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Productivity and cost of cut-to-length and whole-tree harvesting in a mixed-conifer stand.


A field-based study was performed to broaden our knowledge of harvesting productivity, cost (stump-to-truck), and log value recovery between cut-to-length (CTL) and whole-tree (WT) harvesting working side-by-side in two mixed-conifer stands in northern Idaho. Each site included two replications of each of the harvesting options. Hourly harvesting productivity ranged from 1,163 to 5,428 [ft.sup.3] per productive machine hour (PMH) and 1,350 to 6,552 [ft.sup.3]/PMH for the CTL and WT harvesting machines, respectively. This result suggests that the WT harvesting system was more productive than the CTL harvesting because specific tasks were assigned to each machine. Higher productivity of the WT harvesting system resulted in lower harvesting costs, although the hourly machine rate for the WT harvesting system was slightly higher than for the CTL harvesting system. The CTL harvesting system costs were $0.34/[ft.sup.3] and $0.36/[ft.sup.3] while the WT harvesting costs were $0.22/[ft.sup.3] and $0.33/[ft.sup.3] at the two sites. Harvesting costs ($/[ft.sup.3]) were highly influenced by skidding or forwarding distance, log length and diameter, and machine combinations. The WT harvesting recovered more sawlogs (large-end diameter > 8 in) and pulpwood (any snag, decayed log or log below mill specified lengths), but less ton-wood (a log of large-end diameter < 8 in and top minimum diameter of 4 in) than the CTL harvesting. Net revenue from the WT harvesting was higher than that from the CTL harvesting.


Timber harvesting is an essential tool for forestland management. It is used for wood production, wildlife habitat management, and to reduce fuels buildup and the associated wildfire risks in the forest. Harvesting systems and methods used for harvesting activities such as felling, processing, primary transportation to the landing area, loading, and log transportation to the mill greatly affect overall harvesting cost, productivity (volume of logs produced per hour during a harvesting operation), overall profitability of harvesting operations, and returns to a landowner (Han et al. 2004). Currently, about 55 percent of the world's wood harvest is performed manually with a chain saw while the remaining 45 percent is harvested mechanically. Of the mechanically harvested portion, it has been estimated that 65 percent is harvested using the whole-tree (WT) harvesting system and the remaining 35 percent using the cut-to-length (CTL) harvesting system (Ponsse 2005).

In the Pacific Northwest, CTL harvesting systems have been increasingly used for thinning on gentle terrain because they handle small-diameter trees very efficiently, provide a safer, enclosed working environment, and consistently pro duce high-quality end products at a reasonable cost (Kellogg et al. 1992). In CTL harvesting, all of the processing (delimbing and bucking) occurs at the stump (Kellogg and Spong 2004) where trees are cut into mill specified lengths. For example, trees are typically processed into a target length of 16.3 fl in northern Idaho when a fully mechanized CTL harvesting system is used. The harvester fells, delimbs, and cuts trees into logs. A forwarder then transports the logs from the stump area to the landing area (central log collection area in the forest) where trucks take them to the sawmill. The forwarder is also often used to load logs onto the trucks.

A typical mechanized WT harvesting system often includes four machines; feller-buncher, skidder, loader, and processor. The feller-buncher fells and aggregates trees while the skidder drags bunched loads of trees to the landing. At the landing, these trees are processed by a processor into logs that are loaded onto the truck using a loader.

The advantages and disadvantages of CTL and WT harvesting systems have been well documented (Bettinger and Kellogg 1993, Meek and Plamondon 1996). The CTL harvesting systems's greatest disadvantages compared to WT harvesting are the high initial cost of investment of each machine, repair and maintenance of the machine's complex computerized system, and inability of the felling-processing machine to handle stems with stump diameter larger than 22 in (LeDoux and Huyler 2001). However, WT harvesting is at a disadvantage because it requires more woods workers, supervision, and support than CTL harvesting (Gingras 1994, LeDoux and Huyler 2001). In addition, WT harvesting has higher potential for soil compaction and disturbance because the skidder tends to sweep duff and litter during whole-tree extractions, exposing mineral soil (Hartsough et al. 1997).

A cost-efficient harvesting operation improves profitability in timber production and overall competitiveness of the timber production sector (Efthymiou 2001). In some earlier studies, CTL harvesting costs were often comparable to or lower than WT harvesting costs (Lanford and Stokes 1996). In contrast, two other studies found the CTL harvesting system to be 15 to 30 percent more expensive than WT harvesting (Gingras 1994, Yaoxiang et al. 2006). In these studies, harvesting cost differences were primarily affected by machine productivity as influenced by extraction distance and tree size. They also found that WT harvesting was more productive than CTL harvesting (Gingras 1994, Yaoxiang et al. 2006). The higher productivity of WT harvesting in these studies was due to specific tasks assigned to each WT harvesting machine compared to the felling-processing and forwarding functions often performed by two machines in the CTL harvesting system.

CTL and WT harvesting productivity and cost are affected by stand and harvesting variables such as tree size, removal density, extraction distance, and operator skills (Andersson 1994, Egan 1999, Wagner et al. 2000, LeDoux and Huyler 2001, Kellogg and Spong 2004). Primary transportation costs of trees or logs from the stump area to the landing area were found to be the most expensive component of CTL and WT harvesting systems in other studies (Han et al. 2004, Kellogg and Spong 2004).

One of the goals in timber harvesting is to maximize revenue while offsetting costs through high value recovery (Murphy et al. 1996). Value recovery is a process whereby stems are cut into logs according to predetermined specifications with the goal of obtaining the highest possible value (Ian et al. 2004). Value recovery plays an integral part in determining the profitability of harvesting operations since profits are dependent on the volume produced and the unit value of the products (Twaddle and Goulding 1989). In Quebec, Canada, production differences were observed between CTL and WT harvesting. The study demonstrated that CTL harvesting provided a better yield in terms of recovery of green merchantable wood. However, the production differences between the two harvesting systems were due to differences in the volumes of dead and sound wood, the number of windfalls, and the number of unmerchantable trees (Favreau 1997). Contrary to the study in Canada, another study revealed 11 percent higher gross merchantable volume in favor of the WT harvesting system but similar unmerchantable volume for the two harvesting systems. The similar proportion of unmerchantable volume for the two systems suggests the possibility of a comparable merchantable volume recovery by the two harvesting systems (Plamondon and Page 1997).

