Energy in Agriculture, 2 (1983) 307--317 307 Elsevier Science Publishers B.V., Amsterdam -- Printed in The Netherlands

P R O J E C T I N G F O O D - F U E L C O N F L I C T S R E S U L T I N G F R O M BIOMASS E N E R G Y D E V E L O P M E N T IN O H I O

DOUGLAS SOUTHGATE', NORMAN RASK', THOMAS RYAN ~ and STEPHEN L. OTT 3

' Department of Agricultural Economics and Rural Sociology, The Ohio State University, 2120 Fyffe Road, Columbus, OH 43210 (U.S.A.)

Ohio Department of Energy, Columbus, OH 43215 (U.S.A.)

3 Department of Agricultural Economics and Education, California State University, Fresno, CA 93 740 (U.S.A.)

(Accepted 8 April 1983)

ABSTRACT

Southgate, D., Rask, N. and Ott, S.L., 1983. Projecting food-fuel conflicts resulting from biomass energy development in Ohio. Energy Agric., 2: 307--317.

This paper describes research intended to forecast the impacts of alcohol fuel produc- tion on the agricultural economy of the State of Ohio. The mathematical programming techniques used to make projections of state-wide impacts are discussed. Also presented are some results of a model designed to identify the effects of alcohol fuel production on Ohio's cornbelt region. In addition to discussing the structure of the state-wide and cornbelt region models, this paper suggests how the models can be used to quantify complementarities and trade-offs existing between alcohol production goals and land use goals.

INTRODUCTION

Ohio , an industr ia l s ta te w i t h o u t ma jo r oil and gas depos i t s , is heavi ly de- p e n d e n t o n fossil fuels p r o d u c e d ou ts ide its borders . The Ohio D e p a r t m e n t o f Energy ( O D O E ) es t imates t h a t a p p r o x i m a t e l y 10% of the gross s ta te pro- d u c t is used to p a y fo r oil, gas and coal i m p o r t e d f r o m o the r par ts o f the Un i t ed S ta tes and f r o m abroad . F u r t h e r m o r e , d e p e n d e n c e on i m p o r t e d fossil fuels renders the s ta te vu lnerable to supp ly in te r rup t ions . In add i t ion to the shor tages i nduced b y the 1 9 7 3 / 1 9 7 4 arab oil e m b a r g o , Ohio ' s na tura l gas deliveries were severely d i s rup ted dur ing the win te r o f 1 9 7 6 / 1 9 7 7 .

One way to r educe the costs associa ted wi th rel iance on i m p o r t e d fossil fuels is to conve r t l oca l ly -p roduced corn in to a lcohol fuels. Given Ohio ' s c apac i t y to p r o d u c e co rn and given its coal resources , which can be used to p o w e r a lcohol p lan ts , p r o d u c i n g syn the t i c fuel f r o m grain would seem to be an a t t rac t ive o p t i o n for the s ta te . The c o m m i t m e n t to m a n u f a c t u r e large quan t i t i e s o f a lcohol fuels wou ld , however , involve s ignif icant t rade-of fs .

0167-5826/83/$03.00 © 1983 Elsevier Science Publishers B.V.

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Assuming that alcohol production trends in Ohio would parallel production trends in other cornbelt states, the reallocation of agricultrual resources to meet alcohol industry corn demand would result in higher prices for all grains. Increasing feed prices would, in turn, drive up livestock prices. The latter effect would be partially mitigated by increasing supplies of distiller's dried grain and solubles (DDGS) and other livestock feeds, which are by- products of alcohol production processes.

In order to define better the trade-offs implied by large-scale alcohol fuel production, ODOE has contracted with The Department of Agricultural Eco- nomics and Rural Sociology (AERS) of The Ohio State University to con- struct an integrated model of Ohio's agricultural economy. This model will simulate the impacts of different levels of alcohol manufacture in Ohio on grain prices, crop outputs, and livestock production in different parts of the state. These impacts will be forecasted under various scenarios which con- sider, inter alia, non-biomass energy (i.e., food and feed) demand for Ohio- grown crops, prices of energy and inputs derived from petroleum, and public policy.

