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Combining knowledge-guided process models and data-driven machine learning models for enhancing soil carbon modelling in space and time

Objective

Accurate modelling and mapping soil organic carbon (SOC) variations in space and time are crucial for supporting soil health restoration, guiding sustainable land management practices, and contributing to climate change mitigation strategies. Although data-driven approaches, such as machine learning (ML), has attracted many attention in soil mapping tasks for its powerful ability to learn from data, it is better at capturing soil spatial variation than soil temporal dynamics. By contrast, process-based models benefit from mechanistic knowledge to express physical, chemical and biological processes that govern SOC temporal changes. Therefore, integrating two types of models will promise means to represent physically plausible SOC dynamics while retaining the spatial prediction accuracy of ML models. However, a clear research gap remains in applying this concept and designing an appropriate hybrid modelling framework to quantify soil variations in space and time. This HySOC (Hybrid modelling of SOC) project aims to propose a new paradigm of space-time SOC modelling framework that hybrid knowledge-guided process models and data-driven ML models, for fulfilling the needs of the next generation of soil mapping methods and for supporting future carbon–climate feedback projections. By bridging two types of model, the project will go beyond the state of the art by providing high spatial and temporal resolution SOC maps and also the open-access hybrid model architecture, algorithms and calibrated model parameters, thus reducing the uncertainty on our understanding of European soil carbon changes in the past, present, and future. On top of model innovation, the outcomes of HySOC will further help to assess the sensitivity of European soil carbon to multiple global change drivers, and enhance the capacity to diagnose and improve the representations of soil variations in Earth system models, thereby contributing to the goals of the EU Soil Mission and the European Green Deal.

Fields of science (EuroSciVoc)

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Keywords

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Programme(s)

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Topic(s)

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Funding Scheme

Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.

HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships

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Call for proposal

Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.

(opens in new window) HORIZON-MSCA-2025-PF

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Coordinator

INSTITUT NATIONAL DES SCIENCES ET INDUSTRIES DU VIVANT ET DE L'ENVIRONNEMENT - AGROPARISTECH
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 242 260,56
Address
22 PLACE DE L'AGRONOMIE
91120 PALAISEAU
France

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Region
Ile-de-France Ile-de-France Essonne
Activity type
Higher or Secondary Education Establishments
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Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

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Partners (1)

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