Evaluating Supply Control Policies for Frozen Concentrated Orange Juice with an Industrial DynamicsModel* RICHARD c. RAULERSON AND MAX R. LANGHAM Economists are increasingly being asked to study problems that require a systems approach. This paper reports an empirical application of industrial dynamics to a problem of an industry system. A second generation model was developed and used to appraise alternative supply control policies designed to reduce fluctuations in marketings of frozen concentrated orange juice and grower profits. Policies that controlled long-run supplies by constraining tree plant- ings appeared to have the most potential for success. Policies designed to control market sup- plies on a year-to-year basis actually encouraged new tree plantings which created long-run supply problems. T HE Florida citrus industry has been char- acterized by large shifts in supplies of fruit-particularly during and after such freeze years as 1957-58 and 1962-63, when the low supplies caused by the freezes led to high profit levels! and subsequently to large invest- ments in new tree plantings. These new plantings are a potential source of excess supplies at prices that would be acceptable to growers. This paper reports an investigation into the problem of fluctuating orange supplies and grower profits in the frozen concentrated orange juice (FCO]) sector of the Florida citrus indus- try. More specifically, a second generation" in- dustrial dynamics" model was developed and * Florida Agricultural Experiment Station Journal Se- ries No. 3286. This article is based on a thesis by Richard C. Raulerson [15] under a grant from Florida Citrus Mutual, Lakeland, Florida. Without implicating them, the authors would like to express their appreciation for the helpful comments provided by Leo Polopolus, W. K. McPherson, W. W. McPherson, Ray A. Goldberg, and two anonymous reviewers. The authors are also indebted to Roy G. Stout and H. W. Schwarz, both with the Coca-Cola Company, Atlanta, Georgia, for early encour- agement to work on a systems model of the orange sub- sector. 1 "High profit levels" refers to total industry profits without regard to the distribution of these profits. It is certainly recognized that some growers suffered severe economic losses. 2 The first generation model was developed by Jarmain [10]. I The industrial dynamics approach has been well doc- umented by its developer, J. W. Forrester [4]. Ansoff and Slevin [1] developed an expository paper on indus- trial dynamics under a joint commission by the Office of Naval Research and the Army Research Office. Forrester has published a response to this expository paper [5] and a review article on industrial dynamics after the first decade [6]. RICHARD C. RAULERSON is research assistant in agri- cultural economics at the University of Minnesota, and MAx R. LANGHAM is associate professor of agricultural economics at the University of Florida. used to appraise alternative supply control poli- cies which were designed to reduce fluctuations in supplies and grower profits. The study pro- vides an empirical application of industrial dy- namics to an industry policy problem." Industrial Dynamics Forrester [4, p. 13] defines industrial dynam- ics quite broadly as " ... the study of the infor- mation-feedback characteristics of industrial ac- tivity to show how organizational structure, am- plification (in policies), and time delays (in deci- sions and actions) interact to influence the success of the enterprise. It treats the interaction between flows of information, money, orders, materials, personnel, and capital equipment in a company, an industry, or a national economy." Emphasis in industrial dynamics is placed upon information-feedback loops which characterize most decision environments. An information- feedback loop exists whenever the environment leads to a decision that results in action that af- fects the environment and thereby influences fu- ture decisions. Critical to these information-feed- back loops are the amplifications and delays present in the decision-making system. . An example of an information-feedback loop is provided by a simplified model of the intra-year pricing of FCO] (Figure 1). The object here is to arrive at an equilibrium price. However, be- cause of delays in the system, the actual price fluctuates about the equilibrium price. The com- ponents of the information-feedback loops are connected by material flows and information flows. In the figure, flows of FCO] are shown as solid lines. This flow begins with growing and processing activities. The rate at which FCO] 4 Halter and Dean have reported an application of in- dustrial dynamics to a decision problem of a firm [8,9]. For examples of some problems in industrial dynamics, see J armain [11]. 197 at Penn State University (Paterno Lib) on May 8, 2016 http://ajae.oxfordjournals.org/ Downloaded from
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Evaluating Supply Control Policies for Frozen ConcentratedOrange Juice with an Industrial Dynamics Model*
RICHARD c. RAULERSON AND MAX R. LANGHAM
Economists are increasingly being asked to study problems that require a systems approach.This paper reports an empirical application of industrial dynamics to a problem of an industrysystem. A second generation model was developed and used to appraise alternative supplycontrol policies designed to reduce fluctuations in marketings of frozen concentrated orangejuice and grower profits. Policies that controlled long-run supplies by constraining tree plantings appeared to have the most potential for success. Policies designed to control market supplies on a year-to-year basis actually encouraged new tree plantings which created long-runsupply problems.
