United States

Department of

Agriculture

Forest Service

Southern Forest

Experiment Station

New Orleans,

Louisiana

Research Paper

SO-269

September 1992

Harvesting Production in

Uneven-Aged

Loblolly-Shortleaf Pine

Stands: The Crossett Farm

Forestry Forties

R. Kluender, B. Stokes, and S. Woodfin
SUMMARY

Two Arkansas pine stands have been managed using uneven-aged management techniques since 1937. For many years the stands were harvested annually, but they have been harvested every 5 years since the 1970’s. A study to

determine productivity of the harvesting system was completed in the fa11 of

1990. The system consisted of chain saw felling and grapple skidding. Felling

and skidding productivity varied significantly with the diameter of stems or

average stem volume removed from each stand. The diameter distribution of

the removed material caused significant differences in productivity between

the stands.
Harvesting Production in UnevenAged Loblolly-Shortleaf

Pine Stands: The Crossett Farm Forestry Forties

R. Kluender, B. Stokes, and S. Woodfin

INTRODUCTION

Interest in the Good Forty and the Poor Forty (the

Forties) at the U.S. Department of Agriculture Forest

Service’s Southern Forest Experiment Station field

location at Crossett, AB, has remained high over a

long period of time. These two 40-acre tracts were established to determine if previously unmanaged, understocked, second-growth loblolly pine (Pinus tuedu)

stands could be successfully rehabilitated and

managed using uneven-aged management techniques

(Reynolds and others 1984). “Good” and “Poor” refer to

the initial stocking conditions on the tracts when research was established in the 193O’s, not the site

class, yields, or other stand characteristics. Similar

studies comparing yields and silvicultura1 performance of good and poor tracts have been established

elsewhere in the South Central United States (Farrar

and others 1989). The Arkansas Good and Poor Forty

studies have been of particular interest to managers

because the uneven-aged management regimes have

been imposed over a 50-year period. Interest in the

studies continues because of the present debate over

even-age VS. uneven-age management on both public

and private forest lands.

The two 40-acre pine tracts were established in

1937. For many years, they were harvested annually,

but since the late 1970’s partial harvests have been

done every 5 years. These harvests have resulted in

uneven-aged stands of loblolly and shortleaf (P.

echinata) pine. Most published studies of the Forties

have reported silvicultura1 results from harvesting

regimes imposed on the stands. However, no study

has yet reported harvesting production for the two

Forties. In November and December, 1990, the Forties were harvested as a part of the continuing

management plan. At that time, sawtimber volumes

were reduced to residual stocking levels as prescribed

by uneven-aged volume regulation. A field team observed and recorded production information as felling

and skidding progressed on the Forties. Time studies

were made of individual felling and skidding cycles on

each of the two tracts to determine whether merences existed between tracts and to establish baseline

production data for harvesting stands under unevenaged management. Those studies are summarized in

this report.

Because the two 40-acre tracts are only harvested

every 5 years, replication of the study was not possible. Historie records of harvesting at Crossett from

the 1950’s and 1960’s do exist, but harvesting

methods at that time cannot be compared to presentday operations. Earlier operations utilized early

model chain saws and extracted timber with small

crawler tractors in contrast to the current operation,

which used lightweight, high-production chains saws

and large, rubber-tired, grapple skidders.

Comparison of harvesting information across

studies has additional complicating factors. Production is strongly affected by the preharvest diameter

distribution of a stand and the diameter distribution

of trees removed. The current cutting regime consisted of removing a few trees across a range of diameter classes. Additionally, harvesting contractor,

equipment used, terrain, soil, and brush conditions al1

contributed to variability in harvesting rates. Because

of the varying conditions under which the studies

have been conducted, this report cannot be compared

directly with other published studies; however, the

general trends may be contrasted with other studies

where appropriate.

METHODS

Stands

The Good Forty and the Poor Forty were both

marked for harvest by the technical staff stationed at

the Crossett Experimental Forest in accordance with

stated management objectives for the two stands

R. Rluender is an associate professor of Forest Operations, Department of Forest Resources, University of Arkansas at Monticello,

Monticello, AR 71655; B. Stokes is project leader, Engineering Systems for Intensive Forest Management, U.S. Department of Agriculture, Forest Service, Southern Forest Experiment Station, Auburn, AL 36849; S. Woodfin is natural resource specialist, Tennessee Valley

Authority, Norris, TN 37828.