This paper contains a general description of the ODOE model along with the kinds of information it will generate. The following section identifies both the research questions to which the model's analysis is addressed and the methodology the model employs. Then some preliminary results of a model of Ohio's cornbelt region are reported. This model, which employs much of the methodology to be used in the ODOE model, simulates the im- pacts on western and northwestern Ohio of biomass energy development. Finally, the conclusions identify how the ODOE model could be extended to assess a broader range of impacts, both inside and outside the state.

ODOE MODEL

The array of changes expected to occur if corn is used as a feedstock for energy production has been alluded to above. A great deal of research into impacts (especially price impacts) occurring at the national level has been completed (Sanderson, 1981).

ODOE sources indicate that between 380 and 570 million 1 of alcohol pro- duction capacity is scheduled to come on line in Ohio during the next 2--3 years. Assuming that 2.7 kg of corn are needed to produce 1.0 1 of alcohol (Sol. Energy Res. Inst., 1980, p. 45), Ohio alcohol industry corn require- ments will equal 10--15% of the current Ohio corn harvest (Ohio Crop Rep. Serv., 1981, p. 5).

There are a number of ways to meet this added demand for corn. More of that commodi ty can be imported from neighboring states. It might not be possible to service the Ohio industry in this way, though, because biomass energy development in Indiana, Illinois and other midwestern states will aug- ment local demand for those states' corn output . Aside from the option of importing corn, the Ohio alcohol industry can satisfy its corn demand from

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within the state. This could be done in two ways. Land currently being used to grow other crops could be switched to the production of corn. Alterna- tively, land not currently being used for crop production could be planted in corn.

Significant changes in commodi ty prices would be required before either of the latter two adjustments would be observed. In order to see cropland switched from the production of soybeans, wheat, alfalfa hay, and other crops to the production of corn, the per-acre returns of raising corn would have to rise relative to per-acre returns of raising those other crops. Similarly, before pasture-, forest-, or other land is converted to corn production, per- acre corn returns would have to rise relative to the per-acre returns of the goods and services now being produced on that land.

Another set of adjustments in Ohio agriculture would result from the in- creased supplies of DDGS and other livestock feeds (including corn oil and corn gluten feed and meal). Because these feeds are partial or full substitutes for soybean products, the presence of these new supplies offsets some of the oppor tuni ty costs associated with switching lands to corn production. In ad- dition, the cost involved in shipping these feed byproducts provides strong incentives for locating feeding facilities close to alcohol plants. Of course, the extent to which byproduct supplies can either reduce the opportuni ty costs implied by increased com output or promote local livestock produc- tion is limited by the degree to which those byproducts can be substituted in animal diets for soybean products. The choice among alcohol production processes will determine in large part this degree of substitutability (Ott and Rask, 1981).

The pattern of agricultural adjustment to alcohol fuel production will vary from region to region in Ohio. In the cornbelt, which occupies approximate- ly one-third of the state (see Fig. 1), most available land that is suitable for growing crops is employed in that purpose (Sitterley, 1976, 23--46). Accord- ingly, the main feature of the adjustment process in that region will consist of corn production expanding at the expense of output of other crops. On the other hand, in southeastern Ohio, where most of the state's alcohol pro- duction capacity will be located (see Fig. 1), biomass energy development will reverse a long, declining trend in crop production (Sitterley, 1976, pp. 71--83). Also, the presence of alcohol plants will stimulate livestock raising in that area.

How agriculture in different regions of Ohio will respond to the opportun- ity and trade-offs posed by biomass energy development can be forecasted with the aid of the mathematical programming model to be developed for ODOE. The remainder of this section is devoted to explaining the general form of that model, sources of data used to specify the model, and how it can quantify the trade-offs outlined above.

In general, mathematical programming involves setting up a problem in order to identify a mix of activities (processes that convert inputs into out- puts) that maximizes or minimizes an objective function subject to a set of

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Fig. 1. Planned corn ethanol plants, September 1981 (M, million 1 per year)

constraints. Let us consider, in order, the three elements of the ODOE model: (a) the objective function; (b) the matrix of productive activities; and (c) the land resource constraint.

