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LIIRARY
Michigan State
University

 

 

 

This is to certify that the

thesis entitled

An Economic Supply/Demand Balance Sheet
For Forecasting And Simulating
The United States Corn Industry

' presented by

Anthony B. Washington

has been accepted towards fulfillment
of the requirements for

M-S- degree in Agricultunal Economics

 

/ Major professor
Date ffl/Ef/ 7] /i£00

0-7539 MS U is an Affirmative Action/Equal Opportunity Institution

 

 

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MSU

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AN ECONOMIC SUPPLY/DEMAND BALANCE SHEET
FOR FORECASTING AND SIMULATING
THE UNITED STATES CORN INDUSTRY

BY
Anthony B. Washington

A THESIS

Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of

MASTER OF SCIENCE

"Department of Agricultural Economics

1988

 

ABSTRACT

AN ECONOMIC SUPPLY/DEMAND BALANCE SHEET
FOR FORECASTING AND SIMULATING
THE UNITED STATES CORN INDUSTRY

BY
Anthony B. Washington

The price and yield fluctuations faced by agricultural
producers are chief sources of uncertainty. In an effort to
quantify some of the sources of uncertainty, educate
producers where the uncertainty exists, and aid in their
production decisions, an economic supply/demand balance
sheet for use on a microcomputer was designed. The balance
sheet uses time series data to forecast the U.S. supply and
world demand for corn in a recursive model to determine a
0.8. marketing clearing farm price of corn.

The user, be it an educator or producer, can then
select to do scenario analysis with the balance sheet by
altering the exogenous variables or forecasted values and
review the results. Further, the user can chose a
particular scenario to estimate a price probability
distribution. This probability distribution serves to help
producers determine the probable states of nature for risk

aversion/decision analysis.

ACKNOWLEDGMENTS

All praise goes to

 

 

 

 

 

 

 

l .0 ‘
5“ a L
‘ vv ‘
\ /

I am indebted to the people here at Michigan State
University for the opportunity to complete my Masters of
Science degree. I will never forget you.

My major professor, Dr. Jim Hilker, deserves special
thanks for his guidance and patience in supervising my
program and reviewing manuscripts, without his help this
project could never have been undertaken. I am especially
thankful to my committee members, Drs. Roy Black, Gerald
Schwab and Jack Meyer for their valuable comments and input.

My gratitude goes to Eric DeVuyst, Erik Zackmann and
Rob Leland members of MSU's Ag Model, for the technical
assistance they provided.

Finally, I must thank my family members. Each one of
them are educationally oriented and have inspired me to set
lofty goals and stick to them regardless of the hardships

faced.

ii

TABLE OF CONTENTS

Page

CHAPTER I

PROBLEM AND OBJECTIVES...........................1

1.1 Problem Setting..............................1

1.2 Goal And Objectives..........................3

1.3 Project Justification........................3

1.4 Procedure....................................6
CHAPTER II

THEORETICAL AND CONCEPTUAL CONSIDERATIONS

FORMODEL CONSTRUCTIONOOOOOOOOOOOOOOOOOOOOO00.0.08

2.1 Elements Of suPPIYOIOOOOOOOOOOO0.00.00.00.0008

2.1.1 Theoretical Considerations............9
2.1.2 Conceptual Considerations............13

2.2 Elements of Demand..........................29

2.2.1 Theoretical Considerations...........30
2.2.2 Conceptual Considerations............36

2.3 Price Determination.........................44

3.1 Theoretical Considerations...........44
3.

2.
2. 2 Conceptual Considerations............46

CHAPTER III
SIMULATION MODEL CONSTRUCTION...................48
3.1 The Simulation Model........................48
3.2 General Econometric Procedures..............49

3.3 Statistical Concepts
and RandomvariableSOOOOOOO00.0.0000...00.0.51

3.4 Data Source Considerations..................54

3.5 Appraising The Results......................55

iii

TABLE or CONTENTS (cont'd)

Page
3.6 U.S. Corn Supply Model......................57

1 Results For Acres Planted............57
2 Results For Acres Harvested..........61
3 Results For Yield Per Acre...........63
4

3
3
3
3 Production And Supply................65

6.
6.
6.
6.

3.7 U.S. corn Demand “OdeJ-OOOOOOOOOOOOOO0.0.0.0067

3.7.1 Results For Corn Consumed

by Livestock.........................67
3.7.2 Food, Seed, And

Industrial Use of Corn...............83

3.7.3 Results For U.S. Corn Exports ........ 83
3.7.4 Results For Policy And
Free Ending Stocks..................lOl
3.8 U.S. Corn Price Determination...... ...... ..106
CHAPTER IV

THE ECONOMIC SUPPLY/DEMAND BALANCE SHEET WITH
APPLICATIONS.0..0.00.00...0.00.00.00.00...O....109

4.1 The Economic Supply/Demand
Balance SheetOOOOOOOOOOOOOOOOO0.0...0.0.00.109

4.2 Ex POSt Ana1Y818000ooooooooooooooooo0000000112

4.2.1 The 1985-86 Marketing Year..........113
40202 The 1986-87 Marketing year. 0 o o o o o o o o 120
4.2.3 The 1987-88 Marketing Year..........126

4.3 Scenario Analyses And Sample Runs..........134

1 Change In World Income..............136
2 Change In U.S. YieldOOOOOOOOOOOOOOOOIBB
3 Change In Competitor Crop Prices....139
4

4.
4.
4.
4. Change In Alcohol Use...............140

3.
3.
3.
3.
CHAPTER V
SUMMARY, CONCLUSIONS AND RECOMMENDATIONS.......141
5.1 Summary And Conclusions....................141

5.2 Recommendations For Increasing
Balance Sheet Performance............. ..... 143

IV

TABLE OF CONTENTS (cont'd)

Page

5.3 Recommendations For Handling Risk... ..... ..144

5.3.1 The Traditional Analysis............145

5.3.2 The Modern Analysis.................146

5.3.3 Decision Problems...................147

5.3.4 Expected Utility Model..............149
5.3.5 Expected Value-Variance

AnalYSiSOOOOO00.00.000.000.0.0.0.000151

5.4 Conceptual Assistance To
Decision Making............................153

Appendices
1. Data Source Considerations..................155
2. International Regional Groupings............157

3. Supply/Demand Balance Sheet
Documentation.OOOOOOOOOOOOOOOO0.0...00......159

4. Computer Input/Output Code..... ............. 184

Bibliography........................ ..... . ........ ..303

 

 

10.

11.

12.

13.

14.

Table

LIST OF TABLES

Page
USDA Balance Sheet For Corn.................. ..... 4

Ordinary Least Squares Estimates For U.S. Acres
Planted To Corn In Million Acres.................60

Ordinary Least Squares Estimates For U.S. Acres
Harvested In Million Acres.......................64

Ordinary Least Squares Estimate For U.S. Yield
In Bushels/Acre..................................66

Time Relationship Map............................69

Ordinary Least Squares Estimates For Dairy
Animal Corn Consumption Per Head.................71

Ordinary Least Squares Estimates For Cattle On
Feed Corn Consumption Per Head...................73

Ordinary Least Squares Estimates For Other Beef
Cattle Corn Consumption Per Head.................75

Ordinary Least Squares Estimates For Hen, Pullet
And Other Chicken Corn Consumption Per Head......76

Ordinary Least Squares Estimate For Broiler
corn consumption Per HeadOOOOOOOOOOOOO0.00000000078

Ordinary Least Squares Estimate For Turkey
Corn Consumption Per Head........................79

Ordinary Least Squares Estimates For Hog
Corn Consumption Per Head........................81

Ordinary Least Squares Estimate For Other And
Unallocated Corn Consumption.....................82

Ordinary Least Squares Estimates For Importer

Net Imports Per Capita
In Thousand Metric Tons............ ........ . ..... 88

vi

LIST or TABLES (cont'd)

Table Page

15. Ordinary Least Squares Estimates For Importer
Yield In Metric Tons/Hectare.....................91

16. Ordinary Least Squares Estimate For Exporter
Yield In Metric Tons/Hectare.....................94

17. Ordinary Least Squares Estimate For Exporter
Harvested Area In Thousand Hectares..............95

18. Ordinary Least Squares Estimates For Exporter
Consumption Per Capita
In Thousand Metric Tons..........................97

19. Ordinary Least Squares Estimates For Exporter
Ending Stocks Per Capita
In Thousand metric TOIISoooooooooooooooooooooo00.100

20. U.S. Feed Grain Exports In Thousand Bushels.....102

21. Ordinary Least Squares Estimates For A Change
In Policy Stocks In Thousand Bushels............105

22. Ordinary Least Squares Estimate For Free
Stocks In Thousand Bushels......................107

23. Balance Sheet For Corn:
The 1985-86 Marketing Year................. ..... 114

24. U.S. Export Demand:
The 1985-86 marketing YearOOOOOOOOOOOOOOOOOOOO0.118

25. Exporting Region's Net Exports:
The 1985-86 Marketing Year......................118

26. Livestock Consumption Of Corn In The U.S.:
The 1985-86 marketing YearOOOOOOOOOOOOO0.0.0....120

27. Balance Sheet For Corn:
The 1986-87 marketing YearOOOOOOOOOOOOOOOO0.0.0.122

vii

LIST OF TABLES (cont'd)

Table Page

28. U.S. Export Demand:
The 1986-87 Marketing YearOOOOOOOO0000.00.00....125

29. Exporting Region's Net Exports:
The 1986-87 Marketing Year............... ...... .125

30. Livestock Consumption Of Corn In The U.S.:
The 1986-87 Marketing Year......................126

31. Balance Sheet For Corn:
The 1987-88 marketing YearOOOOOOOO0.00.00.00.000128

32. U.S. Export Demand:
The 1987-88 Marketing Year............ .......... 131

33. Exporting Region's Net Exports:
The 1987-88 Marketing Year......................131

34. Livestock Consumption Of Corn In The U.S.:
The 1987-88 Marketing Year......................133

35. Cattle On Feed Consumption Per Head:
The 1987-88 Marketing Year............ .......... 133

36. Other Beef Cattle Consumption Per Head:
The 1987-88 Marketing Year......................133

37. Balance Sheet For Corn:
The 1988-89 Marketing Year......................135

38. Probability Distributions
For Varying Scenarios............. ............ ..137

39. The Decision Table..............................148

40. The Payoff Matrix...............................154

viii

LIST OF FIGURES

Figure Page
1. Acres Harvested To Corn In Million Acres..........62
2. The E-V Boundary.................................152
3. Title Screen.....................................160
4. Main Menu........................................160
5. Load Screen......................................162
6. Save Screen......................................162
7. Exogenous Variable Menu..........................164
8. Exogenous Variable Screen 1......................164
9. Exogenous Variable Screen 2......................164
10. Exogenous Variable Screen 3.....................165
ll. Exogenous Variable Screen 4.....................165
12. Exogenous Variable Screen 5.....................165
13. U.S. Composites Sub Menu........................166
l4. Govt. Program Participant's Revenue Screen......l66
15. Diversion Payments Screen.......................166
16. Deficiency Payments Screen......................167
17. Food, Seed, And Industrial Use Screen...........167
18. Importers Gross Domestic Product Screen 1.......l69
19. Importers Gross Domestic Product Screen 2.......169

20. Importers Gross Domestic Product Screen 3.......169

ix

LIST OF FIGURES (cont'd)

Figure Page

21. Exporters Composites Sub Menu...................170
22. Exporter Crop Revenue Screen....................170
23. Exporter Gross Domestic Product Screen 1........171
24. Exporter Gross Domestic Product Screen 2........171
25. Add Factor Screen 1.............................173
26. Add Factor Screen 2.............................173
27. Balance Sheet Screen 1................. ...... ...174
28. Balance Sheet Screen 2..... ........ . .......... ..174
29. Balance Sheet Screen 3..........................l74
30. Livestock Corn Consumption Menu.................175
31. Corn Consumption Summary Screen....u............175
32. Dairy Animal Consumption Per Head Screen........176
33. Other and Unallocated Consumption Screen........176
34. U.S. Export Sub Menu............................l78
35. Exporters Net Exports Screen....................178
36. U.S. Exports Summary Screen.....................178
37. Solve Options Sub Menu..........................179
38. Single Price Solve Screen.......................179
39. Probability Iteration Screen....................180

40. Price Probability Distribution
Display screenOO...OOOOCOOOOOOOOOOO0......0.0.0.180

41. EXit screeDOOOOCOOOCOO......OOOOOOOOOOOOOOOOOO0.183

CHAPTER I

THE PROBLEM AND OBJECTIVES

1.1 PROBLEM SETTING

Of the many uncertainties facing farmers, yield and
price fluctuations of the products farmers sell comprise the
key sources of uncertainty. The problem is that inputs must
be allocated at one point in time while returns come at a
later date. Because the future cannot be foretold, the
inputs must be allocated without perfect assurance of a
particular return. Over the last decade, dramatic price
movements, both up and down, have increased interest in more
and better information for both policy makers and individual
producers.

When farmers face a planning period, they need to have
an expectation of the price that they will receive for their
efforts. A probability distribution around that price would
also be helpful. This price expectation is what allows
producers to choose how they will allocate their resources
and the probability distribution around the price is what
allows for individual risk behavior to enter the analysis.
With information in this form, decision makers will be

better able to comprehend the degree of uncertainty

2
associated with future events. Furthermore, more accurate

perceptions about the uncertainty associated with future
events will allow decisions to be made that are more
consistent with producers individual objectives and risk
preferences.1

Recalling from classic micro economic theory, we know
that there exist several market structures from which an
industry may be operating. The farming industry is
considered a close representation of a purely
(atomistically) competitive structured market in the sense
that individual farmers are price takers. The structure of
this market dictates that (among other things):

1) There are a large number of firms, each producing
homogeneous products,

2) New firms are free to enter and existing firms are
free to exit the industry,

3) There is generally no nonprice competition, and most
significantly,

4) Each firm is a price taker assuming that its actions
have no effect on market price.

Being a price taker implies two major conditions occur.
First, producers cannot influence the market price of their
product due to the relationship between their production and

the quantity of total supply. Second, in the corn

subsector, producers are unable to create a unique demand

 

1. A. N. Halter, and R. Mason, "Utility Measurement

for Those Who Need to Know," Western Qogrngl Qf Agricultural
E onomics, Vol. 3, No. 2, December 1978, p. 99-109.

3
for their goods because of the homogeneity of their product.

By understanding the structure, comparative static analysis

can be conducted.

1.2 GOAL AND OBJECTIVES

The goal of this project is to develop a microcomputer
economic supply/demand balance sheet for forecasting corn
prices which can be used as a group-interaction teaching
aide.

To reach this goal several objectives must be met. The
primary objective is for the balance sheet to be simple to
use and easy to update or modify yet, comprehensive enough
for educational purposes. Second, nation wide corn supply
and demand relationships need to be estimated from the
available data. Third, these estimated equations will be
combined in an algorithm for use on a microcomputer.
Finally, the algorithm will be menu driven by the user to
display various screens of information which present a

comprehensive balance sheet as exhibited in Table 1.

1.3 PROJECT JUSTIFICATION
The art/science of forecasting corn prices is
imprecise, however most successful market analysts follow an

organized procedure. An economic supply/demand balance

Table l: USDA Balance Sheet For Corn

PROJ. PIOJ.
1981—82 1982-83 1983-88 1988-85 1985-86 1986-87 1987-88

—— — —— ‘— '

(NILLION ACRES)
ACRES SET-ASIDE AND

ACRES PLANTED 88.1 81.8 60.2 80.8 83.2 76.7 66.0
ACRES NARVESTED 75.5 72.7 51.8 71.8 75.1 69.2 59.2

BUINARVESTED ACRE 108.9 113.2 81.0 106.6 118.0 119.3 119.8

 

(NILLION BUSNELS)

BEGINNING STOCKS 1038 2178 3120 723 1688/1 8080 8882
PRODUCTION 8119 8235 8175 7678 8877 8250 7068
IMPORTS 1 1 2 3 11 2 2
III. III. _ I... 88888 I... .3888 II...
TOTAL SUPPLY 9158 10810 7297 8801 10536 12291 11988
USE:
FEED 8202 8521 3737 8117 8095 8715 8900
FOOD, SEED 8
IND. USES 811 898 973 1065 1160 1191 1225
TOTAL DOMESTIC 5013 5819 8710 5182 5255 5906 6125
EXPORTS 1967 1871 1868 1838 1281 1508 1700
III! III. III! 8888 III! 8888 .888
TOTAL USE 6980 7290 6578 7020 6896 7810 7825
TOTAL ENDING STOCKS 2178 3120 723 1381/1 8080 8882 8123
TOTAL. 5 OF USE 31.2 82.8 11.0 19.7 62.2 65.9 52.7

STOCKS IN GOVT. PRGRIN

 

CCC INVENTORY 302 1166 201 280 586 1883
FARMER-OVNED RESERVE 1300 1550 825 883 568 1321
UNDER GOVT. LOAN 385 109 20 567 2736 2279?
10m. TEE? 235% "6:3 12;}; 5533 £335 ----
FRSUSHELS 187 295 77 129 192 -161?
1 OF USE 2.7 8.0 1.2 1.8 3.0 0.0
REGULAR LOAN RATE $2.80 82.55 82.65 82.55 82.55 31.88 81.82
U.S. SEASON AVERAGE ...... ------------ ................................... 87:65:

FARM PRICE. 3/80 $2.50 $2.68 $3.25 82.62 82.35 81.50 81.85

SOURCE: USDA.
111988-85 ending stocks do not 3 1985-86 beginning stocks due to narkctln;
your change.

5
sheet (henceforth called the balance sheet) such as that

shown in Table l, is the most common starting point.2

A balance sheet is similar to a checkbook. It records
how much corn is in the bin at the start of the year, how
much corn is produced during the year, how much grain
disappears during the year, and how much grain is left in
the bin at the end of the year. When the quantities
supplied and demanded are in balance (equilibrium), a market
clearing price is established.

The computerized balance sheet can be used as a .
practical teaching tool by University staff for educating
individuals involved in cash grain enterprises. University
staff can use this mobile balance sheet at extension
meetings to show what factors influence the market price for
corn and how changes in those factors will modify the
estimated market clearing price.

The balance sheet can be used to provide information to
anyone involved in the planning stage of production farming
such as farmers and extension workers. The balance sheet
could assist an individual identify an expected corn price
and the variation around that expected price. Information

about expected price and the variation associated with that

price could aid producers decide upon a crop mixture based

 

2. Unlike the accountant's balance sheet this does not
relate to a point in time but to the balance of flows over a
period of time, generally a year. The term is used in the
same sense as the Food and Agriculture Organization of the
United Nations uses it in their Food Balance Sheets.

6
on capability of diversification and relative prices, while

still maintaining eligibility for government programs.

The balance sheet could provide a portion of the
information needed in the marketing area by supplying both
an expected price and a price probability distribution.
With information in the form of expected price and
probability, producers are better able to determine which
marketing alternatives (i.e. hedge, forward contracts,
options, storage, etc.) will be most in line with their
objectives.

Each supply and demand factor of the balance sheet will
be available for adjustment. This feature of the balance
sheet will allow for quick testing of "what if" situations.
Given an adjustment, a new solution can be found and the
effect on price of that adjustment will be available for
analysis.

Finally, the balance sheet could aid in management
decisions. Whenever an "if" creeps into the statement of a
solution, the concept of risk is implied. By providing a
probability distribution associated with the price,
producers could make management decisions based upon their

own level of risk preference.

1.4 PROCEDURE
In Chapter 2 the theoretical and conceptual
.considerations for model construction are developed.

Elements of supply and demand are presented to provide a

7
basis for static analysis. A review of some of the relevant

literature is also included. Chapter 3 contains the
derivation of the model. The data calculations, equations
used, and empirical results are presented.

The balance sheet will be designed in Chapter 4. The
chapter will present case studies under different scenarios,
and provide documentation on the use of the balance sheet.
Finally, Chapter 5 will present a summary, conclusions and

recommendations for future work on the balance sheet.

CHAPTER II

THEORETICAL AND CONCEPTUAL CONSIDERATIONS

FOR MODEL CONSTRUCTION

This chapter will discuss the necessary economic
theory, and present the conceptual framework needed to
construct equations that will be used in the single

commodity forecasting model.

2.1 ELEMENTS OF SUPPLY

The economic unit for the sale of goods and services is
the firm. The firm can be a small farmer, cultivating his
own land with the help of the members of his family, or it
may be a nationalized organization owned by all and operated
either directly or indirectly by a local or central
authority. In static economic analysis, we are not directly
interested in whether the firm is large or small, or if it
is owned by one or many, but what happens to supply when

firms revise their sales and or purchase plans.

9
2.1.1 Theoretical Considerations

Ryan provides the theoretical background of the firm in
the short run.1 Doll states that the short run is the time
period of such length that at least one resource is varied
while other resources are fixed.2 Thus this is the most
appropriate time period for the analysis in this project.

The production plan of the firm sets the quantities of
each of its products that it plans to sell during any period
of time, and the price per unit at which it hopes or plans
to sell each of them. If 'P' is used to represent the
expected selling price, 'Q' to represent the quantity that
it hopes to sell, and the subscripts 1,2,3,...,n, to
designate particular products, then the production plan will

have the following general form:
P1 * Ql + p2 * Q2 + ... + Pn * on = R,

where R represents the gross revenue that the firm hopes to
get from implementing the plan as a whole. This plan
assumes the state of the arts in technology, a particular
objective in which the firm is pursuing, and a predetermined
length of time.

The revenue 'R' generated by the production plan is the
value of the output produced and is determined by the

expected product prices multiplied by the quantity produced.

 

1. W.J.L. Ryan, Price Theory, Macmillan and Company'
Limited, London, 1958, Chapter 2.

2. John P. Doll, Production Economics: Theory With
Applications, John Wiley & Sons, New York, 1978, p.17.

.—

10
It is assumed (in this industry structure) that the firm

will be able to sell everything produced at a given market
price. It is further assumed that the firm will choose that
level of production which optimizes any cost-benefit
analysis and generates the highest revenue. This assumption
is reasonable because most farmers trade-off between maximum
revenue (benefit) and the degree of risk (cost) associated
with that level of revenue and it seems to approximate
reality.

In order to realize the plan, factors of production are
needed. The quantities of land, labor, and capital, and the
price it expects to pay for them are set out by the purchase
plan. If 'F' is used to represent purchase price, 'X' to
represent quantity to be bought, and the subscripts
1,2,3,...,m, to designate particular inputs that are needed,

then the general form of the purchase plan will be:
Fl * X1 + F2 * X2 + ... + Fm * xm = C,

where C represents the total expenditure that the firm feels
must be incurred to implement the production plan. This
total expenditure must include all opportunity costs to
land, labor, capital, and management used in producing the
outputs that are listed in the production plan.

There are many alternative plans from which the firm
can choose, each with a corresponding purchase plan. In the
short run, alternatives are limited and a budget constraint

is imposed. The budget constraint means that the capital

11
available during the length of run is not sufficient to

allow the firm to use an unlimited amount of factors on each
product. One must assume that the firm will always plan to
produce each product with the quantities of factors that

cost least and meet the objective that the firm is pursuing.

To discover how a firm will revise its production plan
given a change in information, the production possibility
surface must be introduced. The production possibility
surface is the combination of products which can be produced
from a given amount of factors.

The concept of marginal rate of product substitution
(MRPS) refers to the amount by which one product changes in
quantity when another product is increased by one unit along
the product possibility surface ceteris paribus.

The critical part of this analysis is the marginal
criterion for resource allocation. Maximum net revenue from
a limited amount of factors of production occurs when the
value of the marginal product of the factor is the same for

each product.

VMleql VMPanl VMleqz _ VMPanz
_ _ UMleqm _ _ VMIanm

where VMP is the product price multiplied by the marginal.
physical product for each output. This marginal resource

allocation criterion specifies that when all ratios are

12
equal to one, the optimum amount of factors are used to

produce each product given the firm's objectives.

The relationship between planned sales and the expected
selling price is the firm's short run supply schedule. This
supply schedule shows the production plan that the firm
would choose at each expected selling price, with its given
production possibilities, objective and contractual
obligations, and the given prices expected from the purchase
plan.

The supply schedule of each existing firm can be
derived in precisely the same manner. By assuming that the
prices of the variable inputs are the same for all firms,
the industry supply schedule may be obtained simply by
horizontally summing the supply schedules for each firm at
each expected selling price. While each firm operates under
the assumption that the market price for its products is
beyond its control, the total effect of all firms
implementing their production plans is to assist in the
determination of the market price for each product.

The price elasticity of supply measures the responsive-
ness of planned sales to changes in the expected selling
price holding all other factors constant. Elasticity is
valid because it causes the measurement of responsiveness to
be unitless, and only records changes in terms of
percentages. Given a large elasticity, firms show a great
responsiveness to changes in price. With a small

elasticity, firms show little responsiveness. The aggregate

13
short run supply on farm products is extremely inelastic.

Tweeten and Quance estimate that the short run price
elasticity of aggregate supply of farm products in the U.S.
falls somewhere between 0 and 0.2.3 Tweeten goes on to
estimate short run elasticities of supply for selected
commodities and finds the elasticity for feed grains to be
0.4.4

In the next section the conceptual considerations
needed to derive the supply equations for the model are

presented.

2.1.2 Conceptual Considerations

The industry supply of corn for grain in any period is
based upon the amount of corn produced in the period and the
amount of carryover from the previous period plus any
imports. Factors which affect the production are the price
of corn, the ability to incorporate substitute crops given
the relative price differences, the capital flow of the
firm, the biological nature of agricultural production, and
the existence or absence of government programs. The
carryover can be viewed as predetermined, and the imports of
corn grain into the U.S. nearly insignificant.

Soybeans and wheat are major competitors for corn land

and production resources. Therefore the prices of these

 

3. L.G. Tweeten, and C.L. Quance, "Positivistic
Measures of Aggregate Supply Elasticities: Some New
Approaches," AQAE, Vol.51, No.2, 1969, p. 351.

4. L.G. Tweeten, Foundations of Farm Policy,
University of Nebraska Press, Lincoln, 1970, p. 243.

l4
crops are important determinants of expectations for future

prices. If the expected profit of the substitutes is higher
relative to the expected profit of corn the profit maximizer
will substitute one or more of these alternative crops into
production in order to equate the value of marginal product
from an acre of the substitute crop to the value of marginal
product from an acre of corn.

Nerlove examined three works based upon studies of
expectations of the future value of economic variables.5
These studies show that there is widespread underestimation
of actual changes and that forecasters could generally do a
better job at predicting the levels of outcomes if they used
a naive model. In the study of static economic theory of
supply, price changes are usually considered as permanent
changes. It is argued that the profitability of responding
to price changes considered only temporary is likely to be
so limited that entrepreneurs forecast and react primarily
to changes which they consider to be permanent, i.e.,
changes in the average level about which future prices are
expected to fluctuate. This average is what Nerlove refers
to as expected "normal" price. Nerlove raises the question
as to whether entrepreneurs are really trying to forecast a
particular value of an economic variable, or whether, they
try to forecast the "normal" level of future values of the
variable. Nerlove adds, "Any model of expectation formation

should take account of the fact that these expectations

5. Marc Nerlove, The Qypgmigs pf Sppply: Estimation of
Farmers' Response To Price, The John Hopkins Press, 1958.

 

15
probably do not refer to the immediate and temporary future"

(i.e. the immediate and very short run lengths of time).
Expectations of "normal" price are shaped by a
multitude of influences. Expected prices cannot be observed
and must be approximated by observable prices, so that a
representation of expected price as a function of past
prices may merely be a convenient way to summarize the
effects of these many and diverse influences of price
expectations. Eisner suggests that expectations may be
based on a concept of the normal as a starting point in our
development of a model of expectation formation.6
Government policy reflects an attempt to reduce surplus
production, address the problem of low farm income, and the
problem of price instability. Historically, plantings of
corn have been substantially influenced by government
policy. Mc Keon notes that increases in the level of corn
price supports have increased plantings while increases in
diversion payments have reduced plantings. He continues:
"The significant response to diversion programs reflects the
-fact that producers, throughout the U.S., were willing to
remove from production poorer quality crop land in return

for diversion payments. As better land is bid out of

production, however, the response to diversion payments

 

6. Robert Eisner, Erpectatipps, Plans, and Capital
Erpendirures: A Synthesis of Ex Post and Ex Apte Data

presented at the Conference on Expectations, Uncertainty,
and Business Behavior, October 1955.

16

"7 In all works reviewed, the government

would decline.
policy towards crop supply has received much attention.

As discussed earlier, each firms supply curve is shaped
by the expected selling price. Since the farm price in
recent years has been below the guaranteed support prices,
producers, having the option, have found it advantageous to
participate in the government programs. These producer
options for reaction to price stimuli would support the
testable hypothesis, as presented by Houck, Ryan, and
Subotnik,8 that the expected prices can be expressed by a

linear function:

*
P it = 011 Pit—1 + “12 PVlit

where P*it is the expected price of crop i in year t, Pit-1
is the actual price received by farmers for crop i in year
t-l, and PVlit is the price support policy variable for crop
i in year t, the ai's are the coefficients associated with
the explanatory variables.

Price support operations have consisted of guaranteed
support prices, acreage restrictions, and diversion payments
for fallowing land. The guaranteed support prices include a
nonrecourse loan, and direct support payments which vary

according to participation level and historical production

 

7. John Mc Keon, Farm Commodity Prpgrams: Tpgir Effect
On Plantings of Feed Grains and Soypeans, doctoral

dissertation, Michigan State University, 1974, Abstract.

8. J.P. Houck, M.E. Ryan, and A. Subotnik, Spybgans
and Their Products, University on Minnesota Press,
Minneapolis, 1972, p.90.

17
levels. The diversion payments, when in operation, use the

same criteria as the support payments. Because of the
various elements of the price support and diversion
payments, it is difficult to measure the impact of the
government programs on plantings. Houck and Ryan suggest a
means of weighting the announced price support by the
acreage restrictions imposed, to obtain the "effective price
support."9 Similarly, they obtained an "effective diversion
payment" by weighting the announced diversion rate by the

eligible diversion acreage.

a) Acres Planted

A number of researchers have examined supply responses
for various crops. Each of their models had feed grain sub-
models and some disaggregated feed grains into corn specifi-
cally. A short review of some empirical research will
follow.

e eo Model

Mc Keon develops theoretical and empirical estimates of
acres planted to feed grains and soybeans.10 The Mc Keon
study, cites the work of John L. Dillon to develop the
necessary economic theory for calculating the input demand
function. From there he modifies the function into a

general statistical form which follows:

 

9. J.P. Houck, and M.E. Ryan, "Supply Analysis for
Corn in the United States: The Impact of Changing Government
Programs," AQAE, Vol. 54, No. 2, May 1972, p. 184-91.

10. Mc Keon, op. cit.

18
_ 1: a:

where APit is the acreage planted to crop i in year t, P*it
and P*jt are the expected prices of crop i and competing
crop j, respectively, in year t, kat is the price of input
k in t, 2 includes such factors as government programs,
technical change and weather, the 31's are the coefficients
associated with the explanatory variables, and Ut is a
random mean-zero disturbance with finite variance.

This equation offers a guide to construction estimable
equations for supply response. Mc Keon notes that the form
of the price expectations must be presented in observable
terms as must the variables on government policy, technolog-
ical change, weather and input prices.

Following a discussion of the form of the variables
which are used in estimation work, Mc Keon identifies a
typical equation used in his work. The form of the equation

follows:

APCt = fio + £1 PVlCt + 32 PVZCt + 53 PCt_1 + B4 Pkt-l

+ 35 YCt_1 + 56 wt + 57 IPt + BB DV + Ut ,

where APCt is the acreage planted to corn in time t, PV1Ct
and PVZCt are the calculated effective price support and
effective diversion payment (as put forth by Houck and
Ryan), respectively, for corn in time t, PCt_1 is the
average market price of corn in time t-1, Pkt-l is the
average market price of the competing crop k in time t-1,

YCt_1 is the average yield of corn in time t-1, Wt is the

19
average precipitation in April and May, IPt reflects input

prices in time t, DV represents other identifiable shift
factors, and Ut is a random, mean-zero disturbance with
finite variance, the 81's are the coefficients associated
with the explanatory variables.

The Wailgs Model

A later study by Wailes estimates U.S. production for
wheat, soybeans, and feed grains, with feed grains
disaggregated into corn, sorghum, barley, and oats.11

The crop acreage supply relationships specified for his
model were developed out of previous studies by Nerlove,
Houck, and Mc Keon. These empirical estimates of the acres
planted equations provide information on a set of variables
which are hypothesized in explaining agricultural supply.

As noted by Wailes, the approach to analysis of crop
supply functions by concentrating on the independent
estimation of supply relationships for the factors of
production has been developed in the seminal articles by
D. G. Johnson and M. Nerlove in the 1950's. The theoretical
framework is based on neoclassical economic theory which
assumes that firms maximize profits and that the demand for
the factors of production are determined by conditions of
marginal equilibrium.

In the 1960's, Wailes notes, empirical investigations

such as the Interstate Managerial Survey by Johnson

 

11. E. Wailes, "A Simulation Model of United States
Agriculture," doctoral dissertation, Michigan State
University, 1983.

20
(et. a1.) generally supported the expectations hypothesis

but advanced that forward-looking information available to
farmers, not reflected in past prices, was important in the
formulation of farmer's expectations. The concept of
forward prices developed by D. G. Johnson (1947) became
embodied in government programs from the fifties to the
present with the objective to influence crop acreage. The
identification of the impact of forward price levels as
reflected in loan rates and target prices is useful both in
terms of how it indirectly conditions the response to past
market prices and price uncertainty as well as identifies
the direct impact on crop acreage (Houck, et. al.).

The general model specification for the Wailes model
reflects an updating of Mc Keon's equations and the
extension of the policy variable concepts to encompass the
changing policy framework as legislated in the 1973 and 1977
Food and Agriculture Acts. The general acres planted (in

period t) specification used in this study follows:
APt = f( Pt_1 / CI, PVl , pvz , Rt , APt_1 ) ,

where APt = acres planted in period t

Pt-l = vector of lagged market prices

CI = Index of costs, CPI was used in all deflations
PVl = vector of effective support prices, deflated
PV2 = vector of effective diversion payments,

deflated

21
Rt = < Pt-1 - < 2 Pt-1 / 3 ))2 / ( 2 Pt-1 / 3 ). a
vector of market price variances
APt_1 = acres planted, lagged one year.
Wailes notes, "This supply framework provides a straight
forward basis for simulations assuming alternative
government policy strategies and objectives."

The supply sub-model which follows is directly from the
Wailes discourse, and shows acres planted to corn is based
upon the relationship between corn plantings and lagged
corn, soybean and wheat prices and government feed grain
programs

APt = 30 + 31 cpt_1 + 52 SPt_1 + 33 WPt_1 + 34 PVICT

+ 35 PVZCT + 36 TL ,
where APt is acres planted to corn in thousand acres, CPt_1,
SPt_1, and WPt_1 are the deflated corn price, soybean price,
and wheat price received by farmers lagged one year,
respectively, PVICT is the deflated effective support rate
for corn in dollars per bushel, and PVZCT is the deflated
effective diversion payment in dollars per bushel, TL is a
trend, the 81's are the coefficients associated with the
explanatory variables.

The Heady Model

The econometric simulation model by Earl O. Heady
showed an alternative approach.12 Heady designed his model

with five commodity submodels at the national level. Each

 

12. Earl O. Heady, hp Ecohpmetric Simpigtioh Model of

the Q,S, Farm Sector and its Eoiicies and Food Exports,
Vandenhoeck & Ruprecht, 1978.

22
commodity sub-model is divided into three categories,

referred to as the pre-input, input, and output sections,
corresponding to the planning, planting and harvesting
decisions in the production cycle. The first category of
equations is the pre-input section. The initial decisions
which the producer will make, and are included in the pre-
input section, include the number of acres which will be
devoted to production.

Heady models the pre-input section acres planted to

feed grain as:

FG-Act = Bo + 31 FG-PRt_1 + 32 S-PRt_1 + 33 FG-ACDIVt

+ 34 FG-ACt_1

where:
3i = coefficients associated with the explanatory
variables,
FG-ACt = feed grain acreage in million acres,
FG-PRt_1 = feed grain average price received by farmers

deflated by the implicit GNP deflator,

S-PRt_1 = soybean average price received by farmers
deflated by the implicit GNP deflator,
FG-ACDIVt = acreage diverted from production in million

acres,

FG-ACt_1 = feed grain acreage lagged one period.

Following the pre-input section, the variable resource
requirements are estimated in the input section. The levels

of resource demand established in the input section depend

23
upon the levels of the fixed inputs from the pre-input

section as well as the values of endogenous variables from
previous years. For example, variable resource demand in
the current year depend on acreage estimates from the pre-
input section as well as last year's gross income, which
serves as a proxy for a capital constraint. The recursive
structure of the model is preserved, since the solution of
the equations occurs in a sequential manner.

The FAPSIM Model

The Economic Research Service, National Economics
Division developed a report which provides a detailed
description of the structural equations and their
statistical attributes in the current version of the Food
and Agricultural Policy Simulator (FAPSIM).13

As noted by the authors of FAPSIM, a major shortcoming
of previous research has been the failure by economists to
develop acreage response equations that explicitly predict
the level of farmer participation in government commodity
programs. For example, Houck and Ryan (et. a1.) developed
response equations containing government policy variables
such as the effective support price and the effective
diversion payment rate. The authors continue, these
equations can be used to predict total acreage response, but
they cannot predict the level of participation in government

programs. Central to this approach is the development of

 

13. Kenneth E. Gadson, J. Michael Price, and Larry E.
Salathe, U.S. Department of Agriculture, Staff Report
AGE5820506, May 1982.

24

acreage response equations with the construction of

variables that reflect farmer's perceptions of expected

yields and prices.

The acreage response relationships contained in FAPSIM

reflect the relative profitability of either participation

or not participating in a government commodity program.

14

The expected net return per acre for a program participant

producing crop i is:

EPRi

where:
EPR

EPP

EY

VC

SR

PY

SA

DIVi

[(EPPi * EYi - vci) (1.0 - (5A1 + DIVi))]
[SRi * PYi (1.0 - (SAi + DIVi))] ALLOCi

[DRi * PNi * pyi * DIVi]

expected program return per acre for crop i,
the maximum of the loan rate and the expected
market price,

expected yield per acre,

variable cost per acre,

expected deficiency payment rate (announced
target price minus the maximum of the expected
market price and loan rate) per acre,
national program yield,

proportion of each acre required to be
set-aside,

proportion of each acre required to be diverted,

 

14. The acreage response equations contained in FAPSIM
follow from previous research by Robert Bancroft of the
University of Vermont, while he was employed by ERS.

25

DRi diversion payment rate per bushel,

ALLOCi minimum national program allocation factor, and

PNi proportion of acreage eligible for diversion
payments.
The expected net return per acre for a government
program nonparticipant producing crop i is given by the
identity:

EMRi = EMPi * EYi - VCi

where:

EMRi expected market net return per acre for crop i,

EMPo expected market price,

1
VCi = variable cost per acre, and
EYi = expected yield per acre.

The expected net return variables are used to estimate
acreage response by participants and nonparticipants. Total
acreage in the program (planted plus diverted and set-aside
acreage) for crop i is expressed as a behavioral

relationship of the form:

PAi = f ( EPRi/CPI, EMRi/CPI, APPi/CPI, NPi(l.0 - 301))

where:
PAi = program acreage for crop i,
APPi = the average expected net return of competing
crops,
NIi = national program acreage for crop i,

SDi set-aside plus diversion rate for crop i,

26
CPI = the all item consumer price index lagged one

period:
and where EPRi and EMRi are defined as above.

Total acreage planted to a particular crop by program
participants is a function of total program acreage
multiplied by program set-aside and diversion rates.

Acreage set aside and diverted is calculated endogenously as
total program acreage minus acreage planted by participants.
The participation rate is endogenously computed as acreage
planted by participants divided by the sum of acreage
planted by participants and nonparticipants.

Acreage planted to crop i by nonparticipants is a
function of acreage planted to crop i by program
participants, acreage set-aside and diverted, the real
expected net return of competing crops, and the real
expected market return for planting crop i. Acreage planted
in the program, acreage set aside and diverted, and the
real, expected, net return of competing crops all represent
the desirability of using land for alternative purposes.
Thus, acreage planted to crop i by nonparticipants is
inversely related to each of these variables. Acreage
planted by nonparticipants is positively related to the
real, expected, market net return.

The Food Security Act of 1985 calls for a freeze of the
target price at the 1985 level with subsequent reductions in
the future. Along with the freeze, the Secretary of

Agriculture was given authority to lower the loan, which he

27
did. Another change was the formula for calculating base

acres and base yields. According to the Act, acreage bases
will be the planted and considered planted acreage in the
five previous years rather than the previous two years. For
the 1986 and 1987, program yields are frozen at the average
of 1981-85 levels.

These efforts to lower prices and make U.S. corn more
competitive in world markets will result in increased
deficiency payments and encourage more farmers to comply
with the program. A major uncertainty of the 1985 policy is
the implementation of the Gramm-Rudman-Hollings Act to
reduce the federal deficit.

The Ferris Model

Ferris uses an equation for acres planted to corn
formulated in a manner similar to a partial budgeting
approach familiar to many farmers.15 The key independent
variables are: (1) expected net returns from corn over
variable production costs per acre outside the government
program, (2) expected net returns from soybeans over
variable costs per acre, (3) expected net returns on corn
over variable cost per base acre for participants in the
Feed Grain Program (including direct payments), and (4)
percent of base acres that must be put into conserving uses

in order to be in compliance.

 

15. John N. Ferris, Forecestihg Corp Producers'

Response To the 1986 Feed Grain Program, Presented at AAEA
1986.

28
This review shows that there are many alternative

structural approaches to estimating acres planted. In
keeping with the objectives, a simple equation must be
estimated. The common elements of each of the models
reviewed were: a variable for expected corn price, a proxy
for participation in the government program, and a proxy for
revenue received from a competitive crop. This common
structure presents a starting point for estimating the acres

planted equation.

b) Acres Harvested

Acres harvested are estimated as a simple function of
estimated planted acreage. Corn grown for silage, the
ability of producers not to harvest part of the crop or any
damage done to the crop results in the acres harvested being
less than acres planted. The relationship between acres
harvested and acres planted is expected to be positive.

The work done by previous researchers has shown that
acres harvested can be a function with either an intercept
and estimated acres planted, or just estimated acres
planted. The function without the intercept generates a
fixed percentage relationship between acres planted and
acres harvested. When the intercept is included, the value
will explicitly handle silage and abandoned acres in the
processes of estimating acres harvested for grain. This
research will examine both methods for statistical

significance.

29
c) Yield Per Acre

Yield per acre is based upon experience of past yields.
This procedure assumes a base yield on "normal" weather
conditions and incorporates changes in technology. The
amount of yield received from one acre can be affected by
many factors. Producers can use new hybrid seed, use more
or better fertilizers, and of course, the weather plays a
major role on the total yield. Given the level of
technology, expected yields can best be represented by
regressing actual yields on time which assumes a "normal
weather" production period. The technology proxy is
expected to have a positive relationship with yields which

reflect the increase that yields have experienced over time.

d) Production and Supply
Production and total supply are both identities. The
quantity produced is generated by multiplying the number of
acres harvested by the yield per acre. Total supply is
exactly equal to the sum of production in the current period

and the carryover from the previous period plus imports.

2.2 ELEMENTS OF DEMAND

The economic unit for the purchase of the goods and
services of everyday consumption is known as the individual.
It may be a sole person meeting his own individual needs or
it may be a collection of individuals operating together to

gather goods for the sustenance of all. Regardless of the

30
size of the individual, the economic actions pursued are

where the analysis begins.

2.2.1 Theoretical Considerations

Before beginning the analysis of economic action
available to the individual, reference must be made about
individual preferences and the concept of utility. This
discussion provides the framework for the decision theory
analysis.

Economists assume that individuals make choices that
provide some form of satisfaction. It also follows that
those choices are selected by the individual because it
provides the maximum satisfaction given a particular
situation. It is further assumed that these choices conform
to some form of rational behavior, so that a unique ranking
of all possible situations from most to least desirable can
be made. The political theorist Bentham called this ranking
of relative desirabilities utility: more desirable
alternatives offer more utility than do less desirable
ones.16

Since utility measures satisfaction, it is affected by
a variety of factors such as cultural environment and
psychological attitudes. Due to the complex variety of
factors that determine the individual's satisfaction, there
is no interpersonally valid way to measure utility. All

that can be determined by utility theory is that one bundle

 

16. Jeremy Bentham, t uc o to ri ci les 0
horals and Legislation, Hafner, London, 1948, p. 125-31.

 

31
of goods and services is preferred to another, yet, not by

how much.
An individual's preferences can be represented by a

utility function of the form:

U=f( X1, X2, ooo ,Xn)

where X1, X2, ... ,Xn are the quantities of each of n goods
consumed.

Some classical assumptions regarding utility are: 1)
More is better, 2) There exist opportunities for trade-offs,
and 3) Consumers are utility maximizers subject to a budget
constraint.

The assumption that more is better stands because
individuals are insatiable in their wants for all goods.
There is an intuitive feel that when an individual can get
more of one good and not sacrifice any of another that
improvement is made.

A more important aspect of individual taste that must
be noted is the rate of substitution of goods, or how
individuals feel about getting more of some good when they
must give up an amount of some other good given their
current situation. Marshall theorized that it is the
marginal valuation that an individual places on a good that
determines its value.17 In light of the trade-off, the
individual has an unlimited amount of choices of goods and

services which yield the same utility.

17. Alfred Marshall, Priheiplee of Eeonomies,
Macmillan & Co., London, 1920, Book III, Chapter 6.

 

32
Typically, when an individual has a small quantity of

one good relative to another, the individual is willing to
give up a large amount of the excess good to get one more
unit of the first good. This coincides with the notion that
individuals prefer some variety in their mix of goods, i.e.
some balance of consumption is desirable. Intuitively, it
seems clear that an individual will tire of some good as
more of it is consumed which leads to a diminishing marginal
rate of substitution (MRS).

Of particular concern is how an individual allocates a
fixed amount of money in purchasing goods so as to maximize
the utility derived from these goods. Nicholson defines the
optimizing principle for the individual as those quantities
of goods which exhaust his or her total income and for which
the psychic rate of trade-off between any two goods (the
MRS) is equal to the rate at which the goods can be traded
one for the other in the market place.18

Spending all of one's planned consumption expenditure
is required for utility maximization, since extra goods

provide extra utility, which defines the individual's budget

constraint as follows:

where P is the market price of the good, Q is the quantity

of each good purchased, and I is the level of planned

 

18. Walter Nicholson, hiereeeonohic Theory: Pasic

Principles and Extensions, The Dryden Press, Chicago, 1985,
p. 94.

33
consumption expenditure available to spend on the bundle of

goods.

Since the rate at which one good can be traded for
another in the market is given by the ratio of their prices,
this optimizing principle can be restated to say that the
individual will equate the MRS of the goods to the ratio of

the prices of the goods and has the following form:

where U is the utility derived from the bundle of goods, MU
is the marginal utility and Pi is the market price of the
good. This equation says that at the utility maximizing
point, each good purchased should yield the same marginal
utility per dollar spent on that good.

There are many factors which affect the way in which an
individual will adjust the utility maximization point, among
them a change in income and changes in a goods own or
substitutes price are the key elements.

As total expenditures rise, it is natural to expect
that the quantity of each good purchased will also increase.
A normal good (as in most cases) implies as income
increases, purchases increase, yet when, the quantity of a
good deceases as income rises, the good is referred to as an
inferior good.

The effect of price change on the quantity of a good

demanded is somewhat more complex to analyze than is the

34
effect of a change in income. This is because of two

analytical affects which result from a price change: income
and substitution effects

The income effect arises because a price change
necessarily changes an individual's real income. The total
real income will increase when there is a price decrease for
any good and it will decrease when the price of any good
increases. The substitution effect arises from a price
change because consumption patterns are allocated so as to
equate the MRS to the new price ratio.

It has been shown that an individual's demand for a
good depends on the shape of the utility surface and on all

prices and income. Theoretically, this can be shown as:

*
Q 1 = D1( P1, P2, 00. ' Pn' I )

where 0* is the quantity of the good purchased, Pi is the
price of all goods available and I is the desired level of
consumption expenditure, and the subscripts represent the
range of possible prices on each good.

By choosing alternative prices for the good and,
assuming that other determinants of demand are held
constant, the quantities of the good in question can be
mapped out. The relationship between the price of a good
and the quantity of that good purchased is the individual's
demand schedule.

The demand for each potential individual can be

determined in precisely the same manner. Since the prices

35
available to the individual are the same for everyone, and

individual demand is dependent upon his or her own income,
the total or market demand for the good can be obtained by
horizontally summing the demand schedules for each
individual at each potential price.

One of the most important applications of the
elasticity concept is that of price elasticity of demand.
Elasticity of demand records how quantities change (in
percentage terms) in response to a percentage change in own
price holding all other factors constant and is typically
negative for demand functions. Given an "elastic" demand
schedule, a price increase is met by more than a
proportionate quantity decrease. With an inelastic demand
schedule, price increases proportionally more than quantity
decreases.

Another type of elasticity frequently encountered in
economics is the income elasticity of demand. This concept
records the relationship between income changes and quantity
changes. Goods for which income elasticity is greater than
1 might be called luxury goods in the sense that purchases
of these goods increase more rapidly than income. On the
other hand, a good which is a necessity probably has an in-
come elasticity of less than one.

The cross elasticity of demand is the responsiveness of
the good demanded to a change in the price of some other
good. The value of cross elasticity may be positive or

negative, depending on the direction in which the demand

 

36
schedule moves. In general, goods with a positive cross

elasticity of demand are regarded as substitutes for one
another, while goods with negative cross elasticities are
regarded as complements for one another.

The next section presents the conceptual considerations
needed to derive the demand equations used for model

construction.

2.2.2 Conceptual Considerations

Conceptually, corn demand can be typed into one of two
categories. First, there is the immediate demand for
consumption and exports. Second, there is the demand for
storage and speculation.

The immediate demand or disappearance of corn in the
U.S. can be grouped into three classes. First there is the
grain demanded for feed by livestock producers. Second, the
food, seed, and industrial use must be accounted for.
Finally, the amount of utilization in any period is
dependent upon the level of exports. Total disappearance of
corn (both domestic use and exports) has trended upward
during the past two decades. In the past decade, corn
exports grew faster than domestic use, resulting in a 15
percent increase in total disappearance.

Since corn is a seasonally produced commodity and
consumed throughout the year, there is a demand for storage
and speculation. Storage acts as a buffer against price

fluctuations by absorbing excess in years of low prices and

37
releasing stocks in years of high prices. Speculators hold

the commodity in anticipation of a price increase that will

provide them a profit.

a) Corn Consumed By Livestock

In 1982, corn consumed by livestock accounted for 84
percent of the domestic use of corn while also accounting
for 82 percent of all grains fed to livestock. Competition
among feed ingredients depends on relative prices and
relative feed nutrition values. Producers can vary the rate
in which they feed corn based upon the current price and
availability of corn and substitutes (mainly soybean meal),
the revenue generated by their particular enterprise, and
any change in technology that structurally improves the
efficiency of feed conversion.

Feed use of corn, being a derived demand, is positively
related to the number of animal units on feed. By
disaggregating corn consumed by livestock into separate
animal species, and estimating the rate of feeding per head,
an estimate of total corn consumed by livestock can be made
by multiplying the rate per head by the inventory value and
summing across each species. This technique should improve

the overall estimate of corn consumed by livestock.

b) Food, Seed, And Industrial Use Of Corn
Food, seed, and industrial (FSI) use is expected to
continue the upward trend established over the past 10

years. However, evidence is mounting that future increases

38
will not be nearly as large as in the past. In the early

1980's, FSI use increased an average of more than 10 percent
a year, but for the 1986 crop year, only a 2 percent rise is
projected.

The FSI use of corn doubled in the 1970's as a result
of expanding markets for high fructose corn syrup, other
sweetener products, and ethyl alcohol. Each of these
factors will be examined to help explain why this quantity

of demand cannot easily be estimated.

1) FOOD

High fructose corn syrup (HFCS) has held the highest
portion of FSI usage in this decade, and currently ranges
close to 30 percent. HFCS is used in a variety of products
including soft drinks, baking and cereal products, and dairy
and canning products. Soft drinks can use HFCS for nearly
100 percent of their sweetener needs, however, it is not
technically possible for most other products to do so.
Almost all of the products containing HFCS are approaching
their theoretical limits of use, therefore, continued growth
can not be expected without increased future demand for end
products, population growth, or development of new uses for

Hch.19

ii) SEED
Seed use is primarily related to the number of acres to

be planted in the coming season with secondary effects from

 

19. Feed Situation and Outlook Report, August 1986,
p. 11-13.

 

39
trends and from crop prices. Typically, the higher the

market price the higher the seeding rate. Since the seed
used by producers is a special hybrid variety, the demand

for hybrid seed will need to be estimated.

iii) INDUSTRIAL

Alcohol production from agricultural commodities is not
new. During the Great Depression of the 1930's, there was
considerable interest in producing ethanol from grains.
Ethanol currently accounts for nearly 25 percent of FSI
usage.

A great deal of interest in domestically produced oil
substitutes was evoked by the Arab oil embargo in 1973, the
Iranian crisis of the 1980's, and the decontrol of domestic
crude oil prices in 1981. Each of these events caused
gasoline and crude oil prices to increase rapidly. Since
ethanol can be made from renewable resources such as grain
and other agricultural biomass, and serve as a gasoline
extender, major government efforts were undertaken to
stimulate this domestic industry.

There are two types of milling processes for obtaining
ethanol from corn grain. Both yield approximately the same
amount of ethanol per bushel of grain.20 The dry-milling
process is the simpler process, but the wet-milling process
yields more valuable by-products one of which is high

fructose corn syrup.

 

20. Donald R. Hertzmark, "Economic Feasibility of
Agricultural Alcohol Production Within a Biomass System,"
AJAE, Vol. 62, No. 5, December 1980, p. 965-71.

 

 

40
A number of important issues are raised when

considering fuel alcohol production from agricultural
commodities. Whereas ethanol production is seen as a means
to increase farm commodity prices and income, what impacts
will ethanol production have on food prices? What are the
impacts of farming land more intensively, and possibly
bringing more land into production? What will be the
impact on substitute commodity prices such as soybeans and
sorghum?

Given these policy issues and the current worldwide
glut of crude oil, enthusiasm for ethanol and other

alternative fuels has diminished.

c) U.S. Corn Exports

Due to the dominant role of the U.S. corn industry in
the world coarse grain market, the domestic supply, demand,
and U.S. government policy changes, significantly affect
world markets for coarse grain. Since 1970, the level of
U.S. coarse grain exports has been closely tied to the level
of the deficit outside the U.S. The implication is that,
estimates of the deficit outside the U.S., provide a good
handle on what U.S. exports would be.

To model the U.S. export relationship, the world coarse
grain situation must be examined. The assumption made for
this project is that there are three regions in the world:
the U.S., the residual exporting countries, and the .

importing region.

41
Estimates will be made for the net imports on a per

capita basis demanded by the importing region. It is
necessary to include population in the formulations to
account for any changes in demand caused by the annual trend
increases in population.

By assuming that the each country in the exporting
region is working toward internal price stability, exporter
net exports per capita can be an identity which specifies
that the region will export everything not consumed or held
for stocks. This quantity in the international component
should also be estimated on a per capita basis. The
difference between the quantity demanded by the importing
region and the quantity supplied by the exporting region

will be the U.S. exports.

d) Stocks For Storage

Since the 1930's, the U.S. government has used price
supports on many storable commodities and thus have held
stocks to help maintain both orderly marketing, and prices
above equilibrium. The current stock holding programs
include grain purchased under government loan and held in
the Commodity Credit Corporation (CCC) and that owned in
farmer reserve (FOR). Free stocks and those held for
speculative purposes also account for a portion the demand
for stocks. The effect that increased stocks have on U.S.
prices will depend on whether the stocks are stored under

policy rules or as free stocks.

42
Although the government holds stocks to maintain price

by soaking up excess production, the knowledge of the
quantity and the release price for FOR and CCC stocks
discounts its effectiveness. The quantities of grain placed
in FOR and CCC are not residual ending stocks but are
determined by government programs and producer expectations.
Ferris notes that an adjustment must be made to account for
the knowledge of the quantity stored and release price of
grain in FOR and in the CCC.21 Rather than adding all FOR
and CCC grain to the ending stock equation, Ferris deletes
about half of the grain held in FOR and CCC. The rational
is that grain held off the market would tend to raise
prices. However, the existence of grain in the government
program also has a depressing effect on the market. Thus,
the relevant ending stock equation for U.S. policy stocks is
actually somewhere between total ending stocks and free
stocks.22
It is necessary to develop rules for accumulation and
release of FOR and CCC stocks based on producer
expectations, the quantity stored and the release price. If
the farm price is less than the loan rate plus interest and
carrying charges, the producer has no incentive to redeem
his loan and the grain held under FOR and CCC will increase.

The lower the expected price relative to the loan rate, the

 

21. J. Ferris, "Feed Grain, Wheat, and Oilseed Supply
and Demand," MSU Staff Paper, October 1985.

22. Total Ending Stocks = Free stocks + % CCC + % FOR.

43
more grain will be stored as more producers participate and

a greater percentage of acres are committed to the program.

When prices are expected to be above the loan rate plus
interest and storage there is little incentive to
participate in the program and there is little accumulation
of government stocks. If prices rise to a policy
established trigger, the government would begin to release
grain onto the market. The quantity of grain released would
depend upon the upward pressure on price.

Shagam hypothesizes that a cubic function is the form
which could provide a smooth curve with the correct shape to
model the turning points in the accumulation and release of

government policy stocks.23

Shagam notes that one of the
shortcomings of a cubic form for an accumulation and release
of policy stocks equation is its potential to be explosive
if prices are very high or very low.

Although not all inventory positions are not held for
speculation, some stocks are held by individuals in the
anticipation that price will rise during the marketing year.
Assuming free stocks are held for speculative purposes, a
relationship can be made between anticipated use, the
perceived level of policy stocks and prices (relative to
current prices).

When all of the demand quantities (both immediate

consumption and speculative demands) are summed

 

23. Shayle Shagam, "Specification and Structure of an
International Agricultural Trade Model With Linkage
Capabilities," master's thesis, Michigan State University,
1986, p. 51.

 

 

44
horizontally, the total demand for the time period can be

calculated.

2.3 PRICE DETERMINATION
This section will present the theory and
conceptualization needed to forecast the annual average U.S.

farm price of corn.

2.3.1 Theoretical Considerations

To simplify the price determination analysis,
economists use the concept of a perfectly competitive market
to model agricultural product price behavior. The federal
government intervenes in the pricing of grains (as well as
other products) in the U.S., but models which assume
competitive behavior are still useful in establishing a
theoretical norm against which actual behavioral price
effects can be measured. In a purely competitive market, it
is assumed that every firm seeks to maximize profits by
selling at as high a price as possible, and every individual
seeks to maximize utility by obtaining goods at as low a
price as possible. The collective actions of firms and
individuals determine prices.

In the analysis of pricing it is important to decide
the length of time that is to be allowed for a supply
response to changing demand conditions. To further simplify
the analysis, the assumption that the production period for

the firm is the same as the purchase period of the

 

 

 

 

 

 

—_._—

 

 

45
individual is made. If the purchase and production plans of

the firm are made at the beginning of the period, and once
these plans are made, they remain unchanged until the next
period, then, a theoretical static analysis can made.

When the price and quantity relationships for the
firm's short run supply schedule and for the individual's
current demand schedule are equated, an equilibrium price
and quantity are established. An equilibrium price is one
for which the quantity demanded is precisely equal to the
quantity supplied and the market is cleared.

The equilibrium price serves two important functions.
First, this price provides information for firms with which
to decide how much should be produced. The second function
of this price is to ration demand.

There are many reasons why either a supply or demand
schedule might shift. Such shifts may be caused by changes
in prices in other markets, in tastes and preferences, or in
productive technologies. When either a supply or demand
schedule does shift, equilibrium price and quantity will
change. The relative magnitudes of such changes depends
upon the shape of both the supply and demand schedules. The
rise in price that follows any given increase in demand will
be the greater the less is the price elasticity of supply,
and it will be the less the greater is the price elasticity
of supply. Conversely, for any given change in supply, the

ensuing change in price will be the greater the less is the

46
price elasticity of demand, and it will be the less the

greater the price elasticity of demand.
In the next section the conceptual considerations

needed for price determination are presented.

2.3.2 Conceptual Considerations

A common approach to estimating the current period
price is through the ratio of ending stocks to total demand.
Where current supply is a function of lagged price, current
demand is a function of current price, current ending stocks
are the identity current supply less current demand and
lagged ending stocks. This procedure is commonly referred
to as a "disequilibrium" model stemming from the failure of
the price equation to necessarily equate supply and demand.

The FAPSIM model notes that this failure results from
the estimating of the above price equation as a structural,
rather than a reduced form, equation.24 The FAPSIM model
uses an alternative framework where the market price
equation is determined by substituting the supply, demand,
and ending stock functions into the supply/demand identity
and solving for price. Comparison of the two price
equations indicates that the original formulation will not
necessarily provide a market clearing price consistent with
specified demand and supply schedules.

Ferris hypothesizes that in any given year, world

prices will affect U.S. crop exports, seeding rates, and

 

24. FAPSIM, op. cit., p. 14.

 

 

 

 

 

 

 

 

47
feeding rates.25 These changes in world price in turn

modify total domestic utilization and ending stocks. In
essence, the world price is established by determining a
margin over the U.S. loan rate. If the farm price of corn
drops below the loan rate in this computation, the farm
price is set at a level reflecting the influence of the
loan, having the effect of putting a floor under the market.
The loan rate is set exogenously in response to domestic
pressure, and because the U.S. is the dominant world
exporter, this same support price will also act as a floor
on the world price by soaking up and storing excess supply
before it goes into export channels.

These price determination schemes can be used as a
starting point for market clearing (equilibrium) within the

balance sheet.

 

25. J. Ferris, "Feed Grain, Wheat, and Oilseed Supply
and Demand," MSU Staff Paper, October 1985.

CHAPTER III
SIMULATION MODEL CONSTRUCTION

Chapter three examines how economic and statistical
theory can be used to guide in the construction of estimable
equations capable of forecasting the farm price of corn in
the U.S. The data calculations, equations used, and

empirical results are presented.

3.1 THE SIMULATION MODEL

Construction of a simulation model begins with the
specification of a set of individual supply and demand
relationships, which explain the behavior of agricultural
product prices. Applied econometric techniques are used to
estimate specific economic coefficients (parameters) and
provide forecasts of prices or the variables affecting
prices. These quantitative techniques serve the purpose of
making the supply and demand relationships among variables
explicit.

The simulation model should be consistent with the
logic and theory underlying the commodity being analyzed.

According to Tomek and Robinson, model building may be

48

49
viewed as having two parts.1 One involves the specification

of the economic model, that is, the general economic
relationships. Economic theory suggests the general types
of functions that may be appropriate for a particular
research problem. The second part of model building
involves the explicit definition of structural equations
which are to be estimated. These structural issues are
presented with each equation.

Following the development of the conceptual framework,
observations which reflect the hypothesized variables in the
model must be collected. In practice, the specification of
the equations to be estimated is likely to be influenced by
the data available. Finally, coefficients which relate the
variables to one another are estimated, and evaluated

according to statistical procedures.

3.2 GENERAL ECONOMETRIC PROCEDURES

Econometrics is the science of abstracting or modeling
the real world through the use of (typically) quantitative
methods.2 By concentrating on the types of models that can
be expressed in functional form, data can be used to
estimate the parameters of the equations and theoretical

relationships can be tested statistically.

 

1. W.G. Tomek, and K.L. Robinson, Agriculrurel Product
Priees, Cornell University Press, Ithaca, NY, 1972, p. 303.

2. Robert S. Pindyck, and Daniel L. Rubinfeld,

Econometric hodels eng Ecpnomie Porecasrs, McGraw-Hill, New
York, 1981.

50
Conventional economic theory, regardless of form,

postulates exact functional relationships between variables.
Any introductory examination of economic data, however,
indicates that points do not lie exactly on straight lines
or other smooth functions. Thus a stochastic term must be
introduced into economic relationships.

The stochastic term may take on positive or negative
values and helps explain reality by accounting for excluded
variables in the explicit function, unpredictable randomness
in human responses, and errors in measurement of relevant
variables. The important assumptions about the stochastic
term concern its probability distribution. The simplest
assumption is to assume that the term has an expected value
of zero, zero correlation among different observations, and
a finite variance.