1
(Reynolds and th o ers 1984). Both tracts were level,

with no significant slope that would atfect the harvesting effort. Brush conditions on both tracts were

moderate to heavy, typified by honeysuckle, blackberry, and greenbrier. Soils on the tracts are moderately

drained silt loams; site indexes ran fi-om 85 to 100

(base age 50). The preharvest and postharvest quadratic mean diameters (QMD) for the stands were calculated. The QMD of the removed stand portions were

also calculated. The stand distributions were compared on a preharvest and postharvest basis using the

Kolmogorov-Smirnov (K-S) nonparametric test of distribution similarity. The distribution of removed

stand portions were also compared with a K-S test to

determine whether the harvest treatments were

similar.

Felling

The felling crew, consisting of two sawyers subcontracted by the skidding crew, was observed in both

tracts. Sawyers were instructed to fe11 al1 marked

trees within the stand boundaries. Trees were not

directionally felled to optimize skidding. “Hung” trees

occurred severa1 times in both stands. When trees

were hung, the sawyer would stop work and have a

skidder pu11 or push the tree to the ground. Trees

were processed into tree-length stems. Limbing and

topping were generally done at the stump immediately after felling.

A felling cycle was defined as the time it took the

sawyer to walk to a tree (walk), clear the brush and

determine felling direction of the tree (plan), fe11 the

tree (cut), limb and top (limb and top) the tree, and

delays. The field research team members timed each

event in the cycle and recorded the time. Delay time

(delay) and the reason for the delay were recorded.

Walk time started when the sawyer completed limbing and topping the previous tree and ended when the

sawyer reached the tree to be cut. Plan time began at

the end of walk time and ended when the sawyer

began cutting the tree. Cut time began when the saw

flrst touched the tree and ended when the tree was on

the ground or stopped falling because it became

lodged (hung) on an adjacent tree. Limb and top

time started when the tree hit the ground and ended

when the sawyer allowed the chain saw to idle and

began walking to the next tree to be felled. Total

cycle time per tree was calculated as the sum of the

events for each tree.

After a tree was limbed and topped, a researcher

measured the d.b.h. (diameter at breast height) of the

tree. Individual tree volumes were later determined

using a formula developed from a local volume table.

Production rates in cunitshr (100 cubic feet per

productive hour, excluding delays) were calculated for

each observation. Averages for walk time, plan time,

cut time, limb and top time, and delay time were computed by sawyer and by tract and for the overa11

study. An analysis of variance was used to test for differences in production rate by sawyer and tract with

tree diameter as a covariate. A structural regression

equation was developed to test the significance of each

of the relationships of the felling factors to cunits/hr.

Skidding

Two 120-horsepower, turbocharged, John Deere

648-D skidders equipped with two-cord grapples and

34-inch tires skidded the tree-length stems on both

tracts. Skidder operators prebunched enough stems

for a ful1 load before traveling loaded to the landing.

When skidders arrived at the landing, they dropped

their load and pushed the stems into a pile for later

loading. Skidders incurred delays at the deck and in

the woods. At the deck, both skidders sometimes arrived simultaneously; when this occurred, one skidder

waited for the other to fmish dropping the load and

piling the skidded stems. In-the-woods delays occasionally occurred when hung trees needed to be

pulled or pushed down or when sawyers fe11 behind in

their work and there were no stems to skid. No

mechanical delays were observed for the skidders

during the study.

At the deck, skidded trees were measured to obtain

d.b.h., top diameter, and length using tree calipers

and a logger’s tape. For each skidder cycle, travel

empty, travel loaded, bunching, positioning and ungrapple times were recorded. Skidding distances

along skid trails were measured, and colored flagging

was hung in nonharvested trees at measured distances from the deck to aid in measurement of exact skidding distance. Travel empty time began when the

skidder left the deck going back into the woods.

Bunching, positioning, intermediate travel, and grapple/deck times were recorded when the skidder was in

the vicinity of the first stem to be bunched and continued until the skidder had a ful1 grapple and started

back toward the landing with a load. Travel loaded

time began when the skidder left the bunch position

and started moving toward the landing with a load.

Travel loaded time ceased when the grapple was

opened on the deck and the load dropped. Ungrapple

time included time taken to push stems into a pile and

maneuver to return to the woods for another drag.

