Better Understanding Minnesota s Forest-based Economic Development Opportunities: A Draft Model & Draft Analyses

Transcription

1 Better Understanding Minnesota s Forest-based Economic Development Opportunities: A Draft Model & Draft Analyses by Howard M. Hoganson, Professor Department of Forest Resources North Central Research & Outreach Center University of Minnesota 1861 Highway 169 East Grand Rapids, MN hogan001@umn.edu Natalie G. Meyer, Research Assistant Department of Forest Resources University of Minnesota Michael T. Carson, Research Forester North Central Research & Outreach Center University of Minnesota June 30, 2017 Staff Paper Series No. 245 Department of Forest Resources College of Food, Agricultural, and Natural Resource Sciences University of Minnesota, St. Paul, MN i

2 For more information about the Department of Forest Resources and its teaching, research, and outreach programs, contact the department at: Department of Forest Resources University of Minnesota 115 Green Hall 1530 Cleveland Avenue North St. Paul, MN Ph: Fax: The University of Minnesota is committed to the policy that all persons shall have equal access to its programs, facilities, and employment without regard to race, color, creed, religion, national origin, sex, age, marital status, disability, public assistance status, veteran status, or sexual orientation. ii

3 Better Understanding Minnesota s Forest-based Economic Development Opportunities: A Draft Model & Draft Analyses Howard M. Hoganson, Natalie G. Meyer & Michael T. Carson Department of Forest Resources & North Central Research & Outreach Center University of Minnesota Technical Report Interagency Information Cooperative iii

4 Acknowledgements This research was made possible through support from the Blandin Foundation via Grant G , Expanding Statewide Economic Opportunities in Forestry and Horticulture. The project was also supported by the Interagency Information Cooperative of the University of Minnesota, Department of Forest Resources; the University of Minnesota North Central Research and Outreach Center; and the Minnesota Agricultural Experiment Station under Project No i

5 Summary 1. Study Goals and Outcomes This study was based on the assumption that information about Minnesota s future wood supply situation is critical for understanding forest-based economic development. Specifically, how well do potential opportunities fit with Minnesota s forests, Minnesota s rural communities and Minnesota s existing forest industries? Minnesota currently has good statewide forest inventory information, but detailed analyses have been lacking at a state or regional level that project and assess forest conditions forward through time. Goals of this project were to: (1) Develop capabilities to project and analyze potential of the forest to support specific economic development opportunities in northern Minnesota. Analyses must recognize the multi-product and multi-market nature of timber-based industries in the region and use the latest detailed statewide forest inventory information to project forest conditions over time. (2) Project and analyze northern Minnesota forest conditions forward for at least 90 years (approximately 1.5 rotations) assuming all current statewide forest industries are sustained. (3) Compare the projected wood supply situation and projected wood costs for the Grand Rapids market (Itasca County) to those of other Minnesota market locations based on projections of current wood consumption levels in all major markets. (4) Develop and analyze at least two additional forest industry market expansion opportunities that are of interest to the local (Itasca County) community. For each of these scenarios, also address the sensitivity of results to assumptions concerning the future availability of wood from private forest landowners. (5) Document results demonstrating the modeling and analyses capabilities potentially available for addressing other forest-based economic development opportunities. All of these goals have been met. The study is especially timely for helping to address a potential new major forest-based economic development opportunity currently moving forward rapidly. This study addressed the multi-species, multi-market nature of Minnesota, yet focused scenario differences on the aspen resource situation because of the emphasis on this species by Minnesota s forest industries. Scenarios to date have been kept relatively simple, emphasizing learning, with at least a dozen scenarios modeled. Like the initial results for the large and comprehensive Minnesota GEIS analyses completed in 1994, current modeling results are draft results, with plans for potential revisions after review by stakeholders. The University Of Minnesota Department Of Forest Resources Interagency Information Cooperative has committed to funding additional scenario analyses and model refinement based on input by the Minnesota forestry community on draft results. In August of 2016, draft results were presented in detail to leadership of both Minnesota Forest Industries and the Minnesota DNR. Focus has been on concerns about supply of the aspen resource, with the potential need to shift to species other than aspen over time if new development emphasizes aspen, as has been proposed. Draft results raise questions about the potential for Minnesota s forests to support a 600,000 cord annual long-term increase in aspen harvest. Results show that wood supply is especially sensitive to assumptions about availability of aspen from private lands. ii

6 2. Lessons Learned Much was learned from this project that can help future wood supply and future forest planning analyses. Those lessons will be summarized briefly in terms of data needs, model design, and modeling strategy. A. Data Needs: Developing the applications helped identify important data needs for improving future analyses. Clearly important are estimates of future timber growth and yield for specific silvicultural treatment options. This is especially important for aspen in Minnesota, as aspen continues to be the primary species of choice for the largest wood users. For stands in the aspen forest cover type, questions remain as to how best to project future yields, especially for both older aspen stands and for future full rotations of aspen stands. Older stands are difficult to project because tree mortality is difficult to project. Likely even more important, are yield projections for future rotations. Some expect higher yields from future rotations because most aspen stands established in the last 30 years were established with denser stocking (due to aspen suckering) and grow vigorously in good light conditions created by harvesting. Most current older aspen stands were not established with such initial high tree density. This study relied heavily on growth and yield tables used in recent studies, yet substantial effort was made to recognize how tree species mix varies within aspen stands across the state. Not surprising, spruce-fir is much more prevalent in aspen stands in the northeast. Estimates of aspen mortality rates were also adjusted to recognize that aspen tends to be longer-lived in the north. Data available to describe harvest costs is limited and is typically tied to specific harvesting systems. Harvest costs vary by tree species and tree size (stand age) with the mixed-species nature of stands adding complications for estimating costs. Forest planning analyses generally don t take into account harvest costs directly unless multi-market locations are considered. Overall, the imprecise harvest cost estimates used in the analyses likely have minimal impact on most general trends in the results, but certainly caution needs to be exercised in the precision of specific estimates for example, estimates of delivered timber prices for specific markets. B. Model Design: The development of a forest harvest scheduling model built off of a substantial history of modeling work by the PI. The University of Minnesota s Dtran model used for the Minnesota Generic Environmental Impact Statement on Timber Harvesting was updated and changed substantially to add new features developed and used for recent single- market/single-forest studies. The model has added flexibility for defining and tracking important stand-level details. Current model now uses three types of maps: (1) forest conditions, (2) forest products harvested and (3) end-market users. Each of these types of maps can have a variety of layers. Applications of this study have demonstrated the usefulness of the associated flexibility. The modeling system has been designed to emphasize Excel spreadsheets for both model input and model output. Output shells can retain graphics developed from earlier applications with work continuing to fine-tune those features. The model also now utilizes multiple co-processor capabilities of today s personal computers. Because the solution process can decompose and iii

7 solve problems in parts, this process fits especially well with multiple co-processors. Substantial model design effort went into addressing detail of riparian areas. Although this addition seems more technically correct, initial tests show minimal impact on overall results. Generally, few stakeholders seriously question the importance and cost of implementing riparian guidelines in Minnesota. Modeling such detail may not be necessary for strategic planning. C. Modeling Strategy: Models are used for learning, with emphasis on keeping formulations simple, especially in initial applications. Future work will focus on developing scenarios in greater detail to make each more realistic. Initially, focus was on key facets of stakeholder interest. Specifically, inter-related scenarios were tailored to address Minnesota s long-term aspen supply. Initial scenarios lacked detailed management constraints describing harvest limitations of specific ownership groups. However, even under such simplifications, draft results suggest that long-term aspen supply is a likely concern if future economic development emphasizes more aspen use. Model applications also pointed to the importance of assumptions about the behavior of private landowners in terms of the extent to which their lands are likely available for harvest. Initial model applications assumed that landowners of older aspen stands are much less likely to harvest in the near term. Such landowners have been approached multiple times recently about harvesting and have repeatedly turned down the opportunity. Model structure allows for substantial detail about availability assumptions for private landowner. Utilizing this facet of the model seems especially important for future applications. One may question whether this feature should be expanded to public lands, yet public lands are part of owner-specific forest-wide plans. Therefore, for those ownerships it is likely that it would be more realistic to include broader allowable cut constraints for the ownership group. Constraint levels can be varied between scenarios, focusing on either harvest volume or harvest area limits used by the specific ownership group. It may be overly restrictive to break allowable cut constraints down by specific species for individual ownership groups, as those limitations could be addressed at the multi-market level via multi-ownership resource fit for the region. 3. Vibrant Community Outcomes This project has actively blended the Blandin Foundation s strategic priorities of educational attainment, economic vitality, and greater inclusion. It has emphasized that learning about our forest resource s potentials and limitations is vital for major forest-based economic development. Because of the information it is providing, the work has attracted attention from leadership of Minnesota s forest industries and from leadership of multiple divisions of the Minnesota DNR. Separate invited presentations were made to at least six forest stakeholder groups in The project is helping integrate diverse objectives and perspectives. Funding has already become available for further model applications to enhance learning about statewide concerns from this systemic and multi-resource perspective. The project has been especially timely, as there is substantial interest in specific forest-based economic development in the region, with clear concern about how specific potential opportunities fit with Minnesota s forest resources and existing forest industry. iv

8 Table of Contents Acknowledgements... i Summary... ii Table of Contents... v List of Figures... vii List of Tables... ix 1. Purpose and Background University of Minnesota Scheduling Models and Shadow Pricing The Generic Environmental Impact Statement on Timber Harvesting Forest Plans for Minnesota s National Forests The UPM/Blandin Thunderhawk Environmental Impact Statement Modeling for Minnesota County Land Departments Facets of the Updated Multi-market, Statewide Model Analysis Areas with Riparian Areas Treatment Options for Analysis Areas Lands Availability for Harvest Map Layers Condition Sets, Market Sets, and Constraints Allocation of Wood Harvested to End-Markets Planning Horizon and Ending Inventory Values Parallel Processing Option Searches for Estimating Constraint Shadow Prices A Data Set for Northern Minnesota FIA Data for Defining Analysis Areas Ownership Groups & Forest Cover types Analysis Area Management Options Differences in Species Mixes for Aspen Forest Cover Type Thinning Options for Red Pine Plantation Forest Cover Type Stand-level (Analysis Area) Map Layers Market Type Flows End-Markets and Road Networks Transport Costs Harvest Costs Riparian Areas v

9 4. Modelling Draft Scenarios Market Set Flows to Track and Possibly Constrain Forest Condition Sets to Track and Possibly Constrain No-limits Benchmark Scenario (Bench0) Bench0 & Aspen Constraints Scenarios Bench0 & Aspen & Total Volume Constraints Scenarios Bench1 & Aspen & Total Volume Constraints Scenarios Bench1 & Total Volume Constraints & Aspen Departure Scenarios Modeling Results No-limits Benchmark Scenario (Bench0) Bench0 & Aspen Constraints Scenarios Bench0 & Aspen & Total Volume Constraints Scenarios Bench1 & Aspen & Total Volume Constraints Scenarios Bench1 & Total Volume Constraints & Aspen Departure Scenarios Summary & Potential Future Work Literature Cited Appendix A: Bench0 and Aspen 1.7MM Cords/Yr Appendix B: Bench0 and Aspen 1.7MM cords/yr & All-species even-flow Appendix C: Bench1 with Aspen species >= 1.7 Million Cords/ Yr Appendix D: Bench1 & Aspen 2.1 MM Short-term and 1.5 MM Long-term (Cds/yr) Appendix E: Bench1 & Aspen 1.9 MM Short-term and 1.5 MM Long-term (Cds/yr) vi

10 List of Figures Figure 1.1 Timber harvest levels for the three scenarios of the 1994 Minnesota GEIS on timber harvesting and forest management... 5 Figure 1.2 Total statewide Timber Harvest by Ownership over time. Graph obtained from the Minnesota DNR Utilization and Marketing group Figure 1.3 Aspen age class distribution by ownership class for timber land in Minnesota based on the USDA FIA statewide forest inventory Figure 2.1 Facets of the forest management situation Figure 3.1 Spatial distribution of FIA timberland plots used in the model dataset Figure 3.2 Distribution of FIA timberland plots by ownership type Figure 3.3 Distribution of all forested FIA plots by ecological sections Figure 3.4 Distribution of FIA plots by reserved status Figure 3.5 Locations of the seven market centers and the road network Figure 4.1 Recent statewide harvest level estimates for the aspen species group. Data obtained in 2016 from staff of the DNR utilization and marketing group Figure 5.1 Total harvest level by period for the All species market set for the unconstrained benchmark scenario (Bench0) Figure 5.2 Total harvest level by period for the Aspen species market set for the unconstrained benchmark scenario (Bench0) Figure 5.3 Shadow price estimates by period for the Aspen species market set for the Bench0 scenario for alternative minimum annual harvest level targets for the Aspen species market set Figure 5.4 Harvest level by period for the Aspen species market set for the Bench0 scenario with constraints targeting annual harvest of at least 2.0 million cords of the Aspen species market set Figure 5.5 Harvest level by period for the All species market set for the Bench0 scenario with constraints targeting annual harvest of at least 1.7 million cords of Aspen species and with constraints regulating the total volume of All species Figure 5.6 Shadow price estimates for the All species market set for the Bench0 scenario with constraints targeting annual harvest of at least 1.7 million cords of Aspen species and with constraints regulating the total volume of the All species market set each period Figure 5.7 Shadow price estimates for the Aspen species market set for the Bench0 scenario with constraints targeting annual harvest of at least 1.9 million cords of the Aspen species market set and with constraints regulating the total volume of All species each period Figure 5.8 Harvest level estimates by period for the Aspen species market set for the Bench0 scenario with constraints targeting annual harvest of at least 1.9 million cords of Aspen species. and with constraints regulating the total volume of the All species Figure 5.9 Harvest level by forest land ownership for the Aspen species market set for the Bench0 scenario with constraints targeting annual harvest of at least 1.8 million cords for Aspen species and with constraints regulating the volume of All species Figure 5.10 Harvest level estimates end-market for Aspen species for the Bench0 scenario with constraints targeting annual harvest of at least 1.8 million cords of Aspen species and with constraints regulating the total volume of All species Figure 5.11 Area of stands in the aspen forest cover type that are greater than or equal to age 55 in the aspen forest cover type for the Bench0 scenario with constraints targeting annual harvest vii

