01474nam a2200181 a 450000100080000000500110000800800410001910000190006024501150007926001650019452007910035965300310115065300280118165300250120970000190123470000180125370000210127120359202016-02-18       2015    bl uuuu     u00u1 u    #d1 aDART, R. de O.  aDigital soil mapping for soil class prediction in a dry forest of Minas Gerais, Brazil.h[electronic resource]  aIn: CONGRESSO BRASILEIRO DE CIÊNCIA DO SOLO, 35., 2015, Natal. O solo e suas múltiplas funções: anais. Natal: Sociedade Brasileira de Ciência do Soloc2015  aInvestment on soil survey has become scarce over the past decades. Digital Soil Mapping (DSM) techniques emerged as an economic alternative to produce soil maps. We applied a classification tree algorithm to predict soil suborders in a tropical dry forest area with 102 km2 in the north of Minas Gerais state, Brazil. We tested environmental covariates with different spatial resolutions as predictors, and used 361 observations to train the model and 64 independent observations to validate the map. Prediction models included three decision trees and one logistic regression model. The results showed that freely available environmental covariates with coarser spatial resolution can produce as good or better suborder predictions than more expensive covariates with finer resolution.  aÁrvore de classificação  aCovariáveis ambientais  aResolução espacial1 aVASQUES, G. M.1 aCOELHO, M. R.1 aFERNANDES, N. F.