ENVIRONMENTAL IMPACT OF DIFFERENT LOGGING
METHODS IN THE BANKHEAD NATIONAL FOREST, ALABAMA:
A COMPARATIVE ANALYSIS.
by
THOMAS MBELI. TENYAH
A THESIS
Submitted in Partial fulfillment for the requirements for the degree of
Master of Science in the Department of Natural Resources and
Environmental Sciences in the School of Graduate Studies
Alabama A&M University
Normal, AL 35762
May 2009
Submitted by THOMAS MBELI TENYAH in partial fulfillment of the
requirements for the degree of MASTER OF SCIENCE specializing in
FORESTRY/GIS.
Accepted on behalf of the Faculty of the Graduate School by the Thesis
Committee:
Major Advisor
________________________________________ Dean of the Graduate School
_______________________________________ Date
ii
Copyright by
THOMAS MBELI TENYAH
2009
iii
This work is dedicated to my late father Jonas Tenyah Werengoh and to my family for
supporting me through the program.
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ENVIRONMENTAL IMPACT OF DIFFERENT LOGGING METHODS IN THE
BANKHEAD NATIONAL FOREST, ALABAMA: A COMPARATIVE ANALYSIS
Thomas Mbeli Tenyah, M.S. Alabama A&M University, 2009. 103 pp.
Thesis Advisor: Dr. Kozma Naka
The main goal of our study was to evaluate the environmental impacts of two
different harvesting methods on site characteristics. Two different methods, the cut-tolength and the tree-length logging systems were compared. The fundamental question
was which of these two harvesting methods causes less soil disturbances, least residual
tree damage, and was more productive and cost effective than the other. This question
was answered by examining the following specific objectives: (1) compare the effects of
the tree-length and cut-to-length harvesting methods on the soil surface and physical
properties of the harvested areas, (2) compare the residual tree damage between these two
harvesting systems, and (3) compare the productivity and cost-effectiveness of the two
harvesting systems.
To attain these objectives, the environmental impacts caused by these harvesting
operations were measured and compared through statistical analysis. A visual inspection
was conducted before and after harvesting. Pre-harvest data indicated about 98 percent of
the treatment area was undisturbed while post-harvest analysis showed about 79 percent
was disturbed. Statistical analysis using Generalized Linear Model (GLM) showed no
significant difference in the soil disturbance between the two logging methods. However,
there was significant variation in the plot to plot disturbance among the various soil
disturbance classes disturbed with litter (DC2), disturbed with soil exposed (DC3),
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disturbed with rock and stumps (DC5), and disturbed with slash (DC6). There was also
subplot variation in the amount of undisturbed area (DC1), disturbed with litter (DC2),
and disturbed with slash (DC6).
Soil compaction analysis did not indicate any significant difference between the
two harvesting methods, but there was a significant difference between the heavily and
lightly impacted areas within the treatments and there was significant difference between
the disturbed and undisturbed areas. Residual stand damage was determined, by counting
the number of injured trees, and assigning a damage classification before and after
harvesting. Statistical analysis results obtained indicated a significant difference between
the two systems. The cut-to-length (CTL) harvesting system had a higher incident of
residual tree damage than the tree-length (TL) harvesting system.
There was a considerable difference in the productivity and costs analysis of the
two harvesting methods with the CTL system costing less to own and operate than the TL
system but the TL machines had a higher productivity rate per productive machine hour
(PMH) than those of the CTL system. However, the TL harvesting method yielded a
higher net gain to the operator than the CTL system.
KEY WORD: harvesting methods, soil disturbance, residual stand damage, machine
productivity and cost.
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TABLE OF CONTENTS
CERTIFICATE OF APPROVAL ............................................................................ ii
DEDICATION .......................................................................................................iv
ACKNOWLEDGEMENT .......................................................................................xi
CHAPTER I
1.1 Statement of the problem .................................................................................. 3
1.2 Significance of Study ........................................................................................ 5
1.3 Purpose and Objectives ..................................................................................... 6
CHAPETER II
2.1 Environmental Impacts of Logging Operations ................................................ 8
2.2 Soil Impacts ...................................................................................................... 9
2.3 Residual Stand Damage .................................................................................. 13
2.4 Productivity and Cost...................................................................................... 15
CHAPTER III
MATERIAL AND METHODS .............................................................................. 21
3.1 Study Site ........................................................................................................ 21
3.2 Background and Management History of the Research Site .......................... 24
3.3 Experimental Design....................................................................................... 25
3.4. Soil Impact ..................................................................................................... 28
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3.4.1 Visual Estimation:........................................................................................ 29
3.4.2 Soil Compaction: ......................................................................................... 30
3.4.3 Soil Disturbance ........................................................................................... 32
3.4.4 Soil Compaction........................................................................................... 42
CHAPTER IV
RESIDUAL STAND DAMAGE ............................................................................ 47
4.1 Data collection ................................................................................................ 47
4.2 Statistical Analysis .......................................................................................... 48
RESULTS ........................................................................................................... 49
CHAPTER V
PRODUCTIVITY AND COSTS ........................................................................... 58
4.1 Data Collection ............................................................................................... 59
4.3 Data Calculations and Analysis ...................................................................... 63
CHAPTER VI
SUMMARY AND CONCLUSIONS ..................................................................... 74
RECOMMENDATIONS ...................................................................................... 78
APPENDICES .................................................................................................... 80
BIBLIOGRAPHY ................................................................................................. 94
VITA
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LIST OF TABLES
Table
Page
1:
Experimental plot area sizes .................................................................................... 28
2:
Soil Disturbance classes........................................................................................... 30
3:
Percentage of area with surface soil disturbance ..................................................... 36
4:
Percentage of mean soil disturbance for each logging method................................ 37
5:
ANOVA for Surface soil disturbance classes .......................................................... 38
6:
Percent variance accounted for by sources of variance ........................................... 41
7:
Percentage Means by Orientation per each soil disturbance classes ....................... 42
8:
Average soil compaction measured in pounds per square inch (psi) ....................... 44
9:
ANOVA for Soil Compaction ................................................................................. 45
10: Number of trees damage in each class per plot and total number of trees............... 50
11: Residual tree damage of each harvesting method per 100 acres ............................. 52
12: Number of residual tree damaged per treatment plot (Per 100 acres) ..................... 53
13: Contingency table for residual tree damage............................................................. 56
14: Machine Productivity ............................................................................................... 60
15: Total Machine Utilization ........................................................................................ 61
16: Machine rate calculations for TL harvesting system ............................................... 67
17: Operator performance .............................................................................................. 73
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LIST OF FIGURES
Figure
Pages
1:
Location of Bankhead National Forest .................................................................... 23
2:
Distribution of study plots in Bankhead National Forest......................................... 26
3:
Distribution of residual tree damage in to the various classes ................................. 54
4:
Use ArcGIS to determine the number of points on a treatment area . ..................... 80
5:
Selecting Six Sampling plots of 9 points each (total = 54) ...................................... 81
6:
Using DME to measure soil disturbance 10m in each direction .............................. 82
7:
Movement of the Harvester and Forwarder inside Somerville treatment. ............... 83
8:
Skidder……………………………………………………………………………..85
9:
Skidder……………………………………………………………………………..79
10: Feller Buncher……………………………………………………………….......... 85
11: Forwarder…………………………………………………………………………..79
12: Shows pictures of landing areas in the TL system one year after harvesting has been
completed. ................................................................................................................ 92
13: Shows pictures of center of main activity in the CTL system one year after
harvesting has been completed…………………………………………………….93
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ACKNOWLEDGEMENT
First I want to thank the Almighty for giving me the strength to accomplish this
work. I would like to express my appreciation to Dr. Kozma Naka for his tireless advice
and assistance throughout the course of this study. I am also thankful to members of my
advisory committee, Dr. Rory Fraser, Dr. Wubishet Tadesse, and Dr. Monday Mbila for
their valuable suggestions and willingness to serve on committee and not forgetting Dr.
George Brown and Dr. Wang for their tremendous advice on my data analysis. I am also
grateful to the entire faculty members, colleagues, friends, and under-graduate students
who assisted me in one way or the other throughout the period of this study. Many thanks
also go to the loggers – Bobby, Kelly, and Donnie for their cooperation and the staff of
the Forest Service.
Funds for this work came from the School of Agriculture and Environmental
Sciences, through National Science Foundation (NSF) Grant # HRD-042054
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CHAPTER I
INTRODUCTION
All forest operations, especially logging or timber harvesting, have environmental
impacts (Putz et al., 2000). Over the past thirty years, there has been greater scrutiny of
logging operations because of public concerns about the environmental and aesthetic
impacts. While some of these effects might be intended, some others might be
undesirable consequences. For instance, thinning of overstocked stands reduces fuel for
potential forest fires, and provides wood for the timber industry. In addition to harvesting
timber, many logging operations improves the health of the remaining stands by
removing damaged or diseased trees, open the canopy to promote healthier trees, and
promote better habitat for wildlife. (de Wasseige and Defourny, 2004). At the same time,
logging operations can alter species composition and structure of the forest while causing
soil disturbance, damage to residual stands, and disturbance of wildlife habitat. Logging
could also potentially endanger the natural regenerative potential of the forest (de
Wasseige and Defourny, 2004)
1
As a result, soil disturbance and residual stand damage control is required to
retain the potential for natural forest regeneration, especially of the desired commercial
species (Webb, 1997). The extent of damage is largely determined by the felling intensity
and it is also highly dependent on the type of equipment used and the logging method
employed (Hendrison, 1990). Forest managers therefore are often faced with difficult
decisions about harvesting methods, type of equipment, and intensity of harvesting.
Usually, their decisions are based on their estimation of extraction cost, damage to
residual trees, the extent and severity of soil disturbance, and the sustainability of the
forest (Landsberg et al., 2003; de Wasseige and Defourny, 2004). To many loggers, the
choice of harvesting method is dictated by the type of wood desired at the receiving mill,
the slope of the terrain, and potential returns. Often loggers choose the method that
maximizes their short-term economic returns. Much of the logging controversy has been
about whether there is compliance with the Best Management Practices (BMPs) which
are believed to minimize environmental impacts and at the same time achieve high
productivity at low cost (Lanford and Strokes, 1995).
The logging operations can be carried out in different methods of which the
following three are commonly used in the United States; 1) the tree-length logging (TL)
system, 2) the cut-to-length (CLT) system, and 3) the skidder cable system.
The TL and CTL are logging systems used in the Southeast of the United States.
The TL, the most commonly used system involves the use of a feller-buncher and
grapple-skidder while the CLT system includes a harvester and a forwarder (see
Appendix 5). The difference between these two is that in the TL method, the trees are
felled and then skidded on the forest floor to the landing area where they are delimbed
2
and loaded onto the trucks. Branches, wood residue, chips, and barks are accumulated in
piles at roadsides or landing areas. The piles may be left standing or disposed of during or
after the operation. On the other hand, in the CTL method, the trees are felled, delimbed
and bucked to desired log sizes directly at the stump. As a result, slash is distributed all
over the site.
Beside the TL and CTL, another popular logging system used primarily in the
West Coast is cable logging. The cable system requires more complex and expensive
equipments and is more suitable on steep slopes and in swampy or rocky areas
(Heinimann et al., 2001).
Because of the fact that the TL and CTL are the only harvesting methods used in
the northern region of Alabama, this study compares these two systems. Emphasis was
placed on differences in soil impact and residual stand damage because they are the most
common, visible, and easily measurable site characteristics observed after logging
operations. Therefore estimating these environmental impacts caused by mechanized
harvesting equipments can assist logging managers to evaluate the success of the
harvesting operations in a sustainable forest management (Acar and Unver, 2004; Akay
et al., 2006). Productivity and cost effectiveness was also measured and compared, so as
to give an insight of which method cost less to operate and yield more returns to the
logger.
1.1 Statement of the problem
Different logging methods have been used to study the environmental impacts
associated with forest operations. A number of these studies have compared one or more
3
of these methods against each other to see which of them causes more or less soil
disturbance, soil compaction, and residual stand damage than the other. The results of
some of these studies have not been consistent as they may vary depending on the site
characteristics, the type of equipments used, and the personnel or labor force involved.
For instance Lanford and Stokes, (1995) compared the cut-to-length and the tree-length
thinning methods in southern Alabama and concluded that the tree-length method causes
more soil disturbance and compacted the soil more than the cut-to-length method. Carter
et al., (2006) evaluated the site impacts associated with three silvicultural methods in the
upland hardwood of Alabama. Their results indicated that bulk density was highest in the
clear-cut while soil compaction was more in the deferment cut. Shrestha et al., (2008)
compared soil disturbance from horse and mule logging operations coupled with
machines in southern United States. Their study suggested that even though horse and
mule logging was labor intensive, it has low soil disturbance, less residual stand damage,
and less environmentally disruptive in small scale logging operations. On the other hand,
mechanized logging was very efficient for large tracts of timber, and it is often more
disruptive to the soil. Limbeck-Lilienau (2003) measured stand damage from four
different mechanized harvesting methods and found out that the least percentage of stand
damage was generated from the cut-to-length system.
However, no matter what harvesting method is used, there is always going to be
some level of disturbance and damage to the environment. But the question is which of
these harvesting methods provide a physically feasible, economically viable, and
environmentally sound solution. Environmental soundness consists of several impacts,
such as damage to the residual stand, to the soil, or adverse effects on human health and
4
safety (Limbeck-Lilienau, 2003). While many of these studies have been conducted to
evaluate machine solution and to improve harvesting economics, the conservation of
environment becomes even more and more essential in public discussion. Despite these
conflicting results, the essential problem is whether there is a significant difference in
environmental impacts of these logging methods.
In the case of our study on the Bankhead National Forest, local public concern of
these logging operations was taken in consideration, especially in regard to soil
disturbance, residual trees, the type of equipment used , and the general well-being of the
people who live and interact with the forest (Creed, personal communication, 14th
December, 2005). To address these issues, the TL method of logging was proposed in
addition to the CTL. However little consideration was given to the various levels of
impact associated to each of these harvesting methods in the planning process. This could
have been due to the lack of precise and convincing knowledge about the various levels
of environmental impacts caused by the various harvesting system. and no prior research
comparing these two harvesting has been done on the Bankhead National Forest.
1.2 Significance of Study
Public concerns about environmental impacts of logging operations and other
forest practices and regulations have increased lately. This has compelled managers,
planners, and forest-land owners to consider alternative harvesting technologies to
minimize adverse effects on the soil, residual stand, and the entire watershed. However,
to remain viable in the global wood market, woodland owners are seeking logging
systems that are cost effective in meeting standards for timber stand improvement and
5
water quality. At the same time, loggers are facing tremendous economic pressure,
working under conditions of intense competition and constricted profit margins.
Nevertheless, the results of this study will provide definitive evidence to support whether
there is a significant or no significant difference between the CTL and the TL harvesting
systems. This will go a long way to improve decision making by forest managers thereby
reducing the level of environmental impacts.
1.3 Purpose and Objectives
The main goal of this study is to evaluate the impacts of two forest operations on
site characteristics. This was achieved through the following specific objectives:
1) Compare the effects of the tree-length and cut-to-length harvesting methods
on the soil surface and physical properties of the harvested areas.
2) Compare the residual tree damage between the TL and CTL harvesting
systems.
3) Compare the productivity and cost-effectiveness of the TL and CTL
harvesting systems.
