ABSTRACT Title of Thesis: USING TIME DOMAIN …s3.amazonaws.com/zanran_storage/ · ABSTRACT Title of Thesis: USING TIME DOMAIN REFLECTOMETRY TO SCHEDULE IRRIGATION IN SOILLESS SUBSTRATES

ABSTRACT

Title of Thesis: USING TIME DOMAIN REFLECTOMETRY TO

SCHEDULE IRRIGATION IN SOILLESS SUBSTRATES

Degree candidate: Jason Daniel Murray

Degree and year: Master of Science 2001

Thesis directed by: Dr. John D. Lea-Cox Department of Natural Resource Science and Landscape Architecture

Time Domain Reflectometry (TDR) is a moisture sensing system that can be

applied to automatically control cyclic irrigation scheduling in real time. TDR can

be used with pre-determined set points to initiate and terminate irrigation. Previous

research has not provided any data on the variability of TDR probe performance in

soilless substrates, nor any information on calibrating or using this technology in

practical nursery and greenhouse situations. Thus a range of substrates and

container sizes, commonly used in horticulture, were examined to investigate these

effects on substrate volumetric water contents (Wv) and TDR performance. Data

was initially generated to model the relationships between Wv and substrate matric

potential using a modified desorption table. This simultaneously correlated and

calibrated Wv with TDR sensor output for each substrate and container height.

TDR sensor performance was significantly correlated with Wv in all substrates and

most coefficients of variation were below 5%. The relationship between stomatal

conductance and the Wv of each substrate was investigated using Rhododendron

azalea cv.‘Hot Shot’ in a growth chamber study. This allowed for the

determination of irrigation parameters to optimize plant-available water. Sensor

placement issues were then investigated in a greenhouse study, using azalea. This

identified the variability in TDR sensor performance due to the proximity of the

sensor to the drip and/or spray stake emitters. Finally, the use of TDR-mediated

cyclic irrigation scheduling was examined to see whether this technique can

effectively monitor and control irrigation cycles to reduce water leaching in

comparison to standard irrigation scheduling methods.

USING TIME DOMAIN REFLECTOMETRY TO SCHEDULE IRRIGATION IN

SOILLESS SUBSTRATES

By

Jason Daniel Murray

Thesis submitted to the Faculty of the Graduate School of the University of Maryland, College Park in partial fulfillment

of the requirements for the degree of Master of Science

2001

Advisory Committee: Dr. John D. Lea-Cox, Chair Dr. Gerald Deitzer Dr. Bahram Momen Dr. David Ross

©Copyright by

Jason Daniel Murray

2001

TABLE OF CONTENTS

List of Tables iii List of Figures iv Introduction 1 Chapter I: Simultaneous Measurement of Water Release

Curves and Time Domain Reflectometry Calibration for Various Soilless Substrates and Column Heights 16

Chapter II: Determining Set Points for Controlling Irrigation With

Plant Systems 51 Chapter III: Active Control of Irrigation Scheduling by Time

Domain Reflectometry in Soilless Substrates 65 Overall Discussion 80 Appendix A: Economic Assessment of Sensor Technologies 84 Appendix B: Procedures for Tension Table 86 Appendix C: Time Domain Reflectometry Sample Program 87 Glossary 91 Literature Cited 92

LIST OF TABLES

1. Particle Size Distribution of Soilless Substrates

Used in Study 21

2. Descriptive Statistics for the Effect of Column Height on Volumetric Water Content 32

3. Descriptive Statistics for the Relationship of Volumetric Water Content with Time Domain Reflectometry Output 33

4. Descriptive Statistics Concerning Irrigation Set Points 58 5. Descriptive Statistics for the Sensor Placement Study 75 6. Descriptive Statistics for the Irrigation Scheduling Study 76

LIST OF FIGURES 1. Water Availability in Soilless Substrates 6 2. Characteristic Curves of Rockwool and Peat 7 3. Tektronix Time Domain Reflectometry Cable Tester 12 4. Time Domain Reflectometry Sensor Matching Container Height 12 5. Portable Dielectric Probe and Time Domain Reflectometry Probe 20 6. Modified Tension Table 22 7. Sensors Fixed in Tension Table Lids for Calibration Purposes 23 8. Time Domain Reflectometry Wave Trace 27 9-14. Characteristic Curves for Individual Substrates and

Sensor Calibrations 34-45 15. Portable Dielectric Probe Calibration 46-47 16. Growth Chamber Study Conditions 55 17. Change in Stomatal Conductance Due to Increasing

Water Stress for Individual Substrates 59-61 18. Azalea Size Differences by Substrate 62 19. Damage Due to Incipient Water Stress 63 20. Sensor Placement 70 21. Leachate Capturing Design 71

Introduction

The Nursery and Greenhouse Industries are rapidly expanding throughout the

United States, and growers are using increasingly intensive production practices.

To minimize production time, many plant producers use large volumes of water

and nutrients to maximize plant growth rates, which may increase nutrient leaching

and run-off into waterways (Berghage et al., 1999).

The Chesapeake Bay in Maryland is affected by nutrient run-off into the watershed

from non-point (agricultural and urban) sources (Taylor, 2000). Nitrogen (N) and

phosphorus (P), the two primary limiting factors in terrestrial and fresh water

systems (Ryther, 1971), have been targeted for reduction from agricultural systems,

as they can be primary limiting ecological factors in the environment. Container

nursery production utilizes many types of soilless substrates, which usually have a

high nutrient leaching potential. State legislation in Maryland (MDA, 2000) and

new enforcement of the Federal Clean Water Act are primary forces to improve

water and nutrient management in agricultural production systems. Increasing the

efficiency of water and nutrient use is the most pro-active method to minimize

nutrient leaching from container nursery production.

Reducing irrigation duration is one way to reduce nutrient leaching, as soilless

substrates have relatively low water holding capacities (Handreck & Black, 1999;

de Boodt, 1972). Cyclic irrigation techniques have been shown to reduce leaching

1

volumes, i.e. the application of the irrigation volume over several cycles (Fare et

al., 1994; Beeson, 1995; Fare et al., 1996; Tyler et al., 1996). Nutrient applications

should be based on plant growth requirements specific to species and cultivar, but

few data are available on the nutrient use of most woody and herbaceous

ornamental species. Nitrate and soluble ortho-phosphorus tend to move with

leaching water in soilless substrates, since these substrates have little anion

exchange capacity (Handreck & Black, 1999). The basic premise of this thesis is

that reducing the proportion of water leaching from a plant container (ie. leaching

fraction) should reduce the leaching of nitrate and phosphate from the plant root

zone.

The water retention characteristics of any substrate are governed primarily by the

physical properties of the components of the media. Particle size distribution,

particle type, and bulk density affect the ratio of solid material to pore space, which

then determines pore size (Handreck & Black, 1999). The presence of water in a

soilless substrate can be expressed as percent water content by volume or mass, or

it can be characterized in terms of the matric potential of the substrate.

Matric potential is one component of total water potential. Water passively enters

plant roots following an osmotic gradient, moving from a higher water potential to

a lower (more negative) water potential. Briefly, water potential is the sum of

osmotic potential, pressure potential, and matric potential (Taiz & Zeiger, 1998).

2

Osmotic potential is a function of solute concentration of an aqueous solution.

Pressure potential represents the hydrostatic pressure in excess of ambient

atmospheric pressure. Matric potential is a result of the adsorption of water

molecules to substrate particle surfaces. (Taiz & Zeiger, 1998)

Water and air interact dynamically in the pore space during wetting and drying

cycles. Plant uptake of water is passive and is largely determined by the moisture

content and osmotic potential of the water held in the substrate (Handreck & Black,

1999; Taiz & Zeiger, 1998). As the matric potential increases, the plants’ ability to

take up water is decreased. This thesis focuses on availability of water in terms of

matric potential, which composes the largest proportion of the water potential

equation under non-saline conditions. In an aqueous environment, the pressure

potential component of water potential is negligible, since the atmospheric pressure

is consistent with the pressure on the water in a container. If solute concentrations

are then held nearly constant in a range common to plant production practices (1-3

dS/m), water availability can be modeled strictly in terms of matric potential.

Matric potential is expressed in KiloPascals (KPa), and can be induced in step-wise

fashion to designated levels using a tension table. This allows for the

characterization of available plant water in various substrates by plotting

volumetric water content (Wv) versus applied pressure in KiloPascals (Brown et

al., 1989).

3

As a substrate dries, media particles with large pore space lose the majority of the

water most quickly within the substrate, followed by medium and small-sized pore

spaces. Hygroscopic water is adsorbed to the particle surface and is largely

unavailable to plants (Handreck & Black, 1999). Furthermore, as moisture is lost

from the substrate pore space, the osmotic potential of the remaining water is

further increased, inhibiting passive movement of water into the plant root

(Handreck & Black, 1999; Taiz & Zeiger, 1998).

Mineral soils (mixes of clay, sand, and silt) release water gradually and have higher

field capacities due to smaller pore sizes, which minimize free drainage of water

(Handreck & Black, 1999), compared to soilless substrates. Soil desorption is

distributed from 0 to –1500 KPa, with most plants exhibiting permanent wilting at

soil water potential near –1500 KPa (Taiz & Zeiger, 1998).

Soilless substrates in general have very large pore space due to large particle sizes.

Common soilless substrates can consist of ingredients such as composted pine bark,

composted hardwood, peat moss, perlite, rockwool, sand and mixtures of those and

other substrates (Handreck & Black, 1999). These media release the bulk of the

moisture from 0 to -10 KPa (three orders of magnitude less than soils) from the

large pore spaces (de Boodt, 1972; Handreck and Black, 1999; da Silva et al., 1993;

da Silva et al., 1995). Medium and small pore spaces are reduced in comparison to

most soils, resulting in a large proportion of hygroscopic and adsorbed water that is

4

unavailable for plant uptake. Water release curves for these substrates are nearly

asymptotic beyond -10 KPa (de Boodt, 1972). With this understanding, easily-

available water can be generalized for soilless substrates as the initial release of

large volumes of water within the first -10 KPa of matric potential (de Boodt, 1972;

Handreck and Black, 1999; da Silva et al., 1993; da Silva et al., 1995). Substrates

may still appear wet, however water may not be readily available for plant use, as

indicated by nearly asymptotic water release curves (da Silva et al., 1995). For

most soilless substrates, optimal available water is in the range of 0 to –10 KPa for

plant growth (Figure 1). This is therefore the ideal range for irrigation in soilless

media (Figure 2).

5

Fig. 1. Explanation of general water release curves for potting mixes, showing that as extra suction pressure is applied to the mix, the percent volume of water decreases and the volume percent of air increases (After Handreck and Black, 1999).

6

Fig. 2. Examples of characteristic moisture-release curves for rockwool and peat moss substrates. Water content rapidly decreases from 0 to 10 KPa suction pressure, beyond which there is little plant-available water (After da Silva et al., 1995)

7

“Wetting efficiency” is a response of the substrate to the rate at which water is

applied, and is affected by the capillary action of the substrate. Water that is

applied too quickly may channel down the sides of the container, or even vertically

through a porous substrate faster than the water may be distributed horizontally.

Application method and rate, therefore, affect irrigation efficiency to a great extent.