The objective is a hypothesis test that the
relationship among certain variables can be predicted or
explained to a large extent by other variables. Many rules
can be created to define the relationships among variables,
however, the least squares criterion is the procedure said
to give the line of best fit. The line of best fit is
defined by those parameters which minimize the sum of the
squared deviations of the observations and the estimated
relationship. Least squares estimated parameters have the
properties of being linear functions of the actual
observations, and being unbiased linear estimates of the

actual parameter.

51
The procedure of choosing the line of best fit is often

referred to as ordinary least squares (OLS). OLS estimates
of parameters have the minimum variance and conform to the
Gauss-Markov theorem, which states that given the
assumptions about the stochastic term, the parameter
estimates are the best (most efficient) linear unbiased
estimators of the actual parameters. OLS estimates of the
parameters are appropriate due to the recursive nature of
the model and the iterative process used to solve the price
equation.

Using OLS estimating procedures provides information
about the distribution of the parameters, and makes possible
the construction of confidence intervals and testing of

hypotheses concerning those regression parameters.

3.3 STATISTICAL CONCEPTS AND RANDOM VARIABLES

The most important objective of a statistical analysis
is to draw inferences or generalities about a population
from the partial information embodied within sample data.3
The statistical concepts needed to do analysis and make
inferences about a particular population are the sample mean
and sample standard deviation.

The sample mean is a measure of central tendency and
represents the center of a data set. The mean is generated

as:

 

3. Gouri K. Bhattacharyya, and Richard A. Johnson,

Sterisrieal Qeneepts ehd Methogs, Whiley & Sons, New York,
1977.

52
MEAN = (sum of all observations / number of

observations).

The sample standard deviation measures the extent of
variation around the center and serves as a basic measure of
variability. The sample standard deviation gives a measure
of variability in the same unit as the data and is
calculated as the square root of the sample variance. The

sample variance is generated as:

VAR = (sum of (each observation less the MEAN)2) /~

(number of observations less one).

Using these calculations, some inference can be made about
the population of yields and prices from the sample of
yields and prices generated within the model.

The central limit theorem states that when a sample of
observations is large, the distribution of those
observations is approximately normal, regardless of the
shape of the population distribution. The yield of each of
the world regional areas is based on (among other factors)
annual weather patterns. If a single annual weather pattern
constitutes an observation from the population of all annual
weather patterns, it can be assumed that the distribution of
annual weather patterns is approximately normal and each of
the world's regional yields is approximately normal.

Because weather plays such a central role, the actual
yield does not always take on the point forecast estimate,

and is actually a continuous random variable. Continuous

53
random variables have the characteristics of being able to

assume all values in an interval, have a probability
distribution, a mean, a variance and a standard deviation.

The assumption of a normal distribution for yields
allows the use of a standardized random variable for
generating random yields. The standardized random variable
has a mean of zero and a standard deviation of one. It is
customary to denote the standardized random variable by 2.

Knowing the point estimate and the standard deviation
for the yields will allow the world supply to be randomly
shocked. To calculate the random shocks, a computer
generated pseudorandom number can be used. The pseudorandom
number has the property of being a number between zero and
one, inclusive. The pseudorandom number with the prescribed
property will have a population mean of 0.5 and a standard
deviation of the square root of 1/12. By sampling the
pseudorandom number generator a distribution of values
(between 0 and 1, inclusive) is obtained, and a sample mean
can be calculated from this distribution.

To obtain the standard random variable 2, the following

equation may be applied:

2 = (sample mean - population mean) / (standard error /

square root of the number of observations).

Once the 2 value is known, the point estimate and the
standard deviation of the estimated equation are used to

determine the random shock. The equation follows:

54
Shock = standard deviation * z + point estimate.

By assuming yield is a random variable, it holds that
supply will also be a random variable and thus, price will
be a random variable. Using the random shocks the model
will determine a price based on those random supplies and
provide a distribution for price. Once the price
distribution has been generated, statistical confidence
intervals can be placed on the range of prices. These
confidence intervals on the price distribution will be the

basis of the risk aversion/decision analysis.

3.4 DATA SOURCE CONSIDERATIONS

The type of data used in this research is time series.
The sources of each data series are listed in Appendix 1.
As noted in the general econometric procedures, the
consistency of the data is important in the functional form
of the equation and in the level of bias of the estimation
parameters. An attempt was made to obtain observations for
each series from a common and original source.

Another aspect of data sources is the particular time
series selected for estimation purposes. Parameter
estimates generated from different sample periods may be
substantially different because the structural relationship
between the dependent and explanatory variables is subject
to change over time. Given this structure, the sample

period for each equation will be chosen independently.

55
The passage of The Food Security Act of 1985 mandates a

change in the marketing year for corn from October 1 through
September 30 to September 1 through August 31. This change,
as mandated, is due to producers harvesting earlier in the
year and new crop corn being used before October 1. The
change in the feed grain data base has led to changes in the
U.S. supply, disappearance and stocks estimates. The
challenge involved in adjusting to the new data surveys lay
in estimating past years' supply and disappearance balance
for the newly defined periods so that a consistent
historical data base could be developed. Because the
marketing year in 1985 began on October 1, this research
will not use data under the new market year surveys and most

of the equations will be estimated through 1985.

3.5 APPRAISING THE RESULTS

The interpretation of the estimated parameters will
determine the usefulness of a particular result within the
theoretical and conceptual framework developed.

The coefficients of each explanatory variable are
estimates of the net relationship between the respective
variable and the dependent variable. These estimates are
interpreted as the change in the dependent variable given a
one unit change in the respective explanatory variable,
ceteris paribus. For an estimated relationship to have a
high degree of explanatory power, the variance of the

regression and the variance of the coefficients should be as

 

56
small as possible. Small standard errors gives more

confidence that the true, but unknown, parameter is within a
relatively small range of the estimate.

The corrected coefficient of multiple determination is
a measure of the degree of the variation in the dependent
variable associated with the variation in the explanatory
variables. This coefficient, also known as adjusted R-
squared can be used to select among alternative model
specifications.

The estimated parameters must stand the test of logic
according to economic theory. If the sign of an estimated
coefficient is not consistent with the logic of the economic
theory, then the estimated equation is suspected of being
misspecified. Further, the estimated parameters must stand
the statistical evaluation on the Students t test
(henceforth called the t statistic). The t statistic
provides measures of the probability of obtaining an
parameter coefficient when the true parameter is indeed
zero. The t statistic of the estimated parameter is
presented in the tables parenthetically below the respective
estimate.

Given the necessary assumptions about the stochastic
term for OLS, we know that the term is to exhibit a constant
variance over the range of observations. Serial correlation
is the lagged correlation of a series with itself. Such
behavior is usually a sign of specification error in the

estimated relationship which can be caused by a variety of

57
factors. The Durbin Watson test statistic (DW) is a common

test for the existence of first order serial correlation.
Any nonrandom behavior of the error term is a sign of
misspecification and should be investigated.

When one or more lagged endogenous variables are
present in an estimated equation, the DW statistic is no
longer useful. The test statistic to be used in this case
is the Durbin h statistic. The Durbin h statistic is
Aapproximately normally distributed with unit variance and
the test can be done directly by using the normal

distribution table.

3.6 U.S. CORN SUPPLY MODEL

Each structural equation used in the model represents
an attempt to explain how the endogenous variables change
from year to year. The estimation of acres planted contains
the behavioral relationships for corn supply and therefore,
must contain the relevant economic variables to explain this
equation. To facilitate ease of updating and modification,
the equation used to forecast this response should be

relatively simple.

3.6.1 Results For Acres Planted
The structure used to estimate the acres planted in
this project used a variation of the Ferris model discussed

in Chapter 2. The form of the equation is:

53
APCT = f ( REVCOR(-l), REVWHT(-1), PARTREV(-1), DVCRN )

where: APCT = Acres planted to corn in million acres

in the current year,

REVCOR(-1) = Real net corn revenue per acre, lagged one
year,
REVWHT(-1) = Real net wheat revenue per acre, lagged
one year,
PARTREV(-1) = Real net corn revenue for participating in
the government program, lagged one year,
DVCRN = Percent of acreage devoted to conserving

uses .

Revenue received from corn and competing crops provide
expectations for producers to base decisions upon. Since
policy and expected revenue affect the decision of acreage
committed to corn, there must be some variable or proxy of
these included in the equation. The signs on the variables
for the acres planted equation are expected to be positive
on the corn revenue and negative on the substitute's
revenue, the participant's revenue and the conserving acres.

The revenue per base acre for a participant in the
government program is calculated from the gross from the
market given the acreage limitations imposed, revenue from
diversion payments and revenue from any deficiency payment.
With this summation the variable costs are subtracted from
the gross revenue to yield net revenue.

The non-government program participant revenue and

competitor crop revenue received in this estimation is the

59
real farm price multiplied by the yield per acre less the

variable costs. These revenues do not include any income
from government programs nor is it restricted by acreage
limitations and therefore, is strictly a net return per
acre. Each revenue is deflated by the consumer price index.

Equation 1 of Table 2 shows the estimation of acres
planted using net corn revenue for non participants, net
wheat revenue, the participant's net revenue and the
percentage of conservation acres. Dummy variables were used
on two of the observations. The Food and Agriculture Act of
1973 caused an anomaly and the Russian grain embargo of 1979
caused the other. This estimate shows the net revenue for
non participants to be negative which immediately dismissed
the equation from consideration.

The second attempt (equation 2 of Table 2) used net
soybean revenue as the competing crop. Again the negative
sign on the net corn revenue for non participants dismissed
the equation from consideration.

Several attempts (not shown in Table 2) were made at
estimating acres planted using various combinations of
revenues and dummy variables with unsatisfactory results.

By assuming that the revenue for the participants is
the most important variable and that most producers will be
participants in the government program the net corn revenue
for non participants was eliminated from the equation.
Equation 3 of Table 2 shows this formulation. In this

estimation, both dummy variables and net wheat revenue are

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61
used. The t statistic on wheat price tested significant at

the 5% level. The revenue generated when participating in
the government program tested low at the 5% level but due to
its importance, was left in the equation. The explanatory
power of equation 3 was high with adjusted R-squared's of
0.95 and DW statistic's of 2.47, which show inconclusive

results to signs of serial correlation.

3.6.2 Results For Acres Harvested

The Agricultural Act of 1961 which called for specific
acreage diversion programs for corn growers appears in
Figure 1. This legislation can be viewed as a structural
change and makes looking at two estimation periods, 1951 to
1986 and 1961 to 1986, interesting.

The structure used to estimate acres harvested is:

AHCT = f ( APCT, TIME )

where: AHCT

Acres harvested to corn in million acres in

the current year,

APCT = Acres planted to corn in million acres
in the current year,
TIME = Time trend.

Each estimate of acres harvested using the longer time
period was lacking in explanatory power because of the
structural change. This made the 1961 to 1986 time period

more desirable.

62

 

89 L ..—

75
79

65

68

 

 

 

55

 

58 .
1951 1955 1969 1965 1979 1975 -1988 '1985

Figure 1: Acres Harvested To Corn In Million Acres

The vertical line indicates the year (1961) which called
for specific acreage diversion programs. The dashed lines
represent the trend forecasts from (A) 1951 to 1985 and
(B) 1961 to 1985. These dashed lines show a change in

slope and thus a structural change in acres harvested.

63
By using the assumption that only the acres planted is

related to the acres that can be harvested, equation 1 of
Table 3 shows positive serial correlation. The positive
serial correlation indicates a misspecified equation.

If the assumption is that technology also contributes
to the amount of harvested acres, then time can serve as a
proxy. It is assumed that time would have a positive
relationship in this equation to represent increases in
producers ability to incorporate increased cultural
practices.

Equation 2 of the same table shows this relationship.
An adjusted R-squared of 0.98 and a DW of 1.22 show a
greater explanatory power and decreased evidence of positive

serial correlation and make this the equation of choice.

3.6.3 Results For Yield Per Acre

An estimate was made of the actual yield per acre using
one of the simplest approaches in forecasting. By assuming
that events from-the past will continue in the future, a
trend estimate may be used. Unfortunately, forecasts based
on trends miss all turning points for the variable, however
since yield estimates are based on random patterns, this

need not be a factor. The functional form follows:

CORNYT = f ( TIME )

where : CORNYT

Corn yield in bushels per acre in the
current year

TIME Time trend.

64

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65
Here time serves as a proxy to productivity for the

period of 1950 to 1985 (See Table 4). A long time period in
a trend estimate helps to reduce the standard error and
increase degrees of freedom. The estimate shows that an

increase in yield of 2.15 bu/acre per year can be expected.

3.6.4 Production and Supply

Production and supply are both identities. Production
estimates are generated by using the estimate of acres
harvested multiplied by the estimate of the yield per acre.

It takes the following form:

PRODT = AHCT * CORNYT

where: PRODT

Corn production of the current year,

AHCT

Acres harvested in the current year,

CORNYT

Corn yield in the current year.

The supply is the beginning stocks (ending stocks from
the previous year) plus the current production plus any
imports. For this model, the U.S. imports will be
considered predetermined. The supply identity has the

following form:

TSPLYT = PRODT + EST(-1) + IPORTT
where: TSPLYT = Total supply in the current year,

PRODT

Production in the current year,
EST(-1) = Ending stocks lagged one year.

IPORTT

Domestic imports in the current year.

66

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67
3.7 U.S. CORN DEMAND MODEL

The equation used for the demand component is the

identity:
TDMDT = FEEDT + FSIT + XPORTT + STOCKST

Total use (TDMDT) is the sum of corn used for livestock
consumption (FEEDT), food, seed, and industrial use (FSIT),
exports (XPORTT) and ending stocks (STOCKST).

In turn each of these demand components are estimated

functions or identities composed of other variables.

3.7.1 Results For Corn Consumed By Livestock

The livestock feeding rate per head was conceptually
developed to be: that amount of corn which a producer will
feed in any time period based upon the value of output from
feeding, the cost of the feed, the cost of a substitute
feed, and any information regarding technological changes.

The functional form of the feed use equation is as follows:

CONSUMPTION = FEEDING RATE/HEAD * ANIMAL
NUMBERS
where: FEEDING RATE = f ( OUTPUT VALUE, OWN PRICE,
SUBSTITUTE PRICE, TIME )

ANIMAL NUMBERS Inventory values of the animal

species.

By separating each animal species and estimating

individual rates of feeding and multiplying the feeding rate

68
per head by animal inventory numbers, each species can be

summed to give the total combined corn use.

The corn price is expected to be inversely related to
the rate of feeding while the substitute price is expected
to be positively related, which follows from the marginal
rate of substitution discussed earlier. The sign of the
output price is expected to be positive since the short run
response of producers to increases in product price is to
feed animals out more heavily. The technology variable is
expected to have a positive sign, reflecting the increase
over time in the ability of an individual animal to utilize
more feed.

Price series data for this component of demand are
collected with different months (other than the corn
marketing year) establishing the market year. In order to
make meaningful aggregations of prices and quantities, all
time t periods cannot be aggregated together. Because of
this, it is necessary to "overlap" price and inventory data
series where possible. This overlapping means to include
the time period of price and inventory which falls within
the consumption year.

The time map in Table 5 shows the relationships between
the marketing year and the various output price and
inventories. By referring to the time map, the association
between the corn marketing year and relevant price and

inventory observations can be made.

69

TABLE 5: TIME RELATIONSHIP MAP

Calendar year t t+1
------ +------+------+------ ------+------+------+------
JAN APR JUL OCT JAN APR JUL OCT ]
t-1 corn marketing year t
t-1 consumption t
------- +------+----- -------+------+------+----- ------
JAN APR JUL OCT JAN APR JUL OET

dairy animal number
beef cow number
cattle and calves on feed

*
JAN 1

hen, pullet, and other chicken number
hog and pig number
*

DEC 1
broiler production
turkey production
------ +------+------+------]
J APR JUL OCT
milk price t t+1
milk cow price t t+1
steer & heifer price t t+1
beef cow price t t+1
J """ ipfi""3$£""6c§ """ 3111""Xpfi""30£""6ci '''' 1
egg price t t+1
broiler price t t+1
turkey price t t+1
hog price t t+1

-+ ------ + ------ + ------ + ----- + ------------ + ------ +-- --
JAN APR JUL OCT ][ JAN R JUL OCT ][

70
For example, in aggregating the hog consumption per

head, the corn consumed by hogs in corn marketing year t is
divided by the hog and pig number in calendar year t. Dairy
animal consumption per head used corn consumed by dairy
animals in corn marketing year t divided by the dairy animal
number in calendar year t+1.

Likewise, the corn to output price ratios used the
output price in calendar year t+1 divided by the corn price
in marketing year t. This shows an overlapping because the
corn marketing year contained between 9 and 11 months of
output price year t+1. Without using the values that fall
in the corn price marketing year and simply using all time t
periods, the aggregated variable would be biased due to

insufficient or misinformation.

a) Dairy animal consumption per head

The dependent variable dairy animal consumption per
head used dairy animal numbers for milk cows and heifers
that have calved as of January 1.

The estimates of dairy animal consumption used time to
proxy technological changes, real farm price, the real milk
price, and the real wheat price. Several attempts were
tried using various combinations of variables and time
periods. The most favorable equation used the longer time
period which was 1970-84 and had signs that were all correct
(see equation 2 in Table 6). The adjusted R-squared is high

and shows a good fit for the estimate, the standard error is

71

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72
the lowest among the attempts and the DW statistic shows no

sign of serial correlation.

b) Cattle on feed consumption per head

The dependent variable cattle on feed consumption per
head used cattle and calves on feed as of January 1.

The estimates of cattle on feed used time, the real
farm price, the real soybean meal price, the real steer and
heifer price, and a lagged dependent variable. Again
several attempts were tried using these variables. The
estimate which showed the most favorable results used the
time period from 1971-84 and stood the test of logic
(equation 2, Table 7). The sign on the time variable is
negative which could indicate that a substitute for corn as
a feed has been incorporated into the cattle on feed ration
over time. The signs of the other variables are as
expected.

In equation 2, the standard error was the smallest
among all attempts and the adjusted R-squared was the
highest at 0.61. The equation used a lagged dependent
variable so the DW statistic was not applicable. The Durbin
h statistic of 1.31 shows that at the 5 percent level (the
critical value of the normal distribution being 1.645), the
null hypothesis of no serial correlation cannot be rejected

(i.e. not significantly different from zero).

'73

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74
c) Other beef cattle consumption per head

The dependent variable other beef cattle consumption
per head used beef cows and beef cow replacements on January
first.

The estimates of other beef cattle used the beef cow
price, the real farm price, real soybean and wheat prices as.
substitutes. The attempt which showed the best results used
the 1971-84 time period, a lagged substitute price and also
stood the test of logic (equation 2, Table 8).

In this estimation, the adjusted R-squared was highest
(at 0.83) among all attempts while the standard error was
approximately the same for all estimates. The estimate did
not have enough observations to show a critical value on the

DW statistic table.

d) Hen, pullet, and other chicken consumption per
head

The dependent variable HPC consumption per head used
inventories on December 1.

The estimates for hen, pullet and other chicken
consumption used the real egg price, the real farm price,
the real soybean meal price, real wheat price and a lagged
dependent variable. The time period of 1971-84 showed the
best results with logical signs on all variables (equation
2, Table 9). This equation had a smaller standard error and
a higher adjusted R-squared which all indicate a better fit.

Again because of the lagged dependent variable, the Durbin h

75

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77
test statistic must be used. The Durbin h showed that the

null hypothesis of no serial correlation cannot be rejected.

e) Broiler consumption per head

The dependent variable broiler consumption per head
used the number of birds produced from December 1 to
November 30.

The variables used in the broiler estimation were time,
the real farm price of corn and the real wheat price. The
time period chosen was 1971-84 and it showed fair results
with a small number of observations. The signs of all
independent variables were correct for the test of logic
(equation 1, Table 10). The adjusted R-squared showed a
good fit of the data to the dependent variable while the

Durbin Watson indicated no signs of serial correlation.

f) Turkey consumption per head

The dependent variable turkey consumption per head used
annual production counts from December 1 to November 30.

The estimate uses both the real farm price and a lagged
dependent variable, and the real wheat price. With an
estimation period of 1971 to 1984, each variable showed
significant t statistics (equation 1, Table 11). The
explanatory power of the equation is only fair in being able
to attribute less than 64% of the variability of the turkey
consumption to the variability of the independent variables.
The DW is not applicable in this case due to the lagged

dependent variable and the Durbin h is inclusive.

78

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80
g) Hog consumption per head

The dependent variable hog consumption per head used
the hog and pig inventory on December 1.

Hog consumption was estimated using time, real hog
price, and real farm price from 1970-84. The estimate with
the best statistics among those attempted used the 1971-84
time period and had the highest adjusted R-squared and the
lowest standard error (equation 3, Table 12) of all
estimates. The signs were all logical but the DW statistic
showed inclusive results while a pattern existed in the

residual plot.

h) Other and unallocated consumption

To estimate the consumption of each species on a per
head basis, the animal inventories must be known. The only
data obtainable for other and unallocated was sheep and
lambs on feed as of January 1. Since other species of
animals consume corn during the year (i.e. horses, mules,
etc.), the other and unallocated consumption cannot be
estimated on a feeding rate per head basis.
Equation 1 in Table 13 shows the estimate of other and
unallocated consumption. The independent variables of real
corn farm price, real wheat price, and time show good
statistics when dummy variables are used for the years 1982
and 1984. The dummy variables were necessary because an
unusually large amount of corn was placed in this category
during those years. The real farm price was negative while

all other variables were positive which is expected from the

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83
theoretical framework. The t statistic on the price

coefficients are suspect of insignificance yet not extremely
low. The adjusted R-squared shows good explanatory power.
The Durbin Watson showed indeterminate results and was
slightly suspect of showing negative serial correlation
among the error terms however no pattern was evident in the

residual plot of the equation.

3.7.2 Food, Seed, And Industrial Use Of Corn

Given the evidence about FSI in Chapter 2, it would be
difficult to forecast its future demand. It is known that
the quantity consumed by FSI will level off, yet it is not
known when or by how much. Therefore, this model will not
attempt an estimate of PSI and will provide a predetermined

quantity for purposes of total use.

3.7.3 Results For U.S. Corn Exports

a) Assumptions

Recalling the export formulation from Chapter 2, the
assumption is that there are 3 regions. 1) The United
States, 2) Residual Exporters, and 3) Importers (see
Appendix 2 for a listing of the international regional
groupings). Using the three regions, we assume that the
U.S. will provide the balance of the world needs after the
"dumpers" have exported their product.

An equation can be formulated which shows that the U.S.

exports (XPORTT) are equal to importer net imports (INIPC)

84
less exporter net exports (ENEPC). The form of this demand

component follows:

XPORTT = INIPC - ENEPC.

b) Aggregations and Limitations

There is a trade-off involved when aggregating the
consumption and production of the entire world into three
regions. The trade-off involved is: the simplicity of the
model for the purposes of an easy to use educational tool,
versus the precision of a complex system of equations and
linkages. A simple model looses much of: the robustness of
the more complex model, explanatory power, and estimates are
likely to be less reliable, however, the complex system
would be more difficult to maintain and use.

To maintain some consistency in the numerous
international data series, the data for gross domestic
product and consumer price index for each region was indexed
with 1980 equal to 100.

It was necessary to weigh the regional gross domestic
product data by feed grain usage and aggregate across
regional areas. This procedure is essential to represent
the degree of importance feed grain has in each regional
area of the region in relation to their level of wealth. An

example of the weighting procedure follows:

85
Importer Gross Domestic Product (IGDP)=

SUM ((feed grain net imports of the past 5 years /
world net imports of the past 5 years) * real

gross domestic product).

The weight is an average of the past five years net
imports into the regional area divided by the total world
net imports. This product is multiplied by the real gross
domestic product of the regional area. Once the weighted
real gross domestic product has been calculated for each
regional area, the values are summed across each regional
area to obtain the real gross domestic product for the
importing region.

The same type of aggregation was used for the residual
exporter's gross domestic product. Again, a five year
average of the net exports from the dumpers is divided by
the total net exports and multiplied by the residual
exporter's real gross domestic product. Finally, the value
for gross domestic product is summed across the three
countries to get the real gross domestic product for the
exporting region.

Importer and Exporter population was aggregated as the
sum of the population of each region to obtain an importer

and exporter regional population.
SUM (pop i) and SUM (pop j)

Where: i is the importing regions and j is the

exporting regions.

 

86
The prices used in each U.S. export equation reflect

the f.o.b. U.S. Gulf Ports feed grain price perceived by the
respective importing and exporting region. To maintain
consistency with the prices of the other components of U.S.
demand a linkage was needed to relate the U.S farm price of
corn to the export price of feed grain. The linkage is
provided by calculating a simple average of the difference
between the Gulf price and the farm price.

The importer and exporter perceived price variable
called for multiplying the U.S. real feed grain price by a
regional normalized exchange rate. The aggregation is
necessary because the varying in the exchange rate of the
U.S. dollar to foreign currencies could cause the price that
the particular region pays on the world market to vary even
if the Gulf Port price stays constant. The price

aggregation has the following form:

Feed grain and wheat price = the real f.o.b. U.S. Gulf price
* normalized exchange rate for
feed grain and wheat

respectively.

The lagged revenue generated by the crop and
alternative crops affect the amount of acres planted (and
consequently supply) in the current period. This is due to
producers having expectations of the profitability of the_
corp and being able to adjust production accordingly. The

revenue variable generated summed the revenue of the

87
respective crop for each country in the region and has the

following form:

Revenue per acre = The lagged real crop price * the
lagged yield per acre for the crop
* the lagged normalized exchange
rate for the crop all divided by the

lagged local consumer price index.

c) Importers net imports per capita
Theoretical components for a demand function state that
demand is a function of own price, substitute price and
income. The importers net imports per capita equation takes

the form:

INIPC = f ( IFP, IWP, IGDPPC )
where: INIPC = Importers net imports per capita in thousand

metric tons,

IFP Importers feed grain price,
IWP = Importers wheat price,

IGDPPC

Importers gross domestic product per capita.

The theoretical framework for demand developed in
Chapter 2 showed good results. The dependent variable
importer net imports per capita used prices perceived by the
importing region for corn and wheat, and per capita income.
In equation 1 of Table 14, the signs were correct and all
the coefficients were significant. The adjusted R-squared

shows good explanatory power. The Durbin Watson statistic

88

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89
of 1.4 showed indeterminate results. There was however, a

pattern in the residual plot and the most recent observation
having one of largest residuals which is undesirable.

It seems rational to assume that the importers own
supply would have an impact on the amount that they import
in order to fulfill their demand, therefore an indication of
their production can serve as a proxy. The sign of the
production variable is expected to be negative because the
more corn produced in the region, the less imports would be
needed. When importer production is added to the conceptual
formulation (equation 2 of Table 14), the standard error is
large and the coefficient was suspected of insignificance.
The adjusted R-squared of 0.92 showed a slight increase in
explanatory power and the Durbin Watson statistic which,
still tested indeterminate, was lower. Again there was a
pattern to the residual plot and the last observation had
the largest residual.

The final estimate tried (equation 3 of Table 14) used
a correction for first-order serial correlation. This
estimate took 7 iterations for rho to converge. the standard
error was smaller and the adjusted R-squared was larger than

the other estimates which make this the equation of choice.

d) Importers yield
The importers yield was estimated using a time trend.

The estimate took the following form:

90
IYIELD = f ( TIME )

where: IYIELD = Importers yield in metric tons per hectare,

TIME = Time trend.

This estimate is necessary in order to calculate the
importing region's production and to determine the standard
deviation around the yield for purposes of randomly shocking
the world yield. The estimate (see equation 1 of Table 15)
had great explanatory power at 0.97 but the DW statistic
showed negative serial correlation. Equation 2 of Table 15
shows the correction for first-order serial correlatiOn and

was the equation of choice.

e) Exporters net exports per capita
Since the exporters will export everything not consumed
or held for stocks, the exporters net exports per capita is

an identity which takes the following form:

ENEPC = ESPLYPC - ECONPC - EESPC

where: ENEPC Exporters net exports per capita in thousand

metric tons,

ESPLYPC = Exporters supply per capita in thousand
metric tons,
ECONPC = Exporters consumption per capita in thousand
metric tons,
EESPC = Exporters ending stocks per capita in

thousand metric tons.

91

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92
i) Exporter Supply Per Capita

The Exporter supply aggregation was made by summing the
beginning stocks (lagged ending stocks) and the production
of the current period for each country in the region. The

identity follows:

ESPLYPC = EPROPC + EESPC(-1)
where: ESPLYPC = Exporter supply per capita in thousand

metric tons,

EPROPC = Exporter production per capita in thousand
metric tons,
EESPC(-1) = Exporter ending stocks per capita in

thousand metric tons, lagged one year.

ii) Exporters production per capita
By multiplying the estimated yield and acres harvested
together then dividing by the population, the estimation of
the exporter's production per capita can be obtained. The

exporters production per capita has the form:

EPROPC = ( EYIELD * EHA ) / EPOP
where: EPROPC = Exporter production per head in thousand
metric tons,

EYIELD

Exporter yield in metric tons per hectare,
EHA = Exporter harvested area in thousand
hectares,

EPOP = Exporter population.

93
The exporters yield was a function of time to represent

the technology changes made in the production practices.

That equation takes the following form:

EYIELD = f ( TIME )

where: EYIELD Exporters yield in metric tons per hectare,

TIME Time trend.

As expected the sign on the technology proxy was
positive indicating increased cultural practices (as shown
in equation 1 of Table 16). The adjusted R-squared and the
Durbin Watson statistic were 0.89 and 2.03 respectively.
This indicates a good fitting equation with no presence of
serial correlation among the residuals.

To complete the production equation the exporters

harvested area was estimated with the following form:

EHA = f ( EFREV(-l), EWREV(-l), EHA(-l), EES(-l) )

where: EHA = Exporters harvested area in thousand hectares,

EFREV(-1) = Exporters feed grain revenue lagged one year,
EWREV(-1) = Exporters wheat revenue lagged one year,
EHA(-1) = Exporters harvested area lagged one year,
EES(-1) = Exporters ending stocks lagged one year.

Equation 1 of Table 17 Show the estimate of exporter
area harvested. This equation used lagged feed grain
revenue, lagged wheat revenue, lagged harvested acres, and
lagged ending stocks. The signs of all variables were

correct while the standard errors on the feed revenue and

94

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95

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96
ending stock variables were higher than desired, however

within a tolerable range. The Durbin Watson statistic
cannot be used because of a lagged dependent variable in the
equation. The Durbin h test statistic shows that the null
hypothesis of no serial correlation cannot be rejected. The

adjusted R-squared of 0.68 indicates fair explanatory power.

iii) Exporters consumption per capita
As indicated in the theoretical considerations, the .
consumption function is dependent on income and some proxy
for prices. The exporters consumption per capita takes the

form:

ECONPC = f ( EGDPPC, EFP, EWP, ESPLYPC )

where: ECONPC

Exporters consumption per capita in thousand
metric tons,

EGDPPC

Exporters gross domestic product per capita,

EFP

Exporters feed grain price,
EWP = Exporters wheat price,

ESPLYPC Exporters supply per capita in thousand

metric tons.

The estimates of exporter consumption per capita are
shown in Table 18. Equation 1 has problems which is
evidenced by the signs of the own and substitute prices.
Theory says that the own price should have a negative sign

while substitute price should be positive. This equation

97

 

 

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98
has the opposite of what theory would predict. When an

Equation does not stand the test of logic, it must be
dismissed from consideration.

It seems rational that the consumption of the exporting
region is also based upon the amount of corn which is
available for use. This consideration is shown in equation
2 and again the signs of the own and substitute prices are
switched. The formulation must be eliminated but important
information is gained. This equation shows the exporter
supply is significant and helps to reduce the serial
correlation among the residuals, the standard error is
smaller and the adjusted R-squared is larger. This
indicates that the supply variable helps explain the
exporters consumption.

In 1974 the European sector started to subsidize wheat
for feed grain. This policy change marked a structural
change in the formulation of exporters consumption. By
using a spline variable on the years 1974 on, the conceptual
model worked better. The spline variable shows a negative
Sign because that policy caused less corn to be consumed at
the effectively higher price. Equation 3 of Table 18 shows
this formulation and becomes the equation of choice. It had
an adjusted R-squared of 0.93 and a Durbin Watson statistic
of 2.45 indicating inclusive evidence of serial correlation

among the residuals.

99
iv) Exporters ending stocks per captia

The exporters ending stocks was estimated using the

following form:

EESPC = f ( EPROPC, ESUDR(-l) )

where: EESPC

Exporter ending stocks per capita in thousand

metric tons,

EPROPC = Exporter production per capita in thousand
metric tons,
ESUDR(-1) = Exporter ending stocks per capita to exporter

utilization per capita ratio, lagged one

year.

By using the lagged ending stocks to utilization ratio
and the current production to estimate the ending stocks,
all signs were correct (see equation 1 Table 19). The
adjusted R-squared was relatively low but the t statistics
were significant. The DW statistic shows no signs of serial
correlation.

In equation 1, the 1983 observation is a severe
negative outlier. This is because of the U.S. 1983 PIK
program which limited acreage planted and consequently
supply. Due to the reduced U.S. supply, the exporters
"dumped" more grain on the market and hence depleted their
stock holdings. When the 1983 observation is dummied out
(in equation 2 of Table 19), the explanatory power and the t
statistics both improve. Again the DW statistic shows no

serial correlation.

 

100

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101
In equation 3 the ending stock to utilization ratio is

disaggregated. By separating the variable, the equation
becomes richer in the information provided yet it looses one
degree of freedom, which can be sacrificed in this case.

The signs of all the variables were correct and each
variable was significant. The adjusted R-squared was 0.52
and the Durbin Watson statistic indicated a rejection of the
hypothesis of positive serial correlation among the error
terms.

An examination of the U.S. feed grain exports shows
that approximately 86% of the feed grain exported is corn
(see Table 20). Since the international component estimates
feed grain demand, this conversion factor must be provided

to show the actual amount of corn that the U.S. will export.

3.7.4 Results For Policy And Free Ending Stocks

To complete the demand identity, ending stock
quantities must be estimated. The government will hold
stocks in both the CCC and FOR while speculators will hold
free stocks. Since policy stocks are not residual and are
established by policy, some rule must be established for the
accumulation and release of these stocks. Speculative stock
demand is related to prices and use so an estimate of this
is also needed.

a) Policy Stocks
By combining the concepts of both Shagam and Ferris (as

developed in Chapter 2), a function with three major turning

102

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103
points can be developed. The functional form used in

estimating policy stocks follows:

2 3
Pt Pt Pt

CLRt,CLRt ,CLRt

II
P”

Stockst

where: Stockst change in policy ending stocks in time t,

Pt world price of corn in time t,

CLRt

corn loan rate in time t.

Since the loan rate establishes a floor under price,
combining the price and loan rate, creates the ratio needed
to do analysis. The rules used to adjust the cubic function
(as discussed in Chapter 2) were as follows:

A) CCC stocks in the years before 1976 were viewed as
being 10% available. Stocks after 1976 were viewed as being
80% available unless; a) the current stocks accumulated are
less than the previous year and/or b) the price over loan
rate ratio is greater than 1.55 in which case the stocks
were seen as being 10% available and

B) FOR stocks before 1976 were viewed and being non
existent. Stocks after 1976 were viewed as being 75%
available unless; a) the current stocks accumulated are less
than the previous year and/or b) the price over loan rate
ratio is greater than 1.55 in which case the stocks were
seen as being non-existent.

Shagam cites a problem with the policy stock equation
in that the formula is not a smooth functional rule but

rather the choice of one of two percentages. He continues

 

104
that several attempts were made to derive a smooth function

but all attempts failed.

Both of the policy stock equations used the price loan
rate ratio (linearly, squared, and cubed) as the independent
variables. The time period estimated was 1977 to 1997.

Each equation estimated the change in stocks from
accumulation or release as the dependent variable.

The equation for the change in FOR stocks had good
results (equation 1 of Table 21). Again each variable was
significant and the R-squared was 0.96, the estimate was
inconclusive in determining the correlation between residual
error terms.

The equation for the change in CCC stocks also showed
good results (see equation 2 Table 21). Each variable had
significant t statistics, the adjusted R-squared was high at
0.91 and the standard error small. The DW statistic showed

some signs of negative serial correlation.

b) Free Stocks
Once the rules had been established on the release and
accumulation of policy stocks, the equation used to estimate
free stocks could be developed. The functional form needed

for this relationship follows:

Fst = £( Pt, CLRt, ccct, FORt, Utilt )

Free Stocks in time t,

where: FSt

Pt = world price of corn in time t,

cm.

corn load rate in time t,

105

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106

CCCt = estimated CCC stocks in time t,
FORt = estimated FOR stocks in time t,
Utilt = utilization in time t.

The independent variables in this equation were a
summation of the ruled policy stocks to utilization for one
variable and the price over the loan rate to utilization as
the second (see Table 22).

The results show very significant t statistics and the
DW statistic indicates that the null hypothesis of zero
serial correlation can be accepted. The adjusted R-squared

was 0.73 which shows good explanatory power of the equation.

3.8 U.S. CORN PRICE DETERMINATION

The basic model framework used in this approach is:

St = f ( Pt-l )
at f' ( Pt )
smt = f" ( pt )

Pt=f(Pt_1)+f"(Pt_1)'f'(Pt)’f"(Pt)

Total supply in the current year,

where: St

Dt Total demand in the current year,

Pt The average farm price of corn in the current

year,

STt = Total ending stocks in the current year.

By using this system of equations as a recursive model,
and the supply being essentially predetermined by the price

in the previous period, the price can be determined by the

107

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108
procedure outlined in the FAPSIM model. This price equation

structure allows the model to be solved by starting price at
an very high level and successively reducing price until the

quantity demanded equals the amount available.

CHAPTER IV

_ THE ECONOMIC SUPPLY/DEMAND BALANCE SHEET

WITH APPLICATIONS

Chapter four discusses the development of the actual
microcomputer balance sheet. Ex post analysis is performed
on the forecasts and sample runs with analyses under

differing scenarios is provided.

4.1 THE ECONOMIC SUPPLY/DEMAND BALANCE SHEET

The first step in designing an economic supply and
demand balance sheet was to plan a way to input the relevant
variables and to display the relevant information. A
commercial screen generator, SCREEN SCULPTURE, was used in
screen development.1 This screen generator was used to
define the screens, including; the variable names, the
variable types (e.g. real, integer, yes vs. no, etc.) the
default values, the allowable range for input values, and
screen layout.
The screen generator then automatically generated the
input/output computer code associated with the screens in

the Pascal language.

 

1. Screen Sculpture, Copyright 1985, The Software
Bottling, Company of New York.

109

110
The second step was to write an algorithm which would

equate the supply and the demand, determine the equilibrium
corn price, and tied the screens together.2 The algorithm
was written with TURBO PASCAL and utilized the equations

discussed in Chapter 3.3

In the U.S. corn industry, the
annual supply is set for the year and is known with some
certainty which essentially presents an inelastic supply
curve. The level of disappearance is an aggregate of the
domestic consumption, and the exports. The ending stocks
are added to the disappearance to support equilibrium.

Through a series of menu options, the user will input
all of the relevant predetermined and exogenous variables
for the forecast year. The user can then choose to solve
the model with these values and view each of the supply and
demand factors estimated (as discussed in Chapter 3) and the
market clearing farm price. A supply/demand balance sheet
in this form allows the user to perform case studies under
different scenarios by changing the predetermined and
exogenous variables.

A review of the literature on decision-making theory
and agricultural economics research contrasted with a survey

of farm management instructors and Extension specialists

indicates the existence of a large "gap" between theory and

 

2. See Appendix 3 for documentation to the
Supply/Demand Balance Sheet and the input/output code of the
Pascal program.

3. Turbo Pascal System, Copyright 1985, Borland Inc.

111
practice in risky farm decision-making.4 A major step in

closing this gap is to help quantify the probabilities for
these farm decision makers. To this end, one objective of
this project was to develop probabilities for and a
confidence interval of the expected price generated by the
model.

The probability (i.e. the numerical value) assigned to
a particular event reflect the degree of belief that that
event will occur. For example, the model forecasts an
expected farm price, the probability would be an interval of
prices for which there is a 99, 95, 90, 80, 70, ... percent
chance of occurring. The decision maker is then allowed to
use his level of risk preference to make planning decisions.

The accuracy of the probability forecasts could be
improved if the approach provides an opportunity to revise
initial probability estimates when new information becomes
available.

Weather is a predominant cause of yield variations from
year to year. Since long-range weather forecasting is not
yet very dependable we must resort to probability statements

about the weather (and hence price forecasts) by reporting

 

4. Walker, O.L. and A.G. Nelson, Agricultural Research
and Education Related to Decision-Making Under Uncertainty:
An Interpretive Review of Literature, Oklahoma State
University Agricultural Experiment Station Research Report
P-747, March 1977; Walker, O.L. "Teaching Decision Making
Under Uncertainty in the Farm Management Curriculum."
Proceedings of Farm Management Teaching Workshop, Michigan
State University, East Lansing, Michigan, April 14-15 1977,
New York: Agricultural Development Council, Inc., p. 124-
130.

112
yields in the form of probability distributions.5 When the

relevant variables have been inputed, the model will
randomly shock the importer, the exporter and the U.S.
yields. This procedure assumes a normal distribution for
the weather (as it affects yields) since it can be said to
conform to the central limit theorem. The process results
in prices being displayed as probabilities.

A reasonable question at this time would be what does
the user do with this information? The use of any marketing
alternative essentially reshapes the price probability
distribution (by cutting off the tails) that one faces.
Although moving into that area is beyond the scope of this
project, the subject will be addressed in the

recommendations of the next chapter.

4.2 EX POST ANALYSIS

The ex post forecast covers the period between the
estimation period and the present. A distinction can be
made between conditional and unconditional forecasts. In an
unconditional forecast, values for all the explanatory
variables in the forecasting equation are known with
certainty. In a conditional forecast, values for one or
more explanatory variables are not known with certainty, so
that guesses or forecasts of them must be used to produce

the forecast of the dependent variable.

 

5. John Ferris, "Probability Forecasts on U.S. Corn
Prices," Ag Econ Staff Paper No. 82-4, Michigan State
University, December 1983.

113
Using explanatory variables which are known with

certainty provides a measure of the balance sheet’s ability

to forecast accurately.

4.2.1 The 1985-86 Marketing Year

Since most of the equations in the balance sheet are
estimated through 1984, the 1985-86 marketing year would be
the first ex post forecast.

For the 1985-86 marketing year, the simple acres
planted equation did a reasonable job of forecasting
considering the amount of time researchers have committed to
an efficient acres planted equation (See run 1 of Table 23).
The balance sheet forecasted the acres planted as 80.7
million acres which is 3% less than the actual amount of
83.2 million.

Consequently, the acres harvested forecast (which is
essentially a percentage of acres planted) was 5.2% less
than the actual acres harvested. The yield is based on a
time trend and was 5% less than the actual record yield per
acre of 118.0 bushels. These errors in domestic production
resulted in the U.S. production forecast being 10% less than
the actual production and the U.S. supply being 927 million
bushels short of the actual supply.

Given the short supply, the balance sheet estimated a
farm price of $2.65. Based on this price, the feed
consumption was forecasted as 4046 million bushels and the

exports were forecasted as 1797 million bushels. The CCC,

114

TABLE 23: BALANCE SHEET FOR CORN: THE 1985-86 MARKETING YEAR

RUN NUMBER

ACRES SET-ASIDE

ACRES PLANTED

ACRES HARVESTED

YIELD PER ACRE

 

 

BEGINNING STOCKS
PRODUCTION
IMPORTS

TOTAL SUPPLY
USE

FEED

FOOD. SEED &

IND. USES

TOTAL DOMESTIC
EXPORTS

TOTAL USE
TOTAL ENDING STOCKS

TOTAL. X OF USE

U.S. FARM PRICE

STOCKS IN GOVT. PROG.

CCC INVENTORY

FARMER-OUNED RES.

FREE STOCKS

EFFECTIVE LOAN RATE

9609

4046

1160

5206

1797

7004

2604

$2.65

702

I442

459

$2.55

2 3 4
(MILLION ACRES)

5.9 5.9 5.9
83.2 83.2 83.2
75.1 75.1 75.1

118.0 118.0 118.0

(MILLION BUSHELS)

1648 1648 1648
8877 8877 8877
11 ll 11
10536 10536 10536
4187 4095 4115
1160 1160 1160
5347 5255 5275
1825 1241 1811
7173 6496 7087
3362 4040 3448
46.9 62.2 48.6
$2.41 $2.21 $2.53
1102 1580 1172
1945 2434 1999

313 25 276

$2.55 $2.55 $2.69

5 6
5.9 5.9
83.2 83.2
75.1 75.1
118.0 118.0
1648 1648
8877 8877
11 11
10536 10536
4095 4205
1160 1160
5255 5365
1241 1241
6496 6606
4040 3929
62.2 59.5
$2.35 $2.38
1591 1514
2423 2343
24 71
$2.69 $2.69

10536

4095

1160

5255

1241

6496

4040

$2.35

1591

2423

24

$2.69

115
FOR and free stocks were forecasted as 702, 1442 and 459

million bushels respectively.

To correct for the discrepancies in the ex post
forecasts, add factors can be used. The domestic production
is corrected for by adding 2.5 million acres to the acres
planted. Acres harvested needed an add factor of 3.9
million acres and yield needed 6.4 bushels per acre to force
the time trend forecast up to the record yield.

With the domestic supply corrected to equal the actual
supply, the price and disappearance can be analyzed. Given
the corrected supply the balance sheet forecasted a price of
$2.41 (Run 2 of Table 23). As expected, with a lower price,
the disappearance forecasts increased. In this case, the
' feed consumption was 2.2% greater than the actual amount
while exports were 45% greater.

By using the override feature and forcing the feed
consumption and exports equal to the actual amounts, the
balance sheet gives an interesting policy result (Run 3 of
Table 23). Using all the correct supply and demand amounts,
the farm price generated was $2.21. This indicates that the
corn loan rate of $2.55 is not the effective loan rate.
Producers know that there is an amount of corn held off the
market by the government "PIK and ROLL" program. The PIK
certificate has a face value in cash and the number of
bushels a dollar amount will redeem varies with prices.

Thus the producer is indifferent as to the actual farm price

116
of corn if he is covered under the "FIR and ROLL" program

and the price can fall below the loan rate.6

By increasing the corn loan rate to the effective
amount of $2.69 and allowing the disappearance to be
determined by market forces a new analysis can be done (Run
4 of Table 23). A farm price of $2.53 was generated under
this analysis. The feed consumption was less than 1%
greater than the actual amount and exports just over 45%
greater than the actual.

When the feed consumption and export forecasts are
overridden to equal the actual amounts, a farm price of
$2.35 was generated (Run 5 of Table 23). This shows that
the policy of the government program has an affect on the
producer expectation of the effective corn loan rate.

The interesting ex post analysis was the way the
international component forecasted U.S. exports. This
component is a conditional forecast because of the lag in
the publication of international data. By using the
corrected U.S. supply and the effective corn loan rate of
$2.69, the forecasted U.S. exports exceeded the actual
amount by 45 percent (See run 5 of Table 23). Examining the

components of U.S. exports, the balance sheet forecasted

U.S. exports as being exactly equal to the importing

 

6. For discussion on the "FIR and ROLL" program, see
the Agricultural Economics Staff Paper No. 86-90, Michigan
State University, by Jim Hilker and Mary Schultz, November
1986.

117
region's net imports (Run 5 of Table 24). Further analysis

showed the exporting region had zero exports.

The structure of the exporting region's net exports is
that all grain not consumed or held in stocks will be
exported. The equations for consumption and ending stocks
were reasonably consistent with historical data however, the
supply forecast was significantly lower (60% lower) than
historical exporter supply (Run 5 of Table 25).

The exporting region's supply is the sum of the current
production and the lagged ending stocks. The equation used
to forecast the exporter production is a function of lagged
revenues for feed grain and wheat and a lagged price proxy.
With the hyper-inflation that Argentina is experiencing, the
lagged revenues generated were essentially nullified by the
Argentine consumer price index. And thus the overall
production was reduced. To correct for this effect, the
exporter harvested area add factor needed to be used.

Adding 12.8 million hectares to the production of the
exporting region moved the exporter supply in line with the
historical record. Since the exporting region’s consumption
is a function of (among others) the supply, it was necessary
to decrease the exporters consumption through the use of an
add factor. By subtracting 0.109 thousand metric tons per
capita from the exporting region's consumption, the quantity
consumed was more historically accurate. Run 6 of Table 25

shows the corrected exporting region’s net exports.

118

TABLE 24: U.S. EXPORT DEMAND: THE 1985-86 MARKETING YEAR

RUN NUMBER 5 6
"mm-7§4ELEESIEBEQEEET """""""
IMPORTERS NET IMPORTS 1811 2017
EXPORTERS NET EXPORTS 0 776

U.S. EXPORTS 1811 1241

TABLE 25: EXPORTING REGION’S NET EXPORTS:
THE 1985-86 MARKETING YEAR

RUN NUMBER 5 6

(MILLION BUSHELS)

EXPORTERS SUPPLY 752 1900
EXPORTERS CONS. ‘ 820 911
EXPORTERS STOCKS 129 213

EXPORTERS EXPORTS 0 776

119
With the unconditional knowledge that the U.S. exports

were roughly 33% less than the historical average and
assuming that the exporting region is continuing the same
policy of "dumping" their residual grain, the implication is
that the importing region imported much less in the 1985-86
marketing year. The balance sheet forecasted a much lower
net import by the importing region and the use of an add
factor of 0.0012 thousand metric tons per capita was needed
to increase the net imports (Run 6 of Table 24).

The final ex post analysis for the 1985-86 marketing
year examined the forecast of corn consumed by livestock.
By correcting the domestic supply, using the effective corn
loan rate, and adjusting the international component as
discussed, the balance sheet forecast the price as $2.38
(Run 6 of Table 23). With the price higher actual, theory
says that the quantity demanded would be lower, however, the
balance sheet over forecast the quantity of corn consumed by
livestock by 2% (See run 6 of Table 26). By using a
negative add factor of 129.37 million bushels on the other
and unallocated (i.e. residual) consumption the forecast for
corn consumed by livestock reached the actual level of 4095
million bushels (Run 7 of Table 26). Run 7 of Table 23
shows the actual supply and demand factors for the 1985-86
marketing year using all add factors discussed and a market

clearing farm price of $2.35.

120

TABLE 26: LIVESTOCK CONSUMPTION OF CORN IN THE U.S.:
THE 1985-86 MARKETING YEAR

(MILLION BUSHELS)

DAIRY ANIMALS 830 833
CATTLE ON FEED 658 664
OTHER BEEF CATTLE 187 188
HENS, PULLETS, &
OTHER CHICKENS 334 337
BROILERS 481 483
TURKEYS 119 119
HOGS 1445 1449
OTHER AND UNALLOCATED 148 19
TOTAL CONSUMPTION 4205 4095

4.2.2 The 1986-87 Marketing Year

The 1986-87 marketing year is another unconditional
forecast for the balance sheet however, due to the lag in
the reporting of international component statistics, some
of the disaggregated factors of the balance sheet are
conditional upon educated guesses of the exogenous
variables.

In 1986-87 the acres set-aside for participation in the
government program were 13.6 million acres. The acres
planted equation forecasted that 75.0 million acres would be

planted to corn, which again under estimates the amount Of

121
acreage actually committed to corn by 2.2% percent or 1.7

million acres (See run 1 of Table 27).

The acres harvested equation also under estimated the
actual quantity by 4.2% due to the low estimate of acres
planted. The trend used to forecast yield was 113.8 bushels
per acre which is 5.5 bushels per acre less than the actual
record yield of 119.3 bushels. With the reduced acres
planted forecast, the reduced acres harvested forecast and
the low yield forecast, the domestic production was
forecasted as 8.5% lower than the actual amount. This
forecast caused the U.S. supply to be under estimated by 707
million bushels.

Using these figures for domestic supply, the U.S. farm
price was forecasted at $1.99. Since this price was higher
than the actual, both feed consumption and U.S. exports were
under forecast as 4404 million and 1225 million bushels
respectively. The estimates of CCC and FOR stocks were 1765
million and 3001 million bushels respectively while, at this
price, the free stocks were forecasted at a zero level.

When the add factors are imposed on the domestic
production forecasts, the actual production amounts can be
Obtained. An add factor of 1.7 million acres must be added
to the acres planted forecast. The acres harvested needed
an add factor of 2.9 million acres and the yield trend
needed 5.5 bushels per acre added to the equation to equal

the record yield.

122

TABLE 27: BALANCE SHEET FOR CORN: THE 1986-87 MARKETING YEAR

RUN NUMBER 1 2 3 4 5 6 7

 

(MILLION ACRES)

ACRES SET-ASIDE 13.6 13.6 13.6 13.6 13.6 13.6 13.6
ACRES PLANTED 75.0 76.7 76.7 76.7 76.7 76.7 76.7
’ ACRES HARVESTED 66.3 69.2 69.2 69.2 69.2 69.2 69.2
YIELD PER ACRE 113.8 119.3 119.3 119.3 119.3 119.3 119.3

(MILLION BUSHELS)

BEGINNING STOCKS 4040 4040 4040 4040 4040 4040 4040
PRODUCTION 7547 8253 8253 8253 8253 8253 8253
IMPORTS 2 2 2 2 2 2 2
TOTAL SUPPLY 11588 12295 12295 12295 12295 12295 12295
USE:
FEED 4404 4517 4650 4650 4785 4749 4650
FOOD. SEED &
IND. USES 1191 1191 1191 1191 1191 1191 1191
TOTAL DOMESTIC 5595 5708 5841 5841 5976 5940 5840
EXPORTS 1225 1255 1525 1525 1315 1525 1525
TOTAL USE 6821 6963 7366 7366 7292 7465 7366
TOTAL ENDING STOCKS 4766 5331 4929 4929 5002 4829 4928
TOTAL. X OF USE 69.9 76.6 66.9 66.9 68.4 64.7 66.9
U.S. FARM PRICE $1.99 $1.83 $1.94 $1.51 $1.50 $1.54 $1.51

STOCKS IN GOVT. PROG.

CCC INVENTORY 1765 1991 1826 1837 1854 1791 1837
FARMER-OUNED RES. 3001 3339 3102 3091 3132 3038 3090
FREE STOCKS 0 D 0 O O O 0

EFFECTIVE LOAN RATE $1.84 $1.84 $1.84 $1.51 $1.51 $1.51 $1.51

123
Once the domestic production is corrected, a new

analysis can be conducted (Run 2 of Table 27). Given the
corrected production, the supply is now increased to the
actual and the balance sheet forecasts a farm price of
$1.83. With a farm price higher than actual, the quantities
of feed consumption and exports were reduced. In this
analysis the feed consumption was 2.9% lower than the actual
amount and the exports were 17.7% less. This farm price
increased the amount of policy stocks from the original
forecasted amount to 1991 million bushels for CCC holdings
and 3339 million bushels for FOR stocks. Again the free
stocks were forecasted at a zero level.

By overridding all supply and demand factors equal to
the known amounts, the forecasted farm price should equal
the actual farm price Of $1.51. When this scenario is run
the farm price is forecasted as $1.94 which again indicates
that corn loan rate of $1.84 is not the effective corn loan
rate (Run 3 of Table 27). After several runs of trial and
error the effective corn loan rate was determined to be
$1.51 which is essentially a floor under the amount that the
farm price will drop (Run 4 of Table 27).

A new analysis can be conducted using the corrected
supply quantities, the effective corn loan rate and allowing
the demand factors to be determined by market forces (Run 5
of Table 27). In this analysis, the farm price dropped to

$1.50.

124
Since the international component is still conditional

upon the estimates of the exogenous variables some educated
guesses were required for these factors. Examination of the
importing region’s net imports shows that it is low by
historical comparisons (Run 5 of Table 28). An add factor
must be used to increase the importing region’s net imports.
Adding 0.00273 thousand metric tons per capita to the
importing region's net imports brings it into line with
historical values (Run 6 of Table 28).

Run 5 Of Table 29 shows that the exporting region’s net
exports is too low given the add factor used to increase the
importing region’s net imports. Since the exporting
region’s consumption is a function of their supply, an add
factor was needed to reduce the regional consumption.
Subtracting 0.129 thousand metric tons per capita from the
exporting regions consumption creates historical
comparisons. Finally, the exporting region’s ending stocks
needed an add factor of 0.05 thousand metric tons per capita
(Run 6 of Table 29) to complete the exporter net exports.

Once all the corrections have been made on the domestic
supply, the corn loan rate and the international component,
the analysis of corn consumed by livestock can be made.

The farm price for this scenario is $1.54 and the
forecast for feed consumption is 4749 million bushels which
is 2.1% greater than the actual amount (Run 6 of Table 27
and Table 30). A negative add factor Of 126.42 applied to

the other and unallocated consumption would reduce the

125

TABLE 28: U.S. EXPORT DEMAND: THE 1986-87 MARKETING YEAR

RUN NUMBER 5 6
----------TMILLION-BUSHELS; ----------
IMPORTERS NET IMPORTS 2102 2522
EXPORTERS NET EXPORTS 788 996

U.S. EXPORTS 1314 1525

TABLE 29: EXPORTING REGION’S NET EXPORTS:
THE 1986-87 MARKETING YEAR

RUN NUMBER 5 6

(MILLION BUSHELS)

EXPORTERS SUPPLY 2163 2163
EXPORTERS CONS. 1314 973
EXPORTERS STOCKS 62 194

EXPORTERS EXPORTS 788 996

126

TABLE 30: LIVESTOCK CONSUMPTION OF CORN IN THE U.S.:
THE 1986-87 MARKETING YEAR

RUN NUMBER 6 7

(MILLION BUSHELS)

DAIRY ANIMALS 843 845
CATTLE ON FEED 879 892
OTHER BEEF CATTLE 225 227
HENS, PULLETS, &
OTHER CHICKENS 390 392
BROILERS 527 529
TURKEYS 144 145
HOGS 1576 1580
OTHER AND UNALLOCATED 165 40
TOTAL CONSUMPTION 4750 4650

forecast to 4650 million bushels and a market clearing price

of $1.51 (Run 7 Of Table 27 and Table 30).

4.2.3 The 1987-88 Marketing Year

At the time of this publication, the 1987-88 marketing
year supply factors are known with certainty, however the
final demand factors are unknown. This makes the supply
factors an unconditional forecast while the domestic and
international components are conditional upon the estimates
of the user. This creates a situation where the farm price
is also not known with certainty and the USDA provides a

range of prices for the 1987-88 marketing year.

127
In this ex post forecasts the acres planted equation

over estimated the quantity of acres committed to corn (See
run 1 of Table 31). The 1987-88 forecast of acres planted
was 2.6% greater than the actual amount. Due to the
quantity forecasted to be planted, the acres harvested
forecast was also over estimated. This forecast was less
than 1 percent greater than reality. The yield per acre was
a new record of 119.9 bushels and thus the trend yield
forecast was low by 4 bushels per acre.

The 1987-88 forecast for domestic production was under
estimated and is 2.5% less than the actual amount. This
caused the U.S. supply to be forecast as 175 million bushels
less than the actual. Given the short U.S. supply forecast
the balance sheet forecast a farm price of $2.18, 36 cents
over the policy established corn loan rate of $1.82. The
corn consumed by livestock and U.S. exports were forecast as
4252 million and 1414 million bushels respectively. The
total ending stocks as a percent of use were 72.9 percent
and the government held all the stocks (1802 for CCC and
3212 for FOR).

Add factors can be used on the domestic supply with
certainty to determine how well the balance sheet forecasts
given an accurate supply. By subtracting 1.7 million acres
from the acres planted, subtracting 0.5 million acres from
the acres harvested and adding 4.0 bushels per acre to the

yield gives the correct domestic supply (Run 2 of Table 31).

128

TABLE 31: BALANCE SHEET FOR CORN: THE 1987-88 MARKETING YEAR

RUN NUMBER 1 2 3 4 5 6 7 8 9

(MILLION ACRES)

ACRES SET-ASIDE 21.5 21.5 21.5 21.5 21.5 21.5 21.5 21.5 21.5
ACRES PLANTED 67.7 66.0 66.0 66.0 66.0 66.0 66.0 66.0 66.0
ACRES HARVESTED 60.1 59.6 59.6 59.6 59.6 59.6 59.6 59.6 59.6
YIELD PER ACRE 115.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9

(MILLION BUSHELS)

BEGINNING STOCKS 4929 4929 4929 4929 4929 4929 4929 4929 4929
PRODUCTION 6966 7141 7141 7141 7141 7141 7141 7141 7141
IMPORTS 2 2 2 2 2 2 2 2 2
TOTAL SUPPLY 11897 12072 12072 12072 12072 12072 12072 12072 12072
USE:
FEED 4252 4285 4700 4700 4485 4700 4523 4508 4700
FOOD. SEED &
IND. USES 1215 1215 1215 1215 1215 1215 1215 1215 1215
TOTAL DOMESTIC 5467 5500 5915 5915 5640 5915 5738 5723 5915
EXPORTS 1414 1423 1600 1600 1459 1600 1482 1607 1600
TOTAL USE 6882 6924 7515 7515 7099 7515 7221 7330 7515

TOTAL ENDING STOCKS 5015 5147 4557 4557 4972 4557 4850 4741 4556
TOTAL. X OF USE 72.9 74.3 60.6 60.6 70.0 60.9 67.2 64.7 60.6
U.S. FARM PRICE $2.18 $2.13 $2.57 $2.02 $1.93 $1.86 $1.80 $1.82 $1.86

STOCKS IN GOVT. PROG.
CCC INVENTORY 1802 1845 1402 1416 1556 1409 1513 1481 1409

FARMER-DHNED RES. 3212 3302 2346 2331 2619 2339 2536 2456 2339
FREE STOCKS 0 O 807 808 797 808 800 803 808

EFFECTIVE LOAN RATE $1.82 $1.82 $1.82 $1.49 $1.49 $1.39 $1.39 $1.39 $1.39

129
With the new results the analysis shows that the farm

price falls to $2.13 and the livestock consumption and
exports fall. Given the a price Of $2.13, the livestock
consumption was 8.9% lower and the exports were 11% lower
than the USDA forecast of 4700 million and 1600 million
bushels respectively. This lower price also increased
policy stock holdings by 1.4 percent.

When the demand factors are forced to equal the USDA
forecast, the farm price of the balance sheet shows that
$2.57 would be needed to clear the market (Run 3 of Table
31). Under this scenario, the CCC, FOR and free stocks were
1402, 2346 and 807 million bushels respectively.

The analysis could stop here (due to the conditional
nature of the demand factors) except the balance sheet
forecast price does not fall within the range of the USDA
forecast price. Assuming the effective corn loan rate
carries over from the 1986-87 ex post forecast and drops by
two cents as directed by U.S. policy, the 1987-88 effective
corn loan rate Of $1.49 would be used (Run 4 of Table 31).
In this scenario, the domestic supply is corrected with add
factors and the demand factors are overridden to equal the
USDA forecasts. The balance sheet farm price is forecast as
$2.02 and the policy stocks are forecast as 1416 million for
CCC and 2331 million bushels for FOR while the free stocks
are forecast as 808 million bushels. Again the farm price
forecast does not fall within the USDA forecast range.

Using the same corn loan rate assumption and allowing the

130
market to determine the demand factors the farm price clears

the market at $1.93 while both the corn consumed by
livestock and exports are under the USDA forecast by 4.6%
and 8.8% respectively (Run 5 Of Table 31). The total ending
stocks were 70.0% Of use with the free stocks forecast as
797 million bushels.

To establish a farm price within the USDA forecast
range, the effective corn loan rate was reduced ten cents
more (Run 6 of Table 31). With the effective corn loan rate
set at $1.39, using the corrected domestic supply and
overridding the demand factors, the balance sheet forecast a
market clearing farm price of $1.86 which is within the USDA
forecasted range.

When the market determines the demand factors, the
balance sheet forecast a farm price of $1.80 (Run 7 of Table
31). At this price the corn consumed by livestock and
exports were 4523 and 1482 million bushels respectively,
still lower than the USDA forecasts. Ending stocks were
67.2% of total use and free stocks were forecast as 800
million bushels.

The add factors for the international component are
conditional upon the knowledge of the user however,
following historical trends for values leads to reasonable
results. The initial forecast of the importing region's net
imports shows that it is low compared to historical records

(Run 7 of Table 32). The importing region needed an add

131

TABLE 32: U.S. EXPORT DEMAND: THE 1987-88 MARKETING YEAR

RUN NUMBER 7 8
----------TMIELTON-BUSHEIST '''''''''
IMPORTERS NET IMPORTS 2324 2587
EXPORTERS NET EXPORTS 841 987

U.S. EXPORTS 1482 1600

TABLE 33: EXPORTING REGION’S NET EXPORTS:
THE 1986-87 MARKETING YEAR

RUN NUMBER 7 8

(MILLION BUSHELS)

EXPORTERS SUPPLY 2195 2195
EXPORTERS CONS. 1330 1025
EXPORTERS STOCKS 24 183

EXPORTERS EXPORTS 841 987

132
factor of 0.00174 thousand metric tons per capita to show

growth (Run 8 of Table 32).

