Production and cost of
harvesting, processing, and transporting
small-diameter (≤ 5 inches) trees for energy
Fei Pan✳
Han-Sup Han✳
Leonard R. Johnson✳
William J. Elliot
Abstract
Dense, small-diameter stands generally require thinning from below to improve fire-tolerance. The resulting forest biomass
can be used for energy production. The cost of harvesting, processing, and transporting small-diameter trees often exceeds
revenues due to high costs associated with harvesting and transportation and low market values for forest biomass. Productivity
and cost were evaluated in a whole-tree harvesting system on four fuel-reduction thinning treatment units in Arizona. Thinning
required removal of trees less than 5.0 inches in diameter at breast height (DBH). Time studies were applied to evaluate the
harvesting productivities and costs. Sensitivity analyses were performed to test the effects of different variables on costs. Simulations along with break-even analysis were used to examine the economic feasibility of using forest biomass for energy. Harvest
productivity for each machine in the system ranged from 2.31 to 39.32 bone dry tons per productive machine hour (BDT/PMH).
Harvest system costs including transportation for 36 miles or less averaged $55.27 per bone dry ton. Hauling cost represented the
largest component (47.24%) of the total cost. Close to market operations, reduced off-highway hauling, shortened skidding
distance, increased harvest tree size, and improvement in system balance could significantly reduce cost. Below the market value
of $40 per bone dry ton for hog fuel, breaking even or realizing profit would remain difficult. Other values, such as reducing fire
risks, preventing smoke pollutions, and creating renewable energy sources, should increase the attractiveness of harvesting forest
biomass for energy.
D
ense, small-diameter stands of timber have been
viewed as a major issue associated with wildland forest fires
in the interior western United States (USDA Forest Serv.
2001). In the last decade forest fires have increased in Arizona
and New Mexico (Moir et al. 1997). The worst forest fire in
Arizona to date, the Rodeo-Chediski fire in 2002, consumed
467,066 acres of forestland.
Dense, small-diameter stands generally require thinning
from below to improve fire-tolerance (Graham et al. 2002,
Healthy Forests 2002). The amount of woody biomass resulting from increased thinning activities could be substantial. To
ensure a thinning operation reduces fire risk for the residual
stand, the operation must either remove submerchantable
trees and logging slash completely through whole tree removal or carefully burn the fuels using a prescribed fire (Han
et al. 2002). Due to the heavy emissions of smoke and air
pollutants from open burning of biomass residues, prescribed
burning is increasingly limited as a tool for fuel reduction
FOREST PRODUCTS JOURNAL
VOL. 58, NO. 5
(Morris 1999, Bolding 2002). Energy generation creates an
opportunity to use forest biomass that is generated from mechanical fuel reduction treatments (Morris 1999, Sampson et
The authors are, respectively, Graduate Research Assistant, Dept.
of Forest Products, Univ. of Idaho, Moscow, Idaho (feipan@
vandals.uidaho.edu); Associate Professor, Dept. of Forestry and
Wildland Resources, Humboldt State Univ., Arcata, California
(hh30@humboldt.edu); Professor, Dept. of Forest Products, Univ. of
Idaho, Moscow, Idaho (ljohnson@uidaho.edu); and William J. Elliot, Team Leader, USDA Forest Serv., Rocky Mountain Research
Sta., Moscow, Idaho (welliot@fs.efed.us). This study was funded by
a grant from National Fire Plan through USDA Forest Serv., Rocky
Mountain Research Sta. The cooperation of USDA Forest Serv. at
Apache-Sitgreaves National Forest and Walker Brothers Logging
made this study possible. This paper was received for publication in
July 2007. Article No. 10380.
✳Forest Products Society Member.
©Forest Products Society 2008.
Forest Prod. J. 58(5):47-53.
47
al. 2001). In addition, biomass removal saves costs associated
with open-burning of logging slash.
Use of forest biomass will become commonplace only
when it becomes economically advantageous for users (GAO
2005). Harvesting, processing, and transporting forest biomass stems of nonmerchantable size are expensive when using conventional harvesting systems, due to decreased production (Han et al. 2002). Finding methods that will lower the
production cost has become a critical issue in the economic
feasibility of using forest biomass for energy. Many studies
reported harvesting productivities and costs in fuels-reduction treatments that remove trees larger than 5 inches in diameter at breast height (DBH). Halbrook and Han (2005) studied
an integrated harvesting system that processed the tree limbs
and tops resulting from a sawlog production into hog fuel.
