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Notes: Optimal Stocking Levels and Rotation Under Uncertainty

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Decision making under uncertainty involves making decisions in situations where there is no information concerning future events. To resolve this problem in optimal stocking control when the growth function of a species is initially unavailable and the decision maker is risk neutral, adaptive optimization can be used which utilizes the information generated in previous stages to transform the decision problem from the case of uncertainty to risk. At each stage, the growth function is revised by considering newly obtained growth data. The current stocking level which was determined in the latest optimization is used as the initial stocking level for a new optimization. The mean annual increment (MAI) of each stage is calculated, and the stage where the MAI culminates is the optimal rotation. The proposed model is applied to a hypothetical Douglas-fir stand as an illustration. The values of information are calculated from the MAI under certainty, risk, and uncertainty. Forest Sci. 30:921-927.

Keywords: Decision analysis; adaptive optimization; dynamic programming; risk

Document Type: Miscellaneous

Affiliations: Associate Professor, Department of Industrial Management Science, National Cheng Kung University, Tainan, Taiwan, Republic of China

Publication date: 1984-12-01

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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.

    2015 Impact Factor: 1.702
    Ranking: 16 of 66 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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