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Economic externalities in the agricultural use of pesticides and an evaluation of alternative policies

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Title:
Economic externalities in the agricultural use of pesticides and an evaluation of alternative policies
Creator:
Edwards, William Franklin, 1938-
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English
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xi, 210 leaves : maps ; 28 cm.

Subjects

Subjects / Keywords:
Agriculture ( jstor )
Beans ( jstor )
Chlorinated hydrocarbons ( jstor )
Counties ( jstor )
Crops ( jstor )
Pesticides ( jstor )
Phosphates ( jstor )
Potatoes ( jstor )
Radiocarbon ( jstor )
Tomatoes ( jstor )
Dissertations, Academic -- Economics -- UF
Economics thesis Ph. D
Pesticides -- Environmental aspects ( lcsh )
City of Miami ( local )
Genre:
bibliography ( marcgt )
theses ( marcgt )
non-fiction ( marcgt )

Notes

Thesis:
Thesis--University of Florida.
Bibliography:
Bibliography: leaves 206-210.
Additional Physical Form:
Also available online.
General Note:
Manuscript copy.
General Note:
Vita.
Statement of Responsibility:
by William Franklin Edwards

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University of Florida
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University of Florida
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Copyright [name of dissertation author]. Permission granted to the University of Florida to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
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ECONOMIC EXTERNALITIES IN THE AGRICULTURAL

USE OF PESTICIDES AND AN EVALUATION

OF ALTERNATIVE POLICIES











By
WILLIAM FRANKLIN EDWARDS













A DISSERTATION PRESENTED TO THE GRADUATE COUNCIL OF
THE UNIVERSITY OF FLORIDA
IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE
DEGREE OF DOCTOR OF PHILOSOPHY














UNIVERSITY OF FLORIDA
1969




































Copyright by
William Franklin Edwards
1969














ACKNOWLEDGMENTS


The author is indebted to the many persons who have both

encouraged and contributed to his graduate training.

Appreciation is first expressed to the Institute of Food and

Agricultural Sciences and particularly to Dr. K. R. Tefertiller,

Chairman of the Department of Agricultural Economics, for the oppor-

tunity to participate on this research grant.

The financial support of Resources for the Future, Incorporated,

Washington, D.C. is gratefully acknowledged. A special word of thanks

must go to Mr. Blair T. Bower, Dr. Michael F. Brewer, and Dr. Allen V.

Kneese of that organization and to Dr. J. C. Headley of the University

of Missouri for their willingness to read and comment on versions of

this paper. At no time, however, did they attempt to influence in any

way the outcome of the research findings.

My greatest debt of gratitude is owed to Dr. Max R. Langham,

Chairman of the Supervisory Committee, for his continuous guidance and

encouragement during the graduate program, and for his indefatigable

efforts throughout this research study.

Special appreciation is also extended to Dr. Elmo L. Jackson and

Dr. John B. McFerrin, members of the Supervisory Committee, for pro-

viding helpful suggestions.

The author would also like to thank the staffs of the Dade

County Agricultural Agents Office and the Hlomestced Experiment Station

for assistance in gathering data on the area studied. Mr. Richard 1M.

Hunt, Assistant Map'kcting Agent, was especially helpful in establishing

iii












contacts with growers and planning the data gathering phase of the

research. The consulting help of Mr. Fred A. Johnson, an entomologist

in Dade County, is also gratefully acknowledged.

To the many growers in Dade County who supplied information for

this project, I would also like to express my appreciation.

Capable secretarial and clerical assistance was provided by

Mrs. Audra White throughout the project, and the author extends to her

his sincere appreciation for her conscientious service.

The services of the University Computing Center are herein

recognized.

Finally, I would like to thank my wife, Emmy, and our children,

Russell and Carroll, for their patience during this somewhat trying

period. To them, and to my parents, I dedicate this dissertation.














TABLE OF CONTENTS

Page

ACKNtO1 I DGMENTS ............................................... iii

LIST OF TABLES............................ .. ...................... viii

LIST OF FIGURES............................................... .xi

CHAPTER I

INTRODUCTION.............................................. 1

The Problem.......................................... 1
Background of the Problem............................. 2

CHAPTER II

AREA STUDIED............... .............................. ... 7

Choice of an Area......................... ... ............ 7
Agriculture of Dade County............................ 8

CHAPTER III

THE MODEL.......... .............................. ......... .. 19

CHAPTER IV

ESTIMATES OF MODEL COEFFICIENTS.............................. 30

Objective Function....... .......... .................... 30
The Demand Model............................... .30
The Supply Model............................... .34
Shifting the Supply Functions.................. 41
The Externality Functions.......................... 51
Constraints on the Objective Function............... 54
Coefficients of Variables in the Constraints... 55
The Constraint Vector .......................... 56

CHAPTER V

APPLICATION OF AGRICULTURAL PESTICIDES IN DADE COUNTY.... 58

Technology of Pesticide Application in Dade County.. 58
Estimated Quantities of Pesticides Used in Dade
County.......................... ... ................... 60











TABLE OF CONTENTS--Continued.

Page

CHAPTER VI

EXTERNALITIES IN DADE COUNTY................................. 66

The Grower Interview................................... 66
Insurance Claims.................................... .73
Veterinarians....................................... .89
Biologists.......................................... .90
Community Studies on Pesticides......................... 93
Environmental Monitoring................................ 94
Concluding Remarks on Externalities in Dade
County............................................. 100

CHAPTER VII

ANALYTICAL RESULTS, IMPLICATIONS AND F.iC.C 1:i',TIONS..... 104

Analytical Results .................................. 104
Implications........................................ .116
For Policy Makers.............................. .116
For Economic Theory and Methodology............ 117
For Future Research..... ........................ 117

APPENDIX A

NET PROFIT PER ACRE OF SELECTED CROPS IN DADE COUNTY ..... 119

APPENDIX B

ESTIMATES OF PESTICIDE USAGE COMPILED BY A ,.'(;L:T
BIOLOGIST AT THE EVERGLADES NATIONAL PARK.............. 124

APPENDIX C

COTWiOi, CHEMICAL, AND/OR TRADE NAME OF PESTICIDES
IDENTIFIED IN DADE COUNTY .............................. 131

APPENDIX D

ESTiJ''AI.IL) QUANTITIES OF AGRICULTURAL PESTICIDES
USED IN DADE ('i I'U ................................... 139

APPENDIX E

PESTICIDE QUESTIO'N".%JI: !' : I l IN DADE COUNTY .............. 184










TABLE OF CONTENTS--Concluded.

Pag,

APPENDIX F

ENVIROIMCMErTAL MONITORING PROGRAMS............................ 191

Residues in Food and Feed.......................... 193
Pesticides in People.............................. 194
Residues in Fish, Wildlife, and Estuaries...... 195
Pesticides in Water ............................. 196
Pesticides in Soil ............................. 197
Pesticides in Air .............................. 200

APPENDIX G

MATHEMATICAL STATEMENTS OF POLICIES 2A, 2B, AND 2C....... 202

Policy 2A...................................... .203
Policy 2B...................................... .204
Policy 2C....................................... 205

BIBLIOGRAPHY...... ............................................. ... 206













LIST OF. TABLES

Table Page

1 Vegetable, ornamental horticulture, and fruit production,
by commodities, in Dade County, Florida, 1966-67.......... 11

2 Production of livestock and livestock products in Dade
County, Florida, 1967..................................... 12

3 Florida and Dade County population changes for 1950 and 1960 16

4 Empirically estimated relations from which demand functions
were derived.............................................. 35

5 Demand functions used in the model........................... 37

6 Empirically estimated relations from which supply functions
were derived.............................................. 39

7 Recommended insect control measures for tomatoes............ 42

8 Recommended insect control measures for potatoes............ 43

9 Recommended insect control measures for beans............... 44

10 Recommended insect control measures for corn................ 45

11 Estimated average costs of chlorinated hydrocarbons and
organic phosphates used on crops in Dade County, 1966-67.. 48

12 Estimates of average total production costs in Dade County,
1966-67, by crop and pesticide policy...................... 50

13 Supply functions used in the model........................... 52

14 Flexibility constraints used for the empirical model........ 57

15 A summary of grower responses concerning human sickness
from pesticides............ .............................. 68

16 A summary of grower responses concerning damage from
pesticide drift........................................... 74

17 Workmen's compensation claims, State of Florida; work
injuries, days of disability, and cost by industry for
disabling and non-disabling work injuries, 1962, 1963,
1966, and 1967............................................ 79


viii













LIST OF TABLES--Continued.


Table


18 A list of the categories constituting Agency 10, "Poisons
and Infectious Agents .....................................

19 Workmen's compensation claims, State of Florida; work
injuries, days of disability, and cost by agency, for
disabling work injuries, 1962, 1963, 1966, and 1967.......

20 Dollar costs of disabling workmen's compensation claims for
Dade County, Florida, 1966, by kind of payment and agent..

21 Dollar costs of disabling workmen's compensation claims for
Dade County, Florida, 1967, by kind of payment and agent..

22 A summary of data gathered from veterinarians in Dade County

23 A summary of data gathered from the Communities Studies
Program on Pesticides in Miami ............................

24 A summary of the externalities incorporated in the
empirical model, Dade County, 1967 ........................


102


25 Model solution for Policy 1 ................................. 107


26 Model solution for Policy 2A ................................

27 Model solution for Policy 2B ................................

28 Model solution for Policy 2C ................................

29 A comparison of model solutions among policies for z and
g2 coefficients of 0 and -.0301,respectively ..............

30 Net profit per acre for a sample of tomato growers in
Dade County, 1960-61 through 1966-67 ......................

31 Net profit per acre for a sample of potato growers in
Dade County, 1960-61 through 1966-67 ......................

32 Net profit per acre for a sample of pole bean growers in
Dade County, 1960-61 through 1966-67 ......................


108

109

110


113


120


121


122


33 Net profit per acre for a sample of squash growers in
Dade County,1960-61 through 1966-67 ....................... 123

34 Estimates made by Richard Klukas of the quantities of
insecticides used on various crops in Dade County, 1966-67 125


Page










LIST OF TABLES'--Concluded.


Table Page

35 Estimates made by Richard Klukas of the quantities of
fungicides used on various crops in Dade County, 1966-67.. 128

36 Dade County growing seasons and 1967 crop acreages used for
Klukas projections........................................ 130

37 Common names, trade names, and/or chemical names of
pesticides observed in Dade County, Florida, 1966-67...... 132

38 Estimated quantities per acre of certain pesticide
categories used by farmers in Dade County, 1966-67 crop
year, by crop ............................................. 140

39 Estimated quantities per acre of certain pesticide
categories used by farmers in Dade County, 1966-67 crop
year, by crop and month ................................... 142

40 Estimated pesticide usage in Dade County, Florida, by
crop and pesticide, 1966-67 crop year..................... 155

41 Estimated pesticide usage by pesticide, crop, and month
in Dade County, Florida, 1966-67.......................... 163

42 Sample data on pesticide usage, by pesticide and crop,
for growers who used the pesticide, Dade County,
Florida, 1966-67.......................................... 176













LIST OF FIGURES


Figure Page

1 Locational map for Dade County, Florida................... 9

2 Soils of Dade County, Florida............................. 13

3 Population trends in Dade County, Florida................. 17

4 A linear iso-product function for chlorinated hydrocarbons
and organic phosphates.................................. 46

5 A hyperbolic iso-product function for chlorinated
hydrocarbons and organic phosphates..................... 47

6 Point estimate of externalities arising from the
agricultural usage of organic phosphates................ 53

7 A schematic diagram showing data needs in the area of
environmental monitoring................................ 98

8 Hypothesized relations between "welfare," the state of the
arts, and the usage of chlorinated hydrocarbons......... 114

9 U.S. Geological Survey sampling sites for pesticide
residues in aquatic communities of South Florida........ 198

















CHAPTER I


INTRODUCTION


The Problem


This research effort was concerned with the economic external-

ities created by the use of agricultural pesticides and the implications

of such externalities on social welfare. The working hypothesis was

that the externalities could be measured, and the objective was to

measure them for some specified region and time period and to

incorporate them into a model which was developed to aid in the choice

of a pesticide usage policy for the area. The choice criterion was a

measure of social welfare.

The model employed the concept of consumers'-producers' surplus

as a measure of "welfare." For each of two specified policies and

three variations under one of the policies the model maximized this

measure of welfare over production alternatives available to the region.

In each case the objective function explicitly recognized measurable

externalities not accounted for by consumers'-producers' surplus. The

maximization was restrained by flexibility constraints derived from


1This research project is an outgrowth of some earlier work
done by J. C. Headley and J. N. Lewis (23) for Resources for the Future,
Inc.

2YlirtOUl'i. 't the paper this term is to be read as "consumers'
plus producers' surplus."











historical cropping patterns. Finally the policies were ranked by their

maxima.

It was demonstrated that the model could conceptually accommo-

date environmental constraints connecting the levels of pesticide usage

with levels of pesticides monitored in various elements of the environ-

ment. Empirical data to specify such constraints were not available.


Background of the Problem


Environmental modification is not new. In the struggle for

survival, it has been practiced through the ages by most of the

creatures of the Earth. The fox modifies his environment when he digs

a den. Man modifies his environment when he clears a field for culti-

vation, but such environmental modification often has undesirable side

effects. When the farmer clears his land for cultivation, he raises the

probability of soil erosion. The extent of the modification and the

extent of the side effects can become important issues, and herein lie

many of the social problems we face today, one of which concerns the

advantages and disadvantages of using pesticides.

Several factors have played a part in increasing the emphasis

on the pesticide problem. First, world food needs are expanding rapidly.

The world population, estimated at about 500 million in 1650, rose to

1 billion by 1850, and to 1.6 billion by 1900 A.D. Extrapolations of

present growth rates indicate a population of 6.4 billion by 2000 A.D.

(10, pp. 63-71). Present-day nutritionists find that more than half of

the world's inhabitants have a suboptimal diet of less.than 2,200

calories per day (42, pp. 73-80). It is quite possible that thc food

deficit will worsen in future years; the significance of the problem










will depend upon our success at population control and our ability to
3
increase agricultural production.

Second, there is the lurking possibility that the long-run

hazards of persistent pesticides to human health and the Earth's eco-

logical relationships may be more serious than had been imagined

previously. We really know very little about these long-run effects,

but some individuals feel that they may be serious (49, pp. 485, 526,

585, and 684).

Third, it is possible that a nation's values and preferences

change as its standard of living rises. If a large segment of our

population were starving, it is doubtful that the external or side

effects from using pesticides would be given very serious consideration.

It would be worth poisoning a few people if one more than "a few" could

be saved from starving through the use of pesticides. The "pesticide

problem" is a sub-case of the more general "environmental pollution

problem" and a "clean environment" is a luxury public good not demanded

until some minimum quantity of those goods more necessary for sustaining

life is obtained.

Being a public good, an effective demand is not expressed in the

market place. Consequently there are dangers of "social imbalance."

This "social imbalance" was described by J. K. Galbraith more than a

decade ago:

The family which takes its mauve and cerise, air-conditioned,
power-steered, and power-braked automobile out for a tour passes
through cities that are badly paved, made hideous by litter,
blighted buildings, billboards, and posts for wires that
should have long since been put underground. They pass into a


3For two interesting accounts of the world food situation see
Borgstrom (1) and Gunther (22).











countryside that has been rendered largely invisible by commer-
cial art. . They picnic on exquisitely packaged food from a
portable icebox by a polluted stream and go on to spend the night
at a park which is a menace to public health and morals. Just
before dozing off on an air mattress, beneath a nylon tent, amid
the stench of decaying refuse, they may reflect vaguely on the
curious unevenness of their blessings (20, p. 253).

Finally,Rachel Carson's book, Silent Spring (8), did a great

deal to increase public interest in the pesticide issue. Largely as a

result of Miss Carson's book and the interest it created, the United

States Senate, from May, 1963, to July, 1964, conducted extensive

hearings to study the problems created by the increasing use of pesti-

cides and tried to assess their potential hazards (49). The general

conclusion of the Senate investigation was that the benefits from using

pesticides still far outweigh the potential hazards to the environment,

but it was strongly emphasized that additional research is badly needed,

particularly in the area of long-run hazards of pesticide use, where we

have virtually no knowledge.

Stimulated by the Senate Hearings and a growing public concern

about environmental pollution, economists became more interested in the

problem. It is within the purview of the physical sciences to establish

the link between pesticides and various elements of the environment, but

when this is done, the subsequent question, "So what?", still remains to

be answered, and this is left to the social and political sciences. The

benefits must be weighed against the costs; or to put it another way,

relative values must be assessed, and values are the economist's stock

in trade.

The term "pesticides" is commonly used to refer to the whole

family of agricultural chemicals "used to control insects, mites, ticks,

fungi, nematodes, rodents, pest birds, predatory animals, rough fish,










plant diseases, weeds, and also to those which act as regulators of

plant growth, as defoliants, and as desiccants" (49, p. 41). Major

categories have been described as follows (49, p. 41):

1. The chlorinated hydrocarbons containing carbon, hydrogen, and
chlorine are the pesticides used in greatest tonnage. The
most familiar are DDT, dieldrin, aldrin, endrin, toxaphene,
lindane, methoxychlor, chlordane, and heptachlor. Among
those used extensively as herbicides are 2,4-D and 2,4,5-T
for control of broad-leaved weeds in lawns, pastures, cereal
crops, and brush growth along highways and fences.

2. The organic phosphorus compounds, composed of phosphorus,
oxygen, carbon, and hydrogen, are used principally as insec-
ticides and miticides. Parathion, malathion, phosdrin, and
tetraethyl pyrophosphate (TEPP) are examples.

3. Other organic compounds include the carbamates, dinitrophenols,
organic sulfur compounds, organic mercurials, and such natural
products as rotenone, pyrethrum, nicotine, strychnine, and the
anticoagulant rodent poisons.

4. Inorganic substances with a long history of use include copper
sulfate, arsenate of lead, calcium arsenate, compounds of
chlorine and fluorine, zinc phosphide, thallium sulfate, and
sodium fluoroacetate.

Although there is some controversy regarding most of these

groups, the main thrust of the dispute concerns the chlorinated hydro-
4
carbons and the organic phosphates.

The chlorinated hydrocarbons are generally quite persistent in

the environment, and their residues therefore tend to build up in many

elements of the environment, such as soil, water, and plant and animal

tissue. The organic phosphates, on the other hand, decompose very

rapidly into non-harmful substances and thus seldom persist in the

environment for long periods of time. Their detrimental effects tend

to be of an acute rather than chronic nature and have caused many fatal


4Common names, trade names, and chemical names of all pesticides
identified in Dade County are presented in Appendix C ( p. 131).






6


and non-fatal poisonings in man. Education in handling these highly

toxic chemicals can be effective in reducing acute damage.

Current social and legislative trends seem to favor the substi-

tution of non-persistent pesticides for the persistent ones (54, p. 20).

Therefore, alternative policies evaluated in this research project are

oriented toward this objective.

















CHAPTER II


AREA STUDIED


Choice of an Area


The choice of an area for study was initially limited to the

State of Florida because of the location of the researchers. However,

the State as a whole was much too heterogeneous for effective analysis

so a smaller geographic region within the State was chosen. It was

felt that the selected area should be a relatively isolated, heavy

user of pesticides. It was also felt that it would be desirable to have

an area where other scientists had studied the fate and dispersion of

pesticides as well as the effects of pesticides on various elements of

the environment such as wildlife, human health, and domestic animals.

If possible, it should also be an area which would offer the maximum

probability of observing externalities, and it was believed that an area

having a significant interaction of pesticide users with other people

would fulfill this requirement.

The focus rather quickly narrowed to three areas--a small

isolated locale in Northeast Florida (Hastings, Florida) heavily commit-

ted to the production of potatoes and cabbage; an area in Central

Florida (Polk County) which produces primarily citrus; and the winter

vegetable and tropical fruits area of Dade County. The latter area was

the final choice because it had what was believed to be the best work-

able balance of needed characteristics. Since its agriculture is highly










dependent upon pesticides, Dade County uses great quantities of them in

the production of its crops. It is relatively isolated (by the Ever-

glades Swamp, the metropolitan area of Miami, and the Atlantic Ocean),

and there are ample opportunities for urban-rural interaction as the

growing city of Miami encroaches on the agricultural interests in South

Dade County. In addition,several scientists are currently working on

problems related to the use of pesticides in that area.


Agriculture of Dade County


When Dade County was created in 1836, it comprised most of

Southeast Florida. Its first county seat was located at Juno, north of

what is now West Palm Beach. In these early days the industry of the

area consisted of starch production from the native plants, coontie or

zamia. Following the "great freeze" of 1894 (which nearly wiped out the

citrus groves in Central and North Florida) Henry Flagler, seeing Dade

County had escaped all damage, extended the railroad all the way to

Miami and built a hotel there. This was to mark the beginning of the

great development of Dade County.

Today, the county is situated in the southeastern part of the

Florida peninsula, as indicated by Figure 1. It extends about 55 miles

from north to south and 47 miles from east to west. The total area is

2,054 square miles, or 1,314,560 acres. Miami, the county seat, and

other towns are located in the eastern portion of the county near the

coast.

The surface relief is nearly level, and much of the county lies


1I am indebted to the Dade County Agricultural Agents Office
for most of the information contained in this section.



























































DADE
Cu ii;'1 ,


Figure 1.--Locational map for Dade County, Florida.










less than 13 feet above sea level. But a few low ridges in the eastern

portion are slightly higher than 20 feet in elevation. In the western

portion the surface water drains slowly through the peat marshes in a

southerly and southwesterly direction to the Gulf of Mexico. In the

eastern portion the indistinct drainageways of the peat marshes join

many of the canals and ditches which extend through the low sandy and

rocky ridges to the Atlantic Ocean. All major canals in the northeast

and southern part have control-dams for regulating water levels, but

very few pumps are used for controlling water levels in fields except

on several large farm operations. Most of the southwestern part of the

county is included in the Everglades National Park.

Dade County enjoys tropical climatic conditions not found else-

where on the U.S. mainland. It is 500 miles further south than Los

Angeles, but it is not subject to the heat of the tropics due to the

nearness of the Atlantic Ocean, the Gulf of Mexico, and the prevailing

tradewinds. The mean annual temperature at Miami is 75.10 F., and the

mean annual rainfall is 57.8 inches. Most of the rainfall comes during

the summer months. Occasionally disturbances of hurricane type move

across the county during the months from August to November. Killing

frosts may occur from the middle of December to March, but many winters,

sometimes several in succession, pass without damage from freezing

temperatures. Livestock graze the pastures throughout the year and need

little shelter from the weather. Taken together, the climatic con-

ditions of Dade County make it one of the most unusual and productive

farming areas of the nation.

September through March are the months of primary agricultural

activity. Winter vegetables, ornamental horticulture, and tropical










fruit are the three largest activities, representing 44.3, 11.5, and

4.1 million dollars,respectively,as indicated in Table 1. Minor


Table l.--Vegetable, ornamental horticulture, and fruit production, by
commodities, in Dade County, Florida, 1966-67.a



Crop Acres Production Value

.......... Thousands..........
Vegetables
Tomatoes
Fresh 19,000 5,806 crt. $23,050
Processed 1,514 crt. 961
Potatoes 7,660 1,564 cwt. 5,474
Snap beans
Bush 1,470 181 bu. 699
Pole 5,830 1,647 bu. 6,357
Squash 3,080 457 bu. 1,942
Strawberries 670 657 flats 2,214
Corn 1,650 252 crt. 622
Cucumbers 1,290 204 bu. 787
Cabbage 470 189 crt. 312
Okra 400 36 bu. 180
Peas 650 69 bu. 361
Cuban vegetables 1,360 1,102
Other vegetables 390 272
Total vegetables 43,920 $44,333

Ornamental horticulture $11,530
Fruit
Avocados 5,235 220 bu. $ 1,232
Limes 3,585 640 bu. 1,965
Mangos c 1,520 70 bu. 490
Specialty fruit 440 210
Other citrus 365 247
Total fruit 11,145 $ 4,144

aSource of data: Dade County Agricultural Agents Office,
Homestead, Florida.
bIncludes lima beans, cantaloupes, eggplant, escarole, chicory,
lettuce, green peppers, and green onions.
c
Includes lychee, barbados cherries, guava, papyas, and
sapodillas.
dIncludes oranges, grapefruit, tangerines, tangelos, and lemons.










agricultural activities are poultry, dairy, and livestock as shown

in Table 2.


Table 2.--Production of livestock and livestock products in Dade
County, Florida, 1967.a



Value of
Item Unit Quantity production


Dairy (5 farms) $ 1,669,000
Milk Gallons 2,507,112 1,503,000
Cull animals Head 166,000

Poultry (25 farms) $ 3,524,000
Eggs Dozen 5,220,000 2,034,000
Cull birds Head 150,000 40,000
Hatchery chicks Head 10,040,000 1,450,000

Other (including beef cattle,
horses, etc.) Head 5,032,000 $ 796,000

Other farm enterprises 500,000


aSource of data: County Agricultural Agents Office, Homestead,
Florida.


Most of the fruit and vegetable crops in Dade County are grown

on two soil types, marl and rockland, illustrated in Figure 2.

Marl and its various phases comprise a total of about 317,000

acres in Dade County. Surface soil is a light brown or brownish gray

marl of silt loam texture. The subsoil is lighter colored, and the

underlying material is Miami oolite. Native vegetation on these soils

consists of a variable mixture of sawgrass, myrtle, bay, and cypress.

Elevation above sea level of most areas of marl that are suitable for

cultivation ranges from about 8 feet near the rock ridge to 1 to 2 feet

near the shore lines. Soil depth varies from 6 to 60 inches. Marl has






































a
Figure 2.--Soils of Dade County, Florida.a
aReprinted with the permission of the Dade County Agricultural
Agents Office.


poor internal drainage and becomes waterlogged or even flooded during

the rainy season. Occasionally wet conditions occur during the normally

dry winter season; however, this soil may, at times, become too dry

during the winter. Both drainage and irrigation systems are used

extensively for crop production.

Marl is alkaline in reaction,and the application of certain of

the minor elements is imperative for successful crop production. Prior

to discovery of the low availability of manganese, this unknown defi-

ciency limited crop production in the area.

Marl soils are utilized for production of winter vegetables,

including Irish potatoes, tomatoes, bush beans, pole beans, squash,

cucumbers, corn, strawberries, cabbage, beets, lettuce, escarole,










peppers, and eggplant. The total acreage of marl soils cultivated in

these vegetable crops has averaged 18,000 to 20,000 acres annually for

the past 15 years.

There are approximately 279,000 acres of rockland in Dade

County. The rockland area extends roughly from U.S. Highway 1 west to

the Everglades Park boundary and from the Tamiami Trail on the north to

Florida City and Florida Highway 27 leading to the Everglades Park.

The rockland is an oolitic limestone formation with many

solution holes and is relatively soft until it is exposed to the air.

The solution holes below the surface act as storage places for water.

Near the surface these cavities are filled with loam and loamy fine sand

having varying shades of red coloring.

Prior to 1938 farmnning of the rockland was confined to the larger

solution holes and to pockets broken up by dynamiting. Heavy tractors

and scarifying equipment are now used to chisel or "plow" this soil so

that all of the area can be farmed. Vegetable farms are plowed 6 to 8

inches deep over the entire field. Orchards are planted in trenches

that have been cut and crushed to a depth of 20 to 24 inches. The

trenches are back filled after cutting, and young trees are set in the

crushed rock trench that is formed.

Irrigation wells are drilled into the oolitic rock 15 to 40 feet

deep and usually about 300 feet apart. No casing is required. One-half

to an inch of water is applied by overhead irrigation each week. Most

farmers use a single irrigation head on a portable pump, which covers

roughly two acres.

Only vegetables that produce marketable products above the

ground such as tomatoes, corn, squash, beans, cantaloupe, okra, southern










peas, and leaf crops can be grown on rockland. Tree crops in Dade

County, including limes, mangos, and avocados, are grown exclusively on

rockland soils. During the last 15 years, an average of 28,000 acres of

vegetable crops has been produced on rockland soils, and an average of

20,000 acres has been devoted to tree crops.

Both marl and rockland soils are underlaid by Miami oolitic

limestone, which is perforated with numerous vertical solution holes.

The oolite underlies the Atlantic Coastal Ridge south of Boca Raton in

Palm Beach County to an average depth of about 20 feet. Deposits are

thicker near the coast and then fan out to a feather edge in western

Dade and Monroe Counties. It overlies the Tamiami formation, another

porous limestone. These two limestone formations make up the Biscayne

Aquifer, the main source of water for Dade County.

Of the total land area in Dade County (1,314,560 acres) about

117,000 acres or 8.9 percent was apportioned to farms in 1964; in 1959

129,000 acres or 9.8 percent was in farms. Although some growers stated

that suitable farm land is becoming difficult to obtain, it is more

likely that other constraints, such as markets and capital and labor

requirements, are more restrictive than the physical quantity of land.

Metropolitan Dade County is one of the youngest and fastest

growing major metropolitan areas in the United States. Between 1890 and

1962, a span of 72 years, the population increased from 861 to more than

1 million persons. Between 1950 and 1960 the population increased

almost 90 percent as indicated in Table 3.

Currently, the population of the county is estimated at about

1.1 million by the Metropolitan Dade County Planning Department, and

will approach 2.5 million by 1995 if the current growth rates continue.










Table 3.--Florida and Dade County population changes for 1950 and 1960.



Florida Dade County


Total 1950 2,771,305 495,084
Total 1960 4,951,560 935,047
Percent increase 78.7 88.9

Rural 1950 957,415 29,005
Rural 1960 1,290,177 40,705
Percent increase 34.8 40.3

Urban 1950 1,813,930 446,079
Urban 1960 3,661,383 894,392
Percent increase 101.9 91.9


aSource of data: U.S. Bureau of the Census (46).



The Planning Department estimates that 130 additional square miles will

be needed for new homes and apartments to accommodate the additional 1.5

million new residents expected during the next 25 years. The effect of

this growth on the outlying areas is shown in Figure 3.

It is obvious that the projected population growth will have

serious implications for the rural interests in Dade County. Agricul-

tural activities will be driven toward the Everglades Swamp on the west

and southwest. Interdependencies between urban and rural interests will

intensify, and concurrent with this intensification, economic external-

ities arising from such propinquity will increase, giving rise to a

multitude of policy questions such as the one with which this study is

concerned.

Several other characteristics of Dade County and its farmers

should be noted. First, farming in the region requires large amounts of







17






























4


3!






RESLDENT




Figure 3.--Population trends in Dade County, Florida.
a
Reprinted with the permission of the Metropolitan Dade County
Planning Department.


capital relative to other areas of the State. The 1964 Census of

Agriculture indicates that the investment in land and buildings per acre

of farm land is $1,088.12 in Dade County against $285.71 for the State

(47, p. 304-305). For the U.S. as a whole it is $143.81 (48, p. 792).










Second, revenues per acre from farming in Dade County are also very high.

The per acre value of all farm products sold in Dade County is $443.13

against $68.41 for the State (47, p. 332-333). For the U.S. as a whole

this figure is $31.79 (48, p. 802). Third, net returns per acre vary

widely among growers and through time, as indicated in Appendix A

(p. 119). Finally, in the author's opinion, based on observations made

while in the area, the farmers of Dade County are as well educated and

as sophisticated as the entrepreneurs in any vocation. Some of the

larger ones have professional entomologists on their payrolls, and most

of the pesticide firms in the area employ entomologists to sell pesti-

cides and provide consultation to the farmers. The University of

Florida has an experiment station in Homestead, Florida, which is very

active in the area of insect and disease control. In addition, the Dade

County Agricultural Agent's Office, the largest in the State and located

in Homestead, is extremely active in disseminating information to the

farmers.
















CHAPTER III


THE MODEL


The model used for this analysis employed the concept of

consumers'-producers' surplus as a measure of welfare. For each of two

specified pesticide usage policies and three variations under one policy,

the model maximized this measure of welfare over production alternatives

available to the region. In each case the objective function explicitly

recognized measurable externalities not accounted for in the consumers'-

producers' surplus calculation. The maximization was restrained by

flexibility constraints derived from historical cropping patterns.

Finally the policies were ranked by their maxima. The model can be

formally stated as follows:

For a set of subjectively chosen pesticide usage policies, xr,

r = 1, 2A, 2B, 2C, rank the associated estimates of welfare, W where:

n y(t+l)
W = maximum: E f f(y.(t+l)) g(yj (t+l) dy(t+l
r y (t+l) j=l 0

z i(t+l)

m i tl)
Z f [h.(z.(t+l1 dz.(t+i)
i=1 0 L

where the maximization for a given policy r is subject to:

n n n
[I] d(min) E y.(t) Z y.(t-+l) d(max) Z y (t)
j==l j=i j=i











[2] b.(min)y (t) y (t+l) < b (max)y.(t) j=l, .n

n x
r
[3] E a. y.(t+l) z.(t+l) = 0 i=l, .m
j=l 1 1

[4] Cki zi (t+l) < e ki k=l . ..p


[5] y (t+1), zi(t+l) > 0

where:

f (yj (t+l)) = demand function for the jth crop in the (t+l) year.

y.(t+l) = acres of the jth crop in the (t+l) year. For simplifi-

cation the time dimension is omitted in the remaining definitions.
x
r(y.) = supply function for the jth crop under the rth policy
J
alternative.

h.(z.) = a marginal externalityy function," a functional relation-

ship between marginal external effects expressed in dollars, and

the quantity of the ith pesticide.

z. = quantity of the ith pesticide measured in units of 100 percent
1

active material.

d(min) = a minimum flexibility constraint on total farm land.

d(max) = a maximum flexibility constraint on total farm land.

b (min) = a minimum flexibility constraint on the jth crop.

b.(max) = a maximum flexibility constraint on the jth crop.
J

x
r
a.. = the quantity of the ith pesticide used per acre of the jth
Ij

crop under the rth policy.

cki = quantity of the ith pesticide produced in the kth environ-

mental element by 1 unit of the ith pesticide.

eki = an arbitrary upper limit on the ith pesticide in the kth

environmental clement--a parameter to be determined "politically."










There has been a recent revival of interest in the concept of

consumers'-producers' surplus for making policy decisions (37, 45, 55).

Though much criticized (40, pp. 208-209), it is being used in the

literature with increasing frequency to give a rough idea of welfare

aspects of various alternatives. It is defined as the integral of the

demand function minus the integral of the supply function. The integral

of the demand function is, to the user of a product, simply a total

benefits function and the integral of the supply function a total cost

function, so consumers'-producers' surplus as a function of output is a
1
net benefits function.

Buchanan (6, p. 127) argued that the welfare of a group is a

consensus of the preferences of members of the group and that an

observer could introduce a welfare criterion only through his own

estimate of the group's value scales. Hence, the observer can recommend

policy A over policy B given his estimate of the group's preferences,

and the test of his recommendation is to ask whether the given welfare

function reflects the consensus of the group. Even within this frame-

work, however, the question of votes must be resolved before any group

consensus can be revealed. Buchanan (6) further argued that majority

rule does not guarantee consensus or agreement among the parties because

adequate compensation may not be provided for the damaged parties.

At any rate, as a theoretical construct, the social welfare

function is perhaps useful, but for empirical work one generally ends

1The objective function would always be maximized at the
intersection of the demand and supply functions in the absence of the
externality relationship. The introduction of the externalfty relation-
ship will shift the optimum solui ion to the left or to the right
depending on whether the externalities are external economies or external
diseconomies. The externality function will be discussed in later pages.










up using a social welfare function which, to the researcher, represents

a compromise between desired characteristics and empirical feasibility.

Any policy decision concerning pesticide usage is likely to

benefit some parties and hurt others. The model assumes that the
2
income effects of varying prices are small enough to be ignored. Such

an assumption would not seem too unrealistic where the consumer spends

a small proportion of his income on the commodity. The model also

assumes that tastes and preferences remain constant, and that the aggre-

gate demand and supply functions for a crop are independent of those in

other regions, those for other crops, and those in other industries.

The model also assumes that the marginal utility of money is

equal for all parties affected by the policy. Lerner (33, pp. 23-40)

argues that in the absence of knowledge this is the most probable

assumption. Such an argument from ignorance is not very convincing or

comforting.

Nevertheless, the assumption that men are all very much alike is
the foundation of democratic institutions ("We hold these truths
to be self-evident . .") and we certainly do not attempt in
politics to give the voter a number of votes in proportion to
his intelligence and his ability to use them. The one-man-one-
vote principle is forced upon us by the recognition of the
practical impossibility of any other (3, p. 93).

So it is with the issue at hand. We realize that some people probably

derive greater utility than others from an incremental dollar (or

greater disutility from losing a dollar), but it was a practical impos-

sibility to recognize it in the analysis.


A slight variation on this assumption is Hicks' argument that
the marginal utility of money must be assumed constant (25, p. 326).
This assumption seems slightly more restrictive than the one I have
made, but as far as the structure of the model is concerned, it amounts
to the same thing.










The objective function of the model is assumed to be separable.

That is, it is the sum of a number of independent functions in the

variables y. and z.. In the terminology of the problem, this means that

any crop can be grown at any level within the range specified by the

constraints without affecting the demand and supply functions of the

other crops. It also means that the externality functions for chlori-

nated hydrocarbons and organic phosphates are independent. More will be

said about the estimation of these externality functions in later pages.

The concept of a flexibility constraint (see constraints 1 and

2) was used by James M. Henderson (24) in the late 1950's in an attempt

to use linear programming to predict economic behavior of farmers.

Day (11) later applied this idea in his use of recursive programming on

crop production in 11 counties in Mississippi. Considering how a farmer

might allocate his land among various alternative uses, Henderson said

that the farmer's behavior could be explained by two principles. The

first was that the farmer would try to maximize his expected net revenue

and would therefore allocate his land in a manner consistent with

this desire. The second principle said that in allocating his land,

the farmer would also be influenced by the allocation pattern of the

preceding year. This was Henderson's unique contribution. "Speci-

fically, a farmer's acreage plantings for each crop were assumed to be

constrained by maximum and minimum limits which indicated his desire

for diversity and reluctance to depart from an established pattern"

(24, p. 243). There are many factors--economic, technological, insti-

tutional, and sociological--which contribute to this reluctance to

depart from an established pattern. Henderson's problem was to capture

this hypothesis in a uodel without Tmaking the model so complicated that











it could not be used empirically. He did this by placing upper and

lower bounds on allowable acreage changes. The bounds were determined

by observing historical acreage changes and were based on the assumption

that the diverse factors which influenced such changes in the past were

likely to do so in the future with about the same magnitude. He then
3
incorporated these into appropriate linear programming constraints:

bj (min)y (t) J y (t+l) b (max)y (t) j=l, .n

where:

y.(t) = the acreage of the jth crop in period t.

b (min) = one minus the maximum percentage decrease considered

reasonable from observing historical cropping patterns.

b.(max) = one plus the maximum percentage increase considered
J
reasonable from observing historical cropping patterns.

He also had a final restriction on the overall land allocation:

n
Z y.(t+l) -Y
j=l

where:

Y is the total land available for cultivation.

This research effort utilized essentially the same approach.

Maximum and minimum flexibility constraints were applied to the indi-

vidual crops. The overall land restriction differed slightly from

Henderson's. The total quantity of Dade County land which could be

cultivated was not a relevant constraint because it was so much larger

than the quantity that was actually cultivated. Furthermore, the


3Symbols have been changed in order to be consistent with those
of the model.










4
quantity of land allocated to farming appeared to be declining. The

constraint was therefore established to apply to the total land under
5
cultivation, after an adjustment had been made for the other crops.

The form of this constraint was:

n n n
d(min) E y.(t) E- y.(t+l) -< d(max) E yj(t)
j=1 j=l j=l

where:

the d(min) and d(max), as before, represent proportional changes in

total farm land cultivated from one year to the next, as determined
6
from historical cropping patterns.

The third constraint requires little explanation. It was needed

in the model to insure that the quantity of the ith pesticide used on

all crops would coincide with the quantity of the ith pesticide called

for in the solution.

The fourth set of constraints was "environmental." It appears

relatively simple, but it embodies all the physical relationships

between pesticide usage and the environment. When a pesticide is used


4In 1959, 9.8 percent of the land was in farms,while in 1964,
8.9 percent was in farms.

5Other crops were those excluded from the objective function of
the model. These were approximately 20 in number and accounted for
14 percent of planted acreage in 1966-67.
6It has been suggested that the imposition of a new pesticide
policy upon the farmers might disturb the interrelationships which
caused the variations in cropping patterns from which flexibility con-
straints were estimated and that the flexibility constraints would
therefore lose their applicability. Whether or not this is true depends
upon the magnitude of the policy change and the other sources of vari-
ability in cropping patterns. As will be seen in later pages, the
effect of the proposed policy on farmers' costs is relatively small and
is far overshadowed by other exogeneous factors, such as weather, with
which the farmers must contend. It is therefore this writer's opinion
that the flexibility constraints do provide reasonable estimates of
"capacity for change" in the cropping patterns.










on a crop, some of it is taken into the plant, some of it goes into the

soil and water, and some of it into the air. The speed with which a

pesticide breaks down into non-toxic substances varies a great deal

among pesticides. The chlorinated hydrocarbons tend to be very persis-

tent--their residues decompose into non-toxic substances very slowly.

The organic phosphates, on the other hand, tend to break down into non-

toxic substances very rapidly and their residues do not build up

significantly in the environment. Much of the current pesticide

controversy revolves around these physical relations--the residues which

build up and the potential damage which they may represent for the

environment. As will be noted later, large amounts of resources are

being committed to research on these physical relationships, but little

has been done to delineate their role in a decision model for policy

making. For this reason the set of environmental constraints is

included conceptually in the model. It was impossible, however, to

estimate the parameters empirically; therefore, the set is not included

in the succeeding chapter on the estimation of model coefficients. This

was a regrettable compromise because this set of constraints could

possibly be the most important of all in establishing policy. It is

precisely because of pesticides' potential for environmental pollution

that people's emotions become aroused and make them demand policies

regulating the use of pesticides.

The fifth set of constraints simply prevented the occurrence of

negative acreages and pesticide quantities in the solution.

Another aspect of the model which deserves emphasis concerns the

question of property rights, a subject of growing interest in recent

literature on policy issues (9, 12, 41). The model as stated here is











independent of the pattern of legal rights. It is not necessary to

delineate the rights in order to determine the optimal solution, but it

is necessary to delineate the rights in order to evaluate the various

ways of reaching the optimum, a problem with which we are not concerned.

For example, if a policy alternative calling for a tax on chlorinated

hydrocarbons were introduced, then by implication, the farmers do not

have the right to "pollute" the environment and must pay for the privi-

ledge of doing so. On the other hand, if no restraints are placed on

farmers' usage of chlorinated hydrocarbons, then by implication,they do

have the right to "pollute" the environment to some degree. One need

only read a few historical cases in the common law of nuisance to

realize that the question of property rights will vary greatly from time

to time, from place to place, and indeed, from judge to judge (9, p. 22).

Upon reading the following quotation from Prosser, one does not wonder

that the question of property rights is a very elusive concept: a

person is permitted

. to make use of his own property or to conduct his own affairs
at the expense of some harm to his neighbors. He may operate a
factory whose noise and smoke cause some discomfort to others, so
long as he keeps within reasonable bounds. It is only when his
conduct is unreasonable, in the light of its utility and the harm
which results, that it becomes a nuisance . The world must
have factories, smelters, oil refineries, noisy machinery, and
blasting, even at the expense of some inconvenience to those in
the vicinity, and the plaintiff may be required to accept and tol-
lerate some not unreasonable discomfort for the general good (39,
pp. 398, 412).

The phrase in the above quotation, ". . in the light of its

utility and the harm which results . ." (39), illustrates the notion

that property rights are not independent of "the general good" or some

concept of social welfare. To put it another way:

S. The problem which we face in dealing with actions which have
harmful effects is not simply one of restraining those responsible










for them. What has to be decided is whether the gain from pre-
venting the harm is greater than the loss which would be suffered
elsewhere as a result of stopping the action which produces the
harm (9, p. 27).

To summarize, the optimum externality level is independent of

property rights. Property rights become important when we begin trying

to move toward the optimum. The problem of the inter-personal compar-

ison of utility cannot be ignored. The legal profession has been

grappling with it for many years and will continue doing so in the

future. Economists, by training, can bring many valuable tools to bear

on this problem and could be of valuable service to the legal profession

in making such decisions. The fact that policy decisions are difficult

in a world of imperfect knowledge does not justify ignoring them. If

economists refuse to aid in making them, they will be made by others.

Coase (9, pp. 42-43), in his classic article on social cost,

states that in two ways economists have historically fallen short in

helping to make policy decisions. First, they have tended to ignore

considerations which are aesthetic or moral in nature, when,in fact,

these may have a great influence on the final solution. Second, econo-

mists have tended to focus their attention on models that are so simpli-

fied that their solutions are of limited usefulness in the real world.

Conceptually, the model developed here at least partially meets

these criticisms. The environmental constraint allows the introduction

of value judgments through the right-hand-side elements, eki., which

are determined by political compromise. As far as the second criticism

is concerned, it is the writer's opinion that we have not abstracted

from the real world quite as far as most: writers in this area. This is

true because of a slightly different approach. We began with a real







29


problem, for which we needed both a conceptual model and the data to use

it empirically. We retreated or abstracted from reality only to the

point where we felt we could actually satisfy the data requirements.

If we had not been faced with the necessity to use the model empirically,

we might, for example, have developed a dynamic programming formulation

which would have had the advantage of greater realism but for which we

could not obtain the necessary data.

















CHAPTER IV


ESTIMATES OF MODEL COEFFICIENTS


Objective Function


The objective function of the model was based on the concept of

consumers'-producers' surplus, modified to recognize external effects

not included in the surplus calculation. As noted earlier, the area of

consumers'-producers' surplus was defined as the area under the demand

function less the area under the supply function.


The Demand Model

The model which provided the basis for estimating demand

parameters for the objective function was specified as follows:

i] q(t) = T + Tlp(t) + u(t)

subject to:

[2] q(t) q(t-l) = j[q(t) q(t-l 0 < < 2

where:

q(t) = the long-run equilibrium quantity as of period t.

p(t) = price of the commodity in period t.

I(t) = per capital disposable income (deflated) in period t.

q(t) = the quantity demanded in period t.

u(t) = a disturbance term satisfying the classical assumptions,

namely;

E[u(i)] J 0










E[u(i)u(j)] = 0 for i i j, 0a2 for i = j
U
p(t) and I(t) are fixed numbers observed without error.

The matrix of observations on p(t) and I(t) is non-singular.

The constraint, [2], simply describes the adjustment toward the

long-run equilibrium quantity.

Substituting equation [2] into equation [1] generated the
2
following function, which was empirically estimated:

[3] q(t) = T0 + (l-t)q(t-l) + 4Tlp(t) + 21(t) + 4u(t)

The primary purpose of estimating equation [3] was to obtain

estimates of the price elasticity of demand after allowing for the

average effects of I(t) and q(t-l). Data availability necessitated

using State data rather than Dade County data. It was assumed that the

price elasticity for Dade County would be the same as that for the State

since they compete in the same markets and since, in some cases, Dade
3
County represents a relatively high proportion of the State acreages.

Estimates of long-run price elasticity were thus obtained using the

1 rk
If 0 < < 1, q(t) approaches q(t) asymtotically. If = 1,
the entire adjustment is accomplished in one period. If 1 < 4 < 2, q(t)
over-adjusts, but still converges upon 2'(t).
Lagged variables have been increasingly employed in econometric
work in recent years [21, 31, 38].

21n this equation, however, u(t) is no longer independent of
q(t-l), the independent variable (27, p. 212; 35, p. 129). The result
is that the least squares estimates are biased but have the desirable
asymptotic properties of consistency and efficiency.

3Dade County accounted for the following proportions of the
State acreage in 1966-67:
Percent of State acreage
Crop in Dade County

Tomatoes 39
Winter potatoes 64
Beans 12
Corn 3










formula,

E = ip(t) : p
9p(t) 1 -
q q
4
where p and q were the means of the variables from State data. Then,

using this estimate of E and the 1966-67 price and quantity for Dade

County, a linear demand function of the following form was synthesized

for Dade County:

[4] q(t) = 0 + |Ip(t)


41n two cases which will be noted later, the variables were
transformed into logarithms. For these the regression coefficients were
the required estimates of elasticity.

5For each crop the function was derived as follows:
A iA
E = T
q
where:
A
E = estimated long-run price elasticity.
p= average price from State data.
q= average quantity from State data.
T1 = an estimate of T1 in equation [1].
= ^ q_ ^ -^ _qA
[~s1 q*Aj~ q*
LDpjd p* l= p*
where: q
3ql = partial derivative of q with respect to p for Dade County.
|dp
q* = 1966-67 quantity for Dade County.
p* = 1966-67 price for Dade County.
Using the point slope form of a linear function passing through q* and
p* yields:
q*-q(t)= = q*
q p
ap d p*-p(t) 1 P*
q
Sp Tlpq*
q(t) = q* - + p(t) q*(l-E) + j p(t)
q qp* P*
or:
q(t) = 0 + 1P(t)
where: 0 = -qa ,
0 = q*(l-E) and Pi = p*











The average effects of I(t) and q(t-l) on q(t) are, of course,

recognized in these simplified functions since I(t) and q(t-l) were

included as variables in the estimation of equation [3].

Equation [4] was then manipulated algebraically to make q(t) the

independent variable. At the same time the unit of measure on the

quantity variable was converted to acres by multiplying the equation by

the average yield per acre of the past 10 years. This led to demand

equations expressing price as a function of quantity (y) which were used

in the objective function on page 19 as f. (y.), j being the crop desig-

nation.

The conversion of q, units of production, into y, acres, via

the average yield per acre was done in order to express the demand

functions in the same units as the supply functions. Such a conversion

abstracts from the uncertainties of weather on crop yield and rests on

an average expected yield per acre.

Alternative data series (seasonal as well as annual data) were

tried in estimating demand functions. In using seasonal data, the

designated seasons were fall (September through November), winter

(December through February), and spring (March through May). Zero-

one variables were used to allow for the average seasonal effect. If an

observation occurred in the fall, the fall variable was assigned a value

of 1, otherwise 0. If an observation occurred in the spring, the spring
7
variable was assigned a value of 1, otherwise 0. The effect of this

6This, of course, does not necessarily yield the same equation
as one would get had the direction of minimization of the sums of
squares of residuals been in the direction of the p(t) axis. Initially,
p was defined as the independent variable because observations on p were
thought to be more accurate than those on q. Johnston (27, pp. 148-176)
offers a treatment of observational errors in the variables.
7The winter season is recognized in the intercept and therefore
does not have a zero-one variable assigned to it.










dummy variable technique was to shift the intercept of the demand
8
function in an attempt to recognize seasonal shifts in demand. A

logarithmic transformation of the variables was also tried. With the

exception of beans and mangos, these transformations provided little or

no improvement in the fits obtained. Table 4 presents the empirical

results which were selected for derivation of the demand functions

used in the model. Although not of interest for purposes of this study,

short-run price and income elasticities are also presented. Table 5

presents the demand functions which were used for the model.


The Supply Model
x
The second element of the objective function, depicted by g r(y),

was a linear supply function for the jth crop under the rth policy

alternative. The model from which supply parameters for current pesti-

cide usage were estimated was specified as follows:

[5] Y(t) = + E1p(t-l) + u(t)

subject to:

[6] y(t) y(t-l) = 6[y(t) y(t-l) < 6 < 2

where:

y(t) = long-run equilibrium acreage as of period t.

p(t) = price of the commodity in period t.

y(t) = acres planted in period t.

u(t) = a disturbance term satisfying classical assumptions.

As in the demand model, the constraint described the adjustment

toward the long-run equilibrium acreage. Using [6] to eliminate y(t) in


It should be noted that only the intercept changed in this
model. In other words, the demand for fall tomatoes was assumed to
differ from that of winter tomatoes only in level, not in slope.











Table 4.-Empirically estimated relations from which demand functions
were derived.a






Crop Intercept Coefficients of:A
q(t-1) p(t) I(t)/c Sfd


tomatoes


Winter potatoesg

h,i
Beansh


Cornd


Avocados k


Limesm


Mangos,h,i
Mangos


1798.3218


2271.0208


7.3391


969.9456


1130.4805


30.9556


.6427


.3027
(.1394)

.4902
(.2213)

.0930
(.0952)

.2897
(.1674)

.3805
(.1850)

.4280
(.2587)

.0896
(.1805)


- 733.8044
(149.9282)

- 229.7552
(138.8649)

- 1.8514
(.2141)

-1391.9141
(321.5176)

- 44.3303
(13.6422)

- 11.3824
(21.8403)

1.2548
(.2088)


2.8091
(.9227)

-.2824
(.6980)

.2248
(.4506)

1.8613
(.6422)

5.7642
(4.2249)

.1167
(.1344)

1.5275
(1.3004)


-1854.3457
(449.6931)




- .2146
(.0940)

-1141.8203
(332.2517)


sources of data:
1. Tomatoes, potatoes, beans, and corn: Florida Agricultural
Statistics (18).
2. Avocados: Agricultural Statistics (50).
3. Limes:
a. Production and on-tree prices: Florida Agricultural
Statistics (18).
b. Packinghouse prices: Agricultural_ Statisti cs (50).
4. Mangos: Dade County Agricultural Agent's Office, Homestead,
Florida.
Standard errors of the coefficients are shown in parentheses.
CI = per capital income.
S = a "duirmy variable" which equals 1 if the observation

occurred in the fal] and 0 otherwise.
S a "drduiny variable" which equals 1 if the observation occur-
red in the spring and 0 otherwise.
fQuantity measured in thousands of 60 pound crates; price
measured in dollars per crate.











Table 4.--Extended.


se
S


- 797.2398
(335.2292)




.1448
(.0906)

2207.9375
(894.9026)


Number
of
obser-
vations


.8107


.3976


.7492


.9610


.5066


.4832


.9131


gQuantity measured
measured in dollars per ba[
hQuantity measured
dollars per bushel.
iThis function was

JQuantity measured
dollars per crate.
kQuantity measured
mQuantity measured

dollars per box.
nQuantity measured
bushel.


Short-run Long-run Short-run Long-rur


price
elas-
ticity


price
elas-
ti city


income
elas-
ticity


1.4427


- .3094


.2248


1.4705


1.2882


.6272


1.5275


income
elas-
ticity


2.0690


- .6069


.2479


2.0703


2.0795


1.0965


- .9728


- .4856


-1.8514


-1.2703


- .7929


- .1271


-1.2548


-1.3952


- .9525


--2.0410


-1.7881


-1.2800


- .2222


-1.3783


in thousands of 100 pound


Durbin-
Watson
Statistic


1.8095


*}*
1.6151

&
2.3175


1.9689


1.8853


1.8348


1.6778 2.2197



bags; price


in thousands of bushels; price measured in


estimated in natural logs.
in thousands of crates; price measured in


in tons; price measured in dollars per ton.
in thousands of boxes; price measured in


in bushels; price measured in dollars per


Reject the hypothesis that auto-correlation is present at the
95 percent confidence level.
d statisi:ic is inconclusive.


- -4------- 1 i










Table 5.--Demand functions used in the model.a



Crop Function


Tomatoes p = 1521.5660 .0334 y


Potatoes p = 1116.6649 .0747 y2


Beans p = 1152.4799 .0650 y3


Corn p = 764.1282 .1661 Y4


Avocados p = 571.3720 .0479 y5


Limes p = 3285.4824 .7502 Y6


Mangos p = 690.9032 .1911 y7

aFor all functions, price is measured in dollars per acre
and quantity in acres.










[5] yields:

[7] y(t) = 6 0 + (l-6)y(t-l) + 6p(t-l) + 6u(t)

Equation [7] was empirically estimated and the results used to obtain

estimates of the long-run supply elasticity. Results are shown in

Table 6.

As in the development of the demand functions, the estimate of

elasticity and the 1966-67 price and quantity observations for Dade

County were then substituted into the point-slope form of a straight

line to obtain the equations for the supply functions. These equations

were then solved for p in terms of y in order to conform to the require-

ments of the model.

Since equation [5] includes price lagged one time period, the

supply and demand equations constitute a cobweb model which, for

purposes of the study, is assumed to be in equilibrium at the 1966-67

price and quantity. The objective is to determine what happens to this

point of long-run equilibrium when externalities are recognized. There

was no interest in the time path of price-quantity changes but only in

the magnitude of change in the point of long-run equilibrium.9

In the case of avocados, limes, and mangos, data did not exist

to permit the estimation of supply functions, so the acreage for these

crops was constrained at the 1966-67 level. This was probably a


91f the system gets out of equilibrium, cobweb theory (15,
pp. 255-280) states that the time path of price and quantity will be
convergent or divergent depending upon whether the absolute value of the
slope of the supply function is greater or less than that of the demand
function. In the case of potatoes, the relative slope of the supply
function to the demand function would indicate a divergent path. The
negative estimate of the income elasticity for potatoes would aggravate
this problem if income increased. Shifts in the flexibility constraints
might also affect the time path.










Table 6.--Empirically estimated relations from which supply functions
were derived.a


Intercept


y(t-l)


Coefficients of: A


Sf


p(t-l)


Tomatoes


f g
Winter potatoes '


Beansh ,g


CornI


1904.0586


1.9643


3.1023


-4528.8516


.4472
(.1393)

.7367
(.1717)

.6614
(.1636)


1754.8784
(623.8162)


-3727.2373
(1723.1143)


.4033
(.1981)

.2002
(.1659)


.7935 2353.8164
(.1569) (1463.8821)


.0003
(.0708)


1904.8828
(1570.1018)


Limes

Mangos


Acreages for these tree crops were constrained to


aSources of data:
1. Tomatoes, potatoes, beans, and corn: Florida Agricultural
Statistics (18).
2. Avocados: Agricultural Statistics (50).
3. Limes:
a. Production and on-tree prices: Florida Agricultural Statis-
tics (18).
b. Packinghouse prices: Agricultural Statistics (50).
4. Mangos: Dade County Agricultural Agent's Office, Homestead,
Florida.
bStandard errors of the coefficients are shown in parentheses.

CSf = a "dummy variable" which equals 1 if the observation occur-
red in the fall and 0 otherwise.
dS = a "dummy variable which equals 1 if the observation occur-
red in the spring and 0 otherwise.


Crop










Table 6.--Extended.


Number
of Short-run Long-run Durbin-
obser- 2 price price Watson
nations R elasticity elasticity Statistic

S /
s


- 339.7224 33 .6773 .5185 .9379 1.7873
(1392.4358)

17 .5879 .4033 1.5317 1.8363


- .0066 33 .4334 .2002 .5910 2.5493
(.0690)

7843.4375 33 .9648 .3178 1.5390 2.0073
(3283.3203)



be no greater than the 1966-67 levels.


eQuantity measured in acres
per crate
fQuantity measured in acres
per bag.
gVariables were transformed
hQuantity measured in acres
per bushel.
iQuantity measured in acres
per crate.
*
Reject the hypothesis that
95 percent confidence level.


planted; price measured in dollars


planted; price measured in dollars


to natural logs.
planted; price measured in dollars


planted; price measured in dollars


auto-correlation is present at the










somewhat unrealistic restraint for the long-run.


Shifting the Supply Functions

The supply functions developed in the manner described above

pertain to current pesticide usage practices. Those for alternative

pesticide policies could not be observed but had to be synthesized from

information provided by growers, entomologists, and others knowledgeable

about the area.

In order to approximate reality within the data limitations, the

chlorinated hydrocarbons were lumped together as one category and the

organic phosphates as another. This was done on the basis that the

chlorinated hydrocarbons tend to have similar effects on the environ-

ment and that the organic phosphates likewise tend to have similar

effects on the environment. It was also partly a result of political

considerations,since the thrust of the controversy seems to center

mainly around these two categories.

Chlorinated hydrocarbons and organic phosphates are, to a

limited extent, substitutes for each other. This is indicated by Tables

7 through 10 which show alternative insect control measures for the

vegetable crops under consideration. In many cases a given insect can

be controlled by a chlorinated hydrocarbon or an organic phosphate. But

there are also a number of insects for which the only recommended remedy

is a chlorinated hydrocarbon and a number for which the only recommended

remedy is an organic phosphate. Entomologists frequently differ in

their opinions about how to control certain insects. Adding to this

source of variation is the fact that insects' susceptibility to a pesti-

cide seems to vary widely from time to time and place to place.









a
Table 7.--Recommended insect control measures for tomatoes.




Organic Chlorinated Minimum
phosphate hydrocarbon Other days to
Insect Pesticide quantity quantity quantity harvest


Aphids


Armnyworms,
Tomato Fruit-
worms,
Hornworms


Loopers




Leaf Miners


Stinkbugs,
other plant
bugs



Banded Cucumber
Beetle


Dimethoate
Demeton
Parathion
Phosdrin
Thiodan

DDT
Phosdrin
Sevin
TDE (DDD)
Thiodan
**
Dibrom
Parathion
Phosdrin
Thiodan

Diazinon
Dibrom**
Dimethoate
Guthion

Guthion
Parathion
Phosdrin
Sevin
Thiodan

Guthion
Thiodan


aSource of data: Insect Control Guide (17)
with entomologists familiar with the area.


and consultation


bQuantities are expressed in pounds of 100 percent active
material per 100 gallons of water. The gallonage applied per acre will
vary with the size and density of the plants. In the Dade County area
the gallonage usually varies from 100 to 300 gallons.
*
No time limit.
**Dibrom was not observed in Dade County in 1966-67
Dibrom was not observed in Dade County in 1966-~67.


.334
.375
.450
.500


1.000

1.000


1.000
1.000


1.000


.500


2.000
.450
.500


.500
1.000
.334
.500

.500
.450
.250


.500


3
1 *
NTL
1
1

1
3
1
1

1
1
7,*
NTL
*
NTL
3
1
NTL
1
*
NTL
1


1.000


1.000


1.000


1.000










Table 8.-aRecommended insect control measures for potatoes.
Table 8.--Recommended insect control measures for potatoes.


Insect


Aphids


Armyworms,
Loopers, other
caterpillars


Banded Cucumber
Beetle

Leaf-footed
Plant Bug,
Green Stinkbug


Leaf Miners





Wireworms


Pesticide


Demeton
Dimethoate
Meta-Systox-R
Thiodan


Parathion
Phosdrin
Thiodan
Toxaphene

Guthion
Thiodan

Guthion
Parathion
Phosdrin
Thiodan

Diazinon
Dibrom**
Dimethoate
Guthion

Thimet
Parathion


Organic
phosphate
quantity


.375
.500
.375


.300
.500


.500


.500
.300
.250


.500
1.000
.334
.500

3.000
2.000


Chlorinated
hydrocarbon
quantity


1.000


1.000
1.000


1.000


1.000


aSource of data: Insect Control Guide (17) and consultation
with entomologists familiar with the area.

bQuantities are expressed in pounds of 100 percent active
material per 100 gallons of water. The gallonage applied per acre will
vary with the size and density of the plants. In the Dade County area
the gallonage usually varies from 100 to 300 gallons.
A
No time limit.
*Dibrom was not observed in Dade County in 1966-67.
Dibrom was not observed in Dade County in 1966-67.


Soil treatment prior to planting.


Other
quantity


Minimum
days to
harvest


21
7
7
NTL

5
1*
NTL*
NTL

7
NTL

7
5
1
NTL

14 *
NTL
7
7










Table 9.--Recommended insect control measures for beans.a


Organic Chlorinated Minimum
phosphate hydrocarbon Other days to
Insect Pesticide quantity quantity quantity harvest


Aphids


Armyworms,
Corn Earworm


Cowpea Curculio


Bean Leaf-
hopper, Bean
Leafroller





Leaf Miners,
Cucumber
Beetles

Thrips

Stinkbugs


Saltmarsh
Caterpillar

Lima Pod Borer


Demeton
Dimethoate
Parathion
Phosdrin

DDT
Sevin
Toxaphene

Toxaphene
Thiodan

Dimethoate
Guthion
Parathion
Phosdrin
Sevin
Toxaphene

Diazinon
Dimethoate
Guthion

Parathion

Guthion
Parathion
Phosdrin
Sevin

Phosdrin
Toxaphene

Parathion


.334
.500
.300
.500


1.000


1.000


source of data: Insect Control Guide (17) and
with entomologists familiar with the area.


consultation


bQuantities are expressed in pounds of 100 percent active
material per 100 gallons of water. The gallonage applied per acre will
vary with the size and density of the plants. In the Dade County area
the gallonage usually varies from 100 to 300 gallons.

No time limit.


Should not apply Thiodan more than 3 times per season.


.375
.334
.300
.250


1.000


1.000

1.000

1.000
.500


21 *
NTL
3
1

5,
NTL
5

5**
3
*
NTL
7
3
1
NTL
5

7
NTL
7


7
3
1 *
NTL


.500
.334
.500

.225

.500
.300
.250


.500


.300


1.000


1.000










a
Table 10.--Recommended insect control measures for corn.


Organic
phosphate
quantity


Chlorinated
hydrocarbon
quantity


Other
quantity


Minimum
days to
harvest


Aphids, Spider
mites

Fall Armyworms
and Corn Ear-
worm feeding
in bud







Silk-fly

Earworms






Corn Stem
Weevil


Parathion
Phosdrin

DDT
Parathion
Toxaphene
Mixture of
DDT and
Parathion
Mixture of
DDT and
Toxaphene

Parathion

DDT
Sevin
Mixture of
DDT and
Sevin

DDT
Mixture of
DDT and
Toxaphene
Mixture of
DDT and
Toxaphene


aSource of data:


Insect Control Guide (17) and consultation


with entomologists familiar with the area.

bQuantities are expressed in pounds of 100 percent active
material per 100 gallons of water. The gallonage applied per acre will
vary with the size and density of the plants. In the Dade County area
the gallonage usually varies from 100 to 300 gallons.
A*
No specific limitation so long as the usages do not result in
a residue on the edible ears.

tThese amounts should be mixed in 50 gallons of water and
applied to one acre.


Insect


Pesticide


.250


.250
.250


.250


.125


1.000

1.500

1.000


1.000
.750


.250


2.000t


.500


2.000t


2.000


2.000

1.000
1.000

2.000
1.000










Furthermore, if the same pesticide is used constantly for a prolonged

period of time, it raises the probability of immunity on the part of

the insect.

In order to fulfill the data requirements of the model, it was

necessary to synthesize a substitution rate between the chlorinated

hydrocarbons and the organic phosphates. This is not to say that the

consideration is necessarily limited to two dimensions, but these two

categories are so clearly dominant that it is doubtful if the others

could have a significant effect on the average substitution rate.

Entomologists familiar with the problem generally agree that the

substitution rate which maintains equal control is not completely linear,

as shown by the iso-product curve in Figure 4, but is more likely to be

a hyperbolic function somewhat like that of Figure 5.10

Figure 5 merely says that it becomes progressively more

difficult to substitute one for the other, which is in complete agree-

ment with economic theory. It also leads one to the conclusion that

sudden elimination of the chlorinated hydrocarbons may be an unattainable



uo
4J 0~
(0 $




Organic phosphates


Figure 4.--A linear iso-product function for chlorinated
hydrocarbons and organic phosphates.


10Three entomologists provided assistance in developing this
section. Two are located on the University of Florida campus and one in
Dade County.














P 0
0 P




Organic phosphates



Figure 5.--A hyperbolic iso-product function for chlorinated
hydrocarbons and organic phosphates.


policy objective and that reduction is a far more reasonable goal, for

the immediate future, particularly if one is to maintain production

without changing non-pesticide inputs. This doesn't mean that the ulti-

mate goal cannot be elimination or near elimination of the chlorinated

hydrocarbons. It simply means that the first step to be evaluated will

be a reduction policy, and that the ultimate goal, whatever it is, will

be approached asyitotically through a series of policies. It is the

objective of this research effort to trace the welfare implications of

the first step only. The alternative pesticide usage policy for the

analysis was subjectively chosen as a 50 percent reduction in per acre
11
usage of hydrocarbons for each crop.

It was the consensus of entomologists that for policies of 50

percent reduction or less (of chlorinated hydrocarbons) per acre, a one-

pound reduction of chlorinated hydrocarbons would require about .3 to .4


11 There are currently some efforts to make laws banning theuse
of DDT and certain other "hard" pesticides in several states. It is
debatable as to how much effect this would have on reducing the usage of
the "hard" pesticides as a group, for the farmers would be likely to
shift over to some other persistent pesticide not covered by the legis-
lation. Several economists have warreed against the pitfalls of excessive
control in pollution problems (43, pp. 56--58; 30, pp. 17-21).






48


12
additional pounds of organic phosphates. All were careful to specify

that such a statement required gross assumptions about insect infes-

tation, weather conditions, and insect susceptibility. Because of this

great possibility of error, the model was run using rates of .3, .4,

and .5.

Given the substitution rate between chlorinated hydrocarbons and

organic phosphates, it was then necessary to translate this into a shift

in the supply function for the specified policy--a 50 percent reduction

in the per acre usage of chlorinated hydrocarbons for each crop. In

order to do this it was necessary to compute the average costs of chlo-

rinated hydrocarbons and organic phosphates by crops. These are

presented in Table 11. The difference in costs of, say, chlorinated


Table ll.--Estimated average costs of chlorinated hydrocarbons and
organic phosphates used on crops in Dade County, 1966-67.



Chlorinated Organic
Crop hydrocarbons phosphates

... Dollars per pound of 100% active agent.....
Tomatoes 1.09 3.82
Potatoes 2.46 3.01
Beans .99 2.69
Corn .90 1.82
All crops sampled 1.07 2.83



hydrocarbons between tomatoes and potatoes is due to the difference in

mix of chlorinated hydrocarbons between the-two crops. Potatoes tend


12For policies specifying a greater than 50 percent reduction,
none were willing to speculate on the substitution rate,and two indi-
cated that "adequate" control of certain insects would be difficult or
impossible with the current state of the arts.










to use a higher proportion of the more expensive chlorinated hydrocarbons

than do tomatoes, and in computing the average, each chlorinated

hydrocarbon is weighted by its relative importance in the usage mix.

Next, average total costs of chlorinated hydrocarbons plus

organic phosphates for the two alternative policies and the three substi-

tution rates were computed. These are presented in column 3 of Table 12.

In addition to a change in the cost of spray materials, there is

also a possibility that other categories of cost might change under the

alternative pesticide policy. The major ones are those likely to be

affected by a change in the required number of pesticide applications,

since the organic phosphates as a group are less persistent than the

chlorinated hydrocarbons and might require more applications for control.

The entomologists considered this question and came to the conclusion

that there would be very little increase in labor and machinery costs in

Dade County for the crops concerned. The reason for this is that a

fairly rigid fungicide program is required on all crops, and increasing

the number of insecticide applications would merely involve combining

the insecticides with the fungicide applications. Estimates of

increased labor and variable machinery expense were as follows:

Tomatoes 10%

Potatoes 10%

Beans 0

Corn 0

Changes in the cost of spray materials, labor,and variable

machinery cost were then related to the cost per acre of producing the

crops. These are showpin Table 12.

If we assume these changes in average total cost per acre are










Table 12.--Estimates of average total production costs in Dade County,
1966-67, by crop and pesticide policy.


Average
total cost


Cost of chlorinated
hydrocarbons and
organic phosphates


Cost of cultural
labor and variable
cost of machinery
cost of machinery


Tomatoes
Policy
Policy
Policy
Policy

Potatoes
Policy
Policy
Policy
Policy

Beans
Policy
Policy
Policy
Policy

Corn
Policy
Policy
Policy
Policy


repair.


................... Dollars


$866.02e $19.10
881.95 19.27
883.11 20.43
884.28 21.60


$609.24
616.57
616.76
616.95


$754.87c
754.55
755.02
755.48


$399.21'
392.01
395.71
399.42


$20.88
19.88
20.07
20.26


$ 8.27
7.95
8.42
8.88


$48.39
41.19
44.89
48.60


per acre..................


$157.61c
173.37
173.37
173.37


$ 83.32c
91.65
91.65
91.65


$189.80c
189.80
189.80
189.80


$ 47.02c
47.02
47.02
47.02


Includes cultural labor, gas, oil, grease, maintenance, and

bCurrent usage.


CSource of data: Brooke (5).
dA 50 percent reduction in chlorinated hydrocarbons and a sub-
stitution rate of .3 pounds of organic phosphates per pound of chlorinated
hydrocarbons.
eA 50 percent reduction in chlorinated hydrocarbons and a sub-
stitution rate of .4 pounds of organic phosphates per pound of
chlorinated hydrocarbons.
A 50 percent reduction in chlorinated hydrocarbons and a sub-
stitution rate of .5 pounds of organic phosphates per pound of
chlorinated hydrocarbons.










realistic over a wide range of acres, then the change represents a
13
parallel vertical shift in the marginal cost function.13
In summary, it was decided that two categories of cost--(l) cost

of spray materials and (2) cost of labor and variable machinery cost--

were most likely to be affected by the alternative pesticide policy.

The effects of the policy change on average costs per acre were deduced

from cost data currently available and the experience of entomologists

familiar with the region. These cost changes were applied as parallel

shifts in the supply functions used for the first policy. The supply

functions used in the model are presented in Table 13.


The Externality Functions

The explanations for the point estimates of pesticide usage

and externalities are developed in Chapters V and VI respectively. It

is the objective of this section to show how these estimates were used


13Using the functional forms of the model:

given:
TC = A + BY + CY2
then,
MC = B + 2CY
and,
A
AC = A + B + CY
y
where:
TC, MC, and AC are total, marginal, and average cost in dollars
per acre,and Y is the number of acres of output. A parallel vertical
shift, X, in AC gives:
A
AC' = V + (B+A) + CY
for which the corresponding TC and MC become:

TC = A + (B+A)Y + CY2
MC = B + X + 2CY
and the result is a parallel shift in MC.










Table 13.--Supply functions used in the model.a



Function


Tomatoes
Policy 1 p = 83.6862 + .0497 y
Policy 2A dp = 67.6562 + .0497 y
Policy 2B p 66.5962 + .0497 y
Policy 2Ce p = 65.4262 + .0497 yl

Potatoes
Policy 1 p = 189.0995 + .0464 y
Policy 2A p = 196.4295 + .0464 y
Policy 2B p = 196.6195 + .0464 Y2
Policy 2C p = 196.8095 + .0464 Y2

Beans
Policy 1 p = -535.2889 + .2245 Y3
Policy 2A p = -535.6089 + .2245 y3
Policy 2B p = -535.1389 + .2245 y3
Policy 2C p = -534.6789 + .2245 y3

Corn
Policy 1 p = 171.6317 + .1930 Y4
Policy 2A p = 164.4317 + .1930 Y4
Policy 2B p = 168.1317 + .1930 Y4
Policy 2C p = 171.8417 + .1930 y4

Avocados

Limes Acreages on these crops were constrained to be
no greater than the 1966-67 levels.
Mangos


aFor all functions, price is measured in dollars per acre and
quantity in acres.
current usage.

cA 50 percent reduction in chlorinated hydrocarbons and a
substitution rate of .3 pounds of organic phosphates per pound of
chlorinated hydrocarbons.
dA 50 percent reduction in chlorinated hydrocarbons and a
substitution rate of .4 pounds of organic phosphates per pound of
chlorinated hydrocarbons.
e
A 50 percent reduction in chlorinated hydrocarbons and a
substitution rate of .5 pounds of organic phosphates per pound of
chlorinated hydrocarbons.










in the model.

The model required a functional relationship between dollars of

externalities and the quantity of pesticides used. Empirically there

was no way to estimate such a function, so it was necessary to assume

a functional form and rely on sensitivity analysis for some indication

14
about how critical the assumption was to the solution. Figure 6 is

illustrative of the case for organic phosphates. The point estimate of



E
($)


$4,590._____ _





152,348 z2 (organic phosphates)
lbs.


Figure 6.--Point estimate of externalities arising from the
agricultural usage of organic phosphates.


externalities due to the organic phosphates in 1966-67 was $4,590, and

the corresponding usage on the seven crops was 152,348 pounds.15 A

linear functional form passing through the origin was assumed. This

meant that the equation was:

[8] E2 = .0301 z2


14Parametric programming could be employed on both the form of
the function and the parameters of the function. In this paper only the
parameter, or slope, of the linear function was varied.

15Some thought was given to the possibility of measuring the
second-order effects through an input-output technique, but it was later
discovered that the solution was relaLively insensitive to this parameter
and the idea was therefore abandoned.
-x_









The slope of the function was later varied from 0 to 5.0000 in incre-

ments of 1.0 to analyze the criticalness of the assumption.16

The case for chlorinated hydrocarbons presented a more

formidable problem. A point estimate of the chronic externalities could

not be observed, so the function,


E1 = el1z


was used, where e was varied over the interval 0 to 5.0000 in increments

of 1.0.


