ARPA-E recently released a funding opportunity, Systems for Monitoring and Analytics for Renewable Transportation Fuels from Agricultural Resources and Management (SMARTFARM), to develop innovative new technologies for measuring emissions from agricultural feedstock production. We sat down with Dr. David Babson, SMARTFARM’s Program Director, to learn about his vision and the technologies of interest for Phase 2 of the program.

Visit ARPA-E’s SMARTFARM page to learn more about Phase 1, enter the ARPA-E funding portal to submit an application, or watch a recorded webinar on the topic held on April 28th, 2020.

Why is ARPA-E interested in biofuels, and how does the SMARTFARM program fit in?

ARPA-E’s mission is to advance projects that have the potential to radically improve U.S. economic prosperity, national security, and environmental wellbeing. In 2018, biofuels made up nearly 10% of the fuel supply in the transportation sector.[1] In addition to being a strategic energy asset in the U.S., biofuels provide an economic growth benefit to the nation’s farmers and deliver net reductions in emissions associated with transportation fuels.[2] U.S. agriculture could enable sustainable production of 50 billion gallons of biofuels per year,[3] and with new innovations throughout the biofuel supply chain, these fuels could become carbon negative.[4]

Reaching this potential requires feedstock producers to adopt new technologies and management practices that simultaneously improve yield, drive down emissions, and enhance carbon sequestration in soils. Grower adoption of these tactics requires incentives for carbon optimization. While incentives currently exist for downstream biofuel processing, there needs to be an acceptable method of quantifying feedstock-related emissions at the field level in order for the incentives to make their way upstream.

SMARTFARM projects will develop technologies to reliably, accurately (i.e., low uncertainty), and cost-effectively quantify feedstock production lifecycle emissions (in g CO2e/acre) at the field level (i.e., scalable to >80 acres). If successful, the technologies funded by this phase of SMARTFARM will catalyze new market incentives for efficiency in biofuel feedstock production and carbon management, with potential for further emissions reductions if expanded to other agricultural products beyond biofuels.  

How would the technologies built in Phase 2 differ from those developed in Phase 1?

SMARTFARM is focused on the key drivers of net feedstock emissions, with Phase 1 teams doing the complex and laborious work of collecting this information with today’s tools, and Phase 2 teams being encouraged to develop new, scalable methods of getting to the same estimates with the same degree of certainty. Phase 2’s parameters are the main drivers of a feedstock’s net emissions, nitrogen loss as nitrous oxide (N2O), and soil carbon sequestration.

We have been scouting technologies over the last several years to identify advances in agriculture, medicine, and defense that could converge into unique solutions for precision digital agriculture. We anticipate that a “system of systems” will be required to obtain accurate estimates of carbon intensity. These systems may include in-field sensors, UAV and satellite imagery, agronomic data, and modeling/simulation tools, all of which are in need of further development and system integration in order to meet the program’s aggressive targets.

Whether inventing a new sensing modality, utilizing existing sensing capabilities in new ways, or utilizing some combination of in-field sensing, remote sensing, and modeling, all submissions to Phase 2 will be evaluated in terms of cost, operational complexity, and certainty. A tool must be able to work consistently throughout the season, be interoperable with other data tools implemented in the field, not interfere with operations, and deliver all of this at a price point that allows revenue potential for the adopting farmer.

What makes measuring N2O and soil carbon so difficult?

Current technologies and their associated costs severely constrain data collection and limit its potential uses. Our vision is to enable the use of these data to connect individual farm practices to carbon markets, thereby incentivizing more sustainable farming, carbon farming, and ag-sector wide carbon management capacity. These data need to be resolved to a farm-level, highly accurate, and available at a very low cost. Realizing this at the scale, resolution, and cost that our vision demands will require new technologies and new approaches.  

What was the motivation behind your program metrics?

We want the program to focus on the market criteria for adoption – both for the growers who might purchase and install a given sensor system, and the market regulators and verifiers that would rely on these field-level datasets to issue carbon credits and associated payments.

For growers, cost and operational simplicity are paramount; these systems cannot cost more than the potential credit payment, and they can’t disrupt day-to-day operations. From the market perspective, these systems need to be reliable and accurate: the data need to be traceable to the field, and the source needs to be trustworthy.

Where do you see the SMARTFARM program in four years?

In four years, we want to see the majority of the Phase 2 projects successfully demonstrated in the fields of actual farmers, with both farmers and regulators being comfortable with the measurements. We expect the Phase 1 projects will produce rich datasets that will help regulators set baselines and develop new market pathways for upstream carbon credits, which benefits the entire bioenergy supply chain. The Phase 2 technologies will be key to quantifying the emissions impact of different crops and management practices, and validating the performance of individual farms producing bioenergy feedstocks.  As we talk to stakeholders up and down the bioenergy supply chain – in both the emerging carbon market sector and the agriculture sector – the demand for these technologies is clear. We eagerly await proposals from technology developers interested in addressing this need.

[1] Department of Energy, Office of Energy Efficiency & Renewable Energy’s Alternative Fuels Data Center.

[2] Lewandrowski, Jan, et al. "The greenhouse gas benefits of corn ethanol–assessing recent evidence." Biofuels (2019): 1-15.

[3] Langholtz, M. H., B. J. Stokes, and L. M. Eaton. "2016 Billion-ton report: Advancing domestic resources for a thriving bioeconomy, Volume 1: Economic availability of feedstock." Oak Ridge National Laboratory, Oak Ridge, Tennessee, managed by UT-Battelle, LLC for the US Department of Energy 2016 (2016): 1-411.

[4] See DE-FOA-0001565: Rhizosphere Observations Optimizing Terrestrial Sequestration (ROOTS); DE-FOA-0001563: Renewable Energy to Fuels through Utilization of Energy-dense Liquids (REFUEL); DE-FOA-0001211: Transportation Energy Resources from Renewable Agriculture (TERRA); DE-FOA-0000470: Plants Engineered To Replace Oil (PETRO)