mpratt_appliedeconpublication

Article

Supply Response, Marginal Cost, and SoilErosion Implications of Stover-based Biofuels

Juan Sesmero*, Michelle Pratt, and Wallace Tyner

Juan Sesmero is an assistant professor of Agricultural Economics, Michelle Pratt is agraduate student, and Wallace Tyner is the James and Lois Ackerman Professor ofAgricultural Economics, all in the Agricultural Economics Department, PurdueUniversity.

*Correspondence may be sent to: [email protected].

Submitted 12 September 2013; accepted 16 October 2014.

Abstract Existing economic analysis of corn stover as an energy feedstock has notconsidered potential changes in land use associated with different stover prices. Weestimate the response of corn stover supply density to its price driven by changes inland use and examine its implications for a processing plant’s pricing strategy andmarginal cost, as well as associated changes in soil erosion. We find that plants willexploit the intensive margin as well as the extensive margin to secure additionalamounts of stover. Our results show, counterintuitively, that a market for stovermay result in lower soil erosion due to reallocations of land to continuous corn withremoval, which, combined with no-till farming, results in lower soil erosion thanthe baseline without stover removal. Also contrary to expectations, using covercrops with stover removal may result in higher soil erosion due to land use changeswithin the fuel shed associated with optimal pricing.

Key words: Corn Stover, Biofuels, Land use change, Supply response, Soilerosion.

JEL codes: Q15, Q24, Q42.

IntroductionCellulosic biofuels have value for society that is not captured by markets.

For example, biofuels can reduce greenhouse gas emissions (Wang 2005)and contribute to energy security (Moschini, Cui, and Lapan 2012). However,because some of these benefits are “external” to producers, the amount of cel-lulosic biofuel displacing petroleum under free markets may be lower thanthe social optimum. This has, in the United States, motivated governmentsupport to biofuels in the form of a renewable fuel standard (RFS) by whichblenders are obligated to include biofuels in motor vehicle fuel. A significantportion of cellulosic biofuels is expected to come from corn residues given itscost competitiveness relative to other sources such as switchgrass or mis-canthus (National Research Council 2011; Downing et al. 2011; Perrin et al.

# The Author 2014. Published by Oxford University Press on behalf of the Agricultural and AppliedEconomics Association. All rights reserved. For permissions, please e-mail:[email protected]

Applied Economic Perspectives and Policy (2014) volume 0, number 0, pp. 1–22.doi:10.1093/aepp/ppu042

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2012). However, many questions remain open regarding both the economicviability of this fuel source and the soil erosion implications of producingmandated quantities. The former is critical for determining the biofuel price(or subsidy) necessary to induce the production of mandated volumes. Thelatter is important to gauge potential unintended consequences of the policy(Petrolia 2008b; Cruse and Herndl 2009) and, thus the welfare gains accruedby its implementation.

This study sheds light into both issues by estimating (for growing condi-tions in Indiana) the response of stover supply density to its price driven bychanges in land use, by examining its implications for a processing plant’spricing strategy and marginal cost, and by calculating soil erosion asso-ciated with increased biofuel production. We also analyze the effect ofplanting cover crops on cost and soil erosion1 under optimal pricing and,thus, the merits of supporting this management practice through publicpolicy.

The cost at which processing plants can procure corn stover is a criticaldeterminant of the economic viability of corn residue as an energy feed-stock. This cost is determined by the stover supply curve faced by the plant,which in turn hinges upon land use within the fuelshed. If farmers aroundthe plant react to increased stover prices by switching to crop rotations thatproduce more stover, the supply of stover will be upward sloping.2 Anupward-sloping supply means that a plant in need of a certain amount ofbiomass may, if economically convenient, increase its bidding price forstover at the farm gate to achieve an increase in harvest density (i.e., increasein acres harvesting stover) around the plant, and hence, a decrease in sup-plying area and transportation cost. The plant will, however, weigh thisbenefit against the additional cost per ton of stover purchased. In otherwords, there is a tradeoff between the intensive and the extensive marginsthat the plant can exploit to its benefit. The plant’s choice of price and result-ing land use configuration within the fuelshed will also influence soilerosion.

Despite its potential importance, previous economic analyses of cornresidue for energy (Gallagher et al. 2003; Brechbill et al. 2011; Petrolia 2008a;Perrin et al. 2012) have not considered changes in land use within the sup-plying area, nor its implications for plant behavior and resulting soilerosion. Thompson and Tyner (2014) have quantified changes in crop rota-tions associated with increased stover price but they have not modeled thebehavior of a plant facing such a supply response and the resulting soilerosion.3 On the other hand, Petrolia (2008b) has considered soil erosionimplications of stover-based biofuel plants, not driven by changes in land

1Planting cover crops was previously proposed as a management practice that could offset the soil erosioneffects of stover removal, thus increasing the removable rate (Bonner et al. 2014; Mann et al. 2002; Kimand Dale 2005; Fronning et al. 2008).2Farmers may also adjust their removal rates to changes in stover price. Quantification of such elasticitywould require reliable estimates of the cost structure (including intertemporal costs through soil qualityeffects) of varying removal rates. To the best of our knowledge, no such estimation of cost structure is cur-rently available so assuming an elasticity value would be highly speculative. On the other hand, focusingon an extreme case (where harvest density per acre is perfectly inelastic) seems a reasonable startingpoint.3Sesmero and Gramig (2013) recognize this but do not estimate a supply response nor do they quantifysoil erosion. Sesmero (2014) has incorporated a stover supply response in the irrigated Corn Belt but thisresponse is driven by increasing marginal cost of soil water replacement. No changes in land use areinvolved.

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use but by violations of soil erosion constraints at the farm level. This offersa view of the soil erosion effects of biofuels that is complementary to thatdeveloped in this study. Failure to model and quantify changes in land usein response to variations in the price of stover and pricing behavior of aplant facing such a response may seriously handicap the assessment of eco-nomic viability and environmental implications of stover as an energy feed-stock.4 This study attempts to fill these significant gaps in the scholarlyliterature.

