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Integration of decision systems with production information for operations management

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Integration of decision systems with production information for operations management
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Khuri, Ramzi S., 1964-
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English
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x, 149 leaves : ill. ; 29 cm.

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Subjects / Keywords:
Agriculture ( jstor )
Capital costs ( jstor )
Cost control ( jstor )
Databases ( jstor )
Expert systems ( jstor )
Groves ( jstor )
Herbicides ( jstor )
Information storage and retrieval systems ( jstor )
Production costs ( jstor )
Recommendations ( jstor )
Agricultural Engineering thesis Ph. D
Citrus fruit industry -- Cost of operation -- Florida ( lcsh )
Citrus fruit industry -- Costs -- Databases -- Florida ( lcsh )
Citrus fruit industry -- Management -- Florida ( lcsh )
Dissertations, Academic -- Agricultural Engineering -- UF
City of Lake Buena Vista ( local )
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bibliography ( marcgt )
non-fiction ( marcgt )

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Thesis:
Thesis (Ph. D.)--University of Florida, 1990.
Bibliography:
Includes bibliographical references (leaves 143-147)
General Note:
Typescript.
General Note:
Vita.
Statement of Responsibility:
by Ramzi S. Khuri.

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University of Florida
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University of Florida
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Copyright [name of dissertation author]. Permission granted to the University of Florida to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
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25116703 ( OCLC )
AHZ9972 ( NOTIS )
AA00004762_00001 ( sobekcm )

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INTEGRATION OF DECISION SYSTEMS WITH
PRODUCTION INFORMATION FOR OPERATIONS MANAGEMENT









BY

RAMZI S. KHURI


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

UNIVERSITY OF FLORIDA


1990




INTEGRATION OF DECISION SYSTEMS WITH
PRODUCTION INFORMATION FOR OPERATIONS MANAGEMENT
BY
RAMZI S. KHURI
A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY
UNIVERSITY OF FLORIDA
1990


Copyright 1990
by
Ramzi S. Khuri


Dedicated to
my mother Maha, my father Suhail,
my brother Nizar, my uncle Zahi,
and my best friend Wisky


ACKNOWLEDGEMENTS
I would like to express my deep hearted gratitude to Dr.
R. M. Peart, Graduate Research Professor of Agricultural
Engineering, for serving as a chairman for my graduate
committee. I would also like to extend my appreciation to Dr.
Howard Beck of the Department of Agricultural Engineering for
his knowledge, guidance, and supervision throughout this
project. The friendship, support, and guidance of Dr. W. D.
Shoup of the Department of Agricultural Engineering, his help
in formalizing the project, and his serving as cochairman on
my supervisory committee are highly appreciated. I extend a
special thank you to all those who were involved with the
continued financial support I received for this project. I
would also like to extend my gratitude to Dr. Richard Kilmer
of the Department of Food and Resource Economics and Dr. James
Burns of the Department of Industrial and Systems Engineering
for serving on my committee and for their help on this
project.
Mr. Ron Muraro and Dr. Megh Singh of the Citrus Research
and Education Center in Lake Alfred, Florida, deserve my
special thanks for sparing their time and providing me with
valuable information. I also thank all participants of the
cost surveys, and those who helped evaluate the COINS system.
iv


I would like to express very special appreciation and
thanks to my best friend Betsy, whose patience, encouragement,
friendship, and moral support were indispensable during the
completion of this project. I would also like to thank
Betsy's family for their friendship and continuous
encouragement throughout my studies.
My very special thanks are extended to my uncle Waleed,
my uncle Rabie and his family, and my best friends Sami and
Ghassan for their endless support during my five and a half
years of graduate study. My appreciation is also extended to
all my friends in the United States and abroad.
Last but not least, I wish to express my appreciation to
my grandparents whose confidence in me and continuous support
for my efforts helped me achieve my goals.
v


TABLE OF CONTENTS
pages
ACKNOWLEDGEMENTS iv
ABSTRACT ix
CHAPTER
I INTRODUCTION 1
Justification 1
Objectives 3
II REVIEW OF LITERATURE 6
Management Information Systems 6
Agricultural Information Systems 7
Agricultural Technology Transfer 8
Agricultural Cost Systems 9
Decision Support Systems 11
Expert Systems 13
Expert System Applications 14
Database Management Systems 17
Database Design 17
Data Security 19
Integration of Knowledge Systems
and Databases 20
Summary 23
III SYSTEM DESIGN CONSIDERATIONS 24
Preliminary Research 24
Existing Information Sources 24
Existing Agricultural Software 25
Design Requirements 26
The User Interface 2 6
System Hardware and Software
Requirements 27
Functional Requirements 28
IV COINS CITRUS COST INFORMATION SYSTEM 29
An Overview of COINS 29
DBXL as a Database Management and
a Programming Environment 30
vi


pages
Program Development Tools 30
Program Requirements 31
Program Structure 31
Password System 32
The COINS Database 32
Database Structure 32
Data Storage 34
Data Entry Program 38
Program Design 38
Program Functions 38
Entering Costs for a Grove 47
Data Summary and Comparison Program 53
Program Design 53
Program Functions 55
Cost Averaging 55
V SIMON SYSTEMS FOR INTEGRATING MANAGEMENT
AND COST INFORMATION 59
Program Development 60
Program Features 61
Program Development Environment 62
Expert System Environment 62
Expert System Rule Base 65
Knowledge Acquisition 67
Identifying the Experts 67
Preliminary Interview 69
Sources of Information 70
Establishing the Decision Process 71
Follow-up Interviews 84
Program Execution 84
Obtaining a Herbicide Recommendation 84
Obtaining a List of Weeds Controlled
by a Herbicide 90
Comparing Several Herbicides 90
Integration with COINS 92
VI TESTING AND EVALUATION 96
Phase 1 Operational Evaluation 96
Testing and Evaluating the COINS
Cost Programs 96
Testing and Evaluation of the
Herbicide and Weed Guide 99
Phase 2 Professional Evaluation 100
Role of Trade Show Exhibit 100
Role of Citrus and Extension
Specialists 102
Purpose of Evaluation Form 103
Evaluation Procedure 103
vii


pages
Evaluation Results for
Structured Responses 105
Evaluation Results for
Non-structured Responses 110
VII CONCLUSIONS AND RECOMMENDATIONS 122
APPENDICES
A DATABASE FILE FORMATS 126
B SYSTEM EVALUATION 129
REFERENCES 143
BIOGRAPHICAL SKETCH 148
viii


Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy
INTEGRATION OF DECISION SYSTEMS WITH
PRODUCTION INFORMATION FOR OPERATIONS MANAGEMENT
by
RAMZI KHURI
December 1990
Chairman: Dr. R. M. Peart
Major Department: Agricultural Engineering
Production costs for citrus operations in Florida have
been integrated with extension and management recommendations.
This new approach permitted the dissemination of valuable
production, management, and cost information among citrus
growers. It has helped create a dynamic source of information
that would ultimately lead to improved production and
management decision making.
A citrus production cost information system (COINS) with
three components, namely, a cost entry program, a summary and
comparison program, and a decision support system for
extension and management recommendations, has been designed,
developed, and evaluated. The system collects, averages, and
summarizes citrus production costs. It provides a means for
growers to compare their operations with industry averages.
ix


An herbicide and weed guide gives appropriate management
recommendations and associated costs. Citrus growers from
several areas in Florida utilized the system and evaluated its
operational performance and its qualifications as a decision
and management tool. The system's components were found to
operate properly, and the system as a whole was qualified to
be a useful management tool.
The data entry component successfully collected,
organized, and stored production cost information for numerous
grove operations. The summary and comparison component
successfully averaged production costs for specific areas and
types of operations. It allowed for the comparison of
individual grove costs with industry averages. The herbicide
and weed guide integrated cost information with extension and
management recommendations through an expert system. The
successful integration of production costs with management
recommendations demonstrated the system's success as an
effective decision and management tool.
The utilization of a database management environment
provided a comprehensive tool for the development of the
information system. The techniques and design methods used in
development and integration of various components produced a
highly dynamic and flexible system.
x


CHAPTER I
INTRODUCTION
Justification
Citrus growers in Florida are continually striving for
improved methods of production in order to increase yield and
minimize costs. They are challenged by factors such as
weather conditions, consumer demand, market prices, and
foreign competition. They must overcome these obstacles by
using the best production and management practices available.
The cost of producing citrus is partly accrued through
the various grove operations performed during each season.
Production cost records are maintained by most operations as
a means of keeping track of expenditures, and for record
keeping and tax purposes. The amount and detail of cost
information recorded depends largely on the size of the
operation, and on the importance placed on such records by the
operation manager. In some instances grove cost records are
hand written, lack any detail, and are mainly used for year-
end tax purposes. More detailed costs are kept by some
growers who use computers to maintain data on a daily basis.
In some instances, growers owning small operations delegate
the record keeping task to caretaker operations and
cooperatives who usually have the means to maintain large
amounts of data on computer.
1


2
Growers seldom have the opportunity, however, to compare
their production costs with industry averages in a way that
will help identify problem areas. Database systems currently
being used by large operations to help maintain cost
information rarely offer any special data analysis or
comparison features. Most specialty financial software
available for the citrus industry deals strictly with farm
financial records. In most cases the software is used for
record keeping purposes. In some cases financial analysis of
information is performed by the software, and results in an
overview of the operation's financial status, but does not
deal with operational costs of individual grove practices.
Information sharing among citrus growers would enhance
the dissemination of valuable knowledge on production costs
and management practices. A large database system, Florida
Agricultural Information Retrieval Systems (FAIRS) (Johnson
and Beck, 1986), has been developed to provide growers,
researchers, and extension specialists valuable information on
management and extension practices, as well as technical
information on most topics related to citrus. The information
in the database is in the form of text and graphic screens,
and is updated with current data on a regular basis. Other
information sources such as technical publications, extension
specialists, and researchers, also provide growers with a
consistent source of information. According to Smith et al.
(1988) sources of new technology and production methods


3
include farm magazines, discussion with human expert or
neighbor, and extension service.
There is, however, a lack of availability of adeguate
information associated with production costs for citrus.
There is also a need for a means to communicate this
information among growers. Knowledge sharing between citrus
growers is the key to achieving a dynamic database of
information. Using the "dynamic approach would result in a
more specific and more usable database that would result from
simply keeping publications related to a specialist's subject
matter area on-line (Jones and Hoelscher, 1987). Furthermore,
no attempts have been made to integrate citrus production
costs with extension and management recommendations. Such an
integration would enhance growers' ability to make better
management decisions.
There is no reference in the literature to a dynamic
agricultural information system where production costs are
collected, pooled, summarized, and linked to appropriate
extension and management recommendations.
Objectives
The general objective is to provide citrus growers in
Florida with a means of improving their production practices
through the enhancement of knowledge and management decision
making, by providing an economic basis for extension and
management recommendations. This is achieved by developing


4
and integrating a computer based citrus production cost
information system with extension and management
recommendations. Information in the system develops and
expands through the addition of new management and extension
information as well as annually contributed production cost
information.
The specific objectives are as follows:
1) to identify and classify grove operations associated
with citrus production in Florida;
2) to design and develop a dynamic database system for
collecting and storing production cost information for Florida
citrus operations;
3) to design and develop a system to query the cost
database, average and summarize costs, and generate per acre
cost and returns reports;
4) to provide a means for comparing individual grove
costs with industry averages to aid in problem area
identification.
5) to design and develop a prototype decision support
system to integrate cost information with extension and
management recommendations.
6) to verify the information system's capabilities to
enhance the communication of knowledge between citrus growers
through the accessibility of citrus production cost
information and related management and extension
recommendations.


5
The remainder of this dissertation is organized as five
more chapters. The review of literature in Chapter II gives
an overview of management information systems and decision
support systems. It also deals with expert systems and cost
systems as related to agriculture.
Chapter III discusses the design requirements of the
system, including preliminary research, hardware, software,
and functional requirements.
The design and development of the information system
consisting of a database system for handling citrus production
costs and a data summary and comparison are discussed in
Chapter IV.
Chapter V deals with the design and development aspects
of the prototype decision support system for herbicide
application. This chapter also describes the means by which
production cost information is integrated with management
recommendations.
Chapter VI reports the results and procedures of the
evaluation and testing of the coins system. The system was
evaluated by various citrus growers, researchers, and citrus
extension specialists.
Finally, Chapter VII presents conclusions and
recommendations for future work in the area of agricultural
information systems.


CHAPTER II
REVIEW OF LITERATURE
Management Information Systems
Drechsler and Bateson (1986, p. 53) define a management
information system as "an information system that provides a
manager with information on the activities and pertinent
interrelations about the current status of the
production/operation system over which he has control". Smith
et al. (1985, p. 2) define management information systems as
"integrated computer based systems which provide information
to support the operational and decision making functions of
management". Murphy (1989) claims that in today's terms, MIS
(Management Information Systems) are simply databases. This,
he says, is an appropriate title since they are storehouses of
all information about a specific subject.
Information systems have become an important decision aid
and management tool for operation managers. They have become
an essential part of many organizations (Smith et al., 1985).
Both decision support systems and expert systems should be
included as subsystems of an overall management information
system concept.
6


7
Agricultural Information Systems
Today's businesses design and develop powerful
information systems for themselves. The farm community
deserves comparable quality power. To achieve this goal,
farmers must become involved in the design and development of
their systems (Murphy, 1989).
Computer-based systems that are designed specifically for
agriculture must meet the farmers' challenge (Stone et al.,
1986). Agricultural information systems may include a
database representing current knowledge about crop systems,
and may have the capability to access historical data records
and the ability to analyze data and expand on the analysis
presented to a user when requested. An information retrieval
system designed for farm use would encompass a collection of
resources including record keeping facilities such as
accounting information and expert systems (Beck, 1988).
The Jackson County Cooperative Extension Service (CES)
office, for example, uses an information management system to
collect, store, and retrieve extension information, and
provide it to clients in the form most suitable for their
needs (Heatley, 1986). The system saves time and effort that
would have otherwise been used to manually order and store
information, and pass out CES publications. The information
management system uses a database management system to
retrieve blocks of text.


8
Agricultural Technology Transfer
Technology transfer from researchers and other
information sources to producers remains inadequate. The main
objective of agricultural research is to improve the
efficiency of farmers by providing them with valuable
information which they need to make cost effective production
decisions (Smith et al., 1985). The technology of information
communication is made possible through management information
systems. Murphy (1989) predicts that in the next decade or
so, companies will put a lot of effort into 'networks' that
link their information sources together. This will ultimately
enhance the information communication process.
The process of knowledge transfer from researcher to
producer can also be achieved with the construction of
interactive computer based decision support systems (Smith et
al., 1985). Such systems use knowledge to create valuable
information. In order for decision support systems to be an
effective communication tool, various components such as
simulation, information analysis, and problem solving models
must be integrated within a single framework which can be
effectively accessed by different user levels. Lai et al.
(1987) state that an effective means of transmitting knowledge
from technology generator to technology user would be a
process that would permit the end user to question and seek
clarification on the recommendations given.


9
Agricultural Cost Systems
The basic purpose of cost systems is to generate cost
information. Cost information may be used to satisfy certain
managerial requirements. One objective of cost systems is to
assist management in controlling costs (Shah, 1981).
Management is in constant need of timely and reliable
information on costs incurred by various responsibility
factors. Such information will provide management with
decision making support, and aid in the process of cost
reduction. Shah continues to say that a major concern of
management is reducing costs while maximizing profits. A cost
reduction process requires knowledge of significant cost
items, the cost of major activities, the identification of
controllable and non-controllable costs, and the effect of
cost reduction in each activity on revenues and profits.
In agriculture, growers must know their production costs
in order to be competitive and profitable (Tripeppi and
Kucher, 1988). Many microcomputer programs have been
developed to provide growers with various types of management
information. In some cases, analysis of information is
provided in order to give management further insight into the
financial position of their operations.
Olson et al. (1986) discusses an annual report that
summarizes individual farm records for farms in South Eastern
Minnesota. The report shows averages as well as high and low
ranges. Whole-farm information as well as enterprise costs


10
and returns are reported. At year end, individual farmers can
compare their operation to the information provided in the
report to find areas that need management attention and areas
which have above average performance. Some computer aided
farm business analyses is performed annually using IBM FINANX
software by the extension service, and a summary of individual
analyses is prepared.
A microcomputer program has been developed to calculate
production costs based on various inputs involved in growing
crops (Tripepi and Kucher, 1988) The user enters costs for
land, buildings, equipment, general overhead expenses, and
cultural practices. The program then produces cost summaries
for capital requirements, annual fixed costs, variable cost
per hour of equipment, estimated costs of production, and
final price per plant. Using this information, growers can
compare their prices with those of their competitors and can
therefore determine crop profitability. The program saves
hours of manual collating and adding production costs and
eliminates math errors due to human mistakes.
Another computer program, PPCAM was developed to manage
plant production data and enable the user to predict
production and profits (Power et al., 1989). Using input
data, PPCAM generates labor, material, energy, and indirect
cost information. The program can also generate, store, and
retrieve reports, summaries and graphics of various
parameters. PPCAM uses an integrated project-file language


11
using several spreadsheet templates and databases to create a
user friendly environment.
ABC Systems (1989) has created a comprehensive computer
program for agricultural record keeping. The program a mainly
an accounting software package that handles information for
budgets and plans, cash flows, cash forecasts, cost of
production, and offers results and balance sheet analysis.
An economics oriented program is discussed by McGilliard
and Clay (1986). The microcomputer program for decision
analysis (DECAL) was written to provide a relatively easy and
flexible method of analyzing investment decisions. For each
decision, DECAL produces a one page report showing business
measurements of net cash, present, future, period, and annual
values, as well as benefit/cost ratios, return on investment,
and payback period.
Decision Support Systems
Decision support systems are an emerging area of
research. They can combine database management systems and
the branch of artificial intelligence known as knowledge
representation. (Beck, 1988). According to Barrett and Beerel
(1988), conventional computing is concerned with handling
information which might subsequently be used in the decision
making process. Data processing organizes data and transforms
it from one form into another. Here, information is still at
a very basic level. Decision support builds on this by


12
allowing the manager to view data at a level which is
convenient to him.
Producers can use decision support systems to analyze and
help solve agricultural problems (Smith et al., 1985). A
decision support system should be able to provide appropriate
information to decision makers in order for them to operate
from a wider knowledge base than they do at present. A
decision support system discussed by Pruss (1989), utilizes
crop production information in the form of raw data. The
system collects, organizes, and summarizes information which
is later evaluated and interpreted into knowledge used for
crop management decisions.
Decision support systems may include expert systems.
Expert systems help alleviate some of the difficulties of
using decision support systems with a non-expert client.
Expert systems can help users select the most important
information for certain questions (Love, 1988). Expert system
technology can be applied at different points in the decision
support system-user interaction process, data collection,
identifying the problem, interfacing with the decision support
system, guiding the user through decision support system
output, assisting the user in "what if" applications of the
decision support system, helping the user select pertinent
output and decision weights, and analyze output. It is
important to ascertain at what point this application should
occur.


13
Expert Systems
Holt (1988) defines an expert system as a computer
program that enables a computer to mimic the logic of an
expert in diagnosing problems, selecting alternatives, giving
recommendations, and managing operational systems. Expert
systems are rule based and reason from one rule to the next,
gathering information until the system is able to recommend a
decision or provide advice (Helms et al., 1987). Expert
systems are highly interactive and generally easy to use (Lai
et al., 1987). An expert system user can ask the system
questions, change assumptions and even ask for the reasoning
behind answers given.
The biggest barrier to agricultural productivity is the
knowledge gap that lies between researchers and growers. An
expert system approach is ultimately an excellent way to
remove this obstacle (Rudd et al., 1986). Expert systems may
be used to enhance the capabilities of the researchers and
others responsible for the technology transfer process (Lai et
al., 1987).
Expert systems may be used as tools for summarizing
information and knowledge, diagnosing problems, and for
identifying specific objects and conditions such as weeds and
diseases. Expert systems can also be used as a teaching tool
for non-expert users (Holt, 1988).
McKinion and Lemmon (1985) discuss the role of expert
system technology in agriculture. They claim that the first


14
opportunity for using expert system technology in agriculture
is with integrated crop management. Expert systems would take
the form of integrated crop management decision aids which
would encompass such disciplines as irrigation, nutritional
problems, fertilization, weed control, cultivation, herbicide
application, insect control and insecticide and/or nematicide
application.
Expert systems may also be used for economic
applications. Expert systems designed to complement farm
financial records and planning systems hold considerable
promise as decision aids (Love, 1988) Producers can use
expert system technology to assist in synthesizing information
to decide the financial state and performance of their
operations.
Expert System Applications
System development in agricultural and natural resource
management applications has mirrored the growth in recent
years of the development and use of expert systems in product
design, resource management, and logistics (Lambert and Wood,
1988). A survey of agricultural expert systems currently
under development or available for use was completed. The six
major subject areas surveyed were crop and livestock
production, financial analysis, general shells, marketing,
natural resource management,and other areas. Most of the
applications surveyed are diagnostic in nature. However, a


15
few are dedicated to advising users on a variety of concerns
from irrigation scheduling to grain marketing to enterprise
selection. The survey reveals five programs in the area of
financial analysis.
A Financial Analysis Review System (FinARS) (Boggess et
al., 1989) is written in INSIGHT 2+ (Information Builders,
Inc.). The system provides an evaluation of the financial
health of a farm business. It is designed to provide an
initial assessment of the overall financial health of the
business.
In addition to its capabilities as a diagnostic tool for
farmers to provide initial interpretation of their farm's
financial situation, FinARS can also be used as a tool for
teaching financial analysis concepts to students, county
agents, lenders, and farmers.
Lambert and Wood's (1988) survey also included four other
expert system applications related to economics that are worth
mentioning. A Budget Planner written in PASCAL is an
enterprise budget generator that includes a full enterprise
analysis. A farm financial document analyzer written in LEVEL
5 is designed to assist producers with financial management
decisions for Michigan dairy farms. An agricultural financial
analysis expert system is available to give information on a
farm's current year performance, financial condition, and debt
repayment ability. And finally, a farm loan advisor was
written to evaluate farm loan applications.


16
Helms et al. (1987) discuss a farm level expert system
that provides advice regarding farm program participation and
changes in farm policy variable. The description and
application of the Farm Policy Advisor or FPA was demonstrated
on a Southern Blacklands hypothetical farm in Texas.
Richardson et al. (1989) describe an application whereby
mini-expert systems are called to develop data from user
inputs to be used in a database. A main program CARMS
(computer assisted records management system) allows users to
build data sets needed by CIRMAN (crop insurance risk
management analyzer). The expert system CARMS leads the user
through a series of steps to develop a large sophisticated
database required for a simulation model.
Linker et al. (1990) discuss a herbicide recommendation
program that allows the weed managment expert to build a list
of recommended herbicides or herbicide mixtures based on weed
species and soil type. The program, written in C, allows the
user to obtain a herbicide recommendation from a particular
weed problem.
Batchelor et al. (1989) describe a prototype expert
system developed to aid in soybean insect pest management
decision making. Crop status and insect population
information are provided by the user. The decision support
system, SMARTSOY, runs a crop growth model to determine
subsequent damage to the crop. The crop growth model,
SOYGROW, predicts growth and development and final yield of


17
soybean based on daily weather data for specific soils (Jones
and Hoelscher, 1987). Once SOYGROW is run, SMARTSOY gives
cost effectiveness of insecticide applications as well as rate
and type of insecticide are given.
Database Management Systems
Lai et al. (1987) state that the utilization of database
management has made a definite contributions to the technology
process. Database management typically involves the mechanics
of storing and retrieving large amounts of data (Beck et al.,
1987). Efficient database design is essential for file
organization, indexing, rapid transaction and query processing
rates, and allowing multi-user access and sharing of data.
Database management also addresses problems such as data
security, maintenance of data accuracy and integrity, and
creating data storage separate from the application programs
which use the data.
Database Design
Proper database design is imperative when creating an
efficient data management environment. Brathwaite (1985)
discusses some essential reasons behind careful database
design including data redundancy, application performance,
data security, and ease of programming.
Hierarchical Models. Hierarchical data models are based
on tree-like structures made up of nodes and branches, where


18
the highest node is called the root, and succeeding lower
nodes are called children (Brathwaite, 1989). In hierarchical
models, trees are constructed using a father-son approach
(Chorafas, 1989). Hierarchical models can range from fairly
simple such as in the case of one-to-one or one-to-many
relationships, to the more complex many-to-many relationships.
Brathwaite (1989) and Chorafas (1989) both discuss some
advantages and disadvantages of hierarchical models. A major
advantage is the existence of proven database management
systems that utilize the hierarchical model. Another
advantage is the simplicity and ease of use of hierarchical
models which ultimately will facilitate their employment and
utilization by data processing users. Other benefits of
hierarchical models include their ability to reduce data
dependency, and their capability to efficiently represent
decision support system data (Hopple, 1988).
Both authors agree that one of the major disadvantages of
using hierarchical data models as a basic structure is their
lack of flexibility. This presents difficulties during
insertion and deletion operations. Due to strict hierarchical
ordering insertion and deletion of files or entities may
disrupt the tree structure. Also, deletion of the a parent or
father node will result in the deletion of the children
associated with that node. Accessibility of information is
also mentioned as a disadvantage since a child node is
accessible only through its parent node.


19
Relational Models. Relational database models store
information in tables of rows and columns (Singh, 1985) The
association of rows and columns in a table is the
characteristic that gives the relational data model its name
(McNichols and Rushinek, 1988). McNichols and Rushinek
discuss that one advantage of the relational model is that
operations on tables can be defined mathematically, allowing
precise and unambiguous data retrieval. Maier (1983) states
that another advantages of this type of data storage is its
uniformity.
Data Security
An important part of database design is the incorporation
of a system to maintain data security. In an information
system with multiple users, a method of ensuring that only
authorized persons can access certain data is essential. Most
systems use a control of access method whereby a user's
identity is verified, and according to his priority, the
system assigns him access to certain information (Delobel and
Adiba, 1985). Any users attempting to enter the system must
identify themselves and then authenticate the identification
(Brathwaite, 1985).
Harington (1988) states that the first step in on-line
database security is to identify the user. However, in single
microcomputer operation systems there are generally no
individual user accounts or work areas. Therefore, the


20
computer assumes that everyone who has access to the keyboard
is authorized to use the machine. Consequently, security must
be handled at a lower level by the database management system
itself.
DBASE III Plus (Ashton-Tate, Inc.) has no features
specifically designed for data security, but it is possible to
impose some measures of safety through an application program.
However, even with the use of a password system through the
development of an algorithm, there is nothing that will
prevent a knowledgeable user from running DBASE III Plus and
accessing data by using ordinary program commands.
In many businesses, however, DBASE III Plus users have
not been trained to work directly with the database management
system environment. The majority are not computer
professionals, but people trained in other areas who use
computers to help do their jobs. These users work with
application programs whereby interaction with the system is
made through menu-driven interfaces and system screen forms.
In this case, users can be required to supply a password which
governs their access to the program.
Integration of Knowledge Systems and Databases
The integration of expert systems with other conventional
software would be beneficial (Holsapple et al., 1987). Some
expert system environments have a limited ability to import
data from external software. This ability does not, however,


21
replace integration, which provides a comprehensive
environment that makes all business computing abilities
available for use at any time, individually or in tandem.
Expert systems should be viewed as a supplement rather
than a replacement for existing computer technologies like
database management systems (Lai et al., 1987). Stone et al.
(1986) describe a Farm Level Expert System (FLEX). FLEX is an
integrated system made up of expert systems linked through a
common global memory and a database of interrelationships.
The integration process involves several stages (Harmon and
Sawyer, 1989) .
Harmon and Sawyer state that first, it is important to
determine whether or not a database is necessary. Databases
may be used for maintenance purposes whereby they are used to
externalize the parts of a decision support system that change
frequently. Databases can also be used as tools for
information storage, retrieval, searching, and querying. A
database management system is often better suited for
efficient search of large volumes of structured information
than a expert system is. Data sharing with other
applications, and other users, as well as security
maintenance, are other reasons that necessitate the use of a
databases in decision support system.
Second, Harmon and Sawyer say that the role of databases
in decision support systems should be evaluated. An expert
system can be used in two different ways: as a front end for


22
a database, asking questions and then initializing database
queries, or as a back end for a database, taking the results
of the query and analyzing them with rules in order to make
recommendations. Jones and Hoelscher (1987) describe a method
of integration whereby an expert system provides an initial
information sorting process, and then accesses a main database
for more detailed information.
When an expert system is used as a front end for a
database, the consultation proceeds in the normal way, with
the system asking the user questions and using rules to reach
a final conclusion. Once the system reaches a conclusion, it
takes the extra step by constructing a database query and
searching the database for an even more specific
recommendation. The database can hold information that will
further elaborate on the recommendation. Gallagher (1988)
discusses that the processing of data by decision support
systems may be very inefficient if they do not have the
ability to work with information proficiently. A solution to
this problem requires the system to request processing of data
by other programs that are specifically designed to accomplish
that task. That is, ask the data base management system to
search databases and summarize data.
When used as a back end to a database, an expert system
takes the database output and uses the information as input.
It then makes judgements based on it, and presents specific
suggestions to the end user. Back end applications are


23
usually used to take the raw data drawn from a database query,
process it further, and convert it into specific
recommendations. This approach is very effective for
providing support to individuals who have difficulty
interpreting the database query output. Expert systems are
used to build on the results of data processing and decision
support by assisting the user with the interpretation of data
to formulate responses (Barrett and Beerel, 1988).
Summary
The citrus cost information system discussed in this
dissertation integrates several of the technologies addressed
in this chapter. A database management environment is used in
conjunction with an expert system to provide production cost
information and extension and management recommendations.
The cost information system is unique in its ability to
provide industry averages in a way that is useful as a
decision making tool. The expert system provides information
on not only extension and management recommendations, but on
the costs associated with these recommendations.


