Ecophysiology modeling by artificial neural  networks for different spacings in eucalypt

Lafetá, Bruno Oliveira; Santana, Reynaldo Campos; Nogueira, Gilciano Saraiva; Penido, Tamires Mousslech Andrade; Vieira, Diego dos Santos

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dc.creator	Lafetá, Bruno Oliveira
dc.creator	Santana, Reynaldo Campos
dc.creator	Nogueira, Gilciano Saraiva
dc.creator	Penido, Tamires Mousslech Andrade
dc.creator	Vieira, Diego dos Santos
dc.date	2018-11-04
dc.date.accessioned	2023-03-30T19:35:11Z
dc.date.available	2023-03-30T19:35:11Z
dc.identifier	https://comunicatascientiae.com.br/comunicata/article/view/2741
dc.identifier	10.14295/cs.v9i3.2741
dc.identifier.uri	https://biblioteca-repositorio.clacso.edu.ar/handle/CLACSO/246804
dc.description	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.	en-US
dc.description	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.	pt-BR
dc.format	application/pdf
dc.format	application/vnd.openxmlformats-officedocument.wordprocessingml.document
dc.format	image/jpeg
dc.language	eng
dc.language	por
dc.publisher	Federal University of Piauí	en-US
dc.relation	https://comunicatascientiae.com.br/comunicata/article/view/2741/576
dc.relation	https://comunicatascientiae.com.br/comunicata/article/view/2741/709
dc.relation	https://comunicatascientiae.com.br/comunicata/article/view/2741/758
dc.rights	Copyright (c) 2018 Bruno Oliveira Lafetá, Reynaldo Campos Santana, Gilciano Saraiva Nogueira, Tamires Mousslech Andrade Penido, Diego dos Santos Vieira	en-US
dc.source	Comunicata Scientiae; Vol. 9 No. 3 (2018); 438-448	en-US
dc.source	Comunicata Scientiae; v. 9 n. 3 (2018); 438-448	pt-BR
dc.source	2177-5133
dc.source	2176-9079
dc.subject	biomassa de lenho	pt-BR
dc.subject	densidade de plantio	pt-BR
dc.subject	ecofisiologia	pt-BR
dc.subject	prognose	pt-BR
dc.subject	RNA	pt-BR
dc.title	Ecophysiology modeling by artificial neural networks for different spacings in eucalypt	en-US
dc.title	Ecophysiology modeling by artificial neural networks for different spacings in eucalypt	pt-BR
dc.type	info:eu-repo/semantics/article
dc.type	info:eu-repo/semantics/publishedVersion
dc.type	Artigo Científico	pt-BR