Project Details:

Mapping soybean protein and oil quality in farmer fields

Parent Project: Mapping soybean protein and oil quality in farmer fields
Checkoff Organization:North Central Soybean Research Program
Categories:Agronomy, Technology, Seed composition
Organization Project Code:
Project Year:2023
Lead Principal Investigator:Ignacio Ciampitti (Kansas State University)
Co-Principal Investigators:
Aaron Prestholt (Iowa Soybean Association)
Keywords: farm oil map, farm protein map, farmer fields, mapping quality, seed quality

Contributing Organizations

Funding Institutions

Information and Results

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Project Summary

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.

Project Objectives

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.

Project Deliverables

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.

Progress of Work

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 PDF file

Final Project Results

Benefit to Soybean Farmers

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.

Performance Metrics

Field identification, field data collection, preparation of reports, publication, presentation

Project Years