Dominant height projection model with the addition of environmental variables

Abstract

This study investigated the behavior of climatic variables inserted as inclination modifi ers of the Chapman-Richards model for estimating dominant height. Thus, 1507 data pairs from a Continuous Forestry Inventory of clonal eucalyptus stands were used. The stands are located in the States of Espírito Santo and southern Bahia. The climatic variables were inserted in the dominant height model because the model is a key variable in the whole prognosis system. The models were adjusted using 1360 data pairs, where the rest of the data was reserved for model validation. The climatic variables were selected by using the Backward model construction method. The climatic variables indicated by the Backward method and inserted in the model were: mean monthly precipitation and solar radiation. The inclusion of climatic variables in the model resulted in a precision gain of 19.8% for dominant height projection values when compared with the conventional model. The advantage of the method used in this study is the actualization of inventory data contemplating climatic history and productivity estimates in areas without prior plantation.

Impact of climate factors on height growth of Pinus sylvestris var. mongolica

“…Although the height growth of Populus tremuloides was not significantly related to any temporal variables, Picea mariana increased with later stand establishment date, higher average maximum summer temperature between May and August, and higher atmospheric CO 2 concentrations [ 11 ]. Ferraz Filho [ 12 ] reported that the addition of climatic variables (precipitation and solar radiation) to the inclination parameters of the Chapman-Richards model resulted in more precise estimates of dominant height. Leites et al [ 13 ] concluded that the three-year height growth of Pseudototsuga menziesii was most sensitive to the mean temperature in the coldest month.…”