USE OF TIME-DOMAIN REFLECTOMETRY TO MEASURE AND MONITOR MOISTURE

IN WET-STORED PINE AND HARDWOOD LOGS UNDER REDUCED WATER

TREATMENTS

by

MIKIN HEATH RAYBON

(Under the Direction of Laurence R. Schimleck)

ABSTRACT

Wet storage of tree length logs is often used to maintain a steady supply to manufacturers

and preserve wood quality by preventing fungal degradation. Water is continuously applied to

the logs using large amounts of an increasingly scarce and important resource. A reduction in

water use is desired for environmental and economic reasons. In this study, time-domain

reflectometry (TDR) was explored as an option to measure moisture content of logs. Species

specific calibrations were developed to predict moisture content and four studies were completed

that observed response of logs under a 30% reduction in water application rates. Two pine

studies and two hardwood studies were completed with positive results for reducing water use.

The results from all four manipulative studies indicate that a 30% reduction in water is possible

with little to no affect on wood quality.

INDEX WORDS: Time-domain reflectometry, TDR, wood quality, moisture content, water

usage, water quality

USE OF TIME-DOMAIN REFLECTOMETRY TO MEASURE AND MONITOR MOISTURE

IN WET-STORED PINE AND HARDWOOD LOGS UNDER REDUCED WATER

TREATMENTS

by

MIKIN HEATH RAYBON

BSFR, University of Georgia, 2009

A Thesis Submitted to the Graduate Faculty of the University of Georgia in Partial Fulfillment of

the Requirements for the Degree

MASTER OF SCIENCE

ATHENS, GEORGIA

2012

© 2012

Mikin Heath Raybon

All Rights Reserved

USE OF TIME-DOMAIN REFLECTOMETRY TO MEASURE AND MONITOR MOISTURE

IN WET-STORED PINE AND HARDWOOD LOGS UNDER REDUCED WATER

TREATMENTS

by

MIKIN HEATH RAYBON

Major Professor: Laurence Schimleck

Committee: Rebecca Moore Richard Daniels Electronic Version Approved: Maureen Grasso Dean of the Graduate School The University of Georgia May 2012

ACKNOWLEDGEMENTS

I would like to acknowledge Erik Schilling and the National Council for Air and Stream

Improvement for their financial support for this research.

I thank the International Paper Company for the use of their facilities to complete much

of this research. Alastair Twaddle of International Paper, was an integral part to establishing the

project and keeping the project going during wood shortages. Thanks to Frank Wills and Joel

Moore for their help at the International Paper McBean Woodyard. Laurie Smith and Ken Leach

were instrumental in setting up the International Paper (IP) Santee Woodyard study as was Ted

Crane at the IP Dry Creek woodyard study. My gratitude is extended to Bobby Hamilton and

Ricky McKie who worked hands-on with the research team setting up and maintaining the IP

McBean woodyard study.

I thank Rayonier, Inc. for the use of their facilities and Monique Belli and Ryan

Williamson for their help throughout the research at the Rayonier Offerman woodyard hardwood

study.

The U.S. Department of Agriculture Forest Service was very gracious for allowing us to

use their facilities to prepare for these studies. Special gratitude goes out to Edward Andrews and

Mike Thompson with the U.S. Department of Agriculture Forest Service for all of their help with

the project.

This work would not have been possible without the help of Kim Love-Meyers,

Associate Director of the Statistical Consulting Center of the University of Georgia. Her

statistical consultations were extremely valuable in the data analysis.

iv

I must thank Joe Sanders (UGA Research Coordinator, retired 2011) for his hard work

and many hours spent traveling through the duration of this study. I would like to thank Drs.

Laurie Schimleck, Richard Daniels, and Rebecca Moore for their patience and guidance.

Finally, I must give my sincere gratefulness for the love and support from all the

members of my family, especially my parents Pruitt and Kathy Raybon.

v

TABLE OF CONTENTS

Page

ACKNOWLEDGEMENTS ........................................................................................................... iv

LIST OF FIGURES ..................................................................................................................... viii

LIST OF TABLES ......................................................................................................................... ix

CHAPTER

1 INTRODUCTION .........................................................................................................1

2 EXAMINATION OF THE POTENTIAL TO REDUCE WATER APPLICATION

RATES IN PINE WET-DECKS ............................................................................16

Introduction ............................................................................................................16

Methods..................................................................................................................19

Results ....................................................................................................................26

Discussion ..............................................................................................................29

3 EXAMINATION OF THE POTENTIAL TO REDUCE WATER APPLICATION ....

RATES IN HARDWOOD WET-DECKS .............................................................43

Introduction ............................................................................................................43

Methods..................................................................................................................46

Results ....................................................................................................................51

Discussion ..............................................................................................................54

4 CONCLUSIONS..........................................................................................................68

vi

REFERENCES ..............................................................................................................................71

APPENDICES

APPENDIX I ...............................................................................................................73

vii

LIST OF FIGURES

Page

Figure 1.1: An example of a commonly used time-domain reflectometer ....................................14

Figure 2.1: Example of the location of apparent length measurement on a time-domain

reflectometer. .....................................................................................................................32

Figure 2.2: Lower Coastal Plain calibration data plot ...................................................................33

Figure 2.3: Diagram of the layout of an experimental stall ...........................................................34

Figure 2.4: Interaction of treatment and day at the Santee Woodyard ..........................................35

Figure 2.5: Interaction of tier and day at the Santee Woodyard ....................................................36

Figure 2.6: Interaction of location and day at the Santee Woodyard .............................................37

Figure 2.7: Interaction of treatment and day at the Dry Creek Woodyard ....................................38

Figure 2.8: Interaction of tier and day at the Dry Creek Woodyard ..............................................39

Figure 2.9: Interaction of location and day at the Dry Creek Woodyard ......................................40

Figure 3.1: Diagram of the layout of an experimental stall ...........................................................57

Figure 3.2: Interaction of tier and day in yellow poplar logs at the Offerman Woodyard ............58

Figure 3.3: Interaction of treatment and day of sweetgum logs at the Offerman Woodyard ........59

Figure 3.4: Interaction of treatment and day in sweetgum logs at the McBean Woodyard ..........60

Figure 3.5: Interaction of location and day in sweetgum logs at the McBean Woodyard .............61

Figure 3.6: Interaction of treatment and position with respect to day in red oak logs at the

McBean Woodyard ............................................................................................................62

viii

ix

LIST OF TABLES

Page

Table 1.1: Levels of government control over water use regarding quality and quantity……….15

Table 2.1: Type 3 tests of fixed effects for the Santee Woodyard data analysis……………...…41

Table 2.2: Type 3 tests of fixed effects for the Dry Creek Woodyard data analysis…………….42

Table 3.1: Moisture content prediction equations for three hardwood species………………….63

Table 3.2: Type 3 tests of fixed effects for the yellow poplar logs at Offerman Woodyard…….64

Table 3.3: Type 3 tests of fixed effects for the sweetgum logs at Offerman Woodyard. ..............65

Table 3.4: Type 3 tests of fixed effects for the sweetgum logs at McBean Woodyard. ................66

Table 3.5: Type 3 tests of fixed effects for the red oak logs at McBean Woodyard. ....................67

CHAPTER 1

INTRODUCTION

Forest products companies in the Southern United States often purchase more wood than

they can immediately use. The extra wood is stored in wood yards and ensures that

manufacturing facilities maintain a steady supply of logs in the event that harvesting is hindered

by weather, seasonal difficulties or other unforeseeable circumstances. Depending on the

expected period of time the wood will be stored, logs are either kept in dry-storage or wet-

storage decks. If the logs are to be stored for periods beyond 2-3 weeks, they are typically stored

under wet conditions (Malan 2004).

Before the 1940’s wood was stored in large ponds with high maintenance and removal

costs (Koch 1972).The method was abandoned for a much cheaper and simpler alternative;

spraying water over logs with sprinklers or misters. The method is still in use today, with few

changes. Areas that apply water to logs in wet-storage are called wet-decks or wet-yards and can

cover large areas and contain thousands of tons of logs. Most facilities tend to operate with a

continuous application of water, assuming that more water is better to maintain high log

moisture. Water at these facilities is usually pumped from an underground aquifer or directly

from a river, and then into a holding pond which serves as the supply and recollection center for

all of the water sprayed on-site. Water is pumped from the pond through pipes and then

sprinklers are used to spray the logs. Though much of the water is recycled through a series of

pipes and ditches leading to the holding pond, a large portion is lost through evaporation and

some through percolation.

1

Unprotected logs degrade relatively quickly when stored for extended periods of time.

Degradation is mainly due to checking and staining by fungi and is related to the moisture

content of the logs. Fungi are problematic only within a certain moisture range, generally from

20-80 percent (Huisman, et al. 2007). The only ways to stop fungi from colonizing wood is to

dry it below the minimum required moisture content for any fungus to grow, create a shortage of

oxygen by keeping the wood extremely moist (Malan 2004), or by the use of fungicides. Storing

and keeping thousands of tons of dry logs is logistically impossible as is the use of fungicides, so

keeping the logs at a high internal moisture content has become standard practice. Saturating

wood is beneficial, though given sufficient time, anaerobic bacteria can degrade wood (Malan

2004).

Keeping the logs extremely wet maintains the logs at high moisture and creates a host of

benefits for mill facilities seeking to store timber for future use. Wet storage provides protection

against fungal degradation of logs, ensures a steady supply of raw material for the mills and the

high moisture content of the wood slows the dulling of chipper blades (Malan 2004). In addition,

Schmulsky and Taylor (1999) report that logs can exhibit increased permeability, which can be

considered beneficial as wood can be dried at a faster rate. Yet, another benefit of wet-storage is

that prolonged wet-storage of wood is an effective way to prevent powder-post beetle (Lyctus)

infestation in wood used to make furniture (Desch 1981). This method of log storage allows the

cells of the wood to use up any remaining starch which is the food source of powder-post beetles.

There may be an upper limit to the length of time logs can be stored. In the southern US

companies typically store softwoods for a maximum of twelve months while for hardwoods nine

months is considered the upper limit. Schmulsky and Taylor (1999) report increased

permeability in logs, and while this is considered a benefit, it can be a problem if the density of

2

logs is reduced too much. Permeable logs absorb wood treatments well, but boards cut from logs

stored too long can absorb too much chemical when treated with preservatives, causing

difficulties with painting and finishing. Some mill managers have reported that when logs are

stored too long, they break easily when the pile is dismantled for shipment to the mill.

