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Analysis of mechanical thinning productivity and cost for use at the wildland urban interface.

Abstract

Forest management in many parts of the urbanizing Southeastern U.S. is becoming more difficult due to fragmentation, alternative management objectives, and social conflicts with management activities. However, the public benefits from management of these areas are still high. This study compared the productivity and costs of mechanical thinning treatments using conventional thinning and two alternative thinning approaches in even-aged loblolly pine plantations. The alternative treatments removed more stand basal area and were intended to promote transition to uneven-aged stand management. Production studies and cost analyses were completed for conventional, heavy, and strip treatments. The conventional treatment was a fifth row and select removals to 16 [m.sup.2] [ha.sup.- 1] residual basal area. The heavy treatment was a fifth row and select removals to 9 [m.sup.2] [ha.sup.-1] residual basal area. The strip treatment included a conventional thinning treatment with alternating reserve and clearcut strips established on the contour. The resulting residual basal area was 11.5 [m.sup.2] [ha.sup.-1]. The alternative treatments provided substantially lower costs and higher residual values ($1 to 3 [m.sup.-3]) in the 4 ha stands but smaller advantages in 8 and 12 ha stands. The difference from lower harvest costs for the alternative treatments may enable landowners to attract interest in small acreage sales that result from fragmentation.

Conversion of rural lands to urban uses expands the interface between human population and rural land, termed the Wildland Urban Interface (WUI) (Nowicki 2001). The conditions at the WUI limit opportunities for management (Cordell and Macie 2002) which may be required to mitigate potential forest health problems (Bolding et al. 2003). Across the southeastern U.S. growth near population centers is expected to convert forestland to urban uses and increase fragmentation of nearby forestland (Wear and Greis 2002).

Maintaining active management in fragmented forests requires systems that are socially acceptable and economically feasible. Small parcel sizes increase harvest costs because fixed costs are distributed across less volume (Cubbage et al. 1989, Kittredge et al. 1996, Greene et al. 1997). Logging contractors also have greater opportunity cost since they spend more time moving between harvests and less time logging. Marketing high value products is also more difficult since there may not be sufficient quantity. From thinnings and poor quality stands, low product value and potentially high transportation cost further limit gross income from a harvest. Other harvesting problems typical of the WU1 include stand access, log transportation issues, and conflicts with neighbors, all of which are likely to increase harvest costs and further reduce the social and economic feasibility of the operation.

Conventional mechanical thinning practices in even-aged pine plantations in the southeastern U.S. typically combine removal of every third, fourth, or fifth row with thinning from below to some specified residual basal area in the remaining stand. Continued even-aged management may defer, but will not avoid the significant visual impact from the eventual stand regenerating clearcut. One potential alternative to reduce the visual impacts of even-aged regeneration methods would be to promote a transition to unevenaged mixed stands by implementing atypical thinning regimes. A by-product of these alternative treatments is an increase in the tree size and removal intensity that may increase the economic feasibility of these operations.

The overall goal of this study was to examine the harvest cost and residual value (delivered value minus harvest costs) from the typical treatment and two alternative treatments in young loblolly pine (Pinus taeda) stands that will eventually lead to uneven-aged stand conversion. The alternatives considered here include a heavy thinning and strip removal to promote recruitment of another age class within the stand. Although product markets, harvesting systems, and costs are dynamic, increasing the commercial attractiveness of these harvests is an important first step in implementing treatments.

The specific objectives of this study were to: 1) generate production models for the skidder and the feller-buncher; 2) compare harvesting productivity and costs of two alternative treatments to conventional thinning; and 3) evaluate changes in residual value with respect to treatment, stand type, and stand size.

