THE EFFECT OF INFORMAL INFORMATION NETWORKS ON 
THE FLOW AND PROVISION OF AGRICULTURAL 
INFORMATION: THE CASE OF AHAFO ANO SOUTH 
DISTRICT, GHANA. 
 
 
BY 
AIDOO COLECRAFT, DANLEY 
 
 
THIS THESIS IS SUBMITTED TO THE UNIVERSITY OF GHANA, 
LEGON IN PARTIAL FULFILLMENT OF THE REQUIREMENT FOR THE 
AWARD OF MPHIL AGRICULTURAL EXTENSION DEGREE 
 
 
 
 
DEPARTMENT OF AGRICULTURAL EXTENSION 
UNIVERSITY OF GHANA 
LEGON 
 
 
JUNE 2014
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DECLARATION 
I, Danley Colecraft Aidoo do hereby declare that except for the references, which 
have been duly cited, the contents of this thesis for the award of the MPhil. 
Agricultural Extension degree is the result of my own work. No previous submission 
of this study has been presented elsewhere for the award of a degree. 
 
 
..................................................                                 Date.............................................. 
DANLEY COLECRAFT AIDOO                             
 
 
....................................................           Date............................................ 
DR. (MRS) COMFORT K. FREEMAN                      
 
 
...................................................   Date.............................................. 
DR. PASCHAL B. ATENGDEM         
               
        
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ABSTRACT 
To improve agricultural development and to empower farmers to make sound, 
practical decisions, access to reliable agricultural information and knowledge is 
required. Farmers who are unable to access agricultural information from formal 
sources frequently turn to information transmitted through their informal information 
networks. The study examined existing informal agricultural information networks by 
using network characteristics, i.e., Network density, Centralization Measures (Degree 
Centralization and Betweenness Centralization) and explored the implication of such 
characteristics on the flow and provision of information. Specifically the study sought 
to meet the following objectives: find out the existing local information networks for 
the transmission of agricultural information, their characteristics, and the major 
providers of information in the various networks; determine the strength of network 
ties among members of the information networks and how these affect availability of 
agricultural information; and to examine the differences between actors in the various 
information networks and its influence on the availability of information. 
The study was conducted in three communities and the snowball method of sampling 
for network analysis was used in selecting respondents. Key communicators were 
identified by means of the sociometric technique. The networks were presented in the 
form of sociograms in order to reveal the ties between actors in the networks. A high 
density of information ties was observed among a comparatively small group of core 
farmers, demonstrating the existence of a core-periphery structure in all three 
communities. Key communicators were identified on the basis of membership of local 
groups, status in the community, output access to training programs and experience 
acquired over many years of farming; thus serving as conduits between actors in the 
informal networks, spreading available information throughout the farming 
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community. The findings revealed a level of disparity in actor influence in the 
networks, signifying the presence of individuals who had some influence over the 
flow and provision of information. In addition, the networks were heterophilous with 
respect to the ties formed between actors. 
It is recommended that extension service providers should target key communicators 
of informal information networks as they can serve as essential sources of information 
in disseminating information to farmers in a community. The study also suggests the 
use of farmers who can serve as intermediaries by virtue of their links with various 
actors. 
 
 
 
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DEDICATION 
To my loving parents, David and Georgina Colecraft Aidoo, and my brother, Samuel 
Colecraft Aidoo. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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ACKNOWLEDGEMENT 
Words cannot express my indebtedness to my supervisors, Dr. (Mrs) Comfort 
Freeman and Dr. Paschal B. Atengdem who in spite of their challenging schedules 
made sacrifices to help me complete my work. Their concern, enthusiasm, 
encouragement, meaningful contributions, motivation, patience, and wealth of 
experience contributed to the success of this work. Undoubtedly, I could not wish for 
better or more understanding supervisors. I am also grateful to the other lecturers of 
the department, whose doors were always opened to me and ready to make 
suggestions.  
To my parents, I am extremely grateful for the sacrifices, expended resources 
(emotional, material, and otherwise), and all other efforts they made to see me 
through up to this point without disappointments or regrets. I would forever be 
grateful, Dad and Mom! To my brother, I say a big thank you for the concern and love 
that made my work a success. I am also very grateful to my uncle, Samuel Aidoo for 
the fatherly advice and support I enjoyed during my stay with him and his family.  
Abena Konadu Basoah, a true gem is what you are and I will always be grateful to 
you. In addition, I am grateful to the entire Basoah family for their encouragement, 
loving concern and support. May Jehovah richly bless you all for your kindness and 
self-sacrificing spirit. 
Kwasi Antwi Darko Jr., you were awesome throughout this program, unreserved 
appreciation for your brotherly love. You are just too kind.  To the Odjeawo family, 
Grandma Poks and Kwasi Baafi (Trigga) I say thank you for the sumptuous meals, 
concern, and encouragement. Finally to my colleagues, all well meaning friends and 
loved ones, I say thank you very much! 
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TABLE OF CONTENTS 
Content                  Page 
DECLARATION.......................................................................................................... i 
ABSTRACT ................................................................................................................. ii 
DEDICATION............................................................................................................ iv 
ACKNOWLEDGEMENT ...........................................................................................v 
TABLE OF CONTENTS .......................................................................................... vi 
LIST OF FIGURES ................................................................................................... xi 
LIST OF TABLES .................................................................................................... xii 
 
CHAPTER ONE: INTRODUCTION ........................................................................1 
1.0 Background .........................................................................................................1 
1.1 Research Problem ..............................................................................................5 
1.2 Research Questions ............................................................................................6 
1.3 Research Objectives ...........................................................................................7 
1.4 Relevance of Study .............................................................................................7 
1.5 Study Area ..........................................................................................................7 
1.6 Organisation of Thesis .......................................................................................9 
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CHAPTER TWO: LITERATURE REVIEW .........................................................10 
2.0 Introduction ......................................................................................................10 
2.1 Definitions and Meanings of Communication ...............................................10 
2.2 Elements of the Communication Process .......................................................11 
2.2.1 Sender ..........................................................................................................12 
2.2.2 Message .......................................................................................................15 
2.2.3 Recipient ......................................................................................................16 
2.2.4 Feedback .....................................................................................................17 
2.2.5 Channel .......................................................................................................18 
2.3 Socio-Demographic Factors and the Transmission of Agricultural 
Information ......................................................................................................19 
2.3.1 Homophily ...................................................................................................20 
2.3.2 Proximity .....................................................................................................20 
2.3.3 Source Credibility .......................................................................................21 
2.3.4 Status of the Sender ....................................................................................21 
2.4 Levels of Communication ................................................................................21 
2.5 The Relevance of Networks to Agricultural Information Delivery .............22 
2.5.1 Social Networks and Analysis ....................................................................23 
2.5.2 Communication Networks ..........................................................................24 
2.5.3 Communication Network Analysis.............................................................25 
2.6 Homophily Theory and Information networks .............................................28 
2.7 Summary ...........................................................................................................29 
 
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CHAPTER THREE: METHODOLOGY ...............................................................30 
3.1 Research Design................................................................................................30 
3.2 Population .........................................................................................................31 
3.3 Sampling Method .............................................................................................31 
3.4 Data Collection Procedures .............................................................................32 
3.5 Data Collection Instruments ...........................................................................32 
3.6 Data Analysis ....................................................................................................33 
3.6.1 Sociograms ..................................................................................................34 
3.6.2 Measurement of Network Descriptors for Network analysis ....................34 
 
CHAPTER FOUR: RESULTS .................................................................................40 
4.0 Introduction ......................................................................................................40 
4.1 Characteristics of Agricultural Information Networks and Primary 
Sources of Information ...................................................................................40 
4.1.1 Agricultural Information Network in Kunsu Camp..................................41 
4.1.2 Agricultural Network in Afreseni ..............................................................46 
4.1.3 Agricultural Network in Dunyankwanta ...................................................51 
4.3 Similarities and Differences between Actors and the Provision of 
Agricultural Information ................................................................................62 
4.3.1 Distribution of Ties on the basis of Age .....................................................62 
4.3.2 Distribution of ties on the basis of Gender ................................................63 
4.3.3 Distribution of ties on the basis of Level of Education Attained ..............64 
 
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CHAPTER FIVE: DISCUSSION OF RESULTS ...................................................68 
5.0 Introduction ......................................................................................................68 
5.1 Existing Local Agricultural Information Networks and Key 
Communicators ...............................................................................................68 
5.1.1 Implications of Network Characteristics for Provision of Agricultural 
Information .................................................................................................68 
5.1.2 The Role of Key Communicators in Agricultural Information Provision69 
5.2 Structural Properties of Networks and the Provision of Agricultural 
Information ......................................................................................................72 
5.2.1 Network Density and the Flow of Agricultural Information ....................72 
5.2.2 Degree Centralization and the Provision of Agricultural Information ....73 
5.2.3 Betweenness Centrality and the Provision of Agricultural Information .74 
5.3 Homophily and Information Transfer ...........................................................75 
5.3.1 Age ...............................................................................................................76 
5.3.2 Gender .........................................................................................................77 
5.3.3 Level of Education ......................................................................................77 
5.3.4 Local Group Membership...........................................................................78 
5.3.5 Heterogeneity of Key Communicators .......................................................80 
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CHAPTER SIX: SUMMARY, CONCLUSIONS AND RECOMMENDATIONS
......................................................................................................................................83 
6.0 Introduction ......................................................................................................83 
6.1 Summary ...........................................................................................................83 
6.2 Conclusion .........................................................................................................85 
6.3 Recommendations ............................................................................................86 
6.3.1 Recommendations for Agricultural Extension Practice ...........................86 
REFERENCES ...........................................................................................................88 
APPENDIX A -QUESTIONNAIRE FOR FARMERS ........................................105 
APPENDIX B-QUESTIONNAIRE FOR PRIMARY SOURCES OF 
INFORMATION ......................................................................................................110 
 
 
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LIST OF FIGURES 
Figure 2.1 Elements in the Communication Process and how they are related ... 13 
Figure 4.1 Agricultural Information Network in Kunsu Camp, Ahafo Ano South, 
Ghana .......................................................................................................................... 42 
Figure 4.2 Agricultural Information Network in Afreseni, Ahafo Ano South, 
Ghana47 
Figure 4.3 Agricultural Information Network in Dunyankwanta, Ahafo Ano 
South, Ghana .............................................................................................................. 52 
 
 
 
 
 
 
 
 
 
 
 
 
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LIST OF TABLES 
Table 3. 1: Summary of data required and method of data collection .................38 
Table 4. 1: Out-degree statistics for Core and Periphery members (Kunsu).......43 
Table 4. 2: Density of farmer ties between the Core and Periphery groups in the 
Kunsu Agricultural Information Network...........................................44 
Table 4. 3: Selected Background Characteristics of Key Communicators (Kunsu 
Camp) ......................................................................................................45 
Table 4. 4: One-sample statistics of age and output of key communicators in 
Kunsu .......................................................................................................45 
Table 4. 5: One-sample t-test of average age and output of key communicators in 
Kunsu .......................................................................................................46 
Table 4. 6: Out-degree statistics for Core and Periphery members (Afreseni) ...48 
Table 4. 7: Density of Farmer Ties between the Core and Periphery groups in the 
Afreseni Agricultural Information Network .......................................48 
Table 4. 8: Selected Background Characteristics of Key Communicators 
(Afreseni) .................................................................................................50 
Table 4. 9: One-sample statistics of average age and output of key 
communicators in Afreseni ....................................................................50 
Table 4. 10: One-sample t-test of average age and output of key communicators 
in Afreseni ...............................................................................................51 
Table 4. 11: Out-degree statistics for Core and Periphery members in 
Dunyankwanta ........................................................................................53 
Table 4. 12: Density of ties between core and periphery groups in the 
Dunyankwanta Agricultural Information Network ............................54 
Table 4. 13: Selected Background Characteristics of Key Communicators 
(Dunyankwanta) .....................................................................................55 
Table 4. 14: One-sample statistics for Average Age and Output of Key 
Communicators in Dunyankwanta .......................................................56 
Table 4. 15: One-sample t-test for Average Age and Output of Key 
Communicators in Dunyankwanta .......................................................56 
Table 4. 16: Degree Centralization Measures of Kunsu Camp Network .............57 
Table 4. 17: Betweenness Centralization for Kunsu Camp Agricultural 
Information network ..............................................................................58 
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Table 4. 18: Degree Centralization Measures of Afreseni Network ......................58 
Table 4. 19: Betweenness Centralization measures for actors in the Afreseni 
network ....................................................................................................59 
Table 4. 20: Degree Centralization Measures of Dunyankwanta Network ..........60 
Table 4. 21: Betweenness Centralization measures for Dunyankwanta network 60 
Table 4. 22: Mean and (SD) of density, degree, and betweenness parameters 
averaged over the networks ...................................................................61 
Table 4. 23: Distribution of ties within and between groups (age attributes) of 
Agricultural Information Networks .....................................................63 
Table 4. 24: Network Homophily Measures (Age) ..................................................63 
Table 4. 25: Distribution of ties within and between groups (Gender attribute) of 
Agricultural Information Networks .....................................................64 
Table 4. 26: Network Homophily Measures (Gender) ...........................................64 
Table 4. 27: Distribution of ties within and between groups (Educational level) of 
Agricultural Information Networks .....................................................65 
Table 4. 28: Network Homophily Measures (Educational Level) .........................66 
Table 4. 29: Distribution of ties within and between groups (Local Group 
Membership) of Agricultural Information Networks .........................66 
Table 4. 30: Homophily Network Measures (Local Group Membership) ............67 
 