CHAPTER III
SYSTEM DESIGN CONSIDERATIONS
During the implementation and use of the Florida
Agricultural Information Retrieval System (Johnson and Beck,
1986), Florida citrus growers expressed a need for information
on production costs associated with citrus. They needed an
economic basis for extension and management recommendations.
An information system was needed to provide growers with the
means to contribute production cost information for their
operations, and in return receive summaries of industry
averages. The system would not only provide cost summaries,
but would also integrate those costs with appropriate
extension and management recommendations to provide a
comprehensive decision making tool.
Preliminary Research
Existing Information Sources
The first phase in this project was to determine what was
already available for the citrus grower in the form of
production cost information. It was found that an annual cost
survey has been carried out since 1973 by Ron Muraro, a
researcher at CREC (Citrus Research and Education Center) in
Lake Alfred, Florida (Muraro and Matthews, 1988). Muraro's
24


surveys are designed to collect custom charge rate information
from citrus caretakers in different citrus producing areas of
the state. Written surveys are mailed out once a year to
participating caretaker operations. When all information is
collected, the relevant data is organized and summarized in
annual publications. Although other agricultural cost surveys
have been carried out in the past in areas such as potato
production, and woody nursery businesses, Muraro's citrus cost
surveys are the only ones still being carried out on a regular
basis.
Existing Agricultural Software
The next step in the preliminary stages of this project
was to determine what types of software packages were being
written for Florida citrus operations. Several programs were
available through the IFAS (Institute of Food and Agricultural
Sciences) software distribution office. After reviewing what
the IFAS catalog had to offer it was determined that most of
the programs were not written specifically for the citrus
grower. Some of the programs reguired the user to have a
considerable amount of computer experience, and it was often
necessary for the user to have a certain software package or
language such as LOTUS 123 or BASIC to run the programs.
Based on visits to software exhibits, agricultural trade
shows, and discussions with citrus experts, commercial
software proved to be mainly designed for payroll and record
25


26
keeping purposes and did not offer the grower any decision
support capabilities.
Design Requirements
The User Interface
One of the chief design requirements was a user-friendly
yet detailed environment for the user to work in. The
development of a user-friendly interface is an important part
of any system development (Andriole, 1986). Andriole states
that the user interface is the single most important part of
today's microcomputer systems. Its design must be approached
carefully, and should not be left to chance.
Peart (1988) suggests specific guidelines for the design
of user friendly programs. Menu selections should be used to
obtain necessary words or strings from the user, rather than
requiring typing. The user should be given new information
and help when needed. Previously entered information should
be kept on the screen as long as possible. A user should
always have a way reexamine his data and go back and change
it. And finally, files should be designed to allow the user
to refer back to what he did in his last session.
Lai et al. (1990) discuss an interface program designed
to work in conjunction with an expert system. Specially
designed screens use pop-up menus with user defined options
for data selection. The program acts as an information
manager for an integrated decisions support system, FARMSYS.


27
Once data items are defined, they need not be entered again.
This ensures consistency of data and facilitating rule
handling, error-checking, logical decisions, and searches
during program operation.
The system user was assumed to be a novice computer user.
The program had to be easy to use, self explanatory, flexible,
and expandable. Paller and Laska (1990) stress the importance
of ensuring that a system maintains enough flexibility and
responsiveness to meet the real and changing needs of an
organization.
System Hardware and Software Requirements
It was felt that the majority of citrus growers in
Florida that utilized microcomputers in their operations owned
IBM or compatible machines. The system needed to run on the
hardware already available in order to avoid additional
requirements.
Software specifications required a stand-alone system.
As discussed above, a large number of the programs already
available through IFAS required additional software to run.
This often necessitates costly software packages to be
purchased by the user. If such supporting programs were to be
distributed with the information system, then the need for
expensive licenses would arise.


28
Functional Requirements
In order for any computer based system to be successful,
it must fulfill two requirements. It needs to perform the
tasks it was originally designed for, and it must offer the
end user enough benefits to justify the time invested in its
use. Any system must provide the user with a new or improved
method of performing a task by replacing outdated methods or
technologies and enhancing productivity. Software systems
must also provide information that complements or supplements
information already available to the user through conventional
means.
A citrus cost information system would require large
amounts of data to be contributed by the user. It is often a
labor intensive and time consuming task for a grower to
contribute production costs for several of his groves by
entering detailed information into a computer. In the case of
this project, the system needed to provide the user with an
incentive to encourage its utilization. This is achieved by
providing the user with valuable information in the form of
industry averages and management recommendations, and
integrating the two in a unique and beneficial manner. The
system would also offer the user the chance to compare his
operational costs with the industry averages in order to
identify possible problem areas.


COINS
CHAPTER IV
- CITRUS COST INFORMATION SYSTEM
An Overview of COINS
A Citrus Cost Information System (COINS) was developed to
collect, manage, and report costs associated with citrus
production in Florida. The program consists of three main
sections. The first is a data gathering program for
production costs for various areas of the operation. The
second section is a summary and comparison program which
provides detailed summaries of averaged production costs, and
allows the user to compare production costs for one of his
groves with industry averages. The third section integrates
production cost information with extension and management
recommendations. The system for integrating management and
cost information (SIMON) is discussed in more detail in
Chapter V.
The following section discusses COINS in more detail,
including software and computer languages used for
development, the overall program structure, and program
execution.
29


30
DBXL as a Database Management and a Programming Environment
To write the COINS programs, software which combined
database management with a comprehensive programming
environment was needed. Two relational database packages were
considered, DBASE III+, and DBXL (WordTech Systems, Inc.) a
similar database program. Both packages offered a database
management environment, as well as a programming language with
which to develop applications.
DBXL was chosen over DBASE III+ for its increased
flexibility and lower cost. DBXL offers an extended language,
and an added windowing feature which allows multiple windows
to be generated. This feature was found to be helpful in
designing menus, pop-up help screens, error message windows,
and in improving the overall quality of the user interface.
Quicksilver (WordTech Systems, Inc.) was used to compile the
database applications to create fast stand-alone programs.
This allowed COINS to run independently of dBXL. Thus
circulation of the programs would not infringe on any
copyright or distribution laws.
Program Development Tools
COINS was developed on a Compaq 386 computer running
under an MS-DOS environment. A memory resident editor was
used to write the programs. Windowing environments such as
Microsoft Windows 386, and Quarterdeck's Desqview 386 were
used to load and run all the necessary software


31
simultaneously. This allowed the programs to be written with
the editor, tested in a dBXL environment, compiled with
Quicksilver, and tested, with great efficiency.
Program Requirements
The system was designed to run on any IBM-PC, XT, AT and
compatibles as well as 386 based machines. Hardware
requirements include a minimum of 640 kilobytes of free RAM,
a hard disk drive, and a color monitor. The COINS program
files occupy approximately one megabyte of disk space. The
data files require 60 kilobytes of disk space per grove. An
additional 50 kilobytes of free disk space per grove is
recommended to accommodate the temporary files during
execution of the data summary and comparison program.
Program execution speed depends on the hard disk drive
access times, the microprocessor speed of the computer being
used, and on the presence or absence of a coprocessor chip.
Execution is fairly slow on PC and XT machine, and fastest on
386 computers.
Program Structure
COINS incorporates numerous database files and three main
programs, namely a data entry program, a summary and
comparison program, and a decision support system. All
programs are accessed through a main menu program written in
Turbo PASCAL (Borland). When COINS is started, the main menu


32
is loaded into memory and remains in memory until the session
ends. When execution of a program is complete, control
returns to the main menu (Figure 4-1).
Password System
COINS utilizes a password system that serves three
purposes. The first is to protect data files against
unauthorized access. Grove cost information can only be
entered or retrieved when the proper password is used. The
second purpose is to insure that a user has entered cost
information for at least two groves before he is allowed
access to the data summary and comparison program. And
finally, passwords along with grove ID numbers are used as a
means of identifying groves in the database.
The COINS Database
Database Structure
The first step in developing a database to hold citrus
production costs, was to establish a list of grove practices.
Such a list was already available in Muraro's surveys. Muraro
listed ten major grove practices (cost categories), each with
its own list of sub-categories. Using Muraro's list as a
basis, a modified set of cost categories and sub-categories
was created. Each category had five different types of costs
associated with it; overall, labor, machinery, management,
and material costs. Database files used for cost information


33
Figure 4-1
COINS Program Structure


34
storage were designed for maximum flexibility. They allow the
user to enter data at various levels of detail based on the
amount of cost information available. This is achieved by
arranging production cost categories in a hierarchical form.
There are ten main cost categories, namely cultivation,
dusting, spraying, frost protection, young tree care,
irrigation, removing trees, fertilizing, pruning, and other
operations. Each category has one or more levels of sub
categories associated with it (Figure 4-2).
The ten main cost categories appear on the first screen
of the data entry or summary programs. This level of
categories in the hierarchy is referred to by the database as
level 1. Each level 1 main cost category has at least one
level of sub-categories. These subcategories are referred to
as level 2 and level 3. If more detailed information is
available on a particular grove practice, the user may expand
to a sub-category level and enter or view cost information.
Data Storage
Information on grove characteristics and grove costs are
stored in twelve separate data files. One file is specific to
grove characteristics, and is used for verifying passwords, ID
numbers, and to reference information. Production cost
information is stored in eleven main data files; one level 1
file, and ten level 2 files. dBXL data file structures are
shown in Appendix A.


35
- CULTIVATION-
-Hand Hoe/Hand Labor
-Machine Hoe
-Rotovate
-Disc
Mow
-Herbicide
-Temick/Nemacur
- Plow
-Backhoe
Water Furrow Disc
Water Furrow Cleaner
Vine Puller
Miscellaneous
7"
9-10"
Offset/Side
-5-1'
-9-10'
-15-16'
V-Mower
In & Out
Sickle
- DUSTING
Ground Application
Aerial Application
Spot Herb.
Strip/Band
Trunk-Trunk
Chem. Mow
- SPRAYING
- FROST PROTECTION
Ground Application
Aerial Application
General Costs
Wind Machines
Bank/Unbank Trees
Tree Wraps
Grove Heaters
Other Methods
- YOUNG TREE CARE
Reset Care
Planting Trees
Irrigation
Ring Trees
Fertilizing
Dilute
-i 2X
3X
4X
6X
10X
15X
Hand Spr.
Boom Spr.
Other
Fixed Wing
Helicopter
Other
Hand Spreader
Fert. Spreader
- IRRIGATION
General Costs
Repair & Maintenance
Inject Chemicals
Water Pump
Water Slinger
Water Truck
Water Trencher
Rotary Ditcher/Auger
3-Wheeler for Micro.
Miscellaneous
Perm. Overhead
Traveling Vol.
Stationary Vol.
Perfor. Pipe
Microsprinkler
Drip System
Frtil.
Herbicide
Pesticide
Fungicide
Hand Labor
Mechanical
Figure 4-2 Production Cost Hierarchy


36
- REMOVING TREES
Tractor
Bulldozer
Front End Loader
Miscellaneous
- FERTILIZING
Liquid Boom Appl.
Dry (Bulk)
Dry (Bag)
Liquid Nitrogen
Lime and Dolomite
Miscellaneous
Nitrogen
Mix-Fertilizer
Mix Fertilizer
& Herbicide
- PRUNING
General Pruning
Hedging
Topping
Removing Brush
Miscellaneous
Sing
!.e Side
Tractor
Self Prop.
- OTHER OPERATIONS
General Repairs
Mise. Grove Care
Energy Costs
Ground Bypass
Truck with Driver
Tractor with Driver
Power Saw
Front End Loader
Push Brush
Bush Hog
Rotary Ditcher/Auger
Mound Builder
Middle Buster
Skilled Labor
Mechanic Labor
Supervision Costs
Contracted Services
Accounting Services
Miscellaneous
Double Sided
Tractor
Self Prop.
Tractor
Self Prop.
Haul Out Of Grove
Chop Brush
Figure 4-2Continued


37
Supporting database files hold information on fruit
varieties, grove locations, number of categories and sub
categories, and sub-category locations within the database.
Other files include index files and query files. Each cost
information data file has a corresponding temporary file.
Temporary files are used for data entry and modification, and
for data storage after summaries are generated. All user
interaction with the database is done through the temporary
files. This allows for quicker access, and protects the
integrity of the main data files.
When the data entry program is run, appropriate
information from the main data files is copied into the
temporary files. Hence, if information for a new grove is
being entered, information for a grove with costs set to zero
(default grove) is copied from each main data file into its
corresponding temporary file. Upon completion of data entry
and data modification, cost information is appended to the
main data files from the temporary files. On the other hand,
if information from an existing grove is needed, corresponding
cost information for that grove is copied from the main data
files into the temporary files, and displayed for editing.
When editing is complete, the new information on the grove
replaces the old data in the main data files.
Temporary data files are also used when the data summary
program is run. Cost information from the main data files for


38
all groves to be included in the summary is averaged, and the
results placed in the temporary files for viewing.
Data Entry Program
Program Pesian
The data entry program was designed to look and function
like a spreadsheet (Figure 4-3) A cursor bar is used to
select between ten production cost categories. A menu bar is
used to select among ten available functions. Functions are
used to access the different features of the program. To
select a function, the user moves the cursor bar and presses
the enter key. Alternatively, a function may be selected by
pressing a specific highlighted character found in each
function name. An explanation line below the menu options
helps to identify what each function does. The following is
an explanation of the ten functions.
Program Functions
Edit. Data is entered using the edit function. When
edit is selected, the production category on the highlight bar
is highlighted in red, and a help window appears at the bottom
of the screen. Costs for labor, machinery, management, and
materials may be entered for each category. When the editing
is concluded, values in the separate columns are summed, and
the results are placed in the overall cost columns. The
automatic summation may be suppressed by entering a value for


39
Variety:Hamlins,Valencias. Market:Fresh/Process. ID: 1
Grove Practice
Overall
Labor
Mach.
Mngmt Material
Cost
Cost
Cost
Cost
Cost
Cultivation
0.00
0.00
0.00
0.00
0.00
Spraying
0.00
0.00
0.00
0.00
0.00
Dusting
0.00
0.00
0.00
0.00
0.00
Fertilizing
0.00
0.00
0.00
0.00
0.00
Irrigation
0.00
0.00
0.00
0.00
0.00
Removing Trees
0.00
0.00
0.00
0.00
0.00
Pruning
0.00
0.00
0.00
0.00
0.00
Young Tree Care
0.00
0.00
0.00
0.00
0.00
Frost Protection
0.00
0.00
0.00
0.00
0.00
Other Operations
0.00
0.00
0.00
0.00
0.00
New Data File
Edit Help Mode
Product Back
Expand
Return
Locate
Update
Comment
Explanation Enter costs
for this category.
Figure 4-3
Example Initial Data Entry Screen


40
the overall cost, or by using the mode function explained
below.
Help. The data entry program features an on line help
utility. When the help function is selected, an options
window appears and allows the user to select among several
help topics (Figures 4-4a and 4-4b). Help topics include an
introduction to the purpose and uses of the program, a short
tutorial, an overview of the database structure, an
explanation of the ID number to identify groves, and lastly a
help utility that explains each function (Figure 4-5). The
user may also obtain help on functions by moving the menu bar
to the desired function, and pressing the FI key.
Mode and Update. The mode function allows switching
between auto-calculate and manual modes for column summation.
When the program starts up, auto-calculate is on, and whenever
separate costs for labor, machinery, management, or materials
are entered, the program automatically adds these costs, and
places the total in the overall column. The user may select
manual recalculation in order to enter separate costs for the
labor, machinery, management, and materials, as well as a
separate overall cost. This feature is important when the
user reports partial costs for a category, and the overall
cost does not equal the total of the separate costs.
The update function is used to total the costs in the
separate columns when mode is set to manual. The user may


41
Help Menu
General Help Utility.
Help With Functions.
Quit Help.
Use the arrow keys to select a topic and
press . Press to quit.
Figure 4-4a
Main Help Menu


42
Help Menu
General Help Menu
=> What Does This Program Do ?
Getting Familiar With The Program.
Database Structure.
The Grove ID Number.
Quit The General Help Utility.
Use the arrow keys to select and press .
To return to the Main Help Menu press ESCAPE.
Figure 4-4b
General Help Utility Menu


43
Help Menu
Available Functions
Edit
Mode
Expand
Return
Locate
Update
Comment
Product
Back
Press the appropriate letter for a function
to select it. Press to quit.
Figure 4-5 Function Help Menu


44
wish to total all costs, or just those in the current cost
category.
Expand and Return. Each of the ten main cost categories
is divided into one or more levels of sub-categories. The
expand function is used to access the sub-categories for more
detailed data entry. The return function allows the user to
exit from a sub-category screen and return to a higher level
in the hierarchy.
Locate. The locate function serves two purposes. The
first is to show a list of sub-categories available for a
particular cost category. This serves as a "map" for the
operation hierarchy (Figure 4-6). The second usage of the
locate function is an index for all grove practices available
in the database. This function is used to locate the main
category under which a particular grove practice is found.
For example, to find the main cost category for 'Energy and
Fuel Costs', the user searches through the index for the word
mowing. The index will indicate the main category as being
'Other Operations' (Figure 4-7).
Comment and Product. The comment function allows the
user to enter a comment for each cost category. Comments may
be used as notes or reminders. Similarly, the product feature
allows the user to leave a description of the product name, or
material name used in a particular grove practice. This
feature may be important if the program is expanded to include


45
View
Of Operation Hierarchy
Level 1
Level 2
Level 3
Cultivation
Hand Hoe/Hand Labor
Offset or Side
Spraying
Machine Hoe
5-7'
Dusting
Rotovate
9-10'
Fertilizing
Disc
15-16'
Irrigation
MOW
V-Mower
Removing Trees
Herbicide
In & Out
Pruning
Temick/Nemacur
Sickle
Young Tree Care
Plow
Frost Protection
Backhoe
Other Operations
Water Furrow Disc
Water Furrow Cleaner
Vine Puller
Miscellaneous
Figure 4-6 Example for the Locate Function for Sub
categories Under the Level 1 Cultivation
Category, and Level 2 Mow Sub-category.


46
Cost Categories
Use the arrow keys
to browse the list.
=> Energy and Fuel Costs
Fertilize
You may also search
Fertilize Young Trees
by pressing the first
Front End Loader
letter of a desired
category.
Press to
return to the cost
screens.
Frost Protection
Ground Bypass
Grove Heaters
Hedging
Herbicide
Hoe
Inject Chemicals Irr.
Irrigate
Main Cost Category
> Other Operations
Figure 4-7
Example for the Find Function for Energy and
Fuel Costs.


47
collection of names of pests, weeds, chemicals, or other
products used in the citrus operation.
Back. This function is used to return to the COINS main
menu. When the back function is used, all the main category
files are updated using the costs entered during the session,
and control is given to the main menu.
Entering Costs for a Grove
The following section describes the procedure for
starting and using the data entry program to input or edit
costs for a grove. Three options are available when the
program is started. The user may either define and enter
costs for a new grove, view or edit information for an
existing grove, or delete one of his groves from the database.
Each grove is assigned a grove ID number which is
utilized by the user and by COINS to identify the grove.
Along with the ID number, a grove can be identified by the
user's password. The user must have both the a password and
an ID number to enter or edit costs for a grove.
Editing an Existing Grove. If the user chooses to view
or edit information for an existing grove, the program
displays a menu of ID numbers corresponding to available
groves, and prompts for a selection (Figure 4-8). By moving
the menu selection bar to each ID number, the characteristics
of each grove are displayed. When a grove is chosen, the
program searches the database files, retrieves, and displays


48
the cost information found. It is important to note here,
that only those groves associated with the user's own password
can be accessed.
Defining a New Grove. To enter costs for a new grove,
the user must define the characteristics of the grove. These
include size in acres, fruit varieties, fruit market, yield in
boxes of fruit, grove location, and grove ID number. A grove
definition screen is used to enter the necessary data (Figure
4-9) A list of options is available from which the user
selects fruit varieties and grove location (Figures 4-10a and
4-10b). Choosing from a menu of options allows easy
selection, and ensures that no discrepancies occur.
Once the information on a grove is complete, the user is
prompted to enter a grove ID number. This number is unique to
the user's password. Two or more groves with the same ID
number may exist as long as they were defined by different
users.
Entering and Modifying Data. When the spreadsheet is
displayed, the user may enter or change data at any level in
the hierarchy. The arrow keys are used to move around the
spreadsheet, and a menu bar is used to select the functions
described in the previous section. When entry or modification
is complete, the data entry program updates all files, and
returns control to the main menu.
Deleting Grove Information. Often times a grove is no
longer active, or no longer belongs to the person who entered


49
Florida Citrus Production Cost Survey Program
Grove Description
Grove size (acres) ... 5.00
Variety ... Hamlins, Valencias
Yield in boxes .
3745.00
Price per Box ($;
Grove location .
Grove ID number
Figure 4-8
Example of Grove Selection Screen for Viewing
and Editing Costs


50
Florida Citrus Production Cost Survey Program
Grove Description
Grove size (acres) ... 0.00
Variety ...
Yield in boxes .
0.00
Price per Box ($]
0.00
Grove location .
Grove ID number
Figure 4-9
Example Grove Definition Screen


51
Florida Citrus Production Cost Survey Program
Grove Description
Grove size (acres) ... 5.00
Variety ... |
Yield in boxes 0.00
Price per Box ($) 0.00
Grove location
Grove ID number 0
Options
Hamlins
Valencias
Navels
Temples
Early/Mid
Minneolas
Lees
Murcotts
Novas
Marsh
Robinsons
Duncan
Figure 4-10a Fruit Variety Selection Options


52
Florida Citrus Production Cost Survey Program
Grove Description
Grove size (acres) ... 5.00
Variety ... Hamlins, Valencias
Yield in boxes .
2500.00
Price per Box ($;
7.63
Grove location .
Grove ID number
0
Options
Ridge
Interior
Ind. River
Southwest
North
Central
Other
Figure 4-10b Location Selection Options


53
its costs into the system. A grove may be sold, or in light
of the recent freezes, a grove may simply no longer be
productive. In such cases the user may wish to remove this
grove from the database. When the grove deletion option is
chosen, the user needs only to specify the grove ID number,
and the program will remove all costs and grove information
from the database.
Data Summary and Comparison Program
Program Pesian
Like the data entry program, the data summary program was
designed to look and function like a spreadsheet (Figure 4-
11) The main difference between the two programs is that the
data summary program does not support any editing features.
It is simply a tool for averaging costs, and displaying the
results. As in the data entry program, a cursor bar and a
menu bar are used to select categories and menu options
respectively.
The summary screen provides average per acre costs and
returns for the ten main cost categories, as well as both
levels of subcategories. Per acre average labor, machinery,
management, and material costs are also displayed.


54
Averaged Annual Per Acre
Your Grove #: 40
Costs and Returns
Cost Item
Range of Costs
Average
four Grove
Revenue
2786.84
5055.75
Cultivation
76.70 to 194.36
142.26
194.36
Spraying
138.20
to
183.00
167.36
180.88
Dusting
0.00
to
0.00
0.00
0.00
Fertilizing
69.47
to
241.22
173.12
241.22
Irrigation
5.14
to
261.65
169.86
261.65
Removing Trees
0.00
to
1947.98
978.46
1947.98
Pruning
0.00
to
472.70
472.70
0.00
Young Tree Care
71.14
to
270.98
138.48
71.14
Frost Protection
0.00
to
72.08
37.32
2.56
Other Operations
26.55
to
48.44
35.20
26.55
Total Per Acre Expenses
2279.56
2926.34
Per Acre Net Income
507.28
2129.41
Help Options Labor Machinery Management Material
Locate Expand Return Back
Explanation Access the help utility.
Figure 4-11
Example Cost Summary Screen


55
Program Functions
For the purpose of avoiding redundancy, only those
options that differ from the data entry program menu will be
discussed.
Help. The help option is similar to that in the data
entry program, but differs in the contents of the tutorial,
and explanation of functions.
Options. This function accesses a decision support
system discussed in Chapter IV of this dissertation.
Labor. Machinery. Management. Material. When these
options are used, the screen switches from displaying per acre
overall costs and returns to a screen showing only the
selected separate cost.
Cost Averaging
Li et al. (1990) discuss two major methods of retrieving
database information. One is a navigation-based method
whereby the user uses a series of menus to travel from one
area of the database to another. The second method is the
query based access to information. This method uses the users
questions to retrieve information. The COINS data summary and
comparison program uses the data query method to retrieve the
necessary data from the cost databases.
The program requires the user to specify parameters which
determine the types of groves that will be used in the
summary. Maximum and minimum grove sizes, fruit types, fruit


56
market (fresh or process), as well as location of the groves
must be specified (Figure 4-12). The query may be either very
general whereby only one or a few parameters are specified, or
very specific. In this way the user can tailor the cost
comparison to his own needs and interests. The program also
allows the user to specify a grove to compare to the averages.
If a grove is specified, per acre costs for that grove will be
displayed alongside the averages in the summary screen.
Once the query is defined, the program searches the
database for all groves fitting the query and averages the
costs found. All overall, labor, machinery, management, and
material costs for each category are averaged separately.
Data on highest and lowest costs in each category is
maintained and later displayed as a range of costs.
The program is designed to carry out a data summary only
if three or more groves fit the query. This protects the
confidentiality of the data, and prevents the user from
"extracting" costs for a single grove in the database by
specifying its exact characteristics.
Once the summary screen is displayed, the user may choose
to explore the average values in the different levels of the
hierarchy as well as view the individual labor, machinery,
management, and material per acre costs. A data analysis
option allows the user to compare his costs for an operation
to the averaged values, and invoke an expert system that aids


57
Florida Citrus Cost Reporting Program
Grove Information
Minimum grove size ... 0.00
Maximum grove size ... 5.00
Variety ... Any Variety
Grove location Any Region
Grove ID number 40
Cost data is being prepared Wait
Groves
Found
Found 3
Averages:
Size =
5.00 acres
Yield =
2314.67
Price/
Box =
$ 6.02
Figure 4-12
Example of Query Definition Screen During Data
Averaging


58
in the analysis and gives and management recommendations,
analysis option will be discussed in more detail later.
The


CHAPTER V
SIMON SYSTEM FOR INTEGRATING
MANAGEMENT AND COST INFORMATION
A system was developed to integrate management and
extension recommendations with production cost information.
The System for Integrating Management and Cost Information
(SIMON) utilizes an expert system to access a database of
management information for a particular grove practice. Based
on each recommendation, the system determines an ideal cost
for a particular situation and compares that cost to actual
expenditures reported by the grower.
The SIMON concept can be applied to many area of the
citrus operation, or to other crop systems in addition to
citrus. But for the purpose of this project a prototype
system was developed to give management recommendations for
herbicide application and weed control on citrus. The user
defines a weed problem, by identifying categories and names of
weeds. These are chosen from a list of weeds commonly
associated with citrus. The program then searches a database
for appropriate spraying recommendations. The expert system
asks the user a series of guestions to determine the proper
application rates and application times based on conditions
particular to his operation. After a herbicide recommendation
is given, the user has the option of determining the cost of
59


60
controlling the weed problem defined, and making a comparison
to costs in his grove. The system may be used in conjunction
with the COINS data summary program or as a stand-alone system
for citrus herbicide and weed control information.
The following section discusses the herbicide prototype
program and the SIMON concept in more detail. System
development, program execution, integration with the COINS
data summary program, and evaluation will be described.
Program Development
The benefits of an expert system based program for
management recommendations give it a clear advantage over
traditional methods of conveying management recommendations
(Linker, et al.). Information in the system's database can be
easily updated with current information on weeds and
herbicides. Also, the system can narrow down the number of
available recommendations for a particular problem and rank
them by order of effectiveness. This greatly reduces
extension agents' and grower' time and effort in searching
through a long list of recommendations.
There were some advantages to using herbicide application
as a grove practice for the prototype system. Herbicide
application is a year round operation that must be carried out
regardless of exogenous factors such as weather conditions.
Also, the number of weeds most commonly associated with
citrus, and the herbicides recommended for use on Florida


61
citrus are limited in number. Some factors such as soil type,
severity of weed infestation, and in some cases grove location
and rainfall, influence the herbicide application operation.
Nagarajan et al. (1987) say that weed management involves a
number of strategic and technical decisions, such as chemical
control (pre-plant, pre or post emergence) with a choice of
chemicals and formulations. But once these factors were
identified, almost ideal conditions existed for the
development of an expert system based program.
Program Features
The SIMON concept was used in the development of the
prototype herbicide program to integrate management
recommendations with costs for herbicide application. The
program enables the user to abstract an appropriate herbicide
recommendation from an information database, given a
particular weed problem. A cost information summary is then
given to estimate the expenses that may be incurred if the
recommendation is followed. The program also serves as a
directory, providing information on weeds controlled by each
herbicide. Another feature of the program allows the user to
compare the effectiveness and related costs of using several
herbicides on a particular weed problem.


