Investigating Knowledge Transfer for Entrepreneurship in Kenya's 
Agricultural Sector 
William W. Ngugi 
MBA/0240/08 
Submitted In Partial Fulfillment of the Requirements for the Degree of 
Masters in Business Administration at Strathmore University 
Strathmore Business School 
Strathmore University 
Nairobi, Kenya 
January 2018 
This thesis is available for Library use on the understanding that it is copyright material and that 
no quotation from the thesis may be published without proper acknowledgement. 
II 
DECLARATION 
I declare that this work has not been previously submitted and approved for the award of a 
degree by this or any other University. To the best of my knowledge and belief, the thesis 
contains no material previously published or written by another person except where due 
reference is made in the thesis itself. 
sth M 
Approval: 
The thesis of William W. Ngugi was reviewed and approved (for examination) by the following : 
Name of Supervisor: Dr X. N. Iraki . . . .. .. . . ... .. . . .. . ... ... . .. . . .. . . .. ....... ... . .. .. ... .. . 
Faculty Affiliation . .. .. .. ... . .. .. ... . ........ .... . .. .. . . .. .. . ... . .... .... . .... . .......... . .. . . .. . ..... . 
Institution . . . . .. .. . ......... . .. .. ... .. .. ... . ... . . .... .. ... . . . ..... . .. . .. . .. . . . .. .. . .. ...... .. . . . . .... .. . . 
Head of School I Institute I Faculty ..... .. . .. ... ... .. ... . . .. . . ..... ... . ... .. .. . .. .. . . ... ... ... ... .. . . 
School Name ... . . ... . . ...... .. ...... . .. . ... .. . .. . ... .. .. . .. . . ..... ..... ... .. .. . . .. ... .... . . .... . .. .. .. . 
Dean, School of Graduate Studies . . . ... . .... ......... . ..... . ..... . ... .. . . ..... . ..... . ......... .. .. . 
111 
ABSTRACT 
Agriculture remains the backbone of Kenya's economy. About 80% of the population depends 
directly or indirectly on agriculture for both food and employment. Despite this, the small scale 
farmers have largely remained stagnant i.n productivity and tended to focus on subsistence 
farming . This study investigated the knowledge transfer predictors for entrepreneurship in 
Kenya's Agricultural Sector. The objectives were, to first determine the knowledge and skills 
gap in the agricultural sector in Kenya, to determine the relationship between these knowledge 
gaps and successful knowledge transfer (productivity) in the agricultural sector in Kenya and to 
find out the moderating effect of hindrances on the relationship between knowledge gaps and 
successful knowledge transfer in the agricultural sector in Kenya. The knowledge predictors 
identified have shown that with an effective knowledge transfer from various existing 
agricultural and research institutions as well as learning institutions, it is possible for the farmers 
to not only increase their production, but it is also possible to turn their farming activities into 
commercial enterprises, small, medium and large farming enterprises. The initial stage of the 
study used exploratory design followed by descriptive design. Data was collected through self-
administered questionnaires. The questionnaires were structured to minimize variability. The 
sample set was clustered and convenience sampling applied resulting in a sample size of 60 
respondents (30 small-scale farmers and 30 large scale farmers from Laikipia, Kiambu and 
Nakuru counties, purposively sampled). The data was analyzed using descriptive statistics and 
inferential statistics in patiicular regression analysis . The study established that a relationship 
exist between knowledge gaps and knowledge transfer. Five predictors of knowledge transfer 
were identified. The most significant predictors of knowledge transfer according to their impact 
factor were: skills transfer from the large scale farmers to small scale farmers , access to research 
centers (KARl) and extension service providers like Syngeta EA, Osho chemicals, Amiran, 
Bayer EA, Twiga Chemicals, Kenya Seeds Company and others, access to information on 
agriculture relayed via TV and radio, education of the farmers and the courses pursued at college 
level. The study concludes that effective knowledge transfer is tenable through such mediums 
like the government agricultural officers, mass media and agricultural learning institutions which 
will turn the farming activities of small-scale farmers to commercial enterprises. Since 
implementing the predictors of knowledge transfer would entail a financial cost, a key 
IV 
recommendation to other academicians is to determine the return on investment in knowledge 
transfer, by putting into practice the theoretical aspects identified. 
Key words: Strategy, Small-Scale fanners, Large-Scale farmers, Knowledge Transfer 
v 
Table of Contents 
Declaration .............................. ........................................ ..... .. .. ........ .. ........ ............... .... .......... ..... .. iii 
ABSTRACT ... ...... ....... .... ........... ..... ... ..... ..... ....... ........ .. ............ ........... .... ... ............ .... ......... .......... iv 
List of Tables ................. ... ........................................................................................................... viii 
List of Figures .. ......... ................ ...... .. .............................. ............... ......... ........ ... .. ........ .... .... .... .... .. ix 
Acknowledge1nent ................. ...................... ................................. ... ....... ...... ................................. . x 
CHAPTER ONE: INTRODUCTION ........................ .. ........................... ......... ...................... ... ... .. . 1 
1.1 Background ................. .. .... ............................ ..... ........ .......... ....................... ............. ............ 1 
1.2 Proble1n State1nent ............................................................................................................... 3 
1.3 Research Objectives ............................................................. .. .............................................. 5 
1 .4 Research Questions .............................................................................. .... ............................ 5 
1.5 Imp011ance and Significance of The Study .............................................................. .. .......... 5 
1.6 Limitation ofthe study .................... .......... ........................................................................... 7 
1.7 Suininary .................. .. ...... .. ........................................ ... .......... ... .......................................... 7 
CHAPTER TWO: LITERATURE REVIEW ........................................................................... 8 
2.1 Introduction .............................. .... .. ............. ............. .......... .... .. ...... .. .................................... 8 
2.2 Literature Review on knowledge transfer ............................................................................ 8 
2.2.0 The WHAT, HOW, WHO and WHY in knowledge transfer in agriculture .................... 8 
2.2 .1 Strategy in Knowledge Transfer .................................................................................... 10 
2.2.2 Knowledge Transfer in Agriculture Between Developed And Developing Countries .. 12 
2.2.4 Case Studies of Knowledge Transfer in Agriculture in Similar Countries .................... 13 
2.2.5 Agriculture in Kenya with a bias to the Vision 2030 and GDP ..................................... 14 
2.3 E1npirical Review ......... .. .............................. ..... ........ .. ............ ... ............. ..... ...................... 17 
2.4 Research Gap ........................................................................ .... ................. .. ........ ..... .. ....... 18 
2.5 Conceptual Framework ................................................ .. ..... ........ ...... ................................. 20 
2.6 Research Hypothesis ........................................................................................................ .. 21 
2.7 Suininary ...... ............ .... ... ... ........................ ................................. .... ... ......... .. ............. ...... .. 21 
CHAPTER THREE: RESEARCH METHODOLOGY .. .. .. ..................................... .. ...... .. .... . 22 
3 .I Introduction ..................................................... ... ........................................ .. ..................... . 22 
3.2 Research Design ......................................... .. ..................................... .... ...... ....................... 22 
3.3 Target Population ............... .. .............................................................................................. 23 
3.4 Sample and Sampling Method .................................... .. .............. .. .......... ........ .................. . 23 
3.5 Data Collection .... .. ................................................................ .... ........................................ 24 
3.6 Validity .... ... ............................. .... ...................................................................................... 25 
VI 
3.7 Reliability .. ..... ... ... ... ..... ... ...... .. .. ... .... .... ... .. .... .. .... .... ... ....... ........................ ......... .. .... ... .... ... 25 
3.8 Data Analysis ..... ... .. .. ... .. ... .... ... ........... .. ....... .... ... .. ........ ... ...... ... .......... ... .... .. .............. ........ 25 
3.9 Sum1nary ........ .. .. .... ... ... ......... .... ... .. .. ... .... ... ..... .... ... ..... ... ... ....... .. .. .. ........ ............ .... .... ........ 26 
CHAPTER FOUR: RESULTS AND FINDINGS ... .. .. .. ... ..... ...... .. ............. .... ...... ......... ... ... .. .. 27 
4.1 Introduction .... ...... ... ... .. ..... ....... .... .......... ... ..... .. ..... ....... .. .. ... .... .... .. ...... ...... · ........... ...... ...... .. 27 
4.2 Response Rate ....... .. ...... ....... .. .. ... .... .. ..... ...... .... ... ..... ... ...... .... .... ... ......................... .. ...... ..... 27 
4.3 Satnple Profile .. ...... .... .... ... ...... ......... .... .......... ... .. .. ... ... ... ... .... .. .............. ...... .. ...... .......... ..... 27 
4.3 .I Knowledge and skills gap .. ..... .... .. ... ... .. .... .... ..... ... ... ..... ......... ..... ........ .... .. ........ ... .. .... .... 3 6 
4.3.3 Have read or heard of Vision 2030 .... .. ...... ....... .... .. .... .. .......... ....... ... ...... .......... ... ... ....... 39 
4.4 Testing the Research Hypotheses .... ... ....... .. ... .. .. ... .. .... ... ... .. .. ........... ........ ........... ...... ... ...... .... 43 
4.4.1 Relationship between Knowledge Gaps and Knowledge Transfer. ........ ......... .... .. ...... ....... 43 
4.4.2 Relationship between Hindrances and Knowledge Transfer ... .... ...... ..... ... ............. ... .... ...... 45 
4.4.3 The Moderating Effect of Hindrances on the Relationship Between Knowledge Gaps and 
Knowledge Transfer. ...... .... ... ..... .... .. .... ...... .. .. ... ... ... ... ............ ....... ......... ... .... ........ ... .. .... ........ ..... .. 4 7 
4.4.4 Evaluating the Model .... ... .. ...... .................... ..... ......... .. .......... ..... ......... .. ...... ...... .... ... ... .... ... . 48 
4.5 Sun11nary ..... .... .... .. .... ..... ... .. ... .. ....... ..... .... ....... ... ..... .. ... ... .... ... .. .... ... ... .... ... ... .. ........... .. ..... .. 50 
CHAPTER FIVE: DISCUSSION .. .... ............. ...... .......... ... ... ... .............. ..... .. ..... .... ............ .... .. 51 
5.1 Introduction ... ... .... .. .. .. .... .. ...... ... .. ....... .... .............. ... .. ......................... ... ...... .... .. .. .... .. ... ...... 51 
5.2 Discussion on Respondent Profile .. .. .. ... ... .. .... ... ...... ... .. .. .... ..... .. ... ... ........ .. .......... .... .. ... ..... 51 
5.3 Discussion on Knowledge Gaps and Knowledge Transfer. ... ..... .. .. .. .... ... ..... ... ........ ..... ..... 52 
5.4 Discussion the Moderating Effect of Hindrance (Integrated model) .... .... ...... ..... ... ...... .. .. . 55 
5.5 Chapter Summary ..... ... ... ..... ...... .... ................... ....... ....... ..... ....... ..... .. .. .... .... ... .. ... .. ... .... .... 57 
CHAPTER SIX: CONCLUSION AND RECOMMENDATION ..... .... ..... ... .... ..... ... .. ... ....... .. 58 
6.1 : Conclusion ... .... .. ...... ... ... ... ........... .......... ... ...... ... .. ................. ..... .. ... ... ...... ... .... ... ...... ..... ..... 58 
6.2: Recommendation ... ... ........ .... .. ......... ... .... ......... ... ... ... .... ........... ...... ....... ......... ......... ... .... .... 59 
References ... .. .... ... ... ...... ..... ...... ... ... ... ... .... ............ .... ...... ............. ........ .... ............ ... .. .. ... ... ..... .. ...... 61 
APPENDIX I: Tables ..... .. ...... ... .. ...... .. .. .. .. ..... .... ... ... ... ... .... ......... .... ..... .. ... .. .... .. ... ... .. ... .... ... ..... 68 
APPENDIX II : Time line of Activities ............. ... ... ... ...... ...... .. ...... ........... .. .... ........... ........ .. ...... 71 
APPENDIX III : Letter of Introduction ..... .... ....... .. .............. .......... ... ......... .... .... .... .... .... ..... ... ... . 72 
APPENDIX IV: Questionnaire ..... ... .... .. .... ... ...... ........... ........... .. ................. ..... ... ........ ...... ... .. .. 73 
APPENDIX V: Photo Album ...... .... ......... .................... .......... .. .... ... .... .. .... ......... .... ...... ......... .... 81 
VII 
