Economic Modeling of Farm Productionand Conservation Decisions in Responseto Alternative Resource andEnvironmental Policies

John R. Ellis, David W. Hughes, and Walter R. Butcher

Growing concern over resource use and protectionof the environment has prompted greater demandfor agricultural policy analysis at the local, re-gional, and national levels. Faced with decliningsurface-water quality, silting of reservoirs, andcontaminated groundwater supplies, the public hasdemanded greater protection of the environment.With the call for more regulation of agriculturecomes the need for policy analysis, What policy isbest? Should a national standard apply, or do localconditions warrant local standards? Who will ben-efit, and who will lose, and by how much?

Past analyses have examined policy options atall levels. The initial focus here is on current meansof analyzing policy impacts at the farm level. Com-ponents include various means of reflecting the de-cision environment in which agricultural producersoperate as well as alternative behavioral assump-tions within that environment. That decision en-vironment generally includes stochastic productionof both positive (generally crops or livestock) andnegative (primarily pollutants) outputs. Alternativemeans of reflecting production of these biologicaland physical outputs are examined. Selected paststudies are reviewed, coupled with suggestions formethodologies for future research efforts.

Means of aggregating farm-level results to re-gional or national levels are then discussed. Re-source and conservation policies, such as theConservation Reserve Program, ConservationCompliance (Glaser), or the Clean Water Act (Har-rington, Krupnick, and Peskin), were all initiatedat the federal level. Imposition of a national policy,even if enforced locally, has both national and localimplications. In most cases, regulation of resourceuse alters input demand and/or output supply, with

John Ellis is an assistant professor, David Hughes is a postdoctoralresearch associate, and Waker Butcher is a professor, all in the De-patment of Agricultural Economics, Washington State University, Pull-man, WA,

subsequent impacts on prices in those and relatedmarkets. Altered prices on either side of the marketimpact personal incomes, with subsequent impactsthroughout the national and world economies. Thispaper examines selected means of aggregating farm-level results in order to make statements concerningnational impacts of alternative resource polices. Inclosing, the role that concise, accurate modelingof farm-level response may play within policy for-mulation is reemphasized.

Current Modeling Efforts

As available methodologies and technology haveprogressed, researchers have increasingly turned tosimulation andlor mathematical-programmingtechniques in order to evaluate alternative resource-policy strategies. Such tools allow not only thereflection of a rich array of decision objectives andenvironments, but also a large degree of reaIismin depicting the stochastic nature of agriculturalproduction. Lacking the luxury of an actual labo-ratory to test proposed policies, the analyst turnsto the computer as his or her tool of empiricalinvestigation.

Use of the mathematical-programming portionof farm policy analysis has a long history, with thevarious techniques well documented (Hazell andNorton; Agrawal and Heady). Linear programming(LP) studies abound, primarily because of ease ofimplementation and relative flexibility in depictinga large array of economic conditions. Multiperiodstudies using recursive programming techniques havealso been employed in attempts to incorporate be-havioral dynamics (Day and Singh; Ellis, Lace-well, and Reneau; Mapp and Dobbins). Such modelsare also useful when policies to be modeled havevarying parameters over time and one wishes toexamine impacts during the transition to full im-plementation of a policy.

Ellis, Hughes, and Butcher Economic Modeling of Furm Production and Conservation Decisions 99

A host of techniques, including quadratic pro-gramming (QP), MOTAD, Target MOTAD,safety-first, discrete stochastic programming, andchance-constrained programming, have been usedto incorporate the various impacts that risk com-ponents may have on the relevant objective func-tion and constraint set (Anderson, Dillon, andHardaker; Hazell and Norton). Specific uses vary,especially with one’s assumptions concerning therelevant objective function for the producer. Lin,Dean, and Moore found better predictive perfor-mance of actual producer behavior when using util-ity in lieu of profit maximization. Lambert andMeCarl carried this further by incorporating directutility maximization into farm planning models.

Resulting resource usage, and consequently im-pacts of alternative policies, does vary with theassumed producer objective function (Setia; Brinkand MeCarl), Practical policy analysis, however,tends toward use of a profit maximization frame-work (Rola, Chavas, and Harkin; Setia and Mag-leby; Prato and Shi), especially if results are to beaggregated. Use of the risk-oriented methodologiesrequires more, sometimes heroic, assumptions con-cerning variables such as the distribution of risk-aversion parameters or relevant covariance mat-rices among producers.

