Prediction of soil CO2 flux in sugarcane management systems using the Random Forest approach

Detalhes bibliográficos
Autor(a) principal: Tavares, Rose Luiza Moraes
Data de Publicação: 2018
Outros Autores: Oliveira, Stanley Robson de Medeiros, Barros, Flávio Margarito Martins de, Farhate, Camila Viana Vieira, Souza, Zigomar Menezes de, Scala Junior, Newton La
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Scientia Agrícola (Online)
Texto Completo: https://www.revistas.usp.br/sa/article/view/144630
Resumo: The Random Forest algorithm is a data mining technique used for classifying attributes in order of importance to explain the variation in an attribute-target, as soil CO2 flux. This study aimed to identify prediction of soil CO2 flux variables in management systems of sugarcane through the machine-learning algorithm called Random Forest. Two different management areas of sugarcane in the state of São Paulo, Brazil, were selected: burned and green. In each area, we assembled a sampling grid with 81 georeferenced points to assess soil CO2 flux through automated portable soil gas chamber with measuring spectroscopy in the infrared during the dry season of 2011 and the rainy season of 2012. In addition, we sampled the soil to evaluate physical, chemical, and microbiological attributes. For data interpretation, we used the Random Forest algorithm, based on the combination of predicted decision trees (machine learning algorithms) in which every tree depends on the values of a random vector sampled independently with the same distribution to all the trees of the forest. The results indicated that clay content in the soil was the most important attribute to explain the CO2 flux in the areas studied during the evaluated period. The use of the Random Forest algorithm originated a model with a good fit (R2 = 0.80) for predicted and observed values.
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spelling Prediction of soil CO2 flux in sugarcane management systems using the Random Forest approachSaccharum officinarumsoil respirationgreen sugarcaneclay The Random Forest algorithm is a data mining technique used for classifying attributes in order of importance to explain the variation in an attribute-target, as soil CO2 flux. This study aimed to identify prediction of soil CO2 flux variables in management systems of sugarcane through the machine-learning algorithm called Random Forest. Two different management areas of sugarcane in the state of São Paulo, Brazil, were selected: burned and green. In each area, we assembled a sampling grid with 81 georeferenced points to assess soil CO2 flux through automated portable soil gas chamber with measuring spectroscopy in the infrared during the dry season of 2011 and the rainy season of 2012. In addition, we sampled the soil to evaluate physical, chemical, and microbiological attributes. For data interpretation, we used the Random Forest algorithm, based on the combination of predicted decision trees (machine learning algorithms) in which every tree depends on the values of a random vector sampled independently with the same distribution to all the trees of the forest. The results indicated that clay content in the soil was the most important attribute to explain the CO2 flux in the areas studied during the evaluated period. The use of the Random Forest algorithm originated a model with a good fit (R2 = 0.80) for predicted and observed values.Universidade de São Paulo. Escola Superior de Agricultura Luiz de Queiroz2018-08-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://www.revistas.usp.br/sa/article/view/14463010.1590/1678-992x-2017-0095Scientia Agricola; v. 75 n. 4 (2018); 281-287Scientia Agricola; Vol. 75 Núm. 4 (2018); 281-287Scientia Agricola; Vol. 75 No. 4 (2018); 281-2871678-992X0103-9016reponame:Scientia Agrícola (Online)instname:Universidade de São Paulo (USP)instacron:USPenghttps://www.revistas.usp.br/sa/article/view/144630/138937Copyright (c) 2018 Scientia Agricolainfo:eu-repo/semantics/openAccessTavares, Rose Luiza MoraesOliveira, Stanley Robson de MedeirosBarros, Flávio Margarito Martins deFarhate, Camila Viana VieiraSouza, Zigomar Menezes deScala Junior, Newton La2018-03-22T20:13:05Zoai:revistas.usp.br:article/144630Revistahttp://revistas.usp.br/sa/indexPUBhttps://old.scielo.br/oai/scielo-oai.phpscientia@usp.br||alleoni@usp.br1678-992X0103-9016opendoar:2018-03-22T20:13:05Scientia Agrícola (Online) - Universidade de São Paulo (USP)false
dc.title.none.fl_str_mv Prediction of soil CO2 flux in sugarcane management systems using the Random Forest approach
title Prediction of soil CO2 flux in sugarcane management systems using the Random Forest approach
spellingShingle Prediction of soil CO2 flux in sugarcane management systems using the Random Forest approach
Tavares, Rose Luiza Moraes
Saccharum officinarum
soil respiration
green sugarcane
clay
title_short Prediction of soil CO2 flux in sugarcane management systems using the Random Forest approach
title_full Prediction of soil CO2 flux in sugarcane management systems using the Random Forest approach
title_fullStr Prediction of soil CO2 flux in sugarcane management systems using the Random Forest approach
title_full_unstemmed Prediction of soil CO2 flux in sugarcane management systems using the Random Forest approach
title_sort Prediction of soil CO2 flux in sugarcane management systems using the Random Forest approach
author Tavares, Rose Luiza Moraes
author_facet Tavares, Rose Luiza Moraes
Oliveira, Stanley Robson de Medeiros
Barros, Flávio Margarito Martins de
Farhate, Camila Viana Vieira
Souza, Zigomar Menezes de
Scala Junior, Newton La
author_role author
author2 Oliveira, Stanley Robson de Medeiros
Barros, Flávio Margarito Martins de
Farhate, Camila Viana Vieira
Souza, Zigomar Menezes de
Scala Junior, Newton La
author2_role author
author
author
author
author
dc.contributor.author.fl_str_mv Tavares, Rose Luiza Moraes
Oliveira, Stanley Robson de Medeiros
Barros, Flávio Margarito Martins de
Farhate, Camila Viana Vieira
Souza, Zigomar Menezes de
Scala Junior, Newton La
dc.subject.por.fl_str_mv Saccharum officinarum
soil respiration
green sugarcane
clay
topic Saccharum officinarum
soil respiration
green sugarcane
clay
description The Random Forest algorithm is a data mining technique used for classifying attributes in order of importance to explain the variation in an attribute-target, as soil CO2 flux. This study aimed to identify prediction of soil CO2 flux variables in management systems of sugarcane through the machine-learning algorithm called Random Forest. Two different management areas of sugarcane in the state of São Paulo, Brazil, were selected: burned and green. In each area, we assembled a sampling grid with 81 georeferenced points to assess soil CO2 flux through automated portable soil gas chamber with measuring spectroscopy in the infrared during the dry season of 2011 and the rainy season of 2012. In addition, we sampled the soil to evaluate physical, chemical, and microbiological attributes. For data interpretation, we used the Random Forest algorithm, based on the combination of predicted decision trees (machine learning algorithms) in which every tree depends on the values of a random vector sampled independently with the same distribution to all the trees of the forest. The results indicated that clay content in the soil was the most important attribute to explain the CO2 flux in the areas studied during the evaluated period. The use of the Random Forest algorithm originated a model with a good fit (R2 = 0.80) for predicted and observed values.
publishDate 2018
dc.date.none.fl_str_mv 2018-08-01
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/144630
10.1590/1678-992x-2017-0095
url https://www.revistas.usp.br/sa/article/view/144630
identifier_str_mv 10.1590/1678-992x-2017-0095
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv https://www.revistas.usp.br/sa/article/view/144630/138937
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. 4 (2018); 281-287
Scientia Agricola; Vol. 75 Núm. 4 (2018); 281-287
Scientia Agricola; Vol. 75 No. 4 (2018); 281-287
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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