Agricultural impacts of increased alcohol industry demand for corn: the case of Ohio

Energy in Agriculture, 5 (1986) 211-223 211 Elsevier Science Publishers B.V., Amsterdam - - Printed in The Netherlands

Agricul tural Impacts of Increased Alcohol Industry Demand for Corn: the Case of Ohio

DOUGLAS SOUTHGATE l, S T E P H E N OTT;, NORMAN RASK 1 and FRANCIS WALKER I

1Department of Agricultural Economics and Rural Sociology, The Ohio State University, 2120 Fyffe Road, Columbus, 0H43210 (U.S.A.) '-'Department of Agricultural Economics, Georgia Experiment Station, Experiment, GA 30212 (U.S.A.)

(Accepted 6 March 1986)

ABSTRACT

Southgate, D., Ott, S., Rask, N. and Walker, F., 1986. Agricultural impacts of increased alcohol industry demand for corn: the case of Ohio. Energy Agric., 5:211-223.

A mathematical programming model was used to forecast the impacts on Ohio's agricultural sector of a moderate increase in alcohol industry demand for corn. Because converting corn into ethanol yields byproduct feeds that can substitute for soybean meal and other commodities, most of the alcohol industry's demand for corn can be satisfied by switching land from the production of soybeans to the production of corn. For this reason, projected levels of ethanol production do not greatly affect crop or livestock prices. This finding is likely to hold for other corn-growing a r e a s in North America.

I N T R O D U C T I O N

During the past decade, oil price increases as well as disruptions in petro- leum supplies stimulated efforts to increase domestic production of liquid fuels in the United States. From the outset of the search for alternatives to imported oil, converting agricultural commodities into fuel-grade alcohol (or ethanol) held several attractions. Unlike the production of synthetic fuels from coal, economical manufacture of alcohol did not depend on major technological innovation. Also, after the U.S. Environmental Protection Agency (EPA) began considering proposals to phase out sales of leaded gasoline, using ethanol as an octane enhancer in blended fuels appeared to be an effective energy policy alternative ( Hertzmark et al., 1980).

Whether or not converting corn and other commodities into alcohol also benefits farmers, agribusiness and consumers in the United States has been the subject of some debate. There is general agreement that low levels of ethanol

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production would not greatly affect food prices. Much of the 20 X 109 kg of corn used to produce 7.5 × 109 L of alcohol could be grown on land formerly planted to soybeans. This switch would carry a low opportunity cost because ethanol production yields byproduct feeds that substitute for soybean meal (Hertz- mark et al., 1980; Meekhof et al., 1980).

Estimates of the price impacts created as alcohol production rises above 7.5X 109 L are more varied. Using a simulation model of U.S. agriculture, Meekhof et al. (1980) projected that using 15% of the nation's corn crop to produce 15 × 109 L of alcohol could drive up the price of that commodity by as much as 40%. Other researchers, though, anticipate that corn prices would rise by 15% or less if an alcohol production program of this magnitude were initi- ated ( LeBlanc and Prato, 1983).

The debate over the impacts of ethanol manufacture has consequences both for U.S. energy policy and for U.S. agricultural policy. Corn feedstock accounts for a sizable portion of an alcohol plant's operating costs (Braden et al., 1984 ). If large-scale ethanol production were to result in a major run-up in the price of corn, the alcohol industry would grow more slowly. Alternatively, if alcohol production does not greatly affect commodity prices, then it cannot be regarded as an effective tool of agricultural policy.

These questions about the impacts of ethanol production merit close atten- tion given current developments in the market for alcohol fuels. The U.S. ethanol industry is entering a critical growth phase. Given the EPA plan to decrease lead content in gasoline by 90% during 1986, refineries are choosing among several options for upgrading octane levels in lead-free gasoline. Because ethanol is a competitive option, EPA estimates that demand for that commodity will increase from 2.33X 106 L in 1984 to 3.50X 106 L in 1986 (Herman, 1985).

