| Project Detail |
Agricultural and land-based carbon removal, management, and storage is critical to comprehensive climate change mitigation strategies. New technologies to support necessary carbon markets are needed, although robust carbon markets already exist for biofuels. The goal of removing and sequestering more carbon along the biofuel supply chain than it emits requires feedstock producers to adopt new technologies and practices that simultaneously improve yield, drive down production-associated emissions, and enhance carbon sequestration in soils. Carbon management incentives exist downstream in the biofuel supply chain, but not in feedstock production because monitoring and verifying its emissions are too costly to conduct at the field level. Feedstock producers receive the national average for feedstock-production emissions despite significant variations in state and regional averages, as well as field-level estimates. Producers need detailed accounting of biofuel life cycle inputs (e.g., energy, nutrients, chemicals) and outputs (e.g., energy, co-products, emissions) to establish a reliable baseline against which to measure progress.
Project Innovation + Advantages:
The University of Illinois will develop a commercial solution, SYMFONI, to estimate soil organic carbon (SOC) and the dynamics of nitrous oxide (N2O) emissions at an individual field level to promote advanced carbon management and sustainability practices in agricultural systems. The solution can be scaled up to perform per-field estimates for an entire region. SYMFONI integrates (1) synergistic modeling of SOC and N2O; (2) use of novel satellite/airborne data and algorithms; (3) innovative sampling of high-resolution, high-frequency soil moisture; (4) development of physics-guided deep learning; (5) mobile sensing of atmospheric inversion (horizontal layers of air that increase in temperature with height) for further uncertainty reduction at the regional scale; (6) a unique data collection effort studying the N2O and SOC hot spots and hot moments; and (7) thorough system uncertainty quantification. |
