Data/hora: 12/06/2024 03:42:51

Provedor de dados: | 64
País: | Brazil
Título: | Correcting field determination of elemental contents in soils via portable X-ray fluorescence spectrometry
Autores: | Dijair,Thaís Santos Branco; Silva,Fernanda Magno; Teixeira,Anita Fernanda dos Santos; Silva,Sérgio Henrique Godinho; Guilherme,Luiz Roberto Guimarães; Curi,Nilton.
Data: | 2020-01-01
Ano: | 2020
Palavras-chave: | PXRF; Soil moisture; Soil texture; Soil organic matter; Prediction models.
Resumo: | ABSTRACT Portable X-ray fluorescence (pXRF) spectrometry has been useful worldwide for determining soil elemental content under both field and laboratory conditions. However, the field results are influenced by several factors, including soil moisture (M), soil texture (T) and soil organic matter (SOM). Thus, the objective of this work was to create linear mathematical models for conversion of Al2O3, CaO, Fe, K2O, SiO2, V, Ti and Zr contents obtained by pXRF directly in field to those obtained under laboratory conditions, i.e., in air-dried fine earth (ADFE), using M, T and SOM as auxiliary variables, since they influence pXRF results. pXRF analyses in field were performed on 12 soil profiles with different parent materials. From them, 59 samples were collected and also analyzed in the laboratory in ADFE. pXRF field data were used alone or combined to M, T and SOM data as auxiliary variables to create linear regression models to predict pXRF ADFE results. The models accuracy was assessed by the leave-one-out cross-validation method. Except for light-weight elements, field results underestimated the total elemental contents compared with ADFE. Prediction models including T presented higher accuracy to predict Al2O3, SiO2, V, Ti and Zr, while the prediction of Fe and K2O contents was insensitive to the addition of the auxiliary variables. The relative improvement (RI) in the prediction models were greater in predictions of SiO2 (T+SOM: RI=22.29%), V (M+T: RI=18.90%) and Ti (T+SOM: RI=11.18%). This study demonstrates it is possible to correct field pXRF data through linear regression models.
Tipo: | Info:eu-repo/semantics/article
Idioma: | Inglês
Identificador: | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1413-70542020000100211
Editor: | Editora da Universidade Federal de Lavras
Relação: | 10.1590/1413-7054202044002420
Formato: | text/html
Fonte: | Ciência e Agrotecnologia  v.44 2020
Fonte: | info:eu-repo/semantics/openAccess