Woody biomass production in different forest types of
Alabama
by
Xavier Ndona-Makusa
A THESIS
Submitted in partial fulfillment of the requirements
for the degree of Master of Science
in the Department of Natural Resources and Environmental Science
in the School of Graduate Studies
Alabama A&M University
Normal, Alabama 35762
November 2009
Submitted by Xavier Ndona-Makusa in partial fulfillment of the requirements for
the degree of MASTER OF SCIENCE specializing in PLANT AND SOIL SCIENCE.
Accepted on behalf of the Faculty of the Graduate School by the Thesis
committee:
Major Advisor
Dean of the Graduate School
Date
ii
Copyright ©
XAVIER NDONA-MAKUSA
2009
iii
This thesis is dedicated to my beloved father and mother for giving me values that no one
can steal and fire cannot burn. May their souls rest in peace!
iv
WOODY BIOMASS PRODUCTION IN DIFFERENT FOREST TYPES OF
ALABAMA.
Ndona-Makusa, Xavier, M.S., Alabama A&M University, 2009. 68 pp.
Thesis Advisor: Kozma Naka
The quest for renewable sources of energy has opened new opportunities in the forest
industry. What was considered non-merchantable trees for traditional use can now be
marketed as feedstock for energy production. This study was set to evaluate protocols for
producing woody biomass as an added-value production for renewable cleaner energy.
Two forest types (hardwood and pine) were considered as treatments with three
replications. Different aspects of woody biomass production were evaluated including
cost of production, yield per acre, and efficiency to produce electricity. Data were
collected from six in-wood chipping operations in four different counties in Alabama.
The chipping operations were integrated within traditional logging operations. The
specifics of woody biomass production were collecting, loading and chipping logging
residues. A significant difference in cost was found between producing hardwood-chips
($9.68/ton) and producing pine-chips ($7.26/ton). The difference is explained by
temporal factors in chipping hardwood. However, hardwood forests were equally
productive to pine forests in terms of biomass yield per acre per year.
KEY WORDS: Biomass, renewable energy, forest operations.
v
TABLE OF CONTENTS
ABSTRACT.......................................................................Error! Bookmark not defined.
TABLE OF CONTENTS................................................................................................... vi
LIST OF TABLES ........................................................................................................... viii
LIST OF FIGURES ........................................................................................................... ix
LIST ABREVIATIONS...................................................................................................... x
ACKNOWLEDGMENTS ................................................................................................. xi
CHAPTER I INTRODUCTION ........................................................................................ 1
1.2. Significance of study................................................................................................ 4
1.3. Objectives of study .................................................................................................. 5
Chapter II LITERATURE REVIEW ................................................................................ 6
Chapter III MATERIALS AND METHODS ................................................................... 13
3.1. Description study sites ........................................................................................... 13
3.2. Harvesting system .................................................................................................. 16
3.3. Economic analysis ................................................................................................. 17
3.3.1. Equipment productivity ...................................................................................... 18
3.3.2. Cost of harvesting operation ............................................................................... 19
3.3.3. Yield of forest biomass ....................................................................................... 26
3.3.4. Efficiency of wood-based energy. ...................................................................... 26
Chapter IV RESULTS AND DISCUSSION .................................................................... 28
4.1. Productivity analysis .............................................................................................. 28
4.2. Cost analysis .......................................................................................................... 29
4.3. Sensitivity analysis................................................................................................. 36
4.4. Biomass transportation........................................................................................... 38
4.5. Forest biomass yield .............................................................................................. 39
4.5. Efficiency of wood energy ..................................................................................... 42
vi
Chapter V SUMMARY AND CONCLUSION ............................................................... 46
APPENDICES .................................................................................................................. 49
APPENDIX 1 .................................................................................................................... 50
DAILY WOODCHIP PRODUCTION FROM HARDWOOD AND PINE STANDS.... 50
APPENDIX 2 .................................................................................................................... 53
EQUIPMENT USED DURING BIOMASS PRODUCTION OPERATIONS ................ 53
REFERENCES ................................................................................................................. 54
vii
LIST OF TABLES
Table
3.1.
Page
Description of clear-cutting treatments of the different
stands used in the study
16
3.2
Annual depreciation of each machine in $US
23
4.1
System equipment productivity
29
4.2
Forest biomass harvesting machine rate
31
4.3
System operating costs
34
4.4
Statistical values of cost/ton in hardwood and pine forest
operations
4.5
35
Independent t-test on the cost of operations in hardwood and
pine forests
4.6
35
Effect of 10% increase of skidder price on SMH and PMH
36
4.7
Effect of renting skidder on cost per SMH and PMH
38
4.8
Woodchip yield per age and acre
40
4.9
Statistical values of yield of hardwood and pine stands
41
4.10
Independent t-test on the yield of hardwood and pine stands
41
4.11
Electricity generation by species
42
viii
LIST OF FIGURES
Figure
Page
3.1
Counties in Alabama indicating the study area
14
4.1
Combustion process
44
4.2
Gasification process
45
ix
LIST ABREVIATIONS
BTU
: British Thermal Unit
CH
: Chipper
DME
: Distance Measuring Equipments
DOE
: US Department Of Energy
EEA
: European Environmental Agency
FB
: Feller Buncher
FC
: Fair compaction
IEA
: International Energy Agency
HC
: High compaction
HP
: Horse Power
KWH
: Kilowatt Hour
LD
: Loader
MWH
: Megawatt Hour
NC
: No compaction
PMH
: Production Machine Hour
PMM
: Production Machine Minute
SK
: Skidder
SMH
: Scheduled Machine Hour
x
ACKNOWLEDGMENTS
I would like to express my sincere appreciation to the advisory committee
members Dr. James Bukenya, Dr. Luben Dimov and to extend special thanks to Dr.
Kozma Naka for serving as the chairperson. I would also like to thank my family, my
wife Sylvie Munzinga and my son Francis Ndona supporting me during this research. My
appreciations go also to many individuals who helped in conducting this research, Jasper
Lamber. Co. Greg Holmes, Joe King and Tankersley Brothers.
Funding for this research was provided by National Science Foundation (NSF)
Grant HRD-042054 through the School of Agriculture and Environmental Sciences,
Alabama A&M University.
xi
CHAPTER I
INTRODUCTION
The choices for renewable sources of energy include wind, solar, hydro,
geothermal, and biomass. Biomass is the most important source of renewable energy; as
it corresponds to 10.6% of the world’s total energy supply and 79.4% of the total
renewable energy supply (IEA, 2006). In the United States only 7% of domestic energy
production is in the form of renewable energy; and 53% of this renewable energy comes
from biomass. In all, energy from woody biomass represent only 3% (EIA, 2009). In
2005, the production of biomass reached 69.1 quadrillion British Thermal Unit (Btu) of
energy produced in the United States or about 4% of total energy production. Wood,
wood waste and black liquor from pulp mills were the largest biomass sources providing
more than two-thirds of the total biomass energy (DOE, 2006). Over 100MWh ha-1 of
potential bioenergy remains each on-site year as tree tops and slashes after harvesting
operations in pine forests of southeastern United States (Scott and Dean, 2006). In
Alabama forest biomass is the primarily source of renewable energy (AFC, 2002).
Biomass is a general term, and has different meanings in different contexts. The
Energy Information Agency defines biomass as: organic non-fossil material of biological
origin constituting a renewable energy source; this includes forest, agricultural residues
1
and urban wastes (EIA, 2009). Frank and Smith (1998) defined biomass as contemporary
plant matter formed by photosynthetic capture of solar energy and stored as chemical
energy.This definition gives biomass a recyclable characteristic similar to the one
provided by the European Environmental Agency (EED, 2009) stating that biomass is
“all organic matter that derives from the photosynthetic conversion of solar energy.”
In this research the term “forest biomass” is used to describe the current nonmerchantable trees and trees parts such as snags (dead trees), down logs (coarse woody
debris), brush, stumps, small-diameter trees, tops and tree limbs left over after traditional
logging operation. Thus, forest biomass production is the process of collecting and
chipping non-merchantable trees parts, as defined above, during or after traditional
logging operations.
Balancing between crops and energy production in agriculture has been a
controversial debate for scientists, economists, politicians and consumers. The challenge
is how to produce biofuels without creating a negative impact on food production.
Dedicating all US corn and soybean productions to biofuels would meet only 12% of
gasoline demand and 6% of diesel demand (Jason Hill et al., 2006). The production of
biofuel in large scale could have a significant impact on agriculture and patterns of land
use. There are projections indicating that food crops may be partially displaced by the
production of biofuel feedstocks (Thomas et al., 2009).
In contrast to agriculture-based biomass production (plant materials, vegetation or
agricultural waste), forest-based biomass production is not directly related to food
production. The forest can provide renewable natural energy while continuing to provide
traditional wood products (Hubbard et al., 2007). What was once considered “waste” or
2
“non-merchantable product” in the timber industry is now opening new horizons. AngusHankin et al.,(1995) described woody biomass destined for fuelwood as the lowest
valued material left at the recovery site. Currently, tree tops and limbs left over after
logging operations, dead trees, cull trees and trees damaged by wildfire, insects or disease
represent important economic assets. The need for alternative sources of energy is
transforming non-merchantable trees to significant economic values as feedstock for
energy production.