 
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CHAPTER ONE 
INTRODUCTION 
1.0 Background 
 Information is fundamental to our way of life and agriculture is no exception. To be 
successful in agriculture would require gaining, processing, using and evaluating a 
huge quantity of information (policies, markets, new techniques). Agricultural 
information is invaluable to improved agricultural development. In order to make 
sound, practical decisions, farmers need access to reliable agricultural information and 
knowledge.  Such information and knowledge when readily available can play a 
major role in improving agricultural productivity and in the long run, enhance the 
agricultural development of farmers and other users of agricultural information. 
 Information has an impact on individuals’ farming practices within a specific place 
and time, and it arrives from many sources. Consequently, the various sources of 
information combine to create the varied reality of what can be depicted as a person’s 
information environment. This information environment is bounded by the normal 
limitations and restrictions within which people create their farming practices and life 
habits (Sligo, Massey, and Lewis, 2005). A number of authors identify a variety of 
information sources available to farmers. Examples of these sources of agricultural 
information are libraries, internet, extension agents, research organizations, radio, 
television, community based organizations, neighbours, family, fellow farmers 
(farmer to farmer communication), cooperative groups and societies, traders 
(Ngathou, Bukenya, and Chembezi, 2006; Daudu, Chado, and Igbashal,2009; and 
Mwalukasa, 2013). 
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In view of the rapid growth and expansion of communication technologies, there has 
been  a lot of research in the area of communication and agricultural development. 
However, the focal point of researchers have been geared towards modern 
communication media, and how these can contribute to improving agriculture. On the 
other hand, interpersonal communication is invaluable to communication among the 
rural folk who incidentally are at the core of agriculture.  
Several studies highlight the potential of interpersonal communication. For instance, 
Ogbama (2010); Okwu and Daudu (2011); and Lwoga, Stilwell, and Ngulube (2011) 
realized that friends, relatives and neighbours are regularly available and accessible to 
the farmers to gain the agriculture information they need. This is in agreement with 
Ansu-Kyeremeh (1997), that indigenous group structures exist whose credibility, 
effectiveness, organizational and mobilization capacity have been tested over time 
and enjoy the co-operation of rural farmers. Adjaveh and Aborampah (2004) also 
echoed the same point. They found out that cultural and societal networks were 
important in any given society. For example, among the Akans in Ghana, they 
discovered that solidarity among members of the extended family bonded individuals, 
with elders being the major conduits of cultural dissemination. Elders wielded a very 
important status, being responsible for the education of all, younger adults inclusive. 
Mundy and Crompton (1995), explain that local information networks are diverse, 
flexible in time, location specific, suited to local socio-cultural conditions, are already 
in place and so involve less cost, and are accessible to the majority of a community. 
Proper choice of information sources can contribute to development by bringing about 
beneficial changes in the behavior of individuals, groups, and organizations. 
According to Feder and Slade (1985), the dynamics of diffusion methods rely 
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generally on horizontal communication among farmers. Adoption is strongly 
influenced by constituents of the same societal group. New ideas are more easily 
adopted when they come from others who are similar in several respects. Outsiders 
are not entirely trusted, especially in conservative localities. Farmers scrutinize and 
have their perception of other farmers’ skills, practices and performance, and they 
discover more by discussing their own experiences with friends and neighbors. The 
argument that rural farmers prefer farmer-to-farmer communication is supported by 
several studies that indicate that even in communities where social organisation and 
infrastructure exist, farmers favor their colleague farmers as their prime information 
source (Grandstaff and Grandstaff, 1986). Feder and Slade (1985) reveal that while 
farmers in India without contact with formal extension service used farmer-to-farmer 
communication, farmers who had contact with formal extension services also favored 
fellow farmers as their key source of information.  
In line with this, Goswami and Sarkar (2009) identified three different types of social 
relations namely: neighborhood pattern, friendship pattern, and the discussion group 
pattern. Similarly, Soyka and Streiffeler (2003) also established strong linkages 
between farmers in the same neighborhoods and nearby neighborhoods. There were 
also strong network ties between family members. Consequently, farmers often resort 
to more vertical communications to gain access to innovations. Opinion leaders, who 
are also local farmers, are sufficiently heterophilous to be good sources of new 
information and advice. They usually enjoy considerable authority on the way other 
locals think and behave (Rogers, 2003). BenYishay and Mobarak (2012), explain that 
even beyond agriculture, studies indicate that social interactions are  important driving 
forces for the spread of information in a variety of circumstances, including job 
information (e.g., Magruder, 2009; Beaman, 2011), deworming (Miguel and Kremer, 
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2007), HIV testing (Godlonton and Thornton, 2009), and menstruation cup use (Oster 
and Thornton, 2009). 
Chapke and Bhagrat (2011) highlight the significance of local information networks 
in nudging rural masses towards accepting social changes that are being introduced 
through various development agencies. They highlight the following strengths: 
1. The sources of information are familiar to rural audiences since they provide 
opportunity for instant feedback. 
2. The techniques used are simple because they reflect the cultural philosophy of 
the people. 
3. They already play a meaningful role in rural areas, in educating rural folk 
about the consequences of social evils. Consequently, the use of such media 
makes nation building and socio – economic development easier and 
acceptable to rural masses.  
Traditional information networks come from the people and their appeal has by and 
large been both capable and creative. They have always served to amuse, educate to 
buttress existing ideas or ideologies or to transform existing ideals and attitudes. 
Being close to people at the local level, these interpersonal networks are potentially 
useful in the service of social concerns, as determined by local  or national authorities 
themselves. They are, moreover, abundantly present in areas where mass media 
technology has not been fully or effectively developed to capture sustained interest at 
local and national levels. Interpersonal information networks are personal forms of 
entertainment and communication. This is important because behavioural changes are 
most easily brought about by personal interaction. These forms of transmitting 
information are a part of the way of life of a community and provide acceptable 
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means of bringing development issues into the community in its own-terms. They are 
capable of reaching intimate social groups, thus making use of already established 
information networks in the audience. 
1.1 Research Problem 
Developing agriculture by disseminating the right information to the right groups has 
proven to be an effective strategy (Shaffril, Hassan, and Samah, 2009). In Ghana, the 
Ministry of Food and Agriculture (MOFA) which is the major source of information 
to farmers, is faced with several constraints. One of such constraints relevant to this 
study is the low extension – farmer ratio. For example, in the Ahafo Ano South 
district, the extension officer – farmer ratio is 1:2,171 (www.ghanadistricts.com). 
Even though this ratio is better than the national figure which stands at 1:3000, this 
situation negatively affects the availability of agricultural information to farmers in 
the district (www.ghana.gov.gh). The result has been that there is lack of an effective 
farmers’ information support system for the farming activities in the country. As a 
result, farmers rely on their own informal sources for the information they need to 
improve upon their activities. Apart from the low extension agent to farmer ratio, 
another problem is that in instances where there has been some level of agricultural 
information dissemination through the mass communication media, there still exists 
the problem of farmers not having any choice with respect to the kind of information 
made available to them (FAO/DFID/ODI, 2002). This is a serious issue since rural 
farmers certainly have a pivotal need for agricultural information. 
The constraints associated with the use of  ICTs for the provision of agricultural 
information also make it necessary to consider the potential of informal information 
networks. Elijah and Ogunlade’s (2006) study in Nigeria identified several constraints 
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in the implementation of ICTs. The constraints are: lack of access to electricity or 
unstable supply of electricity and lack of adequate technical support. In the same vein, 
Ngwenyama,Andoh-Baidoo, Bollou, and Morawecynski (2006) made the following 
affirmation: “in Africa, three quarters of the population is illiterate and lives in rural 
areas that lack basic facilities such as electricity and to expect effective utilisation of 
communication technologies like the Internet in all areas, by all people, would be 
unreasonable” (p.4). This view is also supported by Ghatak (2007) who mentions 
constraints such as differing literacy, poor technical skills and lack of practical digital 
content. Hassan, Shaffril, Azril, and D’Silva (2009) also indicate that people in the 
rural areas are still hesitant to use the advanced technology that are available to them. 
Consequently these disparities in use of ICTs by farmers, as well as the limited access 
to agricultural extension officers by farmers make it logical to examine the potential 
of local information networks in improving the provision and flow of agricultural 
information. 
Moreover, Sligo et al., (2005) indicate that there is inadequate research into the 
varying interpersonal sources of information that make up the individual’s 
environment. The ways in which these connections help to enhance the individual’s 
“information bank” (from which they can produce insights into their occupation and 
lifestyle) is generally unfamiliar. 
1.2 Research Questions 
 
1. How do the existing local information networks affect the exchange and 
sharing of agricultural information among members of the community? 
2. How do ties among members of information networks influence the provision 
of agricultural information? 
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1.3 Research Objectives 
 
1. To find out the existing local information networks for the transmission of 
agricultural information, their characteristics, and the major providers of 
information in the various networks.  
2. To determine how the structural properties of the existing networks affect the 
flow of agricultural information. 
3. To examine how differences between actors in the various information 
networks influence the provision of agricultural information 
1.4 Relevance of Study 
Rural farmers in general are not adequately-served by the agricultural extension 
services agents as a result of the poor extension – farmer ratio (Entsua-Mensah, 
2005). The inability of these extension service providers to equitably satisfy all rural 
farmers indicates the presence of a gap with regards to fulfilling farmers’ information 
needs. In contrast, information networks are present in rural communities and reach 
comparatively higher number of farmers. Such networks possess potential features 
that can be harnessed for transfer of agricultural information. The contribution made 
by rural farmers in agriculture is invaluable, thus justifying the need to examine how 
their existing information networks satisfy their need for information. This provides 
the foundation and relevance for this research work. 
 
1.5 Study Area 
The study area was the Ahafo Ano South district in the Ashanti Region. The district is 
in the north-western part of the Ashanti Region, bounded to the north by Brong Ahafo 
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Region, to the south by Atwima District, to the west by Ahafo Ano District and to the 
east by the Offinso District. The district lies 32 kilometres northwest of Kumasi on 
the Kumasi - Sunyani road. The capital of the district is Mankranso. The Ahafo Ano 
District covers a total surface area of about 1241km, representing 5.8% of the region’s 
total surface area (www.ghanadistricts.com).  
The district has a population of 133,632.  Males constitute 55.2 percent and females 
represent about 44.8 percent.  Of this population there are only about 20 Agricultural 
Extension Agents in the district. The District is predominantly rural with over 160 
organised settlements. Agriculture is the stronghold of the district’s economy. 
Economic activities are therefore very low with farming as the most important 
productive activity with respect to output, income and employment. Cash crops, such 
as cocoa, citrus, oil palm and food crops like rice, plantain, cassava, cocoyam, maize 
and vegetables (tomatoes and okro) are the main agricultural produce in the 
district.According to the latest population and housing report, just a few households 
have access to electricity (6%). A major part of the population depends on other 
sources of power. 
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1.6 Organisation of Thesis 
The thesis is organized into six chapters. Chapter one has provided a background to 
the study, outlining the research questions and objectives that form the basis of the 
study. In chapter two relevant literature is reviewed, and theories that explain various 
aspects of the study are discussed. The third chapter explains the various techniques 
and methods that were used by the researcher in order to achieve the objectives of the 
study. In Chapter four, the results obtained are presented while these results are 
discusssed in Chapter five. Finally, chapter six presents the conclusions and the 
recommendations that arise from this study. 
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CHAPTER TWO 
LITERATURE REVIEW 
2.0 Introduction 
In this chapter, concepts of communication that underpin the study are reviewed. It 
discusses the elements of the communication process, the forms of communication, 
and the contexts in which communication takes place. Social and communication 
network analysis are explained with practical examples of their use. Finally, theories 
relevant to network analysis are discussed with the focus on their relevance to 
information networks. 
 
2.1 Definitions and Meanings of Communication 
Rogers (1995), defines communication as “a process by which participants create and 
share information with one another in order to reach mutual understanding”. The 
word communication is derived from the Latin verb communicare, which means "to 
share" or "to make common" (Coates, 2009). Considering the function of 
communication, it could basically be viewed as reaching a common ground of 
understanding. According to Chandan (2010), communication can be defined as “the 
process by which a person, group or organization (the sender) transmits some type of 
information (the message) to another person, group or organization (the receiver)”. 
From these definitions, communication can be explained as a process of passing 
information from one person to another. The various definitions also highlight the 
need for recipients of information to properly understand what is transmitted. If the 
message is not understood, then the rationale of communication is defeated. Thus, it 
can be concluded that communication is not just a mere transmission of information 
but it also involves the interpretation and understanding of information. 
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Communication is also shown to be a significant interaction between people such that 
it makes certain that the thoughts conveyed from one person to another is passed on in 
a way that the implication and worth of the message is the same for both sender and 
recipient. This aspect of communication emphasizes how necessary it is for the 
message being communicated to be passed on and fully understood.  
 
2.2 Elements of the Communication Process  
A process is a continuous, non-stationary action. DeVito (1986) says that 
“communication is referredto as a process to emphasize that it is always changing, 
always in motion” (p. 239). Thus the description “process” in relation to 
communication shows that it is an ongoing occurrence. Communication then means a 
procedure of sending out opinions, facts, desires, and sentiments to others in the form 
of a message. This implies that for communication to occur, at least two individuals, a 
sender and recipient, are required. Several researchers identify the elements or 
components of the communication process as being made up of a source or sender, 
the message, the channel of communication, the recipient, and feedback as shown in 
figure 1 (Chandan, 2010; Lunenburg, 2010). The sender commences the 
communication. The sender is the individual who wants to convey a thought or 
concept. The recipient is the individual to whom the message is sent. The sender 
codes the idea by choosing words, symbols, or gestures which would make meaning. 
The message is in the form of oral, nonverbal, or written words. The message is sent 
via a channel. The recipient interprets the received message into significant 
information. Finally, feedback occurs when the receiver reacts to the sender's 
message. Feedback allows the sender to establish whether the message has been 
received and understood. The elements in the communication process determine the 
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quality of communication. A problem in any one of these elements can reduce 
communication effectiveness (Keyton, 2011). 
 
2.2.1 Sender 
The sender is the source of the message or information that is communicated. He 
conveys ideas or concepts to others. The sender can be an individual, group, or 
organization who initiates the communication. As the source of the message, the 
sender is initially responsible for the success of the message. Consequently, he must 
be aware of the purpose of the communication and the recipient's capacity to 
comprehend it’s meaning in terms of language, importance, etc. Thus, the sender's 
experiences, thoughts, familiarity, abilities, observations, and culture influence the 
message. Burnett and Dollar (1989) state that "the written words, spoken words, and 
nonverbal language selected by the sender are paramount in ensuring the receiver 
interprets the message as intended by the sender".  
Persons in any social organization who are sought out for information and guidance 
on broad or precise subject matters are identified as key communicators. These 
individuals are also described as influencers, information leaders, opinion leaders, 
style-setters, gate keepers etc. Rogers and Kincaid (1981) define opinion leadership as 
the degree to which an individual is able to informally influence another’s knowledge, 
attitudes, or obvious behaviour in a desired way with relative regularity. It is believed 
that such individuals are highly esteemed in each social structure to which some 
people turn for suggestions and information and who by means of such discussions, 
influence their behaviour and actions.   
 
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Figure 1. Elements in the Communication Process and how they are related 
 
 (Adapted from Barnlund, 2008)  
 
Rees et al (2000) classifies sources of agricultural information into four general areas. 
They are as follows: 
 Local sources and Community Based Organizations (CBO) e.g. friends, 
relatives, neighbors, farmer cooperatives, water point discussions, etc. 
 Agribusiness (formal and informal) e.g. traders, banks, transporters, etc 
 Government sources e.g. Ministry of Agriculture, Farmer training centers, 
agricultural colleges, etc 
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 NGOs/Church sources e.g. OXFAM, World Vision, Plan Intl. etc 
In Ghana, several sources of information have been identified by various researchers. 
Codjoe, Asuming-Brempong, and Mabe (2013) identified information sources 
generally used by farmers and classified them into four in accordance with their 
similarities, that is: personal information sources, public information sources, private 
information sources and mass media. They explain that in rural communities, farmers 
seek information and knowledge mostly from their fellow farmers, relatives (personal 
sources) as well as from their small radios and television (mass media) more often 
than from extension agents, research institutes and university staff (public); and 
independent sources such as input dealers and Licensed Buying Companies (private). 
In his study conducted in the Atwima Mponua and Amansie West districts in the 
Ashanti region of Ghana, Baah’s (2008) findings agree with the conclusion above. He 
discovered that of the 278 farmers interviewed, 83.8% of farmers had not met an 
extension agent in the year preceding the study. The farmers relied heavily on their 
network of family members and friends for information on cocoa and only 13% 
regarded the extension system as a reliable information source. 
Sumbo (2001) explains that irrespective of whether the sender is an institution or a 
person, there are certain general factors that have a bearing on the reliability of the 
information source. He identifies these factors as communication skills, attitudes, 
level of knowledge, and the position that is occupied by the source within the socio-
cultural system. Boahene (1995) states a number of factors that influence the source 
of information in interpersonal communication among farmers in rural communities. 
Some of these factors are the level of authority and influence of the farmer, level of 
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participation in community activities, and the proximity of the farmer to other farmers 
who can access the type of information required.  
2.2.2 Message 
The message is simply the information the sender wants to communicate to the 
receiver.  The message is usually verbal, but it can also be non-verbal. According to 
Burtis and Turman (2010), a message includes verbal content (i.e., written or spoken 
words, sign language, e-mail, text messages, phone calls etc) and may 
contain nonverbal content (significant actions beyond words: e.g., body movement 
and gestures, eye contact, touch, timing, etc.). Intentionally or not, both verbal and 
nonverbal content form part of the information that is transferred in a message. Bovée 
et al (2004) explain that the message is an invaluable element of the communication 
process because communication is successful only when the message is understood 
and when it stirs up action or encourages the receiver to think in new ways.  
Zins (2007) describes information as a message used by a sender to represent one or 
more concepts within a communication process, intended to increase knowledge in 
recipients. There are two foremost facets of the message communicated: content and 
treatment. The content of the message refers to the statements, opinions, requests, 
topics and ideas which the sender conveys to his recipients. On the other hand, the 
treatment highlights the arrangement or organization of the content by the sender.  
Selassie (2001) defined agricultural information as the data for decision-making and 
as a resource that must be acquired and used in order to make an informed decision. 
Demiryurek, Erdem, Ceyhan, Atasever and Uysal (2008) indicate that agricultural 
information relates with and affects agricultural efficiency in diverse ways. 
Agricultural information is invaluable in decisions concerning land, labour, capital 
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and management.  Umali and Schwartz, (1994) categorize agricultural information 
into two broad groups: pure agricultural information and agricultural information 
naturally tied to new physical inventions. Pure agricultural information refers to any 
category of information which can be utilized without acquiring a particular material 
expertise. Agricultural inventions or technologies occur in the form of agricultural 
inputs, management technologies facilitating farm management, and marketing and 
processing equipment.  On the basis of this classification, agricultural information can 
be deemed as consisting of both agricultural messages transmitted through extension 
and reflected in the form of agricultural technologies that are shared between the 
actors in the agricultural extension system.  
 