Production rates were calculated for each observation. Averages for travel empty, bunching, positioning, intermediate travel, travel loaded, and ungrapple

times were computed by skidder and by tract, and for

the overa11 study. An analysis of variance was used to

test for differences in production rate by skidder and

by tract, with skidded volume and total distance as

covariates. A structural regression equation was

2
developed to test the significance of each of the factors

contributing to total skidding time.

FWXJLTS

Stands

The preharvest and postharvest distributions

(number of trees by d.b.h. class) are shown in figures

1 and 2 for both stands. Number of trees by d.b.h.

class removed from each stand is displayed in figure 3.

The Poor Forty (fig. 1) showed a strongly bimodal distribution with a 13.2-inch QMD preharvest and 12.3

inch QMD postharvest. Postharvest, the Poor Forty

was not as pronounced in its bimodal pattern, but it

still did not have the strong reverse-J tendency

favored in uneven-age management. The Good Forty

displayed a slight reverse-J distribution preharvest,

with a 13.7-inch QMD preharvest and a 12.6-inch

QMD postharvest. Postharvest, the Good Forty approached the traditional reverse-J shape.

The K-S test of preharvest and postharvest stand

distributions between the two Forties showed that the

distributions were similar in the number of trees, or

basal area by d.b.h. class, preharvest and postharvest

(~~0.746). Additionally, both stands had a similar

number of trees removed, and the distributions of

removed trees by d.b.h. class were similar (p=O.483).

Thus, the stands were statistically similar both

preharvest and postharvest and in the harvesting

250

,-

l -

) -

)-

)-

POOR FORTY

POST HARVEST

‘REH ARVEST

0 10 20

D.B.H. (INCHES)

Figure 1. - Number of trees by d.b.h. for the Poor Forty prehare. vest and postharvest.

treatment. This would suggest that there should be no

signifícant differences in harvesting productivity

rates between the two stands. However, the Good

Forty had 60 more trees above a 20-inch d.b.h.

removed than the Poor Forty, whereas the Poor Forty

had 40 more trees removed below a 20-inch d.b.h.

than the Good Forty. The QMD of the 451 trees

removed from the Poor Forty was 16.4 inches,

whereas it was 17.6 inches for the 471 trees from the

Good Forty.

Felling

The analysis of variance of production rate revealed

no production rate differences between sawyers or

sawyers across tracts. However, the covariate, d.b.h.2

of processed trees, was significant in production rate.

Accordingly, al1 118 felling observations of both

sawyers and tracts were lumped together. Mean walk

time was 0.59 minute; mean plan time, 0.11 minute;

mean cut time, 0.68 minute; and, mean limb and top

time, 0.95 minute. Mean production rate for the two

sawyers was 14.41 cunits/hr (table 1).

Delays occurred infrequently; mean delay time was

3.16 minutes. Figure 4 shows that the major delay

was maintenance, wbich included saw filing and

refueling. Hung trees accounted for a small percentage of delay time (3.5 percent), whereas personal time

(operator rest breaks, water breaks, etc.) accounted

for 20 percent of the total delay time. Production

250

GOOD FORTY

0 10 2 0 30

0. B.H. (INCHES)

Figure 2. - Number of trees by d.b.h. for the Good Forty preharvest and postharvest.

3
5 10 15 20 25 30

13.e.H. (INCHES)

Figure 3. - Comparison of distribution of removed trees for the

Good and Poor Fort&.

Table l.-Summary of felling variables for the Good and Poor

Forties

Variable Poor Forty Good Forty

Average

both tracts

Walk

Plan

cut

Limb and top

Total cycle

D.b.h.

Volume per

tree

Productivity

__________________________ Minutes ______________________________

0.340 0.737 0.589

(0.278)* (0.616) (0.550)

0.164 0.072 0.106

(0.186) (0.158) (0.174)

0.437 0.667 0.581

(0.199) (0.476) (0.410)

0.920 0.974 0.954

(0.540) (0.699) (0.642)

1.862 2.438 2.223

(0.765) (1.231) (1.113)

__________________________ Znches ________________________________

15.407 14.028 14.408

(6.654) (12.474) (10.659)

________________________ Cu& feet _____________________________

42.759 45.187 44.282

(16.280) (23.252) (20.884)

---------loo cubicf&per hour----------------------

15.047 14.028 14.408

(6.654) (12.474) (10.659)

*Numbers in parenthesis are standard deviations.