Constraints on the Objective Function


For ease of illustration, the constraints on the objective

function can be written in matrix notation as:

AB C


where:


[-i3i,n
[1i],n

[Fl]n,n_


rx
_ axillm,n
0
mp,n


J0
1 ,


Ol,m

0l,m

n- 3, m

- - - n,11- -
n-m -


[-l]m,m
[k-1
Lki] mp ,mn


16An interesting point could be raised here. Data from the
Industrial Commission indicate that acute externalities have declined.
This decline was probably in the face of rising organic phosphate usage,
which would imply a negative slope to the function of Figure 6. How is
the positive slope logically justified? The function of Figure 6
represents the relation between externalities and organic phosphate
usage, all other factors held constant. But all other factors have not,
of course, remained constant. Education may be the primary example.
The rural inhabitants of Dade County might be learning how to handle the
dangerous pesticides and this is at least a partial explanation of why
externalities have declined in spite of rising usage.









n
-d(min) Z y (t
j=l i,i
n
d(max) yj(t)

B j ( tL j = l C1


Jm 19
B= C I (In V i h (min)y (to
J j n-3,1
[zil m~ 11[bj (max)y (t) n-3,1


[113,1
Om,l

[eki] I


Coefficients of Variables in the Constraints

Matrix A will be considered first. It is the matrix of

coefficients, which had to be empirically determined. As indicated

above, most of the elements of the matrix are ones or zeros. The only

elements which had to be estimated were the a.. The c,
ij k m ,
I m,n L J mp'm
elements would also have to be estimated if the environmental constraints

were maintained in the model, but, as explained previously, the current

state of the arts did not permit estimation of these coefficients. In

the empirical model there were seven crops and two pesticides, so:

X X X
r r r
'x a ll> a12' a 17
a r
nj x x x
in _mn ar ar ar
a2 1 a 2 2 7

Since two pesticide policies with three variations on the second were

considered, the model was run once for r=l and once for r=2A, 2B, and 2C.
xI
The way in which the elements a.. were estimated is explained in
ij









Chapter V. The resulting coefficients were:

Xl 3 6.0719, 1.2860, 3.4304, 40.7003, .0706, .0484, .0205

ijmn .2669, 5.8825, 1.8114, 6.4624, .2239, .1535, .0650j


The Constraint Vector

Of the elements of the constraint vector, C, the first four sets

were flexibility constraints. The underlying philosophy of flexibility

constraints was explained in the previous chapter.

For the estimation of the maximum and minimum flexibility con-

straints, an approach called "the method of average rates" was used (11,

p. 189). For the jth crop, this took the form:

im y (t)
b (max) = E1 for y (t) > y. (t-1)
t=2 (t-l) J
mI1


M 2 y,^)
b (min) = E2 yJ )
t=2 yj (t-l)

m2

where m's were the number of p

total land constraint, it was:


for y (t) < yj (t-1)


periods involved, respectively. For the


p1
d(max) = I
t=2



P2
d(min) = E
t=2


n y (t)


zy _--
j-1 ji

j=l (t
Pl

n y.(t)
Z
j-l Y (t-1)


n
for E y (t) >
j=l J


n
for EZ y.(t) <
j=l J


n
E y.(t-l)
j=l


n
E y.(t-l)
j=l


where p's were the number of periods involved, respectively, and n the

number of crops.

In words, the maximum flexibility constraint was simply one plus

the average of the percentage increases, and the minimum flexibility









constraint was one minus the average of the percentage decreases.17 The

flexibility constraints so generated are presented in Table 14.


Table 14.--Flexibility constraints used for the empirical model.


Crop


Constraints


Total land



Tomatoes

Potatoes


.9022(44843) -



.8284(19000) J

.8527( 7660)

.9037( 5830)

.6881( 1650) j


Beans

Corn


5
j y
j=l


Yl


Avocados


Limes

Mangos


1.0835(44845)



! 1.1140(19000)

1 3.1654( 7660)

S1.1458( 5830)

S2.2872( 1650)

S5235

- 3585

S1520


aDefined as total land under cultivation after allowance for
minor crops (see page 25).


17At least two other possibilities exist for obtaining flexi-
bility constraints. First, one might choose to use the maximum
percentage changes rather than the average percentage change. Second,
one might use a regression approach, fitting the functions:


y (t) = b (max)y.(t-l)
and y j J (t)
and y.(t) b.(mln)y.(t-l)


yj (t) > y (t-l)

yj (t) J y (t-l)


The "regression approach" generated parameters which were very close to
those presented in Table 14.
















CHAPTER V


APPLICATION OF AGRICULTURAL PESTICIDES IN DADE COUNTY


The information contained in this chapter provides the descrip-

tive background and the pesticide usage estimates employed in Chapter IV

for the development of the externality functions.


Technology of Pesticide Application in Dade County


Pesticide spray programs in Dade County vary widely among

growers depending upon the crop involved, the form. of the pesticide

being used, the method of application, and the insect or disease

infestation. There are four dominant pesticide formulations--(1) wet-

table powder, (2) dust, (3) liquid or emulsifiable concentrate, and

(4) granules--and there is a range of concentrations for each.

Parathion, for example, was observed in three different formulations

and seven different concentrations.

The technology of pesticide application ranges from the

relatively primitive hand methods to aerial application using

helicopters, but most growers use ground rigs which hold one, two, three,

or four hundred gallons of liquid. Aerial application is still slightly

more expensive than ground application, but it is more flexible during

adverse weather conditions when ground rigs cannot get into the fields.

Some growers claim that aerial applications are more effective, through

better coverage, than ground rigs. The ground rigs are usually operated

58










at about four miles per hour and three hundred pounds per square inch

nozzle pressure. The number of nozzles varies from three to nine

depending upon the size of plant and desired coverage.

While recommended pesticide dosages are specified on the label

in terms of "amounts per acre," growers tend to make their calculations

in terms of "amounts per one hundred gallons of water" since their spray

rigs hold even multiples of one hundred gallons. They then try to put

the appropriate amount of liquid (water plus pesticide) on an acre so as

not to exceed the recommended dosage. The spray men are, for the most

part, limited in education, so the calculations and mixing are usually

done by the grower,and then the spray man adjusts the speed of the

tractor slightly so that (1) the plants are well covered and (2) a

minimum quantity of spray drips to the ground. In addition, the spray

men try to adjust the discharge slightly so as to run out of spray about

the time they get to the side of the field where the pesticides and

irrigation well are located. In a word, the uniform application of a

given quantity of pesticide to an acre is considerably imprecise.

Aerial spraying is usually done on a contract basis by pilots

who specialize in crop spraying. The farmer purchases the pesticides

and turns them over to the pilot with a mandate to apply a certain

quantity per acre. The pilot, knowing the size and discharge rate of

his spray rig, then mixes the pesticide with water in an appropriate

ratio to achieve the farmer's desired coverage. Boundary lines are

fairly well marked,and experienced pilots familiar with the area can

often spray a field without assistance from the ground. Others use a

helper on the ground to mark off the field boundaries and the spray

progress. Like spraying with ground rigs, aerial T .,.u'g is relatively











imprecise. Wind conditions can effect the coverage significantly, and

the spray apparatus is difficult to turn off at exactly the right moment

as the pilot turns to make another sweep of the field. A survey of

growers and entomologists in the area, conducted by Mr. Richard Hunt of

the Dade County Agricultural Agents Office, indicated that aerial

spraying on the average has increased about 10 to 20 percent over the

past 5 years but that the increase is mainly a function of weather. It

is becoming common practice for the growers to use airplanes after a

rain when it is difficult to get machinery into the fields.

The vagaries of weather are also very important in determining

the frequency with which growers must spray. Not only are certain

weather conditions more propitious than others for disease and insect

damage, but if a grower sprays his field just before a rain he must

often spray it again.right after the rain because the pesticide was

washed off the plant.

In general, it was observed that growers tend to use a pre-

determined spray schedule for disease and a random or "spray-as--needed"

program for insects.


Estimated Quantities of Pesticides Used in Dade County


One might go about estimating Dade County's agricultural usage

of pesticides in several ways. The quickest would be to assume that

growers follow labeled recommendations completely, and then develop the

estimate based on the recommendations, an assumed frequency of appli-

cation, an assumed length of growing season, and a knowl(. of the

acreage of each crop. One such estimate was made by Mr. RichardW.Klukas,










management biologist for the Everglades National Park, and his estimates

are presented in Appendix B (p. 124).

For this study, the quantity of pesticides used for agriculture

in Dade County was estimated by interviewing the growers and by using

records of the growers' pesticide purchases. The interviews provided

background information on spray programs, and, in some cases, the

growers kept individual field records which were made available.

The growers which were interviewed accounted for the following

proportions of harvested acreage in 1966-67:

Tomatoes 73.5%
Potatoes 72.5
Pole beans 65.0
Corn 96.5
Squash 29.2
Other vegetables 43.8
Total vegetables 59.0
Grovesb 12.6


aIncludes okra, peas, cucumbers, cuban pumpkins.

bIncludes avocados, limes, mangos, papayas.



When a pesticide firm makes a sale to a farmer, an invoice is

prepared in triplicate. The farmer receives one copy and the firm keeps

two. At the end of the month, when statements are sent out, another

copy is sent to the farmer along with his bill. The farmers usually

keep this second one for their records. During the interviews, the

farmers were asked for permission to copy these records or to copy the

originals from their pesticide suppliers. If the farmer preferred that

we go to his pesticide supplier, he was asked to sign a permission


We are indebted to Mr. Kliikas for permission to publish these
estimates.









statement for the benefit of the supplier. After making preliminary

visits to the suppliers to explain the research, they were approached

and asked to cooperate. Of the eight major firms in the area, six

agreed to let us copy invoices of sales to customers who had given us

permission to do so. Two refused to cooperate, so the information was

obtained from the grower's copy of the invoice. As noted previously,

there were many different pesticides used in Dade County, and most of

the pesticides were used in a number of different formulations.

Initially,there were 212 separate pesticides and formulations identified.

For a given pesticide the different formulations were converted to units

of 100 percent concentrated material and added together. This reduced

the number of pesticides to 56. The common name, trade name, and/or

chemical name of these are presented in Appendix C (p. 131).

The basic unit of observation for all of the pesticide usage

tables was either a grower or a field. Where a grower had complete

spray information on his fields, each field was considered a separate

observation. If he did not keep field data, the farmer was asked to

designate what crop every pesticide invoice was used on, and it was

assumed that the pesticide was used uniformally on all his land planted

in that crop. It was further assumed that the pesticide was used in

the month in which it was sold.

It was not particularly difficult for the farmers to match the

pesticide with the crop on which it was used since (1) the crops tend

to differ in their basic spray programs both as to ingredients and

timing and (2) most of the farmers tend to specialize in certain crops.

Many of those interviewed grew only one crop and very few grew more than

three. Justification for the assumption that the pesticide was used










in the month in which it was purchased is found in the fact that many

growers stated they were unwilling to tie up capital and warehouse space

maintaining an inventory of pesticides which were dangerous and might,

in some cases, spoil before they were used. Transportation and labor

costs were also saved since the pesticide supplier would deliver directly

to the field where the pesticide was needed.

The crop year for the pesticide usage estimates extended from

July until the following June. Most of the planting was done in

September, October, November, and December, and most of the harvesting

in January, February, March, and April. For an acre of ground, only one

crop per year was grown.

The tables of pesticide usage estimates are presented in Appendix

D (p. 139). Much of the pesticide data presented in the appendix were

not required for the empirical model but they are nevertheless descrip-

tive of pesticide usage in the region and it was felt that they might

also be of use to other researchers concerned with the pesticide issue.

Table 38 (p. 140) summarizes the data for six broad categories

of pesticides---fungicides, organic phosphate insecticides, chlorinated

hydrocarbon insecticides, carbamate insecticides, other insecticides,

and herbicides. Each is reported by crop. Much of the current pesti-

.cide controversy centers around two of the insecticide categories--

the chlorinated hydrocarbons and the organic phosphates. It is the data

on these two categories which were used in the model of Chapter III.

Data on the trend in usage of these categories through the years were not

obtained for Dade County, but the statistical series recently initiated

by the Economic Research Service of the USDA indicates that since 1964

farmers in the Southeast region (South Carolina, Georgia, Florida,










Alabama) have shifted toward the organic phosphates and away from the

chlorinated hydrocarbons while holding the total usage about the

same (51, 52).

Table 39 (p. 142) utilizes the same breakdown of pesticides and

crops,but the data are reported by months for the 1966-67 crop season.

As indicated by the table, usage of pesticides tends to peak out during

the winter months of November, December, January, and February, and

tends to be very low during other months of the year. One might wonder

if the seasonal pattern of pesticide usage combines with the life cycle

of wildlife in the region to cause any particular damage or threat of

damage. There was, however, no evidence to support or refute such an

hypothesis.

Table 40 (p. 155) presents the estimates of annual per acre

pesticide usage for 1966-67 by crop and pesticide. An estimate is also

made of the total pesticide usage for Dade County by pesticide and crop.

Finally, the trend of usage is specified where possible. In a few

isolated cases it was possible to collect data for earlier years, but

the data were not sufficient for making point estimates of pesticide

usage.

Table 41 (p. 163) shows a breakdown by pesticide, crop, and

month for 1966-67. As in Table 39 (p. 142), the intention is to indicate

the seasonal pattern of pesticide usage.

The wide variation of spray programs among growers was noted

early in this chapter. The question arose as to whether this variation

was caused because the giowiers used different pesticides or mainly

because they all used the same pesticides but in greatly varying quanti-

ties per acre. Table 42 (p. 176) was developed to try to "shed some






65


light" on this question. In general, there was a tendency for the

coefficients of variation to fall slightly in this table as compared with

Table 40 (p. 155), but the differences were not particularly striking,

and one is led to the conclusion that not only do the farmers differ

widely in the pesticides they use, but the users of a particular pesti-

cide differ widely in the quantities per acre that they employ.

















CHAPTER VI


EXTERNALITIES IN DADE COUNTY


The information reported in this chapter represents the basis

for the point estimate of externalities used in Chapter IV to synthesize

the externality functions.

For purposes of empirical measurement an externality was defined

as any "cost" which was created by the agricultural use of pesticides

but was not reflected in the supply functions of the producers. This

definition does not preclude the possibility of handling an external

benefit as a negative cost. It is empirically operational and is not

inconsistent with that presented by Buchanan and Stubblebine (7).

The estimation of externalities in Dade County was based on

information gathered from five sources, and the kinds of data needed to

activate the environmental constraints (constraint set 4, page 20) were

considered from a conceptual point of view.


The Grower Interview


A section of the grower questionnaire was devoted to questions

about human sickness, damages to wildlife and domestic animals, and

damage from drift among the growers. This interview, with additional

comments on certain questions, is presented in Appendix E (p. 184).

Most of the growers in Dade County have had some experience with

pesticide sickness. The responses of the growers indicated that such










incidents were not on the increase and possibly were decreasing in the

face of increasing organic phosphate usage. Growers stated that edu-

cation of spray men to the dangers of pesticides and the safe ways of

handling them has been a key factor in stemming the rise of such

incidents. It should be stressed that "safe ways of handling them" did

not always coincide with "recommended ways." This became very evident

from the responses to certain questions (see items 3.28, 3.29, and 3.30

in Appendix E, p. 190) asked of growers. Only one grower stated that he

required his men to wear protective clothing such as boots, gloves, and

masks while spraying. The rest of the growers indicated that they made

such equipment available but could not get their spray men to wear them.

This equipment is very uncomfortable in the hot climate of South Florida,

and most of the spray men would simply prefer to "take their chances"

with the pesticides. Individual tolerance to pesticides among spray men

seemed to vary a great deal. Some spray men were able to handle pesti-

cides with no ill effects, while other spray men tended to be very

sensitive to them. In general, sensitive spray men did not remain spray

men very long. This "natural selection" process is also a possible

explanation for the fact that pesticide sickness does not appear to be

on the increase. It was also reported that some spray men refuse to

apply the highly toxic organic phosphates such as parathion. This was

not, however, observed to be very widespread. Table 15 summarizes the

responses of the growers to questions concerning human sickness.

Grower responses to questions about damage to domestic animals

and wildlife were somewhat vague as to time of occurrence and extent of

damage. In most cases this was probably due to lack of recollection.

Many of the growers acknowledged fish-kills in the canals. Water was

















ro
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frequently taken out of the canals for irrigation and for mixing with

pesticides. Some of this water eventually seeped back into the surface

and ground waters, carrying with it some pesticide residues. It was also

likely that some residues in the surface waters flowed down from the

farming regions just north of Dade County. Growers also acknowledged

that spray rigs were frequently washed out in the canals, increasing the

pesticide residues. Most of the fish found in the canals and drainage

ditches are what conservationists call "rough fish" (not considered suit-

able for human consumption), but the migratory laborers, nevertheless,

ate them, and they were a part of the ecological system of the area.

Some wildlife could, of course, be classified as "pests" and

were purposely poisoned by the growers. This was frequently the case

with rats, blackbirds, crows, and raccoons. One grower stated that the

robin was a pest to strawberry growers in the area. Seagulls are very

plentiful in the area, and several growers stated that they were

sometimes killed by the pesticides. Three growers stated that they had

one dog each killed from drinking polluted water standing in the fields.

It is customary in the area to use honeybees to facillitate

pollination on some of the blooming crops. Squash is the dominant

example. Hives of honeybees are rented from beekeepers by the growers

and placed around the field. One beekeeper, a past president of the

Beekeeper's Association, was contacted about pesticide damage to the

bees. He stated that such damage was very common and that most bee-

keepers assumed they would lose a few bees when they were rented out.

Bees, however, reproduce very quickly and the economic loss was slight.

He further said that most of the growers were using more caution, and

that damage during the past two or three years was much less than it had










been previously. Generally there was no cash settlement above rental

cost when damage occurred.

Damage among growers from pesticide drift appeared to be a very

minor problem, although it did exist. When it occurred, the growers

usually settled the problem informally among themselves with the

damaging party "paying off" in one form or another. Those payments, we

assumed, entered the damaging party's marginal cost function and were

not construed as "externalities." The trends toward fewer crops and

larger crop fields have caused this problem to decline in recent years.

It is conceivable that external benefits may have existed due

to pesticide drift. One grower, located in the center of a number of

growers who sprayed regularly, might not have had to spray as much as

he would otherwise because of the drift from the other growers and the

protective pesticide barrier around his field. This is, however,

speculation, and we were unable to gather any data to substantiate such

an hypothesis. Table 16 is a summary of the drift problem as reported

by the growers.

As a group, the growers were well insured. Not only did almost

all of them carry workmen's compensation on their employees, but they

also carried general liability to protect them in case they damaged the

property of others. They did not, however, have insurance for cases

where others damaged their property. Whether insurance costs should be

considered as external costs will be discussed in a later section.


Insurance Claims


A Florida State law exists which requires all workmen's

compensation claims to be on file at the Florida Industrial Commission
















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in Tallahassee. The Industrial Commission was contacted and they

readily agreed to cooperate on the research. Data for Dade County for

the years 1966 and 1967 were obtained and summarized.

All of the workmen's compensation claims reported to the

Industrial Commission are classified into two groups--disabling and

non-disabling. These terms are defined as follows:

Disabling injury: a work injury resulting in death, permanent
impairment, or loss of time beyond the day or shift of occurrence.

Non-disabling injury: an injury arising out of and in the
course of employment in which there is no loss of time beyond the
day or shift of occurrence (19, 1966 edition).

Very little data are maintained on the non-disabling category. The

number of injuries in this category is much greater than for the dis-

abling category, but the cost involved is much less. Table 17 shows a

comparison between these categories for the state, the agricultural

industry, and commercial farms.

The Industrial Commission categorizes its disabling injuries in

a number of ways. The one which seemed most important for this research

was "disabling work injuries by agency." The "agency of injury" identi-

fies the object, substance, exposure or bodily motion which directly

produced or inflicted the injury (19, 1966 edition). There are 53

agency categories, one of which is called "poisons and infectious agents."

This agency is further divided into the subgroups shown in Table 18.

For the State as a whole the category "poisons and infectious

agents" is declining in importance, as indicated in Table 19. Number of

injuries, days lost, and cost have all declined percentagewise since

1962. The Dade County cost figures for 1966 and 1967, shown in Tables

20 and 21, are consistent with this finding. In 1966 the total cost for

the agency was $175,497 while in 1967 it was $116,450.
































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Table 18.--A list of the categories constituting Agency 10, "Poisons
and Infectious Agents."a



Code
number Agent


10000 Unknown or unreported
10002 Acids, n.e.ccb
10003 Citrus dermatitis
10004 Alcohol
10006 Ammonia
10008 Aniline, other dyes
10010 Arsenic
10012 Beryllium
10014 Carbon dioxide
10016 Carbon monoxide gas fumes
10018 Carbon tetrachloride
10020 Caustics, n.e.c.
10021 Soap
10022 Chlorate of lime
10024 Chlorine
10026 Chromium
10028 Coal tar distillates (naptha, benzol) carbolic acid, creosote
10029 Ethylene gas (fruit coloring)
10030 Di,tetra or perchloroethylene
10031 Dieldrin
10032 Fertilizer, n.e.c.
10033 Food, etc.
10034 Formaldehyde (formain, embalming fluid)
10036 Hydrochloric acid (muriatic)
10038 Hydrocyanic acid
10040 Hydrogen sulphide
10042 Insecticide, n.e.c.
10043 Lead or paint
10044 Malathion
10046 Mercury
10048 Metal fumes (aluminum, monel zinc, welding fumes)
10050 Nitric acid
10052 Parathion
10054 Petroleum distillates (propane, butane, methane, gasoline,
10056 Phosphorus stoddard solvent, kerosene)
10058 Smoke
10060 Sulphuric acid and battery acid
10062 Tar and pitch fumes
10064 Turpentine
10099 Chemicals and poisons, n.e.c.
10100 T.B.
10111 Anthrax
10122 Fungus infections
10133 Larvae migrans (creeping eruption)










Table 18.--Concluded.


Code
number


Agent


10144 Septic infections
10155 Undulant fever (brucellosis)
10200 Dusts
10211 Organic dust (vegetable matter)
10222 Inorganic dust with free silica
10233 Asbestos
10244 Inorganic dust except silica or asbestos
10300 Poison woods or vegetation
10999 Poisonous and infectious agents, n.e.c.


aSource of data: coding form used by the Florida Industrial
Commission.


bNot elsewhere classified.




















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The Southern Farm Bureau was the dominant agricultural insurer

in the area and readily agreed to cooperate on the project. The

following conclusions were drawn from visits with representatives of the

company.

1. Property damage claims would be negligible relative to

workmen's compensation claims.

2. The number of pesticide claims in Dade County has probably

increased in recent years but not as fast as the increase in use of

organic phosphates. Farmers in Dade County are using more caution than

previously.

3. Dade County probably has a smaller amount of pesticide damage

than other areas of the State because its agricultural industry does not

have such a high turnover of farmers and laborers, and the farmers as a

group are relatively sophisticated.

4. In setting premiums, the fact that a farmer does or does not

use pesticides, is not considered. The Farm Bureau takes a "whole risk"

approach. This provides an interesting hypothesis about the behavior

of insurance companies. With regard to a farmer or a region there might

be a "sensitivity threshold" which must be broken before a response is

generated on the part of the insurance company. This "sensitivity

threshold" could be visualized as follows:





.g __ HSensitivity
-Threshold

-1 0
0 ^


Time










When the dollar claims break the sensitivity threshold, the insurance

company responds by raising rates, cancelling policies, or some other

action. If the dollar claims are defined to be the marginal claims due

to pesticides, then external costs are imposed on the insurance company

by the farmer until the threshold is broken and the situation adjusted.


Veterinarians


In order to get additional information on the effects of pesti-

cides on domestic animals, arrangements were made to sample the records

of three veterinarians in the area. Two were located approximately on

the dividing line between the rural and urban areas, while the third was

located in Homestead, the heart of the rural area. Samples which were

as nearly random as possible were taken from each. The frequency of

pesticide calls was noted along with the diagnosis, the specie, and the

treatment. After sampling, a brief conversation was held with each

veterinarian to see if the sample bore out his a priori notions. The

veterinarians all stated, before seeing the sample results, that they

would expect the frequency of pesticide cases to be a small fraction of


IThe first vet numbered his cases sequentially and had about
10,000 of them. Each case contained an average of 4.5 calls. Using a
table of random numbers to determine the starting point, 909 cases were
examined for a total of 4,090 calls. There was no relation between a
case's sequence number and the dates of the calls contained therein.

The second vet maintained his cases alphabetically, by last name
of owner, and did not know how many he had. There were 10 small file
drawers involved so the first and last 150 calls in each drawer were
arbitrarily examined. This vet also kept a separate file for deceased
cases, and all of these were examined.

The third vet maintained his files alphabetically, by last name
of owner, and they did not lend themselves well to sampling. For this
vet, all of the "D's" were examined--a total of 275 cases with 4 calls
per case.




Full Text
24
it could not be used empirically. He did this by placing upper and
lower bounds on allowable acreage changes. The bounds were determined
by observing historical acreage changes and were based on the assumption
that the diverse factors which influenced such changes in the past were
likely to do so in the future with about the same magnitude. He then
3
incorporated these into appropriate linear programming constraints:
b_. (min)y^ (t) y^ (t+1) b^.(max)y^.(t) j=l, . .n
where:
y^ (t) = the acreage of the jth crop in period t.
b^. (min) one minus the maximum percentage decrease considered
reasonable from observing historical cropping patterns.
b.(max) = one plus the maximum percentage increase considered
reasonable from observing historical cropping patterns.
He also had a final restriction on the overall land allocation:
E y (t+1) Y
j=l J
where:
Y is the total land available for cultivation.
This research effort utilized essentially the same approach.
Maximum and minimum flexibility constraints were applied to the indi
vidual crops. The overall land restriction differed slightly from
Henderson's. The total quantity of Dade County land which could be
cultivated was not a relevant constraint because it was so much larger
than the quantity that was actually cultivated. Furthermore, the
3
Symbols have been changed in order to be consistent with those
of the model.


67
incidents were not on the increase and possibly were decreasing in the
face of increasing organic phosphate usage. Growers stated that edu
cation of spray men to the dangers of pesticides and the safe ways of
handling them has been a key factor in stemming the rise of such
incidents. It should be stressed that "safe ways of handling them" did
not always coincide with "recommended ways." This became very evident
from the responses to certain questions (see items 3.28, 3.29, and 3.30
in Appendix E, p. 190) asked of growers. Only one grower stated that he
required his men to wear protective clothing such as boots, gloves, and
masks while spraying. The rest of the growers indicated that they made
such equipment available but could not get their spray men to wear them.
This equipment is very uncomfortable in the hot climate of South Florida,
and most of the spray men would simply prefer to "take their chances"
with the pesticides. Individual tolerance to pesticides among spray men
seemed to vary a great deal. Some spray men were able to handle pesti
cides with no ill effects, while other spray men tended to be very
sensitive to them. In general, sensitive spray men did not remain spray
men very long. This "natural selection" process is also a possible
explanation for the fact that pesticide sickness does not appear to be
on the increase. It was also reported that some spray men refuse to
apply the highly toxic organic phosphates such as parathion. This was
not, however, observed to be very widespread. Table 15 summarizes the
responses of the growers to questions concerning human sickness.
Grower responses to questions about damage to domestic animals
and wildlife were somewhat vague as to time of occurrence and extent of
damage. In most cases this was probably due to lack of recollection.
Many of the growers acknowledged fish-kills in the canals. Water was


190
3.28 What types and quantities of protective equipment and clothing do
you own and make available to your employees? Discuss.
3.29 Do your employees use this equipment regularly? Discuss.
3.30 If NO, what are the major reasons for not doing so? Discuss.


22
up using a_ social welfare function which, to the researcher, represents
a compromise between desired characteristics and empirical feasibility.
Any policy decision concerning pesticide usage is likely to
benefit some parties and hurt others. The model assumes that the
2
income effects of varying prices are small enough to be ignored. Such
an assumption would not seem too unrealistic where the consumer spends
a small proportion of his income on the commodity. The model also
assumes that tastes and preferences remain constant, and that the aggre
gate demand and supply functions for a crop are independent of those in
other regions, those for other crops, and those in other industries.
The model also assumes that the marginal utility of money is
equal for all parties affected by the policy. Lerner (33, pp. 23-40)
argues that in the absence of knowledge this is the most probable
assumption. Such an argument from ignorance is not very convincing or
comforting.
Nevertheless, the assumption that men are all very much alike is
the foundation of democratic institutions ("We hold these truths
to be self-evident . .") and we certainly do not attempt in
politics to give the voter a number of votes in proportion to
his intelligence and his ability to use them. The one-man-one-
vote principle is forced upon us by the recognition of the
practical impossibility of any other (3, p. 93).
So it is with the issue at hand. We realize that some people probably
derive greater utility than others from an incremental dollar (or
greater disutility from losing a dollar), but it was a practical impos
sibility to recognize it in the analysis.
A slight variation on this assumption is Hicks' argument that
the marginal utility of money must be assumed constant (25, p. 326).
This assumption seems slightly more restrictive than the one I have
made, but as far as the structure of the model is concerned, it amounts
to the same thing.


Table 28.Model solution for Policy 2C.a
Solution
vector
->
Objective
function
in dollars
yl
Tomatoes,
. b
xn acres
y2
Potatoes,
b
in acres
y3
Beans ^
in acres
y4
Corn
. c
in acres
y5,y6,y7
Groves ,
d
m acres
Z1
Chlorinated
hydrocarbons
in poundse
Z2
Organic
phosphates
in poundse
Coefficient of:
Z1 z2
0
0
34,148,919.
19,100
7,600
5,800
1,650
10,340
106,494
183,555
0
-.0301
34,143,394.
19,100
7,600
5,800
1,650
10,340
106,494
183,555
0
-1.
33,966,181.
19,000
7,500
5,800
1,600
10,340
105,110
181,624
0
-2.
33,784,895.
19,000
7,500
5,800
1,550
10,340
104,092
180,791
0
-3.
33,604,960.
18,900
7,400
5,800
1,500
10,340
102,708
178,860
0
-4.
33,426,342.
18,800
7,400
5,800
1,450
10,340
101,388
177,549
0
-5.
33,249,130.
18,800
7,300
5,800
1,400
10,340
100,306
176,097
-1.
-.0301
34,037,824.
19,000
7,600
5,800
1,600
10,340
105,174
182,244
-2.
-.0301
33,933,383.
19,000
7,600
5,800
1,550
10,340
104,157
181,412
-3.
-.0301
33,830,079.
19,000
7,600
5,800
1,500
10,340
103,139
180,580
-4.
-.0301
33,728,189.
18,900
7,600
5,800
1,400
10,340
100,802
178,438
-5.
-.0301
33,627,388.
18,900
7,600
5,800
1,400
10,340
100,802
178,438
aA 50 percent reduction for each crop in the per acre usage of chlorinated hydrocarbons and a sub
stitution rate of .5 pounds of organic phosphates per pound of chlorinated hydrocarbons.
^Solution does not differ from the optimum by more than 100 acres.
Q
Solution does not differ from the optimum by more than 50 acres.
Grove acreage is constrained to be no more than the 1966-67 level.
e
All quantities have been converted to units of 100 percent concentrated material.
o


Table 32.Net profit per acre for a sample of pole bean growers in Dade County, 1960-61 through 1966-67
a
Grower
number
1960-1961
1961-1962
1962-1963
1963-1964
1964-1965
1965-1966
1966-1967
Unweighted
Standard
Mean deviation
1
111.95
- 88.22
b
n.a.
n.a.
n.a.
n.a.
n.a.
11.87
141.54
2
131.57
-145.59
- 67.30
187.76
n.a.
n.a.
n.a.
26.61
158.60
3
n. a.
- 80.26
- 48.25
1.56
- 49.84
n.a.
n.a.
- 44.20
33.87
4
n.a.
98.74
n.a.
85.27
n.a.
135.22
n.a.
106.41
25.84
5
n.a.
233.20
n.a.
154.18
270.93
22.55
190.47
174.26
95.53
6
n.a.
80.47
65.52
175.05
11.46
n.a.
- 14.46
63.61
73.35
7
n.a.
-117.90
- 42.60
-249.61
-232.33
n.a.
172.44
- 94.00
171.43
8
n.a.
221.94
233.52
174.62
189.90
194.40
248.97
210.56
28.68
9
n.a.
85.97
n.a.
n.a.
n.a.
24.82
n.a.
55.40
43.24
10
n.a.
n.a.
- 76.59
- 44.21
- 70.96
73.62
- 19.37
- 27.50
60.97
11
n.a.
n.a.
n.a.
125.95
n.a.
135.23
0.74
87.31
75.11
12
n.a.
n.a.
n.a.
n.a.
- 40.21
- 39.15
- 57.90
- 45.75
10.53
. 13
n.a.
n.a.
n.a.
n.a.
n.a.
430.53
181.29
305.91
176.24
Unweighted
mean
121.76
32.04
10.72
67.84
11.28
122.15
87.77
Unweighted
standard
deviation
13.87
144.63
120.56
144.11
169.22
145.62
121.40
g
Source of data: unpublished farm questionnaires collected by Dr. D. L. Brooke of the Department
of Agricultural Economics, University of Florida.
^Data not available.
122


54
The slope of the function was later varied from 0 to 5.0000 in incre-
16
ments of 1.0 to analyze the criticalness of the assumption.
The case for chlorinated hydrocarbons presented a more
formidable problem. A point estimate of the chronic externalities could
not be observed, so the function,
[9] E1 = e1z1
was used, where e was varied over the interval 0 to 5.0000 in increments
of 1.0.
Constraints on the Objective Function
For ease of illustration, the constraints on the objective
function can be written in matrix notation as:
where:
A =
AB C
[-U
l,n
1 ,m
[1]
l,n
1 ,m
{ ^n-_3>j]n~3,n
[1],
e
n-3 ,m
n ,n
6
n^m
x
r
a. .
L_iJJ
[-1]
m,n m,m
G
mp ,n
J^kijmp,m
An interesting point could be raised here. Data from the
Industrial Commission indicate that acute externalities have declined.
This decline was probably in the face of rising organic phosphate usage,
which would imply a negative slope to the function of Figure 6. How is
the positive slope logically justified? Ihe function of Figure 6
represents the relation between externalities and organic phosphate
usage, all other factors held constant. But all other factors have not,
of course, remained constant. Education may be the primary example.
The rural inhabitants of Dade County might be learning how to handle the
dangerous pesticides and this is at least a partial explanation of why
externalities have declined in spite of rising usage.


25
quantity of land allocated to farming appeared to be declining. The
constraint was therefore established to apply to the total land under
cultivation, after an adjustment had been made for the other crops.^
The form of this constraint was:
n n n
d(min) E y.(t) E y.(t+l) d(max) E y.(t)
j=l J j=l J j=l J
where:
the d(min) and d(max), as before, represent proportional changes in
total farm land cultivated from one year to the next, as determined
from historical cropping patterns.^
The third constraint requires little explanation. It was needed
in the model to insure that the quantity of the ith pesticide used on
all crops would coincide with the quantity of the ith pesticide called
for in the solution.
The fourth set of constraints was "environmental." It appears
relatively simple, but it embodies all the physical relationships
between pesticide usage and the environment. When a pesticide is used
^In 1959, 9.8 percent of the land was in farms, while in 1964,
8.9 percent was in farms.
^Other crops were those excluded from the objective function of
the model. These were approximately 20 in number and accounted for
14 percent of planted acreage in 1966-67.
^It has been suggested that the imposition of a new pesticide
policy upon the farmers might disturb the interrelationships which
caused the variations in cropping patterns from which flexibility con
straints were estimated and that the flexibility constraints would
therefore lose their applicability. WTiether or not this is true depends
upon the magnitude of the policy change and the other sources of vari
ability in cropping patterns. As will be seen in later pages, the
effect of the proposed policy on farmers' costs is relatively small and
is far overshadowed by other exogeneous factors, such as weather, with
which the farmers must contend. It is therefore this writer's opinion
that the flexibility constraints do provide reasonable estimates, of
"capacity for change" in the cropping patterns.


Table 39.Concluded
Acres
Tomatoes
10,590
Potatoes
4,584
Pole beans
2,394
Corn
735
Squash
244
Okra
50
Groves
1,247
Other
240
Observations
24
51
93
1
11
2
26
2
154


46
Furthermore, if the same pesticide is used constantly for a prolonged
period of time, it raises the probability of immunity on the part of
the insect.
In order to fulfill the data requirements of the model, it was
necessary to synthesize a substitution rate between the chlorinated
hydrocarbons and the organic phosphates. This is not to say that the
consideration is necessarily limited to two dimensions, but these two
categories are so clearly dominant that it is doubtful if the others
could have a significant effect on the average substitution rate.
Entomologists familiar with the problem generally agree that the
substitution rate which maintains equal control is not completely linear,
as shown by the iso-product curve in Figure 4, but is more likely to be
a hyperbolic function somewhat like that of Figure 5.^
Figure 5 merely says that it becomes progressively more
difficult to substitute one for the other, which is in complete agree
ment with economic theory. It also leads one to the conclusion that
sudden elimination of the chlorinated hydrocarbons may be an unattainable
Figure 4.A linear iso-product function for chlorinated
hydrocarbons and organic phosphates.
Three entomologists provided assistance in developing this
section. Two are located on the University of Florida campus and one in
Dade County.


173
Table 41.Continued.
Pesticide
Toxaphene
Standard
Crop
Month
Mean usage
in pounds
per acre
deviation
pounds
per acre
Pole beans
January
.0042
.0050
Pole beans
March
.0125
.0150
Squash
October
.0410
.0377
Squash
November
1.2108
1.4753
Squash
December
.6627
1.1868
Squash
January
.0887
.1379
Squash
February
.1639
.1508
Squash
March
.1639
.1508
Tomatoes
August
.0139
.1024
Tomatoes
September
.2678
.7886
Tomatoes
October
.3928
.5679
Tomatoes
November
.6734
.9379
Tomatoes
December
.9698
1.0812
Tomatoes
January
.8864
1.0223
Tomatoes
February
.8595
.9022
Tomatoes
March
.6765
.8250
Tomatoes
April
.1149
.3505
Potatoes
December
.0082
.0082
Potatoes
January
.2953
.6370
Potatoes
February
.0556
.3799
Potatoes
May
.0218
.0431
Pole beans
July
.0334
.0207
Pole beans
August
.0568
.0491
Pole beans
September
.0167
.0104
Pole beans
October
.0914
.5140
Pole beans
November
.2657
1.0497
Pole beans
December
.2305
.6938
Pole beans
January
.3702
.8127
Pole beans
February
.5089
1.3013
Pole beans
March
.7113
2.0649
Pole beans
April
.8132
2.8530
Pole beans
May
.0401
.0241
Corn
December
1.5240
.0000
Corn
February
4.7892
.0000
Corn
March
1.9931
.0000
Okra
March
.4000
.7071
Okra
April
.8000
1.4142
Okra
May
1.6000
2.8284
Okra
June
.8000
1.4142
Other
April
4.1667
3.5355
Other
May
.5000
2.1213


174
Table 41.Continued.
Standard
Mean usage deviation
Pesticide
Crop
Month
in pounds
per acre
pounds
per acre
Treflan
Tomatoes
September
.0125
.0413
Tomatoes
October
.0414
.0724
Tomatoes
November
.0589
.1108
Tomatoes
December
.0011
.0021
Pole beans
October
.0125
.0073
Okra
December
.7200
.8485
Other
January
.1500
.6364
Trithion
Groves
May
.1079
.5455
Groves
June
.0181
.1132
Zinc
Tomatoes
September
.0053
.0365
Tomatoes
October
.0020
.0180
Zinc sulfate
Corn
December
.6122
.0000
Corn
February
2.1769
.0000
Corn
March
1.8367
.0000
Corn
April
.2041
.0000
Groves
January
.5995
1.0108
Groves
February
.1512
1.3191
Groves
March
.0742
.3766
Groves
April
.1086
.7087
Groves
May
.3189
1.1791
Groves
June
.4319
1.6547
Zineb
Tomatoes
August
.0118
.3236
Tomatoes
September
.0142
.1518
Tomatoes
October
.0028
.0250
Tomatoes
November
.0095
.2898
Tomatoes
December
.0212
.1914
Tomatoes
February
.1105
.5428
Tomatoes
March
.0674
.3553
Tomatoes
April
.0042
.2863
Potatoes
March
.0062
.0062
Potatoes
May
.0065
.0066
Pole beans
November
.0074
.0688
Pole beans
December
.0147
.1267
Pole beans
January
. 0666
.2440
Pole beans
February
.0764
.3743
Pole beans
March
.0340
.3061
Pole beans
April
.0522
.4176
Squash
October
1.4557
2.8945
Squash
November
2.1562
2.8492
Squash
December
.9677
1.9137
Squash
January
.2668
.3503


114
One trend is currently working to make these estimates of social
cost biased upward. Companies are concentrating their research efforts
on developing new pesticides which are less persistent and more specific
to the target pest. In short, the "state of the arts" is changing in a
way favorable to the substitution which Policies 2A, 2B, and 2C are
designed to accomplish. Thus, we might expect that through time,
cheaper and more effective non-persistent pesticides will be developed
which might mitigate even the small cost differential which exists at
the current state of the arts for the alternative policy. To completely
ignore these trends would be foolish, but we were unable to explicitly
recognize them in the model. The hypothesized changing state of the arts
is illustrated in Figure 8.
(0
4-1
1
43 +
C
H
0)
60 O
tO
W
tO
<0
U
o
0)
¡5
(0
to
o
(1)
0
25
50
75
100
Percentage decrease in use of chlorinated hydrocarbons
Figure 8.Hypothesized relations between "welfare," the
state of the arts, and the usage of
chlorinated hydrocarbons.
Line 1 represents the current state of the arts. Up to some point
farmers can decrease their usage of chlorinated hydrocarbons without
a drastic decrease in welfare, but beyond this point welfare may decline


Table 21.Concluded.
Kind of payment^
Agent
1
2
3
4 5
6
7
8
Total
Petroleum distillates
(propane, butane, methane,
gasoline, Stoddard solvent,
kerosene)
1,048
1,442
492
206
0
0
0
290
3,478
Phosphorus
661
199
15
875
Smoke
259
1,507
992
2,758
Sulphuric acid and battery
acid
825
1,305
51
2,181
Tar and pitch fumes
16
16
Chemicals and poisons, n.e.c.
14,165
8,863
4,701
569
4,607
32,905
T.B.
451
30
481
Fungus infections
Larvae migrans (creeping
117
69
186
eruption)
357
390
248
17
1,012
Septic infections
636
459
77
355
1,527
Dusts
150
689
13
100
952
Poison woods or vegetation
132
359
31
522
Total
$50,405
$34,854
$12,973
$2,455
$ o
$ o
$ o
$15,763
$116,450
Source of data: computer tapes supplied by the Florida Industrial Commission.
^See footnote b of Table 26.
c
Not elsewhere classified


TABLE OF CONTENTSConcluded.
Page
APPENDIX F
ENVIRONMENTAL MONITORING PROGRAMS 191
Residues in Food and Feed 193
Pesticides in People 194
Residues in Fish, Wildlife, and Estuaries 195
Pesticides in Water 196
Pesticides in Soil 197
Pesticides in Air 200
APPENDIX G
MATHEMATICAL STATEMENTS OF POLICIES 2A, 2B, AND 2C 202
Policy 2A 203
Policy 2B 204
Policy 2C 205
BIBLIOGRAPHY 206
vii


Table 39.Continued
November
Crop
Fungicide
Organic
phosphates
Insecticide
Chlorinated
hydrocarbons Carbamates
Other
Herbicide
Total
Tomatoes
2.7878
.4828
.8263
.0725
.0591
.1534
4.3819
Potatoes
1.2088
2.3739
None
None
None
None
3.5827
Pole beans
3.9047
.2036
.2656
None
.1591
None
4.5330
Corn
None
None
None
None
None
None
None
Squash
7.1120
.1967
1.2108
None
None
None
8.5195
Okra
None
None
None
None
None
None
None
Groves
.1363
None
None
None
None
.0161
.1524
Other
None
None
None
None
None
None
None
Total average
usage
2.2231
.7934
.4647
.0369
.0483
.0789
3.6453
152


Table 17.Workmen's compensation claims, State of Florida; work injuries, days of disablity, and cost
by industry for disabling and non-disabling work injuries,
1962, 1963, 1966, and 1967.a
Non-disabling
Disabling
Total
Number
Number
Number
of
of
Days
of
Days
injuries Cost
injuries
lost
Cost
injuries
lost
Cost
1962
State total
150.3
$3,994
56.7
4,274
$26,165
207.0
4,274
$30,159
Agriculture, Forestry,
and fisheries
6.3
148
6.2
277
1,531
12.4
277
1,679
Commercial farms
4.1
100
3.2
175
970
8.0
175
1,070
1963
State total
132.7
3,751
51.9
4,517
29,216
184.7
4,517
32,967
Agriculture, forestry,
and fisheries
5.5
130
5.3
264
1,575
10.8
264
1,705
Commercial farms
3.4
85
2.5
153
945
5.9
153
1,030
1966
State total
176.9
5,045
80.8
7,705
51,599
257.7
7,705
56,644
Agriculture, forestry,
and fisheries
5.8
162
6.8
428
2,541
12.6
428
2,703
Commercial farms
3.8
106
3.5
266
1,589
7.2
266
1,695


Table 42.Continued
Pesticide
Crop
Number of
growers
Groves
1
Lindane
Tomatoes
4
Potatoes
1
Squash
1
Malathion
Tomatoes
1
Groves
1
Maneb
Tomatoes
24
Potatoes
51
Pole beans
93
Corn
2
Squash
6
Okra
1
Manganese
Tomatoes
2
Groves
1
Manganese sulfate
Groves
23
Metaldehyde
Tomatoes
4
Pole beans
1
Nab am
Tomatoes
2
Com
1
Acres
sampled
Mean usage
in pounds
per acre
Standard
deviation
pounds
per acre
600
.0537
.0000
2,075
.1289
.2598
640
.0117
.0000
37
1.8000
.0000
2,890
.0007
.0000
600
.4037
.0000
10,590
19.3253
7.3742
4,584
13.9089
3.4747
2,393
4.4690
4.0941
1,585
5.9295
6.8137
105
11.8921
9.5043
20
.9000
.0000
490
.4451
.0082
10
.3240
.0000
337
4.4070
3.3529
3,800
.1165
.1963
400
.0069
.0000
759
.1406
.0789
735
.5657
.0000
179


72
frequently taken out of the canals for irrigation and for mixing with
pesticides. Some of this water eventually seeped back into the surface
and ground waters, carrying with it some pesticide residues. It was also
likely that some residues in the surface waters flowed down from the
farming regions just north of Dade County. Growers also acknowledged
that spray rigs were frequently washed out in the canals, increasing the
pesticide residues. Most of the fish found in the canals and drainage
ditches are what conservationists call "rough fish" (not considered suit
able for human consumption), but the migratory laborers, nevertheless,
ate them, and they were a part of the ecological system of the area.
Some wildlife could, of course, be classified as "pests" and
were purposely poisoned by the growers. This was frequently the case
with rats, blackbirds, crows, and raccoons. One grower stated that the
robin was a pest to strawberry growers in the area. Seagulls are very
plentiful in the area, and several growers stated that they were
sometimes killed by the pesticides. Three growers stated that they had
one dog each killed from drinking polluted water standing in the fields.
It is customary in the area to use honeybees to facillitate
pollination on some of the blooming crops. Squash is the dominant
example. Hives of honeybees are rented from beekeepers by the growers
and placed around the field. One beekeeper, a past president of the
Beekeeper's Association, was contacted about pesticide damage to the
bees. He stated that such damage was very common and that most bee
keepers assumed they would lose a few bees when they were rented out.
Bees, however, reproduce very quickly and the economic- loss was slight.
He further said that most of the growers were using more caution, and
that damage during the past two or three years was much less than it had


31
E[u(i)u(j) ] = 0 for i j, for i = j
p(t) and I(t) are fixed numbers observed without error.
The matrix of observations on p(t) and I(t) is non-singular.
The constraint, [2], simply describes the adjustment toward the
long-run equilibrium quantity.*'
Substituting equation [2] into equation [1] generated the
2
following function, which was empirically estimated:
[3] q (t) = \|rr0 + (l-ljj)q(t-l) + iJn^pCt) + i^lCt) + l^u(t)
The primary purpose of estimating equation [3] was to obtain
estimates of the price elasticity of demand after allowing for the
average effects of I(t) and q(t-l). Data availability necessitated
using State data rather than Dade County data. It was assumed that the
price elasticity for Dade County would be the same as that for the State
since they compete in the same markets and since, in some cases, Dade
3
County represents a relatively high proportion of the State acreages.
Estimates of long-run price elasticity were thus obtained using the
1 'V
If 0 < ip < 1, q(t) approaches q(t) asymtotically. If ip = 1,
the entire adjustment is accomplished in one period. If 1 < ip < 2, q(t)
over-adjusts, but still converges upon'q(t).
Lagged variables have been increasingly employed in econometric
work in recent years [21, 31, 38].
2
In this equation, however, u(t) is no longer independent of
q(t-l), the independent variable (27, p. 212; 35, p. 129). The result
is that the least squares estimates are biased but have the desirable
asymptotic properties of consistency and efficiency.
Dade County accounted for the following proportions of the
State acreage in 1966-67:
Crop
Percent of State acreage
in Dade County
Tomatoes 39
Winter potatoes 64
Beans 12
Corn 3


93
In summary, the opposing positions seem to reduce to:
Biologist: "Until the chlorinated hydrocarbons are proved
harmless, they should not be used."
Farmer: "Until the chlorinated hydrocarbons are proved harmful,
they may be used."
Community Studies on Pesticides
Another source of information on externalities was the Community
Studies on Pesticides program of the U.S. Public Health Service and the
Florida Department of Public Health, under the direction of John E.
Davies, M.D.
The Community Studies program consisted of a nationwide series
of epidemiological and ecological studies on levels of pesticides in the
human population and environment of selected study areas. The projects
were contractual arrangements with state boards of health and medical
schools or universities whereby the Public Health Service could arrange
for and support investigations of the effects of pesticides upon human
health. The typical program had two major facetsmonitoring and
epidemiology. The monitoring program attempted to determine the levels
of pesticide residues, primarily chlorinated hydrocarbons, in human
tissues and in the environment. The epidemiological facet sought to
detect clinical illness or subtle biochemical changes in occupationally
exposed workers which could be caused by pesticides.
The Dade County program was relatively new, having been set up
in 1964, and did not have a great deal of output useful for our research,
especially in the area of chronic or long-term damage. Their accumulated
knowledge, however, was a valuable source of information on the "human


187
2.10 Have you had evidence of movement of pesticides from one of your
crops to another through the soil, air or water?
Most o the. gnowens msponded "No". Sevenal yeaxs ago tnaces o
Endnin wene {ound in a ew ields whe/ce gnowens claimed no Endnin
had bn used, bat no such incidents wene neponted in the past l
to 3 yeans.
2.11 Do you think that this will become an increasingly important
problem?
No.
2.12 About how often have your crops been checked for residues by the
Florida Department of Agriculture? Discuss.
Thi Elohida Vepantment o Agnicultune is veny actio i in this ama
and pn.acticaJU.ij aJU thi gnowens have had cnop checks. Even thorn
Mho have had violations mm to applaud thi wonk dom by thi
Vepantment.
2.13 Has any other agency ever checked your crops for residues? Which
agency? Discuss.
Most o thi gnowens a/Li mam that "some Eidi/ial Agincy" checles
thein cnops, usually in Atlanta. A m have had limited contact
with thi E.V.A.
2.14 How do you dispose of your pesticide containers? Discuss.
Many o thi gnowens anltted mgliginci on this point. A cu)
exencised cam to bu/in bags and place old ca in a centnal dumping
location, but [/torn this wnlten's obse/tvations, thi disposal o
pesticide container in Vadi County is a veny haphazand pnoces.s.
Ft is not uncomnon to ind thm Simply thnown into thi dnainagi
canats, and omi o thi gnowens adriiltted to dispoing o thm in
this way. Thi magnitude o this pnoblm hould not be undenesti-
mated. A la/ige gnowen can accumulate a gneat many mpty pesticide
contaim/is oven the peniod o one eason. Ft would do no good to
bun.y them, on. the ground waten table is jut below the sunace,
and the "gnound" is olid nock anyway. A a nesult, the gnowe/is
ue orne o the can a/iound the anm to coven innigation wells, etc.,
they tnade in the langen cans to the pesticide inns, they ell as
many as poible to alvage iims, and the balance is inply thnown
in ink holes, canals, on dumped in a cent/ial location.
With mgand to papen bag, at least one gnowen to/ted that he
docs not allow his pnay men to bunn thm because o the dange/LOus
moke.
2.15 Have you ever had pesticide tolerance violations on your products?
Discuss.
Vintually all o the gnowens answened this question in the negative.
This is pa/itly due to the natune o the cnops which wene sampled.
ieay vegetables, such as leituce and cabbage, am the most
lnequent. violatons, and these a/ie veny tninon c)iops in Vade County.


210
BIBLIOGRAPHYConcluded.
54. U.S. President's Science Advisory Committee. Use of Pesticides.
Washington, D.C.: U.S. Government Printing Office, May 15, 1963.
55. Winch, David M. "Consumer's Surplus and the Compensation
Principle," American Economic Review, Vol. LV, June, 1965.
56. Woodwell, George M. "Toxic Substances and Ecological Cycles,"
Scientific American, Vol. CCXVI, No. 3, March, 1967.


Table 40.Continued
Pesticide
Mean usage
in pounds
Crop per acre
Toxaphene
Tomatoes
4.8550
Potatoes
.3809
Pole beans
3.1381
Corn
20.3291
Okra
3.6000
Other
4.6667
Treflan
Tomatoes
.1140
Pole beans
.0125
Okra
.6200
Other
.1500
Trithion
Groves
.1259
Zinc
Tomatoes
.0073
Zinc sulfate
Corn
2.2397
Groves
1.6844
Zineb
Tomatoes
.2416
Potatoes
.0128
Pole beans
.2512
Squash
4.9940
Groves
1.7347
Ziram
Tomatoes
.0013
Pole beans
.0114
Standard
deviation
pounds
per acre
Total
usage
by crops
in pounds
Total
usage
all crops
in pounds
3.8401
.8972
2.8221
17.0029
6.3640
1.4142
.2006
.0073
.8485
.6364
.5575
.0447
3.4150
3.4391
.9191
.0128
.6742
5.5088
2.1649
.0116
.0137
92,245
2,918
18,295
33,543
2,160
10,033
2,166
73
432
323
1,348
139
3,696
18,032
4,590
98
1,464
15,382
18,570
25
66
159,194
2,994
1,348
139
21,728
40,104
91
Trendd
of usage
Up
Down
Uncertain
Uncertain
Uncertain
Uncertain
Down
Uncertain
Uncertain
Uncertain
Uncertain
Uncertain
Uncertain
Uncertain
Down
Uncertain
Uncertain
Uncertain
Uncertain
Uncertain
Down
161


167
Table 41.-
Pesticide
Guthion
Heptachlor
Karathane
Lindane
Malathion
Continued.
Crop
Month
Groves
May
Groves
June
Groves
September
Tomatoes
September
Tomatoes
October
Tomatoes
November
Tomatoes
December
Tomatoes
January
Tomatoes
February
Tomatoes
March
Pole beans
November
Pole beans
January
Pole beans
February
Pole beans
March
Groves
January
Tomatoes
September
Tomatoes
October
Tomatoes
November
Tomatoes
December
Potatoes
March
Squash
December
Squash
January
Squash
February
Squash
March
Groves
January
Tomatoes
Augus t
Tomatoes
September
Tomatoes
October
Tomatoes
November
Tomatoes
December
Tomatoes
January
Tomatoes
February
Tomatoes
March
Tomatoes
April
Potatoes
March
Squash
October
Tomatoes
August
Groves
August
Groves
September
Mean usage
in pounds
per acre
Standard
deviation
pounds
per acre
.8409
3.0636
.7394
4.1934
.0658
.0268
.0774
.2601
.0084
.2518
.0208
.0681
.0944
.2845
.0787
.2493
.0104
.0510
.0028
.0192
.0047
.0888
.0293
.0171
.0543
.0317
.0042
.0024
.0261
.0411
.0312
.0586
.0468
.0944
.0935
.1758
.0002
.0019
.0010
.0010
.0492
.0452
.0984
.0905
.0492
.0452
.0492
.0452
.0258
.0105
.0011
.0052
.0132
.0650
.0011
.0102
.0003
.0025
.0009
.0085
.0038
.0186
.0028
.0139
.0019
.0093
.0001
.0002
.0016
.0016
.2730
.5427
.0002
.0001
.1672
.0682
.0270
.0110


CHAPTER VI
EXTERNALITIES IN DADE COUNTY
The information reported in this chapter represents the basis
for the point estimate of externalities used in Chapter IV to synthesize
the externality functions.
For purposes of empirical measurement an externality was defined
as any "cost" which was created by the agricultural use of pesticides
but was not reflected in the supply functions of the producers. This
definition does not preclude the possibility of handling an external
benefit as a negative cost. It is empirically operational and is not
inconsistent with that presented by Buchanan and Stubblebine (7).
The estimation of externalities in Dade County was based on
information gathered from five sources, and the kinds of data needed to
activate the environmental constraints (constraint set 4, page 20) were
considered from a conceptual point of view.
The Grower Interview
A section of the grower questionnaire was devoted to questions
about human sickness, damages to wildlife and domestic animals, and
damage from drift among the growers. This interview, with additional
comments on certain questions, is presented in Appendix E (p. 184).
Most of the growers in Dade County have had some experience with
pesticide sickness. The responses of the growers indicated that such
66


14
peppers, and eggplant. The total acreage of marl soils cultivated in
these vegetable crops has averaged 18,000 to 20,000 acres annually for
the past 15 years.
There are approximately 279,000 acres of rockland in Dade
County. The rockland area extends roughly from U.S. Highway 1 west to
the Everglades Park boundary and from the Tamiami Trail on the north to
Florida City and Florida Highway 27 leading to the Everglades Park.
The rockland is an oolitic limestone formation with many
solution holes and is relatively soft until it is exposed to the air.
The solution holes below the surface act as storage places for water.
Near the surface these cavities are filled with loam and loamy fine sand
having varying shades of red coloring.
Prior to 1938 farming of the rockland was confined to the larger
solution holes and to pockets broken up by dynamiting. Heavy tractors
and scarifying equipment are now used to chisel or "plow" this soil so
that all of the area can be farmed. Vegetable farms are plowed 6 to 8
inches deep over the entire field. Orchards are planted in trenches
that have been cut and crushed to a depth of 20 to 24 inches. The
trenches are back filled after cutting, and young trees are set in the
crushed rock trench that is formed.
Irrigation wells are drilled into the oolitic rock 15 to 40 feet
deep and usually about 300 feet apart. No casing is required. One-half
to an inch of water is applied by overhead irrigation each week. Most
farmers use a single irrigation head on a portable pump, which covers
roughly two acres.
Only vegetables that produce marketable products above the
ground such as tomatoes, corn, squash, beans, cantaloupe, okra, southern


175
Table 41.Concluded.
Standard
Mean usage
deviation
in pounds
pounds
Pesticide
Crop
Month
per acre
per acre
Squash
February
.1475
.1357
Groves
January
.9405
1.6224
Groves
February
.7563
.4040
Groves
May
.0241
.0098
Groves
June
.0138
.4412
Zirara Tomatoes
September
.0008
.0071
Tomatoes
October
.0005
.0045
Pole beans
March
.0114
.0137
Z. P. rat bait Tomatoes
November
.0002
.0019
Tomatoes
December
.0000
.0000
Tomatoes
January
.0001
.0000
Tomatoes
February
.0001
.0004
Potatoes
January
.0001
.0001
Pole beans
November
.0018
.0013
Pole beans
December
.0033
.0016
Pole beans
January
.0026
.0010
Pole beans
February
.0007
.0004
Pole beans
March
.0017
.0008
Pole beans
April
.0035
.0013
All quantities have been
converted to
units of 100
percent
concentrated material.
^Acres sampled and number
of observations were:
Acres
Observations
Tomatoes
10,590
24
Potatoes
4,584
51
Pole beans
2,394
93
Corn
735
1
Squash
244
11
Okra
50
2
Groves
1,247
26
Other
240
2


15
peas, and leaf crops can be grown on rockland. Tree crops in Dade
County, including limes, mangos, and avocados, are grown exclusively on
rockland soils. During the last 15 years, an average of 28,000 acres of
vegetable crops has been produced on rockland soils, and an average of
20,000 acres has been devoted to tree crops.
Both marl and rockland soils are underlaid by Miami oolitic
limestone, which is perforated with numerous vertical solution holes.
The oolite underlies the Atlantic Coastal Ridge south of Boca Raton in
Palm Beach County to an average depth of about 20 feet. Deposits are
thicker near the coast and then fan out to a feather edge in western
Dade and Monroe Counties. It overlies the Tamiami formation, another
porous limestone. These two limestone formations make up the Biscayne
Aquifer, the main source of water for Dade County.
Of the total land area in Dade County (1,314,560 acres) about
117,000 acres or 8.9 percent was apportioned to farms in 1964; in 1959
129,000 acres or 9.8 percent was in farms. Although some growers stated
that suitable farm land is becoming difficult to obtain, it is more
likely that other constraints, such as markets and capital and labor
requirements, are more restrictive than the physical quantity of land.
Metropolitan Dade County is one of the youngest and fastest
growing major metropolitan areas in the United States. Between 1890 and
1962, a span of 72 years, the population increased from 861 to more than
1 million persons. Between 1950 and 1960 the population increased
almost 90 percent as indicated in Table 3.
Currently, the population of the county is estimated at about
1.1 million by the Metropolitan Dade County Planning Department, and
will approach 2.5 million by 1995 if the current growth rates continue.


Copyright by
William Franklin Edwards
1969


Table 27.Model solution for Policy 2B.a
Solution
vector
->
Objective
function
in dollars
yl
Tomatoes,
. b
in acres
y2
Potatoes^
in acres
y3
Beans
in acres
y4
Corn
c
in acres
y5y6y7
Groves ^
in acres
Z1
Chlorinated
hydrocarbons
in poundse
Z2
Organic
phosphates
in poundse
Coefficient of:
Z1 Z2
0
0
34,181,711.
19,100
7,600
5,800
1,650
10,340
106,494
172,867
0
-.0301
34,176,508.
19,100
7,600
5,800
1,650
10,340
106,494
172,867
0
-1.
34,009,205.
19,000
7,500
5,800
1,600
10,340
105,110
171,074
0
-2.
33,838,131.
19,000
7,500
5,800
1,600
10,340
105,110
171,074
0
-3.
33,668,063.
18,900
7,400
5,800
1,550
10,340
103,726
169,281
0
-4.
33,499,292.
18,900
7,400
5,800
1,500
10,340
102,708
168,551
0
-5.
33,331,483.
18,800
7,300
5,800
1,450
10,340
101,324
166,758
-1.
-.0301
34,070,438.
19,100
7,600
5,800
1,600
10,340
105,477
172,137
-2.
-.0301
33,965,806.
19,100
7,600
5,800
1,550
10,340
104,157
170,958
-3.
-.0301
33,862,312.
19,000
7,600
5,800
1,500
10,340
103,139
170,228
-4.
-.0301
33,760,031.
18,900
7,600 .
5,800
1,450
10,340
101,819
169,049
-5.
-.0301
33,659,114.
18,900
7,600
5,800
1,400
10,340
100,802
168,319
0,
A 50 percent reduction for each crop in the per acre usage of chlorinated hydrocarbons and a
substitution rate of .4 pounds of organic phosphates per pound of chlorinated hydrocarbons.
^Solution does not differ from the optimum by more than 100 acres.
c
Solution does not differ from the optimum by more than 50 acres.
^Grove acreage is constrained to be no more than the 1966-67 level.
0
All quantities have been converted to units of 100 percent concentrated material.
109


Table 39.Continued.
May
Crop
Fungicide
Insecticide
Herbicide
Total
Organic
phosphates
Chlorinated
hydrocarbons
Carbamates
Other
Tomatoes
.0082
None
None
.0272
None
None
.0354
Potatoes
.0262
.0013
.0327
None
None
.0022
.0624
Pole beans
.0656
.0969
.2105
None
None
None
.3730
Corn
None
None
None
None
None
None
None
Squash
None
None
None
None
None
None
None
Okra
13.5000
.3600
2.4000
None
None
None
16.2600
Groves
4.7337
.1079
None
None
.3249
.0064
5.1729
Other
1.1250
None
.7500
None
None
None
1.8750
Total average
usage
.3461
.0187
.0458
.0138
.0194
.0009
.4447
97T


192
In 1961, the Secretaries of Defense, Interior, Agriculture, and
Health, Education, and Welfare undertook the formation of the Federal
Pest Control Review Board with the intent that it would review "... the
various programs conducted by Federal agencies for control of forms of
invertebrate and plant life which adversely affect mans interests, and
shall consider problems and developments in the field of chemical control,
with particular reference to possible adverse effects and the adequacy
of provisions for the proper use of pesticidal chemicals to insure the
greatest public and national benefit" (16, Foreward). The Board was
directed to turn its attention to all aspects of pest control, including
the need (safety to man, domestic animals, wildlife, and the environment
in general) and alternative methods. The Board was instructed to advise
the Departments on modifications in plans that would be in the best
public interest in view of these and related matters. The major impetus
for monitoring, however, arose from the report of the President's
Science Advisory Committee on "Use of Pesticides" in 1963 (54). In
1964, in response to this report, these four Secretaries reorganized the
Board as the Federal Committee on Pest Control. The reorganization was
necessary to expand the collaboration in two directions: first, to
permit the new Committee to cover all aspects of pest controlresearch,
monitoring of the environment for pesticides, and public information
programsas well as to review operational programs; secondly, to extend
their council to all Federal programs involving pests and their control.
The President's Science Advisory Committee also recommended that
the concerned agencies develop a continuing net work to monitor residue


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Table 38.Estimated quantities per acre of certain pesticide categories used by farmers in Dade County,
1966-67 crop year, by crop.k
Crop
Fungicide
Organic
phosphates
Insecticide
Chlorinated
hydrocarbons Carbamates
Other
Herbicide
Total
Tomatoes
24.4666
3.2669
6.0719
2.4925
.1837
.9190
37.4006
Potatoes
15.5264
5.8852
1.2860
.0576
.1034
9.6740
32.5326
Pole beans
61.5815
1.8114
3.4304
None
.0137
.0605
66.8975
Corn
15.8680
6.4624
40.7003
None
None
.8539
63.8846
Squash
43.6890
1.0344
2.6039
None
None
None
47.3273
Okra
34.7124
3.2540
5.0000
None
.7200
None
43.6864
Groves
27.7465
.4424
.1395
3.0345
3.2603
.5611
35.1843
Other
8.1333
.2225
4.9167
None
.1500
None
13.4225
Total average
usage
25.6548
3.5680
5.0308
1.4613
.3943
2.6505
38.7597
aAll
quantities have
been converted
to pounds of
100 percent
concentrated
material.
Acres sampled and number of observations were:


194
and feed is primarily comprised of surveillance programs maintained by
the Food and Drug Administration, U.S. Department of Health, Education,
and Welfare. Data on residues in meat samples are provided by the
Livestock Slaughter Inspection Division, Consumer and Marketing
Service, U.S. Department of Agriculture. The objective of this program
is to determine the levels of pesticide residues in unprocessed and
commercially processed consumer food commodities, animal feeds, and
composites of food items prepared for human consumption. Studies being
carried out to accomplish this objective include (1) a continuing Market
Basket study to assay pesticide residues in the basic 2-week diet of a
19-year-old male, statistically the Nation's largest eater, and (2)
nationwide surveillance of unprocessed food and feed (14, p. 1).
Pesticides in People
The program for assessing pesticide residue levels in the
Nation's populace is being carried out by the Pesticides Program,
National Communicable Disease Center, Bureau of Disease Prevention and
Environmental Control, Public Health Service, U.S. Department of Health,
Education and Welfare.
The purpose of the human monitoring program is to determine on
a national scale the levels and trends of certain more commonly used
pesticide chemicals, both in the general population and in population
segments where the occurrence of more extensive exposure to pesticides
is known or suspected. The present monitoring program will provide
statistical and epidemiological information for use in the evaluation
of the significance of man's total exposure to pesticides.
Monitoring studies are of two types, a limited national survey


Table 15.Concluded.
Case
number
Pesticide
Date
Time in hospital
Comments
23
Parathion
1965
Grower's 17-year-old son spilled "8E liquid Para
thion," one of the strongest concentrations, on his
leg. The grower stripped him immediately, washed him
from the waist down, then sent him home to bathe
again. No problems developed.
24
Thimet
1966
Grower reported that Thimet was used on corn or
potatoes very close to his home, and it caused his
wife and several neighbors to become ill. It caused
nausea, headache, and loss of equilibrium. Grower
stated that the proximity of the usage to his home
was in violation of recommendations.
25
Parathion
Grower reported that over a 20-year period he had 8
or 10 men to get sick from Parathion. The last
incident was in approximately 1960. In no cases did
anyone lose more than a day or two from work.
26
Parathion
1963
"A number of days"
Grower reported that spray man absorbed Parathion
through his skin. He never allowed the man to spray
again.


37
Table 5.Demand functions
used in the model.
a
Crop
Tomatoes
Potatoes
Beans
Corn
Avocados
Limes
Mangos
Function
p = 1521.5660 .0334 y
p 1116.6649 .0747 y2
p = 1152.4799 .0650 y3
p = 764.1282 .1661 y4
p = 571.3720 .0479 y5
p = 3285.4824 .7502 y6
p = 690.9032 .1911 y?
For all functions, price is measured in dollars per acre
and quantity in acres.


Table 39.Continued
Augus t
Crop
Fungicide
Organic
phosphates
Insecticide
Chlorinated
hydrocarbons Carbamates
Other
Herbicide
Total
Tomatoes
.1107
.0864
.0825
.0005
.0090
.2226
.5117
Potatoes
None
None
None
None
None
None
None
Pole beans
None
.0157
.0581
None
None
.0167
.0905
Corn
None
None
None
None
None
None
None
Squash
None
None
None
None
None
None
None
Okra
None
None
None
None
None
None
None
Groves
None
.1672
None
None
.0945
None
.2617
Other
None
None
None
None
None
None
None
Total average
usage
.0563
.0558
.0486
.0002
.0102
.1151
.2862
149


209
BIBLIOGRAPHYContinued.
42. Scrimshaw, Nevin S. "Food," Scientific American, Vol. CCIX,
September, 1963.
43. Stepp, J. M. and Macaulay, H. H. The Pollution Problem, Analysis
No. 16. Washington, D.C.: American Enterprise Institute,
October 10, 1968.
44. Tabor, Elbert C. "Contamination of Urban Air Through the Use of
Insecticides," Transactions of the New York Academy of Sciences,
Vol. XXVIII, Series 2, 1965-1966.
45. Tintner, Gerhard and Patel, Malvika. "Evaluation of Indian
Fertilizer Projects: An Application of Consumer's and Producer's
Surplus," Journal of Farm Economics, Vol. XLVIII, No. 3, Part I,
August, 1966.
46. U.S. Bureau of the Census. U.S. Census of Population: 1960,
Vol. I, Characteristics of the Population, Part II, Florida.
Washington, D.C.: U.S. Government Printing Office, 1963.
47. Census of Agriculture, 1964: Statistics
for the State and Counties, Florida. Washington, D.C.: U.S.
Government Printing Office, 1967.
48. Census of Agriculture, 1964. Statistics
by Subject: Introduction. Washington, D.C.: U.S. Government
Printing Office, 1968.
49. U.S. Congress, Senate, Subcommittee on Reorganizations of the
Committee on Government Operations. Hearings, Interagency
Coordination in Environmental Hazards (Pesticides). 88th Cong.,
1st and 2nd Sessions, Part 1, Appendeces I-V, and Parts 2-11,
May, 1963-July, 1964.
50. U.S. Department of Agriculture. Agricultural Statistics. Edd. 1948
through 1967. Washington, D.C.: U.S. Government Printing Office.
51. U.S. Department of Agriculture, Economic Research Service. Extent
of E'arm Pesticide Use on Crops in 1966. Agricultural Economic
Report No. 147. Washington, D.C.: U.S. Government Printing
Office, October, 1968.
52. Farmers' Expenditures for Pesticides in 1964.
Agricultural Economic Report No. 106. Washington, D.C.: U.S.
Government Printing Office, January, 1967.
53. U.S. Department of the Interior, Fish and Wildlife Service.
Vjildlife Research Problems Programs Progress, 1966. Washington,
D.C.: U.S. Government Printing Office.


169
Table 41.Continued.
Standard
Pesticide
Crop
Month
Mean usage
in pounds
per acre
deviation
pounds
per acre
Metaldehyde
Tomatoes
August
.0090
.1015
Tomatoes
September
.0049
.0177
Tomatoes
October
.0091
.0327
Tomatoes
December
.0188
.0141
Pole beans
September
.0012
.0007
Nabam
Tomatoes
August
.0047
.0230
Tomatoes
September
.0054
.0213
Corn
December
.0629
.0000
Corn
February
.2604
.0000
Corn
March
.2424
.0000
Paraquat
Tomatoes
August
.0004
.0033
Groves
January
.0365
.0831
Groves
February
.0236
.1490
Groves
March
.0066
.0069
Groves
April
.0172
.1078
Groves
May
.0064
.0026
Groves
June
.0151
.1057
Groves
September
.0113
.0046
Groves
October
.0080
.0033
Groves
November
.0161
.0066
Parathion
Tomatoes
August
.0827
.1981
Tomatoes
September
.3971
1.5927
Tomatoes
October
.1639
.5511
Tomatoes
November
.3294
.7291
Tomatoes
December
.3017
.5076
Tomatoes
January
.2304
.6013
Tomatoes
February
.1945
.3587
Tomatoes
March
.0888
.6588
Tomatoes
April
.0108
.1424
Tomatoes
May
.0013
.0013
Potatoes
October
.2291
.2143
Potatoes
November
.8314
1.1065
Potatoes
December
.6690
1.5519
Pole beans
July
.0100
.0062
Pole beans
August
.0167
.0127
Pole beans
October
.0825
.4784
Pole beans
November
.1618
.4869
Pole beans
December
.1614
.3147
Pole beans
January
.1667
.3740
Pole beans
February
.1889
.5567
Pole beans
March
.1907
.5519


189
3.21For each instance of the preceding, please give the following
information:
Approximate Crop(s)
Date Damaged
Approximate Damage ($ and/or
Pesticide physical ternsreduction in yield)
3.22 Did the damaging party pay you for the damage? Discuss.
3.23 Do you have any kind of crop insurance to protect you against these
kinds of risks? Discuss.
3.24 Do you have insurance for the case when some of your pesticide
damages another producer? Discuss.
3.25 If YES, who is the insurance with?
3.26 Have any of your pesticides ever damaged other producer's crops?
3.27 Again, for each instance of the above, please give the following
information:
Approximate Crop(s) Approximate Damage ($ and/or
Date Damaged Pesticide physical termsreduction in yield)


APPENDIX B
ESTIMATES OF PESTICIDE USAGE COMPILED BY
A MANAGEMENT BIOLOGIST AT THE
EVERGLADES NATIONAL PARK


92
the former is now moving along rapidly, but statistical evidence to date
has been inconclusive and, in some cases, contradictory (28, 32, 34, 53,
56). Research on the latter is in its infancy, and results which would
be useful for policy formulation will probably not be available for
several years.
A series of memos describing certain major species in the
Everglades National Park was received from the Park biologists, but
these were written in such general terms that it was impossible for us
to develop population estimates from them. Dr. William B. Robertson,
research biologist, stated that he had observed no distinct relation
between pesticide usage and the population of wildlife species in the
Park.^
From discussions with the biologists this writer was able to
reach two hopefully unbiased conclusions about the position of
biologists.
First, biologists are far more afraid of chlorinated hydrocarbons
as a group than the organic phosphates and would favor policies designed
to encourage the substitution of organic phosphates for chlorinated
hydrocarbons. This stems from a feeling that long-term, sub-lethal
exposure to the chlorinated hydrocarbons is detrimental, primarily to
the reproductive process. Since organic phosphates decompose quickly in
the environment, their effects tend to be acute and are not likely to
have hereditary ramifications.
Second, while there is as yet no conclusive proof of the long-
range detriment of low exposure levels, the circumstantial evidence is
increasing rapidly (29).
2
From a telephone conversation with W. F. Edwards.


