assessing the timber situation in georgia using the …
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ASSESSING THE TIMBER SITUATION IN GEORGIA USING THE MULTI-PRODUCT
SUBREGIONAL TIMBER SUPPLY (MP-SRTS) MODEL: 2005-2025
by
VERNON WESTON HIOTT
(Under the Direction of Michael L. Clutter)
ABSTRACT
The development of the Multi-Product Subregional Timber Supply Model (MP-SRTS)
has expanded the ability to precisely project future quantities of raw forest products. Developed
from the Subregional Timber Supply Model (SRTS), MP-SRTS allows projections to be made
on a product basis. The ability to differentiate among product classifications has allowed
declined demand levels for forest products to be examined by product. Using MP-SRTS allows
market conditions to be assessed more precisely and provides a better understanding of the future
regional outlook for raw forest products within the state of Georgia. The State is projected to
experience depressed levels of raw forest product inventory with stable harvest levels and
increasing upward pressure on raw forest product prices. The largest reduction in raw forest
product inventory is projected to occur in the North Central FIA survey unit and documents the
continued impact of land use change surrounding Atlanta, Georgia.
INDEX WORDS: MP-SRTS, Forest Market Modeling, Timber Product Supply, Supply and
Demand, Georgia, Timber Price
ASSESSING THE TIMBER SITUATION IN GEORGIA USING THE MULTI-PRODUCT
SUBREGIONAL TIMBER SUPPLY (MP-SRTS) MODEL: 2005-2025
by
VERNON WESTON HIOTT
B.S., Clemson University, 2004
A Thesis Submitted to the Graduate Faculty of
The University of Georgia
in Partial Fulfillment of the Requirements for the Degree
MASTER OF SCIENCE
ATHENS, GEORGIA
2006
ASSESSING THE TIMBER SITUATION IN GEORGIA USING THE MULTI-PRODUCT
SUBREGIONAL TIMBER SUPPLY (MP-SRTS) MODEL: 2005-2025
by
VERNON WESTON HIOTT
Major Professor: Michael L. Clutter
Committee: Bruce E. Borders Jacek P. Siry
Electronic Version Approved: Maureen Grasso Dean of the Graduate School The University of Georgia August 2006
DEDICATION
To my father, Craig Hiott, for instilling in me the drive and determination to achieve my
goals, and to my mother, B.J. Hiott for her endless support, love and encouragement.
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ACKNOWLEDGEMENTS
My major professor, Dr. Michael L. Clutter, deserves thanks for providing guidance
during this project and for introducing me to many academic concepts that have influenced my
professional life. My committee members, Dr. Bruce E. Borders and Dr. Jacek P. Siry who have
helped with this project and have taught me so much.
I also extend thanks to my family and friends for all the support they have given me.
Their encouragement throughout the past year and a half is greatly appreciated.
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TABLE OF CONTENTS
Page
ACKNOWLEDGEMENTS.............................................................................................................v
LIST OF TABLES........................................................................................................................ vii
LIST OF FIGURES ..................................................................................................................... viii
CHAPTER
1 Introduction and Literature Review...............................................................................1
Model Development ..................................................................................................6
Current Trends.........................................................................................................10
2 Methods........................................................................................................................18
Market Module ........................................................................................................19
Inventory Module ....................................................................................................22
Model Scenario........................................................................................................24
3 Results..........................................................................................................................27
4 Conclusions..................................................................................................................43
REFERENCES ..............................................................................................................................45
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LIST OF TABLES
Page
Table 1: MP-SRTS Model Softwood Projections by Region ........................................................31
Table 2: MP-SRTS Model Hardwood Projections by Region.......................................................33
vii
LIST OF FIGURES
Page
Figure 1: Georgia Statewide Quarterly Real Stumpage Prices......................................................11
Figure 2: Georgia Statewide Quarterly Percent Change in Pine Product Prices ...........................12
Figure 3: Georgia Statewide Quarterly Percent Change in Hardwood Product Price ...................13
Figure 4: Georgia Softwood Timber Product Output ....................................................................15
Figure 5: Georgia Hardwood Timber Product Output...................................................................15
Figure 6: Forest Service Survey Units for Georgia .......................................................................19
Figure 7: MP-SRTS Market Module .............................................................................................20
Figure 8: MP-SRTS Inventory Module .........................................................................................23
Figure 9: State level hardwood and softwood inventory shifts by product ...................................28
Figure 10: State level hardwood and softwood growth shifts by product .....................................28
Figure 11: State level hardwood and softwood removals by product............................................29
Figure 12: State level raw forest product prices by product ..........................................................29
Figure 13: Softwood inventory shifts by FIA survey unit .............................................................35
Figure 14: Hardwood inventory shifts by FIA survey unit............................................................35
Figure 15: Softwood growth shifts by FIA survey unit .................................................................36
Figure 16: Hardwood growth shifts by FIA survey unit................................................................37
Figure 17: Softwood removals by FIA survey unit .......................................................................38
Figure 18: Hardwood removals by FIA survey unit ......................................................................38
Figure 19: Raw timber product price projections for the Northern survey unit ............................40
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Figure 20: Raw timber product price projections for the North Central survey unit.....................41
Figure 21: Raw timer product price projections for the Central survey unit .................................41
Figure 22: Raw timber product price projections for the Southeastern survey unit ......................42
Figure 23: Raw timber product price projections for the Southeastern survey unit ......................42
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Chapter 1
Introduction and Literature Review
The southern United States produces more timber than any single country in the
world and is projected to remain the dominant producing region for many decades to
come (Prestmon and Abt, 2003). The U.S. South produces approximately 15% of the
industrial roundwood in the world (Smith et al. 2004). The ability to produce this amount
of raw forest products can be contributed in large part to loblolly pine (Pinus taeda L.),
the most economically significant timber species in the world. The range of loblolly pine
reaches across the Atlantic and Gulf Coastal Plains from eastern Texas to southern
Maryland (Wahlenberg, 1960). Loblolly pine in this region consists of more than 68
billion cubic feet in total growing stock (Haynes, 1990).
Loblolly pine is becoming an increasingly important species in Southern forests
as acreage under intensive management increases. During the past thirty years, pine
plantation acres have surpassed natural pine acres; hence loblolly pine plantations have
become the major source of raw forest products (Cost, 1989). As of 2005, the U.S. South
is estimated to contain 177.4 million acres of forestland of which 37.8 million acres
supports pine plantations (Cubbage et al., 2006).