1.1. Problem statement
The opportunity to utilize forest biomass as bioenergy feedstock comes with
many challenges, both economical and environmental. Hall (1997) statedthat the cost of
producing woody biomass fuel typically is two to three times as much as producing coal
in Europe or in the United States. The question therefore is to discern the least expensive
and economically feasible approach to produce woody biomass. Bolding and Lanford
(2001) addressed the issue in terms of what equipment to use to reduce cost under a stand
conversion scenario. Some studies have explained the relationship between the cost of
production and the size and spatial density of biomass to be removed (Hartsough and
Stokes 1995; Kluender et al.1998; Holtzscher and Lanford 1997). Other studies have
linked the cost of production to the high price of machinery and labor costs (Bolding et
al., 2009; Schroeder R. and Jackson B. 2009).
This research compares the costs of production from different forest types with
different species and stand ages. It includes examining equipment productivity, and labor
cost. Sensitivity analysis was performed to indentify the entity that influences the most
the cost of production.
3
1.2. Significance of study
The demand for renewable energy to displace fossil fuels has increased due to
changing price of gasoline and the fear of global climate change. Forest biomass is a
significant feedstock for bioenergy industry. However, the cost of biomass production is
one of the biggest challenges faced by loggers and landowners. As loggers have started to
introduce biomass as part of their products, there is a need to understand what drives the
cost of woody biomass production. To be appealing in the energy market and be
considered viable alternative to fossil fuels, it is important to find the most cost effective
system to produce biomass.
This research is important to state of Alabama, which has 22.9 million acres of
forestland. These forests were evaluated by US Forest service as 45% hardwood (oaks,
hickories, sweetgum, and yellow poplar), 36% pine (longleaf pine, slash pine, loblolly
pine, and shortleaf pine), and 18% mixed oak-pine (Boyce T. et al., 2002). Tree species
in hardwood and pine forests have different characteristics, which can influence biomass
cost and productivity. According to Alabama Forest Commission, Alabama is one of the
thirteen southern states that provide 60% of the US wood supply and a higherpercentage
of wood waste from timber harvesting and processing. Alabama is home to over 1,100
forest manufacturing operations. Timber is the dominant crop harvested in 34 Alabama
counties. The forest industry directly employs approximately 70,000 Alabamians with an
annual payroll of $ 2.2 billion (AFC, 2007).
4
1.3. Objectives of study
This study was undertaken to identify the most cost effective method to produce
forest-based biomass. The specific objectives were: (1) to compare the costs of operation
to produce biomass from hardwood stands versus pine stands; (2) to compare the biomass
yield from both forest types stands.
The following hypotheses were tested:
H0 1 : There is no difference in cost of production between hardwood chips and softwood
chips.
H0 2 : There is no significant difference in the biomass yield in softwood forests and
hardwood forests.
5
Chapter II
LITERATURE REVIEW
Numerous studies have been conducted about forest biomass production. Some
studies focused on the harvesting systems, the productivity of harvesting equipment and
the cost of production, and biomass transportation and utilization. Whereas, others
analyzed the environmental benefits of woody biomass used as feedstock for renewable
energy, and the environment impacts of removing logging residues.
Forest biomass harvesting can be done by using two different methods: one-pass
or two-pass harvesting (Hubbard et al., 2007). In the one-pass method, loggers harvest
roundwood and biomass simultaneously by using conventional timber harvesting
equipment such as feller-bunchers, harvesters, skidders, and forwarders. While, in the
two-pass method, roundwood and biomass are removed separately; starting with round
wood and then biomass (Stokes et al., 1984). Research conducted in Alabama and
Mississippi (Watson et al., 1986; Stokes et al., 1984) found that the one-pass method was
the most cost effective. Most loggers prefer this method because it needs fewer
modifications of the traditional harvesting system. However, Hubbard et al., (2007)
suggested that one benefit of two-pass systems is that it offers the opportunity for
smaller, specialized biomass harvesting contractors to operate if conventional timber
harvesting contractors do not wish to process the biomass. They also found that chipping
6
tops, limbs, and understory vegetation increased the whole operation costs by about
$1/ton; and as chip volume increases, these production costs per ton are expected to
decrease.
Looking at different methods of reducing production cost, Bolding and Lanford
(2001) determined that using a small chipper to harvest energy wood in cut-to-length
system was cost-effective under a stand conversion scenario. Hartsough and Stokes
(1990) suggested that the cost of operation was inversely related to the size of the trees
and the amount of biomass removed in green tons/acre basis. They found that the
removal cost increased as the biomass size was reduced and cost decreased as the total
volume removed per acre was increased. However, the cost is relatively flat when trees
above 12 inches diameter breast height (dbh) were removed (Kluender et al., 1998).
Holtzscher and Lanford (1997) found that the cost per unit for harvesting 4inch-dbh
material in thinning was 1.8 to 2.4 times as much as harvesting 6inch-dbh material, and
over three times the cost of harvesting 10inch-dbh material.
Biomass harvesting systems are usually highly mechanized operations, which
justify the high cost of production, unless trees are manually felled and removed to
reduce cost to the expense of workers safety (Bolding et al., 2003; Schroeder R. and
Jackson B. 2009). In a study by on productivity and cost of manual felling and skidding
in central Appalachian hardwood forests, Wang et al., (2004) reported that the system
could produce 44.63 thousand board feet (MBF)/week at $60.00/MBF. Whereas
Hartsough et al., (1995) found that the capital cost of mechanized biomass harvesting
operations was estimated between 1.2 to 2 million dollars. Nowadays this cost would be
higher due to higher equipment price, labor wage and fuel price.
7
There is limited information or studies comparing biomass cost or yield in
hardwood and pine stands. Bentley and Tony (2008) reported that in the state of Alabama
hardwood trees provide more logging residues than pine trees. Most studies on cost and
productivity analyze specific operations without comparing different forest types.
Bolding et al., (2009) studied productivity and costs of an integrated mechanical forest
fuel reduction in a commercial thinning operation in southwest Oregon. The study found
that on a 20 acre mixed conifer stand, the net cost of removing logging residues was
$968.96 per acre. This operation was a two-pass biomass harvesting, and the cost of
biomass removal was calculated jointly with the cost of conventional logging operation.
Felling and bunching costs were included in biomass harvesting cost. This approach is
known as joint cost approach, which consists of sharing the cost of conventional logging
operation with biomass harvesting operation. Another study by Puttock (2004) conducted
in Ontario, Canada, to determine the cost of biomass removal in integrated operation
revealed that the joint cost approach used in Bolding et al., (2009) tends to increase the
cost of woody biomass production while reducing cost of conventional logging operation.
Instead the marginal approach to estimate the cost of woody biomass harvesting was
advocated. This approach considers logging residues as by product of the primary
product (roundwood) and assumes that logging residues are available at zero cost.
Therefore, the cost of felling and bunching of the trees are not included.
The estimation of forest operation cost can be difficult and complex. The methods
used depend on system objectives and management goals. However, there are two
principle factors that must to be known: the cost of running equipment and the
productivity of the equipment (Visser, 2009). There are multiple methods to estimate the
8
cost of forest operation. Rummer (2008) reviewed four common methods used: (1) expert
opinion method, (2) transaction evidence, (3) accounting method, and (4) engineering
cost analysis and identified critical gaps in calculating and the understanding of operation
cost. The author warned future analyses to be cautious in utilizing cost estimation
approaches from existing literature because each study has its specifics. In most research
the tools used to estimate the cost combine accounting methods and engineering cost
analysis methods. Bolding et al., (2009) used Auburn Harvesting Analyzer spreadsheet to
estimate the cost of an integrated operation while Brinker et al., 2002 used the cost
analysis method, known as machine rate, to estimate the cost of forest operations in terms
of scheduled machine hour (SMH) and productive machine hour (PMH). The notions of
SMH and PMH were extensively explained by Miyata (1980). He combined financial
data (cost of equipment, economic life, labor cost, maintenance cost) and engine data
(horsepower, fuel consumption rate, utilization rate) of a specific equipment to estimate
the cost of using equipment per hour. This information can be replicated and applied in
any harvesting system.
The harvesting system adopted in forest biomass production has economic and
environmental implications. When clearcutting is used, all trees are removed in one
operation and high yields per unit of area and eventually a lower cost of production is
achieved (Kennard, 2009; Nyland, 1996). However, this method increases chances for
erosion and disturbance of the surface litter (Nyland, 1996). Thinnings are intermediate
cuttings to control stand density and enhance the growth of residual trees with enough
resources (Beck, 1986). This method is not as cost effective but has a lower impact on the
environment (Nyland, 1996).