Three important results are revealed by our analysis. First, the price ofstover offered by a cost-minimizing plant increases with scale of production.Therefore, plants will exploit the intensive margin as well as the extensivemargin to procure additional biomass. Second, demand for stover by a cost-minimizing biofuel plant may in fact reduce soil erosion due to the conver-sion of land to continuous corn with stover removal within the fuelshed.This limits negative externalities of cellulosic biofuels and enhances the de-sirability of policies supporting them. Finally, planting cover crops may,counterintuitively, result in higher soil erosion due to lower conversion tocontinuous corn with stover removal. Therefore, policy support to this man-agement practice may not be warranted on the basis of soil conservationconcerns.

Stover SupplyDifferent farmers will likely have different willingness to accept (i.e.,

minimum required stover price) for alternative crop rotations that includestover removal. This will result in an aggregate supply response to increas-ing stover prices (Thompson and Tyner 2014). However, because the marketfor stover has yet to emerge, primary data necessary to estimate this supplyresponse are not available. Therefore, we obtain counterfactual estimates ofsupply response to increased stover price by simulating profit-maximizingland allocation decisions with the Purdue Crop Linear Programming model(PCLP; Doster et al. 2009a).

Users of PCLP specify input data including land, labor, machinery,storage, planting date, crop rotations, expected crop yields, crop prices, andcosts (Doster et al. 2009b). Given these inputs, which are farm-specific,PCLP determines the profit-maximizing allocation of acres to alternativecrop rotations (Doster et al. 2009a). The farmer information used in PCLPcomes from the Top Farmer Crop Workshops held at Purdue University.The data are from several years of the workshop including 2007, 2008, 2009,and 2010. Data from a total of 25 farms located in Indiana were used in thisanalysis. These farms operated a total of 63,336 acres and the mean size ofthe farms was 2,540 acres. The minimum farm size was 550 acres and themaximum farm size was 8,200 acres. Farms had, on average, a mean cornyield of 174 bushels per acre for corn/soybean rotations and 167 bushels peracre for continuous corn rotations.5

High corn-producing counties in Indiana may be particularly attrac-tive locations for cellulosic biofuel plants due to high density and low

4A biomass supply response has been considered by Rosburg et al. (2013) in the case of switchgrass, butnot stover.5No awards are given in this workshop, so no incentives to over-report outcomes or under-report inputusage are in place. Farmers receive feedback from PCLP regarding the marginal value of capital and landin their operations.

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transportation cost. Therefore, a comparison of farm characteristics in oursample with those in high-density counties may be informative of therepresentativeness of our data. This comparison is reported in table A.1 inthe appendix. Values in that table reveal that, while similar to the generalpopulation of farms in high corn-producing counties, farms in oursample are relatively large when compared with the general populationin Indiana. This may result in enhanced flexibility to change crop rota-tions (due to relaxed resource constraints, especially land) and an upwardbias in stover supply. With the caveat of a potential upward bias insupply, it seems that counterfactuals constructed with PCLP may provideuseful information on the response of stover supply to its price, and espe-cially so for high-density counties, which are of particular interest in thisanalysis.

Removing corn stover was added as a cropping alternative in PCLP byThompson (2011), who added stover harvest options into the PCLP modelby creating two new crops: corn/soybean rotation with stover removal, andcontinuous corn with stover removal. Net returns per ton of stover har-vested are determined by subtracting the estimated cost of stover harvest($/ton) from the assumed farm-gate prices for stover ($/ton). The net returnper ton is then multiplied by tons harvested per acre to calculate per acrereturns from harvesting stover. Prices are simulated, but cost is estimated.The next section discusses estimating the total amount of stover harvestedunder alternative crop rotations and calculating harvest cost.

Stover Harvested and Cost

Removing stover may affect soil physical and chemical properties (e.g.,loss of topsoil, reduction in soil organic carbon, increased bulk density),thereby reducing the long run productivity of the soil. Substantial uncer-tainty remains regarding the effects of stover removal on soil propertiesand, in turn, their effect on future yields. As a result, many studies calcu-lated, for different agronomic and climate regimes, an “acceptable” rate ofstover removal, that is, a rate above which long run productivity of the soilmay be reduced.

Lindstrom (1986) calculated, for a no-till system in Minnesota, that soilloss would not be significant at a 30% removal rate; McAloon et al. (2000)calculated the same rate. Other estimates, based on requirements to controlsoil erosion, suggest 30–50% removal rates (Kim and Dale 2004; Grahamet al. 2007). Blanco-Canqui and Lal (2007) conclude that it may only be pos-sible to remove 25% of stover before soil quality indicators are adverselyaffected. A study by Wilhelm et al. (2007) found that the amount of cornstover needed to maintain soil carbon, and thus long-term soil productivity,is between 30–50% of stover produced, depending on crop rotation andgrowing conditions. In conclusion, research suggests that when cover cropsare not planted, the acceptable removal rate of stover is about one-third ofground cover.6 Therefore, we assume that where cover crops are not grown,a stover harvest rate of 33% prevails.

Estimates of harvesting cost for alternative rotations under 33% removalrates were taken from Fiegel (2012) and are reported in table 1. Estimates ofthe cost of harvesting stover in the literature range from $16 per metric ton

6However, it should be borne in mind that acceptable removal rates are site-specific (Karlen and Johnson2014).


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(Gallagher et al. 2003) to $80 per metric ton (Natural Research Council 2011).Many of these estimates are not directly comparable because they evaluatecustom rates and prices at different points in time and adjust certain costs(e.g., nutrient replacement and tillage) to local agronomic conditions.Nonetheless, our estimates of on-farm cost are similar to those in Perrinet al. (2012) and Brechbill et al. (2011). As revealed by table 1, the only differ-ence between harvesting stover under a corn/soybean rotation and continu-ous corn is tillage savings. Continuous corn is usually accompanied bytillage operations because of the significant amount of residues producedevery year. When these residues are (at least partially) removed, one tillageis eliminated. This, in turn, implies savings associated with the cost oftillage operations. Farmers are assumed to apply reduced tillage whenresidue is not removed so tillage savings are calculated as the negative ofthe cost of reduced tillage.

As denoted by table 1 the cost of storage and nutrient replacement are cal-culated on a per ton basis while other cost sources are determined on a peracre basis. In PCLP, the cost of harvesting stover is introduced, for eachfarm, on a weight basis. Therefore, all costs determined on a per acre basisare converted to a weight basis by dividing them by the yield of each farm.So farms with different yields will have different costs of stover removal,which results in different breakeven prices of stover. However, differentyields are not the only variables driving heterogeneity of stover breakevenprices. Differences in input availability and size also introduce differencesin the prices of stover that induce farmers to choose crop rotations thatinclude stover harvest.

