Predicting the proportion of centennially stable soil organic carbon using mid-infrared spectroscopy

• A dataset comprising Rock-Eval® and MIR data on > 2,000 samples have been assembled. • >1800 results were used to build a model to predict stable SOC in French soils. • The proportion of stable carbon can be efficiently predicted by MIR. • Transferring the model to other (German and Finnish) datasets degrades predictions. • Large scale deployment would require calibration transfer and local calibrations. Recent work has shown that it is possible to quantify the proportion of centennially stable soil organic carbon (SOC) by using Rock-Eval® thermal analysis results as input variables for PARTYsoc, a learning model calibrated on long term experiments data. This method of quantifying SOC biogeochemical stability holds promise for improving projections of SOC stock evolutions. Here, we assessed the potential of mid-infrared spectroscopy (MIR) as a lower-cost, higher-throughput technique to facilitate its wide-scale deployment. We compiled a spectral library of over 1,800 records obtained through the scanning of samples from the French Réseau de Mesure de la Qualité des Sols to calibrate a model using MIR data to predict the proportion of centennially stable SOC. The model gave accurate predictions (RMSE = 0.06, RPD = 2.21, RPIQ = 3.26), suggesting that MIR spectra contain relevant information on SOC biogeochemical stability. We then tried to transfer this model directly to datasets acquired using another MIR spectrometer on German and Finnish soil samples. The accuracy of the predictions was degraded, even when using the CORAL data alignment method to harmonise the different spectral datasets. Our results show that it is possible to predict the proportion of centennially stable carbon determined by the PARTYsoc model using MIR spectroscopy. However, we found that the transfer of such models to different soils, scanned with different instruments or different protocols, is difficult. Large-scale deployment of such models will require careful calibration transfer, probably associated to local calibration within a similar spectral space.

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Journal
Geoderma
Published
2025-10-01
DOI
https://doi.org/10.1016/j.geoderma.2025.117536
Citations
1
Primary Topic
Soil Geostatistics and Mapping
Type
article
Field-Weighted Citation Impact
0.80

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article

Predicting the proportion of centennially stable soil organic carbon using mid-infrared spectroscopy

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Predicting the proportion of centennially stable soil organic carbon using mid-infrared spectroscopy

Marcus Schiedung, Sam McNally, Nicolas Saby, Pierre Barré, Kristiina Karhu, Lorenza Pacini, Lauric Cécillon, Christopher Poeplau, Jussi Heinonsalo, Aurélie Cambou, Marija Stojanova, Pierre Roudier, Pierre Arbelet, François Baudin, Pascal Omondiagbe
article en
1 citations

Abstract

• A dataset comprising Rock-Eval® and MIR data on > 2,000 samples have been assembled. • >1800 results were used to build a model to predict stable SOC in French soils. • The proportion of stable carbon can be efficiently predicted by MIR. • Transferring the model to other (German and Finnish) datasets degrades predictions. • Large scale deployment would require calibration transfer and local calibrations. Recent work has shown that it is possible to quantify the proportion of centennially stable soil organic carbon (SOC) by using Rock-Eval® thermal analysis results as input variables for PARTYsoc, a learning model calibrated on long term experiments data. This method of quantifying SOC biogeochemical stability holds promise for improving projections of SOC stock evolutions. Here, we assessed the potential of mid-infrared spectroscopy (MIR) as a lower-cost, higher-throughput technique to facilitate its wide-scale deployment. We compiled a spectral library of over 1,800 records obtained through the scanning of samples from the French Réseau de Mesure de la Qualité des Sols to calibrate a model using MIR data to predict the proportion of centennially stable SOC. The model gave accurate predictions (RMSE = 0.06, RPD = 2.21, RPIQ = 3.26), suggesting that MIR spectra contain relevant information on SOC biogeochemical stability. We then tried to transfer this model directly to datasets acquired using another MIR spectrometer on German and Finnish soil samples. The accuracy of the predictions was degraded, even when using the CORAL data alignment method to harmonise the different spectral datasets. Our results show that it is possible to predict the proportion of centennially stable carbon determined by the PARTYsoc model using MIR spectroscopy. However, we found that the transfer of such models to different soils, scanned with different instruments or different protocols, is difficult. Large-scale deployment of such models will require careful calibration transfer, probably associated to local calibration within a similar spectral space.

GeodermaVol. 462
Université Claude Bernard Lyon 1 (FR), École Normale Supérieure de Lyon (FR), Centre National de la Recherche Scientifique (FR), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (FR), University of Helsinki (FI), Université de Montpellier (FR), Université Paris Sciences et Lettres (FR), École Normale Supérieure - PSL (FR), Sorbonne Université (FR), Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (FR), Ecologie fonctionnelle & biogéochimie des sols & des agro-systèmes (FR), Johann Heinrich von Thünen-Institut (DE), Institut des Sciences de la Terre de Paris (FR), Ministry of Foreign and European Affairs (LU), Soil Science Research Unit (FR), Laboratoire de Géologie de l’École Normale Supérieure (FR), Institut de Recherche pour le Développement (FR), Manaaki Whenua – Landcare Research (NZ)
New Zealand Government, Agence Nationale de la Recherche, Bureau de Recherches Géologiques et Minières, Agence française pour la biodiversité, Agence de la transition écologique, Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement
Openalex Percentile: Top 24%
Soil Geostatistics and Mapping
0.80
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