Irrigation systems such as over-head sprinkler, drip stakes, and spray stakes are

common in horticulture. Generally a system that applies water more slowly, and

directly into the container, will allow more time for water to be held by the

substrate thereby reducing the leaching volume. Cyclic irrigation is popular

because it allows more control of limited application volumes with minimal

adjustment required in management (Bilderback & Fonteno, 1987). Smaller

volumes applied more frequently have a tendency to increase the percentage of

applied water held by the substrate (Bilderback & Fonteno, 1987; Milks et al.,

1989; Milks et al., 1989; Lamack & Niemiera, 1993; Fare et al., 1994; Karam et al.,

1994; Karam, 1994; Beeson, 1995; Fare et al., 1996; Tyler et al., 1996; Tyler et al.,

1996).

Irrigation can theoretically be based theoretically on calculated plant

evapotranspiration over a specified time interval. A container can be weighed at

the maximum water-holding capacity (i.e. container-capacity), and then re-weighed

after a given time. This volume can then be accurately re-applied knowing the

8

emitter discharge rate. This maintains optimum plant-available water, but this is

largely an unmanageable technique in commercial nurseries and greenhouses.

Approximate estimated water loss is usually estimated based on experience and a

knowledge of prior environmental conditions, usually once a day or every few

days. Cyclic irrigation is a technique that attempts to minimize leaching by

allowing the substrate optimal time to disperse water in a uniform wetting pattern.

Application of this required volume of water in two or more cycles typically

reduces leaching volumes. Reduced leaching volume increases the residence time

of nutrients in the container, increases nutrient uptake efficiency, and reduces

nutrient concentrations of any leachate (Rahier, 1989; Ku and Hershey, 1992;

Niemiera et al., 1993; Karam, 1994; Niemiera et al., 1994; Rose et al., 1994; Tyler

et al., 1996).

Sensors could be used to control cyclic irrigation based on maintaining optimum

plant available water (Schmugge et al., 1980; Campbell & Campbell, 1982; Coelho,

1996; Schurer & Hilhorst, 1998). Scheduling irrigation with sensors could replace

irrigation scheduling based on estimated interval water loss, by measuring the

actual water content of a particular substrate. Sensing in soilless substrates is

difficult because the range of available water for optimal plant growth is small (-1

to -10 KPa), so any large variability associated with the sensing system may mask

small differences in substrate water content.

9

Tensiometers and gypsum blocks generate highly variable data when used in

heterogeneous horticultural substrates, though this is not well documented in

research publications. Tensiometers have a slow reaction time to changes in

moisture content. Furthermore, low-tension tensiometers that have been used with

soilless substrates appear to place an equal emphasis on the range of matric

potentials from 0 to –100 KPa (Schmugge et al., 1980). The zero to –10 KPa range

constitutes a very small portion of the defined monitoring range, while –10 to –100

KPa defines the major portion of the tensiometers output gauge (Schmugge et al.,

1980; Testazlaf et al., 1999). As has been shown (Figures 1 & 2), soilless

substrates release nearly all easily-available water in this initial zero to -10 KPa

range, while –10 to –100 KPa tends to represent a very small volume of available

water (Handreck & Black, 1999). Thus the range of these tensiometers does not

match the requirements for irrigation scheduling in most soilless substrates.

Tensiometers also require regular maintenance to ensure continued precision and

reliability, to replace the fluid that equilibrates the tensiometer with the soil water

column. Finally, tensiometers have not been well adapted for use with advanced

monitoring and control systems, which would facilitate more widespread adoption

by the nursery and greenhouse industry.

Time domain reflectometry (TDR) is a wave propagation system that uses a

metallic cable tester (Tektronix 1502 B or C models; Tektronix Beaverton, OR or

Campbell TDR100; Campbell Scientific Inc., Logan, UT) to measure the velocity

10

of a propagated electrical signal, which is then related to substrate water content

(Schmugge et al., 1980; Topp et al., 1980; Topp et al., 1984; Ansoult et al., 1985;

Topp, 1985; Dalton & van Genuchten, 1986; Ledieu et al., 1986; Plakk, 1990;

Brisco et al., 1992; Dirksen & Dasberg, 1993; Amato & Ritchie, 1995). Sensors

may be made in a laboratory (Figure 3) to meet specific criteria, and to reduce cost

compared to purchasing manufactured sensors (Heimovaara, 1993; Evett, 1998).

Non-conductive resins are used to cast the handle of the sensor. The three wave-

guides are manufactured from steel welding rod (308L from All State Welding

Products, Taneytown, MD), wired with RG8 coaxial cable (Alpha Wire Company,

Elizabeth, NJ). TDR probes can be calibrated with wave-guides of length designed

to match the specific container heights (Figure 4). Calibration can be based simply

on substrate water content, or suction or applied positive pressure can be used to

reflect the matric potential of the substrate (Topp & Zebchuk, 1979; Handreck &

Black, 1999; Roth et al., 1990; Roth et al., 1992). Calibrations can be used to set a

minimum and maximum water content at which to trigger and terminate an

automated cyclic irrigation application (Campbell & Campbell, 1982; Topp, 1985;

Coelho, 1996).

11

Figure 3. Tektronix 1502 C Metallic cable tester, with attached TDR sensor that was constructed at the University of Maryland Nursery and Greenhouse Systems Lab.

Figure 4. A TDR sensor constructed to match the container height to be monitored.

12

Wave propagation sensors can be optimized in length to match container height

(Fig. 4). These sensors average the water content over the length of the wave-

guides by measuring the impact of the apparent dielectric constant of the substrate

on the velocity of a signal propagated by the TDR unit (Topp et al., 1980. A pulse

is propagated along the sensor, and the time that it takes to travel out and back to

the TDR unit is indicative of the moisture content as an average over the length of

the sensor. Water has a dielectric constant of 81, while dry substrates have values

from approximately 2 to 7. Water therefore dominates the apparent dielectric

constant of the substrate that is observed by the sensor, and this concept is true for

soils and soilless substrates (Topp et al., 1984; Ansoult et al., 1985; Topp, 1985;

Dalton & van Genuchten, 1986; Ledieu et al., 1986; Plakk, 1990; Herkelrath et al.,

1991; Brisco et al., 1992; Richardson et al., 1992; Anisko et al., 1994; Whalley et

al., 1994; Kelly et al., 1995; da Silva et al., 1998). The velocity of the propagated

signal is affected by the dielectric properties and the amount of water present in the

substrate. More water will attenuate the signal into the soil, in effect slowing the

signal propagation along the wave-guides. A slower signal indicates a higher water

content and thus higher dielectric constant (Topp, 1980). Wave propagation

sensors also have an immediate (response time = 1 to 25 seconds on average

depending on the TDR unit) response to changes in moisture content, facilitating

their use in scheduling irrigations in relatively small containers.

13

A primary disadvantage of the application of TDR systems in greenhouse and

nursery operations is a restriction on the length of cable. RG8 coaxial cable (larger

diameter than RG58) allows a maximum distance from the cable tester to the sensor

of approximately 27 meters, after which the signal becomes overwhelmed by

system noise and produces unreliable data (Baker & Lascano, 1989; Kelly et al.,

1995). Sensors should also be calibrated to match the desired cable length. To

overcome the restriction of cable length, portable dielectric probes (PDP’s)

operating under similar principles can be used (e.g. CS615 probe; Campbell

Scientific, Logan, UT and Theta Probe ML2; Dynamax Inc, Houston, TX). PDP’s

are self-contained units with a built-in signal propagator. PSP sensors also respond

to the dielectric constant, but produce an output in millivolts that is calculated by

the probe. This output is based on an amplitude change of the electromagnetic

wave propagated along metallic wave-guides in the substrate (Schmugge et. al

1980; Brisco et. al 1992), similar to a TDR probe. These sensors operate with a

data logger, via a differential analog channel running up to 100 meters from the

logger to the sensor. The cost of these sensors is much greater than individual TDR

probes (Appendix 1) and they have a fixed wave guide length, which may limit

their use in larger containers unless they are buried. These may also be more ideal

for growing operations that require small numbers of sensors, but when large

numbers are required, the TDR system may be more economic. Little if any data

has been published that directly examines issues of sensor variance for these

various moisture monitoring systems, especially in soilless substrates.

14

The objective of my research was to examine the application of TDR technology to

schedule automated cyclic irrigation in soilless substrates, using a studied, step-

wise approach. A number of soilless substrates prevalent in the nursery and

greenhouse industry were examined independently to assess effectiveness of TDR-

controlled irrigation with an examination of container-height effects on water

retention and irrigation parameters. Firstly, characteristic curves were generated to

model the relationship of percent volumetric water content (Wv) to matric potential

in KPa, with simultaneous correlation and calibration of Wv with TDR output. In

addition, the variability associated with sensor height and sensor type in relation to

container height and substrate properties were examined to insure reliability of the

system. Secondly, the stomatal conductance of a woody perennial species

(Rhododendron azalea cv.‘Hot Shot’) was correlated with substrate water content

to determine parameters for irrigation to optimize plant-available water. Thirdly,

sensor placement was studied to identify any restriction on sensor placement in

cyclic irrigation systems based on the proximity of the sensor to the drip and/or

spray stake emitters. Finally, the use of TDR-mediated cyclic irrigation scheduling

was examined to determine whether this technique could effectively monitor and

control irrigation cycles to reduce leachate volume in comparison to standard

irrigation scheduling methods.

15

Chapter One

Simultaneous Measurement of Water Release Curves and Time Domain

Reflectometry Calibration for Various Soilless Substrates and Column Heights

Introduction

Horticultural soilless substrates generally have a greater proportion of large pore

spaces in comparison to natural soils, with low water holding capacities, and

smaller ranges of easily-available water (EAW) for optimum plant growth

(Handreck and Black, 1999). Plant-available water can be measured by the matric

potential of the substrate in terms of KiloPascals (KPa), if solute concentrations are

held relatively constant in a range of electrical conductivities common to plant

production (ie. 1 to 3 dS/m).

Horticultural media generally have EAW in the range of 0 to -10 KPa (de Boodt,

1972), with the major proportion of water available up to a tension of –5 KPa

(White, 1966; de Boodt, 1972; Topp, 1979; Pokorny, 1984; Handreck & Black,

1999; Bunt, 1986; Pokorny, 1987; Milks, 1989; Plakk, 1990; da Silva et al.,1993;

da Silva et al.,1995). Water-release curves follow a characteristic shape in most

soilless substrates, yet vary according to the substrate composition and adsorptive

qualities of the particles in the media.

16

Container geometry affects the water retention characteristics of a substrate,

especially container height (Handreck & Black, 1999). Gravitational force acts

vertically on the substrate, with increased drainage from taller containers compared

to shorter containers of the same volume. A taller container therefore holds

proportionally less water as a percentage of water content by volume (Wv). Thus

accurate irrigation parameters should be based on specific substrate characteristics,

as well as container height.

Water-release curve data may be used to define irrigation applications precisely if a

method for accurately measuring water content in soilless substrates is available.

More precise irrigation scheduling retains water and nutrients in the root zone (by

increasing the residence time) and can help maximize plant growth, while

minimizing nutrient leaching fractions.

