins. By determining the hydrologic soil group and thus the runoff potential of the
soil, the model is able to address the spatial variability of the soils in Mississippi
agricultural fields.
Figure 1. The MIST model’s field soil data inputs. Soil runoff potential and average
available water capacities are in the final two columns.
MIST calculates usable rain as precipitation that is absorbed by the soil, provided storage
space is available, and subsequently available to the plant. Derivation of usable rain is
determined by taking precipitation in excess of 0.7 in. minus calculated Q. However,
runoff (Q) can be zero for high precipitation events over soil that is very dry and has
ample water storage space. This means rainfall in excess of 0.7 in. is only removed if the
soil profile can no longer receive additional water.
The water loss portion of MIST uses the modified Penman-Monteith equation to
determine daily ET from meteorological weather data. The daily crop water loss is
calculated by multiplying the ET times the appropriate crop coefficient (KC). The final
running water balance (WB) is computed as R+I – (ET*KC) = WB, where R = rainfall
(in.), I = irrigation (in.), ET = reference evapotranspiration (in./day), and KC = daily crop
coefficient. The MIST model makes the assumption that the soil profile is at field
capacity at the time of planting. Field capacity is the amount of water held in the soil
profile after excess water has drained, and it is determined by the physical components of
the soil. For example, a soil high in clay or silt will hold more water than a soil high in
sand. As the percentage of each component changes, so does the potential field capacity.
This relationship between soil texture and water holding capacity does not directly
represent plant available water in the soil profile. MIST assumes field capacity and then
sets the water balance to zero. As daily crop water use is subtracted from the water
balance, it begins to drop. The MIST model will trigger an irrigation event when the
water balance reaches a predetermined deficit, and this deficit is dependent on the field’s
crop and irrigation system.
When MIST indicates that an irrigation event is needed, the user tells the model how
many inches of water were applied to the field. The irrigation event in inches of water is
added to the field’s water balance, reducing the water deficit. The process then continues
with the reduction of the water balance through daily crop water use until the model
MISSISSIPPI SOYBEAN PROMOTION BOARD 14
triggers the need for irrigation again. A negative value in the water balance represents a
reduction in water from the soil profile. For MIST, an irrigation event is triggered by a
maximum negative water value specific to the irrigation system and crop type. For
example, MIST indicates the need for irrigation when a field irrigated with a pivot system
reaches a water balance value greater than -1.0 inches. Similarly, an irrigation event is
indicated when a field with furrow irrigation reaches a value greater than -3.0 inches.
Different water balance values that trigger an irrigation event depend on the capacity of
the irrigation system. While the crop is still growing and without input rainfall or
irrigation, the water balance continues to drop until the water level in the soil reaches the
point at which there is no more plant available water. This is referred to as the permanent
wilting point and varies by soil type.
RESULTS AND DISCUSSION
Results and Discussion
Model application and testing were performed on the 2012 Redgum soybean field under
pivot irrigation and the Jonestown corn field under furrow irrigation. Soil type, crop
type, method of irrigation, years of usable data, and consistency between box readings
were all taken into consideration when selecting these two sites for the purpose of
applying and testing the MIST model. Figures 2 and 3, respectively, show the variation
between total pressure readings (0-36 in.) from data loggers at the 2012 Jonestown corn
furrow and Redgum beans pivot locations.
Figure 2. Comparison of pressure measurements (cbar) for data loggers A and B at
the Jonestown corn furrow field.
MISSISSIPPI SOYBEAN PROMOTION BOARD 15
Figure 3. Comparison of pressure measurements (cbar) for data loggers A and B at
the Redgum soybean pivot field.
The two sites were chosen because they represented both corn and soybean crops and
also furrow and pivot irrigation methods. Each site is easily accessible and is managed at
a high standard by the respective growers, providing an optimal location for data
collection. Cooperation and participation by the producers has been vital to the project.
For example, it has been helpful in analyzing the results to have a record of each field’s
irrigation events, when possible.