Material and methods

Treatments

The conventional thinning (conventional) is a fifth row removal and a thin from below of the remaining rows to a target residual basal area of 16 [m.sup.2] [ha.sup.-1]. The heavy thinning (heavy) requires the same row removal and a more intensive removal from the remaining rows to a target residual basal area of 9 [m.sup.2] [ha.sup.-1]. The strip treatment (strip) creates corridors or strips within the stand that are oriented along the contour and approximately 14 m wide. Leave strips between the removal strips are 36 m wide and are thinned using the conventional thinning method. The strip placement and dimensions could be arranged to obscure view of the cut areas and exact dimensions would be determined by row and tree spacing. Across the strip treatment, the target basal area was 11.5 [m.sup.2] [ha.sup.-1]. Production study

Production data were collected on six harvests across the Piedmont and upper coastal plain regions of Alabama, Georgia, and Mississippi (Table 1). The selection of the harvesting sites was based on thinning treatment, but limited to the following conditions: even-aged loblolly pine plantation; wheeled feller-buncher/skidder/knuckleboom loader systems; first thinning; and gentle terrain with average slope [less than or equal to] 10 percent.

Harvest sites (Table 1) were sampled to describe pre and postharvest stand conditions. On each site we located 10 point samples (10 BAF prism (English)) 50 m apart along random azimuths before and after the harvest. Classification as chip and saw (CNS) considered DBH greater than 25-cm, stem quality (defects, forks, sweep or crook), and merchantable height (7.5 m to a 15 cm top dib). Trees with DBH >25 cm that did not meet specifications for CNS and all trees with DBH 10 to 24 cm were classified as pulpwood.

All six sites were harvested by crews utilizing similar harvesting systems composed of one wheeled feller-buncher, one wheeled grapple skidder, one knuckleboom loader with a pull-through delimber, and three or four crew members. The feller-buncher cut the trees and placed multiple full trees in a bunch. The bunches were skidded to the deck area, where the trees were delimbed and topped by the loader and sorted as pulpwood or CNS.

Gross time study data were collected by observation of the operation for one to 3 days on each site. Gross time study data categories followed those from Miyata et al. (1981).

Cycles from the skidder and the feller-buncher were collected using a video camera system inside the machine cabin. The camera was focused on the grapple in the skidder and the felling head on the feller-buncher. Video was recorded with a digital 8-mm recorder attached to the operator's seat. The videos were replayed, and the cycles and elements recorded and time calculated from the time stamp on the tape. The total cycle time was summed from the elemental time.

The skidder activities were recorded for sample periods of 1.5 hours until approximately 50 skidder cycles were recorded on each site. Five elements were defined for the skidder elemental analysis (travel empty, grapple and load, travel loaded, delays, and delimbing time--when applied). The round-trip cycle distance was obtained using a Garmin GPS 12XL equipped with a remote antenna. Average bunch size (number of stems per bunch) was estimated from gross production data.

A feller-buncher cycle included the machine traveling to a tree(s), grabbing and cutting the tree(s), traveling back to the skid trail, and swinging and placing the tree(s). A cycle ended and started with the swing and place element. About 30 feller-buncher cycles were recorded on each site, 15 in the row removal and 15 from the selection cut in the residual rows.

The analyses of the cycle time data involved graphical and statistical tools to identify trends of total cycle times with independent variables. Dummy variable techniques were used to examine differences in qualitative conditions. Stepwise selection technique (p-value [greater than or equal to] 0.15) identified significant variables from the list in Tables 2 and 3 for the skidder and feller-buncher, respectively. Statistical Analysis System (SAS System for Windows V8.2 1999 to 2001) was used to perform the analyses.

System productivity and cost analysis

To conduct the productivity and cost analysis we selected three harvesting sites as examples of the range of applicable conditions. The "low" productivity site was a high density plantation with low volume per tree, site 2 in Table 1. The "medium" site had density similar to the low site but with a higher volume per tree, site 5 in Table 1. These two sites were considered to be a good representation of first thinning conditions in the Southeast U.S. The "high" site was a deferred first thinning with lower density, higher volume per tree, and consequently a larger volume of CNS, site 1 in Table 1.

