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The “Stover Availability versus Supply” Puzzle and Contracting Options for Cellulosic Biomass 1

Chao Li, Dermot Hayes (, and Keri Jacobs (

The existing US Renewable Fuel Standard (RFS) makes commercial-scale cellulosic ethanol a priority, calling for 16 billion gallons of cellulosic biofuel production by 2022, sourced from grasses, trees, agricultural residues, and municipal waste. The US Department of Energy “US Billion-Ton Update” study (Downing et al. 2011) suggests that to meet the mandates in the RFS, approximately 66 million tons of corn stover may be needed annually. This equates to nearly 50 percent of the total annual stover produced by Illinois, Iowa, Minnesota, and Nebraska (Sesmero et al. 2015).

Stover as an energy crop is appealing because the Midwest produces a lot of it; yet commercialization lags behind the progress made in other cellulosic crops for two significant reasons. First, stover is a crop residue with a high-degree of variability that impacts product quality and processing efficiency, and the most significant cost of production is storage and transport. Tackling the logistical challenges associated with storing and transporting stover and reducing the system costs occupies a substantial portion of the research efforts of agricultural and biosystems engineers working in renewable energy. Second, even as technological innovations advance, commercialization may lag due to farmer participation—cellulosic processors of stover for biofuel report producer participation rates of supplying stover are 20–25 percent, implying that the physical availability of the biomass crop is a poor metric for its supply in the cellulosic biofuel supply chain. If the industry is to achieve scale in cellulosic biofuel from crop residues, particularly stover, it will need to solve both of these issues. Beyond the technological capabilities, the solution is with procurement and pricing contracts.

From a production standpoint, stover is unique from other cellulosic biomass crops. It is a “second crop,” not a dedicated biomass source, and producers do not manage it for yield and quality as they do the primary crop, corn. Also, unlike how producers supply corn and other grains as a standardized commodity at a price revealed daily in the marketplace, stover is not commoditized and no active price discovery mechanisms exist. Producers commonly assign differential values to the stover based on their perception of its contribution to soil quality and productivity, whether collection and transport are likely to interfere with fall field operations, and other factors. Thus, each stover supplier potentially has a unique reservation value at which s/he will participate in the stover supply chain.

On the other side of the transaction, the cellulosic biofuel processor attempting to procure biomass faces the challenge of writing contracts with the heterogeneous suppliers who produce stover. In addition, stover collection outcomes—the product quality characteristics—differ substantially across fields, creating variability in the processor’s conversion process and adding to production costs.

Pricing Challenge: What is an Optimal contract to Procure Stover as a Feedstock?

One procurement option is to offer all stover suppliers (farmers) a single price per ton for biomass delivered to the plant—this is analogous to how grain is priced and leaves transportation costs to the supplier. Alternatively, the processor contracts to collect the stover from field-side locations, bearing the transportation costs and paying each supplier a uniform per-ton price. There are few examples in agriculture where this pricing and procurement option is used. Cellulosic processors using stover feedstock in the Midwest have used both procurement strategies, and in both cases, collection regions (distances in miles) were significantly larger—in some cases up to 75 miles to supply the plant—than anticipated due to low production participation.

Processors have not uniformly adopted either the supplier-delivery pricing model, as is familiar to commodity producers, or a processor-collection pricing model, which prevails in more specialized markets. This leaves open the issue of how procurement markets for stover biomass will emerge on a commercial scale and suggests a third contract option—differentiated pricing based on the supplier’s distance to the plant and reservation values. In other markets where there is a single buyer (seller) transacting with many sellers (buyers), differentiated pricing—or price discrimination in the economics nomenclature—commonly emerges.

Given that the market has not identified a preferred pricing structure, the question remains, what is the optimal procurement and pricing model for this market? We used a simple theory of spatial price discrimination to answer this question, first comparing the supplier-delivery and processor-collection models. Assuming that suppliers and processors face identical transportation costs, we find that farmer (supplier) welfare is greatest under the supplier-delivery model compared with the processor-collection model, even though total feedstock collection expenditures and draw areas (distances) are identical. This is because when supplier-delivery procurement is used, farmers nearest the plant participate more intensely (increased participation) and are able to take advantage of location rents resulting from higher net prices (net of transportation) than they would receive under the processor-collection model. The same happens in the grain markets—farmers nearest to the delivery point have a higher net price than those further away who receive the same price per bushel. When processor-collection is used, the processor cannot capture the supply efficiencies created through increased participation because all suppliers receive an identical field-side price, and it must offer a price based on capturing the feedstock supplied furthest from the plant. Simply put, a tradeoff exists between paying a higher price for feedstock to increase participation near the plant and accepting a greater procurement area (see also Rosburg, Miranowski, and Jacobs 2016).

