1. Continue the development of a multi-state database to allow upscaling of soybean quality predictions to regional levels and benchmark agronomic practices, soybean genetics, and environmental conditions that can lead to large-scale improvements in soybean quality.
2. Communicate the economic value of soybean quality mapping to farmers and agronomists through an online interactive simulation tool, technical publications, and social media.
Year 2 focus: Coordinate, identify, and work with farmers to obtain seed quality samples. In-season data (satellite imagery) will be correlated with final seed quality data. Within-field protein predictions will be explored between the field and remotely sensed quality data. Proposed for this current application with the goal of expanding our farm database and integrating this information in the predictive model.
At the end of this project, the team expects to have the largest dataset on the within-field variation soybean quality at farmer scale across the US North Central Region.
Updated March 27, 2023:
The team will continue working with all the collaborators from multiple states (Ohio, Indiana, South Dakota, Missouri, Iowa, Michigan, Illinois, North Dakota, Nebraska, Iowa, and Kansas), including John Fulton, Shaun Casteel, Peter Kovacs, Andre Borja, Scott Nelson, Mark Seamon and Mani Sing, Randy Pearson, David Kramar and Michael Ostlie, and Laila Puntel.
From our last 2022 season, we have learned several lessons and reports by state and for all farmers collected were produced and released to each our partners. We have achieved all proposed steps, collecting several fields per state, retrieving relevant crop management information, and concluding the analysis of seed quality (protein and oil) from all seeds harvested in those fields.
From the soybean quality tool, the research team is currently working on implementing new improvements.
Currently, we are in preparation and starting the coordination of fields for the 2023 season.
View uploaded report 
This project is important and timely since it will provide relevant information to growers related to segregate quality at the field level, with the ultimate outcome of improving overall profits from the current soybean farming systems.
Field identification, field data collection, preparation of reports, publication, presentation