Characterizing soils via portable X-ray fluorescence spectrometer: 3. Soil reaction (pH)

Sharma, Aakriti; Weindorf, David C.; Man, Titus; Aldabaa, Abdalsamad Abdalsatar Ali; Chakraborty, Somsubhra

doi:10.1016/j.geoderma.2014.05.005

Cited by 125 publications

(63 citation statements)

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“…Here we demonstrated the potential of this equipment for predicting soil properties also in Brazilian soils, in accordance with Piikki et al (2016), who used pXRF coupled with three other sensors to predict results of laboratory soil analyses in Kenya, observing that pXRF was frequently employed in good models. Sharma et al (2014) used pXRF data to predict soil pH from linear regressions. Plots of observed and estimated values resulted from random forest prediction of soil properties for the whole study area.…”

Section: Order Of Importancementioning

confidence: 99%

Multiple linear regression and random forest to predict and map soil properties using data from portable X-ray fluorescence spectrometer (pXRF)

et al. 2017

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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 P 2 O 5 , Zn and Cr. Maps generated using RF from pXRF data had high accuracy for six soil properties, reaching R 2 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.Index terms: Soil analyses; spatial prediction; proximal sensor. RESUMOA determinação de atributos do solo auxilia no correto manejo da sua fertilidade. O equipamento portátil de fluorescência de raios-X (pXRF) foi recentemente adotado para determinar o teor total de elementos químicos em solos, permitindo inferências sobre atributos do solo. No entanto, esses estudos ainda são escassos no Brasil e em outros países. Os objetivos deste trabalho foram prever atributos do solo a partir de dados do pXRF, comparando-se os métodos de regressão linear múltipla stepwise (SMLR) e de random forest (RF), além de mapear e validar atributos do solo. 120 amostras de solo foram coletadas em três profundidades e submetidas a análises laboratoriais. Utilizou-se o pXRF para leitura das amostras e determinou-se o teor total de elementos. A partir dos dados do pXRF, foram utilizadas SMLR e RF para predizer resultados laboratoriais, que refletem atributos do solo, e os modelos foram validados. O melhor método foi utilizado para espacializar os atributos do solo. Utilizando SMLR, os modelos apresentaram valores elevados de R² (≥0,8), porém maior acurácia foi obtida na modelagem com RF. A capacidade de troca de cátions potencial e efetiva, matéria orgânica do solo, pH, saturação por bases e teores trocáveis de Ca, Al e Mg apresentaram ajustes adequados e predições acuradas com RF. Dos dez atributos do solo preditos por RF a partir de dados do pXRF, sete apr...

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Section: Order Of Importancementioning

confidence: 99%

Multiple linear regression and random forest to predict and map soil properties using data from portable X-ray fluorescence spectrometer (pXRF)

et al. 2017

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“…Additionally, another work showed that, by means of multiple linear regression, cation exchange capacity could be adequately estimated from the results of the pXRF, using 450 soil samples from California and Nebraska, USA (Sharma et al, 2015). Similarly, Sharma et al (2014) obtained a significant correlation between soil pH and the elemental composition of soil samples measured by pXRF, since low soil pH values are associated with elements such as Al, and high pH values are associated with elements such as Ca and Mg.…”

Section: Agronomic Applicationsmentioning

confidence: 94%

“…For this emission to occur, the sample must receive enough energy to excite the electrons of the elements that compose the sample. The technique requires that species be irradiated with very energetic photons and the excitation of the atom happens when it is struck by particles such as electrons, protons or ions produced in particle accelerators, electromagnetic waves or through X-ray tubes of Ta/ Au, Rh or Ag (Kalnicky and Singhvi, 2001;Gazley and Fisher, 2014;Sharma et al, 2014;Weindorf et al, 2014a), this latter being the most common process.…”

Section: Principles Of X-ray Fluorescence Analysesmentioning

confidence: 99%

“…When X-rays reach the surface of soil particles, the electrons are displaced from inner to outer shell and, as a consequence, the outer shell electrons move to fill inner-shell vacancies and energy is released in form of fluorescence (Sharma et al, 2014;Weindorf et al, 2014a). Each element has a characteristic spectral signature and the intensity of the energy is proportional to the element concentration in the material, thus the characteristic energy value (keV) is used for elemental identification, and the intensity of fluorescence allows for quantification (Gazley and Fisher, 2014;Weindorf et al, 2014a (Figure 1).…”

Section: Principles Of X-ray Fluorescence Analysesmentioning

confidence: 99%

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Portable X-ray fluorescence (pXRF) applications in tropical Soil Science

Ribeiro

Silva

et al. 2017

Ciênc. agrotec.