Even when the moisture content of wood is extremely high or low, wood degradation can

occur as wood degrading bacteria can remain active. From the middle 1990’s to present,

extensive research has been completed examining bacterial degradation of wood artifacts in very

moist environments. Three types of bacteria are known to degrade wood including cavity,

tunneling, and erosion bacteria. Some erosion bacteria can exist in extremely low oxygen

environments typical of wood submerged underwater or logs stored in wetdecks (Gelbrich et al.

2008). Cavity and tunneling bacteria carve microscopic cavities and tunnels into any of the three

layers (S1, S2, S3) of wood cells. Their damage is usually limited and most of the S3 layer as

well as the middle lamellae remain intact (Blanchette 2000). Erosion bacteria attack the S2 layer

of the cell wall and deplete cellulose and hemicellulose (Blanchette 2000). Tunneling bacteria

are often found in environments similar to soft rot fungi (i.e. higher oxygen levels) while erosion

bacteria are more prolific at low oxygen levels (Gelbrich et al. 2008). If erosion bacteria are

present in wet-deck logs, it may explain the observation that logs stored for too long in wet decks

look sound but are found to be extremely brittle when moved with machinery.

Recent droughts and urbanization in the Southern U.S. have created concern, both

publicly and within companies, over the amount of water being consumed in wet-yards. That

concern has raised some interesting questions from mill facilities about the moisture content of

stored logs. Constant application of water is the current method utilized because mill facilities do

3

not have a way to monitor moisture without an employee actually entering the pile and using

methods that are destructive to the logs.

Forest products companies that utilize wet-storage systems are taking a proactive stance

in reducing the amount of water they apply to logs. There are many reasons to reduce water use

that can be divided into two major issues which relate to the quantity of water used as well as the

quality of water after it is used. The decision to reduce water consumption will impact both, but

as with any decision made by a profitable company, there must be an economic benefit. In the

case of wet-decks, the benefit of being proactive is that companies can prevent economic loss

from multiple of sources including regulation and water quantity requirements needed to operate

woodyards.

Government regulations can come from authorities at any level of government ranging

from federal laws to local city ordinances. Local governments are just as important as federal

regulators because they are controlled by citizens who may directly see the effects of water

storage facilities. Table 1.1 gives examples of regulations, of both quality and quantity of water,

by multiple levels of government.

Federal regulators have the highest level of authority, but that does not suggest they are

the most important regulator. Typically, concern over any issue is raised when there is a problem

that requires a response to prevent serious consequences. Most federal regulation is primarily

concerned with water quality and lacks significant authority to regulate water use in matters of

quantity. Often, those issues are reserved for the states to manage. The majority of mills use

extensive recycling systems including drainage ditches and retention ponds so as to reuse water.

The recycling systems serve as a method to reduce the amount of water pumped from outside

sources, but also prevent pollution from the wood yards from entering natural water systems.

4

This factor makes quality less of an issue for wet yards and the amount of water used the highest

priority.

Discussions with various mill managers revealed that they spend a considerable amount

of their budget supplying power to and maintaining the water pumps that circulate water

throughout a wet-deck system. There would be an instant economic benefit of reducing the

amount of water applied to stored logs if the methodology to reduce water use included shutting

off the pumps during certain periods. The electricity and maintenance costs would decrease by

an amount depending on how much the water application can be decreased. The economic

benefit of reducing the time pumps are run may be important, but pales in comparison to the

economic costs of lacking the volume of water required to keep the pumps going because of

regulation.

The drought from 2007 to 2009 was a major factor behind the concern over the quantity

of water being used across the southeastern U.S. At the height of the drought, the governor of the

state of Georgia mandated that all municipalities in the state reduce water consumption by at

least 10 percent or face stiff penalties (Georgia Institute of Technology 2007). In cases like this,

the sudden reduction in water use was a difficult task to manage and pressure was put on

everyone within the state to take drastic measures to reduce water consumption. Local

governments, which supply most of the drinking water to their communities, were responsible

for making the reduction so local laws became the most critical factor in decisions made by

many companies. Citizen groups can have much influence over local/state leaders making their

opinions on local businesses very important. Some forest products companies in southern

Georgia reported complaints by local citizens over the amount of water being used in some of the

5

wood yards. They may not understand that most of the water used is recycled. Therefore, public

perception became an important driver for change.

The quality of log yard run-off has been examined in several studies, with inconsistent

findings (Jonsson 2008). The differences in results is probably due to variable log yard

conditions with differences in tree species stored, weather conditions, and water application

systems. A feature that many of the studies have in common is that they measured the

biochemical or chemical oxygen demand (BOD or COD) in the run-off as an indirect measure of

the organic material in the water (Jonsson 2008). Since organic material is mainly degraded

aerobically, high amounts can cause oxygen deficiency in the receiving watercourse. Oxygen

deficiency is a serious problem that adversely affects habitat quality, both directly and indirectly,

for water-living organisms (Kalff 2002).

In most wet-deck storage facilities in the U.S., water runoff is of little importance

because the water flows into retaining ponds where it is reused. The highest potential for

pollution in these systems is through percolation of water into groundwater. There are currently

no strict federal regulations on wood yards controlling the amount of pollution in runoff water.

Future regulation could become very important and costly for manufacturing facilities. The

difficulties of cleaning the massive amounts of water used at wet-deck facilities would be great.

It is likely more cost-efficient to reduce water use to try to reduce water pollution problems.

Doing so is difficult without a safe, efficient method to measure moisture in logs.

It would be advantageous to study the moisture content of logs in the pile throughout the

entire storage period and potentially tailor a schedule of water application that would allow wet-

yards to reduce water consumption. Creating a spraying schedule would require an

understanding of the effects of different water application rates. The amount of work required to

6

remove logs from the middle of a pile as well as the inherent dangers are too great to obtain a

gravimetric moisture content by taking samples manually. The high moisture content of stored

logs prevents the use of electrical resistance and capacitance/power-loss moisture meters. These

types of meters can only measure moisture content up to the wood’s fiber saturation point, which

for most species is around 30% (Haygreen and Bowyer 1996).

A review of the literature involving moisture content determination of porous materials

revealed that there are other methods that could potentially be used to monitor variation of

overall log moisture levels as rates of water application are adjusted. Two methods offer

potential to be adapted for use in wet-yards: time-domain reflectometry (TDR) and near infrared

(NIR) spectroscopy. Researchers have successfully used NIR to measure moisture content of

many materials including seeds, grain, soils, etc. NIR has proven useful for measuring moisture

content of trees using core and disk samples (Schimleck et al. 2011) and potentially a NIR-based

fiber optic probe system could be used to monitor moisture levels of logs and trees. NIR is

suitable for measuring moisture levels continuously; however, it typically has been used on cores

or discs and it is impossible to retrieve such samples from wood piles. Fiber optic probes would

have to be inserted into wood samples and these probes could not withstand the extremely hostile

environment inside a log deck. Settling of logs in the pile as well as resin accumulation on

probes could easily destroy them or impair their performance. Another issue with a NIR-based

probe system is that it provides a localized moisture estimation. As there can be variations in

moisture from the pith to the outer portion of the logs, localized measures of moisture may fail to

be representative of the whole tree.

Many studies present evidence that TDR is a promising candidate for measuring moisture

content of wet-stored logs over an extended period of time. Its use of a coaxial cable to transmit

7

a signal, instead of a fragile fiber optic cable as with NIR, makes the TDR method more robust

and suitable for the hostile environment of a wet-yard. TDR has a long history in engineering

and natural sciences and was originally developed to test for faults in cable lines (Cerny 2009).

Its sensitivity to water content makes the technique useful for estimating the amount of water in

any porous material and it has been used to estimate the moisture content of soil, wood, and even

skin (Cerny 2009). TDR systems require just three components; a microwave generator, coaxial

cable, and an oscilloscope (Cerny 2009). The pulse generator launches an electromagnetic wave

that travels down the coaxial cable and returns after striking a fault (break or short-circuit) in the

line (Quinones et al. 2003). The TDR head unit then measures the strength of the returning signal

as well as the time it takes for it to return. The wave form, called a trace, is displayed on the

oscilloscope. Using the trace and the time for the return of the wave, the distance to the fault can

be determined.

The procedures for using TDR in porous, organic materials are similar, even though the

materials may be quite different. The signal is sensitive to the dielectric constant (ε) of the

material, hence the impedance of the signal by the medium it is passing through is measured. The

dielectric constant of water (εw = 80) is much higher than that of air and solids (εair = 1, and εsolid

= 2-5.). Hence in wood, with an average ε of 2, water has a much greater influence on impedance

of the signal than wood making it possible to monitor moisture variation using TDR. Any change

in the εcomposite reflects a change in moisture content (Topp et al. 1980). To measure the change in

ε, typically, two stainless steel rods are used to transfer the electromagnetic wave into the test

material from the coaxial cable. In solid materials, such as wood, the stainless steel probes,

which are connected to a coaxial cable via a brazed connection are driven into pre-drilled pilot

holes (Constanz and Murphy 1990). As the electromagnetic wave travels down the rods, water in

8

the porous material causes the signal to slow, because of the increase in ε and affects the shape of

the trace on the oscilloscope (Constanz and Murphy 1990). The more water in the material, the

longer it takes for the signal to return to the TDR head unit. The cable is of known length, but

owing to the higher moisture content of the wood the length measured by the machine is longer.

The difference between the actual known length and the “apparent” length is used to calculate

moisture content. Careful calibration can result in accurate moisture readings in wood. Figure 1.1

shows an example of a once commonly used TDR for measuring soil moisture.

Topp et al. (1980) published a paper that established the basis for using TDR to estimate

soil moisture content in seconds. Topp et al. (1980) demonstrated how the level of impedance of

the electromagnetic wave, thus the time it takes for the signal to return, in soil varied by the

changes in moisture levels.

Studies of wood followed and it was demonstrated by (Contantz and Murphy 1990) that

TDR could be used in several species of living trees. They attempted to address two issues

before using TDR to measure moisture in logs. First, they acknowledged that the prediction

equation for soils, developed by Topp et al. (1980), could not be applied to wood, while the

second issue was the effect, if any, of the complex morphology of the woody parts of trees.

