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| DE FIGUEIREDO, E. B.; JAYASUNDARA, S.; RONQUIM, C. C.; BORDONAL, R. DE O.; BERCHIELLI, T. T.; REIS, R. A.; WAGNER-RIDDLE, C.; LA SCALA JUNIOR, N.. |
| This study estimates the GHG balance (emissions and sinks) related to the beef cattle production in three contrasting production scenarios on Brachiaria pasture in Brazil: 1) Degraded pasture (DP), 2) Managed pasture (MP), and 3) Crop?livestock?forest integration system (CLFIS). |
| Tipo: Resumo em anais de congresso (ALICE) |
Palavras-chave: Gas emissions. |
| Ano: 2015 |
URL: http://www.alice.cnptia.embrapa.br/handle/doc/1026479 |
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| 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.. |
| 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,... |
| Tipo: Artigo em periódico indexado (ALICE) |
Palavras-chave: Green sugarcane; Mineração de dados; Data mining; Random Forest algorithm; Saccharum Officinarum; Argila; Cana de Açúcar; Soil respiration; Clay; Soil organic carbon; Sugarcane. |
| Ano: 2018 |
URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1092118 |
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