11 of at least 1.8 million cords of Aspen species and with even-flow constraints on All Species Figure 5.12 Harvest levels for the Aspen species for the Bench1 scenario with constraints targeting annual harvest of at least 1.5 million cords of Aspen species and with constraints regulating the total volume of All species Figure 5.13 Shadow price estimates for Aspen species for the Bench1 scenario for alternative minimum annual harvest level targets for Aspen species Figure 5.14 Harvest levels and minimum harvest level targets for the aspen species group for the Bench1 scenario with short-term departures increasing annual aspen harvest levels up to 2.1 million cords for 20 years in the short term Figure 5.15 Shadow price estimates for Aspen species for the Bench1 scenario for minimum annual harvest level targets for the Aspen species market set with short-term harvest levels as high as 2.1 million cords annually as shown in Figure Figure 5.16 Harvest levels and minimum harvest level targets for the aspen species group for the Bench1 scenario with short-term departures increasing annual aspen harvest levels up to 1.9 million cords for 20 years in the short term Figure 5.17 Shadow price estimates for Aspen species for the Bench1 scenario for minimum annual harvest level targets for the Aspen species market set with short-term harvest levels as high as 1.9 million cords annually shown in Figure viii

12 List of Tables Table 3.1 Forest cover types used to classify analysis areas Table 3.2 Minimum and maximum rotation age limits included in the management options for each cover type recognized in the model Table 3.3 Percent volume that is aspen species in the aspen forest cover type by stand age and ecological section Table 3.4 Percent volume that is spruce-fir in the aspen forest cover type by stand age and ecological section Table 3.5 Initial stand basal area (50-year basis) for each site index level for planted red pine.. 23 Table 3.6 Map colors associated with each map layer used to classify analysis areas Table 3.7 Market type flows tracked by the model that have transport costs Table 3.8 Harvest costs ($/cd) for final harvest of pine based on stand age and the volume of wood removed during harvest Table 4.1 Market Sets used for all draft scenarios Table 4.2 Forest Condition Sets tracked for all draft scenarios (T.Land = Timberland) Table 4.3 Additional Forest Condition Sets tracked for all draft scenarios Table 4.4 Minimum rotation age assumptions for the draft scenarios by combinations of forest cover type and site index classes (cover-site classes) Table 4.5 For the Bench1 scenarios, the percent of timberland in each forest cover type considered not available for harvest for each forest ownership class. Percentages are dependent on stand age with table showing maximum age for each age class Table 4.6 For the Bench1 scenarios and for timberland in the private ownership class that is assumed available for harvest, the percent of the land area in each delay class ranging from no delay to a 20-year delay. Percentages depend on stand age with table showing maximum age for each age class ix

13 1. Purpose and Background This paper summarizes modeling work on the Minnesota forest resource situation completed in 2016 from support by the Charles K. Blandin Foundation. The effort was based on the premise that information about Minnesota s future wood supply situation can be critical for forest-based economic development. It is important to understand how well potential economic development opportunities fit with Minnesota s forests, Minnesota s rural communities and Minnesota s existing forest industries. Minnesota currently has good statewide forest inventory information, but recent, detailed analyses have been lacking at a state or regional level that project and assess forest conditions forward through time. Initial goals of this project were to: (1) Develop capabilities to project and analyze potential of the forest to support specific economic development opportunities in northern Minnesota. Analyses must recognize the multi-product and multi-market nature of timber-based industries in the region and use the latest detailed statewide forest inventory information to project forest conditions over time. (2) Project and analyze northern Minnesota forest conditions forward for at least 90 years (approximately 1.5 rotations) assuming all current statewide forest industries are sustained. (3) Compare the projected wood supply situation and projected wood costs for the Grand Rapids market (Itasca County) to those of other Minnesota market locations based on projections of current wood consumption levels in all major markets. (4) Develop and analyze plausible forest industry market expansion opportunities that are of interest to the local (Itasca County) community. For each of these scenarios, also address the sensitivity of results to assumptions concerning the future availability of wood from private forest landowners. (5) Document results demonstrating the modeling and analyses capabilities potentially available for addressing other forest-based economic development opportunities. As in most modeling efforts, data needs are large, and those needs have motivated intentional focus on the most utilized and valuable tree species for forest industry in Minnesota: aspen (Populus tremuloides) and red pine (Pinus resinosa). A main outcome of the project is the development of several basic draft scenarios intended to demonstrate the usefulness of the model for forest planning in Minnesota. In alignment with the interests of the Blandin Foundation, draft scenarios have been chosen to address the potential of Minnesota s forest resource base to support economic development in northern Minnesota. The Interagency Information Cooperative and the University of Minnesota, Department of Forest Resources plan to expand on the draft scenarios presented in this report. Input from Minnesota Forest Industries, the 1

14 Minnesota Forest Resources Council and the Minnesota forest land management organizations like the Minnesota Department of Natural Resources will help guide future work. The ultimate objective of updating the GEIS models is to provide a flexible tool for forest managers in Minnesota that can be applied to a variety of planning scenarios and aid in decision-making regarding the future and sustainability of Minnesota s forest resource. 1.1 University of Minnesota Scheduling Models and Shadow Pricing The University of Minnesota, through the Department of Forest Resources and the North Central Research and Outreach Center, has a long history of work to support analysis for forest planning. For over thirty years, these models have been used extensively for analyses to support forest planning (Hoganson and Rose, 1984; Jaakko Pöyry Consulting, Inc., 1994; Hoganson, 1996; Hoganson et al., 2005, Minnesota Department of Natural Resources. 2006, Hoganson and Reese, 2010, Hoganson, 2013). This project capitalizes on using both model components and data from past efforts. One common thread in these efforts has been the ability to recognize forest inventory information in substantial detail by decomposing very large problems, involving thousands of stands, into stand-level problems that are all linked via the use of shadow prices (Hoganson and Rose, 1984; Paredes and Brodie, 1988; Paredes and Brodie, 1989). The modeling processes search for good estimates of the shadow prices, and once good estimates are found, then management schedules that are optimal to the mathematical formulation of the problem can be found for stand-level management decisions. The University of Minnesota harvest scheduling models have mathematical programming equivalents similar to linear programming formulations currently used by the Minnesota DNR. The shadow price estimates are essentially the same as those found using commercial linear programming solvers. Each constraint in the formulation has an associated shadow price estimate, and this estimate adds insight about the impact of that constraint for the specific model formulation. In this paper we will report shadow price information to help interpret modeling results. Shadow price estimates are marginal cost estimates in that each estimates the impact on the objective function value if the level of its associated constraint is changed by one unit. Shadow prices are thus rates of change in terms of units used to define the objective function and the units used to define the associated constraint. For example, if the objective function is measured in year 0 dollars and the constraint is defined in terms of cords, then the associated shadow price for the constraint would be $/cord. The sign (+ or -) for a shadow price can be less confusing if one realizes that the optimal solutions cannot improve when the size of the feasible region is decreased. Constraint levels that do not impact the value of the optimal solution have a zero shadow price. 2

15 Shadow price estimates can be a key for adding forest level considerations to stand-level problems. For the stand-level analyses, each shadow price estimate is essentially a credit or penalty to apply consistently to evaluations of all stand-level management options so that forestwide considerations are achieved after stand-level options are scheduled and results are totaled for the forest. For example, a shadow price might represent a credit for old-forest production to help achieve a minimum old-forest forest-wide target. Or it might represent a penalty on harvesting an acre if its associated constraint limits acres harvest in an associated planning period. Solutions with large shadow prices (penalties or credits) indicate that potentially large adjustments are needed in valuing stand-level options in order to satisfy the forest-wide constraints. Large shadow price values suggest that stand-level management will shift away from options that would otherwise maximize the objective function when constraints are not included in the model. Forest management is characterized by long planning horizons. Typically, separate constraints are used for each planning period. For example, one might use 20 forest-wide constraints to reflect a desire to harvest at least 30,000 acres in each of 20 planning periods in the planning horizon. Shadow price estimates are developed by the model for each of these 20 periods. These estimates often have a temporal pattern that reflects the initial conditions of the forest. For example, with a forest that is initially older than a regulated forest, credits (shadow prices) are often needed to delay harvest until period 2. And with period 2 harvest options receiving a credit, then even higher credits are likely needed for period 3, so that enough stands are held until period 3. Typically, harvest timings for individual stands can be shifted, with the model selecting harvest in the period that maximizes overall impacts considering contribution to objective function value along with any impacts on constraints as reflected by the shadow price credits/penalties for each constraint. Additional information on shadow prices can be found in most introductory texts to operations research Descriptions of shadow pricing to coordinate stand-level management to achieve forest wide constraints can be found in Hoganson and Rose (1984), Paredes and Brodie (1988), and Paredes and Brodie (1989). 1.2 The Generic Environmental Impact Statement on Timber Harvesting The Generic Environmental Impact Statement (GEIS) on Timber Harvesting and Forest Management (Jaakko Pöyry Consulting, Inc., 1994) was a comprehensive study that cost over $1 million. It addressed many facets of the situation and included fourteen background and technical papers (University of Minnesota, 2016). It was also done in stages including a draft set 3

16 of modeling scenarios followed by a set of final scenarios developed in response to public comment and technical review. The GEIS was conducted in response to growth and projected future growth of the forest industry in the Minnesota at the time. Significant mill expansions had been proposed, the impacts of which were difficult to assess on a mill-by-mill basis. This necessitated the initiation of a statewide study that could model the interactions between these proposed expansions and their effect on the existing timber industry and forest resource base. Many facets of the forest resource situation have changed since the GEIS was completed in It was based on forest inventory data that is now over 25 years old. Yet interests at the time of the GEIS have similarities to today: much of Minnesota s forest lands are financial mature today old to the point where concern arises about natural succession and long-term productivity for supporting forest industry in Minnesota. With the forest generally older and with potential economic development of interest in northern Minnesota, forest-based economic development may be a valuable opportunity for the State. Projected development opportunities addressed in the GEIS were not realized, so to what extent might such opportunities be realized in the near future? The primary goal of this project is similar to that of GEIS: to evaluate potential scenarios for future timber harvest levels in Minnesota given the current forest situation, including the most recent forest inventory information and timber market opportunities. The resource situation is complex, consisting of multiple markets to be modeled over many planning periods. Stands of different forest cover types, varying by species mix, must be addressed alongside considerations of multiple ownership classes, management objectives, and many more variables. This level of complexity emphasizes the need to interpret model scenario results not as predictions, but as insight into the forest resource situation to help aid in the management decision-making process. Yet this study is not funded anywhere to the level of funding for the GEIS. Therefore, focus is primarily on the timber supply situation as measured by available forest inventory information. General environmental conditions will also be reported and tracked as described by age class distributions for specific forest cover types. Here, it is helpful to briefly describe the general framework of the GEIS. Three scenarios were modeled for the GEIS, with scenarios differing in assumed future timber harvest levels over a 50-year planning horizon using 10-year planning periods: a base, medium, and high scenario (Figure 1.1). The base scenario assumed a 4 million cord annual harvest, the 1990 level of statewide timber harvesting, would be maintained throughout the planning horizon. The medium scenario assumed a gradual increase in harvest levels to 4.9 million cords annually by At the time of the GEIS, the estimated timber harvest level for 1995 was 4.9 million cords annually assuming all expected industry expansions occur. Harvest levels for the medium scenario were assumed to remain at 4.9 million cords from 1995 until the end of the planning horizon. The high scenario assumed a gradual increase to a 7 million cord annual harvest level by 2000, and to 4

17 remain at that level for the remainder of the planning horizon. After first-run analyses of these scenarios, the planning horizon was increased to 60 years to better address aspen rotation lengths (Jaakko Pöyry Consulting, Inc., 1992, p. 189). Figure 1.1 Timber harvest levels for the three scenarios of the 1994 Minnesota GEIS on timber harvesting and forest management. Since the GEIS was published, expected industry expansions have not occurred. Instead, the industry has experienced mill closures and decreased harvest levels. From 1995 to 2005, harvest levels remained relatively steady, but dropped significantly in 2006 and have since remained at the lower level. According to the most recent Minnesota Forest Resources Report (MN DNR, 2016), 2.88 million cords were harvested and utilized in 2013, as compared to over 3.8 million cords harvested in The decrease in overall harvest levels across the state is a main concern addressed in this draft GEIS analysis update, as levels are well-below what would be sustainable assuming all species are marketable. Issues that arise include loss in timber quality and value as stands age. This issue is especially notable within the aspen forest cover type and has motivated more detailed modeling of the aspen supply situation, which will be described in the growth and yield modeling section of this report. Another reason for updating modeling efforts is the opportunity for a more nuanced investigation into potential aspen harvest levels using information developed since the GEIS. Analyses for the GEIS assumed a necessary 5

18 20% decline in aspen harvest levels by 2010 in order to meet target harvest levels throughout the planning horizon (Jaakko Pöyry Consulting, Inc., 1992, p.196). Aspen harvest levels have experienced a steady decline since 1999, however, at close to 40%, the decline is nearly double that assumed by the GEIS. It is also important to note that while there has been a decrease in aspen harvest levels in Minnesota, this has not necessarily implied the same decrease in aspen consumption. In 2005 for example, net imports of aspen from the surrounding states and Canada was as high as 371,000 cords, composing 16% of Minnesota s total consumption for that year. Declines in aspen harvest levels in Minnesota are due to several factors including mill closures and substitution for other species by large mills (MN DNR, 2016). The most significant factor, however, is the decline in harvest from private lands over the last 10 years. The majority of aspen acreage across all age classes in Minnesota exists on private lands. Despite an abundance of aspen in Minnesota in the older age classes, potential increases in aspen harvest levels become complicated due to the unpredictability of harvest availability on private lands and the shortlived nature of aspen. Since 1990, acreage in the aspen cover type has declined by over 400,000 acres. Moving forward, aspen potentials are of particular importance to forest management in Minnesota, as increases in aspen harvest levels are central to current discussions of potential industry expansions. 1.3 Forest Plans for Minnesota s National Forests The USDA Forest Service used the planning models of University of Minnesota as the cornerstone for analysis for their most recent forest plans for the Chippewa and Superior National Forests in Minnesota (USDA Forest Service, 2004a; USDA Forest Service. 2004b). University of Minnesota faculty and graduate students were actively involved in modeling to support both their draft and final forest plans, with the process taking five years or more. Each forest developed seven alternatives (scenarios) with substantial stakeholder involvement. Benchmark scenarios were also developed that included fewer constraints to learn more about forest potentials. The draft model used the University of Minnesota Dualplan model with the Final Plan also using the University of Minnesota DPspace model to address spatial arrangement of older forest. In general, the forest plans for the two national forests in Minnesota emphasize sustaining a substantially large component of core area of older forest with planned forest harvest levels (Allowable Sustainable Quantities, [ASQs]) approximately half of sustainable levels if focus was primarily on timber production. Harvest levels implemented by each forest have been substantially below planned levels since plans were completed in Recently, proposals have surfaced concerning some increases in the harvest levels for the National Forests in Minnesota through the USDA Forest Service s newly adopted Good Neighbor Authority policy (USDA Forest Service, 2016). 6