Cover crops have been suggested as a management practice that couldprevent adverse soil impacts of stover removal. But while adoption of covercrops may allow for additional stover removal, it comes at a cost. Pratt(2012) expanded PCLP to incorporate the option of adopting cover crops.7

The benefits of cover crops come from increased stover removal rates.8

Cover crops provide protection to the soil, thus increasing the “acceptable”rate of stover removal.

Table 1 Cost of Harvesting and Storing Stover (33% removal rate)

Cost Component $/acre $/ton

Storage 16.47Net Wrap 10.53Labor 5.79Equipment 12.32Nutrients 12.73Fuel 6.76Corn/soybean rotation 1 Stover, Total 35.40 29.10Tillage Savings 225Continuous corn 1 Stover, Total 10.40 29.10

7Pratt (2012) incorporated three cover crops in PCLP; annual ryegrass, crimson clover, and crimsonclover adjusted to reflect the value of added N. We will only consider annual ryegrass in this study as pre-liminary analysis suggested it is the most profitable option for the farmer.8While there are also some agronomic benefits of planting cover crops, these are compensated by the add-itional removal of stover.


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The data for quantifying the net effect of cover crops and increasedbiomass removed on soil erosion comes from simulations conducted withan integrated modeling system (D. Muth and K. Bryden, Unpublisheddata), which combines the Revised Universal Soil Loss Equation, Version 2(RUSLE2), the Wind Erosion Prediction System (WEPS), and the SoilConditioning Index (SCI). Multiple removal amounts are simulated under arange of soil and weather conditions for different crop rotations and covercrop practices. Simulated soil and weather conditions match those in thestate of Indiana, which is consistent with farm-level data included in PCLP.Indiana is an interesting case for analysis as it has high corn production andthus a large potential for stover removal. Additionally, Indiana is viewed asa leader for cover crop adoption. The management practices specified in theintegrated modeling system include cover crop, residue removal, crop rota-tions, tillage practices, vegetative barriers, and yield drags. Simulated dataare used to estimate a linear approximation to complex biophysical relation-ships embedded in the integrated modeling systems. This linear relation-ship allows us to calculate the additional amount of stover that can beremoved after a cover crop has been planted without increasing erosion rela-tive to a situation without cover crops and 33% removal.

Within the integrated modeling system, RUSLE2 is used to estimateerosion due to water, and WEPS is used to predict erosion due to wind. TheSCI is used to evaluate the effect of conservation practices by estimatingtrends of SOM in a field. Each of these models were extensively validatedand are, as a result, currently used by the United States Department ofAgriculture’s Natural Resources Conservation Services to calculate soilerosion rates under various climate and management conditions (Muthet al. 2012).

Results from simulations suggest that, when cover crops are planted, asubstantially higher amount of stover can be removed (1.9 tons/acre undercontinuous corn and 2.7 tons/acre under corn/soybean) without increasingsoil erosion relative to the case without cover crops and 33% removal.9

However, there are technical limits to stover removal. In particular, studieslike Montross et al. (2003) have shown that it may not be technically possibleto harvest more than 75% of produced stover. Some studies (Petrolia 2008a)have found much lower estimates of maximum technically recoverablestover. Therefore, we analyze two different harvest rates under cover crops:50% and 75%. The benefit of cover crops are composed of the additional re-movable biomass (33%, to either 50% or 75%) and the price of stover.

The cost of a cover crop is added to the cost of harvesting stover becausecover crops are only planted if stover is removed. The cost of a cover cropwas estimated by Pratt (2012) and includes establishment and termination.Furthermore, the stover harvest costs (shown in table 1) estimated by Fiegel(2012) are adjusted to reflect the increase in the stover removal rate. Table 2summarizes the cost of planting a cover crop and the cost of harvestingstover under 75% removal rate and 50% removal rate. Cost figures reportedin tables 2 suggest that the cost (per ton) of harvesting stover with a cover

9The linear approximation estimated by OLS indicate that each ton of biomass removed increases soilerosion by 1.24 tons per acre per year under continuous corn without cover crops, and by 0.72 tons peracre per year under corn/soybean rotation. These estimates also suggest that planting a cover crop reducessoil erosion by 0.8 tons per acre per year under continuous corn, and by 0.46 tons per acre under a corn/soybean rotation.


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crop (under both removal rates) is higher than the cost of removing itwithout a cover crop. The farmer will then plant cover crops only if this costincrement is at least covered by the revenue generated by the additionalremoved biomass. Moreover, note that although tillage savings coincide onper acre basis with the 33% removal situation (table 1) they are significantlylower on a per ton basis. This is because the same per acre amount isdivided by a higher stover tonnage.

Estimated Stover Supply

After cover crop costs and benefits are estimated, various scenarios can beanalyzed using farm-level data in PCLP. The crop rotations considered arecontinuous corn with stover removal with and without cover crops, corn/soybean rotation with stover removal with and without cover crops, con-tinuous corn without stover removal, corn/soybean rotation without stoverremoval, continuous soybeans, and other crop rotations used by farmersthat do not involve corn or soybean. We exploit land allocation decisionssimulated with PCLP to econometrically estimate the response of farmers’participation rate (i.e., the share of land in the supplying area allocated tocorn with stover removal) to stover prices. Econometric estimation of par-ticipation rates proceeds in two steps. First, we use PCLP to calculate, basedon farm-level data, the optimal (i.e., profit-maximizing) allocation of land tocompeting crop rotations for a range of stover prices. Second, we conductsmooth polynomial approximations to the participation rates as a functionof stover prices with and without cover crops. Share equations were esti-mated by seemingly unrelated regressions (SUR) and are reported in the ap-pendix as equations (A1)-(A6).10 Based on these curves, we can endogenize

Table 2 Cost of Harvesting and Storing Stover at 50% and 75% Removal: Cover CropCase

Cost of Stover

75% removalrate

50% removalrate Cost of Cover

CropCost Component $/acre $/ton $/acre $/ton $/acre

Storage 14.33 15.03Net Wrap 23.94 15.96Labor 9.47 7.29Equipment 20.16 15.52Nutrients 12.73 12.73Fuel 11.06 8.51Corn/soybean

rotation 1 Stover,Total

64.63 27.06 47.30 27.76 34.42

Tillage Savings 225.00 225.00Continuous

corn 1 Stover,Total

39.63 27.06 22.30 27.06 34.42

10The order of the polynomial for each crop rotation was determined based on likelihood ratio tests per-formed on SUR estimates of linear, quadratic, and cubic approximations. Though polynomial approxima-tion do not bound shares between zero and one, they performed much better than a logistic typespecification in preliminary analyses. Bounds are imposed in subsequent calculations in the paper.