Forestland ownership in the Southern United States is composed of three major
groupings: private industrial, non-industrial private, and public. The South produces
approximately 60 percent of the Nation’s timber products and almost all of it originates
from private forests (Prestmon and Abt, 2003). Private forestland owners control 93% of
1
the South’s forestland with only 7% being under public control. Within the 93% that is
privately held, forest industry controls 32% and non-industrial private forestland owners
(NIPFs) hold 61% (Siry et al., 2005). The U.S. South has the largest concentration of
both industrial ownership and non-industrial private ownership in the United States
(Clutter et al., 2005).
Non-industrial private forestland owners control the vast majority of forestland in
the Southern U.S. with approximately 110.2 million acres (Cubbage et al., 2006). NIPFs
are a diverse group of forestland owners with a wide array of timberland management
objectives. Members include individuals, family trusts, and S-corporations created to
hold timberland assets. The NIPF ownership classification is expected to become even
more important as ownership trends continue to favor private ownership over C-
corporations (Clutter et al., 2005). Birch (1996a and b) found that within the U.S. South
55% of the forestland controlled by NIPFs had a financially based primary management
objective with 35% being to produce timber. Many NIPFs hold forestland as part of a
larger land holding such as farmland. In these instances the forestland owner may not
view timber management as a primary source of income as owners holding only
forestland. The inventory distribution by management type somewhat reflects this across
the South. For NIPFs throughout the Southern U.S. the majority of forestland is held in
upland hardwood, approximately 42% or 45.7 million acres. Pine plantations under
NIPF ownership only comprise around 12.4% or 13.5 million acres, but these plantations
are not completely uniform monocultures due to their contributing 2.3% of the annual
hardwood timber removals (Cubbage et al., 2006).
2
The forest industry within the Southern U.S. is a management intensive, ever
evolving class of forestland owners. Southern U.S. forestland controlled by the forest
industry is approximately 56.6 million acres and is composed of 22.7 million acres of
pine plantation (Cubbage et al., 2006). The pine plantation component of forest industry
holdings represents 40% of the ownership and reflects the emphasis that is placed on
property to maximize return with intensively managed plantations and also provide a
consistent source of raw forest products to manufacturing facilities. Recent trends have
documented a realization by forest industry that wood can be procured continually on the
open market and has spawned movement of forest ownership away from this ownership
class. From 1996 through 2004 the Southern U.S. experienced movement of 18.4 million
acres, the majority of which transferred ownership from forest industry entities to
institutional ownership (Clutter et al., 2005). The implications for future timber supplies
are unclear at present; however, by examining the relative intensity of NIPF management,
impacts are likely to be negative.
Public ownership of timberland rarely identifies timber production as a primary
management objective. In the Southern U.S. public ownership accounts for only 7% of
the forestland base and of that 7%, only 11% is held as pine plantations (Cubbage et al.,
2006). The management of public forestland in not primarily aligned with financial
principles and therefore is not considered as a significant participant within the forest
sector. In addition, when considering future projections that are based on economic
factors, the modeling assumptions are not appropriate and therefore public holdings are
not examined as a source of future timber supply (Prestmon and Abt, 2003).
3
Similar to the region, the State of Georgia follows the Southern U.S. in forestland
significance and structure. In 2001, Georgia’s forest products industry employed
approximately 204,000 workers, produced an economic output of over $19 billion, and
supported approximately $30.5 billion in economic activity (Riall, 2002). The economic
output of the forest sector represents the direct influence of the industry on the State’s
economy. The $19 billion is created by the operations and transactions related to the raw
forest products. Economic activity resulting from the forest sector amounted to $30.5
billion and is the secondary dispersion of value back into the State’s economy, for
instance, lumber being sold at a home improvement store or a forest industry employee
spending an earned salary. Clearly, the forest industry has a substantial impact on the
economic health of Georgia.
Investment within the forest product sector can be a primary force for growth and
new development across the State. Identifying potential investment opportunities,
whether for an individual purchasing timberland or an industrial entity establishing or
expanding a mill requires careful consideration of current and future raw materials
market conditions. The ability to predict these future relationships is central to business
planning and investment analysis. Assessing the raw material needs of the industry and
predicting the future quantities of these materials is crucial in supporting sustainable
operations and maintaining a healthy forest products sector.
Investment analysis is also used when applying stand level silvicultural
treatments. Silvicultural activities should be justified by producing a sufficient marginal
return. Price fluctuations influence the realized return resulting from silvicultural
applications, and depressed prices result in lower silvicultural investment particularly for
4
non-industrial private forestland owners. Decreased levels of stand investment result in
lower stand production rates and consequently lower supplied levels of raw forest
products in the market place. The ability to reflect these production declines in market
modeling better equips members within the forest sector when formulating a long-term or
strategic plan.
Forecasting timber conditions for the projection period requires projecting
growth, inventory and harvest in the market on an annual basis. The initial point of the
projection period is described as having an established inventory level. This supply level
determines the starting point upon which the following year’s characteristics are
calculated. With the initial inventory established, the annual harvest is applied along
with the amount of growth over the year. At the end of the year, deducting the harvest
and adding the growth to the initial inventory, calculates the net movement in timber
inventory levels. Based on the amount of timber available for harvest and the harvest
level, a price is determined for each product for the year. The calculated price for the
year influences the quantity and distribution of timberland acreage and the assignment of
pine plantation acreage for the area being analyzed. This process is repeated for each
year in the projection period.
One of the major components in expanding the market conditions from the initial
point to the end of the projection period is the amount of growth that is accomplished
over the course of each individual year. The growth of timber is a function of many
factors including the current price level of forest products in the market. In order to
capture the effect of depressed price levels in the projection, adjustments are made to the
annual growth rates applied to the timber inventory level. Due to recent depressed price
5
levels for timber products, as shown in Figure1, this analysis provides an understanding
of the influence of current price levels on projected market conditions for raw forest
products.
Model Development:
Timber supply, demand and price trends have been evaluated for the nation and
for the South (Haynes, 2003; Prestmon and Abt, 2003). Forest modeling has evolved
from multiple state inventory approaches such as the Aggregate Timberland Assessment
System, or ATLAS, to representing subregional inventory and economic implications
based on products as with the Multi-Product Subregional Timber Supply model (MP-
SRTS). Initial modeling attempted and achieved the ability to represent aggregate
inventory levels for a multi-state region such as the Southern United States. As
shortcomings were identified and models were developed to include factors such as land
use change, economic conditions, and product classifications.
