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Hardwood log merchandising and bucking practices in West Virginia.

Abstract

The current state of hardwood log merchandising and bucking practices in West Virginia was examined by on-site interviews with 50 timber harvesting companies. Results indicate that most of the roundwood harvested was merchandized into sawlogs, pulpwood, OSB, peelers, veneer, and scragg. The average daily production of the logging companies was 4.4 truckloads per day with more than 60 percent of the companies producing 1 to 3 truckloads per day. Most sawmills required a small-end diameter between 10 and 12 inches and preferred logs 10 feet in length. Incentives ranging from $10 to $50 per thousand board feet (MBF) in Doyle scale would be needed for some companies to buck logs for grade. Approximately half of the logging companies and 46 percent of the independent loggers were willing to take advantage of training for better bucking decisions and strategies.

Merchandising the tree stem into an optimal array of logs requires simultaneous considerations of species, tree stem quality, tree stem dimensions, log lengths, current market prices, and other factors (Sessions 1988). Maximizing value and volume while minimizing costs has always been a high priority for the forest products industry. However, there are several obstacles that make this goal much more difficult to attain. Value loss from poor bucking was recognized in the United States as early as 1915, but was not sufficiently addressed until the 1950s (Wang et al. 2004). Past studies have shown that manual bucking procedures have generally reduced the value of a tree by 20 percent compared to what is considered good practice (Faaland and Briggs 1984). Value loss related to softwood bucking practices in the U.S. Pacific Northwest and New Zealand ranged between 5 and 26 percent (Geerts and Twaddle 1985, Sessions et al. 1989, Twaddle and Goulding 1989). A study of 166 northern hardwood trees in Michigan indicated that the gross delivered values of optimal solutions were 39 to 55 percent higher than those chosen by the buckers (Pickens et al. 1992). An average value loss of 21 percent was also reported in the Appalachian region of Virginia and West Virginia (Haynes and Visser 2004). Poor bucking decisions translate into real monetary loss, which is, ultimately, the concern of landowners, primary processors, and loggers (Pickens et al. 1992). West Virginia's roundwood markets were examined based on a detailed survey of 30 logging and associated roundwood merchandising operations (Alderman and Luppold 2005). They found that the produc tion of sawlogs or peeler logs appeared to be driving operations. These previous studies provide sufficient evidence that better merchandising and optimal bucking practices could increase revenues for forest products companies.

Because of the difficulty of correcting poor bucking decisions in the later stages of the manufacturing process, bucking control and optimization have been an essential research topic in forest engineering since the 1960s (Kivinen 2004). As technology becomes commonplace and more accessible to the forest products industry, there has been a general shift in timber harvesting from manual to mechanized operations, and as harvesting systems increase in mechanization, there is a general increase in productivity and efficiency. The implementation of computers and computer-aided machines allows foresters to digitally store attributes related to many trees within a stand and aid timber operators in optimal log bucking during harvesting (Murphy et al. 2004).

The hardwood industry is an important component of West Virginia's economy, contributing approximately 4 billion dollars annually (Childs 2005). More than 500 primary and secondary processors are located in West Virginia and employ approximately 29,000 workers. West Virginia's forestland covers more than 11.8 million acres, making it the third most forested state in the nation (USDA Forest Service 2004, Milauskas et al. 2005). Because of the irregular form of many hardwood trees, the diversity of hardwood species existing on an individual site, and variations in the use and value of different species and grades of hardwood logs, tree boles are normally bucked and separated by product before proceeding to future processing instead of being shipped in tree-length form to mills as happens to softwood (Wagner et al. 2004). The complexity of the hardwood grading rules and the variability of hardwood stem form and defects make the bucking decision even harder (Pickens et al. 1992, 1993). There are many possibilities of length and grade combinations and the price differential between grades is quite high for hardwood species (Pickens et al. 1997).

The objective of this study was to investigate the current state of hardwood log merchandising and bucking practices in West Virginia. Specifically, this study examined the type and size of logging companies, the log products hauled off the landing, the number and type of harvesting equipment used, bucking tools as well as the daily production rate of the logging companies in the state.

Methods

On-site interviews with owners/operators of 50 timber harvesting companies were conducted throughout the state (Fig. 1). Logging crews were interviewed during visits for a larger safety program in West Virginia. Selected logging companies were representative of all logging companies in West Virginia. The interviews were targeted at revealing the operating characteristics of each harvesting crew, their equipment spread, and geographic locations of their operations. In addition, questions were raised dealing with log product types, production statistics, bucking and merchandising procedures, and mill requirements for contract-based operations as well as incentives for bucking optimization and training willingness of log buckers in West Virginia.