To address the first issue, a small-scale calibration was conducted with only two sapwood

samples of radiata pine (Pinus radiata D. Don). The samples were saturated before being fitted

with TDR probes and allowed to air-dry. Finally, they were oven dried to obtain the oven dry

weight needed to calculate moisture content. A new moisture content prediction equation was

developed which differed substantially from that reported by Topp and Davies (1985) for soil

moisture. To account for the issue of non-uniform radial moisture distribution in trees, they

adjusted their prediction equation. The adjustment accounted for variation of bark thickness,

9

amount of heartwood, and amount of sapwood along the bole of trees. Following the adjustment,

an extensive field study was conducted in an orchard of English walnut (Juglans regia L.) trees

to observe seasonal moisture content in standing trees. The study successfully used TDR to

measure and monitor moisture levels and seasonal variation of moisture in trees.

Wullschleger et al. (1996) applied TDR to monitor seasonal variation in moisture content

in 4 deciduous hardwood species (2 ring-porous and 2 diffuse-porous) including red maple (Acer

rubrum L.), white oak (Quercus alba L.), chestnut oak (Q. prinus L.), and black gum (Nyssa

sylvatica Marsh.). Much like Contanz and Murphy (1990) they conducted a calibration with

samples of each of the four previously listed species. The samples were dried in the lab over a

period of 2-4 months and then oven dried. A universal second-order quadratic equation was

fitted to the pooled data yielding a prediction equation that differed from the equations developed

by Topp et al. (1980) and Constanz and Murphy (1990).

The study then monitored seasonal variation of stem water content in the four hardwood

species from April to December during the years 1994 and 1995. During 1995, a very dry year,

there was a net movement of water out of the stems, especially in ring-porous species. The

differences were as much a 39% of the total volume of water in the trees. Wullschleger et al

(1996) did note that there are some shortcomings to the method of using TDR to measure stem

moisture content, but recommended that the method is feasible. They also noted that a single

universal calibration should work for all species of trees.

Nadler et al. (2003) used TDR to measure the moisture content of wood and soil

simultaneously in 5-year-old lemon trees (Citrus limon L.) that were subjected to various

irrigation schemes. The trees were located in Negrev, Israel, where the trees are subjected to

severe water stress if not adequately irrigated. Nadler et al. (2003) sought to monitor water

10

storage in wood using a system that could eventually be automated allowing quick adjustment of

irrigation schedules. The research group used combined data from Constanz and Murphy (1990)

and Wullscheleger et al. (1996) to develop an equation to predict stem moisture content. They

noted that temperature can have a substantial effect on TDR readings that utilize short cable

lengths and attempted to compensate during the field study.

In the lemon orchard, Nadler et al. (2003) adjusted irrigation schedules and monitored

stem moisture content as well as soil moisture content and concluded that there is a direct

relation, though stem moisture has a delayed reaction to changes in soil moisture. This lag can be

an issue when tailoring a daily irrigation schedule. The daily temperature fluctuations in the

region also proved to be problematic for TDR readings. Nadler et al. (2003) described that there

are several problems with using TDR, mainly related wood property variations and their effects

on measurements, but concluded that it is a good method of monitoring water levels in tree

stems.

Recently, Schimleck et al. (2011) addressed some questions from Nadler et al. (2003) and

Wullschleger et al. (1996) and attempted to establish the basis for monitoring moisture content in

wet stored pine logs. The previous studies did not address probe rod lengths and assumed that the

same prediction equation can be used for all species of trees. Schimleck et al. (2011) used TDR

to predict moisture content in southern yellow pine stems. The researchers developed a system

for making very robust probes in the lab using stainless steel rods, coaxial cable, and an

extremely durable casting resin. Manufacturing probes allowed the authors to have probes of

multiple rod lengths so they could determine the effect of length. Calibrations specific to

southern yellow pine species were developed using loblolly pine (Pinus taeda L.) samples taken

from the piedmont region of Georgia. Bolt samples approximately 200 mm in length were cut

11

from trees ranging from 120 to 300 mm in diameter. Calibrations were developed using readings

taken at four points along the oscilloscope trace to determine which provided the most accurate

moisture content estimates. (Schimleck et al. 2011) It was determined that taking readings at the

point of inflection of the upward curve was most accurate, which is very similar to the method

described by Topp et al. (1980) for measuring moisture in soils.

Multiple probe rod lengths of 75, 100, and 125 mm were used in the calibration

development phase to determine if the length of the probe rods affected the accuracy of readings.

It was found that calibration accuracy improved as rod length increased. TDR readings from the

125 mm probes yielded the strongest relationship (R2 = 0.94) with measured moisture content. It

was suggested that the longer probes more accurately allow for the moisture variations in the

separate morphological areas across the radial section of a log.

To achieve accurate readings, it is important that the entire length of TDR probe rods be

inserted into the sample wood. Therefore, in trees or logs with diameters too small to permit 125

mm probe rods, smaller 75 mm rods must be used (Schimleck et al. 2011). Diameter was added

as a covariate to the model of the relationship with moisture content for 75 mm probes. This

improved the relationship enough to make it possible to use the shorter rods.

In a more recent study (Schimleck et al. 2012) TDR was used to measure moisture

content over a period of three months in a commercial wet-yard where probes would be installed

into full length trees. The trees were then installed into a standard wet-deck under normal

operating conditions. This was a pilot study aimed at answering several questions prior to

commencing a full-scale study. The study aimed to determine if their probes and cables could

handle being inserted into a wood pile. It also examined the effects of probe position in logs as

well as the position of the study logs with respect to height and distance from a sprinkler head. It

12

was found that if sufficient care was taken when building a wet-deck probes could survive the

harsh environment of wet-decks with no sign of deterioration (Schimleck et al. 2012). The

location of probes within each log was potentially important and it was recommended to have a

probe near the butt end of logs and a probe near the center of mass. It was suggested that the butt

end of logs may dry quicker than the middle of the tree in the event that water application is

insufficient to maintain the moisture content. Log position was important, especially with respect

to vertical position and it was recommended to place study logs in both a lower and upper tier.

Horizontal position with respect to a sprinkler was less important, but because the study was

placed close to the end of a wet-deck, this element could not be explored thoroughly. Schimleck

et al. (2012) provided evidence that TDR can be used to accurately measure log moisture in

commercial wet-decks.

Further research is needed if TDR is going to be utilized widely for monitoring the

moisture content of logs in wet-decks. While Schimleck et al. (2012) demonstrated the potential

of TDR for this purpose they did not monitor moisture throughout an entire cycle of four

seasons. The objective of the research described in this thesis is to use TDR to monitor moisture

in logs in wet-yards over the course of 15 months for southern yellow pines and 12 months for

hardwoods in the southern US and to determine if water application rates can be reduced.

Because wet storage involves pines as well as hardwoods, studies of both types of wet-yards

were completed. To complete the hardwood studies, new calibrations were completed that were

specific to common species found in hardwood wet-yards.

13

Figure 1.1. An example of a commonly used time-domain reflectometer.

14

Table 1.1. Levels of government control over water use regarding quality and quantity.

15

CHAPTER 2

EXAMINATION OF THE POTENTIAL TO REDUCE WATER APPLICATION RATES IN

PINE WET-DECKS

Introduction

Wet storage of large volumes of roundwood under sprinklers is a common practice in the

southeastern US. The aim of wet-storage is to preserve log quality by maintaining consistently

high moisture content which prevents fungal degradation. Recent droughts in the southeastern

United States have raised concerns over the quantity and quality of water being used at

commercial wet-yards. Reducing the amount of water used at these “back-up” storage areas has

become an important issue for many companies.

During storage, it is crucial to prevent the most common and destructive form of decay

which is caused by fungi. Fungi are problematic only within a certain range of moisture levels,

generally from 20-80 percent (Huisman, et al. 2007). Wet-yards maintain a high level of

moisture inside the logs to prevent fungal degradation. Currently, they wish to reduce the amount

of water they are using without reducing the quality of wood in the storage piles. A system to

monitor moisture content of the logs over time is needed to understand and complete the task of

reducing the water applied to the logs. There are several ways to measure the moisture content of

wood, but each method has pros and cons for different applications.

Moisture can be directly measured by weighing a sample of wood before drying it in a

forced-air convection oven at 103°C-105°C until the sample ceases to lose weight. Subtracting

the weight of the oven-dry sample from the weight of moist sample provided the weight of water

16

stored in the wood. Dividing the weight of water in the wood by the weight of the dry sample

yields the moisture content on an oven-dry basis (Skaar 1988). Despite the accuracy of the

method, it is impractical and dangerous to extract wood samples from a functioning log pile; and

an alternative method is required.

One option to measure moisture content in wood is Time-domain reflectometry (TDR),

which has been used to measure moisture in many porous materials. This technique has been

employed by Schimleck et al (2012) to monitor moisture of logs in a commercial wet-yard. TDR

technology was originally developed to test for faults in cable lines (Cerny 2009). The TDR unit

generates a signal that travels down a known length of cable and the strength of the returning

signal as well as the time it takes for the wave to return are measured. The wave form, called a

trace, is displayed on the oscilloscope. Using the trace and the time for the return of the wave,

the distance to the fault can be determined.

The procedures for using TDR in porous, organic materials are similar, though the

materials may be quite different. The dielectric constant of the material is measured and the

dielectric constant of water (εw = 80) is much higher than that of air and solids (εair = 1, and εsolid

= 2-5.) Any change in the composite of dielectric constants reflects a change in moisture content

(Topp et al. 1980). Measuring the change in the dielectric constant, ε, requires that two stainless

steel rods are used to transfer the electromagnetic wave into the test material from the coaxial

cable. The probe rods, which are connected to a coaxial cable via a brazed connection, are driven

into pre-drilled pilot holes (Constanz and Murphy 1990). As the electromagnetic wave travels

down the rods, water in the porous material causes the signal to slow, because of the increase in

ε, and affects the shape of the trace on the oscilloscope (Constanz and Murphy 1990).

17

Using the oscilloscope, a point on the reflection curve can be measured to predict

moisture content. The more water in the material, the longer it takes the signal to return to the

TDR head unit changing the position of the point on the curve. If the cable is of known length,

the higher moisture content of the wood the distance to the point on the curve can be measured

and subtracted from the known cable length which yields and “apparent” length. Careful

calibration, using the apparent length, can result in accurate moisture level readings in any

porous material including wood.

Constanz and Murphy (1990) conducted a small calibration study to create a prediction

equation for moisture content in wood. TDR was later used to monitor seasonal variation in

moisture content in 4 deciduous hardwood species including ring-porous and diffuse-porous

species (Wullschleger et al. 1996). Calibration equations were developed using red maple (Acer

rubrum L.), white oak (Quercus alba L.), chestnut oak (Q. prinus L.), and black gum (Nyssa

sylvatica Marsh.). The data was pooled to create a single prediction equation, though the authors

admitted there may be faults in that method.

