Classification of soil respiration in areas of sugarcane renewal using decision tree

Detalhes bibliográficos
Autor(a) principal: Farhate, Camila Viana Vieira
Data de Publicação: 2018
Outros Autores: Souza, Zigomar Menezes de, Oliveira, Stanley Robson de Medeiros, Carvalho, João Luís Nunes, Scala Júnior, Newton La, Santos, Ana Paula Guimarães
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Scientia Agrícola (Online)
Texto Completo: https://www.revistas.usp.br/sa/article/view/142948
Resumo: The use of data mining is a promising alternative to predict soil respiration from correlated variables. Our objective was to build a model using variable selection and decision tree induction to predict different levels of soil respiration, taking into account physical, chemical and microbiological variables of soil as well as precipitation in renewal of sugarcane areas. The original dataset was composed of 19 variables (18 independent variables and one dependent (or response) variable). The variable-target refers to soil respiration as the target classification. Due to a large number of variables, a procedure for variable selection was conducted to remove those with low correlation with the variable-target. For that purpose, four approaches of variable selection were evaluated: no variable selection, correlation-based feature selection (CFS), chisquare method (χ2) and Wrapper. To classify soil respiration, we used the decision tree induction technique available in the Weka software package. Our results showed that data mining techniques allow the development of a model for soil respiration classification with accuracy of 81 %, resulting in a knowledge base composed of 27 rules for prediction of soil respiration. In particular, the wrapper method for variable selection identified a subset of only five variables out of 18 available in the original dataset, and they had the following order of influence in determining soil respiration: soil temperature >; precipitation >; macroporosity >; soil moisture >; potential acidity.
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spelling Classification of soil respiration in areas of sugarcane renewal using decision treesoil CO2 emissiondata miningvariable selectionsoil temperaturesoil organic matter The use of data mining is a promising alternative to predict soil respiration from correlated variables. Our objective was to build a model using variable selection and decision tree induction to predict different levels of soil respiration, taking into account physical, chemical and microbiological variables of soil as well as precipitation in renewal of sugarcane areas. The original dataset was composed of 19 variables (18 independent variables and one dependent (or response) variable). The variable-target refers to soil respiration as the target classification. Due to a large number of variables, a procedure for variable selection was conducted to remove those with low correlation with the variable-target. For that purpose, four approaches of variable selection were evaluated: no variable selection, correlation-based feature selection (CFS), chisquare method (χ2) and Wrapper. To classify soil respiration, we used the decision tree induction technique available in the Weka software package. Our results showed that data mining techniques allow the development of a model for soil respiration classification with accuracy of 81 %, resulting in a knowledge base composed of 27 rules for prediction of soil respiration. In particular, the wrapper method for variable selection identified a subset of only five variables out of 18 available in the original dataset, and they had the following order of influence in determining soil respiration: soil temperature >; precipitation >; macroporosity >; soil moisture >; potential acidity.Universidade de São Paulo. Escola Superior de Agricultura Luiz de Queiroz2018-05-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://www.revistas.usp.br/sa/article/view/14294810.1590/1678-992x-2016-0473Scientia Agricola; v. 75 n. 3 (2018); 216-224Scientia Agricola; Vol. 75 Núm. 3 (2018); 216-224Scientia Agricola; Vol. 75 No. 3 (2018); 216-2241678-992X0103-9016reponame:Scientia Agrícola (Online)instname:Universidade de São Paulo (USP)instacron:USPenghttps://www.revistas.usp.br/sa/article/view/142948/137810Copyright (c) 2018 Scientia Agricolainfo:eu-repo/semantics/openAccessFarhate, Camila Viana VieiraSouza, Zigomar Menezes deOliveira, Stanley Robson de MedeirosCarvalho, João Luís NunesScala Júnior, Newton LaSantos, Ana Paula Guimarães2018-02-01T17:04:55Zoai:revistas.usp.br:article/142948Revistahttp://revistas.usp.br/sa/indexPUBhttps://old.scielo.br/oai/scielo-oai.phpscientia@usp.br||alleoni@usp.br1678-992X0103-9016opendoar:2018-02-01T17:04:55Scientia Agrícola (Online) - Universidade de São Paulo (USP)false