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the price of stover offered at the farm gate by modeling a cost-minimizingplant facing these stover supply responses.

Cost-minimizing Price and Marginal Cost of StoverA facility’s cost of procuring biomass consists of purchase cost at the

farm plus transportation costs (from the farm to the facility). In studies ofstover-based biofuel considering a circular fuelshed around the processingplant (Perrin et al., 2012; Brechbill et al., 2011; Gallagher et al., 2003), acreageallocated to corn, yield, and harvest practices are assumed fixed and homo-geneously distributed around the delivery point.11 In those studies add-itional deliveries come from an expanding circle around the delivery point,with transportation cost determined by the radial distance to the point ofproduction (Gallagher et al., 2003).

Under a constant density, total cost of procuring biomass increases withtotal quantity not only by the purchase cost but also due to an increase intransportation cost. This is because, under a fixed harvesting density,greater volumes of biomass can only be procured by traveling greaterdistances from the facility. Since increases in stover price always increasecost, the plant offers the minimum possible price of stover which isassumed to be the cost of harvesting, moving stover to the edge of the field,and storing it.

When stover supply responds positively to its price, the plant may, if eco-nomically convenient, increase its biding price for stover at the farm gate toachieve an increase in density and a reduction in radius and transportationcost. In other words, the plant may find it optimal to procure biomassthrough the intensive margin (increase in supply per acre) instead of the ex-tensive margin (increase in the size of the supplying area). Assuming landuse, yields, and harvest rates are homogeneously distributed around the de-livery point and accounting for a supply response results in the followingcost expression:

TC = psQ + 23

t/!!!!!!!!!!pdk,r( ps)

"# $Q3/2, (1)

where Q is total biomass purchased, ps is the price of stover offered by theplant, t represents transportation cost ($/ton/mile), dk,r denotes harvestdensity (tons/square mile) under cover crop practice k (k = cc when a covercrop is planted and k = nc when a cover crop is not planted), and removalrate r (33% under no cover crop and either 75% or 50% under cover crop),which is a function of stover price. Harvest density (a function of stover

price) is defined as dk,r( ps) = Ld%

j sk,rj ( ps)yk,r

j , where Ld denotes total land

“suitable” for corn (i.e., land allocated to either corn or soybean), sk,rj ( ps) are

participation rates that are influenced by stover prices as depicted by func-

tions (A1)-(A6), and yk,rj denotes stover yield under corn-including rotation j

(j = cc for continuous corn and j = cb for corn/soybean rotation), cover croppractice k, and removal rate r.

11Some studies (e.g., Petrolia 2008a and Petrolia 2008b) have not assumed a circular fuelshed and arethus not directly comparable to this study.


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We parameterize our problem for Indiana, where according to data fromthe National Agricultural Statistics Service, planting density of corn andsoybeans was 474 acres per square mile in 2011.12 Based on our farm-leveldata and assumed harvest rates, we estimate stover yields at ! 1.46, ync

cc ynccb !

1.53, ycc,75cc ! 3.33, ycc,75

cb ! 3.48, ycc,50cc ! 2.22, and ycc,50

cb ! 2.32. Stover is har-vested from corn/soybean rotation acres every other year and we capturethis fact by assuming that stover is harvested from only half of the acresunder corn/soybean rotation with stover removal. We combine this infor-mation to derive expressions for harvest density, which can be found in theappendix as equations (A7)–(A9). Finally, transportation cost t in Indiana isassumed to be $3.60 per loaded mile (Brechbill et al. 2011).

Equation (1) captures the fact that increases in the price offered by thefacility for stover will increase purchase costs (the first term in equation (1)),but it will also reduce transportation costs because the second term in equa-tion (1) depends negatively on density, which increases with ps. This modelof supply response assumes the plant offers a price to each farmer at thefarm gate, and the farmers then instantaneously allocate acreage to alterna-tive rotations. In reality, this response may be sluggish but we abstract fromsuch dynamic considerations. Moreover, supply contracts between theplant and farms within the fuelshed may arise. These contracts may besigned to secure a steady flow of supply over time, reduce price risk, and/or incentivize volume and quality according to specific biophysical con-ditions in different farms. We refrain from modeling such bargainingprocesses and assume an open market where the plant uses a uniform deliv-ered pricing strategy and pays for transportation of the feedstock to theplant gate. This pricing strategy has been shown to dominate free-on-boardpricing on a range of empirical situations (Lofgren 1986).

Finally, this model assumes that either all farmers removing stover adopta cover crop (resulting in harvest density dcc,r( ps)), or none of them do(resulting in harvest density dnc,33( ps)). Our objective in this paper is not toconduct a precise calculation of cover crop adoption resulting from a stovermarket, but to examine the extent to which widespread adoption of covercrops within a plant’s fuelshed would influence feedstock cost and soilerosion. Therefore, to simplify the analysis we focus on these two extremecases and refrain from considering the case of “partial” adoption (i.e., somefarmers adopt cover crops and some do not). Potential implications of thisassumption are discussed in the conclusions section.

Positively sloped (with respect to stover price) participation rates areassociated with, among other things, heterogeneous yields. In particular,high-yielding farms achieve, all else being constant, lower stover harvestcosts and are more likely to participate in the stover market at lower prices.However, since we do not have enough information to estimate a specificlink between participation rates and stover yield, we construct supplydensity functions under the assumption of homogeneous yields.13

Therefore, heterogeneity embodied in density functions is captured by par-ticipation rates (i.e., the share components). A potential drawback of thisassumption is that density may be underestimated at low stover prices

12We cannot estimate planting density based on our sample of farmers as they operate in different areas ofthe State.13This is because other factors such as input availability and size also drive heterogeneity in breakevenprices and participation.


