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

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

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 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

PROJETO DE ENSINO DE LÍNGUA ESPANHOLA: INFOGRÁFICO

Neste trabalho, discutimos o papel do letramento científico como forma de construção da cidadania na escola. Fazemos um relato do trabalho desenvolvido no âmbito do Subprojeto do Programa Institucional de Bolsa de Iniciação à Docência - PIBID Letras-Espanhol, na Universidade do Vale do Rio dos Sinos – UNISINOS, buscando socializar os resultados alcançados no desenvolvimento de um projeto de ensino de Língua Espanhola que explora o gênero infográfico. O projeto foi implementado em uma escola de ensino médio em São Leopoldo, Rio Grande do Sul. Para abordar o letramento científico, baseamo-nos em autores como Santos (2007), Demo (2010) e Souza (2009, 2011), que tratam de conhecimentos relacionados à educação científica, à divulgação científica e ao gênero infográfico. O resultado do projeto evidenciou que os alunos foram capazes de captar informações em língua espanhola, transformando-as em infográficos, respeitando as características do gênero e produzindo um novo conhecimento

Crabronidae and Sphecidae (Hymenoptera: Apoidea) type specimens deposited in the Museu de Zoologia da Universidade de São Paulo, Brazil

This catalogue lists the type specimens of Crabronidae and Sphecidae (Hymenoptera: Apoidea) deposited in the Museu de Zoologia da Universidade de São Paulo, Brazil (MZUSP). The collection includes a total of 83 type specimens (17 holotypes, 66 paratypes), 82 of which belong to nine genera and 35 species of Crabronidae and only one of Sphecidae. All labels contents and additional information obtained from other available sources are presented. High resolution photographs of the primary types are also provided

Crabronidae and Sphecidae (Hymenoptera: Apoidea) type specimens deposited in the Museu de Zoologia da Universidade de São Paulo, Brazil

This catalogue lists the type specimens of Crabronidae and Sphecidae (Hymenoptera: Apoidea) deposited in the Museu de Zoologia da Universidade de São Paulo, Brazil (MZUSP). The collection includes a total of 83 type specimens (17 holotypes, 66 paratypes), 82 of which belong to nine genera and 35 species of Crabronidae and only one of Sphecidae. All labels contents and additional information obtained from other available sources are presented. High resolution photographs of the primary types are also provided

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

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

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