Private Timber Harvest Potential In Eastern Oregon

Darius M. Adams and Gregory S. Latta 1

May 15, 2002

Abstract.Growing stock inventory on private lands in eastern Oregon has declined steadily over the past 20 years, as harvesting and mortality losses to insects and disease have outpaced growth. Harvests from both industrial and NIPF ownerships have shown cyclical shifts but little trend over this same period. In the most recent survey (1998-1999) industrial and NIPF inventories differed by less than 8% (1.786 billion cubic feet versus 1.655 billion cubic feet) while the NIPF timberland base was only about 2/3 of the industrial base (1.105 million acres versus 1.603 million acres). The present study employs recent inventories and both even-flow and market-based harvest simulators to develop projections of future harvest potentials. For industrial lands, even-flow and market projections over the next 50 years are about half of the historical average cut over the past 40 years. For NIPF lands the even-flow projection is 20% higher than the historical harvest average, while the market projection indicates potential for a substantial increase in near-term harvest. Inventories on industrial lands would rise under these projections, while NIPF inventories would be roughly stable. Continued loss of land from NIPF ownerships to other owners and uses would have limited impact on the market-based NIPF harvest projection until after 2050. A simulated policy of expanded riparian protection zones would reduce harvest on both ownerships roughly in proportion to the area removed from the harvestable land base. A simulated requirement to retain 30% more residual volume in partially cut stands reduced harvest by roughly 5% on combined private ownerships and lead to a 13% increase in total inventory after 50 years.

Private Timber Harvest Potential In Eastern Oregon

Eastern Oregon’s timber sector has faced an array of resource and policy changes over the past two decades that have put increasing pressure on private lands as timber suppliers. During much of this period, all ownerships have been beset by a major outbreak of bark beetles and defoliators, increasing mortality and slowing growth on the surviving stems. At the same time, growing fuel accumulations, particularly on public lands, have raised the risk of major inventory losses through fire. And, beginning in 1990, management policy shifts reduced timber harvest on national forests to less the 10% of its typical historical level.

These changes, and owners’ responses to them, have had important impacts on the region’s private forest resource. This study examines eastern Oregon’s private forests and offers an assessment of their long-term timber harvest potential. We develop two projections of harvest and management using two markedly different models of harvest behavior—a market-based analysis in which timber demand and supply interact and a volume-flow model that maximizes long-tern even-flow. Rather than focus on a single “most likely” forecast, we hope to develop a clearer understanding of the possible range of future harvest outcomes and of what resource characteristics and aspects of owner behavior most strongly shape future harvest potential.

Beyond the level of harvest, this analysis and the models that support it will also help characterize the possible future conditions of the forest resource itself; its size structure and species composition, growth and inventory levels. These results, in turn, may be of value in assessing future wildlife habitat and biodiversity conditions. Finally, this analysis also considers the harvest impacts of a limited set of changes in public policies that regulate private forest management practices. These results may be of direct use in current policy discussions and will also further illustrate the influence of resource conditions on private owner response to policies.

Relationship to Past Studies

The present analysis differs in several respects from the two previous studies of eastern Oregon timber supply by Beuter et al (1976) and Sessions (1991).

1) Inventory data for all private owners are drawn from a preliminary version of the 1998-99 remeasurement of permanent inventory plots on private lands maintained by the Forest Inventory and Analysis unit of the USDA Forest Service’s Pacific Northwest Research Station. Data cover 529 condition classes on 492 plots on timberland and other forestland.2 Harvest projections are developed for each condition class. The 1991 Oregon study used similar data from an earlier remeasurement with some aggregation of plots. The 1976 report used aggregations of diameter class data at what would be the subregional level in the current study.

2) Yields (for each management regime applied to each condition class in the initial inventory, and for all even-age regimes established during the course of the projections) were developed from the Forest Vegetation Simulator (FVS) model. Maintained by the Forest Management Service Center of the U.S. Forest Service, several calibrated variants of FVS were available for eastern Oregon regions.3 Previous studies used stand table projection (in 1976) and PROGNOSIS (in 1991), the latter a precursor of FVS.

