Uncertainty, risk and forest management on the Tillamook State Forest : a case study Public Deposited

http://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/3x816p61t

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  • Forest management is typically associated with a high degree of uncertainty, since it relies on predictions of natural growth processes over long periods of time. A number of methods exist for mitigating the risk associated with this uncertainty, but few have the ability to explicitly minimize risk. This study will present a case study on dealing with uncertainty and risk in an applied setting. The selected study area was the Tillamook State Forest, located in northwest Oregon. The primary objectives were to quantify the uncertainty and assess its impact on forest management. An additional objective was to assess the application of non-linear probabilistic programming on a large forest management problem. Uncertainty was quantified through regression models that predicted actual outcomes from planned outcomes, as well as the error associated with predictions of actual outcomes. The effects of uncertainty on forest management were assessed through two chance-constrained programming formulations. One maximized the harvest volume under a given level of risk, and the other minimized the maximum level of risk associated with a given forest management plan. Both were subject to sustainable inventory and forest structure constraints. The results showed that these models could substantially increase the probability of achieving a given forest management outcome, at the cost of only a minimal deviation (4 to 6%) from the risk neutral position. These results were however in contrast to an analysis of risk preferences, which showed significant differences in the outcomes associated with various levels of risk. This indicated that uncertainty could not be considered without the decision maker's attitude towards risk. In addition, post-optimality analysis of the model results showed that correlated yield coefficients had an insignificant impact, and that the assumption of zero covariance was justified for this study. Finally, it was also demonstrated that chance- constrained programming can be applied to large scale forest management problems, but that the solvability of these problems were determined by the formulation type.
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