How do we measure soil carbon?

Lately we have seen a growing momentum around “carbon farming”. Behind the buzzword, carbon farming is a set of agricultural practices that aim to increase the carbon stored in farms’ soil and plants. But why do we focus so much on soil carbon in agriculture? And how can we accurately monitor it at the farm level? 

The importance of soil organic carbon

Let’s start with the basics: the carbon (C) found in plant and animal residues is broken down by soil organisms, converting it into soil organic carbon (SOC). Carbon can be stored in agricultural soils for a few months up to several years, depending on agricultural management and environmental conditions. SOC is crucial, for various reasons: 

• At the farm level, it improves soil’s physical, chemical and biological properties, which increases crop yields.

• At the global level, it acts as an important carbon sink by absorbing CO2 from the atmosphere, which is crucial for climate change mitigation. 

SOC can accumulate in a farm’s soils when there is more carbon added into the soil than lost through decomposition or erosion. Farmers can reduce SOC loss or promote SOC accumulation through a variety of agricultural practices, such as leaving crop residues on a field.

Leaving crop residues to decompose on the field increases SOC content, which results in this dark colour. 

How do we measure SOC?

As a core principle reminds us, we can only manage what we can measure. And this is no easy task. SOC dynamics are complex: they are regulated by carbonated inputs and decomposition processes, which in turn are regulated by environmental conditions and agricultural practices.

Estimating SOC in agricultural fields can be done in different ways:

• By collecting soil samples and analysing them with laboratory methods,

• Or through soil models that use soil, climate and management parameters.

The investment of time and money requested for soil sampling make it hard to implement in a systemic way at the farm level. This is why farmers who want to engage in carbon farming typically use MRV platforms that include soil models to estimate their soil carbon stocks and fluxes.

How does a soil model work?

Soil models are mathematical models that can estimate the dynamics of carbon within soil ecosystems. They are fed with various types of data including climate, land use and management practices that influence soil carbon stocks and fluxes. Soil models are crucial for:

• assessing the role of agricultural soils as carbon sinks,

• estimating carbon sequestration in farms, 

• and informing sustainable management practices.

Indeed, by calculating the influence of agricultural practices on SOC, soil models can guide farmers towards the adoption of certain practices for better carbon management.

The AMG Model

The Regen Insight Cropland Methodology uses the AMG soil model, the reference model for SOC in France, which is also used by the governmental Label Bas Carbone methodology.

The AMG model simulates the evolution of soil carbon stock in field crops. It depends on two parameters: the application of exogenous organic matter, and the mineralization rate (the rate at which SOC is converted back into CO2 by soil organisms). The unbalance between these two opposite fluxes determine carbon decline or accumulation in soils.

What makes a good soil model for farm carbon management ?

“Garbage in, garbage out”. That’s a famous saying in computer sciences, showing that when flawed or incomplete data goes into a model, flawed results will come out. Applied to soil carbon management, it means that a soil model shouldn’t ask for inaccessible data because that would imply feeding the algorithm with inaccurate generic data, faulting the results. Typically, the AMG model only asks for easy-to-reach and reliable data, which ensures the results’ integrity. 

This is closely linked to our next point: the model operability. Farmers have limited time and access to data, meaning they should be able to use a model that is practical for them. For instance, while the DNDC model* may have a high level of precision, it requires meteorological data at a daily timestamp, which implies a significant burden and a risk of error from collecting daily data. The AMG model is simpler because it asks for meteorological data at an annual timestep, and does not require calibration.

Finally, we consider a soil model reliable if it has been tested in a large number of sites with contrasted environmental conditions. This is another strength of the AMG model: it has been validated in a wide range of cropping systems across Europe, with varying conditions including soil type, mean annual temperature, rainfall and surface texture, amongst others. 

To sum up, the precision, practicality, and scalability of the AMG model make it an essential tool for farmers aiming to enhance soil carbon storage in their soils. In this way, this tool places them as key players in the fight for healthier soils, and against climate change.

*DeNitrification-DeComposition model developed by the University of New Hampshire

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References

FAO, 2024: RECSOIL: Recarbonization of Global Agricultural Soils. 
What is soil organic carbon (SOC)? | Global Soil Partnership | Food and Agriculture Organization of the United Nations (fao.org) 

Institute for the Study of Earth, Oceans and Space, University of New Hampshire, 2012: User’s Guide for the DNDC model. 
https://www.dndc.sr.unh.edu/model/GuideDNDC95.pdf 

Moreno et al., 2016: Soil organic carbon changes simulated with the AMG model in a high-organic-matter Mollisol. Spanish Journal of Soil Science, Volume 6, pages 212-229.
molisolamg2016.pdf (verdeterreprod.fr) 

Potash et al., 2022: How to estimate soil organic carbon stocks of agricultural fields? Perspectives using ex-ante evaluation. Geoderma, Volume 411.
https://doi.org/10.1016/j.geoderma.2021.115693 

Stockmann et al., 2013: The knowns, known unknowns and unknowns of sequestration of soil organic carbon. Agriculture, Ecosystems & Environment, Volume 164, pages 80-99
https://doi.org/10.1016/j.agee.2012.10.001