Soils of the Brazilian Coastal Plains biome: prediction of chemical attributes via portable X-ray fluorescence (pXRF) spectrometry and robust prediction models
Álvaro José Gomes de Faria A , Sérgio Henrique Godinho Silva A , Leônidas Carrijo Azevedo Melo
A , Renata Andrade A , Marcelo Mancini A , Luiz Felipe Mesquita B , Anita Fernanda dos Santos Teixeira A , Luiz Roberto Guimarães Guilherme A and Nilton Curi A C
+ Author Affiliations
- Author Affiliations
A Federal University of Lavras, Soil Science Department, PO Box 3037, 37200-000, Lavras – MG, Brazil.
B Suzano Papel e Celulose, Espírito Santo, ES, Brazil.
C Corresponding author: Email: niltcuri@ufla.br
Soil Research 58(7) 683-695 https://doi.org/10.1071/SR20136
Submitted: 9 May 2020 Accepted: 24 July 2020 Published: 19 August 2020
Abstract
Portable X-ray fluorescence (pXRF) spectrometry has been successfully used for soil attribute prediction. However, recent studies have shown that accurate predictions may vary according to soil type and environmental conditions, motivating investigations in different biomes. Hence, this work attempted to accurately predict soil pH, sum of bases (SB), cation exchange capacity (CEC) at pH 7.0 and base saturation (BS) using pXRF-obtained data with high variability and robust prediction models in the Brazilian Coastal Plains biome. A total of 285 soil samples were collected to generate prediction models for A (n = 123), B (n = 162) and A+B (n = 285) horizons through stepwise multiple linear regression, support vector machine with linear kernel (SVM) and random forest. Data were divided into calibration (75%) and validation (25%) sets. Accuracy of the predictions was assessed by coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE) and residual prediction deviation (RPD). The A+B horizons dataset had optimal performance, especially for SB predictions using SVM, achieving R2 = 0.82, RMSE = 1.02 cmolc dm–3, MAE = 1.17 cmolc dm–3 and RPD = 2.33. The most important predictor variable was Ca. Predictions using pXRF data were accurate especially for SB. Limitations of the predictions caused by soil classes and environmental conditions should be further investigated in other regions.
Additional keywords: modelling, soil analysis, soil fertility, tropical soils.
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