TH E Florida citrus industry has been characterized by large shifts in supplies offruit-particularly during and after such
freeze years as 1957-58 and 1962-63, when thelow supplies caused by the freezes led to highprofit levels! and subsequently to large investments in new tree plantings. These new plantingsare a potential source of excess supplies at pricesthat would be acceptable to growers.
This paper reports an investigation into theproblem of fluctuating orange supplies andgrower profits in the frozen concentrated orangejuice (FCO]) sector of the Florida citrus industry. More specifically, a second generation" industrial dynamics" model was developed and
*Florida Agricultural Experiment Station Journal Series No. 3286. This article is based on a thesis by RichardC. Raulerson [15] under a grant from Florida CitrusMutual, Lakeland, Florida. Without implicating them,the authors would like to express their appreciation forthe helpful comments provided by Leo Polopolus, W. K.McPherson, W. W. McPherson, Ray A. Goldberg, andtwo anonymous reviewers. The authors are also indebtedto Roy G. Stout and H. W. Schwarz, both with theCoca-Cola Company, Atlanta, Georgia, for early encouragement to work on a systems model of the orange subsector.
1 "High profit levels" refers to total industry profitswithout regard to the distribution of these profits. It iscertainly recognized that some growers suffered severeeconomic losses.
2 The first generation model was developed by Jarmain[10].
I The industrial dynamics approach has been well documented by its developer, J. W. Forrester [4]. Ansoffand Slevin [1] developed an expository paper on industrial dynamics under a joint commission by the Office ofNaval Research and the Army Research Office. Forresterhas published a response to this expository paper [5] anda review article on industrial dynamics after the firstdecade [6].
RICHARD C. RAULERSON is research assistant in agricultural economics at the University of Minnesota, andMAx R. LANGHAM is associate professor of agriculturaleconomics at the University of Florida.
used to appraise alternative supply control policies which were designed to reduce fluctuationsin supplies and grower profits. The study provides an empirical application of industrial dynamics to an industry policy problem."
Industrial Dynamics
Forrester [4, p. 13] defines industrial dynamics quite broadly as "... the study of the information-feedback characteristics of industrial activity to show how organizational structure, amplification (in policies), and time delays (in decisions and actions) interact to influence thesuccess of the enterprise. It treats the interactionbetween flows of information, money, orders,materials, personnel, and capital equipment in acompany, an industry, or a national economy."Emphasis in industrial dynamics is placed uponinformation-feedback loops which characterizemost decision environments. An informationfeedback loop exists whenever the environmentleads to a decision that results in action that affects the environment and thereby influences future decisions. Critical to these information-feedback loops are the amplifications and delayspresent in the decision-making system. .
An example of an information-feedback loop isprovided by a simplified model of the intra-yearpricing of FCO] (Figure 1). The object here isto arrive at an equilibrium price. However, because of delays in the system, the actual pricefluctuates about the equilibrium price. The components of the information-feedback loops areconnected by material flows and informationflows. In the figure, flows of FCO] are shown assolid lines. This flow begins with growing andprocessing activities. The rate at which FCO]
4 Halter and Dean have reported an application of industrial dynamics to a decision problem of a firm [8,9].For examples of some problems in industrial dynamics,see J armain [11].
Figure 1. Intra-year pricing of frozen concentrated orange juice
becomes part of processed inventory is symbolized as an hourglass-shaped valve. Processed inventory moves into retail inventory at anotherrate. Then consumption removes FCOJ from retail inventory at still another rate. Informationflows, upon which the pricing of FCOJ depends,are shown as broken lines. An FOB price is setby processors after a delay while they considerthe level of processed inventory and the rate atwhich it is disappearing. This FOB price dictatesa certain retail price. After a delay in discoveringthis retail price, consumers adjust their rate ofbuying according to their demand schedule. Buying reduces retail inventory and after a delay for
ordering and shipping, FCDJ moves from processed to retail inventory at some given rate.Then the processors reevaluate the level of inventory and rate of disappearance to consider anew FOB price. Then the pricing process is repeated. The dynamic equilibrium price (shown inFigure 1 to be stable) will probably never bereached because the pricing process is continuous. That is, while processors are debating inventories and movements, consumers are stillreacting to the old price. By the time the newprice reaches the consumer, the inventories andrates will have changed and a new price will beforthcoming.