19 The planning efforts for the USDA Forest Service were especially helpful for this study. Investments in planning by the National Forests in Minnesota were substantial, almost certainly more in dollar terms than what was spent on the Minnesota GEIS. The USDA Forest Service s development of riparian area estimates were detailed and were utilized in this study as the best information currently available to help support a regional assessment. A study using USFS planning data (Bixby, 2007) explored the potential value for better coordination in planning and management between large public ownerships. The study found, that for balancing production of timber and core area of older forest, coordinating at a strategic forest level in terms of management objectives is more important than operational on-the-ground coordination along common shared stand boundaries. More specifically, some areas of the National Forests are highly valuable for timber production, while areas of other public lands are better-suited for producing older forest, as their timber productivity potential is relatively low. 1.4 The UPM/Blandin Thunderhawk Environmental Impact Statement The University of Minnesota forest management model (Dualplan) was also used in to help address wood supply as part of an Environmental Impact Statement (EIS) for the proposed mill expansion referred to as the UPM/Blandin Thunderhawk Project (Minnesota Department of Natural Resources. 2006). That study used the statewide FIA inventory of 1999 to At the time, the main concern was about supply of aspen volume, with statewide harvest levels of aspen nearly a million cords higher than harvest levels of today. The study did not consider harvest levels beyond its 40-year planning horizon. Multiple scenarios were modeled including scenarios that varied assumptions about willingness of private landowners to sell and assumptions about potential shifts by the proposer to species other than aspen. Overall results showed aspen supplies especially sensitive to assumptions about willingness of private landowners to harvest. Since 2005 aspen harvest levels have dropped substantially with much of that drop in harvest levels on private lands (Figure 1.2). Since 2005 the estimated total acreage in the aspen type (including balsam poplar) has also declined from approximately 5.5 million acres to 5 million acres. The current estimate of the age class of the aspen forest cover type shows a fairly balanced age class distribution, yet with many acres over age 45, the approximate age of financial maturity for stands in the aspen forest cover type (Figure 1.3). The lower acreage in the youngest aspen age class in Figure 1.3 reflects the recent drop in harvesting in the aspen forest cover type, as acres in this forest cover typically regenerate naturally to aspen after clearcutting. 7

20 Figure 1.2 Total statewide Timber Harvest by Ownership over time. Graph obtained from the Minnesota DNR Utilization and Marketing group. Figure 1.3 Aspen age class distribution by ownership class for timber land in Minnesota based on the USDA FIA statewide forest inventory. 8

21 1.5 Modeling for Minnesota County Land Departments During the last 10 years and with support for the University of Minnesota s Interagency Information Cooperative, the University of Minnesota Department of Forest resources has been engaged in forest planning efforts for three county land departments in Minnesota: Koochiching, Crow Wing and Hubbard Counties (Hoganson and Reese, 2010; Hoganson, 2013), those efforts built off of earlier technical modeling applications to support forest planning. Growth and yield estimates from the Thunderhawk EIS served as basis for developing growth and yield estimates for each county s stand-level inventory data. For applications, there was a need to develop growth and future yield estimates for stands using information available in stand-level inventory information for the counties. Timber yield tables were developed for combinations of forest cover type and site index classes. Empirical yield tables were constructed based on the FIA inventory data and STEMS projections used for the Thunderhawk EIS, as described in the previous section. STEMS projections were used for up to an additional three five-year planning periods in addition to the yield estimate for the immediate period. FIA plots located in fifteen northern Minnesota counties were used. In developing the tables, weights (importance) of data points for each FIA plot were based on the length of its associated growth projection length, with shorter projection lengths receiving greater weight. No STEMS growth projections were used that projected stand growth for more than 19 years. These yield tables were used for developing the growth and yield estimates for this study that involved even-aged management without intermediate harvests. The approach might be described as a compromise approach between conservative empirical yield tables (which are influenced by management activities and recent stand disturbances) and STEMS projections which have been suggested to over-estimate future yields, especially for long projection periods and older stand ages. 9

22 2. Facets of the Updated Multi-market, Statewide Model A primary aspect of this project was to update the Dtran multi-market planning model used for the GEIS analyses (Jaakko Pöyry Consulting, Inc., 1992; Jaakko Pöyry Consulting, Inc., 1994). Work included: (1) addition of new components of the Dualplan model and associated prescription writers that were developed and used for recent Minnesota applications, such as those described in previous section, and (2) new features tailored to important facets of the forest management situation in Minnesota. Forest planning situations are often complex with many facets (Figure 2.1). For applications, it is important that models are simple enough to be useful yet complex enough to realistically address the problem situation. Often, because of the complexity of the forest situation, analysis involves modeling multiple scenarios, varying potentially important assumptions to help learn about their impact. A brief summary of model components of the updated Dtran model is presented in this section. Forest Management Situation Forest Conditions Management Tools Policies, Objectives & (current and Future) (Silviculture) Decsion-making Environment Stand Level Subforest Level Forest Level Rotation lengths Sustainable harvests & revenues Soil conditions Riparian areas Stand age/size Shelterwood harvest Desirable future conditions Cover type Ecological types distributions Selection harvest Mix of forest cover types Stand age Watersheds Diversity of cover types Even-aged thinning systems Mix of stand ages Tree species mix Wildlife habitat Aesthetics Intensity Mixed species stands Tree age mix Cover types Old growth Frequency Mix of patch sizes Tree stocking Stand ages Recreation Species & tree sizes cut Mix of uneven-aged mgmt Woody debris Patch sizes opportunities Uneven-aged management Wildlife habitat Invasive species Patch shapes Forest health Intensity Spatial arrangement Insects Connectivity Area of unevenaged Frequency Recreation and visual quality Tree diseases Wildlife buffers stands Species & tree sizes cut Riparian area guidelines Rare species Sensitive areas Carbon stored Reforestation Climate change & carbon Wildlife present Distances to markets Area of mixedspecies stands Species mix Forest management guidelines Heritage condition Seasonal access Wildlife Tree stocking Prices, costs, markets & budgets Operability Road network populations Site prep Aggregate emphasis areas Accessibility Slope Management area emphasis Fire hazard ratings Fire prevention & control Insect & disease control Multiple owners with differences Linking strategic & operational Wilderness Aspect Genetic tree improvement Data limitations Understory species Road/trail construction Risks & recourse options Figure 2.1 Facets of the forest management situation 10

23 2.1 Analysis Areas with Riparian Areas The Dualplan model decomposes the large forest-wide problem into many small stand-level problems (Hoganson and Rose, 1984). These stands are referred to as analysis areas and represent acres of homogeneous forest condition at an assumed single location. Using statewide FIA inventory information (O Connell et al., 2016), each analysis area represents a stand condition associated with a specific FIA inventory plot. More detail on linkages with FIA data will be described in section 3.1 for the modelled scenarios. Each analysis area is assumed to potentially have a percentage of its area in a riparian area. This is different from recent Dualplan applications where riparian areas were modeled as individual analysis areas. Because riparian areas within stands are generally small, riparian areas typically cannot be managed using different timings of treatments than those used for the stand. In the model now, treatment options for each analysis area (AA) are considered in detail, recognizing that costs and timber flows for the riparian and the nonriparian portions of the AA are different. In analyzing each AA, the management objective is assumed to be to maximize the net present value of the AA, recognizing that some associated cost and revenue flows apply to the entire AA, other flows apply to only the riparian portion of the AA and other flows apply to only the nonriparian portion of the AA. These flows likely have similar timings but differ in actual flows. For example, timber yields per acre will generally be less within the riparian portion of the stand. Also, adding more complications, some benefit flows require transport cost to market while other flows do not. Impacts of transport costs and specific timber market allocation decisions will be described in subsection 2.6. A key strength of the modeling approach is that it is not necessary to enumerate all of potential many shipping options for each treatment option for each AA. 2.2 Treatment Options for Analysis Areas Like with the Dualplan model used in recent Minnesota applications, Dtran uses a Model II approach (Johnson and Scheurman, 1977) where AA treatment options are defined separately for each rotation. This saves enormously on the number of treatment options recognized per AA, especially when planning horizons involve multiple rotations and regeneration treatments include options for changing the forest cover type for the next rotation. Using a Model II format, each AA has its unique set of treatment options for the existing AA. Regeneration options for future rotations are shared across AA s with future rotations defined by the type of associated type of bare land. Bare land can be defined by an ecological land type and a site index value as well as by other map layers used to define AA conditions. Treatment options, whether for existing AA conditions or for future full-rotation regeneration options, are described in terms of market type flows and condition type flows. Condition type flows describe 11

24 the condition of the AA at the end of each planning period if the associated treatment option is selected. Condition type is defined by a combination of stand age, forest cover type and site index This detailed condition information is tracked and summed for each color on each forest map layer. Constraints can be included to describe targeted desired future conditions of the forest for specific forest map layers and map layer colors. More information about forest map layers and map colors can be found in subsection 2.4. Market type flows, as the name implies, include all flows types that are market associated. Each market type flows is assumed to occur at the midpoint of a planning period when management treatments are assumed to occur. Market type flows include both benefits and costs, and in the analysis process, each is multiplied by an associated price/value. Constraints can be included to describe targets for market flows from the forest for specific planning periods. Uneven aged-management options are considered by using long first rotation options, typically with multiple stand entries. Treatment options can include condition and market flows beyond the end of the planning horizon. The modeled planning horizon represents the time over which flows are explicitly tracked and potentially constrained. Net present value estimates for the treatment options are based on an infinite planning horizon, assuming constant values and costs for periods beyond the end of planning horizon. 2.3 Lands Availability for Harvest Forest inventories describe the condition of the forest. FIA uses the term forestland to represent all forested land and the term timberland to refer to forested land potentially available for harvest and capable of tree growth at a minimally acceptable growth rate (~0.25 cords/acre/year). In the model, forestland less timberland equals non-timber land, the portion of forestland that is clearly not available for harvest because of low potential growth or because it cannot be harvested (for example, forestland in MN State Parks) Not all timberland is available for harvest. For each combination of landowner group, forest cover type and forest age class, the user can specify what percentage of the area represented by associated FIA plots are available for harvest. For plots classified as private lands, the model can recognize that some of those acres may not be available for harvest initially. Delays in availability can vary by forest cover type and stand age. For example, on private lands, current landowners of acres of older aspen in Minnesota have likely been approached in recent years about interest in harvesting timber, and those landowners decided not to harvest. One might expect that those landowners are less likely to harvest soon compared to other landowners that have not recently declined inquiries about harvesting. 12

25 2.4 Map Layers The modeling system tracks market type flows and forest condition type flows for all map layers and associated map layer colors. Typical map layers might be an ownership layer and an ecosystem type layer. As an example, county lands might be one color represented in the ownership map layer. These layer colors are not assumed to change color over time. For each color on each map layer for tracking forest condition types, the model tracks, for each planning period, the area of the forest by forest cover type, site index class, and age class. Period 0 refers to forest conditions at the beginning of the planning horizon. Period i refers to forest conditions at the end of period i. For each color on each map layer for tracking market flows, the model tracks, for each planning period, the volume flow of each market type. These flows are all assumed to occur at the midpoint of the planning period. 2.5 Condition Sets, Market Sets, and Constraints The model is extremely detailed in its tracking of forest condition type flows and market type flows. For constraining the model or for helping synthesize results, these flows and conditions can be aggregated into user-defined groups or sets. Condition sets are groups of condition type flows as defined by the user. For example, one condition set might represent all acres of older forest within a specific forest cover type within a specific ecoregion. The area of forestland within each condition set is tracked for each planning period. Condition type flows need not be tied to a single condition set. For example, acres of older aspen could be part of an older forest condition set and an older aspen condition set and an all aspen acres condition set. Market sets are groups of market type flows as defined by the user. For example, one market set might represent all cords of wood harvested from private lands within a specific ecoregion. The volume flows for each market set are tracked for each planning period. Market type flows are not necessarily unique to a single market set. For example, aspen volume flows could be part of an all wood market set and an all aspen market set. Each set as defined by the user need not be associated with a constraint. Constraints applied to a set (market set or condition set) can be any of the following types: (1) equality constraint (2) Demand = f(price) with lower demand when price is high 13

26 (3) lower bound constraint (>=) (4) upper bound constraint (<=) (5) range constraints both upper and lower bounds, both (3) and (4) (6) even-flow with limit for first period flow based on recent flow (period 0 flow) (7) even-flow without limit for first period A separate constraint is assumed to apply for each planning period. The type of constraint can impact the sign of the shadow prices associated with each constraint. For example, >= constraints are requiring a minimally acceptable flow and thus imply the associated shadow price is a reward to encourage an increase in flows. In contrast, <= constraints are setting a ceiling on an acceptable flow and thus imply the associated shadow price is a penalty. 2.6 Allocation of Wood Harvested to End-Markets Some market-type flows are transported to specific end-markets. Like market sets, users can define end-market sets that assign market type flows to specific end-markets. These sets can take into account specific utilization requirements for each market. A key strength of the model is its ability to make allocation decisions directly within the solution process. Rather than enumerate all shipping options as part of AA treatment options, the model uses the estimated shadow prices and any assumed product prices for each market to allocate wood flows to each market. Basically, shadow prices, for specific market type flows to specific end-markets, are like delivered prices for the associated market. In the solution process, shadow price estimates are adjusted to help satisfy end-market constraints. If the shadow price is increased for one market and planning period relative to other markets, the wood procurement zone for that market (and period) increases in size. Past efforts found that wood flow patterns are influenced by the underlying spatial arrangement of the forest in relation to the road network. Specifically there can be major road intersections where a small change in delivered mill prices can cause shipments through that node to all switch end-markets. The model has been enhanced to recognize where near ties are associated with best end-market. For these near-ties it is now assumed that wood shipments for such cases can be re-allocated and split between end-markets rather than ship all flows to the single best market. Currently the model is designed to identify situations where up to three endmarkets are near-ties for a specific stand. The user is allowed to define tolerance associated with near ties. For example, a near-tie might be considered when estimated values per cord differ by less than $0.10 per cord. 14

