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Vol. 12, No. 4, December 2019, pp. 101 - 114 ISSN 2006-1781
Adedayo Olayinka Theodorio, Francisca Jumoke Theodorio, Ganiyat Ranti Bello and Isaac Kehinde Amao (2019), Development of a Communicative Knowledge Management Decision Support System for Rabbitry Farming
© 2019 Afr. J. Comp. & ICT – All Rights Reserved
https://afrjcict.net
Development of a Communicative
Knowledge Management Decision Support
System for Rabbitry Farming
Adedayo Olayinka Theodorio
1, Francisca Jumoke Theodorio
2, Ganiyat Ranti Bello
3 and
Isaac Kehinde Amao4
1,3,4Department of Computer Science,
Oyo State College of Agriculture and Technology, Igboora, Oyo State,
Nigeria. 2Physics Unit,
Oyo State School of Science, Idere, Ibarapa Central Local Government, Oyo State,
Nigeria.
Email: [email protected], [email protected];
ABSTRACT This study examined the current challenges being faced by rabbit breeders using the rabbitry section of Oyo
State College of Agriculture and Technology and a Local Breeder as case studies. The necessity for the study was
borne out of the current need for an electronic platform where experts and users can identify problems,
solutions and recommendations. The instrument used for fact findings include observing day to day practice of
the said section and an interview session with a staff in the section. These were needed in order to acquire
information on the current challenges being experienced. A ‘D’ algorithm, which was later translated into
model, was developed by the author to show the process of problem identification process, as well as the solution
pathway for the system. The system was designed with tools such as HTML, JavaScript and MySQL Database.
It was able to facilitate knowledge through communication between users and experts. The communication
network adopted was client-server mode of communication. The result of the system developed showed that
knowledge was shared, validated and stored for reusable purposes.
Keywords: Network, Model, Knowledge Management, Decision Support System, Rabbitry Farming. _________________________________________________
African Journal of Computing & ICT Reference Format:
Adedayo Olayinka Theodorio, Francisca Jumoke Theodorio, Ganiyat Ranti Bello and Isaac Kehinde Amao (2019), Development of a Communicative Knowledge Management Decision Support System for Rabbitry Farming (2019), Afr. J. Comp. & ICT, Vol.12, No. 4, pp. 101 - 114.
© Afr. J. Comp. & ICT, December 2019; ISSN 2006-1781
101
Vol. 12, No. 4, December 2019, pp. 101 - 114 ISSN 2006-1781
Adedayo Olayinka Theodorio, Francisca Jumoke Theodorio, Ganiyat Ranti Bello and Isaac Kehinde Amao (2019), Development of a Communicative Knowledge Management Decision Support System for Rabbitry Farming
© 2019 Afr. J. Comp. & ICT – All Rights Reserved
https://afrjcict.net
I. INTRODUCTION
For over the years, rabbit rearing has moved from
backyard farming to a commercial-driven business tailored towards white meat production. Rabbits have
been of great advantage to farmers as well as scientists
[14]. These animals can serve as a socio-economic tool
of development that could lead to high income and
quality of livings [11] in [2]. Rabbit rearing has the
capabilities to increase in every two months. A female
rabbit (Doe) can give birth to four to nine babies, as the
case may be. Its commercial values include: laboratory
use, white meat full of protein and low in cholesterol.
Rabbitry is a livestock farming that requires experience
and technical-know how. Unfortunately, most farmers do not have an indept and modernized experience on this.
For instance, a farmer must know how to handle a rabbit
at every stage of their development, how to adopt a
rabbit, relative diseases and gestation periods.
A successful rabbit business requires advocacies from
experts and experienced scientists. This farming
business, on its own, has some shortcomings that need
urgent attentions. Some of these shortcomings include:
lack of technical-know by farmers, lack of modern
technology, diseases outbreaks and lack of quality and
affordable feeds [12] and housing styles [19]. Having a basic and diverse knowledge of how a rabbit is bred still
remain a major problem in Nigeria. Most times, farmers
are condemned to resolve to faith if he makes no
headway in the process of solving a health problem of
his or her rabbits. This problem however remains a
necessity for this study. This study used approaches of
knowledge management system to develop a decision-
support system for information on how to technically
raise a rabbit and how to raise the bar for commercial
rabbitry.
