Download - Calibration of a water content reflectometer and soil water dynamics for an agroforestry practice

Calibration of a water content reflectometer and soil waterdynamics for an agroforestry practice

Ranjith P. Udawatta • Stephen H. Anderson •

Peter P. Motavalli • Harold E. Garrett

Received: 9 March 2010 / Accepted: 19 November 2010 / Published online: 4 December 2010

� Springer Science+Business Media B.V. 2010

Abstract Water content reflectometers allow tem-

poral and continuous assessment of spatial differ-

ences in soil water dynamics. We hypothesized that

volumetric soil water content estimated by the water

content reflectometers (CS616 Campbell Sci. Inc.,

Logan, UT) is influenced by clay content and

temperature and therefore site- and or soil-specific

equations are required for accurate estimations of soil

water. Objectives of the study were to develop

calibration equations and to evaluate soil water

dynamics for an agroforestry system using the

improved calibration equation. Putnam silt loam

(fine, smectitic, mesic Vertic Albaqualfs) and Menfro

silt loam (fine-silty, mixed, superactive, mesic Typic

Hapludalfs) soils were selected with 23–54% clay.

Soils were packed in cylinders and sensors were

monitored at 5, 10, 15, 20, 25, 30, 35, and 40�C.

Calibration equations for volumetric water content

(hv) as a function of sensor measured period,

temperature, and clay content were developed. Coef-

ficient of determination (r2) and root mean square

error (RMSE) were used to compare goodness of fit.

RMSE varied between 0.028 and 0.040 m3 m-3 for

soil specific and soil-combined linear and quadratic

equations with period. Coefficients of determination

ranged between 0.89 and 0.96 for these calibrations.

RMSE decreased and r2 increased as temperature was

included. The effect of temperature varied with water

content, with the strongest effect at high water

contents. Clay content did not contribute significantly

to improve predictability. Water content estimated by

the linear calibration equation with period and

temperature showed differences in hv influenced by

vegetation and soil depth, and closely followed

precipitation events and water use by vegetation.

The field study showed significant differences

between the two treatments. Also the importance of

temperature correction is emphasized during periods

with large diurnal fluctuations and site specific

calibration equations. Results of the study showed

that water content reflectometers can be used to

estimate hv with less than ±4% error and may need

site specific calibration and a temperature correction

to research more precise estimates.

Keywords Corn–soybean � CS616 � CS615 �Sensitivity analyses � Soil water sensors

Abbreviations

CEC Cation exchange capacity

EC Electrical conductivity

K Dielectric constant

R. P. Udawatta (&) � H. E. Garrett

Center for Agroforestry, University of Missouri,

203 Anheuser-Busch Natural Resources Building,

Columbia, MO 65211, USA

e-mail: [email protected]

R. P. Udawatta � S. H. Anderson � P. P. Motavalli

Department of Soil, Environmental and Atmospheric

Sciences, University of Missouri, 302 Anheuser-Busch

Natural Resources Building, Columbia, MO 65211, USA

123

Agroforest Syst (2011) 82:61–75

DOI 10.1007/s10457-010-9362-3

hv Volumetric soil water content m3 m-3

RMSE Root mean square error

C Period

TDR Time domain reflectometry

WCR Water content reflectometer

Introduction

Accurate and continuous estimation of soil water

content is important in many plant–soil–water and

hydrologic studies. Gravimetric, nuclear, electromag-

netic, and tensiometer methods can be used to

estimate soil water content (Zazueta and Xin 1994).

Capacitance sensors (Dean et al. 1987; Kelleners

et al. 2004a), impedance sensors (Hilhorst et al. 1993;

Seyfried and Murdock 2004), and transmission line

oscillators (Campbell and Anderson 1998) are elec-

tromagnetic approaches to measure soil water content

which are often preferred over neutron probe meth-

odology. Relatively inexpensive CS616 water content

reflectometer (WCR) sensors (Campbell Sci. Inc.,

Logan, UT), a type of a transmission line oscillator,

uses a technique similar to time domain reflectom-

eters (TDR) but does not require a separate pulse and

sampling unit (Kelleners et al. 2005). WCR are

increasingly being used in field and laboratory

experiments to research water balance, plant water

use, irrigation, precision farming, and movement of

chemicals and ions (Seyfried and Murdock 2001,

2004; Seobi et al. 2005; Anderson et al. 2009). Some

possible reasons for the preference for these units are

ease of installation, fewer regulatory and safety

concerns, and cost effectiveness. Data can be col-

lected continuously and either stored on-site or

transmitted to a remote computer via a telephone or

radio line (Seyfield and Murdock 2001). Therefore,

they are easier to use in an in-field monitoring

system.

In WCR sensors, two wave guides 30 cm long and

0.32 cm diameter with a 3.2 cm spacing are attached

to a probe head with embedded circuitry; thus

allowing an increase in the distance between the

sensor and a data logger (Seyfried and Murdock

2001; Chandler et al. 2004). Inside the probe head,

voltage pulses are generated and the reflected pulse

triggers the next pulse. The output is proportional to

the number of reflections per second. Reflections are

divided by a scaling factor which can be read by a

data logger as period. Sensors can be vertically

installed to estimate integrated soil profile water

content or horizontally to measure water content by

soil depth.

The wide disparity between dielectric permittivity

(k) of air (1), soil (2.4–3.5), and water (80) is used to

measure water content; thus it is an indirect mea-

surement of soil water content. The WCR technique

measures equilibrium oscillation frequency or period

of an applied voltage, which is directly related to

k. The travel time varies with the k of the medium in

which the wave guide is inserted (Fellner-Feldegg

1969). With an empirical calibration equation, the

measured wave period in microseconds is then related

to volumetric soil water content (hv; Chandler et al.

2004). Dielectric permittivity also varies with tem-

perature. For example, dielectric constants are 87.9,

78.4, and 55.6 for water at 0, 25, and 100�C,

respectively, and 1.0059 for 100�C air.