Much is known about harvesting productivity and how harvesting and stand variables affect operational efficiency of mechanized harvesting systems (Kellogg et al. 1992), but there are still discrepancies in results with respect to cost differences between CTL and WT harvesting systems. Potential revenue generated by these two harvesting systems may be different since each method produces different log products. This study was designed to broaden our knowledge of harvesting productivity, cost, and log value recovery through a side-by-side comparison between CTL and WT harvesting systems.


Study site and system description

The study sites were located near the towns of Princeton and Deary, Latah County, northem Idaho (Fig. 1). The two sites were composed of Douglas-fir (Psuedotsuga menziesii), grand fir (Abies grandis), lodgepole pine (Pinus contorta), ponderosa pine (Pinus ponderosa), western larch (Larix occidentalis), white pine (Pinus strobes), and redcedar (Genus lumis). The stand near Princeton was a 22-acre unit with average tree diameter at breast height (DBH) of 11 in, ground slope range from 3 to 34 percent, and tree age ranging from 35 to 80 years old. The stand near Deary is a 48.1-acre stand with average tree DBH of 11 in, ground slope range from 2 to 32 percent, and tree age ranging from 70 to 130 years.


Each site was divided into four subunits and randomly allocated to CTL and WT harvesting systems. In each subunit, 14 sample plots were laid out to collect preharvesting stand inventory data. In each plot, tree height and DBH were measured to estimate basal area and total tree volume per acre. The experimental design showed similarities in stand and ground conditions between CTL and WT harvesting units at each site (Table 1). There were no significant differences in mean DBH between CTL and WT harvesting systems at the two sites (p < 0.05).

Harvesting systems and operations

At the Princeton site, CTL harvesting operations used a single-grip harvester (Valmet 500T) to fell and process trees for 2 days before the forwarding operation began. This was done for safety reasons and to provide sufficient logs to keep the forwarder (Valmet 890T, with a squirt boom loader) productive throughout the harvesting operation. The forwarding operations were uphill at an average distance of 406 ft. A Prentice 410DX loader was used for loading logs onto the truck. For the WT harvesting operation, a continuous disc-saw feller-buncher (Timbco 455EXL) was used to fell trees for 1 day prior to skidding of trees by a crawler (CAT D-518). Direction of the skidding operations was uphill, at an average skidding distance of 426 ft. A grapple-processor (CAT) and a Link-Belt 240LX log loader were used for tree processing and log loading, respectively. The skidding and forwarding operations were uphill at a slope range from 3 to 34 percent.

At the CTL harvesting site near Deary, the same models of harvester and forwarder were used but with different operators. Forwarding was downhill at an average forwarding distance of 919 ft. For the WT harvesting system, a bar-saw feller-buncher (Timbco Hydro feller-buncher T435) was used to fell trees 3 days prior to primary transportation of trees by a crawler (CAT D-518). Direction of the skidding operation was downhill, at an average skidding distance of 626 ft. A stroke-boom delimber (PC200 Komatsu) was used for processing trees and a log loader (PC220 Komatsu) was used for loading logs onto the truck. The skidding and forwarding operations were downhill at a slope range from 2 to 32 percent.

Hourly machine costs as measured in dollar per scheduled machine hour ($/SMH) were estimated using standard machine rate calculation practices (Miyata 1980). New machine prices were collected from dealers and contractors in northern Idaho. Estimated economic life for all machines was 5 years with 1,800 working hours per year. Salvage value was set at 20 percent of initial purchase cost, interest rate at 8 percent, insurance at 1 percent, and taxes at 1.5 percent; operator wage at $22.00 per hour plus 32 percent benefits. Hourly fuel consumption was estimated based on machine engine size. Percentage utilization and maintenance and repair costs were estimated based on a study by Brinker and others (2002). The resulting hourly costs for the equipment are shown in Table 2. Hourly machine costs ($/SMH) of the CTL harvesting system were slightly higher than those of the WT harvesting systems. In some instances in the inland northwest, there are seasonal shutdowns caused by the thawing of winter moisture, and a CTL harvesting system can often begin operating earlier than a WT harvesting system. A scenario where hourly cost of the CTL harvesting system are based on more working hours per year than a WT harvesting system is evaluated to see how extension of the logging season affects overall logging cost.

Data collection and analysis

A detailed time study using stopwatch techniques was conducted to collect harvesting productivity data and to establish relationships between cycle times, load size, and stand variables. Harvester and feller-buncher operations were evaluated based on 1,000 premarked trees (500 trees in each unit) to determine tree size distribution data of the stand. Each marked tree was numbered and correlated with DBH. Species was also recorded. This way, accurate tree DBH and species data could be collected while observing harvesting operations at a safe distance. The numbering method was also combined with ocular observations during harvesting operation to collect additional data, as it was sometimes difficult to see numbers on tree stems.

Before skidding operations on the WT harvesting units, landing locations and major skid extraction routes were identified by the crawler operator. Distances of tree bunches to the landing were measured with a tape and recorded on stumps, logs, and residual trees. The marked distances on stumps, logs, and residual trees represented travel distances to each bunch when the crawler transported the bunch to the landing. Distances to new bunches located farther from the initial marked distances were estimated using a previously marked distance as a reference point and adding a new distance measured from the marked distances to the new bunch location.

Prior to forwarder machine activity, the harvester created forwarder trails by felling and processing every tree within and around its path. Each forwarder trail was divided into short distances of 50 to 100 ft. The short interval distances were measured and recorded on stumps, logs, and residual trees. These numbers were observed and distances between those marked were estimated at some safe distance from active forwarding operations. The tree size and number of logs manufactured in each processor cycle were collected by ocular estimation of DBH and count of logs produced per tree. The number of pieces per loader cycle was also collected by ocular count of logs during loading onto the truck.

