ENHANCING IRRIGATION SCHEDULING IN THE MISSISSIPPI … · 2015-10-21 · Evaluation Tool (PHAUCET)...

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


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).

http://data.eol.ucar.edu/


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


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.


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


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


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


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


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).


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.


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


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).


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


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


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.


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.


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


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-


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


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

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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.

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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


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.


R2 = 0.9835

R2 = 0.9828

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DSC BoxA

DSC BoxB

C36 BoxA

C36 BoxB

Figure 7. Jonestown sensor-measured water balance converted with composite 36-


R2 = 0.9908

R2 = 0.9916

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DSC BoxA

DSC BoxB

C36 BoxA

C36 BoxB

Figure 8. Redgum sensor-measured water balance converted with composite 36-


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.


R2 = 0.8331

-9.0

-8.0

-7.0

-6.0

-5.0

-4.0

-3.0

-2.0

-1.0

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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

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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.


-1.5

-1.0

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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.


-1.50

-1.00

-0.50

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0.50

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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.


-0.5

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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.

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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.


R2 = 0.8800

-0.5

0.0

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ter

(In

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es)

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

0.0

0.5

1.0

1.5

2.0

2.5

4/21/12 5/1/12 5/11/12 5/21/12 5/31/12 6/10/12 6/20/12 6/30/12 7/10/12 7/20/12

Wa

ter (In

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es)

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.


R2 = 0.9328

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

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

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


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.

0

10

20

30

40

50

60

70

0.0

0.5

1.0

1.5

2.0

2.5

3.0

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4.0

5/21/12 6/15/12 7/10/12 8/4/12 8/29/12

% W

ate

r C

on

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t (P

erc

en

t)

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(In

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es)

Date

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.


0

10

20

30

40

50

60

70

0.0

0.5

1.0

1.5

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% W

ate

r C

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ten

t (P

erc

en

t)

Wa

ter

(In

ch

es)

Date

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.


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.


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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