Schimleck et al. (2011) completed a calibration for TDR use in loblolly pines (Pinus

taeda L.) in an effort to establish a basis for monitoring moisture levels in wet-stored logs. They

also developed probes that could be used in wet yard log piles and tested the probes over a 3

month period in a preliminary pilot study (Schimleck et al. 2012). One potential issue with using

their calibration as a universal pine moisture predictor is that it is based on samples taken from

the Piedmont region of Georgia. Much of the wood in the southeast is located below the

piedmont region in the lower coastal plain region. There is a possible regional effect on TDR

predictions of pine moisture contents. The relationship between the geographic location and

wood properties of pines has been documented since 1960 (Zobel et al. 1960). Because the full

18

effects of morphological variables in trees are not understood, the differences between trees from

the lower coastal plains region and the piedmont should be explored. For this reason, we

developed a calibration specific to the lower coastal plain using samples taken from a storage

yard that would be used to complete one of the manipulative studies.

Methods

Measuring apparent length

Our calibration procedures were similar to those described in Schimleck et al. (2011).

Probes were made with a high density, low dielectric resin that encased two stainless steel rods

spaced 2.54 mm from center to center. The probe rods were brazed to a coaxial cable that exited

the cast probe and was approximately 0.75 meters in length. The exact length of the probes

before being installed into a sample is referred to as the reference length. It is determined by

creating a short-circuit at the base of the steel probes which reflects the exact length of wire and

steel rods combined.

The reference length must be determined for each probe as it is used to determine

apparent length and then to determine moisture content. After placing a probe into a wood

sample, the apparent length is length measured by placing a cursor on the inflection point found

of the oscilloscope of the TDR unit (Schimleck et al., 2011.). Figure 2.1 is an actual screen view

of a TDR unit with the cursor placed at the inflection point of the curve. Subtracting the

reference length from the apparent length gives the value that will be used for the prediction of

moisture content or the prediction length.

Calibration

The pine calibration was tailored to the lower coastal plain (LCP) region due to potential

effects from geographic wood property variation. Calibration samples were collected in

19

November 2010 from a wet yard that supplies logs to the International Paper pulp mill in

Georgetown, SC. The wet yard was also used for our manipulative study making it the ideal

location to collect calibration samples. Samples were collected from logs freshly delivered to the

wet storage yard. This assured that the logs were typical of the raw material coming into the site

during normal business operations.

The selected logs were considered to be in green condition. Nine logs were chosen for

sampling and from each log a bolt 150 to 300 mm in length was cut at approximately 15% to

20% of total log length (measured from the butt of the log). We desired ten samples so one of the

logs was used to provide a second sample large enough to accept a probe with 125mm prongs.

The bolts were labeled, debarked and weighed at the field site. Debarking and weighing

on-site allowed us to calculate the moisture content of the samples immediately after collection.

The bolts were transported to the USDA Forest Service lab in Athens, GA and placed in a

soaking tank for a period of 60 days with the aim of achieving as high a bolt moisture content as

possible. The bolts initially gained moisture rapidly, but the rate of water uptake slowed near the

end of the 60 day period. After 60 days the bolts were absorbing 5-10 grams of water every three

days indicating that the bolts were close to full saturation.

After 60 days the bolts were removed from the tank. Two 3.17 mm guide holes (spaced

25.4 mm apart) were drilled in each bolt and a probe inserted. The probes were checked to

confirm they were working and then returned to the soaking tank for an additional two days (to

negate any drying of the wood in the areas surrounding the probe rods caused by drilling).

After two days, the bolts were taken from the water and allowed to air-dry. TDR readings

and measurements of bolt weight were collected daily for the first 4 days after the samples were

removed from the tank and then on every second day. After 30 days weight loss was negligible

20

and the bolts (with probes removed) were oven-dried to a constant weight over a period of seven

days in a forced-air convection oven at a temperature of 103°C.

The oven-dry weight of each bolt was used to determine the moisture content of the bolts

on each day TDR readings were collected using the following formula: m = (Wm-W0) / W0 , M =

100 * (Wm-W0) / W0. Where Wm = the weight of the sample when measured on any given day

and W0 = the oven dried weight of the sample. See Appendix I for a more detailed description.

After analysis of the data we generated a prediction equation that allows the TDR user to

insert the prediction length TDR value (apparent length minus the reference length) into the

formula and receive an answer in % moisture content. Figure 2.2 shows a plot of the data

collected during bolt drying. The shape of the curve is non-linear and seems to fit a logistic

pattern. A non-linear logistic growth function was used for the analysis and the final 3-parameter

logistic model can be written as:

))(exp(1 γκα

−−+ x

where α is the maximum moisture content, γ is the inflection point of the curve, located where

half the maximum moisture content is obtained, κ is related to the curvature of the model, and x

is the value measured by the TDR.

Manipulative Study – Santee Woodyard

Manipulative studies were completed at two woodyard locations across the southeastern

U.S. The first site (the Santee woodyard) was located near Georgetown, South Carolina.

Installation started on October, 9 2009. Several truckloads of logs were provided by the

woodyard to use in the study. Selected logs were relatively straight with no forks and with a

diameter of at least 150 mm at a distance 4.6 meters from the butt. A diameter less 150 mm

21

would allow probe rods to penetrate through the back side of logs producing inaccurate TDR

moisture estimations.

Space for the manipulative study in the woodyard was limited because the amount of

space needed for each section of the study was large and required logs to be stored for a period

of at least 12 months. Owing to space limitations, only two treatments were used with three

replications of each. Each of the six treatment/replication combinations were located in a

separate compartment or “stall” that was sprayed with water from a single sprinkler head located

at the center of the stall. Stalls are typically 20 meters in width and sprinklers spray water more

than that distance under the pump pressures at the Santee woodyard. Logs can often be sprayed

by water from two sprinklers that have greater than 20 meter throw radii (distance water is

sprayed). In windy conditions, the throw radius of sprinklers can be greatly reduced or expanded

in different directions, calling into question the stability of application rates. To prevent error

caused by windy conditions, sprinklers were moved to 30 meters apart to prevent double-

coverage of logs at all times.

Treatment one consisted of the nominal water application rate for the Santee woodyard. It

was understood that the actual rate of the woodyard may be higher than the nominal rate used in

the study due to the extra width of each stall. The nominal rate used in the study was estimated at

around 100 mm per day. Accurate calculation of application rates is problematic because of

variations in pump pressure which arise when sprinklers in other locations of the wet-deck are

turned on or off (pump pressures decrease and increase in response). The spray patterns of the

sprinkler heads used in the wet-deck are reliant on pressure and the spray patterns directly affect

the volume of water ejected over a given area.

22

Treatment two consisted of a 30% reduction in the nominal water application rate.

Several methods to reduce rates were considered with the only viable option being to shut off the

water supply for 2 days of each week, usually Saturday and Sunday. Typically, water was shut

off to the same three stalls each Friday afternoon and water application resumed the following

Monday.

Each stall contained six logs with three logs positioned in a lower tier located at a height

of two meters and three logs located at a height of five meters. One log in each tier was

positioned three meters to the left of the sprinkler head while another was positioned three

meters to the right. The third log was positioned directly under the sprinkler head. Figure 2.3

shows the relative position of logs under each sprinkler head.

Selected logs were prepared for probe installation by removing the bark at probe

locations so that the probes would sit flush against the wood surface. Probes were place 1.6 and

4.6 m from the butt of each log to analyze potential variation of probe location within the log.

Immediately following installation, TDR readings were taken. Tier one was installed

approximately two weeks before tier two because some time was needed for the woodyard to

stack logs over the first tier to a height of 5 m. Data collection for the second tier started on

October 12, 2009.

Data collection continued for a period of 12 months at which time International Paper

tested the quality of some logs extracted from within our study stalls. Log quality was excellent

and the study was extended for an additional three months. The study pile was dismantled in

January 2011.

23

Manipulative Study – Dry Creek Woodyard

The second manipulative study was located in Prattville, Alabama at the Dry Creek

woodyard. The study was almost identical to the study at the Santee woodyard. The wet-deck

and neighboring mill are located in the lower coastal plain region of the southeast. Installation

began on November 5, 2009. Study logs were provided by the Dry Creek woodyard. Space was

even more limited at the Dry Creek woodyard reducing the study to only two replications of two

treatments.

The sprinkler system at the Dry Creek woodyard consisted of very large water cannons

with spray patterns that extended to 40 meters. Reductions in water rates would be impractical at

that scale and a series of pipe and sprinkler heads were installed at the woodyard to

accommodate the study. Sprinkler heads were placed 30 meters apart and assigned a treatment.

Treatment one consisted of the nominal water application rate where the sprinklers were

left running almost non-stop. It was understood that the actual nominal rate of the woodyard may

be vastly different than the nominal rate used in the study, but the water system at the Dry Creek

woodyard is extremely uncommon. The design built for the study was typical of other

woodyards. The nominal rate used in the study was again estimated at around 100 mm per day

and as noted accurate estimation of application rates is very difficult. Treatment two consisted of

a 30% reduction in the nominal water application rate by shutting off water over the weekend.

Stalls contained six logs with three logs positioned in a lower tier located at a height of

two meters and three logs located at a height of five meters. The position of the logs was the

same as for the Santee woodyard (Figure 2.3).

Logs were prepared for probe installation in the same manner as in the Santee woodyard

by removing the bark at probe locations. Probes were place 1.6 m and 4.6 m from the butt end of

24

the logs to analyze potential variation of probe location within the log. Immediately following

installation, TDR readings were taken for the starting point of the study. Tier one was installed

approximately two weeks before tier two because some time was needed for the woodyard to

stack logs over the first tier to a height of 5 meters. Data collection for the second tier started on

October 12, 2009 and ended 15 months later in February 2011.

Data Analysis

Before beginning analysis of data at both sites, it became apparent that many of the

probes were unable to yield sensible readings throughout the entirety of the study. Plots of each

probe of each reading were generated. Extreme values, low or high, were very obvious and were

removed from the dataset. In some instances, data from entire probes were removed if they

consistently yielded extreme moisture content predictions.