Time study data collected from the field were examined for normality and outliers and were used to develop predictive equations by running an ordinary least squares regression procedure in SAS software (SAS Institute 1999). Independent variables such as DBH, travel distance, and number of trees/ logs, were related to each cycle time.

Harvesting productivities were determined using productivity equations developed for cycle time and production in volume of recovered sawlogs (large-end diameter > 8 in), ton-wood (a log of large-end diameter < 8 in and top minimum diameter of 4 in), and pulpwood (any dead dry log, decayed log, or log below specified mill lengths). Average piece volume ofsawlog, pulpwood, and ton-wood were estimated from log weight data collected at the mills. Each truckload volume was converted to green cubic foot ([ft.sup.3]) and board feet (BF) log volume using the conversion factors of 58.5 pcf, and 4.75 BF/ [ft.sup.3] (Gregg 2002) for mixed conifer species (Spelter 2002).

Results and discussion

Harvesting productivity

The results from the two sites consistently showed higher productivity for the WT harvesting machines than for the CTL harvesting machines (Table 3); these results are also consistent with those observed in other studies (Andersson 1994, David et al. 2005, Yaoxiang et al. 2006). Higher productivity of WT harvesting was partly a result of having a designated task for each machine. For example, the productivity difference between the CTL harvester and the combined WT feller-buncher and processor was high at the Princeton site. However, it was minimal at the Deary site because of low productivity of the feller-buncher equipped with a bar-saw.

Tree size generally affects productivity of harvesting machines (Kellogg and Spong 2004). Time study data for the feller-bunchers and harvesters were used to generate relationships between tree size (DBH) and hourly production (Fig. 2).


Harvesting productivity increased with increasing tree DBH. Productivity of the feller-bunchers increased nonlinearly more than the harvester. This outcome is consistent with the outcome of a previous study where the productivity of a feller-buncher was found to be four times more than that of harvester (Yaoxiang et al. 2006).

The continuous disc-saw feller-buncher used at the Princeton site was more productive than the bar-saw feller-buncher used at the Deary site. Delay-free cycle time for the continuous disc-saw feller-buncher was short (about 40 and 62 percent less than the average delay-free cycle times for the bar-saw feller-buncher and harvester, respectively) with correspondingly higher productivity due to saw type along with machine design elements that allows for high-speed rotation of the saw. From personal communication with feller-buncher operators and machine dealers, the continuous disc-saw feller-bunchers are more productive than bar-saw feller-bunchers in handling tree sizes less than 22 in. However, bar-saw feller-bunchers are still used for forest operations because of their relatively low investment and maintenance costs and their ability to fell a greater range of tree diameters.

Delay summary

Table 4 shows observed delays for the harvester, forwarder, feller-buncher, crawler, and processor. With the CTL harvesting machines, operational delays (e.g., brushing, tree hang-ups, decking, waiting for the log truck, and sorting) accounted for more than 53 percent and 73 percent of total harvester and forwarder delay times respectively at the Princeton and Deary sites. Observed mechanical delays (engine breakdown, chain problem, and engine heating-up) for the harvester and forwarder were minimal. Frequent operational delays and minimal mechanical delays observed for the CTL harvesting machines in this study is similar to observations in a previous study (Kellogg and Spong 2004). Operational delays (removing slash at landing, pushing tree bunches, and waiting) represented the largest delay category for the WT harvesting processor and crawler at both sites. Mechanical delays such as chain problems constituted a greater percentage of processor and bar-saw feller-buncher total delay time, while the continuous disc-saw feller-buncher had some unusual stops when handling large trees (DBH > 22 in). The occasional breakdown was due to operator error in not allowing the rotation speed of the continuous disc-saw to attain its peak before felling large trees.

Generally, administrative delays (talking with harvesting supervisor and machine operators), and personal delays (eating, drinking, etc) constituted a small proportion (< 12.6%) of total delay time, while operational delays constituted the highest proportion of the total delay time. Frequency and total duration of mechanical delays tended to be low, but the average times involved in mechanical delays were higher than for other types of delays once they occurred. Mechanical delays observed in the CTL and WT harvesting machines could be attributed to machine age. Except for the continuous disc-saw feller-buncher used at the Princeton site, the ages of the CTL and WT harvesting machines used in this harvesting operation were beyond what is normally considered the economic life span (5 yr) of harvesting machines (Brinker et al. 2002).

Percent utilization was estimated based on operational delay times as percentage of total cycle times in both sites. Utilization percentages for the harvester were 65 percent and 51 percent, for the forwarder were 97 percent and 94 percent, for the crawler were 83 percent and 58 percent, and for the processor were 59 percent and 55 percent at the Princeton and Deary sites respectively. The utilization percentage for the continuous disc-saw feller-buncher was 43 percent and for the bar-saw feller-buncher, 52 percent. Nonproductive time due to delays can have a significant impact on productivity (LeDoux and Huyler 2001). During the harvesting operation, the observed delays (ranging from 4% to 67%) affected machine productivity. Figure 3 shows 10, 25, and 35 percent reduction in productivity for the CTL and WT harvesting machines with a decreasing utilization percentage from 100 to 65 percent.


Harvesting cost

The stump-to-truck cost for CTL harvesting was $0.34/[ft.sup.3] and $0.36/ [ft.sup.3], while the WT harvesting cost was $0.22/[ft.sup.3] and $0.33/[ft.sup.3] at the Princeton and Deary sites, respectively (Table 5). CTL harvesting cost was more expensive than the WT harvesting at the Princeton and Deary sites. These differences were primarily due to differences in the forwarder and crawler machine costs and productivity in the transportation of trees or logs from the stump area to the landing area.