A third objective alternative includes that of mul-tiple goal programming (MGP). With this ap-proach, efforts may be made to allow for bothutility maximization as well as other goals such asthe well-being of the farm family and increasingfarm size. A narrower definition of MGP is con-cerned with goals in addition to profit maximiza-tion, but it usually excludes models that areprincipally concerned with the trade-off betweenexpected returns and various measures of risk, suchas Target MOTAD.

MGP models maybe categorized based upon thedegree of decision-maker control over relevant de-cision variables. Such models can be divided intotwo types: (1) those that assume all decision vari-ables are under the complete control of the decisionmaker (as characterized by many private decisionmakers) and (2) models where the decision makermay directly control some decision variables butcan only influence other decision variables throughthe use of policy instruments (as characterized bymany public decision makers) (Candler, Fortuny-Amat, and MeCarl). A second categorization ofMGP decision models is between models that re-quire a complete knowledge of the decision mak-er’s preferences, either prior to the model’s creationor in an interactive model with the decision maker,versus models that make no such requirement (Co-hon and Marks; Willis and Perlack).

Techniques that do not require complete knowl-edge about preferences of decision makers are es-pecially amenable whenever a public entity, suchas government policy makers, is the decision-mak-ing group. One such technique is compromise pro-gramming (Zeleny), which has been used to evaluatepublic-policy trade-offs in situations where deci-sion makers’ preferences are not known (Romero,Amador, and Barco; Atwood, Lakshminarayan, andSposito). For example, Atwood, Lakshminarayan,and Sposito used compromise programming to ex-amine the trade-offs between farmer productioncosts, on-farm productivityy loss, and off-site sed-iment damage levels for various soil conservationpolicies for a watershed centered in Iowa. Theyobtained the set of compromise solutions by ini-tially optimizing the model with respect to eachgoal while ignoring all other goals. These resultswere reduced to the Pareto efficient set of solutions,where the obtainment level of one goal cannot beincreased without imposing an opportunity cost onat least one of the other goals through noninferiorset estimation methods. Vector optimization rou-tines were then used to examine trade-offs betweenthe three objectives and filter efficient policy so-lutions to a small enough number of choices (thecompromise set) from which a decision maker couldconceivably choose.

Goal programming (GP) models can be em-ployed under the assumption that the preferencesof the decision maker are completely known (Willisand Perlack). As opposed to linear programmingmodels, the objective function in a GP model seeksto minimize the difference between the desired levelof goal achievement and the level that is actualachievement, as represented by deviation variables(Dobbins and Mapp). Decision makers can eithergive a preference ranking for all goals, or all goalscan be assumed to be of equal value. In the formercase, the GP may result in a lexicographic orderingwhere the initial model solution will exactly obtainthe level of the most desired goal. The obtainedgoal is then treated as a constraint and the secondgoal is exactly solved for. Next, the second goalis added to the constraint set that the model solvedto exactly obtain the third goal, and so forth (Dob-bins and Mapp; Willis and Perlack).

Weights that represent the trade-off between goalsare given to the deviation variables if all goals arenot of equal value. Substitution between goalachievement levels can then occur and solutionscan be obtained through normal LP methods (Dob-bins and Mapp). Dobbins and Mapp used both GPmethods in conjunction with a simulation model toexamine long-run plans for a typical Oklahomafarm. They found model results to be sensitive to

100 April 1991 NJARE

the assumption of equal preference of goals as op-posed to a strict goal preference ranking.

Neely, North, and Fortson used a mixed-integerGP model to examine economic and environmentalgoals for a set of public water projects. Similarly,Thampapillai and Sinden employed a multiple-objective LP model in evaluating possible trade-offs between income maximization and environ-mental quality goals for a region in northern NewSouth Wales, Australia. Both studies concludedthat actual levels of resource use were suboptimalbecause higher levels of both environmental andeconomic goals were obtainable under alternativeplans.

The Thampapillai and Sinden research may suf-fer, however, from a deficiency given that the au-thors optimized the objective function for a publicdecision maker when, in fact, actual use of manyof the appropriate decision variables was controlledby private resource users such as farmers. Candler,Fortuny-Amat, and McCarl have demonstrated thattreating such a two-tiered objective-function prob-lem as a single optimizing problem for a publicdecision maker can lead to erroneous policy rec-ommendations. This especially applies in instanceswhere those public decision makers may be onlyable to indirectly influence the decisions of privateresource users with their own individual objectivefunctions. The same authors demonstrate that op-timizing a two-tiered mathematical programmingmodel, where the public and private decision mak-ers each have separate objective functions, maylead to a local optimum even if all constraints andobjective functions are linear.

