Kevin Boston

The economic impact of green-up constraints in the southeastern United States

Green-up, or adjacency, requirements are a common constraint in forestry. The American Forest and Paper Association has developed a Sustainable Forestry Initiative that includes a green-up constraint which limits the average clearcut opening to 48 ha for 3 years or until the average height of the regenerated trees is >1.4 m. In addition to constraining the average clearcut size, many forestry companies in the southeastern USA voluntarily limit their maximum clearcut size to between 60 and 90 ha. In this research, a heuristic algorithm was used to develop tactical forest plans that consider both the maximum and average clearcut sizes. Economic effects of the green-up constraints were estimated for situations where intensive management can reduce the length of the green-up time from 3 to 2 years on a 21 600 ha ownership in Georgia (USA). For a 60-ha maximum opening size, this reduction in green-up time from 3 to 2 years resulted in an additional US

Combinatorial optimization of elk habitat effectiveness and timber harvest volume

66 600 in present net worth (PNW) over a 10-year analysis period. This corresponds to a US

Forest management decisions for wildlife objectives : system resolution and optimality

10 per harvested ha, or a 0.8% increase in PNW. The benefit gained by reducing the length of the green-up period is less with a 90-ha maximum clearcut size, where PNW increases by US

Valuing and Characterizing Forest Conditions

45 600, or US

Forest and Natural Resource Sustainability

6.70 per harvested ha, a 0.5% increase. While the total volume per period was near the volume goal produced by a strategic forest plan, the spatial restrictions and the desire to maximize net present value resulted in lower volume of timber products (sawlogs and chip-and-saw logs) from older forest stands. A sensitivity analysis showed that an increase in price or yield further reduced the economic incentive for the reduction of the length of the green-up constraint. As price or volume decreased below expectations, however, the incentive to use intensive forest management practices to reduce the length of the green-up constraint became more attractive, since the differences between a 2-year and 3-year green-up time requirement may be large enough to pay for more intensive management practices.

Optimization of Tree- and Stand-Level Objectives

Forest resource planning processes in the western United States have been placing an increasing emphasis on wildlife and fish habitat goals. With this in mind, we developed a method that incorporates a Habitat Effectiveness Index (HEI) for Roosevelt elk (Cervus elaphus roosevelti) into the objective function of a mathematical forest planning model. In addition, a commodity production goal is proposed (maximum timber production), and the habitat and commodity production goals are allowed to act as goals in a multi-objective goal programming planning problem. A heuristic programming technique (tabu search) is used to develop feasible solutions to the resulting non-linear, integer programming problem. Using a hypothetical example, we illustrate results of five scenarios, where the emphasis of the achievement of one or both goals is altered. The main contribution of this approach is the ability to measure and evaluate the trade-offs among achieving a certain level of a complex wildlife goal and achieving commodity production goals. These trade-offs are measured using a flexible model, allowing planners to formulate non-linear spatial goals as objectives of a problem, rather than forcing them to rely on posterior evaluations of the suitability of management plans to goals such as elk HEI.

Forest Supply Chain Management

Abstract Managers of forest wildlife populations make recurring management decisions based on incomplete knowledge of system states. For example, animal population estimates may ignore spatial structure that may influence population viability. We built a spatially-explicit model for a population of birds in a forested landscape. Rates of bird population growth within forest compartments and rates of bird dispersal among compartments were functions of stand age and basal area, compartment population size, and inter-compartment distance. Stand characteristics were imbedded in a dynamic model and assumed perfectly observable and under the complete control of managers. We constructed a genetic algorithm to search for the schedule and spatial distribution of silviculture to maximize total bird abundance at the end of a fixed planning horizon, under combinations of initial habitat and population distribution. We also found policies for a smaller set of population distributions that a manager may only presume to occur (e.g. birds equally distributed among stands), as when managers are only able to observe abundance and not spatial distribution. We investigated the effect of this loss of system resolution on optimality by examining differences in projected population sizes under the two types of policies. That is, we used the set of ‘presumed-state’ policies to project population size from each true initial system state, then we compared these to projections under the best policy for that state. For the planning horizon that we considered, loss in optimality was highly dependent on initial habitat state and on choice of presumed population distribution. Generally, loss in optimality and species extinction rate were both greater for habitat states that were initially poor than initially favorable. For some initial habitat states, population projections based on policies for presumed states often exceeded objective function values for known-state policies, suggesting that the genetic algorithm frequently fell short of finding bona fide optima.

Graphical Solution Techniques for Two-Variable Linear Problems

When conducting management activities across a forest and over a lengthy period of time, managers must understand and quantitatively and qualitatively measure what they expect the future forest to become. In addition, they should evaluate the trade-offs they might experience when they choose one course of action over another. There are a number of common methods for evaluating present and future conditions of a forest. Forest managers can use biological measures, such as basal area, average diameter, average height, mean annual increment, trees per unit area, snags, and tree volume to evaluate the physical structure of an existing or future forest. Forest managers can also use financial criteria such as benefit/cost ratio, equal annual equivalent, net present value, and soil expectation value to evaluate the trade-offs of different actions on a forest. Changes in societal values have forced many natural resource managers to consider how their management activities affect wildlife habitat, recreational opportunities, water resources, air quality, employment, and community stability. Incorporating many of these quantitative and qualitative measures into a forest plan analysis will help forest managers evaluate trade-offs and help them communicate the impact of their forest management plans to private and public landowners.

Scenario Analysis in Support of Strategic Planning

What may seem like one of the hottest topics in natural resource management today actually has been at the forefront of our profession for at least three centuries, only the form of the debate has evolved. From concerns over timber depletion arose the concept of maintaining a stable timber supply through appropriate forest planning and management techniques. As our understanding of other resources grew, the concept of sustainability shifted from commercial products to multiple resource values, then to ecosystems. From one or more perspectives, all of the sustainability concepts are valid and all continue to be used today. Ultimately, the application of sustainability concepts in forest management planning varies by landowner, geographic region, socioeconomic pressure, and resource condition.

Management of Forests and Other Natural Resources

There are at least four physical levels of forest management and planning where decisions must be made: at the tree-level, the stand-level, the forest-level, and the landscape level. With each level, the management issues grow in size and scope, and decisions made at each level may be interconnected as well. However, tree-level decisions and stand-level decisions are not necessarily complementary; neither are stand-level decisions and forest-level decisions. This chapter concerns tree- and stand-level decisions and how a landowner may want to maximize the volume or value produced from a tree or a stand. Forest-level decisions are considered in forthcoming chapters. These typically are developed for one landowner or organization. Landscape plans can involve multiple landowners or organizations. Optimal plans of action are those where the schedule of activities will best meet the objectives of the landowner within the scope of their perceived physical level of forest management. Of course, a number of constraints may guide the development of an optimal plan. In an effort to use resources wisely, we need to understand how to develop guidelines for field implementation of activities, and an optimal solution to a management problem is a reasonable starting point. In addition, landowners not only may want to understand the best course of action for the management of their land, but also to compare the optimal plan to several alternatives in an effort to understand the associated trade-offs.