List of Tables 
Table 4.3: What Did you Study in College ...... ... .... ..... ... ... .. .... ............. .. ....... .. .. ... .. .... ...... .. .... ...... 32 
Table 4.4: What skills or Knowledge Can SSFs Borrow From LSFs ... ....... .. ........ .............. ... .. .. 34 
Table 4.5: Do you listen to radio I TV programs in agriculture ...... .. .......... ..... .. ....... .... .. .. .... ..... .. 35 
Table 4.6: Crosstabulation- Do you listen to radio I TV programs in agriculture ............ ... ... .. ... 35 
Table 4.7: Chi-Square Tests ...... .. ... ...... ............... .... ..... ... ... .. ....... ..... ......... .. ........ ...... ........... ... ...... 36 
Table 4.8: What risks have you experienced ... ............... ... .... ...................... .................. .. ............. 36 
Table 4.9: What is the most important unmet need in your farming activities ...... ......... .. ..... .... ... 37 
Table 4.10: ANOV A .... .... .. .... ...... .. ........... ..... ..... .. .... ..... .. ... ... ... ... ..... ............. ..... .. .... .. ....... ... ...... .. 43 
Table 4.11: Coefficients ofModel4 .... .. .. ... ............... ............ .......... ... .... .. .... ...... ....... ... .... .. ........ .. 44 
Table 4.12: Model Summary .... ....... ..... .... ....... .. .............. .... ....... ....... .... ...... .. .. ... ..... ... .... .............. 45 
Table 4.13 : ANOVA of Model 1, 2 & 3 ............ ... ..... ..... ....... ... .. ........... .............. ... .. ..... ........ ....... 45 
Table 4.14: Model Summary of Hindrances ...... ... .. .... ........... ..................................... ..... ............. 46 
Table 4.15: Coefficients .. ..... ............................... ...... ..... ... .. ....................................... ... .. ...... .. ...... 46 
Table 4.16: ANOV A results of the Moderation Effect.. ..... ... ............................................. ... ....... 4 7 
Table 4.17: Coefficients of the Moderation Effect ............................. .. .......... ............ ........ .......... 48 
Table 4.18: Coefficients of the Moderation Effect .............. .. .. .... ....... .... .... .. ....... .. ..... .... ..... ...... ... 49 
APPENDIX I- TABLES 
Table 2.1: Gross Domestic Product by Activity (Per cent contributions to GOP) ....................... 68 
Table 2.2: Domestic Exp01ts by Broad Economic Category, 2004 -2008 .... .. ... ... ..... ............ ....... 68 
Table 2.3: Wage Employment by Industry and Sector, 2004-2008 ... ............ .. .. .. .... ... ....... ... ...... .. 69 
Table 2.4: Recorded Marketed Production at Current Prices, 2004-2008 (Ksh Million) ...... ... ... . 69 
Table 2.5: Sale to Marketing Boards from Large and Small Farms, 2004-2008 ...... .. ..... ........... .. 70 
VIII 
List of Figures 
Figure 2 .1 : Knowledge Transfer Model (Miesing, Kriger & Slough (2006), pg. 116 .. ........ ...... .. 17 
Figure 2.2: Conceptual model ... .... .. .. ..... .... ...... .... .. ... ..... .. .......... ........ ..... ........... .... ..... .. ....... ..... .. .. 20 
Figure 4.1: Respondents in the respective county ...... ..... .. .... ...... ......... .......... .... ..... .. ....... .. .... ...... 28 
Figure 4.2: Gender of the respondents .. .. ..... ... .... .. ... ........................ ........... .................................. 29 
Figure 4.3: Head of the Household ... ...... ..... .. .. ... .... ... .......... ... .... ... ......... .......... ...... .. .. .. ..... ......... .. 30 
Figure 4.4: Age of the Respondents ... ...... ... .. ..... ........... ..... ........ ...... ..... ....... .... ... ... ....... ... .... ... ...... 30 
Figure 4.5: Primary Activity of the Household head ..... ..... .... .. ..... ........ ... .... ...... .............. .... ... .. ... 31 
Figure 4.6: The Household Head's farming experience ... ... .... .. .................. ..... .. .. ... .. .. .. ..... .... .... ... 31 
Figure 4.7: Level of education of the Household head .. ....... .... ...... ..... .. ........ ............... ...... ... ....... 32 
Figure 4.8: The farm owners ....... ....... ...... ... ... .............. .. .. .... .......... .... .... .. ........... .. .............. ... ... .... 33 
Figure 4.9: Number in working at least once a week ........... .............. ... .... ... ....... ... ... .. ................. 33 
Figure 4.11: The hindrances in knowledge transfer .. .. ... ..... .... .. ....... ... .... .. .... ... .......... ........... ... ..... 3 7 
Figure 4.12: What Farmers can do to reduce food sh011age or crop failure ..... ................... ......... 38 
Figure 4.13: Catalysts of Knowledge transfer ......... ............. ... ........... ... ..... ..... .. ........ ........ .... ... .. .. 39 
Figure 4.14: Have read or heard ofVision 2030 .. .... .... .... ....... ............ ............ ..... .............. ... ... .... 40 
Figure 4.15: Effective knowledge transfer & Productivity ...... ................ ... ..... ........... .. ... ........ .. ... 40 
Figure 4.16: Gap in knowledge following knowledge transfer ............. .. ..................................... 41 
Figure 4.17: Skills for farmers ... ...... ... ... ....... , ... .... .. ............ .. .. ....... .. ................... ... .. ... .... ... ........... 41 
Figure 4.18: Knowledge transfer through reading ..... .... .......... ... .. .. .... .. ...... ..... .. .... ... .. .................. 42 
IX 
Acknowledgement 
First and foremost, I thank the Almighty God for the grace and blessings he has bestowed upon 
me throughout the study period. I remained healthy and strong to face the study period, which 
was difficult and tempting. Secondly, I would like to extend my sincere appreciation and thanks 
to the University fraternity and staff at Strathmore Business School (SBS) for admission at the 
University, suppott and encouragement through-out my study. Special thanks go to my 
supervisor and lecturer, Dr. X. N Iraki, who afforded me all the guidance, help and support 
throughout my project. I cannot forget to acknowledge and appreciate the whole of SBS year 
2008 MBA class for their unwavering encouragement every day; the words "there is no turning 
back" were a daily verse whenever we met in class. My study group, also known as syndicate is 
much appreciated; they all kept encouraging and suppotting me when needed. 
I would also like to acknowledge the moral supp011 of my immediate family members, especially 
my children who kept me on my toes by asking me if I have done my homework, what I got in 
my test and when I will graduate. This push gave me no choice but to continue and complete the 
study. 
I also want to specially acknowledge and appreciate my employer, Syngenta East Africa Limited 
(SEAL) and its management led by Andy Watt, for fully sponsoring my study at SBS. My SEAL 
colleagues connected me to our customers in the field when I went to collect the data and as well 
offered assistance in the data collection. I want to specially appreciate and acknowledge my 
finance colleagues at SEAL who worked extra hours to make up for the time I attended classes in 
the evening, they were extremely understanding and helpful. 
I would also like to acknowledge and thank all the respondents, our customers, farmers, partners, 
and the general public who in one way or another afforded me their valuable time to respond to 
my questionnaire and took time to provide useful insights into my study. 
To all of the above and anyone else who I may have forgotten to mention, I am greatly indebted 
and I pray that God will bless you and do so in a big way; God bless you!. 
X 
CHAPTER ONE: INTRODUCTION 
1.1 Background 
Johnson and Scholes (2006) defined strategy as the direction and scope over the long-term, 
which achieves advantage through the configuration of resources within a challenging 
environment, to meet the needs of markets and to fulfill stakeholder expectations . Strategy, they 
cited, was a careful plan or method, a clever stratagem or the art of devising or employing plans 
or stratagems toward a goal. 
Agriculture in Kenya remams exigent (Njaramba, 2011). Blood and Studdett (2007) defined 
large scale farming as the modern trend to enlarge farms to be a business enterprise rather than as 
a unit size suited to single family management, with the application of modern agricultural 
technologies to improve production while concurrently protecting and enhancing the land 
resources on which production depends. Dumanski et al. , (2006) defined conservation agriculture 
as integrating natural processes into modern agricultural practices while ensuring minimal 
mechanical disturbance ofthe soil. 
Large scale farmers (LSFs) in Kenya are at the top of the pyramid with only a small percentage 
of the population involved in large scale farming or conservation agriculture, as suggested by the 
CAADP Africa Forum (2009) . At the bottom of the Pyramid are small scale farmers (SSFs) and 
the vulnerable population. Potter ( 1996) acknowledged that strategic positioning is about 
performing different activities from rivals or performing similar activities in different ways while 
competitive strategy is about being different. This study intends to examine a strategy for the 
small scale farmers to learn and benefit from the experience and knowledge of the large scale 
farmers. 
Imp01tant weaknesses of small-scale farmers are lack of knowledge about modern markets, 
modern technology and proper use of modern inputs (Pannell, 1999). The author acknowledges 
that modern markets encompass use of approaches like value-chain financing and modern 
technology is the use of equipment for farm use, like spraying, irrigation, harvesting, storage and 
transpotiation. LSFs on the other hand, use superior technology and there is need to transfer 
knowledge between large scale and small scale agriculture and bridge this gap (Lucas, 2006) . 
Malhotra (2002) defined knowledge transfer as a means of finding effective ways to let people 
talk and listen to one another and the successful knowledge transfer involves neither computers 
nor documents but rather interactions between people. Butler, Grice and Reed (2006), 
acknowledged that farmers combine training with their tacit knowledge and through discussions 
with their family, and in some cases with professional contacts, acquire new knowledge initiates 
leading to various degrees of change within businesses. The authors proceeded to state that the 
familial , social and emotion attributes of business relations may determine the extent that 
knowledge is transferred into practice. 
The F AO & World Bank (2000) defined an agricultural knowledge and infonnation systems 
(AKIS) as a system that "links rural people and institutions to promote mutual learning and 
generate, share and utilize agriculture-related technology, knowledge and information'.' . The 
FAO & World Bank (2000) noted that the need to increase production, improve the poor 
linkages between agricultural actors, improve access to market information and agro processing 
and address the limited supply of agricultural information and knowledge for farmers highlighted 
the need to understand the AKISs of small-scale fanners. 
The Economic Survey (2009) repotied that agriculture remains the most impotiant economic 
activity in Kenya, although less than 8% of the land is used for crop and feed production. Less 
than 20% of the land is suitable for cultivation, of which only 12% is classified as high potential 
(with adequate rainfall) agricultural land and about 8% is medium potential land. The rest of the 
land is arid or semiarid . About 80% of the work force engages in agriculture or food processing. 
Farming in Kenya is typically carried out by SSFs who usually cultivate no more than two 
hectares (about five acres) using limited technology. These small farms, operated by about three 
million fanning families, account for 75% of total agricultural production. Although there are 
still important European-owned coffee, tea and sisal plantations, an increasing number of peasant 
farmers grow cash crops. The Encyclopedia of the Nations (20 I 0) noted that agriculture is the 
backbone of Kenya's economy. 80% of the population lives in rural areas and derive their 
2 
livelihoods from agriculture. They are in the bottom of the pyramid while at the top of the 
pyramid are LSFs. There is need to determine an appropriate model for knowledge transfer, in an 
attempt to make sure that the low-income SSFs grow to engage in large scale farming. 
Learning and innovation by the small-scale farmers is essential for economic survival 
Nieuwenhuis (2002) . Knowledge transfer is therefore important. The knowledge is transferred 
through; formal research and development, informal knowledge diffusion through journals, 
organizations, mobility and observation, learning by doing and by using problem-solving 
behavior and purchasing knowledge though machinery and tools. These are impmiant for 
vertical growth of the country' s agricultural GOP contribution. The next section examines the 
significance of agriculture in Kenya and this study will explore more on how learning and 
innovation are connected . 
Ojiambo ( 1989) observed that the problem of inadequate infonnation provision to the rural areas, 
where the bulk of our population lives is an issue which has been addressed but with little 
successes. According to the Economic Survey (2009) Agriculture contributes significantly to 
expmis, employment and income. Despite the dispropmtionate concentration of pove11y in rural 
areas, the provision of knowledge, adequate information and financial access to the poor and 
low-income people has tended to gravitate away from those engaged in conservation agriculture 
and the agriculture borrowers. There is thus a knowledge transfer gap . 
