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.
For the development of a multi-state database in collaboration with other soybean producing states, a protocol for data collection has been developed to obtain the field management information and soybean seed samples from farmer fields that are necessary to identify overall within-field spatial variation of quality. A total of 10 states from the North Central region (excluding WI, MN, and PA) will be involved in this project.
The project will be focused mainly on the collection of soybean seed samples from farmer fields for data quality analyses and corresponding field management information. A complementary project, submitted for a third year of funding from USB, will assist this initiative providing geo-referenced aerial imagery of soybean fields during the growing season. On average, ten soybean fields in each state will be part of this large research project to collect soybean seed samples for quality analyses. A protocol for field seed quality data collection has already been developed (Fig. 3) to obtain within field variation for this relevant seed trait, in addition to connecting this determination with relevant farmer field management (Fig. 2). Fields will be scouted during the season and seed samples (~1-2 lb.) collected before harvest will be analyzed by the Kansas State University Lab.
2. Communicate the economic value of soybean quality mapping to farmers and agronomists through an online interactive simulation tool, accessible publications, and social media.
To communicate the economic value of soybean quality mapping, the second main activity will incorporate the data collected into an online tool “Soybean Quality Differentiation and Economic Simulator” (a beta version, https://analytics.iasoybeans.com/cool-apps/SoybeanQualityPremium/).
The second activity will be comprised of the following sub-activities:
- Utilize the collected information to enhance the tool to enable farmers to select soybean management practices, price points, production costs, cost savings, transportation cost to destinations that provide a soybean quality premium, among other customizable features (side-by-side comparison) that will likely produce the best economic scenario when using spatial crop quality information for soybean marketing.
- Our team will promote the new simulator and expand the reach of this tool. The target audience for our effort will be soybean farmers, the ag technology industry, seed companies, soybean processors and soybean meal consumers.
- Iowa Soybean Association Communication and Marketing departments will help promote the new simulator and expand the reach and influence of this tool. In addition, Kansas State Research and Extension personnel will help to disseminate the use of the web tool and any other relevant information for farmers across the state, and with the PIs at the regional scale.
The first version of the economic simulator allows users to estimate the optimal combination of soybean price and protein premiums while considering possible yield change values. Updates to the tool have included the option to add additional costs associated with planting a higher quality variety, and savings that could be provided by a seed company, soybean crusher or food processor. For example, the tool allows adjustments for the cost of transportation if the soybean is hauled to an alternative processor or elevator, or storage is provided by a seed company. Currently, the tool only simulates the economics of protein premium payments but the option to simulate the same for oil premium payments will be added, specifically high-oleic options such SOYLEIC and others.
Utilizing the database of soybean quality samples, predictive models will be generated to develop a “Spatial Soybean Quality Differentiation Index” to help quantify the economic feasibility of specific field locations to benefit from soybean quality differentiation segregation and marketing. Identifying a field location in the tool could employ the predictive power of such an index to estimate the probability that a given field shows potential for economic benefit from differentiation given the proper market conditions. This would enable translation from simulation into practice, by the user employing the tool to then quantify, simulate, and estimate the potential return per acre or per farm based on estimations of field level soybean protein and oil data produced by the index in conjunction with selected management and price/cost options available within the tool.