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MPratt_AppliedEconPublication

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MPratt_AppliedEconPublication

  1. 1. Article Supply Response, Marginal Cost, and Soil Erosion Implications of Stover-based Biofuels Juan Sesmero*, Michelle Pratt, and Wallace Tyner Juan Sesmero is an assistant professor of Agricultural Economics, Michelle Pratt is a graduate student, and Wallace Tyner is the James and Lois Ackerman Professor of Agricultural Economics, all in the Agricultural Economics Department, Purdue University. *Correspondence may be sent to: jsesmero@purdue.edu. Submitted 12 September 2013; accepted 16 October 2014. Abstract Existing economic analysis of corn stover as an energy feedstock has not considered potential changes in land use associated with different stover prices. We estimate the response of corn stover supply density to its price driven by changes in land use and examine its implications for a processing plant’s pricing strategy and marginal cost, as well as associated changes in soil erosion. We find that plants will exploit the intensive margin as well as the extensive margin to secure additional amounts of stover. Our results show, counterintuitively, that a market for stover may result in lower soil erosion due to reallocations of land to continuous corn with removal, which, combined with no-till farming, results in lower soil erosion than the baseline without stover removal. Also contrary to expectations, using cover crops with stover removal may result in higher soil erosion due to land use changes within the fuel shed associated with optimal pricing. Key words: Corn Stover, Biofuels, Land use change, Supply response, Soil erosion. JEL codes: Q15, Q24, Q42. Introduction Cellulosic 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 than the social optimum. This has, in the United States, motivated government support to biofuels in the form of a renewable fuel standard (RFS) by which blenders are obligated to include biofuels in motor vehicle fuel. A significant portion of cellulosic biofuels is expected to come from corn residues given its cost 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 Applied Economics Association. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com Applied Economic Perspectives and Policy (2014) volume 0, number 0, pp. 1–22. doi:10.1093/aepp/ppu042 1 Applied Economic Perspectives and Policy Advance Access published December 8, 2014 at::onDecember12,2014http://aepp.oxfordjournals.org/Downloadedfrom
  2. 2. 2012). However, many questions remain open regarding both the economic viability of this fuel source and the soil erosion implications of producing mandated quantities. The former is critical for determining the biofuel price (or subsidy) necessary to induce the production of mandated volumes. The latter is important to gauge potential unintended consequences of the policy (Petrolia 2008b; Cruse and Herndl 2009) and, thus the welfare gains accrued by 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 by changes in land use, by examining its implications for a processing plant’s pricing strategy and marginal cost, and by calculating soil erosion asso- ciated with increased biofuel production. We also analyze the effect of planting cover crops on cost and soil erosion1 under optimal pricing and, thus, the merits of supporting this management practice through public policy. The cost at which processing plants can procure corn stover is a critical determinant 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 around the plant react to increased stover prices by switching to crop rotations that produce more stover, the supply of stover will be upward sloping.2 An upward-sloping supply means that a plant in need of a certain amount of biomass may, if economically convenient, increase its bidding price for stover at the farm gate to achieve an increase in harvest density (i.e., increase in acres harvesting stover) around the plant, and hence, a decrease in sup- plying area and transportation cost. The plant will, however, weigh this benefit against the additional cost per ton of stover purchased. In other words, there is a tradeoff between the intensive and the extensive margins that 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 soil erosion. Despite its potential importance, previous economic analyses of corn residue 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 soil erosion. Thompson and Tyner (2014) have quantified changes in crop rota- tions associated with increased stover price but they have not modeled the behavior of a plant facing such a supply response and the resulting soil erosion.3 On the other hand, Petrolia (2008b) has considered soil erosion implications of stover-based biofuel plants, not driven by changes in land 1 Planting cover crops was previously proposed as a management practice that could offset the soil erosion effects of stover removal, thus increasing the removable rate (Bonner et al. 2014; Mann et al. 2002; Kim and Dale 2005; Fronning et al. 2008). 2 Farmers may also adjust their removal rates to changes in stover price. Quantification of such elasticity would require reliable estimates of the cost structure (including intertemporal costs through soil quality effects) 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, focusing on an extreme case (where harvest density per acre is perfectly inelastic) seems a reasonable starting point. 3 Sesmero and Gramig (2013) recognize this but do not estimate a supply response nor do they quantify soil erosion. Sesmero (2014) has incorporated a stover supply response in the irrigated Corn Belt but this response is driven by increasing marginal cost of soil water replacement. No changes in land use are involved. Applied Economic Perspectives and Policy 2 at::onDecember12,2014http://aepp.oxfordjournals.org/Downloadedfrom
