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

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

ABSTRACT: 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.

Saved in:
Bibliographic Details
Main Authors: 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
Format: Digital revista
Language:English
Published: Escola Superior de Agricultura "Luiz de Queiroz" 2018
Online Access:http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162018000400281
Tags: Add Tag
No Tags, Be the first to tag this record!
id oai:scielo:S0103-90162018000400281
record_format ojs
spelling oai:scielo:S0103-901620180004002812018-03-19Prediction of soil CO2 flux in sugarcane management systems using the Random Forest approachTavares,Rose Luiza MoraesOliveira,Stanley Robson de MedeirosBarros,Flávio Margarito Martins deFarhate,Camila Viana VieiraSouza,Zigomar Menezes deScala Junior,Newton La Saccharum officinarum soil respiration green sugarcane clay ABSTRACT: 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.info:eu-repo/semantics/openAccessEscola Superior de Agricultura "Luiz de Queiroz"Scientia Agricola v.75 n.4 20182018-08-01info:eu-repo/semantics/articletext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162018000400281en10.1590/1678-992x-2017-0095
institution SCIELO
collection OJS
country Brasil
countrycode BR
component Revista
access En linea
databasecode rev-scielo-br
tag revista
region America del Sur
libraryname SciELO
language English
format Digital
author 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
spellingShingle 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
Prediction of soil CO2 flux in sugarcane management systems using the Random Forest approach
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_sort Tavares,Rose Luiza Moraes
title Prediction of soil CO2 flux in sugarcane management systems using the Random Forest approach
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
description ABSTRACT: 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.
publisher Escola Superior de Agricultura "Luiz de Queiroz"
publishDate 2018
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162018000400281
work_keys_str_mv AT tavaresroseluizamoraes predictionofsoilco2fluxinsugarcanemanagementsystemsusingtherandomforestapproach
AT oliveirastanleyrobsondemedeiros predictionofsoilco2fluxinsugarcanemanagementsystemsusingtherandomforestapproach
AT barrosflaviomargaritomartinsde predictionofsoilco2fluxinsugarcanemanagementsystemsusingtherandomforestapproach
AT farhatecamilavianavieira predictionofsoilco2fluxinsugarcanemanagementsystemsusingtherandomforestapproach
AT souzazigomarmenezesde predictionofsoilco2fluxinsugarcanemanagementsystemsusingtherandomforestapproach
AT scalajuniornewtonla predictionofsoilco2fluxinsugarcanemanagementsystemsusingtherandomforestapproach
_version_ 1756407138057453568

Similar Items