The ModelOur industrial dynamic model of the FeD] in
dustry was specified by 137 equations [15, pp.111-116] written in the DYNAM05 simulationlanguage. However, the focus here will be on themajor forces that were thought to be operating inthe industry at the time of the study rather thanon detailed mathematical specification of modelstructure.
Economic structure generally enters a DYNAMO Model in simple tabular form throughwhat are termed table functions. For example, inthe model developed for this study, consumer demand for FCO] enters as a schedule of quantities demanded at varying retail prices. Thisschedule was derived from estimates of demandstructure by Langham [12] and Riggan [16] .Population and income projections developed byDaly [3] were used to derive estimates of shiftsin demand over the period of the simulation.These were introduced into the model as a schedule of values based on time. A presentation of tabled values that reflect model assumptions of theeconomic structure would be of little use to thereader without added explanation of the researchon which such assumptions were based, andspace precludes such explanation." Therefore, theunderlying economic structure was submerged,and the model was treated in terms of generalflows of materials and information,"
Major concerns of the detailed specificationwere determining where to close the model andvalidating it as an acceptable representation ofreal world industry behavior. The general criterion used in determining where to close themodel was to obtain sufficient detail of industrybehavior to accommodate testing the marketingpolicies of interest." General criteria used in vali-
6 The DYNAMO language was developed by AlexanderPugh [14]. We found the language to be a flexible instrument for translating a real world problem into amathematical model. There are 58 different types of equations permitted, but these can be used in combinationfor additional model building flexibility.
6 A detailed discussion of each equation in the modelmay be found in Raulerson [15, ch. 3].
1 Our approach is not inconsistent with the philosophyof industrial dynamics. This approach allows a view ofsystem flows and feedbacks that appear to be only indirectly connected to the underlying economic structurewhile the onus of system "performance" ultimately fallson that underlying structure. Even so, our choice has ledto the charge by one anonymous reviewer that we havetended to abandon our role as economists writing forother economists.
8 In reviewing this paper, Professor Ray A. Goldbergcautioned in a letter to the Associate Editor ", • . that
dating the model were internal consistency ofseparate model components and performance ofthe model in simulating actual industry performance during the period 1961-62 through1966-67.
The FCO] industry" and the DYNAMOmodel of it are composed of several interrelatedsectors. The major economic forces that integratethe sectors and make the model go are based onthe actions of two distinct groups of decisionmakers. Production and marketing decisions aresubject to control by growers and processors, andconsumption decisions depend on the behavior ofconsumers. The model is subjected to externalshocks via random freezes which affect bothshort- and long-run supplies of oranges. Theseforces combine to determine the behavior of theFCD] industry, including the level of orangesupplies and grower profits over time. The modelspecification includes informational delays andfeedbacks which represent an attempt to capture-in the model-the actual dynamics of individual decision making and overall system behavior.Additionally, the model is extended to include
there are so many limiting factors that were not takeninto consideration in the model, such as the impact ofsynthetics, major institutional changes such as the growthof cooperatives, long-term contractual arrangements, theadvent of the futures market, and the importance ofmarket orientation of the total industry. If you isolatethe producer from these other interrelated participantsand institutions, you will be doing both the producer andthe total industry a great disservice. This approach is animportant step forward in the thinking of producergroups and public policy makers. Therefore, I would bedisappointed to see it put forth without talking about theimportance of the other participants and the market orientation. Otherwise, producers might get the mistakenidea that they do not have to worry about pricing themselves out of the market, and that they don't have tolearn how to be their own best competitors by developing their own synthetic products to compete with themselves."
An anonymous reviewer indicated that evaluation completely from the growers' viewpoint was myopic.
The comments by both reviewers have validity andrepresent criticisms of the point at which the model wasclosed. Research is under way to expand the model toinclude other variables and the interests of the otherparticipants in the subsector system. However, any modelmust remain an abstraction, and a major benefit of constructing a model is not the numbers that it generates butthe understanding of the system that the effort gives tothose involved in constructing it. This understanding mayin turn become a valuable resource for decision makers.The comments by the reviewers serve to emphasize thedegree of abstraction in the model and to help cautionthe reader in evaluating the actual numbers generated bythe model.
II Goldberg [7] has published a description of theFlorida orange industry system.
Figure 2. Block diagram of major supply feedback loops of the frozen concentrated orange juice industry
the supply management policies defined in thenext section.
