Full proposals due August 15, 2019

This program area priority focuses on data science to enable systems and communities to effectively utilize data, improve resource management, and integrate new technologies and approaches to further U.S. food and agriculture enterprises. The program encourages universitybased research as well as public and private partnerships.

Many challenges are associated with data in agriculture and food production. NIFA stakeholders identified at least a dozen issues that are critical to address including: data infrastructure and management; applications and use of data; entities affected by data; creation, collection, provenance, and characteristics of data; training, programs, student, and knowledge needs around data; principles and protocols associated with data; team, community, and public/private aspects of data; data producers, engineers, scientists, and researchers of data; roles of public, corporate, and commercial entities in data; privacy, security, confidentiality, and quality data; biological and interoperable data systems; bibliometric, altmetrics, text and data mining; and data sharing, repositories, and analysis.

This program area priority will support projects that examine the value of data for small and large farmers, as well as the agricultural and food industries, and gain an understanding of how data can impact the agricultural supply chain, reduce food waste and loss, improve consumer health, environmental and natural resource management, affect the structure of U.S. food and agriculture sectors, and increase U.S. competitiveness. The most competitive proposals will be equally well grounded in the agricultural sciences and data science component.

Applications for research projects must address one or more of the following priorities:

  • Design and Implementation
    • Build scalable data infrastructure and management systems.
    • Conceptualize Open Data FAIR principles: Findable, Accessible, Interoperable, and Re-usable for all experimental and research data.
    • Develop standards and best practices with other government and international organizations.
  • Analysis
    • Develop data-integration and data-quality tools to improve analytic capability.
    • Design and implement new algorithms and methods for depicting massive data.
  • Technologies
  • Connect multiscale data
    • Bridge real-time distributed and parallel data systems.
    • Create new methodologies and frameworks for tracking and processing data.
    • Identify new approaches to data archiving and sharing.
  • Applications and Human-Technology-Data Interactions
    • Examine scientific implications and practical aspects of how agricultural data and computer systems are accessed, designed, and used to improve human-human, human-technology, and human-decision experiences.
    • Integrate visualization with statistical methods and other analytic techniques in order to support discovery and analysis.
    • Engage students and professionals, teams, universities, and public and private sectors.
    • Develop decision-support tools that use diverse data sources and Big Data analytics modeling short-term impacts of various factors to create best value to the U S agricultural enterprise.

Artificial Intelligence (AI) for Precision Agriculture: In support of the Executive Order for Maintaining American Leadership in Artificial Intelligence (https://www.whitehouse.gov/presidential-actions/executive-order-maintaining-americanleadership-artificial-intelligence/ ), the FACT program area priority is particularly seeking projects that apply artificial intelligence and machine learning for monitoring, analytics, and automation in precision crop agriculture and precision livestock farming. Such projects should start their titles as "FACT-AI: full title…." and address one or more of the following:

  • Address changes in conditions through cognitive and machine learning.
  • Facilitate real-time decision making.
  • Make use of frameworks for collecting contextual data with increasing efficiency.
  • Develop open-source platforms to improve affordability, adoption and penetration of AI tools and technologies among the farmers.
  • Provide solutions to AI challenges including testing, validation and effective implementation in agricultural applications.

Applications for integrated or research Coordinated Innovation Networks (CIN) should start their titles as "FACT-CIN: full title…." and must address at least one of the priorities listed immediately above and the following:

  • Synergy: There should be a demonstrable benefit to the existence of a multidisciplinary, multi-sector, or multifunctional CIN that would not otherwise be possible by the participating entities and individuals operating independently.
  • Contribution: Each participating individual or entity should have a unique, meaningful, and active contribution to the network that is critical to the network's functioning, performance, and success in addressing bottlenecks in critical areas.
  • Continuity: There should be a sustainability plan for network persistence beyond the duration of initial grant support (e.g., identification of additional funding sources and/or more formal organizational arrangements).
  • Management: There should be a plan for coordination and oversight including, but not limited to, communication, leadership, advisory boards, milestones, and evolution over time (e.g., new objectives or new participants).

Read the full RFP here (see especially pp. 68-70)

Office of Grants and Contracts

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