Diameter distribution model development of tropical hybrid Eucalyptus clonal plantations in Sumatera, Indonesia: A comparison of estimation methods

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Joni Waldy
John A. Kershaw Jr
Aaron Weiskittel
Mark J. Ducey


3-parameter Weibull, parameter recovery method, moment, percentile, hybrid, Indonesia


Background: Effective forest management and planning often requires information about the distribution of volume by size and product classes. Size-class models describe the diameter distribution and provide information by diameter class, such as the number of trees, basal area, and volume per unit of area. A successful diameter-distribution model requires high flexibility yet robust prediction of its parameters. To our knowledge, there are no studies regarding diameter distribution models for Eucalyptus hybrids in Indonesia. Therefore, the aim of this study was to compare different recovery methods for predicting parameters of the 3-parameter Weibull distribution for characterising diameter distributions of Eucalyptus hybrid clone plantations, on Sumatera Island of Indonesia.

Methods: The parameter recovery approach was proposed to be compatible with stand-average growth and yield models developed based on the same data. Three approaches where compared: moment-based recovery, percentile-based prediction and hybrid methods. The ultimate goal was to recover Weibull parameters from future stand attributes, which were predicted from current stand attributes using regression models.

Results: In this study, the moment method was found to give the overall lowest mean error-index and Kolmogorov– Smirnov (KS) statistic, followed by the hybrid and percentile methods. The moment-based method better fit long tails on both sides of the distribution and exhibited slightly greater flexibility in describing plots with larger variance than the other methods.

Conclusions: The Weibull approach appeared relatively robust in determining diameter distributions of Eucalyptus hybrid clone plantation in Indonesia, yet some refinements may be necessary to characterize more complex distributions.

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