Time Domain Reflectometry (TDR) can be used to monitor soilless substrate

moisture in containers, and has been shown to be a promising technique for

scheduling irrigation. However, few data are available on calibrating sensors in

different substrates, or providing information on the variability of sensor

performance in soilless substrates of different water holding capacities (Ansoult et

al., 1985; Anisko et al., 1994; da Silva et al., 1998).

17

Calibration of sensors based on measuring matric potential with simultaneous water

content and TDR measurements can be used to define irrigation parameters

(Campbell & Campbell, 1982; Topp, 1985).The premise of this approach is that

once the range of EAW is known for a substrate, one can use the TDR values

associated with a certain matric potential to initiate irrigation, and a matric

potential value (eg. –2 KPa) prior to container capacity (0 KPa where free drainage

occurs) to terminate irrigation before leaching occurs. Portable dielectric probes

(PDP) also offer promise for scheduling irrigations based on sensor performance

(Figure 5). PDPs (like TDR sensors) operate in response to the apparent dielectric

constant, yet are based on a change in amplitude of the propagated wave, unlike

TDR, which is based on the travel time of a propagated signal. Each system has

positive and negative attributes. TDR probes can only run up to 27 meters

(Whalley, 1993; Werkhoven, 1998, pers comm. Glenn Jarrell of Campbell

Scientific; Logan UT) from the TDR unit that propagates the signal. PDP probes

have a self-contained propagation mechanism, and thus can run up to 100 meters

from a data logger. If few probes are needed in a system, PDP sensors are more

reasonable in terms of initial cost, but if a large number of sensors are required, the

TDR system can be multiplexed with many probes for a much lower cost per

sensor (Appendix A).

The objectives of this study were to characterize the relationship of substrate

matric potential (KPa) with percent volumetric water content (Wv) and dielectric

18

sensor output (TDR or PDP) in a range of soilless substrates common to the

horticultural industry. In addition, a major objective was to evaluate sensor

performance and variability in each substrate at varying container column heights.

Materials and Methods

Experimental Substrates

Six soilless substrates were selected based on their prevalence in the greenhouse

and nursery industry and/or their differences in particle type: Pro-Mix ‘BX’

(Premier Horticultural Products, Dorval (Quebec, Canada); Pine Bark Mix (The

Conard Pyle Co., Centreville, MD); Hardwood mix (The Conard Pyle Co.,

Centreville, MD); Medium Grade Perlite (Schundler Perlite, Harleysville, PA);

Grodan ‘Talent’ Rockwool slabs (E. C. Geiger, Harleysville, PA); and Sieved

Washed Sand (Quikrete Companies, Atlanta, GA) which served as a uniform

particle size control. Particle size analysis (Table 1) was conducted by the North

Carolina horticultural substrates laboratory (North Carolina State University;

Raleigh, NC). The pine bark mix consisted of equal parts medium pine bark, rice

hulls, peat moss, and sand. The hardwood mix was comprised of equal parts

medium hardwood mulch, medium pine bark, and peat moss. Each substrate was

independently analyzed.

19

Figure 5. Dielectric sensors: Left- PDP Theta Probe ML2 (Dynamax Inc., Houston, TX) with standard 6-cm wave-guides. Right- TDR Sensor constructed in University of Maryland Greenhouse and Nursery Systems Lab. This sensor has 6-cm wave-guides, but TDR sensors may be constructed with different wave-guide lengths.

20

Table 1: Substrate Particle Size Distribution Analysis. Particle sizes are expressed in terms of the weight of each fraction as a percent of the total weight of the sample. Bulk density (Db) is grams/cm3. Substrate (>6.3mm)

(%)

(6.3mm

to

2.0mm)

(%)

(2.0mm

to

0.5mm)

(%)

(<0.5mm)

(%)

Db

(g/cm3)

Pro-Mix ‘BX’ 2.4 63.9 21.5 12.2 0.11

Pine Bark Mix 3.3 35.1 35.2 26.4 0.33

Hardwood Mix 18.9 43.2 26.4 11.5 0.18

Perlite 0.0 55.2 26.4 18.3 0.13

Rockwool 0.0 0.0 0.0 100.0 0.1

Quikrete Sand 0.0 0.4 10.7 88.9 1.38

Volumetric Water (Wv) Release Curves

A modified tension table (Brown et al., 1989) was constructed at the University of

Maryland, Department of Biological Resources Engineering Shop (Figure 6). This

modified tension table was used to generate characteristic water release curves for

each substrate and column height combination, and allowed for simultaneous

calibration of the various TDR sensors and PDP probes. The table consisted of ten

individual schedule 40 polyvinylchloride (PVC) columns, measuring 12.6 cm in

diameter, with interchangeable PVC column heights of 7, 15, 20, 25 cm. The

heights represent the average height of a rockwool slab (7 cm), and the average

21

height of the column of substrate in standard commercial #1, #3, and #5 containers

with heights of 15, 20, and 25 cm. Up to ten columns of one height, or any random

combination of heights could be desorbed simultaneously.

Figure 6: Tension Table used at the University of Maryland Greenhouse and Nursery Systems Lab. Positive incremental air pressure (0 – 100 KPa) forced available water in each column into the graduate cylinders. Tektronix 1502 C is seen in the lower right adjacent to enclosed multiplexers and CR10X data logger. TDR sensors (mounted centrally vertical in each column and attached by black RG8 coaxial cable),were multiplexed to the Tektronix 1502C cable tester and the Campbell CR10X data logger. Pressure regulator is blocked in this view; Mercury and water manometers used to accurately measure applied air pressure, are not in the field of view but are present.

22

Fig. 7: TDR and PDP sensors fixed in column lids (1/2” plexiglass) to measure average moisture content over the height of the column. Sensors are mounted central and vertical. Left: TDR sensor with 6-cm wave-guides. Center: Dynamax Theta Probe ML2 with standard 6-cm wave-guides. Right: TDR sensor with 18-cm wave-guides.

23

To generate a characteristic water-release curve for each substrate (Topp, 1979;

Handreck and Black, 1999), up to ten column replicates of equal height were filled

with completely dry substrate, and were then slowly wetted over four or more

hours from the bottom to force all air out of the column and saturate the substrate.

The substrate was allowed to settle naturally, resembling normal compaction over

time. The columns were then allowed to drain to container capacity; the volumetric

water content (Wv) was the volume of known added water that remained in the

column after free drainage.

Positive air pressure was applied to the top of the column to force water from the

column at a specific matric potential (positive applied pressure). A pressure

regulator (Soil Moisture Corp., Goleta, GA) was used in conjunction with a water

and a mercury manometer to give an accurate assessment of the pressure potential

applied to each column. Columns were sealed to be air tight, using seated gasket o-

rings in a 1mm groove milled in the top and bottom of the PVC columns (Figures

6 and 7). PVC columns and lids were then sealed with a pressure retention 0.45

micron nylon membrane (Osmonics Inc., Minnetonka, MN), and each column was

then clamped down using four quarter-inch nuts that screwed down on quarter-inch

bolts that were approximately 18 inches in length (Fig.7). Vacuum grease was

required to seal the gasket o-rings connecting the lid and base to each PVC column.

24

Pressure on the substrate was increased in one KPa increments from zero to eight

KPa, and then to +10, +20, +40, +60, +80 +100 KPa, respectively. At each

pressure, columns were allowed to equilibrate and drain completely. The volume

of water outflow at each pressure was collected and measured after equilibration

(i.e. no further leaching was observed). This method identified the range of EAW

in each substrate from zero to +10 KPa and up to 100KPa.

Sensor Calibration

Plexiglass column lids were constructed to house TDR sensors of varying lengths.

These sensors were milled and glued into each Plexiglass lid with Virden Perma-

Bilt (Amarillo, TX) non-conductive resin (Figure 3). Lids were constructed with 6,

13, 18, and 23 cm sensors, which were used in 7, 15, 20, 25 cm column heights

respectively. TDR sensors measure the average volumetric water content over the

length of the sensor. Thus for a calibration of equal height columns, the

corresponding equal height sensors lids were used to measure the apparent

dielectric constant in the vertical plane. TDR measurements were taken when Wv

water-release curves were being generated at each matric potential. This allowed

for a simultaneous calibration of the TDR sensors over the range of pressures in

each substrate/column height combination.

25

Moisture Sensors

TDR sensors were constructed of 308L Stainless Tig Welding Rods, set in Virden

Perma-Bilt (Amarillo, TX) non-conductive resin, wired with approximately 4

meters of RG 8 (Alpha Wire Co, Elizabeth, NJ) coaxial cable. A CR10X Campbell

Scientific (Logan, UT) data logger was used in conjunction with a Tektronix 1502C

Metallic Cable (Beaverton, OR) tester, which was multiplexed to match the number

of sensors used in each calibration. The output values from the TDR system

measurement represent the square root of the apparent dielectric constant (Ka).

TDR monitors the moisture based on changes in the time that a signal is propagated

and reflected back to the TDR unit. The apparent length (La) of the sensor as

detected by the TDR unit is related to the actual known length of the sensor (L),

giving La/L. La is detected by the pattern of the wave trace, which is viewed on the

TDR unit. La/L is equal to the apparent dielectric constant of the substrate (Ka)

and equal to the inverse of the velocity of propagation (1/Vp). A signal travels

slower in the presence of water, which attenuates the pulse into the substrate.

Lower velocity is indicating more water present, meaning La appears longer than L,

the apparent dielectric constant is higher, and the moisture content is higher. Ka

equals the apparent length (La) of the wave guides detected by the TDR, divided by

the actual known length (L) of the wave guides (La/L) as illustrated in Figure 8,

La/L equals the reciprocal of the velocity of propagation of the electromagnetic

wave signal (1/Vp) (Topp, 1980; Plakk, 1990; Timlin, 1996; Evett, 1998). Thus

TDR output is La/L = 1/Vp = Ka.

26

Figure 8: TDR sensor wave-guides and corresponding wave-trace. After Evett, 1998

Experimental Analysis

This study quantified the relationship of matric potential in KPa with Wv and TDR

output. All substrates except rockwool were analyzed at three container heights

(15, 20, and 25 cm) in two blocks (ie. a randomized complete block design). Each

block included three replicates of each of the three heights, providing a total of six

replicates for column height. For modeling TDR and Wv versus KPa, all six

measurements were weighted equally and plotted as mean Wv and mean TDR at

each measured pressure (KPa).

We hypothesized that all substrates in taller columns would have lower volumetric

water content at container capacity. For each substrate, several analyses of

27

variance were conducted using the MIXED procedure of SAS (SAS Institute, Cary,

NC) including block effect as a random factor. The effect of height on percent Wv

at zero KPa was analyzed. The plot of the residuals was examined to test for

equality of variances among heights. The same analysis was conducted for Wv

values at 10 KPa (Table 2).

Percent Wv was regressed on TDR output for each height of each substrate using

all six replicates from each measured pressure (90 points per height), using the

REG procedure of SAS. Block effects were not included in this model since the

relationship of Wv to TDR output is not affected by block differences, and the two

variables were directly related. Regression results are for specific column heights

with matching sensor lengths that monitored in the vertical plane (Table 3).

