Author: Farnden Craig
Publisher: Oxford University Press
ISSN: 1464-3626
Source: Forestry, Vol.85, Iss.5, 2012-12, pp. : 621-628
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Abstract
Individual tree growth models are often constructed with much more complexity than is required for many of the tasks to which they might be applied. In many cases, they also have detailed requirements for describing initial conditions, which may necessitate costly data collection. In cases where a large number of model predictions are required for which there is a mismatch between available data and model requirements, an abstracted low-dimension predictive model (LDPM) that uses alternate input variables and that accurately mimics the outcomes of the more complex model for the specified problem may be an attractive option. In this simulation study, several sets of LDPMs are developed as possible replacements for individual tree models in assessing regeneration stocking implications for mixtures of white spruce (
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