Literature on knowledge transfer appears not only extensive, but also highly variegated. Bou-
Liusar & Segarra-Cipre ' s (2006) concluded that the effect of strategic knowledge transfer varies 
depending upon the competitiveness of the sector. Kumar & Ganesh (2008) resolved to 
characterize the knowledge transfer to dimensions while Khamseh & Jolly (2008) determined the 
characteristics of knowledge; the factors related to absorptive capacity; the reciprocal behavior of 
the pminers; and finally, the nature and form of alliance activity . Presented next is the problem 
statement of the research process. 
1.2 Pa·oblem Statement 
According to Kiplang'at (1999), there is lack of knowledge transfer between large scale and 
small scale farmers , defined as the sharing or disseminating of knowledge and providing inputs 
3 
for problem solving. The author continues to state the small scale farmers have largely remained 
stagnant in productivity and tended to focus on subsistence farming hence need to identify 
knowledge transfer predictors that can scale them up entrepreneurially. Kenya has since 
independence relied heavily on the agricultural sector as the mainstream for economic growth, 
employment creation and foreign exchange generation (Kiplang'at, 1999). According to the 
Current National Development Plan (2010) the agricultural sector employs about 82% of the 
labour force, accounts for 27% of the GOP and 64 % of expoti earnings. The sector is also a 
major source of the countty ' s food security and a stimulant to the growth of off-farm 
employment, both of which are of primary concern to the Kenyan government. About 80% of 
Kenya population lives in the rural areas and depends on agriculture for their livelihood. 
Dumanski, Peiretti, Benites, McGarry & Pieri (2006), examined the paradigm of conservation 
agriculture and large scale farming, EPZA (2005) examined the dairy farming sector in Kenya, 
Kenanoglu & Karahan (2002) examined policy implementation for agriculture and the Economic 
Survey (2009) of Kenya examined the contribution of agriculture in Kenya. In all these studies, 
there was limited focus on the strategy for knowledge transfer of commercialization of 
agricultural sector to the small scale farmers . 
Poverty among the farming communities is still very eminent and heavy and there is not much 
training towards improving farming management as studies by FAO & World Bank (2000) 
reveal. This has led to frustrations and frequent protests and riots by small scale farmers . Early 
2010, flower farmers in Naivasha rioted over poor pay and working conditions (Daily Nation, 
201 0). Despite their huge potential to contribute in poverty alleviation, majority of the small 
scale farmers (SSFs) sit at the bottom of the pyramid whilst the large scale farmers (LSFs) are at 
the top. There is need for knowledge transfer and interaction between LSFs like Del Monte in 
Kenya and SSFs like the Nanyuki vegetable farmers , for growth . The study sought to examine 
the knowledge gaps that exist among the farming community especially the SSFs and came up 
with insights for knowledge transfer to help the farmers undetiake their activities as commercial 
enterprises. 
4 
1.3 Research Objectives 
The general objective of the study was to formulate a model for knowledge transfer in Kenya's 
agricultural sector with the aim of commercializing the farming activities especially of small 
scale farmers. The specific objectives of the study were; 
i) To detennine the knowledge and skills gap in the agricultural sector in Kenya 
ii) To determine the relationship between knowledge gaps and successful knowledge 
transfer (productivity) in the agricultural sector in Kenya 
iii) To find out the hindrances for knowledge gaps and successful knowledge transfer in the 
agricultural sector in Kenya. 
1.4 Research Questions 
From the research objectives above, the research questions fonnulated were; 
i) How vast is the knowledge and skills gap in Kenya's agricultural sector? 
ii) Is there a relationship between knowledge gaps and successful knowledge transfer 
(productivity) in the agricultural sector in Kenya? 
iii) What are the hindrances for knowledge gaps and successful knowledge transfer in the 
agricultural sector in Kenya? 
1.5 lmpot·tance and Significance 
From this study, small scale farmers , policy makers and indeed the entire population are set to 
benefit from the findings of my study. Kenya's economy is heavily dependent on agriculture. 
Generally 75% of Kenyans earn their living from farming either directly or indirectly. Kenya is 
among few African countries whose food production has kept pace with its population growth . 
Agriculture usually brings in over 6% of foreign exchange earnings and provides raw materials 
for Kenya's agro-industries, which account for about 70% of all its industrial production. The 
results and findings of the study are imp01tant to the Government, the stakeholders and 
parastatals. As a policy maker, the Government and the parastatals should be able to formulate 
policies and guidelines that will assist the small-scale farmers improve productivity and 
commercialize their farming activities. The objective of the study was to investigate and 
5 
formulate a model for knowledge transfer for entrepreneurship in Kenya 's agricultural sector and 
thus the results and findings will be important to the Government. 
Agriculture has proven to have a stronger impact on povetty reduction than do other sectors. This 
impact is of a direct and indirect nature. Agricultural growth impacts directly on povetty 
reduction by creating rural employment, raising rural incomes, increasing food production and 
food security. Indirectly and directly, agricultural growth contributes to a country ' s GDP, 
especially in countries with agricultural-based economies like Kenya. Increased GDP provides 
the resources for public investments in social sectors such as education and health and in social 
programmes such as social security, roads and other infrastructures. The results and findings will 
be important to the Government of Kenya - Ministry of Agriculture and Ministry of National 
Planning- as Kenya's Vision 2030 seeks to alleviate poverty. 
The study will also provide insights into the small-scale and large scale farmers as regards the 
knowledge and skills gaps and their level of interactions. For effective knowledge transfer and 
remodeling especially of the small scale farms into agricultural enterprises, the results and 
findings of this study will be impottant. Effective knowledge transfer from various knowledge 
and skills centres to the farmers will lead to increased production by farmers' , production levels 
will be higher than their consumption needs. Farmers will seek markets for their excess 
production and farming enterprises will kick-in. The study will be impottant to identify 
investment oppottunities that exist in the agricultural sector, strategy for provision of low cost or 
affordable technologies and equipment for small-scale processmg, oppottunities for 
improvement in technology infrastructure such as packing, storage, and transpottation, 
intensified irrigation and additional value added processing are marketable areas for investment 
and financial support to the sub-sector. 
The study is important and relevant to the government as a source of information for planning 
purposes. To the general public that will want to venture into small-scale or large scale farming, 
the study will be imperative to them while to the scholars and academics, the results and findings 
are impottant to them for fmther research . 
6 
The study will also be impmiant to other stakeholders like Kakuzi limited, Socfinnaf, Delmonte, 
Syngenta and those in flower fanning in Naivasha, considered to be at the top of the pyramid and 
in large-scale farming. The results and findings from this study will be impmiant to these 
stakeholders as it will provide insights on an intervention model for knowledge transfer 
throughout the agricultural pyramid. 
1.6 Limitation of the study 
The main limitation was that the respondents considered some of the infonnation confidential 
and therefore the household heads did not want to fully disclose their information on the number 
of siblings and school going age. In order to get this information and assure the respondents of 
confidentiality, a rappmi was created and approximations on scale numbers were used. There 
were also cultural issues in which the male was considered the head of the household, thereby 
being relied on for information and directions on fanning activities especially among the small 
scale farmers. This was overcome by seeking permission from the male as the household head to 
talk to the female, the male felt recognized and easily asked the female to respond. 
1.7 Summary 
Chapter one presented the background, defined the key terms - strategy, large-scale farming, 
small scale farming and knowledge transfer and enterprise in agriculture. From the introduction 
and background, the research objectives and research questions were formulated , problem 
statement highlighted and significance of the study presented. In order to gain more 
understanding into the subject area, chapter two next examined relevant literature review. 
7 
CHAPTER TWO: LITERATURE REVIEW 
2.1 Introduction 
Chapter two examined relevant literature review to the area of study as suggested by Bailey 
(1987) and Sarantakos (1993) who examined and analyzed the topic of social science, to provide 
insights. Chapter two examined the theoretical review, the empirical review and the conceptual 
framework was then formulated from the literature examined. The conceptual framework was 
formulated to gain more understanding in the subject area and pertained to the research area. 
Within these subject areas, a vast number of authors were cited and acknowledged . The literature 
review examined both the theoretical review and empirical review. It examined the strategy in 
knowledge transfer, knowledge transfer in agriculture between developed and developing 
countries, challenges in knowledge transfer in agriculture, case studies of knowledge transfer in 
agriculture in similar countries and agriculture in Kenya with a bias to the Vision 2030 and GDP. 
2.2 Lite.-atm·e Review on knowledge tr·ansfet· 
2.2.0 The WHAT, HOW, WHO and WHY in knowledge tr·ansfet· in agricultm·e 
In order to fully answer the research questions formulated in chapter one, there was need to first 
examine, WHAT, knowledge is transferred in agriculture, HOW, the processes and channels of 
knowledge transfer (patticularly in agriculture) work, WHO, are involved in knowledge transfer 
and WHY, the effects. Butler et. al (2006) acknowledges that there is a paucity of understanding 
regarding how knowledge gained through vocational training in agriculture is transferred to the 
farm business and effectively applied in practice . The literature on knowledge transfer within 
agriculture tends to be split into two divergent paths. The first examines the processes of 
cognition within the learning environment while the second focuses on the social environment in 
which an individual makes his or her decisions about education within a patticular learning 
8 
process. The decisions that individuals make regarding their personal education is crucial to the 
levels of knowledge transfer in agricultural communities and the prosperity of their business. 
Given the imp01tance of training to the agricultural industry, it is necessary to understand how 
vocational training is transferred as a potential source of knowledge to others involved in their 
businesses or household ; that is their personal business network. The extent of an individual's 
knowledge, described by Polanyi (1967), cited in (Butler et. a!, 2006) as a combination of 
explicit and tacit knowing, and includes a shared dimension depicting knowledge within an 
organization rather than that kept by an individual. Considering shared explicit knowledge first, 
this is information in textbooks and manuals provided by an organization for people to Jearn 
from enabling Delimiting knowledge transfer individuals to acquire additional human capital. In 
terms of vocational training, improving the skills base of farmers and others involved in 
agricultural related businesses may be attained through formal learning situations that impart 
shared explicit knowledge. In agriculture, this tends to be dominated by technical and husbandry 
events rather than more managerial or entrepreneurial forms of training. Conversely, shared tacit 
knowledge remains relatively hidden and may only be accessible to an individual through 
sustained interaction. 
The ability for farmers and other land-workers to pmticipate 111 such training gives them the 
opp01tunity to develop their expe1tise and skills in technical and business related activities at a 
subsidized rate . Many of the training events are conducted on a ' learning-by-doing' or 'learning-
by- interacting' which has a role in innovation particular if augmented to an individual ' s stock of 
tacit knowledge (Taylor & Plummer, 2003). There is evidence that farmers combine training 
with their tacit knowledge as the events that they pmticipate in are frequently closely aligned to 
their business and farm management interests. Through discussions with their family, and in 
some cases with professional contacts this knowledge may initiate change either at a marginal 
level with adjustments to enterprises or through more radical restructuring of their business . 
However, familial, social and emotion attributes of business relations may determine the extent 
that knowledge is transferred in to practice. 
9 
In Kenya, The Government of Kenya, in recognition of the role of private sector in spear heading 
industrialization, has put in place a policy framework to foster the creation of a conducive 
environment for private sector pmticipation in economic development. The agricultural sector 
has potential for spurring substantial growth in the economy. Exports from Kenya enjoy 
preferential access to world markets under a number of special access and duty reduction 
programmes. These include regional markets (EAC, COMESA), EU-African-Caribbean-
Pacific/Lome Convention and the African Growth & Opp01tunity Act (AGOA). Kenya is also a 
member of most major international and regional intellectual property conventions -the World 
Intellectual Property Organization (WIPO), the African Regional Industrial Prope1ty 
Organization, the Paris Convention on the Protection of Industrial Prope1ty, and the Berne 
Convention on the Protection of Literary and A1tistic Works . Located on the East African coast 
and having the p01t of Mombasa, Kenya is strategically located for investors wanting to access 
the East and Central African market. Kenya is also a regional hub for airlines allowing for easy 
access from and to any part of the world. This implies that we woo international investors. This 
is for the growth of our GDP . Agriculture usually brings in over 6% of foreign exchange 
earnings and provides raw materials for Kenya's agro-industries, which account for about 70% of 
all its industrial production. 
2.2.1 Strategy in Knowledge Transfet· 
Strategy is concerned with engaging with, and sometimes trying to 'control', the future (Clegg et 
al., 2004) pmtly on the basis of projections, extrapolations and ideas of what future business 
conditions might be, and hence, how the organization should place itself. Though strategy 
research has moved a long way from its origins as a discipline centered mostly around planning 
(Clegg et a!., 2004 ), this rational and control oriented view of strategy persists in many schools 
of research such as those labeled design, planning or positioning (Mintzberg, 1990). Zijp ( 1994) 
observed that lack of data was a significant constraint in planning and management of rural 
development. In pmticular the author stated that rural populations in Africa have difficulty in 
getting imp01tant information in a timely fashion and an appropriate format, like market produce 
prices or bulletins about pest infestation and most information disseminated was in written form, 
making it difficult for those with low or no literacy skills to benefit from this information. 