  3. 3. use but by violations of soil erosion constraints at the farm level. This offers a view of the soil erosion effects of biofuels that is complementary to that developed in this study. Failure to model and quantify changes in land use in response to variations in the price of stover and pricing behavior of a plant 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 scholarly literature. Three important results are revealed by our analysis. First, the price of stover offered by a cost-minimizing plant increases with scale of production. Therefore, plants will exploit the intensive margin as well as the extensive margin 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 to continuous corn with stover removal. Therefore, policy support to this man- agement practice may not be warranted on the basis of soil conservation concerns. Stover Supply Different farmers will likely have different willingness to accept (i.e., minimum required stover price) for alternative crop rotations that include stover removal. This will result in an aggregate supply response to increas- ing stover prices (Thompson and Tyner 2014). However, because the market for stover has yet to emerge, primary data necessary to estimate this supply response are not available. Therefore, we obtain counterfactual estimates of supply response to increased stover price by simulating profit-maximizing land 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, and costs (Doster et al. 2009b). Given these inputs, which are farm-specific, PCLP determines the profit-maximizing allocation of acres to alternative crop rotations (Doster et al. 2009a). The farmer information used in PCLP comes 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 this analysis. These farms operated a total of 63,336 acres and the mean size of the farms was 2,540 acres. The minimum farm size was 550 acres and the maximum farm size was 8,200 acres. Farms had, on average, a mean corn yield of 174 bushels per acre for corn/soybean rotations and 167 bushels per acre 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 4 A biomass supply response has been considered by Rosburg et al. (2013) in the case of switchgrass, but not stover. 5 No awards are given in this workshop, so no incentives to over-report outcomes or under-report input usage are in place. Farmers receive feedback from PCLP regarding the marginal value of capital and land in their operations. Supply Response, Marginal Cost, and Soil Erosion Implications of Stover-based Biofuels 3 at::onDecember12,2014http://aepp.oxfordjournals.org/Downloadedfrom
  4. 4. transportation cost. Therefore, a comparison of farm characteristics in our sample with those in high-density counties may be informative of the representativeness of our data. This comparison is reported in table A.1 in the appendix. Values in that table reveal that, while similar to the general population of farms in high corn-producing counties, farms in our sample are relatively large when compared with the general population in Indiana. This may result in enhanced flexibility to change crop rota- tions (due to relaxed resource constraints, especially land) and an upward bias in stover supply. With the caveat of a potential upward bias in supply, it seems that counterfactuals constructed with PCLP may provide useful information on the response of stover supply to its price, and espe- cially so for high-density counties, which are of particular interest in this analysis. Removing corn stover was added as a cropping alternative in PCLP by Thompson (2011), who added stover harvest options into the PCLP model by creating two new crops: corn/soybean rotation with stover removal, and continuous 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 return per ton is then multiplied by tons harvested per acre to calculate per acre returns from harvesting stover. Prices are simulated, but cost is estimated. The next section discusses estimating the total amount of stover harvested under 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 properties and, in turn, their effect on future yields. As a result, many studies calcu- lated, for different agronomic and climate regimes, an “acceptable” rate of stover removal, that is, a rate above which long run productivity of the soil may be reduced. Lindstrom (1986) calculated, for a no-till system in Minnesota, that soil loss would not be significant at a 30% removal rate; McAloon et al. (2000) calculated the same rate. Other estimates, based on requirements to control soil erosion, suggest 30–50% removal rates (Kim and Dale 2004; Graham et 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 adversely affected. A study by Wilhelm et al. (2007) found that the amount of corn stover needed to maintain soil carbon, and thus long-term soil productivity, is between 30–50% of stover produced, depending on crop rotation and growing conditions. In conclusion, research suggests that when cover crops are not planted, the acceptable removal rate of stover is about one-third of ground 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% removal rates were taken from Fiegel (2012) and are reported in table 1. Estimates of the cost of harvesting stover in the literature range from $16 per metric ton 6 However, it should be borne in mind that acceptable removal rates are site-specific (Karlen and Johnson 2014). Applied Economic Perspectives and Policy 4 at::onDecember12,2014http://aepp.oxfordjournals.org/Downloadedfrom
  5. 5. (Gallagher et al. 2003) to $80 per metric ton (Natural Research Council 2011). Many of these estimates are not directly comparable because they evaluate custom 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 Perrin et 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 by tillage operations because of the significant amount of residues produced every year. When these residues are (at least partially) removed, one tillage is eliminated. This, in turn, implies savings associated with the cost of tillage operations. Farmers are assumed to apply reduced tillage when residue is not removed so tillage savings are calculated as the negative of the 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 per acre basis. In PCLP, the cost of harvesting stover is introduced, for each farm, on a weight basis. Therefore, all costs determined on a per acre basis are 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, different yields are not the only variables driving heterogeneity of stover breakeven prices. Differences in input availability and size also introduce differences in the prices of stover that induce farmers to choose crop rotations that include stover harvest. Cover crops have been suggested as a management practice that could prevent adverse soil impacts of stover removal. But while adoption of cover crops 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 16.47 Net Wrap 10.53 Labor 5.79 Equipment 12.32 Nutrients 12.73 Fuel 6.76 Corn/soybean rotation 1 Stover, Total 35.40 29.10 Tillage Savings 225 Continuous corn 1 Stover, Total 10.40 29.10 7 Pratt (2012) incorporated three cover crops in PCLP; annual ryegrass, crimson clover, and crimson clover 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. 8 While there are also some agronomic benefits of planting cover crops, these are compensated by the add- itional removal of stover. Supply Response, Marginal Cost, and Soil Erosion Implications of Stover-based Biofuels 5 at::onDecember12,2014http://aepp.oxfordjournals.org/Downloadedfrom
