🎙️ Episode 22: Knowledge-guided machine learning can improve carbon cycle quantification in agroecosystems
🔗 DOI: https://doi.org/10.1038/s41467-023-43860-5

🧠 Abstract
Improving the accuracy and scalability of carbon cycle quantification in agroecosystems is essential for climate mitigation and sustainable agriculture. This episode discusses a new Knowledge-Guided Machine Learning (KGML) framework that integrates process-based models, high-resolution remote sensing, and machine learning to address key limitations in conventional approaches.

📌 Bullet points summary

• Introduces KGML-ag-Carbon, a hybrid model combining process-based simulation (ecosys), remote sensing, and ML to improve carbon cycle modeling in agroecosystems.

• Outperforms traditional models in capturing spatial and temporal carbon dynamics across the U.S. Corn Belt, especially under data-scarce conditions.

• Delivers high-resolution (250m daily) estimates for critical carbon metrics such as GPP, Ra, Rh, NEE, and crop yield, with field-level precision.

• Benefits from pre-training with synthetic data, remote sensing assimilation, and a hierarchical architecture with knowledge-guided loss functions for better accuracy and interpretability.

• Shows promise for broader applications including nutrient cycle modeling, large-scale carbon assessment, and scenario testing under various management and climate conditions.

💡 The Big Idea
KGML-ag-Carbon represents a leap in modeling agroecosystem carbon cycles, blending scientific knowledge with data-driven insights to unlock precision and scalability in climate-smart agriculture.

📖 Citation
Liu, Licheng, et al. "Knowledge-guided machine learning can improve carbon cycle quantification in agroecosystems." Nature Communications 15.1 (2024): 357.