Modelagem da produ??o de povoamentos de eucalipto utilizando diferentes metodologias

Data de aprova??o retirada da vers?o impressa do trabalho.A modelagem ? um procedimento estat?stico empregado por gestores florestais para esbo?ar o desenvolvimento vegetal com precis?o. Informa??es confi?veis do crescimento e da produ??o s?o essenciais para predizer e quantificar a estrutura futura do povoamento. O presente trabalho foi dividido em dois cap?tulos. Os objetivos foram avaliar a efici?ncia de se estimar a altura empregando diferentes modelos hipsom?tricos, crit?rios de estratifica??o e m?todos de ajuste, al?m de comparar tr?s categorias de modelos de crescimento e produ??o (MCP) em planta??es comerciais de eucalipto. Foram definidas quatro unidades de manejo florestal, totalizando 293,43 ha. O invent?rio florestal cont?nuo foi realizado em 34 parcelas permanentes de 400 m2. O espa?amento de plantio foi de 3,0 x 2,5 m. Avaliou-se a precis?o do ajuste de treze modelos hipsom?tricos. Foram treinadas RNA empregando as mesmas vari?veis de resposta e preditoras adotadas nas equa??es selecionadas. As categorias de MCP testadas foram: em n?vel de povoamento (MP), pelo sistema de equa??es simult?neas de Clutter; de distribui??o diam?trica (MDD), pelo ajuste de fun??o densidade de probabilidade de Weibul-2P e de ?rvores individuais (MAI), pelo modelo de Pienaar e Schiver. As equa??es provenientes do modelo de altura em fun??o do di?metro e da altura dominante forneceram estimativas confi?veis da altura para diferentes crit?rios de estratifica??o, demonstrando superioridade em rela??o aos modelos locais. A modelagem por regress?o e redes demonstraram-se adequadas para estimar a altura, com ou sem estratifica??o do banco de dados. A estratifica??o ? um procedimento que pode melhorar a qualidade das estimativas de altura obtidas por regress?o e RNA. As tr?s categorias de modelo proporcionaram estimativas confi?veis da produ??o em volume com casca, aos 36, 48, 60 e 72 meses, para as unidades de manejo estudadas. O MAI foi a categoria mais precisa e consistente na estimativa do volume por hectare. As proje??es com MP e MDD podem gerar estimativas similares de volume para idades al?m daquelas em que se realizou o invent?rio florestal.Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior (CAPES)Disserta??o (Mestrado) ? Programa de P?s-Gradua??o em Ci?ncia Florestal, Universidade Federal dos Vales do Jequitinhonha e Mucuri, 2017.Modeling is a statistical procedure employed by forest managers to sketch plant development with precision. Reliable growth and production information are essential to predict and quantify the future stand structure. The present work was divided in two chapters. The objectives were to evaluate the efficiency of height estimation using different hypsometric models, stratification criteria and adjustment methods, beside to evaluate and compare three categories of growth and yield models (MCP) in commercial eucalypt plantations. Four forest management units were defined, totaling 293.43 ha. The continuous forest inventory was realized in 34 permanent plots of 400 m2. The planting spacing was 3.0 x 2.5 m. The accuracy of the fit of thirteen hypsometric models was evaluated. ANN were trained using the same response e predictive variables adopted in the selected equations. The MCP categories tested were: in level of stand (MP), using Clutter?s simultaneous equations; diameter distribution model (MDD), by adjustment of the Weibull-2P?s probability density function and individual trees (MAI), by Pienaar and Schiver model. The equations from the height model according to the diameter and the dominant height provided reliable height estimates for different stratification criteria, showing superiority in relation to local models. Regression and networks modelling were suitable for estimating height, with or without stratification of the database. Stratification is a procedure that can improve the quality of the estimates obtained by regression and ANN. The three model categories provided reliable estimates of the volume with bark production at 36, 48, 60 and 72 months for the management units studied. MAI was the most accurate and consistent category in estimating volume per hectare. Projections with MP and MDD can generate similar estimates of volume for ages beyond those in which the forest inventory was carried out

Climatic suitability for Eucalyptus cloeziana cultivation in four Brazilian states