Given the add factor increase to the importing region's
net imports, the exporting region’s net export forecast was
tOO low to equal the actual USDA forecast. The exporting
region needed 0.1114 thousand metric tons subtracted from
their regional consumption and 0.059 added to their regional
ending stocks (Run 8 of Table 33) using these corrections tO
the international component brought the balance sheet U.S.
export forecast in line with the USDA forecast.

With the described corrections made to the balance
sheet forecast, the corn consumed by livestock could be
analyzed. Given these corrections the balance sheet
forecast a price Of $1.82 and a livestock consumption
quantity of 4508 million bushels (Run 8 of Table 34). Each
of the feeding rates per head followed historical trends
except the cattle on feed and the other beef cattle figures
(Run 8 of Table 35 and Table 36). These feeding rate per
head forecasts were lower than what would be expected. By
using add factors of 10.0 bushels per head for cattle on
feed and 0.3 for other beef cattle, the forecast went from
52.9 to 61.8 bushels per head in run 9 Of Table 35 and 5.3
to 5.6 bushels per head in run 9 of Table 36. An add factor
of 84.2 was needed on the other and unallocated forecast to
account for the residual quantity of corn unaccounted for by

the other equations (Run 9 Of Table 34).

133

TABLE 34: LIVESTOCK CONSUMPTION OF CORN IN THE U.S.:
THE 1987-88 MARKETING YEAR

RUN NUMBER 8 9

(MILLION BUSHELS)

DAIRY ANIMALS 813 801
CATTLE ON FEED 666 778
OTHER BEEF CATTLE 224 234

HENS. PULLETS. &

OTHER CHICKENS 390 387
BROILERS ‘ 525 523
TURKEYS 156 154
HOGS 1566 1562
OTHER AND UNALLOCATED 168 251

TOTAL CONSUMPTION 4508 4650

TABLE 35: CATTLE ON FEED CONSUMPTION PER HEAD:
THE 1987-88 MARKETING YEAR

RUN NUMBER 8 9
EEELIQEQLE'QEJELB """"""""""""""" £27; """" 5.2"
CATTLE & CALVES ON FEED (MIL. HEAD) 12.6 12.6
CATTLE 0N FEED CONSUMPTION (MIL. 8U.) 666.2 778.1

TABLE 36: OTHER BEEF CATTLE CONSUMPTION PER HEAD:
THE 1987-88 MARKETING YEAR

 

RUN NUMBER 8 9
FEEDING RATE PER HEAD 5.3 5.6
BEEF CONS AND REPLACEMENTS (MIL. HEAD) 42.0 42.0

OTHER BEEF CATTLE CONS. (MIL. BU.) 223.6 234.2

134
After making the corrections to the domestic supply,

the international component and the corn consumed by
livestock, the balance sheet forecasted a clearing farm

price of $1.86 which is within the USDA forecast range.

4.3 SCENARIO ANALYSES AND SAMPLE RUNS

The 1988-89 balance sheet forecast will be a cOmpletely
conditional forecast, and it will use the partially
conditional 1987-88 forecast as the lagged exogenous
variable vector. The first step in making the forecast was
to estimate the exogenous variables for the 1988-89
marketing year. These exogenous variables are based on
historical trends and economic knowledge of the user. Once
the values have been entered into the exogenous variable
section, the model solves for the supply and demand factors
and presents the information in the balance sheet.

The initial forecast for the 1988-89 marketing year
(See run 1 of Table 37) showed an acres planted forecast of
70.5 million acres. The acres harvested was 62.6 million
acres and the yield was forecasted as 118.1 bushels per
acre. In order to bring the acres planted forecast plus the
acres set-aside equal to a 7 year average of total corn
acreage (as estimated by the USDA) an add factor of 4.2
million acres was subtracted form the acres planted.

This add factor adjusted forecast will be known as the
control (See ’Control' run of Table 37). In the control,

the balance sheet forecast a market clearing price of $2.05.

135

TABLE 37: BALANCE SHEET FOR CORN: THE 1988-89 MARKETING YEAR

RUN NUMBER 1 CONTROL 2 3 4 5 6 7 8

(MILLION ACRES)

 

ACRES SET-ASIDE 21.0 21.0 21.0 21.0 21.0 21.0 21.0 21.0 21.0
ACRES PLANTED 70.5 66.3 66.3 66.3 66.3 66.3 66.3 66.3 66.3
ACRES HARVESTED 62.6 58.9 58.9 58.9 58.9 58.9 58.9 58.9 58.9
YIELD PER ACRE 118.1 118.1 118.1 118.1 105.0 124.0 118.1 118.1 118.1

 

(MILLION BUSHELS)

BEGINNING STOCKS 4557 4557 4557 4557 4557 4557 4557 4557 4557
PRODUCTION 7389 6956 6965 6965 6185 7304 6956 6956 6956
IMPORTS 2 2 2 2 2 2 2 2 2
TOTAL SUPPLY 11948 11515 11515 11515 10744 11863 11515 11515 11515
USE
FEED 4602 4518 4699 4244 4273 4588 4562 4516 4490
FOOD. SEED 8
IND. USES 1235 1235 1235 1235 1235 1235 1235 1235 1363
TOTAL DOMESTIC 5837 5753 5934 5479 5508 5823 5797 5751 5853
EXPORTS 1576 1557 619 2465 1495 1573 1632 1557 1551
TOTAL USE 7414 7311 6554 7945 7004 7396 7429 7309 7404

TOTAL ENDING STOCKS 4534 4204 4961 3570 3739 4467 4086 4205 4114

TOTAL. X OF USE 61.2 57.5 75.7 44.9 53.4 60.4 55.0 57.5 55.5

U.S. FARM PRICE $1.93 $2.05 $1.80 $2.49 $2.44 $1.95 $2.10 $2.05 $2.09
STOCKS IN GOVT. PROG
CCC INVENTORY 1593 1464 1777 1041 1103 1568 1419 1464 1428

FARMER-OUNED RES. 2921 2673 3184 1691 1833 2869 2560 2674 2587
FREE STOCKS 20 66 O 838 802 28 106 66 98

EFFECTIVE LOAN RATE $1.80 $1.80 $1.80 $1.80 $1.80 $1.80 $1.80 $1.80 $1.80

136
The corn consumed by livestock was forecast at 4518 million,

exports were forecast at 1557 million, CCC stocks were
forecast at 1464 million, FOR stocks were forecast at 2673
and free stocks were forecast at 66 million bushels. The
probability distribution for this forecast used 100
observations. The Control run of Table 38 shows a price
range Of $1.24 to $2.52 with a mean Of $2.03 and standard
deviation of 0.178.

Because the 1988-89 market year information is unknown,
some scenario’s were chosen to determine their effect on
farm price. The scenarios to be examined include changes in
world income, U.S. yield, competitor crop prices and
industrial alcohol use. These scenarios will be discussed
and compared to the control forecast which used the add

factor on the acres planted equation.

4.3.1 Change In World Income

The first scenario is a change in the rest-of-the-world
income. This action could come about from a change in
exchange rates, a change in the gross domestic product of a
region or a change in the consumer price index of a region.
For purposes of analysis, the exogenous variable importer
and exporter gross domestic product is adjusted by the
desired amounts.

If the rest-Of-the-world income were to decrease by 20%
the analysis shows that the farm price would decrease by 12%

to $1.80 (Run 2 Of Table 37). Corn consumed by livestock is

137

TABLE 38: PROBABILITY DISTRIBUTIONS FOR VARYING SCENARIOS

RUN NUMBER 1 CONTROL 2 3 4 5 6 7 8
PROBABILITY
THAT THE PRICE
HILL BE THAT
LISTED OR BELOH $/BU. $/BU. $/BU. $/BU. $/BU. $/BU. $/BU. $/BU. $/BU.
1 1.24 1.56 1.91 1.74 1.78 1.76
5 1.61 1.61 1.98 1.82 1.80 1.81
10 1.79 1.66 2.05 1.87 1.83 1.88
20 1.85 1.71 2.13 1.92 1.88 1.95
30 1.90 1.74 2.17 1.96 1.92 2.00
40 1.95 1.77 2.22 2.01 1.95 2.04
50 2.00 1.79 2.27 2.06 1.99 2.09
60 2.06 1.82 2.33 2.11 2.02 2.13
70 2.10 1.86 2.51 2.19 2.07 2.18
80 2.14 1.89 2.55 2.25 2.11 2.24
90 2.20 1.95 2.59 2.48 2.15 2.30
95 2.24 1.99 2.62 2.51 2.23 2.50
99 2.52 2.14 2.72 2.63 2.50 2.62
MEAN 2.03 1.81 2.34 2.11 2.03 2.15
STANDARD DEVIATION 0.178 0.114 0.208 0.213 0.158 0.200

forecast to increase by 4% and ending stocks are forecast to
increase by 18.2 percent. If the importing region had 20%
less income, one would assume that U.S. exports world
decrease. The analysis supports this assumption where U.S.
exports drops by over 60 percent. The probability
distribution had a range from $1.56 to $2.14 with a mean
price of $1.81 and standard deviation of 0.114 (Run 2 of
Table 38).

If the rest-Of-the-world income were to increase by 20%
the analysis shows an increase of 21% in farm price to $2.49

(Run 3 of Table 37). Corn consumed by livestock in this

138
case will decrease by 6% and ending stocks will decrease by

15 percent. The assumption that U.S. exports will increase
is supported by the forecast which shows a jump of 58

percent. In this scenario the probability distribution has
a mean of $2.34 and a standard deviation of 0.208 (Run 3 Of

Table 38).

4.3.2 Change In U.S. Yield

The second scenario will examine changes in supply and
demand if the U.S. does and does not continue the record
setting yields of the recent past. To perform the analysis
the override feature of the balance sheet yield per acre is
used.

If the U.S. experiences a yield of 105 bushels per
acre, the analysis shows that the domestic supply would drop
by 6% and farm price would climb 19% to $2.44 (Run 4 of
Table 37). Corn consumed by livestock would drop 5.4% and
U.S. exports would suffer from the inflated price by falling
4 percent. The U.S. government would benefit from this
action in that CCC stocks would fall 25% and FOR stocks
would fall 31 percent. With the inflated price free stocks
climb by 736 million bushels.

If the U.S. were to continue the record making yield of
the recent past, and yield were to reach 124.0 bushels per
acre the analysis shows an increase in domestic supply of 3
percent (Run 5 of Table 37). In this scenario the price

drops 10 cents due to the increased supply and the demand

139
factors increase due to the lower price. The analysis shows

that corn consumed by livestock will increase 1.5 percent,
exports increase 1 percent and ending stocks increase 6.3

percent.

4.3.3 Change In Competitor Crop Prices

In the third scenario a change of competitor crop
prices is examined. In the first case the wheat gulf and
farm price are adjusted in the exogenous variable section.

If the wheat gulf price and farm price increased 16%
and 31% respectively, until they were somewhat comparable to
the 1985-86 marketing year, the analysis shows that corn
farm price rises 2.4 percent (Run 6 of Table 37). If one
were to just examine the corn farm price change the
assumption would be for a decrease in the demand factors.
Yet due to the increase in wheat prices,this scenario shows
the corn consumed by livestock increased over the control by
about 1 percent, the exports increased by almost 5% and
ending stocks dropped by 2.8 percent. The probability
distribution showed a mean farm price of $2.11 and standard
deviation Of 0.213 in 100 Observations (Run 6 of Table 38).

In the second case the soybean meal price (a proxy for
soybeans) falls by 8 percent and is adjusted in the
exogenous variable section. In this analysis, the corn farm
price is $2.05, the same as the control run (Run 7 of Table

37).

140
In this scenario the corn consumed by livestock

decreases by 2 million bushels, while exports and ending
stocks remain the same. This is due to certain livestock
corn consumption equations using soybean meal price as an
exogenous variable. The probability distribution showed a
mean farm price Of $2.03 and standard deviation of 0.158 in

100 observations (Run 7 of Table 38).

4.3.4 Change In Alcohol Use

The final scenario examines an increase in industrial
alcohol use. This action comes about from the increased
interest in using ethyl alcohol as a gasoline extender. The
alcohol use is increased in the exogenous variable section.

By assuming a 40% increase in alcohol production and
all other food, seed, and industrial uses remain the same,
the total FSI use will increase 10 percent. If that
scenario occurs, the farm price is forecast to increase 2
percent (Run 8 Of Table 37). The demand factors drop
slightly with corn consumed by livestock declining 0.6
percent, exports decreasing 0.4% and ending stocks
decreasing 2.2 percent. This makes an interesting policy
analysis because the farm price increases which will make
the producers happy while the government benefits by holding
less policy stocks. The probability distribution shows a
mean price of $2.15 and a standard deviation of 0.200 which
is 2.2 cents above the control standard deviation (Run 8 of

Table 38).

CHAPTER V

SUMMARY, CONCLUSIONS AND RECOMMENDATIONS

This chapter will summarize the Economic Supply/Demand
Balance Sheet project and includes recommendations for
future work. Suggestions include increasing the accuracy of
the balance sheet and discussion on decision theory for

handling risk.

5.1 SUMMARY AND CONCLUSIONS

This project accomplished the goal of developing a
micro-computer economic supply/demand balance sheet for
forecasting corn prices and use as a group-interaction
teaching aide.

In so doing, supply and demand factors for corn were
estimated. The U.S. supply consisted mainly of estimating
the acres that would be planted to corn. Numerous
hypotheses were examined to estimate this equation, yet due
to the dynamic nature of government policy and farmer
expectations an equation to estimate acres planted has never
been accurately developed. The equation provided in this
project cannot necessarily be considered the best, but it

provides ease of use and is good for educational purposes.

141

142
Corn disappearance consists Of the four components

discussed below.

The corn consumed by livestock is disaggregated into
seven animal species and an unallocated (i.e. residual)
amount. Each animal species consumption is estimated on a
per head basis with the unallocated consumption being
estimated based on relative prices and a trend. Estimating
on a per head basis and multiplying by animal inventories
allows a more accurate explanation of corn as a feed grain
disappearance.

Food, seed, and industrial use is not econometrically
estimated but is based upon knowledge provided by the user
as to changing economic conditions for industrial and seed
use and theoretical limits and constraints on food use.

The international component is included by linking a
simple rest-of-the-world supply and demand model with the
U.S. and equating U.S. exports to balance world needs.

Stock holdings were estimated as what the government
holds due to policy and the demand for free stocks held for
speculative purposes. The policy stock equations are based
upon price over the corn loan rate ratios while the free
stock equation is based upon the price over the loan rate
ratio and the policy stocks over utilization ratio.

Attempts were made to overcome the limitations of the
system by allowing the user to adjust the intercept of each
equation with an add factor. If the estimate is known to be

inaccurate of if more current information becomes available,

143
a positive or negative add factor could be included in each

equation to increase the accuracy of the forecast.

An override feature is provided which allows the user
to input a value for any of the supply and demand estimates
using a posteriori information. That override value can
then be used when the model solves which allows the user to
do "what if" scenario analysis by calculating supply and
demand factors using the new value.

The original results of each ex post analysis were
suspect of being inaccurate, yet when add factors are used,
the results became more tolerable. Several scenarios and
analyses were performed on the 1988-89 marketing year. The
add factor adjusted results of that marketing year became

the control from which to compare the rest of the scenarios.

5.2 RECOMMENDATIONS FOR INCREASING BALANCE SHEET PERFORMANCE

The estimated equations should be updated on a regular
basis using the most current data available. This allows
the annual forecasts to be as accurate as possible and will
account for any structural changes that may have occurred
since the last estimate. The food, seed, and industrial use
could be estimated in order to prevent the misinformation of
the user affecting the accuracy of the forecasts.

The farm price forecast could be more accurate if all
feed grains and soybeans were included in the model and the

system of equations were solved simultaneously.

144
Sensitivity analysis could be done on the add factors

to determine the amount of change from one year to another
given an add factor adjustment and if the adjustments used
aid in giving information from year to year.

The computer algorithm calculates all values as whole
numbers whereas the balance sheet presents some information
as integer values. This condition creates rounding errors
to appear in the balance sheet. If the values which are
presented as integers were calculated as such the rounding
errors could be eliminated.

End user input should be incorporated to the design of
the computer balance sheet as it is used by extension
workers to make sure that the information provided is
helpful as well as desired. On line help windows would be
useful to guide the user through the program. Once the
program is mastered, the use of the menus to recalculate a
market clearing price tends to slow the user down. If a
recalculate feature could be included which allows the user
to make changes in the balance sheet, recalculate the supply
and demand factors and resume in the same spot, the analysis

process could be made more user friendly.

5.3 RECOMMENDATIONS FOR HANDLING RISK

The literature shows that it is quite difficult to
develop a universally agreed-upon set of principles of
individual behavior under conditions of uncertainty.

However decision theory provides some principles for making

145
decisions under risk and uncertainty. Decision theory can

be used to determine Optimal strategies when a decision
maker is faced with several decision alternatives and an
uncertain or risk-filled pattern of future events.

Using this information, approaches to incorporate the
data in the balance sheet with decision theory are presented
with the intent that at some future time someone can expand

upon this rudimentary analysis.

5.3.1 The Traditional Analysis

Much of the pioneering works of Frank H. Knight define
the traditional approach to decision theory. Knight divided
uncertain situations into two subclasses: risk and
uncertainty. Knight defines that an event is risky if both,
the possible future outcomes that could result and the
probability associated with each outcome are known.1 Thus,
risk exists when the outcome of the process, say yield per
acre, is a random variable with a known probability
distribution. Risk requires that outcomes can be identified
and a probability can be associated with each outcome. The
probabilities needed to define the risk situation are not
known with certainty but are subject to all the usual
vagaries of empirical estimation. Knight defines
uncertainty as that event in which no probabilities can be
assigned to the outcomes. In this case the possible

outcomes may also be unknown.

 

1. Frank H. Knight, 8 Unc
Houghton-Mifflin, 1921, quoted by Doll, op. cit., p. 238-39.

146
With the theoretical acceptance of subjective knowledge

(Anderson et. al.2), Knight's distinction between risk and
uncertainty is not relevant, and the terms are used
interchangeably. The argument for subjective knowledge
assumes that it is impossible for the manager to know the
alternative actions, the possible outcomes, and their
consequences and yet be entirely ignorant of the
probabilities associated with these outcomes. Since typical
agricultural decision processes focus on the most probable

outcome a more modern analysis is needed.

5.3.2 The Modern Analysis

Modern theorists argue that risky decisions must be
made in uncertain situations even when the decision maker
lacks objective empirical knowledge about outcomes and
probability distributions. In these cases the decision
maker will formulate subjective estimates of possible
outcomes and their probability of occurrence thus making all
risky events subsets of uncertain events.

These probabilities reflect the decision maker's degree
of belief that a particular event will occur. The quality
of information is a consideration when assigning
probabilities. TO the extent that they are an expression of
personal judgment, it is possible, if one decision maker

improperly assesses the quality of information, that two

 

2. J.R. Anderson, J.L. Dillon, and J.B. Hardaker,

Agricultural Decision Analysis, Iowa State University
Press, 1977.

147
decision makers may assign different probabilities to the

same event. However, "When two reasonable men have had
roughly the same experience with a certain kind of event,

they assign roughly the same probability."3

5.3.3 Decision Problems

Modern decision theory has three components applicable
to risky decision making. First the decision maker must
make a choice, or sequence of choices, among various
possible courses of action each with an individual payoff.
Second, the consequence of any course Of action depends on
an unpredictable event or "state of nature". The
unpredictableness of the state of nature is what generates
probabilities that are estimated from the given information.
Finally, the decision maker will choose a strategy for
experimentation and action that is consistent with his level
of risk aversion.4 To illustrate a decision problem, let
$1,...,sm represent the m possible states of nature with
probabilities p(sl),...,p(sm) of occurring. The decision
maker's choices are identified as A1,...,An and each choice
yields a unique outcome oij (i=1,...,m; j=1,...,n) in the
respective state Of nature. These components are shown in

Table 39.

 

3. Schlaifer, Robert, Introduction to Statistics for
Business Decisions, MC Graw-Hill Book Co., 1961, p. 17.

4. Raiffa, Howard, pggigign_gnaly§i§, Addison-Wesley
Publishing Co., Reading, Mass., 1968, Chapter 1.

148
Table 39: The Decision Table

Probability of State And Nature State

p(81) ' ' ' ' - P(sm)

“353323312535; ....... 5.1.---L---;-_-_' _:___ ;_ _ am
A], 011 o . . . . oml
———————— §B___-__------ -81n . T . 0 0 cm

 

 

Having identified the possible choices, the outcomes,
and their likelihoods, the decision maker must compare
outcomes. A common denominator must be established in order
to make comparisons.

One approach is to maximize expected returns. This
approach requires the outcomes to be expressed in dollar
equivalents and again the problem of decision maker
subjectiveness arises. The ordering of choices for the

maximum Of expected returns is based on the index expressed

as follows:

In
E(Aj) = 2

. P(Si)Yij
1

1

where: Aj is the jth choice,

p(si) is the probability for the state of

nature,

149
Yij is the dollar equivalent for the outcome of

the ith state of nature and the jth choice in

Table 39.

5.3.4 Expected Utility Model

Daniel Bernoulli argues that individuals do not care
directly about the dollar payoff of a particular decision
but that they respond to the utility these dollars provide.5
An individual's utility is ranked by their cultural
environment and psychological attitudes. Due to these
factors, the terms risk averse, risk neutral, and risk
preferring do not necessarily describe concave, linear and
convex utility functions. An individual may have
diminishing marginal utility and a preference for risk
taking which, when combined, may result in either a concave
or a convex utility function.

The von Neumann-Morgenstern Theorem developed an
expected utility model in an axiomatic system. The basic
axioms assume "rational" behavior among decision makers and
allow an ordinal ranking of utility functions. At minimum

the conditions include:6

Ordering Qt onoiges: For any two choice A1 and A2 the

decision maker either prefers A1 to A2, prefers A2 to A1, or

 

5. Daniel Bernoulli, "Exposition of a New Theory on
the Measurement of Risk," Econometrica 22, January 1954,
p. 23-36.

6. Robison, L.J., and P.J. Barry, The Competitive

Eirm's Response to Risk, Macmillian, New York, 1987,
Chapter 2.

150
is indifferent.

I s'!'v'! E l .
preferred to A3, then A1 must be preferred to A3.
5 lsl'! !i E l .

some other choice, then a risky choice pAl + (1-p)A3 is

If A1 is preferred to A2, and A2 is

If A1 is preferred to A2, and A3 is

preferred to another risky choice pAz + (1-p)A3, where p is
the probability Of occurrence of A1 or A2.

Qerteinty egniyaient e: eneieee: If A1 is preferred to A2,
and A2 is preferred to A3, then some probability p exists
that the decision maker is indifferent to having A2 for
certain or receiving A1 with probability p and A3 with
probability 1-p. Thus A2 is the certainty equivalent of

pAl + (1—p)A3.

Suppose a decision maker faces a risky event and the
possible outcomes of the event are ranked from least
desirable to most desirable. By arbitrarily assigning a
utility value to the two extremes (for convenience assign 0
the the least desirable and 1 to the most desirable) and
ranking the the values between the extremes based on the
individual's certainty equivalents for differing likelihoods
of gains and losses allows the utility to be ordinally
ranked. This procedure assumes that the utility of the
certainty equivalent (yCE) equals the expected utility of

the risky alternative:

UIYCE) = P(1-0) + (1'P)(0) = P-

151
The index of preference of choices can be constructed

as:

EU(A1)

P(51)U(Y11) + - - - + PIsm)U(Ym1)

E1304“)

p(sl)U(Y1n) + ° ° ° + p(sm)U(Ymn)°

Using this analysis, the optimizing principle is for
the individual to choose that option which maximizes the

expected value of their utility.

5.3.5 Expected Value-Variance Analysis

Expected value—variance (E-V) analysis starts with the
assumption that the decision maker seeks to maximize utility
where utility if a function of expected income and the
variance of expected income. The individual's utility will
increase as expected income increases and decreases as the
variance of expected income increases. Therefore it can be
assumed that a larger expected return is preferred to a
smaller return and a smaller variance is preferred to a
larger variance. Using this information in graphical form,
a boundary which locates the minimum possible variances
associated with each possible level of expected income, can
be created (see Figure 2).

The E-V boundary can be determined conceptually by
computing the expected income and the variance of that
income for all alternative choices and connecting those
points that represent the minimum possible variance for each

expected income level. Points above the E-V boundary may be

152

 

z-u
Bowman: /

A
v

-------'-“""‘)B

V21>-- "’ "“ ‘-

 

 

 

EXPECIED INCOME E E1

Figure 2: The E-V Boundary

If expected income E1 is desired, the least variance that
must be tolerated is V1. Point A represents the expected
income and variance resulting from a particular farm plan,
given the available resoureces. Other points above the
E-V boundary may be attainable, in the sense that the
resources may be available to undertake farm plans with
those particular income and variance values, but because
less variance is preferred to more, a farm plan above the
boundary will never be undertaken. Point B may be attain-
able but is irrational because given expected income E1,

V1 is preferred to V2.

153
attainable given the resources to undertake the production

plan for a particular income and variance value, however
since it is assumed that a producer prefers less variance to
more variance, said producer will never use a production

plan above the boundary.

5.4 CONCEPTUAL ASSISTANCE TO DECISION MAKING

With price information in the form Of probabilities,
decision makers will be better able to comprehend the degree
of uncertainty associated with the states of nature (i.e.
future prices). It is these perceptions about future prices
that determine what and how much farmers produce and when
they sell their production. With more accurate perceptions
about the uncertainty associated with future events,
decisions can be made more consistent with farmers'
individual objectives and risk preferences.7

In the sense that this is an aggregated data model that
forecasts the U.S. farm price, the burden of decision
alternatives is placed on the user. However, the balance
sheet was designed to help in choosing actions or decisions
to reduce risk in three areas. The categories are the
government program participation decisions, crop mixture
decisions and marketing decisions.

The payoff matrix in Table 40 combines all of the

components of modern decision analysis.

 

7. Halter, A.N., and R. Mason, loc. cit.

154
Table 40: The Payoff Matrix

STATES OF NATURE

LOW MEAN HIGH
PRICE PRICE PRICE
----‘BESEEE‘B’Iffff; ....... fa ..... f2- ...?2.. -32 -53- -
d1:Participate in the
Government program + +? ? -? -
dzzIncrease the acres
committed to corn
vs. an alternative - -? ? +? +
d3:Increase the acres
committed to an
alternative vs. corn + +? ? -? -
d4:Repay loan & sell - -? ? +? +
d5:Default loan + +? ? -? -
d :Forward contract
the mean price S3 + +? ? -? -
d7:Hedge + +? ? +? +
(basis narrows)
d8:Spot market - -? ? +? +

 

 

 

 

 

 

The decision alternatives accompany the categories
discussed above. The states of nature (i.e. the price range
and associated probabilities) are provided by the balance
sheet. The outcomes must be assigned by the individual user
but for conceptual assistance, the outcomes should range

from favorable (+) to unfavorable (-) levels of utility.

APPENDICES

Appendix 1

Data Source Considerations

The data for corn consumption Of each animal species
was obtained from the USDA FEED OUTLOOK AND SITUATION
YEARBOOK. The data provided here is in million metric tons
and, to be consistent with other data, was converted to
bushel equivalents.

The animal inventory data was Obtained from
AGRICULTURAL STATISTICS. For most of the animal species
classes, the inventory values are stock variables which
assumes that each animal on inventory day will be carried
throughout the year. However, the broiler and turkey values
are production flow variables and they measure consumption
of all birds produced in the 12 month period.

The price data was obtained from AGRICULTURAL PRICES
and the USDA OUTLOOK AND SITUATION REPORT. The milk, milk
cow, steer & heifer, and beef cow prices are all on a
January to December market year using average of the year in
standard units for each species. Egg, broiler, turkey, and

hog prices are on a December to November market year.

155

156
The data used for the export section was collected from

three sources. All of the commodity data (i.e. regional
consumption, production, ending stocks, net imports and
exports) came from the FOREIGN AGRICULTURE SERVICE and
specifically, the FOREIGN PRODUCTION, SUPPLY, AND
DISTRIBUTION OF AGRICULTURAL COMMODITIES. All price data
comes from the FOREIGN AGRICULTURAL TRADE OF THE UNITED
STATES. All macro economic data (i.e. consumer price index,
population, and exchange rates) comes from the INTERNATIONAL
FINANCIAL STATISTICS.

The marketing year of feed grain in this model is
October 1 to September 30. It is important to note that
future updates of the model will need to reflect the change
in the marketing year from September 1 to August 31,
(starting in calendar year 1986) however, the change in
data collection should not significantly affect this models'

forecasting reliability.

Appendix 2

International Regional Groupings

Resign
United States
Residual Exporters
Canada
Australia
Argentina
Importers
Brazil

Developed Markets

Soviet Bloc

China

Oil-Exporting LDC's

157

W
United States

Canada
Australia

Argentina

Brazil

United Kingdom, Belgium,
Denmark, Netherlands,
Finland, Luxembourg,
Portugal, Ireland, Greece,
Iceland, Austria, France,
West Germany, Italy,
Switzerland, Sweden, Norway,
Malta, Spain, Japan, South
Africa

Albania, Bulgaria, East
Germany, Hungary, Poland,
Romania, Yugoslavia,
Czechoslovakia, USSR

China

Algeria, Ecuador, Indonesia,
Iran, Iraq, Libya, Oman,
Saudia Arabia, Venezuela,
Nigeria, United Arab Emirates,
Kuwait

158
International Regional Groupings (cont'd)

Region Countries
Importers
Newly Industrialized Hong Kong, Singapore,

Malaysia, Taiwan, South Korea

LDC's all others

Appendix 3

Supply/Demand Balance Sheet Documentation

A.1 Getting Started

To use the balance sheet, you will need an IBM or
compatible microcomputer with at least 256K of RAM memory.
At least one disk drive will be needed and an 80-column
monitor.

The balance sheet is started by placing the floppy disk
into the boot up drive and powering on your computer. When
you see the DOS prompt, type "corn" and press return. You
will see some resident application files loaded into memory
and then the title screen. On the bottom Of the title
screen are key codes for performing various Operations. TO
move forward, press F8.

After pressing F8 on the title screen, the main menu
appears. To move through the menu, either select the number
in front of the option or use the direction keys to
highlight an option and press return. Depending on the main
menu selection, you will see another menu or the screen
selected.

The display of the balance sheet is arranged into four

columns of data. The fourth column is always the forecasted

159

160

 

 

AN ECONOMIC SUPPLY/DMD mac: sun
ran :0ch m ammo m
m suns CORN nmusm
by
mom 3. my:

.11): Exam:
neuron sun mm
EAST mam, an 48824

SYSTEM DESIGN AND PROGRAMMING:
BETA TEST VERSION 1.0 ANTHONY B. WASHINGTON
SEPTEMBER, 1987 ROB LELAND

 

n-Holp n-Prcv. Screen Its-Edit Flu-Print Fa-Next Scrun

Figure 3: Title Screen

 

 

MAIN MENU
LOAD CASE STUDY
SAVE CASE STUDY
INITIALIZE EXOGENOUS VARIABLES
INITIALIZE ADD FACTORS
EXAMINE BALANCE SHEET
EXAMINE COMPONENTS OF FEED DEMAND
EXAMINE COMPONENTS OF U.S. EXPORTS
SOLVE

UOQOU.UNH

EXIT

 

 

Figure 4: Main Menu

 

161
vector while column three holds the lagged vector which the

forecast is based. On some screens there are fields for
yes/no responses to the override feature. If you choose to
override an estimated value, the override value column is
highlighted and the new value can be inputted.

Each main menu option will be discussed below.

A.2 Loading and Saving Data

A.2.1 Load Case Study

When the Load screen appears, you are asked to input
the default drive. The historical data is saved on the
balance sheet disk and therefore the return key is entered.
The data files appear on the screen and you are asked if any
of these files will be used. You are then prompted to enter
which column the data should go. You may enter a file name
and by striking return, the vector is loaded. The file
extension is not needed. A message appears at the bottom Of
the screen to confirm the transfer of the file.

You are required to load a lagged vector of historical
data to prevent any errors while running the calculations.
Columns one and two can be used to load historical data or
can be used to hold a particular scenario for on screen
comparisons. In order to update or edit a vector with new

information, it must be loaded into the fourth column.

162

 

LOAD SUPPLY/DEMAND BALANCE SHEET DATA

 

The default drive is:° A:

 

1982-83.BIN 1983-84.BIN 1984-SS.BIN 198$9SG.BIN 1986-S7.BIN
1987-SS.BIN SS-DATA.BIN EEROS.BIN

 

 

Do you wish to use data from one of these files (Y/N)? Y
Which column do you want the data (1-4)? 4

Enter the file name. SS-DATA .SIN

 

Fl-Help Fz-Prev. Screen F3-Edit F7-Print Fa-Next Screen

Figure 5: Load Screen

 

SAVE SUPPLY/DEMAND BALANCE SHEET DATA

 

Do you wish to save the data you entered or modified (Y/N)? Y
The default disk drive is: A:
Name the year you wish to save (1 thru 4): 4

 

1982-83.BIN 1983-S4.BIN 1984-85.3IN 1985-86.BIN 1986-87.BIN
1987-BS.BIN SS-DATA.BIN ZEROS.BIN

 

 

Enter the file name. SS-DNTA .SIN

 

Fi-Help rz-Prev. Screen FS-Sdit F7-Print Fa-Next Screen

Figure 6: Save Screen

 

 

163
A.2.2 Save Case Study

The Save screen is much like the Load screen. You are
asked if any of the data vectors should be saved. You are
then prompted to input which column should be saved and then
a file name. Again, the file extension is not needed.

Since editions are made to the fourth column, it is
generally the one to save. A message at the bottom of the

screen confirms the transfer of the file.

A.3 Initialize Exogenous Variables

In order to forecast the next time period, you must
input the relevant exogenous and predetermined variables.

The Summary Of Exogenous Variables displays all the
variables used and sets the year of the forecast vector.

You begin by entering the marketing year you wish to
forecast and then one-by-one entering the exogenous
variables. When you get to the bottom of the screen, press
F8 to continue.

The other menu options available for initializing the
exogenous variables are essentially work-sheets which carry
the generated value to the exogenous summary. If you wish,
the work-sheets can be bypassed and all relevant inputs done
in the exogenous variable summary.

The U.S. Composites menu option allows you to determine
the gross revenue from participating in the government
program and the food, seed, and industrial use. The U.S..

acres planted to corn is a function of (among others) net

164

 

 

S RETURN TO MENU

EMOGENOUS VARIABLES

1 U.S. COMPOSITES

2 IMFOMTERS GROSS DOMESTIC PRODUCT

4 SUMMARY OF EMDGIMOUS VARIABLES

 

 

Figure 7: Exogenous Variable Menu

 

 

EMOGENOUS VARIABLES
l r

 

 

 

 

 

 

 

 

3:03.
1122 1343-44 1334-37 1347-34 1344-3
0.3. coax 1:30:33 (MILL. In) 11.0 2.0 2.0 2.0
cousunzn 33103 130:: 3.222 3.300 3.403 3.300
73211313 c0333 30a IIIAT (3:: 4:33) 37.33 34.33 33.00 ' 34.30
7331431: 00433 run 00:: (PER 302:) 144.04 144.22 143.00 130.00
GOVT. raconan ranrzczranr°3 asvzluz 270.41 173.41 141.23 144.33
orvrnsxow ruruzxrs 0.00 2.14 33.32 23.30
nerrxczrucx PAYMENTS 30.34 101.17 33.31 102.33
3230231 or 20:23 In 3:: 0.100 0.173 0.200 0.200
3430231 or 3410 1330 orvrnsxou 0.000 0.023 0.130 0.100
coax 1043 313: (403. 30a 31: 4 3011) 2.43 1.31 1.33 1.40

ti-Selp Fa-Srev. Screen FS-Sdit F7-Print FS-Iert Screen
Figure 8: Exogenous Variable Screen 1
330033003 vaaxnanzs

YEAR 1333-44 1344-47 1337-33 135233
soananzxzo lacunae: 34:23 you:

1:300:23: 32:0 034:: 1.4437 1.7000 1.7000 1.3000

rumoursas warn: 1.4113 1.7000 1.7000 1.3000

333033133 7130 02113 1.4037 1.4000 1.4000 1.4000

2230:1133 IIEAT 1.3203 1.3000 1.3000 1.3000
warn: 3310: (0013 taxes 4/nr) 133.00 117.00 113.43 120.00
33223 PRICE (4/30.) 3.14 2.40 2.43 2.43
IIEAT 32:10 (IU./AC) 37.3 34.4 33.2 40.0

 

Fl-Help Fz-Prev. Screen FS-Sdit F7-Print rS-Hext Screen

Figure 9: Exogenous Variable Screen 2

 

 

165

 

 

 

 

 

 

 

 

 

 

 

 

 

330033003 733143133
3300.
1143 1343-44 1344-47 1347-44 1344-43
3111103 3330
31::10:::'4=:&:=:3133 11.177 10.334 10.300 10.000
041113 430 041333 03 3330 11.412 10.303 12.400 12.300
3333 0033 430 3333 003 3331403343314 34.742 33.044 42.000 41.230
3334. 3011313. 430 01033 031033333 334.331 330.000 333.000 340.000
3301133 3300001103 4433.43 4300.00 4330.00 4400.00
103331 3300001103 143.242 203.224 223.343 233.300
300 430 310 303333 32.313 30.340 41.000 31.000
314410 fl-Prev. Screen Mt fl-Print m Screen
Figure 10: Exogenous Variable Screen 3
330033003 733133133

1343 1343-44 1344-47 1347-44 1:5:i4

3013343 3341 33103 (4/103) 133.00 140.00 140.00 143.00
300 33103 (41031) 44.00 31.13 31.00 43.00
3333 31333 33103 (4/031) 37.70 37.73 37.00 37.00
3113 33103 (SIM) 12.73 12.31 12.00 12.23
3333 003 33103 (3/031) 34.30 33.31 33.00 33.30
40333 331-43103 430 01733110 3.3 13.4 21.3 21.0
0.3. 3131303 330130 310033 0.0 0.0 0.0 0.0
3000. 3330 4 1300313141 1140.0 1131.0 1213.0 1233.0
0033 43 4 3330331 03 3330313 0.43333 0.43333 0.43333 0.43333

III-Help PSI-Prev. Screen ”-203 flu-Print PS-lext Screen
Figure 11: Exogenous Variable Screen 4
330033003 333143133

1333 1343-34 1344-47 1347-44 1332343
133031333 3030141103 4333123.0 4330000.0 4400000.0 4433000.0
133031333 03033 00333110 3300001 0.011744 0.012242 0.013324 0.014033
133031333 333v33110 4333 233300.0 230000.0 233000.0 240000.0
333031333 3330 03313 3333303 1.2404 1.2333 0.4444 130030
333031333 33341 3373303 1.1733 1.1233 0.3073 5353‘s
333031333 3030131103 73000.0 74000.0 40000.0 43000.0
333031333 03033 00333110 3300001 0.010777 0.011144 0.011334 0.012331

 

 

n-Ielp n-Prev. Screen n-Sdit fit-Print FS-lest Screen

Figure 12: Exogenous Variable Screen 5

 

 

 

166

 

10mmnunu3wuunmmfi43mnnm
'2mnnun33mmnu3
3nunnnc13mmuu3
43am.4um.um13unnnnnm3

 

 

 

Figure 13: U.S. Composites Sub Menu

 

 

330033003333133133
l I

 

 

 

'm.

1333 1505-00 1505-07 1507-00. 1000-03
0033033313 03:
GOVT. 3300333 33331013333'3 3303303
3330331 0! m II 333 0.100 0.175 0.200 0.200
W 0! PAID 1330 mun: 0.000 0.035 0.150 0.100
3333 33103 03 will OS 1033 3.55 1.04 1.02 1.75
(lbich ever 13 higher)
3 ( 1000 - SIP - DIVERS!“ ) 0.500 0.000 0.350 0.700
3 3300333 11310 110.0 115.1 110.4 110.5

— — — —
- .058 3303 333331 270.01 115.51 141.25 100.19

 

 

n-Selp 32-31-40. Screen 33-3010 fl-Prtnt 33-3430 Screen

Figure 14: Govt. Program Participant's Revenue Screen

 

 

 

 

 

- 3300.
1333 1005-00 1000-07 1007-00 1000-0
mun-3 0?:
DWI” 3313331
PAW II!" 0.00 0.73 2.00 2.00
X 3330331 0? PAID 1330 010333103 0.000 0.025 0.150 0.100
X 3300333 11310 110.0 110.3 110.0 119.5

_ — — —

44 M 333 M82 3033 0.00 2.10 30.02 23.00

 

 

r1-l1e1p n-Prev. Screen 33-3010 ”-3310: "Inner: Screen

Figure 15: Diversion Payments Screen

 

1€Y7

Immansmusr

 

 

 

 

 

 

 

 

 

PROJ.
YEAR 1905-00 1900-07 1907-00 1900-09
COMPONEflTS or: DEEICIENCY RAxNENI
PERCENT or AcREs IN ARR '0.100 0.175 0.200 0.200
PERCENT or RAID LAND DIVERSION 0.000 0.025 0.150 0.100
CORN IARGEI PRICE 3.03 2.90 3.03 2.90
- EARN PRICE or CORN on m 2.55 1.04 1.02 1.75
(Which ever 10 higher)
- DEEICIENC! RAYNENI RAIE 0.40 1.00 1.21 1.23
x ( 1000 - ARR - DIVERSIoN ) 0.900 0.000 0.050 0.700
x PROGRAM YIELD 110.0 119.3 119.4 119.5
— — — _
- 63088 PRE EAsE ACRE 50.90 101.17 93.91 102.09
Fl-Belp rz-Prev. Screen Pa-ldit r7-Print Pa-Next Screen
Figure 16: DefiCIency Payments Screen
000D, SEED, AND INDusIRIAL 00E
P8030
YEAR 1905-00 1900-07 1907-00 1900-09
(811.1110! 30.)
EDOD
HIGH RDCIRDSE CORN SYRUP 330.0 340.0 350.0 355.0
RESIDUAL 501.0 537.0 530.0 541.0
SEED 19.0 19.0 19.0 19.0
INDUSTRIAL 00E
AncoaoD/EUEL 200.0 295.0 310.0 320.0
— — — —
IDEAL 1100.0 1191.0 1215.0 1235.0

 

 

01-0019 ra-Prev. Screen 03-0010 r7-Print rl-Next Screen

Figure 17: Food, Seed, And Industrial Use Screen

 

 

168
corn revenue form government payments. The work-sheets help

you calculate revenues from the market, diversion payments
and any deficiency payment.

The food, seed, and industrial work-sheet disaggregates
FSI use into high fructose corn syrup, seed, industrial use
and a residual. This disaggregation will allow you to more
accurately determine the total FSI use given the conceptual
constraints.

The Importers Gross Domestic Product menu option is a
work-sheet that requires the percentage (or weights) that
each area in the region imports. These weights must be
changed to represent a change in an area's importance to the
total regional imports. Each area CPI and gross domestic
product is needed to calculate the regional gross domestic
product.

The Exporters Composites menu option shows work-sheets
which help you to calculate the exporters crop revenue and
exporters gross domestic product. The balance sheet uses
lagged values for exporters crop revenue, however this menu
option requires user input in order to have values when the
vector being edited is used in the lagged vector column.

The Exporter Gross Domestic Product menu option is a
work-sheet much like the Importers Gross Domestic Product.
It requires the use of weights for area importance as well
as CPI's and gross domestic product.

Once these variables are entered, they can be saved in

a vector for future use or scenario analysis.

 

W 0?:

169

 

Lam WI—
IIIOITIII 0:005 00:25:10 PRODUCT
IEIGIIS: (Percent imported to world inverte)

m

 

 

 

CI 0 . 030 .

00V!” 0100 0.307

WPHG m0 0 . 330

0030 316100 0 . 110

mu! m 0. 113

130023 mm 0. 110

“II. 0 . 003 030.7 .
m 1003-00 1000-07 1007-00 1000-0

m ma 1.01020

”A 110.0 110.0 100.0 110.0

0071!? "DC 130.00 130.00 131.00 131.00

mm m 103.00 130.00 133.00 133.00

one In!“ 133 . 00 133 . 00 130 . 00 133 . 00

 

PI-Ielp fa-Prev. lcreen Pa-ldit 07-0210: rt-lext Screen

Figure 18: Importers Gross Domestic Product Screen 1

 

 

m mun
L I

 

 

 

 

0000 .
3m 1003-00 1000-07 1007-00 1000-0
m ma 110133
m mm 133.00 133.00 133.00 133.00
nun mm 000.0 010.0 000.0 000.0
M11. 3300.0 3300.0 3300.0 3330.0
03003 me moon-r
Gm 1.3000 1.7000 1.0000 1.0000
00V!!! 0106 1.0000 1.3000 1.3000 1.0000
mm m 1. 0000 3 . 0000 3 . 3000 3 . 3000
OPEC ”010' 1 . 0000 3 . 1000 3 . 3000 3 . 3000

 

rl-lelp rz-Prev. screen ra-ldit r7-Pr1nt Pt-lext screen

Figure 19: Importers Gross Domestic Product Screen 2

 

 

 

 

 

 

0000.
m2 1003-00 1000-07 1007-00 1000-0
03000 me mm
m W220 1.0000 3.1000 3.3000 3.3000
1.3003! mm 0.000 0.700 0.000 0.300
“11. 33.000 37.000 00.000 03.000
W 03000 me manor
8 (M0331 0 (6001/0011), 0.011700 0.013303 0.013330 0.010033

 

PI-lelp r2-Rrev. Screen PJ-ldit r7-Print Pt-lext screen

Figure 20: Importers Gross Domestic Product Screen 3

 

 

 

170

 

EXOGENOUS VARIABLES
EXPORTERS CONROSITES

1 EXPORTERS CROP REVENUE

3 EXPORTERS GROSS DOIESTIC PRODUCT

3 RETURN TO NENU

 

 

 

Figure 21: Exporters Composites Sub Menu

 

 

lEXOGENOUS VARIABLEsigi

 

 

 

 

PROJ.
YEAR 1005-00 1000-07 1007-00 -1080-09
CORPONENTB OF: EXPORTERS FEED AND WHEAT REVENUE
EXCHANGE RATE FOR ARGENTINA 0.0010 0.0030 1.3000 1.0000
EXCHANGE RATE FOR AUSTRALIA 0.7000 0.0700 0.3000 0.0000
EXCHANGE RATE FOR CANADA 1.3033 1.3003 1.0000 1.3000
FEEDGRAIN YIELD FOR ARGENTINA 3.3000 3.0000 3.7300 3.0000
FEEDGRAIN YIELD FOR AUSTRALIA 1.3000 1.0000 1.7000 1.0500
FEEDGRAIN YIELD FOR CANADA 3.0000 3.1000 3.3000 3.0500
"HEAT YIELD FOR ARGENTINA 3.3000 3.0000 3.7300 3.0000
WHEAT YIELD FOR AUSTRALIA 1.0000 1.7000 1.7300 1.7500
WHEAT YIELD FOR CANADA 1.7700 1.0000 1.0300 1.0500

 

FI-nelp rz-Prev. Screen FJ-Edit F7-Print rs-Next Screen

Figure 22: Exporter Crop Revenue Screen

 

1371

 

 

:IEXOGENOUS VARIABLEEI

 

COMPONENTS OF: EXPORTERS GROSS DOMESTIC PRODUCT
WEIGSTS: (Percent exported to reet-of-world exporte)

 

 

 

 

 

 

 

 

 

 

 

ARGENTINA 0.503
AUSTRALIA 0.104
CANADA 0.233
_ PROS.
YEAR 1905-00 1900-07 1907-00 1900-09
CONSUMER PRICE INDICEs
ARGENTINA 20000.0 20500.0 21000.0 20959.0
AUSTRALIA 145.00 140.00 150.00 149.00
CANADA 140.00 143.00 145.00 144.00
FI-aelp Fz-Prev. Screen FS-Edit P7-Print PS-Next Screen
Figure 23: Exporter'Gross Domestic Product Screen 1
lExocENOUS VARIABLES";-
. PROJ.
YEAR 1905-00 1900-07 1907-07 1900-09
GROSS DOMESTIC PRODUCT
ARGENTINA 200.00 210.00 220.00 230.00
AUSTRALIA - 1.0000 1.9000 2.0000 2.2000
CANADA 1.0000 1.7500 1.0500 2.0000
EXPORTERS GROSS DOMESTIC PRODUCT
E (NEIcNTj 0 (ODPj/CPIj)) 0.010777 0.011100 0.011534 0.012351

 

 

 

P1-001p Fz-Prev. Screen 03-001: P7-Print F0-Next Screen

Figure 24: Exporter Gross Domestic Product Screen 2

172
A.4 Initialize Add Factors

Each of the estimated equations has a mechanism which
can be used the adjust the intercept of the equation. By
using a positive add factor the function will return a
higher value and vice versa for a negative add factor. This
feature is added so you can adjust each equation with

a posteriori information.

A.5 Examine Balance Sheet

You can choose to examine screens which show a balance
sheet similar to that shown in Table 1 (page 4). This menu
option displays three screens which show U.S. supply and
utilization factors. The first screen shows strictly the
U.S. production situation. The second screen shows the U.S.
supply as well as the estimated and predetermined uses of
corn. This screen also shows the estimated price which is
calculated when supply and usage are equated. The final
screen shows information that relates to the level of ending
stocks. On each of these screens there are places to
override the estimated values presented. If the override is
used, the model will sélve using these new values. This

feature allows you to do "what if” scenarios.

A.6 Examine Components of Feed Demand
This menu option allows you to view screens that show
the livestock cOrn consumption both in aggregate form and on

a per head basis for each livestock species. If you view

173

 

 

 

‘1ADDIQCNWSPOnimEIMmummmyl

 

 

 

 

1000-00 1000-00
SUPPLY ----' CONSUMPTION PER'HEAD ----
ACRES PLANTED -0.300 TURKEYS 0.000
ACRES HARVESTED. 0.000 HUGS .0.000
YIELD 0.000 OTHER 0 UNALIDCATED 0.00
CONSUMPTION PER HEAD INTERNATIONAL COMPONENT
DAIRY ANINAL 0.000 INPORTERS NET IMPORTS 0.00000
CNTTLE ON FEED 0.000 INPORTERS YIELD 0.00
OTHER REEF CATTLE 0.000 EXPORTERS WHEAT YIELD 0.00
HENS, PULLETS, 0 was 0.000 EXPORTERS HARVESTED AREA 0.0
BROILERS 0.000 EXPORTERS CONSUMPTION 0.00000

 

PI-Help rz-Prev. Screen Fa-Edit r7-Print Ps-Next Screen

Figure 25: Add Factor Screen 1

 

 

 

1 ADD FACTORS FOR THE EQUATIONS l

 

 

1000-00

INTERNATIONAL CONPONENT ———-

EXPORTERS ENDING STOCKS 0.00000
POLICY STOCKS

GANGE IN CCC STOCKS 0.00

GANGE IN FOR STOCKS 0.00

FREE STOCRS 0.00

 

 

 

Fl-nelp Fz-Prcv. Screen Pa-Sdit r7-Print PSnNext Screen

Figure 26: Add Factor Screen 2

170

 

SUPPLY/DEMAND BALANCE SHEET FOR CORN IN THE U.S.

m e 0m-
YEAR 1003-00 1000-07 1307-00 1000-0 RIDE ? VALUE

(NILLION ACRES)

ACRES SET-ASIDE 3.0 13.0 31.3 31.0

ACRES PLANTED 03.3 70.7 00.0 00.3 N
ACRES HARVESTED 73.1 00.3 30.0 30.0 N
YIELD PER ACRE 110.0 110.3 110.0 110.1 N

 

 

 

FI-Selp r2-Prev. Screen FS-ldit r7-Print rI-Next Screen

Figure 27: Balance Sheet Screen 1

 

 

 

SUPPLY/DEMAND BALANCE SREET FOR CORN IN THE 0.5.
OVER-
YEAR ' 1905-06 1900-07 1907-00 1900-09 RIDE ? VALUE
(NILLION IUSBELS)
SEGINING STOCKS 1000 0000 0929 0557
PRODUCTION 0977 S253 7101 0950 N
IMPORTS 11 2 2 2
————
TOTAn SUPPL! 10530 12290 12070 11515 N
USE:
FEED 0095 0050 0700 0510 N
FOOD, SEED, 0 INDUSTRY 1100 1191 1215 1235
TOTAL DONESTIC 5255 5001 5915 5753
EXPORTS 1201 1525 1000 1557 N
————
TOTAL USE S090 7300 7515 7311
TOTAL ENDING STOCKS 0040 0929 4557 4204
TOTAL 0 OF USE 02.2 06.9 00.0 57.5
U.S. AVERAGE FAR! PRICE $2.35 01.51 51.00 52.05

 

 

 

r1-uelp r2-Prev. Screen 03-001: r7-Pr1nt FS-Next Screen

Figure 28: Balance Sheet Screen 2

 

SUPPLT/DENAND HALANCE SHEET FOR CORN IN THE U.S.

 

PROJ. OVER-

 

 

YEAR 1003-00 1000-07 1007-00 1000-00 RIDE ? VALUE
(NILLION HUSHELS)
STOCRS IN
GOVERNNENT PROGRAN
CCC INVENTORY 1301 1037 1000 1000 N
FARNER O'NED RESERVE 3033 3000 3330 3073 N
FREE STOCRS 30 0 000 00 N

Enter current
WIDE! GOVT. IOAN 2730 2070 2002 1eve1 here —~ 0

EFFECTIVE CORN ICON RATE 03.00 01.31 01.30 31.00

 

 

 

FI-Selp F2-Prev. Scrcen Fa-ldit F7-Print PS-Next Screen

Figure 29: Balance Sheet Screen 3

175

 

 

HOG CORN CONSUMPTION

RETURN TO MENU

LIVESTOCK CORN CONSUMPTION

1 CORN CONSUMPTION SUMMARY

3 DAIRY ANIMAL CORN CONSUMPTION
3 CATTLE ON FEED CORN CONSUMPTION

0*OTHER REEF CATTLE CORN CONSUMPTION

3 HEN, PULLET AND OTHER CHICEEN CORN CONSUMPTION
0 EROILER CORN CONSUMPTION
7 TUREEY CORN CONSUMPTION
0

0

0

OTHER UNALLOCATED CORN CONSUMPTION

 

Figure 30: Livestock Corn Consumption Menu

 

LIVESTOCK CONSUMPTION OF CORN IN THE U.S.

 

 

 

PROJ.

YEAR 1003-00 1000-07 1007-00 1000-00
DAIRY ANIMALS 033.37 003.10 010.01 701.70
CATTLE ON FEED 000.03 003.01 770.00 733.00
OTHER BEEF CATTLE 100.30 337.11 330.13 310.17
HENS. PULLETS, 0
CHICKENS 337.03 303.07 307.13 377.30
BROILERS 003.03 330.71 333.17 330.17
TURKEYS 110.00 100.31 130.03 137.20
HOGS 1000.31 1300.00 1301.30 1333.11
OTHER AND UNALLOCATED 10.30 30.03 331.11 170.00

— — — _
TOTAL CONSUMPTION (MILL. EU) 0003.00 0030.00 0700.00 0310.35

 

F1-Be1p F2-Prev. Screen FS-Edit r7-Print FS-Next Screen

Figure 31: Corn Consumption Summary Screen

 

 

176

 

DAIRY ANIMAL CONSUMPTION PER HEAD

 

 

 

OVER
PROS. RIDE
YEAR 1003-00 1000-07 1007-00 1000-00 VALUE
FEEDING RATE PER HEAD 70.370 00.000 70.003 70.170 0.000
MILE CONS AND HEIFERS
THAT HAVE CALVED, JAN. 1. 11.177 10.330 10.300 10.000
DAIRY ANIMAL CONSUMPTION 033.37 003.10 010.01 701.70 0.00

(MILLION EU)

 

Fl-Nelp F2-Prev. Screen FS-Edit r7-Pr1nt FS-Next Screen

Figure 32: Dairy Animal Consumption Per Head Screen

 

OTHER AND'UNALLOCATED CONSUMPTION PER HEAD

 

 

 

mam
, an»: tun:
2213 1905-00 1935-97 1997-39 1993-99 VALUE
OTHER AND unarmocarro 19.30 30.03 251.11 170.9 o.o
cnwnmmrum
(HUHJNIBU)

Other end unelloceted ccneunpticn includes hcreee, lulee, oxen, cheep,
deer, pets, etc.

 

Fl-Belp F2-Prev. Screen FS-Edit F7-Rrint FS-Next Screen

Figure 33: Other and Unallocated Consumption Screen

 

 

177
the per head information, there are places to override the

per head estimates for "what if" scenarios.

A.7 Examine Components of U.S. Exports

This menu option allows you to examine the
international import/export component. One sub menu option
shows a screen which disaggregates the exporting regions
supply, consumption, ending stocks and exports. The other
sub menu option shows a screen with the aggregate net world

imports, exports and the U.S. exports.

A.8 Solve

There are two choices for solving the model depending
upon your goals. One solve menu option allows you to solve
for a single price. The second menu option determines a
price probability given a chosen number of iterations.

In the single price option, you have either input the
relevant exogenous and predetermined variables or overridden
an estimated value. You input the initial "high" price and
the price by which to decline on each iteration. When these
inputs have been made, the algorithm will start with the
"high" price and compare the estimated supply to the
estimated demand. If the demand is less than the supply,
the price will drop by the reduction price and recheck the
supply and demand. When the demand is equal to the supply
the algorithm will stop and you can return to the display

portion of the program.

178

 

 

3 Innuimunm

1 EXPORTERS NET EXPORTS

3 U.S. EXPORTS SUMMARY

 

 

Figure 34: U.S. Export Sub Menu

 

 

 

 

EXPORTING RESIDN'S NET EXPORTS
PROS. OVER
YEAR 1003-00 1000-07 1007-00 1000-00 RIDE VALUE
MILLION EU
EXPORTERS SUPPLY 1000.30 3103.30 3103.03 3333.70 N
- EXPORTERS CONSUMPTION 011.30 073.30 1033.01 1370.33 N
- EXPORTERS ENDING STOCES 313.01 103.00 103.00 10.77 N
_———
- EXPORTERS EXPORTS 770.37 000.33 000.30 037.07 N

 

 

Fl-nelp F2-Prev. Screen FS-Edit F7-Pr1nt PS-Next Screen

Figure 35: Exporters Net Exports Screen

 

 

 

 

U.S. EXPORT DENAND
PROS. OVER
YEAR 1003-00 1000-07 1007-00 1000-00 RIDE VALUE
MILLION 0U
IMPORTERS NET IMPORTS 3017.01 3333.03 3300.00 3013.11 N
EXPORTERS NET EXPORTS 770.37 000.33 000.30 037.07
U.S. EXPORTS 1301.0 1333.0 1000.3 1337.0

 

 

r1-Se1p ra-Prev. Screen PS-Edit r7-Pr1nt FS-Next Screen

Figure 36: 0.8. Exports Summary Screen

179

 

SOLVE OPTIONS

1 SOLVE FOR A PRICE
3 PROBABILITY DISTRIBUTION FOR PRICE
3 RETURN TO MENU

 

 

 

Figure 37: Solve Options Sub Menu

 

 

At what price would you like to etert the solve proceee? $2.05
By wnet enount do you went price to drop? $0.03

SOLVINC

 

Fl-Nelp Fz-Prev. Screen Fa-Edit F7-Print FS-Next Screen

Figure 38: Single Price Solve Screen

 

180

 

 

This procedure-will procuce e probehility distribution of the
U.S. corn tern price using rendon yields for the U.S. end the
rest-ot-the-world.

now many times would you like the process to drew tendon yields? 100

SOLVING

PLEASE NAIT

 

Fi-Selp rz-Prev. Screen ra-Edit F7-Print Fa-Next Screen

Figure 39: Probability Iteration Screen

 

 

PROBABILITY THAT THE PRICE NILL

 

 

BE THAT LISTED OR BELOW PRICE $/BU.-
1 1.00
3 1.71

10 1.00
30 1.03
30 ‘ 1.00
00 3.00
30 3.08
00 3.13
70 3.17
00 3.31
00 3.37
03 3.03
00 3.30
MEAN ' 3.10

STD. DEV. I 0.1000

 

Fl-Eelp F2-Prev. Screen FS-Edit r7-Print Fa-Next Screen

Figure 40: Price Probability Distribution Display Screen

 

 

181
A trade-off is involved when choosing the amount by

which to reduce on each iteration. If you choose a low
reduction price, the model will solve slowly. If you are
unsure how your overrides will affect the model, you can set
a high price and a large reduction price. This will allow
the model to solve quickly but the accuracy of the forecast
suffers. By choosing the later alternative, you can wait
until the balance sheet has reached an equilibrium price and
the key codes appear at the bottom of the screen. Choosing
the F3 key code, you will be returned to this solve screen
and the current equilibrium price will be displayed. This
time you can choose a price higher than the previous
reduction price and a new reduction price.

The second solve option allows you to select a number
of iterations for the purpose of calculating and displaying
a range of prices and probabilities based upon random
production shocks. The model will accept 300 or less
iterations. By increasing the number of iterations, the
standard error of the price distribution decreases and the
confidence interval becomes smaller. However depending upon
the number of iterations chosen, the model will take some
time to solve.

When the model solves using this menu option the random
shocks, affect the world production. Each iteration will
have a different equilibrium price a price range is created.
You can move to the price probability display by pressing F8

when the key codes appear at the bottom of the screen. The

182
range of prices have a related probability distribution

which indicates the probability of the price shown being at
or below that percentage. This information provides
subjective probabilities which can be used in the users

decision analysis.

A.9 Exit
This option displays a screen that makes sure you are
ready to exit the program. If not, you inputs the response

for no and the main menu will return.

183

 

 

ARE YOU SURE YOU NISH TO EXIT ? (Y/N) N

 

 

Fl-Eelp F2-Prev. Screen FS-Edit F7-Print FS-Next Screen

Figure 41: Exit Screen

 

Appendix 4

Computer Input/Output Code

PROGRAM FIRST(INPUT,OUTPUT);

(*************************************t*tttttt********fi***********tt*t**tit

* R
* AN ECONOMIC SUPPLY/DEMAND BALANCE SHEET *
t t
* FOR FORECASTING AND SINULATING THE *
* fl
* UNITED STATES CORN INDUSTRY *
* t
t by R
t *
* ANTHONY B. HASHINGTON *
t *
* Submitted to Michigan State University *
* in partial fulfillment of the requirements *
* for the degree of *
t R
* MASTER OF SCIENCE *
* i
* t
* Department of Agricultural Economics *
* *
* 1988 *
t *

******************t*********t*ttt****************t*******************Ritt*)

{Procedures Generated By Screen Sculptor.Version 1.2 4/2/1987 - 23:33z0 }
CONST CopyrightSS='(C)Copyright 80.85 The Software Bottling Company Of New York';
{ DO NOT REMOVE The Above Copyright Notice
This Program may not be used without the above Copyright Notice
}
{ SCREEN SCULPTOR(C) Version 1.2
(C) COPYRIGHT, THE SOFTWARE BOTTLING COMPANY OF NEH YORK. 1980, 1985
WARNING: Do not attempt to make any changes to this procedure unless you
have made a backup. The Software Bottling Company Of New York can not
and will not support such changes made to this program.
Turbo(tm) Pascal Version. Trade Mark 0f Borland International}

{ Global Required Definition of variables and constants }

TYPE STRZ = STRING[2]; STRBO = STRING[80]; STR79 = STRING[79];
resSS = (staySS. prevSS. exitSS. nextSS);

1134»

CONST

11355
ARA . ARRAY[1..4] OF REAL;

{ Esc. Up Arrow Key. Left Arrow Key . Page Up Key }
escSS=i27; uSSs'H'; lSS-‘K'; puSSs'I';

{ Blank. Down Arrow Key. Right Arrow Key. Page Down Key }
blankSSs' '; dSS-‘P'; rSS-‘N'; pdSS-‘O';

{ Function keys Fl-FlO }

flSSs';'; f2$S-'<'; f3$S='-'; f4SS='>'; fSSS='?';
f6$S-'D'; f7SS='A': f8$S-'B': f9$S='C'; flOSS-‘D':

retSS : STR2=";

ES - 1:

***** Beginning of Include file LISTING.INC *****

{SI LISTING.INC}

VAR

actionSS. last_field_actionSS : resSS;

hiSS, loSS : REAL;

vtypeSS. screenSS. use_screenSS. screen-fieldSS. varSS : INTEGER;
file_existSS. last_fieldSS. retrieveSS : BOOLEAN;

rangeSS : STRBD;

BeepOnSS: BOOLEAN;

{ ENDOGENOUS AND EXOGENOUS VARIABLES }
PCNT_OF_XPORTS,

APCT:AR4;

AHCTzAR4;

PYIELD.