1.1 Knowledge Management System and Decision-
Support System for Rabbitry Farming
[16] defined knowledge management as a distinct but
interdependent process of knowledge creation, knowledge
storage, retrieval and knowledge application. Knowledge
Management (KM) is an approach with series of
procedures, strategies and practices that allow an
organization to identify, create, represent, distribute, and
facilitate the adoption of problem-solving solutions that
will culminate cognitive experience. Knowledge
Management is a tool that unlocks knowledge. It may also be referred to as a procedural method of integrating the
technical, organizational and behavioral issues associated
with enterprise knowledge. KM can be described as a
strategic process that captures the ways in which an
organization integrates its information assets with the
processes governing the manipulation of its intellectual
assets. [20] opined that knowledge is developed from a
context, people, culture and technology. The position of [20] was that in order to develop knowledge, it must come
from a practice which involves a day to day interaction
with people who are solving a problem in a context
(which may be in engineering, medical, technology,
education, science, agriculture fields and the likes). This
assertion corroborates [18] believe that knowledge are of
three forms: before knowledge is created, it must be
acquired. Secondly, it must come from a context and
lastly, it must be stored up for reusable purposes through a
medium.
Today, the easiest way of solving a problem is through
knowledge sharing and through consultations [5]. The
world is moving away from the natural resources
dependences to an era of knowledge-based researches and
development. Besides, knowledge has the capability to
link and transform the global world technologically.
Every knowledge acquired is acquired through a process,
which is basically out to solve a problem. All problems on
earth emanated from a source, so does every solution.
Most researchers often move closer to problems in order
to identify the problem, analyze the problem, look at
previous or applied solution and then, proffer a new workable solution. [13] opined that in order to create
knowledge, there must be a problem, a solution must be
sorted for and hence, the process of sorting for solution
creates knowledge.
From the opinions of [8, 9], it could be deduced that a
knowledge could only be attained if and only if an
experiential approach is used. The beauty of
technologically-built knowledge is that, it can be applied
from one domain to another. The intention behind this
study is to proffer solution-driven process through the use of stages of knowledge management system to solve
problems observed in the rabbitry section of the
aforementioned college. A decision-support system helps
organizations, businesses and personnel make a structured
decision about a problem they are currently facing [22].
Most Decision-Support System (DSS) are structured,
interactive and software-based system that combines raw
data and personal experience to form an applicable
solution to problem identified. In DSS, every solution is
stored up in a dataware house. Models, figures as well as
charts are also used to show the solution path to a
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Vol. 12, No. 4, December 2019, pp. 101 - 114 ISSN 2006-1781
Adedayo Olayinka Theodorio, Francisca Jumoke Theodorio, Ganiyat Ranti Bello and Isaac Kehinde Amao (2019), Development of a Communicative Knowledge Management Decision Support System for Rabbitry Farming
© 2019 Afr. J. Comp. & ICT – All Rights Reserved
https://afrjcict.net
problem. A DSS has four stages of application: Design,
Intelligence, Choice and Implementation [22]. In rabbit
production, knowledge or information on house design,
intelligent reports on disease management and choice of
rabbit could be gathered and stored up in a dataware
house for reusable purposes.
II. REVIEW OF SIMILAR INNOVATIVE
LITERATURES
[21] created a model for animal diseases diagnosis expert
system based on Support Vector Machine from the
perspective of animal disease diagnosis. SVM was
established on the principles of structural risk
minimization; this has a strong ability of generalization.
They attempt to make use of its strong generalization
ability to resolve the difficulty of the rapid diagnosis process due to the complexity and diversity of animal
diseases. [3] developed a Collaborative Animal
Diagnostic Systems (CADDS) using the concept of
Content Based Reasoning approaches. The CADDS links
similar problems to similar solutions. Hence, problems are
populated for a collaborative discussions and solutions
from expert.
