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Constraints to technology adoption on small farms in north Florida

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Title:
Constraints to technology adoption on small farms in north Florida
Creator:
Dehm, Bruce A., 1955-
Publication Date:
Language:
English
Physical Description:
x, 164 leaves : ill. ; 28 cm.

Subjects

Subjects / Keywords:
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.
General Note:
Typescript.
General Note:
Vita.
Funding:
Florida Historical Agriculture and Rural Life
Statement of Responsibility:
by Bruce A. Dehm.

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Marston Science Library, George A. Smathers Libraries, University of Florida
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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
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11567492 ( OCLC )
ACM9379 ( NOTIS )

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 H-ildebrand 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 f arms, 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
ACKNOWLEDGME~NTS........................................................ 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
REFERENC,-ES.................... ............................... .. o......157
SUPPLEMENTAL REFERENCES............................................. 159
BIOGRAPHICAL SKETCH.................................................. 164




LIST OF TABLES
TABTP 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................................... ......... 4
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. C attle 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
1? Initial simplex tableau used to compute optimum farm
plan on large farm, Suwannee and Columbia Counties,
Florida, 1982 .............................................. 88
iv




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
v




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
vi




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 sowsf arrow 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
vii




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.
ix




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
x




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, noncommercial 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




2
concentration, corporate agriculture, decay of rural farming communities, limited acess 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. 31.
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




3
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 19401s, 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 definitiion 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].




4
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 increas,-d 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. Althoughthe 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
$=U.S.
A= Florida W= Suwannee County
.0 V 0 0
1870 18A0 1890 1900 1 10 1920 1930 1940 1950 1960 1970 19 0
Figure 1. Farm numbers for U.S., Florida and Suwannee County, 1870 1978..
SOURCE: [22]




Average Farm Size (acres) Acres 400 0 U.S.
* Florida
A= Suwannee County
300
200
100
1870 1880 190 19b0 19:10 1912 1930 Iq40 A50 1960 1970 1980
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
50-99ACres
5,000 ----
100-199 Acres
1,000 200-499 Acres
1945 1950 194 1959 194 1969 1974 1978
Figure 3. Number of farms by acres harvested, Florida.
SOURCE: [22]




OD
9 IR
it rX4
C4
9 V 00
r- (L)
0) r-4
44
H .If .......... 7
T qx
En ro o
En En
44 En
a) t'j :j J, ro
Tt 04
LH
44 >0)
LO
.rj




9
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. 71 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 111. 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




10
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 agressive, innovative farmers adopt new, cost-reducing technology and increase their profit position relative to late or nonadoptors 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 agressive 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 1is biased towards the largescale agricltural 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 famly 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.
1 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.




12
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 Departmen t 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




13
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.




14
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 in land 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.
15




16
0 'K oCOLUMBIA
SUWANNEE *
LKE
CITY
*A
Ri egg w
0 5 so 15 20 AeLOwETEAS
0 5 10 15
Figure 6. Suwannee and Columbia Counties, Florida.




17
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 (221. 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 droughty, nutrient deficient, and susceptible to wind erosion [221. 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 ma% of the nutrients.




18
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 [1611.
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 (1611. 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, 2211. 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 (161. 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
March -July, n=12 7.0 July Sept.. nal
6.0 Average Monthly
5.0..
4.0.
1.0
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 May June July August September
Figure 7. Average monthly and 1982 rainfall as recorded by farmers in Suwannee-and
Columbia Counties, Florida.




20
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, coianded a major portion of the farms'




21
resources. The farm system revolve d 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 ana 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, n what were essentially f arrow 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




22
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 mid1950s. 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 Xs
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 A Index of
Corr Acres150 500 Corn Yield
,'5 0 0 a n d
/ %Tobacco
/ Acres
1 2 5 C o r n Y i e l d
I" / ,\\ */' / \ 400
% % / t/ Corn Acres
100
I .300
75 \Ij / Tobacco Acres
200
50
INDEX: 1930=100
25 Corn Acres = 41653 100
Corn Yield = 8.4 (bu/a)
/ Tobacco Acres = 1123
Suwannee County, Florida
/
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
I
I
II
'wATER 0
MELONS TOBACCO P C CORN NATURE VEGE TABLES U
s AT
T R
T
LABOR
HOUSEHOLD
MARKET
Figure 9. Product and labor flows, traditional full-time, small farm, Suwannee and Columbia
Counties, Florida.




