Existing models to estimate agroecosystem C cycle have large uncertainties. Here, the authors propose a knowledge-guided machine learning framework that improves C cycle quantification in agroecosystems by integrating process-based and machine learning models, and multi-source high-resolution data.
- Licheng Liu
- Wang Zhou
- Zhenong Jin