To determine how U.S. agriculture will be affected by this 50% increase in demand, research into the impacts on agriculture in the state of Ohio of alcohol fuel production was conducted. Ohio provides a representative picture of the response of midwestern U.S. agriculture to development of the ethanol industry. According to Gill and Prato (1983), slightly less than half the existing and planned alcohol production capacity in the United States is located in Ohio and the neighboring states of Illinois, Indiana, Kentucky, and Michigan. The five states produce 40% of the U.S. corn crop. Ohio produces 6% of the nation's corn and has 7% of its existing and planned alcohol production capacity.

The second section of this article contains a brief description of the model. In section three are described the impacts on Ohio's agricultural economy of the development of an alcohol fuels industry. Finally, overviewed in the fourth section are the implications that can be drawn from this study about the effects on the state's and surrounding region's agriculture of increased production of ethanol.

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MATHEMATICAL MODELING METHOD

A mathematical programming model was designed to forecast the effects of shifts in demand and changes in production technology on equilibrium output, prices, and input use in Ohio's agricultural economy. The model's forecast is the equilibrium towards which that economy would move.

Two properties of the model merit special mention at this point. First, when the model was developed, it was assumed that Ohio contributes a constant share of supply to national commodity markets. This 'proportional demand' assumption limits the model's capacity to project the effects of major changes in demand for or supply of commodities. However, assuming proportional demand is appropriate for this study because, as noted above, alcohol fuel development in Ohio has paralleled ethanol industry expansion in neighboring states. Making that assumption is also desirable inasmuch as it allows production and prices of commodities to be projected simultaneously.

Second, an important feature of the model is the way in which substitution between the byproduct feeds of ethanol manufacture and other commodities is handled. In other studies, that substitution, which determines the magnitude of the price impacts generated as alcohol industry corn demand is satisfied, is modelled by assuming a fixed equivalence in animal rations between a given weight of byproduct feeds and a given weight of soybean meal (Meekhof et al., 1980; Webb, 1981; LeBlanc and Prato, 1983 ). By contrast, substitution is identified in this model by searching among various combinations of byproduct feeds and other sources of protein and energy for a set of least-cost livestock rations. This is similar to the approach employed in the 104-region linear programming model Turhollow et al. (1984) used to analyze the effects of alcohol fuel development across the United States.

The detail with which crop and livestock production activities are described in the model used in this study is comparable to the detail found in many farm- scale models. At the same time, the model, like national level models, is price endogenous. Pointing out that this combination of properties allows for improved estimation of impacts generated as corn is converted into alcohol fuel, we now describe the model's objective function, production activities, and restrictions.

Objective function

Competitive equilibrium production (q) of and prices (p) for Ohio's nine principal agricultural products (beef, pork, poultry, eggs, and milk, and exports of corn, wheat, soybeans, and soybean meal) are found by maximizing the following measure of net social payoff (Takayama and Judge, 1964 ) :

1 , z=a'q+~q E q-c'n (1)

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where a and E are the coefficients of the inverted linear demand functions,

p=a+Eq (2)

and c is the vector of constant prices for the input vector, n. Demand for soybean oil is assumed to be perfectly elastic. Demands for all other agricultural commodities are exogenously determined.

To estimate the parameters of the demand equation ( 2 ) and thus the parameters of the first two components of the objective function (equation 1), two assumptions were employed. The first is the proportional demand assumption, described in the second paragraph of this section. The second assumption is that differences between Ohio prices for the four export commodities and prices for those commodities at east coast export terminals equal transportation costs, which are included in the third component of the objective function, c'n. These two assumptions allow for the parameters of the inverted demand functions ( equation 2 ) to be specified as follows:

E = P F Q 1 (3)

were P and Q are 9 x 9 diagonal matrices and F is a matrix of own- and cross- price flexibilities. The diagonal elements of P are average real prices for the nine commodities observed in Ohio since the mid-1970's and the diagonal elements of Q are average Ohio production levels of the same goods during the same period (Ohio Agricultural Statistics, selected annual issues, Columbus, OH). The elements of F were derived from estimates made by Helen (1982) and Ray and Richardson (1978).