170
Table 41.Continued,
Standard
Mean usage deviation
Pesticide
Crop
Month
in pounds
per acre
pounds
per acre
Pole beans
April
.3187
1.4970
Pole beans
May
.0969
.0482
Corn
December
2.9384
.0000
Corn
February
2.1186
.0000
Corn
March
3.4520
.0000
Corn
April
.8369
.0000
Squash
October
.2911
.5789
Squash
November
.1967
.1809
Squash
January
.3279
.3015
Squash
March
.1639
.1508
Okra
February
.8000
1.4142
Okra
April
.8000
1.4142
Okra
June
.1600
.2828
Groves
September
.0962
.0392
Phosdrin
Tomatoes
August
.0009
.0047
Tomatoes
December
.0024
.0213
Tomatoes
February
.0099
.0357
Tomatoes
March
.0019
.0093
Tomatoes
April
.0008
.0037
Potatoes
January
.0218
.1081
Potatoes
February
.2568
.3520
Potatoes
March
.0581
.2445
Pole beans
March
.0063
.0075
Okra
April
.6000
.1178
Okra
May
.3600
.0471
Phosphoric acid
Potatoes
January
.0073
.0640
Potatoes
February
.0120
.0460
Phygon
Pole beans
January
.0528
.0211
Polyram
Potatoes
January
.2199
.4343
Potatoes
February
.1445
.2854
Prolin rat bait
Tomatoes
February
.0098
.0354
Potatoes
March
.0046
.0090
Sevin
Tomatoes
Augus t
.0005
.0022
Tomatoes
November
.0725
.0498
Tomatoes
December
.3021
.2073
Tomatoes
January
.5688
.3904
Tomatoes
February
1.5206
1.0430
Tomatoes
March
.0008
.0014


Table 40.Continued.
Pesticide
Mean usage
in pounds
Crop per acre
Lindane
Tomatoes
.0253
Potatoes
.0016
Squash
.2730
Malathion
Tomatoes
.0002
Groves
.1942
Maneb
Tomatoes
19.3253
Potatoes
13.9089
Pole beans
4.4690
Corn
5.9295
Squash
5.1516
Okra
. 3600
Manganese
Tomatoes
.0206
Groves
.0026
Manganese sulfate
Groves
1.1910
Metaldehyde
Tomatoes
.0418
Pole beans
.0012
Nab am
Tomatoes
.0101
Corn
.2623
Parathion
Tomatoes
1.7992
Potatoes
1.7308
Standard
deviation
pounds
per acre
Total
usage
by crops
in pounds
Total
usage
all crops
in pounds
Trendd
of usage
.1126
481
Uncertain
.0016
12
Uncertain
.5429
841
1,334
Uncertain
.0001
4
Uncertain
.0792
2,079
2,083
Uncertain
7.3742
367,181
Up
3.4747
106,542
Up
4.0941
26,054
Up
6.8137
9,784
Uncertain
7.8050
15,867
Uncertain
.6364
216
525,644
Uncertain
.1256
391
Uncertain
.0635
28
419
Uncertain
3.5145
12,750
12,750
Uncertain
.1169
794
Uncertain
.0007
7
801
Uncertain
.0407
192
Uncertain
.4000
433
625
Uncertain
2.1804
34,185
Uncertain
1.6397
13,258
Uncertain
158


Table 40.Estimated
pesticide usage3
in Dade County,
Florida, by crop and pesticide, 1966-67
b
crop year.
Standard
Total
Total
Mean usage
deviation
usage
usage
in pounds
pounds
by crops
all crops
Trend
Pesticide
Crop
per acre
per acre
in pounds
in pounds
of usage
Agrimycin
Tomatoes
.0291
.1338
553
553
Uncertain
Aldrin
Groves
.1203
.0490
1,287
1,287
Uncertain
Amine 2,4-D
Potatoes
.0313
.0991
240
240
Uncertain
Arsenate of lead
Potatoes
.0842
.0676
645
645
Uncertain
Atrazine
Corn
.8539
.3225
1,409
1,409
Uncertain
Botran
Tomatoes
.0167
.0115
317
Uncertain
Potatoes
.0157
.0158
120
Uncertain
Pole beans
2.8755
4.3910
16,764
17,201
Up
Captan
Tomatoes
.1447
.3802
2,749
Uncertain
Potatoes
1.1003
.7847
8,428
Down
Okra
.4800
.5657
288
Uncertain
Other
.1000
.4243
215
11,680
Uncertain
Chlordane
Tomatoes
.2096
.1929
3,982
Down
Potatoes
.0016
.0013
12
3,994
Uncertain
Citrus oil
Groves
.4970
.3744
5,320
5,320
Uncertain
155


100
they were statistically identifiable. Variation among individuals of a
specie, for example, might be large enough to obscure the relations
depicted in Graphs 1 and 3. Also, the ability of a specie to adapt to
a new environment might make such relationships dynamic and of little
lasting stability.
Environmental monitoring, encompassing the three levels of
knowledge mentioned earler, has not yet generated enough historical
data and has not yet acquired a level of sophistication adequate for
inclusion in the model. Different laboratories analyzing the same
sample still come up with widely differing results, due in part to
differences in testing procedures and equipment. Until these differ
ences are l'econciled, monitoring data will be of limited usefulness to
policy makers. Nevertheless, it was recognized in the theoretical model
because of its anticipated important role in policy decisions of the
future.^
Concluding Remarks on Externalities in Dade County
The problem with many writings on the pesticide issue is that
they quickly degenerate into an enumerative description of incidents in
which pesticides represent either the culprit or the hero depending on
which side of the issue the writer espouses. Conclusions cannot easily
be drawn from such a process. For analytical purposes one would like
to aggregate the incidents with some common measure, and incorporate
them into a benefit-cost analysis.
A brief summary of selected monitoring programs is presented
in Appendix F (p. 191).


TABLE OF CONTENTSContinued.
Page
CHAPTER VI
EXTERNALITIES IN DADE COUNTY 66
The Grower Interview 66
Insurance Claims 73
Veterinarians 89
Biologists 90
Community Studies on Pesticides 93
Environmental Monitoring 94
Concluding Remarks on Externalities in Dade
County 100
CHAPTER VII
ANALYTICAL RESULTS, IMPLICATIONS AND RECOMMENDATIONS 104
Analytical Results 104
Implications 116
For Policy Makers 116
For Economic Theory and Methodology 117
For Future Research 117
APPENDIX A
NET PROFIT PER ACRE OF SELECTED CROPS IN DADE COUNTY 119
APPENDIX B
ESTIMATES OF PESTICIDE USAGE COMPILED BY A MANAGEMENT
BIOLOGIST AT THE EVERGLADES NATIONAL PARK 124
APPENDIX C
COMMON, CHEMICAL, AND/OR TRADE NAME OF PESTICIDES
IDENTIFIED IN DADE COUNTY 131
APPENDIX D
ESTIMATED QUANTITIES OF AGRICULTURAL PESTICIDES
USED IN DADE COUNTY 139
APPENDIX E
PESTICIDE QUESTIONNAIRE USED IN DADE COUNTY 184


Table 16.Continued.
Case
number
Pesticide
Date
Crop
12
Sodium Arsenite
13
Atrazine
1964
Beans and
tomatoes
14
Sodium Arsenite
1963
Beans
15
Sodium Arsenite
1965
Corn
16
Amine 2,4-D
1966
Tomatoes
17
Amine 2,4-D or
1966
Tomatoes
Extent of damage
Comments
Grower reported that he has
occasional small incidents
from potato vine killer. He
said it could not be avoided,
and when the damage is great
enough he settles with the
grower involved.
Not available
Corn growers paid off the bean
and tomato growers.
$1000
Potato grower was the damaging
party.
Slight
Potato grower was the damaging
party. No settlement was made
80 acres
amounting to
$24,000
The grower could not find out
who had done it. Scientists
from the University of Florida
investigated. No settlement
was made.
About 50% of
60 acres was
destroyed
This was the same situation as
#16 above.


99
These diagrams are, of course, grossly oversimplified, but they
nevertheless indicate in their simplest forms the crucial relationships
about which we need more knowledge. Graph 1 represents the first level
of knowledge, statistical series of pesticide levels in certain ecolog
ically important elements of the environment. Graph 2 represents the
second level of knowledge, statistical series of pesticide usage. The
data in these two graphs are then combined to form Graph 3 relating
monitored levels to pesticide usage. Graph 4 represents the third level
of knowledge, the relation between monitored levels and "damage,"
however measured. The functions which have been drawn are for illus
tration only and have no empirical basis. Damage could be measured in
any appropriate units. If the pesticide were detrimental to the
reproductive process, then it might be measured as "decline in egg
fertility." If the pesticide's major effect were acute, then damage
might be measured as "percent kill." At the policy making level, a
value judgement would have to be made specifying the amount of damage
which should not be exceeded. It should be stressed that such a
decision is a social value judgement, not based on scientific knowledge,
but representing the consensus of the citizenry affected. It is there
fore a political decision. Such a value judgement is represented in
Graph 4 by the dashed line. Through the monitored pesticide levels, it
places a ceiling on pesticide usage. The restriction would enter the
model of Chapter III as an "environmental restraint." The actual
mechanics of limiting pesticide usage were not considered in this study.
There would, of course, be statistical difficulties in
estimating the above functions. Aside from the fact that they would be
stocastic rather than exact, there would be a question as to whether


61
management biologist for the Everglades National Park, and his estimates
are presented in Appendix B (p. 124)."*"
For this study, the quantity of pesticides used for agriculture
in Dade County was estimated by interviewing the growers and by using
records of the growers' pesticide purchases. The interviews provided
background information on spray programs, and, in some cases, the
growers kept individual field records which were made available.
The growers which were interviewed accounted for the following
proportions of harvested acreage in 1966-67:
Tomatoes
73.5%
Potatoes
72.5
Pole beans
65.0
Corn
96.5
Squash
Other vegetables
29.2
43.8
Total vegetables
59.0
Groves^
12.6
Includes okra, peas, cucumbers, cuban pumpkins.
^Includes avocados, limes, mangos, papayas.
When a pesticide firm makes a sale to a farmer, an invoice is
prepared in triplicate. The farmer receives one copy and the firm keeps
two. At the end of the month, when statements are sent out, another
copy is sent to the farmer along with his bill. The farmers usually
keep this second one for their records. During the interviews, the
farmers were asked for permission to copy these records or to copy the
originals from their pesticide suppliers. If the farmer preferred that
we go to his pesticide supplier, he was asked to sign a permission
H/e are indebted to Mr. Klukas for permission to publish these
estimates.


199
Location
Dade County, Florida ^
Texas Lower Rio Grande Valley
Western North Carolina
Eastern South Carolina
Central Georgia
Eastern Virginia ^
Monmouth County, New Jersey
Adams County, Pennsylvania
Berrien County, Michigan^
Urbana, Illinois
Western Iowa .
*
Weld County, Colorado
Wenatachee Basin Area, Washington
Kern County California
Tulelake area, California
Crop (s)
Vegetables
Cotton
Apples
Vegetables
Peaches
Peanuts
Vegetables
Fruits
Fruits and vegetables
Corn
Corn and soybeans
Root crops
Fruits and root crops (2 loca
tions)
Cotton, vegetables
Small grains, root crops
*
Soil monitoring sites concide with U.S. Public Health Service
sites to monitor pesticides in human beings.
The Agricultural Research Service has developed a plan for expanding
the national soil monitoring program. The proposed program has been
designed on a statistical basis for the conterminous United States to
provide information that will pinpoint major trouble areas which then
will require additional monitoring. The objectives of the program are:
1. To establish the level of pesticide residues in soils in
reference to major land-use areas in the United States.
2. To continue sampling the same sites over a period of time
to provide information on rates of change of pesticide
residue levels in soils.
The program was initiated in fiscal year 1968. Soil will be collected
from approximately 15,000 sites over the conterminous United States
during a 4-year period.


198
G Surface water, sediment, selected aquatic plants and animals.
O Surface water and sediment.
A Rainfall.
Ground water.
Figure 9.U.S. Geological Survey sampling sites for pesticide residues
in aquatic communities of South Florida.
(Source: U.S. Geological Survey, Miami)


Table 15-Continued.
Case
number
Pesticide
Date
Time in hospital
15
Parathion
1965
1 day
16
Parathion
1967
17
Parathion
1963
1 week
18
Parathion
1965
1 week
19
Parathion
1964
About 2 days
20
Phosdrin
1960
21
Parathion
1966
22
Parathion
1963
Comments
Grower reported that the employee had been discing
new land and that there had been no opportunity for
contact with Parathion while on the job. Neverthe
less the hospital diagnosed it as Parathion poisoning.
Grower reported that employee sprayed with Parathion
on Friday but did not get sick until Sunday. Doctors
diagnosis was Parathion poisoning.
No details.
Spray man was careless during a drenching operation.
He worked all day with a fine mist blowing on his
left leg.
Instead of walking around the spray rig to check the
nozzles, this spray man ran through the mist.
Grower himself got Phosdrin poisoning and had to get
hourly shots at the hospital for several days.
Grower got a slight touch of Parathion sickness.
Doctor prescribed some pills which he took for
several days.
Spray man was putting water into a can of Parathion
with a hose. He withdrew the hose from the can and
took a drink of water, allowing the hose to touch
his lips.


ECONOMIC EXTERNALITIES IN THE AGRICULTURAL
USE OF PESTICIDES AND AN EVALUATION
OF ALTERNATIVE POLICIES
By
WILLIAM FRANKLIN EDWARDS
A DISSERTATION PRESENTED TO THE GRADUATE COUNCIL OF
THE UNIVERSITY OF FLORIDA
IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE
DEGREE OF DOCTOR OF PHILOSOPHY
UNIVERSITY OF FLORIDA
1969

Copyright by
William Franklin Edwards
1969

ACKNOWLEDGMENTS
The author is indebted to the many persons who have both
encouraged and contributed to his graduate training.
Appreciation is first expressed to the Institute of Food and
Agricultural Sciences and particularly to Dr. K. R. Tefertiller,
Chairman of the Department of Agricultural Economics, for the oppor
tunity to participate on this research grant.
The financial support of Resources for the Future, Incorporated,
Washington, D.C. is gratefully acknowledged. A special word of thanks
must go to Mr. Blair T. Bower, Dr. Michael F. Brewer, and Dr. Allen V.
Kneese of that organization and to Dr. J. C. Headley of the University
of Missouri for their willingness to read and comment on versions of
this paper. At no time, however, did they attempt to influence in any
way the outcome of the research findings.
My greatest debt of gratitude is owed to Dr. Max R. Langham,
Chairman of the Supervisory Committee, for his continuous guidance and
encouragement during the graduate program, and for his indefatigable
efforts throughout this research study.
Special appreciation is also extended to Dr. Elmo L. Jackson and
Dr. John B. McFerrin, members of the Supervisory Committee, for pro
viding helpful suggestions.
The author would also like to thank the staffs of the Dade
County Agricultural Agents Office and the Homestead Experiment Station
for assistance in gathering data on the area studied. Mr. Richard M.
Hunt, Assistant Marketing Agent, was especially helpful in establishing
iii

contacts with growers and planning the data gathering phase of the
research. The consulting help of Mr. Fred A. Johnson, an entomologist
in Dade County, is also gratefully acknowledged.
To the many growers in Dade County who supplied information for
this project, I would also like to express my appreciation.
Capable secretarial and clerical assistance was provided by
Mrs. Audra White throughout the project, and the author extends to her
his sincere appreciation for her conscientious service.
The services of the University Computing Center are herein
recognized.
Finally, I would like to thank my wife, Emmy, and our children,
Russell and Carroll, for their patience during this somewhat trying
period. To them, and to my parents, I dedicate this dissertation.
XV

TABLE OF CONTENTS
Page
ACKNOWLEDGMENTS iii
LIST OF TABLES viii
LIST OF FIGURES xi
CHAPTER I
INTRODUCTION 1
The Problem 1
Background of the Problem 2
CHAPTER II
AREA STUDIED 7
Choice of an Area 7
Agriculture of Dade County 8
CHAPTER III
THE MODEL 19
CHAPTER IV
ESTIMATES OF MODEL COEFFICIENTS 30
Objective Function 30
The Demand Model 30
The Supply Model 34
Shifting the Supply Functions 41
The Externality Functions 51
Constraints on the Objective Function 54
Coefficients of Variables in the Constraints... 55
The Constraint Vector 56
CHAPTER V
APPLICATION OF AGRICULTURAL PESTICIDES IN DADE COUNTY.... 58
Technology of Pesticide Application in Dade County.. 58
Estimated Quantities of Pesticides Used in Dade
County 60
v

TABLE OF CONTENTSContinued.
Page
CHAPTER VI
EXTERNALITIES IN DADE COUNTY 66
The Grower Interview 66
Insurance Claims 73
Veterinarians 89
Biologists 90
Community Studies on Pesticides 93
Environmental Monitoring 94
Concluding Remarks on Externalities in Dade
County 100
CHAPTER VII
ANALYTICAL RESULTS, IMPLICATIONS AND RECOMMENDATIONS 104
Analytical Results 104
Implications 116
For Policy Makers 116
For Economic Theory and Methodology 117
For Future Research 117
APPENDIX A
NET PROFIT PER ACRE OF SELECTED CROPS IN DADE COUNTY 119
APPENDIX B
ESTIMATES OF PESTICIDE USAGE COMPILED BY A MANAGEMENT
BIOLOGIST AT THE EVERGLADES NATIONAL PARK 124
APPENDIX C
COMMON, CHEMICAL, AND/OR TRADE NAME OF PESTICIDES
IDENTIFIED IN DADE COUNTY 131
APPENDIX D
ESTIMATED QUANTITIES OF AGRICULTURAL PESTICIDES
USED IN DADE COUNTY 139
APPENDIX E
PESTICIDE QUESTIONNAIRE USED IN DADE COUNTY 184

TABLE OF CONTENTSConcluded.
Page
APPENDIX F
ENVIRONMENTAL MONITORING PROGRAMS 191
Residues in Food and Feed 193
Pesticides in People 194
Residues in Fish, Wildlife, and Estuaries 195
Pesticides in Water 196
Pesticides in Soil 197
Pesticides in Air 200
APPENDIX G
MATHEMATICAL STATEMENTS OF POLICIES 2A, 2B, AND 2C 202
Policy 2A 203
Policy 2B 204
Policy 2C 205
BIBLIOGRAPHY 206
vii

LIST OF. TABLES
Table Page
1 Vegetable, ornamental horticulture, and fruit production,
by commodities, in Dade County, Florida, 1966-67 11
2 Production of livestock and livestock products in Dade
County, Florida, 1967 12
3 Florida and Dade County population changes for 1950 and 1960 16
4 Empirically estimated relations from which demand functions
were derived 35
5 Demand functions used in the model 37
6 Empirically estimated relations from which supply functions
were derived 39
7 Recommended insect control measures for tomatoes 42
8 Recommended insect control measures for potatoes 43
9 Recommended insect control measures for beans 44
10 Recommended insect control measures for corn 45
11 Estimated average costs of chlorinated hydrocarbons and
organic phosphates used on crops in Dade County, 1966-67.. 48
12 Estimates of average total production costs in Dade County,
1966-67, by crop and pesticide policy 50
13 Supply functions used in the model 52
14 Flexibility constraints used for the empirical model 57
15 A summary of grower responses concerning human sickness
from pesticides .' 68
16 A summary of grower responses concerning damage from
pesticide drift 74
17 Workmens compensation claims, State of Florida; work
injuries, days of disability, and cost by industry for
disabling and non-disabling work iniuries, 1962, 1963,
1966, and 1967 79
viii

LIST OF TABLESContinued.
Table Page
18 A list of the categories constituting Agency 10, "Poisons
and Infectious Agents" 81
19 Workmen's compensation claims, State of Florida; work
injuries, days of disability, and cost by agency, for
disabling work injuries, 1962, 1963, 1966, and 1967 83
20 Dollar costs of disabling workmen's compensation claims for
Dade County, Florida, 1966, by kind of payment and agent.. 84
21 Dollar costs of disabling workmen's compensation claims for
Dade County, Florida, 1967, by kind of payment and agent.. 86
22 A summary of data gathered from veterinarians in Dade County 91
23 A summary of data gathered from the Communities Studies
Program on Pesticides in Miami 95
24 A summary of the externalities incorporated in the
empirical model, Dade County, 1967 102
25 Model solution for Policy 1 107
26 Model solution for Policy 2A 108
27 Model solution for Policy 2B 109
28 Model solution for Policy 2C 110
29 A comparison of model solutions among policies for and
z2 coefficients of 0 and -. 0301, respectively 113
30 Net profit per acre for a sample of tomato growers in
Dade County, 1960-61 through 1966-67 120
31 Net profit per acre for a sample of potato growers in
Dade County, 1960-61 through 1966-67..... 121
32 Net profit per acre for a sample of pole bean growers in
Dade County, 1960-61 through 1966-67 122
33 Net profit per acre for a sample of squash growers in
Dade County, 1960-61 through 1966-67 123
34 Estimates made by Richard Klulcas of the quantities of
insecticides used on various crops in Dade County, 1966-67 125
ix

LIST OF TABLES--Concluded.
Table Page
35 Estimates made by Richard Klukas of the quantities of
fungicides used on various crops in Dade County, 1966-67.. 128
36 Dade County growing seasons and 1967 crop acreages used for
Klukas projections 130
37 Common names, trade names, and/or chemical names of
pesticides observed in Dade County, Florida, 1966-67 132
38 Estimated quantities per acre of certain pesticide
categories used by farmers in Dade County, 1966-67 crop
year, by crop 140
39 Estimated quantities per acre of certain pesticide
categories used by farmers in Dade County, 1966-67 crop
year, by crop and month 142
40 Estimated pesticide usage in Dade County, Florida, by
crop and pesticide, 1966-67 crop year 155
41 Estimated pesticide usage by pesticide, crop, and month
in Dade County, Florida, 1966-67 163
42 Sample data on pesticide usage, by pesticide and crop,
for growers who used the pesticide, Dade County,
Florida, 1966-67 176
x

LIST OF FIGURES
Figure Page
1 Locational map for Dade County, Florida 9
2 Soils of Dade County, Florida 13
3 Population trends in Dade County, Florida 17
4 A linear iso-product function for chlorinated hydrocarbons
and organic phosphates 46
5 A hyperbolic iso-product function for chlorinated
hydrocarbons and organic phosphates 47
6 Point estimate of externalities arising from the
agricultural usage of organic phosphates 53
7 A schematic diagram showing data needs in the area of
environmental monitoring 98
8 Hypothesized relations between "welfare," the state of the
arts, and the usage of chlorinated hydrocarbons 114
9 U.S. Geological Survey sampling sites for pesticide
residues in aquatic communities of South Florida 198

CHAPTER I
INTRODUCTION
The Problem
This research effort was concerned with the economic external
ities created by the use of agricultural pesticides and the implications
of such externalities on social welfare. The working hypothesis was
that the externalities could be measured, and the objective was to
measure them for some specified region and time period and to
incorporate them into a model which was developed to aid in the choice
of a pesticide usage policy for the area. The choice criterion was a
measure of social welfare.
2
The model employed the concept of consumers'-producers' surplus
as a measure of "welfare." For each of two specified policies and
three variations under one of the policies the model maximized this
measure of welfare over production alternatives available to the region.
In each case the objective function explicitly recognized measurable
externalities not accounted for by consumers'-producers' surplus. The
maximization was restrained by flexibility constraints derived from
This research project is an outgrowth of some earlier work
done by J. C. Headley and J. N. Lewis (23) for Resources for the Future,
Inc.
2
Throughout the paper this term is to be read as "consumers'
plus producers' surplus."
1

2
historical cropping patterns. Finally the policies were ranked by their
maxima.
It was demonstrated that the model could conceptually accommo
date environmental constraints connecting the levels of pesticide usage
with levels of pesticides monitored in various elements of the environ
ment. Empirical data to specify such constraints were not available.
Background of the Problem
Environmental modification is not new. In the struggle for
survival, it has been practiced through the ages by most of the
creatures of the Earth. The fox modifies his environment when he digs
a den. Man modifies his environment when he clears a field for culti
vation, but such environmental modification often has undesirable side
effects. When the farmer clears his land for cultivation, he raises the
probability of soil erosion. The extent of the modification and the
extent of the side effects can become important issues, and herein lie
many of the social problems we face today, one of which concerns the
advantages and disadvantages of using pesticides.
Several factors have played a part in increasing the emphasis
on the pesticide problem. First, world food needs are expanding rapidly.
The world population, estimated at about 500 million in 1650, rose to
1 billion by 1850, and to 1.6 billion by 1900 A.D. Extrapolations of
present growth rates indicate a population of 6.4 billion by 2000 A.D.
(10, pp. 63-71). Present-day nutritionists find that more than half of
the world's inhabitants have a suboptimal diet of less.than 2,200
calories per day (42, pp. 73-80). It is quite possible that the food
deficit will worsen in future years; the significance of the problem

3
will depend upon our success at population control and our ability to
3
increase agricultural production.
Second, there is the lurking possibility that the long-run
hazards of persistent pesticides to human health and the Earths eco
logical relationships may be more serious than had been imagined
previously. We really know very little about these long-run effects,
but some individuals feel that they may be serious (49, pp. 485, 526,
585, and 684).
Third, it is possible that a nations values and preferences
change as its standard of living rises. If a large segment of our
population were starving, it is doubtful that the external or side
effects from using pesticides would be given very serious consideration.
It would be worth poisoning a few people if one more than "a few" could
be saved from starving through the use of pesticides. The "pesticide
problem" is a sub-case of the more general "environmental pollution
problem" and a "clean environment" is a luxury public good not demanded
until some minimum quantity of those goods more necessary for sustaining
life is obtained.
Being a public good, an effective demand is not expressed in the
market place. Consequently there are dangers of "social imbalance."
This "social imbalance" was described by J. K. Galbraith more than a
decade ago:
The family which takes its mauve and cerise, air-conditioned,
power-steered, and power-braked automobile out for a tour passes
through cities that are badly paved, made hideous by litter,
blighted buildings, billboards, and posts for wires that
should have long since been put underground. They pass into a
3
For two interesting accounts of the world food situation see
Borgstrom (1) and Gunther (22).

4
countryside that has been rendered largely invisible by commer
cial art. . They picnic on exquisitely packaged food from a
portable icebox by a polluted stream and go on to spend the night
at a park which is a menace to public health and morals. Just
before dozing off on an air mattress, beneath a nylon tent, amid
the stench of decaying refuse, they may reflect vaguely on the
curious unevenness of their blessings (20, p. 253).
Finally, Rachel Carson's book, Silent Spring (8), did a great
deal to increase public interest in the pesticide issue. Largely as a
result of Miss Carson's book and the interest it created, the United
States Senate, from May, 1963, to July, 1964, conducted extensive
hearings to study the problems created by the increasing use of pesti
cides and tried to assess their potential hazards (49). The general
conclusion of the Senate investigation was that the benefits from using
pesticides still far outweigh the potential hazards to the environment,
but it was strongly emphasized that additional research is badly needed,
particularly in the area of long-run hazards of pesticide use, where we
have virtually no knowledge.
Stimulated by the Senate Hearings and a growling public concern
about environmental pollution, economists became more interested in the
problem. It is within the purview of the physical sciences to establish
the link between pesticides and various elements of the environment, but
when this is done, the subsequent question, "So what?", still remains to
be answered, and this is left to the social and political sciences. The
benefits must be weighed against the costs; or to put it another way,
relative values must be assessed, and values are the economist's stock
in trade.
The term "pesticides" is commonly used to refer to the whole
family of agricultural chemicals "used to control insects, mites, ticks,
fungi, nematodes, rodents, pest birds, predatory animals, rough fish,

plant diseases, weeds, and also to those which act as regulators of
plant growth, as defoliants, and as desiccants" (49, p. 41). Major
categories have been described as follows (49, p. 41):
1. The chlorinated hydrocarbons containing carbon, hydrogen, and
chlorine are the pesticides used in greatest tonnage. The
most familiar are DDT, dieldrin, aldrin, endrin, toxaphene,
lindane, methoxychlor, chlordane, and heptachlor. Among
those used extensively as herbicides are 2,4-D and 2,4,5-T
for control of broad-leaved weeds in lawns, pastures, cereal
crops, and brush growth along highways and fences.
2. The organic phosphorus compounds, composed of phosphorus,
oxygen, carbon, and hydrogen, are used principally as insec
ticides and miticides. Parathion, malathion, phosdrin, and
tetraethyl pyrophosphate (TEPP) are examples.
3. Other organic compounds include the carbamates, dinitrophenols,
organic sulfur compounds, organic mercurials, and such natural
products as rotenone, pyrethrum, nicotine, strychnine, and the
anticoagulant, rodent poisons.
4. Inorganic substances with a long history of use include copper
sulfate, arsenate of lead, calcium arsenate, compounds of
chlorine and fluorine, zinc phosphide, thallium sulfate, and
sodium fluoroacetate.
Although there is some controversy regarding most of these
groups, the main thrust of the dispute concerns the chlorinated hydro-
4
carbons and the organic phosphates.
The chlorinated hydrocarbons are generally quite persistent in
the environment, and their residues therefore tend to build up in many
elements of the environment, such as soil, water, and plant and animal
tissue. The organic phosphates, on the other hand, decompose very
rapidly into non-harmful substances and thus seldom persist in the
environment for long periods of time. Their detrimental effects tend
to be of an acute rather than chronic nature and have caused many fatal
Common names, trade names, and chemical names of all pesticides
identified in Dade County are presented in Appendix C ( p. 131).

6
and non-fatal poisonings in man. Education in handling these highly
toxic chemicals can be effective in reducing acute damage.
Current social and legislative trends seem to favor the substi
tution of non-persistent pesticides for the persistent ones (54, p. 20).
Therefore, alternative policies evaluated in this research project are
oriented toward this objective.

CHAPTER II
AREA STUDIED
Choice of an Area
The choice of an area for study was initially limited to the
State of Florida because of the location of the researchers. However,
the State as a whole was much too heterogeneous for effective analysis
so a smaller geographic region within the State was chosen. It was
felt that the selected area should be a relatively isolated, heavy
user of pesticides. It was also felt that it would be desirable to have
an area where other scientists had studied the fate and dispersion of
pesticides as well as the effects of pesticides on various elements of
the environment such as wildlife, human health, and domestic animals.
If possible, it should also be an area which would offer the maximum
probability of observing externalities, and it was believed that an area
having a significant interaction of pesticide users with other people
would fulfill this requirement.
The focus rather quickly narrowed to three areasa small
isolated locale in Northeast Florida (Hastings, Florida) heavily commit
ted to the production of potatoes and cabbage; an area in Central
Florida (Polk County) which produces primarily citrus; and the winter
vegetable and tropical fruits area of Dade County. The latter area was
the final choice because it had what was believed to be the best work
able balance of needed characteristics. Siiice its agriculture is highly
7

8
dependent upon pesticides, Dade County uses great quantities of them in
the production of its crops. It is relatively isolated (by the Ever
glades Swamp, the metropolitan area of Miami, and the Atlantic Ocean),
and there are ample opportunities for urban-rural interaction as the
growing city of Miami encroaches on the agricultural interests in South
Dade County. In addition, several scientists are currently working on
problems related to the use of pesticides in that area.
Agriculture of Dade County'*'
When Dade County was created in 1836, it comprised most of
Southeast Florida. Its first county seat was located at Juno, north of
what is now West Palm Beach. In these early days the industry of the
area consisted of starch production from the native plants, coontie or
zamia. Following the "great freeze" of 1894 (which nearly wiped out the
citrus groves in Central and North Florida) Henry Flagler, seeing Dade
County had escaped all damage, extended the railroad all the way to
Miami and built a hotel there. This was to mark the beginning of the
great development of Dade County.
Today, the county is situated in the southeastern part of the
Florida peninsula, as indicated by Figure 1. It extends about 55 miles
from north to south and 47 miles from east to west. The total area is
2,054 square miles, or 1,314,560 acres. Miami, the county seat, and
other towns are located in the eastern portion of the county near the
coast.
The surface relief is nearly level, and much of the county lies
^1 am indebted to the Dade County Agricultural Agents Office
for most of the information contained in this section.

9
Figure 1.Locational map for Dade County, Florida.

10
less than 13 feet above sea level. But a few low ridges in the eastern
portion are slightly higher than 20 feet in elevation. In the western
portion the surface water drains slowly through the peat marshes in a
southerly and southwesterly direction to the Gulf of Mexico. In the
eastern portion the indistinct drainageways of the peat marshes join
many of the canals and ditches which extend through the low sandy and
rocky ridges to the Atlantic Ocean. All major canals in the northeast
and southern part have control-dams for regulating water levels, but
very few pumps are used for controlling water levels in fields except
on several large farm operations. Most of the southwestern part of the
county is included in the Everglades National Park.
Dade County enjoys tropical climatic conditions not found else
where on the U.S. mainland. It is 500 miles further south than Los
Angeles, but it is not subject to the heat of the tropics due to the
nearness of the Atlantic Ocean, the Gulf of Mexico, and the prevailing
tradewinds. The mean annual temperature at Miami is 75.1 F., and the
mean annual rainfall is 57.8 inches. Most of the rainfall comes during
the summer months. Occasionally disturbances of hurricane type move
across the county during the months from August to November. Killing
frosts may occur from the middle of December to March, but many winters,
sometimes several in succession, pass without damage from freezing
temperatures. Livestock graze the pastures throughout the year and need
little shelter from the weather. Taken together, the climatic con
ditions of Dade County make .it one of the most unusual and productive
farming areas of the nation.
September through March are the months of primary agricultural
activity. Winter vegetables, ornamental horticulture, and tropical

11
fruit are the three largest activities, representing 44.3, 11.5, and
4.1 million dollars,respectively,as indicated in Table 1. Minor
Table 1.Vegetable, ornamental horticulture, and fruit production, by
commodities, in Dade County, Florida, 1966-67.a
Crop
Acres
Production
Value
Thousands. .
Vegetables
Tomatoes
Fresh
19,000
5,806 crt.
$23,050
Processed
1,514 crt.
961
Potatoes
7,660
1,564 cwt.
5,474
Snap beans
Bush
1,470
181 bu.
699
Pole
5,830
1,647 bu.
6,357
Squash
3,080
457 bu.
1,942
Strawberries
670
657 flats
2,214
Corn
1,650
252 crt.
622
Cucumbers
1,290
204 bu.
787
Cabbage
470
189 crt.
312
Okra
400
36 bu.
180
Peas
650
69 bu.
361
Cuban vegetables^
1,360
1,102
Other vegetables
390
272
Total vegetables
43,920
$44,333
Ornamental horticulture
$11,530
Fruit
Avocados
5,235
220 bu.
$ 1,232
Limes
3,585
640 bu.
1,965
Mangos
1,520
70 bu.
490
Specialty fruit
440
210
Other citrus^
365
247
Total fruit
11,145
$ 4,144
Source of data: Dade County Agricultural Agents Office,
Homestead, Florida.
^Includes lima beans, cantaloupes, eggplant, escarole, chicory,
lettuce, green peppers, and green onions.
c
Includes lychee, barbados cherries, guava, papyas, and
sapodillas.
^Includes oranges, grapefruit, tangerines, tngelos, and lemons.

agricultural activities are poultry, dairy, and livestock as shown
in Table 2.
12
Table 2.Production of livestock and livestock products in Dade
County, Florida, 1967.a
Item
Unit
Quantity
Value of
production
Dairy (5 farms)
Milk
Gallons
2,507,112
$ 1,669,000
1,503,000
Cull animals
Head
166,000
Poultry (25 farms)
Eggs
Dozen
5,220,000
$ 3,524,000
2,034,000
Cull birds
Head
150,000
40,000
Hatchery chicks
Head
10,040,000
1,450,000
Other (including beef cattle,
horses, etc.)
Head
5,032,000
$ 796,000
Other farm enterprises
500,000
Source of data: County Agricultural Agents Office, Homestead,
Florida.
Most of the fruit and vegetable crops in Dade County are grown
on two soil types, marl and rockland, illustrated in Figure 2.
Marl and its various phases comprise a total of about 317,000
acres in Dade County. Surface soil is a light brown or brownish gray
marl of silt loam texture. The subsoil is lighter colored, and the
underlying material is Miami oolite. Native vegetation on these soils
consists of a variable mixture of sawgrass, myrtle, bay, and cypress.
Elevation above sea level of most areas of marl that are suitable for
cultivation ranges from about 8 feet near the rock ridge to 1 to 2 feet
near the shore lines. Soil depth varies from 6 to 60 inches. Marl has

13
£
Reprinted with the permission of the Dade County Agricultural
Agents Office.
poor internal drainage and becomes waterlogged or even flooded during
the rainy season. Occasionally wet conditions occur during the normally
dry winter season; however, this soil may, at times, become too dry
during the winter. Both drainage and irrigation systems are used
extensively for crop production.
Marl is alkaline in reaction, and the application of certain of
the minor elements is imperative for successful crop production. Prior
to discovery of the low availability of manganese, this unknown defi
ciency limited crop production in the area.
Marl soils are utilized for production of winter vegetables,
including Irish potatoes, tomatoes, bush beans, pole beans, squash,
cucumbers, corn, strawberries, cabbage, beets, lettuce, escarole,

14
peppers, and eggplant. The total acreage of marl soils cultivated in
these vegetable crops has averaged 18,000 to 20,000 acres annually for
the past 15 years.
There are approximately 279,000 acres of rockland in Dade
County. The rockland area extends roughly from U.S. Highway 1 west to
the Everglades Park boundary and from the Tamiami Trail on the north to
Florida City and Florida Highway 27 leading to the Everglades Park.
The rockland is an oolitic limestone formation with many
solution holes and is relatively soft until it is exposed to the air.
The solution holes below the surface act as storage places for water.
Near the surface these cavities are filled with loam and loamy fine sand
having varying shades of red coloring.
Prior to 1938 farming of the rockland was confined to the larger
solution holes and to pockets broken up by dynamiting. Heavy tractors
and scarifying equipment are now used to chisel or "plow" this soil so
that all of the area can be farmed. Vegetable farms are plowed 6 to 8
inches deep over the entire field. Orchards are planted in trenches
that have been cut and crushed to a depth of 20 to 24 inches. The
trenches are back filled after cutting, and young trees are set in the
crushed rock trench that is formed.
Irrigation wells are drilled into the oolitic rock 15 to 40 feet
deep and usually about 300 feet apart. No casing is required. One-half
to an inch of water is applied by overhead irrigation each week. Most
farmers use a single irrigation head on a portable pump, which covers
roughly two acres.
Only vegetables that produce marketable products above the
ground such as tomatoes, corn, squash, beans, cantaloupe, okra, southern

15
peas, and leaf crops can be grown on rockland. Tree crops in Dade
County, including limes, mangos, and avocados, are grown exclusively on
rockland soils. During the last 15 years, an average of 28,000 acres of
vegetable crops has been produced on rockland soils, and an average of
20,000 acres has been devoted to tree crops.
Both marl and rockland soils are underlaid by Miami oolitic
limestone, which is perforated with numerous vertical solution holes.
The oolite underlies the Atlantic Coastal Ridge south of Boca Raton in
Palm Beach County to an average depth of about 20 feet. Deposits are
thicker near the coast and then fan out to a feather edge in western
Dade and Monroe Counties. It overlies the Tamiami formation, another
porous limestone. These two limestone formations make up the Biscayne
Aquifer, the main source of water for Dade County.
Of the total land area in Dade County (1,314,560 acres) about
117,000 acres or 8.9 percent was apportioned to farms in 1964; in 1959
129,000 acres or 9.8 percent was in farms. Although some growers stated
that suitable farm land is becoming difficult to obtain, it is more
likely that other constraints, such as markets and capital and labor
requirements, are more restrictive than the physical quantity of land.
Metropolitan Dade County is one of the youngest and fastest
growing major metropolitan areas in the United States. Between 1890 and
1962, a span of 72 years, the population increased from 861 to more than
1 million persons. Between 1950 and 1960 the population increased
almost 90 percent as indicated in Table 3.
Currently, the population of the county is estimated at about
1.1 million by the Metropolitan Dade County Planning Department, and
will approach 2.5 million by 1995 if the current growth rates continue.

16
Table 3.Florida and Dade County population changes for 1950 and 1960.
a
Florida
Dade County
Total 1950
2,771,305
495,084
Total 1960
4,951,560
935,047
Percent increase
78.7
88.9
Rural 1950
957,415
29,005
Rural 1960
1,290,177
40,705
Percent increase
34.8
40.3
Urban 1950
1,813,930
446,079
Urban 1960
3,661,383
894,392
Percent increase
101.9
91.9
Source of data: U.S. Bureau of the Census (46).
The Planning Department estimates that 130 additional square miles will
be needed for new homes and apartments to accommodate the additional 1.5
million new residents expected during the next 25 years. The effect of
this growth on the outlying areas is shown in Figure 3.
It is obvious that the projected population growth will have
serious implications for the rural interests in Dade County. Agricul
tural activities will be driven toward the Everglades Swamp on the west
and southwest. Interdependencies between urban and rural interests will
intensify, and concurrent with this intensification, economic external
ities arising from such propinquity will increase, giving rise to a
multitude of policy questions such as the one with which this study is
concerned.
Several other characteristics of Dade County and its farmers
should be noted. First, farming in the region requires large amounts of

17
Figure 3.Population trends in Dade County, Florida.
Reprinted with the permission of the Metropolitan Dade County
Planning Department.
capital relative to other areas of the State. The 1964 Census of
Agriculture indicates that the investment in land and buildings per acre
of farm land is $1,088.12 in Dade County against $285.71 for the State
(47, p. 304-305). For the U.S. as a whole it is $143.81 (48, p. 792).

18
Second, revenues per acre from farming in Dade County are also very high.
The per acre value of all farm products sold in Dade County is $443.13
against $68.41 for the State (47, p. 332-333). For the U.S. as a whole
this figure is $31.79 (48, p. 802). Third, net returns per acre vary
widely among growers and through time, as indicated in Appendix A
(p. 119). Finally, in the author's opinion, based on observations made
while in the area, the farmers of Dade County are as well educated and
as sophisticated as the entrepreneurs in any vocation. Some of the
larger ones have professional entomologists on their payrolls, and most
of the pesticide firms in the area employ entomologists to sell pesti
cides and provide consultation to the farmers. The University of
Florida has an experiment station in Homestead, Florida, which is very
active in the area of insect and disease control. In addition, the Dade
County Agricultural Agent's Office, the largest in the State and located
in Homestead, is extremely active in disseminating information to the
farmers.

CHAPTER III
THE MODEL
The model used for this analysis employed the concept of
consumers'-producers' surplus as a measure of welfare. For each of two
specified pesticide usage policies and three variations under one policy,
the model maximized this measure of welfare over production alternatives
available to the region. In each case the objective function explicitly
recognized measurable externalities not accounted for in the consumers'-
producers' surplus calculation. The maximization was restrained by
flexibility constraints derived from historical cropping patterns.
Finally the policies were ranked by their maxima. The model can be
formally stated as follows:
For a set of subjectively chosen pesticide usage policies, x^,
r = 1, 2A, 2B, 2C, rank the associated estimates of welfare, W^, where:
n
W = maximum: E
r y^(t+1) j=l
z^t+l)
m
- E
i=l
where the maximization for a given policy r is subject to:
n n n
[1] d(min) E y.(t) E y.(t+l) d(max) E y.(t)
j-1 J J-l J 3=1 J
Zj (t+1)
/
0
h_. (z^(t+1)
dz.(t+1)
x
yj(t+D
/
o
f j ^yj ^t+1^
gjr)
dy^ (t+1)
19

20
[2]
bj(min)y^(t) y^(t+1) b^(max)y^(t)
j=l,
. .n
n x
[3]
E a.^ y.(t+l) z.(t+l) = 0
j=l ^ J 1
i=l, .
. .m
[A]
c, z. (t+1) e. .
ki i ki
k=l, .
. .p
[5]
y^ (t+1) 2,^ (t+1) 0
where:
fj(yj(t+l)) = demand function for the jth crop in the (t+1) year.
y,(t+l) = acres of the jth crop in the (t+1) year. For simplifi
cation the time dimension is omitted in the remaining definitions,
x
gj (y^) = supply function for the jth crop under the rth policy
alternative.
h <* ) = a marginal "externality function," a functional relation
ship between marginal external effects expressed in dollars, and
the quantity of the ith pesticide.
z^ = quantity of the ith pesticide measured in units of 100 percent
active material.
d(min) = a minimum flexibility constraint on total farm land.
d(max) = a maximum flexibility constraint on total farm land,
bj(min) = a minimum flexibility constraint on the jth crop,
b^(max) = a maximum flexibility constraint on the jth crop.
a = the quantity of the ith pesticide used per acre of the jth
crop under the rth policy.
c^j. = quantity of the ith pesticide produced in the kth environ
mental element by 1 unit of the ith pesticide,
e^ = an arbitrary upper limit on the ith pesticide in the kth
environmental elementa parameter to be determined "politically."

21
There has been a recent revival of interest in the concept of
consumers-producers' surplus for making policy decisions (37, 45, 55).
Though much criticized (40, pp. 208-209), it is being used in the
literature with increasing frequency to give a rough idea of welfare
aspects of various alternatives. It is defined as the integral of the
demand function minus the integral of the supply function. The integral
of the demand function is, to the user of a product, simply a total
benefits function and the integral of the supply function a total cost
function, so consumers'-producers' surplus as a function of output is a
net benefits function.^
Buchanan (6, p. 127) argued that the welfare of a group is a
consensus of the preferences of members of the group and that an
observer could introduce a welfare criterion only through his own
estimate of the group's value scales. Hence, the observer can recommend
policy A over policy B given his estimate of the group's preferences,
and the test of his recommendation is to ask whether the given welfare
function reflects the consensus of the group. Even within this frame
work, however, the question of votes must be resolved before any group
consensus can be revealed. Buchanan (6) further argued that majority
rule does not guarantee consensus or agreement among the parties because
adequate compensation may not be provided for the damaged parties.
At any rate, as a theoretical construct, the social welfare
function is perhaps useful, but for empirical work one generally ends
''The objective function would always be maximized at the
intersection of the demand and supply functions in the absence of the
externality relationship. The introduction of the externality relation
ship will shift the optimum solution to the left or to the right
depending on whether the externalities are external economies or external
diseconomies. The externality function will be discussed in later pages.

22
up using a_ social welfare function which, to the researcher, represents
a compromise between desired characteristics and empirical feasibility.
Any policy decision concerning pesticide usage is likely to
benefit some parties and hurt others. The model assumes that the
2
income effects of varying prices are small enough to be ignored. Such
an assumption would not seem too unrealistic where the consumer spends
a small proportion of his income on the commodity. The model also
assumes that tastes and preferences remain constant, and that the aggre
gate demand and supply functions for a crop are independent of those in
other regions, those for other crops, and those in other industries.
The model also assumes that the marginal utility of money is
equal for all parties affected by the policy. Lerner (33, pp. 23-40)
argues that in the absence of knowledge this is the most probable
assumption. Such an argument from ignorance is not very convincing or
comforting.
Nevertheless, the assumption that men are all very much alike is
the foundation of democratic institutions ("We hold these truths
to be self-evident . .") and we certainly do not attempt in
politics to give the voter a number of votes in proportion to
his intelligence and his ability to use them. The one-man-one-
vote principle is forced upon us by the recognition of the
practical impossibility of any other (3, p. 93).
So it is with the issue at hand. We realize that some people probably
derive greater utility than others from an incremental dollar (or
greater disutility from losing a dollar), but it was a practical impos
sibility to recognize it in the analysis.
A slight variation on this assumption is Hicks' argument that
the marginal utility of money must be assumed constant (25, p. 326).
This assumption seems slightly more restrictive than the one I have
made, but as far as the structure of the model is concerned, it amounts
to the same thing.

23
The objective function of the model is assumed to be separable.
That is, it is the sum of a number of independent functions in the
variables y and z^. In the terminology of the problem, this means that
any crop can be grown at any level within the range specified by the
constraints without affecting the demand and supply functions of the
other crops. It also means that the externality functions for chlori
nated hydrocarbons and organic phosphates are independent. More will be
said about the estimation of these externality functions in later pages.
The concept of a flexibility constraint (see constraints 1 and
2) was used by James M. Henderson (24) in the late 1950's in an attempt
to use linear programming to predict economic behavior of farmers.
Day (11) later applied this idea in his use of recursive programming on
crop production in 11 counties in Mississippi. Considering how a farmer
might allocate his land among various alternative uses, Henderson said
that the farmer's behavior could be explained by two principles. The
first was that the farmer would try to maximize his expected net revenue
and would therefore allocate his land in a manner consistent with
this desire. The second principle said that in allocating his land,
the farmer would also be influenced by the allocation pattern of the
preceding year. This was Henderson's unique contribution. "Speci
fically, a farmer's acreage plantings for each crop were assumed to be
constrained by maximum and minimum limits which indicated his desire
for diversity and reluctance to depart from an established pattern"
(24, p. 243). There are many factorseconomic, technological, insti
tutional, and sociologicalwhich contribute to this reluctance to
depart from an established pattern. Henderson's problem was to capture
this hypothesis in a model without making the model so complicated that

24
it could not be used empirically. He did this by placing upper and
lower bounds on allowable acreage changes. The bounds were determined
by observing historical acreage changes and were based on the assumption
that the diverse factors which influenced such changes in the past were
likely to do so in the future with about the same magnitude. He then
3
incorporated these into appropriate linear programming constraints:
b_. (min)y^ (t) y^ (t+1) b^.(max)y^.(t) j=l, . .n
where:
y^ (t) = the acreage of the jth crop in period t.
b^. (min) one minus the maximum percentage decrease considered
reasonable from observing historical cropping patterns.
b.(max) = one plus the maximum percentage increase considered
reasonable from observing historical cropping patterns.
He also had a final restriction on the overall land allocation:
E y (t+1) Y
j=l J
where:
Y is the total land available for cultivation.
This research effort utilized essentially the same approach.
Maximum and minimum flexibility constraints were applied to the indi
vidual crops. The overall land restriction differed slightly from
Henderson's. The total quantity of Dade County land which could be
cultivated was not a relevant constraint because it was so much larger
than the quantity that was actually cultivated. Furthermore, the
3
Symbols have been changed in order to be consistent with those
of the model.

25
quantity of land allocated to farming appeared to be declining. The
constraint was therefore established to apply to the total land under
cultivation, after an adjustment had been made for the other crops.^
The form of this constraint was:
n n n
d(min) E y.(t) E y.(t+l) d(max) E y.(t)
j=l J j=l J j=l J
where:
the d(min) and d(max), as before, represent proportional changes in
total farm land cultivated from one year to the next, as determined
from historical cropping patterns.^
The third constraint requires little explanation. It was needed
in the model to insure that the quantity of the ith pesticide used on
all crops would coincide with the quantity of the ith pesticide called
for in the solution.
The fourth set of constraints was "environmental." It appears
relatively simple, but it embodies all the physical relationships
between pesticide usage and the environment. When a pesticide is used
^In 1959, 9.8 percent of the land was in farms, while in 1964,
8.9 percent was in farms.
^Other crops were those excluded from the objective function of
the model. These were approximately 20 in number and accounted for
14 percent of planted acreage in 1966-67.
^It has been suggested that the imposition of a new pesticide
policy upon the farmers might disturb the interrelationships which
caused the variations in cropping patterns from which flexibility con
straints were estimated and that the flexibility constraints would
therefore lose their applicability. WTiether or not this is true depends
upon the magnitude of the policy change and the other sources of vari
ability in cropping patterns. As will be seen in later pages, the
effect of the proposed policy on farmers' costs is relatively small and
is far overshadowed by other exogeneous factors, such as weather, with
which the farmers must contend. It is therefore this writer's opinion
that the flexibility constraints do provide reasonable estimates, of
"capacity for change" in the cropping patterns.

26
on a crop, some of it is taken into the plant, some of it goes into the
soil and water, and some of it into the air. The speed with which a
pesticide breaks down into non-toxic substances varies a great deal
among pesticides. The chlorinated hydrocarbons tend to be very persis
tenttheir residues decompose into non-toxic substances very slowly.
The organic phosphates, on the other hand, tend to break down into non
toxic substances very rapidly and their residues do not build up
significantly in the environment. Much of the current pesticide
controversy revolves around these physical relationsthe residues which
build up and the potential damage which they may represent for the
environment. As will be noted later, large amounts of resources are
being committed to research on these physical relationships, but little
has been done to delineate their role in a decision model for policy
making. For this reason the set of environmental constraints is
included conceptually in the model. It was impossible, however, to
estimate the parameters empirically; therefore, the set is not included
in the succeeding chapter on the estimation of model coefficients. This
was a regrettable compromise because this set of constraints could
possibly be the most important of all in establishing policy. It is
precisely because of pesticides' potential for environmental pollution
that people's emotions become aroused and make them demand policies
regulating the use of pesticides.
The fifth set of constraints simply prevented the occurrence of
negative acreages and pesticide quantities in the solution.
Another aspect of the model which deserves emphasis concerns the
question of property rights, a subject of growing interest in recent
literature on policy issues (9, 12, 41). The model as stated here is

27
independent of the pattern of legal rights. It is not necessary to
delineate the rights in order to determine the optimal solution, but it
is necessary to delineate the rights in order to evaluate the various
ways of reaching the optimum, a problem with which we are not concerned.
For example, if a policy alternative calling for a tax on chlorinated
hydrocarbons were introduced, then by implication, the farmers do not
have the right to "pollute" the environment and must pay for the privi-
ledge of doing so. On the other hand, if no restraints are placed on
farmers' usage of chlorinated hydrocarbons, then by implication,they do
have the right to "pollute" the environment to some degree. One need
only read a few historical cases in the common law of nuisance to
realize that the question of property rights will vary greatly from time
to time, from place to place, and indeed, from judge to judge (9, p. 22).
Upon reading the following quotation from Prosser, one does not wonder
that the question of property rights is a very elusive concept: a
person is permitted
... to make use of his own property or to conduct his own affairs
at the expense of some harm to his neighbors. He may operate a
factory whose noise and smoke cause some discomfort to others, so
long as he keeps within reasonable bounds. It is only when his
conduct is unreasonable, in the light of its utility and the harm
which results, that it becomes a nuisance. . The world must
have factories, smelters, oil refineries, noisy machinery, and
blasting, even at the expense of some inconvenience to those in
the vicinity, and the plaintiff may be required to accept and tol-
lerate some not unreasonable discomfort for the general good (39,
pp. 398, 412).
The phrase in the above quotation, ". .in the light of its
utility and the harm which results ..." (39), illustrates the notion
that property rights are not independent of "the general good" or some
concept of social welfare. To put it another way:
. . The problem which we face in dealing with actions which have
harmful effects is not simply one of restraining those responsible

28
for them. What has to be decided is whether the gain from pre
venting the harm is greater than the loss which would be suffered
elsewhere as a result of stopping the action which produces the
harm (9, p. 27).
To summarize, the optimum externality level is independent of
property rights. Property rights become important when we begin trying
to move toward the optimum. The problem of the inter-personal compar
ison of utility cannot be ignored. The legal profession has been
grappling with it for many years and will continue doing so in the
future. Economists, by training, can bring many valuable tools to bear
on this problem and could be of valuable service to the legal profession
in making such decisions. The fact that policy decisions are difficult
in a world of imperfect knowledge does not justify ignoring them. If
economists refuse to aid in making them, they will be made by others.
Coase (9, pp. 42-43), in his classic article on social cost,
states that in two ways economists have historically fallen short in
helping to make policy decisions. First, they have tended to ignore
considerations which are aesthetic or moral in nature, when, in fact,
these may have a great influence on the final solution. Second, econo
mists have tended to focus their attention on models that are so simpli
fied that their solutions are of limited usefulness in the real world.
Conceptually, the model developed here at least partially meets
these criticisms. The environmental constraint allows the introduction
of value judgments through the right-hand-side elements, e, ., which
K1
are determined by political compromise. As far as the second criticism
is concerned, it is the writer's opinion that we have not abstracted
from the real world quite as far as most writers in this area. This is
true because of a slightly different approach. We began with a real

29
problem, for which we needed both a conceptual model and the data to use
it empirically. We retreated or abstracted from reality only to the
point where we felt we could actually satisfy the data requirements.
If we had not been faced with the necessity to use the model empirically,
we might, for example, have developed a dynamic programming formulation
which would have had the advantage of greater realism but for which we
could not obtain the necessary data.

CHAPTER IV
ESTIMATES OF MODEL COEFFICIENTS
Objective Function
The objective function of the model was based on the concept of
consumers'-producers surplus, modified to recognize external effects
not included in the surplus calculation. As noted earlier, the area of
consumers'-producers' surplus was defined as the area under the demand
function less the area under the supply function.
The Demand Model
The model which provided the basis for estimating demand
parameters for the objective function was specified as follows:
II] q(t) = Tq + T-jPt) + u(t)
subject to:
[2]
q(t) q(t-l) = Ip
q(t) -
q(t-l)
0 < ip < 2
where:
q(t) = the long-run equilibrium quantity as of period t.
p(t) = price of the commodity in period t.
I(t) = per capita disposable income (deflated) in period t.
q(t) = the quantity demanded in period t.
u(t) = a disturbance term satisfying the classical assumptions,
namely:
EI u ( i ) ] = 0
30

31
E[u(i)u(j) ] = 0 for i j, for i = j
p(t) and I(t) are fixed numbers observed without error.
The matrix of observations on p(t) and I(t) is non-singular.
The constraint, [2], simply describes the adjustment toward the
long-run equilibrium quantity.*'
Substituting equation [2] into equation [1] generated the
2
following function, which was empirically estimated:
[3] q (t) = \|rr0 + (l-ljj)q(t-l) + iJn^pCt) + i^lCt) + l^u(t)
The primary purpose of estimating equation [3] was to obtain
estimates of the price elasticity of demand after allowing for the
average effects of I(t) and q(t-l). Data availability necessitated
using State data rather than Dade County data. It was assumed that the
price elasticity for Dade County would be the same as that for the State
since they compete in the same markets and since, in some cases, Dade
3
County represents a relatively high proportion of the State acreages.
Estimates of long-run price elasticity were thus obtained using the
1 'V
If 0 < ip < 1, q(t) approaches q(t) asymtotically. If ip = 1,
the entire adjustment is accomplished in one period. If 1 < ip < 2, q(t)
over-adjusts, but still converges upon'q(t).
Lagged variables have been increasingly employed in econometric
work in recent years [21, 31, 38].
2
In this equation, however, u(t) is no longer independent of
q(t-l), the independent variable (27, p. 212; 35, p. 129). The result
is that the least squares estimates are biased but have the desirable
asymptotic properties of consistency and efficiency.
Dade County accounted for the following proportions of the
State acreage in 1966-67:
Crop
Percent of State acreage
in Dade County
Tomatoes 39
Winter potatoes 64
Beans 12
Corn 3

32
formula,
A
E =
where p and q were the means of the variables from State data.' Then,
using this estimate of E and the 1966-67 price and quantity for Dade
County, a linear demand function of the following form was synthesized
for Dade County:"*
[4] q(t) = 3q + BjPt)
3pCt) T1 -
In two cases which will be noted later, the variables were
transformed into logarithms. For these the regression coefficients were
the required estimates of elasticity.
For each crop the function was derived as follows:
where:
q
E = estimated long-run price elasticity,
p = average price from State data,
q = average quantity from State data.
A
= an estimate of in equation [1],
where:
= partial derivative of q with respect to p for Dade County.
q* = 1966-67 quantity for Dade County,
p* = 1966-67 price for Dade County.
Using the point slope form of a linear function passing through q* and
p* yields:
9q q*-q(t) P t q*
3p d p*-p(t) 1 p*
T,pq*
+ p(t) = q*(1-e) +
qp*
or:
q (t) = 3q + 3-j^pCt)
where: e ^
B0 = q* (1-E) and ^ = ¡y
q(t)
l -
V
A

33
The average effects of I(t) and q(t-l) on q(t) are, of course,
recognized in these simplified functions since I(t) and q(t-l) were
included as variables in the estimation of equation [3].
Equation [4] was then manipulated algebraically to make q(t) the
independent variable.^ At the same time the unit of measure on the
quantity variable was converted to acres by multiplying the equation by
the average yield per acre of the past 10 years. This led to demand
equations expressing price as a function of quantity (y) which were used
in the objective function on page 19 as f (y^), j being the crop desig
nation.
The conversion of q, units of production, into y, acres, via
the average yield per acre was done in order to express the demand
functions in the same units as the supply functions. Such a conversion
abstracts from the uncertainties of weather on crop yield and rests on
an average expected yield per acre.
Alternative data series (seasonal as well as annual data) were
tried in estimating demand functions. In using seasonal data, the
designated seasons were fall (September through November), winter
(December through February), and spring (March through May). Zero-
one variables were used to allow for the average seasonal effect. If an
observation occurred in the fall, the fall variable was assigned a value
of 1, otherwise 0. If an observation occurred in the spring, the spring
variable was assigned a value of 1, otherwise 0.^ The effect of this
^This, of course, does not necessarily yield the same equation
as one would get had the direction of minimization of the sums of
squares of residuals been in the direction of the p(t) axis. Initially,
p was defined as the independent variable because observations on p were
thought to be more accurate than those on q. Johnston (27, pp. 148-176)
offers a treatment of observational errors in the variables.
^The winter season is recognized in the intercept and therefore
does not have a zero-one variable assigned to it.

34
dummy variable technique was to shift the intercept of the demand
g
function in an attempt to recognize seasonal shifts in demand. A
logarithmic transformation of the variables was also tried. With the
exception of beans and mangos, these transformations provided little or
no improvement in the fits obtained. Table 4 presents the empirical
results which were selected for derivation of the demand functions
used in the model. Although not of interest for purposes of this study,
short-run price and income elasticities are also presented. Table 5
presents the demand functions which were used for the model.
The Supply Model
r
The second element of the objective function, depicted by g^. (y),
was a linear supply function for the jth crop under the rth policy
alternative. The model from which supply parameters for current pesti
cide usage were estimated was specified as follows:
'V
[5]
subject to:
y (t) = ?0 + ^pit-l) + u (t)
[6]
y(t) y(t-l) = 6
y(t) y(t-l)
, 0 < 6 < 2
where:
'Xj
y(t) = long-run equilibrium acreage as of period t.
p(t) = price of the commodity in period t.
y(t) = acres planted in period t.
u(t) = a disturbance term satisfying classical assumptions.
As in the demand model, the constraint described the adjustment
'Xj
toward the long-run equilibrium acreage. Using [6] to eliminate y(t) in
It should be noted that only the intercept changed in this
model. In other words, the demand for fall tomatoes was assumed to
differ from that of winter tomatoes only in level, not in slope.

35
Table 4.Empirically estimated relations from which demand functions
were derived.3
Crop
*
Intercept
Coefficients of:^
q(t1)
P(t)
I(t)^
Tomatoes^
1798.3218
.3027
(.1394)
- 733.8044
(149.9282)
2.8091
(.9227)
-1854.3457
(449.6931)
g
Winter potatoes
2271.0208
.4902
(.2213)
- 229.7552
(138.8649)
-.2824
(.6980)
Beans*1 *
7.3391
.0930
(.0952)
1.8514
(.2141)
.2248
(.4506)
.2146
(.0940)
Corn^
969.9456
.2897
(.1674)
-1391.9141
(321.5176)
1.8613
(.6422)
-1141.8203
(332.2517)
Avocadosk
1130.4805
.3805
(.1850)
- 44.3303
(13.6422)
5.7642
(4.2249)
Limesm
30.9556
.4280
(.2587)
- 11.3824
(21.8403)
.1167
(.1344)
w n,h,i
Mangos
.6427
.0896
(.1805)
- 1.2548
(.2088)
1.5275
(1.3004)
aSources of data:
1. Tomatoes, potatoes, beans, and corn: Florida Agricultural
Statistics (18).
2. Avocados: Agricultural Statistics (50).
3. Limes:
a. Production and on-tree prices: Florida Agricultural
Statistics (18).
b. Packinghouse prices: Agricultural Statistics (50).
4. Mangos: Dade County Agricultural Agent's Office, Homestead,
Florida.
^Standard errors of the coefficients are shown in parentheses.
cT
I = per capita income.
Sj = a "dummy variable" which equals 1 if the observation
occurred in the fall and 0 otherwise.
0
S = a "dummy variable" which equals 1 if the observation occur
red in the spring and 0 otherwise.
^Quantity measured in thousands of 60 pound crates; price
measured in dollars per crate.

36
Table 4.Extended.
Number
Short-run
Long-run
Short-run
Long-run
of
price
price
income
income
Durbin-
obser-
elas-
elas-
elas-
elas-
Watson
vations
R2
ticity
ticity
ticity
ticity
Statistic
s ^
s
- 797.2398
33
.8107
- .9728
-1.3952
1.4427
2.0690
**
1.8095
(335.2292)
17
.3976
- .4856
- .9525
- .3094
- .6069
**
1.6151
.1448
33
.7492
-1.8514
-2.0410
.2248
.2479
*
2.3175
(.0906)
2207.9375
33
.9610
-1.2701
-1.7881
1.4705
2.0703
1.9689*
(894.9026)
18
.5066
- .7929
-1.2800
1.2882
2.0795
1.8853*'
18
.4832
- .1271
- .2222
.6272
1.0965
1.8348*
12
.9131
-1.2548
-1.3783
1.5275
1.6778
*
2.2197
^Quantity measured in thousands of 100 pound bags; price
measured in dollars per bag.
^Quantity measured in thousands of bushels; price measured in
dollars per bushel.
1This function was estimated in natural logs.
^Quantity measured in thousands of crates; price measured in
dollars per crate.
Quantity measured in tons; price measured in dollars per ton.
mQuantity measured in thousands of boxes; price measured in
dollars per box.
n.
Quantity measured in bushels; price measured in dollars per
bushel.
A
Reject the hypothesis that auto-correlation is present at the
95 percent confidence level.
d statistic is inconclusive.

37
Table 5.Demand functions
used in the model.
a
Crop
Tomatoes
Potatoes
Beans
Corn
Avocados
Limes
Mangos
Function
p = 1521.5660 .0334 y
p 1116.6649 .0747 y2
p = 1152.4799 .0650 y3
p = 764.1282 .1661 y4
p = 571.3720 .0479 y5
p = 3285.4824 .7502 y6
p = 690.9032 .1911 y?
For all functions, price is measured in dollars per acre
and quantity in acres.

38
[5] yields:
[7] y(t) = 6?0 + (l-)y(t-l) + S^pCt-l) + 6u(t)
Equation [7] was empirically estimated and the results used to obtain
estimates of the long-run supply elasticity. Results are shown in
Table 6.
As in the development of the demand functions, the estimate of
elasticity and the 1966-67 price and quantity observations for Dade
County were then substituted into the point-slope form of a straight
line to obtain the equations for the supply functions. These equations
were then solved for p in terms of y in order to conform to the require
ments of the model.
Since equation [5] includes price lagged one time period, the
supply and demand equations constitute a cobweb model which, for
purposes of the study, is assumed to be in equilibrium at the 1966-67
price and quantity. The objective is to determine what happens to this
point of long-run equilibrium when externalities are recognized. There
was no interest in the time path of price-quantity changes but only in
9
the magnitude of change in the point of long-run equilibrium.
In the case of avocados, limes, and mangos, data did not exist
to permit the estimation of supply functions, so the acreage for these
crops was constrained at the 1966-67 level. This was probably a
If the system gets out of equilibrium, cobweb theory (15,
pp. 255-280) states that the time path of price and quantity will be
convergent or divergent depending upon whether the absolute value of the
slope of the supply function is greater or less than that of the demand
function. In the case of potatoes, the relative slope of the supply
function to the demand function would indicate a divergent path. The
negative estimate of the income elasticity for potatoes would aggravate
this problem if income increased. Shifts in the flexibility constraints
might also affect the time path.

39
Table 6.Empirically estimated relations from which supply functions
were derived.3
Crop
Intercept
Coefficients of:^
y(t-i)
p(t-l)
S
0
Tomatoes
1904.0586
.4472
(.1393)
1754.8784
(623.8162)
-3727.2373
(1723.1143)
Winter potatoes >b
1.9643
.7367
(.1717)
.4033
(.1981)
Beans*1
3.1023
.6614
(.1636)
.2002
(.1659)
.0003
(.0708)
Corn1
-4528.8516
.7935
(.1569)
2353.8164
(1463.8821)
1904.8828
(1570.1018)
Avocados )
Limes ?
Acreages for
these tree
crops were constrained to
Mangos J
Sources of data:
1. Tomatoes, potatoes, beans, and corn: Florida Agricultural
Statistics (18).
2. Avocados: Agricultural Statistics (50).
3. Limes:
a. Production and on-tree prices: Florida Agricultural Statis
tics (18).
b. Packinghouse prices: Agricultural Statistics (50).
4. Mangos: Dade County Agricultural Agent's Office, Homestead,
Florida.
^Standard errors of the coefficients are shown in parentheses.
Q
S^ = a "dummy variable" which equals 1 if the observation occur
red in the fall and 0 otherwise.
^S = a "dummy variable which equals 1 if the observation occur
red in the spring and 0 otherwise.

40
Table 6.Extended.
Number
of
obser
vations
R2
Short-run
price
elasticity
Long-run
price
elasticity
Durbin-
Watson
Statistic
s &
s
- 339.7224
(1392.4358)
33
.6773
.5185
.9379
*
1.7873
17
.5879
.4033
1.5317
1.8363*
.0066
(.0690)
33
.4334
.2002
.5910
2.5493*
7843.4375
(3283.3203)
33
.9648
.3178
1.5390
2.0073*
be no greater
than the
1966-67 levels.
0
Quantity measured in acres planted; price measured in dollars
per crate
^Quantity measured in acres planted; price measured in dollars
per bag.
Cr
^Variables were transformed to natural logs.
Quantity measured in acres planted; price measured in dollars
per bushel.
1Quantity measured in acres planted; price measured in dollars
per crate.
*
Reject the hypothesis that auto-correlation is present at the
95 percent confidence level.

41
somewhat unrealistic restraint for the long-run.
Shifting the Supply Functions
The supply functions developed in the manner described above
pertain to current pesticide usage practices. Those for alternative
pesticide policies could not be observed but had to be synthesized from
information provided by growers, entomologists, and others knowledgeable
about the area.
In order to approximate reality within the data limitations, the
chlorinated hydrocarbons were lumped together as one category and the
organic phosphates as another. This was done on the basis that the
chlorinated hydrocarbons tend to have similar effects on the environ
ment and that the organic phosphates likewise tend to have similar
effects on the environment. It was also partly a result of political
considerations,since the thrust of the controversy seems to center
mainly around these two categories.
Chlorinated hydrocarbons and organic phosphates are, to a
limited extent, substitutes for each other. This is indicated by Tables
7 through 10 which show alternative insect control measures for the
vegetable crops under consideration. In many cases a given insect can
be controlled by a chlorinated hydrocarbon or an organic phosphate. But
there are also a number of insects for which the only recommended remedy
is a chlorinated hydrocarbon and a number for which the only recommended
remedy is an organic phosphate. Entomologists frequently differ in
their opinions about how to control certain insects. Adding to this
source of variation is the fact that insects' susceptibility to a pesti
cide seems to vary widely from time to time and place to place.

42
Table 7.Recommended insect
control measures for tomatoes.
a
Insect
Pesticide
Organic
phosphate
quantity^
Chlorinated
hydrocarbon
quantity^
Other
quantity
Minimum
days to
harvest
Aphids
Dimethoate
.334
7
Demeton
.375
3
Parathion
.450
3
Phosdrin
.500
1
Thiodan
1.000
1
Armyworms,
DDT
1.000
3
Tomato Fruit-
Phosdrin
.500
1 *
worms,
Sevin
1.000
NTL
Hornworms
TDE (DDD)
1.000
1
Thiodan
1.000
1
Loopers
Dibrom
2.000
1
Parathion
.450
3
Phosdrin
.500
1
Thiodan
1.000
1
Leaf Miners
Diazinon
.500
1
Dibromx*
1.000
1
Dimethoate
.334
7 *
Guthion
.500
NTL
Stinkbugs,
Guthion
.500
:k
NTL
other plant
Parathion
.450
3
bugs
Phosdrin
.250
1 A
Sevin
1.000
NTL
Thiodan
1.000
1
Banded Cucumber Guthion
.500
&
NTL
Beetle
Thiodan
1.000
1
Source of data: Insect Control Guide (17) and consultation
with entomologists familiar with the area.
Quantities are expressed in pounds of 100 percent active
material per 100 gallons of water. The gallonage applied per acre will
vary with the size and density of the plants. In the Dade County area
the gallonage usually varies from 100 to 300 gallons.
*
No time limit.
AA
Dibrom was not observed in Dade County in 1966-67.

43
Table 8.Recommended insect control measures for potatoes.
a
Insect
Pesticide
Organic
phosphate
quantity^
Chlorinated
hydrocarbon
quantity^
Other
quantity
Minimum
days to
harvest
Aphids
Derneton
.375
21
Dimethoate
.500
7
Meta-Systox-
-R .375
7 *
Thiodan
1.000
NTL
Armyworms,
Parathion
.300
5
Loopers, other
Phosdrin
.500
1 *
caterpillars
Thiodan
1.000
NTL*
Toxaphene
1.000
NTL
Banded Cucumber
Guthion
.500
7 *
Beetle
Thiodan
1.000
NTL
Leaf-footed
Guthion
.500
7
Plant Bug,
Parathion
.300
5
Green Stinkbug
Phosdrin
.250
1 *
Thiodan
1.000
NTL
Leaf Miners
Diazinon
.500
14 *
Dibrom**
1.000
NTL
Dimethoate
.334
7
Guthion
.500
7
Wireworms
Thimet
3.000
***
Parathion
2.000
kkk
3
Source of data: Insect Control Guide (17) and consultation
with entomologists familiar with the area.
Quantities are expressed in pounds of 100 percent active
material per 100 gallons of water. The gallonage applied per acre will
vary with the size and density of the plants. In the Dade County area
the gallonage usually varies from 100 to 300 gallons.
*
No time limit.
kk
Dibrom was not observed in Dade County in 1966-67.
kkk
Soil treatment prior to planting.

44
Table 9.Recommended insect control measures for beans.
a
Organic
Chlorinated
Minimum
phosphate
hydrocarbon
Other
days to
Insect
Pesticide quantity^
quantity^
quantity
harvest
Aphids
Demeton
.375
21 *
Dimethoate
.334
NTL
Parathion
.300
3
Phosdrin
.250
1
Armyworms,
DDT
1.000
5 *
Corn Earworm
Sevin
1.000
NTL
Toxaphene
1.000
5
Cowpea Curculio
Toxaphene
1.000
5**
Thiodan
.500
3
Bean Leaf-
Dimethoate
.334
k
NTL
hopper, Bean
Guthion
.500
7
Leafroller
Parathion
.300
3
Phosdrin
.500
1 *
Sevin
1.000
NTL
Toxaphene
1.000
5
Leaf Miners,
Diazinon
.500
7 *
Cucumber
Dimethoate
.334
NTL5"
Beetles
Guthion
.500
7
Thrips
Parathion
.225
3
Stinkbugs
Guthion
.500
7
Parathion
.300
3
Phosdrin
.250
1 *
Sevin
1.000
NTL
Saltmarsh
Phosdrin
.500
1
Caterpillar
Toxaphene
1.000
5
Lima Pod Borer
Parathion
.300
3
Source of data: Insect Control Guide (17) and consultation
with entomologists familiar with the area.
^Quantities are expressed in pounds of 100 percent active
material per 100 gallons of water. The gallonage applied per acre will
vary with the size and density of the plants. In the Dade County area
the gallonage usually varies from 100 to 300 gallons.
k
No time limit.
k-k
Should not apply Thiodan more than 3 times per season.

45
Table 10.Recommended insect control measures for corn.
a
Insect
Pesticide
Organic
phosphate
quantity*3
Chlorinated
hydrocarbon
quantity**
Other
quantity
Minimum
days to
harvest
Aphids, Spider
Parathion
.250
3
mites
Phosdrin
.250
1
Fall Armyworms
DDT
1.000
**
and Corn Ear-
Parathion
.250
**
worm feeding
Toxaphene
1.500
**
in bud
Mixture of
**
DDT and
1.000
Parathion
.125
Mixture of
**
DDT and
1.000
Toxaphene
.750
Silk-fly
Parathion
.250
3
Earworms
DDT
2.OOOt
**
Sevin
2.OOOt
**
Mixture of
**
DDT and
2.000
Sevin
.500
Corn Stem
DDT
2.000
**
Weevil
Mixture of
**
DDT and
1.000
Toxaphene
1.000
Mixture of
A*
DDT and
2.000
Toxaphene
1.000
Source of data: Insect Control Guide (17) and consultation
with entomologists familiar with the area.
Quantities are expressed in pounds of 100 percent active
material per 100 gallons of water. The gallonage applied per acre will
vary with the size and density of the plants. In the Dade County area
the gallonage usually varies from 100 to 300 gallons.
**
No specific limitation so long as the usages do not result in
a residue on the edible ears.
f
These amounts should be mixed in 50 gallons of water and
applied to one acre.