2.2.3 Recipient 
Also known as the receiver, the recipient puts meaning into and reacts to the 
transmitted message. Tait,  Wibe, and Yarbrough (1980) state that once the recipient 
accepts the message conveyed by the sender the goal of communication has been 
reached. They indicate that the recipient accepts the message by means of attention 
and comprehension. Attention takes place when the recipient listens to, watches or 
tunes in to the message being transmitted. Comprehension transpires when the 
message is understood by the recipient. Yarbrough (1968) proposes that after paying 
attention to and comprehending the message the recipient accepts the message. He 
describes the acceptance of the message as occurring on three levels, namely, 
cognitive (accepting that the message is true), affective (accepts the message as being 
both true and good), and overt action (the recipient believes the message as true and 
good and also takes appropriate action). However, the information seeking decisions 
of the recipient are determined by several factors. Some of these factors that affect the 
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choices made by the recipients with respect to the source of information are discussed 
below. 
Needs: Case (2007) describes a need as “an inner motivational state” that brings about 
thought and action. He goes on to explain an individual’s need for information as a 
recognition that the knowledge he possesses is inadequate to satisfy his goal. One 
characteristic of needs is the expectation of fulfilling a desired goal. Thus when the 
individual becomes aware of a gap in their state of knowledge regarding a specific 
situation or topic he seeks to address it by requesting or seeking information. Bachav 
(2012) shows that farmers need information for various reasons related to agricultural 
activities, and they use diverse sources for access to such information. Consequently, 
farmers would choose sources of information that they believe would help satisfy 
their needs and achieve their goals. 
Perception: Perception is the way an individual thinks about or understands someone 
or something. Individuals are inclined to concentrate on information that they believe 
would meet their desires and interests. Such discriminatory consideration is usually 
instrumental in fulfilling individual requirements and getting things done.  
Other factors such as the recipient’s  power to make use of the information received 
and possession of the skills required to utilize the information received are important 
when deciding which information source to patronize. 
 
2.2.4 Feedback 
 Sending back awareness about the message to the communicator is known as 
feedback. The importance of feedback cannot be overstated. Feedback is a benchmark 
which measures the efficacy of communication and is useful for appraisal of the 
communication process and to amend the message accordingly. A communication 
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process is deemed to have feedback when the recipient responds to the sender’s 
message.  On the other hand the communicator must be aware of how well the 
message has been received by the receiver, and whether it has been understood and 
put to use. To improve communication feedback is desirable. Often, people are 
unaware of the way they communicate. Sending and getting feedback can help to gain 
insight about the communication process. It means that people who communicate 
notify each other about the outcome of the other person’s role in the entire process. 
Georgievska (2011) explains that “we understand  whether communication was 
successful from the feedback that we will obtain, and if  communication was not 
successful the first time, the second time we might consider a different approach.” He 
goes on to explain that the person with the most supple behaviour has the greatest 
influence on others. Consequently, if  an information source repeats the same thing 
that does not work, there likelihood of failure is great. The feedback is thus an 
important reaction, that helps to indicate if communication has been understood or 
not. Guo and Anderson (2005) agree, establishing that feedback should be used to 
improve communication efficacy. It is a successful approach in boosting targets, 
responsiveness, and learning.  
2.2.5 Channel 
Basically, the channel is the medium of communication used to convey the intended 
message. The channel can be thought of as a bridge, connecting the source of the 
message and the recipient. Guo and Anderson (2005), identify two main types of 
channels, verbal channels and non-verbal channels. 
The effectiveness of a communication channel is determined by several dimensions, 
some of which are; 
 Channel Credibility: the reliability of the channel as perceived by the recipient 
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 Channel Feedback: the opportunity a channel provides a recipient to respond 
to a message 
 Complementary nature: the ability to balance the work of other channels  
Generally, the choice of which communication channel to use depends on the type 
of communication. In the light of this study, emphasis would be placed on 
interpersonal communication since it is the main channel of transmitting 
information among informal information networks. 
  
2.3 Socio-Demographic Factors and the Transmission of Agricultural 
Information 
Socio-demographic factors are those features that are related to individual uniqueness. 
Very often, they correspond to the socio-economic characteristics of a population and 
are rendered in statistical form. Some of these factors are sex, age, level of education, 
marital status and household patterns among many others. According to DeNegri, 
Brown, Hernandez, Rosenbaum, and Roter (2012), the communication context is 
affected by the socio-demographic characteristics of the provider of information and 
the recipient, as well as by the environment in which the communication occurs. The 
age, sex, ethnicity, and educational background of both information providers and 
recipients influence how they communicate with each other.  
Banjade (1994) explains that although the mass media assists in creating awareness 
and providing basic information, implementation of the information is most often 
encouraged or discouraged by opinion leaders, close to members of the community, 
who share many of the same characteristics as the target group. 
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Consequently, effective interpersonal communication in a community network is as a 
result of the information source and recepient being aware of the various 
characteristics, cultural values, and societal institutions that characterize the 
community. This study is concerned with factors such as homophily and proximity 
between the information source and recepient, credibility of the information source as 
perceived by the receiver, status of the information source in the community, and the 
needs of the recepient. These factors are briefly highlighted below: 
2.3.1 Homophily 
 It is the tendency of individuals to associate and bond with similar others 
(McPherson, Smith-Lovin, and Cook, 2001). Several studies that have examined 
homophily in some form or another establish that corresponding characteristics breed 
connection. These similarities could be age, gender,class, or organizational role. 
Those in homophilic interactions possess familiar characteristics (beliefs, ethics, level 
of education) that make communication easier.  
2.3.2 Proximity 
Proximity refers to the substantial space between people. People in face-to-face 
interactions command one another’s concentration and are actively occupied with the 
subject of communication. The attention paid to those present makes interactions with 
them more outstanding than exchanges with those far-off (Latane, Liu, Nowak, and 
Bonevento, 1995). Proximity facilitates informal communication since it increases 
attention, social impact, and familiarity (Kiesler and Cummings, 2002). 
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2.3.3 Source Credibility 
 The Source Credibility theory states that people are more likely to be persuaded when 
the source presents itself as credible.  Gilbert, Fiske, and Lindzey (1998) explain that 
even though the theory of source credibility has several elements,  the two general 
elements are the proficiency and trustworthiness of the source. Beebe and Beebe 
(2005) describe trustworthiness as the ability for people to believe that a person is 
reliable and honest.  
2.3.4 Status of the Sender 
 This refers to the relative position that an individual holds in a social chain of 
command based upon honour (“Status,” 2013). In the context of this study, the status 
of the sender is in relation to his/her perceived leadership in the community with 
respect to education, occupation, marital status, and other special qualities gained 
through competition and individual effort (achieved status) or factors such as sex, age, 
race, family relationships, or birth (ascribed status). 
 2.4 Levels of Communication 
According to Sridhar (1988), the settings in which communication takes place can 
serve as a basis for classification. He mentions that communication can be classified 
into formal and informal, interpersonal and intrapersonal, etc. Some authors (Patidar, 
2013; Dunn and Goodnight, 2010) have identified various levels of communication. 
Some of these levels are highlighted below. 
 Interpersonal Communication: This refers to communication between two or 
more people, usually occurring by face to face interaction. It usually occurs in 
two stages: dyads (groups of two) or small groups. Dyadic communicationis 
the interaction between two people. It normally is informal, and entails little or 
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no preplanning. Small-group communicationrefers to exchange of information 
involving three to eight people. In most cases, small-group communication is 
less intimate than dyadic communication and less official than public 
speaking. 
 Intrapersonal Communication: This iscommunication with the individual’s 
self. It involves the person’s consciously sending information to 
himself/herself and includes such activities as self evaluation, analysis of a 
situation, decision making, problem solving, and so on. 
 Mass Communication: This is a description of an individual or organization 
sharing information with a large, diverse audience. Usually the information is 
transmitted via mass media such as journals, television, newspapers, and the 
internet. Verbal communication through mass media usually involves the use 
of microphones or amplifiers while written messages require electric or print 
media. 
 Machine-assisted interpersonal communication: It involves one or more 
people communicating using a mechanical appliance or devices with one or 
more recipients. It combines features of interpersonal and mass 
communication conditions. A typical example of this is the use of the Internet.  
The devices used in this level of communication give the information 
transmitted permanence and/or broaden its range. The source and receiver can 
be separated by time and space.   
2.5 The Relevance of Networks to Agricultural Information Delivery 
This section discusses the theoretical framework on which this study is based, 
highlighting how significant it is to the effective provision of agricultural information. 
 
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2.5.1 Social Networks and Analysis 
According to Wasserman and Faust (1994), a social network  is a social 
construction made up of a set of social actors (such as individuals or organizations) 
and a set of the dyadic (a group of two people) ties between these actors. Serrat 
(2010) also describes social networks as nodes of individuals, groups, organizations, 
and related systems that tie in one or more types of interdependencies such as shared 
values, visions, and ideas; social contacts, kinship, conflict, financial exchanges, 
trade, joint membership in organizations, and group participation in events, among 
numerous other aspects of human relationships. A social networkcan thus be 
expressed as a social structure consisting of persons or organizations called "nodes" 
(or actors), which are connected or linked (tied) by one or more definite types of ties, 
such as friendship, kinship, general interest, or status. This theoretical construct is 
valuable in studying the interactions that exist between individuals, groups, or even 
whole societies. 
The social network theory seeks to explain how the social structure of interactions 
around an individual, group, or organization affects values or behaviour. The theory 
views social relations basically in terms of nodes and ties. The nodes correspond to 
individual actors while the ties stand for the relationships or linkages between the 
actors. Social networks are usually represented as maps of relevant ties between nodes 
that are being examined. These maps are also called sociograms. In sociograms, the 
nodes are represented by points or dots, while the ties are depicted as lines. Social 
networks helps to examine interactions between individuals or groups. 
According to Krebs (2002), "social network analysis is the mapping and measuring of 
relationships and flows between people, groups, organisations, computers or other 
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information/knowledge processing entities." Social Network Analysis (SNA) is a 
technique that facilitates the mapping of interactions between people in order to 
identify the flows of information. It identifies who people in a community seek 
information and facts from and who they share their information and knowledge with.  
This method focuses on informal relationships, indicating how existing informal 
interactions ease or hinder the transfer of information and knowledge. Social network 
analysis has been applied in a wide range of academic disciplines such as 
anthropology, business studies, communication studies,  information science, 
organizational studies, development studies and literature studies (De Nooy, 
2003). The method has also been practically applied in countering money laundering 
and terrorism (Krebs 2002). This study however dwells on the flow of communication 
and how it is aided or hindered by the informal interactions between members of the 
networks. 
 
2.5.2 Communication Networks 
A Communication network refers to a description of the direction and flow of 
information between two or more linked individuals in a community, organization, or 
group. Corman and Scott (1994) explain that the network is a conceptual arrangement 
of perceived communication relationships that function as a set of rules and resources 
actors draw upon in accomplishing communication behavior. Wrench and Punyanunt-
Carter (2012) identify two types of communication networks, namely, Formal and 
Informal communication networks.  
Formal communication networks are networks in which transfer of information occurs 
via official organizational or institutional channels on the basis of established 
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positions of authority. The channels used in formal communication networks are 
established according to the hierarchical configuration of the organization. Wrench 
and Punyanunt-Carter (2012) identify  three directions of information flow in formal 
communication networks. They categorize these as horizontal/lateral flow of 
information (information conveyed to other individuals on the same level of the 
organizational chain of command), downward flow of communication (messages that 
start at the top of the hierarchy and are transmitted down the ladder to the lowest 
pecking order), and upward communication which refers to information that emanates 
from the bottom of the hierarchy and are transmitted up the ladder to the highest rungs 
of the hierarchy. 
In contrast, informal communication networks lack a stable structure, are unplanned 
and situationally derived. Information flow in informal networks is based on 
unofficial associations (such as friendship, membership of a group, the same 
occupation, etc.) and, therefore, is free from all the organisational formalities that 
characterise formal communication networks. Exchange of information at this level 
usually occurs in casual settings. Since it is founded on the basis of social relations 
and interactions, information transmitted through informal communication networks 
can reach a targeted audience quickly and effectively. 
2.5.3 Communication Network Analysis  
A communication network consists of interconnected individuals who are linked by 
patterned flows of information, and analysis of communication networks identifies the 
communication structure within a social system (Rogers and Kincaid, 1981). 
Generally, the focal point of network analysis is to identify the relationships between 
people, instead of their individual characteristics. These relationships include the 
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feelings people have for each other, the transfer of information, or more concrete 
items such as commodities and currency. By expressing these interactions in the form 
of sociograms (a graph that depicts the social relations in a group), network analysis 
helps to reveal the unofficial communication patterns that exist in a community, group 
or organization. As shown in several studies, the patterns that emerge from network 
analysis enables an understanding of some group or societal trends. For instance, the 
relations between employees in an information network (as described by their 
connections or interactions), affects their access to and control over information (Burt, 
1992; Haythornthwaite, 1996). 
Communication network analysis gives meaning to the communication structure of 
the group or organization being studied. It differentiates between the various 
structural characteristics, such as the types of information transmitted through the 
respective communication networks, the roles played by various actors in the 
network, the channels used for different types of information transmitted, and the 
effectiveness of information flow in the network.  The measures of network analysis 
could be in terms of the network, the actors in a network, or the ties that link actors in 
a network. 
Hanneman and Riddle (2005), state that network descriptors that deal with 
connections among actors in a network can be extremely consequential for 
understanding the attributes and behavior of these actors. They further explain that 
actors with more connections is an indication of their exposure to more diverse 
information. Highly connected individuals may be more influential, and may be more 
influenced by others, depending on the direction of information flow. Another method 
of analysing information networks is the use of network descriptors that deal with 
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how far (in terms of social distance) an actor is from others (Izquierdo and 
Hanneman, 2006). According to Katz et al (2004), these descriptors can be generally 
categorised as measures of actor degree centrality (the extent to which actors send or 
receive direct ties), betweenness centrality (the extent to which actors have ties with 
others who are not directly connected), closeness centrality (the extent to which actors 
are directly or indirectly connected to the rest of the actors in the network), reciprocity 
(the extent to which there are mutual ties between actors), or transitivity (the extent to 
which actors who are connected to one another are also connected to the same other 
actors).By means of these descriptors, key actors (referred to as key communicators in 
this study) are identified on the basis of the positions they occupy. 
The actor with most ties to other actors (degree centrality) directs communication and 
resources across the network. These key actors are generally perceived as highly 
knowledgeable than the others or possess a higher status or level of influence than 
others in the community (Valente and Pumpuang, 2007)and are consulted the most for 
any required information. The positions these key actors occupy make the networks 
vulnerable in the event of their absence. 
The other descriptors (betweenness, closeness, reciprocity, etc) determine key actors 
on the basis of their lying between other actors such that if they were removed the 
network could be fragmented, endangering the flow of information between the other 
actors (Burt, 2005). Actors in these positions are indispensable in sealing gaps or 
boundaries imposed by a key actor’s perceived power or status (Gray, 2008). It must 
however be noted that these positions are not mutually exclusive, thus, an actor may 
be a key communicator by virtue of the number of ties initiated with others and still 
be influential in terms of his/her position in the network 
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2.6 Homophily Theory and Information networks 
Monge and Contractor (2003) state that there are ‘families of theories’ that seek to 
explain why people form, sustain, break, or reinforce network ties with others. One of 
such theories fundamental to this study is the theory of homophily. The homophily 
theory is defined as a “principle that contact between similar people occurs at a higher 
rate than among dissimilar people” (McPherson et al, 2001). Hamm (2000) explains 
that likeness in terms of traits and skills possibly eases the process of evaluating, 
communicating with, and even predicting the behavior of others. This phenomenon 
explains why friends, spouses, romantic partners, co-workers, colleagues, and other 
professional and recreational associates all tend to be more similar to each other than 
randomly chosen members of the same population with respect to a variety of 
dimensions, including race, age, gender, socioeconomic status, and education 
(Kossinets and Watts, 2009). Homophily is typical of network systems and 
homogeneity is an attribute of personal networks. Sex, age, religion, and education 
have a strong effect on the structural relationships between individuals, while 
occupation, network position, conduct, and intrapersonal ideals also show substantial 
homophily, but they are more specific to certain forms of networks (McPherson et al, 
2001).  
McPherson and Smith-Lovin (1987) identify two categories of homophily, namely, 
choice homophilyand induced homophily. Choice homophily refers to homophilous 
ties that are formed on the basis of personal preferences. Homophilous ties that exist 
or are formed as a result of the presence of structural opportunities for interaction, 
such as an individual’s vicinity, place of employment, school, etc, are designated as 
induced homophily. According to Handcock, Hoff, and Raftery (2002), these two 
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categories of homophilous ties are not mutually exclusive, instead, they buttress each 
other. Thus, while it is possible to state that an individual selects into an environment 
with the specific aim of meeting the class of people that also exist in those locations, 
it is equally reasonable to establish that the predisposed categorization of individuals 
to structurally nearby locations is itself an outcome of structural limitations.  
Homophily has extensive implications for the delivery of agricultural information in 
informal networks. The fact that homophily facilitates the spread of resources among 
similar people implies that information that flows via informal networks could 
possibly be confined or restricted to a group of similar individuals. As Granovetter 
(1973) indicates, communication among a homophilous group is repeated, 
unchallenging, and speeds up the rate of diffusion in an information network. 
Consequently, homophily in information networks aids the transfer of information, 
not just with the aim of introducing information, but additionally, it supports social 
relations, reinforces existing  networks, and enhances social closeness among farmers. 
2.7 Summary  
Thus far, the review of literature has discussed how important the communication 
process is to the operation of informal information networks. The elements in the 
communication process play various roles to ensure that agricultural information is 
transferred in the network. Consequently, it has been revealed that by means of  
network analysis, key aspects of a network such as identification of influential actors 
(key communicators), and the flow of information among actors can be determined. 
The theories on which this study is grounded have been discussed. This chapter has 
also helped to explain the rationale behind the type of methodology used in the study.  
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CHAPTER THREE 
METHODOLOGY 
3.0 Introduction 
This chapter highlights the study design, sampling and data collection methods. It also 
gives a background to the study area and explains other relevant factors related to 
analysis of the data collected. 
3.1 Research Design 
According to Burkingham and Saunders (2004), a research design is an outline that  
directs data collection and analysis, allowing the investigator to examine and explain 
the problem being studied. In addition, Burns and Grove (2011) add that a research 
design is the plan for the study, which gives the researcher authority over aspects that 
could meddle with the force of the results. It helps the researcher to plan and carry out 
the research in order to accomplish the projected goals.  
For the purpose  of this study, both qualitative and quantitative methods were 
employed.Data was collected primarily by means of questionnaires administered by 
the researcher. The initial respondents were selected by means of the sociometric 
technique. Additional questionnaires were also administered to actors identified as 
key communicators. Data collected was analyzed by means of UCINet 6 (Borgatti, 
Everett, and Freeman, 2002), Pajek (Batagelj and Mrvar, 2007), and the SPSS 
package. 
 