62
Program Development Environment
SIMON consists of two segments; a herbicide program which
includes the user interface, and an expert system which
determines the correct recommendations for herbicide
application. The following section discusses the programming
environment used for development.
The main herbicide program, like COINS, was written using
dBXL's own application development language. Its design
targets the novice user with little or no computer experience.
To run the program, it is only necessary for the user to
follow instructions in a window at the bottom of each screen.
Expert System Environment
Expert System Shells. Expert systems are usually
developed using a programming environment called a "shell".
Although expert systems can be written in any language, it is
often easier and more efficient to use an expert system shell.
Shells provide comprehensive environments for the development
of rule based expert systems. Expert systems developed using
shells utilize an elaborate process of deductive reasoning
called an inference engine to arrive at a conclusion from a
set of circumstances.
In the case of the expert system portion of SIMON, using
a shell would have proved to be somewhat disadvantageous. The
program was intended to be distributed to growers in Florida
along with the complete COINS system. Most software companies


63
do not offer unrestricted distribution rights for applications
developed using their expert system shells. It is often the
case that costly licenses must be purchased for this purpose.
Another disadvantage to using shells was that expert
systems must be run through the development environment with
which they were written. This increases memory requirements
of the programs being run. Also, expert systems usually
interact with other supporting programs in order to swap
information, and gain access to functions not supported by the
shell used for development. For the herbicide expert system,
a database environment would have been needed to store cost
information and make data queries. Running the expert system
separately from its supporting programs would have
necessitated the exchange of large amounts of data between the
two systems causing a decrease in program efficiency, and
requiring an additional amount of memory.
Maintenance of the expert system was a very important
factor to consider. Love (1988) discusses that maintenance of
the expert system may be quite different from that of the
decision support system. If the expertise changes, whether
evolutionary or revolutionary, the developers of an expert
system must consider methods to help assume that the system's
inference is timely. He further states that while the need to
provide expert system maintenance is important for all expert
systems, it may be particularly appropriate to economics
related expert systems. The changing nature of herbicide


64
formulations, recommendations, and effectiveness on weeds,
necessitated high maintenance requirements, and meant that
herbicide and weed data needed to be readily accessible, and
easily modifiable. It was therefore necessary to ensure that
the rules used by the expert system to arrive at
recommendations, and the recommendations themselves, were not
embedded in the program code. Expert systems developed with
shells usually contain all rules and recommendation within the
program code, and hence, any changes that need to be made
would require the program code to be rewritten and recompiled.
This process placed the use of shells at a disadvantage due
their inefficient maintenance requirements.
A custom designed expert system environment was used for
SIMON. The environment utilizes a unique method of rule
storage and inferencing, whereby rules are stored as records
in a database. The concept involved is fairly simple, and is
flexible enough to be applied to virtually any application
requiring a simple rule based expert system.
Environment Advantages. The environment used for SIMON
was developed using dBXL, allowing the expert system to be
compiled and run in conjunction with the rest of SIMON and the
main COINS program. This keeps memory requirements down, and
allows license-free distribution of the program. The expert
system consists of an algorithm that carries out the
simplified inferencing and deduction process, and database
files containing rules and recommendations. The two are


65
completely separate, allowing modifications to be made to the
data files independently of the program code. As new or
changed information on weeds or herbicides is made available,
the data files can be changed by using the dBXL database
environment, and reguires no revisions to be made to the
program code.
Since the expert system part of SIMON is written in dBXL,
it is able to take full advantage of dBXL's functions and
ability to efficiently and easily manipulate large amounts of
information.
Expert System Rule Base
dBXL database files consist of a series of fields and
records. Much like a spreadsheet, fields and records can be
thought of as columns and rows of information. Rules are
stored as records in the database. Each record consists of
three main fields (Figure 5-1); rule number, condition, and
recommendation fields. Rule numbers for each herbicide are
divided into one or more levels of detail allowing easy
referencing. The first level of rule numbers is a whole
number. Rules with decimal values are the next level rules
for a particular condition. File formats for the herbicide
and weed guide are shown in Appendix A.
The deduction process involves an iterative process of
search and selection until a recommendation is found. First,
the expert system displays all condition fields for records


66
NUMBER
CONDITION
RECOMMENDATION
19.00
Areas receiving more than 20"
average annual rainfall
NEXT 19.10
19.00
Areas receiving less than 20"
average annual rainfall
NEXT 19.20
19.00
WEED G-H,
Recommendation
19.10
New plantings.
NEXT 19.11
19.10
Non-bearing established plantings.
Recommendation
19.11
Soil texture is coarse.
Recommendation
19.11
Soil texture is medium
Recommendation
19.11
Soil texture is fine
Recommendation
19.20
New plantings.
NEXT 19.21
19.20
Non-bearing established plantings.
Recommendation
19.21
Soil texture is coarse.
Recommendation
19.21
Soil texture is medium.
or Soil texture is fine with 2-5%
organic matter.
Recommendation
19.21
Soil texture is fine.
or Soil has 5-10% organic matter.
Recommendation
19.21
Soil has 2-5% organic matter.
Recommendation
19.21
Soil has 5-10% organic matter.
Recommendation
Figure 5-1
Example Rules Used for the Herbicide Treflan


67
with first level rule numbers. The user is asked to choose a
condition from the list. The recommendation field for the
chosen condition will either direct the program to search for
another set of conditions, or will contain a recommendation.
In the case of the latter, the program terminates the search,
and displays the recommendation. If a second set of
conditions exist, they are displayed based on their rule
numbers, and the user is asked to choose again. This
procedure is repeated until the program comes across a
recommendation. The program can also accommodate special
cases for rules where only a particular situation within a set
of rules requires a specific recommendation. This is the case
when a particular weed reguires a specific or unique
recommendation. A flow chart illustrating this deduction
process is shown in Figure 5-2.
Knowledge Acquisition
Identifying the Experts
The methods by which the program arrives at management
recommendations are based on the thought processes used by
experts in the field of weed science and particularly in the
area of citrus production. The first step in the design of
the program was to identify the experts.
Two citrus herbicide experts were identified. Dr. Megh
Singh and Dr. David Tucker are both researchers at the
University of Florida's Citrus Research and Education Center


68
Figure 5-2
Flow Chart Illustrating the Deduction Process
Used by SIMON


69
in Lake Alfred, Florida. Both experts had worked closely with
citrus growers and extension agents, and were familiar with
situations that may arise concerning weed infestation in
citrus. They were also current on developments in the
herbicide industry, and hence they provided the complete
knowledge required to develop the system. The herbicide and
weed guide also relies heavily on published materials for
information. However, even with the immense amount of
information available in the form of extension guidelines, the
actual application of this "domain knowledge" to specific
situations had been (and still is) provided primarily by weed
specialists (Holt, 1988).
Preliminary Interview
Determining how the experts arrived at a conclusion from
a series of circumstances was the next logical step in the
program design. Due to the distance between the programmer
and the experts, personal interviews were limited to a
preliminary interview and several follow up interviews. All
other consultations were made over the phone, and through the
Institute of Food and Agricultural Sciences' (IFAS) electronic
mail system. Beck et al. (1987) mention that in general,
extension specialists cannot spend significant portions of
their time working on an expert system project. Furthermore
the experts and engineers are typically quite distant
geographically and cannot meet for intensive interviews on a


70
regular basis. The purpose of the preliminary interview was
to allow the experts to discuss freely the pertinent
considerations of the system (Lacey et al., 1989).
During a preliminary interview the experts were given a
general idea of the concepts behind COINS. A discussion
followed on the steps involved in the development of SIMON,
and the herbicide expert system. Requirements and limitations
of the herbicide expert system were also discussed. It was
determined that only around twenty herbicides were recommended
by I FAS for use on Florida citrus. These herbicides were used
as the basis for the system's herbicide database. It was also
decided that only a certain number of weeds commonly occurred
on Florida citrus, and that the weed database should be
limited to these weeds.
Sources of Information
Information on weed names and classifications were
derived from chemical manufacturers' publications as well as
independent studies conducted by the experts and the herbicide
manufacturers. A list of herbicides recommended for use on
citrus was obtained from the 1990 Citrus Spray Guide (IFAS
Publications, 1990).
The expert system rules used to arrive at the proper
recommendations were derived from several sources. The main
source of the spray recommendations, including rates,
scheduling, and other information, was the 1989 Crop


71
Protection and Chemicals Reference (CPCR). The CPCR contained
product labels for all the recommended herbicides. Product
labels contain all necessary information needed to use each
product, and are used by the grower as a reference.
Additional spray recommendations were obtained from the Citrus
Spray Guide.
Establishing the Decision Process
It was necessary to establish the thought process that
the experts used to analyze a particular weed problem.
Following a discussion with the experts a decision tree was
drawn up that describes the logic used (Figure 5-3).
The first step involved the definition of a weed problem.
Categories as well as names of weeds were selected. Weeds in
three categories, namely grasses, broadleaf weeds, and vines,
were selected to constitute a weed problem. Weed categories
and weed names are listed in Tables 5-la, 5-lb, 5-lc. The
next step was to identify what herbicides control the weeds
selected. This is done by referring to the herbicide product
labels or the citrus spray guide. If no herbicides were found
to control all weeds, the next best herbicides that control as
many weeds as possible were selected.
When the herbicides identification process is completed,
the herbicides were ranked as to their effectiveness on the
weeds selected. The ranking is based on a susceptibility
table that was developed by the experts in conjunction with


72
the chemical manufacturers. It shows the effectiveness of
each herbicide on weeds in each category (Table 5-2). The
following is an explanation of the five susceptibilities
listed in descending order of effectiveness.
S indicates that weeds are susceptible to the herbicide
at germination, and at early seedling stage of growth. Weeds
may also be susceptible at later stages of maturity.
PS indicates that weeds are susceptible only at
germination. Repeat applications may sometimes control
established weeds.
I denotes intermediate control indicating that the degree
of control will be erratic with some plants within a species
population being killed and others not.
T indicates that weeds are tolerant and either showing no
signs of injury or able to recover from injury symptoms. U
indicates that the susceptibility status is unknown due to
lack of experimental data and reliable field observations.
In order to rate the herbicides' effectiveness on a
combination of weeds, a rating system was established by the
experts and the knowledge engineer. The S, PS, I, T, and U
susceptibilities were given equivalent numerical ratings on a
scale of one to five, with five being the highest rating
corresponding to a susceptibility of S. The ratings for all
the weeds selected were summed and resulted in an overall
rating for each herbicide.


73
Figure 5-3
Flow Chart for Experts' Decision Process


74
Table 5-la List of Weeds by Category, a) grass weeds, b)
broadleaf weeds, c) vines
Weed Name
Scientific Name
Bahaigrass
Bermudagrass
Carpetgrass
Cattail
Crabgrass
Paspalum notatum
Cynodon dactylon
Axonopus affinis
Typha sp.
Digitaria adscendens
Crowfootgrass Dactyloctenium aegyptium
Goosegrass
Guineagrass
Johnsongrass
Maidencane
Napiergrass
Natalgrass
Nutsedge
Pangolagrass
Paragrass
Peppergrass
Sandspur
Signalgrass
Texas Panicum
Torpedograss
Eleusine indica
Panicum maximum
Sorghum halepense
Panicum hemitomon
Pennisetum purpureum
Rhynchelytrum repens
Cyperus rotundus
Digitaria decumbens
Panicum purpurascens
Lepidium virginicum
Cenchrus echinatus
Brachiaria piligera
Panicum Texanum
Panicum repens
Vaseygrass Paspalum urvillei
Yellow Foxtail Setaria glauca


75
Table 5-lb List of Weeds by Category, a) grass weeds, b)
broadleaf weeds, c) vines
Weed Name
Scientific Name
Bitter Mint
Black Nightshade
Brazilian Pepper
Camphorweed
Ceaserweed
Common Purslane
Common Ragweed
Creeping Charlie
Cudweed
Dayflower
Dogfennel
Evening Primrose
Flat-topped Goldenrod
Florida Beggarweed
Florida Pusley
Goatweed
Goldenrod
Horseweed
Jerusalem Oak
Lambsquarters
Lantana
Mexican Tea
Pepperweed
Pigweed
Pokeberry
Primrose Willow
Rouge Plant
Rustweed
Saltbush
Seamyrtle
Skunkweed
Sowthistle
Spanish Needles
Spurge
Swampwillow
Teaweed
Virginia Pepperweed
Waxmyrtle
Hyptis mutabilis
Solanum Nigrum
Schinus terebinthifolius
Heterotheca subaxillaris
Urena lobata
Portulaca olercea
Ambrosia artemisiifolia
Lippia nodiflora
Gnaphalium sp.
Commelina benghalensis
Eupatorium capillifolium
Oenethora sp.
Euthamia minor
Desmodium tortuosum
Richardia scabra
Scoparia dulcis
Solidago sp.
Conyza canadensis
Chenopodium botrys
Chenopodium album
Lantana camara
Chenopodium ambrosioides
Lepidium virginicum
Amaranthus sp.
Phytolacca americana
Ludwigia peruviana
Rivina humilis
Polypremum procumbens
Baccharis halimifolia
Baccharis halimifolia
Achyranthes aspera
Sonchus sp.
Bidens pilosa
Chamaesyce hyssopifolia
Salix nigra
Sida acuta
Lepidium verginicum
Myrica cerifera


76
Table 5-lc
List of Weeds by Category, a) grass weeds, b)
broadleaf weeds, c) vines
Weed Name
Scientific Name
Air Potato
Balsam Apple Vine
Bigroot Morningglory
Brazilian Nightshade
Briars
Calico Vine
Cats Claw Vine
Cypress Vine
Maypop (Passion Flower)
Milkweed (Strangler) Vine
Moonvine
Morningglory
Narrow-Leaf Milkweed Vine
Peppervine
Rosary Pea
Virginia Creeper
Wild Grape
Wild Watermelon (Citron)
Woevine
Dioscorea bulbifera
Momordica charantia
Ipomoea pandurata
Solanum seaforthianum
Smilax sp.
Aristolochia littoralis
Bignonia unguis-cati
Ipomoea quamocilt
Passiflora incarnate
Morrenia odorata
Ipomoea alba
Ipomoea sp.
Cynanchum scoparium
Ampelopsis arbrea
Abrus precatorius
Parthenocissus
quinquefolia
Vitis rotundifolia
Citrullus vulgaris
Cassytha filiformis


77
Table 5-2 Weed Susceptibilities
Herbicide Codes8
Weed Name
A
B
c
E
F
G
H
I
J
Bahaiagrass
S
s
s
I
u
T
S
T
U
Bermudagrass
PS
PS
PS
T
u
s
S
T
U
Common Carpetgrass
S
s
s
I
u
u
S
T
U
Cattail
U
u
u
u
u
u
S
T
U
Crabgrass
S
s
s
s
u
s
S
s
s
Crowfootgrass
S
s
s
s
u
PS
S
s
u
Goosegrass
S
s
s
s
u
s
S
s
s
Guineagrass
PS
PS
PS
PS
u
s
S
s
u
Johnsongrass
PS
PS
PS
PS
u
s
S
PS
PS
Maidencane
PS
PS
PS
PS
u
u
S
u
u
Napiergrass
T
T
T
T
u
s
S
u
u
Natalgrass
S
s
s
PS
u
u
S
u
u
Purple Nutsedge
I
I
I
T
u
T
PS
PS
u
Pangolagrass
s
s
s
I
u
u
s
T
u
Paragrass
s
s
s
I
u
s
s
PS
u
Southern Sandspur
s
s
s
s
u
s
s
PS
u
Hairy Signalgrass
PS
PS
PS
I
u
s
s
PS
u
Texas Panicum
s
s
s
u
u
s
s
PS
u
Torpedograss
PS
PS
PS
T
u
I
PS
T
u
Vaseygrass
PS
PS
PS
T
u
s
s
T
u
Yellow Foxtail
s
s
S
s
u
s
s
PS
I
Peppergrass
u
u
u
u
u
u
u
U
u
Florida Beggarweed
s
s
s
u
u
T
s
T
u
Bitter Mint
s
s
s
u
u
T
s
U
u
Black Nightshade
s
s
s
s
u
T
s
PS
u
Brazilian Pepper
u
u
u
u
u
T
I
T
u
Ceasarweed
u
s
s
u
u
T
s
U
u
Camphorweed
s
s
s
s
u
T
s
U
u
Common Purslane
s
s
s
s
u
T
I
T
s
Common Ragweed
s
s
s
PS
u
T
s
PS
I
Creeping Charlie
s
s
s
u
u
T
I
T
u
Cudweed
s
s
s
u
u
T
s
PS
s
Dayflower
s
s
s
s
u
T
PS
T
u
Dogfennel
PS
PS
PS
PS
u
T
S
T
u
Evening Primrose
u
u
u
u
u
T
s
T
u
a. The letters correspond to the following herbicides
A. Bromacil F. EPTC
B. Bromacil & Diuron G. Fluazifop-Butyl
C. Bromacil & Diuron H. Glyphosate
E. Diuron I. Metolchlor
J. Napropamide


78
Table 5-2Continued
Herbicide Codes8
Weed Name
A
B
C
E
F
G
H
I
J
Florida Pusley
S
S
S
PS
U
T
S
T
I
Flat-Topped Goldenrod
PS
PS
PS
PS
U
T
S
T
U
Goatweed
T
S
PS
PS
U
T
I
u
U
Goldenrod
PS
PS
PS
PS
U
T
S
T
u
Horseweed
U
U
U
U
U
T
s
U
u
Jerusalem Oak
PS
PS
PS
I
U
T
PS
T
u
Lambsquarters
S
S
S
PS
U
T
s
PS
s
Lantana
PS
PS
PS
PS
U
T
s
T
u
Mexican Tea
U
U
U
u
U
U
u
u
u
Pepperweed
S
S
s
s
U
T
s
T
u
Pigweed
T
PS
s
s
U
T
s
PS
s
Pokeberry
S
s
s
PS
U
T
s
U
u
Primrose Willow
U
s
s
u
U
T
s
U
u
Rouge Plant
S
s
s
PS
u
T
u
U
u
Rustweed
S
s
s
u
u
T
u
U
u
Saltbush
T
T
T
T
u
T
s
T
u
Seamyrtle
U
U
U
U
u
T
s
T
u
Skunkweed
PS
PS
PS
PS
u
T
u
U
u
Sowthistle
U
S
S
u
u
T
s
T
u
Spanish Needles
T
S
PS
s
u
T
s
T
u
Spurge
S
S
s
PS
u
T
s
T
u
Swampwillow
U
u
u
u
u
T
s
T
u
Teaweed
PS
s
PS
PS
u
T
PS
T
u
Virginia Pepperweed
S
s
s
u
u
T
s
PS
u
Waxmyrtle
u
u
u
u
u
T
I
T
u
Air Potato
T
T
T
T
u
T
PS
T
u
Balsam Apple Vine
PS
S
s
PS
u
T
s
T
u
Bigroot Morningglory
T
U
T
u
u
T
PS
T
u
Brazilian Nightshade
U
u
U
u
u
T
s
T
u
Briars
T
T
T
T
u
T
I
T
u
a. The letters correspond to the following herbicides
A. Bromacil F. EPTC
B. Bromacil & Diuron G. Fluazifop-Butyl
C. Bromacil & Diuron H. Glyphosate
E. Diuron I. Metolchlor
J. Napropamide


79
Table 5-2Continued
Weed Name
A
B
Herbicide Codes8
C E F G H I
J
Calico Vine
U
U
U
U
U
T
I
T
U
Cat'S Claw Vine
T
T
T
T
U
T
PS
T
U
Cypress Vine
U
U
U
U
U
T
s
T
S
Maypop
PS
PS
PS
I
U
T
s
T
U
Milkweed (Strangler)
Vine
PS
PS
PS
PS
U
T
I
T
U
Moonvine
I
I
I
T
U
T
u
T
U
Morningglory
PS
PS
PS
PS
U
T
s
T
S
Narrow-Leaf Milkweed
Vine
T
T
T
T
U
T
I
T
U
Peppervine
T
T
T
T
U
T
I
T
u
Rosarypea
PS
PS
PS
PS
U
T
s
T
u
Virginia Creeper
T
T
T
T
U
T
PS
T
u
Wild Grape
T
T
T
T
U
T
I
T
u
Wild Watermelon
U
PS
PS
U
U
T
s
T
u
Woevine
T
T
T
T
U
T
u
T
u
a. The letters correspond to the following herbicides
A. Bromacil F. EPTC
B. Bromacil & Diuron G. Fluazifop-Butyl
C. Bromacil & Diuron H. Glyphosate
E. Diuron I. Metolchlor
J. Napropamide


80
Table 5-2Continued
Herbicide Codes3
Weed Name
K
L
M
N
0
P
Q
T
Bahaiagrass
PS
PS
T
I
PS
U
T
PS
Bermudagrass
PS
PS
T
I
PS
I
T
PS
Common Carpetgrass
I
s
U
I
s
U
T
S
Cattail
PS
T
U
s
u
u
T
T
Crabgrass
s
s
I
PS
s
u
s
S
Crowfootgrass
PS
s
I
PS
s
u
s
S
Goosegrass
PS
s
I
s
s
s
s
S
Guineagrass
PS
s
I
s
s
u
PS
S
Johnsongrass
PS
PS
I
PS
s
PS
PS
PS
Maidencane
PS
T
U
T
u
u
T
T
Napiergrass
PS
PS
u
I
PS
u
T
PS
Natalgrass
PS
PS
I
u
PS
u
I
PS
Purple Nutsedge
I
T
I
T
T
u
T
T
Pangolagrass
PS
PS
I
PS
PS
u
T
PS
Paragrass
PS
PS
u
T
PS
u
T
PS
Southern Sandspur
PS
s
I
S
s
u
I
S
Hairy Signalgrass
PS
s
u
S
s
u
PS
S
Texas Panicum
PS
s
I
S
s
s
I
S
Torpedograss
s
PS
I
T
PS
u
T
PS
Vaseygrass
PS
PS
u
PS
PS
u
T
PS
Yellow Foxtail
PS
s
I
S
s
s
S
s
Peppergrass
u
u
u
U
u
u
U
u
Florida Beggarweed
PS
T
I
S
T
u
s
T
Bitter Mint
I
T
u
u
u
u
u
T
Black Nightshade
PS
T
s
I
T
u
s
T
Brazilian Pepper
u
T
u
I
T
u
T
T
Ceasarweed
u
T
u
u
U
u
U
T
Camphorweed
I
T
PS
PS
T
u
U
T
Common Purslane
s
S
s
s
S
u
S
S
Common Ragweed
PS
T
I
s
T
u
s
T
Creeping Charlie
u
T
I
s
T
u
s
T
Cudweed
PS
T
PS
s
T
u
s
T
Dayflower
PS
T
I
s
T
u
T
T
Dogfennel
s
T
u
PS
T
u
PS
T
Evening Primrose
I
T
PS
T
T
u
I
T
a. The letters correspond to the following herbicides
K. Norflurazon O. Pendimethalin
L. Oryzalin P. Sethoxydim
M. Oxyfluorfen Q. Simazine
N. Paraquat Dichloride T. Trifluralin


81
Table 5-2Continued
Herbicide Codes3
Weed Name
K
L
M
N
0
P
Q
T
Florida Pusley
PS
S
S
s
s
U
I
S
Flat-Topped Goldenrod
PS
T
U
u
T
U
u
T
Goatweed
PS
T
U
u
T
U
I
T
Goldenrod
PS
T
PS
T
T
U
s
T
Horseweed
I
T
T
PS
T
U
I
T
Jerusalem Oak
I
T
PS
PS
T
U
T
T
Lambsquarters
PS
S
S
s
S
U
s
S
Lantana
T
T
T
I
T
U
T
T
Mexican Tea
U
U
U
u
U
U
u
U
Pepperweed
PS
T
S
PS
T
U
I
T
Pigweed
I
S
S
s
S
u
s
S
Pokeberry
PS
T
U
PS
T
u
s
T
Primrose Willow
PS
T
U
PS
U
u
u
T
Rouge Plant
u
T
U
u
U
u
u
T
Rustweed
I
T
u
u
u
u
u
T
Saltbush
I
T
PS
PS
T
u
T
T
Seamyrtle
I
T
u
PS
U
u
T
T
Skunkweed
u
T
u
u
U
u
U
T
Sowthistle
I
T
s
s
T
u
S
T
Spanish Needles
I
T
PS
s
T
u
S
T
Spurge
s
S
PS
s
S
u
S
S
Swampwillow
u
T
PS
PS
T
u
T
T
Teaweed
PS
T
S
T
T
u
I
T
Virginia Pepperweed
PS
S
s
PS
U
u
I
S
Waxmyrtle
u
T
u
PS
T
u
T
T
Air Potato
u
T
u
u
U
u
T
T
Balsam Apple Vine
PS
T
PS
PS
T
u
S
T
Bigroot Morningglory
I
T
u
PS
T
u
S
T
Brazilian Nightshade
I
T
u
T
T
u
S
T
Briars
T
T
u
PS
T
u
T
T
a. The letters correspond to the following herbicides
K. Norflurazon O. Pendimethalin
L. Oryzalin P. Sethoxydim
M. Oxyfluorfen Q. Simazine
N. Paraquat Dichloride T. Trifluralin


82
Table 5-2Continued
Herbicide Codes8
Weed Name KLMNOPQT
Calico Vine
Cat'S Claw Vine
Cypress Vine
Maypop
Milkweed (Strangler) Vine
Moonvine
Morningglory
Narrow-Leaf Milkweed Vine
Peppervine
Rosarypea
Virginia Creeper
Wild Grape
Wild Watermelon
Woevine
u
T
U
PS
u
U
T
T
u
T
U
T
u
U
T
T
I
T
PS
PS
T
U
T
T
T
T
PS
PS
T
U
T
T
PS
T
PS
PS
T
U
PS
T
u
T
PS
PS
U
U
T
T
PS
T
S
PS
T
U
I
T
u
T
U
PS
T
U
U
T
u
T
U
PS
U
U
T
T
u
T
U
U
U
U
T
T
u
T
U
PS
T
U
T
T
T
T
U
PS
T
U
T
T
I
T
PS
S
T
U
T
T
T
T
U
u
U
U
U
T
a. The letters correspond to the following herbicides
K. Norflurazon
L. Oryzalin
M. Oxyfluorfen
N. Paraquat Dichloride
O. Pendimethalin
P. Sethoxydim
Q. Simazine
T. Trifluralin


83
Apart from the susceptibility ratings, herbicides were
classified into three categories. Pre-emergence herbicides
which are used prior to weed emergence to control potential
problems. Post-emergence herbicides which are used to control
established weeds. And lastly herbicides that offer both pre
emergence and post-emergence control. The classification is
used to determine which types of herbicides are suited for a
particular situation.
After rating and classifying the herbicides, a selection
among alternatives was made. The experts agreed that once all
recommended herbicides have been presented to the user, it is
up to that user to choose which herbicide to utilize. They
were not willing to specify a particular herbicide to a grower
to avoid showing any type of support or endorsement for one
product.
Following the selection of a herbicide, recommendations
for spraying rates were given based on information found in
the product labels. In most cases more than one
recommendation was listed depending on such factors as types
of weeds, soil moisture conditions, and soil type. The final
recommendation depended on these factors. Spraying cost
estimates were then given based on recommended spray rates,
product costs, and treated acreage.


