A pragmatic workflow towards the generation of pXRF datasets for large-scale soil monitoring programs

Xueyu Zhao; Uta Stockmann; Mark Farrell; Senani Karunaratne

doi:10.1071/SR25028

RESEARCH ARTICLE (Open Access)

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A pragmatic workflow towards the generation of pXRF datasets for large-scale soil monitoring programs

Xueyu Zhao

^A ^* , Uta Stockmann ^A , Mark Farrell

^B and Senani Karunaratne

^A

+ Author Affiliations

- Author Affiliations

^A CSIRO Agriculture and Food, GPO Box 1700, Canberra, ACT 2601, Australia.

^B CSIRO Agriculture and Food, PO Box 200, Glenside, Glen Osmond, SA 5065, Australia.

^* Correspondence to: tom.zhao@csiro.au

Handling Editor: Siobhan Staunton

Soil Research 63, SR25028 https://doi.org/10.1071/SR25028

Submitted: 12 February 2025 Accepted: 11 June 2025 Published: 23 June 2025

© 2025 The Author(s) (or their employer(s)). Published by CSIRO Publishing. This is an open access article distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND)

Abstract

Compared to conventional laboratory methods, portable X-ray fluorescence (pXRF) can rapidly estimate total elemental concentrations of soil samples. Developing an optimised and efficient data collection protocol is crucial for processing large sample numbers. Factors such as sample preparation methods, manufacturer calibration modes, and associated analysis times can influence pXRF performance. We evaluated the performance of different containers and detection modes (Soil vs Geochem) to determine the most time-efficient scanning protocol under standard instrument operation while maintaining acceptable accuracy for large-scale total elemental concentration analysis. Using 130 representative soil samples from the CSIRO National Soil Archive, results showed strong correlations for most elements (K, Ca, Ti, Fe, Cu, and Zr) across different containers. However, Mg, which has a low atomic number element, showed poor correlations (R² = 0.05), likely due to the limits of detection (LOD) of the Geochem mode. Al and Si exhibited better R² values but showed a low Lin’s Concordance Correlation Coefficient (LCCC) value of less than 0.1. Among different modes, elements including K, Ca, Ti, Fe, Cu and Zr maintained strong correlations (R² > 0.65 and LCCC > 0.7). Scanning soil samples through plastic bags in Geochem mode is recommended due to its shorter measurement and sample preparation time and ability to detect lighter elements (Mg, Al, and Si). This optimised protocol will support national scale soil monitoring programs with large sample sizes (e.g. n > 3000 soil samples). For future work, elemental data acquired can support for example investigations of how soil mineralogy influences carbon storage capacity and provide insights to the biological-chemical stabilisation of soil organic carbon.

Keywords: containers, detection modes, large-scale soil data collection, portable X-ray fluorescence, proximal sensing, soil monitoring, time-efficient scanning protocol, total elemental concentrations.

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