46
Furthermore, if the same pesticide is used constantly for a prolonged
period of time, it raises the probability of immunity on the part of
the insect.
In order to fulfill the data requirements of the model, it was
necessary to synthesize a substitution rate between the chlorinated
hydrocarbons and the organic phosphates. This is not to say that the
consideration is necessarily limited to two dimensions, but these two
categories are so clearly dominant that it is doubtful if the others
could have a significant effect on the average substitution rate.
Entomologists familiar with the problem generally agree that the
substitution rate which maintains equal control is not completely linear,
as shown by the iso-product curve in Figure 4, but is more likely to be
a hyperbolic function somewhat like that of Figure 5.^
Figure 5 merely says that it becomes progressively more
difficult to substitute one for the other, which is in complete agree
ment with economic theory. It also leads one to the conclusion that
sudden elimination of the chlorinated hydrocarbons may be an unattainable
Figure 4.A linear iso-product function for chlorinated
hydrocarbons and organic phosphates.
Three entomologists provided assistance in developing this
section. Two are located on the University of Florida campus and one in
Dade County.

47
Organic phosphates
Figure 5.A hyperbolic iso-product function for chlorinated
hydrocarbons and organic phosphates.
policy objective and that reduction is a far more reasonable goal, for
the immediate future, particularly if one is to maintain production
without changing non-pesticide inputs. This doesn't mean that the ulti
mate goal cannot be elimination or near elimination of the chlorinated
hydrocarbons. It simply means that the first step to be evaluated will
be a reduction policy, and that the ultimate goal, whatever it is, will
be approached asytotically through a series of policies. It is the
objective of this research effort to trace the welfare implications of
the first step only. The alternative pesticide usage policy for the
analysis was subjectively chosen as a 50 percent reduction in per acre
usage of hydrocarbons for each crop."^
It was the consensus of entomologists that for policies of 50
percent reduction or less (of chlorinated hydrocarbons) per acre, a one-
pound reduction of chlorinated hydrocarbons would require about .3 to .4
There are currently some efforts to make laws banning thg_use
of DDT and certain other "hard" pesticides in several states. It is
debatable as to how much effect this would have on reducing the usage of
the "hard" pesticides as a group, for the farmers would be likely to
shift over to some other persistent pesticide not covered by the legis
lation. Several economists have warned against the pitfalls of excessive
control in pollution problems (43, pp. 56-58; 30, pp. 17-21).

48
additional pounds of organic phosphates. All were careful to specify
that such a statement required gross assumptions about insect infes
tation, weather conditions, and insect susceptibility. Because, of this
great possibility of error, the model was run using rates of .3, .4,
and .5.
Given the substitution rate between chlorinated hydrocarbons and
organic phosphates, it was then necessary to translate this into a shift
in the supply function for the specified policya 50 percent reduction
in the per acre usage of chlorinated hydrocarbons for each crop. In
order to do this it was necessary to compute the average costs of chlo
rinated hydrocarbons and organic phosphates by crops. These are
presented in Table 11. The difference in costs of, say, chlorinated
Table 11.Estimated average costs of chlorinated hydrocarbons and
organic phosphates used on crops in Dade County, 1966-67.
Crop
Chlorinated
hydrocarbons
Organic
phosphates
....Dollars per pound of
100% active
Tomatoes
1.09
3.82
Potatoes
2.46
3.01
Beans
.99
2.69
Corn
.90
1.82
All crops sampled
1.07
2.83
hydrocarbons between tomatoes and potatoes is due to the difference in
mix of chlorinated hydrocarbons between the^two crops. Potatoes tend
For policies specifying a greater than 50 percent reduction,
none were willing to speculate on the substitution rate,and two indi
cated that "adequate" control of certain insects would be difficult or
impossible with the current state of the arts.

49
to use a higher proportion of the more expensive chlorinated hydrocarbons
than do tomatoes, and in computing the average, each chlorinated
hydrocarbon is weighted by its relative importance in the usage mix.
Next, average total costs of chlorinated hydrocarbons plus
organic phosphates for the two alternative policies and the three substi
tution rates were computed. These are presented in column 3 of Table 12.
In addition to a change in the cost of spray materials, there is
also a possibility that other categories of cost might change under the
alternative pesticide policy. The major ones are those likely to be
affected by a change in the required number of pesticide applications,
since the organic phosphates as a group are less persistent than the
chlorinated hydrocarbons and might require more applications for control.
The entomologists considered this question and came to the conclusion
that there would be very little increase in labor and machinery costs in
Dade County for the crops concerned. The reason for this is that a
fairly rigid fungicide program is required on all crops, and increasing
the number of insecticide applications would merely involve combining
the insecticides with the fungicide applications. Estimates of
increased labor and variable machinery expense were as follows:
Tomatoes
10%
Potatoes
10%
Beans
0
Corn
0
Changes in the cost of spray materials, labor, and variable
machinery cost were then related to the cost per acre of producing the
crops. These are showjr'in Table 12.
If we assume these changes in average total cost per acre are

50
Table 12.Estimates of average total production costs in Dade County,
1966-67, by crop and pesticide policy.
Cost of chlorinated
Cost of cultural
Average
hydrocarbons and
labor and variable
total cost
organic phosphates
cost of machinery
Dollars per acre
Tomatoes ,
Policy
1 d
$866.02
$19.10
$157.61
Policy
2Ad
881.95
19.27
173.37
Policy
2Bf
883.11
20.43
173.37
Policy
2C't
884.28
21.60
173.37
Potatoes
Policy
1
$609.24
$20.88
$ 83.32
Policy
2A
616.57
19.88
91.65
Policy
2B
616.76
20.07
91.65
Policy
2C
616.95
20.26
91.65
Beans
Policy
1
$754.87
$ 8.27
$189.80
Policy
2A
754.55
7.95
189.80
Policy
2B
755.02
8.42
189.80
Policy
2C
755.48
8.88
189.80
Corn
Policy
1
$399.21
$48.39
$ 47.02
Policy
2A
392.01
41.19
47.02
Policy
2B
395.71
44.89
47.02
Policy
2C
399.42
48.60
47.02
Includes cultural labor, gas, oil, grease, maintenance, and
repair.
^Current usage.
CSource of data: Brooke (5).
dA 50 percent reduction in chlorinated hydrocarbons and a sub
stitution rate of .3 pounds of organic phosphates per pound of chlorinated
hydrocarbons.
0
A 50 percent reduction in chlorinated hydrocarbons and a sub
stitution rate of .4 pounds of organic phosphates per pound of
chlorinated hydrocarbons.
^A 50 percent reduction in chlorinated hydrocarbons and a sub
stitution rate of .5 pounds of organic phosphates per pound of
chlorinated hydrocarbons.

51
realistic over a wide range of acres, then the change represents a
13
parallel vertical shift in the marginal cost function.
In summary, it was decided that two categories of cost(1) cost
of spray materials and (2) cost of labor and variable machinery cost
were most likely to be affected by the alternative pesticide policy.
The effects of the policy change on average costs per acre were deduced
from cost data currently available and the experience of entomologists
familiar with the region. These cost changes were applied as parallel
shifts in the supply functions used for the first policy. The supply
functions used in the model are presented in Table 13.
The Externality Functions
The explanations for the point estimates of pesticide usage
and externalities are developed in Chapters V and VI respectively. It
is the objective of this section to show how these estimates were used
Using the functional forms of the model:
given:
TC = A + BY + CY2
then,
MC = B + 2CY
and,
AC = y + B + CY
where:
TC, MC, and AC are total, marginal, and average cost in dollars
per acre,and Y is the number of acres of output. A parallel vertical
shift, A, in AC gives:
AC' = I + (B+A) + CY
for which the corresponding TC and MC become:
TC = A + (B+A)Y + CY2
MC = B + A + 2CY
and the result is a parallel shift in MC.

52
Table 13.Supply functions used in the model.
a
Function
Tomatoes
Policy
lu
P
=
- 83.6862
1
.0497
y
Policy
2Ad
P
=
- 67.6562
+
.0497
y
Policy
2Bd
P
=
- 66.5962
+
.0497
y
Policy
2Ce
P
=
- 65.4262
+
.0497
y
Potatoes
Policy
1
P
=
189.0995
+
.0464
y
Policy
2A
P
=
196.4295
+
.0464
y
Policy
2B
P
=
196.6195
+
.0464
y
Policy
2C
P
=
196.8095
+
.0464
y
Beans
Policy
1
P
=
-535.2889
+
.2245
y
Policy
2A
P
=
-535.6089
+
.2245
y
Policy
2B
P
=
-535.1389
+
.2245
y
Policy
2C
P
=
-534.6789
+
.2245
y
Corn
Policy
1
P
=
171.6317
+
.1930
y
Policy
2A
P
=
164.4317
+
.1930
y
Policy
2B
P
=
168.1317
+
.1930
y
Policy
2C
P
=
171.8417
+
.1930
y
Avocados j
Limes
Acreages on these crops were
constrained
to be
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
no greater than the 1966-67 levels.
Mangos
For all functions} price is measured in dollars per acre and
quantity in acres.
Current usage,
c
A 50 percent reduction in chlorinated hydrocarbons and a
substitution rate of .3 pounds of organic phosphates per pound of
chlorinated hydrocarbons.
dA 50 percent reduction in chlorinated hydrocarbons and a
substitution rate of ,4 pounds of organic phosphates per pound of
chlorinated hydrocarbons.
A 50 percent reduction in chlorinated hydrocarbons and a
substitution rate of .5 pounds of organic phosphates per pound of
chlorinated hydrocarbons.

53
in the model.
The model required a functional relationship between dollars of
externalities and the quantity of pesticides used. Empirically there
was no way to estimate such a function, so it was necessary to assume
a functional form and rely on sensitivity analysis for some indication
14
about how critical the assumption was to the solution. Figure 6 is
illustrative of the case for organic phosphates. The point estimate of
E
($)
$4,590.
152,348 z2 (organic phosphates)
lbs.
Figure 6.Point estimate of externalities arising from the
agricultural usage of organic phosphates.
externalities due to the organic phosphates in 1966-67 was $4,590, and
the corresponding usage on the seven crops was 152,348 pounds. ^ A
linear functional form passing through the origin was assumed. This
meant that the equation was:
[8] E2 = .0301 z2
Parametric programming could be employed on both the form of
the function and the parameters of the function. In this paper only the
parameter, or slope, of the linear function was varied.
Some thought was given to the possibility of measuring the
second-order effects through an input-output technique, but it was later
discovered that the solution was relatively insensitive to this parameter
and the idea was therefore abandoned.

54
The slope of the function was later varied from 0 to 5.0000 in incre-
16
ments of 1.0 to analyze the criticalness of the assumption.
The case for chlorinated hydrocarbons presented a more
formidable problem. A point estimate of the chronic externalities could
not be observed, so the function,
[9] E1 = e1z1
was used, where e was varied over the interval 0 to 5.0000 in increments
of 1.0.
Constraints on the Objective Function
For ease of illustration, the constraints on the objective
function can be written in matrix notation as:
where:
A =
AB C
[-U
l,n
1 ,m
[1]
l,n
1 ,m
{ ^n-_3>j]n~3,n
[1],
e
n-3 ,m
n ,n
6
n^m
x
r
a. .
L_iJJ
[-1]
m,n m,m
G
mp ,n
J^kijmp,m
An interesting point could be raised here. Data from the
Industrial Commission indicate that acute externalities have declined.
This decline was probably in the face of rising organic phosphate usage,
which would imply a negative slope to the function of Figure 6. How is
the positive slope logically justified? Ihe function of Figure 6
represents the relation between externalities and organic phosphate
usage, all other factors held constant. But all other factors have not,
of course, remained constant. Education may be the primary example.
The rural inhabitants of Dade County might be learning how to handle the
dangerous pesticides and this is at least a partial explanation of why
externalities have declined in spite of rising usage.

55
C =
n
-d(min) E y. (t)
j=l 3 -
1,1
d(max)
j
-b.(min)y.
=1 J Jl,l
(t)1
J Jn-3,1
b (max)y (t)|
J Jn-3,1
[1]
3,1
^m,l
L J mp, 1
Coefficients of Variables in the Constraints
Matrix A will be considered first. It is the matrix of
coefficients, which had to be empirically determined. As indicated
above, most of the elements of the matrix are ones or zeros. The only
x
r
a. .
ij
The
m.n
M
L J mp,
m
elements which had to be estimated were the
elements would also have to be estimated if the environmental constraints
were maintained in the model, but, as explained previously, the current
state of the arts did not permit estimation of these coefficients. In
the empirical model there were seven crops and two pesticides, so:
x
r
a. .
ij
m,n
XX X
r r r
all a12 ,al7
XX X
r r r
a21 a22 ,a27
Since two pesticide policies with three variations on the second were
considered, the model was run once for r=l and once for r=2A, 2B, and 2C.
X]
The way in which the elements a.. were estimated is explained in

56
Chapter V. The resulting coefficients were:
X1 6.0719, 1.2860, 3.4304, 40.7003, .0706, .0484, .0205
aij 3.2669, 5.8825, 1.8114, 6.4624, .2239, .1535, .0650
m,n
The Constraint Vector
Of the elements of the constraint vector, C, the first four sets
were flexibility constraints. The underlying philosophy of flexibility
constraints was explained in the previous chapter.
For the estimation of the maximum and minimum flexibility con
straints, an approach called "the method of average rates" was used (11,
p. 189). For the jth crop, this took the form:
m-, y. (t)
V"0 tf2 y¡(^T)
for y_. (t) > y^ (t-1)
m.
y.(t)
bJ(mln) 2 P^T)
m
for yj(t) y.(t-i)
where m's were the number of periods involved, respectively. For the
total land constraint, it was:
d(max)
n
E
j=l
y1(t)
pi
d(min)
n y. (t)
y _1_
i-i
n n
for E y. (t) > E y. (t-1)
j-1 J j=l J
n n
for E y.(t) E y (t-1)
j=l J j=l 3
where p's were the number of periods involved, respectively, and n the
number of crops.
In words, the maximum flexibility constraint was simply one plus
the average of the percentage increases, and the minimum flexibility

57
constraint was one minus the average of the percentage decreases."^ The
flexibility constraints so generated are presented in Table 14.
Table 14.Flexibility constraints used for the empirical model.
Crop
Constraints
5
Total land3 .9022(44843) Z y 1.0835(44845)
3=1 3
Tomatoes
Potatoes
Beans
Corn
Avocados
Limes
Mangos
.8284(19000) -
.8527( 7660) -
.9037( 5830) -
.6881( 1650) -
y1 1.1140(19000)
y2 1.1654( 7660)
y3 1.1458( 5830)
y4 2.2872( 1650)
y5 5235
y6 3585
y? 1520
Defined as total land under cultivation after allowance for
minor crops (see page 25).
17
At least two other possibilities exist for obtaining flexi
bility constraints. First, one might choose to use the maximum
percentage changes rather than the average percentage change. Second,
one might use a regression approach, fitting the functions:
y^(t) = b^ (max)y_. (t-1) y..(t) > y^ (t-1)
and y.(t) = b (min)y. (t-1) y.(t) y.(t-l)
3 J 3 3 3
The "regression approach" generated parameters which were very close to
those presented in Table 14.

CHAPTER V
APPLICATION OF AGRICULTURAL PESTICIDES IN DADE COUNTY
The information contained in this chapter provides the descrip
tive background and the pesticide usage estimates employed in Chapter IV
for the development of the externality functions.
Technology of Pesticide Application in Dade County
Pesticide spray programs in Dade County vary widely among
growers depending upon the crop involved, the form, of the pesticide
being used, the method of application, and the insect or disease
infestation. There are four dominant pesticide formulations(1) wet-
table powder, (2) dust, (3) liquid or emulsifiable concentrate, and
(4) granulesand there is a range of concentrations for each.
Parathion, for example, was observed in three different formulations
and seven different concentrations.
The technology of pesticide application ranges from the
relatively primitive hand methods to aerial application using
helicopters, but most growers use ground rigs which hold one, two, three,
or four hundred gallons of liquid. Aerial application is still slightly
more expensive than ground application, but it is more flexible during
adverse weather conditions when ground rigs cannot get into the fields.
Some growers claim that aerial applications are more effective, through
better coverage, than ground rigs. The ground rigs are usually operated
58

59
at about four miles per hour and three hundred pounds per square inch
nozzle pressure. The number of nozzles varies from three to nine
depending upon the size of plant and desired coverage.
While recommended pesticide dosages are specified on the label
in terms of "amounts per acre," growers tend to make their calculations
in terms of "amounts per one hundred gallons of water" since their spray
rigs hold even multiples of one hundred gallons. They then try to put
the appropriate amount of liquid (water plus pesticide) on an acre so as
not to exceed the recommended dosage. The spray men are, for the most
part, limited in education, so the calculations and mixing are usually
done by the grower,and then the spray man adjusts the speed of the
tractor slightly so that (1) the plants are well covered and (2) a
minimum quantity of spray drips to the ground. In addition, the spray
men try to adjust the discharge slightly so as to run out of spray about
the time they get to the side of the field where the pesticides and
irrigation well are located. In a word, the uniform application of a
given quantity of pesticide to an acre is considerably imprecise.
Aerial spraying is usually done on a contract basis by pilots
who specialize in crop spraying. The farmer purchases the pesticides
and turns them over to the pilot with a mandate to apply a certain
quantity per acre. The pilot, knowing the size and discharge rate of
his spray rig, then mixes the pesticide with water in an appropriate
ratio to achieve the farmer's desired coverage. Boundary lines are
fairly well marked, and experienced pilots familiar with the area can
often spray a field without assistance from the ground. Others use a
helper on the ground to mark off the field boundaries and the spray
progress. Like spraying with ground rigs, aerial spraying is relatively

60
imprecise. Wind conditions can effect the coverage significantly, and
the spray apparatus is difficult to turn off at exactly the right moment
as the pilot turns to make another sweep of the field. A survey of
growers and entomologists in the area, conducted by Mr. Richard Hunt of
the Dade County Agricultural Agents Office, indicated that aerial
spraying on the average has increased about 10 to 20 percent over the
past 5 years but that the increase is mainly a function of weather. It
is becoming common practice for the growers to use airplanes after a
rain when it is difficult to get machinery into the fields.
The vagaries of weather are also very important in determining
the frequency with which growers must spray. Not only are certain
weather conditions more propitious than others for disease and insect
damage, but if a grower sprays his field just before a rain he must
often spray it again, right after the rain because the pesticide was
washed off the plant.
In general, it was observed that growers tend to use a pre
determined spray schedule for disease and a random or "spray-as-needed"
program for insects.
Estimated Quantities of Pesticides Used in Dade County
One might go about estimating Dade Countys agricultural usage
of pesticides in several ways. The quickest would be to assume that
growers follow labeled recommendations completely, and then develop the
estimate based on the recommendations, an assumed frequency of appli
cation, an assumed length of growing season, and a knowledge of the
acreage of each crop. One such estimate was made by Mr. Richard W. Klukas,

61
management biologist for the Everglades National Park, and his estimates
are presented in Appendix B (p. 124)."*"
For this study, the quantity of pesticides used for agriculture
in Dade County was estimated by interviewing the growers and by using
records of the growers' pesticide purchases. The interviews provided
background information on spray programs, and, in some cases, the
growers kept individual field records which were made available.
The growers which were interviewed accounted for the following
proportions of harvested acreage in 1966-67:
Tomatoes
73.5%
Potatoes
72.5
Pole beans
65.0
Corn
96.5
Squash
Other vegetables
29.2
43.8
Total vegetables
59.0
Groves^
12.6
Includes okra, peas, cucumbers, cuban pumpkins.
^Includes avocados, limes, mangos, papayas.
When a pesticide firm makes a sale to a farmer, an invoice is
prepared in triplicate. The farmer receives one copy and the firm keeps
two. At the end of the month, when statements are sent out, another
copy is sent to the farmer along with his bill. The farmers usually
keep this second one for their records. During the interviews, the
farmers were asked for permission to copy these records or to copy the
originals from their pesticide suppliers. If the farmer preferred that
we go to his pesticide supplier, he was asked to sign a permission
H/e are indebted to Mr. Klukas for permission to publish these
estimates.

62
statement for the benefit of the supplier. After making preliminary
visits to the suppliers to explain the research, they were approached
and asked to cooperate. Of the eight major firms in the area, six
agreed to let us copy invoices of sales to customers who had given us
permission to do so. Two refused to cooperate, so the information was
obtained from the growers copy of the invoice. As noted previously,
there were many different pesticides used in Dade County, and most of
the pesticides were used in a number of different formulations.
Initially, there were 212 separate pesticides and formulations identified.
For a given pesticide the different formulations were converted to units
of 100 percent concentrated material and added together. This reduced
the number of pesticides to 56. The common name, trade name, and/or
chemical name of these are presented in Appendix C (p. 131).
The basic unit of observation for all of the pesticide usage
tables was either a grower or a field. Where a grower had complete
spray information on his fields, each field was considered a separate
observation. If he did not keep field data, the farmer was asked to
designate what crop every pesticide invoice was used on, and it was
assumed that the pesticide was used uniformally on all his land planted
in that crop. It was further assumed that the pesticide was used in
the month in which it was sold.
It was not particularly difficult for the farmers to match the
pesticide with the crop on which it was used since (1) the crops tend
to differ in their basic spray programs both as to ingredients and
timing and (2) most of the farmers tend to specialize in certain crops.
Many of those interviewed grew only one crop and very few grew more than
three. Justification for the assumption that the pesticide was used

63
in the month in which it was purchased is found in the fact that many
growers stated they were unwilling to tie up capital and warehouse space
maintaining an inventory of pesticides which were dangerous and might,
in some cases, spoil before they were used. Transporation and labor
costs were also saved since the pesticide supplier would deliver directly
to the field where the pesticide was needed.
The crop year for the pesticide usage estimates extended from
July until the following June. Most of the planting was done in
September, October, November, and December, and most of the harvesting
in January, February, March, and April. For an acre of ground, only one
crop per year was grown.
The tables of pesticide usage estimates are presented in Appendix
D (p. 139). Much of the pesticide data presented in the appendix were
not required for the empirical model but they are nevertheless descrip
tive of pesticide usage in the region and it was felt that they might
also be of use to other researchers concerned with the pesticide issue.
Table 38 (p. 140) summarizes the data for six broad categories
of pesticidesfungicides, organic phosphate insecticides, chlorinated
hydrocarbon insecticides, carbamate insecticides, other insecticides,
and herbicides. Each is reported by crop. Much of the current pesti-
.cide controversy centers around two of the insecticide categories
the chlorinated hydrocarbons and the organic phosphates. It is the data
on these two categories which were used in the model of Chapter III.
Data on the trend in usage of these categories through the years were not
obtained for Dade County, but the statistical series recently initiated
by the Economic Research Service of the USDA indicates that since 1964
farmers in the Southeast region (South Carolina, Georgia, Florida,

64
Alabama) have shifted toward the organic phosphates and away from the
chlorinated hydrocarbons while holding the total usage about the
same (51, 52).
Table 39 (p. 142) utilizes the same breakdown of pesticides and
crops,but the data are reported by months for the 1966-67 crop season.
As indicated by the table, usage of pesticides tends to peak out during
the winter months of November, December, January, and February, and
tends to be very low during other months of the year. One might wonder
if the seasonal pattern of pesticide usage combines with the life cycle
of wildlife in the region to cause any particular damage or threat of
damage. There was, however, no evidence to support or refute such an
hypothesis.
Table 40 (p. 155) presents the estimates of annual per acre
pesticide usage for 1966-67 by crop and pesticide. An estimate is also
made of the total pesticide usage for Dade County by pesticide and crop.
Finally, the trend of usage is specified where possible. In a few
isolated cases it was possible to collect data for earlier years, but
the data were not sufficient for making point estimates of pesticide
usage.
Table 41 (p. 163) shows a breakdown by pesticide, crop, and
month for 1966-67. As in Table 39 (p. 142), the intention is to indicate
the seasonal pattern of pesticide usage.
The wide variation of spray programs among growers was noted
early in this chapter. The question arose as to whether this variation
v?as caused because the growers used different pesticides or mainly
because they all used the same pesticides but in greatly varying quanti
ties per acre. Table 42 (p. 176) was developed to try to "shed some

65
light" on this question. In general, there was a tendency for the
coefficients of variation to fall slightly in this table as compared with
Table 40 (p. 155), but the differences were not particularly striking,
and one is led to the conclusion that not only do the farmers differ
widely in the pesticides they use, but the users of a particular pesti
cide differ widely in the quantities per acre that they employ.

CHAPTER VI
EXTERNALITIES IN DADE COUNTY
The information reported in this chapter represents the basis
for the point estimate of externalities used in Chapter IV to synthesize
the externality functions.
For purposes of empirical measurement an externality was defined
as any "cost" which was created by the agricultural use of pesticides
but was not reflected in the supply functions of the producers. This
definition does not preclude the possibility of handling an external
benefit as a negative cost. It is empirically operational and is not
inconsistent with that presented by Buchanan and Stubblebine (7).
The estimation of externalities in Dade County was based on
information gathered from five sources, and the kinds of data needed to
activate the environmental constraints (constraint set 4, page 20) were
considered from a conceptual point of view.
The Grower Interview
A section of the grower questionnaire was devoted to questions
about human sickness, damages to wildlife and domestic animals, and
damage from drift among the growers. This interview, with additional
comments on certain questions, is presented in Appendix E (p. 184).
Most of the growers in Dade County have had some experience with
pesticide sickness. The responses of the growers indicated that such
66

67
incidents were not on the increase and possibly were decreasing in the
face of increasing organic phosphate usage. Growers stated that edu
cation of spray men to the dangers of pesticides and the safe ways of
handling them has been a key factor in stemming the rise of such
incidents. It should be stressed that "safe ways of handling them" did
not always coincide with "recommended ways." This became very evident
from the responses to certain questions (see items 3.28, 3.29, and 3.30
in Appendix E, p. 190) asked of growers. Only one grower stated that he
required his men to wear protective clothing such as boots, gloves, and
masks while spraying. The rest of the growers indicated that they made
such equipment available but could not get their spray men to wear them.
This equipment is very uncomfortable in the hot climate of South Florida,
and most of the spray men would simply prefer to "take their chances"
with the pesticides. Individual tolerance to pesticides among spray men
seemed to vary a great deal. Some spray men were able to handle pesti
cides with no ill effects, while other spray men tended to be very
sensitive to them. In general, sensitive spray men did not remain spray
men very long. This "natural selection" process is also a possible
explanation for the fact that pesticide sickness does not appear to be
on the increase. It was also reported that some spray men refuse to
apply the highly toxic organic phosphates such as parathion. This was
not, however, observed to be very widespread. Table 15 summarizes the
responses of the growers to questions concerning human sickness.
Grower responses to questions about damage to domestic animals
and wildlife were somewhat vague as to time of occurrence and extent of
damage. In most cases this was probably due to lack of recollection.
Many of the growers acknowledged fish-kills in the canals. Water was

Table 15.A summary of grower responses concerning
Case
number Pesticide Date Time in hospital
1 Parathion
2 Parathion 1962
3 Parathion 1958
4
5 Parathion 1964 2 or 3 days
6 Dyrene
7 Parathion 1964 Approximately 1 week
8
Parathion
1959
About 2 weeks
sickness from pesticides.
Comments
The grower reported that through the years he had
built up a sensitivity to Parathion and could no
longer come in close contact with it.
Grower reported that his brother was careless while
spraying in the yard and got some Parathion on his
skin, making him ill.
Grower became sick from Parathion while spraying a
tree in his yard. He was using a hand sprayer and
the chemical drifted down on him.
Grower reported that his workers had considerable
trouble with dermatitis while picking tomatoes}
however, the cause was uncertain and could be due to
the stem fuzz as well as pesticides.
Grower reported that his Mexican foreman, on a very
hot day, accidently got Parathion on his skin.
Grower reported that his men have had some problems
with dermatitis from Dyrene. When this happens he
does not allow the man to spray any more.
Man was spraying corn and got in the drift. He was
not wearing a mask.
No details on this case.
O'
00

Table 15.Continued
Case
number Pesticide Date Time in hospital
9 Thimet 1966
10 Parathion 1960 2 weeks
11 Parathion 1964
12 Phosdrin 1961 1 night in hospital
for each instance
13 Parathion 1964
14
Parathion
1966
3 days
Comments
Grower got sick in spite of fact that he had on
gloves and mask. He went to the Poison Control
Center and tests came back negative but he still
felt that it was the Thimet, and he could no longer
get near the material.
Spray man absorbed the pesticide through his skin.
He almost died in the hospital and thereafter was
not able to use Parathion. In addition to the time
spent in the hospital, he was off work at least a
month.
Three men were involved in this instance. They were
spraying Parathion without face masks. About 2
hours per man were lost from work while a doctor
examined them.
These two instances involved the same man. Each
instance involved skin absorption. After the second
instance, the grower quit using Phosdrin.
Spray man was not using any protective clothing and
apparently got some Parathion on his skin. He has
had no further trouble since then.
Spray man did not wash his hands before eating lunch.
He never reported back to work after release from
the hospital. Grower reported he did not know where
he had gone.
ON
NO

Table 15-Continued.
Case
number
Pesticide
Date
Time in hospital
15
Parathion
1965
1 day
16
Parathion
1967
17
Parathion
1963
1 week
18
Parathion
1965
1 week
19
Parathion
1964
About 2 days
20
Phosdrin
1960
21
Parathion
1966
22
Parathion
1963
Comments
Grower reported that the employee had been discing
new land and that there had been no opportunity for
contact with Parathion while on the job. Neverthe
less the hospital diagnosed it as Parathion poisoning.
Grower reported that employee sprayed with Parathion
on Friday but did not get sick until Sunday. Doctors
diagnosis was Parathion poisoning.
No details.
Spray man was careless during a drenching operation.
He worked all day with a fine mist blowing on his
left leg.
Instead of walking around the spray rig to check the
nozzles, this spray man ran through the mist.
Grower himself got Phosdrin poisoning and had to get
hourly shots at the hospital for several days.
Grower got a slight touch of Parathion sickness.
Doctor prescribed some pills which he took for
several days.
Spray man was putting water into a can of Parathion
with a hose. He withdrew the hose from the can and
took a drink of water, allowing the hose to touch
his lips.

Table 15.Concluded.
Case
number
Pesticide
Date
Time in hospital
Comments
23
Parathion
1965
Grower's 17-year-old son spilled "8E liquid Para
thion," one of the strongest concentrations, on his
leg. The grower stripped him immediately, washed him
from the waist down, then sent him home to bathe
again. No problems developed.
24
Thimet
1966
Grower reported that Thimet was used on corn or
potatoes very close to his home, and it caused his
wife and several neighbors to become ill. It caused
nausea, headache, and loss of equilibrium. Grower
stated that the proximity of the usage to his home
was in violation of recommendations.
25
Parathion
Grower reported that over a 20-year period he had 8
or 10 men to get sick from Parathion. The last
incident was in approximately 1960. In no cases did
anyone lose more than a day or two from work.
26
Parathion
1963
"A number of days"
Grower reported that spray man absorbed Parathion
through his skin. He never allowed the man to spray
again.

72
frequently taken out of the canals for irrigation and for mixing with
pesticides. Some of this water eventually seeped back into the surface
and ground waters, carrying with it some pesticide residues. It was also
likely that some residues in the surface waters flowed down from the
farming regions just north of Dade County. Growers also acknowledged
that spray rigs were frequently washed out in the canals, increasing the
pesticide residues. Most of the fish found in the canals and drainage
ditches are what conservationists call "rough fish" (not considered suit
able for human consumption), but the migratory laborers, nevertheless,
ate them, and they were a part of the ecological system of the area.
Some wildlife could, of course, be classified as "pests" and
were purposely poisoned by the growers. This was frequently the case
with rats, blackbirds, crows, and raccoons. One grower stated that the
robin was a pest to strawberry growers in the area. Seagulls are very
plentiful in the area, and several growers stated that they were
sometimes killed by the pesticides. Three growers stated that they had
one dog each killed from drinking polluted water standing in the fields.
It is customary in the area to use honeybees to facillitate
pollination on some of the blooming crops. Squash is the dominant
example. Hives of honeybees are rented from beekeepers by the growers
and placed around the field. One beekeeper, a past president of the
Beekeeper's Association, was contacted about pesticide damage to the
bees. He stated that such damage was very common and that most bee
keepers assumed they would lose a few bees when they were rented out.
Bees, however, reproduce very quickly and the economic- loss was slight.
He further said that most of the growers were using more caution, and
that damage during the past two or three years was much less than it had

73
been previously. Generally there was no cash settlement above rental
cost when damage occurred.
Damage among growers from pesticide drift appeared to be a very
minor problem, although it did exist. When it occurred, the growers
usually settled the problem informally among themselves with the
damaging party "paying off" in one form or another. Those payments, we
assumed, entered the damaging party's marginal cost, function and were
not construed as "externalities." The trends toward fewer crops and
larger crop fields have caused this problem to decline in recent years.
It is conceivable that external benefits may have existed due
to pesticide drift. One grower, located in the center of a number of
growers who sprayed regularly, might not have had to spray as much as
he would otherwise because of the drift from the other growers and the
protective pesticide barrier around his field. This is, however,
speculation, and we were unable to gather any data to substantiate such
an hypothesis. Table 16 is a summary of the drift problem as reported
by the growers.
As a group, the growers were well insured. Not only did almost
all of them carry workmen's compensation on their employees, but they
also carried general liability to protect them in case they damaged the
property of others. They did not, however, have insurance for cases
where others damaged their property. Whether insurance costs should be
considered as external costs will be discussed in a later section.
Insurance Cla.ims
A Florida State law exists which requires all workmen's
compensation claims to be on file at the Florida Industrial Commission

Table 16.A summary of grower responses concerning damage from pesticide drift.
Case
number
Pesticide
Date
Crop
Extent of damage
Comments
1
Amine 2,4-D
1955
Tomatoes
Slight
Potato grower was the damaging
party. There was no settlement.
2
Sodium Arsenite
1961
Beans
Not available
Potato grower was the damaging
party. A settlement was made.
3
Not available
1958
Tomatoes
Not available
An airplane was spraying
defoliant on soybeans. A small
payment was made and lost
materials, such as fertilizer,
were replaced.
4
Not available
1957 or 1958
Beans
10 acres
Potato grower was the damaging
party. A small settlement was
made.
5
Not available
1958
Tomatoes
10 to 20 acres
This is the same incident as it3
aboveseveral growers were
damaged by the defoliant. No
settlement was made.
6
Not available
1967
Groves
The Seaboard Railroad sprayed
their right-of-way for weed
control, and it damaged this
grower's trees. However, it
turned out that the trees were
on the right-of-way so the
grower agreed not to file suit
if the railroad would not
destroy the trees.

Table 16.Continued.
Case
numb er
Pesticide
Date
Crop
7
Sodium Arsenite
1965
Beans
8
Not available
Not available
Tomatoes
9
Sodium Arsenite
1959
Beans
10
Toxaphene
Not available
Squash
11
Parathion
Not available
None
Extent of damage
Comments
About $300
Potato grower was the damaging
party. Settlement was made by
an insurance company.
10 acres
Grower reported that the county
damaged these tomatoes with an
herbicide. No settlement was
made.
Slight
Potato grower. No settlement
was made.
Not available
Grower damaged his own squash
crop while treating his bean
crop.
Not available
A dusting pilot's hopper hung
up and he accidently dusted
some of the government houses
around the Homestead Air Force
Base. The grower received a
"cease and desist" letter from
Washington ordering him to
quit farming so near the base.

Table 16.Continued.
Case
number
Pesticide
Date
Crop
12
Sodium Arsenite
13
Atrazine
1964
Beans and
tomatoes
14
Sodium Arsenite
1963
Beans
15
Sodium Arsenite
1965
Corn
16
Amine 2,4-D
1966
Tomatoes
17
Amine 2,4-D or
1966
Tomatoes
Extent of damage
Comments
Grower reported that he has
occasional small incidents
from potato vine killer. He
said it could not be avoided,
and when the damage is great
enough he settles with the
grower involved.
Not available
Corn growers paid off the bean
and tomato growers.
$1000
Potato grower was the damaging
party.
Slight
Potato grower was the damaging
party. No settlement was made
80 acres
amounting to
$24,000
The grower could not find out
who had done it. Scientists
from the University of Florida
investigated. No settlement
was made.
About 50% of
60 acres was
destroyed
This was the same situation as
#16 above.

Table 16.Concluded
Case
number Pesticide Date Crop Extent of damage Comments
18
Sodium Arsenite
1957 or 1958
Tomatoes
4 acres
amounting to
$300
Potato grower was the damaging
party. No settlement was made.
19
Atrazine
1967
Beans
3 acres
amounting to
about $1200
A settlement was made but the
identity of the damaging party
was not disclosed.
20
Jet fumes and
oil
1962 or 1963
Potatoes
5 to 10%
reduction in
yield on about
5 acres
Grower stated that he tried to
collect damages from the air
force, but the "red tape got so
involved" that he decided to
drop it.

78
in Tallahassee. The Industrial Commission was contacted and they
readily agreed to cooperate on the research. Data for Dade County for
the years 1966 and 1967 were obtained and summarized.
All of the workmen's compensation claims reported to the
Industrial Commission are classified into two groupsdisabling and
non-disabling. These terms are defined as follows:
Disabling injury: a work injury resulting in death, permanent
impairment, or loss of time beyond the day or shift of occurrence.
Non-disabling injury: an injury arising out of and in the
course of employment in which there is no loss of time beyond the
day or shift of occurrence (19, 1966 edition).
Very little data are maintained on the non-disabling category. The
number of injuries in this category is much greater than for the dis
abling category, but the cost involved is much less. Table 17 shows a
comparison between these categories for the state, the agricultural
industry, and commercial farms.
The Industrial Commission categorizes its disabling injuries in
a number of ways. The one which seemed most important for this research
was "disabling work injuries by agency." The "agency of injury" identi
fies the object, substance, exposure or bodily motion which directly
produced or inflicted the injury (19, 1966 edition). There are 53
agency categories, one of which is called "poisons and infectious agents.
This agency is further divided into the subgroups shown in Table 18.
For the State as a whole the category "poisons and infectious
agents" is declining in importance, as indicated in Table 19. Number of
injuries, days lost, and cost have all declined percentagewise since
1962. The Dade County cost figures for 1966 and 1967, shown in Tables
20 and 21, are consistent with this finding. In 1966 the total cost for
the agency was $175,497 while in 1967 it was $116,450.

Table 17.Workmen's compensation claims, State of Florida; work injuries, days of disablity, and cost
by industry for disabling and non-disabling work injuries,
1962, 1963, 1966, and 1967.a
Non-disabling
Disabling
Total
Number
Number
Number
of
of
Days
of
Days
injuries Cost
injuries
lost
Cost
injuries
lost
Cost
1962
State total
150.3
$3,994
56.7
4,274
$26,165
207.0
4,274
$30,159
Agriculture, Forestry,
and fisheries
6.3
148
6.2
277
1,531
12.4
277
1,679
Commercial farms
4.1
100
3.2
175
970
8.0
175
1,070
1963
State total
132.7
3,751
51.9
4,517
29,216
184.7
4,517
32,967
Agriculture, forestry,
and fisheries
5.5
130
5.3
264
1,575
10.8
264
1,705
Commercial farms
3.4
85
2.5
153
945
5.9
153
1,030
1966
State total
176.9
5,045
80.8
7,705
51,599
257.7
7,705
56,644
Agriculture, forestry,
and fisheries
5.8
162
6.8
428
2,541
12.6
428
2,703
Commercial farms
3.8
106
3.5
266
1,589
7.2
266
1,695

Table 17.Concluded.
Non-disabling
Disabling
Total
Number
of
injuries
Cost
Number
of
injuries
Days
lost
Cost
Number
of
injuries
Days
lost
Cost
1967
State total
161.6
4,565
71.5
6,258 43,115
233.1
6,258
47,680
Agriculture,
forestry,
5.1
139
5.0
317
2,008
10.1
317
2,147
and fisheries
Commercial
farms
3.2
90
2.6
201
1,314
5.8
201
1,404
Source of data: (19).

81
Table 18.A list of the categories constituting Agency 10, "Poisons
and Infectious Agents.3
Code
number Agent
10000 Unknown or unreported
10002 Acids, n.e.c.b
10003 Citrus dermatitis
10004 Alcohol
10006 Ammonia
10008 Aniline, other dyes
10010 Arsenic
10012 Beryllium
10014 Carbon dioxide
10016 Carbon monoxide gas fumes
10018 Carbon tetrachloride
10020 Caustics, n.e.c.
10021 Soap
10022 Chlorate of lime
10024 Chlorine
10026 Chromium
10028 Coal tar distillates (naptha, benzol) carbolic acid, creosote
10029 Ethylene gas (fruit coloring)
10030 Di,tetra or perchloroethylene
10031 Dieldrin
10032 Fertilizer, n.e.c.
10033 Food, etc.
10034 Formaldehyde (formain, embalming fluid)
10036 Hydrochloric acid (muriatic)
10038 Hydrocyanic acid
10040 Hydrogen sulphide
10042 Insecticide, n.e.c.
10043 Lead or paint
10044 Malathion
10046 Mercury
10048 Metal fumes (aluminum, monel zinc, welding fumes)
10050 Nitric acid
10052 Parathion
10054 Petroleum distillates (propane, butane, methane, gasoline,
10056 Phosphorus Stoddard solvent, kerosene)
10058 Smoke
10060 Sulphuric acid and battery acid
10062 Tar and pitch fumes
10064 Turpentine
10099 Chemicals and poisons, n.e.c.
10100 T.B.
10111 Anthrax
10122 Fungus infections
10133 Larvae migrans (creeping eruption)

82
Table 18.Concluded.
Code
number Agent
10144 Septic infections
10155 Undulant fever (brucellosis)
10200 Dusts
10211 Organic dust (vegetable matter)
10222 Inorganic dust with free silica
10233 Asbestos
10244 Inorganic dust except silica or asbestos
10300 Poison woods or vegetation
10999 Poisonous and infectious agents, n.e.c.
£
Source of data: coding form used by the Florida Industrial
Commission.
^Not elsewhere classified.

Table 19.Workmen's compensation claims, State of Florida; work injuries, days of disability,and cost
by agency, for disabling work injuries, 1962, 1963, 1966, and 1967.a
Number of
injuries
Days lost
Cost
1962
State total
56,701
4,274,398
$25,713,086
Poisons and infectious agents
2,082
89,197
516,380
Percent
.036
.0208
.0200
1963
State total
51,297
4,517,191
28,497,999
Poisons and infectious agents
1,747
69,919
517,730
Percent
.033
.0154
.0181
1966
State total
80,803
7,704,996
51,599,084
Poisons and infectious agents
2,622
126,298
831,477
Percent
.032
.0164
.0161
1967
State total
71,490
6,258,464
43,115,224
Poisons and infectious agents
2,339
77,139
665,473
Percent
.033
.0123
.0154
aSource of data: (19).
oo
u>

Table 20.Dollar costs of disabling workmen's compensation claims for Dade County, Florida, 1966,
by kind of payment and agent.a
Agent
1
2
3
4
Unknown or unreported
$4,488
$1,663
$ 257
$ 91
Acids, n.e.c.c
3,810
2,023
856
29
Citrus dermatitis
169
Alcohol
8
Ammonia
1,299
481
64
Carbon monoxide gas fumes
924
524
604
32
Caustics, n.e.c.
7,043
3,233
1,203
223
Soap
4,013
3,145
27
254
Chlorate of lime
9,204
7,653
672
142
Chlorine
133
134
Coal tar distillates
(naptha, benzol) Carbolic
acid, creosote
107
56
Food, etc.
304
360
122
78
Hydrochloric acid (muriatic)
56
188
271
Insecticide, n.e.c.
872
806
60
Lead or paint
1,369
1,345
1,010
118
Metal fumes (aluminum, monel
zinc, welding fumes)
528
510
989
43
Parathion
87
77
728
Kind of payment
Total
Petroleum distillates
(propane, butane, methane,
gasoline, Stoddard solvent,
kerosene)
2,176
2,277
404
Smoke
869
474
40
Sulphuric acid and battery
acid
412
298
85
$ 500
$ 0 $ 0
$ 830
$ 7,829
860
7,578
169
8
411
2,255
255
2,339
1,962
13.664
1,605
9,044
9,521
27,192
267
163
864
515
1,738
190
4,032
355
2,425
892
1,310
6,365
431
1,814
105
900
198

Table 20.Concluded.
Kind of payment^
Agent
1
2
3
4 5 6
7
8
Total
Chemicals and poisons, n.e.c.
22,574
11,903
3,457
874
0
0
0
6,165
44,973
T.B.
2,450
1,050
850
4,350
Fungus infections
484
190
459
1,133
Larvae migrans (creeping
eruption)
422
368
13
803
Septic infections
1,942
846
1,297
14
560
4,659
Dusts
16,585
3,641
275
820
4,500
25,821
Poison woods or vegetation
143
377
520
Total
$83,971
$44,863
$12,946
$3,057
$ 500
$ o
$ 0
$30,160
$175,497
Source of data: computer tapes supplied by the Florida Industrial Commission.
^Kind of payment is coded as follows:
Code
Kind of payment
1
2
3
4
5
6
7
8
Compensation
Medical
Hospital
Artificial members
Burial
Child labor penalty
Attorney fees
First aid
c
Not elsewhere classified.
00
Ln

Table 21.Dollar costs of disabling workmen's compensation claims for Dade County, Florida, 1967,
by kind of payment and agent.a
Kind of payment^
Agent
1
2
3
4 5
6
7
8
Total
Unknown or unreported
$3,815
$3,099
$ 861
$ 199 $ 0
0
0
$1,645
$ 9,619
Acids, n.e.c.c
924
873
232
42
100
2,171
Citrus dermatitis
22
22
Ammonia
608
443
994
151
130
2,326
Carbon Monoxide gas fumes
316
421
529
247
136
1,649
Caustics, n.e.c.
6,354
4,161
672
428
2,237
13,852
Soap
1,420
605
178
357
2,560
Chlorate of lime
13,267
4,771
1,158
225
4,001
23,422
Chlorine
84
439
223
38
784
Chromium
Coal tar distillates
9
9
(naptha, benzol) Carbolic
acid, creosote
17
17
Fertilizer, n.e.c.
32
32
Food, etc.
Formaldehyde (Formain,
2,762
2,211
214
8
1,225
6,420
embalming fluid)
22
6
4
32
Hydrochloric acid (muriatic)
42
70
29
11
152
Hydrocyanic acid
14
14
Insecticide, n.e.c.
547
495
653
27
125
1,847
Lead or paint
Metal fumes (aluminum, monel
719
1,229
240
280
2,468
zinc, welding fumes)
426
188
13
175
802
Nitric acid
437
220
535
1,192
Parathion
167
167

Table 21.Concluded.
Kind of payment^
Agent
1
2
3
4 5
6
7
8
Total
Petroleum distillates
(propane, butane, methane,
gasoline, Stoddard solvent,
kerosene)
1,048
1,442
492
206
0
0
0
290
3,478
Phosphorus
661
199
15
875
Smoke
259
1,507
992
2,758
Sulphuric acid and battery
acid
825
1,305
51
2,181
Tar and pitch fumes
16
16
Chemicals and poisons, n.e.c.
14,165
8,863
4,701
569
4,607
32,905
T.B.
451
30
481
Fungus infections
Larvae migrans (creeping
117
69
186
eruption)
357
390
248
17
1,012
Septic infections
636
459
77
355
1,527
Dusts
150
689
13
100
952
Poison woods or vegetation
132
359
31
522
Total
$50,405
$34,854
$12,973
$2,455
$ o
$ o
$ o
$15,763
$116,450
Source of data: computer tapes supplied by the Florida Industrial Commission.
^See footnote b of Table 26.
c
Not elsewhere classified

88
The Southern Farm Bureau was the dominant agricultural insurer
in the area and readily agreed to cooperate on the project. The
following conclusions were drawn from visits with representatives of the
company.
1. Property damage claims would be negligible relative to
workmen's compensation claims.
2. The number of pesticide claims in Dade County has probably
increased in recent years but not as fast as the increase in use of
organic phosphates. Farmers in Dade County are using more caution than
previously.
3. Dade County probably has a smaller amount of pesticide damage
than other areas of the State because its agricultural industry does not
have such a high turnover of farmers and laborers, and the farmers as a
group are relatively sophisticated.
4. In setting premiums, the fact that a farmer does or does not
use pesticides, is not considered. The Farm Bureau takes a "whole risk"
approach. This provides an interesting hypothesis about the behavior
of insurance companies. With regard to a farmer or a region there might
be a "sensitivity threshold" which must be broken before a response is
generated on the part of the insurance company. This "sensitivity
threshold" could be visualized as follows:
Time

89
When the dollar claims break the sensitivity threshold, the insurance
company responds by raising rates, cancelling policies, or some other
action. If the dollar claims are defined to be the marginal claims due
to pesticides, then external costs are imposed on the insurance company
by the farmer until the threshold is broken and the situation adjusted.
Veterinarians
In order to get additional information on the effects of pesti
cides on domestic animals, arrangements were made to sample the records
of three veterinarians in the area. Two were located approximately on
the dividing line between the rural and urban areas, while the third was
located in Homestead, the heart of the rural area. Samples which were
as nearly random as possible were taken from each.^ The frequency of
pesticide calls was noted along with the diagnosis, the specie, and the
treatment. After sampling, a brief conversation was held with each
veterinarian to see if the sample bore out his a priori notions. The
veterinarians all stated, before seeing the sample results, that they
would expect the frequency of pesticide cases to be a small fraction of
The first vet numbered his cases sequentially and had about
10,000 of them. Each case contained an average of 4.5 calls. Using a
table of random numbers to determine the starting point, 909 cases were
examined for a total of 4,090 calls. There was no relation between a
cases sequence number and the dates of the calls contained therein.
The second vet maintained his cases alphabetically, by last name
of owner, and did not know how many he had. There were 10 small file
drawers involved so the first and last 150 calls in each drawer were
arbitrarily examined. This vet also kept a separate file for deceased
cases, and all of these were examined.
The third vet maintained his files alphabetically, by last name
of owner, and they did not lend themselves well to sampling. For this
vet, all of the "D's" were examineda total of 275 cases with 4 calls
per case.

90
one percent. They further said that they felt the incidence of pesti
cide calls was not increasing. A summary of the sampling experience is
shown in Table 22.
The reason for including toad and lizard poisonings was that
their symptoms are very similar to those of pesticide poisoning, and
a veterinarian sometimes cannot tell the difference. As is indicated
by the footnotes to the table, an effort was made to include all calls
which might have been connected with pesticides even though some were
questionable. Even so, the frequency of pesticide calls was extremely
low and was far overshadowed by:
1. cases where animals were hit by autos
2. cases where an animal swallowed a fish hook
3. cases of tick paralysis
4. cases of dog fights or cat fights
Biologists
Biologists at the Everglades National Park and the research
director for the National Audubon Society, located on Tavernier Key,
were contacted in an effort to gather more information on pesticide
damage to wildlife.
Of all the areas touched upon by the research project, this
areapesticide effects upon wildlifewas probably the most difficult
to assess and the most difficult to speak about definitively.
Our ignorance in this area is twofold. First, we do not under
stand how sub-lethal exposure affects a given specie, and second, if
sub-lethal exposure does affect some specie, we do not know how this
will affect other species through the ecological system. Research on

Table 22.A summary of data gathered from veterinarians in Dade County.
Veterinarian 1
Veterinarian 2
Veterinarian 3
Deceased file
Live file
Total number of calls examined
4090
2082
3500
1100
Number of calls due to pesticide
poisoning
4b
9C
0
4d
Number of calls due to toad or
lizard poisoning
5
5
15e
0
Total calls due to poisoning
9
14
15
4
Proportion poisoned
.0022
.0067
.0043
.0036
£
The files of this veterinarian had
been purged of all
the animals that
died; he did
not keep a
deceased file.
0ne dog ate roach poison; a second was questionable as to the diagnosis.
Q
One dog ate ant poison; two more were questionable; a fourth had no diagnosis but was treated with
atropine, a standard antidote for poison or shock.
d0ne of these was questionable as to diagnosis.
0
One of these was a pet mountain lion poisoned by a lizard.

92
the former is now moving along rapidly, but statistical evidence to date
has been inconclusive and, in some cases, contradictory (28, 32, 34, 53,
56). Research on the latter is in its infancy, and results which would
be useful for policy formulation will probably not be available for
several years.
A series of memos describing certain major species in the
Everglades National Park was received from the Park biologists, but
these were written in such general terms that it was impossible for us
to develop population estimates from them. Dr. William B. Robertson,
research biologist, stated that he had observed no distinct relation
between pesticide usage and the population of wildlife species in the
Park.^
From discussions with the biologists this writer was able to
reach two hopefully unbiased conclusions about the position of
biologists.
First, biologists are far more afraid of chlorinated hydrocarbons
as a group than the organic phosphates and would favor policies designed
to encourage the substitution of organic phosphates for chlorinated
hydrocarbons. This stems from a feeling that long-term, sub-lethal
exposure to the chlorinated hydrocarbons is detrimental, primarily to
the reproductive process. Since organic phosphates decompose quickly in
the environment, their effects tend to be acute and are not likely to
have hereditary ramifications.
Second, while there is as yet no conclusive proof of the long-
range detriment of low exposure levels, the circumstantial evidence is
increasing rapidly (29).
2
From a telephone conversation with W. F. Edwards.

93
In summary, the opposing positions seem to reduce to:
Biologist: "Until the chlorinated hydrocarbons are proved
harmless, they should not be used."
Farmer: "Until the chlorinated hydrocarbons are proved harmful,
they may be used."
Community Studies on Pesticides
Another source of information on externalities was the Community
Studies on Pesticides program of the U.S. Public Health Service and the
Florida Department of Public Health, under the direction of John E.
Davies, M.D.
The Community Studies program consisted of a nationwide series
of epidemiological and ecological studies on levels of pesticides in the
human population and environment of selected study areas. The projects
were contractual arrangements with state boards of health and medical
schools or universities whereby the Public Health Service could arrange
for and support investigations of the effects of pesticides upon human
health. The typical program had two major facetsmonitoring and
epidemiology. The monitoring program attempted to determine the levels
of pesticide residues, primarily chlorinated hydrocarbons, in human
tissues and in the environment. The epidemiological facet sought to
detect clinical illness or subtle biochemical changes in occupationally
exposed workers which could be caused by pesticides.
The Dade County program was relatively new, having been set up
in 1964, and did not have a great deal of output useful for our research,
especially in the area of chronic or long-term damage. Their accumulated
knowledge, however, was a valuable source of information on the "human

94
health" aspect of the pesticide issue. Table 23 summarizes the data
gathered from this office.
Those incidents which were clearly not related to the agricul
tural use of pesticides were omitted from Table 23. Some of those
included are certainly debatable, such as the cases where a pesticide
was brought home from the field by a parent and got into the hands of
a child.
Environmental Monitoring
Every great advance in science seems to have been associated
with a twofold movement. One is the development of a new theoret
ical insight or point of view, a restructuring of the image of the
world, which creates, as it were, evolutionary potential for the
increase of knowledge. The second condition is an improvement in
instrumentation, that is, in the methods by which information
coming from the outside world can be detected, sampled, and
processed (2, p. 22).
Large amounts of resources are being devoted to monitoring the
environment for pesticide residues. New technology in the detection and
measurement of residues is constantly being developed. In short, the
second point of the above quotation is now coming to pass. As sophis
tication increases in the area of environmental monitoring, social
scientists will have a responsibility to determine how to use the data
in policy decisions. The ability to measure precisely the amount of DDT
in the brain of the eagle is of little value if we are unable to put
this information to further use in making policy decisions on the
regulation of DDT.
There are three levels or stages of knowledge needed in order
to incorporate monitoring data into the model.
First, regular statistical series are needed showing the
quantities of pesticides in various environmental elements such as soil,

Table 23.A summary of data gathered from the Communities Studies Program on Pesticides in Miami.
Year
Result
Age
Sex
Race
Pesticide
Comments
1964
Fatal
1
Female
White
Guthion
The father, a migrant worker, brought the
pesticide home from the field.
1964
Non-fatal
20
Male
Negro
Parathion
One week in hospital and another week off work.
1964
Non-fatal
19
Male
Negro
Parathion
One week in hospital and another week off work.
1964
Non-fatal
50
Male
Negro
Parathion
In hospital 2 weeks and off work another 4 weeks
1964
Non-fatal
3
Female
Negro
Parathion
Father brought material home and put it in a
spray gun. Infant ingested it from the spray
gun.
1964
Non-fatal
2
Male
Negro
Parathion
One week in hospital. The material, in powder
form, was brought home to kill roaches.
1964
Non-fatal
23
Male
White
Phosdrin
In hospital 4 days and off work 3 to 4 more days
He was operating a spray rig.
1964
Non-fatal
42
Male
Negro
Parathion
In hospital 1 week; off work another week.
Victim was spraying a potato field.
1964
Non-fatal
2
Female
Negro
Unknown
Became ill after eating treated seed beans.
1965
Fatal
17
Male
White
Zectran
The victim, an employee of a horticultural
nursery, drank the material from a coke bottle.

Table 23.Concluded.
Year
Result
Age
Sex
Race
Pesticide
Comments
1965
Non-fatal
41
Female
Negro
Parathion
Attempted suicide. Victim brought the material
home from the field.
1966
Non-fatal
34
Male
Negro
Guthion
Accidental ingestion.
1966
Non-fatal
23
Male
Negro
Parathion
Victim was spraying field when accident occurred.
1966
Non-fatal
47
Male
Negro
Phosdrin
Accident while spraying.
1966
Non-fatal
36
Male
White
Phosdrin
Accident while spraying.
1966
Non-fatal
27
Male
White
Parathion
Accident while spraying.
1966
Non-fatal
19
Male
White
Parathion
Accident while spraying.
1967
Fatal
50
Male
Negro
Parathion
Victim drank material from a whiskey bottle.
1967
Non-fatal
32
Male
Negro
Parathion
Victim worked for a pesticide manufacturer.
1967
Non-fatal
30
Male
White
Parathion
Victim was a crop duster.
1967
Non-fatal
3
Male
Negro
Parathion
Father brought material home from the field.
Child was in intensive care for 1 week.
1967
Non-fatal
63
Male
Negro
Phosdrin
Victim was using a hand sprayer. He was in the
hospital 2 weeks and off work another 2 weeks.

97
water, air, and various species of plants and animals. This is the area
of monitoring which is currently moving along with the greatest speed,
and a sizeable body of data is being accumulated.
Second, regular statistical series are needed showing the
quantities of pesticides being injected into the environment by the
various groups which use pesticides. It might then be possible to
relate statistically the pattern of usage with the pattern of monitored
observations. Efforts to measure quantities of pesticides injected into
the environment have been very limited. In 1964, the Congress authorized
an expanded program of research on the use of pesticides in agriculture.
One phase of the expanded program was to conduct a periodic farm survey
to obtain information on the use of pesticides in different areas and on
different crops and classes of livestock. These data would provide a
basis for estimating the costs and benefits associated with the use of
pesticides and would serve as a measure of change in pesticide use over
time (52). While this program will no doubt generate very useful data in
the future, its relevance for our research was limited for two reasons.
First, there is approximately a two-year time lag before survey results
are published, and second, no farms were sampled in Dade County and only
a few for the State as a whole.
When one considers the whole area of environmental monitoring,
it does not make much sense to spend large sums of money learning how
to detect residues in various organisms of the environment and then to
neglect the question of what quantities of pesticides are being injec
ted into the environment in the first place. This same position was
recently espoused in Michigan by the Governor's Pesticide Advisory
Panel:

98
The threat of a pesticide to the environment is related, in
part, to the amount and location of its dispersement. The panel
found it difficult to obtain use volume data on materials such as
DDT for the State of Michigan. A mechanism should be developed
by the Department of Agriculture, if necessary through legislation,
for ascertaining the annual total sale and use of toxic pesticides.
This could be accomplished by yearly reports from the pesticide
industry, possibly at the distributor or retail level where sale
of the formulations of the chemical takes place (36, p. 12).
Third, the effect of various levels of pesticides within the
environmental element itself must be understood. If, for example, the
quantity of DDT in the brain of the eagle is 10 ppm, we must know if
this level is harmful, beneficial,or neutral (4, p. 5). Of the three
areas mentioned, this is probably the most difficult to analyze but
vital to making rational policy decisions.
The following schematic diagram is intended to illustrate the
three levels of needed knowledge and how they fit together.
Graph 1
Graph 2 Graph 4
Figure 7.A schematic diagram showing data needs in the area of
environmental monitoring.

99
These diagrams are, of course, grossly oversimplified, but they
nevertheless indicate in their simplest forms the crucial relationships
about which we need more knowledge. Graph 1 represents the first level
of knowledge, statistical series of pesticide levels in certain ecolog
ically important elements of the environment. Graph 2 represents the
second level of knowledge, statistical series of pesticide usage. The
data in these two graphs are then combined to form Graph 3 relating
monitored levels to pesticide usage. Graph 4 represents the third level
of knowledge, the relation between monitored levels and "damage,"
however measured. The functions which have been drawn are for illus
tration only and have no empirical basis. Damage could be measured in
any appropriate units. If the pesticide were detrimental to the
reproductive process, then it might be measured as "decline in egg
fertility." If the pesticide's major effect were acute, then damage
might be measured as "percent kill." At the policy making level, a
value judgement would have to be made specifying the amount of damage
which should not be exceeded. It should be stressed that such a
decision is a social value judgement, not based on scientific knowledge,
but representing the consensus of the citizenry affected. It is there
fore a political decision. Such a value judgement is represented in
Graph 4 by the dashed line. Through the monitored pesticide levels, it
places a ceiling on pesticide usage. The restriction would enter the
model of Chapter III as an "environmental restraint." The actual
mechanics of limiting pesticide usage were not considered in this study.
There would, of course, be statistical difficulties in
estimating the above functions. Aside from the fact that they would be
stocastic rather than exact, there would be a question as to whether

100
they were statistically identifiable. Variation among individuals of a
specie, for example, might be large enough to obscure the relations
depicted in Graphs 1 and 3. Also, the ability of a specie to adapt to
a new environment might make such relationships dynamic and of little
lasting stability.
Environmental monitoring, encompassing the three levels of
knowledge mentioned earler, has not yet generated enough historical
data and has not yet acquired a level of sophistication adequate for
inclusion in the model. Different laboratories analyzing the same
sample still come up with widely differing results, due in part to
differences in testing procedures and equipment. Until these differ
ences are l'econciled, monitoring data will be of limited usefulness to
policy makers. Nevertheless, it was recognized in the theoretical model
because of its anticipated important role in policy decisions of the
future.^
Concluding Remarks on Externalities in Dade County
The problem with many writings on the pesticide issue is that
they quickly degenerate into an enumerative description of incidents in
which pesticides represent either the culprit or the hero depending on
which side of the issue the writer espouses. Conclusions cannot easily
be drawn from such a process. For analytical purposes one would like
to aggregate the incidents with some common measure, and incorporate
them into a benefit-cost analysis.
A brief summary of selected monitoring programs is presented
in Appendix F (p. 191).

101
The activities described in previous pages constituted the
search for externalities in Dade Countythe enumeration process. The
job of reducing and aggregating the incidents to a common measure
required some strong and somewhat arbitrary assumptions. Omissions and
double counting of externalities also aggravated accurate measurement.
Table 24 is a summary of the externality calculation used in the model.
This estimate is subject to three limitations. First, in the
search for externalities, there was no guarantee that all externalities
had been recognized and that none were double counted. It was felt
that the more logical sources of information were exhausted, but this
did not mean that all externalities were uncovered nor did it give a
basis for measuring the confidence one could place in the enumeration.
A second limitation was that, by the nature of things, the list
was probably biased toward external costs relative to external benefits.
External costs were simply easier to observe since they tend to create
controversy. One grower stated, for example, that he hoped the adjacent
grower's spray did drift over to his field (they were growing the same
crop), but there was no way to quantify this phenomenon. The killing
of certain pests, such as rats, might have had a beneficial effect on
human health by holding down disease but, again, quantification was
impossible. The "state of the arts," particularly in the area of eco
logical relationships, simply does not permit such quantification.
The third limitation stems from the fact that all observed
externalities were acute as opposed to chronic. This point was made
implicitly in the section dealing with wildlife. Biologists suspect
that the persistent pesticides are harmful to reproduction, but this
has not been substantiated and therefore cannot enter the empirical

102
Table 24.A summary of the externalities incorporated in the
empirical model, Dade County, 1967.
Nature of externality
Extent
of damage
Damage to humans
Compensation
$l,094b
Medical
662
Hospital
653
Artificial members
27
First aid
125
Total 1
$2,561
Total 2C
$3,227
Total 3d
$3,470
0
Damage to domestic animals
$1,120
Total
$4,590
I
Data from the Florida Industrial Commission, Table 21, serve as
the basis for these figures. The agents, "Parathion" and "Insecticides
not elsewhere classified," were added together.
bThe beginning figure for this estimate was $547. According to
Florida law, the maximum payable compensation is $46 per week. But the
average weekly salary of Florida workers is $86.18. Therefore the
figure, $547, was doubled to try to reflect more accurately the true
dollar loss.
Q
Total 2 represents a 26 percent increase over Total 1 due to
the fact that disabling damages, which must be estimated at the time of
occurrence, have historically been underestimated. For a more complete
explanation of this increase in cost, see the report entitled, "Facts
About Workmen's Compensation," February, 1968, published by the Florida
Industrial Commission, Research and Statistics Department, Caldwell
Building, Tallahassee, Florida 32304.
dTotal 3 represents an increase over Total 2 to reflect the fact
that the dollar cost of disabling claims in agriculture have histori
cally comprised 93 percent of the dollar cost of total claims.
0
From data gathered from the veterinarians. Assumptions:
1. All poisoning calls are included, whether or not they
are designated "pesticide," "toad," or "lizard."
2. A dead animal was assumed to be worth $50.
3. An average veterinarian call was assumed to cost $10.

103
model. If biologists could say that "x" parts per million of some
pesticide in the brain of the eagle were sufficient to limit repro
duction "y" percent, and if we could establish a relationship between
the "x" parts per million and the environmental exposure of the eagle,
then this information could be incorporated in the model as one of the
environmental constraints. The point to be made here is that if we, as
a society, decide to further restrict the usage of the persistent
pesticides without knowing these relations, such a move must be justi
fied on the basis of a social value judgement, not on the basis of any
benefit-cost comparison, for we simply do not know enough about the
costs.

CHAPTER VII
ANALYTICAL RESULTS, IMPLICATIONS
AND RECOMMENDATIONS
Analytical Results
The model was solved"^ four times as follows:
Run 1 Policy 1 (current pesticide usage).
Run 2 Policy 2A (a 50 percent reduction for each crop in the per
acre usage of chlorinated hydrocarbons and a substitution
rate of .3 pounds of organic phosphates per pound of
chlorinated hydrocarbons).
Run 3 Policy 2B (a 50 percent reduction for each crop in the per
acre usage of chlorinated hydrocarbons and a .4 substitution
rate).
Run 4 Policy 2C (a 50 percent reduction for each crop in the per
acre usage of chlorinated hydrocarbons and a .5 substitution
rate).
2 3
For illustration, Run 1 appeared as follows:
Maximize:
1605.2522 y^ .0416 y^ (tomatoes)
+ 927.5654 .0606 y^ (potatoes)
The computer program used for the model was developed by
International Business Machines (26).
2
The mathematical statements of the other three runs are
presented in Appendix G.
3
We have received additional funds to continue our work on this
project through June, 1970. During the next year we hope to explore
the pesticide issue further and evaluate several additional policies.
104

105
+ 1687.7688 y3 .1448
+ 592.4965 y- .1796 y^
+ 571.3720 y5 .0240 y*
+ 3285.4824 y& .3751 y^
+ 690.9032 y? .0956 y^
- elZl
- .0301 z2
Subject to:
7
40459 Z y. 48590
>1 3
15740 y 21166
6532 y2 8927
5269 y3 6680
1135 y4 3774
y5 5235
y6 3585
y? 1520
6.0719 y + 1.2860 y + 3.4304 y3
+ 40.7003 y^ + .0706 y^ + .0484 y^
+ .0205 y7 z^ = 0
3.2669 y + 5.8852 y2 + 1.8114 y3
+ 6.4624 y. + .2239 yc + .1535 y,
4 5 6
+ .0650 y7 z2 = 0
(beans)
(corn)
(avocados)
(limes)
(mangos)
(chlorinated hydrocarbons)
(organic phosphates)
(total farmland)
(tomatoes)
(potatoes)
(beans)
(corn)
(avocados)
(limes)
(mangos)
(chlorinated hydrocarbons)
(organic phosphates)

106
,y7> z2~
For illustration, the tomato portion of the objective function
?
(1605.2522 y .0416 y^) is derived for Run 1 as follows:
yi (t+1)
/ [(1521.5660 .0334 y) (-83.6862 + .0497 y1)3dy1
= 1605.2522 y1 .0416 yj
As stated previously, parametric programming was performed on
the coefficients of z^ and z^. Each was varied from 0 to 5.0 in incre
ments of 1.0.
The results of the four runs are shown in Tables 25 through 28.
As stated in the footnotes to these tables, the grove acreage (avocados,
%
limes, and mangos) was restrained to be no greater than the 1966-67
acreage. These tree crops entered every solution at their maximum
levels, so they were simply added together for the presentation in
Tables 25 through 28.
The footnotes to these tables indicate the solution's degree of
accuracy with respect to the crops in the solution vector. In order to
use the IBM 360 program, it was necessary to break the non-linear objec
tive function up into a series of linear segments in the variables y..
Tomatoes, potatoes, and beans were broken up into 100-acre intervals
while corn was divided into 50-acre intervals. The size of the interval
determines the accuracy of the solution, and any desired accuracy can be
4
achieved by a sufficient reduction in interval size.
It is trivial to show by the Kuhn-Tucker optimality conditions
4
For a detailed discussion of this technique, the interested
reader may consult the IBM Manual (26, pp. 165-173).

Table 25.Model solution for Policy 1
a
Solution
vector
->
Obj ective
function
in dollars
yl
Tomatoes^
in acres
y2
Potatoes^
in acres
y3
Beans ,
. b
in acres
y4
Corn
c
xn acres
y5y6,y7
Groves
in acres
Z1
Chlorinated
hydrocarbons
in poundse
Z2
Organic
phosphates
in poundse
Coefficient of:
Z1 z2
0
0
34,561,979.
19,300
7,700
5,800
1,650
10,340
214,716
131,358
0
-.0301
34,558,025.
19,300
7,700
5,800
1,650
10,340
214,716
131,358
0
-1.
34,431,172.
19,300
7,600
5,800
1,650
10,340
214,587
130,769
0
-2.
34,300,894.
19,200
7,600
5,800
1,600
10,340
211,945
130,119
0
-3.
34,171,289.
19,200
7,500
5,800
1,600
10,340
211,817
129,531
0
-4.
34,041,866.
19,100
7,500
5,800
1,600
10,340
211,209
129,204
0
-5.
33,913,420.
19,100
7,400
5,800
1,550
10,340
209,046
128,292
-1.
-.0301
34,345,977.
19,200
7,600
5,800
1,550
10,340
209,910
129,796
-2.
-.0301
34,138,929.
19,100
7,600
5,800
1,400
10,340
203,198
128,500
-3.
-.0301
33,937,203.
19,100
7,600
5,800
1,300
10,340
199,128
127,854
-4.
-.0301
33,740,433.
19,000
7,600
5,800
1,200
10,340
194,451
126,881
-5.
-.0301
33,547,745.
18,900
7,600
5,800
1,135
10,340
191,198
126,134
£
Current pesticide usage levels.
^Solution does not differ from the optimum by more than 100 acres,
c
Solution does not differ from the optimum by more than 50 acres.
dGroye acreage is constrained to be no more than the 1966^67 level.
0
All quantities have been converted to units of 100 percent concentrated material.
107

Table 26.Model solution for Policy 2A.a
Solution
vector
-y
0b j ective
function
in dollars
yl
Tomatoes,
b
m acres
y2
Potatoes^
in acres
y3
Beans ,
. b
in acres
y4
Corn
in acres
y5,y6y7
Groves ,
, d
in acres
Z1
Chlorinated
hydrocarbons
in poundse
Z2
Organic
phosphates
in poundse
Coefficient of:
21 Z2
0
0
34,212,231.
19,100
7,600
5,800
1,650
10,340
106,686
162,180
0
-.0301
34,207,349.
19,100
7,600
5,800
1,650
10,340
106,686
162,180
0
-1.
34,050,146.
19,000
7,500
5,800
1,650
10,340
106,318
161,154
0
-2.
33,889,449.
19,000
7,500
5,800
1,600
10,340
105,300
160,525
0
-3.
33,729,205.
19,000
7,400
5,800
1,550
10,340
104,218
159,289
0
-4.
33,570,327.
18,900
7,400
5,800
1,550
10,340
103,915
158,871
0
-5.
33,412,100.
18,900
7,300
5,800
1,500
10,340
102,833
157,635
-1.
-.0301
34,100,900.
19,100
7,600
5,800
1,600
10,340
105,668
161,551
-2.
-.0301
33,995,794.
19,000
7,600
5,800
1,550
10,340
104,347
160,505
-3.
-.0301
33,891,922.
19,000
7,600
5,800
1,500
10,340
103,329
159,876
-4.
-.0301
33,789,188.
19,000
7,600
5,800
1,450
10,340
102,312
159,248
-5.
-.0301
33,687,860.
18,900
7,600
5,800
1,400
10,340
100,991
158,202
aA 50 percent reduction for each crop in the per acre usage of chlorinated hydrocarbons and a sub
stitution rate of .3 pounds of organic phosphates per pound of chlorinated hydrocarbons.
^Solution does not differ from the optimum by more than 100 acres,
c
Solution does.not differ from the optimum by more than 50 acres.
^Grove acreage is constrained to be no more than the 1966-67 level,
e
All quantities have been converted to units of 100 percent concentrated material.
108

Table 27.Model solution for Policy 2B.a
Solution
vector
->
Objective
function
in dollars
yl
Tomatoes,
. b
in acres
y2
Potatoes^
in acres
y3
Beans
in acres
y4
Corn
c
in acres
y5y6y7
Groves ^
in acres
Z1
Chlorinated
hydrocarbons
in poundse
Z2
Organic
phosphates
in poundse
Coefficient of:
Z1 Z2
0
0
34,181,711.
19,100
7,600
5,800
1,650
10,340
106,494
172,867
0
-.0301
34,176,508.
19,100
7,600
5,800
1,650
10,340
106,494
172,867
0
-1.
34,009,205.
19,000
7,500
5,800
1,600
10,340
105,110
171,074
0
-2.
33,838,131.
19,000
7,500
5,800
1,600
10,340
105,110
171,074
0
-3.
33,668,063.
18,900
7,400
5,800
1,550
10,340
103,726
169,281
0
-4.
33,499,292.
18,900
7,400
5,800
1,500
10,340
102,708
168,551
0
-5.
33,331,483.
18,800
7,300
5,800
1,450
10,340
101,324
166,758
-1.
-.0301
34,070,438.
19,100
7,600
5,800
1,600
10,340
105,477
172,137
-2.
-.0301
33,965,806.
19,100
7,600
5,800
1,550
10,340
104,157
170,958
-3.
-.0301
33,862,312.
19,000
7,600
5,800
1,500
10,340
103,139
170,228
-4.
-.0301
33,760,031.
18,900
7,600 .
5,800
1,450
10,340
101,819
169,049
-5.
-.0301
33,659,114.
18,900
7,600
5,800
1,400
10,340
100,802
168,319
0,
A 50 percent reduction for each crop in the per acre usage of chlorinated hydrocarbons and a
substitution rate of .4 pounds of organic phosphates per pound of chlorinated hydrocarbons.
^Solution does not differ from the optimum by more than 100 acres.
c
Solution does not differ from the optimum by more than 50 acres.
^Grove acreage is constrained to be no more than the 1966-67 level.
0
All quantities have been converted to units of 100 percent concentrated material.
109

Table 28.Model solution for Policy 2C.a
Solution
vector
->
Objective
function
in dollars
yl
Tomatoes,
. b
xn acres
y2
Potatoes,
b
in acres
y3
Beans ^
in acres
y4
Corn
. c
in acres
y5,y6,y7
Groves ,
d
m acres
Z1
Chlorinated
hydrocarbons
in poundse
Z2
Organic
phosphates
in poundse
Coefficient of:
Z1 z2
0
0
34,148,919.
19,100
7,600
5,800
1,650
10,340
106,494
183,555
0
-.0301
34,143,394.
19,100
7,600
5,800
1,650
10,340
106,494
183,555
0
-1.
33,966,181.
19,000
7,500
5,800
1,600
10,340
105,110
181,624
0
-2.
33,784,895.
19,000
7,500
5,800
1,550
10,340
104,092
180,791
0
-3.
33,604,960.
18,900
7,400
5,800
1,500
10,340
102,708
178,860
0
-4.
33,426,342.
18,800
7,400
5,800
1,450
10,340
101,388
177,549
0
-5.
33,249,130.
18,800
7,300
5,800
1,400
10,340
100,306
176,097
-1.
-.0301
34,037,824.
19,000
7,600
5,800
1,600
10,340
105,174
182,244
-2.
-.0301
33,933,383.
19,000
7,600
5,800
1,550
10,340
104,157
181,412
-3.
-.0301
33,830,079.
19,000
7,600
5,800
1,500
10,340
103,139
180,580
-4.
-.0301
33,728,189.
18,900
7,600
5,800
1,400
10,340
100,802
178,438
-5.
-.0301
33,627,388.
18,900
7,600
5,800
1,400
10,340
100,802
178,438
aA 50 percent reduction for each crop in the per acre usage of chlorinated hydrocarbons and a sub
stitution rate of .5 pounds of organic phosphates per pound of chlorinated hydrocarbons.
^Solution does not differ from the optimum by more than 100 acres.
Q
Solution does not differ from the optimum by more than 50 acres.
Grove acreage is constrained to be no more than the 1966-67 level.
e
All quantities have been converted to units of 100 percent concentrated material.
o

Ill
that the solutions are locally optimal. In addition, because of our
special knowledge about the shape of the objective function (a series of
parabolas opening downward) and the linearity of the constraints, we
know that for this particular problem we must have a global optimum.
Considering Policy 1, the solution acreages were relatively slow
to change. Disregarding externalities entirely, the solution acreages
for current pesticide usage were:
Tomatoes
Potatoes
Beans
Corn
19,300 acres
7,700 acres
5,800 acres
1,650 acres
When the estimated acute externalities from organic phosphates were
recognized (coefficient of = -.0301), the solution acreages remained
the same. The first change in the solution acreages occurred when the
coefficient of z^ was -2.0, or about 6.6 times the estimated level of
externalities. At this point the only changes were a 100-acre reduction
in tomatoes and a 50-acre reduction in corn. When the coefficient of z^
had acquired a value of -5.0, or 16.6 times the estimated level, the
solution acreages were:
Tomatoes 19,100 acres
Potatoes 7,400 acres
Beans 5,800 acres
Corn 1,550 acres
At this point the usage of chlorinated hydrocarbons had only fallen
2.6 percent and that of organic phosphates by 2.3 percent.
%'here are available many good discussions of these conditions.
For one, see Dorfman, Samuelson, and Solow (13, pp. 186-201).