dc.title.none.fl_str_mv Classification of soil respiration in areas of sugarcane renewal using decision tree
title Classification of soil respiration in areas of sugarcane renewal using decision tree
spellingShingle Classification of soil respiration in areas of sugarcane renewal using decision tree
Farhate, Camila Viana Vieira
soil CO2 emission
data mining
variable selection
soil temperature
soil organic matter
title_short Classification of soil respiration in areas of sugarcane renewal using decision tree
title_full Classification of soil respiration in areas of sugarcane renewal using decision tree
title_fullStr Classification of soil respiration in areas of sugarcane renewal using decision tree
title_full_unstemmed Classification of soil respiration in areas of sugarcane renewal using decision tree
title_sort Classification of soil respiration in areas of sugarcane renewal using decision tree
author Farhate, Camila Viana Vieira
author_facet Farhate, Camila Viana Vieira
Souza, Zigomar Menezes de
Oliveira, Stanley Robson de Medeiros
Carvalho, João Luís Nunes
Scala Júnior, Newton La
Santos, Ana Paula Guimarães
author_role author
author2 Souza, Zigomar Menezes de
Oliveira, Stanley Robson de Medeiros
Carvalho, João Luís Nunes
Scala Júnior, Newton La
Santos, Ana Paula Guimarães
author2_role author
author
author
author
author
dc.contributor.author.fl_str_mv Farhate, Camila Viana Vieira
Souza, Zigomar Menezes de
Oliveira, Stanley Robson de Medeiros
Carvalho, João Luís Nunes
Scala Júnior, Newton La
Santos, Ana Paula Guimarães
dc.subject.por.fl_str_mv soil CO2 emission
data mining
variable selection
soil temperature
soil organic matter
topic soil CO2 emission
data mining
variable selection
soil temperature
soil organic matter
description The use of data mining is a promising alternative to predict soil respiration from correlated variables. Our objective was to build a model using variable selection and decision tree induction to predict different levels of soil respiration, taking into account physical, chemical and microbiological variables of soil as well as precipitation in renewal of sugarcane areas. The original dataset was composed of 19 variables (18 independent variables and one dependent (or response) variable). The variable-target refers to soil respiration as the target classification. Due to a large number of variables, a procedure for variable selection was conducted to remove those with low correlation with the variable-target. For that purpose, four approaches of variable selection were evaluated: no variable selection, correlation-based feature selection (CFS), chisquare method (χ2) and Wrapper. To classify soil respiration, we used the decision tree induction technique available in the Weka software package. Our results showed that data mining techniques allow the development of a model for soil respiration classification with accuracy of 81 %, resulting in a knowledge base composed of 27 rules for prediction of soil respiration. In particular, the wrapper method for variable selection identified a subset of only five variables out of 18 available in the original dataset, and they had the following order of influence in determining soil respiration: soil temperature >; precipitation >; macroporosity >; soil moisture >; potential acidity.
publishDate 2018
dc.date.none.fl_str_mv 2018-05-01
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://www.revistas.usp.br/sa/article/view/142948
10.1590/1678-992x-2016-0473
url https://www.revistas.usp.br/sa/article/view/142948
identifier_str_mv 10.1590/1678-992x-2016-0473
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv https://www.revistas.usp.br/sa/article/view/142948/137810
dc.rights.driver.fl_str_mv Copyright (c) 2018 Scientia Agricola
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Copyright (c) 2018 Scientia Agricola
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universidade de São Paulo. Escola Superior de Agricultura Luiz de Queiroz
publisher.none.fl_str_mv Universidade de São Paulo. Escola Superior de Agricultura Luiz de Queiroz
dc.source.none.fl_str_mv Scientia Agricola; v. 75 n. 3 (2018); 216-224
Scientia Agricola; Vol. 75 Núm. 3 (2018); 216-224
Scientia Agricola; Vol. 75 No. 3 (2018); 216-224
1678-992X
0103-9016
reponame:Scientia Agrícola (Online)
instname:Universidade de São Paulo (USP)
instacron:USP
instname_str Universidade de São Paulo (USP)
instacron_str USP
institution USP
reponame_str Scientia Agrícola (Online)
collection Scientia Agrícola (Online)
repository.name.fl_str_mv Scientia Agrícola (Online) - Universidade de São Paulo (USP)
repository.mail.fl_str_mv scientia@usp.br||alleoni@usp.br
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