EQUAÇÕES ALOMÉTRICAS PARA A ESTIMATIVA DE BIOMASSA, CARBONO E NUTRIENTES EM UMA FLORESTA OMBRÓFILA MISTA

• Ano de defesa: 2010
• Autor(a) principal: Ratuchne, Luis Carlos
• Orientador(a): Watzlawick, Luciano Farinha
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: UNICENTRO - Universidade Estadual do Centro Oeste
• Programa de Pós-Graduação: Programa de Pós-Graduação em Agronomia (Mestrado)
• Departamento: Unicentro::Departamento de Agronomia
• País: BR
• Palavras-chave em Português: Floresta com araucária; Regressão linear; stepwise
• Palavras-chave em Inglês: Araucaria forest; Linear regression; Stepwise
• Área do conhecimento CNPq: CIENCIAS AGRARIAS::AGRONOMIA
• Link de acesso: http://localhost:8080/tede/handle/tede/157

The objectives of this study was to adjust and select allometric equations to estimate tree biomass above ground, fixed carbon and nutrient contents in a Mixed Ombrophilous Montane Forest, located in the city of General Carneiro, Paraná. The work was divided into three chapters. In the first chapter has been fitted and the selection of the regression equations to estimate the total biomass and components: stem wood, stem bark, live branches, dead branches, leaves and miscellaneous. In the setting of the equations we used a database containing the determination of biomass of 153 trees of 38 species and 19 families. In the second chapter were selected and adjusted equations to estimate the content of fixed carbon in forest biomass above ground of the study area. The database was composed by the determinations of biomass and carbon content of 91 trees of 38 species and 19 families. The third chapter has been adjusted and selected regression equations to estimate the content of macronutrients (N, P, K, Ca, Mg and S) and micronutrients (B, Mn, Cu, Zn and Fe) stored in above-ground biomass study area, considering the components: stem wood, stem bark, branches, leaves and miscellaneous and the content of the accompanying nutrient. The database used in the setting of the equations was made by the determinations of biomass and nutrient content of 85 trees of 38 species and 19 families. In addition to the determinations of biomass, carbon and nutrients, the databases contained measures dendrometric total height (ht) and the height of the morphological inversion (hm), in meters, and diameter at breast height (1.3 m) (dap) in centimeters, which were used as independent variables in the regression equations. We used 20 models of regression equations commonly used in literature and regression equations provided by the stepwise method. In the setting of the equations we used the least squares method of selecting the best fitting observed statistics adjusted coefficient of determination (R2 aj), standard error of estimate (Syx%), F statistics and graphical analysis of the waste distribution. For biomass, the best results were obtained for total biomass and biomass of stem wood. For the stem bark, live branches, dead branches, leaves and miscellaneous results were less significant and the equations provided estimates with less precision. In the setting of the equations for estimating the carbon content, the best results were obtained for the total carbon and carbon from the wood of the bole, with good determination coefficient and low standard error of the estimate. As for the compartments of the stem bark, branches, leaves and miscellaneous greater difficulties in adjusting the regression curves to the data, producing equations with less precision in estimates. The stepwise method was efficient and provided the best equations for the compartments branches and miscellaneous. Estimates using regression equations may be a good alternative to the indirect method of quantification of fixed carbon in natural forests. In the setting of equations to estimate the nutrient content for most of these equations with better statistics were obtained for the stem bark component. The worst results were obtained for the miscellaneous and foliage components in all nutrients. The stepwise method was efficient in the selection of independent variables to compose the regression equations, providing the best regression equations for various components.