EO-driven carbon farming MRV: linking crop yield prediction to SOC change

Carbon farming can deliver climate mitigation and improved soil health, but credible deployment requires scalable MRV that supports additionality assessment and remains operational at farm scale. We present an EO-driven pipeline that integrates heterogeneous Earth-system data with hybrid modelling (machine learning + process-based physics) to estimate crop yield trajectories, soil organic carbon (SOC) evolution, and economic viability under baseline and regenerative management. A case study illustrates how a crop system can transition toward regenerative farming, demonstrating alignment with EU carbon farming policy. Results show how integrated, data-driven approaches can support quantification of both environmental and financial outcomes, enabling credible carbon accounting and guiding targeted investment in sustainable agriculture.

Multi-sensor satellite time series provide indicators of vegetation dynamics, and management proxies relevant to practice adoption (e.g., seasonal soil cover and surface condition). SoilGrids data provide spatially detailed soil information that helps us capture how soil conditions vary across and within fields, and how sensitive each site is. Climate forcing relies on high-resolution CMCC climate projections, enabling stress-testing of productivity and SOC outcomes under plausible future conditions.

A Random Forest model learns non-linear relationships between yield, EO indicators, soil attributes, and climate predictors to generate baseline yield projections. These projections are translated into carbon input assumptions (e.g., residue returns) and coupled to a RothC-class SOC model to simulate SOC evolution under regenerative scenarios such as cover crops.

Farm-level decision metrics integrate transition costs, yield impacts, potential carbon revenues, and land value appreciation to estimate break-even time and NPV, supporting project design and investment appraisal.