Data/hora: 02/06/2024 16:39:57

Provedor de dados: | 64
País: | Brazil
Título: | Multiple linear regression and random forest to predict and map soil properties using data from portable X-ray fluorescence spectrometer (pXRF)
Autores: | Silva,Sérgio Henrique Godinho; Teixeira,Anita Fernanda dos Santos; Menezes,Michele Duarte de; Guilherme,Luiz Roberto Guimarães; Moreira,Fatima Maria de Souza; Curi,Nilton.
Data: | 2017-12-01
Ano: | 2017
Palavras-chave: | Soil analyses; Spatial prediction; Proximal sensor.
Resumo: | ABSTRACT Determination of soil properties ​​helps in the correct management of soil fertility. The portable X-ray fluorescence spectrometer (pXRF) has been recently adopted to determine total chemical element contents in soils, allowing soil property inferences. However, these studies are still scarce in Brazil and other countries. The objectives of this work were to predict soil properties using pXRF data, comparing stepwise multiple linear regression (SMLR) and random forest (RF) methods, as well as mapping and validating soil properties. 120 soil samples were collected at three depths and submitted to laboratory analyses. pXRF was used in the samples and total element contents were determined. From pXRF data, SMLR and RF were used to predict soil laboratory results, reflecting soil properties, and the models were validated. The best method was used to spatialize soil properties. Using SMLR, models had high values of R² (≥0.8), however the highest accuracy was obtained in RF modeling. Exchangeable Ca, Al, Mg, potential and effective cation exchange capacity, soil organic matter, pH, and base saturation had adequate adjustment and accurate predictions with RF. Eight out of the 10 soil properties predicted by RF using pXRF data had CaO as the most important variable helping predictions, followed by P2O5, Zn and Cr. Maps generated using RF from pXRF data had high accuracy for six soil properties, reaching R2 up to 0.83. pXRF in association with RF can be used to predict soil properties with high accuracy at low cost and time, besides providing variables aiding digital soil mapping.
Tipo: | Info:eu-repo/semantics/article
Idioma: | Inglês
Identificador: | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1413-70542017000600648
Editor: | Editora da Universidade Federal de Lavras
Relação: | 10.1590/1413-70542017416010317
Formato: | text/html
Fonte: | Ciência e Agrotecnologia  v.41 n.6 2017
Fonte: | info:eu-repo/semantics/openAccess