Classification of soil respiration in areas of sugarcane renewal using decision tree
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , , , , |
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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Scientia Agrícola (Online) |
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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 |
_version_ |
1800222793354182656 |