Soybean seed composition quality is receiving increased attention among farmers, agronomists and commodity traders. Higher nutritional content of U.S. soybeans can help in marketing efforts and increase the economic value of each bushel. In the past, measuring soybean protein and oil content required the collection of soybean seed samples and laboratory analyses.
Recent pilot projects in Iowa and Kansas were focused on calibrating an on-the-go protein NIR sensor to produce the first soybean quality maps in the USA. The NIR spectra were collected during the soybean harvest and then calibrated using soybean seed samples collected during the harvest to produce soybean quality maps.

1. Develop 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.
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 1 focus: Soybean quality data from on-farm quality surveys will be the main focus for this year. Coordinate, identify, and work with farmers to obtain seed quality samples. In-season data (aerial from plane and 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.
Year 2 focus: Coordinate, identify, and work with farmers to obtain seed quality samples. In-season data (aerial from plane and 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.
Year 3 focus: Coordinate, identify, and work with farmers to obtain seed quality samples. In-season data (aerial from plane and 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. Finalizing data gathering, creating a final database and the online simulation tool - focus on releasing this service to farmers and to start making field soybean quality predictions.

Updated March 28, 2022:
The team has formalized 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, Greg Luce and John Lory, Scott Nelson, Mark Seamon and Mani Sing, Randy Pearson, David Kramar and Michael Ostlie, and Laila Puntel and Laura Thompson.

Two main goals were achieved from the field coordination, i) all collaborators already committed to contribute to the project and provide between 5-to-15 fields per state (with a target of at least 150 fields per year across the North Central region), and ii) an initial survey, a protocol for data collection has been developed to obtain field data related to management on seed quality.
From the soybean quality tool, the research team discussed new improvements, in addition to have several presentations on this topic during January and February 2022.
Here is the link to the field survey data collection: https://forms.gle/5wBfdj9ZhsoJYsbNA

View uploaded report

Updated November 2, 2022:
The team has accomplished the collection of soybean fields from multiple states (Ohio, Indiana, South Dakota, Missouri, Iowa, Michigan, Illinois, North Dakota, Nebraska, and Kansas), including the main collaborators such as John Fulton, Shaun Casteel, Peter Kyveryga, Greg Luce and John Lory, Scott Nelson, Mark Seamon and Mani Sing, Randy Pearson, David Kramar and Michael Ostlie, and Laila Puntel and Laura Thompson.
From all states, close to 100 fields were collected from the implementation of this project. The final numbers of fields per state are from high to low: Michigan (14), Ohio (13), Indiana (12), Iowa (12), Kansas (11), Nebraska (10), South Dakota (8), Illinois (7), North Dakota (7), and Missouri (4). From all these farmer fields, more than 1,000 seed samples were collected and then further processing for quality (protein and oil), and more than 250 soil samples to characterize the field zones linked to changes in quality. In addition, all the teams from each state are finishing the collection of the main management connected to the fields.
Here is the link to the field survey data collection: https://forms.gle/5wBfdj9ZhsoJYsbNA.
We have also presented information about soybean quality in two meetings during April-May, receiving great feedback on the need of this project and the lack of information about soybean quality. In addition, we did have four summer field days with presentation of this soybean quality project, reaching out close to 150 farmers across our states. Dissemination of soybean quality information obtained from previous survey tools.

View uploaded report

View uploaded report 2

This project completed the first year of a large coordination with 10 states participating and working together to develop the largest farmer database of soybean seed quality around the globe. With close to 100 fields collected in year 1 (and ~1000 seed samples, 250 soil samples), this is an example of the coordination with our teams from K-State and Iowa Soybeans and the other 8 soybean specialists. A sampling protocol was developed (see attached document) in order to provide a large standardization of all the data collected from this project.
All data, seed and soil samples and management information for farmers, will be analyzed and prepared to provide new insights on the relationships and the main drivers of soybean quality across the North Central US region. This project will provide a foundational database to develop predictive models and assist on the possibility for segregating quality at the farmer field level.

At the end of this project, the team expects to have the largest dataset on the within field variation soybean quality at farmer scale around the world, estimating a total close to 500 farmer fields across the US North Central Region. This project is important and timely since it will provide relevant information to growers related to potential capability to learn and segregate quality at the field level, with the ultimate outcome of improving overall profits from the current soybean farming systems.