Exogenizing Agriculture in an Input-Output Model to...

Exogenizing Agriculture in anInput-Output Model to Estimate

Relative Impacts of Different Farm Types

Thomas G. Johnson and Surendra N. Kulshreshtha

In this study, aggregate, provincial level impact for various farm types are estimatedfor Saskatchewan based on an input-output table constructed for the province. Theinput-output table is rectangular with the agriculture sector including 12 farm subsec-tors, treated exogenously. Results indicate that in 1978 agriculture contributed 13.8percent of the provincial gross domestic product directly, and another 18.2 percentindirectly. Among the farm types, the grain farms generated the highest outputmultipliers while cow-calf, dairy and irrigation generated the lowest. The income andvalue added pseudo-multipliers were almost a complete reversal of the output multi-pliers. Although irrigation generated low pseudo-multipliers, the dairy and cow-calfsectors generated higher pseudo-multipliers.

Agricultural policy in Saskatchewan hasoften had as its objective, the diversificationof the agricultural sector. 1 This objective sug-gests a change in the enterprise mix of agri-culture. Until recently, this policy, and oth-ers, such as intensification, development ofnew crops, irrigation development, etc.,have been designed on the basis of micro-level economic analyses of the enterprisesinvolved, but have ignored the aggregate

Thomas G. Johnson is an assistant professor in theDepartment of Agricultural Economics at VirginiaPolytechnic Institute and State University, and Surend-ra N. Kulshreshtha is a professor in the Department ofAgricultural Economics at the University of Saskatche-wan, Saskatoon, Canada. Agricultural Economics Con-tribution Number 1034.

The authors wish to thank Professor K. Rosaasen of theAgricultural Economics Department for his commentsand suggestions on an earlier draft. Also valuable sugges-tions were received from three anonymous reviewers.The authors accept sole responsibility for any remainingerrors and ommissions.

'Although diversification in the context of agriculturerefers to a growing proportion of non-grain enterprises,in the overall context, this also implies diversificationof the primary and secondary production.

economic impact of resulting changes in theenterprise composition of the industry. Sinceeach farm enterprise will, in general, haveunique interrelationships with other sectorsof the economy, changes in the enterprisemix should generate different levels ofeconomic activity in the province.

The objective of the research reported inthis paper was to determine the relative im-pact of different farm types on the provincialeconomy. Information such as this can beused (and has been used) to determine theeffects of irrigation development, droughts,changes in energy prices, and various ag-ricultural policies [Johnson and Kulshresht-ha; Thomas G. Johnson; Kulshreshtha,Tewari, and Johnson]. In this study, theaforementioned impacts were estimated byemploying input-output analysis.

Input-output analysis is ideally suited tothe analysis of economic impacts of changingfinal demands. Sectoral output multipliersindicate the relative impact of changes infinal demand for the various products of aneconomy. However, when the analysis re-lates not to a change in final demand butrather to the impact of intra-sectoral changes,such as a change in the mix of farm types,

187

Western Journal of Agricultural Economics

input-output analysis is somewhat awkwardto use. The problem is comparable to thataddressed by Petkovich and Ching, in whichthe level of sectoral output is, because ofsome exogenous constraint, predeterminedrather than simultaneously determined byfinal and intermediate demand. Petkovichand Ching have suggested a linear pro-gramming solution of the input-output modelas one method of handling constraints onsectoral output. The problem addressed inthe present study is somewhat more generalin that it involves any case in which sectoraloutput and its expenditure pattern is prede-termined. The study employs a more directmethod of incorporating changes in sectoraloutput. The approach involves the redefini-tion of the sector in question as a final de-mand sector rather than an endogenous sec-tor. This approach is employed in this studyto estimate the differential impacts of variousfarm types on the Saskatchewan economy.

Objectives and Scope of the Study

The primary objective of this study was todesign an analysis capable of estimating theimpact of change in agricultural enterprisemix on the aggregate economy of Saskatche-wan. The impact of a change was assessed byestimating both the direct and indirect (in-cluding induced) effects of different types offarms. This objective was carried out with thehelp of a 1974 transactions matrix for theprovince. 2 The model is rectangular3 with 59endogenous sectors and 73 commodities. Thehousehold sector is endogenous and the ag-ricultural sector is exogenous.