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(because participating farms at low prices are likely to achieve higheryields) and overestimated at high stover prices.

The predicted supplies of biomass per square mile under typical condi-tions in Indiana with and without cover crops are plotted in figure 1, whichreveals that stover harvest density is more elastic (i.e., more responsive)with respect to stover price when farmers plant a cover crop, and an in-crease in elasticity is positively related to the rate of stover harvest. On theother hand, the cost of harvesting stover under cover crops is higher so ahigher stover price is needed to induce positive supply. Therefore, thedensity of stover harvest without a cover crop is higher than that with covercrops for prices between $25 and $38 per ton under 75% removal, andbetween $25 and $58 per ton under 50% removal.

Figure 1 also reveals kinks in supply density functions without covercrops and with cover crops under 75% removal. This is because, in thosecases, a slightly higher price is required to trigger stover removal on acresallocated to corn/soybean rotation relative to continuous corn. Therefore, avertical sum of shares would display kinks at the price of stover that triggersremoval under corn/soybean rotation. This is important because it intro-duces non-smoothness in optimal pricing.

The processing plant faces supply curves of stover depicted in figure 1.The plant then chooses the price of stover that minimizes the cost of procur-ing biomass quantity Q:

min ps TC = psQ + 23

t/!!!!!!!!!!!pdk,r ps

& '"# $Q3/2. (2)

An explicit solution to the cost-minimizing price of stover cannot beobtained, so a numerical solution is implemented with the fminsearchroutine in Matlab R2012a.

The marginal cost of stover is an important determinant of its economicviability as an energy feedstock. The fact that the plant faces an upwardsloping supply of stover creates a wedge between the price paid and themarginal cost.14 We calculate marginal cost under three pricing scenarios: 1)

Figure 1 Predicted stover harvest densitya

Note: Superscripta indicates that a combination of cover crop practice and stover removal ratesappear in parentheses.

14This situation is in fact that of a firm acting as a spatial monopsonist.


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optimal stover price when the plant faces supply without a cover crop; 2)optimal stover price when the plant faces supply with a cover crop and 75%removal; 3) and optimal stover price when the plant faces supply with acover crop and 50% removal. Non-smoothness of share equations requiresnumerical evaluations of derivatives. Numerical evaluations at a range ofbiofuel production levels were conducted by creating conditional loops inMatlab R2012a.15 Comparing results under these three scenarios allows usto gauge the importance of incorporating supply response and cover cropsinto the analysis of economic viability of stover for energy.

Optimal Pricing and Marginal Cost of FeedstockThe numerically recovered cost-minimizing price schedules with and

without cover crops are graphed in figure 2 and denominated “optimalprice”, “optimal price with cover crop 75%”, and “optimal price with covercrop 50%”, respectively.16,17 The functions are positively sloped, whichmeans that the facility does find it optimal to increase the price offered forstover to farmers while attempting to secure higher amounts of biomass.Therefore, plants will not only rely on the extensive margin for stover pro-curement but also on the intensive margin.18 Moreover, the cost-minimizingprice of stover is higher when farmers plant cover crops because supplywith cover crops has a lower intercept and a higher slope (figure 1).

The optimal price schedule without cover crops displays a discrete jumparound $35 per ton when cover crops are not grown. This parallels the non-smoothness of supply density around the same price discussed in figure 1.

Figure 2 Optimal pricing and marginal cost (MC) of stovera,b

Notes: Superscripta indicates that a combination of cover crop practice and stover removal ratesappear in parentheses, whileb indicates that marginal cost equals the price plus the effect of achange in quantity on price and transportation cost, that is, the derivative of (1) with respect toQ under optimal pricing p(Q), which is in turn computed from (2).

15As the plant never chooses the price at which the discontinuity occurs, these derivatives exist at theoptimal price.16This and subsequent figures assume a biofuel yield of 80 gallons per ton of biomass.17The simulated production volumes parallel the current range of corn ethanol plants’ production scales.See http://www.neo.ne.gov/statshtml/122.htm.18This result underscores the importance of considering a supply response in the economic analysis ofbiofuel plants. Ignoring such supply response leaves the plant only with the extensive margin, overesti-mating marginal cost.


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At prices around $35 per ton and above, the supply of stover from acres ro-tating corn and soybean gets added to the existing removal from continuouscorn acres. The plant reacts by an abrupt upward adjustment of stover price.Although a vertex in supply density with cover crops also exists around$35, this is not translated into the optimal price schedule because theoptimal price is higher than $35. At prices above $35 per ton, supply densityis always smooth because both sources of stover supply (continuous cornand corn/soybean acres) have already emerged.

Marginal cost curves were also calculated and plotted in figure 2. Lowermarginal costs favor economic viability of stover for energy. Figure 2 revealsthat increased removal rates achieved through planting cover crops mayresult in reduced marginal cost of feedstock. At lower production capacitiesthe marginal cost of procuring stover is lowest under optimal pricing whenfarmers do not plant cover crops. However, marginal cost under cover cropsis lower than marginal cost without cover crops when the removal rate is75%, and for production volumes at or higher than 70 million gallons peryear (MGY). This is because supply is more sensitive to the price of stoverwhen cover crops are planted and 75% of stover is removed, so marginalcost with cover crops increases at a smaller rate than marginal cost withoutcover crops. Marginal cost with cover crops and 75% removal and marginalcost without cover crops are virtually the same when production rangesbetween 45–80 MGY. Figure 2 also shows that any cost savings achievedthrough increased removal rate by planting cover crops depends upon themagnitude of additional removal; that is, the marginal cost of stover withcover crops and 50% removal rate is substantially larger than the marginalcost without cover crops.

The positive slope of price schedules in figure 2 suggests that, as its scaleof production increases, a plant will rely more on the intensive margin. Tounderstand the relative importance of the intensive and extensive marginsin stover procurement, in figure 3 we display the stover supply densitywithin the plant’s supplying area and the size of the circle around the plantsupplying stover under two scenarios: without cover crops, and with covercrops and 75% removal. We concentrate on these two scenarios as they are

Figure 3 Intensive and extensive marginsa

Note: Superscript a indicates that a combination of cover crop practice and stover removal ratesappear in parentheses.