An initial challenge was the vast areas of forestland that were analyzed as a
consistent group or strata in such inventory projection models. Projections were made on
a regional basis where the regions included several states grouped together. This
approach assumes that species, growth, and market behavior remain constant over the
entire region. Without question these factors vary within a single state and are much less
likely to remain unchanged over a multi-state region. The market presence is likely to be
the most varying factor within an area. Market presence is the demand for raw forest
products in a particular area. Depending on the number and capacity of processing
centers in the area, pressure placed on the inventory by product varies. As processing
capacity increases the pressure applied to the surrounding inventory is increased.
6
Consequently in this situation the area will experience a real increase in the price of the
demanded raw material.
The Aggregate Timberland Assessment System-ATLAS was developed by the
U.S. Forest Service and used in conjunction with the 1989 Renewable Resource Planning
Act. The model was developed to address a broad range of policy questions related to
future timber supplies (Mills and Kincaid, 1992). Renewed concern in the potential of
consuming the country’s forest resources fueled the development and use of ATLAS as
an evaluation tool for the forest resources in the United States.
The ATLAS system employs aggregate estimates of inventory, harvest and
growth at various, but course, levels of resolution to describe the region. The inventory
data is collected at the U.S.F.S. Forest Inventory and Analysis survey unit level and
combined to describe the region where the harvest is applied. The model covers a broad
area that is considered as having homogenous growth and market characteristics.
The primary input modules are inventory, management and harvest. The
inventory component establishes the base level of resources available by acre and volume
per acre by age class. This measure sets the base upon which to develop forecasts.
Geographic region, owner, forest type, site and other factors categorize the acreage and
volume per acre quantities. The total inventory for the region is found by combining the
established inventory units that are at a more specific level of resolution. Within the
individual inventory units, five management classifications are applied which reflect
differences in management intensity. The management assignment includes:
regeneration, growth and harvest variables that simulate stand improvement, management
alternatives and area change characteristics. The management component can vary in the
7
levels of each variable applied to simulate differences in species composition. This
allows several stand situations to be represented with varying degrees of management
intensity. The inventory units with management scenarios applied are assigned a harvest
level that is distributed among the separate inventory units.
The ATLAS model approach allows the forest resources for the region to be
assessed and projected based on the established base, assigned growth and yield rates,
and by given levels of harvests. The use of aggregate data allows a prediction to be
formed for the region has a whole but for no smaller resolution and ATLAS lacks product
resolution as well. The applications of the model results are limited due to the large area
that is included and the lack of product specificity.
In order to be more specific and concentrate on the State of Georgia’s timber
situation the Georgia Regional Inventory Timber Supply or GRITS, was developed to
gain understanding regarding the timberland resources within the state of Georgia. The
model uses each of five regions defined by the USDA Forest Service FIA as reporting
levels. GRITS uses estimates from the USDA Forest Service FIA database, which
presents timber inventory by region within the state. The Forest Inventory and Analysis
(FIA) database supplies levels of timberland area, timberland inventory, timber growth
rates, and timber removals (Abt et al., 2000). Using the supplied information GRITS
computes the future levels of timber available within the five regions in Georgia.
The GRITS model expands on the capabilities of ATLAS by being more specific
to a particular area that may perform more similar to a homogeneous unit. This inventory
model provides a methodology for predicting future timber supply based on existing
inventories, management intensities (representing ownership), and current and future
8
harvest levels. A primary flaw in using GRITS is that the current harvest level represents
demand and is adjusted over time by anticipated movements in that harvest level. In
addition, shifts in land area under management are exogenous to the model. The GRITS
model allows the resources for the state of Georgia to be assessed independently from
other states in the Southeastern United States.
Expanding on the GRITS inventory model, the Subregional Timber Supply
(SRTS) model was developed to incorporate impacts from market conditions. The Sub-
Regional Timber Supply model is widely used and accepted for projecting supply,
demand and price trends in the South (Prestmon and Abt, 2003). SRTS was initially
developed at North Carolina State University to provide the southern United States with a
model that did not consider the area as a homogenous timber-producing sector as did
previous models (e.g. ATLAS), but rather a diverse region composed of subunits that
contain wide variation in market conditions (Mills and Kincaid, 1992; Prestmon and Abt,
2003). SRTS provides an economic overlay to traditional inventory models. These
market conditions drive the determination of timberland allocation by area and
management type. The SRTS model applies economic conditions to inventory models
and has been used in conjuncture with both the ATLAS and GRITS models. SRTS
provides useful projections that are specific by region; however, the model does not have
the capability to capture movements based on specific raw materials product categories.
To address product movements individually, the Multi-Product Subregional
Timber Supply (MP-SRTS) model was developed. The MP-SRTS model is a partial-
equilibrium timber market simulation model, and is used to analyze various forest
resource and timber supply scenarios (Abt et al., 2000). The MP-SRTS model provides a
9
tool for examining timber conditions in light of multiple product classifications, land
conversion or land use change, and management intensity. The major implication of
using the MP-SRTS model is that this model is capable of classifying values based on
more descriptive product classes, such as pine pulpwood or hardwood sawtimber, as
opposed to SRTS classifications of growing stock and inventory by hardwood and
softwood classes.
The classifications based on product class and species group allow projections to
be made that identify fluctuations not only in the inventory level as a whole, but as
individual product classes. Separating values by product class allows information to be
provided that is particular to specific area and user. For instance, a pulp and paper
manufacturer would be provided with projected hardwood and softwood pulpwood
values separate from sawtimber projections. Additionally, this model allows
interdependencies between product classes to be simulated. For example, the quantity of
hardwood pulpwood used to produce pulp is directly related to the amount of softwood
pulpwood used as a substitute and is, therefore, indirectly related to softwood sawtimber
harvest in any given year. These projections will be more relevant to many users than
those previously provided by the Southern Forest Resource Assessment, which was based
on the SRTS model (Prestemon and Abt, 2003).
Current Trends
The MP-SRTS model will be used to predict values of timber market conditions
and timberland allocation in Georgia from 2005 to 2025. These projections will reflect
recent trends in raw forest product prices in Georgia, particularly pine pulpwood, and
steadily increasing hardwood stumpage costs, as shown in Figure 1. Considering these
10
price trends and associated timber product output levels presented below, market
responses are sure to occur.
0
10
20
30
40
50
60
1985
1986
1987
1988
1989
1990
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1992
1993
1994
1995
1996
1997
1998
1999
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2001
2002
2003
2004
2005
$/To
n
Pine PW Pine CNS Pine Saw Hd PW Hd Saw
Figure 1. Georgia Statewide Quarterly Real Stumpage Prices Source: Timber-Mart South, 2006
Since 2002, pine pulpwood prices have remained at levels not seen since 1989.
