SOME EFFECTS OF GOVERNMENT PROGRAMS ... ARIZONA UPLAND COTTON ALLOTMENTS by Jeffery John Weber

SOME  EFFECTS  OF  GOVERNMENT  PROGRAMS ... ARIZONA UPLAND  COTTON  ALLOTMENTS by Jeffery  John  Weber

SOME EFFECTS OF GOVERNMENT PROGRAMS ON

ARIZONA UPLAND COTTON ALLOTMENTS by

Jeffery John Weber

A Thesis Submitted to the Faculty of the

DEPARTMENT OF AGRICULTURAL ECONOMICS

In Partial Fulfillment of the Requirements

For the Degree of

MASTER OF SCIENCE

In the Graduate College

THE UNIVERSITY OF ARIZONA

1 9 7 2

STATEMENT BY AUTHOR

This thesis has been submitted in partial fulfill­

ment of requirements for an advanced degree at The

University of Arizona and is deposited in the University

Library to be made available to borrowers under rules of

the Library.

Brief quotations from this thesis are allowable

without special permission, provided that accurate acknowl­ edgment of source is made.

Requests for permission for

extended quotation from or reproduction of this manuscript

in whole or in part may be granted by the head of the major

department or the Dean of the Graduate College when in his

judgment the proposed use of the material is in the inter­ ests of scholarship.

In all other instances, however,

permission must be obtained from the author.

SIGNED:

/ / 7 y

APPROVAL BY THESIS DIRECTOR

This thesis has been approved on the date shown below:

______ _ <L. 7 /^72.

ROBERT S. FIRCH

J

/Date

Professor of Agricultural Economics

ACKNOWLEDGMENTS

I wish to express my appreciation and gratitude to

Dr. Robert S. Firch for his assistance and guidance during

my graduate program.

His help in the development and

preparation of this thesis was invaluable.

Gratitude is also expressed to the many members of

the staff of the Department of Agricultural Economics at

The University of Arizona who provided the author with

information and suggestions during the thesis w o r k .

The author is also indebted to Mr. Vern Englehorn

of the Western Farm Management Company in Phoenix for

providing historical farm sales information.

I would also

like to thank the directors and staffs of the Cochise,

Maricopa, Pima, Pinal, and Yuma County Agricultural

Stabilization and Conservation Service Offices for pro­

viding current yield and ownership information for upland

cotton allotments in their respective counties.

Acknowledgment is also due to the typing and

clerical staff of the Agricultural Economics Department of

The University of Arizona for the assistance and time they

have provided in the preparation of this thesis.

X am very grateful to my wife, L inda, for her help

in editing and typing preliminary drafts of this thesis.

iii

iv

Her encouragement was a source of incentive throughout my

graduate program.

TABLE OF CONTENTS

LIST OF I L L U S T R A T I O N S .............................

LIST

OF

T A B L E S ....................................

ABSTRACT ..........................................

Page vii viii ix

C H A PTE R

I. INTRODUCTION ...............................

Organization and Objectives .............

II. THE POST-WORLD WAR II COTTON SITUATION:

A BRIEF HISTORY OF PRICE MANAGEMENT IN

THE M A R K E T ........................... 4

Basic Theory of Price Regulation . . . .

Effects of Surplus Production Trends . •

Two-Sector Market Model .......... . . .

The Early Y e a r s , 1945-1955

Trial and Error, 1956-1965

The Food and Agriculture Act of • •

S u m m a r y ............................. 24

17

20

1

2

III. ALLOTMENT VALUE: A LOGICAL CONSEQUENCE

OF

PRICE-SUPPORT PROGRAMS ....................

The Influence of Government Programs on the Value of R . ..........

IV. ESTIMATION OF THE PER-ACRE VALUE OF

UPLAND COTTON ALLOTMENTS IN CENTRAL

A R I Z O N A ............................... 36

Source of D a t a ..................... 37

Data P r e p a r a t i o n ................... 37

Analytical Procedure ......................

Investigation of the Distance

V a r i a b l e .....................

Analysis Using Series I Models . . .

Analysis Using Series II . . .

43

28

46

50

26

4l v

H

vi

TABLE OF CONTENTS— Continued

Evaluation of Results of Regression

A n a l y s i s .............................

Comparison of Allotment Income Flows and Allotment Values ..............

V.

AN INVESTIGATION OF YIELD AND OWNERSHIP

PATTERNS FOR ARIZONA UPLAND COTTON

A L L O T M E N T S ...............................

Page

55

57

Analytical . . . .

C o n c l u s i o n .............................

VI. C O N C L U S I O N S .................................

APPENDIX: ADDITIONAL FARM SALES . . .

REFERENCES CITED .................................

6l

62

64

70

77

79

82

86

LIST OF ILLUSTRATIONS

Figure

1 . Basic Market Structure for Upland Cotton . .

2 . Two-Sector Market Model Depicting Market

Situation for Cotton at Close of World

War I I ....................................

3• Supply and Disappearance Trends for Upland

Cotton, 1945-1969 .........................

4. U. S. Mill Consumption and Exports, 1945-

1969

5 . U. S . and World Price L e v e l s ..............

6. Effects of 1956 Program Legislation on

Upland Cotton Markets .....................

7. Effects of a Substantial Reduction in

Price-Support Level and U.S. Supply

L e v e l ...........................

8 . Typical Cost Relationships for an Acre of

Upland Cotton Allotment ...................

9•

Cost and Income Relationships for an Acre

of Domestic Allotment Under the 1966-

1970 P r o g r a m .............................

10. Regression Lines for Log Form and Simple

Inverse Form of Distance Variable

Against Empirical Sale Values ............

11. Extrapolation of SS Coefficients for

Model 1 of Series I .......................

1 2 . Simple Linear Relationship Between

Allotment Acre Value and NC . .

13• Comparison of Per-Acre Allotment Values with

Page

6

9

10

12

13 l 8

23

29

32

44

48

53

60 vii

LIST OF TABLES

Table

1. Per-Pound Price and Subsidy Rates for

U. S. Upland Cotton, 1949-1970

2. Some Statistical Series on U. S. Upland

C o t t o n ...............................

3•

Regression for Series I Approach Using

Per-Acre Sale Price as the Dependent

V a r i a b l e ..................................

4. Per-Acre Allotment Value Estimates for

Years 1 9 6 1 - 1 9 6 7 ...........................

5. Regression Models Using Total Sale Price as the Dependent Variable (Series I I ) . . .

6. Per-Acre Income Above Variable Costs for

Maricopa County Cotton Allotments,

1961-1967 . . ..............................

7. Multiple Regression Analysis Equations

for Yield as a Function of Allotment

Ownership Characteristics (to test if

average yield varies with allotment c o m p o s i t i o n ) ..................

8. ANOVA Results for Individual Counties . . . .

9 . ANOVA Results for 5-County Composite . . . .

10. Classification of Water Districts ...........

11. Farm Sales Data Used for Allotment Value

Regression Analysis .......................

Page l4

15

4?

50

51

58

84

68

71

75

83 viii

ABSTRACT

Analysis of the effects of recent upland cotton

programs on the organization, income, and allotment value

for representative Arizona cotton farms was the focus for

this research study.

Stepwise multiple-linear-regression

analysis was applied to historical farm sales data using

sale price as a dependent variable.

Allotment value

estimates were derived from the regression analysis and

graphically compared to per-acre allotment income estimates

for representative Arizona farms.

This study also analyzed the 1970 allotment yield

and ownership patterns in Arizona's five major cotton-

producing counties.

Multiple regression analysis was used

to investigate the relationship between projected yield and

ownership characteristics for Arizona cotton farms.

The

farms were then classified according to their 1970 payment

level, and analysis of variance techniques were used to

compare group means for ownership variables and projected

yield.

Per-acre allotment income and value in Arizona have

trended downward since 1963.

This decline can probably be

attributed to the increasing uncertainty regarding future

benefits to be derived from the government programs.

ix

Allotment ownership characteristics exhibited

neither a consistent nor a significant relationship to

yield variability.

In each of the counties, and for the

state as a whole, significant differences in average

projected yield existed between farm-size groups.

CHAPTER I

INTRODUCTION

The Federal government has long sought to influence

prices for basic agricultural commodities for the purpose

of maintaining price and income levels for producers of

such commodities.

The basic approach has been price-

support programs in conjunction with production control

measures.

Commodities produced under the auspices of federal

programs generally enjoy favorable price levels and stable

income expectations.

Previous studies have shown that the

production-limiting devices take on values based on the

capitalization of the income flow directly attributable to

the control device.

Such production control devices are

usually in the form of acreage allotments.

The low risk factor and stable income expectations

for these regulated commodities provide ample incentive for

individual producers to increase their production within

the limits of the acreage controls by substituting inputs

other than land into the production process.

The ultimate

consequence of this behavior is excess aggregate supplies,

despite production limitations. In order to maintain price

1

2

levels, these surpluses must be removed from the market.

Removal of such surpluses is costly to the government.

Of the current government agricultural programs, the

program for upland cotton has probably been the most costly

and controversial.

Faced with chronic cotton surpluses,

it has been necessary for the government to reduce allot­ ment acreage over the years to avoid accumulating un­ manageable surplus stocks of cotton.

The impact of these acreage reductions on allotment

values, aggregate production levels, and farm income are of

concern to policy-makers and cotton growers.

In a state

such as Arizona, which relies upon cotton for a substantial

portion of its farm income, any alteration of the profit­

ability of cotton would be expected to have a substantial

impact on the state's agricultural economy.

This study is concerned with analyzing some of the

effects of government programs on income flows to Arizona

cotton growers and the value of Arizona upland cotton

allotments.

Organization and Objectives

The remainder of this thesis is organized in the

following manner.

A narrative of the interplay of the

markets for cotton and government programs in the period

since World War II is presented in Chapter II.

Chapter III

discusses the relationship between income and cotton

programs, and how these factors affect upland cotton allot­ ment value.

Chapter IV presents an analysis of per-acre

upland cotton allotment value trends in central Arizona for

the period 1961-1 9 6 7 . An analysis of the ownership charac­

teristics and productivity of the cotton allotments in the

five major cotton-producing counties of Arizona is the

subject of Chapter V.

In the final chapter, results of the

total analysis are discussed and final conclusions are

presented.

The objectives of this thesis are:

1. To conceptualize the relationship between govern­

ment programs and the income flows to cotton and

value of upland cotton allotment acreage.

2.

To develop estimates for per-acre upland cotton

allotment values in central Arizona and compare

them with estimates of per-acre net incomes for

cotton.

3 .

To analyze the structure of ownership and leasing

of upland cotton allotments for various size farm

operations in order to appraise the potential

effects of programs that limit government payments to individual farmers.