Figure 2 outlines the gross feedback loops affecting supplies for the industry and the model.Long-run supply as a consequence of tree numbers is affected exogenously by weather and endogenously by the level of grower profits. Thislong-run supply influences the general level ofFOB prices. Grower profits are in tum partly dependent on the existing FOB prices. Of specialimportance is the effect of weather as a source ofvariation in long-run supplies.
For any given season, the initial estimate ofshort-run supplies determines the FOB price.
The retail price is determined from the FOBprice; and through the consumer demand schedule, the retail price determines the level of retailsales. Retail sales and the effects of weather combine to influence short-run supply. In turn,short-run supply, when compared by processorsto some desired inventory level, determineswhether a change in the FOB price will occur.The short-run supply information-feedback loopprevents stabilization of prices and flows becauseof delays in information that exist in the system(see discussion of Figure 1).
Figure 3 summarizes the interaction of majorforces hypothesized to be operating in the indus-
try. Implicit in the figure are delays due to timeconsuming physical and decision processes. Thesedelays were of course made explicit in the mathematical specification of the model. Symbols usedin Figure 3 have the following interpretation:
Z represents a flow variable. It represents theaggregate response resulting from decisions made by growers, processors, orconsumers and can be thought of as avalue which restricts or increases a givenflow.
o represents variables that provide information for decision making.
o represents a stock variable. It is the cumulative result of inflows and outflows.
LJ represents a collective decision variable. Itdepicts an industry policy and is not affected by individuals acting alone.
-Er represents an exogenous variable.represents flows of orange concentrate.represents information flows.
Since the relationships depicted involve a system of interdependent information-feedbackloops, the decision as to where to enter the system in order to examine it is rather arbitrary.The production process begins with the plantingof trees and provides a reasonable starting point.
Following Figure 3, individual planting decisions in the model depended on grower profit perbox. Grower profit per box equaled the grower(on-tree) price less production costs. The growerprice was derived from the FOB price. Themodel assumed that individual growers increasedor restricted the rate at which they planted treesas grower profit rose or fell. Historically, it has
been evident that these decisions in the aggregateproduce sizable fluctuations in the number oftrees. More specifically, growers have tended tooverinvest (collectively) in new trees when profits were unusually high. For this reason themodel included a policy alternative to restrictnew plantings when profits were unusually high.
Over time, the planting rate added to thestock of trees. This stock could be reduced instantly by the effect of weather, freezes in particular. A freeze can affect orange supplies in one ofthree possible ways, depending on severity andduration: (1) Trees can be killed or so severelydamaged that they have to be butt-cut." (2)Trees can be damaged enough so that they haveto be hatracked; that is, all but the primary orsecondary scaffold branches have to be pruned.(3) Trees may only suffer yield losses in the current year.
The weather assumptions for simulation runsincluded only the effects of freezes in reducingorange supplies.>' These assumptions were basedon 29 seasons of actual temperature data(1937-38 through 1964-65) obtained from theFederal State Frost Warning Service [17]. Eachof these seasons was assigned (according to theseverity of the winter and corresponding croplosses) a traction of trees or fruit lost by each ofthe three ways discussed above. Random sampling with replacement was used to obtain sixsets of 14 years of weather assumptions from the29. These six sets were used to observe the ef-
10 Butt-cut trees take about as long to come back intoproduction as newly planted trees and were treated inthe model as trees completely lost.
11 Droughts, hurricanes, and other weather conditionsalso have their effect on orange supplies. The effects ofweather on orange supplies is currently undergoing moreintensive study by David W. Parvin, Jr.
.......... reeesecr
Behavior
" f: /-«I i ...~
/ r I .... ndustryI I I Advertisi s
I I \\ ,
\ ,\ ,\~
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~ Supply Control Policies rawer P.O.!. Retal1® --- ----- -- -----®----------_.. ~''"Figure 3. Flow chart showing the major forces operating in the FeD] industry,
including the hypothesized supply policies considered in the study
a Actual freeze effects are shown for seasons 1961-62 through 1966-67;random freeze effects are shown for seasons 1967-68 through 1980-81.
fects of different numbers, timing and severity offreezes on model results. Three such sets ofweather are presented in Table 1.1 2 The first twoof these are used later in the empirical comparison of the policies.
The stock of trees was also reduced as treesbecame unproductive. Although this is a naturalprocess, the rate at which trees become unproductive is influenced by cultural practices. Individual decisions concerning cultural practices depended on grower profits. The crop size for anygiven year was based on the number of trees andtheir yield. The yield was affected by culturalpractices and the influenceof weather.