They reported the biomass harvesting and transportation cost
of $24.37 per green ton for harvesting trees averaging
10.5 inches in DBH. However, the literature lacks information
about the productivity and cost of harvesting trees less than
5 inches in DBH. To improve our knowledge of costreduction methods when harvesting small-diameter trees for
energy, this study investigated the production and cost of harvesting, processing, and transporting small-diameter ponderosa pine trees less than or equal to 5.0 inches in DBH for
energy.
Methodology
Study site and harvesting system
The study sites were located in Springerville and
Black Mesa, Arizona. The two sites were stocked with nearly
100 percent ponderosa pine (Pinus ponderosa) trees. The
ground slope of the sites ranged from 0 percent to 28 percent.
Each site was laid out by global positioning system (GPS)
instruments, forming two 10-acre subunits. Thirty systematic
sampling plots were established throughout each unit to collect pre- and postharvest stand inventory data. The silvicultural prescription required removal of all trees less than or
equal to 5.0 inches in DBH.
A mechanized whole-tree system was used to harvest the
trees. It included a three-wheel hot-saw feller-buncher (Valmet 603) that felled and bunched trees prior to skidding them
with a rubber-tired grapple skidder (CAT 525B). Each unit
had one main skid trail cut by the feller-buncher. The skidder
would choose random skid trails from the main skid trail to
bunched trees. The main skid trail distance ranged from
700 feet to 1,400 feet, with an average of 1,025 feet. A log
loader (Prentice RT-100) was used at the landing to feed the
whole trees into a remote-controlled horizontal grinder (Bandit Beast 3680). The resulting processed hog fuel was loaded
into a chip van (walking floor) directly through the grinder’s
conveyor. Chip vans that could be hooked or disconnected
from the truck were used for landing-to-market hauling. The
hog fuel was sent to the Western Renewable Energy Co. in
Eagar for site 1 and to the RENEGY LLC. in Snowflake for
site 2. The one-way transportation distance ranged from
29.5 to 36 miles.
Data collection and analysis
A preharvest cruise used thirty 0.02-acre systematic, circular sampling plots in each harvesting unit to estimate unit average DBH, tree height, and stand density (stems/acre). To
48
Table 1. — Machine hourly cost ($/SMH) used in the study.
Initial price
Total hourly costa
($)
($/SMHb)
140,000
240,000
66.83
70.00
Prentice RT-100 loader
180,000
58.03
Bandit Beast 3680 grinder
260,000
86.60
Chip van
200,000
71.30
Machine
Valmet 603 feller-buncher
CAT 525B skidder
a
Cost includes labor.
b
SMH: Scheduled machine hour.
allow collection of postharvest stand inventory data, plot centers were staked, numbered, and flagged; plot edges were
painted, and two out-of-plot reference trees were flagged with
angle and distance to the plot center. After harvesting, the
same inventory measurements were recorded again.
Hourly machine costs measured in dollars per scheduled
machine hour ($/SMH) were estimated using a standard machine rate calculation method introduced by Miyata (1980;
Table 1). Initial machine prices, insurance, taxes, interest, lubrication cost, tire and chain cost, and repair and maintenance
cost were obtained from the project contractor. Diesel consumption and utilization rate for each machine were calculated from actual 9-day operations. Diesel prices were determined from local market prices in effect during the study. Estimated economic life for all the machines was set at 5 years
with an assumption of 2,000 scheduled machine hours per
year. Salvage value was set at 20 percent of the initial price.
Overhead and profit allowance were not included in the
hourly machine cost.
Regression equations were developed for machine cycle
time to allow determination of a machine production rate
(bone dry tons per productive machine hour, or BDT/PMH).
A feller-buncher cycle started with a move to the tree, followed by multiple cuttings, and ended with the placement of
the bunch on the ground. A skidder cycle consisted of traveling empty from the landing, positioning, grappling, traveling
loaded to the landing, and unloading in sequence. A log loader
cycle began when grappling the trees from the landing piles,
then swinging to the grinder, in-feeding, and ended when
swinging back to the pile. The grinder worked continuously
until a chip van was fully loaded, the time for loading a chip
van was used as a grinder cycle. A hauling cycle started with
the loading of the chip van, followed by traveling loaded to the
energy plant, unloading, and traveling back empty to the landing. All the variables at each harvesting phase were identified
prior to the start of operations.
Multiple regressions using ordinary least squares estimators were performed in SAS 9.0 program (SAS 1999) to develop these predictive equations. Normality plot, residual
plot, White test, Durbin-Watson test, variance inflation factor,
and condition index were used to detect whether the GaussMarkov assumptions were violated. Generalized least squares
estimators were used given the existence of heteroscedasticity
or autocorrelation. Restricted least squares estimators were
used if multicollinearity was determined to be severe. To validate the developed regression equations, 33 percent of observed data were randomly reserved and all the models developed from 67 percent of the observed data were used to predict the reserved data. A two-sample t-test (␣ = 0.05) was used
to evaluate the predictive regression equations developed.
