Synthetic Data for Soil C Modeling
Dr. Saurav Das
Research Director | Farming Systems Trial | Rodale Institute | Soil Health, Biogeochemistry of Carbon & Nitrogen, Environmental Microbiology, and Data Science | Outreach & Extension | Vibe coding
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My all-time question is, do we need all and precise data from producers (maybe I should be clear: we have enough data to aggregate if everyone wants to share, and there are databases which we can access through APIs and other ways), or can we figure it out with a robust maths and stats pipeline, and now with remote sensing and GIS-tracked tractor and all sorts of other things (function of climate, fertilizer, market, and tradition, and geo-location)! And also let the C model evolve by itself, not parameterize every single step!
Synthetic Data Generation and Hybrid Modeling Frameworks
Process-Based Models as Synthetic Data Engines
Process-based models like ecosys and CLM5 generate synthetic datasets that replicate biogeochemical interactions under varying environmental conditions. These models simulate carbon fluxes, microbial dynamics, and soil physical properties at high spatiotemporal resolutions, producing:
- Parameter-response surfaces linking management practices to SOC dynamics
- Vertical SOC profiles across soil layers
- Multi-decadal projections of carbon stocks under climate scenarios
For example, ecosys generated 14 million synthetic data points spanning 21 years of crop rotations in the U.S. Midwest, capturing daily carbon fluxes (GPP, NEE, Rh) and annual yield variations. This synthetic data costs orders of magnitude less than equivalent field campaigns while preserving process-based relationships between climate drivers and carbon cycling.