Manufacturer provided calibration estimates water

content in sand reasonably well (Seyfried and Mur-

dock 2001). In contrast, studies have shown that

factory calibration overestimates soil water content in

many soils (Seyfried and Murdock 2001; Quinones

et al. 2003; Stangl et al. 2009). The WCR sensors use

15–45 MHz frequency range to estimate hv (Seyfried

and Murdock 2001) where TDR probes use up to

about 1 GHz (Or and Wraith 1999). The frequency

range used in the WCR sensors is affected by

variations in clay content, clay type, and soil

electrical-conductivity (Campbell 1990; Seyfried

and Murdock 2004). However, the effect of clay

content can be corrected by using simple linear or

quadratic functions (Chandler et al. 2004). Further-

more, due to this low frequency range, WCR

estimates are often affected by temperature and

requires soil specific calibrations (Seyfried and

Murdock 2001; Chandler et al. 2004).

Clay content, especially soils containing smectitic

clays found in subsurface horizons of poorly-drained

claypan soils of Major Land Resource Area 113

(USDA-NRCS 1998) or in other regions, may affect

WCR readings. Smectitic clays have relatively high

surface charge which may attenuate the signal from

WCR sensors and affect their ability to estimate

profile hv. In addition, soils with high smectitic clay

can undergo as much as 30% volume change due to

wetting and drying. Therefore, soils characterized by

62 Agroforest Syst (2011) 82:61–75

123

clay-rich subsurface horizons affect water movement,

retention, and hv. These soils often retain water for

extended periods of time and their shrinkage cracks

will seal during wet periods, or channel flow through

these cracks and infiltrate soil water during dry

periods. Therefore, soil profiles with relatively high

water infiltration in surface horizons and relatively

low infiltration in subsurface horizons may need

individual calibration equations to better understand

water dynamics. In support of this, Serrarens et al.

(2000) observed that the measurement error doubled

when a single calibration equation was used in a TDR

calibration study with six soil depths.

A good and accurate understanding of plant–water

use, hydrologic relationships, and soil water dynam-

ics are especially important in agroforestry alley

cropping practices where grass, trees, and crops may

share the same area. Roots of this mixed vegetation

may occupy the same soil volume but with different

densities and distribution patterns. Trees and grass in

agroforestry alley cropping practices use soil water

from different soil depths over a longer period as

compared to crop plants since annual crops have

relatively shallow root systems and shorter growing

seasons than the grass, trees, and shrubs in agrofor-

estry practices (Udawatta et al. 2005; Fernandez et al.

2008). Lack of good calibration relationships

between sensor output and hv may restrict accurate

prediction of soil water dynamics in multi-species

practices such as agroforestry practices. The objec-

tives of this study were to: (1) examine clay content

and temperature effects on WCR hv readings, (2)

develop calibration equations for hv, in soils having

moderate to high clay content, and (3) evaluate

seasonal soil water dynamics for an agroforestry

practice using improved calibration equations that

take into account soil and site factors.

Materials and methods

Soil materials

Two soil types were selected, Putnam soil from the

claypan region and Menfro soil from the Mississippi

valley wooded slopes region, to represent a range in

clay content. The selected two soils have clay content

varying from 23 to 54% (Table 1). Bulk soil material

was obtained from the A and Bt horizons of a Putnam

silt loam (fine, smectitic, mesic, Vertic Albaqualfs) at

the Paired-Watershed study at the Greenley Memorial

Research Center, in Novelty, MO (40�010N,

92�110W). These soils have a montmorillonitic clay

mineralogy. Bulk soil was also obtained from the A

horizon of a Menfro silt loam (fine-silty, mixed,

superactive, mesic Typic Hapludalfs) at the Horti-

culture and Agroforestry Research Center in New

Franklin, MO (HARC; 39�010N, 92�450W). The clay

mineralogy is mixed but dominated by montmoril-

lonite (est. 60–75%) with lesser amounts of illite

(http://www2.ftw.nrcs.usda.gov/osd/dat/M/MENFRO.

html). Soil texture, pH (1:1 soil:water), cation

exchange capacity (CEC), and electrical conductivity

(EC; 1:1 soil:water) were determined at the University

of Missouri Soil Characterization Laboratory using

standard methods for soil survey (Soil Survey Staff

1984).

The poorly drained Putnam silt loam soil occurs in

the northeast region of Missouri. Most areas with this

soil are used for cultivation of corn (Zea mays L.),

soybean (Glycine max (L.) Merr.), and other grain

crops. The deep, well-drained Menfro silt loam soil

occurs along loess bluffs near the Missouri and

Mississippi Rivers. Most areas with this soil are used

for pasture and some areas for grain crops and native

hardwoods. Both Putnam and Menfro are developed

Table 1 Selected soil properties for Putnam and Menfro soils used in the laboratory experiment

Soil Depth, cm Particle size analysis

Clay, % Silt, % Sand, % Fine

silt, %

Textural

class

CEC,

cmol kg-1pHCaCl2

pHH2O Electrical

conductivity,

dS m-1

Putnam A 0–10 23.4 71.4 5.2 46.4 Silt loam 18.0 6.4 5.8 0.12

Menfro A 0–10 32.6 63.8 3.6 28.1 Silty clay loam 22.7 6.4 5.1 0.23

Putnam Bt 30–50 53.9 43.4 2.7 27.0 Silty clay 37.9 5.3 4.8 0.31

Agroforest Syst (2011) 82:61–75 63

123

http://www2.ftw.nrcs.usda.gov/osd/dat/M/MENFRO.html

http://www2.ftw.nrcs.usda.gov/osd/dat/M/MENFRO.html

in loess material. Putnam is underlain by glacial

material.

Laboratory calibration

Two horizons from Putnam soil (0–10 and 30–50 cm

depths) and one horizon from Menfro soil (0–10 cm)

were used for the calibration (Table 1). Nine (some

with eight) volumetric soil water content values

(0.05, 0.10, 0.15, 0.20, 0.25, 0.30, 0.35, 0.40, and

0.45 m3 m-3) and eight temperature levels (5, 10, 15,

20, 25, 30, 35 and 40�C) with three replicates were

used in the laboratory study. Model CS616 WCR

sensors (Campbell Sci. Inc., Logan, UT) were used

for the study. The hv treatment values were target

values with actual hv verified after the laboratory

study was completed. Initial soil water content was

determined and then measured amounts of water were

added and thoroughly mixed to obtain the desired hv.