4

PRODUCTION DELAY

(18.3 PERCENT)

MISCELLANEOUS

(7.4 PERCENT)

PERSONAL

(20.1 PERCENT)

HUNG TREE

(3.5 PERCENT) -ENANCE

(50.7 PERCENT)

Figure 4. - Distribution of felling delay time by cause.

(20.6 PERCENT)

(3.0 PERCENTI

(21.6 PERCENT)

LIME, 8 TOP

(35.7 PERCENT)

Figure 5. - Proportion of total time to fe11 and process a stem by

function.

delays, 18 percent, such as waiting for a skidder to

pu11 processed stems and working around other

sawyers, was the third greatest reason for delay.

The distribution of total time for felling is shown in

figure 5. Note in figure 6 that walk and plan times did

not vary markedly over the fu11 range of diameters observed. However, the proportion of cut and limb and

top times increased with diameter, with limb and top

time increasing faster than cut time. This fits the pattern found by Lanford and others (1972).

The structural regression coefficients for felling is

presented in table 3. Structural regression is used to

determine those independent variables most closely

associated with a dependent variable and is not necessarily used in prediction. Severa1 equation forms were

attempted. Signifícant variables in the determination

of productivity were the natural log of walk, plan, cut,
4.0 c

aLIMB AND TOP

-10 10 12 14 16 16 2 0 2 2 2 4

D . 6. H .

Figure 6. - Distribution of limb and top, cut, plan, and walk

time by d.b.h.

UNGRAPPLE 7

TRAVEL LOADED

(33.1 PERCENT)

TRAVEL EMPTY

(21.3 PERCENT)

PDSITION, 8:

GRAPPLE

(21.1 PERCENT)

INTERMEDIATE TRAVEL = -

(20.3 PERCENT)

Figure 7. - Proportion of total time for skid finction.

and limb, and top time and the natural log of tree

d.b.h.2. The natural log transformation was used to

ensure normal distribution of the variables rather

than the skewed pattern found. Note that al1 regression coeffrcients are signifícant at the 0.01 leve1 and

the adjusted multiple correlation coeffrcient for felling, Rs, is 0.943. The standardized coefficients indicate that diameter squared, an accepted proxy for tree

volume, was over two and a half times as influential

as the next most important variable in determining

productivity. The other important factors in order of

importance were limb and top time, cut time, walk

time, and plan time.

Skidding

(travel loaded, 33 percent and travel empty, 21 percent) consumed the most time whereas building

bunches (position and grapple, 21 percent and intermediate travel, 20 percent) took almost as much tíme.

No delay time occurred for either skidder.

Skidder one averaged more stems per cycle, 2.9 on

the Good Forty and 3.7 on the Peor Forty, than, 2.63

on the Good Forty and 3.2 on the Poor Forty. The

average number of stems skidded by both skidders

was 3.5 on the Poor Forty and 2.75 on the Good Forty

(table 2). Fewer stems were skidded per skid on the

Good Forty (p=O.O09). On the Poor Forty, three stems

were skidded most often (58 percent), with four stems

skidded 35 percent of the time and five stems skidded

6 percent of the time. On the Good Forty, one stem

was hauled 6 percent of the time, two stems skidded

31 percent of the time, three stems skidded 44 percent

of the time, and four stems skidded 19 percent of the

time. Average stem volume skidded was greater

(p=O.O14) on the Good Forty than on the Poor Forty

(56.2 VS. 40.3 cubic feet). On the Good Forty, the

average skid load had fewer stems than on the Poor

Forty, but average stem volume was greater than on

the Poor Forty.

The analysis of variance of each of the skidding

functions tested differences by tract and skidder while

controlling for the effect of volume per skidder cycle

and total travel distance. Position and grapple time

differed significantly by skidder, with skidder two

being consistently faster than skidder one. Field notes

reflect the productivity aggressiveness attitude of the

skidder operator two. Travel loaded rate differed significantly by skidder; skidder two was consistently

faster than skidder one. However, even though skidder two was consistently faster than skidder one for

position and grapple and travel loaded, total production was not significantly different for the two

machines because skidder one averaged consistently

larger loads.