Similarly, pine chip-n-saw and sawtimber have both experienced price declines following
strong levels during the late 1990s and early 2000. Pine sawtimber prices have
strengthened after a substantial reduction during 2000 and 2001. In contrast, hardwood
pulpwood and hardwood sawtimber prices have experienced an upward trend.
Figures 2 and 3 present the statewide quarterly price movements as percentages
and show the fluctuation in the volatility of the timber product prices. Throughout the
1990s, timber product prices fluctuated drastically before settling and becoming more
stable after 2000. Pine product prices, as shown in Figure 2, became less volatile after
11
2002. The pine product prices move in the same direction annually; however, the
severity in movements by product varies with pine pulpwood being most volatile.
-35%
-25%
-15%
-5%
5%
15%
25%
35%
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
Pine Pulp Pine CNS Pine Saw
Figure 2. Georgia Statewide Quarterly Percent Change in Pine Product Prices Source: Timber-Mart South, 2006
The quarterly price movements for hardwood product prices are presented in
Figure 3 and document that hardwood product prices have historically been more volatile
than softwood products. Over the last twenty years, hardwood product price variability
has been greatest in the early to mid nineties. More recent movements, from 2003 to the
first quarter of 2006, have witnessed a heightened level of variability as compared to the
2000 to 2003 period. As with the softwood product prices, hardwood pulpwood
experiences more movement than sawtimber.
12
-35%
-25%
-15%
-5%
5%
15%
25%
35%
45%
55%
65%
1985
1986
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1989
1990
1991
1992
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1994
1995
1996
1997
1998
1999
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2001
2002
2003
2004
2005
Hardwood Sawtimber Hardwood Pulpwood
Figure 3. Georgia Statewide Quarterly Percent Change in Hardwood Product Price Source: Timber-Mart South, 2006
Forest managers and owners relate the volatility in product prices as risk. As the
volatility of these product prices increases, the amount of risk assumed by management is
heightened. Management may be altered to limit the assumed risk, which would impact
stand development and growth negatively. Deferring stand treatments or substituting
with less costly and less effective methods will not allow growth potentials on sites to be
realized. The implication of less silvicultural investment across the State is a significant
reduction in total forest product supplied to the market. The reduction in supply resulting
from increased levels of harvest and declines in growth rates will cause stumpage prices
to increase.
13
As seen in Figure 4, initial production rates were unresponsive to price shifts for
pulpwood from 1999 to 2001; however, during 2003 pulpwood production levels
increased by approximately 100 million cubic feet. Sawtimber production responded to
increasing prices as product output declined by approximately 75 million cubic feet from
1999 to 2003 (USFS, 2006). These product output trends will lead to lower sawtimber
prices and increased stumpage cost for pine pulpwood. Other product output levels of
composite products have increased by approximately 6 million cubic feet among the four
composite panel, or oriented strand board, mills in Georgia (Johnson and Wells, 2005).
The increased production is obviously related to the decline in stumpage of pine
pulpwood and chip-n-saw. Relative to saw logs and pulpwood, all other softwood
products have remained stable with little variation.
Hardwood trends are similar in being responsive to price levels; however,
hardwood prices have steadily strengthened from 1995 to 2003 and with the exception of
slight growth in hardwood sawtimber output, hardwood product output has declined. As
seen in Figure 5, hardwood pulpwood has declined most dramatically with a fifty million
cubic foot reduction in product output. Much of this decline can be contributed to the
increase in pine pulpwood output as a substitute and limited supplies of hardwood fiber.
14
200
300
400
500
600
1995 1997 1999 2001 2003
MM
CF
Saw Logs Pulpwood
Figure 4. Georgia Softwood Timber Product Output Source. USFS FIA, 2006
0
50
100
150
200
1995 1997 1999 2001 2003
MM
CF
Saw Logs Veneer Logs Pulpwood Composite Prod. Fuelwood
Figure 5. Georgia Hardwood Timber Product Output Source: USFS, 2006
15
The movements in prices heavily influence management decisions, particularly,
silvicultural practices. Additionally, price declines reduce the feasibility of retaining
certain acreage in timber management and management intensity. An increasing factor
that magnifies reductions in the timberland base has been coined as urban sprawl. The
Southern Forest Resource Assessment has indicated that urban sprawl has a major impact
on the operations of the forest sector and contributes to losses in southern United States
timberland (Prestmon and Abt, 2003). Urbanization has a significant impact on average
parcel size and if present trends continue, by the year 2010 approximately 95% of the
nation’s private forest ownership will be in parcels of less than 100 acres (Mehmood and
Zhang, 2001; DeCoster, 1998). This increase of owners holding fewer acres is known as
parcelization and generally leads to fragmentation and timberland loss (Mehmood and
Zhang 2001). The implication for the State of Georgia is important when considering
development hotbeds within the State such as areas in and around Atlanta.
Parcelization influences management activities in that the feasibility of
silvicultural applications is related to the treatment unit’s size. The affect of parcelization
is two-fold. Not only is timberland lost as a direct effect of new owner objectives being
outside of timber management but also through the loss of management options that were
viable on the original, larger, tract. In Mississippi and Alabama, proximity to
development and more densely populated areas almost always led to lower harvesting
rates (Barlow et al., 1998). The implication is that with limited management options,
acreage will more rapidly experience land use change or will provide less than optimum
levels of return.
16
Timberland forms the base of an actively growing and contributing sector within
the State. The forest sector provides substantial monetary and social benefits and will
continue to be a strong industry. Modeling techniques have been developed to forecast
market conditions that can aid in planning and investment. Using the MP-SRTS model
the current low price levels can be reflected in these projections to predict the impacts on
future levels of supply, demand and price of forest products and the acreage under timber
management.
17
Chapter 2
Methods
The Multi-Product Subregional Timber Supply model, like SRTS, provides an
economic component to inventory models; however, the implications are examined for
each product. The use of MP-SRTS provides results that are specific to each state survey
unit. This geographical scope allows variation to be captured across the State of Georgia
and among separate market baskets within the State. Figure 6 shows the subregional
survey units that will act as reporting levels to assist in delineating separate timber
markets across the State. MP-SRTS works similar to many inventory models that
consider a particular harvest scenario and allows conditions including potential price
consequences, subregional harvest shifts, and inventory fluctuations to be represented
consistently.