3) The present study examines private lands only. Harvest from public lands is an input to our market-based harvest projection approach, but this volume is treated as exogenous and determined outside the model. Earlier studies devoted considerable attention to national forests and other public lands, since at the time they contributed more than 60% of the eastern Oregon harvest.

4) Previous studies employed variants of “sequential even-flow” analysis to project harvest.4 Harvest in each period is set at the highest level that can be sustained over the look-ahead interval (a typical rotation) starting in the current period and moving sequentially from the first to the last period of the projection. In the present study we project harvest using both market-based and volume flow approaches. The market model simulates the interaction of timber demand and supply over time, including the key forest management investment decisions of private owners. The volume flow model projects the maximum even-flow volume that can be sustained over the full projection period (100 years).

Recent Harvest Trends and Resource Conditions

Beginning in the early 1990’s, shifts in management directions for the national forests brought a significant decline in their timber harvest. From an average of 1.4 billion board feet (BBF) in the late 1980’s, national forest harvest dropped to slightly more than 0.1 BBF by 1999. Since there was little basis in merchantable inventory for any long-term compensating harvest response from private lands, total eastern Oregon harvest fell as well, from about 2.0 BBF to 0.8 BBF for the same time points. Loss of timber supply forced a corresponding reduction in wood products processing capacity. Lumber production dropped from 1.8 BBF in the late 1980’s to 0.9 BBF by 1999, and the number of mills of all sizes declined from 42 in 1988 to 14 in 1998. Plywood production volumes for eastern Oregon are not publicly available. However, it is estimated that log consumption in veneer and plywood mills fell from 235 million board feet (MMBF) in six facilities in 1988 to 119 MMBF in 3 facilities by 1998.5

At the same time, eastern Oregon forests have been subject to major outbreaks of bark beetles and defoliation by the spruce budworm and Douglas-fir tussock moth.6 The effects of these depredations have been to raise mortality and reduce growth on surviving trees. Few reports have attempted to characterize the extent of these impacts on the inventory. Some insight can be gained from comparison of forest surveys for eastern Oregon forests between 1986-7 and an updated inventory in 1992 (see McKay, Mei and Lettman 1994). During this period softwood mortality was nearly 35% of gross growth on all private lands, somewhat higher on NIPF (nonindustrial private forests) and lower on industry. By way of comparison, softwood mortality as a percent of gross growth on private lands in the entire Pacific Coast region (OR, WA, CA, and AK) was only 7% in 1996.7 In an historical context for a period prior to the current outbreaks, mortality as a percent of growth for all private lands in eastern Oregon and Washington averaged about 15% between 1952 and 1976 (USDA, Forest Service, 1982). Thus, from the late 1980s to the early 1990s mortality offset 3 to 5 times as much gross growth in eastern Oregon as in adjacent western regions or during the preceding 20-30 years.

Estimates of the extent of infestations can also be generated from the 1992 eastern Oregon inventory database described by McKay, Mei and Lettman (1994). In this survey, each live stem was examined for the presence and severity of an array of insect and disease attacks. Examining individual stem records (adjusted to represent the total inventory) indicates that 29% of the live trees on industry lands had some manifestation of an insect attack (bark beetle or defoliator) and more than 40% on NIPF lands. Virtually all (80% or more) of the affected trees were lodgepole pine, Douglas-fir, white fir and grand fir. Ponderosa pine accounted for less than 1% of the stems. The data also show that the fraction of total inventory trees with signs of insect attack increased with diameter. As a consequence, the proportion of inventory volume subject to some form of insect attack is higher than the proportion of stems at 39% for industry and more than 50% for NIPF lands.

