Estimated Gross Processor Margin 2009

The value of the products produced from a bushel of soybeans is directly related to the composition of the soybeans that are processed. As presented on the “Average Protein and Oil” information pages, levels of these two major drivers of soybean value can vary considerably. Understanding the economic implications associated with differences in soybean protein and an oil level is complicated by the nature of the relationship between these two components and changing markets.

When trying to better understand complex, multi-faceted situations, the use of mathematical models can be helpful. The Estimate of Gross Processor Margin (EGPM) model utilized here is intended to serve such a purpose.

EGPM is the difference, for a defined set of prices, between the estimated value of the products (i.e. meal + oil + hulls) from a bushel of soybeans and the cost per bushel of soybeans. This gross difference represents the amount of value that is available for the processor to pay all expenses, in addition to that of the soybeans themselves, and provide a profit. The EGPM model can be used to evaluate different soybean composition and/or market pricing scenarios.

For this presentation, one pricing scenario has been applied to the multiple composition scenarios represented by the soybeans in the USDA-NASS 2009 sample set. To allow for an “apples-to-apples” comparison, the same set of assumptions was applied to all samples. The objective here is to gain a better understanding of the economic implications associated with soybean protein and oil variation.

The following tables, data map and histograms present EGPM information calculated from the protein and oil values obtained for the USDA-NASS 2009 sample set. Prices and other assumptions were held constant so observed differences in EGPM are reflective of observed differences in protein and oil. A review of this information indicates that a considerable range in EGPM exists for the pricing scenario presented.  This is the case not only as one moves across broad geographic regions, but within multi-county districts as well. This composition-based variation in economic-value has significant implications for how prices are established for the U.S. soybean crop, both nationally and locally.

Note: For a table with individual district values used to calculate the above table and the data map presented below, use this link: EGPM_Table_2009

Data Map for Observing Trends across Geographic Area

Average EGPM values for 70 multi-county districts are presented in the following map. Each district is identified by a numeric code which is a combination of the respective state and district codes. As an example, district 1710 is District 10 in Illinois. The same code is used in the table of individual district values which can be accessed via the “EGPM Table 2009” link above.

Each district’s average EGPM, $/bu, is represented by its background color using the following color gradient.

Estimate of Gross Processor Margin (EGPM), $/bu

Implications of Variation in Protein, Oil and associated EGPM:

Presented below is a Histogram of all EGPM values for the 2009 dataset. The horizontal axis presents the range of individual sample EGPM’s calculated using the stated set of prices, the same set of assumptions for all samples and each sample’s protein and oil values. The vertical axis corresponds to the number of samples observed to have a given EGPM value. Presented in red on the chart is a vertical line corresponding to the average of all of the samples. The two yellow vertical lines on either side of the average represent the range associated with –1 and +1 Standard Deviation. The Standard Deviation is a statistical measure of variation within a sample set. The smaller the standard deviation is, the more consistent a set of values. The standard deviation for the below dataset is $0.37/bu. By definition, the range above and below the average corresponding to -1 and +1 Standard Deviation should include approximately 68% of all observations. Based on this information, approximately 68% of the samples will fall into a range for EGPM of $0.29 to $1.03 per bushel. Approximately 16% of the samples will be below $0.29/bu while another 16% will be above $1.03/bu.

How a commodity market, which is based on the principal of uniformity, addresses this non-uniformity in the composition of soybeans has significant implications for the value of our domestic crop. To illustrate the implications of compositional variation on the determination of a market bid for soybeans, assume that your job is to determine the price to pay for soybeans which your company will then process into oil and meal to sell at $0.39/lb and $280/ton respectively. You know that to stay in business, you must be able to maintain an EGPM of $0.66/bu. Paying $9.60/bu will, on average, allow you to do so if the soybeans you purchase perfectly correspond to the above set of values.  The problem is that when you go into the commodity marketplace to buy soybeans, you do not know what their composition is. Will you obtain product better than average and be OK or worse than average and eventually be out of a job? Since you will utilize only a small portion of the crop, you cannot assume that things will average out to your favor.  How do you determine the bid you to offer for soybeans to run your operation?

This lack of knowledge regarding soybean composition represents a risk that must be considered when determining your bid for soybeans. The greater the level of compositional variation, the greater the corresponding level of risk. One approach to dealing with risk is to transfer it to someone else. In a market setting, this takes the form of transferring it to the person from whom you purchase the soybeans through a lower price per bushel. This “composition-risk-discount” is not a stated discount, like a moisture discount, but is ultimately reflected in the price paid.

In a composition-blind, commodity market setting, what might this “composition-risk-discount” look like? The above histogram can be used to gain some insight.

• One approach is to trust in averages, in this case no composition-risk-discount would be applied
• Another approach is to use one-standard deviation as your discount. In this case, you will be overpaying for soybeans 16% of the time but underpaying 84% of the time. If the market and competition let you get away with this, this is not a bad place to be. If all participants in the market gravitate toward this approach, this becomes the norm and all soybeans are priced accordingly.
• Ultimately, each group will determine its own approach to this issue. Based on recent experience and market conditions, what this “composition-risk-discount” looks like will be subject to change. However, even relatively small “composition-risk-discounts” per bushel can quickly add up to some significant value when large volumes are involved. The following table projects the impact of different “composition-risk-discounts” costs per bushel relative to the 2009 crop of 3,359,011,000 bushels.

          

Approaches for Reducing the “Risk-Discount”:

The most direct approach to dealing with risk associated with an unknown is the development and use of information which addresses the risk-associated unknown.  One approach to removing the “composition-risk-discount” is to analyze each load of soybeans prior to taking possession and pay on the basis of actual composition. The component based market system that this approach requires would also, over a course of time, encourage the movement of the overall crop toward the upper end of the EGPM value scale. In addition to compositional improvements, this process would simultaneously continue to reduce compositional variation.  Both effects would incrementally add value to the crop. Since all of the samples in this dataset were obtained directly from farmer’s production fields, and the assumption is that these farmers would only plant the most yield competitive varieties available to them, this move could theoretically be affected without any negative impact on yield.

Another approach to reducing the “composition-risk-discount” is to reduce the extent to which variation exists. One approach would be to eliminate soybeans below a given threshold from the marketplace through plant breeding and/or market standard approaches. Even a somewhat modest change would result in significant improvements. As an example, if we were to replace all soybeans below 1 Standard Deviation from the Average in the above histogram (i.e. $0.29) with soybeans having an EGPM of $0.29/bu, the Standard Deviation would decrease to $0.285/bu and the average EGPM of the set would increase to $0.706/bu.

An argument could be made that instead of looking at the “composition-risk-discount” from a total crop basis, we should evaluate it on a more localized basis. The following histogram includes those samples from the state of Iowa. A comparison of results from this scenario to that for the entire sample set above indicates that the extent of variation as defined by the Standard Deviation has been slightly reduced by $0.03/bu.

 

Disclaimer:

All information provided on the U.S. Soy Measurements (USSM) content is provided “as is” and is intended for illustrative purposes only. No warranty, expressed or implied, is provided regarding any information provided in USSM content. All information is provided on the condition that users must make their own determinations regarding any use of this information and must assume all risk associated with any and all use.