84
Follow-up Interviews
Two follow-up interviews were conducted throughout the
program development process. The program was evaluated by the
experts at each follow-up interview with special emphasis on
the new features added since the last interview. Ideas on
improvements and enhancements were exchanged. During the
final follow-up interview, a complete list of program features
was established, and updates on herbicide and weeds
information were made. Once all enhancements and features in
the list were carried out, the final version of the program
was ready for evaluation.
Program Execution
The program proceeds in one of three ways; a herbicide
recommendation is given based on a weed problem, a list of
weeds controlled by a herbicide is identified, or two or more
herbicides are compared as to their effectiveness on a weed
problem.
Obtaining a Herbicide Recommendation
The user defines a weed problem by choosing weeds from
lists in the grass, broadleaf, and vine categories. The
program searches for herbicides that control the weeds
selected. If one or more herbicides are found to control all
weeds selected, they are ranked and displayed on the screen
(Figure 5-4). If no herbicides are found to control all weeds


85
Herbicide and Weed Guide
Available Herbicides for All Weeds Selected
Trade Name
Common Name
Rating
Effect.
Roundup
Glyphosate
6
Post
Devrinol
Napropamide
6
Pre
Solicam
Norflurazon
7
Pre
Goal
Oxyfluorfen
6
Both
Gramoxone
Paraquat Dichloride
8
Post
Susceptibility Rating The susceptibility rating
is a measure of the effectiveness of each herbicide
on the weed combination it controls. It is the sum
of the susceptibiltities of each weed to the
herbicide.
Figure 5-4
Example List of Herbicides Available to
Control The Defined Weed Problem


86
Herbicide and Weed Guide
Note No herbicides were found that control
all weeds selected.
The program can now proceed in two different ways:
1 The program will search for herbicides that control
as many weeds as possible from the list of selected
weeds. One herbicides is selected from a list, and
recommendations and spraying information are given
for that herbicide.
2 The program will search for individual herbicides
for grass weeds, broadleaf weeds, and vines.
Herbicides that control as many weeds as possible
in each category are found. A herbicide is selected
for each category, and recommendations and spraying
information are given for each herbicide.
Figure 5-5
Option Screen When One Herbicide is Not Found
to Control All the Weeds Selected


87
selected, the user is prompted to direct the program in one of
two ways (Figure 5-5). The program can search for the best
herbicide for all weeds selected, or the best herbicides for
weeds in each category. Once herbicides have been identified,
the user may either select a herbicide, or reject the choices,
and direct the program to search for the next best
alternatives. The search continues until the user accepts a
herbicide and requests a recommendation. If herbicides are
recommended separately for each category of weeds, a
combination of two or more herbicides must be used. The
program only lists these herbicides and the individual spray
rates for each. It does not give information on mixing
procedures, compatibility, and precautions. The user must
refer to more detailed information by consulting the product
labels.
Recommendations for all herbicides are stored in a
database, and with the aid of the expert system and some input
from the user, the appropriate recommendation for the selected
herbicide is given (Figure 5-6). At this point in the
program, a cost estimate for the selected herbicide can be
obtained. The total cost for spraying the herbicide at the
recommended rate reflects the material costs for using the
herbicide. Other costs are usually incurred during the
operation such as machinery, labor, and management costs. The
cost per unit of purchasing the herbicide and number of
treated acres to be sprayed are required from the user. If


88
Hyvar X (Bromacil) for use on Texas Panicum
Recommendation
Apply 4-5 lbs. of HYVAR X per acre during the
period from winter to early summer. Alternatively,
make two applications of 3-4 lbs. per acre per
year in spring and summer. Partial control usually
occurs with a single treatment; repeat applications
are required to control perennial weeds. Control
of perennial weeds may be improved by cultivation
prior to treatment; otherwise, avoid working the
soil as long as weed control continues since
effectiveness may be reduced.
Susceptibility Susceptible at germination and
early seedling stage of growth. May also be
susceptible at later stages of maturity.
Figure 5-6
Example Recommendation Screen for Using Hyvar
X Herbicide on Texas Panicum


89
Herbicide Cost Information Krovar I
Cost per gallon for this herbicide ($) 7.35
Number of treated acres to be sprayed 5.00
Rates in pounds per treated acre -
Recommended range 2.00 4.00
Recommended maximum 8.00
Total Cost ($) Range per treated acre 14.70 29.40
Range for this grove 73.50 147.00
Maximum Cost ($) Per treated acre 58.80
For this grove 294.00
Figure 5-7
Example Cost Screen for Krovar I Herbicide


Full Text
UNIVERSITY OF FLORIDA



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INTEGRATION OF DECISION SYSTEMS WITH
PRODUCTION INFORMATION FOR OPERATIONS MANAGEMENT
BY
RAMZI S. KHURI
A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY
UNIVERSITY OF FLORIDA
1990

Copyright 1990
by
Ramzi S. Khuri

Dedicated to
my mother Maha, my father Suhail,
my brother Nizar, my uncle Zahi,
and my best friend Wisky

ACKNOWLEDGEMENTS
I would like to express my deep hearted gratitude to Dr.
R. M. Peart, Graduate Research Professor of Agricultural
Engineering, for serving as a chairman for my graduate
committee. I would also like to extend my appreciation to Dr.
Howard Beck of the Department of Agricultural Engineering for
his knowledge, guidance, and supervision throughout this
project. The friendship, support, and guidance of Dr. W. D.
Shoup of the Department of Agricultural Engineering, his help
in formalizing the project, and his serving as cochairman on
my supervisory committee are highly appreciated. I extend a
special thank you to all those who were involved with the
continued financial support I received for this project. I
would also like to extend my gratitude to Dr. Richard Kilmer
of the Department of Food and Resource Economics and Dr. James
Burns of the Department of Industrial and Systems Engineering
for serving on my committee and for their help on this
project.
Mr. Ron Muraro and Dr. Megh Singh of the Citrus Research
and Education Center in Lake Alfred, Florida, deserve my
special thanks for sparing their time and providing me with
valuable information. I also thank all participants of the
cost surveys, and those who helped evaluate the COINS system.
iv

I would like to express very special appreciation and
thanks to my best friend Betsy, whose patience, encouragement,
friendship, and moral support were indispensable during the
completion of this project. I would also like to thank
Betsy's family for their friendship and continuous
encouragement throughout my studies.
My very special thanks are extended to my uncle Waleed,
my uncle Rabie and his family, and my best friends Sami and
Ghassan for their endless support during my five and a half
years of graduate study. My appreciation is also extended to
all my friends in the United States and abroad.
Last but not least, I wish to express my appreciation to
my grandparents whose confidence in me and continuous support
for my efforts helped me achieve my goals.
v

TABLE OF CONTENTS
pages
ACKNOWLEDGEMENTS iv
ABSTRACT ix
CHAPTER
I INTRODUCTION 1
Justification 1
Objectives 3
II REVIEW OF LITERATURE 6
Management Information Systems 6
Agricultural Information Systems 7
Agricultural Technology Transfer 8
Agricultural Cost Systems 9
Decision Support Systems 11
Expert Systems 13
Expert System Applications 14
Database Management Systems 17
Database Design 17
Data Security 19
Integration of Knowledge Systems
and Databases 20
Summary 23
III SYSTEM DESIGN CONSIDERATIONS 24
Preliminary Research 24
Existing Information Sources 24
Existing Agricultural Software 25
Design Requirements 26
The User Interface 2 6
System Hardware and Software
Requirements 27
Functional Requirements 28
IV COINS - CITRUS COST INFORMATION SYSTEM 29
An Overview of COINS 29
DBXL as a Database Management and
a Programming Environment 30
vi

pages
Program Development Tools 30
Program Requirements 31
Program Structure 31
Password System 32
The COINS Database 32
Database Structure 32
Data Storage 34
Data Entry Program 38
Program Design 38
Program Functions 38
Entering Costs for a Grove 47
Data Summary and Comparison Program 53
Program Design 53
Program Functions 55
Cost Averaging 55
V SIMON - SYSTEMS FOR INTEGRATING MANAGEMENT
AND COST INFORMATION 59
Program Development 60
Program Features 61
Program Development Environment 62
Expert System Environment 62
Expert System Rule Base 65
Knowledge Acquisition 67
Identifying the Experts 67
Preliminary Interview 69
Sources of Information 70
Establishing the Decision Process 71
Follow-up Interviews 84
Program Execution 84
Obtaining a Herbicide Recommendation 84
Obtaining a List of Weeds Controlled
by a Herbicide 90
Comparing Several Herbicides 90
Integration with COINS 92
VI TESTING AND EVALUATION 96
Phase 1 - Operational Evaluation 96
Testing and Evaluating the COINS
Cost Programs 96
Testing and Evaluation of the
Herbicide and Weed Guide 99
Phase 2 - Professional Evaluation 100
Role of Trade Show Exhibit 100
Role of Citrus and Extension
Specialists 102
Purpose of Evaluation Form 103
Evaluation Procedure 103
vii

pages
Evaluation Results for
Structured Responses 105
Evaluation Results for
Non-structured Responses 110
VII CONCLUSIONS AND RECOMMENDATIONS 122
APPENDICES
A DATABASE FILE FORMATS 126
B SYSTEM EVALUATION 129
REFERENCES 143
BIOGRAPHICAL SKETCH 148
viii

Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy
INTEGRATION OF DECISION SYSTEMS WITH
PRODUCTION INFORMATION FOR OPERATIONS MANAGEMENT
by
RAMZI KHURI
December 1990
Chairman: Dr. R. M. Peart
Major Department: Agricultural Engineering
Production costs for citrus operations in Florida have
been integrated with extension and management recommendations.
This new approach permitted the dissemination of valuable
production, management, and cost information among citrus
growers. It has helped create a dynamic source of information
that would ultimately lead to improved production and
management decision making.
A citrus production cost information system (COINS) with
three components, namely, a cost entry program, a summary and
comparison program, and a decision support system for
extension and management recommendations, has been designed,
developed, and evaluated. The system collects, averages, and
summarizes citrus production costs. It provides a means for
growers to compare their operations with industry averages.
ix

An herbicide and weed guide gives appropriate management
recommendations and associated costs. Citrus growers from
several areas in Florida utilized the system and evaluated its
operational performance and its qualifications as a decision
and management tool. The system's components were found to
operate properly, and the system as a whole was qualified to
be a useful management tool.
The data entry component successfully collected,
organized, and stored production cost information for numerous
grove operations. The summary and comparison component
successfully averaged production costs for specific areas and
types of operations. It allowed for the comparison of
individual grove costs with industry averages. The herbicide
and weed guide integrated cost information with extension and
management recommendations through an expert system. The
successful integration of production costs with management
recommendations demonstrated the system's success as an
effective decision and management tool.
The utilization of a database management environment
provided a comprehensive tool for the development of the
information system. The techniques and design methods used in
development and integration of various components produced a
highly dynamic and flexible system.
x

CHAPTER I
INTRODUCTION
Justification
Citrus growers in Florida are continually striving for
improved methods of production in order to increase yield and
minimize costs. They are challenged by factors such as
weather conditions, consumer demand, market prices, and
foreign competition. They must overcome these obstacles by
using the best production and management practices available.
The cost of producing citrus is partly accrued through
the various grove operations performed during each season.
Production cost records are maintained by most operations as
a means of keeping track of expenditures, and for record
keeping and tax purposes. The amount and detail of cost
information recorded depends largely on the size of the
operation, and on the importance placed on such records by the
operation manager. In some instances grove cost records are
hand written, lack any detail, and are mainly used for year-
end tax purposes. More detailed costs are kept by some
growers who use computers to maintain data on a daily basis.
In some instances, growers owning small operations delegate
the record keeping task to caretaker operations and
cooperatives who usually have the means to maintain large
amounts of data on computer.
1

2
Growers seldom have the opportunity, however, to compare
their production costs with industry averages in a way that
will help identify problem areas. Database systems currently
being used by large operations to help maintain cost
information rarely offer any special data analysis or
comparison features. Most specialty financial software
available for the citrus industry deals strictly with farm
financial records. In most cases the software is used for
record keeping purposes. In some cases financial analysis of
information is performed by the software, and results in an
overview of the operation's financial status, but does not
deal with operational costs of individual grove practices.
Information sharing among citrus growers would enhance
the dissemination of valuable knowledge on production costs
and management practices. A large database system, Florida
Agricultural Information Retrieval Systems (FAIRS) (Johnson
and Beck, 1986), has been developed to provide growers,
researchers, and extension specialists valuable information on
management and extension practices, as well as technical
information on most topics related to citrus. The information
in the database is in the form of text and graphic screens,
and is updated with current data on a regular basis. Other
information sources such as technical publications, extension
specialists, and researchers, also provide growers with a
consistent source of information. According to Smith et al.
(1988) sources of new technology and production methods

3
include farm magazines, discussion with human expert or
neighbor, and extension service.
There is, however, a lack of availability of adeguate
information associated with production costs for citrus.
There is also a need for a means to communicate this
information among growers. Knowledge sharing between citrus
growers is the key to achieving a dynamic database of
information. Using the "dynamic” approach would result in a
more specific and more usable database that would result from
simply keeping publications related to a specialist's subject
matter area on-line (Jones and Hoelscher, 1987) . Furthermore,
no attempts have been made to integrate citrus production
costs with extension and management recommendations. Such an
integration would enhance growers' ability to make better
management decisions.
There is no reference in the literature to a dynamic
agricultural information system where production costs are
collected, pooled, summarized, and linked to appropriate
extension and management recommendations.
Objectives
The general objective is to provide citrus growers in
Florida with a means of improving their production practices
through the enhancement of knowledge and management decision
making, by providing an economic basis for extension and
management recommendations. This is achieved by developing

4
and integrating a computer based citrus production cost
information system with extension and management
recommendations. Information in the system develops and
expands through the addition of new management and extension
information as well as annually contributed production cost
information.
The specific objectives are as follows:
1) to identify and classify grove operations associated
with citrus production in Florida;
2) to design and develop a dynamic database system for
collecting and storing production cost information for Florida
citrus operations;
3) to design and develop a system to query the cost
database, average and summarize costs, and generate per acre
cost and returns reports;
4) to provide a means for comparing individual grove
costs with industry averages to aid in problem area
identification.
5) to design and develop a prototype decision support
system to integrate cost information with extension and
management recommendations.
6) to verify the information system's capabilities to
enhance the communication of knowledge between citrus growers
through the accessibility of citrus production cost
information and related management and extension
recommendations.

5
The remainder of this dissertation is organized as five
more chapters. The review of literature in Chapter II gives
an overview of management information systems and decision
support systems. It also deals with expert systems and cost
systems as related to agriculture.
Chapter III discusses the design requirements of the
system, including preliminary research, hardware, software,
and functional requirements.
The design and development of the information system
consisting of a database system for handling citrus production
costs and a data summary and comparison are discussed in
Chapter IV.
Chapter V deals with the design and development aspects
of the prototype decision support system for herbicide
application. This chapter also describes the means by which
production cost information is integrated with management
recommendations.
Chapter VI reports the results and procedures of the
evaluation and testing of the coins system. The system was
evaluated by various citrus growers, researchers, and citrus
extension specialists.
Finally, Chapter VII presents conclusions and
recommendations for future work in the area of agricultural
information systems.

CHAPTER II
REVIEW OF LITERATURE
Management Information Systems
Drechsler and Bateson (1986, p. 53) define a management
information system as "an information system that provides a
manager with information on the activities and pertinent
interrelations about the current status of the
production/operation system over which he has control". Smith
et al. (1985, p. 2) define management information systems as
"integrated computer based systems which provide information
to support the operational and decision making functions of
management". Murphy (1989) claims that in today's terms, MIS
(Management Information Systems) are simply databases. This,
he says, is an appropriate title since they are storehouses of
all information about a specific subject.
Information systems have become an important decision aid
and management tool for operation managers. They have become
an essential part of many organizations (Smith et al., 1985).
Both decision support systems and expert systems should be
included as subsystems of an overall management information
system concept.
6

7
Agricultural Information Systems
Today's businesses design and develop powerful
information systems for themselves. The farm community
deserves comparable quality power. To achieve this goal,
farmers must become involved in the design and development of
their systems (Murphy, 1989).
Computer-based systems that are designed specifically for
agriculture must meet the farmers' challenge (Stone et al.,
1986). Agricultural information systems may include a
database representing current knowledge about crop systems,
and may have the capability to access historical data records
and the ability to analyze data and expand on the analysis
presented to a user when requested. An information retrieval
system designed for farm use would encompass a collection of
resources including record keeping facilities such as
accounting information and expert systems (Beck, 1988).
The Jackson County Cooperative Extension Service (CES)
office, for example, uses an information management system to
collect, store, and retrieve extension information, and
provide it to clients in the form most suitable for their
needs (Heatley, 1986). The system saves time and effort that
would have otherwise been used to manually order and store
information, and pass out CES publications. The information
management system uses a database management system to
retrieve blocks of text.

8
Agricultural Technology Transfer
Technology transfer from researchers and other
information sources to producers remains inadequate. The main
objective of agricultural research is to improve the
efficiency of farmers by providing them with valuable
information which they need to make cost effective production
decisions (Smith et al., 1985). The technology of information
communication is made possible through management information
systems. Murphy (1989) predicts that in the next decade or
so, companies will put a lot of effort into 'networks' that
link their information sources together. This will ultimately
enhance the information communication process.
The process of knowledge transfer from researcher to
producer can also be achieved with the construction of
interactive computer based decision support systems (Smith et
al., 1985). Such systems use knowledge to create valuable
information. In order for decision support systems to be an
effective communication tool, various components such as
simulation, information analysis, and problem solving models
must be integrated within a single framework which can be
effectively accessed by different user levels. Lai et al.
(1987) state that an effective means of transmitting knowledge
from technology generator to technology user would be a
process that would permit the end user to question and seek
clarification on the recommendations given.

9
Agricultural Cost Systems
The basic purpose of cost systems is to generate cost
information. Cost information may be used to satisfy certain
managerial requirements. One objective of cost systems is to
assist management in controlling costs (Shah, 1981).
Management is in constant need of timely and reliable
information on costs incurred by various responsibility
factors. Such information will provide management with
decision making support, and aid in the process of cost
reduction. Shah continues to say that a major concern of
management is reducing costs while maximizing profits. A cost
reduction process requires knowledge of significant cost
items, the cost of major activities, the identification of
controllable and non-controllable costs, and the effect of
cost reduction in each activity on revenues and profits.
In agriculture, growers must know their production costs
in order to be competitive and profitable (Tripeppi and
Kucher, 1988). Many microcomputer programs have been
developed to provide growers with various types of management
information. In some cases, analysis of information is
provided in order to give management further insight into the
financial position of their operations.
Olson et al. (1986) discusses an annual report that
summarizes individual farm records for farms in South Eastern
Minnesota. The report shows averages as well as high and low
ranges. Whole-farm information as well as enterprise costs

10
and returns are reported. At year end, individual farmers can
compare their operation to the information provided in the
report to find areas that need management attention and areas
which have above average performance. Some computer aided
farm business analyses is performed annually using IBM FINANX
software by the extension service, and a summary of individual
analyses is prepared.
A microcomputer program has been developed to calculate
production costs based on various inputs involved in growing
crops (Tripepi and Kucher, 1988) . The user enters costs for
land, buildings, equipment, general overhead expenses, and
cultural practices. The program then produces cost summaries
for capital requirements, annual fixed costs, variable cost
per hour of equipment, estimated costs of production, and
final price per plant. Using this information, growers can
compare their prices with those of their competitors and can
therefore determine crop profitability. The program saves
hours of manual collating and adding production costs and
eliminates math errors due to human mistakes.
Another computer program, PPCAM was developed to manage
plant production data and enable the user to predict
production and profits (Power et al., 1989). Using input
data, PPCAM generates labor, material, energy, and indirect
cost information. The program can also generate, store, and
retrieve reports, summaries and graphics of various
parameters. PPCAM uses an integrated project-file language

11
using several spreadsheet templates and databases to create a
user friendly environment.
ABC Systems (1989) has created a comprehensive computer
program for agricultural record keeping. The program a mainly
an accounting software package that handles information for
budgets and plans, cash flows, cash forecasts, cost of
production, and offers results and balance sheet analysis.
An economics oriented program is discussed by McGilliard
and Clay (1986). The microcomputer program for decision
analysis (DECAL) was written to provide a relatively easy and
flexible method of analyzing investment decisions. For each
decision, DECAL produces a one page report showing business
measurements of net cash, present, future, period, and annual
values, as well as benefit/cost ratios, return on investment,
and payback period.
Decision Support Systems
Decision support systems are an emerging area of
research. They can combine database management systems and
the branch of artificial intelligence known as knowledge
representation. (Beck, 1988). According to Barrett and Beerel
(1988), conventional computing is concerned with handling
information which might subsequently be used in the decision
making process. Data processing organizes data and transforms
it from one form into another. Here, information is still at
a very basic level. Decision support builds on this by

12
allowing the manager to view data at a level which is
convenient to him.
Producers can use decision support systems to analyze and
help solve agricultural problems (Smith et al., 1985). A
decision support system should be able to provide appropriate
information to decision makers in order for them to operate
from a wider knowledge base than they do at present. A
decision support system discussed by Pruss (1989), utilizes
crop production information in the form of raw data. The
system collects, organizes, and summarizes information which
is later evaluated and interpreted into knowledge used for
crop management decisions.
Decision support systems may include expert systems.
Expert systems help alleviate some of the difficulties of
using decision support systems with a non-expert client.
Expert systems can help users select the most important
information for certain questions (Love, 1988). Expert system
technology can be applied at different points in the decision
support system-user interaction process, data collection,
identifying the problem, interfacing with the decision support
system, guiding the user through decision support system
output, assisting the user in "what if" applications of the
decision support system, helping the user select pertinent
output and decision weights, and analyze output. It is
important to ascertain at what point this application should
occur.

13
Expert Systems
Holt (1988) defines an expert system as a computer
program that enables a computer to mimic the logic of an
expert in diagnosing problems, selecting alternatives, giving
recommendations, and managing operational systems. Expert
systems are rule based and reason from one rule to the next,
gathering information until the system is able to recommend a
decision or provide advice (Helms et al., 1987). Expert
systems are highly interactive and generally easy to use (Lai
et al., 1987). An expert system user can ask the system
questions, change assumptions and even ask for the reasoning
behind answers given.
The biggest barrier to agricultural productivity is the
knowledge gap that lies between researchers and growers. An
expert system approach is ultimately an excellent way to
remove this obstacle (Rudd et al., 1986). Expert systems may
be used to enhance the capabilities of the researchers and
others responsible for the technology transfer process (Lai et
al., 1987).
Expert systems may be used as tools for summarizing
information and knowledge, diagnosing problems, and for
identifying specific objects and conditions such as weeds and
diseases. Expert systems can also be used as a teaching tool
for non-expert users (Holt, 1988).
McKinion and Lemmon (1985) discuss the role of expert
system technology in agriculture. They claim that the first

14
opportunity for using expert system technology in agriculture
is with integrated crop management. Expert systems would take
the form of integrated crop management decision aids which
would encompass such disciplines as irrigation, nutritional
problems, fertilization, weed control, cultivation, herbicide
application, insect control and insecticide and/or nematicide
application.
Expert systems may also be used for economic
applications. Expert systems designed to complement farm
financial records and planning systems hold considerable
promise as decision aids (Love, 1988) . Producers can use
expert system technology to assist in synthesizing information
to decide the financial state and performance of their
operations.
Expert System Applications
System development in agricultural and natural resource
management applications has mirrored the growth in recent
years of the development and use of expert systems in product
design, resource management, and logistics (Lambert and Wood,
1988). A survey of agricultural expert systems currently
under development or available for use was completed. The six
major subject areas surveyed were crop and livestock
production, financial analysis, general shells, marketing,
natural resource management,and other areas. Most of the
applications surveyed are diagnostic in nature. However, a

15
few are dedicated to advising users on a variety of concerns
from irrigation scheduling to grain marketing to enterprise
selection. The survey reveals five programs in the area of
financial analysis.
A Financial Analysis Review System (FinARS) (Boggess et
al., 1989) is written in INSIGHT 2+ (Information Builders,
Inc.). The system provides an evaluation of the financial
health of a farm business. It is designed to provide an
initial assessment of the overall financial health of the
business.
In addition to its capabilities as a diagnostic tool for
farmers to provide initial interpretation of their farm's
financial situation, FinARS can also be used as a tool for
teaching financial analysis concepts to students, county
agents, lenders, and farmers.
Lambert and Wood's (1988) survey also included four other
expert system applications related to economics that are worth
mentioning. A Budget Planner written in PASCAL is an
enterprise budget generator that includes a full enterprise
analysis. A farm financial document analyzer written in LEVEL
5 is designed to assist producers with financial management
decisions for Michigan dairy farms. An agricultural financial
analysis expert system is available to give information on a
farm's current year performance, financial condition, and debt
repayment ability. And finally, a farm loan advisor was
written to evaluate farm loan applications.

16
Helms et al. (1987) discuss a farm level expert system
that provides advice regarding farm program participation and
changes in farm policy variable. The description and
application of the Farm Policy Advisor or FPA was demonstrated
on a Southern Blacklands hypothetical farm in Texas.
Richardson et al. (1989) describe an application whereby
mini-expert systems are called to develop data from user
inputs to be used in a database. A main program CARMS
(computer assisted records management system) allows users to
build data sets needed by CIRMAN (crop insurance risk
management analyzer). The expert system CARMS leads the user
through a series of steps to develop a large sophisticated
database required for a simulation model.
Linker et al. (1990) discuss a herbicide recommendation
program that allows the weed managment expert to build a list
of recommended herbicides or herbicide mixtures based on weed
species and soil type. The program, written in C, allows the
user to obtain a herbicide recommendation from a particular
weed problem.
Batchelor et al. (1989) describe a prototype expert
system developed to aid in soybean insect pest management
decision making. Crop status and insect population
information are provided by the user. The decision support
system, SMARTSOY, runs a crop growth model to determine
subsequent damage to the crop. The crop growth model,
SOYGROW, predicts growth and development and final yield of

17
soybean based on daily weather data for specific soils (Jones
and Hoelscher, 1987) . Once SOYGROW is run, SMARTSOY gives
cost effectiveness of insecticide applications as well as rate
and type of insecticide are given.
Database Management Systems
Lai et al. (1987) state that the utilization of database
management has made a definite contributions to the technology
process. Database management typically involves the mechanics
of storing and retrieving large amounts of data (Beck et al.,
1987). Efficient database design is essential for file
organization, indexing, rapid transaction and query processing
rates, and allowing multi-user access and sharing of data.
Database management also addresses problems such as data
security, maintenance of data accuracy and integrity, and
creating data storage separate from the application programs
which use the data.
Database Design
Proper database design is imperative when creating an
efficient data management environment. Brathwaite (1985)
discusses some essential reasons behind careful database
design including data redundancy, application performance,
data security, and ease of programming.
Hierarchical Models. Hierarchical data models are based
on tree-like structures made up of nodes and branches, where

18
the highest node is called the root, and succeeding lower
nodes are called children (Brathwaite, 1989). In hierarchical
models, trees are constructed using a father-son approach
(Chorafas, 1989). Hierarchical models can range from fairly
simple such as in the case of one-to-one or one-to-many
relationships, to the more complex many-to-many relationships.
Brathwaite (1989) and Chorafas (1989) both discuss some
advantages and disadvantages of hierarchical models. A major
advantage is the existence of proven database management
systems that utilize the hierarchical model. Another
advantage is the simplicity and ease of use of hierarchical
models which ultimately will facilitate their employment and
utilization by data processing users. Other benefits of
hierarchical models include their ability to reduce data
dependency, and their capability to efficiently represent
decision support system data (Hopple, 1988).
Both authors agree that one of the major disadvantages of
using hierarchical data models as a basic structure is their
lack of flexibility. This presents difficulties during
insertion and deletion operations. Due to strict hierarchical
ordering insertion and deletion of files or entities may
disrupt the tree structure. Also, deletion of the a parent or
father node will result in the deletion of the children
associated with that node. Accessibility of information is
also mentioned as a disadvantage since a child node is
accessible only through its parent node.