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The X-ray fluorescence (XRF) is an analytical technique for determination of elemental composition of different materials. In soils, the XRF has many pedological, environmental and agronomic applications, mainly after the emergence of portable equipments (pXRF). This technique has been recently adopted and successfully used for soil characterization worldwide, but very rare works have been carried out in soils of developing countries. The soil characterization includes the complete elemental composition determination (nutrients, trace and rare-earth elements) and allows estimating some soil physical and chemical properties. In Brazil, this technique is still incipient, mainly the use of pXRF, however, it can greatly contribute to soil characterization in-field or in-lab conditions and also replacing methods of soil analyses considered non-environmentally friendly. This review summarizes the XRF technique including principles and the main applications of pXRF in soils highlighting its potential for tropical Soil Science.Index terms: Soil analyses; soil morphometrics; soil characterization. RESUMOFluorescência de raios-X (FRX) é uma técnica analítica para determinação da composição elementar de diferentes materiais. Em solos, a FRX apresenta muitas aplicações pedológicas, ambientais e agronômicas, principalmente após a emergência de equipamentos portáteis (pXRF). Essa técnica tem sido utilizada com sucessso no mundo todo para caracterização do solo, entretanto, são raros os trabalhos em solos de países em desenvolvimento. A caracterização do solo inclui a determinação completa da composição elementar (nutrientes, elementos-traço e terras-raras) e permite a estimativa de atributos químicos e físicos do solo. No Brasil, a FRX é ainda incipiente, principalmente o uso do pXRF, entretanto, essa técnica pode contribuir grandemente para a caracterização do solo no campo, em condições laboratoriais e, também, substituindo alguns métodos de análise do solo considerados não prejudicial ao ambiente. Esta revisão sumariza a técnica de FRX incluindo princípios e as principais aplicações do pXRF, destacando seu potencial de uso na Ciência do Solo tropical. Termos para indexação:Análise do solo; morfometria do solo; caracterização do solo.

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“…Previously, PXRF and VisNIR DRS have been independently used to successfully predict a wide range of soil physicochemical properties, including organic C (Morgan et al, 2009;Chakraborty et al, 2013), gypsum content (Weindorf et al, 2009(Weindorf et al, , 2013a, salinity (Swanhart et al, 2014), pH (Sharma et al, 2014), texture (Zhu et al, 2011), cation exchange capacity , diagnostic subsurface horizons and features (Weindorf et al, 2012c), moisture , and organic and inorganic pollutants (Weindorf et al, 2012a(Weindorf et al, , 2013bChakraborty et al, 2010;Paulette et al, 2015). Most importantly, Weindorf et al (2012b) showed that PXRF could be used for enhanced soil horizonation whereby horizons could be differentiated using elemental data from PXRF in soil profiles where morphological differentia were unremarkable.…”

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confidence: 99%

Lithologic Discontinuity Assessment in Soils via Portable X-ray Fluorescence Spectrometry and Visible Near-Infrared Diffuse Reflectance Spectroscopy

Weindorf

Chakraborty

Abdalsatar

et al. 2015

Soil Science Society of America Journal

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Lithologic discontinuity identification can be arduous and erroneous in instances where distinct morphological differences between parent materials are absent. Often, investigators must wait for laboratory data to help differentiate parent materials via physicochemical properties. This study used visible near-infrared diffuse reflectance spectroscopy (VisNIR DRs) and portable X-ray fluorescence (PXRF) spectrometry for establishing parent material differentia more quickly. Ten pedons containing 135 samples were scanned in situ in the united states, Italy, and Hungary, morphologically described by trained pedologists, then sampled for standard laboratory characterization. Compared with laboratory data and/or morphologically described discontinuities, PXRF data were associated with large, abrupt changes in standardized PXRF differences of elements (DEs), noted in data plots as DE maxima and minima-areas of likely discontinuity. standardized VisNIR DRs calculated differences (CDs) in reflectance spectra (350-2500 nm) were also associated with discontinuities based on CD reflectance maxima and minima. Notably within both types of data plots, lithologic discontinuities were not well captured by the proximal sensors when CD or DE values fell in the data plot midsection (i.e., not at maxima or minima within the data plots). Across the pedons evaluated, PXRF was more useful for detecting discontinuities than VisNIR DRs. summarily, PXRF showed good alignment with morphologically established discontinuities in eight out of 10 pedons, while VisNIR DRs showed good alignment in only five pedons. Both PXRF and VisNIR DRs provided useful information for lithologic discontinuity recognition, especially in soils with nondescript morphology.Abbreviations: CD, calculated difference; DE, difference of elements; EC, electrical conductivity; LD, lithologic discontinuity; PCA, principal component analysis; PXRF, portable X-ray fluorescence; SOM, soil organic matter; VisNIR DRS, visible near-infrared diffuse reflectance spectroscopy.

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Characterizing soils via portable X-ray fluorescence spectrometer: 3. Soil reaction (pH)

Cited by 125 publications

References 13 publications

Multiple linear regression and random forest to predict and map soil properties using data from portable X-ray fluorescence spectrometer (pXRF)

Multiple linear regression and random forest to predict and map soil properties using data from portable X-ray fluorescence spectrometer (pXRF)

Portable X-ray fluorescence (pXRF) applications in tropical Soil Science

Lithologic Discontinuity Assessment in Soils via Portable X-ray Fluorescence Spectrometry and Visible Near-Infrared Diffuse Reflectance Spectroscopy

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