• TABLE OF CONTENTS
HIDE
 Title Page
 Acknowledgement
 Table of Contents
 List of Tables
 List of Figures
 Abstract
 Constraints to technology adoption...
 The study area
 Methodology and data base
 Results and discussion
 Conclusions, implications, and...
 Appendix
 Reference
 Supplemental references
 Biographical sketch






Title: Constraints to technology adoption on small farms in north Florida
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Permanent Link: http://ufdc.ufl.edu/UF00055223/00001
 Material Information
Title: Constraints to technology adoption on small farms in north Florida
Physical Description: x, 164 leaves : ill. ; 28 cm.
Language: English
Creator: Dehm, Bruce A., 1955-
Publication Date: 1984
 Subjects
Subject: Farms, Small -- Florida   ( lcsh )
Agricultural innovations -- Florida   ( lcsh )
Agriculture -- Economic aspects -- Florida   ( lcsh )
Food and Resource Economics thesis M.S
Dissertations, Academic -- Food and Resource Economics -- UF
Genre: bibliography   ( marcgt )
non-fiction   ( marcgt )
 Notes
Thesis: Thesis (M.S.)--University of Florida, 1984.
Bibliography: Bibliography: leaves 157-163.
Statement of Responsibility: by Bruce A. Dehm.
General Note: Typescript.
General Note: Vita.
Funding: Florida Historical Agriculture and Rural Life
 Record Information
Bibliographic ID: UF00055223
Volume ID: VID00001
Source Institution: Marston Science Library, George A. Smathers Libraries, University of Florida
Holding Location: Florida Agricultural Experiment Station, Florida Cooperative Extension Service, Florida Department of Agriculture and Consumer Services, and the Engineering and Industrial Experiment Station; Institute for Food and Agricultural Services (IFAS), University of Florida
Rights Management: All rights reserved, Board of Trustees of the University of Florida
Resource Identifier: aleph - 000465275
oclc - 11567492
notis - ACM9379

Table of Contents
    Title Page
        Page i
    Acknowledgement
        Page ii
    Table of Contents
        Page iii
    List of Tables
        Page iv
        Page v
        Page vi
        Page vii
    List of Figures
        Page viii
    Abstract
        Page ix
        Page x
    Constraints to technology adoption on small farms in north Florida
        Page 1
        Introduction
            Page 1
            Page 2
            Page 3
            Page 4
            Page 5
            Page 6
            Page 7
            Page 8
        Problem statement
            Page 9
            Page 10
            Page 11
        Definition of small farms
            Page 12
        Hypothesis
            Page 13
        Objectives
            Page 13
            Page 14
    The study area
        Page 15
        Physical description
            Page 15
            Page 16
            Page 17
            Page 18
            Page 19
        Agricultural systems
            Page 20
            Page 21
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            Page 29
            Page 30
            Page 31
            Page 32
    Methodology and data base
        Page 33
        Procedural overview
            Page 33
            Page 34
        Modelling considerations
            Page 35
            Page 36
            Page 37
            Page 38
            Page 39
        Data base
            Page 40
            Page 41
            Page 42
            Page 43
            Page 44
            Page 45
        Procedure
            Page 46
            Page 47
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            Page 96
    Results and discussion
        Page 97
        Large farm model
            Page 97
            Page 98
            Page 99
        Small farm model
            Page 100
            Page 101
            Page 102
            Page 103
            Page 104
        Merged model: A test of the hypothesis
            Page 105
            Page 106
            Page 107
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    Conclusions, implications, and recommendations
        Page 112
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    Appendix
        Page 115
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    Reference
        Page 157
        Page 158
    Supplemental references
        Page 159
        Page 160
        Page 161
        Page 162
        Page 163
    Biographical sketch
        Page 164
        Page 165
        Page 166
Full Text











CONSTRAINTS TO TECHNOLOGY ADOPTION ON SMALL
FARMS IN NORTH FLORIDA
















BY

BRUCE A. DEHM


A THESIS PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN
PARTIAL FULFILLMENT OF THE REQUIREMENTS
FOR THE DEGREE OF MASTER OF SCIENCE


UNIVERSITY OF FLORIDA


1984















ACKNOWLEDGMENTS


The author wishes to extend heartfelt gratitude to Dr. Peter

Hildebrand for the many unselfish hours of guidance and support

throughout the past three years of academic and research training.

Special thanks are extended to Dr. John Holt for valuable advice

especially in the area of objective function realization, to Dr. Jon

van Blokland for comments and unique insight into the realm of large

farms, and to Dr. Tim Olson for helpful comments and suggestions.

Thanks are also due to Drs. Richard Beilock and Thomas Spreen for their

assistance with the technical aspects of linear programming.

Special acknowledgment is given to Dr. Tito French for providing

support and confidence throughout the author's association with the

North Florida Farming Systems Research and Extension Project.

The author must credit much of his understanding of farming in

North Florida to the many hours of discussion with Dr. Dwight Schmidt

and good friend and colleague, John Wake.

Finally, the author is indebted to the farmers of North Florida

for the time, patience, and generosity given during the many farm

visits, especially to people like Vero and Marjorie Musgrove who have

taught him the real meaning of "southern hospitality".















TABLE OF CONTENTS

PAGE


ACKNOWLEDGMENTS..................................... ......... ... ii

LIST OF TABLES................................................ iv

LIST OF FIGURES ....... ..................................... viii

ABSTRACT........................................................ ix

CHAPTER I CONSTRAINTS TO TECHNOLOGY ADOPTION ON SMALL FARMS
IN NORTH FLORIDA.................................. 1

Introduction...................................... 1
Problem Statement................................. 9
Definition of Small Farms......................... 12
Hypothesis ......................... ............ 13
Objectives....................................... 13

CHAPTER II THE STUDY AREA...................................... 15

Physical Description............................. 15
Agricultural Systems..................... ......... 20

CHAPTER III METHODOLOGY AND DATA BASE......................... 33

Procedural Overview. ................... .......... 33
Modelling Considerations.......................... 35
Data Base ......................................... 40
Procedure......................................... 46

CHAPTER IV RESULTS AND DISCUSSION............................ 97

Large Farm Model................................. 97
Small Farm Model................................. 100
Merged Model: A Test of the Hypothesis........... 105

CHAPTER V CONCLUSIONS, IMPLICATIONS, AND RECOMMENDATIONS.... 112

APPENDIX................. ... .................................... 116

REFERENCES........................ .............................. 157

SUPPLEMENTAL REFERENCES.......... .......................... .. 159

BIOGRAPHICAL SKETCH............................................ 164












LIST OF TABLES


TABT.F PAGE

1 Comparison of cost of technological inputs between
recommended technology and present day small farm
practices for corn production, Suwannee and Columbia
Counties, Florida, 1982................................. 45

2 Initial simplex tableau used to compute optimum farm
plans for small farms in Suwannee and Columbia Counties,
Florida, 1982.......................................... 49

3 Cash flow sub-matrix, savings account and cash transfer
activities............................................... 68

4 Cash flow sub-matrix, farm borrowing activities .......... 68

5 Cash flow sub-matrix, off-farm work transfer activities. 70

6 Cash flow sub-matrix, family living expense activities.. 70

7 Cash flow sub-matrix, production expense transfer
activities................. ................****** 71

8 Cash flow sub-matrix, capital expense transfer activity. 71

9 Yield, labor, cost and returns summary. Crop production:
small farm, Suwannee and Columbia Counties, Florida, 1982 75

10 Hog production: farrow to finish operation. Cost and
returns summary. Small farm, Suwannee and Columbia
Counties, Florida, 1982................................. 77

11 Cost and returns summary. Cattle production--cow/calf
operation, small farms, Suwannee and Columbia Counties,
Florida, 1982...........................................* 79

12 Production sub-matrix, labor hiring activities.......... 81

13 Production sub-matrix, selling activities............... 81

14 Production sub-matrix, feed buying activities........... 83

15 Wheat flexibility restraint.............................. 83

16 Cattle security restraint............................... 85

1i Initial simplex tableau used to compute optimum farm
plan on large farm, Suwannee and Columbia Counties,
Florida, 1982......................................... 88










PAGE


18 Yield, labor, cost, and returns summary. Large farm,
Suwannee and Columbia Counties, Florida, 1982........... 94

19 Optimal Farm Program--Large farm, North Central Florida,
1982.................................................... 98

20 Optimal Farm Program--Wheat excluded, small farm, North
Central Florida, 1982................................... 101

21 Optimal Farm Program--Corn/Wheat ratio = 2:1, wheat
included, small farm, North Central Florida, 1982....... 102

22 Optimal Farm Program--Corn/Wheat ratio unconstrained,
wheat included, small farm, North Central Florida, 1982. 103

23 Optimal Farm Program--Corn/Wheat unrestrained, merged
farm, North Central Florida, 1982....................... 107

24 Optimal Farm Program--Corn/Wheat ratio unrestrained,
soybeans included, merged farm, North Central Florida,
1982..................................................... 108

25 Estimated cost and return of producing one acre of corn,
small farm technology, Suwannee and Columbia Counties,
Florida, 1982... ....................................... 116

26 Corn, Small farm: monthly cash, labor, tractor, and
combine requirements per acre, Suwannee and Columbia
Counties, Florida, 1982................................. 117

27 Estimated cost and return of producing one acre of corn,
large farm technology, Suwannee and Columbia Counties,
1982......................... .. ................... ....... 119

28 Corn, large farm: monthly cash, labor, and tractor
requirements per acre, Suwannee and Columbia Counties,
Florida, 1982 .................. ........................ 120

29 Estimated cost and return of. producing one acre of wheat,
small farm technology, Suwannee and Columbia Counties,
Florida, 1982........................... ................ 122

30 Wheat, small farm: monthly cash, labor, tractor, and
combine requirements per acre, Suwannee and Columbia
Counties, Florida, 1982............................... 123

31 Estimated cost and return of producing one acre of
wheat, grazed, small farm technology, Suwannee and
Columbia Counties, Florida, 1982........................ 125










PAGE


32 Wheat grazed, small farm: monthly cash, labor, tractor,
and combine requirements per acre, Suwannee and Columbia
Counties, Florida, 1982.................................. 126

33 Estimated cost and return of producing one acre of
wheat, large farm technology, Suwannee and Columbia
Counties, Florida, 1982................................. 128

34 Wheat, large farm: monthly cash, labor, and tractor
requirements per acre, Suwannee and Columbia Counties,
Florida, 1982......................................... 129

35 Estimated cost and return of producing one Home
Vegetable Garden, small farm technology, Suwannee and
Columbia Counties, Florida, 1982........................ 131

36 Home vegetable garden, small farm: cash and labor
requirements and cash returns, Suwannee and Columbia
Counties, Florida, 1982................................. 132

37 Estimated cost and return of producing one acre of
soybeans, large farm technology, Suwannee and Columbia
Counties, Florida, 1982................................. 133

38 Soybeans, large farm: monthly cash, labor and tractor
requirements per acre, Suwannee and Columbia Counties,
1982..................................................... 134

39 Estimated cost and return of producing one acre of
peanuts, large farm technology, Suwannee and Columbia
Counties, Florida, 1982................................. 136

40 Peanuts, large farm: monthly cash, labor, and tractor
requirements per acre, Suwannee and Columbia Counties,
Florida, 1982 ......... .......................... 137

41 Estimated cost and return of producing one acre of
tobacco, large farm technology, Suwannee and Columbia
Counties, Florida, 1982................................. 139

42 Tobacco, large farm: monthly cash and labor requirements
per acre, Suwannee and Columbia Counties, Florida, 1982. 140

43 Estimated cost and return of producing one acre of
Bahia grass pasture, small farmt technology, Suwannee
and Columbia Counties, Florida, 1982.................... 142

44 Bahia pasture, small farm: monthly cash and labor
requirements per acre, Suwannee and Columbia Counties,
Florida, 1982....................................... 143










PAGE


45 Estimated cost and return of producing one acre of
ryegrass pasture, small farm technology, Suwannee and
Columbia Counties, Florida, 1982......................... 144

46 Ryegrass pasture, small farm: monthly cash and labor
requirements per acre, Suwannee and Columbia Counties,
Florida, 1982.................................... ...... 145

47 Estimated cost and return of producing hogs (10 sows-
farrow to finish operation), small farm technology,
Suwannee and Columbia Counties, Florida, 1982........... 146

48 Hogs, small farm: monthly cash and labor requirements
per acre, Suwannee and Columbia Counties, Florida, 1982. 147

49 Estimated cost and return of producing one cow/calf unit,
small farm technology, Suwannee and Columbia Counties,
Florida, 1982...................... .................... 148