This approach to the development of the objective function imposes limi- tations on use of the model, three of which should be mentioned at this point:

(1) Determination of equilibrium prices is complicated by disequilibrium in certain markets (e.g., for milk).

(2) To derive the objective function, a linear demand function was esti- mated with price flexibility estimates obtained from non-linear statistical models. As changes in demand and other events drive markets from the origi- hal equilibrium position, the accuracy of the model's predictions is reduced. Any solution to this second problem would change the objective function from a quadratic form to a more complex nonlinear form. This would, in turn, com- plicate the task of finding a solution to the constrained optimization problem.

(3) As is noted in the introduction, the proportional demand assumption detracts from the accuracy with which the model can simulate the impacts of major changes in commodity markets.

Production activities

Linear (fixed-coefficient) functions describe how capital, labor, energy, and land are combined to produce crops, livestock, and other agricultural outputs.

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FIGURE I

OHIO AGRICULTURAL REGIONS

Fig. 1. Ohio agricultural regions.

The price of each non-land input is assumed to be constant. Costs of production differ across the stae because of variations in the productivity of land.

To capture the diversity of crop production in Ohio, Sitterley (1976) and the Soil Conservation Service's (SCS's) maps of major land resource areas have been consulted to divide the state into seven regions (Fig. 1). Regions 1 and 2 contain most of Ohio's cornbelt counties. In Region 6 and 7, there is a transition from cornbelt agriculture to Appalachia (Regions 4 and 5). Dairy production predominates in Region 3, where soils are not as erosive as soils found further south but are also not as productive as cornbelt soils.

Ten cropping options are included in the model: corn, corn silage, soybeans, wheat, alfalfa, oats, sunflowers and grass hay, along with the two double-cropping options of wheat/soybeans and wheat/sunflowers. (Sunflowers are one of the minor crops for which demand is assumed to be exogenously determined - - see discussion of model's restrictions below.) Labor, machinery, seed, and fertilizer inputs were obtained from Ohio crop enterprise budgets (AERS, 1982a). Four energy inputs (gasoline, diesel fuel, liquid propane gas, and elec- tricity) were obtained both from AERS (1982a) and from FEA (1980). Yields for all options depend on the quality of land inputs. Data compiled by Triplett et al. (1973) and by the SCS were used to divide land resources among five productivity classes.

Soybean milling occurs in Regions 1 through 3, corn milling in Region 2, and wheat flower milling in six of the seven regions. Transportation activities include movement of grain by truck or rail among that state's regions, the transfer of grain to export terminals at Cincinnati and Toledo and the alcohol plant at Southpoint, OH, and the shipment of grain by unit train to the east coast. Transportation costs were specified on the basis of industry interviews.

Input requirements for eight types of livestock/poultry-raising activities were obtained from OSU livestock enterprise budgets (AERS, 1982b) and from

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data bases on energy use in U.S. agriculture (FEA, 1980). Built into this model are alternative rations that include varying mixes of the corn gluten meal and corn gluten feed that are byproducts of wet milling, the distillers' dried grains and solids (DDGS) obtained when the dry milling process is employed, and other commodities (Ott, 1981 ). When the model is run, animals' energy and protein needs are satisfied at least-cost. In addition, optimal substitution of byproduct feeds for soybean meal and other products is identified.

Set of restrictions

The objective function (equation 1) is maximized subject to four types of restrictions:

(1) There are minimum production levels for certain minor outputs (e.g., barley, sunflowers, sheep, etc).

(2) With the exeption of exports and in-state feed demands, all crop shipments are determined exogenously. Out-of-state feed demands are set equal to levels observed during the late 1970's (Hennen et al., 1980). Corn demand from high fructose corn sweetener (HFCS) producers has been identified on the basis of industry interviews and alcohol industry corn demand is exogenously determined (see next section).

(3) To reflect inertial tendencies of livestock producers, each region's livestock production levels were constrained to vary by less than 10% from present levels. In general, these constraints were not binding in the model runs dis- cussed below.

