Richard Maltsbarger and Nicholas Kalaitzandonakes
In recent years, there has been much discussion on the costs of segregation and identity preservation (IP). The promise of an expanding menu of quality enhanced crops derived through modern biotechnology has raised expectations for a parallel de-commodification of the agrifood chain and questions about its optimal logistical redesign. More recently, overseas demand for non-genetically modified (GM) grains has also been a major driver for discussions on segregation, IP and relevant costs.
Some recent studies shed light on the size of segregation and IP costs in grain supply chains (Lin, et al., 2000, Bender, et al., 1999, Hurburgh, 1994).
Certain hidden costs of segregation and IP, however, remain unarticulated. For instance, local or regional elevators derive revenues from value-added activities (e.g. grinding) or from carrying spreads, which are relinquished in IP supply chains. Such foregone revenues are real, though somewhat hidden, costs associated with segregation and IP. Similar hidden costs exist all along IP chains.
Accurate assessment of direct and hidden segregation and IP costs is complicated by inherent difficulties in generalizing across grain supply chains. Differences in local supply conditions and asset configurations at the farm, elevator and processor level can produce substantial variation in segregation and IP costs. In this article, estimates for both direct and hidden segregation/IP costs at the elevator level are provided and demonstrate the significance of local supply conditions and asset configuration on their relative size. Attention is focused on elevators as they represent the most significant bottleneck in IP grain supply chains.
Estimating IP costs
In traditional commodity supply chains, the elevator's role is crucial in sourcing sparse farm production for a concentrated processing and milling industry. Over the years, asset configuration and logistics of commodity grain handling have been optimized and elevator managers have built operating margins from scale. However, elevators in IP supply chains accept changing roles. In addition to efficiently equating product supply with demand over time and space, local and regional elevators must generate, store and transfer product and source information. Logistical redesign is necessary and can lead to additional expense. Ultimately, the value of the IP information must net expenses plus a competitive margin. Consequently, the costs of IP are central in determining return-on-investment for any elevator considering participation in an IP supply chain.
To estimate IP costs at the elevator level, the Process & Economic Simulation of IP (PRESIP) was built. The PRESIP model is designed to capture the subtle intricacies of day-to-day operations of segregated handling, chain coordination and opportunity costs in IP supply chains for grains. Its structure is flexible and can be adjusted to simulate any asset configuration that may be encountered.
In the case of an elevator, two integrated modules in PRESIP capture the "essence of riding a bushel of grain from the farm through the elevator." The Elevator Asset Configuration Module mimics the physical configuration of elevator assets, including dumping pits, scales, queues, storage bins and other assets (Figure).
The Elevator Grain Flow Module captures the flow of incoming trucks, movement of trucks through the elevator and within-elevator grain. The combination of these modules tracks all grain (commodity and IP) from elevator arrival to usage -- either outbound shipment or in-house grinding.
Physical data on the movement of grain are fed into the Elevator Economic Analysis Module, which estimates three categories of segregation/IP costs: coordination, segregation and opportunity costs. Coordination costs are incurred as elevator managers find farmers to produce the grain, verify that these farms have the proper production practices and contract for production. For buyer-call contracts, there are additional coordination costs for elevator management meetings and producer calls throughout the year.
Segregation costs may include multi-year depreciable investments such as compositional analysis and other testing equipment as well as necessary asset upgrades and modifications. They may also involve single season expenditures such as incremental labor for segregation and IP handling, additional material and maintenance costs, management of increased farmer dispute and the costs of misgrades (i.e., where IP stock is accidentally stored as commodity).
Hidden or opportunity costs in segregation and IP may also exist. Grind margin loss may be incurred by substituting local production of commodity crops often ground for resale as feed. For every bushel of commodity grain lost to IP, there is the lost potential margin. Storage margin opportunity costs are foregone revenues from under-utilized storage capacity -- a primary concern to 85% of the elevator managers surveyed by E-Markets in its Grain Industry 2000 report (1997). The PRESIP model generates a commodity-only baseline and then compares subsequent IP scenarios to the daily capacity utilization of the baseline. Negative shifts in capacity utilization are multiplied by expected storage margin to assign the lost revenue as a result of IP handling.