Primary transportation cost of trees or logs from the stump area to the landing by the forwarder and crawler accounted for the greatest proportion (36% to 54%) of the total harvesting cost of the CTL and WT harvesting at both study sites.

The cost of operating a crawler is less than the cost of operating a forwarder because the purchase price and all associated ownership costs of a crawler were about 24 percent less than those costs for a forwarder. Hourly production rates for the crawler were also higher than those for the forwarder. On the average, the crawler produced 2,060 [ft.sup.3]/PMH (28% more than the forwarder productivity) at the Princeton site and 1,350 [ft.sup.3]/PMH (14% more than for warder productivity) at the Deary site. However, the impact of increases in skidding distance on crawler productivity was much more significant than the impact of increases in distance on forwarder productivity. At the Deary site, forwarding cost increased by 21 percent because of long forwarding distances, while the crawler harvesting cost increased by 33 percent. This result agrees with other research findings that an increase in average extraction distance beyond 328 ft will increase the cost of WT harvesting more than CTL harvesting (Andersson 1994).

The higher percentage difference (33.3%) in the cost of CTL over WT harvesting at the Princeton site can be explained by differences in machine types used in each system and their harvesting productivities. At both the Princeton and Deary sites, the same forwarder and harvester models were used, resulting in a minimum variation in CTL harvesting machine productivity between the two sites. In contrast, WT harvesting machines used at the Princeton site were more productive than the combination of WT machines used at the Deary site. For example the bar-saw feller-buncher used at the Deary site was less productive than the continuous disc-saw feller-buncher used at the Princeton site.

The method of loading logs also influenced the operating cost of both harvesting systems. Because of the higher productivity of the forwarder, the cost of loading CTL harvested logs using the forwarder machine was 20 percent less than the cost of the WT harvesting loader at the Deary site.

Productivity equations

The productivity equations developed for all harvesting machines contain estimators that have significant F-values (p < 0.05), except for the feller-buncher and loader used at the Princeton and Deary sites (Table 6). The significant F-value for each estimator indicates that the average cycle time equation for each machine can be effectively used to estimate harvesting productivity. Productivity equations for the crawler and forwarder had high values of the training [r.sup.2], developed from 70 percent of the observed data, compared to productivity equations of felling and processing machines. The training [r.sup.2] value is a measure of goodness-of-fit (i.e., how well the estimated regression line fits observed delay-free cycle time). For example, the forwarder equation [r.sup.2] = 0.83) at the Deary site indicates that the independent variables (distance and number of logs) explains 83 percent of variation in delay-free cycle time and that the equation includes variables that are most influential in the forwarding operation. All productivity equations were similar to the range of [r.sup.2] values of previously developed productivity equations (Kellogg and Spong 2004, Yaoxiang et al. 2006).

The combined equations for the forwarder and crawler also showed a significant F-value (p < 0.05) indicating that the direction of forwarding or skidding operation with respect to slope can either increase or decrease delay-free cycle time of machines. The dummy variable (DIRECT) increased the delay-flee cycle time of the forwarder by 3 minutes, 48 seconds and the crawler by 0.11 seconds for uphill harvesting operations.

The significant F-value and [r.sup.2] values are good indicators of the effectiveness of predictive equations. However, they do not necessarily indicate the best equation for prediction of the dependent variable (Kozak and Kozak 2003). In view of this, further analysis (validation) was performed on the productivity equations to ascertain their adequacy. To validate the productivity equations, all developed productivity equations were used to predict the reserved data (30% of observed data not used for developing the productivity equations).

The outcomes of the prediction (i.e., predicted average cycle times) were correlated with the observed cycle times to produce another [r.sup.2] (validated [r.sup.2]). In addition, a two-sample t-test ([alpha] = 0.05) was used to test the differences between the predicted and observed average cycle time. The differences between the observed and predicted cycle time were significant (p < 0.05) for all equations except for forwarder and crawler equations developed at the Deary site, the combined forwarder equation, and the processor equation at the Princeton site. These outcomes suggest that productivity equations developed for the forwarder, crawler, and processor are good predictors of harvesting productivity and thus can be used to predict harvesting productivity in stands with similar characteristics, while the productivity equations developed for other machines are likely to produce estimates of delay-free cycle time that are less satisfactory.

Standardized comparison between CTL and WT harvesting

A standardized comparison was performed to evaluate harvesting cost differences between CTL and WT harvesting under equal site and stand conditions using the productivity equations developed from time study and stand data on both sites. The productivity equations developed for the crawler skidder and forwarder at the Deary site were used because the site involved downhill skidding/forwarding, which is typical of most ground-based harvesting operations. Values of the variables used for the standardized comparison (see Table 7) were 11 in for DBH, 18.6 [ft.sup.3] for log volume, and a range of values between 400 ft and 1150 ft, calculated at a 50-ft interval.

In the standardized comparison, a less productive CTL harvesting (Lc) was compared to less (Lw) and highly (Hw) productive WT harvesting, and a highly productive CTL harvesting (Hc) was compared to highly productive WT harvesting (Hw). CTL harvesting was defined as less productive if a separate loader was used in addition to the harvester and forwarder. It was defined highly productive if the forwarder was used for loading logs onto the truck. WT harvesting was defined less productive if a bar-saw feller-buncher was used. It was defined as highly productive if a continuous disc-saw feller-buncher was used.

The CTL harvesting costs were significantly (p < 0.05) higher than the cost of WT harvesting in the scenario "LcHw" (less-productive CTL harvesting vs. highly productive WT harvesting. For the "HcHw" scenario a lower percentage difference was observed by using the forwarder machine for loading logs instead of a separate loader in the CTL harvesting operations (Table 7).

The effect of feller-buncher type is illustrated in the "LcLw" case. Below a 650-fl extraction distance, the outcome resulted in no harvesting cost differences between the two harvesting systems. Beyond 1,000 ft, WT harvesting becomes more expensive than CTL harvesting. These assumptions represent conditions with the greatest observed differences between the CTL and WT harvesting costs due to variations in extraction distance and machine combinations.

