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AN ECONOMIC COMPARISON OF
BVEN- AND UNEVEN-AGED MANAGEMENT
IN LAKE STATES NORTHERN HRRDWOODS

3!
Jeffrey Neel Nieee

A DISSERTATION
Submitted to
Hichigan state University
in partial fulfillment of the requirements
for the degree of

DOCTOR OF PHILOSOPHY

Department of Forestry
1993

Dr. Karen Potter—Witter, Major Professor

ABSTRACT
AN ICONONIC COMPARISON OF

IVRN- AND UNEVEN-AGED MANAGEMENT
IN LAKE STATES NORTHERN HARDWOODS

BY
Jeffrey Neal liese

Current economic information is inadequate to compare the
financial productivity of even- and uneven-aged management in Lake
States northern hardwood forests. Forest managers need more detailed
economic information on northern hardwood cutting methods from long-term
replicated studies. This study summarises the results of a 40-year
management study in Wisconsin and gives economic returns for 9 even- and
uneven-aged northern hardwood cutting methods on good northern hardwood
sites. It also considers the effects of varying cutting method on the
diversity of northern hardwood tree species and future tree quality.

Under most economic conditions tested, the medium to heavy basal
area cuts produced the highest total economic returns; a heavy diameter-
limit treatment had the highest returns when discount rates were high
and price expectations were low. Even-aged management methods yielded
the highest regeneration diversity, but economic returns were low to
moderate. The heavy selection treatment had the highest net present
values of total lumber yields and harvests. However, the medium
selection treatment gave the best combination of total economic return,
improved tree quality, and regeneration diversity.

Forest managers have a variety of treatment options that can yield
good economic returns. However, this study confirms that, in the long-
term, medium or heavy basal area treatments will provide superior

economic returns in northern hardwood management.

DEDICATION

This dissertation is dedicated to my wife, Rebecca Neal Niece, who
provided me with the encouragement and support to complete this work.
Her patience and love kept me going through the most difficult momenta,
and helped me keep my perspective on life throughout the last four
years. I also dedicate this work to my children--Andrew, Timothy, and
Emily Joy--who have patiently accepted the many hours when Dad couldn‘t
always be with them, even though he wanted to be. Finally, I dedicate
this thesis to my own mother and father, Joan and Ben, who taught me to

dream big dreams and to pursue excellence in all things.

iii

ACKNOWLEDGMENTS

Many people have helped make the completion of this project a
reality. Dr. Karen Potter-Witter, my major professor, gave me expert
guidance on writing, editing and economic methodology. She was always
generous with her time and resources, and her empathetic ear and
expertise helped me negotiate many academic and personal hurdles. I
also would like to thank my other committee members, Dr. Robert Manthy
and Dr. Alan Schmid, for their helpful comments and encouragement.

I am particularly indebted to the 0.8. Forest Service, North
Central Forest Experiment Station for providing the funding to support
my research. The Forest Service's contribution, though, extends beyond
funding to include many people. Special thanks go to Dr. Nike
Vasievich, Economics Project Leader at East Lansing, who gave liberally
of his time and resources, providing the perfect mixture of admonition
and affirmation. Terry Strong served as both research partner and
friend, providing data, helpful comments, and many ”reality checks" to
make sure this work applied to the real world of forest management.
Thanks also to Ed Hansen for his cooperation throughout this project.

Dr. David Baumgartner deserves special tribute: he was always
willing to listen, guide, and give helpful yet gentle criticism. Sharon
Hobrla and Bill Main helped with statistical questions and, along with
Nike, were instrumental in introducing me to the world of computers and
their use in forestry research. Particular thanks are due Sharon for
untangling the difficult quality data set and making it ready for
computer analysis. Donna Paananen assisted with editing and formatting,
and provided a liberal arts perspective in the midst of a technical

analysis.

iv

Carol Hyldahl deserves special praise for her insightful comments
on the economic analysis, and the shared struggle as a graduate student.
Noel Bennett provided expert secretarial support, a listening ear, and
dry wit to help weather the storms. She was especially helpful and
patient in finalizing the manuscript. Thanks are due Bobbe McDaniel and
Michelle Laing for data entry, word processing, and organizing of early
drafts. Jaci Hamberg cheerfully helped in the final process of putting
the thesis together.

I am grateful to Carl Reidel, Gordon Guyer, Larry Tombaugh, and
Larry Leefers for their strong encouragement to pursue this degree, and
faith in my ability to complete it. A final thanks goes to the many
foresters and economists who have shared thoughts on this topic. Their
helpful comments have sharpened my thinking and improved this

manuscript.

TABLE OF CONTENTS

L1.t Of Twl.’ O O O O O O O O O O O O O O O O O O O O O O O

L1.t Of ’19“:.. O C O O O O O O O O O O O O O O O O O O O 0

Chapter 1:

Chapter 2:

Chapter 3:

The Problem . . . . . . . . . . . . . . . . . .
Literature Review . . . . . . . . . . . . . . .
History . . . . . . . . . . . . . . . . . . . .
Uneven-aged Management Methods . . . . . . . .
Even-aged Management Methods . . . . . . . . .

Economics of Cutting Methods . . . . . . . . .

Quality Considerations in Economic Management of

Northern Hardwoods . . . . . . . . . . . .
R‘..arCh “ethOd. O O O O O O O O O O O O O O O 0

Long-term Silvicultural Trials-~Argonne
Experimental Forest . . . . . . . . . . .

Data . . . . . . . . . . . . . . . . . . . . .
Description of Silvicultural Cutting Treatments
Plot Measurements . . . . . . . . . . . . . . .
Modelling Growth and Yield . . . . . . . . . .
Economic Evaluation . . . . . . . . . . . . . .
Hardwood Management Costs . . . . . . . . . . .
Analysis of Hardwood Price Data . . . . . . . .
Quality Change and Economic Return . . . . . .

Application of Hanks' (1976) Tree Grade Lumber
Yield Equations . . . . . . . . . . . . .

Valuation of Lumber Yields . . . . . . . . . .
Overall Economic Evaluation . . . . . . . . . .

Summary . . . . . . . . . . . . . . . . . . . .

vi

Chapter 4:

Chapter 5:

Hardwood Price Expectations . . . . . . . . .
Introduction . . . . . . . . . . . . . . . .
Historical Price Trends . . . . . . . . . . .

Hardwood Markets and Prices--National Demand
and Price Trends . . . . . . . . . . . .

Export Markets . . . . . . . . . . . . .
Hood Household Furniture . . . . . . . .
Hardwood Price Analysis . . . . . . . . . . .
Data . . . . . . . . . . . . . . . . . .
Methods To Estimate Stumpage Value . . .
Derivation of Estimated Price Trends . .
Model Justification . . . . . . . . . .
Results and Discussion, by Species . . . . .
Summary . . . . . . . . . . . . . . . . . . .
Northern Hardwood Management Costs . . . . .
Introduction . . . . . . . . . . . . . . . .
Literature Review . . . . . . . . . . . . . .
Characteristics of Hardwood Management Costs
Methods . . . . . . . . . . . . . . . . . . .
Cost Assumptions . . . . . . . . . . . . . .
Results . . . . . . . . . . . . . . . . . . .

Timber Stand Improvement . . . . . . . .

Post-harvest Removal of Non-commercial Trees

Timber Cruise and Marking . . . . . . .
Thinning Costs . . . . . . . . . . . . .

Clearcut Layout . . . . . . . . . . . .

Cost Variation Between Even- and Uneven-aged Methods.

Application of Cost Estimates to Argonne
Cutting Methods . . . . . . . . . . . .

Application of Thinning and Marking Costs
Application of Clearcut Layout Costs . .

Application of Road Maintenance Costs .

vii

43
43

44

46
47
48
50
SO
52
53
54
S7
72
73
73
73
74
76
76
77
77
77
79
80
81
82

82
82
83
83

Chapter 6:

Chapter 7:

Chapter 8:

Sensitivity Analysis: Selection Marking,
Sale Layout, and T81 Costs . . . . . . . . . . .

Opportunity Costs . . . . . . . . . . . . . . . . . .
Social Costs of Timber Management . . . . . . . . . .
Summary . . . . . . . . . . . . . . . . . . . . . . .
Growth and Yield Predictions . . . . . . . . . . . .
Introduction . . . . . . . . . . . . . . . . . . . .
Methods . . . . . . . . . . . . . . . . . . . . . . .
Characteristics of THIGS . . . . . . . . . . . . . .
Validation of Diameter-growth Predictions . . . . . .
Possible Reasons for Underpredictions . . . . . . . .
Diameter-growth Adjustment Methodology . . . . . . .
Stand Growth Parameters for Running TWIGS . . . . . .
Results After Forty Years of Management . . . . . .
Results of Management Including TWIGS Projections . .
Summary . . . . . . . . . . . . . . . . . . . . . . .

Analysis of Trade-offs Between Tree Species Diversity
and Economic Returns . . . . . . . . . . . . . .

Intranet ion 0 O O O O O O O O O O O O O O O O O O 0
Diversity Measurement . . . . . . . . . . . . . . . .
Results of Management Effects on Stand Diversity . .

Results of Management Effects on Regeneration
Div.r.ity O O O O O O O O O O O O O O O O O O 0

Economic Analysis Methods . . . . . . . . . . . . . .
Economic Results . . . . . . . . . . . . . . . . . .
Discussion . . . . . . . . . . . . . . . . . . . . .
Uneven-aged Management . . . . . . . . . . . . .
Even-aged Management . . . . . . . . . . . . . .
Summary . . . . . . . . . . . . . . . . . . . . . . .

Hardwood Sawlog Quality: Economic Evaluation of
Cutting Method Impacts . . . . . . . . . . . . .

Literature Review . . . . . . . . . . . . . . . . . .

D‘t‘ O O O O O O O O O O O 0 O O O O O O O O O O O 0

viii

85
85
87
88
89
89
90
90
92
93
9‘
98
100
103
104

105
105
106
107

108
113
115
116
116
118
119

121
121
125

Chapter 9:

‘conmic mthOd. O O O O O O O O O O O O O O O O

Silvicultural Results: Changes in Tree Quality
and Grade . . . . . . . . . . . . . . . . .

Results: Regression Analysis of Lumber Quality
Ch.nq.. O O O O O O O O O O O O O O O O O O

Economic Results . . . . . . . . . . . . . . . .
Harvested Lumber Volumes and Cash Flows . .
R‘.1dua1 v.1ue' O O O O O O O O O O O O O O

Marginal Benefit/Cost Analysis of Treatment
Lumber Values . . . . . . . . . . . . .

Discussion . . . . . . . . . . . . . . . . . . .
Changes in Tree Quality . . . . . . . . . .
Economic Returns to Tree Quality Changes . .

Economic Analysis, Results, and Discussion . . .

Results of Analysis for 1952-1992 . . . . . . . .
Base Case . . . . . . . . . . . . . . . . .
Sensitivity to Discount Rate . . . . . . . .
Sensitivity to Changes in Marking Costs . .

Results of Analysis for 1952-2022 with TWIGS
Growth Projection . . . . . . . . . . . . .

3.“. ca" O O O O O O O O O O O O O O O O O
Sensitivity to Discount Rate . . . . . . . .
Sensitivity to Price Changes . . . . . . . .

Sensitivity to Both Price Variation and
Different Discount Rates . . . . . . .

Sensitivity to Road Maintenance Costs . . .

Sensitivity to Individual Tree Marking and
Sale Layout Costs . . . . . . . . . . .

Discussion . . . . . . . . . . . . . . . . . . .
Discussion: 1952-1992 Analysis . . . . . . .
Discussion: Extended (1952-2022) Analysis .

Discussion: Discount Rate Changes,
Extended Analysis . . . . . . . . . . .

sumry O O O O O O O O O O O O O O O O O O O O O

ix

126

128

132
135
135
136

137
137
140
140
141
142
142
142
142
144

145
145
145
146

148
149

150
150
151
153

155

157

Chapter 10: Conclusion and Recommendations . . . . . . . . . . . 159
Future Research Needs . . . . . . . . . . . . . . . . 163

Appendix A: Summaries of TWIGS Growth Projections,
by Treatment and Replication . . . . . . . . . . . . 165

Appendix B: Regression Equations Used to Calculate
Lumber Yields from Graded Hardwood Logs . . . . . . . 192

Bibliogr.Phy O O O O O O O O O O O O O O O O O O O O O O O O O O O 196

Table

Table

Table

Table

Table

Table

Table

Table

Table

Table

Table

Table

Table

Table

Table

3-1e

‘-1e

5-1.
5-2 e

5-3 e

6.10

6-2 s

6-3 e

6.4 e

6-Se

7-2e

LIST OF TABLES

Area of northern hardwood forest types
in the Eastern United States in millions
at .cr.’ O O O O O O O O O O O O O O O O O O O O

Description and stand basal area of
treatments in 1951 and in 1990 . . . . . . . . .

Regression equations used to estimate
Michigan Upper Peninsula sawtimber stumpage
pric. tr.nd. O O O O O O O O O O O O O O O O O O

Regression equations used to estimate
Wisconsin sawtimber stumpage price trends. . . .

Stumpage price scenarios used in analysis,
expressed in average real price increases
(decreases) per year . . . . . . . . . . . . . .

Summary of cost survey results: variable costs .

Average nominal road maintenance costs used
in th. ‘n.ly.i. O O O O O O O O O O O O O O O O

Data used to test sensitivity of treatment
results to changes in marking, T81 and sale
layout c°.ta O O O O O O O O O O O O O O O O O

Comparison of growth predictions for three
species before adjustment . . . . . . . . . . .

Basswood and white ash coefficient adjustments
by dObOhO c1... O O O O O O O O O O O O O O O O

Comparison of THIGS diameter adjustment
coefficients before and after final adjustments

Sawtimber yields by treatment and year,
in Scribner board feet/acre . . . . . . . . . .

Pulpwood yields by treatment and year,
in cords per acre . . . . . . . . . . . . . . .

Species composition by percentage of total
in each of three replicates prior to initial
M"..t. in 1951 O O O O O O O O O O O O O O O O

Shannon's diversity values and changes for

trees 4.6“ d.b.h. and larger in 1951
(before and after cut) and in 1990 . . . . . . .

xi

Page

O 2

32

55

56

S7

78

85

86

93

96

97

101

102

110

111

Table

Table

Table

Table

Table
Table

Table

Table

Table

Table

Table

Table

Table

Table

Table

Table

Table

Table

7.3 e

7-4e

8-1e

8-2.

8-3 .
8“ e

8-7e

8-8.
8.9 e

9-1e

9.2 e

9.3 e

9‘4e

9-5e

9.6 e

9.7 e

Shannon's diversity values of saplings

2.0- to 4.5- d.b.h. by

treatment in 1957, and

in 1990 for all trees and commercial trees . . . . . .

Sapling numbers in trees/acre and frequency

in percent species and

Lumber prices by grade
lumber yields . . . .

Average tree grades by

treatments in 1990 . . . . . .

used to evaluate

treatment in 1971 and

1991, and grade increase from 1971 to 1991 . . . . . .

Percent of trees by grade, 1991 . . . . . . . . . . .

Regression statistics for plot analysis of

variation in #1 common

and better lumber . . . . . . .

Analysis of variance for #1 common and

better lumber . . . .

Increase in #1 common and better lumber,
by plot and treatment (1951-92) . . . . . . . . . . .

Net harvested lumber volumes and total

cash flows, 1971-92 .

Residual values of lumber yields, 1992 . . . . . . . .

Comparison of net present value of the differences
between the control and cutting treatments for
tot‘l 1m: v.1“. yi.ld O O O O O O O O O O O O O O O

Net present values per

acre (1990 S) of

differences between treatments and control

for 1952-1992 analysis

Net present values per
between treatments and
using a 4 percent real

Net present values per
between treatments and
using a 2 percent real

Net present values per
between treatments and
using a 6 percent real

acre (1990 S) of differences
control for 1952-2022 analysis,
discount rate . . . . . . . . .

acre (1990 S) of differences
control for 1952-2022,
discount rate . . . . . . . . .

acre (1990 5) of differences
control for 1952-2022 analysis,
discount rate . . . . . . . . .

Ranking of treatment net present values using a
4 percent real discount rate (1952-2022) . . . . . . .

Ranking of treatment net present values using a
6 percent real discount rate (1952-2022) . . . . . . .

Ranking of treatment net present values using a
2 percent real discount rate {1952-2022) . . . . . . .

xii

112

114

129

130

131

133

134

135

136

136

137

143

145

147

147

155

156

157

Figure

Figure

Figure

Figure

Figure

Figure

Figure

Figure

Figure

Figure

Figure

Figure

Figure

Figure

Figure

Figure

2-1.

3.3 e

3-4e

4-1e

4-2.

4-3 e

4-4 e

4-6.

‘-7e

4-8 e

4-9 e

‘-10e

4-11e

LIST OF FIGURES

Comparison between Arbogast and other
Lake States cutting guides for recommended
reserve growing stock . . . . . . . . . . . . . .

Location of Argonne Experimental Forest
within the Nicolet National Forest, Wisconsin . .

Location of long-term silvicultural trials in
northern and Appalachian hardwood timber types
in the Eastern United States . . . . . . . . . .

Location of the cutting methods study within
the Iron River-Wabeno (IWa and IWb) soil
mapping units on the Argonne Experimental Forest

Sampling design for replication 1 (40 acres),
Argonne cutting methods study (reproduced from an
original drawing) . . . . . . . . . . . . . . . .

Real Michigan Upper Peninsula sugar maple
sawtimber stumpage prices, 1954-91 . . . . . . .

Real Wisconsin sugar maple sawtimber
stumpage prices, 1950-90 . . . . . . . . . . . .

Real Michigan Upper Peninsula basswood
sawtimber stumpage prices, 1954-91 . . . . . . .

Real Wisconsin basswood sawtimber stumpage
DIICOO, 1950-90 e e e e e e e e e e e e e e e e e

Real Michigan Upper Peninsula white ash
sawtimber stumpage prices, 1978-91 . . . . . . .

Real Wisconsin white ash sawtimber stumpage
priCOO ' 1950-90 e e e e e e e e e e e e e e e e 0

Real Michigan Upper Peninsula yellow birch
sawtimber stumpage prices, 1954-91 . . . . . . .

Real Wisconsin yellow birch sawtimber
.tmp‘g. ptiCOO ' 1950-90 e e e e e e e e e e e e

Real Michigan Upper Peninsula red maple
sawtimber stumpage prices, 1954-91 . . . . . . .

Real Wisconsin red maple sawtimber stumpage
pric.‘ ' 1950-90 O O O O O O O O O O O O O O O O O

Real Wisconsin elm sawtimber stumpage
ptiCOO, 1950-90 s e e e e e e e e e e e e e e e e

xiii

Page

12

26

28

29

34

58

58

60

60

62

62

64

64

66

66

68

Figure

Figure

Figure

Figure

Figure

Figure

Figure

Figure

Figure

4-12 e

4-13e

4-14.

4-15.

‘-16e

7-1e

7-2 s

8-1e

9-1e

Real Wisconsin hemlock sawtimber stumpage
FIICOO, 1950-90 e e e e e e e e e e e e e e e e e e e 68

Real Michigan Upper Peninsula mixed hardwood
sawtimber stumpage prices, 1972-91 . . . . . . . . . 70

Real Wisconsin mixed hardwood sawtimber
stumpage prices, 1972-90 . . . . . . . . . . . . . . 70

Real Michigan Upper Peninsula hardwood
pulpwood stumpage prices, 1954-91 . . . . . . . . . . 71

Real Wisconsin mixed hardwood pulpwood
stumpage prices, 1972-1990 . . . . . . . . . . . . . 71

Comparison of 1990 overstory basal area
and Shannon's index of saplings 2.0 to 4.5”
d.b.h. for uneven-aged treatments . . . . . . . . . 109

Comparison of net present value and Shannon's

diversity index of saplings 2.0 to 4.5” d.b.h.

NPV's are based on the differences between the

control and cutting treatments . . . . . . . . . . 117

Comparison of harvested, residual, and total lumber
values for four tree quality study treatments.

Values are for differences between treatments

and control . . . . . . . . . . . . . . . . . . . . 138

Comparative economic returns, 40-year and 70-year

analyses. NPV's reflect differences between control
and cutting treatments with medium prices . . . . . 154

xiv

CHAPTER 18 THE PROBLEM

Northern hardwood forests cover about 40 million acres in the
Eastern United States. These forests stretch from Minnesota, across
southern Canada and New England as far as central Maine, and south to
West Virginia and southern Indiana. These forest types are among the
most valuable and diverse hardwood forests in North America. Sugar
maple (Acer saccharum Marsh.) and red maple (Acer rubrum L.) dominate
most northern hardwood forests. Other primary species include basswood
(Tilia americana L.), American beech (Fagus grandifolia Ehrh.), yellow
birch (Betula alleghaniensis Britten.), and white ash (Fraxinus
americana L.). Most commercial northern hardwood stands are in five
States: Michigan, New York, Wisconsin, Maine, and Pennsylvania
(Table 1-1).

In the Lake States (Minnesota, Wisconsin, and Michigan) northern
hardwoods cover over 11 million acres. They represent 25 percent of the
commercial forestland in these states, yet they also provide wildlife
habitat and a vacation setting for millions of visitors each year.
Because these hardwood forests respond to a variety of silvicultural
techniques, forest managers have many options in managing them for a
variety of resource values (Erdmann 1986). Both even- and uneven-aged
management systems are used to manage northern hardwood forests.
However, there is considerable debate within the forestry community
concerning the most reliable and profitable cutting and regeneration
methods for the various northern hardwood types.

Although commonly called "methods," even- and uneven-aged
management are actually management goals that forest managers work
toward achieving by applying specific cutting methods (Godman 1985). In
even-aged management the forest has a definite beginning and ending

point. Trees are harvested and regenerated at nearly the same time.

2

TABLE 1-1. Area of northern hardwood forest types in the Eastern United
States in millions of acres.’

 

 

 

 

 

 

 

 

 

 

 

 

 

 

, _____I ,
I State Herthern Sugar Maple I
Hardwood Types Types
lNew York (1982) 9.3 4.4
I Michigan (1983) 6.2 5.0
I Maine (1984) 5.0 3.1
I Pennsylvania (1982) 4.3 3.5
Wisconsin (1985) 3.5 3.0
West Virginia (1978) 3.0 2.7
Vermont (1985) 2.7 2.0
New Hampshire (1985) 2.0 1.1
Minnesota (1990) 1.6 1.4
Ohio (1981) 1.5 0.6
Indiana (1988) 1.0 0.7
‘ Total 40.0 ll 27.4

 

 

 

 

 

TOTALS: 40 million acres; about two-thirds in sugar maple types.

' SOURCE: USDA Forest Service, Forest Survey reports for each of the
States listed above. Dates of publication are listed in parentheses.

' ”Sugar maple types“ represent an aggregate of acreage of the following
forest types recognized by the Society of American Foresters: 1) SAF
Type 25 - sugar maple/beech/yellow birch: 2) SAF Type 26 - sugar maple/
basswood; 3) SAF Type 27 - sugar maple. In a few States, all sugar
maple types are lumped into SAF Type 25 above.

NOTE: Tubbs (1977b) said that northern hardwoods, broadly defined,
cover as much as 100 million acres in the eastern United States and
Canada. The above figures came from Forest Survey data and included the
major commercial area for northern hardwoods.

Sturos and Thompson (1986) estimated that northern hardwoods, more
narrowly defined, occupied 32 million acres in the eastern United
States. Their definition probably incorporates only stands where sugar

maple is a prominent species. Northern hardwood stands where red maple,

beech, cherry, hemlock, or red oak are dominant are not necessarily
ncluded.

3

The type of cutting method used at the end of the forest‘s rotation (for
example, clearcutting or shelterwood cutting) defines the silvicultural
method used to achieve even-aged management. The choice of even-aged
management involves managing the entire forest as a single crop. The
usual emphasis in even-aged northern hardwood management is on fiber or
pulp production with sawlogs as a by-product.

An uneven-aged forest, unlike an even-aged forest, cannot be
treated like an agricultural crop to be planted and harvested all at
once. Rather, the uneven-aged forest has no identifiable beginning or
and (Murphy and Guldin 1987). Uneven-aged management of northern
hardwoods involves removing a few of the larger trees at each thinning,
with thinnings occurring on a cutting cycle of 10-20 years. The goal is
to create a variety of sizes of trees, called stand structure.
Eventually, most growth can be harvested as sawtimber, yielding fewer
but larger and more valuable trees. Selective cutting is the method
used to achieve this management goal.

Economically, the choice of uneven-aged management involves
managing the forest for those individual trees that can attain the
highest value in the shortest possible time. The emphasis in uneven-
aged management, then, is on sawlog production, with fiber as a by-
product from improvement cuttings.

Most previous studies of northern hardwood management strategies
have made recommendations based primarily on silvicultural grounds.
However, there is now a critical lack of economic information that
compares the profitability of even- and uneven-aged forest management
strategies in Lake States northern hardwood stands. Today's forest
managers and planners need economic information that considers financial
returns from silvicultural methods practiced on a variety of northern
hardwood site and stand conditions. In short, economic management

guides for northern hardwoods need improvement.

4

Several problems must be overcome to achieve higher economic
returns in northern hardwood forests. First, the overall quality of
northern hardwoods must be improved. Improvements in quality are needed
because most stands originated following large-scale commercial
clearcutting, and many have suffered from periodic high-grading, leaving
forests with many poor quality trees. Second, species composition of
northern hardwood forests should be modified to bring higher economic
returns. Shifts in species composition hold the promise of improving
economic performance, reducing insect and disease risks, and creating
more diverse forest habitats for non-timber uses. Third, new markets
and improved utilization for the maples are required, because they will
remain the dominant northern hardwood species in the Lake States. Both
red and sugar maple are increasing significantly in area and volume
(Spencer et a1. 1988, Spencer and Hahn 1984). Finally, there is a need
to identify those northern hardwood forests and types that are most
productive and concentrate investments on these sites. Underlying the
realisation of all these goals is the need for improved knowledge of the
economic response of northern hardwoods to the various cutting methods
used in their management.

Some current issues underscore the need for improved decision-
making on even- and uneven-aged management of northern hardwoods. Many
special interest groups now Oppose clearcutting. Their negative stance
has pushed agencies such as the USDA Forest Service and State natural
resources departments to look more closely at the potential for wider
application of uneven-aged management of their hardwood forests. This
is particularly true within and near environmentally sensitive areas
such as river corridors, natural areas, and recreation areas. Recent
concern about the negative effects of clearcutting has caused the USDA
Forest Service to change its policy on the use of clearcutting on the

National Forests. Though acknowledged as a proven forest management

5

tool, clearcutting will no longer be the standard method of harvesting
national forest timberh

Some private landowners, particularly large individual and
corporate owners, remain interested in conditions where even-aged
management is feasible in their forests. These private owners have a
renewed interest in even-aged management because of the potential it has
for increasing species diversity, wildlife habitat, and profits. No
matter the type of ownership, all these clients need better information
on the economic trade-offs involved in managing their northern hardwoods
with either even- or uneven-aged methods.

Although there is a body of theoretical knowledge about the
economics of even- and uneven-aged management in hardwood forests, most
of these studies have been based on limited data sets or hypothetical
site conditions. A few studies have compared the economics of cutting
methods in northern hardwoods. But generally they suffer from short-
term data bases or a lack of replication.

At this writing no comparative analysis, based on long-term field
data from a replicated study, has been completed on the economics of
even- and uneven-aged management in northern hardwoods. By answering
forest managers' questions concerning the economic productivity of even-
and uneven-aged management in northern hardwood forests, this study will
help fill this research gap.

This inquiry encompasses three major areas of concern to forest
managers: 1) an analysis of economic returns from nine northern
hardwood cutting methods: 2) an analysis of the contribution of tree
quality improvements to the economic performance of specific cutting

methods; and, 3) an analysis of the trade-offs between achieving

 

'Robertson, F.D. “Ecosystem Management of The National Forests and
Grasslands." Letter from Dale Robertson, Chief, USDA Forest Service, to
Regional Foresters and Station Directors. Dated June 4, 1992. 3 p. plus
enclosures.

6

economic returns and improvements in tree species diversity in northern

hardwood management.

CHAPTER 2: LITERATURE REVIEW

Sugar maple usually comprises a plurality of the stocking in
northern hardwood forest types: often it makes up 70-80 percent or more
of the stems or basal area. Researchers and managers are concerned that
the dominance of sugar maple is increasing in these forests (Stearns
1986), both for financial reasons and for potential losses to forest
health and ecosystem diversity.

The Society of American Foresters recognizes six major cover types
where sugar maple is abundant. They are: SAF Type 25--Sugar
Maple/Beech/Yellow Birch; SAF Type 26--Sugar Maple/Basswood; SAF Type
27--Sugar Maple: SAF Type 28--Black Cherry/Sugar Maple; SAF Type 31--Red
Spruce/Sugar Maple/Beech: and SAF Type 60--Beech/Sugar Maple
(Eyre 1980). A recently established type (Eyre 1980), SAF Type 108--Red
Maple, recognises the growing dominance of red maple in disturbed and
wet-site transitional northern hardwood stands.

American beech is a common associate in eastern Lake States and in
Northeastern hardwood types. Basswood is a common associate throughout
the Lake States, while American elm (Ulmus americana L.) occurs on the
more mesic northern hardwood sites. However, usually the scarcer and
less tolerant northern hardwood species command higher economic returns
and have greater wildlife benefits in northern hardwood forests (Marquis
1967, Della-Bianca and Beck 1985). These species include yellow birch,
white ash, basswood, and northern red oak (Quercus rubra L.). In the
southern areas of northern hardwood types, other high-value species such
as black cherry (Prunes serotina Ehrh.) and black walnut (Juglans nigra
L.) may be present in diverse stands. Intolerant pioneer species that
occur in the early stages of northern hardwood stand development may
include: quaking aspen (Pcpulus tremuloides Michx.), bigtooth aspen
(Populus grandidentata Michx.), paper birch (Betula papyrifera Marsh.),

and pin cherry (Prunes pensylvanica L.f.).

History

Northern hardwood forests have regenerated throughout the North
following large-scale cutting that occurred in the mid-to-late
nineteenth century, and early in the twentieth century. Many of these
forests have been high-graded repeatedly, particularly in the Northeast,
and more recently in parts of the Lake States. Frequently these forests
have regenerated on sites that were formerly occupied by pine and other
softwoods. Poor quality trees often dominate these second-growth
forests as a result of multiple entries and logging practices that took
only the best trees (i.e., high-grading). New markets for lower grade
trees hold the promise of fostering regeneration on these hardwood
forests to increase their value to society (Webster 1986). However,
there has been a long-running debate within the forestry community
concerning the most reliable and profitable regeneration methods for the
various northern hardwood types.

Most early cutting of northern hardwoods, from Maine to Minnesota,
was 'logger's choice." Commercial clearcutting of northern hardwoods
began anywhere from 10-50 years after the big old-growth white pine were
removed from mixed stands. Application of commercial clearcutting meant
that the best trees were cut first, followed by cutting down to a
diameter-limit that could range from 5-10 inches, depending on local
markets. In a few areas such as western Pennsylvania, there were
chemical-wood (charcoal) markets, so these northern hardwood stands
received the equivalent of a complete clearcutting, resulting in stands
dominated by high-value, intolerant trees such as black cherry.

By the time the hardwood industry reached the northern Lake States
in the early 1920's, it was clear that old-growth hardwood resources
were running out. Many managers in the industry began to look at
“selective cutting" as a way to "stretch out“ a dwindling old-growth
northern hardwood resource (Eyre and Zillgitt 1953). Selective logging

not only prolonged the lumber industry's exploitation of old-growth

9

northern hardwood forests, but it also provided a method to harvest only
the largest and best trees that were the only marketable ones in the
region's depressed lumber markets. Truck logging, which had begun to
replace railroad logging in the early 1920's, made selective logging
technically feasible in old-growth stands. Early research by the USDA
Forest Service on the economics of selective logging (Son and Garver
1930, Buell 1937) gave credence to its practice. Unfortunately, the
rise of selective logging (partial cutting) created confusion with the
selection system of silviculture. This confusion persists in the minds
of the public and some in the forestry community to this day (Nelson et
a1. 1975). Selective cutting refers to a harvest operation that takes
only a portion of the sawtimber-sized trees in a stand; this selective
cutting often involves "high-grading," or removal of the best trees and
leaving of the worst. By contrast, the selection system of silviculture
involves all the management steps necessary in scientific uneven-aged
management, of which a selection cut every 10-20 years is only one step.
As forest managers in the Northeast observed the differences in
stand quality between the 'logger's choice“ cuts and the less common
complete clearcuts, it became obvious that even-aged management by
complete clearcutting could result in stands of higher value than
indiscriminate partial cutting. Thus, a trend toward scientific even-
aged management began in the valuable cherry-maple forests of the
Allegheny Plateau and spread throughout the Northeast (Dana 1930). In
the Lake States, however, forest managers had greater difficulty
applying clearcutting successfully, due both to regeneration problems
and the lack of pulpwood and chemical-wood markets. Research in the
Lake States concentrated on partial cutting methods in northern hardwood
stands, with the emergence of the selection system of silviculture in

the 1950's (Arbogast 1957) and a science-based uneven-aged management.

10

uneven-aged Management Methods

Various studies have been conducted on the management and
regeneration of northern hardwood timber types. Most of these studies
have been based on silvicultural objectives. In the Lake States, many
of these studies have concentrated primarily on uneven-aged management.
As early as the 1920's researchers began to compare cutting methods in
old-growth Lake States northern hardwoods. Eyre and zillgitt (1953)
summarised the results of an old-growth cutting methods study that
compared diameter-limit cuts of 5, 12, and 22 inches with light, medium
and heavy selection cuts, and a control. They reported a maximum net
growth rate of 295 board-feet per acre per year (gross growth minus
mortality without deduction for cull) over a 15-year period, with a
residual volume of 6,300 board-feet per acre. They defined optimum
stocking for best growth between 4000 and 7000 board feet or 50 to 75
square feet per acre, on a 15-year cutting cycle. They found that light
selection cuttings were best for maintaining quality, and heavy cuttings
were best for growth. Moderate cuttings reduced tree quality only
slightly while increasing growth rate. This intermediate treatment
yielded premiums for larger quantities of sugar maple whitewood
(sapwood). One treatment in particular (removal of overmature and
defective stems) had the highest average net board foot growth (252
ft./ac./yr.), suggesting the importance of cutting out defective and
cull trees.

Another result of Eyre and Zillgitt's (1953) study was a call for
similar cutting methods research in second-growth northern hardwood
stands, which were rapidly replacing the old-growth in the Lake States.
Much of this research began at the Argonne Experimental Forest in
Wisconsin in the early 1950's.

Arbogast (1957) developed a silvicultural cutting guide for
northern hardwoods that has been widely applied in northern hardwood

stands, especially in the Lake States. His guide called for cutting

11

northern hardwood stands dominated by sugar maple to a residual stocking
of 92 square feet per acre, based upon the following decision criteria,
in their general order of importance: 1) risk: 2) cull; 3) quality; 4)
vigor: 5) species: 6) crown position; and, 7) size. Maximum tree size
recommended by Arbogast was 24 inches, though he suggested that most
northern hardwoods became financially mature in the 20-24 inch size
range (Figure 2-1).

Godman and Erdmann (1981) recommended uneven-aged management for
tolerant hardwoods such as sugar maple because this system produced
greater lumber yields, product grades, merchantable heights and
diameters. Erdmann (1986) cited sugar maple as a species producing
larger yields of high quality material under uneven-aged management
systems, stressing that sugar maple cover types were particularly well
adapted to intensive cultural treatments. Godman and Erdmann (1981)
emphasised that management of northern hardwoods on higher quality sites
should be for producing sustained yields of high grade veneer and
sawlogs, using uneven-aged management.