Portable Dielectric Probes

To compare TDR and PDP performance, the final phase of this study examined the

use of PDP’s in direct comparison to TDR sensors. Theta Probes model ML2

(Dynamax, Houston, TX) were used (Figures 5 & 7), composed of 6-cm wave-

guide sensors that were wired directly to the differential voltage analog channel of

a Campbell CR10X data logger. ML2 sensor output is from 1 to 1000 millivolts,

which is directly proportional to the moisture content, and directly assesses the

apparent dielectric constant of the substrate, and hence water content. ML2 sensors

were mounted into column lids the same way as the TDR sensors (Figure 7), which

28

were glued to the plexiglass with the same non-conductive resins used to cast the

TDR probes (Virden Perma-Bilt, Amarillo, TX).

Similar analyses were conducted with three different sensor systems in 20 cm

columns (different sensors in uniform column heights) using only the Pro-Mix

‘BX’ soilless substrate. This study utilized three blocks, each block consisting of

three replicates each of: 1) 6-cm ML2, 2) 6-cm TDR sensor, and 3) 18-cm TDR

sensor (totaling 9 replicates per sensor type) as illustrated in Figure 7. For each

sensor the output was modeled versus applied pressure. The SAS regression

procedure was used to independently analyze Wv values versus the various sensor

values for each sensor type, to give an estimate of sensor variability. Correlation

coefficients ( r ) are reported since both variables are random, contrary to reporting

r2. The residuals for each sensor type were examined to verify homogeneity of

variance among sensor types. The coefficient of variability (CV) was also

calculated for each sensor at container capacity.

Results

Characteristic Water-release/TDR Curves

All standard TDR and Wv curves for each substrate (Figure 9a, 9b through 14a,

14b) were nearly asymptotic beyond -10 KPa of matric potential. This is evident

from the marginal change in moisture content and sensor output beyond 10 KPa of

applied pressure. These plots illustrate that the matric potential increased sharply,

29

once the nearly asymptotic portion of the curve was reached for all substrates.

Height significantly affected Wv at container capacity (0 KPa) (Table 2), but did

not have a significant effect on Wv at 10 KPa (Table 2). This indicates that there

is a major difference between container heights at the point of container capacity

and is the primary reason that calibration should be based on column height, in

addition to substrate composition.

Sensor Calibration

Table 3 identifies the relationship of Wv to TDR values (Figure 9c – 14c) for each

substrate and column height. The regression yields an associated r-value

correlation coefficient. The regression of Wv on TDR for all data points was

always highly significant (P <0.01). Variances and Coefficients of variation are

reported for each substrate, height, and sensor type (Table 3) at container capacity.

In all cases the CV was low, the standard deviation s generally less than 5% of the

mean Wv at 0 KPa. Rockwool was characterized at the 7-cm height, but was

modeled as rockwool without the plastic shell encasing the slab, because the plastic

casing adversely affects drainage. The homogeneity of variance between heights of

the same substrate appeared to be satisfied by visual inspection of residual plots.

Portable Dielectric Probes

PDP sensors were analyzed using the previously described procedures, and results

are illustrated in Figures 15a – 15c. PDP sensors like TDR sensors had uniform

30

variances in a plot of residuals. R-values and regression functions for PDP probes

are given in Table 3. PDP probes had a lower CV than TDR sensors of the same

length, yet a higher CV than the 18-cm TDR sensors. This indicates that PDP

functions as well as TDR for monitoring moisture in Pro-Mix ‘BX’ soilless

substrate.

Table 2: P values for the effect of container height on Wv. Analysis was conducted for each substrate at 0 KPa and +10 KPa of applied pressure.

Substrate Effect of Height On Wv

at 0 KPa (P-value)

Effect of Height On Wv

at 10 KPa (P-value)

Pro-Mix < 0.01 0.71

Pine Bark Mix < 0.01 0.83

Hardwood Mix < 0.01 0.65

Perlite 0.10 0.79

Sand < 0.01 0.17

31

Table 3: The relationship of Wv to sensor output (TDR or PDP) is shown in the regression equation with the correlation coefficient ( r ) at a significance level of P < 0.01 (n=6). The coefficient of variability was calculated for the six sensors of each treatment and is shown at right expressing the standard deviation as a percent of the mean. PDP sensors appeared to have variability similar to TDR sensors.

Substrate Height (cm) Regression Equation r value CV at 0 KPa Hardwood Mix 15 Wv=-9.7+10.5S 0.96 5.47 Hardwood Mix 20 Wv=-12.4+11.3S 0.97 7.92 Hardwood Mix 25 Wv=-8.7+10.1 0.88 4.05 Pine Bark Mix 15 Wv=-4.4+9.4S 0.92 4.65 Pine Bark Mix 20 Wv=-2.3+8.7S 0.83 2.75 Pine Bark Mix 25 Wv=-3.3+9.0S 0.92 3.62 Pro-Mix 15 Wv=-4.6+10.0S 0.87 2.95 Pro-Mix 20 Wv=-16.5+13.3S 0.88 4.90 Pro-Mix 25 Wv=-9.4+11.6S 0.94 4.25 Perlite 15 Wv=2.5+9.1S 0.90 5.46 Perlite 20 Wv=-12.1+13.0S 0.96 7.92 Perlite 25 Wv=17.9+4.4S 0.52 4.05 Sand 15 Wv=-17.1+10.8S 0.99 0.82 Sand 20 Wv=-16.5+10.9S 0.97 4.11 Sand 25 WV=-18.8+11.6S 0.98 2.19 PDP 6 cm 20 Wv=-54.4+0.1S 0.64 3.51 TDR 18 cm 20 Wv=-1.7+8.9 0.49 2.33 TDR 6 cm 20 Wv=-28.3+13.4S 0.55 6.47 RW no plastic 7 Wv=-70.4+46.5S 0.85 2.55 RW plastic 7 Wv=-9.3+12.2S 0.94 4.96

32

Applied Pressure (KPa)

0 1080 90 100

TDR

Out

put (

Ka-

1/2 )

4.0

5.0

6.0

15-cm Column, 13-cm TDR Sensor20-cm Column, 18-cm TDR Sensor25-cm Column, 23-cm TDR Sensor


0 1080 90 100

Volu

met

ric W

ater

Con

tent

(Wv)

35

45

55

65


Fig. 9a Fig. 9b

Figure 9: Standard (a) Time Domain Reflectometry (TDR) and (b) Volumetric Water Content (Wv) curves for Pro-Mix ‘BX’ soilless substrate in #1, #3, #5 containers, with heights of 15, 20, and 25-cm respectively. Error bars represent the standard error about the mean (n=6).

33

TDR Output (Ka-1/2)

3 4 5 6 7

Vol

umet

ric W

ater

Con

tent

(W

v)

20

30

40

50

60

70

80

9015-cm Column, 13-cm TDR SensorRegression Line20-cm Column, 18-cm TDr SensorRegression Line25-cm Column, 23-cm TDR SensorRegression Line

Figure 9c. Regression of Volumetric Water Content on TDR Output by container height.

34

Figure 10: Standard (a) Time Domain Reflectometry (TDR) and (b) Volumetric Water Content (Wv) curves for Hardwood Mix soilless substrate in #1, #3, #5 containers, with heights of 15, 20, and 25-cm respectively. Error bars represent the standard error about the mean (n=6).


0 1080 90 100

TDR

Out

put (

Ka-

1/2 )

4.0

5.0

6.0



0 1080 90 100

Volu

met

ric W

ater

Con

tent

(Wv)

25

35

45

55


Fig. 10a Fig. 10b

35

Fig.10c. Regression of Volumetric Water Content on TDR Output by container height for hardwood mix.

TD R O utput (Ka-1/2)

3 4 5 6 7

Wv

25303540455055606570

15-cm C olum n, 13-cm TD R SensorRegression L ine20-cm C olum n, 18-cm TD r SensorRegression L ine25-cm C olum n, 23-cm TD R SensorRegression L ine

36

Figure 11: Standard (a) Time Domain Reflectometry (TDR) and (b) Volumetric Water Content (Wv) curves for Pine Bark Mix soilless substrate in #1, #3, #5 containers, with heights of 15, 20, and 25-cm respectively. Error bars represent the standard error about the mean (n=6).


0 1080 90 100

TDR

Out

put (

Ka-

1/2 )

4.0

5.0

6.0



0 1080 90 100

Volu

met

ric W

ater

Con

tent

(Wv)

25

35

45

55


Fig. 11a Fig. 11b

37

TDR Output (Ka-1/2)

3 4 5 6 7

Vol

umet

ric W

ater

Con

tent

(W

v)

25

30

35

40

45

50

55


Fig. 11c. Regression of Volumetric Water Content on TDR Output by container height for pine bark mix.

38

Figure 12: Standard (a) Time Domain Reflectometry (TDR) and (b) Volumetric Water Content (Wv) curves for Perlite soilless substrate in #1, #3, #5 containers, with heights of 15, 20, and 25-cm respectively. Error bars represent the standard error about the mean (n=6).


0 1080 90 100

TDR

Out

put (

Ka-

1/2 )

4.0

5.0

6.0



0 1080 90 100

Volu

met

ric W

ater

Con

tent

(Wv)

35

45

55


Fig. 12a Fig.12b

39

TDR Output (Ka-1/2)

2 3 4 5

Vol

umet

ric W

ater

Con

tent

(W

v)

15

20

25

30

35

40

45


Fig. 12c. Regression of Volumetric Water Content on TDR Output by container height for perlite.

40

Figure 13: Standard (a) Time Domain Reflectometry (TDR) and (b) Volumetric Water Content (Wv) curves for Sand soilless substrate in #1, #3, #5 containers, with heights of 15, 20, and 25-cm respectively. Error bars represent the standard error about the mean (n=6).


0 1080 90 100

TDR

Out

put (

Ka-

1/2 )

2.0

3.0

4.0

5.0



0 1080 90 100

Volu

met

ric W

ater

Con

tent

(Wv)

5

15

25

35


Fig. 13a Fig. 13b

41

TDR Output (Ka-1/2)

2 3 4 5 6

Vol

umet

ric W

ater

Con

tent

(W

v)

5

10

15

20

25

30

35


Fig. 13c. Regression of Volumetric Water Content on TDR Output by container height for sand.

42

Figure 14: Standard (a) Time Domain Reflectometry (TDR) and (b) Volumetric Water Content (Wv) curves for Rockwool soilless substrate in 7-cm height columns, characterized with and without plastic shell. Error bars represent the standard error about the mean (n=6).


0 1080 90 100

TDR

Out

put (

Ka-

1/2 )

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

9.0

7-cm Column, 6-cm TDR Sensor, No Plastic7-cm Column, 6-cm TDR Sensor, With Plastic Shell


0 1080 90 100

Volu

met

ric W

ater

Con

tent

(Wv)

5

15

25

35

45

55

65

75

85

95

7-cm Column, 6-cm TDR Sensor, No Plastic7-cm Column, 6-cm TDR Sensor, Plastic Shell

Fig. 14a Fig. 14b

43

TDR Output (Ka-1/2)

0 2 4 6 8 10 12

Vol

umet

ric W

ater

Con

tent

(W

v)

0

20

40

60

80

100

7-cm Colum n, 6-cm TDR Sensor, No PlasticRegression Line7-cm Colum n, 6-cm TDR Sensor, P lastic ShellRegression Line

Fig. 14c. Regression of Volumetric Water Content on TDR Output by container height for rockwool.