25
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 cornpeanuts 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




26
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, orcorn 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 andas 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 (171. 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




27
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 (171.
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 thebomogeneity 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




28
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 offfarm 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.




_ _M A R K E T
I
WAEO- TOBACCO :PEANUTS 6 PATUEVEGETABLES I0 I)
1 MELONSs -G IL
IIT
A R
IvI
ATT
LABOR
HOUSEHOLD <
I IIMARKET
Figure 10. Product and labor flows, contemporary part-time small farm, Suwannee and Columbia
Counties, Florida.




30
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 tracLors 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 1 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 (171. Closer
1 Following Barkley's definition of high technology being "high" when all effective opportunities to substitute capital for labor have been utilized [2, p. 3091.




MARKET -__r r
I I
WATER- H
MELONS TOBACCO PEANUTS G CORN PASTURE -VEGETABLES 'U I
-- S L ...... IL
,' I C ,sIT a
' A R I
T I' I
aI T
L~j L
E
LABOR
HOUSEHOLD
MARKET
Figure 11. Product and labor flows, contemporary full-time large farm, Suwannee and Columbia
Counties, Florida.




32
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 small 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 variou s levels of resource constraints. Note that the terms constraint, restraint and restriction are usrd 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 activit.4 es on the farms. External restrictions are normally beyond the control of the farm operator, and 33




34
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:




35
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 co-nposed.of three sections:




36
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 Z (P.X C X.), where j-i 1 j3
P is the price received per unit of activity X., and C. is the cash J J J
cost per unit of activity X The number of activities is n.
J
The objective function is subject to this system of constraints:
n
Z a. .X. b. (i =1,2,3...m), where
j-l 1) 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 cotains 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:




37
1) Linearity or proportionality--This assumption exists due to the fact that the amount of resources a.. used for each unit of 1]
activity X is a predetermined and fixed quantity. The relationship is
J
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 =r 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
13 1 3 1
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.




38
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




39
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.




40
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.




41
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.
During 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 e nterprise 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)
7 8) ,
P
CANE 11WATER. 0-) PASTURE EGETABLES U
SYRUP MELONS I ToBAC PEANUT
LABOR 10) T 3)
MARKET
(3))
Figure 12. Aggregate labor and product flows, 10 small farms, Suwannee and Columbia Counties,
Florida.




43
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




44
the University of Florida Institute of Food and Agricitural 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, Agron2omy 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 [191. 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




45
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




46
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. Sundays, 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 da ys 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 grandchildren.
Operator's labor for the large farm was calculated as shown in Table 56, by multiplying 24-26 days per month by B-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




47
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 submatrix, 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.




48
B. Production Sub-Matrix
1. Crop and livestock production activities.
2. LabuL 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 procedureThe first set of columns in the cash flow sub-matrix are the
savings account and cash transfer activities (CSHACT 01 through 12).




49
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
01 Returns Objective Function Dollar N
02 LBROPR 01 Operator's Labor Jan. Hour L 117
03 02 Feb. Hour L 90
04 03 Mar. Hour L 117
05 04 Apr. Hour L 117
06 05 May Hour L 117
07 06 June Hour L 130
08 07 July Hour L 144
09 08 Aug. Hour L 130
10 09 Sept. Hour. L 117
11 10 Oct. Hour L 117
12 11 Nov. Hour L 130
13 12 Dec. Hour L 130
14 LBRHTR 01 Hired Labor Jan. Hour L 40
15 02 Feb. Hour L 40
16 03 Mar. Hour L 40
17 04 Apr. Hour L 40
18 05 May Hour L 40
19 06 June Hour L 40
20 07 July Hour L 40
21 08 Aug. Hour L 40
22 09 Sept. Hour L 40
23 10 Oct. Hour *L 40
24 11 Nov. Hour L 40
25 12 Dec. Hour L 40
26 Land 01 Tillable Land Jan. Acre L 100
27 02 Feb. Acre L 100
28 03 Mar. Acre L 100
29 04 Apr. Acre L 100
30 05 May Acre L 100
31 06 June Acre L 100
32 07 July Acre L 100
33 08 Aug. Acre L 100
34 09 Sept. Acre L 100
35 10 Oct. Acre L 100
36 11 Nov. Acre L 100
37 12 Dec. Acre L 100
38 EXPPMY 01 Family Living Jan,. Dollar G 1145
39 02 Expense Feb. Dollar G 1145
40 03 Mar. Dollar G 1145