The highly productive CTL and WT harvesting scenario (HcHw) (Fig. 4) shows the pattern of cost differences between CTL and WT harvesting at increasing extraction distances. Generally, the percentage by which CTL harvesting cost exceeds WT harvesting cost is reduced (< 6%) with increasing extraction distance. The cost differences between CTL and WT harvesting from this analysis can be described as a cost-difference-circle; below 750 ft, the first half of the cycle begins with CTL harvesting cost increasing at a much higher rate than WT harvesting cost. The reverse is observed beyond 750 ft (second half of circle) where WT harvesting cost increases at a much higher rate than CTL harvesting cost due to increased extraction distances.


Another standardized comparison was also performed to assess a scenario where CTL harvesting systems have more working hours per year than WT harvesting systems. For this analysis, 1,800 hours per year (10 hours per day, 20 days per month, and 9 months per year) and 1,600 hours per year (10 hours per day, 20 days per month, and 8 months per year) were used for CTL and WT harvesting, respectively. Assuming an average extraction distance of 800 ft, WT harvesting operations utilizing a continuous disc-saw feller-buncher, and assuming a forwarder used for loading CTL harvested logs, the CTL harvesting cost of$0.35/ [ft.sup.3] was 3 percent higher than the WT harvesting cost ($0.34/[ft.sup.3]). When the continuous disc-saw feller-buncher is replaced with a bar-saw feller-buncher in the analysis, the CTL harvesting becomes 6 percent less expensive than WT harvesting. Assuming a separate loader or a less productive forwarder for loading the CTL harvesting logs, cost of CTL harvesting would be 12.8 percent higher than WT harvesting operations when a continuous disc-saw feller-buncher is used and 5.1 percent higher if bar-saw feller-buncher is used.

Harvest volume and log value recovery

The harvested sawlog volume accounts for 81 percent and 67 percent of total harvest volume of CTL harvesting and 85 percent and 71 percent of the total harvest of WT harvesting at the Princeton and Deary sites, respectively (Table 8). At both study sites, WT harvesting recovered more sawlog but less ton-wood volume than CTL harvesting. At the Princeton site, no pulpwood was harvested for the timber sale because it was not required of the logging contractors although there is a market for pulpwood in northern Idaho. However, at the Deary site, the contractors were asked to harvest pulpwood and WT harvesting recovered more pulpwood volume than did CTL harvesting. The higher sawlog and pulpwood volume recovered by WT harvesting may be due to differences in manufactured log lengths. WT harvesting recovered the maxi mum possible log volume by manufacturing diverse log lengths. Observed lengths of logs manufactured by CTL harvesting during the study were 16.3 ft and 18.3 ft, while WT harvesting manufactured lengths of 15.5, 16.3, 18.3, 24.3, and 33.3 ft. The variations in lengths translated into higher average log volume in favor of WT harvesting. This scenario might have resulted in differences in sawlog volume between CTL and WT harvesting. A similar outcome was observed in a comparison study between the two harvesting systems where differences in merchantable and unmerchantable volumes were suggested as reasons for the differences in harvested volume of the two harvesting systems (Favreau 1997).

Total values received for the products are shown in Table 9. When all product values are considered, the WT harvesting system generated slightly higher returns from the products at both study sites.


Harvesting productivity and cost of CTL and WT harvesting systems are affected by stand and harvesting variables. The percentage by which CTL harvesting costs exceeded WT harvesting costs ranged from 8.3 to 33.3 percent, depending on average extraction distance, individual machine productivity, and machine combinations. Primary transportation cost (stump-to-landing) was the main reason for cost differences between CTL and WT harvesting. Primary transportation costs accounted for 36 to 37 percent and 42 to 49 percent of the total WT and CTL harvesting cost, respectively, and these costs were very sensitive to extraction distances.

Machine combinations influenced harvesting cost of CTL and WT harvesting. The effect of replacing a less productive bar-saw feller-buncher with a continuous disc-saw feller-buncher was significant on harvesting costs of the WT system, resulting in higher cost differences between the two harvesting systems. In general, all the WT harvesting machines were more productive than the CTL harvesting machines, suggesting that the WT harvesting system completes harvesting work faster than the CTL harvesting system because specific tasks are assigned to each machine in the WT harvesting system.

Harvesting cost can be minimized in CTL harvesting by adequate planning of the harvesting operation to reduce operational delays such as frequent intermediate stops of the forwarder, waiting time at landing to load logs onto trucks, and sorting. In addition, mechanical delays can be reduced through regular servicing of equipment and replacement of machines when frequent mechanical delays are observed. Similarly, adequate planning of the landing area before harvesting operations will reduce imbalances induced by interactions between WT harvesting machines.

Harvesting productivity can be effectively predicted using stand and harvesting variables. Adequacy of each productivity equation was ascertained through validation procedure. The outcome of the validation suggests that productivity equations developed for the forwarder, crawler, and processor are good predictors of harvesting productivity and thus can be used to predict harvesting productivity in stands with similar characteristics, while the productivity equations developed for other machines may have limitations in predicting delay-free average cycle time.

WT harvesting recovered more sawlog volume but less ton-wood volume than CTL harvesting because of variations in manufactured log lengths. As a result of the higher proportion of sawlog volume in the total harvest volume, the net revenue from WT harvesting was higher than for CTL harvesting.

Our study indicated that WT harvesting was more cost-effective system than CTL harvesting but that differences between these systems are highly sensitive to machine productivity and stand variables. In view of this, further research is necessary to explore the effect of other variables not considered in this study such as the effect of other machine combinations (e.g., grapple skidder) and silvicultural prescriptions (e.g., single tree selection) on cost, productivity, and log value recovery of CTL and WT harvesting.

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Adebola B. Adebayo * Han-Sup Han * Leonard Johnson *

The authors are, respectively, Graduate Research Assistant, Division of Forestry, West Virginia Univ., Morgantown, West Virginia (; Associate Professor, Humboldt State Univ., Arcata, California (, and Professor, Dept. of Forest Resources, Univ. of Idaho, Moscow, Idaho (ljohnson@ This paper was received for publication in August 2006. Article No. 10237.