MAY 2008
Table 2. — Pre- and postharvest stand descriptions.
DBH
Stand density
Mean S.D.
Unit 1 Preharvest
Postharvest
Unit 2 Preharvest
Postharvest
(inches)
5.11 5.13
8.72 6.38
5.41 4.06
Mean
S.D.
(stems per acre)
6.98
670 (430) a
264 (62)
3.41
678 (383)
8.76
294 (18)
Tree height
Mean
S.D.
(feet)
27.30 18.19
41.17
26.50
25.43
21.87
14.23
9.07
3.26
3.31
43.34
Unit 3 Preharvest
1.76
1.61
5,898 (5,663) 93.94
10.71
7.06
Postharvest
Unit 4 Preharvest
5.70
1.91
4.76
2.60
225 (87)
4.42
3,274 (3,033) 43.64
20.50
9.13
19.64
10.71
Postharvest
7.84
6.15
29.25
24.63
252 (77)
3.63
Value in () indicates number of trees per acre for trees ⱕ 5 inches in DBH.
a
Average observed values for independent variables provided
by the time study were used in the regression models to predict the cycle times. In addition, a signed rank test was performed to test the highway and unpaved road transportation
speed difference between travel-loaded and travel-empty.
This comparison was not performed for the travel speed difference on spur road as the sample size was too small (n = 2).
The cycle biomass green weight for grinding and hauling
were tracked from the energy plant scaling tickets. An average
moisture content (MC) of 52.75 percent found in an accompanying net energy study (Pan et al. 2008a) was applied to
convert the biomass green weight to ovendry weight. The average ponderosa pine tree dry weight for the felling, skidding,
and loading cycles was calculated using the formula (Jenkins
et al. 2003)
BW = 2.2046 · Exp共−0.4198 + 2.4349 · ln DBH兲
[1]
where:
BW = total aboveground biomass dry weight, pounds,
DBH = tree diameter at breast height, inches,
−0.4198 and 2.4349 are parameters for general pine trees.
Sensitivity analyses were performed to determine the effects of different variables in the developed regression models
while keeping all the other variables constant. The resulting
value change in cycle time was then converted to a production
cost change. Scatter plots showed how the production cost
changed with the corresponding value changes in the tested
variables, which included one-way spur road distance, unpaved road distance, highway distance, and skidding distance.
Simulations were also performed to test the effect of DBH on
production cost by excluding the cycles with inappropriate
DBH. To examine the economic feasibility of using forest
biomass for energy, various scenarios with different site factors were simulated along with a break-even analysis.
Results
Thinning effects and biomass quantities
Due to the removal of most small-diameter trees (DBH ⱕ
5.0 inches), stand characteristics changed dramatically with a
decrease in stand density and increases in both average DBH
and tree height (Table 2). In unit 1, 86 percent of the small
trees were removed. This percentage for units 2, 3, and 4 was
95 percent, 98 percent, and 97 percent, respectively. The
FOREST PRODUCTS JOURNAL
VOL. 58, NO. 5
lower small tree removal rate in unit 1 was caused by the scattered distribution of the larger trees (DBH > 5.0 inches). Difficulties in maneuvering between trees caused the fellerbuncher to occasionally forgo cutting target trees to avoid
damaging leave trees. Some larger trees (DBH 5.1 to
7.0 inches) were removed during the operations. Errors in visual estimates might lead the operator to cut trees slightly
larger than 5.0 inches in DBH as leave trees were not marked.
Sometimes a large tree was cut when it stood in the way and
the operator could not identify a better route.
During the 9-day operation, 336.11 bone dry tons of forest
biomass were removed. From unit 1 to unit 4, removed biomass quantities were 57.31 BDT, 45.54 BDT, 171.54 BDT,
and 61.72 BDT, respectively. The average fuel loading across
the four units before treatment was 8.40 BDT/acre.
Cycle time regression equations
Regression equations (Table 3) developed from the time
study have significant p-values (p < 0.05, ␣ = 0.05) for all the
associated variables. Model validation procedures showed
that the differences between the observed and predicted cycle
times were insignificant (p > 0.05) for all the equations, which
means developed regression equations are good predictors for
the machine cycle time.