Pre-determined weights for each 5 cm increment of

soil were packed to a desired bulk density of

1.25 Mg m-3 in 54 cm long and 10 cm diameter

polyvinyl chloride (PVC) cores with sealed bottoms.

WCR sensors were inserted and the openings of the

PVC tubes were sealed with four layers of saran wrap

and duct tape. Sealed soil PVC columns were placed

horizontally on a laboratory cart to facilitate transport

to a walk-in, temperature-controlled environmental

chamber. Gravimetric soil moisture percentage at the

beginning and end were evaluated to assure no

moisture loss during the study.

Sensors were attached to a multiplexer (Model

AM16/32; Campbell Sci. Inc., Logan, UT) and the

multiplexer was attached to a datalogger (Model

CR23X-4 m; Campbell Sci. Inc., Logan, UT) to

record data at 10-min intervals. The unit was powered

by a 12 V deep cycle marine battery. At each

temperature, volumetric water content estimated by

the manufacturer provided equation and period

readings were collected for two consecutive days

after soil inside the core reached the specified

temperature before starting another horizon.

hv ¼ �0:0663� 0:0063 � C þ 0:0007 � C2 ð1Þ

where hv is volumetric water content estimated by the

manufacturer-provided equation and C is period (ls).

Period and hv estimated by the manufacturer-

provided equation (Eq. 1; Campbell Sci. Inc. (2002)

were downloaded to a laptop computer for analysis.

At the end of each run, three soil samples from each

PVC core were oven-dried to determine gravimetric

water content. The gravimetric water contents were

multiplied by the bulk density to determine volumet-

ric water content. This experimentally measured hv

was used to compare with sensor-measured period

and estimated hv. Relationships between independent

variables (period, temperature, clay content) and

experimentally-measured hv were developed using

linear and quadratic regressions for each soil horizon

and all three horizons combined (Eqs. 2–7; SAS Inst.

1989). Initially period was used in a linear form and

then in a quadratic form. Subsequently temperature

was also incorporated in linear and quadratic forms.

The accuracy of the manufacturer-provided calibra-

tion equation was compared with the measured hv

values. Coefficients of determination and RMSE

were used to evaluate calibration equations for each

soil and all three soils combined to determine the

most suitable equations for the studied soils. The

following relationships were evaluated between the

experimentally measured hv versus period, tempera-

ture, and clay% in various forms.

hv ¼ b0 þ b1 � C ð2Þ

hv ¼ b0 þ b1 � C þ b2 � C2 ð3Þhv ¼ b0 þ b1 � C þ b2 � Temp ð4Þ

hv ¼ b0 þ b1 � C þ b2 � C2 þ b3 � Temp ð5Þ

hv ¼ b0 þ b1 � C þ b2 � C2 þ b3 � Temp þ b4

� Temp2 ð6Þ

hv ¼ b0þ b1 �C þ b2 �Temp þ b3 � clay% ð7Þ

where; hv is experimentally measured volumetric

water content, C is period, Temp temperature, and

clay% clay content (g/g * 100%).

Comparison of vegetation, depth, and temperature

effects on water content

This research utilized an on-going long-term paired

watershed study located at the Greenley Research

Center in Novelty, MO. This study is examining the

effects of agroforestry and grass vegetative buffer

strips on water quality in three adjacent watersheds

with row crop agriculture (Udawatta et al. 2002,

2004, 2006). CS616 WCR sensors and 107B soil

temperature probes (Campbell Sci. Inc., Logan, UT)


123

were installed at four sites within the vegetative

buffers and row crop areas and were placed horizon-

tally at four depths (5, 10, 20, and 40 cm) at each site.

The predominant soil for the watershed study was

Putnam silt loam (fine, smectitic, mesic Vertic

Albaqualfs) which contains a distinct argillic horizon

at depths varying from 10 to 85 cm across the

watersheds. Additional details on soil, site, and

experimental details can be found in Udawatta et al.

(2002). Sensors were attached to a multiplexer and

the multiplexer was attached to a data logger to

record period, hv estimated by the manufacturer

provided equation, and soil temperature at 10-min

intervals.

Soil samples were collected from the field site

during dry periods and wet periods to examine the

fitness of the equations developed in the current

study. Gravimetric water content was measured and

these values were converted to volumetric water

contents by multiplying the bulk density. The period

values and water contents estimated by the manufac-

turer provided equation were recorded at the time of

soil sampling. The period readings were converted to

volumetric water contents by the Eq. 8 developed in

this study.

hv ¼ �0:311þ 0:0193 � C ð8ÞTo understand vegetation, depth, and temperature

effects on soil water dynamics, data were collected

from March 12 to November 19, 2007. Period, hv, and

temperature data were downloaded to a laptop com-

puter for subsequent analysis. Weekly period values

were extracted at 12:00 noon on each seventh day

starting from March 12. The period was converted to

hv using the linear Eq. 9 developed in the laboratory

study (Eq. 4) to compare soil water dynamics between

the two management treatments and depths.

hv ¼ �0:283 þ 0:0199 � C � 0:00198 � Temp ð9Þ

where, hv is volumetric water content estimated by

the linear equation developed in this study, C is

period (ls), and Temp soil temperature (�C).

The effect of diurnal temperature fluctuations on

estimated water content was compared in the row

crop area at the 5 cm depth at 30 min intervals for

July 24, 2007 (date was selected due to the highest

difference in temperature readings during a day for

the year). CS616 sensor-measured period values were

converted to hv with the following four equations:

1. Manufacturer-provided quadratic Eq. 1,

2. Manufacturer-provided quadratic Eq. 1 using the

manufacturer-provided temperature corrected

period (9),

Cc ¼Cunc þ 20 � Tempð Þ ��0:526

� 0:052 � Cuncð Þ þ 0:00136 � C2unc

� �� ð10Þ

where, Cc = corrected period, Cunc = uncorrected

period

3. Calibration Eq. 8 developed in this study with

period only and,

4. Calibration Eq. 9 developed in this study with

period and temperature.

