Soil texture prediction in tropical soils: A portable X-ray fluorescence spectrometry approach

Silva, Sérgio Henrique Godinho; Weindorf, David C.; Pinto, Leandro Campos; Faria, Wilson Missina; Acerbí, Fausto Weimar; Gomide, Lucas Rezende; Mello, José Márcio de; Pádua, Alceu Linares de; Souza, Igor Alexandre de; Teixeira, Anita Fernanda dos Santos; Guilherme, Luiz Roberto Guimarães; Curi, Nilton

doi:10.1016/j.geoderma.2019.114136

Cited by 54 publications

(15 citation statements)

References 35 publications

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“…Also the Roscommon samples had the strongest association (R 2 = 0.94) for Rb with % clay among the 3 counties. Silva et al (2020) state that the stronger correlation between Rb measured by XRF and % clay in the samples studied by Zhu et al (2011) as compared to their Brazilian soils, which showed a poor correlation between Rb with % clay, resulted from the weathering patterns of the soils. In this study, the degree of correlation between Rb and % clay may result from weathering, geology (i.e.…”

Section: Analysis Of Relationship Between Clay Content and Rb Based On Locationmentioning

confidence: 77%

“…Benedet et al (2020) use a random forest algorithm to predict soil texture for Brazilian soils with portable XRF derived results and had RMSE's of the models > 30% for % sand, % silt, and % clay, although the R 2 was > 0.80. In contrast, Silva et al (2020), who also did a similar work with Brazilian soils, had RMSE values for random forest models of the % sand, % clay, and % silt < 10%. Although the two studies were carried out using soils from the same region, the difference is that the latter study used soils of a wide range of soil types unlike the former study.…”

Section: Calibration and Validation Modelsmentioning

confidence: 94%

“…In addition, other elements such as Fe, Si, and Al have been found to strongly correlate with % sand and % clay; thus, correlation between these elements and particle size distribution results from the mineralogy of the soils due to their parent material and weathering-leaching degree (O'Rourke et al 2016;Silva et al 2020;Swetha and Chakraborty 2021). Soils from certain areas are rich in Fe, Si, and Al oxides, hydroxides, and silicate minerals such as phyllosilicates and tectosilicates; also Al and Fe can be found in the clay fractions, with Si in the sand fraction; thus, the chemical composition of the soils is related to soil texture (Zhu et al 2011;O' Rourke et al 2016;Silva et al 2020). It is important to note that although these studies used portable XRF, sample analyses were done under laboratory conditions and not in the field.…”

Section: Introductionmentioning

confidence: 99%

“…Despite XRF measurement having been shown as a suitable technique for soil texture prediction in Indian, Brazilian, and Australian soils (O'Rourke et al 2016;Silva et al 2020;Swetha and Chakraborty 2021), there is limited research on XRF use to predict particle size distribution (PSD) in Irish soils. Geology and climate have a notable influence on soil formation in Ireland; thus, the country has a unique landscape, whereby the soil parent material's chemical and physical properties differ over short distances (Meere et al 2013;Creamer et al 2007).…”

Section: Introductionmentioning

confidence: 99%

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Rubidium measured by XRF as a predictor of soil particle size in limestone and siliceous parent materials

et al. 2021

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Purpose Information about particle size distribution (PSD) and soil texture is essential for understanding soil drainage, porosity, nutrient availability, and trafficability. The sieve-pipette/gravimetric method traditionally used for particle size analysis is labour-intensive and resource-intensive. X-ray fluorescence (XRF) spectrometry may provide a rapid alternative. The study’s aim was to examine the use of XRF for rapid determination of PSD in Irish soils. Methods Soils (n = 355) from existing archives in Ireland were analysed with a benchtop energy-dispersive XRF (EDXRF). Correlation and regression analyses were determined to compare Rb, Fe, Al, and Si concentrations to % clay, % silt, and % sand. Also, linear regression models were developed to compare % clay, % sand, and % silt measured by the gravimetric method to values predicted by EDXRF. Results The relationship between element concentration and PSD was dependent on parent material. Rb, Al, and Fe showed a significant (p < 0.05) correlation (r > 0.50) with % clay and % sand in soils derived from limestone and siliceous stone parent materials. Rb was the best predictor for % clay (R2 = 0.49, RMSE = 10.20) in soils derived from limestone and siliceous stone-derived soils. Conclusion Geochemistry and clay mineralogy of the soils’ parent material strongly influenced the EDXRF’s ability to predict particle size. The EDXRF could predict % clay in soils from parent materials which weather easily, but the opposite was true for soils with parent material recalcitrant to weathering. In conclusion, this study has shown that the EDXRF can screen % clay in soils derived from limestone and siliceous stone parent materials.

