Marc E. McDill

Can mature patch constraints mitigate the fragmenting effects of harvest opening size restrictions

Growing timber is not the primary management objective for many landowners. For example, the national forests must be managed to maintain the populations of all native and desired nonnative plants, fish, and wildlife under the National Forest Management Act. Similarly, only a small percentage of private landowners own land primarily for income generation from timber production (Birch 1996). As the emphasis on ecosystem management goals increases, harvest scheduling models must be modified to incorporate new objectives. When forest ecologists discuss ecosystem management, they are interested in things such as age distribution, patch size distribution, the amount of edge, the amount of interior forest, and the connectivity of the forest (Hunter 1990). With the exception of age distribution, these things are difficult to model directly in a mixed-integer harvest scheduling model. Models have been developed that incorporate a variety of spatial factors such as wildlife dispersal and amount of edge (Hof and Bevers 1998), maximum harvest opening size (Thompson et al. 1973, McDill et al. in press, Barrett et al. 1998, Borges and Hoganson 2000), and minimum habitat patch size constraints (Bettinger et al. 1997 and 2000, Hof and Joyce 1992, Sessions et al. 2000), which require large patches of mature forest to be present at all times. Often, incorporating these factors results in complex models that are difficult to solve to optimality. However, methods for modeling two factors in a spatially-explicit, mixed-integer harvest scheduling model – maximum harvest opening size and minimum patch size – have been proposed that can give optimal solutions to small problems (McDill et al. (in press), Rebain and McDill (in review)). This paper considers how controlling these two factors indirectly affects factors that are not directly controlled, including the age distribution, patch size distribution, and the amount of edge of a forest. The general model used in the study was a mixed-integer harvest scheduling model, with maximizing net present value as the objective. The planning horizon is 60 years, consisting of three 20-year periods. Four possible prescriptions were considered for each stand—harvest in period 1, 2, or 3, or not harvested at all. Flow constraints limit fluctuations in the harvest level from period to period, and ending age constraints are imposed to ensure that a desirable forest will be left at the end of the planning horizon. The yield and economic information used in the model is loosely based on a Pennsylvania oak forest. Ten variants on this general model were developed. One version of the model includes no maximum harvest opening limit and no patch requirements. The remaining nine variants include maximum harvest opening constraints and, in some cases, habitat patch size

An Overview of Forest Management Planning and Information Management

Managing any complex operation requires planning. While this seems obvious enough, it is valuable to consider specifically why planning is important, especially in the context of the management of industrial forest plantations. Most importantly, plans specify what will be done, when, by whom, and for what purpose. The plan ensures that everyone within the organization knows what needs to be done, and it provides a basis for holding both the organization and the individuals within the organization accountable for what is accomplished. Plans should also be the basis for the allocation of scarce resources within an organization, so nearly everyone in the organization has a stake in the planning process. Furthermore, a plan communicates to external stakeholders, such as stockholders and the public, what the organization is doing, what it expects to do in the future, and why. Having a good plan and demonstrating that the plan can and will be implemented gives an organization credibility. Conversely, failing to plan, or having a plan but not following it, increases the likelihood of inefficiency, frustration, and a lack of credibility.

Strategic Management Scheduling

Strategic or long-term management planning plays a key role in the development of forest schedules as the temporal dimension is a determinant characteristic of all forestry production systems. In this chapter we address the representation of industrial forest strategic forest management problems as well as the interpretation of its solution. We start with a simple harvest scheduling model aiming at the maximization of economic returns while addressing concerns with the sustainability of a product flow. This policy scenario is expanded to reflect concerns with the volume in the ending inventory, with the average carbon stock, with the environmental impacts of clearcuts and with the integration of road building and maintenance decisions. Several modeling approaches are considered to represent and solve each problem. An example forest is used to illustrate all problems and how they may be addressed by each modeling approach. Several applications of the modeling approaches to strategic management planning of industrial plantations are briefly described in six Management Planning in Action boxes. A list of problems at the end of the chapter build from the same example forest to support model building, model solving and interpretation of results by readers.

Statistical models for categorical data: Brief review for applications in ecology

A brief review of statistical models for prediction of categorical data is presented, with emphasis on the binary type. Several methods have been adopted to build predictive models for binary and other types of categorical data and response variables. The focus here is on generalized linear models and generalized additive models, widely applied in problems in Ecology, when the goal is to fit a model to data of presence/absence type or any other categorical response. The estimation methods used for generalized linear models and generalized additive models as well its statistical properties are discussed. Some examples in ecology are addressed.

Computational Comparison of Stand-Centered Versus Cover-Constraint Formulations

The area restriction model for harvest scheduling problems can be formulated using mixed integer programs. The three different formulation types are cluster packing, cell aggregation, and the assignment formulation. Within the cell aggregation subgroup there is an exact formulation, the cover constraint (CC) approach; and an inexact formulation, the stand-centered approach. The cover constraint formulation has significantly more constraints than the stand-centered formulation. A computational comparison between these two methods is completed using cutting planes and constraint combinations using the CPLEX solver package. The CC approach as cutting planes was superior based on solution time. If formulation time were included with solution time, then the CC approach remains superior. The maximum harvest area is a significant factor of solution time. In addition, the percentage of total cutting planes used to solve the problem was examined.