Analysis and understanding of the Watermark sensor readings in comparison to the
modeled MIST water balance at these two locations lies within the properties of each
field’s soil profiles. Each field hosts numerous soils types. While the approximate
location for the sets of soil moisture sensors was consistent from year to year, the exact
location of each box set with respect to the multiple soil types found in each field was
unknown for previous years. Specific soil information associated with each individual
soil type is needed to run the MIST model. When analyzing the soil moisture data
collected by the sensors, it is important to understand that the analysis of the collected
data is based on a careful review of each field’s soils data and the assumption that the soil
type in which the sensors were installed is known. Without accurate information on the
soil type(s) in which the sensors were installed, the comparison of the sensor-derived soil
water content can vary from the MIST-calculated water content. For example, at the
Redgum soybean field under pivot irrigation, our sensor sets were located very close to
three different soil types, all having the same curve number designation but different
values for average available water content. Average available water content refers to the
quantity of water that the soil is capable of storing for use by plants, and is determined
from federally conducted soil surveys accessible from the NRCS online soil database
(USDA, 2013b). In Figure 4, it is clear to see how the average available water content
affects the water balance calculated by the MIST model.
MISSISSIPPI SOYBEAN PROMOTION BOARD 16
Figure 4. MIST-calculated field water balance (WB) for three different soil types—
Dowling Clay (Da), Forestdale Silty Clay Loam (Fd), Alligator Clay (Ac)—at the
Redgum soybean pivot field.
Depending on soil data inputs, the MIST calculated water balance can fluctuate by more
than three inches. However, it would require a much more complicated model to
incorporate the spatial variability of the soils found in the Mississippi Delta or any area
that has experienced land leveling or other significant movement of soil. Thus, the runoff
calculation (Q) included in MIST, which uses the soil runoff potential, enables the model
to cover a larger range of soil types and still achieve real-time calculations that are
reasonably accurate. This is especially important when considering implementation of
the model in a production setting – alluvial fields such as those in the Delta are highly
variable, and farmers do not have the time or knowledge to input site-specific detailed
information on soil variability.
In the case of the Redgum field, all three soil types indicated in the graph fall into the
same hydrologic soil group (B), but vary in their average available water capacity. MIST
considers a field’s predominant soil type for the determination of runoff, storage, and
water holding capacity. However, for the purposes of model application and testing, it is
important to know the soil type in which the sensors were installed to correctly compare
the field’s measured water balance to the MIST-calculated water balance. Assumptions
can be made using the 2011 soils data, Global Positioning System (GPS), soil survey
maps, and careful analysis of each field’s soil properties, but verification of each soil type
would require a soil scientist to inspect each site location to confirm its respective soil
type.
A large portion of this work on the MIST project focused on the use of soil moisture
retention curves to convert sensor data to water content and offer insight on behavioral
characteristics of the water balance throughout the soil profile. To compare the recorded
sensor data with the MIST-derived water balance, the pressure readings collected by the
Watermark soil moisture sensors had to be converted to inches of water throughout the
MISSISSIPPI SOYBEAN PROMOTION BOARD 17
36-inch tested soil profile. MIST produces a water balance output in inches of water on a
daily (24-hour) basis by taking the water balance of the previous day and subtracting
water lost through ET and crop water use and adding any additional precipitation or water
from irrigation for the day. Inches of water represent the MIST-calculated water balance
for the soil profile and the plant available water for that particular field.
To compare the MIST-calculated water balance in inches of water to actual inches of
water measured over the growing season, the pressure data collected by the sensors in
centibars was converted to inches of water using curves generated with the Van
Genuchten function (Van Genuchten, 1980). Soil moisture release curves were generated
in 2011 using a 12-inch composite soil sample, but no depth-specific curves were
generated to convert data measured at the various depths at which soil moisture sensors
were placed. The curves generated in 2011 were meant to serve as a general operational
check of the MIST model, and thus soil samples used to create the curves were collected
from approximately the top foot of soil.
The curves generated in 2011 did not take into account the soil physical properties acting
on the sensors or the available water below twelve inches. At each depth in the soil
profile where sensors were placed, the physical properties of the soil can change. Sensors
installed at the Jonestown site were set into a Bosket series soil (fine-loamy, mixed,
active, thermic Mollic Hapludalfs), and sensors were installed into a Forestdale series
(fine, smectic, thermic Typic Endoaqualfs) at the Redgum location. Currently, the use of
soil survey data assumes that input soil data is correct at the resolution provided by the
Web Soil Survey model, and that conventional farming has not altered the soil profile
(Miller, 2012; USDA, 2013b). In assuming this, changes to soil physical properties such
as texture, structure, pore size and bulk density, among others, are expected with depth.