2 The Canadian input-output model is interregional withtables for each province based on what is essentially acensus of firms. The transactions upon which thismodel is based were all those involving Saskatchewanfirms. The model is, therefore, a survey (as opposed tonon-survey) model of the Saskatchewan economy. The1974 transactions data were aggregated in such a wayas to reflect the Saskatchewan economy which is quitedifferent from the total Canadian economy. Resourceand agricultural related industries are highlighted inthe model while manufacturing and most service sec-tors are quite highly aggregated.

188

The Model

In a typical economy, the agriculture in-dustry relies on other sectors in at least twoways: (1) it procures, from the economy,certain farm inputs, such as fertilizer,machinery, labor, etc. and (2) it providesinputs to non-agricultural industries. Al-though it may be easy to visualize the directchanges in the economic health of the ag-ricultural industry as a result of a policymeasure, it is not as easy to visualize theeffects that these direct changes in the indus-try may subsequently have on other sectors.For example, if a government program isinitiated which encourages the establishmentof certain intensive livestock operations, theeffects on producers and the industry arereadily identifiable. The benefits and costs toother sectors or to households are not asobvious. The answers to questions of indirectimpacts lie in an understanding of the in-tersectoral relationships in an economy.

The Rectangular Input-Output Framework

The primary objective of the study wascarried out with the help of a 1974 transac-tion matrix for the province of Saskatchewanwhich basically describes the flow of com-modities from one sector to another. Therectangular input-output model differs fromthe square model in that sectors and com-modities are identified separately with norequirement regarding the correspondencebetween the two classifications. The modelmay recognize any number of commodities- either greater than, less than or equal tothe number of sectors. The important differ-ence between the square and rectangularmodels is that in the latter, any industry mayproduce a positive level of any commodity.

The rectangular model is based on thefollowing accounting equations:

3The rows in the input-output tables are the com-modities being bought whereas the columns stand forindustries or sectors in the provincial economy. Formore details on the rectangular system, see StatisticsCanada, or Chossudousky.

December 1982

Johnson and Kulshreshtha

(6) g=(I- DB)-1De.

where,

q = m x 1 vector of the values of totalcommodity output,

B = m x n matrix of industry technologycoefficients (value of commodity in-puts per $1 of industry output),

g = n x 1 vector of the value of total sec-toral (industry) outputs,

e = mxl vector of final demand (lessimports),

m = number of commodities,n = number of industries.

Equation (1) requires that total output equalsthe sum of intermediate and final demand.The difference is that B relates output levelsof industries to intermediate demands forcommodities.

Commodity output levels are further re-lated by the market shares equation,

(2) g=Dq,

where,D = n X m matrix of market share coeffi-

cientsThe matrix D relates the output levels ofindustries to the sum of its share of eachcommodity,

(3) gi = di, ql + di2 q2

+. .. +dim qm. (i=,. . ,n)

Substituting equation (2) into equation (1)gives

(4) q=BDq+e

which has the solution,

Using the above solutions two multipliermatrices can be defined. From equation (5),one obtains

(7.1) Mc=(I-BD)-1

and from equation (6), one has

(7.2) MI = (I-DB) -D.

The matrix Mc contains the direct plusindirect effects on each commodity of a onedollar change in demand for each commodi-ty. Thus, the element mcij is the direct plusindirect output of commodity i required toproduce one dollar of commodity j for finaldemand. Similarly miij in the matrix MI is thedirect plus indirect output of sector i re-quired to produce one dollar of commodity j.

The major advantage of such a model isthat it allows the industrial definitions to bedeveloped independently of considerationsabout the commodities produced. 4 Thisframework is particularly useful for treatingthe agriculture industry which can be bestviewed as a multi-product industry. The rec-tangular scheme is therefore a more realisticrepresentation of industrial structure. Fur-thermore, the model allows the analyst tomeasure the effects of market shares andtheir changes on the interrelationships be-tween sectors.

The model used in this study was de-veloped in terms of producer prices. This wasdone using coefficients which decomposepurchaser prices into producer prices andvarious margins, using the base year relation-ships.

(5) q= (I-BD)- e.

Alternatively one could substitute equa-tion (1) into equation (2) and solve for thelevel of industry output, as shown by equa-tion (6).

