What is a model?

A numerical model is a tool used to better understand and explore real-world systems. Because reality is complex, a model provides a simplified representation that captures only the most important processes. 

Why are models important?

Models play a key role in soil science for two main purposes: understanding and prediction. First, models help us understand complex systems. 

They allow us to synthesize processes and interactions, explore how different factors influence the system, and guide the design of new experiments. 

Understanding soil carbon and nitrogen cycles

Soil contains large amounts of organic matter, which is made of carbon and nitrogen, among others, originating from plants, roots, microorganisms, and organic residues. 

Modelling soil carbon dynamics 

Because soil processes are complex, researchers often use models to represent them. In most soil carbon models, the soil is represented as a set of conceptual compartments, often called pools.

Building a model 

Once we have formulated the mathematical equations of our model, we can code them in a programming language (like python, R, and matlab), and run the simulations to predict the evolution of soil organic carbon over time. However, models cannot run on their own.

Exploring scenarios through modelling 

Once a model is built and validated, we can use it to explore different scenarios.

Measuring soil carbon

Soil carbon data production is the first step in modelling carbon storage dynamics. Because soils are highly heterogeneous, robust and standardized protocols are required to produce representative and reliable data.

La cartographie des paramètres physico-chimiques des sols (pH, texture, nutriments, matière organique, capacité d’échange cationique, humidité) est devenue un outil stratégique pour comprendre la variabilité des sols, évaluer leur fertilité et anticiper leurs risques. 

Aujourd’hui, les technologies numériques, comme les systèmes d’information géographique (GIS : ArcGIS, QGIS ou autres outils), permettent de produire des cartes précises et rapides, tout en évitant les coûts et la complexité liés à des mesures exhaustives sur le terrain. Ces outils représentent un support essentiel à la prise de décision pour les agriculteurs et les décideurs.

Decision support systems

Models are not only used in research — they can also support real-world decisions. When models are integrated into digital tools, they can become decision support systems. These tools help different stakeholders make informed decisions.

Limits of models

Despite being useful, it is important to remember that models are simplified representations of reality. They help us understand systems and test ideas, but they are not the ground truth: they must always be used carefully.