27 2.7 Planning Horizon and Ending Inventory Values Calculations to value treatment options for specific AA s are based on an infinite planning horizon. A finite planning horizon is used for tracking and applying model constraints. Linear programming (LP) applications can have difficulties with flows in periods near the end of the planning horizon because of assumptions for valuing ending inventory. In some LP applications, timber inventory tends to be liquidated at the end of the planning horizon if the value of ending inventory is not fully recognized. In other LP applications, ending inventory can be high because ending inventory is over-valued. This difficulty of valuing ending inventory is often due to reaching a time when market flow constraints and forest condition constraints are no longer recognized. Dtran allows the user to project shadow price estimates for periods beyond the end of the planning horizon based on shadow prices found for periods near the end of the planning horizon. With this approach there is not an abrupt point in time where market type values or forest condition values change abruptly simply because the end of the constrained period has been reached. This valuation process has worked well in numerous past applications of Dualplan and Dtran. 2.8 Parallel Processing Option Unlike earlier versions of Dualplan and Dtran, the model now offers parallel processing options, taking advantage of how the solution process decomposes the problem into many smaller problems. Parallel processing utilizes the multi-processor capabilities of today s personal computers. Applications to date have not utilized machines with more than eight-processors. Users are allowed to specify the number of co-processors to use. Checks have not found any differences in schedules based on the number of co-processors used. Solution times using eight processors have decreased solution times by a factor of approximately four. The authors are selftrained in computer programming and multi-threading for parallel processing, making additional gains likely if needed for larger problems than those considered to date. 2.9 Searches for Estimating Constraint Shadow Prices A key to solving each problem formulation is the search for estimates of the shadow prices that satisfy the forest-wide constraints of the model. The model basically uses the same search processes as used in past applications of Dualplan with help from recognizing near-ties as described in section 2.6 and the parallel processing feature described in section 2.8. Personal computers are also much improved in terms of processing speed and available memory. Solution time has not been a concern with applications to date of the updated Dtran model for the draft model runs. 15

28 3. A Data Set for Northern Minnesota 3.1 FIA Data for Defining Analysis Areas The baseline data used for projections of future forest conditions comes from the statewide forest inventory dataset, Forest Inventory and Analysis (FIA) program of the USDA Forest Service. The FIA program reports estimates of the status of state and national conditions of all forest lands with statistical confidence (Miles, Brand, & Mielke, 2005). There are two key phases to the program. The survey employs remote sensing and a statewide grid of permanent ground sampling points. Phase 1 identifies forest land area; Phase 2 examines the forested ground sampling points (ie. field plots) to collect measurements of tree, forest, and location descriptors, including information that describes plot condition. The spatial sampling intensity of these ground plots is approximately one plot per 6,000 acres; however, the MN DNR has been working with FIA to double that sampling intensity for Northern Minnesota. Further detail on the sample design and inventory process is provided by Miles, Brand, & Mielke (2005), O Connell et al. (2016), and USDA Forest Service FIA (2014). In 2005 the FIA program shifted from a periodic inventory system to an annual system in which all sample plots in each state are revisited once every survey cycle. In Minnesota, this survey cycle takes 5 years, meaning approximately 20% of the plots are measured each year. At the time of input into the model, the most recent inventory data were from the 2010 to 2014 survey cycle. Since model construction, the 2015 inventory data has been released. Many changes have been made to the FIA program since the 1994 GEIS study was conducted using 1990 inventory data. These include changes to sample and plot design and reclassification of certain plot attributes. More detail on how FIA has changed since the 1990 inventory can be found in Appendix C of the Draft EIS report for the UPM/Blandin Thunderhawk Project (Minnesota Department of Natural Resources, 2006). The differences in inventory procedure between 1990 and now are important to recognize when comparing forest projections from the 1994 GEIS to those produced for this study using the newer FIA data. As part of FIA plot design, individual plots are divided into conditions defined by differences in ownership, land use and vegetation (O Connell, et al., 2016). The proportion of each plot within a condition is recorded and forest and tree inventory data is collected by condition rather than for the plot as a whole. The FIA Condition table was therefore the primary source of forest inventory data for the model. Condition-level attributes of greatest interest to model input included ownership class, forest type, site index, stand age and basal area. Conditions classified as non-forested were excluded from the dataset, as were plots located south of St. Cloud, MN in order to focus on the primary timber market system concentrated in the northern part of the state. Two plot-level attributes were also incorporated into the dataset: plot location (for privacy reasons FIA provides slightly altered coordinates for plots that are guaranteed to be within a

29 acre area of the true plot location) and ecological section. A total of 7,169 FIA forested plot conditions were included in the final dataset from the survey cycle, representing approximately 15.8 million acres of forest land. Of those forested plots, 6,816 of them were classified as timberland and represent about 14.6 million acres. As defined by FIA, timberland is nonreserved forest land capable of producing at least 20 cubic feet of wood volume per acre per year (O Connell, et al., 2016, p.1-4), and is therefore of primary interest to modeling efforts. The approximate spatial distribution of FIA plots used in the model can be seen in Figure 3.1 Figure 3.1 Spatial distribution of FIA timberland plots used in the model dataset 3.2 Ownership Groups & Forest Cover types For model purposes, FIA plot conditions were further divided into analysis areas (AAs) to allow for portions of stands to be classified as riparian areas and to vary time of availability for harvest. Availability is based on ownership, cover type, stand age and site index. Four ownership groups were considered in classifying AAs: federal, state, county and private. The spatial pattern of ownership groups within the dataset can be seen in Figure

30 Figure 3.2 Distribution of FIA timberland plots by ownership type Forest ownership is of particular concern when considering behaviors of non-industrial private forest (NIPF) landowners. While most NIPF lands are eventually harvested, it is not reasonable to assume that private landowners will consistently decide to harvest their lands at the time of financial or ecological maturity. Reasons for this are discussed extensively in the literature and include the high cost of management on smaller stands, preferences for non-timber values and lack of knowledge surrounding timber management (Zhang, Zhang, & Schelhas, 2005). To address this situation in the updated model, portions of the AAs under private ownership are assigned 0 to 20-year delays in their availability for harvest. These availability assumptions vary by cover type and age class. There is also potential within the model to consider a portion of each ownership class as not harvestable. Analysis areas were further classified using twenty forest cover types identified from the FIA dataset. These forest cover types are listed in Table

31 Table 3.1 Forest cover types used to classify analysis areas Jack pine Lowland spruce Aspen 212K SE Red pine natural Tamarack Aspen 212L NE White pine Ash Aspen 212M NC Spruce-fir Lowland hardwoods Aspen 212N CC Oak Cedar Aspen 222M SW Northern hardwoods Other Aspen 222N NW Paper birch Red pine plantation Forest cover type classification follows MN DNR forest type categories, with some additions. As mentioned previously, higher levels of detail for the red pine plantation and aspen cover types have been incorporated into the model. For this purpose, red pine plantations are separated from red pine natural stands and the aspen forest cover type is subdivided by ecological region. Further detail about these forest types are presented below in sections 3.4 and Analysis Area Management Options Five prescription options were provided in the model to account for different management options among cover types. The prescription options included were: 1) red pine thin with clear cut, 2) clear cut without thins, 3) oak shelterwood with removal, 4) uneven-aged management, and 5) no harvest. The forested area assigned to each prescription option in each planning period is tracked by the model as market type flows which are described in a later section. In addition to prescription options, the model also recognizes minimum and maximum rotation ages for each forest cover type by site quality class to ensure stands that modelled harvests are at ages within acceptable Table 3.2 shows minimum and maximum rotation ages by forest cover type. 19

32 Table 3.2 Minimum and maximum rotation age limits included in the management options for each cover type recognized in the model Forest Cover Type Site Index Class Minimum Rotation Age (years) Maximum Rotation Age (years) Jack pine Low & High Red pine natural Low and Medium Red pine natural High White pine All Spruce-fir All Oak Low Oak High Northern hardwoods Low Northern hardwoods High Paper birch All Lowland spruce Low & Medium Lowland spruce High Tamarack Low Tamarack High Ash Low Ash High Lowland Hardwoods Low Lowland Hardwoods High Cedar All Red pine plantation Low Red pine plantation Medium & High Aspen Low & Medium or 100 (in NE MN) Aspen High or 100 (in NE MN) As mentioned previously, red pine and aspen cover types were of particular concern in this study due to their relative high value and greater demand in the market. The aspen forest cover type is 20

33 also the largest forest cover type by area in Minnesota. Red pine has been the most planted tree species, with average growth rates substantially higher than other forest cover types. This prompted a higher level of detail in modeling the red pine and aspen forest types, which is described in the following sections. 3.4 Differences in Species Mixes for Aspen Forest Cover Type Often stands classified in the aspen forest cover type contain a significant mix of species other than aspen. The aspen cover type is also widespread in Minnesota, leading to spatial variations in species mixes by general location. For example, in NE Minnesota aspen stands are recognized as often having a substantial portion of softwood species. To account for this heterogeneity, yield predictions by species for AAs in the aspen cover type are adjusted based on their ecological section and stand age. Yield adjustments for species mix were determined using the most recent FIA data and FIA EVALIDator software (USDA Forest Service 2015). Weighted volume proportions of each species found in the aspen cover type were calculated from these reports for each ecological section and age class. Table 3.3 shows how the percent of total volume that is aspen species in the aspen cover type varies by ecological section and stand age. Table 3.4 provides an example of variation in other species found within the aspen cover type; it shows the percent of total volume that is spruce-fir in the aspen cover type by ecological section. Proportion variations were smoothed in the model so that changes in species mix from period to period would not be reflect as much noise as is typically present in sample data the scheduling model would otherwise attempt to capitalize on what is likely the random error in the sampling results. Delineations of ecological sections are based on the Minnesota Department of Natural Resources Ecological Classification System (ECS). The ECS defines 10 ecological sections in Minnesota, all of which contain stands classified under the aspen cover type according to the 2012 FIA inventory. Due to the limited number of FIA plots in the smaller ecological sections in Minnesota, small sections were combined with larger neighboring sections for model purposes. Specifically, 212Y was combined with 212K and 251A and 251B were combined with 222M. Note from Table 3.3 how aspen percentages decline with increasing age yet not as rapidly in the north and east. Also, the percentage of aspen species in the aspen cover type is higher in the northwestern portion of the state. As expected, spruce fir makes up a larger percentage of the volume in the aspen forest cover type in the northeast and this percentage increases with stand age (Table 3.4). 21

34 Table 3.3 Percent volume that is aspen species in the aspen forest cover type by stand age and ecological section Age Ecological Section K&Y SE 70% 70% 70% 67% 61% 56% 53% 38% 25% 212L NE 65% 65% 64% 63% 63% 61% 58% 54% 50% 212M NC 76% 76% 75% 74% 72% 67% 60% 53% 50% 212N CC 79% 76% 70% 68% 67% 62% 54% 38% 25% 222M; 251A&B SW 76% 75% 70% 67% 66% 63% 55% 38% 25% 222N NW 90% 90% 90% 85% 73% 63% 55% 38% 25% Table 3.4 Percent volume that is spruce-fir in the aspen forest cover type by stand age and ecological section Age Ecological Section K&Y SE 2.7% 2.7% 2.7% 3.0% 3.6% 4.0% 4.3% 5.6% 6.8% 212L NE 15.4% 15.6% 16.0% 16.2% 16.2% 17.1% 18.4% 20.4% 21.9% 212M NC 8.5% 8.7% 8.8% 9.2% 10.1% 11.8% 14.3% 16.6% 17.7% 212N CC 4.6% 5.3% 6.5% 7.0% 7.2% 8.3% 10.0% 13.6% 16.3% 222M; 251A&B SW 1.1% 1.2% 1.4% 1.6% 1.6% 1.8% 2.1% 2.9% 3.5% 222N NW 0.9% 0.9% 0.9% 1.3% 2.4% 3.3% 3.9% 5.4% 6.5% 3.5 Thinning Options for Red Pine Plantation Forest Cover Type Red pine plantations constitute a substantial portion of Minnesota s managed timberland, and is the only cover type commonly thinned, making the updated model results potentially sensitive to the degree of detail considered in red pine yield projections and management options. The ability to incorporate higher levels of detail in red pine plantation management could also provide future opportunities to produce model scenarios exploring red pine s high timber value and investment potential in the larger context of Minnesota s resource and market situation. For this reason a stand-level growth and yield model was used to establish higher resolution management options for planted red pine stands within the 2012 FIA inventory. The model uses a Microsoft Excel application to produce annual harvest and yield predictions based on userdefined stand attributes and management options. The model is adapted from Resinosa, an Excel based harvest and yield model structured using a density management diagram framework (Mack & Burk, 2005). Resinosa uses a system of stand-level volume and growth equations (Mack & 22

35 Burk, 2005, p.12-13). Modifications were made to Resinosa to depart from its stand density based management decision process to provide a range of management options not limited by the density-based management structure. The model was adapted to include input parameters to define the number of thinning treatments, the number of years between each thinning treatment, the residual stand basal area after each thin, and the rotation age for the stand. Additional input parameters describe stand condition at the start of the rotation: site index, stand age, basal area, and trees per acre. Resinosa was used to simulate thinning regimes for a variety of initial stand conditions. Multiple treatment options of different rotation lengths and timing of first thin were developed for each initial stand condition. Each analysis area (AA) in the planted red pine cover type was linked to the set of Resinosa simulations that most closely matched its initial stand conditions. Adjustments were made in first thinning yields to better link the more generic stand conditions simulated with Resinosa to the actual AA conditions. Table 3.5 lists the basal area values used as a starting point for yield projections of treatment options for each site index level. Basal area estimates were calculated based on equations provided in the Manager s Handbook for Red Pine in the North Central States (Benzie, 1977, p.15). The initial stand conditions used in Resinosa all assumed a starting age of 20 years and an initial stand density of 600 trees per acre. Table 3.5 Initial stand basal area (50-year basis) for each site index level for planted red pine Site Index Basal Area (sq. ft.) Stand-level (Analysis Area) Map Layers To reduce model size and increase solution efficiency, the Blandin Foundation Study adopts a version of the Model II LP formulation first described by Johnson and Scheurman (1977). The AAs are aggregated and mapped based on three attribute conditions, referred to in the model as 23