44

Figure 15: Standard (a) Time Domain Reflectometry (TDR) or Portable Dielectric Probe (PDP) and (b) Volumetric Water Content (Wv) curves for Pro-Mix soilless substrate in 20-cm height columns. Error bars represent the standard error about the mean (n=6).


0 1080 90 100

Sens

or O

utpu

t (K

a-1/

2 o

r vol

ts x

10-1

)

4.0

5.0

6.0

7.0

8.0

9.020-cm Column, 6-cm PDP Sensor20-cm Column, 18-cm TDR Sensor20-cm Column, 6-cm TDR Sensor


0 1080 90 100

Volu

met

ric W

ater

Con

tent

(Wv)

25

35

45

55

65

20-cm Column, 6-cm PDP Sensor20-cm Column, 18-cm TDR Sensor20-cm Column, 6-cm TDR Sensor

Fig. 15a Fig. 15b

45

TDR Output (Ka-1/2)

3 4 5 6 7 8 9 10

Vol

umet

ric W

ater

Con

tent

(W

v)

0

20

40

60

80

100

20-cm Column, 6-cm PDP SensorRegression Line20-cm Column, 18-cm TDr SensorRegression Line20-cm Column, 6-cm TDR SensorRegression Line

Fig. 15c. Regression of Volumetric Water Content on TDR Output by container height for PDP study.

46

Discussion

These results provide the first simultaneously derived Wv and TDR

characterization for a range of soilless substrates and column heights in relation to

matric potential. Container height is of primary importance at very low matric

potentials (0 – 2 KPa), and indicates that sensors need to be calibrated based on

height and substrate composition if irrigation water is to be applied using these

sensors. The results also substantiate the range of easily available water in soilless

substrates for optimum plant growth. The curves for each substrate are of similar

shape, determined by matric potential of the substrate, and differ based on

adsorptive qualities and pore space of each substrate affecting volumetric water

content.

Between - 10 KPa and –100 KPa, there is very little change in moisture content,

associated with a drastic increase in matric potential. This illustrates that little of

this remaining moisture is available for plants uptake. Therefore, irrigation

parameters should attempt to maximize plant-available water in the range from 0 to

-10 KPa of applied pressure.

Column height was associated with reduced percent Wv at container capacity (0

KPa applied pressure). At -10 KPa, height was not correlated with decreased Wv in

this system. Matric potential is a function of the height in a natural setting, which

is the case here when draining to container capacity at 0 KPa. Every 10-cm of

47

water column height above a surface is an increase of 1 KPa matric potential. Thus,

in this system with pressure applied, the overall pressure exceeds the maximum

potential by gravity and the matric potential becomes uniform across the height of

the container.

A plot of applied pressure (KPa) versus TDR output (Ka) matches the plot of

applied pressure versus percent Wv. Each plot (Figures 9a,b – 14a,b) easily

identifies the TDR values that match the optimal water contents for optimal plant

growth and irrigation for each substrate. Further, regression analysis of Wv values

versus TDR values can be used to derive a function that can predict Wv from TDR

output. Conversely, the opposite regression would allow TDR values to be

predicted from known water contents for this specific substrate each specific

container height only.

The plots of residuals indicated equal variances in all cases, and the ratio of

maximum variance over minimum variance was low in each situation. Sensors of

differing height appear to exhibit uniform characteristics of variance. However,

each different sensor length corresponds to a certain column height and requires a

unique calibration function equating TDR output to Wv. Correlation coefficients

of Wv and TDR were usually high (p < 0.01).

48

The inspection of variances associated with PDP probes matched values associated

with TDR sensors. The output differs for a PDP sensor; thus the CV was used to

inspect the variances with PDP and TDR probes, as well as residual plots. The

PDPs tend to exhibit less variability than same length TDR sensors, but have

slightly more variance than longer TDR sensors under the same measurement

conditions (Table 3). Thus, different TDR wave-guide lengths can be used

effectively if calibrated to specific heights. In this case, a 6-cm TDR wave-guide

could be used to substitute for an 18-cm wave-guide for a 20-cm height column.

To reiterate, it is important that a specific calibration be derived that correlates the

specific sensor (type and length) with a specific substrate and container height.

TDR and PDP sensors have been shown to be effectively used for monitoring

moisture in various soilless substrates and can discriminate within the range of

easily available water potentials in soilless substrates. Irrigation with the TDR

values corresponding to -10 KPa as an initiation value, and -1 KPa as a termination

value could allow for maximum plant available water. Further investigation may

show that TDR cyclic irrigation may allow for the elimination or strict control of

leaching volume. These characteristic curves can also be used to estimate

application volumes for standard irrigation practices, which manually attempt to

maximize available water with minimal leachate.

49

Chapter Two: Determining Set points for Controlling Irrigation With Plant

Systems

Introduction

Soilless substrates were shown in chapter one to have a narrow range of easily

extractable water by plants. The use of effective sensor technology to automate

irrigation based on the known range of available water should maximize plant-

available water while minimizing leachate (Campbell & Campbell, 1982; Topp,

1985; Coelho, 1996). The hypothesis is that increasing the residence time of

nutrients will reduce leachate nutrient concentrations, increasing plant available

water and nutrient use efficiency.

Chapter one identified accurate calibration procedures for a number of soilless

substrates with known column height, and known TDR sensor length. These

results may be used to schedule automated cyclic irrigation in the range of easily

available water. Irrigation initiation at –10 KPa appears to be an ideal set point

from the various water desorption curve data, but is only ideal if the plant is not

experiencing any water stress at that specific substrate water content. It may be

beneficial to let the substrate dry beyond this point to facilitate a possible plant

osmotic adjustment, which may enhance further growth. Osmotic adjustment is an

active increase in solute concentration of the plant cells as the water content within

the plant decreases with drying substrate conditions (Handreck & Black, 1999; Taiz

50

& Zeiger, 1998). Subsequent wetting of the substrate then maximizes water

movement into the plant due to the differences in cell osmotic potential of the plant

from the substrate.

Below –10 KPa, the increase in substrate matric potential is nearly asymptotic in

relation to changes in moisture content. A possible test of the hypothesis that –10

KPa is a safe level of water deficit without decreasing effects on plant growth,

stomatal conductance may be measured as a signal of any serious water stress. If

stomatal conductance is not decreased significantly at –10 KPa compared to that of

0 KPa, then decreased water availability may not be limiting carbon gain and thus

plant growth rate. Diurnal stomatal conductance is continuously adjusted by the

plant in relation to plant-available water in the substrate. Generally, a plant under

water stressed will minimize stomatal opening to conserve water by limiting

transpiration.

Cyclic irrigation systems are used to maintain high levels of available water with

minimal leaching volumes (Fare et al., 1994; Beeson, 1995; Fare et al., 1996; Tyler

et al., 1996). Sensors may be introduced to a cyclic irrigation system to initiate and

terminate cyclic irrigation at predetermined set points. Chapter one illustrated the

effectiveness of TDR to monitor the moisture content of various soilless substrates.

The calibration procedure in chapter one provided data to define the set points

51

based on the range of easily-available water, with the majority of water available to

–10 KPa.

This study examined the issues in characterizing initiation and termination set

points for TDR controlled cyclic irrigation. Various horticultural soilless substrates

with Rhododendron azalea cv. ‘Hot Shot’ plants will be examined for significant

decreases in stomatal conductance from 0 to –10 KPa over time. The objectives of

this study were to ensure that plants were not stressed in this substrate matric

potential range, and thus show that –10 KPa may be used as an effective initiation

point for irrigation scheduling.

Methodology

Plant Material

Rhododendron azalea ‘Hot Shot’ liners were planted in the University of

Maryland at College Park greenhouse in #3, 20-cm height (Classic 1200 Nursery

Supplies Inc., Fairless Hills, PA) containers and grown for one year in the

following six substrates: Premier Pro-Mix, Hardwood Mix, Pine Bark Mix, Perlite,

Sand and Rockwool. The physical properties of these substrates were given in

chapter one.

52

Study Conditions

Plants were randomly selected from the nursery stock in the greenhouse of each

substrate, and were moved to the controlled environment facility at the University

of Maryland Plant Science Building at College Park, MD. The growth chamber

used in the study was a Conviron model # BDW 36 (Conviron Ltd., Winnipeg,

Canada). The walk-in chamber was fitted with multiple 400 Watt metal halide and

75 Watt incandescent bulbs that could be ramped to increase light intensity.

The study was set up as a randomized complete block design which consisted of

two blocks, with each block containing 4 replicate plants of each substrate (8

replicates in total). No correlation was made between substrates. The chamber was

maintained at a constant 25 C. The photoperiod consisted of an eight-hour dark

period and 16 hours of light on the following schedule, 3 hours at 300 mol/m2/s

(low light), 3 hours at 600 mol/m2/s (mid light), 4 hours at 1000 mol/m2/s (high

light), 3 hours at 600 mol/m2/s, 3 hours at 300 mol/m2/s. The high light intensity

of 1000 mol/m2/s was used to induce high plant water use, and thus was expected

to show signs of water stress before detection at lower light intensities.

Data Collection

TDR monitoring was based on calibrations established in chapter one, using the

regression equation to predict Wv from TDR output. Plants were kept at container

capacity for 3 full days prior to the TDR study. TDR sensors were inserted

53

vertically into each container at half the distance from the plant stem to the

container wall. At the start of day 1 (zero hours into 300 mol/m2/s), plants were

irrigated to container capacity. Two hours into each of the 300 mol/m2/s, 600

mol/m2/s, and 1000 mol/m2/s light levels, Wv and stomatal conductance were

measured. Stomatal conductance was measured with a LiCor 1600 Steady State

Porometer (LiCor, Inc., Lincoln, NE) at known light and temperature.

Measurements of Wv were carried out until wilt approximately 13 days later.

Stomatal conductance measurements were taken until a sharp decline in stomatal

conductance was apparent, due to the start of incipient plant water stress. Mean

Wv and stomatal conductance for all eight replicates are plotted for each substrate

(Figure 17a-f).

Figure 16: The image at left shows azaleas in various substrates in the growth chamber being monitored for substrate moisture content.

54

Characteristic water-release curves from chapter one were used to identify the

water content of the substrate at –10 KPa. ANOVA was conducted on the

conductance values for container capacity versus the conductance values for –10

KPa. Table 4 identifies the probability values for the mean differences, as well as

identifying the water content at container capacity, Wv at –10 KPa, number of days

to reach –10 KPa from container capacity under these conditions, and the day and

water content at which Azalea plants wilted.

Results

Calibrations from chapter one coincided with the monitored Wv and TDR values

for one-year-old Azaleas. The stomatal conductance values for 0 KPa and –10 KPa

did not differ significantly (Table4). This indicates that all Azalea plants did not

experience water stress (based on stomatal conductance) at a Wv of –10 KPa. All

plants wilted by day 13. Plants remained in permanent wilt (did not recover with a

dark period), and after being re-wetted on day 14, resulted in discolored and

damaged leaves, leaf loss, and root damage. However, most plants survived this

stress upon re-watering.

Blocking of the plants did not have any significant affect on Wv or stomatal

conductance. The consistent conditions of the growth chamber resulted in nearly

identical plant response in terms of evapotranspiration and stomatal conductance.