10 
Rees et al. (2000) cited that linkages between research, extension, civil society organizations and 
farmers were weak and that often the new improved technologies did not reach their intended 
beneficiaries . Of the farmers in Kenya, 80% are smallholders, who produce largely for 
subsistence and to some extent for sale. These farmers face many barriers to attaining full 
agricultural production including poor access to agricultural information, low output and 
productivity, weak institutional capacity and coordination, inadequate markets and market 
information (Republic of Kenya, 2006) . 
According to Kiplang'at (1999), rural development demands that rural people get access to 
information they need in forms that they can understand. Without these, development eff011s will 
fail to achieve their potential impact. Kiplang' at (1999) continues to state that inf01mation 
technologies when adopted can increase the effective pa11icipation by the rural folk who are not 
only recipients of information but creators of knowledge based on their own experiences. Butler, 
Le Grice & Reed (2006) acknowledged that a synthesis of knowledge, social network structure 
and trust in relationships provides the basis for a tripa11ite model of knowledge transfer. The 
authors stated that farmers can combine training with their tacit knowledge and through 
discussions with their family, and in some cases with professional contacts, new knowledge is 
initiated. The familial, social and emotion attributes of business relations may determine the 
extent that knowledge is transferred into practice. 
Ministry of Agriculture of Kenya, (2009), admonished the fact that in Africa, the potential of 
agriculture is underutilized while in Asia, agriculture has transformed national economies out of 
poverty. It proceeded to state that smallholders should be supp011ed to become commercially 
viable producers and dependencies on handouts need to be reduced with the key strategies being, 
availing access to markets, inputs and credit to farmers, increasing the area under irrigation and 
strengthening institutions. 
Conservation agriculture and zero tillage are now in practice worldwide and it is important to 
also examine the knowledge transfer in conservation large-scale farming. According to 
11 
Dumanski, Peiretti, Benites, McGarry & Pieri (2006), the practice of conservation agriculture on 
a large scale emerged out of Brazil and Argentina, although similar developments were occurring 
in many other areas of the world, notably North America in zero tillage, and Africa and Asia 
with technologies such as agro forestry . Zero tillage is now applied on more than 95 million 
hectares worldwide, primarily in North and South America (Derpsch, 2005) attributed to 
knowledge transfer. An important aspect would be to examine the knowledge transfer with a bias 
to agriculture between developed and developing countries. This is presented next. 
2.2.2 Knowledge Transfet· in Agriculture between Developed and Developing Countries 
The UK educational policy asserted that the economic future depended on productivity as a 
nation and that required a labour force with the skills to match the best in the world (Department 
for Education and Skills, 2006). Within this picture, the agricultural sector was directed by 
policy to undergo a 'radical re-direction' (Ministry of Agriculture, Fisheries and Food, 2001) and 
adapt to changing markets, more sustainable systems of production and enterprise that would 
suppott the wider rural economy. Training and knowledge transfer was made a pillar of supp011. 
The decisions that individuals make regarding training is crucial to the levels of knowledge 
transfer in agricultural communities and the prosperity of their business. Indeed, the ability of a 
business to survive and develop is influenced strongly by its capacity for innovation (Warren, 
2003). A rational approach is for a farmer to weigh up the costs and benefits of training and 
decide whether it is in his or her personal interest (Learning and Skills Development Agency, 
2002). Pat1 of this reasoning is that training may be seen as a cost rather than an investment in 
future development (Matlay, 1999; Matlay & Hyland, 1999). Any decision to patticipate in 
vocational training with the intention of improving profitability, efficiency or the adoption of 
new technology is likely to be strongly correlated to a farmer's previous training and education 
experiences (Kilpatrick, 1998a, b, 2002). 
2.2.3 Challenges in Knowledge Transfet· in Agriculture 
Diederen et a!. ( 1999) define knowledge transfer in agriculture as the spread in time of an 
innovation among the members of a social system . In agriculture, many studies have been 
conducted on the diffusion processes of innovation (Smulders et.al, 1998). There is however a 
12 
paucity of understanding regarding how knowledge gained through vocational training 111 
agriculture is transferred to the farm business and effectively applied in practice (Butler, Grice & 
Reed, 2006). 
Shared tacit knowledge remains relatively hidden and according to Spender (1994) may only be 
accessible to an individual through sustained interaction. Yet to acquire additional knowledge 
through participation in education and training is only part of the process of knowledge transfer 
as this will only equip farmers for what is presently known (Taylor & Plummer, 2003). 
2.2.4 Case Studies of Knowledge Tr·ansfer in Agriculture in Similar Countries 
According to Zakaria and Nagata (2009), the Japanese agriculture has experienced several 
phases of reforms and modernization for more than a century ago and since the end of the World 
War II, Japan sta1ted to embark on a concerted effmt to revitalize its agriculture sector in order to 
boost production to meet the escalating demand for food. The central and prefectural 
governments worked closely to enhance the training of farmers to uplift their technical and 
managerial skills and to ensure sustainability, and this was remarkably carried out through the 
activities and programs by the agricultural extension services. 
The Japanese extension system for agriculture which stmted in 1948 was meant for helping 
farmers to acquire useful, appropriate, and practical knowledge in the domain of agriculture 
(Fujita, n.d.). This system was adapted from the Western extension system into the Japanese 
culture to suit their local needs and requirements. Traditionally, extension focuses on 
disseminating information from research laboratories to farmers (Roling, 1990), providing 
farmers with technical advice as a guide to improved fanning methods (Zakaria et. al, 2009), 
training of new, youth and women farmers as well as community reorganization. 
The Japanese agriculture has thus far been remarkably successful and sustainable especially in 
terms of its technical development. This "suggests that farm decision makers have either been 
using more and better information or becoming more knowledgeable" (Jones et al., 1987). 
In Turkey, according to Kenanoglu and Karahan (2002), some committees were established to 
carry out activities on development and application of agriculture, with knowledge transfer in 
13 
mind. The authors proceed to state that now nearly all organic agricultural products from Turkey 
are vegetal crops, attributed to the knowledge transfer throughout the years. 
According to Nieuwenhuis (2002), within Dutch agriculture, links between the educational 
system and the economic system have existed ever since the emergence of agricultural education 
during the years around the turn of the twentieth century. 
2.2.5 Agricultut·e in Kenya, Vision 2030 and GDP 
The Kenya Vision 2030 sector plan, (Republic of Kenya, 2008-2012), prescribes a step-by-step 
process measurable over five years, to improve the quality of life and living standards of the 
citizenry, in which agriculture is given prominence. Agriculture is acknowledged as the lifeline 
to the economy. The agricultural sector plays a critical role in wealth creation and employment 
and accounts for about 25% of the GOP (Republic of Kenya, 2008a). Agriculture provides raw 
materials to the manufacturing sector and stimulates indirect growth. The agricultural sector 
employs more than 80% of Kenya's workforce and contributes 57% of national income 
(Republic of Kenya, 2006). Despite its importance, growth in the sector has declined over the 
years from 24.4% in the year 1996 to a record 22.4% in 2000 (Republic of Kenya, 2006) . 
According to the Economic Survey (2009), the GOP for Kenya increased from 5.1 % in 2004 to 
7.1 % in 2008 . Table 2.1 (Appendix I- Tables) presents the percentage share contribution to 
GOP ofthe various sectors ofthe economy. The contribution of agriculture and forestry to GOP 
is the highest at 23.4% in 2008, followed by manufacturing at 10.6% in 2008 and transport and 
communication at 10.2% also in 2008 . However, the contribution of agriculture and forestry to 
GOP declined from 23.4% in 2006 to 21.6% in 2007 . The high costs of agricultural inputs like 
fertilizers and seeds worsened the situation thereby leading to the low production of both food 
and industrial crops. However, as indicated in Table 2.1 the contribution of agriculture and 
forestry to GOP increased from 21.6% in 2007 to 23.4 in 2008 (Economic Survey, 2009). 
With regards to the contribution of agriculture to exports in Kenya, the summary of composition 
of Kenya's domestic exports by Broad Economic Category (BEC) is presented in Table 2 .2 
(Appendix I- Tables) Export earnings from food and beverages accounted for 40.4% of the total 
14 
domestic exp01t earnings in 2008, almost the share recorded in 2007, according to the Economic 
Survey (2009) and the Kenya Bureau of Statistics (2009). 
The export earnings from food and beverages increased from Kshs. 97,801 million in 2006 to 
Kshs. 130,273 million in 2008 recording an increase of 23.4% from Kshs. I 05 ,549 million in 
2007 to Kshs. 130,273 million in 2008. This increase was attributed to the increase in earnings 
from primary food and beverages for household and consumption. 
With regards to the contribution of agriculture to employment in Kenya, as indicated in Table 2.3 
(Appendix I- Tables) the wages employment from the private sector rose from 1,107.300 million 
jobs in 2004 to 1,305,500 million jobs in 2008 . The leading sectors in providing private sector 
employrnent were: community, social and personal services; agriculture and forestry; and 
manufacturing with contributions of23 .5%, 22.2% and 18.2% respectively. 
The public sector wage employment registered negative growth from 657.4 thousands in 2004 to 
628.1 thousands in 2007. However, it registered marginal growth of 1.6% in 2008 (Table 2.3). 
Community, social and personal services registered the highest employment recording 464.7 
thousand in 2008, followed by agriculture and forestry with 51 ,000 jobs. 
With regards to the contribution of agriculture to marketed production and income from 
agriculture in Kenya, Table 2.4 (Appendix I -Tables) presents the value of marketed production 
in the agricultural sector for the period 2004 to 2008. The total value of marketed production at 
current prices increased marginally from Kshs. 178,634.9 million in 2007 to Kshs. 178,856.6 
million in 2008. Tea, maize, fruits and sugarcane contributed to the marginal increase in the 
value of marketed output. The aggregate value of livestock and products marketed increased by 
3.2 per cent from Kshs. 29,691.4 million in 2007 to Kshs. 30,629.1 million in 2008. The value 
of marketed cereals declined by 8.3 percent from Kshs. 14,617.6 million in 2007 to Kshs. 
13 ,398.4 million in 2008. 
The value of marketed maize increased by 4.5 % from Kshs. 7,969.2 million in 2007 to Kshs. 
8,326 million in 2008, as a result of high prices offered in the market. The value of marketed 
horticultural produce declined by 13.8% from Kshs. 67,253.7 million in 2007 to Kshs. 57,965 .8 
million in 2008 mainly due to lower unit prices for horticulture in the exp01t market. The decline 
in h01ticultural export earnings in the year under review is attributed to a 27.8% slump in the 
value of vegetables. The high tea prices resulted in the value of marketed tea increasing by 26.2 
IS 
%to Kshs. 55,383 .1 million in 2008. The value of marketed dairy produce decreased by 1.1% to 
Kshs. 8,368.7 million in 2008 as a result of reduced milk production. 
With regards to the sales to the marketing boards in Kenya by the small farms and large farms, as 
presented in Table 2.5 (Appendix I- Tables) the share of sales to marketing boards by small 
farms declined from 75 .9% in 2007 to 73.1% in 2008 in line with lower production from the 
smallholder farms (Economic Survey 2009). 
The share of large farms sale to marketing boards increased by 11.8% from Kshs. 43 ,053 million 
in 2007 to Kshs. 48,148 million in 2008 . In spite of the decline in the share of small farms sale 
to marketing boards by 3.6% from Kshs. 135,591 million in 2007 to Kshs. 130,709 million in 
2008, the aggregate value of sales by the small farmers remained more than double , the value 
sold by the large farms . 
Therefore as suggested by Kip lang' at (1999), accelerated growth in agriculture will increase 
employment opp01tunities, enhance foreign exchange earnings and act as a catalyst to improve 
the standard of living of the people. However, the author suggests that for increased agricultural 
production in any developing country, there needs to be improved rural roads, technological 
supp01t to farmers in terms of better seeds, research and extension services, mechanization 
services, plant protection and animal health, irrigation and drainage and credit. 