The final opportunity cost is a result of scheduled deliveries from the elevator to another intermediary or the end-user. Each scheduled delivery forces the elevator manager to release stock at specific times thereby relinquishing the option to hold grain for carrying spreads. Opportunity costs exist for deliveries in which there is "spread" in the market -- a positive net difference between current price and expected future price less cumulative storage and lost interest costs. Using historical spread information (indexed on a constant dollar basis), the PRESIP model assigns an estimated gross spread on each delivery date. Deliveries made on dates with positive spreads are assigned the lost revenue the manager would realize from carrying.
Early case studies
To provide a perspective on the size of IP costs, both direct and hidden, estimates from PRESIP are presented here for three case elevators. The case elevators, located in Missouri and Illinois, were selected to represent different sizes and functions that are common in the Midwest. In this way, the variation of IP costs across different asset configurations is illustrated. For each PRESIP simulation, actual operating data were obtained through personal interviews with the elevator managers. All three cases involve segregation of high-oil corn (HOC).
The first elevator (Elevator No. 1) is a smaller-size Missouri farm supply/grain-merchandising facility. Its operating capacity is 306,800 bu. with annual throughput of 1.25 million bushels. Elevator No. 1 has two bins of approximately 100,000 bu. used for terminal storage with 14 other smaller bins (2,400-18,000 bu.) used both for terminal storage and temporary turning of stocks. This elevator has only one pit area in which there are two dumping pits -- Pit No. 1 is assigned to soybeans, and Pit No. 2 is shared by corn and milo, often causing significant changeover delays at harvest. Soybean margin is derived from merchandising; grinding margin is derived from corn and milo. Elevator No. 1 is restricted by geography to outbound delivery by truck only.
Elevator No. 2 is a larger cooperative, merchandising elevator in Illinois and handles IP crops without using dedicated facilities. Elevator No. 2 operates at a capacity of 1.513 million bushels with an annual throughput of 3 million bushels and does not have grinding facilities. This elevator has three dumping pits in two pit areas -- primarily one crop type per pit without changeover. There are 12 small bins (5,000-30,000 bu.) for turning and drying operations. A "honeycomb" of 13 storage bins of 28,000 bu. per bin is used for terminal storage as well as two 125,000 bu. flats and one 480,000 bu. bin. Elevator No. 2 can deliver outbound loads of grain by truck or rail.
Elevator No. 3 is a large, river terminal elevator located on the Missouri River. Its capacity is 2.665 million bu. with an annual throughput of 4.5 million bushels. There are two pit areas (two pits per area). Most bins at this facility are very large, with one holding flat of 1.8 million bushels and one large bin of 400,000 bu. There are four terminal bins of approximately 100,000 bu. and then a few turning bins below this capacity.
Finally, elevator No. 3 can deliver grain directly from the bin by truck or rail. For barge delivery, the elevator must dump the grain into a truck and move the grain over a levy into the barge (i.e., the barge cannot be loaded directly from storage bins).
Multiple scenarios of bin filling schedules, crop-to-bin assignments, incoming volumes and other key parameters were simulated using PRESIP to derive efficient IP handling.
Accordingly, the costs presented here are the lowest per bushel costs for which all constraints of contractual delivery, practical bin filling patterns, management designated checks and volume patterns are met. Five distinct scenarios for each of the case study elevators are presented here:
* 100,000 bu. of incoming HOC delivered during peak harvest (100K HD);
* 100,000 bu. of incoming HOC delivered through buyer call (100K BCD);
* 200,000 bu. of incoming HOC delivered during peak harvest (200K HD);
* 200,000 bu. of incoming HOC delivered through buyer call (200K BCD), and
* 500,000 bushels of incoming HOC delivered during peak harvest (500K).
In each of these scenarios, the first scheduled delivery of HOC to the end-user occurs Dec. 1. The assumption is that the end-user has 10,000 bu. of temporary storage to accept HOC delivery and receives such an amount every seven days.
To reduce the volume of empirical results, details are presented for Elevator No. 2, while more aggregate results are presented for Elevators Nos. 1 and 3 (Table 1).
Coordination costs are rather stable at less than 3 cents/bu. and increase only slightly with buyer call contracting. Segregation costs are somewhat higher with testing being a large portion of these costs. At the 100K volume, the elevator finds that, rather than purchase analysis equipment, it is cheaper to ship samples via a national carrier (e.g., Federal Express) to a third-party certification site. At the 200K and 500K volumes, the elevator finds more efficiency in purchasing the analysis unit.