CORNYT:AR4:

UHTYT.
GROSS.

ARP.

DIVERSION.

BAL.

DVPCN.
DVRCN.

DIVPAY.

DPAY,

DEFPAY.
BEGINSTOCKS.
MIN_FREE_STOCKS.
END_STOCKS.
PRODUCT:AR4:
IMPORTS.
TSUPPLY.
ONE:AR4;

DNEPH.

TUD:AR4;

TUOPH.

THREEzAR4;
THREEPH.
FOUR:AR4;
FOURPH.
FIVEzAR4;
FIVEPH.
SIX:AR4;

186

SIXPH.
SEVEN:AR4;
SEVENPH.
EIGHTzAR4;
DANUMzAR4;
COFNUMzAR4;
DBCNUMzARd;
HPCNUMzAR4:
DRONUMzAR4;
TURNUM:AR4;
HOGNUM:AR4;
DAIRYCONS.
CATONFED.
OTHERBEEFCAT,
CHICKENCONS.
BROILERCONS.
TURKEYCONS.
HOGCONS.
OTHERCONS.
FEEDT:AR4;
HFCS.
SEED_USE.
RESIDUAL.
FUEL_USE.
FSI_USE.
DOMESTIC_USE.
SETASIDE_ACRES:AR4;
DV73:REAL;
DV82:REAL;
DV83:REAL:
DV84:REAL;
DV740NzREAL:
VCCNzAR4;
VCHU:AR4;
C_TARGETP:AR4;
CDRN_LDAN_RATE:AR4;
PARREV:AR4;
REVCOR:AR4;
REVHHTzAR4;
YEAR:AR4;
YEAR18.YEARZB.YEAR38.YEARAB:REAL:
YEARB.
CORNPTzARd;
RFARMP.
MILKPzAR4:
UHTPTzAR4;
BEFSTRPzAR4:
BCOUPzAR4;
SMPTzAR4;
HOGPzAR4;
IPOPzAR4;
IGDPzAR4;
IHA.

IYIELD.

187

IPRO:AR4;
IFP.

1UP.
IGDPPC.
IPROPC.
INIPC:AR4;
INI_IN_BU.
NXXRF:AR4;
NXXRUzARA;
NMXRF:AR4;
NMXRH:AR4;
CHHT.SBUT.DMUT.LOUT.NIUT.LDHT.BRHT.ARUT.AUUT.CAHT:REAL;
CPISBzAR4:
CPIDMzAR4;
CPILO:AR4:
CPIleARd;
CPILDzARd;
CPIBR:AR4;
CPIAR:AR4;
CPIAU:AR4;
CPICAzAR4;
CPICH:AR4;
CPITzAR4;
GDPCH.
GDPSB.
GDPDM.
GDPLO.
GDPNI.
GDPLD.
GDPBR.
GDPAR,
GDPAU.
GDPCA.

FP.

HP.
EYIELDzAR4;
EFREV.
EUREV:AR4:
UYAR.
UYAU.
UYCA.
FYAR.
FYAU.
FYCA.
XRAR.
XRAU.
XRCA.
E_END_STK,
E_NET_EXPORT.
EHAzAR4;
ECON.
ECON_IN_BU.
ECONPC:AR4:
EES.

188

EES_IN_BU.
EESPC:AR4;
ESPLY_IN_BU.
ESPLYPC.
EPRO.
EPROPC.
EUDPC.
ENE:AR4;
ENE_IN_BU.
EXPORTS:AR4;
EGDP:AR4;
EPOPzAR4;
EHYIELDzAR4:
TOTAL_USE:AR4:
PERCENT_USE:AR4:
FPLR.
FPLRZ.
FPLR3:REAL:
CCCSTOCKS.
DCCCSTOCKS.
FORSTOCKS.
DFORSTOCKS:AR4;
FREESTOCKS:AR4;
GOVTSTOCKS:AR4;
POLICY_STOCKS:AR4;
TRIGGERP:REAL;

{ ADD FACTOR VARIABLES }
APCT_AF.
AHCT_AF.
CORNYT_AF.
ONEPH_AF.
TUOPH_AF,
THREEPH_AF,
FOURPH_AF,
FIVEPH_AF.
SIXPH_AF.
SEVENPH_AF.
EIGHT_AF.
IYIELD_AF.
INIPC_AF,
EYIELD_AF.
EUYIELD_AF,
EHA_AF.
ECONPC_AF.
EESPC_AF.
DCCCSTOCKS_AF.
DFORSTOCKS_AF:AR4;
FREESTOCKS_AF:AR4;

{ OVERRIDE VARIABLES }
CORNYT_0R.
APCT_OR.
AHCT_OR.
BEGINSTOCKS_OR.
PRODUCT_OR.

189

TSUPPLY_OR.
INPORTS_DR.
ONEPN_OR.
DAIRYCONS_DR.
TUOPH_OR.
CATONFED_OR.
THREEPH_DR.
OTHERBEEFCALOR.
FOURPN_OR.
CNICKENCONS_OR.
FIVEPN_OR.
BROILERCONS_OR.
SIXPH_OR.
TURKEYCONS_OR.
SEVENPH_OR.
HOGCONS_OR.
OTHER_UNALLOC_OR.
FEEDT_OR.
DONESTIC_USE_OR.
INI_IN_BU_OR.
ESPLY_IN_BU_OR.
ECON_IN_BU_OR.
EES_IN_BU_OR.
ENE_IN_BU_OR.
EXPORTS_OR.
TOTAL_USE_DR.
CCCSTOCKS_OR.
FORSTOCKS_OR.
FREESTOCKS_OR.
ENDSTOCKS_OR:AR4;

{ VARIABLES FOR THE RANDOM SECTION }
ALPHA.ALPHA1.ALPHA22ARRAY[1..30] OF REAL;
BETA.BETAI.BETA2.

GANHA.GAMNA1.GAMMA2.
YIELD.YIELDI.YIELDZ.
2.21.22:REAL;
REDUCE_PRICE:REAL:
INFO:STRING[7];
PAUSE:STRING[11];
RANDON_PULLS:REAL;

{ FLAG VARIABLES FOR OVERRIDE VALUES }
CORNYT.FLAG.
APCT_FLAG:STRING[1]:
AHCT_FLAG.

BEGINSTOCKS_FLAG.
PRODUCT_FLAG.

TSUPPLY_FLAG.

INPORTS_FLAG.

FEEDT_FLAG.
DOMESTIC_USE_FLAG:STRING[1]:
INI_IN_BU_FLAG.
ESPLY_IN_BU_FLAG.

IEBCI

ECON_IN_BU_FLAG.
EES_IN_BU_FLAG.
ENE_IN_BU_FLAG.
EXPORTS_FLAG.
TOTAL_USE_FLAG.
CCCSTOCKS_FLAG.
FORSTOCKS_FLAG. .
FREESTOCKS_FLA6:STRING[I];
ENDSTOCKS_FLA6:STRING[I];

{ MISCELLANEOUS VARIABLES }
F6.
86.
FLAG.
UA.I.S.F:INTEGER;
P.J:REAL:
NYzINTEGER;
USCORNPT:ARRAY[I..BOO] OF REAL:
EQUILIBRIUMzREAL:
PERCENTILE_ANSHER:REAL:

***** End of Include file LISTING.INC

*ittt

{SV-.C-.R-} { Pascal Directives. See Compiler Manual }
{SI TURPROCS.TUR Include Procedures In This File. See Manual }

(it

PROCEDIRE BEEP(BeepOn: BOOLEAN) ;
PROCEDURE CLEAR_KBD:

PROCEDURE COLOR(foregr.backgrzBYTE);
PROCEDLRE WITECUtext: STRBO);
FUNCTION SET_MONITOR_TYPE: INTEGER;
{ Display A Screen Sculptor Screen }

This is a sumary of the procedures in TLRPROCSJUR

{ Sound Beep if BeepOnsTRUE }
{ Clear Keyboard Buffer }

{ Set Color }

{ Hrite Chars Using Color }

{ Determine Monitor Type }

{ 2=Color. 3-Mono }

PROCEDURE DISPLAY_SCREEN(screen_name: STRBD; VAR file_existSS: BOOLEAN);
{ Display And Get An Item From Screen. See Detailed Desription In Manual }

PROCEDURE GETITEM(

COL.LIN,LEN : BYTE;
ITVPE : CHAR;
VAR HITEM : STRBD;
PICT : STRBO;
ITEM_LOU.ITEM_HIGH : STRBD;
VAR RET : STRZ;
RETRIEVE : BOOLEAN:
FGR_COLOR.BGR_COLOR : BYTE
): EXTERN;

*)

PROCEDURE RET_STATUS:

{ Column. Line. Length }

{ Type- C. N. D. Y. M }

{ Variable Name }

{ Picture x. U. L. 9. B l }

{ Range - Numerics/Date Only}
{ Returned Code }

{ False-Disp Only. TruecGet }
{ Colors Foregr. Backgr }

{ Check Status Of Variable retSS and return a code in 'actionSS' & set 'varSS'
This procedure is called imnediately following GETITEM }

{ Input to this procedure:

when retSS is length 1 the values are any of the ASCII chars

1591.

when retSS is length 2 the values are uSS. lSS. puSS. pdSS. function keys
dSS. rSS
( See CONST Section For Meanings ) }
{ Output:
The following codes are returned in actionSS : nextSS. prevSS.
exitSS. staySS }
{ Based upon 'actionSS' this procedure will then set 'varSS' to an integer.
which represents the next item (variable ) to get. }
BEGIN
last_field_actionSS:=exitSS;
actionSS:=nextSS; { Initialize Action Code }
if retrieveSS then { Is retrieveSS TRUE? }
begin
if ord(retSS[D])=2 then { Is retSS length 2 ? }
begin
CASE retSS[2] of
{ Action to be taken depending on the last key pressed }
uSS. lSS: actionSS:=prevSS; { Up Key. Left Key }
dSS. rSS: actionSS:=nextSS; {Down Key. Right Key}
puSS: actionSS:s { Page Up }exitSS;
pdSS: actionSS:= { Page Down }exitSS;
f1$S.f2$S.f3$S.f4SS.fSSS.
f6$S.f7SS.fBSS.f9$S.fIOSS: actionSS:=staySS; { Function Key }
END { Case ret };
end else { retSS is length I }
begin
if retSSsescSS { Escape Key } then actionSS:=exitSS
end;
{ Any other key not in the above list will keep actionSS=nextSS }
end; {retrieveSS}
CASE actionSS of
staySS: ;
nextSS: begin
varSS:=varSS+l; if varSS>screen_fieldSS then varSS:=l;
if last_fieldSS and retrieveSS then actionSS:-last_field_actionSS
end;
prevSS: begin varSS:=varSS-I; if varSS<l then varSS:cscreen_fieldSS end;
exitSS: ;
END; { CASE }
END; {PROCEDURE RET_STATUS}

PROCEDURE GETNUM(

COL.LIN.LEN : BYTE; { Column. Line. Length }
ITYPE : CHAR; { Types C. N, D. Y. M }

VAR VITEM : REAL; { Numerci Variable Name }
PICT : STRBD; { Picture X. U. L. 9. 8 I }
ITEM_LOU.ITEM_HIGH : REAL; { Range - Numerics/Date Only}

VAR RET : STRZ; { Returned Code }
RETRIEVE : BOOLEAN; { False-Disp Only. TruezGet }
FGR_COLOR.BGR_COLOR : BYTE { Colors Foregr. Backgr }

I;

{ This Procedure converts numeric to string before calling GETITEM }
{ It then converts the result back to numeric }
VAR numSS.numloSS.numhiSS: STRBO; errorcodeSS.dec_posSS: INTEGER;

1£92

BEGIN
{ Get I of Decimal Positions }
dec_posSS:-ord(pict[O])-pos('.'.pict);
{ Convert item. low and high range to string }
STR(witem:O:dec_posSS.numSS);
STR(itam_low:0:dec_posSS.numloSS);
STR(itam_high:O:dec_posSS.numhiS$);
GETITEN(col.lin.len.itype.numSS.pict.numlDSS.numhiSS.

ret.retrieve.fgr_color.bgr_color);

{ Convert string to numeric item }
VAL(numSS. witem. errorcodeSS);

END; { GETNUM }

"*** Beginning of Include file MENU.UTL *’*“
{SI MENU.UTL}
const
IOerr: boolean - false;

type
strZSS . string[255];
str3 - string[3];
strB - string[8]:
strI -string[l];
char3 I array [1..3] of char;

var
{“ variables used for whatnext **}
Bscreen: integer;
response: char;
{** variables used for datasp.scr and the Exit screen **}
Option.Exit_Response: STRI;

{*‘ variables used in LOAD **}
DefaultDrive.
LoadYn.
SaveYn: STRI;
FileName: STRING[80];
FileExt: STR3;
bactive.
edit.
exit: boolean;
ub: integer;

Procedure INIT_VAR_UTIL;
(*‘ Procedure to initialize the above variables *‘}
Begin
FileExt:-'BIN';
Option:-'N';
Exit_Response:-'N';
getdir(O.DefaultDrive);
LoadYn:-'N';
Filename:-";
SaveYn:-'Y';

1533

bscreen:-1;

bactivez-false;

exit:-false;

editzsfalse;
End;

Procedure Flashup(f lash_comnand:$tr255):

{This procedure receives a string and performs a FLASHUP HINDOU function
to it. The write stmt. is the procedure which activates the flash

command.
See a FLASUP UINDOU manual (Software Bottling company)}

var tilde: char;

begin

gotoxy(1.l):

tilde:-chr(126);
write(tilde.flash_command.'/');
end;

{**************t*ittt*itttttiiiittttit*ittt*tifft****i**t**********t**.t******

* FUNCTION FreeDiskSpace uses M5005 and functions of MsDos to get the am-
* aunt of diskspace available on a disk. This function returns a negative
* number if no diskspace is available. It also returns a negative number
* if the drive bieng accessed is not available on the computer.

i

t

*

****t*********t****t******tt******t*titttttit*tfit*tfi*****i****fi*****tt***tttt}

FUNCTION FreeDiskSpace( filespec : str2 ) : real;

tYPe
regpack 8 record
ax. bx. cx. dx. bp. si. di. ds. es. flags : integer;
end;
var
regs : regpack;
drivenum.
colon : integer;
availclusters.
totalclusters.
bytespersect.
sectpercluster : real;
ch : char;
begin
drivenum ;. 0; {start with default drive}
colon :: pos( ”z'. filespec );
if colon <> 0 then begin {if drive in filespec then...)

ch :3 copy( filespec. colon-l. I );
drivenum :- ord( ch ) - 64;
end { if };
with regs do begin
ax :8 $3600;
dx :- drivenum;
msdos( regs );
if ax - SFFFF then begin {got an invalid drive}
freediskspace ;. -99999.0;

IlSl4

end { then }
else begin
availclusters :- bx;
totalclusters :- dx;
bytespersect :- cx;
sectpercluster :- ax;
freediskspace :- bytespersect * sectpercluster * availclusters;
end; { if I:
end { with };
end; { FreeDiskSpace }

{tfitfitttttttttttfifitttfittfitfit*tfifttttt*fiiittfifiOttiittfttifitflfttttttifit

‘ PROCEDURE LineZS writes the string-parameter passed to it on the *
* 25th line of the screen. *
itfifittfififiifitiit.ttttfitt......ttfittfitit.tittttitfitffititttttttfifiittfittl
procedure Line25(bottom : strBD);
BEGIN

gotoxy(l.25);

clreol;

gotoxy(((BO-length(bottom)) div 2).25);

write(bottam);
END;

{tittttifii*tttttttttt*9.it...itit...*ttfit*ttfitttttttitttfitttttttttit!
* PROCUDURE Presskey loops until the user presses any key. '
* fi

*it.*t*ttit*****tttfifiittitttti...!!!tiff...ttfifi*fittitttttfittfitttittt}

procedure Presskey;
VAR

cmd : char;

test : boolean;

BEGIN
repeat
if keypressed then testz-true;
until test;
read(kbd.cmd);
END;

{tfififitttifitttttt*tit!tit*tttititt*ttttittttititttttiiitfittittttittfitt

‘ PROCEDURE Continue writes a message on line 25 by calling the ’
* procedure Line25. It then waits for a response by calling the *
* procedure Presskey. *
tifittfi*§§.**ittittii*titttttttfitttttfitififititttitttt*fiii*itifiiifttfiit}
procedure Continue;
BEGIN

Line25('Press any key to continue...');

Presskey;

gotoxy(l.25);

clreol;
END:

{fiftittfittfiitttttttfit*ttttttttfitttfitttttti*I'ttti'tttttttt'itifittttt

11955

* PROCEDURE Function_Key_Pressed awaits the user input and then *
* emulates the pressing of <RETURN>. *

fittfitttttttttitit.t*******t*ititt9**fiittittitfitt****t*i*******i****}

procedure Function_Key_Pressed(VAR retSS:Str2);

VAR
answerSS:string[1];

BEGIN
answerSS:sblankSS; .
GetItem(78.25.1.'C'.answerSS.'U'.".".retSS.TRUE.7.0);
if ord(retSS[O])=2
then
retSS:-retSS[2]
else
retSS:-blankSS;
END;

{**********************ttit*ti*ttitttitt*tt*******t***********i******

*

PROCEDURE IOcheck checks for a number of input/output errors as
listed below. In order for this procedure to be valuable the
Operating systems input/output error checking system must be
temporarily turned off. This is done by placing $I- in parenth
* before IOcheck is called. To turn the system back on use $I+.
* The procedure writes an error message on line 25 of the screen

and awaits user input to continue. *
****t*tfi*****************itRitttR**ttt*****t****************t*******t}

*
t
i

I i * d i I

*

procedure IOcheck(pass: integer);
VAR
IOcode:Integer;

BEGIN

IOcodez-IOresult;

IOerr:=(IOcode<>O);

If IOerr

then begin

Case IOcode of
$01 :Line25('File does not exist-Press any key to continue');
$02 :LineZS('File not open for input-Press any key to continue');
$03 :Line25('File not open for output-Press any key to continue');
$04 :Line25('File not open-Press any key to continue');
$10 :Line25('Error in numeric format-Press any key to continue');
$20 :line25('Operation not allowed on logical device-Press any key to continue');
$21 :Line25('Not allowed in direct mode-Press any key to continue');
$22 :Line25('Assign to standard files not allowed-Press any key to continue');
$9O :Line25('Record length mismatch-Press any key to continue');
$91 :Line25('Seek beyond end-of-file -Press any key to continue');
$99 :LineZSI'Unexpected end-of-file -Press any key to continue');
SFO :Line25('Disk write error-Press any key to continue');
$FI :Line25('Directory is full-Press any key to continue');
$F2 :Line25('File size overflow-Press any key to continue');
$FF :Line25('File disapeared-Press any key to continue')
else
Line25('Unknown I/O error-Press any key to continue');

15965

end; {case}
Presskey;{pressing of any key to continue}
end {then begin}
else begin
if (pass - 2)
then begin
Line25('File transfer successful-Press any key to continue');
Presskey;{pressing of any key to continue}
end;
end; {else if}
Line25(' 'I;
end;{of proc IOcheck}

{fit.tififiititttffiitt*ititfiiififittit!ifQt!ififiiiftfltttitfitifiitttfiitflitit

‘ Procedure Nelpscreen prints the the screen file HELP1.SCR *
* and NELP2.SCR to the monitor. if they exist. *

tfl.***i*t*t***it*fiiti*fitit.tittt*ttt*9t.***it*i**t**i*t***fi***ttttiff}

PROCEDURE Helpscreen;

BEGIN
DISPLAY_SCREEN('HELP1.SCR'.file_existSS); {display screen}
if not file_existSS then
BEGIN
gotoxy(I.I);
write('Missing screen Helpscreen');
end;
Continue;
DISPLAY_SCREEN('HELP2.SCR'.file_existSS); {display screen}
if not file_existSS then
BEGIN
gotoxy(I.I);
write('Missing screen Helpscreen');
end;
Continue:
screenSS:-IOO;
end; {Helpscreen}

{ *Qfiiitifftiitttfittt*tittttiitttfitii*tttfflifi.*tt*t*fi*ii*****fifltittit

* Print: This routine is to be used to emulate the pressing of ‘
* the <SHIFT> and <PrtSc> keys. It works by using the system *
* interupt number five (5) in memory location hex COOS. *

tiff...****i**i**tt**t**tt**ttfit*ttifl*fitttfittfit*tttfitttttttitittt*ti}

overlay procedure Print;
TYPE
regpack u record
ax.bx.cx.dx.bp.si.di.ds.es.flags: integer;

END;
VAR
recpack : regpack; {assign record}
BEGIN
intr($cdDS.recpack); {call interrupt}

END;

1597'

{t***t**it*********t****************t*******************i*****t*******it****

* PROCEDURE EXITSCREEN DETERMINES IF USER ACTUALLY INTENDED ON EXITING THE *
* PROGRAM-IF NOT ALLOHS USER TO RETURN TO PREVIOUS SCREEN. *

***t****************t*tttttit***i*t*********ittttt***t*********t**********t}

overlay procedure ExitScreen;

Begin
exit_response:-'N';
flashup('S-exit');
gotoxy(1.25);
clreol;
GETITEM(63.9.1.'Y'.EXIT_RESPONSE.
'U'.".".retSS.TRUE.O.7);
if (Exit_Response = 'Y') then
begin
exitzs true; { Exits the program }
textcolor(15);
textbackground(0);
clrscr;
end;
END; { PROCEDURE ExitScreen }

{****************tttt**********titt**********************************

*

PROCEDURE HhatMenu functions to allow smooth passage from one *
* screen to another. It also allows editing of input values. calls *
* the procedure Print to print the screen bieng viewed to a print- *
* er. allows exiting from the program and calls the Help Screens. *

************it*ttfi**********t****************t**************tt******}

PROCEDURE UhatMenu(VAR screenzinteger; var bactive: boolean; var ub: integer);

VAR
retSS: StrZ;

BEGIN
Line25(' F1=Help F2=Prev. Screen F3=Edit F7=Print F8=Next Screen ‘);
if (not bactive)
then ubz-l;
repeat
Function_Key_Pressed(retSS);
CASE retSS[I] of
flss: Helpscreen;
f2ss: if (screen > 1)
then screen:=screen-l { previous screen }

else begin
bactive:- false;
screen:=l;
end;

f3ss: edit:- true;

f7ss: Print;

sts: if (screen < Ub)
then screenz-screen+l {next screen}
else begin

119E!

bactive:-false;
screenzsl;
end;
else Beep(true);
END;
until retSS[1] in [flss.fZSs.f3$s.sts];
if retSS[1]<>f3ss
then editzsfalse;

gotoxy(1.25);
clreol;
END;

{******************t****fi****t******tttt*****fi***********fi***t***t***

* Procedure Directory checks the specified defaultdrive for the ‘
files with the given file extension. The procedure uses MsDos to *
search and find the specified files. The procedure then prints the*
file names on the screen. This procedure was taken from the Turbo *

Tutor manual section 20-4 *
*******t***tttiitt*tttttt*iititttttiiititttttt****t******t***t******}

fiifii

procedure Directory(DefaultDrive:StrZ;
fileextzstr3;

ymin.ymax:integer);

TYPE
CharIZarr = array [ I..12 ] of Char;
StringZO = string[ 20 ];
RegRec =
record

AX. BX. CX. DX. BP. SI. DI. 05, ES. Flags : Integer;
END;

VAR
Regs : RegRec;
DTA : array [ I..43 ] of Byte;
Mask : CharlZarr;
NamR : StringZO;
Error. 1. X. Y : Integer;
originaldirectory : StrBO;
ext : char3;

BEGIN { main body of PROCEDURE Directory }
for i:- 1 to 3 do
ext[i]:= fileext[i];

FillChar(DTA.SizeOf(DTA).O); { Initialize the DTA buffer }
FillChar(Mask.SizeOf(Mask).O); { Initialize the mask }
FillChar(NamR.SizeOf(NamR).O); { Initialize the file name }
getdir(O.originaldirectory); { Get the original directory}
Chdir(DefaultDrive);

Regs.AX :- SIAOO; { Function used to set the DTA }

119$!

Regs.DS :- Seg(DTA); { store the parameter segment in OS }
Regs.Dx :- Ofs(DTA); { ” ” ” offset in DX }
MSDos(Regs); { Set DTA location }

Error ;. D;
Mask :- '7???????.???'; { Use global search }

mask[10] :- ext[1];
mask[ll] :- ext[2];
mask[12] :- ext[3]:

Regs.AX :- $4EOO: { Get first directory entry }

Regs.DS :- Seg(Mask); { Point to the file Mask }

Regs.DX :- Ofs(Mask);

Regs.CX :- 22; { Store the Option }

MSDos(Regs); { Execute MSDos call }
{IOcheck;}

Error :- Regs.AX and SEE; { Get Error return }

I :- I; { initialize 'I' to the first element }
xz-S;
yt'ymin;
if (Error e D) then
repeat
NamR[I] ;- Chr(Mem[Seg(OTA):Ofs{DTA)+29*I]);
I :- I + 1:
until not (NamR[I-I] in [' '..'~']) or (I>ZO);

NamR[O] :- Chr(I-I); { set string length because assigning }
{ by element does not set length }

gotoxy(x.y):
write(namr);

x:-x+15;

while (Error - 0) do BEGIN

Error ;. O;
Regs.AX ;. $4FOO; _ { Function used to get the next }
{ directory entry }
Regs.CX :- 22; { Set the file Option }
MSDos( Regs ); { Call MSDos }
Error :- Regs.AX and SFF; { get the Error return }
I :- I;
repeat
NamR[I] ;. Chr(Mem[Seg(DTA):Ofs(DTA)+29+I]);
I :- I + 1;

until not (NamR[I-1] in [' '..'~'] ) or (I > 20);
NamR[D] :- Chr(I-I);
if (Error - D)
then
BEGIN
if x>65
then
BEGIN
x:-5;
y==y+1:

ZADCI

END:
if y>ymax
then
BEGIN
Line25('Press any key to continue ..');
presskey;
window(2.ymin.79.ymax):
clrscr:
window(I.I.BO.25):
xz-S;
y:=ymin;
END:
gotoxy(x.y):
write(namr);
x:-x+15:
END;
END:
chdir(originaldirectory);
END; { of PROCEDURE Directory }

{**********fi***i*****t****t******t****************fit*****t**********ti

* Procedure LoadBEV gets the specified file on the specified de- *
* fault drive and puts it into a record or variables. Conmands to *
* turn off the systems input/output error checking routine are found *
* in this procedure in order to keep fatal program terminating errors*
* from occuring. Procedure IOcheck replaces the systems I/O check. *
***t**********t****itttttti*********i*ttt*****t**********************}
overlay procedure LoadBEV(FileName:strBO;DefaultDrive:Str1);
{$I-} {turns off system I/D error check procedure}
VAR

BevFile: text;

Drive: StrZ;

OriginalDirectory: StrBO;

izinteger;

BEGIN
GetDir(O.originaldirectory);
Drive:-DefaultDrive+':';
ChDir(Drive);
FileName:-FileName+'.'+fileext;
assign(BevFile.FileName);
reset(BevFile);
IOcheck(I);
If not IOerr then
begin
{ REQUIRES READLN STATEMENTS OF UHAT I HILL LOAD }
{ ENDOGENOUS AND EXOGENOUS VARIABLES }

readln(BevFile.PCNT_OF_XPORTS[S]);
readln(BevFile.APCT[S].AHCT[S].PYIELD[S].CORNYT[S].UHTYT[S].GROSS[S]);
readln(BevFile.BAL[S].ARP[S].DIVERSION[S]);
readln(BevFile.DVPCN[S].DVRCN[S].DIVPAY[S].DPAY[S].DEFPAY[S].BEGINSTDCKS[S]):
readln(BevFile.MIN_FREE_STOCKS[S].ENO_STOCKS[S].PRODUCT[S].IMPORTS[S]);
readln(BevFile.TSUPPLY[S].ONEPH[S].TUOPH[S].THREEPH[S]);

12(11

readln(BevFile.FDURPH[S].FIVEPH[S].SIXPH[S]);
reedln(BevFile.SEVENPH[S].DANUM[S].COFNUM[S].OBCNUM[S].HPCNUM[S]);
reedln(BevFile.BRONUM[S].TURNUM[S].HDGNUM[S].DAIRYCDNS[S].CATDNFED[S]);
reedln(BevFile.DTHERBEEFCAT[S].CHICKENCDNS[S].BROILERCDNS[S].TURKEYCONS[S]):
reedln(BevFile.HDGCONS[S].OTHERCONS[S].FEEDT[S].HFCS[S].SEED’USE[S].FSI_USE[S]);
reedln(BevFile.DDMESTIC_USE[S].SETASIDE_ACRES[S].VCCN[S].VCHU[S].C_TARGETP[S]);
reedln(BevFiTe.CDRN_LOAN_RATE[S].PARREV[S].REVCDR[S].REVHHT[S].YEAR[S]);
readln(BevFile.YEARB[S]I;
readln(BevFile.CDRNPT[S].RFARMP[S].MILKP[S].HHTPT[S].BEFSTRP[S].BCDUP[S]);
reedln(BevFile.SMPT[S].NDGP[S].IPOP[S].IGDP[S].IPRO[S].IFP[S]);
readln(BevFile.IHA[S]);
reedln(BevFile.IUP[S].IGDPPC[S].IPROPC[S].INIPC[S].NXXRF[S]);
readln(BevFile.NXXRH[S]);
reedlnIBevFile.NMXRF[S].NMXRU[S].CPISB[S].CPIDM[S].CPILD[S].CPINI[5]);
readln(BevFile.CPILD[S]);
readln(BevFile.CPIBR[S].CPIAR[S].CPIAU[S].CPICA[S]);
reedln(BevFile.CPICH[S].CPIT[S].GDPCH[S]);
reedln(BevFile.GDPSB[S].GDPDM[S].GDPLO[S].GDPNI[S].GDPLD[S].GDPBR[S]);
readln(BevFile.GDPAR[S]); '
readln(BevFile.GDPAU[S].GDPCA[S].FP[S].HP[S].EYIELD[S].EFREV[S]);
readln(BevFile.EUREV[S]);
readln(BevFile.UYAR[S].UYAU[S].HYCA[S].FYAR[S].FYAU[S].FYCA[S]);
readln(BevFile.XRAR[S].XRAU[S]);
readln(BevFile.XRCA[S].E_END_STK[S].E_NET_EXPORT[S].EMA[S].ECON[S].ECONPC[S]);
readln(BevFile.EES[S].EESPC[S].ESPLYPC[S].EPRO[S].EPROPC[S]);
readln(BevFile.EUDPC[S]);

readln(BevFile.ENE[S].EXPORTS[S].EGDP[S].EPDP[S]);
readln(BevFile.EUYIELD[S]);
reedln(BevFile.TOTAL_USE[S].PERCENT_USE[S].CCCSTOCKS[S].DCCCSTDCKS[S]);
readln(BevFile.FORSTOCKS[S].DFORSTOCKS[S].FREESTOCKS[S].GDVTSTOCKS[S]):
readln(BevFile.POLICY_STOCKS[S]);

readln(BevFile.DNE[S].TUD[S].THREE[S]);
readln(BevFile.FOUR[S].FIVE[S].SIX[S]);
readln(BevFile.SEVEN[S].EIGHT[S].RESIDUAL[S].FUEL_USE[S]);
readln(BevFile.INI_IN_BU[S].ECON_IN_BU[S].EES.IN_BU[S]);
readln(BevFile.ESPLY_IN_BU[S].ENE_IN_BU[S]);

readln(BevFile.DV73.DV82.DV83.DV84.DV740N);
readln(BevFile.YEARIB.YEARZB.YEAR38.YEAR4B);
readln(BevFile.CHHT.SBUT.DMUT.LOUT.NIUT);
readln(BevFile.LDUT.BRUT.ARHT.AUUT.CAUT);
readln(BevFile.FPLR.FPLR2.FPLR3);
readln(BevFile.TRIGGERP);

{ ADD FACTOR VARIABLES }
readln(BevFile.APCT_AF[S].AMCT_AF[S].CORNYT_AF[S].ONEPM_AF[S].TUOPN_AF[S));
readln(BevFile.THREEPH_AF[S]);
readln(BevFile.FOURPH_AF[S]);
readln(BevFile.FIVEPH_AF[S].SIXPH_AF[S].SEVENPH_AF[S].EIGMT_AF[S].INIPC_AF[S]);
readln(BevFile.EYIELD_AF[S]);
readln(BevFile.EUYIELD_AF[S].EHA_AF[S].ECONPC_AF[S].EESPC_AF[S].DCCCSTOCKS_AF[S]);
readln(BevFile.DFORSTOCKS.AF[S].FREESTOCKS_AF[S]);

{ OVERRIDE VARIABLES }

12(12

readln(BevFile.CDRNYT_DR[S].APCT_DR[S].AHCT_DR[S].BEGINSTDCKS_DR[S].PRDDUCT_DR[S]);
readln(BevFile.TSUPPLY_OR[S]);
readln(BevFile.IMPDRTS_OR[S].ONEPM_OR[S].DAIRYCONS_OR[S].TUOPM_DR[S].CATONFED_OR[S]);
readln(BevFile.TNREEPH_OR[S]);
readln(BevFiIe.DTHERBEEFCAT_DR[S].FDURPH_DR[S].CHICKENCDNS_DR[S].FIVEPH_DR[S]);
readln(BevFile.BROILERCONS_OR[S]);
readln(BevFile.SIXPN_OR[S].TURKEYCONS_OR[S].SEVENPM_DR[S].MOGCONS_OR[S]);
readln(BevFile.OTHER_UNALLOC_OR[S]);

readln(BevFile.FEEDT_OR[S].DOMESTIC_USE_OR[S]);

readln(BevFile.INI_IN_BU_OR[S].ESPLY IN Bu OR[S].ECON_IN_BU_OR[S]);

readln(BevFile.EES_IN_BU_OR[S].ENE_IN_BU_OR[S]);
readln(BevFile.EXPORTS_OR[S].TOTAL_USE_OR[S].CCCSTOCKS_OR[S].FORSTOCKS_DR[S]);
readln(BevFile.FREESTOCKS_OR[S]);

readln(BevFile.ENDSTOCKS_OR[S]);

‘ { FLAG VARIABLES FOR OVERRIDE VALUES }
readln(BevFile.CORNYT_FLAG);
readln(BevFile.APCT_FLAG);
readln(BevFile.AHCT_FLAG);
readln(BevFile.BEGINSTDCKS_FLAG);
readln(BevFile.PRODUCT_FLAG);
readln(BevFile.TSUPPLY_FLAG);
readln(BevFile.IMPORTS_FLAG);
readln(BevFile.FEEDT_FLAG);
readln(BevFile.DOMESTIC_USE_FLAG);
readln(BevFile.INI_IN_BU_FLAG);
readln(BevFile.ESPLY_IN_BU_FLAG);
readln(BevFile.ECON_IN_BU_FLAG);
readln(BevFile.EES_IN_BU_FLAG);
readln(BevFile.ENE_IN_BU_FLAG);
readln(BevFi le. EXPORTS_FLAG) ;
readln(BevFile.TOTAL_USE_FLAG);
readln(BevFile.CCCSTOCKS_FLAG);
readln(BevFile.FORSTOCKS_FLAG):
readln(BevFile.FREESTDCKS_FLAG);
readln(BevFile.ENDSTOCKS_FLAG);

IOcheck(Z);
end;
close(BevFile);

{81+} {turns system I/O error check back on}
ChDir(originaldirectory);
END;

{eneeeeeeeeeteeeeeeeeeeeneeeeeeeeeeeeeeeeeeemeeneeeeeeenemeeee

‘ Procedure SaveBEV functions the same as Load exept that SaveBEV *
* creates and writes to a specified file. *

"it‘tfltttfitflfifififliifltt.*tttfiti*ttitif“itimtftflittflfittttttttttt}

overlay procedure SaveBEV(FileName:str80;DefaultDrive:StrI):

VAR
BevFile: text;
Drive: StrZ;

.2()3

originaldirectory: StrBO;
fileexists :boolean;
overwriteyn :strI;
izinteger;

BEGIN
fileexists:-false; _
GetDir(O.originaldirectory);
Drive:-DefaultDrive+':';
ChDir(drive);
FileName:-FileName+'.'+fileext;
{$I-} {turns system I/O error trapping procedure off}
assign(BevFile.FileName);
reset(bevfile);
if IOresult-O
then begin {file exists}
Line25('The file exists. Do you wish to OVERHRITE it? (Y/N)');
GETITEM (75.25.I.'Y'.overwriteyn.
'U'.".".retSS.true.O.7);
if overwriteyn-‘N'
then begin
fileexistsz-true:
close (BEVFILE);
end;
end;
if (not fileexists)
then begin
rewrite(BevFile);
IOcheck(I):
if not IOerr then
begin
{ REQUIRES URITELN STATEMENTS FOR UHAT HILL BE SAVED }
{ ENDOGENOUS AND EXOGENOUS VARIABLES }

writeln(BevFiIe.PCNT_OF_XPORTS[S]:9:S);
writeln(BevFiTe.APCT[S]:9:2.AHCT[S]:9:2.PYIELD[S]:9:2.CDRNYT[S]:9:2.HHTYT[S]:9:2.GRDSS[S]:9:2):
writeln(BevFile.BAL[S]:9:3.ARP[S]:9:3.DIVERSIDN[S]:9:3);
writeTn(BevFiIe.DVPCN[S]:9:2.DVRCN[S]:9:3.DIVPAY[S]:9:3.DPAY[S]:9:2.DEFPAY[S]:9:3.BEGINSTDCKS[S]:9:2
):

writeln(BevFiIe.MIN_FREE_STDCKS[S]:9:2.END_STDCKS[S]:9:2.PRDDUCT[S]:9:2.IMPDRTS[S]:9:2);
writeln(BevFile.TSUPPLY[S]:9:2.0NEPH[S]:9:3.TUOPH[S]:9:3.THREEPH[S]:9:3);
writeln(BevFile.FOURPN[S]:9:3.FIVEPH[S]:9:3.SIXPH[S]:9:3);
writeln(BevFile.SEVENPH[S]:9:3.DANUM[S]:9:3.COFNUM[S]:9:3.0BCNUM[S]:9:3.HPCNUM[S]:9:3);
writeln(BevFiTe.BRDNUM[S]:9:3.TURNUM[S]:9:3.HDGNUM[S]:9:3.DAIRYCDNS[S]:9:2.CATDNFED[S]:9:2);
writeln(BevFile.DTHERBEEFCAT[S]:9:2.CHICKENCONS[S]:9:2.BRDILERCDNS[S]:9:2.TURKEYCDNS[S]:9:2);
writeln(BevFiTe.HDGCDNS[S]:9:2.0THERCONS[S]:9:2.FEEDT[S]:9:2.HFCS[S]:9:2.SEED_USE[S]:9:2.FSI_USE[S]:
9:2);
writeln(BevFile.DDMESTIC_USE[S]:9:2.SETASIDE_ACRES[S]:9:2.VCCN[S]:9:2.VCHH[S]:9:2.C_TARGETP[S]:9:2);
writeln(BevFile.CORN_LOAN_RATE[S]:9:2.PARREV[S]:9:2.REVCOR[S]:9:2.REVUHT[S]:9:2.YEAR[S]:3:O);
writeln(BevFile.YEARB[S]:3:O);
writeln(BevFile.CORNPT[S]:9:2.RFARMP[S]:9:2.MILKP[S]:9:2.UHTPT[S]:9:2.BEFSTRP[S]:9:2.BCOUP[S]:9:2);
writeln(BevFiTe.SMPT[S]:9:2.HDGP[S]:9:Z.IPOP[S]:ll:2.ISDP[S]:11:B.IPRD[S]:II:2.IFP[S]:9:3);
writeln(BevFile.IHA[S]:II:2);
writeln(BevFiIe.IHP[S]:9:3.IGDPPC[S]:II:6.IPROPC[S]:11:6.INIPC[S]:11:3.NXXRF[S]:9:6);

12(14

writeln(BevFile.NXXRU[S]:9:6);
writeln(BevFile.NMXRF[S]:9:G.NMXRU[S]:9:6.CPISB[S]:9:3.CPIDM[S]:9:3.CPILO[S]:9:3.CPINI[S]:9:3);
writeln(BevFile.CPILD[S]:9:3);
writeln(BevFile.CPIBR[S]:9:3.CPIAR[S]:Il:2.CPIAU[S]:11:2.CPICA[S]:Il:2);
writeln(BevFile.CPICH[S]:9:3.CPIT[S]:9:3.GDPCH[S]:9:6);
writeln(BevFile.BDPSB[S]:9:6.GDPDM[S]:9:5.GDPLO[S]:9:6.GDPNI[S]:9:B.GDPLD[S]:9:6.GDPBR[S]:9:3);
writeln(BevFile.GDPAR[S]:9z3);
writeln(BevFile.BDPAU[S]:9:6.GDPCA[S]:9:5.FP[S]:9:3.HP[S]:9:3.EYIELD[S]:9:6.EFREV[S-I]:9:6);
writeln(BevFile.EUREV[S-I]:9:G);
writeln(BevFile.UYAR[S]:II:5.UYAU[S]:11:5.UYCA[S]:11:5.FYAR[S]:II:5.FYAU[S]:II:5.FYCA[S]:II:5);
writeln(BevFile.XRAR[S]:II:5.XRAU[S]:11:5);
writeln(BevFile.XRCA[S]:7:5.E_END_STK[S]:9:2.E_NET_EXPDRT[S]:9:2.EHA[S]:11:2.ECDN[S]:Il:2.ECONPC[S]:
11:3);

writeln(BevFile.EES[S]:II:2.EESPC[S]:II:6.ESPLYPC[S]:11:6.EPRO[S]:II:2.EPROPC[S]:9:6);
writeln(BevFile.EUDPC[S]:Ilz6);
writeln(BevFile.ENE[S]:II:Z.EXPDRTS[S]:9:2.E6DP[S]:II:5.EPDP[S]:II:2);
writeln(BevFile.EUYIELD[S]:9:6);
writeln(BevFiTe.TOTAL_USE[S]:9:2.PERCENT_USE[S]:9:2.CCCSTDCKS[S]:9:2.DCCCSTDCKS[S]:9:2);
writeIn(BevFiIe.FDRSTDCKS[S]:9:2.DFDRSTDCKS[S]:9:2.FREESTDCKS[S]:9:2.GOVTSTOCKS[S]:9:2);
writeln(BevFile.POLICY_STOCKS[S]:9:2);

writeln(BevFile.ONE[S]:9:3.TUO[S]:9:3.THREE[S]:9:3);
writeln(BevFile.FOUR[S]:9:3.FIVE[S]:9:3.SIX[S]:9:3);
writeln(BevFile.SEVEN[S]:9:3.EIGHT[S]:9:3.RESIDUAL[S]:9:2.FUEL_USE[S]:9:2);
writeln(BevFile.INI_IN_BU[S]:9:2.ECON_IN_BU[S]:9:2.EES_IN_BU[S]:9:2);
writeln(BevFile.ESPLY_IN_BU[S]:9:2.ENE_IN_BU[S]:9:2);

writeln(BevFile.DV73:9:2.DV82:9:2.0V83:9:2.0V84:9:2.0V740N:9:2);
writeln(BevFile.YEARIB:9:2.YEARZB:9:2.YEAR3B:9:2.YEARAB:9:2);
writeln(BevFile.CMHT:9:4.SBHT29:4.DMUT:9:4.LOUT:9:4.NIUT:9z4);
writeln(BevFile.LDHT:9z4.BRUT:9:4.ARHT:9:4.AUHT:9:4.CAUT:9:4);
writeln(BevFile.FPLR:9:2.FPLR2:9:2.FPLR3:9:2);
writeln(BevFile.TRIGGERP:9:2);

{ ADD FACTOR VARIABLES }
writeln(BevFile.APCT_AF[S]:9:3.AHCT_AF[S]:9:3.CORNYT_AF[S]:9:3.0NEPH_AF[S]:9:3.TVOPH_AF[S]:9:3);
writeln(BevFile.THREEPM_AF[S]:9:3);
writeln(BevFile.FOURPH_AF[S]:9:3);
writeln(BevFile.FIVEPH_AF[S]:9:3.SIXPH_AF[S]:9:3.SEVENPH_AF[S]:9:3.EIGHT_AF[S]:9:3.INIPC_AF[S]:9:5);
writeln(BevFile.EYIELD_AF[S]:9:3);
writeln(BevFiIe.EUYIELD_AF[S]:9:3.EHA_AF[S]:Il:3.ECDNPC_AF[S]:9:5.EESPC_AF[S]:9:5.DCCCSTDCKS_AF[S]:9
:3):
writeln(BevFile.DFDRSTOCKS_AF[S]:9:3.FREESTDCKS_AF[S]:9:3);

{ OVERRIDE VARIABLES }
writeln(BevFiTe.CORNYT_OR[S]:9:2.APCT_DR[S]:9:2.AHCT_DR[S]:9:2.BEGINSTDCKS_DR[S]:9:2.PRODUCT_OR[S]:9
:2):
writeln(BevFile.TSUPPLY_OR[S]:9:2);
writeln(BevFile.IMPORTS_OR[S]:9:2.0NEPH_OR[S]:9:3.DAIRYCONS_OR[S]:9:2.TUOPH_OR[S]:9:3.CATONFED_OR[S]
:9:2);
writeln(BevFile.THREEPH_OR[S]:9z3);
writeln(BevFile.DTHERBEEFCAT_DR[S]:9:2.FDURPH_DR[S]:9:3.CHICKENCDNS_DR[S]:9:Z.FIVEPH_OR[S]:9z3);
writeln(BevFile.BROILERCONS_OR[S]:9:2);
writeln(BevFiTe.SIXPH_DR[S]:9:3.TURKEYCDNS_DR[S]:9:2.SEVENPH_DR[S]:9:3.HDGCDNS_OR[S]:9:2);
writeln(BevFile.OTHER_UNALLOC_0R[S]:9:2);

21355

writeln(BevFiTe.FEEDT_DR[S]:9:2.DOMESTIC_USE_DR[S]:9:2);
writeln(BevFile.INI_IN_BU_OR[S]:9:2.ESPLY_IN_BU_OR[S]:9:2.ECON_IN_BU_OR[S]:9:2);
writeln(BevFile.EES_IN_BU.OR[S]:9:2.ENE_IN_BU_OR[S]:9:2);
writeln(BevFiIe.EXPORTS_DR[S]:9:2.TDTAL-USE_DR[S]:9:2.CCCSTDCKS_DR[S]:9:2.FDRSTDCKS_DR[S]:9:2);
writeln(BevFile.FREESTOCKS_OR[S]:9:2);

writeln(BevFile.ENDSTOCKS_OR[S]:9:2);

{ FLAG VARIABLES FDR OVERRIDE VALUES }
writeln(BevFile.CORNYT_FLAG);
writeln(BevFile.APCT_FLAG);
writeln(BevFile.AHCT_FLAG);
writeln(BevFile.BEGINSTOCKS_FLAG);
writeln(BevFile.PRODUCT_FLAG):
writeln(BevFile.TSUPPLY_FLAG);
writeln(BevFile.IMPORTS_FLAG);
writeln(BevFile.FEEDT_FLAG);
writeln(BevFile.DOMESTIC_USE_FLAG);
writeln(BevFile.INI_IN_BU.FLAG);
writeln(BevFile.ESPLY_IN_BU_FLAG);
writeln(BevFile.ECON_IN_BU_FLAG);
writeln(BevFile.EES_IN_BU_FLAG);
writeln(BevFile.ENE_IN_BU_FLAG);
writeln(BevFile.EXPORTS_FLAG);
writeln(BevFile.TOTAL_USE_FLAG);
writeln(BevFile.CCCSTOCKS_FLAG);
writeln(BevFile.FORSTOCKS_FLAG);
writeln(BevFile.FREESTOCKS_FLAG);
writeln(BevFile.ENDSTOCKS_FLAG);

IOcheck(Z):
end;
close(BevFile);

end;

{51+} {turns system error trapping back on}

ChDir(originaldirectory);

Line25(' ');
END;

{******fi*tfi*****i************t*****ttitttittiittifi*ti****************

* PROCEDURE CheckDisk checks if the specified defaultdrive is a *
* legal drive. If it is it will call the Procedure Directory and *
* continue normally. Else the procedure will write an error mes- *

t
’ age.
it**********i****************************tt***t************t*******t}

overlay procedure CheckDisk(defaultdrive:strl);
Var drive: Str2;

result:real;

temp: Strl;

Begin {checkdisk}
drive:-defaultdrive+':';
result:-freediskspace(drive);
GetDir(O.temp):

12(M6

if((result<-O)or(defaultdrive>'E'))and((result<-O)or(defaultdrive<>temp))
then begin
Line25('Disk Full/Invalid Drive-press any key to contiue');
Presskey;
gotoxy(l.25);
clreol;
if (retrieveSS) then varSS:-screen_fieldSS;
end
else
if (retrieveSS) then Directory (drive.fileext.B.16);
end;{checkdisk}

{overlay Procedure end_on_line_help;}

{ begin}

{ flashup('C-ALL');}

{ flashup('K-CLEAR');}

{ if retrieveSS then Line25('Press <ESC> for Comand Line');}
{ end:}

{overlay Procedure on_line_help(window_name: strB);}

(*i begin}

{ if retrieveSS}

{ then begin}

{ flashup('K-{FIO}.'+window_name);}

{ Line25('On line help available. Press <FIO>');}
{ end:}

{ end:}*)

***** End of Include file MENU.UTL *****
*'**‘ Beginning of Include file INITIAL.INC *****
{$1 initial.inc}
overlay procedure initial; { A PROCEDURE TO INITIALIZE ALL VARIABLES TO ZERO }
begin
for izsl to 4 do
begin

PCNT_OF_XPORTS[i]:=0.0;

APCT[i]:=O.D:

AHCT[i]:¢0.0;

PYIELO[i]:-0.0;

CORNYT[i]:=0.0;

UHTYT[i]:8O.D;

GROSS[i]:-0.0;

ARP[i]:-D.O;

DIVERSION[i]:=0.0;

BAL{i]:-0.0:

DVPCN[i]:=0.0;

DVRCN[i]:-O.D;

DIVPAY[i]:-D.O;

DPAY[i]:-D.O;

DEFPAY[i]:=O.D;

BEGINSTOCKS[i]:-0.0;

MIN_FREE_STOCKS[i]:-O.D;

END_STOCKS[i]:=0.0;

PRODUCT[i]:=0.0;

2(17

IMPORTS[i]:-0.0:
TSUPPLY[i]:-D.D;
ONE[i]:=0.0:
DNEPH[i]:=O.D;
THO[i]:=0.0;
THOPH[i]:=0.0;
THREE[1]:=D.D:
THREEPH[i]:-O.D;
FDUR[i]:80.D;
FOURPH[i]:-0.0;
FIVE[i]:=0.0:
FIVEPH[1]:-O.D;
SIX[i]:s0.0:
SIXPH[I]:'D.D:
$EVEN[i]:RD.D:
SEVENPH[i]:=0.0;
EIGHT[i]:=0.0;
DANUM[i]:-0.0:
CDFNUM[1]:'D.D;
DBCNUM[i]:=0.0;
HPCNUM[i]:-0.0;
BRDNUM[1]:=D.D;
TURNUM[i]:=O.D;
HDGNUM[i]:=D.D;
DAIRYCONS[i]:=D.D;
CATDNFED[1]:=D.D;
DTHERBEEFCAT[T]:=D.D;
CHICKENCONS[i]:=O.D;
BROILERCONS[i]:=D.D;
TURKEYCDNS[i]:=D.D;
HOGCONS[i]:=0.0:
DTHERCDNS[1]:=O.D;
FEEOT[i]:=0.0;
HFCS[i]:=O.D;
SEED_USE [i] :=0.0:
RESIDUAL[i]:-D.O;
FUEL_USE [1] :sD.O;
FSI_USE[i] :-0.0:
DOMESTIC_USE [i] :-0.0;
SETASIDE_ACRES[i]:=0.0;
VCCN[i]:*D.D:
VCHU[i]:=D.D;
C_TARGETP[i]:=0.0:
CORN_LOAN_RATE [i] ;=0 . o:
PARREV[i]:=O.D:
REVCOR[i]:=0.0:
REVUHT[i]:-D.D;
YEAR[i]:-0.0;
YEARB[1]:=D.D;
CDRNPT[I]:=D.D;
RFARMP[i]:=D.D;
MILKP[i]:=O.D:
HHTPT[i]:=0.0:
BEFSTRP[i]:-0.0;

BCDHP[i]:'D.O;
SMPT[i]:=0.0;
HOGP[i]:-0.0:
IPOP[i]:-0.0:
IGDP[1]:8D.D;
IHA[i]:8O.O;
IYIELD[1]:-D.D;
IPRD[1]:BD.D;
IFP[i]:-0.0:
IHP[i]:=0.0;
IGDPPC[i]:8O.O;
IPRDPC[i]:=O.D;
INIPC[i]:-0.0;

INI_IN_BU[i]:=0.0;

NXXRF[i]:-O.D:
NXXRH[i]:-D.
NMXRF[i]:-O.
NMXRH[1]:'D.
CPISB[i]: O.
CPIDM[1]:IO.
CPILO[i]: O.
CPINI[i]: O.
CPILO[i]: D.
CPIBR[1]:=O.
CPIAR[i]:=O.
CPIAU[i]: O.
CPICA[i]: O.
CPICH[i]:=O.D:
CPIT[1]:=O.D:
GDPCH[i]:'D.D:
GDPSB[i]:=D.D:
GDPDM[i]:-0.0;
GDPLD[i]:-O.D;
GDPNI[i]:-0.0;
GDPLD[i]:-D.D;
GDPBR[i]:-0.0;
GDPAR[i]:-0.0:
GDPAU[1]:'O.D;
GDPCA[i]:-O.D;
FP[i]:-0.0:
UP[i]:-0.0:
EYIELD[i]:-D.O:
EFREV[l]:-D.D:
EHREV[i]:IO.O;
NYAR[i]:-0.0:
UYAU[i]:=D.D;
HYCA[i]:-D.O;
FYAR[i]:-D.O;
FYAU[i]:t0.0;
FYCA[i]:-0.0;
XRAR[i]:-0.0:
XRAU[i]:=0.0;
XRCA[i]:-0.0;

I
Ce .0 .e

OOOOOOOOOOOO

E_END_STK[i]:-0.0;

.2(18

2(39i

E_NET_EXPORT [i] ;-O.O;
EHA[i]:=D.Di
ECDN[i]:-D.D;
ECON_IN_BD[ i] ;-o . o;
ECONPC[i]:-0.0;
EES[i]:-0.0;
EES_IN_BU[i]:-0.0;
EESPC[i]:-0.0;
ESPLY_IN_BU[i] :-0.0;
ESPLYPC[i]:IO.O;
EPRO[i]:-0.0;
EPROPC[i]:.D.D;
EUDPC[i]:-0.0;
ENE[i]:-0.0:
ENE_IN_BU[i] :-O.D:
EXPORTS[i]:-D.D;
EGDP[i]:-D.O:
EPOP[i]:-O.D:
ENYIELO[i]:=0.0:
TOTAL_USE[i]:=D.O;
PERCENT_USE[i]:-D.O;
CCCSTDCKS[i]:'D.0;
DCCCSTOCKS[i]:'D.D;
FORSTOCKS[i]:-0.0;
DFORSTDCKS[1]:=D.D;
FREESTDCKS[I]:=D.O;
GOVTSTOCKS[i]:=0.0;
PDLICY_STDCKS[i]:-O.D;
{ ADD FACTORS }
APCT_AF[i] ;-D.O;
AHCT_AF[i] :-0.0;
CORNYT_AF [i] :-D. o;
ONEPH_AF[i]:=0.0;
THOPH_AF[i] ;-o.o;
THREEPH_AF[i] ;=D.o;
FOURPH_AF [i] :-O.D;
FIVEPH_AF[i]:-D.D;
SIXPH_AF[i] :-o.o;
SEVENPH_AF[i]:-0.0;
EIGHT_AF[i] :-o.o;
INIPC_AF[i] :s0.0;
IYIELD_AF[i]:-0.0:
EYIELD_AF[i]:=0.0:
EHYIELD_AF[i]:-0.0;
EHA_AF[i] :-0.0;
ECONPC_AF[i]:-0.0;
EESPC_AF[i] ;-0.0;
DCCCSTOCKS_AF [i] :=0. 0;
DFDRSTDCKS_AF[i]:8D.D;
FREESTOCKS_AF[i] ;-O.o;
{ OVERRIDE VARIABLES }
CORNYT_OR[i] :-D.0:
APCT_OR[i] ;-0.0:
AHCT_OR[i] ;-O.o;

210

BEGINSTOCKS_OR[i]:=0.0;
PRODUCT_OR[i]:-0.0:
TSUPPLY_OR[i]:-D.O;
IMPORTS_OR[i]:-0.0;
ONEPH_OR[i]:-O.D:
DAIRYCONS_OR[i]:8O.O;
TUOPM_OR[i] :=O.D:
CATONFED_OR[i]:=0.0;
THREEPM_OR[i]:-O.D;
OTHERBEEFCAT_OR[i]:=0.0;
FOLRPH_OR[i] z-0.0:
CMICKENCONS_OR[i]:=0.0:
FIVEPH_OR[i]:-0.0;
BROILERCONS_OR[i]:=O.D;
SIXPH_OR[i]:-0.0:
TURKEYCONS_OR[i]:=O.D;
SEVENPH_OR[i]:-0.0;
HOGCONS_OR[i]:-O.D:
DTHER_UNALLDC_OR[i]:=D.D;
FEEDT_OR[i]:=0.0:
DDMESTIC_USE_OR[i]:=0.0;
INI_IN_BU_OR[i]:=D.O:
ESPLY_IN_BU_OR[i]:=0.0;
ECON_IN_BU_OR[i]:=0.0;
EES_IN_BU_OR[i]:=0.0;
ENE_IN_BU_OR[i]:=0.0;
EXPORTS_OR[i]:=O.D;
TOTAL_USE_OR[i]:=0.0;
CCCSTOCKS_OR[i]:=0.0;
FORSTOCKS_OR[i]:=0.0;
FREESTOCKS_OR[i]:-0.0:
ENDSTOCKS_OR[i]:=D.O;
end;
{ VARIABLES FOR THE RANDOM SECTION }
FOR I:-I TO 30 DO
BEGIN
ALPHA[I] :-O. 0:
ALPHA1[I]:-0.0:
ALPHA2[I]:-0.0:
END;
BETA:-0.0;
BETAI:-0.0:
BETA2:-O.D:
GAMMA:-0.0;
GAMMAI:'D.O;
GAMMA2:-0.0;
YIELD:-0.0;
YIELDl:-0.0:
YIELDZ:-0.0:
Z:=0.0;
21:-0.0;
22:-0.0;
DV73:-0.0;
OV82z-0.0;

 

211

DV83:=0.0:
DV84z-D.D:
DV740N:-D.O:
YEARIB:-0.0:
YEARZB:-0.0:
YEAR3B:-0.0:
YEAR4B:-0.0;
CHHT:=0.0;
SBUT:8O.O;
DMHT:=0.0;
LOHT:-0.0;
NIUT:=0.0;
LDUT:=0.0;
BRVT:=0.0;
ARUT:=0.0;
AUVT:=0.0;
CAVT:-0.0;
FPLR:-0.0:
FPLR2:-0.0:
FPLR3:-0.0:
TRIGGERP:-O.D:
{ MISCELLANEOUS VARIABLES }
FG:-15;
BG:=D;
flag:=O;
NYz-A;
CORNPT[4]:-3.DO:
REDUCE_PRICE:-0.03:
INFO:8'SOLVING':
end;
’**** End of Include file INITIAL.INC *****
***** Beginning of Include file RECALC.INC *****
{$1 RECALC.INC}
overlay procedure RE_CALCULATE; { THE FORECAST CALCULATIONS }

var conserved_acres: real; { A LOCAL VARIABLE FOR CONSERVING ACRES
(ARP + DIVERSION) }

procedure FREESTOCK_PROC;
{ A PROCEDURE TO CALCULATE THE FREE STOCKS BASED ON
HEIGHTS USED FOR POLICY }

VAR
CCCUT. FORHT: REAL:

BEGIN
{ RULE ccc HEIGHT FOR HEIGHT }
{ -------------------- }
{ 1) YEAR <- 75 0.10 0.0 }
{ 2) YEAR >- 77 0.80 0.75 }
{ 3) YEAR >- 77. CCCt <= CCCt-I. }
{ TRIGGER PRICE > 31.55 0.10 0.75 }
{ 4) YEAR >- 77. FORt <= FORt-I. }

22112
{ TRIGGER PRICE > $1.55 0.80 0.0 }

IF ( year[NY] <- 76 ) THEN CCCUT:- 0.1 ELSE CCCHT:- 0.8:

IF ( year[NY] >- 77 ) AND ( CCCstocks[NY] <- CCCstocks[NY-I] )
AND ( fplr > 1.55 ) THEN CCCUTzl 0.1;

IF ( year[NY] <- 76 ) THEN FORUTzs 0.0 ELSE FORUTz- 0.75:

IF ( FORstocks[NY] <2 FORstocks[NY-I] ) AND ( fplr > 1.55 ) THEN
FORUT:- 0.0;

FREESTOCKS[NY]:-IIZ7.423I — 1873.6898 * (( CCCVT * CCCSTOCKS[NY]
+ FORUT * FORSTOCKS[NY] ) / TOTAL_USE[NY] )
- 1301390 2 * ( FPLR / TOTAL_USE[NY] ) + FREESTOCKS_AF[NY];
IF FREESTOCKS[NY] < MIN_FREE_STOCKS[NY] THEN FREESTOCKS[NY]:-MIN-FREE_STOCKS[NY];

END; { AREA UHICH CALCULATES FUNCTION Fx FOR GIVEN HEIGHTS }

BEGIN
{ DOMESTIC SUPPLY }

{ APCT = f ( DV83. LAGGED CORN REVENUE(NON-PARTICIPANTS). HHEAT REVENUE.
LAGGED CORN REVENUE(PARTICIPANTS) }

CONSERVED_ACRES;=ARP[NY]+DIVERSION[NY];
IF APCT_FLAG-'N' THEN

APCT[NY]:- 90.993360 {-6.539 * dv73 + 4.04 * dv79} - 0.1262719 *
(((VHTPT[NY—1] * HHTYT[NY-1]) - VCHH[NY-1]) / CPIT[NY-I]) - 0.0281170 *
(((PARREV[NY-I] + DIVPAY[NY-I] + DEFPAY[NY-l]) - VCCN[NY—1]) / CPIT[NY—I])
- 60.333270 * CONSERVED_ACRES + APCT_AF[NY]

ELSE APCT[NY]:- APCT_OR[NY];

IF AHCT_FLAG='N' THEN
AHCT[NY];- -B.412272 + 0.8729 * APCT[NY] + 0.1078 * YEAR[NY] + AHCT_AF[NY]
ELSE AHCT[NY]:- AHCT_OR{NY];

IF CORNYT_FLAG-'N' THEN
CORNYT[NY]:= -7I.3758 + 2.153 * YEAR[NY] + CORNYT_AF[NY]
ELSE CORNYT[NY]:- CORNYT_OR[NY];

if year[ny]-85 then beginstocks[NY]:-164B.O
else BEGINSTOCKS[NY]:-END_STOCKS[NY-I]:

IF PRODUCT_FLAG-'N' THEN
PRODUCT[NY]:= AHCT[NY] * CORNYT[NY]
ELSE PRODUCT[NY]:IPRODUCT_OR[NY];

IF IMPORTSuFLAGs'Y' THEN INPDRTSINY]:= IMPORTS_0R[NY]:

211£3

IF TSUPPLY_FLAG='N' THEN
TSUPPLY[NY]:- BEGINSTOCKS[NY] + PROOUCT[NY] + IMPORTS[NY]

ELSE TSUPPLY[NY]:8TSUPPLY_OR[NY];

{ FEED USE }

{ OACON/HEAD ' f ( TIME. REAL MILK PRICE. REAL CORN FARM PRICE. REAL UHEAT PRICE ) }

if oneph_or[ny]=0.0 then

begin

ONEPH[NY]:- -86.515 + 1.832 * YEAR[NY] + 3.415 * (MILKP[NY] / CPIT[NY])

- 25.61 * (CORNPT[NY] / CPIT[NY]) + 10.746 * {UHTPT[NY] I CPIT[NY]) + ONEPH_AF[NY];
dairycons[NY]:=oneph[NY]*danum[NY];

end

else dairycons[ny]:-oneph_or[ny]*danum[ny];

{ COFCON/HEAO I f ( TIME. BEEF-STEER/CORN RATIO. LAGGED DEPENDENT ) }

if twoph_or[ny]=0.0 then

begin
THOPH[NY]:= 240.119 - 2.126 * YEAR[NY] + 1.665 ’ (BEFSTRP[NY] / CORNPT[NY])

- 0.659 * TVDPH[NY-1] + THDPH_AF[NY];
catonfed[NY]:-twoph[NY]*cofnum[NY];{

end

else catonfed[ny]:stwoph_or[ny]*cofnum[ny];

{ OBCCON/HEAD 8 f ( BEEF CON/CORN RATIO. LAGGED REAL SOYBEAN MEAL PRICE ) }

if threeph_or[ny]=0.0 then

begin
THREEPH[NY]:= 2.875 + 0.115 * (BCOHP[NY] I CORNPT[NY])

+ 0.00487 * (SMPT[NY-1] / CPIT[NY-1]) + THREEPH_AF[NY];
otherbeefcat[NY]:-threeph[NY]*obcnum[NY];

end
else otherbeefcat[ny]:-threeph_or[ny]*obcnum[ny];

{ HPCCON/HEAO I f ( REAL CORN FARM PRICE. REAL SOYBEAN MEAL PRICE. REAL UHEAT PRICE.
LAGGED DEPENDENT }

if fourph_or[ny]=0.0 then

begin

FOURPH[NY]:= 0.9349 - 0.724 * (CORNPT[NY] / CPIT[NY]) + 0.00121

* (SMPT[NY] / CPIT[NY]) + 0.3409 * (HHTPT[NY] / CPIT[NY]) + 0.222 * FOURPH[NY-I]
+ FOURPH_AF[NY];

chickencons[NY]:sfourph[NY]*hpcnum[NY];

end

else chickencons[ny]:-fourph_or[ny]*hpcnum[ny];

{BROCON/HEAD 8 f ( TIME. REAL CORN FARM PRICE. REAL UHEAT PRICE ) }

if fiveph_or[ny]s0.0 then

begin

FIVEPH[NY]:- 0.00245 + 0.00143 * YEAR[NY] - 0.0431 * (CORNPT[NY] / CPIT[NY])
+ 0.0162 ' (UHTPT[NY] / CPIT[NY]) + FIVEPH_AF[NY];

:IJuC

broilercons[NY]:Ifiveph[NY]*bronum[NY];

end
else broilercons[ny]:Ifiveph_or[ny]'bronum[ny];

{ TURCON/HEAD I f ( REAL CORN FARM PRICE. REAL HHEAT PRICE. LAGGED DEPENDENT ) }

if sixph_or[ny]I0.0 then

begin
SIXPH[NY]:I 0.709 - 0.3428 ‘ (CORNPT[NY] / CPIT[NY]) + 0.126 * (UHTPT[NY] / CPIT[NY])

+ 0.104 * SIXPH[NY-I] + SIXPH_AF[NY];
turkeycons[NY]:Isixph[NY]*turnum[NY];

end

else turkeycons[ny]:Isixph_or[ny]*turnum[ny];

{ HOGCON/HEAD I f ( TIME. REAL HOG PRICE. REAL CORN FARM PRICE. ) }

if sevenph_or[ny]I0.0 then

begin
SEVENPH[NY]:I -14.624 + 0.5 * YEAR[NY] + 0.394 ‘ (HOGP[NY] / CPIT[NY])

- 7.612 ' (CORNPT[NY] / CPIT[NY]) + SEVENPH_AF[NY];
hogcons[NY]:Isevenph[NY]'hognum[NY];

end

else hogcons[ny]:Isevenph_or[ny]'hognum[ny];

{ DUNCON I f ( TIME. REAL CORN FARM PRICE. REAL HHEAT PRICE. DVBZ. DV84 ) }

if other_unalloc_or[ny]-0.0 then
othercons[NY]:I -412.497 + 6.84 ' YEAR[NY] - 63.265 ' (CORNPT[NY] / CPIT[NY])

+ 26.481 ' (HHTPT[NY] / CPIT[NY]) + 326.601 * OV82
+ 189.265 * DV84 + EIGHT_AF[NY]
else othercons[ny]:Iother_unalloc_or[ny];

IF FEEDT-FLAGI'N' THEN
FEEDT[NY]:I dairycons[NY] + catonfed[NY] + otherbeefcat[NY] + chickencons[NY]

+ broilercons[NY] + turkeycons[NY] + hogcons[NY] + othercons[NY]
ELSE FEEDT[NY]:I FEEDT_0R[NY];

1F DOMESTIC_USE_FLAGI'N' THEN

DDMESTIC_USE[NY]:IFEEDT[NY] + FSI_USE[NY]

ELSE DOMESTIC_USE[NY]:IDOMESTIC_USE_0R[NY];
{ EXPORT COMPONENT }

{ INIPC uses generalized differencing and is adjusted below. }

{ THE SIMPLE LINKAGE BETWEEN FEEDGRAIN PRICE (S/BU.) AND
FARM PRICE IS AN ADDITIONAL $0.42 }

FOR I:- 1 TO MY DD
BEGIN
FP[I]:I CORNPT[I] + 0.42:
END:

IFP[NY]:I {( (FP[NY]/0.0254) / CPIT[NY] ) ' NMXRF[NY] );

12115
{ THIS TRANSFORMATION PUTS FP 1N S/MT }

IUP[NY]:I (( HP[NY] / CPIT[NY] ) * NMXRU[NY] );
IYIELD[NY]:I-0.8943774 + 0.0328114 * YEAR[NY] + IYIELD_AF[NY];
{ Hhere: IYIELD[NY]* I IYIELD[4] - 0.3820334 * IYIELD[3].
YEAR[4]* I YEAR[4] - 0.3820334 ’ YEAR[3].
In this case. Rho is equal to 0.3820334. }

IPRO[NY]:I(1Y1ELO[NY] * IHA[NY]);

INIPC[NY]:I 0.0028 - 0.0000425 * IFP[NY] + 0.0000478 ‘ IHP[NY]
+ 9209967.0 * (IGDP[NY]/1POP[NY]) - 0.143 * (IPRO[NY]/IPOP[NY]) + INIPC_AF[NY];

{ Hhere: INIPC[NY]* I INIPC[4] - 0.7277196 ' INIPC[3].
IFP[4]* I IFP[4] - 0.7277196 * IFP[3].

