MACRONUTRIENTS USE EFFICIENCY IN EUCALYPT BY NON-DESTRUCTIVE METHODS ESTIMATED BY ARTIFICIAL NEURAL NETWORKS
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | Ciência Florestal (Online) |
Texto Completo: | https://periodicos.ufsm.br/cienciaflorestal/article/view/32049 |
Resumo: | 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. |
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Ciência Florestal (Online) |
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MACRONUTRIENTS USE EFFICIENCY IN EUCALYPT BY NON-DESTRUCTIVE METHODS ESTIMATED BY ARTIFICIAL NEURAL NETWORKSEFICIÊNCIA DE UTILIZAÇÃO DE MACRONUTRIENTES EM EUCALIPTO POR MÉTODO NÃO DESTRUTIVO ESTIMADOS POR REDES NEURAIS ARTIFICIAISCBUANNnon-destructive samplingplanting density.CUBRNAamostragem não destrutivadensidade de plantioThe 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.Universidade Federal de Santa Maria2018-06-29info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://periodicos.ufsm.br/cienciaflorestal/article/view/3204910.5902/1980509832049Ciência Florestal; Vol. 28 No. 2 (2018); 613-623Ciência Florestal; v. 28 n. 2 (2018); 613-6231980-50980103-9954reponame:Ciência Florestal (Online)instname:Universidade Federal de Santa Maria (UFSM)instacron:UFSMporhttps://periodicos.ufsm.br/cienciaflorestal/article/view/32049/pdfCopyright (c) 2018 Ciência Florestalinfo:eu-repo/semantics/openAccessLafetá, Bruno OliveiraSantana, Reynaldo CamposNogueira, Gilciano SaraivaNeves, Júlio César LimaPenido, Tamires Mousslech Andrade2018-06-29T11:07:47Zoai:ojs.pkp.sfu.ca:article/32049Revistahttp://www.ufsm.br/cienciaflorestal/ONGhttps://old.scielo.br/oai/scielo-oai.php||cienciaflorestal@ufsm.br|| cienciaflorestal@gmail.com|| cf@smail.ufsm.br1980-50980103-9954opendoar:2018-06-29T11:07:47Ciência Florestal (Online) - Universidade Federal de Santa Maria (UFSM)false |
dc.title.none.fl_str_mv |
MACRONUTRIENTS USE EFFICIENCY IN EUCALYPT BY NON-DESTRUCTIVE METHODS ESTIMATED BY ARTIFICIAL NEURAL NETWORKS EFICIÊNCIA DE UTILIZAÇÃO DE MACRONUTRIENTES EM EUCALIPTO POR MÉTODO NÃO DESTRUTIVO ESTIMADOS POR REDES NEURAIS ARTIFICIAIS |
title |
MACRONUTRIENTS USE EFFICIENCY IN EUCALYPT BY NON-DESTRUCTIVE METHODS ESTIMATED BY ARTIFICIAL NEURAL NETWORKS |
spellingShingle |
MACRONUTRIENTS USE EFFICIENCY IN EUCALYPT BY NON-DESTRUCTIVE METHODS ESTIMATED BY ARTIFICIAL NEURAL NETWORKS Lafetá, Bruno Oliveira CBU ANN non-destructive sampling planting density. CUB RNA amostragem não destrutiva densidade de plantio |
title_short |
MACRONUTRIENTS USE EFFICIENCY IN EUCALYPT BY NON-DESTRUCTIVE METHODS ESTIMATED BY ARTIFICIAL NEURAL NETWORKS |
title_full |
MACRONUTRIENTS USE EFFICIENCY IN EUCALYPT BY NON-DESTRUCTIVE METHODS ESTIMATED BY ARTIFICIAL NEURAL NETWORKS |
title_fullStr |
MACRONUTRIENTS USE EFFICIENCY IN EUCALYPT BY NON-DESTRUCTIVE METHODS ESTIMATED BY ARTIFICIAL NEURAL NETWORKS |
title_full_unstemmed |
MACRONUTRIENTS USE EFFICIENCY IN EUCALYPT BY NON-DESTRUCTIVE METHODS ESTIMATED BY ARTIFICIAL NEURAL NETWORKS |
title_sort |
MACRONUTRIENTS USE EFFICIENCY IN EUCALYPT BY NON-DESTRUCTIVE METHODS ESTIMATED BY ARTIFICIAL NEURAL NETWORKS |
author |
Lafetá, Bruno Oliveira |
author_facet |
Lafetá, Bruno Oliveira Santana, Reynaldo Campos Nogueira, Gilciano Saraiva Neves, Júlio César Lima Penido, Tamires Mousslech Andrade |
author_role |
author |
author2 |
Santana, Reynaldo Campos Nogueira, Gilciano Saraiva Neves, Júlio César Lima Penido, Tamires Mousslech Andrade |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Lafetá, Bruno Oliveira Santana, Reynaldo Campos Nogueira, Gilciano Saraiva Neves, Júlio César Lima Penido, Tamires Mousslech Andrade |
dc.subject.por.fl_str_mv |
CBU ANN non-destructive sampling planting density. CUB RNA amostragem não destrutiva densidade de plantio |
topic |
CBU ANN non-destructive sampling planting density. CUB RNA amostragem não destrutiva densidade de plantio |
description |
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. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-06-29 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://periodicos.ufsm.br/cienciaflorestal/article/view/32049 10.5902/1980509832049 |
url |
https://periodicos.ufsm.br/cienciaflorestal/article/view/32049 |
identifier_str_mv |
10.5902/1980509832049 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
https://periodicos.ufsm.br/cienciaflorestal/article/view/32049/pdf |
dc.rights.driver.fl_str_mv |
Copyright (c) 2018 Ciência Florestal info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2018 Ciência Florestal |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Universidade Federal de Santa Maria |
publisher.none.fl_str_mv |
Universidade Federal de Santa Maria |
dc.source.none.fl_str_mv |
Ciência Florestal; Vol. 28 No. 2 (2018); 613-623 Ciência Florestal; v. 28 n. 2 (2018); 613-623 1980-5098 0103-9954 reponame:Ciência Florestal (Online) instname:Universidade Federal de Santa Maria (UFSM) instacron:UFSM |
instname_str |
Universidade Federal de Santa Maria (UFSM) |
instacron_str |
UFSM |
institution |
UFSM |
reponame_str |
Ciência Florestal (Online) |
collection |
Ciência Florestal (Online) |
repository.name.fl_str_mv |
Ciência Florestal (Online) - Universidade Federal de Santa Maria (UFSM) |
repository.mail.fl_str_mv |
||cienciaflorestal@ufsm.br|| cienciaflorestal@gmail.com|| cf@smail.ufsm.br |
_version_ |
1799944133291278336 |