According to Kiplang' at (1999), the key to increased agricultural production ultimately lies with 
the Nation's ability to disseminate relevant information to the farming community to facilitate 
the effective adoption of new production techniques and knowledge transfer from large-scale 
fanners. With regards to knowledge transfer, Miesing, Kriger & Slough (2006), hypothesized 
that transferring best practices between subsidiary and parent, and between organizational units, 
requires three activities: creating, sharing, and using knowledge. The authors synthesized the 
relevant extant literature on organizations to further develop the model in Figure 2.1 . 
16 
KNOWLEDGE KNOWLEDGE KNOWLEDGE 
CREATION SHARING USE 
I FLEXIBLE WORLD I 
VIEWS I I RELATIONSHIP I 
BONDS I 
I ABSORPTIVE I 
CAPACITY I 
~-, / ~ 
Figure 2. I: Knowledge Transfer Model (Miesing, Kriger & Slough (2006), pg. 116 
2.3 Empil·ical Review 
2.3.1 Tr·ansfer of Knowledge in the Agricultural Sector by Universities, Resear·ch 
Institutes and Traditions 
Atchoarena & Holmes (2004) noted that universities can potentially become showcases of local 
traditions and knowledge, reflecting the regional , cultural, and ethical traditions of their society, 
as well as global movements and forces. In reinforcing their roles as contributors to a culture of 
learning and rural development, the authors emphasized that Higher Agricultural Education 
institutions need to engage more directly and more effectively in pat1nerships and dialogue with 
other local educational institutions and their surrounding communities, in order to achieve 
knowledge transfer. 
Innovative universities have conceived themselves to have three complementary missions, 
namely research, teaching and service to the community (Maguire 2002). In Kenya, agricultural 
universities have adjusted their programs to accommodate new topics, new pa11nerships and 
continuous dialogue with policy-makers in dissemination of knowledge to small scale - farmers. 
17 
Mbithi (2004), of the University ofNairobi, placed rural and agricultural development within the 
context of overall national development after examining the production, adaptation and transfer 
of technical knowledge in the field of agriculture in Kenya. In Kenya, Jomo Kenyatta University 
of Agriculture and Technology (JKUAT) pa1tnered with the University of Nairobi on a 
programme on knowledge transformation and intellectual prope1ty transfer aimed at providing an 
enabling environment for the management and commercialization of intellectual prope1ty and 
supp01ting the activities related to commercialization and popularization of R&D, according to a 
rep011 by UNECA (2009). 
Universities and research institutes have therefore done much in knowledge transfer and this 
study determines the knowledge gaps existing among the farmers and how the same can be 
bridged for higher productivity and commercialization of especially the small-scale farms . Lucas 
(2006) argues that the issue of culture, individualism I collectivism, unce1tainty avoidance, and 
masculinity I femininity , will significantly impact the possibility of knowledge transfer. The 
study will formulate a likely appropriate model for knowledge transfer that can possibly allow 
smooth knowledge and skills transfer among the farmers, boost the productivity of small-scale 
farmers beyond their consumption requirement and lead to the commercialization of their 
farming activities. 
2.4 Research Gap 
Dumanski, Peiretti, Benites, McGarry & Pieri (2006), examined the paradigm of conservation 
agriculture and large scale farming, EPZA (2005) examined the dairy farming sector in Kenya, 
Kenanoglu & Karahan (2002) examined policy implementation for agriculture and the Economic 
Survey (2009) of Kenya examined the contribution of agriculture in Kenya . From the theoretical 
review, the authors examined the strategic model for knowledge transfer and generally 
acknowledged that since strategy was concerned with extrapolations and ideas of what the future 
business conditions might be, it may be important for small-scale fanners to devise a way to 
alleviate their challenges . The authors acknowledged that a synthesis of knowledge, social 
network structure and trust in relationships provides the basis for a tripmtite model for 
knowledge transfer. However, the authors did not expound on the likely appropriate strategy to 
empower small-scale farmers in Kenya, as sought by the study. 
18 
The authors also highlighted the challenges in knowledge transfer in the agricultural sector and 
all cited a paucity of understanding regarding how knowledge gained through vocational training 
in agriculture is transferred to the farm business and effectively applied in practice. The study, 
therefore sought to determine the knowledge and skills gaps, come up with solutions to the 
hindrances for knowledge and skills transfer, come up with likely appropriate model for smooth 
knowledge transfer for increased productivity and commercialization of Kenya's agricultural 
sector, especially among the small scale farmers. 
In the empirical review examined, the authors examined the mediums of knowledge transfer. 
They examined these mediums as contributors to a culture of learning and rural development. 
However, there was a gap in understanding how these mediums of knowledge and skills transfer 
will infiltrate the small-and large-scale farmers in Kenya. 
19 
• 
• 
• 
• 
• 
2.5 Conceptual Framewor·k 
The literature review examined the what, how, who and why in knowledge transfer in 
agriculture, the strategy in knowledge transfer, knowledge transfer in agriculture between 
developed and developing countries, case studies of knowledge transfer in agriculture in similar 
countries, agriculture in Kenya with a bias to the vision 2030 and GOP. Based on this the 
reviewed literature, the study proposed the conceptual framework, which is a 1 to 1 relationship 
within the variables, is shown in Figure 2.2 . The independent variable is knowledge gaps, 
hindrances to knowledge transfer is the moderating variable, while successful knowledge transfer 
is the dependent variable. 
Hindr·ances to Knowledge Transfer· 
• Risk associated with weather 
• Education level 
• Unmet needs 
• Cultural issues 
Hz 
Knowledge Gaps HJ 
Access to agrochemicals 
Access to leaning institutions Successful Knowledge Transfer· 
Access to capital Ht Indicators 
Access to skills and Increased productivity • knowledge 
• Increased product sales Access to insurance 
Figure 2.2: Conceptual model 
It was proposed that knowledge gaps have a direct causal effect on successful knowledge 
transfer, but that this effect is moderated by hindrances such as risk resulting from 
unpredictability of weather. The study proposed four sources of knowledge gaps as including: 
access to agrochemicals, access to learning institutions, access to capital, access to skills and 
knowledge, cultural issues and demographics. Successful knowledge transfer was defined by 
increased productivity and increased product sales of the small scale farmers 
20 
2.6 Research Hypothesis 
From the literature review examined, the conceptual framework was fonnulated and the 
following hypothesis formulated : 
H1: There is a significant relationship between knowledge gaps and successful knowledge 
transfer 
H2 : Hindrance has a significant effect on successful knowledge transfer 
H3 : The relationship between knowledge gaps and successful knowledge transfer is 
moderated by hindrance 
2.7 Summary 
Chapter two examined relevant literature review with a view to gain more understanding on the 
subject area. It examined the theoretical and empirical review and from this, the conceptual 
framework was formulated and hypotheses extracted. From the research gaps highlighted, there 
was need to design the approach to collect primary data that will exhaustively answer the 
research questions . 
21 
CHAPTER THREE: RESEARCH METHODOLOGY 
3.1 Intr·oduction 
This chapter details the research methodology used in the study. The chapter presents the 
research design, the data collection instruments, target population of the study, the sampling 
method, study setting, data analysis, pre-testing, validity test the and research ethics. 
To answer the research questions formulated and to affirm or reject the hypotheses, it was 
impmtant to define and design a detailed plan for obtaining information, which could smooth the 
progress for the resolution of the problem . Central to this was the development of an effective 
research design . 
3.2 Research Design 
Based on the nature of the study, the research objectives and the target population, exploratory 
and descriptive research design was used. Exploratory approach allowed the researcher to 
interview the farmers and gain more insight of the prevailing of knowledge gaps. While the 
descriptive design permitted accurate estimation of the population parameters and subsequent 
hypotheses testing leading to generalization. This approach allowed for the use of quantitative 
approach in data analysis, and enabled explanation of how knowledge can be transferred to and 
among the farmers, pmticularly the small scale farmers. 
The survey method was used and a questionnaire administered face-to-face to the respondents 
and farmers. The survey method was advantageous as it allowed for the comprehensive 
accumulation and aggregation of statistical data that was easy to analyze and interpret. 
In order to measure the knowledge gap and hindrances to knowledge transfer, a structured 
questionnaire was administered face-to-face which sought to solicit their level of knowledge. 
The questionnaire allowed them to list the predictors of knowledge transfer. This was then 
analyzed and repmted descriptively. 
22 
3.3 Target Population 
The target respondents of the study were the large scale farmers', small-scale farmers, 
stakeholders in the agricultural sector and the Government. The study focused on farmers in 
Nanyuki, Naivasha, Thika and Ruiru due to the volume of agribusiness and agriculture practiced 
there by both large and small scale farmers . According to the Ministry of Agriculture (2012), the 
population of the farmers, both large and small scale in the areas covered by the study was 
approximately 500 to 1 000 farmers. 
3.4 Sample and Sampling Method 
In a study by Orodho and Kombo (2002), sampling was described as the procedure used to 
gather things, places or people to study, the study used the non-probability sampling technique. 
The sampling technique employed in this study was a combination of cluster sampling followed 
by convenience sampling method . 
The farmers were clustered into three geographic areas (county) and from each county 
respondent were picked by using convenience sampling method. Convenience sampling was 
prefe.rred because the farmers could not be found in one central point; instead the questionnaires 
had to be administered to them in the fields where they were working. In this method, 
researchers have the freedom to choose whomever they find, hence the name "convenience." 
Although a convenience sample has no controls to ensure precision, it is still useful for testing 
ideas or gaining ideas about a subject of interest (Cooper and Schindler, 2008). A total of 60 
respondents formed the sample, following this rationale, as presented in the table 3.1 next. 
The rationale for the counties and numbers were purposive. The economic Survey (2009) posit 
that the large scale farmers are mostly from Laikipia, Kiambu and Nakuru Counties and hence by 
purposively selecting these counties, we can examine the knowledge transfer between these large 
scale farmers and the small scale farmers in these counties. Purposive Sampling was cited by 
Cooper and Schindler (2008) as advantageous and most appropriate because of the judgmental 
decision that it would be universe hence the rationale for the choice. 
23 
Table 3.1: Sample Size 
Cluster Number of respondents 
County District Large Scale Small Scale Total Fa1·mers Percentage (%) 
Farmers Farmers of sample 
Laikipia Nanyuki town 10 10 20 33.3% 
Kiambu Thika I Ruiru area 10 10 20 33.3% 
Nakuru Naivasha 10 10 20 33.3% 
TOTAL 30 30 60 100% 
3.5 Data Collection 
The study used a questionnaire as the main instrument to collect primary data from the 
respondents. Quantitative data was collected that was related to the variables in the conceptual 
framework . Questionnaires were considered appropriate given the research objectives, there cost 
effectiveness (Mellenbergh, 2008), respondents are given time to fill-in the questionnaires, do 
not require as much effort as for the verbal and telephone interviews and are easier to classify the 
data given in the closed ended questions making it easier to compile data. The questionnaire 
consisted of a demographic section that allowed profiling the respondents and categorizing them 
for cross analysis . To recruit the respondents, the list of large and SSFs that represent the 
agricultural sector in Kenya was selected randomly from the official telephone directory, 2012. 
The selected respondents were then called and appointments made. In order to collect accurate 
information, the respondents were explained to who large scale farmers and small scale farmers 
were. Pictures were used and in addition, they were explained to as those who use modern large 
scale equipment for farm work like tractors, versus those who use simple hoes for digging and 
cannot use tractors. 
For data integrity, they were first greeted; a rapp011 created and then briefed on the purpose of 
the research, the duration of the interview and then the questionnaire was administered. The 
interview was conducted in a quiet, comf011able place that the respondent perceived suitable, 
without disrupting his or her normal chores. Quality checks were also being put in place to 
ensure accuracy of the data. The data was counterchecked and incomplete or incomprehensible 
questionnaires reconfirmed with the respondents. 
24 
3.6 Validity 
Validity was cited as the degree by which the sample of the test items represents the content the 
test is designed to measure (Rousson, Gasser and Seifer, 2002). The study undertook two validity 
tests. Face validity and internal construct validity. The face validity test was undertaken by 
administering the questionnaire to I 0 farmers prior to field work. Their feedback was used to 
improve the questionnaire. 
To establish the internal construct validity of the study, the construct validity test was used as 
reported under the data analysis section . Internal construct validity is concerned with the extent 
to which a particular measure relates to other measures in a way that is consistent with 
theoretically derived hypotheses concerning the concept. Construct validity defines how well a 
test or experiment measures up to its claims (Mugenda, 2008) . 
3.7 Reliability 
The data collected using the questionnaire was tested for reliability. Reliability is a measure of 
degree to which research instrument yields consistent results or data after repeated trials. The 
instrument in Appendix III was subjected to Cronbach's alpha test and the 81 item instrument 
resulted a.= 0.861 , meaning the questionnaire was very reliable . 
