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The USDA Agricultural Research Service AI Center of Excellence (AI-COE) AI Innovation Fund for FY26 supports 4-6 proposals at up to $100,000 each to encourage and promote AI-related research in agriculture. Priority projects must either develop or adapt AI/ML methods addressing agricultural research questions, or create prototype digital products serving producers or researchers.
Proposals must demonstrate real-world applications with defined deliverables and utilize SCINet high-performance computing resources. Training, workshop, and working group activities are excluded. Submissions require a 2-page project description with challenge statement, proposed method, team capability, and deliverables, plus a detailed budget using REE form 455.
Funds must be spent or obligated by end of FY2026.
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Search similar grants →Based on current listing details, eligibility includes: ARS Category 1, 4, or 6 scientists with Research Leader or supervisor approval. One lead investigator proposal per scientist, though researchers may participate in multiple team proposals. Applicants should confirm final requirements in the official notice before submission.
Current published award information indicates Up to $100,000 per project (4-6 awards expected; total $400,000-$600,000) Always verify allowable costs, matching requirements, and funding caps directly in the sponsor documentation.
The current target date is February 6, 2026. Build your timeline backwards from this date to cover registrations, approvals, attachments, and final submission checks.
Federal grant success rates typically range from 10-30%, varying by agency and program. Build a strong proposal with clear objectives, measurable outcomes, and a well-justified budget to improve your chances.
Requirements vary by sponsor, but typically include a project narrative, budget justification, organizational capability statement, and key personnel CVs. Check the official notice for the complete list of required attachments.
Yes — AI tools like Granted can help research funders, draft proposal sections, and check compliance. However, always review and customize AI-generated content to reflect your organization's unique strengths and the specific requirements of the solicitation.
Review timelines vary by funder. Federal agencies typically take 3-6 months from submission to award notification. Foundation grants may be faster, often 1-3 months. Check the program's timeline in the official solicitation for specific dates.
Many federal programs offer multi-year funding or allow competitive renewals. Check the official solicitation for continuation and renewal policies. Non-competing continuation applications are common for multi-year awards.
PFAS Fate and Remediation in Agricultural Systems: Developing Conservation Assistance for Landowners (Interagency Reimbursable Agreement) is sponsored by USDA Agricultural Research Service (ARS). This is an ongoing research project within USDA ARS focused on understanding the fate and remediation of PFAS in agricultural systems, with the goal of developing conservation assistance for landowners.
The USDA ARS AI Innovation Fund is an annual intramural funding program from the Agricultural Research Service's Artificial Intelligence Center of Excellence (AI-COE) that directly supports the development and adoption of AI and machine learning tools in agricultural research. The FY2026 cycle funded 4-6 proposals at up to $100,000 each. The program supports two primary categories of projects: (1) development or adaptation of AI/ML methods to help ARS scientists address agricultural research questions, and (2) creation of prototype digital products using AI/ML technologies for producers or agricultural researchers. Projects should utilize SCINet computing resources including the Ceres and Atlas high-performance computing clusters. Proposals are limited to 2 pages plus an abstract (maximum 1,500 characters), and funds must be spent or obligated by end of the fiscal year. The program represents USDA's commitment to integrating AI into agricultural research across areas including crop science, soil health monitoring, livestock management, pest detection, and climate-smart agriculture. While eligibility is limited to ARS scientists, the program produces open research outputs that benefit the broader agricultural AI community.