Digital Twin for Climate-Smart Agriculture: Unraveling Environment and Management Effects on Field-level Carbon Dynamics

Tracking agroecosystem carbon flows is crucial to assess greenhouse gas emissions and improve crop production. This project will develop a digital twin framework integrating remote sensing, agroecosystem modeling, and ground data via process-guided machine learning to scalably track field-level carbon dynamics in Denmark. It will unravel environment and management impact on field greenhouse gas emissions for fair carbon credit and tax accounting. The grant will fund two PhD students and two postdocs.