[FIGURE 1 OMITTED]

The general linear model (GLM) was applied to the survey data to examine the impacts of individual factors as well as their interactions on merchandizing and bucking practices in West Virginia.

[Y.sub.ijkl] = [mu] + [B.sub.1] + [C.sub.j] + [T.sub.k] + [B.sub.i] * [C.sub.j] + [B.sub.i] * [T.sub.k] + [C.sub.j] * [T.sub.k] + [[epsilon].sub.ijkl] i = 1.2 j = 1,2,3 k = l,2

Where [Y.sub.ijkl]represents the [l.sub.th] observation of the average daily production rate or the expected bucking incentives of the logging companies and [mu] is the overall mean of the response variable. [B.sub.i] is the effect of [i.sup.th] bucking basis (by volume or by value). [C.subj] is the effect of [j.sup.th] logging company type (contract-based, company crew, and independent). [T.sub.k] is the effect of [k.sup.th] bucking tool (by chain saw or by sawbuck), [[epsilon].sub.ijkl] is an error component that represents all uncontrolled variability.

Results

Logging operations

Logging companies interviewed were located throughout each of the six West Virginia Division of Forestry Districts, with 70 percent from north and central West Virginia (Fig. 1). A majority of the logging companies were contract-based, which accounted for 72 percent of the total number of companies intmwiewed. The remaining were independent loggers (18%) and company crews (10%). There was an average of five workers (including owner and machine operators) per logging company, ranging from 2 to 10 people. The survey also revealed that about 50 percent of the company owners/ operators were also the primary log buckers.

Skidder operators accounted for 28 percent of the total number of workers, followed by manual chain saw fellers (22%), loader/sawbuck operators (19%), and bulldozer operators (16%) (Fig. 2). Only one logging company reported using forwarders for log extraction which represented less than 1 percent of the total workers for the companies sampled.

[FIGURE 2 OMITTED]

To facilitate the analysis, logging companies were grouped into three classes based on the average daily production: small (1 to 3 truckloads), medium (>3 to 6 truckloads), and large (>6.0 truckloads) (one truckload was assumed as 5.5 MBF). The target daily production rate ranged from 1.3 to 10 truckloads with an average of 4.4 truckloads for the logging companies interviewed. Daily production ranged from 1.3 to 9.0 with an average of 3.5 truckloads per day. Approximately one-fifth of the logging companies met their target daily production rate while the majority fell below their goal. More than 90 percent of the logging companies sampled were small to medium size, with the daily production of less than 6 truckloads. For the contract-based logging companies, small, medium, and large size companies accounted for 49, 40, and 11 percent of totals, respectively (Table 1). However, all of the independent loggers and company crews were in either the small or medium group and all of the large companies were contract-based. The actual daily production was 2.53, 4.29, and 7.49 truckloads for small, medium, and large categories, respectively (Fig. 3). This was 5, 4, and 12 percent lower than the target production for the corresponding group.

The major log products hauled off landings were sawlogs (22%), hard pulpwood (17%), OSB (15%), peelers (14%), veneer (13%), and scragg wood (10%) (Table 2). Post/rails accounted for less than 10 percent, while only one company reported firewood as one of its products. For sawlogs, the target small-end diameter was 10 to 12 inches for most species due to mill requirements. However, small-end diameters of 8 to 9 inches was acceptable for black cherry and red oak logs. The average target length of sawlogs was 10 feet, although 8 to 16 foot logs were acceptable at sawmills.

Manual felling was still dominant with 78 percent of the logging companies utilizing more than one chain saw for felling, while mechanical felling with feller-buncher accounted for 28 percent (Table 3). Most of the companies used either chain saws or feller-bunchers for felling while 6 percent used both. STIHL and HUSQVARNA were the two most commonly used chain saw models, which accounted for more than 70 percent of the chain saws utilized in West Virginia. More than 90 percent of the feller-bunchers used in this region were Timbco T425 or T445 and 8 percent of them were Bell-Serpent feller-bunchers. About 78 percent of the logging companies employed cable skidders for extraction while 38 percent of them used grapple skidders. John Deere series cable skidders (JD440, JD540, and JD640) were the most commonly used skidders (71%) in the region, followed by Timberjack series (T J240, T J360, and T J380) (18%). Sixteen percent of the logging companies used both cable and grapple skidders for extraction.