Differences in hv between row crop and agrofor-

estry treatments and by soil depth were declared

significant at the a = 0.05 level using least signifi-

cance difference (LSD) at each measured date.

Results and discussion

Soil properties

Properties for the collected bulk soils for the labo-

ratory study differed in clay and silt content and other

chemical properties (Table 1). The textural classes of

the Putnam soil horizons from the Greenley Center

were silt loam and silty clay, respectively, while the

Menfro soil from the HARC in New Franklin was a

silty clay loam. Soil clay content, fine silt content,

EC, CEC, and pH measured in 0.01 M CaCl2 and d-

H2O varied among horizons. The surface horizon of

the Putnam soil had 23.4% clay. The Putnam Bt

horizon (30–50 cm) had 2.3 times more clay (53.9%)

than the Putnam surface A horizon (0–10 cm). CEC

and EC of the Putnam Bt horizon were higher than

the Putnam surface horizon but soil pHCaCl2and

pHH2O were lower.

The 32.6% clay content of the Menfro A horizon

(0–10 cm) was in-between the clay contents of the

two Putnam soil horizons. CEC followed a similar

pattern. Values of pHCaCl2for the Putnam surface soil

and the Menfro soil were similar. However, the

Putnam surface soil had a higher pHH2O than that of

the Menfro soil. Soil organic carbon content of these

soils were \3%. Organic matter can affect the


123

dielectric response of soil (Campbell Sci. Inc. 2002).

A study by Khele et al. (2008) observed a linear

increase of dielectric constant from 3.1 to 7.6 within

the 8–11 GHz range when organic matter content was

increased from 0 to 20%.

Measured and sensor-estimated water content

The manufacturer-provided equation estimated sig-

nificantly higher water content as compared to

measured values, especially for higher hv (Fig. 1).

The difference between measured- and estimated-hv

increased with increasing water content for all three

soils irrespective of clay content or other physical

properties. At the highest water content levels within

each soil, the manufacturer-provided equation esti-

mated 0.65, 0.70, and 0.77 m3 m-3 hv for Putnam A,

Menfro A, and Putnam Bt, respectively, while

measured values were 0.46, 0.41, and 0.45 m3 m-3

hv, respectively. The bulk density of these soil cores

was 1.25 Mg m-3 and therefore the porosity was

approximately 0.52 m3 m-3. The manufacturer-pro-

vided equation estimated 25, 35, and 48% more

water-filled pore volumes than the estimated soil

porosity for Putnam A, Menfro A, and Putnam Bt

respective soils. Therefore, the manufacturer-provided

equation was less suited to estimate soil water in these

soils. Similar to the results of this study, in a soil

moisture sensor comparison research, Walker et al.

(2004) observed that CS615 sensors predicted greater

soil water content near saturation than the soil

porosity. In a laboratory calibration study, Stenger

et al. (2005) observed 15–19% overestimation in

Australia with manufacturer-provided equation.

VWC = -0.31 + 0.019*period r² = 0.92

Period (microsec)

VWC = -0.31 + 0.019*period r² = 0.92

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Vo

lum

etric

Wat

er C

ont

ent

(m3

m-3

)

VWC = -0.34 + 0.019*period r² = 0.89

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

15 20 25 30 35 40

Vo

lum

etric

Wat

er C

ont

ent

(m3

m-3

)

Period (microsec)

VWC = -0.28 + 0.018*period r² = 0.96

Menfro A

Putnam A Putnam Bt

Combined

15 20 25 30 35 40

Fig. 1 Relationships

between sensor-measured

period and volumetric soil

water content (VWC) for

Putnam A, Menfro A, and

Putnam Bt and all three

horizons combined. Filledand empty circles denote

measured and

manufacturer-provided

calibration estimated water

content, respectively. The

distribution of sensor-

measured period for each

measured volumetric water

content indicates the

temperature effect


123

Stangl et al. (2009) also noticed overestimation

anomalies as high as 67%. In contrast, soil water

content was underestimated for volcanic soils (Stenger

et al. 2005). Capacitance sensors behave the same

way, water content was underestimated at lower hv and

overestimated at higher hv; as high as 80% (Kelleners

et al. 2004b).

Individual sensor precision appears to be high as

indicated by the narrow range in measured values. All

sensors responded to differences in hv similarly

among the three horizons. The three replicated

sensors for each hv had a very small standard error

for period (\0.02), for values between 17 and 40 ls.

Similar to our results Stangl et al. (2009) also

observed low sensor variability for CS 615. When

sensors were removed it was also noticed that none of

the rods were either bent or tips were touching to

cause larger differences in reading.

Sensor measured period had a significant relation-

ship with measured soil water for the three soils

individually and the three soils combined (Table 2;

Fig. 1). Period alone accounted for 89–96% of the

variation in measured-hv of individual soils in linear

and quadratic calibration equations (Table 2). Among

the three soils, the Putnam Bt soil had the best

relationship (r2 = 0.96 and RMSE = 0.028 m3 m-3)

between period and measured-hv. A quadratic regres-

sion equation improved the r2 of Menfro soil, but there

was no change for Putnam soils as compared to the

linear calibration. This corroborates with previous

studies which showed that differences between linear

versus quadratic and cubic calibrations were either

small or did not improve estimated water contents

(Chandler et al. 2004; Stenger et al. 2005; Stangl et al.

2009). Table 2 shows that linear and quadratic cali-

brations estimate hv within ±0.028–0.040 m3 m-3 for

these three soils.

Slopes and intercepts among the three soils of the

linear calibration were not significantly different and

therefore sensor-measured period and hv were com-

bined to develop a single calibration for the three

soils (Table 2; Fig. 1). Sensor period in linear and

quadratic calibrations explained 92% of the variation

in hv for the three soils combined. This 92%

predictability could be attributed to several factors.