112
Parametric programming on the coefficient for chlorinated
hydrocarbons, z^, caused more extensive changes. At estimated levels of
acute externalities (z^ coefficient = -.0301), and as the coefficient of
z^ was decreased,the solution acreages for tomatoes and corn declined
progressively while that for potatoes and beans hardly changed.
Tomatoes declined from 19,300 to 18,900 acres and corn from 1,650 to
1,135, the lower limit imposed by the flexibility constraint. At the
point where the zcoefficient equalled -5.0 and the z^ coefficient,
-.0301, the usage of chlorinated hydrocarbons had fallen by 11 percent
and organic phosphates by 4 percent.
With Policies 2A, 2B, and 2C, three characteristics stand out.
First, as in Policy 1, the solution acreages tended to be "stable" as
the coefficients of z^ and z^ were varied. In general, very large
changes in external "damage" levels caused very small changes in
solution acreages. This was at least partial justification for ignoring
the "second-order effects" of the externalities, a point mentioned in
footnote 15, page 53.
Second, the solution acreages for estimated externality levels
did not change at all from Policy 2A through Policy 2C, leading to the
conclusion that the solution acreage (but not the value of the objective
function) is relatively insensitive to the substitution rate between
chlorinated hydrocarbons and organic phosphates. This is significant
because it reduces the importance of error in this parameter, a matter
of considerable concern to the entomologists.
Third, the value of the objective function did not change very
much from one policy to the next. Including Policy 1 in the comparison,
the maximum value of the objective function was $34,561,979 (Policy 1,

113
zero externalities) and the minimum was $33,249,130 (Policy 2C maximum
externalities from organic phosphates), a difference of only 3.8 percent.
For observed externality levels (z^ coefficient = 0 and z^ coefficient
= -.0301), the difference among policies was even smaller, as shown in
Table 29. The objective function of Policy 2C represented only a 1.2
Table 29.A comparison of model solutions among policies for z^ and
z^ coefficients of 0 and -.0301, respectively.
Policy 1
Policy 2A
Policy 2B
Policy 2C
Objective function
$34,558,025
$34,207,349
$34,176,508
$34,143,394
Tomatoes (acres)
19,300
19,100
19,100
19,100
Potatoes (acres)
7,700
7,600
7,600
7,600
Beans (acres)
5,800
5,800
5,800
5,800
Corn (acres)
1,650
1,650
1,650
1,650
Groves (acres)
10,340
10,340
10,340
10,340
z^ (pounds)
214,716
106,686
106,494
106,494
z^ (pounds)
131,358
162,180
172,867
183,555
percent change from Policy 1. In other words, if we are willing to
accept the measure of welfare used in the model, we can say that
"welfare" would fall by $414,631 or 1.2 percent under Policy 2C. Under
Policy 2A it would fall by only $350,676 or about 1 percent. Is this
too high a price to pay to reduce the usage of chlorinated hydrocarbons
and hence the potential environmental hazard? That is a question which
must be answered by the people through their elected representatives.
The role of the economist stops short of such a recommendation. It is
his responsibility to explore the ramifications of various alternatives,
but the ultimate decision is a political one.

114
One trend is currently working to make these estimates of social
cost biased upward. Companies are concentrating their research efforts
on developing new pesticides which are less persistent and more specific
to the target pest. In short, the "state of the arts" is changing in a
way favorable to the substitution which Policies 2A, 2B, and 2C are
designed to accomplish. Thus, we might expect that through time,
cheaper and more effective non-persistent pesticides will be developed
which might mitigate even the small cost differential which exists at
the current state of the arts for the alternative policy. To completely
ignore these trends would be foolish, but we were unable to explicitly
recognize them in the model. The hypothesized changing state of the arts
is illustrated in Figure 8.
(0
4-1
1
43 +
C
H
0)
60 O
tO
W
tO
<0
U
o
0)
¡5
(0
to
o
(1)
0
25
50
75
100
Percentage decrease in use of chlorinated hydrocarbons
Figure 8.Hypothesized relations between "welfare," the
state of the arts, and the usage of
chlorinated hydrocarbons.
Line 1 represents the current state of the arts. Up to some point
farmers can decrease their usage of chlorinated hydrocarbons without
a drastic decrease in welfare, but beyond this point welfare may decline

115
more radically. Line 2 represents some future state of the arts. Again,
beyond some point welfare may decline more drastically, but the point
has moved further out. By the same reasoning lines 3 and 4 represent
successive points in the future.
At the point in time represented by line 4 farmers are able to
substitute non-persistent for persistent pesticides very easily with
virtually no decrease in welfare because of the new non-persistent pesti
cides which have been developed during the intervening period of time.
Even then, however, it might be that a few pests still require persis
tent materials. Their level of usage, however, would present no serious
threat to the quality of the environment.
The lines in Figure 8 reflect the current judgments of
entomologists with regard to policies which require large, decreases in
the use of persistent pesticides. However,the changing state of the
arts is shifting the curves representing increasing social costs
downward to the right. This hypothesis which these shifts represent
suggest a multi-stage versus a single stage approach to reducing the
usage of chlorinated hydrocarbons. In the multi-stage approach we
evaluate, say, a 50 percent reduction policy. If it is not "too
detrimental" to welfare, we pursue it. When it is accomplished, we
again evaluate a 50 percent reduction policy and again pursue it if it
is not "too detrimental" to welfare. This process continues as long as
welfare is improved or until the governing body decides the price of
further reduction is "too high." At each stage of the process a new
state of the arts prevails which the analyst may recognize in his model.
The writer is therefore recommending the multi-stage approach on the
ground that it would leave more flexibility for adjustment, more freedom

116
for the farmers, and would permit the accumulation of valuable knowledge
as the process continues. Such an approach would also contain less
predictive error simply because the near future is easier to forecast.
This approach does not imply that many years would be required
to achieve a great reduction in the usage of chlorinated hydrocarbons.
On the contrary, it appears that considerable reductions (on the order
of 40 to 60 percent) could be made in the usage of chlorinated hydro
carbons at the present time without a serious reduction (1 to 2 percent
or less) in the net social benefits, as defined, from the crops evaluated.
Implications
For Policy Makers
Aside from the thoughts presented in the previous paragraphs,
there are two other implications for policy makers which should be
explicitly stated even though they have been implied in earlier pages.
First, the pesticide issue is far from "cut and dried." Persuasive
evidence can be marshalled for either side. Furthermore, at this point
there is little known with regard to the long-run effects of pesticide
exposure.
Second, the role of value judgments in the pesticide issue has
been emphasized, and this role should be constantly borne in mind by
policy makers and legislators when considering the pesticide problem.
No amount of research and/or data gathering can bypass the need for
value judgements. Research can point out characteristics and perhaps
effects of alternative value judgments, and it might be able to uncover
other alternatives, but the ultimate choice is still a value judgment
which, argued Buchanan (6), should reflect the consensus of the
citizenry affected by the decision.

117
For Economic Theory and Methodology
Classical and neo-classical economists have for the most part
tended to ignore or assume away the problem of "external effects,"
although in recent years it has received much more attention in the
literature. It is this writer's opinion that externalities may be
crucial in many social problems of the future. If this comes to pass,
economic theory needs to be refined in order to deal with these problems.
Not only does the theory need attention, but the empirical problems of
identifying and measuring externalities need much work. This writer
is, frankly, somewhat pessimistic about transferring the knowledge
gained in this research project on identifying and measuring external
ities to other problems. The decision rule which was used (any "cost"
not included in the farmers' marginal cost functions was considered to
be an externality) may have some transferability,but beyond this the
problems encountered in any particular situation may be quite unique.
For Future Research
In order to improve the information available to decision
makers, research should be aggressively pursued on several fronts.
First, regular statistical series should be developed to show the
amounts of pesticides being used in various locales. Retail firms could
report sales data regularly from which statistical series of usage could
be developed. As mentioned earlier in this report, it does not make
much sense to consume so many resources in the monitoring of plant and
animal organisms and to ignore the quantities of pesticides which are
being injected into the environment in the first place. This is not to
say that we should decrease the monitoring of various elements of the

118
environment. It should probably be increased and especially concen
trated on the ecologically critical species of plants and animals.
Furthermore, it is not enough to know how much pesticide residue exists
in various elements of the environment. We must know if it is "bad," or
"good," or "neutral." In other words, the residue must be empirically
linked to some measure of damage.
Second, research in ecology .must be expanded. Aside from a
descriptive understanding of food chains or networks, an understanding
is needed about how pesticide residues are passed around in the food
networks. Such knowledge is a prerequisite to understanding the effects
of given exposure levels on the environment.
Finally, more research is needed to bring all the pieces of
information together into a solution generating model. This research
effort is an approximation to what is ultimately needed. Continued
refinement in model specification and parameter estimation are needed.
Better communication among the disciplines should be fostered by
economists0 to point toward needed research and to delineate the roles
of the various disciplines (including the governing bodies, with whom
the ultimate decisions will lie). An inter-disciplinary problem requires
an inter-disciplinary effort if viable solutions are to be found.
An effort was made in this project to improve communication
among the interested disciplines. For the most part we found them
eager to understand the economists' approach to the problem and how the
various aspects of the problem might be combined into one model. Most
seemed to be aware that economists have an important role to play in the
issue, and most seemed to be aware that research efforts to date have
been fragmentary and piecemeal, hence needing a unifying model or
systems approach.

APPENDIX A
NET PROFIT PER ACRE OF SELECTED CROPS IN
DADE COUNTY

Table 30.
-Net profit
per acre
for a sample
of tomato
growers in
Dade County,
1960-61
through 1966-
-67.a
Unweighted
Grower
Standard
number
1960-1961
1961-1962
1962-1963
1963-1964
1964-1965
1965-1966
1966-1967
Mean
deviation
1
287.51
b
n.a.
-122.78
n.a.
n.a.
n.a.
n.a.
82.36
290.12
2
-107.48
-352.23
-129.21
15.98
n.a.
- 93.15
n.a.
-133.22
134.64
3
149.13
208.12
- 61.35
52.79
-166.49
111.14
-108.84
26.36
140.92
4
- 65.89
n.a.
-117.61
-165.71
n.a.
-207.89
n.a.
-139.28
61.27
5
- 20.40
n.a.
- 84.22
494.42
- 88.84
-233.24
-352.67
- 47.49
291.58
6
- 32.30
348.37
11.34
296.71
- 14.52
- 77.14
144.94
96.77
169.40
7
79.78
- 31.50
-107.59
n.a.
n.a.
n.a.
n.a.
- 19.77
94.23
8
6.30
122.38
76.59
115.67
-105.33
-125.39
n.a.
15.04
109.29
9
- 46.50
- 25.16
89.01
-154.55
-417.35
n.a.
n.a.
-110.91
191.89
10
n.a.
442.61
133.10
-141.89
- 62.94
-120.37
121.39
61.98
220.86
11
n. a.
88.85
- 32.53
-152.47
-308.51
n.a.
n.a.
-101.16
169.75
12
n.a.
278.15
189.49
255.76
70.33
n.a.
n.a.
198.43
93.33
. 13
n.a.
217.66
- 97.09
- 68.28
-233.68
n.a.
270.45
17.81
216.58
14
n.a.
88.84
-103.73
- 88.15
-232.21
-214.11
-328.07
-146.24
145.26
15
n.a.
n.a.
n.a.
n.a.
-263.65
-195.34
n.a.
-229.49
48.30
16
n.a.
n.a.
n.a.
n.a.
n.a.
-100.70
- 27.55
- 64.13
51.72
Unweighted
mean
27.79
126.01
-25.47
38.36
-165.74
-125.62
- 40.05
Unweighted
standard
deviation
124.43
216.13
106.60
214.01
141.79
100.64
238.84
Source of data: unpublished farm questionnaires collected by Dr. D. L. Brooke of the Department
of Agricultural Economics, University of Florida.
^Data not available.
120

Table 31.
Net profit
per acre for a sample of potato
growers in
Dade County, 1960-61 through 1966-67.3
Grower
number
1960-1961
1961-1962 1962-1963 1963-1964
1964-1965
Unweighted
Standard
1965-1966 1966-1967 Mean deviation
1
-110.16
168.72
- 13.34
214.36
139.21
156.50
251.72
115.29
129.70
' 2
-177.53
n.a.k
- 55.11
5.09
176.24
-167.34
- 21.72
- 40.06
129.99
3
- 82.48
25.33
124.36
354.58
410.98
245.38
57.43
162.23
181.12
4
-176.07
50.59
62.96
288.23
259.18
208.27
188.72
125.98
161.07
5
90.04
217.81
88.71
243.35
478.36
468.45
160.73
249.64
163.56
6
-176.77
166.86
- 31.12
74.67
380.05
54.27
65.41
76.20
171.59
7
n. a.
69.48
26.50
539.19
289.28
-151.47
n.a.
154.60
266.17
8
n. a.
95.72
-129.37
n.a.
n.a.
n.a.
n.a.
- 16.83
159.16
9
n. a.
150.79
n.a.
103.56
258.14
102.59
152.05
153.43
63.33
10
n. a.
59.41
n.a.
n.a.
n.a.
395.05
n.a.
227.23
237.33
11
n.a.
82.97
- 24.92
471.96
458.08
93.59
35.64
186.22
220.02
12
n. a.
n.a.
n.a.
56.86
438.24
21.17
- 54.59
115.42
220.17
Unweighted
mean
-105.50
108.77
5.41
235.19
328.78
129.68
92.82
Unweighted
standard
deviation
103.96
63.06
78.49
180.70
120.67
198.05
101.67
2
Source of data: unpublished farm questionnaires collected by Dr. D. L. Brooke of the Department
of Agricultural Economics, University of Florida.
^Data not available.
121

Table 32.Net profit per acre for a sample of pole bean growers in Dade County, 1960-61 through 1966-67
a
Grower
number
1960-1961
1961-1962
1962-1963
1963-1964
1964-1965
1965-1966
1966-1967
Unweighted
Standard
Mean deviation
1
111.95
- 88.22
b
n.a.
n.a.
n.a.
n.a.
n.a.
11.87
141.54
2
131.57
-145.59
- 67.30
187.76
n.a.
n.a.
n.a.
26.61
158.60
3
n. a.
- 80.26
- 48.25
1.56
- 49.84
n.a.
n.a.
- 44.20
33.87
4
n.a.
98.74
n.a.
85.27
n.a.
135.22
n.a.
106.41
25.84
5
n.a.
233.20
n.a.
154.18
270.93
22.55
190.47
174.26
95.53
6
n.a.
80.47
65.52
175.05
11.46
n.a.
- 14.46
63.61
73.35
7
n.a.
-117.90
- 42.60
-249.61
-232.33
n.a.
172.44
- 94.00
171.43
8
n.a.
221.94
233.52
174.62
189.90
194.40
248.97
210.56
28.68
9
n.a.
85.97
n.a.
n.a.
n.a.
24.82
n.a.
55.40
43.24
10
n.a.
n.a.
- 76.59
- 44.21
- 70.96
73.62
- 19.37
- 27.50
60.97
11
n.a.
n.a.
n.a.
125.95
n.a.
135.23
0.74
87.31
75.11
12
n.a.
n.a.
n.a.
n.a.
- 40.21
- 39.15
- 57.90
- 45.75
10.53
. 13
n.a.
n.a.
n.a.
n.a.
n.a.
430.53
181.29
305.91
176.24
Unweighted
mean
121.76
32.04
10.72
67.84
11.28
122.15
87.77
Unweighted
standard
deviation
13.87
144.63
120.56
144.11
169.22
145.62
121.40
g
Source of data: unpublished farm questionnaires collected by Dr. D. L. Brooke of the Department
of Agricultural Economics, University of Florida.
^Data not available.
122

Table 33.
-Net profit
per acre
for a sample
of squash
growers in
Dade County
1960-61 through 1966-
67.a
Unweighted
Grower
Standard
number
1960-1961
1961-1962
1962-1963
1963-1964
1964-1965
1965-1966
1966-1967
Mean
deviation
1
- 69.39
-124.28
b
n.a.
n.a.
n.a.
n.a.
n.a.
- 96.84
38.81
2
283.65
91.24
38.88
n.a.
n.a.
n.a.
n.a.
137.92
128.89
3
- 76.62
57.84
-117.61
-102.51
-235.28
-150.62
n.a.
-104.13
96.47
4
n.a.
1.44
-177.12
n.a.
- 68.29
n.a.
n.a.
- 81.32
89.99
5
n.a.
- 67.15
55.17
- 49.92
n.a.
n.a.
91.27
7.34
77.80
6
n.a.
67.62
49.04
77.62
276.20
276.40
238.25
164.19
110.18
7
n.a.
n.a.
- 16.88
-116.27
73.35
- 40.41
n.a.
- 25.05
78.12
8
n.a.
n.a.
n.a.
59.56
n.a.
-172.28
-168.82
- 93.85
132.87
9
n.a.
n.a.
n.a.
n.a.
66.74
127.57
322.64
172.32
133.69
10
n.a.
n.a.
n.a.
n.a.
n.a.
136.62
- 40.96
47.83
125.57
Unweighted
mean
45.88
4.45
- 28.09
- 26.30
22.54
29.55
88.48
Unweighted
standard
deviation
205.95
84.95
97.69
90.32
189.47
178.94
200.14
Source of data: unpublished farm questionnaires collected by Dr. D. L. Brooke of the Department
of Agricultural Economics, University of Florida.
^Data not available.
123

APPENDIX B
ESTIMATES OF PESTICIDE USAGE COMPILED BY
A MANAGEMENT BIOLOGIST AT THE
EVERGLADES NATIONAL PARK

Table 34.
Estimates made by
Richard Klukas3 of the
Dade County,
quantities
1966-67.b
of insecticide
used on
various crops
in
Insecticide
Crop
Insect
Pound
per acre
Number of
applications
Total
pounds
per acre
Total
pounds
per crop
Total
pounds
all crops
Cygon
Tomatoes
Aphid
.33
1
.33
5,795
Potatoes
Aphid
.54
1
.54
4,140
Pole beans
Aphid
.25
1
.25
2,250
12,185
DDT
Corn
Fall Armyworm
1.00
38
38.00
76,000
Southern peas
Cowpea Curculio
.50
4
2.00
2,000
78,000
Diazinon
Potatoes
Wireworm
2.00
1
2.00
15,200
15,200
Dieldrin
Sweet potatoes
Sweet Potato Weevil
1.50
4
6.00
6,000
6,000
Guthion
Tomatoes
Leaf Minor
.50
11
5.50
96,580
Potatoes
Leaf Minor
.25
5
1.25
9,500
Pole beans
Bean Leafroller
.50
5
2.50
22,500
Squash
Leaf Minor
.50
6
3.00
10,500
Canteloupe
Leaf Minor
.50
6
3.00
750
Cucumbers
Leaf Minor
.50
6
3.00
6,000
Watermelons
Leaf Minor
.50
6
3.00
300
146,130
Kelthane
Strawberries
Spider Mite
.32
10
3.20
1,760
1,760
Parathion
Tomatoes
Aphid
.40
6
2.40
42,144
Potatoes
Aphid and Armyworm
.30
12
3.60
27,360
Pole beans
Aphid
.30
4
1.20
10,800
Squash
Aphid
.30
10
3.00
10,500
Okra
Aphid and Leaf Minor .30
7
2.10
1,575
125

Table 34.Continued.
Insecticide
Phosdrin
Sevin
Crop
Insect
Cabbage
Aphid and Cabbage
Looper
Canteloupe
Aphid
Collards
Aphid and Cabbage
Looper
Cucumbers
Aphid
Strawberries
Pamera
Strawberries
Bud Nematode
Turnips
Aphid
Watermelons
Aphid
Pole beans
Aphid
Cabbage
Aphid and Cabbage
Looper
Collards
Aphid and Cabbage
Looper
Tomatoes
Stinkbug Worm
Corn Earworm and
Fall Armyworm
Corn Corn Earworm
Toxaphene
Total Total
Pounds Number of pounds pounds
per acre applications per acre per crop
Total
pounds
all crops
Per season
.50
10
5.00
2,500
.30
10
3.00
750
.25
17
4.25
425
.30
10
3.00
6,000
.30
26
7.80
.30
1
.30
4,455
.30
7
2.10
420
.30
10
3.00
300
107,229
.25
1
.25
2,250
.25
2
.50
250
.25
17
4.25
425
2,925
1.00
4
4.00
70,240
2.00
5
10.00
20,000
90,240
1.50
38
57.00
114,000
126

Table 34.Concluded.
Total
Total
Total
Pounds
Number of
pounds
pounds
pounds
Insecticide
Crop
Insect
per acre
applications
per acre
per crop
all crops
Per season
Southern peas
Cowpea Curculio
1.00
4
4.00
Southern peas
Bean Leafroller and
Bean Leafhopper
1.00
4
4.00
8
Management Biologist of the National Park Service, Everglades National Park.
^All quantities have been converted to units of 100 percent concentrated material. Table 36
presents the assumed growing seasons and estimated acreage for the calculations.
127

Table 35.-
-Estimates made
by Richard Klukas of the
Dade County,
quantities
1966-67.b
of fungicides
used on
various crops
in
Total
Total
Total
Pound
Number of
pounds
pounds
pounds
Fungicide
Crop
Disease
per acre
applications
per acre per crop
all crops
....Per
season
Captan
Strawberries
Leaf Spot
1.00
14
14.00
7,700
7,700
Dyrene
Tomatoes
Gray Leaf Spot
.75
3
2.25
39,510
39,510
Karathane
Squash
Powdery Mildew
.75
11
8.25
28,875
Canteloupe
Powdery Mildew
.75
11
8.25
2,200
Cucumbers
Powdery mildew
.75
11
8.25
17,600
48,675
Maneb
Tomatoes
Late Blight
1.20
11
13.20
231,792
Potatoes
Late Blight
1.20
14
16.80
127,680
Pole beans
Rust, Root Rots,
and mildew
1.20
6
7.20
64,800
Corn
Leaf Blight
1.20
11
13.20
26,400
Okra
Powdery Mildew and
Verticilium Wilt
1.50
2
3.00
2,250
452,922
Metallic
copper
Tomatoes
Bacterial Spot
2.00
4
8.00
140,480
140,480
Nab am
Tomatoes
Soil born disease
45.00
1
45.00
790,200
790,200
Zineb
Squash
Anthracnose and
Downy Mildew
1.50
6
9.00
31,500
Okra
Powdery Mildew and
Verticilium Wilt
1.50
2
3.00
2,250
128

Table 35.Concluded.
Fungicide
Crop
Disease
Pound
per acre
Number of
applications
Total
pounds
per acre
Total
pounds
per crop
Total
pounds
all crops
Canteloupe
Anthracnose and
Downy Mildew
1.50
11
16.50
4,125
Cucumbers
Anthrcnose and
Downy Mildew
1.50
11
16.50
33,000
70,875
Management Biologist of the National Park Service, Everglades National Park.
All quantities have been converted to units of 100 percent concentrated material.
129

Table 36.Dade County growing seasons and 1967 crop acreages used for Klukas projections.
Crop
1967 acreage
Planting, growing, harvesting period
Tomatoes
17,560
August 15 April 30
Potatoes
7,600
October 15 April 30
Pole beans
9,000
September 15 April 30
Corn
2,000
December 15 May 31
Squash
3,500
Continuous
Okra
750
February September
Cabbage
500
September 15 March 1
Cantaloupe
250
August 30 May 1
Collards
100
December 1 April 1
Cucumbers
2,000
August 30 April 30
Southern peas
1,000
December June
Strawberries
550
September 15 April 15
Sweet potatoes
1,000
Continuous
Turnips
200
November April
Watermelons
100
December 15 April 30
130

APPENDIX C
COMMON, CHEMICAL, AND/OR TRADE NAME OF
PESTICIDES IDENTIFIED IN
DADE COUNTY

Table 37.Common names, trade names, and/or chemical names of pesticides observed in Dade County,
Florida, 1966-67.
Common name
Trade name
Chemical name
INSECTICIDES
Chlorinated hydrocarbons
Aldrin
Chlordane Chlorokil
DDD (or TDE) Rhothane D-3
DDT Niatox
Dieldrin
Endosulfan Thiodan
Endrin
Not less than 95% of 1,2,3,4,10,10-
hexachloro-1,4,4a,5,8,8a-hexahydro-l,4-
endo, exo-5, 8-dimethanonaphthalene.
1,2,3,4,5,6,7,8,8-octachloro-2,3,3a,4,7,
7a-hexahydro-4,7-methanoindene.
Dichloro Diphenyl Dichloroethane.
Dichloro Diphenyl Trichloroethane.
1.2.3.4.10.10-hexachloro-6,7-epoxy-l,4,
4a,5 6,7,8,8a-octahydro-l,4-endo, exo-5,
8-dimethanonaphthalene.
6.7.8.9.10.10-hexachloro-l,5,5a,6,9,9a-
hexahydro-6,9-methano-2,4,3-benzodiox-
athiepin-3-oxide.
1.2.3.4.10.10-hexachloro-6,7=epoxy-l,4,
4a,5,6,7,8,8a-octahydro-l,4-endo-endo-5,
8-dimethanonaphthalene.
132

Table 37.Continued.
Common name Trade name
Heptachlor
Lindane
Toxaphene
Organic phosphates
Azinphosmethyl
Guthion
Carbophenothion
Trithion
Demeton
Systox
Dimethoate
Cygon
Malathion
Malaphos
Mevinphos
Phosdrin
Chemical name
3a,4,5,6,7,8,8-hep tachloro-3a,4,7,7a-
tetrahydro-4,7-methanoindene.
Gamma isomer of benzene hexachloride.
Chlorinated camphene containing 67-69%
chlorine.
0,O-dimethyl S-4-oxo-l,2,3-benzotriazin-
3(4H)-ylmethyl phosphorodithioate.
0,0-diethyl S-(p-chlorophenylthiomethyl)
phosphorodithionate.
0,0-diethyl 0(and S)-ethylthioethyl phos-
phorothioate.
0,0-dimethyl S-N methylcarbamoylmethyl
phosphorodithioate.
0,0-dimethyl dithiophosphate of diethyl
mercaptosuccinate.
2-carbomethoxy-l-propen-2-yl dimethyl
phosphate.
133

Table 37.Continued.
Common name
Parathion (ethyl)
Phorate
Other
Carbaryl
Citrus oil
Ethion
Manganese
Manganese sulfate
Metaldehyde
Phosphoric acid
Trade name
Alkron, Genthion, Niran,
Orthophos, Thiophos
Thimet
Sevin, Aqua 5 Sevin
Slug-tox
Treflan
Chemical name
0,0-diethyl O-p-nitrophenyl phosphor-
othioate.
0,0-diethyl S-ethylchlomethyl phosphor-
othioate.
N-napthyl N-methylcarbamate.
A mixture of hydrocarbons distilled from
petroleum.
0,0,0',0'-tetraethyl-S-S'-methylenebis
(phosphorodithioate).
Acetaldehyde polymer.

Table 37.Continued.
Common name
Trade name
Chemical name
FUNGICIDES
Other
Botran
2,6-dichloro-4-nitroaniline.
Captan
Orthocide, Stauffer Captan,
Ortho Captan
N-trichloromethylthiotetrahydrophthalli-
mide OR N-trichloromethyl mercapto-4-
cyclohexene-1,2-dicarboximide.
Copper compounds
Mang-Z-Kop, Nutri Sperse,
CPCS
These are composed of 33% copper and
miscellaneous formulae not available.
Dexon
Sodium P-dimethylaminobenzenediazo sulfo
nate, diacetoxy propene (not acceptable)
should be 2-propene-l,1-diol diacetate.
Dichlone
Phygon
2,4-dichloro-l,4-n-aphthoquinone.
Dyrene
Kemate
2,4-dichloro-6-(2-chloroanilino)-s-
triazine.
Ferbam Fermate, Karbam Black, Stauffer Ferric dimethyl dithiocarbamate.
Ferbam, Carbamate
Karathane
Dinitro (1-methylheptyl) phenyl crotonate.

Table 37.Continued
Common name
Trade name
Maneb
Dithane M-22, Manzate, Miller
6582, Manzate D, Dithane M-45
Nab am
Nabam, Dithane A-40
Polyram
Streptomycin
Agrimycin, Agrimycin 100,
Agristrep, Phytomycin,
Agrimycin 500, Agristrep 500
Sulfur (wettable)
Micro Nutri Sperse, Micro
Sperse, Sulfobrite, Enduro
Sulfur
Ziram
Z. C. Spray, Karbam, Zerlante
Zinc
Zinc sulfate
Zineb
Parzate C, Dithane Z-78,
Ortho Zineb
Chemical name
Ethylenebis (dithiocarbamate) manganese;
Dithane M-45 is a coordination product of
zinc ion and maneb.
Disodium ethylene bisdithiocarbamate.
Zinc polyethylene thiuram disulfide
complex.
2,4-diguanidina-3,5,6-trihydroxycyclo-
hexyl 5-deoxy-2-0-(2-deoxy-2-methylamino-
a-glucopyranosyl)-3-formyl pentofuranoside.
Contains 15% streptomycin and 40% copper.
Zinc dimethyl dithiocarbamate.
Ethylenebis (dithiocarbamate) zinc.

Table 37.Continued.
Common name Trade name
HERBICIDES
Other
2,4-D
Amine 2,4-D
Atrazine
Atrazine
Dacthal
DCPA
Diphenamid
Dymid, Dymid D, Enide
Paraquat
Penite
Kill All, Miller Kill All
Chem-Sen
Simizine
Solan
Solan
RODENTICIDES
Other
Warfarin
Prolin Rat Bait
Chemical name
2,4-dichlorophenoxy acetic acid.
2-chloro-4-ethylamino-6-isopropylamino-
1,3,5-triazine.
Dimethyl 2,3,5,6-tetrachlorterephthalate.
N, N-dimethyl-2, 2-diphenylacetamide.
1,1-dimethy1-4, 4-bipyridinium dichloride.
Sodium arsenite.
2-chloro-4,6-bis (ethylamino)-1,3,5-
triazine.
N-(3-chloro-4-methylphenyl)-2-methyl-
pentanamide.
Alpha-acetoneylfurfuryl-4-hydroxycoumarin.
137

Table 37.Concluded.
Common name
Trade name
Chemical name
Zinc phosphide
Z. P. Rat Bait
Zinc 2-pyridinethiol-l-oxide.
MITICIDES
Other
PCNB
Terrachlor
Pentachloronitrobenzene.
Tetradifon
Tedion
Tetradithion 2,4,5,4-tetrachlorodiphenyl
sulfone.

APPENDIX D
ESTIMATED QUANTITIES OF AGRICULTURAL PESTICIDES
USED IN DADE COUNTY

Table 38.Estimated quantities per acre of certain pesticide categories used by farmers in Dade County,
1966-67 crop year, by crop.k
Crop
Fungicide
Organic
phosphates
Insecticide
Chlorinated
hydrocarbons Carbamates
Other
Herbicide
Total
Tomatoes
24.4666
3.2669
6.0719
2.4925
.1837
.9190
37.4006
Potatoes
15.5264
5.8852
1.2860
.0576
.1034
9.6740
32.5326
Pole beans
61.5815
1.8114
3.4304
None
.0137
.0605
66.8975
Corn
15.8680
6.4624
40.7003
None
None
.8539
63.8846
Squash
43.6890
1.0344
2.6039
None
None
None
47.3273
Okra
34.7124
3.2540
5.0000
None
.7200
None
43.6864
Groves
27.7465
.4424
.1395
3.0345
3.2603
.5611
35.1843
Other
8.1333
.2225
4.9167
None
.1500
None
13.4225
Total average
usage
25.6548
3.5680
5.0308
1.4613
.3943
2.6505
38.7597
aAll
quantities have
been converted
to pounds of
100 percent
concentrated
material.
Acres sampled and number of observations were:

Table 38.Concluded.
Acres
Tomatoes
10,590
Potatoes
4,584
Pole beans
2,394
Corn
1,585
Squash
244
Okra
50
Groves
1,247
Other
240
c
Sulfur compounds
comprised 53.8829 pounds.
Observations
24
51
93
2
11
2
26
2
m

Table 39.
-Estimated quantities
a per acre
1966-
of certain pesticide
67 crop year, by crop
categories
and month.
used by
b
farmers in Dade
County,
January
Crop
Fungicide
Organic
phosphates
Insecticide
Chlorinated
hydrocarbons Carbamates
Other
Herbicide
Total
Tomatoes
4.8433
.6177
1.0695
.5688
.0001
.0136
7.1130
Potatoes
6.3642
.5190
.5822
None
.0288
.6726
8.1668
Pole beans
19.4178
.2543
.3743
None
.0874
None
20.1338
Corn
None
None
None
None
None
None
None
Squash
24.4520
.3279
.0887
None
None
None
24.8686
Okra
.4800
None
None
None
None
None
.4800
Groves
10.8815
.0261
None
.1797
1.9079
.1562
13.1514
Other
.1000
None
None
None
.1500
None
.2500
Total average
usage 7.0330
.4628
.7158
.2999
.1323
.1643
8.8081
142

Table 39.Continued.
February
Crop
Fungicide
Insecticide
Herbicide
Total
Organic
phosphates
Chlorinated
hydrocarbons
Carbamates
Other
Tomatoes
6.3334
.6435
.9459
1.5207
.0099
None
9.4534
Potatoes
4.5131
.6156
.5217
None
.0120
3.8978
9.5602
Pole beans
9.2554
.2996
.5088
None
.1432
None
10.2070
Corn
2.9272
2.4670
11.4323
None
None
.1632
16.9898
Squash
.1967
None
.1639
None
None
None
.3606
Okra
None
1.0670
None
None
None
None
1.0670
Groves
3.4069
None
.1203
.3079
.3090
.1703
4.3144
Other
1.1250
None
None
None
None
None
1.1250
Total average
usage
5.5980
.5866
1.0665
.7914
.0426
.8736
8.9587

Table 39.Continued.
March
Crop
Fungicide
Organic
phosphates
Insecticide
Chlorinated
hydrocarbons Carbamates
Other
Herbicide
Total
Tomatoes
1.3239
.1456
.8046
.0008
None
None
2.2749
Potatoes
.7997
.0611
.1226
None
.0046
4.5556
5.5436
Pole beans
12.2596
.2862
.7237
None
.0289
None
13.2984
Corn
6.8941
3.4521
16.7774
None
None
.0815
27.2053
Squash
.0492
.2187
.1639
None
None
None
.4318
Okra
1.9224
.2670
.6000
None
None
None
2.7894
Groves
3.2403
None
.0192
2.5469
.0778
.0643
5.9485
Other
None
None
None
None
None
None
None
Total average
usage
2.7000
.2453
1.1155
.1528
.0090
1.0091
5.2317
m

Table 39.Continued.
April
Crop
Fungicide
Insecticide
Herbicide
Total
Organic
phosphates
Chlorinated
hydrocarbons
Carbamates
Other
Tomatoes
.1024
.0166
.1285
None
None
None
.2475
Potatoes
None
None
None
None
None
.2356
.2356
Pole beans
5.2683
.3409
.9166
None
.0035
None
6.5293
Corn
.2040
.8369
2.3775
None
None
None
3.4187
Squash
None
None
None
None
None
None
None
Okra
16.1100
1.4000
.8000
None
None
None
18.3100
Groves
1.7401
None
None
None
.1086
.1134
1.9621
Other
5.7833
.2225
4.1667
None
None
None
10.1725
Total average
usage
.8741
.0830
.3044
None
.0069
.0586
1.3270
S7T

Table 39.Continued.
May
Crop
Fungicide
Insecticide
Herbicide
Total
Organic
phosphates
Chlorinated
hydrocarbons
Carbamates
Other
Tomatoes
.0082
None
None
.0272
None
None
.0354
Potatoes
.0262
.0013
.0327
None
None
.0022
.0624
Pole beans
.0656
.0969
.2105
None
None
None
.3730
Corn
None
None
None
None
None
None
None
Squash
None
None
None
None
None
None
None
Okra
13.5000
.3600
2.4000
None
None
None
16.2600
Groves
4.7337
.1079
None
None
.3249
.0064
5.1729
Other
1.1250
None
.7500
None
None
None
1.8750
Total average
usage
.3461
.0187
.0458
.0138
.0194
.0009
.4447
97T

Table 39.Continued
June
Crop
Fungicide
Insecticide
Herbicide
Total
Organic
phosphates
Chlorinated
hydrocarbons
Carbamates
Other
Tomatoes
None
None
None
None
None
None
None
Potatoes
None
None
None
None
None
None
None
Pole beans
None
None
None
None
None
None
None
Corn
None
None
None
None
None
None
None
Squash
None
None
None
None
None
None
None
Okra
2.7000
.1600
1.2000
None
None
None
4.0600
Groves
3.4270
.0181
None
None
.2936
.0151
3.7538
Other
None
None
None
None
None
None
None
Total average
usage
.2116
.0015
.0029
None
.0176
.0009
.2345
147

Table 39.Continued
July
Crop
Fungicide
Insecticide
Herbicide
Total
Organic
phosphates
Chlorinated
hydrocarbons
Carbamates
Other
Tomatoes
None
None
None
None
None
None
None
Potatoes
None
None
None
None
None
None
None
Pole beans
None
.0100
.0334
None
None
None
.0434
Corn
None
None
None
None
None
None
None
Squash
None
None
None
None
None
None
None
Okra
None
None
None
None
None
None
None
Groves
None
None
None
None
None
None
None
Other
None
None
None
None
None
None
None
Total average
usage
None
.0012
.0038
None
None
None
.0050
m

Table 39.Continued
Augus t
Crop
Fungicide
Organic
phosphates
Insecticide
Chlorinated
hydrocarbons Carbamates
Other
Herbicide
Total
Tomatoes
.1107
.0864
.0825
.0005
.0090
.2226
.5117
Potatoes
None
None
None
None
None
None
None
Pole beans
None
.0157
.0581
None
None
.0167
.0905
Corn
None
None
None
None
None
None
None
Squash
None
None
None
None
None
None
None
Okra
None
None
None
None
None
None
None
Groves
None
.1672
None
None
.0945
None
.2617
Other
None
None
None
None
None
None
None
Total average
usage
.0563
.0558
.0486
.0002
.0102
.1151
.2862
149

Table 39.Continued
September
Crop
Fungicide
Insecticide
Herbicide
Total
Organic
phosphates
Chlorinated
hydrocarbons Carbamates
Other
Tomatoes
3.4464
.5048
.5113
None
.0375
.2644
4.7644
Potatoes
None
None
None
None
None
None
None
Pole beans
.0341
.0112
.0171
None
.0012
None
.0636
Corn
None
None
None
None
None
None
None
Squash
None
None
None
None
None
None
None
Okra
None
None
None
None
None
None
None
Groves
.0658
.1233
None
None
.1559
.0113
.3563
Other
None
None
None
None
None
None
None
Total average
usage
1.7597
.2653
.2619
None
.0285
.1351
2.4505
350

Table 39.Continued
October
Crop
Fungicide
Insecticide
Herbicide
Total
Organic
phosphates
Chlorinated
hydrocarbons
Carbamates
Other
Tomatoes
1.6478
.2051
.5374
None
.0583
.2074
2.6560
Potatoes
.1691
.4411
None
None
None
None
.6102
Pole beans
.8552
.1075
.0914
None
.0287
.0301
1.1129
Corn
None
None
None
None
None
None
None
Squash
4.0761
.2911
.3139
None
None
None
4.6811
Okra
None
None
None
None
None
None
None
Groves
.1147
None
None
None
None
.0080
.1227
Other
None
None
None
None
None
None
None
Total average
usage
1.0276
.2170
.2874
None
.0329
.1093
1.6742
151

Table 39.Continued
November
Crop
Fungicide
Organic
phosphates
Insecticide
Chlorinated
hydrocarbons Carbamates
Other
Herbicide
Total
Tomatoes
2.7878
.4828
.8263
.0725
.0591
.1534
4.3819
Potatoes
1.2088
2.3739
None
None
None
None
3.5827
Pole beans
3.9047
.2036
.2656
None
.1591
None
4.5330
Corn
None
None
None
None
None
None
None
Squash
7.1120
.1967
1.2108
None
None
None
8.5195
Okra
None
None
None
None
None
None
None
Groves
.1363
None
None
None
None
.0161
.1524
Other
None
None
None
None
None
None
None
Total average
usage
2.2231
.7934
.4647
.0369
.0483
.0789
3.6453
152

Table 39.Continued
December
Crop
Fungicide
Insecticide
Herbicide
Total
Organic
phosphates
Chlorinated
hydrocarbons
Carbamates
Other
Tomatoes
3.8639
.5645
1.1661
.3021
.0200
.0474
5.9640
Potatoes
2.4452
1.8731
.0268
.0576
.0628
.3054
4.7709
Pole beans
10.5142
.1841
.2305
None
.1708
None
11.0996
Corn
.6752
3.3221
2.2861
None
None
.3811
6.6642
Squash
7.8030
None
.6627
None
None
None
8.4657
Okra
None
None
None
None
.7200
None
.7200
Groves
None
None
None
None
.0208
None
.0208
Other
None
None
None
None
None
None
None
Total average
usage
3.8253
.8374
.7135
.1663
.0466
.1047
5.6938
aAll
quantities have
been converted
to pounds of
100
percent
concentrated
material.
T.
0Acres sampled and number of observations were:
153

Table 39.Concluded
Acres
Tomatoes
10,590
Potatoes
4,584
Pole beans
2,394
Corn
735
Squash
244
Okra
50
Groves
1,247
Other
240
Observations
24
51
93
1
11
2
26
2
154

Table 40.Estimated
pesticide usage3
in Dade County,
Florida, by crop and pesticide, 1966-67
b
crop year.
Standard
Total
Total
Mean usage
deviation
usage
usage
in pounds
pounds
by crops
all crops
Trend
Pesticide
Crop
per acre
per acre
in pounds
in pounds
of usage
Agrimycin
Tomatoes
.0291
.1338
553
553
Uncertain
Aldrin
Groves
.1203
.0490
1,287
1,287
Uncertain
Amine 2,4-D
Potatoes
.0313
.0991
240
240
Uncertain
Arsenate of lead
Potatoes
.0842
.0676
645
645
Uncertain
Atrazine
Corn
.8539
.3225
1,409
1,409
Uncertain
Botran
Tomatoes
.0167
.0115
317
Uncertain
Potatoes
.0157
.0158
120
Uncertain
Pole beans
2.8755
4.3910
16,764
17,201
Up
Captan
Tomatoes
.1447
.3802
2,749
Uncertain
Potatoes
1.1003
.7847
8,428
Down
Okra
.4800
.5657
288
Uncertain
Other
.1000
.4243
215
11,680
Uncertain
Chlordane
Tomatoes
.2096
.1929
3,982
Down
Potatoes
.0016
.0013
12
3,994
Uncertain
Citrus oil
Groves
.4970
.3744
5,320
5,320
Uncertain
155

Table 40.Continued
Pesticide
Mean usage
in pounds
Crop per acre
Copper compounds
Tomatoes
2.1128
Potatoes
.1037
Pole beans
.0365
Squash
.1126
Okra
.5724
Groves
8.5688
Cygon
Tomatoes
1.1586
Potatoes
.7698
Pole beans
.3184
Squash
.0547
Okra
.5340
Other
.2225
Dacthal
Pole beans
.0301
DDD
Tomatoes
.1428
DDT
Tomatoes
.4929
Potatoes
.2082
Pole beans
.2480
Corn
20.3712
Okra
1.4000
Groves
.0192
Other
.2500
Demeton
Potatoes
.1371
Standard
deviation
pounds
per acre
Total
usage
by crops
in pounds
Total
usage
all crops
in pounds
7.8153
.1745
.0438
.2089
1.0119
40
3.2718
91
. 6464
22
.4250
5
.1230
.0503
.9440
.1888
.0361
1
.4569
2
1.3517
9
.3834
1
.1400
1
5.9330
2.4749
.0078
1.0607
33
.2965
1
143
794
213
347
343
729
133,569
013
897
856
168
320
478
30,732
175
175
713
2,713
365
595
446
612
840
205
538
47,601
050
1,050
r. ,d
Trend
of usage
Down
Uncertain
Down
Uncertain
Uncertain
Uncertain
Down
Up
Uncertain
Uncertain
Uncertain
Uncertain
Uncertain
Uncertain
Up
Down
Uncertain
Uncertain
Uncertain
Uncertain
Uncertain
Down
156

Table 40.Continued
Pesticide
Mean usage
in pounds
Crop per acre
Dexon
Pole beans
.0022
Dieldrin
Tomatoes
.0237
Pole beans
.0017
Diphenamid
Tomatoes
.7724
Dyrene
Tomatoes
1.7251
Endrin
Potatoes
.1149
Pole beans
.0259
Ethion
Groves
1.5697
Ferbam
Tomatoes
.0267
Groves
3.0412
Guthion
Tomatoes
.2930
Pole beans
.0925
Groves
.0261
Keptachlor
Tomatoes
.1714
Karathane
Tomatoes
.0002
Potatoes
.0010
Squash
.2459
Groves
.0258
Standard
deviation
pounds
per acre
Total
usage
by crops
in pounds
Total
usage
all crops
in pounds
Trend
of usage
.0013
13
13
Uncertain
.1005
450
Uncertain
.0010
10
460
Uncertain
.6179
14,676
14,676
Up
1.6692
32,777
32,777
Down
.0717
880
Down
.0151
151
1,031
Uncertain
3.1691
16,804
16,804
Uncertain
.2405
507
Uncertain
10.5389
32,556
33,063
Uncertain
.8092
5,567
Uncertain
.1017
539
Uncertain
.0411
279
6,385
Uncertain
.2996
3,257
3,257
Down
.0019
4
Uncertain
.0010
8
Uncertain
.2261
757
Uncertain
.0105
276
1,045
Uncertain
157

Table 40.Continued.
Pesticide
Mean usage
in pounds
Crop per acre
Lindane
Tomatoes
.0253
Potatoes
.0016
Squash
.2730
Malathion
Tomatoes
.0002
Groves
.1942
Maneb
Tomatoes
19.3253
Potatoes
13.9089
Pole beans
4.4690
Corn
5.9295
Squash
5.1516
Okra
. 3600
Manganese
Tomatoes
.0206
Groves
.0026
Manganese sulfate
Groves
1.1910
Metaldehyde
Tomatoes
.0418
Pole beans
.0012
Nab am
Tomatoes
.0101
Corn
.2623
Parathion
Tomatoes
1.7992
Potatoes
1.7308
Standard
deviation
pounds
per acre
Total
usage
by crops
in pounds
Total
usage
all crops
in pounds
Trendd
of usage
.1126
481
Uncertain
.0016
12
Uncertain
.5429
841
1,334
Uncertain
.0001
4
Uncertain
.0792
2,079
2,083
Uncertain
7.3742
367,181
Up
3.4747
106,542
Up
4.0941
26,054
Up
6.8137
9,784
Uncertain
7.8050
15,867
Uncertain
.6364
216
525,644
Uncertain
.1256
391
Uncertain
.0635
28
419
Uncertain
3.5145
12,750
12,750
Uncertain
.1169
794
Uncertain
.0007
7
801
Uncertain
.0407
192
Uncertain
.4000
433
625
Uncertain
2.1804
34,185
Uncertain
1.6397
13,258
Uncertain
158

Table 40.Continued
Pesticide
Crop
Mean usage
in pounds
per acre
Pole beans
1.3942
Corn
5.0964
Squash
.9797
Okra
1.7600
Groves
.0962
Paraquat
Tomatoes
.0004
Groves
.1407
Phosdrin
Tomatoes
.0159
Potatoes
.3367
Pole beans
.0063
Okra
.9600
Phosphoric acid
Potatoes
.0192
Phygon
Pole beans
.0528
Polyram
Potatoes
.3644
Prolin rat bait
Tomatoes
.0098
Potatoes
.0046
Sevin
Tomatoes
2.4925
Potatoes
.0576
Groves
3.0345
Simizine
Groves
.4204
Standard
deviation
pounds
per acre
Total
usage
by crops
in pounds
Total
usage
all crops
in pounds
Trend^
of usage
1.4190
8,128
Uncertain
6.0096
8,409
Uncertain
.8141
3,017
Uncertain
3.1113
1,056
Uncertain
.0392
1,030
69,083
Uncertain
.0033
8
Uncertain
.2777
1,506
1,514
Uncertain
.0434
302
Uncertain
.3831
2,579
Uncertain
.0075
37
Uncertain
.0707
576
3,494
Uncertain
.0924
147
147
Down
.0211
308
308
Uncertain
.7196
2,791
2,791
Uncertain
.0354
186
Uncertain
.0090
35
221
Uncertain
1.6889
47,358
Down
.0588
441
Uncertain
1.2369
32,484
80,283
Uncertain
.0328
4,500
4,500
Uncertain
159

Table 40.Continued
Pesticide
Mean usage
in pounds
Crop per acre
Sodium arsenite
Potatoes
9.6380
Pole beans
.0167
Solan
Tomatoes
.1360
Sulfur
Tomatoes
.8330
Potatoes
.0196
Pole beans
53.8829
Corn
2.1066
Squash
33.1848
Okra
33.3000
Groves
12.6916
Other
8.0333
Tedion
Pole beans
.0002
Groves
.0328
Terrachlor
Pole beans
.5952
Tnimet
Potatoes
2.9108
Corn
1.3660
Thiodan
Tomatoes
.1512
Potatoes
.5788
Pole beans
.0167
Squash
2.3310
Standard
deviation
pounds
per acre
3.0033
.0104
.1059
2.4743
.0197
39.5020
3.2130
16.1244
16.4402
13.2781
19.9121
.0058
.1471
.9618
1.2717
.8966
.3972
.6770
.0201
2.0005
Total Total
usage
by crops
in pounds
usage
all crops
in pounds
,d
Trend
of usage
73,827
Up
97
73,924
Uncertain
2,584
2,584
Uncertain
15,827
Down
150
Uncertain
314,137
Down
3,476
Uncertain
102,209
Uncertain
19,980
Uncertain
135,864
Uncertain
17,272
608,915
Uncertain
1
Uncertain
351
352
Uncertain
3,470
3,470
Uncertain
22,297
Up
2,254
24,551
Uncertain
2,873
Uncertain
4,434
Up
97
Uncertain
7,179
14,583
Uncertain
160

Table 40.Continued
Pesticide
Mean usage
in pounds
Crop per acre
Toxaphene
Tomatoes
4.8550
Potatoes
.3809
Pole beans
3.1381
Corn
20.3291
Okra
3.6000
Other
4.6667
Treflan
Tomatoes
.1140
Pole beans
.0125
Okra
.6200
Other
.1500
Trithion
Groves
.1259
Zinc
Tomatoes
.0073
Zinc sulfate
Corn
2.2397
Groves
1.6844
Zineb
Tomatoes
.2416
Potatoes
.0128
Pole beans
.2512
Squash
4.9940
Groves
1.7347
Ziram
Tomatoes
.0013
Pole beans
.0114
Standard
deviation
pounds
per acre
Total
usage
by crops
in pounds
Total
usage
all crops
in pounds
3.8401
.8972
2.8221
17.0029
6.3640
1.4142
.2006
.0073
.8485
.6364
.5575
.0447
3.4150
3.4391
.9191
.0128
.6742
5.5088
2.1649
.0116
.0137
92,245
2,918
18,295
33,543
2,160
10,033
2,166
73
432
323
1,348
139
3,696
18,032
4,590
98
1,464
15,382
18,570
25
66
159,194
2,994
1,348
139
21,728
40,104
91
Trendd
of usage
Up
Down
Uncertain
Uncertain
Uncertain
Uncertain
Down
Uncertain
Uncertain
Uncertain
Uncertain
Uncertain
Uncertain
Uncertain
Down
Uncertain
Uncertain
Uncertain
Uncertain
Uncertain
Down
161

Table 40.Concluded.
Pesticide
Crop
Mean usage
in pounds
per acre
Standard
deviation
pounds
per acre
Total
usage
by crops
in pounds
Total
usage
all crops
in pounds
Trend^
of usage
Z. P. rat bait
Tomatoes
.0004
.0019
8
Uncertain
Potatoes
.0001
.0001
1
Uncertain
Pole beans
.0137
.0054
80
89
Uncertain
All quantities have been converted to units of 100 percent concentrated material.
Acres sampled and number of observations were:
Acres
Observations
Tomatoes
10,590
24
Potatoes
4,584
51
Pole beans
2,394
93
Corn
1,585
2
Squash
244
11
Okra
50
2
Groves
1,247
26
Other
240
2
Q
These figures were computed by multiplying the usage per acre by the number of acres of the crop
grown in Dade County in 1966-67.
^This column presents the trend of usage per acre from 1965-66 to 1966-67. The information on
1965-66 was limited and not sufficient for making point estimates of usage. It should be noted that usage
from one year to another is greatly affected by insect and disease infestation and therefore a trend
projection on two years' data is at best crude and subject to considerable error.
ON
to

163
Table 41.Estimated pesticide usage by pesticide,
in Dade County, Florida, 1966-67.^
crop, and month
Standard
Mean usage deviation
Pesticide
Crop
Month
in pounds
per acre
pounds
per acre
Agrimycin
Tomatoes
August
.0054
.0346
Tomatoes
September
.0159
.0782
Tomatoes
October
.0070
.0343
Tomatoes
November
.0008
.0069
Aldrin
Groves
February
.1203
.0490
Groves
June
.0000
.0010
Amine 2,4-D
Potatoes
January
.0182
.0986
Potatoes
February
.0131
.0100
Arsenate of lead
Potatoes
December
.0628
.0504
Potatoes
January
.0214
.0172
Atrazine
Corn
December
.3810
.0000
Corn
February
.1633
.0000
Corn
March
.0816
.0000
Botran
Tomatoes
January
.0113
.0078
Tomatoes
February
.0054
.0037
Potatoes
February
.0157
.0158
Pole beans
December
.3735
1.8958
Pole beans
January
1.5933
4.0390
Pole beans
February
.7041
1.9559
Pole beans
March
.1045
.7466
Captan
Tomatoes
August
.0121
.0732
Tomatoes
September
.0160
.0513
Tomatoes
October
.0272
.1205
Tomatoes
November
.0045
.0218
Tomatoes
January
.0378
.1856
Tomatoes
February
.0283
.0586
Tomatoes
March
.0189
.0928
Potatoes
October
.1691
.1110
Potatoes
November
.7376
.6289
Potatoes
December
.1936
.1592
Okra
January
.4800
.5657
Other
January
.1000
.4243
Chlordane
Tomatoes
August
.0437
.1428
Tomatoes
September
.0464
.1033
Tomatoes
October
.0343
.0368
Tomatoes
November
.0111
.0069

164
Table 41.Continued.
Standard
Mean usage deviation
Pesticide
Crop
Month
in pounds
per acre
pounds
per acre
Tomatoes
December
.0284
.0213
Tomatoes
January
.0485
.0861
Potatoes
January
.0016
.0013
Citrus oil
Groves
January
.2325
.3508
Groves
February
.0140
.0651
Groves
August
.0945
.0385
Groves
September
.1559
.0635
Copper compounds
Tomatoes
August
.0431
.3588
Tomatoes
September
1.4384
4.8120
Tomatoes
October
.3831
2.7442
Tomatoes
November
.1634
1.3154
Tomatoes
December
.0368
.5316
Tomatoes
January
.0465
1.0559
Tomatoes
March
.0015
.1531
Potatoes
February
.1037
.1745
Pole beans
December
.0196
.0236
Pole beans
January
.0168
.0202
Squash
December
.0490
.1618
Squash
January
.0636
.1868
Okra
March
.5724
1.0119
Groves
January
.6678
1.1212
Groves
February
1.2322
.5224
Groves
March
.4498
.1833
Groves
April
1.1144
.4542
Groves
May
2.9004
1.1822
Groves
June
2.0254
.7356
Groves
October
.0425
.0173
Groves
November
.1363
.0556
Cygon
Tomatoes
August
.0025
.0462
Tomatoes
September
.0303
.1300
Tomatoes
October
.0328
.0315
Tomatoes
November
.1326
.1330
Tomatoes
December
.1660
.1740
Tomatoes
January
.3085
.2125
Tomatoes
February
.4287
.2550
Tomatoes
March
.0521
.0732
Tomatoes
April
.0051
.0050
Potatoes
December
.0477
.0787
Potatoes
January
.4407
.2683
Potatoes
February
.2813
.2014

165
Table 41.-
Pesticide
Dacthal
DDD
DDT
-Continued.
Standard
Crop
Month
Mean usage
in pounds
per acre
deviation
pounds
per acre
Pole beans
September
.0112
.0075
Pole beans
October
.0250
.1068
Pole beans
November
.0371
.0797
Pole beans
December
.0228
.0555
Pole beans
January
.0584
.1243
Pole beans
February
.0565
.1019
Pole beans
March
.0851
.0989
Pole beans
April
.0223
.0138
Squash
March
.0547
.0503
Okra
February
.2670
.4720
Okra
March
.2670
.4720
Other
April
.2225
.1888
Pole beans
October
.0301
.0361
Tomatoes
September
.0453
.2338
Tomatoes
October
.0068
.0612
Tomatoes
December
.0567
.2041
Tomatoes
January
.0340
.1225
Tomatoes
Augus t
.0125
.0605
Tomatoes
September
.0710
.4777
Tomatoes
October
.0491
.2354
Tomatoes
November
.0311
.1855
Tomatoes
December
.0755
.3321
Tomatoes
January
.0793
.2054
Tomatoes
February
.0483
.0907
Tomatoes
March
.1146
.0917
Tomatoes
April
.0115
.0516
Potatoes
January
.1296
.2965
Potatoes
February
.0262
.1400
Potatoes
March
.0414
.0416
Potatoes
May
.0109
.0215
Pole beans
April
.1035
.0601
Pole beans
May
.1445
.0801
Corn
December
.7620
.0000
Corn
February
6.6429
.0000
Corn
March
14.7840
.0000
Corn
April
2.3776
.0000
Okra
March
.2000
.3536
Okra
May
.8000
1.4142
Okra
June
.4000
.7071
Groves
March
.0192
.0078
Other
May
.2500
1.0607

166
Table 41.Continued.
Pesticide Crop
Demeton
Potatoes
Potatoes
Potatoes
Potatoes
Dexon
Pole beans
Dieldrin
Tomatoes
Tomatoes
Tomatoes
Pole beans
Pole beans
Diphenamid
Tomatoes
Tomatoes
Tomatoes
Tomatoes
Tomatoes
Tmateos
Dyrene
Tomatoes
Tomatoes
Tomatoes
Tomatoes
Tomatoes
Tomatoes
Tomatoes
Endrin
Potatoes
Potatoes
Potatoes
Pole beans
Ethion
Groves
Groves
Groves
Ferbam
Tomatoes
Tomatoes
Groves
Groves
Groves
Groves
Month
Mean usage
in pounds
per acre
Standard
deviation
pounds
per acre
December
.0002
.0025
January
.0564
.2317
February
.0775
.1600
March
.0031
.0366
May
.0022
.0013
August
.0114
.0481
September
.0109
.0562
October
.0014
.0125
August
.0013
.0008
September
.0004
.0003
August
.2203
.4617
September
.1775
.3910
October
.1677
.1003
November
.1458
.0835
December
.0474
.0441
January
.0136
.0093
October
.0340
.1680
November
.1541
.2364
December
.2659
.6230
January
.5295
.4843
February
.6619
.4671
March
.0775
.1368
April
.0023
.0042
December
.0186
.0186
January
.0575
.0394
February
.0389
.0352
May
.0259
.0151
January
1.4223
3.1208
February
.1438
.5789
March
.0036
.0883
September
.0155
.1396
October
.0112
.1008
January
.0088
.5366
February
.3668
3.4160
March
.6107
1.6842
April
.4088
2.3490

167
Table 41.-
Pesticide
Guthion
Heptachlor
Karathane
Lindane
Malathion
Continued.
Crop
Month
Groves
May
Groves
June
Groves
September
Tomatoes
September
Tomatoes
October
Tomatoes
November
Tomatoes
December
Tomatoes
January
Tomatoes
February
Tomatoes
March
Pole beans
November
Pole beans
January
Pole beans
February
Pole beans
March
Groves
January
Tomatoes
September
Tomatoes
October
Tomatoes
November
Tomatoes
December
Potatoes
March
Squash
December
Squash
January
Squash
February
Squash
March
Groves
January
Tomatoes
Augus t
Tomatoes
September
Tomatoes
October
Tomatoes
November
Tomatoes
December
Tomatoes
January
Tomatoes
February
Tomatoes
March
Tomatoes
April
Potatoes
March
Squash
October
Tomatoes
August
Groves
August
Groves
September
Mean usage
in pounds
per acre
Standard
deviation
pounds
per acre
.8409
3.0636
.7394
4.1934
.0658
.0268
.0774
.2601
.0084
.2518
.0208
.0681
.0944
.2845
.0787
.2493
.0104
.0510
.0028
.0192
.0047
.0888
.0293
.0171
.0543
.0317
.0042
.0024
.0261
.0411
.0312
.0586
.0468
.0944
.0935
.1758
.0002
.0019
.0010
.0010
.0492
.0452
.0984
.0905
.0492
.0452
.0492
.0452
.0258
.0105
.0011
.0052
.0132
.0650
.0011
.0102
.0003
.0025
.0009
.0085
.0038
.0186
.0028
.0139
.0019
.0093
.0001
.0002
.0016
.0016
.2730
.5427
.0002
.0001
.1672
.0682
.0270
.0110

168
Table 41.Continued.
Pesticide
Maneb
Manganese
Manganese sulfate
Crop
Month
Tomatoes
August
Tomatoes
September
Tomatoes
October
Tomatoes
November
Tomatoes
December
Tomatoes
January
Tomatoes
February
Tomatoes
March
Tomatoes
April
Tomatoes
May
Potatoes
November
Potatoes
December
Potatoes
January
Potatoes
February
Potatoes
March
Pole beans
September
Pole beans
October
Pole beans
November
Pole beans
December
Pole beans
January
Pole beans
February
Pole beans
March
Pole beans
April
Pole beans
May
Corn
February
Corn
March
Squash
October
Squash
November
Squash
December
Squash
January
Okra
April
Tomatoes
September
Tomatoes
October
Groves
March
Groves
January
Groves
February
Groves
March
Groves
April
Groves
May
Groves
June
Mean usage
in pounds
per acre
Standard
deviation
pounds
per acre
.0335
.2668
1.9275
4.6441
1.1026
3.0119
2.2556
3.3812
3.4164
3.3938
3.9943
4.3011
5.4243
3.3173
1.0672
1.6151
.0960
.3739
.0082
.0153
.4712
.6049
2.2516
2.7184
6.1443
2.4776
4.2492
3.1435
.7926
1.5944
.0341
.0222
.3227
1.8608
.2928
.7094
.2750
.3520
.2971
.2402
.8160
.9491
1.4982
2.6937
.9261
2.5595
.0070
.0041
.4898
.0000
.2721
.0000
2.6203
5.2101
1.5440
2.8656
.8729
1.7475
.1145
.3363
.3600
.6364
.0148
.0979
.0058
.0521
.0026
.0635
.2531
1.0212
.1512
1.3191
.0716
.3777
.1086
.7087
.3189
1.1791
.2875
1.6643

169
Table 41.Continued.
Standard
Pesticide
Crop
Month
Mean usage
in pounds
per acre
deviation
pounds
per acre
Metaldehyde
Tomatoes
August
.0090
.1015
Tomatoes
September
.0049
.0177
Tomatoes
October
.0091
.0327
Tomatoes
December
.0188
.0141
Pole beans
September
.0012
.0007
Nabam
Tomatoes
August
.0047
.0230
Tomatoes
September
.0054
.0213
Corn
December
.0629
.0000
Corn
February
.2604
.0000
Corn
March
.2424
.0000
Paraquat
Tomatoes
August
.0004
.0033
Groves
January
.0365
.0831
Groves
February
.0236
.1490
Groves
March
.0066
.0069
Groves
April
.0172
.1078
Groves
May
.0064
.0026
Groves
June
.0151
.1057
Groves
September
.0113
.0046
Groves
October
.0080
.0033
Groves
November
.0161
.0066
Parathion
Tomatoes
August
.0827
.1981
Tomatoes
September
.3971
1.5927
Tomatoes
October
.1639
.5511
Tomatoes
November
.3294
.7291
Tomatoes
December
.3017
.5076
Tomatoes
January
.2304
.6013
Tomatoes
February
.1945
.3587
Tomatoes
March
.0888
.6588
Tomatoes
April
.0108
.1424
Tomatoes
May
.0013
.0013
Potatoes
October
.2291
.2143
Potatoes
November
.8314
1.1065
Potatoes
December
.6690
1.5519
Pole beans
July
.0100
.0062
Pole beans
August
.0167
.0127
Pole beans
October
.0825
.4784
Pole beans
November
.1618
.4869
Pole beans
December
.1614
.3147
Pole beans
January
.1667
.3740
Pole beans
February
.1889
.5567
Pole beans
March
.1907
.5519

170
Table 41.Continued,
Standard
Mean usage deviation
Pesticide
Crop
Month
in pounds
per acre
pounds
per acre
Pole beans
April
.3187
1.4970
Pole beans
May
.0969
.0482
Corn
December
2.9384
.0000
Corn
February
2.1186
.0000
Corn
March
3.4520
.0000
Corn
April
.8369
.0000
Squash
October
.2911
.5789
Squash
November
.1967
.1809
Squash
January
.3279
.3015
Squash
March
.1639
.1508
Okra
February
.8000
1.4142
Okra
April
.8000
1.4142
Okra
June
.1600
.2828
Groves
September
.0962
.0392
Phosdrin
Tomatoes
August
.0009
.0047
Tomatoes
December
.0024
.0213
Tomatoes
February
.0099
.0357
Tomatoes
March
.0019
.0093
Tomatoes
April
.0008
.0037
Potatoes
January
.0218
.1081
Potatoes
February
.2568
.3520
Potatoes
March
.0581
.2445
Pole beans
March
.0063
.0075
Okra
April
.6000
.1178
Okra
May
.3600
.0471
Phosphoric acid
Potatoes
January
.0073
.0640
Potatoes
February
.0120
.0460
Phygon
Pole beans
January
.0528
.0211
Polyram
Potatoes
January
.2199
.4343
Potatoes
February
.1445
.2854
Prolin rat bait
Tomatoes
February
.0098
.0354
Potatoes
March
.0046
.0090
Sevin
Tomatoes
Augus t
.0005
.0022
Tomatoes
November
.0725
.0498
Tomatoes
December
.3021
.2073
Tomatoes
January
.5688
.3904
Tomatoes
February
1.5206
1.0430
Tomatoes
March
.0008
.0014

171
Table 41.Continued.
Pesticide
Crop
Month
Mean usage
in pounds
per acre
Standard
deviation
pounds
per acre
Tomatoes
May
.0272
.0511
Potatoes
December
.0576
.0588
Groves
January
.1797
.0732
Groves
February
.3079
.1255
Groves
March
2.5469
1.0381
Simizine
Groves
January
.1196
.6962
Groves
February
.1468
.7310
Groves
March
.0577
.0235
Groves
April
.0962
.0392
Sodium arsenite
Potatoes
December
.3054
.8169
Potatoes
January
.6545
.8402
Potatoes
February
3.8847
5.6074
Potatoes
March
4.5556
4.5440
Potatoes
April
.2356
.1877
Potatoes
May
.0022
.0028
Pole beans
August
.0167
.0104
Solan
Tomatoes
August
.0019
.0014
Tomatoes
September
.0869
.0650
Tomatoes
October
.0396
.0426
Tomatoes
November
.0076
.0182
Sulfur
Tomatoes
September
.0127
.0864
Tomatoes
October
.0793
.5375
Tomatoes
November
.1997
1.6063
Tomatoes
December
.1232
.7166
Tomatoes
January
.2238
.8646
Tomatoes
February
.1028
.7484
Tomatoes
March
.0915
.7562
Potatoes
May
.0196
.0197
Pole beans
October
.5327
.2771
Pole beans
November
3.6050
12.3486
Pole beans
December
9.7826
33.8602
Pole beans
January
17.3437
31.3943
Pole beans
February
7.6603
15.9311
Pole beans
March
10.6132
12.0082
Pole beans
April
4.2908
11.8783
Pole beans
May
.0564
.0329
Corn
March
4.5429
.0000
Squash
November
3.4119
6.7840
Squash
December
5.8642
7.0100
Squash
January
23.9087
18.6662

172
Table 41.Continued.
Standard
Pesticide
Crop
Month
Mean usage
in pounds
per acre
deviation
pounds
per acre
Okra
March
1.3500
2.3865
Okra
April
15.7500
6.6291
Okra
May
13.5000
15.9099
Okra
June
2.7000
4.7730
Groves
January
8.6391
3.7553
Groves
February
.9004
10.2565
Groves
March
2.1057
9.9994
Groves
April
.1083
.0441
Groves
May
.6496
.2648
Groves
June
.2165
.1765
Groves
October
.0722
.0294
Other
February
1.1250
4.7730
Other
April
5.7833
10.3662
Other
May
1.1250
4.7730
Tedion
Pole beans
March
.0002
.0058
Groves
May
.0059
.0024
Groves
June
.0060
.1471
Groves
December
.0208
.0085
Terrachlor
Pole beans
October
.0162
.2652
Pole beans
November
.1572
.7065
Pole beans
December
.1675
.7222
Pole beans
January
.0848
.5405
Pole beans
February
.1425
.8441
Pole beans
March
.0270
.4737
Thimet
Potatoes
October
.2120
.7272
Potatoes
November
1.5425
1.6285
Potatoes
December
1.1562
1.6187
Corn
February
.3483
.0000
Corn
December
.3837
.0000
Thiodan
Tomatoes
September
.0255
.1393
Tomatoes
October
.0051
.0245
Tomatoes
November
.0170
.0944
Tomatoes
December
.0347
.1052
Tomatoes
January
.0202
.0771
Tomatoes
February
.0352
.2703
Tomatoes
March
.0116
.1645
Tomatoes
April
.0019
.0093
Potatoes
January
.0982
.2710
Potatoes
February
.4010
.6527
Potatoes
March
.0796
.3906

173
Table 41.Continued.
Pesticide
Toxaphene
Standard
Crop
Month
Mean usage
in pounds
per acre
deviation
pounds
per acre
Pole beans
January
.0042
.0050
Pole beans
March
.0125
.0150
Squash
October
.0410
.0377
Squash
November
1.2108
1.4753
Squash
December
.6627
1.1868
Squash
January
.0887
.1379
Squash
February
.1639
.1508
Squash
March
.1639
.1508
Tomatoes
August
.0139
.1024
Tomatoes
September
.2678
.7886
Tomatoes
October
.3928
.5679
Tomatoes
November
.6734
.9379
Tomatoes
December
.9698
1.0812
Tomatoes
January
.8864
1.0223
Tomatoes
February
.8595
.9022
Tomatoes
March
.6765
.8250
Tomatoes
April
.1149
.3505
Potatoes
December
.0082
.0082
Potatoes
January
.2953
.6370
Potatoes
February
.0556
.3799
Potatoes
May
.0218
.0431
Pole beans
July
.0334
.0207
Pole beans
August
.0568
.0491
Pole beans
September
.0167
.0104
Pole beans
October
.0914
.5140
Pole beans
November
.2657
1.0497
Pole beans
December
.2305
.6938
Pole beans
January
.3702
.8127
Pole beans
February
.5089
1.3013
Pole beans
March
.7113
2.0649
Pole beans
April
.8132
2.8530
Pole beans
May
.0401
.0241
Corn
December
1.5240
.0000
Corn
February
4.7892
.0000
Corn
March
1.9931
.0000
Okra
March
.4000
.7071
Okra
April
.8000
1.4142
Okra
May
1.6000
2.8284
Okra
June
.8000
1.4142
Other
April
4.1667
3.5355
Other
May
.5000
2.1213

174
Table 41.Continued.
Standard
Mean usage deviation
Pesticide
Crop
Month
in pounds
per acre
pounds
per acre
Treflan
Tomatoes
September
.0125
.0413
Tomatoes
October
.0414
.0724
Tomatoes
November
.0589
.1108
Tomatoes
December
.0011
.0021
Pole beans
October
.0125
.0073
Okra
December
.7200
.8485
Other
January
.1500
.6364
Trithion
Groves
May
.1079
.5455
Groves
June
.0181
.1132
Zinc
Tomatoes
September
.0053
.0365
Tomatoes
October
.0020
.0180
Zinc sulfate
Corn
December
.6122
.0000
Corn
February
2.1769
.0000
Corn
March
1.8367
.0000
Corn
April
.2041
.0000
Groves
January
.5995
1.0108
Groves
February
.1512
1.3191
Groves
March
.0742
.3766
Groves
April
.1086
.7087
Groves
May
.3189
1.1791
Groves
June
.4319
1.6547
Zineb
Tomatoes
August
.0118
.3236
Tomatoes
September
.0142
.1518
Tomatoes
October
.0028
.0250
Tomatoes
November
.0095
.2898
Tomatoes
December
.0212
.1914
Tomatoes
February
.1105
.5428
Tomatoes
March
.0674
.3553
Tomatoes
April
.0042
.2863
Potatoes
March
.0062
.0062
Potatoes
May
.0065
.0066
Pole beans
November
.0074
.0688
Pole beans
December
.0147
.1267
Pole beans
January
. 0666
.2440
Pole beans
February
.0764
.3743
Pole beans
March
.0340
.3061
Pole beans
April
.0522
.4176
Squash
October
1.4557
2.8945
Squash
November
2.1562
2.8492
Squash
December
.9677
1.9137
Squash
January
.2668
.3503

175
Table 41.Concluded.
Standard
Mean usage
deviation
in pounds
pounds
Pesticide
Crop
Month
per acre
per acre
Squash
February
.1475
.1357
Groves
January
.9405
1.6224
Groves
February
.7563
.4040
Groves
May
.0241
.0098
Groves
June
.0138
.4412
Zirara Tomatoes
September
.0008
.0071
Tomatoes
October
.0005
.0045
Pole beans
March
.0114
.0137
Z. P. rat bait Tomatoes
November
.0002
.0019
Tomatoes
December
.0000
.0000
Tomatoes
January
.0001
.0000
Tomatoes
February
.0001
.0004
Potatoes
January
.0001
.0001
Pole beans
November
.0018
.0013
Pole beans
December
.0033
.0016
Pole beans
January
.0026
.0010
Pole beans
February
.0007
.0004
Pole beans
March
.0017
.0008
Pole beans
April
.0035
.0013
All quantities have been
converted to
units of 100
percent
concentrated material.
^Acres sampled and number
of observations were:
Acres
Observations
Tomatoes
10,590
24
Potatoes
4,584
51
Pole beans
2,394
93
Corn
735
1
Squash
244
11
Okra
50
2
Groves
1,247
26
Other
240
2