The emphasis in this paper is on modeling theeffect of private decisions on natural-resource con-servation and environmental quality. However, wehave been unable to find any empirical studies thatexamined environmental effects and resource con-servation decisions by private decision makers us-ing an MGP approach. This research gap existsdespite the demonstrated importance of noneco-nomic goals, such as bequeathing a sound farmoperation to heirs (Carlson and Dillman), on re-source conservation decisions at the farm level.

This research gap also exists in the area of ap-plied studies employing adaptive economics (Day;Baum). The complexity of actual human decisionbehavior is better reflected in adaptive economictheory, yet applied studies are even less tract-able than those that can be addressed using themore standard static mathematical programmingapproaches. Even recursive schemes employingmathematical programming have difficulty reflect-ing Kenneth Boulding’s additional decision factorsof love/altruism and coercion (Troub).

In practice, firm-level programming models havedifficulty in predicting producer behavior. Alter-native goals, as well as correctly specifying allrelevant resource constraints, are likely the mainculprits. Producers also likely operate with multipletime horizons for varying goals. Reflecting such adecision framework is no easy task, and, in prac-tice, profit maximization or univariate utility be-comes the objective by default.

Process Models

A host of physical process models have been de-veloped by physical scientists to model processessuch as wind and water erosion, chemical loading,and crop and livestock growth. Such models havebecome key components of current efforts at mod-eling the stochastic nature of agricultural produc-tion of both commodities and pollutants. A partiallist of models and their realm of application appearsin Table 1. In general, such process models providephysical-response data for use in more compre-hensive mathematical programming or farm-firmsimulation models.

Agricultural economists have the most experi-ence (Musser and Tew) with the biophysicalcrop-growth models such as EPIC or CERES.Probability distributions of yield under stochasticweather conditions and varying tillage/irrigationpractices are the general output of such models. Amore recent survey (Joyce and Kickert) lists forty-two crop-growth models for single-crop species.Multiple-crop models, such as EPIC, sacrifice someprecision in modeling crop growth for particularcrops in exchange for greater flexibility in mod-eling crop rotations. Applied studies employing EPICinclude analyses of wind erosion impacts on pro-duction (Lee, Ellis, and Lacewell), risk impacts ofalternative cotton irrigation schemes (Ellis), andpotential greenhouse-effect impacts (Robertson etal. ). EPIC’s greatest use to date has been in the1985 RCA resource appraisal (Putman, Williams,and Sawyers).

Additional process models (Table 1) have beendeveloped to reflect sediment and pollutant loadingin streams and groundwater. EPIC has limited ca-pabilities in performing these functions. Appliedstudies are numerous, including the use of SWRRB(Arnold et al.) by the National Oceanic and At-mospheric Administration to estimate nonpoint-source loadings from nonurban lands in all coastalcounties of the U.S. (Singer et al.). Additionalstudies (Braden et al.; Lee, Lovejoy, and Beasley)employ process models to avoid the constant dam-age-to-delivery ratios of past work (Montgomery;

Ellis, Hughes, and Butcher Economic Modeling of Farm Production and Conservation Decisions 101

Table 1. Selected Process Models

Name Application Level of Resolution Reference

EPIC(Erosion productivity Impact

Calculator)

CERES-CEREAL

CREAMS(ChemicaIs, Runoff, and

Erosion from Agr. Mgmt.Systems)

GLEAMS(Groundwater Loading

Effects of Agr, Mgmt.Systems)

SWRRB(Simulator for Water

Resources in RuralBasins)

AGNPS(Agricultural Nonpoint

Source)

Crop growthWind and water erosion

Crop growth

Chemical and sediment fateand transport (surfacewater)

Chemical and sediment fateand transpofi(groundwater)

Crop growth, hydrology,mnoff

Erosion, sedimentation

Multiple crop, field size

Multiple cereal crops, fieldsize

Field size

Watershed

Watershed

Watershed

Williams et al.

Singh et al.

Knisel

Leonard et al,

Arnold et al.

Young et al

Taylor and Frohberg). Prato and Shi employ a geo-graphical information system (GIS), coupled witha simplified erosion model, to evaluate alternativeerosion management practices ina watershed.