MP-SRTS is applicable to several inventory models including ATLAS and
GRITS. The inventory module used for this analysis was modeled after the GRITS
model (Cubbage et al. 1990). The inventory and market modules are the two major
modeling components. Beginning with the market, which is composed of the subregional
survey units, the base price equilibrium is calculated using various market statistics.
Subregional movements in price and inventory are used to determine the distribution of
harvest intensity by subregion. Within Georgia there are five FIA survey units, as shown
in Figure 6, and when analyzed by forest industry and NIPF owners there are ten separate
owner / areas that exist in the model (5 subregions * 2 ownership classes). As discussed
18
earlier, public ownership is irrelevant in this analysis since management decisions are not
usually based on economic principles but rather on a wide array of social and
environmental management objectives.
Figure 6. – Forest Service Survey Units for Georgia. Source: Thompson, 1998
Market Module
The MP-SRTS modeling approach is designed to link to inventory modules that
establish the harvest characteristics under some assumed base case scenario. The model
is used to reflect movements in price and quantity as they relate to varying manipulations
of available supply and harvest. Given the harvest intensity for a region, the harvest is
19
distributed among the more specific subregional units and the inherent demand, price,
and subregional harvest shifts are calculated. Figure 7 depicts the MP-SRTS market
module and the relative positioning of the inventory module.
DemandPrice orHarvestProjection
by Product
DemandElasticities
by Product
SupplyPrice andInventoryElasticities
by Productby Owner
InventoryShifts
by Product-Owner-Unit
Equilibrium
Price byProduct
Harvest byProduct-Owner-Unit
Goal Program
Inventory Module
Multi-Product Equilibrium
Figure 7. MP-SRTS Market Module
The MP-SRTS algorithm determines the annual harvest based on the quantity
supplied and demanded for each year during the projection period. The harvest level is
assigned at the aggregate region level. For this analysis the aggregate region is the State
of Georgia. Timber supply is a function of several factors with the largest influences
being made by the product prices and inventory levels for the given year. The demand of
raw forest products is a function primarily of price levels at that given time and an array
20
of other influencing factors including: input prices, technological change, land quality,
management, and landowner characteristics. Harvest levels for a given year are based on
the raw forest product prices, initial annual inventory levels, and other supply and
demand shifting variables including management and landowner characteristics. As
harvest levels increase they are assumed to produce a marginal cost per unit. This
implies that the harvest supply function is positively sloping. The initial annual inventory
of merchantable raw forest products positively influences t year’s harvest with constant
elasticity.
In MP-SRTS, modeled inventory changes are used to compute the price, demand,
and supply shifts when the harvest level is assigned to the projection as an exogenous
variable (the most common method used to produce MP-SRTS simulations). The region
is assumed to be at equilibrium at the base year. At this point the demand and supply
variables are known and are used to solve for the price levels of raw forest products and
the inherent demand shift. On the subregional level, the proportion of harvest relative to
the assigned regional harvest level is calculated using the regional price movements and
subregional inventory shifts. The subregional harvest quantities are then adjusted in
order to sum to the amount of the regional harvest. The need for the adjustment comes
from the application of the Cobb-Douglas functional form, which is not additive. The
model can be run assuming that subregional specifications hold and that the aggregate
price is found by using a binary search algorithm that determines the market-clearing
price by summing the supply response across subregions and owners. In addition to
harvest scenarios, timber demand or price can be assigned as exogenous variables where
the remaining market conditions or equilibrium parameters are solved by the model. For
21
this analysis a top-down approach is used and the technique maintains the aggregate
market relationships.
The primary model assumption is that within the region the market is competitive
with no price discrimination between the two ownership classifications. Both NIPF
owners and forest industry owners alike face the same price trends consistent with
economic theory. MP-SRTS represents subregional market conditions that vary
according to regional price levels. Demand is assumed to move between subregions in
response to price movements and comparative advantages among subregional units. For
the life of the projection period, all owners and regions are exposed to the same general
price trend; however, the levels experienced may be different. Comparative advantages
determine the shifts in harvest among owner classes and subregions.
Inventory Module
The GRITS model forms the basis of inventory projections for MP-SRTS. Figure
8 depicts the layout of the MP-SRTS inventory module and the relative positioning of the
market module. The modified internal inventory model allows inventories to be formed
from USDA Forest Service FIA estimates of timberland characteristics. These estimates
include timber removals, growth and inventory, and timberland area. Timber and
timberland estimates are made by five year age class, species and product (softwood
pulpwood, hardwood sawtimber, etc.). Timberland characteristics include being
associated with one of five management types. These management types are: planted
pine, natural pine, oak-pine, upland hardwood, and bottomland hardwood). FIA data by
ten-year age class, species group, product and forest management type are summarized
for each of Georgia’s five subregions and for the State as a single unit.
22
Figure 8. MP-SRTS Inventory Module
Growth. – MP-SRTS, in order to project growth, uses five-year age classes that are
described by species, product, subregion, owner, and management type. Growth is
estimated by a regression equation where growth is a function of the subregion,
ownership class, age and an allowance for interaction between the ownership class and
age. The growth function is modeled as a cubic age relationship. This cubic age
relationship allows the growth to be modeled for the entire state but allows the quantity to
vary by subregion and ownership class.
Harvest. – The approach to harvest allocation within MP-SRTS initiates with distributing
the regional aggregate harvest quantities among the subregions by ownership. The
harvests are distributed based on subregional supply shifts and is part of market
23
equilibrium calculations. On the subregional/owner level, exogenous parameters
allocate harvests by management type and ten-year age class and allow harvests to reflect
historical trends, inventory levels, growth or any weighted combination of these. Timber
harvesting can be distributed by age class within the management type through
proportions of total subregional/owner harvest. For example, higher proportions being
assigned to the older age classifications accomplish an older first approach. Similarly,
assigning the same proportion evenly across age classes allows harvests to be distributed
evenly across all age classes. Abt and others (2000) found through empirical
examination of harvest allocations in the FIA data that for all management types other
than pine plantations, harvest allocations across age classes are highly correlated with
inventory age class distributions.
Model Scenario
The MP-SRTS model can be applied to timber markets at various levels of
resolution for the private forest sector. For the purpose of this analysis the State of
Georgia was considered as a whole and at the FIA survey unit level. The raw timber
product base was established using the 1997 FIA survey, which represents the most
recently completed survey for the area. The simulation is dictated by a depressed level of
growth in demand for raw forest products, which reflects the current timber markets
across the State.