50
Table 2 --continued.
Row Row Row
No. Name Description Unit Type Constraint
41 EXPFMY 04 Family Living Apr. Dollar G 1145
42 05 Expense May Dollar G 1145
43 06 June Dollar G 1145
44 07 July Dollar G 1145
45 08 Aug. Dollar G 1145
46 09 Sept. Dollar G 1145
47 10 Oct. Dollar G 1145
48 11 Nov. Dollar G 1145
49 12 Dec. Dollar G 1145
50 EXPCSH 01 Cash Production Jan. Dollar E 0
51 02 Expenses Feb. Dollar E 0
52 03 Mar. Dollar E 0
53 04 Apr. Dollar E 0
54 05 May Dollar E 0
55 06 June, Dollar E 0
56 07 July Dollar E 0
57 .08 Aug. Dollar E 0
58 09 Sept. Dollar E 0
59 10 Oct. Dollar E 0
60 11 Nov. Dollar E 0
.61 12- Dec. Dollar E 0
62 Loan 01 Borrowing Limit Jan. Dollar L 5000
63 02 Feb. Dollar L 5000
64 03 Mar. Dollar L 50QO
65 04 Apr. Dollar L 5000
66 05 May Dollar L 5000
67 06 June Dol-lar L 5000
68 07 July Dollar L 5000
69 08 Aug. Dollar L 5000
70 09 Sept. Dollar L 5000
71 10 Oct. Dollar L 5000
72 11 Nov. Dollar L 5000
73 12 Dec. Dollar L 5000
74 CSHACC 01 Savings and Cash Jan. Dollar E 0
75 02 Account Feb. Dollar E 0
76 03 Mar. Dollar E 0
77 04 Apr. Dollar E 0
78 05 May Dollar E 0
79 06 June Dollar E 0
80 07 July' Dollar E 0
81 08 Aug. Dollar E 0
82 09 Sept. Dollar E 0
83 10 Oct. Dollar E 0
84 11 Nov. Dollar E 0
85 12 Dec. Dollar E 0




51
Table 2--continued.
Row Row Row
No. Name Description Unit Type Constraint
86 CSHEND Cash Ending Balance Dollar L 0
87 OFW 01 Off-Farm Work Jan. Dollar L 1
88 02 Feb. Dollar L 1
89 03 Mar. Dollar L 1
90 04 Apr. Dollar L 1
91 05 May Dollar L 1
92 06 June Dollar L 1
93 07 July Dollar L 1
94 08 Aug. Dollar L 1
95 09 Sept. Dollar L 1
96 10 Oct. Dollar L 1
97 11 Nov. Dollar L 1
98 12 Dec. Dollar L 1
99 TRCSM 01 Small Tractor Time Jan. Hour L 364
100 02 Feb. Hour L 364
101 03 Mar. Hour L 364
102 04 Apr. Hour L 364
103 05 May Hour L 364
104 06 June Hour L 364
105 07 July Hour L 364
106 08 Aug. Hour L 364
107 09 Sept. Hour L 364
108 10 Oct. Hour L 364
109 11 Nov. Hour L 364
110 12 Dec. Hour L 364
111 CMB 05 Combine Time May Hour L 230
112 CMB 09 Combine Time Sept. Hour L 230
113 CRNACC Corn Grain Account Bu L 0
114 CNSACC Corn Stubble Account Acre L 0
115 WHTACC Wheat Account Bu L 0
116 VEGACC Vegetable Account Garden L 0
117 RYEACC Rye Pasture Account Acre L 0
118 BAHACC Bahia Pasture Account Acre L 0
119 HAYACC Hay Account 60 lb Bale L 0
120 HOGACC Hog Account Cwt L 0
121 CTLACC Cattle Account Lb L 0
122 WHTFLEX Wheat Flexibility Acre L 0
123 WHTGRAZ Wheat Grazing Account Acre L 0
124 CTLSEC 01 Cattle Security Jan. Dollar L 0
125 02 Account Feb. Dollar L 0
126 03 Mar. Dollar L 0
127 04 Apr. Dollar L 0
128 05 May Dollar L 0
129 06 June Dollar L 0




