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Improving Value in Soybean Processing

Monday, August 16, 2021
Improving Value in Soybean Processing

Profitability is a concern for all stakeholders in the soybean production and processing chain, and it's influenced by more than just yield: Oil and protein levels also affect profits. Geographic differences in soybeans have economic implications for grain elevators, but these are typically masked by random errors in the receiving and analysis process.

In research published in Applied Engineering in Agriculture, a team from Iowa State University sought to better understand the geographic variations and to address the problem of random errors. Their work will facilitate the marketing of soybeans based on oil and protein levels, and thus improve profitability.  

Maximizing Processing Value with Selective Handling Strategies: An Analysis of Soybeans Received at Iowa Elevators

Bennett E. Barr, Charles R. Hurburgh, Gretchen A. Mosher

Published in Applied Engineering in Agriculture 37(4): 583-594 (doi: 10.13031/aea.14534)
Copyright 2021 American Society of Agricultural and Biological Engineers

Abstract: A better ability to understand and use geographic variations in protein and oil is one way to maximize the value potential of soybeans for handlers and processors. An Iowa cooperative had been sourcing soybeans for processing from nearby elevator locations and wanted to know whether this strategy was maximizing the net processing value of the soybeans. Random and systematic errors from testing and measurement instruments also impact marketing decisions and were investigated as part of this project. During the Fall 2018 soybean harvest, soybean samples were collected from 32 country elevator locations belonging to one Iowa-based cooperative which has its own soybean processing plant. Samples were analyzed using near-infrared spectroscopy (NIR), and protein and oil content data were entered into an Estimated Processing Value (EPV) model to determine value differences of soybeans among elevator locations. Results showed substantial variability among locations that represented a $0.23/bushel EPV spread. No significant variation was found in soybean quality over the harvest season, suggesting that marketing decisions can be made at the beginning of the season. 

To determine the incidence of random errors, a simulated Excel-based model was used with three test cases. The introduction of random error lowered value gaps between locations, which made the discrimination of high-value locations from average or low-value locations difficult. Although protein and oil measurement with the NIR instrument was feasible even on busy harvest season days, the validity of marketing decisions using these data depended highly on the error involved in sample analysis. Future studies should identify specific sources of error and attempt to eliminate them. Specifically, one of the largest sources of error in a commodity-based market system is in the measuring units. The ability to isolate and quantify measurement error will improve the capability of the commodity-based soybean market system to focus trade decisions on end use traits, maximizing soybean value and providing incentive for improvement.