Brazilian Soil Bulk Density Prediction Based on a Committee of Neural Regressors
Diego B. Haddad, Laura Silva de Assis, Luís Domingues Tomé Jardim Tarrataca, Andréa da Silva Gomes, Marcos Bacis Ceddia, Rosane Ferreira de Oliveira, Jurair R. de P., Diego Nunes Brandão · 2018
Computer models have been an important tool to determine soil bulk density. This soil property is fundamental to estimate soil carbon reserves and consequently to understand the global carbon cycle. The estimation of soil bulk density is not a trivial task since it demands an intensive and often impractical work. The purpose of this paper is to evaluate the performance of a pedotransfer function against an Artificial Neural Networks to estimate soil bulk density for soils at Brazilian biomes. The first one consists of a linear model composed of a Least Square method. The latter employs a robust committee of multilayer perceptron networks and a model selection procedure based on k-fold cross-validation. The data are composed of 3404 soil layers distributed in different Brazilian regions and with different uses. The proposed non-linear regressor presents higher precision when compared to the linear model, and requires less information to do so. Additionally, the developed solution brings to light the assumed relationship between soil bulk density and some soil chemical properties.