# A new machine-learning model to partition soil organic carbon into its centennialily stable and active fractions based on Rock-Eval(r) thermal analysis

Marija Stojanova, Pierre Barré, Hugues Clivot, Lauric Cécillon, François

Baudin, Thomas Kätterer, Bent T. Christensen, Claire Chenu, Ines Merbach, Adrián Andriulo, et al.

<figure><img src="image_1.png" /></figure>

## To cite this version:

Marija Stojanova, Pierre Barré, Hugues Clivot, Lauric Cécillon, François Baudin, et al. A new machine-learning model to partition soil organic carbon into its centennialily stable and active fractions based on Rock-Eval(r) thermal analysis. EGU General Assembly 2024, Apr 2024, Vienne, Austria. p. EGU24-11107, 2024, 10.5194/egusphere-egu24-11107 . hal-04612125

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<figure><img src="image_2.png" /></figure>
# PARTYsoc v3: A new machine-learning model to partition soil organic carbon into its centennialily stable and active fractions based on Rock-Eval® thermal analysis

Marija Stojanova ♦ Pierre Barré ♦ Hugues Clivot ♠ Adrian Andriulo ‡ François Baudin ♦ Lauric Cécillon ▽ Claire Chenu ♡ Bent T Christensen △ Sabine Houot ♡ Thomas Kätterer ○ Ines Merbach ▼ Fabien Ferchaud †

Laboratoire de Géologie, École Normale Supérieure, CNRS, PSL University, IPSL, Paris, France

Buenos Aires

<figure><img src="image_1.png" /></figure>

ISTeP, UMR 7193 Sorbonne Université, CNRS, Paris

<figure><img src="image_2.png" /></figure>

Research and innovation office, Ministry of agriculture and food sovereignty, Paris, France

<figure><img src="image_3.png" /></figure>

UMR ECOSYS, INRAE, AgroParisTech, Université Paris Saclay,

Palaiseau, France

<figure><img src="image_4.png" /></figure>

Aarhus University, Department of Agroecology - Soil Fertility

<figure><img src="image_5.png" /></figure>

Swedish University of Agricultural Sciences

<figure><img src="image_6.png" /></figure>

Helmholtz-Centre for Environmental Research

<figure><img src="image_7.png" /></figure>

UMR Eco&Sols, Univ Montpellier, CIRAD, INRAE,

IRD, InstitutAgro Montpellier, Montpellier

## Introduction

* Accurately simulating soil organic carbon (SOC) stock evolution is crucial for assessing the role of soil in climate regulation.

* SOC evolution is often modeled by dividing SOC into kinetic pools with contrasting residence times. Initializing such kinetic pools remains a complex task and a source of uncertainty in SOC simulations.

* In a previous study, Cécillon et al. [1] developed a machine-learning model (PARTYsoc v2) that uses Rock-Eval® thermal analysis results as input features to quantify the proportion of centennialily stable and active SOC fractions.

* These proportions have been shown to be particularly effective for initializing the AMG model, enabling very accurate simulations of SOC stock evolutions for a dozen French agricultural long-term experiments [2].

* This present work builds a **new version of PARTYsoc**, validated on a larger data set, and extends the usefulness of the AMG model initialized with PARTYsoc to different parts of the world.

## Materials and methods

### Data

* Data collection of 18 sites with known crop yields and SOC stock evolutions and archived soil samples available for Rock-Eval® characterization.

* Sites are located in Europe and South America.

* The list of Rock-Eval® parameters [3] represents our potential **set of features**.

* The stable SOC stock size for each site is determined by optimization of the AMG model as in Clivot et al. (2019) [4].

* Using the optimal stable SOC stock values, we calculate the stable SOC proportion of each sample from each site, representing our **target variable**.

* We reserve 1/3 of the data for testing, and use 2/3 for training.

### Feature engineering and model training

* We remove the highly correlated Rock-Eval® features using Spearman's correlation and a threshold of 0.9.

* We use AutoFeatureCorrelation and permutation importance to rank the features according to their impact on the predicted target.

* We run 30 repetitions of 5-fold cross-validation to find the **optimal number of features** for prediction on the test set and in Leave-One-Site-Out (LOSO) validation.

* The final model is a **combination of a Support Vector Machine (SVM) and a Beta regression**, trained using 10 Rock-Eval® features.