The last two decades have also seen an increasing risk of catastrophic loss due to fire for both public and private lands. Fuel accumulation resulting from the effective exclusion of fire since the early 1900’s has created an unprecedented hazard. Most attention has been focused on the national forests because of their extent and because they harbor the greatest hazard. But fire is no respecter of property boundaries, and risk of loss on private lands has risen as fuel conditions have worsened on adjacent public lands.

Historical timber harvest in eastern Oregon by owner group and ecoregion is shown in Figure 1a and Figure 1b. Ecoregion boundaries and the counties used to approximate ecoregions in the harvest statistics are shown in Figure 2. At the half-state level (lowest panel in Figure 1a), industrial and NIPF harvests show considerable year-to-year variation but no clear trend. Neither private group showed a discernable shift in response to the major decline in national forest harvest, despite the large increase in prices. Harvest detail at the ecoregion level (upper two panels in Figure 1b), however, suggests that stability at the half-state level has resulted from compensating shifts between ecoregions. Harvest on both private ownerships in the Blue Mountain region has increased since the early 1980’s, while there is a declining trend in private harvests in the eastern Cascades.

It appears that the decline in national forest harvest has also forced more extensive commerce in logs between the eastern Oregon ecoregions. In 1988, eastern Cascades mills obtained only 7% of their log receipts from the Blue Mountains, while only 2% of receipts at Blue Mountain mills came from the eastern Cascades. By 1998, Blue Mountain mills obtained 9% of their log supplies from the eastern Cascades, and eastern Cascades mills obtained more than 20% of their supplies from the Blue Mountains.8 The absolute volumes of both types of flows were also larger in 1998 that in 1988.

The basic time trends in the area of private timberland ownership in eastern Oregon are similar to those in most other forested regions of the U.S. The area of industrial ownership has risen irregularly since the early 1960s (Figure 3), though there has been a continual change in the identities of these owners. There has also been a gradual shift in the composition of the industrial class toward a larger fraction of owners that are not integrated to processing facilities but hold land for purposes of commercial timber production. At the same time the area of NIPF ownership has declined steadily since 1978, by some 155,000 acres or an annual rate of nearly 7,800 acres. This land was lost to other land uses, such as agriculture, urbanization, infrastructure, and in some cases sale to other forest owners. For example, between 1988 and 1999, Azuma et al (in press) estimate that NIPF owners in eastern Oregon lost 40,000 acres while industrial ownerships expanded by 59,000 acres. The net gain for industry was the result of 1,000 acres gained from other owners and uses, plus 58,000 acres acquired from NIPF. The NIPF net loss was the result in part of transfers to industry and other owners and part (19,000 acres) from shifts to urban uses.

Unlike their counterparts in western Oregon, both private owner groups in eastern Oregon have experienced declining growing stock inventories over the past 20 years. As illustrated in Figure 4, industry inventory has declined more than 25% since 1978 while NIPF stock has dropped by nearly 18%. For NIPF owners, part of this decline reflects loss of timberland area and associated volume to other owners and uses. An additional portion is due to mortality that, as discussed above, has been accelerated by insect and disease attacks. The largest part, however, reflects a high rate of removals. On industry lands Azuma et al (in press) estimate that removals were 36% higher than gross growth over the 1988 to 1999 period. NIPF removals were more than 80% of gross growth.

Figure 5a and Figure 5b provide a more detailed view of private inventories, showing the current (1999) distributions of total trees and volume by diameter class. Figure 5a indicates that industrial ownerships have a higher proportion of total stems in the smallest diameter classes than do NIPF lands (65% of stems are less than 4.9 inches on industry lands compared to 54% on NIPF owners). This is also true for volume (Figure 5b), where 59% of industry volume is in trees less than 15 inches while 44% of NIPF volume falls in these classes.9

In a dynamic context, harvest and mortality have had a dramatic impact on the size composition of private forest inventories over the past decade. Figure 6a and Figure 6b show numbers of trees by diameter class for industry and NIPF owners for the inventory years 1987 and 1999. Note the large drop in trees in the smaller classes on NIPF ownerships. Volume shifts are shown in Figure 7a and Figure 7b. For diameter classes below 25 inches, the absolute reductions on NIPF lands are larger than those for industry owners in nearly all cases.

