Production and cost of harvesting, processing, and transporting small-diameter ( 5 inches) trees for energy

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1 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 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. Dense, 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 (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 Article No Forest Products Society Member. Forest Products Society Forest Prod. J. 58(5): FOREST PRODUCTS JOURNAL VOL. 58, NO. 5 47

2 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 Table 1. Machine hourly cost ($/SMH) used in the study. Machine Initial price Total hourly cost a ($) ($/SMH b ) Valmet 603 feller-buncher 140, CAT 525B skidder 240, Prentice RT-100 loader 180, Bandit Beast 3680 grinder 260, Chip van 200, 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 Gauss- Markov 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. 48 MAY 2008

3 Table 2. Pre- and postharvest stand descriptions. DBH Stand density Tree height Mean S.D. Mean S.D. Mean S.D. (inches) (stems per acre) (feet) Unit 1 Preharvest (430) a Postharvest (62) Unit 2 Preharvest (383) Postharvest (18) Unit 3 Preharvest ,898 (5,663) Postharvest (87) Unit 4 Preharvest ,274 (3,033) Postharvest (77) a Value in () indicates number of trees per acre for trees 5 inches in DBH. 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 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) where: BW = Exp ln DBH [1] BW = total aboveground biomass dry weight, pounds, DBH = tree diameter at breast height, inches, and 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 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 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, bone dry tons of forest biomass were removed. From unit 1 to unit 4, removed biomass quantities were BDT, BDT, BDT, and 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 = and p < , 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 FOREST PRODUCTS JOURNAL VOL. 58, NO. 5 49

4 Table 3. Delay-free average cycle time equations for harvesting machines. All variables included in the equations have significant p-values less than Machine Average cycle time estimator (centi-min.) Variable range Mean r 2 n a Validation p-value b Feller-buncher = (move to tree distance in feet) 2 to (number of cuts per cycle) 1 to (intermediate travel distance in feet) 5 to (move to bunch distance in feet) 0 to Skidder = (travel empty distance in feet) 22 to 1, (positioning distance in feet) 10 to (number of trees per cycle) 8 to (travel loaded distance in feet) 20 to 1, Loader = (number of trees per cycle) 1 to (DBH in inches) 1 to (swing to grinder degree) 30 to (swing back to pile degree) 5 to Grinder = (hog fuel weight per chip van load in green pounds) 22,600 to 58, Chip van = (hog fuel weight per chip van load in green pounds) 22,600 to 58, (one-way highway distance in miles) 8.5 to (one-way unpaved road distance in miles) 0 to , (one-way spur road distance in miles) 0 to 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. Table 4. Predicted delay-free average cycle time and production rate. Feller-buncher Skidder Loader Grinder Chip van Cycle time Prod. rate Cycle time Prod. rate Cycle time Prod. rate Cycle time Prod. rate Cycle time Prod. rate (min) (BDT a /PMH b ) (min) (BDT/PMH) (min) (BDT/PMH) (min) (BDT/PMH) (min) (BDT/PMH) Unit Unit Unit Unit Overall a BDT: bone dry ton. b PMH: productive machine hour. 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 = DBH, r 2 = 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 BDT/PMH for the loader (Table 4). Despite shorter 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). 50 MAY 2008

5 Table 5. Road type, one-way distance, and average speed. Spur road b Unpaved road c Paved road d Highway e Total (miles) Unit Unit Unit Unit Average speed (miles/hour) a 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 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 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 FOREST PRODUCTS JOURNAL VOL. 58, NO. 5 51

6 Table 6. Summary of delays for the 9-day operation. Operational delay Mechanical delay Personal & others a Total delay Total production time Utilization rate (min) (percent) Feller-buncher 14 (3.5) b 331 (82.5) 56 (14.0) 401 (100) 2, (88.1) c Skidder 881 (87.5) 117 (11.6) 9 (0.9) 1,007 (100) 1, (94.0) Loader 978 (86.6) 149 (13.2) 3 (0.2) 1,130 (100) 1, (92.2) Grinder 965 (83.8) 173 (15.0) 13 (1.2) 1,151 (100) 1, (90.6) Chip van 1,237 (83.9) 212 (14.4) 25 (1.7) 1,474 (100) 5, (95.7) 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 ($/BDT) and percentage in total. Feller-buncher Skidder Loader Grinder Chip van Total Cost of total ($/BDT) ($/BDT) ($/BDT) ($/BDT) ($/BDT) ($/BDT) Unit Unit Unit Unit Overall Table 8. Stand descriptive variables and the corresponding cost of hog fuel production. Preharvest avg. stand DBH Preharvest stand density Avg. skidding distance One-way hauling distance Cost (inches) (per acre) (feet) (miles) ($/BDT) Unit Unit Unit , Unit , Overall , Table 9. Simulated production costs by DBH limit. DBH limit (inches) Fellerbuncher Skidder Loader Grinder Chip van Scenario Total ($/BDT) to to to 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. 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. Figure 1. Effect of various road distances on production cost. 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. 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. Conclusion This study evaluated harvesting productivity and cost of a mechanized fuel reduction thinning of small-diameter trees to 52 MAY 2008

7 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 Unit Unit Unit Overall Figure 3. Break-even analysis for simulated harvesting scenarios. create forest biomass for energy. Under the conditions of target tree size (DBH) 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 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 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 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 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) 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 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 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 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 Economic feasibility of an integrated harvesting system for small-diameter trees in southwest Idaho. Forest Prod. J. 54(2): Healthy Forests An initiative for wildfire prevention and stronger communities. Healthy_Forests_v2.pdf. 22 pp. Accessed January 2, Jenkins, J.C., D.C. Chojnacky, L.S. Heath, and R.A. Birdsey National-scale biomass estimation for United States tree species. Forest Sci. 49(1): Miyata, E.S 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 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 Morris, G The value of the benefits of U.S. biomass power. NREL/SR 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. 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