Classification of soil respiration in areas of sugarcane renewal using decision treecultural
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , , , , |
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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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 |
|
_version_ |
1808128840644427776 |