The CADDS developed was able to retrieve similar
experience, solutions and problems from users and expert.
Also, it also uses the concept of indexing to fetch out
solutions to similar problems. [15] used agricultural information system theory to analyze the current
information system used by organic and in-organic
hazelnut producers and found that the information
systems for the two groups of farmers were largely
separated and supply of integrated pest management
information. Moreso, [7] developed and implemented a
herbal therapy knowledge management system using the
concept of collaboration and case-based reasoning
technique to solve problems herbal doctors inability to
identify ailment. The result obtained was a self-solving to
self-knowing method of identification and solution conceptions. [1] researched on analysis of factors
influencing farmers’ adoption of improved rabbit
production technologies: a case of Nyamira County,
Kenya. They stated level of awareness of improved rabbit
production technologies and adoption of improved rabbit
technologies as research questions. Questionnaires were
developed as research instrument and used for a sampled
population.
The result of their research showed that the four regions
involved have two years and above experience of rabbit
farming. They also have a high percentage of rabbit
rearing awareness (76.6%). Their adoption of technology
adoption in rabbit rearing is very low (Frequency low,
40%) and Frequency high (19%). From the result, there
was a low rate of adoption. [2] studied challenges of
rabbit farming in Ogba/Egbema/Ndoni Local Government Area of River State. Their methodology involved the use
of a sampled forty respondents from twenty contact
farmers and twenty non-contact farmers. A personal
interview and direct discussion with farmers was used to
elicit responses from the farmers.
The result of their study showed that rabbit farming was
introduced as a pet animal and not as a business animal to
make profit. Also, they discovered that adoption of rabbit
for production was very low with maximum breeding of
ten rabbits. The study also revealed that production of rabbit feeds and drugs has not been adopted by the
farmers. Lastly, the findings revealed that lack of proper
awareness, techniques involved in business, inadequate
knowledge and information about the advantages of
eating rabbit meat are some of the problems identified in
the study. Conclusively, [10] wrote on the disease
management practices among rabbit farmers in Enugu
State, Nigeria. A structured interview was used for data
collection and validated by four academic staff of the
Department of Agricultural Extension, University of
Nigeria, Nsukka.
The results of the study revealed that majority of the
farmers are small-scale farmers with stock of size 1-10.
They mostly have in stock, New Zealand and Californian
White Breeds. The study further revealed that mange, ear
canker, sniffle, diarrhea were some of the diseases
recorded in the findings. To source for drugs for these
diseases, the study showed that most farmers source for
appropriate drugs from fellow farmers: a confirmation
that information sharing played a major role for this
study.
III. METHODOLOGY AND RESULTS
The method employed for information gathering on the
problem involved the use of observation and interview
session. A ‘D’ algorithm was used to elicit the solution
pathway for the system developed. Also, a Use-Case
diagram was designed from the algorithm to show the
graphical interactions among elements in the system.
Further, a Process State Diagram was used to show the
processes and states of execution of the system
developed. A Client-Server Communication network is
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Vol. 12, No. 4, December 2019, pp. 101 - 114 ISSN 2006-1781
Adedayo Olayinka Theodorio, Francisca Jumoke Theodorio, Ganiyat Ranti Bello and Isaac Kehinde Amao (2019), Development of a Communicative Knowledge Management Decision Support System for Rabbitry Farming
© 2019 Afr. J. Comp. & ICT – All Rights Reserved
https://afrjcict.net
used to show the distribution of resources on the system.
Finally, few samples of the system outputs were
presented as evidence of system developed.