These soil physical properties can affect a soil’s water holding capacity. For the 36-inch
tested soil depth, the field profiles at each location have three separate soil horizons as
shown in Table 1. Within each soil horizon, the physical properties can change with
depth, assuming some integrity remains despite modern farming practices.
Table 1. Test Site Soil Series Horizon(a)
Depths
Site Jonestown Redgum
Soil Series Bosket Forestdale
Horizons and Depth
Ap - 0-9 inches Ap - 0-6 inches
AB - 9-25 inches Btg1 - 6-26 inches
Bt1 - 25-48 inches Btg2 - 26-60 inches
(a) Horizon separates soil layers by obvious physical features, chiefly color
and texture. Information available at: soilseries.sc.egov.usda.gov.
Initial observation indicated large amounts of water recorded by the sensors that did not
match the modeled usable rainfall. To eliminate potential error as a result of data
conversion through the use of non-depth-specific SMRCs, new SMRCs were generated
for the soil found at each sensor depth. In doing so, it was thought that a much more
accurate picture of the soil water balance could be obtained. The generation of depth-
MISSISSIPPI SOYBEAN PROMOTION BOARD 18
specific SMRCs required detailed soil sampling at both sites to determine the bulk
density of the soil samples being taken at each level in the profile. Bulk density data is
used to compute shrink-swell potential, available water capacity, total pore space, and
other soil properties. Higher bulk densities indicate that the soil is more restrictive on
water storage and root penetration. Depending on soil texture, a bulk density of more
than 1.4 can restrict water storage and root penetration (USDA, 2013b). Table 2 shows
the changes in measured bulk density throughout the depth of each soil profile and how
they are generally higher than normal for each of the respective soil types.
Table 2. Test Bulk Density for Calibration Sites
Jonestown Bosket Series Redgum Forestdale Series
Depth (in.)
Field
Bulk
Density
NRCS(a)
Soil
Data Bulk
Density
Depth (in.)
Field
Bulk
Density
NRCS(a)
Soil
Data Bulk
Density
6 1.55 1.47 (0-9in.)
6 1.57 1.53 (0-6in.)
12 1.57 12 1.62
1.55 (6-26in.) 18 1.43 1.50 (9-25in.)
18 1.59
24 1.54 24 1.54
30 1.44 1.48 (25-48in.)
30 1.55 1.50 (26-60in.)
36 1.28 36 1.55 (a)
Natural Resource Conservation Service; NRCS Soil Data available at:
www.soilseries.sc.egov.usda.gov
Given the location and land use, this is likely an indication of compaction in the soil
profile as a result of farming. The table shows that the bulk density of the first twelve
inches in both soils is different from that of the soil below twelve inches. This means that
the water holding capacities of the soil around the sensors below the 12-inch sensor is
different than that of the soil in the first 12 inches of the profile. Therefore, continued
use of the 2011 SMRCs to convert the sensors’ centibar readings to inches of water
would result in calculated amounts that incorrectly represent 36 inches of the soil profile.
Curves generated for soil samples taken at the specific depth of each sensor provide a
more accurate representation of water quantities actually being held or released
throughout the 36-inch soil profile. In the determination of the soil’s water balance, it is
assumed that each sensor is reading the correct soil moisture status of a six-inch range
extending above the sensor.
Next, the new depth-specific SMRCs were then used to calculate the total soil water
balance in the profiles for the Jonestown and Redgum sites during the 2012 growing
season, and this was graphed in Figures 5 and 6, respectively, alongside the water balance
created with the composite 12-inch SMRCs for the same time period. The cumulative
soil water balance for each site was calculated by first determining inches of water for the
six-inch range at the depth of each sensor using the depth-specific SMRCs. Recorded
values for the six sensors were then summed to determine the soil water balance for the
36-inch depth. Figures 5 and 6 indicate that there was indeed a soil moisture retention
difference within the lower soil horizons. The retention difference implied a change in
MISSISSIPPI SOYBEAN PROMOTION BOARD 19
the water balance of the soil profile, which did result in a difference in total inches of
water measured for the growing season. In Figure 5, depth-specific curves at Jonestown
indicated a daily difference of approximately one inch of water. Figure 6 shows a daily
difference of five inches of water for the profile when new depth-specific curves were
applied to the Redgum sensor values.
0
5
10
15
20
25
5/21/12 6/5/12 6/20/12 7/5/12 7/20/12 8/4/12 8/19/12
Wa
ter
in.