Regression coefficients for skidding are given in

table 3. The adjusted multiple correlation coefficient,

Rs, is 0.643. Signifícant variables in the best-fit structural equation were a dummy variable for tract,

natural logarithm of skidded volume, and the natural

logarithm of total travel distance. Total travel distance is approximately one and two-fifths times as im-

,portant in contributing to productivity as the next

most important regressor, skidded volume. The tract

variable, acting as an intercept shifter, indicates that

significantly higher production was observed on the

Good Forty than the Poor Forty when other factors

are taken into account. Al1 regression coefficients

were significant at the 0.01 level.

Figure 7 shows the average percentage of time expended in each of the skidding functions. Travel
Table 2.-Structuml regression coeficients for felling and skidding on tke G’ood and Poor Forties

Variable Coefficient Standard Standard P

error coefficient

Felling (a&mt.ed R2 = 0.943)

constant -5.060 0.357 .OOO 0.000

Lnwalk -0.200 0.020 4.035 0.000

Lu plan -0.082 0.200 -0.127 0.003

Lncut -0.311 0.049 -0.384 0.000

Ia limb and top -0.457 0.052 -0.451 0.000

Ln d.b.h.+ 1.258 0.062 1.137 0.000

Skidding (adjusted R2 = 0.643)

comnt 1.324 0.965 0.000 0.180

Tra& 0.194 0.068 0.518 0.008

Ln skid volume 1.134 0.177 0.716 0.000

Lu travel distance -0.661 0.119 -1.061 0.000

*Felling and skidding dependent variable in In cunitsk.

TA dummy variable acting as an intercept shifker. Tract = 1 for the Cood Forty and 0 for the Poor

Forty.

DISCUSSION.

Variance of felling production was high on both

tracts; the Good Forty had a significantly higher

production rate variance, than did the Poor Forty

(standard deviation of 6.65 for the Poor Forty and

12.47 for the Good Forty, chi-square equal to 18.128,

and p = 0.000). Additionally, there was not a significant difference in felling productivity by tract

(p=O.514) due to the high variability of the felling observations and consequent inability to separate the

means in the analysis of variance and structural

regression procedures. However, felling productivity

was significantly influenced by the covariate,

diameter squared, (p=O.O05) in the analysis of variante. This variable was also the most significant in

the structural regression. This underscores the strong

dependency of production rate on tree diameter for

felling, even though other induced variance precluded

mean separation for the two tracts.

Skidding production rate was influenced by total

distance and volume per skid. The combination of

fewer stems per skid and greater stem volume on the

Good Forty increased productivity on that tract for

two reasons. First, a lower proportion of total time

was dedicated to building bunches (intermediate

travel and position and grapple) on the Good Forty

(p=O.O04), leading to lower total time per skid when

total travel distance was taken into account. Second,

the significantly greater average stem volume

(p=O.OM) on the Good Forty led to significantly higher

volumes per skid. Consequently, when total travel

distance was controlled, productivity (cunits/HR) was

influenced by the difference in average-stem volume

by tract (p=O.O56). Note that operator productivity aggressiveness, often cited as a contributing factor to

higher productivity, did not significantly influente the

outcome.

CONCLUSION

The most important result of our research on the

Crossett Good and Poor Forties is that despite the

relatively low number of observations and high variability in both felling and skidding, productivity rate

varied significantly with the diameter of stems or

average stem volume removed from each stand. The

distributional similarity of the stands before and after

harvest and the distributions of the harvested trees

would imply production similarities in harvest. This

however, was not the case. In this study, a slight

change in the distribution of the removed material

was sufficient to cause a significant difference in

productivity Figure 3 shows the distribution of trees

removed from the two stands. The Poor Forty had

more small diameter trees removed than the Good

Forty. Figure 8 shows the relationship of d.b.h.

squared on felling productivity. Note that while there

is no significant statistical difference in the productivity rates on the two tracts, there is a recognizable

difference in the productivity rates experienced on

them. It is our opinion that more observations (with a

corresponding increase in the degrees of freedom) are

necessary to explain the differences between the two

tracts, and the difference would become statistically

significant.

