Arva Intelligence Awarded Department of Energy ARPA-E Innovation Grant

by | 12.7.20

Arva Intelligence Awarded Department of Energy ARPA-E Innovation Grant
Establishing Validation Sites for Field-level Emissions Quantification of Agriculture Biofuel Feedstock Production.

Park City, Utah — Announced in a press release yesterday, the Department of Energy’s Advanced Research Projects Agency-Energy (ARPA-E) awarded Arva Intelligence up to $2,950,000 to lead an AI-enhanced quantification study of greenhouse gas emissions on biofuel feedstock production. Arva is partnered with Lawrence Berkeley National Laboratory and the University of Arkansas to conduct research on farms in diverse states, including the Arva Research Farm in Humphrey, Arkansas, with the goal to implement commercial applications for artificial intelligence in precision agriculture. “This project furthers our objectives of marrying sustainability and profitability in agriculture. Improving nitrogen efficiency helps farmers achieve higher profitability and reduce greenhouse gas emissions. We are thrilled to continue our collaboration with scientist at Berkeley Labs and the University of Arkansas,” says Arva Intelligence CEO Jay McEntire.

ARPA-E describes the participants selected for this topic will “work to establish validation sites for field-level emissions quantification of agricultural bioenergy feedstock production. These teams will work towards the development of ‘ground truth’ solutions to establish measurements and protocols for emissions monitoring at the field level to create publically available, open-source, high-resolution datasets to support testing and validation of emerging biofuel production monitoring technologies. The projects will also compliment selections in ARPA-E’s full SMARTFARM program, further supporting and validating the selections made through this full funding opportunity… By establishing sites and protocols for measuring the impact on yield increasing and emissions reducing technologies, these teams will bridge the technology gap between feedstock producers and existing market incentives to de-risk sustainable management practices, defray the cost of monitoring their impact, reduce biofuel feedstock production emissions, and broadly enable a future carbon farming industry.”

Arva’s project, titled Rice & Grits: Quantifying Environmental Benefits of Bioenergy Crops Through Complete Carbon and Nitrogen Accounting, will establish validation sites where dedicated energy crops (corn-soy or sorghum) and crop residues (straw/stover) are used to produce domestic, sustainable, carbon-negative biofuels (i.e., ethanol, biodiesel, or biogas) and measure carbon and nitrogen fluxes using state-of-the-art high-frequency commercial-scale monitoring towers to assess carbon dioxide, nitrous oxide, and methane emissions at sub-second resolution yearlong. All deployed farm equipment is highly instrumented, and will measure fuel, electricity, and fertilizer use, in addition to crop yield and management practices. This data will allow Arva’s artificial intelligence platform to construct a generative model for biofuel yield and life cycle emissions.

Alex Whitley, Managing Director of Sales and Marketing for Arva, says, “Until recently there has been assumptions regarding sustainable and/or regenerative practices as mutually exclusive from increased production and profitability. We are excited to objectively demonstrate that these sustainable practices can contribute to the economic benefit of renewable energy and profitability of row crops.”

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To learn more about Arva Intelligence’s machine learning empowered field trial process, contact Matt Rohlik at

Arva Intelligence is an agriculture technology company that partners with farmers to augment their ability to make the best decisions for their land and maximize the value between environmental stewardship and economic profit. Using cutting-edge machine learning and artificial intelligence, Arva leverages field data to improve yield, lower cost, reduce risk, and achieve sustainability goals for farmers. For more information, visit


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