IHP[4]* I IHP[4] - 0.7277196 * IHP[3].

IGDPPC[4]* I IGDPPC[4] - 0.7277196 * IGDPPC[3].

and IPRDPC[4]* I IPRDPC[4] - 0.7277196 ' IPRDPC[3].

In this case. Rho is equal to 0.7277196. }

IF INI_IN_BU_FLAGI'N' THEN
INI_in_bu[NY]:I ((((INIPC[NY] * IPOP[NY])*39.368)/1000.0)*PCNT_0F_XPORTS[NY])
ELSE INI_IN_BU[NY]:IINI_IN_BU_0R[NY];
EYIELD[NY]:I -I.26 + 0.0460 * YEAR[NY] + EYIELD_AF[NY]; { EXPORTERS FEEDGRAIN YIELD }
EVYIELD[NY];- 0.2247 + 0.0175 * YEAR[NY] + EUYIELD_AF[NY]; { EXPORTERS UHEAT YIELD }
{ EXPORTERS HARVESTED ACRES - f ( LAGGED EXPORTERS FEEDGRAIN REVENUE. LAGGED
EXPORTERS vHEAT REVENUE. LAGGED HARVESTED ACRES. LAGGED ENDING STOCKS I
EFREV[NY-1]:- ((( {FP[NY-1]/0.0254) / CPIT[NY-l] ) * XRAR[NY-I] * FYAR[NY-l])/CPIAR[NY-I]
+ (( (FP[NY-1]/0.0254) / CPIT[NY—I] I * XRAU[NY-1] * FYAU[NY-I])/CPIAU[NY-I]
+ (( (FP[NY-I]/0.0254) / CPIT[NY-I] ) * XRCA[NY-I] * FYCA[NY-1])/CPICA[NY-I]);
EVREV[NY-1]:- ((( VP[NY-1] / CPIT[NY-I] ) * XRAR[NY-1] * VYAR[NY-1])/CPIAR[NY-I]
+ (( UP[NY-1] / CPIT[NY-I] ) * XRAU[NY-1] * UYAU[NY-1])/CPIAU[NY-1]
+ (( UP[NY-I] / CPIT[NY—I] ) * XRCA[NY-I] * vYCA[NY-1])/CPICA[NY-1]I;

EHA[NY]:I 8508.919 + 204.545 * EFREV[NY-1] - 343.169 * EUREV[NY-1]
+ 0.698 * EHA[NY-I] - 0.300 * EES[NY-1] + EHA_AF[NY];

{ EXPORTER PRODUCTION PER HEAD }

EPROPC[NY]:I ( (EHA[NY] * EYIELD[NY]) / EPOP[NY] );
EPRO[NY]:I ( EYIELO[NY] * EHA[NY] );

ESPLYPCINY]:-EPR0Pc[NY]+EESPc[NY-1];

{ EXPORTERS CONSUMPTION/HEAD I f ( EXPORTERS GROSS DOMESTIC PRODUCT. EXPORTERS

leti

FEEDGRAIN PRICE. EXPORTERS HHEAT PRICE. EXPORTERS SUPPLY PER CAP.. SPLINE
FROM 1974 ON ) }

ECONPC[NY]:I 0.0578 + 1222063.5 ’ (EGDP[NY]/EPOP[NY]) - 0.001
* (( (FP[NY]/0.0254) / CPIT[NY] ) * NXXRF[NY]) + 0.00083

* (( UP[NY] / CPIT[NY] ) * MXXRU[NY]) + 0.314 ‘ ESPLYPC[NY]

- 0.0793 * DV74ON + ECONPC_AF[NY];

{ EXPORTER ENDING STOCKS PER HEAD }

EESPC[NY]:I 0.02932 + 0.0728 * EPROPC[NY] + 0.3516 * (EES[NY-1]/EPOP[NY-I])
- 0.1338 ‘ EUDPC[NY-I] - 0.0932 * DV83 + EESPC_AF[NY];

EES[NY]:IEESPC[NY] * EPOP[NY];

EUDPC[NY]:I ESPLYPC[NY] - EESPC[NY];

IF ECON_IN_BU_FLAG='N' THEN

ECON_IN_BU[NY]:I((((ECONPC[NY] * EPOP[NY])*39.368)/1000.0)*PCNT_DF_XPORTS[NY])
ELSE ECON_IN_BU[NY];=ECON_IN_BU_0R[NY];

IF EES_IN_BU_ELAG='N' THEN

EES_IN_BU[NY]:={(((EESPC[NY] ' EPOP[NY])‘39.36B)/IOOD.O)*PCNT_OF_XPDRTS[NY])
ELSE EES_IN_BU[NY]:IEES_IN_BU_0R[NY];

IF ESPLY_IN_BU_FLAGI'N' THEN
esply_in_bu[NY]:I((((esplypc[NY]*EP0P[NY])*39.368)/1000.0)*PCNT_OF_XPORTS[NY])
ELSE ESPLY_IN_BU[NY] -ESPLY_IN_BU_0R[NY];

1F (ENE_IN_BU_FLAG='N') THEN

ENE_IN_BU[NY]:I ESPLY_IN_8U[NY] - ECON_IN_BU[NY] - EES_IN_BU[NY]

ELSE ENE_IN_BU[NY]:IENE_IN_BU_0R[NY];

if ene_in_bu[ny] < 0.0 then ene_in_bu[ny]:I0.0;

IF EXPORTS_FLAG='N' THEN

EXPORTS[NY]:I ( INI_IN_BU[NY] - ENE_IN_BU[NY] )

ELSE EXPORTS[NY]:I EXPORTS_DR[NY];

T0TAL_USE[NY]:I DOMESTIC_USE[NY] + EXPORTS[NY];

{ STOCKS IN GOVERNMENT PROGRAM }

FPLRzI FP[NY] / CORN_LOAN_RATE[NY];
FPLR2:I FPLR * FPLR;
FPLR3:I FPLR2 * FPLR:
{ CCC INVENTORY (t) I CCC INVENTORY (t-I) + CHANGE IN CCC INVENTORY }

DCCCSTOCKS[NY]:I 34330.230 - 68153.126 ' FPLR + 45967.279 * FPLR2
- 10569.075 * FPLR3:

CCCSTOCKS[NY]:= CCCSTOCKS[NY-I] + OCCCSTOCKS[NY];

221$?

IF CCCSTOCKS[NY] < 0.0 THEN CCCSTOCKS[NY]:I 0.0;
IF CCCSTOCKS_FLAGI'Y' THEN CCCSTOCKS[NY]:I CCCSTOCKS_OR[NY];

{ { FOR INVENTORY (t) I FOR INVENTORY (t-I) + CHANGE IN FOR INVENTORY }

OFORSTOCKS[NY]:I 28637.804 - 53497.38 ' FPLR + 35532.478 ' FPLR2
- 8431.0044 * FPLR3:

F0RSTOCKS[NY]:I FORSTOCKS[NY-I] + DFORSTOCKS[NY];

IF FORSTOCKS[NY] >- ( ( TSUPPLY[NY] - TOTAL_USE[NY] ) - ( MIN_FREE_STOCKS[NY] .
CCCSTOCKS[NY] ) ) THEN FORSTOCKS[NY]:-
( ( TSUPPLY[NY] - TOTAL_USE[NY] ) - ( MIN_FREE_STOCKS[NY] .
CCCSTOCKS[NY] ) );

IF FORSTOCKS[NY] < 0.0 THEN F0RST0CKS[NY]:I 0.0;
IF FORSTOCKS_FLAGI'Y' THEN F0RST0CKS[NY]:I FORSTOCKS_OR[NY];

FREESTOCK_PROC;
IF FREESTOCKS_FLAGI'Y' THEN FREESTOCKS[NY]:I FREESTOCKS_0R[NY];

IF ((TOTAL_USE[NY]+CCCSTOCKS[NY]+FORSTOCKS[NY]+FREESTOCKS[NY])>TSUPPLY[NY]) THEN
FORSTOCKS[NY]:I(TSUPPLY[NY]-(CCCSTOCKS[NY]+FREEST0CKS[NY]+TOTAL_USE[NY]));

{ TOTAL ENDING STOCKS EQUATION }

END_ST0CKS[NY]:I ( CCCSTOCKS[NY] + FORSTOCKS[NY] + FREESTOCKS[NY] );
{ TOTAL. PERCENT OF USE }

PERCENT_USE[NY]:I (END_STOCKS[NY] / TOTAL_USE[NY]) * 100;

END; { PROCEDURE RE CALCULATE }

"“’ End of Include file RECALC.INC '*‘**

"**' Beginning of Include file CACULATE.INC ’*'**
{SI CACULATE.INC} ,
overlay procedure CALCULATIONS(corn_price_in_year_t: real);
{ THE FORECAST CALCULATIONS USING RANDOM SHOCKS }

var conserved_acres:real; { A LOCAL VARIABLE USED FOR PERCENT OF CONSERVATION ACRES
( ARP + DIVERSION) }

procedure FREESTOCK_PROC;
{ A PROCEDURE T0 CALCULATE THE FREE STOCKS BASED
ON HEIGHTS USED FOR POLICY }

VAR
CCCHT. FORHT: REAL:

BEGIN
{ RULE CCC HEIGHT FOR HEIGHT }

{ -------------------- i
{ 1) YEAR <I 76 0.10 0.0 }

ZILEI

2) YEAR >I 77 0.80 0.75
3) YEAR >I 77. CCCt <I CCCt-l.

TRIGGER PRICE > $1.55 0.10 0.75
4) YEAR >I 77. FORt <I FORt-I.

TRIGGER PRICE > 51.55 0.80 0.0

VVVVV

IF { year[NY] <I 76 ) THEN CCCHT:I 0.1 ELSE CCCHT:I 0.8:

IF ( year[NY] >I 77 ) AND ( CCCstocks[NY] <I CCCstocks[NY-I] )
AND ( fplr > 1.55 ) THEN CCCHT:I 0.1;

IF ( year[NY] <I 76 ) THEN FORHT:I 0.0 ELSE FORHT:I 0.75;

IF ( FORstocks[NY] <I FORstocks[NY-I] ) AND ( fplr > 1.55 ) THEN
FORHT:I 0.0;

FREESTOCKS[NY]:I1127.4231 — 1873.6898 * (( CCCUT * CCCSTOCKS[NY]
4 FORHT ' FORSTOCKS[NY] ) I TOTAL_USE[NY] )
- 1301390.2 * ( FPLR / TOTAL_USE[NY] ) + FREESTOCKS_AF[NY];
IF FREESTOCKS[NY] < MIN_FREFNSTOCKS[NY] THEN FREESTOCKS[NY]:IMIN_FREE_STOCKS[NY];

END; { AREA HHICH CALCULATES FUNCTION Fx FOR GIVEN HEIGHTS }

BEGIN
{ DOMESTIC SUPPLY }

{ APCT I f ( DV83. LAGGED CORN REVENUE(NON-PARTICIPANTS). HHEAT REVENUE.
LAGGED CORN REVENUE(PARTICIPANTS) }

CDNSERVED_ACRES:IARP[NY]+DIVERSIDM[NY];

IF APCT_FLAGI'N' THEN
APCT[NY]:I 90.993360 {+4.044 ’ dv79 - 6.54 * dv73} - 0.1262719 '
{((HHTPT[NY-I] * HHTYT[NY-I]) - VCHH[NY-I]) / CPIT[NY'IJI ' 0.0281170 *
(({PARREV[NY'1] + DIVFAY[MY-I] + DEFFAY[NY-I]} ' VCCN[NY-I]} / CPIT[NY'IJI
- 60.333270 ' CONSERVED_ACRES + APCT_AF[NY]

ELSE APCT[NY]:IAPCT_OR[NY];

IF AHCT_FLAGI'N' THEN

AHCT[MY]:I '8.412272 + 0.8729 ' APCT[NY] + 0.1078 * YEAR[NY] + AHCT_AF[NY]
ELSE AHCT[NY]:IAHCT_OR[NY];

IF CORNYT_FLAGI'N' THEN

CDRNYT[NY]:I -71.3758 + 2.153 ' YEAR[NY] + CORNYT_AF[NY]

ELSE CORNYT[NY]:ICORNYT_0R[NY];

if year[nyJIBS then beginstocks[NY]:IIG48.0
else BEGINSTOCKS[NY]:IEND_STOCKS[NY-1];

YIELD:I7.661092 ‘ Z + CORNYT[NY];

21151

IF PRODUCT_FLAGI'N' THEN
PRODUCT[NY]:I AHCT[NY] * YIELD
ELSE PROOUCT[NY]:IPROOUCT_OR[NY];

IF IMPORTS_FLAGI'Y' THEN IMPORTS[NY]:IIMPORTS_OR[NY];

IF TSUPPLY_FLAGI'N' THEN
TSUPPLY[NY]:I BEGINSTOCKS[NY] + PROOUCT[NY] + IMPORTS[NY]
ELSE TSUPPLY[NY]:ITSUPPLY_OR[NY];

{ FEED USE }

{ DACON/HEAD I f ( TIME. REAL MILK PRICE. REAL CORN FARM PRICE. REAL HHEAT PRICE ) }

if oneph_or[ny]I0.0 then

begin

ONEPH[NY]:I -86.515 + 1.832 ' YEAR[NY] + 3.415 ‘ (MILKP[NY] / CPIT[NY])

- 25.61 ‘ (CORNPT[NY] / CPIT[NY]) + 10.746 * (HHTPT[NY] / CPIT[NY]) + ONEPH_AF[NY];
dairycons[NY]:Ioneph[NY]‘danum[NY];

end

else dairycons[ny]:Ioneph_or[ny]'danum[ny];

{ COFCON/HEAD I f ( TIME. BEEF-STEER/CORN RATIO. LAGGED DEPENDENT ) }

if twoph_or[ny]I0.0 then

begin
THOPH[NY]:I 240.119 - 2.126 ' YEAR[NY] + 1.665 * (BEFSTRP[NY] / CORNPT[NY])

- 0.659 * THOPH[NY-I] + THOPH_AF[NY];
catonfed[NY]:Itwoph[NY]*cofnum[NY];

end

else catonfed[ny]:Itwoph_or[ny]‘cofnum[ny];

{ OBCCON/HEAO I f ( BEEF COH/CORN RATIO. LAGGEO REAL SOYBEAN MEAL PRICE ) }

if threeph_or[ny]I0.0 then

begin .
THREEPH[NY]:I 2.875 + 0.115 * (BCOHP[NY] / CORNPT[NY])

+ 0.00487 * (SMPT[NY-I] / CPIT[NY-I]) + THREEPH_AF[NY];
otherbeefcat[NY]:Ithreeph[NY]*obcnum[NY];

end

else otherbeefcat[ny]:Ithreeph_pr[ny]'obcnum[ny];

{ HPCCON/HEAD I f ( REAL CORN FARM PRICE. REAL SOYBEAN MEAL PRICE. REAL HHEAT PRICE.
LAGGED DEPENDENT }

if fourph_or[ny]I0.0 then

begin

F0URPH[NY]:I 0.9349 - 0.724 * (CORNPT[NY] / CPIT[NY]) + 0.00121

* (SMPT[NY] / CPIT[NY]) + 0.3409 * (HHTPT[NY] / CPIT[NY]) + 0.222 * FOURPH[NY-I]
+ FOURPH_AF[NY];

chickencons[NY]:Ifourph[NY]*hpcnum[NY];

end

else chickencons[ny]:Ifourph_or[ny]‘hpcnum[ny];

212(1
{BROCON/HEAD I f ( TIME. REAL CORN FARM PRICE. REAL HHEAT PRICE ) }

if fiveph_or[ny]I0.0 then

begin

FIVEPH[NY]:I 0.00245 + 0.00143 * YEAR[NY] - 0.0431 * (CORNPT[NY] / CPIT[NY])
+ 0.0162 * {HHTPT[NY] / CPIT[NY]) + FIVEPH_AF[NY];

broi lercons [NY] : If iveph [NY] 'bronun [NY] ; '

end

else broilercons[ny]:Ifiveph_or[ny]*bronum[ny];

{ TURCON/HEAD I f ( REAL CORN FARM PRICE. REAL HHEAT PRICE. LAGGEO DEPENDENT ) }

if sixph_or[ny]I0.0 then

begin

SIXPH[NY]:I 0.709 - 0.3428 * (CORNPT[NY] / CPIT[NY]) + 0.126 ‘ (HHTPT[NY] / CPIT[NY])
+ 0.104 * SIXPH[NY-1] + SIXPH_AF[NY];

turkeycons[NY]:Isixph[NY]*turnum[NY];

end

else turkeycons[ny]:Isixph_or[ny]*turnum[ny];

{ HOGCON/HEAD I f ( TIME. REAL HOG PRICE. REAL CORN FARM PRICE. 1 I

if sevenph_or[ny]=0.0 then

begin

SEVENPH[NY]:I -14.624 + 0.5 * YEAR[NY] + 0.394 * (HOGP[NY] / CPIT[NY])
- 7.612 * (CORNPT[NY] / CPIT[NY]) + SEVENPH_AF[NY];
hogcons[NY]:Isevenph[NY]*hognum[NY];

end

else hogcons[ny]:Isevenph_or[ny]*hognum[ny];

{ DUNCON I f ( TIME. REAL CORN FARM PRICE. REAL HHEAT PRICE. DV82. OV84 ) }

if other_unalloc_or[ny]I0.O then

othercons[NY]:I -412.497 + 6.84 * YEAR[NY] - 63.265 * (CORNPT[NY] / CPIT[NY])
+ 26.481 * {HHTPT[NY] / CPIT[NY]) + 326.601 ' DV82
+ 189.265 ' DV84 + EIGHT_AF[NY]

else othercons[ny]:Iother_unalloc_or[ny];

IF FEEDT_FLAGI'N' THEN

FEEDT[NY]:I dairycons[NY] + catonfed[NY] + otherbeefcat[NY] + chickencons[NY]
+ broilercons[NY] + turkeycons[NY] + hogcons[NY] + othercons[NY]

ELSE FEEDT[NY]:IFEEDT_OR[NY];

1F DOMEST1C_USE_FLAG='N' THEN

DOMESTIC_USE[NY]:IFEEDT[NY] + FSI_USE[NY]

ELSE DOMESTIC_USE[NY] IDOMESTIC_USE_0R[NY];
{ ExPORT COMPONENT }

{ INIPC uses generalized differencing and is adjusted below. }

{ THE SIMPLE LINKAGE BETHEEN FEEDGRAIN PRICE ($/BU.) AND
FARM PRICE IS AN ADDITIONAL $0.42 }

12221

FOR I:I 1 TO NY D0
BEGIN
FP[I]:I CORN_PRICE_IN_YEAR_T + 0.42:
END;

IFP[NY]:I (( (FP[NY]/0.0254) / CPIT[NY] ) ' NMXRF[NY] );
{ THIS TRANSFORMATION PUTS FP IN $/MT }

IHP[NY]:I (( HP[NY] / CPIT[NY] ) * NMXRH[NY] );
IYIELD[NY]:I-0.8943774 + 0.0328114 ‘ YEAR[NY] + 1Y1ELO_AF[NY];

{ Hhere: IYIELD[NY]‘ I IYIELD[4] - 0.3820334 ‘ IYIELO[3].
YEAR[4]* I YEAR[4] - 0.3820334 ‘ YEAR[3].
In this case. Rho is equal to 0.3820334. }

YIEL02:I 0.045231 ' 22 + IYIELD[NY];
IPRO[NY]:I( YIEL02 * IHA[NY] );

INIPC[NY]:I 0.0026 - 0.0000425 * IFP[NY] + 0.0000476 * IHP[NY]
. 9209967 0 * (IGDP[NY]/IPOP[NY]) - 0.143 * (IPRO[NY]/IPOP[NY]) + INIPC_AF[NY];

{ Hhere: INIPC[NY]* I INIPC[NY] - 0.7277196 ' INIPC[NY-l].
IFP[NY]* I IFP[NY] - 0.7277196 * IFP[NY-I].

IHP[NY]* I IHP[NY] - 0.7277196 * IHP[NY-l].

IGDPPC[NY]* I IGDPPC[NY] - 0.7277196 * IGDPPC[NY-1].

and IPROPC[NY]* I IPROPC[NY] - 0.7277196 * IPROPC[NY-I].
In this case. Rho is equal to 0.7277196. }

IF INI_IN_BU_FLAGI'N' THEN

{ corn is about 0.86 X of exports }
INI_IN_BU[NY]:- ((((INIPC[NY]*IPOP[NY]I* 39.368)/1000.0)*PCNT_0F_XPORTS[NY])
ELSE INI_IN_BU[NY]:IINI_IN_BU_OR[NY];

EYIELD[NY]:I -1.26 + 0.0460 ' YEAR[NY] + EYIELD_AF[NY]; { EXPORTERS FEEDGRAIN YIELD }
YIEL01:I 0.113202 ' 21 + EYIELD[NY];
EHYIELD[NY]:I 0.2247 + 0.0175 * YEAR[NY] + EHYIELO_AF[NY]; { EXPORTERS HHEAT YIELD }

{ EXPORTERS HARVESTED ACRES I f ( LAGGED EXPORTERS FEEDGRAIN REVENUE. LAGGED
EXPORTERS HHEAT REVENUE. LAGGED HARVESTED ACRES. LAGGED ENDING STOCKS }

EFREV[NY-I]:I ((( (FP[NY-I]/0.0254) / CPIT[NY-I] I * XRAR[NY-I] . FYAR[NY-I])/CPIAR[NY-I]
+ (( (FP[NY-I]/0.0254) / CPIT[NY-I] I * XRAU[NY-I] * FYAU[NY-1]1/CPIAU[NY-1]
. (( (FP[NY-1]/0.0254) / CPIT[NY-I] I * XRCA[NY-I] * FYCA[NY-I])/CPICA[NY-I]);
EvREV[NY-1]:- ((( vP[NY-1] / CPIT[NY-I] I * XRAR[NY-I] * HYAR[NY-l])/CPIAR[NY-I]
+ (( UP[NY-I] / CPIT[NY-I] I . XRAU[NY-1] * HYAU[NY-I])/CPIAU[NY-I]
+ (( HP[NY-I] / CPIT[NY-I] I * XRCA[NY-1] * HYCAENY-I])/CPICA[NY-I]I;

EHA[NY]:I 8508.919 + 204.545 * EFREV[NY-I] - 343.169 ' EHREV[NY-1]
+ 0.698 ’ EHA[NY-l] - 0.300 * EES[NY-l] + EHA_AF[NY];

2:32

{ EXPORTER PRODUCTION PER HEAD }

EPROPC[NY]:I ( (EHA[NY] * YIELDI) / EPOP[NY] );
EPRO[NY]:I ( YIELDI * EHA[NY] );

ESPLYPC[NY]:I EPROPC[NY]+EESPC[NY-I]:

{ EXPORTERS CONSUMPTION/HEAD I f ( EXPORTERS GROSS DOMESTIC PRODUCT. EXPORTERS
FEEDGRAIN PRICE. EXPORTERS HHEAT PRICE. EXPORTERS SUPPLY PER CAP.. SPLINE
FROM 1974 ON ) }

ECONPC[NY]:I 0.0576 + 1222063.5 * (EGDP[NY]/EPOP[NY]) - 0.001
* (( (FP[NY]/0.0254) / CPIT[NY] I * NXXRF[NY]) + 0.00063

* (( HP{NY] / CPIT[NY] I * NXXRH[NY]) + 0.314 * esplypc[NY]
- 0.0793 * DV740N + ECONPC_AF[NY];

{ EXPORTER ENDING STOCKS PER HEAD }

EESPC[NY]:I 0.02932 + 0.0728 ’ EPROPC[NY] + 0.3516 ‘ (EES[NY-l]/EPOP[NY-1])
- 0.1338 * EUDPC[NY-1] - 0.0932 * OV83 + EESPC_AF[NY];

EES[NY]:I EESPC[NY] ' EPOP[NY];
EUDPC[NY]:IESPLYPC[NY] - EESPC[NY];

IF ECON_IN_BU_FLAGI'N' THEN
ECON_IN_BU[NY]:I ((((ECONPC[NY] * EPOP[NY])*39.3681/1000.0)'PCNT_OF_XPORTS[NY])
ELSE ECON_IN_BU[NY]:IECON_IN_BU_0R[NY];

IF EES_IN_BU_FLAGI'N' THEN
EES_IN_BU[NY]:- ((((EESPC[NY] * EPOP[NY]I*39.366I/1000.0I*PCNT_OP_XP0RTS[NY]I
ELSE EES_IN_BU[NY]:IEES_IN_BU_0R[NY];

IF ESPLY_IN_BU_FLAGI'N' THEN
ESPLY_IN_BU[NY];-((((espiypc[NY]*EP0P[NY]I*39.366I/1000.0)*PCNT_0F_XP0RTS[NY]I
ELSE ESPLY_IN_BU[NY]:IESPLY_IN_BU_OR[NY];

IF ENE_IN_BU_FLAGI'N' THEN
ENE_IN_BU[NY]:I ESPLY_IN_Bu[NY] - ECON_IN_BU[NY] - EES_IN_BU[NY]
ELSE ENE_1N_BU[NY]:IENE_1N_BU_0R[NY];

if ene_in_bu[ny] < 0.0 then ene_in_bu[ny] :I0.0;

IF EXPORTS_FLAGI'N' THEN
EXPORTS[NY]:I ( INI_IN_BU[NY] - ENE_IN_BU[NY] I
ELSE EXPORTS[NY] -EXPORTS_0R[NY];

TOTAL-USE[NY]:I DOMESTIC_USE[NY] + EXPORTS[NY];
{ STOCKS IN GOVERNMENT PROGRAM I

FPLR . FP[NY] / CORN_L0AN_RATE[NY]:
FPLR2:I FPLR * FPLR:
FPLR3:I FPLR2 * FPLR:

12213

{ CCC INVENTORY (t) I CCC INVENTORY (t-I) + CHANGE IN CCC INVENTORY }

DCCCSTOCKS[NY]:I 34330.230 - 68153.126 * FPLR + 45967.279 * FPLR2
- 10569.075 * FPLR3:

CCCSTOCKS[NY]:I CCCSTOCKS[NY-l] + DCCCSTOCKS[NY];

1F CCCSTOCKS[NY] < 0.0 THEN CCCSTOCKS[NY]:I 0.0;
IF CCCSTOCKS_FLAG='Y' THEN CCCSTOCKS[NY]:ICCCSTOCKS_OR[NY];

{ FOR INVENTORY (t) I FOR INVENTORY (t-l) + CHANGE IN FOR INVENTORY }

DFORSTOCKS[NY]:I 28637.804 - 53497.38 * FPLR + 35532.478 * FPLR2
- 8431.0044 * FPLR3:

FORSTOCKS[NY]:I FORSTOCKS[NY-I] + DFORSTOCKS[NY];

IF FORSTOCKS[NY] < 0.0 THEN FORSTOCKS[NY]:I 0.0;
IF FORSTOCKS_FLAGI'Y' THEN FORSTOCKS[NY]:IFORSTOCKS_OR[NY];

FREESTOCK_PROC;
IF FREESTOCKS_FLAG='Y' THEN FREESTOCKS[NY]:IFREESTOCKS_0R[NY];

{ TOTAL ENDING STOCKS EQUATION }

END_STOCKS[NY]:I ( CCCSTOCKS[NY] + FORSTOCKS[NY] + FREESTOCKS[NY] );
{ EQUILIBRIUM CONDITION }

EQUILIBRIUM:I(TOTAL_USE[NY] + END_STOCKS[NY]) - TSUPPLY[NY];

END; { PROCEDURE CALCULATIONS }

***** End of Include file CACULATE.INC *****
***** Beginning of Include file SOLUTION.INC *****

{$1 SOLUTION.INC}
OVERLAY PROCEDURE SOLUTION_SCREEN: { A PROCEDURE TO RUN 'RE_CALCULATE' }
BEGIN

screenSS:II; screen_fieldSS:I3; varSS:II;

retrieveSS:-FALSE; last_fieldSS:-FALSE;
DISPLAY_SCREEN('SOLVESCR.SCR'.file_existSS); { Display Screen }
if not file_existSS then

begin gotoxy(I.I); write(‘Missing Screen SOLVESCR') end;

retSS:I";

{ Display Items. Change retrieveSS to TRUE and INPUT items}
REPEAT { until actionSS I exitSS }

CASE varSS of

GETNUM(68.5.4.'N'.CORNPT[NY].
'l.ll'.0.00.9.99.retSS.retrieveSS.15.0);

GETNUM(59.7.4.'N'.REDUCE_PRICE.

2224

'I.Il'.0.00.0.99.retSS.retrieveSS.15.0);
3: BEGIN
IF RETRIEVESSIFALSE THEN
FG:IO
ELSE
FGzIIS:
GETITEM(36.I4.7.'C'.INFO.
'XXXXXXX'.".".retSS.FALSE.FG.O);
END;
END; { CASE }

if varSSIscreen_fieldSS then last_fieldSS:-TRUE;
RET_STATUS; { Check the code in ”retSS". Set "varSS" and ”actionSS" }

{ Check to see whether to switch retrieveSS to true }
if last_fieldSS and (not retrieveSS) then
begin
retrieveSS:ITRUE; last_field$S:-FALSE; actionSS:=staySS; varSS:=I;
end else
last_fieldSS:-FALSE;

UNTIL actionSS-exitSS
END; { PROCEDURE SCREEN_I }

***** End of Include file SOLUTION.INC *'***
***'* Beginning of Include file REMAIN.INC ***'*
{$1 remain.INC}
procedure remain; { A PROCEDURE TO DETERMINE MARKET CLEARING PRICE
FOR THE 'RE_CALCULATE' PROCEDURE }
begin
SOLUTION_SCREEN:
repeat
re_calculate;
cornpt[NY]:Icornpt[NY]-REDUCE_PRICE;
until((total_use[NY] + end_stocks[NY]) >I tsupply[NY]) or (cornpt[ny] <= 0.10);
cornpt[NY]:Icornpt[NY]+REDUCE_PR1CE:
beep(true);
beeponss:Ifalse;
end; { procedure remain }
*"" End of Include file REMAIN.INC *****
“**' Beginning of Include file HOTT.INC ’*"*

{$1 HOTT.INC} { add factors }
OVERLAY PROCEDURE ADD_FACTOR_SCREEN_1: I B:AFACTOR1 }
BEGIN

screenSS:I2; screen_fieldSS:-20; varSS:-I;

retrieveSS:-FALSE; last_fieldSS:-FALSE:

DISPLAY_SCREEN('AFACTORI.SCR'.file_existSS); { Display Screen }

if not file_existSS then

begin gotoxy(I.I); write(‘Missing Screen AFACTORI') end;
retSS:I":

{ Display Items. Change retrieveSS to TRUE and INPUT items}
REPEAT { until actionSS I exitSS }

21255

CASE varSS of
I: GETNUM(34.3.2.'N'.YEAR[NY].
'Il'.0.0.99.0.retSS.FALSE.15.0);
2: BEGIN
YEARB[NY]:IYEAR[NY]+1;
GETNUM(37.3.2.'N'.YEARB[NY].
'll'.0.0.99.0.retSS.FALSE.15.0);
END;
3: GETNUM(73.3.2.'N‘.YEAR[NY].
'II'.0.0.99.0.retSS.FALSE.15.0);
4: GETNUM(76.3.2.'N'.YEARB[NY].
'll'.0.0.99.0.retSS.FALSE.15.0);
5: GETNUM(32.6.7.'N'.APCT_AF[NY].
'#II.#ll'.-99.000.999.000.retSS.retrieveSS.15.0);
6: GETNUM(32.8.7.'N'.AHCT_AF[NY].
'lil.ll!'.-99.999.999.999.retSS.retrieveSS.15.0);
7: GETNUM(32.10.7.'N'.CORNYT_AF[NY].
'Ill.lll'.-99.999.999.999.retSS.retrieveSS.15.0);
8: GETNUM(32.15.7.'N'.ONEPH_AF[NY].
'lll.lil'.-99.999.999.999.retSS.retrieveSS.15.0);
9: GETNUM(32.17.7.'N'.THOPH_AF[NY].
'III.III'.-99.999.999.999.retSS.retrieveSS.15.0);
10: GETNUM(32.19.7.'N'.THREEPH_AF[NY].
'Ill.lll'.-99.999.999.999.retSS.retrieveSS.15.0);
ll: GETNUM(32.21.7.'N'.FOURPH_AF[NY].
’li#.lll'.-99.999.999.999.retSS.retrieveSS.15.0);
12: GETNUM(32.23.7.'N'.FIVEPH_AF[NY].
'III.II#'.-99.999.999.999.retSS.retrieveSS.15.0);
13: GETNUM(71.6.7.'N'.SIXPH_AF[NY].
’Ill.l#l'.-99.999.999.999.retSS.retrieveSS.15.0);
I4: GETNUM(71.8.7.'N'.SEVENPH_AF[NY].
'll!.#ll’.-99.999.999.999.retSS.retrieveSS.15.0);
15: GETNUM(71.10.7.'N'.EIGHT_AF[NY].
'llil.l#'.-999.99.9999.99.retSS.retrieveSS.15.0);
16: GETNUM(70.15.8.'N'.INIPC_AF[NY].
'51.!!lll'.-9.99999.99.99999.retSS.retrieveSS.15.0);
I7: GETNUM(73.17.5.'N'.IY1ELD_AF[NY].
'll.ll'.-9.99.99.99.retSS.retrieveSS.15.0);
18: GETNUM(73.19.5.'N'.EY1ELD_AF[NY].
'l#.li'.-9.99.99.99.retSS.retrieveSS.15.0);
19: GETNUM(71.21.7.'N'.EHA_AF[NY].
'l!!!l.l'.-9999.9.99999.9.retSS.retrieveSS.15.0);
20: GETNUM(70.23.8.'N'.ECONPC_AF[NY].
'll.lli!!'.-9.99999.99.99999.retSS.retrieveSS.15.0);
END; { CASE }

if varSS-screen_fieldSS then last_fieldSS:-TRUE:
RET_STATUS; { Check the code in "retSS". Set "varSS" and "actionSS" }

{ Check to see whether to switch retrieveSS to true }
if last_fieldSS and (not retrieveSS) then
begin
retrieveSS:-TRUE; last_fieldSS:IFALSE; actionSS:=staySS; varSS:=1;
end else

212GB
last_fieldSS:IFALSE;

UNTIL actionSSIexitSS
END; { PROCEDURE ADD_FACTOR_SCREEN_I }

OVERLAY PROCEDURE ADO_FACTOR_SCREEN_2: { B:AFACTOR2 }
BEGIN

screenSS:I3; screen_fieldSS:I6; varSS:II;

retrieveSS:-FALSE; last_fieldSS:IFALSE;

DISPLAY_SCREEN('AFACTOR2.SCR'.file_existSS); { Display Screen }

if not file_existSS then

begin gotoxy(I.I); write(‘Missing Screen AFACTORZ') end;
retSS:-";

{ Display Items. Change retrieveSS to TRUE and INPUT items}
REPEAT { until actionSS I exitSS }
CASE varSS of
I: GETNUM(57.6.2.'N'.YEAR[NY].
'II'.0.0.99.0.retSS.FALSE.15.0);
2: GETNUM(60.6.2.'N'.YEARB[NY].
'll'.0.0.99.0.retSS.FALSE.15.0);
3: GETNUM(55.10.7.'N'.EESPC_AF[NY].
'Il.lll!l'.-0.99999.9.99999.retSS.retrieveSS.15.0);
4: GETNUM(55.18.B.'N'.DCCCSTOCKS_AF[NY].
'Il!!!.il'.-9999.99.99999.99.retSS.retrieveSS.15.0);
5: GETNUM(55.20.8.'N'.DFORSTOCKS_AF[NY].
'lllll.ll'.-9999.99.99999.99.retSS.retrieveSS.15.0);
6: GETNUM(55.22.8.'N'.FREESTOCKS_AF[NY].
'IIIII.II'.-9999.99.99999.99.retSS.retrieveSS.15.0);
END; { CASE }

if varSS-screen_fieldSS then last_fieldSS:ITRUE;
RET_STATUS; { Check the code in "retSS". Set ”varSS" and "actionSS" }

{ Check to see whether to switch retrieveSS to true }
if last_fieldSS and (not retrieveSS) then
begin
retrieveSS:-TRUE; last_fieldSS:IFALSE; actionSS:IstaySS; varSS:II;
end else
last_fieldSS:IFALSE;

UNTIL actionSS-exitSS
END; { PROCEDURE ADD_FACTOR_SCREEN_2 }
*‘*** End of Include file HOTT.INC *****
***** Beginning of Include file INTRO.1NC *****
{$1 intro.inc} { ALLOHS THE LOAD AND SAVE PROCESS }
overlay procedure LOAD;
BEGIN
I:Il:
screen_fieldSS:-5;
varSS:I1;
retrieveSS:IFALSE;
last_fieldSS:IFALSE;

.2137

DISPLAY_SCREEN('LOAD.SCR'.file_existSS); { Display Screen }
if not file_existSS then
BEGIN
gotoxy(I.I);
write('Missing Screen LOAD')
END;
retSS:I";

{ Display Items. Change retrieveSS to TRUE and INPUT items}
REPEAT { until actionSS I exitSS }
CASE varSS of
1: BEGIN
GetItem(27.4.47.'C'.Default0rive.
'U:UUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUU'.".".retSS.retrieveSS.0.7);
if (length(defaultdrive) I3) then delete(defaultdrive. 3. I);
Checkdisk(defaultdrive);
END;
2: BEGIN
LoadYn:-'Y';
GetItem(60.20.1.'Y'.LoadYn.
'U'.".".retSS.retrieveSS.0.7);
If (retrieveSS) and (upcase(LoadYn)I'N') then varSS:Iscreen_fieldSS:
END;
3: begin
GETNUM(46.21.I.'N'.J.
'l'.1.4.retSS.retrieveSS.0.7);
S:ITRUNC(J);
end;
4: BEGIN

GetItem(26.23.8.'C'.FileName.
'UUUUUUUU'.".".retSS.retrieveSS.0.7);
If (retrievess) then Loadbev(FileName.DefaultDrive);

END;

5: begin

GetItem(35.23.3.'C'.FileExt.
'UUU'.".".retSS.FALSE.15.0);
if (retrieveSS-true) and (S<>3) then varSS:-1;

end;

END; { CASE }

if varSS-screen_fieldSS then last_fieldSS:ITRUE;
RET_STATUS; { Check the code in ”retSS". Set ”varSS" and "actionSS" }

{ Check to see whether to switch retrieveSS to true }
if last_fieldSS and (not retrieveSS) then
begin
retrieveSS:ITRUE; last_fieldSS:IFALSE; actionSS:IstaySS; varSS:II;
end else
last_fieldSS:IFALSE:

UNTIL actionSS-exitSS
END; { PROCEDURE LOAD }

2:28

overlay procedure SAVE;
BEGIN
th4:
screen_fieldSS:-5:
varSS:I1;
retrieveSS:IFALSE; last_fieldSS:IFALSE;
DISPLAY_SCREEN('SAVE.SCR'.file_existSS); { Display Screen }
if not file_existSS then
BEGIN
gotoxy(I.I);
write('Missing Screen SAVE')
END;
retSS:I";

{ Display Items. Change retrieveSS to TRUE and INPUT items}
REPEAT { until actionSS I exitSS }
CASE varSS of
1: BEGIN
GetItem(65.4.l.'Y'.SAVEYN.
'U'.".".retSS.retrieveSS.O.7);
If (retrieveSS) AND (upcase(Saveyn)-'N')
Then varSS :I screen_fieldSS;
END;
2: BEGIN
GetItem(32.5.47.'C'.DefaultDrive.
'U:XUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUU'.".".retSS.retrieveSS.O.7);
if (length(defaultdrive) I3) then delete(defaultdrive. 3. I);
Checkdisk(defaultdrive);
END;
3: BEGIN

GETNUM(4B.6.I.'N'.J.
'I'.1.4.retSS.retrieveSS.0.7);
S:ITRUNC(J):

END;
4: begin
{ on_line_help( 'SAVEFILE'): }

GetItem(26.23.8.'C'.FileName.
‘UUUUUUUU'.".".retSS.retrieveSS.0.7);
end;
5: BEGIN
GetItem(35.23.3.'C'.FileExt.
'UUU'.".".retSS.FALSE.15.0);
If retrieveSS
Then Savebev(FileName.Default0rive);
END;
END; { CASE }

if varSS-screen_fieldSS then last_fieldSS:ITRUE;
RET_STATUS; { Check the code in ”retSS”. Set ”varSS" and "actionSS" }

{ Check to see whether to switch retrieveSS to true }

 

229I

if last_fieldSS and (not retrieveSS) then
begin

retrieveSS:ITRUE; last_fieldSS:IFALSE; actionSS:IstaySS; varSS:II;
end else

last_fieldSS:IFALSE:

UNTIL actionSS-exitSS
END; { PROCEDURE LOAD }

"**' End of Include file INTRO.INC *“**

""‘ Beginning of Include file BALSHEET.INC '****
{$1 BALSHEET.INC}
overlay PROCEDURE SCREEN_12: { B:SDBSCUS }
BEGIN

screenSS:-4; screen_field$S:-30; varSS:I1:

retrieveSS:IFALSE; last_fieldSS:IFALSE;

DISPLAY_SCREEN('SDBSCUS.SCR'.file_existSS): { Display Screen I

if not file_existSS then

begin gotoxy(I.I); write('Missing Screen SDBSCUS') end;
retSS:I";

{ Display Items. Change retrieveSS to TRUE and INPUT items}
REPEAT { until actionSS I exitSS }
CASE varSS of

1: GETNUM(25.7.2.'N'.YEAR[NY-3].
'll'.0.0.99.0.retSS.FALSE.15.0);

2: begin
yearb[NY-3]:Iyear[NY-3]+I;
GETNUM(28.7.2.'N'.YEARB[NY-3].
'II'.0.0.99.0.retSS.FALSE.15.0);
end;

3: GETNUM(36.7.2.'N'.YEAR[NY-2].
'I!’.0.0.99.0.retSS.FALSE.15.0):

4: begin
yearb[NY-2]:Iyear[NY-2]+I;
GETNUM(39.7.2.'N'.YEARB[NY-2].
'll'.0.0.99.0.retSS.FALSE.15.0);
end:

5: GETNUM(46.7.2.'N'.YEAR[NY-I].
'II'.0.0.99.0.retSS.FALSE.15.0);

6: begin
yearb[NY-I]:Iyear[NY-I]+I:
GETNUM(49.7.2.'N'.YEARB[NY-I].
'II'.0.0.99.0.retSS.FALSE.15.0);
end;

7: GETNUM(56.7.2.'N'.YEAR[NY].
'li'.0.0.99.0.retSS.FALSE.15.0);

B: begin
yearb[NY]:-year[NY]+I;
GETNUM(59.7.2.'N'.YEARB[NY].
'll'.0.0.99.0.retSS.FALSE.15.0)1
end:

9: GETNUM(26.II.4.'N'.SETASIDE_ACRES[NY-3].

10:

11:

12:

13:

14:

15:

16:

17:

18:

19:

20:

21:

22:

23:

24:

25:

26:

27:

28:

213(1

'II.I'.0.0.99.9.retSS.FALSE.15.0);
GETNUM(26.14.4.'N'.APCT[NY-3].
'l#.l'.0.0.99.0.retSS.FALSE.15.0);
GETNUM(26.17.4.'N'.AHCT[NY-3].
'II.!'.0.0.99.9.retSS.FALSE.15.0);
GETNUM(25.20.5.'N'.CORNYT[NY-3].
'Ili.l'.0.0.999.9.retSS.FALSE.15.0);
GETNUM(37.11.4.'N'.SETASIDE_ACRES[NY-2].
'Il.l’.0.0.99.9.retSS.FALSE.15.0);
GETNUM(37.14.4.'N'.APCT[NY-2].
'l#.l'.0.0.99.9.retSS.FALSE.15.0);
GETNUM(37.17.4.'N'.AHCT[NY-2].
'll.l'.0.0.99.9.retSS.FALSE.15.0);
GETNUM(36.20.5.'N'.CORNYT[NY-2].
'lll.l'.0.0.999.9.retSS.FALSE.15.0);
GETNUM(47.11.4.'N'.SETASIDE_ACRES[NY-1].
'li.l'.0.0.99.9.retSS.FALSE.15.0);
GETNUM(47.I4.4.'N'.APCT[NY-1].
'Il.i'.0.0.99.9.retSS.FALSE.15.0);
GETNUM(47.17.4.'N'.AHCT[NY-I].
'll.l'.0.0.99.9.retSS.FALSE.15.0);
GETNUM(46.20.5.'N'.CORNYT[NY-1].
'lll.l'.0.0.999.9.retSS.FALSE.15.0);
GETNUM(57.II.4.'N'.SETASIDE_ACRES[NY].
'Il.l'.0.0.99.9.retSS.FALSE.15.0);
GETNUM(57.14.4.'N'.APCT[NY].
'II.I'.0.0.99.9.retSS.FALSE.15.0);
GETNUM(57.17.4.'N'.AHCT[NY].
'Il.l'.0.0.99.9.retSS.FALSE.15.0);
GETNUM(56.20.5.'N'.CORNYT[NY].
'#ll.!'.0.0.999.9.retSS.FALSE.15.0);
GETITEM(66.I4.1.'Y'.APCT_FLAG.
'U'.".".retSS.retrieveSS.IS.0);
BEGIN
if (retrieveSS-true) and (apct_flag='Y') then
begin
fgzIIS;
bg:IO;
GETNUM(72.14.6.'N’.APCT_OR[NY].
'Il.##l'.0.000.99.999.retSS.retrieveSS.fg.bg);
end
else
begin
fgzIO;
bg:I0;
6ETNUM(72.14.6.'N'.APCT_OR[NY].
'##.l#i'.0.000.99.999.retSS.false.fg.bg);
end;
END;
GETITEM(66.17.1.'Y'.AHCT_FLAG.
'U'.".".retSS.retrieveSS.15.0);
BEGIN
if (retrieveSSItrue) and (ahct_flag='Y') then

begin

22331

fgzIIS:
bg:I0;
GETNUM(72.I7.6.'N'.AHCT_0R[NY].
'l#.lil'.0.000.99.999.retSS.retrieveSS.fg.bg);
end
else
begin
fgzIO:
bgzIO:
GETNUM(72.I7.6.'N'.AHCT_0R[NY].
'll.lll'.0.000.99.999.retSS.false.fg.bg);
end;
END:
29: GETITEM(66.20.I.'Y'.CORNYT_FLAG.
'U'.".".retSS.retrieveSS.IS.0);
30: BEGIN
if (retrieveSS-true) and (cornyt_flag='Y') then
begin
fg:I15:
bgzIO;
GETNUM(73.20.5.'N'.CORNYT_OR[NY].
'l!!.l'.0.0.999.9.retSS.retrieveSS.fg.bg);
end
else
begin
fgzIO;
bgzIO;
GETNUM(73.20.5.'N'.CORNYT_OR[NY].
'lll.l'.0.0.999.9.retSS.false.fg.bg);
end;
END;
END; { CASE }
color(white.black);

if varSS-screen_fieldSS then last_fieldSS:ITRUE;
RET_STATUS; { Check the code in "retSS”. Set ”varSS" and "actionSS" }

{ Check to see whether to switch retrieveSS to true }
if last_fieldSS and (not retrieveSS) then
begin
retrieveSS:ITRUE; last_fieldSS:IFALSE; actionSS:IstaySS; varSS:=l;
end else
last_fieldSS:IFALSE:

UNTIL actionSS-exitSS
END; { PROCEDURE SCREEN_12 }

overlay PROCEDURE SCREEN_I3: { B:SDBSCUSA I

BEGIN
screenSS:I5; screen_field$S:-64; varSS:II;
retrieveSS:IFALSE: last_fieldSS:IFALSE;
DISPLAY_SCREEN('SDBSCUSA.SCR'.file_existSS); { Display Screen }
if not file_existSS then
begin gotoxy(I.I); write('Missing Screen SDBSCUSA') end;

 

.2132
retSS:=";

{ Display Items. Change retrieveSS to TRUE and INPUT items}
REPEAT { until actionSS I exitSS }
CASE varSS of

1: GETNUM(30.4.2.'N'.YEAR[NY-3].
'il'.0.0.99.0.retSS.FALSE.15.0);

2: GETNUM(33.4.2.'N'.YEARB[MY-3].
'Ii'.0.0.99.0.retSS.FALSE.15.0);

3: GETNUM(40.4.2.'N'.YEAR[NY-2].
'll'.0.0.99.0.retSS.FALSE.15.0);

4: GETNUM(43.4.2.'N'.YEARB[NY-2].
'l".0.0.99.0.retSS.FALSE.15.0);

5: GETNUM(50.4.2.'N'.YEAR[NY-1].
'II'.0.0.99.0.retSS.FALSE.15.0);

6: GETNUM(53.4.2.'N'.YEARB[NY-1].
'EI'.D.O.99.0.retSS.FALSE.15.0);

7: GETNUM{GO.4.2.'N'.YEAR[NY].
'II'.0.0.99.0.retSS.FALSE.15.0);

B: GETNUM(63.4.2.'N'.YEARB[NY].
'II'.0.0.99.0.retSS.FALSE.15.0):

9: GETNUM(3I.7.4.'N'.8EGINSTOCKS[NY-3].
'R'EI'.0.0.9999.0.retSS.FALSE.15.0};

IO: GETNUM(3I.8.4.'N'.PRODUCT[NY-3].
'RIII'.0.0.9999.0.retSS.FALSE.15.0):

11: GETNUM(33.9.2.'N'.IMPORTS[NY-3].
'Rl'.0.0.99.0.retSS.FALSE.15.0);

12: GETNUM(30.11.5.'N'.TSUPPLY[NY-3].
'IIIII'.0.0.99999.0.retSS.FALSE.15.0);

13: GETNUM(31.I3.4.'N'.FEEDT[NY-3].
'llii'.0.0.9999.0.retSS.FALSE.15.0);

l4: GETNUM(3I.14.4.'N'.FSI_USE[NY-3].
'IIIP'.0.0.9999.0.retSS.FALSE.15.0);

15: GETNUM(31.I6.4.'N'.DOMESTIC_USE[NY-3].
'IPII'.0.0.9999.0.retSS.FALSE.15.0);

16: GETNUM(31.I7.4.'N'.EXPDRTS[NY-3].
'IIII'.0.0.9999.0.ret$S.FALSE.15.0);

I7: GETNUM(31.19.4.'N'.TOTAL_USE[NY-3].
'IIIE'.0.0.9999.0.retSS.FALSE.15.0);

18: GETNUM(31.20.4.'N'.END_STOCKS[NY-3].
'iiil'.0.0.9999.0.retSS.FALSE.15.0);

19: GETNUM(30.21.5.'N'.PERCENT_USE[NY-3].
'Ill.l'.0.0.IO0.0.retSS.FALSE.15.0);

20: GETNUM(31.23.4.'N'.CORNPT[NY-3].
'I."'.0.00.9.99.ret$S.FALSE.15.0);

21: GETNUM(41.7.4.'N'.BEGINSTDCKS[NY-2].
'II'I'.0.0.9999.0.retSS.FALSE.15.0);

22: GETNUM(41.B.4.'N'.PRODUCT[NY-2].
'IIII'.0.0.9999.0.retSS.FALSE.15.0);

23: GETNUM(43.9.2.'N'.IMPORTS[NY-2].
'li'.0.0.99.0.retSS.FALSE.15.0);

24: GETNUM(40.11.5.'N'.TSUPPLY[NY-2].
'IIIIE'.0.0.99999.0.retSS.FALSE.15.0);

25: GETNUM(4I.I3.4.'N'.FEEDT[NY-2].

26:

27:

28:

29:

30:

31:

32:

33:

34:

35:

36:

37:

38:

39:

40:

41:

42:

43:

44:

45:

46:

47:

49:

50:

SI:

52:

233

'lill'.0.0.9999.0.retSS.FALSE.15.0);
GETNUM(4I.14.4.'N'.FSI_USE[NY-2].
'IIII'.0.0.9999.0.retSS.FALSE.15.0);
GETNUM(41.16.4.'N'.DOMEST1C_USE[NY-2].
'IPII'.0.0.9999.0.retSS.FALSE.15.0);
GETNUM(41.17.4.'N'.EXPORTS[NY-2].
'IIIF'.0.0.9999.0.retSS.FALSE.15.01:
GETNUM(4I.19.4.'N'.TOTAL_USE[NY-2].
'llll'.0.0.9999.0.retSS.FALSE.15.0):
GETNUM(41.20.4.'N'.END_STOCKS[NY-2].
'illl'.0.0.9999.0.retSS.FALSE.15.0);
GETNUM(40.21.5.'N'.PERCENT_USE[NY-2].
'lll.l'.0.0.100.0.retSS.FALSE.15.0):
GETNUM(4I.23.4.'N'.CORNPT[NY-2].
'l.ll'.0.00.9.99.retSS.FALSE.15.0);
GETNUM(51.7.4.'N'.BEGINSTOCKS[NY-1].
'llll'.0.0.9999.0.retSS.FALSE.15.0);
GETNUM(51.B.4.'N'.PRODUCT[NY-I].
'llll'.0.0.9999.0.retSS.FALSE.15.0):
GETNUM(S3.9.2.'N'.IMPORTS[NY-1].
'II'.0.0.99.0.retSS.FALSE.15.0);
GETNUM(50.II.5.'N'.TSUPPLY[NY-I].
'lllll'.0.0.99999.0.retSS.FALSE.15.0);
GETNUM(51.13.4.'N'.FEEDT[NY-1].
'llll'.0.0.9999.0.retSS.FALSE.15.0);
GETNUM(SI.14.4.'N'.FSI_USE[NY-I].
'lll!’.0.0.9999.0.retSS.FALSE.15.0);
GETNUM(51.16.4.'N'.DOMESTIC_USE[NY-l].
'l!!!’.0.0.9999.0.retSS.FALSE.15.0);
GETNUM(51.I7.4.'N'.EXPORTS[NY-1].
'IIII'.0.0.9999.0.retSS.FALSE.15.0):
GETNUM(51.I9.4.'N'.TOTAL_USE[NY-1].
'llll'.0.0.9999.0.retSS.FALSE.15.0):
GETNUM(51.20.4.'N'.ENO_STOCKS[NY-I].
'IIPI'.0.0.9999.0.retSS.FALSE.15.0);
GETNUM(SO.21.5.'N'.PERCENT_USE[NY-l].
'Ill.l'.0.0.100.0.retSS.FALSE.15.0):
GETNUM(51.23.4.'N'.CORNPT[NY-I].
'I.II'.0.00.9.99.retSS.FALSE.15.0);
GETNUM(61.7.4.'N'.BEGINSTOCKS[NY].
'Illl'.0.0.9999.0.retSS.FALSE.15.0);
GETNUM(60.8.5.'N'.PRODUCT[NY].
'lllll'.0.0.99999.0.retSS.FALSE.15.0);
GETNUM(63.9.2.'N'.IMPORTS[NY].
'll'.0.0.99.0.retSS.FALSE.15.0);
GETNUM(60.II.5.'N'.TSUPPLY[NY].
'lllll'.0.0.99999.0.retSS.FALSE.15.0);
GETNUM(61.13.4.'N’.FEEDT[NY].
'llll'.0.0.9999.0.retSS.FALSE.15.0);
GETNUM(61.14.4.'N'.FSI_USE[NY].
'Illl'.0.0.9999.0.retSS.FALSE.15.0);
GETNUM(61.16.4.'N'.DOMESTIC_USE[NY].
'llll'.0.0.9999.0.retSS.FALSE.15.0):
GETNUM(61.17.4.'N'.EXPORTS[NY].