 
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3.2 Population 
The population for this study encompasses all natives and resident farmers in the 
Ahafo Ano South District. The respondents were drawn from the population. The 
District is predominantly rural with over 160 organised settlements. The district has a 
population of 133,632 (2000 population and housing census) of which 48.2% live in 
the 20 larger settlements. The major crops grown by farmers in the area are cash 
crops, such as cocoa, citrus, oil palm and food crops like rice, plantain, cassava, 
cocoyam, maize and vegetables (tomatoes and okro). Farmers in the district belong to 
various groups, agricultural and non-agricultural. Some of the agricultural groups are 
locally formed (eg. Nnoboa groups) while the others are formed by external agencies, 
non governmental organizations (NGOs), and produce buying companies. Traditional 
methods of farming (use of cutlasses, hoes, slash and burn are very common. In view 
of the high cost of hired labour, a large number of farmers form nnoboa groups 
(groups formed on the basis of communal labour) to work on each farm. 
3.3 Sampling Method 
The study adopts the snowball method of sampling for network analysis as proposed 
by Hanneman and Riddle (2005). The snowball method begins with a central actor or 
set of actors. Each of these actors is asked to name some or all of their ties to other 
actors. Then, all the actors identified (who were not part of the initial listing) are 
tracked down and asked to name their ties. The process continues until no new actors 
are identified, or until the researcher decides to stop (usually for reasons of time and 
resources, or because the new actors being named are very marginal to the group 
being studied). 
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The study area was purposively selected due to practical limitations (i.e., distance, 
finance and transportation). Specifically, respondents were drawn from 
Dunyankwanta, Afreseni, and Kunso camp. These three communities were 
purposively selected on the basis of their being predominantly rural farming 
communities. The communities also have various formal and informal groups.  
3.4 Data Collection Procedures 
The Sociometric technique was used to identify the key communicators. This 
technique, developed by Moreno (1951) studies human connectedness. The 
respondents were asked to indicate their choices from whom they receive information 
regarding agricultural activities (Moreno, 1960; Goswami and Sarkar, 2009). 
Respondents were required to make unlimited choices without any specified 
categories. The choices were directed in nature, i.e. they distinguished between 
seeking information and providing information. The number of received choices by 
the respondents were ranked. Key communicators identified from the analysis of 
responses collected with the aid of the initial questionnaires were consulted for a 
second round of interviews to determine their sources of information, information 
sharing habits, and their positions or status in the community. Pretesting of 
questionnaire was done to ensure the validity and reliability of the instruments used in 
the data collection process.  
3.5 Data Collection Instruments 
Data was collected primarily by means of questionnaires that were administered to 
respondents. The questionnaires sought information on individual characteristics of 
the respondents, their choice of information sources, and their membership of local 
farmer groups. Another set of questionnaires were administered to key 
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communicators, seeking information on their output, level of experience, training 
programs attended, and status in their respective communities. 
3.6 Data Analysis 
Social network analysis usually involves the use of questionnaires and/or interviews 
to obtain data about the interactions within a distinct cluster or network of people. The 
data collected is then charted with the aid of a software instrument designed for that 
specific purpose. Social network analysis expresses social interactions using network 
theory terms, comprising nodes (which symbolize individual actors within the 
network) and ties (which also stand for relationships between the individuals in the 
network) such as friendship, kinship, organizational position, sexual relationships, etc 
(D’andrea, 2009; Pinheiro, 2011).  
Farmer responses were coded in terms of dual variables (distinguishing between the 
presence or absence of  ties), to allow entry into a name-based adjacency matrix 
(Hanneman and Riddle, 2005). The coded responses were entered into Pajek (Batagelj 
and Mrvar, 2007) to create socio-grams and  analysed with the aid of the software 
Ucinet 6 for Windows (Borgatti et al., 2002). The sociograms were created according 
to the core-periphery model (Cattani and Ferriani, 2008). The Core-periphery model 
generally arranges actors in a network into two categories, one category for highly 
sought farmers and the other for farmers who are less consulted. Basically, network 
members who occupy core positions possess relatively many interconnected ties, 
whereas occupants of the peripheral positions in a network have relatively few 
connections to the core and to each other (Borgatti and Everett 1999).  The networks 
were also analysed in terms of network descriptors such as degree, density, in/out-
degree links, network size, and network centrality. Personal characteristics of key 
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communicators were presented with the aid of charts, graphs, and tables generated 
from SPSS. 
3.6.1 Sociograms 
Sociograms, are graphic pictures, or  descriptions, of a type of social connection. 
Sociograms show aspects of a relationship at a given point in time (Durland, 2003).  
A sociogram is composed of nodes (or actors or points) connected by lines (or 
relations or ties) and usually represents types of relations among the actors 
(Hanneman and Riddle, 2005). The sociogram shows whether there are numerous or a 
small number of ties between members in a network, the general pattern of the 
existing ties, and where every individual member is located in the network (Katz et al, 
2004). In this study, the ties that made up the sociograms were directed with the aid of 
arrows to reveal the flow of information (i.e from information source to target). The 
ties were reciprocal (X chose Y and Y chose X) in which case such ties were 
represented with a double-headed arrow, or non-reciprocal (only one member chose 
the other). In this study, information about ties were entered into Pajek (Batagelj and 
Mrvar, 2003) in order to obtain sociograms. 
3.6.2 Measurement of Network Descriptors for Network analysis 
This section discusses the various measures of network analysis that are relevant to 
this study. These variables were measured with the aid of UCINET 6 (Borgatti et al., 
2002). 
i. Degree of Nodes: According to Brass (1995) degree as used in network 
analysis deals with the number of direct links with other actors. This measure 
of analysis helps to identify key communicators or major information sources. 
The key communicators possess the highest number of “out-going” links, 
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indicating the flow of information from them to other actors in the network 
(Wrench and Punyanunt-Carter, 2012).  
ii. Network Size: This refers to the number of interconnected individuals in a 
network. This was determined by literally counting the members of the 
network. 
iii. Network Connectedness: This measure of social network analysis refers to the 
percentage of the theoretical number of connections that have been achieved 
(Zhang et al, 2013). Network connectedness primarily focuses on evaluating 
networks (groups of actors) in terms of the number of ties that link different 
networks. This study explored network connectedness in terms of network 
density. 
 Network Density: Density of a network is the ratio of the actual number of 
relationships that exist in a network to the total number of potential 
relationships in the network (Thaden and Rotolo, 2009). The density of a 
network gives meaning to trends such as the rate at which information 
diffuses among the nodes. According to Wasserman and Faust (1994), if a 
network of size x contains y relationships, the density of the network is 
defined as:   
iv. Network Centrality Measures: These refer to the overall centrality present in a 
network and calculate the extent to which ties in a network are situated in the 
region of one or a few middle network members. High network centrality 
values are an indication of information flow in the network being reliant on 
few individual actors (Izquierdo and Hanneman, 2006). For the purpose of this 
study the networks were evaluated on the basis of degree centrality and 
betweenness centrality measures. 
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 Degree Centrality: The underlying principle for this centrality measure is 
that actors with more ties are the most important (Izquierdo and 
Hanneman, 2006). Actors who possess extraordinarily high out-degree ties 
are deemed capable of transmitting information to many others in the 
network. Actors who display high out-degree centrality are often said to be 
influential actors (Hanneman and Riddle, 2005). Freeman's graph 
centralization measures as found in UCINet 6 (Borgatti et al., 2002), also 
determine the total centralization measures for the entire network. 
According to Hanneman and Riddle (2005), these network measures reveal 
the degree of disparity or variance in the network as compared to an 
equivalent perfect network where all possible ties are present. On the basis 
of those network measures conclusions can be drawn regarding the level of 
centralization in the entire network.  
 Betweenness Centrality: This centrality measure identifies information 
sources and gatekeepers that exist in a network. According to Müller-
Prothmann (2007), it determines whether an actor plays a comparatively 
central role as a mediator or gatekeeper of information flows with a high 
potential of indirectly controlling the relations of other actors in the 
network. The study adopts the Flow approach to betweenness centrality as 
found in UCINet 6 (Borgatti et al., 2002). The flow approach to centrality 
assumes that actors will use all pathways that connect them, proportionally 
to the length of the connecting paths. Accordingly, the flow approach 
calculates the contribution of an actor to all possible maximum flows. 
(Hanneman and Riddle, 2005). Additionally, it calculates a network flow 
centralization measure for the entire network, indicating the degree of 
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inequality, or concentration in the distribution of flow betweenness 
centralities among the actors relative to that of a perfect network of same 
size. 
v. Homophily scores: This determines measures of homophily in the entire 
network on the basis of a specific partition. The homophily measures are 
expressed in terms of three values: the E-I  (external – internal) Index, the 
PBSC (point biserial coefficient), and the Yule’s Q.  
 The E-I index is the number of ties outside the groups minus the number of 
ties that are within the group divided by the total number of ties. The 
possible values range from 1 to -1 with -1 being the highest degree of 
homophily and +1 the highest heterophily possible.  
 PBSC is used to determine correlation based homophily measures by 
correlating the dichotomous variable "is in the same group as" with the 
corresponding entry in the attribute matrix (UCINet 6, Borgatti et al., 
2002). It ranges from 0 to 1 indicating a positive relationship (i.e, in the 
same direction), and 0 to -1 indicating a negative relationship (i.e, opposite 
directions). The higher the value, the stronger the relationship.  
 Yule’s Q is a nominal level measure that determines the strength and 
direction of  association between two possibly related dichotomous 
variables (Baddie and Fred, 1995). The values range from -1 to 1. When Q 
= -1, it indicates a perfect negative correlation, meaning that when one 
variable occurs it invariably leads to the non-occurrence of the other and 
vice-versa. There is no relationship between the two variables when Q = 0. 
However values that are close to zero are indications of weak associations. 
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Table 3.1 summarizes the relevant information required for achieving each objective 
as well as the sources of information, how data was collected, and the type of analysis 
undertaken. 
Table 3.1 Summary of data required and method of data collection 
OBJECTIVE 
OF 
RESEARCH 
INFORMATION 
REQUIRED 
SOURCE 
OF 
INFORM
ATION 
METHOD 
OF DATA 
COLLECTI
ON 
METHO
D OF 
ANALY
SIS 
To find out the 
existing local 
information 
networks for 
the 
transmission of 
agricultural 
information, 
their 
characteristics, 
and the major 
providers of 
information in 
the various 
networks.  
 
Sources of 
agricultural 
information in the 
communities, mode 
of transmission of 
agricultural 
information, mode of 
contacting sources of 
information and 
mode of seeking 
further clarification 
or sending feedback 
 
Farmers in 
the study 
area 
 
Researcher-
administered 
questionnaires 
Descripti
ve 
Statistics, 
Sociogra
ms 
(identific
ation of 
local 
informati
on 
networks
), degree 
of nodes, 
determin
ation of 
network 
size and 
network 
density of 
core/peri
phery 
class 
members 
To determine 
how the 
structural 
properties of 
the existing 
networks affect 
the flow of 
agricultural 
information. 
 
The primary sources 
of information 
contacted by the 
respondents, the type 
of information 
received, and the 
level of clarity and 
trustworthiness of the 
information received 
from their sources 
Farmers in 
the study 
area 
Researcher-
administered 
questionnaires 
Network 
density, 
Centraliz
ation 
measures 
(Degree 
Centraliz
ation and 
Between
ness 
Centraliz
ation) 
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To determine 
how 
differences 
between actors 
in the various 
information 
networks 
influence the 
provision of 
agricultural 
information 
 
Personal 
characteristics of 
primary sources, 
types of agricultural 
information 
transmitted, types of 
training programs 
received, interactions 
with AEAs, types of 
crops grown, output 
per season 
Farmers in 
the study 
area 
 
Researcher-
administered 
questionnaires  
Homophi
ly scores: 
E-I Index 
(External 
ties – 
Internal 
ties 
index), 
PBSC 
(Point 
Biserial 
Coefficie
nt), and 
Yule’s Q 
 
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CHAPTER FOUR 
RESULTS 
4.0 Introduction 
In this chapter, the results are presented according to the communities and the types of 
agricultural information networks that exist in them.First, a brief description of the 
structural properties of the various information networks is given. References are 
made to the sociograms in order to help explain these structural properties. The 
network characteristics that are discussed are the network size, the core-periphery 
structure, network density, network degree centralization, network betweenness and 
homophily. Personal characteristics of members who belong to the core (key 
communicators) are summarized to highlight possible similarities. 
4.1 Characteristics of Agricultural Information Networks and Primary Sources 
of Information 
In this section, the results are presented across the various communities. The mode of 
information seeking and sending feedback was by oral communication that took place 
during home visits, farm visits, informal conversations, group meetings, etc.The 
sociograms portray the social relations existing in each community, revealing the 
unofficial communication patterns that exist in the community. In addition, results 
from analysis of other network properties such as core-periphery structures, network 
size, and network density are shown. Key communicator characteristics as well as 
results of t-tests conducted on these characteristicsare also presented.. 
 
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4.1.1 Agricultural Information Network in Kunsu Camp 
The agricultural information network found in Kunsu Camp is presented in the form 
of a sociogram (Figure 2). The information network in Kunsu has a network size of 
thirty-two. The network transmits information regarding fertilizer application, weed, 
pest, and disease control, harvesting, and marketing. Six key communicators were 
identified in the network. 
Core/Periphery Class Memberships  
The core-periphery model identifies actors who have high number of ties within the 
network by sharing information with most members of the network (the core), and 
another set of actors who have very low number of ties with members of the networks 
(the periphery).  Consequently, actors in the core are at a structural advantage in terms 
of exchanging relations with actors in the periphery. The core-periphery structure of 
the network indicates that respondents 1, 2, 3, 4, 10, and 11 form the core and are the 
key communicators in Kunsu camp community. Table 4.1 indicates the role each node 
plays as a source of information. The sum is the total number of ties that consult an 
actor for information. Taking into account the statistics, the core members are 
influential (key communicators), considering the number of ties (information 
transmitted to other actors) they have with other farmers. From the table, actor 1 is 
most influential (18 out-ties) with actors 10 (17 out-ties), 11 (15 out-ties), 2 (12 out-
ties), 4 (9 out-ties) and 3 (7 out-ties) following respectively. 
 
 
 
 
 
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Figure 4.1 Agricultural Information Network in Kunsu Camp, Ahafo Ano South, 
Ghana 
 
 
NB: Direction of arrows denotes direction of information flow. Double-ended arrows 
symbolize reciprocal ties. 
 