19
Relational Models. Relational database models store
information in tables of rows and columns (Singh, 1985) . The
association of rows and columns in a table is the
characteristic that gives the relational data model its name
(McNichols and Rushinek, 1988). McNichols and Rushinek
discuss that one advantage of the relational model is that
operations on tables can be defined mathematically, allowing
precise and unambiguous data retrieval. Maier (1983) states
that another advantages of this type of data storage is its
uniformity.
Data Security
An important part of database design is the incorporation
of a system to maintain data security. In an information
system with multiple users, a method of ensuring that only
authorized persons can access certain data is essential. Most
systems use a control of access method whereby a user's
identity is verified, and according to his priority, the
system assigns him access to certain information (Delobel and
Adiba, 1985). Any users attempting to enter the system must
identify themselves and then authenticate the identification
(Brathwaite, 1985).
Harington (1988) states that the first step in on-line
database security is to identify the user. However, in single
microcomputer operation systems there are generally no
individual user accounts or work areas. Therefore, the

20
computer assumes that everyone who has access to the keyboard
is authorized to use the machine. Consequently, security must
be handled at a lower level by the database management system
itself.
DBASE III Plus (Ashton-Tate, Inc.) has no features
specifically designed for data security, but it is possible to
impose some measures of safety through an application program.
However, even with the use of a password system through the
development of an algorithm, there is nothing that will
prevent a knowledgeable user from running DBASE III Plus and
accessing data by using ordinary program commands.
In many businesses, however, DBASE III Plus users have
not been trained to work directly with the database management
system environment. The majority are not computer
professionals, but people trained in other areas who use
computers to help do their jobs. These users work with
application programs whereby interaction with the system is
made through menu-driven interfaces and system screen forms.
In this case, users can be required to supply a password which
governs their access to the program.
Integration of Knowledge Systems and Databases
The integration of expert systems with other conventional
software would be beneficial (Holsapple et al., 1987). Some
expert system environments have a limited ability to import
data from external software. This ability does not, however,

21
replace integration, which provides a comprehensive
environment that makes all business computing abilities
available for use at any time, individually or in tandem.
Expert systems should be viewed as a supplement rather
than a replacement for existing computer technologies like
database management systems (Lai et al., 1987). Stone et al.
(1986) describe a Farm Level Expert System (FLEX). FLEX is an
integrated system made up of expert systems linked through a
common global memory and a database of interrelationships.
The integration process involves several stages (Harmon and
Sawyer, 1989) .
Harmon and Sawyer state that first, it is important to
determine whether or not a database is necessary. Databases
may be used for maintenance purposes whereby they are used to
externalize the parts of a decision support system that change
frequently. Databases can also be used as tools for
information storage, retrieval, searching, and querying. A
database management system is often better suited for
efficient search of large volumes of structured information
than a expert system is. Data sharing with other
applications, and other users, as well as security
maintenance, are other reasons that necessitate the use of a
databases in decision support system.
Second, Harmon and Sawyer say that the role of databases
in decision support systems should be evaluated. An expert
system can be used in two different ways: as a front end for

22
a database, asking questions and then initializing database
queries, or as a back end for a database, taking the results
of the query and analyzing them with rules in order to make
recommendations. Jones and Hoelscher (1987) describe a method
of integration whereby an expert system provides an initial
information sorting process, and then accesses a main database
for more detailed information.
When an expert system is used as a front end for a
database, the consultation proceeds in the normal way, with
the system asking the user questions and using rules to reach
a final conclusion. Once the system reaches a conclusion, it
takes the extra step by constructing a database query and
searching the database for an even more specific
recommendation. The database can hold information that will
further elaborate on the recommendation. Gallagher (1988)
discusses that the processing of data by decision support
systems may be very inefficient if they do not have the
ability to work with information proficiently. A solution to
this problem requires the system to request processing of data
by other programs that are specifically designed to accomplish
that task. That is, ask the data base management system to
search databases and summarize data.
When used as a back end to a database, an expert system
takes the database output and uses the information as input.
It then makes judgements based on it, and presents specific
suggestions to the end user. Back end applications are

23
usually used to take the raw data drawn from a database query,
process it further, and convert it into specific
recommendations. This approach is very effective for
providing support to individuals who have difficulty
interpreting the database query output. Expert systems are
used to build on the results of data processing and decision
support by assisting the user with the interpretation of data
to formulate responses (Barrett and Beerel, 1988).
Summary
The citrus cost information system discussed in this
dissertation integrates several of the technologies addressed
in this chapter. A database management environment is used in
conjunction with an expert system to provide production cost
information and extension and management recommendations.
The cost information system is unique in its ability to
provide industry averages in a way that is useful as a
decision making tool. The expert system provides information
on not only extension and management recommendations, but on
the costs associated with these recommendations.

CHAPTER III
SYSTEM DESIGN CONSIDERATIONS
During the implementation and use of the Florida
Agricultural Information Retrieval System (Johnson and Beck,
1986), Florida citrus growers expressed a need for information
on production costs associated with citrus. They needed an
economic basis for extension and management recommendations.
An information system was needed to provide growers with the
means to contribute production cost information for their
operations, and in return receive summaries of industry
averages. The system would not only provide cost summaries,
but would also integrate those costs with appropriate
extension and management recommendations to provide a
comprehensive decision making tool.
Preliminary Research
Existing Information Sources
The first phase in this project was to determine what was
already available for the citrus grower in the form of
production cost information. It was found that an annual cost
survey has been carried out since 1973 by Ron Muraro, a
researcher at CREC (Citrus Research and Education Center) in
Lake Alfred, Florida (Muraro and Matthews, 1988). Muraro's
24

surveys are designed to collect custom charge rate information
from citrus caretakers in different citrus producing areas of
the state. Written surveys are mailed out once a year to
participating caretaker operations. When all information is
collected, the relevant data is organized and summarized in
annual publications. Although other agricultural cost surveys
have been carried out in the past in areas such as potato
production, and woody nursery businesses, Muraro's citrus cost
surveys are the only ones still being carried out on a regular
basis.
Existing Agricultural Software
The next step in the preliminary stages of this project
was to determine what types of software packages were being
written for Florida citrus operations. Several programs were
available through the IFAS (Institute of Food and Agricultural
Sciences) software distribution office. After reviewing what
the IFAS catalog had to offer it was determined that most of
the programs were not written specifically for the citrus
grower. Some of the programs reguired the user to have a
considerable amount of computer experience, and it was often
necessary for the user to have a certain software package or
language such as LOTUS 123 or BASIC to run the programs.
Based on visits to software exhibits, agricultural trade
shows, and discussions with citrus experts, commercial
software proved to be mainly designed for payroll and record
25

26
keeping purposes and did not offer the grower any decision
support capabilities.
Design Requirements
The User Interface
One of the chief design requirements was a user-friendly
yet detailed environment for the user to work in. The
development of a user-friendly interface is an important part
of any system development (Andriole, 1986). Andriole states
that the user interface is the single most important part of
today's microcomputer systems. Its design must be approached
carefully, and should not be left to chance.
Peart (1988) suggests specific guidelines for the design
of user friendly programs. Menu selections should be used to
obtain necessary words or strings from the user, rather than
requiring typing. The user should be given new information
and help when needed. Previously entered information should
be kept on the screen as long as possible. A user should
always have a way reexamine his data and go back and change
it. And finally, files should be designed to allow the user
to refer back to what he did in his last session.
Lai et al. (1990) discuss an interface program designed
to work in conjunction with an expert system. Specially
designed screens use pop-up menus with user defined options
for data selection. The program acts as an information
manager for an integrated decisions support system, FARMSYS.

27
Once data items are defined, they need not be entered again.
This ensures consistency of data and facilitating rule
handling, error-checking, logical decisions, and searches
during program operation.
The system user was assumed to be a novice computer user.
The program had to be easy to use, self explanatory, flexible,
and expandable. Paller and Laska (1990) stress the importance
of ensuring that a system maintains enough flexibility and
responsiveness to meet the real and changing needs of an
organization.
System Hardware and Software Requirements
It was felt that the majority of citrus growers in
Florida that utilized microcomputers in their operations owned
IBM or compatible machines. The system needed to run on the
hardware already available in order to avoid additional
requirements.
Software specifications required a stand-alone system.
As discussed above, a large number of the programs already
available through IFAS required additional software to run.
This often necessitates costly software packages to be
purchased by the user. If such supporting programs were to be
distributed with the information system, then the need for
expensive licenses would arise.

28
Functional Requirements
In order for any computer based system to be successful,
it must fulfill two requirements. It needs to perform the
tasks it was originally designed for, and it must offer the
end user enough benefits to justify the time invested in its
use. Any system must provide the user with a new or improved
method of performing a task by replacing outdated methods or
technologies and enhancing productivity. Software systems
must also provide information that complements or supplements
information already available to the user through conventional
means.
A citrus cost information system would require large
amounts of data to be contributed by the user. It is often a
labor intensive and time consuming task for a grower to
contribute production costs for several of his groves by
entering detailed information into a computer. In the case of
this project, the system needed to provide the user with an
incentive to encourage its utilization. This is achieved by
providing the user with valuable information in the form of
industry averages and management recommendations, and
integrating the two in a unique and beneficial manner. The
system would also offer the user the chance to compare his
operational costs with the industry averages in order to
identify possible problem areas.

COINS
CHAPTER IV
- CITRUS COST INFORMATION SYSTEM
An Overview of COINS
A Citrus Cost Information System (COINS) was developed to
collect, manage, and report costs associated with citrus
production in Florida. The program consists of three main
sections. The first is a data gathering program for
production costs for various areas of the operation. The
second section is a summary and comparison program which
provides detailed summaries of averaged production costs, and
allows the user to compare production costs for one of his
groves with industry averages. The third section integrates
production cost information with extension and management
recommendations. The system for integrating management and
cost information (SIMON) is discussed in more detail in
Chapter V.
The following section discusses COINS in more detail,
including software and computer languages used for
development, the overall program structure, and program
execution.
29

30
DBXL as a Database Management and a Programming Environment
To write the COINS programs, software which combined
database management with a comprehensive programming
environment was needed. Two relational database packages were
considered, DBASE III+, and DBXL (WordTech Systems, Inc.) a
similar database program. Both packages offered a database
management environment, as well as a programming language with
which to develop applications.
DBXL was chosen over DBASE III+ for its increased
flexibility and lower cost. DBXL offers an extended language,
and an added windowing feature which allows multiple windows
to be generated. This feature was found to be helpful in
designing menus, pop-up help screens, error message windows,
and in improving the overall quality of the user interface.
Quicksilver (WordTech Systems, Inc.) was used to compile the
database applications to create fast stand-alone programs.
This allowed COINS to run independently of dBXL. Thus
circulation of the programs would not infringe on any
copyright or distribution laws.
Program Development Tools
COINS was developed on a Compaq 386 computer running
under an MS-DOS environment. A memory resident editor was
used to write the programs. Windowing environments such as
Microsoft Windows 386, and Quarterdeck's Desqview 386 were
used to load and run all the necessary software

31
simultaneously. This allowed the programs to be written with
the editor, tested in a dBXL environment, compiled with
Quicksilver, and tested, with great efficiency.
Program Requirements
The system was designed to run on any IBM-PC, XT, AT and
compatibles as well as 386 based machines. Hardware
requirements include a minimum of 640 kilobytes of free RAM,
a hard disk drive, and a color monitor. The COINS program
files occupy approximately one megabyte of disk space. The
data files require 60 kilobytes of disk space per grove. An
additional 50 kilobytes of free disk space per grove is
recommended to accommodate the temporary files during
execution of the data summary and comparison program.
Program execution speed depends on the hard disk drive
access times, the microprocessor speed of the computer being
used, and on the presence or absence of a coprocessor chip.
Execution is fairly slow on PC and XT machine, and fastest on
386 computers.
Program Structure
COINS incorporates numerous database files and three main
programs, namely a data entry program, a summary and
comparison program, and a decision support system. All
programs are accessed through a main menu program written in
Turbo PASCAL (Borland). When COINS is started, the main menu

32
is loaded into memory and remains in memory until the session
ends. When execution of a program is complete, control
returns to the main menu (Figure 4-1).
Password System
COINS utilizes a password system that serves three
purposes. The first is to protect data files against
unauthorized access. Grove cost information can only be
entered or retrieved when the proper password is used. The
second purpose is to insure that a user has entered cost
information for at least two groves before he is allowed
access to the data summary and comparison program. And
finally, passwords along with grove ID numbers are used as a
means of identifying groves in the database.
The COINS Database
Database Structure
The first step in developing a database to hold citrus
production costs, was to establish a list of grove practices.
Such a list was already available in Muraro's surveys. Muraro
listed ten major grove practices (cost categories), each with
its own list of sub-categories. Using Muraro's list as a
basis, a modified set of cost categories and sub-categories
was created. Each category had five different types of costs
associated with it; overall, labor, machinery, management,
and material costs. Database files used for cost information

33
Figure 4-1
COINS Program Structure

34
storage were designed for maximum flexibility. They allow the
user to enter data at various levels of detail based on the
amount of cost information available. This is achieved by
arranging production cost categories in a hierarchical form.
There are ten main cost categories, namely cultivation,
dusting, spraying, frost protection, young tree care,
irrigation, removing trees, fertilizing, pruning, and other
operations. Each category has one or more levels of sub¬
categories associated with it (Figure 4-2).
The ten main cost categories appear on the first screen
of the data entry or summary programs. This level of
categories in the hierarchy is referred to by the database as
level 1. Each level 1 main cost category has at least one
level of sub-categories. These subcategories are referred to
as level 2 and level 3. If more detailed information is
available on a particular grove practice, the user may expand
to a sub-category level and enter or view cost information.
Data Storage
Information on grove characteristics and grove costs are
stored in twelve separate data files. One file is specific to
grove characteristics, and is used for verifying passwords, ID
numbers, and to reference information. Production cost
information is stored in eleven main data files; one level 1
file, and ten level 2 files. dBXL data file structures are
shown in Appendix A.

35
- CULTIVATION-
•Hand Hoe/Hand Labor
â– Machine Hoe
-Rotovate
•Disc
â– Mow
â– Herbicide
•Temick/Nemacur
• Plow
•Backhoe
â– Water Furrow Disc
Water Furrow Cleaner
â– Vine Puller
â– Miscellaneous
7"
9-10"
Offset/Side
-5-1'
-9-10'
-15-16'
— V-Mower
— In & Out
— Sickle
- DUSTING
Ground Application
Aerial Application
Spot Herb.
Strip/Band
Trunk-Trunk
Chem. Mow
- SPRAYING
- FROST PROTECTION
Ground Application—
Aerial Application—
General Costs
Wind Machines
Bank/Unbank Trees
Tree Wraps
Grove Heaters
Other Methods
- YOUNG TREE CARE
Reset Care
Planting Trees
Irrigation
Ring Trees
Fertilizing
Dilute
-i 2X
3X
4X
6X
10X
15X
—Hand Spr.
— Boom Spr.
Other
Fixed Wing
Helicopter
Other
Hand Spreader
Fert. Spreader
- IRRIGATION
General Costs
Repair & Maintenance
Inject Chemicals
Water Pump
Water Slinger
Water Truck
Water Trencher
Rotary Ditcher/Auger
3-Wheeler for Micro.
Miscellaneous
Perm. Overhead
Traveling Vol.
— Stationary Vol.
Perfor. Pipe
Microsprinkler
Drip System
Fértil.
— Herbicide
Pesticide
Fungicide
Hand Labor
Mechanical
Figure 4-2 Production Cost Hierarchy

36
- REMOVING TREES
Tractor
Bulldozer
Front End Loader
Miscellaneous
- FERTILIZING
Liquid Boom Appl.
Dry (Bulk)
Dry (Bag)
Liquid Nitrogen
Lime and Dolomite
Miscellaneous
Nitrogen
Mix-Fertilizer
Mix Fertilizer
& Herbicide
- PRUNING
General Pruning
Hedging
Topping
Removing Brush—
Miscellaneous
Sing
!.e Side
—Tractor
— Self Prop.
- OTHER OPERATIONS
General Repairs
Mise. Grove Care
Energy Costs
Ground Bypass
Truck with Driver
Tractor with Driver
Power Saw
Front End Loader
Push Brush
Bush Hog
Rotary Ditcher/Auger
Mound Builder
Middle Buster
Skilled Labor
Mechanic Labor
Supervision Costs
Contracted Services
Accounting Services
Miscellaneous
Double Sided
—Tractor
— Self Prop.
Tractor
Self Prop.
Haul Out Of Grove
Chop Brush
Figure 4-2—Continued

37
Supporting database files hold information on fruit
varieties, grove locations, number of categories and sub¬
categories, and sub-category locations within the database.
Other files include index files and query files. Each cost
information data file has a corresponding temporary file.
Temporary files are used for data entry and modification, and
for data storage after summaries are generated. All user
interaction with the database is done through the temporary
files. This allows for quicker access, and protects the
integrity of the main data files.
When the data entry program is run, appropriate
information from the main data files is copied into the
temporary files. Hence, if information for a new grove is
being entered, information for a grove with costs set to zero
(default grove) is copied from each main data file into its
corresponding temporary file. Upon completion of data entry
and data modification, cost information is appended to the
main data files from the temporary files. On the other hand,
if information from an existing grove is needed, corresponding
cost information for that grove is copied from the main data
files into the temporary files, and displayed for editing.
When editing is complete, the new information on the grove
replaces the old data in the main data files.
Temporary data files are also used when the data summary
program is run. Cost information from the main data files for

38
all groves to be included in the summary is averaged, and the
results placed in the temporary files for viewing.
Data Entry Program
Program Pesian
The data entry program was designed to look and function
like a spreadsheet (Figure 4-3) . A cursor bar is used to
select between ten production cost categories. A menu bar is
used to select among ten available functions. Functions are
used to access the different features of the program. To
select a function, the user moves the cursor bar and presses
the enter key. Alternatively, a function may be selected by
pressing a specific highlighted character found in each
function name. An explanation line below the menu options
helps to identify what each function does. The following is
an explanation of the ten functions.
Program Functions
Edit. Data is entered using the edit function. When
edit is selected, the production category on the highlight bar
is highlighted in red, and a help window appears at the bottom
of the screen. Costs for labor, machinery, management, and
materials may be entered for each category. When the editing
is concluded, values in the separate columns are summed, and
the results are placed in the overall cost columns. The
automatic summation may be suppressed by entering a value for

39
Variety:Hamlins,Valencias. Market:Fresh/Process. ID: 1
Grove Practice
Overall
Labor
Mach.
Mngmt Material
Cost
Cost
Cost
Cost
Cost
Cultivation
0.00
0.00
0.00
0.00
0.00
Spraying
0.00
0.00
0.00
0.00
0.00
Dusting
0.00
0.00
0.00
0.00
0.00
Fertilizing
0.00
0.00
0.00
0.00
0.00
Irrigation
0.00
0.00
0.00
0.00
0.00
Removing Trees
0.00
0.00
0.00
0.00
0.00
Pruning
0.00
0.00
0.00
0.00
0.00
Young Tree Care
0.00
0.00
0.00
0.00
0.00
Frost Protection
0.00
0.00
0.00
0.00
0.00
Other Operations
0.00
0.00
0.00
0.00
0.00
New Data File
Edit Help Mode
Product Back
Expand
Return
Locate
Update
Comment
Explanation - Enter costs
for this category.
Figure 4-3
Example Initial Data Entry Screen

40
the overall cost, or by using the mode function explained
below.
Help. The data entry program features an on line help
utility. When the help function is selected, an options
window appears and allows the user to select among several
help topics (Figures 4-4a and 4-4b). Help topics include an
introduction to the purpose and uses of the program, a short
tutorial, an overview of the database structure, an
explanation of the ID number to identify groves, and lastly a
help utility that explains each function (Figure 4-5). The
user may also obtain help on functions by moving the menu bar
to the desired function, and pressing the FI key.
Mode and Update. The mode function allows switching
between auto-calculate and manual modes for column summation.
When the program starts up, auto-calculate is on, and whenever
separate costs for labor, machinery, management, or materials
are entered, the program automatically adds these costs, and
places the total in the overall column. The user may select
manual recalculation in order to enter separate costs for the
labor, machinery, management, and materials, as well as a
separate overall cost. This feature is important when the
user reports partial costs for a category, and the overall
cost does not equal the total of the separate costs.
The update function is used to total the costs in the
separate columns when mode is set to manual. The user may

41
Help Menu
General Help Utility.
Help With Functions.
Quit Help.
Use the arrow keys to select a topic and
press . Press to quit.
Figure 4-4a
Main Help Menu

42
Help Menu
General Help Menu
=> What Does This Program Do ?
Getting Familiar With The Program.
Database Structure.
The Grove ID Number.
Quit The General Help Utility.
Use the arrow keys to select and press .
To return to the Main Help Menu press ESCAPE.
Figure 4-4b
General Help Utility Menu

43
Help Menu
Available Functions
Edit
Mode
Expand
Return
Locate
Update
Comment
Product
Back
Press the appropriate letter for a function
to select it. Press to quit.
Figure 4-5 Function Help Menu

44
wish to total all costs, or just those in the current cost
category.
Expand and Return. Each of the ten main cost categories
is divided into one or more levels of sub-categories. The
expand function is used to access the sub-categories for more
detailed data entry. The return function allows the user to
exit from a sub-category screen and return to a higher level
in the hierarchy.
Locate. The locate function serves two purposes. The
first is to show a list of sub-categories available for a
particular cost category. This serves as a "map" for the
operation hierarchy (Figure 4-6). The second usage of the
locate function is an index for all grove practices available
in the database. This function is used to locate the main
category under which a particular grove practice is found.
For example, to find the main cost category for 'Energy and
Fuel Costs', the user searches through the index for the word
mowing. The index will indicate the main category as being
'Other Operations' (Figure 4-7).
Comment and Product. The comment function allows the
user to enter a comment for each cost category. Comments may
be used as notes or reminders. Similarly, the product feature
allows the user to leave a description of the product name, or
material name used in a particular grove practice. This
feature may be important if the program is expanded to include

45
View
Of Operation Hierarchy
Level 1
Level 2
Level 3
Cultivation
Hand Hoe/Hand Labor
Offset or Side
Spraying
Machine Hoe
5-7'
Dusting
Rotovate
9-10'
Fertilizing
Disc
15-16'
Irrigation
MOW
V-Mower
Removing Trees
Herbicide
In & Out
Pruning
Temick/Nemacur
Sickle
Young Tree Care
Plow
Frost Protection
Backhoe
Other Operations
Water Furrow Disc
Water Furrow Cleaner
Vine Puller
Miscellaneous
Figure 4-6 Example for the Locate Function for Sub¬
categories Under the Level 1 Cultivation
Category, and Level 2 Mow Sub-category.

46
Cost Categories
Use the arrow keys
to browse the list.
=> Energy and Fuel Costs
Fertilize
You may also search
Fertilize Young Trees
by pressing the first
Front End Loader
letter of a desired
category.
Press to
return to the cost
screens.
Frost Protection
Ground Bypass
Grove Heaters
Hedging
Herbicide
Hoe
Inject Chemicals - Irr.
Irrigate
Main Cost Category
—> Other Operations
Figure 4-7
Example for the Find Function for Energy and
Fuel Costs.

47
collection of names of pests, weeds, chemicals, or other
products used in the citrus operation.
Back. This function is used to return to the COINS main
menu. When the back function is used, all the main category
files are updated using the costs entered during the session,
and control is given to the main menu.
Entering Costs for a Grove
The following section describes the procedure for
starting and using the data entry program to input or edit
costs for a grove. Three options are available when the
program is started. The user may either define and enter
costs for a new grove, view or edit information for an
existing grove, or delete one of his groves from the database.
Each grove is assigned a grove ID number which is
utilized by the user and by COINS to identify the grove.
Along with the ID number, a grove can be identified by the
user's password. The user must have both the a password and
an ID number to enter or edit costs for a grove.
Editing an Existing Grove. If the user chooses to view
or edit information for an existing grove, the program
displays a menu of ID numbers corresponding to available
groves, and prompts for a selection (Figure 4-8). By moving
the menu selection bar to each ID number, the characteristics
of each grove are displayed. When a grove is chosen, the
program searches the database files, retrieves, and displays

48
the cost information found. It is important to note here,
that only those groves associated with the user's own password
can be accessed.
Defining a New Grove. To enter costs for a new grove,
the user must define the characteristics of the grove. These
include size in acres, fruit varieties, fruit market, yield in
boxes of fruit, grove location, and grove ID number. A grove
definition screen is used to enter the necessary data (Figure
4-9) . A list of options is available from which the user
selects fruit varieties and grove location (Figures 4-10a and
4-10b). Choosing from a menu of options allows easy
selection, and ensures that no discrepancies occur.
Once the information on a grove is complete, the user is
prompted to enter a grove ID number. This number is unique to
the user's password. Two or more groves with the same ID
number may exist as long as they were defined by different
users.
Entering and Modifying Data. When the spreadsheet is
displayed, the user may enter or change data at any level in
the hierarchy. The arrow keys are used to move around the
spreadsheet, and a menu bar is used to select the functions
described in the previous section. When entry or modification
is complete, the data entry program updates all files, and
returns control to the main menu.
Deleting Grove Information. Often times a grove is no
longer active, or no longer belongs to the person who entered

49
Florida Citrus Production Cost Survey Program
Grove Description
Grove size (acres) ... 5.00
Variety ... Hamlins, Valencias
Yield in boxes .
3745.00
Price per Box ($]
Grove location .
Grove ID number
Figure 4-8
Example of Grove Selection Screen for Viewing
and Editing Costs

50
Florida Citrus Production Cost Survey Program
Grove Description
Grove size (acres) ... 0.00
Variety ...
Yield in boxes .
0.00
Price per Box ($]
0.00
Grove location .
Grove ID number
Figure 4-9
Example Grove Definition Screen

51
Florida Citrus Production Cost Survey Program
Options
Hamlins
Valencias
Navels
Temples
Early/Mid
Minneolas
Lees
Murcotts
Novas
Marsh
Robinsons
Duncan
Figure 4-10a Fruit Variety Selection Options

52
Florida Citrus Production Cost Survey Program
Grove Description
Grove size (acres) ... 5.00
Variety ... Hamlins, Valencias
Yield in boxes .
2500.00
Price per Box ($;
7.63
Grove location .
Grove ID number
0
Options
Ridge
Interior
Ind. River
Southwest
North
Central
Other
Figure 4-10b Location Selection Options

53
its costs into the system. A grove may be sold, or in light
of the recent freezes, a grove may simply no longer be
productive. In such cases the user may wish to remove this
grove from the database. When the grove deletion option is
chosen, the user needs only to specify the grove ID number,
and the program will remove all costs and grove information
from the database.
Data Summary and Comparison Program
Program Pesian
Like the data entry program, the data summary program was
designed to look and function like a spreadsheet (Figure 4-
11) . The main difference between the two programs is that the
data summary program does not support any editing features.
It is simply a tool for averaging costs, and displaying the
results. As in the data entry program, a cursor bar and a
menu bar are used to select categories and menu options
respectively.
The summary screen provides average per acre costs and
returns for the ten main cost categories, as well as both
levels of subcategories. Per acre average labor, machinery,
management, and material costs are also displayed.