53:

S4:

55:

56:

57:

58:

59:

60:

61:

62:

234

'Illi'.0.0.9999.0.retSS.FALSE.15.0);
GETNUM(61.19.4.'N'.TOTAL_USE[NY].
'lill'.0.0.9999.0.retSS.FALSE.15.0);
GETNUM(61.20.4.'N'.END_STOCKS[NY].
'llli'.0.0.9999.0.retSS.FALSE.15.0);
GETNUM(60.21.5.'N'.PERCENT_USE[NY].
'lll.l'.0.0.100.0.retSS.FALSE.15.0);
GETNUM(61.23.4.'N'.CORNPT[NY].
'l.ll'.0.00.9.99.retSS.FALSE.15.0);
GETITEM(69.8.I.'Y',PRODUCT_FLAG.
'U'.".".retSS.retrieveSS.15.0);
BEGIN
IF (retrieveSS-true) AND (product-flagI'Y') then
begin
fgzI15:
bg:I0;
GETNUM(73.8.5.'N'.PRODUCT_OR[NY].
'Ilill'.0.0.99999.9.retSS.retrieveSS.fg.bg);
end
else
begin
fgzIO;
bg:I0;
GETNUM(73.8.5.'N'.PRODUCT_OR[NY].
'lilli'.0.0.99999.9.retSS.false.fg.bg);
end;
END;
GETITEM(69.II.I.'Y'.TSUPPLY_FLAG.
'U'.".".retSS.retrieveSS.IS.0);
BEGIN
if (retrieveSS-true) and (TSUPPLY_flag='Y‘) then
begin
fg:I15:
bg:I0;
GETNUM(73.II.5.'N'.TSUPPLY_0R[NY].
'lilil'.0.0.99999.0.retSS.retrieveSS.fg.bg);
end
else
begin
fg:I0;
bg:I0;
GETNUM(73.II.S.'N'.TSUPPLY_OR[NY].
'lllll'.0.0.99999.0.retSS.false.fg.bg);
end;
END:
GETITEM(69.13.I.'Y'.FEEDT_FLAG.
’U'.".".retSS.retrieveSS.15.0);
BEGIN
if (retrieveSS-true) and (FEEDT_flagI'Y') then
begin
fg:I15:
bg:I0;
GETNUM(74.13.4.’N'.FEEDT_OR[NY].
'llll'.0.0.9999.0.retSS.retrieveSS.fg.bg);

21355

end
else
begin
fg:I0:
bg:I0;
GETNUM(74.I3.4.'N'.FEEDT_OR[NY].
'lfil'.0.0.9999.0.retSS.false.fg.bg);
end;
END;
63: GETITEM(69.17.1.'Y'.EXPORTS_FLAG.
'U'.".".retSS.retrieveSS.15.0);
64: BEGIN
if (retrieveSSItrue) and (EXPORTS_flag='Y') then
begin
fgzIIS:
bg:I0;
GETNUM(74.17.4.'N'.EXPORTS_0R[NY].
'IIII'.0.0.9999.0.retSS.retrieveSS.fg.bg);
end
else
begin
fg:I0:
bg:I0;
GETNUM(74.I7.4.'N'.EXPORTS.OR[NY].
'Ilil'.0.0.9999.0.retSS.false.fg.bg);
end;
END;
END; { CASE }
color(white.black);

if varSS-screen_fieldSS then last_fieldSS:ITRUE;
RET_STATUS; { Check the code in ”retSS". Set "varSS" and "actionSS" }

{ Check to see whether to switch retrieveSS to true )
if last_fieldSS and (not retrieveSS) then
begin
retrieveSS:ITRUE; last_fieldSS:IFALSE: actionSS:IstaySS; varSS:-1;
end else
last_fieldSS:IFALSE:

UNTIL actionSS-exitSS
END; { PROCEDURE SCREEN_13 }

overlay PROCEDURE SCREEN_I4; { B:SDBSCUSB }
BEGIN

screenSS:I6; screen_fieldSS:I34; varSS:Il;

retrieveSS:IFALSE; last_fieldSS:IFALSE;

DISPLAY_SCREEN('SDBSCUSB.SCR'.file_existSS); { Display Screen }

if not file_existSS then

begin gotoxy(I.I); write('Missing Screen SDBSCUSB') end;
retSS:I";

{ Display Items. Change retrieveSS to TRUE and INPUT items}

23345

REPEAT { until actionSS I exitSS }
CASE varSS of

I: GETNUM(30.5.2.'N'.YEAR[NY-3].
'll'.0.0.99.0.retSS.FALSE.15.0);

2: GETNUM(33.5.2.'N'.YEARB[NY-3].
'II'.0.0.99.0.retSS.FALSE.15.0);

3: GETNUM(40.5.2.'N'.YEAR[NY-2].
'll'.0.0.99.0.retSS.FALSE.15.0);

4: GETNUM(43.S.2.'N'.YEARB{NY-2].
'li'.0.0.99.0.retSS.FALSE.15.0);

5: GETNUM(50.5.2.'N'.YEAR[NY-1].
'II’.0.0.99.0.retSS.FALSE.15.0);

6: GETNUM(S3.5.2.'N'.YEARB[NY-l].
'll'.0.0.99.0.retSS.FALSE.IS.0);

7: GETNUM(60.5.2.'N'.YEAR[NY].
'II'.0.0.99.0.retSS.FALSE.15.0);

8: GETNUM(63.5.2.'N'.YEARB[NY].
'II'.0.0.99.0.retSS.FALSE.15.0);

9: GETNUM(31.11.4.'N'.CCCSTOCKS[NY-3].
'lill'.0.0.9999.0.retSS.FALSE.15.0);

10: GETNUM(31.13.4.'N'.FORSTOCKS[NY-3].
'ltll'.0.0.9999.0.retSS.FALSE.15.0);

11: GETNUM(31.15.4.'N'.FREESTOCKS[NY-3].
'll1l'.0.0.9999.0.retSS.FALSE.15.0);

12: GETNUM(31.20.4.'N'.GOVTSTOCKS[NY-3].
'Illi'.0.0.9999.0.retSS.FALSE.15.0);

13: GETNUM(3I.22.4.'N'.CORN_LOAN_RATE[NY-3].
'I.Il'.0.00.9.99.retSS.FALSE.15.0);

14: GETNUM(4I.11.4.'N'.CCCSTOCKS[NY-2].
'Illl'.0.0.9999.0.retSS.FALSE.15.0);

15: GETNUM(41.13.4.'N'.FORSTOCKS[NY-2].
'Plil'.0.0.9999.0.retSS.FALSE.15.0);

16: GETNUM(41.15.4.'N'.FREESTOCKS[NY-2].
'lill'.0.0.9999.0.retSS.FALSE.15.0);

I7: GETNUM(4I.20.4.'N'.GOVTSTOCKS[NY-2].
'IIII'.0.0.9999.0.retSS.FALSE.15.0);

18: GETNUM(41.22.4.'N'.CORN_LOAN_RATE[NY-2].
'l.ll'.0.00.9.99.retSS.FALSE.15.0);

19: GETNUM(SI.II.4.'N'.CCCSTOCKS[NY-1].
'llii'.0.0.9999.0.retSS.FALSE.15.0);

20: GETNUM(51.13.4.'N'.FORSTOCKS[NY-l].
'IIII'.0.0.9999.0.retSS.FALSE.15.0);

21: GETNUM(51.15.4.'N'.FREESTOCKS[NY-I].
'III!’.0.0.9999.0.retSS.FALSE.15.0);

22: GETNUM(SI.20.4.'N'.GOVTSTOCKS[NY-I].
'Illl'.0.0.9999.0.retSS.FALSE.15.0);

23: GETNUM(51.22.4.'N'.CORN_LOAN_RATE[NY-I].
'l.l#'.0.00.9.99.retSS.FALSE.15.0);

24: GETNUM(60.11.4.'N'.CCCSTOCKS[NY].
'lll!‘.0.0.9999.0.retSS.FALSE.15.0);

25: GETNUM(60.13.4.'N'.FORSTOCKS[NY].
'llll'.0.0.9999.0.retSS.FALSE.15.0);

26: GETNUM(60.15.4.'N'.FREESTOCKS[NY].
'llfll'.0.0.9999.0.retSS.FALSE.15.0);

 

I F. em..___

 

1T 1'7 ,

27:

28:

29:

30:

31:

32:

33:

2237

GETNUM(60.22.4.'N'.CORN_LOAN_RATE[NY].
'l.ll'.0.00.9.99.retSS.FALSE.15.0);
GETITEM(70.II.I.'Y'.CCCSTOCKS_FLAG.
'U'.".".retSS.retrieveSS.15.0);
BEGIN
if (retrieveSS-true) and (CCCSTOCKS_flag-'Y') then
begin
fgzIIS:
bg:I0;
GETNUM(74.11.4.'N'.CCCSTOCKS_0R[NY].
'llll'.0.0.9999.0.retSS.retrieveSS.fg.bg);
end
else
begin
fg:I0:
bg:I0;
GETNUM(74.II.4.'N'.CCCSTOCKS_0R[NY].
'Illl'.0.0.9999.0.retSS.false.fg.bg);
end;
END;
GETITEM(70.I3.I.'Y'.FORSTOCKS_FLAG.
'U'.".".retSS.retrieveSS.15.0);
BEGIN
if (retrieveSS-true) and (F0RSTOCKS_flagI'Y’) then
begin
fg:I15:
bg:I0;
GETNUM(74.13.4.'N'.FORSTOCKS_OR[NY],
'llll'.0.0.9999.0.retSS.retrieveSS.fg.bg);
end
else
begin
fgzIO:
bg:I0;
GETNUM(74.13.4.'N'.FORSTOCKS_0R[NY].
'lili'.0.0.9999.0.retSS.false.fg.bg);
end;
END;
GETITEM(70.IS.1.'Y'.FREESTOCKS_FLAG.
'U'.".".retSS.retrieveSS.15.0);
BEGIN
if (retrieveSSItrue) and (FREESTOCKS_flagI'Y') then
begin
fgzIIS:
bg:IO:
GETNUM(74.15.4.'N'.FREESTOCKS_OR[NY].
'IIOI'.0.0.9999.0.retSS.retrieveSS.fg.bg);
end
else
begin
fg:I0:
bg:I0;
GETNUM(74.15.4.'N‘.FREESTOCKS_0R[NY].
'lill'.0.0.9999.0.retSS.false.fg.bg);

2238

end;
END;

34: GETNUM(74.20.4.'N'.GOVTSTOCKS[NY].
'IIII'.0.0.9999.0.retSS.retrieveSS.15.0);

END; { CASE }

if varSS-screen_fieldSS then last_fieldSS:ITRUE;
RET_STATUS; { Check the code in ”retSS". Set “varSS" and "actionSS" }

{ Check to see whether to switch retrieveSS to true }
if last_fieldSS and (not retrieveSS) then
begin
retrieveSS:ITRUE; last_fieldSS:IFALSE; actionSS:IstaySS; varSS:Il:
end else
last_fieldSS:IFALSE:

UNTIL actionSS-exitSS
END; { PROCEDURE SCREEN_14 I

***** End of Include file BALSHEET.INC '****
"*" Beginning of Include file ZEBRAC.INC '****
{$1 ZEBRAC.INC}
PROCEDURE zebrac; { A PROCEDURE HHICH USES THE EQUATION BI-SECTION
METHOD TO DETERMINE MARKET CLEARING PRICE FOR THE
'CALCULATE' PROCEDURE }
var
y0.x.xl.xu.es.xr.xn.aa.ea. temp1.temp2.temp3: real;
ni.im: integer;

begin

XL:I0.5; XU:I5.OO: ES:I0.1:

im:I20;

xr:-(xl+xu)/2.0;

nizIO:

repeat
ni:-ni+1:
calculations(xl);
templzquuilibrium;
calculations(xr);
temp2:quuilibrium:
aa:-temp1 * tempz;
if aa < 0.0 then xuzIxr;
if aa > 0.0 then xlzIxr;
xn:I(xl+xu)/2.0;
if (xn <> 0.0) then ea:Iabs((xn-xr)/xn)*100.0 else ea:I999.99;
xerxn;
uscornpt[f]:-(xr+xn)/2;

until ((ea < es) or (ni > im));

end;

"*** End of Include file ZEBRAC.INC *****
****‘ Beginning of Include file RANDOMIZE.INC ****’

22359

{$1 RANDOMIZE.INC}
overlay PROCEDURE RANDOMIZE_YIELD_SCREEN_I: { RAND }
VAR { Variables Section For RAND }

jump.m.n : integer;

temp : real;

alldone : boolean:

a: real;

l.kount: integer;

BEGIN
RANDOM_PULLS:I25.0: INFOzI'SOLVING': PAUSEzI'PLEASE HAIT';
screenSS:-7; screen_fieldSS:I3; varSS:II:
retrieveSS:IFALSE; last_fieldSS:IFALSE;
DISPLAY_SCREEN('RAND.SCR'.file_existSS); { Display Screen }
if not file_existSS then
begin gotoxy(I.I); write('Missing Screen RAND') end;
retSS:I";

{ Display Items. Change retrieveSS to TRUE and INPUT items}
REPEAT { until actionSS I exitSS }
CASE varSS of
1: BEGIN
GETNUM(70.8.3.'N'.RANDOM_PULL5.
'lll'.I.O.999.0.retSS.retrieveSS.15.0);
wa:-TRUNC(RANDOM_PULLS):
END;
2: BEGIN
IF RETRIEVESSIFALSE THEN
FG:I0
ELSE FG:IIS:
GETITEM(36.12.7.'C'.INFO.
'XXXXXXX'.".".retSS.FALSE.FG.O):
END;
3: BEGIN
IF RETRIEVESSIFALSE THEN
FG:I0
ELSE FG:IIS:
GETITEM(34.15.11.'C'.PAUSE.
'XXXXXXXXXXX'.".".retSS.FALSE.FG.0);
END;
END; { CASE }

if varSS-screen_fieldSS then last_fieldSS:ITRUE;
RET_STATUS; { Check the code in ”retSS". Set "varSS" and "actionSS" }

{ Check to see whether to switch retrieveSS to true }
if last_fieldSS and (not retrieveSS) then
begin
retrieveSS:ITRUE; last_fieldSS:IFALSE; actionSS:IstaySS; varSS:=I;
end else
last_fieldSS:IFALSE:

UNTIL actionSS-exitSS;

 

240

BEGIN
FOR fzI 1 TO we DO
BEGIN
{ CORNPT[NY]:I2.SO;}
BETA:IO.0; { THE NAME IS THE US YIELD }
BETA1:I0.0; { THE NAMEI IS THE EXPORTERS FEEDGRAIN YIELD }
BETA2:IO.0; { THE NAMEZ IS THE IMPORTERS FEEDGRAIN YIELD }
RANDOMIZE:
FOR I:Il T0 30 DO
BEGIN
ALPHA[I]:IRANOOM:
ALPHA1[I]:IRANDOM:
ALPHA2[I]:IRANDOM:
BETA:IBETA+ALPHA[I];
BETA1:IBETA1+ALPHA1[I];
BETA2:IBETA2+ALPHA2[I]:
END;
GAMMAzIBETA/30:
GAMMAlzIBETAI/3O:
GAMMA2:IBETA2/3O:
Z:I((GAMMA-0.5)l0.052705); { ZI(rnd-mu)/(sigma/sqrt(n)) by the central limit therom }
ZI:I((GAMMA1-0.5)/0.052705);
ZZ:I((GAMMA2-0.5)/0.052705):

ZEBRAC:

END;
END; { PROCEDURE RANDOMIZE_YIELD_SCREEN_1 }
{itiitttitttitttttt'fliitittttttttt*ttttttttttttttttttttti‘ttttttttttttttt
* PROCEDURE SORT '
* This procedure sorts an array. T. of length L using the diminishing *
* increment sorting procedure developed by Shell. This code is a *
* modification of that presented on pp. 158-63 of the revised edition ‘
* "Programming in PASCAL" by Peter Grogono. *
**itfittiifitiiiti***tttitiiit*tf‘tittittifittttfittittitttttttititit...titt}
begin {procedure Sort}
l:Iwa;
JUMD1II:
while (Jump>1) do
begin
jump:Ijump div 2:
repeat
alldonezItrue;
for m:Il to l-jump do
begin
nzIm+jump:
if (uscornpt [m] >uscornpt [n] )
then
begin
tenp : Iuscornpt [m] ;
uscornpt [m] :Iuscornpt [n] ;
uscornpt[n]:Itemp;
alldonezIfalse;
end;

 

 

2411.

end;
until alldone:
end;
end: {Procedure Sort}

for i:I1 to 3 do
begin
beep(true);
end;
beeponss:-false;

END; {PROCEDURE RANDOMIZE_YIELO_SCREEN_1}

overlay procedure DISPLAY-RANDOMIZE_SCREEN;

VAR { Variables Section For BzPROBDISP }
USCPRICEI: REAL; USCPRICEZ: REAL; USCPRICE3: REAL; USCPRICE4: REAL:
USCPRICES: REAL: USCPRICEG: REAL: USCPRICE7: REAL; USCPRICEB: REAL:
USCPRICEQ: REAL; USCPRICEIO: REAL: USCPRICEII: REAL: USCPRICEIZ: REAL:
temp. holdl. mean. std_dev: real;

{ttfiiitttttitifittttitfit*tfittittttfiifitfitfitititttttfittttttttittttitttt3*it

‘ PROCEDURE FINO_PERCENTILES *
‘ A PROCEDURE HHICH USES LINEAR INTERPOLATION TO CONSTRUCT THE *
‘ FREQUENCY DISTRIBUTION. IT USES THE METHOD SIMULAR T0 THAT ‘
* DESCRIBED IN "STATISTICAL CONCEPTS AND METHODS” PAGES 14-24 *
* BY BHATTACHARYYA AND JOHNSON. ’

ittfififiifiififiitifitiiitfitttfitttttiit'tI.*ttfitifittttfifiitttitttfiiiitittttiii}

PROCEDURE FIND_PERCENTILES:

VAR cum_freq. frequency.interval:array[1..10] of real;
lowprice. highprice. spread. classlength:real;
counter.obs.fractionzreal;
q.i.l.J:integer;

begin
lowprice:Iuscornpt[I]-0.005;
highpricequscornpt[wa]+0.005:
spread:Ihighprice-lowprice;
classlength:Ispread/IO;
percentile_answer:I0.0;
for i:I1 to 10 do

begin
interval[i]:Ilowprice+classlength;
counter:-0.0;
for j:I1 to we do
if (uscornpt[j] > lowprice) and (uscornpt[j] < interval[i]) then
begin
counter:Icounter+l;
end;
frequency[i]:Icounter;
if jII then cum_freq[i]:Ifrequency[i]+0.0
else cum_freq[i]:Ifrequency[i]+cum_freq[i-I];
lowprice:Iinterval[i];
end:
obszIwa'(p/100);

 

41mm ... .

 

2412

i:I I;
while i<11 do
begin
if cum_freq[i]>Iobs then
begin
qui;
i:IIO;
end;
1:- i+1;
end;
fraction:I((obs-cum_freq[q-I])/frequency[q]);
percentile_answer:I(interval[q]-classlength)+(fraction'classlength);
end;

BEGIN
temp:IO.0;
for f:II to wa do
begin
temszuscornpt[f]+temp;
end;
meanzItemp/wa;

temsz0.0;
for f:I1 to we do
begin
temp:I(uscornpt[f]-mean)‘(uscornpt[f]-mean) + temp;
end;
std_dev:Isqrt(temp/(wa-I)):

screenSS:I8; screen_fieldSS:IIS; varSS:II;

retrieveSS:IFALSE; last_fieldSS:IFALSE;

DISPLAY_SCREEN('PROBDISP.SCR'.file.existSS): { Display Screen I

if not file_existSS then

begin gotoxy(I.I); write('Missing Screen PROBDISP') end;
retSS:I";

{ Display Items. Change retrieveSS to TRUE and INPUT items}
REPEAT { until actionSS I exitSS }
CASE varSS of
1: BEGIN
P:I1;
F1ND_PERCENTILES:
USCPRICEI:IPERCENTILE_ANSHER:
GETNUM(60.7.4.'N'.USCPRICE1.
'l.ll'.0.00.9.99.retSS.FALSE.15.0);
END;
2: BEGIN
P:I5;
FIND_PERCENTILES;
USCPRICEZ:IPERCENTILE_ANSHER:
GETNUM(60.8.4.'N'.USCPRICE2.
'l.il'.0.00.9.99.retSS.FALSE.15.0);
END:

 

 

10:

BEGIN

P:I10:

FIND_PERCENTILES:

USCPRICE3:IPERCENTILE_ANSHER:

GETNUM(60.9.4.'N'.USCPRICE3.
'I.Il'.0.00.9.99.retSS.FALSE.15.0);
END;

BEGIN

P:I20:

FIND_PERCENTILES:

USCPRICE4:IPERCENTILE_ANSHER:

GETNUM(60.10.4.'N'.USCPRICE4.
'I.II'.0.00.9.99.retSS.FALSE.15.0);
END;

BEGIN

P:I30:

FIND_PERCENTILES:

USCPRICES:IPERCENTILE_ANSHER;

GETNUM(60.11.4.'N'.USCPRICE5.
'I.Il'.0.00.9.99.retSS.FALSE.15.0);
END;

BEGIN

P:I4O:

FIND_PERCENTILES:

USCPRICEG:IPERCENTILE_ANSHER:

GETNUM(60.12.4.'N'.USCPRICE6.
'I.II'.0.00.9.99.retSS.FALSE.15.0);
END;

BEGIN

P:I50:

FIND_PERCENTILES:

USCPRICE7:IPERCENTILE_ANSHER;

GETNUM(60.13.4.'N'.USCPRICE7.
'l.li'.0.00.9.99.retSS.FALSE.15.0);
END:

BEGIN

P:I60:

FIND_PERCENTILES:

USCPRICEB:IPERCENTILE_ANSHER:

GETNUM(60.14.4.'N'.USCPRICE8.
'!.li'.0.00.9.99.retSS.FALSE.15.0);
END;

BEGIN

P:I70;

FIND.PERCENTILES:

USCPRICE9:IPERCENTILE_ANSHER;

GETNUM(60.15.4.'N'.USCPRICE9.
'l.!l'.0.00.9.99.retSS.FALSE.15.0);
END;
BEGIN

P:I80;

FIND_PERCENTILES:

USCPRICEIO:IPERCENTILE_ANSHER:

GETNUM(60.16.4.'N'.USCPRICE10.

2413

 

 

2414:

'l.ll'.0.00.9.99.retSS.FALSE.15.0);
END;
11: BEGIN
P:I90;
FIND_PERCENTILES:
USCPRICEII:IPERCENTILE_ANSHER:
GETNUM(60.17.4.'N'.USCPRICE11.
'i.ll'.0.00.9.99.retSS.FALSE.15.0);
END;
12: BEGIN
P:I95;
FIND_PERCENTILES:
USCPRICEIZ:IPERCENTILE_ANSHER:
GETNUM(60.IB.4.'N'.USCPR1CE12.
'I.Il'.0.00.9.99.retSS.FALSE.15.0);
END
13: GETNUM(60.19.4.'N'.uscornpt[wa].
'l.ll'.0.00.9.99.retSS.FALSE.15.0):
14: GETNUM(4S.20.4.'N'.MEAN,
'I.II'.0.00.9.99.retSS.FALSE.15.0);
15: GETNUM(45.21.6.'N'.STD_DEV.
'l.llll'.0.0000.9.9999.retSS.FALSE.15.0);
END; { CASE }

if varSS-screen_fieldSS then last_fieldSS:ITRUE;
RET_STATUS; { Check the code in "retSS". Set "varSS" and "actionSS" }

{ Check to see whether to switch retrieveSS to true }
if last_fieldSS and (not retrieveSS) then
begin
retrieveSS:ITRUE; last_fieldSS:IFALSE; actionSS:IstaySS; varSS:II;
end else
last_fieldSS:IFALSE;

UNTIL actionSS-exitSS
END; { PROCEDURE SCREEN_I }
*‘**‘ End of Include file RANDOMIZE.INC *****
overlay procedure EXOGENOUS_VARIABLE-SUB_MENU: { A SUB MENU UNDER THE MAIN MENU }

"*“ Beginning of Include file MAGICI.INC "*’*

{$1 MAGIC1.INC} { EXOGENOUS VARIABLE SUMMARY }
OVERLAY PROCEDURE FSI_USE_SCREEN: { BzFSIUSE }
BEGIN

screenSS:I9; screen_fieldSS:IZB; varSS:Il;

retrieveSS:IFALSE; last_fieldSS:IFALSE:

DISPLAY_SCREEN('FSIUSE.SCR'.file_existSS); { Display Screen }

if not file_existSS then

begin gotoxy(I.I); write('Missing Screen FSIUSE') end;
retSS:I";

{ Display Items. Change retrieveSS to TRUE and INPUT items}
REPEAT { until actionSS I exitSS }
CASE varSS of

 

 

10:

II:

12:

I3:

14:

15:

16:

17:

18:

19:

20:

21:

22:

23:

24:

25:

26:

27:

24155

GETNUM(41.6.2.'N'.YEAR[NY-3].
'll'.0.0.99.0.retSS.FALSE.15.0);
GETNUM(44.6.2.'N'.YEARB[NY-3].
'll'.0.0.99.0.retSS.FALSE.15.0);
GETNUM(51.6.2.'N’.YEAR[NY-2].
'Il'.0.0.99.0.retSS.FALSE.15.0):
GETNUM(54.6.2.'N'.YEARB[NY-2].
'll'.0.0.99.0.retSS.FALSE.15.0);
GETNUM(61.6.2.'N'.YEAR[NY-I].
'II'.0.0.99.0.retSS.FALSE.15.0);
GETNUM(64.6.2.'N’.YEARB[NY-1].
'il'.0.0.99.0.retSS.FALSE.15.0);
GETNUM(7I.6.2.'N'.YEAR[NY].
'Il'.0.0.99.0.retSS.FALSE.IS.0):
GETNUM(74.6.2.'N'.YEARB[NY].
'll'.0.0.99.0.retSS.FALSE.15.0);
GETNUM(41.11.5.'N'.HFCS[NY-3].
'lII.I'.0.0.999.9.retSS.FALSE.15.0);
GETNUM(4I.I3.5.'N'.RESIDUAL[NY-3].
'Ill.l'.0.0.999.9.retSS.FALSE.15.0);
GETNUM(42.IS.4.'N'.SEED_USE[NY-3].
'll.l'.0.0.99.9.retSS.FALSE.15.0);
GETNUM(4I . 19.5. 'N' .FUEL_USE [NY-3].
'Ill.l'.0.0.999.9.retSS.FALSE.15.0);
GETNUM(40.21.6.'N'.FSI_USE[NY-3].
'Illl.i'.0.0.9999.9.retSS.FALSE.15.0);
GETNUM(SI.II.S.'N'.HFCS[NY-2].
'lll.l'.0.0.999.9.retSS.FALSE.15.0);
GETNUM(51.13.5.'N'.RESIOUAL[NY-2].
'Ill.l'.0.0.999.9.retSS.FALSE.15.0);
GETNUM(52.15.4.'N'.SEED_USE[NY-Z].
'll.l'.0.0.99.9.retSS.FALSE.15.0):
GETNUM(51.I9.S.'N'.FUEL_USE[NY-2].
'l!#.l'.0.0.999.9.retSS.FALSE.15.0);
GETNUM(50.21.6.'N'.FSI_USE[NY-2].
'Illl.l'.0.0.9999.9.retSS.FALSE.15.0);
GETNUM(GI.II.5.'N'.HFCS[NY-I].
'ill.l'.0.0.999.9.retSS.FALSE.15.0);
GETNUM(61.I3.5.'N'.RESIDUAL[NY-1].
'Ill.l'.0.0.999.9.retSS.FALSE.15.0);
GETNUM(62.15.4.'N'.SEED_USE[NY-1].
'll.l'.0.0.99.9.retSS.FALSE.15.0):
GETNUM(61.19.5.'N'.FUEL_USE[NY-I].
'III.I'.0.0.999.9.retSS.FALSE.15.0);
GETNUM(60.21.6.'N'.FSI_USE[NY—1].
'llll.l'.0.0.9999.9.retSS.FALSE.15.0);
GETNUM(7I.11.5.'N'.HFCS[NY].
’lll.l'.0.0.999.9.retSS.retrieveSS.15.0);
GETNUM(71.13.5.'N'.RESIDUAL[NY].
'Ill.l’.0.0.999.9.retSS.retrieveSS.15.0);
GETNUM(72.15.4.'N'.SEED_USE[NY].
'll.l'.0.0.99.9.retSS.retrieveSS.15.0);
GETNUM(71.19.5.'N'.FUEL-USE[NY].
'lfil.l'.0.0.999.9.retSS.retrieveSS.I5.0);

 

 

22445

28: BEGIN
FSI_USE[NY]:IHFCS[NY]+RESIDUAL[NY]+SEED_USE[NY]+FUEL_USE[NY];
GETNUM(70.21.6.'N'.FSI_USE[NY].

'Illl.!'.0.0.9999.9.retSS.retrieveSS.15.0);
END:
END; { CASE }

if varSS-screen_fieldSS then last_fieldSS:ITRUE;
RET_STATUS; { Check the code in ”retSS”. Set ”varSS" and ”actionSS" }

{ Check to see whether to switch retrieveSS to true }
if last_fieldSS and (not retrieveSS) then
begin
retrieveSS:ITRUE; last_fieldSS:IFALSE: actionSS:IstaySS; varSS:Il:
end else
last_fieldSS:IFALSE:

UNTIL actionSSIexitSS
END; { OVERLAY PROCEDURE FSI USE SCREEN_I }

OVERLAY PROCEDURE SCREEN_I: { EXOGENOUS VARIABLE SUMMARY SCREEN 1 }
BEGIN

screenSS:-IO; screen_fieldSS:I48; varSS:I1;

retrieveSS:IFALSE; last_fieldSS:IFALSE:

DISPLAY_SCREEN('EXOGVARI.SCR'.file_existSS): { Display Screen I

if not file_existSS then

begin gotoxy(I.I); write('Missing Screen EXOGVARI') end;
retSS:I";

{ Display Items. Change retrieveSS to TRUE and INPUT items}
REPEAT { until actionSS I exitSS }
CASE varSS of
I: GETNUM(43.3.2.'N'.YEAR[NY-3].
'll'.0.0.99.0.retSS.FALSE.15.0);
2: BEGIN
YEARB[NY-3]:IYEAR[NY-3]+1:
GETNUM(46.3.2.'N'.YEARB[NY-3].
'Il'.0.0.99.0.retSS.FALSE.15.0);
END;
3: GETNUM(54.3.2.'N'.YEAR[NY-2].
'li'.0.0.99.0.retSS.FAL$E.15.0)i
4: BEGIN
YEARB[NY-Z]:IYEAR[NY-2]+I:
GETNUM(57.3.2.'N'.YEARB[NY-2].
'Il'.0.0.99.0.retSS.FALSE.15.0):
END;
5: GETNUM(64.3.2.'N'.YEAR[NY-I].
'll'.0.0.99.0.retSS.FALSE.15.0);
6: BEGIN
YEARB[NY-I]:IYEAR[NY-I]*I:
GETNUM(67.3.2.'N'.YEARB[NY-1].
'II'.0.0.99.0.retSS.FALSE.15.0):
END;

7:

9:

10:

11:

12:

13:

14:

15:

16:

I7:

18:

19:

20:

21:

22:

23:

24:

25:

26:

27:

28:

29:

30:

31:

32:

247

GETNUM(74.3.2.'N'.YEAR[NY].
'll'.0.0.99.0.retSS.retrieveSS.15.0);
BEGIN

YEARB[NY]:IYEAR[NY]+1;

GETNUM(77.3.2.'N'.YEARB[NY].

’Il'.0.0.99.0.ret$S.FALSE.15.0);
END;

GETNUM(44.5.4.'N'.IMPORTS[NY-3].

'II.I'.0.0.99.9.retSS.FALSE.15.0);
GETNUM(43.7.5.'N'.CPIT[NY-3].
'l.#ll'.0.000.9.999.retSS.FALSE.15.0);
GETNUM(43.9.5.'N'.VCHH[NY-3].
'II.II'.0.00.99.99.retSS.FALSE.15.0);
GETNUM(42.II.6.'N'.VCCN[NY-3].
'lll.#l'.0.00.999.99.retSS.FALSE.15.0);
GETNUM(42.13.6.'N'.PARREV[NY-3].
'll!.#l'.0.00.999.99.retSS.FALSE.15.0);
GETNUM(42.IS.6.'N'.DIVPAY[NY-3].
'lll.ll’.0.00.999.99.retSS.FALSE.15.0);
GETNUM(42.17.6.'N'.DEFPAY[NY-3].
'lli.ll'.0.00.999.99.retSS.FALSE.15.0);
GETNUM(43.19.5.'N'.ARP[NY-3].
'l.lll'.0.0.I.0.retSS.FALSE.15.0);
GETNUM(43.21.S.'N'.DIVERSION[NY-3].
'l.lll'.0.0.1.0.retSS.FALSE.IS.0);
GETNUM(44.23.4.'N'.CORN_LOAN_RATE[NY-3].
'!.!l',0.00.9.99.retSS.FALSE.15.0);
GETNUM(55.5.4.'N'.IMPORTS[NY-2].

'll.l'.0.0.99.9.retSS.FALSE.15.0);
GETNUM(54.7.5.'N'.CPIT[NY-2].
'l.lll'.0.000.9.999.retSS.FALSE.15.0);
GETNUM(S4.9.5.'N'.VCHH[NY-2].
'l!.ll'.0.00.99.99.retSS.FALSE.15.0);
GETNUM(53.11.6.'N'.VCCN[NY-2].
'III.II'.0.00.999.99.retSS.FALSE.15.0);
GETNUM(53.13.6.'N'.PARREV[NY-2].
'll#.!l'.0.00.999.99.retSS.FALSE.15.0);
GETNUM(53.15.6.'N'.DIVPAY[NY-2].
'lll.il'.0.00.999.99.retSS.FALSE.15.0);
GETNUM(53.17.6.'N'.DEFPAY[NY-2].
'lll.ll'.0.00.999.99.retSS.FALSE.15.0);
GETNUM(54.19.5.'N'.ARP[NY-2].
'l.lii'.0.0.1.0.retSS.FALSE.15.0);
GETNUM(54.21.5.'N'.DIVERSION[NY-2].
'l.!ll'.0.0.1.0.retSS.FALSE.15.0);
GETNUM(55.23.4.'N'.CORN_LOAN_RATE[NY-2].
'l.ll'.0.00.9.99.retSS.FALSE.15.0);
GETNUM(65.5.4.'N'.IMPORTS[NY-I].

'll.I'.0.0.99.9.retSS.FALSE.15.0);
GETNUM(64.7.5.'N'.CPIT[NY-1].
'I.lll'.0.000.9.999.retSS.FALSE.15.0):
GETNUM(64.9.5.'N'.VCHH[NY-1].
'll.ll'.0.00.99.99.retSS.FALSE.15.0);
GETNUM(63.11.6.'N'.VCCN[NY-1].

24153

'III.II'.0.00.999.99.retSS.FALSE.15.0);
33: GETNUM(63.13.6.'N'.PARREV[NY-I].
'll!.ll'.0.00.999.99.retSS.FALSE.15.0):
34: GETNUM(63.15.6.'N'.DIVPAY[NY-I].
'III.II'.0.00.999.99.retSS.FALSE.15.0);
35: GETNUM(63.17.6.'N'.OEFPAY[NY-1].
'lll.!l'.0.00.999.99.retSS.FALSE.15.0):
36: GETNUM(64.19.5.'N'.ARP[NY-1].
'l.ll!'.0.0.1.0.retSS.FALSE.15.0);
37: GETNUM(64.21.5.'N'.DIVERSION[NY-l].
'l.#ll'.0.0.l.0.retSS.FALSE.IS.0);
38: GETNUM(65.23.4.'N'.CORN_LOAN_RATE[NY-1].
'l.l#'.0.00.9.99.retSS.FALSE.15.0);
39: GETNUM(75.S.4.'N'.IMPORTS[NY].
’ll.l'.0.0.99.9.retSS.retrieveSS.15.0);
40: GETNUM(74.7.5.'N'.CPIT[NY].
'l.lll'.0.000.9.999.retSS.retrieveSS.15.0);
41: GETNUM(74.9.5.'N'.VCHH[NY].
'Il.ll'.0.00.99.99.retSS.retrieveSS.15.0);
42: GETNUM(73.II.6.'N'.VCCN[NY].
'III.II'.0.00.999.99.retSS.retrieveSS.15.0);
43: GETNUM(73.13.6.'N'.PARREV[NY].
'lll.ll'.0.00.999.99.retSS.retrieveSS.15.0);
44: GETNUM(73.15.6.'N'.OIVPAY[NY].
'Ili.ll'.0.00.999.99.retSS.retrieveSS.15.0);
45: GETNUM(73.17.6.'N'.DEFPAY[NY].
'III.II'.0.00.999.99.retSS.retrieveSS.15.0);
46: GETNUM(74.19.5.'N'.ARP[NY].
'l.lll'.0.0.I.0.retSS.retrieveSS.15.0);
47: GETNUM(74.21.5.'N'.DIVERSION[NY].
'l.lil'.0.0.I.0.retSS.retrieveSS.15.0);
48: GETNUM(75.23.4.'N'.CORN_LOAN_RATE[NY].
'l.ll°.0.00.9.99.retSS.retrieveSS.15.0);
END; { CASE }

if varSS-screen_fieldSS then last_fieldSS:ITRUE;
RET_STATUS; { Check the Code in ”retSS". Set "varSS” and "actionSS" }

{ Check to see whether to switch retrieveSS to true }
if last_fieldSS and (not retrieveSS) then
begin
retrieveSS:ITRUE; last_fieldSS:IFALSE; actionSS:IstaySS; varSS =1;
end else
last_fieldSS:IFALSE;

UNTIL actionSS-exitSS
END; { OVERLAY PROCEDURE SCREEN_I }

OVERLAY PROCEDURE SCREEN_Z: { EXOGENOUS VARIABLE SUMMARY SCREEN 2 }
BEGIN

screenSS:IIl; screen_fieldSS:I36; varSS II;
retrieveSS:IFALSE: last_fieldSS:IFALSE;
DISPLAY_SCREEN('EXOGVAR2.SCR'.file_existSS); { Display Screen }

21459

if not file_existSS then
begin gotoxy(I.I); write(’Missing Screen EXOGVARZ') end;
retSS:I";

{ Display Items. Change retrieveSS to TRUE and INPUT items}
REPEAT { until actionSS I exitSS }
CASE varSS of

1: GETNUM(43.5.2.'N'.YEAR[NY-3].
'Il'.0.0.99.0.retSS.FALSE.15.0);

2: GETNUM(46.5.2.'N'.YEARB[NY-3].
'Il'.0.0.99.0.retSS.FALSE.15.0);

3: GETNUM(S4.5.2.'N'.YEAR[NY-2].
'll'.0.0.99.0.retSS.FALSE.15.0);

4: GETNUM(57.5.2.'N'.YEARB[NY-2].
'll'.0.0.99.0.retSS.FALSE.15.0);

5: GETNUM(64.5.2.'N'.YEAR[NY*1].
'Il'.0.0.99.0.retSS.FALSE.15.0);

6: GETNUM(67.5.2.'N'.YEARB[NY-I].
'li'.0.0.99.0.retSS.FALSE.15.0);

7: GETNUM(74.5.2.'N'.YEAR[NY].
'll'.0.0.99.0.retSS.FALSE.15.0);

8: GETNUM(77.5.2.'N'.YEARB[NY].
'il'.0.0.99.0.retSS.FALSE.15.0);

9: GETNUM(42.10.6.'N'.NMXRF[NY-3].
'l.llll'.0.0000.9.9999.retSS.FALSE.15.0);

10: GETNUM(42.12.6.'N'.NMXRH[NY-3].
'l.llll'.0.0000.9.9999.retSS.FALSE.15.0);

11: GETNUM(42.I4.6.'N'.NXXRF[NY—3].
'I.l#ll'.0.0000.9.9999.retSS.FALSE.15.0);

12: GETNUM(42.16.6.'N'.NXXRH[NY-3].
'l.llll'.0.0000.9.9999.retSS.FALSE.15.0);

13: GETNUM(4I.18.7.'N'.HP[NY-3].
'IIII.II'.0000.00.9999.99.retSS.FALSE.15.0);

I4: GETNUM(44.20.4.'N'.HHTPT[NY-3].
'l.ll'.0.00.9.99.retSS.FALSE.15.0);

15: GETNUM(44.22.4.'N'.HHTYT[NY-3].
'Il.l'.0.0.99.9.retSS.EALSE.15.0);

16: GETNUM(53.10.6.'N'.NMXRF[NY-2].
'l.#lll'.0.0000.9.9999.retSS.FALSE.15.0);

I7: GETNUM(53.12.6.'N'.NMXRH[NY-2].
'l.llil'.0.0000.9.9999.retSS.FALSE.15.0);

18: GETNUM(53.I4.6.'N'.NXXRF[NY-2].
'l.llll'.0.0000.9.9999.retSS.FALSE.15.0);

19: GETNUM(S3.16.6.'N'.NXXRH[NY-2].
'I.III!'.0.0000.9.9999.retSS.FALSE.15.0);

20: GETNUM(52.IB.7.'N'.HP[NY-2].
'Illl.ll'.0000.00.9999.99.retSS.FALSE.15.0);

21: GETNUM(55.20.4.'N'.HHTPT[NY-2].
'l.li'.0.00.9.99.retSS.FALSE.15.0);

22: GETNUM(55.22.4.'N'.HHTYT[NY-2].
'iI.I'.0.0.99.9.retSS.FALSE.15.0);

23: GETNUM(63.10.6.'N'.NMXRF[NY-I].
'l.illl'.0.0000.9.9999.retSS.FALSE.15.0);

24: GETNUM(63.12.6.'N'.NMXRH[NY-I].

225C)

'l.llli'.0.0000.9.9999.retSS.FALSE.15.0);

25: GETNUM(63.14.6.'N'.NXXRF[NY-1].
'l.llll'.0.0000.9.9999.retSS.FALSE.15.0);

26: GETNUM(63.16.G.'N'.NXXRH[NY-I].
‘I.llll'.0.0000.9.9999.retSS.FALSE.15.0);

27: GETNUM(62.18.7.'N'.HP[NY-I].
'llll.’i'.0000.00.9999.99.retSS.FALSE.15.0):

28: GETNUM(65.20.4.'N'.HHTPT[NY-1].
'l.ll'.0.00.9.99.retSS.FALSE.15.0);

29: GETNUM(65.22.4.'N'.HHTYT[NY-1].
'I!.l'.0.0.99.9.retSS.FALSE.15.0):

30: GETNUM(73.10.6.'N'.NMXRF[NY]. ,
'l.llll'.0.0000.9.9999.retSS.retrieveSS.15.0);

31: GETNUM(73.IZ.G.'N'.NMXRH[NY].
'l.llll'.0.0000.9.9999.retSS.retrieveSS.15.0);

32: GETNUM(73.I4.6.'N'.NXXRF[NY].
'l.!lll'.0.0000.9.9999.retSS.retrieveSS.15.0);

33: GETNUM(73.16.6.'N'.NXXRH[NY].
'I.Illl'.0.0000.9.9999.retSS.retrieveSS.15.0);

34: GETNUM(72.18.7.'N'.HP[NY].
'llll.li'.0000.00.9999.99.retSS.retrieveSS.15.0);

35: GETNUM(75.20.4.'N'.HHTPT[NY].
'l.ll'.0.00.9.99.retSS.retrieveSS.15.0);

36: GETNUM(75.22.4.'N'.HHTYT[NY].
'll.l'.0.0.99.9.retSS.retrieveSS.15.0);

END; { CASE }

if varSS-screen_fieldSS then last_fieldSS:ITRUE;
RET_STATUS; { Check the code in "retSS". Set "varSS" and "actionSS" }

{ Check to see whether to switch retrieveSS to true }
if last_fieldSS and (not retrieveSS) then
begin
retrieveSS:ITRUE; last_fieldSS:IFALSE; actionSS IstaySS; varSS:=I:
end else
last_fieldSS:IFALSE:

UNTIL actionSSIexitSS
END; { OVERLAY PROCEDURE SCREEN_Z }

OVERLAY PROCEDURE SCREEN_B; { EXOGENOUS VARIABLE SUMMARY SCREEN 3 }
BEGIN

screenSS:-12: screen_fieldSS:I36; varSS:I1;

retrieveSS:IFALSE; last_fieldSS:IFALSE;

DISPLAY_SCREEN('EXOGVAR3.SCR'.file_existSS): { Display Screen }

if not file_existSS then

begin gotoxy(I.I); write('Missing Screen EXOGVARS') end;
retSS:I";

{ Display Items. Change retrieveSS to TRUE and INPUT items}
REPEAT { until actionSS I exitSS }

CASE varSS of
1: GETNUM(43.5.2.'N'.YEAR[NY-3].

251

'Il'.0.0.99.0.retSS.FALSE.15.0);
GETNUM(46.5.2.'N'.YEARB[NY-3].
'll'.0.0.99.0.retSS.FALSE.15.0):
GETNUM(54.5.2.'N’.YEAR[NY-2].
'II'.0.0.99.0.retSS.FALSE.15.0):
GETNUM(57.5.2.'N'.YEARB[NY-2].
'il'.0.0.99.0.retSS.FALSE.15.0);
GETNUM(64.5.2.'N'.YEAR[NY-1].
'll'.0.0.99.0.retSS.FALSE.15.0):
GETNUM(67.5.2.'N'.YEARB[NY-1].
'II'.0.0.99.0.retSS.FALSE.15.0):
GETNUM(74.5.2.'N'.YEAR[NY].
'll'.0.0.99.0.retSS.FALSE.15.0):
GETNUM(77.5.2.'N'.YEARB[NY].
'il'.0.0.99.0.retSS.FALSE.15.0);

9: GETNUM(42.10.6.'N'.DANUM[NY-3].

10:

11:

12:

13:

14:

15:

16:

17:

18:

19:

20:

21:

22:

23:

24:

25:

26:

27:

28:

'Il.#!l'.0.000.99.999.retSS.FALSE.15.0);
GETNUM(42.12.6.'N'.COFNUM[NY-3].
'Ii.l#l'.0.000.99.999.retSS.FALSE.15.0);
GETNUM(42.14.6.'N'.OBCNUM[NY-3].
'Ii.l#l'.0.000.99.999.retSS.FALSE.15.0);
GETNUM(41.16.7.'N'.HPCNUM[NY-3].
'lll.lll'.0.000.999.999.retSS.FALSE.15.0);
GETNUM(41.IB.7.'N'.BRONUM[NY-3].
'lll#.#l'.0.00.9999.99.retSS.FALSE.15.0);
GETNUM(4I.20.7.'N'.TURNUM[NY-3].
'III.!#I'.0.000.999.999.retSS.FALSE.15.0);
GETNUM(42.22.6.'N'.HOGNUM[NY-3].
'II.IIF'.0.000.99.999.retSS.FALSE.15.0):
GETNUM(53.IO.6.'N'.DANUM[NY-2].
'Il.lll'.0.000.99.999.retSS.FALSE.15.0);
GETNUM(53.12.6.'N'.COFNUM[NY-2].
'll.lil'.0.000.99.999.retSS.FALSE.15.0);
GETNUM(53.I4.6.'N'.OBCNUM[NY-2].
'Il.l#l'.0.000.99.999.retSS.FALSE.15.0);
GETNUM(52.16.7.'N'.HPCNUM[NY-2].
'iii.lll'.0.000.999.999.retSS.FALSE.15.0);
GETNUM(52.18.7.'N'.BRONUM[NY-2].
'llll.li'.0.00.9999.99.retSS.FALSE.15.0);
GETNUM(52.20.7.'N'.TURNUM[NY-2].
'lll.#fl'.0.000.999.999.retSS.FALSE.15.0);
GETNUM(53.22.6.'N'.HOGNUM[NY-2].
'fil.lll'.0.000.99.999.retSS.FALSE.15.0);
GETNUM(63.10.6.'N'.DANUM[NY-l].
'II.#II'.0.000.99.999.retSS.FALSE.15.0);
GETNUM(63.12.6.'N'.COFNUM[NY-1].
'll.l#l'.0.000.99.999.retSS.FALSE.15.0);
GETNUM(63.14.6.'N'.OBCNUM[NY-I].
'II.III'.0.000.99.999.retSS.FALSE.15.0);
GETNUM(62.16.7.'N'.HPCNUM[NY-1].
'Iil.lll'.0.000.999.999.ret$S.FALSE.15.0);
GETNUM(62.18.7.'N'.BRONUM[NY-l].
'Illl.l#'.0.00.9999.99.retSS.FALSE.15.0);
GETNUM(62.20.7.'N'.TURNUM[NY-1].

 

2552

'lll.lll'.0.000.999.999.retSS.FALSE.15.0):
29: GETNUM(63.22.6.'N'.HOGNUM[NY-1].
'l!.ll!'.0.000.99.999.retSS.FALSE.15.0);
30: GETNUM(73.10.6.'N'.DANUM[NY].
'll.lll'.0.000.99.999.retSS.retrieveSS.15.0);
31: GETNUM(73.12.6.'N'.COFNUM[NY].
'l!.lll'.0.000.99.999.retSS.retrieveSS.15.0);
32: GETNUM(73.14.6.'N'.OBCNUM[NY].
'll.ll0'.0.000.99.999.retSS.retrieveSS.15.0);
33: GETNUM(72.16.7.'N'.HPCNUM[NY].
'lll.lll'.0.000.999.999.retSS.retrieveSS.15.0);
34: GETNUM(72.18.7.'N'.BRONUM[NY].
'Illl.ll'.0.00.9999.99.retSS.retrieveSS.15.0);
35: GETNUM(72.20.7.’N'.TURNUM[NY].
'lll.lll'.0.000.999.999.retSS.retrieveSS.15.0);
36: GETNUM(73.22.6.'N'.HOGNUM[NY].
'01.!!l'.0.000.99.999.retSS.retrieveSS.15.0);
END; { CASE }

if varSS-screen_fieldSS then last_fieldSS:ITRUE;
RET_STATUS; { Check the code in "retSS”. Set "varSS" and "actionSS" }

{ Check to see whether to switch retrieveSS to true }
if last_fieldSS and (not retrieveSS) then
begin
retrieveSS:ITRUE; last_fieldSS:IFALSE; actionSS:IstaySS; varSS:=l;
end else
last_fieldSS:IFALSE;

UNTIL actionSSIexitSS
END; { OVERLAY PROCEDURE SCREEN_3 }

OVERLAY PROCEDURE SCREEN_3A: {B:EXOGVAR 3A I
BEGIN

screenSS:-13; screen_fieldSS:I44; varSS:Il:

retrieveSS:IFALSE; last_fieldSS:IFALSE;

DISPLAY_SCREEN('EXOVAR3A.SCR'.file_existSS); { Display Screen }

if not file_existSS then

begin gotoxy(I.I); write('Missing Screen EXOVAR3A') end;
retSS:I";

{ Display Items. Change retrieveSS to TRUE and INPUT items}
REPEAT { until actionSS I exitSS }
CASE varSS of
1: GETNUM(43.5.2.'N'.YEAR[NY-3].
'll'.0.0.99.0.retSS.FALSE.15.0);
2: GETNUM(46.5.2.'N'.YEARB[NY-3].
'Il'.0.0.99.0.retSS.FALSE.15.0);
3: GETNUM(54.5.2.'N'.YEAR[NY-2].
'Il'.0.0.99.0.retSS.FALSE.15.0);
4: GETNUM(57.5.2.'N'.YEARB[NY-2].
'll'.0.0.99.0.retSS.FALSE.15.0);
5: GETNUM(64.5.2.'N'.YEAR[NY-1].

IO:

11:

12:

I3:

14:

15:

16:

I7:

18:

19:

20:

21:

22:

23:

24:

25:

26:

27:

28:

29:

30:

31:

32:

.2553

'll'.0.0.99.0.retSS.FALSE.15.0);
GETNUH(67.5.2.'N'.YEARB[NY-1].
'll'.0.0.99.0.retSS.FALSE.15.0);

: GETNUM(74.5.2.'N'.YEAR[NY],

'll'.0.0.99.0.retSS.FALSE.15.0):
GETNUM(77.5.2.'N'.YEARB[NY].
'Il'.0.0.99.0.retSS.FALSE.15.0);
GETNUM(42.7.G.'N'.SMPT[NY-3].
'lll.ll'.0.00.999.99.retSS.FALSE.15.0):
GETNUM(43.9.5.'N'.HOGP[NY-3].
'OI.II'.0.00.99.99.retSS.FALSE.15.0);
GETNUM(43.11.5.'N'.BEFSTRP[NY-3].
'll.!l'.0.00.99.99.retSS.FALSE.15.0):
GETNUM(43.13.5.'N'.MILKP[NY-3].
'll.ll'.0.00.99.99.retSS.FALSE.15.0):
GETNUN(43.IS.5.'N'.BC0vP[NY-3].
'll.ll'.0.00.99.99.retSS.FALSE.15.0);
GETNUM(44.17.4.'N'.SETASIDE_ACRES[NY-3].
'II.I'.0.0.99.9.retSS.FALSE.15.0);
GETNUM(43.19.5.'N'.MIN_FREE_STOCKS[NY—3].
'Ill.l'.0.0.999.9.retSS.FALSE.15.0):
GETNUM(42.ZI.6.'N'.FSI_USE[NY-3].
'lll#.l'.0.0.9999.9.retSS.FALSE.15.0);
GETNUM(41.23.7.'N'.PCNT_OF_XPORTS[NY-3].
'l.lllil'.0.0.I.0.retSS.FALSE.15.0):
GETNUM(53.7.6.'N'.SMPT[NY-2].
’lll.ll'.0.00.999.99.retSS.FALSE.15.0);
GETNUM(54.9.5.'N'.HOGP[NY-2].
'll.ll’.0.00.99.99.retSS.FALSE.IS.0);
GETNUM(54.11.S.'N'.BEFSTRP[NY-2].
'll.ll'.0.00.99.99.retSS.FALSE.15.0);
GETNUM(S4.13.5.'N'.MILKP[NY-2].
'Il.ll'.0.00.99.99.retSS.FALSE.15.0);
GETNUM(54.15.S.'N'.BCOHP[NY-2].
'll.ll'.0.00.99.99.retSS.FALSE.15.0):
GETNUN(55.17.4.'N'.SETASIDE_ACRES[NY-2].
'll.l'.0.0.99.9.retSS.FALSE.15.0);
GETNUM(54.I9.5.'N'.MIN_FREE_STOCKS[NY-2].
'Iil.l'.0.0.999.9.retSS.FALSE.15.0);
GETNUM(53.21.6.'N'.FSI_USE[NY-2].
'IIII.I'.0.0.9999.9.retss.FALSE.15.0);
GETNUH(52.23.7.'N'.PCNT_0F_XP0RTS[NY-2].
'l.lllll'.0.0.I.0.retSS.FALSE.15.0);
GETNUM(G3.7.6.'N'.SMPT[NY-I].
'Ill.l!'.0.00.999.99.retSS.FALSE.15.0);
GETNUM(G4.9.5.'N'.HOGP[NY-I].
'tt.tl'.0.00.99.99.retSS.FALSE.15.0);
GETNUM(64.II.5.'N'.BEFSTRP[NY-I].
'll.ll'.0.00.99.99.retSS.FALSE.15.0);
GETNUM(64.13.5.'N'.MILKP[NY-I].
'to.rt'.0.00.99.99.retSS.FALSE.15.0);
GETNUM(64.15.5.'N'.BCOHP[NY-I].
'll.ll'.0.00.99.99.retSS.FALSE.15.0);
GETNUM(65.I7.4.'N'.SETASIDE_ACRES[NY-I].

 

.2554

'Il.l'.0.0.99.9.retSS.FALSE.15.0);

33: GETNUM(64.19.5.'N'.MIN_FREE_STOCKS[NY-l].
'III.I'.0.0.999.9.retSS.FALSE.15.0);

34: GETNUM(63.21.6.'N'.FSI_USE[NY-1].
'Iiil.l'.0.0.9999.9.retSS.FALSE.15.0);

35: GETNUM(62.23.7.'N'.PCNT_OF_XPORTS[NY-l].
'I.illll'.0.0.I.0.retSS.FALSE.15.0);

36: GETNUM(73.7.6.'N'.SMPT[NY].
'lll.ll'.0.00.999.99.retSS.retrieveSS.15.0);

37: GETNUM(74.9.5.'N'.HOGP[NY].
'll.ll'.0.00.99.99.retSS.retrieveSS.15.0);

38: GETNUM(74.11.5.'N'.BEFSTRP[NY].
'll.ll'.0.00.99.99.retSS.retrieveSS.15.0);

39: GETNUM(74.13.5.'N'.MILKP[NY].
'li.ll'.0.00.99.99.retSS.retrieveSS.15.0);

40: GETNUM(74.IS.5.'N'.BCOHP[NY].
'II.II'.0.00.99.99.retSS.retrieveSS.15.0);

41: GETNUM(75.I7.4.'N'.SETASIOE_ACRES[MY].
'l!.l'.0.0.99.9.retSS.retrieveSS.15.0);

42: GETNUM(74.19.5.'N'.MIN_FREE_STOCKS[NY].
'Ill.l'.0.0.999.9.ret$$.retrieveSS.15.0);

43: GETNUM(73.21.6.'N'.FSI_USE[NY].
'IlII.I'.0.0.9999.9.retSS.retrieveSS.15.0);

44: GETNUM(72.23.7.'N'.PCNT_OF_XPORTS[NY].
'I.!llii'.0.0.1.0.retSS.retrieveSS.15.0);

END; { CASE }

if varSS-screen_fieldSS then last_fieldSS:ITRUE;
RET_STATUS; { Check the code in ”retSS”. Set "varSS" and "actionSS" }

{ Check to see whether to switch retrieveSS to true }
if last_fieldSS and (not retrieveSS) then
begin
retrieveSS:ITRUE; last_fieldSS:IFALSE; actionSS:IstaySS; varSS:=1;
end else
last_fieldSS:IFALSE;

UNTIL actionSS-exitSS
END; { OVERLAY PROCEDURE SCREEN_3A }

OVERLAY PROCEDURE SCREEN_4; { EXOGENOUS VARIABLE SUMMARY SCREEN 4 }
BEGIN

screenSS:I14; screen_fieldSS:I34; varSS:II;

retrieveSS:IFALSE; last_fieldSS:IFALSE;
DISPLAY_SCREEN('EXOGVAR4.SCR'.file_existSS); { Display Screen }
if not file_existSS then

begin gotoxy(I.I); write('Missing Screen EXOGVAR4') end;
’retSS:I";

{ Display Items. Change retrieveSS to TRUE and INPUT items}
REPEAT { until actionSS I exitSS }

CASE varSS of
I: GETNUM(44.5.2.'N'.YEAR[NY-3].

 

10:

11:

12:

13:

14:

15:

16:

17:

18:

19:

20:

21:

22:

23:

24:

25:

26:

27:

28:

255

'II'.0.0.99.0.retSS.FALSE.15.0);
GETNUM(47.5.2.'N'.YEARB[NY-3].
'll'.0.0.99.0.retSS.FALSE.15.0);
GETNUM(54.5.2.'N'.YEAR[NY-2].
'll'.0.0.99.0.retSS.FALSE.15.0);
GETNUM(57.5.2.'N'.YEARB[NY-2].
'II'.0.0.99.0.retSS.FALSE.15.0);
GETNUM(64.5.2.'N'.YEAR[NY-1].
'Il'.0.0.99.0.retSS.FALSE.15.0);
GETNUM(67.5.2.'N'.YEARB[NY-1].
'Il'.0.0.99.0.retSS.FALSE.15.0);
GETNUM(74.5.2.'N'.YEAR[NY].
'll'.0.0.99.0.retSS.FALSE.15.0);
GETNUM(77.5.2.'N'.YEARB[NY].
'll'.0.0.99.0.retSS.FALSE.15.0);
GETNUM(40.7.9.'N’.IPOP[NY-3].
'lllilll.l'.0.0.9999999.9.retSS.FALSE.15.0);
GETNUM(41.9.8.'N'.IGOP[NY-3].
'I.IIIIII'.0.000000.9.999999.retSS.FALSE.15.0);
GETNUM(41.11.8.'N'.IHA[NY-3].
'ICIIII.I'.0.0.999999.9.retSS.FALSE.15.0);
GETNUM(43.14.6.'N'.EFREV[NY-3].
'l.lil#'.0.0000.9.9999.retSS.FALSE.15.0);
GETNUM(43.16.6.'N'.EHREV[NY-3].
'I.I#li'.0.0000.9.9999.retSS.FALSE.15.0);
GETNUM(42.18.7.'N'.EPOP[NY-3].
'lllll.l'.0.0.99999.9.retSS.FALSE.15.0);
GETNUM(41.20.8.'N'.EGOP[NY-3].
’I.llllll'.0.000000.9.999999.retSS.FALSE.15.0);
GETNUM(50.7.9.'N'.IPOP[NY-2].
'IIIIIII.I'.0.0.9999999.9.retSS.FALSE.15.0);
GETNUM(51.9.8.'N'.IGDP[NY-2].
'l.lillll'.0.000000.9.999999.retSS.FALSE.15.0);
GETNUM(51.11.B.'N'.IHA[NY-2].
'lllill.l'.0.0.999999.9.retSS.FALSE.15.0);
GETNUM(53.14.6.'N'.EFREV[NY-2].
'IIPIII'.0.0000.9.9999.retSS.FALSE.15.0);
GETNUM(53.16.6.'N'.EHREV[NY-2].
'l.llll'.0.9999.9.9999.retSS.FALSE.15.0);
GETNUM(52.18.7.’N'.EPOP[NY-2].
'lllll.l'.0.0.99999.9.retSS.FALSE.15.0);
GETNUM(51.20.8.'N'.EGDP[NY-2].
'I.I#Illl'.0.000000.9.999999.retSS.FALSE.15.0);
GETNUM(60.7.9.'N'.IPOP[NY-1].
'Illilll.l'.0.0.9999999.9.retSS.FALSE.15.0);
GETNUM(61.9.8.'N'.IGDP[NY-1].
'#.Illlll'.0.000000.9.999999.retSS.FALSE.15.0);
GETNUM(61.11.8.'N'.IHA[NY-1].
'IIIIII.I'.0.0.999999.9.retSS.FALSE.15.0);
GETNUM(63.14.6.'N'.EFREV[NY-I].
'l.llll'.0.0000.9.9999.retSS.FALSE.15.0);
GETNUM(63.16.6.'N'.EHREV[NY-1].
'l.llll'.0.0000.9.9999.retSS.FALSE.15.0);
GETNUM(62.18.7.'N'.EPOP[NY-I].