The objective of this work was to identify zones with climatic potential for Eucalyptus cloeziana cultivation in four Brazilian states (Bahia – BA, Mato Grosso do Sul – MS, Minas Gerais – MG e São Paulo – SP). 490 records of this species in Australia were obtained. Current prediction of the distribution of habitat suitability was based on climatic conditions recorded between 1960 and 1990. For the future projections of 2050, four scenarios were used: RCP 2.6 W/m2, RCP 4.5 W/m2, RCP 6.0 W/m2 and RCP 8.5 W/m2. MaxEnt was used in modeling, and only climatic information was used as predictor variables. The modeling was robust and presented high values of AUC (> 0.95). Annual precipitation and isothermal were the variables that contributed the most for the quality of the models. It was concluded that the Brazilian mesoregions of Itapetininga (SP), Litoral Sul Paulista (SP) and Zona da Mata (MG) presented the most climatically suitable sites for E. cloeziana cultivation. Climatic changes may restrict the distribution of suitable zones for E. cloeziana cultivation. The negative effect of global warming was more prominent in MG

Ecophysiology modeling by artificial neural networks for different spacings in eucalypt

Growth and production models are widely used to predict yields and support forestry decisions. Artificial Neural Networks (ANN) are computational models that simulate the brain and nervous system human functions, with a memory capable of establishing mathematical relationships between independent variables to estimate the dependent variables. This work aimed to evaluate the efficiency of eucalypt biomass modeling under different spacings using Multilayer Perceptron networks, trained through the backpropagation algorithm. The experiment was installed in randomized block, and the effect of five planting spacings was studied in three blocks: T1 – 3.0 x 0.5 m; T2 – 3.0 x 1.0 m; T3 – 3.0 x 1.5 m; T4 – 3.0 x 2.0 m e T5 – 3.0 x 3.0 m. A continuous forest inventory was carried out at the ages of 48, 61, 73, 85 and 101 months. The leaf area, leaf perimeter and specific leaf area were measured at 101 months in one sample tree per experimental unit. Two thousand ANN were trained, using all inventoried trees, to estimate the eco-physiological attributes and the prognosis of the wood biomass. The artificial neural networks modeling was adequate to estimate eucalypt wood biomass, according to age and under different spacings, using the diameter-at-breast-height and leaf perimeter as predictor variables.Growth and production models are widely used to predict yields and support forestry decisions. Artificial Neural Networks (ANN) are computational models that simulate the brain and nervous system human functions, with a memory capable of establishing mathematical relationships between independent variables to estimate the dependent variables. This work aimed to evaluate the efficiency of eucalypt biomass modeling under different spacings using Multilayer Perceptron networks, trained through the backpropagation algorithm. The experiment was installed in randomized block, and the effect of five planting spacings was studied in three blocks: T1 – 3.0 x 0.5 m; T2 – 3.0 x 1.0 m; T3 – 3.0 x 1.5 m; T4 – 3.0 x 2.0 m e T5 – 3.0 x 3.0 m. A continuous forest inventory was carried out at the ages of 48, 61, 73, 85 and 101 months. The leaf area, leaf perimeter and specific leaf area were measured at 101 months in one sample tree per experimental unit. Two thousand ANN were trained, using all inventoried trees, to estimate the eco-physiological attributes and the prognosis of the wood biomass. The artificial neural networks modeling was adequate to estimate eucalypt wood biomass, according to age and under different spacings, using the diameter-at-breast-height and leaf perimeter as predictor variables

CROWN MORPHOMETRIC INDEXES OF EUCALYPT ESTIMATED BY LOGISTIC REGRESSION AND SUPPORT VECTOR MACHINES

The proper choice of the modeling method for morphometric tree crown estimates is important to optimize measurement and support silvicultural decision-making. This study aims to evaluate the efficiency of interdimensional morphometric relationships modeling of eucalypt crown under different spacings using logistic regression and Support Vector Machines (SVM). The experiment was set up with four spacings (T1: 3.0 × 0.5 m; T2: 3.0 × 1.0 m; T3: 3.0 × 1.5 m and T4: 3.0 × 2.0 m). A continuous forest inventory was carried out at the ages of 24, 37, 48, 59 and 72 months. Two modeling methods, one using nonlinear regression (logistic model) and the other using SVM, were tested. The range, salience and vital space indexes decreased with increasing tree stem dimensions, tending to stabilization. The logistic model was satisfactorily adapted to the problems, more specifically in prediction of the first two indexes. SVM modeling using radial base Kernel function can be used with good precision for crown morphometric indexes estimation of eucalypt, simultaneously, for different planting spacings

Monitoring the understory in eucalyptus plantations using airborne laser scanning