Stand sizes of 4, 8, and 12 ha were selected for the analysis because the vast majority of the private forest ownerships in the Southern U.S. are less than 20 ha (49 acres) (Birch 1997). Assumptions for generating the distribution of round-trip skid distances included square shaped stands with a single landing located in the middle of one side.

The system that was modeled consisted of three machines and operators, one wheeled feller-buncher, one wheeled grapple skidder, and one knuckleboom loader with a pull-through delimber without gate delimbing. Machine productivity was estimated using the total cycle time equations for the skidder and the feller-buncher. System productivity was determined by the limiting machine on a daily basis where the scheduled machine hours (SMH) of each machine and operator were the same. The maximum utilization rates (UR) used for analysis were 79 percent and 76 percent for the feller-buncher and the skidder, respectively. The equations derived from the production study determined cycle times where the values of the site interactions were 0. Loader/delimber productivity was estimated from the gross production data at 30 [m.sup.3] SM[H.sup.1] and was not a limiting factor in production.

To determine feller-buncher productivity the analysis specified the proportion of the diameter distribution removed and the proportion of trees harvested from removed rows vs. the residual stand. In the conventional and heavy treatments, removed rows accounted for the harvest of 20 percent of trees across all DBH classes. Additional trees were removed from smaller DBH classes until the respective target basal areas of 16 and 9 [m.sup.2] [ha.sup.-1] were achieved. In the strip treatment, removed rows accounted for the harvest of 40 percent of trees across all DBH classes for the strips and throughout the residual stand (60%) trees were removed from smaller DBH classes. The target residual basal area for the strip treatment (including both strips and the residual stand) was 11.5 [m.sup.2] [ha.sup.-1]. Trees per cycle (FNtrees) were determined from observed data and volume per cycle was calculated from tree size information from the site.

To determine skidder productivity we sampled from the distribution of skid distances (Distance), including short and long distances in each of the study sites, so hourly productivity would be even as the harvest progressed. Trees per skidder cycle (SNtrees) were based on observed data. Average volume per tree was estimated from the site data. The distribution of round-trip skid distances generated average skidding productivity (cycles per productive machine hours (PMH)). The production rate was calculated from cycles per PMH, average volume per tree, and observed trees per cycle.

[FIGURE 1 OMITTED]

Hourly costs were estimated using an after-tax cash flow method (Tufts and Mills 1982). The method was incorporated into a spreadsheet developed by Tufts (unpublished). Cost assumptions and the annual equivalent cost of year 1 are in Table 4.

Moving and transportation costs were added to logging costs. Moving cost referred to the cost of moving the harvesting crew and equipment to the harvest site, and includes direct costs for moving equipment and labor time, and indirect costs of lost equipment operation and operational overhead costs during the move. Moving costs were a flat after tax rate of $1,725 per move and covered the expense required to move an average distance of 50 km (Greene et al. 1988). For wood transportation an after tax rate of $3.88 [m.sup.-3] was used.

Twenty-seven scenarios resulted from 3 treatments, 3 sites, and 3 stand sizes. Average gross revenue, logging cost, and residual value ($ [m.sup.-3], [stand.sup.-1], and $ [ha.sup.-1]) were calculated for each combination. Gross revenue or the value of the wood at the mill was assumed to be $25.00 [m.sup.-3] for pulpwood and $33.00 [m.sup.-3] for CNS.

Results and discussion

Production study

Stepwise selection of total skidder cycle time without the delimbing and delay elements returned the equation in Table 2. The model is typical of previous results, where travel time increased with higher skid distance (Tufts et al. 1988, Lanford and Stokes 1996, Kluender et al. 1997). Total skid distance and the interaction of skid distance and the average number of trees per bunch were prominent in the total cycle time model. No treatment effect was indicated by the selected model for total cycle time, but site effect was indicated by the interaction with skid distance in 50 percent of the study sites. Significance of interaction terms may suggest differences due to system bottlenecks, machine operator, site, or some combination.

