Height-diameter models in forestry with inclusion of covariates

Abstract

The main difficulty in selecting height-diameter relationships is the large number of variables involved. Techniques for decomposition of model parameters with inclusion of covariates relating to individual trees and to the stand collectively can improve model precision. This study aimed to evaluate quality improvement in the fit of height-diameter models by inclusion of covariates. The data in this study was obtained from commercial Eucalyptus sp. plantations in southern Bahia state. Firstly two reduced models were fitted, one linear and another nonlinear, considering the same trend of height variation as a function of diameter, for all genetic materials being studied. Between the two, the logistic model presented the best performance for the relevant database. After fitting parameters for the selected model, the complete formulation was fit with inclusion of variables relating to individual trees, which improved model precision. A reduction of 17% was observed in the residual standard error value when comparing reduced model and complete model, with inclusion of covariates.