50 Cattle, small farm: monthly cash and labor requirements
per cow/calf unit, Suwannee and Columbia Counties,
Florida, 1982........................................... 149

51 Modified large farm labor and tractor time for merged
activity model. Corn, Suwannee and Columbia Counties,
Florida, 1982........................................... 150

52 Modified large farm labor and tractor time for merged
activity model. Wheat, Suwannee and Columbia Counties,
Florida, 1982................... ....................... 151

53 Modified large farm labor and tractor time for merged
activity model. Soybeans, Suwannee and Columbia
Counties, Florida, 1982..-;........... ....... ....... 152

54 Modified large farm labor and tractor time for merged
activity model. Peanuts, Suwannee and Columbia
Counties, Florida, 1982.................................. 153

55 Operator labor availability, small, part-time family
farm, North Florida.... ................................ 154

56 Operator labor availability, large, full-time farm...... 155

57 Cost of family consumption in nonmetropolitan areas of
the Southern States, 1981....................... ..... 156















LIST OF FIGURES


FIGURE PAGE

1. Farm numbers for U.S., Florida and Suwannee County,
1870 1978............................................ 5

2 Average farm size in acres for U.S., Florida, and
Suwannee County................................... 6

3 Number of farms by acres harvested, Florida............ 7

4 Farms by value of sales: Florida, 1978............... 8

5 Value of products sold by size of sales: Florida, 1978 8

6 Suwannee and Columbia Counties, Florida................. 16

7 Average monthly and 1982 rainfall as recorded by
farmers in Suwannee and Columbia Counties, Florida..... 19

8 Index of corn acres, corn yield and tobacco acres,
Suwannee County, Florida............................... 23

9 Product and labor flows, traditional full-time, small
farm, Suwannee and Columbia Counties, Florida.......... 24

10 Product and labor flows, contemporary part-time small
farm, Suwannee and Columbia Counties, Florida........... 29

11 Product and labor flows, contemporary full-time large
farm, Suwannee and Columbia Counties, Florida........... 31

12 Aggregate labor and product flows, 10 small farms,
Suwannee and Columbia Counties, Florida................ 42


viii















Abstract of Thesis Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Master of Science



CONSTRAINTS TO TECHNOLOGY ADOPTION ON SMALL
FARMS IN NORTH FLORIDA


BY

BRUCE A. DEHM

April 1984

Chairman: Peter E. Hildebrand
Major Department: Food and Resource Economics

In North Florida, the traditional, homogeneous small family farm

of the past has evolved into two distinct farming systems. The first

comprises those farmers who have been able consistently to utilize new

technology to become high volume, capital intensive producers

characterized as "modern and efficient." The second, defined as small

family farmers, have been in the past, and are presently unable or

unwilling to adopt many of the recommended new farming practices and

technology, particularly at recommended levels. As an example, in

1982 on corn, small farmers sampled used 39, 73 and 69% of recommended

seed, base fertilizer and nitrogen, respectively, and they used no

herbicides or insecticides. The focus of this thesis is to determine

the special constraints facing small family farmers that prevent them

from adopting recommended farming practices and suggest necessary

characteristics of new technology for small farmers in north central

Florida.











Whole-farm, multi-period linear programming models were used to

simulate a typical large farm using recommended farming practices and

a typical small part-time family farm system using present small farm

technology. A third model utilized small farm constraints and merged

the large and small farm activities to simulate the availability of

recommended farming practices to the small family farmer.

Findings indicate that small farmers do not adopt most large farm

technology because of the high cash requirements of input intensive

practices, which also require more labor and management time than is

available to the part-time farmer. Yields are low but cash cost per

unit produced is often lower than for large farms. Even so, limited

volumes keep farm income low. Risk reduction strategies such as

diversification into livestock reduce further the profitability of the

small farm system.






















Chairman















CHAPTER I
CONSTRAINTS TO TECHNOLOGY ADOPTION ON SMALL FARMS IN NORTH FLORIDA


Introduction


In the environment of competitive markets-and dynamic technical

change, the processes.involved with the production of food and fiber

have undergone tremendous change. The widespread application of

science and technology to agriculture has greatly increased the

efficiency of production factors such as labor and land in the United

States in the last 50 years. Never before in the history of humankind

have so few people produced food for so many. Since 1933, the volume

of labor engaged in agricultural production has decreased more than

70%, while the average per acre yields in corn and wheat have increased

more than 340 and 220%, respectively [6]. America enjoys one of the

highest standards of living ever achieved and much of the credit lies

in the high productivity of its agricultural base.

These achievements have not come without certain costs, however.

Agriculture in the late 20th century has become a highly technical,

capital intensive complex in our industrial society. The technical,

financial and managerial revolutions that have transformed U.S. farms

from the 19th century Jeffersonian concept of a self-sufficient, non-

commercial entity into the present day agricultural firm that is input

intensive and profit oriented have produced many social, economic and

environmental ramifications. We are surrounded almost daily with

public concern of the social and economic consequences of resource















CHAPTER I
CONSTRAINTS TO TECHNOLOGY ADOPTION ON SMALL FARMS IN NORTH FLORIDA


Introduction


In the environment of competitive markets-and dynamic technical

change, the processes.involved with the production of food and fiber

have undergone tremendous change. The widespread application of

science and technology to agriculture has greatly increased the

efficiency of production factors such as labor and land in the United

States in the last 50 years. Never before in the history of humankind

have so few people produced food for so many. Since 1933, the volume

of labor engaged in agricultural production has decreased more than

70%, while the average per acre yields in corn and wheat have increased

more than 340 and 220%, respectively [6]. America enjoys one of the

highest standards of living ever achieved and much of the credit lies

in the high productivity of its agricultural base.

These achievements have not come without certain costs, however.

Agriculture in the late 20th century has become a highly technical,

capital intensive complex in our industrial society. The technical,

financial and managerial revolutions that have transformed U.S. farms

from the 19th century Jeffersonian concept of a self-sufficient, non-

commercial entity into the present day agricultural firm that is input

intensive and profit oriented have produced many social, economic and

environmental ramifications. We are surrounded almost daily with

public concern of the social and economic consequences of resource










concentration, corporate agriculture, decay of rural farming

communities, limited access to land and capital, and long range

environmental effects of chemical fertilizers, pesticides and

herbicides. Arguments concerning the positive and negative aspects of

the social and economic consequences, and whether or not public policy

can or should be used to direct these changes has precipitated into

what has become known as "the structure issue" [13]. Farm structure is

defined as "the control and organization of resources needed for farm

production. Its dimensions include the number and sizes of farms by

commodities and by regions, the degree of specialization in production

and the technology employed, the ownership and control of productive

resources, barriers to entry and exit in farming, and the social,

economic and political situation of farmers" [15, p. 3].

The attendant farm structure controversies are complex and

interrelated. Furthermore, economic considerations are no longer the

criteria in the formulation of solutions. Legal, ethical and

philosophical principles have also come to play an important role in

determining public policy in structural issues [18].

The role and importance of the American family farm is one

controversy in particular that has received much attention in recent

years. Undoubtedly, the survival of the family farm in modern American

is a complicated issue that involved deep emotional beliefs as well as

economic and political elements. The American family farm evokes

visions of hardworking and godfearing men, women and children who

worked to tame a new land with their staunch individualism and

independent value system. Over the years, however, the concept and










definition of the family farm has changed considerably. During the

17th and 18th centuries, hired labor and labor-saving technology were

limited, which meant that most farms "rarely expanded beyond a size

that could be operated by a single family" [4, p. 18]. The phrase

"family farm' itself may not have been part of American vocabulary

until the early 20th century, but concepts and beliefs of economic

self-sufficiency and wide distribution of land were important socially

and politically as a means to ensure responsible citizenship and a

republican form of government. These values have carried over into the

20th century and account for the notion that family farmers are the

backbone of democracy [4].

In the early 20th century, family farms were considered those

farms that were capable of supporting and fully employing a family. In

the 1940's, this definition was expanded to include a moderate amount

of outside labor to help maintain the farm plant but still excluded,

farms that received off-farm income as part of the total farm-family

income. During the 1950s and 1960s, however, total farm numbers in

general declined dramatically with part-time farms being the only group

to increase in absolute number. Political expediency deemed it

necessary to include these part-time farms in the family farm

definition to mitigate the drastic decline in the number of family

farms. The modern definition has eliminated the concepts of economic

self-sufficiency. "The family farm is a primary agricultural business

in which the operator is a risk-taking manager, who with his family

does most of the farmwork and performs most of the managerial

activities" [4, p. 21].











The dramatic decline in the numbers of medium and small family

farms in the past few decades has alarmed many Americans. Nationally,

as Figure 1 indicates, the total number of farms has decreased 40% to

2.7 million between 1935 and 1978. During the same period, average

farm size (Figure 2) has increased from 140 acres to nearly 400 acres.

The concentration of production has also changed greatly over the past

few decades. In 1978, farms that sold less than $40,000 of produce

represented 78% of all farms, but only 18% of all sales. Farms with

sales above $100,000 constituted 7.1% of all farms, yet generated 56%

of the total sales. In terms of acreage, 6.6% of the farms encompass

54.1% of the land in farms. These data indicate that production is

being concentrated into a relatively small number of large farms [23].

United States census of Agriculture and USDA data indicate that

structural changes in Florida are similar to national trends. As

illustrated in Figures 1 and 2, Florida farm numbers have decreased

39.5% while average acreage per farm has increased 363% during the

1935-1978 period. Although the Florida figures show a recent (since

1978) surge in farm numbers and decrease in farm size, these trends are

due mainly to an increase in small "hobby" or "retirement" farms,

locally known as "ranchettes". As indicated in Figure 3, and Census

data, a majority of new farms are in the less than 10 acre category.

These figures do not speak for the small and medium sized family farms,

but indicate rather the recent migration from urban to rural areas in

Florida.

Concentration of productive resources is higher in Florida than

nationally. Figures 4 and 5 show that in 1978, 82% of all farms sold












U.S. Florida Suwannee


Number of farms
0= u.s.
*= Florida
A= Suwannee County


Figure 1. Farm numbers for U.S., Florida and Suwannee County, 1870 1978..

SOURCE: [22]











Average Farm Size (acres)
Acres 400 = U.S.
1 I -= Florida
A= Suwannee County


300






200






100







1870


Figure 2.


Average farm size in acres for U.S., Florida, and Suwannee County.

SOURCE: [22]



















40,000
Farm
Numbers
By Acres
Harvested









20,000






10,000

5-99Ares

5,000 ---
100-199 Acres
1,000 00-499 Acres C
1,000 -
1945 1950 1954 1959 1964 1969 1974 19



Figure 3. Number of farms by acres harvested, Florida.


SOURCE: [22]

















'M than 2500
36.2%










$250 to $4,999
14.4%


$100,000 or mre
--------------6%

I $40,000 to $99999
8.3%


$20.000 to $3,999
&8.%



$10,000 to $19,999
-----------10.$6%



$5,000 to $9,999
12.4%


Figure 4. Farms by value of sales: Florida, 1978.


$10,000 to $19,999
2.2%
$20,000 to $39.999 3.5%
$'0m00 to s9,s 9
7.5%


___ L than $10,000
2.5%




* ;,.*. '-.
* .- ,. \







* .: *.---.,


__$100,000 or mof
84.4%


US Depar nt of Commerce BUREAU F TM CENSUS


Figure 5.


Value
1978.


of products sold by size of sales: Florida,











less than $40,000 of produce, but represented only 8% of total sales.

Farms with sales above $100,000 constituted 9.6% of all farms, yet

generated 84.4% of total sales.

Concern for the welfare of the family farm has been broad based.

Congressional affirmation of "the family farm system (as being)

essential to the social well-being of the Nation" [21, p. 7] is a clear

indication of public support and concern for a troubled agricultural

sector. Likewise, the Florida legislature has shown its concern for

the family farm with the recent passage of a "Right to Farm" bill which

strengthens the position of long-term farmers faced with complaints

from encroaching urban residents over noise and odors from agricultural

operations [12; James Wershow, personal communication]. In 1981, the

Florida legislature authorized and funded a Governor's Conference on

the Future of Small Farms to inform political leaders of the small

farmer situation so that "public policy (can be managed) in ways that

will encourage small farms to continue in profitable operation" [Lt.

Gov. Wayne Mixon in 11]. Structural issues that require further study

and resolution abound. These range from arriving at a general

consensus on the exact nature of farmer problems, to farm size

definition, tax, credit, marketing, income equity and agricultural

trade and pricing problems.

