Soil Organic Carbon Networked Measurement System (SOCNET)

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Salt Lake City,
Project Term:
01/13/2021 - 01/12/2024

Critical Need:

Crop-based biofuels have the potential to supply up to ~5% of U.S. energy demand. Making the biofuel supply chain carbon negative—i.e., removing or sequestering more carbon than it emits—would greatly improve biofuel’s benefit to the broader economy and environment. Reaching this potential requires that feedstock producers adopt new technologies and management practices that simultaneously improve yield, drive down production-associated emissions, and enhance carbon sequestration in soils. To encourage adoption of these new technologies and practices, feedstock producers need incentives beyond yield. Carbon management incentive structures exist elsewhere in the biofuel supply chain, but do not extend to feedstock production because monitoring and verifying its emissions is too costly to conduct at the field level. Instead, feedstock producers are given the national average for feedstock-production emissions despite significant variations in state or regional averages, let alone field-level estimates. Producers need detailed accounting of inputs (e.g. energy, nutrients, chemicals) and outputs (e.g. energy, co-products, emissions) of the biofuel life cycle to establish a reliable baseline against which to measure progress.

Project Innovation + Advantages:

The University of Utah aims to develop and deploy a distributed carbon sensor system that is buried into the soil, capable of locally stimulating a surrounding volume of soils at multiple depths, and sensing carbon and carbon flux at ultra-low operational cost. The sensors will enable high-accuracy and real-time decision data for cost-effective carbon removal, storage, and management to promote climate change mitigation via agriculture and managed land systems. The team aims to develop (1) a UV-based non-destructive CO2 sampling technique, (2) low-cost, wideband, and high-selectivity CO2 sensors, enabling accurate quantification of CO2 among gas mixtures (3) an artificial intelligence-based auto-calibration technique by combining environmental information and infrared spectra to quantify the sensor data, (4) machine learning-based geo-statistical mapping of CO2 distribution and flux over time, with an operational cost of <$10/acre/year on a commercial scale.

Potential Impact:

Reducing the uncertainty of emissions quantification is critical to realizing the revenue potential of carbon management markets.


New technologies will maintain U.S. leadership in sustainable biofuel production and advanced carbon removal and management.


These technologies will help incentivize continued emissions reductions throughout the biofuel and bioeconomy supply chains while enabling new opportunities to leverage agriculture and managed land systems to perform carbon removal, management, and storage to address climate change.


Enabling producers to participate in carbon management markets would complement yield-based revenues with economic incentives for input efficiency, climate change mitigation, and restorative practices.


ARPA-E Program Director:
Dr. David Babson
Project Contact:
Dr. Hanseup Kim
Press and General Inquiries Email:
Project Contact Email:


University of Nebraska, Lincoln

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