 
 
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Table 4. 1: Out-degree statistics for Core and Periphery members (Kunsu) 
Connections from Core to others Connections from Periphery to Others 
Actor Mean Sum Actor Mean Sum 
V1 0.581 18 V 8, 9, 12, 14, 16 0.00 0 
V2 0.387 12 V 17, 18, 20, 21, 22 0.00 0 
V3 0.226 7 V 25, 26, 27, 28, 29 0.00 0 
V4 0.290 9 V 5, 6, 15, 19, 23, 24, 
30 
0.032 1 
V10 0.548 17 V7, V31, V32 0.065 2 
V11 0.484 15 V13 0.129 4 
 
Network Density: The density values indicate the quantity of potential ties in the 
network that are really existent. The values range from 0 to 1, where the lower limit 
corresponds to networks with few relationships carried out and the upper limit stands 
for dense portions of the network with interconnected nodes.  
Table 4.2 shows that farmers in the core group sought advice from other core 
acquaintances more often than from farmers in the periphery, resulting in a high level 
of communication within the smaller core group (0.733). Generally, the 
comparatively small, dense cluster of farmers (core) was consulted by the larger 
population for agricultural information. Farmers in the core group sought advice from 
other core acquaintances more often than from farmers in the periphery, resulting in a 
high level of communication within the smaller core group (0.733). Generally, the 
comparatively small, dense cluster of farmers (core) was consulted by the larger 
population for agricultural information. 
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Table 4. 2: Density of farmer ties between the Core and Periphery groups in the 
Kunsu Agricultural Information Network 
 CORE PERIPHERY 
CORE 0.733 0.359 
PERIPHERY 0.013 0.023 
 
Kunsu Camp – Key Communicators 
The network in Kunsu Camp had 6 key communicators (Table 4.3). They are all 
married, with children. All six key communicators are members of local farmer 
groups and occupy various leadership positions in the community. Ibrahim Zakaria is 
the Unit Committee Chairman of Kunsu Camp and the Chairman of the “Koroye 
Nkabom” farmers group. Adam Mohammed Zakaria, Abukari Yakubu, and Stephen 
Boadi are prominent members of the “Koroye Nkabom” farmers group. Yaw Manu 
and Kojo Manu are also members of a local farmers group sponsored by Armajaro 
Ghana. Yaw Manu is the head of farmers, while Kojo Manu is the Financial 
Secretary. All six key communicators have been previously sponsored by Armajaro 
Ghana to receive training geared towards use of good planting materials, improved 
planting methods, good cultural practices related to cocoa production and disease 
control. Ibrahim Zakaria is also a beneficiary of a JICA training program aimed  at 
improving proper weeds, pests, and disease control in rice and proper fertilizer 
application. These key communicators have been farming for at least twenty years. 
The key communicators in Kunsu cited older family members (father and 
grandfather), interactions with other skilled farmers, interactions with the Agricultural 
Extension Officer (AEA), and training from other organizations. The key 
communicators stand out with regards to the acreage per crop grown and their output 
per acre each season (Table 4.3). 
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Table 4. 3: Selected Background Characteristics of Key Communicators (Kunsu 
Camp) 
Name Age 
(Years) 
Years Of 
Farming(Years) 
Acreage For 
Cocoa (Acres) 
Output Per 
Season (Bags 
Per Acre) 
Ibrahim Zakaria 37 20 6  7 
Stephen Boadi 50 25 7  7  
Adam Mohammed 
Zakaria 
39 20 6  6.5 
Kojo Manu 52 31 5 6.5 
Yaw Manu 50 30 6 8 
Abubakari Yakubu 58 30 5 8  
 
A one-sample t-test was run to test the mean value of the average age of the key 
communicators against the community.From the one-sample t-test in Table 4.4, the 
key communicators were significantly older (47.67) than the population norm of 38.8 
years. Also, the difference in mean output between the key communicators and the 
population norm was statistically significant (Table 4.5). 
Table 4. 4: One-sample statistics of age and output of key communicators in 
Kunsu 
 N Mean Std. Deviation Std. Error Mean 
Average Age  6 47.67 8.066 3.293 
Average Output 6 7.1667 .68313 .27889 
 
 
 
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Table 4. 5: One-sample t-test of average age and output of key communicators in 
Kunsu 
 Test Value = 38.8 
T Df Sig. (2-
Tailed) 
Mean 
Difference 
95% Confidence Interval 
Of The Difference 
Lower Upper 
Average 
Age  
2.693* 5 .043 8.867 .40 17.33 
 
Average 
Output 
 Test Value = 4.52 
9.490* 5 .000 2.647 1.9298 3.3636 
* p < .05 
4.1.2 Agricultural Network in Afreseni 
The Afreseni information network presented in Figure 3 indicates that there are fifty-
eight members in the network. The information network in Afreseni transmits 
information concerning fertilizer application, weed, pest, and disease control, 
harvesting, and marketing. From the network eight key communicators were 
identified.  
Core/Periphery Class Memberships: By means of the core/periphery structure, 
influential actors in terms of agricultural information ties sent out are identified. From 
the core/periphery structure in the Afreseni network, eight key communicators stand 
out. The key communicators identified are: 1, 4, 8, 13, 18, 21, 32, and 43. Table 4.6 
indicates how influential each node is as a source of information. The sum is the total 
number of ties that consult an actor for information. Considering the statistics, the 
core members are influential on the basis of the number of ties (information 
transmitted to other actors) they have with other farmers. From  Table 4.6, actors 1 
and 43 are the most influential (19 out-ties each) with actors 18 and 21 (18 out-ties 
each), 8 (15 out-ties), 32 (14 out-ties), 4 (11 out-ties) and 13 (8 out-ties) following in 
that order respectively. 
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Figure 4.2 Agricultural Information Network in Afreseni, Ahafo Ano South, 
Ghana 
 
 
NB: Direction of arrows denotes direction of information flow. Double-ended arrows 
symbolize reciprocal ties. 
 
 
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Table 4.6: Out-degree statistics for Core and Periphery members (Afreseni) 
Connections from Core to others Connections from Periphery to Others 
Actor Mean Sum Actor Mean Sum 
V1 0.333 19 V16, 17, 19, 23, 25, 27 
- 29 
0.00 0 
V4 0.193 11 V3, 6, 9, 11, 12, 24, 26, 
30, 31 
0.018 1 
V8 0.263 15 V37, 39, 40, 42, 50, 52, 
53, 54, 57 
0.018 1 
V13 0.140 8 V2, 14, 15, 20, 34, 35 0.035 2 
V18 0.316 18 V36, 38, 45, 48, 51, 56 0.035 2 
V21 0.316 18 V5,10, 41, 44, 55, 58 0.053 3 
V32 0.246 14 V7, 22, 33, 46, 47, 49 0.070 4 
V43 0.333 19 
 
The density of ties between the groups in a network is an indication of the rate at 
which information spreads among members of the groups. Thus, denser portions of a 
network ensure that information flows readily among the nodes in that part of the 
network. From Table 4.6, farmers in the core group sought advice from other core 
acquaintances more often than from farmers in the periphery, resulting in a high level 
of communication within the smaller core group (0.411). Generally, the core 
transmitted agricultural information to the larger population. 
Table 4. 7: Density of Farmer Ties between the Core and Periphery groups in the 
Afreseni Agricultural Information Network 
 CORE PERIPHERY 
CORE 0.411 0.237 
PERIPHERY 0.023 0.032 
 
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Afreseni – Key Communicators 
Analysis of the Afreseni information network indicated that there were 8 key 
communicators. They are all married, with children. Only four key communicators are 
members of local farmer groups while all of them occupy various leadership positions 
in the community. Paul Opoku Anane is the Unit Committee Chairman of Afreseni. 
Paul Wisdom Femeyibor is the Assemblyman in the area and a prominent member of 
“Odo Kuo”, the local farmers group. Joseph Kusi is the treasurer of the local farmers 
group, “Odo Kuo”. Samuel Obiri Addo and Osei Yaw are also members of the local 
farmers group, “Odo Kuo”. Samuel Obiri Addo is a “gang supervisor” (cocoa 
spraying exercise), while Osei Yaw is the Chief’s linguist. Kojo Nsiah is the chief 
cocoa farmer, Elizabeth Aniko is the queen mother of Afreseni and Nana Akwasi 
Owusu is the chief of Afreseni. Some of the key communicators have received 
agricultural related training from various organizations. Osei Yaw, Kojo Nsiah, and 
Joseph Kusi are beneficiaries of a training program sponsored by the Ministry of Food 
and Agriculture to help improve cocoa spraying practices. Paul Femeyibor and Nana 
Akwasi Owusu are beneficiaries of training programs by Care International – Ghana, 
which sought to improve cocoa production through use of healthy planting material, 
fertilizer application, and weed control. They were also sponsored by Armajaro 
Ghana for similar training programs.  
All the key communicators in Afreseni cited older family members (father, uncle and 
grandfather), interactions with other skilled farmers, contact with the Agricultural 
Extension Officer (AEA), and training from other organizations. The key 
communicators stand out with regards to the acreage per crop grown and their output 
per acre each season. Their figures are shown in Table 4.8. 
 
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Table 4. 8: Selected Background Characteristics of Key Communicators 
(Afreseni) 
Name Age Years Of 
Farming 
(Years) 
Acreage For 
Cocoa (Acres) 
Output Per 
Season (Bags Per 
Acre) 
Osei Yaw 52 10 10 7.5 
Joseph Kusi 39 15 15 7 
Paul Wisdom 
Femeyibor 
49 7 6 7 
Nana Akwasi 
Owusu 
62 30 16 7.5 
Elizabeth Aniko 52 30 10 6.5 
Kojo Nsiah 36 16 12 8  
Paul Opoku 
Anane 
48 12 8 7  
Samuel Obiri 
Addo 
61 12 7.5  7 
 
A one-sample t-test was run to test the mean value of the average age and the average 
output of the key communicators against the community.The t test showed that the 
difference in ages between the key communicators and the population (36.6) was 
statistically significant (Table 4.10). Similarly, the difference in mean output between 
the key communicators and the population norm was statistically significant. 
Table 4. 9: One-sample statistics of average age and output of key 
communicators in Afreseni 
 N Mean Std. Deviation Std. Error Mean 
Average Age  8 49.88 9.219 3.259 
Average Output 8 7.1875 .45806 .16195 
 
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Table 4. 10: One-sample t-test of average age and output of key communicators 
in Afreseni 
 Test Value = 36.6 
T Df Sig. (2-
Tailed) 
Mean 
Difference 
95% 
Confidence 
Interval Of The 
Difference 
Lower Upper 
Average Age  4.073* 7 .005 13.275 5.57 20.98 
 
Average 
Output 
Test Value = 4.71 
15.298* 7 .000 2.47750 2.0946 2.8604 
* p < .05. 
 
4.1.3 Agricultural Network in Dunyankwanta 
The information network as presented in the sociogram(Figure 4) indicates that there 
are seventy-two members in the network. The information network in Dunyankwanta 
transmits information concerning fertilizer application, weed, pest, and disease 
control, harvesting, and marketing.From the network nine key communicators were 
identified.  
Core/Periphery Class Memberships: The core/periphery structure of the network 
identified influential actors in terms of those consulted for agricultural information. 
From the core/periphery structure, nine key communicators stand out. The key 
communicators identified as core farmers and key communicators are: 1, 2, 10, 13, 
15, 21, 38, 48 and 54. 
 
 
 
 
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Figure 2.3 Agricultural Information Network in Dunyankwanta, Ahafo Ano 
South, Ghana 
 
NB: Direction of arrows denotes direction of information flow. Double-ended arrows 
symbolize reciprocal ties. 
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Table 4.11 indicates the prominence of each node as a source of information. The sum 
displays the total number of actors that consult an actor for information. The statistics 
show how influential core members are on the basis of the number of out-degree ties 
(information transmitted to other actors) they have with other farmers. From the table, 
actor 2 is the most influential (21 out-ties) with actors 10 (19 out-ties), 38 (17 out-
ties), 15 and 21 (13 out-ties each), 13 and 48 (12 out-ties each), 1 (11 out-ties) and 13 
(6 out-ties) following in that order respectively. 
Table 4.11: Out-degree statistics for Core and Periphery members in 
Dunyankwanta 
Connections from Core to others Connections from Periphery to Others 
Actor Mean Sum Actor Mean Sum 
V2 0.296 21 V3, 4, 62 0.070 5 
V10 0.268 19 V55, 69 0.056 4 
V38 0.239 17 V12, 34, 35, 36, 37, 41, 
50, 52, 56, 67 
0.042 3 
V15 0.183 13 V8, 16, 18, 29, 32, 39, 40  0.028 2 
V21 0.183 13 V43, 51, 57, 60, 61, 71 0.028 2 
V13 0.169 12 V5, 9, 14, 26, 28, 30, 31, 
33, 42, 44  
0.014 1 
V48 0.169 12 V45, 46, 47, 49, 53, 63, 
64, 65, 70 
0.014 1 
V1 0.155 11 V6, 7, 11, 15, 17, 19, 20, 
22 
0.000 0 
V54 0.085 6 V23, 24, 27, 58, 59, 66, 
68, 72 
0.000 0 
 
Network Density: The density of a section of a network is an indication of the speed 
at which information is disseminated among the nodes that exist in that segment of the 
network. Consequently, denser portions of a network would ensure that information 
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flows readily among the nodes in that network. Table 4.12  presents the network 
density values for the Dunyankwanta network. Actors who form the core group 
sought advice from fellow core actors regularly than from farmers in the periphery, 
resulting in a high level of communication within the smaller core group (0.444). On 
the whole, the core group was prominent in the transmission of agricultural 
information to the larger population. 
Table 4.12: Density of ties between core and periphery groups in the 
Dunyankwanta Agricultural Information Network 
 CORE PERIPHERY 
CORE 0.444 0.162 
PERIPHERY 0.009 0.024 
 
Dunyankwanta – Key Communicators 
The information network in Dunyankwanta has 9 key communicators. They are all 
married, except one, who is a widower. Seven out of the nine key communicators are 
members of local farmer groups while several of them occupy various leadership 
positions in the community. Opanin Kwaku Duruyeh is the Chief Cocoa Farmer in 
Dunyankwanta and a member of the local farmer group sponsored by Care 
International - Ghana. Eric Kofi Asenso is the Chief Rice farmer in Dunyankwanta 
and the current winner of the Best Rice Farmer award in the district. He is also the 
secretary of the Abrabopa Rice Growers Association, a local rice farmer group in 
Dunyankwanta. Agnes Achiaa and Francis Brenyah are members of the local farmer 
group sponsored by Care International – Ghana. J.K. Sarfo is a member of the Care 
International – Ghana sponsored group and a treasurer of the Abrabopa group, 
Andrews Donkor is a Community Emulator in the Care – International group and the 
Chairman of the Cocoa Abrabopa group while Kwame Sekyereh is the chairman of 
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the Abrabopa Rice Growers group. Some of the key communicators have received 
agricultural related training from various organizations. Eric Kofi Asenso, Kwame 
Sekyereh, and Andrews Donkor are beneficiaries of training programs sponsored by 
the Ministry of Food and Agriculture to help improve cocoa production. Agnes 
Achiaa and Andrews Donkor are also beneficiaries of training programs by Kuapa 
Cocoa, which sought to improve cocoa production through use of healthy planting 
material, proper spacing of crops, timing of fertilizer application, and effective weed 
control. In addition, the key communicators in Dunyankwanta cited older family 
members (parents and guardians), interactions with other skilled farmers, contact with 
the Agricultural Extension Officer (AEA), and training from other organizations. . 
The key communicators are prominent with regards to the acreage per crop grown and 
their output per acre each season (Table 4.13). 
Table 4.13: Selected Background Characteristics of Key Communicators 
(Dunyankwanta) 
Name Age Years Of 
Farming 
(Years) 
Acreage For 
Cocoa 
(Acres) 
Output Per 
Season (Bags 
Per Acre) 
Opanin Kwaku 
Duruyeh 
78 40 8 7 
Agnes Achiaa 47 7 5 6 
Eric Kofi Asenso 59 6 5 8 
Andrews Donkor 34 15 6  7 
Francis Brenyah 53 11 4  6.5 
J. K. Sarfo  55 30 6 6 
Yaw Attah 48 15 8 6.5 
Mallam Adamu 53 14 5 6 
Kwame Sekyere 49 10 4 6 
 
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A one-sample t-test was run to compare the means of the ages and output of the key 
communicators against the population. The results are presented in Tables 4.14 and 
4.15. From the t-test, the difference in ages between the key communicators and the 
population was statistically significant (Table 4.15). Correspondingly, the difference 
in mean output between the key communicators and the population norm was 
statistically significant. 
 