Harvest Projection Methods

The basic building blocks of any harvest projection are: (i) inventory data, (ii) assumptions about, or projections of, future management (silvicultural) investment, (iii) estimates of stand growth under each management option, (iv) projections of future changes in the harvestable land base, and (v) a harvest decision simulator that computes the volumes to be removed.

Inventory. In the present analysis, inventory data derive from the Forest Service’s 1998 remeasurement of 504 permanent plots on private forestland in eastern Oregon. Our work employed a preliminary version of this inventory, which differs somewhat from the draft final release [see Azuma et al. (in press) for a description of inventory methods and broad results]. The primary sources of difference lie in recomputation of site index values, assignment of vegetation type for some plots and expansion factors. Table 1 contrasts alternate estimates of land area by site productivity class from our analysis and the draft release by the Forest Service.

Table 1. Area of forest land by site quality class from current study and draft USFS eastern Oregon inventory report (Azuma et al., in press).
Cubic feet per acre per year growth
Ownership 165-224 120-164 85-119 50-84 20-49 <20 Total
(excl <20)
Industry-current 0 22 91 450 948 174 1511
NIPF-current 8 8 85 365 633 257 1099
Industry-USFS 0 17 104 591 892 0 1603
NIPF-USFS 0 29 73 366 636 0 1105

Management intensity classes. Management practices were divided into reserve (no harvest), even and uneven aged classes with three increasing levels of intensity in non-reserve groups, termed management intensity classes (MIC):

Clearcut if stand volume at least 12 MBF/acre
Natural regeneration
Cut if stand volume at least 7 MBF/acre,
leaving 4 MBF/acre residual in trees 7" and larger
Clearcut if stand volume at least 13 MBF/acre
Plant to 250 trees/acre
Cut if stand volume at least 9 MBF/acre,
leaving 5 MBF/acre residual in trees 7" and larger
Clearcut if stand volume at least 16 MBF/acre
Plant to 250 trees/acre, and thin to 175
trees/acre when stand height at least 15 feet
Cut if stand volume at least 9 MBF/acre,
leaving 4 MBF/acre residual in trees 7" and larger,
underplant 100-150 trees per acre

These regimes were adapted from work by Bare et al. (1995) in an analysis of harvest potential in roughly comparable forest types in eastern Washington.

Estimates of the current (1998) allocation of private lands to these several classes were derived from responses to surveys of industrial owners and Oregon Department of Forestry field foresters regarding current and prospective future management actions on private lands in Oregon.10 This initial allocation can be forced on the model solution and is termed an "exogenous" allocation to initial MIC classes. As noted below, our projection model also allows endogenous determination (by procedures within the model) of the initial MIC allocation based on the specific objective of the projection. Differences in harvest projections arising from the exogenous and endogenous initial allocations are discussed in a later section.

Yield projection. Estimates of current and future inventory volumes and stand conditions for all MIC’s were derived from the Forest Service’s FVS stand projection system (see Three variants corresponding to vegetation zones in eastern Oregon were employed.

Since our initial (1998) stand volumes do not come directly from the inventory data, but are computed by FVS from the tree records, there are some differences between the draft Forest Service inventory report [Azuma et al. (in press)] and our values. Table 2 compares these values for different land types. Our values are consistently lower but are within 5% of the Forest Service estimates in all cases.