Hidden or opportunity costs are the most prohibitive costs to IP. In particular, Elevator No. 2's spread opportunity costs, ranging from 7 to 19 cents/bu., are significant. In addition, the highest of these costs occurs at the 200K volumes. The variation illustrates the significance of local supply conditions. For the case elevator, spread opportunity costs at the 100K volume reflect substitution of HOC from 100,000 bu. of local, commodity corn supply. For the 200K volume scenarios, substitution of HOC from the next 100,000 bu. is taken primarily from local soybean supply, resulting in higher opportunity costs. Similar substitution effects exist at the 500K volume, but larger volume allows better allocation of costs. At 16 cents to 27 cents/bu., the sum of coordination, segregation and opportunity costs is clearly not trivial and, therefore, important in the management's decision to participate in IP supply chains.
In all scenarios, Elevator No. 2 has a significant advantage driven by significantly lower opportunity costs (Table 2). It also has the lowest mean per bushel cost of $0.164 at the 500K volume.
The "honeycomb" configuration of 30 storage bins of approximately 28,000-bu. offers Elevator No. 2 greater flexibility in filling patterns to maximize storage utilization within the batch-processing IP system. Elevator No. 1's primary disadvantage is significantly decreased carry revenue, up to 21 cents/bu., as its local producers substitute out soybean production. Elevator No. 1's most interesting scenario is at 500K in which the entire facility is dedicated to HOC, yet this dedicated scenario does not produce the lowest per bushel cost. Potential operational efficiencies from a dedicated facility are offset by grinding and spread opportunity costs.
Elevator No. 3 faces two primary problems with IP. The first is significant carry opportunity costs ranging from 7 cents to 22 cents/bu. with a local production-driven cost spike in the 200K scenarios similar to Elevator No. 2. However, underutilized capacity is the largest issue for Elevator No. 3. With low incoming HOC volumes in relation to its historical commodity stocks, Elevator No. 3 finds that it cannot fill its large bins (100,000-1.800 million bushels) as quickly as it can with commodity stocks. Consequently, its storage margin opportunity costs range from 6 cents to 15 cents/bu.
The three case studies of segregation costs at the elevator level suggest the importance of hidden or opportunity costs that can occur from adapting current commodity operations to IP. Potential revenue streams may change drastically as bin filling patterns shift, grind margins are lost and carry returns are foregone under specific contractual agreements. Especially important are the effects of local supply conditions and asset configuration on IP costs.
Logistical efficiencies are fundamental to the diffusion of value-added grain and oilseed products for commercial use. The estimated costs of segregation presented here suggest that as agro-biotechnology products with increased end-user value are marketed, innovative schemes of intermediation in IP grain supply chains will be essential to their success.
It is also important to clearly define the scope of the results presented here. In the listed case studies, PRESIP simulations were developed under a 5% allowable threshold of contamination from other varieties/hybrids. As such, these costs do not strictly apply to supply chains that are currently attempting to segregate non-GM corn hybrids and soybean varieties. If zero- or near-zero thresholds for contamination are enforced, the costs to segregation used in this study will underestimate the costs of more stringent IP.
Within the bounds of less than 1% percent thresholds, new operational challenges and testing requirements are created. Additional operational changes, such as sealed bins, additional cleaning of pollen and grain residue, dedicated delivery dates, insurance or non-compliance penalties, will add extra costs to IP not accounted for in this study. In addition, testing the presence of GM material at the protein and DNA levels is more expensive than for value-added compositional analysis used here.
Bender, Karen; Hill, Lowell; Wenzel, Benjamin, and Hornbaker, Robert. Alternative Market Channels for Specialty Corn and Soybeans. Department of Agricultural and Consumer Economics, Agricultural Experiment Station, University of Illinois at Urbana-Champaign, AE4726, February 1999.
Lin, William W.; Chambers, William, and Harwood, Joy. "Biotechnology: U.S. Grain Handlers Look Ahead." Agricultural Outlook. Economic Research Service, U.S. Department of Agriculture, AGO-270, April 2000.
Hurburgh, Charles R. "Identification and Segregation of High-Value Soybeans at a Country Elevator," Journal of American Oil-Chemists Society. 1994. 71:1073-1078.
E-Markets and Context Consulting. Grain Industry 2000. E-Markets, Ames, Iowa, and Context Consulting, West Des Moines, Iowa. April 1997.
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Last Updated on 9/15/00