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

Detalhes bibliográficos
Autor(a) principal: Vieira Farhate, Camila Viana
Data de Publicação: 2018
Outros Autores: Souza, Zigomar Menezes de, Medeiros Oliveira, Stanley Robson de, Nunes Carvalho, Joao Luis, La Scala Junior, Newton [UNESP], Guimaraes Santos, Ana Paula
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1590/1678-992X-2016-0473
http://hdl.handle.net/11449/163821
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 (chi(2)) 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 treeculturalsoil CO2 emissiondata miningvariable selectionsoil temperaturesoil organic matterThe 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 (chi(2)) 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.Univ Estadual Campinas, FEAGRI, Av Candido Rondon 501, BR-13083875 Campinas, SP, BrazilEmbrapa Agr Informat, Computat Intelligence Lab, Av Andre Tosello 209, BR-13083886 Campinas, SP, BrazilBrazilian Ctr Res Energy & Mat, Brazilian Bioethanol Sci & Technol Lab, R Giuseppe Maximo Scolfaro 10000, BR-13083100 Campinas, SP, BrazilSao Paulo State Univ, Dept Exact Sci, Via Acesso Prof Paulo Donato Castellane S-N, BR-14884900 Jaboticabal, SP, BrazilSao Paulo State Univ, Dept Exact Sci, Via Acesso Prof Paulo Donato Castellane S-N, BR-14884900 Jaboticabal, SP, BrazilUniv Sao PaoloUniversidade Estadual de Campinas (UNICAMP)Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA)Brazilian Ctr Res Energy & MatUniversidade Estadual Paulista (Unesp)Vieira Farhate, Camila VianaSouza, Zigomar Menezes deMedeiros Oliveira, Stanley Robson deNunes Carvalho, Joao LuisLa Scala Junior, Newton [UNESP]Guimaraes Santos, Ana Paula2018-11-26T17:45:06Z2018-11-26T17:45:06Z2018-05-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article216-224application/pdfhttp://dx.doi.org/10.1590/1678-992X-2016-0473Scientia Agricola. Cerquera Cesar: Univ Sao Paolo, v. 75, n. 3, p. 216-224, 2018.1678-992Xhttp://hdl.handle.net/11449/16382110.1590/1678-992X-2016-0473S0103-90162018000300216WOS:000424389600006S0103-90162018000300216.pdfWeb of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengScientia Agricolainfo:eu-repo/semantics/openAccess2024-06-06T13:42:49Zoai:repositorio.unesp.br:11449/163821Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T17:39:37.958747Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Classification of soil respiration in areas of sugarcane renewal using decision treecultural
title Classification of soil respiration in areas of sugarcane renewal using decision treecultural
spellingShingle Classification of soil respiration in areas of sugarcane renewal using decision treecultural
Vieira Farhate, Camila Viana
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 treecultural
title_full Classification of soil respiration in areas of sugarcane renewal using decision treecultural
title_fullStr Classification of soil respiration in areas of sugarcane renewal using decision treecultural
title_full_unstemmed Classification of soil respiration in areas of sugarcane renewal using decision treecultural
title_sort Classification of soil respiration in areas of sugarcane renewal using decision treecultural
author Vieira Farhate, Camila Viana
author_facet Vieira Farhate, Camila Viana
Souza, Zigomar Menezes de
Medeiros Oliveira, Stanley Robson de
Nunes Carvalho, Joao Luis
La Scala Junior, Newton [UNESP]
Guimaraes Santos, Ana Paula
author_role author
author2 Souza, Zigomar Menezes de
Medeiros Oliveira, Stanley Robson de
Nunes Carvalho, Joao Luis
La Scala Junior, Newton [UNESP]
Guimaraes Santos, Ana Paula
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Universidade Estadual de Campinas (UNICAMP)
Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA)
Brazilian Ctr Res Energy & Mat
Universidade Estadual Paulista (Unesp)
dc.contributor.author.fl_str_mv Vieira Farhate, Camila Viana
Souza, Zigomar Menezes de
Medeiros Oliveira, Stanley Robson de
Nunes Carvalho, Joao Luis
La Scala Junior, Newton [UNESP]
Guimaraes Santos, Ana Paula
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 (chi(2)) 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 2018-11-26T17:45:06Z
2018-11-26T17:45:06Z
2018-05-01
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1590/1678-992X-2016-0473
Scientia Agricola. Cerquera Cesar: Univ Sao Paolo, v. 75, n. 3, p. 216-224, 2018.
1678-992X
http://hdl.handle.net/11449/163821
10.1590/1678-992X-2016-0473
S0103-90162018000300216
WOS:000424389600006
S0103-90162018000300216.pdf
url http://dx.doi.org/10.1590/1678-992X-2016-0473
http://hdl.handle.net/11449/163821
identifier_str_mv Scientia Agricola. Cerquera Cesar: Univ Sao Paolo, v. 75, n. 3, p. 216-224, 2018.
1678-992X
10.1590/1678-992X-2016-0473
S0103-90162018000300216
WOS:000424389600006
S0103-90162018000300216.pdf
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Scientia Agricola
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 216-224
application/pdf
dc.publisher.none.fl_str_mv Univ Sao Paolo
publisher.none.fl_str_mv Univ Sao Paolo
dc.source.none.fl_str_mv Web of Science
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
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