Table 2. Estimates of total growing stock inventory on private ownership in eastern Oregon by land type from current study and draft USFS inventory report (Azuma et al., in press).
/Land Type
Present study USFS Draft Present study USFS Draft
Timberland Timberland and
Other Forest Land
Million Cubic Feet
Industry 1637 1655 1699 1782
NIPF 1713 1786 1912 1949

Land base. As illustrated in Figure 4 there have been fairly clear trends in the area of timberland in NIPF ownership in eastern Oregon over the past two decades. In recent years, shifts to non-forest uses have been an important part of NIPF losses [Azuma et al. (in press)]. Of course, past trends need not accurately characterize future land base changes, but there is a strong popular belief that the NIPF area base will continue to decline. To examine the impacts of further NIPF timberland losses, we develop projections under both a constant and declining NIPF land base. The declining area simulation assumes periodic losses over the next three decades (through 2028) equivalent to the trend shift over the past two decades, with a stable base thereafter. This amounts to a loss of some 199,300 acres over the period from 1998 to 2028, a decline of about 15% relative to the NIPF 1998 land base.11 Unlike the past, we assume that all of this area is lost to non-forest uses and a portion is not transferred to industrial owners.

Harvest simulation. The harvest simulator combines the initial inventory, land base and estimates of future growth by MIC class to project timber harvests by owner group. In the present analysis the harvest projection can take two forms: (i) maximum long-term even-flow, which finds the highest harvest level that can be maintained over the projection period within some prespecified bounds of variation, and (ii) harvests based on the simulation of demand and supply interactions in the market for softwood timber.

The volume-flow model is similar in general outline to approaches used in many other studies (see, for example, Sessions (1991) or Beuter (1976)), except that we employ linear programming to find the optimal solution directly rather than some form of successive approximation such a binary search. This allows the imposition of an array of important restrictions or constraints on aspects of the volume flow or resource characteristics over time that are often difficult to examine in the binary search approach.

The market simulation approach finds, in effect, the harvest quantities and prices that balance demand and supply for softwood sawlogs in eastern Oregon in all periods of the projection. Demand originates from sawmills and plywood producers for logs delivered to their mills. Supply represents the harvest decisions of industrial and nonindustrial timberland owners. Functions representing the demand for delivered sawlogs were derived by econometric methods from historical data on sawlog use in eastern Oregon mills.12 Private log producer supply is an implicit function of the costs of growing timber over time, harvest, and delivery to the point of utilization. Private suppliers are seen in this context as wealth or present net worth maximizers in a market where their supply actions (harvest or absence of harvest) influence current and future log prices.

Initial and future management regimes. In both the market and volume-flow models, future management investment decisions in even-aged stands are determined as part of the simulation (i.e., they are endogenous). They need not be preset or predetermined as in past studies. In the market model this means that investments (choices of MIC) will be consistent with the intertemporal wealth maximization objective. In the volume-flow model, they will be chosen so as to optimally enhance the volume maximization objective.

For stands initially allocated to some form of uneven-aged management, the MIC is fixed at the initial allocation for all future time periods. For a given stand, movement between even and uneven-aged management forms is a potentially complex process, since both the timing of the shift and the cutting cycle or rotation of the subsequent stand are variable. These changes are not considered in the present study. Areas allocated to even and uneven-aged management in the initial period remain in these management forms for the entire projection.

The MIC classification of existing stands at the start of the projection can either be set exogenously (based on prior knowledge of current management practices such as the 1998 land owner survey described above) or endogenously in an optimal fashion by the models. This latter option is useful when examining policy alternatives that may induce timberland owners to modify their current management practices. In many past studies, the MIC distribution of existing stands has been fixed. As a result, owners were unable to adjust management regimes in the simulations when policies changed until after the first rotation in the case of even-aged stands. This limitation in the management response may influence the estimated impacts of policies compared to the case were initial management regimes are flexible. The effects of free versus fixed initial MIC allocations are examined in a later section.

Model specification. The linear programming structure developed for our models also differs from past approaches in the way in which it defines management options or activities. In broad outline, the structure is that of Johnson and Scheurman’s (1977) “model II.” Past applications of model II forms have generally defined activities (harvest timing and management combinations) at the stratum level, where a stratum describes a homogeneous grouping of the inventory (commonly species, age class, site, owner, etc.). The basic inventory data are reaggregated from plots into strata with growth projections developed at the stratum level.