3.1 Instrumentation
The instrument used to gather current problems faced by farmers was the use of open, face-face interview with an
animal scientist in the Department of Animal Science
and Production, Oyo State College of Agriculture and
Technology, Igboora and a local rabbit breeder in Idere,
a suburb of Igboora. The interview questions used were:
1. What are the breeds of your rabbit?
2. How many rabbits do you have now?
3. What is the general health condition of the animals?
4. How often do you observe them?
5. Do you employ the services of a veterinarian or animal scientists for treatment?
6. What are the ailments these animals experience?
7. What housing style do you use for these animals?
8. What is your feeding programme for these animals?
9. Is there any electronic platform where animal
scientists discuss solutions to rabbit problems?
3.2 CASE STUDIES
CASE 1:
It was observed that a problem of space competition
exists in the Rabbitry section of Animal Health and Technology Department, Oyo State College of
Agriculture and Technology, Igboora. The rabbits have
multiplied more than the available spaces, and thus,
diseases such as mange are being transferred from one
animal unto another. Also, a staff of Animal Health and
Production Department of the College affirmed that the
college currently has more than forty animals comprising
New Zealand and Chinchilla. The strength of the
Department was that there are more than a good number
of animal scientists in the department as well as
veterinary doctors, only few specializes in rabbitry.
The problem at hand is that most of the veterinary
doctors and scientists do not live in the community; they
are sometimes called to attend to emergency. She also
confirmed that some drugs could not be seen easily in
the community except if bought from advanced
neighboring town. Moreso, the housing style of the
rabbitry section needs to be updated as there is problem
of space in the section. This problem led to disease
transfer among the animals. Some of the diseases
identified were Diarrhea, Pneumonia, Mange and
coccidiosis. Among these ailments, the mange is now
very difficult to treat because the animals are kept
together. She further suggested that with the current
style of breeding, the college may continue to experience
the current challenges except if the latest technological
method of breeding rabbit is adopted. Also, that rabbit breeding approaches are of different methods and
practices. She opined that the use of technology in
breeding will assist in showcasing the different
approaches of identifying problems as well as proffering
solutions.
CASE 2: A local breeder in Idere, Ibarapa Central Local
Government, Oyo State complained about non-access to
doctor in the days of emergency. He also complained
about his inability to determine the exact disease
affecting his rabbits, and if he is lucky to get information online, he does not know the required drugs or
preventions to be applied.
3.3 The ‘D’ ALGORITHM
Match target problem to a pack of solutions (knowledge)
Where
T=Target problem
M=Matching
S=Solution
B= Case Base
C=Collaboration N= New Solution
S≤B∀T (S is less than B for every T)
Where S B (where Solution (S) is fetched
from Case Base(B) N := T = S < B (New
Solution (N) is equal to new Target Problem
(T); where Solution is found in Case Base
(B))Where S C (where Solution is fetched
through Collaboration)
N := T≥S≥B≤C ( New Solution is equal to
solution fetched from Collaboration, greater
than solution not seen from Case Base)
The D algorithm stands for Discussions to Yield Solution
Algorithm. We conceptualized the algorithm to produce
solutions from experts to be stored in a data warehouse
(Case Base) where a solution to a target problem is not
seen [6]. The Algorithm follows the principles of
104
Vol. 12, No. 4, December 2019, pp. 101 - 114 ISSN 2006-1781
Adedayo Olayinka Theodorio, Francisca Jumoke Theodorio, Ganiyat Ranti Bello and Isaac Kehinde Amao (2019), Development of a Communicative Knowledge Management Decision Support System for Rabbitry Farming
© 2019 Afr. J. Comp. & ICT – All Rights Reserved
https://afrjcict.net
matching a problem in a solution base. Else, it is
discussed by experts.
Technically, For Matched Solution
N := T = S < B…………………………Solution
Pathway 1
For Collaborated Solution
N := T ≥ S ≥ B ≤C…………………………..Solution
Pathway 2. For this study, emphasis was laid on the
collaborated solution for the system developed.