Knuckleboom loaders were utilized by all of the companies sampled, and 98 percent used dozers for road building (Table 3). About 80 percent of the logging companies in West Virginia have their own trucks while 27 percent of them used contracted trucks, and 7 percent of them used their own trucks as well as contracted trucks. Tractor trailers were the most commonly used truck type, which accounted for 40 percent of the trucks used, followed by triaxial trucks (36%), triaxials with pup trailers (24%), and triaxials with loader (28%). Twenty-eight percent of the logging companies used at least two types of trucks.

Bucking practices

All of the bucking operations in West Virginia were done at the landing using either chain saws or sawbucks. Bucking with a sawbuck is a typical practice in West Virginia, which accounted for 82 and 86 percent of the bucking operations for small and medium companies, respectively (Table 4). Accordingly, manual bucking with chain saws at the landing accounted for 18 and 14 percent for small and medium companies, respectively. However, the survey revealed that all of the large companies used sawbucks for bucking operations. It was also noticed that bucking by sawbucks increased 5 and 16 percent when the company size changed from small to medium, and medium to large, respectively. This indicates that the larger the company, the more mechanized the bucking operation.

Seventy percent of the logging companies bucked logs based on the value (grade) and 30 percent of log bucking was based on volume (or daily production rate in truckloads/day). Bucking based on value was dominant for small and medium size logging companies in West Virginia. About two-thirds of the medium logging companies bucked logs based on the value (grade) and the portion was increased to three-fourths for the small size companies (Table 4). Half of the large companies bucked logs based on value, and the other half based on volume. As the company size increased from small to medium and to large, the percentage of bucking by volume increased steadily from 28 to 40 and to 50 percent, respectively. During grading, sweep, crook, forks, and rot were the commonly recognized defects.

Fifty percent of the logging companies who were currently bucking for volume indicated that some incentives ranging from $10 to $50/MBF would be needed for them to buck for grade (Table 4). If bucking incentives for grade are divided into three groups (<$20/MBF, $20-$30/MBF, and >$30/ MBF), 32 and 68 percent of the small companies would be willing to conduct bucking for grade if they could be paid <$20/MBF and >$30/MBF more, respectively. Bucking for grade would be considered by 65 and 35 percent of the logging companies of medium size if the bucking incentives of $20-$30/MBF and >$30/MBF were paid, respectively. If more than S30/MBF bucking incentive could be paid, all the large companies would conduct the bucking for grade. While approximately half of the small and large logging companies sampled expressed the willingness to take advantage of bucking training, only 33 percent of the medium sized companies would be willing (Table 4).

Fifty-four percent of the independent loggers thought that it was not necessary for them to get additional assistance in bucking sawlogs, such as training and information transfer, while 46 percent of them expressed they would welcome the assistance in sawlog bucking decisions and strategies. Bucking for volume or grade (F = 0.04: df = 1, 50; p =

0.8387), company type (F - 0.23; df= 2, 50;p = 0.7985), and bucking tools (F = 0.00; df = 1, 50; p = 0.9722) did not significantly affect the actual daily production (Table 5). Similarly, these three class variables did not significantly affect the target daily production, which was 25 percent higher than the actual daily production for the logging companies in West Virginia. Bucking incentives differed significantly between bucking by value and bucking by volume for the logging companies (F = 11.82; df = 1,50;p - 0.0034). However, it was not significantly different among contract-based, independent companies (F = 0.19; df = 1, 50; p = 0.6717) and between bucking with chain saws and bucking with sawbucks (F = 0.04; df = 1, 50; p = 0.8387) (Table 5).

Conclusions and discussion

Sawlogs, pulpwood, OSB, peelers, veneer, and scragg were the major products hauled off the landing in West Virginia. Manual harvesting using a chain saw for felling and cable skidder for extraction was the dominant system in the region, followed by a feller-buncher and grapple skidder system. The average daily production of logging companies in West Virginia was 4.4 truckloads with more than 50 percent of the companies producing 3 to 6 truckloads per day. Only 20 percent of the logging companies could physically reach their target daily production rate.

Bucking logs based on the value (grade) is dominant in West Virginia, which account for 70 percent of the logging companies, while 30 percent of log bucking in the region was based on volume (production). Target small-end diameter of 10 to 12 inches was normally required for considering careful bucking by the mills in West Virginia and the preferred log length was 10 feet. Among the companies currently bucking based on volume, 50, 33, and 47 percent of them were willing to take advantage of training that would enable their company to make better bucking decisions for large, medium, and small companies, respectively. Forty-six percent of the independent loggers expressed their willingness to accept assistance in sawlog bucking decision-making.