 

22565

'llill.l'.0.0.99999.9.retSS.FALSE.15.0);

29: GETNUM(61.20.B.'N'.EGDP[NY-I].
'l.llllll'.0.000000.9.999999.retSS.FALSE.15.0);

30: GETNUM(70.7.9.'N'.IPOP[NY].
'iliilll.l'.0.0.9999999.9.retSS.retrieveSS.15.0);

31: GETNUM(71.9.8.'N'.IGOP[NY].
'l.llllll’.0.000000.9.999999.retSS.retrieveSS.15.0);

32: GETNUM(71.11.8.'N'.IHA[NY].
'Iillll.l'.0.0.999999.9.retSS.retrieveSS.15.0);

33: GETNUM(72.18.7.'N'.EPOP[NY].
'Illll.l'.0.0.99999.9.retSS.retrieveSS.15.0);

34: GETNUM(71.20.8.'N'.EGDP[NY].
'I.IIIIII'.0.000000.9.999999.retSS.retrieveSS.15.0);

END; { CASE }

if varSSIscreen_fieldSS then last_fieldSS:ITRUE;
RET_STATUS; { Check the code in "retSS". Set "varSS" and "actionSS" }

{ Check to see whether to switch retrieveSS to true }
if last_fieldSS and (not retrieveSS) then
begin
retrieveSS:ITRUE; last_fieldSS:IFALSE; actionSS:IstaySS; varSS:Il;
end else
last_fieldSS:IFALSE;

UNTIL actionSSIexitSS
END; { OVERLAY PROCEDURE SCREEN_4 }
***** End of Include file MAGICI.INC *****
OVERLAY procedure US_COMPOSITE_VARIABLES; { A SUB MENU UNDER EXOGENOUS VARIABLE SUB MENU }

***** Beginning of Include file MAGICZ.INC *****

{$1 MAGIC2.INC} { US COMPOSITES }
OVERLAY PROCEDURE US_COMPOSITES_SCREEN_1: { B:EXOGVARS }
BEGIN

screenSS:I15; screen_fieldSS:I32; varSS:II;

retrieveSS:IFALSE; last_fieldSS:IFALSE;

DISPLAY_SCREEN('EXOGVAR5.SCR'.file_existSS): { Display Screen }

if not file_existSS then

begin gotoxy(I.I); write('Missing Screen EXOGVARS') end;
retSS:I";

{ Display Items. Change retrieveSS to TRUE and INPUT items}
REPEAT { until actionSS I exitSS }
CASE varSS of
1: GETNUM(41.4.2.'N'.YEAR[NY-3].
'li'.0.0.99.0.retSS.FALSE.15.0);

2: BEGIN
YEARB[NY-3]:IYEAR[NY-3]+1;
GETNUM(44,4.2.'N'.YEARB[NY-3].
'li'.0.0.99.0.retSS.FALSE.15.0);

END;

10:

11:

12:

13:

14:

15:

16:

17:

18:

19:

20:

2557

GETNUM(52.4.2.'N'.YEAR[NY-2].
'II'.0.0.99.0.retSS.FALSE.15.0);
BEGIN
YEARB[NY-2]:IYEAR[NY-2]+1;
GETNUM(55.4.2.'N'.YEARB[NY-2].
'II'.0.0.99.0.retSS.FALSE.15.0);
END;
GETNUM(63.4.2.'N'.YEAR[NY-I].
'li'.0.0.99.0.retSS.FALSE.15.0);
BEGIN
YEARB[NY-1]:IYEAR[NY-1]+1;
GETNUM(66.4.2.'N'.YEARB[NY-l].
'Il'.0.0.99.0.retSS.FALSE.15.0);
END;
GETNUM(74.4.2.'N'.YEAR[NY].
'll'.0.0.99.0.retSS.FALSE.15.0);
BEGIN
YEARB[NY]:IYEAR[NY]+I;
GETNUM(77.4.2.'N'.YEARB[NY].
'li'.0.0.99.0.retSS.FALSE.15.0);
END;
GETNUM(4I.II.5.'N'.ARP[NY-3].
'l.l#l'.0.000.9.999.retSS.FALSE.15.0);
GETNUM(41.13.5.'N'.DIVERSION[NY-3].
'I.lli'.0.000.9.999.retSS.FALSE.15.0);
GETNUM(42.15.4.'N'.GROSS[NY-3].
'I.I#'.0.00.9.99.retSS.FALSE.15.0);
BEGIN
BAL[NY-3]:I(I.O - (ARP[NY-3] + DIVERSION[NY-3]));
GETNUM(41.IB.S.'N'.BAL[NY-3].
'!.Il#'.0.000.9.999.retSS.FALSE.15.0);
END;
GETNUM(4I.20.5.'N'.PYIELD[NY-3].
'ill.l'.0.0.999.9.retSS.FALSE.15.0);
BEGIN
PARREV[NY-3]:IGROSS[NY-3]*BAL[NY-3]*PYIELD[NY-3];
GETNUM(40.22.6.'N'.PARREV[NY-3].
'Il#.##'.000.00.999.99.retSS.FALSE.15.0);
END;
GETNUM(52.11.5.'N'.ARP[NY-2].
'!.I#l'.0.000.9.999.retSS.FALSE.15.0);
GETNUM(52.13.5.'N'.DIVERSION[NY-2].
'l.l#l'.0.000.9.999.retSS.FALSE.15.0);
GETNUM(53.15.4.'N'.GROSS[NY-2].
'I.II'.0.00.9.99.retSS.FALSE.15.0);
BEGIN
BAL[NY-2]:I(I.0 - (ARP[NY—Z] + DIVERSION[NY-2]));
GETNUM(52.18.5.'N'.BAL[NY-2].
'I.lll'.0.000.9.999.retSS.FALSE.15.0);
END;
GETNUM(52.20.5.'N'.PYIELD[NY-2].
'##f.l'.0.0.999.9.retSS.FALSE.15.0);
BEGIN
PARREV[NY-2]:IGROSS[NY-2]*BAL[NY-2]*PYIELD[NY-2];

 

2558

GETNUM(51.22.6.'N'.PARREV[NY-2].
'lll.ll'.0.00.999.99.retSS.FALSE.15.0);
END;
21: GETNUM(63.11.5.'N'.ARP[NY-1].
'I.Ill'.0.000.9.999.retSS.FALSE.15.0);
22: GETNUM(63.13.5.'N'.DIVERSION[NY-l].
'l.#ll'.0.000.9.999.retSS.FALSE.15.0);
23: GETNUM(64.15.4.'N'.GROSS[NY-1].
'l.l#'.0.00.9.99.retSS.FALSE.15.0);

24: BEGIN
BAL[NY-I]:I(1.0 - (ARP[NY-I] + DIVERSION[NY-1]));
GETNUM(63.18.5.'N'.BAL[NY-1].
'l.lll'.0.000.9.999.retSS.FALSE.IS.0);

END;
25: GETNUM(63.20.5.'N'.PYIELD[NY-1].
'Ill.l'.0.0.999.9.retSS.FALSE.15.0);

26: BEGIN
PARREV[NY-1]:IGROSS[NY-I]*BAL[NY-1]*PYIELD[NY-1];
GETNUM(62.22.6.'N'.PARREV[NY-I].
'lll.#l'.0.00.999.99.retSS.FALSE.15.0);

END;
27: GETNUM(74.11.5.'N'.ARP[NY],
'I.lll'.0.000.9.999.retSS.retrieveSS.15.0);
28: GETNUM(74.13.5.'N'.DIVERSION[NY].
'I.Il#'.0.000.9.999.retSS.retrieveSS.15.0);
29: GETNUM(75.15.4.'N'.GROSS[NY].
'I.II'.0.00.9.99.retSS.retrieveSS.15.0);

30: BEGIN
BAL[NY]:I{1.O - (ARP[NY] + DIVERSION[NY]));
GETNUM(74.IB.5.'N'.BAL[NY].
'I.ill'.0.000.9.999.retSS.FALSE.15.0);

END;
31: GETNUM(74.20.5.'N'.PYIELD[NY].
'lil.l'.0.0,999.9.retSS.retrieveSS.15.0);

32: BEGIN
PARREV[NY]:IGROSS[NY]*BAL[NY]*PYIELD[NY];
GETNUM(73.22.6.'N'.PARREV[NY].
'IFI.##'.0.00.999.99.retSS.FALSE.15.0);

END:
END; { CASE }

if varSS-screen_fieldSS then last_fieldSS:ITRUE;
RET_STATUS; { Check the code in "retSS”. Set ”varSS" and "actionSS" }

{ Check to see whether to switch retrieveSS to true }
if last_fieldSS and (not retrieveSS) then
begin
retrieveSS:ITRUE; last_fieldSS:IFALSE; actionSS:IstaySS; varSS:I1;
end else
last_fieldSS:IFALSE;

UNTIL actionSSIexitSS
END; { OVERLAY PROCEDURE US_COMPOSITES_SCREEN_I }

 

2559I

OVERLAY PROCEDURE US_COMPOSITES_SCREEN_2; { BzEXOVARSA }
BEGIN

screenSS:I16; screen_fieldSS:I24; varSS:-I;

retrieveSS:IFALSE: last_fieldSS:IFALSE;

DISPLAY_SCREEN('EXOVARSA.SCR'.file_existSS): { Display Screen I

if not file_existSS then

begin gotoxy(I.I); write('Missing Screen EXOVARSA') end;
retSS:I";

{ Display Items. Change retrieveSS to TRUE and INPUT items}
REPEAT { until actionSS I exitSS }
CASE varSS of

I: GETNUM(41.5.2.'N'.YEAR[NY-3].
'Il'.0.0.99.0.retSS.FALSE.15.0);

2: GETNUM(44.5.2.'N'.YEARB[NY-3].
'II'.0.0.99.0.retSS.FALSE.15.0):

3: GETNUM(52.5.2.'N'.YEAR[NY-2].
'll'.0.0.99.0.retSS.FALSE.15.0);

4: GETNUM(55.5.2.'N'.YEARB[NY-2].
'II'.0.0.99.0.retSS.FALSE.15.0);

5: GETNUM(63.5.2.'N'.YEAR[NY-I].
'II'.0.0.99.0.retSS.FALSE.15.0);

6: GETNUM(66.5.2.'N'.YEARB[NY-I].
'II'.0.0.99.0.retSS.FALSE.15.0);

7: GETNUM(74.5.2.'N'.YEAR[NY].
'II'.0.0.99.0.retSS.FALSE.15.0);

B: GETNUM(77.5.2.'N'.YEARB[NY].
'Il'.0.0.99.0.retSS.FALSE.15.0);

9: GETNUM(42.13.4.'N'.DVPCN[NY-3].
'l.ll'.0.00.9.99.retSS.FALSE.15.0};

IO: GETNUM(41.16.5.'N'.DIVERSION[NY-3].
".!l".0.000.9.999.retSS.FALSE.15.013

11: GETNUM(41.19.5.'N'.PYIELD[NY-3].
'III.".0.0.999.9.retSS.FALSE.15.0);

12: BEGIN
DIVPAY[NY-3]:IDVPCN[NY-3]*DIVERSION[NY-3]*PYIELD[NY-3]:
GETNUM(40.21.6.'N'.DIVPAY[NY-3].
'I'I.ll'.O.DO.999.99.retSS.FALSE.15.0);

END;
13: GETNUM(53.13.4.'M'.DVPCN[NY-2].
".'I'.0.00.9.99.retSS.FALSE.15.0);
I4: GETNUM(52.16.5.'N'.DIVERSION[NY-2].
'l.lll'.0.000.9.999.retSS.FALSE.15.0);
15: GETNUM(52.19.5.'N'.PYIELD[NY-2].
'II!.I'.0.0.999.9.retSS.FALSE.15.0);

16: BEGIN
DIVPAY[NY-2]:IDVPCN[NY-2]*DIVERSION[NY-2]*PYIELD[NY-2];
GETNUM(51.21.6.'N'.DIVPAY[NY-2].
'lll.ll'.0.00.999.99.retSS.FALSE.15.0):

END:
17: GETNUM(64.I3.4.'N'.DVPCN[NY-1].

215C)

'l.ll'.0.00.9.99.retSS.FALSE.15.0):

18: GETNUM(63.16.5.'N'.DIVERSION[NY-I].

'I.III'.0.000.9.999.retSS.FALSE.15.0);
19: GETNUM(63.19.5.‘N'.PYIELD[NY-I].
’lll.l'.0.0.999.9.retSS.FALSE.15.0):

20: BEGIN
DIVPAY[NY-1]:IDVPCN[NY-I]*DIVERSION[NY-1]'PYIELD[NY-1];
GETNUM(62.21.6.'N'.DIVPAY[NY-I].
'Ill.ll'.0.00.999.99.retSS.FALSE.15.0);

END;
21: GETNUM(75.13.4.'N'.OVPCN[NY].
'l.ll'.0.00.9.99.retSS.retrieveSS.15.0);
22: GETNUM(74.16.5.'N'.OIVERSION[NY].
'I.III'.0.000.9.999.retSS.retrieveSS.15.0);
23: GETNUM(74.I9.5.'N'.PY1ELO[NY].
'lll.l'.0.0,999.9.retSS.retrieveSS.15.0);

24: BEGIN
DIVPAY[NY]:IDVPCN[NY]'DIVERSION[NY]*PY1ELD[NY];
GETNUM(73.21.6.'N'.DIVPAY[NY].
’Ill.ll'.0.00.999.99.retSS.FALSE.15.0);

END;
END; { CASE }

if varSS-screen_fieldSS then last_fieldSS:ITRUE;
RET_STATUS; { Check the code in ”retSS”. Set ”varSS" and "actionSS" }

{ Check to see whether to switch retrieveSS to true }
if last_fieldSS and (not retrieveSS) then
begin
retrieveSS:ITRUE; last_fieldSS:IFALSE; actionSS:IstaySS; varSS I1;
end else
last_fieldSS:IFALSE;

UNTIL actionSS-exitSS
END; { OVERLAY PROCEDURE US_COMPOSITES_SCREEN_2 }

OVERLAY PROCEDURE US_COMPOSITES_SCREEN_3: { B:EXOVARSB }
BEGIN

screenSS:II7; screen_fieldSS:I40; varSS:I1;

retrieveSS:IFALSE; last_fieldSS:IFALSE;

DISPLAY_SCREEN('EXOVARSB.SCR'.file_existSS); { Display Screen }

if not file_existSS then

begin gotoxy(I.I); write('Missing Screen EXOVARSB') end;
retSS:I";

{ Display Items. Change retrieveSS to TRUE and INPUT items}
REPEAT { until actionSS I exitSS }
CASE varSS of
I: GETNUM(4I.4.2.'N'.YEAR[NY-3].
'Il'.0.0.99.0.retSS.FALSE.15.0);
Z: GETNUM(44.4.2.'N'.YEARB[NY-3].

 

 

IO:

11:

12:

13:

14:

IS:

16:

17:

18:

19:

20:

21:

22:

261

'll'.0.0.99.0.retSS.FALSE.15.0);
GETNUM(52.4.2.'N'.YEAR[NY-2].
'll'.0.0.99.0.retSS.FALSE.15.0);
GETNUM(55.4.2.'N'.YEARB[NY-2].
'll'.0.D.99.0.ret$S.FALSE.15.0);
GETNUM(63.4.2.'N'.YEAR[NY-I].
'll'.0.0.99.0.retSS.FALSE.15.0);
GETNUM(66.4.2.'N'.YEARB[NY-1].
'II'.0.0.99.0.retSS.FALSE.15.0);
GETNUM(74.4.2.'N'.YEAR[NY].
'll'.0.0.99.0.retSS.FALSE.15.0);
GETNUM(77.4.2.'N'.YEARB[NY].
'll'.0.0.99.0.retSS.FALSE.15.0);
GETNUM(41.B.5.'N'.ARP[NY-3].

'l.ll!'.0.000.9.999.retSS.FALSE.15.0):

GETNUM(41.10.5.'N'.DIVERSION[NY-3].

'I.lll'.0.000.9.999.retSS.FALSE.15.0);

GETNUM(42.12.4.'N'.C_TARGETP[NY-3].

'l.l#'.0.00.9.99.retSS.FALSE.15.0);

GETNUM(42.I4.4.'N'.GROSS[NY-3].

'l.ll'.0.00.9.99.retSS.FALSE.15.0);

BEGIN
DPAY[NY-3]:IC_TARGETP[NY-3]-GROSS[NY-3];
IF DPAY[NY-3] < 0.0 THEN DPAY[NY-3]:IO.0;
GETNUM(42.I7.4.'N'.DPAY[NY-3].
'l.ll'.0.00.9.99.retSS.FALSE.15.0);

END;

BEGIN .

BAL[NY-3]:I(I.0 - (ARP[NY-3] + DIVERSION[NY-3]));
GETNUM(41.19.5.'N'.BAL[NY-3].
'l.lll'.0.000.9.999.retSS.FALSE.15.0);

END;

GETNUM(4I.21.5.'N'.PYIELO[NY-3].

'III.I'.0.0.999.9.retSS.FALSE.15.0);

BEGIN
DEFPAY[NY-3]:IDPAY[NY-3]'BAL[NY-3]*PYIELD[NY-3];
GETNUM(40.23.6.'N'.DEFPAY[NY-3].
'lll.ll'.0.00.999.99.retSS.FALSE.15.0):

END;

GETNUM(52.B.S.'N'.ARP[NY-2].

'l.lll'.0.000.9.999.retSS.FALSE.15.0);

GETNUM(52.10.5.'N'.DIVERSION[NY-2].

'I.#ll'.0.000.9.999.retSS.FALSE.15.0);

GETNUM(53.12.4.'N'.C_TARGETP[NY-2].

'l.ll'.0.00.9.99.retSS.FALSE.15.0);

GETNUM(53.14.4.'N'.GROSS[NY-2].

'I.Il'.0.00.9.99.retSS.FALSE.15.0);

BEGIN
DPAY[NY-2]:IC_TARGETP[NY-2]-GROSS[NY-2];
1F DPAY[NY-2] < 0.0 THEN DPAY[NY-2]:I0.O;
GETNUM(53.17.4.'N'.0PAY[NY-2].
'I.#l'.0.00.9.99.retSS.FALSE.15.0);

END:

BEGIN

23:

24:

25:

26:

27:

28:

29:

30:

31:

32:

33:

34:

35:

36:

37:

38:

262

BAL[NY-2]:I(I.0 - (ARP[NY-Z] + DIVERSION[NY-2]));

GETNUM(52.19.5.'N'.BAL[NY-2].
'l.lll'.0.000.9.999.retSS.FALSE.15.0);

END;

GETNUM(52.21.5.'N'.PYIELD[NY-2].

'lfl.l'.0.0.999.9.retSS.FALSE.15.0);

BEGIN
DEFPAY[NY-2]:IDPAY[NY-2]*BAL[NY-2]*PYIELD[NY-2];
GETNUM(51.23.6.'N'.DEFPAY[NY-2].
'lli.l#'.0.00.999.99.retSS.FALSE.15.0);

END;

GETNUM(63.B.5.'N'.ARP[NY-1].

'l.lll'.0.000.9.999.retSS.FALSE.15.0);

GETNUM(63.10.5.'N'.DIVERSION[NY-1].

’I.III'.0.000.9.999.retSS.FALSE.15.0);

GETNUM(64.12.4.'N'.C_TARGETP[NY-I].

'l.li'.0.00.9.99.retSS.FALSE.15.0);

GETNUM(64.14.4.'N'.GROSS[NY-I].

'l.ll'.0.00.9.99.retSS.FALSE.15.0);

BEGIN
DPAY[NY—I]:=C_TARGETP[NY-I]-GROSS[NY-l]:
1F DPAY[NY-1] < 0.0 THEN DPAY[NY-l]:I0.0;
GETNUM(64.I7.4.'N'.DPAY[NY-1].
'l.l#'.0.00.9.99.retSS.FALSE.15.0);

END;

BEGIN

BAL[NY-1]:I(1.0 - (ARP[NY-I] + DIVERSION[NY-l]));

GETNUM(63.19.5.'N'.BAL[NY-I].
'l.li#'.0.000.9.999.retSS.FALSE.15.0);

END;

GETNUM(63.21.5.'N'.PYIELD[NY-I].

'#ll.#'.0.0.999.9.retSS.FALSE.15.0);

BEGIN
DEFPAY[NY-1]:IOPAY[NY-1]*BAL[NY-I]*PYIELD[NY-I];
GETNUM(62.23.6.'N'.DEFPAY[NY-1].
'lli.ii'.0.00.999.99.retSS.FALSE.15.0);

END;

GETNUM(74.B.S.'N'.ARP[NY].

'I.Il#'.0.000.9.999.retSS.retrieveSS.15.0);

GETNUM(74.10.5.'N'.DIVERSION[NY].

'l.ll#'.0.000.9.999.retSS.retrieveSS.15.0);

GETNUM(7S.12.4.'N'.C_TARGETP[NY].

'l.##'.0.00.9.99.retSS.retrieveSS.15.0);

GETNUM(75.14.4.'N'.GROSS[NY].

'I.ll'.0.00.9.99.retSS.retrieveSS.15.0);

BEGIN
DPAY[NY]:IC_TARGETP[NY]-GROSS[NY];

IF DPAY[NY] < 0.0 THEN DPAY[NY]:I0.0:
GETNUM(75.17.4.'N'.DPAY[NY].
'I.Il'.0.00.9.99.retSS.FALSE.15.0);

END;

BEGIN
BAL[NY]:I(1.O - (ARP[NY] + DIVERSION[NY]));
GETNUM(74.19.S.'N’.BAL[NY].

ivk

 

 

E
39: G

2453

'I.II!'.0.000.9.999.retSS.FALSE.15.0);
ND:
ETNUM(74.21.5.'N'.PY1ELD[NY].

'l#l.l'.0.0.999.9.retSS.retrieveSS.15.0);

40: B

E
END;

if v

EGIN
DEFPAY[NY]:IDPAY[NY]*BAL[NY]*PYIELD[NY];
GETNUM(73.23.6.'N'.DEFPAY[NY].
'III.II'.0.00.999.99.retSS.FALSE.15.0);

ND;

{ CASE }

arSSIscreen_fieldSS then last_fieldSS:ITRUE;

RET_STATUS; { Check the code in ”retSS". Set ”varSS" and ”actionSS" }

{ Check to see whether to switch retrieveSS to true }
if last_fieldSS and (not retrieveSS) then
begin
retrieveSS:ITRUE; last_fieldSS:IFALSE; actionSS:IstaySS; varSS:=1;
end else
last_fieldSS:=FALSE;
UNTIL actionSS=exitSS
END; { OVERLAY PROCEDURE US COMPOSITES SCREEN 3 }
***** End of Include file MAGIC2.INC *****
BEGIN
REPEAT
IF (EDIT=FALSE) AND (BACTIVE=FALSE)
THEN BEGIN
COLOR(HHITE.BLACK);
CLRSCR;
FLASHUP('H=USCOMPOS.NOESC.NOMOVE');
GOTOXY(I.25);
REAO(RESPONSE);
CLREOL:
FLASHUP('CIALL');
END:
CASE (RESPONSE) OF
'1': US_COMPOSITES_SCREEN_I: { GOVT PARTICIPANTS REV. }
'2': US_COMPOSITES_SCREEN_2; { DIVERSION PAYMENTS }
'3': US_COMPOSITES_SCREEN_3: { DEFFICIENCY PAYMENTS }
'4': FSI_USE_SCREEN;
'5'::
END; {CASE}
1F (RESPONSE <> '5') THEN
HHATMENU(BSCREEN.BACTIVE.UB);
UNTIL (RESPONSE I '5');
END; { PROCEDURE US COMPOSITES }
***** Beginning of Include file MAGIC3.INC *****
{$1 MAGIC3.INC} { IMPORTERS GDP }

OVERLAY PROCEDURE IMPORTERS_GDP_SCREEN_I; { B:EXOGVAR6 }

BEGIN

 

2554

screenSS:I18: screen_fieldSS:I31: varSS:I1;

retrieveSS:IFALSE; last_fieldSS:IFALSE;

DISPLAY_SCREEN('EXOGVAR6.SCR'.file_existSS): { Display Screen I

if not file_existSS then

begin gotoxy(I.I); write('Missing Screen EXOGVARG') end;
retSS:I";

{ Display Items. Change retrieveSS to TRUE and INPUT items}
REPEAT { until actionSS I exitSS }
CASE varSS of

I: GETNUM(3I.6.S.'N'.CHHT.
'l.lli'.0.000.I.000.retSS.retrieveSS.15.0);

2: GETNUM(31.7.5.'N'.SBHT.
'l.ll#'.0.000.I.000.retSS.retrieveSS.15.0);

3: GETNUM(31.8.5.'N'.DMHT.
'l.lll'.0.000.1.000.retSS.retrieveSS.15.0);

4: GETNUM(31.9.5.'N'.LOHT.
'I.lll'.0.000.1.000.retSS.retrieveSS.15.0);

5: GETNUM(3I.IO.S.'N’.NIHT.
'l.!ll'.0.000.1.000.retSS.retrieveSS.15.0);

6: GETNUM(31.II.S.'N'.LDHT.
'I.III'.0.000.1.000.retSS.retrieveSS.15.0):

7: GETNUM(31.12.5.'N'.BRHT.
'l.lil'.0.000.I.000.retSS.retrieveSS.15.0);

8: GETNUM(41.13.2.'N'.YEAR[NY-3].
'il'.0.0.99.0.retSS.FALSE.15.0);

9: GETNUM(44.I3.2.'N'.YEARB[NY-3].
'li'.0.0.99.0.retSS.FALSE.15.0);

10: GETNUM(52.13.2.'N'.YEAR[NY-2].
'll'.0.0.99.0.retSS.FALSE.15.0);

II: GETNUM(55.13.2.'N'.YEARB[NY-2].
'll'.0.0.99.0.retSS.FALSE.15.0);

12: GETNUM(63.13.2.'N'.YEAR[NY-I].
'Il'.0.0.99.0.retSS.FALSE.15.0);

13: GETNUM(66.I3.2.'N'.YEARB[NY-I].
'll'.0.0.99.0.retSS.FALSE.15.0);

I4: GETNUM(74.I3.2.'N'.YEAR[NY].
'll'.0.0.99.0.retSS.FALSE.15.0);

15: GETNUM(77.13.2.'N'.YEARB[NY].
'll'.0.0.99.0.retSS.FALSE.15.0);

16: GETNUM(41.17.5.'N'.CPICH[NY-3].
'lll.l'.0.0.999.9.retSS.FALSE.15.0);

17: GETNUM(40.19.6.'N'.CPISB[NY-3].
'lll.ll'.0.00.999.99.retSS.FALSE.15.0):

18: GETNUM(40.21.6.'N'.CPIDM[NY-3].
'1II.I!'.0.00.999.99.retSS.FALSE.15.0);

19: GETNUM(40.23.6.'N'.CPILO[NY-3].
'III.II’.0.00.999.99.retSS.FALSE.15.0);

20: GETNUM(52.I7.S.'N'.CPICH[NY-2].
'III.!'.0.0.999.9.retSS.FALSE.15.0):

21: GETNUM(SI.I9.6.'N'.CPISB[NY-2].
'lil.ll'.0.00.999.99.retSS.FALSE.15.0);

22: GETNUM(51.21.6.'N'.CPIDM[NY-2].
'III.II'.0.00.999.99.retSS.FALSE.15.0);

2555i

23: GETNUM(51.23.6.'N'.CPILO[NY-2].
'III.II'.0.00.999.99.retSS.FALSE.15.0);

24: GETNUM(63.I7.5.'N'.CPICH[NY-1].
'lll.l'.0.0.999.9.retSS.FALSE.15.0);

25: GETNUM(62.19.6.'N'.CPISB[NY-1].
'lll.ll'.0.00.999.99.retSS.FALSE.15.0);

26: GETNUM(62.21.G.'N'.CPIDM[NY-1].
'll!.li'.0.00.999.99.retSS.FALSE.15.0);

27: GETNUM(62.23.6.'N'.CP1LO[NY-I].
’lll.ll'.0.00.999.99.retSS.FALSE.15.0);

28: GETNUM(74.17.5.'N'.CPICH[NY].
'Ill.l'.0.0.999.9.retSS.retrieveSS.15.0);

29: GETNUM(73.19.6.'N'.CPISB[NY].
'#l#.ll'.0.00.999.99.retSS.retrieveSS.15.0);

30: GETNUM(73.21.6.'N'.CPIDM[NY].
'Ill.ll’.0.00,999.99.retSS.retrieveSS.15.0);

31: GETNUM(73.23.6.'N'.CPILO[NY].
'#l#.ll'.0.00.999.99.retSS.retrieveSS.15.0);

END; { CASE }

if varSS-screen_fieldSS then last_fieldSS:ITRUE;
RET_STATUS; { Check the code in "retSS". Set ”varSS" and "actionSS" }

{ Check to see whether to switch retrieveSS to true }
if last_fieldSS and (not retrieveSS) then
begin
retrieveSS:ITRUE; last_fieldSS:IFALSE; actionSS:IstaySS; varSS:=1;
end else
last_fieldSS:=FALSE;

UNTIL actionSS=exitSS
END; { OVERLAY PROCEDURE SCREEN_G }

OVERLAY PROCEDURE IMPORTERS_GOP_SCREEN_2; { B:EXOGVAR7 }
BEGIN

screenSS:I19; screen_fieldSS:IBG; varSS:I1;

retrieveSS:IFALSE; last_fieldSS:IFALSE;

DISPLAY_SCREEN('EXOGVAR7.SCR'.file_existSS); { Display Screen }

if not file_existSS then

begin gotoxy(I.I); write('Missing Screen EXOGVAR7') end;
retSS:I";

{ Display Items. Change retrieveSS to TRUE and INPUT items}
REPEAT { until actionSS I exitSS }
CASE varSS of

1: GETNUM(41.4.2.'N'.YEAR[NY-3].
'Il'.0.0.99.0.retSS.FALSE.15.0);

2: GETNUM(44.4.2.'N'.YEARB[NY-3].
'Il'.0.0.99.0.retSS.FALSE.15.0);

3: GETNUM(52.4.2.'N'.YEAR[NY-2].
'll'.0.0.99.0.retSS.FALSE.15.0);

 

IO:

11:

12:

13:

14:

15:

16:

17:

18:

I9:

20:

21:

22:

23:

24:

25:

26:

27:

28:

29:

30:

266

GETNUM(55.4.2.'N'.YEARB[NY-2].
'll'.0.0.99.0.retSS.FALSE.15.0);
GETNUM(63.4.2.'N'.YEAR[NY-1].
'II'.0.0.99.0.retSS.FALSE.15.0);
GETNUM(66.4.2.'N'.YEARB[NY-1].
'Il'.0.0.99.0.retSS.FALSE.15.0);
GETNUM(74.4.2.'N'.YEAR[NY].
'Il'.0.0.99.0.retSS.FALSE.15.0);
GETNUM(77.4.2.'N'.YEARB[NY].
'Ii'.0.0.99.0.retSS.FALSE.15.0);
GETNUM(40.8.6.'N'.CPINI[NY-3].
'II!.II'.0.00.999.99.retSS.FALSE.15.0);
GETNUM(40.10.6.'N'.CPILO[NY-3].
'IIII.I'.0.0.9999.9.retSS.FALSE.15.0);
GETNUM(40.12.6.'N'.CPIBR[NY-3].
'llll.l'.0.0.9999.9.retSS.FALSE.15.0);
GETNUM(40.17.6.'N'.GDPCH[NY-3].
'I.IIII'.0.0000.9.9999.retSS.FALSE.15.0);
GETNUM(40.19.6.'N'.GDPSB[NY-3].
'I.IIIF'.0.0000.9.9999.retSS.FALSE.15.0);
GETNUM(40.21.6.'N'.GDPOM[NY-3].
'I.IIII'.0.0000.9.9999.retSS.FALSE.15.0);
GETNUM(40.23.6.'N'.GDPLO[NY-3].
'l.l#Il'.0.0000.9.9999.retSS.FALSE.15.0);
GETNUM(51.8.6.'N'.CPINI[NY-2].
'lll.l!'.0.00.999.99.retSS.FALSE.15.0);
GETNUM(51.IO.6.'N'.CP1LO[NY-2].
'l2ll.l'.0.0.9999.9.retSS.FALSE.15.0);
GETNUM(51.12.6.'N'.CPIBR[NY-2].
'Ill#.l'.0.0.9999.9.retSS.FALSE.15.0);
GETNUM(51.17.6.'N'.GDPCH[NY-2].
'l.llli'.0.0000.9.9999.retSS.FALSE.15.0);
GETNUM(51.19.6.'N'.GDPSB[NY-2].
'l.lill'.0.0000.9.9999.retSS.FALSE.15.0);
GETNUM(51.21.6.'N'.GDPOM[NY-2].
'l.ll#I'.0.0000.9.9999.retSS.FALSE.15.0);
GETNUM(51.23.6.'N'.GDPLO[NY-2].
'I.IIII'.0.0000.9.9999.retSS.FALSE.15.0);
GETNUM(62.B.6.'N'.CPINI[NY-1].
'III.II'.0.00.999.99.retSS.FALSE.15.0);
GETNUM(62.10.6.'N'.CPILD[NY-1].
'llll.#'.0.0.9999.9.retSS.FALSE.15.0):
GETNUM(62.12.6.'N'.CPIBR[NY-1].
'llll.l'.0.0.9999.9.retSS.FALSE.15.0);
GETNUM(62.17.6.'N'.GDPCH[NY-1].
'l.lI#l'.0.0000.9.9999.retSS.FALSE.15.0);
GETNUM(62.19.6.'N'.GDPSB[NY-1].
'l.#lll'.0.0000.9.9999.retSS.FALSE.15.0);
GETNUM(62.21.6.'N'.GDPDM[NY-1].
'l.lii#'.0.0000.9.9999.retSS.FALSE.15.0):
GETNUM(62.23.6.'N'.GOPLO[NY-1].
'#.IIII'.0.0000.9.9999.retSS.FALSE.15.0);
GETNUM(73.8.6.'N'.CPINI[NY].
'Ill.ll'.0.00.999.99.retSS.retrieveSS.15.0);

 

.2(57

31: GETNUM(73.IO.6.'N'.CP1LD[NY].
'Illl.l'.0.0.9999.9.retSS.retrieveSS.15.0);
32: GETNUM(73.12.6.'N'.CPIBR[NY].
'IIII.I'.0.0.9999.9.retSS.retrieveSS.15.0);
33: GETNUM(73.17.6.'N'.GDPCH[NY].
'l.llll'.0.0000.9.9999.retSS.retrieveSS.15.0);
34: GETNUM(73.19.6.'N'.GOPSB[NY].
'l.ll#l'.0.0000.9.9999.retSS.retrieveSS.15.0);
35: GETNUM(73.21.6.'N'.GDPDM[NY].
'l.l#i#'.0.0000.9.9999.retSS.retrieveSS.15.0);
36: GETNUM(73.23.6.'N'.GDPLO[NY].
'i.llll'.0.0000.9.9999.retSS.retrieveSS.15.0);
END; { CASE }

if varSS-screen_fieldSS then last_fieldSS:ITRUE;
RET_STATUS; { Check the code in ”retSS”. Set ”varSS" and "actionSS" }

{ Check to see whether to switch retrieveSS to true }
if last_fieldSS and (not retrieveSS) then
begin
retrieveSS:ITRUE; last_fieldSS:IFALSE; actionSS:IstaySS; varSS:=I;
end else
last_fieldSS:IFALSE;

UNTIL actionSSIexitSS
END; { OVERLAY PROCEDURE SCREEN_7 }

OVERLAY PROCEDURE IMPORTERS_GOP_SCREEN_3; { B:EXOGVAR8 }
BEGIN

screenSS:I20; screen_fieldSS:I24; varSS:I1;

retrieveSS:IFALSE; last_fieldSS:IFALSE;

DISPLAY_SCREEN('EXOGVAR8.SCR'.file_existSS); { Display Screen }

if not file_existSS then

begin gotoxy(I.I); write('Missing Screen EXOGVARB') end;
retSS:I";

{ Display Items. Change retrieveSS to TRUE and INPUT items}
REPEAT { until actionSS I exitSS }
CASE varSS of
1: GETNUM(41.5.2.'N'.YEAR[NY-3].
'll'.0.0.99.0.retSS.FALSE.15.0);
2: GETNUM(44.5.2.'N'.YEARB[NY-3].
'Il'.0.0.99.0.retSS.FALSE.15.0);
3: GETNUM(52.5.2.'N'.YEAR[NY-2].
'll'.0.0.99.0.retSS.FALSE.15.0);
4: GETNUM(55.5.2.'N'.YEARB[NY-2].
'il'.0.0.99.0.retSS.FALSE.15.0);
5: GETNUM(63.5.2.'N'.YEAR[NY-1].
'll'.0.0.99.0.retSS.FALSE.15.0);
6: GETNUM(66.5.2.'N'.YEARB[NY-I].
'II'.0.0.99.0.retSS.FALSE.15.0);

 

.2i58

7: GETNUM(74.5.2.'N'.YEAR[NY].
'II'.0.0.99.0.retSS.FALSE.15.0);
B: GETNUM(77.S.2.'N'.YEARB[NY].
'II'.0.0.99.0.retSS.FALSE.15.0);
9: GETNUM(40.9.6.'N'.GDPNI[NY-3].
'I.Illl'.0.000D.9.9999.retSS.FALSE.15.0);
10: GETNUM(40.11.6.'N'.GDPLD[NY-3].
'!#.##l'.0.000.99.999.retSS.FALSE.15.0);
11: GETNUM(40.13.6.'N'.GDPBR[NY-3].
'Il.lll'.0.000.99.999.retSS.FALSE.15.0);
12; GETNUM(38.21.8.'N'.IGDP[NY-3].
'l.llll#l'.0.000000.9.999999.retSS.FALSE.15.0);
13: GETNUH(51.9.6.'N'.GDPNI[NY-2].
't.tt:t'.0.0000.9.9999.retSS.FALSE.15.0);
14; GETNUM(51.11.6.'N'.GDPLD[NY-2].
'II.III'.0.000.99.999.retSS.FALSE.15.0);
15: GETNUM(51.13.6.'N'.GDPBR[NY-2].
'li.lll'.0.000.99.999.retSS.FALSE.15.0);
16: GETNUM(49.21.B.’N'.IGDP[NY-2].
'l.lillii'.0.000000.9.999999.retSS.FALSE.15.0);
17: GETNUM(62.9.6.’N'.GDPNI[NY-I].
'l.llll'.0.0000.9.9999.retSS.FALSE.15.0);
18: GETNUM(62.II.6.'N'.GDPLD[NY-1].
'll.lll'.0.000.99.999.retSS.FALSE.15.0);
19; GETNUM(62.13.6.'N'.GDPBR[NY-I].
'Il.ll#'.0.000.99.999.retSS.FALSE.15.0);
20: GETNUM(60.21.8.'N'.IGDP[NY-I].
'l.lillll'.0.000000.9.999999.retSS.FALSE.15.0);
21; GETNUM(73.9.6.'N'.GDPNI[NY].
'I.IIII'.0.0000.9.9999.retSS.retrieveSS.15.0);
22: GETNUM(73.11.6.'N'.GDPLD[NY].
'II.III'.0.000.99.999.retSS.retrieveSS.15.0);
23; GETNUM(73.13.6.'N'.GDPBR[NY].
'Il.lll'.0.000.99.999.retSS.retrieveSS.15.0);
24: BEGIN
if retrieveSS=true then begin
IGDP[NY]:I((CHHT*GDPCH[NY])/CPICH[NY])
+ ((SBVT*GDPSB[NY])/CPISB[NY]I + ((DMHT*GDPDM[NY])/CPIDM[NY])
+ ((LOHT*GDPLO[NY])ICPILO[NY]) + ((NIHT*GDPN1[NY])/CPINI[NY])
+ ((LDHT*GDPLD[NY])/CPILD[NY]) + ((6RVT*GDPBR[NY])/CPIBR[NY]I;
GETNUM(7I.21.8.'N'.IGDP[NY].
'l.llfill'.0.000000.9.999999.retSS.retrieveSS.15.0);
end;
END;
END; { CASE I

if varSS-screen_fieldSS then last_fieldSS:ITRUE;
RET_STATUS; { Check the code in "retSS”. Set "varSS" and "actionSS" }

{ Check to see whether to switch retrieveSS to true }
if last_fieldSS and (not retrieveSS) then
begin
retrieveSS:ITRUE; last_fieldSS:=FALSE; actionSS:IstaySS; varSS:=1;
end else

 

2(59
last_fieldSS:IFALSE;

UNTIL actionSS-exitSS
END; { OVERLAY PROCEDURE SCREEN_B }

***** End of Include file MAGIC3.INC *****
OVERLAY procedure EXPORTERS_COMPOSITES; { A SUB MENU UNDER EXOGENOUS VARIABLE SUB MENU }

*‘*** Beginning of Include file MAGIC4.INC *****

{$1 MAGIC4.INC} { EXPORTERS GDP }
OVERLAY PROCEDURE EXPORTERS_GDP_SCREEN_1: { B:EXOGVAR9 }
BEGIN

screenSS:I21; screen_fieldSS:I23; varSS:II;

retrieveSS:IFALSE; last_fieldSS:IFALSE;

DISPLAY_SCREEN('EXOGVAR9.SCR'.file_existSS); { Display Screen }

if not file_existSS then

begin gotoxy(I.I); write('Missing Screen EXOGVAR9') end;
retSS:I";

{ Display Items. Change retrieveSS to TRUE and INPUT items}
REPEAT { until actionSS I exitSS }
CASE varSS of

1: GETNUM(31.7.5.'N'.ARHT.
'I.III'.0.000.1.000.retSS.retrieveSS.15.0);

2: GETNUM(3I.9.S.'N'.AUHT.
'I.lll'.0.000.I.000.retSS.retrieveSS.15.0);

3: GETNUM(31.11.5.'N'.CAHT.
'l.#ll'.0.000.I.OOO.retSS.retrieveSS.15.0);

4: GETNUM(41.14.2.'N'.YEAR[NY-3].
'II'.0.0.99.0.retSS.FALSE.15.0);

5: GETNUM(44.14.2.'N'.YEARB[NY-3].
'Il'.0.0.99.0.retSS.FALSE.15.0);

6: GETNUM(52.14.2.'N'.YEAR[NY-2].
'II'.0.0.99.0.retSS.FALSE.15.0);

7: GETNUM(55.14.2.'N'.YEARB[NY-2].
'l!’.0.0.99.0.retSS.FALSE.15.0);

8: GETNUM(63.14.2.'N'.YEAR[NY-I].
'II'.0.0.99.0.retSS.FALSE.15.0);

9: GETNUM(66.14.2.'N'.YEARB[NY-1].
'Il'.0.0.99.0.retSS.FALSE.15.0);

10: GETNUM(74.14.2.'N'.YEAR[NY].
'll'.0.0.99.0.retSS.FALSE.15.0);

11: GETNUM(77.I4.2.'N'.YEARB[NY].
'll'.0.0.99.0.retSS.FALSE.15.0);

12: GETNUM(39.18.7.'N'.CPIAR[NY-3].
'llili.l'.0.0.99999.9.retSS.FALSE.15.0);

13: GETNUM(40.20.6.'N'.CPIAU[NY-3].
'IRI.II'.0.00.999.99.retSS.FALSE.15.0);

14: GETNUM(40.22.6.'N'.CPICA[NY-3].
'Pll.ll'.0.00.999.99.retSS.FALSE.15.0);

15: GETNUM(50.18.7.'N'.CPIAR[NY-2].
'IIIII.I'.0.0.99999.9.retSS.FALSE.15.0);

 

2F7i)

16: GETNUM(51.20.6.'N'.CPIAU[NY-2].
'Ill.ll'.0.00.999.99.retSS.FALSE.15.0);

I7: GETNUM(51.22.6.'N'.CPICA[NY-2].
'III.II'.0.00.999.99.retSS.FALSE.15.0);

18: GETNUM(61.18.7.'N'.CPIAR[NY-I].
'IIIFI.I'.0.0.99999.9.retSS.FALSE.15.0);

19: GETNUM(62.20.6.'N'.CP1AU[NY-1].
'Il#.ll'.0.00.999.99.retSS.FALSE.15.0);

20: GETNUM(62.22.6.'N'.CPICA[NY-l].
'Iii.li'.0.00.999.99.retSS.FALSE.15.0);

21: GETNUM(72.18.7.'N’.CPIAR[NY].
'li#l#.l'.0.0.99999.9.retSS.retrieveSS.15.0);

22: GETNUM(73.20.6.'N'.CPIAU[NY].
'III.#I'.0.00.999.99.retSS.retrieveSS.15.0);

23: GETNUM(73.22.6.'N'.CPICA[NY].
'III.II'.0.00.999.99.retSS.retrieveSS.15.0);

ENO; { CASE }

if varSSsscreen_fieldSS then last_fieldSS:=TRUE;
RET_STATUS; { Check the code in ”retSS". Set "varSS" and "actionSS" }

{ Check to see whether to switch retrieveSS to true }
if last_fieldSS and (not retrieveSS) then
begin
retrieveSS:ITRUE; last_fieldSS:=FALSE; actionSS:=staySS; varSS:=1;
end else
last_fieldSS:=FALSE;

UNTIL actionSS=exitSS
END; { OVERLAY PROCEDURE SCREEN_9 }

OVERLAY PROCEDURE EXPORTERS_GDP_SCREEN_2; { B:EXOVARIO }
BEGIN

screenSS:I22; screen_fieldSS:I24; varSS:I1;

retrieveSS:IFALSE; last_fieldSS:IFALSE;

DISPLAY_SCREEN('EXOVAR10.SCR’.file_existSS); { Display Screen }

if not file_existSS then

begin gotoxy(I.I); write('Missing Screen EXOVARIO') end;
retSS:I";

{ Display Items. Change retrieveSS to TRUE and INPUT items}
REPEAT { until actionSS I exitSS }
CASE varSS of
I: GETNUM(41.5.2.'N'.YEAR[NY-3].
'I".0.0.99.0.retSS.FALSE.15.0);
2: GETNUM(44.5.2.'N'.YEARB[NY-3].
'Il'.0.0.99.0.retSS.FALSE.15.0);
3: GETNUM(52.5.2.'N'.YEAR[NY-2].
'll'.0.0.99.0.retSS.FALSE.15.0);
4: GETNUM(55.5.2.'N'.YEARB[NY-2].
'II'.0.0.99.0.retSS.FALSE.15.0);
5: GETNUM(63.5.2.'N'.YEAR[NY-1].

 

Ir

2Y7IL

'l!’.0.0.99.0.retSS.FALSE.15.0);
6: GETNUM(66.5.2.'N'.YEAR[NY-I].
'll'.0.0.99.0.retSS.FALSE.15.0);
7: GETNUM(74.5.2.'N'.YEAR[NY].
'II'.D.0.99.0.retSS.FALSE.15.0);
8: GETNUM(77.5.2.'N'.YEARB[NY].
'll'.0.0.99.0.retSS.FALSE.15.0);
9: GETNUM(40.9.6.'N'.GDPAR[NY-3].
'ill.ll'.0.00.999.99.retSS.FALSE.15.0);
10: GETNUM(40.11.6.'N'.GDPAU[NY—3].
'I.l#ll’.0.0000.9.9999.retSS.FALSE.15.0);
II: GETNUM(40.13.6.'N'.GDPCA[NY-3].
'l.l!ll'.0.0000.9.9999.retSS.FALSE.15.0);
12: GETNUM(38.21.8.'N'.EGDP[NY-3].
'I.llilll'.0.000000.9.999999.retSS.FALSE.15.0);
13: GETNUM(51.9.6.'N'.GDPAR[NY-2].
'lll.ll'.0.00.999.99.retSS.FALSE.15.0);
I4: GETNUM(51.11.6.'N'.GDPAU[NY-2].
'I.IIII'.0.0000.9.9999.retSS.FALSE.15.0);
15: GETNUM(51.13.6.'N'.GDPCA[NY-2].
'I.Ill!'.0.0000.9.9999.retSS.FALSE.15.0);
16: GETNUM(49.21.8.’N'.EGDP[NY-2].
'l.l!ll#l'.0.000000.9.999999.retSS.FALSE.15.0);
l7: GETNUM(62.9.6.'N'.GDPAR[NY-I].
'lll.l#'.0.00.999.99.retSS.FALSE.15.0);
18: GETNUM(62.11.6.'N'.GDPAU[NY-l].
'#.IIII'.0.0000.9.9999.retSS.FALSE.15.0);
19: GETNUM(62.13.6.'N’.GDPCA[NY-1].
'I.Illl'.0.0000.9.9999.retSS.FALSE.15.0);
20: GETNUM(60.21.8.'N'.EGDP[NY-I].
'l.llllll'.0.000000.9.999999.retSS.FALSE.15.0);
21: GETNUM(73.9.6.'N'.GDPAR[NY].
'lll.il'.0.00.999.99.retSS.retrieveSS.15.0);
22: GETNUM(73.II.6.'N'.GDPAU[NY].
'I.llll'.0.0000.9.9999.retSS.retrieveSS.15.0);
23: GETNUM(73.I3.6.'N'.GDPCA[NY].
'I.IIII'.0.0000.9.9999.retSS.retrieveSS.15.0);
24: BEGIN
if retrieveSS-true then begin
EGDP[NY]:I((ARHT*GDPAR[NY])/CPIAR[NY])
+ ((AUHT*GDPAU[NY])/CPIAU[NY]) + ((CAHT'GDPCA[NY])/CPICA[NY]);
GETNUM(7I.21.8.'N'.EGDP[NY].
'l.#l!lil'.0.000000.9.999999.retSS.retrieveSS.15.0);
end;
END;
END; { CASE }

if varSS-screen_fieldSS then last_fieldSS:ITRUE;
RET_STATUS; { Check the code in "retSS". Set "varSS" and "actionSS" }

{ Check to see whether to switch retrieveSS to true }
if last_fieldSS and (not retrieveSS) then
begin
retrieveSS:ITRUE; last_fieldSS:IFALSE; actionSS:IstaySS; varSS:=1;

 

.2172

end else
last_fieldSS:=FALSE;

UNTIL actionSSIexitSS
END; { OVERLAY PROCEDURE SCREEN_IO }

***** End of Include file MAGIC4.INC *****

***** Beginning of Include file MAGIC5.INC *****
{$1 MAGIC5.INC} { EXPORTERS REVENUE }
OVERLAY PROCEDURE EXPORTER_CROP_REVENUE; { B:EXOVARII }
BEGIN

screenSS:I23; screen_fieldSS:I44; varSS:II;

retrieveSS:IFALSE; last_fieldSS:IFALSE;

DISPLAY_SCREEN('EXOVAR11.SCR'.file_existSS); { Display Screen }

if not file_existSS then

begin gotoxy(I.I); write('Missing Screen EXOVARII') end;
retSS:I";

{ Display Items. Change retrieveSS to TRUE and INPUT items}
REPEAT { until actionSS I exitSS }
CASE varSS of

I: GETNUM(41.3.2.'N'.YEAR[NY-3].
'II'.0.0.99.0.retSS.FALSE.15.0);

2: GETNUM(44.3.2.'N'.YEARB[NY-3].
'II'.0.0.99.0.retSS.FALSE.15.0);

3: GETNUM(52.3.2.'N'.YEAR[NY-2].
'll'.0.0.99.0.retSS.FALSE.15.0);

4: GETNUM(55.3.2.'N'.YEARB[NY-2].
'I!’.0.0.99.0.retSS.FALSE.15.0);

5: GETNUM(63.3.2.'N'.YEAR[NY-1].
'll'.0.0.99.0.retSS.FALSE.15.0);

6: GETNUM(66.3.2.'N'.YEARB[NY-1].
'Il'.0.0.99.0.retSS.FALSE.15.0);

7: GETNUM(74.3.2.'N'.YEAR[NY].
'll'.0.0.99.0.retSS.FALSE.15.0);

8: GETNUM(77.3.2.'N'.YEARB[NY].
'll'.0.0.99.0.retSS.FALSE.15.0);

9: GETNUM(40.7.6.'N'.XRAR[NY-3].
'l.lili'.0.0000.9.9999.retSS.FALSE.15.0);

10: GETNUM(40.9.6.'N'.XRAU[NY-3].
'I.#III'.0.0000.9.9999.retSS.FALSE.15.0);

11: GETNUM(40.11.6.'N'.XRCA[NY-3].
'f.l'll'.0.0000.9.9999.retSS.FALSE.15.0);

12: GETNUM(40.I3.6.'N'.FYAR[NY-3].
'l.llli'.0.0000.9.9999.retSS.FALSE.15.0);

13: GETNUM(40.15.6.'N'.FYAU[NY-3].
'I.IPIP'.0.0000.9.9999.retSS.FALSE.15.0);

I4: GETNUM(40.17.6.'N'.FYCA[NY-3].
'I.IIII'.0.0000.9.9999.retSS.FALSE.15.0);

15: GETNUM(40.19.6.'N'.HYAR[NY-3].
'#.lfll'.0.0000.9.9999.retSS.FALSE.15.0);

16: GETNUM(40.21.6.'N'.HYAU[NY-3].
'I.IIII'.0.0000.9.9999.retSS.FALSE.15.0);

 

17:

18:

19:

20:

21:

22:

23:

24:

25:

26:

27:

28:

29:

30:

31:

32:

33:

34:

35:

36:

37:

38:

39:

40:

41:

42:

43:

GETNUM(40.23.6.'N'.HYCA[NY-3].

273

'l.l!ll'.0.0000.9.9999.retSS.FALSE.15.0);

GETNUM(51.7.6.'N'.XRAR[NY-2].

'l.llll'.0.0000.9.9999.retSS.FALSE.15.0);

GETNUM(SI.9.6.'N'.XRAU[NY-2].

'I.Il#l'.0.0000.9.9999.retSS.FALSE.15.0):

GETNUM(51.11.6.'N'.XRCA[NY-2].

'l.llll'.0.0000.9.9999.retSS.FALSE.15.0);

GETNUM(51.13.6.'N'.FYAR[NY-2].

'I.IIII'.0.0000.9.9999.retSS.FALSE.15.0);

GETNUM(51.15.6.'N'.FYAU[NY-2].

'l.llll'.0.0000.9.9999.retSS.FALSE.15.0);

GETNUM(51.17.6.'N’.FYCA[NY-2].

'I.Illl'.0.0000.9.9999.retSS.FALSE.15.0);

GETNUM(51.19.6.'N'.HYAR[NY-2].

'i.llll'.0.0000.9.9999.retSS.FALSE.15.0):

GETNUM(SI.21.6.'N'.HYAU[NY-2].

'I.ll!l'.0.0000.9.9999.retSS.FALSE.15.0);

GETNUM(51.23.6.'N'.HYCA[NY-2].

'i.llll'.0.0000.9.9999.retSS.FALSE.15.0);

GETNUM(62.7.6.'N'.XRAR[NY-I].

'l.llIl'.0.0000.9.9999.retSS.FALSE.15.0);

GETNUM(62.9.6.'N'.XRAU[NY-I].

'I.llll'.0.0000.9.9999.retSS.FALSE.15.0);

GETNUM(62.11.6.'N'.XRCA[NY-1].

'l.llll'.0.0000.9.9999.retSS.FALSE.15.0);

GETNUM(62.13.6.'N'.FYAR[NY-I].

'l.llll'.0.0000.9.9999.retSS.FALSE.15.0):

GETNUM(62.15.6.'N'.FYAU[NY-1].

'I.llll'.0.0000.9.9999.retSS.FALSE.15.0);

GETNUM(62.17.6.'N'.FYCA[NY-1].

'I.IIII'.0.0000.9.9999.retS$.FALSE.15.0);

GETNUM(62.19.6.'N'.HYAR[NY-1].

'l.llll'.0.0000.9.9999.retSS.FALSE.15.0):

GETNUM(62.21.6.'N'.HYAU[NY-1].

'l.llil'.0.0000.9.9999.retSS.FALSE.15.0):

GETNUM(62.23.6.'N'.HYCA[NY-l].

'l.llll'.0.0000.9.9999.retSS.FALSE.15.0);

GETNUM(73.7.6.'N'.XRAR[NY].

'I.llll'.0.0000.9.9999.retSS.retrieveSS.15.0);

GETNUM(73.9.6.'N'.XRAU[NY].

'I.Illl'.0.0000.9.9999.retSS.retrieveSS.15.0);

GETNUM(73.II.6.'N'.XRCA[NY].

'l.llil'.0.0000.9.9999.retSS.retrieveSS.15.0);

GETNUM(73.13.6.'N'.FYAR[NY].

'O.lill'.0.0000.9.9999.retSS.retrieveSS.15.0);

GETNUM(73.15.6.'N'.FYAU[NY].

'I.IIII'.0.0000.9.9999.retSS.retrieveSS.15.0);

GETNUM(73.17.6.'N'.FYCA[NY].

'I.l'll'.0.0000.9.9999.retSS.retrieveSS.15.0);

GETNUM(73.I9.6.'N'.HYAR[NY].

'I.III#'.0.0000.9.9999.retSS.retrieveSS.15.0);

GETNUM(73.21.6.'N'.HYAU[NY].

’l.l!ll'.0.0000.9.9999.retSS.retrieveSS.15.0);

 

 

2774.

44; GETNUM(73.23.6.'N'.vYCA[NY].
'l.llll'.0.0000.9.9999.retSS.retrieveSS.15.0);
END; { CASE I

if varSS-screen_fieldSS then last_fieldSS:=TRUE;
RET_STATUS; { Check the code in "retSS". Set "varSS" and "actionSS" }

{ Check to see whether to switch retrieveSS to true }
if last_fieldSS and (not retrieveSS) then
begin
retrieveSS:ITRUE; last_fieldSS:IFALSE; actionSS:IstaySS; varSS:Il;
end else
last_fieldSS:IFALSE;

UNTIL actionSS-exitSS
END; { OVERLAY PROCEDURE SCREEN_II }

"*** End of Include file MAGIC5.INC *****
BEGIN

REPEAT
IF (EDIT-FALSE) AND (BACTIVEIFALSE)
THEN BEGIN
COLOR(HHITE.BLACK);
CLRSCR;
FLASHUP('HIEXPORTER.NOESC.NOMOVE');
GOTOXY(1.25I;
READ(RESPONSE);
CLREOL:
FLASHUP('CIALL');
END:
CASE (RESPONSE) OF
'1':EXPORTER_CROP_REVENUE:
'2':BEGIN { EXPORTERS_GDP }
UB:I2:
BACTIVE:ITRUE:
CASE (BSCREEN) OF
1: EXPORTERS_GDP_SCREEN_1:
2: EXPORTERS_GDP_SCREEN_2:
END; {CASE BSCREEN 0F}
END;
'3':;
END; {CASE}
IF (RESPONSE <> '3') THEN
vHATMENu(BSCREEN.BACTIVE.UB);
UNTIL (RESPONSE - '3'):
END: { PROCEDURE EXPORTERS COMPOSITES }

BEGIN
REPEAT
IF (EDIT-FALSE) AND (BACTIVEIFALSE) THEN
BEGIN

2'755

COLOR(HHITE.BLACK);

CLRSCR;

FLASHUP( 'HIEXOGSUMM. NOESC . NOMOVE ' ) ;

GOTOXY(1.25);

READ(RESPONSE);

CLREOL:

FLASHUP(‘C-ALL');

END:

CASE (RESPONSE) OF
'I':US_COMPOSITE_VARIABLES:
'2':BEGIN { IMPORTERS_GDP }

UB:I3;

BACTIVEzITRUE:

CASE (BSCREEN) OF
I: IMPORTERS_GDP_SCREEN_1:
2: 1HPORTERS_GDP_SCREEN_2:
3: IHPORTERS_GDP_SCREEN_3:

END; { CASE BSCREEN OF }

END;
'3':EXPORTERS_COMPOSITES:
'4':BEGIN { EXOGENOUS_VARIABLE_SUMMARY }
UBzIS;

BACTIVEzITRUE:
CASE (BSCREEN) OF

' SCREEN_I:
SCREEN_3A:
SCREEN_Z:
SCREEN_3:
. SCREEN_4;

END; { CASE BSCREEN OF }
END;
'5':;

END; {CASE}
IF (RESPONSE <> '5') THEN
HHATMENU(BSCREEN.BACTIVE.UB);

UIwaO-I

UNTIL (RESPONSE I '5');
END; { PROCEDURE EXOGENOUS VARIABLE SUB MENU }

overlay procedure EXPORT_VARIABLE_SUB_MENU;

‘***' Beginning of Include file HETT.INC ***’*
{$1 HETT.INC} { exporters net exports and us exports }

(. mtti'tiit’itfiiiiiititflflfiiittiitittiiitttfltttfittiiitfittittittt * i i t

{ Initialize Variables To Default Values }

BEGIN { formula (((x’39.368)/1000)*0.85995 }

{ corn I 0.85995 as a ' X ' of exports }
esply_in_bu[1]:I1890.21; { actual values }
econ_in_bu[l]:-9I4.68: { ECON[1]:I27017.96; I
ees_in_bu[l]:I294.47; { EES[I]:I8698.DI; I
ene_in_bu[l]:I681.06; { ENE[1]:I19112.01; I

eha[1]:I19038.0:

esply_in_bu[2]:I1824.19;

 

257E;

econ_in_bu[2]:8986.76; { ECON[2]:-29146.99; }
ees_in_bu[2]:-168.59; { EES[2]:=4979.98; }
ene_in_bu[Z]:=668.84; { ENE[2]:8ZI37D.DD; }
eha[2]:-19065.D;

esply_in_bu[3]:-1840.ID;

econ_in_bu[3]:-961.l3; ( ECON[3]:t28390.02; }
ees_in_bu[3]:-l71.34; { EES[3]:-5060.98; }
ene_in_bu[3]:-7D7.63; { ENE[3]:-20425.0D; }

eha[3]:-19424.0;

INI_IN_BU[l]:-2552.D3;
INI_IN_BU[Z]:-2532.85;
INI_IN_BU[B]:-254S.63:

i***t*********fi****t**i*ttt*******ttttt******ii*************tttttttt *)

procedure EXPORTERS_NET_EXPORTS_SCREEN; { B:ENEXPORT }
BEGIN

screenSS:-24; screen_fieldSS:=32; varSS:=l;

retrieveSS:8FALSE; last_fieldSS:=FALSE;

DISPLAY_SCREEN('ENEXPORT.SCR'.file_exist$$); { Display Screen }

if not file_existSS then

begin gotoxy(I.I); write('Missing Screen ENEXPDRT') end;
retSS:-";

{ Display Items. Change retrieveSS to TRUE and INPUT items}
REPEAT { until actionSS - exitSS }
CASE varSS of
I: GETNUM(31.6.2.'N'.YEAR[NY-3].
'II'.D.O.99.0.retSS.FALSE.15.0);
2: begin
yearb[NY-3]:syear[NY-3]+l;
GETNUM(34.6.2.'N'.YEAR8[NY-3].
'II'.0.0.99.0.retSS.FALSE.15.0);
end;
3: GETNUM(41.6.2.'N'.YEAR[NY-Z].
'll'.D.0.99.0.retSS.FALSE,15.0);
4: begin
yearb[NY-2]:-year[NY-2]+l;
GETNUM(44.6.2.'N'.YEARB[NY-2],
'Il'.0.0.99.D,retSS.FALSE,15.0);
end;
5: GETNUM(51.6.2.'N'.YEAR[NY-1].
'll'.O.D.99.D.retSS,FALSE.15,0);
6: begin
yearb[NY-l]:Iyear[NY-1]+1;
6ETNUH(54.6.2.'N'.YEARB[NY-l].
'II'.D.D.99.D.retSS.FALSE.15.0)z
end;
7: GETNUM(61.6.2.'N'.YEAR[NY].
'll'.0.D.99.D.retSS.FALSE.15.0);
8: begin
yearb[NY]:-year[NY]+1;

 

 

 

10:

11:

12:

13:

14:

15:

16:

17:

18:

19:

20:

21:

22:

23:

24:

25:

26:

27:

127“?

GETNUM(64,6.2.'N'.YEARB[NY].
'Il'.0.0.99.0,retSS.FALSE.15.0);
end;
GETNUM(28.11.B.'N'.ESPLY_IN_BU[NY-3].
’Illll.ll'.OOOD.OO.99999.99.retSS.FALSE.15,0);
GETNUM(28.15.8.'N'.ECON_IN_BU[NY-3].
'illll.ll'.DDOO.DO.99999.99.retSS.FALSE.15.0);
GETNUM(29.19.7.'N'.EES_IN_BU[NY-3].
'llll.!l'.OO0.00.9999.99.retSS,FALSE.15.0);
GETNUM(29.22.7.'N'.ENE_IN_BU[NY-3].
'l#ll.il'.0.00.9999.99.retSS.FALSE.15.0);
GETNUM(38.11.B.'N'.ESPLY_IN_BU[NY-2].
'IIIII.II'.OOO0.00.99999.99.retSS.FALSE.15.0);
GETNUM(38.15.8,'N'.ECON_IN_BU[NY-2].
'lllil.’i'.OOOD.OD.99999.99.retSS.FALSE.15.0);
GETNUM(39.19.7.'N'.EES_IN_BU[NY~2].
'llll.ll'.OOO.DO.9999.99.retSS.FALSE.15.0);
GETNUM(39.22.7.'N'.ENE_IN_BU[NY-2],
'Illl.ll'.0.0D.9999.99.retSS.FALSE.15.0):
GETNUM(48.11.8.'N'.ESPLY_IN_BU[NY-1].
'lllll.ll'.ODDD.OD.99999.99.retSS.FALSE.15.0);
GETNUM(48.15.8.'N'.ECDN_IN_BU[NY-l].
'lll!#.!l'.OOO0.00.99999.99.retSS.FALSE.15.0);
GETNUM(49.19.7.'N'.EES_IN_BU[NY—l].
'llll.ll'.ODD.DD.9999.99.retSS.FALSE.15.0);
GETNUM(49.22.7.'N'.ENE_IN_BU[NY-l],
'llll.#l'.0.00.9999.99.retSS,FALSE.15.0);
GETNUM(58.11.8.'N'.ESPLY_IN_BU[NY],
'llill.ll'.ODO0.00.99999.99.retSS.FALSE.15.0);
GETNUM(58.1S.8.'N'.ECON_IN_BU[NY].
'llil#.ll'.ODOO.DO.99999.99.retSS.FALSE.15.0);
GETNUM(59.19.7.'N'.EES_IN_BU[NY],
'illl.li'.OO0.0D.9999.99.retSS.FALSE.15.0):
GETNUM(59.22.7.'N'.ENE_IN_BU[NY].
’lll!.ll'.0.00.9999.99.retSS.FALSE.15.0);
GETITEN(7D.11.1.'Y'.ESPLY_IN_BU_FLAG.
'U'.’ '.' '.retSS.retrieveSS.IS.D);
BEGIN
1F (RETRIEVESS=TRUE) AND (ESPLY_IN_BU_FLAG='Y') THEN
BEGIN
FGz-IS;
86:80;
GETNUM(74.11.5.'N'.ESPLY_IN_BU_DR[NY].
'illll'.D.O;99999.9.retSS.RETRIEVESS.FG.BG);
END
ELSE
BEGIN
FG:=O;
BstO;
GETNUM(74.11.5.'N'.ESPLY_IN_BU_OR[NY],
'lllll‘.0.0.99999.9.retSS.FALSE.FG.BG);
END;
END;
GETITEH(70.15.1.'Y'.ECON_IN_BU_FLAG.

278

'U'.’ '.’ '.retSS.retrieveSS.15.0);
28: BEGIN
IF (RETRIEVESSsTRUE) AND (ECON_IN_BU_FLAG='Y') THEN
BEGIN
FG:-15:
BG:-O;
GETNUM(74.IS.5.'N'.ECON_IN_BU_OR[NY].
'IIIII'.0.0.99999.9.retSS.RETRIEVESS.FG.BG):
END
ELSE
BEGIN
FG:=O;
86:80:
GETNUM(74.15.5.'N'.ECON_IN_BU_OR[NY],
'IIIII'.0.0.99999.9.retSS.FALSE.FG.BG);
END;
END;
29: GETITEN(70.19.1.'Y'.EES_IN_BU_FLAG.
'U'.’ '.' '.retSS.retrieveSS.15.D);
30: BEGIN
IF (RETRIEVESS=TRUE) AND (EES_IN_BU_FLAG=’Y') THEN
BEGIN
FG:=15;
BG:=O;
GETNUM(74.19,5,'N'.EES_IN_BU_OR[NY].
'lilll'.0.0.99999.9.retSS.RETRIEVESS,FG.BG);
END
ELSE
BEGIN
FG:I0;
BG:-0;
GETNUM(74.19.5.'N'.EES_IN_BU_OR[NY].
'lllll'.D.D.99999.9.retSS.FALSE.FG.BG);
END;
END;
31: GETITEH(70.22.1.'Y'.ENE_IN_BU_FLAG,
'U'.’ '.' '.retSS.retrieveSS.15.D);
32: BEGIN
IF (RETRIEVESS=TRUE) AND (ENE_IN_BU_FLAG='Y') THEN
BEGIN
FG:=15;
BGz-O;
GETNUM(74.22.5.'N'.ENE_IN_BU_0R[NY],
'Olill'.0.0.99999.9.retSS.RETRIEVESS.FG.BG);
END
ELSE
BEGIN
FG:-O;
861-0;
GETNUM(74.22.5.'N'.ENE_IN_BU_0R[NY].
'Illll'.0.0.99999.9.retSS.FALSE.FG,BG);
END;
END;
END; { CASE }

 

.2179
COLOR(HHITE.BLACK);

if varSS-screen_fieldSS then last_fieldSS:=TRUE;
RET_STATUS; { Check the code in "retSS". Set "varSS" and "actionSS" }

{ Check to see whether to switch retrieveSS to true }
if last_fieldSS and (not retrieveSS) then
begin
retrieveSS:-TRUE; last_fieldSS:-FALSE; actionSS:-staySS; varSS:-I;
end else
last_fieldSS:=FALSE;

UNTIL actionSS-exitSS
END; { PROCEDURE EXPORTERS NET EXPORTS SCREEN_I }

procedure US_EXPORTS_SCREEN; { B:XXPORT }
BEGIN

screenSS:-25; screen_fieldSS:-22; varSS:-l;

retrieveSS:IFALSE; last_fieldSS:cFALSE:

DISPLAY_SCREEN('XXPORT.SCR'.file_existSS); { Display Screen }

if not file_existSS then

begin gotoxy(I.I); write('Missing Screen XXPORT') end;
retSS:-";

{ Display Items. Change retrieveSS to TRUE and INPUT items}
REPEAT { until actionSS - exitSS )
CASE varSS of
l: GETNUM(3D.5.2.'N'.YEAR[NY-3].
'II'.D.D.99.0.retSS.FALSE.15.0);
2: begin
yearb[NY-3]:-year[NY-3]+I;
GETNUH(33.5.2.'N'.YEARB[NY-3].
'll'.O.D.99.D.retSS.FALSE.15.0);
end;
3: GETNUM(4D.5.Z.'N',YEAR[NY-2].
'll'.D.O.99.0.retSS.FALSE.15.0);
4: begin
yearb[NY-2]:uyear[NY-2]+I;
GETNUM(43.5.2.'N'.YEARB[NY-2].
'll'.D.0.99.D.retSS.FALSE.15,0);
end;
5: GETNUM(50.5.2.'N'.YEAR[NY-l].
'll'.0.0.99.0.retSS.FALSE.15.0);
6: begin
yearb[NY-1]:-year[NY-1]+l;
GETNUH(53.5.2.'N'.YEARB[NY-1].
'l!’.0.0.99.0.retSS.FALSE.15.0);
end;
7: GETNUM(GD.5.2.'N',YEAR[NY].
'Il'.0.0.99.0.retSS.FALSE.15.0)z

 

EFF-—

IO:

11:

12:

13:

14:

15:

16:

17:

18:

19:

20:

21:

22:

213C)

begin
yearb[NY]:-year[NY]+l;
GETNUM(63.5.2.'N'.YEARB[NY],
'll'.D.D.99.D.retSS.FALSE.15.0);
end;

GETNUM(28.11.7.'N'.INI_IN_BU[NY-3].