Table 42.Sample
data on pesticide usage,'
Dade
a
by pesticide and
County, Florida,
crops for growers
1966-67.
who used the pesticide,
Pesticide
Crop
Number of
growers
Acres
sampled
Mean usage
in pounds
per acre
Standard
deviation
pounds
per acre
Agrimycin
Tomatoes
6
1,574
.1955
.1734
Aidrin
Groves
2
610
.2460
.1732
Amine 2,4-D
Potatoes
7
1,712
.0837
.2128
Arsenate of lead
Potatoes
1
800
.4825
.0000
Atrazine
Corn
2
1,585
.8539
.3225
Botran
Tomatoes
1
3,150
.0562
.0000
Potatoes
1
640
.1125
.0000
Pole beans
69
1,962
3.3853
3.4251
Captan
Tomatoes
7
4,914
.3119
.5797
Potatoes
4
2,265
2.2268
1.9402
Okra
1
30
.8000
.0000
Other
1
40
.6000
.0000
Chlordane
Tomatoes
13
9,874
.2248
.1923
Potatoes
1
800
.0094
.0000
Citrus oil
Groves
11
805
.7699
.3132
Copper compounds
Tomatoes
22
10,145
2.2055
7.9230
176

Table 42.Continued
Pesticide
Crop
Number of
growers
Potatoes
2
Pole beans
1
Squash
5
Okra
1
Groves
12
Cygon
Tomatoes
20
Potatoes
33
Pole beans
89
Squash
1
Okra
1
Other
1
Dacthal
Pole beans
1
DDD
Tomatoes
3
DDT
Tomatoes
7
Potatoes
8
Pole beans
2
Corn
2
Okra
1
Groves
1
Other
1
Demeton
Potatoes
21
Acres
sampled
Mean usage
in pounds
per acre
Standard
deviation
pounds
per acre
825
.5760
.7754
207
.4220
.0000
68
.4000
.0004
20
1.4310
.0000
1,105
9.6699
4.2968
9,695
1.2656
.6479
3,816
.9247
.3945
2,364
.3223
.1135
80
.1669
.0000
20
1.3350
.0000
200
.2670
.0000
207
.3478
.0000
1,060
1.4264
.8507
4,575
1.1411
1.8361
1,372
.6955
.5423
632
.9394
.8799
1,585
20.3712
5.9330
20
3.5000
.0000
600
.0400
.0000
40
1.5000
.0000
2,445
.2571
.3148
Vj

Table 42.Continued
Pesticide
Crop
Number of
growers
Dexon
Pole beans
1
Dieldrin
Tomatoes
5
Pole beans
1
Diphenamid
Tomatoes
15
Dyrene
Tomatoes
12
Endrin
Potatoes
3
Pole beans
1
Ethion
Groves
12
Ferbam
Tomatoes
1
Groves
24
Guthion
Tomatoes
10
Pole beans
4
Groves
2
Keptachlor
Tomatoes
2
Karathane
Tomatoes
1
Potatoes
2
Squash
1
Mean usage
in pounds
per acre
Acres
sampled
Standard
deviation
pounds
per acre
425
.0124
.0000
1,245
.2015
.1285
400
.0100
.0000
10,160
.8051
.5758
7,374
2.4776
2.0724
1,680
.3136
.0969
425
.1459
.0000
815
2.4017
3.1543
240
1.1780
.0000
887
4.2755
10.5116
2,209
1.4044
.9401
447
.4945
.0029
660
.0493
.1237
1,590
1.1418
.7497
245
.0092
.0000
640
.0070
.0000
80
.7500
.0000
178

Table 42.Continued
Pesticide
Crop
Number of
growers
Groves
1
Lindane
Tomatoes
4
Potatoes
1
Squash
1
Malathion
Tomatoes
1
Groves
1
Maneb
Tomatoes
24
Potatoes
51
Pole beans
93
Corn
2
Squash
6
Okra
1
Manganese
Tomatoes
2
Groves
1
Manganese sulfate
Groves
23
Metaldehyde
Tomatoes
4
Pole beans
1
Nab am
Tomatoes
2
Com
1
Acres
sampled
Mean usage
in pounds
per acre
Standard
deviation
pounds
per acre
600
.0537
.0000
2,075
.1289
.2598
640
.0117
.0000
37
1.8000
.0000
2,890
.0007
.0000
600
.4037
.0000
10,590
19.3253
7.3742
4,584
13.9089
3.4747
2,393
4.4690
4.0941
1,585
5.9295
6.8137
105
11.8921
9.5043
20
.9000
.0000
490
.4451
.0082
10
.3240
.0000
337
4.4070
3.3529
3,800
.1165
.1963
400
.0069
.0000
759
.1406
.0789
735
.5657
.0000
179

Table 42.Continued
Pesticide
Crop
Number of
growers
Parathion
Tomatoes
23
Potatoes
25
Pole beans
93
Corn
2
Squash
2
Okra
1
Groves
1
Paraquat
Tomatoes
1
Groves
13
Phosdrin
Tomatoes
3
Potatoes
41
Pole beans
1
Okra
2
Phosphoric acid
Potatoes
6
Phygon
Pole beans
3
Polyram
Potatoes
1
Prolin rat bait
Tomatoes
1
Potatoes
1
Sevin
Tomatoes
3
Acres
sampled
Mean usage
in pounds
per acre
Standard
deviation
pounds
per acre
10,340
1.8427
2.1149
2,982
2.6606
.5274
2,393
1.3942
1.4190
1,585
5.0964
6.0096
117
2.0431
.1273
20
4.4000
.0000
600
.2000
.0000
250
.0160
.0000
812
.2161
.3143
1,280
.1313
.0469
2,544
. 6067
.2846
207
.0725
.0000
50
.9600
.0707
407
.2166
.1047
1,032
.1226
.0240
325
5.1397
.0000
600
.1736
.0000
325
.0646
.0000
4,750
5.5572
4.7037
180

Table 42.Continued
Pesticide
Crop
Number of
growers
Simizine
Groves
11
Sodium arsenite
Potatoes
49
Pole beans
1
Solan
Tomatoes
2
Sulfur
Tomatoes
15
Potatoes
1
Pole beans
89
Corn
1
Squash
10
Okra
2
Groves
13
Other
2
Tedion
Pole beans
1
Groves
O
4
Terrachlor
Pole beans
63
Thimet
Potatoes
45
Corn
2
Thiodan
Tomatoes
14
Mean usage
Acres in pounds
sampled per acre
Standard
deviation
pounds
per acre
797
.6577
.7319
4,527
9.7594
2.2932
400
.1000
.0000
3,340
.4311
.1346
5,309
1.6616
2.4365
640
.1407
.0000
2,364
54.5483
37.9918
735
4.5429
.0000
164
49.3725
5.7794
50
33.3000
16.4402
1,107
14.2967
11.3919
240
8.0333
19.9121
9
.0563
.0000
610
.0670
.4910
735
1.9357
.3247
4,177
3.1944
.7098
1,585
1.3660
.8966
5,551
.2884
.2921
CO

Table 42.Continued
Pesticide
Crop
Number of
growers
Potatoes
42
Pole beans
1
Squash
7
Toxaphene
Tomatoes
20
Potatoes
17
Pole beans
93
Corn
2
Okra
1
Other
2
Treflan
Tomatoes
3
Pole beans
1
Okra
1
Other
1
Trithion
Groves
10
Zinc
Tomatoes
2
Zinc sulfate
Corn
1
Groves
26
Zineb
Tomatoes
12
Potatoes
1
Pole beans
55
Acres
sampled
Mean usage
in pounds
per acre
Standard
deviation
pounds
per acre
3,807
.6969
.5290
207
.1934
.0000
185
3.0628
1.7309
9,976
5.1536
3.7823
1,725
1.0122
1.1992
2,393
3.1381
2.8221
1,585
20.3291
17.0029
20
9.0000
.0000
240
4.6667
1.4142
1,840
.6559
.4827
425
.0704
.0000
30
1.2000
.0000
40
.9000
.0000
147
1.0681
.3656
490
.1580
.0209
735
4.8299
.0000
1,247
1.6844
3.4391
1,586
1.6132
.9948
640
.0194
.0000
649
.9262
.6195
M
00
NJ

Table 42.Concluded.
Pesticide
Crop
Number of
growers
Acres
sampled
Mean usage
in pounds
per acre
Standard
deviation
pounds
per acre
Squash
7
185
6.5619
5.8972
Groves
3
609
3.5520
3.9272
Ziram
Tomatoes
1
240
.0569
.0000
Pole beans
1
207
.1322
.0000
Z. P. rat bait
Tomatoes
3
4,000
.0011
.0048
Potatoes
1
640
.0009
.0000
Pole beans
3
1,175
.0279
.0124
All quantities
have been converted
to units of 100
percent concentrated
material.
183

APPENDIX E
PESTICIDE QUESTIONNAIRE USED IN DADE COUNTY

UNIVERSITY OF FLORIDA
DEPARTMENT OF AGRICULTURAL ECONOMICS
PESTICIDE QUESTIONNAIRE
SECTION I: GENERAL INFORMATION
1.1 Interview # .
1.2 Date.
kit tnteAviejMA weAe token dueling the. 1967-1968 gAowing -6 ecu on
and pertained to the 1966-1967 gAoMing ecuon.
1.3 Number of acres operated .
1.4 Number of acres owned .
1.5 Land Use 1966-1967 crop year (August 1, 1966 through July 31, 1967)
Double Cropped
Use Acres Proportion Other Crop
Vegetables
Tomatoes
Potatoes, Irish
Pole Beans
Corn
Squash
Other
Fruit
Avocados
Limes
Other
Pasture
185

186
SECTION II: MAJOR INTERNAL DATA
2.1 Crops to which this section applies .
2.2 Total production : .
2.3 Do you usually follow a predetermined fungicide program? Discuss.
The gnowens nesponded that they do genenally tny to follow a pne-
detenmlned pnognam {¡oh. disease, modified as necessany fon weathen
conditions.
2.4 If YES, are there any particular factors which cause you to deviate
from the schedule? Examples: insect outbreak, rain, etc. Discuss.
The main factons one: (7) Aain, which tends to wash off the
fungicide and (2) -tewpeAotuAe oa molstune conditions favonable {¡on
disease.
2.5 Do you usually follow a predetermined insecticide program? Discuss.
In genenaJL, gnoweJis nesponded that they tend to use a spnay-as-
needed" pnognm {¡on Insects. They Inspect theln flelds eveny day,
o{ten accompanied by an entomologist and take necessany "on-ti\e-
spot" action.
2.6 In a normal crop year would it be impossible to produce, without
some fungicide?
All o{ -the gnowens nesponded that It would be physically Impossible
to pnoduce a cnop In Vade County without fungicide.
2.7 In a normal crop year would it be impossible to produce without
some insecticide?
The gnowens nesponded that It would be Impossible to pnoduce a
manket.able cnop In Vade County without Insecticides. In a good
yean some pnoduce could be bnought to the manket but at best It
would have Insect dmage and would not be acceptable to the hmenican
housewl{e.
2.8 In a normal crop year would it be impossible to produce without
some herbicide?
The gnowens one not unanimous In theln use o{ henblcldes, but the
usage o{ henblcldes seems to be on the Incnease In Vade County.
In most cases henblcldes one a substllut.e {on labon. The necent
minimum wage Incneases and legislation on {¡anm labon tmlgnatlon
encounage the substitution o{¡ henblcldes {¡on labon.
2.9 Are tolerance levels of the pesticides a. very important consid
eration in your application decisions?
Be, f one answenlng, each gnowen wees nerlnded that fils Individual
answen would be confidential. By and lange, the a ns wens to this
question wene negative. Thene Is a lange vanlety o{ pesticides
among which the gnowens can altennate.

187
2.10 Have you had evidence of movement of pesticides from one of your
crops to another through the soil, air or water?
Most o the. gnowens msponded "No". Sevenal yeaxs ago tnaces o
Endnin wene {ound in a ew ields whe/ce gnowens claimed no Endnin
had bn used, bat no such incidents wene neponted in the past l
to 3 yeans.
2.11 Do you think that this will become an increasingly important
problem?
No.
2.12 About how often have your crops been checked for residues by the
Florida Department of Agriculture? Discuss.
Thi Elohida Vepantment o Agnicultune is veny actio i in this ama
and pn.acticaJU.ij aJU thi gnowens have had cnop checks. Even thorn
Mho have had violations mm to applaud thi wonk dom by thi
Vepantment.
2.13 Has any other agency ever checked your crops for residues? Which
agency? Discuss.
Most o thi gnowens a/Li mam that "some Eidi/ial Agincy" checles
thein cnops, usually in Atlanta. A m have had limited contact
with thi E.V.A.
2.14 How do you dispose of your pesticide containers? Discuss.
Many o thi gnowens anltted mgliginci on this point. A cu)
exencised cam to bu/in bags and place old ca in a centnal dumping
location, but [/torn this wnlten's obse/tvations, thi disposal o
pesticide container in Vadi County is a veny haphazand pnoces.s.
Ft is not uncomnon to ind thm Simply thnown into thi dnainagi
canats, and omi o thi gnowens adriiltted to dispoing o thm in
this way. Thi magnitude o this pnoblm hould not be undenesti-
mated. A la/ige gnowen can accumulate a gneat many mpty pesticide
contaim/is oven the peniod o one eason. Ft would do no good to
bun.y them, on. the ground waten table is jut below the sunace,
and the "gnound" is olid nock anyway. A a nesult, the gnowe/is
ue orne o the can a/iound the anm to coven innigation wells, etc.,
they tnade in the langen cans to the pesticide inns, they ell as
many as poible to alvage iims, and the balance is inply thnown
in ink holes, canals, on dumped in a cent/ial location.
With mgand to papen bag, at least one gnowen to/ted that he
docs not allow his pnay men to bunn thm because o the dange/LOus
moke.
2.15 Have you ever had pesticide tolerance violations on your products?
Discuss.
Vintually all o the gnowens answened this question in the negative.
This is pa/itly due to the natune o the cnops which wene sampled.
ieay vegetables, such as leituce and cabbage, am the most
lnequent. violatons, and these a/ie veny tninon c)iops in Vade County.

188
SECTION III: EXTERNAL DATA
(Th& eetion am diicued and umria/Uzed in Chaptex vi, -6 o the.
author'* edito >tai comments axe omitted.)
3.1 Have you or any of your employees ever experienced sickness from
contact with pesticides or farm chemicals? Discuss.
3.2 When was this approximately?
3.3 What pesticide(s) were involved?
3.4 Did you visit a doctor or hospital on any of these occasions? If
so, please give the name of doctor or hospital.
3.5 Was any time lost from work on these occasions?
3.6 How much time was lost?
3.7 Approximately what was the hourly value of the labor lost?
3.8 Do you have any kind of insurance to cover accidents such as these?
3.9 If YES, who is it with?
3.10 Do you know of any instances where children or housewives have been
poisoned or sickened through contact with pesticides? Describe.
3.11 Approximately when were the incident(s)?
3.12 What pesticide(s) were involved?
3.13 What doctor, if any, was called?
3.14 Do you know of any instances where household pets have been
poisoned by pesticides? Please describe and estimate value.
3.15 When did this occur?
3.16 What pesticide(s) were involved?
3.17 What was the form of the pesticide, i.e., dust, spray, etc.?
3.18 Are you aware of any wildlife deaths from pesticides?
3.19 In your opinion had the pesticide been improperly used?
3.20 Have the pesticides used by other producers ever drifted onto your
crops and caused damage? Discuss.

189
3.21For each instance of the preceding, please give the following
information:
Approximate Crop(s)
Date Damaged
Approximate Damage ($ and/or
Pesticide physical ternsreduction in yield)
3.22 Did the damaging party pay you for the damage? Discuss.
3.23 Do you have any kind of crop insurance to protect you against these
kinds of risks? Discuss.
3.24 Do you have insurance for the case when some of your pesticide
damages another producer? Discuss.
3.25 If YES, who is the insurance with?
3.26 Have any of your pesticides ever damaged other producer's crops?
3.27 Again, for each instance of the above, please give the following
information:
Approximate Crop(s) Approximate Damage ($ and/or
Date Damaged Pesticide physical termsreduction in yield)

190
3.28 What types and quantities of protective equipment and clothing do
you own and make available to your employees? Discuss.
3.29 Do your employees use this equipment regularly? Discuss.
3.30 If NO, what are the major reasons for not doing so? Discuss.

APPENDIX F
ENVIRONMENTAL MONITORING PROGRAMS

192
In 1961, the Secretaries of Defense, Interior, Agriculture, and
Health, Education, and Welfare undertook the formation of the Federal
Pest Control Review Board with the intent that it would review "... the
various programs conducted by Federal agencies for control of forms of
invertebrate and plant life which adversely affect mans interests, and
shall consider problems and developments in the field of chemical control,
with particular reference to possible adverse effects and the adequacy
of provisions for the proper use of pesticidal chemicals to insure the
greatest public and national benefit" (16, Foreward). The Board was
directed to turn its attention to all aspects of pest control, including
the need (safety to man, domestic animals, wildlife, and the environment
in general) and alternative methods. The Board was instructed to advise
the Departments on modifications in plans that would be in the best
public interest in view of these and related matters. The major impetus
for monitoring, however, arose from the report of the President's
Science Advisory Committee on "Use of Pesticides" in 1963 (54). In
1964, in response to this report, these four Secretaries reorganized the
Board as the Federal Committee on Pest Control. The reorganization was
necessary to expand the collaboration in two directions: first, to
permit the new Committee to cover all aspects of pest controlresearch,
monitoring of the environment for pesticides, and public information
programsas well as to review operational programs; secondly, to extend
their council to all Federal programs involving pests and their control.
The President's Science Advisory Committee also recommended that
the concerned agencies develop a continuing net work to monitor residue

193
levels in air, water, man, wildlife, and fish. To implement this
recommendation, the Federal Committee on Pest Control established a
Subcommittee on Pesticide Monitoring which periodically evaluates the
activities in this area throughout the Nation. Much of the work of
monitoring levels of pesticides in the environment is being done by
universities, state agricultural experiment station, conservation groups,
and other non-Federal agencies. The results of many of these studies
are either not published or appear in journals or individual reports
that are scattered and difficult to locate. For this reason, the
Subcommittee recommended that a journal be established to assure acces
sibility of monitoring data to the scientists who not only provide
information on the present levels of pesticide residues in various
elements of the environment including man, but would provide a base line
from which we can determine whether these levels are increasing,
decreasing, or remaining substantially unchanged. The Pesticides
Monitoring Journal was established for this purpose.'*' It serves as a
publishing medium for essentially all pesticide monitoring efforts in
this country.
Pesticide monitoring, as defined by the Federal Committee on
Pest Control, involves two facets, the distribution of pesticides in
various elements of the environment, and the changes in these levels
through time.
Residues in Food and Feed
The Federal Program for monitoring pesticide residues in food
1
Much of the information in this section was gathered from the
Pesticides Monitoring Journal (16).

194
and feed is primarily comprised of surveillance programs maintained by
the Food and Drug Administration, U.S. Department of Health, Education,
and Welfare. Data on residues in meat samples are provided by the
Livestock Slaughter Inspection Division, Consumer and Marketing
Service, U.S. Department of Agriculture. The objective of this program
is to determine the levels of pesticide residues in unprocessed and
commercially processed consumer food commodities, animal feeds, and
composites of food items prepared for human consumption. Studies being
carried out to accomplish this objective include (1) a continuing Market
Basket study to assay pesticide residues in the basic 2-week diet of a
19-year-old male, statistically the Nation's largest eater, and (2)
nationwide surveillance of unprocessed food and feed (14, p. 1).
Pesticides in People
The program for assessing pesticide residue levels in the
Nation's populace is being carried out by the Pesticides Program,
National Communicable Disease Center, Bureau of Disease Prevention and
Environmental Control, Public Health Service, U.S. Department of Health,
Education and Welfare.
The purpose of the human monitoring program is to determine on
a national scale the levels and trends of certain more commonly used
pesticide chemicals, both in the general population and in population
segments where the occurrence of more extensive exposure to pesticides
is known or suspected. The present monitoring program will provide
statistical and epidemiological information for use in the evaluation
of the significance of man's total exposure to pesticides.
Monitoring studies are of two types, a limited national survey

195
of the general population and an in-depth study of selected communities
in high-use areas. In-depth community studies, including monitoring,
are in progress at these locations:
Arizona
Pima and Maricopa Counties
California
State-wide
Colorado
Weld County
Florida
Dade County
Hawaii
Island of Oahu
Iowa
Johnson County
Louisiana
LaForche and Jefferson Parishes
Michigan
Berrien County
New Jersey
Monmouth County
Texas
Cameron and Hidalgo Counties
Washington
Wenatchee and Quincy Basins
Residues in Fish, Wildlife, and Estuaries
Federal efforts to determine pesticide levels in fish and
wildlife are being carried out by the Bureau of Sport Fisheries and
Wildlife, U.S. Department of the Interior. Monitoring estuarine pesti
cide levels in clams, oysters, and sediments is a joint endeavor of the
Bureau of Commercial Fisheries, U.S. Department of the Interior, and the
Water Supply and Sea Resources Program of the National Center for Urban
and Industrial Health, Public Health Service, U.S. Department of Health,
Education, and Welfare. The objective of this program is to ascertain
on a national scale, and independent of specific treatments, the levels
and trends of certain pesticidal chemicals in the bodies of selected
forms of animals and in estaurine sediments. The following locations
are currently being sampled in Florida.
Location
Element being sampled
St. Johns River, Welaka, Florida
St. Lucie Canal, Indiantown, Florida
Tampa Bay, Tampa, Florida
Apalachicola Bay, Apalachicola, Florida
Fish
Fish
Shell fish and sediment
Shell fish and sediment

196
Wildlife monitoring efforts have tended to center primarily,
though not exclusively, oil ducks, starlings, and bald and golden eagles.
Major considerations in the choice of specie are ubiquity and relative
position in the food change.
Pesticides in Water
This program for continuous surveillance of pesticides in surface
waters was proposed for joint operation by the Federal Water Pollution
Control Administration and the Geological Survey of the U.S. Department
of the Interior. The proposal has been partially implemented. The
purpose of this program is to provide continuing information on the
overall extent of pesticide contamination of the Nation's water sampling
resources. The objective has been to develop the minimum program that
will enable an adequate assessment of conditions. Within this objective,
monitoring currently is confined to the examination of surface waters
in the major drainage rivers of the United States through a nationwide
network of sampling locations. Over a period of years, it is expected
that data obtained from this network will reflect important changes in
pesticide levels in these rivers.
Rivers flowing into or through Florida that are being sampled
are the Pee Dee River, Altamaha River, St. Johns River, Suwannee River,
Apalachicola River.
The Geological Survey in Miami has, to date, performed a con
siderable amount of monitoring of Dade County's water and sediment on a
2
non-continuous basis. Efforts are now underway to develop a regular
2
We are indebted to Dr. Milton C. Kolipinski and Aaron L. Higer
for information on the program of the U.S. Geological Survey.

197
sampling program and expand the breadth of the sample to include selected
acquatic plants and animals. Figure 9 illustrates the locations of the
sampling sites which they hope to include in the regular program. A
report will be forthcoming in the summer of 1969 detailing their findings
to date.
Pesticides in Soil
Much of the soil monitoring program is being carried out by the
U.S. Department of Agriculture as an established program. Other phases
of monitoring are conducted by the USDA in cooperation with State and
other Federal agencies.
The objective of this program is to determine existing levels of
pesticide residues in soils of selected areas in the conterminous United
States and to detect any significant changes in these levels. Soil
monitoring sites were chosen wherever possible to coincide with sampling
sites of other agencies in the Federal pesticide monitoring network so
that soil data may be correlated with pesticide levels in other environ
mental media.
Intensive study areas were at single locations in Alabama,
Arizona, and in the Red River Valley of North Dakota. Studies in these
areas were set up to run for a 3-year period and were phased out at the
end of the 1967 season. Operations at Stuttgart, Arkansas; Greenville,
Mississippi; and Utica, Mississippi, were phased out in the fall of 1966
after a 3-year sampling period was completed. Special soil monitoring
activities have been extended to numberous other areas of the country
where pesticides are extensively employed in agriculture. Five farms
are included at each location. These areas, and the principal crops
they produce, include:

198
G Surface water, sediment, selected aquatic plants and animals.
O Surface water and sediment.
A Rainfall.
Ground water.
Figure 9.U.S. Geological Survey sampling sites for pesticide residues
in aquatic communities of South Florida.
(Source: U.S. Geological Survey, Miami)

199
Location
Dade County, Florida ^
Texas Lower Rio Grande Valley
Western North Carolina
Eastern South Carolina
Central Georgia
Eastern Virginia ^
Monmouth County, New Jersey
Adams County, Pennsylvania
Berrien County, Michigan^
Urbana, Illinois
Western Iowa .
*
Weld County, Colorado
Wenatachee Basin Area, Washington
Kern County California
Tulelake area, California
Crop (s)
Vegetables
Cotton
Apples
Vegetables
Peaches
Peanuts
Vegetables
Fruits
Fruits and vegetables
Corn
Corn and soybeans
Root crops
Fruits and root crops (2 loca
tions)
Cotton, vegetables
Small grains, root crops
*
Soil monitoring sites concide with U.S. Public Health Service
sites to monitor pesticides in human beings.
The Agricultural Research Service has developed a plan for expanding
the national soil monitoring program. The proposed program has been
designed on a statistical basis for the conterminous United States to
provide information that will pinpoint major trouble areas which then
will require additional monitoring. The objectives of the program are:
1. To establish the level of pesticide residues in soils in
reference to major land-use areas in the United States.
2. To continue sampling the same sites over a period of time
to provide information on rates of change of pesticide
residue levels in soils.
The program was initiated in fiscal year 1968. Soil will be collected
from approximately 15,000 sites over the conterminous United States
during a 4-year period.

200
Pesticides in Air
Air pollution studies are being conducted by the Air Quality Sec
tion of the Public Health Service. Opportunities for finding pesti
cide-polluted atmospheres are greatest in communities surrounded by
agricultural areas where large amounts of these chemicals are applied
during the growing season or in communities conducting pest control
programs. A number of communities in several different agricultural
areas were selected for sampling to ensure information on a wide variety
of insecticides. The communities having pest control programs were
selected on the basis of the type program and ability to cooperate in
the sampling studies. The following seven agricultural communities
were selected for investigation.
1. Fort Valley, Peach County, Georgia, (population 8,300) is
completely surrounded by large peach orchards. A pesticide-formulating
plant is operated in Fort Valley. Sampling was carried out in May and
June, when the applications of pesticides to the peach crop were
heaviest, and again in September, after the peach harvest, to detect
the contribution of the formulating plant.
2. Inman, Spartanburg County, South Carolina, (population 1,500)
also is completely surrounded by extensive peach orchards. Sampling was
conducted in May and June.
3. Leland, Washington County, Mississippi, (population 6,300)
was completely surrounded by large cotton fields, which received many
applications of various pesticides during the growing season. Sampling
was carried on from July through September.
3
For additional information on air sampling for pesticide
residues, see Tabor (44).

201
4. Newellton, Tensas Parish, Louisiana, (population 1,500) is
located in the cotton-growing area of the Mississippi delta. Extensive
plantings of cotton were located to the north, west, and south of the
community, with somewhat less to the east. Samples were collected from
July through September.
5. Florida City, Dade County, Florida, (population 4,100) is
surrounded by vegetable fields, which are planted in late fall. Samples
were collected from October through December.
6. Lake Alfred, Polk County, Florida, (population 2,600) is
surrounded by orange groves. Samples were collected from June through
August.
7. Lake Apopka area, Orange County, Florida, has no great con
centration of population but appeared suitable for investigation because
of the frequent application of insecticides to the sweet corn and other
vegetables grown a few miles north of the lake. Numerous protests
had been heard from local sportsmen regarding the frequent drift of
pesticide clouds over the lake. Samples were collected from April
through July.

APPENDIX G
MATHEMATICAL STATEMENTS OF POLICIES 2A, 2B, AND 2C

203
Policy 2A
Maximize:
1589.2222 y
+ 920.2354 y2
+ 1688.0888 y3
+ 599.6965 y4
+ 571.3720 y5
+ 3285.4824 y&
+ 690.9032 y?
el Z1
- .0301 z
Subject to:
7
40459 I y.
J-l J
- .0416 yj
- .0606 y^
- .1448 y^
- -1796 y4
- .0240 y*
- .3751 yjl
- .0956 y^
- 48590
(tomatoes)
(potatoes)
(beans)
(corn)
(avocados)
(limes)
(mangos)
(chlorinated hydrocarbons)
(organic phosphates)
(total land)
15740 y
6532 y
5269 y
1135 y
y
y
y
3.0359 y1 + .6430 y2
+ .0353 y^ + .0242 y^
1 21166
2 8927
3 6680
. 3774
4
- 5235
, 3585
D
? 1520
+ 1.7152 y3
+ .0103 y?
+ 20.3501
" Z1 = o
y4
(tomatoes)
(potatoes)
(beans)
(corn)
(avocados)
(limes)
(mangos)
(chlorinated hydrocarbons)
4.1807 y + 6.0781 y2 + 2.3260 y3 + 12.5674 y4
+ .2345 y^ + .1608 y^ + .0681 y? z2 = 0 (organic phosphates)
yv y2, . ,y?, z,*~ 0

204
Policy 2B
Maximize:
1588.1620 y .0416 y^
+ 920.0454 y2 .0606 y2
+ 1687.6188 y3 .1448 y^
+ 595.9965 y, .1796 y^
+ 571.3720 y5 .0240 y^
+ 3285.4824 y6 .3751 y^
+ 690.9032 y? .0956 y^
- e;L
- .0301 z
Subject to:
40459 E y. 48590
j-1 3
15740 -
6532 -
5269 -
1135 -
y 21166
y2 8927
y3 6680
y4 3774
y5 5235
y6 3585
y? 1520
3.0359 y3 + .6430 y2 +1.7152 y3 + 20.3501 y4
+ .0353 y5 + .0242 y6 + .0103 y? z = 0
4.4853 yx + 6.1424 y2 + 2.4975 y3 + 14.6024 y4
+ .2380'y + .1632 y& + .0691 y? z2 = 0
(tomatoes)
(potatoes)
(beans)
(corn)
(avocados)
(limes)
(mangos)
(chlorinated hydrocarbons)
(organic phosphates)
(total land)
(tomatoes)
(potatoes)
(beans)
(corn)
(avocados)
(limes)
(mangos)
(chlorinated hydrocarbons)
(organic phosphates)
yl y2'
,yr v z2 0

205
Policy 2C
Maximize:
1586.9922 y
+ 919.8554 y2
+ 1687.1588 y3
+ 592.2865 y^
+ 571.3720 y5
+ 3285.4824 y&
+ 690.9032 y?
- e;L ^
- .0301 z2
Subject to:
7
40459 Z y
- .0416 y^
- .0606 y2
- .1448 y^
- .1796 y^
- .0240 y2
- .3751 y2
- .0956 y2
~ 48590
(tomatoes)
(potatoes)
(beans)
(corn)
(avocados)
(limes)
(mangos)
(chlorinated hydrocarbons)
(organic phosphates)
(total land)
6532 -
5269 -
1135 -
y2 8927
y3 6680
- 3774
- 5235
- 3585
y4
y6
y? 1520
3.0359 y + .6430 y£ + 1.7152 y3 +
+ .0353 y5 + .0242 y& + .0103 y? -
4.7899 y1 + 6.2067 y2 + 2.6690 y3 + 16.6374 y^
+ .2416 y5 + .1656 y6 + .0702 y? z2 = 0
(tomatoes)
(potatoes)
(beans)
(corn)
(avocados)
(limes)
(mangos)
(chlorinated hydrocarbons)
(organic phosphates)
>
yr y2>
>y7> z2
o

BIBLIOGRAPHY
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of Famine. New York: Macmillan Co., 1965.
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11. Day, Richard H. Contributions to Economic Analysis: Recursive
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12. Demsetz, Harold. "Toward a Theory of Property Rights," American
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13. Dorfman, Robert, Samuelson, Paul A. and Solow, Robert M. Linear
Programming and Economic Analysis. New York: McGraw-Hill Book
Co., Inc., 1958.
14. Duggan, R. E. and McFarland, F. J. "Residues in Food and Feed:
Assessments Include Raw Food and Feed Commodities, Market
Basket Items Prepared for Consumption, Meat Samples Taken at
Slaughter," Pesticides Monitoring Journal, Vol. I, No. 1,
June, 1967.
206

207
BIBLIOGRAPHYContinued.
15. Ezekiel, Mordecai. "The Cobweb Theorem," Quarterly Journal of
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16. Federal Committee on Pest Control and its Subcommittee on Pesticide
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17. Florida Agricultural Extension Service. Insect Control Guide.
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18. Florida Department of Agriculture, in cooperation with the U.S.
Department of Agriculture and Agricultural Experiment Stations
of the University of Florida. Florida Agricultural Statistics:
Vegetable Summary, 1967. Orlando, Florida: Florida Crop and
Livestock Reporting Service, 1967.
19. Florida Industrial Commission. Analysis of Work Injuries Covered
by Workmen's Compensation, edd. for 1962-1963, 1966, and 1967.
Tallahassee, Florida.
20. Galbraith, John K. The Affluent Society. Boston: Houghton Mifflin,
Co., 1958.
21. Goldberger, Arthur S. Econometric Theory. New York: John Wiley
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22. Gunther, F. A. and Jeppson, L. R. Modern Insecticides and World
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23. Headley, J. C. and Lewis, J. N. The Pesticide Problem: An
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24. Henderson, James M. "The Utilization of Agricultural Land, A
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25. Hicks, John R. "The Rehabilitation of Consumers' Surplus,"
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26. International Business Machines Corporation. IBM Application
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208
BIBLIOGRAPHYContinued.
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29. Kimball, Thomas L. "Changing Trends in Insect Control, "N.A.C.
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32. Lehner, Philip N., Boswell, Thomas 0., and Copeland, Frank.
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209
BIBLIOGRAPHYContinued.
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210
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55. Winch, David M. "Consumer's Surplus and the Compensation
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Scientific American, Vol. CCXVI, No. 3, March, 1967.

BIOGRAPHICAL SKETCH
The author was born February 11, 1938, in Memphis, Tennessee.
He entered Clemson University in 1955 and graduated in 1959 with a B.S.
degree in Industrial Management. From 1959 to 1961 he worked in the
sportswear manufacturing industry. From 1961 to 1963 he attended
Indiana University, from which he received an M.B.A. degree. Following
graduation from Indiana University,he joined the research department of
the Federal Reserve Bank of Atlanta and worked there until 1964 when he
entered the doctoral program at the University of Florida. While
working on the Ph.D., he has been employed as an instructor.
The author's wife, Emmy, is a graduate of Furman University.
They have two children, Carroll and Russell, aged 7 and 5 years old
respectively.

This dissertation was prepared under the direction of the
chairman of the candidate's supervisory committee and has been
approved by all members of that committee. It was submitted to the
Dean of the College of Business Administration and to the Graduate
Council, and was approved as partial fulfillment of the requirements
for the degree of Doctor of Philosophy.
Dean, Graduate School
Supervisory Committee:



90
one percent. They further said that they felt the incidence of pesti
cide calls was not increasing. A summary of the sampling experience is
shown in Table 22.
The reason for including toad and lizard poisonings was that
their symptoms are very similar to those of pesticide poisoning, and
a veterinarian sometimes cannot tell the difference. As is indicated
by the footnotes to the table, an effort was made to include all calls
which might have been connected with pesticides even though some were
questionable. Even so, the frequency of pesticide calls was extremely
low and was far overshadowed by:
1. cases where animals were hit by autos
2. cases where an animal swallowed a fish hook
3. cases of tick paralysis
4. cases of dog fights or cat fights
Biologists
Biologists at the Everglades National Park and the research
director for the National Audubon Society, located on Tavernier Key,
were contacted in an effort to gather more information on pesticide
damage to wildlife.
Of all the areas touched upon by the research project, this
areapesticide effects upon wildlifewas probably the most difficult
to assess and the most difficult to speak about definitively.
Our ignorance in this area is twofold. First, we do not under
stand how sub-lethal exposure affects a given specie, and second, if
sub-lethal exposure does affect some specie, we do not know how this
will affect other species through the ecological system. Research on


LIST OF FIGURES
Figure Page
1 Locational map for Dade County, Florida 9
2 Soils of Dade County, Florida 13
3 Population trends in Dade County, Florida 17
4 A linear iso-product function for chlorinated hydrocarbons
and organic phosphates 46
5 A hyperbolic iso-product function for chlorinated
hydrocarbons and organic phosphates 47
6 Point estimate of externalities arising from the
agricultural usage of organic phosphates 53
7 A schematic diagram showing data needs in the area of
environmental monitoring 98
8 Hypothesized relations between "welfare," the state of the
arts, and the usage of chlorinated hydrocarbons 114
9 U.S. Geological Survey sampling sites for pesticide
residues in aquatic communities of South Florida 198


Table 34.Continued.
Insecticide
Phosdrin
Sevin
Crop
Insect
Cabbage
Aphid and Cabbage
Looper
Canteloupe
Aphid
Collards
Aphid and Cabbage
Looper
Cucumbers
Aphid
Strawberries
Pamera
Strawberries
Bud Nematode
Turnips
Aphid
Watermelons
Aphid
Pole beans
Aphid
Cabbage
Aphid and Cabbage
Looper
Collards
Aphid and Cabbage
Looper
Tomatoes
Stinkbug Worm
Corn Earworm and
Fall Armyworm
Corn Corn Earworm
Toxaphene
Total Total
Pounds Number of pounds pounds
per acre applications per acre per crop
Total
pounds
all crops
Per season
.50
10
5.00
2,500
.30
10
3.00
750
.25
17
4.25
425
.30
10
3.00
6,000
.30
26
7.80
.30
1
.30
4,455
.30
7
2.10
420
.30
10
3.00
300
107,229
.25
1
.25
2,250
.25
2
.50
250
.25
17
4.25
425
2,925
1.00
4
4.00
70,240
2.00
5
10.00
20,000
90,240
1.50
38
57.00
114,000
126


BIOGRAPHICAL SKETCH
The author was born February 11, 1938, in Memphis, Tennessee.
He entered Clemson University in 1955 and graduated in 1959 with a B.S.
degree in Industrial Management. From 1959 to 1961 he worked in the
sportswear manufacturing industry. From 1961 to 1963 he attended
Indiana University, from which he received an M.B.A. degree. Following
graduation from Indiana University,he joined the research department of
the Federal Reserve Bank of Atlanta and worked there until 1964 when he
entered the doctoral program at the University of Florida. While
working on the Ph.D., he has been employed as an instructor.
The author's wife, Emmy, is a graduate of Furman University.
They have two children, Carroll and Russell, aged 7 and 5 years old
respectively.


Table 34.Concluded.
Total
Total
Total
Pounds
Number of
pounds
pounds
pounds
Insecticide
Crop
Insect
per acre
applications
per acre
per crop
all crops
Per season
Southern peas
Cowpea Curculio
1.00
4
4.00
Southern peas
Bean Leafroller and
Bean Leafhopper
1.00
4
4.00
8
Management Biologist of the National Park Service, Everglades National Park.
^All quantities have been converted to units of 100 percent concentrated material. Table 36
presents the assumed growing seasons and estimated acreage for the calculations.
127


LIST OF TABLES--Concluded.
Table Page
35 Estimates made by Richard Klukas of the quantities of
fungicides used on various crops in Dade County, 1966-67.. 128
36 Dade County growing seasons and 1967 crop acreages used for
Klukas projections 130
37 Common names, trade names, and/or chemical names of
pesticides observed in Dade County, Florida, 1966-67 132
38 Estimated quantities per acre of certain pesticide
categories used by farmers in Dade County, 1966-67 crop
year, by crop 140
39 Estimated quantities per acre of certain pesticide
categories used by farmers in Dade County, 1966-67 crop
year, by crop and month 142
40 Estimated pesticide usage in Dade County, Florida, by
crop and pesticide, 1966-67 crop year 155
41 Estimated pesticide usage by pesticide, crop, and month
in Dade County, Florida, 1966-67 163
42 Sample data on pesticide usage, by pesticide and crop,
for growers who used the pesticide, Dade County,
Florida, 1966-67 176
x


51
realistic over a wide range of acres, then the change represents a
13
parallel vertical shift in the marginal cost function.
In summary, it was decided that two categories of cost(1) cost
of spray materials and (2) cost of labor and variable machinery cost
were most likely to be affected by the alternative pesticide policy.
The effects of the policy change on average costs per acre were deduced
from cost data currently available and the experience of entomologists
familiar with the region. These cost changes were applied as parallel
shifts in the supply functions used for the first policy. The supply
functions used in the model are presented in Table 13.
The Externality Functions
The explanations for the point estimates of pesticide usage
and externalities are developed in Chapters V and VI respectively. It
is the objective of this section to show how these estimates were used
Using the functional forms of the model:
given:
TC = A + BY + CY2
then,
MC = B + 2CY
and,
AC = y + B + CY
where:
TC, MC, and AC are total, marginal, and average cost in dollars
per acre,and Y is the number of acres of output. A parallel vertical
shift, A, in AC gives:
AC' = I + (B+A) + CY
for which the corresponding TC and MC become:
TC = A + (B+A)Y + CY2
MC = B + A + 2CY
and the result is a parallel shift in MC.


Table 39.Continued.
February
Crop
Fungicide
Insecticide
Herbicide
Total
Organic
phosphates
Chlorinated
hydrocarbons
Carbamates
Other
Tomatoes
6.3334
.6435
.9459
1.5207
.0099
None
9.4534
Potatoes
4.5131
.6156
.5217
None
.0120
3.8978
9.5602
Pole beans
9.2554
.2996
.5088
None
.1432
None
10.2070
Corn
2.9272
2.4670
11.4323
None
None
.1632
16.9898
Squash
.1967
None
.1639
None
None
None
.3606
Okra
None
1.0670
None
None
None
None
1.0670
Groves
3.4069
None
.1203
.3079
.3090
.1703
4.3144
Other
1.1250
None
None
None
None
None
1.1250
Total average
usage
5.5980
.5866
1.0665
.7914
.0426
.8736
8.9587


193
levels in air, water, man, wildlife, and fish. To implement this
recommendation, the Federal Committee on Pest Control established a
Subcommittee on Pesticide Monitoring which periodically evaluates the
activities in this area throughout the Nation. Much of the work of
monitoring levels of pesticides in the environment is being done by
universities, state agricultural experiment station, conservation groups,
and other non-Federal agencies. The results of many of these studies
are either not published or appear in journals or individual reports
that are scattered and difficult to locate. For this reason, the
Subcommittee recommended that a journal be established to assure acces
sibility of monitoring data to the scientists who not only provide
information on the present levels of pesticide residues in various
elements of the environment including man, but would provide a base line
from which we can determine whether these levels are increasing,
decreasing, or remaining substantially unchanged. The Pesticides
Monitoring Journal was established for this purpose.'*' It serves as a
publishing medium for essentially all pesticide monitoring efforts in
this country.
Pesticide monitoring, as defined by the Federal Committee on
Pest Control, involves two facets, the distribution of pesticides in
various elements of the environment, and the changes in these levels
through time.
Residues in Food and Feed
The Federal Program for monitoring pesticide residues in food
1
Much of the information in this section was gathered from the
Pesticides Monitoring Journal (16).


164
Table 41.Continued.
Standard
Mean usage deviation
Pesticide
Crop
Month
in pounds
per acre
pounds
per acre
Tomatoes
December
.0284
.0213
Tomatoes
January
.0485
.0861
Potatoes
January
.0016
.0013
Citrus oil
Groves
January
.2325
.3508
Groves
February
.0140
.0651
Groves
August
.0945
.0385
Groves
September
.1559
.0635
Copper compounds
Tomatoes
August
.0431
.3588
Tomatoes
September
1.4384
4.8120
Tomatoes
October
.3831
2.7442
Tomatoes
November
.1634
1.3154
Tomatoes
December
.0368
.5316
Tomatoes
January
.0465
1.0559
Tomatoes
March
.0015
.1531
Potatoes
February
.1037
.1745
Pole beans
December
.0196
.0236
Pole beans
January
.0168
.0202
Squash
December
.0490
.1618
Squash
January
.0636
.1868
Okra
March
.5724
1.0119
Groves
January
.6678
1.1212
Groves
February
1.2322
.5224
Groves
March
.4498
.1833
Groves
April
1.1144
.4542
Groves
May
2.9004
1.1822
Groves
June
2.0254
.7356
Groves
October
.0425
.0173
Groves
November
.1363
.0556
Cygon
Tomatoes
August
.0025
.0462
Tomatoes
September
.0303
.1300
Tomatoes
October
.0328
.0315
Tomatoes
November
.1326
.1330
Tomatoes
December
.1660
.1740
Tomatoes
January
.3085
.2125
Tomatoes
February
.4287
.2550
Tomatoes
March
.0521
.0732
Tomatoes
April
.0051
.0050
Potatoes
December
.0477
.0787
Potatoes
January
.4407
.2683
Potatoes
February
.2813
.2014


LIST OF. TABLES
Table Page
1 Vegetable, ornamental horticulture, and fruit production,
by commodities, in Dade County, Florida, 1966-67 11
2 Production of livestock and livestock products in Dade
County, Florida, 1967 12
3 Florida and Dade County population changes for 1950 and 1960 16
4 Empirically estimated relations from which demand functions
were derived 35
5 Demand functions used in the model 37
6 Empirically estimated relations from which supply functions
were derived 39
7 Recommended insect control measures for tomatoes 42
8 Recommended insect control measures for potatoes 43
9 Recommended insect control measures for beans 44
10 Recommended insect control measures for corn 45
11 Estimated average costs of chlorinated hydrocarbons and
organic phosphates used on crops in Dade County, 1966-67.. 48
12 Estimates of average total production costs in Dade County,
1966-67, by crop and pesticide policy 50
13 Supply functions used in the model 52
14 Flexibility constraints used for the empirical model 57
15 A summary of grower responses concerning human sickness
from pesticides .' 68
16 A summary of grower responses concerning damage from
pesticide drift 74
17 Workmens compensation claims, State of Florida; work
injuries, days of disability, and cost by industry for
disabling and non-disabling work iniuries, 1962, 1963,
1966, and 1967 79
viii


171
Table 41.Continued.
Pesticide
Crop
Month
Mean usage
in pounds
per acre
Standard
deviation
pounds
per acre
Tomatoes
May
.0272
.0511
Potatoes
December
.0576
.0588
Groves
January
.1797
.0732
Groves
February
.3079
.1255
Groves
March
2.5469
1.0381
Simizine
Groves
January
.1196
.6962
Groves
February
.1468
.7310
Groves
March
.0577
.0235
Groves
April
.0962
.0392
Sodium arsenite
Potatoes
December
.3054
.8169
Potatoes
January
.6545
.8402
Potatoes
February
3.8847
5.6074
Potatoes
March
4.5556
4.5440
Potatoes
April
.2356
.1877
Potatoes
May
.0022
.0028
Pole beans
August
.0167
.0104
Solan
Tomatoes
August
.0019
.0014
Tomatoes
September
.0869
.0650
Tomatoes
October
.0396
.0426
Tomatoes
November
.0076
.0182
Sulfur
Tomatoes
September
.0127
.0864
Tomatoes
October
.0793
.5375
Tomatoes
November
.1997
1.6063
Tomatoes
December
.1232
.7166
Tomatoes
January
.2238
.8646
Tomatoes
February
.1028
.7484
Tomatoes
March
.0915
.7562
Potatoes
May
.0196
.0197
Pole beans
October
.5327
.2771
Pole beans
November
3.6050
12.3486
Pole beans
December
9.7826
33.8602
Pole beans
January
17.3437
31.3943
Pole beans
February
7.6603
15.9311
Pole beans
March
10.6132
12.0082
Pole beans
April
4.2908
11.8783
Pole beans
May
.0564
.0329
Corn
March
4.5429
.0000
Squash
November
3.4119
6.7840
Squash
December
5.8642
7.0100
Squash
January
23.9087
18.6662


98
The threat of a pesticide to the environment is related, in
part, to the amount and location of its dispersement. The panel
found it difficult to obtain use volume data on materials such as
DDT for the State of Michigan. A mechanism should be developed
by the Department of Agriculture, if necessary through legislation,
for ascertaining the annual total sale and use of toxic pesticides.
This could be accomplished by yearly reports from the pesticide
industry, possibly at the distributor or retail level where sale
of the formulations of the chemical takes place (36, p. 12).
Third, the effect of various levels of pesticides within the
environmental element itself must be understood. If, for example, the
quantity of DDT in the brain of the eagle is 10 ppm, we must know if
this level is harmful, beneficial,or neutral (4, p. 5). Of the three
areas mentioned, this is probably the most difficult to analyze but
vital to making rational policy decisions.
The following schematic diagram is intended to illustrate the
three levels of needed knowledge and how they fit together.
Graph 1
Graph 2 Graph 4
Figure 7.A schematic diagram showing data needs in the area of
environmental monitoring.


47
Organic phosphates
Figure 5.A hyperbolic iso-product function for chlorinated
hydrocarbons and organic phosphates.
policy objective and that reduction is a far more reasonable goal, for
the immediate future, particularly if one is to maintain production
without changing non-pesticide inputs. This doesn't mean that the ulti
mate goal cannot be elimination or near elimination of the chlorinated
hydrocarbons. It simply means that the first step to be evaluated will
be a reduction policy, and that the ultimate goal, whatever it is, will
be approached asytotically through a series of policies. It is the
objective of this research effort to trace the welfare implications of
the first step only. The alternative pesticide usage policy for the
analysis was subjectively chosen as a 50 percent reduction in per acre
usage of hydrocarbons for each crop."^
It was the consensus of entomologists that for policies of 50
percent reduction or less (of chlorinated hydrocarbons) per acre, a one-
pound reduction of chlorinated hydrocarbons would require about .3 to .4
There are currently some efforts to make laws banning thg_use
of DDT and certain other "hard" pesticides in several states. It is
debatable as to how much effect this would have on reducing the usage of
the "hard" pesticides as a group, for the farmers would be likely to
shift over to some other persistent pesticide not covered by the legis
lation. Several economists have warned against the pitfalls of excessive
control in pollution problems (43, pp. 56-58; 30, pp. 17-21).


CHAPTER VII
ANALYTICAL RESULTS, IMPLICATIONS
AND RECOMMENDATIONS
Analytical Results
The model was solved"^ four times as follows:
Run 1 Policy 1 (current pesticide usage).
Run 2 Policy 2A (a 50 percent reduction for each crop in the per
acre usage of chlorinated hydrocarbons and a substitution
rate of .3 pounds of organic phosphates per pound of
chlorinated hydrocarbons).
Run 3 Policy 2B (a 50 percent reduction for each crop in the per
acre usage of chlorinated hydrocarbons and a .4 substitution
rate).
Run 4 Policy 2C (a 50 percent reduction for each crop in the per
acre usage of chlorinated hydrocarbons and a .5 substitution
rate).
2 3
For illustration, Run 1 appeared as follows:
Maximize:
1605.2522 y^ .0416 y^ (tomatoes)
+ 927.5654 .0606 y^ (potatoes)
The computer program used for the model was developed by
International Business Machines (26).
2
The mathematical statements of the other three runs are
presented in Appendix G.
3
We have received additional funds to continue our work on this
project through June, 1970. During the next year we hope to explore
the pesticide issue further and evaluate several additional policies.
104


Table 37.Concluded.
Common name
Trade name
Chemical name
Zinc phosphide
Z. P. Rat Bait
Zinc 2-pyridinethiol-l-oxide.
MITICIDES
Other
PCNB
Terrachlor
Pentachloronitrobenzene.
Tetradifon
Tedion
Tetradithion 2,4,5,4-tetrachlorodiphenyl
sulfone.


9
Figure 1.Locational map for Dade County, Florida.


Table 17.Concluded.
Non-disabling
Disabling
Total
Number
of
injuries
Cost
Number
of
injuries
Days
lost
Cost
Number
of
injuries
Days
lost
Cost
1967
State total
161.6
4,565
71.5
6,258 43,115
233.1
6,258
47,680
Agriculture,
forestry,
5.1
139
5.0
317
2,008
10.1
317
2,147
and fisheries
Commercial
farms
3.2
90
2.6
201
1,314
5.8
201
1,404
Source of data: (19).


Table 42.Continued
Pesticide
Crop
Number of
growers
Simizine
Groves
11
Sodium arsenite
Potatoes
49
Pole beans
1
Solan
Tomatoes
2
Sulfur
Tomatoes
15
Potatoes
1
Pole beans
89
Corn
1
Squash
10
Okra
2
Groves
13
Other
2
Tedion
Pole beans
1
Groves
O
4
Terrachlor
Pole beans
63
Thimet
Potatoes
45
Corn
2
Thiodan
Tomatoes
14
Mean usage
Acres in pounds
sampled per acre
Standard
deviation
pounds
per acre
797
.6577
.7319
4,527
9.7594
2.2932
400
.1000
.0000
3,340
.4311
.1346
5,309
1.6616
2.4365
640
.1407
.0000
2,364
54.5483
37.9918
735
4.5429
.0000
164
49.3725
5.7794
50
33.3000
16.4402
1,107
14.2967
11.3919
240
8.0333
19.9121
9
.0563
.0000
610
.0670
.4910
735
1.9357
.3247
4,177
3.1944
.7098
1,585
1.3660
.8966
5,551
.2884
.2921
CO


64
Alabama) have shifted toward the organic phosphates and away from the
chlorinated hydrocarbons while holding the total usage about the
same (51, 52).
Table 39 (p. 142) utilizes the same breakdown of pesticides and
crops,but the data are reported by months for the 1966-67 crop season.
As indicated by the table, usage of pesticides tends to peak out during
the winter months of November, December, January, and February, and
tends to be very low during other months of the year. One might wonder
if the seasonal pattern of pesticide usage combines with the life cycle
of wildlife in the region to cause any particular damage or threat of
damage. There was, however, no evidence to support or refute such an
hypothesis.
Table 40 (p. 155) presents the estimates of annual per acre
pesticide usage for 1966-67 by crop and pesticide. An estimate is also
made of the total pesticide usage for Dade County by pesticide and crop.
Finally, the trend of usage is specified where possible. In a few
isolated cases it was possible to collect data for earlier years, but
the data were not sufficient for making point estimates of pesticide
usage.
Table 41 (p. 163) shows a breakdown by pesticide, crop, and
month for 1966-67. As in Table 39 (p. 142), the intention is to indicate
the seasonal pattern of pesticide usage.
The wide variation of spray programs among growers was noted
early in this chapter. The question arose as to whether this variation
v?as caused because the growers used different pesticides or mainly
because they all used the same pesticides but in greatly varying quanti
ties per acre. Table 42 (p. 176) was developed to try to "shed some


186
SECTION II: MAJOR INTERNAL DATA
2.1 Crops to which this section applies .
2.2 Total production : .
2.3 Do you usually follow a predetermined fungicide program? Discuss.
The gnowens nesponded that they do genenally tny to follow a pne-
detenmlned pnognam {¡oh. disease, modified as necessany fon weathen
conditions.
2.4 If YES, are there any particular factors which cause you to deviate
from the schedule? Examples: insect outbreak, rain, etc. Discuss.
The main factons one: (7) Aain, which tends to wash off the
fungicide and (2) -tewpeAotuAe oa molstune conditions favonable {¡on
disease.
2.5 Do you usually follow a predetermined insecticide program? Discuss.
In genenaJL, gnoweJis nesponded that they tend to use a spnay-as-
needed" pnognm {¡on Insects. They Inspect theln flelds eveny day,
o{ten accompanied by an entomologist and take necessany "on-ti\e-
spot" action.
2.6 In a normal crop year would it be impossible to produce, without
some fungicide?
All o{ -the gnowens nesponded that It would be physically Impossible
to pnoduce a cnop In Vade County without fungicide.
2.7 In a normal crop year would it be impossible to produce without
some insecticide?
The gnowens nesponded that It would be Impossible to pnoduce a
manket.able cnop In Vade County without Insecticides. In a good
yean some pnoduce could be bnought to the manket but at best It
would have Insect dmage and would not be acceptable to the hmenican
housewl{e.
2.8 In a normal crop year would it be impossible to produce without
some herbicide?
The gnowens one not unanimous In theln use o{ henblcldes, but the
usage o{ henblcldes seems to be on the Incnease In Vade County.
In most cases henblcldes one a substllut.e {on labon. The necent
minimum wage Incneases and legislation on {¡anm labon tmlgnatlon
encounage the substitution o{¡ henblcldes {¡on labon.
2.9 Are tolerance levels of the pesticides a. very important consid
eration in your application decisions?
Be, f one answenlng, each gnowen wees nerlnded that fils Individual
answen would be confidential. By and lange, the a ns wens to this
question wene negative. Thene Is a lange vanlety o{ pesticides
among which the gnowens can altennate.


plant diseases, weeds, and also to those which act as regulators of
plant growth, as defoliants, and as desiccants" (49, p. 41). Major
categories have been described as follows (49, p. 41):
1. The chlorinated hydrocarbons containing carbon, hydrogen, and
chlorine are the pesticides used in greatest tonnage. The
most familiar are DDT, dieldrin, aldrin, endrin, toxaphene,
lindane, methoxychlor, chlordane, and heptachlor. Among
those used extensively as herbicides are 2,4-D and 2,4,5-T
for control of broad-leaved weeds in lawns, pastures, cereal
crops, and brush growth along highways and fences.
2. The organic phosphorus compounds, composed of phosphorus,
oxygen, carbon, and hydrogen, are used principally as insec
ticides and miticides. Parathion, malathion, phosdrin, and
tetraethyl pyrophosphate (TEPP) are examples.
3. Other organic compounds include the carbamates, dinitrophenols,
organic sulfur compounds, organic mercurials, and such natural
products as rotenone, pyrethrum, nicotine, strychnine, and the
anticoagulant, rodent poisons.
4. Inorganic substances with a long history of use include copper
sulfate, arsenate of lead, calcium arsenate, compounds of
chlorine and fluorine, zinc phosphide, thallium sulfate, and
sodium fluoroacetate.
Although there is some controversy regarding most of these
groups, the main thrust of the dispute concerns the chlorinated hydro-
4
carbons and the organic phosphates.
The chlorinated hydrocarbons are generally quite persistent in
the environment, and their residues therefore tend to build up in many
elements of the environment, such as soil, water, and plant and animal
tissue. The organic phosphates, on the other hand, decompose very
rapidly into non-harmful substances and thus seldom persist in the
environment for long periods of time. Their detrimental effects tend
to be of an acute rather than chronic nature and have caused many fatal
Common names, trade names, and chemical names of all pesticides
identified in Dade County are presented in Appendix C ( p. 131).


197
sampling program and expand the breadth of the sample to include selected
acquatic plants and animals. Figure 9 illustrates the locations of the
sampling sites which they hope to include in the regular program. A
report will be forthcoming in the summer of 1969 detailing their findings
to date.
Pesticides in Soil
Much of the soil monitoring program is being carried out by the
U.S. Department of Agriculture as an established program. Other phases
of monitoring are conducted by the USDA in cooperation with State and
other Federal agencies.
The objective of this program is to determine existing levels of
pesticide residues in soils of selected areas in the conterminous United
States and to detect any significant changes in these levels. Soil
monitoring sites were chosen wherever possible to coincide with sampling
sites of other agencies in the Federal pesticide monitoring network so
that soil data may be correlated with pesticide levels in other environ
mental media.
Intensive study areas were at single locations in Alabama,
Arizona, and in the Red River Valley of North Dakota. Studies in these
areas were set up to run for a 3-year period and were phased out at the
end of the 1967 season. Operations at Stuttgart, Arkansas; Greenville,
Mississippi; and Utica, Mississippi, were phased out in the fall of 1966
after a 3-year sampling period was completed. Special soil monitoring
activities have been extended to numberous other areas of the country
where pesticides are extensively employed in agriculture. Five farms
are included at each location. These areas, and the principal crops
they produce, include:


Table 33.
-Net profit
per acre
for a sample
of squash
growers in
Dade County
1960-61 through 1966-
67.a
Unweighted
Grower
Standard
number
1960-1961
1961-1962
1962-1963
1963-1964
1964-1965
1965-1966
1966-1967
Mean
deviation
1
- 69.39
-124.28
b
n.a.
n.a.
n.a.
n.a.
n.a.
- 96.84
38.81
2
283.65
91.24
38.88
n.a.
n.a.
n.a.
n.a.
137.92
128.89
3
- 76.62
57.84
-117.61
-102.51
-235.28
-150.62
n.a.
-104.13
96.47
4
n.a.
1.44
-177.12
n.a.
- 68.29
n.a.
n.a.
- 81.32
89.99
5
n.a.
- 67.15
55.17
- 49.92
n.a.
n.a.
91.27
7.34
77.80
6
n.a.
67.62
49.04
77.62
276.20
276.40
238.25
164.19
110.18
7
n.a.
n.a.
- 16.88
-116.27
73.35
- 40.41
n.a.
- 25.05
78.12
8
n.a.
n.a.
n.a.
59.56
n.a.
-172.28
-168.82
- 93.85
132.87
9
n.a.
n.a.
n.a.
n.a.
66.74
127.57
322.64
172.32
133.69
10
n.a.
n.a.
n.a.
n.a.
n.a.
136.62
- 40.96
47.83
125.57
Unweighted
mean
45.88
4.45
- 28.09
- 26.30
22.54
29.55
88.48
Unweighted
standard
deviation
205.95
84.95
97.69
90.32
189.47
178.94
200.14
Source of data: unpublished farm questionnaires collected by Dr. D. L. Brooke of the Department
of Agricultural Economics, University of Florida.
^Data not available.
123


APPENDIX E
PESTICIDE QUESTIONNAIRE USED IN DADE COUNTY


48
additional pounds of organic phosphates. All were careful to specify
that such a statement required gross assumptions about insect infes
tation, weather conditions, and insect susceptibility. Because, of this
great possibility of error, the model was run using rates of .3, .4,
and .5.
Given the substitution rate between chlorinated hydrocarbons and
organic phosphates, it was then necessary to translate this into a shift
in the supply function for the specified policya 50 percent reduction
in the per acre usage of chlorinated hydrocarbons for each crop. In
order to do this it was necessary to compute the average costs of chlo
rinated hydrocarbons and organic phosphates by crops. These are
presented in Table 11. The difference in costs of, say, chlorinated
Table 11.Estimated average costs of chlorinated hydrocarbons and
organic phosphates used on crops in Dade County, 1966-67.
Crop
Chlorinated
hydrocarbons
Organic
phosphates
....Dollars per pound of
100% active
Tomatoes
1.09
3.82
Potatoes
2.46
3.01
Beans
.99
2.69
Corn
.90
1.82
All crops sampled
1.07
2.83
hydrocarbons between tomatoes and potatoes is due to the difference in
mix of chlorinated hydrocarbons between the^two crops. Potatoes tend
For policies specifying a greater than 50 percent reduction,
none were willing to speculate on the substitution rate,and two indi
cated that "adequate" control of certain insects would be difficult or
impossible with the current state of the arts.


200
Pesticides in Air
Air pollution studies are being conducted by the Air Quality Sec
tion of the Public Health Service. Opportunities for finding pesti
cide-polluted atmospheres are greatest in communities surrounded by
agricultural areas where large amounts of these chemicals are applied
during the growing season or in communities conducting pest control
programs. A number of communities in several different agricultural
areas were selected for sampling to ensure information on a wide variety
of insecticides. The communities having pest control programs were
selected on the basis of the type program and ability to cooperate in
the sampling studies. The following seven agricultural communities
were selected for investigation.
1. Fort Valley, Peach County, Georgia, (population 8,300) is
completely surrounded by large peach orchards. A pesticide-formulating
plant is operated in Fort Valley. Sampling was carried out in May and
June, when the applications of pesticides to the peach crop were
heaviest, and again in September, after the peach harvest, to detect
the contribution of the formulating plant.
2. Inman, Spartanburg County, South Carolina, (population 1,500)
also is completely surrounded by extensive peach orchards. Sampling was
conducted in May and June.
3. Leland, Washington County, Mississippi, (population 6,300)
was completely surrounded by large cotton fields, which received many
applications of various pesticides during the growing season. Sampling
was carried on from July through September.
3
For additional information on air sampling for pesticide
residues, see Tabor (44).


27
independent of the pattern of legal rights. It is not necessary to
delineate the rights in order to determine the optimal solution, but it
is necessary to delineate the rights in order to evaluate the various
ways of reaching the optimum, a problem with which we are not concerned.
For example, if a policy alternative calling for a tax on chlorinated
hydrocarbons were introduced, then by implication, the farmers do not
have the right to "pollute" the environment and must pay for the privi-
ledge of doing so. On the other hand, if no restraints are placed on
farmers' usage of chlorinated hydrocarbons, then by implication,they do
have the right to "pollute" the environment to some degree. One need
only read a few historical cases in the common law of nuisance to
realize that the question of property rights will vary greatly from time
to time, from place to place, and indeed, from judge to judge (9, p. 22).
Upon reading the following quotation from Prosser, one does not wonder
that the question of property rights is a very elusive concept: a
person is permitted
... to make use of his own property or to conduct his own affairs
at the expense of some harm to his neighbors. He may operate a
factory whose noise and smoke cause some discomfort to others, so
long as he keeps within reasonable bounds. It is only when his
conduct is unreasonable, in the light of its utility and the harm
which results, that it becomes a nuisance. . The world must
have factories, smelters, oil refineries, noisy machinery, and
blasting, even at the expense of some inconvenience to those in
the vicinity, and the plaintiff may be required to accept and tol-
lerate some not unreasonable discomfort for the general good (39,
pp. 398, 412).
The phrase in the above quotation, ". .in the light of its
utility and the harm which results ..." (39), illustrates the notion
that property rights are not independent of "the general good" or some
concept of social welfare. To put it another way:
. . The problem which we face in dealing with actions which have
harmful effects is not simply one of restraining those responsible


APPENDIX F
ENVIRONMENTAL MONITORING PROGRAMS


Table 42.Continued
Pesticide
Crop
Number of
growers
Parathion
Tomatoes
23
Potatoes
25
Pole beans
93
Corn
2
Squash
2
Okra
1
Groves
1
Paraquat
Tomatoes
1
Groves
13
Phosdrin
Tomatoes
3
Potatoes
41
Pole beans
1
Okra
2
Phosphoric acid
Potatoes
6
Phygon
Pole beans
3
Polyram
Potatoes
1
Prolin rat bait
Tomatoes
1
Potatoes
1
Sevin
Tomatoes
3
Acres
sampled
Mean usage
in pounds
per acre
Standard
deviation
pounds
per acre
10,340
1.8427
2.1149
2,982
2.6606
.5274
2,393
1.3942
1.4190
1,585
5.0964
6.0096
117
2.0431
.1273
20
4.4000
.0000
600
.2000
.0000
250
.0160
.0000
812
.2161
.3143
1,280
.1313
.0469
2,544
. 6067
.2846
207
.0725
.0000
50
.9600
.0707
407
.2166
.1047
1,032
.1226
.0240
325
5.1397
.0000
600
.1736
.0000
325
.0646
.0000
4,750
5.5572
4.7037
180


Table 16.Concluded
Case
number Pesticide Date Crop Extent of damage Comments
18
Sodium Arsenite
1957 or 1958
Tomatoes
4 acres
amounting to
$300
Potato grower was the damaging
party. No settlement was made.
19
Atrazine
1967
Beans
3 acres
amounting to
about $1200
A settlement was made but the
identity of the damaging party
was not disclosed.
20
Jet fumes and
oil
1962 or 1963
Potatoes
5 to 10%
reduction in
yield on about
5 acres
Grower stated that he tried to
collect damages from the air
force, but the "red tape got so
involved" that he decided to
drop it.


Table 37.Common names, trade names, and/or chemical names of pesticides observed in Dade County,
Florida, 1966-67.
Common name
Trade name
Chemical name
INSECTICIDES
Chlorinated hydrocarbons
Aldrin
Chlordane Chlorokil
DDD (or TDE) Rhothane D-3
DDT Niatox
Dieldrin
Endosulfan Thiodan
Endrin
Not less than 95% of 1,2,3,4,10,10-
hexachloro-1,4,4a,5,8,8a-hexahydro-l,4-
endo, exo-5, 8-dimethanonaphthalene.
1,2,3,4,5,6,7,8,8-octachloro-2,3,3a,4,7,
7a-hexahydro-4,7-methanoindene.
Dichloro Diphenyl Dichloroethane.
Dichloro Diphenyl Trichloroethane.
1.2.3.4.10.10-hexachloro-6,7-epoxy-l,4,
4a,5 6,7,8,8a-octahydro-l,4-endo, exo-5,
8-dimethanonaphthalene.
6.7.8.9.10.10-hexachloro-l,5,5a,6,9,9a-
hexahydro-6,9-methano-2,4,3-benzodiox-
athiepin-3-oxide.
1.2.3.4.10.10-hexachloro-6,7=epoxy-l,4,
4a,5,6,7,8,8a-octahydro-l,4-endo-endo-5,
8-dimethanonaphthalene.
132


APPENDIX D
ESTIMATED QUANTITIES OF AGRICULTURAL PESTICIDES
USED IN DADE COUNTY


Table 31.
Net profit
per acre for a sample of potato
growers in
Dade County, 1960-61 through 1966-67.3
Grower
number
1960-1961
1961-1962 1962-1963 1963-1964
1964-1965
Unweighted
Standard
1965-1966 1966-1967 Mean deviation
1
-110.16
168.72
- 13.34
214.36
139.21
156.50
251.72
115.29
129.70
' 2
-177.53
n.a.k
- 55.11
5.09
176.24
-167.34
- 21.72
- 40.06
129.99
3
- 82.48
25.33
124.36
354.58
410.98
245.38
57.43
162.23
181.12
4
-176.07
50.59
62.96
288.23
259.18
208.27
188.72
125.98
161.07
5
90.04
217.81
88.71
243.35
478.36
468.45
160.73
249.64
163.56
6
-176.77
166.86
- 31.12
74.67
380.05
54.27
65.41
76.20
171.59
7
n. a.
69.48
26.50
539.19
289.28
-151.47
n.a.
154.60
266.17
8
n. a.
95.72
-129.37
n.a.
n.a.
n.a.
n.a.
- 16.83
159.16
9
n. a.
150.79
n.a.
103.56
258.14
102.59
152.05
153.43
63.33
10
n. a.
59.41
n.a.
n.a.
n.a.
395.05
n.a.
227.23
237.33
11
n.a.
82.97
- 24.92
471.96
458.08
93.59
35.64
186.22
220.02
12
n. a.
n.a.
n.a.
56.86
438.24
21.17
- 54.59
115.42
220.17
Unweighted
mean
-105.50
108.77
5.41
235.19
328.78
129.68
92.82
Unweighted
standard
deviation
103.96
63.06
78.49
180.70
120.67
198.05
101.67
2
Source of data: unpublished farm questionnaires collected by Dr. D. L. Brooke of the Department
of Agricultural Economics, University of Florida.
^Data not available.
121


106
,y7> z2~
For illustration, the tomato portion of the objective function
?
(1605.2522 y .0416 y^) is derived for Run 1 as follows:
yi (t+1)
/ [(1521.5660 .0334 y) (-83.6862 + .0497 y1)3dy1
= 1605.2522 y1 .0416 yj
As stated previously, parametric programming was performed on
the coefficients of z^ and z^. Each was varied from 0 to 5.0 in incre
ments of 1.0.
The results of the four runs are shown in Tables 25 through 28.
As stated in the footnotes to these tables, the grove acreage (avocados,
%
limes, and mangos) was restrained to be no greater than the 1966-67
acreage. These tree crops entered every solution at their maximum
levels, so they were simply added together for the presentation in
Tables 25 through 28.
The footnotes to these tables indicate the solution's degree of
accuracy with respect to the crops in the solution vector. In order to
use the IBM 360 program, it was necessary to break the non-linear objec
tive function up into a series of linear segments in the variables y..
Tomatoes, potatoes, and beans were broken up into 100-acre intervals
while corn was divided into 50-acre intervals. The size of the interval
determines the accuracy of the solution, and any desired accuracy can be
4
achieved by a sufficient reduction in interval size.
It is trivial to show by the Kuhn-Tucker optimality conditions
4
For a detailed discussion of this technique, the interested
reader may consult the IBM Manual (26, pp. 165-173).


Table 26.Model solution for Policy 2A.a
Solution
vector
-y
0b j ective
function
in dollars
yl
Tomatoes,
b
m acres
y2
Potatoes^
in acres
y3
Beans ,
. b
in acres
y4
Corn
in acres
y5,y6y7
Groves ,
, d
in acres
Z1
Chlorinated
hydrocarbons
in poundse
Z2
Organic
phosphates
in poundse
Coefficient of:
21 Z2
0
0
34,212,231.
19,100
7,600
5,800
1,650
10,340
106,686
162,180
0
-.0301
34,207,349.
19,100
7,600
5,800
1,650
10,340
106,686
162,180
0
-1.
34,050,146.
19,000
7,500
5,800
1,650
10,340
106,318
161,154
0
-2.
33,889,449.
19,000
7,500
5,800
1,600
10,340
105,300
160,525
0
-3.
33,729,205.
19,000
7,400
5,800
1,550
10,340
104,218
159,289
0
-4.
33,570,327.
18,900
7,400
5,800
1,550
10,340
103,915
158,871
0
-5.
33,412,100.
18,900
7,300
5,800
1,500
10,340
102,833
157,635
-1.
-.0301
34,100,900.
19,100
7,600
5,800
1,600
10,340
105,668
161,551
-2.
-.0301
33,995,794.
19,000
7,600
5,800
1,550
10,340
104,347
160,505
-3.
-.0301
33,891,922.
19,000
7,600
5,800
1,500
10,340
103,329
159,876
-4.
-.0301
33,789,188.
19,000
7,600
5,800
1,450
10,340
102,312
159,248
-5.
-.0301
33,687,860.
18,900
7,600
5,800
1,400
10,340
100,991
158,202
aA 50 percent reduction for each crop in the per acre usage of chlorinated hydrocarbons and a sub
stitution rate of .3 pounds of organic phosphates per pound of chlorinated hydrocarbons.
^Solution does not differ from the optimum by more than 100 acres,
c
Solution does.not differ from the optimum by more than 50 acres.
^Grove acreage is constrained to be no more than the 1966-67 level,
e
All quantities have been converted to units of 100 percent concentrated material.
108


This dissertation was prepared under the direction of the
chairman of the candidate's supervisory committee and has been
approved by all members of that committee. It was submitted to the
Dean of the College of Business Administration and to the Graduate
Council, and was approved as partial fulfillment of the requirements
for the degree of Doctor of Philosophy.
Dean, Graduate School
Supervisory Committee:


105
+ 1687.7688 y3 .1448
+ 592.4965 y- .1796 y^
+ 571.3720 y5 .0240 y*
+ 3285.4824 y& .3751 y^
+ 690.9032 y? .0956 y^
- elZl
- .0301 z2
Subject to:
7
40459 Z y. 48590
>1 3
15740 y 21166
6532 y2 8927
5269 y3 6680
1135 y4 3774
y5 5235
y6 3585
y? 1520
6.0719 y + 1.2860 y + 3.4304 y3
+ 40.7003 y^ + .0706 y^ + .0484 y^
+ .0205 y7 z^ = 0
3.2669 y + 5.8852 y2 + 1.8114 y3
+ 6.4624 y. + .2239 yc + .1535 y,
4 5 6
+ .0650 y7 z2 = 0
(beans)
(corn)
(avocados)
(limes)
(mangos)
(chlorinated hydrocarbons)
(organic phosphates)
(total farmland)
(tomatoes)
(potatoes)
(beans)
(corn)
(avocados)
(limes)
(mangos)
(chlorinated hydrocarbons)
(organic phosphates)


11
fruit are the three largest activities, representing 44.3, 11.5, and
4.1 million dollars,respectively,as indicated in Table 1. Minor
Table 1.Vegetable, ornamental horticulture, and fruit production, by
commodities, in Dade County, Florida, 1966-67.a
Crop
Acres
Production
Value
Thousands. .
Vegetables
Tomatoes
Fresh
19,000
5,806 crt.
$23,050
Processed
1,514 crt.
961
Potatoes
7,660
1,564 cwt.
5,474
Snap beans
Bush
1,470
181 bu.
699
Pole
5,830
1,647 bu.
6,357
Squash
3,080
457 bu.
1,942
Strawberries
670
657 flats
2,214
Corn
1,650
252 crt.
622
Cucumbers
1,290
204 bu.
787
Cabbage
470
189 crt.
312
Okra
400
36 bu.
180
Peas
650
69 bu.
361
Cuban vegetables^
1,360
1,102
Other vegetables
390
272
Total vegetables
43,920
$44,333
Ornamental horticulture
$11,530
Fruit
Avocados
5,235
220 bu.
$ 1,232
Limes
3,585
640 bu.
1,965
Mangos
1,520
70 bu.
490
Specialty fruit
440
210
Other citrus^
365
247
Total fruit
11,145
$ 4,144
Source of data: Dade County Agricultural Agents Office,
Homestead, Florida.
^Includes lima beans, cantaloupes, eggplant, escarole, chicory,
lettuce, green peppers, and green onions.
c
Includes lychee, barbados cherries, guava, papyas, and
sapodillas.
^Includes oranges, grapefruit, tangerines, tngelos, and lemons.