36 map layers, each with a set of possible map colors. Map layers include ecological region, ownership type, and management zone. For the draft scenarios, six ecological regions, eight ownership types, and two management zones were considered, as enumerated in Table 3.6 below. Figure 3.3 and Figure 3.4 show the FIA plot distribution among ecological regions and management zones. The distribution among ownership types were shown in Figure 3.2. Specific stand conditions for each map layer and map color are tracked over the entire planning horizon. AAs that have undergone a final harvest in a particular planning period are aggregated into bare land classes, for which a set of management decision variables regarding regeneration and site conversion options are also enumerated. Table 3.6 Map colors associated with each map layer used to classify analysis areas Ecological Regions Ownership Types Management Zones Southeast (212K) Northeast (212L) North Central (212M) Central (212N) Southwest (222M; 251A&B) Northwest (222N) Federal Timberland State Timberland County Timberland Private Timberland Federal Non-Timberland State Non-Timberland County Non-Timberland Private Non-Timberland Timberland (not reserved) Non-timberland (reserved) 24

37 Figure 3.3 Distribution of all forested FIA plots by ecological sections Figure 3.4 Distribution of FIA plots by reserved status 25

38 3.7 Market Type Flows There are 20 market types included in the model, with differentiation between market types that are transported and those that are not. Market types with transport costs are timber products that are harvested from stands. Transport cost estimates are based on stand location and distance to a particular market center. Total delivered costs to market are thus based on assumed stand location, harvest cost estimates, transport cost estimates, and stumpage price assumptions. Market types with transport costs are listed in Table 3.7 below. Table 3.7 Market type flows tracked by the model that have transport costs Spruce-fir pulp Oak Pulp Tamarack pulp Red pine logs Oak Logs firewood Jack pine logs Aspen pulp Other pulp Pine pulp Hardwood pulp Other Logs Non-transported market types tracked by the model for each stand describe facets such as the number of acres allocated to specific treatment options and associated harvest and stand regeneration costs. Stand harvest costs are not linked directly to individual market type flows as harvests involve joint production with inseparable costs. 3.8 End-Markets and Road Networks It was necessary to incorporate the 2012 FIA data into a spatial transport model that included timber market centers. This was achieved using ArcMap software to overlay 2012 FIA plots onto a road network downloaded from the Minnesota Department of Transportation. Locations of the seven Minnesota timber markets in the state were also included to determine distances from plots to market. The seven market locations considered were the Minnesota towns of Duluth/Cloquet, Bemidji, Brainerd, Cook, Grand Rapids, International Falls, and Grand Marais. Six of these market locations were also included in the GEIS; Grand Marais was added to this study as the location of a sawmill that consumes a relatively large amount of valuable pine. Figure 5 below shows the locations of market centers in the context of the road network used in the transport model. The shortest distance from each plot to the nearest road segment was calculated and recorded in order to estimate off-road transport costs of timber from harvest site to road access. Distances along the road network from each plot s nearest road segment to each mill location were also calculated and recorded to help estimate road transport costs. These distance data allow for a dynamic spatial transport model able to assess changes in timber flows to markets based on transport costs and timber prices at individual markets. Consideration of transportation costs is a significant aspect of a spatially dynamic harvest scheduling model. The 26

39 location of a particular stand (AA) and its subsequent distance to each market can impact when the stand will be scheduled for harvest and how its multiple timber products are allocated among markets. Figure 3.5 Locations of the seven market centers and the road network 3.9 Transport Costs There are three parameters included in transport cost calculations within the model: loading cost, distance to road cost, and distance to market cost. The base scenarios use a loading cost of $5/cd, a distance to road cost of $0.60/cd/mile, and a distance to market cost of $0.30/cd/mile. These costs have been compared to and found consistent with other transport cost estimates in Minnesota (T. O Hara, NewPage Corporation, personal correspondence, 2016). However, the model provides flexibility for the user to change these costs if desired. The distance to market cost is of particular concern to the model as it contributes to determining where timber types from each stand is shipped based on its distance from each market center and the delivered price at that market. 27

40 3.10 Harvest Costs Harvest cost tables were also developed to aid the updated model in scheduling harvest prescriptions for individual AAs. The Harvesting Systems Background Paper published with the 1994 GEIS study states that the main factors influencing harvest costs include harvest treatment type (partial or final harvest), tree size, merchantable standing volume, and merchantable harvested volume, (Jaakko Pöyry Consulting, Inc., 1992b). To represent these factors, the developed tables vary harvest costs based on stand age and volume removed from the stand. Separate harvest cost tables were produced to account for general forest type and harvest treatment. In total, six tables were created: partial harvest for pine final harvest for pine partial harvest for hardwoods final harvest for hardwoods final harvest for spruce-fir final harvest for aspen As an example, Table 3.8 shows harvest cost trends for the final harvest for pine. Table 3.8 Harvest costs ($/cd) for final harvest of pine based on stand age and the volume of wood removed during harvest Stand Age Volume Removed (cds/ac)

41 Harvest costs trends are loosely based on information from the 1994 GEIS study and the 2015 Wisconsin Forest Practices Study (Steigerwaldt Land Services, Inc., 2015). Substantial data on harvest cost trends is not readily available; therefore the cost values included in the draft scenarios should be taken more as relative values based on the factors mentioned above. Costs associated with site conversion and restoration and timber sale administration costs are comparable to values used for the USDA Forest Service planning and other studies (USDA Forest Service 2004, Minnesota DNR 2006, Hoganson 2010, Hoganson and Reese 2013). Site conversion opportunities were not included in the draft scenarios Riparian Areas This study incorporated considerable detail in addressing riparian areas. Outside data sources were necessary to consider riparian areas because the FIA inventory data does not provide spatial detail at the stand level. This study utilized information from USDA Forest Service planning for National Forests in Minnesota (USDA Forest Service 2004) to estimate the percent of each forest cover type that falls within riparian areas. The model divides each FIA condition (subplot) into three analysis areas (AAs), each with either 0% or 10%, or 20% of its area classified as a riparian area. The estimated area in these three classifications for each condition plot are distributed such that the weighted average of percent riparian area equals the estimated average percent for that condition plot s associated forest cover type. As an example, consider an FIA condition plot in the aspen cover type. The model splits this FIA condition plot into three AAs, one having 0% riparian, one having 10% riparian and one having 20% riparian. The proportion of area represented within each AA as riparian is assigned such that the weighted average across all three AAs associated with this FIA condition plot equals 2%, because the example FIA plot represents the aspen cover type and the estimated statewide average riparian area for the aspen cover type is 2%. For options for managing riparian areas, this study follows the Voluntary Site-Level Forest Management Guidelines for Minnesota (Minnesota Forest Resources Council, 2012). Model consideration of AAs identified with a proportion of their acreage classified as riparian generally have lower per acre harvest and transport costs and lower per acre yield because of associated riparian area management limitations. By modeling riparian areas as separate analysis units in previous studies, management decisions for those areas were made separate from the greater stand in which the riparian area was located. Tracking riparian areas as part of their larger associated AA forces the model to consider the influences riparian management has on stand value and harvest timings when evaluating management options for the entire stand. This better represents reality, as riparian areas are relatively small areas with lower timber availability and would rarely be considered for management if not included as a part within a larger stand. 29

42 4. Modelling Draft Scenarios A strategy often used in forest planning is to develop a series of model scenarios and then compare results for those scenarios to help learn about the forest management situation. Results can be compared at multiple scales with it often desirable to aggregate some of the data to help define modeling constraints and to help focus some of the comparisons. For such data aggregations, the Dtran model allows users to define market sets and forest condition sets which are then tracked for each planning period for each scenario. For the scenarios described in this report, twenty planning periods are used, each 5 years in length. In discounting to calculate the net present value of management options, a 4% annual discount rate was used for all scenarios. The planning horizon is assumed to start in year 2012, using the Minnesota statewide FIA inventory to describe the forest 4.1 Market Set Flows to Track and Possibly Constrain Market sets are aggregated totals for each planning period for one or more of the market type flows recognized in the stand-level treatment options. Flows of market types are the specific timber product and management cost flows used to describe the treatment options for each analysis area. Each market type flow can be part of any number of market sets. And a market type need not be included in any market set. Market types are tracked by the model for each color of each market map layer. Market sets can be used to help focus on important details associated with market type flows. It is through the market sets that market flow constraints are applied. Flows for each market set are tracked and potentially constrained for each planning period. If one wants to define a constraint for a single market type flow, then that market type would also be a market set. Only ten market sets were used for the draft scenarios. These market sets are listed in Table 4.1. When constraints are included in the model for a market set, then each constraint for each planning period has an associated shadow price, with the estimated value of the shadow price likely dependent on the assumed constraint level. Table 4.1 Market Sets used for all draft scenarios. Set # Market Set Label Set # Market Set Label 1 All Volume All Species 6 All Volume Aspen Species 2 All Volume from Fed 7 All Aspen Species Volume from Fed 3 All Volume from State 8 All Aspen Species Volume from State 4 All Volume from Counties 9 All Aspen Species Volume from Counties 5 All Volume from Private 10 All Aspen Species Volume from Private 30

43 4.2 Forest Condition Sets to Track and Possibly Constrain Forest condition sets are like market sets, summarizing conditions of the forest by planning period. Through forest condition types, substantial detail is tracked about the condition of the forest over time. Forest condition sets allow the user to aggregate combinations of condition types that are of interest. Each forest condition type is a unique combination of forest cover type, stand site index class and stand 5-year age class. Area in each condition type is tracked for each color of each forest condition map layer. The number of forest condition type and map layer and color combinations found throughout the forest can be especially large. It is through the aggregated forest condition sets that forest condition constraints are applied. If one wants to define a constraint for a single forest condition type, then that condition type would also be defined as a forest condition set. For the draft scenarios, 168 forest condition sets were defined and tracked. In the draft scenarios, no flows for any of these forest condition sets were constrained. These 168 sets are listed in Table 4.2 and Table 4.3. When constraints are included in the model for a forest condition set, then each associated constraint has a shadow price associated with the constraint level assumed for the constraint. If the constraint is not a binding constraint, then its associated shadow price is zero, reflecting that there is no cost for this constraint at the assumed constraint level. Shadow price estimates often help substantially in identifying the costly constraints. Actual flow levels for the forest condition sets can help identify condition sets that may be desirable to constrain in future model runs. 4.3 No-limits Benchmark Scenario (Bench0) The No-limits Benchmark Scenario also referred to as the Bench0 scenario is an unconstrained scenario, finding a maximum net present value solution for the forest. Because this scenario has no forest-wide constraints, its solution does not involve searching for shadow price estimates and an optimal solution is simply the sum of the maximum NPV solution for each analysis area. The Bench0 scenario is constrained only in terms of what is considered allowable practices for stand-level management. Specifically, riparian area harvest guidelines are implemented for each analysis area following procedures described in subsection 2.1. Minimum rotation ages assumed by cover type and site index class are summarized in Table

