The importance of soybean seed quality and composition is receiving increased attention among farmers, agronomists, and commodity traders. A higher nutritional content of US (United States) soybean provides a competitive edge that can be widely exploited to produce increased economic value through the targeted marketing of each bushel. Within-field knowledge of soybean quality will enable and encourage value chain disruption both up and downstream via more leveraged negotiations.
This work will expand knowledge of soybean protein and oil quality in farmer fields in 10 states across the North Central region of the US. This larger database will be incorporated into a decision support tool developed with funding requested from USB for mapping soybean quality across the US. A simplified framework is presented below. This project will provide the base level of ground-truth field data required (Fig. 1) for integration with remote sensing provided by multi-spectral satellite imagery to build predictive models that will provide new insights derived from soybean quality spatial variation.

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

Updated October 29, 2023:
The team have worked with all the collaborators from multiple states (Ohio, Indiana, South Dakota, Missouri, Iowa, Michigan, Illinois, North Dakota, Nebraska, Iowa, and Kansas), to complete all field selection for 2023 season. Seed data collection has been completed across all states involved on the project, samples are currently being sent to our lab for seed quality analysis and final processing will be completed on the coming month. For 2023 season, we did collect more than 100 fields with those to be added to the 2022 season, +100 fields, to continue the process of completing the largest on-farm database on soybean seed quality. The compilation of the database follows the same step as previous year, from data collection (survey), protocol for seed and soil sampling from each farmer field and connecting seed quality variation with weather conditions. The next steps are to prepare the reports to all states and add the information to the main database.

View uploaded report

The team is making substantial progress for developing a multistate database to allow upscaling of soybean quality predictions to regional levels and benchmark agronomic practices, soybean genetics, management, and environmental conditions that can lead to large-scale improvements in soybean quality.

Data collection across all states is presented for the complete dataset using 2022 season. The team is in the process of developing a similar outcome for 2023 and combining both seasons. In overall, when yield data was divided in terciles, the lower tercile clearly reflected a greater protein concentration, while both lower and middle yield ranges presented similar protein variation. The variation in oil concentration was less sensitive to yield levels.

The effect of soil and weather variables on soybean oil and protein concentration were evaluated for all the on-farm data collected. For protein concentration, farmer fields with high yield presented greater protein levels linked to the levels of soil organic matter. For oil concentration, the levels of this seed quality trait were mainly linked to weather conditions during the seed filling period and in less extent with the type of soils. This a preliminary analysis based on only one year dataset but with more than 100 fields included for the evaluation.

From our last 2022 season, we have achieved all proposed steps, collecting several fields per state, retrieving relevant crop management information, and concluding the analysis of seed quality from all fields. Similar steps are now underway for all the 2023 season, all seeds are starting to be received and analyzed for seed quality traits and reports by state and for all farmers will be produced and released to each our partners on the coming months.
An initial publication will be prepared synthesizing all the data from 2022 and 2023 seasons, new field reports and extension newsletters will be released as a part of this effort.

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.