The relationship between k and hv is directly propor-

tional to the free water content such as with sand

(Zazueta and Xin 1994; Kelleners et al. 2004a). The

soils used in this study contained up to 54% clay. In

fine textured soils, the presence of bound water

causes high dielectric loss affecting measured hv

(Jones et al. 2005; Kelleners et al. 2004b). According

to Chandler et al. (2004) and Seyfried and Murdock

(2001), variation between sensors due to scatter also

affects the reading. In their studies, calibration of

individual sensors reduced the scatter and improved

the predictability. In the current study packing also

may have contributed to sensor readings. Compac-

tion, air gaps, porosity, and spatial variability affect

travel time and thereby the measured hv (Serrarens

et al. 2000; Vaz and Hopmans 2001; Stangl et al.

2009).

Table 2 Relationships

between CS616 sensor-

measured period (C; in

microsec) and measured

volumetric soil water

content (m3 m-3) for

Putnam A, Menfro A, and

Putnam Bt and all three

horizons combined

Regression Relationship Coefficient of

determination, r2Significance

level,

P [ F

Root mean

square

error, m3 m-3

Putnam A

hv = -0.309 ? 0.0197 * C 0.92 0.001 0.039

hv = -0.387 ? 0.0259 * C - 0.0011 * C2 0.92 0.001 0.039

Menfro A

hv = -0.339 ? 0.0198 * C 0.89 0.001 0.040

hv = -0.0673 - 0.0115 * C ? 0.0058 * C2 0.91 0.001 0.038

Putnam Bt

hv = -0.283 ? 0.0183 * C 0.96 0.001 0.028

hv = -0.282 ? 0.0182 * C ? 0.00002 * C2 0.96 0.001 0.029

Combined

hv = -0.311 ? 0.0193 * C 0.92 0.001 0.038

hv = -0.283 ? 0.0182 * C ? 0.00002 * C2 0.92 0.001 0.039


123

Root mean square error (RMSE) for linear and

quadratic equations with period was 0.038 and

0.039 m3 m-3, respectively, for the three soils com-

bined as compared to 0.15 m3 m-3 for the manufac-

turer-provided equation. RMSE is preferred as an

additional measure of quality of models (Stenger

et al. 2005). It is a measure of goodness of fit

compared to correlation and regression coefficients,

and these values indicated that period alone can be

used to estimate hv with less than ±0.04 m3 m-3

difference in measured- and estimated-hv.

Coefficients of determination and RMSEs for both

linear and quadratic calibrations presented in Table 2

were similar and, therefore, the linear equation may

be preferable because it is simpler to use. Results

strongly suggest the importance of development of

site or soil specific equations as compared to the

manufacturer-provided equation for more precise

estimates of hv. Equations presented in Table 2 may

be useful to estimate hv for soils with similar clay

types as found in northern and central Missouri. It

should be noted that clay mineralogy affects dielec-

tric properties and therefore, further research is

required to understand the effects of clay mineralogy

on sensor performance.

Temperature effect

Figure 2 shows a representative example for the

Putnam A horizon of relationships between hv and

period for selected temperature values. The other two

soils examined in this study also exhibited this

pattern (data not presented). As hv increases, the

period increased up to *0.36 m3 m-3 hv for all

temperature values. Slope steepness was higher for

higher temperatures and lower for lower temperatures

for hv \ 0.36 m3 m-3. Slope steepness was signifi-

cantly reduced beyond this water content and the

increasing water content had a small effect on the

measured period.

The effect of temperature on measured period and

hv was positive and linear (Fig. 3). The measured

period was always higher with higher temperatures

for the same hv irrespective of soil horizon. At lower

hv values, the slope steepness with increasing

temperature was low and concomitant differences in

estimated hv were smaller as compared to higher hv.

For example, at the 0.03 m3 m-3 hv, Putnam Bt soils

indicated a 0.725 ls increment in measured period,

from an increase in temperature from 5 to 40�C. This

represents 0.013 m3 m-3 change in estimated-hv

using the manufacturer-provided quadratic equation

between these two temperature values. Similar to the

results for soils in this study, Campbell Inc. (2002)

reported -0.8 to 1.8% water content error for

0.12 m3 m-3 hv between 10 and 40�C. Studying the

temperature effects on Lolalita sandy loam, Searla

loam, and Larimer loam soils at 0.10 m3 m-3, Seyfried

and Murdock (2001) observed a 0.09 m3 m-3 differ-

ence in estimated hv over a temperature range of 5 and

45�C when soil specified equations were used. Clay

contents for the three soils in their study ranged from 5

to 29% whereas the Menfro and Putnam soils in this

research ranged from 23 to 54% clay. The variation in

the observed effects of temperature between hv in this

study, Campbell Inc. (2002), and Seyfried and Mur-

dock (2001) could be possibly due to differences in

clay contents and clay mineralogy of the soils used in

each study.

In nearly saturated soils, the hv was greatly

influenced by the temperature. Period values for

Putnam Bt were 35.54 and 39.15 ls at 5 and 40�C,

respectively, for the 0.45 m3 m-3 hv. The correspond-

ing difference in estimated hv was 0.17 m3 m-3 hv,

with the manufacturer-provided quadratic equation.

Campbell Inc. (2002) observed a 9% change in water

15

20

25

30

35

40

0.0 0.1 0.2 0.3 0.4 0.5

Cam

pbel

l CS

616

Per

iod

(mic

rose

c)

Volumetric Water Content (m3 m-3)

5C 10C

15C 20C

25C 30C

35C 40C

Fig. 2 Sensor-measured period versus measured volumetric

soil water content for the eight selected temperature values for

Putnam A horizon material


123

content error for a soil with 0.30 m3 m-3 hv between

10 and 40�C. In another study with 0.30 m3 m-3 hv,

the difference in hv was 0.155 m3 m-3 between 5 and

40�C (Seyfried and Murdock 2001).