Our research question was which of these harvesting method causes less soil disturbance,
least residual tree damage, and is more productive and cost effective?
This study tested the following alternative hypotheses:
H 1: The CTL system creates less soil surface disturbance than the TL system.
H 2 : The CTL system has less incidences of residual tree damage than the TL
system.
H 3 : The CTL system has higher unit production cost than the TL system.
6
The obtained results enabled us to answer the research question above.
7
CHAPTER II
LITERATURE REVIEW
2.1 Environmental Impacts of Logging Operations
Environmental impact assessment of logging operations in a number of different
countries clearly demonstrate that uncontrolled logging using heavy machines reduces the
forest’s ability to carry out vital environmental and ecological functions (Putz et al.,
2000). The impacts of logging on the environment can be divided into two broad
categories; 1) those caused by the timber harvesting itself, that is, the removal of trees
from the forest thereby creating gaps in the forest, and 2) the impact caused by logging
operations such as felling and dragging trees and maneuvering machinery in the forest.
Removal of trees alters species composition, structure of the forest and can cause
nutrient depletion (Putz et al., 2000). On the other hand, removal may provide
opportunities for some species to grow while creating a loss of opportunity for others (de
Wasseige and Defourny, 2004). Trees provide shade to streams and may alter stream
temperature either by preventing the sun from shining directly on the water during the
8
day, or by preventing water from radiating the heat at night. When this phenomenon
occurs on a large enough scale, precipitation is affected. Some changes in local rainfall
patterns have resulted when large forest cover is removed (Webb, 1997). Meanwhile
changes in transpiration may results in a greater intensity of rainfall, enhancing both runoff and erosion.
Modern ground based logging operations require the use of heavy machinery in
the forest. In some areas, roads must be built which often destroy the natural habitats,
resulting in loss of biodiversity and sometimes leading to local and possibly global
extinction of some endanger species (Putz et al., 2000). The use of heavy machinery in
the forest can cause disturbance on soil surface and damage residual stand. It can also
cause soil compaction and resistance to root growth which can eventually affects the
productivity of the forest (Carter et al., 2000). Harvesting on steep slopes can lead to soil
erosion, landslides, and water turbidity. Logging on saturated soils can cause ruts and
change in drainage patterns. Forest machines use oils which, if not handled carefully, can
cause pollution. The severity of these impacts depends on a number of factors which
include the method of harvesting, the type of equipment used and intensity of harvesting,
slope of the terrain, soil type, harvesting season, species and size of tree harvested,
number of trees cut, tract size, and the experience of the operator. (Hendrison, 1990;
Webb, 1997, and Botz et al., 2001; Shrestha et al., 2008).
2.2 Soil Impacts
Soil disturbance has been identified as the principal impact of most forest
operations (Rummer, 2002). It results from road or trail construction, equipment traffic,
9
and the dragging of material on the forest floor. The effects of these actions include
physical dislocation of and loosening of the soil, litter and topsoil removal, soil
compaction, or puddling, and disruption of activities of soil microorganisms.
Soil
compaction is the process of soil particles rearrangement into a denser state, thus
reducing the air-filled fraction of soil pores. Extensive studies have been carried out on
the impacts of forest management on soil characteristics especially soil compaction from
harvesting equipments (McDonald et al., 2002). Most of these studies have tended to
approach the problem of forest harvesting impact on the soil from two distinctive
perspectives: (i) direct measurement of compaction at a series of sampling points
(Perumpral, 1987; Carter et al., 2000, and Miller et al., 2001) and (ii) a stand-level
estimation of visible disturbance (visual assessment) as in Landford and Strokes (1995),
and Aust et al. (1998). The difference between the two approaches is that direct
measurement of soil compaction provides an insight into the relationship between
management or equipment related factors and soil characteristics meanwhile stand-level
studies evaluate relative measures of harvested areas impacted in one of several
qualitative categories. These categories are assigned based on visual estimates and are
widely used to determine the level of impact that occurred during a harvesting operation
(Aust et al., 1998 and McDonald et al., 2002). The investigator decides to term whether a
portion is highly disturbed, moderately disturbed, lightly disturbed, or not disturbed at all.
Although this method is widely used, it has certain limitations. It is highly subjective as
the categorization is based on the investigator’s discretion. Often, he or she is guided by
the severity of the disturbance (Jensen and Visser, 2005). Furthermore, it is difficult to
capture the entire area occupied by these disturbances classes. In an attempt to overcome
10
this difficulty, Jensen and Visser (2005) used line transects spaced 20 meters apart
throughout the harvested area. These transect were placed perpendicular to the expected
path of the skid trails and were used to collect both pre-harvest and post-harvest data.
Even with this design, it is still difficult to collect the data, especially if the area is too
large and more than one replication has to be measured. Moreover, using a graduated tape
to measure the distances in the forest is difficult particularly in areas where the slash piles
are heavy or the terrain is rough.
Visual estimates are not enough to measure the impact of harvesting system on
soil disturbance (Carter et al., and McDonald et al., 2002). This is because the procedure
estimates the impact only on the soil surface and not the impact of machine trafficking on
other soil characteristics such as soil compaction. Increases in soil compaction reduce the
permeability of soil to air and water. The main causes of soil compaction are external
loads from heavy machinery traffic (Carter et al., 2000). A typical measure of compaction
is soil bulk density, expressed in grams per cubic centimeter (g/cm3). The most common
method for measuring compaction is to determine cone index values using static
penetrometers (Herrick and Jones, 2002). Static penetrometers are designed to measure
the force required to push a probe (usually a cone or blunt tip) through the soil at a
constant (static) velocity. These probes rely on one or more discrete applications of
kinetic energy to advance the probe. Cone indices from penetrometer data have been used
to characterize soil compaction and resistance variability to tillage effects and root
growth (Carter et al., 2000), and wheel traffic effect (McDonald et al., 20002). According
to Fritton (1990), the values depend on cone properties (i.e. diameter, height, and
included angle), as well as soil properties such bulk density, shear strength, water
11
content, and texture. Even though manually operated static penetrometers are widely used
to measure soil compaction and resistance, they suffer from several limitations (Herrick
and Jones, 2002). They are relatively expensive to buy and in the course of using them,
they must be moved through the soil at a constant velocity. This procedure is always hard
to achieve in most cases. Furthermore, each piece of equipment is designed for a
relatively limited range of soil resistance and must be recalibrated on a regular basis in
order to generate consistent and repeatable measurements. The dynamic penetrometer has
been developed to overcome these limitations (Perumpral, 1987). With the dynamic
penetrometer, there is no need to push the penetrometer through the soil at a constant
velocity, and continuous force. As such, the dynamic penetrometers are not subject to the
operator’s variability since they do not rely on constant penetration velocity, and the
kinetic energy applied by these devices is mechanically controlled (i.e., fixed hammer
mass and drop heights). Presently, available dynamic penetrometers are relatively cheap
($200 – $300) and new designs include some that are dropped onto the soil from a
specific height and others that are driven into the soil with repeated hammer blows.
A number of studies have been conducted to compare soil compaction on
different harvesting methods caused by machine trafficking. Carter et al. (2006)
compared soil disturbance patterns and associated changes in soil physical properties of
clearcut, strip cut, and deferment cut in an upland hardwood stand in northern Alabama.
Their findings showed less soil surface disturbances in the strip cut site. Bulk density was
highest in clear-cut site while soil compaction was highest in the deferment cut. The
differences in these results were related to different trafficking patterns on each site.
12
To summarize, the factors that affect soil disturbance include the method of
harvesting, the type of equipments used which will include tire sizes, weight of the
machines, and their movement in the forest. Heavy machines with large tire sizes will
cause more soil disturbance, compaction, and more residual stand damage. High intensity
of harvesting causes more soil disturbances than low intensity because of high
trafficking. Harvesting on steep slope will not only reduce the productivity of the
operator but also cause more residual stand damage. The experience of the operator
greatly affects the ability to maneuver insider the trees thereby affecting the number of
stand damage and the productivity of the system.
2.3 Residual Stand Damage
Residual stand damage in the form of canopy removal and residual stem damage
is another major impact of forest harvesting operations. It is mostly caused by the
maneuvering of heavy harvesting machines around the residual trees. Clatterbuck (2006)
indicated that more than 43% of the trees damaged or destroyed are caused by harvesting
operations. The majority of residual stand damage is located along skid trails where most
harvesting activities occur. Wounding can cause stem deformity and decay and
significantly affect final crop volume and value (Solgi and Najafi, 2007). Therefore,
controlling residual stand damage during logging is important in maintaining the health
of the trees and it is also an important factor in retaining the potential for natural
regeneration (Webb, 1997). Akay et al., (2006) reported that the damage on residual
stand can be reduced by using the cut-to-length thinning systems. They also concluded
13
that well trained and motivated operators can be a significant factor in keeping the
amount of residual stand damage under tolerable level.
Residual stand damage is calculated by counting the number of standing trees
whose stems were scratched and crowns destroyed during the harvesting process. In a
study conducted on a small Appalachian mixed-hardwood woodlot in Virginia, Jensen
and Visser (2005) calculated residual stand damage by counting the number of damaged
trees that were within 2 m (1m in each side) of each transect during post-harvest
sampling. This number was then used to estimate the number of damaged trees per acre.
A damage class was assigned to each damaged tree. These classes were based on the size
of the damage: minor stem damage (< 10 cm2), major stem damage (>10 cm2), minor
crown damage (<1/3 crown destroyed), major crown damage (>1/3 crown destroyed),
and undamaged. They found only14 trees per acre with minor stem damage and 4 trees
per acre with minor crown damage and concluded that residual stand damage was
minimal. No tree was found with major stem damage. The limitation of this method was
that even though the size of the damage on the stem was calculated, categorization of the
classes was subjective and there was no comparison to a standard or another system of
harvesting.
Johns et al. (1996) compared logging damage on residual stand in planned and
unplanned harvesting operations in the Paragominas region of eastern Amazonia. Tree
damage was found in five logging phases: tree felling, machine maneuvering, skidding
logs to landing, constructing log landings, and constructing logging roads. Crown damage
was calculated by adding the total number of open spaces and comparing them between
the logging operations. The result showed more residual stand and crown damage in the
14
unplanned than planned logging operation. The limitations in this method were that they
counted only the number of damage trees in each phase of the operation and little
consideration was given to the intensity of the harvest and the severity of the damage.
Furthermore, the distances calculated around a stump may not be all whole gaps (open
area) and may not be easy to determine whether the gap was caused only by logging
operations. In our study, the total crown and stem damage was considered by estimating
the amount of branches destroyed as well as the degree of stem damage.
2.4 Productivity and Cost
Timber harvesting costs have a significant influence on the application and
potential use of any logging system (Bjorn et al., 2000). These costs are strongly related
to the time needed for the logging operations. Most harvesting systems involve a heavy
initial capital investment mainly to buy machines needed for logging operations. Hence
loggers must manage their system efficiently to ensure profitability (Boltz et al., 2001).
Estimating cost and predicting the performance of harvesting systems is a difficult task.
The machines themselves are very complex and work in a constantly changing
environment interacting with other machines and are influenced by a range of site and
stand factors (Gingras, 1988; Brinker, 2002). There are two major types of costs incurred
in a harvesting operation: a) operating costs and b) ownership costs.
Operating costs involve expenditures on fuel, lubrication oils, repairs and
maintenance, labor wages, salaries, and benefits. Ownership costs (fixed or overhead
cost) include depreciation, interest, insurance, and taxes. Determining accurate logging
costs for any harvesting operation can be difficult (Holtzscher and Lanford, 1997), but
15
two factors must be taken into account: the total cost of running a machine or system
quantified in dollars ($) and the production of the machine or system (measured in tons).
The unit cost of harvesting expressed as dollars per ton ($/ton) can be obtained by
dividing the total cost by production.
There are two main methods to determine logging cost: the cash flow and the
machine rate. The cash flow method uses the loggers past data to establish a logging rate.
This is done by adding all business related costs from previous years and dividing it by
all tons of timber delivered for that period. This will give the average logging cost
($/ton). The merit of this method is that the cost is easy to calculate but it has the
disadvantage of spreading out larger cost such as equipment or major equipment repair
over the entire period.
With the machine rate method, the hourly system cost is taken and dividing by the
estimated hourly productivity to predict a harvest rate ($/ton). The hourly harvesting
system cost is obtained by calculating the hourly machine cost of each piece of
equipment in the system and adding them up. Other costs such as overhead, workshop,
storage, and service vehicles are also added in the calculations.
Productivity on the other hand is calculated by averaging the number of tons of
wood delivered per day or week and dividing it by the number of Scheduled Machine
Hour (tons/SMH). The machine rate method has a limitation in that productivity not only
varies to a great extent with the logging machines or harvesting system used, but also
with the stand and site characteristics of the harvested area (Visser, 2007). As such, all
values are estimates and the output is only good as its inputs. Nevertheless, it still
provides an easier way to get an approximate idea of productivity of a logging system.
16
According to Visser (2007), the most accurate way of obtaining reliable productivity
information for a given machine or system at a given site was to conduct a time and
motion study. This could be achieved either at the entire system level or with a specific
machine level. However, in this study, we used the machine rate method because of
convenience and the type of data collected.
A very common method of determining a specific logging machine cost is to use a
machine costing spreadsheet (Brinker, 2002; Visser, 2007). The spreadsheet allows the
user to enter some machine cost values which it uses to calculate the fixed operating cost,
the running cost, and the labor cost for the machine. The resultant cost estimate is per
scheduled machine hour (SMH) that is, the cost per every hour the machine is scheduled
to work including down-time. The advantage of this method is that it is very useful for
predicting approximate hourly costs of a machine and can be used to make a cost
comparison between two machines. However, the result of the spreadsheet model
depends on the accuracy of the inputs (Holtzscher and Lanford, 1997). Hourly cost per
productive machine hour (PMH) is obtained by dividing the dollar amount spent per
SMH by the utilization rate. This would reflect the cost if the machine is compensated
only when it is working. In reality, the actual cost would include the final costs associated
with the spreadsheet input parameters, including state, and federal taxes paid for that
year.
There are several studies that have tried to measure and estimate the productivity
and cost of harvesting systems. Wang et al. (2004), measured productivity and cost of
harvesting system in a central Appalachian hardwood forest using a handheld data logger
to record time and other harvesting related factors. When the data logger was not used,
17
time and operational variables were measured using a stopwatch and recorded on paper.
Variables measured included start and end time, distance travel from landing to tree and
back, time taken to cut a tree, delimbing and loading it on a truck, tree species, and
diameter of each tree at breast height (dbh). Chainsaws and cable skidders were used to
perform the logging operation. All the data collected were analyzed using a general linear
model (GML).
In a similar study conducted by Jensen and Visser (2005) in the Shenandoah
Valley, Virginia, productivity of the harvesting system was determined by conducting a
time and motion study during the harvesting operation. The time study was based on the
method used by Kluender and Strokes (1994). Variables recorded in each extraction
cycle were time travel from deck to woods and back, bunching, choking, and unhooking
logs. Distance along skid trails, length of each tree and dbh were also measured using a
logger’s tape. A manual chainsaw and a four wheel tractor equipped with a skidder plate
and a pulling winch was used for felling and transporting the trees.