Table 4.14: One-sample statistics for Average Age and Output of Key 
Communicators in Dunyankwanta 
 N Mean Std. Deviation Std. Error Mean 
Average Age  9 52.89 11.741 3.914 
Average Output 9 6.5556 .68211 .22737 
 
Table 4.15 One-sample t-test for Average Age and Output of Key 
Communicators in Dunyankwanta 
 Test Value = 40.2 
T Df Sig. (2-
Tailed) 
Mean 
Difference 
95% Confidence 
Interval Of The 
Difference 
Lower Upper 
Average Age  3.242* 8 .012 12.689 3.66 21.71 
 
Average 
Output 
Test Value = 5.10 
6.402* 8 .000 1.45556 .9312 1.979
9 
* p < .05. 
4.2 Structural Properties of Networks and their Role in the flow of Agricultural 
Information 
This section presents results about the structural properties of the networks in the 
various communities. These properties are, network density, degree centralization, 
and betweenness centralization. The means of these network values are compared 
across communities for similarities. A network centralization measure is an indication 
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of how compactly the network is structured around its most central point. The 
centrality measures are Degree Centralization and Betweenness Centralization. 
 Degree Centralization measures express the level of variability in the degrees of 
actors in a network. This is done by expressing the extent of inequality or variance in 
the network as a percentage of thatof a perfect network of the same size. The whole 
network values of degree centralization in terms of outdegree (number of ties sent 
out) and indegree (number of ties received) are presented. 
Betweeeness Centralization measures determine influential actors in terms of their 
control over the flow of information by virtue of their position as intermediaries 
connecting many pairs of other actors in the network. The betweenness centrality of 
the individual actors are presented alongside the network centralization measure. 
4.2.1 Structural Properties of the Agricultural Information Network in Kunsu 
Camp 
Table 4.16 indicates that the out-degree network centralization is 50.05% and the in-
degree network centralization is 6.764%, indicating that a significant quantity of 
centralization exists in the entire network. However, it also confirms that the influence 
of individual actors differs considerably, and influential actors are quite randomly 
dispersed in the network. 
Table 4.16: Degree Centralization Measures of Kunsu Camp Network 
Out-Degree (%) In-Degree (%) 
50.052 6.764 
 
Results in table 4.17 show that the actors 10, 1, 2, 11, 4, 13, 3, 7, 5 respectively are 
obviously the most significant mediators in the Kunsu network. These actors are most 
involved in the flow of information between the other pairs of actors. On the other 
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hand, the concentration in the placement of betweenness centralities among the actors 
is low as indicated by the Network Centralization Index (5.866%). The descriptive 
statistics in table 4.17 also indicate that there is a high disparity in individual actor 
betweenness (from zero to 113.00). 
Table 4.17 Betweenness Centralization for Kunsu Camp Agricultural 
Information network 
Vertex Number Flow Betweenness 
v1 113.000 
v2 99.000 
v3 40.000 
v4 57.000 
v5 10.000 
          v7         . 19.000 
v10 117.000 
v11 63.000 
v13 46.000 
v15 7.000 
v19 9.000 
v23 6.000 
v24 9.000 
v30 13.000 
v6, 8, 9, 12, 14, 16, 17, 18, 20 0.00 
v21, 22, 25, 26, 27, 28, 29, 31, 32 0.00 
Network Centralization Index = 5.866% 
4.2.2 Structural Properties of the Agricultural Information Network in Afreseni 
From Table 4.18, the out-degree graph centralization is 27.578% and the in-degree 
graph centralization is 4.371%. This demonstrates a significant disparity in the level 
of power wielded by actors in the network on the basis of direct ties with other actors.  
Table 4.18 Degree Centralization Measures of Afreseni Network 
Out-Degree (%) In-Degree (%) 
27.578 4.371 
 
Table 4.19 shows actors 22, 58, 21, 7, 8, 26, 15, 20, 47, 54, 10, 49, 1, 18, and 40 
respectively as the most significant mediators in the Afreseni network. These actors 
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are most involved in the flow of information between the other pairs of actors. 
However on a whole, the concentration in the placement of betweenness centralities 
among actors in the network is low (13.237%), suggesting that some actors in the 
network are more central than others. A very high disparity in individual actor 
betweenness (from zero to 478.267) is observed, indicating the presence of actors who 
are influential as intermediaries in the dissemination of information in the network. 
Table 4.19 Betweenness Centralization measures for actors in the Afreseni 
network 
Vertex No Flow Betweenness Vertex No Flow 
Betweenness 
V22 478.267 V46 85.733 
V58 240.633 V44 77.750 
V21 238.300 V43 75.683 
V7 218.017 V5 65.000 
V8 206.750 V36 62.667 
V26 178.600 V57 58.000 
V15 163.083 V38 55.517 
V20 159.017 V55, v35, v33, v13 30.000 – 49.999 
V47 145.033 V34, v32, v4, v41, v6 10.000 – 29.999 
V54 136.100 V 56, v53, v52, v51,v50, 
v48 
0.900 – 9.999 
V10 132.400 V45, v42, v39, v30, v14, 
v12 
0.900 – 9.999 
V49 125.700 V3, v2 0.900 – 9.999 
V1 122.517 V37 0.800 
V18 119.550 V9, v11, v16, v17, v19, 
v25 
0.000 
V40 113.400 V27, v28, v29 0.000 
Network Centralization Index = 13.237% 
4.2.3 Structural Properties of the Agricultural Information Network in 
Dunyankwanta 
From Table 4.20 the network centralization measures of 25.956% (out-degree) and 
5.674% (in-degree) observed are an indication of a substantial variance across actors 
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in the network in terms of out-degree and in-degree ties. The low percentages indicate 
that there is an unequal distribution of power among actors in the network. 
Table 4.20 Degree Centralization Measures of Dunyankwanta Network 
Out-Degree (%) In-Degree (%) 
25.956 5.674 
 
From Table 4.21 actors 10, 3, 2, 9, 30, 13, 8, 15, 48, 21 and 4 respectively are thekey 
intermediaries in the Dunyankwanta network. These actors are most involved in the 
flow of information between the other pairs of actors thus, indicating their status as 
central figures in the information network. On a whole, the low concentration in the 
placement of betweenness centralities among actors in the network (10.451%) is 
indicative of an unequal distribution of central positions in the network. This is 
reinforced by the high disparity in individual actor betweenness (from zero to 
551.550) observed, indicating that some actors are more prominent in the 
dissemination of information in the network. 
Table 4. 21 Betweenness Centralization measures for Dunyankwanta network 
Actor Flow 
Betweenness 
Actor Flow 
Betweenness 
V10 551.550 V4 72.383 
V3 447.983 V38 57.650 
V2 283.683 V53 57.200 
V9 231.383 V18, 34, 50, 54, 61 30.000 – 49.000 
V30 183.633 V5, 26, 28, 35, 39, 41, 42, 51, 
64, 67 
10.000 – 29.000 
V13 140.383 V12, 14, 16, 29, 31, 32, 33, 36, 
37, 44 
1.000 – 9.000 
V8 114.767 V45, 46, 47, 49, 56, 63, 65, 70, 
71 
1.000 – 9.000 
V15 99.300 V1, 6, 7, 11, 17, 18, 20, 22, 23, 
24, 25 
0.000 
V48 90.800 V27, 40, 43, 52, 55, 57, 58, 59, 
60, 62 
0.000 
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V21 84.250 V66, 68, 69, 72 0.000 
Network Centralization Index = 10.451% 
 
4.2.4 Comparison of Structures in the Three Agricultural Information Networks 
Table 4.22 shows considerable variability among the networks in terms of density, 
degree centralization, and betweenness centralization. The density across the 
networks decreases as the size of the network increases. This can be seen in the 
differences in density between Kunsu which is the smallest network and 
Dunyankwanta which is the largest. The degree centrality means explain the 
distribution of the degree centrality scores of the network actors. It is observed that 
there is more variability across the actors in terms of outdegree than indegree 
(standard deviations). The variability of actor degree aids in describing the 
heterogenous features of a network in terms of structural positions occupied by 
individual actors. The central tendency values (mean and standard deviation) for 
betweenness centrality also display a relatively high variation in actor betweenness 
(comparing the standard deviations to the means). This is logical, since not all actors 
are influential in the provision of information. Consequently, the level of influence of 
actors with respect to positions in the network vary greatly. 
Table 4.22 Mean and (SD) of density, degree, and betweenness parameters 
averaged over the networks 
 
Community 
 
Density 
Degree  
Betweenness InDegree OutDegree 
Kunsu 0.096 2.969 (5.199) 2.969 (1.262) 9.482 (18.008) 
Afreseni 0.062 3.552 (5.018) 3.552 (1.262) 63.021 (87.732) 
Dunyankwanta 0.043 3.083 (4.499) 3.083 (1.898) 39.325 (94.349) 
 
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4.3 Similarities and Differences between Actors and the Provision of Agricultural 
Information 
Similarities and differences between actors in information networks are determined 
using homophily scores and the level of ties formed between and within various 
categorizations of farmers in the networks. For each community, the distribution of 
ties across the various groupings on the basis of attributes such as age, gender, level 
of education, and membership of local group.are presented along with the homophily 
scores. 
4.3.1 Distribution of Ties on the basis of Age 
The results presented in this section show the distribution of information ties between 
actors in the various agricultural information network across age categories. In 
addition, the homophily scores (E-I Index, PBSC, and Yule’s Q values) are also 
indicated. 
Actors in the various networks were grouped on the basis of age into three categories; 
30 – 40 years, 41 – 50 years, and 51 years and above (Table 4.23). In the Kunsu 
network, a total of 95 ties are distributed within and between the three different age 
groups. Actors who fall in the age range of 41 – 50 years are prominent in terms of 
sending out information within and between the groups. The Afreseni network has 
206 ties distributed within and between the three different age groups. Actors in the 
30 – 40 year group and those 51 years and older are major sources of information. 
The Dunyankwanta network has  222 ties dispersed within and between the three age 
groups. Actors aged 51 and above are prominent sources of information in the 
network. 
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Table 4.23 Distribution of ties within and between groups (age attributes) of 
Agricultural Information Networks 
NETWORK  30 – 40 yrs 41 – 50 yrs 51 yrs and 
above 
Kunsu 30 – 40 yrs 7 15 13 
41 – 50 yrs 12 14 11 
51 yrs and 
above 
8 8 7 
Afreseni 30 – 40 yrs 25 30 17 
41 – 50 yrs 22 30 10 
51 yrs and 
above 
25 33 14 
Dunyankwanta 30 – 40 yrs 5 19 12 
41 – 50 yrs 14 28 24 
51 yrs and 
above 
21 63 30 
 
The E-I index values in table 4.24 show that on the basis of the proportion of ties 
within and between the groups, there is a prevalence of external than internal ties for 
the three networks. This suggests that actors in the various age groups are more likely 
to have out-group ties instead of ties within their age groups. The point biserial 
coefficient values also indicate a negative relationship between age and homophily in 
the networks. The Yule’s Q values presented exhibit a negative association between 
age and homophily in all three networks.      
Table 4.24 Network Homophily Measures (Age) 
NETWORK E-I INDEX PBSC YULE’S Q 
Kunsu 0.42 -0.02 -0.06 
Afreseni 0.33 -0.01 -0.05 
Dunyankwanta 0.38 -0.03 -0.15 
 
4.3.2 Distribution of ties on the basis of Gender 
Actors in the three networks were grouped on the basis of gender into two categories; 
male or female. The results show the distribution of ties across the gender groupings 
and the homophily scores for the distribution observed in the various networks. In all 
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three networks, males were dominant over females in terms of ties sent out (Table 
4.25). 
Table 4.25 Distribution of ties within and between groups (Gender attribute) of 
Agricultural Information Networks 
Network  Male Female 
Kunsu Male 67 23 
Female 1 4 
Afreseni Male 145 46 
Female 7 8 
Dunyankwanta Male 127 49 
Female 20 26 
 
The E-I index values in table 4.26 suggest a high occurrence of internal than external 
ties for the networks. This implies that actors of both sexes are more likely to have 
ties among themselves instead of with the opposite sex. However, the point biserial 
coefficients demonstrate a positive relationship between gender and homophily in the 
networks. There is a weak positive association between gender and network 
homophily as indicated by the Yule’s Q values. 
Table 4.26 Network Homophily Measures (Gender) 
NETWORK E-I INDEX PBSC YULE’S Q 
Kunsu -0.50 0.11 0.39 
Afreseni -0.49 0.04 0.19 
Dunyankwanta -0.38 0.04 0.21 
 
4.3.3 Distribution of ties on the basis of Level of Education Attained 
Network actors were grouped on the basis of level of educationattained into three 
categories; no formal education, basic education, and secondary education. The 
results in Table 4.27 show the distribution of ties across the different levels of 
education attained and the homophily scores for the distribution of ties observed. 
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Farmers educated to the secondary level are dominant over the other categories in 
terms of sending out information in the network 
Table 4.27 Distribution of ties within and between groups (Educational level) of 
Agricultural Information Networks 
NETWORK  No Formal 
Education 
Basic 
Education 
Secondary 
Education 
Kunsu No Formal 
Education 
6 4 7 
Basic Education 5 8 9 
Secondary 
Education 
12 24 20 
Afreseni No Formal 
Education 
10 10 3 
Basic Education 13 26 41 
Secondary 
Education 
17 43 43 
Dunyankwanta No Formal 
Education 
23 22 20 
Basic Education 15 19 17 
Secondary 
Education 
22 37 47 
 
The E-I index results in table 4.28 show that considering the number of ties within 
and between the three groups of different educational levels across the three networks, 
there is a high incidence of internal than external ties for the entire network. This 
signifies that actors were more likely to have ties within their group instead of outside 
their respective groups. However, the point biserial coefficient valuesshow a positive 
relationship between educational level and homophily in the network. This is 
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supported by the Yule’s Q value which also points to a positive association between 
actors’ level of education and homophily, albeit weak. 
Table 4.28 Network Homophily Measures (Educational Level) 
NETWORK E-I INDEX PBSC YULE’S Q 
Kunsu -0.50 0.11 0.39 
Afreseni -0.49 0.04 0.19 
Dunyankwanta -0.38 0.04 0.21 
 
4.3.4 Distribution of ties on the basis of Membership of local groups 
Actors in the network were grouped according to their membership of local groups or 
otherwise. Farmers belonging to local groups are dominant over non-members of 
groups in terms of sending out information in the Kunsu and Dunyankwanta 
networks.The Afreseni network shows an even distribution of ties sent out by both 
members and non-members of local farmer groups (Table 4.29). 
Table 4.29 Distribution of ties within and between groups (Local Group 
Membership) of Agricultural Information Networks 
Network  Local Group 
Members 
Non-
Members 
Kunsu Local Group 
Members 
41 40 
Non-Members 5 9 
Afreseni Local Group 
Members 
41 61 
Non-Members 65 39 
Dunyankwanta Local Group 
Members 
96 53 
Non-Members 40 33 
 
The Kunsu and Dunyankwanta networks exhibit a low occurrence of internal ties than 
external ties as evidenced by the E-I Index results (Table 4.30). The point biserial 
coefficients for the two networks also demonstrate a positive relationship between 
local group membership and homophily. In addition, the Yule’s Q values suggest a 
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moderately weak but positive relationship between membership of a local farmer 
group and homophily. In contrast, the Afreseni network shows a low occurrence of 
external ties as compared to internal ties. There is a negative relationship between 
local group membership and homophily in the network as indicated by the point 
biserial coefficients and the Yule’s Q values. 
Table 4.30 Homophily Network Measures (Local Group Membership) 
NETWORK E-I INDEX PBSC YULE’S Q 
Kunsu -0.05 0.01 0.02 
Afreseni 0.22 -0.06 -0.25 
Dunyankwanta -0.16 0.04 0.18 
 