  6. 6. The data for quantifying the net effect of cover crops and increased biomass removed on soil erosion comes from simulations conducted with an integrated modeling system (D. Muth and K. Bryden, Unpublished data), which combines the Revised Universal Soil Loss Equation, Version 2 (RUSLE2), the Wind Erosion Prediction System (WEPS), and the Soil Conditioning Index (SCI). Multiple removal amounts are simulated under a range of soil and weather conditions for different crop rotations and cover crop practices. Simulated soil and weather conditions match those in the state 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 and thus a large potential for stover removal. Additionally, Indiana is viewed as a leader for cover crop adoption. The management practices specified in the integrated modeling system include cover crop, residue removal, crop rota- tions, tillage practices, vegetative barriers, and yield drags. Simulated data are 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 be removed 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 estimate erosion due to water, and WEPS is used to predict erosion due to wind. The SCI is used to evaluate the effect of conservation practices by estimating trends of SOM in a field. Each of these models were extensively validated and are, as a result, currently used by the United States Department of Agriculture’s Natural Resources Conservation Services to calculate soil erosion rates under various climate and management conditions (Muth et al. 2012). Results from simulations suggest that, when cover crops are planted, a substantially higher amount of stover can be removed (1.9 tons/acre under continuous corn and 2.7 tons/acre under corn/soybean) without increasing soil erosion relative to the case without cover crops and 33% removal.9 However, there are technical limits to stover removal. In particular, studies like Montross et al. (2003) have shown that it may not be technically possible to harvest more than 75% of produced stover. Some studies (Petrolia 2008a) have found much lower estimates of maximum technically recoverable stover. 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 because cover crops are only planted if stover is removed. The cost of a cover crop was 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 2 summarizes the cost of planting a cover crop and the cost of harvesting stover under 75% removal rate and 50% removal rate. Cost figures reported in tables 2 suggest that the cost (per ton) of harvesting stover with a cover 9 The linear approximation estimated by OLS indicate that each ton of biomass removed increases soil erosion by 1.24 tons per acre per year under continuous corn without cover crops, and by 0.72 tons per acre per year under corn/soybean rotation. These estimates also suggest that planting a cover crop reduces soil erosion by 0.8 tons per acre per year under continuous corn, and by 0.46 tons per acre under a corn/ soybean rotation. Applied Economic Perspectives and Policy 6 at::onDecember12,2014http://aepp.oxfordjournals.org/Downloadedfrom
  7. 7. crop (under both removal rates) is higher than the cost of removing it without a cover crop. The farmer will then plant cover crops only if this cost increment is at least covered by the revenue generated by the additional removed biomass. Moreover, note that although tillage savings coincide on per acre basis with the 33% removal situation (table 1) they are significantly lower on a per ton basis. This is because the same per acre amount is divided by a higher stover tonnage. Estimated Stover Supply After cover crop costs and benefits are estimated, various scenarios can be analyzed using farm-level data in PCLP. The crop rotations considered are continuous 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 stover removal, continuous soybeans, and other crop rotations used by farmers that do not involve corn or soybean. We exploit land allocation decisions simulated with PCLP to econometrically estimate the response of farmers’ participation rate (i.e., the share of land in the supplying area allocated to corn with stover removal) to stover prices. Econometric estimation of par- ticipation rates proceeds in two steps. First, we use PCLP to calculate, based on farm-level data, the optimal (i.e., profit-maximizing) allocation of land to competing crop rotations for a range of stover prices. Second, we conduct smooth polynomial approximations to the participation rates as a function of 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 Crop Case Cost of Stover 75% removal rate 50% removal rate Cost of Cover Crop Cost Component $/acre $/ton $/acre $/ton $/acre Storage 14.33 15.03 Net Wrap 23.94 15.96 Labor 9.47 7.29 Equipment 20.16 15.52 Nutrients 12.73 12.73 Fuel 11.06 8.51 Corn/soybean rotation 1 Stover, Total 64.63 27.06 47.30 27.76 34.42 Tillage Savings 225.00 225.00 Continuous corn 1 Stover, Total 39.63 27.06 22.30 27.06 34.42 10 The 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 type specification in preliminary analyses. Bounds are imposed in subsequent calculations in the paper. Supply Response, Marginal Cost, and Soil Erosion Implications of Stover-based Biofuels 7 at::onDecember12,2014http://aepp.oxfordjournals.org/Downloadedfrom
  8. 8. the price of stover offered at the farm gate by modeling a cost-minimizing plant facing these stover supply responses. Cost-minimizing Price and Marginal Cost of Stover A facility’s cost of procuring biomass consists of purchase cost at the farm plus transportation costs (from the farm to the facility). In studies of stover-based biofuel considering a circular fuelshed around the processing plant (Perrin et al., 2012; Brechbill et al., 2011; Gallagher et al., 2003), acreage allocated 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 of production (Gallagher et al., 2003). Under a constant density, total cost of procuring biomass increases with total quantity not only by the purchase cost but also due to an increase in transportation cost. This is because, under a fixed harvesting density, greater volumes of biomass can only be procured by traveling greater distances from the facility. Since increases in stover price always increase cost, the plant offers the minimum possible price of stover which is assumed 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 to achieve an increase in density and a reduction in radius and transportation cost. In other words, the plant may find it optimal to procure biomass through the intensive margin (increase in supply per acre) instead of the ex- tensive margin (increase in the size of the supplying area). Assuming land use, yields, and harvest rates are homogeneously distributed around the de- livery point and accounting for a supply response results in the following cost expression: TC = psQ + 2 3 t/ pdk,r( ps) Q3/2 , (1) where Q is total biomass purchased, ps is the price of stover offered by the plant, t represents transportation cost ($/ton/mile), dk,r denotes harvest density (tons/square mile) under cover crop practice k (k = cc when a cover crop is planted and k = nc when a cover crop is not planted), and removal rate 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,r j ( ps)yk,r j , where Ld denotes total land “suitable” for corn (i.e., land allocated to either corn or soybean), sk,r j ( ps) are participation rates that are influenced by stover prices as depicted by func- tions (A1)-(A6), and yk,r j denotes stover yield under corn-including rotation j (j = cc for continuous corn and j = cb for corn/soybean rotation), cover crop practice k, and removal rate r. 11 Some studies (e.g., Petrolia 2008a and Petrolia 2008b) have not assumed a circular fuelshed and are thus not directly comparable to this study. Applied Economic Perspectives and Policy 8 at::onDecember12,2014http://aepp.oxfordjournals.org/Downloadedfrom