The measurement of efficiency and accuracy using this method can be
problematic because of possible human errors in measuring distances and time in the
woods. Recent technological advancement such as the Haglof DMEs and the MultiDAT
increase convenience and accuracy compare with using the logger tape and the
stopwatch.
A number of studies have compared effects of different harvesting methods such
as the TL, cable logging, and the CLT. The results have caused forest managers to pay
closer attention to the various harvesting techniques as well as their environmental
impacts (Clydes et al., 1999). Gingras (1994) concluded that the CTL system causes
18
minimum damage to the residual stand and to the soil because logging slash is distributed
throughout the harvest site rather than left piled at the landing areas as in the TL system.
Lanford and Stokes (1995, 1996) compared both systems in a loblolly pine plantation in
southern Alabama and found no significant differences in productivity and costs, though
the CTL system had less impact on stands and sites. LeDoux and Huyler (2000)
compared three harvesting methods in northern hardwood stands and found that the CTL
system had higher costs than the TL skidder system and the cable logging system. Clyde
et al. (1999) also compared the full-tree and CTL systems in central Louisiana. They
found that the advantage of the CTL method included reduced damage on the residual
stand and less soil compaction, even though the system had the disadvantage of higher
equipment costs. Bjorn et al. (2000) examined productivity and cost of a shelterwood
harvesting system in Norway spruce (Picea abies) stands of northern Sweden and found
out that harvesting cost has a significant influence on the application of and potential use
of the shelterwood system. Comparing this system to the clearcut system, they found out
that the time per tree and the time per cubic meter were higher in shelterwood than in
clearcutting. Most of the increase was due to longer driving times because fewer trees
were harvested. Hence, they concluded that the longer the driving time, the higher the
logging costs in the shelterwood system compared with the clearcutting system. Boltz et
al. (2001) compared Reduced-Impact Logging (RIL) and TL techniques in selected
tropical forest region. Their findings showed that RIL demonstrated clear ecological
benefits by reducing harvest impacts on residual stems and more effectively maintaining
forest structure relative to the TL practices. However, the financial competitiveness of
RIL was less conclusive. When they conducted a comparative analysis of financial
19
returns to one and two cutting-cycle logging entries for representative RIL and TL
operations of the eastern Amazonia, they observed that RIL harvesting operations
generated higher returns relative to TL for a wide range of discount rates due to gains in
harvesting efficiency and forest conservation.
Despite the fact that similar studies have been carried also in Alabama, the use of
latest equipment such as the DME and MultiDat will give better results. These results
will provide definitive evidence as to which method of logging causes less environmental
impacts or alternatively if there is no significant difference between the two harvesting
methods. This will assist forest managers not only in the Bankhead National Forest
(BNF), but throughout southern United States where these two methods are popular. In
that way, these managers will be able to evaluate which logging system is more
environmentally friendly and sustainable so as to reduce public out cry. Lastly, the results
could serve as guidelines for subsequent selection for the most appropriate harvesting
method on the Bankhead National.
20
CHAPTER III
MATERIAL AND METHODS
3.1 Study Site
Our study was conducted at the BNF, which is located in Lawrence, Winston, and
Franklin counties of northwestern Alabama (Fig. 1). Geographically, the BNF is in the
southern Cumberland Plateau region of the United States The forest type is a mixed
pine-hardwood dominated by planted even-aged loblolly pines (Pinus taeda L.) and
includes other species of mesophytic forest which makes it among the most biologically
diverse ecosystems in the United States (USDA, 1988). This forest supports a wide
variety of plant communities and species because of the variety of landforms, soil, and
moisture conditions found in the area. The topography is characterized by a gently rolling
plateau that is dissected by a well developed drainage pattern with canyon-like bluff and
narrow flood plain. The elevation of the terrain ranges between 165 m and 325 m. The
climate of the area is characterized by long, moderately hot summers and short mild
winters (Smalley, 1979). The average monthly temperatures range between 5oc in
January to 25oc in July. The average annual precipitation is 145 cm, fairly distributed
21
throughout the year with March being the wettest month and October being the driest
month (Smalley, 1979). Most of the soils in the area are highly leached ultisols. Soils in
the southern and northwestern part of the forest are primarily shaley silt loams that have
developed in sandstone and shale of the Pottsville formation.
Beside the type of harvesting equipment used, all logging methods are influenced
by the intensity of harvesting, topography, species composition, soil type, seasonality,
rainfall, and temperature of the location. The study site was selected because it was
appropriate for the research question to be evaluated properly as the researcher was able
to control these factors. There was no difference in the topography and species
composition between the plots. The soil type had a well developed drainage pattern and
the data for the study was collected in the spring of 2007 and 2008 when the temperatures
with almost the same, hence no seasonality factor. However, the site location of this
study could be different from the previous studied locations and this might explain why
the results might be different from previous studies.
22
Figure 1: Location of Bankhead National Forest
23
3.2 Background and Management History of the Research Site
The BNF was established in 1914 as a result of the Week’s Act, for the primary
purpose of helping to protect the nation’s watersheds and streams (USDA Forest Service,
2003). Today, the 72,800 ha forest serves as a multiple use area, providing many
recreational opportunities for the public, as well as forest products for the timber industry
(Gaines and Creed, 2003). In the 1960s, the Forest Service initiated efforts to improve
forest economic yields by replacing some upland hardwood forests with faster growing
loblolly pine. This was because at the time, loblolly pine offered the best chance of high
survival and success in reforestation (USDA Forest Service, 2003). These efforts coupled
with favorable natural conditions led to the establishment of approximately 79,000 acres
of loblolly pine on the BNF. Currently, the BNF is comprised of 51 % southern pines and
49 % hardwoods.
Over the past decades, the BNF has been experiencing significant infestation of
the southern pine beetle (SPB). The infestation reached its peak in the summer of 2000
and remained high through 2001, killing an estimated 18,600 acres of pine forest. Most
of the mortality occurred in the Sipsey Wilderness, leaving large areas of standing dead
trees that were a public safety hazard along roads and trails. These areas have also
accumulated considerable forest fuel loads, which have increased the risk of damaging
wildfires in the future (USDA Forest Service, 2003). In 2003, the Forest Service took
action by developing and implementing the Forest Health Restoration Project (FHRP)
which includes (1) thinning of 9,452 acres of overstocked loblolly pine stands, to reduce
the SPB epidemic in the BNF, (2) restoration of 7,382 acres of SPB damaged stands that
need to be restored through reforestation, and (3) restoring a native pine and upland
24
hardwood forest that will provide an ecologically richer habitat in the southern
Cumberland Plateau. In an effort to attain these goals, portions of the forest were selected
and placed on timber sale for commercial harvesting. The bid was offered to the highest
bidder. Unfortunately in the planning process, the Forest Service gave little or no
consideration to the harvesting method to be used, probably because they did not think it
was necessary or they were not very much informed about the level of environmental
impact caused by the various harvesting methods. This is why in the initial planning
process, the CTL system or any other alternative systems were not included as a
requirement in harvesting. The CTL harvesting method was later included due to public
concerns but was never followed through the end. Nevertheless, a study like this will
likely arouse the awareness of forest managers and subsequent harvesting decisions will
take the results into consideration.
3.3 Experimental Design
This study was conducted in conjunction with FHRP. Treatment plots were
established in the selected stands of the BNF, which are located on upland sites and
composed of 20 to 35 year old loblolly pine mixed with some hardwood. The topography
is undulating with slope between 20 to 30 percent. Soil type for all the plots was uniform
leached ultisols. The treatment plots were set-up in accordance with those jointly set by
the BNF and Center for Forest and Ecosystems Assessment (CFEA) researchers. The
research design was a completely randomized block (CRB) with three levels of nesting.
The average treatment plot size was about 13 hectares (31 acres). Half of each stand
25
(about 6.5 ha) was planned to be using the TL system and the other half using the CTL
system.
According to Gaines and Creed (2003), most of these treatment plots were
comprised of about 60% pine (loblolly pine or Virginia pine, Pinus virginiana), with the
remainder consisting of mainly oak species (Quercus spp.). Thinning treatments were 11
m2 ha-1 of residual basal area and 17 m2 ha-1 residual basal area; which are about 50% to
75% retention rate. However, in this study, stands with 11 m2 ha-1 of residual basal area
were selected for consistency and be able to control the factor of intensity which might
affect our results. Hardwoods were preferentially retained because that was one of the
objectives of the FHRP.
Figure 2: Distribution of study plots in Bankhead National Forest.
26
After the first two treatments were harvested (Block 1 Treatment 5 for TL and
Block 1 Treatment 4 for CTL), the CTL contractor lost the bid and pulled out, causing us
to make some adjustments. We located another plot in Somerville, AL (about 35 miles
northeast of BNF) with similar stand and site characteristics.
Treatments were replicated three times on three different plots (three treatments
for CTL and three for TL). TL included Block 1 treatment 5 (B1T5), Block 4 treatment 9
(B4T9), and Block 4 treatment 4 (B4T4) while CTL included Block 1 treatment 4
(B1T4), Block 4 treatment 10 (B4T10- this treatment was numbered by us because it was
not part of the CFEA study plots), and the plot located in Somerville which was not also
among the study plots set by the BNF and CFEA. Since we did not have enough
replications for the CTL method in BNF and Somerville is located reasonably close to
BNF, we decided to include it in our study.
B1T5 and B1T4 were harvested in the spring of 2007 and data collected that same
spring. The rest of the plots were harvested in the spring of 2008 and data collected
thereafter. Prior to harvesting in spring, the sampling plots were marked in the summer
with flagging. Pre-harvesting surveys for the soil disturbance and tree damage was done
and data was recorded. For the soil compaction, data was collected after harvesting in
areas that were disturbed and undisturbed. After harvesting, post-harvesting data were
collected by measuring the same sampling plots. The machines used in the harvesting
included a JD 548G-II skidder with tire size 28Lx26, a Tigercat 845B feller-buncher with
tire size 24Lx26 for the TL and Timbco TF820-D forwarder with tire size 28Lx26, and
CAT 550 harvester with tire size 600/65x34 for the CTL. All tires were standard sizes
from the manufacturer.
27
Table 1 shows the entire plots and their respective area after harvesting has been
completed.
Table 1: Experimental plot area sizes
Method
TL
CTL
Plots
Area (Acres)
B1T5
26
B4T4
63
B4T9
44
B1T4
25
Somerville
11
B4T10
19
In each of these treatment plots, six subplots of 40 m x 40 m were located and 9
points were located (20m x 20m). Each of these points was measured 10 m in the four
cardinal directions (North, East, South, and West) for soil disturbance data collection (see
Appendix 2). For soil compaction, three line transects were located and compaction
readings were taken at points 10 m apart along these transects and data separated between
disturbed and undisturbed areas. The points that were measured in the undisturbed area
were used as a control to be compared with those in the disturbed areas.
3.4. Soil Impact
Soil impact is the most prevalent to observe after timber harvesting. Impacts
include changes in the bulk density, soil compaction, porosity, soil organic matter and top
28
soil removal (Knoepp and Swant, 1997). We used the visual estimation method proposed
by Jensen and Visser, (2005) to measure the soil surface disturbance, while the dynamic
cone penetrometer proposed by Miller et al., (2001) was used to measure soil
compaction.
3.4.1 Visual Estimation:
To estimate the impact of soil surface disturbance, a visual inspection was
conducted before and after harvesting along line transects spaced 20 m apart. Pre- and
post-harvest data for soil disturbance were collected using six sampling plots in each
treatment area. Sub-sampling was used because the treatment areas were large (average
of about 13 hectares ) and would have been time consuming to measure the entire area of
the three replications. The ArcGIS software (ESRI, Redland, CA) was used to plot
regular points on the treatment areas at an interval of 20 x 20 m. This provided about
1,000 points for each treatment area (see Appendix 1). ArcGIS was also used to plot a
rectangular grid on the treatment area, at intervals of 100 x 100 m. The six sampling plots
were placed randomly within the 100 x 100 m grids and each of them had nine points
(see Appendix 2). A total of 54 points were selected on each treatment area (see formula
in Appendix 3). Coordinates for each point were recorded at transect intersections using a
submeter Global Positioning System (GPS) unit to allow for relocation of the points.
Haglof DMEs (Distance Measuring Equipments) were used instead of a graduated tape to
measure the distance of soil disturbance attributes at a 10 m distance in each direction
(See appendix 4). The Haglof DME (Distance Measuring Equipment), ArGIS software,
and the GPS unit are some of the innovations used in this study to improve the speed and
accuracy of the measurements. The distance of each disturbance class was recorded. The
29
following disturbance classes were used in the order of increasing severity of the impact.
These classes were adopted from Aust et al. (1998) with some modifications but were
later combined for analysis purposes.
Table 2: Soil Disturbance classes
Class Type
Disturbance Class
Description
1
Undisturbed
No sign of trafficking or logging
2a
Disturbed with litter
Trafficking with litter still in place
2b
Disturbed with soil
Trafficking with litter removed and
little soil exposed
3
Mineral soil
Large area of soils exposed
4
Rutting
Narrow paths or channels caused by
machine wheels
5
Rock/Stump
Rock or stump exposed
6
Slash
Branches and stems left in piles on
the floor
7
Trail & Deck
7a
Main road
Constructed roads in the forest
7b
Secondary roads
Frequent paths used to transport
timber to the roadside or landing area
3.4.2 Soil Compaction:
Soil compaction caused by machine trafficking is most often estimated through
changes in soil bulk density, typically expressed in grams per cubic centimeter (g/cm3).
30
Soil compaction is also related to soil strength, which can be measured using a dynamic
or static cone penetrometer much more rapidly than collecting bulk density samples
(Miller et al., 2001).
In this study, we used a static cone penetrometer tester to measure the compaction
(firmness) of the soil. The instrument is supplied with two tips; a ½ inch tip for use in
firm soil and a ¾ inch tip for use in soft soil. The dial indicator has two scales (one for
each tip) that are calibrated in pounds per square inch (psi) of the base area of the cone
(tip) and are color coded for reference: green (0 – 200 psi), yellow (200 – 300 psi), and
red (300 + psi). The green range is considered as good for plant growth, fair in the yellow
range and poor in the red range. For this study we used the ½ inch tip and the depth of the
soil was 10 cm since the soil was hard to measure for compaction at different depths
using the cone penetrometer
The split-plot was used here within the completely randomized block with three
levels of nesting (method, plots, range, and status). Each treatment plot was divided into
three line transects; transect one (T1), transect two (T2), and transect three (T3). ArcGIS
software was used to generate these three line transects running from north to south of the
treatment area. These lines transects were measured 100 m apart across each treatment
plot starting from the center of intensive activity away to the areas of less intensive
activity. Soil compaction readings were taken every 10 m along these transects giving a
total of 15 points. Each of these points was classified into disturbed and undisturbed area
depending on whether it falls on a traffic lane (see Appendix 5).Those readings on the
undisturbed areas were used in the analysis as a control for the disturbed areas. A total of
45 readings were taken on each treatment and GPS coordinates of each point were
31
recorded. In our analysis, each of these line transects were called ranges (Range 1, Range
2, and Range 3). Range 1 included areas of high activity, range 2 included areas of
moderate activity, and range 3 included areas of low activity corresponding to T1, T2,
and T3 respectively (see Appendix 5). This was done both on the three plots replicated
for the CTL and TL harvesting methods. This information was downloaded into ArcGIS
together with the positional movements of the machines to see whether there is any
correlation between the movement of the machines and the level of compaction. The data
for plots B1T5 and B1T4 were collected in the spring of 2007 after harvesting had been
completed. Those for the other plots were collected in the spring of 2008 still after
harvesting was completed.