(4) The fourth restriction on agricultural production arises because the stock of land is finite. Shown in Table 1 are the areas in different productivity classes in each of the seven regions. Also shown there are corn yields in each productivity class. Note that the cornbelt regions feature a large and highly productive stock of land. Both cropland areas and yields are lower in Region 3 (the north- east dairy belt) and the two transition regions (Regions 6 and 7). Areas and yields are lowest in Appalachia (Regions 4 and 5), where alcohol production is concentrated.

Model validation

In order to determine how accurately the model simulates performance of Ohio's agricultural economy, a base run, in which alcohol production was held to zero, was compared with actual crop and livestock outputs and prices for the period, 1978 through 1982 ( see Table 2 ). In general, the base run corresponded closely to actual performance. Simulated area planted to the state's three major crops - - corn, soybeans, and wheat - - was only 0.7% greater than average area planted during the period, 1978 through 1982 (3.68 × l0 s ha versus 3.66 × 106 ha). Also, base run crop yields were very close to recent state averages. For

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TABLE 1

Cropland by region and land productivity class (ha)

Region Productivity class Total

1 2 3 4 5

1 488 305 258 93 19 1163 2 439 782 52 158 43 1474 3 13 222 280 141 18 674 4 17 70 121 147 45 400 5 26 61 69 49 55 260 6 69 193 168 19 47 496 7 49 275 58 11 34 427

State 1101 1908 1006 618 Crop yield 8.16 7.22 6.28 5.34 (103 kg/ha)

261 4894 4.39

Sources: SCS data, Triplett et al. (1973), Bureau of Census.

instance, simulated corn production divided by base run corn acreage equalled 6.9 X 10 3 kg/ha, which compares to state-wide averages of between 7.0 and 7.2 X 10 3 kg/ha in three out of the five base-period years (Crop Reporting

TABLE 2

Comparison of base run with actual performance of Ohio's agricultural economy during 1978-1982

Item Actual Base average run 1978-82"

Land use (103 ha) corn 1.53 1.59 soybeans 1.53 1.49 wheat 0.54 0.53 wheat/soybeans - - 0.07 Crop production (109 kg) corn 10.52 10.98 soybeans 13.54 3.69

Commodity prices** ($/kg) ($/kg)

corn 0.12 0.12 soybeans 0.27 0.27 beef 1.37 1.43 pork 1.15 1.10

*Source: Crop Reporting Service. **In 1982 dollars, at farm gate.

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Service). In general, simulated livestock outputs were about 95% of average output

during the period 1978 to 1982. Livestock production has not been highly prof- itable during recent years. Actual production remained higher than it would have been had all costs, not just variable costs, been taken into account. There are some exceptions to this t rend - - poultry, for example. Those exceptions were reflected in the base run by outputs that exceed recent actual production.

Finally, simulated prices were close to the levels that one would observe in a year when both yields and cropped area are normal (Table 2 ). Beef and pork prices were also close to recent levels.

RESULTS

The initiation of alcohol production causes two direct effects on a region's agriculture. First, there is a new outlet for corn. Second, alcohol production increases the supply of byproduct feeds. All farmers in the region are not affected uniformly by these two events. Corn for the new alcohol plants is drawn first from farms located close to alcohol plants. Similarly, those plants seek out local customers for byproduct feeds so that t ransporat ion costs can be reduced.

The impacts of alcohol fuel production do not end with the two direct effects. An increase in demand for corn increases derived demand for agricultural inputs. Land currently used to produce other crops (particularly soybeans) is converted to corn production. Finally, some pasture, forest, and other land not presently devoted to crop agriculture is used to raise corn and other field crops. This last impact is of special interest in Ohio since most of the state 's planned alcohol production capacity is located outside the cornbelt, in southern Ohio (Region 5).