The feller-buncher equation indicated that the cycle time
was influenced by all kinds of moving distances. A denser
stand requiring shorter moving distances would result shorter
cycle times. The skidder regression function excluded DBH
due to severe multicollinearity with number of stems per skidding cycle and its statistical insignificance (p > 0.05) in predicting the skidding cycle time. The equation implied that a
decrease in skidding distance would reduce cycle time. A
shorter cycle time could also be achieved by dragging more
trees per cycle, which would require the feller-buncher to
make larger bunches.
Tree size and swing degrees positively impacted the loading cycle time, meaning that with trees decked close to the
grinder, requiring a small swing angle and by loading small
trees, cycle time would be reduced. In the loader equation,
both number of trees per loading cycle and DBH were significant (p = 0.004 and p < 0.0001, respectively); therefore, they
were kept in the equation despite moderate multicollinearity.
Total biomass weight processed by the grinder was the only
factor influencing grinding cycle time. Tree size and MC became insignificant (p > 0.05) for grinding trees less than
5.0 inches in DBH in relatively short period of the observed
harvesting operations. The signed rank test revealed no average speed difference between travel-loaded and travel-empty
on highway (p = 0.062) and unpaved road (p = 0.062), in part
because travel-loaded was on favorable grade and travelempty was on adverse grade. The transportation distance on
various road types positively affected the hauling cycle time.
The regression coefficients suggested that given the same distance, spur road distance had the greatest effect on cycle time,
while the influence of highway distance was less.
Cycle biomass weight
Due to the difficulties in counting multiple small stems in
the felling cycles, the number of trees per skidding cycle was
used to estimate the number of trees per felling cycle, as field
observation verified the feller-buncher generally needed two
49
Table 3. — Delay-free average cycle time equations for harvesting machines. All variables included in the equations have
significant p-values less than 0.05.
Machine
Average cycle time estimator (centi-min.)
Feller-buncher
Variable range
Mean
= 1.336
Skidder
Loader
+ 0.379 (move to tree distance in feet)
+ 7.165 (number of cuts per cycle)
2 to 210
1 to 25
26.8
7.0
+ 0.315 (intermediate travel distance in feet)
5 to 600
83.7
+ 0.399 (move to bunch distance in feet)
0 to 250
22.8
= 111.950
+ 0.125 (travel empty distance in feet)
22 to 1,385
531.7
+ 0.688 (positioning distance in feet)
10 to 200
47.7
– 0.588 (number of trees per cycle)
+ 0.089 (travel loaded distance in feet)
8 to 172
20 to 1,347
50.9
516.2
= 7.902
+ 0.062 (number of trees per cycle)
+ 0.471 (DBH in inches)
+ 0.159 (swing to grinder degree)
+ 0.094 (swing back to pile degree)
Grinder
= – 1133.400
+ 0.151 (hog fuel weight per chip van load in green pounds)
Chip van
1 to 53
10.3
1 to 8
30 to 150
2.9
81.8
5 to 165
81.7
22,600 to 58,060
45011
22,600 to 58,060
8.5 to 36
0 to 13.5
45011
27.07
2.34
= 2623.000
+ 0.142 (hog fuel weight per chip van load in green pounds)
+ 231.580 (one-way highway distance in miles)
+ 849.100 (one-way unpaved road distance in miles)
+ 10,711.600 (one-way spur road distance in miles)
0 to 1.5
r2
na
Validation p-valueb
0.90
422
0.18
0.86
100
0.62
0.53
718
0.22
0.88
20
0.59
0.98
19
0.11
0.07
a
67 percent of the total observed data that used for model training.
p-value provided by two-sample t-test between predicted and observed cycle times.
b
Table 4. — Predicted delay-free average cycle time and production rate.
Feller-buncher
Cycle time
Skidder
Prod. rate
a
b
Loader
Grinder
Chip van
Cycle time
Prod. rate
Cycle time
Prod. rate
Cycle time
Prod. rate
Cycle time
Prod. rate
Unit 1
(min)
1.11
(BDT /PMH )
12.17
(min)
3.17
(BDT/PMH)
9.57
(min)
0.31
(BDT/PMH)
39.32
(min)
70.06
(BDT/PMH)
10.91
(min)
309.48
(BDT/PMH)
2.31
Unit 2
Unit 3
1.08
0.78
15.16
10.18
2.93
1.86
15.22
18.08
0.33
0.29
20.01
17.65
56.63
54.23
11.80
11.35
223.88
158.01
2.78
3.80
Unit 4
Overall
1.01
0.97
9.96
11.92
1.83
2.27
27.98
17.41
0.32
0.31
24.03
23.13
55.18
56.63
11.32
11.27
151.89
178.55
4.28
3.49
a
BDT: bone dry ton.