55

Plants in different substrates exhibited a drop in stomatal conductance at different

days. Sand, perlite, and rockwool grown plants behaved similarly in the high light

levels where stomatal conductance did not notably exceed the stomatal conductance

at measured intermediate light levels. In general, plants grown in identical

circumstances varied morphologically with substrate. Sand, Perlite, and Rockwool

yielded far smaller plants, with decreased leaf area (the approximate average total

fresh weight of leaves and stems as Pro-Mix = 300 grams, Hardwood Mix = 325g,

Pine Bark =300g, Perlite =250g, Sand = 150g, and Rockwool = 150g (illustrated in

Figure 18).

56

Table 4. Wv values at 0, -10 KPa matric potential, for six horticultural substrates. TP values are for the test of significance of KPa on stomatal conductance. No plant in any substrate exhibited stress at –10 KPa as measured by stomatal conductance.

Substrate P value Wv at

0 KPa

Wv at

-10 KPa

Day when

-10 KPa was

reached

Hardwood 0.97 NS 52.8 34.6 4

Pine Bark 0.82 NS 44.7 22.7 5

Rockwool 0.39 NS 85.5 58.7 3

Sand 0.74 NS 30.5 15.4 9

Perlite 0.23 NS 33.2 26.9 3

Pro-Mix 0.59 NS 61.3 37.7 5

57

59

Figure 17: a) Pro-Mix, b) Pine Bark Mix, c) Hardwood Mix, d) Perlite, e) Sand, f) Rockwool. Plots illustrate the relationship of stomatal conductance at high (1000 umol/m2/s), mid (600 umol/m2/s), and low (300 umol/m2/s) light levels each day after irrigation to container capacity on day one. None of the substrates induced plant stress until beyond –10 KPa. Arrrows indicate when –10 KPa was reached

Day

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Figure 18: The image below shows an example of the differences in growth of the Azaleas grown in different substrates under greenhouse conditions, nine months after planting.

61

Figure 19:Azalea plants in Hardwood Mix (HB) substrate at container capacity (W=wet) and at nine days after last irrigation (D=dry) well beyond a –10 KPa matric potential. However, no water stress can be seen in the HB-D plant even visually observed after nine days.

Discussion

Stomatal conductance did not decrease significantly until beyond –10 KPa in all

substrates. This verifies that for Azalea ‘Hot Shot’ under these circumstances,

irrigation could start at a substrate Wv equivalent to –10 KPa. Thus irrigations

would be initiated before plants exhibited water stress, thus maximizing plant

growth based on an accurate measurement of plant-available water in each

substrate.

62

The accuracy of TDR calibrations measured in the first study (chapter one) were

verified by this growth chamber data, derived with real plants in each soilless

substrate.

Substrates reached a matric potential of –10 KPa on different days. This was

presumably due to differences in leaf mass in the various substrates which affected

evapotranspiration and total plant water use differently. Plant leaf mass in

Hardwood Mix, Pine Bark Mix, and Pro-Mix greatly exceeded the leaf mass of

Azaleas in other substrates. Sand acted as a drought-inducing substrate. Stomatal

conductance at high light did not proportionally exceed intermediate light values,

and stomatal conductance values were generally lower than in other substrates.

Sand holds very little easily available water, primarily due to high bulk density and

minimal surface charge to interact with water. Plant water use was minimal for

sand compared to other substrates due to reduced leaf mass, and did not reach a Wv

of –10 KPa until day 9, much later than any other substrate.

Defining –10 KPa as an irrigation initiation set point is supported by the range

easily-available water in these diverse soilless substrates. For nursery stock such as

Azalea ‘Hot Shot’, this study illustrated in the growth chamber a decrease in

stomatal conductance was not realized before a Wv reading of –10 KPa was

reached. Thus, it can be safely assumed that this matric potential can be used as a

general guideline for initiating irrigation to minimize plant water stress.

63

Chapter Three: Active Control of Irrigation Scheduling by TDR in Soilless

Substrates

Introduction

Soilless substrates have low anion exchange capacities; thus nutrients have high

potential for leaching with irrigation water (Handreck & Black, 1999). To

minimize leaching of soluble nutrients from container nursery operations, excessive

irrigation durations should be avoided. Increasing the residence time of nutrients in

the root zone will maximize the efficiency of plant nutrient and water use. The use

of sensors such as TDR and PDP may offer great promise in improving cyclic

irrigation practices, which are already known to facilitate optimal plant available

water while reducing leaching of nutrients and water (Schmugge et al., 1980;

Topp, 1985).

Traditional irrigation scheduling uses estimated irrigation durations to ensure the

adequate application of enough irrigation water to plants. This is presently the only

practical way for growers to maintain adequate moisture in the root zone to

maximize plant growth. Characteristic curves which relate Wv to substrate matric

potential can now identify the range of easily available water in soilless substrates,

and can be used to automatically schedule irrigation events, based upon actual plant

water use. Any standard timed irrigation system does not act dynamically with

diurnal or seasonal changes in the environment. Traditional timed irrigation

64

systems have a designated watering time, and often apply equal volumes of water

on cool-cloudy and warm-sunny days. This inability to respond to changes in

weather and to specific plant water leads to inaccurate irrigation volume

applications at least some of the time. This excess application of water also has

implications for nutrient run-off from nurseries and greenhouses.

A consistent moisture monitoring system combined with an improved irrigation

system is one that can monitor and respond in real time to the conditions

experienced by plant roots (Campbell & Campbell, 1982; Topp, 1985). Results

from chapter one illustrate the ability of TDR and PDP to effectively monitor

moisture in soilless substrates. Calibrations defined by the procedures in chapter

one can be used to define set points for irrigation initiation and termination.

Moisture contents should be maintained that provide optimal available water,

minimize plant water stress (chapter two), and minimize leaching volumes.

Properly managed cyclic irrigation practices are known to reduce irrigation

volumes, and reduced application rate allows for improved distribution of water in

the wetting zone, minimizing leaching (Beeson, 1995; Fare et al., 1994; Tyler et al.,

1996). When sensors are incorporated with irrigation emitters that do not provide

uniform wetting throughout the container, placement of the sensor is critical

(Coelho, 1996). Monitoring a portion of the container that does not receive an

equal portion of the irrigation water may lead to excessive applications of water, if

65

the calibration procedures for the sensor are based on uniform wetting conditions,

such as in a tension table (Chapter one).

In this set op experiments, I examined the application of dynamic irrigation

scheduling using TDR in various soilless substrates. In the first study, the issue of

sensor placement was examined in association with drip and spray stake emitters to

determine accurate monitoring of the wetting zone. Subsequently, the use of TDR

controlled irrigation scheduling was examined to see whether leaching volumes

could be controlled in comparison to irrigation scheduling based on precisely

calculated plant evapotranspiration water use.

Methodology

Plant Material

Azalea ‘Hot Shot’ liners were planted in the University of Maryland at College

Park greenhouse in #3 containers (approximately 25-cm diameter, 23-cm tall, with

an average substrate column height of 20-cm) (Classic 1200 Nursery Products,

PA), and grown for one year in the following six substrates: Premier Pro-Mix,

Hardwood Mix, Pine Bark Mix, Perlite, Sand and in Rockwool Slabs (sources and

physical properties were noted in chapter one). All plants were irrigated during a

one-year growth period with drip stakes, except sand and perlite, which used spray

stakes for a more uniform wetting distribution.

66

Equipment

A Campbell TDR system using a Campbell TDR100 Metallic Cable Tester was

used in this study in conjunction with a Campbell CR10X data logger, that

measured individual sensors at approximately one per second. The TDR100

measured ten replicates in our treatments in approximately ten seconds. This

response time is ideal for use with spray stakes, which tend to apply water at rates

higher than drip emitters. This is in contrast to a Campbell Scientific TDR system

using a Tektronix 1502, which measured ten sensors in approximately three

minutes; though this time may be adequate for use in conjunction with slower

application drip emitters. PDP sensors were not used in this study, but they exhibit

sensor-reading times of one second (based on data logger logistics).

Forty TDR sensors were constructed identical to the 18-cm sensors described in

chapter one. The calibration of sensor output to volumetric content procedures

from chapter one (Appendix B; Brown et al., 1989) were used, generating a

function specific to the TDR100. The moisture content and TDR output values

were identified for 0, -1, and -2, and –10 KPa. Irrigation cycles were initiated at –

10 KPa values, based on results from chapter two, that found that azaleas were not

under water stress at this matric potential. The irrigation termination matric

potential (Wv) was chosen based on examination of the substrate characteristic

curves (chapter one). A –1 KPa set point was selected to maximize available water

67

while minimizing leaching (Figures 9 – 14). All leaching volumes in this study

were quantified as mean leaching volumes (ml/irrigation event/plant).

Sensor Placement (Study One)

TDR sensor placement was examined in relation to a drip or spray stake emitter

placement and water output. A two by two factorial in a completely randomized

design was used to determine the effect of emitter type and sensor placement

combinations on leaching volume. Factor one was emitter type: drip or spray stake.

Both emitters were spike shaped to insert into the substrate. All emitters were

placed at one half the radius, mid-way between the stem and container wall. The

drip stakes emitted an average of 41 ml per minute. Spray stakes emitted at an

average of 130 ml per minute. Spray stakes were directed toward the center of the

container and water did not spray beyond the container wall. Factor two was the

positioning of the 18-cm TDR sensor in relation to the emitter: Vertical or Diagonal

(V or D) placement as illustrated in Figure 20. Vertical placement was having the

sensor directly in the vertical plane of the drip or spray stake. Diagonal placement

was insertion of the sensor directly on the opposite side of the stem from the

emitter at half the radius, and at a 45 degree angle with the bottom of the sensor

coming toward the center of the container, thus intersecting the plane of the emitter

near the bottom of the container.

68

Figure 20: Drip or spray stakes are represented by the blue line and spike at the left of the plant stem. The placement at left represents vertical sensor placement in the plane of the emitter (Drip Vertical or Spray Vertical). At right is the diagonal placement (Drip Diagonal or Spray Diagonal).

These two factors generate four irrigation management sensor placement

combinations that were randomly assigned to 40 plants (ten plants in each

treatment combination) for each of five substrates individually examined

(excluding rockwool) (n=40 per substrate, totaling 200 plants). Drip Vertical is

intended to represents sensor placement within the wetting zone for drip irrigation.

Drip Diagonal intends to represents monitoring outside the wetting zone for drip

stakes. For spray stakes, both vertical and diagonal may be understood to be within

the wetting zone of those emitters.

69

Figure 21: The photograph below shows an Azalea grown in pine bark mix, with a drip stake and sensor placed vertically in the plane of the emitter (Drip Vertical). The tray below was used to collect leaching volume for measurement.

