Use este identificador para citar ou linkar para este item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1092118
Título: Prediction of soil CO2 flux in sugarcane management systems using the Random Forest approach.
Autoria: TAVARES, R. L. M.
OLIVEIRA, S. R. de M.
BARROS, F. M. M. de
FARHATE, C. V. V.
SOUZA, Z. M. de
LA SCALA JUNIOR, N.
Afiliação: ROSE 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.
Ano de publicação: 2018
Referência: Scientia Agricola, Piracicaba, v. 74, n. 4, p. 281-287, July/Aug. 2018.
Conteúdo: 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.
Thesagro: Saccharum Officinarum
Argila
Cana de Açúcar
NAL Thesaurus: Soil respiration
Clay
Soil organic carbon
Sugarcane
Palavras-chave: Green sugarcane
Mineração de dados
Data mining
Random Forest algorithm
Digital Object Identifier: http://dx.doi.org/10.1590/1678-992X-2017-0095
Tipo do material: Artigo de periódico
Acesso: openAccess
Aparece nas coleções:Artigo em periódico indexado (CNPTIA)

Arquivos associados a este item:
Arquivo Descrição TamanhoFormato 
APPredictionTavaresetal.pdf506,43 kBAdobe PDFThumbnail
Visualizar/Abrir

FacebookTwitterDeliciousLinkedInGoogle BookmarksMySpace