Problem Statement


The focus of this thesis will include a structural issue that is

concerned with the relationship between available technology and farm

size. Many factors contribute to the previously indicated trends in

the U.S. farm structure. These include the price level of agricultural










products and factors of production, and the availability of credit,

competitive markets and information. Technology also plays an

important role in these trends in that land augmenting technology

(hybrids, seeds, chemical fertilizers, etc.) has allowed huge increases

in yields per acre, while labor augmenting technology (tractors and

other mechanical equipment) has allowed individuals to cultivate more

land with less labor. The "technological treadmill" [6] theory

suggests that aggressive, innovative farmers adopt new, cost-reducing

technology and increase their profit position relative to late or non-

adoptors of new technology. In a competitive free market system, the

farmers who are able to reduce per unit costs the most in the long run

are able to survive because as output expands, product price falls and

squeezes the high cost producers out of farming. The process is

referred to as a treadmill because new technology is continually made

available to farmers, and since there are always aggressive and

innovative early adoptors, the remaining farmers must follow suit and

adopt the new cost reducing technology or eventually be forced out of

farming. As a result, technical change "frequently provides an

advantage to those who seek or can readily adapt to change; but at the

same time, it usually puts some at a disadvantage--those who do not or

cannot readily adapt to change" [24, p. 127]. However, this is not to

suggest that the treadmill is the sole inducement behind the changes in

farm structure. The treadmill process as described can be considered a

"push" process in that it eliminates farmers against their will.

However, concurrent with U.S. post World War II development have come

many off-farm employment opportunities in the industrial, professional










and service sectors of the economy. The amenities offered by such

opportunities have served to "pull" people from rural areas and allow

them alternative life-styles that the farm could not provide. Thus, we

see that changes in the farm structure are due to elements that both

"push" and "pull" farmers out of agriculture.

In light of the technological treadmill theory and of the

structural trends in U.S. agriculture, questions arise about the nature

of new technologies being produced for farmers. Is it possible that

much of the new agricultural technology is biased towards the large-

scale agricultural firm that is managed by the aggressive, early

adoptor, and thus exacerbates the technological treadmill? Could much

of this new technology contain inherent prerequisites for successful

application that are beyond the grasp of most small and medium-sized

family farms? Many of the new farming practices and technologies are

purported to be "scale neutral", implying proportionate availability

and equal value to both large and small scale farms. To what degree is

this statement true in North Florida? This thesis will investigate and

attempt to answer these questions in the context of contemporary

farming systems found in the north central region of Florida.










Technology as defined here applies not only to the use per se of
specific inputs, but also to other practices associated with the
inputs such as timing, quantities used, method of use, etc.











Definition of Small Farms


The task of defining the terms small, medium and large scale farms

is not easy. Many researchers follow the United States Department of

Agriculture method of categorizing farm size based on gross cash farm

sales, as was done earlier in this paper. Although these types of

definitions are useful when describing general trends, they fail to

consider many charactersitics that qualitatively differentiate farms by

size. For this study, size, either in terms of product sales or land

area is only marginally useful in separating large and small farming

systems. Other important classification criteria lie in the production

strategies, the patterns of household consumption, and the interaction

between production and consumption activities, with the various

beliefs, value systems and goals held by family and larger than family

farmers being the underpinnings of such strategies and activities.

Even though each farmer has his own motivation for farming, there

seems to be sufficient homogeneity between farms of similar farming

systems to suggest that in general, motivational factors, production

strategies and consumption activities are different between the small

and medium sized family farms on the one hand and the larger than

family sized farms on the other. The assumption for this thesis will

be that larger than family farms operate more as a business and rely on

the farm primarily as a means to produce income, wealth and profit. In

North Florida, these farming systems are often characterized by fewer

and larger farm enterprises that require hired labor and six and eight

row machinery. The production strategy is calculated for high yields

on large acreages with the use of large amounts of fixed and operating










capital as a means of insuring successful crops (i.e., irrigation,

chemical weed and pest control). To a large degree, production

decisions on the farm enterprises are made separately from consumption

decisions in the household and vise versa.

Medium and small family farmers, on the other hand, perceive their

farm as a home first, a desirable and cherished lifestyle that they

wish to maintain. Family living expenses and production costs are more

likely to compete for the same scarce resources, so that production

decisions on the farm and consumption patterns in the household are

more closely linked. Production strategy is calculated for yields that

are roughly two-thirds to one-half the size of those on larger than

family farms for many crops in North Florida. Two and four row

equipment, enterprise diversity, integration between crops and

livestock, and a high utilization of farm products by the household

typify many of these farming systems.


Hypothesis


Economic, social and environmental constraints prevent small and

medium scale family farmers in North Florida from adopting agricultural

production techniques employed by large-scale farmers.


Objectives


1. Identify typical large scale farming systems that use

recommended.practices and typical small scale farming systems in

Suwannee and Columbia Counties of North Florida. This will include

describing the nature of the farming systems and defining the

production and consumption activities that occur within the systems.










capital as a means of insuring successful crops (i.e., irrigation,

chemical weed and pest control). To a large degree, production

decisions on the farm enterprises are made separately from consumption

decisions in the household and vise versa.

Medium and small family farmers, on the other hand, perceive their

farm as a home first, a desirable and cherished lifestyle that they

wish to maintain. Family living expenses and production costs are more

likely to compete for the same scarce resources, so that production

decisions on the farm and consumption patterns in the household are

more closely linked. Production strategy is calculated for yields that

are roughly two-thirds to one-half the size of those on larger than

family farms for many crops in North Florida. Two and four row

equipment, enterprise diversity, integration between crops and

livestock, and a high utilization of farm products by the household

typify many of these farming systems.


Hypothesis


Economic, social and environmental constraints prevent small and

medium scale family farmers in North Florida from adopting agricultural

production techniques employed by large-scale farmers.


Objectives


1. Identify typical large scale farming systems that use

recommended.practices and typical small scale farming systems in

Suwannee and Columbia Counties of North Florida. This will include

describing the nature of the farming systems and defining the

production and consumption activities that occur within the systems.










2. Identify and quantify the major social, economic and

biophysical constraints facing both small family farms and large scale

farms in the two county region.

3. Determine the constraints that limit the adoption by small

farms of recommended farming practices and technologies that are

utilized on large farms in the study area.

4. Based on the findings of a linear programming analysis,

determine why family farms have not adopted the technology used on

large scale farms, and to make recommendations pertaining to the

necessary characteristics of new technology for small farms in the

study area.
















CHAPTER II
THE STUDY AREA


Physical Description


The area studied in this thesis is a two county region located in

north central Florida (Figure 6) encompassing Suwannee and Columbia

counties. Agricultural production and farming systems in these

counties are similar because of historical parallels inland

settlement, land tenure and market opportunities, as well as a

similitude in soils, topography, precipitation and temperature.

Roads and other infrastructure are well developed in the region.

Live Oak and Lake City, the county seats of Suwannee and Columbia

Counties, respectively, are the major marketing centers in the area.

Live Oak boasts the largest tobacco market in the state, while Lake

City has two livestock markets and Live Oak one. Some hog farmers

access the feeder pig market in Gainesville. Goldkist, in Live Oak, is

the major market for grains in Suwannee and Grainex, in Lake City,

services grain producers in Columbia County. Smaller grain dealers are

located in O'Brien, McAlpin, Live Oa-;, Branford, Columbia City and

other points around the counties. There are no vegetable buyers or

packing plants in either county which forces growers to sell produce at

roadside stands or to travel to the markets in Jacksonville or

Valdosta.
















CHAPTER II
THE STUDY AREA


Physical Description


The area studied in this thesis is a two county region located in

north central Florida (Figure 6) encompassing Suwannee and Columbia

counties. Agricultural production and farming systems in these

counties are similar because of historical parallels inland

settlement, land tenure and market opportunities, as well as a

similitude in soils, topography, precipitation and temperature.

Roads and other infrastructure are well developed in the region.

Live Oak and Lake City, the county seats of Suwannee and Columbia

Counties, respectively, are the major marketing centers in the area.

Live Oak boasts the largest tobacco market in the state, while Lake

City has two livestock markets and Live Oak one. Some hog farmers

access the feeder pig market in Gainesville. Goldkist, in Live Oak, is

the major market for grains in Suwannee and Grainex, in Lake City,

services grain producers in Columbia County. Smaller grain dealers are

located in O'Brien, McAlpin, Live Oa-;, Branford, Columbia City and

other points around the counties. There are no vegetable buyers or

packing plants in either county which forces growers to sell produce at

roadside stands or to travel to the markets in Jacksonville or

Valdosta.






















































































0 5 so 1 20 8i
0 LOETOEaS

0 5 to 15


Figure 6. Suwannee and Columbia Counties, Florida.


_I











Topography and Soils


Suwannee and Columbia Counties are part of the Central Florida

Ridge of the Atlantic Coastal Plain. Limestone formations underlie the

entire region and serve as a vast reservoir for fresh water that fills

the underground caves and solution pores [22]. The soils that cover

the limestone formations are recent deposits by seas and are sandy in

nature. The terrain is low in elevation with areas of gently rolling

hills which are well-drained to excessively well-drained. Areas in

northwest Suwannee and north Columbia Counties are low-lying, flat and

poorly drained. The soils range in thickness from less than one to

more than twenty feet in depth and are characterized as drought,

nutrient deficient, and susceptible to wind erosion [22]. One

advantage these soils provide farmers is that land preparation, seeding

and cultivation activities can usually be carried out within hours

after a rainfall increasing available field time and decreasing

timeliness losses due to inaccessible fields.


Climate and Weather


The climate is temperate and humid, with long warm summers and

mild winters. Precipitation during the late spring, summer and early

fall is due primarily to the differential heating of air over

peninsular land and coastal waters causing convectional rainfall.

These thunderstorms tend to be localized and violent, often dropping

more than an inch of rain in one afternoon. It is not uncommon to see

a drenching rainfall in one field and no precipitation on an area

nearby. After a warm summer shower, the water tends to move down

through the sandy soil quickly, causing leaching of many of the nutrients.











During the late fall, winter and early spring, rainfall is

generally produced as cold, high pressure fronts move down from the

north. This precipitation is less intense and more evenly distributed

than the convectional rainfall, and is more likely to recharge the

underground storage levels [16].

Gusty and enduring March winds erode sand and organic matter, and

can cause severe stress to young corn, tobacco and watermelon crops.

Farmers have adjusted by planting strips of rye in watermelon and

tobacco fields to act as wind-breaks, and have tried to mitigate the

damage to young corn by planting deep in the furrow.

Even though North Florida receives an average of 51 inches of

rainfall each year, water is still one of the most limiting factors of

production for summer crops. A recent thesis found a correlation of

0.76 between rainfall and corn yields in North Florida over a 12-year

period [16]. The rapid runoff from summer downpours exacerbates the

drouthiness of these sandy soils. In addition to this problem, many

years have a 2-4 week dry spell in April and May during the critical

flowering stage of summer crops, especially corn (16, 22]. These dry

spells can greatly reduce yield.

Water requirements during silking and tasseling of corn are 0.2 to

0.3 inches per day, or 20-25 inches during the entire growing period,

and it has been estimated that 25 inches of rainfall are necessary to

produce 40-60 bushel corn [16]. Figure 7 indicates the amount of

rainfall measured by 12 farmers in Suwannee and Columbia Counties

during 1982. No farmer reported any rain during the first half of May

and only scant amounts were recorded during the last two weeks of








Inches
Rainfall


7.01

6.0 ..


March July, n=12
July Sept., n=l

-Average Monthly


16-30 1-15 1.6-30 1-15 16-31 1-15 16-30 1-15 16-31 1,15 16-31 1-15 15-30


March April


June


July August September


Figure 7. Average monthly and 1982 rainfall as recorded by farmers in Suwannee and
Columbia Counties, Florida.


N>


-7
-,

I .










April. Other than this dry period, rainfall was adequate for the 1982

corn crop, generally above 6.5 inches per month.


Agricultural Systems


Traditional Farming Systems


Farming in Suwannee and Columbia Counties has long been of the

yeoman tradition--a land owning rural populace producing agricultural

products for subsistence and sale [17]. Labor intensive, high value

cash crops have been the mainstay of farms in the area since the late

1800s. A diverse mixture of other crops and livestock has been

integrated into the agricultural systems in an effort to add stability

to the farms' ability to maintain the farm family.