(In
ch
es)
Date
Jonestown Corn Furrow
DSC BoxA
DSC BoxB
C12 BoxA
C12 BoxB
Figure 5. Jonestown sensor-measured water balance converted with composite 12-
inch (C12) and depth-specific (DSC) soil water release curves.
0
5
10
15
20
25
5/21/12 6/15/12 7/10/12 8/4/12 8/29/12
Wa
ter in
. (I
nch
es)
Date
Redgum Soybean Pivot
DSC BoxA
DSC BoxB
C12 BoxA
C12 BoxB
Figure 6. Redgum sensor-measured water balance converted with composite 12-
inch (C12) and depth-specific (DSC) soil water curves.
For both sites, this indicates that for the 36-inch tested soil profile, there is more water
being held than previously thought. The larger differences in water balance seen in the
Redgum soil vs. the Jonestown soil are due to the different water holding capacities of the
two soil types. Differences in soil properties, texture, and percentage of sand, silt, and
clay result in differences in average water holding capacities. Jonestown sensors
installed in the Bosket series soil were surrounded by very fine sandy loam, typically
MISSISSIPPI SOYBEAN PROMOTION BOARD 20
having 18-30% clay and over 30% sand (USDA, 2013a). The Bosket soil’s physical
properties indicate large pore spaces with easily accessible water but less total water
holding capacity than that of a soil with a higher clay content. Redgum sensors were
installed in a Forestdale series soil consisting of a silty clay loam. The Forestdale series
commonly consists of 35-60% clay with less than 20% sand (USDA, 2002). The higher
clay content in the Forestdale soil indicates smaller soil particles creating smaller but
more abundant pore space for water retention.
Differing water holding capacities due to soil property differences can clearly be seen
where the soil at the Redgum site (Forestdale) holds 20 inches of water early in the
season, while the soil at the Jonestown site (Bosket) peaks around 13 inches. However,
despite the greater water holding capacity of the Forestdale series at the Redgum site, the
plant available water can still be minimal due to the extreme amount of work (pressure)
required by the plant to remove water from smaller pore spaces. In Figure 6, the larger
difference between the depth-specific vs. the composite 12-inch curve totals are more
pronounced and can be explained by smaller pore spaces, which are a result of clay
particle size. The change in water holding capacity found at each of the lower sensor
depths results in a compounded difference over the total depth of the profile. The larger
difference illustrated in Figure 6 could indicate that, due to their unique water holding
capabilities, soils with a high clay content may require special attention in the MIST
model calibration or when using soil moisture sensors to schedule irrigation.
Composite samples to a depth of 36 inches were also used to create SMRCs for the 2012
Jonestown corn furrow and Redgum soybean pivot fields. These samples were then
graphed beside the depth-specific curves for Jonestown and Redgum, respectively, as
shown in Figures 7 and 8. The goal was to determine if a single 36-inch composite
sample with an average bulk density for the soil profile could be used to create SMRCs
for other sites, eliminating the need for extensive depth-specific soil sampling at each
MIST test site. Results shown in Figures 7 and 8 indicate that it may be feasible to use a
composite 36-inch sample to generate a SMRC for other MIST test sites or production
fields. The R2 values for Boxes A and B at the Jonestown Corn Furrow site are 0.9828
and 0.9835, respectively, comparing water balance conversions using depth-specific
SMRCs and a 36-inch composite SMRC. The R2 values for Boxes A and B at the
Redgum Soybean Pivot site are 0.9908 and 0.9916, respectively, when comparing the use
of depth-specific versus 36-inch composite SMRCs.
MISSISSIPPI SOYBEAN PROMOTION BOARD 21
R2 = 0.9835
R2 = 0.9828
0
5
10
15
20
25
5/11/12 5/26/12 6/10/12 6/25/12 7/10/12 7/25/12 8/9/12 8/24/12
Wa
ter
in.
(In
ch
es)
Date
Jonestown Corn Furrow
DSC BoxA
DSC BoxB
C36 BoxA
C36 BoxB
Figure 7. Jonestown sensor-measured water balance converted with composite 36-
inch (C36) and depth-specific (DSC) soil water release curves.
R2 = 0.9908
R2 = 0.9916
0
5
10
15
20
25
5/21/12 6/15/12 7/10/12 8/4/12 8/29/12 9/23/12
Wa
ter
in.
(In
ch
es)
Date
Redgum Soybean Pivot
DSC BoxA
DSC BoxB
C36 BoxA
C36 BoxB
Figure 8. Redgum sensor-measured water balance converted with composite 36-
inch (C36) and depth-specific (DSC) soil water release curves.