Mixed effects analysis of variance (AVOVA) models were used to analyze data from

both studies. The variables examined for significant effect on moisture content at the study sites

include the replication, treatment, tier, position (location of log under sprinkler head), location

(distance of probe from the butt of tree), and the day of measurement. All possible interaction

effects were considered as part of the model building process. The full statistical model

(developed in cooperation with Kim Love-Meyers of the Statistical Consulting Center of The

University of Georgia) can be written as follows:

Yiklmn = µ + wi + eij + tk + pl + wtik + wpil + tpkl + wtpikl + e*ijkl + lm + wlim + tlkm + pllm + wtlikm + wplilm + tplklm + oijklm + dn + wdkn + tdkn + pdln + ldmn + wtdikn + wdlinm + tdlknm + wdniln + tpdkln + pldlmn + o*ijklmn

Where Yiklmn is the predicted moisture content in %, µ is the intercept of the model, wi is

the water treatment (i= nominal or 30% reduced), eij is the error introduced by replicate j of

treatment i, tk is the tier (k = upper or lower), pl is the position relative to the sprinkler (l = left,

center, or right of sprinkler), wtik is the interation between water treatment and tier, wpil is the

25

interaction between water treatment and position, tpkl is the interaction of tier and position, wtpikl

is the three-way interaction of water treatment, tier, and position of probe relative to the

sprinkler, e*ijkl is the error introduced by log in tier k in position l for replicate j of treatment i, lm

is the location of the probe within the logs (m = 1.6 m or 4.6 m), wlim is the interaction of water

treatment and location of probe within log, tlkm is the interaction of tier and location of probe

within log, pllm is the interaction of position of log relative to sprinkler and location of probe

within log, wtlikm is the three-way interaction of water treatment, tier, and location of probe in

log, wplilm is the three-way interaction of water treatment, position, and location of probe in log,

tplklm is the interaction of tier, position, and probe location, oijklm is the error introduced by

location m of probe of the log in tier k in position l for replicate j of treatment i (residual error),

dn is the day of TDR measurement, wdkn is the interaction of water treatment and day, tdkn is the

interaction of tier and day, pdln is the interaction of position of log with day, ldmn is the

interaction of location of probe within log and day, wtdikn is the three-way interaction of water

treatment, tier, and day, wdlinm is the three-way interaction of water treatment, day, and location

of probe within log, tdlknm is the three-way interaction of tier, day, and location of probe, wdpinl is

the three-way interaction of water treatment, day, and position of log, tpdkln is the interaction of

tier, position of log, and day, pldlmn interaction of position, location of probe, and day, and

o*ijklmn is the error error introduced by day n measurement at location m of the log in tier k in

position l for replicate j of treatment i (residual error). Because the measurements from each

probe were taken over time, it was appropriate to include an autocovariance structure to account

for the similarities between measurements within a single probe. The week of measurement was

therefore analyzed as a repeated factor with an AR(1) covariance structure. This was the most

appropriate covariance structure, based on the fit statistics of the resulting model.

26

Results

Lower Coastal Plain Calibration

Based on the data plotted in Figure 2.2 a logistic model was chosen for the prediction of

moisture content of southern yellow pine logs.

The final model for moisture prediction of pines in the lower coastal plain is:

))4198.1(0549.3exp(1

14.129−−+

=x

MC

The output value for the equation is given as a percentage water weight compared to the

dry weight of a log.

Santee Woodyard

The variables examined for significant effect on moisture content at the Santee woodyard

study include the replication, treatment, tier, position (location of log under sprinkler head),

location (location of probe within the tree), and the day of measurement. Significant moisture

differences occurred between tiers by day, treatment by day, and location by day, though further

analysis revealed that the most significant differences in each interaction occurred at the

initiation of the study. Table 2.1 lists the significant interactions with their associated p-values.

Figure 2.4 demonstrates that the logs in the two different treatments show differences in moisture

content for the first few data collection days and only sporadically thereafter. Despite the

significant differences, the moisture content of both treatments remained well above 80% (lower

limit to prevent fungal degradation) moisture content and both averaged near 120% at the end of

the study.

Figure 2.5 demonstrates that the moisture content of logs in tier one were lower than tier

two for the entire study, but the difference was only significant from day 0 to day 85. Again, it

27

should be noted that during this period the average moisture content across tier 1 was above the

desired 80% threshold. The same can be said for the interaction between location and day in

Figure 2.6 where location 5 represents probes located 1.6 m from the butt end of the logs while

location 15 represents logs positions 4.6 m from the butt end of the logs. Day 0 to day 85 saw

higher moisture estimates for probes at location 5 compared to those at location 15.

Table 2.1 also shows that there is a difference between moisture contents between tiers

by location. It appears that in tier 1, probes located 4.6 m from the butt end of the tree are drier

than the logs only 1.6 m from the butt end. The table also shows that there is a position*location

effect meaning moisture contents are variable in different positions by location. Again, with both

of these interactions, the significance of effects is limited to the beginning part of the study and is

only sporadic thereafter. The interactions were not a major factor affecting moisture content of

stored logs because as with all other significant moisture differences, the moisture content of

logs was sufficiently high enough to prevent decay.

At the completion of the Santee Woodyard study, samples were taken from study logs to

calculate actual moisture content values and compare those with the average predicted values

from the TDR which was 123.0%. The average of the calculated values was 116.5%, slightly

lower than the predicted values.

Dry Creek Woodyard

Analysis of the data showed that there were several important interactions. A treatment

by day effect occurred meaning there was a significant difference in moisture contents of logs in

different treatments determined by data collection day. The significant difference was only

observed at the initiation of the study and after installation the logs in both treatments quickly

gained moisture and reached nearly equivalent moisture contents (Figure 2.7).

28

The two tiers at the Dry Creek woodyard had statistically different moisture contents at

various data collection times but generally they were not statistically different from each other.

The differences were noticed across both treatments and were not located at the initiation of the

study as with the treatment effect. Instead, the differences were apparent on random

measurement days. In general, tier 2 did have a lower moisture content than tier 1.Statistically,

the tier effect is limited to certain days and has little effect on moisture content. Figure 2.8

demonstrates the differences observed during the study.

Finally, probes placed in different locations (positions within each log) occasionally

showed significant variations in moisture estimates. In Figure 2.9, the difference in average

moisture content observed between probe locations is not significant until the upper moisture

range is reached after 100 days. At that time, the significance is sporadic with the moisture

content of both locations fluctuating around 120%.

At the completion of the study, moisture contents were taken of some of the logs used in

the study to determine if the average moisture content of the stored logs was comparable to the

values predicted by the TDR. The overall average moisture content of logs whose MC was

directly calculated was 125.2%. The overall MC predicted by the TDR at the last reading was

123.9% which is in close agreement to the calculated moisture contents.

Discussion

Pine Calibration

Time-domain reflectometry can be used to measure moisture content of pine logs located

in wet-decks. Careful calibration led to a logistic model that produces quick, accurate

measurements of log moisture. This model is intuitive because it includes an upper and lower

29

asymptote for moisture content prediction. Real moisture values will never be below 0% and

every species would be expected to have an upper moisture limit.

Manipulative Studies

The goal of the studies was to determine if water application rates at commercial wet-

decks could be reduced without causing harm to the quality of stored wood. The data collected

from the Santee woodyard and the Dry Creek woodyard indicate that water application rates can

be reduced by at least 30% with little to no increase in the potential for log degradation. While

moisture content is affected by treatment at Santee and Dry Creek, the effect was clearly only

seen at the very beginning of the studies when the logs were first installed with probes. Although

the exact reason for this is unclear, it can be attributed to the logs having different moisture

contents when the study commenced. Additionally, the moisture content of the logs at the

initiation of the study were below 100%. This can allow a small difference in moisture content to

show a more significant effect than when the logs have a higher moisture content at their upper

moisture limit.

Moisture content was also affected by tier location of logs. Again, the differences were

noticed at the beginning of the Santee study and appeared throughout the Dry Creek study on

random reading days. Removing the initial data when log moisture is rapidly increasing would

remove most of the effects, but would also remove data that is important in observing what

happens to log moisture content when log piles are established. In both studies, log moisture

quickly increased and remained well above 80%. There was never any sign that the logs were in

danger of slipping below that level, even when water application rates were reduced by 30%.

Internal pulping trials conducted by International Paper using logs from the Santee Woodyard

location showed that logs stored in both treatments produced nearly the same quantity of pulp.

30

Moisture content predictions were compared with measured moisture contents of sample

logs in both locations with favorable results. At Santee the calculated moisture content was

116.5% while the overall average TDR predicted moisture content was 123%. The small

difference is not concerning and can likely be accounted for by the warm, windy weather

conditions at the time the samples were weighed for moisture content calculation. There was

likely some drying of the cut samples during the 30 minute time interval between when they

were cut and when they were weighed. At the Dry Creek Woodyard, samples were cut and

immediately placed on a scale to be weighed. The moisture difference between TDR predicted

and calculated moisture was small with only a 1.3% difference. Measuring a moisture content so

close to the TDR predicted moisture content gives confidence in the ability of TDR to be used in

monitoring moisture of wet-stored logs over long periods of time.

These two studies were reduced to two treatments and only three replications at Santee

with two replications at Dry Creek Woodyard. Ideally, more treatments with greater reductions

in water rates would have been installed with three or more replications of each. The issue with

additional treatments and replications is that the scale of these studies is massive with respect to

the size of a typical woodyard. In both pine sites, the studies were nearly terminated months

before the scheduled dates. A sudden increase in demand for pulp, coupled with rainy weather

across the south, decreased supply to the point where the wet-storage areas were empty of all

logs but the study logs. International Paper made great efforts, at considerable costs, to continue

the studies but if a greater volume of logs had been committed, owing to more treatments and

replications, then it is probable that International Paper would have terminated at least part of the

study.

31

Figure 2.1. Example of the location of apparent length measurement on a time-domain

reflectometer.

32

Figure 2.2. Lower Coastal Plain calibration data plot.

33

Figure 2.3. Diagram of the layout of an experimental stall.

34

Figure 2.4. Interaction of treatment and day at the Santee Woodyard.

35

Figure 2.5. Interaction of tier and day at the Santee Woodyard.

36

Figure 2.6. Interaction of location and day at the Santee Woodyard.

37

Figure 2.7. Interaction of treatment and day at the Dry Creek Woodyard.

38

Figure 2.8. Interaction of tier and day at the Dry Creek Woodyard.

39

Figure 2.9. Interaction of location and day at the Dry Creek Woodyard.

40

Table 2.1. Type 3 tests of fixed effects for the Santee Woodyard data analysis.