The form of this model's objective function is of the type proposed by Takayama and Judge (1964, p. 73) for spatial equilibrium problems in which prices as well as production and transportation of commodities are endo- geneously determined. The price endogeneity of the objective function re- flects the assumption of proportional demand -- that Ohio accounts for a constant percentage of national supplies of agricultural products. If this as- sumption were dropped (which in this case would amount to assuming that neighboring states' agricultural economies would not adjust to local alcohol fuel development in the same way that Ohio's agricultural economy does), demand for Ohio's output would be perfectly elastic.

A variety of nonlinear programming packages could be used to obtain a solution to the problem (price endogeneity renders the model nonlinear). Howeve'r, the "grid linearization" technique proposed by Miller (1963) will be employed to convert the objective function into a series of linear activi- ties. Duloy and Norton (1975) have used this approach, which allows for the solution of price-endogeneous spatial equilibrium models to be obtained by using the highly efficient Simplex Method.

The second element of information needed for the ODOE model is the matrix that describes how restricted inputs (i.e., land resources) and unre-

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stricted inputs (labor, machinery, and energy) are combined to form out- puts, both in Ohio agriculture and in the emerging alcohol industry. By as- suming that all production is characterized by constant returns to scale, each activity will feature proportionate relationships between employment of inputs and output yields. In addition, all inputs will be expressed in physi- cal, rather than value terms. Thus, any user of the ODOE model will be able to project alcohol production impacts using the set of constant dollar prices that he or she feels most accurately represents economic conditions.

Data used for specification of the ODOE model's activity matrix will come from a variety of sources. Crop, dairy, and livestock budgets prepared by AERS researchers will be used to identify labor and machinery inputs for agricultural activities. In order to capture some of the diversity of Ohio agri- culture, the model will divide the state into three regions: (a) the western/ northwestern cornbelt, in which crop agriculture predominates; ( b ) t h e northeast, where dairying activity is concentrated; and (c) the southeast, an unglaciated area that is covered mostly by grazing land and forests. Scientists from the Ohio State University Department of Agronomy will be consulted in order to specify crop yields associated with different rotations practiced in each of these areas. The costs of transporting grain from each region to terminals and alcohol production plants as well as the cost of shipping DDGS and other byproducts from the plants to each region will be determined on the basis of research currently being conducted by AERS scientists. I npu t - output relationships in the alcohol industry will be specified on the basis of material flow data for several types of manufacturing processes.

Finally, energy will be treated as a separate class of input in this model. Both energy consumed in a direct form (i.e., diesel fuel, gas and electricity in the case of farms and coal in the case of alcohol plants) as well as the energy used to produce fertilizers and chemicals used on the farm will be accounted for. The 1974 Agricultural Energy Data Base (Fed. Energy Adm., 1977, p. 183) will be consulted in order to identify the gallons of liquid fuel, cubic feet of natural gas, and kilowatt hours of natural gas required for each type of agricultural activity.

The third element of the ODOE model will be the constraint set. Aside from the requirement that a certain quanti ty of corn be delivered to the alcohol industry (the program user will be able to vary that quantity), the constraint set consists of the limits on agricultural production existing be- cause of the natural productivity of the state's soils. These natural resource production constraints will be identified on the basis of information con- tained in the 1978 Census of Agriculture (Bur. Census, 1980) as well as on the basis of Soil Conservation Service (SCS) data on the state's "major land resource areas".

Given what the activity matrix and land resource constraints say about production possibilities in different parts of Ohio as well as what the objec- tive funct ion tells us about the price effects following from changes in delive- ries of Ohio-produced crops to traditional markets, the ODOE model will be

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able to identify an optimal pattern of adjustment to alcohol industry's de- mand for corn and supply of livestock feeds. By varying price parameters, the sensitivity of these forecasts to, for example, changes in energy prices or increases in export demand for Ohio-grown crops can be demonstrated.