52
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. 010 03 04 05
01 -58.76 -73.93 -59.45 -73.95 -110.61
02 0.10
03 0.80 0.80 0.10 0.10
04 1.21 12
05 0.41 0.41
06 0.67 0.67 0.80 0.30 0.30
10 1.07 0.10
11 0.68 0.68 0.68
12 0.49 0.49 0.69
13 0.10
26 1.00 1.00
27 1.00 1.00 1.00 1.00
28 1.00 1.00 1.00 1.00
29 1.00 1.00 1.00 1.00
30 1.00 1.00 0.50 0.50
31 1.00 1.00
32 1.00 1.00
33 1.00 1.0034 1.00 1.00
36 0.50 0.50
37 1.00 1.00
38 1.40
51 2.10 2.10 25.13 25.13 22.75
52 31.274 31.74 19.10 19.10
53 0.70 0.70
54 20.08 20.08 2.20 16.70 20.00
57 0.13 0.13
58 4.28 15.17
59 10.82 10.82 15.38
60 1.20 1.20 35.33




53
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
ill 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




54
Table 2--continued.
Row Vegetable Garden Rye Pasture Bahia Pasture
No. 06 ,07 08
38 60.00 15.80
39 60.00 6.60
40 60.00
41 60.00 35.70
42 76.00 0.26
43 76.00 0.26
44 60.00
45 60.00 0.27
46 60.00 1.50
47 76.00 10.66
48 76.00 15.10
49 76.37
51 9.47
52 12.33
53 16.03
54 3.26
56 4.57
57 2.58
58 15.61
59 16.04
117 -1.00
118 -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




55
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 Labor
No. 01 02 03 04 05 06 07 08 09 10 11 12
01 -3.50 -3.50 -3.50 -3.50 -3.50 -3.50 -3.50 -3.50 -3.50 -3.50 -3.50 -3.50
02 -1
03 -1
04 -1
05 -1
06 -1
07 -1
08 -1
09 -1
10 -1
11 -1
12 -1
13 -1
14 1
15 1
16 1
17 1
18 1
19 1
20 1
21 1
22 1
23
24
25
(J1




Table 2--continued.
Row Hired Labort
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.
un
-j




58
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.
Un




Table 2--continued.
Row Buying Activitlest 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 44 1
45 1
46
47 1
48 1
49 1
tColumns 54 through 65.
o)




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
63 1
64 1
65 1
66 1
67 1
68 1
69 1
70
71 1
72 1
73
74 -1
75 1.011 -1
76 1.011 -1
77 1.011 -1
78 1.011 -1
79 1.011 -1
80 1.011 -1
81 1.011 -1
82 1.011 -1
83 1.011 -1
84 1.011 -1
85 1.011 -1
86 1.011
tColumns 101 through 112




Table 2--continued.
Row Borrowing Activitiest
No. 01 02 03 04 05 06 07 08 09 C10 11 12
124 1
125 1
126 1
127
128
129 1
130
131 1
132 1
133 1
134 1
135 1
tColumns 101 through 112.
o)




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 -3000
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 3000
85 -770.85
87 1
88 1
89 1
90
91
92
93 1
94 1
95
96
97
98
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
01
50 -1
51 -1
52 -1
53 -1
54 -1
55 -1
56 -1
57 -1
58 -1
59 -1
60 -1
61 -1
74 1
75 1
76 1
77 1
78 1
79 1
80 1
81 1
82 1
83
84
85 1
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.




67
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 1oan 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




68
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 02 1.011 -1 0
Rows
03 1.011 -1 0
04 1.011 -1 0
Loan 01 1 5000
Restraint 02 1 5000
Rows
03 1 5000
04 1 5000




69
farm manager to enter the farm system. This set of twelve activities designated as OFWT01 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, so cial 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




70
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 0 705
Rows 0 705
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-l-- l
Cash 01 1 0.
Account 02 10
Rows
03 1 0
04 1- 0
Family 01 1 1145
Expense 0 114
Restraint 02114
Rows 03 1 1145
04 1 1145




71
Table 7. Cash flow sub-matrix, production expense transfer activities.
Month
01 02 03 04 RHS
Returns Row
C sh 01 -1 0
Accoun
Rows02 0
03 -1 0
04 -1 0
Production 01 1 0
Expense 02 1 0
Rows
03 1 0
04 1 0
Table 8. Cash flow sub-matrix, capital expense transfer activity.
Month RHS
Returns Row -3000
Cash
Account 11 3000 0
Row




72
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




73
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.