Effect

Num DF

Den DF

F Value Pr > F

treatment 1 3.68 0.26 0.6378tier 1 26.8 6.05 0.0206

position 2 26.3 0.67 0.5193location 1 26.1 0.12 0.7329

tier*location 1 26 16.67 0.0004position*location 2 25.9 4.34 0.0237

day 21 953 87.16 <.0001treatment*day 21 952 1.97 0.0058

tier*day 21 953 2.57 0.0001location*day 21 953 4.38 <.0001

41

Table 2.2. Type 3 tests of fixed effects for the Dry Creek Woodyard data analysis.

Effect Num DF

Den DF

F Value Pr > F

Treatment 1 1.78 0.22 0.688 Tier 1 19.5 6.06 0.0232

location 1 21.1 5.65 0.027 day 17 513 70.3 <.0001

Treatment*day 17 513 3.37 <.0001 Tier*day 17 514 2.79 0.0002

location*day 17 513 3.76 <.0001

42

CHAPTER 3

EXAMINATION OF THE POTENTIAL TO REDUCE WATER APPLICATION RATES IN

HARDWOOD WET-DECKS

Introduction

The wet storage of large volumes of roundwood under sprinklers is a common practice in

the southeastern US. The aim of wet storage is to preserve log quality by maintaining

consistently high moisture content. Recent droughts in the southeastern United States have raised

questions regarding the quantity and quality of water being applied to whole logs in wet-storage

at commercial wet-yards. These wet-yards are used by wood products companies to store wood

until it is needed by a mill and reducing the amount of water used at these “back-up” storage

areas has become an important issue for many companies.

The prevention of wood decay is of utmost importance when storing wood. Storage times

are typically 9 to 12 months and for this period of time, decay is most often caused by fungi

which are generally most active within a range of moisture from 20%-80% (Huisman, et al.

2007). To prevent degradation, wet-yards apply water using sprinklers which maintain the stored

logs at moisture content greater than 80%. Typically, logs are sprayed constantly with the only

breaks in water application occurring when the logs are removed from the pile. In response to

concerns regarding the volume of water being used in wet yards interest exists in reducing the

amount of water applied.

Little is known about the relationship between the amount of water applied to logs in

wet-storage and their subsequent moisture content. To be able to reduce the amount of water

43

used, it is necessary to be able to monitor the moisture content of the logs under storage to be

sure it is above 80%. There are several methods to measure wood moisture content but the most

common scientific methods are not applicable to wet-storage systems. The simplest method is to

take a sample of wood and weigh it soon after collection. Then, the sample is dried in an oven at

103°C-105°C until its weight is stable to ensure that no water remains. The moisture content of

wood is usually expressed as a fraction or percent of its dry weight (Skaar 1988). This is an

impractical way to measure moisture content in wet-yards because of the difficulty in extracting

samples from the center of the pile and the time required to dry the samples. A rapid

nondestructive method is required, and of the possible options time-domain reflectometry (TDR)

shows the greatest promise.

TDR technology was originally developed to test for faults in cables (Cerny 2009). It is a

relatively simple concept and TDR systems require just three components including a microwave

generator, coaxial cable, and an oscilloscope (Cerny 2009). The pulse generator launches an

electromagnetic wave that travels down the coaxial cable which returns after striking a fault

(break or short-circuit) in the line (Quinones et al. 2003). The TDR head unit then measures the

strength of the returning signal as well as the time it takes for the wave to return. The wave form,

called a trace, is displayed on the oscilloscope. Using the trace and the time for the return of the

wave, the distance to the fault can be determined.

The procedures for using TDR in porous, organic materials are similar, even though the

materials may be quite different. The signal is sensitive to the dielectric constant (ε) of the

material, hence the impedance of the signal by the medium it is passing through. The dielectric

constant of water (εw = 80) is much higher than that of air and solids (εair = 1, and εsolid = 2-5). A

typical value for εwood is 2-3. Any change in the εcomposite (i.e. ε for wood and water) reflects a

44

change in moisture content (Topp et al. 1980). To measure the change in the dielectric constant,

ε, typically, two stainless steel rods are used to transfer the electromagnetic wave into the test

material from the coaxial cable. In solid materials, such as wood, the stainless steel probes,

which are connected to a coaxial cable via a brazed connection, are driven into pre-drilled pilot

holes (Constanz and Murphy 1990). As the electromagnetic wave travels down the rods, water in

the porous material causes the signal to slow, because of the increase in ε, which affects the

shape of the trace on the oscilloscope (Constanz and Murphy 1990). The more water in the

material, the longer it takes the signal to return to the TDR head unit. The cable is of known

length, but owing to the higher moisture content of the wood the length measured by the machine

is longer. The difference between the actual known length and the “apparent” length is used to

calculate moisture content. Careful calibration can result in accurate moisture level readings in

any porous material including wood.

Previous studies have investigated and derived calibrations/prediction curves for moisture

using TDR. These studies include a wide range of species including radiata pine (Pinus radiata

D.Don) and hardwoods such as red maple (Acer rubrum L.), white oak (Quercus alba L.),

chestnut oak (Q. prinus L.), and black gum (Nyssa sylvatica Marsh.).

TDR calibrations to predict moisture content were completed by Love-Meyers et al.

(2012) for four hardwood species that are commonly found in wet yards in the southeast. The

species include red oak (Quercus spp.), white oak (Quercus alba L.), sweetgum (Liquidambar

styraciflua L.), and yellow poplar (Liriodendron, tulipifera L.). Separate 3-parameter logistic

models were developed for each of the four species and can be written as:

))(exp(1 γκα

−−+ x

45

where x is the apparent distance as measured on the TDR and both α and γ were allowed to

vary randomly based on the tree/bolt from which the measurements came. In the study α is the

maximum moisture content, γ is the inflection point of the curve, located where half the

maximum moisture content is obtained, and κ is related to the curvature of the model. The

objectives of this study were to use the prediction equations from Love-Meyers et al. (2012) for

the most common species of hardwoods found in wet-yards to monitor the moisture of logs

under different watering regimes and to examine if it is possible to reduce the amount of water

used at wet-yard facilities while maintaining high log moisture contents. Only three species were

used in this study; red oak, sweetgum, and yellow poplar. Table 3.1 lists the prediction model

parameter values for each of the species used in this study.

Methods

Hardwood Manipulative Study – Offerman Woodyard

With the aim of examining if the rate of water application could be reduced for hardwood

species two studies were conducted at wetyards in the southeastern US. The sites were located at

a Rayonier wet-storage facility in Offerman, GA (Offerrman Woodyard) and an International

Paper wet-storage facility in Augusta, GA (McBean Woodyard).

The Offerman wet-yard was the location of the first manipulative study. Probe

installation began August 10, 2010 and was completed the next day. The species examined were

yellow poplar and sweetgum as they are most common species stored at Offerman. The wet-yard

crew provided a full truckload of logs of both species so that candidates for study logs could be

chosen. Candidate logs were isolated on the basis that they were not forked and that the diameter

at 4.6 m was at least 150 mm to allow for the 125 mm probes to be installed properly.

46

Twenty-four logs of each species were fitted with two probes each. Probes were located

at approximately 1.6 and 4.6 m from the butt of each log. Log moisture readings were taken

immediately after probe installation and the values were used as a beginning point for moisture

estimations. After the initial readings were taken, the log pile was built and study logs were

positioned.

Two water treatments were applied in the study. Logs in one treatment received the

nominal water application rate for the wood-yard, while the other treatment received 30% less

water. A 30% reduction was attained by shutting off the sprinklers for two days each week; the

water was usually turned off over the weekend. Application rates in terms of millimeters of water

per day or volumes are very difficult to calculate for wet-storage facilities. Each wet-yard has a

different pumping system with varying pump pressures which are affected by the number of

sprinkler heads running at any given time. Estimates of the nominal application rates in mm per

day using water pressures and throw radii of sprinkler heads produced values from 50 mm to 120

mm of water applied over the surface of the logs each day.

Space was limited in the wet-decks for various reasons leaving room for only two

replications of each treatment. A single sprinkler head applied water to each replicate or “stall”

with sprinkler heads spaced 18 meters apart. Four sprinkler heads were used in total to maintain

moisture in two stalls for each treatment. Six logs of each species were placed in each stall for a

total of 12 logs under each sprinkler head and 48 logs in total for the Offerman study. In each

stall, there were two tiers, one located approximately two meters above the ground and the other

located approximately three meters above that, to account for any significant effects related to

the height of logs within the wet-deck (relative to the ground). In each tier there were three logs

47

of each species one log of each directly under the sprinkler head, and the other two placed two

meters on either side of the sprinkler head. See Figure 3.1 for a diagram of a stall.

TDR measurements were taken at two to three week intervals, with some deviation, for a

period of eight months which is considered the maximum amount of time hardwoods can be

stored in wet-storage. After completion of the study several logs were pulled from the pile to

determine wood quality. The logs showed little sign of decay and the study continued for an

additional 7 months to give a total of fifteen months of measurements. Measurement intervals

were extended to once monthly and the study wet-deck was dismantled in December 2011.

Hardwood Manipulative Study- McBean Woodyard

The second study location was the McBean woodyard, a large wet-storage facility

operated by International Paper in Augusta, GA. The design of the McBean woodyard study was

essentially the same as the Offerman study with just a few key differences. The first major

difference was that yellow poplar is not readily available in the wood basket employed by the

International Paper Augusta mill. Sweetgum is predominant along with red oak and consequently

the moisture content of these species was examined.

There were two replications of two water treatments: the nominal application rate and a

30% reduction in water. Each stall included six trees of each species placed in two tiers located

two meters and five meters above the ground. One log of each species was place three meters on

either side, as well as directly under, each sprinkler head for a total of 48 logs. Each study log

was fitted with a probe approximately 1.6 m and 4.6 m from the butt of each tree.

A second key difference with the McBean study was that the installation occurred in two

stages. The lower tier was installed on September 23, 2010. Over the following two weeks, the

woodyard worked to buy enough wood to fill all four study stalls with the wood needed to install

48

the upper tier located 4.6 m above the ground. During this time, no water was applied to the logs

which allowed them the potentially lose some moisture. The second tier was installed on October

12, 2010 and water application started very soon after. TDR measurements were taken as close

to bi-weekly as possible for the initial eight months and monthly thereafter for a period of fifteen

months. The final measurements were taken January 31, 2012.

Data Analysis

Mixed effects analysis of variance (AVOVA) models were used to analyze data from

both studies. In both studies, each species was treated as a different set of data and no

comparison between data sets were made. The upper moisture limit varied by species making

any meaningful comparison very difficult.