Processed inventory was accumulated as theresult of processing rate decisions. Since matureoranges can be stored on the tree, it may bethought that the on-tree price and other factorswill influence the processing rate. This may betrue to some degree, but historically the processing rate has been quite consistent from year toyear. Evidently, the gains from attempting tooperate processing facilities at peak capacity outweigh other forces that influence processing decisions. Thus, the schedule of processing rates during a season were fixed in the model. A policythat would divert FCDJ into the secondary market was included. This action was presumed totake place whenever the grower profit per boxreached the minimum allowable (for policy pur-
12 All simulation runs actually had a duration of 20years (1961-62 through 1980-81). Actual weather for thefirst six years (through 1966-67) was used in order toprovide a basis for validation of the model.
poses) level (15 cents per box). The policy wasdesigned to reduce short-run supplies and toraise the price structure and growerprofits.
All disappearing processed inventories notchanneled into the secondary market was accumulated as retail inventory. The flow into retailinventory was regulated by retail ordering decisions based on the current FOB price and levelof retail inventory. Retail inventory was depletedas consumer buying decisions regulated the outflow of FCDJ into final consumption. The consumer buying decisions were based on an estimated demand function. Demand shifters in theform of income, population, and industry advertising were estimated (the latter quite crudely)and incorporated into the model. Retail price wasderived from the FOB price. Industry advertisingwas specified as a collective decision and its levelwas tied to a per-box tax on the fruit processed.
This completes discussion of the physical flowof FCOJ in the model, which began with the initial decisions to plant trees and ended withFCOJ flowing into final consumption. Main interest in the study was with grower profits, andall policy decisionsadded to the model were basedon the goal of stabilizing grower profits at acceptable levels. These policies are defined in thenext section.
Market Policies
The model was used to test the effectiveness ofthe following policies in stabilizing orange supplies and growerprofits:
1. "Free market" policy. The FCDJ industrywas simulated with its current policy of no explicit supply controls.
2. Allocation of FCDJ to two separate markets. This policy assumed that two separablemarkets for FCOJ either existedor could be established. The economic reasoning behind this policy was that if different price elasticities of demand existed in two different markets, then totalrevenue could possibly be increased in the shortrun by shifting FCOJ from the market with thesmaller (in an absolute sense) elasticity coefficient to the market with the higher elasticitycoefficient. In this study the two markets weredesigned as primary and secondary markets, respectively. The normal retail market was considered to be the primary market, while such possible markets as school lunch, vending machines,and relief were considered together as the secondary market. All FCDJ was considered sold inthe primary market when grower profits wereabove 15 cents per box. When profits were belowthis level, an amount of FCOJ necessary to raise
profits to 15 cents per box was shifted to the secondary market. It was assumed that FCO] moving into the secondary market faced a perfectlyelastic demand at a price of $1.00 per box andthat entry into and exit from the secondary market was possible.
3. Elimination of fully productive trees. Thispolicy was designed to stabilize profits above the15 cents per box level. To accomplish this goal, acertain portion of the existing productive treeswere destroyed whenever grower profits droppedbelow 15 cents per box. When the policy was effective, trees were removed at a rate that wouldeventually raise grower profits to $1.00 per box.However, the policy would never actually bringprofits to that level since the policy was no longer effective when grower profits rose above 15cents per box.
4. Curtailment of new tree plantings. Thispolicy was formulated so that no new orangetrees could be planted whenever grower profitswere above $1.00 per box. The rationale behindthis policy was derived from the known tendencyof orange growers to overinvest in new treeplantings when grower profits were at an unusually high level (e.g., following a freeze, whensupplies were very low and prices were high).Due to the nature of its control over supplies,
this policy was perhaps more forward lookingthan the other policies. Instead of representingan attempt to correct a low grower profit situation, the policy was designed to help keep such alow profit situation from developing.
5. Allocation of FCOJ to two separate markets, used in conjunction with elimination offully productive trees.
6. Allocation of FeOJ to two separate markets, used in conjunction with curtailment of newtree plantings.
Simulation runs with alternative sets of randomly selected weather assumptions indicatedthat weather had little effect on the relative performance of the six policies. Generally, the"best" policy became more valuable as weatherincreased fluctuations in orange supplies. An empirical comparison of the policies under two setsof weather assumptions is presented in the nextsection.