The manufacturer-provided temperature correction

(Eq. 3) was evaluated with estimated and measured hv.

This quadratic equation corrects the period, and the

corrected period is used to estimate hv using the

manufacturer-provided quadratic equation. The mea-

sured period was less than 23 ls for the entire

temperature range for the three soils with

hv \ 0.15 m3 m-3 (Fig. 3). For the 0.10 m3 m-3 hv

at 5�C, both Menfro and Putnam Bt period values were

*20 ls and the corrected period was 20.45 ls. The

estimated hv, using the 20 and 20.45 ls period in the

quadratic equation, were 0.088 and 0.098 m3 m-3,

respectively, whereas the measured hv was 0.10

m3 m-3. The temperature corrected period values

were 41.48 and 27.65 ls for Putnam Bt at 5 and 40�C

for the 0.45 m3 m-3 hv. The manufacturer-provided

equation estimated 0.877 and 0.295 m3 m-3 water

contents for these periods, respectively. Irrespective of

the clay content, the manufacturer-provided quadratic

equation estimated similar hv values with corrected and

uncorrected periods at 20�C.

Since the temperature effect appears to be linear

and uniform at low volumetric water contents, the

temperature response can be easily incorporated into

calibration equations to estimate volumetric water

content using period (Figs. 2 and 3). Although period

values leveled off at higher water contents, when

temperature was included in the calibration, RMSE

decreased for the linear and quadratic equations

(Tables 2 and 3). Equations developed using the

period and temperature explained 93–97% of the

variability in hv for soils with 23–54% clay (Table 3).

When the data for all three soils were combined, 95%

of the variation in hv was accounted for by period and

temperature in a linear calibration. The RMSE was

0.13 m3 m-3 for the three soils combined with the

manufacturer-provided equation. The laboratory hv

data were reasonably and well represented by linear

and quadratic calibration equations for these soil

data; the predictability was better than with the

manufacturer-provided equation.

The temperature effects on dielectric properties are

complex (Seyfried and Murdock 2004) and may be

due to the interactive effects of temperature and the

amount of bound water, clay mineralogy, and ion

valence. It should be noted that the dielectric constant

is directly proportional to the free water of the media

(Zazueta and Xin 1994). Therefore, these sensors

may cause errors in measurement, particularly for

areas with large diurnal fluctuations. Results also

show that the temperature effect was small for

smaller hv and higher for higher hv. However,

inclusion of a temperature correction might be

impractical until temperature-moisture combo sen-

sors become available since it would require instal-

lation of temperature sensors at each depth and

location where hv is being measured. According to

Seyfried and Murdock (2004), the temperature

effect should be acknowledged and included when

using the sensors.

Effect of clay content

The clay content among the three soils varied

between 23 and 54% and accounted for only 1–3%

of the variation in hv while period alone explained

92% of the variation in hv (Table 4; Fig. 1). The best

calibration equation with clay, temperature, and

period accounted for 95% of the variation (Table 4).

Adding squared terms each for period, temperature,

or clay content did not improve r2 or reduce RMSE.

Although clay content influences the dielectric prop-

erties due to variations in charge, period accounted

for most of the variation in hv. Research shows that

clay content affects period and requires soil specific

calibrations to improve the estimate for a given hv

(Seyfried and Murdock 2001; Chandler et al. 2004).

15

20

25

30

35

40

0 5 10 15 20 25 30 35 40 45 50

Cam

pbel

l CS

616

Per

iod

(mic

rose

c)

Temperature (°C)

0.45

0.36

0.31

0.25

0.20

0.14

0.10

0.03

Fig. 3 Campbell CS616 sensor-measured period versus tem-

perature for Putnam Bt soil with soil water content values

between 0.03 and 0.45 m3 m-3


123

For example, comparing three calibration methods

with TDR sensors, Quinones et al. (2003) stated that

non-continuous wetting, continuous wetting, and

sensors at known soil water levels had consistent

relationships. In contrast, Seyfield and Murdock

(1996) found that a single equation can be used to

describe differences in soil water for the same soils

used in their study. Another factor that may have

affected the lower contribution by the clay content

could be the small (23–54%) range in clay content for

soils used in this study; i.e., soils with very low and

very high clay contents were not included. Seyfried

and Murdock (2001) had clay contents as low as 5

and 10% compared to 23% in this study. In Australia,

Stangl et al. (2009) used 64–89% clay soils with six

CS615 sensors to develop calibration relationships

and found slopes and intercepts were different among

soils and horizons. However, these equations cannot

be compared directly as those were developed for

CS615 sensors. Period reading for the Stenger et al.

(2005) and Stangl et al. (2009) were between 0.6 and

2.2 ms as compared to 15–40 ms in the current study.

Period and volumetric soil moisture data from the

Stenger et al. (2005) were used to compare the

quadratic equation developed in this study. Volumet-

ric water contents was converted to period using

hv = -0.283 ? 0.0182 * C ? 0.00002 * C2. Period

values were regressed to examine whether an equa-

tion developed for high clay is comparable to the

quadratic equation developed in the current study.

Regression coefficient was 0.98 between the period

values of Stenger et al. and the current study.

However, further studies may be required to validate

the accuracy of the equation when VMC is predicted

for clay percentages higher than values in the current

study.

