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Optimizing Species Composition in Uneven-Aged Forest Stands

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Abstract:

This article describes an approach for determining the optimal sustainable equilibrium diameter distribution and species composition for a mixed-species forest stand. Using the Prognosis Model-a single tree distance independent growth model and its attendant regeneration subsystem--the maximization objective is formulated in terms of three decision variables per species: (1) the scale and shape parameters of a Weibull distribution function, and (2) the total number of trees per acre. A direct search, derivative-free, constrained nonlinear programming algorithm is used to optimize the growth model under a sustainable equilibrium constraint. To facilitate optimization, the stochastic features of the Prognosis Model are transformed to their deterministic counterparts. Results are presented for the Abies lasiocarpa/Clintonia uniflora habitat type found in northern Idaho. For. Sci. 33(4):958-970.

Keywords: Diameter distributions; Investment efficiency; Prognosis Model; Weibull distribution function

Document Type: Journal Article

Affiliations: Research Assistant, College of Forest Resources and Center for Quantitative Science in Forestry, Fisheries and Wildlife, University of Washington, Seattle, WA 98195

Publication date: December 1, 1987

More about this publication?
  • 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

    Also published by SAF:
    Journal of Forestry
    Other SAF Publications
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