CHAPTER II

THE POST-WORLD WAR II COTTON SITUATION: A

BRIEF HISTORY OF PRICE MANAGEMENT

IN THE MARKETl

The traditional objective of government programs for

cotton has been the stabilization and maintenance of the

level of cotton prices.

Under certain circumstances, the

stabilization of per-unit prices acts to stabilize individual

farm incomes.

The government pursues these objectives in

the fashion described in the following section.

Basic Theory of Price Regulation

Price is ultimately a function of market supply and

demand conditions. It is beyond the scope of this dis­

cussion to investigate all of the factors which influence

supply levels and demand levels, or to attempt to precisely

define the nature of the cotton market.

Rather, government

influence on the workings of the market mechanism are of

interest h e r e .

1 .

For a more detailed discussion of the history

of U. S. Upland Cotton Programs, see the following sources

which provided the historical information for this chapter:

United States Department of Agriculture, Economic Research

Service, Cotton Situation. Washington, D.C., a bimonthly

publication.

Cotton and Other Fiber Problems and Policies

and Fiber, Washington, D.C., 1967, especially "The Cotton

Surplus Problem," by Rodney Whitaker, p p . 8l-l66 .

k

5

The demand curve for cotton is typically price-

inelastic.

As such, small percentage changes in the cotton

supply bring about relatively large percentage changes in

cotton prices.

Assuming that the level of demand is known

beforehand, theoretically supply levels can be adjusted so

as to achieve a balance with demand at the desired price

levels.

In the case of cotton, the adjustment is ultimately

achieved with acreage allotments.

Any fluctuations in

supply from the desired level can be dampened by government

purchasing or by the disposal of cotton surpluses.

Figure 1 depicts the relationships in this model.

The demand line (D D ) is initially assumed to be constant

and price-inelastic.

The desired price-support level is

P p , and Qp represents the quantity that the market will

demand at price .

Line SS represents a supply level that

will achieve price level P^ without government purchase of

cotton.

In this m o d e l , the supply curve represents the

output response to variation in price, while acres planted

and the state of the arts are held constant.

This supply

curve will tend to drift to the right as higher yielding

varieties and improved production practices are brought

into use over a period of years.

An increase in supply level as shown by line S ' S •

brings about a proportionately larger reduction in price

(Pp to P 1) than in quantity ( to Q 1). The government

% T

Figure 1. Basic Market Structure for Upland Cotton

6

can maintain

by standing ready to purchase cotton at

price P p , and thus remove quantity Q q

Q '

7

available for purchase at price P^ when supply levels fail

to reach Q^, thus keeping the price level of cotton very

near P^.

Effects of Surplus Production Trends

Implicit in this theoretical construct is the

assumption that, in a long-run context, quantities supplied

will equal quantities demanded at the desired price level.

In other w ords, price and supply levels should be adjusted

so as to maintain a long-run supply and demand equilibrium.

Should a long-term period of excess production occur, the

government would accumulate large stocks of cotton.

The

stabilization of prices at levels favorable to the producer

encourages individual producers to accelerate the forces

leading to increased per-acre yields, which compounds the

problem of aggregate surplus production levels.

The government may relieve this problem of chronic

surplus production by periodically reducing total produc­

tion, either by reducing the total allotment size or by

lowering the price-support level.

The former measure

effectively shifts the supply curve to the left; the lower

support price has the effect of increasing the quantity

demanded while reducing the quantity supplied and reducing

farm income.

8

Two-Sector Market Model

The market for U . S . cotton is best described with

a model showing two separate markets, as illustrated in

Figure 2 .

In this model, the U . S. market situation and

the Free Foreign World (FFW) market situation are shown as

two separate markets, each with its own supply and demand

curves.

Each sector has the same scales on the price and

quantity axes.

These two markets can reach an equilibrium

with trade.

Assuming that no costs or institutional

barriers are associated with trade, the equilibrium price

will be the same in both markets.

The market with the

lower "no-trade" equilibrium price (U. S.) will export to

the market with the higher "no-trade" equilibrium price

(FFW) until the price equilizes and the quantity exported

is equal to the quantity imported .

Since the complex behavior of both markets pre­

cludes a detailed analysis of their behavior, market trends

will be emphasized.

Particular attention will be directed

toward the market conditions that precipitated major U. S.

program legislation.

Figure 3 is a graphical representation of U. S.

supply and disappearance for the period, 19^5-1969•

Supply

is defined as production plus the carryover from the

Free Foreign World

Figure 2 .

Two-Sector Market Model Depicting Market Situation for Cotton at Close

of World War II vo

MILLIONS

RUNNING

BALES

Supply

Disappearance

YEAR (Beginning Aug. 1)

Figure 3 .

Supply and Disappearance Trends for Upland

Cotton, 1945-1969 — Source: Table 2.

10

11 previous year's supplies.

Disappearance is the sum of the

two components shown in Figure 4, exports and domestic mill

consumption.

The vertical difference between the supply

line and the disappearance line in Figure 3 is the carry­ over stock for the following y e a r .

Figure 5 shows the average price/pound received by

U.S. farmers for upland cotton from 1945-1969 and the

average annual price for a specified grade of U.S. cotton

in the Liverpool market from 1953 to 1969 •

The Liverpool

price is regarded as the FFW market price for U.S. upland

cotton.

Table 1 lists the average seasonal price for U.S.

upland cotton and the average payment and subsidy levels

to U.S. growers, domestic users, and exporters throughout

the period 1949-1970 .

Table 2 summarizes numerical data for supply, dis­

appearance, carryover, domestic mill consumption, exports,

and Liverpool price.

The Early Years. 1945-1955

At the close of World War XI, the market conditions

for cotton were similar to the situation illustrated by the

model in Figure 2.

Production-stimulating measures of the

war y e ars, including the absence of controls and high

price-support levels, were continued during the period 1945-

1949• The high world price stimulated production throughout

12

MILLION

BALES

YEAR

Mill Consumption of Cotton in the United States, 19^5-1969

10

8

MILLION 6

BALES

2

0

1945 1950 1955 1960 1965

YEAR

Exports of Cotton from the United States, 19^5-1969

1970

Figure 4.

U. S . Mill Consumption and Exports, 1945-1969 --

Source: Table 2 .

13

CENTS

POUND

YEAR

Average Annual Price, 1 "M, GIF, Liverpool, England, 1953“

1969

CENTS

POUND

YEAR

Prices Received by Farmers for Cotton in the United States,

1945-1969

Figure 5• U . S. and World Price Levels — Source: Table 1 .

Ik

Table 1.

Per-Pound Price and Subsidy Rates for U. S .

Upland Cotton, 1949-1970

Year

Season

Average

Pric e

Subsidy

Payment Rate Domestic for Exports Subsidy x C G X A O U.XA

J

Support

Payment Rate

1949

1950

1951

1952

1953

1954

1955

1956

1957

1958

1959

I960

1961

1962

1963

1964

1965

1966

1967

1968

1969

1970

28.57

39.90

37.69

34.17

32.10

33.52

32.57

31.63

29.46

33.09

31.56

30.08

32.80

31.74

32.02

29.62

28.03

20.64

2 5 0 9

22.02

20.60

7-50a

7 .2la

6 .19a

6.50

8.00

6 .00

8.50

8.50

8.50

6.50

5.75

6.50

5-75

3* 5°£

4.35^

9.42

11.53

12.24

14.73

16.80

^Difference between CCC export sale price and

average price for middling 1 -inch cotton in the designated

spot markets .

b .

Paid to small producers and domestic allotments only.

Source: United States Department of Agriculture, Economic

Research Service.

Cotton Production and Farm

Income Estimates Under Selected Alternative Farm

Programs, Agricultural Economics Report 2 1 2 ,

Strickland, P. L ., et a l ., Washington, D . C .,

1971 (Table 3 , p. 7)•

Year

19 ,i5

1946

1947

1948

1949

1950

1951

1952

1953

1954

1955

1956

1957

1958

1959 i960

1961

1962

1963

1964

1965

1966

1967

1968

1969

Table 2. Some Statistical Series on U. S. Upland Cotton

Disappear­ ance^

12,650

13,288

10,960

12,349

14,376

14,447

1 4,46i

12,089

12,181

11,118

16,233

13,459

11,227

15,774

1.4,511

13,616

11,474

14 ,024

1 3,124

12,300

13,786

13,390

10,738

10,605

Carry­ over^

Supply^

(Carryover plus

Production)

Upland

M i l l 2

Consumption Exports^

Price l"/l

Middling GIF

Liverpool

(cents/pound)

11,006

7,165

2,392

2,988

5,216

19,815

15,680

13,948

17,565

21 ,121

6,745

2,144

2,709

5,478

9,550

16,591

17,170

17,567

21,731

23,127

25,500

10,999

14,382

11,251

8,592

8,718

7,390

7,078

27,484

22,052

19,945

23,164

21,589

21,341

7,725

11,005

12,1 10

14 ,018

22,479

26,134

27,142

28,866

16,565

26,056 '

12,270

. 19,637

6,246

17,085

6,34?

16,214

8,946

9,755

9,101

•7,629

8,666

10,452

9,011

9,280

8,439

8,705

8,987 •

8,591

7,855

8,535

8,854

8,084

8,863

8,198

8,384

9,000

9,338

9,298

8,923

8,067

7,838

3,613

3,544

1,963

4,746

5,771

4,108

5,515

3,048

3,760

3,445

2,214

7,598

5,717

2,789

7,182

6,632

4,915

3,351

5,662

4,o6o

2,942

4,669

4,206

2,731

2,768

38.42

39.13

38.91

33.17

30.62

30.48

26.92

27.03

28.81

28.62

27.29

26.96

26.75

25.40

25.71.

28.22

25.53

Sources: 1 •

United States Department of Agriculture, Economic Research Ser v i c e ,

Statistics on Cotton and Other Related D a t a . Statistical Bulletin

♦ Washington, 51 C , April 1963 and supplements, Table 100,

p. 99.

2 . I b i d . . T a b l e 1 2 , p. 1 1 .

3. I bid. . Table 9, p. 8.

4. I b i d .. Table 240, p. 215.

H

VI

the world, and increased supply levels in the FFW sector

reduced the size of the available export market as shown

16

In the United States, a sharp reduction in acres

planted resulted from the imposition of acreage controls

under the price-support program for the 1950 crop. Reduced

supplies, coupled with increased domestic demands brought

on by the Korean conflict, caused a sharp rise in market

prices , thus temporarily obscuring the U.S. cotton surplus

problem.

High price-support levels were continued and

acreage limitations were relaxed during the period 1951-

1955, despite overall declines in domestic mill consumption

and exports (see Figure 4).

The result of this combination

of circumstances is shown in Figure 3, where disappearance

declined and supply (production plus carryover) increased

during this period.