Figures 9 and 10, respectively, show the measured water balance as compared to the
MIST-calculated water balance for the 2012 Jonestown corn furrow and Redgum
soybean pivot fields. Debits and credits to the water balance can be seen in relative
proximity to one another, and there are correctly modeled changes to the water balance at
both sites. The differences in MIST-modeled and sensor-measured water balances were
greater at the Redgum soybean pivot site than at the Jonestown corn furrow site.
MISSISSIPPI SOYBEAN PROMOTION BOARD 22
R2 = 0.8331
-9.0
-8.0
-7.0
-6.0
-5.0
-4.0
-3.0
-2.0
-1.0
0.0
1.0
2.0
4/21/12 5/11/12 5/31/12 6/20/12 7/10/12 7/30/12 8/19/12 9/8/12
Wa
ter
in.
(In
ch
es)
Date
Jonestown Corn Furrow
Measured WB
Modeled WB
Figure 9. Sensor-measured field water balance (WB) and MIST-modeled water
balance for the Jonestown corn furrow field.
R2 = 0.3620
-12.0
-10.0
-8.0
-6.0
-4.0
-2.0
0.0
2.0
4/21/12 5/11/12 5/31/12 6/20/12 7/10/12 7/30/12 8/19/12 9/8/12
Wa
ter
in.
(In
ch
es)
Date
Redgum Soybean Pivot
Measured WB
Modeled WB
Figure 10. Sensor-measured field water balance (WB) and MIST-modeled water
balance for the Redgum soybean pivot field.
For the application and testing of the MIST model, the water balance equation was split
into input and output processes. The goal was to look at input and output separately to
determine which processes showed the most consistency with the sensor-recorded data.
In Figures 11 and 12, the water balance for each site has been separated into precipitation
and irrigation as inputs versus crop water use as output, as recorded by each field’s set of
soil moisture sensors. Figure 11 shows Jonestown’s daily water loss averages around 0.2
inches per day, closely matching recorded ET from local Soil Climate Analysis Network
(SCAN) sites.
MISSISSIPPI SOYBEAN PROMOTION BOARD 23
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
4/26/12 5/21/12 6/15/12 7/10/12 8/4/12 8/29/12
Wa
ter
(In
ch
es)
Date
Jonestown Corn Furrow
Sensor Measured
Precip/IrrigationSensor Measured ET
& Crop Water Use
Figure 11. Daily Jonestown corn furrow sensor-measured soil profile water balance
inputs (Precip/Irrigation) and losses (ET).
The Bosket series soil at the Jonestown site is low in clay and does not have the smectitic
shrink swell properties of soils such as the Forestdale and other high clay content soils
found at the Redgum site. Thus, it is assumed that sensors at the Jonestown location had
optimal soil to sensor contact for the duration of the growing season. The soil to sensor
contact is an important consideration in the analysis and interpretation of data results
collected from any of the MIST test sites. In soils with high clay content, the smectitic
shrinking and swelling of the soils creates large cracks extending from the soil surface
downward. In addition to open cracks forming around sensors within the upper soil
layers and allowing increased infiltration rates, the shrinking soil likely creates spaces
around the sensors resulting in an absence of the soil to sensor contact that is needed for
accurate sensor measurements and data collection. In Figure 12, the rapid wetting and
drying of the soil profile can clearly be seen where the measured water balance gains 1.25
inches on June 22nd
and then loses 1.26 inches over the subsequent 24-hour period. This
is a considerable loss, when taking into account the early stage of the soybean crop and a
recorded ET for the Redgum location of 0.25 inches for the same 24-hour period.
MISSISSIPPI SOYBEAN PROMOTION BOARD 24
-1.50
-1.00
-0.50
0.00
0.50
1.00
1.50
2.00
2.50
3.00
5/11/12 5/31/12 6/20/12 7/10/12 7/30/12 8/19/12 9/8/12
Wa
ter (
Inch
es)
Date
Redgum Soybean Pivot
Sensor Measured
Precip/Irrigation
Sensor Measured ET
& Crop Water Use
Figure 12. Daily Redgum soybean pivot sensor-measured soil profile water balance
inputs (Precip/Irrigation) and losses (ET).