There were no in-woods bucking practices with chain saws reported based on the bucking operations investigated. Bucking from the loader without ground inspection is commonly practiced in West Virginia, which was reported by 71 percent of the logging companies. For higher quality stems, the bucking operators with sawbucks usually turned logs and looked them over for grading defects from the cab prior to bucking and then made the bucking decisions. In these cases, the bucking operators generally believed that they could make the right decisions without leaving the cab. However, for veneer potential logs, the operator usually got off the loader and measured and marked the logs manually before bucking. The rest of the logging companies reported that the loader operator measured and marked logs with paint on the ground and then bucked them with a sawbuck in order to maximize the log value. In some cases, loader operators measured and marked logs and then made the cuts with chain saws. Some companies also indicated that veneer potential tree stems were inspected and bucked to a target of 23 inches to maximize profit. According to the survey about 50 percent of the company owners/operators were also primary log buckers, which minimized the operator related bucking value losses for these companies.

Results regarding incentives for bucking optimization and training willingness of log buckers in West Virginia would be helpful to logging managers and researchers to design an appropriate log merchandising program to improve value recovery of hardwood logs in the state. Some potential value recovery through bucking central Appalachian hardwood logs can be achieved through improved bucking practices. Future research can concentrate on optimal bucking program, procedures, and implementation of the program on field PCs. The comparisons between the patterns generated by optimal program and field studies should be conducted to examine how efficient the log merchandising and bucking practices are in West Virginia. Training is essential for log merchandising, which incurs the corresponding costs for logging companies. Additionally, labor and other operating costs could be another concern since more time would be required for careful log merchandising. Training on optimal bucking along with field workshops can be used for buckers to develop a better understanding of grading, scaling, and bucking skills. An appropriate incentive model needs to be established between loggers and forest products companies to enhance the merchandising processes and maximize the value of hardwood log products.

Literature cited

Alderman, D. and W. Luppold. 2005. Examination of regional round-wood markets in West Virginia. Forest Prod. J. 55(12): 153-157.

Childs, R.A. 2005. West Virginia's forests: Growing West Virginia's future. Bureau of Business and Economic Res., College of Business and Economics, West Virginia Univ., Morgantown, West Virginia. June 2005. 14 pp.

Faaland, B. and D. Briggs. 1984. Log bucking and lumber manufacturing using dynamic programming. Manage. Sci. 30(2):245-257.

Geerts, J.M.P. and A.A. Twaddle. 1985. A method to assess log value loss caused by cross-cutting practice on the skidsite. New Zealand J. For. 9(2): 173-184.

Haynes, H.J.G. and R.J.M. Visser. 2004. An applied hardwood value recovery study in the Appalachian region of Virginia and West Virginia. Inter. J. of Forest Engineering. 15(1):25-31.

Kivinen, V.P. 2004. A genetic algorithm approach to tree bucking optimization. Forest Sci. 50(5):696-710.

Milauskas, S.J., R.B. Anderson, and J. McNeel. 2005. Hardwood industry research priorities in West Virginia. Forest Prod. J. 55(1):28-32.

Murphy, G., H. Marshall, and M.C. Bolding. 2004. Adaptive control of bucking on harvesters to meet order book constraints. Forest Prod. J. 54(12):114-121.

Pickens, J.B., A. Lee, and G. Lyon. 1992. Optimal bucking of northern hardwoods. Northern J. of Applied Forestry 9(4): 149-152.

--, S.A. Throop, and J.O. Frendewey. 1997. Choosing prices to optimally buck hardwood logs with multiple log-length demand restrictions. Forest Sci. 43(3):403-413.

--, G.W. Lyon, A. Lee, and W.E. Frayer. 1993. HW-Buck game improves hardwood bucking skills. J. of Forestry 91 (8):4244.

Sessions, J. 1988. Making better tree-bucking decisions in the Woods--An introduction to optimal bucking. J. of Forestry 86(10):43-45.

--, E. Olsen, and J. Garland. 1989. Tree bucking for optimal stand value with log allocation constraints. Forest Sci. 35(1):271-276.

Twaddle, A.A. and C.J. Goulding. 1989. Improving profitability by optimizing log-making. New Zealand J. of Forestry. 34:17-23.

USDA Forest Serv. 2004. Forest Inventory and Analysis Data Available at www.fia.fs.fed.us/.