Stepwise selection variables and two-way interactions for feller-buncher cycle without delay returned the equation in Table 3. The model indicates that selection thinning took more time than thinning the removal row which is consistent with previous results (Greene et al. 1987, Lanford and Stokes 1996). Cycle time also increased as the number of trees cut in a cycle increased. No treatment effect (heavy or conventional) was indicated by the analysis. Significant interactions were present between the principal continuous variable (FNtrees) and most sites. Again interactions may indicate differences due to the system bottlenecks, machine operator, the site, or some combination.

System productivity and cost analysis

The skidder productivity estimates are summarized in Table 5. Skidder productivity decreases with increasing stand size because of the longer assumed skid distance. Using a single landing and landing placement for all stand sizes addresses likely restrictions in harvest planning, established landing areas, and no allocation of road building costs in move-in costs. Increased tree size was responsible for productivity increasing from low to medium to high sites. Larger trees meant fewer trees, larger volume per bunch, and consequently fewer bunches per cycle which reduced cycle time and increased productivity.

The feller-buncher productivity was greatest for the strip treatment due to the higher productivity of the row removal and the greater percentage of the site volume removed in rows (Table 6). Also, the larger average volume per tree increased productivity within the medium and high sites. In all scenarios skidding limited hourly or daily production, and the fellerbuncher and the loader were not fully utilized. There is a fairly wide range in feller-buncher productivity and a narrower range for the skidder.

Residual value was calculated as total delivered value minus total harvesting cost. Residual value would include timber buyer and logger profit, logger overhead, and stumpage. With the low site the residual value increased $1 to 2 [m.sup.-3] when comparing the conventional treatment to the heavy and patch treatment (Fig. l).The larger stands resulted in higher residual value. Residual value in the 12 ha stand was adversely effected by lower skidding productivity in spite of lower per unit move-in costs. With the medium site the relationships are similar but the residual values overall are nearly $2 [m.sup.-3] higher than the low site (Fig. 2). The higher residual values due to higher productivity and lower move-in cost from greater volume per hectare. The high site showed the largest effect due to treatment (Fig. 3). The delivered value was nearly $1 [m.sup.-3] greater than the low and medium with the increased amount of CNS. Within the high site the residual value increased $2 to 3 [m.sup.-3] when comparing the conventional treatment to the heavy and strip treatments. Most of the treatment differences were again due to lower volume per unit move-in costs. Because the high site was less dense, incremental increases in volume removal had greater impact on costs. High skidding productivity due to bigger bunch volume yielded nearly equivalent residual value for 8 and 12 ha stands.

[FIGURE 2 OMITTED]

[FIGURE 3 OMITTED]

Table 7 shows volume removal and revenue on a stand and per hectare basis. One objective of these alternative prescriptions was to increase the revenue and financial attractiveness of smaller harvests typical at the WUI. The stand residual value of $2400 to $2600 for the low and high, 4 ha sites would not likely cover the logger risk and pay a minimal stumpage value. The incremental increase in volume per hectare within the medium site is enough to improve residual value to over $4800.

The difference in values between the heavy and strip treatment reflect the more intense removal in the heavy treatment. This difference could be easily narrowed by changing the parameters on the strip treatments (e.g., wider strips or heavier thinning in leave strips). The increased felling productivity in the strips provided minimal benefit since skidding limited production.

Conclusions

We analyzed alternative treatments for the harvesting of small loblolly pine plantations at the Wildland Urban Interface (WUI) using fully mechanized commercial thinning. Consideration in selection of alternative methods was given to harvesting productivity, economics, aesthetics, and practical concerns for long term stand management at the WUI. Skidder productivity limited system productivity on all combinations. Productivity of the skidder was affected by skid distance (stand size) and tree size. Feller-buncher productivity increased as row removal was increased in the strip treatment. Overall, the heavy and the strip treatments resulted in similar residual value. Treatment differences were greatest in the smallest stand size (4 ha) where the difference in volume removed resulted in an increase of up to $3.20 [m.sup.-3] in residual value.