Cotton was king early in Suwannee's history until it was

devastated by the Mexican boll weevil in the mid-1920s. Before its

demise, there were a record number of farmers in the county--2570, with

an average land holding of 113.5 acres (U.S. Census Data). In five

short years, the number of farms fell by 38% to 1575 as the boll weevil

made its impact felt. By 1930, tobacco began to replace cotton as the

chief earner of farm cash and farm numbers again began to rise, but

never reached their former strength. The number of farms steadily

increased from 1930 to 1950 and this increase in number was accompanied

by moderate increases in farm size (Figure 2). Agricultural

production, markets and technology were quite stable during this 20-25

year period, giving rise to what will be defined here as the

traditional farming system of Suwannee and Columbia Counties.

As the chief cash crop, tobacco paid the highest marginal return

for farm resources, and thus, coiimanded a major portion of the farms'











resources. The farm system revolved around the requirements of tobacco

because of its singular importance in providing the family with cash

for purchasing goods that could not be produced on the farm. However,

the livelihood of the family farm rarely depended wholly on a single

cash crop. Livestock, especially hogs were an important part of the

farming system for reasons such as risk aversion or improved cash flow.

In 1945, Bostwick [3] described many of the farms in Suwannee County as

being tobacco-hog systems. In 1952, Ellis [7] found that 98% of 132

farms in Suwannee and Columbia Counties produced tobacco and 92% of the

same farms reported having hogs. Hogs were incorporated within the

farm system to take advantage of excess resources such as crop and

household residues, abundant foraging land, and non-peak period labor.

In return for resources used, the hogs provided a means.by which

farmers could accumulate and store livestock on the farm for future use

as food for the family or cash for family or production expenses.

The number of hogs per farm were generally low until the late

1950s. Before that time, large farms maintained four brood sows and

small farms two, in what were essentially farrow to finish operations

[3]. In the 1950s, hogs generated 12.5% of the gross farm receipts

while tobacco supplied 52%. The remainder of the farm receipts were

supplied by watermelon, poultry, vegetables, cattle and other

miscellaneous crops such as pecans and cane syrup.

In the traditional farming system, feeding hogs was a matter of

providing various forages throughout the year and allowing the animals

to graze or "hog-off" the crops while supplying minimal amounts of corn

grain to the animals. Crops grazed during the fall and winter months










included rye, standing corn and corn residues, velvet beans, cowpeas

and beggarweed. Peanuts for hogging-off have been used extensively for

fattening hogs but later this practice was penalized in the marketplace

because of the greasiness of the meat produced.

It was in the production of feed for both. the hogs and workstock

(horses, mules, that corn became important to the farm system. Corn

was utilized almost entirely inside the farm gate up until the mid-

1950s. Since at least the early 1900s, corn acreage has been higher

than any other crop in North Florida, but.only in the last twenty years

has it become used widely as a cash crop.

Until the 1940s, corn was rarely fertilized with commercial

sources of fertilizer. Yields remained under ten bushels per acre

until the mid-1950s, (Figure 8) and extension agents actively promoted

the intercropping of legumes such as velvet beans, cowpeas, soybeans,

crotalaria, peanuts and beggarweed for purposes of improving soil

fertility and livestock feed [17]. The amazing stability of corn

production technology and yield throughout the first half of this

century despite the advent of tractors, fertilizers and hybrid corn in

the mid-1920s, and yields of 38 bushels per acre by Florida "corn club"

members [10] is evidence of social and economic constraints limiting

its production.

The traditional farming system as depicted in Figure 9 is

characterized by a large number of enterprises with a high degree of

interaction between components. One of the main advantages of such

diversification is that a farmer can feel confident that at least some

of his enterprises will be profitable enough to maintain the economic


viability of the farm in any one year.












Inde. of
150
Corn Acres


125





100





75





50





25


/ \


I
/
I
I
I


II I
I V C

I





I


A
- '


SCorn Yield



\- Corn Acres
*1
I---


"\Tobacco Acres


INDEX: 1930=100
Corn Acres = 41653
Corn Yield = 8.4 (bu/a)
Tobacco Acres = 1123

Suwannee County, Florida


Index of
Corn Yield
. 500
and
Tobacco
Acres



. 400






- 300






- 200






- 100


I I I II & 1 I I Ia
1925 1930 1935 1940 1945 1950 1954 1959 1964 1969 1974 1978

Figure 8. Index of corn acres, corn yield, and tobacco acres, Suwannee County, Florida.

SOURCE: [22]













MARKET












MELONS TOBACCO PEANTS- CORN PASTURE VEGE TABLES U
L C E










IHOIIUSEHOLD





MARKET




Figure 9. Product and labor flows, traditional full-time, small farm, Suwannee and Columbia
Counties, Florida.










This tobacco-hog-corn system was prevalent and homogeneous in

Suwannee and Columbia Counties until the 1950s. There were, however,

variations in the size of farms. In the early 1940s, a large farm

averaged 3.5 acres of tobacco, 35 acres of corn-peanut intercrop, 25

acres of sole cropped corn and 4 brood sows. The typical small farm

during the same period produced 1.8 acres of tobacco, 8 acres of corn-

peanuts and 7 acres of corn with an average of 2 brood sows. The main

difference between large and small farms were simply the size of the

various enterprises. Large farms required more labor, land and

capital, but the organization and interaction of enterprises on both

large and small farms were quite similar. Today, there are many

differences between large and small farms. Large farms typically

specialize in a few enterprises that require high amounts of capital

investment and there is little interaction between enterprises on the

farm. Smaller farms, on the other hand, often have maintained some of

the enterprise diversity and interaction that typified farms of the

past.


The Changing Technical and Economic Environment


The second World War dramatically altered the rate of technical

change in industry and agriculture in the U.S. The effects reached

North Florida by the mid-1950s and fundamental changes took place in

the nature of farming. The equilibrium that had been reached between

technology, labor supply, market prices and the physical environment

started to erode, putting the tobacco-hog system into a state of flux.

Relationships and interdependencies between farm enterprises that had











evolved over the previous twenty-five years were now changing as

tractors replaced mules and people, and fertilizers and hybrids

increased yields. Many farmers saw a decline in the ability of small

tobacco allotments to provide the family with the cash necessary for

the increasing standard of living. The results of these various

factors was a trend towards specialization of farm enterprises and the

use of technology (irrigation, chemicals, etc.) rather than

diversification as a risk reducing mechanism. Some farms became

specialized in operations such as corn, or corn and hogs because these

were traditional crops whose production was well understood.

The development of portable feed grinders and mixers allowed

expansion of hog enterprises and as a result, encouraged further

specialization in hog production (feeder pigs vs. farrow-finish, for

example). This trend was augmented by the opening of a feeder pig

market in the early 1960s in Live Oak. Corn acreage increased during

the period from the mid-1950s to the late 1960s. At the same time,

average yields increased from 15 to 35 bushels per acre. This

increased production occurred while farm numbers, acres of tobacco and

the number of hogs decreased, indicating that corn grew in importance

as a cash crop.

Other farmers specialized in tobacco or peanuts, through allotment

consolidations or through renting a large number of allotments (17].

The number of farmers producing tobacco has declined each year since

1950. During the early 1960s, the introduction of bulk curing barns

presented a dilemma to many producers: should they invest the required

sum for purchase of a bulk barn (which greatly reduced labor in the











curing process), or should they leave tobacco production behind? The

increasing cost and decreasing availability of labor, and the expansion

of off-farm employment opportunities influenced many farmers to alter

their traditional system to the emerging trends of specialization,

mechanization and commercialization. A number of farms continued to

survive on small peanut or tobacco allotments and remained diversified

to some degree, but the specialization trend was generally noted across

all farm sizes [17].

While this transformation of farming from the traditional to the

specialized systems included new technology and production methods,

other changes in the economic and social networks also took place.

Improved transportation expanded markets beyond the local and regional

level and tied North Florida producers to national and international

markets where prices are determined for crops, produce and livestock.

The competitive-nature of farming became more acute as Suwannee and

Columbia County farmers found themselves facing the same product and

input market prices that farmers faced in the more productive soils of

the Midwest and Northeast.


Contemporary Farming Systems


The farming systems presently found in Suwannee and Columbia

Counties are the evolutionary result of the traditional farming system

responses to new economic and technical environments. Contemporary

farm systems lack the homogeneity that was characteristic of the

traditional large and small farms. Due to factors such as education,

access to land, labor and capital, management ability, and personal










goals and values, farmers reacted differently to the conditions facing

them. The traditional corn-swine-tobacco system has given way to the

more specialized corn-swine, tobacco-cattle or tobacco-soybeans-wheat

systems. Further divergence of the farm systems is apparent between

farmers which derive all or most of the farm family income from the

farm alone, and farmers which utilize full or part-time employment off-

farm to support the farm family.

Figure 10 depicts the product and labor flows of a small part-time

farm typical of the study area. Due to increasing competition, the

decreasing ability of major cash crops to maintain an adequate family

income or decreasing availability of family or kin labor, many farmers

have been forced to work off-farm for a dependable source of income.

These part-time family farms follow many traditional farming

activities, but on a reduced scale or with less diversification than

was seen on the traditional, full-time farm. It should be noted that

these farms are not to be considered "hobby" farms, "weekend" farms or

ranchettes since most of the farm families have a long history of

farming and maintain extensive kinship networks in the area. Profit is

still a strong incentive for production and sucessive farm losses can

force these farms out of business altogether. Personal communication

indicates that the goal of many of these farmers is to simply maintain

an agrarian lifestyle. Great satisfaction is derived from the process

of producing crops and livestock and although the financial reward of a

profitable crop is appreciated and necessary for economic viability, it

does not seem to be the major motivational factor for small farmers.















MARKET

S4






IP
H _

AMELS TOBACCO i PEANUTS <-- CORN PASTURE VEGETABLES 1 U
I MELONS _Gj : ..... _- : U
F-- I L
S C IT
A IP
T IY
T
L .
E


LABOR


HOUSEHOLD




MARKET


Figure 10.


Product and labor flows, contemporary part-time small farm, Suwannee and Columbia
Counties, Florida.










Generally speaking, the labor intensive and high management crops

have been dropped from the traditional farm system leaving the

"secondary" low labor, low management enterprises intact. Cash earned

through off-farm employment is frequently used to finance enterprises

on the farm or to help repay short-term liabilities for seed,

fertilizer, and fuel. The volume of production is low and tractors and

equipment tend to be old and fully depreciated, making repair costs

high, but intermediate liabilities on new equipment are low. It is

estimated that 60% of the farmers in Suwannee and Columbia Counties

fall into the definition of small farms, which in addition, gross less

than $40,000, the USDA definition of a small farm (Personal

communication, Marilyn Swisher, Multicounty Agent, Suwannee and

Columbia Counties).

Farmers that have continued to specialize their farming operations

and have evolved into "modern, efficient" agricultural producers might

typically be depicted by Figure 11. These farms are characterized by

fewer enterprises and large acreages of specialized crops. The large

farms employ high technology in both the labor and land augmenting

categories to insure high yields of high quality product. Farming is a

full time occupation for these farmers and because they produce large

volumes of output they have greater marketing and financing

flexibility. Large-scale farmers are a preferred credit risk because

their payback ability is better than smaller farmers and transaction

costs per dollar borrowed is lower than small farm loans [17]. Closer



Following Barkley's definition of high technology being "high" when
all effective opportunities to substitute capital for labor have been
utilized [2, p. 309].






















MARKET


SHATER -
MELONS


r--1
H I
I------',H'
PEANUTS G
IG




L
l- --J


HOUSEHOLD


MARKET


Figure 11.


r CORN .1
, CORN
- -------


*VEGETABLES '
L.... ...J


r -i
IIp
10 1
SI

IL i
IT i
iR I
iY


Product and labor flows, contemporary full-time large farm, Suwannee and Columbia
Counties, Florida.











ties with Cooperative Extension agents and the use of institutional

risk reducing mechanisms such as futures market, and crop insurance are

also typical of large farmers.

Growth oriented, large volume farms are more likely to have high

leverage ratios (total liabilities/total assets especially if

production and growth strategies require newer, more labor efficient

equipment and more land. As a result, long term and intermediate debt

will be much higher than that found on a small farm with a low growth

orientation and depreciated equipment.

The agricultural systems of North Florida have undergone sweeping

changes in the past 40 years in response to new conditions set by the

evolving economic and social environments. As these environments

continue to change in the future, so must the character and nature of

the present day farming systems. The importance of off-farm work and

the emergence of the part-time family farmer may aid in the continuance

of the small scale land holder and independent agricultural producer.