54
Averaged Annual Per Acre
Your Grove #: 40
Costs and Returns
Cost Item
Range of Costs
Average
¡four Grove
Revenue
2786.84
5055.75
Cultivation
76.70 to 194.36
142.26
194.36
Spraying
138.20
to
183.00
167.36
180.88
Dusting
0.00
to
0.00
0.00
0.00
Fertilizing
69.47
to
241.22
173.12
241.22
Irrigation
5.14
to
261.65
169.86
261.65
Removing Trees
0.00
to
1947.98
978.46
1947.98
Pruning
0.00
to
472.70
472.70
0.00
Young Tree Care
71.14
to
270.98
138.48
71.14
Frost Protection
0.00
to
72.08
37.32
2.56
Other Operations
26.55
to
48.44
35.20
26.55
Total Per Acre Expenses
2279.56
2926.34
Per Acre Net Income
507.28
2129.41
Help Options Labor Machinery Management Material
Locate Expand Return Back
Explanation - Access the help utility.
Figure 4-11
Example Cost Summary Screen

55
Program Functions
For the purpose of avoiding redundancy, only those
options that differ from the data entry program menu will be
discussed.
Help. The help option is similar to that in the data
entry program, but differs in the contents of the tutorial,
and explanation of functions.
Options. This function accesses a decision support
system discussed in Chapter IV of this dissertation.
Labor. Machinery. Management. Material. When these
options are used, the screen switches from displaying per acre
overall costs and returns to a screen showing only the
selected separate cost.
Cost Averaging
Li et al. (1990) discuss two major methods of retrieving
database information. One is a navigation-based method
whereby the user uses a series of menus to travel from one
area of the database to another. The second method is the
query based access to information. This method uses the users
questions to retrieve information. The COINS data summary and
comparison program uses the data query method to retrieve the
necessary data from the cost databases.
The program requires the user to specify parameters which
determine the types of groves that will be used in the
summary. Maximum and minimum grove sizes, fruit types, fruit

56
market (fresh or process), as well as location of the groves
must be specified (Figure 4-12). The query may be either very
general whereby only one or a few parameters are specified, or
very specific. In this way the user can tailor the cost
comparison to his own needs and interests. The program also
allows the user to specify a grove to compare to the averages.
If a grove is specified, per acre costs for that grove will be
displayed alongside the averages in the summary screen.
Once the query is defined, the program searches the
database for all groves fitting the query and averages the
costs found. All overall, labor, machinery, management, and
material costs for each category are averaged separately.
Data on highest and lowest costs in each category is
maintained and later displayed as a range of costs.
The program is designed to carry out a data summary only
if three or more groves fit the query. This protects the
confidentiality of the data, and prevents the user from
"extracting" costs for a single grove in the database by
specifying its exact characteristics.
Once the summary screen is displayed, the user may choose
to explore the average values in the different levels of the
hierarchy as well as view the individual labor, machinery,
management, and material per acre costs. A data analysis
option allows the user to compare his costs for an operation
to the averaged values, and invoke an expert system that aids

57
Florida Citrus Cost Reporting Program
Grove Information
Minimum grove size ... 0.00
Maximum grove size ... 5.00
Variety ... Any Variety
Grove location Any Region
Grove ID number 40
Cost data is being prepared - Wait
Groves
Found
Found - 3
Averages:
• Size =
5.00 acres
• Yield =
2314.67
• Price/
Box =
$ 6.02
Figure 4-12
Example of Query Definition Screen During Data
Averaging

58
in the analysis and gives and management recommendations,
analysis option will be discussed in more detail later.
The

CHAPTER V
SIMON - SYSTEM FOR INTEGRATING
MANAGEMENT AND COST INFORMATION
A system was developed to integrate management and
extension recommendations with production cost information.
The System for Integrating Management and Cost Information
(SIMON) utilizes an expert system to access a database of
management information for a particular grove practice. Based
on each recommendation, the system determines an ideal cost
for a particular situation and compares that cost to actual
expenditures reported by the grower.
The SIMON concept can be applied to many area of the
citrus operation, or to other crop systems in addition to
citrus. But for the purpose of this project a prototype
system was developed to give management recommendations for
herbicide application and weed control on citrus. The user
defines a weed problem, by identifying categories and names of
weeds. These are chosen from a list of weeds commonly
associated with citrus. The program then searches a database
for appropriate spraying recommendations. The expert system
asks the user a series of guestions to determine the proper
application rates and application times based on conditions
particular to his operation. After a herbicide recommendation
is given, the user has the option of determining the cost of
59

60
controlling the weed problem defined, and making a comparison
to costs in his grove. The system may be used in conjunction
with the COINS data summary program or as a stand-alone system
for citrus herbicide and weed control information.
The following section discusses the herbicide prototype
program and the SIMON concept in more detail. System
development, program execution, integration with the COINS
data summary program, and evaluation will be described.
Program Development
The benefits of an expert system based program for
management recommendations give it a clear advantage over
traditional methods of conveying management recommendations
(Linker, et al.). Information in the system's database can be
easily updated with current information on weeds and
herbicides. Also, the system can narrow down the number of
available recommendations for a particular problem and rank
them by order of effectiveness. This greatly reduces
extension agents' and grower' time and effort in searching
through a long list of recommendations.
There were some advantages to using herbicide application
as a grove practice for the prototype system. Herbicide
application is a year round operation that must be carried out
regardless of exogenous factors such as weather conditions.
Also, the number of weeds most commonly associated with
citrus, and the herbicides recommended for use on Florida

61
citrus are limited in number. Some factors such as soil type,
severity of weed infestation, and in some cases grove location
and rainfall, influence the herbicide application operation.
Nagarajan et al. (1987) say that weed management involves a
number of strategic and technical decisions, such as chemical
control (pre-plant, pre or post emergence) with a choice of
chemicals and formulations. But once these factors were
identified, almost ideal conditions existed for the
development of an expert system based program.
Program Features
The SIMON concept was used in the development of the
prototype herbicide program to integrate management
recommendations with costs for herbicide application. The
program enables the user to abstract an appropriate herbicide
recommendation from an information database, given a
particular weed problem. A cost information summary is then
given to estimate the expenses that may be incurred if the
recommendation is followed. The program also serves as a
directory, providing information on weeds controlled by each
herbicide. Another feature of the program allows the user to
compare the effectiveness and related costs of using several
herbicides on a particular weed problem.

62
Program Development Environment
SIMON consists of two segments; a herbicide program which
includes the user interface, and an expert system which
determines the correct recommendations for herbicide
application. The following section discusses the programming
environment used for development.
The main herbicide program, like COINS, was written using
dBXL's own application development language. Its design
targets the novice user with little or no computer experience.
To run the program, it is only necessary for the user to
follow instructions in a window at the bottom of each screen.
Expert System Environment
Expert System Shells. Expert systems are usually
developed using a programming environment called a "shell".
Although expert systems can be written in any language, it is
often easier and more efficient to use an expert system shell.
Shells provide comprehensive environments for the development
of rule based expert systems. Expert systems developed using
shells utilize an elaborate process of deductive reasoning
called an inference engine to arrive at a conclusion from a
set of circumstances.
In the case of the expert system portion of SIMON, using
a shell would have proved to be somewhat disadvantageous. The
program was intended to be distributed to growers in Florida
along with the complete COINS system. Most software companies

63
do not offer unrestricted distribution rights for applications
developed using their expert system shells. It is often the
case that costly licenses must be purchased for this purpose.
Another disadvantage to using shells was that expert
systems must be run through the development environment with
which they were written. This increases memory requirements
of the programs being run. Also, expert systems usually
interact with other supporting programs in order to swap
information, and gain access to functions not supported by the
shell used for development. For the herbicide expert system,
a database environment would have been needed to store cost
information and make data queries. Running the expert system
separately from its supporting programs would have
necessitated the exchange of large amounts of data between the
two systems causing a decrease in program efficiency, and
requiring an additional amount of memory.
Maintenance of the expert system was a very important
factor to consider. Love (1988) discusses that maintenance of
the expert system may be quite different from that of the
decision support system. If the expertise changes, whether
evolutionary or revolutionary, the developers of an expert
system must consider methods to help assume that the system's
inference is timely. He further states that while the need to
provide expert system maintenance is important for all expert
systems, it may be particularly appropriate to economics
related expert systems. The changing nature of herbicide

64
formulations, recommendations, and effectiveness on weeds,
necessitated high maintenance requirements, and meant that
herbicide and weed data needed to be readily accessible, and
easily modifiable. It was therefore necessary to ensure that
the rules used by the expert system to arrive at
recommendations, and the recommendations themselves, were not
embedded in the program code. Expert systems developed with
shells usually contain all rules and recommendation within the
program code, and hence, any changes that need to be made
would require the program code to be rewritten and recompiled.
This process placed the use of shells at a disadvantage due
their inefficient maintenance requirements.
A custom designed expert system environment was used for
SIMON. The environment utilizes a unique method of rule
storage and inferencing, whereby rules are stored as records
in a database. The concept involved is fairly simple, and is
flexible enough to be applied to virtually any application
requiring a simple rule based expert system.
Environment Advantages. The environment used for SIMON
was developed using dBXL, allowing the expert system to be
compiled and run in conjunction with the rest of SIMON and the
main COINS program. This keeps memory requirements down, and
allows license-free distribution of the program. The expert
system consists of an algorithm that carries out the
simplified inferencing and deduction process, and database
files containing rules and recommendations. The two are

65
completely separate, allowing modifications to be made to the
data files independently of the program code. As new or
changed information on weeds or herbicides is made available,
the data files can be changed by using the dBXL database
environment, and reguires no revisions to be made to the
program code.
Since the expert system part of SIMON is written in dBXL,
it is able to take full advantage of dBXL's functions and
ability to efficiently and easily manipulate large amounts of
information.
Expert System Rule Base
dBXL database files consist of a series of fields and
records. Much like a spreadsheet, fields and records can be
thought of as columns and rows of information. Rules are
stored as records in the database. Each record consists of
three main fields (Figure 5-1); rule number, condition, and
recommendation fields. Rule numbers for each herbicide are
divided into one or more levels of detail allowing easy
referencing. The first level of rule numbers is a whole
number. Rules with decimal values are the next level rules
for a particular condition. File formats for the herbicide
and weed guide are shown in Appendix A.
The deduction process involves an iterative process of
search and selection until a recommendation is found. First,
the expert system displays all condition fields for records

66
NUMBER
CONDITION
RECOMMENDATION
19.00
Areas receiving more than 20"
average annual rainfall
NEXT 19.10
19.00
Areas receiving less than 20"
average annual rainfall
NEXT 19.20
19.00
WEED G-H,
Recommendation
19.10
New plantings.
NEXT 19.11
19.10
Non-bearing established plantings.
Recommendation
19.11
Soil texture is coarse.
Recommendation
19.11
Soil texture is medium
Recommendation
19.11
Soil texture is fine
Recommendation
19.20
New plantings.
NEXT 19.21
19.20
Non-bearing established plantings.
Recommendation
19.21
Soil texture is coarse.
Recommendation
19.21
Soil texture is medium.
or Soil texture is fine with 2-5%
organic matter.
Recommendation
19.21
Soil texture is fine.
or Soil has 5-10% organic matter.
Recommendation
19.21
Soil has 2-5% organic matter.
Recommendation
19.21
Soil has 5-10% organic matter.
Recommendation
Figure 5-1
Example Rules Used for the Herbicide Treflan

67
with first level rule numbers. The user is asked to choose a
condition from the list. The recommendation field for the
chosen condition will either direct the program to search for
another set of conditions, or will contain a recommendation.
In the case of the latter, the program terminates the search,
and displays the recommendation. If a second set of
conditions exist, they are displayed based on their rule
numbers, and the user is asked to choose again. This
procedure is repeated until the program comes across a
recommendation. The program can also accommodate special
cases for rules where only a particular situation within a set
of rules requires a specific recommendation. This is the case
when a particular weed requires a specific or unique
recommendation. A flow chart illustrating this deduction
process is shown in Figure 5-2.
Knowledge Acquisition
Identifying the Experts
The methods by which the program arrives at management
recommendations are based on the thought processes used by
experts in the field of weed science and particularly in the
area of citrus production. The first step in the design of
the program was to identify the experts.
Two citrus herbicide experts were identified. Dr. Megh
Singh and Dr. David Tucker are both researchers at the
University of Florida's Citrus Research and Education Center

68
Figure 5-2
Flow Chart Illustrating the Deduction Process
Used by SIMON

69
in Lake Alfred, Florida. Both experts had worked closely with
citrus growers and extension agents, and were familiar with
situations that may arise concerning weed infestation in
citrus. They were also current on developments in the
herbicide industry, and hence they provided the complete
knowledge required to develop the system. The herbicide and
weed guide also relies heavily on published materials for
information. However, even with the immense amount of
information available in the form of extension guidelines, the
actual application of this "domain knowledge" to specific
situations had been (and still is) provided primarily by weed
specialists (Holt, 1988).
Preliminary Interview
Determining how the experts arrived at a conclusion from
a series of circumstances was the next logical step in the
program design. Due to the distance between the programmer
and the experts, personal interviews were limited to a
preliminary interview and several follow up interviews. All
other consultations were made over the phone, and through the
Institute of Food and Agricultural Sciences' (IFAS) electronic
mail system. Beck et al. (1987) mention that in general,
extension specialists cannot spend significant portions of
their time working on an expert system project. Furthermore
the experts and engineers are typically quite distant
geographically and cannot meet for intensive interviews on a

70
regular basis. The purpose of the preliminary interview was
to allow the experts to discuss freely the pertinent
considerations of the system (Lacey et al., 1989).
During a preliminary interview the experts were given a
general idea of the concepts behind COINS. A discussion
followed on the steps involved in the development of SIMON,
and the herbicide expert system. Requirements and limitations
of the herbicide expert system were also discussed. It was
determined that only around twenty herbicides were recommended
by I FAS for use on Florida citrus. These herbicides were used
as the basis for the system's herbicide database. It was also
decided that only a certain number of weeds commonly occurred
on Florida citrus, and that the weed database should be
limited to these weeds.
Sources of Information
Information on weed names and classifications were
derived from chemical manufacturers' publications as well as
independent studies conducted by the experts and the herbicide
manufacturers. A list of herbicides recommended for use on
citrus was obtained from the 1990 Citrus Spray Guide (IFAS
Publications, 1990).
The expert system rules used to arrive at the proper
recommendations were derived from several sources. The main
source of the spray recommendations, including rates,
scheduling, and other information, was the 1989 Crop

71
Protection and Chemicals Reference (CPCR). The CPCR contained
product labels for all the recommended herbicides. Product
labels contain all necessary information needed to use each
product, and are used by the grower as a reference.
Additional spray recommendations were obtained from the Citrus
Spray Guide.
Establishing the Decision Process
It was necessary to establish the thought process that
the experts used to analyze a particular weed problem.
Following a discussion with the experts a decision tree was
drawn up that describes the logic used (Figure 5-3).
The first step involved the definition of a weed problem.
Categories as well as names of weeds were selected. Weeds in
three categories, namely grasses, broadleaf weeds, and vines,
were selected to constitute a weed problem. Weed categories
and weed names are listed in Tables 5-la, 5-lb, 5-lc. The
next step was to identify what herbicides control the weeds
selected. This is done by referring to the herbicide product
labels or the citrus spray guide. If no herbicides were found
to control all weeds, the next best herbicides that control as
many weeds as possible were selected.
When the herbicides identification process is completed,
the herbicides were ranked as to their effectiveness on the
weeds selected. The ranking is based on a susceptibility
table that was developed by the experts in conjunction with

72
the chemical manufacturers. It shows the effectiveness of
each herbicide on weeds in each category (Table 5-2). The
following is an explanation of the five susceptibilities
listed in descending order of effectiveness.
S indicates that weeds are susceptible to the herbicide
at germination, and at early seedling stage of growth. Weeds
may also be susceptible at later stages of maturity.
PS indicates that weeds are susceptible only at
germination. Repeat applications may sometimes control
established weeds.
I denotes intermediate control indicating that the degree
of control will be erratic with some plants within a species
population being killed and others not.
T indicates that weeds are tolerant and either showing no
signs of injury or able to recover from injury symptoms. U
indicates that the susceptibility status is unknown due to
lack of experimental data and reliable field observations.
In order to rate the herbicides' effectiveness on a
combination of weeds, a rating system was established by the
experts and the knowledge engineer. The S, PS, I, T, and U
susceptibilities were given equivalent numerical ratings on a
scale of one to five, with five being the highest rating
corresponding to a susceptibility of S. The ratings for all
the weeds selected were summed and resulted in an overall
rating for each herbicide.

73
Figure 5-3
Flow Chart for Experts' Decision Process

74
Table 5-la List of Weeds by Category, a) grass weeds, b)
broadleaf weeds, c) vines
Weed Name
Scientific Name
Bahaigrass
Bermudagrass
Carpetgrass
Cattail
Crabgrass
Paspalum notatum
Cynodon dactylon
Axonopus affinis
Typha sp.
Digitaria adscendens
Crowfootgrass Dactyloctenium aegyptium
Goosegrass
Guineagrass
Johnsongrass
Maidencane
Napiergrass
Natalgrass
Nutsedge
Pangolagrass
Paragrass
Peppergrass
Sandspur
Signalgrass
Texas Panicum
Torpedograss
Eleusine indica
Panicum maximum
Sorghum halepense
Panicum hemitomon
Pennisetum purpureum
Rhynchelytrum repens
Cyperus rotundus
Digitaria decumbens
Panicum purpurascens
Lepidium virginicum
Cenchrus echinatus
Brachiaria piligera
Panicum Texanum
Panicum repens
Vaseygrass Paspalum urvillei
Yellow Foxtail Setaria glauca

75
Table 5-lb List of Weeds by Category, a) grass weeds, b)
broadleaf weeds, c) vines
Weed Name
Scientific Name
Bitter Mint
Black Nightshade
Brazilian Pepper
Camphorweed
Ceaserweed
Common Purslane
Common Ragweed
Creeping Charlie
Cudweed
Dayflower
Dogfennel
Evening Primrose
Flat-topped Goldenrod
Florida Beggarweed
Florida Pusley
Goatweed
Goldenrod
Horseweed
Jerusalem Oak
Lambsquarters
Lantana
Mexican Tea
Pepperweed
Pigweed
Pokeberry
Primrose Willow
Rouge Plant
Rustweed
Saltbush
Seamyrtle
Skunkweed
Sowthistle
Spanish Needles
Spurge
Swampwillow
Teaweed
Virginia Pepperweed
Waxmyrtle
Hyptis mutabilis
Solanum Nigrum
Schinus terebinthifolius
Heterotheca subaxillaris
Urena lobata
Portulaca olerácea
Ambrosia artemisiifolia
Lippia nodiflora
Gnaphalium sp.
Commelina benghalensis
Eupatorium capillifolium
Oenethora sp.
Euthamia minor
Desmodium tortuosum
Richardia scabra
Scoparia dulcis
Solidago sp.
Conyza canadensis
Chenopodium botrys
Chenopodium album
Lantana camara
Chenopodium ambrosioides
Lepidium virginicum
Amaranthus sp.
Phytolacca americana
Ludwigia peruviana
Rivina humilis
Polypremum procumbens
Baccharis halimifolia
Baccharis halimifolia
Achyranthes aspera
Sonchus sp.
Bidens pilosa
Chamaesyce hyssopifolia
Salix nigra
Sida acuta
Lepidium verginicum
Myrica cerifera

76
Table 5-lc
List of Weeds by Category, a) grass weeds, b)
broadleaf weeds, c) vines
Weed Name
Scientific Name
Air Potato
Balsam Apple Vine
Bigroot Morningglory
Brazilian Nightshade
Briars
Calico Vine
Cats Claw Vine
Cypress Vine
Maypop (Passion Flower)
Milkweed (Strangler) Vine
Moonvine
Morningglory
Narrow-Leaf Milkweed Vine
Peppervine
Rosary Pea
Virginia Creeper
Wild Grape
Wild Watermelon (Citron)
Woevine
Dioscorea bulbifera
Momordica charantia
Ipomoea pandurata
Solanum seaforthianum
Smilax sp.
Aristolochia littoralis
Bignonia unguis-cati
Ipomoea quamocilt
Passiflora incarnate
Morrenia odorata
Ipomoea alba
Ipomoea sp.
Cynanchum scoparium
Ampelopsis arbórea
Abrus precatorius
Parthenocissus
quinquefolia
Vitis rotundifolia
Citrullus vulgaris
Cassytha filiformis

77
Table 5-2 Weed Susceptibilities
Herbicide Codes8
Weed Name
A
B
c
E
F
G
H
I
J
Bahaiagrass
S
s
s
I
u
T
S
T
U
Bermudagrass
PS
PS
PS
T
u
s
S
T
U
Common Carpetgrass
S
s
s
I
u
u
S
T
U
Cattail
U
u
u
u
u
u
S
T
U
Crabgrass
S
s
s
s
u
s
S
s
s
Crowfootgrass
S
s
s
s
u
PS
S
s
u
Goosegrass
S
s
s
s
u
s
S
s
s
Guineagrass
PS
PS
PS
PS
u
s
S
s
u
J ohnsongrass
PS
PS
PS
PS
u
s
S
PS
PS
Maidencane
PS
PS
PS
PS
u
u
S
u
u
Napiergrass
T
T
T
T
u
s
S
u
u
Natalgrass
S
s
s
PS
u
u
S
u
u
Purple Nutsedge
I
I
I
T
u
T
PS
PS
u
Pangolagrass
s
s
s
I
u
u
S
T
u
Paragrass
s
s
s
I
u
s
s
PS
u
Southern Sandspur
s
s
s
s
u
s
s
PS
u
Hairy Signalgrass
PS
PS
PS
I
u
s
s
PS
u
Texas Panicum
s
s
s
u
u
s
s
PS
u
Torpedograss
PS
PS
PS
T
u
I
PS
T
u
Vaseygrass
PS
PS
PS
T
u
s
s
T
u
Yellow Foxtail
s
s
s
s
u
s
s
PS
I
Peppergrass
u
u
u
u
u
u
u
U
u
Florida Beggarweed
s
s
s
u
u
T
s
T
u
Bitter Mint
s
s
s
u
u
T
s
U
u
Black Nightshade
s
s
s
s
u
T
s
PS
u
Brazilian Pepper
u
u
u
u
u
T
I
T
u
Ceasarweed
u
s
s
u
u
T
s
U
u
Camphorweed
s
s
s
s
u
T
s
U
u
Common Purslane
s
s
s
s
u
T
I
T
s
Common Ragweed
s
s
s
PS
u
T
s
PS
I
Creeping Charlie
s
s
s
u
u
T
I
T
u
Cudweed
s
s
s
u
u
T
s
PS
s
Dayflower
s
s
s
s
u
T
PS
T
u
Dogfennel
PS
PS
PS
PS
u
T
S
T
u
Evening Primrose
u
u
u
u
u
T
s
T
u
a. The letters correspond to the following herbicides
A. Bromacil F. EPTC
B. Bromacil & Diuron G. Fluazifop-Butyl
C. Bromacil & Diuron H. Glyphosate
E. Diuron I. Metolchlor
J. Napropamide

78
Table 5-2—Continued
Herbicide Codes8
Weed Name
A
B
C
E
F
G
H
I
J
Florida Pusley
S
S
S
PS
U
T
S
T
I
Flat-Topped Goldenrod
PS
PS
PS
PS
U
T
S
T
U
Goatweed
T
S
PS
PS
U
T
I
u
U
Goldenrod
PS
PS
PS
PS
U
T
S
T
u
Horseweed
U
U
u
U
U
T
S
U
u
Jerusalem Oak
PS
PS
PS
I
U
T
PS
T
u
Lambsquarters
S
S
s
PS
U
T
s
PS
s
Lantana
PS
PS
PS
PS
U
T
s
T
u
Mexican Tea
U
U
u
u
U
U
u
u
u
Pepperweed
S
S
s
s
U
T
s
T
u
Pigweed
T
PS
s
s
U
T
s
PS
s
Pokeberry
S
s
s
PS
U
T
s
U
u
Primrose Willow
U
s
s
u
u
T
s
U
u
Rouge Plant
S
s
s
PS
u
T
u
U
u
Rustweed
S
s
s
u
u
T
u
U
u
Saltbush
T
T
T
T
u
T
s
T
u
Seamyrtle
U
U
u
U
u
T
s
T
u
Skunkweed
PS
PS
PS
PS
u
T
u
U
u
Sowthistle
U
S
s
u
u
T
s
T
u
Spanish Needles
T
S
PS
s
u
T
s
T
u
Spurge
S
S
s
PS
u
T
s
T
u
Swampwillow
U
u
u
u
u
T
s
T
u
Teaweed
PS
s
PS
PS
u
T
PS
T
u
Virginia Pepperweed
S
s
s
u
u
T
s
PS
u
Waxmyrtle
u
u
u
u
u
T
I
T
u
Air Potato
T
T
T
T
u
T
PS
T
u
Balsam Apple Vine
PS
S
s
PS
u
T
s
T
u
Bigroot Morningglory
T
U
T
u
u
T
PS
T
u
Brazilian Nightshade
U
u
U
u
u
T
s
T
u
Briars
T
T
T
T
u
T
I
T
u
a. The letters correspond to the following herbicides
A. Bromacil F. EPTC
B. Bromacil & Diuron G. Fluazifop-Butyl
C. Bromacil & Diuron H. Glyphosate
E. Diuron I. Metolchlor
J. Napropamide

79
Table 5-2—Continued
Weed Name
A
B
Herbicide Codes8
C E F G H I
J
Calico Vine
U
U
U
U
U
T
I
T
U
Cat'S Claw Vine
T
T
T
T
U
T
PS
T
U
Cypress Vine
U
U
U
U
U
T
s
T
S
Maypop
PS
PS
PS
I
U
T
s
T
U
Milkweed (Strangler)
Vine
PS
PS
PS
PS
U
T
I
T
U
Moonvine
I
I
I
T
U
T
u
T
U
Morningglory
PS
PS
PS
PS
U
T
s
T
S
Narrow-Leaf Milkweed
Vine
T
T
T
T
U
T
I
T
U
Peppervine
T
T
T
T
U
T
I
T
U
Rosarypea
PS
PS
PS
PS
U
T
s
T
u
Virginia Creeper
T
T
T
T
U
T
PS
T
u
Wild Grape
T
T
T
T
U
T
I
T
u
Wild Watermelon
U
PS
PS
U
U
T
s
T
u
Woevine
T
T
T
T
U
T
u
T
u
a. The letters correspond to the following herbicides
A. Bromacil F. EPTC
B. Bromacil & Diuron G. Fluazifop-Butyl
C. Bromacil & Diuron H. Glyphosate
E. Diuron I. Metolchlor
J. Napropamide

80
Table 5-2—Continued
Herbicide Codes3
Weed Name
K
L
M
N
0
P
Q
T
Bahaiagrass
PS
PS
T
I
PS
U
T
PS
Bermudagrass
PS
PS
T
I
PS
I
T
PS
Common Carpetgrass
I
s
U
I
s
U
T
S
Cattail
PS
T
U
s
u
u
T
T
Crabgrass
s
s
I
PS
s
u
S
S
Crowfootgrass
PS
s
I
PS
s
u
s
S
Goosegrass
PS
s
I
s
s
s
s
S
Guineagrass
PS
s
I
s
s
u
PS
S
Johnsongrass
PS
PS
I
PS
s
PS
PS
PS
Maidencane
PS
T
U
T
u
u
T
T
Napiergrass
PS
PS
u
I
PS
u
T
PS
Natalgrass
PS
PS
I
u
PS
u
I
PS
Purple Nutsedge
I
T
I
T
T
u
T
T
Pangolagrass
PS
PS
I
PS
PS
u
T
PS
Paragrass
PS
PS
u
T
PS
u
T
PS
Southern Sandspur
PS
s
I
S
s
u
I
S
Hairy Signalgrass
PS
s
u
S
s
u
PS
S
Texas Panicum
PS
s
I
S
s
s
I
S
Torpedograss
s
PS
I
T
PS
u
T
PS
Vaseygrass
PS
PS
u
PS
PS
u
T
PS
Yellow Foxtail
PS
s
I
S
s
s
S
S
Peppergrass
u
u
u
U
u
u
U
u
Florida Beggarweed
PS
T
I
S
T
u
s
T
Bitter Mint
I
T
u
u
u
u
u
T
Black Nightshade
PS
T
s
I
T
u
s
T
Brazilian Pepper
u
T
u
I
T
u
T
T
Ceasarweed
u
T
u
u
U
u
U
T
Camphorweed
I
T
PS
PS
T
u
U
T
Common Purslane
s
S
s
s
S
u
S
S
Common Ragweed
PS
T
I
s
T
u
s
T
Creeping Charlie
u
T
I
s
T
u
s
T
Cudweed
PS
T
PS
s
T
u
s
T
Dayflower
PS
T
I
s
T
u
T
T
Dogfennel
s
T
u
PS
T
u
PS
T
Evening Primrose
I
T
PS
T
T
u
I
T
a. The letters correspond to the following herbicides
K. Norflurazon O. Pendimethalin
L. Oryzalin P. Sethoxydim
M. Oxyfluorfen Q. Simazine
N. Paraquat Dichloride T. Trifluralin

81
Table 5-2—Continued
Herbicide Codes3
Weed Name
K
L
M
N
0
P
Q
T
Florida Pusley
PS
S
S
s
s
U
I
S
Flat-Topped Goldenrod
PS
T
U
u
T
U
u
T
Goatweed
PS
T
U
u
T
U
I
T
Goldenrod
PS
T
PS
T
T
U
s
T
Horseweed
I
T
T
PS
T
U
I
T
Jerusalem Oak
I
T
PS
PS
T
U
T
T
Lambsquarters
PS
S
S
s
S
U
s
S
Lantana
T
T
T
I
T
U
T
T
Mexican Tea
U
U
U
u
U
U
u
U
Pepperweed
PS
T
S
PS
T
U
I
T
Pigweed
I
S
S
s
S
u
s
S
Pokeberry
PS
T
U
PS
T
u
s
T
Primrose Willow
PS
T
U
PS
U
u
u
T
Rouge Plant
u
T
U
u
U
u
u
T
Rustweed
I
T
u
u
u
u
u
T
Saltbush
I
T
PS
PS
T
u
T
T
Seamyrtle
I
T
u
PS
U
u
T
T
Skunkweed
u
T
u
u
U
u
U
T
Sowthistle
I
T
s
s
T
u
S
T
Spanish Needles
I
T
PS
s
T
u
S
T
Spurge
s
S
PS
s
S
u
S
S
Swampwillow
u
T
PS
PS
T
u
T
T
Teaweed
PS
T
S
T
T
u
I
T
Virginia Pepperweed
PS
S
s
PS
U
u
I
S
Waxmyrtle
u
T
u
PS
T
u
T
T
Air Potato
u
T
u
u
U
u
T
T
Balsam Apple Vine
PS
T
PS
PS
T
u
S
T
Bigroot Morningglory
I
T
u
PS
T
u
S
T
Brazilian Nightshade
I
T
u
T
T
u
S
T
Briars
T
T
u
PS
T
u
T
T
a. The letters correspond to the following herbicides
K. Norflurazon O. Pendimethalin
L. Oryzalin P. Sethoxydim
M. Oxyfluorfen Q. Simazine
N. Paraquat Dichloride T. Trifluralin

82
Table 5-2—Continued
Herbicide Codes3
Weed Name KLMNOPQT
Calico Vine
Cat'S Claw Vine
Cypress Vine
Maypop
Milkweed (Strangler) Vine
Moonvine
Morningglory
Narrow-Leaf Milkweed Vine
Peppervine
Rosarypea
Virginia Creeper
Wild Grape
Wild Watermelon
Woevine
u
T
U
PS
u
U
T
T
u
T
U
T
u
U
T
T
I
T
PS
PS
T
U
T
T
T
T
PS
PS
T
U
T
T
PS
T
PS
PS
T
U
PS
T
u
T
PS
PS
U
U
T
T
PS
T
S
PS
T
U
I
T
u
T
U
PS
T
U
U
T
u
T
U
PS
U
U
T
T
u
T
U
U
U
U
T
T
u
T
U
PS
T
U
T
T
T
T
U
PS
T
U
T
T
I
T
PS
S
T
U
T
T
T
T
U
U
U
U
U
T
a. The letters correspond to the following herbicides
K. Norflurazon
L. Oryzalin
M. Oxyfluorfen
N. Paraquat Dichloride
O. Pendimethalin
P. Sethoxydim
Q. Simazine
T. Trifluralin

83
Apart from the susceptibility ratings, herbicides were
classified into three categories. Pre-emergence herbicides
which are used prior to weed emergence to control potential
problems. Post-emergence herbicides which are used to control
established weeds. And lastly herbicides that offer both pre¬
emergence and post-emergence control. The classification is
used to determine which types of herbicides are suited for a
particular situation.
After rating and classifying the herbicides, a selection
among alternatives was made. The experts agreed that once all
recommended herbicides have been presented to the user, it is
up to that user to choose which herbicide to utilize. They
were not willing to specify a particular herbicide to a grower
to avoid showing any type of support or endorsement for one
product.
Following the selection of a herbicide, recommendations
for spraying rates were given based on information found in
the product labels. In most cases more than one
recommendation was listed depending on such factors as types
of weeds, soil moisture conditions, and soil type. The final
recommendation depended on these factors. Spraying cost
estimates were then given based on recommended spray rates,
product costs, and treated acreage.

