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Predictions of Cu, Zn, and Cd Concentrations in Soil Using Portable X-Ray Fluorescence Measurements

Adler, Karl and Piikki, Kristin and Söderström, Mats and Eriksson, Jan and Alshihabi, Omran (2020). Predictions of Cu, Zn, and Cd Concentrations in Soil Using Portable X-Ray Fluorescence Measurements. Sensors (Basel, Switzerland). 20 , 1-15
[Journal article]

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Abstract

Portable X-ray fluorescence (PXRF) measurements on 1520 soil samples were used to create national prediction models for copper (Cu), zinc (Zn), and cadmium (Cd) concentrations in agricultural soil. The models were validated at both national and farm scales. Multiple linear regression (MLR), random forest (RF), and multivariate adaptive regression spline (MARS) models were created and compared. National scale cross-validation of the models gave the following R-2 values for predictions of Cu (R-2 = 0.63), Zn (R-2 = 0.92), and Cd (R-2 = 0.70) concentrations. Independent validation at the farm scale revealed that Zn predictions were relatively successful regardless of the model used (R-2 > 0.90), showing that a simple MLR model can be sufficient for certain predictions. However, predictions at the farm scale revealed that the non-linear models, especially MARS, were more accurate than MLR for Cu (R-2 = 0.94) and Cd (R-2 = 0.80). These results show that multivariate modelling can compensate for some of the shortcomings of the PXRF device (e.g., high limits of detection for certain elements and some elements not being directly measurable), making PXRF sensors capable of predicting elemental concentrations in soil at comparable levels of accuracy to conventional laboratory analyses.

Authors/Creators:Adler, Karl and Piikki, Kristin and Söderström, Mats and Eriksson, Jan and Alshihabi, Omran
Title:Predictions of Cu, Zn, and Cd Concentrations in Soil Using Portable X-Ray Fluorescence Measurements
Year of publishing :2020
Volume:20
Page range:1-15
Number of Pages:15
Publisher:MDPI
Language:English
Publication Type:Journal article
Article category:Scientific peer reviewed
Version:Published version
Copyright:Creative Commons: Attribution 4.0
Full Text Status:Public
Subjects:(A) Swedish standard research categories 2011 > 4 Agricultural Sciences > 401 Agricultural, Forestry and Fisheries > Soil Science
Keywords:PXRF, soil, copper, zinc, cadmium, machine learning, precision agriculture
URN:NBN:urn:nbn:se:slu:epsilon-p-105071
Permanent URL:
http://urn.kb.se/resolve?urn=urn:nbn:se:slu:epsilon-p-105071
Additional ID:
Type of IDID
DOI10.3390/s20020474
Web of Science (WoS)000517790100148
ID Code:16837
Faculty:NJ - Fakulteten för naturresurser och jordbruksvetenskap
Department:(NL, NJ) > Dept. of Soil and Environment
(S) > Dept. of Soil and Environment
Deposited By: SLUpub Connector
Deposited On:04 May 2020 11:58
Metadata Last Modified:04 May 2020 11:58

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