PMH: productive machine hour.
b
cycles to make a bunch for the skidder. In the skidder cycle
time regression, multicollinearity was detected between trees
per cycle and average DBH: trees per skidding cycle =
106.87–18.253·DBH, r2 = 0.48. The average DBH for the observed felling cycles was 2.21 inches, which meant an average
of 33 trees or 387 dry pounds biomass were cut per cycle.
Using Eq. [1], the biomass dry weight per cycle for the skidding and loading were calculated to be 1,317 dry pounds and
236 dry pounds, respectively. Energy plant scaling tickets
showed the grinding and hauling cycle biomass weights
ranged from 42,351 to 50,380 green pounds, averaging
43,898 green pounds, or 20,742 dry pounds.
Production rates
Combining the predicted cycle time with the cycle biomass
weight, production rates for all the machines were determined. They ranged from 2.31 BDT/PMH for the chip van to
39.32 BDT/PMH for the loader (Table 4). Despite shorter
50
cycle times in units 3 and 4, the feller-buncher had lower production rates compared to units 1 and 2 due to lower cycle
biomass weights. For the same reason, the loader had the lowest production rate in unit 3 although the shortest loading
cycle time also appeared there. Increasing cycle biomass
weight by harvesting larger trees is more important than reducing the cycle time for improving the felling and loading
production rates. The skidding production rate was the lowest
at unit 4 where the average skidding distance was 884 feet and
was the highest at unit 1 where the average skidding distance
was 347 feet. The grinding production rate was quite uniform
across the four units as the grinding productivity was only
influenced by the weight of processed hog fuel. The transportation production rates were lower in units 1 and 2 compared
with units 3 and 4. Although units 1 and 2 enjoyed shorter
highway distances to the energy plant, they had to cover 8 and
10.5 miles of unpaved road plus 1.5 extra miles of spur road in
unit 1 (Table 5).
MAY 2008
Table 5. — Road type, one-way distance, and average speed.
Spur
roadb
Unpaved
roadc
Paved
roadd
Highwaye
Total
- - - - - - - - - - - - - - - - - - - - - (miles) - - - - - - - - - - - - - - - - - - - - Unit 1
Unit 2
1.5
0
Unit 3
0
Unit 4
Average speed
(miles/hour)a
0
2.67
8
10.5
2.5
1
17.5
22.5
29.5
34
0
0
35
35
0
0
36
36
13.83
14.40
44.14
--
a
The average speed was calculated using the road distance divided by the time
traveling on it. The signed rank test did not find significant speed difference
between travel loaded and travel empty for highway (p = 0.062, ␣ = 0.05) and
unpaved road (p = 0.062, ␣ = 0.05).
b
Spur road: one lane forest road without gravel top or pavement.
c
Unpaved road: one-lane, graveled forest road.
d
Paved road: one-lane road with pavement, connects highway and unpaved
road.
e
Highway: two-lane state highway.
Delays
Delay types, associated delay time, and utilization rate for
the equipment are presented in Table 6. Machine cooling due
to hot season operations, teeth replacement because of rocks,
and diesel refueling caused 38.4 percent, 18.1 percent, and
29.6 percent of the feller-buncher delay time, respectively.
Because the feller-buncher could work independently from
other machines, it achieved the highest utilization rate of
88.1 percent in the system.
The skidder, loader, grinder, and chip van were operated as
a “hot” system, meaning one component of the system could
be affected by the productivity of another component. The
9-day shift-level data showed that waiting for truck caused the
largest portion of the skidder, loader, and grinder delay. Regression analysis verified that the predicted cycle times for
hauling and grinding were around 179 minutes and 57 minutes. The use of more than one trailer in the system did not
increase the hauling productivity enough to match the productivities of other system parts. Hauling productivity was low
because the chip van was not designed for off-highway hauling and only one highway truck driver was hired during the
operations in units 1, 2, and most of the time in unit 3.
Production costs
The total production costs by cutting unit ranged from
$49.20/BDT to $72.18/BDT. The overall production cost
was $55.27/BDT (Table 7). The transportation cost of
$26.11/BDT represented 47.24 percent of the total cost and
was the largest component of the total system cost, suggesting
the importance of maintaining operations close to the market.
The grinding cost was the second highest in the system followed by felling, skidding, and loading in sequence. Another
study (Han et al. 2004) indicated that skidding cost is usually
higher than felling cost. Felling cost was higher than skidding
cost in this study because the small-diameter trees required
increased felling/bunching time.
Table 8 summarizes the site factors by cutting unit and the
associated total production costs. The production costs related
positively to the average DBH and skidding distance, but
negatively to the stand density and one-way hauling distance.