Watermark sensors require complete contact with the soil in order to operate correctly,
and this operational limitation of the Watermark soil moisture sensors may offer an
explanation for the additional perceived water loss. The dynamic response of the
Watermark Model 200 sensors appears to perform well during typical soil drying cycles
following complete rewetting, but the response to rapid drying or partial soil rewetting is
slow or non-existent (McCann et al., 1992). Partial soil rewetting in this case would be
any irrigation or precipitation that did not achieve soil saturation. This could indicate that
the rapid increase and decrease of the soil water content at the Redgum site (Figure 12),
and the inconsistent response of the sensors to precipitation and irrigation in Figure 13,
could most likely be explained by a lack of soil to sensor contact. A closer look at
individual sensors installed at the Redgum site indicated large wetting and drying
fluctuations at the 6-, 12-, and 18-inch depths. Measurements recorded by the 12-in
sensor are shown in Figure 14, adding credibility to the lack of soil to sensor contact
theory. Past a depth of eighteen inches, the large wetting and drying fluctuations ceased,
indicating that the soil remained moist enough past that depth to maintain soil to sensor
contact. This is a challenge in working with soils with high clay content.
MISSISSIPPI SOYBEAN PROMOTION BOARD 25
-0.5
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Wa
ter (In
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es)
Date
Redgum Soybean Pivot
Sensor Measured
Precip/Irrigation
Nexrad 2012
Figure 13. Sensor-measured water balance inputs through 36-inch profile compared
to Nexrad-recorded precipitation for the 4x4 kilometer grid over the Redgum
soybean pivot field.
0
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Redgum Soybean Pivot
BoxA in.
BoxB in.
BoxA %WC
BoxB %WC
Figure 14. Redgum soybean pivot twelve-inch sensor-calculated water balance for
data loggers A and B.
Due to the data inconsistencies through the loss of soil to sensor contact found within the
first 18 inches of the Redgum data, only the Jonestown data were used for further
analysis of MIST-modeled input and output processes to determine consistency with
sensor-derived data. For all comparisons to the MIST-modeled water balance, the
sensor-measured water balance is represented as an average of the measured water
balance values from boxes A and B. In Figure 15, the additional precipitation recorded
by the sensors at the Jonestown field on May 30th
and July 8th
(with 12-inch composite
soil samples used to calculate water balance) during the 2012 growing season can clearly
be seen rising above the modeled inputs for the same time frame. It was thought that the
differences were related to the use of the SMRCs derived from the 12-inch composite soil
samples.
MISSISSIPPI SOYBEAN PROMOTION BOARD 26
R2 = 0.8800
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(In
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Date
Jonestown Corn Furrow: Input Comparison
C12 InputsMIST InputsNexrad 2012
Figure 15. Sensor-measured water balance inputs (derived from composite 12-inch
SMRCs) compared to MIST–calculated water balance inputs and Nexrad
precipitation data for the Jonestown corn furrow site.
This assumption was verified by comparing measured water balance inputs converted
with the composite 12-inch curves to water balance inputs converted with the depth-
specific curves for the Jonestown 2012 corn furrow field. Figure 16 illustrates the change
in measured inches of water for the Jonestown field water balance using data converted
with the composite 12-inch curves and the depth-specific curves.
-0.5
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ter (In
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Date
Effects of Soil Moisture Release Curves: 12-in. Composite vs. Depth-Specific
C12 Inputs
DSC Inputs
Figure 16. Comparison of the sensor-measured water balance inputs converted with
composite 12-inch SMRCS with those converted using depth-specific SMRCs at the
Jonestown study site.
In Figure 17, water balance inputs converted with depth-specific SMRCs are graphed
alongside the MIST model inputs and Nexrad precipitation data for the 2012 growing
season. Figure 17 shows that MIST is able to produce water balance inputs for the
Jonestown site that closely match sensor-measured data for the same time frame. The
three remaining anomalies are assumed to be furrow irrigation events.
MISSISSIPPI SOYBEAN PROMOTION BOARD 27
R2 = 0.9328
-0.5
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Wa
ter
(In
ch
es)
Date
MIST vs Measured Inputs using Depth-Specific SMRCs
MIST Inputs
Nexrad 2012
DSC Inputs
Figure 17. Jonestown corn furrow sensor-calculated water balance inputs derived
from depth-specific SMRCs compared to MIST-derived water balance inputs and
Nexrad precipitation data.