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more cost effective than planting cover crops and removing 50% of stover.We will consider the 50% removal scenario again when we compare the soilerosion implication of these harvesting options.

First, the non-smoothness of supply density and pricing schedule in thecase without cover crops translates into non-smoothness of area. When pro-duction is around 56MGY and the optimal price jumps to $35 per ton, thesupply density experiences an upward adjustment and the area a down-ward adjustment, which means an increase in the relative importance of theintensive margin. Second, cover crops seem to have a significant impact onthe relative importance of the intensive and extensive margins in stover pro-curement. In particular, farmers’ planting of cover crops increases the rela-tive importance of the intensive margin over the extensive margin asrevealed by higher supply density and smaller area for all productionvolumes. This implies a significant change in land use around the plant,which may have important implications for soil erosion. We now turn ourattention to this issue.

Soil Erosion Implications of Increasing CellulosicBiofuel Production

Average yearly soil erosion per acre has been calculated for continuouscorn and corn rotated with soybeans with stover removal. Soil erosion peracre has also been calculated for a baseline situation without stover removalunder both crop rotations. Estimated erosion values are reported in table 3.Erosion values reported in table 3 are average (across soil types) erosionvalues obtained through simulations with the integrated modeling system.

Estimates in table 3 reveal that, all else being constant, switching fromcorn/soybean rotation to continuous corn reduces soil erosion because cornresidues provide more effective soil cover than soybean residue. In addition,planting cover crops reduces soil erosion across crop rotations. This isbecause under cover crops, the rate of stover removal that would keep soilerosion constant exceeds 75%, but only up to 75% of stover is technically re-coverable. Therefore, at maximum recoverable rates, soil erosion with covercrops is lower than without cover crops.

We now analyze changes in land use associated with increased cellulosicbiofuel production and its implications on overall soil erosion vis-a-viserosion under a baseline scenario in which no stover is removed. The

Table 3 Soil Erosion Values

Crop RotationCover Crop (AnnualRyegrass)

RemovalRate

Mean Soil Erosion(tons/acre/year)

Continuous Corn No 33% 2.03Yes 75% 1.54Yes 50% 1.05

Corn/SoybeanRotation

No 33% 3.93Yes 75% 3.55Yes 50% 3.25

Continuous Corn No 0% 1.35Corn/Soybean

RotationNo 0% 3.55


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baseline case consists of observed land allocations by farmers in oursample.19 These allocations will change as growing corn with stoverremoval becomes more profitable due to an increase in stover price. Threeforces drive differences in soil erosion under alternative land cover scen-arios. First, a higher share of acres under corn/soybean rotation withremoval (as opposed to continuous corn with removal) results in higher soilerosion. Second, planting cover crops will tend to reduce erosion per acre.Third, given rotation and cover crop practice, stover removal increaseserosion so a decrease in the number of acres removing stover will tend to de-crease overall erosion.

Regarding the first force, the shares of cropland within the supplying areaallocated to alternative rotations under optimal pricing with and withoutcover crops are plotted in figure 4. The profitability of continuous corn withremoval relative to corn/soybean with removal decreases when cover cropsare planted. Therefore, the share of acres allocated to continuous corn ishighest when farmers do not plant cover crops (figure 4).20 This, based onerosion values in table 3, suggests lower soil erosion in the fuelshed withoutcover crops. However, the other two forces driving erosion described abovework in the opposite direction. Since more stover is harvested from an acrewith a cover crop relative to one without a cover crop, the number of acresfrom which stover needs to be harvested to satisfy a given biomass require-ment is significantly smaller when cover crops are planted. Moreover, soilerosion caused by stover removal is lower when cover crops are planted.The net effect of these opposing forces is now quantified and discussed.

Figure 4 Cover crops and land usea

Note: Superscript a indicates that a combination of cover crop practice and stover removal ratesappear in parentheses.

19Farms that provide data for PCLP operate under no market for stover. The market for stover is simulatedin PCLP based on these data. Therefore, in the baseline scenario, no stover removal takes place and alloca-tion of acres to continuous corn or corn/soybean is purely based on corn and soybean profitabilitywithout stover considerations.20The non-smoothness of pricing and density translates into non-smoothness of shares without covercrops.


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In the baseline scenario (no stover removal), farmers growing corn orsoybean in our sample have allocated 25% of their land to continuous cornwithout removal (which results in 1.35 tons of erosion per acre) and 75% tocorn/soybean rotation without removal (which results in 3.55 tons oferosion per acre). The change in soil erosion from stover removal under al-ternative cover crop scenarios relative to the baseline is calculated as:

DTSEk,r!b = {[sk,rcc ( pk,r"

s (Q)) " sek,rcc + sk,r

cb ( pk,r"s (Q)) " sek,r

cb ]

! [0.25 " 1.35 + 0.75 " 3.55]} " ak,r(3)

where sk,rcc ( pk,r"

s (Q)) represents the share of cropland allocated to continuouscorn with optimal pricing under cover crop practice k and removal rate r,sk,r

cb ( pk,r"s (Q)) is the share of acreage in the fuelshed allocated to corn/

soybean rotation with optimal pricing under cover crop practice k andremoval rate r, ak,r is the total number of acres of cropland in the supplyingarea under cover crop practice k and removal rate r, sek,r

cc denotes soil erosion(tons/acre/year) under continuous corn with removal, cover crop practice kand removal rate r, and sek

cb is erosion under corn/soybean with removal,cover crop practice k and removal rate r (values in table 4).21

Figure 5 displays the change in soil erosion (relative to the baseline)resulting from stover removal under alternative cover crop/removal ratescenarios. Figure 5 reveals two counterintuitive results. First, all practicesare associated with a reduction in soil erosion relative to the baseline. This isbecause many acres are converted from corn/soybean rotation withoutstover removal to continuous corn with stover removal. The latter rotationresults (given assumed harvest rates) in lower soil erosion than the formerunder all cover crop practices.

Figure 5 Stover removal and soil erosiona

Note: Superscripta indicates that a combination of cover crop practice and stover removal ratesappear in parentheses.

21Total number of acres in the supplying area result from the product of planting density and the area ofthe stover supplying circle. Area, in turn, is a function of radius. Different cover crop/removal rate scen-arios will have different radii because they are associated with different supply densities. Areas for differ-ent biomass requirements under alternative scenarios were plotted in Figure 3.