3.8 Data Analysis 
The data collected was analyzed using Ms Excel and Scientific Package for Social Sciences 
(SPSS) . Data analysis was unde1taken using two statistical tests: descriptive statistics and 
regression analysis. The independent variable (knowledge gaps) was regressed against the 
dependent variable (successful knowledge transfer) . Then the hindrances to knowledge transfer 
were introduced in the relationship to examine its moderating effect. The linear regression model 
adopted took the mathematical form below; 
25 
From the equation, SKT = successful knowledge transfer, a.O = constant, !31 , !32 ,(33 . ... (3; 
coefficient of the independent variables X1, X2, X3 . ... X;, respectively and Eo= error term. 
Here; 
X1 =Access to agrochemicals 
X2 =Access to leaning institutions 
XJ =Access to capital 
X4 =Access to skills and knowledge 
X5 =Access to insurance 
These variables were operationalized and analyzed by the type of variable. The nominal 
variables, access to learning institutions, access to skills and knowledge, the ordinal variables 
access to capital, insurance, were applied various statistical statistics. Inferential statistics and 
differential statistics were employed for their means, mode and medians. These were then 
repot1ed in graphical format and presented in the findings section . 
Before the data was analyzed, data checks and data cleaning were exhaustively carried out. Any 
unusual data check repot1s were re-confirmed and where need arouse, the respondent was called 
to countercheck the data. The treatment for missing data used in the study was, exclude cases 
pair wise method. The analyzed data was then used to prepare the final repm1 and the results and 
findings presented in graphs and chat1 output. 
For rigour in the analysis, and based on the nature of the study, there was need to run a 
regression model and analyse the analysis of variance (ANOVA) in order to determine which 
factor fosters transfer of knowledge and hindrance. 
3.9 Summat·y 
Defining and designing a proper approach for data collection and analysis was important, as the 
results, conclusions and recommendations were dependent on this. The research design presented 
the approach in data collection and the draft questionnaire is presented in Appendix III. 
26 
CHAPTER FOUR: RESULTS AND FINDINGS 
4.1 Introduction 
In this chapter data is analyzed, presented and the interpretation made of the findings . The data 
presented includes the response rate, the demographic information and the findings of the 
research . Descriptive statistics is used to explain the variables and correlate bivariate 
relationships; factor analysis is used to reduce the many variables associated with knowledge 
gaps into a few factors that can be easily explained. Regression analysis is used to determine the 
associated strength between the dependent and independent variables and while at the same time 
indicating the predictive power of the emerging model. Linear regression, correlation and cross 
tabulation were applied in order to relate variables and provide more in-depth insights for the 
correct conclusion. 
4.2 Response Rate 
Table 4.1 shows that a total of 60 questionnaires were administered to the farmers, out of which 
45 were returned and found usable resulting in a sample size of n = 45 and a 75% response rate 
which was considered very good for data analysis. According to Mugenda and Mugenda (2003) 
a 50% response rate is adequate, 60% good and above 70% rated very good. This implied that 
basing on this asse1tion; the response rate in this case of 100% was very good . 
4.3 Sample Pr·ofile 
This section presents the respondent profile and descriptive statistics of the study variables. The 
demographic information collected was on; the gender, age, the head of household, his/her 
primary economic activities, farming activities, and level of education, the farm owners, number 
of household members involved in agricultural activities, those attending school, assisting in the 
farm and opting to relocate to the city . 
27 
l'al....*l.l ru 
K.iambu 
Laikipia 
Embu 
Nyaud.:trua 
!\ Ien.1 
Respondents in the respective county 
---------------· 25.0'?/0 
------------· 20.0% 
10.0% 
--- 5.0% 
--- 5.0% 
Nairobi City - 2.5% 
Nyeri - 2 . 5 ~'o 
30.0% 
0.0% 5.0% 10.0"/o 15.0"/o 20.0"/o 25.0"/o 30.0"/o 35.0% 
Figure 4.1: Respondents in the respective county 
The study area sampled three counties countrywide. Figure 4.1 shows a majority, 30%, were 
from Nakuru County since Nakuru heavily relies on agriculture and has horticulture and large 
scale farming. Nakuru was also part of the former white highlands and still have a good number 
of large farms under whites. A good reason to select the counties ofNakuru, Laikipia and Embu 
was that they as well have a good combination of both large scale and small scale farmers. These 
reasons made these areas appropriate for the study. A key finding was that there is agricultural 
activity in each county, namely large scale farming and small-scale farming as well as 
agricultural oriented organizations like NGOs and offices for multinational agricultural 
companies . The findings were therefore comparable to the Ministry of Agriculture, 2012. 
According to Haggblade (2011), Africa's agribusinesses stand poised for exceptionally rapid 
growth over the coming 40 years. The author noted that accelerating urbanization means that city 
populations will surpass rural inhabitants and the resulting urban population gains, coupled with 
growing income and changing tastes, will propel rapid growth in the marketed share of Africa' s 
agricultural production. The author notes that majority of the population in the city are fairly 
young as compared to those remaining to farm in the large scale farms. 
From Table 4.2, majority, 80% of the respondents were male. The main finding was that the 
nature of work and task involved was tough and thus most that opted to do farming were male. 
There was a similar finding in the gender of the household head. 70 % of the respondents 
28 
affirmed that the male is the household head while 12 % affirmed it was the female. Less than a 
third, 15 %, stated that they were equal partners as household head. These findings were 
consistent with the works of Haggblade et al., (1989) who noted that gender roles have changed 
over time as commercialization accelerates . 
Gender of the re~ltOD«lents 
Female 
Figure 4.2: Gender of the respondents 
From the demographic profile, majority of the males were the head of household, aged 31 to 40 
years and primary activity was agriculture. Culture and the socio cultural influence play a great 
role in farming. According to Onyekwere et al., (1989) and Nweke et al., (2002), from the 
African tradition, men own farms and lead in the agricultural activities. Traditionally, men are 
the household heads and family spokespersons. This is also a finding in the study. 
29 
12% 
Equal partners 
Head of the Household 
Didntwant 
Figure 4.3: Head of the Household 
Male 
70% 
Table 4.2 also shows that a majority of the respondents that were engaging in agriculture as an 
economic activity were aged between 31 to 40 years. Figure 4.4 shows that close to half of 
them, 45% were in the age group of 31 to 40 years. This could be attributed to the fact that at the 
ages of between 31 to 40 years, the respondents were settled withwhat they want to do in life. 
13.25 
Years 
Age ot' the re1!-]londents 
22.5 % 22. .5 % 
26- 30 
Ye<lrS 
31-35 36- 40 
Ye<1rs 
41-50 Above 50 Didllw<J nl 
Years Ye~ r.s years revea l eel 
Figure 4.4: Age of the Respondents 
Another explanation could be that agriculture as an activity has started attracting younger 
population after university graduation. In some cases, these respondents are employed and it is 
clear that the employers prefer younger energetic workforce. 
30 
J>rim::~ry acthrify of the Household lu;.ul 
• Rt'fus -·d t.-. 
Sai<Jr ied 
Em plovees 
8% 
disdos.:: 
10% 
Figure 4.5: Primary Activity ofthe Household head 
ult re 
From the general objective of the study, the study targeted farmers and indeed, most, 82% were 
in agriculture as their primary activity (Figure 4.5) with over eleven years of farming experience, 
and most, 35% having being educated to tertiary I college level as shown in Figure 4.5. 
GO .O 
50 .0 
·'1 0.0 
:,10 .0 
20 .0 
·•o .o 
0.0 
The llout.:chold hcad•t.:t'at·tnlllg CXIJt<CI'Iencc 
s _o 'Yu 
l c£. ~ l ha n 1 
'/<~.-,,-
2 - 6 Ye a rs 
V L~~ r~. 
Figure 4.6: The Household Head's farming experience 
31 
2.5 ·.:.-~ 
Refu ~cd lo 
d i ,;.-: J .-,~,.. 
LeYel of Education of tbe Household bead 
35.0 % 
Prim ary 5-8 Seconda ry Te rtiary Training Unive rsity 
I College 
Figure 4.7: Level of education of the Household head 
2.5 % 
Re fused to 
answe r 
Most of the farmers as shown in Table 4.3 had studied agriculture while at college, implying they 
had the basic skills of the trade. 
Table 4.3: What Did you Study in College 
Cumulative 
Study area Frequency Percent Percent 
Agriculture 20 50.0 52.5 
Information technology 2 5.0 62.5 
Accounting 1 2.5 2.5 
Electrical 1 2.5 55.0 
Teaching 1 2.5 57.5 
Numerous units 
(Accounting/ Animal 1 2.5 65.0 
husbandry/ Agriculture) 
No response 
14 35.0 100.0 
Total 40 100.0 
Only 47.5 % of the respondents owned the farms where they engaged in farming activities . It 
was observed that most of the large scale farms are company owned (Figure 4.8), with the 
32 
farmers residing and reporting to work daily, a finding similar to Lanjouw (2007). According to 
the author, the poverty impact of shifting nonfarm employment reveals more variability. 
The Farm owners 
Refused to 
disclose ------~=--. The farm is 
company 
owned 
42% 
Farm Owner 
48% 
5% 
Figure 4.8: The farm owners 
relative 
5% 
Figure 4.9 shows that 74.8% of the workforce were aged between 21-50 years and reside in the 
compound and work in the firm once a week. Similarly of those aged of21- 50 years, a majority 
lived away but assisted in the farm activities as evidenced by Figure 4.10. 
80.0 
70.0 
60.0 
50.0 
40 .0 
30.0 
20.0 
10.0 
0 .0 
Nuntbet· residing in the coiUJ)QUnd and ~1ctually 
'"orldng on the fanu at leaf)'t once a 'veek 
< 20 years 21-50 >50 years Number of 
children 
attending 
school 
Number of 
siblings 
opting to do 
farming 
Figure 4.9 : Number in working at least once a week 
33 
Number of 
siblings 
opting for 
the city 
This could be attributed to the fact that below the ages of 21 would be school going while above 
50 would be advanced in age and retreated to the farms. This also reveals the connection 
between those living in town and cities and those living in farms. Those living away could be in 
colleges and universities, or even working, but they go home most of the weekend and give some 
kind of assistance in farming. 
Table 4.4 shows that knowledge transfer from Small Scale Farmers (SSFs) to Large scale 
farmers' (LSF) is more likely to take place in terms of skills required for the appropriate 
agrochemical to use on crops (37.7%).This is followed by need to understand the market related 
information. This is interpreted to mean that skills in agrochemicals can boost output while 
market knowledge will make it possible for the SSFs to sell more. 
Table 4.4: What skills or Knowledge Can SSFs Borrow From LSFs 
Cumulative 
Skill Frequency Percent Percent 
Required agrochemicals for 15 37.5 37.5 
various crops 
Market information 8 20.0 72.5 
Research and development in new 5 12.5 85.0 
crops 
Agricultural management skills 4 10.0 95.0 
Application of agrochemicals 3 7.5 45.0 
Weather knowledge 3 7.5 52.5 
None really 2 5.0 100.0 
Total 40 100.0 
A majority (52.5%) of respondents get agricultural information by listening to radio and TV. 
While the other 32.5% source information from other sources as shown in Table 4.5. Information 
is critical for success in agri-business and entrepreneurial farmers seek information from all 
sources including the internet, professional networks and others. 
34 
Table 4.5: Do you listen to radio I TV programs in agriculture 
Cumulative 
Channel Frequency Percent Percent 
21 
Yes 
52.5 52.5 
No 
13 32.5 85.0 
No response 
6 15.0 100.0 
Total 
40 100.0 
When asked the channel listened to the most, a majority as shown in Table 4.6 indicated Citizen 
Radio. A cross tabulation of the source of information and channel of information confirms the 
same. They preferred this station because it communicates in local dialect that makes it possible 
for them to understand. Government stations (KBC) are not so popular or preferred therefore as 
presented next 
Table 4.6: Do you listen to radio I TV programs in agriculture * Radio I TV programs in 
agriculture listened to 
Cross tabulation 
Radio I TV programs in agriculture 
listened to 
Radio: 
KBC Can't recall 
Swahili Radio: fully I Not 
service Citizen applicable Total 
Yes 1 19 1 21 
Do you listen to radio I 
TV programs in No 0 0 13 13 
agriculture 
No Response 0 0 6 6 
Total 1 19 20 40 
The study sought to examine the significance of this relationship as used the Chi-Square test for 
independence of association and the output is displayed in Table 4.7 below. The resulting 
35 
Pearson Chi-Square indicates Chi-Square value (X) = 36.190, p = 0.000. This means there is 
statistical significant association between listening to radio I TV programs in agriculture and the 
Radio I TV programs in agriculture listened to . Hence farmers who listen to -radio/TV may end 
up getting more information than those who don't. 