44 Table 4.2 Forest Condition Sets tracked for all draft scenarios (T.Land = Timberland). Set # Condition Set Label Set # Condition Set Label 1 Aspen Cover Type: Age 0 to < 5 Yrs: T.Land 43 LowLand Hdwds: Age 0 to < 5 Yrs : T.Land 2 Aspen Cover Type: Age >= 55 Yrs: T.Land 44 All Types: Age 0 to < 5 Yrs : T.Land 3 Aspen Cover Type: Age >= 80 Yrs: T.Land 45 Fed: Jack Pine: Age 0 to < 5 Yrs : T.Land 4 Aspen Cover Type: All Ages: T.Land 46 Fed: Red Pine Nat: Age 0 to < 5 Yrs : T.Land 5 Aspen Cover Type: Age 0 to < 5 Yrs: For.Land 47 Fed: White Pine: Age 0 to < 5 Yrs : T.Land 6 Aspen Cover Type: Age >= 55 Yrs: For.Land 48 Fed: Spruce/fir: Age 0 to < 5 Yrs : T.Land 7 Aspen Cover Type: Age >= 80 Yrs: For.Land 49 Fed: Oak: Age 0 to < 5 Yrs : T.Land 8 Aspen Cover Type: All Ages: For.Land 50 Fed: N. hardwoods: Age 0 to < 5 Yrs : T.Land 9 Aspen212K_SE: Age 0 to < 5 Yrs : T.Land 51 Fed: Paper Birch: Age 0 to < 5 Yrs : T.Land 10 Aspen212L_NE: Age 0 to < 5 Yrs : T.Land 52 Fed: Lowland Spruce: Age 0 to < 5 Yrs : T.Land 11 Aspen212M_NC: Age 0 to < 5 Yrs : T.Land 53 Fed: Tamarack: Age 0 to < 5 Yrs : T.Land 12 Aspen212N_CC: Age 0 to < 5 Yrs : T.Land 54 Fed: Ash: Age 0 to < 5 Yrs : T.Land 13 Aspen222M_SW: Age 0 to < 5 Yrs : T.Land 55 Fed: LowLand Hdwds: Age 0 to < 5 Yrs : T.Land 14 Aspen222N_NW: Age 0 to < 5 Yrs : T.Land 56 Fed: All Types: Age 0 to < 5 Yrs : T.Land 15 Aspen Federal: Age 0 to < 5 Yrs : T.Land 57 State: Jack Pine: Age 0 to < 5 Yrs : T.Land 16 Aspen State: Age 0 to < 5 Yrs : T.Land 58 State: Red Pine Nat: Age 0 to < 5 Yrs : T.Land 17 Aspen County: Age 0 to < 5 Yrs : T.Land 59 State: White Pine: Age 0 to < 5 Yrs : T.Land 18 Aspen Private: Age 0 to < 5 Yrs : T.Land 60 State: Spruce/fir: Age 0 to < 5 Yrs : T.Land 19 Red Pine PLT Type: Age 0 to < 5 Yrs : T.Land 61 State: Oak: Age 0 to < 5 Yrs : T.Land 20 Red Pine PLT Type: Age 65 Yrs and older : T.Land 62 State: N. hardwoods: Age 0 to < 5 Yrs : T.Land 21 Red Pine PLT Type: Age 90 Yrs and older : T.Land 63 State: Paper Birch: Age 0 to < 5 Yrs : T.Land 22 Red Pine PLT Type: All Ages : T.Land : T.Land 64 State: Lowland Spruce: Age 0 to < 5 Yrs : T.Land 23 Eco212K_SE: R.PinePLT Age 0 to < 5 Yrs : T.Land 65 State: Tamarack: Age 0 to < 5 Yrs : T.Land 24 Eco212L_NE:R.PinePLT Age 0 to < 5 Yrs : T.Land 66 State: Ash: Age 0 to < 5 Yrs : T.Land 25 Eco212M_NC: R.PinePLT Age 0 to < 5 Yrs : T.Land 67 State: LowLand Hdwds: Age 0 to < 5 Yrs : T.Land 26 Eco212N_CC: R.PinePLT Age 0 to < 5 Yrs : T.Land 68 State: All Types: Age 0 to < 5 Yrs : T.Land 27 Eco222M_SW: R.PinePLT Age 0 to < 5 Yrs : T.Land 69 County: Jack Pine: Age 0 to < 5 Yrs : T.Land 28 Eco222N_NW: R.PinePLT Age 0 to < 5 Yrs : T.Land 70 County: Red Pine Nat: Age 0 to < 5 Yrs : T.Land 29 R.PinePLT Federal: Age 0 to < 5 Yrs: T.Land 71 County: White Pine: Age 0 to < 5 Yrs : T.Land 30 R.PinePLT State: Age 0 to < 5 Yrs: T.Land 72 County: Spruce/fir: Age 0 to < 5 Yrs : T.Land 31 R.PinePLT County: Age 0 to < 5 Yrs: T.Land 73 County: Oak: Age 0 to < 5 Yrs : T.Land 32 R.PinePLT Private: Age 0 to < 5 Yrs: T.Land 74 County: N. hardwoods: Age 0 to < 5 Yrs : T.Land 33 Jack Pine: Age 0 to < 5 Yrs : T.Land 75 County: Paper Birch: Age 0 to < 5 Yrs : T.Land 34 Red Pine Nat: Age 0 to < 5 Yrs : T.Land 76 County: Lowland Spruce: Age 0 to < 5 Yrs : T.Land 35 White Pine: Age 0 to < 5 Yrs : T.Land 77 County: Tamarack: Age 0 to < 5 Yrs : T.Land 36 Spruce/fir: Age 0 to < 5 Yrs : T.Land 78 County: Ash: Age 0 to < 5 Yrs : T.Land 37 Oak: Age 0 to < 5 Yrs : T.Land 79 County: LowLand Hdwds: Age 0 to < 5 Yrs : T.Land 38 N. hardwoods: Age 0 to < 5 Yrs : T.Land 80 County: All Types: Age 0 to < 5 Yrs : T.Land 39 Paper Birch: Age 0 to < 5 Yrs : T.Land 81 Private: Jack Pine: Age 0 to < 5 Yrs : T.Land 40 Lowland Spruce: Age 0 to < 5 Yrs : T.Land 82 Private: Red Pine Nat: Age 0 to < 5 Yrs : T.Land 41 Tamarack: Age 0 to < 5 Yrs : T.Land 83 Private: White Pine: Age 0 to < 5 Yrs : T.Land 42 Ash: Age 0 to < 5 Yrs : T.Land 84 Private: Spruce/fir: Age 0 to < 5 Yrs : T.Land 32

45 Table 4.3 Additional Forest Condition Sets tracked for all draft scenarios Set # Condition Set Label Set # Condition Set Label 85 Private: Oak: Age 0 to < 5 Yrs : T.Land 127 Fed: Paper Birch: All Ages : T.Land 86 Private: N. hardwoods: Age 0 to < 5 Yrs : T.Land 128 Fed: Lowland Spruce: All Ages : T.Land 87 Private: Paper Birch: Age 0 to < 5 Yrs : T.Land 129 Fed: Tamarack: All Ages : T.Land 88 Private: Lowland Spruce: Age 0 to < 5 Yrs : T.Land 130 Fed: Ash: All Ages : T.Land 89 Private: Tamarack: Age 0 to < 5 Yrs : T.Land 131 Fed: LowLand Hdwds: All Ages : T.Land 90 Private: Ash: Age 0 to < 5 Yrs : T.Land 132 Fed: All Types: All Ages : T.Land 91 Private: LowLand Hdwds: Age 0 to < 5 Yrs: T.Land 133 State: Jack Pine: All Ages : T.Land 92 Private: All Types: Age 0 to < 5 Yrs : T.Land 134 State: Red Pine Nat: All Ages : T.Land 93 Fed: Aspen Cover Type: Age >=55 Yrs: T.Land 135 State: White Pine: All Ages : T.Land 94 Fed: Aspen Cover Type: Age >= 80 Yrs: T.Land 136 State: Spruce/fir: All Ages : T.Land 95 Fed: Aspen Cover Type: All Ages: T.Land 137 State: Oak: All Ages : T.Land 96 Fed: Aspen Cover Type: Age >= 55 Yrs: For.Land 138 State: N. hardwoods: All Ages : T.Land 97 Fed: Aspen Cover Type: Age >= 80 Yrs: For.Land 139 State: Paper Birch: All Ages : T.Land 98 Fed: Aspen Cover Type: All Ages: For.Land 140 State: Lowland Spruce: All Ages : T.Land 99 State: Aspen Cover Type: Age >= 55 Yrs: T.Land 141 State: Tamarack: All Ages : T.Land 100 State: Aspen Cover Type: Age >= 80 Yrs: T.Land 142 State: Ash: All Ages : T.Land 101 State: Aspen Cover Type: All Ages: T.Land 143 State: LowLand Hdwds: All Ages : T.Land 102 State: Aspen Cover Type: Age >= 55 Yrs: For.Land 144 State: All Types: All Ages : T.Land 103 State: Aspen Cover Type: Age >= 80 Yrs: For.Land 145 County: Jack Pine: All Ages : T.Land 104 State: Aspen Cover Type: All Ages: For.Land 146 County: Red Pine Nat: All Ages : T.Land 105 County: Aspen Cover Type: Age >= 55 Yrs: T.Land 147 County: White Pine: All Ages : T.Land 106 County: Aspen Cover Type: Age >= 80 Yrs: T.Land 148 County: Spruce/fir: All Ages : T.Land 107 County: Aspen Cover Type: All Ages: T.Land 149 County: Oak: All Ages : T.Land 108 County: Aspen Cover Type: Age >= 55 Yrs: For.Land 150 County: N. hardwoods: All Ages : T.Land 109 County: Aspen Cover Type: Age >= 80 Yrs: For.Land 151 County: Paper Birch: All Ages : T.Land 110 County: Aspen Cover Type: All Ages: For.Land 152 County: Lowland Spruce: All Ages : T.Land 111 Private: Aspen Cover Type: Age >= 55 Yrs: T.Land 153 County: Tamarack: All Ages : T.Land 112 Private: Aspen Cover Type: Age >= 80 Yrs: T.Land 154 County: Ash: All Ages : T.Land 113 Private: Aspen Cover Type: All Ages: T.Land 155 County: LowLand Hdwds: All Ages : T.Land 114 Private: Aspen Cover Type: Age >= 55 Yrs: For.Land 156 County: All Types: All Ages : T.Land 115 PrivateAspen Cover Type: Age >= 80 Yrs: For.Land 157 Private: Jack Pine: All Ages : T.Land 116 Private: Aspen Cover Type: All Ages: For.Land 158 Private: Red Pine Nat: All Ages : T.Land 117 All Federal Nontimber 159 Private: White Pine: All Ages : T.Land 118 All State Nontimber 160 Private: Spruce/fir: All Ages : T.Land 119 All County Nontimber 161 Private: Oak: All Ages : T.Land 120 All Private Nontimber 162 Private: N. hardwoods: All Ages : T.Land 121 Fed: Jack Pine: All Ages : T.Land 163 Private: Paper Birch: All Ages : T.Land 122 Fed: Red Pine Nat: All Ages : T.Land 164 Private: Lowland Spruce: All Ages : T.Land 123 Fed: White Pine: All Ages : T.Land 165 Private: Tamarack: All Ages : T.Land 124 Fed: Spruce/fir: All Ages : T.Land 166 Private: Ash: All Ages : T.Land 125 Fed: Oak: All Ages : T.Land 167 Private: LowLand Hdwds: All Ages: T.Land 126 Fed: N. hardwoods: All Ages : T.Land 168 Private: All Types: All Ages : T.Land 33

46 Table 4.4 Minimum rotation age assumptions for the draft scenarios by combinations of forest cover type and site index classes (cover-site classes). Cover Type # Cover-site # Cover-site Label Min rotation age 1 1 Jack Pine Low Jack Pine High Red Pine Nat L&M Red Pine Nat High White Pine Spruce-fir Low Spruce-fir High Oak Low Oak High N Hdwds Low N Hdwds High Paper Birch Low Paper Birch High LowLSpruce Low-Med LowLSpruce High Tamarack Low Tamarack High Ash Low Ash High Lowland Hdwds Low Lowland Hdwds High Cedar Open RP Plantation Low RP Plantation Med RP Plantation High Aspen212K_SE Low Aspen212K_SE Medium Aspen212K_SE High Aspen212L_NE Low Aspen212L_NE Medium Aspen212L_NE High Aspen212M_NC Low Aspen212M_NC Medium Aspen212M_NC High Aspen212N_CC Low Aspen212N_CC Medium Aspen212N_CC High Aspen222M_SW Low Aspen222M_SW Medium Aspen222M_SW High Aspen222N_NW Low Aspen222N_NW Medium Aspen222N_NW High 40 34

47 4.4 Bench0 & Aspen Constraints Scenarios Overall intent was to keep scenarios simple by not including many constraints, especially in the first scenarios examined. The Bench0 & Aspen Constraints Scenarios are the same as the Bench0 scenario (no constraints scenario) with constraints added to force the total species volume of aspen harvested per year to a constant level that is sustained over all twenty planning periods. Scenarios in this set differ from each other only in terms of this constant harvest level assumed for aspen. Aspen volume included trembling aspen (Populus tremuloides), bigtooth aspen (Populus grandidentata) and balm of gilead (Pupulus balsamifera). The minimum aspen harvest level modelled was 1.5 million cords per year, a volume approximately equal to estimates of the recent harvest level for aspen in Minnesota. Figure 4.1 shows estimates of recent statewide harvest levels for aspen. Statewide harvest volumes of aspen have declined in recent years with most of the decline occurring on private lands. Intent in modeling these scenarios is to help better understand potential sustainable harvest levels for aspen, assuming timber harvesting is a priority on all ownerships. Figure 4.1 Recent statewide harvest level estimates for the aspen species group. Data obtained in 2016 from staff of the DNR utilization and marketing group. 35

48 4.5 Bench0 & Aspen & Total Volume Constraints Scenarios This set of scenarios adds, to the previous set of scenarios, constraints on the total volume harvested over all species. Under this set of scenarios, period 1, annual harvest level must between 3.8 and 4.2 million cords. And this range increases to 4.0 to 4.5 million cords for period 2 thru period 20. Statewide harvest level in recent years has dropped to under 3 million cords. 4.6 Bench1 & Aspen & Total Volume Constraints Scenarios The Bench1 set of scenarios is similar to the previous set of scenarios except assumptions were changed regarding the availability of timberland for harvest. This set of scenarios changes the Bench0 assumptions in two ways. First, it does not assume that all timberland (as classified by the FIA inventory) are available for harvest. For these scenarios the percentage of the land not available for harvest for each FIA plot is made dependent based on the FIA plots forest cover type, ownership class and age. Table 4.5 summarizes the percentage of land not considered available for harvest based on these stand parameters. Generally the percentage of land in the privately owned land class and in financially over-mature age classes have a greater percentage of their respective forest area not considered available for harvest because owners of these acres likely declined harvest opportunities in recent years. For private lands considered available for harvest, each FIA plot was subdivided for analyses into as many as 5 classes, with each class varying in terms of its assumed delay in potential time of harvest. Delay options included 0, 5, 10, 15 and 20 years. Table 4.6 identifies the percent of the forest area within each delay class for forest cover type and age class combinations. 36

49 Table 4.5 For the Bench1 scenarios, the percent of timberland in each forest cover type considered not available for harvest for each forest ownership class. Percentages are dependent on stand age with table showing maximum age for each age class. CoverType Age Mx Federal State County Private Jack Pine Jack Pine Red Pine Red Pine White Pine White Pine Spruce/fir Spruce/fir Oak Oak N. hardwoods N. hardwoods Paper Birch Paper Birch Lowland Spruce Lowland Spruce Tamarack Tamarack Ash Ash LowLand Hdwds LowLand Hdwds Cedar Other RP Plantation RP Plantation Aspen Aspen Aspen

50 Table 4.6 For the Bench1 scenarios and for timberland in the private ownership class that is assumed available for harvest, the percent of the land area in each delay class ranging from no delay to a 20-year delay. Percentages depend on stand age with table showing maximum age for each age class. CoverType Age Mx Delay 0 yrs Delay 5 yrs Delay 10 yrs Delay 15 yrs Delay 20 yrs Sum Jack Pine Jack Pine Red Pine Red Pine White Pine White Pine Spruce/fir Spruce/fir Oak Oak N. hardwoods N. hardwoods Paper Birch Paper Birch Lowland Spruce Lowland Spruce Tamarack Tamarack Ash Ash LowLand Hdwds LowLand Hdwds Cedar Other RP Plantation RP Plantation Aspen Aspen Aspen