 
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CHAPTER FIVE 
DISCUSSION OF RESULTS 
5.0 Introduction 
In this chapter the findings presented in the previous chapter are discussed. The 
discussions are guided by the research objectives set out in this study and are 
presented as folllows:  First, the existing agricultural information networks and their 
characteristics are discussed. Then the structural properties of the networks as well as 
the network descriptors (network density, network centralization, etc.) are also 
considered. The factors that influence homophily are then discussed together with the 
implications of the structural properties of the various networks on agricultural 
information flow. 
5.1 Existing Local Agricultural Information Networks and Key Communicators 
Three agricultural information networks were observed as playing an important role 
in the provision of information. These networks are discussed along with their 
characteristics. The implications of these characteristics for information provision in 
networks are also considered. 
5.1.1 Implications of Network Characteristics for Provision of Agricultural 
Information  
A total of three networks were observed in the three communities studied. These 
networks had varying sizes yet shared common characteristics. All the three networks 
displayed a core-periphery structure. Core farmers were consulted by other core and 
periphery farmers more often than the other farmers, leading to a high number of 
information ties originating from within the small group (core farmers). 
Consequently, tie density was found to be very low between the core and periphery 
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groups. Generally, a comparatively little, dense group of farmers was sought by the 
larger farming community for information on farming practices. This finding agrees 
with Borgatti and Everett (1999), who observed that in a core-periphery network there 
exists a group of agents that is densely connected internally, while all remaining 
network members are sparingly connected among themselves. Barsky (1999) also 
proposes that core-periphery models in information networks develop from a 
persistent choice of exact actors who possess exclusive characteristics that enhance 
their entry into the core membership group.  
The core-periphery concept in networks has implications on the provision of 
information in a network. As Csermeley, London, Wu, and Uzzi (2013) point out, the 
forcefulness and solidity of core members in a network is as a result of their affluent 
link configuration which helps them to almost guarantee support and the ability to 
provide numerous alternatives of directing information in a network when desired. 
Importantly, the presence of a core group facilitates a smooth transfer of information 
within an information network. Consequently, at any rate,  whether information 
originates from external sources or inside the community, informal ties between the 
core and periphery are used as a vital mode of conveying agricultural information to 
farmers in the networks. This effectively results in stability in the network, in view of 
the variety of information sources for farmers, in addition to maintaining a balance 
between formal and informal sources of information within  networks (Isaac, 
Erickson, Quashie-Sam, and Trimmer, 2007). 
5.1.2 The Role of Key Communicators in Agricultural Information Provision 
A total of 23 major providers of information were identified in the three networks. 
These ones, referred to as key communicators in this study were observed to be 
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invaluable to the provision of information to members of the network. The 
characteristics of actors identified as key communicators were studied in comparison 
to the characteristics of the population. 
Key communicators in all three networks were found to be significantly older than the 
rest of the population when the means of both groups were compared by means of a 
one-sample t-test. Thus key communicators who were consulted most turned out to be 
comparatively older. This observation is consistent with the explanation of Oakley 
and Garforth, (1997) that even though younger people tend to have different values, 
attitudes, and aims, elderly people are treated with great respect and their advice is 
listened to carefully. Mittal and Mehar (2013) also indicate that older farmers are 
more likely to be consulted by younger farmers for agricultural information. 
The key communicators also had significantly higher output per acreage when 
compared to the population by means of the t-test. Thus farmers who were perceived 
as successful were contacted by other farmers in the various networks. This is 
consistent with the findings of several authors who posit that farmers who have higher 
yields and are seen as successful by other farmers tend to provide information to other 
farmers and are seen as role models (Faust, 1997; Borgatti, 2005; Goswami and Basu, 
2010). A possible reason for this could be the training received by the key 
communicators by various extension service providers. All 23 actors had benefitted 
from training programs in areas like choice of good planting material (cocoa), weed 
and disease control, fertilizer application (cocoa), proper record keeping, and proper 
use of chemicals (fertilizers, pesticides, weedicides, etc.) in rice production. 
Consequently, they had connections to external sources of information that gave them 
an advantage over the other actors. This is consistent with Gueye’s findings (2009) in 
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his study of poultry farmers that noteworthy progress in poultry production can be 
realized by means of properly designed and implemented training programs that equip 
farmers with essential knowledge and skills. He further explains that training farmers 
allows them to acquire and spread knowledge, views, experiences as well as research 
and development results with other farmers so as to bring about required changes to 
farms. 
In addition, the key communicators identified in the various information networks 
occupied various leadership positions in their communities. It was observed that by 
virtue of their status in the community they were seen as role models to their 
colleague farmers. As a result they were regularly consulted for help to problems 
encountered on the various farms. The provision of information in informal 
information networks is highly dependent on the presence of leaders who are 
perceived as role models due to their status in the communities in which they live. 
These individuals have attained elevated degrees of trust and reliance with others in 
the community and are usually willing to do more to help others out. 
Having used the sociometric method to identify opinion leaders, Flodgren, Parmelli, 
Doumit, Gattellari, O’Brien, Grimshaw, and Eccles (2011) discovered that 
dissemination and implementation of new evidence-based activities by local leaders 
was a promising approach to bridge observed gaps in practice. They explain that the 
leaders identified are people who are seen as likeable, reliable and prominent. As a 
result of their influence, it is thought that they are capable of helping and persuading 
the target audience in the local community. This view is also supported by Rogers 
(2010), who stated that farmers in leadership positions in their local communities are 
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indispensable with respect to providing agricultural information to farmers and 
influencing adoption of new agricultural technologies. 
5.2 Structural Properties of Networks and the Provision of Agricultural 
Information 
In this section, the various structural properties of the various networks studied are 
discussed with emphasis on how they influence the flow of agricultural information. 
The structural properties are discussed individually across the various communities. 
5.2.1 Network Density and the Flow of Agricultural Information 
Density of a network is the ratio of the actual number of relationships that exist in a 
network and the total number of potential relationships in the network (Thaden and 
Rotolo, 2009). If all farmers had direct ties with all other farmers, then the density 
would be 1. The density of a network gives meaning to trends such as the rate at 
which information diffuses among the nodes. In the three networks studied, the 
density values averaged over the networks established that a higher density of ties was 
present among the core farmer group, than between the core and periphery, periphery 
and core, and periphery and periphery. This is understandable, considering the fact 
that most of the ties originated from the core group. In connection with the whole 
network values, it was observed that the whole density values decreased as network 
size increased. Thus the larger the network, the less dense the network. 
Leonard (2008) states that the closer the density value is to zero, the flow of 
information in the network becomes slower. Noor, Mrvar, and Batagelj (2005), also 
explain that density is inversely related to the size of a network; the larger the social 
network, the lower the density because the number of possible ties increases rapidly 
with the number of actors, whereas the number of ties which each person can 
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maintain is limited. Thus larger networks are generally characterised by lower 
densities. Consequently, Yu and Bao (2011) confirm that smaller networks with 
higher densities possess stronger relationship ties, richer interactions, and exchange 
more resources and information. Thus smaller information networks are preferable 
when seeking to strengthen adoption of agricultural technologies as compared to 
larger networks where the strength of relationship between actors is reduced. 
However, networks with low densities do not necessarily indicate a disadvantage. A 
low network density could imply that the network’s sources of information will be 
varied or unique (Burt, 1992). In these types of networks, ties are often "weak," 
meaning that they signify interactions concerning fairly low familiarity and irregular 
contact (Granovetter, 1973). Thus weak ties or low network density opens up 
networks to diverse information sources. 
5.2.2 Degree Centralization and the Provision of Agricultural Information 
Whole network degree centrality (in-degree and out-degree) measures refer to the 
number of direct links between farmers, indicated per individual by number of ties 
and in total as a percentage of a completely centralized network where one person 
would be at the center of a star like structure with all others in the network connected 
only to the centralactors. Thus, the higher the percentage, the higher the degree of 
network centralization (Marks, Barnett, Foulker, Hawe, and Allender, 2013) . With 
the exception of the Kunsu network that had a higher percentage (50.052%) than the 
other networks, the networks did not show any considerable differences with respect 
to the whole network centrality measures. The low degree centrality measures indicate 
that despite the presence of a significant quantity of centralization within the 
networks, the influence of the various actors differ and there is a considerable 
disparity in the amount of power wielded by influential actors in the networks. This is 
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explained by the fact that the key communicators who possess high amounts of ties 
are comparatively few as compared to the other actors.  
Marks et al (2013) explained that low network degree centralization measures (less 
than 50%) as seen in the Afreseni and Dunyankwanta networks indicate that actors in 
the network share frequent and valuable information with one another through 
relationships with several central individual actors rather than through one centralized 
person or position, thus reducing the danger of information flow being dependent on 
just one central actor. Mandarano (2007) adds that such network degree centralization 
values can be  used to point out gaps in resource exchange networks. Individuals that 
have higher access to the required information can be identified by means of their 
high ties sent out to other actors. This implies that these actors are vital and can be 
engaged in collaborative efforts to disseminate information (Mendel, Damberg, 
Sorbero, Varda, and Farley, 2009). Thus,  on the basis of the whole network degree 
centralization measures, conclusions can be drawn as to how important the central 
actors are. As Rana (2010) explains, high degree network centralization measures 
indicate that central actors are critical to providing information to the network, 
whereas, low whole network degree centralization measures show that central actors 
are not very crucial to the provision of information. 
5.2.3 Betweenness Centrality and the Provision of Agricultural Information 
Betweenness centrality measures explain the extent to which an actor is positioned 
between other actors in a network. Therefore, actors with high betweenness centrality, 
are capable of directly or indirectly influencing the flow of information to others. A 
node with high betweenness centrality has the ability to control the transfer of 
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information within a network, by assisting, obstructing, or even manipulating the 
communication between others (Newman, 2003). 
In the three networks, the network betweenness centralization were very low, with the 
Dunyankwanta network (10.45%) exhibiting the highest network betweenness 
centralization value. As Hopkins (2011) explains, low betweenness scores indicates 
that the actors are situated between few pairs of other actors. The low mean 
betweenness scores of all three networks, the large number of actors with a 
betweenness measure of zero, and the extremely low network betweenness 
centralization index across all three networks indicate that there are few information 
brokers within the network. Marks et al (2013), highlight an advantage of low 
network betweenness centralities by pointing out that such low scores indicate the 
absence of “information gatekeepers” who could obstruct or alter information from 
reaching other actors. 
However, Hopkins (2011) explains that betweenness centrality measures can improve 
understanding of knowledge acquisition. Since the sociometric method is focused on 
learning by direct questioning, knowledge acquired through circumstantial learning 
and unsolicited information cannot be accounted for. That could explain the reason 
for the low betweenness centralization measures. 
5.3 Homophily and Information Transfer 
The study examined homophily of the various networks using several characteristics 
of actors in the various networks. The characteristics examined were age, sex, level of 
education, and membership of local groups. In this section, these characteristics are 
discussed with the aim of examining the level of influence they may have on the 
transfer of information in the various networks. 
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5.3.1 Age  
In all three networks that were examined, it was realized that there was a prevalence 
of external ties over internal ties when actors were grouped on the basis of age. All 
three networks demonstrated a negative association between age and homophily. This 
phenomenon can be explained in the light of the age of the key communicators in the 
networks. The key communicators in the networks averaged 50.17 years. Since most 
of the ties originate with the key communicators, it is logical that the provision of 
information is not restricted within groups. This means that individuals in various age 
categories sought information beyond farmers who were in the same age bracket as 
they were. Considering the average age of the key communicators with whom most of 
the information ties originate, it explains that key communicators shared information 
not just within their age category, but also with younger farmers. 
These findings agree with Louch (2000) who realized that the probability that two 
actors who are not kinsmen will be connected decreases with age difference. Marsden 
(1988) also found an interesting pattern of age homophily for various age groupings. 
With respect to formation of ties, he observed that there existed a greater distance 
between the middle age categories and other age categories than there was between 
other age categories. Middle age groups usually exhibit noteworthy levels of out ties. 
Older people often connect with younger confidants, an indication of the significance 
of older, experienced individuals. Smitha, McPherson, and Smith-Lovin (2014),  also 
found a significant relationship between age and homophily. Interestingly, they 
discovered that in contrast, the young had few ties with older individuals. However, 
they explain that changes in economic inequalities, cultural boundaries, and other 
factors could be responsible for this trend. 
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5.3.2 Gender 
The three networks were homophilous with regard to gender. A positive correlation 
and association between gender and homophily was observed. This means that male 
farmers had the tendency to share agricultural information among themselves while 
the same was true in the case of females. Consequently, that accounted for the 
prevalence of internal ties over external ties within both gender categories. 
Several research works discovered comparatively higher gender homophily in 
communication ties than in other characteristics such as age, religion, and level of 
education  (Ibarra, 1992; McPherson, et al., 2001). Ibarra (1997) found that men and 
women search for and utilize informal networks in unusual ways, and suggests a 
reason: Whereas men formed ties and relations with other men in their networks, 
women appeared to form twofold networks: One network with other women to gratify 
the need for friendships and support, and another network with males to fulfill the 
need for information and organizational knowledge. In contrast, Brashears’ (2008) 
study revealed that males and females vary in terms of choice of formation of ties 
across the sexes, thus pointing to a lack of homophily in connection with gender. In 
spite of the unbalanced distribution of gender across the various occupations, the 
support networks of actors were found to consist of ties between actors mainly of the 
same gender. Gender homophily is thus asignificant factor in the formation of 
network ties. 
5.3.3 Level of Education  
The three networks were heterophilous on the basis of the level of education. The 
various actors fell into three different groups: those with no formal education, farmers 
who had been educated to basic level and farmers who had been educated to the 
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secondary school level. The distribution of ties showed a higher proportion of external 
ties (ties sent outside groups) than internal ties (ties within groups). This is reflected 
in the educational levels of the key communicators responsible for a majority of out-
degree ties. These individuals were evenly distributed across all three groups, leading 
to majority of information ties being formed with other actors in different educational 
level categories. This is understandable considering that unlike the previous 
dimensions of homophily which are ascribed or inherited, the level of an individual’s 
education is acquired.  
These findings are in agreement with the findings of Louch (2010) who discovered 
that education was unlikely to create links between confidants than most other 
characteristics (religion, race, etc.). Mittal and Mehar (2013) also found level of 
education to be significantly related to homophily with regards to the formation of 
network ties among farmers. However they found that the level of homophily reduced 
considerably with farmers  who had higher levels of education. Demiryurek et al 
(2008) explain further that farmers who acquire a considerable level of knowledge do 
not consult their colleague farmers for information, rather they seek out more detailed 
information from external sources, thus the reason for decreased homophily in 
network members with higher levels of education. 
5.3.4 Local Group Membership 
Membership of local groups accounted for a large number of network ties observed in 
all three communities. Two of the communities (Dunyankwanta and Kunsu) 
demonstrated homophily with respect to the membership of groups. Unlike the other 
two information networks, the Afreseni network demonstrated heterophily on the 
basis of group membership. The differences in group homophily of the networks 
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could be as a result of the inequalities in the networks studied. The structural features 
of a social network considerably influence the information seeking behavior of its 
constituent actors. Thus, while members of local groups in Dunyankwanta and Kunsu 
sought information from members of their local groups, members of groups in 
Afreseni were hesitant to seek advice within the local group and rather sought 
information from others. This is understandable, since a majority of the key 
communicators in Afreseni were not members of local groups as compared to the 
other networks (Dunyankwanta and Kunsu) where key communicators belonged to 
various local farmer groups. 
Darr and Pretzsch (2008) explain that farmer groups are one of the most successful 
conduits of information dissemination since they efficiently facilitate the spread of 
innovation within the group. However, this is dependent on the membership of the 
group as well as the strength and cohesion within the group. 
Membership of local groups was responsible for a large number of network ties 
observed. In all three communities, key communicators and other network actors were 
members of the same community-based local groups. The local groups observed in 
the communities were those formed by the farmers in the community for the welfare 
of the local farmers and groups formed by external organizations (produce buying 
companies, NGOs, etc). Some farmers belonged to more than one group, thus 
allowing them further opportunities to pass on or obtain information since they had 
high prospects of additional network ties. Those groups established by the local 
farmers for their welfare and improved production ensured regular communication 
among group members. There were regular meetings where issues related to 
agricultural and social problems were handled. Farmers interviewed indicated that at 
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one point or the other, they had received helpful agricultural information at a group 
meeting. The groups established by external organizations also improved network ties 
among farmers in the communities since there were opportunities for discussions 
among group members. Another observation that was made was the presence of 
several undefined, informal labour groups, also known as the “nnoboa”. These groups 
exist mainly with the aim of providing unpaid labour services to member farmers. 
There are no fixed members; instead, the group is composed of farmers who are 
available and willing to help on a colleague’s farm as and when such help is needed. 
During those sessions, farmers take the opportunity to seek agricultural information 
from their colleagues, thus strengthening their personal network ties. 
These findings agree with Soyka and Streiffeler (2003) who discovered that farmers 
ask for advice and concentrate their information seeking activities within small groups 
in their neighborhoods, consulting farmers with similar interests. Madukwe (2006) 
agrees when he says that farmer groups allow the passing on of agricultural 
technology to farmers. Since farmer groups are constituted by farmers working 
together with each other towards accomplishing a joint goal, generally, there exists an 
interaction between constituents of the group. Consequently, farmer groups are 
invaluable to information transfer. A key advantage of local farmer groups is that the 
farmers help one another to gain knowledge and skills. Network ties are thus 
strengthened among members of local farmer groups. 
5.3.5 Heterogeneity of Key Communicators 
The actors identified as key communicators in the various were found to be 
heterogeneous as a group. They differed in terms of age, gender, level of education, 
membership of local group, religion, etc. The actors identified as key communicators 
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exhibited varying characteristics.The implication of this finding is that in making use 
of key communicators to aid extension delivery, characteristics of key communicators 
should be considered and examined before selecting any as contact farmers. Perry-
Smith (2006) posits that among key communicators in a network, there are 
differences in levels of knowledge concerning required information, differences in 
perspectives, and strength of ties with external sources.   
5.4 Summary 
In summary, the networks had a number of key communicators who serve as primary 
sources of information related to cocoa and rice production. Transfer of information 
in these networks were influenced by experience acquired over the years by key 
communicators, leadership positions/status in the community and farmer training 
programs. Ties among actors in the network were fairly distributed on the basis of 
age, gender, level of education, and membership of local groups. The networks 
displayed a considerable amount of homophily in terms of gender and membership of 
local groups.  
By means of network density a core-periphery structure was identified, indicating that 
a small group of specific farmers were responsible for most of the network ties 
observed. These farmers were chosen due to their possession of various characteristics 
that appealed to other network actors. This was reflected in the network structure 
which revealed a separate group of farmers that received a high quantity of inquiries 
from network members. The centrality measures showed no significant differences 
across the three networks. Even though the measures indicated a level of 
centralization within the networks, the central actors were unequally distributed in the 
various networks with just a few being positioned in between other actors. Those 
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identified as key communicators were heterogeneous even though this did not have 
any significant effect on the flow of information in the network. 
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CHAPTER SIX 
SUMMARY, CONCLUSIONS AND RECOMMENDATIONS 
6.0 Introduction 
In this chapter the conclusions and recommendations arrived at the end of the study 
are stated. 
6.1 Summary 
The objectives of the study were: to find out the existing local information networks 
for the transmission of agricultural information, their characteristics, and the major 
providers of information in the various networks; to determine the strength of network 
ties among members of the information networks and how these affect availability of 
agricultural information; and to examine the differences between actors in the various 
information networks and its influence on the availability of information. 
From the study, three local information networks were identified. One in each of the 
three communities (Afreseni, Dunyankwanta, and Kunsu).The network sizes were 
thirty-two (Kunsu), fifty-eight (Afreseni) and seventy-two (Dunyankwanta). The 
networks had a total of twenty-three key communicators made up of six from Kunsu, 
eight from Afreseni, and nine from Dunyankwanta. These farmers provided 
information on fertilizer application, weed control, pest control, choice of planting 
materials, and harvesting of cocoa and rice. Farmers in the network consulted the key 
communicators in order to fulfill their need for information. However, they consulted 
people they perceived to be successful in the community, farmers who were 
experienced and had access to training and information from credible external 
sources.  
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All three information networks were heterogeneous on the basis of age, gender, 
religion, level of education, membership of local farmer groups, and contact with 
agricultural extension officers. The key communicators in the networks displayed a 
level of homogeneity in terms of crops grown, contact with extension officers and 
status/leadership positions occupied in the community. However, the key 
communicators were heterogeneous in terms of age, religion, gender, level of 
education, membership of local groups and family size. 
The mode of information transfer was primarily through oral communication and 
mainly occurred in informal settings. The twenty-three key communicators turned out 
to be major providers of information in the network. These had received training from 
various organizations in the past. Others had acquired a wealth of experience over the 
years and were deemed knowledgeable enough to provide helpful information to fill 
the needs of the other actors. 
Degree centrality measures indicated that actors who had many ties to other actors 
occupied privileged points in the network. The low network degree centralities 
however showed that actors in the network share frequent and valuable information 
with one another through relationships with several central individual actors rather 
than through one centralized person or position.  
Betweenness centrality measures also indicated the strength of ties present in the 
network on the basis of how centrally placed actors in the network were. The three 
communities displayed a level of significant tie strength, albeit fairly weak. However, 
it also showed that aside the key communicators present in the network, there were 
other central actors in the network who facilitated the dissemination of information 
throughout the network. In view of the low betweeness centrality measures across the 
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three networks, it can be said that there a few intermediaries who have the ability to 
hinder or improve the flow of information in a network. 
The networks generally displayed a significant lack of homophily. The networks were 
heterophilous on the basis of age, level of education, and membership of local groups. 
The only characteristic where there seemed to be a level of homophily was regarding 
gender. The networks were sufficiently heterophilous with respect to age and level of 
education attained. This was largely as a result of the key communicators falling 
across different age groups and educational level categories. Consequently, the 
interactions in the network were dominated by external ties rather than internal ties or 
ties within age/educational level categories. There were also significant differences 
with regards to membership of local farmer groups. The networks were not 
sufficiently homophilous enough and generally displayed heterophily. 
6.2 Conclusion 
The study found a local information network for transmitting agricultural information 
in each of the three communities studied. The mode of information was primarily 
oral. The major providers of information were homogenous in terms of ages, gender, 
contact with extension and status in community. 
Significant but weak network ties were found among members of the networks 
suggesting that other actors also facilitated the flow of information through the 
network. It can be concluded that the informal information networks in the three 
communities had access to a variety of agricultural information. Actors in the 
networks were generally heterophilous indicating that individuals were exposed to a 
variety of information sources. 
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6.3 Recommendations 
In the light of the observations made and ensuing conclusions arrived at certain 
recommendations have been made. These are discussed in terms of recommendations 
for agricultural extension practice and further research. 
6.3.1 Recommendations for Agricultural Extension Practice 
 In introducing, transferring, and implementing specialized formal information 
or technologies, extension agents should seek out not just successful or “rich” 
farmers. Rather, farmers who are highly sought after or consulted by their 
peers should also be contacted. 
 It is recommended that extension agents and other extension service providers 
should target key communicators of informal information networks as they 
can serve as essential sources of  information in disseminating information to 
farmers in a community. 
 In view of the potential of local farmer groups, extension service providers 
should take special interest in local farmer groups. This could strengthen and 
promote any possible informal information networks that could be harnessed 
to improve agricultural delivery. 
 Considering the characteristics of informal agricultural information networks 
such as ease of access, affordability, regularity of use by farmers, providing 
means for sending and receiving feedback, etc. it can be said that such 
agricultural information networks have very high prospects in terms of 
disseminating or introducing new information and technologies. Extension 
agents would need to determine structural properties of existing networks to 
identify key actors for the exchange and provision of agricultural information. 
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 The presence of intermediaries or information brokers as indicated by the 
betweeness centrality measures indicate the importance of these mediators. It 
is recommended that in disseminating information, extension service providers 
should identify not just actors who have numerous ties with many others 
(central actors), but should also make use of farmers who can serve as 
intermediaries by virtue of their links between actors. 
6.3.2 Recommendations for Future Research 
 The study made generalizations for agricultural knowledge-concentrated 
interactions. These generalizations may not be applicable when considering 
interactions that are not knowledge related, e.g. transfer of agricultural 
resources, etc. Future research can be geared towards examining interactions 
involving the exchange of physical/material ties. 
 The study used a representative model of a district in one region of the 
country. The surveyed farmers engaged in rice and cocoa farming. Further 
studies could be designed towards gaining insight into the networks that exist 
in districts with more crop diversity. 
 In analyzing the differences in characteristics, emphasis was placed on 
homophily. It is recommended that further studies be conducted into the 
patterns of heterophily, investigating why individuals in the same community 
would avoid other community members or develop few ties with them. 
 The focus of this study was on network measures that were studied at a 
particular point in time. Subsequent research may possibly investigate how 
network measures and their influence on information availability vary over 
time. 
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APPENDIX A -QUESTIONNAIRE FOR FARMERS 
CHARACTERISTICS OF RESPONDENTS 
1. Name: ………………………………………………………………….. 
2. Sex : [  ] Male     [  ] Female  
3. Age: …………………..  
4. Marital Status:  [  ] Married    [  ] Single   [   ] Divorced   [   ] Separated   [  ] 
Widowed 
5. Religion:  [ ] Christian [ ] Islam   [ ] Traditional   [ ] Other (please 
indicate)…………………………….. 
6. Level of education: [  ] No formal education [  ] Basic education [  ] Secondary 
level [  ] Tertiary 
7. What is the size of your household? ………………………….. 
8. Are you the household head? [  ] Yes  [  ] No 
9. If no, what is your relationship with the household head? …………………… 
10. What is your major occupation? …………………………………………………. 
11. What are the major crops you grow? 
[ ] Cocoa [  ] Maize [  ] Tomato [  ] Cassava [  ] Yam [  ] Beans 
12. How is the farm owned?  [  ] Sole proprietorship [  ] Partnership  [  ] Family 
owned 
13. Which type of land tenure system do you use? [  ] Rent [  ] Outright purchase [   ] 
Shared rights [   ] Other (please specify) ……………………………………….. 
14. Do you face any problems in producing these crops? [  ] Yes  [  ] No 
PROBLEM SOLUTION 
  
