Abstract
Multiple ecological, economic, social, and political facets influence forest-planning decisions. Decision models have been widely used in forest management planning, but most are deterministic models. However, long-term forest planning problems are surrounded by potential uncertainties. To begin to account for uncertainty surrounding growth and yield under climate change conditions, a stochastic forest planning model was developed and tested. The intent of the model is to help identify potential current forest management actions that will perform well over a range of plausible climate change scenarios (futures). The stages of the model address how uncertainty about the future might unfold, with model solutions providing immediate management actions plus detailed contingency (recourse) plans for each future. The use of specialized decomposition methods of operations research has allowed for testing the model in a detailed and large application. Results from the case study showed that planning for an average deterministic case produces a misleading solution, underestimating the potential impact of climate change. On the other hand, only planning for a worst-case scenario ignores the potential value of management opportunities under other likely futures in which harvesting benefits could be greater. Overall, results advance our understanding of recognizing forest-wide uncertainty in forest management planning models. Study Implications Stand-level decisions often have forest-wide implications. Forest planning helps coordinate management of stands to address ecological, economic, and social aspects. Decision models are often used, but most assume all the information is known. However, long-term forest planning is surrounded by potential uncertainties, such as climate change. We developed a model to identify current forest management actions that will perform well over a range of plausible climate change scenarios instead of just one. The novelty lies in how we solve the problem. Breaking it into smaller subproblems allows us to include more stand-level details while still tackling a large problem.