4In reality, of course, most sectors are identified by arange of products such as "leather and textile products"or "other petroleum and coal products". The firmsincluded in these sectors are defined as those whosemajor products (50 percent of value) come from thedefinition in question.

189

(1) q=Bg+e,

Exogenizing Agriculture



Input-output analysis implicitly assumesthat all endogenous sectors can produce anylevel of output required to meet final de-mands. Given this assumption, changes infinal demand can be introduced to the input-output model and the total effects on eachsector calculated. As Petkovich and Chingpoint out, in some cases, the output of acertain sector is not determined by demandbut by capacity constraints. Under these cir-cumstances simple final demand driven solu-tions to the input-output model are inade-quate. Petkovich and Ching propose anddemonstrate an iterative linear programmingsolution to the model which allows the incor-poration of predetermined levels for the sec-tor in question.

The capacity reduction scenario above isreally a special case of the more generalscenario in which the output of a given sectoris, in the short run at least, restricted to somepredetermined level. This more generalscenario includes those cases where output isreduced because of policy changes [Bromley,Blanch, and Stoevener], depletion of rawmaterials [Petkovich and Ching; Jones,Casey and Lacewell], or drought [Hoppe].On the other hand, it also includes thosecases where production is increased becauseof irrigation, weather modification, etc.[Mamer, Goldman and Wallace, 1973a and1973b; Maki, et al.; Jerome E. Johnson; Bur-ris; Bark; Bark, Buller and Vanderlip; andThomas G. Johnson]. In fact, this scenarioincludes all those cases where the objective isto determine the impact, not of changes infinal demand, but changes in total output.

The approach used in the present study ismuch more direct than the Petkovich andChing solution in that it sets the level of asector's output at the exogenously deter-mined level and then solves the input-outputmodel in a more or less normal fashion.

For simplicity define the m X m matrix Aas follows

(8) A=BD.

Then

(9)

(9.1)

q=Aq+e, or

q = (I-A)- e.

If a subset of the sectors are, as describedabove, restricted to some predeterminedlevel we may partition the matrices as fol-lows:

(10) [q] = [r'Al l q] + Feljq2 = A21:A22J + e2

where the subscript 1 refers to those sectorswhose outputs are endogenously determinedwhile subscript 2 refers to those whose out-puts are exogenous. In this scenario, e1 andq2 are known but q1 and e2 are unknown.Solving for these unknowns

(11) q = (I-A 1 l) - (A12 q2+ e1)

and,

(12) e2 = (I - A 22) q2- A21 q1 .

If, as in the present paper, one is interestedin the impact of some known level of sectoroutput in a given sector, e1 can be assumed tobe zero. Then

(13) q=(I -A1 l)- A12q2.

Final demand, e2, is then calculated as aresidual. Total economy wide output is qlil+q2i2 where the i's are the appropriatelydimensioned identity vectors.

Notice that once e2 is known, the input-output model can be solved in the usualfashion resulting in estimates of q1 and q2equivalent to those above. However, in thescenario described above, e2 and not q2 arethe unknowns.

In general, if the predetermined level of q2(or even a predetermined change in q2) isincorrectly multiplied by the multiplier ma-trix, as is often done, the estimated impact isexaggerated, by the amount (I -A)- (q - e).

190

December 1982


Notice that A 2q2 in equation (13) is simplythe first round expenditures of the sectors inquestion. In the following sections, the in-put-output model is "open" with respect toagriculture and the sector's expenditures aretreated like final demand.5

Calculation of Multipliers

Input-output allows the analyst to developmany different types of multipliers - outputmultipliers, income multipliers, value addedmultipliers, employment multipliers andothers. Each of these may be refered to asType I - direct plus indirect effects, or TypeII - direct plus indirect plus induced ef-fects. Direct effects are the initial shock ordisturbance being studied. The indirect ef-fects include all subsequent changes whichresult from the several rounds of purchases ofintermediate outputs, but exclude purchaseswhich result from the respending of incomesearned as a result of the initial shock. Thislatter effect is called the induced effect andcan be measured only if households are en-dogenous to the input-output model.

