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Open AccessArticle

Assessing Soil Key Fertility Attributes Using a Portable X-ray Fluorescence: A Simple Method to Overcome Matrix Effect

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Laboratory of Precision Agriculture (LAP), Department of Biosystems Engineering, “Luiz de Queiroz” College of Agriculture (ESALQ), University of São Paulo (USP), Piracicaba 13418900, São Paulo, Brazil
2
Precision Soil and Crop Engineering Group (Precision SCoRing), Department of Environment, Faculty of Bioscience Engineering, Ghent University, Coupure Links 653, Blok B, 1st Floor, 9000 Gent, Belgium
3
Laboratory of 14 Carbon (LC14), Center for Nuclear Energy in Agriculture (CENA), University of São Paulo (USP), Piracicaba 13416000, São Paulo, Brazil
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Laboratory of Applied Nuclear Physics (LFNA), Department of Physics, Londrina State University (UEL), Londrina 86057970, Paraná, Brazil
5
Laboratory of Nuclear Instrumentation (LIN), Center for Nuclear Energy in Agriculture (CENA), University of São Paulo (USP), Piracicaba 13416000, São Paulo, Brazil
*
Author to whom correspondence should be addressed.
Agronomy 2020, 10(6), 787; https://doi.org/10.3390/agronomy10060787
Received: 28 April 2020 / Revised: 24 May 2020 / Accepted: 26 May 2020 / Published: 1 June 2020
(This article belongs to the Special Issue Precision Agriculture for Sustainability)
The matrix effect is one of the challenges to be overcome for a successful analysis of soil samples using X-ray fluorescence (XRF) sensors. This work aimed at evaluation of a simple modeling approach consisted of Compton normalization (CN) and multivariate regressions (e.g., multiple linear regressions (MLR) and partial least squares regression (PLSR)) to overcome the soil matrix effect, and subsequently improve the prediction accuracy of key soil fertility attributes. A portable XRF was used for analyzing 102 soil samples collected from two agricultural fields with contrasting soil matrices. Using the intensity of emission lines as input, preprocessing methods included with and without the CN. Univariate regression models for the prediction of clay, cation exchange capacity (CEC), and exchangeable (ex-) K and Ca were compared with the corresponding MLR models to assess matrix effect mitigation. The MLR and PLSR models improved the prediction results of the univariate models for both preprocessing methods, proving to be promising strategies for mitigating the matrix effect. In turn, the CN also mitigated part of the matrix effect for ex-K, ex-Ca, and CEC predictions, by improving the predictive performance of these elements when used in univariate and multivariate models. The CN has not improved the prediction accuracy of clay. The prediction performances obtained using MLR and PLSR were comparable for all evaluated attributes. The combined use of CN with multivariate regressions (MLR or PLSR) achieved excellent prediction results for CEC (R2 = 0.87), ex-K (R2 ≥ 0.94), and ex-Ca (R2 ≥ 0.96), whereas clay predictions were comparable with and without CN (0.89 ≤ R2 ≤ 0.92). We suggest using multivariate regressions (MLR or PLSR) combined with the CN to remove the soil matrix effects and consequently result in optimal prediction results of the studied key soil fertility attributes. The prediction performance observed for this solution showed comparable results to the approach based on the preprogrammed measurement package tested (Geo Exploration package, Bruker AXS, Madison, WI, USA). View Full-Text
Keywords: precision agriculture; proximal soil sensing; hybrid laboratories; Compton normalization; multivariate regressions precision agriculture; proximal soil sensing; hybrid laboratories; Compton normalization; multivariate regressions
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Tavares, T.R.; Mouazen, A.M.; Alves, E.E.N.; dos Santos, F.R.; Melquiades, F.L.; Pereira de Carvalho, H.W.; Molin, J.P. Assessing Soil Key Fertility Attributes Using a Portable X-ray Fluorescence: A Simple Method to Overcome Matrix Effect. Agronomy 2020, 10, 787.

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