CHAPTER III
METHODOLOGY AND DATA BASE


Procedural Overview


To test the hypothesis, linear programming was utilized in a

whole-farm modeling procedure to describe the activities, constraints

and objectives of two separate contemporary farming systems found in

Suwannee and Columbia Counties of North Florida. The Mathematical

Programming System (MPS) installed at the Northeast Regional Data

Center of the University of Florida campus was used in compiling and

executing the linear programming models.

Linear programming was used first to simulate two predetermined,

representative farm systems: the first was a part-time smal-1 family

farm and the second was a full-time, large farm. The whole-farm models

take into account the various types of decision criteria, management

practices and technologies used within the two representative systems

with variation in the input/output coefficients and various levels of

resource constraints. Note that the terms constraint, restraint and

restriction are used interchangeably throughout this thesis.

Constraints in the models were divided into the following classes:

1) resource restrictions, 2) external restrictions, and 3) subjective

restrictions. The first category, resource or input restrictions

determine the availability of land, labor, and capital for the

production, management and marketing activities on the farms. External

restrictions are normally beyond the control of the farm operator, and















CHAPTER III
METHODOLOGY AND DATA BASE


Procedural Overview


To test the hypothesis, linear programming was utilized in a

whole-farm modeling procedure to describe the activities, constraints

and objectives of two separate contemporary farming systems found in

Suwannee and Columbia Counties of North Florida. The Mathematical

Programming System (MPS) installed at the Northeast Regional Data

Center of the University of Florida campus was used in compiling and

executing the linear programming models.

Linear programming was used first to simulate two predetermined,

representative farm systems: the first was a part-time smal-1 family

farm and the second was a full-time, large farm. The whole-farm models

take into account the various types of decision criteria, management

practices and technologies used within the two representative systems

with variation in the input/output coefficients and various levels of

resource constraints. Note that the terms constraint, restraint and

restriction are used interchangeably throughout this thesis.

Constraints in the models were divided into the following classes:

1) resource restrictions, 2) external restrictions, and 3) subjective

restrictions. The first category, resource or input restrictions

determine the availability of land, labor, and capital for the

production, management and marketing activities on the farms. External

restrictions are normally beyond the control of the farm operator, and










include the level of government tobacco and peanut allotments and the

amount of credit lenders are willing to extend. The third class of

constraints, subjective restrictions, are imposed by the operators

themselves and reflect the farmers' personal decisions on which crops

to produce, the size of certain farm enterprises, the amount of labor

he is willing to hire and amount of debt he is willing to carry. These

characteristics along with the small farmer's full time off-farm job

differentiated a small family farm as defined here from the larger

family farm.

Each model considered a single annual production/consumption cycle

that was divided into twelve monthly periods. The objective function

for each .system was assumed to be identical in that operators act to

maximize gross margin (total revenue-variable expenses).

Once the descriptive models were completed and refined to reflect

as acurrately as possible the "real world", the hypothesis was tested.

The procedure for determining whether or not small farmers are

prevented from adopting high technology because of their constraints

was quite simple. In essence, the small farm system was allowed to

choose between its present technology or the higher technology used on

large farms, given its present (small farm) constraints. This was

accomplished by creating a merged model that combined the large farm

and small farm activities into one matrix. The constraints and

objective function for the merged model are those used in the small

part-time farm descriptive model. The process followed this two step

pattern:











Small Farm Large Farm
Activities Activities

Step 1: Small Small Large Large
Farm Farm Farm Farm
Constraints Model Constraints Model





Small and Large Farm Activities

Step 2: Small
Farm Merged Farm Model
Constraints




The merged model simulates the availability of recommended

technology to the small farmer, and allows him the choice of activities

that maximize his gross margin given his small farm constraints. As a

test of the hypothesis, if the optimized merged activity model fails to

include large farm activities, the hypothesis will not be rejected.

In addition to hypothesis testing, the small farm descriptive

model was used to assess the impact of winter wheat varieties that have

recently been introduced into North Florida, and will continue to be

refined to test the appropriateness of new technology for small farms

in the study area.


Modelling Considerations


The Linear Programming Model


Linear programming uses sets of linear equations in an

optimization procedure that allocates scarce resources among competing

alternatives in the best possible way. The standard form of a linear

programming model is composed of three sections:










1. The objective function,

2. Resource constraints,

3. Activities or competing alternatives.

The model used in this thesis is a maximization procedure whose

objective function maximizes returns to cash costs (gross margin). In

matrix notation the objective of the model is to:


n
MAX Z = (P.X. C.X.), where
j-1 3 3 3 3



P. is the price received per unit of activity X., and C. is the cash

cost per unit of activity X.. The number of activities is n.

The objective function is subject to this system of constraints:


n
Z a. .X. b. (i = 1,2,3...m), where
j-1 13 j 1



a.. equals the quantity of resource i used per unit of activity j, and

b. equals the set of available resources i for the X. activities.
*1 3
One further restriction in the model, known as the non-negativity

restraint assures a rational solution in that all activities mdst be in

positive quantities. It states that:



X > 0.




The general form of the linear programming model contains four

implicit assumptions that must be considered since each tends to

distort some aspect of the real life farm system being modeled. These

assumptions are:









1) Linearity or proportionality--This assumption exists due to

the fact that the amount of resources a.. used for each unit of
13
activity X. is a predetermined and fixed quantity. The relationship is

linear in nature and in constant proportion over the entire range of

the X activity. The linearity assumption rules out the possibility of
3
increasing zr decreasing marginal products and limits output responses

to constant returns to scale.

2) Divisibility--The assumption here is that resources can be

used and activities can be divided into fractional units. Noninteger

values for units of activities and resources used are often contained

in the final solution of the linear program. This problem (e.g., of

producing 16.6 head of cattle) is commonly solved by rounding to the

nearest integer value without causing serious decision-making errors

[9].

3) Additivity--This assumes that there can be no interaction

between activities that could change the proportion of resources input

to activity output. The total use of resource b. must equal the sum of
1

the resource b. used by each activity.
1

4) Certainty--This assumption requires all parameters in the

model (a..'s, b.'s P.'s and C.'s) to be known constants. The

parameters are derived or assumed in a number of ways and can rarely be

regarded as certain over the entire population. This leads to the

conclusion that model predictions will inevitably contain some degree

of uncertainty especially where agriculture is concerned because of its

biological variability and complexity.










In addition to the implicit assumptions inherent in any linear

programming model, there are several explicit assumptions made in

determination of the objective function and choice of model activities.

The objective functions in both the large and small farm system

are linear expressions for profit maximization. They state that the

operator of each farm will act only in ways that maximize the

difference between total revenue and variable expenses. Although the

assumption is often made that farmers behave as profit maximizers, it

has rarely been proven to be true. In the case of small part-time

farmers the difficulties with this assumption may be even more

pronounced because of stated non-pecuniary goals associated with

farming. Optimization implies that certain combinations or resources

are more important than others. When the objective is to optimize

profit, the importance of these combinations is in pecuniary terms

only. The attitudes and objectives of farmers are often more diverse

and complex than this simple assumption allows for. Taking some of the

non-pecuniary goals into consideration in the objective function of a

linear program, however, is beyond the scope of this thesis.


Normative vs. Positive Uses of Linear Programming


Heady and Candler [9] make the assertion that linear programming

is a tool whose main use is in providing normative or "ought to"

answers to formulated problems. Given an objective function,

alternative activities and a set of resource restrictions, the linear

program will provide a normative plan that indicates how available

resources should be combined to obtain an optimum solution. It does











not explain why in fact producers follow a different pattern of

resource use and production.

The attempt has been made in this thesis to use linear programming

as a positive tool. This is opposed to the normative use in that the

mix of activities in the solution is defined ex ante. The linear

program endeavors to restructure the existing system's organization by

adjusting the objectives and the resource and subjective constraints to

fit a predetermined farming system. The question being asked of the

model is why do farmers combine their resources the way they do to

obtain an optimum solution, not how should they combine them to obtain

the optimum.

Asking the why question is critical in this thesis because it is

the constraints of a farmer or farm system that determine whether or

not certain technology can be utilized by a farmer.

Determination of the constraints of small farmers is not a simple

enumeration of land, labor and capital. Given the host of interactions

between the physical, economic, biological and social environments,

interdependencies between various sub-systems of the farm are numerous

and complex. The process of adjusting resource restrictions and

subjective constraints to obtain a predetermined enterprise mix is one

of the methods used in helping to identify some of the more important

constraints facing a farm system. This cannot be accomplished if

linear programming is used only as a normative tool.










Data Base


Small Farm Practices


To gather data on corn and wheat production on small farms, the

North Florida Farming Systems Research (FSR/E) project of the

University of Florida included an on-farm enterprise record keeping

project as one of its activities. Information gathered during the 1982

corn crop and 1982-83 wheat crop was used to generate the labor and

cash requirements for those crops used in this study.

The enterprise budgets and cropping records were acquired through

direct farmer participation throughout the cropping seasons. Seventeen

corn and 22 wheat producers initially agreed to keep records on their

respective crops during 1982-83. Soil samples were taken from each

prospective corn and wheat field and analysis and fertilizer

recommendations from the University Soil Testing Laboratory were

returned to the farmers before the crops were planted. Rain gauges

were also distributed to each farmer so that rainfall data could abe

collected.

To collect unbiased information of farmer practices, no advise or

persuasion was given on fertilizer recommendations or any other

cropping practice. To insure accuracy of the data collected,

cooperating farmers were visited often during peak work periods. In

*all, 101 farm visits were made for the 1982 corn crop data and 88

visits for the 1982-83 wheat data. Each farm was visited from 4 to 8

times with visits lasting 5 minutes to well over an hour. Some farmers

kept accurate records themselves while others were assisted by the

FSR/E personnel.










The basic information requested from the farmers included

quantities of inputs used such as fuel, seed, fertilizers and amounts

of hired and operator labor. Prices paid by the farmers for inputs

were collected at the time of the farm visits. The types and ages of

machinery used in producing the crop were ascertained, but other costs

such as depreciation, taxes, insurance and shelter were not estimated.

Cropping records and agronomic data included varieties, planting

densities, types and amounts of fertilizers used, timing of management

practices and any special problems that may have occurred because of

insects, disease, weeds or weather.

SDuring farmer interviews, information concerning the reasons

behind management practices were gathered. These included such things

as what value.does the farmer place on his corn or wheat, what use do

the grains serve in the farm system, how does a farmer cope with

drought, storage problems, insect damage, labor constraints or lack of

sufficient operating capital.

Of the seventeen original farmers contacted to keep corn records,

five were discontinued because of disinterest or because of persistent

difficulties in contacting. Two other farmers passed away during the

summer, leaving 10 sets of completed corn records. Of these 10

cooperators, 2 considered their crop a failure because of varietal and

management difficulties and one farmer irrigated his corn. This left

seven sets of records that were recognized as homogeneous in nature

(i.e., rain-fed, non-failure corn), for use in determining the small

farm corn enterprise budgets.

A labor and product flow chart similar to the example given in

Figure 10 was constructed for each of the ten small farms. Figure 12













MARKET

(2) (1) (' 2) (3) (6) (8) (2) (1)





17)
I I IP
WATER- 0 7) 0
MELONS TOBACCO PEANUTS CRN PASTURE GETABLES U
S AL
T
(2) 2) t3) T ) 9) 8)








MARKET _



MARKET



Figure 12. Aggregate labor and product flows, 10 small farms, Suwannee and Columbia Counties,
Florida.











aggregates all ten farm systems to provide a basis for the small farm

linear program model. The number in the parenthesis represents the

number of farms out of the total that engaged in that specific

activity. The small farm model which will be discussed later was

derived from this figure by determining which activities were most

typical of the small farms. A majority of the ten small farms produced

hogs, cattle, corn for hog and cattle feed, summer and winter pastures

for cattle, and a home vegetable garden. Less common activities were

cane syrup, watermelons, tobacco, peanuts, corn as a cash crop,

vegetables for market and poultry production. The average size of the

corn crop was 37 acres, with cash costs for non-irrigated corn ranging

from $34 to $85 per acre. These farms had an estimated 1982 gross

sales below $40,000, which falls under the USDA definition of a small

farm.

For the 1982-83 wheat crop, 14 of the original 22 farm cooperators

ended up with completed wheat records. The economic data from the

wheat and corn records are summarized in Tables 25, 26, 29, and 30 of

the Appendix. These data as discussed earlier, represent approximately

60% of the farmers in the study area.