Simulation Results
Validation of modelFigures 4 and 5 provide a view of the corre
spondence of model behavior'" to that of the ac-
13 All simulation runs were identical during the firstsix years.
~ Simulated
o Actual
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Figure 4. Simulated and actual grower profits of frozen concentrated orangejuice, 1961-62 through 1966-67
a Estimated.Source: Florida Canners Association Statistical Summary, Season of 1965-66, Winter
Simulated and actual retail pack of frozen concentrated orange juice,1961-62 through 1966-67
1963-64 1964-65
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90
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a Based on 45° Brix.b Estimated.Source: Florida Canners Association Statistical Summary Season of 1965-66, Winter
Haven, Florida.
tual system with respect to pack of FCOJ andgrower profits per gallon. These variables arerepresentative of the other variables in the modelinsofar as model behavior versus actual industrybehavior is concerned. The model and the actualsystem exhibited the same modes of behavior forthe six seasons, 1961-62 through 1966-67. Thecycles of the variables have the same periods,and the amplitudes (absolute change from a highpoint on a curve to a low point) are similar. Theability of the model to represent turning pointsin the movement of variables during this six-yearperiod supported the notion that the model couldsatisfactorily simulate the time-varying behaviorof the industry system.
Simulation of the "free market" policy withno freezes (weather One) is presented in Figure6.14 This run, like all runs, was started with theinitial conditions corresponding to the start ofthe 1961-62 season. Crop size (for FCOJ only)was 55.9 million boxes, FOB price per dozensix-ounce cans was $1.44, grower profits averaged73 cents per box, and productive trees (not shown
14 The behavior of only three variables is traced. TheDYNAMO simulation program will plot and present intabular form the data that traces the movement of anyvariable in the model.
in Figure 6) numbered 13.9 million.Violent shifts in the variables in the early
years were a consequence of the 1962-63 freeze.Beginning with the 1964-65 season growerprofits fell continuously and reached zero in the1966-67 season. During this same period cropsize rose to 69.7 million boxes, the FOB price fellto about $1.15, and the number of productivetrees rose to over 18 million. Grower profits didnot become positive again until the 1975-76 season after which they followed a general upwardtrend with slight seasonal variation.
Analysis of alternative policies
Method of comparing.-Supply control policies were compared on the basis of their ability tostabilize supplies and grower profits at acceptable levels. Weather conditions One and Two(Table 1) were each used to simulate each ofthe six policies. As indicated earlier, differentweather conditions had little effect on the desirability of the six policies in terms of an ordinalranking. However, as will be shown later, theabsolute performance of the six policies was sensitive to the number and severity of freezes thatoccurred.
and relative variability of grower profits." Thecomparison used assumed that industry utility(from the growers' point of view) was an increasing function of total grower profits and adeclining function of variability of profits.
Comparison using weather One.-The "freemarket" policy appeared to be the least desirableof the six studied from the growers' point of view.It produced the lowest total grower profits andhad the highest rel-variance (Table 2). However,at the end of the simulation it ranked third in thelevel of grower profits. This result is consistentwith the well-known facts that a free market iscapable of correcting a low grower profit situation without the aid of supply-oriented policiesbut that the time required to correct such a situation can be quite long. The hypothesized utilityfunction suggests that some type of direct programwould be preferred. The time required for profitsto recover in a "free market" environment if nofreezes occur can be seen in Figure 6. Note thatthe period of high profits following the freezelasted only four years, while it took twelve yearsfor profits to come back to pre-freeze levels oncethey declined. This situation is common amongseveral important tree crops (see Bateman [2]).The difference in investment between a nurserytree and a fully productive tree probably contributes to the longer period of depressed pricesand grower profits. Growers seem to be morewilling to plant new trees during periods of high
15 Grower profits were defined as on-tree price lessaverage production cost of 85 cents per box.
profits than to destroy productive trees duringperiods of low profts. These differences ingrowers' response substantiate the propositionthat large fluctuations in crop size and growerprofits tend to lower long-run profits because ofthe stimulant to planting during periods of highgrower profits.