The impacts of ethanol production on crop and livestock production in Ohio can be seen by comparing two runs of the model: (a) the base run, in which there is no alcohol production, and (b) a run in which ethanol output in southern Ohio rises to an annual total of 380 X 106 L, which represents a moderate expansion in the size of the industry. For the latter case, it is assumed that the Ohio alcohol industry obtains its corn inputs from within the state and that the dry milling process is employed. This would be consistent with current industry practice. In addition, the case that 1 X 109 kg of Ohio-grown corn are used to manufacture alcohol merits investigation because expansion in the Ohio industry, which would draw in corn from outside the state, is expected to be matched by expansion in alcohol production capacity in neighboring states. The latter increase would cause Ohio farmers to increase grain shipments across state boundaries.

To produce 380X 106 L, area planted to corn would have to increase by 127 X 103 ha, or by about 8% (Table 3 ). At this level of ethanol output, byproduct feeds would substi tute for soybean meal and a small amount of corn. Sub-

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TABLE3

Simulated land use and agricultural production, with and without ethanol production

Item Base run - - 380X 106 L of no alcohol alcohol produced produced (dry milling process)

Land use (106 ha) corn 1.59 1.72 soybeans 1.49 1.38 wheat 0.53 0.52 wheat/soybean 0.07 0.07 hay 0.23 0.22

Total 3.91 3.91 Crop production (109 kg)

corn produced 2.56 2.54 feed (Ohio) 1.57 1.56 export 5.83 5.62 alcohol - - 1.02 corn sweetener 1.02 1.02 total 10.98 11.75

soybeans produced processed 1.97 1.76 exported 1.09 1.88 Total 3.87 3.65

Livestock numbers dairy cows 347 346 beef 284 280 cow/calf 395 389 swine 219 219

*Difference in shipments of corn from Ohio to other states is accounted for by shipments of DDGS.

s t i tu t ion away f rom s oybean mea l would re lease 107 X 103 ha, which equals 84% of the l and needed to m e e t the alcohol i n d u s t r y ' s d e m a n d for corn. T h e r e m a i n - ing a rea for corn p r o d u c t i o n would come f rom reduc t ions in a rea p l a n t e d to o the r crops and f rom the conve r s ion of p a s t u r e to row crop agr icul ture .

T h e r e is v i r tua l ly no idle c rop l and in the co rnbe l t (i.e., in Regions 1 a n d 2 ). Also, p rac t i ca l ly all agr icu l tura l l and resources the re are used for the in tense p roduc t i on of corn, soybeans , a n d whea t . The re fo re , as is ind ica ted by the esti- m a t e s of regional i m p a c t s r epo r t ed in Tab l e 4, m o s t of the changes in the agri- cu l tura l sec tor needed to a c c o m o d a t e d e v e l o p m e n t of the new indus t ry would be obse rved a t the ex tens ive m a r g i n of row crop agr icul ture . T h e r e would be a cons iderable e x p a n s i o n of a rea p l a n t e d to corn in the v ic in i ty of the a lcohol indust ry . In addi t ion, in Region 6, in which the re is a t r a n s i t i o n f rom the mid- wes te rn cornbe l t to the A p p a l a c h i a n hills, a ma jo r increase in corn p r o d u c t i o n

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TABLE 4

Regional land use impacts of alcohol production*

Region Corn Soybeans Small grains and Pasture

1 0 0 0 0 2 0 0 0 0 3 +23 - 7 0 0 0 4 + 3 0 + 3 - 4 5 + 35 0 - 22 - 10 6 + 30 - 90 + 12 0 7 0 0 + 3 - 1

*Percentage change in area caused by using 1.02 × 109 kg of corn to produce 380 X 10 6 L of alcohol.

would be observed. This would be accomplished principally through a reduction in soybean production.

Of course, these changes in the spatial organization of Ohio agriculture would occur as a result of farmers responding to altered price incentives. Channeling 1X 109 kg of corn to ethanol production would cause the average farm-gate price of corn to rise by less than $0.003/kg. Given the opportunities for substitution between byproduct feeds and feeds derived from soybeans, the reduction in area planted to soybeans would result in a minor increase in the soybean price: approximately $0.01/kg. The most pronounced price impacts would be observed in the vicinity of alcohol plants. In southern Ohio, corn prices would rise by $0.006/kg, or by 5%.