74.
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 ai-e 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. Crop production: small farm, Suwannee
and Columbia Counties, Florida, 1982.
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
'n




76
The linear programming itodel 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 f arrow 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.
Costs: Cash Cost Pounds Live Wt.
$/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 $55.00
CWT ($)
Total Income per $1519.10
Sow ($)




78
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.1 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.
1This figure may be optimistic. Researchi is currently being carried out to better estimate the level of substitution between wheat and rye for grazing.




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Table 11. Cost and returns summary. Cattle production--cow/calf
operation, small farms, Suw'annee and Columbia Counties,
Florida, 1982.
$/Cow-calf Unit
Costs:
Misc. Maintenance 27.60
Rye Pasture Cost 41.03
Bahia Pasture Cost 64.63
Hay Purchased 22.50
Total Cash Cost $155.76
Returns:
Pounds Marketed per 2.7562
Cow/calf unit (CWT)
Price Recieved per CWT $70.00
Total Income per $192.93
Cow/calf unit
Cash Cost per CWT $ 56.51
Marketed




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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 proauzed in the cow/calf operation are born in the winter months and marketed in October at $0.70 per pound.




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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
Labor021B Restraint02-B Rows 03 -1B
04 -lB
Hired 01 1B
Labor 02 1 B
Restraint
Rows 03 1B
04 1B
Cash 01 3.50 0
Account 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 5.00.70
Cash Account 03 -27.50
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




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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 purc hased 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 parttime 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 constrai:.-t (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




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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
Cach 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




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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




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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




86
only bejustified 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 institution al 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,




87
this payment is made once a year in November and primarily acts to reduce the objective function by $35,000.
The production sub-matrix for the large farm contains sections
for producing and selling crops and for hiring labor. The selling and hiring activities are similar to those in the small farm matrix and will not be discussed further. The only difference betwen small and large farm hiring activities is that the hourly wage on the large farm is $4.50 as compared to $3.50 on the small farm. This price differential is justified because the small farm hires only part-time labor that is often a neighbor or family member while the large farmer is paying wages for a full-time employee that requires higher compensation for his time.
The large farm is given the choice to produce five crops that are sold immediately after harvest for cash. Corn, wheat, soybeans, tobacco and peanuts are all crops commonly produced by farms in the study area. Table 17 illustrates part of the 'Initial tableau for the large farm that summarizes the cash,.labor and machinery requirements necessary for each unit of activity. Yielas, cash costs, gross margin, prices received and labor requirements for large farm activities are summarized in Table 18. In general, the large farm activities use six or eight row equipment with tractor sizes over 125 horsepower. As a result, field labor requirements for the large farm are lower than for comparable enterprises on the small farm. The five large farm activities require the use of fertilizers, herbicides, pesticides and planting densities recommended by the local extension agents. Enterprise budgets and cash and labor flows for the five




88
Table 17. Initial simplex tableau used to compute optimum farm plan on
large farm, Suwannee and Columbia Counties, Florida, 1982.
Row Row Row
No. Name Description .Unit Type Constraint
01 Returns Objective Function Dollar N
02 LBROPR 01 Operator's Labor -Jan. Hour L 208
03 02 Feb. Hour L 192
04 03 Mar. Hour L 208
05 04 Apr. Hour L 234
06 05 May Hour L 234
07 06 June Hour L 260
08 .07 July Hour L 260
09 08 Aug. Hour L 260
10 09 Sept. Hour L 260
11 10 Oct. Hour L 234
12 11 Nov. Hour L 234
13 12 Dec. Hour L 208
14 LBRHIR 01 Hired Labor Jan. Hour L 720
15 02 Feb. Hour L 720
16 03 Mar. Hour L 720
17 04 Apr. Hour L 720
18 05 May Hour L 720
19 06 June Hour L 720
20 07 July Hour L 720
21 .08 Aug. Hour L 720
22 09 Sept. Hour L 720
23 10 Oct. Hour L 720
24 11 Nov. Hour L 720
25 12 Dec., Hour L 720
26 Land 01 Tillable Land Jan. Acre L 450
27 02 Feb. Acre L 450
28 03 Mar. Acre L 450
29 04 Apr. Acre L 450
30 05 May Acre L 450
31 06 June Acre L 450
32 07 July Acre. L450
33 08 Aug. Acre L 450
34 09 Sept. Acre L 450
35 10 Oct. Acre L 450
36 11 Nov. Acre L 450
37 12 Dec. Acre L 450
38 Land TOB Tobacco Allotment Acre L 50
39 Land PNT Peanut Allotment Acre L 100
40 EXPFMY 01 Family Living Exp.-Jan. Dollar G 1819
41 02 Feb. Dollar G 1819
42 03 Mar. Dollar G 1819