Many analysts use different names for thesame multiplier (i.e. final demand versusoutput) while others use the same name fordifferent multipliers (i.e. income multiplieris used to indicate total change in incomecaused by a one dollar change in income or aone dollar change in demand). 6 As a result,

5The effect of exogenizing agriculture is not unlike thatof exogenizing the household sector. When the level ofhousehold expenditure is known or for some reason notexpected to change (i.e., it is exogenous), then it isappropriate to include it in final demand. When itslevel is not known ex ante, but is determined by thelevels of other sectors, it should be endogenous. Whilehouseholds are frequently made exogenous when ap-propriate, the approach has seldom (if ever) beenextended to production sectors.

6A common error committed is to calculate the Type IIoutput multipliers by simply adding all elements in theappropriate column of the multiplier matrix includingthe household sector. The problem with this approachis that if the value is compared with the type I multi-plier, the entire household income level is attributedto the induced effect, when in reality most of it is directand indirect.

there is a great deal of misunderstandingsurrounding the concept of multipliers.

In this study five multipliers (all type II)are defined. The output multiplier is definedas the ratio of total (direct plus indirect plusinduced) output to direct output (or finaldemand). This multiplier, when multipliedby a given level of final demand will indicatetotal production required to deliver that finaldemand. The income (and valued added)multipliers are defined as the ratio of totalincome (value added) to direct income (valueadded). These multiplier must be multipliedby income (value added) rather than output.Because it is easier to multiply multipliers bydirect output (as opposed to direct income orvalue added), two additional psuedo-multipliers 7 can be identified. The income(value added) psuedo-multiplier is defined asthe ratio of total income (value added) todirect output. These multipliers are designedto be multiplied by direct output, in contrastto final demand.

Sources of Data for theAgricultural Sector

In the 1974 Canadian input-output tables,the agricultural industry is treated as a singlesector. For studies related to agriculture,such a model is of limited value. In thisstudy, therefore, the agricultural sector wasdivided into 12 sub-sectors, based on enter-

7The term pseudo-multiplier is coined and offered hereto distinguish those multipliers which have the sameunits in the denominator and numerator, from those"multipliers" which have different units, as in the caseof the pseudo-multipliers in this study. Considerableconfusion is possible when a term such as "incomemultiplier" refers to both types. Schaffer, for example,reports income multipliers whose units are total in-come per dollar of direct output. The unsuspectingreader may be surprised to find that essentially all ofthese multipliers fall between 0.0 and 1.0. Further-more, the distinction should alert the users of thesemultipliers to consider which of the alternatives is theappropriate one for their purposes. The authors areundoubtedly not alone in having their income multi-pliers misused by practitioners who multiply them by adirect output change rather than a direct incomechange.

191



prise type and soil zone. 8 The cross-classification by soil types was carried out toincrease the homogeneity of the farms withineach enterprise type. The criterion for thiscross-classification was a non-statistical evalu-ation of differences in coefficients.9 The finalclassification included: (1) Mixed farms -Brown and dark brown soil zones; (2) Mixedfarms - Black soil zones; (3) Cow-calf; (4)Feeder; (5) Dairy; (6) Hogs; (7) Cereal -Brown soil zone; (8) Cereal - Dark brownsoil zone; (9) Cereal - Black soil zone; (10)Oilseed; (11) Poultry; and (12) Irrigation -South Saskatchewan Irrigation project.

The major data source was a magnetic tapecontaining the 1978 consumption and pro-duction records kept by the CANFARM sys-tem (a national farm record keeping service).There is reason to believe that the distribu-tion of CANFARM subscribers and the dis-tribution of Saskatchewan farmers by size aredissimilar. This led to the need for some testof the sample as compared with the popula-tion. These tests resulted in support of theabove contention. As a result, the CAN-FARM data were categorized on the basis ofsize, and weights calculated from the 1976

SThe classification was based on the same criterion usedby Statistics Canada to classify industries. Cereal farmsreceived at least 50 percent of total revenues fromcereal grains sales, cow-calf farms received at least 50percent of total revenues from the sale of cows, bull,calves and feeder animals, feeder operations receivedat least 50 percent of total revenues from slaughtersteers and heifers, etc. Mixed farms were those whichdid not have a dominating enterprise. Irrigation farmsfrom the South Saskatchewan Irrigation Project (theonly major concentration of irrigated acreages in theprovince) have a range of products but receive a majorportion of total revenue from irrigated crops.

