Modeling dominant height growth of eucalyptus plantations with parameters conditioned to climatic variations

Abstract

Dominant height growth equations, which given at some base age is defined as site index, is usually used to assess site quality. A flexible and accurate way to represent the potential productive capacity of forest stands of Eucalyptus spp. was developed. The generalized algebraic difference method was used, in which 15 dynamic equations were tested for modeling dominant height growth. The models were fitted to a data set derived from permanent plots located in the states of Bahia (BA) and Espirito Santo (ES), Brazil, with clonal eucalyptus plantations. The database was analyzed separately for the clear-cut and coppice regimes. The selection of the best-fitting model for each management regime was based on statistical fitting, predictive validation, and graphical analysis. After selection of the best model, one of its parameters were expanded with the addition of climatic variables that allowed for the creation of scenarios. The polymorphic modified Von Bertalanffy-Richards model with a single asymptote performed the best for the two management regimes. For clear-cut management, conditioning the slope parameter by the mean monthly precipitation obtained the best performance. For coppice management, the asymptote parameter conditioned by the mean monthly precipitation and its distribution throughout the year provided the best performance. The inclusion of the climate modifiers added flexibility for the models, which was represented by the interannual variations of precipitation. Expansions of the parameters did not mischaracterize the behavior of the modified Von Bertalanffy-Richards model for the management regimes studied. Climatic conditioning of the parameters of the slope and asymptote for the two management regimes led to accuracy gains in the estimates. Additionally, this enabled the generation of productivity scenarios based on the amount and distribution of the total precipitation for the areas under study.