  9. 9. We parameterize our problem for Indiana, where according to data from the National Agricultural Statistics Service, planting density of corn and soybeans was 474 acres per square mile in 2011.12 Based on our farm-level data and assumed harvest rates, we estimate stover yields at ¼ 1.46, ync cc ync cb ¼ 1.53, ycc,75 cc ¼ 3.33, ycc,75 cb ¼ 3.48, ycc,50 cc ¼ 2.22, and ycc,50 cb ¼ 2.32. Stover is har- vested from corn/soybean rotation acres every other year and we capture this fact by assuming that stover is harvested from only half of the acres under corn/soybean rotation with stover removal. We combine this infor- mation to derive expressions for harvest density, which can be found in the appendix as equations (A7)–(A9). Finally, transportation cost t in Indiana is assumed to be $3.60 per loaded mile (Brechbill et al. 2011). Equation (1) captures the fact that increases in the price offered by the facility 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 model of supply response assumes the plant offers a price to each farmer at the farm gate, and the farmers then instantaneously allocate acreage to alterna- tive rotations. In reality, this response may be sluggish but we abstract from such dynamic considerations. Moreover, supply contracts between the plant and farms within the fuelshed may arise. These contracts may be signed 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 bargaining processes and assume an open market where the plant uses a uniform deliv- ered pricing strategy and pays for transportation of the feedstock to the plant gate. This pricing strategy has been shown to dominate free-on-board pricing on a range of empirical situations (Lo¨fgren 1986). Finally, this model assumes that either all farmers removing stover adopt a 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 to conduct a precise calculation of cover crop adoption resulting from a stover market, but to examine the extent to which widespread adoption of cover crops within a plant’s fuelshed would influence feedstock cost and soil erosion. Therefore, to simplify the analysis we focus on these two extreme cases and refrain from considering the case of “partial” adoption (i.e., some farmers adopt cover crops and some do not). Potential implications of this assumption are discussed in the conclusions section. Positively sloped (with respect to stover price) participation rates are associated with, among other things, heterogeneous yields. In particular, high-yielding farms achieve, all else being constant, lower stover harvest costs and are more likely to participate in the stover market at lower prices. However, since we do not have enough information to estimate a specific link between participation rates and stover yield, we construct supply density 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 this assumption is that density may be underestimated at low stover prices 12 We cannot estimate planting density based on our sample of farmers as they operate in different areas of the State. 13 This is because other factors such as input availability and size also drive heterogeneity in breakeven prices and participation. Supply Response, Marginal Cost, and Soil Erosion Implications of Stover-based Biofuels 9 at::onDecember12,2014http://aepp.oxfordjournals.org/Downloadedfrom
  10. 10. (because participating farms at low prices are likely to achieve higher yields) 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, which reveals 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 the other hand, the cost of harvesting stover under cover crops is higher so a higher stover price is needed to induce positive supply. Therefore, the density of stover harvest without a cover crop is higher than that with cover crops for prices between $25 and $38 per ton under 75% removal, and between $25 and $58 per ton under 50% removal. Figure 1 also reveals kinks in supply density functions without cover crops and with cover crops under 75% removal. This is because, in those cases, a slightly higher price is required to trigger stover removal on acres allocated to corn/soybean rotation relative to continuous corn. Therefore, a vertical sum of shares would display kinks at the price of stover that triggers removal 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: minps TC = psQ + 2 3 t/ pdk,r ps Q3/2 . (2) An explicit solution to the cost-minimizing price of stover cannot be obtained, so a numerical solution is implemented with the fminsearch routine in Matlab R2012a. The marginal cost of stover is an important determinant of its economic viability as an energy feedstock. The fact that the plant faces an upward sloping supply of stover creates a wedge between the price paid and the marginal 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 rates appear in parentheses. 14 This situation is in fact that of a firm acting as a spatial monopsonist. Applied Economic Perspectives and Policy 10 at::onDecember12,2014http://aepp.oxfordjournals.org/Downloadedfrom
  11. 11. 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 a cover crop and 50% removal. Non-smoothness of share equations requires numerical evaluations of derivatives. Numerical evaluations at a range of biofuel production levels were conducted by creating conditional loops in Matlab R2012a.15 Comparing results under these three scenarios allows us to gauge the importance of incorporating supply response and cover crops into the analysis of economic viability of stover for energy. Optimal Pricing and Marginal Cost of Feedstock The numerically recovered cost-minimizing price schedules with and without cover crops are graphed in figure 2 and denominated “optimal price”, “optimal price with cover crop 75%”, and “optimal price with cover crop 50%”, respectively.16,17 The functions are positively sloped, which means that the facility does find it optimal to increase the price offered for stover 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-minimizing price of stover is higher when farmers plant cover crops because supply with cover crops has a lower intercept and a higher slope (figure 1). The optimal price schedule without cover crops displays a discrete jump around $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 rates appear in parentheses, whileb indicates that marginal cost equals the price plus the effect of a change in quantity on price and transportation cost, that is, the derivative of (1) with respect to Q under optimal pricing p(Q), which is in turn computed from (2). 