Wagner, J.E., B. Smalley, and W. Luppold. 2004. Factors affecting the merchandising of hardwood logs in the southern tier of New York. Forest Prod. J. 54(11):98-102.

Wang, J., C.B. Ledoux, and J. McNeel. 2004. Optimal tree-stem bucking of northeastern species of China. Forest Prod. J. 54(2):45-52.

The authors are, respectively, Associate Professor, Assistant Director of AHC, Former Research Associates (currently Associates Professor, Northeast Forestry Univ., Harbin, China) and Professor/ Director, West Virginia Univ., Division of Forestry and Natural Resources, Morgantown, West Virginia (jxwang@wvu.edu@;sgrushec@wvu.cdu; yaoxiangl@yahoo.com; jmcneel@wvu.edu). The authors would like to thank Mr. Curt Hassler and Mr. Tim Pahl for field data collection. This manuscript is published with the approval of the Director of West Virginia Agricultural and Forestry Experimental Station as Scientific Article No. 2974. This paper was received for publication in April 2006. Article no. 10193

Jingxin Wang*

*Forest Products Society Member. [c]Forest Products Society 2007. Forest Prod. J. 57(3):71-75.
Figure 3.--Daily production rate of the logging companies in West
Virginia.

 Actual Daily Production Target Daily Production

Small 2.53 2.65
Medium 4.29 4.48
Large 7.49 8.52

Note: Table made from bar graph.

Table 1.--Type of the logging companies in West Virginia by
production capacity.

 Production Company type
 capacity
Size (truckloads/ Logging Contract- Company
class day) company based Independent crew

 (%)

Small <3.0 57 49 89 60
Medium 3.0 to 6.0 35 40 11 40
Large >6.0 8 11 0 0

Table 2.--Types of log products hauled off landings.

 Cumulative
Product type Percentage percentage

 (%)

Sawlogs 22 22
Hard pulpwood (pulp & paper) 17 39
Soft pulpwood (OSB) 15 54
Peelers 14 68
Veneer 13 81
Scragg wood 10 91
Post/rails 8 99
Firewood 1 100

Table 3.--Harvesting and trucking equipment employed by West Virginia's
logging companies.

 Logging companies
 using selected equipment

Type of equipment Small Medium Large Total

 (%) (a)

Felting
 Chain saw 48 28 2 78
 Feller-buncher 14 8 6 28
Skidding
 Cable skidder 46 30 2 78
 Grapple skidder 12 18 8 38
 Forwarder 0 2 0 2
 Bulldozer 14 10 2 26
Bucking
 Chain saw 8 2 0 10
 Sawbuck 48 34 8 90
Loading
 Knuckleboom loader 52 38 10 100
Road building
 Dozer 55 34 9 98
 Excavator 0 2 0 2
Trucking
 Triaxial 22 12 2 36
 Triaxial with loader 20 6 2 28
 Triaxial with pup 8 14 2 24
 Tractor trailer 24 18 8 40

(a) If the total percentage is greater than 100 for a category, it
simply means that some of the companies use more than one machine in
that category.

Table 4.--Bucking tools, basis, and incentives for logging companies
in West Virginia.

 Bucking
 incentives
 Bucking tools Bucking basis for grade

Size Class Chain saw Sawbuck Value Volume <$20/MBF

 (%)

Small 18 82 72 28 32
Medium 14 86 60 40 0
Large 0 100 50 50 0

 Bucking incentives Training
 for grade willingness

Size Class $20 to 30/MBF >$30/MBF Yes No

Small 0 68 47 53
Medium 65 35 33 67
Large 0 100 50 50

Table 5.--Means and significant levels of bucking practices in West
Virginia. (a)

 Actual daily Target daily Bucking
 production production incentives
 (loads) (loads) ($/MBF)

Bucking basis
 Bucking by value 3.21 A 4.09 A 16.43 B
 Bucking by volume 3.78 A 4.61 A 28.32 A
Company type
 Contract-based 3.61 A 4.49 A 21.73 A
 Company crew 3.20 A 3.80 A --
 Independent 2.56 A 3.50 A 20.00 A
Bucking Tool
 Chain saw 3.44 A 4.14 A 25.0 A
 Sawbuck 3.07 A 4.28 A 21.5 A

(a) Means containing the same letter in a column of a group are not
significantly different at the 5 percent level with Duncan's
Multiple-Range Test.
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Author:Wang, Jingxin; Grushecky, Shawn; Li, Yaoxiang; McNeel, Joseph
Publication:Forest Products Journal
Geographic Code:1U5WV
Date:Mar 1, 2007
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