9
The exploitation of forest biomass is driven by the search for renewable sources
of energy. Though we are dependent upon them now, fossil fuels were not humanity’s
first source of energy. In fact, fuelwood, charcoal, and plant residues have been used
since the discovery of fire. During World War I, woody gasifiers were used to run
vehicles for transportation (Rosillo-Calle et al., 2007). Today, Hall et al., (1998) noted
that the fear surrounding global the warming phenomenon resonates with a growing
consensus that renewable energy must progressively displace the use of fossil fuels.
The benefits of using woody biomass as feedstock for energy production were
evaluated by Gan and Smith (2007) in Eastern Texas. Their analysis revealed that using
logging residues instead of coal in power generation would displace about 2.44 metric ton
of CO 2 , create jobs for local communities and reduce the cost of the site preparation by
saving $200–250 ha-1.
Kumara et al (2008) suggested that processing fuelwood fosters “the development
of engineering and operating expertise in biomass power plants” that bioenergy needs to
compete with other sources of energy. However, biomass has the ability to provide
storable and transportable solid, liquid and gaseous fuels (Hall et al., 1998). In particular,
using woody biomass as feedstock has greater environmental and net energy benefits than
other form of biomass, such as annual arable crops, which are short-term alternative
feedstock for fuels (Hall, 1997).
The utilization of forest biomass as a feedstock for energy production can yield
environmental benefits related to the reduction of greenhouse gases. Hall and House
(1995) suggest that the use of biomass for energy is one of the means of offset the
emissions of CO 2 from the fossil fuels. Comparing CO 2 emissions from forest biomass
10
short-rotation to the CO 2 emissions from coal in Sweden, Börjesson (1996) found that net
emissions of CO 2 from short-rotation forest production, including transporting 50 km by
truck, are 35 to 40 times lower than when fossil-fuel inputs are used. Clearly from public
perception biofuel is an environmentally friendly energy; as mitigation of global warming
becomes the largest single incentive to use biofuels (Groscurth et al., 2000). Manley and
Richardson (1995) noticed an increase of forest biomass consumption for energy in
Europe and America, and recommended the development of a "comprehensive
sustainable strategies" to capitalize on potential numerous environmental and economic
benefits.
The current technology of biomass production and the market conditions
dominated by fossil fuels industry make bioenergy industry less competitive (Hall 1997).
Nevertheless, bioenergy has many social benefits such as jobs creation that provides a
strong incentive to justify government support for forest bioenergy development.
Government regulations have a big impact in the development of renewable energy. Earl
(1975) suggested that, as energy production is a big factor of economic growth, the
government should have invested interest in energy laws.
However, it appears that the development of such a market depends on the
existence of others energy markets and the cost of biomass production. A study
conducted by Mitchell et al., (1995) suggested that the “electricity production is only
possible where feed costs are minimal or electricity purchase prices are high.”.
Compared to other energy markets, Hall (1997) found that the price of wood fuel would
be competitive with fossil fuels if taxes regimes are favorable to woody biomass
production.
11
The development of a wood-based energy industry yields also enormous social
benefits for local communities. The additional activities related to biomass harvesting,
fuel processing and market expansion will create employment and enhance the
development of local economy. A study conducted by Gan and Smith (2007) in East
Texas suggests all activities related to biomass power plant would generate about 1,340
new jobs and $215 million in value-added annually.
The quest for renewable source of energy is a good opportunity for foresters.
Despite the impact forest biomass production may have on forest ecosystem, it has been
demonstrated that forest bioenergy industry has significant environmental, social and
economic benefits. However, the cost of forest biomass production is the biggest barrier.
Bolding and Lanford (2001) addressed the issue in term what equipment to use to reduce
cost under a stand conversion scenario. Some studies have explained the relationship
between the cost of production and the size and spatial density of biomass to be removed
(Hartsough and Stokes 1995; Kluender et al.1998; Holtzscher and Lanford 1997). Other
studies have linked the cost of production to the high price of machinery and labor costs
(Bolding et al., 2009; Schroeder R. and Jackson B. 2009).
This research was conducted in two different forest types (hardwood and pine) to
analyze the cost of woody biomass production, the biomass yield per acre, the efficiency
for different biomass types to generate energy. The analysis also includes the indirect cost
related to the environmental impact of forest biomass harvesting.
12
Chapter III
MATERIALS AND METHODS
3.1. Description of study sites
This study was conducted in four different locations throughout the state of
Alabama: Winston County, Jefferson County, Blount County and Cullman County
(Figure 3.1). The stands were named by the logger after the landowners (Soterra, Burson,
Burton, Ellison). Description of each stand is summarized in table 3.1. Jasper Lumber Co.
who purchases timbers throughout the state of Alabama for wood production. Tankersley
Brothers (a logging company) based Hanceville, Alabama, won the bid to integrate
biomass harvesting into their traditional logging operations and did the work in all
locations with the same equipment and the same crew. Tankersley Brothers was assigned
to produce timber as primary product and chip the logging residues for biomass fuel. All
the chips’ loads were delivered to International Paper, in Courtland, Alabama.
In Winston County, the land belongs to Soterra LLC, which is a sister company of Greif,
leader of the wood packing industry since the 1940’s. Greif purchased nearly 300,000
acres of timberland to provide raw materials to make wooden barrels, casks and kegs.
Although currently packaging industry does not use wooden materials anymore, Greif
still owns and manages forests located in the southeastern United States and in Canada.
The Soterra stand has 126 acres of 30 year old hardwood (red oak, white oak and
13
yellow poplar), with 115 trees per acre and an average basal area of 100 square feet per
acre. The Soterra track was divided into 2 stands, Soterra1 (70 acres) and Soterra2 (56
acres). Soil in this area were formed mainly in residuum weathered from limestones. As
part of the Tennessee this soil was weathered from pure limestones and are mainly red
clayey soils with silt loam surface textures (NRCS, 2009). The average slope for both
stands was evaluated at 15%.
Figure 3.1. Counties in Alabama indicating the study area.
14
In Jefferson County, the name of the landowner is Burson. The stand is located in
Parlmedale, Alabama at the intersection of US Highway 75 and Palmerdale Road. This
property is located at the trace where the government is about to start a new highway
construction. The landowner was forced to sell the forest land to timber companies as the
area was declared being in eminent domain. Jasper Lumber purchased 30 acres of 25 year
old hardwood, mostly populated with oak trees with 152 trees per acre and an average
basal area of 110 square feet per acre. Soil in this area were derived from alkaline, Selma
chalk, or acid marine clays. Sumter soils, which are typical of the alkaline soils, are
clayey throughout and have a dark- colored surface layer and a yellowish colored subsoil
(NRCS, 2009). The terrain was mostly flat with 9% slope.
In Blount County, Burton stand has 35 acres of mostly loblolly and long leaf
pines. This was a 20 year old plantation with an average basal are of 100 square feet per
acre. The slope was evaluated at 6%. The area is surrounded by private residences and
small farms near Snead, Alabama. Elison was located about half mile from Burton, and
had 25 acres loblolly and Virginia pines plantation of 23 year old with an average basal
area of 120 square foot per acre. The slope was evaluated at 5%. In Blount county soils
are derived from alluvium deposited by the streams. The Cahaba, Annemaine, and Urbo
series represent major soils of this area (NRCS, 2009).
In Cullman County, Felthan stand had 19 acres of 20 year old plantations of mix
longleaf and loblolly pines. The average basal area was evaluated at 110 square feet per
acre. The stand was clearcut to convert the land to agricultural land and pasture. The area
was almost flat with a slope of 5%. Most of the soils in Cullman County are derived from
granite, hornblende, and mica schists. Madison, Pacolet, and Cecil soils, which have a
15
red, clayey subsoil and a sandy loam or clay loam surface layer, are prominent in this
area (NRCS, 2009).
Table 3.1. Description of clear-cutting treatments of the different stands used in the
study
Basal area
St
Stand
ID name
Area in
Age in Species
acres
years
Location/
in square feet County
per acre
Distance
to the mill
in miles
1
Soterra1
70
30
Hardwood
110
Winston
55
2
Soterra 2
56
30
Hardwood
110
Winston
55
3
Burson
30
25
Hardwood
110
Jefferson
107
4
Ellison
25
23
Pine
100
Blount
88
5
Burton
35
20
Pine
120
Blount
87
6
Feltham
19
20
Pine
110
Cullman
58
3.2. Harvesting system
Harvesting operations were carried out by Thankersley Brothers based in
Hanceville, Alabama with more than 30 years of logging experience. With a crew of five
people, forest biomass harvesting was integrated in the traditional round wood logging
operation. The equipment used included: one John Deree 843J feller buncher wheeled
225 HP; two John Deere Grapple Skidders 748GII 169 HP; two Prentice 384 Loaders
with 160 HP equipped with delimbers, and one Morbark chipper 30/36 model 500 HP
(Appendix 2).