One use of the model will be a quantitative analysis of the interfaces exist- ing between public policies that encourage production and use of alcohol fuels (e.g., exemption of gasohol from state gasoline taxes, a 10% investment tax credit for alcohol plant construction undertaken before 1985, etc.) and existing or proposed policies that either affect traditional demand for crops or affect crop production. Included among the latter set of policies are rules or incentives intended to contain soil erosion. If that goal were to be reached in southeastern Ohio, that region's ability to meet local alcohol plant's corn demand might be limited. Similarly, policies that promote or inhibit popula- t ion growth in the cornbelt 's SMSA's will affect agricultural production po- tential in that area. By varying land resource constraints, possible comple- mentarities and conflicts between alcohol production goals and soil conserva- t ion and land use planning goals can be identified.

The following section of this paper describes the results of this type of exercise. A mathematical programming model of Ohio's cornbelt region has been used to simulate agricultural adjustments to the emergence of an alco- hol industry under a variety of assumptions regarding prices, land availabil- ity, and other economic variables.

RESULTS OF THE MODEL OF OHIO'S CORNBELT REGION

Ott has developed a quadratic programming (~4P) model of the agricultural economy of Ohio's cornbelt region (Ott and Rask, 1981). He has used it to forecast the effects on allocation of land resources and on food and feed prices of different levels of alcohol industry demand for the region's corn. This section briefly describes the model and reviews some of its major fore- casts.

The approach recommended by Meister et al. to obtain a QP model from a "primal-dual" mathematical program has been used (Meister et al., 1978, pp. 7--23). In order to identify the QP objective function, the assumption of proportional demand referred to above has been employed. The demand elasticities for livestock feeds contained in the POLYSIM model (Ray and Richardson, 1975, pp. 25--32) and George and King's (1971, pp. 53--66) estimates of retail-level food price elasticities are then used to simulate grain price effects.

The cornbelt model's activity matrix describes how one restricted input (land) and three types of unrestricted inputs (energy, labor and capital) are combined to produce agricultural products and alcohol. Land availability is specified on the basis of the 1978 Census of Agriculture (Bur. Census, 1980). Energy has been expressed in physical terms in order to facilitate sensitivity analyses. Labor and capital inputs are expressed in constant dollar equiva- lents.

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In o r d e r t o keep the m o d e l f r o m choos ing a c o r n e r s o l u t i o n , in which the r eg ion spec ia l izes in one or t w o ac t iv i t ies , a n u m b e r o f c o n s t r a i n t s have been bu i l t in to t he m o d e l . By inc lud ing hay in da i ry and l ives tock r a t ions , the m o d e l s imu la t e s t h a t some m i n i m u m a m o u n t o f hay will be p r o d u c e d . Also, a series of c o n s t r a i n t s l imi t s the degree to which D D G S can be s u b s t i t u t e d fo r s o y b e a n p r o d u c t s in l i ves tock ra t ions . The a l fa l fa and feed ing r a t i o n con- s t r a in t s l imi t t he a m o u n t o f c r o p l a n d p l aced in to c o n t i n u o u s co rn r o t a t i o n and t h e r e b y h e i g h t e n the i m p a c t on co rn pr ices o f a l c oho l fuel p r o d u c t i o n .

In o r d e r to ver i fy t he m o d e l ' s a ccu racy , a base run was m a d e in which al- c o h o l i n d u s t r y d e m a n d fo r co rn was he ld equa l to ze ro . A perusa l o f Table I i nd ica t e s t h a t , w i th t he e x c e p t i o n o f h a y , t he m o d e l s i m u l a t e d c rop and live-

TABLEI

Base model compared with three-year-averages of actual production and prices, 1977--1979

Commodity Base model 3-year average model/3-year average

Quantity Beef (million kg) 126.53 126.73 1.00 Pork (million kg) 223.12 226.5 0.99 Lamb (million kg) 4.34 4.29 1.01 Chicken (million kg) 16.31 15.92 1.02 Turkey (million kg) 22.49 22.13 1.02 Eggs (million) 1 465 1 443 1.02 Milk (million kg) 705.80 720.63 0.98 Corn grain (ha) 1 032 849 1 015 067 1.02 Corn silage (ha) 82 543 45 932 1.80 Soybean (ha) 1 242 718 1 234 264 1.01 Wheat (ha) 423 578 422 177 1.00 Oats (ha) 60 479 64 832 0.93 Total hay (ha) 79 937 170 896 0.47 Total acreage (ha) 2 922 106 2 953 170 0.99