49
to use a higher proportion of the more expensive chlorinated hydrocarbons
than do tomatoes, and in computing the average, each chlorinated
hydrocarbon is weighted by its relative importance in the usage mix.
Next, average total costs of chlorinated hydrocarbons plus
organic phosphates for the two alternative policies and the three substi
tution rates were computed. These are presented in column 3 of Table 12.
In addition to a change in the cost of spray materials, there is
also a possibility that other categories of cost might change under the
alternative pesticide policy. The major ones are those likely to be
affected by a change in the required number of pesticide applications,
since the organic phosphates as a group are less persistent than the
chlorinated hydrocarbons and might require more applications for control.
The entomologists considered this question and came to the conclusion
that there would be very little increase in labor and machinery costs in
Dade County for the crops concerned. The reason for this is that a
fairly rigid fungicide program is required on all crops, and increasing
the number of insecticide applications would merely involve combining
the insecticides with the fungicide applications. Estimates of
increased labor and variable machinery expense were as follows:
Tomatoes
10%
Potatoes
10%
Beans
0
Corn
0
Changes in the cost of spray materials, labor, and variable
machinery cost were then related to the cost per acre of producing the
crops. These are showjr'in Table 12.
If we assume these changes in average total cost per acre are


Table 37.Continued.
Common name Trade name
HERBICIDES
Other
2,4-D
Amine 2,4-D
Atrazine
Atrazine
Dacthal
DCPA
Diphenamid
Dymid, Dymid D, Enide
Paraquat
Penite
Kill All, Miller Kill All
Chem-Sen
Simizine
Solan
Solan
RODENTICIDES
Other
Warfarin
Prolin Rat Bait
Chemical name
2,4-dichlorophenoxy acetic acid.
2-chloro-4-ethylamino-6-isopropylamino-
1,3,5-triazine.
Dimethyl 2,3,5,6-tetrachlorterephthalate.
N, N-dimethyl-2, 2-diphenylacetamide.
1,1-dimethy1-4, 4-bipyridinium dichloride.
Sodium arsenite.
2-chloro-4,6-bis (ethylamino)-1,3,5-
triazine.
N-(3-chloro-4-methylphenyl)-2-methyl-
pentanamide.
Alpha-acetoneylfurfuryl-4-hydroxycoumarin.
137


188
SECTION III: EXTERNAL DATA
(Th& eetion am diicued and umria/Uzed in Chaptex vi, -6 o the.
author'* edito >tai comments axe omitted.)
3.1 Have you or any of your employees ever experienced sickness from
contact with pesticides or farm chemicals? Discuss.
3.2 When was this approximately?
3.3 What pesticide(s) were involved?
3.4 Did you visit a doctor or hospital on any of these occasions? If
so, please give the name of doctor or hospital.
3.5 Was any time lost from work on these occasions?
3.6 How much time was lost?
3.7 Approximately what was the hourly value of the labor lost?
3.8 Do you have any kind of insurance to cover accidents such as these?
3.9 If YES, who is it with?
3.10 Do you know of any instances where children or housewives have been
poisoned or sickened through contact with pesticides? Describe.
3.11 Approximately when were the incident(s)?
3.12 What pesticide(s) were involved?
3.13 What doctor, if any, was called?
3.14 Do you know of any instances where household pets have been
poisoned by pesticides? Please describe and estimate value.
3.15 When did this occur?
3.16 What pesticide(s) were involved?
3.17 What was the form of the pesticide, i.e., dust, spray, etc.?
3.18 Are you aware of any wildlife deaths from pesticides?
3.19 In your opinion had the pesticide been improperly used?
3.20 Have the pesticides used by other producers ever drifted onto your
crops and caused damage? Discuss.


Table 34.
Estimates made by
Richard Klukas3 of the
Dade County,
quantities
1966-67.b
of insecticide
used on
various crops
in
Insecticide
Crop
Insect
Pound
per acre
Number of
applications
Total
pounds
per acre
Total
pounds
per crop
Total
pounds
all crops
Cygon
Tomatoes
Aphid
.33
1
.33
5,795
Potatoes
Aphid
.54
1
.54
4,140
Pole beans
Aphid
.25
1
.25
2,250
12,185
DDT
Corn
Fall Armyworm
1.00
38
38.00
76,000
Southern peas
Cowpea Curculio
.50
4
2.00
2,000
78,000
Diazinon
Potatoes
Wireworm
2.00
1
2.00
15,200
15,200
Dieldrin
Sweet potatoes
Sweet Potato Weevil
1.50
4
6.00
6,000
6,000
Guthion
Tomatoes
Leaf Minor
.50
11
5.50
96,580
Potatoes
Leaf Minor
.25
5
1.25
9,500
Pole beans
Bean Leafroller
.50
5
2.50
22,500
Squash
Leaf Minor
.50
6
3.00
10,500
Canteloupe
Leaf Minor
.50
6
3.00
750
Cucumbers
Leaf Minor
.50
6
3.00
6,000
Watermelons
Leaf Minor
.50
6
3.00
300
146,130
Kelthane
Strawberries
Spider Mite
.32
10
3.20
1,760
1,760
Parathion
Tomatoes
Aphid
.40
6
2.40
42,144
Potatoes
Aphid and Armyworm
.30
12
3.60
27,360
Pole beans
Aphid
.30
4
1.20
10,800
Squash
Aphid
.30
10
3.00
10,500
Okra
Aphid and Leaf Minor .30
7
2.10
1,575
125


21
There has been a recent revival of interest in the concept of
consumers-producers' surplus for making policy decisions (37, 45, 55).
Though much criticized (40, pp. 208-209), it is being used in the
literature with increasing frequency to give a rough idea of welfare
aspects of various alternatives. It is defined as the integral of the
demand function minus the integral of the supply function. The integral
of the demand function is, to the user of a product, simply a total
benefits function and the integral of the supply function a total cost
function, so consumers'-producers' surplus as a function of output is a
net benefits function.^
Buchanan (6, p. 127) argued that the welfare of a group is a
consensus of the preferences of members of the group and that an
observer could introduce a welfare criterion only through his own
estimate of the group's value scales. Hence, the observer can recommend
policy A over policy B given his estimate of the group's preferences,
and the test of his recommendation is to ask whether the given welfare
function reflects the consensus of the group. Even within this frame
work, however, the question of votes must be resolved before any group
consensus can be revealed. Buchanan (6) further argued that majority
rule does not guarantee consensus or agreement among the parties because
adequate compensation may not be provided for the damaged parties.
At any rate, as a theoretical construct, the social welfare
function is perhaps useful, but for empirical work one generally ends
''The objective function would always be maximized at the
intersection of the demand and supply functions in the absence of the
externality relationship. The introduction of the externality relation
ship will shift the optimum solution to the left or to the right
depending on whether the externalities are external economies or external
diseconomies. The externality function will be discussed in later pages.


196
Wildlife monitoring efforts have tended to center primarily,
though not exclusively, oil ducks, starlings, and bald and golden eagles.
Major considerations in the choice of specie are ubiquity and relative
position in the food change.
Pesticides in Water
This program for continuous surveillance of pesticides in surface
waters was proposed for joint operation by the Federal Water Pollution
Control Administration and the Geological Survey of the U.S. Department
of the Interior. The proposal has been partially implemented. The
purpose of this program is to provide continuing information on the
overall extent of pesticide contamination of the Nation's water sampling
resources. The objective has been to develop the minimum program that
will enable an adequate assessment of conditions. Within this objective,
monitoring currently is confined to the examination of surface waters
in the major drainage rivers of the United States through a nationwide
network of sampling locations. Over a period of years, it is expected
that data obtained from this network will reflect important changes in
pesticide levels in these rivers.
Rivers flowing into or through Florida that are being sampled
are the Pee Dee River, Altamaha River, St. Johns River, Suwannee River,
Apalachicola River.
The Geological Survey in Miami has, to date, performed a con
siderable amount of monitoring of Dade County's water and sediment on a
2
non-continuous basis. Efforts are now underway to develop a regular
2
We are indebted to Dr. Milton C. Kolipinski and Aaron L. Higer
for information on the program of the U.S. Geological Survey.


53
in the model.
The model required a functional relationship between dollars of
externalities and the quantity of pesticides used. Empirically there
was no way to estimate such a function, so it was necessary to assume
a functional form and rely on sensitivity analysis for some indication
14
about how critical the assumption was to the solution. Figure 6 is
illustrative of the case for organic phosphates. The point estimate of
E
($)
$4,590.
152,348 z2 (organic phosphates)
lbs.
Figure 6.Point estimate of externalities arising from the
agricultural usage of organic phosphates.
externalities due to the organic phosphates in 1966-67 was $4,590, and
the corresponding usage on the seven crops was 152,348 pounds. ^ A
linear functional form passing through the origin was assumed. This
meant that the equation was:
[8] E2 = .0301 z2
Parametric programming could be employed on both the form of
the function and the parameters of the function. In this paper only the
parameter, or slope, of the linear function was varied.
Some thought was given to the possibility of measuring the
second-order effects through an input-output technique, but it was later
discovered that the solution was relatively insensitive to this parameter
and the idea was therefore abandoned.


18
Second, revenues per acre from farming in Dade County are also very high.
The per acre value of all farm products sold in Dade County is $443.13
against $68.41 for the State (47, p. 332-333). For the U.S. as a whole
this figure is $31.79 (48, p. 802). Third, net returns per acre vary
widely among growers and through time, as indicated in Appendix A
(p. 119). Finally, in the author's opinion, based on observations made
while in the area, the farmers of Dade County are as well educated and
as sophisticated as the entrepreneurs in any vocation. Some of the
larger ones have professional entomologists on their payrolls, and most
of the pesticide firms in the area employ entomologists to sell pesti
cides and provide consultation to the farmers. The University of
Florida has an experiment station in Homestead, Florida, which is very
active in the area of insect and disease control. In addition, the Dade
County Agricultural Agent's Office, the largest in the State and located
in Homestead, is extremely active in disseminating information to the
farmers.


57
constraint was one minus the average of the percentage decreases."^ The
flexibility constraints so generated are presented in Table 14.
Table 14.Flexibility constraints used for the empirical model.
Crop
Constraints
5
Total land3 .9022(44843) Z y 1.0835(44845)
3=1 3
Tomatoes
Potatoes
Beans
Corn
Avocados
Limes
Mangos
.8284(19000) -
.8527( 7660) -
.9037( 5830) -
.6881( 1650) -
y1 1.1140(19000)
y2 1.1654( 7660)
y3 1.1458( 5830)
y4 2.2872( 1650)
y5 5235
y6 3585
y? 1520
Defined as total land under cultivation after allowance for
minor crops (see page 25).
17
At least two other possibilities exist for obtaining flexi
bility constraints. First, one might choose to use the maximum
percentage changes rather than the average percentage change. Second,
one might use a regression approach, fitting the functions:
y^(t) = b^ (max)y_. (t-1) y..(t) > y^ (t-1)
and y.(t) = b (min)y. (t-1) y.(t) y.(t-l)
3 J 3 3 3
The "regression approach" generated parameters which were very close to
those presented in Table 14.


118
environment. It should probably be increased and especially concen
trated on the ecologically critical species of plants and animals.
Furthermore, it is not enough to know how much pesticide residue exists
in various elements of the environment. We must know if it is "bad," or
"good," or "neutral." In other words, the residue must be empirically
linked to some measure of damage.
Second, research in ecology .must be expanded. Aside from a
descriptive understanding of food chains or networks, an understanding
is needed about how pesticide residues are passed around in the food
networks. Such knowledge is a prerequisite to understanding the effects
of given exposure levels on the environment.
Finally, more research is needed to bring all the pieces of
information together into a solution generating model. This research
effort is an approximation to what is ultimately needed. Continued
refinement in model specification and parameter estimation are needed.
Better communication among the disciplines should be fostered by
economists0 to point toward needed research and to delineate the roles
of the various disciplines (including the governing bodies, with whom
the ultimate decisions will lie). An inter-disciplinary problem requires
an inter-disciplinary effort if viable solutions are to be found.
An effort was made in this project to improve communication
among the interested disciplines. For the most part we found them
eager to understand the economists' approach to the problem and how the
various aspects of the problem might be combined into one model. Most
seemed to be aware that economists have an important role to play in the
issue, and most seemed to be aware that research efforts to date have
been fragmentary and piecemeal, hence needing a unifying model or
systems approach.


62
statement for the benefit of the supplier. After making preliminary
visits to the suppliers to explain the research, they were approached
and asked to cooperate. Of the eight major firms in the area, six
agreed to let us copy invoices of sales to customers who had given us
permission to do so. Two refused to cooperate, so the information was
obtained from the growers copy of the invoice. As noted previously,
there were many different pesticides used in Dade County, and most of
the pesticides were used in a number of different formulations.
Initially, there were 212 separate pesticides and formulations identified.
For a given pesticide the different formulations were converted to units
of 100 percent concentrated material and added together. This reduced
the number of pesticides to 56. The common name, trade name, and/or
chemical name of these are presented in Appendix C (p. 131).
The basic unit of observation for all of the pesticide usage
tables was either a grower or a field. Where a grower had complete
spray information on his fields, each field was considered a separate
observation. If he did not keep field data, the farmer was asked to
designate what crop every pesticide invoice was used on, and it was
assumed that the pesticide was used uniformally on all his land planted
in that crop. It was further assumed that the pesticide was used in
the month in which it was sold.
It was not particularly difficult for the farmers to match the
pesticide with the crop on which it was used since (1) the crops tend
to differ in their basic spray programs both as to ingredients and
timing and (2) most of the farmers tend to specialize in certain crops.
Many of those interviewed grew only one crop and very few grew more than
three. Justification for the assumption that the pesticide was used


APPENDIX G
MATHEMATICAL STATEMENTS OF POLICIES 2A, 2B, AND 2C


Table 38.Concluded.
Acres
Tomatoes
10,590
Potatoes
4,584
Pole beans
2,394
Corn
1,585
Squash
244
Okra
50
Groves
1,247
Other
240
c
Sulfur compounds
comprised 53.8829 pounds.
Observations
24
51
93
2
11
2
26
2
m


101
The activities described in previous pages constituted the
search for externalities in Dade Countythe enumeration process. The
job of reducing and aggregating the incidents to a common measure
required some strong and somewhat arbitrary assumptions. Omissions and
double counting of externalities also aggravated accurate measurement.
Table 24 is a summary of the externality calculation used in the model.
This estimate is subject to three limitations. First, in the
search for externalities, there was no guarantee that all externalities
had been recognized and that none were double counted. It was felt
that the more logical sources of information were exhausted, but this
did not mean that all externalities were uncovered nor did it give a
basis for measuring the confidence one could place in the enumeration.
A second limitation was that, by the nature of things, the list
was probably biased toward external costs relative to external benefits.
External costs were simply easier to observe since they tend to create
controversy. One grower stated, for example, that he hoped the adjacent
grower's spray did drift over to his field (they were growing the same
crop), but there was no way to quantify this phenomenon. The killing
of certain pests, such as rats, might have had a beneficial effect on
human health by holding down disease but, again, quantification was
impossible. The "state of the arts," particularly in the area of eco
logical relationships, simply does not permit such quantification.
The third limitation stems from the fact that all observed
externalities were acute as opposed to chronic. This point was made
implicitly in the section dealing with wildlife. Biologists suspect
that the persistent pesticides are harmful to reproduction, but this
has not been substantiated and therefore cannot enter the empirical


65
light" on this question. In general, there was a tendency for the
coefficients of variation to fall slightly in this table as compared with
Table 40 (p. 155), but the differences were not particularly striking,
and one is led to the conclusion that not only do the farmers differ
widely in the pesticides they use, but the users of a particular pesti
cide differ widely in the quantities per acre that they employ.


Table 20.Concluded.
Kind of payment^
Agent
1
2
3
4 5 6
7
8
Total
Chemicals and poisons, n.e.c.
22,574
11,903
3,457
874
0
0
0
6,165
44,973
T.B.
2,450
1,050
850
4,350
Fungus infections
484
190
459
1,133
Larvae migrans (creeping
eruption)
422
368
13
803
Septic infections
1,942
846
1,297
14
560
4,659
Dusts
16,585
3,641
275
820
4,500
25,821
Poison woods or vegetation
143
377
520
Total
$83,971
$44,863
$12,946
$3,057
$ 500
$ o
$ 0
$30,160
$175,497
Source of data: computer tapes supplied by the Florida Industrial Commission.
^Kind of payment is coded as follows:
Code
Kind of payment
1
2
3
4
5
6
7
8
Compensation
Medical
Hospital
Artificial members
Burial
Child labor penalty
Attorney fees
First aid
c
Not elsewhere classified.
00
Ln


Table 40.Continued
Pesticide
Mean usage
in pounds
Crop per acre
Sodium arsenite
Potatoes
9.6380
Pole beans
.0167
Solan
Tomatoes
.1360
Sulfur
Tomatoes
.8330
Potatoes
.0196
Pole beans
53.8829
Corn
2.1066
Squash
33.1848
Okra
33.3000
Groves
12.6916
Other
8.0333
Tedion
Pole beans
.0002
Groves
.0328
Terrachlor
Pole beans
.5952
Tnimet
Potatoes
2.9108
Corn
1.3660
Thiodan
Tomatoes
.1512
Potatoes
.5788
Pole beans
.0167
Squash
2.3310
Standard
deviation
pounds
per acre
3.0033
.0104
.1059
2.4743
.0197
39.5020
3.2130
16.1244
16.4402
13.2781
19.9121
.0058
.1471
.9618
1.2717
.8966
.3972
.6770
.0201
2.0005
Total Total
usage
by crops
in pounds
usage
all crops
in pounds
,d
Trend
of usage
73,827
Up
97
73,924
Uncertain
2,584
2,584
Uncertain
15,827
Down
150
Uncertain
314,137
Down
3,476
Uncertain
102,209
Uncertain
19,980
Uncertain
135,864
Uncertain
17,272
608,915
Uncertain
1
Uncertain
351
352
Uncertain
3,470
3,470
Uncertain
22,297
Up
2,254
24,551
Uncertain
2,873
Uncertain
4,434
Up
97
Uncertain
7,179
14,583
Uncertain
160


Table 23.Concluded.
Year
Result
Age
Sex
Race
Pesticide
Comments
1965
Non-fatal
41
Female
Negro
Parathion
Attempted suicide. Victim brought the material
home from the field.
1966
Non-fatal
34
Male
Negro
Guthion
Accidental ingestion.
1966
Non-fatal
23
Male
Negro
Parathion
Victim was spraying field when accident occurred.
1966
Non-fatal
47
Male
Negro
Phosdrin
Accident while spraying.
1966
Non-fatal
36
Male
White
Phosdrin
Accident while spraying.
1966
Non-fatal
27
Male
White
Parathion
Accident while spraying.
1966
Non-fatal
19
Male
White
Parathion
Accident while spraying.
1967
Fatal
50
Male
Negro
Parathion
Victim drank material from a whiskey bottle.
1967
Non-fatal
32
Male
Negro
Parathion
Victim worked for a pesticide manufacturer.
1967
Non-fatal
30
Male
White
Parathion
Victim was a crop duster.
1967
Non-fatal
3
Male
Negro
Parathion
Father brought material home from the field.
Child was in intensive care for 1 week.
1967
Non-fatal
63
Male
Negro
Phosdrin
Victim was using a hand sprayer. He was in the
hospital 2 weeks and off work another 2 weeks.


Table 37.Continued
Common name
Trade name
Maneb
Dithane M-22, Manzate, Miller
6582, Manzate D, Dithane M-45
Nab am
Nabam, Dithane A-40
Polyram
Streptomycin
Agrimycin, Agrimycin 100,
Agristrep, Phytomycin,
Agrimycin 500, Agristrep 500
Sulfur (wettable)
Micro Nutri Sperse, Micro
Sperse, Sulfobrite, Enduro
Sulfur
Ziram
Z. C. Spray, Karbam, Zerlante
Zinc
Zinc sulfate
Zineb
Parzate C, Dithane Z-78,
Ortho Zineb
Chemical name
Ethylenebis (dithiocarbamate) manganese;
Dithane M-45 is a coordination product of
zinc ion and maneb.
Disodium ethylene bisdithiocarbamate.
Zinc polyethylene thiuram disulfide
complex.
2,4-diguanidina-3,5,6-trihydroxycyclo-
hexyl 5-deoxy-2-0-(2-deoxy-2-methylamino-
a-glucopyranosyl)-3-formyl pentofuranoside.
Contains 15% streptomycin and 40% copper.
Zinc dimethyl dithiocarbamate.
Ethylenebis (dithiocarbamate) zinc.


Ill
that the solutions are locally optimal. In addition, because of our
special knowledge about the shape of the objective function (a series of
parabolas opening downward) and the linearity of the constraints, we
know that for this particular problem we must have a global optimum.
Considering Policy 1, the solution acreages were relatively slow
to change. Disregarding externalities entirely, the solution acreages
for current pesticide usage were:
Tomatoes
Potatoes
Beans
Corn
19,300 acres
7,700 acres
5,800 acres
1,650 acres
When the estimated acute externalities from organic phosphates were
recognized (coefficient of = -.0301), the solution acreages remained
the same. The first change in the solution acreages occurred when the
coefficient of z^ was -2.0, or about 6.6 times the estimated level of
externalities. At this point the only changes were a 100-acre reduction
in tomatoes and a 50-acre reduction in corn. When the coefficient of z^
had acquired a value of -5.0, or 16.6 times the estimated level, the
solution acreages were:
Tomatoes 19,100 acres
Potatoes 7,400 acres
Beans 5,800 acres
Corn 1,550 acres
At this point the usage of chlorinated hydrocarbons had only fallen
2.6 percent and that of organic phosphates by 2.3 percent.
%'here are available many good discussions of these conditions.
For one, see Dorfman, Samuelson, and Solow (13, pp. 186-201).


Table 16.A summary of grower responses concerning damage from pesticide drift.
Case
number
Pesticide
Date
Crop
Extent of damage
Comments
1
Amine 2,4-D
1955
Tomatoes
Slight
Potato grower was the damaging
party. There was no settlement.
2
Sodium Arsenite
1961
Beans
Not available
Potato grower was the damaging
party. A settlement was made.
3
Not available
1958
Tomatoes
Not available
An airplane was spraying
defoliant on soybeans. A small
payment was made and lost
materials, such as fertilizer,
were replaced.
4
Not available
1957 or 1958
Beans
10 acres
Potato grower was the damaging
party. A small settlement was
made.
5
Not available
1958
Tomatoes
10 to 20 acres
This is the same incident as it3
aboveseveral growers were
damaged by the defoliant. No
settlement was made.
6
Not available
1967
Groves
The Seaboard Railroad sprayed
their right-of-way for weed
control, and it damaged this
grower's trees. However, it
turned out that the trees were
on the right-of-way so the
grower agreed not to file suit
if the railroad would not
destroy the trees.


4
countryside that has been rendered largely invisible by commer
cial art. . They picnic on exquisitely packaged food from a
portable icebox by a polluted stream and go on to spend the night
at a park which is a menace to public health and morals. Just
before dozing off on an air mattress, beneath a nylon tent, amid
the stench of decaying refuse, they may reflect vaguely on the
curious unevenness of their blessings (20, p. 253).
Finally, Rachel Carson's book, Silent Spring (8), did a great
deal to increase public interest in the pesticide issue. Largely as a
result of Miss Carson's book and the interest it created, the United
States Senate, from May, 1963, to July, 1964, conducted extensive
hearings to study the problems created by the increasing use of pesti
cides and tried to assess their potential hazards (49). The general
conclusion of the Senate investigation was that the benefits from using
pesticides still far outweigh the potential hazards to the environment,
but it was strongly emphasized that additional research is badly needed,
particularly in the area of long-run hazards of pesticide use, where we
have virtually no knowledge.
Stimulated by the Senate Hearings and a growling public concern
about environmental pollution, economists became more interested in the
problem. It is within the purview of the physical sciences to establish
the link between pesticides and various elements of the environment, but
when this is done, the subsequent question, "So what?", still remains to
be answered, and this is left to the social and political sciences. The
benefits must be weighed against the costs; or to put it another way,
relative values must be assessed, and values are the economist's stock
in trade.
The term "pesticides" is commonly used to refer to the whole
family of agricultural chemicals "used to control insects, mites, ticks,
fungi, nematodes, rodents, pest birds, predatory animals, rough fish,


88
The Southern Farm Bureau was the dominant agricultural insurer
in the area and readily agreed to cooperate on the project. The
following conclusions were drawn from visits with representatives of the
company.
1. Property damage claims would be negligible relative to
workmen's compensation claims.
2. The number of pesticide claims in Dade County has probably
increased in recent years but not as fast as the increase in use of
organic phosphates. Farmers in Dade County are using more caution than
previously.
3. Dade County probably has a smaller amount of pesticide damage
than other areas of the State because its agricultural industry does not
have such a high turnover of farmers and laborers, and the farmers as a
group are relatively sophisticated.
4. In setting premiums, the fact that a farmer does or does not
use pesticides, is not considered. The Farm Bureau takes a "whole risk"
approach. This provides an interesting hypothesis about the behavior
of insurance companies. With regard to a farmer or a region there might
be a "sensitivity threshold" which must be broken before a response is
generated on the part of the insurance company. This "sensitivity
threshold" could be visualized as follows:
Time


Table 40.Concluded.
Pesticide
Crop
Mean usage
in pounds
per acre
Standard
deviation
pounds
per acre
Total
usage
by crops
in pounds
Total
usage
all crops
in pounds
Trend^
of usage
Z. P. rat bait
Tomatoes
.0004
.0019
8
Uncertain
Potatoes
.0001
.0001
1
Uncertain
Pole beans
.0137
.0054
80
89
Uncertain
All quantities have been converted to units of 100 percent concentrated material.
Acres sampled and number of observations were:
Acres
Observations
Tomatoes
10,590
24
Potatoes
4,584
51
Pole beans
2,394
93
Corn
1,585
2
Squash
244
11
Okra
50
2
Groves
1,247
26
Other
240
2
Q
These figures were computed by multiplying the usage per acre by the number of acres of the crop
grown in Dade County in 1966-67.
^This column presents the trend of usage per acre from 1965-66 to 1966-67. The information on
1965-66 was limited and not sufficient for making point estimates of usage. It should be noted that usage
from one year to another is greatly affected by insect and disease infestation and therefore a trend
projection on two years' data is at best crude and subject to considerable error.
ON
to


42
Table 7.Recommended insect
control measures for tomatoes.
a
Insect
Pesticide
Organic
phosphate
quantity^
Chlorinated
hydrocarbon
quantity^
Other
quantity
Minimum
days to
harvest
Aphids
Dimethoate
.334
7
Demeton
.375
3
Parathion
.450
3
Phosdrin
.500
1
Thiodan
1.000
1
Armyworms,
DDT
1.000
3
Tomato Fruit-
Phosdrin
.500
1 *
worms,
Sevin
1.000
NTL
Hornworms
TDE (DDD)
1.000
1
Thiodan
1.000
1
Loopers
Dibrom
2.000
1
Parathion
.450
3
Phosdrin
.500
1
Thiodan
1.000
1
Leaf Miners
Diazinon
.500
1
Dibromx*
1.000
1
Dimethoate
.334
7 *
Guthion
.500
NTL
Stinkbugs,
Guthion
.500
:k
NTL
other plant
Parathion
.450
3
bugs
Phosdrin
.250
1 A
Sevin
1.000
NTL
Thiodan
1.000
1
Banded Cucumber Guthion
.500
&
NTL
Beetle
Thiodan
1.000
1
Source of data: Insect Control Guide (17) and consultation
with entomologists familiar with the area.
Quantities are expressed in pounds of 100 percent active
material per 100 gallons of water. The gallonage applied per acre will
vary with the size and density of the plants. In the Dade County area
the gallonage usually varies from 100 to 300 gallons.
*
No time limit.
AA
Dibrom was not observed in Dade County in 1966-67.


BIBLIOGRAPHY
1. Borgstrom, Georg. The Hungry Planet; The Modern World at the Edge
of Famine. New York: Macmillan Co., 1965.
2. Boulding, Kenneth. The Impact of the Social Sciences. New
Brunswick, New Jersey: Rutgers University Press, 1966.
3. Principles of Economic Policy. Englewood
Cliffs, New Jersey: Prentice-Hall, Inc., 1958.
4. Brinkley, Park C. "A Report to the Membership," N.A.C. News,
Vol. XXVII, No. 1, October, 1968 .
5. Brooke, Donald L. Cost and Returns from Vegetable Crops in Florida:
Season 1964-1965 with Comparisons, Agricultural Economics Mimeo
Report EC 66-10. Department of Agricultural Economics, University
of Florida, Gainesville, Florida, February, 1966.
6. Buchanan, James M. "Positive Economics, Welfare Economics, and
Political Economy," The Journal of Law and Economics, Vol. II,
October, 1959.
7. Buchanan, James M. and Stubblebine, William Craig. "Externality,"
Econmica, Vol. XXIX, November, 1962.
8. Carson, Rachel Louise. Silent Spring. Boston: Houghton Mifflin
Co., 1962.
9. Coase, R. H. "The Problem of Social Cost," The Journal of Law and
Economics, Vol. Ill, October, 1960.
10. Davis, K. Scientific American, Vol. CCIX, September, 1963.
11. Day, Richard H. Contributions to Economic Analysis: Recursive
Programming and Production Response, ed. R. Strotz et al.
Amsterdam: North-Holland Publishing Co., 1963.
12. Demsetz, Harold. "Toward a Theory of Property Rights," American
Economic Review, Vol. LVII, No. 2, May, 1967.
13. Dorfman, Robert, Samuelson, Paul A. and Solow, Robert M. Linear
Programming and Economic Analysis. New York: McGraw-Hill Book
Co., Inc., 1958.
14. Duggan, R. E. and McFarland, F. J. "Residues in Food and Feed:
Assessments Include Raw Food and Feed Commodities, Market
Basket Items Prepared for Consumption, Meat Samples Taken at
Slaughter," Pesticides Monitoring Journal, Vol. I, No. 1,
June, 1967.
206


TABLE OF CONTENTS
Page
ACKNOWLEDGMENTS iii
LIST OF TABLES viii
LIST OF FIGURES xi
CHAPTER I
INTRODUCTION 1
The Problem 1
Background of the Problem 2
CHAPTER II
AREA STUDIED 7
Choice of an Area 7
Agriculture of Dade County 8
CHAPTER III
THE MODEL 19
CHAPTER IV
ESTIMATES OF MODEL COEFFICIENTS 30
Objective Function 30
The Demand Model 30
The Supply Model 34
Shifting the Supply Functions 41
The Externality Functions 51
Constraints on the Objective Function 54
Coefficients of Variables in the Constraints... 55
The Constraint Vector 56
CHAPTER V
APPLICATION OF AGRICULTURAL PESTICIDES IN DADE COUNTY.... 58
Technology of Pesticide Application in Dade County.. 58
Estimated Quantities of Pesticides Used in Dade
County 60
v


LIST OF TABLESContinued.
Table Page
18 A list of the categories constituting Agency 10, "Poisons
and Infectious Agents" 81
19 Workmen's compensation claims, State of Florida; work
injuries, days of disability, and cost by agency, for
disabling work injuries, 1962, 1963, 1966, and 1967 83
20 Dollar costs of disabling workmen's compensation claims for
Dade County, Florida, 1966, by kind of payment and agent.. 84
21 Dollar costs of disabling workmen's compensation claims for
Dade County, Florida, 1967, by kind of payment and agent.. 86
22 A summary of data gathered from veterinarians in Dade County 91
23 A summary of data gathered from the Communities Studies
Program on Pesticides in Miami 95
24 A summary of the externalities incorporated in the
empirical model, Dade County, 1967 102
25 Model solution for Policy 1 107
26 Model solution for Policy 2A 108
27 Model solution for Policy 2B 109
28 Model solution for Policy 2C 110
29 A comparison of model solutions among policies for and
z2 coefficients of 0 and -. 0301, respectively 113
30 Net profit per acre for a sample of tomato growers in
Dade County, 1960-61 through 1966-67 120
31 Net profit per acre for a sample of potato growers in
Dade County, 1960-61 through 1966-67..... 121
32 Net profit per acre for a sample of pole bean growers in
Dade County, 1960-61 through 1966-67 122
33 Net profit per acre for a sample of squash growers in
Dade County, 1960-61 through 1966-67 123
34 Estimates made by Richard Klulcas of the quantities of
insecticides used on various crops in Dade County, 1966-67 125
ix


Table 39.Continued
December
Crop
Fungicide
Insecticide
Herbicide
Total
Organic
phosphates
Chlorinated
hydrocarbons
Carbamates
Other
Tomatoes
3.8639
.5645
1.1661
.3021
.0200
.0474
5.9640
Potatoes
2.4452
1.8731
.0268
.0576
.0628
.3054
4.7709
Pole beans
10.5142
.1841
.2305
None
.1708
None
11.0996
Corn
.6752
3.3221
2.2861
None
None
.3811
6.6642
Squash
7.8030
None
.6627
None
None
None
8.4657
Okra
None
None
None
None
.7200
None
.7200
Groves
None
None
None
None
.0208
None
.0208
Other
None
None
None
None
None
None
None
Total average
usage
3.8253
.8374
.7135
.1663
.0466
.1047
5.6938
aAll
quantities have
been converted
to pounds of
100
percent
concentrated
material.
T.
0Acres sampled and number of observations were:
153


23
The objective function of the model is assumed to be separable.
That is, it is the sum of a number of independent functions in the
variables y and z^. In the terminology of the problem, this means that
any crop can be grown at any level within the range specified by the
constraints without affecting the demand and supply functions of the
other crops. It also means that the externality functions for chlori
nated hydrocarbons and organic phosphates are independent. More will be
said about the estimation of these externality functions in later pages.
The concept of a flexibility constraint (see constraints 1 and
2) was used by James M. Henderson (24) in the late 1950's in an attempt
to use linear programming to predict economic behavior of farmers.
Day (11) later applied this idea in his use of recursive programming on
crop production in 11 counties in Mississippi. Considering how a farmer
might allocate his land among various alternative uses, Henderson said
that the farmer's behavior could be explained by two principles. The
first was that the farmer would try to maximize his expected net revenue
and would therefore allocate his land in a manner consistent with
this desire. The second principle said that in allocating his land,
the farmer would also be influenced by the allocation pattern of the
preceding year. This was Henderson's unique contribution. "Speci
fically, a farmer's acreage plantings for each crop were assumed to be
constrained by maximum and minimum limits which indicated his desire
for diversity and reluctance to depart from an established pattern"
(24, p. 243). There are many factorseconomic, technological, insti
tutional, and sociologicalwhich contribute to this reluctance to
depart from an established pattern. Henderson's problem was to capture
this hypothesis in a model without making the model so complicated that


Table 42.Concluded.
Pesticide
Crop
Number of
growers
Acres
sampled
Mean usage
in pounds
per acre
Standard
deviation
pounds
per acre
Squash
7
185
6.5619
5.8972
Groves
3
609
3.5520
3.9272
Ziram
Tomatoes
1
240
.0569
.0000
Pole beans
1
207
.1322
.0000
Z. P. rat bait
Tomatoes
3
4,000
.0011
.0048
Potatoes
1
640
.0009
.0000
Pole beans
3
1,175
.0279
.0124
All quantities
have been converted
to units of 100
percent concentrated
material.
183


Table 39.
-Estimated quantities
a per acre
1966-
of certain pesticide
67 crop year, by crop
categories
and month.
used by
b
farmers in Dade
County,
January
Crop
Fungicide
Organic
phosphates
Insecticide
Chlorinated
hydrocarbons Carbamates
Other
Herbicide
Total
Tomatoes
4.8433
.6177
1.0695
.5688
.0001
.0136
7.1130
Potatoes
6.3642
.5190
.5822
None
.0288
.6726
8.1668
Pole beans
19.4178
.2543
.3743
None
.0874
None
20.1338
Corn
None
None
None
None
None
None
None
Squash
24.4520
.3279
.0887
None
None
None
24.8686
Okra
.4800
None
None
None
None
None
.4800
Groves
10.8815
.0261
None
.1797
1.9079
.1562
13.1514
Other
.1000
None
None
None
.1500
None
.2500
Total average
usage 7.0330
.4628
.7158
.2999
.1323
.1643
8.8081
142


Table 35.-
-Estimates made
by Richard Klukas of the
Dade County,
quantities
1966-67.b
of fungicides
used on
various crops
in
Total
Total
Total
Pound
Number of
pounds
pounds
pounds
Fungicide
Crop
Disease
per acre
applications
per acre per crop
all crops
....Per
season
Captan
Strawberries
Leaf Spot
1.00
14
14.00
7,700
7,700
Dyrene
Tomatoes
Gray Leaf Spot
.75
3
2.25
39,510
39,510
Karathane
Squash
Powdery Mildew
.75
11
8.25
28,875
Canteloupe
Powdery Mildew
.75
11
8.25
2,200
Cucumbers
Powdery mildew
.75
11
8.25
17,600
48,675
Maneb
Tomatoes
Late Blight
1.20
11
13.20
231,792
Potatoes
Late Blight
1.20
14
16.80
127,680
Pole beans
Rust, Root Rots,
and mildew
1.20
6
7.20
64,800
Corn
Leaf Blight
1.20
11
13.20
26,400
Okra
Powdery Mildew and
Verticilium Wilt
1.50
2
3.00
2,250
452,922
Metallic
copper
Tomatoes
Bacterial Spot
2.00
4
8.00
140,480
140,480
Nab am
Tomatoes
Soil born disease
45.00
1
45.00
790,200
790,200
Zineb
Squash
Anthracnose and
Downy Mildew
1.50
6
9.00
31,500
Okra
Powdery Mildew and
Verticilium Wilt
1.50
2
3.00
2,250
128


ACKNOWLEDGMENTS
The author is indebted to the many persons who have both
encouraged and contributed to his graduate training.
Appreciation is first expressed to the Institute of Food and
Agricultural Sciences and particularly to Dr. K. R. Tefertiller,
Chairman of the Department of Agricultural Economics, for the oppor
tunity to participate on this research grant.
The financial support of Resources for the Future, Incorporated,
Washington, D.C. is gratefully acknowledged. A special word of thanks
must go to Mr. Blair T. Bower, Dr. Michael F. Brewer, and Dr. Allen V.
Kneese of that organization and to Dr. J. C. Headley of the University
of Missouri for their willingness to read and comment on versions of
this paper. At no time, however, did they attempt to influence in any
way the outcome of the research findings.
My greatest debt of gratitude is owed to Dr. Max R. Langham,
Chairman of the Supervisory Committee, for his continuous guidance and
encouragement during the graduate program, and for his indefatigable
efforts throughout this research study.
Special appreciation is also extended to Dr. Elmo L. Jackson and
Dr. John B. McFerrin, members of the Supervisory Committee, for pro
viding helpful suggestions.
The author would also like to thank the staffs of the Dade
County Agricultural Agents Office and the Homestead Experiment Station
for assistance in gathering data on the area studied. Mr. Richard M.
Hunt, Assistant Marketing Agent, was especially helpful in establishing
iii


55
C =
n
-d(min) E y. (t)
j=l 3 -
1,1
d(max)
j
-b.(min)y.
=1 J Jl,l
(t)1
J Jn-3,1
b (max)y (t)|
J Jn-3,1
[1]
3,1
^m,l
L J mp, 1
Coefficients of Variables in the Constraints
Matrix A will be considered first. It is the matrix of
coefficients, which had to be empirically determined. As indicated
above, most of the elements of the matrix are ones or zeros. The only
x
r
a. .
ij
The
m.n
M
L J mp,
m
elements which had to be estimated were the
elements would also have to be estimated if the environmental constraints
were maintained in the model, but, as explained previously, the current
state of the arts did not permit estimation of these coefficients. In
the empirical model there were seven crops and two pesticides, so:
x
r
a. .
ij
m,n
XX X
r r r
all a12 ,al7
XX X
r r r
a21 a22 ,a27
Since two pesticide policies with three variations on the second were
considered, the model was run once for r=l and once for r=2A, 2B, and 2C.
X]
The way in which the elements a.. were estimated is explained in


32
formula,
A
E =
where p and q were the means of the variables from State data.' Then,
using this estimate of E and the 1966-67 price and quantity for Dade
County, a linear demand function of the following form was synthesized
for Dade County:"*
[4] q(t) = 3q + BjPt)
3pCt) T1 -
In two cases which will be noted later, the variables were
transformed into logarithms. For these the regression coefficients were
the required estimates of elasticity.
For each crop the function was derived as follows:
where:
q
E = estimated long-run price elasticity,
p = average price from State data,
q = average quantity from State data.
A
= an estimate of in equation [1],
where:
= partial derivative of q with respect to p for Dade County.
q* = 1966-67 quantity for Dade County,
p* = 1966-67 price for Dade County.
Using the point slope form of a linear function passing through q* and
p* yields:
9q q*-q(t) P t q*
3p d p*-p(t) 1 p*
T,pq*
+ p(t) = q*(1-e) +
qp*
or:
q (t) = 3q + 3-j^pCt)
where: e ^
B0 = q* (1-E) and ^ = ¡y
q(t)
l -
V
A


contacts with growers and planning the data gathering phase of the
research. The consulting help of Mr. Fred A. Johnson, an entomologist
in Dade County, is also gratefully acknowledged.
To the many growers in Dade County who supplied information for
this project, I would also like to express my appreciation.
Capable secretarial and clerical assistance was provided by
Mrs. Audra White throughout the project, and the author extends to her
his sincere appreciation for her conscientious service.
The services of the University Computing Center are herein
recognized.
Finally, I would like to thank my wife, Emmy, and our children,
Russell and Carroll, for their patience during this somewhat trying
period. To them, and to my parents, I dedicate this dissertation.
XV


73
been previously. Generally there was no cash settlement above rental
cost when damage occurred.
Damage among growers from pesticide drift appeared to be a very
minor problem, although it did exist. When it occurred, the growers
usually settled the problem informally among themselves with the
damaging party "paying off" in one form or another. Those payments, we
assumed, entered the damaging party's marginal cost, function and were
not construed as "externalities." The trends toward fewer crops and
larger crop fields have caused this problem to decline in recent years.
It is conceivable that external benefits may have existed due
to pesticide drift. One grower, located in the center of a number of
growers who sprayed regularly, might not have had to spray as much as
he would otherwise because of the drift from the other growers and the
protective pesticide barrier around his field. This is, however,
speculation, and we were unable to gather any data to substantiate such
an hypothesis. Table 16 is a summary of the drift problem as reported
by the growers.
As a group, the growers were well insured. Not only did almost
all of them carry workmen's compensation on their employees, but they
also carried general liability to protect them in case they damaged the
property of others. They did not, however, have insurance for cases
where others damaged their property. Whether insurance costs should be
considered as external costs will be discussed in a later section.
Insurance Cla.ims
A Florida State law exists which requires all workmen's
compensation claims to be on file at the Florida Industrial Commission


Table 40.Continued
Pesticide
Mean usage
in pounds
Crop per acre
Dexon
Pole beans
.0022
Dieldrin
Tomatoes
.0237
Pole beans
.0017
Diphenamid
Tomatoes
.7724
Dyrene
Tomatoes
1.7251
Endrin
Potatoes
.1149
Pole beans
.0259
Ethion
Groves
1.5697
Ferbam
Tomatoes
.0267
Groves
3.0412
Guthion
Tomatoes
.2930
Pole beans
.0925
Groves
.0261
Keptachlor
Tomatoes
.1714
Karathane
Tomatoes
.0002
Potatoes
.0010
Squash
.2459
Groves
.0258
Standard
deviation
pounds
per acre
Total
usage
by crops
in pounds
Total
usage
all crops
in pounds
Trend
of usage
.0013
13
13
Uncertain
.1005
450
Uncertain
.0010
10
460
Uncertain
.6179
14,676
14,676
Up
1.6692
32,777
32,777
Down
.0717
880
Down
.0151
151
1,031
Uncertain
3.1691
16,804
16,804
Uncertain
.2405
507
Uncertain
10.5389
32,556
33,063
Uncertain
.8092
5,567
Uncertain
.1017
539
Uncertain
.0411
279
6,385
Uncertain
.2996
3,257
3,257
Down
.0019
4
Uncertain
.0010
8
Uncertain
.2261
757
Uncertain
.0105
276
1,045
Uncertain
157


63
in the month in which it was purchased is found in the fact that many
growers stated they were unwilling to tie up capital and warehouse space
maintaining an inventory of pesticides which were dangerous and might,
in some cases, spoil before they were used. Transporation and labor
costs were also saved since the pesticide supplier would deliver directly
to the field where the pesticide was needed.
The crop year for the pesticide usage estimates extended from
July until the following June. Most of the planting was done in
September, October, November, and December, and most of the harvesting
in January, February, March, and April. For an acre of ground, only one
crop per year was grown.
The tables of pesticide usage estimates are presented in Appendix
D (p. 139). Much of the pesticide data presented in the appendix were
not required for the empirical model but they are nevertheless descrip
tive of pesticide usage in the region and it was felt that they might
also be of use to other researchers concerned with the pesticide issue.
Table 38 (p. 140) summarizes the data for six broad categories
of pesticidesfungicides, organic phosphate insecticides, chlorinated
hydrocarbon insecticides, carbamate insecticides, other insecticides,
and herbicides. Each is reported by crop. Much of the current pesti-
.cide controversy centers around two of the insecticide categories
the chlorinated hydrocarbons and the organic phosphates. It is the data
on these two categories which were used in the model of Chapter III.
Data on the trend in usage of these categories through the years were not
obtained for Dade County, but the statistical series recently initiated
by the Economic Research Service of the USDA indicates that since 1964
farmers in the Southeast region (South Carolina, Georgia, Florida,


Table 19.Workmen's compensation claims, State of Florida; work injuries, days of disability,and cost
by agency, for disabling work injuries, 1962, 1963, 1966, and 1967.a
Number of
injuries
Days lost
Cost
1962
State total
56,701
4,274,398
$25,713,086
Poisons and infectious agents
2,082
89,197
516,380
Percent
.036
.0208
.0200
1963
State total
51,297
4,517,191
28,497,999
Poisons and infectious agents
1,747
69,919
517,730
Percent
.033
.0154
.0181
1966
State total
80,803
7,704,996
51,599,084
Poisons and infectious agents
2,622
126,298
831,477
Percent
.032
.0164
.0161
1967
State total
71,490
6,258,464
43,115,224
Poisons and infectious agents
2,339
77,139
665,473
Percent
.033
.0123
.0154
aSource of data: (19).
oo
u>


Table 39.Continued
October
Crop
Fungicide
Insecticide
Herbicide
Total
Organic
phosphates
Chlorinated
hydrocarbons
Carbamates
Other
Tomatoes
1.6478
.2051
.5374
None
.0583
.2074
2.6560
Potatoes
.1691
.4411
None
None
None
None
.6102
Pole beans
.8552
.1075
.0914
None
.0287
.0301
1.1129
Corn
None
None
None
None
None
None
None
Squash
4.0761
.2911
.3139
None
None
None
4.6811
Okra
None
None
None
None
None
None
None
Groves
.1147
None
None
None
None
.0080
.1227
Other
None
None
None
None
None
None
None
Total average
usage
1.0276
.2170
.2874
None
.0329
.1093
1.6742
151


Table 23.A summary of data gathered from the Communities Studies Program on Pesticides in Miami.
Year
Result
Age
Sex
Race
Pesticide
Comments
1964
Fatal
1
Female
White
Guthion
The father, a migrant worker, brought the
pesticide home from the field.
1964
Non-fatal
20
Male
Negro
Parathion
One week in hospital and another week off work.
1964
Non-fatal
19
Male
Negro
Parathion
One week in hospital and another week off work.
1964
Non-fatal
50
Male
Negro
Parathion
In hospital 2 weeks and off work another 4 weeks
1964
Non-fatal
3
Female
Negro
Parathion
Father brought material home and put it in a
spray gun. Infant ingested it from the spray
gun.
1964
Non-fatal
2
Male
Negro
Parathion
One week in hospital. The material, in powder
form, was brought home to kill roaches.
1964
Non-fatal
23
Male
White
Phosdrin
In hospital 4 days and off work 3 to 4 more days
He was operating a spray rig.
1964
Non-fatal
42
Male
Negro
Parathion
In hospital 1 week; off work another week.
Victim was spraying a potato field.
1964
Non-fatal
2
Female
Negro
Unknown
Became ill after eating treated seed beans.
1965
Fatal
17
Male
White
Zectran
The victim, an employee of a horticultural
nursery, drank the material from a coke bottle.


CHAPTER IV
ESTIMATES OF MODEL COEFFICIENTS
Objective Function
The objective function of the model was based on the concept of
consumers'-producers surplus, modified to recognize external effects
not included in the surplus calculation. As noted earlier, the area of
consumers'-producers' surplus was defined as the area under the demand
function less the area under the supply function.
The Demand Model
The model which provided the basis for estimating demand
parameters for the objective function was specified as follows:
II] q(t) = Tq + T-jPt) + u(t)
subject to:
[2]
q(t) q(t-l) = Ip
q(t) -
q(t-l)
0 < ip < 2
where:
q(t) = the long-run equilibrium quantity as of period t.
p(t) = price of the commodity in period t.
I(t) = per capita disposable income (deflated) in period t.
q(t) = the quantity demanded in period t.
u(t) = a disturbance term satisfying the classical assumptions,
namely:
EI u ( i ) ] = 0
30


Table 37.Continued.
Common name
Trade name
Chemical name
FUNGICIDES
Other
Botran
2,6-dichloro-4-nitroaniline.
Captan
Orthocide, Stauffer Captan,
Ortho Captan
N-trichloromethylthiotetrahydrophthalli-
mide OR N-trichloromethyl mercapto-4-
cyclohexene-1,2-dicarboximide.
Copper compounds
Mang-Z-Kop, Nutri Sperse,
CPCS
These are composed of 33% copper and
miscellaneous formulae not available.
Dexon
Sodium P-dimethylaminobenzenediazo sulfo
nate, diacetoxy propene (not acceptable)
should be 2-propene-l,1-diol diacetate.
Dichlone
Phygon
2,4-dichloro-l,4-n-aphthoquinone.
Dyrene
Kemate
2,4-dichloro-6-(2-chloroanilino)-s-
triazine.
Ferbam Fermate, Karbam Black, Stauffer Ferric dimethyl dithiocarbamate.
Ferbam, Carbamate
Karathane
Dinitro (1-methylheptyl) phenyl crotonate.


Table 39.Continued.
March
Crop
Fungicide
Organic
phosphates
Insecticide
Chlorinated
hydrocarbons Carbamates
Other
Herbicide
Total
Tomatoes
1.3239
.1456
.8046
.0008
None
None
2.2749
Potatoes
.7997
.0611
.1226
None
.0046
4.5556
5.5436
Pole beans
12.2596
.2862
.7237
None
.0289
None
13.2984
Corn
6.8941
3.4521
16.7774
None
None
.0815
27.2053
Squash
.0492
.2187
.1639
None
None
None
.4318
Okra
1.9224
.2670
.6000
None
None
None
2.7894
Groves
3.2403
None
.0192
2.5469
.0778
.0643
5.9485
Other
None
None
None
None
None
None
None
Total average
usage
2.7000
.2453
1.1155
.1528
.0090
1.0091
5.2317
m


Table 15.Continued
Case
number Pesticide Date Time in hospital
9 Thimet 1966
10 Parathion 1960 2 weeks
11 Parathion 1964
12 Phosdrin 1961 1 night in hospital
for each instance
13 Parathion 1964
14
Parathion
1966
3 days
Comments
Grower got sick in spite of fact that he had on
gloves and mask. He went to the Poison Control
Center and tests came back negative but he still
felt that it was the Thimet, and he could no longer
get near the material.
Spray man absorbed the pesticide through his skin.
He almost died in the hospital and thereafter was
not able to use Parathion. In addition to the time
spent in the hospital, he was off work at least a
month.
Three men were involved in this instance. They were
spraying Parathion without face masks. About 2
hours per man were lost from work while a doctor
examined them.
These two instances involved the same man. Each
instance involved skin absorption. After the second
instance, the grower quit using Phosdrin.
Spray man was not using any protective clothing and
apparently got some Parathion on his skin. He has
had no further trouble since then.
Spray man did not wash his hands before eating lunch.
He never reported back to work after release from
the hospital. Grower reported he did not know where
he had gone.
ON
NO


APPENDIX C
COMMON, CHEMICAL, AND/OR TRADE NAME OF
PESTICIDES IDENTIFIED IN
DADE COUNTY


29
problem, for which we needed both a conceptual model and the data to use
it empirically. We retreated or abstracted from reality only to the
point where we felt we could actually satisfy the data requirements.
If we had not been faced with the necessity to use the model empirically,
we might, for example, have developed a dynamic programming formulation
which would have had the advantage of greater realism but for which we
could not obtain the necessary data.


CHAPTER V
APPLICATION OF AGRICULTURAL PESTICIDES IN DADE COUNTY
The information contained in this chapter provides the descrip
tive background and the pesticide usage estimates employed in Chapter IV
for the development of the externality functions.
Technology of Pesticide Application in Dade County
Pesticide spray programs in Dade County vary widely among
growers depending upon the crop involved, the form, of the pesticide
being used, the method of application, and the insect or disease
infestation. There are four dominant pesticide formulations(1) wet-
table powder, (2) dust, (3) liquid or emulsifiable concentrate, and
(4) granulesand there is a range of concentrations for each.
Parathion, for example, was observed in three different formulations
and seven different concentrations.
The technology of pesticide application ranges from the
relatively primitive hand methods to aerial application using
helicopters, but most growers use ground rigs which hold one, two, three,
or four hundred gallons of liquid. Aerial application is still slightly
more expensive than ground application, but it is more flexible during
adverse weather conditions when ground rigs cannot get into the fields.
Some growers claim that aerial applications are more effective, through
better coverage, than ground rigs. The ground rigs are usually operated
58


56
Chapter V. The resulting coefficients were:
X1 6.0719, 1.2860, 3.4304, 40.7003, .0706, .0484, .0205
aij 3.2669, 5.8825, 1.8114, 6.4624, .2239, .1535, .0650
m,n
The Constraint Vector
Of the elements of the constraint vector, C, the first four sets
were flexibility constraints. The underlying philosophy of flexibility
constraints was explained in the previous chapter.
For the estimation of the maximum and minimum flexibility con
straints, an approach called "the method of average rates" was used (11,
p. 189). For the jth crop, this took the form:
m-, y. (t)
V"0 tf2 y¡(^T)
for y_. (t) > y^ (t-1)
m.
y.(t)
bJ(mln) 2 P^T)
m
for yj(t) y.(t-i)
where m's were the number of periods involved, respectively. For the
total land constraint, it was:
d(max)
n
E
j=l
y1(t)
pi
d(min)
n y. (t)
y _1_
i-i
n n
for E y. (t) > E y. (t-1)
j-1 J j=l J
n n
for E y.(t) E y (t-1)
j=l J j=l 3
where p's were the number of periods involved, respectively, and n the
number of crops.
In words, the maximum flexibility constraint was simply one plus
the average of the percentage increases, and the minimum flexibility


44
Table 9.Recommended insect control measures for beans.
a
Organic
Chlorinated
Minimum
phosphate
hydrocarbon
Other
days to
Insect
Pesticide quantity^
quantity^
quantity
harvest
Aphids
Demeton
.375
21 *
Dimethoate
.334
NTL
Parathion
.300
3
Phosdrin
.250
1
Armyworms,
DDT
1.000
5 *
Corn Earworm
Sevin
1.000
NTL
Toxaphene
1.000
5
Cowpea Curculio
Toxaphene
1.000
5**
Thiodan
.500
3
Bean Leaf-
Dimethoate
.334
k
NTL
hopper, Bean
Guthion
.500
7
Leafroller
Parathion
.300
3
Phosdrin
.500
1 *
Sevin
1.000
NTL
Toxaphene
1.000
5
Leaf Miners,
Diazinon
.500
7 *
Cucumber
Dimethoate
.334
NTL5"
Beetles
Guthion
.500
7
Thrips
Parathion
.225
3
Stinkbugs
Guthion
.500
7
Parathion
.300
3
Phosdrin
.250
1 *
Sevin
1.000
NTL
Saltmarsh
Phosdrin
.500
1
Caterpillar
Toxaphene
1.000
5
Lima Pod Borer
Parathion
.300
3
Source of data: Insect Control Guide (17) and consultation
with entomologists familiar with the area.
^Quantities are expressed in pounds of 100 percent active
material per 100 gallons of water. The gallonage applied per acre will
vary with the size and density of the plants. In the Dade County area
the gallonage usually varies from 100 to 300 gallons.
k
No time limit.
k-k
Should not apply Thiodan more than 3 times per season.


Table 40.Continued
Pesticide
Crop
Mean usage
in pounds
per acre
Pole beans
1.3942
Corn
5.0964
Squash
.9797
Okra
1.7600
Groves
.0962
Paraquat
Tomatoes
.0004
Groves
.1407
Phosdrin
Tomatoes
.0159
Potatoes
.3367
Pole beans
.0063
Okra
.9600
Phosphoric acid
Potatoes
.0192
Phygon
Pole beans
.0528
Polyram
Potatoes
.3644
Prolin rat bait
Tomatoes
.0098
Potatoes
.0046
Sevin
Tomatoes
2.4925
Potatoes
.0576
Groves
3.0345
Simizine
Groves
.4204
Standard
deviation
pounds
per acre
Total
usage
by crops
in pounds
Total
usage
all crops
in pounds
Trend^
of usage
1.4190
8,128
Uncertain
6.0096
8,409
Uncertain
.8141
3,017
Uncertain
3.1113
1,056
Uncertain
.0392
1,030
69,083
Uncertain
.0033
8
Uncertain
.2777
1,506
1,514
Uncertain
.0434
302
Uncertain
.3831
2,579
Uncertain
.0075
37
Uncertain
.0707
576
3,494
Uncertain
.0924
147
147
Down
.0211
308
308
Uncertain
.7196
2,791
2,791
Uncertain
.0354
186
Uncertain
.0090
35
221
Uncertain
1.6889
47,358
Down
.0588
441
Uncertain
1.2369
32,484
80,283
Uncertain
.0328
4,500
4,500
Uncertain
159


38
[5] yields:
[7] y(t) = 6?0 + (l-)y(t-l) + S^pCt-l) + 6u(t)
Equation [7] was empirically estimated and the results used to obtain
estimates of the long-run supply elasticity. Results are shown in
Table 6.
As in the development of the demand functions, the estimate of
elasticity and the 1966-67 price and quantity observations for Dade
County were then substituted into the point-slope form of a straight
line to obtain the equations for the supply functions. These equations
were then solved for p in terms of y in order to conform to the require
ments of the model.
Since equation [5] includes price lagged one time period, the
supply and demand equations constitute a cobweb model which, for
purposes of the study, is assumed to be in equilibrium at the 1966-67
price and quantity. The objective is to determine what happens to this
point of long-run equilibrium when externalities are recognized. There
was no interest in the time path of price-quantity changes but only in
9
the magnitude of change in the point of long-run equilibrium.
In the case of avocados, limes, and mangos, data did not exist
to permit the estimation of supply functions, so the acreage for these
crops was constrained at the 1966-67 level. This was probably a
If the system gets out of equilibrium, cobweb theory (15,
pp. 255-280) states that the time path of price and quantity will be
convergent or divergent depending upon whether the absolute value of the
slope of the supply function is greater or less than that of the demand
function. In the case of potatoes, the relative slope of the supply
function to the demand function would indicate a divergent path. The
negative estimate of the income elasticity for potatoes would aggravate
this problem if income increased. Shifts in the flexibility constraints
might also affect the time path.


Table 35.Concluded.
Fungicide
Crop
Disease
Pound
per acre
Number of
applications
Total
pounds
per acre
Total
pounds
per crop
Total
pounds
all crops
Canteloupe
Anthracnose and
Downy Mildew
1.50
11
16.50
4,125
Cucumbers
Anthrcnose and
Downy Mildew
1.50
11
16.50
33,000
70,875
Management Biologist of the National Park Service, Everglades National Park.
All quantities have been converted to units of 100 percent concentrated material.
129


115
more radically. Line 2 represents some future state of the arts. Again,
beyond some point welfare may decline more drastically, but the point
has moved further out. By the same reasoning lines 3 and 4 represent
successive points in the future.
At the point in time represented by line 4 farmers are able to
substitute non-persistent for persistent pesticides very easily with
virtually no decrease in welfare because of the new non-persistent pesti
cides which have been developed during the intervening period of time.
Even then, however, it might be that a few pests still require persis
tent materials. Their level of usage, however, would present no serious
threat to the quality of the environment.
The lines in Figure 8 reflect the current judgments of
entomologists with regard to policies which require large, decreases in
the use of persistent pesticides. However,the changing state of the
arts is shifting the curves representing increasing social costs
downward to the right. This hypothesis which these shifts represent
suggest a multi-stage versus a single stage approach to reducing the
usage of chlorinated hydrocarbons. In the multi-stage approach we
evaluate, say, a 50 percent reduction policy. If it is not "too
detrimental" to welfare, we pursue it. When it is accomplished, we
again evaluate a 50 percent reduction policy and again pursue it if it
is not "too detrimental" to welfare. This process continues as long as
welfare is improved or until the governing body decides the price of
further reduction is "too high." At each stage of the process a new
state of the arts prevails which the analyst may recognize in his model.
The writer is therefore recommending the multi-stage approach on the
ground that it would leave more flexibility for adjustment, more freedom


Table 39.Continued
June
Crop
Fungicide
Insecticide
Herbicide
Total
Organic
phosphates
Chlorinated
hydrocarbons
Carbamates
Other
Tomatoes
None
None
None
None
None
None
None
Potatoes
None
None
None
None
None
None
None
Pole beans
None
None
None
None
None
None
None
Corn
None
None
None
None
None
None
None
Squash
None
None
None
None
None
None
None
Okra
2.7000
.1600
1.2000
None
None
None
4.0600
Groves
3.4270
.0181
None
None
.2936
.0151
3.7538
Other
None
None
None
None
None
None
None
Total average
usage
.2116
.0015
.0029
None
.0176
.0009
.2345
147


81
Table 18.A list of the categories constituting Agency 10, "Poisons
and Infectious Agents.3
Code
number Agent
10000 Unknown or unreported
10002 Acids, n.e.c.b
10003 Citrus dermatitis
10004 Alcohol
10006 Ammonia
10008 Aniline, other dyes
10010 Arsenic
10012 Beryllium
10014 Carbon dioxide
10016 Carbon monoxide gas fumes
10018 Carbon tetrachloride
10020 Caustics, n.e.c.
10021 Soap
10022 Chlorate of lime
10024 Chlorine
10026 Chromium
10028 Coal tar distillates (naptha, benzol) carbolic acid, creosote
10029 Ethylene gas (fruit coloring)
10030 Di,tetra or perchloroethylene
10031 Dieldrin
10032 Fertilizer, n.e.c.
10033 Food, etc.
10034 Formaldehyde (formain, embalming fluid)
10036 Hydrochloric acid (muriatic)
10038 Hydrocyanic acid
10040 Hydrogen sulphide
10042 Insecticide, n.e.c.
10043 Lead or paint
10044 Malathion
10046 Mercury
10048 Metal fumes (aluminum, monel zinc, welding fumes)
10050 Nitric acid
10052 Parathion
10054 Petroleum distillates (propane, butane, methane, gasoline,
10056 Phosphorus Stoddard solvent, kerosene)
10058 Smoke
10060 Sulphuric acid and battery acid
10062 Tar and pitch fumes
10064 Turpentine
10099 Chemicals and poisons, n.e.c.
10100 T.B.
10111 Anthrax
10122 Fungus infections
10133 Larvae migrans (creeping eruption)


102
Table 24.A summary of the externalities incorporated in the
empirical model, Dade County, 1967.
Nature of externality
Extent
of damage
Damage to humans
Compensation
$l,094b
Medical
662
Hospital
653
Artificial members
27
First aid
125
Total 1
$2,561
Total 2C
$3,227
Total 3d
$3,470
0
Damage to domestic animals
$1,120
Total
$4,590
I
Data from the Florida Industrial Commission, Table 21, serve as
the basis for these figures. The agents, "Parathion" and "Insecticides
not elsewhere classified," were added together.
bThe beginning figure for this estimate was $547. According to
Florida law, the maximum payable compensation is $46 per week. But the
average weekly salary of Florida workers is $86.18. Therefore the
figure, $547, was doubled to try to reflect more accurately the true
dollar loss.
Q
Total 2 represents a 26 percent increase over Total 1 due to
the fact that disabling damages, which must be estimated at the time of
occurrence, have historically been underestimated. For a more complete
explanation of this increase in cost, see the report entitled, "Facts
About Workmen's Compensation," February, 1968, published by the Florida
Industrial Commission, Research and Statistics Department, Caldwell
Building, Tallahassee, Florida 32304.
dTotal 3 represents an increase over Total 2 to reflect the fact
that the dollar cost of disabling claims in agriculture have histori
cally comprised 93 percent of the dollar cost of total claims.
0
From data gathered from the veterinarians. Assumptions:
1. All poisoning calls are included, whether or not they
are designated "pesticide," "toad," or "lizard."
2. A dead animal was assumed to be worth $50.
3. An average veterinarian call was assumed to cost $10.


43
Table 8.Recommended insect control measures for potatoes.
a
Insect
Pesticide
Organic
phosphate
quantity^
Chlorinated
hydrocarbon
quantity^
Other
quantity
Minimum
days to
harvest
Aphids
Derneton
.375
21
Dimethoate
.500
7
Meta-Systox-
-R .375
7 *
Thiodan
1.000
NTL
Armyworms,
Parathion
.300
5
Loopers, other
Phosdrin
.500
1 *
caterpillars
Thiodan
1.000
NTL*
Toxaphene
1.000
NTL
Banded Cucumber
Guthion
.500
7 *
Beetle
Thiodan
1.000
NTL
Leaf-footed
Guthion
.500
7
Plant Bug,
Parathion
.300
5
Green Stinkbug
Phosdrin
.250
1 *
Thiodan
1.000
NTL
Leaf Miners
Diazinon
.500
14 *
Dibrom**
1.000
NTL
Dimethoate
.334
7
Guthion
.500
7
Wireworms
Thimet
3.000
***
Parathion
2.000
kkk
3
Source of data: Insect Control Guide (17) and consultation
with entomologists familiar with the area.
Quantities are expressed in pounds of 100 percent active
material per 100 gallons of water. The gallonage applied per acre will
vary with the size and density of the plants. In the Dade County area
the gallonage usually varies from 100 to 300 gallons.
*
No time limit.
kk
Dibrom was not observed in Dade County in 1966-67.
kkk
Soil treatment prior to planting.


33
The average effects of I(t) and q(t-l) on q(t) are, of course,
recognized in these simplified functions since I(t) and q(t-l) were
included as variables in the estimation of equation [3].
Equation [4] was then manipulated algebraically to make q(t) the
independent variable.^ At the same time the unit of measure on the
quantity variable was converted to acres by multiplying the equation by
the average yield per acre of the past 10 years. This led to demand
equations expressing price as a function of quantity (y) which were used
in the objective function on page 19 as f (y^), j being the crop desig
nation.
The conversion of q, units of production, into y, acres, via
the average yield per acre was done in order to express the demand
functions in the same units as the supply functions. Such a conversion
abstracts from the uncertainties of weather on crop yield and rests on
an average expected yield per acre.
Alternative data series (seasonal as well as annual data) were
tried in estimating demand functions. In using seasonal data, the
designated seasons were fall (September through November), winter
(December through February), and spring (March through May). Zero-
one variables were used to allow for the average seasonal effect. If an
observation occurred in the fall, the fall variable was assigned a value
of 1, otherwise 0. If an observation occurred in the spring, the spring
variable was assigned a value of 1, otherwise 0.^ The effect of this
^This, of course, does not necessarily yield the same equation
as one would get had the direction of minimization of the sums of
squares of residuals been in the direction of the p(t) axis. Initially,
p was defined as the independent variable because observations on p were
thought to be more accurate than those on q. Johnston (27, pp. 148-176)
offers a treatment of observational errors in the variables.
^The winter season is recognized in the intercept and therefore
does not have a zero-one variable assigned to it.


Table 39.Continued
July
Crop
Fungicide
Insecticide
Herbicide
Total
Organic
phosphates
Chlorinated
hydrocarbons
Carbamates
Other
Tomatoes
None
None
None
None
None
None
None
Potatoes
None
None
None
None
None
None
None
Pole beans
None
.0100
.0334
None
None
None
.0434
Corn
None
None
None
None
None
None
None
Squash
None
None
None
None
None
None
None
Okra
None
None
None
None
None
None
None
Groves
None
None
None
None
None
None
None
Other
None
None
None
None
None
None
None
Total average
usage
None
.0012
.0038
None
None
None
.0050
m


165
Table 41.-
Pesticide
Dacthal
DDD
DDT
-Continued.
Standard
Crop
Month
Mean usage
in pounds
per acre
deviation
pounds
per acre
Pole beans
September
.0112
.0075
Pole beans
October
.0250
.1068
Pole beans
November
.0371
.0797
Pole beans
December
.0228
.0555
Pole beans
January
.0584
.1243
Pole beans
February
.0565
.1019
Pole beans
March
.0851
.0989
Pole beans
April
.0223
.0138
Squash
March
.0547
.0503
Okra
February
.2670
.4720
Okra
March
.2670
.4720
Other
April
.2225
.1888
Pole beans
October
.0301
.0361
Tomatoes
September
.0453
.2338
Tomatoes
October
.0068
.0612
Tomatoes
December
.0567
.2041
Tomatoes
January
.0340
.1225
Tomatoes
Augus t
.0125
.0605
Tomatoes
September
.0710
.4777
Tomatoes
October
.0491
.2354
Tomatoes
November
.0311
.1855
Tomatoes
December
.0755
.3321
Tomatoes
January
.0793
.2054
Tomatoes
February
.0483
.0907
Tomatoes
March
.1146
.0917
Tomatoes
April
.0115
.0516
Potatoes
January
.1296
.2965
Potatoes
February
.0262
.1400
Potatoes
March
.0414
.0416
Potatoes
May
.0109
.0215
Pole beans
April
.1035
.0601
Pole beans
May
.1445
.0801
Corn
December
.7620
.0000
Corn
February
6.6429
.0000
Corn
March
14.7840
.0000
Corn
April
2.3776
.0000
Okra
March
.2000
.3536
Okra
May
.8000
1.4142
Okra
June
.4000
.7071
Groves
March
.0192
.0078
Other
May
.2500
1.0607


Table 20.Dollar costs of disabling workmen's compensation claims for Dade County, Florida, 1966,
by kind of payment and agent.a
Agent
1
2
3
4
Unknown or unreported
$4,488
$1,663
$ 257
$ 91
Acids, n.e.c.c
3,810
2,023
856
29
Citrus dermatitis
169
Alcohol
8
Ammonia
1,299
481
64
Carbon monoxide gas fumes
924
524
604
32
Caustics, n.e.c.
7,043
3,233
1,203
223
Soap
4,013
3,145
27
254
Chlorate of lime
9,204
7,653
672
142
Chlorine
133
134
Coal tar distillates
(naptha, benzol) Carbolic
acid, creosote
107
56
Food, etc.
304
360
122
78
Hydrochloric acid (muriatic)
56
188
271
Insecticide, n.e.c.
872
806
60
Lead or paint
1,369
1,345
1,010
118
Metal fumes (aluminum, monel
zinc, welding fumes)
528
510
989
43
Parathion
87
77
728
Kind of payment
Total
Petroleum distillates
(propane, butane, methane,
gasoline, Stoddard solvent,
kerosene)
2,176
2,277
404
Smoke
869
474
40
Sulphuric acid and battery
acid
412
298
85
$ 500
$ 0 $ 0
$ 830
$ 7,829
860
7,578
169
8
411
2,255
255
2,339
1,962
13.664
1,605
9,044
9,521
27,192
267
163
864
515
1,738
190
4,032
355
2,425
892
1,310
6,365
431
1,814
105
900
198


166
Table 41.Continued.
Pesticide Crop
Demeton
Potatoes
Potatoes
Potatoes
Potatoes
Dexon
Pole beans
Dieldrin
Tomatoes
Tomatoes
Tomatoes
Pole beans
Pole beans
Diphenamid
Tomatoes
Tomatoes
Tomatoes
Tomatoes
Tomatoes
Tmateos
Dyrene
Tomatoes
Tomatoes
Tomatoes
Tomatoes
Tomatoes
Tomatoes
Tomatoes
Endrin
Potatoes
Potatoes
Potatoes
Pole beans
Ethion
Groves
Groves
Groves
Ferbam
Tomatoes
Tomatoes
Groves
Groves
Groves
Groves
Month
Mean usage
in pounds
per acre
Standard
deviation
pounds
per acre
December
.0002
.0025
January
.0564
.2317
February
.0775
.1600
March
.0031
.0366
May
.0022
.0013
August
.0114
.0481
September
.0109
.0562
October
.0014
.0125
August
.0013
.0008
September
.0004
.0003
August
.2203
.4617
September
.1775
.3910
October
.1677
.1003
November
.1458
.0835
December
.0474
.0441
January
.0136
.0093
October
.0340
.1680
November
.1541
.2364
December
.2659
.6230
January
.5295
.4843
February
.6619
.4671
March
.0775
.1368
April
.0023
.0042
December
.0186
.0186
January
.0575
.0394
February
.0389
.0352
May
.0259
.0151
January
1.4223
3.1208
February
.1438
.5789
March
.0036
.0883
September
.0155
.1396
October
.0112
.1008
January
.0088
.5366
February
.3668
3.4160
March
.6107
1.6842
April
.4088
2.3490


17
Figure 3.Population trends in Dade County, Florida.
Reprinted with the permission of the Metropolitan Dade County
Planning Department.
capital relative to other areas of the State. The 1964 Census of
Agriculture indicates that the investment in land and buildings per acre
of farm land is $1,088.12 in Dade County against $285.71 for the State
(47, p. 304-305). For the U.S. as a whole it is $143.81 (48, p. 792).


Table 22.A summary of data gathered from veterinarians in Dade County.
Veterinarian 1
Veterinarian 2
Veterinarian 3
Deceased file
Live file
Total number of calls examined
4090
2082
3500
1100
Number of calls due to pesticide
poisoning
4b
9C
0
4d
Number of calls due to toad or
lizard poisoning
5
5
15e
0
Total calls due to poisoning
9
14
15
4
Proportion poisoned
.0022
.0067
.0043
.0036
£
The files of this veterinarian had
been purged of all
the animals that
died; he did
not keep a
deceased file.
0ne dog ate roach poison; a second was questionable as to the diagnosis.
Q
One dog ate ant poison; two more were questionable; a fourth had no diagnosis but was treated with
atropine, a standard antidote for poison or shock.
d0ne of these was questionable as to diagnosis.
0
One of these was a pet mountain lion poisoned by a lizard.