Large Farm Recommended Practices


Labor and cash flow data for the large farm enterprise activities

were derived from the University of Florida's FARM System Budget

Generator and enterprise budgets originating from "high management

level farms" in West Florida "that follow recommended practices" (Tim

Hewitt, Area Extension Economist, Personal communication). Although











the University of Florida Institute of Food and Agricltural Sciences

and the Florida Cooperative Extension Service use the term

"recommended" in their Agronomy Facts publications, the suggested

practices are open to interpretation and change by individual county

extension directors based on local conditions (Charles Dean, Agronomy

Department Chairman, Personal communication). Following this practice,

the large farm budgets from West Florida were modified with the

assistance of the Columbia County Extension Director to more closely

approximate the practices and yields found on large farms in the study

area. These modified budgets will be referred to here as the

"recommended" practices. An example of the differences in small farm

and "recommended" technology is shown for corn in Table 1.


Family Living Expense and Off-Farm Income


Information concerning family living expenses and the level of

income derived from off-farm employment was taken from Florida

Statistical Abstract for 1982 [19]. The source of family living

expense, or "cost of family consumption" was the U.S. Department of

Labor Bureau of Labor Statistics for the Autumn of 1981, for

nonmetropolitan areas of the Southern United States. It was assumed

for this thesis that the small farm family expenses were the same as

those given for the "Lower Budget", while the large farm family had a

higher cost of living and used data from the "Intermediate Budget".

Table 57 of the Appendix gives an itemized budget for each farm size.

The off-farm income earned by the small, part-time farmer was the

average annual pay of employees covered by state and federal














Table 1. Comparison of cost of technological inputs between
recommended technology and present day small farm practices
for corn production, Suwannee and Columbia.Counties, Florida,
1982.



Item Small Farm Practices Recommended Practices


$/Acre

Seed 5.67 14.40

Complete Fertilizer 24.00 33.00

Nitrogen (Sidedress) 16.20 23.40

Herbicide 8.00

Insecticide 9.50










unemployment insurance programs in Suwannee County for 1980. This

salary amounted to $9247 annually and equalled $770.58 per monthly

payment.


Operator's Labor


The amount of operator's labor available to perform farm work was

determined for the small farm as shown in Table 55. Sunday, off-farm

work days and 2 to 3 "rain-days" were subtracted from the number of

days in each month. This figure became the number of 10 hour days the

operator would be available for field work and ranged from 4-6 per

month. Many part-time farmers also do field work after they arrive

home from their off-farm jobs. A total of 12 three hour days was

allotted per month. The ten hour and three hour days were converted to

total hours available for field work per month and increased by 33% to

account for labor contributed by the farmer's spouse and.children.

Operator's labor for the large farm was calculated as shown in

Table 56, by multiplying 24-26 days per month by 8-10 hours per day.


Procedure


Initial Tableaus


Three initial tableaus or matrices were created to represent the

small, large and merged activity farm models. Each matrix followed a

similar design with respect to cash flow and monthly allocations and

requirements of resources. The large farm model contains cash-flow

activities similar to those in the small farm system, but does not have










off-farm work, livestock or feed buying activities. The merged model

was similar in all respects to the small farm model except that five

large farm activity columns for producing and selling corn, wheat,

soybeans, peanuts, and tobacco were incorporated into it.


Small Farm Tableau


The small farm tableau contains 10 sets of activity columns and

11 sets of contraint rows. Conceptually, this matrix can be separated

into two sub-matrices. The first matrix, called the production sub-

matrix, contains activities and constraints dealing with the

production of crops and livestock, including the buying of inputs,

hiring of labor and the selling of farm produce. The second section

involves flow of cash on the farm. This cash flow sub-matrix includes

activities for borrowing and saving cash, paying 'for family living

expenses, regulating cash earned from off-farm work and paying fixed

and variable farm expenses.

These sub-matrices are sectioned as follows:

A. Cash-Flow Sub-Matrix

1. Savings account and-cash transfer activities.

2. Farm borrowing activities.

3. Off-farm work transfer activities.

4. Family living expense activities.

5. Production expense transfer activities.

6. Capital expense transfer activity.










B. Production Sub-Matrix

1. Crop and livestock production activities.

2. Labor hiring activities.

3. Crop and livestock selling activities.

4. Feed purchasing activities.

Detailed descriptions of the small farm initial tableau will

begin with the cash flow sub-matrix. Each of the six sections will be

discussed separately, with a portion of the initial tableau

illustrated to enhance clarity. With the exception of section six,

the capital expense transfer activity, each set of activities in the

cash flow sub-matrix consists of twelve columns numbered 01 through 12

corresponding to the months January through December. Due to space

limitations, the illustrations will display the first four months (01

through 04), assuming that the activities for months 05 through 12

follow the same illustrated pattern.


The cash flow sub-matrix (small farm)


The heart of the cash flow sub-matrix is the set of twelve

monthly cash accounting rows (numbers 74 through 85) in the initial

tableau (Table 2). Cash from all activities on the farm flows either

into or out of these accounting rows, each row representing a specific

month of the year. The cash account rows are set equal to zero to

ensure that no cash remains in any account during the optimization

procedure-

The first set of columns in the cash flow sub-matrix are the

savings account and cash transfer activities (CSHACT 01 through 12).















Table 2 .


Initial simplex tableau used to compute optimum farm plans for
small farms in Suwannee and Columbia Counties, Florida, 1982.


Row Row Row
No. Name Description Unit Type Constraint


Returns
LBROPR 01
02
03
04
05
06
07
08
09
10
11
12
LBRHIR 01
02
03
04
05
06
07
08
09
10
11
12
Land 01
02
03
04
05
06
07
08
09
10
11
12
EXPRMY 01
02
03


Objective Function
Operator's Labor














Hired Labor














Tillable Land













Family Living
Expense


Jan.
Feb.
Mar.
Apr.
May
June
July
Aug.
Sept.
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
June
July
Aug.
Sept.
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
June
July
Aug.
Sept.
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.


Dollar
Hour
Hour
Hour
Hour
Hour
Hour
Hour
Hour
Hour
Hour
Hour
Hour
Hour
Hour
Hour
Hour
Hour
Hour
Hour
Hour
Hour
Hour
Hour
Hour
Acre
Acre
Acre
Acre
Acre
Acre
Acre
Acre
Acre
Acre
Acre
Acre
Dollar
Dollar
Dollar


117
90
117
117
117
130
144
130
117
117
130
130
40
40
40
40
40
40
40
40
40
40
40
40
100
100
100
100
100
100
100
100
100
100
100
100
1145
1145
1145











Table 2--continued.


Row Row Row
No. Name Description Unit Type Constraint


EXPFMY 04
05
06
07
08
09
10
11
12
EXPCSH 01
02
03
04
05
06
07
08
09
10
11
12
Loan 01
02
03
04
05
06
07
08
09
10
11
12
CSHACC 01
02
03
04
05
06
07
08
09
10
11
12


Family Living
Expense









Cash Production
Expenses












Borrowing Limit













Savings and Cash
Account


Apr.
May
June
July
Aug.
Sept.
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
June
July
Aug.
Sept.
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
June
July
Aug.
Sept.
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
June
July
Aug.
Sept.
Oct.
Nov.
Dec.


Dollar
Dollar
Dollar
Dollar
Dollar
Dollar
Dollar
Dollar
Dollar
Dollar
Dollar
Dollar
Dollar
Dollar
Dollar
Dollar
Dollar
Dollar
Dollar
Dollar
Dollar
Dollar
Dollar
Dollar
Dollar
Dollar
Dollar
Dollar
Dollar
Dollar
Dollar
Dollar
Dollar
Dollar
Dollar
Dollar
Dollar
Dollar
Dollar
Dollar
Dollar
Dollar
Dollar
Dollar
Dollar


1145
1145
1145
1145
1145
1145
1145
1145
1145
0
0
0
0
0
0
0
0
0
0
0
0
5000
5000
50QO
5000
5000
5000
5000
5000
5000
5000
5000
5000
0
0
0
0
0
0
0
0
0
0
0
0











Table 2--continued.


Row Row Row
No. Name Description Unit Type Constraint


86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129


CSHEND
OFW













TRCSM













CMB
CMB
CRNACC
CNSACC
WHTACC
VEGACC
RYEACC
BAHACC
HAYACC
HOGACC
CTLACC
WHTFLEX
WHTGRAZ
CTLSEC


Cash Ending Balance
Off-Farm Work













Small Tractor Time













Combine Time
Combine Time
Corn Grain Account
Corn Stubble Accoun
Wheat Account
Vegetable Account
Rye Pasture Account
Bahia Pasture Accou
Hay Account
Hog Account
Cattle Account
Wheat Flexibility
Wheat Grazing Accou
Cattle Security
Account


Dollar
Jan. Dollar
Feb. Dollar
Mar. Dollar
Apr. Dollar
May Dollar
June Dollar
July Dollar
Aug. Dollar
Sept. Dollar
Oct. Dollar
Nov. Dollar
Dec. Dollar
Jan. Hour
Feb. Hour
Mar. Hour
Apr. Hour
May Hour
June Hour
July Hour
Aug. Hour
Sept. Hour
Oct. Hour
Nov. Hour
Dec. Hour
May Hour
Sept. Hour
Bu
It Acre
Bu
Garden
Acre
mt Acre
60 Ib Bale
Cwt
Lb
Acre
mt Acre
Jan. Dollar
Feb. Dollar
Mar. Dollar
Apr. Dollar
May Dollar
June Dollar


0
1
1
1
1
1
1
1
1
1
1
1
1
364
364
364
364
364
364
364
364
364
364
364
364
230
230
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0












Table 2--continued.


Row Row Row
No. Name Description Unit Type Constraint


130 CTLSEC 07 Cattle Security July Dollar L 0
131 08 Account Aug. Dollar L 0
132 09 Sept. Dollar L 0
133 10 Oct. Dollar L 0
134 11 Nov. Dollar L 0
135 12 Dec. Dollar L 0



Corn Production Wheat Production
Own Custom Own Custom Wheat
Row Harvest Harvest Harvest Harvest Grazed
No. 01 02 03 04 05


-58.76

0.80
1.21
0.41
0.67
1.07





1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00


2.10
31.2174
0.70
20.08
0.13
4.28


-73.93

0.80
1.21
0.41
0.67
0.10





1.00
1.00
1.00
1.00
1.00
1.00
1.00
S1.00



2.10
31.74
0.70
20.08
0.13
15.17


-59.45

0.10


0.80

0.68
0.49

1.00
1.00
1.00
1.00
0.50





0.50
1.00

25.13
19.10

2.20


10.82
1.20


-73.95


-110.61
0.10


0.10


0.30

0.68
0.49

1.00
1.00
1.00
1.00
0.50


0.50
1.00

25.13
19.10

16.70


10.82
1.20


0.30

0.68
0.69
0.10


1.40
22.75


20.00


15.38
35.33











Table 2--continued.


Corn Production Wheat Production
Own Custom Own Custom Wheat
Row Harvest Harvest Harvest Harvest Grazed
No. 01 02 03 04 05

61 15.75
99 0.05
100 1.70 1.70 0.10 0.10
101 1.21 1.21
102 0.41 0.41
103 0.42 0.42 0.20 0.20 0.20
107 0.185 0.10
108 0.58 0.58 0.58
109 0.44 0.44 0.64
110 0.05
111 0.50
112 0.64
113 -35.00 -35.00
114 -1.00 -1.00
115 -32.00 -32.00 -29.00
122 -1.00 -1.00 1.00 1.00
123 1.00



Row Vegetable Garden Rye Pasture Bahia Pasture
No. 06 07 08

01 720.48 -41.03 -43.09
02 0.10
05 0.20
06 0.10
07 0.10
09 0.10
10 0.40
11 0.30
12 0.10
26 1.00 1.00
27 1.00 1.00
28 1.00 1.00
29 1.00
30 1.00
31 1.00
32 1.00
33 1.00
34 1.00
35 1.00 1.00
36 1.00 1.00
37 1.00 1.00











Table 2--continued.


