Development of a Communicative Knowledge Management ... · Adedayo Olayinka Theodorio1, Francisca Jumoke Theodorio2, Ganiyat Ranti Bello3 and Isaac Kehinde Amao4 1,3,4Department of

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];

[email protected],

[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





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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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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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

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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

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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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Figure 1: Use-case Diagram for CKMDSRF

VIEW SOLUTIONS

PROVIDE SOLUTION

FOR UNSEEN

PROBLEM

LOGIN

REPORT PROBLEM

PROFFER SOLUTIONS

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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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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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Figure 5: CKMDSRF Users’ Question Page

Figure 6: CKMDSRF Communication Page

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4.3 Snapshots of CKMDSRF Data Warehouse

Figure 7: Diseases Data Warehouse (Populated)

112





Figure 8: CKMDSRF Database Structure

113





Figure 9: Experts’ Solution Warehouse (Discussion).

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Documents

Development of a Communicative Knowledge Management ... · Adedayo Olayinka Theodorio1, Francisca Jumoke Theodorio2, Ganiyat Ranti Bello3 and Isaac Kehinde Amao4 1,3,4Department of