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Another counterintuitive result is that, under optimal pricing, removingstover with a cover crop does not necessarily result in lower erosion withinthe fuelshed. Differences in land use changes with and without cover cropsare behind this phenomenon. Under optimal pricing by the processingplant, cover crops shift land use towards reduced prevalence of continuouscorn (as revealed by figure 4) which results in higher erosion.22 Moreover,while planting cover crops and removing only 50% of residue can result inlower soil erosion relative to a scenario without cover crops, it only does sofor plants producing more than 70 MGY. Recall that this stover removalscenario resulted in a significantly higher marginal cost of feedstock, thusdiminishing its attractiveness relative to other scenarios.

In combination, our results suggest a tradeoff between private and socialgoals at high production levels. For instance, the lowest marginal cost offeedstock for a cellulosic biofuel plant producing 100 million gallons peryear is achieved under cover crops and 75% removal (see figure 2).However, this harvest scenario results in the highest soil erosion (figure 5).On the other hand, the lowest soil erosion is achieved under cover crops and50% removal (figure 5), but this harvest scenario results in the highest mar-ginal cost of feedstock (figure 2).

A potentially important parameter in our analysis is transportation cost,and understanding the robustness of our results to changes in this param-eter is informative. Results from a sensitivity analysis are reported in the ap-pendix (table A.2). This analysis reveals that, without cover crops, adoubling of transportation cost increases the price that a 100 MGY plantwould offer for stover, and it also increases marginal cost. Land use changestriggered by the change in stover price result in a 10% increase in soilerosion (2% with cover crops and 75% removal). On the other hand, ahalving of transportation cost decreases stover price and marginal cost, andreduces soil erosion by 24% (19% with cover crops and 75% removal).

Policy ImplicationsOur analysis shows, contrary to expectations, that under plausible man-

agement practices, stover-based biofuels may trigger land use changes thatresult in lower soil erosion. Although concerns about the soil erosion effectsof stover-based biofuels may be well-founded, our analysis shows that sucheffects will be, in part, shaped by land use changes associated with a marketfor stover. Consequently, predictions of soil implications of stover-basedbiofuels should be informed by an evaluation of potential land use changeswithin the fuelshed. The fact that increases in soil erosion are not a foregoneconclusion of stover-based biofuels enhances the merits of biofuel policiesas welfare-increasing government interventions.

Our simulations have also revealed that, in a given acre and for a givencrop rotation, soil erosion with cover crops and 75% removal is lower thanerosion without a cover crop and 33% removal. As a result, policymakersmay be tempted to support the planting of cover crops with soil conserva-tion purposes. However, our analysis demonstrates that land use changesassociated with the use of cover crops with 75% removal may in fact result

22The slight upward movement of erosion reduction in the case without cover crops is due to the emer-gence of stover removal from corn/soybean acres which are associated with higher soil erosion than con-tinuous corn. This of course occurs at 56MGY production which coincides with jumps on optimal priceschedule and supply density.


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in increased erosion, thereby undermining the policy objective. Our resultsshow that despite these land use changes, planting cover crops can reduceoverall soil erosion if it is accompanied by a relatively low stover removalrate (i.e., lower than 50%) as reported in figure 5. On the other hand, such areduction in the removal rate greatly increases marginal cost of feedstock(figure 2). These results reveal a tension between biofuel policies (aimed atreducing biofuel cost of production) and soil conservation policies (aimedat incentivizing cover crops with low stover removal rates).

ConclusionsDue to a lack of primary data, previous economic analysis of stover-based

biofuels did not consider the effect of changes in the price of stover offered bythe plant on land use and soil erosion. We generate supply responses to arange of stover prices using mathematical programming (PCLP) based onfarm-level data. Based on these simulated data, we econometrically estimatedstover supply curves with and without cover crops for typical conditions inIndiana, and examined its economic and environmental implications.

An important insight emerging from this analysis is that plants increasestover price and exploit both the intensive and extensive margins to procurestover as they increase the scale of production. Therefore, a lack of consider-ation regarding a supply response may result in overestimating the effect ofincreased scale of production on the marginal cost of feedstock. Moreover,contrary to expectations, a market for stover may trigger reallocations ofland to continuous corn with removal which, in combination with no-tillfarming, results in lower soil erosion than corn/soybean rotation withoutremoval and tillage. Consequently, a market for stover results in a reductionin soil erosion relative to the baseline without stover removal.

Also counterintuitively, our analysis found that planting cover crops doesnot necessarily reduce soil erosion since its effects are offset by increasedprevalence of corn/soybean rotation relative to continuous corn within thefuelshed. The effectiveness of cover crops as a means of reducing soilerosion is substantially diminished as the rate of stover removal under covercrops increases (figure 5) due to associated crop mix changes. On the otherhand, increases in the rate of removal reduce the marginal cost of feedstock(figure 2). This suggests a tradeoff between private marginal cost and soilerosion. Thus, without additional limitations on removal rates, supportingcover crops through policy as a measure to offset soil erosion implicationsof stover harvest may have unintended consequences and result in an evenhigher level of soil erosion.

Several dimensions not considered by our analysis deserve more atten-tion. Our analysis takes the existence of an operating plant as exogenousand does not investigate the likelihood of such occurrence. Biofuel priceshave to be sufficiently high to induce investment, and current market condi-tions have proven insufficient to trigger massive entry into the market.Previous studies have asserted that significant barriers to entry into thismarket still remain (National Research Council 2011), suggesting there isstill much to be done policy-wise to alleviate the effect of such barriers.Insights from this study aim at informing policy makers on the economicand environmental implications of policy-induced entry.

We have analyzed the situation of a spatial monopsonist. However, ourresults suggest that in searching for stover producers with a low breakeven


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price, optimal pricing may result in a large supplying area. Hence, supply-ing areas of different plants may overlap triggering spatial competition forstover. The implications of such a change in market structure for the mar-ginal cost of stover deserves attention. Moreover, this study has not consid-ered the case of partial adoption of cover crops. Allowing for partialadoption entails relaxing a constraint that could result in changes in stoversupply elasticity and land cover configuration within the fuelshed. Otherenvironmental consequences such as carbon emissions associated withstover transportation and potential carbon sequestration benefits of plantingcover crops may be important and were also ignored here.