Row Vegetable Garden Rye Pasture Bahia Pasture
No. 06 07 08


38
39
40
41
42
43
44
45
46
47
48
49
51
52
53
54
56
57
58
59
117
118


60.00
60.00
60.00
60.00
76.00
76.00
60.00
60.00
60.00
76.00
76.00
76.37
9.47
12.33
16.03
3.26
4.57
2.58
15.61
16.04


15.80


6.60

35.70
0.26
0.26

0.27


1.50
10.66
15.10


-1.00


-1.00


Hog Production Cattle Production
Row Fed Corn Fed Wheat Traditional Wheat Grazed
No. 09 10 11 12

01 -675.72 -675.72 -27.60 -27.60
02 4.80 4.80 0.252 0.252
03 4.80 4.80 0.292 0.292
04 4.80 4.80 0.332 0.332
05 4.80 4.80 0.292 0.292
06 4.80 4.80 0.252 0.252
07 4.80 4.80 0.252 0.252
08 4.80 4.80 0.252 0.252
09 4.80 4.80 0.292 0.292
10 4.80 4.80 0.332 0.332
11 4.80 4.80 0.292 0.292
12 4.80 4.80 0.252 0.252
13 4.80 4.80 0.252 0.252
50 56.31 56.31 3.75 3.75
51 56.31 56.31 5.25 5.25
52 56.31 56.31











Table 2--continued.


Hog Production Cattle Production
Row Fed Corn Fed Wheat Traditional Wheat Grazed
No. 09 10 11 12

53 56.31 56.31 12.30 12.30
54 -56.31 56.31
55 56.31 56.31 4.80 4.80
56 56.31 56.31
57 56.31 56.31
58 56.31 56.31
59 56.31 56.31
60 56.31 56.31
61 56.31 56.31 1.50 1.50
113 177.00
114 0.50
115 165.20
117 1.00 0.33
118 1.50 1.50
120 -27.62 -27.62
121 -275.62 -275.62
123 1.00











Table 2--continued.


Row Hired Labort
No. 01 02 03 04 05 06 07 08 09 10 11 12


-3.50
-1


-3.50 -3.50 -3.50 -3.50 -3.50 -3.50 -3.50 -3.50 -3.50 -3.50 -3.50

-1
-1


1


-- --










Table 2--continued.


Row Hired Laborf
No. 01 02 03 04 05 06 07 08 09 10 11 12


74 3.50
75 3.50
76 3.50
77 3.50
78 3.50
79 3.50
80 3.50
81 3.50
82 3.50
83 3.50
84 3.50
85 3.50


tColumns 13 through 24.












Table 2--continued.


Row Selling Activitiest
No. Corn Wheat Hogs Cattle

01 2.73 3.10 0.55 0.70
74
75
76 -27.50
77
78 -3.10
79
80 -2.73
81
82 -27.50
83 -0.70
84
85
113 1
115 1
119
120 1
121 1


tColumns 25 through 28.










Table 2--continued.


Row Buying Activitiest Corn
No. Hay 01 02 03 04 05 06 07 08 09 10 11 12

01 -2.25 -3.70 -3.70 -3.70 -3.70 -3.70 -3.70 -3.70 -3.70 -3.70 -3.70 -3.70 -3.70

74 2.25 3.70
75 3.70
76 3.70
77 3.70
78 3.70
79 3.70
80 3.70
81 3.70
82 3.70
83 3.70
84 3.70
85 3.70

113 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1
119 -1


tColumns 29 through 53.










Table 2--continued.


Row Buying Activitiest Wheat
No. 01 02 03 04 05 06 07 08 09 10 11 12


01 -3.70 -3.70 -3.70 -3.70 -3.70 -3.70 -3.70 -3.70 -3.70 -3.70 -3.70 -3.70
74 3.70
75 3.70
76 3.70
77 3.70
78 3.70
79 3.70
80 3.70
81 3.70
82 3.70
83 3.70
84 3.70
85 3.70

115 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1


tColumns 29 through 53.











Table 2--continued.


Row Family Living Expenset
No. 01 02 03 04 05 06 07 08 09 10 11 12


01 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1
38 1
39 1
40 1
41 1
42 1
43 i
44 1
45 1
46
47 1
48
49 1


tColumns 54 through 65.










Table 2--continued.


Row Borrowing Activitiest
No. 01 02 03 04 05 06 07 08 09 10 11 12

01 --0.011 -0.011 -0.011 -0.011 -0.011 -0.011 -0.011 -0.011 -0.011 -0.011 -0.011 -0.011


62 1


-1
1.011 -1
1.011 -1
1.011 -1
1.011 -1
1.011 -1
1.011 -1
1.011 -1
1.011 -1
1.011 -1
1.011 -1
1.011


-1
1.011


tColumns 101 through 112










Table 2--continued.


Row Borrowing Activitiest
No. 01 02 03 04 05 06 07 08 09 1C 11 12

124 1
125 1
126 1
127 1
128 1
129 1
130 1
131 1
132 1
133 1
134 1
135 1


tColumns 101 through 112.










Table 2--continued.


Row Off-Farm Workt Fixedf
No. 01 02 03 04 05 06 07 08 09 10 11 12 Expense


01 770.85 770.85 770.85 770.85 770.85 770.85 770.85 770.85 770.85 770.85 770.85 770.85

74 -770.85
75 -770.85
76 -770.85
77 -770.85
78 -770.85
79 -770.85
80 -770.85
81 -770.85
82 -770.85
83 -770.85
84 -770.85
85 -770.85


-3000


3000


tColumns 113 through 124.
tColumn 125.










Table 2--continued.


Row Production Expense Transfert
No. 01 02 03 04 05 06 07 08 09 10 11 12


tColumns 66 through 77.










Table 2--continued.


Row Savings and Cash Transfert
No. 01 02 03 04 05 06 07 08 09 10 11 12


01 0.0065 0.0065 0.0065 0.0065 0.0065 0.0065 0.0065 0.0065 0.0065 0.0065 0.0065 0.0065

74 1
75 -1.0065 1
76 -1.0065 1
77 -1.0065 1
78 -1.0065 1
79 -1.0065 1
80 -1.0065 1
81 -1.0065 1
82 -1.0065 1
83 -1.0065 1
84 -1.0065 1
85 -1.0065 1
86 -1.0065


tColumns 78 through 100.











This set of activities has the dual purpose of directing the flow of

cash sequentially through the twelve monthly periods while

simultaneously providing an interest bearing savings account (Table

3). This procedure allows the program to value excess cash in the

system and provides a disincentive for cash to be deposited elsewhere.

Each of the activities transfers excess cash from one month to the

next, adding an interest value equal to one twelfth of the annual

savings rate of 7.8%. The earned interest value is then added to the

objective function and cash account row, and is available for farm

use.

The farmer is provided with the opportunity of receiving loans

for short term credit through a second set of cash sub-matrix

activities designated as LOAN activities (Table 4). These activities

intersect with the cash account rows and allow money to be withdrawn

from interest charging accounts if there is no cash available in the

savings account. If the cash account is unable to pay the loan back

in the next month because total expenses are still greater than total

revenue, the money is reborrowed at one twelfth of the annual lending

rate of 13.2% (Personal communication, Thomas Spreen).

The LOAN activities are regulated by a set of rows designated as

LOANSM (loan, small farm) constraints. Setting the level of the loan

constraint allows the modeler to incorporate external.restrictions in

the model such as lending limits imposed by creditors or subjective

restrictions like a farmer's willingness to borrow.

Section three of the cash-flow sub-matrix makes provisions in the

model for money earned through full time, off-farm employment of the











Table 3. Cash flow sub-matrix, savings account and cash transfer
activities.



Month
01 02 03 04 RHS


Returns Row 0.0065 0.0065 0.0065 0.0065

Cash 01 1 0
Account
Rows 02 -1.0065 1 0

03 -1.0065 1 0

04 -1.0065 1 0









Table 4. Cash flow sub-matrix, farm borrowing activities.




Month
01 02 03 04 RHS

Returns Row -0.011 -0.011 -0.011 -0.011

Cash 01 -1 0
Account
Account 02 1.011 -1 0
Rows
03 1.011 -1 0

04 1.011 -1 0

Loan 01 1 5000
Restraint
S 02 1 5000
Rows
03 1 5000

04 1 5000











farm manager to enter the farm system. This set of twelve activities

designated as OFWTO1 through OFWT12 (for Off-Farm Work Transfer)

allows the farmer's cash salary to enter both the cash accounting rows

and the objective function of the linear programming model (Table 5).

The monthly salary enters each column once only as controlled by the

twelve Off-Farm Work constraint rows. Once this cash enters the cash

account rows it can be used for any purpose on the farm, including

loan repayment, production expense or family living expense.

Family Living Expense Transfer (EXPFY 01 through 12) activities

require a specific amount of cash each month to be allocated for

family needs including housing, food, transportation, clothing,

personal care, medical care, social security and disability payments,

personal taxes and other family consumption items (Table 6). The

implicit assumption of this method of incorporating family living

expenses with the farm production expenses is that financial support

of the farm family takes priority over other cash using activities on

the farm. The farm family must receive its required monthly living

allowance even if this necessitates borrowing money. This aspect of

the farm model incorporates the concept of competition for cash

between production expenses and family cash requirements.

Section 5 of the cash flow sub-matrix allows funds to be

transferred from the cash account to crop and livestock production

activities (Table 7) and is designated EXPCSH 01 through 12 (Expense,

Cash).

These simple transfer activities in the optimal solution indicate

the sum of cash expenses for crop and livestock activities on a












Table 5. Cash flow sub-matrix, off-farm work transfer activities.




Month
01 02 03 04 RHS


Returns Row 770.58 770.58 770.58 770.58

Cash 01 -770.58 G
Account
Account 02 -770.58 0
Rows
03 -770.58 0

04 -770.58 0

01 1 1

02 1 1

03 1 1

04 1 1




Table 6. Cash flow sub-matrix, family living expense activities.



Month
01 02 03 04 RHS

Returns Row -1 -1 -1 -1
Cash 01 1 0
Account
02 1 0
Rows
03 1 0

04 1 0

Family 01 1 1145
Expense 02 1 1145
Restraint
Rows 03 1 1145

04 1 1145














Table 7. Cash flow sub-matrix, production expense transfer activities.



Month
01 02 03 04 RHS


Table 8. Cash flow sub-matrix, capital expense transfer activity.




Month RHS


Returns Row -3000

Cash
Account 11 3000 0
Row











monthly basis. All cash required for farm production enterprises is

obtained through these activities and may originate from several

sources, depending on the balance in the cash account rows. Off-farm

work, loans, savings or the sale of farm products may all contribute

to the supply of cash for farm production expenses.

The final section in the cash flow sub-matrix allows for a once a

year medium or long term principal and interest payment on land,

buildings or machinery (Table 8). This activity designated as Capital

Expense for the Small Farm (EXPCAPSM) is a single payment from the

cash account to a financial intermediary or lending institution, and

primarily reduced the objective function and savings account by $3000

in November. A positive cash balance is most likely to be found on

the farm at this time because all crops and livestock have been

marketed. This reduces the probability of competition for cash

between long term debt and short term production credit requirements,

and assumes that a farmer and his lender agree that payment by made

when the farmer is best able to afford it. The result of this payment

is that the RETURNS row of the objective function is actually

depicting returns to labor and management, not simply gross margin.


The production sub-matrix (small farm)


The production sub-matrix is comprised of four sets of activity

columns which intersect with eight sets of constraint or accounting

rows. The first section of the production sub-matrix includes

thirteen separate crop and livestock production activities. This is

the set of activities that the farmer chooses from to organize the











farm system. These thirteen production activities are listed below

and are followed by a brief explanation of each activity. The

resources required for each unit of these thirteen activities are

located in Table 2, the initial tableau for the small farm.

1. Corn production, own combine.

2. Corn production, custom combine.

3. Wheat production, own combine.

4. Wheat production, custom combine.

5. Wheat, grazing, own combine.

6. Wheat, grazing, custom combine.

7. Home vegetable garden.

8. Rye pasture production.

9. Bahia pasture production.

10. Hogs fed corn.

11. Hogs fed wheat.

12. Cattle grazed traditionally.

13. Cattle grazing wheat.

The two corn production activities are similar in all respects

except that one assumes the farmer owns and operates his own combine

and the other allows him to have his corn custom combined. The custom

harvest activity has a lower labor requirement for corn production in

September, but a higher cash cost in the same month because custom

rates include a charge for operator labor, depreciation, taxes and

other fixed costs associated with owning a combine. Each corn acre

adds 35 bushels of corn into a corn account row which can either be

sold for cash or used as hog feed. Corn production also supplies one

acre of crop residue which can be utilized by livestock on the farm.











Four separate activities exist for wheat production. Two

activities are defined similarly in that the farm operator has the

choice of having his wheat custom combined or owning and operating his

own equipment. The trade off between cash and labor for wheat

production occurs during the May harvest. Each acre of wheat yields

32 bushels of grain which enters a wheat grain account to be either

sold as a cash crop of fed to on-farm hogs.