Partly because of the above research findings, Lake States
foresters often manage northern hardwood stands with single tree
selection (Eyre and Zillgitt 1953, Arbogast 1957). This method
develops well-regulated stands dominated by sugar maple and other
tolerant species such as American beech, red maple, and Eastern hemlock
(Tubbs 1977b, Erdmann 1986). Most of the high-value but less tolerant
species (the oaks, cherry, yellow birch, white ash) tend to be lost from
these managed stands (Zillgitt and Eyre 1945). However, Erdmann's
(1987) work has shown that smaller (less than 15 percent of each
species) components of non-maple species can be maintained and managed

in selection stands.

12

Trees Per Acre

 

140

120—— ------------------------------------------

100— ----------------------------------------

60— ————————————————————————————————————————
40— --------- — ————————————————————————————

20— ————————————————————————————————————————

 

 

011111 '11.
2 4 681012141618202224

Diameter class (inches)

 

*Arbogast +Adams 8. ER '3‘? Martin "' Bars 8. Opalach * Lyon 8. Reed

Figure 2-1. Comparison between Arbogast and other Lake States
uneven-aged cutting guides for recommended reserve
growing stock.

13

Managers using the single-tree selection method must be concerned
with three factors that impinge on silvicultural development and
economic yields. They are: stand diameter distribution (the 'q'
factor--the average quotient between number of trees in successively
smaller diameter classes, Sammi 1961), residual basal area, length of
cutting cycle, and the largest diameter tree (LDT) that will be allowed
to develOp before cutting. The existing diameter distribution in
unmanaged second-growth stands depends primarily on how the original
old-growth stand was cut. Early logging operations that took only the
best grades have left many high-graded stands with an uneven-aged
character. Their diameter distributions have the typical inverse J-
shaped curve associated with uneven-aged stands. Recommended uneven-
aged stand structure (Erdmann 1987, Arbogast 1957) can be achieved in
these stands within two cutting cycles if they are cut to residual basal
areas of 90, 75, or 60 square feet per acre (Reed of a1. 1986).

Schultz2 studied management opportunities for a high-quality sugar
maple stand partially within a national scenic river management area of
northern Wisconsin. He found that uneven-aged management by single tree
selection would yield the highest economic returns. Also, this cutting
protected the sensitive visual resources of the area and critical
nesting habitat of the bald eagle. Schultz's study emphasised the need
for further research on: 1) the trade-offs in timber quality and value
between even- and all-aged management: and, 2) the need to determine
the economic trade-offs of management at different "q" levels. The
group selection method of cutting is an uneven-aged technique that calls
for the removal of small groups of trees one-quarter to two acres in
sise (Smith 1962). This cutting method has met with mixed success in

northern hardwoods because it makes regulation of the cut and management

 

2Schultz, J.R. Silvicultural prescription: stand number 2,
Namekagon River Tract. Unpublished paper for silvicultural
certification. 1981. Milwaukee, WI: 0.8. Department of Agriculture,
Forest Service, Region 9, Chequamegon National Forest. 81 p.

14

for sustained yield very difficult, if not impossible, to achieve (Roach
1974, Tubbs 1977a). Studies in New England demonstrated the
effectiveness of group selection in regenerating white ash and yellow
birch (Leak and Filip 1977). Group selection, however, has typically
regenerated undesirable tolerant species such as ironwood (Ostrya
virginiana Mill.) x. Roch) in the Lake States (Erdmann 1986).
Therefore, it is not a viable cutting method in this region.
Diameter-limit cuttings are attractive to forest managers because
they offer high initial financial returns and are easily applied to
northern hardwood stands (Crew 1985). However, stand development, net
growth and long-term financial returns are often less than with other
cutting methods (Erdmann 1986, Niese and Strong 1992). An 8-inch
diameter-limit cut applied to a productive (Site Index 50 c 65)
Wisconsin northern hardwood stand resulted in fewer than 25 potential
crop trees per acre after 35 years and no merchantable timber until 40
years after application (Erdmann 1986). Other studies (Eyre and
Sillgitt 1953, Reed et a1. 1986) have reported similar results. When
diameter-limit cuts of any diameter are strictly followed, stand quality
will gradually decline because small, low-quality, and defective trees
are not cut (Crew 1985). Erdmann (1986) recommended that if they must
be used, diameter-limits should be set at 16 inches or higher so that
good quality sawlogs can be produced. In Appalachian mixed hardwoods
containing sugar maple, Smith and Miller (1987) recommend use of a
flexible diameter-limit cut, but they also cautioned against using

diameter-limit cuts with lower than a 16-inch limit.

Even-aged Management Methods

Northern hardwood stands also can be managed successfully using
even-aged methods. However, there are varying degrees of success with
even-aged methods, especially clearcutting, depending on geographic

location, stand condition, and regeneration conditions.

15

Old-growth stands that were commercially clearcut, a more common
treatment than above, are mostly even-aged stands today. Their diameter
distributions tend to follow the normal bell-shaped frequency curve
associated with even-aged stands. Sometimes these hardwood stands,
though even-aged, are a mixture of fast- and slow-growing species,
creating an even-aged stand with an uneven-aged diameter distribution.
In either case, these stands can be managed as even-aged (when the
manager desires to maintain valuable intolerant species), or converted
to uneven-aged management with three selection cuts applied at about 10-
year intervals (Erdmann 1987).

Research has shown that clearcutting Lake States northern
hardwoods is desirable only for reproducing stands of sugar maple when
adequate advanced regeneration is already present (Godman 1985). These
types of cuts are sometimes called "one-cut shelterwoods' to prevent
misuse of clearcutting. One study on clearcutting northern hardwoods in
Wisconsin resulted in shrub and grass-dominated communities, despite
abundant small advance reproduction present before cutting (Metsger and
Tubbs 1971). Clearcutting without adequate established regeneration
present can increase northern hardwood rotation length by 10-15 years
(Metsger and Tubbs 1971). Yet, in the Northeast, species such as white
ash, black cherry, and other intolerants regenerate successfully under
clearcutting, with a reduction of shade-tolerant beech and sugar maple
(Walters and Nyland 1989).

Contrary to conventional forestry wisdom, Godman and Erdmann
(1981) found that even-aged management could be applied to all hardwood
species in Lake States northern hardwood types. They reported, however,
that growing high quality holes with even-aged methods was more
difficult than with uneven-aged methods. Their work suggested a one-
half to one leg loss in merchantable height was possible when using
these methods in the Lake States. A later report (Godman 1985) showed

that the two-cut shelterwood silvicultural system was the most reliable

16

way to regenerate even-aged Lake States northern hardwoods, especially
small-seeded species such as yellow and paper birch and hemlock.
Economic benefits of shelterwood management can also be important.
Niese and Strong (1992) reported a two-cut shelterwood treatment as
having the highest net present values of three even-aged methods in a
Wisconsin cutting methods study.

With the increased interest in even-aged hardwood management,
shelterwood methods of regeneration have received closer attention,
particularly in the Lake States (Tubbs 1987). In the last decade
researchers have learned that shelterwood and clearcutting methods can
sometimes increase both the value and diversity of hardwood resources
(Erdmann 1983, Niese and Strong 1992).

Hannah (1988,1991) also found renewed interest in shelterwood
regeneration methods because selection methods often do not achieve
regeneration and stand structure objectives, and because clearcutting
has proven aesthetically unacceptable, especially in hardwoods. Higher
costs of implementation, lack of experience among foresters, and private
landowner reluctance were limiting factors in the application of
shelterwood in hardwoods. Research (Helty and Nyland 1981) also has
shown that, in some areas, heavy deer browsing and undesirable beech
regeneration must be overcome before shelterwood cutting can be used
successfully.

Many of the finest hardwood stands are even-aged, such as the
cherry/maple forests of the Allegheny plateau. Smith (1987) cited this
historical precedent as a compelling reason for even-aged hardwood
management. He described most northeastern hardwood stands as
vertically-stratified, even-age mixtures often misdiagnosed as
uneven-age stands, a portrayal confirmed by Oliver (1980). Smith found
that in most of these hardwood mixtures, moisture supplies were
sufficient to manage the upper-stratum less tolerant species separately

from the tolerant lower-stratum species, using a modified shelterwood

17

system. Such a two-tiered management regime may be more appropriate in
the Northeast than in the droughtier Lake States (Smith 1987).

Leak and Gottsacker (1985) reported that new approaches were
making even-aged management systems more feasible in New England. They
showed that second-growth hardwood stands with lower proportions of
sawtimber produced better net growth responses than stands with higher
percentages of sawtimber.

BlashockJ recomended even-aged management for maximising
financial returns and providing a mix of other benefits in a maple-
basswood stand in northern Michigan. He cited the need for research on
artificial regeneration of oak in northern hardwood types. He suggested
that managers could use oak enrichment plantings as economic options to
improve species diversity and grow larger diameter sawtimber trees.

A study by Oliver (1978) found that in mixed, even-aged central
New England hardwood stands regeneration of maples and birches greatly
outnumbered and outgrew oaks during the first 20 years. Eventually,
however, the oaks came to dominate the stand. Many of these mixed,
vertically stratified yet even-aged hardwood stands have been managed as
though they were uneven-aged stands (Oliver 1980). This improper
assessment has led to a variety of management problems. By careful
management of openings in these mixed, even-aged stands oak species can
be regenerated to create improved stand diversity and potential value.

Forest managers and researchers have long been aware of the fact
that regeneration requirements of the various northern hardwood species
vary greatly. Soil temperature and light availability at germination,
amount of scarification, insect pests of seeds and seedlings, and
predation by rodents and browsing by herbivores are particularly

important factors governing future stand composition (Godman 1985).

 

’Blashock, L.F. Mackenzie National Ski Trail--silvicultural
options. Unpublished paper for silvicultural certification. 1983.
Milwaukee, WI: 0.8. Department of Agriculture, Forest Service, Region 9,
Huron-Manistee National Forest. 59 p.

18

In northern hardwoods, the dominance of sugar maple is another factor
mitigating against managers' efforts to increase tree diversity through
careful regeneration practices.

Fre-settlement stands in the Lake States had a greater species
diversity than managers find in today's stands (Stearns 1986), which are
often 70-90 percent sugar maple. Past high-grading, protection from
fire, a greatly increased deer herd, and succession to tolerant species
have accelerated the trend toward lower species diversity in Lake States
hardwoods (Stearns 1986). The increased dominance of sugar maple under
these conditions has made it difficult for forest managers to maintain
the less tolerant species in Lake States northern hardwood stands.

Maintaining or enhancing species diversity in northern hardwood
stands is often dependent upon meeting the specific regeneration
requirements of the less common associated species. Varying the timing
and intensity of cutting can improve the manager's chances of reaching
diversity goals in stand management.

Both even- and uneven-aged management can be used to regenerate
northern hardwood stands (Godman 1985). Conventional wisdom holds that
even-aged management produces more diverse stands than uneven-aged
management. However, either system can increase or reduce species
diversity under varying site conditions (Stearns 1986).

Jacobs (1974) evaluated a two-cut shelterwood applied to an
uneven-aged northern hardwood stand in northeastern Wisconsin. He found
species composition unchanged three years later, with sugar maple
comprising 87 percent of the understory stems and white ash 8 percent.
However, shelterwood cutting alone does not necessarily increase species
diversity. A shelterwood with scarification increased species diversity
in a similar Wisconsin study (Godman 1985), particularly of
light-seeded species such as hemlock, white birch, and yellow birch.

Group selection cuts of 0.1 to 0.2 acres in size have been

successful in regenerating white ash and yellow birch in New England

19

(Leak and Filip 1977). However, group selection has regenerated
undesirable tolerant species such as ironwood, and few desirable
species, in the Lake States (Erdmann 1986).

Basel area treatments in northern hardwoods typically are cut to
residual stand densities of 60-90 ftnz/acre. As partial cuttings geared
to sawlog production, these often favor the development of sugar maple
and other shade-tolerant species (Tubbs 1977b). However, where site
conditions are favorable for other species, more aggressive basal area

cuts can favor the establishment of less tolerant species (Tubbs 1977a).

Economics of Cutting Methods

Relatively few studies have considered the economic returns of
various treatments applied to hardwood stands. Eyre and Zillgitt's
(1953) classic cutting methods study of old-growth northern hardwoods in
Michigan's Upper Peninsula found silviculturally optimum residual
stocking for maximum growth occurred at 5,500 to 6,000 board feet per
acre (BA 60-65) in sawtimber-sized trees. However, the economically
optimal stocking was much lower, 3,500 board feet per acre, yielding a
4.7 percent real rate of return on a 15-year cutting cycle. Eyre and
Sillgitt (1953) suggested that forest managers compromise with residual
stocking of 4,500 to 5,000 feet, which would approach optimum growth
while yielding a 3-4 percent real rate of return.

A 22-year financial evaluation of cutting methods in Michigan
northern hardwoods (Reed et a1. 1986) found that a 16-inch diameter-
limit out had the greatest financial return among nine different
treatments. A lack of data on quality ingrowth in the basal area
treatments was a possible factor causing results to favor the diameter-
limit treatments. The authors said that there would be a loss of
quality from diameter-limit cutting, and improvements in quality from

the basal area cuts that would change the financial returns. Also, this

20

study only considered periodic harvest revenues, not total stand value,
in making its economic comparisons.

A recent evaluation of the same study with ten more years of
management (Erickson et a1. 1990) confirms this observation. Though the
diameter-limit out still had the highest financial ranking, the light
improvement treatment had improved its ranking from sixth to third of
the eight treatments studied.

A 34-year Appalachian cutting methods study in a sugar
maple/yellow poplar/black locust stand (Smith and Miller 1987) compared
the silvicultural and economic performance of four methods--a 16-inch
diameter-limit, commercial clearcutting, and two selection treatments.
The diameter-limit cut had the greatest value of periodic revenues, but
total treatment value, including residual stand value, was comparable
between the diameter-limit and the selection cuts for sawtimber and
pole-sized trees.

A few studies of northern hardwoods management have considered the
rates of return from various cutting treatments. The premise of these
studies is that an individual tree should earn the discount rate or
greater to be left in the stand in a selection cut. If the tree's rate
of value growth (Gansner and Herrick 1987) falls below the discount
rate, it should be cut (Murphy and Guldin 1987, Duerr et a1. 1956).

Marquis, et a1. (1984) noted that there were no analyses on the
trade-offs between diameter growth and quality sufficient to suggest
that investments in precommercial thinning of northern hardwoods are
economically feasible. Several recent studies show that commercial
thinnings in northern hardwood stands can provide some very attractive
rates of return. Johnson (1986) found that thinning overstocked sugar
maple pole stands in Michigan resulted in an average 45 percent
reduction of time needed to reach maturity. Thiede (1986) found rates
of return of 6.4-25 percent for hardwood thinnings on State lands in

Michigan, assuming that timber did not have to cover land purchase

21

costs. Webster (1986) also noted real rates of return of 9-12 percent
for northern hardwood management. Large acreage of northern hardwoods
with commercial thinning investment opportunities have been partially
responsible for prompting Michigan to initiate a Forest Development
Fund. The State expects to generate sufficient capital to pay for these
forest improvements that yield competitive rates of return (Murray
1989).

Martin (1982) developed a set of investment-efficient stocking
guides (Figure 2-1) for northern hardwood forests based on prior work
done by Adams and Ek (1974) and Adams (1976). Because of the
opportunity costs of holding growing stock between harvests, Martin
recommended that managers should leave larger numbers of small diameter
trees (6-14 inches) and fewer large diameter trees (16-24 inches) than
was recommended in previous guides published by Arbogast (1957) and
Tubbs (1977b). However, Martin's investment-efficient stocking guides
make no assumptions about the higher stumpage prices obtainable for
large sawtimber trees. The lack of information on the economics of
quality differences between various northern hardwood stands is a
critical factor in evaluating returns to management. Martin recognised
this need and recommended that future research was needed to quantify
the economic effects of these differences in quality.

Haya and Buongiorno (1987, 1989) developed a Markovian cutting
guide for northern hardwoods that considers the uncertain growth of
forest stands and the variation in product prices. Their guide differs
from the Arbogast guide and deterministic economic guides because
instead of prescribing a fixed residual stand and a constant cutting
cycle, the Markovian guide suggests a decision that depends on the state
of the stand and on product price when the stand is examined.
Significantly, Raya and Buongiorno (1989) found that northern hardwood
stands could be managed more economically by concentrating growth and

harvest in the larger sawtimber classes, while minimizing the cutting of

22

pulpwood, which they found uneconomical to harvest from even-aged
stands. However, the limiting of harvest treatments to saleable
sawtimber classes will not maintain a stable stand structure (Roach
1974, Erdmann 1986). Without cutting of smaller pole-sized trees,
crowding will develop in the middle diameter classes. This form of
diameter-limit management will effectively reduce future returns to the
landowner, and appears to perpetuate stand high-grading. As in Martin's
investment-efficient stocking guide, Maya and Buongiorno's work does not
reflect the potential impact that cutting of hardwood pulpwood and cull
has on quality growth in current and future sawtimber classes.

Haya and Buongiorno's (1989) work raises an interesting question,
one debated in many forestry circles today. Should forest economics be
based upon scientific silvicultural decision-making, or should
silvicultural options be based upon economic considerations, and in what
time frame? Whose time preferences should count, and when, and on what
lands? Current debates make it clear that economic management regimes
for northern hardwoods must be based on biologically sustainable
systems, yet silvicultural efforts must also be responsive to economic
priorities. The two disciplines must inform each other; indeed, they

must be accountable to one another in forest management.

Quality Considerations in Economic Management of Northern Hardwoods
Several past studies (Erdmann 1986, Nyland 1986, Tubbs 1977b, Eyre
and Sillgitt 1953) cite management of northern hardwoods for the
sustained production of high grade veneer and sawlogs as an important
objective. However, there is scant economic evidence to support these
assertions. Most studies on tree quality have been theoretical or
silvicultural in nature (McCay and DeBald 1973, Brisbin and Dale 1987).
Economic evaluation of long-term (i.e., greater than 20 years) quality
response to treatment has not been possible previously due to lack of

data. However, surveys of research priorities for Eastern hardwoods

23

(McLintock 1987) have shown that improved prediction of tree quality
changes resulting from silvicultural practices is one of the greatest
research needs.

A few hardwood studies have found that aggressive timber stand
improvement practices result in favorable changes in tree grade and
species composition, with favorable economic results. In an uneven-aged
mixed hardwood stand in Illinois, White and Campbell (1988) found these
changes were sufficient to increase the economically optimal largest
diameter tree (LDT) from 16 to 22 inches with an average price scenario,
to as high as 26 inches under an enhanced price scenario. Their work
confirms the need for an active timber stand improvement (TSI) effort
that assures uneven-aged crop trees are of the highest quality and the
most marketable species.

In medium- to high-value hardwoods, research has confirmed that
quality change over time is at least as important, if not more
important, than diameter and height growth in contributing to timber
stand value (Rast et a1. 1987, Sonderman 1986). In Lake States northern
hardwoods, increases in tree grade over a 10-year period consistently
added greater value than either gains in merchantable height or diameter
growth (Godman and Mendel 1978). Previous work in Ohio oak stands by
McCay and DeBald (1973) also confirmed the financial superiority of
grade increases over height or diameter gains alone; their study showed
a doubling in rate of value increase for 18-inch red oak trees that
changed from a butt-log grade 2 to grade 1, versus those with no change.

Mautiyal (1983) has proposed a theoretical model for managing
uneven-aged selection hardwood forests based on the theory of financial
maturity. Using tree vigor as a criterion, he derived optimal rotations
from crops of trees that have increasing stumpage price functions, thus
effectively ”safeguarding" the penultimate (next-to-last) crop before
financial maturity. Increases in tree grade or size caused most of

these increasing stumpage price functions. Nautiyal's work underscores

24

the importance of including hardwood tree quality considerations in
economic evaluation of hardwood management, because tree grade
improvements tend to lengthen financial maturity (Nautiyal 1982, Godman
and Mendel 1978).

Miller (1991) used a linear programming model similar to one
devised by Buongiorno and Michie (1980) to create optimal stand
structure goals for single tree selection management in an Appalachian
hardwood stand. In his work, Miller compared grade yields and financial
profitability between diameter-limit and selection cutting regimes. He
calculated a maximum net present value of $433 per acre (for periodic
revenues) with a 22-inch diameter limit cut, while the best single-tree
selection management model produced a NPV of $404 per acre using a q-
value of 1.2, largest diameter tree of 20 inches, and a residual basal
area of 75 square feet per acre. However, he also found that the
selection management treatments experienced distinct improvements in
residual tree quality over the 30-year period, while the diameter-limit
and control treatments did not. Based on these results and the similar
economic returns between the optimal diameter-limit and selection
practices, Miller (1991) recommended that managers consider single tree
selection management to promote higher quality and total stand value.

In the Lake States, no definitive work has been done to compare
the economic profitability of even- and uneven-aged cutting methods.
Many cutting guides and models have been developed, but no research has
yet confirmed the economic superiority of one or more cutting methods in
northern hardwoods, under various conditions. The lack of data on the
long-term effects of cutting methods on tree quality has increased the
difficulty of achieving accurate estimates of economic returns under the
various methods. However, current work holds the promise of overcoming
the above obstacles, and should give forest managers much improved data

for decision-making in northern hardwoods management.

CHAPTER 3: RESEARCH METHODS

This study has three objectives that parallel the research

components (page 6) described earlier:

1. Determine which cutting methods are profitable and maintain or
enhance tree species diversity.

2. Estimate the economic returns that occur because of changes in
tree quality due to cutting method.

3. Determine total economic returns from nine cutting methods over
both 40- and 70-year investment periods.

Three research hypotheses were tested in this study. They are:

1. IL: Diversity of tree regeneration is not affected by cutting
method.
2. 15: ‘There is no difference among economic returns for northern

hardwood stands under different management methods.
3. 1%: ‘Total economic return is not affected by varying even- and
uneven-aged cutting methods.
This paper addressed the above research objectives and hypotheses
generally, and specifically in Chapters 7, 8, and 9, respectively.

The study is based on data collected over a 40-year period on the
Argonne Experimental Forest in north central Wisconsin (Figure 3-1).
The data originate from a northern hardwoods cutting methods study
sponsored by the USDA Forest Service, North Central Forest Experiment
Station (NH-25). Previous work on the study centered around
silvicultural options for managers of northern hardwoods. Various
aspects of this silvicultural work were summarized by Stoecklerh

Arbogast (1957), Godman and Books (1971), Metzger and Tubbs (1971),

 

|Stoeckler, J.H. Establishment report for cutting methods study
(A-3) in pulpwood-sized northern hardwoods. Unpublished manuscript
(dated January 1955). St. Paul, MN: U.S. Department of Agriculture,
Forest Service, Lake States Forest Experiment Station, Argonne
Experimental Forest. 60 p.

25

26

Argonne Experimental Forest

 

Nicolet National Forest

 

Figure 3-1. Location of the Argonne Experimental Forest within
the Nicolet National Forest, Wisconsin.

27
Erdmann and Oberg (1973), Jacobs (1974), Tubbs (1977a, 1977b), Godman
(1985), Erdmann (1986), and Niese and Strong (1992).

Prior research (Niese and Vasievich 1990) identified five
information needs that must be addressed successfully to compare the
economic returns of even- and uneven-aged management. These research
needs are listed in order below:

1. Long-term silvicultural trials.
2. Definition of silvicultural strategies.

3. Stand growth responses to management and harvesting treatments,
especially effects on timber quality.

4. Future timber demands and prices.

5. Costs of different forest management practices.

Long-term Silvicultural Trials--Argonne Experimental Forest

The economic analysis of a cutting methods study must begin with a
well-defined and documented biological production function. Long-term
silvicultural trials are necessary to produce such a function. There
are at least seven long-term silvicultural trials in northern and
Appalachian hardwoods in the Eastern United States. The Argonne cutting
methods study is the only replicated study (Figure 3-2).

In the current study, biological data were first summarised from
the three managed northern hardwood stands that compose the cutting
methods study (NH-25) in the Argonne Experimental Forest. The original
study had been established in 1951, to assess the effects of nine
different cutting methods on tree and stand development in a 45-year
old, even-aged, pole-sised northern hardwood stand. Early researchers
laid out three 40-acre treatment replicates along a low ridge north of
Route 32 near Hiles, Wisconsin (Figure 3-3).

The study plots were second-growth northern hardwood stands
typical of those found throughout the northern Lake States. They had
developed from a period of cuts in mixed, old-growth stands in the early
1900's: first removing white pine (Pinus strobus L.), then removing
hemlock (Tsuga canadensis L.), and finally removing the hardwoods with

commercial clearcutting.

28

WV; /m

Figure 3-2. Location of long-term silvicultural trials in northern
and Appalachian hardwood timber types in the
Eastern United States.

 

 

 

 

29

FR 2183

   

 

............
I

 

 

 

 

 

 

 

 

 

 

 

State Hwy 32

Figure 3-3. Location of the cutting methods study within the Iron-River
Wabeno (Ma and Mia) soil mapping units on the Argonne
Experimental Forest.

30

By 1950, regeneration from this series of cuts had developed into

fully stocked pole-sized stands, with the following characteristics:

0 Average diameter: 6.0 to 9.2 in.

0 Trees per acre: 237 (z 4.6 in. d.b.h.).

o Basal area: 85-114 square feet/acre.

0 Average age: 45-50 years.

a Site index, sugar maple: 65 feet (50 years).

0 Species composition: 63% sugar maple, 4-10t each of basswood,

white ash, yellow birch, Eastern hemlock, and red maple.

Northern red oak, black cherry, paper birch, aspen, black ash
(Fraxinus nigra), and American elm (Ulmus americana) were also present,
but in insignificant (0.1-1.8 percent) quantities at the time.

Scattered in the stands were occasional residual trees that had grown
into large-diameter, heavy-branched, poor-quality trees. The Acer-
Viola-Osmorbisa habitat type (Coffman et a1. 1984) and Iron-River Wabeno
stony loam soils occurred over most (90 percent) of the study sites

(Figure 3-3).

Data
Most of the study data were in hand-written, tabular or card
format when received in 1990. Included in the data were:

0 Individual tree records for approximately 3000 trees in six
uneven-aged treatments and three replications. The data included
measurements taken every five growing seasons since 1951 on
d.b.h., form class, crown class, number of logs and/or bolts,
merchantable and total heights, mortality, ingrowth, reproduction,
and tree grade if harvested.

0 Tree quality data that included log and/or tree grade and defect
data from about 400 individual trees dating back to 1951.

0 Individual tree records for a total of 272 crop trees from three
replications of a crop tree treatment installed in 1951.
Researchers remeasured these trees in 1953, 1958, 1961, and 1966.
(This study did not include these data due to difficulty in
interpreting the changing definition for crop trees).

0 Cutting history and financial records for all treatments, both
even- and uneven-aged. (The economic data were so site-specific
that they were not useful for this analysis).

31

0 Regeneration, reproduction, and regrowth data for the even-aged
treatments, beginning in 1958, including clearcut and shelterwood
blocks.

0 Data on the species diversity of both woody and non-woody
species.

Data from a tree quality study, superimposed on the original cutting
methods study in 1971, was received in 1991. About the same time,
additional plot data on the even-aged treatments became available, and

new regeneration measurements were taken for a tree diversity study.

Description of Silvicultural Cutting Treatments

This study evaluated nine treatments including the control. They are:

1. Control (no cutting)

2. Selection to residual basal area of 90 square feet/acre
3. Selection to basal area 75

4. Selection to basal area 60

5. Crop tree selection to basal area 60

6. Eight-in. stump diameter limit cut
7. Two-cut shelterwood with residual basal area 55 (after first cut)
8. Block clearcut with five-in. limit
9. Block clearcut with one-in. limit

In treatments 1-7, each block treated was 2.5 acres; while in
treatments 8 and 9, each block treated was 5 acres in size. Initial
stand basal areas ranged from 85-114 square feet per acre (Table 3-1),
but were not significantly different.

The three basal area treatments were cut to light, medium, and
heavy selection levels. These treatments left 90, 75, and 60 square
feet of basal area, respectively, in trees 4.6 inches d.b.h. (diameter
breast height) and larger. The cuttings favored commercial hardwood
species while discriminating against balsam fir, ironwood, hemlock, and

all cull trees (Stoecklerfi.

 

2Stoeckler, J.H. Cutting methods in pulpwood-sized northern
hardwoods (A-3). Unpublished work plan (dated October 19, 1951). St.
Paul, MN: U.S. Department of Agriculture, Forest Service, Lake States
Forest Experiment Station. 18 p.

32

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

TABLE 3-1. Description and stand basal area of treatments (ft’lac) in
1951 and in 1990.
1951 1951
Treatment Description Before out After out 1990
Control Uncut through
entire period 94 94 147
Light Cut to residual
selection basal area of
90 ft’lacre 104 as 106
Medium Cut to residual
selection basal area of
75 ftz/acre 98 77 87
Heavy Cut to residual
selection basal area of
60 ft’lacre 85 62 73
Crop tree Crown-released
30-50 crop
trees/acre:
residual basal
area was between
60-75 ft’lacre 87 63 87
Diameter Cut all trees
limit with stump
diameters of 8
inches and larger 84 23 124
Shelterwood* Cut to a residual
basal area of 55
ftz/acre (88%
crown cover), 114 55 30
Commercial Cut all trees
clearcut 4.6” d.b.h. and
larger 89 14 115
Complete Cut all trees
clearcut 1.0" d.b.h. and
L—i larger 89 0 93
1 I
* First cut in shelterwood occurred in 1958.

 

 

33

In the crop tree treatment, marking crews left 25-40 crop trees
per acre. Then, a crown thinning to a residual basal area of 60-75
square feet per acre allowed these dominant or co-dominant trees room to
grow. Small trees were left adjacent to the crop trees as trainers
where needed (Stoecklerfi. The diameter-limit cut was to an 8-in. stump
diameter, taken at a 12-in. stump height, a common commercial treatment
at the time the Argonne study began. The clearcutting treatments
removed the stand in blocks of 5 acres. Cuttings left trees 5 in. and
smaller in the commercial treatment and 1 in. or less in the
silvicultural treatment. Foresters prefer the silvicultural clearcut
because it does not allow poorly formed non-commercial trees to become
dominant over the more desirable regeneration (Marquis 1967).

The study added a shelterwood treatment in 1957, six years after
the other treatments. Researchers prescribed a two-cut shelterwood
system (Godman 1985) with 55 square feet of basal area left in the
original cut (1958). Removal cuts occurred in 1965 and 1975, once the

mixed hardwood regeneration was 4 to 8 feet high.

Plot Measurements

Each treatment block, 2.5 acres in size, was sampled by five 0.1
acre circular sample plots (Figure 3-4). The 5-acre clearcut blocks
were each sampled with two 0.1 acre plots. Research workers made a 100
percent inventory of each 0.1 acre growth plot before and after cutting
in the winter of 1951-1952, and estimated cull and saw log grade
recovery from the volume cut in 1951-1952. End of the growing season
inventories were repeated in 1957, 1961 before and after out, 1966, 1971
before and after cut, 1977, 1981 before and after cut, and 1986. All
trees 4.6 in. d.b.h. and larger were measured to the nearest 0.1 in. and

tallied by species.

 

’Stoeckler, J.H. Establishment report for cutting methods study
(A-3) in pulpwood-sized northern hardwoods, p. 6.

34

 

/

L
C .
Q9 00 .

n» F O . o

m JG Block
1 K‘) C709 Clearcuts

521232. \2 “3°

 

Commerczal O Silvrcultural

--.---“

    

I
l
\

 

 

 

Sheiterwood

 

 

 

 

 

 

 

 

 

 

 

 

Figure 3-4. Sampling design for replication 1 (40 acres), Argonne.
cutting methods study. Each of the smaller treatment
blocks is 2.5 ac., with five 0.1 ac. sample plots
(reproduced from an original drawing).

35

Trees were felled and sectioned in 1952 and 1969 for local volume
table construction. Measurements for cubic foot volume included
diameters inside bark beginning at a 1-foot stump to the nearest 0.1 in.
at 8.3 feet intervals to a 4.0-in. top and total tree heights to the
nearest foot. In addition, measurements on saw log trees (9.6 in. d.b.h.
and larger) included Scribner board foot volumes to a variable
merchantable top diameter that was limited by branches, defect, or other
deformity, but not less than an 8.0-in. diameter inside bark (d.i.b.)
top. Total cubic foot volumes inside bark were computed for each tree
over 4.6 in. d.b.h. using Smalian's formula for the following tree
portions: stump, stem in 8.3 foot lengths to a 4.0-in. d.i.b. top, the
tip, and branches to 4.0-in. d.i.b. tops. Then, following Spurr's
(1954) volume line method, workers constructed separate local cubic foot
volume tables for sugar maple, basswood, yellow birch, and white ash
(Erdmann and Oberg 1973). The study used sugar maple volume-line
equations for calculating cubic foot volumes of other less commonly
associated species such as red maple and American elm. Merchantable
board feet and cordwood volumes were derived from the total cubic-volume
estimates by use of appropriate converting factors for different mean
stand diameters (Spurr 1954). The study defined a cord to contain 79
cubic feet of solid wood (Erdmann and Oberg 1973).

A lack of data on merchantable heights made the use of the volume
line equation method (Erdmann and Oberg 1973) described above necessary
to determine stand volumes. However, beginning in 1972, the study
included calculations of individual tree volumes for all harvests and
residual stands, using merchantable height measurements, diameters, and

appropriate deductions for cull.

Modelling Growth and Yield (1992-2022)

This study used the Lake States version of TWIGS (Belcher 1982,
Shifley 1987) to model future growth and yield. Because of poor
predictability for white ash and basswood, the author revised the TWIGS

36

equations for these species following the additive method developed by
Holdaway (1985). The methods used for this adjustment are described

elsewhere in this report (Chapter 6, pp. 92-98).

Economic Evaluation

For each of the above treatments, the author evaluated growth data
to determine economic returns resulting from the various stand
manipulations. The economic model estimated returns to treatment, given
initial stand conditions, cost and price expectations, and changes in
tree quality over time. This model was based on regression analysis of
the variables that affected both residual stand value in 1992 after 40
years of treatment, and total stand value in 1992 (cut plus residual).
In addition to treatment effects, the author tested the following
variables for their effects on 1992 lumber value: number of white ash
stems in initial stands, trees per acre in 1951, basal area of white ash
at the end of the study, and the average tree size in 1951 for each
treatment replicate.

A benefit/cost analysis framework was used to compare economics of
cutting strategies. A marginal economic analysis was conducted using
Quick-Silver version 2.0 (Vasievich et a1. 1984), a forestry investment
software program. All costs, prices, volumes, and stand conditions were
entered into Quick-Silver version 2.0 (Vasievich et a1. 1984).
Sensitivity analyses in Quick-Silver determined the response of each

treatment to changes in discount rate, costs, and price expectations.

Hardwood Management Costs

Only direct or variable costs of management were used to compare,
economic performance of the cutting methods. Since the fixed costs of
the operation as a whole did not affect relative choice, they were
excluded in this comparative analysis (Davis 1966). Thus, the financial
performance of each treatment was compared with the control or no

management treatment.

37

The study used cost survey data specifically from Lake States
northern hardwood management to construct estimated costs of treatments.
Variable costs included in the analysis were: cruising and marking for
partial cuts: clearcut sale layout: post-harvest removal of non-
commercial trees; periodic road maintenance; and annual administrative
costs. Sensitivity to cost variation was tested with the following low,
medium, and high cost assumptions, on a per acre basis (indicated in
parentheses, respectively): cruise/mark for partial cut ($16,20,24):
clearcut sale layout ($12,26,40); and periodic road maintenance
(53,5,7).

