Please use this identifier to cite or link to this item: http://repositorio.ufla.br/jspui/handle/1/48052
metadata.artigo.dc.title: Soils of the Brazilian Coastal Plains biome: prediction of chemical attributes via portable X-ray fluorescence (pXRF) spectrometry and robust prediction models
metadata.artigo.dc.creator: Faria, Álvaro José Gomes de
Silva, Sérgio Henrique Godinho
Melo, Leônidas Carrijo Azevedo
Andrade, Renata
Mancini, Marcelo
Mesquita, Luiz Felipe
Teixeira, Anita Fernanda dos Santos
Guilherme, Luiz Roberto Guimarães
Curi, Nilton
metadata.artigo.dc.subject: Soil analysis
Soil fertility
Tropical soils
Modelling
Solos - Análise
Solos - Fertilidade
Solos tropicais
metadata.artigo.dc.publisher: CSIRO
metadata.artigo.dc.date.issued: 2020
metadata.artigo.dc.identifier.citation: FARIA, A. J. G. de. Soils of the Brazilian Coastal Plains biome: prediction of chemical attributes via portable X-ray fluorescence (pXRF) spectrometry and robust prediction models. Soil Research, Rome, 2020. DOI: 10.1071/SR20136.
metadata.artigo.dc.description.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.
metadata.artigo.dc.identifier.uri: https://doi.org/10.1071/SR20136
http://repositorio.ufla.br/jspui/handle/1/48052
metadata.artigo.dc.language: en_US
Appears in Collections:DCS - Artigos publicados em periódicos

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