The variables examined for significant effect on moisture content at the Offerman and

McBean woodyard studies include replication, treatment, tier, position (location of log under

sprinkler head), location (distance of probe from the butt of tree), and the day of measurement.

All possible interaction effects were considered as part of the model building process. Kim Love-

Meyers with the Statistical Consulting Center at the University of Georgia helped develop the

full statistical model which can be written as follows:

Yiklmn = µ + wi + eij + tk + pl + wtik + wpil + tpkl + wtpikl + e*ijkl + lm + wlim + tlkm + pllm + wtlikm + wplilm + tplklm + oijklm + dn + wdkn + tdkn + pdln + ldmn + wtdikn + wdlinm + tdlknm + wdniln + tpdkln + pldlmn + o*ijklmn where Yiklmn is the predicted moisture content in %, µ is the intercept of the model, wi is the

water treatment (i= nominal or 30% reduced), eij is the error introduced by replicate j of

treatment i, tk is the tier (k = upper or lower), pl is the position relative to the sprinkler (l = left,

center, or right of sprinkler), wtik is the interation between water treatment and tier, wpil is the

interaction between water treatment and position, tpkl is the interaction of tier and position, wtpikl

49

is the three-way interaction of water treatment, tier, and position of probe relative to the

sprinkler, e*ijkl is the error introduced by log in tier k in position l for replicate j of treatment i, lm

is the location of the probe within the logs (m = 1.6 m or 4.6 m), wlim is the interaction of water

treatment and location of probe within log, tlkm is the interaction of tier and location of probe

within log, pllm is the interaction of position of log relative to sprinkler and location of probe

within log, wtlikm is the three-way interaction of water treatment, tier, and location of probe in

log, wplilm is the three-way interaction of water treatment, position, and location of probe in log,

tplklm is the interaction of tier, position, and probe location, oijklm is the error introduced by

location m of probe of the log in tier k in position l for replicate j of treatment i (residual error),

dn is the day of TDR measurement, wdkn is the interaction of water treatment and day, tdkn is the

interaction of tier and day, pdln is the interaction of position of log with day, ldmn is the

interaction of location of probe within log and day, wtdikn is the three-way interaction of water

treatment, tier, and day, wdlinm is the three-way interaction of water treatment, day, and location

of probe within log, tdlknm is the three-way interaction of tier, day, and location of probe, wdpinl is

the three-way interaction of water treatment, day, and position of log, tpdkln is the interaction of

tier, position of log, and day, pldlmn interaction of position, location of probe, and day, and

o*ijklmn is the error error introduced by day n measurement at location m of the log in tier k in

position l for replicate j of treatment i (residual error).

Because the measurements from each probe were taken over time, it was appropriate to

include an autocovariance structure to account for the similarities between measurements within

a single probe. The week of measurement was therefore analyzed as a repeated factor with an

AR(1) covariance structure This was the most appropriate covariance structure, based on the fit

statistics of the resulting model.

50

Results

Offerman Woodyard

The moisture content of the yellow poplar logs averaged 117.0% on a dry weight basis

for logs in treatment one (nominal treatment) which is near the upper limit of 118% found in the

logistic model for yellow poplar logs. The logs in treatment two (reduced treatment) had an

average moisture content of 116.5%, very close to the logs under treatment one. Analysis of the

data revealed that there was no significant interaction between treatment and the moisture

content of the logs. Table 3.2 provides a full list of significant variables and interactions for

yellow poplar logs. The only significant interaction in yellow poplar logs was between tier and

day meaning that statistical differences exist in moisture content between logs located in tier one

and tier two. Tests of effect slices reveal that a significant difference only exists on day 0 of the

study, at the α=0.05 level, or the initial reading. Figure 3.2 shows average moisture content of all

logs separated by tier. It is unknown why logs in tier 1 had different average moisture contents

than logs in tier two on day zero; however, Figure 3.2 shows that both tiers had nearly identical

moisture contents by day 50. From Figure 3.2 and the test of effect slices the assumption can be

made that a more significant difference was forming near the end of the study period, but it was

not enough to signal an interaction effect at that time. Despite the interaction effect of tier*day at

day zero, there seems to be no significant difference between average moisture content because

of tier location. At day zero, when there is a difference, the moisture content of tier two (lower

MC) is around 93% which is well above the lower 80% threshold for fungal decay.

The sweetgum logs at the Offerman Woodyard revealed that there was a significant

treatment effect by day, but only on day zero, i.e. the logs had different moisture contents when

the study commenced (Table 3.3). Within 50 days of the commencement of the study, the

51

moisture level of logs in treatment two averaged only slightly lower than treatment one but at no

time did the moisture content fall below 135% (Figure 3.3).

At the end of the study, logs containing probes were removed from the wet-deck so

probes could be removed and samples could be taken to calculate an average moisture content

value for a sample of the logs. The average calculated moisture content of sweetgum logs was

140%, while for yellow poplar logs the average calculated moisture content was 109.5%. The

average moisture content value calculated by TDR for sweetgum was 139.6% and for yellow

poplar it was 115.2%. Both TDR predicted averages are reasonably close to the measured values.

McBean Woodyard

The moisture content for sweetgum logs at the McBean woodyard were slightly affected

by treatment overall and only significantly affected for two readings days. See Table 3.4 for the

list of significant interactions. Those occurred at day 60 and day 327 and it is unknown why the

variation between moisture was significant on those days only. Logs stored under treatment one

(nominal water application rate) averaged slightly higher moisture content than logs under

treatment two. Figure 3.4 is a graph of moisture content of logs in both treatments. Sweetgum

logs under both treatments maintained moisture levels well above 80%. Within three data

collection periods, the overall average moisture content for logs in the lower tier was above

130% where it would stay for the duration of the study.

A second effect on moisture was observed by location which is related to the position of

the probe within the log; these were 1.6 m and 4.6 m from the butts of the logs and called

location 5 and location 15 respectively. Figure 3.5 is a graph of the log moisture by location and

day. The two lines track together in the same pattern with the location 5 position having slightly

lower moisture contents over both treatments. Tests of effect slices reveal that only at day 222

52

was the difference in moisture contents significant. At days 0 and 19 (the first two measurement

periods), the opposite was seen with the moisture content of logs at the location 5 position being

significantly higher than the other logs.

Red oak study logs at the McBean site had more complicated interactions including a

three-way interaction between treatment, day, and position, i.e. the significance of treatment and

position changed by day (Table 3.5). Figure 3.6 shows the three-way interaction with position 1

being probe to the left of the sprinkler head, position 2 being probes directly beneath the

sprinkler, and position three being probes located to the right of the sprinkler head. Treatment

one is the nominal treatment and treatment two is the reduced treatment. The largest interaction

occurs at the second reading period on day 19 when there is a large drop in moisture in logs in

position 3.

The effect of position of probes was significant by itself as well because of the huge drop

in moisture content of logs in the position to the right of the sprinkler heads. This mainly

occurred at the beginning of the study and only in treatment two. After that initial variation, there

were only slight differences in overall average moisture contents between probes in the two

positions. Despite the significant effects on moisture content, the moisture level in the red oak

logs never fell below 80% in any of the logs in the entire study.

At the end of the McBean study, samples were taken from logs located in the study areas

so a comparison could be made between actual moisture contents and TDR predicted moisture

contents. The overall predicted moisture content on the last day of TDR readings for sweetgum

and red oak logs were 145.9% and 90.9% respectively. These compare well to the measured

moisture contents of 143.9% for sweetgum and 89.0% for red oak logs.

53

Discussion

Offerman Woodyard

The results from the Offerman woodyard study indicate that water application rates can

be reduced by 30% with little chance of the moisture contents of the logs falling below 80%, the

threshold below which fungal decay can begin. In the study, all yellow poplar average moisture

contents remained between 110% and 120%.

A slight tier effect was noticed in yellow poplar logs that was only significant on day zero

when the study commenced. As no treatment had been applied this means the logs were at

different moisture contents when probes were installed. It is possible that the logs from each tier

were harvested at different times allowing one group to dry more than the other group. The

important factor is that all logs absorbed water quickly and soon attained high moisture contents.

The data for sweetgum logs show an effect from treatment where logs in treatment two

(30% reduced water application) had a lower moisture content than logs under treatment one, but

at no time was the difference significant. In fact, the moisture contents of the sweetgum logs

remained well above the lower moisture limit of 80%. With such high moisture contents, it is

possible that water rates could be reduced even further.

McBean Woodyard

The results at the McBean Woodyard were similar to the results from the Offerman

Woodyard. Analysis of the data from sweetgum logs revealed a treatment effect where the

moisture content of logs subjected to treatment one (nominal application rate) had a slightly

higher moisture than logs under treatment two. The difference was significant, but small.

Location of the probe within sweetgum logs also appeared to affect moisture content, but the

differences were not consistent enough to state that one portion of the logs was wetter than

54

another portion. Location within the log varied depending on when the interaction was

significant.

For red oak logs at the McBean Woodyard log position, relative to the sprinkler heads,

had the most significant impact on moisture content. This effect arose because of difference in

moisture content at the beginning of the study. From Figure 3.6, it is evident that logs in

treatment two positioned to the right side of the sprinkler (position 3) experienced a drying event

that caused them to lose around 10% of their moisture between the installation of the study and

the next reading at day 19. This single drop in moisture accounts for most of the significant

effect in the three-way interaction between position, treatment, and day and the interaction of

position and day. The moisture content of treatment two, position two fell to a level that is

considered potentially dangerous to wood quality, but quickly increased after day 19 to over 80%

within 90 days. The maximum moisture content for red oak is 92% which is not much over 80%.

Care must be taken when utilizing red oak logs in a wet-storage area as small changes in

moisture content has the potential cause degradation.

Species in both studies all exhibited high moisture content, above 80%, after around 90

days. There were treatment effects that caused moisture contents in treatment two logs to be

lower than in treatment one, but effects were negligible and limited to a short period of time. The

same can be said for position, location, and tier for both study locations and all species included

in the study. Removing the TDR reading taken at the onset of the studies would almost certainly

remove the majority of the interactions at the sites. The best option in comparing data would be

to wait until the logs in the study have reached their upper moisture ranges for the given

treatments, positions, probe locations, and tier locations. However, this would not accurately

represent what happens in woodyards where logs are delivered with a range of moisture contents.

55

This would also give a narrow view of moisture content patterns over time, which is why initial

TDR readings were retained in our datasets.