Establishing a base inventory using the FIA data, harvests were allocated for
softwoods by proportions based on management type at a rate of 70% inventory and 30%
growth. This allocation assigns removals to originate from the initial years inventory and
the annual growth proportionately. The softwood harvests were assigned to age classes
24
based on inventory levels for 70% and by an oldest first approach for 30%. Harvest
allocations for hardwood were not assigned based on growth or age class but purely by
the inventory distribution. The harvest levels were adjusted down by 17% in the initial
year (1997-1998) of the projection in order to better reflect realized harvest levels.
The simulation spans from the base year of 1997 to 2025 to encompass a twenty-
eight year projection life. The course of the projection was determined by an annual
growth in the demand for raw forest products at an assigned rate of .5% annually. This
level of demand growth is less than that of previous analysis using SRTS by Abt and
others, 2000, which was assigned at 1.6% based on previous FIA trends. This lower level
of demand growth better reflects the current market for raw forest products for the State
of Georgia.
An increasingly influential factor in the availability of raw forest products in
Georgia is the productivity of pine plantations. Due to variations in management
intensity and development, growth rates are assigned separately to NIPF and industry
ownership classes. Over the life of the projection period, plantations under industry
ownership are assumed to realize a 30% increase in plantation growth rates while a 15%
increase in plantation growth rates for NIPF owners. The growth rate increases are
applied so that the majority is realized in the first half of the projection period and is
assumed to impact all age classes. In addition to the growth rates increasing over the life
of the projection period, the growth rates were increased by 10% initially to reflect
current plantation growth rates.
The timberland base is ever changing as real estate is converted to and from forest
applications. Fluctuations in land use impact the contribution of an area to the timber
25
market and are influenced by factors such as population growth, aggregate U.S. economic
growth, and agricultural and residential land rents. Within the model, movement in land
use is determined based on the regional raw timber product prices. The elasticity of land
use conversion to raw timber product price is assumed to be approximately .3 based on
the findings of Hardie and others (2000). Timber management has aided in the
conversion of natural and mixed pine management types to pine plantations. For the life
of the projection, acres of timberland in pine plantation were held constant at
approximately 26% of the privately held timberland area. Acres under natural and mixed
pine were converted to pine plantation to retain the 26% pine plantation based on the
relative abundance of each within the survey unit. The allowance for conversion among
management types allows the amount of total timberland across the life of the projection
to remain constant.
26
Chapter 3
Results
Figure 9 presents the statewide inventory results. Inventories are seen to decline
throughout the projection period with the largest reductions being in pulpwood and large
sawtimber. The growth that is supported from these inventories and influenced by
subregion, ownership class and age is presented in Figure 10. The growth is distributed
around a general flat trend by product; however, the variation in annual growth increases
over the projection life. The removals for the State are presented in Figure 11 and remain
unresponsive with the largest shift being in large sawtimber, which declined 16% over
the period. The initial drop in pulpwood removals is a result of pulpwood harvest levels
being adjusted by the administrator prior to performing the simulation. From the 1998
initial harvest level, the projection of pulpwood removals is more closely aligned with
current trends.
The timber product prices are measured in real terms and are presented in Figure
12. The trends are formed using a price index where the price of raw forest products
during the initial year serves as the base and the movement in price is presented as a
percent increase or decrease from the original value. The raw timber product prices
strengthen for the life of the projection period. Sawtimber and large sawtimber
experience the largest increases in product value with approximate increases of 90%.
27
0
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Figure 9. State level hardwood and softwood inventory shifts by product.
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Figure 10. State level hardwood and softwood growth shifts by product.
28
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Figure 11. State level hardwood and softwood removals by product.
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Figure 12. State level raw forest product prices by product.
29
The MP-SRTS projections of inventory, growth and removals for softwoods are
presented in Table 1. The projections are categorized by State, FIA survey unit and
product. Data are characterized by product as being pulpwood, small sawtimber,
sawtimber and large sawtimber. The small sawtimber category under softwood products
is commonly referred to as chip-n-saw.
The hardwood data presented in Table 2 is similar to the softwood grouping
however there is no small sawtimber or chip-n-saw category. These projections represent
the potential development of raw forest product inventories throughout Georgia under a
situation of low demand. These forecasts pertain only to timberland holdings under
private ownership and do not consider the implications of publicly owned timberland.
30
Table 1. MP-SRTS Model Softwood Projections by Region 1997 2004 2011 2018 2025
StateInventory Pulpwood 250,928 229,477 216,855 206,503 202,542
Small Saw 213,215 196,753 194,009 183,079 177,520 Sawtimber 99,226 81,516 75,430 69,728 61,012 Large Saw 189,659 150,103 114,678 95,273 83,421