Historically, the area around Ohio's alcohol plants (Region 5) has been a net exporter of grain. After the initiation of alcohol production, demand for corn within the region would exceed local supply. With corn being imported to reduce the local deficit, local producers, who incur a low cost to move their output to market, would secure a locational rent. This locational rent and the $0.006/kg increase in the price of corn observed in Region 5 (which is needed to bring marginal land in the area into corn production) would combine to cause a 25% average increase in local cropland values, which historically have been low.

Since the ethanol industry's demand for corn would not have a major overall impact on grain prices, the costs of meeting the nutritional requirements of livestock and poultry would not be greatly affected. Hence, moderate expansion of the alcohol industry would not cause prices for beef, pork, poultry, eggs, or milk to rise significantly. Neither would production fall by any considerable degree (Table 3 ).

Finally, changes in input use would reflect the fact that corn is being planted on land previously planted to soybeans. Nitrogen use would increase by about 5%, phosphorus use would remain constant, and potash use would decline

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slightly. While total energy employed would remain nearly constant, liquid propane gas use would increase in order to dry the larger corn crop.

CONCLUSIONS

At the levels likely to be observed in Ohio during the next few years, alcohol production will place no great stress on the state's agricultural sector. Byprod- uct feeds yielded by ethanol manufacture will substitute for soybean meal. Accordingly, most of the land needed to satisfy increased demand for corn will be obtained through a reduction in soybean production. Substitution between these two crops is likely to be consentrated in areas located close to ethanol plants. There, the largest changes in corn prices, crop production, and agricultural land values will be observed. Overall, neither crop prices nor livestock prices will change much because 8 percent of the state's corn crop is used to produce fuel-grade alcohol.

These results carry implications for other corn-producing states in the midwestern U.S. Being located close to east coast markets, Ohio's farmers are specializing increasingly in cash grain production. The state's livestock industry is in secular decline. If substitution of byproduct feeds for soybean meal cushions the grain price impacts of alcohol manufacture under these economic circumstances, using a similar share of the corn crop in the western part of the cornbelt, where local feed demand is stronger, cannot be expected to cause major increases in corn and soybean prices. Implicitly, this study corroborates other researchers' findings that 10-15% of the nation's corn crop could be converted to alcohol without greatly disrupting commodity markets (Her tzmark et al., 1980; LeBlanc and Prato, 1983).

Increasing alcohol industry demand for corn far beyond this level, however, would cause more pronounced price impacts. As DDGS accounts for a larger and larger share of cattle rations, it ceases to be a source of additional protein, substituting for soybean meal, and becomes instead a source of energy, substituting for corn. That is, as more DDGS is mixed into cattle feed, its incremen- tal value declines. This, in turn, raises the opportunity costs associated with supplying corn feedstock to the ethanol industry. An earlier study indicates that the trade-offs in the agricultural sector caused by alcohol industry expansion would be more acute if more than 25% of Ohio's corn crop were used to make liquid fuels. Those trade-offs would translate into major increases in commodity prices (Ott, 1981 ).

A concern raised by this study's results is that ethanol production could exacerbate soil loss problems. Erosion will decrease on land that is switched from the cultivation of soybeans to the cultivation of corn. However, even the relatively moderate expansion in corn demand that is the subject of this research would shift outward the extensive margin of midwestern row crop agriculture. Much of the new land brought into production would be more erosive. The

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mode l de sc r ibed in th i s p a p e r has b e e n a l t e r e d t o e s t i m a t e t he i m p a c t s o n soil r e sou rces c a u s e d by e x p a n d e d i n d u s t r i a l d e m a n d for c o r n ( N a b a e e - T a b r i z et al., 1984) .

ACKNOWLEDGEMENTS

T h e r e s e a r c h r e p o r t e d in t h i s a r t i c l e was f u n d e d b y t h e O h i o D e p a r t m e n t o f Ene rgy . Mr. M i c h a e l M c C u l l o u g h a n d Dr . S a e e d N a b a e e - T a b r i z c o n t r i b u t e d to th i s r e s e a r c h effor t . O f course , a n y e r ro r s a n d o m i s s i o n s in t h i s m a n u s c r i p t are t he a u t h o r s ' r e spons ib i l i ty .