<figure><img src="image_8.png" /></figure>

## Results

<figure><img src="image_9.png" /></figure>

* On average, we obtain an $R^2 = 0.78$ and RMSE = 0.07 in internal validation, and $R^2 = 0.59$ and RMSE = 0.09 in LOSO validation.

<figure><img src="image_10.png" /></figure>

* Using PARTYsoc v3 (LOSO) values to initialize AMG simulations offers comparable results to initializing with optimal AMG values.

## Conclusions and forthcoming research

* The proposed new PARTYsoc v3 builds upon and improves the original PARTYsoc v2 by incorporating a larger data set covering a **wider geographic area**, and a **more parsimonious machine-learning model** (10 vs. 18 Rock-Eval® features in PARTYsoc v2).

* We are currently working on extending the data set as well as stabilizing the processes of feature selection and model parameterization.

## References

[1] L. Cécillon, F. Baudin, C. Chenu, B. T. Christensen, U. Franko, S. Houot, E. Kanari, T. Kätterer, I. Merbach, F. Van Oort, et al., "Partitioning soil organic carbon into its centennialily stable and active fractions with machine-learning models based on Rock-Eval (R) thermal analysis," *Geoscientific Model Development*, 2021.

[2] E. Kanari, L. Cécillon, F. Baudin, H. Clivot, F. Ferchaud, S. Houot, F. Levavasseur, B. Mary, L. Soucémarianadin, C. Chenu, et al., "A robust initialization method for accurate soil organic carbon simulations," *Biogeosciences*, vol. 19, no. 2, pp. 375-387, 2022.

[3] L. Pacini, T. Adatte, P. Barré, M. Boussafir, N. Bouton, L. Cécillon, V. Lamoureux-Var, D. Sebag, E. Verrecchia, A. Wattripont, et al., "Reproducibility of Rock-Eval® thermal analysis for soil organic matter characterization," *Organic Geochemistry*, vol. 186, p. 104687, 2023.

[4] H. Clivot, J.-C. Mouny, A. Duparque, J.-L. Dinh, P. Denoroy, S. Houot, F. Vertès, R. Trochard, A. Bouthier, S. Sagot, et al., "Modeling soil organic carbon evolution in long-term arable experiments with AMG model," *Environmental modelling & software*, 2019.

## Acknowledgements

We thank Florence Savignac for her help in conducting Rock-Eval measurements. This work would not have been possible without all the people who helped initiate and maintain these invaluable long-term trials. The analyses have been funded by the CNRS-INRAE "IMMORTAL" project.
# PARTYsoc v3: A new machine-learning model to partition soil organic carbon into its centennialily stable and active fractions based on Rock-Eval® thermal analysis

Marija Stojanova ♦ Pierre Barré ♦ Hugues Clivot ♠ Adrian Andriulo ‡ François Baudin ♦ Lauric Cécillon ▽ Claire Chenu ♡ Bent T Christensen △ Sabine Houot ♡ Thomas Kätterer ○ Ines Merbach ▼ Fabien Ferchaud †

Laboratoire de Géologie, École Normale Supérieure, CNRS, PSL University, IPSL, Paris, France

Buenos Aires

<figure><img src="image_3.png" /></figure>

ISTeP, UMR 7193 Sorbonne Université, CNRS, Paris

<figure><img src="image_4.png" /></figure>

Research and innovation office, Ministry of agriculture and food sovereignty, Paris, France

<figure><img src="image_5.png" /></figure>

UMR ECOSYS, INRAE, AgroParisTech, Université Paris Saclay,

Palaiseau, France

<figure><img src="image_6.png" /></figure>

Aarhus University, Department of Agroecology - Soil Fertility

<figure><img src="image_7.png" /></figure>

Swedish University of Agricultural Sciences

<figure><img src="image_8.png" /></figure>

Helmholtz-Centre for Environmental Research

<figure><img src="image_9.png" /></figure>

UMR Eco&Sols, Univ Montpellier, CIRAD, INRAE,

IRD, InstitutAgro Montpellier, Montpellier

## Introduction

* Accurately simulating soil organic carbon (SOC) stock evolution is crucial for assessing the role of soil in climate regulation.