In the present study, activities are defined on the basis of the condition class.13 This allows greater flexibility in making projections that depend on, or are constrained by, various characteristics of the resource base, since virtually every descriptor collected in the field survey can be employed in identifying the condition class. It also markedly reduces the size of the linear programming problem by reducing the potential number of activities. When activities are based on strata, increasing the number of dimensions on which strata are defined exponentially increases the number of potential strata and activities. When activities are defined at the condition class level, information at the stratum level (where strata can be defined with any number of dimensions) can be obtained by simply sorting and summing condition class information. This can be used for reporting purposes, e.g., to obtain area by age, site and owner class, or to define constraints in the optimization.

Projections Under Current Policies

Industrial owners. Industrial inventory has been declining in eastern Oregon for at least the past 20 years while harvest has been relatively stable at the half-state level. In the most recent inventory cycle (1988-1999, taken as 11 years), gross growth averaged 54 MMCF per year, mortality 11 MMCF per year and removals 74 MMCF per year for a net annual inventory reduction of some 31 MMCF per year. Reflecting this long-term inventory reduction, projected eastern Oregon harvest drops dramatically in the initial and all subsequent periods relative to historical levels (Figure 8). The even-flow projection from the present study is about half of the 40-year historical average. The market projection shows no large near-term increase relative to historical levels, indicating that there is essentially no excess inventory growing less rapidly that the interest rate. Sessions’ (1991) projection (as shown in Figure 8) dropped well below the Beuter et al (1976) level, and ours falls an additional 30%.

With the projected slowing in future harvest, inventories build quickly (Figure 9), though in the market projection this does not continue nor can this increase return harvest to historical levels. In the even-flow case, the inventory hiatus that controls the long-term, even-flow level lies in the present period. Since the harvest can neither rise nor fall in this type of projection, the inventory builds steadily. We have also examined a more flexible volume-flow schedule in which cut could move up over time by 5% per decade. In this case the long-term harvest on industrial lands rose with the rising inventory, though return to average historical levels required more than 100 years.

Using the option of an endogenous initial MIC allocation, Figure 10a illustrates the initial distribution between even and uneven-aged MICs and the projected shifts within even-aged stands for the market projection(Figure 10b). Uneven-aged stands cannot change management type after the start of the projection. In the even-aged stands, a larger fraction of future stands are managed under the low intensity class than in the initial distribution.

NIPF owners. Harvest potentials for eastern Oregon NIPF owners represent a marked contrast to the industrial case even though NIPF inventories, like their industry counterparts, have been falling in recent years (Figure 11). In this case the high initial level of the market projection suggests the availability of sizable levels of merchantable volume in the current inventory (growing at less than the discount rate). The projected even-flow level in this case is about 20% higher than the 40-year historical average. The long-term market level (from about 2008 on) is nearly equal to the 40-year historical average (96%).

Both the market and even-flow projections (Figure 12) lead to inventory reductions in the initial part of the projection. Inventories under both projections ultimately converge, however, to nearly the same level, about 10% below current volumes. Thus NIPF owners would be able to maintain current harvest levels with little change in their inventories in the long-term.

From the endogenously determined initial MIC distribution, only a small fraction of NIPF land would be managed on an even-aged basis. As illustrated in Figure 13a and Figure 13b, projected optimal management of even-aged stands involves shifting a portion of these lands into less intensive regimes.