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Table 17--continued.
Row Row Row
No. Name Description Unit Type Constraint
43 EXPFMY 04 Family Living Exp.- Apr. Dollar G 1819
44 05 May Dollar G 1819
45 06 June Dollar G 1819
46 07 July Dollar G 1819
47 08 Aug. Dollar G 1819
48 09 Sept. Dollar G 1819
49 10 Oct. Dollar G 1819
50 11 Nov. Dollar G 1819
51 12 Dec. Dollar G 1819
52 Loan 01 Borrowing Limit Jan. Dollar L 50,000
53 02 Feb. Dollar L 50,000
54 03 Mar. Dollar L 50,000
55 04 Apr. Dollar L 50,000
56 05 May Dollar L 50,000
57 06 June Dollar L 50,000
58 07 July Dollar L 50,000
59 08 Aug. Dollar L 50,000
60 09 Sept. Dollar L 50,000
61 10 Oct. Dollar L 50,000
62 11 Nov. Dollar L 50,000
63 12 Dec. Dolalr L 50,000
64 EXPCSH 01 Cash Production Jan. Dollar E 0
65 02 Expenses Feb. Dollar E 0
66 03 Mar. Dollar E 0
67 04 Apr. Dollar E 0
68 05 May Dollar E 0
69 06 June Dollar E 0
70 07 July Dollar E 0
71 08 Aug. Dollar E 0
72 09 Sept. Dollar E 0
73 10 Oct. Dollar E 0
74 11 Nov. Dollar E 0
75 12 Dec. Dollar E 0
.76- CSHACC 01 Savings and Cash Jan. Dollar E 0
77. 02 Account Feb. Dollar E 0
78 03 Mar. Dollar E 0
79 04 Apr. Dollar E 0
80 05 May Dollar E 0
81 06 June Dollar E 0
82 07 July Dollar E 0
83 08 Aug. Dollar E 0
84 09 Sept. Dollar E 0
85 10 Oct. Dollar E 0
86 11 Nov. Dollar E 0
87 12 Dec. Dollar E 0




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Table 17--continued.
Row Row Row
No. Name Description Unit Type Constraint
88 CSHEND Cash Ending Balance Dollar L 0
89 TRLLG 01 Large Tractor Time-Jan. Hour L 364
90 02 Feb. Hour L 364
91 03 Mar. Hour L 364
92 04 Apr. Hour L 364
93 05 May Hour L 364
94 06 June Hour L 364
95 07 July Hour L 364
96 08 Aug. Hour L 364
97 09 Sept. Hour L 364
98 10 Oct. Hour L 364
99 11 Nov. Hour L 364
100 12 Dec. Hour L 364
101 TRCSM 01 Small Tractor time-Jan. Hour L 364
102 02 Feb. Hour L 364
103 03 Mar. Hour L 364
104 04 Apr. Hour L 364
105 05 May Hour L 364
106 06 June Hour L 364
107 07 July Hour L 364
108 08 Aug. Hour L 364
109 09 Sept. Hour L 364
110 10 Oct. Hour L 364
il 11 Nov. Hour L 364
112 12 Dec. Hour L 364
113 CRNACC Corn Account Acre L 0
114 WHTAC Wheat Account Acre L 0
115 SBNACC Soybean Account Acre L 0
116 PNTACC Peanut Account lb L 0
117 TOBACC Tobacco Account lb L 0