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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