In summary, the results of this study suggest that there is a potential for landowners and land managers to benefit from alternative thinning treatments. The additional income may make harvesting more available to landowners and attractive to buyers. From the aesthetic and long term management perspectives, these treatments may be suitable to land managers at the WUI. The continuous tree cover aspect of the heavy and strip treatments may result in less visual impact. However, the decision to implement a treatment depends primarily on the landowner objectives. If the alternative treatments proposed here satisfy the objectives they could play an important role in marketing small stands.

Literature cited

Birch, T.W. 1997. Private forest-land owners of the southern United States. 1994. Resource Bulletin NE-138. USDA Forest Serv., Northeastern Forest Expt. Sta. Newtown Square, Pennsylvania. 195 pp.

Bolding, M.C., B.L. Lanford, and L.D. Kellogg. 2003. Forest fuel reduction: Current methods and future possibilities. In: Proc. 26th Council of Forest Engineering Annual Meeting, Bar Harbor, Maine.

Cordell, H.K. and E.A. Macie. 2002. Human influences on forest ecosystems; the southern wildland-urban interface assessment. Gen. Tech. Rept. SRS-55. USDA Forest Serv., Southern Res. Sta. Ashville, North Carolina. pp. 11-35.

Cubbage, F.W., W.D. Greene, and J.P. Lyon. 1989. Tree size and species, stand volume, and tract size: Effects on southern harvesting costs. S. J. Appl. For. 13(3):145-152.

Greene, W.D, F.W. Cubbage, and J.F. McNeel. 1988. Characteristics of independent loggers in Georgia. Forest Prod. J. 38(7/8):51-56.

--, T.G. Harris, Jr., C.E. DeForest, and J. Wang. 1997. Harvesting cost implications of changes in the size of timber sales in Georgia. S. J. Appl. For. 21(4):193-198.

--, B.L. Lanford, and E.F. Mykytka. 1987. Stand and operating effects on feller-buncher productivity in second thinnings of southern pine. Forest Prod. J. 37(3):27-34.

Kittredge, D.B., Jr., M.J. Mauri, and E.J. McGuire. 1996. Decreasing woodlot size and the future of timber sales in Massachusetts: When is an operation too small? N. J. of Appl. For. 13(2):96-101.

Kluender, R., D. Lortz, W. McCoy, B. Stokes, and J. Klepac. 1997. Productivity of rubber-tired skidders in Southern pine forests. Forest Prod. J. 47(1):53-58.

Lanford, B.L., and B.J. Stokes. 1996. Comparison of two thinning systems. Part 2. Productivity and costs. Forest Prod. J. 46(1 l/1 2):47-53.

Miyata, E.S., M.H. Steinhilb, and S.A. Winsauer. 1981. Using work sampling to analyze logging operations. Res. Pap. NC; 213. USDA Forest Serv., North Central Forest Expt. Sta., St. Paul, Minnesota. 8 pp.

Nowicki, B. 2001. Protecting communities from forest fires--effectively treating the wildland urban interface. Southwest Forest Alliance, Flagstaff, Arizona. 11 pp.

Tufts, R.A. and W.L. Mills, Jr. 1982. Financial analysis of equipment replacement forest harvesting. Forest Prod. J. 32(10):45-52.

--, B.J. Stokes, and B.L. Lanford. 1988. Productivity of grapple skidders in Southern pines. Forest Prod. J. 38(10):24-30.

Wear, D.N. and J.G. Greis. 2002. Southern forest resource assessment. Gen. Tech. Rept. SRS-53. USDA Forest Serv., Southern Res. Sta. Asheville, North Carolina. 635 pp.

Bruno S. Folegacti Mathew F. Smidt * Edward F. Loewenstein Emily Carter Timothy P. McDonald

* Forest Products Society Member.