Most estimates of variable management costs for the nine
treatments in the study were obtained from a recent Lake States study
(Vasievich, Potter-Witter”. These costs were compared with other cost
data from the Nicolet National Forest in Wisconsin, and cost data from
previous Lake States studies (Winebar and Gunter 1984, Olson et a1.
1978, Hilliker et a1. 1969). The author also checked these costs for
accuracy with costs incurred by consulting foresters in the northern
Lake States. Administrative costs ($0.50/ac in 1952) were also assigned
to each treatment and applied annually with a 3 percent annual increase.
All nominal costs were adjusted to constant 1990 values using the
Producer Price Index for all commodities (1990 a 100). Therefore, all
economic analyses were done in real or constant dollar terms. Income

and property taxes and land costs were not included in the analysis.

Analysis of Hardwood Price Data
Prices for northern hardwoods stumpage were obtained from 1954-91

reported prices from Michigan's Upper Peninsula (Michigan Department of

 

‘Vasievich, J.H.: Potter-Witter E. Costs of timber management
practices in the Lake States, 1987-88. Unpublished draft manuscript
(dated 8/16/90) of a cooperative study. East Lansing, MI: U.S.
Department of Agriculture, Forest Service, North Central Forest
Experiment Station and Michigan State University, Department of
Forestry: 16-19. (In author's personal file.)

38

Natural Resources 1954-91) and the Wisconsin Forest Products Price
Review (Peterson, various dates). Of particular concern were the five
major Argonne species--sugar maple, white ash, basswood, yellow birch,
and red maple--although price series from Wisconsin (Peterson, various
dates) were also analysed for Eastern hemlock, American elm, and mixed
hardwoods. These prices were compared with transaction evidence from
local sales in the Eagle River Ranger District of the Nicolet National
Forest to assure comparability. The Producer Price Index (1990 - 100)
was used to adjust these nominal Wisconsin stumpage prices for
inflation.

This study used transaction evidence because it is the method that
most closely approximates market value. FORSight (Vasievich et a1.
1988), a timber price analysis software program, analyzed the long,
medium, and short-term price trends for eight northern hardwood species.
Each price series was analyzed using a natural log transformation model
of the form:

ln(P year) s a + b (Year)

Where ln(P) - natural log of the reported stumpage price, and the
corresponding year is the dependent variable. The b parameter is
interpreted as the annual rate of compound price change. The author
evaluated each cutting treatment using these estimated prices for the
four major hardwood species and Eastern hemlock present on the study
sites. Other hardwood sawtimber‘ comprised less than 6 percent of stand
volume. Also, hemlock sawtimber from the early years (1951-72) of the
study was priced at softwood pulpwood stumpage prices.

These hardwood price trends were considered in the context of
exogenous factors influencing hardwood prices, derived from the Hardwood

Assessment Market Model (HAMM, Cardellichio and Binkley 1984), RPA

 

’Other sawtimber included small (<1.5t of basal area) amounts of
American elm (Ulmus Americana L.), white birch (Betula papyrifera
Marsh.), black ash (Fraxinus Nigra Marsh.), black cherry (Prunus
serotina Ehrh.), and northern red oak (Quercus rubra L.).

39

timber assessment reports (Haynes 1990), and other sources. Finally,
three hardwood price scenarios were chosen. These scenarios were the
ones considered most appropriate for the five major species in northern
Lake States hardwood markets (Chapter 4, p. 57). Then, the inflation-
adjusted price estimates were applied by product to the cutting
treatment yields.

Sensitivity to price changes was incorporated in the analytical
model with these low, medium, and high real price change assumptions:
sugar maple (0, 1.8, 4 percent): basswood (0, 1.5, 4 percent); white ash
(2, 3.8, 6 percent): yellow birch (-l, 0, 2 percent), red maple and
mixed hardwoods (0, 2, 4 percent); mixed hardwood pulpwood (0, 1, 3
percent): and softwood pulpwood (-2, 0, 1 percent).

Quality Change and Economic Return

The financial value attributable to tree quality changes has two
dimensions: value from trees already harvested, and the value of
residual trees. In valuation of residual trees, each tree left has an
opportunity cost, and must earn more than the discount rate to justify
being retained (Murphy and Guldin 1987). This study evaluated both
components of tree quality value for each cutting method.

The average tree grades (Hanks 1976) of both harvested and
residual sawlog trees, by treatment, were calculated for 1971, 1981, and
1991. Analysis of variance (ANOVA) tested which treatments had
significant changes in quality due to silvicultural treatment. When
these grade improvements occur, sawlog trees often have high value
increases, and the decision to leave them for future growth can be
justified (Nautiyal 1983). The economic analysis of tree quality
changes included five uneven-aged treatments plus the control, but did
not include the shelterwood, or either of the clearcut treatments,
because quality data was not available for them.

The framework for quality analysis considered the trees before
quality treatments (1971) and after treatment (1992), after three

40

10-year periodic harvests had been made. Regression equations for
factory lumber yields from graded hardwood trees, first developed by
Hanks (1976), were used. In Hanks' (1976) equations, the dependent
variable was lumber grade, and the independent variables were a constant
term, (d.b.h.)’, merchantable height, and [(d.b.h.)’ x merchantable
height). These regression equations are shown in Appendix 8. Hanks
(1976) did not derive lumber yield equations for white ash: however,
because of similarity between basswood, elm, and white ash form class
and taper functions (unpublished data on file at the Argonne
Experimental Forest!) the basswood lumber yield equations were also used
for white ash and American elm.

Lumber volumes were calculated for the following grades: FAS, FAS-
1F, Selects, #1 Common, #2A, #28, #2 Common, #3A, #38, and #3 Common.
However, not all grades apply to all species. For example, in this
study, the #2A and #28 grades applied only to red maple; and the #3A and
#38 grades applied only to sugar and red maple, yellow birch, and paper
birch. Because of very small variations in value, the grades FAS, FAS-
lF, and Selects were combined into one grade (total FAS) for economic
evaluation. Similarly, the #2A and #28 red maple grades were combined
into the #2 Common grade for pricing outputs. Six grades were used in
the final economic evaluation of hardwood tree quality--Total FAS, #1

Common, #2 Common, #3A, #38, and #3 Common.

Application of Hanks'(1976) Tree Grade Lumber Yield Equations

Cut and residual tree files were exported from Paradox. v.3.5 to
Lotus‘”. The data included tree grade, d.b.h., merchantable length, and
species. Gross board feet volumes (Scribner) for each tree were derived

using the following equation:

 

‘Strong, Terry F.) Erdmann, Gayne G. Unpublished data on file at
the Argonne Experimental Forest, Rhinelander, Wisconsin.

iMention of trade names does not constitute endorsement by the U.S.
Department of Agriculture.

41

(l/merch. length) + 0.723 (109» merch. length)]

Het board foot volumes were then calculated by multiplying gross volume
by percentage of cull in each tree in the spreadsheet. All data in the
spreadsheet were sorted by replicate, species, and plot. Then, the
Hanks (1976) equations for each lumber grade and species were entered
into a Microsoft Excel. spreadsheet and volumes by grade were calculated
for each tree. Finally, the author sorted the data again by treatments

and replicate for final valuation.

Valuation of Lumber Yields

Lumber yields for each treatment were evaluated using prices from
the Hardwood Market Report (Lemsky, 1971, 1981, 1991) and the Wisconsin
Forest Products Price Review, lumber edition (Peterson 1981, 1991).
Each sawtimber ( > 9.5 in. d.b.h.) tree's lumber yield value was
calculated in 1971 and in 1991 for residual stands, and in 1971, 1981,
and 1991 for harvested trees. Thus, the value gain or loss from grade
changes was shown for each individual tree, and by summation, for each

treatment over time.

Overall Economic lvaluation

Net treatment values were calculated from the value of all
harvested stumpage, plus residual values of all commercial (> 4.6 in.
d.b.h.) trees in 1992, less treatment costs. A total of nine treatment
case files (not including sensitivity analyses) were created in Quick-
Silver version 2.0 (Vasievich et a1. 1984).

Quick-Silver has a simplified method for conducting marginal
analyses. This method is called a combined case, where two separate
Quick-Silver cases are entered, one for the treatment in question, and
one for the control treatment. Quick-Silver then automatically changes

all control transaction quantities to negative values for comparative

42

analysis. Thus, the program treats control revenues as opportunity
costs (negative values) and control costs as opportunity revenues.

Analysis of the combined cases showed the differences in financial
performance between each treatment and the control. In this study, the
difference in net present value (NPV) was the desired decision
criterion. Analysts consider net present value to be a superior
investment criterion in most benefit/cost applications (Randall 1981,
Davis 1966). Net present values were calculated using real discount
rates of 2, 4, and 6 percent and adjusted to 1990 U.S. dollars for
reporting.

Values of the residual merchantable stands in 1992 were included
in all analyses to reflect total value for each treatment, after 40
years of management. Although a few other cutting methods studies do
not include residual value in their economic analyses (Reed et a1. 1986,
Erickson st a1. 1990), this study explicitly included residual value.
Comparison of stand treatments over time must include changes in total

treatment value, not just periodic revenues.

Summary

A benefit/cost analytical framework was used to compare total
economic returns for nine northern hardwood cutting methods. Cutting
yields and returns were analyzed for 40-year (1952-92) and 70-year
(1952-2022) investment horizons. Tree quality changes were explicitly
included in an analysis of treatment lumber values that considered
potential grade and yield of every sawtimber-sized tree for 1971-92.
Species diversity trade-offs were also analyzed using Shannon's
diversity index to measure hardwood regeneration (Chapter 7).
Regression analysis was used to find price trends from time series data
and to analyse changes in hardwood tree quality and lumber yield. Data
from surveys were used to estimate hardwood treatment costs.
Sensitivity analyses tested economic results under changing price, cost,

and discount rate assumptions.

CHAPTER ‘8 HARDWOOD PRICI EXPECTAIIOIB

Introduction

This section presents real stumpage price trends in Wisconsin and
Upper Hichigan for selected hardwood species and products for the period
1950 to 1991. It also reviews past research on hardwood prices and
expectations of future hardwood prices. Based on these sources likely
hardwood price scenarios are reported for the commercially important
northern hardwood species occurring on the study site.

In a market economy, prices provide an intricate system of signals
and incentives to coordinate the activities of firms and individuals.
Market prices serve as signals to producers and consumers alike, showing
scarcity or abundance, regulating production and consumption. 8ut
market prices may not always be efficient prices, because suppliers of
resources often have insufficient information to assess the future.
Timber markets often contain imperfect information, making them
inefficient. However, the advent of stumpage price reporting by various
agencies (Rosen 1984, Hichie and kamets 1987) and widespread
dissemination of price information has improved this situation in recent
years.

Stumpage price is the price that a buyer, usually a logger, pays to
the owner of forest land for the privilege of cutting and taking timber.
This payment is the timber's scarcity rent (Tietenberg 1988). Stumpage
can also be defined as the value of standing timber. It is determined
by the complex interaction of timber stand, location, and market
characteristics.

Since the characteristics determining stumpage price are site
specific and vary over time, price forecasting is difficult. However,
current and past prices inevitably shape landowners' expectations of
future timber prices. These future expectations about stumpage prices

can have a dramatic effect on forest cutting decisions both now and in

43

44

the future (Dennis and Remington 1985). For northern hardwoods,
stumpage price expectations can influence the cutting system chosen
(even- or uneven-aged), the particular methods used, and economic
returns achieved. Therefore, it is important to consider hardwood

stumpage price expectations in this study.

Historical Price Trends

Historically, forest products have been atypical of most natural
resource commodities because they have had real long-term price
increases (Hanthy 1978, Potter and Christie 1962). Host natural
resource industries have had stable commodity prices and decreasing
employment-per-unit-of-output ratios, suggesting widespread adoption of
capital-intensive technology (Hanthy 1978). Forest products, and
sawlogs and lumber in particular, are resource commodities that have not
always followed these patterns.

Hanthy's (1978) composite index of forest product prices showed
rising real prices between 1870 and 1950, relatively stable prices
between 1950 and 1970, and rising real prices since then. He attributed
most increases in real forest product prices to sawlogs. Hanthy
described sawlog prices for the 100-year period 1873-1973 as a rising
ratcheting trend, with 1950-70 being a fairly stable period.

Barnett and Horse (1963) examined the real cost trends for
sawlogs, and the trend of cost of net sawlog output relative to a
deflator of Gross National Product for non-extractive industries. They
found that the relative cost of sawlogs rose substantially, a fact they
cited as confirmation of the historical scarcity of sawlogs.

Potter and Christy's (1962) work on lumber prices found similar
price trends to those reported earlier for sawlogs. They suggested that
the rising real price trend for lumber may have been understated. This
occurred because for many years the shift of the industry from the East

to the West Coast had put the output (priced at the mill) farther from

45

the market. Lagging productivity had been cited as the chief cause of
lumber price increases until the 1950's (Duerr 1960).

Irland's (1974) work showed that increases in lumber prices had
not come steadily, but mainly in building booms and inflationary periods
following several wars in this century. His interpretation of the
rising real prices for lumber parallels Manthy's ratcheting rising price
trend for sawlog prices. Irland also cautioned that lumber prices may
not be a good proxy for timber prices. For example, from about 1860 to
1900, lumber prices were stable while timber prices rose. Scarcity
shown in the rising price of timber had been camouflaged by declining
transportation and milling costs.

Recent work by Potter-Witter and Lacksen (1993) has found rising
real sawtimber stumpage prices for red maple, sugar maple, and basswood
in Hichigan between 1954 and 1991. They documented somewhat different
market trends between Hichigan's Upper and Lower Peninsulas, with most
of the larger price increases occurring in the Upper Peninsula.

In contrast to sawtimber (and lumber), pulpwood prices have been
remarkably stable since 1900. Hanthy (1978) noted this real price
stability as unusual, given rapidly rising consumption of pulp and
paper. The pulp and paper industry, unlike the lumber industry,
increased its productivity (output per unit of labor) by an average 133
percent in the 40 years prior to World War II, blunting the effect of
increasing wages and costs (Duerr 1960).

However, a few researchers have recently documented significant
real price increases for some pulpwood species and real price declines
for others. In New Hampshire real stumpage prices for hardwood pulpwood
increased an average of 3.9 percent annually while spruce/fir pulpwood
declined 3 percent annually between 1964 and 1983 (Dennis and Remington
1985). Further work (Dennis 1989) found that real prices for both
hardwood and spruce/fir pulpwood declined 0.5 percent for the 1979-88
period.

46

These trends reflect the substitution of hardwood for softwood
pulp as the Eastern U.S. region has shifted from sulfite mills to semi-
chemical mills. Other regions of the U.S. have been making similar
changes in pulp and paper making industries, with similar changes in
relative pulpwood stumpage prices. The result has been short-term
periods of rising real prices for hardwood pulpwood and declining real
prices for softwood pulpwood, followed by a stabilizing trend as Dennis
(1989) observed in New Hampshire.

Despite varying interpretations of the past century of stumpage
and lumber prices, most analysts conclude that, in the long run,
pulpwood prices have generally remained stable, while sawlog and lumber
prices have seen real price increases averaging between 1.5 and 2.0
percent per year. Analysis of historical stumpage prices for this study
is made in the context of these more general trends.

These historical price trends are pertinent to recent work done on
the demand for hardwood stumpage and hardwood lumber. The reason is
that hardwood price expectations are always shaped by the current and
expected supply and demand for hardwood stumpage and lumber. The
following sections examine the status of hardwood lumber markets in the
United States and the various factors that influence the supply and
demand for hardwood products and, therefore, hardwood stumpage prices.

Hardwood Markets and Prices-~Wational Demand and Price Trends

In the past 15 years hardwood lumber markets have had generally
strong growth (Dempsey and Luppold 1992). Demand for hardwood lumber
increased especially strongly in the period 1982-89, from 8 billion
board feet to 11.2 billion board feet. In the decade of 1977-87,
overall hardwood lumber use increased by 38 percent. However, these
overall upward trends include dramatic changes in end uses of hardwood
lumber. For example, in the same lO-year period hardwood lumber exports
were up by 287 percent, while hardwood lumber usage in railroad ties and

47

timbers (-46 percent) and wood household furniture (-15 percent) both
declined. Some current research (Dempsey and Luppold 1992) suggests
these may be long-term trends.

The major domestic uses of hardwood lumber in the past two decades
have been pellets and containers, hardwood dimension, wood household
furniture, and millwork. These four industries combined accounted for
7.6 billion board feet or nearly three-fourths of domestic consumption
in 1987 (Dempsey and Luppold 1992). Thus, the future of hardwood
stumpage prices is, to a large extent, determined by the outlook for
these four industries. The following sections explain the hardwood

exports and wood household furniture sectors in greater detail.

W
The export market nearly tripled its consumption of hardwood

lumber between 1977 and 1987, and continued a positive real growth rate
through 1990 and 1991, despite a U.S. recession. Host hardwood exports
are in high quality logs and lumber, particularly of premium species
such as red and white oak, white ash, and black cherry. Sugar maple,
though a valuable hardwood, has not shared this status as a ”glamour"
species. However, this situation may be changing. Sugar maple exports
have shown a strong increase since 1989, as Japanese and Duropean buyers
and brokers have begun to purchase large volumes of high-grade logs in
the northern Lake States. lxports of hard maple logs to Korea have
shown particularly strong growth in the past five years (LuppoldU.
Interest in bird's-eye maple has been particularly strong. Prices for
Michigan's Upper Peninsula bird's-eye have gone as high as $28,000 per

thousand board feet, or more (Hros et a1. 1990). Demand for bird's-eye

 

'Luppold, W. Personal Communication, October, 6, 1992.

48

maple may be contributing to upward price pressure on woods-run sugar

maple (lrdmann’) .

need_lenaehel§_znrnitnrs
The wood household furniture industry is the third largest

domestic user of hardwood lumber, and the largest end user for total
value. Hew housing construction feeds demand for hardwood lumber,
though there is also a significant market for wood furniture in
remodeled homes.

Two opposing trends have affected hardwood demand for household
furniture. First, the amount of hardwood lumber used in each unit of
furniture has been reduced by half as less expensive reconstituted wood
products such as hardwood plywood, particle board, and medium density
fiberboard have replaced solid wood components. This trend reflects the
ingenious nature of furniture manufacturers to find less expensive
substitutes for hardwood lumber (Cardellichio and Binkley 1984).

Second, the actual number of furniture units produced has more
than doubled between 1940 and 1980, and has shown continued strong
growth since then. If the decreasing use factor is combined with the
growth in furniture production, there is a slight upward trend in the
consumption of hardwood lumber for furniture for 1940-80, a decline of
15 percent for 1977-87, and a stable trend in household furniture demand
since 1987 (Cardellichio and Binkley 1984, Dempsey and Luppold 1992).
Recovery from the 1990-91 recession has resulted in a modest upward
trend in consumption of hardwood lumber for furniture.

For many years, perceptions of the hardwood sector have been
shaped by the belief that sawtimber of the select species is declining
in availability. These perceptions are not always confirmed by official
reports of hardwood sawtimber availability but recent studies have

 

aErdmann, 0.0., Consulting Forester. Personal Communication,
July 26, 1992.

49

confirmed forest industry's beliefs that the availability of quality
hardwood sawlogs is in fact declining (Dennis and Remington 1985,
Kingsley and De8ald 1987). Real prices in the Lake States have
increased for several furniture-grade hardwood species, including red
oak, white ash, and black cherry. Declining real prices seen in the
1960's and 1970's for red and sugar maple and basswood have stabilized
and increased since about 1975.

The quality issue has dominated most discussions of the future of
hardwood markets. However, increases in the production of low-grade
hardwood products (pallets, 088, fuelwood, etc.) and substitution of
hardwoods for softwoods (e.g., pulp and paper) also may be a driving
force in increasing hardwood demand (Haynes 1990). Therefore, the
species most able to meet a variety of product specifications may be
those that sustain future real price increases.

During the 1980's hardwood harvest from growing stock grew by over
40 percent (Haynes 1990), and the share of harvest used as pulpwood is
expected to increase to nearly 50 percent by 2040. Analysts expect
continued growth in hardwood timber inventories will prevent stumpage
price increases for hardwoods, except for sawtimber, especially in the
north (Haynes 1990). These forecasts also show that tree quality is
important in determining future hardwood price increases.

The world furniture industry generates most demand for high-value
U.S. hardwood sawlogs. Several global economic trends are also
affecting supply and demand for U.S. hardwood forests and the U.S.
furniture industry. Host of these trends have developed rapidly since
the late 1970's. They include: increased U.S. wood furniture imports:
increasing substitution of American hardwoods for Southeast Asian
hardwoods by Pacific Rim manufacturers: development of a strong "baby-
boomer“ market for high-end furniture: strong interest by Asian
furniture producers in buying U.S. hardwood timber supplies; and changes

in global exchange rates that have increased economic incentives for

SO

U.S. firms to export the better hardwood logs (Smith and West 1990).
These trends point to an emerging global demand, and possibly higher
prices for, the better-quality U.S. hardwood timber resources.

Hardwood Price Analysis
an

The basic price data analysed for this study came from stumpage
data from Hichigan's Upper Peninsula. Statewide stumpage sales from
Wisconsin and local sales from the Nicolet National Forest were also
considered for comparative purposes. The Hichigan data were used
because the study area is within the Upper Peninsula market area.

The Wisconsin data originated from Hinnesota and Wisconsin Forest
Product Prices: A Historical Review 1950—1980 (Lothner et a1. 1982),
and from the University of Wisconsin Extension (UWEX) Forest Products
Price Review, Timber Edition (Peterson, various dates). The Lothner
document summarises past trends in forest products prices for stumpage,
harvested wood, and lumber sold in Hinnesota and Wisconsin. The UWEX
price review newsletters report similar information by species in a
market news format. Both sources report ranges and average prices for
stumpage. The prices reported in these sources (and used in this
research) are median stumpage prices, unweighted by sales volume. All
reported prices represent timber sales throughout the state of Wisconsin
and reflect stumpage prices at the time of harvest. All stumpage prices
are on a Scribner Decimal C log rule.

The major data source used in the analysis was the Region 1 (Upper
Peninsula) stumpage data provided by the Hichigan Department of Natural
Resources. Sugar maple stumpage price data from the Nicolet National
Forest (1977-91) were checked for comparative purposes only.

The Hichigan Upper Peninsula data are for the region just to the
north and east of the study site. These data generally are for the

1954-91 period, from competitively-bid government sales. The Nicolet

51

data are from the National Forest area that surrounds the Argonne
Experimental Forest. They are also competitive stumpage prices for
locally sold, public timber sales.

The Forest Economics Research Unit (of the USDA Forest Service
North Central Forest Experiment Station, in East Lansing, Hichigan)
compiled price data into a uniform reporting format and coded into ASCII
data files. This research project made liberal use of this consolidated
data base.

Price series for nine species and product combinations were
analyzed, representing nearly all of the commercial volume on the

cutting methods study site. These species and product combinations are:

W

1. Sugar maple 6. American elm

2. Basswood 7. Hixed hardwoods
3. Yellow birch 8. Eastern hemlock?
4. White ash Eglpgggg

5. Red maple 9. Hixed hardwood

The mixed hardwood category was used to estimate price trends for
other hardwood sawtimber on the Argonne. These other hardwood species
included small (< 1 percent of basal area) amounts of white birch, black
ash, black cherry, and northern red oak. Except for American beech (not
present on the study site), the above species, in various combinations,
are typical of northern hardwood forest types in the northern Lake

States.

 

’Though not a hardwood, hemlock prices were analyzed because
Eastern hemlock is a significant component of this and other northern
hardwood forests.

52

W

Three different methods are commonly used to evaluate stumpage: l)
transaction or market evidence: 2) value in use or cost/income; or,

3) residual value approach. This study used transaction evidence for
several reasons, discussed below.

The transactions evidence approach to stumpage valuation is based
on statistical analysis of past timber sales in the same area. It
involves obtaining the actual sales records from sales of similar and
nearby timber. This is generally the preferred method because it
directly measures what actual stumpage has sold for, in the market.
Transactions evidence, also called 'appraisal-by-comparison,' has been
used effectively to value stumpage for public and private timber sales
(Anderson 1976).

Legally, transactions evidence has been the method most often
upheld by judges in court cases, rather than expert opinion (Sizemore
et a1. 1965). One Kentucky judge summed up the practical advantages of
transactions evidence this way:

”Of course market value is the price at which an

article sells in the open market. This price is

fixed by sales actually consummated. Such sales,

when made under normal and fair conditions, are

necessarily a better test of the market value than

the speculative opinions of witnesses; for truly

here is when 'money talks." (Sizemore et a1. 1965)

To be valid for valuation purposes, a transaction evidence price must
represent the high bid in a competitive market. The market defines
competition as a case in which there are at least two parties bidding
for the same stumpage. Advantages of the transaction evidence approach
include: its reliance on existing data from past timber sales,
simplicity and ease of operation, low cost, and less reliance on end
product valuation (Vasievich et a1. 1988).

The cost or income approach has limited application, mostly to the
valuation of immature timber, and is not appropriate for short-term

timber sale contracts. The residual value approach is sometimes used by

53

the USDA Forest Service. It works backward from an estimate of the
value of the manufactured end product such as lumber. The value of
various costs of logging, hauling, manufacturing, and profit and risk
margins are subtracted from end product value, leaving a ”residual”
value for the stumpage.

A major problem with the residual value approach is that it is
costly and time consuming to use (USDA Forest Service 1988). It also
has the disadvantage of overlooking competitive factors that can lead to
higher or lower prices than the average. Being based on what the firm
of ”average“ efficiency can afford to pay for stumpage, the derived
residual value approach does not necessarily evaluate market price where
marginal costs equal marginal revenue but instead, price at a lower
level (Buongiorno and Young 1984). Since the concern here is with fair
market value rather than appraised value, the derived residual value
approach was not appropriate to this study. Given the availability of
appropriate data and its acceptance by the research community,

transactions evidence was deemed the best method to use.

W
Long, medium, and short-term price trends (Tables 4-1, 4-2) for

the nine northern hardwood species product combinations were analyzed
using FORSight (Vasievich et a1. 1988), a timber price analysis software
program. FORSight generates a variety of useful statistics. These
include mean values and standard deviations for model variables,
t-statistics, F-statistics, slope and intercept coefficients, ANOVA,
and R-squared and adjusted R-squared values.

Unless otherwise specified, the long trend was for 1950-90 or
1954-90, the medium trend for 1970-90, and the short one for 1980-90.
Each price series was analyzed in FORSight using a natural log

transformation model of the form:

54

ln(P,.) - a + b (Year)

Where ln(P) - natural log of the reported stumpage price, and the
corresponding year is the dependent variable. The b parameter is
interpreted as the annual rate of compound price change.

In the above model, a regression coefficient (b parameter) above
zero indicates that an increase (decrease) in the independent variable
(time) results in an increase (decrease) in the dependent variable
(natural log of real price). The Student's t-statistic is used to test
the null hypothesis that the b parameter is statistically equal to zero.
The model's significance is tested similarly with F-values.

W

In determining price changes, the regression method is preferable
to merely taking prices at the beginning and end of the period(s) and
calculating the annual average increase. By incorporating timber price
variation in all years of the series, the regression method includes
more information in the price estimates (Holmes et a1. 1990).

The natural log transformation is used because it transforms the
multiplicative coefficients into a more convenient sum that can easily
be estimated by using linear regression. Once transformed, the
regression is kept in its log form because of the ease of interpreting
the b coefficient, because the change in the log of x (yearly price) is

equal to the relative change in x itself (Wonnacott and Wennacott 1984).

RIBLB 4-1.

Peninsula sawtimber stumpage price trends.

 

Regression equations used to estimate Hichigan Upper

“

 

 

 

 

 

 

 

 

 

 

 

 

Dependent Independent variables
Variable:
natural log of
reported stumpage Trend
price for: Period Constant (a) Year (b)
Sugar maple 1954-91 3.0201 0.0180**
1970-91 1.1788 0.0404**
1980-91 -S.1971 0.1141**
Basswood 1954-91 3.2966 0.0150*
1970-91 -0.7068 0.0576**
1980-91 -5.9920 0.1191**
White ash 1980-91 -0.8889 0.0560**
Yellow birch 1954-91 4.6797 -0.0008
1970-91 3.1703 0.0174*
1980-91 2.1083 0.0295*
Red maple 1954-91 1.5022 0.0290**
1970-91 0.5613 0.0406**
1980-91 4.2974 -0.0026
Hixed hardwoods 1972-91 -2.3720 0.0722**
1980-91 -6.2461 0.1170**
Hixed hardwood 1957-91 -0.5393 0.0221**
pulpwood 1970-91 -2.9327 0.0510**
1980-91 -11.5075 0.1511ff m“
* - Significant at the a - .05 level

** - Significant at the a - .01 level

 

 

56

TABLE 4-2. Regression equations used to estimate Wisconsin sawtimber
stumpage price trends.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

I

Dependent Independent Variables

Variable:

natural log of

reported stumpage Trend

price for: Period Constant (a) Year (b)

Sugar Haple 1950-90 5.3328 -0.0105*
1970-90 4.4318 0.0004
1980-90 1.1377 0.0388*

Basswood 1950-90 5.5337 -0.0143**
1970-90 4.5518 -0.0023
1980-90 -0.4563 0.0562**

White ash 1967-90 1.8179 0.0307**
1980-90 -2.3108 0.0791**

Yellow birch 1950-90 6.2273 -0.0209**
1970-90 6.6328 -0.0263**
1980-90 2.2098 0.0254*

Red maple 1950-90 4.2845 -0.0001
1970-90 3.6568 0.0074
1980-90 -0.0330 0.0504**

American elm 1950-90 3.8064 0.0055*
1970-90 1.7425 0.0310**
1980-90 -3.8647 0.0965**

Hixed hardwood 1972-90 5.2692 -0.0129
1980-90 3.7212 0.0049

Eastern hemlock 1950-90 6.0567 -0.0310**
1970-90 5.9851 -0.0302**
1980-90 3.3975 -0.0003

Hixed hardwood 1972-90 -l.1111 0.0323*

pulpwood 1980-90 1.0342 0.0074

* 8 Significant at the u e .05 level

** - Significant at the a - .01 level

57

TABLE 4-3. Stumpage price scenarios used in analysis, expressed
in average real price increases (decreases) per year.

   

 

 

 

 

 

 

 

 

 

Low Price Hedium Price High Price
Scenario Scenario Scenario

Sugar maple 0% 1.8% 4%
sawtimber
Basswood 0% 1.5% 4‘
sawtimber
White ash 2% 3.8% 6%
sawtimber
Yellow birch -1% 0% 2%
sawtimber
Red maple 0% 2% 4%
sawtimber
Hixed hardwood 0% 2% 4%
sawtimber
Hixed hardwood 0% 1% 3t
pulpwood
Softwood pulpwood
inc. hemlock -2% 0% 1%
sawtimber

 

 

 

 

Results and Discussion

W
Price trends for sugar maple were especially important to this

study, because 60-85 percent of the study site's individual trees and
volumes were sugar maple. Over the 1954-1991 period, real sugar maple
sawtimber prices in Hichigan's Upper Peninsula showed a real price
increase of 1.80 percent, while 1970-91 was up 4.04 percent annually and
the last decade (1980-91) increased by 11.41 percent (Figure 4-1 and
Table 4-1). The Nicolet National Forest data showed that sugar maple
sawtimber had an average annual real increase of 3.33 percent for the
period 1979-90. In Wisconsin, sugar maple prices for 1950-90 declined
1.05 percent (Figure 4-2 and Table 4-2). However, despite the long-term

decline in Wisconsin sugar maple sawtimber prices, when the 40-year

58

WBF Scribner

 

200

150

100

 

50

 

 

 

0
50 55 60 65 70 75 80 85 91
Year

Figure 4-1. Reel (1990 S) Michigan Upper Peninsula sugar maple sawtimber stumpage
prices. 19544991 (Michigan Department at Natural Resources data).

SIMBF Scribner

 

200

150

100

 

 

50

 

 

 

0
50 55 60 65 70 75 80 85 91
Year

Figure 4.2. Reel (1990 S) Wisconsin sugar maple sawtimber stumpage prices.
1950-1990 (Wisconsin Forest Products Price Review).

S9

analysis period was divided into two 20-year periods, a different price
trend picture emerged. Then, real sugar maple stumpage prices were
nearly stable (+0.04 percent) for the 1970-90 period, and showed an
average 3.88 percent annual increase for the 1980-90 period.

Based on current market activity, sugar maple will experience the
greatest demand for pallet wood and for high-quality furniture and
export logs. The development of specialty markets such as bird's-eye
maple is also indicative of the growing interest in the sugar maple
resource. Demand for the mid-grades of sugar maple logs and lumber will
not be as great, because the end users of these grades (such as flooring
mills) have not experienced rapid growth. However, there is a large
surplus of #2 sugar maple sawlogs in the region. New technologies that
can produce high-quality furniture parts from lower-quality materials
may create added demands on this currently under-used resource.

Price scenarios for sugar maple were difficult to predict because
of the resource's current abundant supply, yet its rapidly growing
demand. However, price trends since 1970 were probably more indicative
of future prices than the long-range price series. The author estimated
that real sugar maple sawtimber prices would have a low scenario of a 0
percent increase, a medium scenario of a 1.8 percent increase, and a

high scenario of a 4 percent increase (Table 4-3).

am

Real basswood sawtimber prices showed a 1.50 percent increase in
the U.P. for 1954-91 (Figure 4-3), but an average 1.43 percent decline
in Wisconsin for 1950-90 (Figure 4-4). In the past two decades,
basswood showed a 5.76 percent increase in the U.P., but a slight
decrease (-0.23 percent) in Wisconsin. Since 1980 prices have shown
significant annual increases both in Wisconsin (+5.62 percent) and in

the U.P. (+1l.9l percent).

6O

$IMBF Scribner

 

200

150

100

50

 

 

 

 

0
50 55 60 65 70 75 80 85 91
Year

Figure 4-3. Reel (1990 3) Michigan Upper Peninsula basswood sawtimber stumpage
prices, 1954-1991 (Michigan Department of Natural Resources data).

SIMBF Scribner

 

200

 

 

 

 

0
50 55 60 65 70 75 80 85 91
Year

Figure 4-4. Real (1990 3) Wisconsin basswood sawtimber stumpage prices. 1950-90
(Wisconsin Forest Products Price Review).

61

Basswood has had increased demand for carving stock and, to a
lesser extent, keystock in the past few years. Current technological
trends may favor greater demand for basswood in the future. Hany mills
now have improved debarking machinery that is making it easier to remove
basswood's stringy bark. Also, basswood's versatility makes it usable
for pulp, pallets, and solid wood products.

Based on the more recent (1970-91) price history for basswood, the
author expects at least modest real price increases in the future.
Therefore, real basswood sawtimber prices were projected to have a
stable low price scenario, a 1.5 percent increase medium scenario, and a

4 percent increase high scenario.

My
White ash was the only species that showed consistently high real

price increases in both Hichigan's Upper Peninsula and Wisconsin. In
the Upper Peninsula on state lands, white ash showed an average real
price increase of 5.60 percent for 1980-91 (Figure 4-5). In Wisconsin,
median white ash prices had average annual real increases of 3.07
percent between 1967 and 1990, and 7.91 percent annually for 1980-90
(Figure 4-6).

As red oak sawtimber removals continue to approach or exceed
annual growth (Cooney 1992), and as oak types convert to maple and other
species, white ash will provide an increasingly attractive alternative
to scarce red oak. White ash has several desirable qualities: fast
growth, fairly easy regeneration in both even- and uneven-aged
management (Godman 1985), and excellent machining and finishing
properties. Because it can substitute for red or white oak, its price

often follows the price of red oak, but at a lower level.

