Eligible Entities
• State agricultural experiment stations;
• Colleges and universities;
• University research foundations;
• Other research institutions and organizations;
• Federal agencies;
• National laboratories;
• Private organizations, foundations, or corporations;
• Individuals; or
• Any group consisting of two or more of the entities described above (consortium applications encouraged)
USDA will not accept applications for grants and cooperative agreements submitted for dangerous gain-of-function research.
Eligible Activities
The Agricultural Genome to Phenome Initiative supports interdisciplinary research projects that advance agricultural productivity, efficiency, and data-driven innovation across crop and animal systems. Priorities include integrating genetics and genomics with computational and engineering approaches, improving agricultural data infrastructure and interoperability, developing advanced data collection and analysis tools, and strengthening crop and animal genetic resources important to U.S. agriculture. The purpose of the program is to:
• Study agriculturally significant crops and animals in production environments to achieve sustainable agricultural production.
• Ensure that gaps in existing knowledge of agricultural crop and animal genetics and phenomics are filled.
• Identify and develop a functional understanding of relevant genes from animals and agronomically relevant genes from crops that are of importance to the U.S.’ agriculture sector.
• Ensure future genetic improvement of crops and animals of importance to the U.S.’ agriculture sector.
• Study the relevance of diverse germplasm as a source of unique genes that may be of importance in the future.
• Enhance genetics to reduce the economic impact of pathogens on crops and animals of importance to the U.S.’ agriculture sector.
• Disseminate findings to relevant audiences.
Applications are encouraged to address at least two of the five goals:
• Develop or expand agricultural benchmark datasets for use in predictive analytics and data science applications.
• Integrate genomic, phenotypic, and environmental data to improve understanding and prediction of crop and animal performance.
• Develop high-throughput, on-farm trait recording methods, including AI, machine learning, and phenomics approaches.
• Improve agricultural data infrastructure, storage, harmonization, and interoperability.
• Support workforce development through research training and mentorship
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