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Section: Analysis Of Relationship Between Clay Content and Rb Based On Locationmentioning

confidence: 77%

Section: Calibration and Validation Modelsmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Rubidium measured by XRF as a predictor of soil particle size in limestone and siliceous parent materials

et al. 2021

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“…These soil properties illustrate some of the problems in the land after the gold mining from the physical and chemical properties of the soil. The soil texture class is classified as sand because soil particles are dominated by the sand fraction which reaches 91.53%, while the silt and clay fractions are only 8.11% and 0.36%, respectively [14]. The percentage of the sand fraction that reaches more than 90% characterizes sandy soils or in mining terms it is called tailings.…”

Section: Characteristic Of Post-mining Landmentioning

confidence: 99%

Coastal Sediment as an Ameliorant in Post-Mining Land Management

Sulakhudin¹,

Suswati²

2021

Coastal Environments

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Coastal sediment is a sediment resulting from sedimentation of eroded materials from up land through river flows that are deposited around the coast. It usually contains a lot of alkaline cations, especially Na so that it is good enough to decrease soil acidity. The use of coastal sediment must be considered carefully because it has a high level of salinity, which can inhibit plant growth and even cause death. Coastal sediment as an ameliorant can replace the role of lime in increasing the pH and base saturation of soil. Applying coastal sediment to sandy or post-gold mining soils can reduce soil acidity, increase soil CEC and soil base saturation, as well as the availability of nutrients, especially nutrients, phosphorus, potassium, calcium and magnesium. Improvement of some of these soil properties will encourage increased growth and crop yields in post gold mining land.

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Optimization of sample preparation and data evaluation techniques for X‐ray fluorescence prediction of soil texture, pH, and cation exchange capacity of loess soils

Greenberg,

Sawallisch,

Stelling

et al. 2023

Soil Science Soc of Amer J

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Use of X‐ray fluorescence (XRF) spectrometry for estimation of soil texture, pH, and cation exchange capacity (CEC) is desirable given the time‐consuming nature of traditional methods. Recent studies have shown promising results; however, further investigation is required to determine the effects of sample preparation and data evaluation techniques on accuracy. Our objective was to compare (I) a simple but well‐founded approach, combining measurement of powder samples and modeling with elemental contents as predictors in stepwise multiple linear regressions (MLR), with alternative approaches including (II) use of partial least squares regression (PLSR), (III) sample preparation as a pressed pellet, and (IV) spectral intensities as predictors (20 kV, 40 kV, and concatenated 20 + 40 kV). A total of 395 loess soils from three arable fields were used with a fivefold random training‐testing approach and a hold‐one‐site‐out training‐testing approach. With random partitioning, clay, silt, and sand accuracy with approach I was excellent (ratio of performance to interquartile distance in validation (RPIQv) = 8.5–12.9), while pH and CEC estimations were satisfactory to excellent (RPIQv = 2.0–2.5 and 2.2–3.3, respectively). Differences between MLR and PLSR were negligible. Increases in accuracy with pellet samples were 1%–13% of RPIQv for 20 kV intensities, but effects were inconsistent for other predictors. The optimal predictor varied by property, and differences ranged from 3% to 13% of RPIQv. Improvements to accuracy from Approach I to the best alternative were largest for texture (10%–15%) but may be superfluous given the excellent accuracy across all approaches. Although the leave‐one‐site‐out training resulted in variable performance, inclusion of soils from the target site in training assured reliable accuracy.

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Soil texture prediction in tropical soils: A portable X-ray fluorescence spectrometry approach

Cited by 54 publications

References 35 publications

Rubidium measured by XRF as a predictor of soil particle size in limestone and siliceous parent materials

Rubidium measured by XRF as a predictor of soil particle size in limestone and siliceous parent materials

Coastal Sediment as an Ameliorant in Post-Mining Land Management

Optimization of sample preparation and data evaluation techniques for X‐ray fluorescence prediction of soil texture, pH, and cation exchange capacity of loess soils

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