* Forest Products Society Member.

[c]Forest Products Society 2007. Forest Prod. J. 57(5):59-69.
Table 1.--Descriptions of stand characteristics at the Princeton and
Deary sites.

 Princetosite Deary site

Characteristics CTL WT CTL WT

Total area (ac.) 8.2 9.3 22.0 26.1
Average DBH (in) 11.0 11.0 11.5 11.0
Average height (ft) 66.0 70.0 67.0 68.0
Average basal area ([ft.sup.2]/ac.) 161.0 189.0 116.0 117.0
Trees per acre 241.0 276.0 191.0 176.0
Harvest volume (MBF (a)/ac.) 14.4 12.0 13.3 14.2
Species composition (%)
 Douglas-fir 39 25 27 31
 grand fir 25 20 30 50
 lodgepole pine 14 21 42 15
 ponderosa pine 4 2 0 0
 red cedar 11 27 1 4
 western larch 6 5 0 0
 white pine 1 0 0 0

(a) Thousand board feet.

Table 2.--Hourly cost ($/SMH (a)) for harvesting system and equipment.

 CTL harvesting system

Site Harvester Forwarder Loader Total

Princeton 138.4 143.3 81.2 362.9

Deary 138.4 143.3 143.3 42.0

 WT harvesting system

Site Feller- Crawler Processor Loader Total
location buncher


Princeton 110.3 108.8 106.3 91.6 417.0

Deary 99.2 108.8 131.2 90.8 430.0

(a) Scheduled machine hour based on utilization rates for each
machine: 65 percent for harvester, forwarder, crawler, feller-buncher,
processor, and 50 percent for loader (Brinker et al. 2002).

Table 3.--Average and standard deviation of delay-free cycle times and
harvesting productivity observed for cut-to-length and whole-tree
harvesting at the two study sites.

 Average cycle
 time (a) Turn size
Site and machines (min.) (# pieces/cycle (b))

Princeton site
 CTL harvesting
 Harvester 0.8 (0.05) 2.4 (1.0)
 Forwarder 22.2 (7.4) 59.0 (16.5)
 Loader 28.7 (6.8) 120.0 (13.9)
 WT harvesting
 Feller-buncher (c) 0.3 (0.2) 1.4 (0.6)
 Crawler 4.8 (0.25) 8.5 (2.0)
 Processor 0.6 (0.5) 1.4 (0.6)
 Loader 22.2 (6.3) 70.0 (14.6)
Deary site
 CTL harvesting
 Harvester 0.7 (0.1) 2.3 (1.1)
 Forwarder 26.0 (12.5) 60.0 (32.0)
 Loader (f) 10.4 (1.3) 112.3 (50.0)
 WT harvesting
 Feller-buncher (g) 0.5 (0.2) 1.3 (0.5)
 Crawler 6.1 (2.4) 7.5 (2.0)
 Processor 0.7 (0.4) 1.3 (0.5)
 Loader 21.3 (4.3) 65.0 (17.0)

 Piece sizes (c) productivity
Site and machines ([ft.sup.3]/piece) ([ft.sup.3]/PMH (d))

Princeton site
 CTL harvesting
 Harvester 9.3 1,762
 Forwarder 9.3 1,483
 Loader 9.3 2,333
 WT harvesting
 Feller-buncher (c) 19.5 6,552
 Crawler 19.5 2,060
 Processor 19.5 2,824
 Loader 19.5 3,673
Deary site
 CTL harvesting
 Harvester 8.4 1,610
 Forwarder 8.4 1,163
 Loader (f) 8.4 5,428
 WT harvesting
 Feller-buncher (g) 18.3 2,745
 Crawler 18.3 1,350
 Processor 18.3 1,983
 Loader 18.3 3,351

(a) Delay-free cycle time in minutes. Values in parentheses indicate
standard deviation.

(b) Number of log pieces handled each cycle.

(c) Log volume based on log scaling at the mill and number of piece
per truck that was collected during time study in field.

(d) Productive machine hour.

(e) Continuous disc-saw feller-buncher.

(f) Forwarder machine loading log truck at landing.

(g) Bar-saw feller-buncher.

Table 4.--Summary of delays by harvesting machine at the Princeton
and Deary sites. Values in parenthese inidicate number of harvesting
operation days.

 Princeton site

Machine/delay type Frequency Average time Percent (a)
 (min. delay)

CTL harvesting
 Harvester (5)
 Administrative (b) 1 1.42 1.5
 Mechanical (c) 18 1.39 26.2
 Operational (d) 132 0.43 60.0
 Personal (e) 7 1.70 12.5
 % utilization (f) -- -- 65
 Forwarder (4)
 Administrative 3 7.00 11.0
 Mechanical -1 0.00 0.0
 Operational 262 0.39 89.0
 Personal 0 0.00 0.0
 % utilization (f) -- -- 97
WT harvesting
 Feller-buncher (3)
 Administrative 3 2.30 8.1
 Mechanical 6 2.60 18.6
 Operational 155 0.40 73.3
 Personal 0 0.00 0.0
 % utilization (f) -- -- 43
 Crawler (5)
 Administrative 8 8.79 31.9
 Mechanical 2 40.50 36.7
 Operational 51 1.00 23.3
 Personal 1 18.00 8.2
 % utilization (f) -- -- 83
 Processor (3)
 Administrative 4 1.29 4.0
 Mechanical 1 10.50 8.1
 Operational 31 3.69 88.0
 Personal 0 0.0 0.0
 % utilization (f) -- -- 59

 Dean site

Machine/delay type Frequency Average time Percent (a)
 (min. delay)