16
The first skidder (SK1) was assigned to drag whole trees, from cutting area to the
landing area. Trees were delimbed and topped, and logs were loaded in a truck stationed
a couple yards from the first loader (LD1). Then, tree limbs and tops were dragged and
piled up by the second skidder (SK2) about 20 m away where the second loader (LD2)
grabbed and pushed them into the chipper. The chipper was equipped with knives that
crush logging residues into chips of about three inches or less. The chipper had the
capacity to blow chips directly into a truck trailer.
In this system only FB, SK1 and SK2 were in motion. To increase the
productivity and reduce moving time, the FB and SK1 were moving in a 0.5 mile radius
from the landing area where LD1, LD2 and chipper were positioned. The SK2 was
assigned to move logging residues from LD1 to LD2. The distance between LD1 and
LD2 was about 30 m.
Forest biomass harvesting operation consisted of moving logging residues from
LD1 to LD2, and chipping them. Activities from FB to LD1 were considered traditional
logging operations, which include felling and bunching trees, skidding trees and loading
logs into truck.
3.3. Economic analysis
Forest biomass can be produced as primary product by whole tree chipping or as
secondary product from logging residues chipping. In the system described above, forest
biomass is produced from logging residues. However, there is a debate regarding the
approach to use when it comes to estimate the productivity and cost of the biomass
harvesting operation. The first approach, known as marginal costing, treats wood biomass
17
as a by-product of the production of the primary products (roundwood) and assumes that
the logging residues are available at zero cost. The second approach, known as joint
product cost, advocates share of costs of operation between the conventional forest
products and forest biomass (Puttock, 1994). According to Puttock, the second approach
has received little attention in integrated harvesting studies.
3.3.1. Equipment productivity
In this study, the marginal cost method was used because logging residues were
considered to be collected at no cost. Thus, only equipments involved in biomass
harvesting operation were studied. This includes skidding logging residues from LD1 to
LD2, loading trees limbs and tops, and chipping trees limbs and tops. To evaluate the
overall productivity of the operation, the productivity of each individual machine was
studied. The ratio between the time and the output of each individual operation was used
to calculate individual machine productivity. The operations were videotaped. The
videotape method has been used in different studies (see Lanford and Strokes (1996) and
Bolding et al., (2009)). From the videotapes timing, we calculated the delay-free time
required for skidder (SK2) to move logging residues from LD1 to LD2, the time required
for LD2 to feed the chipper with residues, and the time required for the chipper to chip
and load a trailer truck.
The value of the output was estimated based on the weight of one load of truck
trailer. Thus, the productivity of the skidder is expressed as time required to SK2 to move
enough logging residues to load one trailer truck with chips. Productivity of LD2 equals
to the time required to LD2 to feed chipper enough logging residues to load one truck
18
trailer with chips. Productivity of the chipper equals to time required for the chipper to
chip enough logging residues to for one track trailer load. The truck load weight was
obtained from the average of weight of all observations (78 loads of hardwood chips and
31 loads of softwood chips).
SK
Skidding time per ton (min/ton) =
= Average weight one load of chips (41’ trailer truck)
Loading time per ton (min/ton) =
LD2
Truck load
(3.2)
= Delay-free time to feed chipper with logging residues
Truck load
CH
(3.1)
= Delay-free time dragging tops and limbs to LD2
Truck load
LD2
SK
Truck load
= Average Weight one load of chips (41’ trailer truck)
Chipping time per ton (min/ton) =
CH
Truck load
(3.3)
: Delay free-time to chip and load into truck
Truck load
: Average Weight one load of chips (41’ trailer truck)
In this study 6 operations were studied (3 on softwood stands and 3 on hardwood
stands). Equipment productivity in each operation was calculated to evaluate the
performance of each piece of equipment in different forest types.
3.3.2. Cost of harvesting operation
To estimate the cost of the operation, the cost analysis method, known as
“machine rate” was used in this study. This method combines fixed and variable costs
19
with a machine’s lifetime average hourly cost (Brinker et al.2002). It gives details about
each component of the system of production, and allows to calculate the Scheduled
Machine Hour (SMH) and Productive Machine Hour (PMH).
SMH is the time during which the equipment is supposed to work. PMH is the time
during which the equipment is doing productive work.
To calculate machine rate, the following information was collected from loggers
and estimated based the model formulas:
1. Input data: Combination of financial and engine data.
-
Net purchase price (P): includes equipment costs, initial investment, sales taxes
and freight.
-
Machine horsepower rating (hp): the power of the engine, to estimate fuel
consumption.
-
Machine life (N): the economic life of equipment measured in terms of years. It is
defined, as the period over which the equipment can operate at an acceptable cost
and productivity (Miyata, 1980). The calculations were done based on 5 years of
machine life.
-
Salvage value rate: the amount that equipment can be sold for at the time of its
disposal. It is expressed as a percentage of the initial investment. In this study we
estimated salvage value rate at 20% of equipment price as rule of thumb (Miyata,
1980).
-
Utilization rate: the percentage of time a piece of equipment spends doing
assigned work. The following rates were used: 75% for chipper, 65% for skidder,
and 65% for hydraulic loader (Brinker et al., 2002).
20
-
Repair and maintenance: all charges from scheduled maintenance (as indicated in
user’s manual) and periodic engine breakdowns, transmission, brake or other
equipment repairs (90% of depreciation, as suggested by Brinker et al., 2002)
-
Interest rate, insurance and tax rate: since this information was lacking, we
applied 12% for interest and 3% for insurance and tax as rule of thumb (Miyata
1980). Brinker et al., (2002) applied 10% for equipment loan and 4% for the
insurance.
The interest rate of business equipment loan depends on many
elements including the term length, business balance sheet, credit history, and
equipment type (personnel communication,
Wells Fargo and Regions
representatives, 11/23/2009). Currently Wells Fargo applies between 8 to 15%
APR (personnel communication with Brian Wells Fargo representative,
11/23/2009).
-
Fuel consumption rate per hour (fcr) was obtained by the following formula
(Miyata 1980):
𝐹𝑐𝑟 =
0.40 x 0.65
7.08
x hp (3.4)
= 0.037 x hp
Where:
Frc: Machine fuel consumption rate per hour.
0.40 = Pounds of diesel fuel consumed per hp/ hour.
0.65 = The ratio of average net horsepower used to net hp available
7.08 = Weight (in pound) of diesel fuel per gallon.
hp = Net horsepower at rated maximum condition engine speed
-
Fuel cost: The cost of diesel was estimated at $2.5/gallon, average local price.
21
-
Lubricant and oil (% of fuel cost): was set to 36.77% of fuel cost (Brinker et al.,
2002).
-
Operator wage and benefit (WB): This wage depends on operator experience.
Operators were reluctant to tell say how much they were paid. Tankersly Brothers
is a family company run by siblings and cousins, all crew members. We estimated
WB at $19.00/ hour based on the wages of similar operations in the region.
-
Schedule Machine Hours (SMH): is the time during which equipment is
scheduled to do the productive work. This includes productive and nonproductive delay time (Brinker et al., 2002). To determine SMH we considered
that equipment is scheduled to be used 8 hours a day. Possible working days in a
year are evaluated at 250 days, then SMH = 8 hours x 250 days = 2,000 hours/
year (Miyata 1980)
2. Calculations: Computation of financial and engine data.
-
Salvage value (S) was obtained by multiplying equipment cost by salvage value
rate.
-
Annual depreciation of each machine was calculated using the straight-line
method (Table 3.2). This method assumes that the value of equipment decreases
at a constant rate for each year of its economic life (Miyata 1980). The following
formula was used to calculate the depreciation charges per year in US$:
𝐷=
𝑃−𝑆
D
= yearly depreciation charge
Where:
𝑁
(3.5)
22
The
P
= cost of equipment
S
= salvage value
N
= economic life years
purchase
price
(P)
of
equipments
collected
from
loggers
and
www.machenerytrader.com were:
-
John Deere Grapple Skidders 748G at $124,000,00 (2005);
-
Prentice 384 Loader at $161,500,00 (2006)
-
Morbark chipper 30/36 model $245,000,00 (2009).
Table 3.2: Annual depreciation of each machine in US$ :
Year
0
1
2
3
4
5
Skidder
Depr.
charge
19,840
19,840
19,840
19,840
19,840
Grapple
Undepr.
value
124,000
104,160
84,320
64,480
44,640
24,800
Depr.
charge
25,840
25,840
25,840
25,840
25,840
Loader
Undepr.
value
161,500
135,660
109,820
83,980
58,140
32,300
Chipper
Depr.
Undepr.
charge
value
245,000
39,200
205,800
39,200
166600
39,200
127400
39,200
88200
39,200
49,000
Average yearly investment was obtained from the following formula (Miyata 1980):
Where :
𝐴𝑌𝐼 =
(𝑃 − 𝑆)(𝑁 + 1)
+ 𝑆
2𝑁
(3.6)
AYI
= Average value of yearly investment over economic life.
P
= Equipment purchase price
S
= Salvage value
23
from
N
= Economic life in years
Productive Machine Hours (PMH): The actual time machine was working. It was
obtained by multiplying the schedule machine hours (SMH) by the utilization rate. This is
to correct any delay time (breaks, repairs, weathers, etc) that occurs during the operation.