Public Law 480 was passed in 1954.

Although this

legislation did not directly affect cotton support prices,

it did list among its objectives the stimulation of exports

of agricultural commodities, including cotton.

Despite

the program, the downtrend in cotton exports continued.

In 1955, exports fell to their lowest level since 194?

(see Table 2 ).

Total carryover stocks climbed to over 11

million running bales in 1955, with 9•6 million bales being

held in public storage by the government (see Table 2 ).

17

Trial and Error. 1956-1965

The Agricultural Act of 1956 represented a sig­

nificant legislative effort to cope with the cotton surplus

problem.

Faced with adverse supply and demand conditions,

the government offered an export subsidy, reduced price-

support levels, and called for reductions in crop acreage.

Prior to the legislation (pre-1956), supply and

demand levels in both market sectors took on the character­ istics illustrated by the model in Figure 6 .

Despite the

need for downward price or supply adjustment designed to

bring production into balance with consumption, the U . S .

maintained price level for the 1955 crop y e a r .

At this

price level, the U . S . was generating annual excess supplies

of Q^Qg.

The FFW market had an annual excess demand equal

to Q'Q", a substantially smaller quantity than the U.S.

excess supply of Q^Qg at this price level.

The 1956 program sought to reduce supply levels

with the establishment of a "Soil Bank," a feature

designed to divert allotment acreage into soil-conserving

uses.

This action is represented by a shift to the left

in the supply curve from SS to S *S'

This has

the effect of reducing excess supply levels from Q^Qg to

quantity .

The second provision of this program was a reduc­ tion in price-support levels.

The effects of this action

on quantities of excess supply and excess demand can be

F.F.W.

Figure 6 . Effects of 1956 Program Legislation on Upland Cotton Markets

% T

H

CO

demonstrated with the model in Figure 6 .

A movement in

19

demand in the FFW sector to something greater than Q'Q",

while reducing the quantity of excess supply in the U.S.

market to something less than .

For the purpose of reducing surplus stocks, an

export subsidy provision was included in the program legis­ lation. The subsidy effectively lowered the price of

U . S .

cotton in the FFW market.

The effects on the FFW sector

are the same as previously discussed price level reduction

effects.

An export subsidy has no direct effect on the

U . S .

market price in this model as long as the export

subsidy does not cause excess demand in the FFW sector to

exceed the excess supply in the

U . S .

market sector at the

given price-support level.

The result's of the 1956 program conform quite

closely to what the model would predict.

U . S .

price and

supply levels declined (see Figures 3 and 5), and exports

increased sharply, reaching 7*6 million bales in 1956

(Table 2 ).

Carryover stocks were reduced from the record

August 31, 1956, level of l4.4 million bales to 11.3

million bales in 1957 (Table 2 ).

By i960 the national cotton situation had improved

substantially from the 1955-1956 surplus crisis.

Except

for a slump in 1 9 5 8 , exports exceeded 6 million running

bales in each year of the period 1956-1960, as is shown in

20

Figure 4.

The surge in production levels that accompanied

the return to regular allotments for the 1959 crop year was

absorbed by the strong export market.

Price-support levels

were raised in i9 6 0 , and the stage was set for a replay of

what occurred during the early 1950's.

The higher price

level encouraged production throughout the world, increasing

supply levels in both the U . S . and FFW sectors.

Despite the

continuation of an export subsidy, the export market for

U.S. cotton weakened with effects similar to those shown by

the model in Figure 2 .

Excess supply levels increased in

the U.S. market and excess demand levels in the FFW market

declined.

By 1965, total carryover stocks had increased to

a level of l4 million running bales.

This trend continued

and carryover stocks reached a record 16.56 million running

bales in 1966 (Table 2 ).

Disappearance declined in 1964 and

1965 (Tables 1 and 2 ), despite the existence of a direct

subsidy to domestic cotton users and cotton exporters.

The Food and Agriculture Act of 196?

In an effort to alleviate the surplus problem,

Congress passed The Food and Agriculture Act of 1965.

This

legislation called for a sharp reduction in U.S. market

price to encourage domestic consumption and to provide

incentives for producers to cut back on production.

The

Food and Agriculture Act of 1965 provided for a reduction

21

in the national acreage allotment for upland cotton to a

minimum of 16 million acres.

The magnitude of the price drop resulting from the

substantial cuts in price-support levels is shown by the

statistics in Table 1 .

With the price-support level

effectively lowered below market price levels, the need for

subsidies for exporters and domestic consumers was

eliminated.

To compensate for the loss of farm income due to the

low price-support level, the program provided for direct

payments to producers based on the expected production of

sixty-five per cent of each grower's regular allotment

acreage.

This portion of the allotment was designated as

domestic allotment.

Participants in the program were

required to divert not less than 12.5 per cent, but could

divert up to thirty-five per cent, of their regular allot­

ment acreage from production and earn additional government

payments for their diverted acreage.

In the early years of the program, the combination

of low price-support levels and attractive diversion pay­

ments prompted many growers to restrict their planted

acreage to their domestic allotment, which was the required

minimum planted acreage for full participation in the

program.

In addition to this decrease in planted acreage

levels to approximately sixty-five per cent of the regular

allotment, per-acre yields declined sharply because of

adverse growing conditions.

Total production was reduced

significantly in 1966.

Low price levels sharply curtailed U.S. cotton

22 ments.

Many growers planted only ninety per cent of the

domestic allotment (sixty-five per cent of the total allot­

ment) , which qualified them for their maximum allowable

diversion payment in addition to their price-support payment.

The effects of reduced price levels for U.S. cotton

and a decline in U.S. supply levels can be shown with the

two-sector model in Figure 7*

It can be seen that, if the

U.S. demand had remained constant, U.S. stocks would not

only have rapidly declined, but also there would have likely

been higher market prices and expanding U.S. production by

1970.

During this period, U.S. demand was drifting to the

left because of displacement by man-made fibers and part of

the potential benefit of the 19&5 program was cancelled out.

Aside from direct payments, the government's role

in the market was reduced by the 1965 legislation.

The

program replaced the domestic user and export subsidy

programs with a direct payment to the grower.

The direct

payment aspect of the program drew sharp criticism.

It

was argued that payments to large producers were not

justifiable from an income maintenance standpoint.

Consequently, for the first time, program legisla­ tion for the 1971-1973 crops placed limits on the magnitude

F.F.W.

Figure 7•

Effects of a Substantial Reduction in Price-Support Level and U.S.

Supply Level

% T

N

V)

24

of direct payments at $55,000 per commodity per producer.

Except for this provision for payment limitations, the 1970

legislation is in its effects essentially the same as the

1965-1970 program.

Summary

Since 1945, cotton has become less competitive in

domestic and foreign fiber markets.

Ultimately the price

level for cotton is a function of market supply and demand

conditions.

It is apparent that cotton prices should

•weaken in a market characterized by increasing supply levels

and declining demand levels, and declining prices bring

about declining farm income levels.

For various reasons, U.S. policy-makers have elected

to maintain farm income via price-support programs.

The

criteria for setting price-support levels can be faulted

for not recognizing changes in input and productivity

relationships.

Consequently, price-support levels have been

excessively high, stimulating increased production and

leading to excessive supply levels instead of adjusting

supplies downward as demands slackened.

When unmanageable surpluses accumulate, sharp down­

ward price adjustments and supply level adjustments in the

form of allotment reductions are necessary. The 1965 pro­

gram of legislation was such a measure. More significantly,

the program represented a substantial departure from the

25 basic price-support policy of the earlier programs.

Rather

than attempting to define a new market equilibrium at some

relatively high price-support level, the program limits

acreages to hold U.S. supply levels in check, while main­ taining farm income through direct payments to producers.

The imposition of payment limitations may portend a further

withdrawal of government influence in the market by reducing

the support of farm income levels for cotton producers.

This study is concerned with some of the effects

that this program may have on Arizona allotment values and

the organization of the cotton industry.

CHAPTER III

ALLOTMENT VALUE: A LOGICAL CONSEQUENCE

OF PRICE-SUPPORT PROGRAMS

Allotment value is an empirical fact; farms with

cotton allotments command higher sale prices than comparable

farms sold without allotments.

A logical explanation exists

for this phenomenon.

The allotment represents a right to participate in

government cotton programs and receive whatever benefits

the programs provide.

The allotment can be viewed as an income-producing asset. The values of such assets are commonly determined by the capitalized annual income stream that the assets provide for the owners. The magnitude and potential duration of the benefit forms the basis for allotment v a lue. The most general capitalization formula for the asset value assumes that the annual income flow is const ant and known with certainty into perpetuity. The formula is:

2

V = — r

(I)

2. E. 0. Heady, Economics of Agricultural Produc­

26

27

In this formula, the annual income is represented by R , the

market rate of interest is r, and the present value of the

asset is V.

Extraordinary risk can be compensated for by

inflating the value of r.

Where uncertainty necessitates that future planning be limited to a finite number (t) of production periods

(years), the basic capitalization formula is modified to a discount formula and expressed as follows:

3

R .

R t

V = ---- --- — + . . . + ----

-

-- = t = 0 . . . T (II)

(l + r) (l + r)

Assuming that R and r are constant, the value of V

will be smaller in Formula II than in Formula I if T is less

that infinity.

Fluctuations in R that can be anticipated with

certainty can be discounted with Formula II.

It should be

apparent that lower values of R bring about lower values for

V, all else being equal.

The value of allotment acreage can be estimated

with the discount formula, if R, r , and T are known.

In

this context, the economist defines income (R) as the

residual income remaining after all other factors of pro­

duction have been paid their acquisition cost or alternative

3. Ibid. , p. 386 .

28

Per-acre yields can be easily estimated, which

leaves price as the critical variable in determining gross

income.

To the extent that the government stabilizes price

through commodity programs, gross income is correspondingly

stabilized as long as there has been no previous inverse

correlation of price and quantity.

Assuming that costs are

relatively stable, income above costs (R in the discount

formula) can be estimated.

Futhermore, cotton programs

guarantee income levels (R) via price-support levels for

some specified length of time (T in the formula)•

T h u s , the owner of the allotment implicitly attaches

some value (V) to the allotment based upon his knowledge of

the values of R, r , and T, with R and T being functions of

the existing program legislation.

The Influence of Government Programs

on the Value of R

Given the knowledge of how an income stream becomes

capitalized (or discounted) into an allotment value, it is

of interest to examine how the magnitude of that income

stream (R) is influenced by changes in government programs.

By using a per-unit cost and revenue model for an

upland cotton allotment acre (see Figure 8), it is possible

to analyze the effects of price-support level changes,

allotment size changes, direct subsidy payments, and

limitations on direct subsidy payments with respect to per-

acre residual income (R in the discount formula).