Table 1
Table 1. Simulation Results Comparing Collection Distances, Supplier Participation and Prices for Three Pricing and Collection Mechanisms

The third procurement option—differentiated pricing—was evaluated alongside the other two options using simulations. The modeling assumptions were based on industry engineering and cost factors for a cellulosic ethanol facility requiring 300,000 metric tons of stover per year, and we include transportation costs of $0.65 per ton per mile. Table 1 shows, under varying degrees of price-responsiveness by stover suppliers, how feedstock collection distances from the plant, producer participation (supply), and feedstock per ton prices vary for a fixed plant to meet its feedstock needs. As producers become more price-responsive, collection distances fall and per-ton feedstock prices increase, inducing greater participation in supplying feedstock. The processor-collection and supplier-delivery models generate the same collection distances and total costs (not shown), but increased participation by suppliers closer to the plant increases the overall welfare to suppliers of stover.

Perhaps the most significant result relative to understanding efficient contracting as a mechanism for commercialization is that, at all levels of price responsiveness, the collection distance to meet plant capacity is smallest when the processor is able to set differential prices with suppliers. In that case overall collection costs are reduced, and as a consequence, overall cellulosic ethanol production costs are lowest of the three options. The highlighted row represents the current participation rates and collection distances observed by processors, and suggests that improvements are possible using a price-differentiated approach.

Figure 1
Figure 1. Illustration of simulation on stover collection areas under different pricing mechanisms

Figure 1 depicts hypothetical draw regions as they might exist in Iowa when there is no competition for stover (no overlap of stover collection areas by different processors), based on plant locations in central and north-central Iowa and recently-experienced producer participation outcomes. When processors are able to use differentiated price contracts, collection distances fall along with transportation costs, which are primary determinants of the economic feasibility of cellulosic ethanol from stover.

These results are important to policy discussion surrounding the RFS, both in terms of relative feedstock use and costs and also with regard to the marketing mechanisms and contracts that may arise as the industry commercializes. From industry experiences, we know that the availability of a non-dedicated feedstock is not equal to supply and processors are drawing feedstock from significantly larger areas than early studies estimated would be needed based on stover production. It is likely that this market will continue to show low participation by suppliers if processors are compelled to use single-price contracts, which result in larger procurement regions and lower stover prices per ton. One solution to this is price differentiation based on a spatial factors (i.e., suppliers’ distances to the processor) and also stover-specific and field-specific characteristics that influence processing quality and quantity.

As a consequence of the current procurement and pricing challenges to commercializing stover as feedstock, meeting the RFS mandate for cellulosic stocks will likely continue to require the use of feedstocks such as grasses and other “high-cost” feedstocks that were previously bid out of the early feedstock and supply cost models.


1. This is a summary of research emerging from chapter 2 of Li’s 2017 dissertation “The Supply Curve for Cellulosic Ethanol,” in Three essays on agricultural economics, Iowa State University.


Downing, M., L.M. Eaton, R.L. Graham, M.H. Langholtz, R.D. Perlack, A.F. Turhollow, B. Stover, and C.C. Brandt. 2011. “U.S. Billion-Ton Update: Biomass Supply for a Bioenergy and Bioproducts Industry.” Report ORNL/TM-2011-224, Oak Ridge Laboratory, Oak Ridge, TN.

Rosburg, A., J. Miranowski, and K. Jacobs. 2016. “Modeling Biomass Procurement Tradeoffs within a Cellulosic Biofuel Cost Model.” Energy Economics 58: 77–83.

Sesmero, J.P., J.V. Balagtas, M. Pratt. 2015. “The Economics of Spatial Competition for Corn Stover.” Journal of Agricultural and Resource Economics 40(3): 425–441.