62

$IMBF Scribner

 

200

150

100

50

 

 

 

0
50 55 60 65 70 75 80 85 91
Year

Figure 4-5. Real (1990 S) Michigan Upper Peninsula white ash sawtimber stumpage
prices, 1978-91 (Michigan Department of Natural Resources data).

$IMBF Scribner

 

 

 

 

200

150

100‘ A?‘
l r '

A ‘ '

so 1' 1 I ,

0

50 55 60 65 7O 75 80 85 91

Year

Figure 4-6. Real (1990 S) Wisconsin white ash sawtimber stumpage prices. 1950-1990.
(Wisconsin Forest Products Price Review).

63

Significant real white ash price increases should be the norm for
the northern Lake States. The following scenarios were based upon price
performance since 1967: a low scenario of +2 percent, a medium scenario

of +3.8 percent, and a high scenario of +6 percent.

111.191.111.911
Yellow birch sawtimber prices were essentially stable (-0.08

percent) for 1954-91 in the U.P. (Figure 4-7), but declined
significantly for the long trend period in Wisconsin (-2.09 percent,
Figure 4-8). For 1970-91 U.P. birch prices climbed 1.74, while in
Wisconsin they declined at an average annual rate of 2.63 percent.
Prices have appeared to ”bottom out," evidenced by rising real prices
both in the U.P. (+2.95 percent) and in Wisconsin (+2.54 percent) for
the last decade. However, median real prices (1990 $) for yellow birch
stumpage in 1990 were only $77.00/H8F (Scribner) in Wisconsin compared
with $153.46 in 1957. In the Upper Peninsula median real yellow birch
prices (1990 $) had surpassed their 1957 historic high of $140.00,
reaching $148.17 by 1991 (Figure 4-8). Scarcity of yellow birch sawlogs
may contribute to modest future price increases for this species.

Only a generation ago yellow birch was the premium northern
hardwood species of the northern Lake States. It was used mainly
for furniture, veneers, airplane parts, and doors from the late 1940's
to the 1960's. Today the yellow birch resource and its economy are
barely viable compared to their past prominence, the victims of
excessive harvesting by industry and a lack of regeneration amid
competition by sugar maple. Though among northern hardwoods yellow
birch has slipped from second to fifth place in commercial importance on
the Argonne and in the region, it is still a high-value species.
Despite a long-term (1950-80) real decline in birch prices for

Wisconsin, yellow birch prices have stabilized in the region.

64

$IMBF Scribner

 

200

/ WM

50

 

 

 

0
50 55 60 65 70 75 80 85 91
Year

Figure 4-7. Real (1990 S) Michigan yellow birch sawtimber stumpage prices. 1954-91.
(Michigan Department 01 Natural Resources data for Upper Peninsula).

 

 

 

 

SIMBF Scribner
200
150 VAN 1
"I" i ‘
100" .“~'\ 4
V ’1’ <
M

50

0

50 55 60 65 70 75 80 85 91

Year

Figure 4-8. Reel (1990 6) Wisconsin yellow birch sawtimber stumpage prices.
1950-1990 (Wisconsin Forest Products Price Review).

65

The success of current yellow birch regeneration efforts will
decide the future viability of these markets and the species' price
performance. Price scenarios for yellow birch sawtimber were estimated
as a 1 percent real decline (low), a stable scenario (medium), and a 2.0

percent increase (high).

mm
In the Upper Peninsula, a 37-year trend (1954-91) showed an

average annual 2.90 percent increase (Figure 4-9) in real red maple
prices. By contrast, Wisconsin real red maple sawtimber prices were
virtually stable (-0.01 percent) over the 40-year period 1950-90 (Figure
4-10). The U.P. had a 4.06 percent increase between 1970 and 1990, more
than five times as great as Wisconsin's (0.74 percent). However, while
Wisconsin showed a highly significant 5.04 percent increase for the most
recent decade, the U.P. showed a slight (-0.26 percent) decline.
Obviously, there are local variations in markets that strongly influence
red maple price behavior.

Red maple has increased faster in volume and acreage since 1968
than any other species (or type) in the Lake States (Spencer and Hahn
1984, Spencer et a1. 1988). It has also seen greatly increased usage in
products ranging from 088 to upholstered furniture to flooring. Along
with its increased usage and volumes red maple has experienced marked
changes in its price behaviors.

Potter-Witter and Lacksen's (1993) work showed average annual real
price increases of 3.2 percent for Upper Peninsula red maple since the
late 1950's. This analysis showed similar (+2.9 percent) real price
increases for a slightly longer period. Wisconsin's long-term red maple
series was stable, but annual compound price increases of 0.74 percent

and 5.04 percent were observed for 1970-90 and 1980-90, respectively.

66

SIMBF Scribner

 

200

150

100

50

 

 

 

 

50 55 60 65 70 75 80 85 91
Year

Figure 4-9. Reel (1990 S) Michigan Upper Peninsula red maple sawtimber stumpage prices.
1954-1991 (Michigan Department 01 Natural Resources data).

SIMBF Scribner

 

200
1 50

100A». RNA/me. n .A
WV U

. /\«.
w u V341"

 

 

 

0
50 55 60 65 70 75 80 85 91
Year

Figure 4-10. Real (1990 8) Wisconsin red maple sawtimber stumpage prices. 1950-1990.
(Wisconsin Forest Products Price Review).

67

Based on the increase in its marketability, and on the proximity
of the study site to Upper Peninsula markets, the author expects red
maple to have price scenarios of stable (low), +2 percent (medium), and

+4 percent (high).

W

American elm prices in Wisconsin were on an upward trend for all
three projection periods. Between 1950 and 1990, prices rose an average
of 0.55 percent annually (Figure 4-11). The increase was 3.10 percent
for 1970-90 and 9.65 percent for 1980-90. Prices were essentially
stable for 1950-78 (-0.24 percent), but large real price increases have
occurred since then. Dutch elm disease has created a scarcity of elm
sawtimber, driving up prices since it swept the northern Lake States in
the 1970's.

Although American elm is a minor component in today's northern
hardwood forests, many stands still have an elm component, particularly
those stands on more mesic sites. Due to the scarcity of mature elm and
continued demand, American elm scenarios were estimated as: +1 percent

(low), +3 percent (medium), and +5 percent (high).

W

Eastern hemlock in Wisconsin had average annual real price
declines of 3.10 percent for 1950-90, and 3.02 percent for 1970-90
(Figure 4-12). A steep 30-year decline in real price may have ”bottomed
out" by 1980, because prices have been nearly stable (-0.03 percent)
since then. This long-term decline in real hemlock prices was even more
pronounced than the real decline in yellow birch prices. No new
commercial markets are expected for hemlock sawtimber. Hemlock price
scenarios were estimated as: -2 percent (low), stable (medium), and a

1 percent increase (high).

68

SIMBF Scribner

 

200
1 50

100

501 VVYYY VW

0
50 55 60 65 70 75 80 85 . 91
Year

 

 

 

 

Figure 4-11. Real (1990 3) Wisconsin elm sawtimber stumpage prices, 1950-1990.
(Wisconsin Forest Products Price Review).

SIMBF Scribner

 

200

150

100

50

 

 

 

 

0 .
50 55 60 65 7O 75 80 85 91
Year

Figure 4-12. Reel (1990 8) Wisconsin hemlock sawtimber stumpage prices. 1950-1990.
(Wisconsin Forest Products Price Review).

69

W
Hixed hardwood sawtimber displayed contrasting price behavior in

Wisconsin and the Upper Peninsula. This disparity may be due to
changing definitions and composition of sales of ”mixed” hardwood.
Upper Peninsula mixed hardwood sawtimber prices increased 7.22 percent
annually for the period 1972-91 (Figure 4-13). But in Wisconsin, mixed
hardwood sawtimber declined in value an average of 1.29 percent (Figure
4-14). For 1980-91, Hichigan's Upper Peninsula saw a very substantial
increase (11.70 percent, Table 4-1), while Wisconsin's mixed hardwood
sawtimber showed a slight annual increase in real price (0.49).

Because of the difficulty of defining mixed hardwood, and because
the observed trends in the U.P. and Wisconsin were distinctly different,
price scenarios were estimated as: stable (low), a real increase of 2

percent (medium), and a real increase of 4 percent (high).

W

In Hichigan's Upper Peninsula (Figure 4-15), mixed hardwood
pulpwood prices showed a 2.21 percent average annual increase for 1957-
91, a 5.10 percent increase for 1970-91, and a 15.11 percent real price
increase for 1980-91. Available data for Wisconsin showed a 3.23
percent real increase in mixed hardwood pulp prices from 1972-90 (Figure
4-16). Real price increases (+0.74 percent) appear to have leveled off
since 1980. Hichigan Upper Peninsula real prices (1990 S) were in the
$1.75-2.75 per cord range from 1970-80, whereas Wisconsin's average real
prices were in the $3.00-5.00 range for the same period. Since 1980,
the steep climb in Upper Peninsula real prices has made them comparable
to Wisconsin hardwood pulp prices (Figure 4-15 and Figure 4-16).

Hardwood pulpwood stumpage markets have played an important part
in the recent development of forest products industry in the northern
Lake States. Several new mills and mill expansions led to significant

real price increases (3-15 percent), especially in the 1970's in

7O

 

 

 

 

 

SIMBF Scribner
200
1 50
1 00
50
0
50 55 60 65 70 75 80 85 91

Year

Figure 4-13. Reel (1990 S) Michigan Upper Peninsula mixed hardwood sawtimber stumpage
prices, 1972-1991 (Michigan Department of Natural Resources data).

SIMBF Scribner

 

150

100

 

 

 

50 55 80 85 70 75 80 85 91

Year

Figure 4-14. Reel (1990 5) Wisconsin mixed hardwood sawtimber stumpage prices.
1972-1990 (Wisconsin Forest Products'Price Review).

71

$lCord

 

10

4 ’1‘

‘ 4
2 ‘V'

50 55 60 65 70 75 80 85 91

Year

 

 

 

Figure 4-15. Real (1990 S) Michigan Upper Peninsula Hardwood pulpwood stumpage
prices, 1954-1991 (Michigan Department of Natural Resources data).

$lCord

 

10

 

 

 

0
50 55 60 65 70 75 80 85 91

Year

figure 4-16. Reel (1990 6) Wisconsin mixed hardwood pulpwood stumpage prices.
1972-1990 (Wisconsin Forest Products Price Review).

72

Wisconsin, and in the 1980's in the Upper Peninsula. It is very
doubtful that these price increases will be sustained. At this time it
is unlikely that another large hardwood mill will locate in the region.
Also, cheap eucalyptus fiber from Brazil and elsewhere can easily be
substituted for northern hardwood fiber, especially in the production of
high-quality printing papers, which are the economic mainstay of most
northern mills (Stier 1990). Huch of the Lake States paper industry has
an older plant infrastructure and heavy (38 percent) reliance on
purchased woodpulp. Therefore, it is quite possible that cheaper
imported fiber will have some future impact on Lake States paper
producers (Stier 1990), exerting downward pressure on real prices.

Despite recent real increases in hardwood pulp prices, the author
expects them to stabilize within a few years. Price scenarios for mixed
hardwood pulpwood were forecast as: stable (low), +1 percent (medium),
and +2 percent (high).

summary

Northern hardwood sawtimber stumpage prices for the northern Lake
States have generally shown real price increases of 0.5-7.5 percent
since the early 1970's. Sugar maple, the species of primary interest,
has shown price scenarios ranging from stable (Wisconsin 1970-90) to a
real increase of over 11 percent (Upper Peninsula, 1980-91). Because of
high value and excellent growth rates, price projections for basswood
and white ash are also of primary interest. All three species are
likely to have real price increases in the next 20 to 30 years, ranging
from a low of 1-3 percent for sugar maple sawtimber stumpage to a high
of 5-8 percent for white ash. Hardwood pulpwood markets are more
uncertain, and may have modest (1 percent) increases or decreases in the

future, with a stable real price trend quite possible.

CRAPTIR 58 "081383. IARDWOOD “IMAGINE“! 00828

This section presents available cost estimates for various
northern hardwood management practices. These estimates are applied to
the eight treatments in the Argonne cutting methods study. A review of
the scanty northern hardwood cost literature follows. Results of recent
studies and the derived cost estimates are then made in the light of the

literature.

Introduction

Land managers need information on the costs of northern hardwood
timber management to assure full knowledge of forest management options
and to make efficient resource allocation decisions. Landowners and
managers need this cost information when choosing between even- and
uneven-aged management and the various cutting methods available to
achieve their silvicultural goals. Unfortunately, there have been few
studies on hardwood management costs in the northern United States. The
lack of published information has contributed to uncertainty and
imprecise estimates of the costs and returns in northern hardwoods

management.

Literature Review

Hardwood timber management is different from conventional
enterprises since it has a very long time horizon. Production costs are
affected by land productivity, resource or stand characteristics,
available capital, available technology, local, regional, and global
markets, and by landowner preferences (Duerr 1960, Davis 1966, Davis and
Johnson 1987). Costs of timber production are also affected by social
and institutional factors including zoning and land use statutes,
taxation policies, forest practice acts, traditions, ownership patterns,

and environmental concerns.

73

74

Host empirical work on timber management costs has involved
softwoods, particularly in the South (Straka et a1. 1989, DuBois at al.
1991). Recent southern studies suggest several important overall cost
trends: 1) Forest management costs in the South (1982-88) increased by
less than inflation, as measured by the implicit Gross National Product
price deflator: 2) labor costs are the most significant factor
associated with recent increases in forest management costs: and,

3) costs associated with regeneration of forest sites account for a very
high proportion of a forestry firm's total operation expenditures
(DuBois at al. 1991).

Characteristics of Hardwood Hanagement Costs

The above results are general trends in costs of softwood
management in the South. Although some softwood management cost
categories are common to hardwood management, there are several
differences in costs of management between the two. Usually, hardwood
management involves moderate or no site preparation for regeneration,
while softwoods often require intensive site preparation. Natural
regeneration is common in hardwood management: artificial regeneration
in softwoods. Hardwoods often require additional costs for crop tending
or quality development. Establishment costs for natural hardwoods are
less than for softwoods, while tending costs are higher (Smith 1976,
Erdmann and Godman 1981).

Host variable costs faced by owners of northern hardwood forests
are from labor-intensive practices (Winebar and Gunter 1984). Included
in these high labor cost practices are non-commercial tree removal,
timber sale layout, timber stand improvement, timber marking, and pre-
commercial thinning. These are the costs of primary concern in this
study.

Limited research on Lake States forest management costs has been

accomplished. Researchers in Wisconsin first tried to establish cost

75

relationships for common forest management practices by regression
analysis, using time and materials data from forest management agency
records. They constructed dollar-cost estimates from the physical cost
relationships (Hilliker at al. 1969). Another study (Olson et a1. 1978)
developed cost equations for the most common forest management
practices, using agency data from across the Lake States. Recent
efforts to document costs have concentrated on surveys. In these
studies, researchers have asked both public and private forest managers
to provide detailed responses on the actual or estimated costs of their
operations (Winebar and Gunter 1984, Vasievich and Potter-Witterfl.

Hardwood management costs may vary depending on whether the
silvicultural system chosen is even-aged or uneven-aged. Hanaging
forests under even- or uneven-aged management systems usually leads to
different cost functions. Variation in stand structure and ‘
silvicultural goals leads to differential timing of stand entries and,
therefore, treatment costs. The uneven-aged methods have the greatest
number of stand operations, and thus higher periodic costs. The even-
aged methods have fewer operations and thus lower periodic costs, but
regeneration treatments are riskier and are often more costly. This is
especially true where clearcutting is the harvest method. Annual costs
(e.g., taxes, administration) are generally the same for both even- and
uneven-aged methods.

One recent study (Lewiifi attempted to assess if there were
differences in stumpage price received for timber sales using even-aged

versus uneven-aged cutting methods. This study would show if there were

 

'Vasievich, J.H.; Potter-Witter R. Costs of timber management
practices in the Lake States, 1987-88. Unpublished draft manuscript
(dated 8/16/90) of a cooperative study. East Lansing, HI: U.S.
Department of Agriculture, Forest Service, North Central Forest
Experiment Station and Hichigan State University, Department of
Forestry: 16-19. (In author's personal file.)

?Lewis, Paul J. Unpublished. The impact of cutting method on
northern hardwood stumpage prices. East Lansing, HI: Hichigan State
University, Department of Forestry. H.S. thesis. 1990. 155 p.

76

cost differences between even- and uneven-aged methods. The study found
that, usually, the stumpage price received varied because of the species
being out, not the cost savings of a particular method. There were no
significant effects on stumpage value from shelterwood cuts, selection
cuts, overstory removals, and improvement cuts. Buongiorno and Young
(1984) also found that individual species volume was the most important
factor in predicting the high bid for northern hardwood timber sales.

Hethods

Only direct or variable costs of management were analyzed to
compare the economic performance of the eight cutting methods in this
study. The advantage of only including direct costs is that the fixed
costs of the operation as a whole do not affect relative choice and thus
can be excluded in a comparative analysis (Davis 1966, Davis and Johnson
1987). The financial performance of each treatment is then compared
with the control or no management treatment.

This analysis relied heavily on a few studies of Lake States
forest management costs (Olsen et a1. 1978, Winebar and Gunter 1984, and
Vasievich and Potter-Witter”. Survey data specific to northern
hardwoods management were used to construct variable cost estimates of
treatments. Costs not identified as common to northern hardwoods
management were not included. Since nearly 90 percent of the commercial
northern hardwood forest types in the Lake States are found in Hichigan
and Wisconsin (Eyre 1980, Spencer 1983, Spencer et a1. 1988), cost

survey responses from Hinnesota were not included.

Cost Assumptions
Several cost assumptions were made in this analysis. First, costs
were discounted at 4 percent, a rate that represents the real, long-term

opportunity cost of capital (Row et a1. 1981). All costs are expressed

 

3Vasievich and Potter-Witter, pp. 16-19.

77

in real 1990 dollars, unless otherwise noted. Land purchase costs are
not included in the analysis, because the author assumed that owners
purchase land for non-timber reasons (Young st a1. 1985), or evaluate
timber management marginally where land is a sunk cost. Taxes are also
not included in the analysis. Harvesting costs were assumed to be

reflected in the stumpage prices of competitively-bid sales.

Results

Results of gleaning cost estimates from several surveys and
studies are summarized below. Because there were a small number of
forest managers who responded to the various surveys, results by
management treatment varied greatly. Accordingly, numbers of responses
and standard deviation are given for each treatment cost category

(TBblO 5-1).

We:
Though timber stand improvement (TSI) can involve both chemical

and non-chemical means, in Lake States hardwoods landowners most often
use non-chemical methods. Labor is the most expensive cost component,
accounting for 76 percent of all TSI costs in one study (Winebar and
Gunter 1984). Wisconsin managers reported average TSI costs of $15 per
acre in 1987, while Hichigan forest managers had higher average costs of
$22 per acre (Vasievich and Potter-Witter‘). Earlier work in Hichigan

(Winebar and Gunter 1984) reported average per acre TSI costs of $36.76.

W

Felling and killing of unmerchantable trees is an important albeit
labor-intensive silvicultural practice in many forest types, both
following commercial clearcuts and partial cuts. Its purpose is to

improve regeneration and promote stand growth. This practice had

 

‘Vasievich and Potter-Witter, pp. 16-19.

78
reported average costs in 1987 of $24 per acre in Wisconsin and $16 per
acre in Hichigan (Vasievich and Potter-Witter“. Another study (Winebar
and Gunter 1984) reported that removal of non-commercial trees after a

harvest had labor costs averaging 69 percent of total costs. Total

costs averaged $24.51 per acre.

TABLE 5-1. Summary of cost survey results: variable costs, number of
observations, and standard deviation by category.

 

 

 

 

 

 

 

— L ‘l — 1
Type of variable Costs variable Costs Variable Costs
Treatment Wisconsin* Hichigan* Lake States**

(per acre) (per acre)
Timber Stand $15 (3,510) 522 (7,517) $36.76
Improvement (13,$17.60)
Fell/kill 524 (12,518) $16 (9,514) -------
unmerch. stems
Cruise/mark $16 (3,54) $23 (5,518) -------

(partial cut
Cruise/timber 516 (3,54) 517 (26,514) 515.63 (37,512,
mark, imp. cut mark only)
Clearcut Layout 584 (6,5156) 516 (25,516) 58.97 (26,511.6)
Periodic road $256 (9,5189) 5871 (7,51031) --------
maintenance

 

Reduced current/ $0.50 (8,50.560 $0.47 (2,50.25) --------
special use tax

 

 

 

 

 

 

Yield tax rate 7.55 percent 10 percent (7,0 10 percent
(8,3.67 percent)
percent) L
W— .

 

* Vasievich and Potter-Witter (unpublished)
** Winebar and Gunter (1984)

79
Writing

These important practices are often lumped together as a single
cost category because they occur simultaneously in stand management
(Vasievich and Potter-Witterfi. Some studies, however, report them as
two separate cost categories (Winebar and Gunter 1984). Because of the
similarity in these cost categories for improvement and partial cuts,
the following data is reported for both types of cuts.

Individual tree selection marking/cruising is perhaps the single
most important cost component in uneven-aged northern hardwood
management. Harking stands for commercial thinning and partial sawlog
cuts occurs every 10-20 years in uneven-aged management, and occurs at
least once, about one-half to two-thirds of the way to rotation age, in
even-aged management. Because of the differences in timing and
frequency, marking is one key cost that differentiates uneven-aged
management from even-aged management. Therefore, it is important to pay
particular attention to cost estimates for this practice.

Winebar and Gunter (1984) reported an average 1982 total cost for
selection marking of 512.62 per acre in Wisconsin, slightly less than
the average cost of $13.85 per acre reported for Hichigan (37
responses). These figures compare favorably with selection marking

costs reported in another study (Vasievich and Potter-Witterfi. That

work showed an average cost for cruising and selection marking of 516
per acre in Wisconsin, with a range of $12 to $20. The cruising portion
of this estimate is about $2.75 per acre, leaving $13.25 as an average
for marking. In real terms (net of inflation during 1982-87) the two
cost estimates are virtually identical.

There is a significant degree of variability between selection
marking costs as reported by various agencies and types of ownerships.

Costs of selection marking in one of the studies ranged from a low of

 

’Vasievich and Potter-Witter, pp. 16-19.

80

$9.31 reported by industry sources to a high of $24.26 per acre reported
by Federal agencies. In all ownerships labor costs were about three-
quarters of total selection marking costs (Winebar and Gunter 1984).

A few researchers have cited the potential for diameter-limit
cutting in northern hardwood stands because of the reduced marking costs
(Lyon 1986). Based on work with forest industry, these workers believe
that marking costs could be avoided altogether when corporate foresters
and logging contractors work closely together. At least two large
corporate landowners in Hichigan have tried this practice, but its long-
term feasibility has yet to be proven, particularly on non-industrial

lands.

Thinnina_£2111
Olson at al. (1978) derived equations for manual thinning based

upon thirty-eight operations in Hinnesota and Hichigan state forests.
With an average basal area of 38 square feet removed (BAR), their

estimated total project cost equation was:

r - 120.29 + (0.418(8AR)] so

For a 40-acre tract of northern hardwoods this would translate into a
cost of 513-14 per acre (1978 5), or about 519-21 (1990 5). The cost
compares well with the $18-19 per acre cruising/marking cost reported by
Vasievich and Potter-Witter‘. Cost of lighter or heavier thinning would
vary accordingly. Since the Argonne cutting methods averaged 25-30
square feet of basal area removed, estimated thinning costs would be
somewhat less in this study than in Olson et a1. (1978).

Thiede (1986) reported that northern hardwood thinning costs on
.Hichigan state forests had been about $30 per acre in 1974. The
development of commercial firewood markets in the mid- to late-1970's

 

‘Vasievich and Potter-Witter, pp. 16-19.

81

caused thinning costs to drop to about $16 per acre in 1978. By 1984,
nearly all hardwood thinning on state lands with access to good firewood
markets was being done profitably, through commercial timber sales.

Here, a net cost had become a net return.

£1!!!£H&_LIIQES
Reported costs for laying out clearcut timber sales averaged $5.15

per acre for Wisconsin and $8.14 for Hichigan in the early 1980's
(Winebar and Gunter 1984). Laying out clearcuts ranged from a low of
$3.15 per acre for state and industry to a high of 519.77 per acre for
the federal agencies, where the use of landscape architects and wildlife
biologists, and higher overhead, add appreciably to sale layout costs
(Blumfl.

By 1988, the latter survey reported cruise and sale layout costs
averaging 516 per acre for Hichigan and $84 per acre for Wisconsin
(Vasievich and Potter-Witter‘). These figures include clearcut sale
layout costs and 52-3 per acre for cruising costs. These costs are
significantly higher than costs for the same practice reported in the
earlier survey. Because clearcutting is becoming more controversial,
and a variety of expertise is now commonly used before clearcuts take
place, this study will use an estimate intermediate to the extremes
listed above. A cost of 526 per acre (the mean for all respondents in
the most recent Lake States survey) is a reasonable compromise,
reflecting the increased labor and environmental compliance costs for

clearcut management.

 

’Blum, John. Assistant Ranger, Timber Hanagement. Baldwin District,
Hanistee National Forest. Personal communication.

'Vasievich and Potter-Witter, pp. 16-19.

82

Cost variation Between Even- and Uneven-aged Hethods

In this analysis, three methods (BA 60, 75, 90) are clearly
uneven-aged. Two of the methods (clearcutting and shelterwood) are
even-aged. The crop tree cutting method, though classified by
Stoeckler’ as uneven-aged, is closer to an even-aged or a two-aged
method because of the stand structure it creates (Erdmann 1986).
Similarly, the diameter-limit method was called an uneven-aged method
(Stoeckler’), but results in stands that are structurally quite similar
to the commercial clearcut (Erdmann”). Hardwood management costs occur
each time there is a stand entry: thus, costs are more frequent and

usually higher for uneven-aged management.

Application of Cost Estimates to Argonne Cutting Hethods

Cost estimates from above were applied to the Argonne cutting
treatments. All costs are variable costs, and were expressed in 1990
dollars, then adjusted for inflation by the Producer Price Index for all
commodities (1990 8 100). Finally, all costs were discounted with a

4 percent discount rate.

Wham
This cost is consistent with Olson st a1.'s (1978) analysis. Each

basal area treatment was selectively marked every ten years. Average
cruising and marking costs from the three studies was $17.60 (1987 5)
per acre for both improvement and partial cut marking; this is about $20
per acre in 1990 dollars. Therefore, a cruising and marking cost of $20

per acre was assigned to each basal area treatment.

 

’Stoeckler, J.H. Establishment report for cutting methods study
(A-3) in pulpwood sized northern hardwoods. Unpublished manuscript
(dated January 1955). St. Paul, HN: U.S. Department of Agriculture,
Forest Service, North Central Forest Experiment Station, Argonne
Experimental Forest. 60 p.

I‘Erdmann, G.G. July 17, 1992. Personal communication.

83

W
This study included two types of clearcut treatments: a

silvicultural clearcut with a l-in. residual limit, and a commercial
clearcut with a s-in. limit. Eoth treatments were out only in 1952.
Though reported costs for clearcut layout in Visconsin averaged $84 per
acre in 1987, this cost category had only five responses. An average of
57 responses from the three surveys indicates that a cost of $26 per
acre (1990 $) is a figure reflecting current operational costs.
Post-harvest of non-commercial trees is a cost that needs to be
assigned to the silvicultural clearcut treatment, where l-S in. non-
merchantable stems were removed. In 1990 dollars, this would be about

$23 per acre (Vasievich and Potter-Witter").

MW
The Argonne cutting methods study is adjacent to three unpaved

USDA Forest Service roads. The study replications are no more than one-
half mile from these dirt access roads, and approximately one to two
miles from Wisconsin Route 32, a paved, all-weather secondary state
highway (Figure 3-2, page 32).

Typical logging access conditions in the Lake States northern
hardwoods region vary widely; there is really no such thing as a
“typical“ logging chance in the Lake States. Some areas, such as the
uplands of the northern Lower Peninsula of Michigan, are crisscrossed
with logging roads that make logging access low in cost (if trespassing
is not a problem and rights-of—way can be obtained). Other areas, such
as the central and western parts of the Upper Peninsula, have more
difficult physical access that occasionally require access roads of two
to five miles in length. Spencer (1983), reported that 39 percent of

commercial forestland in Hichigan is within one-quarter mile of a

 

"Vasievich and Potter-Witter, pp. 16-19.

84

maintained road, and SS percent is within one mile. Northern Wisconsin
has average logging access conditions that tend to be intermediate
between these two extremes.

Documentation of differences in road costs for even-aged and
uneven-aged hardwood management is poor. Initially, road costs for
even-aged systems tend to be less per unit of wood harvested than for
uneven-aged systems, yet limited research indicates that in the long run
the type of harvest system has little influence on road costs for
similar site conditions (Smith and DeBald 1978). Given this poor
information, and the marginal cost framework of this analysis,
differences in road costs between the two systems are assumed to be
minimal. Therefore, road costs are not included in this analysis, with
one exception discussed below.

Read maintenance, a variable cost, is an important cost category,
particularly for uneven-aged management, where stand entries occur every
10-15 years. Vasievich and Potter-Witter's'2 Lake States study found
average annual Wisconsin road maintenance costs ranging from $110 to
$330 per mile, with 5190-200 the average for most conditions. Variation
in these costs may be a function of different state, agency, and
corporate standards (Winebar and Gunter 1984). Based upon common
practice and ”average“ conditions, a maintenance cost of $200 per mile
was included every ten years (stand entry interval), for the uneven-aged
methods. This cost averages about $5.00 per acre, with a $3.00-7.00
range, assuming an SO-acre parcel size (Table 5—2). Because even-aged
management requires only two or three entries per rotation, road
maintenance costs in these treatments were applied only for final

harvest and thinnings.

 

“Vasievich and Potter-Witter, pp. 16-19.

85

TABLE 5-2. Average nominal road maintenance costs used in the
analysis. (These costs are on a per-acre basis,
assuming an SO-acre parcel sise.)

 

 

 

 

 

 

 

 

 

 

Year Low ledium Eigh
Costs Costs Costs
2022 $7.25 $12.10 $17.00
2012 5.40 9.00 12.65
2002 4.05 6.70 9.40
1992 3.00 5.00 7.00
1982 2.55 4.30 6.00
1972 1.00 1.70 2.40
1962 0.80 1.35 1.90
1952 0.75 1.20 1.65

 

 

 

 

 

Sensitivity Analysis: Selection Harking, Sale Layout, and TSI Costs
Results of the 1952-2022 analysis were tested for sensitivity to
changes in individual tree selection marking, TSI, and sale layout

costs. Table 5-3 lists these variations in cost.

Opportunity Costs

Opportunity costs measure the cost of making an investment in
terms of lost opportunities or alternative courses of action. In
forestry, opportunity cost is an important concept because cutting
decisions, once made, affect future forest conditions for decades. The
”wrong“ decision can lead to high opportunity costs for forest managers
and forest users. Once a stand is high-graded, marked too heavily, out
below a specified diameter-limit, or fails to regenerate, the landowner
pays a substantial opportunity cost for many years to come. Those
landowners with large acreage (industry, state, and national) are less
adversely affected by these decisions than smaller, non-industrial
private landowners who have fewer acres to manage and fewer alternatives

to choose. One problem in dealing with opportunity costs is that they

86

are not easily anticipated and cannot be quantified readily.
Uncertainty about future timber markets and forest conditions

complicates landowners' ability to anticipate Opportunity costs.

 

 

 

 

 

 

 

 

 
 
 
  

TABLE 5-3. Data used to test sensitivity of treatment results to
changes in marking, TSI, and sale layout costs; per acre
(1990 S).
_—_ _

Cost Category Low Wedium Bigh

Interval Cost Cost Cost
Cruise/Hark Every
for Partial 10 years $16 $20 $24
Cut 1952-2022 I
Shelterwood 1957, 1965, 16 20 24

2022
Clearcut
Sale 1952, 2002, 12 26 40
Layout 2022
Post-Harvest
Removal Non- 1952, 2022 20 23 26
Comm. Stems (Silv. CC)
Periodic
Woods 10 years or 3 5 7
Road before cuts ($120/mi) ($200/mi) ($280/mi)
Maintenance

-

 

 

 

 

 

Opportunity cost is also important when comparing even- and
uneven-aged management because timber growing stock functions
differently in the two systems. In even-aged management, the growing
stock is merely the crop to be harvested, while in uneven-aged
management, the reserve growing stock is both the crop and the "factory”
that produces the crop. In either case, the reserve growing stock
represents accumulated invested capital. A landowner's marginal cost of
capital decides the level of growing stock allowed to accumulate. In
economic theory, if a landowner cannot achieve a rate of return on
growing stock that is above his or her alternative rate, then s/he will

harvest the excess growing stock (Davis 1966).

87

Social Costs of Timber Wanagement

Because the great majority of commercial hardwood resources are in
the Eastern United States where population pressures are greatest,
people demand that hardwood forests provide a greater variety of
resource benefits than other forest types. Thus, social concerns can
have a greater impact on costs in hardwood management than in softwood
management, particularly in the Worth, where hardwood resources are
often close to large population centers.

Costs associated with environmental regulation of timber
management, though important, are poorly documented. A few forest
management practices, such as clearcutting, have been restricted or
eliminated entirely in certain areas. The federal government, for
example, recently enacted a policy that restricts clearcuts larger than
40 acres, and eliminates clearcutting as the standard silvicultural
technique on most national forests (Robertsonu).

Legislative restrictions on cutting practices impose higher costs,
yet few regions have consistent evidence of the extent of these costs.
Many states have enacted forest practices acts that add to timber
management costs, particularly for non-industrial private forest owners
whose transaction and administrative costs for complying with forest
practice acts are much higher than state government (Ellefson 1992). At
this writing a forest practice act has been proposed for Michigan, but
has not been passed. Neither Wisconsin nor Hinnesota has a forest
practices act yet, nor anticipates enacting one in the near future.

Evaluating the impact of higher social costs in hardwood timber
management is next to impossible. However, sensitivity analysis can be

used to allow for much of this uncertainty.

 

I’Robertson, F.D. “Ecosystem Hanagement of The National Forests and
Grasslands.“ Letter from Dale Robertson, Chief, USDA Forest Service, to
Regional Foresters and Station Directors. Dated June 4, 1992. 3 p. plus
enclosures.

Suuary

Hardwood management costs from four Lake States studies were
reviewed and compared. Costs from the two more recent studies (Winebar
and Gunter 1984, Vasievich and Potter-Witter“) were used in this
analysis. Variable costs included in this cutting methods study were:
sale layout, timber stand improvement, individual tree selection
marking, and periodic road maintenance. These variable costs were used
as inputs to the marginal economic analyses reported in Chapters 7-9.

These highly variable and site specific hardwood management costs
provide a basis for the sensitivity analysis in this study. However,
since there are very limited empirical data from northern studies,
future northern hardwood cost research is crucial, and may change the

results of this study.

 

“Vasievich and Potter-Witter, pp. 16-19.

CHAPTER ‘8 GROWTH AND YIILD P38010110"!

Introduction

Forest managers have a variety of programs available to project
northern hardwoods growth and yield in the Lake States. This study used
a 30-year growth and yield projection (1992-2022) to supplement the
original 40 years (1952-1992) of data on cutting methods. The growth
and yield projection was needed for a more complete description of stand
condition and response to treatment. Instead of only 40 years, a
management horizon close to northern hardwood rotation age was desired.