29

^ L B YIELD

.YIELD

Figure 8.

Typical Cost Relationships for an Acre of Upland

Cotton Allotment

30

Cotton is sold in a competitive market and the

individual cotton producer cannot influence the price for

a given quality of cotton.

Relevant costs in the model are

average variable costs, average total costs, and marginal

cost.

For a typical acre of upland cotton allotment, the

per-unit cost curves would look like the curves shown in

Figure 8.

The horizontal axis represents production in

pounds, and the vertical axis is the price or cost in

cents/pound.

Marginal cost

(MC) is the cost associated with

each additional pound of production.

Rational behavior

dictates that a producer will produce at an output where MC

equals price in the short run, as long as price is equal to

or greater than average variable costs.

Average total cost

(ATC) is the sum of average fixed costs and average

variable costs at a given output level.

The government, however, has chosen to influence

cotton prices for the purpose of maintaining farm income

levels.

From 195^ to 19^5, this influence was in the form

of price supports.

At some hypothetical support price (P^) above the

equilibrium price (P ), the grower expands output to a

higher level Qg .

Although the total cost of production

increases as a result of this expanded production (from

OP^aQ^ to OcdQ^), it is more than compensated"for by the

31 larger gross income (OP^bQ^) that can be realized.

The

gross income in excess of total costs is defined as net

income.

Such an anticipated net income stream, discounted

to the present moment, represents the allotment value.

Program changes which alter the level of the support

price (Pg) bring about altered production levels at which

price equals marginal cost (MC) .

Net income levels are

correspondingly affected, leading to changes in allotment

value.

Adjustments in the size of the national allotment

base effectively shrink or expand the size of our hypo­ thetical allotment acre.

Shifting the cost curves to the

right in the model depicts the effects on net production

and net income that would result from an expansion of the

allotment base.

The effects of a reduction in the allotment

base can be shown by shifting the curves to the left.

Federal programs for the 1966-1970 cotton crops

provided direct price-support payments on a portion of the

price-support levels.

As pointed out in Chapter II, the

lowered price-support level dampened the growers 1

enthusiasm for planting acreage which could not qualify

for the subsidy payments.

The effects on income levels and

production for allotment acreage qualified to receive

subsidy payments can be illustrated with the model shown

in Figure 9•

MC

Figure 9•

Cost and Income Relationships for an Acre of Domestic Allotment Under

the 1966-1970 Program

V) to

33

Price level P g represents a hypothetical "high"

price level for cotton under pre-1966 programs.

At price

level P g , production is OQ^ (where price and marginal

revenue equal marginal cost).

Assuming as one extreme case

that the government fixed the projected yield at OQ^ at the

inception of the 1965 program and provided a fixed per-

pound subsidy payment rate of PPg to augment the lower

price-support level of P, then the grower can expect to

receive a total subsidy payment equal to P g abP for the

duration of the program.

The program required that

production be carried out on ninety per cent of acres

qualifying for payment in order to receive the full subsidy

payment.

If the grower continues to produce at O Q g , he

incurs a loss of P'cbP and realizes a net income of

Pga c P 1

It is clear that the producer could lower his

total costs by reducing output to something less than OQ^

and still receive a total subsidy payment based on O Q ^ ,

assuming that projected yield is fixed at O Q ^ .

The optimum

production level under these conditions is OQ^, which

minimizes losses on production (fdgP).

This minimum loss

balanced against a fixed total subsidy income P gabP, shown

as P"hgP in the model, leaves a maximum residual income to

the allotment acre of P"hdf.

This income stream is for a

domestic allotment acre that qualifies for payments.

Thus ,

the income and, ultimately, the value of a regular

34

allotment acre would be sixty-five per cent of the income

and value of a domestic allotment acre.

Relaxing the assumption that projected yield is

fixed at OQ^ alters the size of the total subsidy payment.

As the second extreme case, assume that the payment is

based on actual yield in the same year and the per-pound

subsidy rate is constant at PP^.

In this situation,

production levels will revert to O Q ^ , the same level as

when price-support levels are pegged at P^ for an acre of

domestic allotment.

Income and value for a full acre of

allotment will again be sixty-five per cent of that income

and value associated with an acre of domestic allotment.

The program defines projected yield as a moving

three-year average of the allotment's actual yield.

At

the outset of the program, a producer would be tempted to

restrict his production to OQ^ and receive a payment based

on his projected yield of O Q ^ •

However, this would reduce

the size of his total subsidy payment in the following

year, and, all else being equal, would mean a loss of net

income.

Maintaining a production level of OQ^ would be an

optimal solution only if the payment were based on actual

production in the same year or in the case where the rate

of payment is expected to continue into the indefinite

future and the discount rate of future earnings is zero.

Faced with uncertainty regarding future programs,

it is reasonable to suggest that producers elected to

35

maintain production and projected yield values nearer OQ^

than 0Q1 early in the program.

As the program neared its

end, producers probably tended to restrict production to a

level closer to O Q ^ .

In either case, the program offers

benefits substantial enough to provide a net income stream

which becomes capitalized into an allotment value.

A l s o ,

the lower price expectations are probably a significant

factor in explaining the decline in per acre cotton yields

that have been observed in recent years.

Should an acre of domestic allotment fail to

qualify for payments because of the 1971 provision for

payment limitations, the producer faces a price of P and

no hope of deriving any positive net income.

With a

negative net income stream, the allotment ceases to be of

positive value (i .e . , V = p = 0) while retained in the

original owner's control.

This chapter has explored some theoretical rela­

tionships among programs, prices , income, and allotment

v alue.

The following chapters are concerned with exploring

the empirical relationships between income and allotment

value for allotment acreage in central Arizona.

The

potential effects of payment limitations on allotment

income and farm organization in Arizona will also be

explored.

CHAPTER IV

ESTIMATION OF THE PER-ACRE VALUE OF UPLAND

COTTON ALLOTMENTS IN CENTRAL ARIZONA

The purpose of this chapter is to empirically

estimate the per-acre values of cotton allotments in

central Arizona.

The hypothesis that upland cotton allotments have a

capitalized value was tested in work completed by Howard

Barfels in 1 9 6 7 •^ Barfels applied multiple regression

analysis to historical sales data to explore relationships

between selected independent variables and the sales values

of farms. This research confirmed the existence of sub­

stantial values associated with upland cotton allotments in

the early i960 ’s .

The Food and Agriculture Act of 1965 allowed the

transfer of allotments exclusive of land. However, allot­

ments sold in conjunction with farmland would be expected

to have some value that would be reflected in the sale

price of the farm.

By developing appropriate regression models, it

4. Howard R. Barfels, "The Value of Cotton Allot­

ments in Arizona," unpublished Master's Thesis, The

University of Arizona, Tucson, Arizona, 1967, p . 2.

36

37

value of central Arizona upland cotton allotments in any

given y e a r .

The regression coefficients associated with

independent variables in the models can be interpreted as

marginal values for the variables.

The specific objective

of this analysis is to develop marginal value estimates for

annual allotment values during the period 1961-1967•

These

estimates will be compared to annual income estimates

obtained with budgetary analysis techniques.

Source of Data

The Western Farm Management Company in Phoenix

maintains files on individual sales of rural property in

Arizona that it uses as background information for preparing

farm appraisals.

Their files were an excellent source of

primary data for sales that occurred during the period 196l

through 1968.

Information on the location of a parcel, its

upland allotment acreage, its total acreage and land use

classification, total price, water supply, value of improve­ ments, and date of sale were obtained from these files.

Data Preparation

The only sales that were included in this analysis

were sales of rural farmland that had cotton allotments

attached to them.

Furthermore, sales had to be "bona fide"

transfers of assets.

Any cases where it was determined

that the price paid did not solely represent payment for

the land and its associated use were not included.

38

Examples of this are transfers of estate property between

family members, sales of state lease land to tenants with

a long history of occupancy, and condemnations for public

ownership.

Initial examination of the data indicated that

information was available on relatively few sales of

property in Pinal County during the first few years of

the period 1961-1968, which made it unsuitable for

analysis as a separate area.

Pinal County observations

could have been included with Maricopa County sales, but

again, examination of the data indicated a wide divergence

in sale value for farmland between the two counties.

In

light of this , it was decided that all analysis would be

done with sales of farmland in Maricopa County.

Because of limitations with respect to our ability

to observe and classify variables, it was necessary to

define variables that would reflect influences on property

value of other variables that could not be easily quanti­ fied.

It was expected that improvement value was a

variable that would substantially affect sales prices.

This variable was expressed as a "0, 1" variable in the

models.

Where the sales reports indicated that the value

of improvements on a specific farm were substantially above

normal for farms of similar size, this variable was given

a value of "one." A similar variable was defined for

below-normal improvement values.

1

39

Water is essential for agricultural production in

the cotton growing areas of Arizona, and the conditions of

its availability would be expected to be an important

variable in determining the sale price of a farm.

Barfels

attempted to express water supply in terms of the cost per

acre-foot in his w o r k .

In addition to cost, however,

quality and dependability of supply are important factors

in determining the relative desirability and value for

various water sources.

Ideally, each water district and pumping field

should be treated as an independent variable.

Because of

the limited number of sales in any given district, meaning­

ful statistical results could not be obtained using this

approach.

Therefore, water supply was expressed as three

"0, 1" variables.

Each water district was reviewed with

respect to cost per acre-foot, quality, and dependability

of supply, and was assigned to one of three classifications

good (Salt River Project), fair (Roosevelt Irrigation

District), and poor (well water).

(See Appendix, Table 10,

for classifications.)

Speculative value, or the potential for shifting

the use of the land for purposes other than farming, was

expected to exert a strong influence on property values.

Urban development is the major speculative factor in

Maricopa County.

Although difficult to quantify, the

speculative influence would be expected to be correlated

4o with the location of the property.

A location variable

was included and expressed simply as the distance in miles

from the city limits of metropolitan Phoenix to the

specific land parcel.

The removal of the effects of abnormal improvement

levels, water supply conditions, and speculative influences

on sale price is necessary to isolate the influence of

allotment acreage and net crop acreage on sale price, which

is of primary interest in this analysis.

The problem of

isolating the effects of specific variables is readily

dealt with by using multiple regression techniques.

Net crop acreage in this analysis is defined as

crop acres less allotment acres. This means that an allot­

ment acre, as defined h e r e , includes the rights under the

government program to plant an acre of cotton and also an

acre of land suitable for growing cotton.

By treating

these variables in this way, it may be possible--by a

process explained later in the chapter— to estimate the

value of an acre of allotment as distinct from an acre of

land that may be part of the same farm sale price.

One

acreage variable was defined for each year that had sales

data recorded by the source.