  
  
 
15. Who in your household is involved in making decisions concerning your farming 
activities? 
 
RELATIONSHIP 
 
 
 
 
 
 
 
 
 
 
 
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SOURCES OF AGRICULTURAL INFORMATION 
16. From which individuals do you get information about activities, skills and 
practices related to farming? 
 
NAME OF       
SOURCE 
ACTIVITY/SKILL/ 
PRACTICE 
RELATIONSHIP FREQUENCY 
    
    
    
    
    
 Key: For frequency choose from the following: a) Daily b) Weekly c) Fortnightly d) 
Monthly e) Yearly 
17. What are your mode(s) of contacting these source(s) of information and what 
problems do you face in receiving information from them? 
INFORMATION 
SOURCE 
MODE OF 
CONTACT 
PROBLEM FACED 
   
   
   
   
   
 
18. Do you reside in the same community with your source(s) of information? 
[  ] Yes [  ] No 
19. How useful is the information that you receive from these sources? 
[  ] Not at all useful [  ] Not useful [  ] Average [  ] Useful [   ] Very useful 
20. How strong is your trust in the information you receive from your source(s)? 
[  ] Very weak [  ] Weak [  ] Moderate [  ] Strong [  ] Very strong 
SOURCE STRENGTH OF TRUST 
  
  
  
  
 
21. Do you use the information your source(s) of information provide? [  ] Yes [  ] No 
22. If yes, why do you do so? 
…………………………………………………………………………………
…………………………………………………………………………………
………… 
If no, why not? 
…………………………………………………………………………………
…………………………………………………………………………………
………… 
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23. If yes, is the information provided clear enough for you to implement? [  ] Yes [  ] 
No 
If no, what alternative sources of information do you patronize? 
…………………………………………………………………………………
…………………………………………………………………………………
………… 
24. What is the relationship between you and those who consult you for agricultural 
information, if any? Please indicate type of information provided 
NAME RELATIONSHIP TYPE OF 
INFORMATION 
   
   
   
   
   
 
25. Do you know others in this community who serve as sources of agricultural 
information to other people? [  ] Yes [  ] No 
NAME TYPE OF INFORMATION 
  
  
  
  
  
 
MODE OF INFORMATION FLOW 
26. Are you able to seek clarification or any answers to additional questions you 
might have? [  ] Yes [  ] No 
27. How do you seek such clarification/answers/send feedback from/to your 
informants? 
………………………………………………………………………………………
……………………………………………………………………………………… 
28. How do you provide others with the information they ask from you? 
…………………………………………………………………………………
…………………………………………………………………………………
………… 
 
29. Do you receive any information from the Agricultural Extension Agent (AEA)?    
[  ] Yes [  ] No 
30. What kind of information do you receive from the AEA? 
…………………………………………………………………………………
…………………………………………………………………………………
……….. 
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…………………………………………………………………………………
….. 
31. Do you receive any agricultural information from the mass media (radio, TV, 
internet, etc)? [  ] Yes [  ] No 
 
If yes, indicate 
TYPE OF MEDIA INFORMATION RECEIVED 
  
  
  
  
  
 
 
LOCAL GROUP MEMBERSHIP 
32. Do you belong to 
any group in the community? [  ] Yes [  ] No 
33. If yes, please name 
the group(s) you belong to and position(s) held if any 
 
GROUP POSITION HELD ACTIVITIES 
ENGAGED IN 
   
   
   
   
   
 
34. Do people from outside the community join your group(s) [  ] Yes [  ] No 
 
35. Are there similar groups in other communities? [  ] Yes [  ] No 
If yes, please indicate the 
communities……………………….................................. 
36. If yes, do you communicate with members of those group(s)? [  ] Yes [  ] No 
37. Do you discuss agriculture related topics with members of your group(s)?  [  ] Yes 
[  ] No 
GROUP MEMBER AGRICULTURAL 
INFORMATION 
  
  
  
  
  
 
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38. Have you received any new agriculture related skills, knowledge, or practices 
from members of your group? [  ] Yes [  ] No 
If yes, please indicate 
…………………………………………………………………………………
…………………………………………………………………………………
………… 
39. Have you provided members of your group with any new agriculture related 
skills, knowledge, or practices? [  ] Yes [  ] No 
If yes, please specify 
…………………………………………………………………………………
…………………………………………………………………………………
………………………… 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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APPENDIX B-QUESTIONNAIRE FOR PRIMARY SOURCES OF 
INFORMATION 
1. Name: ………………………………………………………………….. 
2. Sex : [  ] Male     [  ] Female  
3. Age: …………………..  
4. Marital Status:  [  ] Married    [  ] Single   [   ] Divorced   [   ] Separated   [  ] 
Widowed 
5. Religion:  [ ] Christian [ ] Islam   [ ] Traditional   [ ] Other (please 
indicate)…………………………….. 
6. Level of education: [  ] No formal education [  ] Basic education [  ] 
Secondary level [  ] Tertiary 
7. What is the size of your household? ………………………….. 
8. Are you the household head? [  ] Yes  [  ] No 
9. If no, what is your relationship with the household head? …………………… 
10. What specific agricultural skills, practices or knowledge do people consult you 
for agricultural information? 
..............................................................................................................................
..............................................................................................................................
... 
11. For how long have you been using these skills, practices, or knowledge? 
…………………………………………………………………………..............
..............................................................................................................................
..............................................................................................................................
......... 
12. From whom did you receive these skills, knowledge, and practices firsthand? 
…………………………………………………………………………………
…………………………………………………………………………………
…………………………………………………………………………………
……………… 
13. Have you undergone any kind of training to acquire any of the skills, 
practices, or knowledge in (1) above? [  ] Yes [  ] No 
If yes, please indicate 
TRAINER/TRAINING 
ORGANIZATION(S) 
SKILLS, KNOWLEDGE, 
PRACTICES ACQUIRED 
  
  
  
  
 
 
 
14. On what basis were you chosen for the training? 
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…………………………………………………………………………………
…………………………………………………………………………………I
f no, how did you acquire the agricultural skills, knowledge, and practices that 
you share with others? 
…………………………………………………………………………………
…………………………………………………………………………………
………… 
15. Do you receive any information from the Agricultural Extension Agent 
(AEA)?    [  ] Yes [  ] No 
16. What kind of information do you receive from the AEA? 
…………………………………………………………………………………
…………………………………………………………………………………
……….. 
…………………………………………………………………………………
….. 
17. Excluding the AEA and the mass media, is there any other individual(s) who 
pass(es) on agricultural information to other farmers in the community?  
[  ] Yes [  ] No 
If yes, please indicate 
…………………………………………………………………………………
…………………………………………………………………………………
……….. 
18. If yes, do you consult this/these individuals for agricultural information?  
[  ] Yes [  ] No 
19. If yes, what agricultural skills, knowledge, or/and practices do you consult 
them for?  
…………………………………………………………………………………
…………………………………………………………………………………
………… 
 
 
LOCAL GROUP MEMBERSHIP 
20. Do you belong to 
any group in the community? [  ] Yes [  ] No 
21. If yes, please name 
the group(s) you belong to, any position held if any, and  
GROUP ACTIVITIES 
ENGAGED 
IN 
POSITION 
HELD 
RESPONSIBILITIES 
    
    
    
    
    
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22. Do people from outside the community join your group(s) [  ] Yes [  ] No 
If yes, please name them 
…………………………………………………………………………………
…………………………………………………………………………………
………... 
23. Are there similar groups in other communities? [  ] Yes [  ] No 
If yes, please indicate the 
communities……………………….................................. 
24. If yes, do you communicate with members of those group(s)? [  ] Yes [  ] No 
25. Do you pass on agricultural information to members of your group(s)?   
[  ] Yes [  ] No 
26. If yes, what are some of the agricultural topics you pass on to them? 
GROUP MEMBER AGRICULTURAL 
INFORMATION 
  
  
  
  
  
 
STATUS IN THE COMMUNITY 
27. What crops do you grow? 
…………………………………………………………………………………
…………………………………………………………………………………
………… 
28. What is the acreage per crop grown? 
CROP ACREAGE 
  
  
  
  
 
29. On the average, what is your average output per crop each season? 
CROP YIELD 
  
  
  
  
  
  
 
30. What positions do you occupy in the community? 
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…………………………………………………………………………………
…………………………………………………………………………………
………………………… 
…………………………………………………………………………………
…………………………………………………………………………………
………………………… 
 
 
31. Have you received any award(s) or recognition for your farming activities in 
this community? [  ] Yes [  ] No 
 
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