3.4 The CKMDSRF Use-Case Graphical Interactions
A Use-case diagram was used to show interactions among
the two types of user; a user and expert. As shown in
Figure 1 below, the system has two ends:
The Users end (knowledge finders) and the Experts end:
the system revealed that a user will first be prompted to register his/her details before gaining access to the
system. From Figure 1 above, there are two types of users
that interact with the system. First, a user who needs
information and an expert who shares information through
the collaboration forum. The user interacts with the
system by quarrying a problem and fetches the solution
while an expert interact with the system by proffering
solutions to unseen solutions; view solutions provided by
others as well as fetch solutions as a user. If the
information needed is not found, the system automatically
prompts a query for communication among experts.
3.5 CKMDSRF Flowchart Diagram
The flowchart diagram shown in figure 2 below shows the
steps by steps process of arriving at solutions of the
system developed.
From figure 2 above, a user is required to login with his
registration details (same with experts) before being
allowed to search for solution(s) from the dataware house.
If the solution is not seen, the system automatically
pushes the query for experts to discuss and bring out
solutions. The solution(s) given by the experts is also automatically populated into the database as possible new
solutions to the problem queried.
3.6 CKMDSRF Communication Network
The communication network adopted for the system is
Client-Server network. [4] described this type of network
as computers on a network that are connected, monitored
and centralized by a server.
From figure 3, it can be seen that the two users are
fetching for solutions or proffering solutions through the
internet. The internet gives room for wider and easy
communication. The system developed adopted the
method of client-server communication. They both source for information, the only difference is that they all
controlled by a system. For instance, experts’ information
are captured and stored up while access is given through a
central computer/administrator. The function of the
administrator is to coordinate activities of the experts as
well arranged their comments as they come in. Clients are
free to retrieve any information through the internet after
completing registration procedure.
IV. SYSTEM OUTPUTS
The System was developed with Hypertext Text Markup
Language, Windows 8.1 O.S, JavaScript and MySQL
Database. Below are samples of system outputs.
4.1 Welcome Page
The system welcome page has features to capture
information of a new user, grant entry for registered user,
get information from data ware house and get feedback
from experts.
4.2 Users PAGE
The CKMDSRF has a page for already registered users
to ask experts questions that solutions could not be
fetched.
4.3 Communication Page
The experts’ communicate through a chat page on the
system. Access is given only to registered experts. This
page allows users to moderate, constructively criticize a
solution, identify ailments and solutions together and to
unanimously reach a consensus on any solution. Their solutions are automatically stored up in a data warehouse.
V. CONCLUSION
The study of knowledge management system is a
procedural process that solves problems for a wider
consumption. The knowledge developed is however
subject to applicability in any context, locally or globally.
In this study, a decision support system for rabbitry
farming was developed to aid the decision making of users and experts. A Jennex and Olfman KMS Success
Model (2004)[17] was used to validate the system
105
Vol. 12, No. 4, December 2019, pp. 101 - 114 ISSN 2006-1781
Adedayo Olayinka Theodorio, Francisca Jumoke Theodorio, Ganiyat Ranti Bello and Isaac Kehinde Amao (2019), Development of a Communicative Knowledge Management Decision Support System for Rabbitry Farming
© 2019 Afr. J. Comp. & ICT – All Rights Reserved
https://afrjcict.net
developed. Jennex and Olfman [17] opined that a good
Knowledge Management System must have a good
system quality, knowledge and Information quality and
Service Quality together with the intent to use/perceived
benefits and Users satisfaction. Prior to the development
of the system, interview sessions were conducted in order to provide a justification for the study.