Unlike the data collected from weather stations, the Nexrad data does not have large gaps
of missing data and provides MIST with a precipitation estimate for a 4 x 4 kilometer
area around each modeled field site. With the integration of Nexrad’s precipitation data
and MIST’s runoff (Q) calculation utilizing field soil properties, the model is providing
sharper estimates of water balance inputs for Jonestown and sites with similar soil
properties. In Figure 17, MIST-modeled input amounts (total usable rainfall and
irrigation absorbed by the soil profile) closely match recorded Nexrad precipitation
amounts for the 2012 season. This would indicate that all precipitation was absorbed by
the soil profile as usable rain. Bosket series soil has a runoff potential of B, indicating
moderate infiltration with a water transmission rate of approximately 0.15-0.30 inches
per hour (USDA, 2013a). These values are incorporated within the model and used in the
runoff (Q) equation to determine initial abstraction. The model determines the amount of
water in the soil to be dry, average, or wet depending on the previous five days’
precipitation. Upon determination of the soil moisture status, the model then determines
the amount of precipitation the soil can take before reaching saturation and resulting in
runoff (Q). For the Bosket soil at the Jonestown site, the infiltration or water
transmission rate would allow for 0.15-0.30 inches of water per hour into the soil profile
until reaching saturation.
On May 30, 2012, the sensors recorded a large precipitation event, where Nexrad rainfall
totaled 1.24 inches over a 6-hour interval. During this 6-hour interval, the soil only had
to infiltrate and transmit water at a rate of 0.2 inches of water per hour, a value that is
below the 0.3 inches per hour for which the soil is capable. Additionally, the soil had not
received precipitation for nine days prior to the May 30th
rainfall event. This means that
despite the largest precipitation event recorded by Nexrad during the 2012 growing
season, the rainfall rate should not have exceeded the soil infiltration rate or water storage
potential. However, Figure 17 indicates that the field’s measured water balance reached
saturation at 0.96 in., resulting in 0.28 in. of runoff that MIST did not model. The runoff
unaccounted for by MIST is likely the result of the bulk density changes found during
MISSISSIPPI SOYBEAN PROMOTION BOARD 28
soil testing. As Jonestown’s soil bulk density increases, the soil’s water holding capacity
is reduced, creating runoff that would have otherwise been held within the profile. This
means that MIST is likely modeling the correct runoff, water holding, and storage
capacity of the survey soil characteristic of the Bosket series soil at the Jonestown field.
The system of categorizing soil moisture status dependent on the previous five days’
precipitation allows MIST to correctly model all the season’s precipitation as usable
rainfall for the Jonestown field and fields under similar soil conditions. For fields with
similar physical soil properties to hydrologic soil group B, MIST appears to be delivering
accurate water balance inputs.
The second step was to look at the water loss portion of the water balance equation.
MIST calculated water loss is determined by multiplying the calculated daily ET times
the crop Kc, which gives the calculated daily crop water use. The depth-specific water
balance recorded by each sensor was graphed separately to determine the activity at each
depth. The first question regarding water loss investigated the possibility of profiles
losing water as drainage out of the bottom of the thirty-six inch tested soil profile.
Analysis of the Redgum data suggested that cracks in the soil were only apparent to the
depth of eighteen inches, indicating that soil moisture sensors past eighteen inches had
optimal soil to sensor contact for data collection. Figures 18 and 19 illustrate the water
balance from 30 to 36 inches as recorded by the 36-inch sensors for the Jonestown and
Redgum fields, respectively. Soil water content percentages are approximately 49%
during the early portion of the season for Jonestown. The soil profile was most likely
very moist. Then, as summer progressed, the soil surrounding the sensor rapidly dried
out, as seen in Figure 18. This is consistent with the soil water retention characteristics of
a sandy soil.
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70
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Water Balance at 36-inch Sensor Depth for Jonestown Site
BoxA in.
BoxB in.
BoxA %WC
BoxB %WC
Figure 18. Water balance at 30-36 inches in the soil profile from data loggers A and
B at the Jonestown corn furrow site.
MISSISSIPPI SOYBEAN PROMOTION BOARD 29
0
10
20
30
40
50
60
70
0.0
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Water Balance at 36-inch Sensor Depth for Redgum Site
BoxA in.
BoxB in.
BoxA %WC
BoxB %WC
Figure 19. Redgum soybean pivot sensor-calculated water balance from data
loggers A and B at the 30-36 inch depth of the soil profile.