In eucalyptus plantations, the presence of understory increases the risk of fires, acts as an obstacle to forest operations, and leads to yield losses due to competition. The objective of this study was to develop an approach to discriminate the presence or absence of understory in eucalyptus plantations based on airborne laser scanning surveys. The bimodal canopy height profile was modeled by two Weibull density functions: one to model the canopy, and other to model the understory. The parameters used as predictor in the logistic model successfully discriminated the presence or absence of understory. The logistic model composed by gcanopy, gunderstory, and gunderstory showed higher values of accuracy (0.96) and kappa (0.92), which means an adequate classification of presence of understory and absence of understory. Weibull parameters could be used as input in the logistic regression to effectively identify the presence and absence of understory in eucalyptus plantation

SAMPLING OF CHEMICAL ATTRIBUTES IN FOREST SOILS

Information about sample adequacy that represents soil chemical attributes distribution are fundamental for a better rationalization of the use of correctives and fertilizers. The objective was to evaluate the variability of these attributes and to size the minimum number of composite samples to represent the fertility of forest soils. The total area planted was 9,101ha, constituted of 265 commercial eucalypt stands. The 687 soil composite samples obtained were for chemical analysis. It was evaluated the performance of two exploratory analysis techniques and six sampling procedures. The attributes P, K, Ca, Mg and S presented higher coefficient of variation (>35%). In contrast, the distributions of Al, organic matter and, mainly, pH were the most homogeneous. The sample error was smaller as the amount of composite samples increased. The representative of all chemical attributes (sample error of 5%) was achieved with a minimum of 309 (one each 29ha, 1:29) and 295 (1:31) composite samples from sampling procedures simple casual and stratified by altitude class, respectively. Both procedures were promising for soil sampling, especially, when applying the boxplot for identification and removal of outliers

Crown projection area of Licania tomentosa (Benth.) Fritsch (Chrysobalanaceae), estimated by linear regression / Área de projeção da copa de Licania tomentosa (Benth.) Fritsch (Chrysobalanaceae), estimada por regressão linear

Knowledge of the Crown Projection Area (CPA) allows to make inferences about the shading and to know space occupied by a tree. However, crown measurements are more time-consuming and laborious when compared to those of Circumference Breast Height (CBH). Thus, this work aimed to evaluate regression models and present the most suitable to CPA estimate of Licania tomentosa, in an urban area of São João Evangelista municipality, Brazil. Fifty trees distributed over 7 public roads were sampled. CBH and Crown Diameter (CD, m) were measured for later calculation of its projection area (CPA, m2). Four regression models were tested in order to estimate CPA as a function of CBH alone. The equation derived from of the model “” showed a homoscedastic distribution of the percentage residues, with closer deviations around the abscissa axis. It is concluded that the equation obtained with the adjustment of the simple linear model was the most efficient to estimate of the crown projection area of L. tomentosa. This projection area increased as the stem of the trees thickened

EFICIÊNCIA DE UTILIZAÇÃO DE MACRONUTRIENTES EM EUCALIPTO POR MÉTODO NÃO DESTRUTIVO ESTIMADOS POR REDES NEURAIS ARTIFICIAIS