Three scenarios were simulated based on the pooled data from
unit 1 to unit 4 to test the impact of DBH on the production
FOREST PRODUCTS JOURNAL
VOL. 58, NO. 5
cost. These scenarios included harvesting trees 2 to 5 inches,
3 to 5 inches, and 4 to 5 inches. The grinding and hauling costs
were assumed to remain at the observed values since their
productivities were independent of DBH. Table 9 shows that
a decrease in average DBH results in increased total costs.
From unit 1 to unit 4, spur road and unpaved road distances
were reduced, but the increase of longer highway distance
caused the increase of the total hauling distance. Due to the
stronger cost effect of spur road and unpaved road compared
with that of highway for the same distance, the total hauling
cost was reduced. The unit 3 and unit 4 had similar site parameters, but significantly different production cost. This was
due to the hiring of the second truck driver in unit 4, which
increased the system utilization rate and productivity.
Effects of hauling distance and
skidding distance on harvesting cost
The effect of hauling distance on the total cost was determined by setting one-way distance to 5, 10, 15, and 20 miles
for highway, unpaved road, and spur road, respectively. The
sensitivity test showed that overall production cost changed at
rates of $0.17, $0.62, and $7.86 per bone dry ton for each
additional mile of highway, unpaved road, and spur road, respectively (Fig. 1). The greater influence of spur road and
unpaved road indicates that reducing off-highway hauling
should receive more attention in efforts to reduce production
cost.
The overall average skidding distance when traveling
empty and traveling loaded were 532 feet and 516 feet, respectively. Cost changes were calculated for both skidding
distance reductions and increases of 100 feet and 200 feet. The
sensitivity test showed that the overall production cost would
increase $0.58/BDT for every 100 feet skidding distance increase (Fig. 2). The effect of 100 feet skidding distance on the
cost is nearly equivalent to the cost change of travel on 1 mile
of unpaved road. Shortening the skidding distance should factor highly in the harvest planning.
Cost reduction potentials
Operational delays have a potential to be minimized by
knowing the productivities of system parts. Table 6 summarizes the utilization rates for an assumed balanced system by
eliminating the “waiting on truck” time. The loading cost was
reduced most significantly due to the highest increase in its
utilization rate. The overall production cost (Table 10) was
reduced to $43.20/BDT, or 78 percent of the overall production cost in the unbalanced condition.
Recovering forest biomass piled at landings can avoid felling and skidding costs and thereby reduce the total production
cost. If the felling and skidding cost were excluded in this
study, the remaining cost would be $42.82/BDT, or 77 percent
of the observed cost. If the system was balanced and the felling and skidding costs were excluded, the production cost
would be $32.55/BDT, or 59 percent of the observed cost.
Economic analysis
Figure 3 shows a break-even analysis for the simulated scenarios based on various tree sizes, skidding distances, and
one-way highway transportation distances. Other variables
were kept at average observed values. The market price of hog
fuel was set at $40/BDT. Scenario one represented the most
favorable operation conditions with the largest average stand
DBH and shortest skidding distance. When one-way highway
51
Table 6. — Summary of delays for the 9-day operation.
Operational delay
Personal & othersa
Mechanical delay
Total delay
Total production time
Utilization rate
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - (min) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 331 (82.5)
56 (14.0)
401 (100)
2,960
14 (3.5)b
Feller-buncher
Skidder
881 (87.5)
117 (11.6)
9 (0.9)
1,007 (100)
1,963
66.1 (94.0)
978 (86.6)
965 (83.8)
149 (13.2)
173 (15.0)
3 (0.2)
13 (1.2)
1,130 (100)
1,151 (100)
1,803
1,788
61.5 (92.2)
60.8 (90.6)
1,237 (83.9)
212 (14.4)
25 (1.7)
1,474 (100)
5,334
78.3 (95.7)
Loader
Grinder
Chip van
(percent)
88.1 (88.1)c
a
Other delay includes research delay.
b
Value in () indicates % of total.
c
Value in () indicates utilization rate without “waiting on truck” or operational delay time.
Table 7. — Stump-to-market production cost ($/BDT) and percentage in total.