One should note that a great deaI of expertise isrequired to properly use such models, and theseauthors highly recommend a team approach utiliz-ing close cooperation between physical scientistsand policy analysts or economists. Mere calibra-tion of the models, especially crop-growth models,should not be left to the uninformed (Bryant andLacewell).

Once calibrated, such process models are oftenused conjunctively with some form of economicanalysis or formal farm-level programming model.The FLIPSIM farm simulation model (Richardsonand Nixon) is probably the best known of suchmodels. Aspects of government farm programs,family withdrawals, machinery replacement, andincome taxes may be reflected in multiple-year sim-ulations employing stochastic or fixed prices andyields. Ellis used EPIC-generated crop yields alongwith FLIPSIM to simulate the impacts of differentirrigation schemes on firm survival. Standards forcontinuing operation of the firm (i.e, a maximumdebt-to-equity ratio) may be used to reflect firmfailure. One may then estimate the probabilities offirm survival under alternative policies using mul-tiple simulations under different initial debt loads.FLIPSIM also has a yearly crop-mix planner em-ploying LP or QP techniques.

Combining Systems

Cole and English provide an excellent review ofthe decision process used when linking several sub-components into an overall policy-analysis system.In their first stage of selection, they considered thefollowing:

1. Generalized commodity selection: The modelshould be flexible in the types and combinationsof commodities that can be analyzed. Single-commodity models or specific crop models are notacceptable.

2. Crop-oriented: The model should be crop-onented but not crop-specific.

3. Current time period: Only recently developedmodels will be considered. Earlier models wouldrequire substantial updating to be useful.

4. Transferable: The model must be transferableor have a general model structure and database toenable it to be easily transferred to other users andregions for analytical purposes.

5. Multiperiod time horizon: The model must bedynamic and must reflect impacts of the decision-making process over a specified simulation period.

6. Alternative-sized farms: The model must beable to handle various farm sizes.

After performing an extensive literature review,the models that were deemed acceptable by theabove criteria were subjected to a second stage ofcriticism. Qualities sought in that stage includedthe following:

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1. Government policy: The model must permitincorporation of various agricultural commodity andresource programs.

2. Cash-@w analysis: The model must have acapability to simulate the cash-flow process in thefarm operation decision-making routine. This wouldinclude such items as refinancing, depreciation, andtaxes.

3. Decision-making or adaptive process: Themodel must have some type of decision-makingsimulation routine such as when to buy land, re-finance loans, or which agronomic practices to se-lect.

4. Resource use and availability: The model mustinclude provisions for resource endowments anddistribution. Ultimately, what is achievable will beinfluenced by the level and location of resourcesavailable for production. Knowledge of land quan-tity and quality, initial cash endowments, and ma-chinery and buildings available for productionpurposes is necessary if analysis at tile farm levelis to be meaningful.

5. Professional acceptance: The model must havebeen tested and determined to be reliable.

Upon completion of their review, they chose afour-component system comprised of a biologicalsimulator, a farm budget generator, an optimizer,and a whole-farm simulation model. The resultingsystem employed the EPIC and FLIPSIM simula-tion models coupled with the North Carolina BudgetPlanner (Hoag) and two possible optimizers. Thelatter two components consisted of the LINDOpackage (Schrage) for some applications and theNorth Carolina Crop Planner (Edmund, Rogers,and Hoag) for others.

The resulting system, MOAPS (Micro-OrientedAgricultural Production System), was designed toanalyze farm-level impacts of resource-policy is-sues, including low-input sustainable agriculture,cross-compliance policies, and other environmen-tal issues, An immediate application occurred inan analysis of the 1985 Food Security Act Con-servation Compliance Standards (Thompson et al.).

Subsequently, MOAPS was incorporated into aneven more comprehensive system of models knownas CEEPES (Comprehensive Economic PolicyEvaluation System). This model was developedthrough the cooperation of the Environmental Pro-tection Agency (EPA) and the Center for Agricul-tural and Rural Development (CARD) at Iowa StateUniversity. The major application to date involvesstudy of the banning of corn rootworm insecticidesin Iowa (Cole et al.). CEEPES is easily the mostcomprehensive modeling system built to date. WithinCEEPES a suite of simulation models has beenassembled to address a variety of resource-policyquestions (CARD and U.S. EPA). The develop-

ment group is currently attempting to simulate al-ternative policies toward atrazine use in agriculture.The system is designed to account for market con-sequences as well as transport of atrazine and itssubstitutes in multiple media (air, land, and water).Final evaluations of the consequences to humanhealth and the environment are also key compo-nents.