AcknowledgmentsThe authors would like to thank two anonymous referees for their helpful com-ments, and extend special thanks to the editor, Terrance Hurley, for thoughtfulcomments and editorial assistance.

References

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Appendix

Comparison of Farms in PCLP with Distribution of Farms in the Stateof Indiana

As shown in table A.1., 70% of cropland in the two highest corn produ-cing counties in Indiana (Jasper and White) are operated by farms withover 1,000 acres (this figure is 53% for the State of Indiana). In oursample of farms, that figure is 87%. Moreover, in Jasper and White coun-ties, 40% of cropland is operated by farms with over 2,000 acres (27% inIndiana). That figure increases to 48% in our sample. The average cornyield in Jasper and White counties during the 2007–2010 period was 173bushels per acre, which is very similar to the average yield in oursample.

Share of Land Allocated to Alternative Crop Rotations

Estimated share equations are as follows (t-ratios appear under each coef-ficient and all coefficients are statistically significant at the 1% level):

snc,33cc = !0.45

(!5.35) +(0.0227)ps

(6.06) ! (0.00025)p2s

(!4.85) + (0.00000107)p3s

(4.69) (A1)

snc,33cb = !0.57

(!5.15) +(0.021)ps

(6.48) ! (0.00012)p2s

(!5.64) (A2)

scc,75cc = !0.26

(!10.99) +(0.0078)ps

(26.49) (A3)

scc,75cb = !0.55

(!4.83) +(0.02)ps

(5.93) ! (0.00012)p2s

(!5.35) (A4)

scc,50cc = !0.22

(!11.03) +(0.0061)ps

(24.61) (A5)

scc,50cb = !0.53

(!4.63) +(0.0174)ps

(5.25) ! (0.00009)p2s

(!4.17) , (A6)

where sk,rj denotes the share of acres in the supplying area under corn-

including rotation j (j = cc for continuous corn and j = cb for corn/soybeanrotation), cover crop practice k (k = cc when a cover crop is planted andk = nc when a cover crop is not planted), and removal rate r (33% under nocover crop and either 75% or 50% under cover crop), and ps is the price ofstover.


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Stover Harvest Density

Using density and yield information from Indiana, the density equationsunder alternative management practices are as follows:

dnc,33 = 474[1.46 " min(max(snccc ( ps), 0), 1) + 1.53 " 0.5 " min(max(snc

cb ( ps), 0)](A7)

dcc,75=474[3.33"min(max(scc,75cc (ps),0),1)+3.48"0.5"min(max(scc,75

cb (ps),0),1)](A8)

dcc,50=474[2.22"min(max(scc,50cc (ps),0),1)+2.32"0.5"min(max(scc,50

cb (ps),0),1)],(A9)

where total acres suitable for corn per square mile (474 acres/square mile) ismultiplied by participation rates sk,r

j (ps), which are defined by (A1)-(A6) inthis appendix, and their corresponding stover yields (ync

cc ! 1.46, ynccb ! 1.53,

ycc,75cc ! 3.33, ycc,75

cb ! 3.48, ycc,50cc ! 2.22, and ycc,50

cb ! 2.32), where yk,rj . denotes

stover yield under corn-including rotation j, cover crop practice k, andremoval rate r. The minimum and maximum operators bound sharesbetween zero and one. Multiplying land suitable for corn by sk,r

j (ps) yieldsthe number of acres within the fuelshed allocated to corn-including rotationj, cover crop practice k, and removal rate r. Mplying these figures by theirrespective yields result in the total amount of stover supplied by each rota-tion within the fuelshed.

Note that supply response is driven by the effect that stover price has onthe share of land allocated to different crop rotations including stoverremoval, sk,r

j ( ps). Also note that predicted shares have been boundedbetween zero and one.

Sensitivity with Respect to Transportation Cost

Results from a sensitivity analysis reported in table A.2 reveal that,without cover crops, a doubling of transportation cost increases the pricethat a 100 MGY plant would offer for stover by 10% (7% under cover cropsand 75% removal), and it also increases marginal costs by 28% (21% undercover crops and 75% removal). Soil erosion increases by 10% (2% undercover crops and 75% removal). On the other hand, a halving of transportation

Table A.1 Representativeness of our Sample of Farms

Region Category Indiana White and Jasper counties Farms in PCLP

1,000 or more acres(land in farms, % oftotal)

53 71 87

2,000 or more acres(land in farms, % oftotal)

27 40 48

Yield (bushels peracre)

161 173 174


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cost reduces stover price by 12% (5% under cover crops and 75% removal)and marginal cost by 20% (15% under cover crops and 75% removal), andreduces erosion by 24% (19% under cover crops and 75% removal).

To understand the effect of changes in transportation cost on soil erosion,we need to trace their effects on land use configurations. In particular, therise in stover price associated with an increase in transportation costincreases the share of land within the fuelshed allocated to continuous cornwith removal from 9–12% (5–7% under cover crops and 75% removal) andthe share of land allocated to corn/soybean with removal from 2–6%(4–7% under cover crops and 75% removal). Higher reliance on the inten-sive margin is associated with a decrease in the radius of the fuelshed from74–60 miles (58–47 miles under cover crops and 75% removal) which, com-bined with the aforementioned changes in land use within the fuelshed,result in increased soil erosion. Moreover, the decrease in stover price asso-ciated with a decrease in transportation cost reduces the share of land undercontinuous corn with removal from 9–5% (from 5–3.5% under cover cropsand 75% removal) and increases the size of the fuelshed. The combinationof these changes in land use result in a reduction of soil erosion.

Table A.2 Impact of Transportation Cost on Key Performance Indicators

TransportationCost Index

Stover Price IndexMarginal CostIndex Soil Erosion Index

Nocovercrops

Covercrops (75%removal)

Nocovercrops


Nocovercrops


200 110 107 128 121 110 10250 88 95 80 85 76 81

Note: All indices are calculated relative to figures with estimated transportation cost. For instance, atransportation cost index of 200 indicates a doubling of transportation cost relative to the estimatedfigure. Similarly, a stover price index of 110 means a 10% increase relative to stover price at theestimated transportation cost.


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