15 As the plant never chooses the price at which the discontinuity occurs, these derivatives exist at the optimal price. 16 This and subsequent figures assume a biofuel yield of 80 gallons per ton of biomass. 17 The simulated production volumes parallel the current range of corn ethanol plants’ production scales. See http://www.neo.ne.gov/statshtml/122.htm. 18 This result underscores the importance of considering a supply response in the economic analysis of biofuel plants. Ignoring such supply response leaves the plant only with the extensive margin, overesti- mating marginal cost. Supply Response, Marginal Cost, and Soil Erosion Implications of Stover-based Biofuels 11 at::onDecember12,2014http://aepp.oxfordjournals.org/Downloadedfrom
  12. 12. 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 continuous corn 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 the optimal price is higher than $35. At prices above $35 per ton, supply density is always smooth because both sources of stover supply (continuous corn and corn/soybean acres) have already emerged. Marginal cost curves were also calculated and plotted in figure 2. Lower marginal costs favor economic viability of stover for energy. Figure 2 reveals that increased removal rates achieved through planting cover crops may result in reduced marginal cost of feedstock. At lower production capacities the marginal cost of procuring stover is lowest under optimal pricing when farmers do not plant cover crops. However, marginal cost under cover crops is lower than marginal cost without cover crops when the removal rate is 75%, and for production volumes at or higher than 70 million gallons per year (MGY). This is because supply is more sensitive to the price of stover when cover crops are planted and 75% of stover is removed, so marginal cost with cover crops increases at a smaller rate than marginal cost without cover crops. Marginal cost with cover crops and 75% removal and marginal cost without cover crops are virtually the same when production ranges between 45–80 MGY. Figure 2 also shows that any cost savings achieved through increased removal rate by planting cover crops depends upon the magnitude of additional removal; that is, the marginal cost of stover with cover crops and 50% removal rate is substantially larger than the marginal cost without cover crops. The positive slope of price schedules in figure 2 suggests that, as its scale of production increases, a plant will rely more on the intensive margin. To understand the relative importance of the intensive and extensive margins in stover procurement, in figure 3 we display the stover supply density within the plant’s supplying area and the size of the circle around the plant supplying stover under two scenarios: without cover crops, and with cover crops 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 rates appear in parentheses. Applied Economic Perspectives and Policy 12 at::onDecember12,2014http://aepp.oxfordjournals.org/Downloadedfrom
  13. 13. more cost effective than planting cover crops and removing 50% of stover. We will consider the 50% removal scenario again when we compare the soil erosion implication of these harvesting options. First, the non-smoothness of supply density and pricing schedule in the case without cover crops translates into non-smoothness of area. When pro- duction is around 56MGY and the optimal price jumps to $35 per ton, the supply density experiences an upward adjustment and the area a down- ward adjustment, which means an increase in the relative importance of the intensive margin. Second, cover crops seem to have a significant impact on the 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 as revealed by higher supply density and smaller area for all production volumes. This implies a significant change in land use around the plant, which may have important implications for soil erosion. We now turn our attention to this issue. Soil Erosion Implications of Increasing Cellulosic Biofuel Production Average yearly soil erosion per acre has been calculated for continuous corn and corn rotated with soybeans with stover removal. Soil erosion per acre has also been calculated for a baseline situation without stover removal under both crop rotations. Estimated erosion values are reported in table 3. Erosion values reported in table 3 are average (across soil types) erosion values obtained through simulations with the integrated modeling system. Estimates in table 3 reveal that, all else being constant, switching from corn/soybean rotation to continuous corn reduces soil erosion because corn residues provide more effective soil cover than soybean residue. In addition, planting cover crops reduces soil erosion across crop rotations. This is because under cover crops, the rate of stover removal that would keep soil erosion constant exceeds 75%, but only up to 75% of stover is technically re- coverable. Therefore, at maximum recoverable rates, soil erosion with cover crops is lower than without cover crops. We now analyze changes in land use associated with increased cellulosic biofuel production and its implications on overall soil erosion vis-a`-vis erosion under a baseline scenario in which no stover is removed. The Table 3 Soil Erosion Values Crop Rotation Cover Crop (Annual Ryegrass) Removal Rate Mean Soil Erosion (tons/acre/year) Continuous Corn No 33% 2.03 Yes 75% 1.54 Yes 50% 1.05 Corn/Soybean Rotation No 33% 3.93 Yes 75% 3.55 Yes 50% 3.25 Continuous Corn No 0% 1.35 Corn/Soybean Rotation No 0% 3.55 Supply Response, Marginal Cost, and Soil Erosion Implications of Stover-based Biofuels 13 at::onDecember12,2014http://aepp.oxfordjournals.org/Downloadedfrom