Comparing even- and uneven-aged management in northern hardwood
forests is complex because cutting cycles, stand entries, and stand
volumes vary between the two systems. In this study, the stand that
originated in 1905 was even-aged with most of the oldest trees about 90
years old. Even though there were 40 years of data (1952-92) on nine
cutting methods, the data base was inadequate to compare even-aged with
uneven-aged management. Rotation ages for even-aged management of Lake
States northern hardwoods are usually 110-120 years for sawtimber and
50-70 years for pulpwood (Erdmann 1986). Cutting cycles for uneven-aged
management in northern hardwoods range from 10 to 20 years (Erdmann
1986, Tubbs 1977b, Arbogast 1957). The available data were only
sufficient to observe the silvicultural effects of four or five periodic
cuts in the uneven-aged treatments and about one-half of a sawtimber
rotation in the even-aged treatments.

Growth and yield models, developed since the early 1970's, can
help close this knowledge gap. A variety of models exist for projecting
growth and yield of mixed hardwood stands (Belcher 1982, Belcher et a1.
1983, Brand 1981, Adams and Ek 1974, Hoser and Hall 1969, Hoser et a1.
1979). Available growth and yield models can be divided into two major
categories: stand level and individual tree models. Stand level models

are used where individual tree data are not generally available.

89

90

Individual tree models are further classified into three types:
individual tree, distance-independent: individual tree, distance-
dependent: and deterministic models. Because this study's data are
specific to individual trees on plots, the individual tree, distance-

independent models are of greatest interest.

Wethods

Lake States TWIGS (The Woodsman's Ideal Growth Projection System)
(Belcher 1982) was used in this study. Experts in hardwood growth and
yield (Hiner et a1. 1988, Belcher 1982) suggest that for short-term
projections of up to 30 years where individual tree data is available,
the STEHS or TWIGS growth and yield model is very appropriate.

TWIGS is an individual tree, distance-independent growth and yield
simulation program developed by the USDA Forest Service's Worth Central
Forest Experiment Station. TWIGS projects the diameter-at-breast-height
growth and the mortality of individual trees in the context of a stand,
whether of single or mixed species and sizes. Thus the computer program
grows the forest stand through time based on the characteristics of the
stand (Hiner et a1. 1988, Kowalski and Gertner 1989). TWIGS is actually
the microcomputer version of a main-frame growth and yield program
developed earlier, called STEHS (the Stand Tree Evaluation and Modeling
System, Belcher 1981, Belcher et a1. 1983, Holdaway and Brand 1986).

One of the specific purposes of TWIGS is to evaluate the productivity
and economic effects of different silvicultural prescriptions, a purpose

consistent with this research.

Characteristics of TWIGS

TWIGS and its predecessor, STEHS, a mainframe computer model, are
both regional growth projection systems. Data used to develop the Lake
States version of TWIGS came from Hichigan, Minnesota, and Wisconsin

(Hiner et a1. 1988). Nearly all of the data for northern hardwoods came

91
from Wisconsin and Hichigan's Upper Peninsula (BrandH. Because the
TWIGS data came from across the region, it results in average species
growth and mortality for the Lake States region; it will not necessarily
give accurate localised estimates. When localised estimates are
required, the developers of TWIGS recommend the use of methods for
improving estimates with additional diameter-growth information (Smith
1983, Holdaway 1985).

The TWIGS growth function equation has three subcomponents:
potential diameter growth, a competition modifier, and a diameter
adjustment factor. These three components of growth are related as
follows: annual diameter growth equals potential diameter growth times
the competition modifier plus the diameter adjustment factor.
Projections from each diameter growth model are used in each succeeding
projection. Each component is modelled in TWIGS based upon a set of
equations developed for that purpose. In Lake States TWIGS, estimated
board-foot, cord, and cubic feet volumes are based on Upper Peninsula of
Hichigan equations developed for the program (Raile and Smith 1982).
Volume equations were developed from measurements of d.b.h.,
merchantable height, top diameter outside bark, site index, and tree
class. There are also three condition classes that trees can be
assigned in TWIGS. They are: l) acceptable, 2) undesirable, and
3) cull. In this study, all trees were given acceptable condition
classes except for ironwood, which was classed as undesirable, and
obvious cull trees. By 1992, after 40 years of management, virtually
all cull trees were gone from the treatment study plots. Therefore, the
1992-2022 projections were done with all trees classed as acceptable.

Assuming reasonable accuracy of the projections, this combination
of 40 years of actual data with 30 years of projection creates an
expanded view of stand development. It gives the study about three-

fourths of a typical sawtimber rotation, and 20 years longer than a

 

'Brand, Gary. November 5, 1992. Personal Communication.

92

fairly typical pulpwood rotation. With this longer analysis period,
based on actual and projected growth, even- and uneven-aged methods can

be evaluated on a more comparable basis.

Validation of Diameter-growth Predictions

Diameter-growth predictions of TWIGS were validated by projecting
sample plots from all cutting treatments for the 1951-81 growth period.
Actual plot growth was compared with the performance of TWIGS over 30
years. A total of 17 sample plots were tested from the three
replications; most were from replications one and two, which had more
white ash and basswood. Average annual prediction errors for sugar
maple were -0.015, -0.0008, and -0.012 for the 10, 20, and 30-year
growth periods tested, respectively. This level of prediction error was
well within the plus or minus 0.020 acceptable prediction error
calculated by Holdaway (1985). Therefore, no adjustment of growth
coefficients was made for sugar maple.

However, basswood and white ash exhibited much higher prediction
errors than sugar maple, particularly for the 9, 11, and 13 in. diameter
classes. Annual prediction errors for white ash ranged from a low of
-.025 in. per year to a high of -0.093 in. per year, depending on
diameter class (Table 6-1). Diameter growth for sawlog-sized white ash
had an average underprediction of nearly two inches within a 20-year
period. Growth and yield for basswood was also strongly underpredicted
for the Argonne sites. Twenty-year prediction errors for basswood
ranged from a low of -.027 in. per year to a high of -.074 in. per year
(Table 6-1).

93

TABLE 6-1.- Comparison of growth predictions for three species before
adjustment (in inches, measured at d.b.h.).

20- ear annual rediction error Potential underprediction
Species Low High Ten years Twenty years

 

 

 

 

 

 

 

 

 

 

Sugar maple -.006 -.015 -0.15 -0.30
—

Possible Reasons for Underpredictions

Holdaway (1985) suggested the causes of poor predictions of tree
diameter growth in TWIGS (or STBHS) growth models could include several
factors: limitations in the original data base; the preponderance of
experimental plots, which are usually carefully managed; changes in
climate and environment; and the fact that 90 percent of the hardwood
trees used to calibrate the model were between 5-11 inches. Another
factor Holdaway (1985) cited was the aging of the model development
data. Researchers took most initial measurements between 1945 and 1960,
and final remeasurements in 1975 and 1976. Changes in general forest
conditions since 1976, such as droughts, pest outbreaks, and warming
trends, could lead to over or underprediction of d.b.h. growth for
several species. For example, in some forests high mortality in
American elm could have led to other hardwood species having greater-
than-expected growth during a 20-30 year period. Another factor is the
limited amount of data for white ash and basswood used to calibrate and
adjust the model. Finally, it is likely that site indices for basswood
and white ash were higher at the Argonne (8.1. 70-72) than for the
region-wide average originally used to calibrate TWIGS.

Although basswood and white ash together made up only 15 percent
of the study's trees in 1951, both species had high growth rates, good
quality, and higher-than-average prices. For these reasons, they

contributed disproportionately to stand value. Therefore, an adjustment

94

or correction to their growth rates was necessary before the growth

projection could be completed.

Diameter-growth Adjustment Hethodology

When making adjustments to growth and yield programs, there are
two methods: additive and multiplicative. Holdaway's (1985) additive
method was chosen here for several reasons. First, it provides more
realistic projections of overall stand characteristics than the
multiplicative adjustment model. Second, it has the flexibility to
increase or alter growth for large diameter trees, a trait lacking in
the multiplicative adjustment models. Finally, it has a proven record
of reducing d.b.h. prediction errors specifically for Wisconsin forests
(Holdaway 1985).

Prediction errors for basswood and white ash were calculated for a
20-year period by comparing actual field data with individual tree
growth predicted by TWIGS in each treatment plot for these periods. The
20—year prediction errors were used because this was the best compromise
between having a short growth interval for accuracy and a longer period
to maximise sample points (Holdawayfi. When lO-year diameter correction
coefficients were used initially, 10-year corrections obtained were good
for both species, but 20 and 30-year growth, especially for white ash,
were overpredicted.

Prediction errors were stratified across treatments to determine
if there were differences in errors due to treatment effects. An
analysis of variance was run on all the 20-year growth prediction errors
in HIWITABO. Though there were some differences due to treatment, they
were not found to be significant at the a - .05 level. Although one
might expect treatment differences on an individual tree basis, this was
not the case, perhaps because basswood and white ash trees are both

somewhat tolerant of shade, and if they survive heavy early competition,

 

aHoldaway, Hargaret. October 5, 1992. Personal communication.

95
can achieve good growth rates regardless of cutting history. This
effect was seen in several of the control plots, where white ash in
particular exhibited excellent growth.

Because no significant treatment effects were found, two
prediction equations were fitted to the data, one for basswood and one
for white ash. For each tree in each species, the midpoint d.b.h. was
calculated by: (final observed d.b.h. - initial d.b.h.) / 2. Each
observation was placed in the appropriate 2-in. diameter class; e.g., 5,
7, 9, 11, 13, and 15 in. classes. The annual growth error was found by:
(predicted - observed growth) divided by the number of years (10, 20,
30) in the growth period. The average adjustment for each diameter

class was thus the average growth error, or:

§2m_i29§i_:_2£29i1
Eq.1: Avg. adj. 8 s
no. obs. in d.b.h. class

The average 20-year adjustment was calculated for each diameter class
for basswood and white ash. These "new“ adjustments were added to the
'old' adjustments calculated by Holdaway (1985) to yield a “final”
adjustment for each diameter class. Average calculated adjustments for
basswood and white ash, by d.b.h. classes, are shown below (Table 6-2).
Once the above average adjustment was calculated for each diameter

class, a polynomial function of the form:

«.2: Adj. - a + b(d.b.h.) + c(d.b.h.)’

was fitted to the above data using HINITABO. The fitted value was
calculated at each 2-in. d.b.h. class, then added to the old adjustment
value. Finally, another polynomial function of the same form as above

was fitted to this data to yield the new adjustment coefficients. These

coefficients were entered into the TWIGS coefficient file (LS-TWIGS.dat)

96

in place of the previous values for basswood and white ash, following

instructions given by one of the designers of TWIGS (Brandfl.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

   
 
  

  
    

 

  

 

  

 

  

 

  

 

  

 

 

 

 

    

TABLE 6-2. Basswood and white ash coefficient adjustments
by d.b.h. class.
Basswood:
fl
Old adjustment New adjustment Final
d.b.h class Holdaway 1985 Twenty-year data adjustment
5 -.035 .003 -.032 l
7 -.034 .042 .008
9 -.031 .066 .035
11 -.029 .056 .027
13 -.025 .074 .049
White ash:
Old adjustment New adjustment Final
d.b.h. class Holdaway 1985 Twenty-year data adjustment
5 -.042 .025 -.017
7 -.049 .055 .006
9 -.050 .070 .020
‘_ -

 

 

 

The final fitted equations for basswood and white ash were as

follows:
3.1.3: W...
Eq.4:

 

- 0.0312 d.b.h.-0.00123(d.b.h.)’ -0.154

mm - 0.0007 d.b.h.-0.00036(d.b.h.)’ -0.24.

’Brand, Gary. November 5, 1992. Personal Communication.

97

The above prediction equations were highly significant at the
a -.05 level. The F-value for the fitted basswood equation was 20.38
with an R, of 93.1 percent (adjusted R, . 88.6 percent). The F-value for
the final fitted white ash equation was 17.92 with an R, of 92.3 percent
(adjusted R, I 87.1 percent).

For comparative purposes, the growth adjustment coefficients from
these equations are displayed with Holdaway's (1985) basswood and white
ash adjustment coefficients to the original STEHS (Belcher 1981) model
(Table 6-3).

TABLE 6-3. Comparison of TWIGS diameter adjustment coefficients before
(Holdaway 1985) and after final adjustments.

 

 

 

 

 

 

 

 

- Coefficientle
lSpecies Term Before After I
Basswood a, 0.00000 0.03120 I
a, 0.00007 -0.00123
a, -0.03700 -0.15400
White ash a, -0.01130 0.00070
a2 1.00066 -0.00036
-0.00200 -0.02400

 

 

 

 

Once TWIGS was modified with the new coefficients, the modified
program was run to determine the level of prediction improvement for
basswood and white ash. The 20-year white ash prediction error, that
before adjustment had averaged -0.083 annually, now averaged only
-0.01137 annually, based upon 22 observations from nine different sample
plots. The 20-year basswood prediction error, that had averaged -0.059
before correction, was now a slight overprediction of 0.0176 annually
(23 observations). Another result of the change in coefficients was a
slightly improved predictability for sugar maple, even though its

prediction errors were already within acceptable limits for the program

98

before the changes. The overall result of the changed coefficients was
a substantially improved version of TWIGS for the prediction of white

ash and basswood growth on the Argonne study site.

Stand Growth Parameters for Eunning TWIGS

A number of stand growth parameters must be specified before TWIGS
can be run. Due to the 5-year (growing season) remeasurement period
throughout this study, a 5-year projection interval was chosen. This is
the frequency at which TWIGS applies the growth and mortality equations.
The TWIGS technical guide (Niner et a1. 1988) also suggests a projection
interval of not over five years to maintain accuracy. A related
parameter, years in growth cycle, was also set for a 5-year interval.
This parameter determines the frequency of stand and stocking table
generation, and allows for the management Option. In the uneven-aged
treatments, management only occurs on a 10-year interval, but the 5-year
growth interval allows for checking remeasurement data on a basis
consistent with the study's original design.

For each 0.1 acre sample plot included in the study, trees present
in 1992 were entered into a companion computer program called TRBBGEN
(Brand 1981). A total of 27 plots were projected; nine treatments were
projected for each of three replications in the study. Host of the
treatments in each replication had five 0.1 acre sample plots, and these
were aggregated for each tree list. Consequently, an expansion factor
of two was entered to put all treatment projections on a per acre basis
for each of the following treatments: the light, medium, and heavy
selection, crop tree, control, diameter-limit, and shelterwood. For the
commercial and silvicultural clearcut treatments, only two 0.1 acre
sample plots were available in each 5-acre clearcut block. Therefore,
an expansion factor of five was used to put these treatments on a per
acre basis. To give even-aged treatments about three-fourths of a

sawtimber rotation, six 5-year growth cycles were chosen over 30 years

99

(1992-2022), with cuts occurring every 10 years in the uneven-aged
treatments. In the prior TWIGS validation, six S-year cycles were also
chosen over 30 years (1951-81). Other relevant parameters chosen before
running TWIGS were as follows: management option on; economic
evaluation option Off; volume option chosen was Scribner Decimal C rule;
and five diameter classes were chosen.

The economic evaluation Option was kept Off in this study because
all growth and yield results were later evaluated with the use of
Quick-Silver v. 2.0 (Vasievich and Frebis 1984), a forest investment
program. The Scribner Decimal C volume rule was used for consistency
with study measurements and northern Lake States' protocol. Also, five
diameter classes were chosen for all TWIGS simulations, to be consistent
with the study's diameter classes and to give greater detail Of
projections.

In the validation all management activities were done on an
actual, individual tree basis. If a 7.5 in. d.b.h. red maple was cut,
it was also 'cut' in TWIGS. For the 1992-2022 stand projections,
uneven-aged treatments were managed through a thinning from above to a
specified residual basal area (BA 60, 75, 90). This TWIGS treatment
option simulates the harvest of all volume growth in the form Of large
sawlogs, an acceptable treatment once the selection stand is fully
regulated.

The crop tree treatment was cut by individual tree selection,
favoring existing crop trees and leaving a residual basal area Of 85
square feet per acre after each cut. This treatment differed slightly
from the original crop tree treatment imposed in 1951. The original
crop tree treatment had thinned trees tO BA 60-70, following New England
guidelines, which later proved to be too heavy.

Because it was cut in 1992, no out was modelled in the TWIGS
projections for the 8-in. diameter-limit treatment. The growth of the

control treatment by definition was modelled with no changes. Due to

100

the small sise Of the residual stands, the commercial and silvicultural
clearcuts only received treatments in 1952. Similarly, the shelterwood
was cut only in 1957, 1965, and 1975. Of these three treatments, only
the commercial clearcut had a stand nearly ready for commercial
treatment in 1992. A regeneration cut to a 5-in. residual diameter was
simulated for the commercial clearcut in 2002, while a similar cut was
simulated for the silvicultural clearcut in 2022, the end Of the
projection period. The shelterwood plots were not cut in 2022, because

they had not reached commercial sise.

Results after Forty Years of Nanagement

Over the 40-year management period, the diameter-limit treatment
had the greatest total volume Of sawtimber (8069 board feet per acre)
removed (Table 6-4). The shelterwood treatment ranked second in total
sawtimber removals (7664 feet per acre) while the heavy selection
treatment ranked third at 7101 board feet removed per acre. The medium
selection treatment ranked fourth at 6775 board feet, the light
selection next at 5475 board feet, and the crop tree treatment came next
at 5119 board feet removed. The silvicultural clearcut had 3510 board
feet removed (all in 1952), while the commercial clearcut ranked last
(in 1992) at only 3325 board feet harvested (Table 6-4).

Total pulpwood removals were greatest over the 40-year period in
the diameter-limit treatment at 25.4 cords per acre removed. The medium
selection treatment ranked a close second at 25.1 cords. The heavy and
light selection treatments ranked third and fourth at 21.5 and 20.9
cords, respectively. Crop tree pulpwood removals were 12.5 cords, and
shelterwood removals were 10.6 cords. The silvicultural clearcut to a
1 in. limit had 8.2 cords removed through 1992, while the commercial

clearcut ranked last with 6.7 cords removed (Table 6-5).

101

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102

 

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103

Results of Nanagement including TWIGS Projections

Total treatment harvests and rankings changed when the results Of
30 years (1992-2022) of projections were added to the 40 years of actual
management (1952-1992). The medium selection treatment now had the
highest sawtimber production at 14,708 board feet per acre, followed
closely by the heavy selection (14,594), the light selection (13,847),
and the crop tree (13,100) treatments (Table 6-4). The commercial
clearcut was highest Of the even-aged treatments and fifth overall at
11,593 board feet per acre, followed by the silvicultural clearcut with
a production of 11,397 board feet per acre. The diameter-limit cut
ranked next at 8069 board feet per acre, and the shelterwood treatment
was last with 7664 board feet per acre.

Rankings for total production Of pulpwood also changed.
Including TWIGS projections caused the commercial clearcut tO move up to
third for total pulpwood production (39.1 cords per acre) and the
silvicultural clearcut to fourth (38.9 cords per acre). The medium
selection treatment now had the highest pulpwood production at 42.7
cords, with the light selection now second (40.0 cords). The heavy
selection was fifth (38.0 cords), the crop tree sixth (32.1 cords), and
the diameter-limit had fallen to seventh (26.3 cords). The shelterwood
treatment remained last for total pulpwood production (Table 6-5).

Several interesting management trends emerge from the projections.
First, the commercial clearcut was ready for harvesting again in the
year 2002, and yielded a great deal Of low-grade (mostly sugar maple)
sawtimber in that cut--an average Of 8268 board feet per acre across the
three replications. The projected cut ranged from 5936 (replication 1)
tO 9512 (replication 2) board feet per acre. Pulpwood removals in 2002
totalled 32.4 cords per acre. Average tree size in the cut was 8.8 in.,
with a range Of 5.5 to 21.7 in. d.b.h. Species composition Of the
sawtimber cut was overwhelmingly sugar maple (82 percent Of cut BA),

with much smaller amounts Of basswood (7 percent Of BA), white ash (6

104

percent Of BA), red maple (3 percent of BA), and bigtooth aspen (2
percent Of BA) harvested. By contrast, the simulated silvicultural
clearcut in 2022 had most (> 65%) Of its sawtimber volume in valuable
white ash.

Another intriguing development shown by the TWIGS projections is
the sustainability Of all three selection cuts. Each cut harvested only
the larger diameter trees after 1992, yet the residual stand diameter
distributions were sustained. Only the heavy selection cuts in
Replications 2 and 3 had diameters Of harvested trees that decreased tO
below 20 in. when thinned. In actual management practice, however,
although large trees will continue tO be harvested on the basis Of
financial maturity, some smaller trees will also be selected for reasons
of high risk, etc. Therefore, actual volumes harvested in the next 30

years may be less than shown here for the projected selection cuts.

Summary

Lake States TWIGS was tested for accuracy in predicting the growth
and yield Of the Argonne study treatments for 1951-81. Host species
were predicted reasonably well, but there were substantial and
consistent underpredictions for white ash and basswood. Using
Holdaway's (1985) additive adjustment methodology, diameter coefficient
adjustments were made for these two species, and an adjusted TWIGS model
created. The adjusted model performed well for the 30-year (1992-2022)
period needed in this study. Growth and yield results for treatments
were consistent with past growth Of similar-sized stands Obtained in
actual silvicultural trials. Therefore, the modified TWIGS results were

used in the extended economic analysis in this paper.

CHAPTER 78 “1818 0? TRADE-0P1! BETWEEN TREE IPECIES

DIVERSITY IND ECONOMIC RETURN.

Forest ecologists have long believed that greater tree species
diversity in northern hardwood forests reduces biological risk, but
researchers have not yet linked diversification with economic returns
for managed hardwood forests. This chapter shows how management Of
northern hardwood forests affects tree species diversity and economic
returns. Shannon's index (Drawer and far 1984) was used to measure
overstory and regeneration diversity for the nine even- and uneven-aged
cutting methods from this 40-year study on the Argonne Experimental
Forest. These indices Of tree species diversity were then compared with

the potential economic returns for each cutting method.

Introduction

There has recently been a rapid development Of interest in
ecosystem management among forest managers (Salwasser 1990).
Haintaining biological diversity in forested ecosystems has become an
important resource management issue. Bcologists consider biological
diversity at four different levels: genetic, species, ecosystem, and
landscape (HcNeely et a1. 1990, Cleland and Scott 1990). Yet economists
have not studied the implications Of preserving diversity until
recently. The paucity Of economic studies results from difficulty in
defining and measuring biological diversity on an economic scale.
Lacking clear definition, resource allocations for biodiversity will be
less than Optimal (Leefers 1990, Randall 1988).

This analysis defines and measures species diversity as a
parameter Of average rarity within a forest stand (Patil and Taillie
1982). In a diverse community, average rarity is the probability that a

particular species will be comparatively rare, whereas in a single

105

106

species community such as a red pine plantation the average rarity or
diversity measure is expected tO be near serO.

As a portion Of the overall economic analysis, this chapter
addresses tree species diversity in a northern Wisconsin hardwood stand.
The Objective is to learn which cutting methods are profitable, yet also
maintain or enhance tree species diversity.

Both even- and uneven-aged management can be used to regenerate
northern hardwood stands (Tubbs 1987, Godman 1985). Conventional wisdom
holds that even-aged management produces more diverse stands than
uneven-aged management. However, either system can increase or reduce
species diversity under varying site conditions (Stearns 1986). Past
research on the various cutting methods used in northern hardwoods
management has shown that tree diversity is strongly affected by the
choice and timing of a specific cutting method (Chapter 2, pp. 17-19).
Initial tree species composition also affects potential stand diversity.
Species composition of the stands in this study in 1951 was 63 percent
sugar maple and 4-9 percent each Of basswood, yellow birch, white ash,
red maple and hemlock (Table 3-1, p. 32).

Diversity Neasurement

The Shannon diversity index is derived from information theory.
Its premise is that natural system diversity can be measured similarly
to the information contained in a code or message (Hagurran 1988). The

Shannon measure of rarity of species, H', is calculated by:

3.1.1: H'I-Zpflogmpi

where p" -log.a (pg, reflect the degree Of uncertainty Of the identity
Of an individual chosen at random from a community. Shannon index

values range from 0 to l; the higher the index value, the more diverse

107

the forest community“. The Shannon index satisfies all essential
criteria needed for a diversity index to be useful (Hunter 1990): 1)
the diversity index should be maximized when the proportion Of each
species is equal (an even community): 2) for two completely even
communities, the one with the greater number Of species should have the
larger index value; 3) for a community that can be classified in more
than one way, the sum Of the index values in each classification should
equal the index value using all levels Of classification: 4) diversity
indices should increase with transfers of abundance from one species to
other less abundant species within the same community.

Analysis Of variance was used to test differences between
treatment means. Differences between individual mean values were
calculated with Duncan's multiple range test when the p-value was less

than 0.05.

Results of Management Effects on Stand Diversity

Initial (1951) stand basal area Of trees 4.6 in. d.b.h. and larger
did not differ significantly between treatments and averaged 92 ftZ/acre
(Table 3-1). Sugar maple was the largest component in all three
replications but was more abundant in replications 2 and 3 (Table 7-1).
Replication 1 was much more diverse than replications 2 and 3 (Shannon
indices Of 0.864, 0.358, and 0.408, respectively, Table 7-1). Forty
years later, basal area differed by treatment and ranged from 147 ft? in
the control plots to 73 ft? in the heavy selection cut plots.

Initial cutting decreased the diversity Of trees 4.6 in. and
larger only in the diameter limit cut (Table 7-2). Shannon's index

dropped from 0.310 before cutting to 0.219 after cutting. The change in

 

‘Some researchers use log base e (natural logarithms) for scaling
the Shannon index; however, either method is acceptable (Drawer and Zar
1984).

108

diversity from the initial cutting was not significant for the other
treatments.

After 40 years Of management, diversity had decreased in all
uneven-aged treatments (Table 7-2). The change in diversity was not
significantly different between treatments and no pattern Of change was
apparent. Shannon's diversity index decreased the greatest in the
medium selection cut (0.220) and the least in the light selection cut
(0.107).

In 1992 basal area Of species other than sugar maple (as a
percentage Of total residual basal area after cutting) was by far the
highest in the silvicultural clearcut treatment (85 percent). This was
followed by the shelterwood (56 percent), the light selection (45
percent), the commercial clearcut (38 percent), the control and crop
tree treatments (37 percent), and the medium selection (34 percent).
The heavy selection (19 percent) and the diameter limit (18 percent)
cuts had the lowest percentages Of basal area in trees other than sugar

maple.

Results of Management Effects on Regeneration Diversity

Saplings at the time the study was established were almost
nonexistent. In 1957, some seedlings had grown into the sapling size
class, but diversity was low and not significantly different among
treatments (Table 7-3). Shannon's diversity indices in the uneven-aged
treatments ranged from 0.100 in the heavy selection tO 0.207 in the
light selection.

By 1990, significant differences in diversity occurred among
treatments. Shannon's diversity indices ranged from 0.080 in the
control to 0.350 in the heavy selection plots for all trees (Table 7-3).
Generally, the heavier the cutting or less the basal area in 1990, the
greater the diversity. A strong negative correlation (p < 0.01) exists

between current basal area and Shannon's diversity index (Figure 7-1).

109

 

 

0.4 .,
Heavy selection
6 ' I I Medium selection
.2 0 3 In
_ I Light selection
>..
.1: - Crop tree I
a)
In-
0 e e
.2 0-2 ‘ Diameter limit
'0 I
.0) q
C
8
c: 0 1 " Control
(0 I
S .
U)
0 I I I I I I E U
70 90 1 10 130 150
1990 stand basal area (ftz)

Figure 7-1 Comparison of 1990 overstory basal area and Shannon's
index of saplings 2.0 to 4.5' d.b.h. for uneven-aged
treatments.

110

TABLE 7-1. Species composition by percentage of total in each of
three replicates prior to initial harvests in 1951.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

I Species Rep 1 (t) Rep 2 (8) Rep 3 (S) Nean (8)

I Sugar maple 32.2 79.9 77.7 63.3
White ash 9.3 3.9 1.6 4.9
Basswood 15.3 6.8 3.0 8.4
Yellow Birch 11.1 5.3 2.7 6.4
Red maple 12.0 0.0 0.0 4.0
Hemlock 10.3 0.1 4.1 4.8
Paper Birch 3.7 0.2 0.0 1.3
American Elm 1.9 1.8 1.6 1.8
Black cherry, 0.0 0.0 1.0 0.3
Black ash 1.9 0.0 0.0 0.6 I
N. Red oak 0.1 0.0 0.0 0.0 I
Balsam fir 0.2 0.2 0.8 0.4 I
Ironwood 1.2 1.8 7.5 3.5
Aspen 0.5 0.0 0.0 0.2
White cedar 0.2 0.0 0.0 0.1
Shannon's index 0.864 0.358 0.408 0.622

 

 

111

TABLE 7—2. Shannon's diversity values and changes for trees 4.6“ d.b.h.
and larger in 1951 (before and after cut) and in 1990.

Changes from
initial condition

 

 

 

 

 

 

 

 

 

 

Partial 1951 1951 After

cut Before After cut

treatments cut cut 1990 1951 1990

Control 0.438 0.438 ab| 0.305 0.000 0.133

Diameter 0.310 0.219 c 0.184 0.091 0.126

Limit

Crop tree 0.509 0.501 a 0.305 0.008 0.204

Light

selection 0.433 0.424 ab 0.326 0.009 0.107

Hedium

Heavy

selection 0.326 0.311 abc 0.198 0.016 0.128

p value 0.205 0.014 0.364 0.001 0.169
— _

 

 

 

 

 

'Heans followed by the same letter are not significantly different at
the 0.05 probability level based on Duncan's multiple range test.

TABLE 1-3.

112

by treatment in 1957, and in 1990 for all trees and

commercial trees.

Shannon's diversity values of saplings 2.0” to 4.5“ d.b.h.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Partial out All trees Commercial trees
treatments 1957 1990 1990

Control 0.128 0.080 c' 0.038 a
Diameter limit 0.122 0.183 be 0.144 ab

Crop tree 0.138 0.277 ab 0.175 b

Light selection 0.207 0.250 ab 0.141 ab h
Medium selection 0.118 0.345 a 0.238 b

Heavy selection 0.100 0.350 a 0.229 b

p value 0.3640 0.0001 0.0013

Even-aged All trees Commercial trees
treatments 2.0“ 8 larger 2.0” 8 larger
Shelterwood --- .441 .351
Silvicultural

clearcut --- .465 .322

Commercial

clearcut --- .477 .368
_ i

 

 

 

 

'Means followed by the same letter are not significantly different at
the 0.05 probability level based on Duncan's multiple range test.

Sugar maple comprised greater than 50 percent Of the saplings in
all treatments except the medium selection, shelterwood, and complete
clearcut. The proportion Of sugar maple was related to harvest
intensity: the heavier the cut, the less sugar maple, except in the
diameter limit cut.

Stocking of saplings was greatest in the medium selection cut and
least in the control (171 and 41 saplings/acre, respectively, Table
7-4). Considering the stocking Of commercial trees only, the heavy
selection had the highest number Of saplings at 134 per acre. Comparing
the regenerated stands, stocking is about the same in the complete
clearcut and shelterwood (693 and 734 trees/acre, respectively) and much

less in the commercial clearcut (347 trees/acre). The shelterwood had

113

more uniform stocking (plot stocking levels ranging from 640 to 920
trees/acre compared to the complete clearcut (ranging from 150 to 1210
trees/acre, Table 7-4). Diversity values were similar for all
regenerated treatments with diversity indices ranging from 0.441 to
0.477 for the shelterwood and commercial clearcut, respectively (Table
7-3), as compared with the entire study area's initial average index Of
0.622 (Table 7-1). Species composition in these three even-aged
treatments varied. The high percentage Of white ash (50.7 percent) in
the complete clearcut and black cherry (20.1 percent) in the shelterwood

were Of particular interest (Table 7-4).

Economic Analysis Methods
Price estimates for each species were Obtained from reported

prices (Chapter 4, pp. 62-67) using the linear regression model:

Eq. 2: ln(P”.) s a + b(Year)

Where ln(P)- natural log Of the reported stumpage price, and the
corresponding year is the dependent variable. The b parameter is
interpreted as the annual rate Of compound price change. Each cutting
treatment was evaluated using these estimated prices for the four major
hardwood species and hemlock present on the study sites. Other hardwood
sawtimber2 comprised less than 6 percent Of stand volume. Management

costs for cutting treatments were derived as discussed in Chapter 5.

 

2Other sawtimber included small (<1.5t Of basal area) amounts Of
American elm (Ulmus Americana L.), white birch, black ash (Fraxinus
Nigra Marsh.), black cherry (Prunus serotina Bhrh.), and northern red
oak (Quercus rubra L.).

114

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115

Volumes for the 1952 and 1962 cuts were derived using the volume line
equation method (Erdmann and Oberg 1973), due to a lack of merchantable
height data early in the study. Individual tree volumes were calculated
in the 1972, 1982, and 1992 cuts from merchantable height measurements,
diameters, and appropriate deductions for cull.

A marginal analysis was conducted using Quick-Silver v. 2.0
(Vasievich et a1. 1984). Financial performance for each treatment was
compared to the control (no management) treatment. Values of the
residual merchantable stands in 1992 were included in all analyses to
reflect total value for each treatment. Economic returns did not
include tree quality changes due to treatments except as reflected in
residual stand values. Net present values were calculated using a
4 percent real discount rate and adjusted to 1990 U.S. dollars for

reporting.

Economic Results

Net present values (1990 U.S. $ per acre) for the differences
among the seven treatments and the control ranged widely (Table 9-1,
p. 143). The medium selection had the highest net present value
($54/ac) followed closely by the heavy selection ($51/ac) and the
diameter-limit treatment ($48/ac). The light selection ($40/ac) and the
commercial clearcut ($32/ac) ranked fourth and fifth, respectively.

Although the diameter-limit cut ranked third among uneven-aged
treatments, its actual value was probably lower due to two reasons: the
heavy early harvest took small sawlog material before it had reached
financial maturity, and the residual was degraded primarily to a low-
value pulpwood stand. A subsequent analysis considered the economic
effects Of these changes in stand tree quality (Chapter 8).

The high residual sawtimber volume Of the medium selection and
heavy selection treatments combined with the low percentage Of
below-grade material in their residual stands helped give them high

116

financial rankings. The lack Of a commercial residual in the
shelterwood ($14/ac) and commercial clearcuts ($32/ac) caused their
rankings to be much lower. Financial performance for these even-aged
treatments may improve when current volumes attain commercial size in

the next few decades.

Discussion

Traditional views in the forestry community have held that, when
selecting a silvicultural system to manage a stand, the first criteria
should be the Objectives Of the landowner (Smith 1986). Economic
concerns Often dictate these objectives. Aesthetics and tree species
diversity, however, are Of increasing concern in forest land management.
The discussion Of these results focuses on the trade-offs between these

Objectives (Figure 7-2).

W

The 8-in. diameter-limit resulted in the least amount Of total
regeneration diversity Of any treatment except the control. Northern
hardwood stands that begin as diverse mixtures can easily become sugar
maple monocultures through diameter-limit cutting. Sugar maple
monocultures also may develop under light selection treatments, but
diameter-limit cutting brings on the near-monoculture condition faster
than any other treatment except the control. Heavy cutting Of mature
intolerant hardwoods appears to have led tO ideal conditions for sugar
maple reproduction. Many Of today's nearly-pure stands Of sugar maple

may have been created by diameter-limit cutting in the past.

117

 

 

 

 

0.5
l I

X Shelterwood Clearcut
.8 0.4 __ ....................................................................
5 Heavy
> I s
’3 Medium
2 0'3 _ ............................... . ................... . ................
0)
.2 a
‘0 ......................................................
C 0.2 ..............
O
C Diameter .
3 Limit
z 0.1 L— ....................................................................
CD

0 2 Control , 1 L , 1

0 10 20 30 40 50 60

NPV in $Iac.

Figure 7-2. Comparison of net present value and Shannon's diversity index
of saplings 2.0 to 4.5“ d.b.h. NPVs are based on the differences
between the control and cutting treatments.