84
Follow-up Interviews
Two follow-up interviews were conducted throughout the
program development process. The program was evaluated by the
experts at each follow-up interview with special emphasis on
the new features added since the last interview. Ideas on
improvements and enhancements were exchanged. During the
final follow-up interview, a complete list of program features
was established, and updates on herbicide and weeds
information were made. Once all enhancements and features in
the list were carried out, the final version of the program
was ready for evaluation.
Program Execution
The program proceeds in one of three ways; a herbicide
recommendation is given based on a weed problem, a list of
weeds controlled by a herbicide is identified, or two or more
herbicides are compared as to their effectiveness on a weed
problem.
Obtaining a Herbicide Recommendation
The user defines a weed problem by choosing weeds from
lists in the grass, broadleaf, and vine categories. The
program searches for herbicides that control the weeds
selected. If one or more herbicides are found to control all
weeds selected, they are ranked and displayed on the screen
(Figure 5-4). If no herbicides are found to control all weeds

85
Herbicide and Weed Guide
Available Herbicides for All Weeds Selected
Trade Name
Common Name
Rating
Effect.
Roundup
Glyphosate
6
Post
Devrinol
Napropamide
6
Pre
Solicam
Norflurazon
7
Pre
Goal
Oxyfluorfen
6
Both
Gramoxone
Paraquat Dichloride
8
Post
Susceptibility Rating - The susceptibility rating
is a measure of the effectiveness of each herbicide
on the weed combination it controls. It is the sum
of the susceptibiltities of each weed to the
herbicide.
Figure 5-4
Example List of Herbicides Available to
Control The Defined Weed Problem

86
Herbicide and Weed Guide
Note - No herbicides were found that control
all weeds selected.
The program can now proceed in two different ways:
1 - The program will search for herbicides that control
as many weeds as possible from the list of selected
weeds. One herbicides is selected from a list, and
recommendations and spraying information are given
for that herbicide.
2 - The program will search for individual herbicides
for grass weeds, broadleaf weeds, and vines.
Herbicides that control as many weeds as possible
in each category are found. A herbicide is selected
for each category, and recommendations and spraying
information are given for each herbicide.
Figure 5-5
Option Screen When One Herbicide is Not Found
to Control All the Weeds Selected

87
selected, the user is prompted to direct the program in one of
two ways (Figure 5-5). The program can search for the best
herbicide for all weeds selected, or the best herbicides for
weeds in each category. Once herbicides have been identified,
the user may either select a herbicide, or reject the choices,
and direct the program to search for the next best
alternatives. The search continues until the user accepts a
herbicide and requests a recommendation. If herbicides are
recommended separately for each category of weeds, a
combination of two or more herbicides must be used. The
program only lists these herbicides and the individual spray
rates for each. It does not give information on mixing
procedures, compatibility, and precautions. The user must
refer to more detailed information by consulting the product
labels.
Recommendations for all herbicides are stored in a
database, and with the aid of the expert system and some input
from the user, the appropriate recommendation for the selected
herbicide is given (Figure 5-6). At this point in the
program, a cost estimate for the selected herbicide can be
obtained. The total cost for spraying the herbicide at the
recommended rate reflects the material costs for using the
herbicide. Other costs are usually incurred during the
operation such as machinery, labor, and management costs. The
cost per unit of purchasing the herbicide and number of
treated acres to be sprayed are required from the user. If

88
Hyvar X - (Bromacil) for use on Texas Panicum
Recommendation
Apply 4-5 lbs. of HYVAR X per acre during the
period from winter to early summer. Alternatively,
make two applications of 3-4 lbs. per acre per
year in spring and summer. Partial control usually
occurs with a single treatment; repeat applications
are required to control perennial weeds. Control
of perennial weeds may be improved by cultivation
prior to treatment; otherwise, avoid working the
soil as long as weed control continues since
effectiveness may be reduced.
Susceptibility - Susceptible at germination and
early seedling stage of growth. May also be
susceptible at later stages of maturity.
Figure 5-6
Example Recommendation Screen for Using Hyvar
X Herbicide on Texas Panicum

89
Herbicide Cost Information - Krovar I
Cost per gallon for this herbicide ($) - 7.35
Number of treated acres to be sprayed - 5.00
Rates in pounds per treated acre -
Recommended range - 2.00 - 4.00
Recommended maximum - 8.00
Total Cost ($) - Range per treated acre - 14.70 - 29.40
Range for this grove - 73.50 - 147.00
Maximum Cost ($) - Per treated acre - 58.80
For this grove - 294.00
Figure 5-7
Example Cost Screen for Krovar I Herbicide

90
additional spray material such as oils or surfactants are to
be used, the user must specify the cost of these materials.
When all information is provided, the program calculates the
cost per acre and total grove cost for the herbicide based on
the recommendation and the user's inputs (Figure 5-7).
Obtaining a List of Weeds Controlled bv a Herbicide
The user chooses a herbicide from a list of all
herbicides in the database (Table 5-3). The program searches
for weeds controlled by that herbicide and provides lists of
weeds in the grass, broadleaf, and vine categories. The user
may select any weed from the lists and obtain appropriate
spraying recommendations and cost information.
Comparing Several Herbicides
It is often the case that a grower has several choices of
herbicides to use on his weed problem. A is very difficult to
assess the advantages of using one herbicide over another. An
accurate management decision could lead to a reduction in
costs of spraying while maintaining an acceptable level of
weed control. A feature of the program allows a comparison to
be made between several herbicides resulting in a better
management decision. Their effectiveness on a particular weed
problem as well as the cost of using each herbicide are
compared. After selecting the herbicides from a list, and
defining a weed problem, a summary screen is displayed for

91
Table 5-3
List of Herbicides Recommended for Use on
Florida Citrus
Trade Name
Common Name
Hyvar X/L
Krovar I
Krovar II
Karmex, Direx
Eptam
Fusilade
Roundup
Dual
Devrinol
Solicam
Surflan
Goal
Gramoxone
Prowl
Poast
Princep
Treflan
Bromacil
Bromacil & Diuron
Bromacil & Diuron
Diuron
EPTC
Fluazifop-Butyl
Glyphosate
Metolachlor
Napropamide
Norflurazon
Oryzalin
Oxyfluorfen
Paraquat Dichloride
Pendimethalin
Sethoxydim
Simazine
Trifluralin

92
each selected herbicide. The user is required to go through
the spraying recommendations and cost information portions of
the program to gather all the necessary data. Once
recommendations and costs for the herbicides have been
gathered a comparison can be made using the summary screens.
Each summary (Figure 5-8a) contains information on the number
of weeds controlled by a herbicide (Figure 5-8b), weeds not
controlled, and cost per acre as well as total cost for the
grove.
Integration with COINS
The herbicide and weed guide can be accessed either from
the main menu of COINS or through the summary and comparison
program. If the user wishes to compare his herbicide
application costs with costs for recommended rates, the
herbicide and weed guide must be accessed through the summary
program. From the summary program, the user runs the
herbicide and weed guide to obtain application recommendations
and associated costs. Overall and material costs from the
summary program are then displayed alongside the recommended
rates and costs (Figure 5-9). The user may then evaluate his
herbicide application operation and make management decisions
based on differences in herbicides, rates, and associated
costs.

93
Herbicide Summary Number of Herbicides Selected - 2
1. Fusilada - Fluazifop-Butyl
Number of weeds controlled - 4 of 13
Cost Information -
Total Cost -
Range per treated acre - 30.19 - 40.25
Range for this grove - 150.94 - 201.25
2. Roundup - Glyphosate
Number of weeds controlled - 6 of 13
Cost Information -
Total Cost -
Range per treated acre - 18.30 - 73.20
Range for this grove - 91.50 - 366.00
Maximum Cost - Per treated acre - 192.15
For this grove - 960.75
Figure 5-8a
Example Summery Screens for Comparing Fusilade
and Roundup Herbicides

94
Herbicide Summary Number of Herbicides Selected - 2
Roundup - Glyphosate on Combination Of Weeds
Weeds Not Controlled
Type
Bahaigrass
Grass
Bermudagrass
Grass
Guineagrass
Grass
Bitter Mint
Broadleaf
Black Nightshade
Broadleaf
Brazilian Pepper
Broadleaf
Cudweed
Broadleaf
Figure 5-8b
Example of Seven WeedsNot Controlled By
Roundup

95
Herbicide Cost Information - Krovar I
Cost per gallon for this herbicide ($) - 7.35
Number of treated acres to be sprayed - 5.00
Rates
in pounds
per treated acre -
Recommended range - 2.00 - 4.00
Recommended maximum - 8.00
Total
Cost ($) -
Range per treated acre - 14.70 - 29.40
Range for this grove - 73.50 - 147.00
Maximum Cost ($)
- Per treated acre - 58.80
For this grove - 294.00
Total
Herbicide
Costs for Grove #40 ($) -
Overall Cost - 356.00
Material Cost - 227.13
Figure 5-9
Example Cost Screen for Krovar I Herbicide
Including Grower Overall and Material Costs

CHAPTER VI
TESTING AND EVALUATION
The performance of the COINS system was tested and
evaluated during the developmental stages and again at the
completion of the project. The testing and evaluation process
involved two major phases. The first phase involved debugging
the system and testing its performance with real data. The
purpose of this phase, termed operational evaluation, was to
determine whether or not the system performed according to
design standards, and requirements. The second phase of the
evaluation involved determining the system's qualifications as
an information and management tool for the Florida citrus
industry. This phase, termed professional evaluation, was
carried out by allowing a group of Florida citrus growers,
extension specialists, county agents, and citrus specialists
to utilize and evaluate the system. Evaluation results are
shown in tables 6-1 and 6-2.
Phase 1 - Operational Evaluation
Testing and Evaluation of the COINS Cost Programs
Preliminary Testing and Debugging. When COINS was being
developed, cost information was needed to test the various
routines that constituted the data entry and summary programs.
96

97
A data set was created from the published reports of caretaker
charges to mock actual grower cost data. The cost information
generated was not accurate, and was used only to debug the
programs and test the functionality of routines within the
programs.
Testing COINS With Actual Data. New application programs
must be thoroughly debugged against "real" data (Harrington,
1988) . One of the prime sources for accidental destruction of
data in a database is the testing and use of new application
programs that have not been thoroughly debugged.
In order to properly test the overall performance of the
COINS programs, actual cost information was needed. Within
the constraints of time and resources, it was decided that an
initial group of ten growers would be contacted. The names of
ten potential participants was drawn up from a list of growers
and citrus cooperatives that had already expressed an interest
in working with information systems, by taking part in the
FAIRS program (Florida Agricultural Information Retrieval
System). Phone contact was made with the growers in order to
explain the purpose of the project, and to solicit
cooperation. Arrangements to visit the growers were made.
Six of the ten growers contacted, agreed to participate in a
short information session that included a demonstration of the
program, and a brief explanation of the overall objectives of
the project. Two of the ten expressed no desire to help with
the project due to lack of time, and two growers agreed to

98
look over an information package on the project rather than
sit in on a meeting. Written information packages containing
an explanation of the project objectives, and two cost survey
forms were mailed out to each of the two growers.
Following the information sessions, written cost survey
packets were mailed out to two growers, a citrus cooperative,
and a caretaker operation that also maintained grove records
for customers. Through these final four participants,
production cost information for 50 groves was collected.
Each of the two growers supplied information on one of
their groves by filling out the written survey, the citrus
cooperative supplied information on two groves through a
computer printout of costs, and the caretaker operation
supplied cost information for 46 groves belonging to several
customers, by downloading the necessary data from an IBM
System 36 to a 3.5" floppy disk. The data was transferred
using a utility that converted data from the IBM System 36
software package being used by the caretaker to a file in
DBASE III+ format.
The cost information supplied by the growers and
cooperative was entered into the COINS database through the
data entry program. Information supplied by the caretaker
operation was stored in two data files on disk. The first
file held information on grove characteristics, and was easily
appended to the COINS database with a few modifications. The
second file held the actual cost data for the grove. A hard

99
copy of this information was made and used to input the
information into COINS through the data entry program. Since
the grove characteristics were not entered by hand in this
case, this data collection method proved to be time efficient.
However, the cost information file could not be appended
directly to the COINS databases due to discrepancies in the
names of some cost categories. For this reason manual entry
of the cost data was needed.
Some categories were modified during data entry in order
to accommodate the different category names used by the
growers. Also, additional categories not covered by the
original caretaker surveys were added. It was estimated that
data entry took approximately 10 to 15 minutes per grove to
complete.
Testing and Evaluation of the Herbicide and Weed Guide
All rules used to reach recommendations were derived from
the herbicide product labels and hence were very accurate.
For this reason evaluation of these rules was not necessary.
It was, however, necessary to evaluate the procedures the
program used to arrive at its conclusions. The purpose of the
evaluation was to determine whether the program properly goes
through the logic steps used by the experts to reach a
recommendation. The accuracy of the recommendations (i.e.
which herbicide to use) , and not the spraying rates and
herbicide information, were also evaluated.

100
The completed version of the herbicide and weed guide was
demonstrated to the experts for final evaluation. All program
features were demonstrated and several runs were carried out
to test the various functions. Following the evaluation, the
experts agreed that the program ran all the applications
satisfactorily. The experts carried out several runs to match
their recommendations with those of the program. It is
important to note here that a final recommendation for a
herbicide is often given based on the user's choice from a
list of recommended herbicides. The experts cannot make the
choice for the user. The program's ability to obtain accurate
lists of appropriate herbicides beginning with a weed problem,
was also found to be acceptable by the experts.
The program in its final form was also installed on one
of the experts' computers for further evaluation. The purpose
of this evaluation was to determine what future modifications
and enhancements could be made to the program.
Phase 2 - Professional Evaluation
Role of Trade Show Exhibit
The main concern prior to the professional evaluation was
to recruit enough growers and citrus industry professionals to
use and evaluate the system. Success with phone solicitation
was found to be limited due to the growers' lack of full
knowledge of the system's capabilities. Most growers
contacted cited lack of time and limited computer expertise as

101
the principal problems when declining to participate in the
evaluation.
It was decided that the best way to obtain participation
would be to demonstrate the program to a group of growers,
showing its potential benefits and usefulness as a management
tool. COINS was demonstrated at a citrus trade show in
Lakeland, Florida. The trade show is held every year and is
attended by thousands of citrus industry professionals,
including vendors, growers, caretakers, managers, college
professors, and extension specialists. A booth was set up at
the show with a demonstration version of COINS. Those
interested in the program were asked to sit through a five
minute demonstration that outlined the COINS system's
capabilities. Following the demonstration, they were given a
two page information pamphlet with brief details on the main
purpose, features, and requirements of the system (Appendix
B). The second page of the information sheet was a software
order form. It indicated that the software was available free
of charge on a trial basis in exchange for an evaluation of
the system. Those interested in ordering the software were
asked to fill out the order form and mail it in, or simply
leave it at the exhibit table. Information required from the
participants included name, address, phone number, and best
times to be contacted. It was also important to get
information on the type of computer and floppy disk drive that
will be used to install and run the system.

102
The exhibit and demonstrations were very successful.
Approximately thirty people stopped by the booth for
information during a two day period. The demonstration was
found to be an effective tool in generating enthusiasm, and
arousing interest in COINS. Short discussions followed some
demonstrations where participants gave their views and
comments on the COINS system.
Citrus growers, and operation managers living in various
regions of Florida, the United States, and some foreign
countries expressed an interest in receiving a copy of COINS.
Several specialists and citrus industry professionals also
expressed interest in examining and evaluating the system.
Those included a former county agent, a professor of citrus at
South Florida Community College, two professors of the Fruit
Crops Department at the University of Florida, a citrus
researcher and economics specialist at the University of
Florida's Citrus Research and Education and Center, and a
professor at the Department of Food and Resource Economics.
Role of Citrus and Extension Specialists
The main purpose of COINS was to provide information to
citrus growers as well as other professionals in the citrus
industry. Hence, program evaluation by extension and citrus
specialists was important, and provided a different
perspective.

103
Extension specialists and county agents helped provide
additional names of growers that were potential users of the
system. These growers were contacted, and those interested,
were mailed the COINS software package.
Purpose of the Evaluation Form
The evaluation form (Appendix B) used was designed to
serve two purposes. The first was to evaluate the usefulness
of the program's features as decision tool, and to evaluate
COINS overall functional performance as a software package.
The form was divided into structured responses, whereby the
evaluator rated a certain aspect of the program's usefulness
or performance, and non-structured responses which were aimed
at determining strong points and possible improvements.
Evaluation Procedures
Evaluations were carried out using two methods. The
software was either mailed out to participants along with a
cover letter, a quick reference guide and an evaluation form
(Appendix B), or a demonstration and evaluation session was
set up with the participant at his or her convenience. When
growers were involved, it was more beneficial for them to
receive the software by mail and evaluate it on their own
time. This also allowed the program to be tested completely
by the grower avoiding any bias to the evaluation by having an

104
expert user present, as in the case with the information
session.
Software Sent bv Mail. The software was mailed out on
either two 720K 3V diskettes or three 360K 5V floppy
diskettes. Transferring the COINS system from the
distribution diskettes required little or no computer
knowledge. An install program that transferred all the
necessary files to a sub-directory on the user's hard disk
drive was included with the program.
The program itself uses approximately 5 megabytes of hard
disk space. Up to an additional 5 megabytes of hard disk
space were reguired during cost averaging due to the use of
temporary files for data storage. The exact amount of
additional memory required depended on the number of groves
being averaged. In order to reduce the number of files and
hence diskettes required to mail to participants, a special
program was used to compress the required programs into
special archived files. The install program creates a COINS
sub-directory on the users hard drive and copies the
compressed files to it. The install program then decompresses
the archived files on the hard drive, thus creating the full
running version of COINS.
The quick reference guide (Appendix B) was included with
the software package. It contained all information necessary
to install, and run the program. The user was asked to

105
evaluate the program by returning the completed evaluation
forms that were also included in the packages.
Personal Contact Sessions. Personal contact sessions
with growers and other specialists were used as a means of
evaluating COINS. Meetings included a detailed demonstration
of COINS followed by a discussion and evaluation. During the
meetings COINS was installed on the participant's computer.
Each of the two cost programs were demonstrated using examples
from the database to show cost information for groves. The
herbicide and weed guide was also demonstrated thoroughly with
specific examples.
During the discussion that followed the demonstration,
participants were encouraged to comment on positives and
negatives aspects of the system. At the conclusion of the
meeting, the participants were given the COINS quick reference
guide, and asked to contact the programmer in case of any
problems. They were also provided with a COINS software
request form to give to any persons interested in receiving
the program.
Evaluation Results for Structured Responses
Frequency of Use. The herbicide and weed guide received
the highest ratings for frequency of use. The cost summary
and comparison program was used frequently, and the data entry
program less frequently.

106
Ratina of General Aspects. The quick reference guide
provided all necessary information for the installation and
execution of all program features. System installation was
rated as excellent. The quality, contents, and context
sensitivity of the help utilities was rated as good. General
ease of use of the system was rated as good, and improved with
increased use of the programs.
The speed with which the programs access information was
found to be satisfactory with the exception of the cost
averaging process that takes place before summaries are
displayed. This part of the program involves lengthy data
compilation and calculations as well as frequent hard disk
access, and hence requires considerably longer to run than
other applications.
Data Entry Program Evaluation. Grove definition
parameters namely grove size, type of fruit, yield, price per
box, fruit varieties, fruit market (fresh, processed, or
both), and grove location where found to constitute an
acceptable description for a grove. Other parameters were
suggested as possible additions to the existing ones. These
were tree spacing or tree set, tree count per acre, year of
production, general weather conditions and occurrences of
unusual weather patterns during the growing season, average
age of the grove, location of bodies of water near the groves,
and the nature of each grove operations (contracted out, or
performed by the grower).

107
The data entry program was found to be useful for storing
cost information, but did not replace existing packages that
specialized in grove accounting. The rating for thoroughness
and usefulness of the cost categories and sub-categories
varied among growers. Those growers who kept records of
detailed costs found the sub-categories to be useful during
data entry. Lack of detailed costs for many groves was found
to decrease the usefulness of the sub-categories in the
summary and comparison program.
Usefulness of menu options was rated as good. And the
program as a whole was found to be relatively easy to use.
The user interface including, menu driven functions and
screens, help menus, and the spreadsheet type data entry
screens were rated as excellent.
Summary and Comparison Program Evaluation. Search
parameters were found to be fairly comprehensive, and allowed
flexibility in determining the types and amount of information
to be summarized. Some additional search parameters were
recommended such as year of production, average age of the
grove, general condition and guality of trees, and weather
conditions during the particular growing season. In general
the program was found to be easy to use, and usefulness of
menu functions was rated a good.
Averaged data provided in the summaries was found to be
a good source of production cost information. Accuracy of the
cost data was a major concern, but it was established that the

108
cost information was to be used only as a general indicator of
industry averages. Comparisons between industry averages and
grower costs were found to be very useful. The comparisons
were found to give a general idea of how a grower's expenses
compared to industry averages. However, the accuracy of the
comparisons depended on the specificity of the data summary
parameters given at the start of the summary program. The
more general the parameters, the less accurate the comparison
data.
The program was reported to aid citrus growers in
determining possible problem areas in their operations. The
summary column for the growers' own operations was a helpful
indicator of per grove costs and revenues. It was suggested
that a grower be able to compare several years of production
for his own operation in order to study trends and potential
problems. This may be achieved by incorporating a year of
production parameter when collecting the grove costs. In
conclusion, the cost summary and comparison program was found
to be an effective aid for management decision making.
Herbicide and Weed Guide Evaluation. The general ease of
use of the herbicide and weed guide was rated as good.
Although the program offers no help screens, the menu driven
user interface was found to be adequate. Thoroughness of
herbicide and weed lists was rated as excellent. Growers
normally referred to the Florida Citrus Spray Guide for
information and lists of recommended herbicides. The program

109
comprised a comprehensive and easily accessible source of
information. Lists of weeds controlled by each herbicide and
subsequent recommendations were useful.
The program was found to be an excellent tool for
obtaining herbicide recommendations. Arriving at a herbicide
recommendation from a set of circumstances was found to be
highly useful. The recommendations given by the program were
not very detailed but supplied the necessary information on
spray rates.
In many cases, growers are faced with making a decision
on which herbicide to purchase from several vendors bidding
for his business. Or, the program may recommend several
choices of herbicides to be used on weed problem. The ability
to compare the herbicides was found to be particularly useful
as a decision making tool.
The usefulness of the integration of the spraying rates
with material cost estimates was rated as good. Cost
summaries for herbicide recommendations and for herbicide
comparisons, along with printed reports constituted a
comprehensive decision making tool for weed control on citrus.
Information Sharing. One of the major concerns since the
beginning of this project has been the reluctance of growers
to share their cost information. The main strategy used to
overcome this potential problem was ensuring the
confidentiality and integrity of cost data. A password system
was developed to protect the integrity of files. Sources of

110
cost information distributed to growers were blanketed by
using an identical password for all groves. Also, no less
than three groves can be averaged to produce a summary. All
these safety features were explained to the evaluation
participants. Most growers who evaluated the system indicated
they would have no problem sharing their data as long as
confidentiality was ensured.
Evaluation Results for Non-structured Responses
The results of the non-structured evaluation yielded
favorable results. The program's main strengths were in its
ability to store and retrieve useful production cost
information. The system provided growers with a means to
evaluate their operation's performance and gain valuable
insight into possible problem areas. Information sharing in
the form of cost data as well as extension and management
recommendations were also listed as program strengths. The
herbicide and weed guide was found to be a particularly useful
decision making tool.
Some recommendations were made for the overall
improvement of COINS. It was suggested that more grove
definition parameters be added. An increase in the total
number of groves in the system, and the areas covered by the
survey was also desired. The inclusion of cost information
for several growing seasons and the ability to compare those
costs was suggested by some growers as a possible enhancement

Ill
to the program. More accurate comparison criteria were also
recommended. And finally, it was suggested that decision
support systems cover other areas in addition to the herbicide
category.

112
Table 6-1 Responses to Written Evaluations
Responses
Statements/Questions3
1
2
3
Frequently
1
0
4
Sometimes
3
4
2
Rarely
2
2
0
Total
6
6
6
a. The numbers correspond to the following statements
1. Extent of use of the data entry program.
2. Extent of use of the summary and comparison program.
3. Extent of use of the herbicide and weed guide.