4l

Analytical Procedure

Two basic series of multiple regression models

were used in this analysis.

The primary difference between

the models was the form of the dependent variable:

Series X = S/A: The average sale price per acre

Series II for the parcel.

= $: The total-doliar sale price of the parcel.

Independent variables included in both series of

models are defined below:

D - The distance in miles of each parcel from the city limits of metropolitan Phoenix.

SS - That part of the parcel which is upland cotton

(6l-68)

(i.e., short-staple) allotment.

NC - Net crop acreage in parcel.**

(6l-68)

NNC - Non-cropland acreage in parcel. The residual

(61-68)

after subtracting net crop acres and upland

allotment acres from the total acreage in the

5 .

Variable is expressed as a percentage in Series

I models and as actual number of acres in Series II models.

Subscript denotes year in which acreage was sold, with each

year represented by an individual variable.

42

Pos -

Improvement valuation is substantially above

normal for a farm, all else being equal;

expressed as a "zero-one" variable.

Neg -

Improvement valuation is below normal for a

farm, all else being equal; expressed as a

"zero-one" variable.

SRP -

Good water supply.

expressed as "0, 1.

RID -

Fair water supply, expressed as "0, 1.

Well Poor water supply, expressed as "0, 1.

a - Constant term which embodies the net influence

on the dependent variable of variables not

explicitly included in the model.

The significance of coefficients associated with

the independent variables was judged by the following

statistical tests:

1.

The "t" test was used to evaluate the significance

of estimated regression coefficients.

Computed "t"

values appear in parentheses below each regression

coefficient.

Significance at the five per cent

and ten per cent level is denoted by ** and *,

respectively.

2.

The coefficient of multiple determination (R^)

gives a measure of the proportion of the variation

in the dependent variable that is explained by

variation in the independent variables.

/

43

An initial Series I model which included all of the

above variables was tested.

This model suggested that the coefficients for all

NNC variables were not significant, as well as the SS^g and

Cgg variable coefficients.

These variables were not

included in subsequent models, thus eliminating 1968 from

the analysis.

Investigation of the Distance Variable

The residuals from this initial regression suggested

that the relationship between sale price and the independent

variable representing distance was not a linear function.

In order to better identify the influence of

distance, this variable was next expressed as a logarithm

and then as the reciprocal of the distance.

The water

supply variables were deleted from these models because of

the high positive intercorrelation between the independent

variables of "D" and "SRP.M

When graphed, the functions described in the two

models coincided at a point 15 miles from the metropolitan

Phoenix city limits.

The values for the dependent

variable were then plotted on the same graph in order to

determine which form of the distance variable best

measured this empirical relationship (see Figure 10).

From zero to fifteen miles, the logarithmic function was a

better estimate; the reciprocal function gave a better fit

D O L L A R S P E R A C R E

S A L E P R I C E

Inverse

99ml.

99ml

D I S T A N C E F R O M C I T Y LIMITS O F M E T R O P O L I T A N P H O E N I X IN M I L E S

Figure 10 •

Regression Li nes for Log Form and Simple Inverse Form of Distance

Variable Against Empirical Sale Values

45 to the data beyond fifteen miles.

Using the coefficients

from these two models, the dependent variable (the sale

price) was adjusted to remove the effect due to location

and speculative influence.

From an empirical standpoint, this adjustment has

the effect of adjusting the farm values to a location an

infinite distance from Phoenix.

In fact, the adjustment

of per-acre values beyond fifteen miles is not very large,

as would be expected in light of the nature of the recip­ rocal function.

These adjusted values of the dependent variable were

run in a model which had no independent variable for dis­ tance.

The results were not satisfactory, and this approach

was abandoned.

The distance variable was retained in its

original form in subsequent models.

This work with the distance variable did reveal

that some of the parcels located in close proximity to

metropolitan Phoenix were being sold at prices far higher

than any conceivable value for agricultural purposes.

Per-

acre prices for these sales remained very high even after

the previously described adjustment procedure was performed.

It was concluded that the speculative effects so completely

dominated the value of land located very near Phoenix that

the value of allotment acreage could probably not be

identified.

Therefore, all observations located less than

2.5 miles from metropolitan Phoenix city limits were

deleted from the data, leaving 51 individual sales in

Maricopa County over the period 1961-1967•

These 51 sales

constituted the data with which the remainder of the

analysis was performed.

These data are summarized in Table

11 of the Appendix.

Analysis Using Series I Models

Using S/acre as the dependent variable, the regres­ sion models in Table 3 were estimated.

With this form of

the m odel, the regression coefficients associated with the

allotment variables represent changes in per-acre values as

the proportion of the farm covered by allotment is increased

by one per cent.

T h u s , to determine the value of an acre of

allotment, it is necessary to multiply the regression co­ efficient by a factor of one hundred.

Figure 11 illustrates

the principle involved.

With zero per cent of a farm having

allotment, the intercept of the vertical axis gives the

value of land without any allotment. The regression co­

efficient gives the slope of the line and provides the basis

for projecting the line to one hundred per cent of the farm

having allotment.

The change in price in passing from zero

to one hundred per cent represents the estimate of the

value of an acre of allotment.

The SRP variable has an effect on land values that

is significant at the five per cent level.

Its regression

coefficient can be interpreted as the average added

Table

3*

Regression for Series I Approach Using Per-Acre Sale Price as the

Dependent Variable

M o d e l 1 * coefficients t-values

R 2 = .589

D.F. residual a

589

+ D

-9.12

+ SS 6l

13.26

(2.33)** (1.51)

SRP

+ ss62 +

8.38

ss63 + ss64 +

14.69

7.81

455

(2.17)**

(3.90)**

(2.30)**

(3.85)**

M o d e l 2.

Y 1

= a + ss6i + ss 62

" SS 63

+ ss64 *

SS65

+ ss66 coefficients 378 13.86

15.79

21.51

12.88

11.44

10.33

t-values

(1.33) (2.73)**

(3.54)**

(2.58)**

(1.59)

(.74)

R 2 = .565

D.F. residual = 42

•Significant at 5% level

S S g 7 + SRP

9.24 558

(1.16) (4.76)**

48

Per-acre

Dollar

Value

600

-

Percent of acre that Is allotment

Figure 11.

Extrapolation of SS Coefficients for Model 1 of

49

value/acre occurring because a particular farm is located

in a good water district rather than outside of it.

The relatively higher value ($558/acre) for the

variable in Model 2 can be explained by the strong negative

correlation between the distance variable and the SRP

variable.

The exclusion of the distance variable in Model

2 has the effect of inflating the SRP variable, in that the

SRP variable is performing in part as a proxy for the

distance variable.

The constant term reflects the average value of the

dependent variable that is not explained by the independent

variables in the models.

The only statistically significant regression co­

efficients on the allotment variables in these models are

the 1 9 6 2 , 1 9 6 3 , and 1964 allotment coefficients.

According to these estimates, farm property in the

Salt River Project, or a comparable water district,

commands a substantially greater per-acre price than does

comparable rural property outside of the Salt River Project

water district.

The coefficient associated with the distance

variable in Model 1 indicates that for every additional

mile removed from the city limits of metropolitan Phoenix

the average per-acre value of a parcel of land is expected

to decrease by approximately $9•

50

Average annual allotment value estimates obtained

by the procedure discussed earlier for interpreting allot­ ment variable coefficients are summarized in Table 4.

Table 4.

Per-Acre Allotment Value

1961-1967

Estimates for Years

Model

Year

1961

1962

1963

1964

1965

1966

1967

Series I Series 11

#1

#2

#1

$1,326

838**

1,469**

781**

$1,386

1,579**

2 ,151**

1 ,288**

1,144

$1,000

1,205

1,220

1,105

920

1,033

924

R 2 = .565

R 2 = .589

**Significant at the 5% level.

735

545

R 2 = .949

#2

$ 960

1,190

1,220

1,110

950

745

550

R 2 = .940

Analysis Using Series

II Models

Using total sale price as the dependent variable,

the regression models in Table 5 were estimated. The co­

efficients for allotment acreage and net crop acreage

variables were expressed as the actual number of allotment

acres and net crop acres contained in each parcel.

Table 5.

Regression Models Using Total Sale Price as the Dependent Variable

(Series II) t - v c l u e e

T2 e a e

0

<2*.99))

(-796)

* NC61

(1677)

♦ ,s6a + SS6)

♦ *c6* 4

"6,

(2722) (2)87)-

(1202) (-9120)

4 *C65

05)9)

4 **66

(0770)

4 *c6?

(9*1)

(1.97)* (2.27)**

(12.5*)** (6.21)**

().%))**

(-a.)))** ().6*)*« (a.ao)**

(9.1*)**

4

P e e

(25.76))

(22.7*9)

(1.86)*

♦ S R P

<1.*4)

R2 • .9*9

O . F . r e e l d u a l

• >9

1*1 2.

c e e f f I d e a t e

<87,998) e

D

(9)))

(2.19)**

* *<61 ♦ **62

(1680)

(2710)

(21.))** (11.71)**

♦ ss6)

W 2

• .9*0

O . F . r e e l d u a l e

*0

♦ *c66

4 *c6)

(1190)

(1067)

().06)*»

(11.69)**

4 **66 4 *<67

(17*6) (928)

(2.0*)"

(*.75)**

F e e e

S R P

(27,07*)

(20,*)#)

0.8))* 0.2))

•Significant at 10%

level

••Significant at 5% level,

52

The estimated coefficients for allotment acreage

variables in this series of models represent the combined

value of an acre of cropland and the value of an acre of

allotment that was associated with that acre of cropland.

To remove the segment of the value that is contributed by

the cropland, it is necessary to subtract the appropriate

net cropland coefficient from the allotment value co­ efficient.

This procedure yields the residual value that

can be attributed to the allotment.

Because the models do not yield meaningful co­

efficients for all of the allotment and net cropland

variables, it is necessary to extrapolate in order to

obtain values for each of the coefficients.

This can be

done by plotting the meaningful coefficient values for each

set of variables against time, and connecting the points

with straight lines.

The vertical distance between the

lines at a given year can be interpreted as the per-acre

value of allotments for that year.

Table 4 presents annual per-acre allotment value

estimates obtained by applying this procedure to coeffi­ cients derived from Models 1 and 2.

The graphs for each of

the models are shown in Figure 12.

The results for Model 1

ignore the unexplainable coefficients for the 1965 allot­ ment variable and the 1965 cropland variable.

Model 2 differs from Model 1 only in that the 1965

allotment variable is omitted in Model 2. When the shapes

D O L L A R S

P E R

A C R E

2 9 0 0

Model 1

Model 2

53

Y E A R

Figure 12. Simple Linear Relationship Between Allotment

54

of net cropland graphs are compared for both models, the

Model 2 graph suggests a curvilinear trend in net crop acre

values.