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Vol. 12, No. 4, December 2019, pp. 101 - 114 ISSN 2006-1781
Adedayo Olayinka Theodorio, Francisca Jumoke Theodorio, Ganiyat Ranti Bello and Isaac Kehinde Amao (2019), Development of a Communicative Knowledge Management Decision Support System for Rabbitry Farming
© 2019 Afr. J. Comp. & ICT – All Rights Reserved
https://afrjcict.net
[17] M.E. Jennex and L. Olfman. Assessing management
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Vol. 12, No. 4, December 2019, pp. 101 - 114 ISSN 2006-1781
Adedayo Olayinka Theodorio, Francisca Jumoke Theodorio, Ganiyat Ranti Bello and Isaac Kehinde Amao (2019), Development of a Communicative Knowledge Management Decision Support System for Rabbitry Farming
© 2019 Afr. J. Comp. & ICT – All Rights Reserved
https://afrjcict.net
Figure 1: Use-case Diagram for CKMDSRF
VIEW SOLUTIONS
PROVIDE SOLUTION
FOR UNSEEN
PROBLEM
LOGIN
REPORT PROBLEM
PROFFER SOLUTIONS
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Vol. 12, No. 4, December 2019, pp. 101 - 114 ISSN 2006-1781
Adedayo Olayinka Theodorio, Francisca Jumoke Theodorio, Ganiyat Ranti Bello and Isaac Kehinde Amao (2019), Development of a Communicative Knowledge Management Decision Support System for Rabbitry Farming
© 2019 Afr. J. Comp. & ICT – All Rights Reserved
https://afrjcict.net
Figure 2: Flowchart for CKMDSRF
Start
User
Registration
Login
Verified
No
Yes
Problem Solution
Look Up
Is Solution
Fetched?
Yes
No
Analyze and Discuss
Problem to give
applicable solution
Stop
Retrieve
Solution
ataware
House
User Login
User Logout
Push to experts’
forum
Experts
Section
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Vol. 12, No. 4, December 2019, pp. 101 - 114 ISSN 2006-1781
Adedayo Olayinka Theodorio, Francisca Jumoke Theodorio, Ganiyat Ranti Bello and Isaac Kehinde Amao (2019), Development of a Communicative Knowledge Management Decision Support System for Rabbitry Farming
© 2019 Afr. J. Comp. & ICT – All Rights Reserved
https://afrjcict.net
Figure 3: Communication Network Diagram for CKMDSRF
Figure 4: CKMDSRF Welcome Page
CLIENT
CLIENT
CLIENT
INTERNET
PLATFORM
EXPER
T
EXPER
T
EXPERT
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Vol. 12, No. 4, December 2019, pp. 101 - 114 ISSN 2006-1781
Adedayo Olayinka Theodorio, Francisca Jumoke Theodorio, Ganiyat Ranti Bello and Isaac Kehinde Amao (2019), Development of a Communicative Knowledge Management Decision Support System for Rabbitry Farming
© 2019 Afr. J. Comp. & ICT – All Rights Reserved
https://afrjcict.net
Figure 5: CKMDSRF Users’ Question Page
Figure 6: CKMDSRF Communication Page
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Vol. 12, No. 4, December 2019, pp. 101 - 114 ISSN 2006-1781
Adedayo Olayinka Theodorio, Francisca Jumoke Theodorio, Ganiyat Ranti Bello and Isaac Kehinde Amao (2019), Development of a Communicative Knowledge Management Decision Support System for Rabbitry Farming
© 2019 Afr. J. Comp. & ICT – All Rights Reserved
https://afrjcict.net
4.3 Snapshots of CKMDSRF Data Warehouse
Figure 7: Diseases Data Warehouse (Populated)
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Vol. 12, No. 4, December 2019, pp. 101 - 114 ISSN 2006-1781
Adedayo Olayinka Theodorio, Francisca Jumoke Theodorio, Ganiyat Ranti Bello and Isaac Kehinde Amao (2019), Development of a Communicative Knowledge Management Decision Support System for Rabbitry Farming
© 2019 Afr. J. Comp. & ICT – All Rights Reserved
https://afrjcict.net
Figure 8: CKMDSRF Database Structure
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Vol. 12, No. 4, December 2019, pp. 101 - 114 ISSN 2006-1781
Adedayo Olayinka Theodorio, Francisca Jumoke Theodorio, Ganiyat Ranti Bello and Isaac Kehinde Amao (2019), Development of a Communicative Knowledge Management Decision Support System for Rabbitry Farming
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Figure 9: Experts’ Solution Warehouse (Discussion).
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