2022
Mapping soybean protein and oil quality in farmer fields
Category:
Sustainable Production
Keywords:
DiseaseField management Pest
Parent Project:
This is the first year of this project.
Lead Principal Investigator:
Ignacio Ciampitti, Kansas State University
Co-Principal Investigators:
Scott Nelson, Iowa Soybean Association
Aaron Prestholt, Iowa Soybean Association
Leonardo Bastos, Kansas State University
Mark Seamon, Michigan Soybean Committee
Maninder Singh, Michigan State University
Greg Luce, Missouri Soybean Merchandising Council
David Kramar, North Dakota State University
Shaun Casteel, Purdue University
Peter Kovacs, South Dakota State University
Randall Pearson, Southern Illinois University
John Fulton, The Ohio State University
John Lory, University of Missouri
Laila Puntel, University of Nebraska at Lincoln
Laura Thompson, University of Nebraska at Lincoln
+13 More
Project Code:
Contributing Organization (Checkoff):
Leveraged Funding (Non-Checkoff):
Iowa Soybean Association check-off imagery calibration project. Research collaboration with commercial imagery providers such as Ceres, IntelinAir Descartes Lab and PlanetLab will provide support on accessing to satellite imagery data.
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Institution Funded:
Brief Project Summary:
Attention to soybean seed quality is increasing among farmers, agronomists and commodity traders. Higher nutritional content of U.S. soybeans can help in marketing and increase their economic value. Measuring soybean protein and oil content used to require laboratory analysis. Previous projects involved calibrating NIR sensors to produce soybean quality maps. This project will develop a database to benchmark agronomic practices, genetics, management and environmental conditions for soybean quality predictions at regional levels that can lead to large-scale quality improvements. The team intends to have an interactive simulation tool to show in-field predictions based on remote-sensed data collected from the sites.
Key Beneficiaries:
#agronomists, #farmers, #marketers
Unique Keywords:
#agronomy, #protein, oil, #remote sensing, #soybean quality
Information And Results
Project Summary

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.

Project Objectives

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.

Project Deliverables

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.

Progress Of Work

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 PDF file

Final Project Results

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 PDF file

View uploaded report 2 PDF file

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.

Benefit To Soybean Farmers

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.

The United Soybean Research Retention policy will display final reports with the project once completed but working files will be purged after three years. And financial information after seven years. All pertinent information is in the final report or if you want more information, please contact the project lead at your state soybean organization or principal investigator listed on the project.