Table 3 Relationships between CS616 sensor-measured period (C; in microsec), soil temperature (�C), and measured volumetric

soil water content (m3 m-3) for Putnam A, Menfro A, and Putnam Bt and all three horizons combined

Regression relationship Coefficient of


level, P [ F

Root mean square

error, m3 m-3

Putnam A

hv = -0.278 ? 0.0206 * C - 0.0024 * Temp 0.96 0.001 0.029

hv = -0.193 ? 0.0141 * C ? 0.00012 * C2 - 0.0026 * Temp 0.96 0.001 0.029

Menfro A

hv = -0.315 ? 0.0206 * C - 0.0020 * Temp 0.93 0.001 0.034

hv = 0.283 - 0.0249 * C ? 0.00084 * C2 - 0.0025 * Temp 0.96 0.001 0.026

Putnam Bt

hv = -0.258 ? 0.0186 * C - 0.00157 * Temp 0.97 0.001 0.022

hv = -0.211 ? 0.0151 * C ? 0.00006 * C2 - 0.00159 * Temp 0.97 0.001 0.022

Combined

hv = -0.283 ? 0.0199 * C - 0.00198 * Temp 0.95 0.001 0.031

hv = -0.129 ? 0.0083 * C ? 0.00021 * C2 - 0.00213 * Temp 0.95 0.001 0.030

Table 4 Relationships between CS616 sensor-measured period (C; in microsec), soil temperature (�C), and clay (%) with measured

volumetric soil water content (m3 m-3) for all three horizons combined

Regression relationship Coefficient of


level, P [ F

Root mean square

error, m3 m-3

hv = 0.279 - 0.0011 * clay 0.01 0.172 0.133

hv = 0.544 - 0.0162 * clay ? 0.000193 * clay2 0.03 0.074 0.132

hv = -0.297 ? 0.0192 * C - 0.0003 * clay 0.92 0.001 0.038

hv = -0.270 ? 0.0198 * C - 0.00198 * Temp - 0.00031 * clay 0.95 0.001 0.031


123

Although sensors could provide hv for compari-

sons, a soil specific calibration is required to obtain a

high degree of accuracy in hv (Leib et al. 2003). This

is especially true when the same soil volume is

utilized by multi-species vegetation with different

lengths of growing season, root distribution patterns,

and moisture requirements, such as in agroforestry

systems. Accurate information on parameters such as

water use, profile moisture patterns, and peak demand

are important for developing multi-species manage-

ment practices for environmental and economic

benefits. In spite of proper calibrations and frequent

maintenance and checking, Zazueta and Xin (1994)

questioned the long-term stability of the calibration.

Sensor-estimated field water content

for agroforestry and crop treatments by depth

The equation developed in this study estimated field

soil volumetric water content better than the water

contents estimated by the manufacturer provided

equation (Fig. 4). Slopes were 0.96 and 1.90 for the

equations developed in this study and the manufac-

turer provided relationships with the field measured

water content values. The slopes of the two equations

were significantly different. The manufacturer pro-

vided equation estimated much higher water contents

especially for higher water content values. The

equation developed in the study estimated water

contents similar to soil porosity.

The annual rainfall at the Greenley Center in 2007

was 893 mm which corresponds to 97% of the long-

term mean annual rainfall. Rain occurred on 77 days

of the 253-day (March 12 and November 19) study

period with amounts ranging from 0.25 to 58 mm

(Fig. 5). Precipitation amount received on day 2, 3,

and 6 during this period were between, 25–30, 30–35,

and 20–25 mm, respectively. About 42% of the

rainfall occurred during the March to May period

when the evapotranspiration demand was relatively

low. Approximately 33% of the rainfall occurred

during the period between June 8 and October 26.

This period corresponds with the cropping period of

the watershed.

The experimental design of a long-term study that

evaluates soil water dynamics of an agroforestry

system was used to examine management and depth

effects on hv. Soil water content was higher due to

winter recharge and reduced evapotranspiration at the

beginning of the analysis for both treatments and four

depths (Fig. 5). Soil water content was slightly higher

for all four depths in the agroforestry treatment as

compared to the crop treatment until mid-April. This

could possibly be due to the beneficial effects of

perennial vegetation on soil physical properties, such

as increased porosity and carbon accumulation and

reduced bulk density (Seobi et al. 2005; Anderson

et al. 2009; Udawatta and Anderson 2008). Porosity

values for agroforestry soil were 0.53, 0.49, 0.52, and

0.57 m3 m-3 for the 10 cm depth increments as

compared to 0.49, 0.46, 0.48, and 0.59 m3 m-3 for

the respective depths of the crop treatment (Seobi

et al. 2005).

y = 0.9659x + 0.0368R² = 0.886

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

0.50

VW

C e

stim

ated

by

the

Equ

atio

n de

velo

ped

in th

is

stud

y (m

3 m

-3)

Measured Volumetric Water Content (m3 m-3)

y = 1.9038x -0.0577R² = 0.8677

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

0.10 0.20 0.30 0.40 0.50

VW

C e

stim

ated

by

Cam

pbel

l Equ

atio

n(m

3 m

-3)

0.10 0.20 0.30 0.40 0.50

Fig. 4 Field measured volumetric water content and volumet-

ric water contents (VWC) estimated by the manufacturer

provided equation (a) and equation developed in this study (b).

Please note that the scale of Y axis is different for the two

figures


123

As the vegetation became active and began to

transpire, soil water content decreased. The agrofor-

estry treatment lost more hv compared to the crop

treatment until the crop was established. However,

these differences were not significant. Statistically

lower (P \ 0.05) hv persisted in the agroforestry

treatment compared to the row crop treatment within

each depth during the crop period. Among the

measured 37 sampling dates during the study period,

significant differences were found between crop and

agroforestry treatments for 13, 17, 18, and 18

sampling dates for 5, 10, 20, and 40 cm depths,

respectively. This was attributed to the greater

transpiration from the trees in the agroforestry buffer

treatment compared to the soybeans in the row crop

treatment. In Missouri, bud break for oaks occurs

during the March–April period and trees start to use

soil water. Thus, the trees with higher leaf areas in

PP

T (

mm

)0

20

40

60

5 cm depth

0.1

0.2

0.3

0.4

0.5

10 cm depth

0.1

0.2

0.3

0.4

0.5

20 cm depth

0.1

0.2

0.3

0.4

0.5

40 cm depth

VW

C (

m3 m

-3)

0.1

0.2

0.3

0.4

0.5

CropAgroforestry

Julian Date March April May June July August Sept. Oct. Nov.