3. Ownership costs:
Ownership cost also known as fixed costs are those accumulated from the length
of equipment ownership. They do not depend on operations costs but occur constantly
even when the machines are not working. They are generally due to depreciation, interest,
insurance and taxes.
Interest, insurance and taxes: were obtained by multiplying the sum of interest, insurance
and tax rate (12% +3%) by the Average Yearly Investment (AYI). Yearly ownership cost
(YF$): was obtained by the sum of annual depreciation, interest, insurance, and taxes.
Ownership cost per SMH (F$SMH): Yearly ownership cost (YF$) divided by Scheduled
Machine Hours (SMH). Ownership cost per PMH (F$PMH) : Yearly ownership cost
(YF$) divided by Production Machine Hours (PMH)
4. Operating costs
Operation costs are all charges related to the productive work of equipment.
Fuel cost (F) was obtained by multiplying fuel consumption rate by fuel price. Lubricant
cost (L): was obtained from cost multiplied by 36.77% (Brinker et al., 2002). Repair and
24
maintenance cost (RM): (AD*rm%/ PMH), Annual depreciation (AD) multiplied by
Repair and maintenance rate (rm%), and all divided by Production Machine
Hours(PMH). Operator labor and benefits costs : (WB/ut%), Operator labor and benefice
rate (WB) divided by divided by Utilization rate (ut%). Operating cost per PMH
(V$PMH): The sum of Fuel cost (F), Lube cost (L), Repair and maintenance (RM) and
Operator labor and benefits cost (WB/ut%). Operating cost per SMH (V$SMH):
Operating cost per PMH (V$PMH) multiplied by Utilization rate (ut%).
5. Total Machine Costs
The total machine costs is the sum of all charges above including variable and
fixes charges expressed per SMH and PMH: Total cost per SMH (T$SMH): The sum of
ownership cost per SMH (F$SMH) and operating cost per SMH (V$SMH). Total cost per
PMH (TSPMH): The sum of ownership cost per PMH (F$PMH) and operating cost per
PMH (V$PMH).
The application of machine rate requires caution on the part of the users. Since the
result of the machine rate calculation is an average cost, actual cash expenditures of
ownership costs will be different than estimated costs early in the machine’s life, and will
be greater or lesser than estimated in later years (Brinker et al., 2002). It is also important
to state that productivity not only varies greatly with the logging machine or system
chosen, but also with stand and site characteristics of the harvest area (Visser, 2009).
The cost analysis method expressed the results as cost per SMH and PMH. The
cost per SMH is the cost of equipment per hour for the time equipment is supposed to
work. It does not include operating charges. The cost per PMH is the cost of equipment
25
per hour for the time equipment is working. The cost per PMH includes all costs involved
in the operation, raison why it was used to estimate the total cost of the operation.
3.3.3. Yield of forest biomass
To calculate biomass yield, the weight of every single load of chip delivered to
power plant was collected from load ticket report. The productivity of each stand was
evaluated per acre basis and then standardize per age. Statistical analysis t- test was used
to evaluate the difference between wood chip yields in pine stands and hardwood stands.
Statistical Package for the Social Sciences (SPSS 11.0.1) software was used to compute
the results.
3.3.4. Efficiency of wood-based energy.
Chips produced in these operations were delivered to International Paper in
Courtland, Alabama. This plant produces paper from clear chips and uses fuel wood
chips (dirty chips) to produce electricity. We visited the power plant and were briefed but
not allow the see the actual process of transforming wood chips to electricity.
The density is the most important parameter to evaluate the energy efficiency of
wood, considering the structure materials; for instance chips, sawdust and logs do not
have the same density (Cassidy, 2008). As different tree species have different density,
the wood density was considered as a factor to differentiate the energy efficiency of
hardwood and pine. The amount of energy generated by logging residues was estimated
using the following formula (Gan and Smith, 2007):
26
E=
1
ηϕ D B
3.6
(3.7)
Where:
E
= Amount of electricity generated in Megawatt hour (MWh);
D
= Density of dry woody biomass (t m-3)
B
= Volume of logging residues (m3);
ϕ
= Energy content of dry woody biomass in Gigajoules per ton (GJ t-1);
η
= Efficiency of power conversion from biomass to electricity;
3.6
= Unit conversion from GJ to MWh (1 MWh = 3.6 GJ).
27
Chapter IV
RESULTS AND DISCUSSION
4.1. Productivity analysis
In this analysis, we calculated the delay-free time required for skidder (SK2) to
move logging residues from LD1 to LD2, the time required for LD2 to feed the chipper
with residues, and the time required for the chipper to chip and load a truck trailer of 52
feet 6 inches. The productivity is expressed as ratio between the time required to produce
one truck load of chips and the weight of the load. In hardwood stands, 78 loads of chips
were produced with an average of 27.29 tons a load, whereas in pine stands 31 loads of
chips were produced with an average of 28.24 tons a load (Table 4.1).
The skidder’s productivity in average was evaluated at 29.60 minutes (30 minutes
were considered; 15 trips of 2 minutes), which is the average time required to move
enough logging residues from LD1 to LD2. The same skidding time was observed in pine
and hardwood operations; however the average loads were different: 27.29 tons of
hardwood-chips and 28.24 tons of pine-chips. The skidder was the only equipment in
motion, and for that fact, it was assigned others duties including cleaning up the ways
around landing area. This added additional time, which was difficult to estimate.
However, as part of the system, the skidder’s speed was dependent of other equipment’s
speed in the system.
28
Table 4.1: System equipment productivity
Skidder
Loader
Chipper
Time in
min.
30 +
45
45
Pine-chips
Load in
tons
28.24
28.24
28.24
Harwood-chips
Minute per Time in
Load in
Minute per
ton
min.
tons
ton
1.06
30+
27.29
1.09
1.59
60
27.29
2.19
1.59
60
27.29
2.19
The loader was assigned to feed the chipper with logging residues. The time
required to perform this task was evaluated at 44.55 minutes or 45 minutes for 28.24 tons
of pine-chips and 60 minutes for 27.29 tons of hardwood-chips. The productivity in
minutes was evaluated at 1.59 minutes per ton of pine-chips and 2.19 minutes per ton of
hardwood-chips. The chipper’s work was to chip residues and load them directly into the
trailer truck. The time observed in chipping process was the same as in loading since the
same operator controlled both equipments. The loader and the chipper highly depend on
each other. The loader’s operator is required keeping a reasonable pace in feeding the
shipper to prevent residues to clog into the chipper’s blades. This depends on the operator
experience, which is critical in this task.
4.2. Cost analysis
The machine rate of each piece of equipment involved in the operation is shown
in Table 4.2. It combines fixed and variables costs of each machine. The current prices
for equipments are evaluated at $124,000 for John Deere Skidder Grapple model 848H;
$161,500 for Prentice Model 384 loader, and $245,000 for Morbark chipper 30/36. The
prices of the skidder and the loader were obtained from dealers of used equipment (see
www.machinetrader.com). The price of the chipper was given by the manufacturer. Five
years were assigned for economic life on all equipments. The salvage values for the
29
skidder, the loader and the chipper were evaluated respectively at $24,800, $32,300 and
$49,000. The annual depreciation was evaluated at $19,840 for the skidder, $25,840 for
the loader and $39,200 for the chipper. The yearly ownership costs do not depend on
operations cost but occur constantly even if the machines are not working. These costs
were evaluated at $32,488 for skidder, $42,313 for loader and $61,340 for chipper. The
ownership costs were then estimated based on scheduled machine hours (SMH) and
production machine hours (PMH). The following values were obtained: $16.24/SMH and
$24.99/PMH for the skidder; $21.15/SMH and $32.54/PMH for the loader; and
$24.74/SMH and $32.99/PMH for the chipper. Interest, taxes and insurance rate were
evaluated at 15%, for a value of $12,648 for the skidder, $16,473 for loader and $22,140
for the chipper.
The operating costs are those resulting from the actual utilization of the
equipment during the operation. They include all charges related to fuel and lubricant
consumption, repair and maintenance cost; and charges related to personnel wage and
benefits. The fuel consumption of a machine per hour depends on the engine’s power
known as horsepower (HP), while the fuel cost depends on the fuel consumption rate and
the fuel price in the market. The fuel consumption rates for equipment were evaluated as
6.25 gallon of diesel per hour for the 169 HP skidder engine (John Deere), 5.92 gallon of
diesel per hour for the 160 HP loader engine (Prentice 348) and 18.3 gallon of diesel per
hour for the 500 HP chipper (Morbark 30/36). The average price of a gallon of diesel was
$2.5 in the local market. The fuel cost of the operation was evaluated as $15.63 per hour
to skid logging residues, $14.8 per hour for the loader to feed the chipper with residues,
and $18.5 per hour for the chipper to chip them and load them in a truck trailer. The
30
lubricant cost was considered to be 36.77% of fuel cost (Brinker et al., 2002). Thus the
cost for lubricant was calculated at $5.75 for the skidder, $5.44 for the loader and $6.8 for
the chipper.