Table 4 7· Chi-Square Tests ..
Asymp. Sig. (2-
Value Df sided) 
Pearson Chi-Square 36.190a 4 .000 
Likelihood Ratio 47.411 4 .000 
Linear-by-Linear Association 7.245 1 .007 
N ofValid Cases 40 
.. 
a. 0 cells (.0%) have expected count less than 5. The mm1mum expected count IS .15. 
4.3.1 Objective 1: Knowledge and skills gap 
A majority of the farmers (57.5%) identified doughty as a major hindrance to knowledge transfer 
as shown in Table 4.8 below. The other hindrances were floods, pests and diseases. The 
hindrance were described by the farmers as occurrences which inhibit them from enjoying the 
fruits of knowledge transfer especially when they experiment with new seeds or they try new 
technologies. 
Table 4.8: What risks have you experienced 
Risk Frequency Percent Cumulative Percent 
Droughts, dry spells 23 57.5 57.5 
Flooding 2 5.0 62.5 
Pests 9 22.5 85.0 
Diseases 3 7.5 92.5 
Figure 4.11 showed that the major challenge in adopting knowledge, from knowledge transfer is 
the change in weather patterns as stated by close to two-thirds, 70%. About 10% stated that the 
prices of recommended inputs and equipment and their current knowledge in agriculture is a 
hindrance. The policy makers need to put deliberate effort to ease access to farm inputs, 
equipments and knowledge on agrochemicals. 
36 
Hindrances in knowledge transfer 
Prices of both 
Lack of 
agriculture 
inputs 
recommended 
5% 
inputs and 
equipments 
recommended 
10% 
Present 
Knowledge in 
agriculture 
10% 
Doesn't really 
know 
5% 
The weather 
patterns e\oen 
when all 
knowledge 
acquired is 
used 
70% 
Figure 4.11: The hindrances in knowledge transfer 
Table 4.9 defines many needs that farmers have that hinder knowledge transfer. The major unmet 
need was the unaffordable prices of inputs and implements (35%) followed by market 
accessibility (27.5%). These variables serve to reduce the farmer's potentiality to gain from 
knowledge transfer. 
Table 4.9: What is the most important unmet need in your farming activities 
Cumulative 
Fr~quency Percent Percent 
Lack of Knowledge 7 17.5 17.5 
Lack of inputs (chemicals, seeds, etc) 3 7.5 25.0 
Lack of equipment 1 2.5 27.5 
Prices of inputs and equipment 14 35.0 62.5 
Market inaccessibility 11 27.5 90.0 
No response 4 10.0 100.0 
Total 40 100.0 
Figure 4.12 shows what the respondents suggested that farmers can do to reduce food shortage. 
Many (15%) suggested that access to irrigation facilities was the best option to food shortage. 
The farmers can be more productive on their farms if they sought the assistance of the 
37 
agricultural extension workers for farming knowledge, also if they attend field days usually 
organized by multinationals and other stake holders. Harvesting of rain water for simple 
irrigation, usage of simple green houses now available even for SSFs will increase the 
productivity in agriculture. 
What Farmer has done to reduce food shortage or crop fuilure 
Refused to 
disclose, 55% 
Multiple of 
solutions , 12.5 
15% 
Green Houses, 
2.5% 
Attendance of }----
field days, 7.5% 
Sought 
assistance of 
extension 
officers , 2.5% 
Figure 4.12: What Farmers can do to reduce food shortage or crop failure 
4.3.2 Objective 2: Relationship between knowledge gaps and successful knowledge 
transfer 
Knowledge transfer is prescribed to entail idea creation, sharing, evaluation, dissemination and 
adoption. Close to half of the respondents, 44%, affirm that the catalysts for knowledge transfer 
are the government, schools and colleges, media, individual effort and money. The most 
effective mediums for knowledge transfer are agricultural radio programmes followed with 
agricultural extension officers visiting the farmers for demonstrations. Multinationals and other 
organizations trading in agrochemicals also need to relax their controls on their products' 
information. The knowledge transfer implies the vehicles that drive information or sources 
through which the respondents acquire new skills. From the findings and indeed from 
Onyekwere et al., (1989) and Nweke et al., (2002), the government plays a key role in 
agriculture and knowledge transfer. This is presented in Figure 4.13 below. 
38 
Catalysts of Knowledge Transfer 
Nothing really 
5°/o 
All the above ____ , 
44% 
Figure 4.13: Catalysts ofKnowledge transfer 
Government 
8°/o 
Money 
3% 
Individual effort 
17% 
The farmers also need to put individual efforts to acquire farming knowledge - they need to 
attend field days, they need to read books, they need to educate their children who in tum will 
help in knowledge transfer. Farmers can also benefit a lot from agricultural radio programmes 
now being organized by multinationals (like Syngenta East Africa and others). So farmers' 
individual efforts remain very important, the government can only do so much. The media and 
the government can accelerate knowledge transfer in agriculture 
4.3.3 Have read or heard of Vision 2030 
The variables of interest were the productivity of the farmers based on Kenya's Vision 2030 plan 
that outlines the growth of small-scale farmers, the reduction of the productivity gap between the 
small-scale farmers and the large - scale farmers, the reduction of the knowledge and skills gap 
and the presence of a strategy and model for knowledge and skills transfer. 
From the findings, 47% (Figure 4.14) of the respondents have read or heard ofthe Kenya Vision 
2030 plan and 67.5% (Figure 4.15) affirmed that indeed, effective knowledge transfer in the 
agricultural sector would increase the productivity of the farming community. 
39 
Have read or heard of Vision 2030 
No idea------
18% 
Figure 4.14: Have read or heard of Vision 2030 
A g ree 
47% 
Figure 4.15 shows that knowledge transfer is a key pillar in the agricultural sector. Majority of 
the respondents strongly agreed effective knowledge transfer is the main means of boosting 
productivity in agriculture, especially among the SSFs. The respondents reported that unless an 
effective models for transferring the farming knowledge and skills to those who need it are 
designed then the knowledge will continue existing in books and benefiting only a few Large 
scale farmers ' and the elite farmers . 
80.0 
70.0 
60.0 
50.0 
40.0 
30.0 
20.0 
10.0 
0.0 
Would effective knowledge transfer in agriculture increase the 
productivity of small scale furmers 
?<:; 
.....-------. 
Strongly 
disagree 
?<:; 
.....-------. 
Disagree 
1 f.b 
r--1 
I I 
Neither 
disagree nor 
agree 
ov.v 
I I 
Agree 
67.5 
r-
t--
I--
I--
I--
I--
Strongly 
Agree 
Figure 4.15: Effective knowledge transfer & Productivity 
40 
The agricultural knowledge and skills gap implies that each new medium of knowledge transfer 
decreases the gap between the information rich and information poor. Differences in access to 
the mediums (like radio, television, extension workers, farmers forum), and control over their 
use, among other factors has contributed to this knowledge and skills gap. 
Would tne eap in know redee reduce if skills and 
l<nowledge was transferred from large scale to small 
scale farmers 
so_o% 
l7. S. '.~ 
A g rr.:r.: N•-•idc<J 
Figure 4.16: Gap in knowledge following knowledge transfer 
Figure 4_16 shows that if skills or knowledge is transferred to and among the farmers, both large 
and small scale farmers, the gap in the pyramid will reduce. 50% of the respondents agree that 
knowledge transfer will definitely reduce the knowledge and skills gap among the farmers. 
Small-scale farmers need new skills, research and 
development (e.g. from KARl) to boost exportation 
volumes 
Figure 4.17: Skills for farmers 
41 
A g ree 
85% 
Figure 4.17 shows that a majority (85%) of the farmers assert that indeed, large and Small-Scale 
farmers (SSFs) need new skills, new research knowledge and development, like from KARl to 
boost exportation volumes, hence if the hindrances in knowledge and skills transfer to and 
among the farmers are overcome, exportation volumes for Kenya will increase. Knowledge here 
includes market infmmation, quality information as well as pricing information. Information on 
crops that are more weather resistant, planting methods, and crop diseases control etc need to be 
disseminated to the farming community at the right time. 
Figure 4.18 shows that only 45% have bought or read books in agriculture. An intervention 
model needs to encompass material for knowledge dissemination in order to improve the 
productivity of small-scale farmers in Kenya. Knowledge transfer seeks to organize, create, 
capture or distribute knowledge and ensure its availability for future users. 
Knowledge Transfer 
Refused to 
disclose 
20% 
Has never 
bought I read a 
book in 
agriculture 
35% 
Figure 4.18: Knowledge transfer through reading 
42 
Has bought I 
read a book in 
agriculture 
45% 
4.4 Testing the Research Hypotheses 
In reference to the conceptual model in Figure 2.2 and the proposed hypotheses, this section 
presents the results of the hypotheses test. The study assumed a linear relationship between the 
predictors and dependent variables. Liner regression was used to examine the relationships and 
predictive ability of the independent variables. The mathematical form of the regression model 
was, SKT = ao + [31X2 +[32X2 +[33X3 . ... [)iXi +Eo as earlier described . 
4.4.1 Relationship between Knowledge Gaps and Knowledge Tr·ansfet· 
While the previous descriptive analysis described the variables in tenns of their frequencies, a 
determination of the magnitude of their impact was sought. The first research hypothesis was: 
H,: There is a significant relationsh;p between knowledge gaps and successful knowledge 
transfer 
A linear regression was used to enter the predictor variables in steps. The stepwise regression 
method resulted in four models as shown in Table 4.10 below. The one way analysis of variance 
(ANOVA) test results were used to assess the significance of the regression. 
Table 4.10: ANOVA 1 
Sum of Mean 
Model Squares df Square F Sig. 
I Regression 818 .695 2 409.348 3.562 .038b 
Residual 4252.068 37 114.921 
Total 5070.763 39 
2 Regression 2595 .174 3 865.058 12.580 .oooc 
Residual 2475 .589 36 68.766 
Total 5070.763 39 
3 Regression 4059 .656 4 I 014 .914 35.132 .oooct 
Residual 1011.107 35 28.889 
Total 5070.763 39 
4 Regression 4621.267 9 513.474 34.270 .oooe 
Residual 449.496 30 14.983 
Total 5070.763 39 
a. Dependent Variable: Successful Knowledge Transfer 
43 
The ANOV A Table 4.1 0 shows that model 1, 2, 3 and 4 were all significant (p ::; 0.05) m 
explaining the relationship between knowledge gaps and successful knowledge transfer. 
Table 4.12 presents the model summary reflective of the four models generated. The coefficient 
of determination R square (12 ) column shows model 4 provides the best fit (12 = 0.911 ). The study 
therefore proceeds to interpret the coefficients related to model 4 in Table 4.12 below. 
Table 4.11: Coefficients of Model 4 
95.0% 
Unstandardized Standardized Confidence 
Coefficients Coefficients Interval for B 
Std. Lower Upper 
Model B Error Beta t Sig. Bound Bound 
4 (Constant) 
19.002 5.347 3.554 .001 8.082 29.921 
What is your highest level of education 
-2.555 1.194 -.213 - .041 -4.994 -.117 2. 140 
What did you study in college 
-.041 .023 -.172 - .050 -.089 .006 1.781 
Where do you sell your crops 
.023 .059 .030 .384 .703 -.097 .142 
What skills or knowledge can SSFs borrow from 
LSFs .675 .318 1.260 2.120 .042 .025 1.325 
Have you ever bought I read a book in 
.025 .020 .086 1.225 .230 -.017 .066 agricu lture 
Do you listen to radio I TV programs in 
.088 .023 .273 3.840 .001 .041 .135 agriculture 
Have you heard about KARl , coffee research 
foundation or any other research inst itute and 
.152 .042 .349 3.6 18 .001 .066 .238 
what they do? 
Have you heard about Syngeta EA, Du Pont, 
Osho chemicals, Am iran , Bayer EA, SeedCo., 
Twiga Chemicals, Murphy chemicals, Kenya 
-.582 .453 -1.065 - .209 -1.507 .344 
Seeds Company, Monsato, Pioneer Seed? 1.284 
What in your opinion facilitates knowledge 
transfer in Kenya .087 .277 .160 .3 15 .755 -.478 .652 
a. Dependent Yanable: Successfi.d Knowledge Transfer 
Table 4.11 shows that under model 4, there are five variables with significant p values (p::; 0.05). 
