A Linear Programming Framework for Models of Forest Management Strategy

Abstract

Results found in this thesis draw attention to limitations in the conventional approach to modelling forest management strategy, where models have insufficient spatial resolution and ignore industry. Addressing these limitations, a Model One linear programming framework was developed in which models built can model strategically relevant spatial resolution, and include industry representation. In a case-study on Nova Scotia's Crown Central Forest, models from this framework were compared with Woodstock\texttrademark, a commercial modelling framework. When strategically relevant spatial resolution was modelled, these models found solutions in substantially less time than Woodstock. Of further interest, the framework's industry representation allows novel analysis to be performed. A comparison between a model that includes industry and a conventional model demonstrates that the conventional model schedules unprofitable stands for harvest. Then, models with industry representation are used to demonstrate industry based analysis, such as assessing the cost of a clearcut restriction policy and investigating the benefit of industrial expansion. Taken together, the results herein contained make an argument for modelling forest management strategy at strategically relevant spatial resolution, and including industry representation in modelling.