Table 4.2. Forest biomass harvesting machine rate
1. Input data
Purchase price (P in $)
Machine horsepower rate (hp)
Machine life (yrs)
Salvage value (%)
Utilization rate (%)
Repair and maintenance
Interest, insurance and tax rate (%)
Fuel consumption rate (gallon/hr)
Fuel cost ($/gal)
Lube and oil (%)
Operator wage and benefit rate ($/hr)
Schedule machine (hrs/year)
2. Calculations
Salvage value ($)
Annual depreciation ($)
Average Yearly Investment (AYI, $)
Productive machine hours (hrs/ year)
3. Ownership costs
Interest, Insurance and tax cost /year
Yearly ownership cost ($)
Ownership cost per SMH ($/hrs)
Ownership cost per PMH ($/hrs)
4. Operating costs
Fuel costs ($/hr)
Lube cost ($/hr)
Repair and maintenance cost ($/hr)
Operator labor and benefit cost ($hr)
Operating cost per PMH ($/hr)
Operating cost per SMH ($/hr)
5. Total machine costs
Total cost per SMH ($/hr)
Total cost per PMH ($/hr)
Skidder
(2005)
124,000
169
5
20
65
0.90
15
6.25
2.5
36.8
19.00
2,000
Loader
(2006)
161,500
160
5
20
65
0.90
15
5.92
2.5
36.8
19.00
2,000
Chipper
(2009)
245,000
500
5
20
75
0.90
15
18.3
2.5
36.8
0.00
2,000
24,800
19,840
84,320
1,300
32,300
25,840
109,820
1,300
49,000
39,200
68,600
1,500
12,648
32,488
16.24
24.99
16,473
42,313
21.15
32.54
10,290
49,490
24.74
32.99
15.63
5.75
13.73
29.23
64.34
41.82
14.8
5.44
17.88
29.23
67.35
43.77
18.5
6.8
23.52
0.00
48.82
65.10
58.06
89.33
64.92
99.89
89.83
81.81
31
The charges per hour related to repair and maintenance of equipment were evaluated at
$13.73 for the skidder, $17.88 for the loader and $23.52 for the chipper. These values
were obtained by multiplying the annual depreciation of equipment by the repair and
maintenance rate, and all divided by PMH. The annual depreciation (20% of the
equipment cost) was calculated at $19,840 for the skidder, $25,840 for the loader, and
$39,200 for the chipper. PMH excludes repair and maintenance (tire change, engine
repair, etc) and breaks time. PMH is related to the utilization rate of equipment which
was evaluated at 65% for the skidder, 65% of the loader and 75% of the chipper.
Dividing the SMH of each equipment by the utilization rate provided the PMH of
equipment per year: the skidder and the loader had 1,300 PMH/year and chipper had
1,500 PMH/year. The operator labor and benefit cost were evaluated at $29 per hour for
skidder and loader, and labor cost for the chipper was considered null since it did not
need an operator except to turn it off or on. These values were obtained by dividing the
operator wage ($19/hour) by utilization rate of each machine. The operating cost per
PMH was obtained by adding up fuel costs, lube costs, repair and maintenance costs, and
operator labor costs. The following values were found: $64.34 for the skidder, $67.35 for
the loader and $48.82 for the chipper. Multiplying the operating cost per PMH by the
utilization rate provided the operating cost per SMH.
The total machine cost was expressed in terms of SMH and PMH. The total cost
per SMH is the sum of ownership cost per SMH and operating cost per SMH, evaluated
at $58.06 for the skidder, $64.92 for the loader and $89.83 for the chipper. The total cost
per PMH is the sum of ownership cost per PMH and the operating cost per PMH,
32
evaluated at $89.33 per hour for the skidder, $99.89 per hour for the loader and $81.81
per hour for the chipper.
To evaluate the total amount of money spent for this operation, in this study, we
considered only the cost per PMH because the cost per SMH does not involve variable
costs. The cost per PMH expresses the cost of the actual delay-free time equipment was
doing productive work. The sum of cost per PMH of all three activities: skidding
($89.33), loading ($99.89), and chipping ($81.81) gives the total cost of the whole
operation ($271.03 per hour). Since the same crew used the same equipments in
hardwood stand and pine stand as well, the cost per PMH remained the same $271.03 per
hour.
However, from the productivity analysis the system worked faster producing
pine-chips than hardwood-chips. It took 45 minutes for the system to produce 28.24 tons
of pine chips, while it took 60 minutes to produce 27.29 tons of hardwood chips. In the
cost analysis average weights of pine-chips loads and hardwood-chips loads were
considered equal since the difference of 0.95 ton was not significant. Therefore, the
evaluation of cost differences on both stands was focused on the 15 minutes time
difference to load a truck of the same weight.
33
Table 4.3. System operating costs
Chip type
Hardwood-
Time per
Load
System cost
Cost per
Cost per
load in
weight in
per min
load
green ton
min
ton
(PMM)
60
28
$4.52
$271.03
$9.68
45
28
$4.52
$203.27
$7.26
chips
Pine-chips
The cost of skidding, loading and chipping logging residues in this system was
calculated at $271.03 per hour, which equals to the time required to produce one load of
hardwood-chip. Thus, the production of 28 tons of hardwood-chips in this system costs
$271.03, or $9.68/ton. The production cost of one ton of pine-chips was calculated by
converting the cost per PMH to the cost per Production Machine Minute (PMM) to the
system’s cost per minute. The conversion gave $4.52 per minute, which equals to
$203.27 for 45 minutes; cost and time required to produce 28 tons of pine-chips. Thus,
the production of 28 tons of pine-chips in this system costs $203.27, or $7.26/ton (Table
4.3).
4.3. Statistical analysis of cost differences
The cost analysis was based on the productivity of each stand. The averages of
operations’ productivity in hardwood and pines forests were broken down to find the
productivity of each operation. The following were found: 61 minutes/ton Soterra1 and
Soterra2; 59 minutes/ton for Burson, 48 minutes/ton for Ellison, 43 minutes/ton for
34
Burton and 45 minutes/ton for Feltham. The cost of each operation by multiplying the
system cost per minutes ($4.52) by the productivity. The following numbers were found:
was evaluated as: $275.72/ton for Soterra1 and Soterra2, $266.68/ton for Burson,
$216.96/ton for Ellison, $194.36/ton and $203.4/ton for Feltham.
Table 4.4. Statistical values of cost/ton in hardwood and pine forest operations
Cost/ton mean
Forest type
Hardwood
N
3
Pine
3
272.7067
Std
Deviation
5.21925
Std. Error
Mean
3.01333
204.9067
11.37508
6.56741
Table 4.5. Independent t-test on the cost of operations in hardwood and pine forests
Equality of
variance
Equal
variances
assumed
Equal
variances
not assumed
t – test for equality of means
F
Sig.
t
df
Sig.
(2-tailed)
1.362
0.308
9.383
4
0.001*
67.800
7.22572
95%
confidence
interval of
difference
Lower upper
47.738 87.86
-
-
9.383
2.8
0.003
67.800
7.22572
43.880
Mean
diff.
Std.
Err diff.
91.71
*P-value = 0.001
The results (Table 4.4 and Table 4.5) with P-value = 0.001 show that there is significant
difference in cost of producing biomass in hardwood and pine forests.
35
4.3. Sensitivity analysis
The objective in sensitivity analysis was to determine what factor influence the
most the production cost, expressed in cost per SMH and cost per PMH. The assumptions
were made on 10% increase and decrease in the cost of the skidder, operator wage and
fuel. Keeping other prices unchanged, the increase of skidder’s price from $124,000 to
$136,400 had changed the total cost per SMH from $58.06 to $60.57 and the total cost
per PMH from $89.33 to $93.19. This change in skidder’s price represents 4.32%
increase in total charge. Whereas if workers received wage increase from $19/hour to
$20.9/hour, it will result in the increase of cost per SMH from $58.06 to $59.95 and cost
per PMH from $89.33 to $92.25. This change in labor wage represents 3.2% increase of
total cost. And if the fuel diesel increases from $2.5/gallon to $2.75/gallon, it will the
total cost per SMH from $58.06 to $59.44 and cost per PMH from $89.33 to $91.46,
which will be a 2.38% increase of total cost.
Table 4.6. Effect of 10% increase of price on cost per SMH and cost per PMH of
skidder.
?
SMH
Skidder price
($60.57)
4.32%
Labor rate
($59.95)
3.26%
Fuel price
($59.44)
2.38%
($58.06)
($58.06)
PMH
($93.19)
($89.33)
4.32%
($92.25)
($89.33)
36
($58.06)
3.26%
($91.46)
($89.33)
2.38%
This results (Table 4.4) show equipment price is the most influential factor of
operation cost with the high% increase rate of 4.32%, followed by change in labor cost
3.26% and fuel price 2.38%. The same% variations in were observed in decreasing these
charges at 10%. This will certainly help managers to understand how the change in the
price of equipment, labor cost or fuel cost will affect the production, in order to make
strategic planning decisions.