9Livestock farms, for example, had similar coefficientsregardless of soil type. Oilseed farms were almost alllocated in the black soil zone and were therefore notcross-classified by soil zone. The cross-classification ofmixed farms would have left the sample size unaccept-ably low and the brown and dark brown were thereforeleft undistinguished.

192

Census of Agriculture. 0

The next task was to calculate input andoutput coefficients from the weighted data.These expenditures were classified into Sta-tistics Canada input-output classifications(see Table 1).

Coefficients for poultry farms are based oncost of production formulas provided by thevarious marketing agencies involved. The ir-rigation farm coefficients are based onbudgets prepared by the Outlook IrrigationBranch of the Saskatchewan Department ofAgriculture.

Empirical Results

From the agricultural input-output coeffi-cients in Table 2, 1979 final demand levelswere calculated for each type of farm. Thisinformation was used to calculate relevantmultipliers for the twelve farm types, whichare shown in Table 2. The output multipliersindicate the total provincial production aris-ing from each dollar of production in any ofthe twelve subsectors. The output multiplierof 2.03 for the total sector is reasonably largefor an economy as open as that of Saskatche-wan. Of the subsectors, irrigation farms (inthe Outlook, Saskatchewan, area) have thesmallest output multiplier (1.95) whileoilseed farms have the highest (2.09).

The value-added multiplier for agricultureis estimated at 2.42. This indicates that foreach dollar of value added generated in agri-culture, $1.42 of additional value added isgenerated elsewhere in the economy. Thecow-calf farms generate the lowest multiplierof this type (2.25) while poultry farms gener-ate the highest (3.47). These multipliers con-vey very little comparative information how-ever. The poultry farms have the highestvalue simply because they generate the low-est direct value added.

°The test referred to was a Hotellings T2 test of sevenaverage attributes of the provincial farms versus theCANFARM sample. After weighting the sample forfarm size, the T2 statistic was very insignificant indicat-ing no difference between the population and thesample.

December 1982


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TABLE 2. Various Input-Output Multipliers by Agriculture Subsectors.

ValueValue Added Income

Agriculture Output Added Pseudo- Income Pseudo-Subsector Multiplier Multiplier Multiplier Multiplier Multiplier

Mixed Br.-D.Br. 2.03256 2.41609 1.18674 1.61256 0.758207Mixed Black 2.04274 2.50715 1.19390 1.62205 0.755626Cow-Calf 1.96809 2.24792 1.24974 1.51941 0.821513Feeder 2.01984 2.37808 1.21474 1.59240 0.787743Dairy 1.98067 2.25649 1.25800 1.49883 0.864450Hogs 2.05863 2.44437 1.10292 1.62395 0.707264Cereal, Br. 1.99493 2.29443 1.24107 1.54893 0.818248Cereal, D.Br. 2.00816 2.34568 1.21797 1.57469 0.788229Cereal, Black 2.07666 2.63044 1.15976 1.69218 0.727517Oilseed 2.08864 2.72862 1.14298 1.71977 0.701595Poultry 2.01422 3.46913 0.63133 2.26510 0.356255Irrigation 1.94842 2.48804 1.09880 1.98241 0.512301Total (All Farm) 2.02556 2.41782 1.19736 1.60136 0.770025

The value added pseudo-multipliers areperhaps the most meaningful among the vari-ous multipliers. The aggregate multiplier of1.20 indicates that agriculture has an excep-tionally large impact on the economy. It sug-gests that for each dollar of production inagriculture, $1.20 in value added occurs inthe province. A multiplier of this magnitudeis possible only if the sector in question isclosely related to the household sector.Under-scoring the ambiguity of the valueadded multiplier, the cow-calf subsector,with a value added pseudo-multiplier of 1.25is second only to the dairy farms with 1.26.The lowest is the poultry subsector with0.63.

The income multipliers, like the valueadded multipliers must be interpreted care-fully. The aggregate income multiplier of1.60 indicates that for each dollar of farmincome, $0.60 of income is generated else-where. The income pseudo-multiplier of 0.77indicates that for each dollar of production inagriculture provincial income rises $0.77. Interms of the last multiplier, the dairy andcow-calf subsectors again rank high (0.86 and0.82) while poultry is the lowest (0.36).