  14. 14. baseline case consists of observed land allocations by farmers in our sample.19 These allocations will change as growing corn with stover removal becomes more profitable due to an increase in stover price. Three forces drive differences in soil erosion under alternative land cover scen- arios. First, a higher share of acres under corn/soybean rotation with removal (as opposed to continuous corn with removal) results in higher soil erosion. Second, planting cover crops will tend to reduce erosion per acre. Third, given rotation and cover crop practice, stover removal increases erosion 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 area allocated to alternative rotations under optimal pricing with and without cover crops are plotted in figure 4. The profitability of continuous corn with removal relative to corn/soybean with removal decreases when cover crops are planted. Therefore, the share of acres allocated to continuous corn is highest when farmers do not plant cover crops (figure 4).20 This, based on erosion values in table 3, suggests lower soil erosion in the fuelshed without cover crops. However, the other two forces driving erosion described above work in the opposite direction. Since more stover is harvested from an acre with a cover crop relative to one without a cover crop, the number of acres from which stover needs to be harvested to satisfy a given biomass require- ment is significantly smaller when cover crops are planted. Moreover, soil erosion 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 rates appear in parentheses. 19 Farms that provide data for PCLP operate under no market for stover. The market for stover is simulated in 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 profitability without stover considerations. 20 The non-smoothness of pricing and density translates into non-smoothness of shares without cover crops. Applied Economic Perspectives and Policy 14 at::onDecember12,2014http://aepp.oxfordjournals.org/Downloadedfrom
  15. 15. In the baseline scenario (no stover removal), farmers growing corn or soybean in our sample have allocated 25% of their land to continuous corn without removal (which results in 1.35 tons of erosion per acre) and 75% to corn/soybean rotation without removal (which results in 3.55 tons of erosion 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,r cc ( pk,r∗ s (Q)) ∗ sek,r cc + sk,r cb ( pk,r∗ s (Q)) ∗ sek,r cb ] − [0.25 ∗ 1.35 + 0.75 ∗ 3.55]} ∗ ak,r (3) where sk,r cc ( pk,r∗ s (Q)) represents the share of cropland allocated to continuous corn 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 and removal rate r, ak,r is the total number of acres of cropland in the supplying area 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 k and 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 rate scenarios. Figure 5 reveals two counterintuitive results. First, all practices are associated with a reduction in soil erosion relative to the baseline. This is because many acres are converted from corn/soybean rotation without stover removal to continuous corn with stover removal. The latter rotation results (given assumed harvest rates) in lower soil erosion than the former under all cover crop practices. Figure 5 Stover removal and soil erosiona Note: Superscripta indicates that a combination of cover crop practice and stover removal rates appear in parentheses. 21 Total number of acres in the supplying area result from the product of planting density and the area of the 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. Supply Response, Marginal Cost, and Soil Erosion Implications of Stover-based Biofuels 15 at::onDecember12,2014http://aepp.oxfordjournals.org/Downloadedfrom
  16. 16. Another counterintuitive result is that, under optimal pricing, removing stover with a cover crop does not necessarily result in lower erosion within the fuelshed. Differences in land use changes with and without cover crops are behind this phenomenon. Under optimal pricing by the processing plant, cover crops shift land use towards reduced prevalence of continuous corn (as revealed by figure 4) which results in higher erosion.22 Moreover, while planting cover crops and removing only 50% of residue can result in lower soil erosion relative to a scenario without cover crops, it only does so for plants producing more than 70 MGY. Recall that this stover removal scenario resulted in a significantly higher marginal cost of feedstock, thus diminishing its attractiveness relative to other scenarios. In combination, our results suggest a tradeoff between private and social goals at high production levels. For instance, the lowest marginal cost of feedstock for a cellulosic biofuel plant producing 100 million gallons per year 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 and 50% 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, a doubling of transportation cost increases the price that a 100 MGY plant would offer for stover, and it also increases marginal cost. Land use changes triggered by the change in stover price result in a 10% increase in soil erosion (2% with cover crops and 75% removal). On the other hand, a halving of transportation cost decreases stover price and marginal cost, and reduces soil erosion by 24% (19% with cover crops and 75% removal). Policy Implications Our analysis shows, contrary to expectations, that under plausible man- agement practices, stover-based biofuels may trigger land use changes that result in lower soil erosion. Although concerns about the soil erosion effects of stover-based biofuels may be well-founded, our analysis shows that such effects will be, in part, shaped by land use changes associated with a market for stover. Consequently, predictions of soil implications of stover-based biofuels should be informed by an evaluation of potential land use changes within the fuelshed. The fact that increases in soil erosion are not a foregone conclusion of stover-based biofuels enhances the merits of biofuel policies as welfare-increasing government interventions. Our simulations have also revealed that, in a given acre and for a given crop rotation, soil erosion with cover crops and 75% removal is lower than erosion without a cover crop and 33% removal. As a result, policymakers may be tempted to support the planting of cover crops with soil conserva- tion purposes. However, our analysis demonstrates that land use changes associated with the use of cover crops with 75% removal may in fact result 22 The 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 price schedule and supply density. Applied Economic Perspectives and Policy 16 at::onDecember12,2014http://aepp.oxfordjournals.org/Downloadedfrom
  17. 17. in increased erosion, thereby undermining the policy objective. Our results show that despite these land use changes, planting cover crops can reduce overall soil erosion if it is accompanied by a relatively low stover removal rate (i.e., lower than 50%) as reported in figure 5. On the other hand, such a reduction in the removal rate greatly increases marginal cost of feedstock (figure 2). These results reveal a tension between biofuel policies (aimed at reducing biofuel cost of production) and soil conservation policies (aimed at incentivizing cover crops with low stover removal rates). Conclusions Due 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 by the plant on land use and soil erosion. We generate supply responses to a range of stover prices using mathematical programming (PCLP) based on farm-level data. Based on these simulated data, we econometrically estimated stover supply curves with and without cover crops for typical conditions in Indiana, and examined its economic and environmental implications. An