2

The coefficient of multiple determination (R ) is

reasonably close to 1.00 for both models.

This indicates

that a major part of the variability in the dependent

variable is explained by variability in the independent

variables.

The sizeable constant term in both models signifies

that a substantial amount of a farm's total value is not

accounted for by the independent variables explicitly

included in these models.

The coefficient for the distance variable is

significant at the five per cent level in Model 2.

The

interpretation is that for each additional mile that the

average farm is removed from the city limits of metro­

politan Phoenix the total sale value will drop $933 on the

average.

The coefficient for the "positive improvement"

variable in Model 1 is significant at the ten per cent

level.

The magnitude of the coefficient associated with

the "positive improvement" variable in each of the models

is substantial, indicating that this variable makes a large

contribution to the total sale price of farms having this characteristic.

55

The coefficient for the SRP variable in each of the

models, although not significant at the ten per cent level,

is substantial in magnitude.

This suggests that the

possession of SRP water rights may contribute heavily to

the total sale value of a farm.

Evaluation of Results of

Regression Analysis

The results obtained from the Series I models should not be regarded too heavily. Both Model 1 and Model 2 of

this series have a low coefficient of multiple determination

2

(R ) v alue.

Very few of the independent variables in these

models have statistically significant coefficients associ­ ated with them.

The Series I estimates probably over-state the

value of an acre of cotton allotment, particularly in the

earlier years. This occurs because the estimating pro­

cedure allows only one value for net crop acres for all

years, while this value seems to have declined during the

period.

For an early year in the series, the intercept is

forced through a lower value than would be the case if free

to find its own level and this causes the regression line

on proportion of allotment to be steeper than it would be

if each year's data were fitted independently.

Regressions were attempted with models including

net crop variables, but the lack of independence between

allotment acreage and net crop acreage variables gave

56 unsatisfactory results.

The models do indicate a peak

allotment value for the year 1963 and continuously

declining values thereafter.

These findings are consistent

with the results of the Series IX models.

The allotment variable estimates obtained with the

Series II regression models are more dependable from a statistical standpoint. Both models in this series meet

2

the criteria for high R values, indicating that the models

"explain" a large proportion of the variability of the

dependent variable. Many of the coefficients on the

independent variables are significantly different from

zero, as confirmed by the "t" test.

Problems of inter-

correlation among the independent variables representing

net crop acreage and allotment acreage for the same year

prevented the development of a regression model with all of

these variables in it. The exclusion of some of the allot­

ment and net crop acreage variables does not exclude the

influence on the dependent variable associated with these

omitted variables because of their high correlation.

The

influence of these deleted variables would be to over­

state the value of the net crop acres and understate the

value of the allotment variables.

Because the estimate of

pure allotment value depends upon subtracting the estimate

of net crop value from allotment v alue, the net effect

should be to understate the value of pure allotment.

57

Comparison of Allotment Income Flows

and Allotment Values

Per-acre income above variable costs was calculated

for active cotton allotment acres in Maricopa County during

each year of the period 1961-1967•

These trends in income

levels were then compared with allotment value trends.

Per-acre variable cost figures for a representative 320-

acre Maricopa crop farm were obtained from an unpublished

Master's thesis.^

Average gross income-per-acre figures were derived

by dividing the number of "active" allotment acres in

Maricopa County in a given year into the cash receipts

(value, of lint plus payments) for cotton for the same year.

"Active" allotment acreage is essentially planted

acreage in 1961, 1962, and 1963•

For 1964 and 1965, it is

planted acreage plus diverted acreage under the voluntary

program.

In 1966 and 1967 the definition is acres partici­

pating in the program, including planted acres and acres

diverted under the program.

These statistics are summarized

in Table 6 .

The average income above variable costs is found by

subtracting the variable cost figure from the per-acre

average income figure. The income above variable cost

6 .

Richard C. Shane, "Risk and Diversification in

Arizona Crop Farm Production," unpublished Master's Thesis,

The University of Arizona, Tucson, Arizona, 1971, p • 7*1 •

Table 6.

Per-Acre Income Above Variable Costs for Maricopa County Cotton

Allotments, 1961-1967

1961

1962

1963

1964

1965

1966

1967

Acres

Planted3

137,670

135,200

121,900

1 2 1 ,4ood

121,777;?

122,917^

124,436°

Value of k

Production

$48,594,975

52,932,035

44,821,600

36,414,000

38,364,040

17,612,925

21,949,175

Payments0

$123,586

668,883

13,814,606

21,476,445

Gross

Income

Per Acre

$353

391

368

301

321

256

349

Variable6

Costs

Per Acre

Income Above

Variable Costs

$215

221

220

218

223

221

217

$138

170

148

83

98

35

132 a.

Arizona Crop and Livestock Reporting Service, Arizona Agricultural

Statistics. 1970. Bulletin S - 5 , Phoenix, Arizona, M a r c h , 1970, p. 1 4 .

b. Ibid.

c .

Agricultural Stabilization and Conservation Service, Arizona Annual

Report. Phoenix, Arizona, 1964, p . 32; 1965, p . 34; 1966, p . 27; 1 9 ^ 7 , p p • 27-28.

d . Includes diverted acreage eligible for payments. Source: Ibid.

1 e. Richard C. S hane, "Risk and Diversification in Arizona Crop Farm

Production," unpublished Master's Thesis, The University of Arizona, 1971, p • 7 ^ .

59

figures are graphed in Figure 13 along with the allotment

value trends.

With the exception of a minor uptrend in income in

1965, the per-acre income above the variable costs peaked

in 1962, fell steadily through 1966, and finally jumped

sharply in 1967 because of a sharp rise in the price of

cotton.

Allotment value trends seem to generally lag one

year behind the income trends.

Allotment values peaked in

1963, and then fell sharply through 1967•

The 1965 upswing in income did not stem from the

falling allotment value trend for 1966.

The uncertainty

surrounding the 1965 cotton program which became effective

in 1966, may be the reason why allotment values did not

respond to the improved income picture for 1965•

It is

also possible that the response was obscured in the regres­ sion analysis because of the data limitations.

This comparison indicates that allotment values are

closely linked to income levels.

Income at the producer

level in turn is strongly influenced by government programs.

This would imply that changes in allotment valuation are

closely linked with cotton program modifications which

affect per-acre income flows.

6o

Per-acre value

of allotment

DOLLARS

Per-acre Income

above variable

costs

YEAR

Figure 13« Comparison of Per-Acre Allotment Values with

Income Above Variable Costs by Year — Sources:

Table 6 and Table 4.

CHAPTER V

AN INVESTIGATION OF YIELD AND OWNERSHIP

PATTERNS FOR ARIZONA UPLAND

COTTON ALLOTMENTS

If no reorganization of upland allotments took place, payment limitations would substantially reduce

7 aggregate income to Arizona cotton producers.

Based on

the results of the study presented in the previous chapter,

allotment values would be expected to decline from their

present levels because of the reduced income.

The payment limitation in the Agriculture Act of

1970, however, does not preclude a variety of potential

reorganization schemes for allotments.

By using various

legal methods, producers can reorganize their allotment

holdings for the purpose of avoiding the loss of payments

on allotment acreage that was eligible to receive payments

prior to the limitation legislation.

The nature of any farm reorganization, and the ease

with which it could be accomplished, would depend heavily

upon current allotment ownership and lease patterns for

7 .

Robert S. Firch and Jeffery J . Weber, "Upland

Cotton Allotments in Arizona and Potential Effects of

Government Payment Limitations Based Upon 1970 Allotments,"

Agricultural Economics Department, The University of

Arizona, Tucson, Arizona, October 1 0 , 1970, Table 4.

61

62

Arizona cotton farms.

An analysis of allotment ownership

and lease patterns may serve to offer some insight into the

nature and direction of potential allotment reorganization.

Because payments under the Agriculture Act of 1970

will continue to be based upon projected yield, the per-

acre income will be affected importantly by changes in

projected yield. The question arises as to whether r e ­

organization of allotment acreage into smaller farming

units, in order to avoid the payment limitation of $5 5 ,0 0 0 ,

will have important effects on projected yields and, thus,

on income and allotment values.

Source and Preparation of Data

This study is an analysis of allotment ownership

and yield patterns on cotton farms in Cochise, Maricopa,

Pima (including the one allotment planted in Santa Cruz

County), Pinal, and Yuma Counties for the 1970 crop y e a r .

These five counties contain well over ninety-five per cent

of Arizona's domestic allotment acreage, and account for an

even larger proportion of the state's production by virtue of their relatively high per-acrc yield levels.

g

Information for this study was taken from individual

farm record cards in each of the five county Agricultural

Stabilization and Conservation Service Offices. The cards

8 .

Arizona Crop and Livestock Reporting Service,

Arizona Agricultural Statistics, Bulletin S -5 v Phoenix,

Arizona, 1970, p p . 14-17.

contained names and addresses of operators, as well as a

63 list of all owners of land recorded on that card.

For a given farm, the upland allotment acreage

owned by the operator was clearly defined, as well as

allotment acreage leased from the state and from the

Indians.

If an allotment lessor had the same surname as

the operator, that portion of the tract was arbitrarily

classified as "family lease." The residual acreage,

remaining after the subtraction of allotment acres identi­

fied as owner-operated, leased from Indians, leased from

state, and leased from family was classified as "leased

from others." It should be noted that the definition of

"family lease" does not include those cases where allotment

was leased from relatives with different surnames.

This

tends to understate the "family lease" variable and to

overstate the "leased from others" category.

The farm record card also provided a projected yield

figure (hereafter called yield) for that tract which

represents a moving three-year average of the actual yield

for the tract.

In cases where it could be confirmed that the same

operator was farming allotments listed on more than one

farm record c a r d , the information on the cards was combined

and treated as a single farm.

A weighted projected yield

figure was calculated in these instances.

64

It was not possible to identify cases where a

single operator farmed allotments in more than one county.

In such cases, the farms were recorded as independent

farming operations.

In this manner, data from 1,370 individual farming

operations in the five counties was recorded on IBM cards.

The 1970 payment for each farm was calculated using the

1970 payment level of l6.8 cents/pound of projected yield

on the domestic allotment acreage.

The following formula

was used:

(Total Allotment Acres) x

(Projected Yield) x

(S.168) x (.6 5 ) = Payment.

Analytical Procedure and Results

Variables of interest in this study are the

following:

00

LF

Owner-operated allotment acres

Allotment acreage classified as leased from

LO

LS

LI

Y family members

Allotment acreage leased from private owners

other than family, or leased from others

Allotment acreage leased from the State of

Arizona

Allotment acreage leased from Indians

Projected yield in pounds.