Price Beef ($/kg) 1.78 1.19 1.50 Pork ($/kg) 1.12 1.04 1.08 Lamb ($/kg) 3.06 1.46 2.10 Chicken ($/kg) 0.59 0.60 0.98 Turkey ($/kg) 0.95 0.97 0.98 Eggs (S/dozen) 0.58 0.61 0.96 Milk ($/kg) 0.31 0.26 1.19 Corn ($/kg) 0.105 0.098 0.93 Soybeans ($/kg) 0.244 0.254 0.96 Wheat ($/kg) 0.160 0.121 1.32 Oats ($/kg) 0.165 0.092 1.79 Soybean meal ($/kg) 0.21 0.22 0.95 Soybean oil ($/kg) 0.58 0.62 0.94 Cropland rent (S/ha) 216.39 219.08 0.99

Sources: Ohio Crop Rep. Serv., (1981)

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s tock p r o d u c t i o n wi th in 10% of the average levels observed dur ing the years , 1977 t h r o u g h 1979. F u r t h e r m o r e , the prices s imula ted by the mode l ap- p r o x i m a t e d fairly well prices observed for the principal f o o d p ro d u c t s and agricul tural land in the region.

Table II r epor t s some impacts on c rop o r o d u c t i o n and prices observed whe n the a lcohol indus t ry util izes d i f fe ren t a m o u n t s o f the region 's corn c rop . The a m o u n t s co r r e spond to fou r levels o f a lcohol o u t p u t : 0 .3785 , 0 .757 , 1 .136 and 1 .514 X 109 1 (100, 200, 300 and 400 mill ion gal). Using the convers ion ra t io c i ted above (i.e., tha t 2.7 kg o f co rn yield 1.0 1 o f alco- hol) , these o u t p u t levels would require rough ly 1 .022, 2 .044, 3 .047 and 4 .088 × 109 kg of corn , respect ively .

TABLE II

Effects of alcohol fuel production on crop production and prices -- Base alcohol fuel model

Commodity Alcohol fuel production level (109 l)

0.3785 0.757 1.136 1.514

Percent change in production

Corn grain +12.6 +23.7 +34.6 +42.0 Soybeans -10.1 - 18.3 -25.5 -27.9 Wheat -0.7 -2.5 -6.0 -11.9 Oats -1.4 -7.0 -15.9 -36.1 Corn silage -- -0.5 -1.1 -2.6 Hay -0.8 -2.7 -5.7 -17.0

Percent change in price

Corn grain +1.1 +3.4 +9.4 +21.4 Soybeans +1.2 +4.7 +11.3 +25.9

The co rnbe l t mode l co r robo ra t e s m u c h o f what has been wr i t t en a b o u t the ef fec ts o f a lcohol fuel deve lopmen t . Land and o th e r resources are di- ve r ted f r om the p r o d u c t i o n o f soybeans , hay and o th e r crops to the p roduc- t ion o f corn . Diversion would be even greater if the feeding rations, and hay acreage cons t ra in ts were n o t in place. Biomass energy d e v e l o p m e n t also en- tails s ignif icant price effects . Corn prices rise with increases in a lcohol in- dus t ry d e m a n d fo r t ha t c o m m o d i t y . Also, the upward pressure on soybean prices caused by the supply decreases t ha t occu r when land is swi tched f ro m soybean to co rn p r o d u c t i o n more than counter -ba lances the d o w n w a r d pres- sure on prices resul t ing f rom increased availabili ty o f DDGS. Finally, Table III indicates t ha t c rop land values rise in response to the increasing prices in grains.