The equipment leasing over purchasing was considered to offset the cost of
biomass production. This assumption was made to see how equipment rent can influence
the total cost per SMH and PMH. According to a local equipment dealer, Warrior Tractor
Inc, renting logging equipment is not a common practice. However, if a John Deere
Skidder would have to be rented, it would be at about $5,000.00 per month. This price is
subjective and depends on the relationship between the parties involved (Warrior Tractor
representative, personnel communication, 11/17/2009).
Using machine rate method, the monthly SMH was calculated at 240 hours: 8
hours of work/day multiplied by 30 days. The monthly PMH was calculated at 156 hours:
SMH multiplied by utilization rate (65%). The ownership cost per SMH and PMH were
substituted by rental cost. Thus, the rent per SMH and PMH were evaluated respectively
at $20.83 and $32.05 per hour. The operating cost per PMH was calculated at $50.61 and
$32.89 per SMH. The repair and maintenance costs covered by the lender were not
included in the operating cost. The total cost per SMH and PMH were calculated at
$53.72 and $82.66.
37
Table 4.7. Effect of renting skidder on cost per SMH and PMH.
?
Skidder purchase Skidder leased at Decrease in cost
at $124,000
$5000 /month
SMH
$58.06
$53.72
7.47%
PMH
$89.33
$82.66
7.47%
Leasing of skidder at $5,000.00 a month instead of purchasing it at $124,000.00
would reduce the cost per SMH and PMH by 7.47%. This result (Table 4.5) was even
better than reducing the price of skidder at 10%. Leasing is an alternative to purchasing
and many companies have found leasing more attractive because the cost appears to be
lower (Nevitt and Fabozzi, 2000). In addition to previous studies’ suggestions (Bolding et
al., 2009; Schroeder R. and Jackson B. 2009) about using small equipment or cutting
overstock forests to reduce the cost of biomass production, equipment leasing is one of
the approaches loggers should consider.
4.4. Biomass transportation
In this research transportation cost was not included in the production cost due to
the fact this cost was the same ($0.12/mile/ton) for hardwood and pine chips. However
transportation cost is one of the most important factors influencing the cost of biomass
production and the cost of energy produced in the power plant (Angus-Hankin et al.,
1995). Transportation of wood fiber accounts for about 25 to 50% of the total delivered
costs and highly depends on fuel prices, distance to the power plan, and vehicle capacity
38
(Hubbard et al., 2007). To reduce the cost of transportation, Sandra et al., (2009)
recommended biomass production field within 50 miles radius of power plant. The
authors justified the concentration of ethanol produced in the Midwest of the USA by the
proximity to the corn fields. In addition, a low bulk density increases the cost of
transportation because air and water in the wood are major components of the transported
volume (Hubbard et al., 2007). This is the main reason power plant facilities prefer a
material of low moisture content to increase the heat value and reduce the cost of energy
per MWh (Angus-Hankin et al., 1995).
4.5. Forest biomass yield
The number of loads and their weight were recorded daily in every operation. The
following data were registered: 802.63 tons of chips with 30 loads for Sotera1, 556.61
tons with 21 loads for Soterra2 of, 770.00 tons with 27 loads for Burson, 380.03 tons with
13 loads for Burton, 234.36 tons with 9 loads for Ellison and 261.30 tons with 9 loads for
Feltham (Appendix 1). All the chips (hardwood and pine) were sold at the same price to
the International Paper power plant in Courland, Alabama.
39
Table 4.8. Woodchip yield per age and acre
Hardwood
Stand
Softwood
Stand
Tons/
Acres
Age
Yield
in tons
Soterra1
70
30
802.63
Ton/
Acres
year/acre
Age
Yield
year/acre
in tons
0.382
Ellison
25
23
380.0
0.660
3
Soterra2
56
30
556.61
0.331
Burton
35
20
234.3
0.330
6
Borson
30
25
770.00
1.02
Feltham
19
20
261.3
0.687
0
Total
156
2,129.2
Total
1.733
4
79
875.6
1.677
9
Table 4.6 shows that biomass yield in hardwood stands was 0.382 ton/year/acre
for Soterra1; 0.331 tone/year/acre for Soterra2; and 1.02 ton/year/acre for Borson. In pine
stands, the biomass yield was 0.66 ton/year/acre for Ellison; 0.330 ton/year/acre for
Burton and 0.687 ton/year/acre for Feltham. In average hardwood stands had 0.580
ton/year/acre and pine stands had 0.559 ton/year/acre.
A statistical analysis t-test was performed with SPSS 11.0.1 to compare the yield
per acres in the six stands above. The results (Table 4.7 and Table 4.8) showed that there
was no significant difference in terms of yield of biomass/year/acre in the two forest
types. The table 4.6 displays the treatment yield/year/acre means, standard deviations and
standard errors.
40
Table 4.9. Statistical values of yield of hardwood and pine stands
Hardwood-chip
N
3
Mean/year
/acre
0.5807
Pinewood-chip
3
0.5590
Std
Deviation
0.39000
Std. Error
Mean
0.22517
0.19878
0.11476
Table 4.10. Independent t-test on the yield of hardwood and pine stands
Equality of
variance
Equal
variances
assumed
Equal
variances
not assumed
t – test for equality of means
F
Sig.
t
df
Sig.
(2-tailed)
2.935
0.162
0.086
4
0.936*
0.0217
0.25273
95%
confidence
interval of
difference
Lower upper
-0.680 0.723
-
-
0.086
2.9
0.937
0.0217
0.25273
-0.786
Mean
diff.
Std.
Err diff.
0.830
*P-value = 0.936
The results (Table 4.9 and Table 4.10) show two group means of softwood-chip
yield and hardwood-chip yield as independent samples. A low significance value for the
P (typically less than 0.05) indicates that there is a significant difference between the two
group means. In this case the value of P-value is 0.936 which, indicates there is no
significant difference between the two groups of means.
Regarding other energy crops, switchgrass has been reported to be one of the most
productive energy crops. Oak Ridge National Laboratory has reported yield of
switchgrass to reach a one-year record of 15 tons per acre (DOE, 2009). In another study
Sharma et al., (2003) reported a yield of 10.27 tons/ha (4.58 ton/acre). Compared to
forest biomass yield, switchgrass seems to have advantage as a short rotation crop for
41
bioenergy. The highest yield of forest biomass in this study was evaluated at 1.733 green
ton/year/acre while lowest yield in switchgrass has reported above by Sharma et al.,
(2003) is 4.58 ton/year/acre.
4.5. Efficiency of wood energy
The amount of electricity generated by the woodchips was calculated considering
the wood density as an important factor. The formula (3.7) proposed by Gan and Smith
(2007) was used and the results show (Table 4.11) that amount of electricity generated
with a cubic meter of pine-chip was estimated at 0.95 MWh, while the same volume of
hardwood-chip would provide 1.19 MWh. The energy content of logging residues is
assumed to be 19.19 GJ t-1 (Graham et al., 1995) and the energy efficiency of the power
plants has been evaluated to be about 35% by the US Department of energy (IEA, 2002).
The difference in energy efficiency is due to different wood density in tree species. The
density of pine wood and hardwood (in metric tons per m3) were respectively evaluated at
0.510 and 0.639 (Gan and Smith, 2007).
Table 4.11. Electricity generation by species
Feedstock
Density
Moisture
Electricity in
Electricity
content
MWh/cubic
per ton
meter
Pine-chip
0.510
50%
0.95
1.862 MWh
Hardwood-chip
0.639
40%
1.19
1.862 MWh
42
Other researches reveal that wood composition and moisture content are two of
the more important properties of woody biomass in terms of utilization potential and
energy yield. Moisture plays a significant role in the type of conversion process used and,
at high levels, reduces energy yield (Hubbard et al., 2007; Gan and Smith, 2007). The
amount of electricity per ton were obtained by dividing the electricity in MWh by the
moisture content. Both type feedstook provided the same amount of electricity per ton
(1.862): 0.95/0.516 for pine-chip and 1.19/0.639 for hardwood-chip.
In this research we were interested in following the transformation of woody
biomass to energy in a power plant. Unfortunately we were not allowed to see the actual
energy production process but were briefed about the process. Woody biomass can be
converted into useful forms of energy, solid, liquid or gaseous, through bio-chemical and
thermochemical processes (Hubbard et al., 2007). The International Paper power plant in
Courtland, Alabama generates electricity through combustion and gasification, which are
parts of the thermochemical processes. This process depends on the relationship between
heat and chemical reactions to produce energy from woodchip.
The combustion (Figure 4.1) is the rapid oxidation of biomass in high temperature
up to 1800 degrees Fahrenheit. Woody biomass is burned in a boiler to produce highpressure steam that is then pumped into a turbine, over a series of blades that rotate,
powering an electric generator (Hubbard et al., 2007).
