O objetivo deste trabalho foi comparar métodos para obtenção do índice de sítio para povoamentos de eucalipto (Eucalyptus spp.), bem como avaliar seus impactos na estabilidade desse índice em bases de dados com e sem a presença de “outliers”. Foram testados três métodos, com uso de regressão linear, regressão quantílica e rede neural artificial. Foram utilizadas 22 parcelas permanentes de inventário florestal contínuo, medidas em árvores com idade de 23 a 83 meses. Os outliers foram identificados com uso de gráfico de boxplot. A rede neural artificial proporcionou melhores resultados que as regressões linear e quantílica, tanto para as estimativas de altura dominante quanto do índice de sítio. A estabilidade da classificação do índice de sítio obtida pela rede neural artificial também foi melhor que a obtida com os outros métodos, independentemente da presença ou da ausência de outliers na base de dados. Isso indica que a rede neural artificial é uma técnica sólida de modelagem na presença de outliers. Quando a causa da presença de outliers na base de dados não é conhecida, eles podem ser mantidos nela se técnicas como as de redes neurais artificiais ou de regressão quantílica forem utilizadas.

The site production capacity in forest modeling is represented by the site index or projection of dominant height at the reference age. The site rating is the key to the whole prognosis system. However, methods related with increase of accuracy and flexibility of equations are important to create productivity scenarios due to climate changes. The overall objective of this study was to relate forest development with climate variables. Firstly, we aimed to propose a flexible model seeking to represent the dominant height growth. Then, we sought to identify climatic variations that affect the site capacity in wood production, and how. The research was related with the development of eucalyptus stands under the tall trees and coppice management regimes in Midwest Region of the State of Espirito Santo and Southern Bahia. Data were obtained on 299 permanent plots of stands of Eucalyptus urograndis, from which 155 are related with the coppice management regime and 144 are related with high tree-trunk management regime. Climatic data were obtained from 31 meteorological stations distributed in the area under study. The polymorphic modified Von-Bertallanfy Richards model with common asymptote showed the best performance for both two management regimes. The mean monthly rainfall and distribution of rainy days were used to expand the model parameters. The climatic conditioning of coefficients brought gains in terms of accuracy of estimates. In addition, it allowed the generation of development scenarios from interannual climate variations. This survey also showed that the mean monthly rainfall and number of rainy days were the most related variables with the site productive capacity. These factors showed positive linear effect on the site capacity to produce wood. Therefore, this methodology allows managers to predict forest productivity scenarios on interannual climate changes, as well as the expectation of growth for sites without historical data of plantations.

This study aimed at evaluating the performance of different models based on Artificial neural networks (ANN) to estimate the total height of eucalyptus trees (Eucalyptus spp.), reducing the number of measurements in the field. Forty-eight ANN were tested, different from each other by the number of trees used as training sample, number of trees used to calculate the dominant height and use of variables (a) categorical, (b) categorical and continuous and (c) continuous, except for the diameter at 1.30 meters above the ground (DBH), used in all combinations. Estimates of height obtained by ANN were compared with values observed and estimates obtained by a hypsometric model. The ANN that showed the best results were used for the height estimation in forest inventory data for further application in the Schumacher and Hall volumetric model. The proposed models were efficient to estimate the total height of eucalyptus trees and allowed the expressive reduction of the number of trees to be measured in forest inventory. The best model found is composed of five trees as training sample, one as test sample and one as validation sample; dominant height coming from the height of the tallest tree in the plot; categorical variable Clone and continuous variables DBH, DBH dominant and basal area of the plot.

The use of techniques for determining the productive capacity of a forest site depends on its sensitivity to evaluate or to predict the interaction between the genetic factors of the species and the environment (climate, soil, physiography). This work was developed in the southern part of Rio Grande do Sul state and aimed to evaluate the influence of soil properties in different soil classes, with the production capacity by the dominant height (h100) for Eucalyptus saligna (clone 32864) and seminal plantations of Eucalyptus dunnii. Thus, we selected 16 inventory plots based on genetic material and dendrometric variables as the dominant height (h100), mean annual increment (MAI), volume of wood with bark (m3/ ha-1) designed for seven years. Form these 16 plots, nine plots were E. dunnii and seven plots were E. Saligna. In each selected site trenches were opened to classify the soil, to collect soil samples for chemical (pH, MO, Ca, Mg, Al, P, S, K, Zn, Cu, B, CTC,ph7, V) and physical analysis ( particle size, particle density and soil porosity, macro and microporosity, soil water retention curve , saturated hydraulic conductivity) and to collect litter. Profiles/plots showed variations in soil classes, site index, source material, morphological, chemical and physical soil characteristics and amount of litter. We analyzed four classes of representative soils of the study region: Argissolos (PVe, PVd, PBACd, PBACe, PAd), Neossolos (RRd), Luvissolos (TCo) and Cambissolos (CXbd). The dominant height (h100) showed a minimum height of 22.9 m ( Profile 13 - RRd) and maximum height of 29.3 m (Profile 10-TCo) for E. dunnii and for the clone E. saligna the IS showed a minimum height of 22.4 m (Profile 11- PAd) and maximum of 28.7 m (Profile 16-PVe). The statistical analysis between dominant height (h100) and the physical, chemical and morphological soil characteristics was performed by the use of multiple regression analysis, which aimed to verify the significance of the input variables to determine the dominant height. The equations that best correlated the dominant height to soil attributes were derived from the layers 10-20 cm and 60-80 cm when were considered both genetic materials. However, when genetic materials were evaluated separately the equations that best correlated the dominant height to soil attributes were derived from the layers 10-20 cm and 20-40 cm for E. dunnii, and from the layers 40 - 60 cm and 60 - 80 cm for clone E. saligna, which shows the different relations between the genetic material and the analyzed soil profiles. The depth of the A + B horizons, the silt, the organic matter and the concentration of boron, potassium and calcium were the attributes that gave the highest contribution in the models to explain the productive capacity of the profiles analyzed by dominant height.