%0 Journal Article
%T Effect of Climate Variables on Monthly Growth in Modeling Biological Yield of Araucaria angustifolia and Pinus taeda in the Juvenile Phase
%A Naiara Teodoro Zamin
%A Sebasti£żo do Amaral Machado
%A Afonso Figueiredo Filho
%A Henrique Soares Koehler
%J International Journal of Forestry Research
%D 2013
%I Hindawi Publishing Corporation
%R 10.1155/2013/646759
%X The aim of this study was to investigate the effect of climate variables on monthly growth in diameter and height of Araucaria angustifolia (Bert.) O. Kuntze and of Pinus taeda L., over a six-year period, as well as verifing the contribution of these variables in the composition of the Chapman-Richards model. To this end, we selected 30 trees of each species and measured monthly the diameter and height, between June 2006 and August 2012. The climate variables were obtained from two SIMEPAR meteorological stations near the plantings. A correlation matrix was constructed to determine the effect of climate variables on the monthly growth. Next a principal component analysis (PCA) was conducted to determine the climate variables to be included in the fit of the Chapman-Richards model. The results indicated that the climate variables with the highest correlation (about 0.6) with monthly growth in diameter and height of the species were temperature, photoperiod and atmospheric pressure, and precipitation for some years of the study. The fitted model that included climate variables showed reduced Syx% of about 0.8% compared to the traditional biological model. However, ANOVA showed no statistical difference between the production estimates obtained by both models. 1. Introduction In forestry planning knowledge of the present and future growth and yield of trees and forest stands is important as it aids in the implementation of appropriate management regimes, when the quality of the final product is the goal for an increasingly demanding consumer timber market. In this context, biological modeling becomes a useful tool in planning forest production. Moreover, Maestri [1] pointed out that modeling growth is possible only if a perfect match between the measurements of stands in forest inventory and environmental variables is carefully aligned in spatial and temporal terms. However, environmental characteristics are not reflected in traditional biological growth models, which only consider tree growth as a function of the size and age of the individuals. In this sense, statistical methodologies have evolved in recent years, enabling a breakthrough in forest data modeling, and thereby allowing researchers to arrive at estimates that represent reality with increasing accuracy, a fact that has motivated numerous attempts to model the environmental factors in association with biological growth and yield. This fact suggests that establishing a link using modeling between environmental variables and the productive capacity of the forest stand seems to contribute to the
%U http://www.hindawi.com/journals/ijfr/2013/646759/