The third and fourth activities are wheat grazing activities

which allow farmers to reduce rye acreage by two-thirds when wheat is

grazed by cattle during late December and early January. If the

cattle are removed before the wheat begins elongation of the growing

point, a grain yield is also possible. The introduction of wheat

grazing into the system improves the efficiency of rye pasture by

relieving grazing pressure when rye growth is slow and increasing

pressure in the late winter and spring when rye production usually

outstrips cattle demand. One activity allows for custom combining of

the wheat and the other has the farmer combining his own crop. The

trade-off for labor and cash aie similar to the wheat-for-grain

activities. The cash flow and labor requirements are higher for

grazing and vary from the wheat-for-grain production activities as

seen in Columns 3 and 4 of Table 2. Grain from the wheat grazing

activities enters the wheat account and can likewise be used for hog

feed or sold as a cash crop. A summary of yield, labor requirements,

prices received, cash cost and gross margin for each of the field

crops produced on the small farm is given in Table 9.












Table 9. Yield, labor, cost and returns summary.
and Columbia Counties, Florida, 1982.


Crop production: small farm, Suwannee


Cash Price Gross Cash
Yield Cost Received Margin Labor Cost
(bu/ac) ($/ac) ($/bu) ($/ac) (hours/ac) ($/bu)

Corn

Own Combine 35 58.76 2.73 36.79 4.16 1.68

Custon Combine 35 73.93 2.73 21.62 3.19 2.11

Wheat

Own Combine 32 59.45 3.10 39.75 2.07 1.86
Custom Combine 32 73.95 3.10 25.25 1.57 2.31

Rye Pasture 41.03 .98

Bahia Pasture -43.09 .50










The linear programming model incorporates a home vegetable garden

(Tables 35 and 36) as part of the small farm system. Gardens provide

the farm family with a number of vegetables throughout the year when

properly processed and stored, and reduce the cash requirement

necessary for food purchased off-farm. The benefits from the

vegetable garden enter the program in two places; the first being the

objective function and the second the family expense restraint rows

(Table 2). The value of the family vegetable garden in the objective

function is equal to the opportunity cost of purchasing the product at

a supermarket, minus the cost of production. As the value of the

garden product enters the family expense restraint rows, it reduces

the family living expense by an amount equal to the opportunity cost

of purchasing those goods at the market. The bounding procedure of

the MPS program is used to limit the garden activity to one unit.

Although vegetable gardens require a sizeable amount of time, it is

assumed in this model that the spouse and children provide the

necessary labor and thus no requirement is given for operator's labor.

Land and tractor time are negligible in the home vegetable garden so

no requirements have been defined in those rows.

Two hog producing activities have been provided for the small

farm system and both define a farrow to finish enterprise where the

unit of activity is one sow (Table 2). Under the assumptions given in

Tables 47 and 48, each unit of activity produces 2762 pounds of

marketed live weight. That figure includes losses due to mortality

and a replacement value for the sow. A summary of production costs

for hogs under various feed procuring alternatives is given in Table

10.










Table 10. Hog production: farrow to finish operation. Cost and returns summary. Small farm, Suwanee
and Columbia Counties, Florida, 1982.



Cash Cost Pounds Live Wt.
costs: $/CWT Hog $/Sow Marketed per Sow


Excluding Feed Cost: 24.50 675.20 2762


Feed Required Cost of Feed Feeding Cost Total Cost Break-Even
Per Sow (CWT) ($/CWT Feed) ($/Sow) ($/Sow) Cash Cost
($/CWT)

Including Feed Cost:
Corn:
Own Harvest 99.12 3.00 297.36 972.56 35.21
Custom Harvest 99.12 3.77 373.68 1048.88 37.97
Purchased Feed 99.12 6.17 611.57 1286.77 46.59
Wheat:
Own Harvest 99.12 3.10 307.27 982.47 35.57
Custom Harvest 99.12 3.85 381.61 1056.81 38.26
Purchased Feed 99.12 6.17 611.51 1286.77 46.59

Returns:
Live wt. Marketed
27.62
per sow (CWT)

Price received per
CWT ($)
Total Income per $1519.10
Sow ($)










The two hog production activities differ only in the source of

feed, one being corn, the other wheat. Corn and wheat can be

substituted for each other in hog rations on a pound for pound basis

with little or no problem. The source of these feeds for the hog

activities are the corn and wheat accounting rows which acquire the

grains from either on farm production activities or off-farm

suppliers. Marketed hogs enter a hog account row and are transferred

to hog selling activities, described later.

Cattle production is somewhat more complicated than hog

production because each unit of the cow/calf activity also requires

winter and summer pastures. Two separate cattle production activities

have been defined. One requires one acre of winter rye pasture per

cow/calf unit in addition to one and one half acres of bahia grass

(summer) pasture and purchased hay. The second cattle activity cuts

the rye pasture requirement to one third acre per cow/calf unit and

substitutes one acre of wheat to be grazed in early winter.

Identical amounts of bahia and hay are required for the wheat and rye

grazing activities. Each cattle production activity assumes that one

450 lb. calf per cow is produced and sold each year in October.

Values for herd calving efficiency and cow.replacement are subtracted

from the 450 pound weight to obtain a net weight for marketing. These

data are summarized in Table 11.




This figure may be optimistic. Research is currently being carried
out to better estimate the level of substitution between wheat and rye
for grazing.














Table 11.


Cost and returns summary. Cattle production--cow/calf
operation, small farms, Suwannee and Columbia Counties,
Florida, 1982.


$/Cow-calf Unit


Costs:


27.60

41.03

64.63

22.50

$155.76


Misc. Maintenance

Rye Pasture Cost

Bahia Pasture Cost

Hay Purchased

Total Cash Cost

Returns:

Pounds Marketed per
Cow/calf unit (CWT)

Price Recieved per CWT
Total Income per
Cow/calf unit

Cash Cost per CWT
Marketed


2.7562

$70.00

$192.93


$ 56.51











The second section of the production sub-matrix provides the -

farmer with the opportunity to hire labor when the operator's labor is

exhausted in any month. Table 12 illustrates the first four months of

the labor hiring activities. Since the operator's labor has no

explicit cost in the model, it is utilized first in the production

activities. Once operator labor has been depleted, additional hired

labor can be acquired. Each unit of hired labor reduces the objective

function by the hourly wage of $3.50 which is also taken from the cash

account. The total monthly amount of operator and hired labor

available for use on the farm is set by the right hand side values

(RHS) of the labor restraint rows.

The third section of the production sub-matrix provides the

farmer with market access for crops and livestock produced on the

farm. Four selling activities were created (Table 13) for the small

farm model for the marketing of corn, wheat, hogs and cattle.' It is

assumed in this model that farmers sell their crops at harvest time.

Cash from the sale of farm products enters the cash account during the

month that the crops are harvested. Alternately, the farmers may

decide to feed wheat or corn to hogs, and not sell them as cash crops.

Hogs produced in the farrow to finish operation are assumed to be sold

twice a year in March and September. The market price per hundred

weight of hogs sold is divided equally between the two months

indicating that each sow unit produced has two identical litters per

year. Feeder calves produced in the cow/calf operation are born in

the winter months and marketed in October at $0.70 per pound.











Table 12. Production sub-matrix, labor hiring activities.



Month
01 02 03 04 RHS

Returns Row -3.50 -3.50 -3.50 -3.50

Operators C! -1 B
Labor
02 -1 B
Restraint
Rows 03 -1 B

04 -1 B

Hired 01 1 B
Labor
02 1 B
Restraint
Rows 03 1 B

04 1 B

Cash 01 3.50 0
Account
ou 02 3.50 0
Rows
03 3.50 0

04 3.50 0




Table 13. Production sub-matrix, selling activities.




Corn Wheat Hogs Cattle


Returns Row 2.73 3.10 55.00 0.70

Cash Account 03 -27.50
Rows
Rows 05 -3.10

07 -2.73

09 -27.50

10 -0.70

Corn Account 1

Wheat Account 1

Hog Account 1

Cattle Account 1











To provide the small farm operator with the alternative of

purchasing corn or wheat as a hog feed instead of producing them on

the farm, buying activities have been incorporated as part of the farm

model (Table 14). Corn and wheat may be purchased in any month if

necessary. Each bushel purchased reduces the objective function by

$3.70 and is also registered in the cash account row as an expense.

Purchased grain enters the corn or wheat accounting row and becomes

available for feeding to the hogs. Each of the cattle production

activities requires 600 pounds of hay per cow as a feed supplement

source. This hay must be purchased from off-farm sources and thus a

hay buying activity is part of the farm model. All hay required to

feed the livestock is purchased in January at $2.25 per 60 pound bale.

Two additional sets of constraints have been added to the part-

time small family farm model that differ from the resource

restrictions discussed previously. These subjective restrictions have

been included in the linear programming model to incorporate risk

reduction strategies used by many small farmers in the study area.

Since wheat is a relatively new crop to farmers in North Florida,

a two step scenario for the adoption of wheat into the small farm

system was envisioned. As a new crop, it is assumed that farmers will

be willing to plant a limited amount of wheat 'the first few years

until familiarity and satisfaction with the crop as a feed source or

cash crop is obtained. To simulate this behavior, a wheat flexibility

constraint (WHTFLEX) as depicted in Table 15 was incorporated into the

model. This constraint limits the amount of wheat acreage a farmer is

willing to grow to one half of the corn acreage he produces. As the












Table 14. Production sub-matrix, feed buying activities.



Corn Wheat Hay
01 02 01 02 01


Returns Row -3.70 -3.70 -3.70 -3.70 -2.25

ach 01 3.70 3.70 2.25
Account 02 3.70 3.70
Rows
Corn Account -1 -1

Wheat Account -1 -1

Hay Account -1


Table 15. Wheat flexibility restraint.


Corn Wheat
Production Production RHS


Wheat
Flexibility -1 2 0
Restraint
Row











farmer becomes more confident in the ability of wheat to provide

stable yields of quality grain, he will then produce an amount of

wheat that optimizes gross margin on the farm. To simulate this

action, the wheat flexibility restraint is removed in a new solution.

The second set of subjective restraints built into the small and

merged farm models are known as the cattle-security constraints

(CTLSEC, rows 124-135 in Table 16). This set of constraints requires

a small farmer to have one cow/calf activity for every $350 the farmer

borrows from a financial intermediary for collateral. However, the

absolute level of borrowing is set by the right hand side of the loan

restraints.

The cattle-security restraints were developed in an effort to

incorporate some of the value of cattle production that is not

directly expressed by the objective function of the linear programming

model. Other than being able to generate cash from the sale of

calves, livestock serve as hedge against risk, and as a source of

income between sales of cash crops [7]. Farmers indicate this

importance by saying such things as "I only plant as much as I can

afford to lose," and by selling some of the cattle herd to pay

creditors rather than being forced to sell land in a bad year.

Under this set of constraints, small farmers are being forced to

produce a relatively less profitable mix of activities because they

must also produce an extra amount to hold as security, or risk

insurance. The large farm reduces risk more efficiently through the

use of large farm technology. Chemical fertilizer, herbicides,

pesticides, hybrid varieties and various implements whose expense can














Table 16. Cattle security restraint.


Cattle Loan Loan Loan Loan
Production 01 02 03 04


Cattle 01 -350 1
Security 02 -350
Rows
03 -350 1
04 -350 1











only be justified by large farmers on large acreages all tend to

reduce yield variability. Both the institutional and technological

risk reducing mechanisms are more effective and more appropriate for

farmers who produce large volumes. The transaction costs for

institutional mechanisms are high in terms of education, training and

time required for effective use. Since these costs.are the same for

large and small volume farmers, the advantage goes to the large farmer

who can spread costs over more units of production. Small farms that

are unable to afford the institutional and technological risk reducing

mechanisms contain built-in stability factors that buffer the firm

from extreme variations in profitability. The small farm is simply

unable to maximize profit as a large farm can because it is limited by

the need to produce a less profitable enterprise such as cattle.


Large Farm Initial Tableau


The large farm tableau consists of eight sets of activity columns

and ten sets of constraint rows. It differs from the small farm

tableau in that the two sets of columns for off-farm work and

purchasing of livestock feed and the set of rows for off-farm work are

not included in the large farm model.

The cash flow sub-matrix of the large farm is identical to the

cash flow sub-matrix described for the small farm with the exception

of the off-farm work transfer rows, and the amount required to pay for

fixed capital expense. A rough estimation of $35,000 for principal

and interest payments for medium and long term debt on a typical 450

acre crop farm in Columbia County was made. As with the small farm,




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