REFERENCES

AERS, 1982a. Crop Enterprise Budgets. Department of Agricultural Economics and Rural Soci- ology, Ohio State University, Columbus, OH, 17 pp.

AERS, 1982b. Livestock Enterprise Budgets. Department of Agricultural Economics and Rural Sociology, Ohio State University, Columbus, OH, 24 pp.

Braden, J., Leiner, F. and Wilhour, R., 1984. A financial analysis of an integrated ethanol-livestock production enterprise. North Central J. Agric. Econ., 6: 1-7.

FEA, 1980. Energy and U.S. Agriculture: 1974 Data Base, Vol. I. Federal Energy Administration, Washington, DC, 260 pp.

Gill, M. and Prato, A., 1983. Situation and outlook for alcohol fuels from grain. Economic Research Service, U.S. Department of Agriculture, Washington, DC.

Heien, D.M., 1982. The structure of food demand: Interrelatedness and duality. Am. J. Agric. Econ., 64: 213-221.

Hennen, G., Baldwin, E.D., Larson, D.W. and Sharp, J.W., 1980. Ohio grain flows by mode of transportation and type of grain firms for 1970 and 1977: a comparison. Res. Bull. 1124, Ohio Agricultural Research and Development Center, Wooster, OH, 56 pp.

Herman, M.J., 1985. The Environmental Protection Agency's lead in gasoline rule and its impact on the alcohol fuels industry { address). 1985 Washington Conference on Alcohol, 14 Novem- ber 1985, Arlington, VA.

Hertzmark, D., Flaim, S., Ray, D. and Parvin, G., 1980. Economic feasibility of agricultural production within a biomass system. Am. J. Agric. Econ., 62: 965-971.

LeBlanc, M. and Prato, A., 1983. Ethanol production from grain in the United States: Agricultural impacts and economic feasibility. Can. J. Agric. Econ., 31: 223-232.

Meekhof, R., Tyner, W. and Holland, F.D., 1980. U.S. agricultural policy and gasohol: a policy simulation. Am. J. Agric. Econ., 62: 408-415.

Nabaee-Tabriz, S., Rask, N. and Southgate, D., 1984. The impacts of increased industrial demand for corn on the Midwest's land resources. Selected Paper, Annual Meeting of the American Agricultural Economics Association, Ithaca, NY.

Ott, S., 1981. The economics of producing fuel-grade alcohol from corn in midwestern Ohio. Ph.D. Diss., Department of Agricultural Economics and Rural Sociology, Ohio State University, Columbus, OH, 161 pp.

Ray, D.E. and Richardson, J.W., 1978. Detailed description of polysim. Tech. Bull. T-151, Okla- homa State University Agricultural Experiment Station, Stillwater, OK, 159 pp.

Sitterly, J.H., 1976. Land use in Ohio, 1900-1970. Res. Bull. 1084, Ohio Agricultural Research and Development Center, Wooster, OH, 92 pp.

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Takayama, T. and Judge, G.C., 1964. Spatial equilibrium and quadratic programming, J. Farm Econ., 46: 67-93.

Triplett, G.B., Jr., Van Doren, D.M., Jr. and Bone, S.W., 1973. An evaluation of Ohio soils in relation to no-tillage corn production. Res. Bull. 1068, Ohio Agricultural Research and Devel- opment Center, Wooster, OH, 20 pp.

Turhollow, A.F., Jr., Christensen, D.A. and Heady, E.O., 1984. The potential impacts of large- scale fuel alcohol production from corn, grain sorghum, and crop residues under varying tech- nologies and crop export levels. CARD Rep. 126, Center for Agricultural and Rural Develop- ment, Iowa State University, Ames, IA, 89 pp.

Webb, S., 1981. The impact of increased alcohol production on the agricultural sector: a simulation study. Am. J. Agric. Econ., 63: 532-537.

Agricultural impacts of increased alcohol industry demand for corn: the case of Ohio

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