'llll.Ii'.DDOD.DD.9999.99.retSS,FALSE.15.0);
GETNUM(28.15,7.'N'.ENE_IN_BU[NY-3].
'llll.#l'.OOOD.OO.9999.99.retSS.FALSE.15.0);
begin

exports[NY-3]:-ini_in_bu[NY-3]-ene_in_bu[NY-3];
GETNUM(29.19.6.'N'.EXPORTS[NY-3].
'llll.l'.D.O.9999.9.retSS.FALSE.15.0);
end;
GETNUM(38.11.7.'N'.INI_IN-BU[NY-2].
'llll.ll'.ODOD.DD.9999.99,retSS.FALSE.15.0);
GETNUM(38.15.7.'N'.ENE_IN_BU[NY-2].
'llll.ll'.DDDD.DD.9999.99.retSS.FALSE.15.0);
begin
exports[NY-2]:-ini_in_bu[NY-2]-ene_in_bu[NY-2];
GETNUM(39.19.6.'N'.EXPDRTS[NY-2].
'Illl.l'.D.O.9999.9.retSS,FALSE.15.0);
end;
GETNUM(48.II.7.'N'.INI_IN_BU[NY-I].
'llll.li'.0000.DO.9999.99.retSS.FALSE.15.0);
GETNUM(48.15.7.'N'.ENE_IN_BU[NY-l].
'Illl.ll'.0000.0D.9999.99.retSS.FALSE.15.0);
begin

exports[NY-I]:Iini_in_bu[NY-1]-ene_in_bu[NY-l];
GETNUM(49.19,G.'N'.EXPORTS[NY-I].
'lill.l'.D.O.9999.9.retSS.FALSE.15.0);
end;
GETNUM(SB.II.7.'N’.INI_IN_BU[NY].
'IIII.I".0000.DD.9999.99.retSS.FAL$E.15.0);
GETNUM(58.15.7.'N'.ENE_IN_BU[NY].
'Illf.ll'.ODO0.0D.9999.99.retSS.FALSE.15.0);
begin
exports[NY]:sini_in_bu[NY]-ene_in_bu[NY];
GETNUM(59.19.6.'N'.EXPDRTS[NY],
'il'l.l'.D.O.9999.9.retSS.FALSE.15.0);
end;
GETITEN(70.II.1.'Y'.INI_IN_BU_FLAG.
'U'.’ '.' '.retSS.retrieveSS.15.D);
BEGIN
IF (RETRIEVESSsTRUE) AND (INI_IN_BU_FLAG='Y') THEN
BEGIN
FGt-IS;
BGz-O;
GETNUM(74.II.5.'N'.INI_IN_BU_OR[NY].
’Illll'.D.D.99999.9.retSS.RETRIEVESS,FG.BG);
END
ELSE
BEGIN

 

2E3].

FG:=O;
BG:-O;
GETNUM(74.II.5.’N'.INI_IN_BU_OR[NY],
'll!!!’.0.0.99999.9.retSS.FALSE.FG.BG);
END;
END;

END; { CASE }
COLOR(HHITE.BLACK);

if varSS-screen_fieldSS then last_fieldSS:=TRUE;
RET_STATUS; { Check the code in ”retSS". Set ”varSS” and "actionSS" }

{ Check to see whether to switch retrieveSS to true }
if last_fieldSS and (not retrieveSS) then
begin
retrieveSS:-TRUE; last_fieldSS:=FALSE; actionSS:=staySS; varSS:=1;
end else
last_fieldSS:=FALSE;

UNTIL actionSS-exitSS
END; { PROCEDURE US EXPORTS SCREEN }

***** End of Include file HETT.INC *****

BEGIN
REPEAT
IF (EDIT=FALSE) AND (BACTIVE=FALSE) THEN
BEGIN
COLOR(HHITE.BLACK);
CLRSCR;
FLASHUP('HIXPORTSUN.NOESC.NOMOVE');
GOTOXY(1.25);
READ(RESPONSE);
CLREOL:
FLASHUP(‘C-ALL'):
END;
CASE (RESPONSE) OF
'1': EXPORTERS_NET_EXPORTS_SCREEN; { EXPORTER NET EXPORT }
'2': US_EXPORTS_SCREEN; { US EXPORT SUMMARY }
'3':;
END; { CASE }
IF (RESPONSE <> '3') THEN
UHATHENU(BSCREEN.BACTIVE.UB);

UNTIL (RESPONSE I '3');
END; { PROCEDURE EXPORTER VARIABLE SUB HENU }
overlay procedure feed_grain_demand;
*’*** Beginning of Include file FEEDTOR.INC *****

{SI FEEDTOR.INC}
OVERLAY PROCEDURE DAIRY_ANINAL_CONS_PERHEAD; { BzDACDN }

 

BE

.2E32
GIN

screenSS:-26; screen_fieldSS:-22; varSS:-I;
retrieveSS:=FALSE; last_fieldSS:=FALSE;

DISPLAY_SCREEN('DACDN.SCR'.file_existSS); { Display Screen }

if not file_existSS then
begin gotoxy(I.I); write('Missing Screen DACON') end;

retSS:-";

{
RE

1:

10:

11:

12:

13:

14:

Display Items. Change retrieveSS to TRUE and INPUT items}

PEAT { until actionSS - exitSS }

CASE varSS of

GETNUM(31.6.2.'N'.YEAR[NY-3].
'll'.O.D.99.0.retSS.FALSE.15.0);

begin

yearb[NY—3]:-year[NY-3]+1;

GETNUM(34.6.2.'N'.YEARB[NY-3].

'l!’.D.O,99.D.retSS.FALSE,15.0);

end;

GETNUM(41.6.2.'N',YEAR[NY-2],
'll',0.0,99.D,retSS.FALSE.15.0);

begin

YEARB[NY-2]:=year[NY-2]+l;

GETNUM(44.6.2.'N',YEARB[NY-2].
'll',O.D.99.0.retSS.FALSE.15.0);
end;

GETNUM(51.6.2.'N'.YEAR[NY-l].
'll'.D.O.99.0.retSS.FALSE.15.0);

begin

YEARB[NY-l]:syear[NY-1]+l;

GETNUM(54.6.2,'N'.YEARB[NY-1].
'Il'.D.D.99.0,retSS,FALSE.15.0);
end;

GETNUM(61.6.2.'N'.YEAR[NY].
'll'.0.0.99.0.retSS.FALSE.15.0);

begin

YEARB[NY]:=year[NY]+1;

GETNUM(64.6.2.'N'.YEARB[NY],
'li'.0.0.99.0.retSS.FALSE.15.0);
end;

GETNUM(3D.9.6.'N',ONEPH[NY-3].
'll.lll'.OD.DDD.99.999.retSS.FALSE.15.0);
GETNUM(30.15.6.'N'.DANUH[NY-3].
'I#.lll',0.000.99.999.retSS.FALSE.15.0);
BEGIN

DAIRYCONS[NY-3]:=ONEPH[NY-3]*DANUH[NY-3];

GETNUM(30,20.6.'N'.DAIRYCONS[NY-3].

'lll.#l'.0.00,999.99,retSS,FALSE.15.0);
END;
GETNUM(40.9.6.'N'.ONEPH[NY-2].
'II.III',OD.OOD.99.999.retSS.FALSE.15.0);
GETNUM(40.15.6.'N'.DANUH[NY-2].
'll.lli',D.OOD.99.999.retSS.FALSE.15.0);
BEGIN

 

.2£33

DAIRYCDNS[NY-2]:IDNEPH[NY-2]'DANUM[NY-2];
GETNUM(AO.ZO.G.'N'.DAIRYCONS[NY-2].
'lll.il'.0.00.999.99.retSS.FALSE.15.0);
END;
15: GETNUM(50.9.6.'N'.DNEPH[NY-l].
'll.lll'.O0.000.99.999.retSS.FALSE.15.0);
16: GETNUM(SO.15.6.'N'.OANUM[NY-l].
'll.lll'.0.000.99.999.retSS.FALSE.15.0);
l7: BEGIN
OAIRYCONS[NY—l]z-ONEPH[NY-l]*DANUM[NY-1];
GETNUM(SO.ZO.G.'N'.DAIRYCONS[NY-l].
'Ill.ll'.D.OO,999.99,retSS.FALSE.15.0};
END;
18: GETNUM(60.9.G.'N'.ONEPH[NY],
'll.lll'.D0.000.99.999.retSS.FALSE.15.0};
19: GETNUM(60.15.6.'N'.DANUM[NY].
'l'.lll'.0.0DO.99.999.retSS.FALSE.15.0);
20: BEGIN
DAIRYCONS[NY]:80NEPH[NY]*DANUM[NY];
GETNUM(GO.ZD.6.'N'.DAIRYCONS[NY].
'III.II'.D.OO.999.99.retSS.FALSE,15.0};
END;
21: GETNUM(71.9.6.'N'.ONEPH_OR[NY].
'll.lll'.O0.000.99.999,retSS.retrieveSS.15.0);
22: BEGIN
1r ONEPH_OR[NY]<>O.D THEN
DAIRYCDNS-DR[NY]:’0NEPH_OR[NY]*DANUH[NY]
ELSE DAIRYCDNS_0R[NY]:-0.0;
GETNUM(72.ZD.6.'N'.DAIRYCONS_OR[NY].
'lil.ll'.0.00.999.99.retSS.FALSE,15.0);
END;
END; { CASE }

1

if varSS-screen_fieldSS then last_fieldSS:=TRUE;
RET_STATUS; { Check the code in ”retSS". Set "varSS" and "actionSS" }

{ Check to see whether to switch retrieveSS to true }
if last_fieldSS and (not retrieveSS) then
begin
retrieveSS:-TRUE; last_fieldSS:-FALSE; actionSS:-staySS; varSS:=I;
end else
last_fieldSS:=FALSE;

UNTIL actionSS-exitSS
END; { OVERLAY PROCEDURE DAIRY ANIMAL CONS PERHEAD }

OVERLAY PROCEDURE CATTLE_ON_FEED_CONS_PERHEAD; { B:COFCON }
BEGIN

screenSS:-27; screen_fieldSS:IZZ; varSS:=l;

retrieveSS:IFALSE; last_fieldSS:IFALSE;
DISPLAY_SCREEN('COFCON.SCR',file_existSS); { Display Screen }
if not file_existSS then

 

12£l4

begin gotoxy(I.I); write('Missing Screen COFCDN') end;
retSS:-";

{ Display Items. Change retrieveSS to TRUE and INPUT items}
REPEAT { until actionSS - exitSS }
CASE varSS of
I: GETNUM(33.6.2.'N'.YEAR[NY-3].
'IX'.D.0.9.D.retSS.FALSE.15.D);
2: GETNUM(36.6.2.'N'.YEARB[NY-3].
'II'.D.O.99.D.retSS.FALSE.15.0};
3: GETNUM(43,6.2,'N'.YEAR[NY-2],
'II'.0.D.99.0,retSS.FALSE.15.0);
4: GETNUM(46.6.2.'N'.YEARB[NY-2].
'll'.0.0.99.D,retSS,FALSE.15.0};
5: GETNUM(S3.6.Z.'N'.YEAR[NY-1].
'll'.0.0.99.D.retSS.FALSE.15.0);
6: GETNUM(56.6.Z.'N'.YEARB[NY-l].
'II'.O.D,99.D.retSS.FALSE.15.0):
7: GETNUM(63.5.2.'N',YEAR[NY],
'Ol'.D.D.99.D.retSS.FALSE.15.0}i
B: GETNUM(66.5,Z.'N'.YEARB[NY].
'Il'.O.D.99.D.retSS.FALSE.15.0);
9: GETNUM(31.9.7.'N'.TUDPH[NY-3].
'PI.#II'.DD.OOO,99.999.retSS.FALSE.15.0};
10: GETNUM(32.15.B.'N'.COFNUM[NY-3],
'll.l!!'.O.DOD.99.999.retSS.FALSE,15.0};
ll: BEGIN
CATONFED[NY-3]:tTUOPH[NY-3]*CDFNUM[NY-3];
GETNUM(32.20.6.'N'.CATONFED[NY-3].
'lll.ll'.D.DO.999.99,retSS.FALSE.15.0};
END;
12: GETNUM(4I.9.7.'N'.TUOPH[NY-2].
'II.III',DD.ODD.99.999,retSS.FALSE.15,0);
l3: GETNUM(42.15.6.'N'.CDFNUM[NY-2].
'II.IPI'.D.ODO.99.999.retSS.FALSE.15.0};
l4: BEGIN
CATONFED[NY-Z]:8TU0PH[NY*Z]*CDFNUM[NY-2];
GETNUM(42.ZD.6.'N'.CATDNFED[NY-2].
'l'l.ll'.D.DD.999.99.retSS.FALSE.15.0};
END;
15: GETNUM(51.9.7.'N'.TUOPH[NY-l].
'PI.#II'.00.000.99.999,retSS.FALSE.15.0);
16: GETNUM(52.15.6.'N'.CDFNUH[NY-I].
'll."l'.D.DDO.99.999.retSS.FALSE.15.0};
l7: BEGIN
CATONFED[NY-I]:=TUDPH[NY-l]*CDFNUH[NY-1];
GETNUM(52.20.5.'N'.CATONFED[NY-l].
'BIP.II'.D.DO.999.99.retSS.FALSE.15.0);
END;
18: GETNUM(61.9.7.'N'.TUDPH[NY].
"P.lll'.DO.DOO.99.999.retSS.FALSE.15.0);
19: GETNUM(62.15.6,'N'.CDFNUM[NY],
'll.'ll'.D.000.99.999.retSS.FALSE.15.0};
20: BEGIN

 

21355

CATONFEO[NY]:-TUOPH[NY]*COFNUM[NY];
GETNUM(62.20.6.'N'.CATONFED[NY].
'lll.ll'.D.OO.999.99.retSS.FALSE.15.0);
END;
21: GETNUM(72.9.7.'N'.TUOPH_OR[NY],
'll.#ll'.O0.0DO.99.999.retSS.retrieveSS.15,0);
22: BEGIN
IF THOPH_OR[NY]<>0.0 THEN
CATONFEO_0R[NY]:-THOPH_OR[NY]*COFNUM[NY]
ELSE CATONFED_OR[NY]:-0.0;
GETNUM(73.ZD.6.'N'.CATONFED_OR[NY].
'Il!.ll',0.00.999.99.retSS.FALSE.15.0);
END;
END; { CASE }

if varSSsscreen_fieldSS then last_fieldSS:ITRUE;
RET_STATUS; { Check the code in ”retSS". Set "varSS" and "actionSS" }

{ Check to see whether to switch retrieveSS to true }
if last_fieldSS and (not retrieveSS) then
begin
retrieveSS:ITRUE; last_fieldSS:=FALSE; actionSS:=staySS; varSS:=l;
end else
last_fieldSS:=FALSE;

UNTIL actionSSaexitSS
END; { OVERLAY PROCEDURE CATTLE_ON_FEED_CONS_PERHEAD }

OVERLAY PROCEDURE OTHER_BEEF_CATTLE_CONS_PERHEAD; { B:OBCCON }
BEGIN

screenSS:-28; screen_fieldSS:-22; varSS:-I;

retrieveSS:-FALSE; last_fieldSS:=FALSE;

DISPLAY_SCREEN('OBCCON.SCR'.file_existSS); { Display Screen }

if not file_existSS then

begin gotoxy(I.I); write('Missing Screen OBCCON') end;
retSS:-";

{ Display Items. Change retrieveSS to TRUE and INPUT items}
REPEAT { until actionSS - exitSS }
CASE varSS of
1: GETNUM(34.6.2.'N'.YEAR[NY-3].
'II'.0.0,99.D.retSS.FALSE.15.0);
2: GETNUM(37.6.2.'N'.YEARB[NY-3].
'II'.0.0.99.0.retSS.FALSE.15.0);
3: GETNUM(44.6.2.'N'.YEAR[NY-2].
'li'.0.0.99.D.retSS.FALSE.15.0);
4: GETNUM(47,6.Z.'N'.YEARB[NY-2].
'll'.D.O.99.D.retSS.FALSE.15.0);
5: GETNUM(54.6.2.'N'.YEAR[NY-l],
'll'.D.O.99.D.retSS.FALSE.15.0);
G: GETNUM(57.6.2.'N'.YEARB[NY-l].
'll'.D.O.99.0.retSS.FALSE.15.0);

 

JZEMS

7: GETNUM(64.6.2.'N'.YEAR[NY],
'II'.0.0.99.0.retSS.FALSE.15.0);

8: GETNUM(67.6.2.'N'.YEARB[NY].
'll',D.D.99.D.retSS.FALSE,15.0);

9: GETNUM(32.9.7.'N'.THREEPH[NY—3].
'l.lll'.O.DOO.9.999.retSS.FALSE.15.0);

ID: GETNUM(33.IS.6.'N'.OBCNUM[NY-3].
'll.lll'.0.000.99.999.retSS.FALSE.15.0);

II: BEGIN
DTHERBEEFCAT[NY-3]:ITHREEPH[NY-3]*DBCNUM[NY-3];
GETNUM(33.ZO.6.'N'.OTHERBEEFCAT[NY-3].
'lll.li'.0.00.999.99.retSS.FALSE.15.0};

END;
12: GETNUM(42,9.7.'N',THREEPH[NY-2].
'I.III'.0.000.9.999.retSS.FALSE.15.0);
13: GETNUM(43,15.B.'N'.DBCNUM[NY—2].
'Il.lll'.0.000.99.999,retSS,FALSE,15.0);

14: BEGIN
0THERBEEFCAT[NY-2]:=THREEPH[NY-2]*OBCNUM[NY-2];
GETNUM(43.ZO.6.'N'.OTHERBEEFCAT[NY-2].
'lll.il'.0.00.999.99.retSS.FALSE.15.0);

END;
15: GETNUM(52.9.7.'N'.THREEPH[NY-l].
'l.lll'.0.000.9.999.retSS,FALSE.15,0);
16: GETNUM(53.15.6.'N'.OBCNUM[NY-l].
'Il.lll'.D.DOO.99.999.retSS.FALSE.15.0);

17: BEGIN
OTHERBEEFCAT[NY-l]:-THREEPH[NY-I]*OBCNUM[NY-1];
GETNUM(53.20.6.'N'.OTHERBEEFCAT[NY-1],
'Ill.ll'.D.OD.999.99.retSS.FALSE.15.0);

END;
18: GETNUM(62.9.7.‘N'.THREEPH[NY],
'l.lll'.O.DOO.9.999.retSS.FALSE.15.0);
19: GETNUM(63.15.6.'N'.DBCNUM[NY],
'll.lll'.D.DDO.99.999.retSS.FALSE.15.0);

20: BEGIN
OTHERBEEFCAT[NY]z-THREEPH[NY]*OBCNUM[NY];
GETNUM(63.20.6.'N'.DTHERBEEFCAT[NY].
'lil.ll'.D.OO.999.99.retSS,FALSE.15.0);

END;
21: GETNUM(72.9.7.'N'.THREEPH_OR[NY],
'l.ill'.0.00D.9.999.retSS.retrieveSS.15.0);

22: BEGIN
IF THREEPH_OR[NY]<>D.O THEN
OTHERBEEFCAT_OR[NY]z-THREEPH_OR[NY]*OBCNUM[NY]
ELSE OTHERBEEFCAT_OR[NY]z-O.D;
GETNUM(73.ZD.6.'N'.OTHERBEEFCAT_OR[NY].
'lli.ll'.0.0D.999.99.retSS.FALSE.15.0);

END;
END; { CASE }

if varSSescreen_fieldSS then last_fieldSS:=TRUE;
RET_STATUS; { Check the code in "retSS". Set ”varSS" and "actionSS" }

 

.2£¥7

{ Check to see whether to switch retrieveSS to true }
if last_fieldSS and (not retrieveSS) then
begin
retrieveSS:ITRUE; last_fieldSS:IFALSE; actionSS:IstaySS; varSS:I1;
end else
last_fieldSS:IFALSE;

UNTIL actionSSIexitSS
END; { OVERLAY PROCEDURE OTHER_BEEF_CATTLE_CDNS_PERHEAD }

OVERLAY PROCEDURE CHICKEN_CONS_PERHEAO; { BzHPCCON }
BEGIN

screenSS:I29; screen_fieldSS:IZZ; varSS:I1;

retrieveSS:IFALSE; last_fieldSS:IFALSE;

DISPLAY_SCREEN('HPCCDN.SCR'.file_existSS): { Display Screen }

if not file_existSS then

begin gotoxy(I.I); write('Missing Screen HPCCON') end;
retSS:I";

{ Display Items. Change retrieveSS to TRUE and INPUT items}
REPEAT { until actionSS I exitSS }
CASE varSS of
l: GETNUM(29.6.2.'N'.YEAR[NY-3].
'Il'.0.0.99.0.retSS.FALSE.15.0);
2: GETNUM(32.G.2.'N'.YEARB[NY-3].
'II'.0.0.99.0.retSS.FALSE.15.0};
3: GETNUM(41.6.2.'N'.YEAR[NY-2].
'll',0.0,99.D.retSS.FALSE.15.0};
4: GETNUM(44,6.2.'N'.YEARB[NY-2].
'Il'.0.0.99£O.retSS.FALSE.15.0);
5: GETNUM(53.6.2.'N'.YEAR[NY-l].
'Il'.0.0.99.0.retSS.FALSE.15.0};
6: GETNUM(56.6.2.'N'.YEARB[NY-l].
'll',0.0,99.0.retSS.FALSE.15.0};
7: GETNUM(65.6.2.'N'.YEAR[NY].
'II'.O.D.99.0.retSS.FALSE.15.0);
8: GETNUM(GB.6.2.'N'.YEARB[NY].
'Il'.0.0.99.0.retSS.FALSE.15.0);
9: GETNUM(29.9.5.'N'.FDURPH[NY-3],
'l.'ll'.0.000.9.999.retSS.FALSE.15.0);
IO: GETNUM(27.15,7.'N'.HPCNUM[NY-3].
'lil.l#l'.0.000.999.999.retSS.FALSE.15.0);
ll: BEGIN
CHICKENCDNS[NY-3]:IFOURPH[NY-3]*HPCNUH[NY-3];
GETNUM(ZB.20.6.'N'.CHICKENCDNS[NY-3].
'lll.ll'.0.0D.999.99,retSS.FALSE.15.0};
END;
12: GETNUM(41.9.5.'N'.FOURPH[NY-2].
'I.Ill'.O.DOO.9.999.retSS.FALSE.15.0};
13: GETNUM(39.15.7.'N'.HPCNUH[NY-2].
'III.III'.D.DOD.999.999,retSS.FALSE.15.0);
14: BEGIN

 

JZEMB

CHICKENCONS[NY-Z]:IFOURPH[NY-2]*HPCNUM[NY-2];
GETNUM(40,ZO.6.'N'.CHICKENCONS[NY-2],
'lll.ll',0.00.999.99.retSS.FALSE,15.0);
END;
15: GETNUM(53.9.5.'N'.FOURPH[NY-1].
'I.III'.0.000.9.999.retSS.FALSE.15.0);
16: GETNUM(51.15.7.'N'.HPCNUM[NY-1].
'lll.lli'.0.000.999.999.retSS.FALSE.15.0};
17: BEGIN
CHICKENCONS[NY-l]:IFOURPH[NY-I]*HPCNUM[NY-I];
GETNUM(52.20.G.'N'.CHICKENCONS[NY-l].
'Ill.li'.0.00.999.99.retSS.FALSE.15.0);
END;
18: GETNUM(65.9.5.'N'.FOURPH[NY],
'l.lll'.0.000.9.999,retSS.FALSE.15.0);
19: GETNUM(63,15,7.’N'.HPCNUM[NY].
'lll.lli'.0.000.999.999.retSS.FALSE.15.0);
20: BEGIN
CHICKENCONS[NY]:IFOURPH[NY]*HPCNUM[NY];
GETNUM(64.ZO.6.'N'.CHICKENCONS[NY].
'lll.ll'.0.00.999.99,retSS.FALSE,15.0);
END;
21: GETNUM(74.9.5.'N'.FOURPH_OR[NY],
'I.I#I'.0.000.9.999.retSS.retrieveSS.15.0};
22: BEGIN
IF FOURPH_OR[NY]<>0.0 THEN
CHICKENCONS_DR[NY]:IFOURPH_OR[NY]*HPCNUM[NY]
ELSE CHICKENCONS_OR[NY]:=0.0;
GETNUM(73,ZO.G.'N'.CHICKENCONS_OR[NY],
”##I.Il'.0.00.999.99.retSS,FALSE.15.0);
END;
END; { CASE }

if varSSIscreen_fieldSS then last_fieldSS:ITRUE;
RET_STATUS; { Check the code in ”retSS". Set "varSS" and "actionSS" }

{ Check to see whether to switch retrieveSS to true }
if last_fieldSS and (not retrieveSS) then
begin
retrieveSS:ITRUE; last_fieldSS:IFALSE; actionSS:IstaySS; varSS:=l;
end else
last_fieldSS:IFALSE;

UNTIL actionSS-exitSS
END; { OVERLAY PROCEDURE CHICKEN CONS PERHEAD }

OVERLAY PROCEDURE BROILER_CONS_PERHEAD; { B:BROCON }
BEGIN

screenSS:IBD; screen_fieldSS:IZZ; varSS:Il;
retrieveSS:IFALSE; last_fieldSS:IFALSE;
DISPLAY_SCREEN('BROCDN.SCR'.file_existSS); { Display Screen }

 

.2£39

if not file_existSS then
begin gotoxy(I.I); write('Missing Screen BROCON') end;
retSS:I";

{ Display Items. Change retrieveSS to TRUE and INPUT items}
REPEAT { until actionSS I exitSS }
CASE varSS of
1: GETNUM(29.G.Z,'N'.YEAR[NY-3].
'Il'.0.0.99.0.retSS.FALSE.15.0};
2: GETNUM(32.G.2,'N'.YEARB[NY-3].
'IP'.0.0.99.0.retSS.FALSE.15.0};
3: GETNUM(4I.6.2.'N'.YEAR[NY-2].
'll'.0.0.99.0.retSS.FALSE.15.0);
4: GETNUM(44.G.2.'N',YEARB[NY-2].
'II'.0.0.99.0.retSS.FALSE.15.0);
5: GETNUM(53.6.2.'N'.YEAR[NY-l].
'll'.O.D.99.D.retSS.FALSE.15.0);
6: GETNUM(56.6.2.'N',YEARB[NY-I],
'Il'.0.0.99.0.retSS.FALSE.15,0};
7: GETNUM(64,6.Z.'N'.YEAR[NY],
'II'.D.D.99.D.retSS.FALSE.15.0);
8: GETNUM(67.6.Z.'N'.YEARB[NY].
'Il'.D.O,99.0,retSS,FALSE.15.0};
9: GETNUM(28.9.6.'N'.FIVEPH[NY-3].
'I.IIII'.0.DDOO.9.9999.retSS.FALSE.15.0};
IO: GETNUM(26.15.B.'N'.BRDNUM[NY-3].
'IIII.III'.D.OOO.9999.999.retSS.FALSE.15.0);
11: BEGIN
BR0ILERCONS[NY-3]:IFIVEPH[NY-3]*BRDNUM[NY-3];
GETNUM(ZB,ZD.G.'N'.BRDILERCONS[NY-3].
'Il'.ll'.0.00.999.99.retSS.FALSE.15.0};
END;
12: GETNUM(40.9.G.'N'.FIVEPH[NY-2].
'I.llll'.0.0000.9.9999.retSS.FALSE.15.0);
13: GETNUM(38.15,8.'N'.BRONUM[NY-Z].
'IIIP.PII'.0.000.9999.999.retSS.FAL$E.15.0};
14: BEGIN
BROILERCDNS[NY-2]:3FIVEPH[NY-2]*BRONUM[NY-2];
GETNUM(40.ZD.6.'N'.BRDILERCONS[NY-2].
'III.II'.0.00.999.99.retSS.FALSE.15.0);
END;
15: GETNUM(52.9.6.'N',FIVEPH[NY-l].
'l.llll'.0.00DO.9.9999.retSS.FALSE.15.0};
16: GETNUM(SO.15,B.'N'.BRDNUM[NY-l].
'IIII.III'.0.000.9999.999.retSS.FALSE.15.0);
17: BEGIN
BROILERCONS[NY-l]:IFIVEPH[NY-l]*BRONUH[NY-l];
GETNUM(52.20.6.'N'.BRDILERCDNS[NY-I].
'III.II'.0.00.999.99.retSS.FALSE.15.0};
END;
18: GETNUM(63.9.6.'N'.FIVEPH[NY].
'l.llll'.0.00DD.9.9999.retSS.FAL$E.15.0);
19: GETNUM(61.15.B.'N'.BRONUM[NY],
'Ilfl.lll'.0.000.9999.999.retSS.FALSE.15,0};

 

ZHBCI

20: BEGIN
BROILERCONS[NY]:IFIVEPH[NY]*BRONUM[NY];
GETNUM(63.ZO.6.'N'.BROILERCONS[NY].
'lli.ll'.0.00.999.99,retSS.FALSE.15.0);
END;
21: GETNUM(73.9.6.'N'.FIVEPH_OR[NY].
'I.IIII',0.0000.9.9999.retSS.retrieveSS.15,0);
22: BEGIN
IF FIVEPH_OR[NY]<>0.0 THEN
BROILERCONS_OR[NY]:IFIVEPH_OR[NY]*BRONUM[NY]
ELSE BROILERCONS_OR[NY]:IO.O;
GETNUM(73.20.G.'N'.BROILERCONS_OR[NY].
'lll.ll'.0.00,999.99.retSS.FALSE.15.0);
END;
END; { CASE }

if varSS-screen_fieldSS then last_fieldSS:ITRUE;
RET_STATUS; { Check the code in "retSS". Set ”varSS" and "actionSS" }

I Check to see whether to switch retrieveSS to true }
if last_fieldSS and (not retrieveSS) then
begin
retrieveSS:ITRUE; last_fieldSS:IFALSE; actionSS:IstaySS; varSS:=1;
end else
last_fieldSS:IFALSE;

UNTIL actionSSIexitSS
END; { OVERLAY PROCEDURE BROILER CONS PERHEAD }

OVERLAY PROCEDURE TURKEY_CDNS_PERHEAD; { BzTURCON }
BEGIN

screenSS:IBI; screen_fieldSS:IZZ; varSS:-l;

retrieveSS:IFALSE; last_fieldSS:IFALSE;

DISPLAY_SCREEN('TURCON.SCR',file_existSS); { Display Screen }

if not file_existSS then

begin gotoxy(I.I); write('Missing Screen TURCDN') end;
retSS:-";

{ Display Items. Change retrieveSS to TRUE and INPUT items}
REPEAT { until actionSS I exitSS }
CASE varSS of
l: GETNUM(31.6.2.'N'.YEAR[NY-3].
'Il'.0.0.99.0.retSS.FALSE.15.0);
2: GETNUM(34.6.2.'N'.YEARB[NY-3].
'II'.0.0.99.0.retSS.FALSE.15,0};
3: GETNUM(42.G.2.'N'.YEAR[NY-2].
'Il'.0.0.99.0.retSS.FALSE.15,0);
4: GETNUM(45.6.2.'N'.YEARB[NY-2].
'II'.0.0.99.0,retSS.FALSE.15.0};
5: GETNUM(53.6.2.'N'.YEAR[NY-l].
'l".0.0.99.0.retSS.FALSE.15.0);
6: GETNUM(56.6.2.'N',YEARB[NY-I].

 

12511

'i!’.0.0.99.0.retSS.FALSE.15.0};
7: GETNUM(64.6.2.'N'.YEAR[NY].
'II'.0.0.99.0.retSS.FALSE,15.0};
8: GETNUM(67.6.2.'N'.YEARB[NY].
'Ii'.D.O.99.0.retSS.FALSE.15.0);
9: GETNUM(30.9.6.'N'.SIXPH[NY-3].
'l.llll'.0.0DOO.9.9999.retSS.FALSE.15.0);
IO: GETNUM(29.15.7.'N'.TURNUM[NY-3].
'lll.!ll'.0.000.999.999.retSS.FALSE.15.0);
ll: BEGIN
TURKEYCONS[NY-3]:ISIXPH[NY-3]*TURNUM[NY-3];
GETNUM(30.20.6.'N'.TURKEYCONS[NY-3],
'l##.li'.0.00,999.99.retSS.FALSE.15,0);
END;
12: GETNUM(41.9.B.'N'.SIXPH[NY-2].
'l.llil'.0.0000.9.9999.retSS.FALSE.15,0);
13: GETNUM(40.15.7.'N',TURNUM[NY-2].
'Ill.ill'.0.0DO,999.999.retSS.FALSE.15.0);
I4: BEGIN
TURKEYCONS[NY-2]:ISIXPH[NY-2]*TURNUM[NY-2];
GETNUM(4I.20.6.'N'.TURKEYCONS[NY-2].
'Ill.il'.0.00.999.99.retSS.FALSE.15.0);
END;
15: GETNUM(52.9.6.'N'.SIXPH[NY-1].
'l.llli'.0.0000.9.9999.retSS.FALSE.15.0);
16: GETNUM(51.15.7.'N'.TURNUH[NY-l].
'Ill.Ill'.0.000,999.999.retSS.FALSE.15.0);
17: BEGIN
TURKEYCONS[NY-1]:ISIXPH[NY-1]*TURNUM[NY-I];
GETNUM(52.ZO,6.'N'.TURKEYCONS[NY-I].
'Oll.ll'.O.DO.999.99.retSS.FALSE.15,0);
END;
18: GETNUM(63,9.6.'N'.SIXPH[NY].
'l.lli#'.0.0000.9.9999.retSS.FALSE.15.0);
19: GETNUM(62.15.7.'N'.TURNUM[NY].
'l#l.i#l'.0.000.999.999.retSS.FALSE.15.0);
20: BEGIN
TURKEYCONS[NY]:ISIXPH[NY]*TURNUM[NY];
GETNUM(63.ZO,6.'N'.TURKEYCONS[NY].
'ill.!#'.0.00.999.99.retSS.FALSE.15.0);
END;
21: GETNUM(73.9.6.'N'.SIXPH_OR[NY],
'#.#iil'.0.0000.9.9999.retSS.retrieveSS.15.0);
22: BEGIN
IF SIXPH_OR[NY]<>D.D THEN
TURKEYCONS_OR[NY]:ISIXPH_OR[NY]*TURNUM[NY]
ELSE TURKEYCONS_OR[NY]:IO.O;
GETNUM(73.ZO.6.'N'.TURKEYCONS_OR[NY].
'II!.II'.0.00.999.99.retSS.FALSE.15.0);
END;
END; { CASE }

if varSSIscreen_fieldSS then last_fieldSS:ITRUE;
RET_STATUS; { Check the code in ”retSS". Set ”varSS" and "actionSS" }

 

 

.2592

{ Check to see whether to switch retrieveSS to true }
if last_fieldSS and (not retrieveSS) then
begin
retrieveSS:ITRUE; last_fieldSS:IFALSE; actionSS:IstaySS; varSS:=1;
end else
last_fieldSS:IFALSE;

UNTIL actionSSIexitSS
END; { OVERLAY PROCEDURE TURKEY CONS PERHEAD }

OVERLAY PROCEDURE HOG_CONS_PERHEAD; { BzHOGCON }
BEGIN

screenSS:I32; screen_fieldSS:IZZ; varSS:Il;

retrieveSS:IFALSE; last_fieldSS:IFALSE;

DISPLAY_SCREEN('HOGCON.SCR'.file_existSS); { Display Screen }

if not file_existSS then

begin gotoxy(I.I); write('Missing Screen HOGCON') end;
retSS:-";

{ Display Items. Change retrieveSS to TRUE and INPUT items}
REPEAT { until actionSS I exitSS }
CASE varSS of
l: GETNUM(31.5.2.'N'.YEAR[NY-3].
'll'.D.O.99.0.ret$S.FALSE,15.0};
2: GETNUM(34.6.2.'N'.YEARB[NY-3],
'Il'.0.0,99.0.retSS.FALSE.15.0);
3: GETNUM(42,5.2.'N'.YEAR[NY-2].
'll'.0.0.99.0.retSS.FALSE.15.0);
4: GETNUM(45.6.2.'N'.YEARB[NY-2].
'Pl'.0.0.99.0.retSS.FALSE.15.0};
5: GETNUM(53.6.2,'N'.YEAR[NY-l].
'Il'.D.O.99.0.retSS.FALSE.15.0};
6: GETNUM(55.6,Z.'N',YEARB[NY-l].
'll'.O.D.99.0.retSS.FALSE.15.0};
7: GETNUM(64,6.2.'N'.YEAR[NY],
'll'.0.0.99.0.retSS.FALSE.15.0};
B: GETNUM(B7.6.2.'N'.YEARB[NY].
'll'.0.0.99.0.retSS.FALSE.15.0};
9: GETNUM(3D.9.B.'N'.SEVENPH[NY-3].
'II.III'.0.000.99.999.retSS.FALSE.15.0};
IO: GETNUM(30.15.6.'N'.HOGNUH[NY-3].
'PI.IPI'.O.DDD.99.999.retSS,FALSE,15,0);
ll: BEGIN
HOGCDNS[NY-3]:ISEVENPH[NY-3]*HOGNUH[NY-3];
GETNUM(29.ZO.7.'N'.HOGCONS[NY-3].
'llll.li',0.00.9999.99.retSS.FALSE.15.0);
END;
12: GETNUM(41.9.6.'N'.SEVENPH[NY-Z].
'll.lll'.0.000.99.999.retSS.FALSE.15.0);
l3: GETNUM(41.15.6.'N'.HOGNUM[NY-Z].
'II.III'.0.000.99.999.retSS.FALSE.15.0):

2593

I4: BEGIN
HOGCONS[NY-Z]:ISEVENPH[NY-2]*HOGNUM[NY-2];
GETNUM(40.20.7.'N'.HOGCONS[NY—2].
'Illt.ll',0.0D.9999.99.retSS.FALSE.15.0);

END;
15: GETNUM(52.9.B.'N'.SEVENPH[NY-I].
'li.ill'.0.000.99.999.retSS.FALSE.15.0);
16: GETNUM(52.15.G.'N'.HOGNUM[NY-1].
'll.lll'.0.000.99.999.retSS.FALSE.15.0);

17: BEGIN
HOGCONS[NY-I]:ISEVENPH[NY-1]*HOGNUM[NY-1];
GETNUM(SI.20.7.'N'.HOGCONS[NY-I].
'llli.ll'.0.00.9999.99.retSS.FALSE.15.0);

END;
18: GETNUM(63.9.6.'N'.SEVENPH[NY].
'll.lll'.0.000.99.999.retSS,FALSE.15.0);
19: GETNUM(63.15.6.'N'.HOGNUM[NY],
'll.ll#'.0.000.99.999,retSS.FALSE.15.0);

20: BEGIN
HOGCONS[NY]:ISEVENPH[NY]*HOGNUH[NY];
GETNUM(GZ.ZO.7.'N'.HOGCONS[NY].
'IIII.II'.0.00.9999.99.retSS.FALSE.15.0);

END;
21: GETNUM(73.9.6.'N'.SEVENPH_OR[NY].
'll.Ill'.0.000.99.999.retSS.retrieveSS.15.0);

22: BEGIN
IF SEVENPH_OR[NY]<>0.0 THEN
HOGCONS_OR[NY]:ISEVENPH_OR[NY]’HOGNUM[NY]
ELSE HOGCONS_OR[NY]:IO.O;
GETNUM(72.ZO.7.'N'.HOGCONS_OR[NY],
'Ill!.'l'.0.00.9999.99.retSS.FALSE.15.0);

END;
END; { CASE }

if varSS-screen_fieldSS then last_fieldSS:ITRUE;
RET_STATUS; { Check the code in "retSS”. Set "varSS” and "actionSS" }

{ Check to see whether to switch retrieveSS to true }
if last_fieldSS and (not retrieveSS) then
begin
retrieveSS:ITRUE; last_fieldSS:IFALSE; actionSS:IstaySS; varSS:=1;
end else
last_fieldSS:IFALSE;

UNTIL actionSSIexitSS
END; { OVERLAY PROCEDURE HOG CONS PERHEAD }

OVERLAY PROCEDURE OTHER_UNALLOCATED_CONS; { B:OUNCON }
BEGIN

screenSS:I33; screen_fieldSS:II3; varSS:I1;
retrieveSS:IFALSE; last_fieldSS:IFALSE;

 

.2514

DISPLAY_SCREEN('OUNCON.SCR'.file_existSS); { Display Screen }

if not file_existSS then

begin gotoxy(I.I); write('Missing Screen OUNCDN') end;
retSS:I";

{ Display Items. Change retrieveSS to TRUE and INPUT items}

REPEAT { until actionSS I exitSS }

CASE varSS of

1: GETNUM(29.B.2.'N'.YEAR[NY-3].
'll'.0.0.99.0.retSS.FALSE.15.0);

2: GETNUM(32.B,2.'N',YEARB[NY-3].
'll'.0.0.99.0.retSS.FALSE.15.0};

3: GETNUM(40.B.2.'N'.YEAR[NY-2].
'II'.0.0.99.0.retSS.FALSE.15.0};

4: GETNUM(43.B.2.'N',YEARB[NY-2].
'll'.0.0.99.0.retSS.FALSE.15.0};

5: GETNUM(51.B.2.'N'.YEAR[NY-l].
'Il'.0.0,99.0,retSS.FALSE,15.0};

6: GETNUM(54,B.2.'N'.YEARB[NY-I].
'il'.0.0.99.0.retSS.FALSE.15.0};

7: GETNUM(62,B.2.'N'.YEAR[NY],
'Il'.0.0.99.0.retSS.FALSE.15.0};

B: GETNUM(65.B.2.'N',YEARB[NY].
'il'.0.0.99.0.retSS.FALSE.15.0};

9: GETNUM(Z7.14.7,'N'.OTHERCONS[NY—3].
'IIII.II',0.00.9999.99.retSS.FALSE.15.0};

IO: GETNUM(38.14.7.'N'.OTHERCDNS[NY-2].
'llll.li'.0.00.9999.99.retSS.FALSE.15.0);

11: GETNUM(49.I4.7.'N'.DTHERCONS[NY-l].
'IIII.II'.0.00.9999.99,retSS.FALSE,15.0);

12: GETNUM(60.14.7,'N'.DTHERCONS[NY],
'il'l.l'.0.0.9999.9.retSS.FALSE.15.0};

13: GETNUM(71 . 14. 7. 'N' ,OTHER_UNALLOC_OR [NY].
'llll.!'.D.D.9999.9.retSS.retrieveSS.15.0);

END; { CASE }

if varSS-screen_fieldSS then last_fieldSS:ITRUE;
RET_STATUS; { Check the code in "retSS". Set "varSS" and "actionSS" }

{ Check to see whether to switch retrieveSS to true }
if last_fieldSS and (not retrieveSS) then
begin
retrieveSS:ITRUE; last_fieldSS:IFALSE; actionSS:IstaySS; varSS:=I;
end else
last_fieldSS:IFALSE;

UNTIL actionSSIexitSS
END; { OVERLAY PROCEDURE OTHER UNALLOCATED CONS }
OVERLAY PROCEDURE SCREEN_IS; { THE CORN CONSUMPTION SUMMARY SCREEN }

BEGIN

screenSS:I34; screen_fieldSS:I44; varSS:Il;

 

25955

retrieveSS:IFALSE; last_fieldSS:IFALSE;

DISPLAY_SCREEN('CORNCONS.SCR'.file_existSS); { Display Screen }

if not file_existSS then

begin gotoxy(I.I); write('Missing Screen CORNCONS') end;
retSS:-";

{ Display Items. Change retrieveSS to TRUE and INPUT items}
REPEAT { until actionSS I exitSS }
CASE varSS of
l: GETNUM(36.4.2.'N'.YEAR[NY-3],
'Ii'.0.0.99.0.retSS.FALSE.15.0)z
2: BEGIN
YEARB[NY-3]:8YEAR[NY-3]+I;
GETNUM(39.4.Z.'N'.YEARB[NY-3].
'Il'.0.0.99.0.retSS.FALSE.15.0);
END;
3: GETNUM(47.4.Z.'N'.YEAR[NY-Z].
'PI'.0.0.99.0.retSS.FALSE.15.0);
4: BEGIN
YEARB[NY-2]:IYEAR[NY-2]+1;
GETNUM(SO,4.2.'N'.YEARB[NY-Z].
'II'.0.0.99.0.retSS.FALSE.15.0);
END;
5: GETNUM(58.4.2.'N'.YEAR[NY-I],
'II'.O.D.99.0.retSS.FALSE.15.0};
6: BEGIN
YEARB[NY-I]:IYEAR[NY-I]+l;
GETNUM(61.4.2.'N'.YEARB[NY-l].
'll'.0.0,99.0.retSS.FALSE.15.0)z
END;
7: GETNUM(69.4.2.'N'.YEAR[NY].
'Il'.0.0.99.0.retSS.FALSE.15.0};
8: BEGIN
YEARB[NY]:=YEAR[NY]+I;
GETNUM(72.4.2.'N'.YEARB[NY],
'Il'.0.0.99.0.retSS,FALSE.15.0);
END; .
9: GETNUM(3S.6.6.'N'.DAIRYCONS[NY-3].
'Ill.ll'.0.00.999.99.retSS.FALSE.15.0);
IO: GETNUM(34,B.7.'N'.CATONFED[NY-3].
'l'll.ll'.0.00.9999.99.retSS.FALSE.15.0);
11: GETNUM(3S.IO.6.'N'.OTHERBEEFCAT[NY-3].
'lll.ll'.0.00.999.99,retSS.FALSE.15.0);
IZ: GETNUM(35.I3.6.'N'.CHICKENCONS[NY—3].
'Iil.ll'.0.00.999.99.retSS.FALSE.15.0);
I3: GETNUM(35.15.6.'N'.BROILERCONS[NY-3].
'l'l.ll'.0.00.999.99.retSS.FALSE.15.0};
I4: GETNUM(35.17.6.'N'.TURKEYCONS[NY-3].
'lll.ll'.0.00.999.99.retSS.FALSE.15.0};
15: GETNUM(34.19.7.'N'.HOGCONS[NY-3].
'l'll.ll'.0.00.9999.99.retSS.FALSE.15.0};
16: GETNUM(35.21.G.'N'.OTHERCONS[NY-3].
'lll.ll'.0.00.999.99.retSS.FALSE.15.0);
l7: BEGIN

18:

19:

20:

21:

22:

23:

24:

25:

26:

27:

28:

29:

30:

31:

32:

33:

34:

35:

36:

JZEMG

FEEDT[NY-B]:IDAIRYCONS[NY-3]+CATONFED[NY-3]+OTHERBEEFCAT[NY-3]
+CHICKENCONS[NY-3]+BROILERCONS[NY-3]+TURKEYCONS[NY-3]
+HOGCONS[NY-3]+OTHERCONS[NY-3];

GETNUM(34.23.7.'N'.FEEDT[NY-3],

'IIII.II'.D.OO.9999.99.retSS.FALSE.15.0);

END;

GETNUM(46.6.6.'N'.OAIRYCONS[NY-2],

'lil.ll'.0.00.999.99.retSS.FALSE.15.0);

GETNUM(45.B.7.'N'.CATONFED[NY-2].

'Illl.ll'.0.0D.9999.99.retSS.FALSE.15.0);

GETNUM(46.IO.6.'N'.DTHERBEEFCAT[NY-2].

'lll.ll'.0.00.999.99.retSS.FALSE.15.0);

GETNUM(46.I3.6.'N'.CHICKENCONS[NY-2].

'lll.ll'.0.00.999.99.retSS.FALSE,15.0);

GETNUM(46.IS.6.'N'.BROILERCONS[NY-2].

'lll.ll'.0.0D.999.99.retSS.FALSE.15.0);

GETNUM(46.17.6.'N'.TURKEYCONS[NY-2].

'III.II'.0.00.999.99,retSS.FALSE.15.0);

GETNUM(45.19.7.'N'.HOGCONS[NY-2].

'illl.il'.0.00.9999.99.retSS.FALSE.15.0):

GETNUM(46.21.6.'N'.OTHERCONS[NY—2].

'Iil.ll'.0.00.999.99.retSS.FALSE.15.0);

BEGIN
FEEDT[NY-Z]:IDAIRYCONS[NY-2]+CATONFED[NY-2]+OTHERBEEFCAT[NY-2]

+CHICKENCONS[NY-2]+BROILERCONS[NY-2]+TURKEYCONS[NY-2]
+HOGCONS[NY-2]+OTHERCONS[NY-Z];

GETNUM(4S,23.7.'N'.FEEDT[NY-2].

'IIII.II'.0.00,9999.99.retSS.FALSE.15.0);

END;

GETNUM(57.6.6.'N'.DAIRYCONS[NY-1].

'Ill.ll'.0.0D.999.99.retSS.FALSE.15.0);

GETNUM(56.B.7.'N'.CATONFED[NY-I].

'Illl.ll'.0.00.9999.99.retSS.FALSE.15.0);

GETNUM(57.IO.6.'N'.DTHERBEEFCAT[NY-1].

'lll.li'.0.00.999.99.retSS.FALSE.15.0);

GETNUM(57.13.6.'N'.CHICKENCONS[NY-l].

'ill.ll'.0.00.999.99.retSS.FALSE.15.0);

GETNUM(57.15.6.'N'.BROILERCONS[NY-I].

'lll.ll'.0.00.999.99.retSS.FALSE.15.0);

GETNUM(57.17.6.'N'.TURKEYCONS[NY-l].

'li!.ll'.0.00.999.99.retSS.FALSE.15.0);

GETNUM(56.19.7.'N'.HOGCONS[NY-I].

'lill.l#'.0.00.9999.99.retSS.FALSE.15.0);

GETNUM(57.21.G.'N'.OTHERCONS[NY-1].

'lll.ll'.D.OO.999.99.retSS,FALSE.15.0);

BEGIN
FEEDT[NY-I]:IDAIRYCONS[NY-l]+CATONFED[NY-1]+OTHERBEEFCAT[NY-1]
+CHICKENCONS[NY-1]+BROILERCONS[NY-1]+TURKEYCONS[NY-I]+HOGCONS[NY-I]
+OTHERCONS[NY-l];

GETNUM(56.23.7.'N'.FEEDT[NY—l].

'Ill!.li'.D.OO.9999.99.retSS.FALSE,15.0);

END;

begin

if dairycons_DR[NY]<>D.O then

37:

38:

39:

40:

41:

42:

43:

44:

12517

dairycons[NY]:Idairycons_OR[NY];
GETNUM(68.6.6.'N'.DAIRYCONS[NY].
'll!.ll'.D.DO.999.99.retSS.FALSE.15.0);
end;
begin
if catonfed_OR[NY]<>O.D then
catonfed[NY]:Icatonfed_OR[NY];
GETNUM(67,8.7.'N',CATONFED[NY],
'lill.ll'.D.OO.9999.99,retSS.FALSE.15.0);
end;
begin
if otherbeefcat_OR[NY]<>O.D then
otherbeefcat[NY]:Iotherbeefcat_0R[NY];
GETNUM(68.IO.6.'N'.OTHERBEEFCAT[NY],
'lll.ll'.0.00.999.99.retSS.FALSE.15.0};
end;
begin
if chickencons_OR[NY]<>O.D then
chickencons[NY]:Ichickencons_OR[NY];
GETNUM(68,13.6.'N'.CHICKENCONS[NY].
'lll.ll'.O.DO.999.99.retSS.FALSE.15.0);
end;
begin
if broilercons_OR[NY]<>0.0 then
broilercons[NY]:Ibroilercons_OR[NY];
GETNUM(68.15.6.'N'.BROILERCONS[NY].
'lll.ll'.0.00,999.99.retSS.FALSE.15.0};
end;
begin
if turkeycons_DR[NY]<>O.D then
turkeycons[NY]:Iturkeycons_OR[NY];
GETNUM(68.I7.6.'N',TURKEYCONS[NY].
'il!.ll'.D.DO.999.99.retSS.FALSE.15.0);
end;
begin
if hogcons_OR[NY]<>D.O then
hogcons[NY]:Ihogcons_OR[NY];
GETNUM(67.19.7. 'N' .HOGCONS[NY],
'Ilil.ll'.0.00.9999.99.retSS.FALSE.15.0);
end;
begin
if other_unalloc_OR[NY]<>D.D then
othercons[NY]:Iother_unalloc_OR[NY];
GETNUM(68.21.6.'N'.DTHERCONS[NY].
'l'l.ll'.0.00.999.99.retSS.FALSE.15.0};
end;
BEGIN
FEEDT[NY]:IDAIRYCONS[NY]+CATONFED[NY]+OTHERBEEFCAT[NY]+CHICKENCONS[NY]
+BROILERCONS[NY]+TURKEYCONS[NY]+HOGCONS[NY]+OTHERCONS[NY];
GETNUM(67.23.7.'N'.FEEDT[NY].
'l#ll.ll'.0.00.9999.99.retSS.FALSE.15.0);
END;

END: { CASE }

 

.2538

if varSS-screen_fieldSS then last_fieldSS:ITRUE;
RET_STATUS; { Check the code in "retSS". Set ”varSS" and "actionSS" }

{ Check to see whether to switch retrieveSS to true }
if last_fieldSS and (not retrieveSS) then
begin
retrieveSS:ITRUE; last_fieldSS:IFALSE; actionSS:IstaySS; varSS:-1;
end else
last_fieldSS:IFALSE;

UNTIL actionSSIexitSS
END; { OVERLAY PROCEDURE SCREEN_IS }
*’*" End of Include file FEEDTOR.INC ‘*'*‘

BEGIN
REPEAT
IF (EDIT-FALSE) AND (BACTIVEIFALSE) THEN
BEGIN

color(Hhite.Black);

ClrScr;

FLASHUP('HIfeedt.NOESC.NOHOVE');

GOTOXY(1.25);

READ(RESPONSE);

CLREOL:

FLASHUP(‘CIALL');

END;

CASE (RESPONSE) OF
'1': screen_lS; { feed grain summary }
'2': dairy_animal_cons_perhead;
'3': cattle_on_feed_cons_perhead;
'4': other_beef_cattle_cons_perhead;
'5': chicken_cons_perhead;
'6': broiler_cons_perhead;
'7': turkey_cons__perhead;
'8': hog_cons_perhead;
'9 ' : other_unal located_cons;
'O': ;

END; { CASE }

IF (response <> '0') THEN
HHATMENU(BSCREEN.BACTIVE.UB);

UNTIL (RESPONSE - '0');
END;

VAR RESPONSEI:CHAR;

BEGIN { Main Test Program }
vtypeSS:ISET_HONITOR_TYPE; { ZIColor. 3IMono }
screenSS:IO; use_screenSS:I1; BeepOnSS:IFALSE;

initial;

INIT_VAR_UTIL: { USED IN THE LOAD AND SAVE PROCEDURES }
FLASHUP(‘X:LITLPSCR');

FLASHUP(‘LITLPLIB');

.25T9

FLASHUP(‘S-TITLE');
UHATMENU(BSCREEN.BACTIVE.UB);
REPEAT
IF (EDIT-FALSE) AND (BACTIVEIFALSE)
THEN BEGIN
COLOR(HHITE.BLACK);
CLRSCR;
FLASHUP('UIMAINMENU.NOESC.NOMOVE');
GOTOXY(1.25);
READ(RESPONSE);
CLREOL:
FLASHUP(‘CIALL');
ENO;

CASE (RESPONSE) OF

'1':LOAD;

'2':SAVE;

'3':EXOGENOUS_VARIABLE_SUB_MENU:

'4':BEGIN { ADD_FACTOR }
UBzIZ;

BACTIVE:-TRUE;
CASE (BSCREEN) OF
I: ADD_FACTOR_SCREEN_1:
2: ADO_FACTOR_SCREEN_2:
END; { CASE BSCREEN OF }
END;
'5':BEGIN { BALANCE SHEET }
UB:=3;
BACTIVEzITRUE:
CASE (BSCREEN) OF
I: SCREEN_IZ;
2: SCREEN_IB;
3: SCREEN_I4;
END; { CASE BSCREEN OF }
END;
'6':feed_grain_demand; { DOMESTIC FEED DEMAND }
'7':EXPORT4VARIABLE_SUB_MENU; { EXPORT_DEMAND }
'8':begin
REPEAT
IF (EDIT=FALSE) AND (BACTIVE=FALSE) THEN
BEGIN
COLOR(HHITE.BLACK);
CLRSCR;
FLASHUP('HISOLUTION.NOESC.NOMOVE');
GOTOXY(1.25);
READ(RESPONSEI);
CLREOL:
FLASHUP(‘CIALL');
END;
CASE (RESPONSEI) OF
'1': remain;
'2': BEGIN
UBzIZ;
BACTIVEzITRUE;

 

3(3C)

CASE (BSCREEN) OF
I: RANDOMIZE_YIELD_SCREEN_1: {SOLVES FOR A SINGLE PRICE}

2: DISPLAY_RANDOMIZE_SCREEN; {SOLVES HITH RANDOM SHOCKS}
END; { CASE BSCREEN OF }
END;
'3':;
end; { CASE }
IF (RESPONSEI <> '3') THEN
UHATMENU(BSCREEN.BACTIVE.UB);
UNTIL(RESPONSE1I'3');
END;
'9':EXITSCREEN:
END; {CASE}
IF (NOT EXIT)
THEN HHATMENU(BSCREEN.BACTIVE.UB);
UNTIL (EXIT-TRUE);
END. { PROGRAM FIRST.PAS }

 

PROCEDURE NAME

ADD_FACTOR_SCREEN_1
ADD_FACTDR_SCREEN_2
BROILER_CONS_PERHEAD
CALCULATIONS
CATTLE_DN_FEED_CDNS_PERHEAD
CHICKEN_CONS_PERHEAD
CHECKDISK

CONTINUE
DAIRY_ANIMAL_CONS-PERHEAD
DISPLAY_RANDDMIZE_SCREEN
DIRECTORY
EXOGENOUS_VARIABLE_SUB_MENU
EXPORTERS_COMPOSITES
EXPORTERS_GDP_SCREEN_1
EXPORTERS_GDP_SCREEN_2
EXPORTERS_NET_EXPORTS_SCREEN
EXPORTER_CROP_REVENUE
EXPORT_VARIABLE_SUB_MENU
EXITSCREEN
FIND_PERCENTILES
FREESTOCK_PRDC
FREESTOCK_PROC
FSI_USE_SCREEN
FEED_GRAIN_DEMAND

FLASHUP
FUNCTION_KEY_PRESSED
GETNUM

HOG_CONS_PERHEAD
HELPSCREEN
IMPORTERS_GDP_SCREEN_I
IMPORTERS_GDP_SCREEN_2
IMPORTERS_GDP_SCREEN_3
INIT_VAR_UTIL

IOCHECK

INITIAL

LOAD

LINE25

LDADBEV
OTHER_BEEF_CATTLE_CONS_PERHEAD
OTHER_UNALLOCATED_CDNS
PRESSKEY

PRINT
RANDOMIZE_YIELD_SCREEN_1
RET_STATUS

RE_CALCULATE

REMAIN

SAVE

SCREEN_I

SCREEN_Iz

SCREEN_13

ALPHABETIC SORT OF PROCEDURES

LISTING FILE

HOTT.INC
HOTT.INC
FEEDTOR.INC
CACULATE.INC
FEEDTOR.INC
FEEDTOR.INC
MENU.UTL
MENU.UTL
FEEDTOR.INC
RANDOMIZE.INC
MENU.UTL
FIRST.PAS
FIRST.PAS
MAGIC4.INC
MAGIC4.INC
UETT.INC
MAGIC5.INC
FIRST.PAS
MENU.UTL
RANDOMIZE.INC
CACULATE.INC
RECALC.INC
MAGICI.INC
FIRST.PAS
MENU.UTL
MENU.UTL
FIRST.PAS
FEEDTOR.INC
MENU.UTL
MAGIC3.INC
MAGIC3.INC
MAGIC3.INC
MENU.UTL
MENU.UTL
INITIAL.INC
INTRO.INC
MENU.UTL
MENU.UTL
FEEDTOR.INC
FEEDTOR.INC
MENU.UTL
MENU.UTL
RANDOMIZE.INC
FIRST.PAS
RECALC.INC
REMAIN.INC
INTRO.INC
MAGICI.INC
BALSHEET.INC
BALSHEET.INC

PROCEDURE NAME

SCREEN_14
SCREEN_IS

SCREEN_z

SCREEN_3

SCREEN_3A

SCREEN_4
SOLUTION_SCREEN

SAVEBEV
TURKEY_CONS_PERHEAD
US_COMPOSITES_SCREEN_1
US_CDMPOSITES_SCREEN_2
US_CDMPDSITES_SCREEN_3
US_COMPOSITES_VARIABLES
US_EXPORTS_SCREEN
UNATNENU

ZEBRAC

 

302

ALPHABETIC SORT OF PROCEDURES (Cont'd)

LISTING FILE

BALSHEET.INC
FEEDTOR.INC
MAGICI.INC
MAGICI.INC
MAGICI.INC
MAGICI.INC
SOLUTION.INC
MENU.UTL
FEEDTOR.INC
MAGIC2.INC
MAGIC2.INC
MAGIC2.INC
FIRST.PAS
UETT.INC
MENU.UTL
ZEBRAC.INC

 

BIBLIOGRAPHY

 

BIBLIOGRAPHY

Anderson, J.R., J.L. Dillon, and J.B. Hardaker.

A9ri2ulturel_negi§ign_5nalxei§- Ames= Iowa state
University Press, 1977.
Bentham, Jeremy. Intrggggtign to the Principles of Morals

and Legislation. Hafner, London, 1948.

Bernoulli, Daniel. "Exposition of a New Theory on the
Measurement of Risk." Econometrica 22, January 1954.

Bhattacharyya, Gouri K., and Richard A. Johnson. §§atistical
Qoncepts and Methods. Whiley & Sons, New York, 1977.

Dell. John P- 2r2du2ti9n.£22n2micei_1be2rx_flith
Applicatiggg. John Wiley 8 Sons, New York, 1978.

Eisner. Robert. Er2e2tatign§1_21en§1_end_§epitel
Expenditures: A Synthesis of Ex Post and Ex Ante Data.

presented at the Conference on Expectations,
Uncertainty, and Business Behavior, October 1955.

Feed Situation and Outlook Report. United States Department
of Agriculture, August 1986.

Ferris, John N. Egrecgsting ngn Producers' Besponse to
tne_12§§_Eeed_§rain_£regram- Presented at AAEA 1986.

------ , John. "Probability Forecasts on U.S. Corn Prices."
Ag Econ Staff Paper #82—4, Michigan State University,
December 1983.

------ , J. "Feed Grain, Wheat, and Oilseed Supply and
Demand." MSU Staff Paper, October 1985.

Gadson, Kenneth E., J. Michael Price, and Larry E. Salathe,
U.S. Department of Agriculture, Staff Report
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