This Exploratory Topic works 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.
Ethanol production is one of the largest consumers of domestic grain in the U.S., and developing sustainable production methods for ethanol and bio-based fuels has great potential to both reduce emissions and potentially provide a net emissions-free source of energy. While the economic and emissions impacts of ethanol production nationally are clear, field-level contributions remain unclear. The lack of understanding of field-level feedstock emissions, combined with the absence of economic incentives beyond yield, leaves feedstock producers to estimate and assume risks to their primary revenue stream by new management practices. 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.
Projects funded within this Exploratory Topic will work concurrently with teams selected under the following ARPA-E program(s):
Projects Funded Within This Exploratory Topic
OKLAHOMA STATE UNIVERSITY
ESTABLISHING VALIDATION SITES FOR FIELD-LEVEL EMISSIONS QUANTIFICATION FROM GRAIN SORGHUM IN SOUTHERN GREAT PLAINS
Oklahoma State University (OSU) will synthesize scientific principles from eddy covariance (a method enabling observation of gas and energy exchange between ecosystems at earth’s surface and the atmosphere), plant and soil science, remote sensing, and crop modeling to measure field-level emissions. The OSU-led team will collect data for field-level emissions of carbon dioxide, nitrous oxide, and methane in grain sorghum production systems in Texas, Oklahoma, and Kansas. Current estimates are from point-based measurements extrapolated using modeling approaches that lack field-scale validation or comprehensive ground truth data. The project’s final deliverable will be gold-standard, publicly-available data sets for quantifying field-level greenhouse gas emissions from grain sorghum production systems.
UNIVERSITY OF NEBRASKA, LINCOLN
NOVEL COMMERCIAL FARM-FIELD NETWORK TO QUANTIFY EMISSIONS AND CARBON STORAGE FROM AGRICULTURE BIOENERGY FEEDSTOCK PRODUCTION
The University of Nebraska, Lincoln (UNL) will leverage existing data sets and new data collection methodologies to quantify fertilizer- and biomass-induced emissions, biomass nitrogen content, carbon dioxide uptake, and soil organic carbon sequestered—while providing agronomic management insights to farmers, farming communities, and agricultural supply chains. This team will use eddy covariance flux towers and static chamber methods to quantify field-scale emissions, while using active chambers to quantify fertilizer and soil surface biomass emissions. UNL will combine the data with site-specific data collected through telemetrically connected agricultural equipment to understand management, soil, greenhouse gas, water quality, and productivity interactions in feedstock production for biofuels.
UNIVERSITY OF ILLINOIS
MIDWEST BIOENERGY CROP LANDSCAPE LABORATORY (MBC-LAB): CAPTURING SPATIO-TEMPORAL AND MANAGERIAL VARIATIONS TO PROVIDE A GOLD STANDARD DATA AND PLATFORM FOR VALIDATING FIELD-LEVEL EMISSIONS FROM BIOENERGY CROPS
The University of Illinois will produce field-level emissions data from commercial bioenergy crops managed by Illinois farmers. The project team will 1) collect emissions data from three commercial bioenergy feedstock sites, using ground and remote sensing measurements, 2) develop protocols for data processing and storage, and an online portal for users to access emissions datasets, 3) develop cyberinfrastructure to enable emissions data visualization, including real-time eddy covariance data, in a timely manner, and 4) actively engage stakeholders regarding emissions data usage. The project will reduce energy-related emissions by enabling technology development for managing bioenergy crops, improving yield, reducing over-fertilization, and designing and validating remote sensors and decision support tools for smart farms.
RICE N’ GRITS: QUANTIFYING ENVIRONMENTAL BENEFITS OF BIOENERGY CROPS THROUGH COMPLETE CARBON AND NITROGEN ACCOUNTING
Arva 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). Arva will 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.
LAWRENCE BERKELEY NATIONAL LABORATORY
CARBON STANDARD: CARBON ACCOUNTING TO REDEFINE BIOFEEDSTOCK OPERATION NORMALITY USING SENSING TECHNOLOGY ASSISTED BY NUMERICAL AND DATA ANALYTICS FOR RELIABLE DETECTION
The Lawrence Berkeley National Lab (LBLN) CARBON STANDARD team will develop advanced machine learning tools for a cross-scale quantification of carbon intensity (CI) during biofuel feedstock production. LBL will act as the integrator across all SMARTFARM teams to analyze complex, multi-physics, and multi-scale datasets, and develop scaling approaches across the variety of CI monitoring fields.