Payment per farm was used solely as a classifica­

65 tion variable.

All acreage figures for the ownership and

leasing variables were expressed as proportions of the

total acreage in a given farm.

The first objective of this study was to determine

if the average yield varied significantly with changes in

the ownership composition of a farm's allotment acreage.

The independent variables were expressed as proportions of

a given farm's allotment acreage.

In other words, what is

the contribution of an added acre of allotment, be it leased

or bought, to the average yield of a farm?

Stepwise multiple-linear-regression analysis was

the technique used for investigating this question.

By

regressing the ownership variables on the projected yield

variable, coefficients could be obtained for the ownership

and lease variables which would indicate the magnitude and

arithmetic sign of a given variable's contribution to

average yield.

Because the sum of the explanatory variables for a

particular observation add to one, it is impossible to

include all variables in the regression analysis at the

same time.

The owner-operated variable was omitted in the

equation because of the high proportion of owner-operated

allotment acreage. By doing this, it was hoped that any

66

remaining yield variability would be identified with the

appropriate lease classification variable.

The general regression equations was the following:

Yield = a + b(LF) + c(LO) + d(LS) + f(LI).

Cochise County had no LI allotment; therefore, the

LI variable was omitted from the model for that county.

The constant and the mean of the dependent variable can be

directly interpreted as average per-acre yield in pounds.

The coefficients on the independent variables can be inter­

preted as deviations from the average yield of owner-

operated allotments expressed in the constant.

For example,

if the regression constant has a value of 1,000 and the

regression coefficient on the LS variable has a value of

-2 0 0 , this would indicate that the average acre of owner-

operated allotment in that category had a projected yield

of 1,000 pounds, while an acre of allotment on land leased

from the state had a yield of 800 pounds.

Coefficients were evaluated for significance with

the "t" test.

No attempt was made to run regression models

beyond a point where additional variables would be added at

very low levels of significance.

Models were run for each county except Pima County,

and a composite model was also run using only Maricopa and

Pinal County observations (928 of the 1,370 farms).

Pima

County was not analyzed in this way because of- the

relatively small number of observations (farms) in the

county and the unusually high proportion of LS allotment

67 operated by many of the producers in the county.

The results of the study are summarized in Table 7•

Blank spots in the tables are cases where variables did not enter the model before the regression was terminated.

2

Each of the models has a very low R v a l u e , which

indicates that the independent variables offer little

explanation of the variability in the dependent variable.

The constant values closely approximate the mean of the

dependent variable.

Yield on the owner-operated allotment

acreage is probably the primary factor involved in deter­ mining the county average yield.

In looking at the coefficients for the independent

variables, Indian lease allotment has a definite negative

effect on projected yield in Maricopa and Pinal Counties.

The LF and LS variables have a positive effect on average

yield in Cochise County.

Yuma County is characterized by

very high yield levels and intensive farming, which may

explain the strong positive effect of the LO variable on

the projected yield levels for farms in that county.

In the second phase of this analysis, interest was

focused on the variability of yield and allotment owner­

ship characteristics between various farm-size groups.

Specifically, the study sought to test the hypothesis that

the average yield and the average proportions of allotment

Table 7• Multiple Regression Analysis Equations for Yield as a Function of

Allotment Ownership Characteristics (to test if average yield varies

with allotment composition)

County

R 2

.030

Av. County

Yield = a

(constant)

+ b(LF)

7 8 0

765

+ c(L0) + d(LS) + f(LI)

Cochis e

302

(1.80)

Maricopa .030

lOOti

1033

— —

Pinal

.013

.042

1132 1136

Yuma

1431

1375

181

(3.10)

Maricopa

& Pinal

.029

1051

1077

-62

(3.11) t-value of coefficient in parentheses.

t-value for ten per cent level of

significance = 1.645; five per cent level of significance = 1 .9 6 .

(3-99)

-201

(2.09)

-257

(4.60)

<T\

CO

69 acreage owned or leased from various sources significantly varied between farm-size groups.

The same data that was used for the regression analysis was utilized for this phase of the study. Payment level was the criteria for classifying farms into size groups. The potential payment level figures of $55,000 and $20,000 per farm were logical levels for dividing farms into groups because of the congressional debate about these levels of payment limitation. Group 1 included all farms receiving 0 -$2 0 ,000 direct payments in 1970. Group 2 consisted of farms receiving $2 0 ,000-$55,000 payments, and

Group 3 included farms receiving more than $55,000.

One-way analysis of variance was the statistical technique chosen for comparing the group means of each set

9 of variables.

The Student-Newman-Kuhl statisticy was the

test applied to determine the significance of the difference

between the group means.

For the S-N-K test, group means

are ranked in order and the two extreme means initially

tested.

If the difference between the two extreme means

is significant, further tests can be made comparing means

which are closer together in the rankings.

This permits

the researcher to better identify any significant variation

among a series of group m eans. The S-N-K test adjusts for

9 *

R • G . D . Steel and J. H . Torrie, Principles and

Procedures of Statistics (New York: McGraw-Hill, i960) , p. 1 1 0 .

70

the number of means being tested across, as well as for. the

number of observations in each of the two groups being

compared.

Test criteria were the one per cent level of

significance and the five per cent level of significance.

Analysis of variance was run on each of the follow­ ing variables: Y, 00, L F , L 0 , L S , and LI. This procedure

was carried out for each of the five counties for which

data were available, as well as for the entire five counties

considered as a whole.

The results are summarized in

Tables 8 and 9«

In all cases, group means are ranked lowest to

highest.

The numbers in the parentheses behind the group

number for yield represent the number of farms, or observa­ tions, in each group (treatment).

Results of Analysis of Variance

Looking first at the yield variable, the five-

county composite indicates a significant difference in

average yield between every possible combination of farm-

size group at the one per cent level of significance.

The

ranking is an important point because it indicates a

significantly larger yield as farm size increases.

With

the exception of Pima County, the ranking persisted in all

counties. Pima County was the only county where no signif­ icant difference in yield existed between any of the

Y

Y

Table 8. ANOVA Results for Individual Counties

Variable

00/total

LF/total

LO/total

LS/total

Ll/total

00/total

LF/total

LO/tot al

1(396)

2(143)

3(64)

2

3

1

3

2

1

1

3

Treatment

Rank

Treatment

Mean

1(153)

2 (21)

3(2)

1

2

3

1

2

3

3

2

1

3 l

2

.019

.076

No LI in Cochise County

Cochise

725

1114

1455

.245

. 4l8

.618

.021

.029

.120

.342

.477

.635

Maricopa

1032

1123

1221

.466

.536

.566

.046

.068

.069

.338

.366

Significanc e

1 vs. 3 *

1 v s . 2 *

2 vs. 3

3 vs. 1

1 vs. 3

1

V S .

3

3 vs. 2

1

V S .

3*

1 v s . 2 *

2 v s . 3*

2 vs. 1 * *

3 vs. 1

1 vs. 2

72

Table 8.--Continued ANOVA Results for Individual Counties

Variable

LS/total

Ll/total

Y

00/total

LF/total

LO/total

LS/total

Ll/total

Y

00/total

Treatment

Rank

Treatment

Mean l

2

3

1

2

3

.006

.006

.013

.021

.023

.046

3 (1 0 )

1 (1 6 )

2 (2 1 )

3

2

1

Pima

907

955

980

.358

.608

.636

2

3

1

.024

.068

.125

l

2

3

1

2

3

.239

.310

.376

.000

•059

.198

No LI allotment in Pima County

Significanc e

1 vs. 3

1

V S .

3

3 vs. 2

3 vs. 1

2 vs. 1

1 vs. 3

1

V S .

3

1 (102)

2(129)

3(94)

2

1

3

Pinal

999

1191

1196

.647

.653

.661

1 vs. 3 *

1 vs. 2 *

2 vs. 3

2 vs. 3

73

Table 8.--Continued ANOVA Results for Individual Counties

Variable

LF/total

LO/total

LS/total

Ll/total

Y

00/total

LF/total

LO/total

LS/total

Treatment

1

2

3

3

1

2

2

3

1

3

1

2

Rank

1(134)

2(53)

3(32)

3

2

1

3

1

2

1

2

3

3

1

2

Treatment

Mean

.028

.070

.073

.223

.245

.256

.001

.033

.050

.015

.020

.023

Yuma

1354

1489

1657

.358

.495

• 541

.009

.026

.045

.274

.327

.426

.016

.020

.036

Significanc

3 vs. 2

2 vs. 1

1

V S .

3 * *

——

3 vs. 2

1 vs. 3 *

1 vs. 2 *

2 vs. 3

3 vs. 1

3 vs. 2

1

V S .

3

3 vs . 2

74

Table 8,— Continued ANOVA Results for Individual Counties

Variable

Ll/total

Treatment

Rank

2

1

3

Treatment

Mean

.097

.iko

.192

Significance

2 v s . 3 the one per cent level of significance.

* *Indicat es group means significantly different at

the five per cent level of significance, but not signifi­ cantly different at the one per cent level of significance•

75

Table

ANOVA Results for 5-County Composite

Variable

Y

00/total

LF/total

LO/total

LS/total

Ll/total

Treatment

Ranking

1

3

2

3

1

2

1

2

3

2

1

3

2

3

1

1 (801)

2 (367)

3(202)

Treatment

Means

971 lbs .

1191

1265

•539

.554

.584

.034

.054

.062

.315

.322

.338

.010

.030

.037

.031

.052

Comparison of Means

1 vs. 3*

1 vs. 2*

2 vs. 3*

2 v s . 1

——

3 vs. 2

1 vs. 2

----

1 vs. 3*

1 vs . 2*

2 vs. 3

2 vs. 3

* Indicates group means significantly different at

the one per cent level of significance.

76 farm-size groups.

This is probably a result of the smaller

number of farms (4?) spread rather evenly according to size.

It can be seen that no significant difference in

average yield existed between Group 2 and Group 3 farms in

Cochise, Yuma, and Pinal Counties at the one per cent

level.

This suggests that, on the average, no sacrifice

in yield would be expected to accompany allotment transfers

from Group 3 to Group 2 farms in these counties.

Group 1

farms, however, had significantly lower yields than Group 2

or 3 farms in all counties except P i m a .

This indicates

that, with the level of management and other things held

constant, yield losses would be expected with the transfer

of land from larger farms to farms currently receiving less

than $20,000 in payments.

The only other variable exhibiting any significant

difference at the one per cent level was the LS variable.

The Group 1 farms had a significantly smaller proportion

of LS allotment on the average than the Group 2 or Group 3

farms.

The group ranking for this variable in each county

bears this out, despite the absence of any significant

difference between group means at the county level for the

LS variable at the one per cent level.