The authors are, respectively, Research Assistant, Associate Professor, Assistant Professor, School of Forestry and Wildlife Science, Auburn University, Auburn, Alabama (folegbs@auburn.edu; smidtmf@auburn.edu; loewenstein@auburn.edu); Research Soil Science, USDA Forest Service, Auburn University, Auburn, Alabama (eacarter@fs.fed.us); and Associate Professor, Department of Biosvstems Engineering, Auburn University, Auburn, Alabama (mcdontp@eng.auburn.edu). Funding for this project was provided by the USDA Forest Service, Southern Research Station. In addition, we acknowledge the support of Alabama Power Company and the six logging crews who allowed us to study their operations. This paper was received for publication in March 2007. Article No. 10330.
Table 1.--Stand characteristics for continuous timing study of
conventional (Cony.) and heavy treatments.

 Stand Tree
Site age Treatment Trees volume
 ([ha.sup.-1]) ([m.sup.3])

 1 18 Conv. 1102 0.11
 2 14 Conv. 1849 0.07
 3 18 Conv. 1683 0.13
 4 13 Heavy 1318 0.1
 5 14 Heavy 1834 0.11
 6 25 Heavy 813 0.22

 Pulpwood CNS Basal area
Site volume volume Pre-harvest Post-harvest
 --(%)-- --([m.sup.2] [ha.sup.-1])--

 1 81 19 29.4 14.2
 2 97 3 32.6 17.0
 3 93 7 43.6 14.9
 4 95 5 32.6 8.5
 5 93 7 38.8 10.1
 6 43 57 34.2 10.2

Table 2.--Skidder production study variables and regression model
from stepwise selection (p [greater than or equal to] 0.15).

 Name Definition

Variables Trt Dummy variable for treatment
 [S.sub.1] Dummy variable for logging site
 (i = 1 to 6)
 SNtrees Average number of trees/bunch
 (daily average)
 Distance Total round-trip skid distance for a
 cycle (m)
 DistTrt Interaction distance X Trt
 DistSNtrees Interaction distance X SNtrees
 Dist[S.sub.1] Interaction distance X site

Model Cycle (see) = 171.934 + 0.179 X Distance + 0.0116 X
 DistSNtrees + 0.0458 X Dist[S.sub.3] +0.0883 X
 Dist[S.sub.4] + 0.1477 X Dist[S.sub.5]

 F-value = 55.8; p-value < 0.0001; [R.sup.2] = 0.49;
 n = 297, MSE = 10906

Table 3.--Feller-buncher production study variables and regression
model from step-wise selection (p [greater than or equal to] 0.15).

 Name Definition

Variable Trt Dummy variable for treatment
 [S.sub.i] Dummy variable for logging site
 (i = 1 to 6)
 FNtrees Number of trees cut in a cycle
 DBH Average site DBH (cm)
 Method Dummy variable for row vs. selection
 felling
 FNtrees[S.sub.1] Interaction FNtrees x site

Model Cycle (sec) = 15.411 + 9.456 x Method + 7.6938 x
 FNtrees + 1.5784 x FNtrees[S.sub.1] -0.9831 x
 FNtrees[S.sub.2] + 2.2461 x FNtrees[S.sub.3] +
 0.7998 x FNtreeS[S.sub.4] + 0.4388 x FNtrees[S.sub.6]

 F-value 2= 553.7; p-value < 0.0001; [R.sup.2] = 0.66;
 n = 195; MSE = 3 24

Table 4.--Machine cost assumptions and after tax cost calculations
for the first year of machine ownership.

 Skidder Feller-buncher Loader

Purchase price ($) 195,361 204,604 135,816
Economic life (years) 6 6 6
Scheduled machine hours
 (SMH/year) 2000 2000 2000
Insurance (% of machine value) 6 6 6
Salvage value (% of purchase) 20 20 20
Fringe benefit (% of labor rate) 30 30 30
Discount rate (%) 7 7 7
Finance APR (%) 7 7 7
Marginal tax rate (%) 25 25 25
Utilization rate (%) 70 70 70
Labor cost ($/SMH) (after tax) 17.95 17.95 17.95
Fixed cost ($/SMH) (after tax) 17.85 19.13 11.10
Variable cost ($/SMH) (after tax) 21.60 21.26 16.17

Table 5.--Skidder productivity estimates ([m.sup.3] PM[H.sup.-1]) for
treatment, site (low, medium, and high), and stand size (4, 8, and
12 ha).