important insight emerging from this analysis is that plants increase stover price and exploit both the intensive and extensive margins to procure stover as they increase the scale of production. Therefore, a lack of consider- ation regarding a supply response may result in overestimating the effect of increased scale of production on the marginal cost of feedstock. Moreover, contrary to expectations, a market for stover may trigger reallocations of land to continuous corn with removal which, in combination with no-till farming, results in lower soil erosion than corn/soybean rotation without removal and tillage. Consequently, a market for stover results in a reduction in soil erosion relative to the baseline without stover removal. Also counterintuitively, our analysis found that planting cover crops does not necessarily reduce soil erosion since its effects are offset by increased prevalence of corn/soybean rotation relative to continuous corn within the fuelshed. The effectiveness of cover crops as a means of reducing soil erosion is substantially diminished as the rate of stover removal under cover crops increases (figure 5) due to associated crop mix changes. On the other hand, increases in the rate of removal reduce the marginal cost of feedstock (figure 2). This suggests a tradeoff between private marginal cost and soil erosion. Thus, without additional limitations on removal rates, supporting cover crops through policy as a measure to offset soil erosion implications of stover harvest may have unintended consequences and result in an even higher 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 exogenous and does not investigate the likelihood of such occurrence. Biofuel prices have 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 this market still remain (National Research Council 2011), suggesting there is still much to be done policy-wise to alleviate the effect of such barriers. Insights from this study aim at informing policy makers on the economic and environmental implications of policy-induced entry. We have analyzed the situation of a spatial monopsonist. However, our results suggest that in searching for stover producers with a low breakeven Supply Response, Marginal Cost, and Soil Erosion Implications of Stover-based Biofuels 17 at::onDecember12,2014http://aepp.oxfordjournals.org/Downloadedfrom
  18. 18. price, optimal pricing may result in a large supplying area. Hence, supply- ing areas of different plants may overlap triggering spatial competition for stover. 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 partial adoption entails relaxing a constraint that could result in changes in stover supply elasticity and land cover configuration within the fuelshed. Other environmental consequences such as carbon emissions associated with stover transportation and potential carbon sequestration benefits of planting cover crops may be important and were also ignored here. Acknowledgments The authors would like to thank two anonymous referees for their helpful com- ments, and extend special thanks to the editor, Terrance Hurley, for thoughtful comments and editorial assistance. References Blanco-Canqui, H., and R. Lal. 2007. Soil and Crop Response to Harvesting Corn Residues for Biofuel Production. Geoderma 141: 355–62. Bonner, I.J., D.J. Muth, Jr., J.B. Koch, and D.L. Karlen. 2014. Modeled Impacts of Cover Crops and Vegetative Barriers on Corn Stover Availability and Soil Quality. BioEnergy Research 7 (2): 576–89. Brechbill, S.C., W.E. Tyner, and K.E. Ileleji. 2011. The Economics of Biomass Collection and Transportation and Its Supply to Indiana Cellulosic and Electric Utility Facilities. BioEnergy Research 4 (2): 141–52. Cruse, R.M., and C.G. Herndl. 2009. Balancing Corn Stover Harvest for Biofuels with Soil and Water Conservation. Journal of Soil and Water Conservation 64 (4): 286–91. Doster, D.H., C.L. Dobbins, and T.W. Griffin. 2009a. B-21 Input Form Guide Book. Department of Agricultural Economics, Purdue University, June 2009. Doster, D.H., C.L. Dobbins, G.F. Patrick, W.A. Miller, and P.V. Preckel, 2009b. Purdue PC-LP Farm Plan. Department of Agricultural Economics, Purdue University, July 2009. Downing, M., L.M. Eaton, R.L. Graham, M.H. Langholtz, R.D. Perlack, A.F. Turhollow, Jr., B. Stokes, and C.C. Brandt. 2011. U.S. Billion-Ton Update: Biomass Supply for a Bioenergy and Bioproducts Industry. No. ORNL/TM-2011/224. Oak Ridge National Laboratory (ORNL). Fiegel, J. 2012. Development of a Viable Corn Stover Market: Impacts on Corn and Soybean Markets. Master’s thesis, Purdue University. Fronning, B.E., K.D. Thelen, and D.H. Min. 2008. Use of Manure, Compost, and Cover Crops to Supplant Crop Residue Carbon in Corn Stover Removed Cropping Systems. Agronomy Journal 100 (6): 1703–10. Gallagher, P., M. Dikeman, J. Fritz, E. Wailes, W. Gauthier, and H. Shapouri. 2003. Supply and Social Cost Estimates for Biomass from Crop Residues in the United States. Environmental and Resource Economics 24: 335–8. Graham, R.L., R. Nelson, J. Sheehan, R.D. Perlack, and L.L. Wright. 2007. Current and Potential U.S. Corn Stover Supplies. Agronomy Journal 99 (1): 1–11. Karlen, D.L., and J.M.F. Johnson. 2014. Crop Residue Considerations for Sustainable Bioenergy Feedstock Supplies. BioEnergy Research 7 (2): 465–67. Kim, S., and B.E. Dale. 2004. Global Potential Bioethanol Production from Waster Crops and Crop Residues. Biomass and Bioenergy 26: 361–75. Applied Economic Perspectives and Policy 18 at::onDecember12,2014http://aepp.oxfordjournals.org/Downloadedfrom
  19. 19. Kim, S., and B. Dale. 2005. Life Cycle Assessment of Various Cropping Systems Utilized for Producing Biofuels: Bioethanol and Biodiesel. Biomass and Bioenergy 29: 426–39. Lindstrom, M.J. 1896. Effects of Residue Harvesting on Water Runoff, Soil Erosion and Nutrient Loss. Agriculture, Ecosystems & Environment 16 (2): 103–12. Lo¨fgren, K.G. 1986. The Spatial Monopsony: A Theoretical Analysis. Journal of Regional Science 26 (4): 707–30. Mann, L., V. Tolbert, and J. Cushman. 2002. Potential Environmental Effects of Corn Stover Removal with Emphasis on Soil Organic Matter and Erosion. Agriculture, Ecosystems & Environment 89 (3): 149–66. McAloon, A., F. Taylor, W. Yee, K. Ibsen, and R. Wooley. 2000. Determining the Cost of Producing Ethanol from Corn Starch and Lignocellulosic Feedstocks. NREL/ TP-580-28893. National Renewable Energy Lab., Golden, CO. Montross, M.D., R. Prewitt, S.A. Shearer, T.S. Stombaugh, S.G. McNeil, and S. Sokhansanj. 2003. Economics of Collection and Transportation of Corn Stover. American Society of Agricultural Engineers Annual International Meeting, No. 036081. Moschini, G., J. Cui, and H. Lapan. 2012. Economics of Biofuels: An Overview of Policies, Impacts and Prospects. Bio-based and Applied Economics 1 (3): 269–96. Muth, D.J., D.S. McCorkle, J.B. Koch, and K.M. Bryden. 