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Prediction and mapping of erodibility factors (USLE and WEPP) by magnetic susceptibility in basalt-derived soils in northeastern São Paulo state, Brazil

Barbosa, Ronny Sobreira, Marques Júnior, José, Barrón, Vidal, Martins Filho, Marcílio Vieira, Siqueira, Diego Silva, Peluco, Rafael Gonçalves, Camargo, Lívia Arantes, Silva, Laércio Santos
Environmental earth sciences 2019 v.78 no.1 pp. 12
Oxisols, Universal Soil Loss Equation, Water Erosion Prediction Project, chemical analysis, erodibility, geostatistics, iron, magnetism, monitoring, pedotransfer functions, prediction, regression analysis, soil sampling, sugarcane, tropics, water erosion, Brazil
Spatial assessment of soil erosion is essential for the adaptation of agricultural practices and monitoring of soil losses. In this sense, this study aims to assess the efficiency of magnetic susceptibility (MS) as a predictor of soil erodibility factors (K for USLE model; Kᵢ and Kᵣ for WEPP model) fora detailed mapping of Oxisols with different iron contents in northeastern São Paulo State, Brazil. This study was carried out in an area of 380 hectares under sugarcane cultivation in São Paulo State. Soil samples were collected in a sampling grid (150) and in a transect (86) and physical and chemical analyses and calculations of the erodibility factors/parameters K, Kᵢ, and Kᵣ were performed. Pedotransfer functions (PTFs) were calibrated using simple linear regression analysis to predict the factors/parameters K and Kᵢ using MS as a predictor variable. The observed values of MS and the predicted values of the factors/parameters K, Kᵢ, and Kᵣ were submitted to geostatistical analysis for constructing maps. Magnetic susceptibility can be used as a predictor of erodibility factors (K for USLE model; Kᵢ and Kᵣ for WEPP model) for Oxisols with total iron content ranging from 1 to 20% Fe₂O₃, with a precision of up to 60% and an accuracy of up to 85%. The results can guide future studies on water erosion in a tropical environment using magnetic soil data as an environmental covariate in the modeling process for large areas.