118

Landowners with short time horizons may favor diameter-limit
cutting because it gives quick financial returns, and avoid the more
labor-intensive and expensive basal area cuts. Other studies (Reed
et a1. 1986, trickson et a1. 1990) have also found diameter-limit cuts
to be economically attractive for short-term management regimes.

The light selection treatment in this study was not as profitable
as the other uneven-aged cutting methods. This result comes as no
surprise, since others (e.g., Lyon 1986 and Reed et a1. 1986) have
demonstrated the economic infeasibility of these cuts. Also, the light
selection was the only uneven-aged treatment other than the control that
lost diversity in the commercial sapling class (Table 7-3).

After four cuts, stand structure in the medium selection treatment
came very close to the structure suggested by Arbogast (1957). The
Arbogast guide is widely applied in selection cutting throughout the
Lake States. This guide produces sustained yields of around 250 board
feet per year for 50 years once the desired structure is reached (Crow
et a1. 1981). Rays and Buongiorno (1989) suggested alternative economic
cutting strategies, based upon the uncertain growth of forest stands and
fluctuations of product prices. These results, however, suggest the
medium selection treatment not only sustains yield but has the highest
economic value of the treatments in this study. Furthermore, tree
sapling diversity is greater in the medium selection than all other
uneven-aged treatments. Initial heavier cutting and creating of canopy
gaps in unmanaged hardwood stands (Erdmann 1986) may increase sapling

diversity even further.

I!!!:l§l§.fl£§l§!l£ni

Shelterwood and clearcutting both maintain or enhance tree species
diversity, while diameter-limit cutting will not increase diversity in
hardwood forests (Table 7-3). Also, shelterwood's potential for

enhancing diversity can be increased by modifying the shelterwood to

119

include scarification (Godman 1985) and by controlling deer densities
when needed through heavy hunting (Relty and Nyland 1981). The higher
the value placed on tree species diversity, the more appealing
shelterwood will be as a silvicultural system. However, overall
financial performance of the shelterwood in this study falls
considerably short of the uneven-aged methods (Figure 7-2).

Past research has shown clearcutting to produce highly variable
regeneration results in Lake States northern hardwoods (Tubbs 1977a).
Our results also suggest that clearcutting may not be as financially
attractive as other methods; however, the Argonne clearcuts occurred in
pulpwood and small sawlog stands that may not represent commercial

stands today.

Summary

This study shows that there are clear differences in economic
returns and regeneration diversity under various cutting methods. The
medium selection treatment produced the highest diversity index of all
uneven-aged treatments and the highest economic returns. Production of
quality sawlogs and compatibility with aesthetic objectives are
additional benefits. Results also show that regeneration diversity in
the diameter-limit cut was half that in medium and heavy basal area
cuts.

Both the medium and heavy selection treatments also provided
greater economic returns and species diversity than the diameter limit
cut (Figure 7-2). Results indicate that the crop tree method may be
profitable if landowners are willing to wait. For even-aged treatments,
the commercial clearcut produced moderate financial returns and high
species diversity. Shelterwood cutting did provide high diversity and
initial revenues but little commercial value after 40 years.

If landowners have tree species diversity as their primary

objective and financial return is secondary, then clearcutting is the

120

preferred treatment in Lake States northern hardwood management.
However, if higher economic returns are the key objective, with tree
species diversity as a secondary management goal, then a medium

selection treatment produces the best combination of both economic and

diversity results.

CHAPTER .8 HARDWOOD IAWLOO CHILI!!!

DCOIONIC EVILUAIIOI OP CUTTIDO METHOD IMPACTS

Lake States northern hardwood stands today are largely second-
growth, resulting from heavy commercial cutting at or near the turn of
the past century. Tree quality has been a concern in these forests
because many stands have suffered degradation due to high-grading.
These stands could produce far better quality than in the past, yet
higher tree quality depends upon strong markets to remove low quality
hardwoods from heavily overstocked stands. Recent development of
vigorous pulp and fuelwood markets in many areas of the Lake States has
created a new economic opportunity for silvicultural treatments to
improve tree quality (Webster 1986).

Forest managers already have most of the silvicultural techniques
needed to produce higher quality in northern hardwoods (Erdmann 1986).
What they lack is adequate information on the returns they can expect
from silvicultural treatments that may improve tree quality. Managers
need to know if investments in tree quality are worth the time and
expense involved, and what methods are most profitable under specific
stand and site conditions.

This chapter answers some of the above questions, based upon a
20-year tree quality study. In 1971 this study was superimposed on the
Argonne cutting methods study. Changes in the silvicultural and
economic yields due to improvements in individual tree quality were

analysed for six of the original study's nine cutting methods.

Literature Review

Several past studies (lrdmann 1986, Nyland 1986, Tubbs 1977b, Eyre
and Zillgitt 1953) suggested that northern hardwoods on good sites are
best managed for the sustained production of high grade veneer and

sawlogs. However, there is scant economic evidence to support these

121

122

assertions. Most studies on tree quality have been theoretical or
silvicultural in nature (lcCay and Denald 1973, Brisbin and Dale 1987).
tconomic evaluation of long—term tree quality response to treatment has
not been possible previously due to a lack of adequate data.

Accurate evaluation of hardwood tree quality is crucial to the
economic management of high-value hardwoods, because the differential in
price between high-quality and low-quality end products is large. For
example, a recent issue of the weekly fig;§ugg§_figgigw (March 1991)
reported northern white ash 4/4 green lumber prices (delivered) of $775
PAS grade and only $215 IZA grade. Price differentials for northern
basswood were similar--$625 FAS grade and $175 #23 grade. Available
data for delivered logs by quality, though scarce, also show that prime
and #1 hardwood logs often command more than twice the price of #2 or #3
logs (Hoover'). Obviously, improvement in log quality of one or more
grades could produce handsome financial returns.

However, tree quality can only be estimated accurately after end
products to be produced are defined (Rast et. a1. 1987). Hardwood tree
quality, therefore, is a function of tree and wood characteristics
(Brisbin 1985), the most important of which are: 1) growth rate and
bole form of the tree; 2) knots and limb-related defects; 3) decay and
insect damage; and, 4) size of tree.

Prior research has shown that much of the improvement in stand
tree quality is the result of increasing sise of hardwood sawlogs.
Larger siaes make increased yield and utilisation possible (Brisbin and
Dale 1987, Sonderman 1987, Banks 1976, Boyce and Carpenter 1968).
Improved growth rate in response to crown release and thinnings has
three important functions relative to tree quality. First, it makes
individual grade improvements due to size increases possible much sooner

(trdmann 1986, Johnson 1986). Second, it improves the overall stand

 

'Hoover, W.L. Unpublished data on delivered log prices according to
grade. West Lafayette, IN: Purdue University, Department of Forestry.

123

quality through increased vigor. Finally, it can stimulate growth that
can cover up or reduce bole defects (Godman and Books 1971). Yet there
is a definite trade-off between stand growth response and stem quality--
thinnings that are too heavy will stimulate epicormic sprouting and
lowering of crowns, increasing defect and decreasing quality and value
(Godman and Books 1971, Crow 1985).

Although there is a close relationship between diameter growth and
tree grade improvement, grade improvement does not depend on diameter
increases alone. Diameter growth is a necessary but not a sufficient
condition for grade improvements in hardwoods (Herrick 1952, Godman and
Mendel 1978). Loss of quality may occur because of crook, rot, branch
defects, sweep, seams, mineral streak, bird peck, insect holes, limbs,
knots, and bud clusters (Banks 1976). The species of tree or inadequate
growth between stand entries also can contribute to trees that do not
improve in tree grade.

In medium- to high-value hardwoods, research has confirmed that
quality change over time is at least as important, if not more
important, than diameter and height growth in contributing to timber
stand value (Rast et a1. 1987, Sonderman 1986). In Lake States northern
hardwoods, increases in tree grade over a 10-year period consistently
added greater value than either gains in merchantable height or diameter
growth (Godman and Mendel 1978). Previous work in Ohio oak stands by
McCay and Denald (1973) also confirmed the financial superiority of
grade increases over height or diameter gains alone. Their study showed
a doubling in the rate of value increase for lB-in. red oak trees that
changed from a butt-log grade 2 to grade 1, versus those with no change.

On a stand basis, particularly in vigorous pole-sized hardwoods,
diameter growth is the primary contributor to grade improvements.
Properly timed thinnings greatly reduce the time necessary for northern
hardwoods to reach larger sizes and, therefore, improvements in tree

grade. Johnson (1986), for example, found that the time required to

124

reach minimum financial maturity (16 in.) was 45 percent less on thinned
versus unthinned trees in over-stocked pole-sized northern hardwoods of
Michigan's Upper Peninsula. Such reductions in maturation time can
yield significant increases in rates of return to hardwood management.

Merchantable height differences also occur between even- and
uneven-aged hardwood management treatments. Prior research has not
considered the variation in hardwood quality and yield because of these
differences. Erdmann (1986) and Crow et a1. (1981) mention this
difference, but little empirical data has been available to consider
economic impacts of this growth difference between even- and uneven-aged
management. One ”rule of thumb” sometimes used by managers is that
uneven-aged management can produce an extra half-log of merchantable
height in Lake States northern hardwoods. However, this judgment has
not been verified by research.

Mautiyal (1983) has proposed a theoretical model for managing
uneven-aged selection hardwood forests based on the theory of financial
maturity. Using tree vigor as a criterion, he derived optimal rotations
from crops of trees that have increasing stumpage price functions, thus
effectively “safeguarding" the penultimate (next-to-last) crop before
financial maturity. Tree grade and size changes caused most of these
increasing stumpage prices. Nautiyal's work ties in with studies on
hardwood tree quality because tree grade improvements lengthen financial
maturity (Godman and Mendel 1978).

The financial value attributable to tree quality changes has two
components: value from trees already harvested, where treatment may
have affected quality; and the value of residual trees, which may or may
not change in quality in response to treatments. Higher quality logs
yield a greater proportion of higher value lumber, the key factor in
valuing changes in tree quality. Herrick (1956) used log and lumber
price relatives rather than dollar values to analyse differences in tree

and log quality. Expressing the prices of the various grades as ratios,

125

using log or lumber grades as reference grade, Herrick found that
differences in tree and log value could be calculated more quickly and
easily than with other methods.

Hanks (1976) developed equations that gave graded lumber yields
for tree-graded hardwood logs. He developed these equations for 17
hardwood species. Hanks (1976) used hundreds of logs from the Lake
States, Midwest, Appalachians, and Hortheast. These lumber yield
equations have become the standard for developing hardwood yield
estimates from graded trees throughout the last. This analysis used

these equations as a principal tool in this portion of the study.

Data

Diameter at 4.5 feet (d.b.h.) was measured on 5-year intervals
beginning in 1951 from five, 0.5 acre sample plots within each treatment
plot. Sawlog trees (trees larger than 9.6 in. d.b.h.) were graded
according to Hanks' tree grading system in 1971 and 1991 (Hanks 1976).
Cull percent was calculated by techniques reported by Gevorkiantz
(1947). Merchantable sawlog heights (number of 16-foot logs) to a
diameter inside bark of 7.6 in. on the small end, or to a merchantable
log stopper, were recorded in 1971 and 1991.

The stand in 1951 primarily consisted of pole-sized trees
(4.6-9.5 in.). Scattered among the treatments were some larger diameter
residual trees from the early 1900's logging. Because grade changes are
unlikely in these residual trees and most often these trees are poor
quality, only those trees that were pole-sized in 1951 and had grown
into sawlog-sized trees by 1991 were analyzed for tree grade
improvement. The silvicultural analysis of tree quality did not include
trees cut during the period. Final economic analysis, however, included
these large holdover trees because of their contribution to total value.

For the silvicultural data (Tables 8-2 and 8-3), analysis of

variance tested differences between treatment means. Differences

126

between individual means were calculated with Least Significant
Difference when the p-value was less than 0.05.

The silvicultural data were originally in a variety of hand-
written formats. They were put into an individual tree data base
computer format using PARADOXO v. 3.5 (Borland International 1985,1990),
a relational database management software package. The query and table
functions in PARADOX. made it possible to link tree grade, diameter
growth, and species data for each treatment.

Tree grades for the 1952 and 1962 stand entries were not
available. However, log grades were available for 332 trees from 1951
that were in the original stand. Of these 332 trees, about 85 were
trees that were harvested in 1951 or 1961. Analysis of log grades for
these 85 trees showed that most (60) of these graded trees were either
of small diameter or grade three. Therefore, they contributed very
little #1 common and better lumber to the analysis. However, 25 trees
in the selection and diameter-limit cut were of log grade 1 or 2 or were
grade 3 trees above 15 inches in diameter. Tree grades were derived for
these 25 trees so their contribution of upper—quality lumber could be

included in the regression analysis of lumber quality changes.

Bconomic Hetbods

Reported Wisconsin hardwood lumber prices were used for economic
analysis of cutting method treatment values. These are the prices paid
for five grades of green, 4/4 hardwood lumber at the mill--FAS, selects,
#1 common, #2 common, and #3 common. Three sources of Wisconsin data
were consulted--the Wisconsin Forest Products Price Review, Lumber
edition (Peterson 1981, 1991), the Hardwood Market Report, Northern
Market (Lemsky 1971-91) and the Weekly Hardwood Review (1971-92).

Gevorkiants' and Olsen's (1955) volume equations calculated

individual tree gross board foot yields, according to the formula:

127
volume (Scribner) I AHTILOO 10 {-.121 - 4.21(1/d.b.h.) + l.16(loglo

d.b.h.) - .306(l/merch. length) + .723(loglo merch. length)).

Het board foot yields were determined by multiplying gross board foot
yield derived from the above equation by the percent cull for each tree,
a variable measured in the study.

Tree grades (Hanks 1976) of both harvested and residual sawlog
trees, by treatment, were measured for 1971, 1981, and 1991 following
Hanks' (1976) methodology. Merchantable lengths of each log were also
measured, and used to derive factory grade lumber yields for each major
treatment. These yields were developed by applying Hanks' (1976) yield
equations to treatment data from the Argonne cutting methods study in
1972, 1982, and 1992. Treatment lumber yields were calculated for 1972
and 1982, while both treatment lumber yields and residual stand quality
lumber grade distributions were calculated for 1992.

A few changes were made in Hanks' (1976) lumber grade yields to
make treatment comparison more feasible. Instead of using seven or
eight lumber grades, this study used only six. A single FAS grade
included the grades FAS, FASlF, and Selects. This change was made
because differentials are very small between these grades, generally
only 35-20 per thousand board feet. Similarly, the grades 2A and 28
were combined into a single 20 grade for red maple, because it was the
only study species not having a single #2 grade, and because red maple
yields were less than 2 percent of this study's volumes. However,
because of large reported price differentials between grades 3A and 38
(Peterson 1981, 1991), particularly for sugar maple, these grades were
included in the analysis for all species. The five grades used in the

analysis were: FAS, #1 Common, #2 Common, #3A, and #38 (or #3C). These

128

grades and their prices per thousand board feet (Scribner) by species
and pertinent year are shown below (Table 8-1).

Economic values of treatment yields were calculated before the
1972 cut and after the 1992 cut, to determine the effect of quality
treatment on value yield. Total economic values also included residual
treatment values that reflected gains in the quality of the residual
stands. Prices for the graded hardwood lumber yields came from the
Wisconsin Forest Products Review, Lumber edition (Peterson 1981, 1991)
and the Hardwood Market Report (Lemsky 1971, 1981, 1991).

Only variable costs for the 20-year period were used in the
analysis (Chapter 5, pp. 82-85). A marginal cash flow analysis was
conducted using Quick-Silver v. 2.0 (Vasievich et a1. 1984), where each

treatment's performance was compared with the control.

Silvicultural Results: Changes in Tree Quality and Grade

Because grade improvement is the primary contributor to increased
value (Godman and Mendel 1978), discussion of silvicultural improvements
in quality focused on grade changes (Tables 8-2 and 8—3).

Average tree grade improved the most and was highest in 1991 for
the medium selection treatment (Table 8-2). The medium selection was
the only treatment that averaged more than a full grade in improvement.
It also had the most (32 percent) grade 1 trees in 1992 after cutting
and the most (66 percent) trees in grade 1 and 2 combined (Table 8-3).

129

TABLB 8-1. Lumber prices by grade used to evaluate lumber yields
(per 1000 MBF, 4/4 lumber F.0.B.).

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

* Prices for ash in 1971 were quoted for brown ash, the trade name for
black ash. In most markets white and black ash lumber were mixed, with
the same price in 1971.

Notes: Above are median prices at the mill. These are nominal prices.
FAS, PAS IF, and Selects combined into a single FAS grade. #2A and #28
red maple were also combined into a single #2C grade. Grade premiums of
380-100 per MBF were applied for FAS/Selects alone grades for sugar
maple, basswood, and yellow birch in 1981 and 1991.

Species ,7 FAS #IC #2C I #3AI #38
Hbvember 1971

Sugar maple 270 180 90 70 65
White ash* 250 175 78 --- 65
Basswood 260 157 80 --- 59
Yellow birch 345 175 100 83 65
Red maple 240 180 85 --- 65
November 1981

Sugar maple 450 335 210 190 140
White ash 540 350 182 152 127
Basswood 462 295 136 145 100
Yellow birch 585 325 210 192 137
Red maple 370 207 160 140 127
Hovember 1991

Sugar maple 620 415 305 285 165
White ash 755 515 215 170 140
Basswood 650 295 170 110 100
Yellow birch 625 325 220 190 150
Red maple _____ 485 335 __ 200 160 150

 

130

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132

Grade improvement was slightly less than one grade for the crop
tree treatment, and 0.8 grade for both the light and heavy selection.
The control (2.1) and diameter-limit (2.3) had by far the lowest average
tree grades in 1991, but their increases (0.7) were not significantly
different from the heavy or light selection treatments.

After 20 years of quality management, the light selection had the
same number (32 percent) of grade 1 trees as the medium selection, but
fewer grade 2 trees. The heavy selection had more grade 2 trees (43
percent), but fewer (19 percent) grade 1 trees. The diameter-limit had
the fewest grade 1 (5 percent) and grade 2 (14 percent) but the most
grade 3 (50 percent) and total number of trees that were grade 3 and
below (81 percent). The control treatment had the highest number (37
percent) of below grade trees, while the heavy selection had only two
percent of its trees below grade. These results strongly confirm the
effects of silvicultural treatments that remove cull and low-grade trees

in selection management?.

Results: Regression Analysis of Lumber Quality Changes

In order to proceed with the economic analysis, it was first
determined whether there were, in fact, significant changes in treatment
quality. A linear regression model was used where the change in #1
common and better lumber was analysed by treatment and plot.
Independent variables were the cutting treatments, stocking of white ash
and basswood, and average initial tree size. The dependent variable was
the total change in #1 common and better lumber (both harvested and

residual).

 

’Strong, T.F.; Erdmann, G.G.; Niese, J.H. (in preparation). Forty
years of alternative management practices in second growth, pole-sized
northern hardwoods. St. Paul, MN: U.S. Department of Agriculture, Forest
Service, North Central Forest Experiment Station (unpublished
manuscript).

133

In the best regression model, treatment effects explained 32

percent of the variation in #1 common and better lumber.

Adding a term

for percent initial stocking of white ash and basswood improved the

explanatory power of the model to 51 percent.

A third term, average

initial (1951) tree sise, boosted the model's explanatory power to 59

percent.

In this model, five of the six treatments had significant

t-ratios (Table 8-4). The analysis of variance is presented in

EARLS I-C.

IQUAIION:

Regression statistics for plot analysis of variation in
#1 common and better lumber.

Total #1c* :- «19 + 184 LIT! + 123 use + 176 m + 31.: case
- 108 nus + 11.a avatars + 20.3 ans 1. ass

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

—_ — _1
Independent Coefficient Standard t-ratio p-value
Variable Deviation
Constant -418.90 124.40 -3.37** 0.001
Light 183.65 55.89 3.29** 0.002
Medium 123.15 56.53 2.18* 0.032
Heavy 175.54 56.22 3.12** 0.003
Crop 87.21 55.03 1.58 H.S. 0.117
Diameter -108.07 54.93 -1.97* 0.053
Limit
Average 77.80 15.07 5.16** 0.000
tree sise
Basswood] 20.29 3.64 5.57** 0.000
White ash
s - 140.4 a’ - 58.7t Adjusted a” - 55.0:

* - significant at the a - .05 level
** - significant at the a - .01 level

 

 

134

Analysis of variance for #1 common and better lumber.

 

 

 

 

m
Mean
Source DF Sum of Squared F-Value p-Value
Squares Brror
Regression 7 2212976 316140 16.04 0.000
Srror 80 1557239 19712
Total 87 3770216
T _

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Source DF SEQ Sum of
Squares I

Light 1 476757
Medium 1 224590

IHeavy 1 221386
Crop 1 164855

I Dia.-limit 1 107693

IAvg. tree 1 405081
Basswood 1 612614
/ash

 

 

 

The regression model above indicated that changes in #1 common and
better lumber were significant. Then, a one-way analysis of variance
was conducted to determine how significant the changes in #1 common and
better lumber were by treatment (Table 8-6). The light selection
treatment had the greatest change (562 Bdft/plot) in #1 common and
better lumber over the study period. Changes for the medium (477
Bdft/plot) and heavy (449 Bdft/plot) selection treatments were also
significantly (a - .05) higher than the other three treatments, but did
not differ from each other. The crop tree (374 Bdft/plot) and control
(351 Bdft/plot) did not differ from each other, but were also
significantly greater than the diameter-limit treatment (214 Bdft/plot).
For comparison, these data are also shown on a per acre basis

(Table s-s).

135

TAILS S-6. Increase in #1 common and better lumber, by plot and
treatment (1951-92).

 

 

 

 

 
  
 

 

 

 

 

 

 

 

 

— l i
Mean change in Increase in
#1C 8 better #IC 6 better
lumber, Standard lumber, avg.
Treatment 8d.ft[plot Deviation 8d.ft[ac
Light 561.6 195.5 5616
Selection
Medium 477.1 182.9 4771
Selection
Heavy 449.4 175.2 4494
Selection
F Crop 374.1 218.1 3741
Tree
Diameter 213.8 115.0 2138
Limit
rControl 350.6 250.4 3506

 

 

 

 

 

The above results showed that there were significant changes in lumber
quality variation by cutting method. This result gave credence to the

economic analysis that follows.

Sconomic Results
new

The diameter-limit treatment had the greatest net harvested volume
for the study period, 4452 net board feet (HBF) per acre (Table 8-7).
Its lumber value cash flows averaged $1763 per acre. The heavy
selection treatment was second with 3216 net board feet harvested per
acre with cash flows of $1405/ac. Ranking third was the medium
selection with 2180 NBF/ac and $1079/ac. Fourth ranking went to the
light selection (2175 HBF/ac.) with $933/ac. Finally the crop tree
treatment had the lowest net harvested volumes, 1916 HBF/ac., and total
cash flows of $9l8/ac.

136

TAILS 8-7. Met harvested lumber volumes and total cash flows, 1971-92
(per acre basis, 1990 S).

 

 

 

 

 

 

 

 

 

 

— F

Treatment Met volume Total Cash Harvested

Flows Lumber Value

Discounted

at(4t)
Diameter-limit 4452 $1763 $805 I
Heavy 3216 1405 953 I
Medium 2180 1079 738 I
Light 2175 933 583 I

I Crop 1916 918 827

— —

 

TABLS 8-8. Residual values of lumber yields, 1992 (1990 S).

 

 

 

 

 

 

 

 

 

 

 

nu-

[Treatment Residual Value I
Light selection $3173
Medium selection 2671
Heavy selection 2182
Crop tree 2329
Diameter-limit 0
Control 2899 I

We”

Residual lumber value (real 1990 S) was greatest in 1992 in the
light selection treatment ($3173/ac) (Table 8-8). The control treatment
ranked next ($2899/ac), followed closely by the medium selection
($267l/ac), then the crop tree ($2329/ac). The heavy selection ranked
last of the four uneven-aged treatments in residual lumber value
($2182/ac). The diameter-limit treatment (SO/ac) had no residual lumber
value in 1992 because all sawlog-sized material was harvested in that

year.

137

The above cash flows from harvests and residual lumber values were
combined and then discounted to give a proper measure of economic
values. Marginal costs (see pp. 82-85) were also applied to obtain the

net present value rankings below.

WW
Marginal analysis (1971—92) of the remaining treatments as

investments gave top ranking to the heavy selection ($542/ac) treatment
(Table 8-9). The medium selection ($488/ac) came next, while the light
selection ranked third ($443/ac). Met present value was lowest for the
diameter-limit cut (-$247/ac). Lumber value in the diameter-limit was

far below the value of lumber in the control treatment.

TABLE 8-9. Comparison of net present value of the difference between
the control and cutting treatments for total lumber value
yield (1990 8, 4‘ discount rate).

I—
Met present value,
Treatment Marginal revenues

 

 

 

 

Heavy selection $542 I
Medium selection 488 I
Light selection 443 I

 

 

 

Diameter limit -247 I

 

Discussion

Considering the need to balance both harvested and residual lumber
values, both the heavy and medium selection treatments clearly rank
ahead of the others. The heavy selection treatment had the highest
total value of marginal revenues (Table 8-9, Figure 8-1), the highest
discounted lumber value of harvests (Table 8-7), and the lowest

percentage of poor quality trees in 1992 (2 percent). The medium

138

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

NPV $/acre
600/
400 "
’ / / _ . ..
/// ///
1:: _ W / / , /
0 . / :2:::::::..:: ::: ’
-200 " f / 7 7
/ / / /
/ / / /
Heavy Medium Light DL
Figure 8-1. Comparison of harvested. residual. and total lumber

values (1971-92) for four tree quality study treatments.

 

 

I Total Value
Residual
Harvest

 

Values are for differences between treatments and control.

 

139

selection treatment ranked second in MPV of marginal revenues and had
the greatest improvements in tree grade, merchantable height, and number
of residual grade 1 and 2 trees (Table 8-3). It also had the most
favorable distribution of size classes that approached recommended
(Arbogast 1957, Crow et a1. 1981) stand structure guidelines (Figure
2-1). Though it also had a high total lumber value, the light
selection's value was heavily weighted by its residual value, and
harvests tended to be light and marginally economic from an operability
viewpoint. The crop tree treatment had a high total lumber value, but
over 70 percent of its harvested value came in the first cut, which gave
it excessive weight under discounting assumptions. Also, the crop tree
treatment was overcut in 1972 at a level that was not sustainable and
may have resulted in subsequent wind damage and mortality to the
residual stand.

The diameter-limit treatment had the highest volumes and cash flow
for its harvested lumber, but was only third in present value of lumber
(Table 8-7). Met present value of marginal revenues (-$247/ac) was
lowest for the diameter-limit cut, and it had by far the highest (81
percent) number of poor-quality (grade 3 and below) trees before harvest
in 1992.

The light selection had the highest percentage (71 percent) of its
total lumber value in the residual stand (other than the control). The
medium selection had 62 percent of total lumber value in its residual,
the crop tree 57 percent, and the heavy selection only 51 percent. The
diameter-limit out had none of its total lumber value in its residual
stand, because a complete harvest of all sawlog-sized material had taken
place in 1992.

These economic results can be used to elaborate on results of the
overall economic analysis (Chapter 9). When the economic returns to

tree quality change are included, then the economic performance of the

140

medium and heavy selection treatments improves, relative to the
diameter-limit and clear-cut treatments.

The gap in economic performance between the medium and heavy
selection treatments and the diameter-limit is much greater because this
quality analysis explicitly includes information on low-value, below-
grade sawtimber, while the overall analysis does not. Also, this
quality analysis only includes relative lumber value returns of sawlogs,
and does not include pulpwood, which is the primary product of the most
heavily-cut treatments. The quality results do not include potential
returns to veneer production, although veneer trees may number 50-75t of
residual large sawlog trees in the carefully-managed selection
treatments.

These quality analysis results emphasise the importance of one's
management perspective and underlying assumptions. Taking a long-term
view of the forest enterprise makes the realisation of these much higher
returns to quality improvement possible. A shorter-term view will favor
a greater proportion of pulpwood production, as shown in the diameter-
lbmit results of Chapter 9. This analysis of tree quality returns does
show that with current high prices in hardwood lumber markets, long-term

management for tree quality can yield excellent returns for some owners.

Summary
W

Analysis of six cutting treatments regressed on total change in
upper-quality lumber showed that there were significant differences in
hardwood tree quality of the six cutting methods over a 20-year period.
Initial stocking of white ash and basswood, and average initial tree

size, also helped explain differences between treatments.

141
new

The heavy selection treatment had the highest economic returns in
this study, yet its silvicultural quality--in terms of tree grade,
merchantable heights, and lumber quality--improved nearly as much as the
more lightly cut selection treatments (Strong et al. in preparation).
The medium selection treatment also had high total economic returns, but
a greater proportion of its value was in the residual stand, and it had
greater improvements in silvicultural quality. Diameter-limit cutting
in this study resulted in surprisingly low net revenues, and by far the
lowest lumber and stand quality.

For managers primarily desiring the highest economic return while
enhancing tree quality, the heavy selection treatment was best in this
analysis. More conservative managers desiring the greatest improvements
in silvicultural quality, but also high total value, will find the
medium selection treatment desirable. Under no situations where quality

management is a goal is the diameter-limit treatment recommended.

CHAPTII ’8 ICOMOMIC IMILIOII, IISULII, IMD DISCUBSIOM

The following sections contain the results of the overall economic
analysis for the nine cutting methods. All analytical results are
stated in terms of marginal revenues that accrue to treatments as
compared to the financial performance of the control treatment. The
base analysis covered the 40-year (1952-92) period at a 4 percent real
discount rate. Later sections discuss sensitivity to discount rate and
marking cost changes. Then, the analysis was extended to include the
30-year (1992-2022) growth and yield projection. The extended analysis
tested results for their sensitivity to the low, medium, and high price
scenarios defined previously. Also, a sensitivity analysis was done to
test the extended treatment response to changes in marking, sale layout,
and road maintenance costs. Finally, the treatments were ranked and
discussed according to their financial performance under the various

economic conditions.

Results of Analysis for 1952-1992
Mn

The medium selection treatment ($54 per acre) had the highest net
present value of difference with the control, using a 4 percent real
discount rate (Table 9—1). Ranking second was the heavy selection
($51/ac) followed by the diameter-limit ($48/ac), the light selection
($40/ac), the commercial clearcut ($32/ac), and the crop tree selection
($30/ac). The shelterwood treatment ($14/ac) ranked last of all the
treatments considered. The silvicultural clearcut was not included in

this analysis due to lack of species-specific volume data in 1952.

W
Both the medium ($70/ac) and the heavy ($58/ac) selection

treatments still ranked highest with a low discount rate of 2 percent.

142

143

However, the light selection treatment ($57/ac) now ranked far ahead of
the diameter-limit treatment ($30/ac). The crop tree treatment ($30/ac)
was about the same in financial return as the diameter-limit. The
commercial clearcut ($20/ac) and shelterwood (-$19/ac) were at the
bottom of the rankings under the low discount rate assumption.

Raising the discount rate to 6 percent (real) changed the
treatment rankings for the 1952-1992 investment period. The diameter-
limit cut now had the highest financial return ($56/ac), followed by the
medium ($45/ac) and heavy ($45/ac) selection treatments, in a tie for
second ranking. Mext came the commercial clearcut ($38/ac), the light
selection ($31/ac), the crop tree ($28/ac), and the shelterwood
($25/ac).

TABLI 9-1. Met present values per acre (1990 S) of differences between
treatments and control for 1952-1992 analysis.

Discount Rate

 

 

 

 

 

 

 

 

 

 

 

Treatment 2‘ 4t 65
Light $57 $40 $31

I Medium 70 54 45
Heavy 58 51 45
Crop 30 30 28 I
Diameter-limit 30 48 56
Commercial CC 20 32 38
Shelterwood -19 14 25

--

 

 

 

The medium selection treatment was best at two of the chosen
discount rates (2 percent and 4 percent) and tied for second ranking at
the high discount rate. The diameter-limit cut ranked first at the high
rate, third at the medium (4 percent) rate, and fifth at the low rate.
The heavy selection treatment was second at all three discount rates
(Table 9-1). The light selection, commercial clearcut, and crop tree

generally fell in the middle of the rankings for all discount rates,

144

although the commercial clearcut improved its ranking at the high rate,
while the light selection improved at the low rate. At all three
discount rates, the shelterwood consistently ranked last of all

treatments analyzed.

W
The 1952-92 HPV results were tested for sensitivity to changes in

marking costs. For the basal area cuts, individual tree marking is the
single highest cost category, while sales costs for all other treatments
are lower, and less frequently applied. The medium cost for selection
marking was $20 per acre in 1992 (1990 S). In the sensitivity analysis,
this was varied by 20 percent to reflect a high cost of $24 per acre, or
a low cost of $16 per acre.

Higher marking costs for the basal area cuts (4 percent discount
rate) caused the gap between the first-ranked medium selection cut and
the diameter-limit cut to decrease by a marginal NPV of $2/ac. Rankings
between the medium selection (first), heavy selection (second), and
diameter-limit remained the same. Only by allowing sales costs for the
diameter-limit to fall to low levels, while keeping selection marking
costs high, did rankings change. Then, the diameter-limit was second,
the heavy selection fell to third, and the medium selection still had
the highest economic return.

Financial ranking for the other treatments did not change when
marking costs were varied, with one exception. Applying high marking
costs to the selection treatments with a 6 percent discount rate caused
the shelterwood treatment to move from last ranking to fifth, while the

crop tree fell to last and the light selection fell to sixth.

145
Results of Analysis for 1952-2022 with TWIGS Growth Projection
3:11.9111

The medium selection treatment had the highest net present value
($75/ac) in the 70-year (1952-2022) base case, using medium price and
cost assumptions and a 4 percent real discount rats (Table 9-2).
Following it were the heavy selection ($68/ac), the diameter-limit cut
($65/ac), and the light selection ($61/ac). These rankings parallel
those of the base case for the 40-year analysis (1952-1992), discussed
previously (Table 9-1).

The commercial clearcut ($53/ac) ranked fifth overall and highest
of the even-aged treatments. Rounding out the rankings were the crop
tree ($51/ac), the silvicultural clearcut ($46/ac), and finally the
shelterwood ($27/ac).

TABLS 9-2. Met present values per acre (1990 S) of differences between
treatments and control for 1952-2022 analysis, using a
4 percent real discount rate.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

---L, l --r
Treatment Low Prices Medium Prices High Prices
Light $45 $61 $153
Medium 61 75 131
Heavy 57 68 104 I
Crop 40 51 119
Diameter-limit 64 65 11 I
Commercial CC 51 53 32
Silv. CC 45 46 73 I
Shelterwood — 49 27 -9 J

 

WW

Treatment rankings changed when discount rates were changed. At a
6 percent real discount rate, the diameter-limit treatment ($7l/ac) was
first, followed by the heavy (SSS/ac) and medium ($54/ac) selection

treatments. Both the commercial ($51/ac) and silvicultural ($46/ac)

146

clearcuts now had higher rankings than the light selection ($39/ac)
treatment. As before, the shelterwood treatment ($36/ac) was last
(Table 9-3).

With a lower (2 percent) discount rate, rankings changed again in
favor of the selection treatments. Again, the medium selection
($12l/ac) had the highest returns, followed this time by the light
selection ($110/ac), heavy selection ($88/ac), and crop tree ($87/ac)
treatments. The silvicultural ($40/ac) and commercial ($38/ac)
clearcuts both were ahead of the diameter-limit cut ($24/ac). The

shelterwood (-$36/ac) was last of all treatments (Table 9-4).