CTL harvesting
 Harvester (8)
 Administrative (b) 4 5.90 12.0
 Mechanical (c) 8 6.67 27.0
 Operational (d) 136 0.78 53.7
 Personal (e) 2 7.26 7.4
 % utilization (f) -- -- 51
 Forwarder (8)
 Administrative 19 2.43 5.0
 Mechanical 16 11.38 21.0
 Operational 375 1.73 73.0
 Personal 5 1.55 1.0
 % utilization (f) -- -- 94
WT harvesting
 Feller-buncher (5)
 Administrative ? 3.75 6.4
 Mechanical 9 9.80 75.6
 Operational 11 1.82 17.2
 Personal 1 0.95 0.8
 % utilization (f) -- -- 52
 Crawler (8)
 Administrative 12 6.95 7.1
 Mechanical 15 6.89 8.9
 Operational 200 4.84 82.3
 Personal 4 5.40 1.8
 % utilization (f) -- -- 58
 Processor (5)
 Administrative 4 1.78 5.8
 Mechanical 12 6.30 61.8
 Operational 64 0.57 29.9
 Personal 2 1.38 2.3
 % utilization (f) -- -- 55

(a) Percent of total delay time lot specific machine based on weighted

(b) Administrative delay includes talking with harvesting supervisor
and machine operators.

(c) Mechanical delay includes chain problems, machine maintenance,
machine breakdown, and breakdown of harvester or processor processing

(d) Operational delay includes removimg slash at landing, brushing,
log sorting, decking of log, tree hang-up, obstructions, and waiting
at landing (e.g., forwarder waiting for log truck, crawler waiting
for processor to create space to deliver tree bunches and processor
waiting while crawler clears slash at landing).

(e) Personal delay includes lunch time, personal time, and talks not
relevant to work.

(f) Percentage utilization based on delay-free cycle time and observed
operational delays.

Table 5.--Stump-to-truck cost ($/[ft.sup.3]) of cut-to-length and
whole-tree harvesting.

 Machine cost Hourly production
 (S/PMH (a)) ([ft.sup.3]/PMH)

Princeton site
 CTL: Harvester 197.83 1,762
 Forwarder 220.49 1,483
 Loader 162.43 2,333
Total 580.75
 WT: Feller-bancher (b) 169.68 6,552
 Crawler 167.45 2,060
 Processor 163.55 2,824
 Loader 183.20 3,673
Total 683.90
Deary site
 CTL: Harvester 197.83 1,610
 Forwarder 220.49 1,163
 Loader (c) 220.49 5,428
Total 638.81
 WT: Feller-bancher (d) 152.60 2,745
 Crawler 167.45 1,350
 Processor 200.04 1,983
 Loader 183.66 3,351
Total 714.26

 Harvesting Percent of difference
 cost total cost from WT
 ($/[ft.sup.3]) (%) (%)

Princeton site
 CTL: Harvester 0.11 34
 Forwarder 0.15 45
 Loader 0.07 21
Total 0.34 100 33.3
 WT: Feller-bancher (b) 0.03 14
 Crawler 0.08 36
 Processor 0.06 27
 Loader 0.05 23
Total 0.22 100
Deary site
 CTL: Harvester 0.12 35
 Forwarder 0.19 54
 Loader (c) 0.04 11
Total 0.36 100 8.3
 WT: Feller-bancher (d) 0.06 18
 Crawler 0.12 37
 Processor 0.10 30
 Loader 0.05 15
Total 0.33 100

(a) Productive machine hour.

(b) Continuous disc-saw feller-bancher.

(c) Forwarder machine used for loading log truck

(d) Bar-saw feller-bancher.

Table 6.--Delay-free average cycle time equations for cut-to-length
and whole tree harvesting machines.

Site/Machines Average cycle time estimator (a)
Princeton site (centi-minutes)

 CTL: Harvester 30.04 + 0.2[DBH.sup.2] + 8.3LOG
 Forwarder 599.89 + 1.35TED - 0.064ITD + 126TLD
 + 9.63PIECE
 WT: Feller-buncher 18.22 + 0.037[DBH.sup.2]
 Crawler 192.85-0.181TED + 0.87TLD - 3.092TREE
 Processor exp (d) (2.81 + 0.064DBH + 0.27LOG)
Deary site
 CTL: Harvester 27.63 + 0.48 [DBH.sup.2] + 1.05LOG
 Forwarder 634.44 + 0.64TED + 0.427TLD + 0.64ITD
 + 11.87PIECE
 WT:Feller-buncher 29.32 + 0.77DBH + 1.769DISTANCE
 Crawler 399.31 + 0.00046[TLD.sup.2]
 + 0.000015[TED.sup.2] + 4.29TREE
 Processor 0.43 + 3.34DBH + 34.02LOG
 Loader 1882 + 8.362PIECE
 CTL: Harvester 33.30 + 0.069DBH + 0.11LOG- 0.16DIRECT
 Forwarder 318.81 + 0.954TED + 0.5591TD + 0.44TLD
 + 11.04PIECE + 80.79DIRECT
 WT:Feller-buncher exp (d) (3.58 + 0.024DBH -0.08SAW)
 Crawler + 0.65TLD + 0.87TED + 6.24TREE + 11.07DIRECT

Princeton site Training (b) Validated (c)

 CTL: Harvester 0.31 0.13
 Forwarder 0.73 0.50

 WT: Feller-buncher 0.16 0.27
 Crawler 0.61 0.60
 Processor 0.66 0.66
Deary site
 CTL: Harvester 0.45 0.16
 Forwarder 0.83 0.72

 WT:Feller-buncher 0.64 0.42
 Crawler 0.75 0.76

 Processor 0.54 0.39
 Loader 0.15 ND (c)
 CTL: Harvester 0.40 0.10
 Forwarder 0.74 0.65

 WT:Feller-buncher 0.51 0.04
 Crawler 0.68 0.78

Princeton site f-value p-value n

 CTL: Harvester 14.19 0.0003 450
 Forwarder 23.20 0.0001 35

 WT: Feller-buncher 1.88 0.7450 350
 Crawler 57.22 0.0001 133
 Processor 225.5 0.0001 224
Deary site
 CTL: Harvester 45.00 0.0001 450
 Forwarder 58.45 0.0001 60