Larger pores associated with sandy soils will quickly and easily release plant available
water unlike high clay soils whose smaller pore space requires much more work
(pressure) to remove the same amount of water. The slow release of water out of the high
clay soil can clearly be seen in Figure 19 as the percent water content at the 36-inch
sensor slowly fell from a water content of approximately 55% early in the season to a
water content of approximately 38% by harvest. In Figures 18 and 19, the percent water
content falls to a level at which there is very little available water between 30-36 inches
after the middle portion of the growing season.
The sensors at the Jonestown site recorded a percent water content status of 14% by July
1st, with Redgum sensors recording a 40% water content around the same time frame.
While indicating a much different value for inches of water within the profile, the plant
available water status is similar for both sites. Sensor measurements at thirty-six inches
were often similar for both sites. On July 2nd
at 22:00, the 36-inch sensor at Jonestown
had a pressure reading of 44 centibars, while the sensor at the same depth at Redgum
recorded a pressure of 47 centibars. Figures 18 and 19 also show that the percent water
content does not indicate large fluctuations at the thirty-six inch depth as the season
progresses. While fractions of a percentage are noticeable, both Figures 18 and 19
remain at a relatively constant lower water content after mid-season, indicating a dry or
drying soil. This demonstrates that for both tested sites, precipitation and irrigation
inputs are not being lost from the water balance as drainage out of the bottom of the soil
profile. All water balance inputs (precipitation and irrigation) are being consumed as
crop water use or remaining in the soil profile. This confirms the MIST design to model
water loss as a result of ET and Kc.
MISSISSIPPI SOYBEAN PROMOTION BOARD 30
CONCLUSIONS
Future testing of the MIST model will likely start with incorporation of irrigation data
provided by the producer. This irrigation data will confirm or deny irrigation as the cause
for the threes spikes measured by the sensors in Figure 17 for the Jonestown 2012 corn
furrow.
While MIST appears to model inputs representative of the Jonestown field water balance,
the model may need to consider an alternate runoff coefficient or provide users an option
to manually change the average available water capacity of their fields. This would allow
users to account for soil compaction as a result of conventional farming practices. A
slight increase in the runoff coefficient could create the small amount of runoff that the
model is not accounting for as a result of soil compaction.
While sites with high clay content were unable to be fully evaluated, the model appears to
calculate water balance inputs correctly based on the usable data at those locations.
Further testing in high clay soils may require a change in sensor arrangement to address
the smectic properties associated with these soils. Options such as installing the sensors
without PVC could reduce open space created around the PVC as a result of soil
shrinking away from the pipe. This could cut off free flowing macro pore transmission to
the sensors. Another option to consider would be installing sensors horizontally from the
furrow for the 6, 12, and 18-inch depths.
This study shows that water is not lost through drainage out of the bottom of the tested
soil profiles, and that all losses to the water balance equation are a result of crop water
use (ET*Kc). Therefore, in comparison to the close relationship between the measured
and modeled water balance inputs for the Jonestown 2012 corn furrow site in Figure 17,
compared to the total measured and modeled water balance in Figure 10, the differences
seen in Figure 10 should be a result of discrepancies within the calculation of crop water
use. In Figure 11, two of the larger daily measured losses at the Jonestown site during
the 2012 growing season occur on June 3rd
and July 9th
, which are also within 24 hr of the
two coolest recorded temperatures for that year’s growing season. While not definitive,
this could indicate that crop coefficients used in the model may need to consider
including an adjustment for temperature to address cool or hot weather, which can have
an effect on crop transpiration.
SMRCs created from the 36-inch composite samples and bulk densities from Web Soil
Survey appear to provide the same volumetric water content as depth-specific samples
and bulk density measurements developed for this study. In future testing, a 36-inch
composite sample collected in the same manner should provide a simplified method to
retrieve soil data needed to generate SMRCs for the use of moisture data from other
MIST study sites. This also has implications for producers who plan to use soil moisture
sensors for monitoring soil water balance. A 36-inch composite sample for the
development of SMRCs would provide substantial benefit in interpreting and applying
soil moisture readings from in-field sensors, while minimizing costs for soil sampling.
MISSISSIPPI SOYBEAN PROMOTION BOARD 31
This research shows the value in an irrigation scheduling tool designed for the humid
climate and spatial soil variability Mississippi producers face. MIST provides an easily
adaptable method of managing irrigation that addresses both water resource concerns and
water use management objectives.
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