ASSESSING THE TIMBER SITUATION IN GEORGIA USING THE MULTI-PRODUCT SUBREGIONAL TIMBER SUPPLY (MP-SRTS) MODEL: VERNON WESTON HIOTT

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1 ASSESSING THE TIMBER SITUATION IN GEORGIA USING THE MULTI-PRODUCT SUBREGIONAL TIMBER SUPPLY (MP-SRTS) MODEL: 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

2 ASSESSING THE TIMBER SITUATION IN GEORGIA USING THE MULTI-PRODUCT SUBREGIONAL TIMBER SUPPLY (MP-SRTS) MODEL: 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

3 2006 Vernon Weston Hiott All Rights Reserved

4 ASSESSING THE TIMBER SITUATION IN GEORGIA USING THE MULTI-PRODUCT SUBREGIONAL TIMBER SUPPLY (MP-SRTS) MODEL: by VERNON WESTON HIOTT Major Professor: Committee: Michael L. Clutter Bruce E. Borders Jacek P. Siry Electronic Version Approved: Maureen Grasso Dean of the Graduate School The University of Georgia August 2006

5 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. iv

6 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. v

7 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 Methods...18 Market Module...19 Inventory Module...22 Model Scenario Results Conclusions...43 REFERENCES...45 vi

8 LIST OF TABLES Table 1: MP-SRTS Model Softwood Projections by Region...31 Table 2: MP-SRTS Model Hardwood Projections by Region...33 Page vii

9 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 viii

10 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 ix

11 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 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

12 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 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

13 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

14 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

15 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

16 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

17 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

18 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

19 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 partialequilibrium 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

20 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 or hardwood, 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 values separate from projections. Additionally, this model allows interdependencies between product classes to be simulated. For example, the quantity of hardwood used to produce pulp is directly related to the amount of softwood used as a substitute and is, therefore, indirectly related to softwood 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 These projections will reflect recent trends in raw forest product prices in Georgia, particularly pine, and steadily increasing hardwood stumpage costs, as shown in Figure 1. Considering these 10

21 price trends and associated timber product output levels presented below, market responses are sure to occur $/Ton 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 prices have remained at levels not seen since Similarly, pine chip-n-saw and have both experienced price declines following strong levels during the late 1990s and early Pine prices have strengthened after a substantial reduction during 2000 and In contrast, hardwood and hardwood 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 Pine product prices, as shown in Figure 2, became less volatile after 11

22 2002. The pine product prices move in the same direction annually; however, the severity in movements by product varies with pine being most volatile. 35% 25% 15% 5% -5% -15% -25% -35% 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 experiences more movement than. 12

23 65% 55% 45% 35% 25% 15% 5% -5% -15% -25% -35% 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

24 As seen in Figure 4, initial production rates were unresponsive to price shifts for from 1999 to 2001; however, during 2003 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 prices and increased stumpage cost for pine. 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 and chip-n-saw. Relative to saw logs and, 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 output, hardwood product output has declined. As seen in Figure 5, hardwood 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 output as a substitute and limited supplies of hardwood fiber. 14

25 MMCF Saw Logs Pulpwood Figure 4. Georgia Softwood Timber Product Output Source. USFS FIA, MMCF Saw Logs Veneer Logs Pulpwood Composite Prod. Fuelwood Figure 5. Georgia Hardwood Timber Product Output Source: USFS,

26 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

27 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

28 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

29 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

30 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. Demand Price or Harvest Projection by Product Supply Price and Inventory Elasticities by Product by Owner Demand Elasticities by Product Inventory Shifts by Product- Owner-Unit Multi-Product Equilibrium Equilibrium Price by Product Harvest by Product- Owner-Unit Goal Program Inventory Module 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

31 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

32 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, hardwood, 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

33 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

34 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

35 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 ( ) 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 twentyeight 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

36 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

37 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 and large. 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, which declined 16% over the period. The initial drop in removals is a result of harvest levels being adjusted by the administrator prior to performing the simulation. From the 1998 initial harvest level, the projection of 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 experience the largest increases in product value with approximate increases of 90%. 27

38 Million Cubic Feet Year Large Pulp Sawtimber Small Figure 9. State level hardwood and softwood inventory shifts by product Million Cubic Feet Year Large Pulp Sawtimber Small Figure 10. State level hardwood and softwood growth shifts by product. 28

39 Million Cubic Feet Large Pulp Sawtimber Small Figure 11. State level hardwood and softwood removals by product Large Pulp Sawtimber Small Figure 12. State level raw forest product prices by product. 29

40 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, small, and large. The small 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 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

41 Table 1. MP-SRTS Model Softwood Projections by Region State Thousand Cubic Feet - - Inventory Pulpwood 250, , , , ,542 Small Saw 213, , , , ,520 Sawtimber 99,226 81,516 75,430 69,728 61,012 Large Saw 189, , ,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 North 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 Inventory 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 Small Saw , Sawtimber Large Saw 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 Large Saw 1, Northcentral Inventory 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, ,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 31

42 Table 1. MP-SRTS Model Softwood Projections by Region (conti ) State Thousand Cubic Feet - - Inventory 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 Southeast 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 Inventory 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 Southwest 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 ,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 Inventory 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 ,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 32

43 Table 2. MP-SRTS Hardwood Model Projections by Region State Thousand Cubic Feet - - Inventory Pulpwood 641, , , , ,551 Sawtimber 55,146 54,078 53,236 52,402 51,207 Large Saw 301, , , , ,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 North 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 Inventory Pulpwood 108, , , , ,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, ,563 1, Sawtimber Large Saw , Removals Pulpwood Sawtimber Large Saw Northcentral Inventory Pulpwood 151, , , , ,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, Sawtimber Large Saw 2,238 2,783 1, ,067 Central Removals Pulpwood 3,342 2,821 2,892 2,861 2,774 Sawtimber Large Saw 1,480 1,540 1,602 1,611 1,576 Inventory Pulpwood 189, , , , ,810 Sawtimber 16,373 16,860 17,392 17,950 17,904 Large Saw 88,233 94, , , ,665 Growth Pulpwood 6,362 6,386 7,486 6,769 5,511 Sawtimber 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 Large Saw 3,084 3,208 3,340 3,489 3,575 33