* SOC evolution is often modeled by dividing SOC into kinetic pools with contrasting residence times. Initializing such kinetic pools remains a complex task and a source of uncertainty in SOC simulations.

* In a previous study, Cécillon et al. [1] developed a machine-learning model (PARTYsoc v2) that uses Rock-Eval® thermal analysis results as input features to quantify the proportion of centennialily stable and active SOC fractions.

* These proportions have been shown to be particularly effective for initializing the AMG model, enabling very accurate simulations of SOC stock evolutions for a dozen French agricultural long-term experiments [2].

* This present work builds a **new version of PARTYsoc**, validated on a larger data set, and extends the usefulness of the AMG model initialized with PARTYsoc to different parts of the world.

## Materials and methods

### Data

* Data collection of 18 sites with known crop yields and SOC stock evolutions and archived soil samples available for Rock-Eval® characterization.

* Sites are located in Europe and South America.

* The list of Rock-Eval® parameters [3] represents our potential **set of features**.

* The stable SOC stock size for each site is determined by optimization of the AMG model as in Clivot et al. (2019) [4].

* Using the optimal stable SOC stock values, we calculate the stable SOC proportion of each sample from each site, representing our **target variable**.

* We reserve 1/3 of the data for testing, and use 2/3 for training.

### Feature engineering and model training

* We remove the highly correlated Rock-Eval® features using Spearman's correlation and a threshold of 0.9.

* We use AutoFeatureCorrelation and permutation importance to rank the features according to their impact on the predicted target.

* We run 30 repetitions of 5-fold cross-validation to find the **optimal number of features** for prediction on the test set and in Leave-One-Site-Out (LOSO) validation.

* The final model is a **combination of a Support Vector Machine (SVM) and a Beta regression**, trained using 10 Rock-Eval® features.

<figure><img src="image_10.png" /></figure>

## Results

<figure><img src="image_11.png" /></figure>

* On average, we obtain an $R^2 = 0.78$ and RMSE = 0.07 in internal validation, and $R^2 = 0.59$ and RMSE = 0.09 in LOSO validation.

<figure><img src="image_12.png" /></figure>

* Using PARTYsoc v3 (LOSO) values to initialize AMG simulations offers comparable results to initializing with optimal AMG values.

## Conclusions and forthcoming research

* The proposed new PARTYsoc v3 builds upon and improves the original PARTYsoc v2 by incorporating a larger data set covering a **wider geographic area**, and a **more parsimonious machine-learning model** (10 vs. 18 Rock-Eval® features in PARTYsoc v2).

* We are currently working on extending the data set as well as stabilizing the processes of feature selection and model parameterization.

## References

[1] L. Cécillon, F. Baudin, C. Chenu, B. T. Christensen, U. Franko, S. Houot, E. Kanari, T. Kätterer, I. Merbach, F. Van Oort, et al., "Partitioning soil organic carbon into its centennialily stable and active fractions with machine-learning models based on Rock-Eval (R) thermal analysis," *Geoscientific Model Development*, 2021.

[2] E. Kanari, L. Cécillon, F. Baudin, H. Clivot, F. Ferchaud, S. Houot, F. Levavasseur, B. Mary, L. Soucémarianadin, C. Chenu, et al., "A robust initialization method for accurate soil organic carbon simulations," *Biogeosciences*, vol. 19, no. 2, pp. 375-387, 2022.

[3] L. Pacini, T. Adatte, P. Barré, M. Boussafir, N. Bouton, L. Cécillon, V. Lamoureux-Var, D. Sebag, E. Verrecchia, A. Wattripont, et al., "Reproducibility of Rock-Eval® thermal analysis for soil organic matter characterization," *Organic Geochemistry*, vol. 186, p. 104687, 2023.

[4] H. Clivot, J.-C. Mouny, A. Duparque, J.-L. Dinh, P. Denoroy, S. Houot, F. Vertès, R. Trochard, A. Bouthier, S. Sagot, et al., "Modeling soil organic carbon evolution in long-term arable experiments with AMG model," *Environmental modelling & software*, 2019.

## Acknowledgements

We thank Florence Savignac for her help in conducting Rock-Eval measurements. This work would not have been possible without all the people who helped initiate and maintain these invaluable long-term trials. The analyses have been funded by the CNRS-INRAE "IMMORTAL" project.