The LS variable

was significant at the five per cent level in Pinal County.

The other variables exhibited no significant dif­

ference at the one per cent level between group means in

any of the analyses. At the five per cent level of

77 significance, the 00 variable exhibited a significant dif­

ference between group means for the Group 1 and Group 2

farms.

Looking at the group rankings, it can be seen that

smaller farms generally have a higher proportion of owner-

operated allotment, whereas larger farms have a larger pro­ portion of leased allotment.

This would suggest that larger

farms, especially those which earned more than $55,000 in

cotton payments in 1970, could adjust their allotment

acreage more easily than smaller operations by simply not

renewing their allotment leases.

This generalization

cannot be proven from a statistical standpoint, but is

merely a suggestion based upon the observed patterns in

allotment ownership.

Conclusion

The regression analysis indicates that allotment

ownership patterns have no consistent and significant

influence on yield levels.

The analysis of variance went

on to demonstrate that significant yield differences exist

between farms receiving less than $20,000 in government

payments and larger farms.

The analysis of variance also

showed that allotment ownership patterns did not vary

significantly among farm-size groups.

Factors other than

the allotment ownership patterns seem to influence yield

levels significantly as farm size increases.

78

Payment limitations will necessitate some r e ­ organization of allotments to avoid payment l o s s .

Allotment

transfer will probably begin with the transfer of relatively

mobile, leased allotment acreage.

Regardless of what type

of allotment is transferred, this study indicates a possible

decline in average yield for transfers from larger opera­ tions to smaller operations.

Transfer to farms currently

receiving less than $20,000 in payments would mean, all

else being equal, significant reductions in yield levels

and reduced income for Arizona upland cotton growers.

CHAPTER VI

CONCLUSIONS

As a federally regulated crop, upland cotton has

provided farmers with relatively stable income levels over

the past twenty-five years.

Throughout this period of time

continuing gains in productivity, declining markets, and

an inability to find some generally accepted means of

distributing program benefits have caused the government to

periodically revise upland cotton programs.

Arizona cotton producers, with some of the larger

farms and the highest per-acre yields in the nation, are

affected significantly by these changes. Substantial pro­

gram modifications in 1965, and again in 1970, raise some

serious questions concerning the future profitability of

upland cotton and potential changes in allotment values.

Historical allotment sales data indicate a downward

trend in Arizona allotment values following the 1965 program

legislation.

Furthermore, with the imposition of a $55,000

payment limitation, a substantial portion of the state's

allotment acreage could be excluded from receiving subsidy

payments, meaning loss of income and further declines in

allotment valuation.

The potential effects of a $55,000

limitation will be buffered by reorganization and transfer

79

8o

of allotment acreage for the purpose of preserving the

eligibility of the allotments to receive payments.

In some cases, reorganization will be a mere legal

manipulation, having no effect on the physical makeup of

the farm.

But with 202 of the 1,370 farms analyzed having

potential payments in excess of the limit, it is doubtful

that reorganization will be either complete or painless.

Some of the current domestic allotment acreage may become

ineligible for payments, which means a loss of income.

Some allotments will be transferred, probably at reduced

value, to smaller farms, which may well realize less per-

acre income as a consequence of lower per-acre yield levels.

This again means loss of income for the cotton producers.

It is difficult to speculate on future aggregate

yield and income levels for Arizona without full knowledge

of the factors which influence yield.

This study, however,

indicates that higher yields are associated with larger

upland cotton farms.

Allotment value is closely linked to income.

To the

extent that program legislation and yield variability affect

per-acre income, a corresponding change in the allotment

value can be expected.

The magnitude of the change will

depend on how the individual producer chooses to discount

the expected income flow.

This study suggests that any

movement toward smaller-scale cotton farming operations

8l

would be expected to result in reduced per-acre income

levels and lowered per-acre allotment values.

The changes that a $55,000 limitation would

necessitate are expected to have an adverse impact on

income and allotment value in the state. A $20,000 limita­

tion would probably cause substantial loss of payments in

Arizona.

The $20,000 limitation would affect 569 of the

1,370 farms studied, and well over one-half of the 1970

domestic allotment acreage in Arizona would be ineligible

for payments at this lower level if the limitation were

effectively enforced.

There is no question that a transfer

of allotment acreage to farms which earned less than $20,000

in payments in 1970 would mean significant reductions in

yield levels.

APPENDIX

ADDITIONAL FARM SALES

INFORMATION

82

Table 1 0 . Classification of Water Districts

Classification

Good

Poor

District

Salt River Project

Arlington Canal Company

Roosevelt Irrigation District

Buckeye Irrigation Company

Buckeye Water Conservation and Drainage

District

Roosevelt Water Conservation District

Saint John's Irrigation District

McMicken Irrigation District

Maricopa County Municipal Water Conserva­

tion District

Private Well

83

Table 11. Farm Sales Data Used for Allotment Value Regression Analysis

Sale

#

Total

Price

1 900,000

2 48,000

3

25 ,000

4 448,000

5

6

53,000

6 6 ,ooo

7

8

80,000

76,000

9

511,000

10 128,000

11

12

90,000

36,300

13

14

59,400

350,000

15

55,250

16 100 ,000

17

18

200 ,000

40,000

72,000

19

20

21

22

4 o ,000

23,500

65,000

23

112,500

24 160,000

25

145,000

26 77,500

27

28

208,000

46,142

Dis -

tanc e

7

63

99

22

3

13

17

12

13

13

4

7

8

17

17

18

24

32

11

3

15

15

13

4o

11

16

5

3

Upland

Allotment

Acres

29

43

9

13

54

12

16

34

9

15

13

10

122

22

26

145

15

45

23

26

182

19

50

61

53

23

45 l4

Net

Crop

Acres

45

27

55

23

78

29

22

56

4l

4l

24

501

38

46

282

23

71

46

39

434

17

19

96

98

197

54

57

23

Non-

Crop

Acres Sold

17

20

1961

1961

128

1961

13

1961

2 1962

4 1962

1 1962

16 1962

24 1962

6

1962

2 1962

1 1962

4 1962

8 1962

4

1963

2

1963

10

1963

0

1963

4

1963

3 1963

7 1963

2

1963

4

1963

1

1963

30

1963

1

1963

2

1963

2

1963

Positive

Improve­ ments

Negative

Improve­ ments

Water

Situa­ tion

1

1

1

1

1

1

1

1

1

SRP

Well

Well

Well

SRP

SRP

RID

Well

RID

RID

RID

RID

RID

Well

Well

SRP

SRP

Well

RID

Well

Well

Well

SRP

SRP

SRP

SRP

SRP

SRP

00

Table 1 1 •— Continued

Sale

#

Total

Price

29

30

31

32

10,000

148,000

190,755

130,980

33 112,459

3'i

126,000

33

36

50,000

75,000

37 l44 ,000

38

28,000

39 l4o,ooo

'10 224,000

'll 448,000

42 334,260

43

44

86,050

167,500

43

46

47

56,000

48

102,900

49

50

102,000

35,000

4 o ,000

181,000

51

209,500

52a i4o ,000

53a

312,000

?4a

135,000

55a

240,000

56

30,000

Dis­ tance

3

16

12

8

11

14

17

19

3

18

14

18

22

23

24

17

13

7

19 l4

11

21

10

2

1

2

1

2

Upland

Allotment

Acres

67

105

210

62

44

50

51

6

8

30

12

125

130

3.6

7

48

84

50

46

22

18

20

49

12

60

13.5

50

9.7

Net

Crop

Acres

99

65

39

72

89

28

175

160

39

212

38 i4o

20

2

100

148

87

80

52

21

18

111

28

133

215

430

160

68

Non-

Crop

Acres

1 1964

2 1964

4

1964

11 1964

7

1964

1 1964

1 1964

2

1964

0

1965

0

1965

0

1965

0

1965

0

1965

0

1965

73 1965

1

I.965

54

1966

22 1966

0 1966

53

1967

0

1967

4o

1967

40

1967

1

1961

48

1963

2

1964

20

1965

0

1966

Positive

Year Improve-

Sold ments

Negative

Improve­ ments

1

1

1

1

1

1

1

1

1

1

1

1

1

1

Water

Situa­ tion

Well

RID

RID

RID

RID

Well

Well

Well

RID

RID

RID

Well

Well

Well •

Well

RID

Well

SRP

RID

Well

RID

Well

Well

Well

SRP

SRP

SRP

SRP

aSales which were omitted from final analysis because of nearness to

metropolitan Phoenix city limits (i.e., less than 2•5 miles).

oo

U l

REFERENCES CITED

Agricultural Stabilization and Conservation Service.

Arizona Annual Report. Phoenix. Arizona. 1964, p . 32; 1965, p • 34; 1966, p. 2 7 ; and 1967, p p • 27-

28.

Arizona Crop and Livestock Reporting Service. Arizona

Agricultural Statistics. 1970«

Bulletin S -5•

Phoenix, Arizona, March 1970, p p . 14-17•

Barfels, Howard R.

"The Value of Cotton Allotments in

Arizona," unpublished Master's thesis, The

University of Arizona, Tucson, Arizona, 1967, p . 2 .

Firch, Robert S ., and Jeffery J . W eber.

"Upland Cotton

Allotments in Arizona and Potential Effects of

Government Payment Limitations Based Upon 1970

Allotments," Agricultural Economics Department,

The University of Arizona. Tucson, Arizona,

October 1 0 , 1970, Table 4, p . 6 .

Heady, E . 0. Economics of Agricultural Production and

U s e . Prentice-Hall, New York, 1952, p p • 38b, 396.

Shane, Richard C.

"Risk and Diversification in Arizona

Crop Farm Production." unpublished Master's

thesis, The University of Arizona, Tucson,

Arizona, 1971, p • 74.

Steel, R. G . D ., and J . H . Torrie. Principles and

Procedures of Statistics. McGraw-Hill, New Y o r k , i9 6 0 , p. 1 1 0 .

United States Department of Agriculture, Economic Research

Service. Cotton Production and Farm Income Esti­

mates Under Selected Alternative Farm Programs.

Agricultural Economics Report 212, Strickland,

P.L., et a l ., Washington, D. C ., 1971, p • 7*

United States Department of Agriculture, Economic Research

Service. Cotton Situation.

Washington, D . C .,

various issues .

86

87

United States Department of Agriculture, Economic Research

Service.

Statistics on Cotton and Other Related

Data.

Statistical Bulletin 329, 19&3, and supple-

m e n t s , Washington, D . C.

Whitaker, Rodney. "The Cotton Surplus Problem," Cotton

and Other Fiber Problems and Policies in the United

Statos.

National Advisory Commission on Food and

Fiber, Washington, D . C ., 1967, p p • 8l-l6 6 .

42

6

7 05

5

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