The Agriculture and Food Research Initiative's Data Science for Food and Agricultural Systems program area priority seeks to catalyze activities at the intersection between data science/artificial intelligence (AI) and agricultural areas, in order 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.
DSFAS 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. 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 and food 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.
DSFAS applications must address one or more of the following data science priorities in relation to food and agricultural systems:
- Analysis of Agricultural Data
- Develop data-integration and data-quality algorithms and tools to improve analytic capability.
- Design, validate and implement new algorithms and methods for depicting and leveraging massive data.
- Connect Multi-scale, Multi-domain or Multi-format Agricultural Data
- Bridge real-time distributed and parallel data systems;
- Create new methodologies and frameworks for tracking and processing data; and/or
- Identify new approaches to data archiving and sharing that support Findable, Accessible, Interoperable, and Re-usable (FAIR) standards.
- Agricultural Applications and Human-Technology-Data Interactions
- Examine new 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 the public and private sectors; and /or
- Develop decision-support tools that use diverse data sources and Big Data analytics modeling of short-term impacts of various factors to create best value to the U. S. agricultural enterprise.
For more information, please read the DSFAS program area priority description in Part I, C of the AFRI Foundational and Applied Science RFA.
DSFAS Funding Opportunity
- AFRI Foundational and Applied Science RFA within Crosscutting Programs