Wanna

Two other price scenarios were used in the analysis--a low price
case and a high price case. In the low price case, the dominant species
(sugar maple, basswood, and hardwood pulp) had stable real prices for
the 70-year period. White ash had a real 2 percent price increase,
while yellow birch had a 1 percent decrease.

With the low prices and a 4 percent discount rate, the diameter-
limit ($64/ac) had highest ranking followed closely by the medium
selection ($61/ac). Then came the heavy selection ($57/ac), commercial
clearcut ($51/ac), shelterwood ($49/ac), silvicultural clearcut
($45/ac), and light selection ($45/ac). The crop tree treatment had the
lowest returns ($40/ac) (Table 9-2).

The high price scenario assumed real 4 percent annual price
increases for the major species (basswood and sugar maple), a 6 percent
increase for white ash, and a 1 percent increase for yellow birch.
Because of the variation in species composition and timing of harvests,

the high price scenario had a major effect on treatment rankings.

147

TAIL! 9-3. Wet present values per acre (1990 8) of differences between
treatments and control for 1952-2022 analysis, using a
6 percent real discount rate.

 

 

 

 

 

 

 

 

 

 

Treatment Low Prices Wedium Prices gighiPrices
Light $32 $39 $79
Medium 47 S4 84
Heavy 46 55 77
Crop 32 38 63
Diameter-limit 62 71 59
Commercial CC 47 51 S4
Silv. CC 45 46 52
Shelterwood 43 36 46

 

 

 

 

 

TABLI 9-4. Wet present values per acre (1990 8) of differences between
treatments and control for 1952-2022, using a 2 percent
real discount rate.

 

 

 

 

 

 

 

 

 

 

 

 

 

F"""""""""r""""" """"'

Treatment Low Prices Medium Prices nigh Prices
Light $75 $110 $341
Medium 90 121 219
Heavy 73 88 111
Crop $8 87 281
Diameter-limit 58 24 -233
Commercial cc 53 38 -133
Silv. CC 48 40 152
Shelterwood 42 -36 -276

 

 

With a 4 percent discount rate and high prices, the light
selection treatment ($153/ac) had highest ranking. The medium selection
($131/ac) and crop tree (5119/ac) followed: then came the heavy
selection ($104/ac) and the silvicultural clearcut ($73/ac). Much

farther behind were the commercial clearcut ($32/ac) and the diameter-

148

limit cut ($11/ac). The shelterwood treatment ranked last and had a

negative value (-$9/ac) when compared to the control (Table 9-2).

W
Low prices and a low (2 percent) discount rate gave the medium

selection ($90/ac) a clearly superior ranking over the next treatments--
the light ($75/ac) and heavy ($73/ac) selection cuts. Next came the
crop tree ($58/ac), and the diameter-limit (SSS/ac). The commercial
($53/ac) and silvicultural ($48/ac) clearcuts followed. The shelterwood
ranked last of all treatments again ($42/ac) (Table 9-3).

High prices and the 2 percent discount rate favored the more
lightly cut selection treatments. The light selection ($341/ac) and the
crop tree ($281/ac) had, by far, the highest rankings under this very
optimistic scenario (Table 9-3). They were followed by the medium
selection ($219/ac), the silvicultural clearcut ($152/ac), and the heavy
selection (Sllllac). Because the control had a residual stand with many
valuable white ash, it had a higher value than the most heavily cut
treatments. Accordingly, the commercial clearcut (-$133/ac), the
diameter-limit (-$233/ac), and the shelterwood (-$275/ac) all had
negative marginal values in this scenario.

Higher real discount rates (6 percent) and a low price scenario
again gave the diameter-limit cut (562/ac) the most favorable ranking
(Table 9-4). The medium selection ($47/ac) and commercial clearcut
($47/ac) tied for second ranking, followed closely by the heavy
selection ($46/ac), and silvicultural clearcut ($45/ac) treatments. The
shelterwood treatment ($43/ac) moved from last to sixth ranking, while
the light selection ($32/ac) and crop tree ($32/ac) tied for last
ranking under the low price, high discount rate assumptions.

With a high price scenario and a high discount rate, the medium
selection treatment again had the greatest financial return ($84/ac)
(Table 9-4). The light selection ($79/ac) and heavy selection ($77) had

149

a virtual tie for second ranking, followed by the crop tree ($63/ac)
treatment. The diameter-limit ($59/ac) fell to fifth ranking, followed
closely by the commercial ($54/ac) and silvicultural ($52/ac) clearcut
treatments. The shelterwood ($46/ac) treatment was last under the high
price, high discount rate scenario.

In this extended analysis (1952-2022), the discount rate and price
assumptions caused frequent shifts in rankings, especially between the
medium selection and diameter-limit cut treatments. The diameter-limit
treatment tended to be first under low price, high discount rate
scenarios (three 1st rankings), while the medium selection was first
under most of the medium and high price scenarios and at medium or low
discount rates (four 1st rankings). The light selection only had a
first ranking under the high price assumption cases with low or medium
discount rates.

Overall, the medium selection treatment ranked either first or
second in seven of the nine price/discount rate scenarios. The
diameter-limit ranked first or second in only three of the nine

scenarios, while no other treatment was first in more than one case.

W

The treatments were tested for sensitivity to changes in road
maintenance costs. The medium (base) cost was SS/ac in 1992 (1990 S)
and was varied to reflect low ($3/ac) and high (37/ac) road maintenance
costs. Because the selection treatments had costs applied at more
frequent (lo-year) intervals, the change in their net present values was
greater (53-4 range) than for the even-aged treatments where these costs
were applied infrequently (Si-2 change in HPV's). The overall treatment

rankings, however, were not sensitive to these changes.

150

W
The extended (1952-2022) results were tested for their sensitivity

to higher or lower marking and sale layout costs. Costs have a range of
20 percent for this analysis.

Varying marking costs for the base (4 percent discount rate)
analysis caused WPV for the selection treatments to fluctuate over a $5
range (e.g., between $75/ac and SBO/ac for the medium selection). In
the medium price scenario, variation in marking costs did not change
rankings unless the diameter-limit sale layout cost fell to low levels
while the selection marking cost was at high levels. Here, similarly to
the 1952-1992 sensitivity analysis, the diameter-limit moved to second
ranking while the heavy selection fell to third. The medium selection
(first) remained unchanged.

Changes in marking and sale layout costs with a high price
scenario did not change investment rankings. With a low price scenario,
the diameter-limit (first) increased its relative performance over the
medium (second) and heavy (third) selection treatments, but rankings of
the first five treatments remained unchanged. The silvicultural
clearcut, however, did move past the light selection treatment into

sixth ranking when marking costs were at high levels.

Discussion

Several considerations must be kept in mind as a framework for the
discussion of these results. first, all of the analyses used total
economic value: that is, the value of both harvested and residual trees.
Some analyses of hardwood management regimes have only accounted for the
values of periodic revenues. Under such an analytical framework
different results and treatment rankings may be obtained than those
presented here. This author strongly believes that total economic
valuation is the proper basis for forest management, particularly as

scientists and managers struggle to make ecosystem management decisions.

151

secondly, these results do not reflect value differences obtained
from improved or degraded tree quality. They assume that a buyer would
pay the same amount for sawtimber from a poorly-managed stand (e.g.,
diameter-limit) as from a well-managed stand (e.g., heavy or medium
selection) over the long term. However, both empirical research
(Nautiyal 1982) and experience have shown that buyers do pay premiums
for quality hardwood logs. A 20-year economic analysis of hardwood
quality considered these value premiums explicitly (Chapter 8).

Finally, these results must be considered from a long-term
perspective. Porest land managed on a short-term or “cut-out and get-
out' basis is not included in this analytical framework. These results
apply to owners and managers who have a long-term (i.e., a generation or

more) perspective for their forest management goals.

W

The medium selection had the highest net present value of all
treatments at both 2 percent and 4 percent real discount rates. With a
6 percent discount rate, the diameter-limit out had the highest NPV,
while the medium and heavy selection treatments were tied for second.

These results should not be surprising to most foresters. Owners
and managers with short-time horizons (e.g., high real discount rates)
will tend to favor management regimes where as much timber as feasible
is harvested early. This often means heavy diameter-limit cuts. With
more moderate or low time preferences, landowners will be more willing
to pursue management regimes balancing current revenues against future
gains.

Both the medium and the heavy selection treatments provided
similar high total financial returns that exceeded the diameter-limit
cut on all but the highest discount rate (6 percent) analyzed. The
light selection treatment did not perform well at medium and high

discount rates, but nearly matched the performance of the heavy

152
selection at the 2 percent rate (Table 9-1). Light selection
treatments, therefore, in northern hardwood timber types are only
economically feasible for landowners with very low discount rates and
very long time horizons. Results for the light selection treatment are
tempered by the fact that its replicates had a high initial endowment of
valuable white ash and yellow birth. This comparative advantage was
still not enough to give the light selection higher than a third ranking
at the lower discount rate.

Because of differences in the timing of cash flows, the range of
returns varied by treatment across the three discount rates in the
analysis. The narrowest range ($28-30) of return for 1952-92 was for
the crop tree, while the greatest range was for the shelterwood
treatment (-$19-25). Variation in range of returns was also high for
the light selection (331-57) and diameter-limit (530-56) cuts, but
moderate for the commercial clearcut (520-38) and the heavy selection
(545-58). These ranges in economic returns are important. some
treatments (light selection) will have a large shift in economic return
given varying time preferences, while others (heavy selection) will not.

The even-aged treatments (commercial clearcut and shelterwood)
generally had low economic returns for the 1952-1992 analysis period.

It is fair to question whether a different set of initial conditions, or
a different analysis period, would have changed their investment
rankings. for the 40-year period, all revenues from these cuts came at
the beginning of the study, and since the stands were mostly pole-sized
in 1951, returns were low. If the stands had been large sawtimber in
1951, then the commercial clearcut would have had a much higher ranking,
perhaps the highest under certain conditions.

It is obvious that the 40-year time horizon is not sufficient to
evaluate the even-aged treatments comparably with the uneven-aged
treatments. Therefore, the discussion of the 70-year analysis (1952-

2022) attempted to address this concern.

153
Winn

In the extended analysis, a 30-year growth and yield projection
was added to the original 40 years of data. Similar (2, 4, and 6
percent) discount rates were used, but low, medium, and high price
scenarios were now included in the analysis. The 70-year analysis
helped to account for the fact that at the 40—year (1992) mark, many of
the even-aged stands were of pre-commercial size.

Investment rankings of the 70-year base case (4 percent discount
rate, medium prices) were nearly the same as for 1952-1992. The medium
selection, heavy selection, and diameter-limit cuts still ranked first,
second, and third, respectively (Figure 9-1, Table 9-5). The only
difference was that the silvicultural clearcut was added as an eighth
treatment, and it ranked number seven, ahead of the shelterwood (Tables
9-2, 9-5). Net present values for all treatments were somewhat higher
than for 1952-1992. Host differences between treatments were also
similar to those in 1952-1992, except that the commercial clearcut
improved substantially in itsWPV, while the medium selection slightly
increased the favorable gap in its performance over the heavy selection
and diameter-limit treatments (Table 9-2). This result shows that
investment rankings remained basically unchanged between the 40-year and
70-year horizons (Figure 9-1).

A high price scenario at the 4 percent rate still favored the
selection treatments, but the light selection was best, followed by the
medium selection and crop tree treatments (Table 9-2, 9-5). High prices
and high projected residual sawtimber volumes gave the light selection
top ranking. However, the residual stands contained nearly all of this
value. High prices caused the diameter-limit cut to fall greatly in NPV
and in ranking. The silvicultural clearcut, however, rose in its rank
(8th to 5th) and in NPV ($46 to $73). This is mostly because the
silvicultural clearcut had a projected stand in year 2022 with a large,

valuable white ash sawtimber component.

NPV per acre (1990 S)

 

80 “Em-yr. returns I
.40-yr. returns '

______J

    
       

60

“C

3 ,

40

"V‘W’VV‘ V"
o 0’. "e
a”. .{og’

O

20

O

.0

V

9:

 

 

Shelt CCC SivaC Light Medium Heavy Crop 8"DL

Figure 9-1. Comparative economic returns, 40-year and 70-year analyses (4% discount rate)
NPVs reflect differences between control and cutting treatments with medium prices.

155

real discount rate (1952-2022).

I Low Prices

Ranking of treatment net present values using a 4 percent

 

Medium Prices

High Prices

 

 

 

 

 

 

 

 

Diameter-limit* Medium Light I
Medium* Heavy Medium l
Heavy Diameter-limit Crop I
Commercial CC Light Heavy I
Shelterwood Commercial CC* Silv CC I
Light Crop* Commercial CC

I Silv. CC Silv CC Diameter-limit I
Cro Shelterwood Shelterwood

 

* Shows treatments having nearly the same NPV.

 

Low prices at a 4 percent discount rate gave the diameter-limit

cut a slight edge over the medium selection treatment (Table 9-5).
other treatment rankings were similar to the medium price case, but the
shelterwood performed well, given the low price assumption.

in performance is because low prices do not favor treatments that build

up large sawtimber volumes and because the shelterwood, similar to the

diameter-limit, had most of its volume removed early.

We

The diameter-limit out had the highest economic

discount rate was raised to 6 percent, similar to the

(Table 9-6).
low and the medium price

medium, light, and heavy selection treatments, and crop tree treatment,

This top ranking for the diameter-limit

scenarios.

ranking when the
40-year analysis

was for both the

respectively, all ranked ahead of the diameter—limit (Table 9-6).

Most

This change

Under the high prices, however, the

156

TABLI ’-6. Ranking of treatment net present values using a
6 percent real discount rate (1952-2022).

 

 

 

 

 

 

 

 

 

Low prices Medium prices High prices I
Diameter-limit Diameter-limit Medium I
Commercial CC* Heavy* Light*
Medium' Medium* Heavy*

IHeavy Commercial CC Crop
Silv. CC Silv. CC Diameter-limit
Shelterwood Light Commercial CC*
Light Crop Silv. CC*
Cro Shelterwood Shelterwood

 

 

 

 

* Shows treatments having nearly the same NPV.

The commercial clearcut fluctuated from second ranking (low
prices) to sixth ranking (high prices) depending on price assumptions.
The even-aged treatments, in general, ranked better with the low price
scenarios (Table 9-6).

The shelterwood treatment was last in nearly every analysis except
for the extended (1952-2022) analysis at low prices with the 6 percent
discount rate. This result was surprising because the same treatment
had ranked highest of all even-aged treatments in a previous analysis
(Niese and Strong 1992) covering 30 years. However, the poor financial
performance over the longer investment horizon is at least partly
because the shelterwood was the only treatment with a non-commercial
projected residual stand in 2022.

Lower (2 percent) discount rates caused all of the uneven-aged
selection treatments to move ahead of the even-aged treatments (Table
9-7), regardless of price scenario. The only exception was the
silvicultural clearcut, which ranked fourth (ahead of the heavy

selection) when the high prices were assumed at the low rate.

157

The preference for uneven-aged treatments, given low discount
rates, is not surprising. Landowners with such time preferences are
more willing to allow growing stock to accumulate, growing larger trees
to harvest at some future date.

The financial performances of three treatments, in particular,
deserve mention. The medium selection was the overall best-ranked
treatment at the low rate, while the shelterwood remained the worst.
The silvicultural clearcut ranked highest of the even-aged treatments

with either medium or high prices, despite higher costs.

TABLI 9-7. Ranking of treatment net present values using a 2 percent
real discount rate (1952-2022).

 

 

 

 

 

 

 

 

 

 

 

[:Prices Medium Prices High Prices
I Medium Medium Light
Light* Light Crop
Heavy* Heavy* Medium
Crop** Crop* Silv. CC I
Diameter-limit** Silv. CC** Heavy
Commercial CC Commercial CC** Commercial CC
Silv. CC Diameter-limit Diameter-limit
Shelterwood Shelterwood Shelterwood

 

* or ** indicates a virtual tie with nearest treatment having a
similar sign.

Summary

Marginal benefit/cost analysis of the nine treatments in this

study showed that, in general, medium or heavy selection treatments have

higher total economic returns than either the more lightly-cut selection

treatments or the heavily-cut even-aged treatments. The diameter-limit

cut had the best financial ranking in most cases with higher discount

rates or with low price assumptions. Clearcut treatments tended to rank

in the lower third or middle, both in the 40-year and 70-year analyses.

158

Silvicultural clearcutting was preferable to commercial clearcutting
under high price expectations. Commercial clearcutting, however,
performed better with high discount rates or low prices, similar to the
diameter-limit. In nearly every analysis, the shelterwood ranked last
of all treatments, while in most analyses, except those with the most
optimistic price/discount rate assumptions, the light selection or crop
tree had the poorest financial performance of the uneven-aged
treatments. These overall results confirm the findings of Chapter 8,
that also showed medium and heavy selection treatments had the best

economic returns when tree quality changes were included.

CHIP!!! 10: CONCLUSIOI IND RICONNINDIIIONB

Forest managers have known for many years that northern hardwoods
can.be managed by both even-aged and uneven-aged methods. What has not
always been clear is which silvicultural system is economically
preferable under various conditions. Depending on their objectives,
managers can use the results of this paper as a basis for making future
northern hardwood management decisions.

Results of this analysis indicate that under most conditions of
long-term management, uneven-aged management will consistently produce
higher total economic returns than even-aged management, on good
(8.1. > 60) hardwood sites. In almost every case, medium and heavy
selection treatments like those used in this study will generate higher
economic returns than clearcutting treatments. Diameter-limit cutting
can provide landowners with short-term profits, yet under many economic
conditions its total economic return falls short of the best selection
treatments.

The medium and heavy selection treatments were first and second
choices for total economic returns whenever discount rates were in the 2
to 5 percent range. At higher real discount rates (6 percent) these
treatments were economically superior to the others only when owners
expected high prices for their timber. Considering effects of tree
quality, the heavy selection had the highest economic returns while the
medium selection was second; however, the medium treatment had the
greatest overall improvements in tree quality.

The heavy (B-inch) diameter-limit treatment was the best of the '
heavily-cut treatments. When price expectations were low, it
outperformed all other treatments at medium and high discount rates. It
also was first-ranked in most cases where discount rates were high.
Tree quality for diameter-limit cutting, though, was very low, with 81

percent of its trees becoming grade three quality or below-grade.

159

160

A light selection treatment had favorable economic returns only
under very optimistic assumptions—-when discount rates were low and
price expectations were high. In most management cases, clearcut
treatments consistently had returns in the middle of the rankings. The
silvicultural clearcut, however, outperformed the more common commercial
clearcut whenever price expectations were high. Both the crop tree and
the shelterwood treatments had consistently low total economic returns,
and cannot be recommended.

The study also shows that for even- and uneven-aged management,
there are very tangible trade-offs between producing high economic
returns and high species diversity. Managers who desire the highest
tree species diversity must practice some form of even-aged management
that produced only low to moderate economic returns in this study.
Managers who choose medium or heavy basal area cutting to maximize
economic returns will only maintain moderate species diversity. Similar
trade-offs exist between even- and uneven-aged management for diversity,
stand structure and aesthetics. Regeneration cuts in even-aged
management enhance long-term diversity, but temporarily impair
aesthetics. Selection management systems provide continuous forest
cover, and favor tolerant sugar maple, producing high-quality sawlogs
and sustaining the "big tree look” (Erdmann and Oberg 1973) desired by
many nature lovers. Yet pursuing this aesthetic management goal is
costly, because tree species diversity can be reduced.

The overall results of this study show that medium to heavy basal
area cuts that enhance both growth and tree quality are the economically
preferable methods. Diameter-limit cutting that lowers stand quality
and yields was preferred only when prices were low or discount rates
high. Ho other treatments tested were comparable to these three for
economic returns.

These results do not reflect the reality of most northern

hardwoods management today. Because most hardwood forests owners do not

161

employ professional foresters, they may use or allow inappropriate
cutting methods on their property (Hiese and Vasievich 1988, Hyland
1992). Without professional forestry assistance, many landowners will
make harvesting decisions with imperfect information. They may expect
low prices for their hardwood resources when, in fact, prices are
rising. As shown in this analysis, these low expectations may lead to
excessive diameter-limit cutting or to the clearcutting of potentially
high-value but small-diameter stands. Such an imperfect decision-making
environment exacts high opportunity costs.

However, when prices become high (as happened to oak in the
1980's, and to sugar maple in the Lake States in the early 1990's),
landowners also may liquidate their hardwood resources through heavy
cutting. This is particularly true for non-industrial private owners
who often have high discount rates.

lconomic returns to hardwood management are most sensitive to
price expectations. This analysis has shown that real prices for the
major northern hardwood species in the northern Lake States have
increased at close to their historic (2 percent) rates over 40 years,
and at considerably higher rates (3 to 11 percent) from 1970 to 1991.
These higher real prices are indicators of economic scarcity for the
sawtimber of most northern hardwood species. The author believes that
the combination of a global economy and the lack of substitutes for fine
hardwoods will cause these higher real prices to continue; such prices
may become a permanent part of the forest products economy.

Ho other country has a temperate hardwood resource comparable to
that of the Iastern United States-~either in quantity or in quality.

The current trend away from consumption of non-renewable tropical
hardwoods is already focusing global attention on the United States'
renewable hardwood resources. One large domestic furniture company, for
example, recently changed one of its lines of fine furniture from using

tropical hardwood to temperate northern hardwoods. A papular periodical

162

publicized this shift (Whodruff 1991) and cited it as an example of
corporate environmental stewardship. last Asian manufacturers are also
changing many of their product lines from tropical to temperate hardwood
resources (Dempsey and Luppold 1992).

What do these global trends and this study's results mean to the
current owner of an overstocked and under-managed northern hardwood
forest? Changing economic conditions generally mean that there will be
good markets for northern hardwoods in the future. Low quality hardwood
pulpwood and fuelwood can be thinned to provide current revenues that
will release high-quality trees for faster diameter growth and quality
improvement. Landowners who can afford to wait have an excellent
opportunity to improve their rates of return through these carefully
controlled but heavy basal area cuts. Because most cutting will
continue to be of the opportunistic diameter-limit variety, those
landowners who are patient may control an increasingly scarce, high-
quality northern hardwood resource.

Many northern hardwood landowners will continue to have short-term
horizons and high real discount rates. Through ignorance and non-
competitive timber sales they also may expect low prices. These owners
probably will prefer some form of heavy diameter-limit cutting or
commercial clearcutting. But there also will be a minority of owners
who will wisely choose a longer investment horizon, who have low-to-
moderate discount rates, who expect current moderate-to-high price
trends to continue. For these owners, medium or heavy basal area
treatments will consistently provide superior economic returns in

northern hardwood management.

163

Future Research Weeds

Research needs identified by this author fall into three
categories. They are: 1) research on trade-offs between hardwood
timber and non-timber outputs, 2) research on hardwood timber quality,
and, 3) research related to the application and transfer of these
results to northern hardwoods across the northern forest region.

Future research is needed that specifies the non-timber
opportunity costs of hardwood cutting methods. Managers need to know
which wildlife habitats are lost or altered under certain cutting
methods, and which cutting methods maintain or enhance habitats for
unusual plant or animal species. In addition to research on the costs
of foregoing non-timber outputs, managers need specific research tying
economic returns of hardwood cutting methods to existing ecological land
classifications. Preliminary research (Coffman et al. 1984) has shown
that growth and yield of northern hardwoods vary by habitat class;
however, this work needs to be refined and explicitly include economic
returns. Another non-timber benefit that needs further research is
species diversity. Some northern hardwood forests are highly diverse,
others are nearly all sugar maple. A study could be initiated that
would compare economic returns of the same cutting methods practiced on
a diverse versus a homogenous northern hardwood forest. Managers might
then obtain better information on the conditions where management for
diversity could result in improved economic returns.

Research on timber quality changes in northern hardwoods is still
in its infancy because of the lack of good data. This study has shown
that either of two basal area methods may be optimal, depending on
whether objectives are defined primarily as economic or silvicultural in
nature. This result needs to be refined through more careful
observation and analysis. One question that needs to be answered is to
what extent decreases in merchantable height may affect the (currently)

economically optimal heavy selection treatment results. The economic

164

trade-offs and costs of maintaining specific levels of high-quality
hardwood growing stock, under the varying cutting methods, also need
further research.

Technology transfer is needed to complete the northern hardwood
economics research cycle. One way that these and other research results
could be made more available to managers is through-specific northern
hardwood cutting method profiles. Costs, benefits, and net yields of
the most promising treatments could be summarized in short technical
publications, such as Morthern Hardwood Motes, one that already reaches
many managers. Finally, both the silvicultural and the economic results
of this 40-year study need to be published jointly, in a general

document accessible to the forestry community.

APPENDIX A

APPENDIX A

Summaries of TWIGS Growth

and Yield Projections

165

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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we Nnmn ON vaN hmmm mafia v.0 mma on non mzomwm Nag BHON
no nab“ «N Noon omOm «mm m.h emu on Mac mmommm bod NuON
«v MNwH MN vmhm vav who N.h ova me ave machmm NOH NOON
an mvmn ON menu mfimm mmo m.m mma hm mmv mmOhmm hm NOON
hm nee“ ma nnvn vmvN mhv m.m MN” eh «mm mmommm NO mama
mm coma ma mafia hmON mom N.m Nag O mNo mucumm hm Nome
mZOu. . hm OKOU . Pm . 8m . him zma mmoc OH H O M>HA maadhm EUC Edwin
. so . so . om . :o m>¢ >3 5:. 53. are:
ACDOHmmm OOOZOAOA mOOQZCm

NNON ad acouucao .oswdmmm unslsmsns:

o.mo .xsocn even came: unosm .usqusmm women seam

em .smm Asqudcn Nmma answuusnoum no use» duwuwcn

H “sowwsowamem

modirmmuiwllilila u on cause

.«Id Mundsmmt

166

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

O O O O O O O. O ee O mask: can NNON
Nm OFHN 0N ONO— thm sea. O. OON O Ovv POO hnfl NNON
Nm OhnN vN ONO— tho mean 0.0 OON cc Oev MOOhmO bud NNON
mv ONON ON mHON "fine men“ m.h mm" mm vmv maOmmm Nnu FHON
me vmmu hm mvoN mmhn Hum ~.h “be Oh mnm mmOamm hOn NnON
we nnmu mN mFHN «man can v.o emu mm mom mmOmmm NOn bOON
co more mN mama meN mm p.m Non OO~ new mmommm hm NOON
vv amen ON mom“ O O ~.m mNn mm hmh szhmm No bmmfi
no neon vn OOH" O O m.v Nag O mom szbmm hm Nmmn
mZOF .._..m OzCU .9... . a...“ . km .55 mags OMHO m>Ha~ mOFCBm Maud Kama
. so . no . a: . so ":2 2m 5:. cm... are:
JCOOHmmm OOOZOJOO mOCO3<m

NNON :4 usuncoao .OOddmmd adolomscsz

O.mo .xoccu seem same: woman assessmm xsvsu swam

hm acme Andaman Nmmn acaduosnoum no use» dnwudcn

.codwosnOum zozoum con acoacmsccd_u:uuno~o ~snsu~suw>~dn N .mom mauza HO ausllsm

4 x~ozmmm<

N .co«ueo«~mom

 

.Nlm xuocsmmm

 

 

 

 

 

 

 

 

167

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

O O O O O O O. O me O zmem< can NNON
me NeON mu NNn— eanH eemN O. “as O hON BOD bun NNON
me NvON ha NNnH vmon~ vemN v.O~ Hm“ OH th mmOmmm on” NNON
mo Hem“ be men" mmvan hmnN s.m mo“ ma nmN mzommm Nae haON
av OObu 5H mmnn meme mmha O.m va ON Hon mmommm he” NHON
on menu mu comm mmnp coon n.m Ova «N uNn mmommm NOH hOON
mm mvva mm v~¢~ new: mom m.h 0N: mN men mzommm hm NOON
nn Nona ma Oeug hfibn Ohm m.o Nan an «en szhmm NO hmmu
On HNNH Na Oom mNmN Hem H.o me O mOe mmommm hm Nmmn
mz©fi firm OKOU .km .Pm .%h _=:. mzu< OMHO m>~A mOh<Pm £04 Ktma
. :o . =0 . on . =0 m>< 2n 5:. 5:. .58:
.HCDOHmm—z C8392...— wCOJZ’am

NNON om nooummdo .csdammd ussflsmscsx

O.mo .xoccu ovum cams: ummsm .nsdosmm mourn ovum

so some usuuacn Neon accumusnoum we know usdawcu

n .noaucuuunom
mquOauommo ocean

 

.=O«u00aonm :uSOum ocs unseemccme usuuoONU unusuazuq>~dn n .90: MOHSH «O husfilsm .nl‘ Mdvdommfl

4 XHOZHOQC

168

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

on “em h «mm moon wan n.o on n can access on” «now
on one o no. amen mop o.p no u no" needed and snow
nu Omm m nmn mONN Nee h.e me N no“ memmm nod N~ON
on can n ~v~ ohm” emu o.m on n no" exodus «on poem
on are a ecu «no and o.m an on an: «name he «com
«a anon o" Hum omen mean 0. ma o no“ ecu so «oou
on mono «H mam moon menu n.n pan on «ma muoaun be «com
on one” «a sea «a». new m.o can an own accuse am been
an spam an men «men can o.m cod 0 one arouse no «man
mzoa .ah axon .ua .em .eu man are: ammo n>mn unseen uo< mama
.oo .90 .on .oo m>< \rn rue «as are:
neanumne nooznoaa mooazcm

.nouuusnoun museum one anemones-I aroussuo unwouslloo u .msm menxa no musllsm

NOON ma .:.n.o ca s>ons use xm.v noon» «an usuussao avedummm ans-suede:
.xouau can»
.ou: nausea:

O.mm
hm

fl Knaznmmfl

same: ususm

Noun

.usdusmm xenon seam

.noduusnoum no ussw «seamen
a .ooaueuwumsm
mmqmmmumumaiumqum

.eum xdvcsmmm

169

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

an ac. n «an omen ova 0.6: me a pm uzoauu ham "won
on can a one «new . onm p.m an a on access and soon
o «fin n ma" one” ncn 5.9 on a on «momma so" «non
» mmu n mun soon «Nu s.» an a co exodus «on soon
o «Na n ecu mac up o.o an an me «name pa «oo«
No «man on «own poem one" 6. end 6 new you so «can
no menu on puma mono” wean o.o nee pm «On axons: he «com
me can“ an open coco ovum o.a «on an oan unease «a no»:
no new: cu can" even «and n.o me” a con excuse so Noon
mzoe .na expo .aa .eu .9: ran neon anon uses maaeem no: ¢<us
.ao .50 .an .au n>< \au rue <aa are:
grandam: noozanaa anagram
momma»
NOON ca .:.n.m cu seems one um.e seen» use usoussao .osunmmm ussIOOsns:

sums: ususm .esuusmm women seem
Neon .eusnoam no use» uneven"

N acouasuqdmsm
muqumduumwaiuoemu

o.me .uocau code
so .06: «cause:

.noueosnonm museum one ansssmsnsl anon-ens «sauusIIou N .msm manta mo musllsm .mid namesake

¢ unozummt

170

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

mm «on m «o. man» can m.o on a no muons: pan «won
«a on. n can «now on. p.o «v a no excuse «an ado"
«a mac v mun oooa can o.e an a on uzoaun son «now
a won a new Need now u.» an a nu uzosnn «on «can
a man n onu «oo o»: c.o cu on «a «use: so «can
on menu «a sea «can com" o. and 6 «em you re «oou
we can" an menu «own one: ~.o men on on" mucosa pa «can
a. one: cu and" have mom" ~.a on" on can agenda «a noon
on pend on «nun «use eon” 0.9 on: a new access no «man
mzoa .au neoo .ea .ea .aa mun emu: neon u>ea unseen nee «an»
.au .ao .au .90 n>¢ \an ass any are:
acsannux accesses anagram
nomad»
«oou an .:.n.v ca o>onu cc. .o.e noon» on. usuuuono .ooaaaua vacuum-an:

.sOqusnoum museum one uselemsnsl asunssuo usuouellou a .mem manta no musllsm

same: woman .uswusmm lemon swam
«man .uoonoaa do an.» Andaman

n .dONwIONAQsm
auqmuduuuwaiuqqum

o.mo .uovuu cues
so .oe¢ uneven“

.oue «ecu-nae

¢ KHOZflamfl

 

 

170

in‘

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

no «on m gov nnnn com m.o on n on seeing sun -o~
«H as. m own «moN one s.o me a so axosmn «an anon
no mac c Han soon omn a.» en a on nmoaun so: «Hon
a men n no" ~oem now «.p an H on nuance «on room
o men n on” «no or: o.o mm on an ensue so noon
on we“: «a mom ~oho ommu 0. had 0 we" mmw so «oou
co com“ mm mwwo vmmo when p.a men on can axonun ea «can
a. when «H has: boom mMma H.a one a“ emn accuse rmw soon
on he": on «an: v~oo «on: o.o on: 0 men neouun no wan:

III‘ ['1 ll.

mzoe .9: amoo .au .em .au mun ago: anon used unseen use m<u»
.au .ao .an .so u>¢ \cn emu any axe:
H
q<=aommz noosagaa anagram
“magma
NOON cu .:.n.v :4 seems one um.9 sssuu Hus vsonssuu .vswummd unslensssi

.dONOUsnoum museum use unslsmsnsl asuussuo unquusIIOU n .nem manta no musllsm

sums: venom .nsuosfim Recon swam
Nmmn .aoenonm no user Neuuunn

n anodesuqnmsm
gagging

O.mw .MIUIN Dada
so .ou¢ agenda:

.ml¢ Hammond:

‘ NHOZHNN‘

171

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

mm omna ma mama moao anaa m.m nma ca mom sconce paa «won
an mnau ma mama mane com 0.» sea ma mam excuse aaa mama
an mama «a mama mama can m.o ama mm «mm sconce boa aaoa
we mama aa nooa avaa mun o.o «ma mm «mm smegma aoa moon
ma umoa ma nova mam «ea m.m maa ma moo nzoaum «m «can
an ooua ma unaa maa an o.m mm ma mum neoaum am mmma
ma oaoa «a mam o o m.¢ am o ave umoamm mm amma
mzoe .aa axon .aa .aa .aa can have mean n>an ashram . mom can»
.oo .50 .on .60 n>¢ \en can aae are:
seesaw»: noozaoaa neonate
madam»

scoz .msaammd unslsmsns:

O.mm .wsmnu swam sans: noosm .usqosmm xenon seam

mm .omc a-aaaaa amma .aoauoonoua no use» a-auana

N

.slsm meson

mmdumdwummmlumqmm

.nOawosnoum museum one unslemsnsl coosueaasmn a .msm manna no mnsllsm .mtd mandamus

¢ ”Nozummfi

172

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

ON ONNN mN vmmN hmo NNN m.m NNN NN Nov mmommm hNN NNON
mN mom NN hOmN Nen mm v.0 mNN mN mam mmommm NNN NNON
NN mNm mN QNmN mm NN m.m VON ON nnm mehmm NON NNON
MN ONO eN NONN O O v.m mm wN men mmOhmm NON NOON
me mnhN N nmN O O m.e Np MN new mmommm mm NOON
Nn ONNN N mNN O O N.v em NN Ohm afloamm Nm NOON
NN mam N Nm O O e.n en O mmm mmommm hm NmmN
mZOH .ah OKOO .Hh .8h .9m sz mdU‘ ONNO m>Ha maatam mud ¢¢mu
.60 .90 .mm .ao m>¢ \cn any any are:
AdOONmmx OOOZNAON mooa3¢m
madam»

scoz .szNmmd unslsmsns:

O.me .xsvnN seam sans: usoom .esaosmm xssz seam

so .oma a-auaua amma .uoaaoonoam no use» a-auaaa

N .noavsoaNmsm
mmdwmdummmaludqdu

.noauuenoum masonm mos unslsmsoel oooxusuNsms N .msm mauxh no husllsm .mtm xstemmm

¢ KNOZHNN£

173

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

on mvna mN nmNN vaN OVN o.m ova me mom mmommm eaN NNON
nn mNNN ON NNON nee mma O.m mnN mm NNm mmommm NNN NNON
On ONNN «N mmma mom Nm m.m NNN Nm mum m¢Ommm mON NNON
mm NmmN ON own no mN m.e mON Nn One memmm NON NOON
ve ONON m mmm O O v.e Om ON Nos mmommm mm NOON
nn NNNN e Noe O O m.n Np mN Nmp mmommm Nm mama
eN nae v mmN O O n.n em O hem memmm mm NmmN
@208 .Bh OKOU .ak .Hh .Hh TOO ”£04 OHNO m>NA mahthm ”0‘ Ktu»
.OU ..OU .OO .90 n>¢ \dm ¢mfl (m8 920:
ACOONmmd OOO3LAON mOOQZCm
madam»

scoz .szNmmd enslsmsns!