43
Figure 4.1. Combustion process (from Hubbard et al., 2007).
At the end of combustion process, the system produces ash, and releases Nitrogen
oxides (NOx) and Sulfur oxides (SOx), which are air pollutants. Ash from woody
biomass comes from the minerals present in the tissues of woody plants and from
possible soil contamination. The major emissions of air pollutants from biomass power
plant concern particulate matter (PM), carbon monoxide (CO), volatile organic
compounds (VOC), and nitrogen oxides (NOx). However, the emissions of sulfur dioxide
(SO 2 ) are typically low because of the low amount of sulfur usually found in biomass
(Bain et al., 2003).
Gasification (Figure 4.2) is a special combustion process, in which biomass solids
are turned directly into biogas. Complex hydrocarbons of wood are decomposed into
hydrogen, carbon monoxide, and carbon dioxide at high temperature heat.
44
Figure 4.2 Gasification process (From Hubbard et al., 2007).
At the end of gasification process, the system produces ash, char, tars, methane,
and other hydrocarbons. The International paper power plant used both processes:
combustion and gasification. However, gasification has been proven to be more efficient
in power production than combustion. It provides as much as 20% more efficiency than
combustion. (Bain and Amos, 2003).
45
Chapter V
SUMMARY AND CONCLUSION
This study was set to evaluate efficient way to produce woody biomass as
feedstock for electricity production. Two forest types (hardwood and pine forest) were
considered as treatments with three replications. Different aspects in woody biomass
production were evaluated including cost of production, yield per acre, and efficiency to
produce electricity. Data were collected from six in-wood chipping operations in four
different counties in Alabama. The chipping operations were integrated within traditional
logging operations. The specifics of woody biomass production were, collecting, loading
and chipping logging residues. All operations were conducted by the same crew using the
same equipment and the same harvesting system. To answer this study’s questions and
hypotheses, data were collected and analyzed using conventional scientific methods and
techniques.
The first objective was to evaluate and compare the cost of production of
hardwood-chips and pine-chips. The results of productivity analysis indicate that it took
15 more minutes to produce one load of hardwood-chips than pine-chips. In terms of
cost, it required $271.03 to produce 28 tons of hardwood-chips or $9.68 per ton. On the
other hand, it costs $203.27 to produce the same amount of pine-chips or $7.26/ton. The
$2.42 per ton difference in cost is certainly explained by the 15 minutes delay registered
in chipping hardwood. This delay is due to the structure of the wood. Softwoods such as
46
pine trees have higher moisture content and less density which makes chipping pines
faster than hardwood such red oak trees. Therefore, these findings allow the rejection of
the null-hypothesis since the difference is significant.
The study was also concerned with identifying the most influential factor in the
cost of production. Among the three factors evaluated (price of equipment, labor cost and
fuel cost), the price of equipment was identified as the most sensitive. The changes in
equipment cost had greater impact on the total cost of SMH and PMH. An increase of
10% in equipment price, while other prices remained unchanged, resulted 4.32% increase
of the total cost. Whereas the same increase in labor wage and fuel price changed the
total cost to 3.26% and 2.38% respectively. In addition, leasing logging equipment was
considered to be a good alternative to purchasing. The lease of a skidder reduced the total
cost per SMH and PMH by 7.47% which is even better than reducing the price of skidder
at 10%.
The evaluation chip production, in tons per acre, from each operation was the
second objective of this study.
In hardwood forest, 78 loads hardwood-chips were
produced on 156 acres, and in pine forests, 31 loads of pine-chips were produced on 79
acres. In statistical analysis the difference between 15.68 tons of hardwood-chip per acre
and 11.08 tons of pine-chips per acre was found to be non-significant, therefore the nullhypothesis is accepted cannot be rejected. The difference of 4.6 tons per acre was found
to be not significant in this case.
Efficiency of wood chip to produce electricity was evaluated. We were not
allowed to see the gasification and combustion processes at the power plant where the
wood-chip was delivered. However, explanations received from engineers on the site
47
were helpful to understand the process of converting woody biomass to electricity, and to
evaluate environmental benefits and potential concerns. Since no information about the
specific of the power plant was given, we relied on formula found in Gan and Smith,
(2007) to estimate the amount of electricity from the hardwood and pine chips. The result
shows that hardwood-chip is more efficient to produce electricity than pine-chip. The
amount of electricity generated with a cubic meter of pine-chip was estimated at 0.95
MWh, while the same volume of hardwood-chip would provide 1.19 MWh. The
difference in energy efficiency is due to different physical propriety of wood such as bulk
density. The density of pine wood and hardwood (in metric ton per m3) are respectively
evaluated at 0.510 and 0.639 (Gan and Smith, 2007).
In conclusion there is a significant difference between the cost of producing
hardwood-chips and pine-chips, while there is no significant different in yield from both
forest types.
Despite the impact forest biomass production may have on forest ecosystem depletion of soil nutrients, destruction of wildlife habitat and soil disturbance- it has been
demonstrated that the utilization for woody biomass for energy yields more
environmental benefits compared to the use of fossil fuels. The reduction of greenhouse
gases emission to mitigate global warming is the most important environmental benefits.
However, the development of renewable energy industry is set back by multiples barriers.
The market conditions (dominated by fossil fuels industry), the current technology (not
yet developed for mass production of bioenergy), and the current laws and regulations
give little incentives to help an embryonic bio-energy industry.
48
APPENDICES
49
APPENDIX 1
DAILY WOODCHIP PRODUCTION FROM HARDWOOD AND PINE STANDS
Daily woodchips production
Daily woodchips production
Tract Name: Soterra 1 (Winston County)
Tract Name: Soterra 2 (Winston County)
Date
6/4/09
6/5/09
6/7/09
6/8/09
6/11/09
6/12/09
6/13/09
6/14/09
6/15/09
6/16/09
6/17/09
6/18/09
6/22/09
6/23/09
Total
Ticket number
326544
326415
326810
326868
326999
327359
327504
327625
327780
328825
329229
323590
329486
329761
329916
330002
330104
330395
330465
330763
331006
331417
331529
330831
332708
332587
333277
333177
333328
333145
Tons
18.97
33.35
24.01
22.45
23.28
32.45
26.65
25.55
34.99
24.72
34.51
22.92
25.22
25.79
32.33
28.50
25.63
26.41
25.64
26.74
25.83
24.51
24.65
26.55
26.35
26.58
25.31
28.20
29.15
25.39
802.63
Date
6/24/09
6/25/09
6/29/09
6/30/09
7/1/09
Total
50
Ticket number
333598
333684
333827
333722
334154
334275
334380
334372
334249
334835
334840
335022
335058
335392
335431
335524
335570
335840
335877
335969
336015
Tons
27.42
28.49
28.89
27.05
26.22
25.10
24.60
28.89
27.89
24.03
26.56
26.50
28.33
16.82
24.24
29.27
31.07
24.26
28.02
25.73
27.23
566.61
Daily woodchips production
Tract Name: Burson (Jefferson County)
Date
7/15/09
7/16/09
7/17/09
7/18/09
7/20/09
7/21/09
7/22/09
7/23/09
7/24/09
7/27/09
Total
Ticket number
340708
340545
8/10/2832
340804
341126
341383
341427
341555
341650
341725
341934
342166
341977
342199
342494
342381
342643
343150
342999
343499
343648
343816
344071
344512
344698
344921
344831
Tons
29.81
26.85
23.36
26.49
33.48
31.79
31.80
34.65
30.39
30.70
26.49
27.22
27.10
29.36
27.11
28.60
28.84
30.49
28.85
31.01
29.27
29.78
27.30
23.83
25.12
27.27
23.04
770
51
Daily woodchips production
Tract Name: Burton (Blount County)
Date
7/28/09
7/29/09
7/30/09
7/31/09
8/3/09
8/4/09
8/5/09
8/6/09
Total
Ticket number
345361
345507
345694
345836
346131
346460
347167
347168
347672
347383
347871
348177
348362
Daily woodchips production
Tract Name: Ellison Cross Roads (Blount co)
Tons
28.22
32.11
30.57
31.42
31.62
33.32
31.04
30.73
27.35
26.67
25.89
27.17
23.92
380.03
Date
8/10/09
8/11/09
8/12/09
8/13/09
Total
Daily woodchips production
Tract Name: Feltham (Cullman County)
Date
9/4/09
9/8/09
9/9/09
9/10/09
Total
Ticket number
358336
358181
358301
359592
359733
359554
359952
360152
360272
Tons
29.66
29.31
29.22
28.35
27.47
31.71
26.92
30.31
28.35
261.3
52
Ticket number
349473
349590
349415
349497
349847
349728
349893
350182
350556
Tons
21.30
26.31
24.50
23.37
24.62
28.11
28.44
29.06
28.65
234.36
APPENDIX 2
EQUIPMENT USED DURING BIOMASS PRODUCTION OPERATIONS
I
Morbark chipper 30/36 model
John Deere Skidder
Prentice Loader
53
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