Growth Pulpwood 16,219 19,507 15,960 20,338 18,311 Small Saw 14,641 21,065 18,573 18,274 20,242 Sawtimber 6,799 7,619 8,415 6,762 7,800 Large Saw 12,309 9,605 9,919 10,844 10,806
Removals Pulpwood 23,926 19,702 19,627 19,575 19,847 Small Saw 20,747 20,344 20,652 20,514 20,714 Sawtimber 9,712 8,994 8,863 8,770 8,319 Large Saw 17,378 15,450 13,730 12,965 12,456
NorthInventory Pulpwood 26,306 23,875 22,415 21,201 18,968
Small Saw 23,779 21,061 20,022 18,682 15,376 Sawtimber 11,883 9,670 9,624 9,039 9,383 Large Saw 19,455 17,050 14,872 13,090 12,318
Growth Pulpwood 780 890 939 998 589 Small Saw 761 992 1,311 696 904 Sawtimber 117 595 413 583 565 Large Saw 726 507 709 660 887
Removals Pulpwood 1,353 1,095 1,109 1,114 1,084 Small Saw 1,359 1,316 1,337 1,333 1,246 Sawtimber 533 504 508 496 509 Large Saw 1,018 963 938 898 881
NorthcentralInventory Pulpwood 43,897 32,096 21,122 16,749 14,146
Small Saw 36,841 26,571 17,472 13,425 12,030 Sawtimber 18,463 12,702 7,815 5,312 4,040 Large Saw 36,864 25,813 15,485 11,102 8,283
Growth Pulpwood 1,920 1,345 1,209 2,119 1,878 Small Saw 1,599 1,705 1,416 2,246 1,976 Sawtimber 1,026 804 873 1,021 1,112 Large Saw 1,946 1,126 1,579 1,670 1,896
Removals Pulpwood 3,878 2,973 2,587 2,361 2,283 Small Saw 3,332 3,038 2,604 2,388 2,366 Sawtimber 1,857 1,639 1,397 1,235 1,128 Large Saw 3,256 2,850 2,369 2,130 1,980
- - Thousand Cubic Feet - -
31
Table 1. MP-SRTS Model Softwood Projections by Region (conti…) 1997 2004 2011 2018 2025
StateInventory Pulpwood 69,875 68,544 68,801 67,733 70,562
Small Saw 59,587 62,726 66,577 62,654 62,913 Sawtimber 26,156 21,735 22,321 25,517 22,531 Large Saw 48,008 32,516 24,574 26,800 29,733
Growth Pulpwood 5,547 6,083 5,349 6,785 6,226 Small Saw 5,189 7,115 6,024 5,909 6,372 Sawtimber 2,221 2,613 3,329 3,204 2,799 Large Saw 3,356 2,867 4,139 4,988 5,202
Removals Pulpwood 6,718 5,726 5,883 5,984 6,190 Small Saw 5,716 6,055 6,317 6,244 6,366 Sawtimber 3,084 2,864 2,908 3,166 3,028 Large Saw 5,567 4,786 4,276 4,557 4,909
SoutheastInventory Pulpwood 29,093 26,834 27,131 27,067 27,557
Small Saw 20,418 18,874 22,361 24,002 24,228 Sawtimber 13,113 9,995 8,325 8,213 7,854 Large Saw 37,584 30,004 21,259 17,090 16,011
Growth Pulpwood 1,799 2,648 1,826 2,432 2,356 Small Saw 1,188 2,687 2,314 2,167 2,368 Sawtimber 729 774 877 980 1,054 Large Saw 2,184 1,482 1,389 2,170 2,271
Removals Pulpwood 2,644 2,191 2,237 2,277 2,359 Small Saw 1,973 1,947 2,100 2,198 2,267 Sawtimber 1,166 1,083 1,032 1,038 1,039 Large Saw 2,982 2,797 2,507 2,337 2,344
SouthwestInventory Pulpwood 29,093 26,754 26,882 26,188 26,222
Small Saw 20,418 18,833 22,286 23,746 23,449 Sawtimber 13,113 9,965 8,214 7,917 7,534 Large Saw 37,584 29,846 20,650 15,302 14,431
Growth Pulpwood 1,799 2,626 1,811 2,291 2,286 Small Saw 1,188 2,671 2,314 2,098 2,309 Sawtimber 729 760 885 915 1,046 Large Saw 2,184 1,427 1,341 1,882 2,167
Removals Pulpwood 2,644 2,189 2,231 2,261 2,328 Small Saw 1,973 1,946 2,098 2,193 2,249 Sawtimber 1,166 1,082 1,028 1,029 1,029 Large Saw 2,982 2,793 2,487 2,267 2,271
- - Thousand Cubic Feet - -
32
Table 2. MP-SRTS Hardwood Model Projections by Region 1997 2004 2011 2018 2025
StateInventory Pulpwood 641,932 624,402 617,602 608,480 595,551
Sawtimber 55,146 54,078 53,236 52,402 51,207 Large Saw 301,720 309,217 311,782 312,476 310,355
Growth Pulpwood 12,096 12,551 16,567 10,772 9,065 Sawtimber 1,099 1,250 1,646 1,172 1,015 Large Saw 6,778 8,434 9,430 6,903 6,176
Removals Pulpwood 17,270 14,221 14,291 14,346 14,348 Sawtimber 1,456 1,447 1,448 1,454 1,452 Large Saw 7,722 7,862 7,942 8,018 8,054
NorthInventory Pulpwood 108,204 110,268 111,449 111,893 114,193
Sawtimber 9,840 10,165 10,229 10,199 10,416 Large Saw 51,833 55,469 58,041 59,227 61,036
Growth Pulpwood 1,292 856 1,563 1,356 603 Sawtimber 169 61 154 150 75 Large Saw 798 970 1,051 776 441
Removals Pulpwood 944 799 816 834 856 Sawtimber 83 85 87 88 91 Large Saw 411 428 441 452 465
NorthcentralInventory Pulpwood 151,492 156,660 161,842 153,932 140,661
Sawtimber 13,516 14,002 14,541 13,962 12,747 Large Saw 72,848 79,943 85,456 85,121 80,960
Growth Pulpwood 4,095 3,986 2,369 835 892 Sawtimber 332 422 266 112 127 Large Saw 2,238 2,783 1,508 946 1,067
Removals Pulpwood 3,342 2,821 2,892 2,861 2,774 Sawtimber 294 300 306 305 296 Large Saw 1,480 1,540 1,602 1,611 1,576
CentralInventory Pulpwood 189,439 193,339 200,928 208,701 207,810
Sawtimber 16,373 16,860 17,392 17,950 17,904 Large Saw 88,233 94,454 100,218 107,867 110,665
Growth Pulpwood 6,362 6,386 7,486 6,769 5,511 Sawtimber 548 681 737 729 612 Large Saw 3,090 4,124 4,307 4,166 3,825
Removals Pulpwood 6,802 5,793 5,982 6,199 6,304 Sawtimber 607 626 645 667 682 Large Saw 3,084 3,208 3,340 3,489 3,575
- - Thousand Cubic Feet - -
33
Table 2. MP-SRTS Model Hardwood Projections by Region (conti…) 1997 2004 2011 2018 2025
StateInventory Pulpwood 65,287 68,174 72,653 75,952 77,475
Sawtimber 5,663 5,971 6,306 6,597 6,825 Large Saw 27,187 30,027 33,661 35,823 37,272
Growth Pulpwood 1,749 1,905 2,031 1,541 1,673 Sawtimber 162 182 204 182 195 Large Saw 870 1,107 1,291 889 1,074
Removals Pulpwood 1,576 1,340 1,385 1,424 1,452 Sawtimber 140 145 149 154 157 Large Saw 684 716 751 777 798
SouthwestInventory Pulpwood 65,287 64,857 68,027 68,380 67,498
Sawtimber 5,663 5,682 5,914 5,940 5,947 Large Saw 27,187 28,561 31,594 32,463 33,091
Growth Pulpwood 1,749 1,146 1,890 637 1,507 Sawtimber 162 125 188 101 167 Large Saw 870 801 1,202 465 997
Removals Pulpwood 1,576 1,317 1,355 1,377 1,390 Sawtimber 140 143 146 148 150 Large Saw 684 703 735 752 767
- - Thousand Cubic Feet - -
Figure 13 documents the softwood inventory levels across the projection period
by FIA survey unit. The Central FIA survey unit contains the majority of the State’s raw
forest products, particularly pulpwood and small sawtimber. The inventory in the Central
region is relatively unchanged throughout the period with the exception of the large
sawtimber. The North Central region experiences the largest amount of inventory loss
across all product classes due to land conversion. Similarly, Figure 14 documents the
hardwood inventory shifts by product and survey unit. Hardwood inventories remain
unchanged or are seen to increase for the life of the projection period in all regions with
the exception of the North Central region.
