Data/hora: 27/07/2024 01:40:17

Biblioteca(s): 	Embrapa Agricultura Digital.	
Data corrente: 	01/06/2018	
Data da última atualização: 	06/06/2018	
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.	
Título: 	Prediction of soil CO2 flux in sugarcane management systems using the Random Forest approach.	
Ano de publicação: 	2018	
Fonte/Imprenta: 	Scientia Agricola, Piracicaba, v. 74, n. 4, p. 281-287, July/Aug. 2018.	
DOI: 	http://dx.doi.org/10.1590/1678-992X-2017-0095	
Idioma: 	Inglês	
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.	
Palavras-Chave: 	Data mining; Green sugarcane; Mineração de dados; Random Forest algorithm.	
Thesagro: 	Argila; Cana de Açúcar; Saccharum Officinarum.	
Thesaurus Nal: 	Clay; Soil organic carbon; Soil respiration; Sugarcane.	
Categoria do assunto: 	X Pesquisa, Tecnologia e Engenharia	
URL: 	http://ainfo.cnptia.embrapa.br/digital/bitstream/item/177973/1/AP-Prediction-Tavares-etal.pdf	
Registro original: 	Embrapa Agricultura Digital (CNPTIA)	
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