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Prediction of Topsoil Texture Through Regression Trees and Multiple Linear Regressions Rev. Bras. Ciênc. Solo
Pinheiro,Helena Saraiva Koenow; Carvalho Junior,Waldir de; Chagas,César da Silva; Anjos,Lúcia Helena Cunha dos; Owens,Phillip Ray.
ABSTRACT: Users of soil survey products are mostly interested in understanding how soil properties vary in space and time. The aim of digital soil mapping (DSM) is to represent the spatial variability of soil properties quantitatively to support decision-making. The goal of this study is to evaluate DSM techniques (Regression Trees - RT and Multiple Linear Regressions - MLR) and the ability of these tools to predict mineral fraction content under a wide variability of landscapes. The study site was the entire Guapi-Macacu watershed (1,250.78 km2) in the state of Rio de Janeiro in the Southeast region of Brazil. Terrain attributes and remote sensing data (with 30 m of spatial resolution) were used to represent landscape co-variables selected as an input in...
Tipo: Info:eu-repo/semantics/article Palavras-chave: Terrain attributes; Soil depth functions; Digital soil mapping; Regression models.
Ano: 2018 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-06832018000100304
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