The two policies, planting restriction (PR)and planting restriction combined with secondarymarket (PRSM), produced similar total growerprofits. However, the behavior of industry variables for the two policies was somewhat differentover the 20-year simulation period. Yearlygrower profits were higher for PRSM during seasons 1966-67 through 1972-73. This occurredbecause the secondary market part of the policylimited supplies earlier than a planting policy byitself. However, for the balance of the simulationthe policy (PR), which called for planting restrictions, alone had higher yearly profits. Sinceno immediate supply removal policy was used toraise the grower profits, with this single policy,the planting restriction was in effect longer. Inaddition, the lower profits restricted the plantingof more trees, which resulted in lower suppliesand higher profits later. An examination of theprofits for the latter years of the simulation revealed that the planting restriction only policy(PR) was gaining on the dual policy (PRSM) atthe rate of over three million dollars per year.Since the total grower profits were 773.9 and771.0 million dollars for the dual and single policy, respectively, grower profits for the single
85
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Figure 6. Average grower proflt, FOB price, and crop size for a 20-year simulation with weather assumption One and a "free market" policy, 196162 through 1980-81
Table 2. Total grower profits, rel-varianees,grower profits for the 20th year,and total retail sales for 20-yearsimulations of the frozen concentrated orange juice industry, 196162 through 1980-81
a Policies are ranked according to their performanceunder weather One. FM = "free market," policy 1 (as defined in text); SM = secondary market, policy 2; TR = treeremoval, policy 3; PR=planting restriction, policy 4;TRSM = tree removal and secondary market, policy 5;PRSM =planting restriction and secondary market,policy 6.
b Each case contains 48 six-ounce cans of concentrate.o Ranks differ from weather assumption One.
policy would probably be greater had the simulation been extended one more year.
The policies that included removal of productive trees-tree removal (TR) and tree removalcombined with secondary market (TRSM)-hadrel-variances slightly lower than the policies thatrestricted plantings, but produced lower growerprofits. Total grower profits for TRSM were almost identical to those for the single tree removal policy, TR (Table 2). Other major industry variables reacted analogously over time forTR and TRSM. The tree removal and secondarymarket policies were both designed to bringgrower profits up to 15 cents per box. Thus, theaddition of the secondary market policy added
very little to the short-run effects of the tree removal policy. In later years of the simulation thegrower profits were above 15 cents per box sothat the secondary market part of the policy wasnot in effect. Thus, it appears that a secondarymarket policy used in tandem with a tree removal policy has little advantage over a tree removal policy used by itself.
The secondary market policy (SM) used byitself had the lowest total grower profits and thehighest reI-variance of all policies except the"free market" policy. At the end of the simulation it produced lower grower profits than anyother policy (Table 2). Its influence on industryvariables was to raise profits in the short runwhile limiting long-run gains. This type of policyattacks only the symptoms of the problem andallows the cause to go unchecked. A secondarymarket policy adjusts supplies on a year-to-yearbasis. However, only oranges are removed. Inorder to affect future supplies, the trees must becontrolled in some manner. Model experimentation showed that curtailing short-run supply witha secondary market scheme encouraged theplanting of trees; hence, long-run supply problems were actually magnified.
Comparison using weather Two.-The relative desirability of the six policies studied underweather assumption One remained unchangedunder weather condition Two. However, the absolute and percentage differences in grower profits between the leading policies showed some increase under weather assumption Two.
Under weather condition One, the total growerprofits for the policies involving the removal ofproductive trees were about 90 percent of thetotal grower profits for the policies that restricted new plantings. However, this figuredropped to about 78 percent for weather condition Two (Table 3). This implies that the moreexogenous fluctuations in supply (freezes) thatoccur, the more valuable the planting policy becomes. This is reasonable, considering the argument presented earlier that growers are more responsive to high than to low prices. Consequently, intermittent periods of high growerprofits induced larger supplies unless a restrictionon planting was imposed.
Relative costs of alternative policies
The policies discussed have been judged onlyon their capability to stabilize grower profits andsupplies of oranges at specified levels. Implementing any policy program will necessitate somecosts. The two dual policies-planting restrictions plus secondary market, and tree removal
plus secondary market-would certainly costmore than their counterparts-planting restrictions and tree removal. Since the single policiesperformed as well as the dual policies, the singlepolicies would probably be preferred on the basisof cost considerations.
The elimination of dual policies leaves fourpolicies-"free market" policy, the secondarymarket, tree removal, and planting restrictionsto be considered. Of these, the "free market"policy is obviously the least expensive to employ,but it produced the highest tel-variance and thelowest total grower profits. The planting restriction policy added most to grower profits and wouldprobably be the least expensive to administer.The secondary market idea would probably bethe most expensive to administer because of therecord supervision required to assure that supplies would be channeled into the correct market.This policy also ranked third among the four single policies in terms of total grower profits andvariability of profits. The tree removal policyprobably falls between the planting restrictionand secondary market policies in terms of cost,as well as in terms of grower profits. Thus, it appears that a consideration of the cost of alternative supply policies probably would not alter therelative desirability of these policies.