Projections with land loss. An alternative base projection was developed simulating a further loss of NIPF land of some 199,300 acres over the next three decades (as described above). It was assumed that all this land was shifted to non-forest uses and lost from the harvestable forest base. Results for industrial and NIPF harvest are shown in Figures Figures 14 and Figure 15. The constant and reduced land base projections differ little over the first 50 years. Average annual industrial cut falls by -.1% while NIPF cut declines by –2.7%. Because of the limited volume of growing stock on industrial lands there is little basis for a response to the NIPF decline, and industrial cut is nearly unchanged over the projection. The largest departures of NIPF harvest from the constant land base levels occur 50 years into the projection (see Figure 15). This lag may reflect the long cutting cycles in the uneven-aged management regimes (given the slow growth rate on much of the NIPF land base) and changes in harvest timing of condition classes by the market harvest simulator to minimize the effects of the land base reduction.

Alternative Policies and Scenarios

To examine the sensitivity of the projections to changes in management and resource conditions, we have simulated two types of hypothetical policy changes.

Expanded streamside buffers. In the first example, it is assumed that requirements for streamside buffers to protect riparian habitat are expanded. We consider a case in which no harvest of any kind is permitted within 100 feet of a stream course for both perennial and intermittent streams. Current riparian protection requirements are variable depending on the size of the stream and whether it is fish-bearing. Since our database allows identification only of the permanence of stream flow and not a stream’s size or fish-bearing status, we have assumed a 20 foot no-cut buffer on all streams as a rough average in the base case. The present scenario, therefore, represents a quintupling of the width of the no-cut corridor on each side of streams.

As illustrated in Figure 16, both industry market and even-flow harvest projections fall relative to the base case. The market projection declines irregularly over time, averaging about 11% below the base. The even-flow projection falls by roughly 12%. The area lost to harvesting in the expanded buffer is about 10% of the industrial base. NIPF harvest, in Figure 17, declines by 19% in the market projection and by 18% in the even-flow case. The NIPF area loss in the expanded buffer is 18% of the NIPF base. As we found in similar scenarios conducted in western Oregon (Adams et al 2002), the proportional reduction in eastern Oregon harvest is close to the area reduction. This suggests that the areas removed from harvest in the expanded riparian zones may represent something like an average of all stands in the two private ownerships in terms of both current inventory and future harvest potential and may not be concentrated in either particularly productive or unproductive types.

Higher residual stocking. A second policy example was suggested by forest practice developments in California and by recent discussion in Oregon concerned with lengthening the period between harvest disturbances and moderating the extent of the disturbance. In this instance we raise the minimum post-harvest residual stocking volume in partial cutting by 30% relative to the base case. For example, in the least intensive uneven-age MIC the base case allows a minimum of 3 MBF/acre after harvest, while the higher stocking scenario requires a minimum of 4 MBF/acre. The effect is to reduce the available volume of harvestable timber.

Looking at the combined harvest for both owners (Figure 18), to filter some of the short-term variability, the average harvest reduction over the 50 year projection is about 5%. In the initial portion of the projection, harvest departs gradually from the base case. After 2040 projected harvest begins to rise as timber inventory builds and by the final period exceeds the base level. Inventory, again summed across both owners, is some 13% higher by the end of the projection period (in 2058). The differences from the base case for each owner are shown in the lower right portion of Figure 18. There is a noticeable difference between groups. Industry harvest falls by an average of 2% per period while NIPF cut is some 7% lower. The absolute NIPF decline is larger as well at –12 million board feet compared to –9 million board feet for industrial lands.

Endogenous Versus Exogenous Initial MIC Allocation

In the preceding comparisons, the initial (first period) allocations of lands to MIC classes on both ownerships were determined (endogenously) within the projection model. Under a policy change, the potential impacts are perfectly foreseen by the model and the initial MIC allocation adjusted to minimize the effects of the policy on the market (or even-flow) objective. This endogenous approach is consistent with the view that many aspects of a given MIC allocation at the start of the projection could in fact be readily modified if policy conditions were to vary and expected future market conditions change. For example, any stand managed on an uneven-aged basis could be reallocated to any other uneven-aged MIC by appropriate reduction of residual volume at time of harvest or by lengthening the waiting period until first harvest. Except for natural versus plantation origin, even-aged stands could also be shifted between MIC’s by varying the minimum harvest threshold. In analyses of alternative policies with the endogenous allocation, one might anticipate smaller estimates of policy impacts than in the more typical analysis where the initial allocation of MICs is fixed and based on exogenous information.