Aggregate Analysis

Increased interest in national policy effects duringthe 1970s prompted a major emphasis on devel-opment of sector models to reflect aggregate re-sponse to alternative policies (Lee). One approachentailed cost-minimizing models such as the CARDmodel (English et al.). Such models, reviewed inHeady and Srivistava, generally divide the totalregion of study into numerous subregions. Rep-resentative farm-level models are imbedded at thesubregion level, and the cost of meeting exoge-nously determined total demand for one or morecommodities is minimized. Marketing linkages be-tween subregions and semiaggregate resource con-straints across subsets of the total sector may alsobe reflected. Typically such models exhibit a some-what poor replication of regional crop mix (Schaller;Young), primarily due to a lack of detail concer-ningmicrolevel response.

Earlier versions of this methodology relied onfixed prices, ignoring any quantity impacts on inputor output prices. Later versions, including theCARD-RCA model used in the 1985 RCA resourceinventory analysis, employed an iterative proce-dure to determine equilibrium prices and quantities(Huang, English, and Quinby). For an initial setof prices, a demand (econometric) model is solvedfor commodity quantities, The aggregated pro-gramming model is then solved such that the de-sired demand is produced at minimum cost, Shadowprices on the demand constraints from the pro-gramming model then serve as prices in the demandmodel, and the process continues until the pricesin the demand model are approximately equal toshadow prices in the supply model.

Later refinements in this methodology includedfully price-endogenous programming models thatsought to maximize welfare instead of minimizingcosts (Takayama and Judge). The equilibrium con-ditions of supply equal to demand imply that totalsurplus, or the sum of producer and consumer sur-plus, will be maximized at the point of equilibrium.McCarl and Spreen review numerous studies uti-lizing this approach. Quadratic-programming for-mulations are numerous (Judge and Takayama; Hallet al.), and Duloy and Norton employed separable-programming techniques to maximize a linearized

Ellis, Hughes, and Butcher Economic Modeling of Farm Production and Conservation Decisions 103

version of the quadratic objective function impliedwhen maximizing total surplus with linear de-mands. Applied work employing these methodol-ogies includes Meister, Chen, and Heady’s studyof price supports or storage policy. Several studies(Taylor and Frohberg; Taylor and Swanson; andMeister, Chen, and Heady) employ such ap-proaches to examine the impacts of limiting fertil-izer use. Langley, Heady, and Olsen examined themacro implications of a transition to organic farm-ing, while Burton and Martin investigated the im-pacts of restricted herbicide use.

Despite fairly rigorous development, the sectormodels noted above often exhibit quite differentresponse characteristics from actual aggregate out-put because of unrealistic crop specialization andresource mobility (Baker and McCarl). An alter-native approach is to simultaneously model a largegroup of homogeneous farms and/or aggregatesubgroups of heterogeneous farms, and to aggre-gate the results of the numerous optimized farmmodels. On a national or regional basis such ag-gregation is obviously impossible. The requiredinput data and computing time are too onerous.McCarl proffers one partial solution by employinga formulation involving Dantzig-Wolfe decompo-sition principles. Once firms within each subregionhave been classified into similar groups based uponselected criteria, such as primary production activ-ity, resources, or firm size (Anderson and Stryg;Johansson), representative farm models are run fora multitude of potential input and output price com-binations. Results are then aggregated across firmclassifications for each price vector. This processproduces extreme points of the feasible space fora possible sector model shown below,

Max C.Xsubject to X – Z Xi At = O,

~hj=l, X= O,hia OfOr alli.

Here Xi’s are the extreme points obtained by ag-gregating the output of the farm models, and hirepresents the proportion allotted to each of theextreme-point solutions.

In practice all extreme points will not be known,and the full Dantzig-Wolfe (D-W) decompositionalgorithm requires choosing a set of shadow pricesfor the convexity constraints, solving the represen-tative farm models using the chosen shadow pricesas objective-function coefficients, and inserting theresulting extreme-point solutions into the sectorformulation. One may then solve the sector modelfor a new set of shadow prices and resolve thesubproblems with the updated shadow prices. Asecond set of extreme points is generated and isadded to the sector model. The process continuesuntil the prices and/or subproblem solutions remain

unchanged between two iterations. The algorithmalso provides for upper and lower bounds on theobjective-function value at each step (Dantzig andWolfe).