6
Table 3.-Summary of skidding variable for Good and Poor Forties

Variable Poor Forty Good Forty

Average

both tracts

Travel empty

Position and

grwple

Intermediate

travel

Travel loaded

Ungrapple

Total cycle

Average total

distance

Volume per

skid

Stems per skid

Average stem

volume

Productivity

----------________________ Minutes ____________________________

1.262 2.148 1.694

(0x4)* (0.832) (0.811)

1.227 2.138 1.669

(0.643) (1.881) (1.442)

1.716 1.506 1.614

(0.961) (1.501) (1.237)

2.265 3.007 2.625

(1.185) (0.931) (1.118)

0.618 0.038 0.337

(0.773) (0.069) (0.622)

7.092 8.837 7.938

(2.317) (2.485) (2.522)

_____________________________ Feet _______________________________

830 2100 1446

(291) (703) (831)

_____________________ 100 cubic feet __________________________

1.35 1.41 1.38

(0.27) (0.39) (0.33)

__________________________ Number ____________________________

2.75 3.47 3.12

(0.87) (0.62) (0.82)

_______________________ Cubic feet ____________________________

40.26 56.20 47.99

(11.56) (22.27) (19.10)

_______________ 100 cubic feet per hour ___________________

12.45 10.35 ll.43

(4.28) (4.27) (4.32)

*Numbersin parenthesis are standard deviations.

For skidding, production rates (after correction for

skidding distance and skidded volume) were higher

for the Good Forty than for the Poor Forty (fig. 9). At

total travel distances of less than 750 feet, distance

was more significar-k in the determination of skidding

productivity than was average tree size. However, at

total travel distances beyond 750 feet, the larger

average tree size observed on the- Good Forty

produced higher production rates.

Recall that although there was no significant difference in the removed portions of the two stands,

there was a 1.2 inch difference in the QMXkThus, for

felling, we conclude that for the conditions of this

0

0 GOOD FORTY

l POOR FORTY

Qo .

I 0e

.

. .

. I

m- .

0

100 200 300 400 500 600

TREE SIZE (D.B.H.2)

Figure 8. - Relationship of felling production to tree size for the

Good Forty and the Poor Forty, with the best-fit curve

for each tract.

I I I I I I

1,000 2,000 3,000

TOTAL TRAVEL DISTANCE (FEET)

Figure 9. -&lationship of skidding production to total travel

distance for the Good Forty and the Poor Forty, with

the best-fit curve for each tract.
study the d.b.h. distribution of trees removed in a

selection harvest is the single most important factor

influencing productivity rate. Similarly, for skidding,

the distribution of trees removed in a selection harvest is the third most important factor in influencing

harvesting productivity rate, ranking only behind

skidding distance and skidded volume. A removal distribution weighted to larger d.b.h. trees will produce

higher productivity rates than a distribution weighted

to lower d.b.h. classes. Size distribution of the

removed stems and skidded volume are both included,

instead of average stem volume alone, because a skidder may not always travel with a ful1 grapple load.

This research suggests that the difference in the removal diameter distributions between two stands

caused a signifícant difference in productivity rates.

This further implies that harvesting cost differences

will occur for relatively minor changes in removal distributions.

LITERM’URE CITED

Farrar, R.M.; Straka, T.J.; Burkhardt, C.E. 1989. A

quarter-century of selection management on the

Mississippi State Farm Forestry Forties. MAFES

Bull. No. 164. Mississippi State, MS: Mississippi

Agricultura1 and Forestry Experiment Station. 24

P*

Lanford, B.L.; Cunia, T.; Haver, G.F. 1972. Handbook

of production tables. APA Harvesting Research

Project. Washington, DC: Ameritan Pulpwood Association. 523 p.

Reynolds, R.R.; Baker, J.B.; Ku, T.T. 1984. Four

decades of selection management on the Crossett

Farm Forestry Forties. Bulletin No. 872. Fayetteville, AR: Agricultura1 Experiment Station, Division of Agriculture, University of Arkansas. 43 p.
Kluender, R.; Stokes, B.; Woodf’m, S. 1992. Harvesting production in

uneven-aged loblolly-shortleaf pine stands: the Crossett Farm

Forestry Forties. Res. Pap. SO-269, New Orleans, LA: U.S. Department of Agriculture, Forest Service, Southern Forest Experiment

Station. 8 p.

Two stands managed using uneven-aged techniques were harvested as par-t of a 5-year entry schedule. Felling and skidding

productivity varied significantly with average stem diameter and

volume and was affected by diameter distribution of the removed

material.

Keywords: Felling, skidding.

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