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| Farhate,Camila Viana Vieira; Souza,Zigomar Menezes de; Oliveira,Stanley Robson de Medeiros; Carvalho,João Luís Nunes; Scala Júnior,Newton La; Santos,Ana Paula Guimarães. |
| ABSTRACT: The use of data mining is a promising alternative to predict soil respiration from correlated variables. Our objective was to build a model using variable selection and decision tree induction to predict different levels of soil respiration, taking into account physical, chemical and microbiological variables of soil as well as precipitation in renewal of sugarcane areas. The original dataset was composed of 19 variables (18 independent variables and one dependent (or response) variable). The variable-target refers to soil respiration as the target classification. Due to a large number of variables, a procedure for variable selection was conducted to remove those with low correlation with the variable-target. For that purpose, four approaches of... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Soil CO2 emission; Data mining; Variable selection; Soil temperature; Soil organic matter. |
| Ano: 2018 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162018000300216 |
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