The multipliers indicate only the relativeeffects per dollar of output. In absolute termsthe cereal farms predominate, as Table 3

indicates. From the provincial economy's po-sition, the direct plus indirect value addedlevels are the best indicators of a subsector'simportance. Overall, the model predicts thatnearly $3 billion dollars of the provincialgross domestic product can be directly orindirectly attributed to agriculture. To putthis in perspective, consider that the pro-vinces gross domestic product (at factor cost)in 1978 was 8,865 million. Therefore, whileagriculture contributed only 13.8 percent ofgross domestic product directly, indirectly itcontributed another 18.2 percent for a totalof 33.4 percent. l This indicates, vividly, therelative importance of the industry in theprovince of Saskatchewan.

A Comparison of theAgricultural Subsectors

The first major observation regarding theagriculture subsectors is that, with the excep-tion of poultry, the subsectors are relativelysimilar. The rather incongruous results dis-played by the poultry subsector may be due

"Using the Saskatchewan Bureau of Statistics estimateof agriculture's contribution to GDP and the IO esti-mate of the value added multiplier the direct and totalcontributions are 18.0 percent and 43.4 percent re-spectively.

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to one or more of the following reasons.First, the input-output coefficients are notbased on empirical observations as are thosefor most other sub-sectors, but rather on costof production formulas used in pricing poul-try. Actual costs may be lower and net in-comes higher than what the data indicated.Second, the subsector is highly concentratedwith large gross revenues per farm. Thisresults in relatively low net returns per dollarof production. Third, unlike the other sub-sectors, the poultry subsector does not pro-duce a mixture of products. The mixture ofproducts in the other subsectors tend to keepthe multipliers homogeneous.

In general, the grain farms generated thehighest output multipliers while cow-calf,dairy and irrigation generated the lowest.This is most likely because the grain farmstend to purchase more goods and services(particularly goods) from domestic sources.This is probably because the grain farm inputsupply sectors are highly developed in theprairie provinces. The dairy, cow-calf andirrigation subsectors are somewhat smaller.This is probably because these two sectorspurchase more inputs than any others fromother farms. Since agriculture is not assumedto respond to increased demand, the multi-plier effect is low. The hogs subsector on theother hand purchases more prepared feedswhich gives it a higher multiplier.

The income and value added pseudo-multipliers are almost a complete reversal ofthe output multipliers. Irrigation generatesquite low multipliers as before, but the dairyand cow-calf sectors generate somewhathigher pseudo-multipliers than any of theothers. One explanation for this may be thatcow-calf and dairy generate higher levels ofincome per dollar of sales than other subsec-tors. 12 This contributes directly to householdand to income and value-added. The abovecomparisons should not be extended beyondtheir valid ranges. For instance, one should

2Income here includes return to operator labour andmanagement, hired labour, land ownership and certainland rentals.

not conclude from this that irrigation shouldbe discouraged. On the contrary, earlier ap-plications of this model to irrigation budgetssuggest very important secondary effectsfrom this development strategy. If one was togenerate ratios of direct plus indirect incomeor value added per acre, it would be notedthat irrigation has a much larger impact thanany dryland farm since a larger value of pro-duction is generated per acre relative to dry-land farming. Similarly, the hog, dairy andpoultry subsectors would be favoured in sucha comparison. If one was to compare thedirect plus indirect income or value-addedper dollar of investment one would get yetanother ranking.

Conclusions

This paper demonstrates the inappropri-ateness of introducing changes in the level ofsectoral output through the final demandvector. A simple method is proposd and usedto introduce the changes in output directlyinto the solution. The method is a convenientway of comparing the aggregate impact ofvarious types of farms on the Saskatchewaneconomy.

The analysis above allows one to drawseveral conclusions regarding the impact ofdifferent types of farms. First, the variousfarm types do not have profoundly differenteffects on the aggregate levels of output,income or value added when compared on aper dollar of output basis. Poultry is the onlysubsector whose multipliers are considerablydifferent from that of the overall mean. Thedifferences displayed by this subsector, whilelarge in relative terms, are small in absoluteterms because of the relatively small numberof producers in the group. Overall, the multi-pliers for agricultural subsectors are large incomparison with those of other sectors.