One should note in the simplified example abovethat output prices are exogenous, Endogenous pricescould easily be incorporated by employing a quad-ratic objective function to maximize total surplus.In addition, measures of relevant externalities, suchas soil loss or groundwater contamination, couldbe aggregated across the optimal farm models andincorporated into the sector model via an additionalaccounting constraint. The optimal sector-modelsolution would then also provide the correspondingtotal externality generated (assuming additivity isappropriate).

Onal and McCarl note that a great deal of detailand effort is required to generate the numerousfarm-level LP solutions across the relevant rangesof output and input prices. An alternative approachis to use historically observed crop mixes for theregions under consideration. These are assumed tobe aggregates of feasible solutions for the unknownfirm models in each region. Aggregate output andcrop mix for the various regions are usually avail-able in county or state statistics. Estimates ofaggregate inputs are less reliable, yet use of rep-resentative budgets on known acreage may providea reasonable approximation. Some aggregation biasis likely to occur because the historical crop mixesmay not span the full set of possible aggregatedoptimal extreme points. Proposed policy changesthat might alter the production choice set farmersface would render this approach less attractive sincehistorical regional crop mixes may be incomplete.

Theoretical presentations of this methodologyappear in Onal and McCarl as well as the 1982article by McCarl. The major applied study to date(Hamilton, McCarl, and Adams) evaluates the eco-nomic effects of reduced ozone-pollution levels inthe Corn Belt under alternative aggregate-modelassumptions. The authors compare various aggre-gation schemes and their relative performance, Ingeneral, the Dantzig-Wolfe procedure, using eithera large array of pregenerated whole-farm plans orhistorical crop mixes, strongly outperformed theuse of detailed firm-level LP models with constantprices as well as a quadratic sector model withregional land and labor constraints.

Overall Evaluation of Policies

Agricultural and environmental policy also requiresadditional evaluation in terms of its administrativecost, feasibility, and institutional requirements. Suchconsiderations about policy implementation range

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from the costs and feasibility of invoking a givenpolicy under the current set of policy institutionsto using a new set of institutions in carrying out apolicy.

Administrative costs include any additional ex-penditures in equipment and personnel necessaryto enforce the policy in question. For example,strict adherence to soil conservation and com-modity program cross-compliance provisions mayrequire additional funding of local and state Ag-ricultural Stabilization and Conservation Service(ASCS) and Soil Conservation Service (SCS) of-fices. Feasibility problems include the probabilityof farmers eluding poIicy regulations (enforcementslippage) as well as the probability of successfulcourt challenges to the policy. The accuracy ofmodels used to implement and track the implica-tions of policies is an especially important concernin the latter case (Hauser). A broader question isthe desirability of using the current ASCS insti-tutional complex to administer a relatively stricterosion- and pollution-control policy. The ASCS,with its tradition of subsidizing, rather than regu-lating agriculture, and with farmer control of itslocal and state offices, may be an inappropriatetool for carrying out environmental policies(Lovejoy).

Less mundane, but equally important policy cri-teria include equity and fairness concerns, prop-erty-rights implications of the policy, and a socialdesire to maintain the family farm. Ceterus paribus,society generally favors policies that narrow ratherthan expand income distribution (Seitz). A policywhere more wealthy farmers would receive a dis-proportionate share of government subsidies or apolicy where relatively poor farmers may be bur-dened with a disproportionate portion of costs mayviolate this equity criterion. Fairness concerns in-clude the notion of shared consequences, wheresociety alleviates some of the hardships imposedon individuals by government policies that meet asocial end (Seitz), and the concept of earned re-wards that hold “firms and individuals should re-ceive what they pay for and should pay others forthe damage they cause” (Seitz, p. 318). Sharedconsequences and earned rewards may together im-ply that while farmers should not bear all costs ofreducing farm-source environmental pollution, nei-ther should taxpayers pay all pollution-abatementand resource-conservation costs.

The form that policies take also has property-rights implications. For example, under subsidypolicy, farmers are given implied pollution rightsbecause a given action involves bribing the farmernot to pollute (purchasing the right) and societybears the costs of pollution abatement. Regulatory

policies, on the other hand, imply that pollutionrights rest with society if farmers are forced to bearthe lion’s share of pollution-abatement costs.

