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Additivity on Nonlinear Stem Taper Functions: A Case for Corsican Pine in Northern Spain

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Abstract:

A system of additive equations was developed to predict whole-tree volume and the different components of Corsican pine. In this work, the nonlinear seemingly unrelated regression (NSUR) approach, which guarantees additivity in nonlinear equations, was evaluated. The effect of bark thickness on the accuracy of the results for all tree components was also assessed. Data for 351 trees, ranging in age from 10 to 72 years, were collected from 65 public and private sites. The volume estimates show average biases that range in absolute values from 2.19 to 31.02 dm3 for whole-tree, from 1.41 to 27.31 dm3 for wood, and from 1.05 to 16.52 dm3 for bark volume components. Errors in volume predictions were relatively small, representing less than 3% of the average observed wood volume and less than 6% of the average observed bark volume. This research showed that satisfactory predictions can be obtained from forcing additivity using NSUR approach with a minimal number of easily measurable tree variables, such as dbh and total height.

Keywords: Corsican pine; Seemingly Unrelated Regression; additivity; bark thickness; stem form; volume equations

Document Type: Research Article

DOI: https://doi.org/10.5849/forsci.12-023

Publication date: 2013-08-21

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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