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dc.contributor.authorDART, R. de O.pt_BR
dc.contributor.authorVASQUES, G. M.pt_BR
dc.contributor.authorCOELHO, M. R.pt_BR
dc.contributor.authorFERNANDES, N. F.pt_BR
dc.date.accessioned2016-02-02T11:11:11Zpt_BR
dc.date.available2016-02-02T11:11:11Zpt_BR
dc.date.created2016-02-02pt_BR
dc.date.issued2015pt_BR
dc.identifier.citationIn: CONGRESSO BRASILEIRO DE CIÊNCIA DO SOLO, 35., 2015, Natal. O solo e suas múltiplas funções: anais. Natal: Sociedade Brasileira de Ciência do Solo, 2015.pt_BR
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1035920pt_BR
dc.descriptionInvestment 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.pt_BR
dc.language.isoengeng
dc.rightsopenAccesseng
dc.subjectCovariáveis ambientaispt_BR
dc.subjectÁrvore de classificaçãopt_BR
dc.subjectResolução espacialpt_BR
dc.titleDigital soil mapping for soil class prediction in a dry forest of Minas Gerais, Brazil.pt_BR
dc.typeArtigo em anais e proceedingspt_BR
dc.date.updated2016-02-18T11:11:11Zpt_BR
riaa.ainfo.id1035920pt_BR
riaa.ainfo.lastupdate2016-02-18pt_BR
dc.contributor.institutionRICARDO DE OLIVEIRA DART, CNPS; GUSTAVO DE MATTOS VASQUES, CNPS; MAURICIO RIZZATO COELHO, CNPS; NELSON FERREIRA FERNANDES, UFRJ.pt_BR
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