2012. Modeling Sustainable Agricultural Residue Removal at the Subfield Scale. Agronomy Journal 104 (4): 970–81. National Research Council. 2011. Renewable Fuel Standard: Potential Economic and Environmental Effects of US Biofuel Policy. Committee on Economic and Environmental Impacts of Increasing Biofuels Production. Washington DC, National Academies Press. Perrin, R., J. Sesmero, K. Wamisho, and D. Bacha. 2012. Biomass Supply Schedules for Great Plains Delivery Points. Biomass and Bioenergy 37: 213–20. Petrolia, D.R. 2008a. The Economics of Harvesting and Transporting Corn Stover for Conversion to Fuel Ethanol: A Case Study for Minnesota. Biomass and Bioenergy 32: 603–12. ———. 2008b. An Analysis of the Relationship between Demand for Corn Stover as an Ethanol Feedstock and Soil Erosion. Review of Agricultural Economics 30 (4): 677–91. Pratt, M. 2012. Synergies Between Cover Crops and Corn Stover Removal. Master’s thesis, Purdue University. Rosburg, A., J. Miranowski, and K. Jacobs. 2013. Cellosic Biofuel Potential Under Land Constraints: Locations, Plant Sizes, and Feedstock Supply Costs. Working paper #13014, Department of Economics, Iowa State University. Thompson, J., and W.E. Tyner. 2014. Corn Stover for Bioenergy Production: Cost Estimates and Farmer Supply Response. Biomass and Bioenergy 62 (2014): 166–73. Thompson, J.L. 2011. Corn Stover for Bioenergy Corn Stover for Bioenergy Production: Cost Estimates and Farmer Supply Response. Master’s thesis, Purdue University. Wang, M. 2005. Updated Energy and Greenhouse Gas Emission Results of Fuel Ethanol. Presented on the 15th International Symposium on Alcohol Fuels. San Diego, CA. Wilhelm, W.W., J.M.F. Johnson, D.L. Karlen, and D.T. Lightle. 2007. Corn Stover to Sustain Soil Organic Carbon Further Constrains Biomass Supply. Agronomy Journal 99: 1665–67. Supply Response, Marginal Cost, and Soil Erosion Implications of Stover-based Biofuels 19 at::onDecember12,2014http://aepp.oxfordjournals.org/Downloadedfrom
  20. 20. Appendix Comparison of Farms in PCLP with Distribution of Farms in the State of 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 with over 1,000 acres (this figure is 53% for the State of Indiana). In our sample 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% in Indiana). That figure increases to 48% in our sample. The average corn yield in Jasper and White counties during the 2007–2010 period was 173 bushels per acre, which is very similar to the average yield in our sample. 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,33 cc = −0.45 (−5.35) + (0.0227)ps (6.06) − (0.00025)p2 s (−4.85) + (0.00000107)p3 s (4.69) (A1) snc,33 cb = −0.57 (−5.15) + (0.021)ps (6.48) − (0.00012)p2 s (−5.64) (A2) scc,75 cc = −0.26 (−10.99) + (0.0078)ps (26.49) (A3) scc,75 cb = −0.55 (−4.83) + (0.02)ps (5.93) − (0.00012)p2 s (−5.35) (A4) scc,50 cc = −0.22 (−11.03) + (0.0061)ps (24.61) (A5) scc,50 cb = −0.53 (−4.63) + (0.0174)ps (5.25) − (0.00009)p2 s (−4.17) , (A6) where sk,r j denotes the share of acres in the supplying area under corn- including rotation j (j = cc for continuous corn and j = cb for corn/soybean rotation), cover crop practice k (k = cc when a cover crop is planted and k = nc when a cover crop is not planted), and removal rate r (33% under no cover crop and either 75% or 50% under cover crop), and ps is the price of stover. Applied Economic Perspectives and Policy 20 at::onDecember12,2014http://aepp.oxfordjournals.org/Downloadedfrom
  21. 21. Stover Harvest Density Using density and yield information from Indiana, the density equations under alternative management practices are as follows: dnc,33 = 474[1.46 ∗ min(max(snc cc ( ps), 0), 1) + 1.53 ∗ 0.5 ∗ min(max(snc cb ( ps), 0)] (A7) dcc,75 =474[3.33∗min(max(scc,75 cc (ps),0),1)+3.48∗0.5∗min(max(scc,75 cb (ps),0),1)] (A8) dcc,50 =474[2.22∗min(max(scc,50 cc (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) is multiplied by participation rates sk,r j (ps), which are defined by (A1)-(A6) in this appendix, and their corresponding stover yields (ync cc ¼ 1.46, ync cb ¼ 1.53, ycc,75 cc ¼ 3.33, ycc,75 cb ¼ 3.48, ycc,50 cc ¼ 2.22, and ycc,50 cb ¼ 2.32), where yk,r j . denotes stover yield under corn-including rotation j, cover crop practice k, and removal rate r. The minimum and maximum operators bound shares between zero and one. Multiplying land suitable for corn by sk,r j (ps) yields the number of acres within the fuelshed allocated to corn-including rotation j, cover crop practice k, and removal rate r. Mplying these figures by their respective 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 on the share of land allocated to different crop rotations including stover removal, sk,r j ( ps). Also note that predicted shares have been bounded between 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 price that a 100 MGY plant would offer for stover by 10% (7% under cover crops and 75% removal), and it also increases marginal costs by 28% (21% under cover crops and 75% removal). Soil erosion increases by 10% (2% under cover 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, % of total) 53 71 87 2,000 or more acres (land in farms, % of total) 27 40 48 Yield (bushels per acre) 161 173 174 Supply Response, Marginal Cost, and Soil Erosion Implications of Stover-based Biofuels 21 at::onDecember12,2014http://aepp.oxfordjournals.org/Downloadedfrom
  22. 22. 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), and reduces 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, the rise in stover price associated with an increase in transportation cost increases the share of land within the fuelshed allocated to continuous corn with removal from 9–12% (5–7% under cover crops and 75% removal) and the 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 from 74–60 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 under continuous corn with removal from 9–5% (from 5–3.5% under cover crops and 75% removal) and increases the size of the fuelshed. The combination of these changes in land use result in a reduction of soil erosion. Table A.2 Impact of Transportation Cost on Key Performance Indicators Transportation Cost Index Stover Price Index Marginal Cost Index Soil Erosion Index No cover crops Cover crops (75% removal) No cover crops Cover crops (75% removal) No cover crops Cover crops (75% removal) 200 110 107 128 121 110 102 50 88 95 80 85 76 81 Note: All indices are calculated relative to figures with estimated transportation cost. For instance, a transportation cost index of 200 indicates a doubling of transportation cost relative to the estimated figure. Similarly, a stover price index of 110 means a 10% increase relative to stover price at the estimated transportation cost. Applied Economic Perspectives and Policy 22 at::onDecember12,2014http://aepp.oxfordjournals.org/Downloadedfrom

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