Finally, society holds goals for agriculture, in-cluding a supply of food that satisfies the wantsand needs of American consumers at reasonablecosts. A second goal is preserving the family farmbecause it provides a blueprint for social behaviorin a democracy (Thompson). A third goal that af-fects farm policy is that of efficiency in governmentexpenditures. Fewer government expenditures aredesirable as long as the other overriding goals ofthe policy are maintained. Accordingly, environ-mental policies that are perceived as threateningthe family farm, or requiring large increases in foodcosts or government expenditures may be rejectedby the public.

Role of Farm-Level Analysis

Given the preceding discourse, one might questionwhat role farm-level analysis has within policy for-mulation. John E. Lee, director of the EconomicResearch Service, summarized the micromodelingneeds of that agency as:

● To understand likely responses of farms in var-ious regional, commodity, size, financial, andother situations to various market conditionsand policy provisions in order to qualitativelyunderstand, but not necessarily quantify, likelyaggregate responses;

● To understand likely distributive effects andfarmer responses to various policy and marketsituations; and

● To use micromodels with macro- and econo-metric models to help provide additionaldetailed information and likely behavioral re-sponses not well specified in the macromodels(Lee).

Lee sees little reason to use micromodels to esti-mate aggregate behavior due to the large data re-quirements and aggregation problems. Theseconcerns are likely doubly emphasized given thestatus of the federal budget.

Demand for micromodeling of farm-level re-sponse to alternative environmental policies maygrow, however, at the state or regional level. Greaterconcern over resource use at that level has promptedmany states to more closely regulate agriculture.The “Big Green” proposal currently under con-sideration that would phase out much or all agri-cultural chemical use in California is one example.Detailed knowledge of the resource base, farmcharacteristics, and producer decision environment

Ellis, Hughes, and Butcher Economic Modeling of Farm Production and Conservation Decisions 105

can only help in such analyses. Estimates of localeffects of alternative policies can also aid in tar-geting areas for treatment.

One might argue that farm-level modeling alsohas a place in serving individual agricultural clien-tele. Experience, however, has shown little deriveddemand for such analyses by agricultural manage-ment firms or individual producers. Use of highlydetaiIed process and optimization models similarto those described here may not be possible for themajority of individual producers. Use of subcom-ponents, however, such as the crop-growth models,is already common among more progressive pro-ducers in several states. Their efforts may even-tually encompass self-analysis of resource useemploying many or all of the tools examined here.Such efforts may become more common as expert-systems applications mature. One program (CARMSby Richardson et al.) serves as a front-end datainput and matrix generator for FLIPSIM. Thesetypes of applications greatly reduce the process ofbuilding detailed farm-level models.

Summary and Conclusions

Agricultural economists, working in cooperationwith their fellow agricultural scientists, have madegreat strides in reflecting the decision environmentfaced by producers as well as the stochastic natureof agricultural production. Advances in computertechnology, optimization algorithms, and knowl-edgetreflection of biological and physical processeshave allowed us to examine more complex prob-lems and evaluate more comprehensive resource-policy options. As expertise has been gained inusing such optimization and process models, thetendency to develop more comprehensive policy-analysis systems has occurred, resulting in the“marriage” of seemingly diverse components. Theseefforts have extended from firm-level response toaggregate national price and safety impacts.

Such gains should not be accepted blindly. Ourability to accurately predict producer decisionbehavior is still somewhat poor, likely due toincorrect objective and constraint specification.Process-model development continues with phys-ical responses, such as the deterioration of cropresidues, effects of alternative tillage practices, andmeans of accurately reflecting rotational consid-erations, requiring more work. Dealing with thelarge quantities of physical data necessary to modelpollutant transport, for example, is still a problemdespite the use of GIS and other advanced tech-nologies.

In practice, regional effects will likely have greater

emphasis due to the regional nature of many re-source problems. Policies mandated at the nationallevel will likely be too general to have large effectsunless very specific standards, etc., are employed.If highly specific national policies are used, re-gional differences will be exaggerated and relativecomparative advantage among regions greatlychanged. Research into what level and form ofpolicy administration is appropriate for a particularresource problem is greatly needed. Care shouldbe taken to account for unintended spillover effectsof a given policy. Some very challenging tasks arebefore us. Let us hope that we are not too late inrecognizing symptoms of past excesses.

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