51 4.7 Bench1 & Total Volume Constraints & Aspen Departure Scenarios This set of scenarios includes only two scenarios. Each is similar to the scenarios of the previous set except assumptions were changed regarding limits on aspen harvest volume flows. Much of the aspen resource at the start of the planning horizon is financially mature. These scenarios set the aspen harvest volume level higher only for the first 30 years (6 periods). One scenario raises this short term minimum harvest level to 2.1 million cords/year and the other raises it to 1.9 million cords/year. For these scenarios with a short term and temporary increase in the aspen harvest volume level, the long-term minimum harvest volume level for aspen is 1.5 million cords/year (estimated current statewide aspen level) for periods For each of these two short-term departure scenarios, the period 1 and period 6 aspen minimum harvest levels are averages of 1.5 million cords and the assumed maximum annual harvest level -- so as to walk the aspen harvest level up and down more gradually than a large abrupt increase. For example, for the scenario increasing the annual aspen harvest level to 1.9 million cords, the assumed minimum harvest level was 1.7 million cords for period 1 and period 6. The forest-wide constraints of these scenarios on total volume harvested are identical to those used for the previous set of Bench1 scenarios. 39

52 5. Modeling Results To help keep the interpretation of results easier, model formulations were kept simple for the draft scenarios. Constraints included for all scenarios were relatively few and focused primarily on the potential production of aspen, as supply of aspen volume seems of most concern when considering current mill expansion opportunities in Minnesota. Results presented here focus on harvest levels and their associated shadow price estimates. It is important to note that many forest resource conditions were tracked for each scenario (Table 4.2 and Table 4.3) with a subset of those tracks presented in the appendices for five of the scenarios. 5.1 No-limits Benchmark Scenario (Bench0) The No-limits Benchmark Scenario (Bench0) is the unconstrained scenario. Resulting harvest level volumes for all species (Figure 5.1) and for aspen (Figure 5.2) have annual harvest levels in the first 5-year planning period that are 5-6 times those of the next eight 5-year planning periods. For aspen, harvest levels for periods 2-8 are approximately 200,000 to 400,000 cords below the current 1.5 million cord aspen harvest level, so some of the nearly 8 million cords of financially mature wood harvested in period 1 will be need to be held for later periods. Longterm for aspen, the cycle in higher aspen volumes reflects the assumed minimum rotation age of aspen of 40 to 45 years, varying by site index. The peak volume flows in future cycles for aspen (period 10 and period 19) are not as high as period 1 because period 1, on average, involve harvesting older and higher volume aspen stands, plus stands in the aspen cover type with a high site index are following a 40-year cycle while other aspen stands are on a 45-year cycle. 40

53 Figure 5.1 Total harvest level by period for the All species market set for the unconstrained benchmark scenario (Bench0) Figure 5.2 Total harvest level by period for the Aspen species market set for the unconstrained benchmark scenario (Bench0) 41

54 5.2 Bench0 & Aspen Constraints Scenarios This set of scenarios added a set of constraints to the Bench0 scenario to require a minimum harvest level for the aspen species group for each 5-year period. Intent was to use a series of these scenarios examining how results change, as this minimum level is increased from the estimated current harvest level (1.5MM cords) for the aspen species group. Figure 5.3 compares the shadow prices associated with the aspen harvest level constraints for this set of scenarios. Figure 5.3 Shadow price estimates by period for the Aspen species market set for the Bench0 scenario for alternative minimum annual harvest level targets for the Aspen species market set. Basically, at a 2 million cord annual harvest level, the model has hit a wall where even with high shadow prices this harvest level cannot be met (light blue in Figure 5.3). Even at a 1.8 million cord annual harvest level, shadow prices are high, especially in later periods. Shadow prices in these scenarios are like per cord harvesting subsidies used to move wood into later periods. They generally increase over time for the first eight planning periods, reflecting that much of the forest is currently financially mature with a subsidy needed to encourage harvesting in a period later than period 1. For example, shadow prices for period 2 need to be higher than period 1 to offer greater stimulus for period 2 harvesting over period 1 harvesting. This same domino effect applies over the first eight periods -- to a time when aspen stands regenerated in period 1 can be harvested again in period 9. Figure 5.4 shows a graph of the harvest levels associated with the scenario with aspen minimum harvest volume targets set to a 2.0 million cord 42

55 level. The graph shows harvest volumes substantially below this minimum level for periods 12 to 19, indicating that for the assumptions of this model formulation, a 2.0 million cord level, is not sustainable for 100 years. In Figure 5.4, the small spike in period 1 aspen volume likely reflects that there is more than 2 million cords of aspen volume available in period 1 that would contribute little in later periods because it will have substantial aspen mortality. Harvesting stands in period 1 likely helps increase aspen volumes in period 9 and beyond. Figure 5.4 Harvest level by period for the Aspen species market set for the Bench0 scenario with constraints targeting annual harvest of at least 2.0 million cords of the Aspen species market set. 5.3 Bench0 & Aspen & Total Volume Constraints Scenarios This set of scenarios makes the formulations more realistic in that it adds both upper and lower bounds on the total volumes harvested in each period. Generally there is relatively low demand for timber volumes for most species other than aspen, with it likely impractical to obtain aspen volume as a minor volume component from harvesting other forest cover types. Figure 5.5 shows the total harvest volume for all species and those volumes in relation to the assumed upper and lower bounds on total volume harvested when the aspen harvest level is required to be 1.7 million cords annually. 43

56 Figure 5.5 Harvest level by period for the All species market set for the Bench0 scenario with constraints targeting annual harvest of at least 1.7 million cords of Aspen species and with constraints regulating the total volume of All species These constraints also have an estimated shadow price for each planning period (Figure 5.6) with a notable pattern in those shadow prices over time. 44

57 Figure 5.6 Shadow price estimates for the All species market set for the Bench0 scenario with constraints targeting annual harvest of at least 1.7 million cords of Aspen species and with constraints regulating the total volume of the All species market set each period. For early periods, these shadow prices act like penalties for harvesting, keeping wood flows lower. Penalties decline over time eventually becoming subsidies for harvest in later periods. Like with the prior set of scenarios, a series of scenarios were run to look at effects of greater harvest levels for the aspen species group. Shadow prices for aspen basically hit the wall for this scenario set at a minimum annual harvest of 1.9 million cords (Figure 5.7). Raising them to even higher values would add little additional aspen volume to the harvest totals. 45

58 Figure 5.7 Shadow price estimates for the Aspen species market set for the Bench0 scenario with constraints targeting annual harvest of at least 1.9 million cords of the Aspen species market set and with constraints regulating the total volume of All species each period. The planning periods falling short of the 1.9 million cord target tended to be later periods (Figure 5.8) 46

59 Figure 5.8 Harvest level estimates by period for the Aspen species market set for the Bench0 scenario with constraints targeting annual harvest of at least 1.9 million cords of Aspen species. and with constraints regulating the total volume of the All species. For each scenario, the model tracks far more than just harvest volumes by planning period. A breakdown of aspen volume by forest ownership is shown for this scenario set in Figure 5.9 when the minimum aspen harvest volume was constrained to 1.8 million cords. As Figure 5.9 shows, the total harvest level each period is very close to 1.8 million cords annually. As shown earlier, attempting to achieve 1.9 million cords annually could not be achieved each period (Figure 5.8) even when very large shadow price estimates (subsidies) were used to encourage more harvesting. 47

60 Figure 5.9 Harvest level by forest land ownership for the Aspen species market set for the Bench0 scenario with constraints targeting annual harvest of at least 1.8 million cords for Aspen species and with constraints regulating the volume of All species. Figure 5.9 shows clearly that the private land ownership group is a major contributor to total volume harvest. Relative changes in volume harvested by ownership between period 1 and period 2 reflects the relative amount of older aspen owned by each ownership group. This result reflects that the Federal ownership group, composed almost entirely of USDA National Forest lands in Minnesota, has a larger proportion of its aspen forest cover type in older age classes than other ownership groups. To help keep draft scenarios simple, all draft scenarios assumed delivered timber prices at each market location were equal. Under this simplifying assumption, it is interesting to examine wood shipment patterns to specific market centers. Figure 5.10 shows these patterns for the aspen species group when the annual aspen minimum harvest level by period is at 1.8 million cords. 48

61 Figure 5.10 Harvest level estimates end-market for Aspen species for the Bench0 scenario with constraints targeting annual harvest of at least 1.8 million cords of Aspen species and with constraints regulating the total volume of All species. Average quantities to specific markets over time suggest Bemidji as a relatively good location in relation to the aspen resource. In contrast, International Falls, which currently consumes approximately 400,000 cords annually, averages less than 200,000 cords annually. Also of note is the relatively large volume for the Grand Marais market in period 1. This is not surprising as Grand Marias does not have an aspen pulpwood user and much of the timberland near Grand Marias is Federal land. For this same scenario described above, Figure 5.11 shows a track of the amount of the aspen forest cover type that is older than 55 years. Both forest land and timberland are tracked with most of this older aspen primarily on forestland by the end of the planning horizon. 49

62 Figure 5.11 Area of stands in the aspen forest cover type that are greater than or equal to age 55 in the aspen forest cover type for the Bench0 scenario with constraints targeting annual harvest of at least 1.8 million cords of Aspen species and with even-flow constraints on All Species. 5.4 Bench1 & Aspen & Total Volume Constraints Scenarios This set of scenarios is similar to the previous set of scenarios except assumptions were changed regarding the availability of timberland for harvest, especially from private landowners. Delays on availability of private forest landowners were also considered. A graph of the annual aspen harvest when the minimum annual aspen harvest volumes were set at 1.5 million cords (Figure 5.12) shows substantial delays in harvesting the large volume of aspen otherwise financially mature in period 1 (see Figure 5.2), reflecting the delay assumptions for private lands for the Bench1 scenarios. Under the assumptions for this set of scenarios, the model hits the wall of increasing shadow prices at an annual harvest level of 1.8 million cords (Figure 5.13). Due to the not available and delayed available assumptions of Bench1 scenarios, the aspen harvest level where harvests are not sustainable is at a lower harvest level than the Bench0 scenarios where shadow prices hit the wall at an annual Aspen species harvest level of about 1.9 million cords. 50

63 Figure 5.12 Harvest levels for the Aspen species for the Bench1 scenario with constraints targeting annual harvest of at least 1.5 million cords of Aspen species and with constraints regulating the total volume of All species. Figure 5.13 Shadow price estimates for Aspen species for the Bench1 scenario for alternative minimum annual harvest level targets for Aspen species. 51

64 5.5 Bench1 & Total Volume Constraints & Aspen Departure Scenarios This set of scenarios includes only two scenarios. Each is similar to the scenarios of the previous set except assumptions were changed regarding limits on aspen harvest volume flows to consider only short term increases in aspen harvest levels. Figure 5.14 shows the aspen targets and associated harvest flows when the short term departure target is as high as 2.1 million cords. Figure 5.14 Harvest levels and minimum harvest level targets for the aspen species group for the Bench1 scenario with short-term departures increasing annual aspen harvest levels up to 2.1 million cords for 20 years in the short term. Longer term, aspen harvest levels rebound above the 1.5 million cord level suggesting that the decline in aspen harvest level is potentially temporary. Aspen shadow prices are fairly high short-term for this scenario, with period 1 having a positive shadow price (Figure 5.15). 52

65 Figure 5.15 Shadow price estimates for Aspen species for the Bench1 scenario for minimum annual harvest level targets for the Aspen species market set with short-term harvest levels as high as 2.1 million cords annually as shown in Figure Later periods in Figure 5.15 have shadow prices at zero because the minimum harvest levels for later periods can be met without subsidizing aspen production. Figure 5.16 shows aspen harvest volumes when the short-term departure targets are modelled at 1.9 million cords annually. Long-term aspen volumes are again well above 1.5 million cords. Figure 5.16 Harvest levels and minimum harvest level targets for the aspen species group for the Bench1 scenario with short-term departures increasing annual aspen harvest levels up to 1.9 million cords for 20 years in the short term. 53

66 Associated shadow prices for aspen volume constraints for this short-term departure scenario show much lower shadow prices (Figure 5.17), with none over $15 per cord in any period. Figure 5.17 Shadow price estimates for Aspen species for the Bench1 scenario for minimum annual harvest level targets for the Aspen species market set with short-term harvest levels as high as 1.9 million cords annually shown in Figure These relatively low shadow price estimates suggest a short-term increase of approximately 400,000 cords annually of the aspen species market set seems plausible under the assumptions of the Bench1 scenario 54

67 6. Summary & Potential Future Work This study updated a modeling system similar to the modeling system used for the GEIS in Minnesota. The modeling system recognizes substantial spatial detail regarding interactions between markets and details of existing road networks. It offers substantial flexibility in terms of defining specific market demands by timber species as well as opportunities to track important detail regarding a wide range of stand conditions. It recognizes substantial detail regarding riparian management at the stand-level. It can recognize multiple map layers for addressing forest conditions over time. It offers opportunities to address complexities regarding assumptions about private landowner behavior with those assumptions potentially sensitive to forest cover type and stand age Test applications added insight regarding the current timber supply situation in northern Minnesota. Focus was on aspen species supply as aspen is a valuable species, with great interest in the potential for market expansion based on aspen use. Draft results suggest that opportunities for increasing aspen harvests above recent harvest levels may be more short term in nature, reflecting the current age class imbalance of the aspen forest cover type. Although much has been learned from the draft analyses, results suggest opportunities for future work along multiple lines: a. Information describing the future growth and yield the aspen forest cover type is important, as aspen is valuable and aspen harvest levels are potentially already close to their sustainable long-term levels. b. Age class imbalances are prevalent for most forest cover type in Minnesota. Timber does not store well on the stump and concerns exist about future lost opportunities and longterm productivity for older stands that are not harvested and regenerated. c. There is substantial interest in additional model applications related to red pine. Red pine reforestation investments have declined in recent years, with questions arising about potentials to increase long-term allowable cuts for the forest as a whole via investments in faster-growing, more valuable species. d. Wood supply from private lands is a concern, as much of the forest managed by nonindustrial private forest landowners is old and likely to be lost from the market if it is not harvested relatively soon. e. There is substantial interest in the potential for forest-based economic development in Minnesota. Understanding more of the specifics of those opportunities could be helpful. This likely relates to also better understanding the situation for specific market centers considering exiting demands by major market centers and their interactions. 55

68 f. Opportunities likely exist to better link analyses of the statewide situation to specific planning applications for public agencies like the USDA Forest Service, the Minnesota Department of Natural Resources, and various county land ownership groups. g. Forest managers are clearly concerned about the impact of new invasive species that could cause large timber losses like those that are occurring in other parts of the continent. Future modeling applications might address detail concerning costs associated with developing more diverse mixes of forest cover types and ages on the landscape to help promote general resilience to future invasive species outbreaks. These are but a few examples of potential future model applications. Plans are to address at least some of these opportunities via support from the University of Minnesota, Department of Forest Resources Interagency Information Center and the University of Minnesota North Central Research and Outreach Center. Stakeholders are encouraged to communicate with the University regarding specific interests, as this type of work is central to the land grant mission of the University of Minnesota. 56