Concluding Remarks
Before the FeO] industry adopts any supplyoriented policy, it should resolve some basic conflicts of interest regarding the desired level of orange supplies and the length of time the proposed policy should be in effect. Furthermore, itshould also recognize that any policy will undoubtedly change the industry environmentwhich created the need for the policy. The industry should therefore be cautioned to continuallyevaluate both the tangible benefits of any policyand the structural changes in the industry resulting from such a policy.
The price elasticity of demand is more elasticfor the processor than for the grower sector ofthe FCOJ industry at any given level of cropsize. This situation is common in agriculture andresults from the addition of processing and marketing charges to the value of raw fruit. Thesedifferent price elasticities imply that a crop sizethat maximizes net revenue for the grower mayproduce less than the maximum net revenue forthe processor.
One solution to any conflict that may exist orarise between growers and processors concerningoptimum crop size is vertical integration in theFCO] industry. Historically, vertical integration
Table 3. Simulated total grower profits forsix policy decisions under twoweather conditions expressed as apercent of total grower profits ofleading policy under each weatherassumption
a Total grower profits as percent of PRSM under eachweather assumption.
has been occurring in the industry. The continuation of a "free market" policy will probablyprovide greater impetus to more integration thanthe other policies considered in this study.
The study shows that policies designed to control supplies on a year-to-year basis may createadditional supply problems in the future. If so,there may be a general conflict between shortand long-run policy goals. The implication forthe industry is that careful study should be givento any policy to determine whether such a conflict exists.
Even though a supply policy is successful instabilizing grower profits and supplies of orangesat an acceptable level, industry decision makersshould be aware of possible unforeseen dangers.It was pointed out earlier that a policy maychange the industry environment in such a waythat producers will respond in an unpredicted orunpredictable manner. For example, when the Israeli government established a program of guaranteed forward prices to help the poultry industry, farmers increased egg production an estimated 81.3 percent above normal production forthat price because the price was no longer uncertain (Mundlak, [13, p. 98]). If such a situationdeveloped in the FCO] industry, the results ofthis study would tend to be optimistic for all policies tested, except"free market."
A final word of caution. Before the FCOJ industry adopts any policy it must recognize the irreversibility of the industry supply function. Thetime lag of grower response to price increases ismuch shorter than to price decreases. Thus, anypolicy that improves prices in the short run anddoes not attempt to control tree numbers appearsto lower average grower profits in the long run.
[6] --, "Industrial Dynamics-After the First Decade," Mgt. Sci. 14 :398-415, March 1968.
[7] GOLDBERG, R A., Agribusiness Coordination, ASystems Approach to Wheat, Soybeans, and FloridaOrange Economics, Boston, Division of Research,Harvard Business School, 1968.
[8] HALTER, A. N., AND G. W. DEAN, Simulation of aCalifornia Range Feedlot Operation, Giannini Foundation Res. Rep. 282, University of California, May1965.
[9] ---, "Use of Simulation in Evaluating Management Policies Under Uncertainty: Application to aLarge-Scale Ranch," J. Farm Econ. 47:557-573,Aug. 1965.
[10] JARYAIN, W. E., "Dynamics of the Florida FrozenOrange Cencentrate Industry," unpublished M. S.thesis, Massachusetts Institute of Technology, 1962.
[11] ---, ed., Problems in Industrial Dynamics,Cambridge, MIT Press, 1963.
[12] LANGHAM, M. R, "On-Tree and In-Store CitrusPrice Relationships," in Proceedings of the SecondAnnual Citrus Business Conference, Florida CitrusCommission, Nov. 1965.
[13] MUNDLAK, Y., An Economic Analysis of Established Family Farms in Israel, 1953-58, The FolkProject for Economic Research in Israel, Jerusalem,The Jerusalem Post Press, July 1964.
[14] PUGH, A. L., III, DYNAMO User's Manual, 2nded., Cambridge, MIT Press, May 1963.
[15] RAULERSON, R C., "A Study of Supply-OrientedMarketing Policies for Frozen Concentrated OrangeJuice: An Application of DYNAMO Simulation,"unpublished M.S. thesis, University of Florida,1967.
[16] RIGGAN, W. B., "Demand for Florida. Oranges,"unpublished Ph.D. thesis, North Carolina StateUniversity, 1965.
[17] U.S. Department of Commerce, Weather Bureau,Winter Minimum Temperatures in Peninsular Florida, Summary of 20 Seasons, 1937-57, and AnnualReports, 1958 through 1965, Federal-State FrostWarning Service, Lakeland, Florida.