 Conventional Heavy

Stand size (ha) 4 8 12 4 8 12 4

Site
 Low 21.0 18.4 15.5 23.4 20.4 17.2 23.7
 Medium 23.4 20.7 17.7 25.9 22.9 19.6 25.4
 High 23.8 21.1 18.0 27.6 24.5 20.9 27.2

Stand size (ha) 4 8 12

Site
 Low 23.7 20.8 17.5
 Medium 25.4 22.5 19.2
 High 27.3 24.1 20.6

Table 6.--Feller-buncher productivity estimates ([m.sup.3]
PM[H.sup.-1]) for treatment and site. Average productivity
is the weighted average from row and selection components determined
by the treatment.

 Conventional Heavy

 Selection Row Average Selection Row Average
Site
 Low 23.6 27.5 25.5 26.2 30.6 27.7
 Medium 32.1 37.1 34.6 35.6 41.0 37.4
 High 41.5 49.8 45.6 48.1 57.6 51.3

 Strip

 Selection Row Average
Site
 Low 26.6 31.1 29.6
 Medium 34.9 40.2 38.4
 High 47.4 56.8 53.7

Table 7.--Total volume removed and residual stand value and residual
value per hectare for all scenarios.

 Volume removed

Site Size Conv. Heavy Strip
 (ha) --([m.sup.3] [stand.sup.-1])--

Low 4 348 508 456
 8 696 1016 912
 12 1044 1524 1368
Average ([m.sup.3] 87 127 114
 [ha.sup.-1])

Medium 4 488 668 604
 8 976 1336 1208
 12 1464 2004 1812
Average ([m.sup.3] 122 167 151
 [ha.sup.-1])

High 4 288 464 400
 8 576 928 800
 12 864 1392 1200

 Average ([m.sup.3] 72 116 100
 [ha.sup.-1])

value per hectare for all scenarios.

 Residual value

Site Size Conv. Heavy Strip Conv. Heavy Strip
 (ha) --($ [m.sup.3])-- --($ [ha.sup.-1])--

Low 4 7.35 9.67 9.66 640 1228 1102
 8 8.71 10.36 10.35 758 1315 1180
 12 7.86 9.42 9.19 684 1196 1047
Average ([m.sup.3]
 [ha.sup.-1])

Medium 4 9.88 11.72 11.34 1206 1957 1712
 8 10.74 12.20 11.93 1310 2037 1802
 12 9.97 11.40 11.26 1217 1905 1701
Average ([m.sup.3]
 [ha.sup.-1])

High 4 8.34 11.66 11.18 600 1353 1118
 8 10.47 12.77 12.56 754 1481 1256
 12 10.16 12.25 12.12 731 1421 1212

 Average ([m.sup.3]
 [ha.sup.-1])

value per hectare for all scenarios.

 Residual value

Site Size Conv. Heavy Strip
 (ha) --($ [stand.sup.-1])--

Low 4 2559 4912 4406
 8 6061 10523 9442
 12 8204 14351 12568
Average ([m.sup.3]
 [ha.sup.-1])

Medium 4 4823 7829 6848
 8 10483 16296 14415
 12 14601 22855 20409
Average ([m.sup.3]
 [ha.sup.-1])

High 4 2401 5412 4473
 8 6032 11847 10051
 12 8775 17048 14549

 Average ([m.sup.3]
 [ha.sup.-1])
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Author:Folegatti, Bruno S.; Smidt, Mathew F.; Loewenstein, Edward F.; Carter, Emily; McDonald, Timothy P.
Publication:Forest Products Journal
Geographic Code:1USA
Date:Nov 1, 2007
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