Statistical Analysis
3.4.3 Soil Disturbance
Area percentages of each disturbance class (DC) by treatment between the two
harvesting methods were calculated by summing the total area covered by each DC in
each treatment and dividing it by the number of points measured over all replications.
The generalized linear model (GLM) was used to test whether the CTL system
creates less soil disturbance than the TL system. The advantage of using the GLM is that
it has a mechanism to handle unbalanced data set in the model (Heninger et al., 2002;
Carter et al., 2006). This model was used to examine the variation in the different soil
disturbance classes in the various treatment sites and between the two harvesting
methods. The interaction between the methods, plots, subplots, points, and orientation
32
was also examined. The statistical design was a completely randomized block (CRB)
with four levels of nesting. These levels include two methods of harvesting (CTL and
TL), three plots for each method. Each plot had six sub plots, each of these subplots had
nine points, and each of these points is measured in four directions. The following model
was used with the disturbance classes as the dependent variables while the methods of
harvesting were the independent variables:
DC ijklm = f (MT + PL + SP + PT + OR) ……………………………………. (Equation. 1)
DC ijklmn = Observed disturbance classes
i = Number of Replications
l = Points
j = Method
m = Direction (Orientation)
k = Subplots
MT = Method
PT = Point
PL = Plot
OR = Orientation
SP = Subplot
DC = Disturbance Classes (These classes include DC1, DC2, DC3, DC4, DC5, and DC6)
Where:
DC1 = Disturbance class 1 (Undisturbed)
DC2 = Disturbance class 2 (Disturbed with litter)
DC3 = Disturbance class 3 (Disturbed with soil, mineral soil, and main road)
DC4 = Disturbance class 4 (Disturbed with rutting and secondary roads)
DC5 = Disturbance class 5 (Disturbed with rocks/stumps)
DC6 = Disturbance class 6 (Disturbed with slash)
33
(Some of the disturbance classifications in Table 1.1 have been merged for analysis
purposes).
Model: [DC1 DC2 DC3 DC4 DC5 DC6] = Method, Plot, Sub Plots, Points, and
Orientation.
Where:
Plot = (method)
Sub Plots = Sub plot (method *plot)
Points = points (method * plot * sub plots)
Orientation = Orientation (method * plot * sub plots * points)
Test H = M E = plot (method)
The F-Distribution test with α = 0.05 was used to test the hypothesis (h) to see if
the CTL system creates less soil disturbance than the TL system and also if there is a
significant difference in the disturbance classes between the two harvesting methods (M).
In the case plot was used as error term (E) for method.
The GLM and area percentages were used to analyze and compare the level of
soil compaction due to trafficking between the two logging systems. These averages were
calculated by adding the cone readings in each range and dividing it by the number of
points to get the means. This was done on each plot in each method. The results were
compared within the treatments and by each method. The t-test was used to test whether
there was any significant difference in the levels of soil compaction within the treatments
and between these two systems. The following GLM procedure was used to compute the
results:
Model: Compaction = method, plot, range, and status.
34
(Method = CTL, TL; plots = 1, 2, 3; range = low, moderate, high intensity of trafficking;
status = disturbed and undisturbed areas)
Where:
Plot = (method)
Range = (method*plot)
Status = (method*plot*range)
(The test to check for hypothesis – H and error – E)
Test H = method E = method*plot
The t-test with α = 0.05 was used to test if there is any significant difference in the level
of compaction between the two harvesting methods. In this model, plot*method was used
as the error term (E) for method.
All of these statistical analyses were performed in SAS programming software.
Results
Descriptive statistics were used to summarize and understand the nature of the
surface soil disturbance before and after logging. The results were tabulated and
compared between the damage classes and also the two harvesting methods (see Table 3).
Pre-harvest soil disturbance data indicated that about 97 % of the treatment areas were
generally undisturbed before logging began. The only few disturbances that could be
seen were spots of bare soil (DC3) mostly caused by horse trails or erosion and a few
stretches of slash (DC6) resulting from fallen trees that have been infected by the SPB.
35
Both the TL and CTL pre-harvest treatments were not much different except for more
areas of slash (DC6) in the TL.
Table 3: Percentage of area with surface soil disturbance
Pre-Harvest
Post-Harvest
Damage classes
CTL
TL
CTL
TL
DC1- undisturbed
97.7
95.4
21.5
20.5
DC2- disturbed with litter
0.0
0.7
50.4
49.9
DC3- disturbance with soils exposed
0.7
0.7
6.8
7.2
DC4- disturbed with rutting/sec. roads
0.0
0.2
3.8
2.3
DC5- disturbed with rock/stumps
0.0
0.0
0.4
0.6
DC6- disturbed with slash
1.7
3.1
17.0
19.4
100.0
100.0
100.0
100.0
Total
Post-harvest data indicated that the treatments plots were highly disturbed after
harvesting. Only about 22 % of the treatment area in the CTL system remained
undisturbed while about 78 % was disturbed. This results conforms to 89 % obtained by
Jensen and Visser, (2005) and 75 % obtained by Shrestha et al., (2008). Disturbed with
litter (DC2) was the most visible disturbance class in both harvesting systems accounting
for about 50 % of the disturbance. Disturbance coming from road construction (DC5) was
the least in both CTL and TL, 0.4 % and 0.6 % respectively. We found more exposed
soils (DC3) and more slash (DC6) in the TL than CTL because of the dragging (skidding)
36
of trees on the forest floor to the landing area and slash was piled in heaps in the TL
system.
Comparing the means of soil disturbance between the two harvesting systems, we
found out that means for disturbed with litter (DC2) in the both systems were high,
though that for TL was higher, 4.99 and 4.16 for CTL respectively with a standard
deviation of 3.56 for the TL and 3.58 for the CTL (see Table 4). This implies that
disturbance with litter was the most prevalent disturbance class that characterized the two
harvesting systems. Disturbed with rock/stump (DC5), disturbed with rutting/secondary
roads (DC4), and disturbed with soil exposed (DC3) had the lowest means and standard
deviations respectively.
Table 4: Percentage of mean soil disturbance for each logging method
CTL
TL
Damage Classes
Mean
Std
Mean
Std
DC1-undisturbed
2.53
3.64
2.05
3.12
DC2- disturbed with litter
4.16
3.58
4.99
3.56
DC3- disturbed with soil exposed
0.80
1.93
0.72
1.75
DC4- disturbed with rutting/sec. roads
0.45
1.51
0.24
1.24
DC5- disturbed with rocks/stumps
0.05
0.24
0.06
0.19
DC6- disturbed with slash
2.00
2.56
1.94
2.62
.
37
Statistical analysis for soil disturbance was done in GLM and the ANOVA results are summarized in the table 5
Table 5: ANOVA for Surface soil disturbance classes
DC1
DC2
F-
Source
DF
SS
MS
DC3
F-
P-value
SS
MS
Value
FP-value
SS
MS
value
P-value
Value
Method
1
74.41
74.41
0.70
0.45
225.98
225.98
0.60
0.48
2.18
2.18
0.32
0.60
Plot (Method)
4
453.38
113.34
2.30
0.08
1505.65
376.41
5.10
0.003**
275.79
68.95
7.60
0.0002**
Subplot (method*plot)
30
1494.23
49.81
2.20
0.0005** 2218.36
73.95
3.70
0.000**
271.06
9.04
1.40
0.09
288
6507.39
22.60
5803.92
20.15
1923.74
6.68
972
6417.02
Point
38
(method*plot*subplot)
Orientation
(method*plot*subplot*point)
6995.76
1913.05
Table 5 (continue)
DC4
DC5
DC6
Source
DF
SS
MS
F-Value
P- value
SS
MS
F-Value
P- Value
SS
MS
F-Value
P-Value
Method
1
14.67
14.67
0.80
0.422
0.04
0.04
0.39
0.566
1.22
1.22
0.02
0.894
Plot (Method)
4
74.18
18.55
3.50
0.012**
0.45
0.11
1.70
0.176
307.01
76.75
3.32
0.023**
Subplot (method*plot)
30
157.79
5.26
1.80
0.008**
1.97
0.70
1.38
0.095
694.06
23.14
2.23
0.0004**
Point
288
852.17
2.96
13.70
0.05
2987.26
10.37
972
1393.78
(method*plot*subplot)
39
Orientation
(method*plot*subplot*point)
* Significant at α = 0.05 (Table six is a continuation of table 5)
42.87
4695.86
A total of 1296 observations were measured from a total of 324 points in the six
different treatment sites. These measurements were grouped in the six different
disturbance classes as mentioned earlier.
Statistical evaluation of post-harvest disturbance did not detect any significant
differences in the level of surface soil disturbance between the two harvesting systems at
α = 0.05. The interaction between the plots also indicated no significant difference
between the two harvesting systems. This can be explained by the fact that the soil type
in the plots was leached ultisols which are well drained soil. Thus the movement of the
machines on these plots could not have caused any significant disturbance differences
between the two harvesting methods. However, from the analysis, when we examined the
interaction between the different disturbance classes, we found some significant variation
from plot to plot in the two harvesting methods. These significant variations could be
observed within all the disturbance classes except the undisturbed areas in both
harvesting method. The interaction from subplots to subplots showed significant variation
only in DC1 (undisturbed), DC2 (disturbed with litter), and DC6 (disturbed with slash).
There were also differences in the sources of the variation of these individual
classes (Table 5). ANOVA reveals that, the most significant sources of these variations
between the treatment plots and subplots were orientation (direction of measurement) and
the point to point differences. This can be explained by the pattern of harvesting and the
minor differences in topography. Even though the slope of the treatment areas was
between 20 to 30 %, the subplots had some minor differences and this may account for
the variations. The loggers used directional pattern of harvesting following the direction
of the slope and wind. This pattern is highly recommended by the BMPs as a means to
40
mitigate environmental impacts. For the undisturbed areas (DC1), these two factors
contributed 42.90% and 43.50% of the variation respectively (see Table 6) while
disturbed with litter (DC2) orientation accounted for 42%, and point 35%. In disturbed
with soil exposed class (DC3), orientation accounted for 43.62% and point to point
43.86%; disturbed with rutting/secondary road - DC4 (orientation 55.92%, point
34.19%); disturbed with rocks/stumps - DC5 (orientation 72.62%, point 23.21%); and
slash - DC6 (orientation 54.67 %, point 34.39%). The main effect of method, plots, and
subplots contributed very little to the surface soil disturbance between the two systems.
Table 6: Percent variance accounted for by sources of variance
Source
DC1
DC2
DC3
DC4
DC5
DC6
Method
0.50
1.30
0.05
0.59
0.07
0.03
Plot (method)
3.00
9.00
6.00
3.00
0.20
0.34
Subplot (plot*method)
10.00
13.24
6.18
6.33
3.34
8.00
43.50
34..65
43.86
34.19
23.21
34.39
42.90
41.80
43.62
55.92
72.62
54.67
Point
(plot*method*subplot)
Orientation
(plot*method*subplots*point)
Even though orientation was the greatest source of variation, the mean distribution of
surface soil disturbance by orientation indicated there was no significant difference in
orientation between North, East, South and West (Table 7). However, our method of
collecting data in the cardinal points direction happens to have cut across these
41
disturbances in the direction of harvesting and we were fortunate to have capture it. This
might explain the reason why we happened to catch soil surface disturbance by
orientation being the greatest source of the disturbance.
Table 7: Percentage Means by Orientation per each soil disturbance classes
Orientation
Damage
North
East
South
West
Mean Std
Mean Std
Mean Std
Mean Std
Dev
Dev
Dev
classes
Dev.
DC1
2.29
3.42
2.33
3.33
2.27
3.36
2.27
3.48
DC2
4.46
3.52
4.44
3.56
4.67
3.61
4.73
3.71
DC3
0.75
1.76
0.79
1.91
0.76
1.81
0.75
1.89
DC4
0.29
1.08
0.37
1.50
0.35
1.31
0.35
1.61
DC5
0.05
0.12
0.08
0.34
0.06
0.19
0.04
0.14
DC6
2.21
2.70
1.92
2.54
1.92
2.62
1.84
2.49
Further analysis to see if there were any differences between the means of the orientation
from point to point in the two harvesting systems revealed that there was no significant
difference in the directions that these soil disturbance data were collected. However, we
had to keep the direction constant for consistency.
3.4.4 Soil Compaction
Soil compaction was analyzed in an analysis of variance (PROC GLM, α = 0.05)
with the harvesting impact range (T1 = High, T2 = Moderate, and T3 = Low) as a split
42
plot. Soil compaction readings were classified under the status of disturbed and
undisturbed and the means calculated to show the relationship between soil compaction
and the level of trafficking. The method, plots, ranges, status, and their interactions were
tested for significance.
In general, the results for soil compaction measured an average of 171 pound per
square inch (psi) throughout the study area (see Table 8). This is generally good soil for
plant growth. The various ranges (Low, Moderate, and High intensities of trafficking) in
the CTL indicated higher compaction averages (156, 170, and 190 psi respectively with a
total mean of 172 psi) than in the TL (147, 163, and 178 psi respectively with a total
mean of 163 psi). This implies the CTL system causes more compaction than the TL
system.
43
Table 8: Average soil compaction measured in pounds per square inch (psi)
CTL
Plots
TL
Low
Moderate
High
Low
Moderate
High
(T3)
(T2)
(T1)
Total
(T3)
(T2)
(T1)
Total
Rep 1
165
175
181
174
141
160
193
165
Rep 2
130
157
161
149
146
172
174
164
Rep 3
174
177
228
193
153
155
172
160
Means
Grand Means
172
156
170
190
163
147
163
178
171
CLT (Rep 1 = B1T4; Rep 2 = Somerville; Rep 3 B4T10)
TL (Rep 1 = B1T5; Rep 2 = B4T4; Rep 3 B4T9).
The results between the plots also revealed some slight differences in the soil
compaction. In the CTL, plot three (Rep 3) indicated a higher soil compaction with 193
psi, followed by plot one (Rep 1, 174 psi), and plot two (Rep 2) with the least (149 psi).
For the TL, plot one (Rep 1) had the highest compaction reading (165 psi), next was plot
two (Rep 2, 164 psi), and last was plot three (Rep 3) with 160 psi.
44
Table 9: ANOVA for Soil Compaction
45
Source
DF
Sum of Squares
Mean Square
F-Value
P –Value
Method
1
2165.43
2165.43
0.48
0.527
Plot (method)
4
18165.08
4541.27
3.35
0.046**
Range (method*plot)
12
16262.24
1355.19
0.047
0.999
Status (method*plot*range)
18
518770.73
28820.60
26.77
0.000**
Error
234
251922.60
1076.59
** Significant at α = 0.05
Statistical evaluation (GLM ) of soil compaction indicated that there was no
significant difference in the level of soil compaction caused by the two harvesting
methods (P = 0.5278). This result was contrary to that obtained by Landford and Stokes
(1995) who found out that the TL system causes significantly more compaction than the
TL system at all levels of disturbance. This could be explained by the fact that they used
the bulk density and we used the cone penetrometer to measure the level of compaction.
