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Growth Model Predictions as Affected by Alternative Sampling-Unit Designs

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This study analyzed the effect on growth-model predictions of introducing differential design error to predictor variables that are influenced by the sampling-unit design, such as basal area per acre. Computer simulations of five forest stands were generated from random spatial distributions and field data coveting a range of stand conditions. Two variable-radius cluster designs and four sizes of fixed-radius circular plots were used to sample the generated stands. Sample data were used to predict single-period gross basal area growth rates in both a single-tree and a whole-stand growth model. The sampling-unit design used to develop each model was defined as the standard error-free method of measuring the predictor variables. For both models, the average of gross basal-area growth rate predictions from 50 samples of each alternative sampling-unit design were not significantly different from the standard design. However, using a different sampling-unit design may produce large differences in individual predictions, particularly when small sampling units are used for application of models developed using relatively large sampling units. Such differences may affect decisions about treatment of a stand and could complicate the validation of a growth-and-yield model. For. Sci. 37(6):1641-1655.
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Keywords: DFSIM; ORGANON; Differential design error; computer simulation; growth-and-yield models

Document Type: Journal Article

Affiliations: Former Graduate Research Assistant, Department of Forest Resources, College of Forestry, Oregon State University, Corvallis, OR

Publication date: 1991-12-01

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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