Agroecosystem models are essential tools for understanding and optimizing agricultural systems, providing insights into crop yields, soil nitrogen dynamics, greenhouse gas emissions, and the impacts of various management practices. Traditionally, process-based models (PBMs) such as LandscapeDNDC have been used to simulate complex biogeochemical and biophysical processes, allowing researchers to explore interactions between soil, climate, and management decisions. While PBMs are invaluable for predicting agroecosystem responses under changing environmental conditions, they often require significant computational resources, particularly when applied at large spatial scales or used for optimization tasks.

To address these challenges, machine learning (ML) techniques have emerged as powerful alternatives, offering computational efficiency and the ability to capture complex nonlinear relationships without predefined assumptions. ML models, including deep learning approaches, can accelerate agroecosystem simulations, improve predictive accuracy, and assist in optimizing mitigation strategies. This is particularly relevant for large-scale assessments of climate adaptation and emission reduction strategies, where traditional PBMs may be too computationally demanding. By leveraging ML as a surrogate modeling approach, researchers can explore a wider range of management scenarios and identify optimal solutions more efficiently.

This Ph.D. study aims to integrate process-based and machine learning models to enhance the assessment of greenhouse gas mitigation strategies in Danish agriculture. By training ML models on outputs from PBMs, the aim is to develop a scalable approach for identifying emission reduction hotspots while maintaining agricultural productivity. This hybrid modeling framework will provide policymakers and stakeholders with data-driven insights to support sustainable land management practices and inform national greenhouse gas inventories.