ECONOMIC EXTERNALITIES IN THE AGRICULTURAL
USE OF PESTICIDES AND AN EVALUATION
OF ALTERNATIVE POLICIES
By
WILLIAM FRANKLIN EDWARDS
A DISSERTATION PRESENTED TO THE GRADUATE COUNCIL OF
THE UNIVERSITY OF FLORIDA
IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE
DEGREE OF DOCTOR OF PHILOSOPHY
UNIVERSITY OF FLORIDA
1969


195
of the general population and an in-depth study of selected communities
in high-use areas. In-depth community studies, including monitoring,
are in progress at these locations:
Arizona
Pima and Maricopa Counties
California
State-wide
Colorado
Weld County
Florida
Dade County
Hawaii
Island of Oahu
Iowa
Johnson County
Louisiana
LaForche and Jefferson Parishes
Michigan
Berrien County
New Jersey
Monmouth County
Texas
Cameron and Hidalgo Counties
Washington
Wenatchee and Quincy Basins
Residues in Fish, Wildlife, and Estuaries
Federal efforts to determine pesticide levels in fish and
wildlife are being carried out by the Bureau of Sport Fisheries and
Wildlife, U.S. Department of the Interior. Monitoring estuarine pesti
cide levels in clams, oysters, and sediments is a joint endeavor of the
Bureau of Commercial Fisheries, U.S. Department of the Interior, and the
Water Supply and Sea Resources Program of the National Center for Urban
and Industrial Health, Public Health Service, U.S. Department of Health,
Education, and Welfare. The objective of this program is to ascertain
on a national scale, and independent of specific treatments, the levels
and trends of certain pesticidal chemicals in the bodies of selected
forms of animals and in estaurine sediments. The following locations
are currently being sampled in Florida.
Location
Element being sampled
St. Johns River, Welaka, Florida
St. Lucie Canal, Indiantown, Florida
Tampa Bay, Tampa, Florida
Apalachicola Bay, Apalachicola, Florida
Fish
Fish
Shell fish and sediment
Shell fish and sediment


UNIVERSITY OF FLORIDA
DEPARTMENT OF AGRICULTURAL ECONOMICS
PESTICIDE QUESTIONNAIRE
SECTION I: GENERAL INFORMATION
1.1 Interview # .
1.2 Date.
kit tnteAviejMA weAe token dueling the. 1967-1968 gAowing -6 ecu on
and pertained to the 1966-1967 gAoMing ecuon.
1.3 Number of acres operated .
1.4 Number of acres owned .
1.5 Land Use 1966-1967 crop year (August 1, 1966 through July 31, 1967)
Double Cropped
Use Acres Proportion Other Crop
Vegetables
Tomatoes
Potatoes, Irish
Pole Beans
Corn
Squash
Other
Fruit
Avocados
Limes
Other
Pasture
185


10
less than 13 feet above sea level. But a few low ridges in the eastern
portion are slightly higher than 20 feet in elevation. In the western
portion the surface water drains slowly through the peat marshes in a
southerly and southwesterly direction to the Gulf of Mexico. In the
eastern portion the indistinct drainageways of the peat marshes join
many of the canals and ditches which extend through the low sandy and
rocky ridges to the Atlantic Ocean. All major canals in the northeast
and southern part have control-dams for regulating water levels, but
very few pumps are used for controlling water levels in fields except
on several large farm operations. Most of the southwestern part of the
county is included in the Everglades National Park.
Dade County enjoys tropical climatic conditions not found else
where on the U.S. mainland. It is 500 miles further south than Los
Angeles, but it is not subject to the heat of the tropics due to the
nearness of the Atlantic Ocean, the Gulf of Mexico, and the prevailing
tradewinds. The mean annual temperature at Miami is 75.1 F., and the
mean annual rainfall is 57.8 inches. Most of the rainfall comes during
the summer months. Occasionally disturbances of hurricane type move
across the county during the months from August to November. Killing
frosts may occur from the middle of December to March, but many winters,
sometimes several in succession, pass without damage from freezing
temperatures. Livestock graze the pastures throughout the year and need
little shelter from the weather. Taken together, the climatic con
ditions of Dade County make .it one of the most unusual and productive
farming areas of the nation.
September through March are the months of primary agricultural
activity. Winter vegetables, ornamental horticulture, and tropical


82
Table 18.Concluded.
Code
number Agent
10144 Septic infections
10155 Undulant fever (brucellosis)
10200 Dusts
10211 Organic dust (vegetable matter)
10222 Inorganic dust with free silica
10233 Asbestos
10244 Inorganic dust except silica or asbestos
10300 Poison woods or vegetation
10999 Poisonous and infectious agents, n.e.c.
£
Source of data: coding form used by the Florida Industrial
Commission.
^Not elsewhere classified.


Table 16.Continued.
Case
numb er
Pesticide
Date
Crop
7
Sodium Arsenite
1965
Beans
8
Not available
Not available
Tomatoes
9
Sodium Arsenite
1959
Beans
10
Toxaphene
Not available
Squash
11
Parathion
Not available
None
Extent of damage
Comments
About $300
Potato grower was the damaging
party. Settlement was made by
an insurance company.
10 acres
Grower reported that the county
damaged these tomatoes with an
herbicide. No settlement was
made.
Slight
Potato grower. No settlement
was made.
Not available
Grower damaged his own squash
crop while treating his bean
crop.
Not available
A dusting pilot's hopper hung
up and he accidently dusted
some of the government houses
around the Homestead Air Force
Base. The grower received a
"cease and desist" letter from
Washington ordering him to
quit farming so near the base.


8
dependent upon pesticides, Dade County uses great quantities of them in
the production of its crops. It is relatively isolated (by the Ever
glades Swamp, the metropolitan area of Miami, and the Atlantic Ocean),
and there are ample opportunities for urban-rural interaction as the
growing city of Miami encroaches on the agricultural interests in South
Dade County. In addition, several scientists are currently working on
problems related to the use of pesticides in that area.
Agriculture of Dade County'*'
When Dade County was created in 1836, it comprised most of
Southeast Florida. Its first county seat was located at Juno, north of
what is now West Palm Beach. In these early days the industry of the
area consisted of starch production from the native plants, coontie or
zamia. Following the "great freeze" of 1894 (which nearly wiped out the
citrus groves in Central and North Florida) Henry Flagler, seeing Dade
County had escaped all damage, extended the railroad all the way to
Miami and built a hotel there. This was to mark the beginning of the
great development of Dade County.
Today, the county is situated in the southeastern part of the
Florida peninsula, as indicated by Figure 1. It extends about 55 miles
from north to south and 47 miles from east to west. The total area is
2,054 square miles, or 1,314,560 acres. Miami, the county seat, and
other towns are located in the eastern portion of the county near the
coast.
The surface relief is nearly level, and much of the county lies
^1 am indebted to the Dade County Agricultural Agents Office
for most of the information contained in this section.


204
Policy 2B
Maximize:
1588.1620 y .0416 y^
+ 920.0454 y2 .0606 y2
+ 1687.6188 y3 .1448 y^
+ 595.9965 y, .1796 y^
+ 571.3720 y5 .0240 y^
+ 3285.4824 y6 .3751 y^
+ 690.9032 y? .0956 y^
- e;L
- .0301 z
Subject to:
40459 E y. 48590
j-1 3
15740 -
6532 -
5269 -
1135 -
y 21166
y2 8927
y3 6680
y4 3774
y5 5235
y6 3585
y? 1520
3.0359 y3 + .6430 y2 +1.7152 y3 + 20.3501 y4
+ .0353 y5 + .0242 y6 + .0103 y? z = 0
4.4853 yx + 6.1424 y2 + 2.4975 y3 + 14.6024 y4
+ .2380'y + .1632 y& + .0691 y? z2 = 0
(tomatoes)
(potatoes)
(beans)
(corn)
(avocados)
(limes)
(mangos)
(chlorinated hydrocarbons)
(organic phosphates)
(total land)
(tomatoes)
(potatoes)
(beans)
(corn)
(avocados)
(limes)
(mangos)
(chlorinated hydrocarbons)
(organic phosphates)
yl y2'
,yr v z2 0


94
health" aspect of the pesticide issue. Table 23 summarizes the data
gathered from this office.
Those incidents which were clearly not related to the agricul
tural use of pesticides were omitted from Table 23. Some of those
included are certainly debatable, such as the cases where a pesticide
was brought home from the field by a parent and got into the hands of
a child.
Environmental Monitoring
Every great advance in science seems to have been associated
with a twofold movement. One is the development of a new theoret
ical insight or point of view, a restructuring of the image of the
world, which creates, as it were, evolutionary potential for the
increase of knowledge. The second condition is an improvement in
instrumentation, that is, in the methods by which information
coming from the outside world can be detected, sampled, and
processed (2, p. 22).
Large amounts of resources are being devoted to monitoring the
environment for pesticide residues. New technology in the detection and
measurement of residues is constantly being developed. In short, the
second point of the above quotation is now coming to pass. As sophis
tication increases in the area of environmental monitoring, social
scientists will have a responsibility to determine how to use the data
in policy decisions. The ability to measure precisely the amount of DDT
in the brain of the eagle is of little value if we are unable to put
this information to further use in making policy decisions on the
regulation of DDT.
There are three levels or stages of knowledge needed in order
to incorporate monitoring data into the model.
First, regular statistical series are needed showing the
quantities of pesticides in various environmental elements such as soil,


CHAPTER II
AREA STUDIED
Choice of an Area
The choice of an area for study was initially limited to the
State of Florida because of the location of the researchers. However,
the State as a whole was much too heterogeneous for effective analysis
so a smaller geographic region within the State was chosen. It was
felt that the selected area should be a relatively isolated, heavy
user of pesticides. It was also felt that it would be desirable to have
an area where other scientists had studied the fate and dispersion of
pesticides as well as the effects of pesticides on various elements of
the environment such as wildlife, human health, and domestic animals.
If possible, it should also be an area which would offer the maximum
probability of observing externalities, and it was believed that an area
having a significant interaction of pesticide users with other people
would fulfill this requirement.
The focus rather quickly narrowed to three areasa small
isolated locale in Northeast Florida (Hastings, Florida) heavily commit
ted to the production of potatoes and cabbage; an area in Central
Florida (Polk County) which produces primarily citrus; and the winter
vegetable and tropical fruits area of Dade County. The latter area was
the final choice because it had what was believed to be the best work
able balance of needed characteristics. Siiice its agriculture is highly
7


Table 25.Model solution for Policy 1
a
Solution
vector
->
Obj ective
function
in dollars
yl
Tomatoes^
in acres
y2
Potatoes^
in acres
y3
Beans ,
. b
in acres
y4
Corn
c
xn acres
y5y6,y7
Groves
in acres
Z1
Chlorinated
hydrocarbons
in poundse
Z2
Organic
phosphates
in poundse
Coefficient of:
Z1 z2
0
0
34,561,979.
19,300
7,700
5,800
1,650
10,340
214,716
131,358
0
-.0301
34,558,025.
19,300
7,700
5,800
1,650
10,340
214,716
131,358
0
-1.
34,431,172.
19,300
7,600
5,800
1,650
10,340
214,587
130,769
0
-2.
34,300,894.
19,200
7,600
5,800
1,600
10,340
211,945
130,119
0
-3.
34,171,289.
19,200
7,500
5,800
1,600
10,340
211,817
129,531
0
-4.
34,041,866.
19,100
7,500
5,800
1,600
10,340
211,209
129,204
0
-5.
33,913,420.
19,100
7,400
5,800
1,550
10,340
209,046
128,292
-1.
-.0301
34,345,977.
19,200
7,600
5,800
1,550
10,340
209,910
129,796
-2.
-.0301
34,138,929.
19,100
7,600
5,800
1,400
10,340
203,198
128,500
-3.
-.0301
33,937,203.
19,100
7,600
5,800
1,300
10,340
199,128
127,854
-4.
-.0301
33,740,433.
19,000
7,600
5,800
1,200
10,340
194,451
126,881
-5.
-.0301
33,547,745.
18,900
7,600
5,800
1,135
10,340
191,198
126,134
£
Current pesticide usage levels.
^Solution does not differ from the optimum by more than 100 acres,
c
Solution does not differ from the optimum by more than 50 acres.
dGroye acreage is constrained to be no more than the 1966^67 level.
0
All quantities have been converted to units of 100 percent concentrated material.
107


Table 40.Continued
Pesticide
Mean usage
in pounds
Crop per acre
Copper compounds
Tomatoes
2.1128
Potatoes
.1037
Pole beans
.0365
Squash
.1126
Okra
.5724
Groves
8.5688
Cygon
Tomatoes
1.1586
Potatoes
.7698
Pole beans
.3184
Squash
.0547
Okra
.5340
Other
.2225
Dacthal
Pole beans
.0301
DDD
Tomatoes
.1428
DDT
Tomatoes
.4929
Potatoes
.2082
Pole beans
.2480
Corn
20.3712
Okra
1.4000
Groves
.0192
Other
.2500
Demeton
Potatoes
.1371
Standard
deviation
pounds
per acre
Total
usage
by crops
in pounds
Total
usage
all crops
in pounds
7.8153
.1745
.0438
.2089
1.0119
40
3.2718
91
. 6464
22
.4250
5
.1230
.0503
.9440
.1888
.0361
1
.4569
2
1.3517
9
.3834
1
.1400
1
5.9330
2.4749
.0078
1.0607
33
.2965
1
143
794
213
347
343
729
133,569
013
897
856
168
320
478
30,732
175
175
713
2,713
365
595
446
612
840
205
538
47,601
050
1,050
r. ,d
Trend
of usage
Down
Uncertain
Down
Uncertain
Uncertain
Uncertain
Down
Up
Uncertain
Uncertain
Uncertain
Uncertain
Uncertain
Uncertain
Up
Down
Uncertain
Uncertain
Uncertain
Uncertain
Uncertain
Down
156


Table 37.Continued.
Common name Trade name
Heptachlor
Lindane
Toxaphene
Organic phosphates
Azinphosmethyl
Guthion
Carbophenothion
Trithion
Demeton
Systox
Dimethoate
Cygon
Malathion
Malaphos
Mevinphos
Phosdrin
Chemical name
3a,4,5,6,7,8,8-hep tachloro-3a,4,7,7a-
tetrahydro-4,7-methanoindene.
Gamma isomer of benzene hexachloride.
Chlorinated camphene containing 67-69%
chlorine.
0,O-dimethyl S-4-oxo-l,2,3-benzotriazin-
3(4H)-ylmethyl phosphorodithioate.
0,0-diethyl S-(p-chlorophenylthiomethyl)
phosphorodithionate.
0,0-diethyl 0(and S)-ethylthioethyl phos-
phorothioate.
0,0-dimethyl S-N methylcarbamoylmethyl
phosphorodithioate.
0,0-dimethyl dithiophosphate of diethyl
mercaptosuccinate.
2-carbomethoxy-l-propen-2-yl dimethyl
phosphate.
133


20
[2]
bj(min)y^(t) y^(t+1) b^(max)y^(t)
j=l,
. .n
n x
[3]
E a.^ y.(t+l) z.(t+l) = 0
j=l ^ J 1
i=l, .
. .m
[A]
c, z. (t+1) e. .
ki i ki
k=l, .
. .p
[5]
y^ (t+1) 2,^ (t+1) 0
where:
fj(yj(t+l)) = demand function for the jth crop in the (t+1) year.
y,(t+l) = acres of the jth crop in the (t+1) year. For simplifi
cation the time dimension is omitted in the remaining definitions,
x
gj (y^) = supply function for the jth crop under the rth policy
alternative.
h <* ) = a marginal "externality function," a functional relation
ship between marginal external effects expressed in dollars, and
the quantity of the ith pesticide.
z^ = quantity of the ith pesticide measured in units of 100 percent
active material.
d(min) = a minimum flexibility constraint on total farm land.
d(max) = a maximum flexibility constraint on total farm land,
bj(min) = a minimum flexibility constraint on the jth crop,
b^(max) = a maximum flexibility constraint on the jth crop.
a = the quantity of the ith pesticide used per acre of the jth
crop under the rth policy.
c^j. = quantity of the ith pesticide produced in the kth environ
mental element by 1 unit of the ith pesticide,
e^ = an arbitrary upper limit on the ith pesticide in the kth
environmental elementa parameter to be determined "politically."


Table 36.Dade County growing seasons and 1967 crop acreages used for Klukas projections.
Crop
1967 acreage
Planting, growing, harvesting period
Tomatoes
17,560
August 15 April 30
Potatoes
7,600
October 15 April 30
Pole beans
9,000
September 15 April 30
Corn
2,000
December 15 May 31
Squash
3,500
Continuous
Okra
750
February September
Cabbage
500
September 15 March 1
Cantaloupe
250
August 30 May 1
Collards
100
December 1 April 1
Cucumbers
2,000
August 30 April 30
Southern peas
1,000
December June
Strawberries
550
September 15 April 15
Sweet potatoes
1,000
Continuous
Turnips
200
November April
Watermelons
100
December 15 April 30
130


Table 42.Continued
Pesticide
Crop
Number of
growers
Dexon
Pole beans
1
Dieldrin
Tomatoes
5
Pole beans
1
Diphenamid
Tomatoes
15
Dyrene
Tomatoes
12
Endrin
Potatoes
3
Pole beans
1
Ethion
Groves
12
Ferbam
Tomatoes
1
Groves
24
Guthion
Tomatoes
10
Pole beans
4
Groves
2
Keptachlor
Tomatoes
2
Karathane
Tomatoes
1
Potatoes
2
Squash
1
Mean usage
in pounds
per acre
Acres
sampled
Standard
deviation
pounds
per acre
425
.0124
.0000
1,245
.2015
.1285
400
.0100
.0000
10,160
.8051
.5758
7,374
2.4776
2.0724
1,680
.3136
.0969
425
.1459
.0000
815
2.4017
3.1543
240
1.1780
.0000
887
4.2755
10.5116
2,209
1.4044
.9401
447
.4945
.0029
660
.0493
.1237
1,590
1.1418
.7497
245
.0092
.0000
640
.0070
.0000
80
.7500
.0000
178


Table 37.Continued.
Common name
Parathion (ethyl)
Phorate
Other
Carbaryl
Citrus oil
Ethion
Manganese
Manganese sulfate
Metaldehyde
Phosphoric acid
Trade name
Alkron, Genthion, Niran,
Orthophos, Thiophos
Thimet
Sevin, Aqua 5 Sevin
Slug-tox
Treflan
Chemical name
0,0-diethyl O-p-nitrophenyl phosphor-
othioate.
0,0-diethyl S-ethylchlomethyl phosphor-
othioate.
N-napthyl N-methylcarbamate.
A mixture of hydrocarbons distilled from
petroleum.
0,0,0',0'-tetraethyl-S-S'-methylenebis
(phosphorodithioate).
Acetaldehyde polymer.


39
Table 6.Empirically estimated relations from which supply functions
were derived.3
Crop
Intercept
Coefficients of:^
y(t-i)
p(t-l)
S
0
Tomatoes
1904.0586
.4472
(.1393)
1754.8784
(623.8162)
-3727.2373
(1723.1143)
Winter potatoes >b
1.9643
.7367
(.1717)
.4033
(.1981)
Beans*1
3.1023
.6614
(.1636)
.2002
(.1659)
.0003
(.0708)
Corn1
-4528.8516
.7935
(.1569)
2353.8164
(1463.8821)
1904.8828
(1570.1018)
Avocados )
Limes ?
Acreages for
these tree
crops were constrained to
Mangos J
Sources of data:
1. Tomatoes, potatoes, beans, and corn: Florida Agricultural
Statistics (18).
2. Avocados: Agricultural Statistics (50).
3. Limes:
a. Production and on-tree prices: Florida Agricultural Statis
tics (18).
b. Packinghouse prices: Agricultural Statistics (50).
4. Mangos: Dade County Agricultural Agent's Office, Homestead,
Florida.
^Standard errors of the coefficients are shown in parentheses.
Q
S^ = a "dummy variable" which equals 1 if the observation occur
red in the fall and 0 otherwise.
^S = a "dummy variable which equals 1 if the observation occur
red in the spring and 0 otherwise.


113
zero externalities) and the minimum was $33,249,130 (Policy 2C maximum
externalities from organic phosphates), a difference of only 3.8 percent.
For observed externality levels (z^ coefficient = 0 and z^ coefficient
= -.0301), the difference among policies was even smaller, as shown in
Table 29. The objective function of Policy 2C represented only a 1.2
Table 29.A comparison of model solutions among policies for z^ and
z^ coefficients of 0 and -.0301, respectively.
Policy 1
Policy 2A
Policy 2B
Policy 2C
Objective function
$34,558,025
$34,207,349
$34,176,508
$34,143,394
Tomatoes (acres)
19,300
19,100
19,100
19,100
Potatoes (acres)
7,700
7,600
7,600
7,600
Beans (acres)
5,800
5,800
5,800
5,800
Corn (acres)
1,650
1,650
1,650
1,650
Groves (acres)
10,340
10,340
10,340
10,340
z^ (pounds)
214,716
106,686
106,494
106,494
z^ (pounds)
131,358
162,180
172,867
183,555
percent change from Policy 1. In other words, if we are willing to
accept the measure of welfare used in the model, we can say that
"welfare" would fall by $414,631 or 1.2 percent under Policy 2C. Under
Policy 2A it would fall by only $350,676 or about 1 percent. Is this
too high a price to pay to reduce the usage of chlorinated hydrocarbons
and hence the potential environmental hazard? That is a question which
must be answered by the people through their elected representatives.
The role of the economist stops short of such a recommendation. It is
his responsibility to explore the ramifications of various alternatives,
but the ultimate decision is a political one.


Table 39.Continued
September
Crop
Fungicide
Insecticide
Herbicide
Total
Organic
phosphates
Chlorinated
hydrocarbons Carbamates
Other
Tomatoes
3.4464
.5048
.5113
None
.0375
.2644
4.7644
Potatoes
None
None
None
None
None
None
None
Pole beans
.0341
.0112
.0171
None
.0012
None
.0636
Corn
None
None
None
None
None
None
None
Squash
None
None
None
None
None
None
None
Okra
None
None
None
None
None
None
None
Groves
.0658
.1233
None
None
.1559
.0113
.3563
Other
None
None
None
None
None
None
None
Total average
usage
1.7597
.2653
.2619
None
.0285
.1351
2.4505
350


agricultural activities are poultry, dairy, and livestock as shown
in Table 2.
12
Table 2.Production of livestock and livestock products in Dade
County, Florida, 1967.a
Item
Unit
Quantity
Value of
production
Dairy (5 farms)
Milk
Gallons
2,507,112
$ 1,669,000
1,503,000
Cull animals
Head
166,000
Poultry (25 farms)
Eggs
Dozen
5,220,000
$ 3,524,000
2,034,000
Cull birds
Head
150,000
40,000
Hatchery chicks
Head
10,040,000
1,450,000
Other (including beef cattle,
horses, etc.)
Head
5,032,000
$ 796,000
Other farm enterprises
500,000
Source of data: County Agricultural Agents Office, Homestead,
Florida.
Most of the fruit and vegetable crops in Dade County are grown
on two soil types, marl and rockland, illustrated in Figure 2.
Marl and its various phases comprise a total of about 317,000
acres in Dade County. Surface soil is a light brown or brownish gray
marl of silt loam texture. The subsoil is lighter colored, and the
underlying material is Miami oolite. Native vegetation on these soils
consists of a variable mixture of sawgrass, myrtle, bay, and cypress.
Elevation above sea level of most areas of marl that are suitable for
cultivation ranges from about 8 feet near the rock ridge to 1 to 2 feet
near the shore lines. Soil depth varies from 6 to 60 inches. Marl has


89
When the dollar claims break the sensitivity threshold, the insurance
company responds by raising rates, cancelling policies, or some other
action. If the dollar claims are defined to be the marginal claims due
to pesticides, then external costs are imposed on the insurance company
by the farmer until the threshold is broken and the situation adjusted.
Veterinarians
In order to get additional information on the effects of pesti
cides on domestic animals, arrangements were made to sample the records
of three veterinarians in the area. Two were located approximately on
the dividing line between the rural and urban areas, while the third was
located in Homestead, the heart of the rural area. Samples which were
as nearly random as possible were taken from each.^ The frequency of
pesticide calls was noted along with the diagnosis, the specie, and the
treatment. After sampling, a brief conversation was held with each
veterinarian to see if the sample bore out his a priori notions. The
veterinarians all stated, before seeing the sample results, that they
would expect the frequency of pesticide cases to be a small fraction of
The first vet numbered his cases sequentially and had about
10,000 of them. Each case contained an average of 4.5 calls. Using a
table of random numbers to determine the starting point, 909 cases were
examined for a total of 4,090 calls. There was no relation between a
cases sequence number and the dates of the calls contained therein.
The second vet maintained his cases alphabetically, by last name
of owner, and did not know how many he had. There were 10 small file
drawers involved so the first and last 150 calls in each drawer were
arbitrarily examined. This vet also kept a separate file for deceased
cases, and all of these were examined.
The third vet maintained his files alphabetically, by last name
of owner, and they did not lend themselves well to sampling. For this
vet, all of the "D's" were examineda total of 275 cases with 4 calls
per case.


103
model. If biologists could say that "x" parts per million of some
pesticide in the brain of the eagle were sufficient to limit repro
duction "y" percent, and if we could establish a relationship between
the "x" parts per million and the environmental exposure of the eagle,
then this information could be incorporated in the model as one of the
environmental constraints. The point to be made here is that if we, as
a society, decide to further restrict the usage of the persistent
pesticides without knowing these relations, such a move must be justi
fied on the basis of a social value judgement, not on the basis of any
benefit-cost comparison, for we simply do not know enough about the
costs.


117
For Economic Theory and Methodology
Classical and neo-classical economists have for the most part
tended to ignore or assume away the problem of "external effects,"
although in recent years it has received much more attention in the
literature. It is this writer's opinion that externalities may be
crucial in many social problems of the future. If this comes to pass,
economic theory needs to be refined in order to deal with these problems.
Not only does the theory need attention, but the empirical problems of
identifying and measuring externalities need much work. This writer
is, frankly, somewhat pessimistic about transferring the knowledge
gained in this research project on identifying and measuring external
ities to other problems. The decision rule which was used (any "cost"
not included in the farmers' marginal cost functions was considered to
be an externality) may have some transferability,but beyond this the
problems encountered in any particular situation may be quite unique.
For Future Research
In order to improve the information available to decision
makers, research should be aggressively pursued on several fronts.
First, regular statistical series should be developed to show the
amounts of pesticides being used in various locales. Retail firms could
report sales data regularly from which statistical series of usage could
be developed. As mentioned earlier in this report, it does not make
much sense to consume so many resources in the monitoring of plant and
animal organisms and to ignore the quantities of pesticides which are
being injected into the environment in the first place. This is not to
say that we should decrease the monitoring of various elements of the


45
Table 10.Recommended insect control measures for corn.
a
Insect
Pesticide
Organic
phosphate
quantity*3
Chlorinated
hydrocarbon
quantity**
Other
quantity
Minimum
days to
harvest
Aphids, Spider
Parathion
.250
3
mites
Phosdrin
.250
1
Fall Armyworms
DDT
1.000
**
and Corn Ear-
Parathion
.250
**
worm feeding
Toxaphene
1.500
**
in bud
Mixture of
**
DDT and
1.000
Parathion
.125
Mixture of
**
DDT and
1.000
Toxaphene
.750
Silk-fly
Parathion
.250
3
Earworms
DDT
2.OOOt
**
Sevin
2.OOOt
**
Mixture of
**
DDT and
2.000
Sevin
.500
Corn Stem
DDT
2.000
**
Weevil
Mixture of
**
DDT and
1.000
Toxaphene
1.000
Mixture of
A*
DDT and
2.000
Toxaphene
1.000
Source of data: Insect Control Guide (17) and consultation
with entomologists familiar with the area.
Quantities are expressed in pounds of 100 percent active
material per 100 gallons of water. The gallonage applied per acre will
vary with the size and density of the plants. In the Dade County area
the gallonage usually varies from 100 to 300 gallons.
**
No specific limitation so long as the usages do not result in
a residue on the edible ears.
f
These amounts should be mixed in 50 gallons of water and
applied to one acre.


172
Table 41.Continued.
Standard
Pesticide
Crop
Month
Mean usage
in pounds
per acre
deviation
pounds
per acre
Okra
March
1.3500
2.3865
Okra
April
15.7500
6.6291
Okra
May
13.5000
15.9099
Okra
June
2.7000
4.7730
Groves
January
8.6391
3.7553
Groves
February
.9004
10.2565
Groves
March
2.1057
9.9994
Groves
April
.1083
.0441
Groves
May
.6496
.2648
Groves
June
.2165
.1765
Groves
October
.0722
.0294
Other
February
1.1250
4.7730
Other
April
5.7833
10.3662
Other
May
1.1250
4.7730
Tedion
Pole beans
March
.0002
.0058
Groves
May
.0059
.0024
Groves
June
.0060
.1471
Groves
December
.0208
.0085
Terrachlor
Pole beans
October
.0162
.2652
Pole beans
November
.1572
.7065
Pole beans
December
.1675
.7222
Pole beans
January
.0848
.5405
Pole beans
February
.1425
.8441
Pole beans
March
.0270
.4737
Thimet
Potatoes
October
.2120
.7272
Potatoes
November
1.5425
1.6285
Potatoes
December
1.1562
1.6187
Corn
February
.3483
.0000
Corn
December
.3837
.0000
Thiodan
Tomatoes
September
.0255
.1393
Tomatoes
October
.0051
.0245
Tomatoes
November
.0170
.0944
Tomatoes
December
.0347
.1052
Tomatoes
January
.0202
.0771
Tomatoes
February
.0352
.2703
Tomatoes
March
.0116
.1645
Tomatoes
April
.0019
.0093
Potatoes
January
.0982
.2710
Potatoes
February
.4010
.6527
Potatoes
March
.0796
.3906


Table 21.Dollar costs of disabling workmen's compensation claims for Dade County, Florida, 1967,
by kind of payment and agent.a
Kind of payment^
Agent
1
2
3
4 5
6
7
8
Total
Unknown or unreported
$3,815
$3,099
$ 861
$ 199 $ 0
0
0
$1,645
$ 9,619
Acids, n.e.c.c
924
873
232
42
100
2,171
Citrus dermatitis
22
22
Ammonia
608
443
994
151
130
2,326
Carbon Monoxide gas fumes
316
421
529
247
136
1,649
Caustics, n.e.c.
6,354
4,161
672
428
2,237
13,852
Soap
1,420
605
178
357
2,560
Chlorate of lime
13,267
4,771
1,158
225
4,001
23,422
Chlorine
84
439
223
38
784
Chromium
Coal tar distillates
9
9
(naptha, benzol) Carbolic
acid, creosote
17
17
Fertilizer, n.e.c.
32
32
Food, etc.
Formaldehyde (Formain,
2,762
2,211
214
8
1,225
6,420
embalming fluid)
22
6
4
32
Hydrochloric acid (muriatic)
42
70
29
11
152
Hydrocyanic acid
14
14
Insecticide, n.e.c.
547
495
653
27
125
1,847
Lead or paint
Metal fumes (aluminum, monel
719
1,229
240
280
2,468
zinc, welding fumes)
426
188
13
175
802
Nitric acid
437
220
535
1,192
Parathion
167
167


CHAPTER III
THE MODEL
The model used for this analysis employed the concept of
consumers'-producers' surplus as a measure of welfare. For each of two
specified pesticide usage policies and three variations under one policy,
the model maximized this measure of welfare over production alternatives
available to the region. In each case the objective function explicitly
recognized measurable externalities not accounted for in the consumers'-
producers' surplus calculation. The maximization was restrained by
flexibility constraints derived from historical cropping patterns.
Finally the policies were ranked by their maxima. The model can be
formally stated as follows:
For a set of subjectively chosen pesticide usage policies, x^,
r = 1, 2A, 2B, 2C, rank the associated estimates of welfare, W^, where:
n
W = maximum: E
r y^(t+1) j=l
z^t+l)
m
- E
i=l
where the maximization for a given policy r is subject to:
n n n
[1] d(min) E y.(t) E y.(t+l) d(max) E y.(t)
j-1 J J-l J 3=1 J
Zj (t+1)
/
0
h_. (z^(t+1)
dz.(t+1)
x
yj(t+D
/
o
f j ^yj ^t+1^
gjr)
dy^ (t+1)
19


112
Parametric programming on the coefficient for chlorinated
hydrocarbons, z^, caused more extensive changes. At estimated levels of
acute externalities (z^ coefficient = -.0301), and as the coefficient of
z^ was decreased,the solution acreages for tomatoes and corn declined
progressively while that for potatoes and beans hardly changed.
Tomatoes declined from 19,300 to 18,900 acres and corn from 1,650 to
1,135, the lower limit imposed by the flexibility constraint. At the
point where the zcoefficient equalled -5.0 and the z^ coefficient,
-.0301, the usage of chlorinated hydrocarbons had fallen by 11 percent
and organic phosphates by 4 percent.
With Policies 2A, 2B, and 2C, three characteristics stand out.
First, as in Policy 1, the solution acreages tended to be "stable" as
the coefficients of z^ and z^ were varied. In general, very large
changes in external "damage" levels caused very small changes in
solution acreages. This was at least partial justification for ignoring
the "second-order effects" of the externalities, a point mentioned in
footnote 15, page 53.
Second, the solution acreages for estimated externality levels
did not change at all from Policy 2A through Policy 2C, leading to the
conclusion that the solution acreage (but not the value of the objective
function) is relatively insensitive to the substitution rate between
chlorinated hydrocarbons and organic phosphates. This is significant
because it reduces the importance of error in this parameter, a matter
of considerable concern to the entomologists.
Third, the value of the objective function did not change very
much from one policy to the next. Including Policy 1 in the comparison,
the maximum value of the objective function was $34,561,979 (Policy 1,


60
imprecise. Wind conditions can effect the coverage significantly, and
the spray apparatus is difficult to turn off at exactly the right moment
as the pilot turns to make another sweep of the field. A survey of
growers and entomologists in the area, conducted by Mr. Richard Hunt of
the Dade County Agricultural Agents Office, indicated that aerial
spraying on the average has increased about 10 to 20 percent over the
past 5 years but that the increase is mainly a function of weather. It
is becoming common practice for the growers to use airplanes after a
rain when it is difficult to get machinery into the fields.
The vagaries of weather are also very important in determining
the frequency with which growers must spray. Not only are certain
weather conditions more propitious than others for disease and insect
damage, but if a grower sprays his field just before a rain he must
often spray it again, right after the rain because the pesticide was
washed off the plant.
In general, it was observed that growers tend to use a pre
determined spray schedule for disease and a random or "spray-as-needed"
program for insects.
Estimated Quantities of Pesticides Used in Dade County
One might go about estimating Dade Countys agricultural usage
of pesticides in several ways. The quickest would be to assume that
growers follow labeled recommendations completely, and then develop the
estimate based on the recommendations, an assumed frequency of appli
cation, an assumed length of growing season, and a knowledge of the
acreage of each crop. One such estimate was made by Mr. Richard W. Klukas,


2
historical cropping patterns. Finally the policies were ranked by their
maxima.
It was demonstrated that the model could conceptually accommo
date environmental constraints connecting the levels of pesticide usage
with levels of pesticides monitored in various elements of the environ
ment. Empirical data to specify such constraints were not available.
Background of the Problem
Environmental modification is not new. In the struggle for
survival, it has been practiced through the ages by most of the
creatures of the Earth. The fox modifies his environment when he digs
a den. Man modifies his environment when he clears a field for culti
vation, but such environmental modification often has undesirable side
effects. When the farmer clears his land for cultivation, he raises the
probability of soil erosion. The extent of the modification and the
extent of the side effects can become important issues, and herein lie
many of the social problems we face today, one of which concerns the
advantages and disadvantages of using pesticides.
Several factors have played a part in increasing the emphasis
on the pesticide problem. First, world food needs are expanding rapidly.
The world population, estimated at about 500 million in 1650, rose to
1 billion by 1850, and to 1.6 billion by 1900 A.D. Extrapolations of
present growth rates indicate a population of 6.4 billion by 2000 A.D.
(10, pp. 63-71). Present-day nutritionists find that more than half of
the world's inhabitants have a suboptimal diet of less.than 2,200
calories per day (42, pp. 73-80). It is quite possible that the food
deficit will worsen in future years; the significance of the problem


35
Table 4.Empirically estimated relations from which demand functions
were derived.3
Crop
*
Intercept
Coefficients of:^
q(t1)
P(t)
I(t)^
Tomatoes^
1798.3218
.3027
(.1394)
- 733.8044
(149.9282)
2.8091
(.9227)
-1854.3457
(449.6931)
g
Winter potatoes
2271.0208
.4902
(.2213)
- 229.7552
(138.8649)
-.2824
(.6980)
Beans*1 *
7.3391
.0930
(.0952)
1.8514
(.2141)
.2248
(.4506)
.2146
(.0940)
Corn^
969.9456
.2897
(.1674)
-1391.9141
(321.5176)
1.8613
(.6422)
-1141.8203
(332.2517)
Avocadosk
1130.4805
.3805
(.1850)
- 44.3303
(13.6422)
5.7642
(4.2249)
Limesm
30.9556
.4280
(.2587)
- 11.3824
(21.8403)
.1167
(.1344)
w n,h,i
Mangos
.6427
.0896
(.1805)
- 1.2548
(.2088)
1.5275
(1.3004)
aSources of data:
1. Tomatoes, potatoes, beans, and corn: Florida Agricultural
Statistics (18).
2. Avocados: Agricultural Statistics (50).
3. Limes:
a. Production and on-tree prices: Florida Agricultural
Statistics (18).
b. Packinghouse prices: Agricultural Statistics (50).
4. Mangos: Dade County Agricultural Agent's Office, Homestead,
Florida.
^Standard errors of the coefficients are shown in parentheses.
cT
I = per capita income.
Sj = a "dummy variable" which equals 1 if the observation
occurred in the fall and 0 otherwise.
0
S = a "dummy variable" which equals 1 if the observation occur
red in the spring and 0 otherwise.
^Quantity measured in thousands of 60 pound crates; price
measured in dollars per crate.


50
Table 12.Estimates of average total production costs in Dade County,
1966-67, by crop and pesticide policy.
Cost of chlorinated
Cost of cultural
Average
hydrocarbons and
labor and variable
total cost
organic phosphates
cost of machinery
Dollars per acre
Tomatoes ,
Policy
1 d
$866.02
$19.10
$157.61
Policy
2Ad
881.95
19.27
173.37
Policy
2Bf
883.11
20.43
173.37
Policy
2C't
884.28
21.60
173.37
Potatoes
Policy
1
$609.24
$20.88
$ 83.32
Policy
2A
616.57
19.88
91.65
Policy
2B
616.76
20.07
91.65
Policy
2C
616.95
20.26
91.65
Beans
Policy
1
$754.87
$ 8.27
$189.80
Policy
2A
754.55
7.95
189.80
Policy
2B
755.02
8.42
189.80
Policy
2C
755.48
8.88
189.80
Corn
Policy
1
$399.21
$48.39
$ 47.02
Policy
2A
392.01
41.19
47.02
Policy
2B
395.71
44.89
47.02
Policy
2C
399.42
48.60
47.02
Includes cultural labor, gas, oil, grease, maintenance, and
repair.
^Current usage.
CSource of data: Brooke (5).
dA 50 percent reduction in chlorinated hydrocarbons and a sub
stitution rate of .3 pounds of organic phosphates per pound of chlorinated
hydrocarbons.
0
A 50 percent reduction in chlorinated hydrocarbons and a sub
stitution rate of .4 pounds of organic phosphates per pound of
chlorinated hydrocarbons.
^A 50 percent reduction in chlorinated hydrocarbons and a sub
stitution rate of .5 pounds of organic phosphates per pound of
chlorinated hydrocarbons.


205
Policy 2C
Maximize:
1586.9922 y
+ 919.8554 y2
+ 1687.1588 y3
+ 592.2865 y^
+ 571.3720 y5
+ 3285.4824 y&
+ 690.9032 y?
- e;L ^
- .0301 z2
Subject to:
7
40459 Z y
- .0416 y^
- .0606 y2
- .1448 y^
- .1796 y^
- .0240 y2
- .3751 y2
- .0956 y2
~ 48590
(tomatoes)
(potatoes)
(beans)
(corn)
(avocados)
(limes)
(mangos)
(chlorinated hydrocarbons)
(organic phosphates)
(total land)
6532 -
5269 -
1135 -
y2 8927
y3 6680
- 3774
- 5235
- 3585
y4
y6
y? 1520
3.0359 y + .6430 y£ + 1.7152 y3 +
+ .0353 y5 + .0242 y& + .0103 y? -
4.7899 y1 + 6.2067 y2 + 2.6690 y3 + 16.6374 y^
+ .2416 y5 + .1656 y6 + .0702 y? z2 = 0
(tomatoes)
(potatoes)
(beans)
(corn)
(avocados)
(limes)
(mangos)
(chlorinated hydrocarbons)
(organic phosphates)
>
yr y2>
>y7> z2
o


40
Table 6.Extended.
Number
of
obser
vations
R2
Short-run
price
elasticity
Long-run
price
elasticity
Durbin-
Watson
Statistic
s &
s
- 339.7224
(1392.4358)
33
.6773
.5185
.9379
*
1.7873
17
.5879
.4033
1.5317
1.8363*
.0066
(.0690)
33
.4334
.2002
.5910
2.5493*
7843.4375
(3283.3203)
33
.9648
.3178
1.5390
2.0073*
be no greater
than the
1966-67 levels.
0
Quantity measured in acres planted; price measured in dollars
per crate
^Quantity measured in acres planted; price measured in dollars
per bag.
Cr
^Variables were transformed to natural logs.
Quantity measured in acres planted; price measured in dollars
per bushel.
1Quantity measured in acres planted; price measured in dollars
per crate.
*
Reject the hypothesis that auto-correlation is present at the
95 percent confidence level.


116
for the farmers, and would permit the accumulation of valuable knowledge
as the process continues. Such an approach would also contain less
predictive error simply because the near future is easier to forecast.
This approach does not imply that many years would be required
to achieve a great reduction in the usage of chlorinated hydrocarbons.
On the contrary, it appears that considerable reductions (on the order
of 40 to 60 percent) could be made in the usage of chlorinated hydro
carbons at the present time without a serious reduction (1 to 2 percent
or less) in the net social benefits, as defined, from the crops evaluated.
Implications
For Policy Makers
Aside from the thoughts presented in the previous paragraphs,
there are two other implications for policy makers which should be
explicitly stated even though they have been implied in earlier pages.
First, the pesticide issue is far from "cut and dried." Persuasive
evidence can be marshalled for either side. Furthermore, at this point
there is little known with regard to the long-run effects of pesticide
exposure.
Second, the role of value judgments in the pesticide issue has
been emphasized, and this role should be constantly borne in mind by
policy makers and legislators when considering the pesticide problem.
No amount of research and/or data gathering can bypass the need for
value judgements. Research can point out characteristics and perhaps
effects of alternative value judgments, and it might be able to uncover
other alternatives, but the ultimate choice is still a value judgment
which, argued Buchanan (6), should reflect the consensus of the
citizenry affected by the decision.


36
Table 4.Extended.
Number
Short-run
Long-run
Short-run
Long-run
of
price
price
income
income
Durbin-
obser-
elas-
elas-
elas-
elas-
Watson
vations
R2
ticity
ticity
ticity
ticity
Statistic
s ^
s
- 797.2398
33
.8107
- .9728
-1.3952
1.4427
2.0690
**
1.8095
(335.2292)
17
.3976
- .4856
- .9525
- .3094
- .6069
**
1.6151
.1448
33
.7492
-1.8514
-2.0410
.2248
.2479
*
2.3175
(.0906)
2207.9375
33
.9610
-1.2701
-1.7881
1.4705
2.0703
1.9689*
(894.9026)
18
.5066
- .7929
-1.2800
1.2882
2.0795
1.8853*'
18
.4832
- .1271
- .2222
.6272
1.0965
1.8348*
12
.9131
-1.2548
-1.3783
1.5275
1.6778
*
2.2197
^Quantity measured in thousands of 100 pound bags; price
measured in dollars per bag.
^Quantity measured in thousands of bushels; price measured in
dollars per bushel.
1This function was estimated in natural logs.
^Quantity measured in thousands of crates; price measured in
dollars per crate.
Quantity measured in tons; price measured in dollars per ton.
mQuantity measured in thousands of boxes; price measured in
dollars per box.
n.
Quantity measured in bushels; price measured in dollars per
bushel.
A
Reject the hypothesis that auto-correlation is present at the
95 percent confidence level.
d statistic is inconclusive.


26
on a crop, some of it is taken into the plant, some of it goes into the
soil and water, and some of it into the air. The speed with which a
pesticide breaks down into non-toxic substances varies a great deal
among pesticides. The chlorinated hydrocarbons tend to be very persis
tenttheir residues decompose into non-toxic substances very slowly.
The organic phosphates, on the other hand, tend to break down into non
toxic substances very rapidly and their residues do not build up
significantly in the environment. Much of the current pesticide
controversy revolves around these physical relationsthe residues which
build up and the potential damage which they may represent for the
environment. As will be noted later, large amounts of resources are
being committed to research on these physical relationships, but little
has been done to delineate their role in a decision model for policy
making. For this reason the set of environmental constraints is
included conceptually in the model. It was impossible, however, to
estimate the parameters empirically; therefore, the set is not included
in the succeeding chapter on the estimation of model coefficients. This
was a regrettable compromise because this set of constraints could
possibly be the most important of all in establishing policy. It is
precisely because of pesticides' potential for environmental pollution
that people's emotions become aroused and make them demand policies
regulating the use of pesticides.
The fifth set of constraints simply prevented the occurrence of
negative acreages and pesticide quantities in the solution.
Another aspect of the model which deserves emphasis concerns the
question of property rights, a subject of growing interest in recent
literature on policy issues (9, 12, 41). The model as stated here is


59
at about four miles per hour and three hundred pounds per square inch
nozzle pressure. The number of nozzles varies from three to nine
depending upon the size of plant and desired coverage.
While recommended pesticide dosages are specified on the label
in terms of "amounts per acre," growers tend to make their calculations
in terms of "amounts per one hundred gallons of water" since their spray
rigs hold even multiples of one hundred gallons. They then try to put
the appropriate amount of liquid (water plus pesticide) on an acre so as
not to exceed the recommended dosage. The spray men are, for the most
part, limited in education, so the calculations and mixing are usually
done by the grower,and then the spray man adjusts the speed of the
tractor slightly so that (1) the plants are well covered and (2) a
minimum quantity of spray drips to the ground. In addition, the spray
men try to adjust the discharge slightly so as to run out of spray about
the time they get to the side of the field where the pesticides and
irrigation well are located. In a word, the uniform application of a
given quantity of pesticide to an acre is considerably imprecise.
Aerial spraying is usually done on a contract basis by pilots
who specialize in crop spraying. The farmer purchases the pesticides
and turns them over to the pilot with a mandate to apply a certain
quantity per acre. The pilot, knowing the size and discharge rate of
his spray rig, then mixes the pesticide with water in an appropriate
ratio to achieve the farmer's desired coverage. Boundary lines are
fairly well marked, and experienced pilots familiar with the area can
often spray a field without assistance from the ground. Others use a
helper on the ground to mark off the field boundaries and the spray
progress. Like spraying with ground rigs, aerial spraying is relatively


201
4. Newellton, Tensas Parish, Louisiana, (population 1,500) is
located in the cotton-growing area of the Mississippi delta. Extensive
plantings of cotton were located to the north, west, and south of the
community, with somewhat less to the east. Samples were collected from
July through September.
5. Florida City, Dade County, Florida, (population 4,100) is
surrounded by vegetable fields, which are planted in late fall. Samples
were collected from October through December.
6. Lake Alfred, Polk County, Florida, (population 2,600) is
surrounded by orange groves. Samples were collected from June through
August.
7. Lake Apopka area, Orange County, Florida, has no great con
centration of population but appeared suitable for investigation because
of the frequent application of insecticides to the sweet corn and other
vegetables grown a few miles north of the lake. Numerous protests
had been heard from local sportsmen regarding the frequent drift of
pesticide clouds over the lake. Samples were collected from April
through July.


13
£
Reprinted with the permission of the Dade County Agricultural
Agents Office.
poor internal drainage and becomes waterlogged or even flooded during
the rainy season. Occasionally wet conditions occur during the normally
dry winter season; however, this soil may, at times, become too dry
during the winter. Both drainage and irrigation systems are used
extensively for crop production.
Marl is alkaline in reaction, and the application of certain of
the minor elements is imperative for successful crop production. Prior
to discovery of the low availability of manganese, this unknown defi
ciency limited crop production in the area.
Marl soils are utilized for production of winter vegetables,
including Irish potatoes, tomatoes, bush beans, pole beans, squash,
cucumbers, corn, strawberries, cabbage, beets, lettuce, escarole,


207
BIBLIOGRAPHYContinued.
15. Ezekiel, Mordecai. "The Cobweb Theorem," Quarterly Journal of
Economics, Vol. LII, 1938.
16. Federal Committee on Pest Control and its Subcommittee on Pesticide
Monitoring. Pesticides Monitoring Journal. Washington, D.C.:
U.S. Government Printing Office.
17. Florida Agricultural Extension Service. Insect Control Guide.
Institute of Food and Agricultural Sciences, University of
Florida, Gainesville, Florida.
18. Florida Department of Agriculture, in cooperation with the U.S.
Department of Agriculture and Agricultural Experiment Stations
of the University of Florida. Florida Agricultural Statistics:
Vegetable Summary, 1967. Orlando, Florida: Florida Crop and
Livestock Reporting Service, 1967.
19. Florida Industrial Commission. Analysis of Work Injuries Covered
by Workmen's Compensation, edd. for 1962-1963, 1966, and 1967.
Tallahassee, Florida.
20. Galbraith, John K. The Affluent Society. Boston: Houghton Mifflin,
Co., 1958.
21. Goldberger, Arthur S. Econometric Theory. New York: John Wiley
and Sons, Inc., 1964.
22. Gunther, F. A. and Jeppson, L. R. Modern Insecticides and World
Food Production. New York: John Wiley and Sons, Inc., 1960.
23. Headley, J. C. and Lewis, J. N. The Pesticide Problem: An
Economic Approach to Public Policy. (Printed for Resources for
the Future, Inc.). Baltimore, Maryland: The Johns Hopkins
Press, 1967.
24. Henderson, James M. "The Utilization of Agricultural Land, A
Theoretical and Empirical Inquiry," Review of Economics and
Statistics, Vol. XLI, No. 3, August, 1959.
25. Hicks, John R. "The Rehabilitation of Consumers' Surplus,"
Readings in Welfare Economics, eds. Kenneth J. Arrow and Tibor
Scitovsky, Vol. XII, 1969.
26. International Business Machines Corporation. IBM Application
Program: Mathematical Programming System/360 (360A-C0-14X,
Linear and Separable ProgrammingUser's Manual. White Plains,
New York: Technical Publications Department, 1967.
27. Johnston, J. Econometric Methods. New York: McGraw-Hill Book
Co., Inc., 1963.


203
Policy 2A
Maximize:
1589.2222 y
+ 920.2354 y2
+ 1688.0888 y3
+ 599.6965 y4
+ 571.3720 y5
+ 3285.4824 y&
+ 690.9032 y?
el Z1
- .0301 z
Subject to:
7
40459 I y.
J-l J
- .0416 yj
- .0606 y^
- .1448 y^
- -1796 y4
- .0240 y*
- .3751 yjl
- .0956 y^
- 48590
(tomatoes)
(potatoes)
(beans)
(corn)
(avocados)
(limes)
(mangos)
(chlorinated hydrocarbons)
(organic phosphates)
(total land)
15740 y
6532 y
5269 y
1135 y
y
y
y
3.0359 y1 + .6430 y2
+ .0353 y^ + .0242 y^
1 21166
2 8927
3 6680
. 3774
4
- 5235
, 3585
D
? 1520
+ 1.7152 y3
+ .0103 y?
+ 20.3501
" Z1 = o
y4
(tomatoes)
(potatoes)
(beans)
(corn)
(avocados)
(limes)
(mangos)
(chlorinated hydrocarbons)
4.1807 y + 6.0781 y2 + 2.3260 y3 + 12.5674 y4
+ .2345 y^ + .1608 y^ + .0681 y? z2 = 0 (organic phosphates)
yv y2, . ,y?, z,*~ 0


CHAPTER I
INTRODUCTION
The Problem
This research effort was concerned with the economic external
ities created by the use of agricultural pesticides and the implications
of such externalities on social welfare. The working hypothesis was
that the externalities could be measured, and the objective was to
measure them for some specified region and time period and to
incorporate them into a model which was developed to aid in the choice
of a pesticide usage policy for the area. The choice criterion was a
measure of social welfare.
2
The model employed the concept of consumers'-producers' surplus
as a measure of "welfare." For each of two specified policies and
three variations under one of the policies the model maximized this
measure of welfare over production alternatives available to the region.
In each case the objective function explicitly recognized measurable
externalities not accounted for by consumers'-producers' surplus. The
maximization was restrained by flexibility constraints derived from
This research project is an outgrowth of some earlier work
done by J. C. Headley and J. N. Lewis (23) for Resources for the Future,
Inc.
2
Throughout the paper this term is to be read as "consumers'
plus producers' surplus."
1


Table 42.Sample
data on pesticide usage,'
Dade
a
by pesticide and
County, Florida,
crops for growers
1966-67.
who used the pesticide,
Pesticide
Crop
Number of
growers
Acres
sampled
Mean usage
in pounds
per acre
Standard
deviation
pounds
per acre
Agrimycin
Tomatoes
6
1,574
.1955
.1734
Aidrin
Groves
2
610
.2460
.1732
Amine 2,4-D
Potatoes
7
1,712
.0837
.2128
Arsenate of lead
Potatoes
1
800
.4825
.0000
Atrazine
Corn
2
1,585
.8539
.3225
Botran
Tomatoes
1
3,150
.0562
.0000
Potatoes
1
640
.1125
.0000
Pole beans
69
1,962
3.3853
3.4251
Captan
Tomatoes
7
4,914
.3119
.5797
Potatoes
4
2,265
2.2268
1.9402
Okra
1
30
.8000
.0000
Other
1
40
.6000
.0000
Chlordane
Tomatoes
13
9,874
.2248
.1923
Potatoes
1
800
.0094
.0000
Citrus oil
Groves
11
805
.7699
.3132
Copper compounds
Tomatoes
22
10,145
2.2055
7.9230
176


3
will depend upon our success at population control and our ability to
3
increase agricultural production.
Second, there is the lurking possibility that the long-run
hazards of persistent pesticides to human health and the Earths eco
logical relationships may be more serious than had been imagined
previously. We really know very little about these long-run effects,
but some individuals feel that they may be serious (49, pp. 485, 526,
585, and 684).
Third, it is possible that a nations values and preferences
change as its standard of living rises. If a large segment of our
population were starving, it is doubtful that the external or side
effects from using pesticides would be given very serious consideration.
It would be worth poisoning a few people if one more than "a few" could
be saved from starving through the use of pesticides. The "pesticide
problem" is a sub-case of the more general "environmental pollution
problem" and a "clean environment" is a luxury public good not demanded
until some minimum quantity of those goods more necessary for sustaining
life is obtained.
Being a public good, an effective demand is not expressed in the
market place. Consequently there are dangers of "social imbalance."
This "social imbalance" was described by J. K. Galbraith more than a
decade ago:
The family which takes its mauve and cerise, air-conditioned,
power-steered, and power-braked automobile out for a tour passes
through cities that are badly paved, made hideous by litter,
blighted buildings, billboards, and posts for wires that
should have long since been put underground. They pass into a
3
For two interesting accounts of the world food situation see
Borgstrom (1) and Gunther (22).


52
Table 13.Supply functions used in the model.
a
Function
Tomatoes
Policy
lu
P
=
- 83.6862
1
.0497
y
Policy
2Ad
P
=
- 67.6562
+
.0497
y
Policy
2Bd
P
=
- 66.5962
+
.0497
y
Policy
2Ce
P
=
- 65.4262
+
.0497
y
Potatoes
Policy
1
P
=
189.0995
+
.0464
y
Policy
2A
P
=
196.4295
+
.0464
y
Policy
2B
P
=
196.6195
+
.0464
y
Policy
2C
P
=
196.8095
+
.0464
y
Beans
Policy
1
P
=
-535.2889
+
.2245
y
Policy
2A
P
=
-535.6089
+
.2245
y
Policy
2B
P
=
-535.1389
+
.2245
y
Policy
2C
P
=
-534.6789
+
.2245
y
Corn
Policy
1
P
=
171.6317
+
.1930
y
Policy
2A
P
=
164.4317
+
.1930
y
Policy
2B
P
=
168.1317
+
.1930
y
Policy
2C
P
=
171.8417
+
.1930
y
Avocados j
Limes
Acreages on these crops were
constrained
to be
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
no greater than the 1966-67 levels.
Mangos
For all functions} price is measured in dollars per acre and
quantity in acres.
Current usage,
c
A 50 percent reduction in chlorinated hydrocarbons and a
substitution rate of .3 pounds of organic phosphates per pound of
chlorinated hydrocarbons.
dA 50 percent reduction in chlorinated hydrocarbons and a
substitution rate of ,4 pounds of organic phosphates per pound of
chlorinated hydrocarbons.
A 50 percent reduction in chlorinated hydrocarbons and a
substitution rate of .5 pounds of organic phosphates per pound of
chlorinated hydrocarbons.


163
Table 41.Estimated pesticide usage by pesticide,
in Dade County, Florida, 1966-67.^
crop, and month
Standard
Mean usage deviation
Pesticide
Crop
Month
in pounds
per acre
pounds
per acre
Agrimycin
Tomatoes
August
.0054
.0346
Tomatoes
September
.0159
.0782
Tomatoes
October
.0070
.0343
Tomatoes
November
.0008
.0069
Aldrin
Groves
February
.1203
.0490
Groves
June
.0000
.0010
Amine 2,4-D
Potatoes
January
.0182
.0986
Potatoes
February
.0131
.0100
Arsenate of lead
Potatoes
December
.0628
.0504
Potatoes
January
.0214
.0172
Atrazine
Corn
December
.3810
.0000
Corn
February
.1633
.0000
Corn
March
.0816
.0000
Botran
Tomatoes
January
.0113
.0078
Tomatoes
February
.0054
.0037
Potatoes
February
.0157
.0158
Pole beans
December
.3735
1.8958
Pole beans
January
1.5933
4.0390
Pole beans
February
.7041
1.9559
Pole beans
March
.1045
.7466
Captan
Tomatoes
August
.0121
.0732
Tomatoes
September
.0160
.0513
Tomatoes
October
.0272
.1205
Tomatoes
November
.0045
.0218
Tomatoes
January
.0378
.1856
Tomatoes
February
.0283
.0586
Tomatoes
March
.0189
.0928
Potatoes
October
.1691
.1110
Potatoes
November
.7376
.6289
Potatoes
December
.1936
.1592
Okra
January
.4800
.5657
Other
January
.1000
.4243
Chlordane
Tomatoes
August
.0437
.1428
Tomatoes
September
.0464
.1033
Tomatoes
October
.0343
.0368
Tomatoes
November
.0111
.0069


Table 42.Continued
Pesticide
Crop
Number of
growers
Potatoes
42
Pole beans
1
Squash
7
Toxaphene
Tomatoes
20
Potatoes
17
Pole beans
93
Corn
2
Okra
1
Other
2
Treflan
Tomatoes
3
Pole beans
1
Okra
1
Other
1
Trithion
Groves
10
Zinc
Tomatoes
2
Zinc sulfate
Corn
1
Groves
26
Zineb
Tomatoes
12
Potatoes
1
Pole beans
55
Acres
sampled
Mean usage
in pounds
per acre
Standard
deviation
pounds
per acre
3,807
.6969
.5290
207
.1934
.0000
185
3.0628
1.7309
9,976
5.1536
3.7823
1,725
1.0122
1.1992
2,393
3.1381
2.8221
1,585
20.3291
17.0029
20
9.0000
.0000
240
4.6667
1.4142
1,840
.6559
.4827
425
.0704
.0000
30
1.2000
.0000
40
.9000
.0000
147
1.0681
.3656
490
.1580
.0209
735
4.8299
.0000
1,247
1.6844
3.4391
1,586
1.6132
.9948
640
.0194
.0000
649
.9262
.6195
M
00
NJ


6
and non-fatal poisonings in man. Education in handling these highly
toxic chemicals can be effective in reducing acute damage.
Current social and legislative trends seem to favor the substi
tution of non-persistent pesticides for the persistent ones (54, p. 20).
Therefore, alternative policies evaluated in this research project are
oriented toward this objective.


208
BIBLIOGRAPHYContinued.
28. Keith, J. A. "Reproduction in a Population of Herring Gulls
(Larus Argentatus) Contaminated by DDT," Journal of Applied
Ecology, Vol. Ill (Supplement), 1966.
29. Kimball, Thomas L. "Changing Trends in Insect Control, "N.A.C.
News, Vol. XXVII, No. 1, October, 1968. (Remarks before the
35th Annual Meeting of the National Agricultural Chemicals
Association, White Sulphur Springs, West Virginia, September 23,
1968).
30. Kneese, Allen V. The Economics of Water Quality Management.
(Published for Resources for the Future, Inc.) Baltimore, Maryland
The Johns Hopkins Press, 1964.
31. Koyck, L. M. Distributed Lags and Investment Analysis. Amsterdam:
North-Holland Publishing Co., 1954.
32. Lehner, Philip N., Boswell, Thomas 0., and Copeland, Frank.
"An Evaluation of the Effects of the Aedes aegypti Eradication
Program on Wildlife in South Florida," Pesticide Monitoring
Journal, Vol. I, No. 2, September, 1967.
33. Lerner, Abba P. The. Economics of Control: Principles of Welfare
Economics. New York: The Macmillan Co., 1946.
34. Locke, Louis N., Chura, Nicholas J. and Stewart, Paul A. "Sperma
togenesis in Bald Eagles Experimentally Fed a Diet Containing
DDT," The Condor, Vol. LXVIII, No. 5, September-October, 1966.
35. Malinvaud, E. Statistical Methods of Econometrics. Trans. Mrs. A.
Silvey. Amsterdam: North-Holland Publishing Co., 1966.
36. Michigan. Report of the Governor's Pesticide Advisory Panel, 1968.
37. Mishan, E. J. "A Survey of Welfare Economics, 1939-59," The
Economic Journal, Vol. LXX, June, 1960.
38. Nerlove, Marc. Distributed Lags and Demand Analysis for Agri
cultural and Other Commodities, Agricultural Handbook 141, U.S.
Department of Agriculture, 1958.
39. Prosser, William L. Handbook of the Law of Torts. 2nd ed.
St. Paul, Minn.: West Publishing Co., 1955.
40. Samuelson, Paul Anthony. Foundations of Economic Anatysis.
New York: Atheneum, 1965.
41. Schmid, A. Allan. "Nonmarket Values and Efficiency of Public
Investments in Water Resources," American Economic Review,
Vol. LVII, No. 2, May, 1967.


97
water, air, and various species of plants and animals. This is the area
of monitoring which is currently moving along with the greatest speed,
and a sizeable body of data is being accumulated.
Second, regular statistical series are needed showing the
quantities of pesticides being injected into the environment by the
various groups which use pesticides. It might then be possible to
relate statistically the pattern of usage with the pattern of monitored
observations. Efforts to measure quantities of pesticides injected into
the environment have been very limited. In 1964, the Congress authorized
an expanded program of research on the use of pesticides in agriculture.
One phase of the expanded program was to conduct a periodic farm survey
to obtain information on the use of pesticides in different areas and on
different crops and classes of livestock. These data would provide a
basis for estimating the costs and benefits associated with the use of
pesticides and would serve as a measure of change in pesticide use over
time (52). While this program will no doubt generate very useful data in
the future, its relevance for our research was limited for two reasons.
First, there is approximately a two-year time lag before survey results
are published, and second, no farms were sampled in Dade County and only
a few for the State as a whole.
When one considers the whole area of environmental monitoring,
it does not make much sense to spend large sums of money learning how
to detect residues in various organisms of the environment and then to
neglect the question of what quantities of pesticides are being injec
ted into the environment in the first place. This same position was
recently espoused in Michigan by the Governor's Pesticide Advisory
Panel:


Table 42.Continued
Pesticide
Crop
Number of
growers
Potatoes
2
Pole beans
1
Squash
5
Okra
1
Groves
12
Cygon
Tomatoes
20
Potatoes
33
Pole beans
89
Squash
1
Okra
1
Other
1
Dacthal
Pole beans
1
DDD
Tomatoes
3
DDT
Tomatoes
7
Potatoes
8
Pole beans
2
Corn
2
Okra
1
Groves
1
Other
1
Demeton
Potatoes
21
Acres
sampled
Mean usage
in pounds
per acre
Standard
deviation
pounds
per acre
825
.5760
.7754
207
.4220
.0000
68
.4000
.0004
20
1.4310
.0000
1,105
9.6699
4.2968
9,695
1.2656
.6479
3,816
.9247
.3945
2,364
.3223
.1135
80
.1669
.0000
20
1.3350
.0000
200
.2670
.0000
207
.3478
.0000
1,060
1.4264
.8507
4,575
1.1411
1.8361
1,372
.6955
.5423
632
.9394
.8799
1,585
20.3712
5.9330
20
3.5000
.0000
600
.0400
.0000
40
1.5000
.0000
2,445
.2571
.3148
Vj


41
somewhat unrealistic restraint for the long-run.
Shifting the Supply Functions
The supply functions developed in the manner described above
pertain to current pesticide usage practices. Those for alternative
pesticide policies could not be observed but had to be synthesized from
information provided by growers, entomologists, and others knowledgeable
about the area.
In order to approximate reality within the data limitations, the
chlorinated hydrocarbons were lumped together as one category and the
organic phosphates as another. This was done on the basis that the
chlorinated hydrocarbons tend to have similar effects on the environ
ment and that the organic phosphates likewise tend to have similar
effects on the environment. It was also partly a result of political
considerations,since the thrust of the controversy seems to center
mainly around these two categories.
Chlorinated hydrocarbons and organic phosphates are, to a
limited extent, substitutes for each other. This is indicated by Tables
7 through 10 which show alternative insect control measures for the
vegetable crops under consideration. In many cases a given insect can
be controlled by a chlorinated hydrocarbon or an organic phosphate. But
there are also a number of insects for which the only recommended remedy
is a chlorinated hydrocarbon and a number for which the only recommended
remedy is an organic phosphate. Entomologists frequently differ in
their opinions about how to control certain insects. Adding to this
source of variation is the fact that insects' susceptibility to a pesti
cide seems to vary widely from time to time and place to place.


Table 30.
-Net profit
per acre
for a sample
of tomato
growers in
Dade County,
1960-61
through 1966-
-67.a
Unweighted
Grower
Standard
number
1960-1961
1961-1962
1962-1963
1963-1964
1964-1965
1965-1966
1966-1967
Mean
deviation
1
287.51
b
n.a.
-122.78
n.a.
n.a.
n.a.
n.a.
82.36
290.12
2
-107.48
-352.23
-129.21
15.98
n.a.
- 93.15
n.a.
-133.22
134.64
3
149.13
208.12
- 61.35
52.79
-166.49
111.14
-108.84
26.36
140.92
4
- 65.89
n.a.
-117.61
-165.71
n.a.
-207.89
n.a.
-139.28
61.27
5
- 20.40
n.a.
- 84.22
494.42
- 88.84
-233.24
-352.67
- 47.49
291.58
6
- 32.30
348.37
11.34
296.71
- 14.52
- 77.14
144.94
96.77
169.40
7
79.78
- 31.50
-107.59
n.a.
n.a.
n.a.
n.a.
- 19.77
94.23
8
6.30
122.38
76.59
115.67
-105.33
-125.39
n.a.
15.04
109.29
9
- 46.50
- 25.16
89.01
-154.55
-417.35
n.a.
n.a.
-110.91
191.89
10
n.a.
442.61
133.10
-141.89
- 62.94
-120.37
121.39
61.98
220.86
11
n. a.
88.85
- 32.53
-152.47
-308.51
n.a.
n.a.
-101.16
169.75
12
n.a.
278.15
189.49
255.76
70.33
n.a.
n.a.
198.43
93.33
. 13
n.a.
217.66
- 97.09
- 68.28
-233.68
n.a.
270.45
17.81
216.58
14
n.a.
88.84
-103.73
- 88.15
-232.21
-214.11
-328.07
-146.24
145.26
15
n.a.
n.a.
n.a.
n.a.
-263.65
-195.34
n.a.
-229.49
48.30
16
n.a.
n.a.
n.a.
n.a.
n.a.
-100.70
- 27.55
- 64.13
51.72
Unweighted
mean
27.79
126.01
-25.47
38.36
-165.74
-125.62
- 40.05
Unweighted
standard
deviation
124.43
216.13
106.60
214.01
141.79
100.64
238.84
Source of data: unpublished farm questionnaires collected by Dr. D. L. Brooke of the Department
of Agricultural Economics, University of Florida.
^Data not available.
120


Table 15.A summary of grower responses concerning
Case
number Pesticide Date Time in hospital
1 Parathion
2 Parathion 1962
3 Parathion 1958
4
5 Parathion 1964 2 or 3 days
6 Dyrene
7 Parathion 1964 Approximately 1 week
8
Parathion
1959
About 2 weeks
sickness from pesticides.
Comments
The grower reported that through the years he had
built up a sensitivity to Parathion and could no
longer come in close contact with it.
Grower reported that his brother was careless while
spraying in the yard and got some Parathion on his
skin, making him ill.
Grower became sick from Parathion while spraying a
tree in his yard. He was using a hand sprayer and
the chemical drifted down on him.
Grower reported that his workers had considerable
trouble with dermatitis while picking tomatoes}
however, the cause was uncertain and could be due to
the stem fuzz as well as pesticides.
Grower reported that his Mexican foreman, on a very
hot day, accidently got Parathion on his skin.
Grower reported that his men have had some problems
with dermatitis from Dyrene. When this happens he
does not allow the man to spray any more.
Man was spraying corn and got in the drift. He was
not wearing a mask.
No details on this case.
O'
00


APPENDIX A
NET PROFIT PER ACRE OF SELECTED CROPS IN
DADE COUNTY


34
dummy variable technique was to shift the intercept of the demand
g
function in an attempt to recognize seasonal shifts in demand. A
logarithmic transformation of the variables was also tried. With the
exception of beans and mangos, these transformations provided little or
no improvement in the fits obtained. Table 4 presents the empirical
results which were selected for derivation of the demand functions
used in the model. Although not of interest for purposes of this study,
short-run price and income elasticities are also presented. Table 5
presents the demand functions which were used for the model.
The Supply Model
r
The second element of the objective function, depicted by g^. (y),
was a linear supply function for the jth crop under the rth policy
alternative. The model from which supply parameters for current pesti
cide usage were estimated was specified as follows:
'V
[5]
subject to:
y (t) = ?0 + ^pit-l) + u (t)
[6]
y(t) y(t-l) = 6
y(t) y(t-l)
, 0 < 6 < 2
where:
'Xj
y(t) = long-run equilibrium acreage as of period t.
p(t) = price of the commodity in period t.
y(t) = acres planted in period t.
u(t) = a disturbance term satisfying classical assumptions.
As in the demand model, the constraint described the adjustment
'Xj
toward the long-run equilibrium acreage. Using [6] to eliminate y(t) in
It should be noted that only the intercept changed in this
model. In other words, the demand for fall tomatoes was assumed to
differ from that of winter tomatoes only in level, not in slope.


28
for them. What has to be decided is whether the gain from pre
venting the harm is greater than the loss which would be suffered
elsewhere as a result of stopping the action which produces the
harm (9, p. 27).
To summarize, the optimum externality level is independent of
property rights. Property rights become important when we begin trying
to move toward the optimum. The problem of the inter-personal compar
ison of utility cannot be ignored. The legal profession has been
grappling with it for many years and will continue doing so in the
future. Economists, by training, can bring many valuable tools to bear
on this problem and could be of valuable service to the legal profession
in making such decisions. The fact that policy decisions are difficult
in a world of imperfect knowledge does not justify ignoring them. If
economists refuse to aid in making them, they will be made by others.
Coase (9, pp. 42-43), in his classic article on social cost,
states that in two ways economists have historically fallen short in
helping to make policy decisions. First, they have tended to ignore
considerations which are aesthetic or moral in nature, when, in fact,
these may have a great influence on the final solution. Second, econo
mists have tended to focus their attention on models that are so simpli
fied that their solutions are of limited usefulness in the real world.
Conceptually, the model developed here at least partially meets
these criticisms. The environmental constraint allows the introduction
of value judgments through the right-hand-side elements, e, ., which
K1
are determined by political compromise. As far as the second criticism
is concerned, it is the writer's opinion that we have not abstracted
from the real world quite as far as most writers in this area. This is
true because of a slightly different approach. We began with a real


16
Table 3.Florida and Dade County population changes for 1950 and 1960.
a
Florida
Dade County
Total 1950
2,771,305
495,084
Total 1960
4,951,560
935,047
Percent increase
78.7
88.9
Rural 1950
957,415
29,005
Rural 1960
1,290,177
40,705
Percent increase
34.8
40.3
Urban 1950
1,813,930
446,079
Urban 1960
3,661,383
894,392
Percent increase
101.9
91.9
Source of data: U.S. Bureau of the Census (46).
The Planning Department estimates that 130 additional square miles will
be needed for new homes and apartments to accommodate the additional 1.5
million new residents expected during the next 25 years. The effect of
this growth on the outlying areas is shown in Figure 3.
It is obvious that the projected population growth will have
serious implications for the rural interests in Dade County. Agricul
tural activities will be driven toward the Everglades Swamp on the west
and southwest. Interdependencies between urban and rural interests will
intensify, and concurrent with this intensification, economic external
ities arising from such propinquity will increase, giving rise to a
multitude of policy questions such as the one with which this study is
concerned.
Several other characteristics of Dade County and its farmers
should be noted. First, farming in the region requires large amounts of


168
Table 41.Continued.
Pesticide
Maneb
Manganese
Manganese sulfate
Crop
Month
Tomatoes
August
Tomatoes
September
Tomatoes
October
Tomatoes
November
Tomatoes
December
Tomatoes
January
Tomatoes
February
Tomatoes
March
Tomatoes
April
Tomatoes
May
Potatoes
November
Potatoes
December
Potatoes
January
Potatoes
February
Potatoes
March
Pole beans
September
Pole beans
October
Pole beans
November
Pole beans
December
Pole beans
January
Pole beans
February
Pole beans
March
Pole beans
April
Pole beans
May
Corn
February
Corn
March
Squash
October
Squash
November
Squash
December
Squash
January
Okra
April
Tomatoes
September
Tomatoes
October
Groves
March
Groves
January
Groves
February
Groves
March
Groves
April
Groves
May
Groves
June
Mean usage
in pounds
per acre
Standard
deviation
pounds
per acre
.0335
.2668
1.9275
4.6441
1.1026
3.0119
2.2556
3.3812
3.4164
3.3938
3.9943
4.3011
5.4243
3.3173
1.0672
1.6151
.0960
.3739
.0082
.0153
.4712
.6049
2.2516
2.7184
6.1443
2.4776
4.2492
3.1435
.7926
1.5944
.0341
.0222
.3227
1.8608
.2928
.7094
.2750
.3520
.2971
.2402
.8160
.9491
1.4982
2.6937
.9261
2.5595
.0070
.0041
.4898
.0000
.2721
.0000
2.6203
5.2101
1.5440
2.8656
.8729
1.7475
.1145
.3363
.3600
.6364
.0148
.0979
.0058
.0521
.0026
.0635
.2531
1.0212
.1512
1.3191
.0716
.3777
.1086
.7087
.3189
1.1791
.2875
1.6643


78
in Tallahassee. The Industrial Commission was contacted and they
readily agreed to cooperate on the research. Data for Dade County for
the years 1966 and 1967 were obtained and summarized.
All of the workmen's compensation claims reported to the
Industrial Commission are classified into two groupsdisabling and
non-disabling. These terms are defined as follows:
Disabling injury: a work injury resulting in death, permanent
impairment, or loss of time beyond the day or shift of occurrence.
Non-disabling injury: an injury arising out of and in the
course of employment in which there is no loss of time beyond the
day or shift of occurrence (19, 1966 edition).
Very little data are maintained on the non-disabling category. The
number of injuries in this category is much greater than for the dis
abling category, but the cost involved is much less. Table 17 shows a
comparison between these categories for the state, the agricultural
industry, and commercial farms.
The Industrial Commission categorizes its disabling injuries in
a number of ways. The one which seemed most important for this research
was "disabling work injuries by agency." The "agency of injury" identi
fies the object, substance, exposure or bodily motion which directly
produced or inflicted the injury (19, 1966 edition). There are 53
agency categories, one of which is called "poisons and infectious agents.
This agency is further divided into the subgroups shown in Table 18.
For the State as a whole the category "poisons and infectious
agents" is declining in importance, as indicated in Table 19. Number of
injuries, days lost, and cost have all declined percentagewise since
1962. The Dade County cost figures for 1966 and 1967, shown in Tables
20 and 21, are consistent with this finding. In 1966 the total cost for
the agency was $175,497 while in 1967 it was $116,450.


Table 39.Continued.
April
Crop
Fungicide
Insecticide
Herbicide
Total
Organic
phosphates
Chlorinated
hydrocarbons
Carbamates
Other
Tomatoes
.1024
.0166
.1285
None
None
None
.2475
Potatoes
None
None
None
None
None
.2356
.2356
Pole beans
5.2683
.3409
.9166
None
.0035
None
6.5293
Corn
.2040
.8369
2.3775
None
None
None
3.4187
Squash
None
None
None
None
None
None
None
Okra
16.1100
1.4000
.8000
None
None
None
18.3100
Groves
1.7401
None
None
None
.1086
.1134
1.9621
Other
5.7833
.2225
4.1667
None
None
None
10.1725
Total average
usage
.8741
.0830
.3044
None
.0069
.0586
1.3270
S7T