Furthermore, the soil type (leached ultisols) for our study area was well drained soils and
could not have caused any significant difference in the soil compaction between the two
harvesting method. Thus, the different soil types could be another factor that can explain
for these differences in the results. Again, the tire sizes of the machine and the movement
of these machines inside the plots can also explain the results we got for this study.
However, the t-test indicated a significant difference (P = 0.0001) between the disturbed
and the non-disturbed areas. The difference in the mean was 208.42 psi for the disturbed
areas and 114.66 psi for the undisturbed areas. This shows a high correlation between
high trafficking areas and high compaction areas in the treatment plots. The passage of
these heavy equipments on the soil surface compact the soil more than in those areas of
the forest where there was no machine movement. The results obtained were contrary to
our expectation because with a large landing area in the TL system, we expected higher
soil compaction levels than in the CTL system. However, this can be explained by the
fact that in the TL unlike the CTL, the center of activities (that is the landing area) most
of the slash was left in heaps and sometimes spread along the path for the machines to
trample on thereby causing less impact on the soil below.
46
CHAPTER IV
RESIDUAL STAND DAMAGE
4.1 Data collection
A field survey was done to collect data of all damaged trees after the thinning
operation was finished. Residual stand damage was determined by counting the number
of injured trees all over the entire area harvested. For every damaged tree, the dbh, scar
length and height from ground level were measured using a logger’s tape. The crown
damage was assessed visually and separated from the stem damage. For every single
damage the location on the stem, the wound size and intensity were measured. A class
number was assigned to each damage classification based on the depth of the bark
removal and crown damage. This was done according to the method used by USDA
Forest Service (2003). The following damage classes (DC) and description of the damage
was assigned:
•
Class 1: minor stem damage (51-100% of bark present over wound);
•
Class 2: intermediate stem damage (less the 50% of bark present);
•
Class 3: major stem damage ( no bark present);
•
Class 4: minor crown damage (< 50% );
47
•
Class 5: major crown damage (>50%).
•
Class 6: undamaged crown but have stem damage.
According to Limbeck-Lilienau (2003) and Solgi and Najafi (2007), damages in
classes one and two pose no immediate threat to the trees but increases the likelihood of
attack by insects or diseases. However, the probability that wounded rot fungi appear as a
result of real wood injury (DC 3) is about 40 to 50% compared with the other damage
categories.
4.2 Statistical Analysis
To find whether the CTL system has less incidences of residual tree damage than
the TL system, the contingency tables ( Χ 2 ) tests was used to compare the level of
damage on the residual trees between the two harvesting systems. The following formula
was used:
2
^


 f ij − f ij 
 ……………………………………………………… (Equation 2)
Χ 2 = ΣΣ 
^
f ij
Where
Χ 2 = Chi Square
ΣΣ = Computation of grand total
f ij = Frequency of observed in row i and column j
^
f ij = Frequency expected in row i and column j
This was done using the SPSS statistical analysis software.
48
Percentage, frequency, and means were also calculated and used to compare the
level of damage between the stem and crown within the two harvesting methods. Since
the harvested plots were of different sizes (Table 1.), we standardized the data by
dividing the number of damaged trees in each class by the plot size and multiply it by 100
to get the number of damaged trees on a per 100 acres basis.
Results
A total of 474 residual trees were found damaged in the two harvesting systems.
Out of these, 124 trees were found in the treatments that were harvested using the CTL
method while 350 trees were found on those plots harvested using the TL method (Table
11).
49
Table 10: Number of trees damage in each class per plot and total number of trees
Method
Plot
DMC1 DMC2
DMC3 Total
DMC4 DMC5 DMC6 Total
Undamaged Total
Minor
Interm. Major
Stem
Minor
Major
U-CN
Crown
Trees
16
18
14
48
37
9
2
48
1616
1712
50
CTL
Somm
CTL
B4T10 5
13
26
44
20
10
14
44
2466
2554
CTL
B1T4
6
5
21
32
21
5
6
32
3076
3140
27
36
61
78
24
22
Total
7157
TL
B1T5
4
10
36
50
41
9
0
50
3134
3234
TL
B4T9
22
32
103
157
130
25
2
157
6673
6987
TL
B4T4
36
46
58
143
100
30
13
143
11054
11340
65
88
197
271
64
15
Total
20862
The highest number of residual tree damage in the CTL harvesting method were found on
treatment Somerville (48 trees) and the lowest number on B1T4 (32 trees); while in the
TL method, the highest number of damaged residual trees was found on treatment B4T9
(157 trees) and the lowest on treatment B1T5 (50 trees). Looking at these raw figures we
cannot compare the tree damage because of the different sizes of the plots. Therefore, we
converted them on a per 100 acres basis to standardize the data before comparing them.
The results are presented in Table 12.
51
Table 11: Residual tree damage of each harvesting method per 100 acres
Damage Classes
DMC1
DMC2
DMC3
Total
DMC4
DMC5
DMC6
Total
Undamaged
Grand Total
CTL
196
252
348
796
526
154
116
796
40764
43948
TL
127
184
465
776
612
139
25
776
45544
48648
Grand Total
323
436
813
1138
293
141
86308
92596
52
Method
The CTL had the highest number of residual damaged tree (796 tree per 100
acres) than the TL (776 tree per 100 acres). However, the TL harvesting method caused
more major stem damage (DMC3-465 trees per 100 acres) than the CTL (348 trees per
100 acres). On the contrary, the CTL harvesting method caused more minor (DMC1) and
intermediate (DMC2) than the TL. This because the dragging of wood in between the
trees in the TL harvesting method to the landing area obviously caused more major
damage while the minor and intermediate stem damages are mostly caused by machine
maneuverability and machine tires. The TL had the lowest undamaged crown because the
feller-buncher could cut the trees and drop them in an open area thereby preventing much
crown damage.
Table 12: Number of residual tree damaged per treatment plot (Per 100 acres)
Site
Size (acres)
Method
No. of residual trees damaged
% Damaged
B1T4
25
CTL
128
1
Somerville
11
CTL
436
3
B4T10
19
CTL
232
2
B1T5
26
TL
192
2
B4T9
44
TL
357
2
B4T4
63
TL
227
1
After standardization, CTL in Somerville still had the highest number of damaged
trees (436 trees per 100 acres) and B4T9 for the TL method (357 trees per 100 acres)
53
Total No. of Damaged Trees/100
acre
Residual Tree Damage
700
600
500
400
300
200
100
0
DM
DM
C
1
(b
ac
k
51
-1
00
C
)
2
(b
ar
k
<5
DM
0)
C
3
(N
o
ba
DM
rk
)
C
4
(C
r<
50
DM
)
C
DM
5
(C
C
r>
6
(C
50
rN
)
o
da
m
ag
e)
CTL
TL
Damage Class
Figure 3: Distribution of residual tree damage in to the various classes
•
DMC1, DMC2, and DMC3 = Stem damage
•
DMC4, DMC5 = Crown damage
•
DMC6 =Undamaged crown
Generally, the results show that approximately 49% (776) of all incidents of residual
tree damage were caused by the TL harvesting method while the CTL accounted for 51%
(796). Major stem damage (DMC3) per 100 acres (465 trees) occurred in the TL system
as compared to 348 trees in the CTL. The highest numbers of minor (DMC1) and
intermediate (DMC2) stem damage occurred in the CTL system (196 versus 127 and 252
versus 184 trees respectively) for the TL harvesting method. The results also shows that
the CTL system caused more crown damage that the TL system (DMC4 – 526 trees;
54
DMC5 – 144 trees; and undamaged crown (DMC6 116 trees compared to 612, 139, and
25 trees respectively for TL). These results are contrary to those obtained by Solgi and
Najafi (2007) who reported that the TL system causes more wounding (80%) to residual
trees than the forwarder in the CTL system (20%). Scarring damage can be more serious
than other types of wounding because, although it may not affect tree diameter growth, it
can decrease future log value.
Damaged trees in the TL system were highly concentrated along skid trails. Out of
the 776 residual trees damaged in TL harvesting system, 461 of them were found along
the major tails. This represents about 60 % of the total stem damage in this system. The
greatest number damage occurred within 2 m from the centerline of the skid trail and
most of them with within 1 m above the ground. This high number can be explained by
the act of dragging the felled trees by the skidder to the landing area. Han-Sup and Loren
(2000) reported the greatest damage occurred within the first 10 ft from the centerline of
the skid trails or skyline. Froese and Han-Sup (2002) reported highest total scarring
damage (73.7%) occurred within 15 feet of the nearest skid trail centerline.
Chi Square statistical analysis (P = 0.000) revealed that there is a significant different in
the level of residual tree damage between the two harvesting system (Table 15).
55
Table 13: Contingency table for residual tree damage
CTL
DMC
TL
Observed No. Expected No.
Observed No. Expected No.
1
196
153
127
170
2
252
206
184
229
3
348
385
465
428
4
526
539
612
599
5
154
139
139
154
6
116
67
25
74
Undamaged
40764
40867
45544
45441
Total
42356
42356
47096
47096
Test Statistics
Chi Square DF Probability
Tree Damage 121
6
0.000
Separating the stem from the crown damage, also indicated a significant difference in the
level of damage caused to residual trees between the two harvesting systems (P = 0.00)
for stem damage and P = 0.00 for crown damage. The implication of this is that the TL
harvesting method causes less residual damage than the CTL method.
Limbeck-Lilienau (2003) reported that the main reasons for the differences in the
level of residual tree damage between the TL and the CTL systems were the harvesting
intensity and the slope. Even though with the differences in the slope, she also reported
56
that the correlation between terrain slope and damage level has not yet been statistically
proven but a trend was reported by other authors (Stampfer et al., 2001). However, in our
case, since the harvesting intensity (50% retention) and the average slope (20 to 30%)
were the same for all the research plots, the disparity in the level of residual tree damage
could be ascribed mainly to the different methods of harvesting. The skidding system of
dragging trees to a central location (landing area) for delimbing resulted in more damage
along skid trails as oppose to forwarder system. Also, different levels of operator
experience cannot account for this disparity. The CTL contractor was the only operator of
his machines (the forwarder and harvester) and has been in the logging business for over
36 years. He was expected to be more careful in maneuvering the machines within the
trees, thus causing less damage to the standing trees. On the other hand, the TL contractor
had employed two operators who had 15 and 18 years of experience respectively.
However, the higher incidences of tree damage in the CTL system might have been
caused by delimbing the trees in the woods unlike in the landing area as in the TL system
can cause more damage to the residual trees. All of these are possible explanations why
we have more residual tree damage in the CTL harvesting system than the TL system.
57
CHAPTER V
PRODUCTIVITY AND COSTS
Productivity and cost are essential components of a harvesting operation. In our
study, we used two methods to evaluate cost and productivity of the CTL and TL
harvesting methods. These methods include the machine rate method using the cost
spreadsheet in Brinker et al., (2002) and the system productivity method (Visser, 2007).
The first approach calculates the cost per PMH and SMH for each machine while the
second calculates productivity based on what the system currently delivers on the average
per day or week divided by the PMH (tons/PMH) or SMH (tons/SMH). Machine costs
are sometimes very difficult to calculate because the loggers are reluctant to release
information they considered as sensitive while the second is much easier to calculate
based on the actual production.
58
4.1 Data Collection
A time study during the harvesting operation was conducted to determine the
productivity of the two systems. The Machine Rate Method was used to calculate the
hourly costs and estimate the productivity for each specific machine in the CTL and TL
harvesting methods. These machines include a harvester and a forwarder for the CTL,
and a feller-buncher and a skidder for the TL systems. Data were collected using a
MultiDAT (a multi-purpose datalogger) placed on each of these machines for a duration
of two weeks. These data were downloaded every three days during the length of the twoweek period and were used to calculate productive and non-productive time and to
monitor interactions between equipment; operator and harvesting operations (see Table
14). Out of the two weeks the actual number of days worked were 10 days and the data
was collected on a 24x7 168 hours schedule. However, the actual scheduled hours to
work were 8 hours per day, 5 days per week. Other data collected included level of
experience of the operators (number of years in operation both for the owners and
employees), amount of fuel used per day, the price of fuel during that period, the
purchase prices of the various machines, the horsepower and number of hours worked,
and cost of labor per hour.
59
Table 14: Machine Productivity
Method Machine
Total Recorded Recorder Working Short Idle
Total
time
Data (H)
Active
(H)
(H)
Recorded Recorder Total
stops
Time
working Working
Active
Production
Time
(H)
Time
(%)
(%)
(ton)
(H)
(%)
Skidder
336.
324.22
28.18
20.22
2.78
301.22 6.02
6.24
71.74
TL
Feller-
336
324.29
235.14
16.00
0.05
307.77 4.76
4.93
6.80
60
TL
Buncher
1,120
CTL
Forwarder 431
431
20.01
16.14
1.19
413.67 3.74
3.74
80.65
CTL
Harvester
431
10.35
7.58
0.34
423.07 1.76
1.76
73.25
431
* These numbers were obtained from the MultiData for duration of two weeks
280
Table 15: Total Machine Utilization
Method Machine
Total
Recorded Scheduled Engine
Engine
61
* These numbers were obtained from the MultiData for duration of two week
Work
Work in %
Work in %
Idling in
time
Data (H)
Time (H)
running
Idling
Time
of recorded
of schedule
% of
(H)
(H)
(H)
data (%)
time (%)
engine
time (%)
TL
Skidder
336.
324.22
336.00
28.18
7.96
20.22
6.24
6.02
28.26
TL
FellerBuncher
336
324.29
336.00
235.14
219.14
16.00
4.93
4.76
93.20
CTL
Forwarder
455
455.00
456.00
22.57
4.31
18.25
4.01
4.00
19.12
CTL
Harvester
455
455.00
456.00
10.35
2.77
7.58
1.67
1.66
26.75
62
4.3 Data Calculations and Analysis
After these data were collected, they were entered and calculated using a
spreadsheet put together with information obtained from Brinker et al., (2002). This
spreadsheet is shown in Appendix 7. The purchase prices of the machines, and the
horsepower were obtained from the owners. The machine life span (expected life) was set
at 5years (IRS standard for tax purposes).for the TL machines (feller-buncher and
skidder) while the harvester and forwarder in the CTL system had been paid off (the
owner had been using them for over 11 years). The salvage value rate, which is a
percentage of the purchase price of each machine, was calculated by dividing the
purchase price of the machine by the life span and dividing the quotient by the purchase
price of the machine. The salvage value was obtained by multiplying the purchase price
by the salvage value rate. The utilization rate was calculated based on the actual hours
worked divided by the scheduled Machine hours (SMH). The actual hours worked were
obtained from the MultiDAT (see Tables 14 and 15) and the SMH was set at 80 hours
(calculated based on 8 hours of work per day and 5 days per week for the two weeks that
the machines were working). Repair and maintenance was set at 1%, interest, insurance,
tax rates, and lube and oil (percent of fuel cost) were set at 4% each (from Brinker et al.,
2002). Fuel cost at the time of our study was $2.30 per gallon and the fuel consumption
rate for each machine horsepower was calculated by dividing the number of gallons
consumed per hour by the respective horsepower. Annual depreciation was calculated by
subtracting the salvage value from the price of the machine and dividing the difference by
the life span. Average yearly investment (AYI) was obtained by subtracting the salvage
value (S) from the purchase price of the machine (P) and multiplying the difference by
63
the life span (n) plus 1 and dividing the product by 2 times the life span plus the salvage
value.(AYI = (((((P-S) x (n+1))/(2 x n))+S)/50) x 2) However, since the machine only
work for two weeks, the annual depreciation and average yearly investment were divided
by 50 weeks in a year (50 working weeks in a year to get amount per week) times 2 (two
weeks) to get the actual amount of depreciation and investment for that period of time.