Feller-buncher
Skidder
Percent
of total
Cost
($/BDT)
Loader
Percent
of total
Cost
($/BDT)
Grinder
Percent
of total
Cost
($/BDT)
Cost
Percent
of total
($/BDT)
Chip van
Cost
Percent
of total
($/BDT)
Total
Percent
of total
Cost
($/BDT)
Unit 1
Unit 2
6.23
5.01
8.63
8.15
11.06
6.96
15.32
11.33
2.40
4.72
3.33
7.68
13.05
12.06
18.08
19.63
39.44
32.70
54.64
53.21
72.18
61.45
100
100
Unit 3
7.45
13.51
5.86
10.63
5.35
9.70
12.54
22.74
23.95
43.43
55.15
100
Unit 4
Overall
7.62
6.37
15.49
11.53
3.79
6.08
7.70
11.00
3.93
4.08
7.99
7.38
12.58
12.63
25.57
22.85
21.28
26.11
43.25
47.24
49.20
55.27
100
100
Table 8. — Stand descriptive variables and the corresponding
cost of hog fuel production.
Preharvest
avg. stand DBH
Preharvest
stand
density
Avg.
skidding
distance
One-way
hauling
distance
Cost
(inches)
(per acre)
(feet)
(miles)
($/BDT)
Unit 1
Unit 2
Unit 3
5.11
5.41
1.76
670
678
5,898
884
693
370
29.5
34
35
72.18
61.45
55.15
Unit 4
1.91
3,274
347
36
49.20
Overall
2.25
2,630
574
33.63
55.27
Table 9. — Simulated production costs by DBH limit.
Scenario
DBH
limit
Fellerbuncher Skidder Loader Grinder
Chip
van
Figure 1. — Effect of various road distances on production
cost.
Total
(inches) - - - - - - - - - - - - - - - - - - - ($/BDT) - - - - - - - - - - - - - - - - - - - ⱕ5.0
6.37
6.08
4.08
12.63 26.11 55.27
2.0 to 5.0
4.65
6.06
3.90
12.63 26.11 53.35
3.0 to 5.0
3.37
5.67
3.37
12.63 26.11 51.15
4.0 to 5.0
2.94
5.60
3.03
12.63 26.11 50.31
Applying developed regression equations in other site conditions need extra cautions. The predictive regression equations were developed over finite intervals for the values of
independent variables. Applications in the conditions where
the values are outside these intervals need a process of bias
correction (Pan et al. 2008b), particularly for the independent
variables other than distances.
distance was 10 miles, the scenario one had the lowest production of $44.35/BDT among the three scenarios, which is
still difficult to break-even with costs or to realize a profit.
Biomass fuel should not be viewed as the only product resulting from fuel reduction treatments. A whole-tree harvesting system can effectively reduce site preparation costs in the
future. More importantly, harvesting forest biomass for energy helps reduce fire risks in overstocked forests, prevent air
from being polluted by the smoke of prescribed fires, and create a source of clean, renewable energy. These values should
be considered in addition to the market value of biomass fuel.
1
2
3
4
Discussion
Tree size is not a significant factor in predicting the time of
felling, skidding, and grinding trees less than or equal to
5.0 inches in DBH. This should not be viewed as a common
feature of most harvesting operations, where felling, skidding,
and grinding productivities often increase with the increase of
the tree size. The insignificance of tree size in this study is
combined effect of small tree size and high machine capability.
52
Conclusion
This study evaluated harvesting productivity and cost of a
mechanized fuel reduction thinning of small-diameter trees to
MAY 2008
Figure 2. — Effect of average skidding distance on production
cost.
Table 10. — Stump-to-market production cost without “waiting
on truck” time.
Feller-buncher
Skidder
Loader
Grinder
Chip van
Total
- - - - - - - - - - - - - - - - - - - - - - - - - ($/BDT) - - - - - - - - - - - - - - - - - - - - - - - - Unit 1
Unit 2
6.23
5.01
7.78
4.89
1.60
3.15
8.76
8.10
32.25
26.80
56.63
47.95
Unit 3
Unit 4
7.45
7.62
4.12
2.66
3.57
2.62
8.42
8.44
19.61
17.41
43.16
38.75
Overall
6.37
4.28
2.72
8.48
21.35
43.20
Figure 3. — Break-even analysis for simulated harvesting
scenarios.
create forest biomass for energy. Under the conditions of target tree size (DBH) ⱕ 5.0 inches, stand densities of 670 to
5,898 trees per acre, skidding distances of 347 to 884 feet, and
one-way hauling distance of 29.5 to 36 miles, hourly productivity for different machines varied from 2.31 to 39.32 BDT/
PMH, and the production costs including transportation
ranged from $49.20 to $72.18/BDT, averaging $55.27/BDT.
