Location: Cereal Crops Improvement Research
Project Number: 3060-21000-046-051-S
Project Type: Non-Assistance Cooperative Agreement
Start Date: Aug 1, 2026
End Date: Jul 31, 2027
Objective:
The Oat Data Analytics Core (ODAC) will serve as a national platform for breeding data management, data analytics, and decision support capacity to strengthen U.S. oat improvement efforts. The project emphasizes harmonized data resources, streamlined workflows, and breeder oriented tools that support more efficient use of existing breeding information.
The project will capitalize on advances in artificial intelligence, machine learning, quantitative genetics, and data science to provide breeders with actionable tools for evaluating germplasm, predicting performance, and identifying superior breeding selections.
1. Use breeding program data to increase the projected rate of genetic gain for oats intended for human consumption, with particular emphasis on improving grain quality, nutritional characteristics, disease resistance, agronomic performance, and other traits important to oat breeding programs.
2. Integrate seamlessly into existing breeding pipelines in a manner that remains sustainable after the funding period concludes. Solutions should establish reproducible and scalable workflows that initially may require human oversight but become increasingly automated and operationally efficient over the course of the project.
3. Result in routine deposition of phenotypic, genotypic, pedigree, and quality trait data into T3/Oat and related community databases. Data may remain private (embargoed) for up to five years before public release. Project activities should leverage T3/Oat, Breeding Insight, and associated cyberinfrastructure to improve data management, interoperability, and collaboration among oat breeding programs.
Approach:
Oat breeding programs increasingly rely on large, complex datasets. ODAC will provide a coordinated, stakeholder driven framework for data harmonization, predictive analytics, and decision support. USDA ARS will contribute scientific and technical expertise from Raj Nandety (Cereal Crops Improvement Research Unit, Fargo, ND), Jean-Luc Jannink (Plant, Soil, and Nutrition Research Unit, Ithaca, NY), relevant USDA-ARS oat quality and breeding programs, and the Breeding Insight team.
A. Decision-Support Systems and Data Integration
The project will establish collaborative partnerships with oat breeder Dr. Melanie Caffe at South Dakota State University to identify priority breeding challenges and translate them into practical analytical solutions. The analytical frameworks will interface with T3/Oat, Breeding Insight, and USDA-ARS breeding databases to generate breeder-ready outputs, including:
Harmonized datasets from participating breeding programs will be incorporated to facilitate cross-program analyses and maximize the value of community breeding resources.
B. Breeding Data Infrastructure and Automation
The project will develop standardized workflows for data collection, quality control, integration, and analysis. Particular emphasis will be placed on creating sustainable pipelines that reduce manual data handling and improve consistency across breeding programs.