34
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Figure 13. Softwood inventory shifts by FIA survey unit
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1997 2004 2011 2018 2025
Figure 14. Hardwood inventory shifts by FIA survey unit
35
Figure 15 documents the amount of growth experienced by survey unit and
product class for softwood. Much of the growth is concentrated in the Central region,
which corresponds to the proportion of inventory that the region holds. In most cases the
level of growth is increasing over the projection period as would be expected from the
assigned rates of growth increase in pine plantations. Figure 16 documents the hardwood
growth during the projection period. Unlike softwood trends, hardwood growth over the
projection is forecasted to decline. The most dramatic movements in the level of growth
over the projection period occur in pulpwood, which decreases in four of the five survey
units.
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1997 2004 2011 2018 2024
Figure 15. Softwood growth shifts by FIA survey unit
36
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1997 2004 2011 2018 2024
Figure 16. Hardwood growth shifts by FIA survey unit The inventory coupled with the annual growth can depict trends that present how
and in which regions the timber resources in Georgia are plentiful. With Figures 17 and
18 the amount of pressure that is being placed on these inventories is evident. These
expected timber harvest trends document where opportunities exist for increased use of
raw forest products. Figure 17 presents the softwood harvest levels for the projection
period by FIA survey unit. In most cases the harvest levels remain relatively unchanged.
The North Central survey unit, unlike the other regions, experiences sharp declines in
harvest levels for all product classes. In Figure 18 the hardwood harvest levels are
presented. These levels remain constant for all regions except for the Central survey unit
which experiences increasing levels of harvest over the projection interval.
37
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1997 2004 2011 2018 2025
Figure 17. Softwood removals by FIA survey unit
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1997 2004 2011 2018 2025
Figure 18. Hardwood removals by FIA survey unit
38
The implication of movements in inventory and growth, and the pressure that is
applied to the inventories through harvest is the resulting price of raw forest products.
Product prices dictate much of the activity that takes place within the industry and impact
the strategic planning of many entities within the forest sector. By exploring the
projected price levels, movements within the industry can be identified and possibly
addressed through governmental policy. Price forecasting in this fashion allows users of
the forest resources to better plan and invest capital efficiently. The administrator prior
to performing the simulation adjusted the pulpwood projections in this analysis. The
initial drop in year one (1997-1998) represents a realized reduction in harvest levels of
17%. The manipulation allows projections to reflect harvest levels that have been
realized thus far in the projection period.
Figure 19 presents the projected price levels for forest products for the Northern
survey unit. The pulpwood price level has been adjusted in year one as explained earlier.
The price of sawtimber peaks in 2002 and begins to decline till the end of the period.
Pulpwood and small sawtimber prices increase for the entire period while large
sawtimber prices increase most noticeably during the first half of the projection period.
Figure 20 documents the raw timber product price projection for the North
Central region, which are all increasing for the life of the projection. Figure 21
represents the price projections for the Central region. This area as presented previously
has the largest amount of inventory in the State and the product prices within the region
remain relatively unchanged over the projection period.
In Figure 22, the price projections pertain to the Southeastern survey unit. The
product price for sawtimber and large sawtimber increase rapidly until 2015 when they
39
both decelerate and gradually continue to increase toward the end of the period. Small
sawtimber and pulpwood products remain relatively unchanged over the period. The
Southwestern region experiences similar price movements over the period with
sawtimber and large sawtimber increasing in real terms.
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Figure 19. Raw timber product price projections for the Northern survey unit.
40
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Figure 20. Raw timber product price projections for the North Central survey unit.
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Figure 21. Raw timber product price projections for the Central survey unit.
41
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Figure 22. Raw timber product price projections for the Southeastern survey unit.
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Figure 23. Raw timber product price projections for the Southwestern survey unit.
42
Chapter 4
Conclusions
The MP-SRTS model is a strategic planning tool with the capabilities of
identifying opportunities for expansion/contraction within the forest sector and possible
needs for protection from or encouragement for modified forest policy. The model has
evolved from previous approaches to better project the forest market conditions of more
specific areas, which has increased the model’s applications and benefits. The MP-SRTS
model allows inventory conditions to be given an economic overlay that is capable of
forecasting future conditions by specific area and product class and has allowed
information to be tailored for individual entities within the forest sector.
This analysis has examined the response of the Georgia regional and subregional
markets to the conditions aligned with a lower demand scenario for raw forest products.
The low demand scenario still results in a statewide decline in raw forest product
inventories which gives rise to increased prices over the period of the projection for most
products. The harvest levels are predicted to remain level for the projection period.
Regional responses vary among survey units and between species class, from
drastic reductions in softwood inventory as seen in the North Central region to increasing
hardwood inventories such as in the Central region. Fluctuations are predicted to be
more drastic in softwood inventories with hardwood levels remaining relatively constant
if not increasing. The Central survey unit contains the largest amount of timber resources
within the State. This region is predicted to supply the largest amount of raw forest
43
products to the forest industry by sustaining the largest projected harvest. Hardwood and
softwood harvests are expected to return to 1997 harvest levels by the end of the
projection period.
As the inventory, growth, and harvest levels vary the price levels fluctuate widely
among the five survey units and may indicate operational opportunities. The North
Central region experiences dramatic price increases over the period of the projection and
is influenced by the growing metropolis, Atlanta. In most cases the price projected for
raw forest products increases over the projection period. This is an expected outcome in
that the inventory declines and harvests are relatively constant which applies upward
pressure on the price of raw forest products.
There are several weaknesses in the MP-SRTS approach. Projecting future
market conditions using MP-SRTS assumes that historical trends hold true, which may
not be the case in some areas, particularly those with high levels of urbanization.
Projecting inventory levels using product specifications generates issues in that stands are
assumed to contain a historically consistent distribution of products when being “grown”
in the inventory model. As management techniques are implemented, such as thinning
treatments, stands will undoubtedly be changed to support a larger amount of higher end
products. This shift from previous product distributions to being proportionally skewed
toward sawtimber is not fully reflected in the inventory component of the MP-SRTS
model. The MP-SRTS model however is a useful tool and can be adapted to address
future needs of the forest sector. The use of MP-SRTS provides entities within the forest
sector with a tool for management and long term planning.
44
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