Productive machine hours (PMH) was calculated by multiplying SMH by utilization rate
(or could be got directly from MultiDAT Table 15)
Ownership cost was calculated based on interest, insurance, and tax costs based
on rates obtained from Brinker et al., (2002). The total dollar amounts were calculated by
multiplying the average yearly investment by their rates. The yearly ownership cost was
calculated by adding the annual depreciation to the interest, insurance, and tax costs.
Ownership cost per SMH was obtained by dividing the yearly ownership cost by the
SMH and the ownership cost per PMH was calculated by dividing the yearly ownership
cost by the PMH. Note should be taken that when we adjusted the annual depreciation
and the average yearly investment to the two weeks period of operation, ownership costs
are all presented only for that two weeks period.
Operating costs were calculated as follows, total fuel cost for each machine was
obtained by multiplying the horsepower by the fuel cost and fuel consumption rate. Lube
(engine oil, hydraulic, grease, and other lubricants) cost per hour was calculated by
multiplying the fuel cost per hour by the rate of fuel cost. Repair and maintenance cost
was obtained by multiplying annual depreciation by rate of depreciation and dividing the
product by PMH. Operator labor and benefit cost per hour was calculated by dividing
operator wage and benefit rate by utilization rate. Operating cost per PMH was calculated
64
by adding fuel cost plus lube cost plus repair and maintenance cost and dividing the sum
by operate labor and benefit cost per hour. Operating cost per SMH was obtained by
multiplying the operating cost per PMH by the utilization rate. The total cost per SMH
was obtained by adding the ownership cost per SMH and the operating cost per SMH.
Finally the total cost per PMH was calculated by adding the ownership cost per PMH and
the operating cost per PMH. The detail spreadsheet calculations for all the machines are
in Appendices 7 to 10.
Results
The results of the machine rate calculations are shown in Table 16. For the TL
harvesting method, the purchase price for the feller-buncher was $140,000 and $185,000
for the skidder. The purchase prices for the harvester and the forwarder in the CTL
harvesting method were $248,000 and $200,000 respectively. Thus the total cost for
equipment for the CTL machines was $448,000 as opposed to the TL machines which
was $325,000. However, the CTL operator had already paid off for his equipment and
incurred not equipment cost. Depreciation (decline in value of a machine due to wear,
obsolescence, and weathering) for the machines in the TL harvesting method was set at 5
years even though from personal conversation with the owner, these machines could last
more than 5 years depending on their usage and maintenance. Salvage value (the
estimated value of an asset at the end of its useful life) of the machines was calculated to
be 2 percent of purchase price and the respective dollar amounts were as follows;
$34,820 for the feller-buncher, $37,000 for the skidder, $22,545.45 for the harvester, and
$18,181.82 for the forwarder In Table 16 these figures for the harvester and forwarder
65
appear as zero for the computation of ownership cost but were used to calculate taxes,
insurance, and repair and maintenance. Annual depreciation cost for the two weeks
operation period was calculated to be $841.44 for the feller-buncher, $1,184.00 for the
skidder, $0.00 for the harvester, and $0.00 for the forwarder. The values are zero because
equipments have been paid off but they were still calculated to know the cost of
maintenance – $819.83 for the harvester and $661.16 for the forwarder. The average
yearly investment was $3,917.12 for the feller-buncher, for the skidder $5,032.00, and
those for the harvester and forwarder were $0.00.
The PMH (the actual hours the various machines worked) were calculated to be
16 hours for the feller-buncher, 20.22 hours for the skidder, 7.58 hour for the harvester,
and 16.14 hours for the forwarder. Operator wage and benefit rate was set at 30 percent
and cost was calculated by adding one to rate and multiplying the sum by the hourly rate,
which was $12.00 per hour. Thus, the total cost of labor and fringes per person for each
machine in the two harvesting method was $15.60 per hour.
66
Table 16: Machine rate calculations for TL harvesting system for two weeks
Input Data
Purchase price ($)
Machine horsepower rate (hp)
Machine life (yrs)
Salvage value, (% of purchase price)
Utilization rate (%)
Repair & maintenance (% of depreciation)
Interest rate
Insurance and tax rate (%)
Fuel consumption rate (gal/hp-hr)
Fuel cost ($ per gal)
Lube and oil (% of fuel cost)
Operator wage and benefit rate ($/hr)
Scheduled machine (hrs/10 days)
2. Calculations
Salvage value ($)
Annual depreciation ($/10 days)
Average year investment ($/10 days)
Productive machine hours (hrs/10 days)
3. Ownership costs
Interest cost ($/10 days)
Insurance and tax cost ($/10 days)
Yearly ownership cost ($/10 days)
Ownership cost per SMH ($/hr)
Ownership cost per PMH ($/hr)
4. Operating costs
Fuel cost ($/hr)
Lube cost ($/hr)
Repair and Maintenance cost ($/hr)
Operator labor and benefit cost ($/hr)
Operating cost per PMH ($)
Operating cost per SMH ($/hr)
5. Total Machine Costs
Total cost per SMH ($/hr)
Total cost per PMH ($/hr)
F-Buncher
140,000
170
5
0.25
0.20
0.01
0.045
0.045
0.033
2.30
0.04
15.60
80
Skidder
185,000
119
5
0.2
0.25
0.01
0.05
0.05
0.05
2.30
0.04
15.60
80
Harvester
0.00
163
0
0.00
0.095
0.01
0.04
0.04
0.034
2.30
0.04
15.60
80
Forwarder
0.00
116
0
0.00
0.20
0.01
0.04
0.04
0.034
2.30
0.04
15.60
80
34,820
841.44
3,917.12
16.00
37,000
1,184.00
5,032.00
20.22
0.00
0.00
0.00
7.58
0.00
0.00
0.00
16.14
176.27
176.27
1,193.98
14.92
74.62
251.60
251.60
1,687.20
21.09
83.44
0.00
232.83
0.00
0.00
30.72
0.00
187.77
0.00
0.00
11.63
11.50
0.46
0.53
78.00
90.49
18.10
13.80
0.55
0.59
61.72
76.66
19.38
12.65
0.51
1.08
164.64
177.80
16.85
9.20
0.37
0.41
77.32
86.52
17.46
33.02
165.11
40.47
160.10
16.85
208.52
17.46
98.16
Interest, insurance and taxes rates were set at 4.5 percent for the feller-buncher, 5
percent for the skidder, and 4 percent each for the harvester and the forwarder.(from
Brinker et al., (2002). Therefore their respective costs for the two weeks period were as
67
follows; interest cost for the feller-buncher was $176.27, that for the skidder was
$251.60, and those for the harvester and the forwarder were $0.00 each. Taxes and
insurance costs were $176.27 for the feller-buncher, $251.60 for the skidder, $232.83 for
the harvester, and $187.77 and forwarder. The yearly annual ownership for the same
period was $1,193.98 for the feller-buncher, $1,687.20 for the skidder, and 0.00 for the
harvester and the forwarder. Ownership cost per SMH was calculated to be $14.92 for the
feller-buncher, $21.09 for the skidder, and $0.00 for the harvester and the forwarder.
Ownership cost per PMH for the feller-buncher was $74.62, $83.44 for the skidder,
$30.72 for the harvester and, $11.63 for the forwarder. The ownership costs for the TL
harvesting method were lower than those for the CTL harvesting method because they
included only taxes and insurance as all equipment had been paid off.
For the operating costs, fuel and lube cost were calculated at $11.50 and $0.46
respectively for the feller buncher; $13.80 and $0.55 for the skidder; $12.65 and 0.51 for
the harvester; $9.20 and $0.37 for the forwarder. Repair and maintenance cost for the
feller-buncher was $0.53, $0.59 for the skidder, $1.08 for the harvester, and $0.41 for the
forwarder. Operator labor and benefit cost for each machine were as follows $78.00 for
the feller-buncher, $61.72 for the skidder, $164.64 for the harvester, and $77.32 for the
forwarder. Operating cost per PMH for each machine was $90.49 for the feller-buncher,
$76.66 for the skidder, $177.80 for the harvester, and $98.52 for the forwarder while
operating cost per SMH for the feller-buncher was $18.10, $19.38 for the skidder, $16.85
for the harvester, and $17.46 for the forwarder.
The total machine cost per SMH (ownership costs plus operating costs) was
$33.02 for the feller-buncher, $40.47 for the skidder, $16.85 for the harvester, and $17.46
68
for the forwarder. The total cost per PMH was calculated at $165.11 for the fellerbuncher, $160.10 for the skidder, $208.52 for the harvester, and $98.16 for the forwarder.
To assess the actual total cost for each machine in each harvesting method, we
considered only PMH. In order to obtain the total dollar amount spent for the two weeks
of operation when we collected the data, we computed average investment (AYI) plus
operating cost per PMH multiply by the PMH and add the cost of transporting the wood
to the mill. The calculations are as follows:
Feller-buncher: - AYI ($3,917.12) + Operating cost ($90.49/PMH) x PMH (16
hrs) = $5,364.89.
Skidder: - AYI ($5,032.00) + Operating cost ($ 76.66/PMH) x PMH (20.22 hrs)
= $6.582.04
Harvester: - AYI (0) + Operating cost ($177.80/PMH) x PMH (7.58 hrs) + Taxes
and insurance ($232.83) + RM ($1.08) = $1,581.63
Forwarder: - AYI (0) + Operating cost ($98.16/PMH) x PMH (16.14 hrs) + taxes
and insurance ($187.77) + RM (0.41) = $1,772.48.
Total cost for TL (without transportation) = $11,946.93 ($5,364.89 +$ 6,582.04)
Total cost for CTL (without transportation) = $3,354.11 (1,581.63 + $1,772.48)
Transportation cost for the TL system was contracted out at $8.50 per ton. Thus,
the total cost for transporting the wood to the mill cost $9,520 (1,120 tons times
$8.50/ton). For the CTL system, the wood was transported to the mill by the owner using
his truck. The truck cost him $75,000 and he had been using it for 11 years. It had been
paid off and the salvage value was calculated at $6,618.18. The mill was located about 50
miles from the harvesting plots and the truck used to transport the wood consumed 5
69
gallons per every 50 mile. Therefore to and from the mill, the truck used 10 gallons per
day. For the two weeks, the truck consumed 100 gallons multiplied by the price of gas at
that time ($2.30 per gallon), the total cost of fuel was $230.00 ($2.30 x 100 gallons).
Lube rate for the truck was 0.04% of fuel, giving a total cost of $9.20 (0.04 x $230.00).
Taxes and insurance for the two weeks was $70.41 (using the spreadsheet based on
salvage value). Operator labor and benefit cost for the truck was $260.05 ($15.60 for
hourly wage x 16.67 PMH). Repairs and maintenance cost was $0.15 (calculated using
the spreadsheet). Total cost for operating the truck for the two weeks was $569.81`
($230.00 + $9.20 + $70.41 + $0.15 + $260.05).
The final formula to calculate production cost for each machine in each harvesting
method was average yearly (AYI) plus Operating cost per PMH multiplied by PMH plus
transportation cost. Based on our calculations, the final total cost for the feller-buncher
and the skidder in the TL harvesting system including transportation was $21,466.93
($11,946.93 + $9,520) while that for the harvester and forwarder in the CTL system was
$3,923.92 ($3,354.11 + $569.81). Our analysis based on this study indicated that the CTL
harvesting system cost less to operate than the TL system. This result differed from that
obtained by Lanford and Stokes (1995) who stated that the CTL harvesting system has a
higher cost than the TL system due to initial cost of equipment. The explanation in our
study was that the CTL operator had paid off for all his equipment and was only incurring
operating costs plus the cost of insurance and taxes while the TL operator was incurring
both ownership costs and operating costs.
The productivity of each harvesting method was calculated using the system
productivity method. According to Visser, (2007), this is calculated by getting the
70
number of tons of wood each harvesting system currently delivers per day or week and
divides that number by the total machine SMH. This gives an indicative productivity
(tons/SMH) for each harvesting system can be compared. However, in our study we
divided the total tonnage produced by the PMH to obtain the actual productivity.
Actual production from the TL system averaged four trucks per day and each
truck contained about 28 tons (information collected from the loggers). Thus, the average
daily production was 112 tons per day. Thus for the 10 days within the two weeks of
operation, 1,120 ton (112 tons/day x 10) were delivered at the mill. The PMH for the
feller-buncher was 16 hours and 20.22 hours for the skidder over the two weeks in TL
harvesting method. Thus, the productivity for the feller-buncher was 70.tons per PMH
(1,120 tons divided by 16 hours) and that for the skidder was 56 tons per PMH.
For the CTL system, since the owner was working alone, the average daily
production was only one truck per day (about 28 tons/day). Within the time of operation,
the CTL operator worked for 10 days and produced 10 trucks load of wood (average of
one truck per day) Thus, the total production was 280 tons (28 tons/day x 10 days). The
PMH for the harvester was 36 tons per PMH and 17 tons per PMH for the forwarder.
After calculating the total cost (costs of ownership and operation) and
productivity for each harvesting method, we calculated the returns (profits) for each
system to evaluate which of them yielded more profits to the operator. This was
calculated by subtracting total cost from total revenue obtained during the two weeks of
operation. From personal conversation with the loggers, the price paid per ton of wood at
the mill was different. The mill paid the TL contractor $38/ton and the CTL, $35 per ton
because the mills could get more wood from the TL system than the CTL. Total revenue
71
obtained from the TL harvesting method was $42,560.00 (1120 tons*$38.00/ton) and
$9,800.00 (280 tons*$35.00/ton) for the CTL system. Hence, the total profit obtained
from the TL harvesting was $21,093.07 ($42,560.00 – $21,466.93) and $5,876.08
($9,800 - $3,923.92) for the CTL system.
These results were contrary to our expectation as the CTL harvesting method
proved to be less expensive to operate and yielded less profit to the owner. Nevertheless
this could be explained by way the two systems were operating under different
circumstances with CTL operator working alone. If he had not paid off for all his
equipment, it would have been less cost effective for him to be operating the two
machines and the truck alone. The only way this method would have made profits was to
have more than one operator operating the tow machines in the system as was in the case
of the TL harvesting method.
Furthermore, the operator performance (see Table 17) was low in the CTL
harvesting method. Even thought with his longevity in the profession (about 38 years in
the business), we expected to have a high performance than that of the operators in the
TL system.
72
Table 17: Operator performance
Method Machine
Logged
Engine
Work
Work in % of
Idling
Time (H) Running (H) Time (H) Logged Time (%) (H)
TL
Skidder
TL
FellBuncher
324
28.18
20.22
6.24
7.96
324.29
235.14
16.00
4.93
21.14
CTL
Forwarder 431.00
20.01
16.14
3.74
3.87
CTL
Harvester
10.35
7.58
1.76
2.77
431.00
Looking at (table 20) the TL operators were performing much better than the CTL
operator on all the two machines in each system. The performance of the operator on the
skidder was 6.24 percent and feller-buncher was 4.93 percent compared to that of the
forwarder 3.74 percent and harvester 1.76 percent. May be if the operator was working
with another worker, he might have been motivated to work harder. Again since he was
running the business as a family business and all of his equipment was paid off, he was
not under any pressure to increase performance. As a consequence, the CTL harvesting
method in our study did not prove to be the most effective and high profit yielding.
