Abstract

LAFETA, Bruno Oliveira et al. Modeling alternatives for height estimation in eucalypt tree species. Kurú [online]. 2023, vol.20, n.47, pp.1-8. ISSN 2215-2504.  http://dx.doi.org/10.18845/rfmk.v20i47.6816.

This study aimed to evaluate alternatives for general hypsometric modeling of eucalypt through linear regression, with inclusion of distance-independent competition indices as a predictor variable. The data was collected from 34 plots distributed in four 72-month-old forest management units. Seven distance-independent competition indices were calculated. The study evaluated the predictive performance of 16 general hypsometric models, and 14 double-entry models (DAP and competition index) were proposed. All equations generated were biologically consistent. Given the lack of information on the height of dominant trees, general modeling can be better employed with inclusion of the IC4 index, which represents basal area of neighboring competing trees. It is concluded that the inclusion of competition indices in hypsometric models increases generalization capacity of equations for eucalyptus stands with different productive capacities. The IC4 competition index enhances fit quality and predictive performance of the hypsometric modeling in the study sites.

Keywords : Competition; generalization; hypsometric relationship; linear regression.

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