Summary
This study assessed the use of portable spectrometry techniques (pXRF and DRIFT-MIR) paired with machine learning algorithms as a scalable, cost-effective approach for comprehensive nutrient characterisation of organic amendments. Both forest regression and extreme gradient boosting demonstrated similar capabilities in estimating nutrient concentrations, with pXRF showing particular utility for detecting both nutrients and contaminants. The findings suggest that this integrated methodology could provide rapid quality assessment and nutrient profiling systems suitable for field-scale deployment.
UK applicability
This methodology could support UK organic farming operations and compost quality assurance schemes by enabling rapid, non-destructive nutrient analysis of locally produced amendments. The technique's cost-effectiveness and portability align with UK smallholder and regenerative farming interests in soil amendment monitoring.
Key measures
Concentrations of carbon, nitrogen, macro- and micronutrient elements; model accuracy via cross-validation; suitability of pXRF versus DRIFT-MIR for nutrient and contaminant estimation
Outcomes reported
The study evaluated portable X-ray fluorescence (pXRF) and Diffuse Reflectance Fourier Transformed Mid-Infrared (DRIFT-MIR) spectroscopy combined with machine learning algorithms to quantify macro- and micronutrient concentrations in organic amendments. Both forest regression and extreme gradient boosting methods were assessed for their ability to estimate nutrient and contaminant profiles with generalizable accuracy.
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