The average transportation cost of $26.11/BDT represented
47.24 percent of the total production cost. Maintaining an operation close to markets could reduce the total cost due to
lower hauling costs and improved system balance. Spur road
distance had the strongest effect on transportation cost, followed by distances of unpaved road and highway. Reducing
off-highway hauling should receive more attention in planning than overall distance. Well planned skidding placement
is critical in harvest planning since shorter skidding distances
decrease the skidding cost and thereby the total production
FOREST PRODUCTS JOURNAL
VOL. 58, NO. 5
cost. The low biomass weight per cycle due to small-diameter
trees in the felling and loading operations overrode the effect
of short cycle time and made their production costs relatively
high. Grinding cost was quite uniform across the four units
since the production and cost were independent of most site
variables and related only to the weight of processed hog fuel.
System balancing by reducing the operational delays
greatly improved the production rate and lowered the cost. If
operations could rely totally on the biomass piled at the landing through some other operation, the cost of recovering biomass for energy would be further reduced. A market rate of
$40/BDT for biomass fuel is insufficient to break-even and
realize profit when harvesting, processing and transporting
small-diameter trees for energy. Other values, such as reducing fire risks, preventing smoke pollutions, and creating renewable energy sources, should increase the attractiveness of
harvesting forest biomass for energy.
Literature cited
Bolding, M.C. 2002. Forest fuel reduction and energywood production
using a CTL / small chipper harvesting system. MS thesis. Auburn
Univ., Alabama. 111 pp.
U.S. Government Accountability Office (GAO). 2005. Federal agencies
are engaged in various efforts to promote the utilization of woody biomass, but significant obstacles to its use remain. 17 pp.
Graham, R.T., A. Harvey, T. Jain, and J. Tonn. 2002. The effects of
thinning and similar stand treatments on forest fire behavior in western
forests. PNW-BDTR-463. USDA Forest Serv., Pacific Northwest Res.
Sta., Portland, Oregon. 27 pp.
Halbrook, J. and H.-S. Han. 2005. Costs and constraints of fuel reduction
treatments in a recreational area. Gen. Tech. Rept. PSW-GTR-194.
USDA Forest Serv. 13 pp.
Han, H.-S., J. Hinson, G. Jackson, R. Folk, H. Lee, L. Johnson, and T.
Gorman. 2002. Economic feasibility of small wood harvesting and utilization on the Boise National Forest, Cascade, Idaho City, Emmett
Ranger Districts: A report for Gem County Commissioners. Univ. of
Idaho, Moscow, Idaho. 62 pp.
, H.W. Lee, and L.R. Johnson. 2004. Economic feasibility of
an integrated harvesting system for small-diameter trees in southwest
Idaho. Forest Prod. J. 54(2):21-27.
Healthy Forests. 2002. An initiative for wildfire prevention and stronger
communities. www.whitehouse.gov/infocus/healthyforests/
Healthy_Forests_v2.pdf. 22 pp. Accessed January 2, 2007.
Jenkins, J.C., D.C. Chojnacky, L.S. Heath, and R.A. Birdsey. 2003. National-scale biomass estimation for United States tree species. Forest
Sci. 49(1):12-35.
Miyata, E.S. 1980. Determining fixed and variable costs of logging
equipment. Gen. Tech. Rept. NC-55. USDA Forest Serv., North Central Forest Exp. Sta., St. Paul, Minnesota. 20 pp.
Moir, W.H., B. Geils, M.A. Benoit, and D. Scurlock. 1997. Ecology of
Southwestern ponderosa pine forests. In: Songbird Ecology in Southwestern Ponderosa Pine Forests: A Literature Review. W.M. Block and
D.M. Finch, technical eds. Gen. Tech. Rept. RM-GTR-292. USDA
Forest Serv., Rocky Mountain Forest and Range Exp. Sta., Fort Collins, Colorado. pp. 3-27.
Morris, G. 1999. The value of the benefits of U.S. biomass power.
NREL/SR-570-27541. 32 pp.
Pan, F., H.-S. Han, L.R. Johnson, and W.J. Elliot. 2008a. Net energy
output from harvesting small-diameter trees using a mechanized system. Forest Prod. J. 58(1):25-30.
, L.R. Johnson, and C.J. Williams. 2008b. A method for using multiple linear regression equations to make predictions. Inter. J. of
Forest Engineering (submitted).
Sampson, R.N., M.S. Smith, and S.B. Gann. 2001. Western forest health
and bioenergy potential: A report to the Oregon Office of Energy. The
Sampson Group, Inc., Alexandria, Virginia. 53 pp.
SAS Inst. Inc. (SAS). 1999. SAS/STAT user’s guide, Version 8. SAS,
Cary, North Carolina.
USDA Forest Serv. 2001. Small-diameter timber on display at 2002
Winter Olympics. Forest Prod. Lab., Madison, Wisconsin. 2 pp.
53