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Compatible Basal Area Growth and Yield Models Consistent With Forest Growth Theory

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Compatible growth and yield models often include constraints which prevent them from fitting the sample data as well as empirically derived equations. However, the superiority of such constrained equations can be demonstrated by examining their fit to the complementary stand characteristic. For example, if parameter estimation has been performed using growth as the dependent variable, the compatible yield equations should be evaluated as part of the process of choosing the best model. Thus chances are reduced of either selecting a model that has been strongly influenced by unusual aberrations in the sample data or selecting a model based on inappropriate constraints. To illustrate, one empirical and two constrained basal area growth models, have been fitted to a common data set obtained from red pine (Pinus resinosa Ait.) plantations that were subject to a variety of thinning regimes. One constrained model in which an inverse relationship between growth and current basal area was specified, gave the poorest fit to the growth data used for parameter estimation, however, the compatible yield form estimated future yields the best of all three models. This anomalous result provides some justification for using the new model for growth and yield estimation. Forest Sci. 29:279-288.
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Keywords: Pinus resinosa; basal area increment; constrained models; density; growth models; stand growth; thinning; yield estimates

Document Type: Journal Article

Affiliations: Faculty of Forestry, University of Toronto, Toronto, Ontario M5S 1A1, Canada

Publication date: 1983-06-01

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.

    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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