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Optimizing the Management of Uneven-Aged Forest Stands: A Stochastic Approach

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Mathematical programs used for management and policy decisions in natural resources normally contain at least one underlying component which is stochastic. A technique is presented that allows marginal, conditional, and empirical confidence regions to be calculated for a widely known model of optimal uneven-aged stand structure. The technique uses the nonparametric bootstrap to approximate the joint sampling distribution for the decision variables of the nonlinear programming model. Subsequently, multivariate normal theory is used to obtain 95% confidence statements on the decision variables and functions thereof. Results show that the optimal steady-state investment-efficient diameter distribution for uneven-aged northern hardwood stands is an imprecise estimate given the data used for growth model calibration and the assumptions of the mathematical model. However, confidence statements found using this methodology are only approximate as they rely on an estimate of the sampling distribution for the optimal diameter distribution, not on classical statistical theory. These findings suggest a very real need for modelers, managers, and policy makers to begin considering the role of stochastic model components in mathematical programming models in natural resources. For. Sci. 38(3):623-640.
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Keywords: Nonlinear optimization; Weibull distribution; confidence intervals; multivariate normal distribution; northern hardwoods

Document Type: Journal Article

Affiliations: Assistant Professor of Forest Resources Management, School of Forest Resources, Pennsylvania State University, University Park, PA 16802

Publication date: 1992-08-01

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    Forest Science is a peer-reviewed journal publishing fundamental and applied research that explores all aspects of natural and social sciences as they apply to the function and management of the forested ecosystems of the world. Topics include silviculture, forest management, biometrics, economics, entomology & pathology, fire & fuels management, forest ecology, genetics & tree improvement, geospatial technologies, harvesting & utilization, landscape ecology, operations research, forest policy, physiology, recreation, social sciences, soils & hydrology, and wildlife management.
    Forest Science is published bimonthly in February, April, June, August, October, and December.

    2016 Impact Factor: 1.782 (Rank 17/64 in forestry)

    Average time from submission to first decision: 62.5 days*
    June 1, 2016 to Feb. 28, 2017

    Also published by SAF:
    Journal of Forestry
    Other SAF Publications
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