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Using Numerical Optimization for Specifying Individual-Tree Competition Models

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Abstract:

In this article we present a method that combines maximum likelihood estimation and nonlinear programming in growth modeling. The method of Hooke and Jeeves is used to discover the optimal specification of a particular competition index type, while statistical software is used to fit the regression model with the given competition index type. The log-likelihood computed by the statistical software is fed back to the optimization algorithm, which alters the specification of the competition index type based on the changes in the log-likelihood. This approach was tested for a mixture of Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies [L.] Karst.). The characteristics of five different competition index types were optimized. The best model included an index computed from vertical angles formed by a horizontal plane and the tops of competitors. The elevation of the horizontal plane was computed with a species-specific linear regression model using height of the subject tree as the predictor. Pine competitors nearer than 6 m and spruce competitors nearer than 9--10 m were included in the optimal competition index. This study showed that the approach used here is highly efficient. For. Sci. 46(2):277-283.

Keywords: Competition index; Norway spruce; Scots pine; mixed stands; nonlinear optimization

Document Type: Journal Article

Publication date: May 1, 2000

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.
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