Precipitation

71 99 127 155 183 211 239 267 295 323

5 cm depth

10 cm depth

20 cm depth

40 cm depth

Precipitation

VW

C (

m3 m

-3)

VW

C (

m3 m

-3)

VW

C (

m3 m

-3)

Fig. 5 Daily precipitation and volumetric soil water content

estimated with the linear calibration at 12:00 noon (n = 4) for

crop and agroforestry treatments at the paired watershed study

for 5, 10, 20, and 40 cm depth during 2007. The gray area

shows the crop period for soybeans. Bars on the 40-cm depth

graph indicate LSD values for significant differences in water

content between crop and agroforestry treatments at the

a = 0.05 level


123

early spring would have transpired more water

relative to row crops. Although precipitation replen-

ished soil water resulting in small losses from the soil

profile, soil water depletion occurred from all four

depths during the growing season.

The pattern of changes in hv closely followed the

rainfall distribution (Fig. 5). Rain events recharged

the soil profile on both treatments; the effect was

more dominant on the two surface depths. Soils at 20

and 40 cm depths started to lose water after mid-

August for the crop treatment while the differences

were smaller in the agroforestry treatment. Rain

events did not completely recharge the profile until

the 58-mm rain event in October. Although soil water

content remained high in the crop treatment while the

soil water content in the agroforestry treatment

continued to decrease, differences between the two

treatments and depths were not significant after this

recharge. This pattern could be attributed to rainfall

and reduced evapotranspiration demand. Soil water

dynamics were parallel to rainfall distribution and

evapotranspiration demand. The analysis indicated

that perennial vegetation with deeper roots used more

water and maintained lower soil water profile than the

annual crops. In this study, with lower soil water

content for the agroforestry treatment and an associ-

ated increased soil water storage potential, the

agroforestry buffer may reduce runoff during precip-

itation events.

Figure 6 shows an example of the differences in hv

at the 5 cm depth on July 24 which had the highest

recorded diurnal temperature difference in 2007 in the

field study. Soil temperature values were 19.5 and

32.2�C at 6:00 a.m. and 2:50 p.m., respectively.

Volumetric water contents estimated by manufac-

turer-provided equations showed the largest fluctua-

tions. The difference in volumetric water content

between the maximum and minimum for July 24 were

0.023 and 0.043 m3 m-3 for the two manufacturer-

provided equations without temperature correction

and with temperature correction, respectively (Fig. 6).

Without temperature correction, the manufacturer-

provided equation estimated larger values during the

day while the temperature corrected manufacturer-

provided equation estimated smaller values. The

largest (0.335 m3 m-3; without temperature correc-

tion) and the smallest (0.272 m3 m-3; with temper-

ature correction) estimated hv were recorded between

2:30 and 3:30 p.m. for the respective manufacturer-

provided curves. The higher temperature appeared to

have significantly influenced the manufacturer-pro-

vided equation and its estimate of hv.

The linear calibration developed (period only) in

this study had a 0.013 m3 m-3 difference between the

maximum and minimum hv. The highest (0.246 m3

m-3) hv was at 3:00 p.m. and the smallest (0.233 m3

m-3) hv was between 5:30 and 7:00 a.m. It closely

followed the maximum and minimum temperature

values. The calibration with period and temperature

had the smallest difference (0.011 m3 m-3) between

the highest (0.239 m3 m-3) and the smallest (0.228

m3 m-3) hv. Equations developed using period alone

and period with temperature showed smaller fluctua-

tions as compared to the manufacturer-provided equa-

tions. The results of the analysis also suggest that

sensor-estimated period values should be used to

estimate volumetric water contents during the times

when temperature fluctuation was relatively small.

Potential application and summary

The study was designed to examine clay content and

temperature effects on WCR sensor-measured soil

water content since accurate values are required to

understand potential water use by a combination of

trees, crops, and grass to evaluate soil and water

conservation practices. Within these systems, non-

15

20

25

30

35

0.20

0.25

0.30

0.35

0 3 6 9 12 15 18 21 24

Tem

pera

ture

(°C

)

Volu

met

ric W

ater

Con

ten

t (m

3m

-3)

Time (Hour)

Calibration 1 Calibration 2 Calibration 3

Calibration 4 Temperature

Fig. 6 Soil temperature and volumetric water content at 0.5 h

intervals for the 5 cm soil depth in the row crop treatment on

July 24, 2007 at the Greenley Center Paired Watershed. Linesdenote water content estimated using manufacturer-provided

relationship (Calibration 1), manufacturer-provided relation-

ship with temperature correction (Calibration 2), linear

calibration developed in this study with period (Calibration

3), and linear calibration developed in this study with period

and temperature (Calibration 4)


123

uniform water use and highly dynamic changes in soil

water content could occur in the root zone and

therefore, shallow rooted plants may not receive the

required amount of soil water leading to yield

reductions.

WCR sensors can be used for continuous monitor-

ing of volumetric water content. Development of site

specific calibrations ensures greater accuracy of soil

moisture measurements over general calibration equa-

tions. The equations developed from this study

estimated soil water content with less than ±4% error

thus providing an estimation of soil water dynamics.

These levels of errors may be tolerable for many

applications. The analysis also indicated that sites with

higher temperature fluctuations require a temperature

correction, especially during times with hv above

0.15 m3 m-3. Calibration equations developed in this

study may be used for similar soils and temperature

conditions to estimate changes in soil water content for

irrigation scheduling; to study root zone water dynam-

ics and water balance; and to evaluate soil water and

solute movement within the profile. These calibration

equations may result in reduced error in hv estimates

and significant differences in volumetric water content

among studies and/or management attributes.

Frequent checking with a TDR system and/or

gravimetric sampling can be employed to correct

WCR estimated values. WCR soil water monitoring

with these procedures reduces the cost and allows

estimation of continuous soil water dynamics in

several locations and depths. The required precision,

frequency, and spatial density of data collection for a

specific cropping system and soil type and the cost

associated with instrumentation are among several

factors to be considered when designing an appro-

priate calibration procedure or a soil water monitor-

ing system. Future studies may be needed to examine

whether the initial calibration equation for the WCR

sensor holds for extended periods of time and to

determine the effects of clay mineralogy.

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Florida Cooperative Extension Service, Inst. Food and

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