2 results

Prediction of Topsoil Texture Through Regression Trees and Multiple Linear Regressions

Helena Saraiva Koenow Pinheiro, Waldir de Carvalho, César da Silva Chagas, Lúcia Helena Cunha dos Anjos, Phillip Ray Owens

04/Apr/2018

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 […]

Pedotransfer functions related to spatial variability of water retention attributes for lowland soils

Álvaro Luiz Carvalho Nebel, Luís Carlos Timm, Wim Cornelis, Donald Gabriels, Klaus Reichardt, Leandro Sanzi Aquino, [...]

01/Jun/2010

The estimation of non available soil variables through the knowledge of other related measured variables can be achieved through pedotransfer functions (PTF) mainly saving time and reducing cost. Great differences among soils, however, can yield non desirable results when applying this method. This study discusses the application of developed PTFs by several authors using a variety of soils of different characteristics, to evaluate soil water contents of two Brazilian lowland soils. Comparisons are made between PTF evaluated data and field […]