Building a random forest machine learning model for carbon budget estimation in agricultural fields using discontinuous atmospheric Eddy Covariance measurements

Abstract. Atmospheric CO₂ exchanges in agroecosystems are strongly controlled by plant phenology, management and microclimatic conditions, yet obtaining finely resolved flux estimates across heterogeneous agricultural landscapes remains challenging. This study evaluates the ability of a single Eddy Covariance (EC) system combined with wind-sector partitioning and Random Forest (RF) modelling to estimate annual carbon budgets of adjacent fields (wheat, mixed-grain, permanent grassland) in the Marais Poitevin wetland. Fluxes were measured over the period 2023-01-01–2024-01-31 and attributed to the different fields by wind sectors. Two modelling strategies were compared (i) a single global RF trained on all sectors and (ii) sector-specific RFs for each adjacent field. RF models globally showed good overall performance (R² ≈ 0.68–0.95 depending on sector), while the sectoral approach better reproduced phenological dynamics and responses to management events (harvest, grazing) than the global model, which tended to smooth site-specific signals. Annual carbon budgets estimated from the sectoral models indicate that the permanent grassland and the wheat field acted as net sinks (-259 and -216 g C m^-2 yr^-1;, respectively), whereas the mixed grain and the hybrid field behaved as net sources (+182 and +231 g C m^-2 yr^-1). Main limitations include spatial attribution uncertainty related to the EC footprint under stable conditions, flux disturbances during stormy episodes, and the limited one-year observation period. This study highlights the novelty and practical value of coupling a single EC system with wind-sector partitioning and machine learning approaches to resolve carbon fluxes at the field scale within heterogeneous agricultural landscapes. This integrated approach provides a cost-effective alternative to traditional multi-tower setups, offering new opportunities to monitor spatial carbon dynamics and management effects in real agricultural mosaics. Beyond methodological innovation, the goal of this work is to establish a comprehensive carbon budget not merely for a single agroecosystem, but for the terrestrial component of a wetland area, capturing the complexity of its ecological and biogeochemical interactions.