113
Table 6-1 — Continued
Responses
Statements/Questions8
1
2
3
4
5
Excellent
4
4
0
0
1
Good
2
1
3
5
4
Fair
0
1
2
1
1
Poor
0
0
1
0
0
Total
6
6
6
6
6
8. The numbers
correspond
to the
following
statements
Rating for general aspect of COINS.
1. Ease of installation
2. Directions for use
3. Overall speed of accessing information
4. General ease of use of programs
5. Quality and usefulness of help utility

114
Table 6-1 — Continued
Responses
Statements/Questions8
1
2
3
4
5
6
7
Excellent
1
1
0
0
0
1
3
Good
4
2
6
4
6
4
2
Fair
1
3
0
2
0
1
1
Poor
0
0
0
0
0
0
0
Total
6
6
6
6
6
6
6
a. The numbers correspond to the following statements
Rating for characteristics of the data entry program.
1. Thoroughness of grove definition parameters
2. Usefulness as a data entry and storage tool
3. Thoroughness of cost categories
4. Usefulness of sub-categories
5. Thoroughness of sub-categories
6. Usefulness of menu functions
7. General ease of use of program

115
Table 6-1 — Continued
Responses
Statements/Questions3
1
2
3
4
5
6
7
Excellent
0
0
0
0
0
0
3
Good
3
3
3
2
3
6
1
Fair
3
3
3
3
1
1
0
Poor
0
0
0
1
2
0
0
Total
6
6
6
6
6
6
6
a. The numbers correspond to the following statements
Rating for the chracteristics of the summary and
comparison program.
1. Thoroughness of search parameters
2. Usefulness as an information tool
3. Usefulness as a cost comparison tool
4. Usefulness as a decision making tool
5. Usefulness of information to your operation
6. Usefulness of menu functions
7. General ease of use of program

116
Table 6-1 — Continued
Responses
Statements/Questions8
1
2
3
4
5
6
7
8
9
10
Excellent
2
3
2
3
1
0
1
1
2
2
Good
4
3
3
3
5
5
2
4
3
3
Fair
0
0
1
0
0
1
0
1
0
1
Poor
0
0
0
0
0
0
0
0
1
0
Total
6
6
6
6
6
6
3
6
6
6
a. The numbers correspond to the following statements
Rating for chracteristics of the herbicide and weed
guide.
1. Thoroughness of list of herbicides
2. Thoroughness of list of weeds
3. Usefulness of herbicide recommendations
4. Usefulness of comparing several herbicides
5. Usefulness in showing weeds controlled by a herbicide
6. Usefulness of cost information on herbicides
7. Usefulness of printed reports
8. Usefulness as a decision making tool
9. Usefulness of information to your operation
10. General ease of use of program

117
Table 6-2 Estimated Responses to Discussions
Responses
Statements/Questions3
1
2
3
Frequently
0
0
3
Sometimes
2
3
0
Rarely
1
0
0
Total
3
3
3
a. The numbers correspond to the following statements
1. Extent of use of the data entry program.
2. Extent of use of the summary and comparison program.
3. Extent of use of the herbicide and weed guide.

118
Table 6-2 — Continued
Responses
Statements/Questions3
1
2
3
4
5
Excellent
4
0
0
0
2
Good
1
5
2
5
2
Fair
0
0
2
0
1
Poor
0
0
1
0
0
Total
5
5
5
5
5
a. The numbers
correspond
to the
following
statements
Rating for general aspect of COINS.
1. Ease of installation
2. Directions for use
3. Overall speed of accessing information
4. General ease of use of programs
5. Quality and usefulness of help utility

119
Table 6-2 — Continued
Responses
Statements/Questions8
1
2
3
4
5
6
7
Excellent
0
0
0
0
0
0
1
Good
1
3
5
3
5
5
4
Fair
4
2
0
2
0
0
0
Poor
0
0
0
0
0
0
0
Total
5
5
5
5
5
5
5
a. The numbers correspond to the following statements
Rating for characteristics of the data entry program.
1. Thoroughness of grove definition parameters
2. Usefulness as a data entry and storage tool
3. Thoroughness of cost categories
4. Usefulness of sub-categories
5. Thoroughness of sub-categories
6. Usefulness of menu functions
7. General ease of use of program

120
Table 6-2 — Continued
Responses
Statements/Questions8
1
2
3
4
5
6
7
Excellent
0
0
0
1
0
0
0
Good
0
4
3
2
0
4
5
Fair
4
1
2
2
3
1
0
Poor
1
0
0
0
0
0
0
Total
5
5
5
5
3
5
5
8. The numbers correspond to the following statements
Rating for the chracteristics of the summary and
comparison program.
1. Thoroughness of search parameters
2. Usefulness as an information tool
3. Usefulness as a cost comparison tool
4. Usefulness as a decision making tool
5. Usefulness of information to your operation
6. Usefulness of menu functions
7. General ease of use of program

121
Table 6-2 — Continued
Responses
Statements/Questions8
1
2
3
4
5
6
7
8
9
10
Excellent
4
3
5
2
1
2
0
0
0
4
Good
1
2
0
3
2
3
2
5
3
1
Fair
0
0
0
0
2
0
0
0
0
0
Poor
0
0
0
0
0
0
0
0
0
0
Total
5
5
5
5
5
5
2
5
3
5
a. The numbers correspond to the following statements
Rating for chracteristics of the herbicide and weed
guide.
1. Thoroughness of list of herbicides
2. Thoroughness of list of weeds
3. Usefulness of herbicide recommendations
4. Usefulness of comparing several herbicides
5. Usefulness in showing weeds controlled by a herbicide
6. Usefulness of cost information on herbicides
7. Usefulness of printed reports
8. Usefulness as a decision making tool
9. Usefulness of information to your operation
10. General ease of use of program

CHAPTER VII
CONCLUSIONS AND RECOMMENDATIONS
The primary objective of this dissertation was to provide
citrus growers in Florida with a means of improving their
production practices through the enhancement of knowledge and
management decision making, by providing an economic basis for
extension and management recommendations. This goal was
successfully achieved by developing and integrating a computer
based citrus production cost information system with extension
and management recommendations. The techniques used have
allowed the dissemination of valuable knowledge among citrus
growers in Florida. It has helped create a dynamic source of
citrus production cost information.
The utilization of a database management environment
provided a comprehensive method for the development of an
information system that is both dynamic and flexible. The
environment allowed for the efficient manipulation of large
amounts of information in a way that facilitates maintenance
and information updating.
A system that collects and summarizes citrus production
costs for groves in Florida was designed and developed. The
Citrus Production Cost Information System (COINS) allows
comparisons to be made between individual grove costs and
122

123
industry averages. Information retrieval can be customized to
include a wide or narrow information domain.
A herbicide and weed guide was designed and developed as
a prototype for a system that integrates production cost
information with extension and management recommendations
(SIMON). The decision support system incorporates an expert
system that uses rules to arrive at management
recommendations. The expert system was designed to utilize a
unique method of deduction and information storage that allows
easy access to rules and data, allowing efficient maintenance
and updating.
COINS was tested using real grove cost information from
fifty individual groves in Florida. System evaluation yielded
encouraging responses from citrus growers who installed and
used the system on their own computers. Evaluations were also
carried out by citrus specialists, county agents, consultants,
and extension specialists. The concept of a dynamic database
of cost information integrated with extension and management
recommendations can be used to enhance communication of
information among citrus growers in Florida. The concepts
used in the herbicide and weed guide may be applied to other
areas of the citrus operation, and the overall concept of the
COINS system can be applied to virtually any agricultural
operation.

124
Some recommendations to further strengthen COINS as a
decision support system for citrus production costs are
discussed below.
1) The prototype system developed to demonstrate the
SIMON concept currently deals with the herbicide operation.
The development of other systems to handle other production
areas such as pesticide application, irrigation,
fertilization, and frost protection would complement the
existing system.
2) When defining a grove, the user provides information
on size, yield, price per box, type of fruit, fruit market,
and grove location. The addition of such criteria as tree
spacing, tree count per acre, average age of the grove, nature
of operation (contracted out or performed by the grower), and
types of chemicals used, would allow for a more comprehensive
description of a grove. It would also allow for more accurate
comparisons with the industry averages to be made.
3) Production costs are currently compared on a same year
basis. The COINS database would be greatly enhanced with the
inclusion of data for more than one growing season. Such
information would allow comparisons to be made over several
years of production.
4) COINS is currently designed to reside on a growers
computer. All necessary files and programs are located on his
system. With a growing database, and the need for more
efficient maintenance, the possibility of an on-line system

125
whereby COINS is located at a central location with users
accessing the system through modems should be explored.
5) The integration of COINS with other agricultural
information systems would greatly enhance the systems
capabilities as a tool for agricultural technology transfer.
6) The transfer of information from source to user has an
endless potential for growth and improvement. The COINS
system is currently available for citrus growers in Florida.
The concepts discussed in this dissertation should be
implemented in other areas of agriculture.

APPENDIX A
DATABASE FILE FORMATS

File format for production cost files:
Field
Field Name
1
OPERATION
2
ID
3
OVERALL
4
LABOR
5
MACHINERY
6
MANAGEMENT
7
MATERIAL
8
LEVEL
9
COMMENT
10
PRODUCT
11
SEPARATE
12
OVERRANGE
13
LABORANGE
14
MACHRANGE
15
MANARANGE
16
MATERANGE
17
GROWERCODE
Type Length Dec
Character
20
Numeric
5
Numeric
9
2
Numeric
8
2
Numeric
8
2
Numeric
8
2
Numeric
8
2
Numeric
1
Character
60
Character
60
Logical
1
Character
24
Character
24
Character
24
Character
24
Character
24
Character
20
Description
Operation name
Grove ID number
Overall cost
Labor cost
Machinery cost
Management cost
Material cost
Level in hierarchy
Comment
Product description
Separate cost access
Overall cost range
Labor cost range
Machinery cost range
Management cost range
Material cost range
Password
File format for weed files:
FieId Field Name
1 NAME
2 SCINAME
3 CODE
4 SELECT
5 TYPE
Type Length
Character 40
Character 40
Character 2
Logical 1
Character 1
Description
Weed name
Scientific name
Herbicide code
Selection flag
Classification
127

1
2
3
4
7
8
9
10
11
12
14
15
16
17
18
es
Lid
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
128
format for
Field Name
NAME
TRADE
HCODE
NUMBER
BROAD
BNAME
GRASS
GNAME
VINE
VNAME
PRICE
UNIT
FLAG
PICK
EFFECT
format for
Field Name
NAME
TRADE
NUMBER
CONDITION
LINK
CONDITION2
RECOMI
RECOM2
NOTE1
NOTE2
FLAG
UNIT
LOW
HIGH
MAX
OTHER
ALTERNATE
NUMALT
ALOW
AHIGH
AMAX
AOTHER
MAXN
herbicide file:
Type Length
Dec
Description
Character
30
Herbicide name
Character
30
Trade name
Character
2
Herbicide code
Numeric
5
2
Herbicide number
Logical
1
Controls broadleaf
Character
60
Broadleaf codes
Logical
1
Controls grasses
Character
60
Grass codes
Logical
1
Controls vines
Character
60
Vine codes
Numeric
6
2
Price (not used)
Character
3
Price unit
Logical
1
Selection flag
Logical
1
Selection flag
Character
4
Effectiveness
herbicide recommendations file:
Tvoe Lenath/Dec
Character
30
Character
30
Numeric
5 2
Character
60
Character
5
Character
60
Character
254
Character
254
Character
254
Character
254
Logical
1
Character
4
Numeric
6 2
Numeric
6 2
Numeric
6 2
Numeric
6 2
Logical
1
Numeric
1
Numeric
6 2
Numeric
6 2
Numeric
6 2
Numeric
6 2
Numeric
2
Description
Herbicide name
Trade name
Rule number
First condition
Linker (AND, OR)
Second condition
First recommendation
Second recommendation
Special note
Special note
Selection flag
Unit(lbs.,qts.,gal. )
Low application rate
High application rate
Maximum rate
Other chemical
Second recommendation
Number of application
Low application rate
High application rate
Maximum rate
Other chemical needed
Maximum number of
applications

APPENDIX B
SYSTEM EVALUATION

University of Florida
Agricultural Engineering Department
COINS - citrus Cost Information System
Purpose: COINS is a computer program designed to provide
industry average citrus production costs. It also
features decision support systems for various
operations. A herbicide and weed guide is
currently available.
Features:
• Data Entry - A series of spreadsheets allow you to store
production cost information for your grove in ten
categories: cultivation, dusting, spraying, frost
protection, young tree care, irrigation, tree removal,
fertilizer application, pruning, and other operations.
Costs may be entered for several different groves at
varying levels of detail.
• Data Retrieval - You give a description of grove size,
type of fruit, and location, and the program will
retrieve information for all available groves that fit
the description.
• Cost Summary - Information found in the data retrieval
program is averaged, and then displayed in a per acre
costs and returns summary. You may also do an on screen
comparison between the averages and costs for one of your
groves.
• Herbicide and Weed Guide -
• Displays a list of weeds controlled by a particular
herbicide.
• Compares the effectiveness of two or more
herbicides on a particular weed problem.
• Gives herbicide recommendations for weed problems,
including spraying information and application
rates.
• Gives a summary of costs associated with applying a
herbicide.
Requirements:
The programs require an IBM compatible microcomputer with
640K of RAM memory, a hard disk drive, and a color
monitor. COINS requires at least 5 Megabytes of free
hard disk space. The Herbicide and Weed Guide requires
at least 500K of free hard disk space. The program can
run on any IBM compatible PC, XT, AT or 386, but
execution on slower machines may be very time consuming.
130

131
University of Florida
Agricultural Engineering Department
COINS - Citrus Cost Information System
COINS is currently being tested and is available free of
charge to Florida citrus growers. To receive a copy of COINS,
including the data entry, retrieval, and summary programs as
well as the Herbicide and Weed Guide, simply fill out this
form and mail it to:
Ramzi Khuri
Agricultural Engineering Department
Frazier Rogers Hall
University of Florida
Gainesville, FL 32611
Tel. (904) 392-5979
After receiving your form, I will contact you and set up
a one or two hour demonstration session in your office
involving a demonstration and evaluation. Following the
demonstration, you will receive your copy of COINS.
COINS Software Request Form
Name: ___ .
Company: .
Address: .
Phone: i ) - .
When is the best time to reach you by phone ? .
When is the best time to meet with you ? .
What type of computer do you own ? PC XT AT 386
What type of disk drive(s) does your computer have ? 5% 3%

132
COINS
Citrus Cost information System
Thank you for agreeing to evaluate the COINS program.
We hope that you will benefit from the use of this program.
Please take the time to read the Quick Reference Guide for
general information and installation procedures.
COINS is still under development. It has been made
available to you as part of a test which will help us improve
the program to better fit your needs. We ask that you take
some time to familiarize yourself with the program, and to use
as many of its features as possible. An evaluation form is
included with your package. Please take the time to fill it
out, and return it to us as soon as possible.
COINS is based on a database of grove cost information.
The program currently holds information on 50 groves growing
several varieties of citrus in the Ridge area of Florida
gathered in 1988. We are hoping the database will grow to
include many more groves. Please use the Data Entry program
to enter production costs for your operation. Remember: Your
cost information will remain in the strictest confidence. We
will not add your data to the database without your
authorization.
Thank you again for using COINS and for participating in
this evaluation. If you have any problems or questions please
contact Ramzi Khuri at (904) 392-5979 or (904) 392-1864 and
leave a message. For further information please write to:
Ramzi Khuri
Agricultural Engineering Department
Frazier Rogers Hall
University of Florida
Gainesville, FL 32611

133
COINS
Citrus Cost Information System
Quick Reference Guide
1990

134
General Information
A Citrus Cost Information System (COINS) was developed to
collect, manage, and report costs associated with citrus
production in Florida. The program consists of three main
sections. The first is a data gathering program for
production costs for various areas of the operation. The
second section is a summary and reporting program which
provides detailed summaries of averaged production costs, and
allows a comparison to be made between production costs for a
single grove, and industry averages. The third part is a
decision support system that allows you to link extension and
management recommendations with the appropriate production
costs. Currently, a herbicide and weed guide is available to
make management recommendations and provide information on
related production costs.
How to Install COINS
COINS comes with its own install program. The program
will be installed on your hard drive in a directory called
C:\COINS. To run the install program, simply place the COINS
INSTALL disk in your floppy drive and type INSTALL drive> destination drive> (ex. INSTALL A: C:), then press
return. To run the COINS program, go to the COINS directory
on your hard drive, type COINS and press return.
Special Note; When using COINS, please make sure you exit the
program through the menus. Do Not turn the computer off
before properly exiting the COINS program. This will ensure
that no information will be lost from the files.

135
The Data Entry Program
The following section describes the procedure for
starting and using the data entry program to input or edit
costs for a grove. Two options are available when the program
is started. You may either define and enter costs for a new
grove, or view and edit information for an existing grove.
From the main menu, select the Enter/Edit Costs option.
You will then be asked for a password. Enter your password
and press return. If this is the first time you use the
system, enter NEW and press return. The program will ask you
for a password. Enter a password of 20 characters or less in
length. Write down your password and remember it. Your
password is your key to using the system.
If you are not a new user, the program will ask you if
you want to enter costs for a new grove, or modify information
for an existing grove. Select one of the two options.
• Defining a New Grove. To enter costs for a new grove, you
must define the characteristics of the grove. These include
size in acres, fruit varieties, fruit market, yield in boxes
of fruit, grove location, and grove ID number. A grove
definition screen is used to enter the necessary data. A list
of options is available from which you can select fruit
varieties and grove locations. Once you have entered the
required information, you must provide an ID number to
identify the grove. This number is unique to your password.
No one else will have access to this information.
The program will now display a spreadsheet of production
costs categories. You are now ready to enter your grove
costs. Use the arrow keys to move the blue highlight bar up
and down to select categories, and right and left to select
program functions. Please refer to the Program Functions
section below for details.
• Editing an Existing Grove. If you choose to view or edit
information for an existing grove, the program displays a menu
of ID numbers corresponding to groves for which you had
previously entered costs. By moving the menu selection bar to
each ID number, the characteristics of each grove are
displayed. Press return to select a grove. The program will
display the cost information for that grove, and will allow
you to view and modify the data. Please refer to the Program
Functions section below for details.
• Program Functions
Edit. Data is entered using the edit function. When
edit is selected, the production category on the highlight bar
is highlighted in red, and a help window appears at the bottom
of the screen. Costs for labor, machinery, management, and
materials may be entered for each category. When editing is

136
completed, values in the separate cost columns are summed, and
the results are placed in the overall cost column. The
automatic summation may be suppressed by entering a value for
the overall cost, or by using the mode function explained
below.
Help. The data entry program features an on line help
utility. When the help function is selected, an options
window appears and allows you to select among several help
topics. Help topics include an introduction to the purpose
and uses of the program, a short tutorial, an overview of the
database structure, an explanation of the ID number to
identify groves, and lastly a help utility that explains each
program function. The user may also obtain help on functions
by moving the menu bar to the desired function, and pressing
the FI key.
Mode and Update. The mode function allows switching
between auto-calculate and manual modes for column summation.
When the program starts up, auto-calculate is on, and whenever
separate costs for labor, machinery, management, or materials
are entered, the program automatically adds these costs, and
places the total in the overall column. The user may select
manual recalculation in order to enter separate costs for the
labor, machinery, management, and materials, as well as a
separate overall cost. This feature is important when the
user reports partial costs for a category, and the overall
cost does not equal the total of the separate costs.
The update function is used to total the costs in the
separate columns when mode is set to manual. The user may
wish to total all costs, or just those in the current cost
category.
Expand and Return. Each of the ten main cost categories
is divided into one or more levels of sub-categories. The
expand function is used to access the sub-categories for more
detailed data entry. The return function allows the user to
exit from a sub-category screen and return to a higher level
in the hierarchy. A white triangle will appear to the left of
a sub-category to indicate that further sub-categories are
available.
Locate. The locate function serves two purposes. The
first is to show a list of sub-categories available for a
particular cost category. The second usage of the locate
function is an index for all grove practices available in the
database. This function is used to locate the main category
under which a particular grove practice is found. For
example, to find the main cost category for 'Herbicide
Application1, the user searches through the index for the word
herbicide. The index will indicate the main category as being
'Cultivation*.
Comment and Product. The comment function allows you to
enter a comment for each cost category. Comments may be used
as notes or reminders. Similarly, the product feature allows
you to enter a description of the product name, or material

137
being used in a particular grove practice. weeds, chemicals,
or other products used in the citrus operation.
Back. Use the back function to return to the main menu.
All changes you have made or information you have entered will
be saved.
• To Delete a Grove from the Database: Select Enter/Edit
Costs from the main menu. Then select the Delete a Grove
option. Enter a grove ID number to delete and press return.
When the grove is deleted, press any key to return to the main
menu.

138
nata summary and Reporting program
The data summary and comparison program provides industry
averages for citrus production costs. It also provides a
means for comparing your grove costs to the industry averages.
The summary screen provides average per acre costs and
returns for the ten main cost categories, as well as both
levels of subcategories. A separate column for the range of
costs is also provided. Per acre average labor, machinery,
management, and material costs are also displayed. The
following section describes the procedure for starting and
using the data summary program to average, view, and compare
costs.
• Cost Averaging.
From the main menu, select the Data Summary option. You
will be asked for a password. Enter your password and press
return. If you have not yet entered any grove costs using the
data entry program, enter NEW and press return.
The program will now display a grove description screen.
You are required to specify parameters which determine the
types of groves that will be used in the summary. Maximum and
minimum grove sizes, fruit types, fruit market (fresh or
process) , as well as location of the groves must be specified.
You may be either very general whereby only one or a few
parameters are specified, or very specific.
The program also allows you to specify one of your groves
to compare to the averages. If a grove is specified, per acre
costs for that grove will be displayed alongside the averages
in the summary screen. NOTE: If you have not yet entered
grove costs using the data entry program, you will not be able
to make a comparison.
Once all the necessary information has been entered, the
program will begin to average the information found. This may
take a few minutes depending on how many groves were found
that fit your description. Please Be Patient. If you wish to
exit this program at any time press F10.
Once the summary screen is displayed, you may choose to
explore the different average values in the different sub¬
category levels as well as view the individual labor,
machinery, management, and material per acre costs. If you
have chosen to compare the averages to costs from one of your
groves, your per acre grove costs will be displayed on the
column to the far right of the screen. Small yellow arrows to
the right of a cost indicate categories showing higher cost
than the average.
• Program Functions
For the purpose of avoiding repetition, only those
functions that differ from the data entry program menu will be
discussed. Please refer to the program functions under the
Data Entry section above for more details.

139
Options. The options function is not active at this
time. Additional features will be added to the program in the
future.
Labor. Machinery. Management. Material. These options
are used to display separate per acre overall costs and
returns.

140
Herbicide and Weed Guide
The herbicide and weed guide provides herbicide
recommendations and related cost information. It also
provides a way to compare the effectiveness of several
herbicides on a weed problem. A listing of weeds controlled
by each herbicide is also available.
The program is fairly simple to use and requires you only
to follow the instructions on the screen. In most cases,
there is an instruction window at the bottom of the screen.
The ESCAPE key will almost always return you to a previous
screen, and the P10 key will abort the program.
The Herbicide and Weed Guide has several features. The
following is a brief description and instruction for each
feature.
. to get a list of weeds controlled by a particular herbicide:
1) Select the Herbicide option from the main menu. 2) Move
the cursor bar to the desired herbicide and press return. 3)
Select a weed category. 4) Use the cursor bar to browse the
list of weeds. 5) Press return to access the recommendations
program. 6) You will need to answer a few questions to get
recommendation and cost information on control of this weed.
• To compare the effectiveness of several herbicides on weeds:
1) Select the Herbicide option from the main menu. 2) Move
the cursor bar to each herbicide desired and press the space
bar to select. 3) Press return when done selecting
herbicides. 4) Select a weed category or combination of
categories. 5) Select weeds in each category. 6) A summary
screen for each herbicide will be displayed. 7) Use the up
and down arrow keys to select herbicides. 8) Press return to
access the recommendations program. 9) You will need to
answer a few questions to get recommendations and cost
information for each herbicide.
• To get a herbicide recommendation:
1) Select the Weed option from the main menu. 2) Select a
weed category or combination of categories. 3) Select weeds
in each category. 4) If one herbicide is recommended, press
return to get recommendations and cost information. 5) If
more than one herbicide is recommended, select the desired
herbicide and press return to access the recommendations
program. 6) You will need to answer a few questions to get
recommendations and cost information. 7) If no herbicides are
found to control all weeds selected, you may choose the best
herbicide for all weeds selected, or the best herbicide for
weeds selected in each category. Once the desired herbicide
is found, press return to get recommendations and cost
information.

141
EVALUATION
COINS - Citrus Cost Information System
We would like to have your comments concerning COINS. This
will help use to improve the program. We appreciate your help
and ideas on making COINS a more useful information tool. In
responding to the questions, please make comments and
suggestions where appropriate.
1. Please indicate the extent to which you feel you will use
the following components. (l=frequently; 2=sometimes;
3=rarely; 4=never use)
2.
3.
Data entry program
Summary and comparison
Herbicide and weed guide
Using the scale provided, please rate COINS for the
following general aspects. (l=excellent; 2=good; 3=fair;
4=poor)
Ease of installation
Directions for use
Overall speed of accessing information
General ease of use of programs
Quality and usefulness of help utility
Please rate the Data Entry Program for the
characteristics listed. (l=excellent; 2=good; 3=fair;
4=poor)
Thoroughness of grove definition parameters
Usefulness as a data entry and storage tool
Thoroughness of cost categories
Usefulness of sub-categories
Thoroughness of sub-categories
Usefulness of menu functions
General ease of use of program
4. Please rate the Summary and Comparison Program for the
characteristics listed. (l=excellent; 2=good; 3=fair;
4=poor)
Thoroughness of search parameters
Usefulness as an information tool
Usefulness as a cost comparison tool
Usefulness as a decision making tool
Usefulness of information to your operation
Usefulness of menu functions
General ease of use of program

Please rate the Herbicide and Weed Guide for the
characteristics listed. (l=excellent; 2=good; 3=fair;
4=poor)
Thoroughness of list of herbicides
Thoroughness of list of weeds
Usefulness of herbicide recommendations
Usefulness of comparing several herbicides
Usefulness in showing weeds controlled by a
herbicide
Usefulness of cost information on herbicides
Usefulness of printed reports
Usefulness as a decision making tool
Usefulness of information to your operation
General ease of use of program
Will you be willing to share your own cost information
with other growers provided the confidentiality of your
name and operation are assured? YES NO
In summary what do you see as the strong points of COINS.
How can COINS be made more useful to you?
Please provide us with the following information:
Name:
Company:
Position (grower, manager, etc.):
Size of operation:
Type of operation:
County in which groves are located:

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BIOGRAPHICAL SKETCH
Ramzi Khuri was born May 9, 1964, in Beirut, Lebanon. In
June 1982, he graduated from the International College in
Beirut. He received his Bachelor of Science degree in
agriculture from the American University of Beirut, Lebanon,
in June 1985.
In August 1985, Ramzi enrolled as a graduate student in
the Agricultural Engineering Department at the University of
Florida. He was employed as a graduate research assistant for
Dr. W. D. Shoup. His duties involved working with Florida
citrus growers to improve citrus harvest management
techniques. He graduated in August, 1987 with a Master of
Science degree (agricultural operations management).
He was accepted as a Ph.D. student in agricultural
engineering at the University of Florida in 1987. Ramzi was
again employed as a graduate research assistant, this time for
Dr. Howard Beck. His research responsibilities included the
design and development of a citrus production cost information
system.
Ramzi has accepted a job at EXXON U.S.A. in Houston,
Texas, as an Associate Staff Systems Analyst in the
Information and Communication Systems department. His
responsibilities will include work with information systems,
148

149
database management systems, expert systems, and end-user
support.

I certify that I have read this study and that in my
opinion it conforms to acceptable standards of scholarly
presentation and is fully adequate, in scope and quality, as
dissertation for the deqree of Doctor of Philosophy.
R. M. Peart, Chairman
Graduate Research Professor of
Agricultural Engineering
I certify that I have read this study and that in my
opinion it conforms to acceptable standards of scholarly
presentation and is fully adequate, in scope and quality, as
dissertation for the degree of Doctor of Philosophy.
wT 5"! Shoup, Cochairman
Professor of Agricultural
Engineering
I certify that I have read this study and that in my
opinion it conforms to acceptable standards of scholarly
presentation and is fully adequate, in scope and quality, as
dissertation for the degree of Doctor of Philosophy.
- U
H. W. Beck
Assistant Professor of
Agricultural Engineering
I certify that I have read this study and that in my
opinion it conforms to acceptable standards of scholarly
presentation and is fully adequate, in scope and quality, as
dissertation for the degree of Doctor of Philosophy.
R. P. Kilmer
Professor of Food and Resource
Economics

I certify that I have read this study and that in my
opinion it conforms to acceptable standards of scholarly
presentation and is fully adequate, in scope and quality, as
dissertation for the degree of Doctor of Philosophy.
rrS ll
F. Burns
Professor of Industrial and
Systems Engineering
This dissertation was submitted to the Graduate Faculty
of the College of Agriculture and to the Graduate School and
was accepted as partial fulfillment of the requirements for
the degree of Doctor of Philosophy.
December 1990
ctcA df.
r<ícu'.
Dean,
lege of Agriculture
Dean, Graduate School

UNIVERSITY OF FLORIDA