The study leaves many questions unre-solved, however. A comparison of aggregateeffects per dollar of output is only the firststep in comparing the impact of various sub-sectors. While the aggregate multipliers foragriculture are similar, their impact onspecific sectors may not be. The preliminary

197



findings have not indicated the relative im-pact that different sectors have on specificnon-agricultural sectors such as finance,trade, feed manufacturers, etc. In addition,more research is needed to determine theimpact of replacing an extensive enterprisesuch as cow-calf with a more intensive feederoperation. Before it is possible to guide gov-ernment policy related to diversification, in-tensification and similar structural changes,more comparative research is necessary. Themodel does, however, provide the necessarytool.

Johnson, Jerome E. The Effects of Added Rainfall Dur-ing the Growing Season in North Dakota. AgriculturalExperiment Station, North Dakota State University,Fargo, Number 52, August, 1974.

Johnson, Thomas G. The Environmental and EconomicEffects of Weather Modification in Saskatchewan.Research Bulletin BL:82-02. Saskatoon: Departmentof Agricultural Economics, University of Saskatche-wan, March, 1982.

Johnson, Thomas G. and S. N. Kulshreshtha, Nature ofIntersectoral Relations of Saskatchean Agriculture:An Input-Output Analysis, Department of Agricultur-al Economics, University of Saskatchewan, ResearchReport 81-06, November, 1981.

Jones, Lonnie L.; James E. Casey and Ronald D.Lacewell. "Use of an Integrated Input-Output andOptimization Model to Estimate the RegionalEconomic Effects of Resource Depletion." Mimeo,Texas Agriculture Experiment Station, Texas A&M(Undated).

References

Bark, L. Dean, A Study of the Effects of Altering thePrecipitation Pattern on the Economy and Environ-ment of Kansas. Department of Physics, Kansas StateUniversity Agricultural Experiment Station. Manhat-tan, Kansas. Departmental Report 5-425, October,1978.

Bark, L. D.; O. H. Buller; and R. L. Vanderlip. CloudSeeding. Potential Benefits for Kansas Agriculture.Agricultural Experiment Station, Kansas State Uni-versity, Manhattan, Bulletin #628, May, 1979.

Bromley, D. W.; G. E. Blanch; and H. H. Stoevener.Effects of Selected Changes in Federal Land Use on aRural Economy. Station Bulletin #604, AgriculturalExperiment Station, Oregon State University, March,1968.

Burris, Martin J. Impacts of Induced Rainfall on theGreat Plains of Montana. Montana Agricultural Ex-periment Station, Montana State University, Boze-man. Bulletin 670, August, 1973.

Chossudousky, Michel. Input Output Analysis. Re-search Paper No. 7708. University of Ottawa, March,1977.

Hoppe, Robert. Building a Non-Metropolitan Input-Output Model: Minnesota's Region Six East. Agricul-tural Experiment Station, University of Minnesota,Technical Bulletin #313, 1978.

198

Kulshreshtha, S. N.; D. D. Tewari; and Thomas G.Johnson. "Impact of Rising Energy Cost Upon Ag-ricultural Production and Regional Economy: A CaseStudy of Saskatchewan," Prepared for Presentation atthe International Conference of Agricultural Econo-mists, Jakarta, September, 1982.

Maki, Wilbur R.; Leonard A. Laulainen, Jr.; MasonChen, and Donald R. Newell. Economic Impact ofIrrigated Agriculture in West Minnesota. MinneapolisAgricultural Experiment Station, University of Min-nesota.

Mamer, John W.; George Goldman; and L. T. Wal-lace. (a) "Economic Effects of the Expansion or Irriga-tion in Colusa County." Publication I-O 6. Coopera-tive Extension, University of California, September,1973.

Mamer, John W.; George E. Goldman; and L. T. Wal-lace. (b) "Economic Impact of the Expansion of Irriga-tion in Glenn County." Publication I-O 7. Coopera-tive Extension, University of California, November,1973.

Petkovich, M. D. and C. T. K. Ching, "Modifying a OneRegion Leontief Input-Output Model to Show SectorCapacity Constraints", Western Journal ofAgricultur-al Economics, 3(1978):173-79.

Schaffer, William A., ed. On the Use of Input-OutputModels for Regional Planning. Leiden, Netherlands:Martinus Nyhoff Social Science Division, 1976.

Statistics Canada, The Input-Output Structure of theCanadian Economy, 1966, Ottawa, August, 1969.

December 1982

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