73
CHAPTER VI
SUMMARY AND CONCLUSIONS
This study focuses on evaluating and comparing the environmental impact of two
popular harvesting systems (CTL and TL) used in North Alabama. The study site was
chosen for the research because it provided the appropriate conditions for the researcher
to conduct the experiments. The site had similar characteristics and some were side-byside. Care was taken by the researcher to make sure each harvesting system was
conducted in similar conditions even though we later encounter some technical
difficulties. We had to look for a new treatment to complete the third CTL replication
after the contractor lost his bid. Furthermore, the CTL system had only one operator for
both the forwarder and harvester as oppose to two in the TL system. This made
comparing the productivity of the two systems more complicated. Again, we had to
replace the antenna for the MultiDAT on feller-buncher about three times because it kept
getting cut as the feller-buncher was maneuvering in the woods and scrubbing it against
the trees thereby making it difficult to track the positional movement of the machine
inside the treatments. In spite of all these problems, we were able to overcome some and
carry out our research successfully and were able to answer our research questions.
74
Forest operations, particularly harvesting activities greatly affect the health of residual
trees and causes huge soil disturbance which is not only a concern to environmentalists
but also forest managers. All of these operations are not void of negative consequences
ecologically but researchers are interested in researching to find out which system causes
less impact and find solutions that will mitigate these adverse effects thereby promoting a
more sustainable environment.
The first objective of this study was to evaluate and compare the effects of the
CTL and TL harvesting systems on soil surface and some soil physical properties. The
results of our analysis indicated no significant differences in the soil disturbance caused
by the two harvesting methods. However, we experienced some variation in the various
soil disturbance classes between the two harvesting systems. The movement of heavy
equipments and the dragging of harvested trees to the landing areas caused a lot of soil
disturbances along trafficking trails. Based on our analysis in GLM, there was a
significant variation in the level of point to point surface soil disturbance classes within
the two harvesting systems except for DC5 (Road construction). Disturbance from road
construction was the least in both harvesting systems because in most of the treatments,
only one major road was usually constructed to serve as an outlet. Again, our analysis
also revealed that the direction of logging (orientation) causes significant variation in the
surface soil disturbance classes between the two systems. The interaction between
methods, plots, and subplots did not show any significant variation in the level of surface
soil disturbance among the various soil disturbance classes. Surface soil disturbance is
important because it affects the rate of surface runoff (erosion) and consequently the
75
productivity of the residual trees. Slash left in piles like in the TL system also the
regeneration of the forest particularly around the landing areas (see Appendix 8).
Soil compaction results revealed that overall; there was no significant difference
between the two systems. However, there was variation in the range of activities (center
of high activities and that of low or moderate activities). In the TL system, these areas
were along the major trail where harvested trees were skidded to the landing areas and
around the landing areas while in the CTL systems the center of activity was along the
major outlet route. The t-test indicated a significant difference between the disturbed and
non-disturbed surfaces thereby showing a high correlation between areas of high
trafficking and no trafficking. Soil compaction is very important to the tree root
penetration into the ground and also the absorption of nutrients (Ampoorter et al., 2007).
Since the movement of heavy equipment inside the forest also causes residual tree
damage, our second objective was to compare the two harvesting methods and see which
of them causes more residual stand damage than the other. Residual tree damage is
important because it affects the health of the trees (Clatterbuck, 2006). The results of our
statistical analysis revealed that residual tree damage was significantly higher for the TL
system than in the CTL system. This was evident in all the damage classes but for
intermediate stem damage (170 trees observed for the CTL against 154 trees for the TL).
Most of the damage trees in the TL system were observed along the major skidding trail
representing 72% of the total damage. A majority of the scars in the two harvesting
system were found less than 1 m above the ground level.
Cost and productivity are important components that determine the profitability of
a logging system. Cost influences the type of harvesting system and equipments an
76
operator needs to buy. Thus, our third objective was to evaluate and compare which of
these harvest systems will cost less to own and operate and at the same time have high
productivity rate, thereby yielding higher profits to the operator. A combination of these
factors will allow the operator to stay competitive in the business. The results of our
analysis indicated that the CTL harvesting system has a lower total cost to own and
operate than the TL harvesting system ($208.52/PMH for the harvester and $98.16/PMH
for the forwarder in the CTL system, and $165.11/PMH for the feller-buncher and
$160.10/PMH in the TL system). The feller-buncher and the skidder in the TL harvesting
system had a higher productivity rate (70 tons/PMH and 56 tons/PMH respectively) than
that of the harvester and forwarder (36 tons/PMH and 17 tons/PMH respectively) in the
CTL system. This was because the CTL operator was working alone. However, with the
current price offered at the mill for the full tree ($38 per ton of wood) as oppose to
$35.00 per ton for the already cut logs from the CTL system, the TL operator was making
a higher net gain of about $15,216.99 more than the CTL operator. If the CTL operator
had not paid off for his equipment, he would have been making enormous losses. This
analysis can explain why most operators around northern Alabama will prefer the TL
system over the CTL. The only thing that can change this trend will be for forest manager
to factor CTL harvesting method into their management objectives and give some
consideration to the CTL operators in the contract bidding process so that they can
compete the TL operators.
77
RECOMMENDATIONS
In light of the results of this study, the following recommendations should be
taken into consideration in order to ensure that any forest operation will mitigate its
impact on the environments and also ensure sustainability.
Since in our statistical analysis of soil disturbance, we found out that orientation
(direction of harvesting) was significant, directional felling of trees should be taken
seriously. This is a situation whereby trees are felled to fall perpendicular to the main
direction of the slope as this will reduce the impact of raindrops and runoff. Directional
felling is important not only because it increases production but it also reduces residual
stand damage. This will entail the need for pre-harvest planning. During pre-harvest
planning, the skid trails should be laid out to reduce residual tree damage, increase
efficiency and reduce overall skid distance, property boundaries, and stream management
zones (SMZs) or any other control points should be located so they can be avoided
without slowing the operation down. More important in this pre-harvesting planning
process, the researcher should be able to communicate with the operators as what kind of
measurements are to be carried out, equipment to be placed on the machines, and also
make sure all bid deals have been finalized. This will prevent the situation we ran into
where the CTL contractor pulled out. Finally, based on the results of this study, even
though the CTL harvesting system has a higher initial investment and operating cost and
reduced productivity, it is still highly recommendable because: 1) it is more suitable for
directional tree felling and minimizes residual stand damage than the TL harvesting
system, 2) the harvester in the CTL system is highly flexible in that it can be used as a
feller-buncher where whole trees are felled and bunched to an open location for
78
delimbing thereby avoiding much crown damage, and 3) the CTL system can be
programmed to process trees into specific log lengths that meet specific factory-grade log
requirements or pulpwood length.
In conclusion, the decision as to which harvesting method ought to be used should
be based on the management goals. This should be factored into the management plan
before a bid is offered. In this way, this very important management decision is not left in
the hands of the highest bidder. If this happens, then obviously the TL contractor will
take precedence as seen by the results of our analysis. However, any management
decision should seriously consider the environmental consequences of any forest
operation as the obvious goal should be environmental sustainability.
79
APPENDICES
Appendix 1
Figure 4: Use ArcGIS to determine the number of points on a treatment area (about
1000).
80
Appendix 2
Figure 5: Selecting Six Sampling plots of 9 points each (total = 54)
Appendix 3
Determining the required sample size the following formula was used
n=
1
2
 A 
1
 (t )(cv)  + N


……………………………………………… (Equation 3)
n - Sample size (units/points)
A - Allowable error (expressed as a % of the mean). A 25% allowable error was chosen
t - Confidence level (95 % confidence level = 2)
81
cv - Coefficient of variation (this number is gotten from pre-sampling = 94)
N - Population (total number of point estimated to be treatment area = 1000)
n=
1
2
 25 
1
 (2)(94)  + 1000


= 54 points
Appendix 4
00(N)
2700
10 m
900(E)
(W)
10 m
1800 (S)
Figure 6: Using DME to measure soil disturbance 10m in each direction
82
Appendix 5
Figure 7: Movement of the Harvester and Forwarder inside Somerville treatment.
83
Appendix 6: Harvesting equipment used in these operations
Feller-buncher
A felle- buncher is a mobile machine, either rubber tired or tracked, with a power
engine, operator enclosure, and an articulating extensible arm onto which a felling head is
attached. The felling head consists of grappling devices and either a disc saw or a chain
saw. The operator moves the machine into position in front of a tree and maneuvers the
felling head to the tree trunk. The grappling devices wrap around the tree and the saw
severs the tree from the stump. The machine then takes the severed vertical tree and
lowers it into a horizontal position onto a pile or bunch of trees on the ground, hence the
term feller-buncher.
Skidder:
A grapple skidder is a rubber tired four-wheel-drive machine consisting of a
power engine, operator enclosure, forward dozer blade and a maneuverable grappling
device at the back of the machine. These machines are generally used where fellerbuncher machines are working. The grapple skidder backs into position adjacent to
previously felled piles (bunches) of trees. The operator opens and lowers the grapple onto
the trunks of the trees and then closes the grapple and raises the tree trunks slightly off
the ground. The machine then moves the trees through the woods to the landing and
drops them off.
84
Figure 8: Skidder
Figure 9: Skidder
Figure 10: Feller Buncher
Figure 11: Forwarder
Harvester
A harvester is a machine that combines the features and abilities of the feller
buncher and processor and that may or may not have a bunk to store and then forward the
trees or cut logs to the landing.
85
Forwarder
A forwarder is a tracked or rubber tired machine consisting of a power plant,
operator enclosure, dozer blade, articulating grapple, and a bunk to the rear. This machine
usually follows the processor and picks up the cut-to-length logs, places them in the bunk
and then takes the logs out of the woods and piles them at the landing. It then moves back
into the woods to repeat the process. A forwarder may also be used to pick up bunched
trees and forward them to the landing where a machine called a delimber is used to
remove the limbs, cut off the tops, and pile the logs.
Delimbers:
Dangle-head Delimber.
The dangle head sits at the end of a boom on a “wrist-like” swivel. The head has
wheels in which pull the tree through, delimbs, measures, and then cuts to length. These
machines can process approximately 2 to 4 trees per minute depending on timber type
and operator experience, with the operator fully enclosed in a protective cab. They
require less landing space than the stroke delimber.
Stroke Delimber
The stroke delimber does not have a moveable head; it rather grabs the tree and
pulls it through the main body as it removes the limbs and cuts to the desired length. A
stroke delimber grasp the tree at the butt end, extend the boom and sharp knives/arms
conform to the bole and cut off the limbs as the stroke arm extends. There is a bucking
86
saw that tops the tree at the minimum preset diameter. The bole is then drawn into the
machine, a calibrated wheel/gear determines when the right log length is reached, and the
bucking saw does its work.
