Kaiyu Guan , Zhenong Jin, Evan H. DeLucia, Paul West, Bin Peng, Jinyun Tang, Chongya Jiang, Sheng Wang, Taegon Kim, Wang Zhou, Timothy Griffis, Licheng Liu, Ziqi Qin, Andrew J. Margenot, Vipin Kumar, Carl J Bernacchi, Wendy H. Yang, DoKyoung Lee, Jonathan W Coppess, James Gerber, Molly Jahn, Madhu Khanna, Shang-Jen Yang

Agriculture contributes nearly a quarter of global greenhouse gas (GHG) emissions, which is motivating interest in certain farming practices that have the potential to reduce GHG emissions or sequester carbon in soil. The related GHG emission (including N2O and CH4) and changes in soil carbon stock are defined here as “agricultural carbon outcomes”. Accurate quantification of agricultural carbon outcomes is the basis for achieving emission reductions for agriculture, but existing approaches for measuring carbon outcomes (including direct measurements, emission factors, process-based modeling) fall short of achieving the required accuracy and scalability necessary to support credible, verifiable, and cost-effective measurement and improvement of these carbon outcomes. Here we propose a foundational and scalable framework to quantify field-level carbon outcomes for farmland, which is based on the holistic carbon balance of the agroecosystem: Agroecosystem Carbon Outcomes = Crops (C) × Management (M) × Environment (E). Following a comprehensive review of the scientific challenges associated with existing approaches, as well as their tradeoffs between cost and accuracy, we propose that the most viable path for the quantification of field-level carbon outcomes in agricultural land is through an effective integration of various approaches (e.g. diverse observations, sensor/in-situ data, modeling), defined as the “system- of-systems” solution. Such a “system-of-systems” solution should simultaneously comprise the following components: (1) scalable collection of ground truth data and cross-scale sensing of crop conditions (C), management practices (M), and environment (E) at the local field level; (2) advanced modeling with necessary processes to support the quantification of carbon outcomes; (3) systematic Model-Data Fusion (MDF), i.e. robust and efficient methods to integrate sensing data and models at each local farmland level; (4) high computation efficiency and artificial intelligence (AI) to scale to millions of individual fields with low cost; and (5) robust and multi-tier validation systems and infrastructures to ensure solution fidelity and true scalability, i.e. the ability of a solution to perform robustly with accepted accuracy on all targeted fields. In this regard, we provide here the detailed scientific rationale, current progress, and future R&D priorities to achieve different components of the “system-of-systems” solution, thus accomplishing the Crop×Management×Environment framework to quantify field-level agricultural carbon outcomes.