Goal
- Develop a scalable monitoring and modeling framework to track field-level carbon dynamics for Danish cropland.
- Improve measuring, monitoring, reporting, and verification (MMRV) of cropland CO₂ fluxes for both organic and mineral soil fields.
- Support accurate carbon credit accounting for farmers adopting climate-smart management to improve crop production and environmental sustainability.
Plan
- Synthesize field measurement data related to cropland carbon dynamics across Denmark.
- Develop cross-scale sensing technologies to quantify agroecosystem variables.
- Utilize agroecosystem models to simulate carbon budgets under varying environmental and man-agement conditions.
- Implement model-data fusion via advanced knowledge-guided machine learning to integrate remote sensing and agroecosystem modeling to accurately quantify carbon budgets.
- Conduct scenario analyses to assess mitigation strategies for reducing greenhouse gas emissions and increasing carbon sequestration.
Expected results
- A scalable framework for monitoring CO₂ emissions and sequestration in Danish croplands.
- High-resolution estimates of field-level carbon fluxes to improve carbon credit accounting.
- A decision-support tool for policymakers and farmers to optimize climate-smart agriculture practices.
- Scientific advancements in knowledge-guided machine learning for agroecosystem sensing and modeling.