The moisture content of each of the 40 plants were automatically measured with

TDR sensors every two hours for 7 days, and the Campbell CR10X data logger was

programmed to decide in real time to initiate an irrigation event. As an example the

70

data logger measured ten replicates of Drip Vertical, determined the average Wv,

and then initiated an irrigation event if the observed moisture content was below

the –10 KPa calibration value. If irrigation was initiated, the system would

continue to monitor those ten plants and average the moisture contents; once the

mean Wv exceeded the –1 KPa calibration value, then the irrigation event

terminated. The next treatment (Drip Diagonal) would then be monitored

sequentially and again an irrigation event would be initiated if necessary, before the

TDR measured the next treatment. Thus the true treatment was the active initiation

and termination of irrigation based on the Wv measured by the emitter and

placement combination. Each treatment had 10 replicates in a completely

randomized design of the four treatment combinations. The response variable

analyzed was mean leaching volume (ml/event/plant).

Statistical Analysis

Treatments that showed almost no leaching resemble a poisson distribution and did

not meet homogeneity of variance assumptions, and thus require nonparametric

analysis. Proc NPAR1WAY was used to analyze the data using a rank

transformation procedure. The P value for overall treatment combination effect

was reported along with the mean ml/event/plant and standard error. Using SAS

Proc NPAR1WAY, the interactions of the 2x2 factorial were not examined; instead

the data was treated as a simple completely randomized design with four balanced

treatment levels.

71

Irrigation Management Study

The second study was set up to investigate the effectiveness of TDR for irrigation

scheduling compared to irrigation scheduling based on calculated plant

evapotranspiration over a specified time interval. Sand and Perlite used spray

stakes with the sensor in the diagonal plane (SD); rockwool had vertical 6-cm

sensors, and Pro-Mix, Pine Bark, and Hardwood mix were drip irrigated with the

sensor in the vertical plane of the emitter (DV). Two treatment levels were

implemented. The first used TDR sensors with the chosen placements, and

irrigation from –10 KPa to –1 KPa as described in study 1 above. Four containers

were randomly selected to be monitored for Wv, initiating and terminating

irrigation for this treatment at the set points based on the average of the four

containers. The average of those four containers constitutes the treatment as mean

Wv with upper and lower set points for irrigation. Ten individual replicates of the

TDR sensor treatment (S) were randomized with the second treatment, which also

contained ten replicates.

The second treatment was applying a calculated evapotranspiration water loss at a

known application rate for a calculated time, known as timed irrigation (T). To

determine the irrigation duration for the “T” treatment, four plants of each substrate

were placed in the greenhouse in a manner identical to the first study conditions.

The containers were weighed at container capacity and reweighed five days later at

the same time of day. This allowed for varying environmental conditions over

72

those five days as a good approximation of water to apply. This known amount of

water was divided by 5 to get the average water lost per day, and then was applied

in two applications split in half at 6 AM and 10 AM daily. The leaching volume

per plant per irrigation event was measured for each treatment.

Statistical analysis

The data required nonparametric analysis, due to the poison characteristics and lack

of homogeneity of variance of some data sets. Proc NPAR1WAY was used to

detect significant effects of management on leaching volumes using a rank

transformation process.

Results & Discussion

From the sensor placement, leaching volumes were significantly affected by emitter

and sensor placement combination as illustrated by Table 5. In all substrates the

treatment did significantly affect leaching volumes. The mean and standard

deviation of each treatment combination are listed in Table 5. Treatments with

minimal leaching volumes per irrigation event per plant were considered more

effective at reducing leaching volumes. The primary conclusion is that placement is

very important when using drip stakes. When the sensor was not in the plane of

wetting by the drip stake represented in this study as DD, leaching volume was

significantly higher.

73

The irrigation management study concluded that TDR is successful for managing

irrigation on/off scheduling compared to applications based on calculated

evapotranspiration values applied in two cycles. Leaching volumes from Pine Bark

and sand substrates (Table 6) were significantly affected by management, with

sensors reducing leaching volumes. All other substrates showed no significant

difference between sensor based irrigation and precisely calculated water loss

irrigation scheduling.

Table 5: The effect of combination of emitter and sensor placement (Drip V, Drip D, Spray V, Spray D) on mean leaching volume in milliliters per irrigation event per plant (ml/event/plant) is reported here with a p value of significance. The mean(standard error) is reported for each combination and individual substrate in ml/event/plant.

Substrate Significance

(P)

Drip V Drip D Spray V Spray D

Pro-Mix < 0.01 33(13) 276(38) 0(0) 27(27)

Hardwood Mix < 0.01 0(0) 37(9) 1(1) 12(8)

Pine Bark Mix < 0.01 0(0) 623(42) 14(8) 32(9)

Perlite < 0.01 0(0) 168(30) 0(0) 6(4)

Sand 0.05 8(4) 57(15) 70(27) 9(5)

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Table 6: The effect of irrigation management practices (S = TDR Sensor, T = Timed) on mean leaching volume in milliliters per irrigation event per plant (ml/event/plant) is reported here with a p value of significance. The means(standard error) of both treatments are reported for each substrate.

Substrate Significance (P) Sensor Timed

Pro-Mix 0.60 1(1) 2(1)

Hardwood Mix 0.95 2(2) 2(2)

Pine Bark Mix < 0.01 0(0) 5(2)

Perlite 0.87 16(12) 14(5)

Sand < 0.01 0(0) 45(7)

Rockwool 0.49 44(17) 25(12)

The TDR irrigation system functionally controlled irrigation events, with

initiation and termination of cyclic irrigation events at predetermined set points of –

10 KPa and –1 KPa. The placement results (study one) conclusively identified that

when the wetting zone is restricted, as with drip irrigation, sensors need to be

placed within the wetting zone to minimize leaching volumes, since drip emitters

are point source applicators. Placement appears to be less important in spray stake

irrigation. However, spray stakes have a tendency in some substrates to apply

water faster than the substrate can infiltrate, leading to undesirable extension of the

irrigation cycle. Diagonal placement of the sensor with spray stakes may improve

this by decreasing the distance water must travel to reach the sensor and terminate

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irrigation. It appears that sensors should be kept in the direct wetting area of the

emitter, especially with drip irrigation and non-uniform wetting patterns.

TDR controlled irrigation also appeared to match or exceed the precision afforded

by calculating evapotranspiration water loss and timed application methods as

irrigation management practices. Sensor controlled treatments significantly

reduced leaching volumes in pine bark mix and sand, and the four remaining

substrates were not found to be significantly different than timed irrigation as a

management practice, only because exact measurements were made of plant water

use over time. However, under practical situation, this would not be possible, as

plants cannot be weighed and re-weighed every few weeks.

Typically growers do not have the time or resources to actively manage irrigation

using calculated evapotranspiration water loss methods. This is especially due to

changes in environmental conditions that require the application time to be

continuously updated. Sensor controlled systems function to irrigate as plants need

the water, and do not irrigate when there is sufficient moisture in the substrate.

This system reflects actual plant available water over time, and thus may be used to

integrate changes in environmental conditions and plant growth over time.

Four sensors were used in the second experiment to initiate and terminate irrigation

for a block of ten plants. Few sensors are required for optimal management if the

conditions of the crop are very uniform. Sensors should be randomized in the plot

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to account for variability in light intensity, temperature, air movement, plant size,

etc., as they affect plant water use.

TDR has therefore been demonstrated to be an accurate and precise system to

monitor and control water applications that optimize plant growth and minimize

leaching volumes. Sensors can be calibrated to any substrate in terms of Wv and

KPa at any height, and can be effectively implemented to control automated cyclic

irrigation.

Further investigation is necessary to advance the state of the applied technology.

Plant growth could be analyzed for different irrigation parameters. Irrigation could

be initiated at moisture contents beyond –10 KPa if drier conditions are desired. A

drier period may improve or impede growth of some horticultural crops and not

others. Further tests using irrigation termination at lower potentials than –1 KPa,

such as –2.5 KPa, or –5 KPa may result in reduced growth. Finally, various other

horticultural crops should be studied, such as herbaceous high water users like

cucumbers and tomatoes. Plants which use water at higher rates may be more

sensitive to changes in moisture content, and illustrate the ability of TDR irrigation

to perform in more extreme circumstances.

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Overall Discussion

Irrigation control that minimizes leaching volumes is a pro-active way to minimize

nutrient run-off, as well as conserve water. TDR and PDP’s offer promise as

sensor technology that has the capability of maintaining moisture content in a

soilless environment, within a narrow range of easily available water for plant to

passively take up water. Whether sensor technology is implemented, or reduced

irrigation volume is applied manually, nutrient fertilization regime needs to be

altered. The effect of eliminating leaching volume is to increase the residence time

of nutrients in the container. In these circumstances, fewer nutrients are needed

since less leaches and runs off site.

Calibration by generating characteristic curves with simultaneous TDR sensing

proved valid in terms of use in a plant system. Calibration only involves the

substrate, which is altered by regular irrigation and plant growth. All substrates

settled during the one-year growth period, yet the Calibration functions remained

directly correlated with the values found with the plants used. Each TDR system,

Tektronix 1502 or TDR100, should be calibrated separately for the same substrate

and container circumstances. This eliminates bias caused by instrumentation.

Calibration procedures as in chapter one require great care and time to ensure a

closed airtight system, in which the membrane at the lower surface is the only point

of release in a positive pressure system. To obtain the most accurate measure of the

78

characteristic curve, several blocks should be independently characterized and

averaged. This accounts for differences in equipment operation, substrate packing

differences, and any general experimental error to be minimized by incorporating

block effects.

Rockwool exhibited a strong difference when characterized in desorption with and

without the plastic shell remaining. When sliced for drainage as intended for use in

irrigation, the plastic shell of the rockwool slab still held a large volume of water

compared to the rockwool desorbed without the plastic shell (Figure 14 a,b).

The Campbell Tektronix system operated without error. The Campbell TDR100

system operated much faster in measuring sensors, but may occasionally generate

erroneous values leading to an irrigation event not being stopped and causing

leaching. This is a random occurrence that in general does not appear to be

common to all individual TDR100 units, but occasionally appears for unknown

reasons.

Irrigation initiation at –10 KPa appears to prevent plant stress in terms of stomatal

conductance. High light intensities showed a decline in stomatal conductance

(Figure 16) for each substrate well beyond –10 KPa values (Table 4). An irrigation

termination set point of –1 KPa was selected and appeared to successfully

minimize leaching fractions while maintaining optimal water content.

79

Multiple sensors should be averaged to use as a basis for automated irrigation. This

is due to variability associated with plant location effects on water use, such as

sunnier locations, warmer, windier, etc. The TDR sensors tend to have a CV in the

range of 2 to 9, centered around 5% (Table 3). This is adequate sensitivity to

changes in Water content, yet different plants in the same conditions may behave

differently, thus multiple sensors should be average for control mechanisms.

These calibration functions derived in chapter one, and applied in chapters two and

three, are specific to these circumstances. Differences in sensor construction, cable

length or type, TDR unit, specific substrate, crop type, growth stage, etc., may

influence the output of the sensors. A calibration should be performed for any

specific circumstance, paying particular attention to substrate and container height.

TDR and PDP technology can be applied to various crops, and could be used to

model stomatal conductance or other physiological mechanisms while easily

monitoring substrate moisture content. More sensitive crops with high water use

rates should be examined for applying sensor controlled cyclic irrigation to crops

such as cucumber and tomato.

Unique irrigation parameters that facilitate further drying or high moisture contents

can be examined for effects on crop growth rate. Certain crops may do better with

80

an increased drying cycle between wettings, and this may be quantified by growth

rate analysis.