O.mo .stnN euNm same: woman .ueNuemm xevna swam

so .om¢ auauaaa amma .aoauoonoua no uses a-aaaua

.nONaosnoua mvzoum one unslsmsnsd costustsma n .msm manta no musllsm

4 KNOZflmmd

n .ooaasuaamsm
mdammammmmaiuuqmm

.mi¢ sacs-doc

174

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

ow mmOa m man mmmm mmma m.ea cm a mo «mesa maa «man
u mmu a an mmmn one c. an o m eao maa «now
an vnna m ame mmmma maau o.ma oaa a em mucosa maa «Non
mu mmaa m men «maaa moma m.oa ooa a mm sesame «aa maoa
ma aeoa e «an ammm omoa m.na cm a em «mane mOa Naou
o mma a no ommu «no a. an o a you moa Naoa
an ouna m men mmvua muaa m.oa aaa a co meonnm boa NaoN
ow amaa v cmn momma omoa m.na ooa a mo excuse «ca moo“
«a neoa c mom «mam enoa o.na cm a we «use: mm «men
s men a me mama can o. mm o m use mm noon
Nn nmna m oav mmoua omam m.na maa a mm excuse mm Noam
mm anaa m «me vmmoa nema a.na «ca a pm excuse am mmma
cu aoOa o mac memo coma n.~a mm e um axonnn mo amma

mzoe .an neon .nn .ea .en :mm axe: mean n>an «semen nor can»

.oo .30 .om .oo u>< \cn can can are:
Atacamaz moozmoam mooozmm

NNON .NNON .NOON ca noun Nessa no menu snssvn on on s>oos Eouu .cazh .OONNmmt enslsmnns:
O.me .xsmna seam sans: unosm .ssNusmm xscna suNm

mm .om: a-auaua amma .aoauo-Noua no anon a-auaea

N .noausuNNmsm

mmammammmmmiumumu

.noNausnoun muzoum fins unslsmsnsl noNaosNeu JAONN N .msm mONxH no musllsm .ONim xansmmm

d NnOZflhh‘

 

175

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

ha nooa v can mmmm mvoa m.va cm a a» «use: maa «New
m man a mo mama men a. ea c w you baa «won
an mama m nun maaaa «aoa «.ma moa a as excess maa anon
ma maaa c own mmmoa aema m.ea mm a ma wzonum «aa mama
mu coca c «an oaom omoa m.na cm n on «use: moa «aou
m man a av coca non o. ma 6 m use moa aaom
an naaa v nmn «mmaa anon m.ea moa a mo excuse moa «aoa
mu moaa m «on amaoa Nona e.na mm a mo seamen aoa boon
ma «mm m hon comm omma o.«a cm N am «can: em «mom
a mma a cm oaan so. a. a" o m use am Noon
an apaa o ave mamaa anon m.na aaa a mm sconce pm «com
on amaa m mac vaaoa coma m.«a «ca a «ma mucosa um mmma
ma mama o «me name vmca o.~a am 6 «ca neonmm mm amma
mzoa .aa mmou .aa .en .ea 2mm emu: anam n>an unseen no: ¢<m»
.oo .ao .on .nu n>¢ \rn can gas are:
ncamauum noozmnam moonaam
NNON .NNON .NOON ca noun Nsenn NO ussu susove on o» moons some .cazh .osNNmmd anslsmnns:

.nOqusnonn nesonm one wnsnsmsnsl noNuusta ummNN N .mem mONTH no musllnm

O.mm
hm

.uosua ouau

.omc a-aaaaa

¢ NNOZNBG¢

N

sans: usosm

OON

.usNoemm msonN stm

.nONausnonm no use» NsNuNnN
N .nONusUNNmsm
qmammauuuuaimcqmm

.aau¢ nave-one

 

176

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

mu mama p can mmmo mmca m.ea cm a we enema maa «wow
u naN a mma vaoa mnu 0. ea c m ecu maa «Non
mm onua m mum naooa mama m.ma 00a a am nuance baa «won
an caaa a mom mmom mama m.va mm a um amonmm Naa mama
ON NNON h mvm envm VNvN O.NN Om N am «meme mON NNON
m ONN N Ov mOmN mmn O. ON O m BOO mON NNON
mm enaa m vmm «noaa vama o.ea moa a an excuse pea «Non
an uaaa 5 man oamm cmoa o.na mm a an ozone: «ca moon
on n00a b can aoem oava a.na om N No anen< hm Noon
oa can a mma comm mmv 0. mm a ma you mm «com
on avna m «mo eohaa cuma a.va maa « «m excuse am Noon
mm anua m «on aoeoa papa v.na mOa N cm accuse um mmma
an eaaa m 5mm. emam ooma o.~a mm 6 mm excuse mo «mma
mzoe .en axon .mn .en .eu can scum onao n>an unease no: «as»
.90 .90 .on .90 m>¢ \rm (as mas are:
Atacama: moosmnam mooozem

NNON .NNON .NOON ca noun Nessa «0 none cusses OO 0» cache Eonu .caca
eNOn: unosm

.nONuusnoun avBOuO one unelsOsnnl nONuuste uoONN m .mem manta no husllsm

O.mo

mm

d KNOZflmmt

.xscna oaau
.ome a-auaua

NOON

.osNNmmt enslsOsns!
.usNusmm xsonN seam
.noNuoenonm no nee» NsNuNnN

n .noausuNNmsm

O w

an

.aai¢ savanna:

177

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

ma mom a ama mmcm aava o.aa me a on «name paa aaoa
m com a om amma me. o. ma o a «so baa aaoa
ma maaa a pan mevaa aoma v.ca «m a an excuse . aaa aaoa
ma omm a can mmmm avoa o.aa mm a an unease aaa aaoa
aa one c man meme mama a.aa ma a we «sang aoa aaoa
a mom a he mama we. a. an o oa you moa aaoa
ma enaa m can mecca ovma m.na mm a no axoamm moa aaoa
ma mom a man moom amma m.aa mm a co mzoamm aoa sooa
an omm . can mace oena m.aa ma a om enema am noon
0 ama a maa mmaa man 6. na o m ecu am aooa
ma aoaa o mac oamm mama m.~a mm a mm smegma am aooa
ma nooa m mo. comm mpqa m.aa so a am uzoanm am amma
«a mom m «a. mono aoaa o.aa ea c mm seeing so amma
2.2. .ea 9.8 .E a... .aa :3 axon mean ”53 3.23 sea in»
. =0 . DO . an . 90 g‘ \Cn 5.9—. flan. 920!
aaaoammm moozaaaa mooozcm
aaoa .aaoa .aooa ca noun aauan «0 good cause. me on osona eoua .caze .eoaann¢ «announcer

.noNausnonm autonO one anslsOsnsn noaaueNse lsNosl N .mem moNxa no musllsm

O.mo
hm

.weonN saNm
.sOm NsNaNnN

¢ KNOZflmht

sts: usOsm
NOON

assNusnm xsonN swam
.nONuosnonm no use» NsNuNnN

N .noausuNNmsm

O

on O nu

.naia uavaonaa

 

178

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

ON OOO N OON ONNO ONON N.ON ON N OO Kuhn: NNN NNON
O OON N MO ONMN MOM O. NN O O _ BOO NNN NNON
ON NOON n MMN OOONN OOON 0.0N NO N Om ”momma NNN NNON
ON NOO M NON OONON OONN 0.0N OO N Om muchwm NNN NNON
ON OOO M OON OMNO MOON 0.0N ON N Om Knahd NON NNON
O ONN N OO OOON OOM O. ON O O BOO NON NNON
OM ONON M MON MNNNN NOON 0.0N MO N MO udOhmm NON NNON
ON NOO M MMN NNOO MNON 0.0N Om N MO Hicham NON NOON
MN OOO M NMN NONO NOON O.MN ON N OO “Hahfl NO NOON
O OMN N GO OOON MOM O. ON O O 990 NO NOON
ON OOON O OOM OOOON OMON 0.0N OO N ON muchmm NO NOON
ON ONO O OON OOOO ONON O.MN no N NN udOumm NO NOON
MN OOO O NON . OOON NmMN O.MN ON O NN afloamm NO NOON
OZOB .Hh OKOO .NL .Hh .Bu man 0‘ OONO m>NN OON‘BO m0¢ mdua
.30 .50 .OO .50 m>¢ \tm HKON \muflmfi 8:0:
NdOONOHK OOOCONOO mooattm

NNON .NNON .NOON ca sous Nance uo assu.sussve ON 0» s>ons eouu .cara .osNNmmn unslsOsns:
0.00 .xeonN seam same: unOsm .usNusmm xeonN suNm
mm .sOm NsNaNnN NOON anoNuosnona No use» NsNuNnN
N .nONunuNNmsm
a one o a

.nonosnonm museum one anslsOsnsl nowaustu lsNosl N .msm meaxh no musllsm .ONIO xNonsmmm

¢ an2flmm¢ .

 

179

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

ea bmm a mom name «noa m.ma mm a am enema aaa aaoa
o boa a me «man son a. ma o m aau aaa «mom
on emoa a can oeoaa aoma «.ma mm a pm uzoamm eaa «non
ha omm v mma mmom mama m.ma em a mm. nzoumm aaa naoa
ea mam a oma mmam enea o.va mm a mm «use; moa «aoa
m can a me mama mmn o. ma o a you aoa aaoa
on mooa a nma onmoa mama «.ma mm a me access aoa aaoa
ma amm e man mmvm mama m.va em a no nuance aoa soon
«a man a mma ppm» mmna m.na mm a so «mane am «com
o ama a om mama mac 0. on o a emu am noon
on mmoa v awn omaoa mama m.va mm a on agenda am noon
ma omm v won comm mooa o.na mm a ma nachos am amma
ma emm e mom amma mmna e.aa mm c or season so amma
mace .an once .9: .ea .ea an: acne mean u>aa «page» no: can»
.60 .60 .am .90 u>< \rm «an ans are:
aaaoamnx moosmaam mooazam
aaoa .aaoa .aooa ca «on. aauan do noon nuance cm on usage eoua .cana .coaannc vacuum-ac:

.noauosnonm masouO one ensssOsnnl nONuoste IsNosn M .msm neuxu no musllsm

0.00
NO

.uooaa ouau
.sme a-auaaa

d KNOZHOO¢

sans: unocm
NOON

.ueNusOm xsonN swam
.noavusnonm No use» NstNnN

M .nONasuNNmsm

ON»

One s0

.ONI¢ MNQdOOh‘

180

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

ma men n mnn mnmm amm n.aa on a am «mean naa anon
m com o nn oonn nmn a. ea c n use naa «non
nn men n non mnnn mana o.na on a no meouun naa anon
on men v nmn onmo onaa n.aa no a mo nuance naa naon
ma non e nan encm nnm n.aa on a an unea< nOa «aon
o mma o «N «nan onn o. na o m use noa naon
nu mom e mnn nvnn mvna n.aa nn a mn umonun noa «ago
on mnn v man aamo ooaa n.aa mm a on «momma noa noon
na mno e - mma ovum nnm m.oa on a an «meme nm noon
n man a nv omen nnm c. on 0 na ecu nm «can
mm «mm c onn «one evma a.na mm a nm muoann nm «can
an «em v aon anon nmna n.aa an a mm neonum am nmma
ma men v oan comm noaa n.aa on 0 mm acumen nm «mma
mzou .ua nmoo .ea .an .eu can exec mma: n>ea manna» nor can»
.8 .8 .8 .co m>¢ 2m ‘2. 5:. are:
anamamuc moozmnam moonzam

«non .naon .noon ca «on. among no uoou cease.

.noNuusnoun museum one wnessOsnsl nONuosNee mason N .mem manta no musnlsm

0.00
NO

.uucea suan
.ome a-auana

fl KNOZQOOC

OO 0» s>onn Eouu .cana .osNNmmd unslsOsns:
stnz unOsm .ssNusmm xeonN swam

NOON .noNvusnouu no use» NsNuNnN

N .noausoaNmsm

mmammdummumimmnmm

.eaie xNonsnnm

181

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

ma nee n can onne neaa e.ma me a an suede naa anon
e nma a on nean men o. ea c n eao naa anon
en ene n oen nmme emea e.ea en a on neonue naa «won
an nnn n can aenn eana e.ea me a me museum naa naou
ma nne n eon nave anaa e.ea me a me enema naa aaon
e «an o en omen ooe o. ea c m use noa «aon
en eme n men came anea n.ea en a me excuse naa «aon
«a con n man «man anna n.ea me a me nuance Noa noon
ma eee n «nu amme ana a.na me a me «near nm noon
n acn o mu nana «me c. an o «a you nm noon
en mom n aen vmee nvea o.ea an a on seamen nm noon
an «on n new econ emna o.na an a an excuse nm nmma
ma ane e mnn acne enoa o.na ae o «n axonme no amma
e28. :5 9.8 are .a... .E zen neon mean 9a.. 3.23 use mean
.ao .au .me .ao n>¢ \an ran eme are:
amamaeux moozmaaa «oedema

NNON .NNON .NOON ca nous Needs «0 ussu sussvn cm on s>ons noun .caoe .osNNmmm unsnsOsnsI
O.mO .wsonN saNm stm: usosm .esNosmm xsonN suNm

ne .om: aaaaana nmma .aoauuonoun do an.» a-auaaa

N .noNusUNNmsm

mmammqumuumimmmmm

.noNausnoum avxouO one unsIsOsnsl noNuosts mason N .msm moNza we musllnm .NNIO wNonsmmd

£ KNOZOON¢

 

182

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

ma anaa a nna neOn eeaa e.ea me a we mean: naa «non
e eaaa a nv enan ean 0. ea c m use naa «new
on oona n nna neam aoea m.ea en a nv museum naa «non
an eeaa n «an aemn «nna e.ea me a no vacuum naa naca
ma nnoa n ann anee nnaa n.aa me a on «near noa naon
e men a we enaa nan 6. ea c e ecu naa aaoa
en aona n nna nene evea eo.ea en a ne vacuum noa naoa
«a enaa n ova mven nena v.ea me a ne muonme «ca noon
ma coca n can anee maaa e.na me a on «name nm noon
m «an a ne neon emn a. ea o q ecu nm noon
ea nena n ann «nee nava n.aa en a an excuse nm noon
an anna n enn «man mana e.na ne a me vacuum nm nmma
ma aeoa e enn maee enm n.aa me o oe seamen ne nmma
ezoa .en anon .en .eu .en zen neon mean u>aa unease non «run
.90 .ao .ae .ou u>< \rn can man are:
mandamus moozmaam moonzce
NNON .NNON .NOON :N nous Nessa no menu sumsve OO O» s>oan Eouu .caza .osNNmmm anslsOsns:

.nONeusnoum nuSOuO one uncleOsnnl nONvuste mass: M .mem mONxa No NusIInm

0.00
NO

.wsonN suNm
.oma aaauaea

t KNOZOOO¢

nNms: unOom
NOON

ansNosmm neonN suNm
.nONuosnoum No use» NnNuNnN

M .noausUNNmsm
noau nous nu

.eann navaonoc

 

 

 

 

 

 

 

 

 

 

183

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

en nooa n eon aenoa eona m.ea no a on xenon naa nnon
e man o on emen nmn o. ma o m you naa nnon
an nnna e oon oenna ooan n.ea ooa a me oooooo naa nnon
on aoaa n non aonaa mmoa o.na em a ee oooooo naa naon
en aom n men oaem eeea e.va no a ne «one: noa naon
e enn a ee nnnn mon o. ma o na poo noa naon
on nona e emn nooaa eeon m.ea ooa a mn ozonoo noa naon
nn eooa e omn menoa maoa o.oa em a oo oooooo noa noon
on nem o eon nneo oeea n.na mo n no «one: nm noon
e nnn o on mann aoo o. on o na noo nm noon
on enna e non emnaa oeon n.na moa n mm noonoo nm noon
nn moaa e nnn nnem mena m.na om n ooa amouno nm nmma
on enm m man. nnen eeea o.na oo o noa noonoo no nmma

ozoe .eu oooo .na .eo .en zoo noon ooao o>aa eonoae no: one»

.oo .oo .oo .oo n>< xco one one are:
acooaeuo ooozmaom oooazoo

nnon .naon .noon ca «one aouon no name cause. no on case .ooaanno vacuum-nu:

O.mO .xsonN saNm stn: unOoO .eeNusmm xsonN saNm
Nm .smm NsNUNnN NOON .nONaosnoum No uusm NsNUNnN

N .nONasONNmsm

gunman

.nONuosnoum muxouO one unslsOsnsl ssua mouu N .msm moNxa no mus-Inn .Oaum xNonsmfim

fl KNozummfi

184

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

NN OOO O OOO ONOO OOMN O.NN mO N OO mmhhd NNN NNON
O ONN O MM MOON OOO O. ON O NN HOD NNN NNON
MM OONN N OMO NNOON OONN O.NN OON N Om OOOEOO NNN NNON
OM OOON O NOO MONO OOON 0.0N OO N OO OOOOOO NNN NNON
NN NNO O OOO OOON NNON O.mN OO N OO Omahd NON NNON
O OMN N NO MOON OOO O. ON O O BOO NON NNON
MM NONN O NOO NOMON NNON 0.0N OON N OO NOOOOO NON NNON
ON ONON O OOO ONOO NNON O.mN OO N ON OOOOOO NON NOON
ON OOO O OMO NNON NOMN 0.0N OO N ON OOOOOO NO NOON
NN NNO N ONO OOOO OOON O.MN ON N NN mmOhmO NO NOON
ON OOO N NOO OMNO OOO O.NN OO O NN OOOOMO NO NOON
OZON. . ab GOOD . .N...N . 8h . .NLN OOO mmoc Om N O W>N.N OOHOHO OOO ”NOON
.30 .90 .OO .90 O>4 \dm OOH (OH 9202
Adan—H mam OOOSQADQ OOONZCVJ

.nONwosNoun avSOuO one unsIsOsnsl ssuu mono N chem OONOH uo husllnm

NNON .NNON ca nous Nonmn no comm muoovn mm on case
came: unasm

.mmus Nonmn 3ON ou moo NOON cN unannaou ozv

0.00
NO

axeonN szm
.ome aaauaoa

d XNOZNOO¢

NOON

N

aoONNmOO unslsOsns!
ansNoomm neonN suNm
anONuusnoum no use» NsNuNnN

.nOstONNmsm

 

noNu Nuunsn onsum

.oNin xaonsmmO

 

18S

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

NN OOON O NOO NNMO MOON N.ON OO N NM amend NNN NNON
O OON O NM OONN NOM O. NN O ON HOD NNN NNON
NM OMNN O OMO ONONN OOON N.ON NON N OO OdOhmO NNN NNON
ON ONNN O OOM NOMON OONN N.ON MO N NO OOOOOO NNN NNON
ON NNON O OOM ONNO OOON N.NN OO N NO OONOO NON NNON
O ONN N OO NOMN NOM O. ON O N HOD NON NNON
NM OMNN O NNO NOONN OOON N.ON MON N OO OOOOOO NON NNON
ON ONNN O ONM NMNON NNNN N.NN OO. N OO HOOKOO NON NOON
ON OOO O NMM NMOO NMON M.ON OO N OO Omhhc NO NOON
O ONN N MO NONN OOM O. ON O MN BOO NO NOON
NM OONN O ONM ONONN NNON M.ON OON N OO OOOOOO NO NOON
ON ONON O OOM ONMO OOON 0.0N OO N OO OOOOOO NO NOON
ON OOO O OMO OOON MNMN 0.0N OO O ON HOOOOO NO NOON
O28 . 9h GOOD ..N..N . uh . .N... NNOO ”OOO ONNO gNA OOHOHO n04 OOON
.90 .30 .OO .90 fl>¢ \Om (OH COB N20:
NOOONOOO OOOSONON mooaztm

.nONwosnouO mesouO ons enslsOsnsI ssua mono M .nem moNxH No musllnm

.uoua aauun 30a on one noon ca mcaccaeu oz.

 

NNON .NNON nN sous Nsnso no ussu sussvn mm on cash .osNNmmc unsIsOsns:
o.nO .xsonN szm sans: usOsm .usNusmm xsonN suNm

nm .sOO NsNeNnN NOON .noNuusnoua no ussw NsNuNnN

M .nONusONNOsm

nmamnauummmlunumm

.anun savanna:

fl NNOZOOOO

186

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

MN OOO N NON NNOM MON O.NN OO N NO mmommm NNN NNON
NN OOO O ONM NONN OOO O.NN OM N MO Odommm NNN NNON
O ONM O MOO NOO OON M.ON NM N OO mOOmmm NON NNON
N OON O NMO OON OO N.O ON N OO memmm NON NOON
O NNN O OOM O O 0.0 ON N OO mOOEOO NO NOON
M NNN M mMN O O 0.0 ON N OO mmommm NO NOON
M ONN N MNN O O 0.0 ON O OO mmoamm NO NOON
mach . an 5.8 . Hm . N.O . .N... mma aunt OHNO O> NA OONONO mo: OOO»
.ou .oo .oo .oo use \oo one one one:
AOOONOOO OOOSOOOA OOONZOO
scoz aosNNOOO unsIsOsnsz
O.OO .xsonN saNm sans: usoom .usNusmm xsonN suNm

.noNuosnoum masouO one

NO

unessOsnsn uNINNIusesIsNo :

.ome a-auaua

t KNOZHOO<

anoNuosnoum No use» NsNUNnN
N .nONusuNNmsm
mmdmmdmummmlflwmmu

NOON

o a .noe nears no nun-Ian .nnue nave-nan

187

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

ON ONO N OMN OOOO ONO O.MN OO N OO mOOhmm NNN NNON
NN MNO M MNN OOOM OMO N.NN OO N OO mOommm NNN NNON
ON MMM O NOM OOON ONM 0.0N MM N OO mOOEmm NON NNON
N NON O OOO OON OO N.O ON N OO mOommm NON NOON
O ONN O MOM O O O.m ON N NO mOommm NO NOON
M ONN M NON O O O.N ON N NO mmommm NO NOON
M OO N ONN O O N.O O O NO mOommm NO NOON
Ozoa .Hh OOOO .Bh .HO .HO OOO OOOO OONO u>oa OOHOHO OOO OOO»
.oo .oo .oo .oo n>¢ \co one may one:
JOOONOOO OOO3ONOO OOOA3¢O
scoz .osNNmO¢ unsleOsns:
O.OO .xsonN suNm sans: usosm aesNosOm xsonN suNm

.nONaosnouO avloum one

NO

vnsIsOsnsl UNINNIusasIsNo :

.om¢ aaaoaua

O KNOZHOOO

.nONausnoum No use» NsNUNnN
N .noNusuNNOsm
OONmmNmmmuNIOmumm

NOON

o n .noo usage on nuns-so .nnue savanna:

188

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

ON OOO N NON OONO ONO O.NN OO o OO uuoaun NNN NNoN
NN Non N OON NNon moo N.NN on 0 OO uncanu NNN NNoN
a ONN O can OONN ONN o.NN Na 0 OO uuoaun NoN NNoN
N oON O Non can Oo O.N ON N OO uzoauu NoN NooN
O NNN O OON o o n.o ON N NO Oceans No NooN
n NNN N ONN o o N.N ON N NO uzoaun Na NONN
N on N ONN o o N.O a o OO uzoann No NONN
uses .9; nzgu .NL .au .Nu an: u¢o¢ auNn n>Na mapsau uoO «ONN
.au .50 .nn .ao n>¢ \On ¢ua :na are:
a¢aaNaa¢ noozaaaa mooaz¢u

NNON .NNON .NOON :N can. Nun-n no uoou ou¢9Uu OO 0» o>on¢ EONO .chH .GONNOGC unclonaulx

0.00 .uovaN OONO INna:,uao:O N.ONOOOO unvaN IUNO
NO .om: N-NuNaN NNON .uoNaooNoua No use» N-NaNuN

M .uONquNNaon

umquuquuqqanuuquu

.QON»00noua £03090 can uuolouaudl aNINNIuIOOIINv :O n .mox Ooqu no manila. .ONud xdvuoant

< RHOZflhat

189

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

OO OOMN NN ONNN MONON NNON O.NN ONN ON NNN OOOOOO NNN NNON
OO ONNN NN OONN OOOMN NOON O.NN OON O NNN OOOOOO NNN NNON
OO OONN NN ONNN OMONN OONN O.NN OON O OON OOOOOO NON NNON
NO NNON MN NONN OOONN NNON O.NN NON O OMN OOOOOO NON NOON
OO OOON NN NONN NOOO NOON O.NN NON N OON OOOOOO NO NOON
OO OOON ON NMON OOON ONON 0.0N ONN N MON OOOOOO NO NOON
NO OOON NN OONN OOOO OONN 0.0N OON O OON OOOOOO NO NOON
OZOH .Bh nxOO .ak .Bh .Hh ZOO Hunt aflNn n>Nq @5848“ ”0‘ m‘u»
.90 .au .nn .ao n>¢ \<m «my ONN are:
A‘OONOOO OOOSONOO OOON3¢O

0:02 .OONNGQO vuolonnaax

O.OO .uovaN IUNO ONOO: uuosm .OONOOOO novaN IuNO

NO .OOO NuNuNaN NOON .aoNu00nouO no nae» NuNuNaN

.u0N000noua auxoum can vacuum-uni Nouuuoo N .mox OONIH no huallsu

¢ x~nzamm¢

N .nONuIONNmox
qumuqumuumluquwu

.ONu¢ uNOuomm¢

 

 

 

 

190

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

NO OOON MN OOON OOOON OOON 0.0N NNN O OON OOOLOO NNN NNON
OO NOON NN MONN nMMNN ONON 0.0N OON O OON OOOOOO NNN NNON
NO ONMN NN OONN OOOON NNON 0.0N OON O OON OOOOOO NON NNON
NO ONNN NN OOON OOOON ONON 0.0N OON n NON HOOOHO NON NOON
MO ONON ON ONON ONNMN MMNN N.MN ONN n NON OOOOOO NO NOON
OO O OOON ON ONON NOONN ONON N.MN NON M MON OOOOOO NO NOON
OO ONON NN OMMN ONNON ONNN O.NN OON O OON OOOOOO NO NOOfi
mack .Hh OdOO .Hh .Hh .Hh mma udot nmNo M>NN mahdhm n04 zdna
.DD .30 .an .50 fl>‘ \‘fl fink (OH 820:
Atannmux OOOZLADO mooaxtm
ocoz NGONNOOO uaOIOOauI:
O.OO .uoqu ouNO ONOO: uaosm .OONOOOO nounN ouNO

.uONu00noum no ado» N.NuNaN
N .aONucuNNOox

NO .001 NINUNlN NOON

 

.uONuuonouO avaouO on. OOOIOOOOII Nouuuoo N .Oox OoNza «o nun-Isa .ONIO xNvuoam‘

d xHoznmmt

191

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

OO OONN NN NOON NMOON OOMN 0.0N NON O ONN OOOOOO NNN NNON
NO NNNN NN ONON OOOMN MMNN 0.0N OON M ONN OOOOOO NNN NNON
OO ONON ON NOON, OOONN OONN 0.0N NNN M NNN OOOOMO NON NNON
NO ONON ON NOON NOONN NOON 0.0N OON M OMN NOOOOO NON NOON
OO MOON ON OOON OONNN NOON O.MN OON M MMN OOOOOO NO NOON
OO ONON NN MNMN OOOON ONNN M.MN OON N OMN OOOOOO NO NOON
NO OOON ON ONNN NOOO OOON O.NN OMN O OMN HOOOOO NO NOON
OZOH .9& GOOD .NL .Bh .Hh sun ndot OHNO fl>flu ODBCHO n04 z‘u»
.50 .20 .nn .ao n>¢ Nan «ma ONN are:
N‘DONmfld Ooozmdaa mooqxdm
0:02 NOONNOOO unaloucnl:
O.OO .uovaN ouNO ONOO: NOOOO .OONOOOO noqu ouNO

.u0N000noaO no an.» NINuNaN
M .OONaIuNNOox

No .ou¢ NONuNuN NONN

 

.uONNOInoum avxouO can OBOIOOOQII Nouuaoo M .nox OONIH No Nhnllsn .NNI¢ quuoamd

4 xNoZHOOC

APPENDIX B

APPENDIX B

Rog:osoion Equations usod to Calculoto
bulb-r Yioldo fro. Grodod Hardwood Logs

””‘di‘ .- 1 a

192

BPPIIDI! B

Rogroaaion oquationa for anon: NAPLI.

 

Independent Variabloa

 

 

 

 

Dopondont
variablo : Dbhrx
lumbar grad. Dbh2 Horchantablo Horohantabla
height height
TREE GRAD! 1
PBS -13.7 0.04397 -0.5012 0.003110
PASIF -S.1 .09560 .2343 --
Select. 8.7 -- -- --
No. 1c 64.4 -.08478 -2.S900 .009585
No. 2c -20.2 .17790 .6335 -.000416
No. 3A 17.9 -.02044 -.0217 .001842
No. 38 -49.2 .15884 1.7636 -.002061
TREE GRAD! 2
PAS -1.1 0.02014 -0.1342 0.001130
1381! -26.2 .10385 .5039 --
Select. 7.6 -- -- --
No. 1c -47.9 .23097 .2012 .002139
No. 2C -34.0 .14299 1.1578 .000284
No. 3A -9.6 .01312 .8717 .000694
No. 38 33.7 -.09204 -.3045 .003507
TREE GRADE 3
:35 3.6 -0.01538 -0.2051 0.001103
P381! -9.9 .06014 .1904 -.000541
Solocta -- .01602 -- --
No. 1C -32.4 .19398 .4660 --
No. 20 17.1 -.03144 -.8246 .006521
No. BA -40.0 .15978 1.7973 -.002380
No. 38 .4 -.01007 1.1382 .001620

 

193

APPENDIX I

Appendix 8-2. legreeeion eguatione for nasswooofl

 

Independent variables

 

 

 

 

Dependent
variable: Dbh1 x
lumber grade Constant Dbh2 Merchantable Merchantable
height height
TRIS GRADE 1
PAS 109.2 -0.28506 -3.8748 0.013515
Selecte 46.2 -.05390 -.6958 .003807
No. 30 -94.0 .32304 1.6243 -.003752
TRI! GRADE 2
PAS 34.4 -0.14665 -1.2592 0.006328
Selecte 2.7 .02116 -.5684 .004110
No. 20 -31.4 .14176 2.4489 -.001547
No. 3c 37.8 -.06258 -l.7140 .006367
TREE GRAD!
PAS -5.1 0.00443 0.1479 0.000396
Selecte 10.4 -.07589 -.3836 .003898
No. 10 -27.7 .11870 .8929 --
No. 20 -29.8 .23438 1.2059 .001412
No. 3C 14.8 -.01777 -.4513 .003582

 

* Theee equations also

used for white aeh and American elm.

APPOIldiX B- 3 e

194

APPINDII I

Regression equations for YELLOW BIRCH.

 

Independent variables

 

 

 

 

Dependent
variable: Dbh2 x
lumber grade Constant Dbh2 Merchantable Merchantable
height height
TREE GRADE 1
PAS -81.6 0.25084 0.9478 --
PASlr 4.9 .06269 -- --
Selects .9 .00458 .1614 --
No. 1c 42.7 -.07439 -2.2l97 0.009403
No. 20 75.0 -.13688 -1.5430 .006463
No. 3A 29.6 -.07673 -.4814 .003595
No. 38 -l6.1 .06006 .8727 -.000553
TREE GRADE 2
PAS -31.3 0.10494 0.5159 -0.000243
3A51F -.15.9 .06511 .4257 -.000395
Selecte 3.7 .00242 .0495 --
No. 10 -52.7 .25423 1.7080 -.003374
No. 20 -5.2 .01883 .4377 .002703
No. 3A -16.0 .06754 1.1508 -.000963
No. 38 -4.8 .02837 .1856 .001751
TREE GRADE 3
PAS 0.9 -0.00197 -0.0494 0.000301
FASlP -.4 .00701 -.0677 .000735
Selects 1.3 .00229 -- --
No. 1c -35.8 .19206 .6504 --
No. 20 .9 -.06329 .4244 .005634
No. 3A —32.7 .15298 1.5680 —.002739
No. 38 .2 08238 .5935 —.001215

 

”pndix 3“ e

195

APPENDIX 3

Regression equations for RED NAPLE.

 

Independent variables

 

 

 

 

Dependent
variable : DbhI x
lumber grade Constant Dbh2 Merchantable Merchantable
height height
TREE GRADE 1
PAS 107.0 -0.32016 -3.2156 0.013067
FASIF -55.7 .17503 1.2690 -.001008
Selecte 33.9 -.09531 -.6961 .002937
No. 1C -58.6 .26467 .5604 -.000106
No. 2A -59.0 .21877 1.7695 -.002140
No. 28 23.1 -- .6110 --
No. 3A -3.1 -- .5536 --
No. 38 -18.8 .02797 .4187 .000139
TREE GRADE 2
EAS 50.1 -0.19679 -1.9356 0.009389
FASIE 36.0 -.11245 -1.1578 .006010
Select.9 7.9 -.02157 -.0667 .001043
80. 1C -35.9 .21697 .4824 -.000330
No. 2A -62.8 .25951 2.5908 -.004669
No. 28 20.6 .00823 .6919 -—
No. 3A -5.6 .03546 .3150 ~-
No. 38 -31.3 .12511 .4-546 -.001094
TREE GRADE*3
PAS -3.6 0.03176 0.0160 --
EASlE 3.3 -.03651 -.2691 0.002968
Selecte -9.2 .03157 .3783 -.000581
No. 10 -21.0 .18789 .1210 ~-
80. 2A -17.1 .06125 .6576 .002633
No. 28 -10.6 .02622 1.6818 --
No. 3A -2.3 .04847 .1145 .000231
No. 38 -2.9 .04757 -- --

 

IIDLIOGRAPIY

Adams, D.N.: Ek, A.R. 1974. Optimizing the management of uneven-aged
forest stands. Canadian Journal of Forest Research 4(3): 274-287.

Adams, D.N. 1976. A note on the interdependence of stand structure and
best stocking in a selection forest. Forest Science. 22(2):
180-184.

Anderson W.C. 1976. A method for appraising multiple-product sales of
southern pine stumpage. Res. Pap. 80-126. New Orleans, LA: U.S.
Department of Agriculture, Forest Service, Southern Forest
Experiment Station. 9 p.

Arbogast, C. Jr. 1957. Marking guides for northern hardwoods under the
selection system. Sta. Pap. 56. St. Paul, MN: U.S. Department of
Agriculture, Forest Service, Lake States Experiment Station. 20 p.

Barnett, 8.J.; Norse, C. 1963. Scarcity and growth: the economics of
natural resource availability. Baltimore, ND: Johns Hopkins
University Press. 288 p.

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