87
Appendix 7: Machine rate calculation for feller-buncher for two weeks
Feller-buncher
1. Input Data
Purchase price (P) $ ________
Machine horsepower rating (hp) ________hp
Machine life (n) ________yrs
Salvage value, percent of purchase price (rv%) ________%
Utilization rate (ut%) ________%
Repair and maintenance, percent of depreciation (rm%) ________%
Interest rate (in%) ________%
Insurance and tax rate (it%) ________%
Fuel consumption rate (fcr) ________gal/hp-hr
Fuel cost (fcg) $ ________per gal
Lube and oil, percent of fuel cost (lo%) ________%
Operator wage and benefit rate (WB) $ ________hr
Scheduled machine hours (SMH) ________hrs/10 days
2. Calculations
Salvage value (S) = (P*rv%) $ ________
Annual depreciation (AD) = ((P-S)/n) $ ________/10 days
Average yearly investment (AYI) = ((((P-S)*(n+1))/(2*n))+S) $
________/10 days
Productive machine hours (PMH) = (SMH*ut%) ________hrs/10 days
3. Ownership costs
Interest cost (IN) = (in%*AYI) $ ________10 days
Insurance and tax cost (IT) = (it%*AYI) $ ________10 days
Yearly ownership cost (YF$) = (AD+IN+IT) $ ________10 days
Ownership cost per SMH (F$SMH) = (YF$/SMH) $ ________hr
Ownership cost per PMH (F$PMH) = (YF$/PMH) $ ________hr
4. Operating costs
Fuel cost (F) = (hp*fcr*fcg) $ ________hr
Lube cost (L) = (F*lo%) $ ________hr
Repair and Maintenance cost (RM) = (AD*rm%/PMH) $ ________hr
Operator labor and benefit cost (WB/ut%) $ ________hr
Operating cost per PMH (V$PMH) = (F+L+RM+(WB/ut%)) $
________hr
Operating cost per SMH (V$SMH) = (V$PMH*ut%) $ ________hr
5. Total Machine Costs
Total cost per SMH (T$SMH) = (F$SMH+V$SMH) $ ________hr
Total cost per PMH (T$PMH) = (F$PMH + V$PMH) $ ________hr
88
140,000
170
5
0.248714286
0.2
0.01
0.045
0.045
0.03
2.3
0.04
15.6
80
34820
841.44
3917.12
16
176.27
176.27
1193.98
14.92
74.62
11.5
0.46
0.53
78.00
90.49
18.10
33.02
165.11
Appendix 8: Machine rate calculation for skidder for two weeks
Skidder
1. Input Data
Purchase price (P) $ ________
Machine horsepower rating (hp) ________hp
Machine life (n) ________yrs
Salvage value, percent of purchase price (rv%) ________%
Utilization rate (ut%) ________%
Repair and maintenance, percent of depreciation (rm%) ________%
Interest rate (in%) ________%
Insurance and tax rate (it%) ________%
Fuel consumption rate (fcr) ________gal/hp-hr
Fuel cost (fcg) $ ________per gal
Lube and oil, percent of fuel cost (lo%) ________%
Operator wage and benefit rate (WB) $ ________hr
Scheduled machine hours (SMH) ________hrs/10 days
2. Calculations
Salvage value (S) = (P*rv%) $ ________
Annual depreciation (AD) = ((P-S)/n) $ ________10 days
Average yearly investment (AYI) = ((((P-S)*(n+1))/(2*n))+S) $ ___10days
Productive machine hours (PMH) = (SMH*ut%) ________hrs/10 days
3. Ownership costs
Interest cost (IN) = (in%*AYI) $ ________10 days
Insurance and tax cost (IT) = (it%*AYI) $ ________10 days
Yearly ownership cost (YF$) = (AD+IN+IT) $ ________10 days
Ownership cost per SMH (F$SMH) = (YF$/SMH) $ ________hr
Ownership cost per PMH (F$PMH) = (YF$/PMH) $ ________hr
4. Operating costs
Fuel cost (F) = (hp*fcr*fcg) $ ________hr
Lube cost (L) = (F*lo%) $ ________hr
Repair and Maintenance cost (RM) = (AD*rm%/PMH) $ ________hr
Operator labor and benefit cost (WB/ut%) $ ________hr
Operating cost per PMH (V$PMH) = (F+L+RM+(WB/ut%)) $ ________hr
Operating cost per SMH (V$SMH) = (V$PMH*ut%) $ ________hr
5. Total Machine Costs
Total cost per SMH (T$SMH) = (F$SMH+V$SMH) $ ________hr
Total cost per PMH (T$PMH) = (F$PMH + V$PMH) $ ________hr
89
185,000
119
5
0.2
0.25
0.01
0.05
0.05
0.05
2.3
0.04
15.6
80
37000
1184.00
5032.00
20.22
251.60
251.60
1687.20
21.09
83.44
13.80
0.55
0.59
61.72
76.66
19.38
40.47
160.10
Appendix 9: Machine rate calculation for harvester for two weeks
Harvester
1. Input Data
Purchase price (P) $ ________
Machine horsepower rating (hp) ________hp
Machine life (n) ________yrs
Salvage value, percent of purchase price (rv%) ________%
Utilization rate (ut%) ________%
Repair and maintenance, percent of depreciation (rm%) ________%
Interest rate (in%) ________%
Insurance and tax rate (it%) ________%
Fuel consumption rate (fcr) ________gal/hp-hr
Fuel cost (fcg) $ ________per gal
Lube and oil, percent of fuel cost (lo%) ________%
Operator wage and benefit rate (WB) $ ________hr
Scheduled machine hours (SMH) ________hrs/10 days
2. Calculations
Salvage value (S) = (P*rv%) $ ________
Annual depreciation (AD) = ((P-S)/n) $ ________10 days
Average yearly investment (AYI) = ((((P-S)*(n+1))/(2*n))+S) $ ___10 days
Productive machine hours (PMH) = (SMH*ut%) ________hrs/10 days
3. Ownership costs
Interest cost (IN) = (in%*AYI) $ ________10 days
Insurance and tax cost (IT) = (it%*AYI) $ ________10 days
Yearly ownership cost (YF$) = (AD+IN+IT) $ ________10 days
Ownership cost per SMH (F$SMH) = (YF$/SMH) $ ________hr
Ownership cost per PMH (F$PMH) = (YF$/PMH) $ ________hr
4. Operating costs
Fuel cost (F) = (hp*fcr*fcg) $ ________hr
Lube cost (L) = (F*lo%) $ ________hr
Repair and Maintenance cost (RM) = (AD*rm%/PMH) $ ________hr
Operator labor and benefit cost (WB/ut%) $ ________hr
Operating cost per PMH (V$PMH) = (F+L+RM+(WB/ut%)) $ ________hr
Operating cost per SMH (V$SMH) = (V$PMH*ut%) $ ________hr
5. Total Machine Costs
Total cost per SMH (T$SMH) = (F$SMH+V$SMH) $ ________hr
Total cost per PMH (T$PMH) = (F$PMH + V$PMH) $ ________hr
90
0
163
0
0.09
0.095
0.01
0.04
0.04
0.034
2.3
0.04
15.6
80
0
0
0
7.58
0
232.83
0
0
30.72
12.65
0.506
0
164.64
177.80
16.85
16.85
208.52
Appendix 10: Machine rate calculation for forwarder for two weeks
Forwarder
1. Input Data
Purchase price (P) $ ________
Machine horsepower rating (hp) ________hp
Machine life (n) ________yrs
Salvage value, percent of purchase price (rv%) ________%
Utilization rate (ut%) ________%
Repair and maintenance, percent of depreciation (rm%) ________%
Interest rate (in%) ________%
Insurance and tax rate (it%) ________%
Fuel consumption rate (fcr) ________gal/hp-hr
Fuel cost (fcg) $ ________per gal
Lube and oil, percent of fuel cost (lo%) ________%
Operator wage and benefit rate (WB) $ ________hr
Scheduled machine hours (SMH) ________hrs/10 days
2. Calculations
Salvage value (S) = (P*rv%) $ ________
Annual depreciation (AD) = ((P-S)/n) $ ________10 days
Average yearly investment (AYI) = ((((P-S)*(n+1))/(2*n))+S) $ __10 days
Productive machine hours (PMH) = (SMH*ut%) ________hrs/10 days
3. Ownership costs
Interest cost (IN) = (in%*AYI) $ ________10 days
Insurance and tax cost (IT) = (it%*AYI) $ ________10 days
Yearly ownership cost (YF$) = (AD+IN+IT) $ ________10 days
Ownership cost per SMH (F$SMH) = (YF$/SMH) $ ________hr
Ownership cost per PMH (F$PMH) = (YF$/PMH) $ ________hr
4. Operating costs
Fuel cost (F) = (hp*fcr*fcg) $ ________hr
Lube cost (L) = (F*lo%) $ ________hr
Repair and Maintenance cost (RM) = (AD*rm%/PMH) $ ________hr
Operator labor and benefit cost (WB/ut%) $ ________hr
Operating cost per PMH (V$PMH) = (F+L+RM+(WB/ut%)) $ ________hr
Operating cost per SMH (V$SMH) = (V$PMH*ut%) $ ________hr
5. Total Machine Costs
Total cost per SMH (T$SMH) = (F$SMH+V$SMH) $ ________hr
Total cost per PMH (T$PMH) = (F$PMH + V$PMH) $ ________hr
91
0
116
0
0.09
0.20
0.01
0.04
0.04
0.034
2.3
0.04
15.6
80
0
0
0
16.14
0
187.77
0
0
11.63
9.2
0.368
0
77.32
86.52
17.46
17.46
98.16
Appendix 11
91
Figure 12: Shows pictures of landing areas in the TL system one year after harvesting has been completed.
92
Figure 13: Shows pictures of center of main activity in the CTL system one year after harvesting has been completed.
BIBLIOGRAPHY
Acar, H.H. and S. Unver, 2004. Determination of harmful effects on wooden raw
materials regarding technical and environmental aspects and recommendations for
the solution. J. ZKU Bartin For. Fac., vol. 2, pg 165 – 173.
Akay, E Abdullah, M. Yilmaz, and F. Tonguc, 2006. Impact of Mechanized Harvesting
on Forest Ecosystem: Residual Stand Damage. Journal of Applied Sciences vol. 6
No. 11, pg 2414 – 2419.
Ampoorter, E., R. Goris, W.M. Cornelis, K. Verheyen, 2007. Impact of mechanized
logging on compaction status of sandy forest soils. Forest Ecology and
management, vol. 241, pg. 162 – 174.
Aust, W.M., J.A. Burger, D.P. Carter, D.P. Preston, and S.C. Patter, 1998. Visually
Determined Soil Disturbance Classes Used as Indices of Forest Harvesting
Disturbance. Southern Journal of Applied Forestry, vol. 22, No. 4, pg. 245-250.
Bjorn, H., T. Nordfjell, and L. Eliasson. 2000. Productivity and Cost in Shelterwood
Harvesting. Scand. J. For. Res., vol. 15, p. 561 – 569.
Blake, G.R., and K.H. Hartge. 1986. Bulk density in A. Klute (ed.) Methods of Soil
analysis, Part 1, Physical and mineralogical methods. Ed. 2. Am. Soc. Agron.,
Soil Science Society of America, Madison, WI., pg 363 – 376.
Boltz, F., D.R. Carter, T.P. Holmes, and R. Pereira Jr., 2001. Financial returns under
uncertainty for conventional and reduced-impact logging in permanent production
forests of the Brazilian Amazon. Ecological Economics, vol. 39, pg 387 – 398.
Brinker, R.W., J. Kinard, and B. Rummer, 2002. Machine Rates for Slected Harvesting
Machines. Circular 296 (revised). Alabama Agricultural Experimental Station,
Auburn University, Alabama.
Carter, E.A., R.B. Rummer, and B.J. Strokes. 2006. Evaluation of site impacts associated
with three silvicultural prescriptions in an upland hardwood stand in northern
Alabama, USA. Biomass and Bioenergy, vol. 30, pg 1025 -1034.
Carter, E., T. McDonald, and J. Torbert. 2000. Assessement of soil strength variability in
a harvested loblolly pine plantation in the Piedmont region of Alabama, United
States. New Zealand Journal of Forestry Science 30(1/2): 237 – 249.
94
Clatterbuck, K.W., 2006 Logging damage to residual tree following commercial
harvesting to different over story retention levels in a mature hardwood stand in
Tennessee. Gen. Tech. Rep. SRS-92. Asheville, NC: U.S. Department of
Agriculture, Forest Service, Southern Research Station, pp: 640.
Clyde, G.V., C. deHoop, and L.B. Lanford. 1999. Assessment of site and stand
disturbance from cut-to-length harvesting. Paper presented at the 10th Biennial
Southern Silvicultural Research Conference, Shreveport LA.
Danielson, R.E. and P.L. Sutherland, 1986. Porosity. P. 443 – 462 in A. Klute (ed.),
Methods of Soil analysis, Part 1, Physical and mineralogical methods. Ed. 2. Am.
Soc. Agron., Soil Science Society of America, Madison, WI.
de Wasseige, C., and P. Defourny. 2004. Remote sensing of selective logging impact for
tropical forest management. Forest Ecology and Management. Vol. 188, pg 161173.
Fitton, D.D. 1990. A Standard for interpreting soil PEN measurements. Soil Science. Vol.
150, Pg 542 – 551.
Froese, K. and H. Han-Sup, 2002. Residual damage in a conifer stand thinned with a CTL
system. Department of Forest Products College of Natural Resources, University
of Idaho.
Gaines, G., and J. Creed. 2003. Bankhead Liaison Panel and Monitoring Work Groups
Meeting Summary, Double Springs, Alabama.
Gingras, J.F. 1988. The effects of site and site factors on Feller Buncher performance.
Tech. Rep. No. TR-81. For. Eng. Res. Inst. of Canada.
Gingras, J.F. 1994. A comparison of full-tree versus cut-to-length system in a Manitoba
model forest. For. Eng. Res. Inst. of Canada Spec. Rep. SR-92
Han-Sup, H. and K.D. Loren, 2000. Damage characteristics in young Douglas-fir stands
from commercial thinning with four timber harvesting systems. West J. Applied
forest, vol. 15, pg. 1-7
Heinimann, H.R., K. Stampfer, J. Loschek, and L. Caminada. 2001. Perspectives on
Central European Cable Yarding Systems. Internacional Mountain Logging and
11th Pacific Northwest Skyline Symposium, Dec. 10 -12, 2001, Seatle,
Washington, USA.
Hendrison, J. 1990. Damage-Controlled Logging in a Manager Tropical Rain Forest in
Suriname. Wageningen Agricultural University, The Netherelands, 204 pp.
95
Heninger, R., W. Scott, A Dobkowski, R. Miller, H. Anderson, and S. Duke, 2002. Soil
disturbance and 10-year growth response of coast Douglas-fir on nontilled and
tilled skid trails in the Oregon Cascades. Can. J. For. Res. 32: 233-246.
Herrick, J.E., and T.L. Jones 2002. A Dynamic Cone Penetrometer for measuring soil
penetration resistance. Soil Sci.Soc. Am. J. vol. 66 pg 1320 - 1324
Holtzscher, M.A., and B.L. Lanford. 1996. Tree Diameter Effectson Cost and
Productivity of Cut-to-Length System. Forest Product Journal, vol. 47, No. 3.
Jensen, K., and Visser, R. 2005. Low Impact Forest Harvesting at the Urban Interface.
Department of Forestry, Virginia Tech, Blacksburg, VA.
Johns, J.S., P. Barreto, and C. Uhl, 1996. Logging damage during planned and unplanned
logging operation in Eastern Amazon. Forest Ecology and Management, Vol. 99,
Pg 59 -77.
Kluender, R.A., and B.J. Stokes. 1994. Productivity and Cost of Three Harvesting
Methods. Southern Journal of Applied Forestry, 18 (4), 168 – 174.
Landsberg, J.D., R.E. Miller, H.W. Anderson, and J.S. Tepp. 2003. Bulk density and soil
resistance to penetration as affected by commercial thinning in northeastern
Washington. Res. Pap. PNW-RP-551. USDA Forest Service Pacific Northwest
Research Station. Portland, OR 35 p.
Lanford, B.L. and B. J. Stokes. 1995. Comparison of Two Thinning Systems. Part 1.
Stand and Site Impacts. Forest Product Journal 45(5): 74-79.
Lanford, B.L., and B. J. Stokes. Comparison of Two Thinning Systems. Part 2.
Productivity and Costs. Forest Product Journal 46 (11/12): 47-53.
LeDoux, C.B., and N.K. Huyler. 2000. Cost Comparisons for Three Harvesting Systems
Operating in Northern Hardwood Stands. USDA Forest Service Northeastern
Research Station Research Paper NE-715.
Limbecl-Lilienau, B. 2003. Residual Stand Damage caused by Mechanized Harvesting
System. In Proceedings of the Austro2003 meeting: High Tech Forest Operations
for Mountainous Terrain. Schlaegl – Austria. 11p.
McDonald, T.P., E.A. Carter, and S.E. Taylor. 2002. Using the global positioning system
to map disturbance patterns of forest harvesting machinery. Canadian Journal of
Forest Resources, vol. 32, pg 310 319.
Miller, R.E., J. Hazard, and S. Howes. 2001. Precision, accuracy, and efficiency of four
tools for measuring soil bulk density or strength. Res. Pap. PNW-RP-532.
Portland, OR: USDA Forest Service, Pacific Northwest Station. 16 p.
96
Perumpral, J.V. 1987. Cone penetrometer application – a review. Transaction of
American Society of Agricultural Engineers. 30 (4): 939 – 944.
Putz, F.E., K.H. Redford, J.G. Robinson, R. Fimbel, and G.M. Blate. 2000. Biodiversity
conservation in the context of tropical forest management. World Bank
Environment Department paper No. 75, Washington, DC. 80pp.
Rummer, R.B. 2002. Impact of forest operations. In Wear D. N. and J. G. Greis. 2002.
Southern Forest Resource Assessment. Final Technical Report. USDA Forest
Service. <http://www.srs.fs.usda.gov/sustain/report/timbr3/timbr3.htm > (4
September 2007).
Shrestha, S.P., B.L. Landford, R. Rummer, and M. Dubois, 2008. Soil Disturbance from
Horse/Mule logging Operations Coupled with Machines in the Southern United
States. International Journal of Forest Engineering, vol. 19, No. 1, pg 17 – 23.
Solgi, A and A. Najafi. 2007. Investigating of Residual Tree Damage During GroundBased Skidding. Pakistan Journal of Biological Sciences, 10 (10): 1755 – 1758.
Stampfer, K., T. Steinmuller, and T. Svaton, 2001. Grenzen der Steigfahigkeit,
Osterreichische Forstzeitung (Arbeit in Wald), 112, 3: 1-3.
USDA Forest Service, 2003. Final Environmental Impact Statement: Forest Health and
Restoration Project of Bankhead National Forest. Management Bulletin R8-MB
110B.
Visser,
R.
2007.
Logging
Cost
Analyzes.
Tech. http://www.cnr.vt.edu/harvestingsystems/Costing.htm
Virginia
Wang, J., C. Long, J. McNeel, and J. Baumgras. 2004. Productivity and dost of manual
felling and cable Skidding in Central Appalachian hardwood forest. Forest
Products Journal, vol. 54, no. 12, pg 45 – 51.
Webb, E.L. 1997. Canopy removal and residual stand damage during controlled selective
logging in lowland swamp forest of northeast Costa Rica. Forestry Ecology and
Management vol. 95, Pg 11 – 129.
97