Application of this study to larger block sizes may further reveal the strengths or

weaknesses of the system. The number of sensors required is affected by the

dissimilarity of the plot crop location. Four sensors were ideal in this case to

significantly reduce leaching volumes. Ten plants were controlled by the mean

moisture content of four randomly selected containers to be monitored. A block of

100 or even 1000 containers may need more sensors, but if conditions are relatively

constant less than four may be successful.

Overall, TDR appears to be a tool available to the nursery and greenhouse industry

for improving irrigation management in soilless substrates. Conserving water and

minimizing nutrient leachate are extremely beneficial outcomes that are

economically and environmentally sound. PDP sensors also exhibit the ability to

monitor moisture content accurately and reliably. These systems are currently

expensive as scientific products. Manufacturing for the industry at reduced prices

may accelerate the adoption of advanced irrigation management by the nursery and

greenhouse industry.

81

Appendix A

Cost Comparison of PDP and TDR

A Hypothetical Situation: Research study requires estimated 128 sensors (16 half blocks with 8 sensors each, estimate based on preliminary study and literature) (Topp 1985) Less sensors per block may be adequate, but a single multiplexer that is

dedicated to a block has 8 channels (Campbell SDM) or 16 channels (Dynamax), which can facilitate use of more or less sensors depending on the uniformity of the plot

Chapter three results revealed that four sensors were adequate to minimize leaching volumes, in which case one multiplexer may be divided for two or more plots, reducing the cost

PDP has steady increase in cost with each sensor TDR has initial cost of cable tester, after which each sensor is quite inexpensive

PDP Theta-Probe ML2

Each = $495 128 at quantity rate of $325 each = $41,600 Data logger = $2,560 for Campbell CR10X Extra Cable approximately $1200 Multiplexer’s approximately $600 Total = $45,960

Time Domain Reflectometry Dynamax

Tektronix 1502 B/C New = $10,000 (Used $4,000 to $7,000) Dedicated Laptop PC = $1,500+ (Used if available) 16 Channel Multiplexers: 9 at $550 each = 4,950 (one mux per 2

blocks) Essential Multiplexer and logger cables = $300 TDR control software = $600 128 Sensors we manufacture approximately $25 each = $3200 Extra cable approximately $1,200 Total = $21,750

82

Campbell Scientific

Tektronix 1502 B/C New = $10,000 (Used $4,000 to $7,000) CR10X data logger = $2,560 8 Channel Multiplexers: 19 at $445 each = $8,455 (one mux per block) Logger to TDR Communication Interface = $325 Logger to multiplexer cables approximately $100 128 Sensors we manufacture approximately $25 each = $3200 Extra cable approximately $1,200 Total = $25,840 (with the purchase of a new, unused Tektronix 1502

B,C)

2000 Release of Campbell TDR100 Cable Tester to Replace Tektronix TDR100 = $3,700 Otherwise Similar Costs Total = $19,540

83

Appendix B Procedure for Tension Table Measurements

All parts of the table should be thoroughly cleaned prior to use to make it easier to get an air tight seal.

Place filter membrane on the bottom piece of ½” plexiglass (the piece with many small holes. Wet the filter paper to keep it in place.

Use vacuum grease on o-rings. First grease lower ring, insert into groove in column, and place the column on

the filter paper with the o-ring adjoining the two. Pack column with dry substrate, using reasonable pressure to minimize settling. Grease and insert o-ring for the top of the column. Slide plexiglass lid and steel lid cover over bolts evenly down to column. Use washers, and then hand tighten nuts. After hand tightening, use a wrench

and add ½ to ¾ of a turn to snug the bolts, but do not use excessive pressure or the plexiglass will break.

Attach funnel tubes from the bottom. The higher the funnel is above the table the more efficiently the substrate will wet.

Measure accurately all water you add to the column, until the water reaches the top of the column as seen through the plexiglass lid. Vacuum may have to be applied to initiate wetting through the membrane.

Release the hose when wetting is complete, and allow to drain at least 4 hours, or to the next morning. Measure the outflow volume. Calculate the remaining volume of water in the container. The volume of water remaining divided by the total volume of the column is the percent volumetric water content expressed as a decimal, or multiply by 100 to get percent.

The air source in the lab hooks to the pressure regulator. The outflow from the pressure regulator is split between the manifold for the table and the water (low pressure, 1-8 KPa) or mercury (10 to 100 KPa) manometer. Never use this table beyond 100 KPa, it is designed to go to 200 KPa without mounted sensors…only to 100 KPa with sensors mounted in the lids.

Any column not in use needs to have the hose turned off at the manifold valve to maintain pressure.

Increase pressure stepwise, and collect outflow volume when equilibrium is established with each pressure. For free drainage, allow 4 or more hours, for 1 and 2 KPa drainage allow at least 1.5 to 2 hours each, from there it takes less time since the majority of water is released in the first 10 KPa.

Measure TDR values for each column at each pressure

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Appendix C ;{CR10X} *Table 1 Program 01: 1800 0 Execution Interval (seconds) 1: TDR100 Measurement (P119) 1: 22 SDM Address 2: 0 La/L 3: 1106 MMMP Mux & Probe Selection 4: 1 Waveform Averaging 5: 1 Vp 6: 250 Points 7: 18.5 Cable Length (meters) 8: 2 Window Length (meters) 9: .18 Probe Length (meters) 10: .025 Probe Offset (meters) 11: 1 Loc [ _________ ] 12: 1 Mult 13: 0 Offset 2: Spatial Average (P51) 1: 6 Swath 2: 1 First Loc [ _________ ] 3: 41 Avg Loc [ _________ ] 3: TDR100 Measurement (P119) 1: 22 SDM Address 2: 0 La/L 3: 4104 MMMP Mux & Probe Selection 4: 1 Waveform Averaging 5: 1 Vp 6: 250 Points 7: 18.5 Cable Length (meters) 8: 2 Window Length (meters) 9: .18 Probe Length (meters) 10: .025 Probe Offset (meters) 11: 13 Loc [ _________ ] 12: 1 Mult 13: 0 Offset 4: Spatial Average (P51) 1: 4 Swath 2: 13 First Loc [ _________ ] 3: 43 Avg Loc [ _________ ] 5: If (X<=>Y) (P88) 1: 43 X Loc [ _________ ] 2: 4 < 3: 49 Y Loc [ _________ ] 4: 30 Then Do

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6: Beginning of Loop (P87) 1: 0 Delay 2: 0 Loop Count 7: Do (P86) 1: 47 Set Port 7 High 8: TDR100 Measurement (P119) 1: 22 SDM Address 2: 0 La/L 3: 4104 MMMP Mux & Probe Selection 4: 1 Waveform Averaging 5: 1 Vp 6: 250 Points 7: 18.5 Cable Length (meters) 8: 2 Window Length (meters) 9: .18 Probe Length (meters) 10: .025 Probe Offset (meters) 11: 13 Loc [ _________ ] 12: 1 Mult 13: 0 Offset 9: Spatial Average (P51) 1: 4 Swath 2: 13 First Loc [ _________ ] 3: 43 Avg Loc [ _________ ] 10: If (X<=>Y) (P88) 1: 43 X Loc [ _________ ] 2: 3 >= 3: 50 Y Loc [ _________ ] 4: 31 Exit Loop if True 11: End (P95) 12: Do (P86) 1: 57 Set Port 7 Low 13: Else (P94) 14: Do (P86) 1: 57 Set Port 7 Low 15: End (P95) 16: TDR100 Measurement (P119) 1: 22 SDM Address 2: 0 La/L 3: 3103 MMMP Mux & Probe Selection 4: 1 Waveform Averaging 5: 1 Vp 6: 250 Points

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7: 11.5 Cable Length (meters) 8: 1 Window Length (meters) 9: .06 Probe Length (meters) 10: .025 Probe Offset (meters) 11: 19 Loc [ _________ ] 12: 1 Mult 13: 0 Offset 17: Spatial Average (P51) 1: 3 Swath 2: 19 First Loc [ _________ ] 3: 44 Avg Loc [ _________ ] 18: If (X<=>Y) (P88) 1: 44 X Loc [ _________ ] 2: 4 < 3: 51 Y Loc [ _________ ] 4: 30 Then Do 19: Beginning of Loop (P87) 1: 0 Delay 2: 0 Loop Count 20: Do (P86) 1: 48 Set Port 8 High 21: TDR100 Measurement (P119) 1: 22 SDM Address 2: 0 La/L 3: 3103 MMMP Mux & Probe Selection 4: 1 Waveform Averaging 5: 1 Vp 6: 250 Points 7: 11.5 Cable Length (meters) 8: 1 Window Length (meters) 9: .06 Probe Length (meters) 10: .025 Probe Offset (meters) 11: 19 Loc [ _________ ] 12: 1 Mult 13: 0 Offset 22: Spatial Average (P51) 1: 3 Swath 2: 19 First Loc [ _________ ] 3: 44 Avg Loc [ _________ ] 23: If (X<=>Y) (P88) 1: 44 X Loc [ _________ ] 2: 3 >= 3: 52 Y Loc [ _________ ] 4: 31 Exit Loop if True

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24: End (P95) 25: Do (P86) 1: 58 Set Port 8 Low 26: Else (P94) 27: Do (P86) 1: 58 Set Port 8 Low 28: End (P95) 29: Do (P86) 1: 10 Set Output Flag High (Flag 0) 30: Real Time (P77) 1: 1110 Year,Day,Hour/Minute (midnight = 0000) 31: Sample (P70) 1: 44 Reps 2: 1 Loc [ _________ ] *Table 2 Program 01: 0.0000 Execution Interval (seconds) *Table 3 Subroutines End Program

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Glossary of Terms

Apparent Dielectric Constant: A value characterizing the charge interaction of liquid and solid phase media. Water has an apparent dielectric constant of approximately 81, while dry substrates are nearly 2. Water dominates the apparent dielectric constant of the substrate when moist. Characteristic Curve: A model of volumetric water content (Wv) plotted versus substrate matric potential (KPa). Matric Potential: A term used to account for the reduction in free energy of water when it exists as a thin surface layer adsorbed onto the surface of soil/substrate molecules expressed in KiloPascals (KPa). More negative values indicate water more tightly held to substrate particle surfaces and less available to the plant. Portable Dielectric Probe (PDP): A sensor technology that monitors the moisture content of a substrate in units of millivolts. Stomatal Conductance: A measure of the rate of carbon assimilation through the stomata measured in mmol/m2/s of CO2. Time Domain Reflectometry (TDR): A sensor technology that monitors the moisture content of a substrate in terms of the square root of the apparent dielectric constant of the substrate Volumetric Water Content (Wv): The volume of water in a container divided by the volume of the container, expressed as percent volumetric water content Water Release Curve: Similar to a characteristic curve, but may either plot moisture versus KPa, or moisture versus time as the substrate dries. Wetting Efficiency: The ability of a substrate to absorb water due to the rate at which water is applied. Slower application of water tends to improve wetting efficiency.

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Documents

ABSTRACT Title of Thesis: USING TIME DOMAIN …s3.amazonaws.com/zanran_storage/ · ABSTRACT Title of Thesis: USING TIME DOMAIN REFLECTOMETRY TO SCHEDULE IRRIGATION IN SOILLESS SUBSTRATES