To examine the effects of the endogenous initial MIC allocation we compare the base case market and expanded streamside protection zone simulations with and without endogenous allocation. For the exogenous initial allocation we used the ODF-OFIC management survey discussed earlier. Figure 19a and Figure 19b compare the initial allocations from the endogenous and exogenous runs. While the differences between the overall allocations to even and uneven-aged groups are modest, there are some large differences in the split across the low to high range in uneven-aged systems. For both owners little or no land is placed in the medium intensity uneven-aged class in the endogenous allocation.

As shown in Figure 20a the impacts of a 100 foot no-cut zone on industrial harvest are similar under both endogenous and exogenous initial MIC allocations. On NIPF lands (Figure 20b), in contrast, the impacts under the endogenous allocation are larger in most periods than for the exogenous allocation, though the average harvest reductions over the first 50 years are close at –19.3% for the endogenous and –18.5% for the exogenous. This seems at first to be counterintuitive and contrary to our earlier expectation.

The reasons for this result relate to the nature of the two types of runs. From the perspective of the harvest scheduling model, all simulations under the exogenous initial MIC allocation are suboptimal. Endogenous initial MIC allocation should allow higher NIPF harvests in both the base and 100 foot no-cut simulations. This is clearly the case, as illustrated in Figure 21. The endogenous runs give higher harvests in nearly all years (compare the pair of solid lines and the pair of dashed lines). Since the exogenous runs begin with essentially arbitrary MIC allocations, there is in fact no reason to expect that the difference between the exogenous base and 100 foot no-cut runs should be larger than that between the endogenous base and 100 foot no-cut runs. Both exogenous runs are suboptimal, so the differences between them might be of any size. We conclude that it is not in general possible to judge a priori how the use of a fixed versus variable initial MIC allocation will impact the evaluation of policy impacts.


The most dramatic result of this analysis is the finding that industrial harvest potential in eastern Oregon could fall by as much as 50% over the next 50 years. Past harvests have steadily reduced the industrial inventory base, shifting the concentrations of both numbers of trees and volume into the smaller diameter classes. The result has been lower aggregate growth and reduced long-term harvest potential. At the same time, despite this reduced inventory, industrial harvest potential does not appear to be more sensitive that its NIPF counterpart to the two forms of policy shifts examined here. Expansion in industrial harvest would be possible in the long-term with “inventory savings” (harvest less than growth) but return to historical average harvests would require many decades.

Harvest potential on nonindustrial ownerships in eastern Oregon appears to be similar in many respects to our findings for NIPF lands in western Oregon (Adams et al 2002). Despite declining inventories over the past decade, a substantial volume of merchantable timber remains on these lands. Our even-flow base projection was above the long-term historical average and the market projection showed very large near-term harvests that would be concentrated in the currently merchantable surplus. Our simulation of continued trends in land area loss from this ownership had only modest impacts on harvest, with the biggest changes coming 50 or more years into the future.

The hypothetical 100 foot no-cut buffer simulation suggests that both ownerships will respond in similar fashion to this type of restriction. Harvests fell roughly in proportion to the area removed from operation. Increasing the minimum post-harvest stocking, in contrast, had a larger proportional and absolute impact on NIPF harvest. This latter result might reflect differences in growth rates or the distribution of volume across stands between the two owner groups.

Literature Cited

Adams, D.M., R. S. Schillinger, G. Latta, and A. Van Nalts. (in press) Timber Harvest Projections for Private Land in Western Oregon. Oregon State University, Forest Research Laboratory, Research Paper. Corvallis, OR.

Azuma, D. L. et al. (in press) Timber Resources Statistics for Eastern Oregon, 1999. USDA, Forest Service, PNW Research Station, Resource Bulletin. Portland, OR.

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