Accurate and cost-effective quantification of the carbon cycle for agroecosystems at decision-
relevant scales is critical to mitigating climate change and ensuring sustainable food
production. However, conventional process-based or data-driven modeling approaches
alone have large prediction uncertainties due to the complex biogeochemical processes to
model and the lack of observations to constrain many key state and flux variables. Here we
propose a Knowledge-Guided Machine Learning (KGML) framework that addresses the …