The two studies were reduced to two treatments with two replications of each. Ideally,

more treatments and replications of each would have been used in the design of the studies but

they are on a large scale and require valuable sections of wet-storage areas. Logs cannot be

removed from the piles once the studies commence, so storage facilities are reluctant to give any

more space than we received for these two studies.

56

Figure 3.1. Diagram of the layout of an experimental stall.

57

Figure 3.2. Interaction of tier and day in yellow poplar logs at the Offerman Woodyard.

58

Figure 3.3. Interaction of treatment and day of sweetgum logs at the Offerman Woodyard.

59

Figure 3.4. Interaction of treatment and day in sweetgum logs at the McBean Woodyard.

60

Figure 3.5. Interaction of location and day in sweetgum logs at the McBean Woodyard.

61

Figure 3.6. Interaction of treatment and position with respect to day in red oak logs at the McBean Woodyard.

62

Table 3.1. Moisture content prediction equations for three hardwood species.

Species α κ

Sweetgum 154.94 3.3479 TDR length 1.4492

Yellow Poplar

118.61 4.3688 TDR length 1.3065

Red Oak 92.06 4.9571 TDR length 1.291

63

Table 3.2. Type 3 tests of fixed effects for the yellow poplar logs at Offerman Woodyard.

Effect Num

DF Den DF

F Value Pr > F

Tier 1 18.9 1.07 0.3135 day 13 363 42.52 <.0001

Tier*day 13 363 4.9 <.0001

64

Table 3.3. Type 3 tests of fixed effects for the sweetgum logs at Offerman Woodyard.

Effect Num DF

Den DF

F Value Pr > F

Treatment 1 1.95 2.95 0.2312 Tier 1 13.3 6.39 0.0249

Treatment*Tier 1 13.3 12.27 0.0038 day 13 362 122.14 <.0001

Treatment*day 13 362 2.57 0.0021

65

Table 3.4. Type 3 tests of fixed effects for the sweetgum logs at McBean Woodyard.

Effect DF DF F

Value Pr > FTreatment 1 2.03 4.12 0.1775

CblLth 1 22.5 0.5 0.4879day 14 470 41.94 <.0001

Treatment*day 14 470 1.84 0.0308CblLth*day 14 472 2.71 0.0007

66

Table 3.5. Type 3 tests of fixed effects for the red oak logs at McBean Woodyard.

Effect Num DF Den DF

F Value Pr > F

treatment 1 2.04 0.01 0.9456 position 2 15.7 5.44 0.0161

day 14 405 20.58 <.0001 treatment*day 14 405 3.63 <.0001

position*day 28 432 3.18 <.0001

treatment*position*day 30 377 2.04 0.0013

67

CHAPTER 4

CONCLUSIONS

Environmental awareness has raised concern over the large volumes of water used at wet-

storage facilities and created interest in monitoring moisture in wet-stored logs so that reductions

in water application rates could be examined. Previous research (Nadler, et al. 2003,

Wullschleger, et al. 1996, and Schimleck, et al. 2011) has shown that time-domain reflectometry

can be used to accurately predict moisture content of wood and whole trees. Pines are frequently

stored in wet-decks in the southeastern United States and were the first species to be tested in a

set of two manipulative studies.

A calibration equation specific to the geographic location of the study sites was created to

predict the moisture content of pine logs. The two manipulative studies were to be located in the

Lower Coastal Plain so samples were taken from that area to be used in the calibration. The

prediction formula was used to predict moisture in logs in the manipulative study where water

application rates were reduced by 30% by shutting off the water 2 days each week. Both

manipulative studies showed that water rates can be reduced by 30% with very little effect on the

moisture content of stored logs.

Hardwoods are more commonly stored in the southeast than are pines. As a result, two

hardwood manipulative studies were completed to examine the possibility of reducing the

volume of water applied. The two species that are most commonly found in those facilities were

used in each study (yellow poplar and sweetgum at Offerman and red oak and sweetgum at

McBean). For all species and treatments at both sites, moisture content stayed above the lower

limit of approximately 80%. The Offerman Woodyard and the McBean Woodyard both provide

68

strong evidence for using time-domain reflectometry to measure and monitor moisture levels in

wet-stored hardwood logs.

In all manipulative studies (pine and hardwoods), a 30% reduction is easily attainable

simply by shutting off water sprinklers over the weekend. Moisture contents of logs remain high

over the course of two days without water, even in the hot, dry summer periods. Despite this fact,

there are some unanswered questions that deserve attention, but lie outside the scope of this

research. The most important question is how much can we reduce the water application rates

before wood degradation begins? Further reductions in water rates need to be analyzed, but the

difficulty in this research lies in the potential economic losses if less than the minimum amount

of water is applied for too long. Hundreds of thousands of dollars of logs could be susceptible to

fungal decay and may be useless for pulp or sawtimber production. The answer is complex as

each wet-storage facility will have individual aspects that affect the overall water application

requirements. These aspects included pump pressures, weather patterns in different wet yard

locations, and species composition of hardwood wet-decks.

Another issue is with the methods of water rate reductions. It is difficult to turn off the

pumping system to an entire wet-deck. Perhaps the most useful method would be to turn off

certain sections at varying times of the day or night. There are endless possibilities for reducing

the water applied to logs and many need to be explored with careful observation at each wet-

deck in order to tailor a watering schedule that utilizes the optimum amount of water.

The objectives of this study were achieved with time-domain reflectometry showing that

it could be used to monitor moisture in wet-stored pine and hardwood logs. It was shown that

water rates can be decreased by at least 30% in the project study areas. Reductions of 30% are

69

likely possible in other wet-storage facilities though the actual greatest water reductions are

unknown due to the scale of the research needed to answer more complex questions.

70

REFERENCES

Blanchett, R. 2000. A Review of microbial deterioration found in archaeological wood from different environments. Int. Biodeterior. Biodegrad. 46: 189-204 Cerný, R. 2009. Time-domain reflectometry method and its application for measuring moisture content in porous materials: A review. Measurement. 42(3): 329-336. Constantz, J. and Murphy, F. 1990. Monitoring moisture storage in trees using time domain reflectometry. J. Hydrol. 119(1-4): 31-42. Desch, H. 1981. Timber, Its structure, properties, and utilisation. 6th edition. The Macmillan Press LTd. , London. Gelbrich, J., Mai, C., and Militz, H. 2008. Chemical changed in wood degraded by bacteria. Int. Biodeterior. Biodegrad. 61:24-32 Georgia Institute of Technology. 2007. Preliminary report of the campus water conservation task force. Technical Report. Haygreen, J., and Bowyer, J. 1996. Forest Products and Wood Science: An Introduction. Iowa State University Press. 3rd Edition. Huisman, D.J., Manders, M., Kretschmar, E., Klaassen, R.K.W.M., Lamersdorf, N. 2007. Burial conditions and wood degradation at archaelogical sites in the Netherlands. Int. Biodeteri. and Biodegrad. 62: 33-44 Jonsson, M. 2004. Wet Storage of roundwood -- Effects effects on wood properties and treatment of run-off water. Doctoral dissertation. Swedish University of Agricultural Sciences Silvestria, 1401-6230 ; 319 Kalff, J. 2002. Limnoecology. Prentice-Hall, Upper Saddle River, 592pp. Koch, P. 1972. Utilization of Southern Pines. U.S. Dep. Agric. Agric. Handb. No. 420 Love-Meyers, K., Schimleck, L.R., Raybon, H., Sanders, J., Daniels, R., Andrews, E., Schilling, E. 2012. Time domain reflectometry based calibrations for estimating the moisture content of green hardwood logs. Paper in Preparation. Malan, F. 2004. Some notes on the effect of wet-storage on timber: Review paper. South. Forests. 202: 77-82.

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Nadler, A., Raveh, E., Yermiyahub, U., and Green, A. R. 2003. Evaluation of TDR Use to Monitor Water Content in Stem of Lemon Trees and Soil and Their Response to Water Stress. Soil Sci. Soc. Am. J. 67(2): 437-448. Quinones, H., Ruelle, P., Nemith, I. 2003. Comparison of three calibration procedures for TDR soil moisture sensors. Irrigat. Drain. 52(3): 203-217. Schimleck, L., Love-Meyers, K., Sanders, J., Mahon, J., Raybon, H., Daniels, R., Andrews, E., Schilling, E. 2011. Measuring the moisture content of green wood using time-domain reflectometry. For. Prod. J. 61(6):428-434. Schimleck, L., Love-Meyers, K., Sanders, J., Raybon, H., Daniels, R., Andrews, E., Schilling, E. 2012. Examination of moisture content variation within an operationalwetdeck. Tappi J. Currently under Review. Shmulsky, R., Taylor, F. 1999. Effect of water spray storage of southern pine logs on the drying and shrinkage of lumber. For. Prod. J. 49:(11/12) 75-77. Skaar, C. (1988) Wood Water Relations. Springer-Verlag, Berlin ; New York. 3rd Edition. Topp, G., Davies, J., Anaan, A. 1980. Electromagnetic determination of soil water content: Measurements in coaxial transmission lines. Water Resour. Res. (16):574:582. Topp, G. and Davies, J. 1985. Measurements of soil water content using time domains reflectometry (TDR): A field evaluation. Soil Sci. Soc. Am. J. 49(1): 19-24 Wullshleger, S.D., Hanson, P.J., and Todd, D.E. (1996). Measuring stem water content in four deciduous hardwoods with a time-domain reflectometer. Tree Physiol.(16): 809-815. Zobel, B., Thorbjornsen, E., and Henson, F. 1960. Georgraphic, site and individual tree variation in wood properties of loblolly pine. Silvae Genet. 9(6): 149-158.

72

73

APPENDIX I

MOISTURE CONTENT CALCULATION

The moisture content of wood is usually expressed as a fraction m or percent M of its dry

weight (Skaar 1988).

m = Ww / W0

M = 100 * m,

Where Ww is the weight of water and W0 is the weight of the wood material. The weight

of water is obtained from the difference between the moist Wm and oven-dry weight of wood.

m = (Wm-W0) / W0

M = 100 * (Wm-W0) / W0.

In green wood water is located in the cell wall (typically referred to as bound water) or

cell lumen (includes liquid water and water vapor and is referred to as free water. When the cell

walls are saturated with moisture and the cell lumen contains no free water, the wood sample is

considered to be at fiber saturation point Mf. Fiber saturation point is an important concept with

regards to the strength properties of wood. It is generally accepted that as moisture content

decreases below the fiber saturation point, mechanical properties increase (Skaar 1988).