NSF Org:	ITE Innovation and Technology Ecosystems
Recipient:	UNIVERSITY OF ARKANSAS
Initial Amendment Date:	December 9, 2022
Latest Amendment Date:	December 9, 2022
Award Number:	2236302
Award Instrument:	Standard Grant
Program Manager:	Michael Reksulak mreksula@nsf.gov  (703)292-8326 ITE  Innovation and Technology Ecosystems TIP  Directorate for Technology, Innovation, and Partnerships
Start Date:	December 15, 2022
End Date:	November 30, 2025 (Estimated)
Total Intended Award Amount:	$743,651.00
Total Awarded Amount to Date:	$743,651.00
Funds Obligated to Date:	FY 2023 = $743,651.00
History of Investigator:	Meredith Adkins (Principal Investigator) mmckee@uark.edu Chase Rainwater (Co-Principal Investigator) Kristen Gibson (Co-Principal Investigator) Ngan Le (Co-Principal Investigator) Yasser Sanad (Co-Principal Investigator)
Recipient Sponsored Research Office:	University of Arkansas 1125 W MAPLE ST STE 316 FAYETTEVILLE AR  US  72701-3124 (479)575-3845
Sponsor Congressional District:	03
Primary Place of Performance:	University of Arkansas 481 N. Shiloh Drive FAYETTEVILLE AR  US  72704-7552
Primary Place of Performance Congressional District:	03
Unique Entity Identifier (UEI):	MECEHTM8DB17
Parent UEI:
NSF Program(s):	Convergence Accelerator Resrch
Primary Program Source:	01002324DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s):
Program Element Code(s):	131Y00
Award Agency Code:	4900
Fund Agency Code:	4900
Assistance Listing Number(s):	47.084

ABSTRACT

Global environmental changes and pandemic have exposed vulnerabilities in the food system, exacerbating food insecurity, especially for underserved communities who already experience disproportionate access to safe and affordable food and nutrition. The need for resilient local food supply has refocused efforts on domestic sourcing of food in the United States. Yet, logistical and market knowledge barriers limit the viability of productive local food systems. The convergence of multiple scientific research fields and modern technological innovations such as artificial intelligence and machine learning can improve supply and demand efficiencies by extending small farmers' access to market insights. This project will empower regional food producers to understand the economic value of the specialty crop assortment and food animals on their farms in comparison to market demand for institutional sales (e.g., retailers, food hubs, distributors, grocers, restaurants, hospitals, schools or colleges). The end-platform's data-driven market and financial insights will enhance regional food producers' abilities to obtain procurement contracts with institutional buyers, supporting local farms to bring their products to store shelves, restaurant tables, and cafeterias.

Addressing the challenges of regional food systems will have broad societal implications for the economic livelihoods of small farmers and local businesses, and for the increased availability of safe and nutritious local food that will support metabolic health, particularly for disadvantaged communities. Furthermore, enhanced knowledge of sales channels will reduce food losses and enhance crop diversity, thus creating income streams for farmers practicing regenerative agricultural techniques such as mixed farming and crop diversification. Ultimately, this project advances the health and prosperity of the United States' population, as well as environmental stewardship, through its focus on food and nutrition security.

This project assesses user needs to design a scalable technology platform that provides market insights to small farmers. The primary research objectives are: (a) to understand the knowledge barriers that small farmers experience to sell to institutional markets; and (b) to converge use-inspired research of multiple scientific disciplines and novel data-driven techniques to develop the conceptual design for a software platform that would support small farmers to access the relevant market information. The research methods include: (a) user discovery with small farmers and other stakeholders about the barriers that they face; (b) market analysis; (c) data collection that contributes to the conceptual design and data feeds for computational models in the platform. Data collection includes: (i) inventory assessments and interviews with institutional buyers in the underserved pilot regions to identify local demand for food products; (ii) product-level data collected on-farm via robotics, remote sensing, satellite data or drone, including both existing datasets and data collected from growers; (iii) assessment of low-cost validated on-farm preventative controls and detection of microbial risk for analysis of food safety economic risk models to support production decisions.

Central to the translation of research discovery to market impact, this project will identify the barriers that small farmers experience to understand institutional market demand and sell to institutional buyers. By identifying where gaps in knowledge contribute to supply and demand inefficiencies, it will also extend understanding of the data across scientific fields that could be integrated to inform business decisions, leveraging artificial intelligence (AI) and machine learning (ML) techniques, such as computer vision, to price farm products and create predictive models to anticipate future food demand and pricing. This work will advance the field for data-driven agriculture for small producers, supporting their livelihoods and local economic growth and food security.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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