One o f the mode l ' s results t ha t seems puzzl ing at first glance is tha t soy-

TABLE III

Land value increases accompanying different levels of alcohol production

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Alcohol production level (109 l)

Increase (%) in land value

0.3785 3 0.757 12 1.136 30 1.514 68

bean pr ices exh ib i t sha rper relat ive rises t h a n do co rn prices. This wou ld seem to be incons i s t en t wi th the increases in co rn p r o d u c t i o n and decreases in s o y b e a n o u t p u t a c c o m p a n y i n g increased a lcohol o u t p u t . However , this resul t is u n d e r s t o o d b y cons ider ing the e f fec t s o f increasing land values (see Tab le I I I ) . The pe rcen tage o f s o y b e a n p r o d u c t i o n costs a c c o u n t e d fo r by land ren ts exceeds the pe r cen t age o f corn p r o d u c t i o n costs a c c o u n t e d for by t h a t expense . Given tha t land values increase a t a g rea te r ra te t h a n e i ther co rn or s o y b e a n pr ices as a resul t o f e x p a n d e d a lcohol p r o d u c t i o n , the per- acre marg ins assoc ia ted with corn p r o d u c t i o n rise relat ive to per -acre soy- bean margins . This, in tu rn , encourages the swi tch o f resources to corn crops .

Final ly , Tab le IV r epo r t s some o f the sensi t iv i ty analyses p e r f o r m e d with t he c o r n b e l t mode l . A series o f pr ice indices r e p o r t e d the re show the change in pr ices obse rved as the d e m a n d and supp ly cond i t i ons prevai l ing original ly in t he c o r n b e l t agr icul tural e c o n o m y are mod i f i ed . As does Tab le II , the first r o w o f Tab le IV shows the f o o d pr ice i m p a c t s o f increasing a lcohol indus t ry d e m a n d . The n e x t t h ree rows show t h a t t hose i m p a c t s are ame l io r a t ed as m o r e c r o p l a n d is b r o u g h t in to p r o d u c t i o n . Final ly , the last t w o rows indica te

TABLE IV

Food price indexes for various models and alcohol fuel production levels -- Base model-100

Model Alcohol fuel production level (109 l)

0.3785 0.757 1.136 1.514

Base alcohol fuel I 100.1 102.0 104.7 112.1

Increased cropland +1% II-A 99.6 100.8 103.1 110.4 +3% II-B 99.6 100.4 101.0 105.4 +5% II-C 99.6 100.4 100.0 102.1

Increased export demand +10% III-A 105.7 108.3 111.7 122.1 +20% III-B 113.0 115.6 119.0 134.8

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how food price effects are exacerbated if shipments outside the United States are expanded. In this table, an X% increase in exports is taken to mean that foreign demand for Ohio cornbelt grains increases sufficiently to produce an X% boost in quant i ty demanded at old market prices.

CONCLUSIONS

The model discussed in the previous section provides some interesting forecasts of the impacts of biomass energy development on Ohio's cornbelt . It shows that alcohol industry corn demand would push up prices for the principal crops (corn and soybeans) of the Midwest. Reacting to changing produc t and land prices, farzners in the Ohio cornbelt , like farmers in neigh- boring states, would switch land from the product ion of soybeans, hay, and other crops to the product ion of corn.

That the regional model can be used to identify major impacts on the cornbelt 's agricultural economy resulting from the emergence of an alcohol fuels industry promps an interest in studying the same industry 's effects in o ther parts of the state. Plans to locate a number of alcohol plants in south- eastern Ohio suggest that that area's agriculture might experience even more dramatic changes. Clearly, there is a need to predict those changes.

Construct ion of a model for the state of Ohio should also aid multi-state and national level research into the effects of biomass energy development. Because of the heterogenei ty of its agriculture and natural resources, Ohio serves as an ideal laboratory for gauging the impacts of this new phenom- enon. Hopeful ly , in modelling different types of agricultural activities and resource supply conditions, this research effor t will develop some insights that will aid similar undertakings.

Finally, it can be anticipated that there will be many ways to improve the ODOE model. It will be possible to improve specification of the activity matrix, resource constraints, and objective function. More important ly , per- haps, with some modificat ion, the model 's capacity to quantify trade-offs and complementari t ies existing between alcohol product ion, on the one hand, and changes in public policy and economic conditions on the other, can be enhanced. As pointed out above, there is a need to express in quanti- tative terms the interfaces between policies that affect the product ion po- tential of Ohio's soil resources and the policies that encourage biomass ener- gy development in the state.

It is our hope, then, that the ODOE model will provide useful information about the effects of alcohol fuel product ion on Ohio's agricultural economy and will stimulate research into a broad range of policy questions.

R E F E R E N C E S

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

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