The Non-Destructive Sampling (NDS) provides an efficient, simple and safe characterization of chemical properties of the plant, as the Coefficient of Biological Use (CBU). The association of NDS with the technique of Artificial Neural Networks (ANN) can be a potential alternative to replace the regression equations and the traditional methods of interpolation. Therefore, this work aimed to evaluate the efficiency of ANN and non-destructive sampling for the efficiency of nutrient use in the trunk. The research plot was installed in a randomized block being studied, in three blocks, the effect of five planting spacing: T1 – 3,0 m x 0,5 m, T2 – 3,0 m x 1,0 m, T3 – 3,0 m x 1,5 m, T4 – 3,0 m x 2,0 m e T5 – 3,0 m x 3,0 m. A sample-tree was felled to make the cubage and quantify the dry bark and wood per experimental plot, totaling 15 trees. The sample-trees were weighed in the field and subsamples of bark and wood were collected along the stem to form a composite sample per tree. Also removed was a single sample of each component obtained with the aid of a chisel and hammer in DBH in the same sample-trees. The samples were dried at 65°C until constant weight. The material was ground and subjected chemical analysis. Adjusted regression models and application of ANN to estimation of CBUTrunk from the CBUDBH Bark and CBUDBH Wood. The ANN had a higher accuracy and reliability of the regression. Modeling by artificial neural networks using only sample in the DBH region proved to be adequate for estimating the coefficient of biological use of stem.A Amostragem Não Destrutiva (AND) permite uma caracterização eficiente, simples e segura das propriedades químicas do vegetal, como o Coeficiente de Utilização Biológico (CUB). A associação da AND com a técnica de Redes Neurais Artificiais (RNA) pode ser uma alternativa potencial em substituição às equações de regressão e aos métodos tradicionais de interpolação. Portanto, o presente trabalho objetivou avaliar a eficiência da RNA e da amostragem não destrutiva para estimar a eficiência de uso de nutrientes no tronco. O experimento foi instalado em blocos ao acaso, sendo estudado, em três blocos, o efeito de cinco espaçamentos de plantio: T1 – 3,0 m x 0,5 m; T2 – 3,0 m x 1,0 m; T3 – 3,0 m x 1,5 m; T4 – 3,0 m x 2,0 m e T5 – 3,0 m x 3,0 m. Uma árvore-amostra foi abatida para realizar a cubagem rigorosa e quantificar a matéria seca de casca e lenho por unidade experimental, totalizando-se 15 árvores. As árvores-amostras foram pesadas no campo e subamostras de casca e lenho foram coletadas ao longo do fuste para compor uma amostra composta por árvore. Também foi retirada uma amostra simples de cada componente obtidas com auxílio de um formão e martelo na região do DAP nas mesmas árvores-amostras. As amostras foram secas a 65ºC até peso constante. O material vegetal foi moído e submetido à análise química. Ajustaram-se modelos de regressão e aplicação de RNA para estimação do CUBTronco a partir do CUBDAP Casca e CUBDAP Lenho. As RNA apresentaram maior precisão e confiabilidade do que a regressão. A modelagem por redes neurais artificiais utilizando-se apenas uma amostra da casca na região do DAP demonstrou ser adequada para a estimativa do coeficiente de utilização biológico do tronco

Artificial Neural Networks to Estimate Nutrient Use Efficiency in Eucalypt

Background: Nutrient use efficiency (NUE) is the basis for fertilizer recommendations in eucalypt plantations in Brazil needs to be calculate individually for each nutrient and spacing. The possibility of superior performance to conventional models of regression and interpolation can be obtained by Artificial Neural Networks (ANN) enabling its use for solve complex problems. The ANN are being used in environmental science, but still studies on forest nutrition are poor. Objective: To evaluate the efficiency of NUE estimation in the Eucalyptus stem, under different spacing using ANN. Results: The nonlinear activation functions in the hidden layer generating local receptive fields were observed in all networks. Specific leaf area contributed to capture the biological realism and increased the ability of generalization of MLP's networks. Its generalization capability and connectivity allowed use only one network to perform the estimation of the stem's NUE.Conclusion: The modeling by ANN using multilayer perceptron architecture is a suitable alternative, accurate and biologically realistic to estimate the NUE by macronutrient, used in different spacings

ALTURA DE MUDAS DA Tibouchina granulosa COGN. (MELASTOMATACEAE) ESTIMADA POR REDES NEURAIS ARTIFICIAIS

O objetivo do presente trabalho foi avaliar a eficiência da estimação da altura de mudas da Tibouchina granulosa em função do diâmetro do coleto, sob diferentes composições de substrato, empregando Redes Neurais Artificiais (RNA). Foram selecionadas 72 mudas produzidas via tubetes para a repicagem em baldes de 25 litros. Adotou-se delineamento experimental inteiramente casualizado, com três repetições, sendo os tratamentos constituídos por quatro composições de substrato. Cada unidade experimental foi composta por seis mudas. Aos 13 meses de idade foram mensurados o Diâmetro à Altura do Coleto (DAC) e a altura total (H) de todas as mudas. Foram treinadas 200 RNA para estimar a H, sendo 100 Multilayer Perceptron (MLP) e 100 Radial Basis Function (RBF). As variáveis utilizadas como entrada das RNA para estimação da altura das mudas foram numéricas (DAC e H) e categórica (T: Substrato 1 – T1; Substrato 2 – T2; Substrato 3 – T3 e Substrato 4 – T4). Conclui-se, assim, que a modelagem por RNA utilizando arquitetura MLP é adequada e precisa para estimar a altura de mudas da Tibouchina granulosa