69 7. Literature Cited Benzie, J.W., Manager's handbook for red pine in the north-central states. General Technical Report NC-33, USDA Forest Service, NC Exp. Sta., St Paul, MN Bixby, J., Spatially-explicit landscape modeling: A case study on gains from coordinating forest management across ownerships. M.S. Thesis. University of Minnesota. 114 p. Duloy, J.H., and R.D. Norton Prices and incomes in linear programming models. American Journal of Agricultural Economics. 57(4): Hoganson, H.M Using the DTRAN model for the Minnesota Generic Environmental Impact Statement on timber harvesting and management. in R. Paivinen, L. Roihuvuo and M. Siitoinen (Eds.) Large-Scale Forestry Scenario Models: Experiences and Requirements, EFI Proceedings no. 5, p International Conference on Large Scale Forest Modeling, June 15-22, European Forest Institute. Joensuu, Finland. Hoganson, H.M Integrating Harvest Plans Across Forest Cover Types: An Analysis of Crow Wing County Managed Timberland. Interagency Information Cooperative Report and Staff Paper Series No St. Paul, MN. University of Minnesota, Department of Forest Resources. 25 p. Hoganson, H. and J. Borges Using dynamic programming and overlapping subproblems to address adjacency in large harvest scheduling problems. Forest Science: 44(4): Hoganson, H.M. and N.G. Meyer Constrained optimization for addressing forest-wide timber production. Current Forestry Reports, 1: Springer Publishing, London. Hoganson, H.M. and J.L. Reese Sustaining Timber Harvesting and Older Forest Conditions: A Harvest Scheduling Analysis for Koochiching County's 2010 Forest Plan. Staff Series Paper 2010, Department of Forest Resources, University of Minnesota, 102 p. Hoganson, H.M. and D.W. Rose A simulation approach for optimal timber management scheduling. Forest Science, 30(1): Hoganson, H.M. and D.W. Rose A model for recognizing forest-wide risk in timber management scheduling. Forest Science. 33(2): Hoganson, H., C. Vanderschaaf, and T. O'Hara Insights from harvest scheduling applications in Minnesota. Department of Forest Resources Staff Series Paper #227. University of Minnesota 57

70 Hoganson, H., Y. Wei and R. Hokans Integrating spatial objectives into forest plans for Minnesota s national forests. In: Bevers, M.; Barrett, T. comps. Systems Analysis in Forest Resources: Proceedings of the 2003 Symposium. General Technical Report PNW-GTR-656. Portland, OR. US Department of Agriculture, Forest Service, Pacific Northwest Research Station Hrubes, R. J., and D. I. Navon Application of linear programing to downward sloping demand problems in timber production. USDA Forest Serv. Res. Note PSW- 315, 6 p., Pacific Southwest Forest and Range Exp. Stn., Berkeley, Calif. Jaakko Pöyry Consulting, Inc. 1992a. Maintaining productivity and the forest resource base. A technical paper for a generic environmental impact statement on timber harvesting and forest management in Minnesota. Prepared for the Minnesota Environmental Quality Board. 305 p. plus appendices. Jaakko Pöyry Consulting, Inc. 1992b. Harvesting Systems: A Background Paper for a Generic Environmental Impact Statement on Timber Harvesting and Forest Management in Minnesota. St. Paul, MN: Minnesota Environmental Quality Board. Jaakko Pöyry Consulting, Inc Generic Environmental Impact Statement on Timber Harvesting and Forest Management in Minnesota. Prepared for the Minnesota Environmental Quality Board. 813 p. plus appendices. Johnson, K., & Scheurman, H Techniques for Prescribing Optimal Timber Harvest and Investment Under Different Objectives - Discussion and Synthesis. Forest Science Monograph, 18, 31p. Kilgore, M. and A. Ek Minnesota Forest Age Class Distribution, Minnesota Forestry Research Note 295, St. Paul, MN: Department of Forest Resources, University of Minnesota. 3p Kilgore, M., A. Ek, K. Buhr, L. Frelich, H. Hanowski, C. Hibbard, A. Finley, L. Rathbun, N. Danz, J. Lind, and G. Niemi Minnesota timber harvesting GEIS: An assessment of the first 10 years. Staff Paper Series No St. Paul, MN: Department of Forest Resources, University of Minnesota. 178p. Lappi, J. and Lempinen R A linear programming algorithm and software for forest-level planning problems including factories. Scan. J. For. Res., 29 (Supplement 1), Mack, T.J. and Burk, T.E., A model-based approach to developing density management diagrams illustrated with Lake States red pine. Northern Journal of Applied Forestry, 22(2), pp Miles, P. D., G. J. Brand, and M. E. Mielke Minnesota s forest resources in U. S. Department of Agriculture, Forest Service, North Central Research Station. Resource Bulletin NC-246. St. Paul, MN. 36 p. 58

71 Minnesota Department of Natural Resources Final Environmental Impact Statement on UPM/Blandin Paper Thunderhawk Project. St. Paul, MN: MN Department of Natural Resources. Minnesota Department of Natural Resources Minnesota s Forest Resources p. Minnesota Forest Resources Council Sustaining Minnesota Forest Resources: Voluntary Site-Level Forest Management Guidelines for Landowners, Loggers and Resource Managers. O Connell, B., B. Conkling, A. Wilson, E. Burrill, J. Turner, S. Pugh, G. Christiansen, T. Ridley, J. Menlove The Forest Inventory and Analysis Database: Database description and user guide version 6.1 for Phase 2. U.S. Department of Agriculture, Forest Service. 892 p. documentation/ Paredes V. and J. Brodie Land value and the linkage between stand and forest level analyses. Land Economics. 65(2): Paredes, V. and J. Brodie Activity analysis in forest Planning. Forest Science. 34(1):3-18. Russell, M., M. Kilgore and C. Blinn Characterizing salvage operations on public forests in Minnesota and Wisconsin, USA. International Journal of Forest Engineering. Schwalm, C Forest Harvest Levels in Minnesota: Effects of Selected Forest Management Practices on Sustained Timber Yields. Staff Paper Series No St. Paul, MN: Department of Forest Resources, University of Minnesota. 14 p. plus appendices. Schweitzer, D., R. Sassaman, and C. Schallau Allowable cut effect, some physical and economic implications. Journal of Forestry. 70: Steigerwaldt Land Services, Inc Wood Supply Chain Component Costs Analysis: A Comparison of Wisconsin and U.S. Regional Costs 2015 Update. Rhinelander, WI: Great Lakes Timber Professionals Association. University of Minnesota, Department of Forest Resources USDA Forest Service. 2004a. Forest Plan Chippewa National Forest. USDA Forest Service, Eastern Region, Milwaukee Wisconsin. 59

72 USDA Forest Service. 2004b. Proposed Forest Plan Superior National Forest. USDA Forest Service, Eastern Region, Milwaukee Wisconsin. USDA Forest Service EVALIDator Version USDA Forest Service U.S. Forest Service and Department of Natural resources to utilize Good Neighbor Authority to restore forest and watershed. Walters, D., and A. Ek Whole stand yield and density equations for fourteen forest types in Minnesota. Northern Journal of Applied Forestry 10(2):75-85 Zhang, Yaoqi, Daowei Zhang, and John Schelhas Small-scale non-industrial private forest ownership in the United States: rationale and implications for forest management. Zobel, J. and A. Ek The Wildlife Habitat Indicator for Native Genera and Species (WHINGS): Methodology and Application. Staff Paper Series Number 231, St. Paul, MN: Department of Forest Resources, University of Minnesota. 14p plus 2 appendices. Zobel, J, A. Ek and T. O'Hara Description and implementation of a single cohort and lifespan yield and mortality model for forest stands in Minnesota. Minnesota Forestry Research Notes No St. Paul, MN: Department of Forest Resources, University of Minnesota. 6p. 60

73 8. Appendix A: Bench0 and Aspen 1.7MM Cords/Yr Figure A1. Acres regeneration harvested during each 5-year planning period for aspen forest cover type by forest ownership class. 61

74 Figure A2. Acres regeneration harvested during each 5-year planning period for the aspen forest cover type and for all other forest types combined. 62

75 Figure A3. Acres regeneration harvested during each 5-year planning period for forest cover types other than the aspen forest cover type. 63

76 Figure A4. Acres of older aspen by 5-year planning period. Figure A5. Acres of older planted red pine by 5-year planning period. 64

77 Figure A6. Total annual harvest level (all species) by ownership class Figure A7. Annual harvest level of aspen species by ownership class 65

78 Figure A8. Annual harvest level of aspen species by end-market Figure A9. Annual harvest level of oak species by end-market 66

79 Figure A10. Annual harvest level of other hardwoods by end-market Figure A11. Annual harvest level of red pine saw logs by end-market 67

80 Figure A12. Annual harvest level of jack pine saw logs by end-market Figure A13. Total annual harvest level of pine pulp by end-market 68

81 Figure A14. Annual harvest level of spruce-fir by end-market Figure A15. Annual harvest level of tamarack by end-market 69

82 9. Appendix B: Bench0 and Aspen 1.7MM cords/yr & Allspecies even-flow Figure B1. Acres regeneration harvested during each 5-year planning period for aspen forest cover type by forest ownership class. 70

83 Figure B2. Acres regeneration harvested during each 5-year planning period for the aspen forest cover type and for all other forest types combined. 71

84 Figure B3. Acres regeneration harvested during each 5-year planning period for forest cover types other than the aspen forest cover type. 72

85 Figure B4. Acres of older aspen by 5-year planning period. Figure B5. Acres of older planted red pine by 5-year planning period. 73

86 Figure B6. Total annual harvest level (all species) by ownership class Figure B7. Annual harvest level of aspen species by ownership class 74

87 Figure B8. Annual harvest level of aspen species by end-market Figure B9. Annual harvest level of oak species by end-market 75

88 Figure B10. Annual harvest level of other hardwoods by end-market Figure B11. Annual harvest level of red pine saw logs by end-market 76

89 Figure B12. Annual harvest level of jack pine saw logs by end-market Figure B13. Total annual harvest level of pine pulp by end-market 77

90 Figure B14. Annual harvest level of spruce-fir by end-market Figure B15. Annual harvest level of tamarack by end-market 78

91 10. Appendix C: Bench1 with Aspen species >= 1.7 Million Cords/ Yr Figure C1. Acres regeneration harvested during each 5-year planning period for aspen forest cover type by forest ownership class. 79

92 Figure C2. Acres regeneration harvested during each 5-year planning period for the aspen forest cover type and for all other forest types combined. 80

93 Figure C3. Acres regeneration harvested during each 5-year planning period for forest cover types other than the aspen forest cover type. 81

94 Figure C4. Acres of older aspen by 5-year planning period. Figure C5. Acres of older planted red pine by 5-year planning period. 82

95 Figure C6. Total annual harvest level (all species) by ownership class Figure C7. Annual harvest level of aspen species by ownership class 83

96 Figure C8. Annual harvest level of aspen species by end-market Figure C9. Annual harvest level of oak species by end-market 84

97 Figure C10. Annual harvest level of other hardwoods by end-market Figure C11. Annual harvest level of red pine saw logs by end-market 85

98 Figure C12. Annual harvest level of jack pine saw logs by end-market Figure C13. Total annual harvest level of pine pulp by end-market 86

99 Figure C14. Annual harvest level of spruce-fir by end-market Figure C15. Annual harvest level of tamarack by end-market 87

100 11. Appendix D: Bench1 & Aspen 2.1 MM Short-term and 1.5 MM Long-term (Cds/yr) Figure D1. Acres regeneration harvested during each 5-year planning period for aspen forest cover type by forest ownership class. 88

101 Figure D2. Acres regeneration harvested during each 5-year planning period for the aspen forest cover type and for all other forest types combined. 89

102 Figure D3. Acres regeneration harvested during each 5-year planning period for forest cover types other than the aspen forest cover type. 90

103 Figure D4. Acres of older aspen by 5-year planning period. Figure D5. Acres of older planted red pine by 5-year planning period. 91

104 Figure D6. Total annual harvest level (all species) by ownership class Harvest Volume (MM cords/yr) Private Lands County Lands State Lands Federal Lands 5-Year Planning Period Figure D7. Annual harvest level of aspen species by ownership class 92

105 2500 Harvest Volume (M cords/yr) year Planning Period I. Falls Grand Rapids Grand Marais Duluth Cook Brainerd Bemidji Figure D8. Annual harvest level of aspen species by end-market 500 Harvest Volume (M cords/yr) Year Plannig Period I. Falls Grand Rapids Grand Marais Duluth Cook Brainerd Bemidji Figure D9. Annual harvest level of oak species by end-market 93

106 Figure D10. Annual harvest level of other hardwoods by end-market 1200 Harvest Volume (M cords/yr) I. Falls Grand Rapids Grand Marais Duluth Cook Brainerd Bemidji Year Planning Period Figure D11. Annual harvest level of red pine saw logs by end-market 94

107 Figure D12. Annual harvest level of jack pine saw logs by end-market Figure D13. Total annual harvest level of pine pulp by end-market 95

108 Figure D14. Annual harvest level of spruce-fir by end-market Figure D15. Annual harvest level of tamarack by end-market 96

109 12. Appendix E: Bench1 & Aspen 1.9 MM Short-term and 1.5 MM Long-term (Cds/yr) Figure E1. Acres regeneration harvested during each 5-year planning period for aspen forest cover type by forest ownership class. 97

110 Figure E2. Acres regeneration harvested during each 5-year planning period for the aspen forest cover type and for all other forest types combined. 98

111 Figure E3. Acres regeneration harvested during each 5-year planning period for forest cover types other than the aspen forest cover type. 99

112 Figure E4. Acres of older aspen by 5-year planning period. Figure E5. Acres of older planted red pine by 5-year planning period. 100

113 Figure E6. Total annual harvest level (all species) by ownership class Figure E7. Annual harvest level of aspen species by ownership class 101

114 Figure E8. Annual harvest level of aspen species by end-market Figure E9. Annual harvest level of oak species by end-market 102