 WT:Feller-buncher 103.1 0.0001 297
 Crawler 180.3 0.0001 314

 Processor 137 0.0001 181
 Loader 2.3 0.1410 26
 CTL: Harvester 15.30 0.0001 900
 Forwarder 42.7 0.0001 97

 WT:Feller-buncher 9.83 0.0019 394
 Crawler 201 0.0001 62

(a) TED = travel empty distance measured from the landing: ITD =
intermediate travel distance traveled by forwarder between stops while
picking logs; TLD = distance traveled with full load of trees or logs;
DBH = diameter at breast height: PIECE = # logs pieces carried per
trip; TREE = # trees being carried per trip. DIRECT = "1" if it was
uphill skidding or forwarding or harvesting "0" otherwise. SAW = "1"
if continuous disc-saw was used, "0" otherwise.

(b) [r.sup.2] developed from 70 percent of observed data.

(c) [r.sup.2] produced from using productivity equations to predict
the reserved data (30%).

(d) exp = 2.71828.

(e) ND-no data for validation.

Table 7.--Standardized (a) comparison of cut-to-length and whole-tree
harvesting cost.

Extraction Harvesting cost
 (ft) CTL (c) CTL (d) WT (e) WT (f)


 400 0.31 0.29 0.28 0.31
 450 0.31 0.29 0.28 0.31
 500 0.32 0.30 0.29 0.32
 550 0.32 0.30 0.29 0.32
 600 0.33 0.31 0.30 0.33
 650 0.34 0.32 0.30 0.33
 700 0.34 0.32 0.31 0.34
 750 0.35 0.33 0.31 0.34
 800 0.35 0.33 0.32 0.35
 850 0.36 0.34 0.32 0.35
 900 0.36 0.34 0.33 0.36
 950 0.37 0.35 0.34 0.37
 1,000 0.37 0.35 0.35 0.38
 1,050 0.38 0.36 0.35 0.38
 1,100 0.38 0.36 0.36 0.39
 1,150 0.39 0.37 0.37 0.40

Extraction Difference (b)
 (ft) LcHw (g) HcHw LcLw


 400 6.45 3.45 0.00
 450 6.45 3.45 0.00
 500 6.25 3.33 0.00
 550 6.25 3.33 0.00
 600 6.06 3.23 0.00
 650 5.88 6.25 2.94
 700 5.88 3.13 0.00
 750 5.71 6.06 2.86
 800 5.71 3.03 0.00
 850 5.56 5.88 2.78
 900 5.56 2.94 0.00
 950 5.41 2.86 0.00
 1,000 5.41 0.00 -2.70
 1,050 5.26 2.78 0.00
 1,100 5.26 0.00 -2.63
 1,150 5.13 0.00 -2.56

(a) Standardized comparison variables equations include crawler,
forwarder, harvester, Komatsu loader, and bar-saw feller-bancher
equations developed in Deary, and continuous disc-saw feller-bancher
equation developed in Princeton. The same average DBH (11 in), log
volume, and extraction distance range from 400 to 1, 150 ft was used.

(b) Differences in percentage of the CTL harvesting over the WT

(c) Harvester, forwarder, and loader.

(d) Harvester and forwarder (used for log transportation and loading
onto truck).

(e) Continuous disc-saw feller-bancher, crawler, processor, and

(f) Bar-saw feller-bancher, crawler, processor, and loader.

(g) LcHw: cost difference between CTL (c) and WT (e) is significant
(p < 0.05); LcLw: cost difference between CTL (c) and WT (f) is not
significant (p < 0.05); HcHw: cost difference between CTL (d) and
WT (f) is not significant (p < 0.05).

Table 8.--Total harvest volume from cut-to-length and whole-tree
harvesting at Princeton and Deary sites.

 Product volume recovers
Site and
Product CTL WT CIL WT Difference (%)

 ([ft.sup.3]/ac.) (MBF (a)/ac.)
Princeton site
 Sawlog 1,674 (81%) 1,662 (85%) 6.8 7.9 13.9 (c)
 Ton-wood 383 (19%) 282 (15%) -- -- 26 (f)
Deary site
 Sawlog (b) 1,455 (67%) 1,663 (71%) 7.4 7.5 1.3 (e)
 Ton-wood (c) 327 (15%) 281 (12%) -- -- 14 (f)
 Pulpwood (d) 376 (17%) 382 (16%) -- -- 2 (f)

(a) Thousand board feet (MBF) based on scale data collected from mills
in Idaho. At the Deary site, each truck-load volume was converted to
green cubic foot ([ft.sup.3]) and board feet (BF) log volume using the
conversion factors (58.5 pcf and 4.75 BF/[ft.sup.3] (Gregg 2002) for
mixed conifer species) (Speller 2002).

(b) A log of large-end diameter > 8 in.

(c) A log of large-end diameter < 8 in and top minimum diameter of 4

(d) Any snag, decayed log of log below mill specified lengths.

(e) Difference over CTL harvesting volume based on MBF/ac.

(f) Difference based on [ft.sup.3]/ac.

Table 9.--Log value recovery from cut-to-length and whole tree
harvesting at the Princeton and Deary sites.

 Princeton site Dears site

Product Price (a) CTL WT CTL WT

 ($/ac.) ($/ac.)

Sawlog $470/MBF 3,290 3,572 3,196 3,713
Ton-brood $45 ton 450 360 432 369
Pulpwood $26 ton NA (b) NA (b) 286 291
Total 3,740 3,932 3,914 4,373

(a) Delivered price at mills in northern Idaho.

(b) No pulpwood harvested at the Princeton site for the timber sale.
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Title Annotation:research and statistics of harvesting
Author:Adebayo, Adebola B.; Han, Han-Sup; Johnson, Leonard
Publication:Forest Products Journal
Article Type:Report
Geographic Code:1CANA
Date:Jun 1, 2007
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