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

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dc.contributorROSE LUIZA MORAES TAVARES, Rio Verde University; STANLEY ROBSON DE MEDEIROS OLIVEIRA, CNPTIA; FLÁVIO MARGARITO MARTINS DE BARROS, Feagri/Unicamp; CAMILA VIANA VIEIRA FARHATE, Feagri/Unicamp; ZIGOMAR MENEZES DE SOUZA, Feagri/Unicamp; NEWTON LA SCALA JUNIOR, FCAV/Unesp.
dc.creatorTAVARES, R. L. M.
dc.creatorOLIVEIRA, S. R. de M.
dc.creatorBARROS, F. M. M. de
dc.creatorFARHATE, C. V. V.
dc.creatorSOUZA, Z. M. de
dc.creatorLA SCALA JUNIOR, N.
dc.date2018-06-02T00:35:29Z
dc.date2018-06-02T00:35:29Z
dc.date2018-06-01
dc.date2018
dc.date2018-06-06T11:11:11Z
dc.date.accessioned2026-07-07T04:15:36Z
dc.descriptionABSTRACT: 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.
dc.identifierScientia Agricola, Piracicaba, v. 74, n. 4, p. 281-287, July/Aug. 2018.
dc.identifierhttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1092118
dc.identifierhttp://dx.doi.org/10.1590/1678-992X-2017-0095
dc.identifier.urihttp://hdl.handle.net/123456789/455804
dc.languageeng
dc.rightsopenAccess
dc.subjectGreen sugarcane
dc.subjectMineração de dados
dc.subjectData mining
dc.subjectRandom Forest algorithm
dc.subjectSaccharum Officinarum
dc.subjectArgila
dc.subjectCana de Açúcar
dc.subjectSoil respiration
dc.subjectClay
dc.subjectSoil organic carbon
dc.subjectSugarcane
dc.titlePrediction of soil CO2 flux in sugarcane management systems using the Random Forest approach.
dc.typeArtigo de periódico

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