These are: What is your highest level of education (p=0.041 ), what did you study in college 
(p=0.050), what skills or knowledge can SSFs borrow from LSFs (p=0.042), do you listen to 
44 
radio I TV programs in agriculture (p=O.OO I), have you heard about KARl, coffee research 
foundation or any other research institute and what they do (p=O. 00 1 ). 
Table 4.12:Model Summary 
Std. Change Statistics 
Error of 
R Adjusted the R Square F Sig. F Durbin-
Model R Square R Square Estimate Change Change dil df2 Change Watson 
I .402" .161 .116 10 .720 11 .161 3.562 2 37 .038 
2 .7J5b .512 .471 8.29255 .350 25.834 I 36 .000 
~ 
.895c .801 .778 5.3 7483 .289 50.694 I 35 .000 .) 
4 .955d .9 11 .885 3.87081 . Ill 7.497 5 30 .000 1.797 
e. Dependent Vanable : Successful Knowledge Transfer 
4.4.2 Relationship between Hindr·ances and Knowledge Tr·ansfer 
The second research hypothesis was: 
H2: Hindrance has a significant effect on successjitl knowledge transfer 
The ANOVA Table 4.13 shows that model 1, model 2 and model 3 are all significant (p::; 0.05) 
in explaining the relationship between hindrance and successful knowledge transfer. 
Table 4.13: ANOVA of Model 1, 2 & 3 
Sum of 
Model Squares df Mean Square F Sig. 
I Regression 1733.016 I 1733.016 19.730 .ooob 
Residual 3337.747 38 87.835 
Total 5070.763 39 
2 Regression 1836.331 2 918.165 10.503 .oooc 
Residual 3234.433 37 87.417 
Total 5070.763 39 
3 Regression 1836.519 3 612.173 6.814 .OOld 
Residual 3234.244 36 89.840 
Total 5070.763 39 
a. Dependent Vanable: Successful Knowledge Transfer 
45 
The model summary in Table 4.14 provides a description of the three models generated. The 
significance column shows that only model 1 and model 2 are significant. Model 2 has a higher 
t2 = 0.362 as compared with model 1 (r2 = 0.342). Because model 2 has a better fit, the study 
proceeds to interpret the coefficients related to model 2 in Table 4.14 below. 
Table 4 14·Model Summary of Hindrances 
Std. Error Chanoe Statistics 
R Adj usted of the R Square Sig. F Durbin-
Model R Square R Square Estimate Change F Change dfl df2 Change Watson 
I .585• .342 .324 9.37206 .342 19.730 I 38 .000 
2 .602b .362 .328 9.34971 .020 1.182 I 37 .028 
~ 
.602c .362 .309 9.47840 .000 0.002 I 36 .964 1.8245292 .J 
a. Dependent Vanable: Successfl.tl Knowledge Transfer 
Table 4.15 shows that under model 2, one predictor (What would you say are the maJOr 
challenges in your farming operations) was significant with p value= 0.000. The study therefore 
fails to reject H2 at 95% C.I and concludes that hindrance has a statistically significant effect on 
successful knowledge transfer. 
Table 4 15: Coefficients 
95 .0% 
Unstandardized Standardized Confidence 
Coefficients Coefficients Interval for B 
Std. Lower Upper 
Model B Error Beta t Sio. Bound Bound 
(Constant) 9.524 1.550 6.146 .000 6.387 12.661 
I 
What would you say are the major challenges in 0.3!0 0.070 .585 4.442 .000 0.169 .451 your farming operations 
(Constant) 10.067 1.625 6.196 .000 6.775 13.360 
2 What would you say are the major challenges in .380 .095 .717 4.00! .000 . 188 .572 your farming operations 
What is the most important unmet need in your 
-.077 .070 -0.195 - .284 -.219 0.066 farming activities 1.087 
(Constant) 10.054 1.672 6.0!3 .000 6.663 13.445 
What would you say are the major challenges in 
.376 .!36 .709 2.755 .009 .099 .652 your farming operations 
3 What is the most important unmet need in your 
-.076 .071 -. 195 - .29 1 -.22! .068 farming activities 1.071 
What risks have you experienced .004 .098 0 .010 0.046 .964 -0 .194 .203 
a. Dependent Vanable. Successflli Knowledge Transfer 
46 
4.4.3 The Moderating Effect of Hindrances on the Relationship Between Knowledge Gaps 
and Knowledge Transfer 
After establishing the existence of a significant relationships between knowledge gaps and 
successful knowledge transfer (HI), and also that hindrances significantly influence successful 
knowledge transfer (H2), the study sought to examine the mediating effect of hindrance by 
testing the third research hypothesis: 
H3: The relationship between knowledge gaps and successful knowledge transfer is 
moderated by hindrance 
Using stepwise regression process, the five variables that were significant in the relationship 
between knowledge gaps and successful knowledge transfer (HJ) were entered leading to 
generation of Model 1 in Tables 4 .16, Table 4.17 and Table 4.18. The variables that define 
hindrance were then entered step by step leading to the generation of the other models 2, 3 and 4. 
The ANOV A Table 4.16 shows that model 1, model 2, model 3 and model 4 are all significant (p 
::; 0.05) in explaining the relationship between hindrance and successful knowledge transfer. 
Table 4.16:ANOV A Results of the Moderation Effect 
Mean 
Model Sum of Squares df Square F Sig. 
I Regression 4565.869 5 913.174 61.494 .ooob 
Residual 504.894 34 14.850 
Total 5070.763 39 
2 Regression 4568.319 6 761.387 50.007 .oooc 
Residual 502.444 11 15.226 .).) 
Total 5070.763 39 
3 Regression 4586.246 7 655 . 178 43 .271 .oood 
Residual 484.517 32 15.141 
Total 5070.763 39 
4 Regression 4586.381 8 573.298 36.690 .oooe 
Residual 484.382 31 15.625 
Total 5070.763 39 
a. Dependent Vanable: Successful Knowledge Transfer 
47 
The model summary in Table 4.18 provides a description of the four models. The significance 
column shows that only model 1 is significant with a p = 0.000. Models 2, 3 and 4 all were 
representing variables that defined hindrances were not significant. The study therefore fails to 
accept H3 which means that hindrances do not moderate the relationship between knowledge 
gaps and successful knowledge transfer. Model 1 is therefore adopted in this study as providing 
the best description of the relationship between the variables and is interpreted as follows. 
Table 4.17: Coefficients of the Moderation Effect 
95.0% 
Unstandardized Standardized Confidence 
Coefficients Coefficients Interval for B 
Std. Lower Upper 
Model B Error Beta t Sig. Bound Bound 
I (Constant) 21.609 4.898 4.412 .000 11.655 31.563 
-What is your highest level of education -3.195 1.085 -.266 2.945 .006 -5 .399 -.990 
-What did you study in college -.058 .021 -.241 2.812 .008 -.100 -.016 
What skills or knowledge can SSFs 
.2 15 .052 .401 4. 124 .000 .109 .321 borrow from LSFs 
Do you listen to radio I TV programs in 
.099 .021 .306 4.694 .000 .056 .14 1 
agriculture 
Have you heard about KARl, coffee 
research foundation or any other .147 .04 1 .338 3.567 .001 .063 .231 
research institute and what they do? 
a. Dependent Yanable: Successful Knowledge Transfer 
4.4.4 Evaluating the Model 
Following the outcome above, model I (one) in Table 4.18 is evaluated. Model I has a coefficient 
of determination (R2) = 0.900. This means 90.0% of the variance in the dependent variable 
(successful knowledge transfer) is explained by five independent variables (level of education of 
the farmers, what the farmer studied in college, the type of skills that small farmers can borrow 
from the large farmers, access to agricultural information from radio and TV and access to 
research institutes like KARl). The model provides a very strong fit, implying the five variables 
are identified as the largest areas of knowledge gaps amongst small scale entrepreneurial farmers 
in Kenya. 
48 
Table 4.18 Model Summary of the Moderation Effect 
Std. Chan~ e Statistics 
Error of 
R Adjusted the R Square F Sig. F Durbin-
Model R Square R Square Estimate Change Change dfl df2 Change Watson 
1 .949" .900 .886 3.85355 .900 61.494 5 34 .000 
2 .949b .901 .883 3.90200 .000 .161 I ~~ .691 .).) 
3 .951 ° .904 .884 3.89117 .004 1.184 I 32 .285 
4 .951 d .904 .880 3.95288 .000 .009 I 31 .927 1.964 
e. Dependent Vanable: Successful Knowledge Transfer 
The relationship between the variables presented in coefficients Table 4 .17 and the model 
summary Table 4.18, led to the derivation of a linear regression model of the form : SKT = ao + 
~~X2 +~2X2 +~3X3 . .. -~iXi +Eo, as earlier proposed. The resulting model was; 
SKT = 21.609- 3.195E - 0.058C+0.125S+0.99I+0.147R 
n = 60 
12 = 0.900 
Where: SKT = Successful knowledge transfer 
E =Education level of the farmers 
C =College training 
S = Skills transfer from LSFs to small scale farmers 
= Information on agriculture accessed by radio and TV 
R =Research Centers accessibility 
The standardized coefficients column Table 4.18 shows that, skills transfer from LSFs to SSFs 
had the highest P = 0.401. Skills transfer from LSFs to SSFs therefore had the greatest impact 
factor on successful knowledge transfer. This meant that successful skill transfer depended on 
skills transfer from LSFs to SSFs 40.1% of the times. The regression model shows a positive 
relationship between skills transfer from LSFs to SSFs and successful skill transfer. This meant 
that the more the SSFs acquired skills from the LSFs, the more successful the knowledge 
transfer. 
49 
4.5 Summuy 
The data collected was profiled, analyzed and presented . Chapter four provided descriptive 
statistics of the sampled data, examined relationship amongst variables, tested research 
hypotheses and determined the predictive effect of the independent variable (knowledge transfer) 
on the dependent variable (successful knowledge transfer). 
50 
CHAPTER FIVE: DISCUSSION 
5.1 Introduction 
In this chapter, a discussion of the major results is presented. Initially the respondent profile with 
significant implications to the study is discussed, followed by a discussion relating to the three 
research objectives . Using interviews, exploratory data was gathered and this data is infused in 
the discussion in this chapter. This study sought to determine the skills and knowledge fanners 
require in order to run their farms as business enterprises, identify knowledge and skills gap 
among the small and large-scale farmers, determine the hindrances in knowledge transfer, and 
establish an appropriate model for knowledge and skills transfer that will allow SSFs acquire the 
pre-requisite knowledge and skills for operating an agricultural enterprise. 
5.2 Respondent Pr·ofile 
The demographic profile shows that most of the farmers were of the male gender, indicative of 
the labor intensive nature of farming that makes it more attractive to the male gender. It was 
noted that a majority of the heads of family households happen to be men and that most of the 
farmers who own land were men. While this is consistent with the African customs where men 
own almost all the productive resources in an African set up, this meant the female gender 
played an insignificant role in farming decision making and that cultural factors in such 
circumstances acted as a deterrence to knowledge and skills transfer. This observation was 
consistent to the works of Onyekwere et al., ( 1989) and also Nweke et al., (2002) who 
acknowledged men have assumed an increasingly impmtant role in agriculture processing and 
distribution system, in Africa. 
The study established that a majority of those involved in farming were in the age bracket of 31-
40 years , indicating youthfulness of the agricultural entrepreneurs. Few of the younger 
generation and even fewer of the older generation were involved in farming. This trend is in line 
with the observation that agriculture is labor intensive and requires energetic individuals. 
51 
Majority, 74.8 % of those involved in farming reside in the compound and work in the farm at 
least once a week, with a similar age group of 21 -50 years living away (non-resident) but 
assisting in the farm activities. This meant the respondents were to a large extent fully employed 
in agriculture . Hence confirming the trend earlier presented in Table 2.3, which shows an 
increase in number of wage rate for those employed in Agriculture overtime as capture in the 
Economic Survey (2009) . 
Over 60% of the households had nonresident members (family members living in schools, 
colleges and university most of the time) aged below 20 years, while the nonresident members 
met the criteria of being young and energetic, their absence most of the time meant, they were 
not accessible for knowledge transfer. This age set further indicates agricultural knowledge 
transfer would be more effective if targeted at farmer's aged 31-40 years. Most of the farmers 
had more than 1 I years of farming experience and were well educated with many having attained 
college level education. Literacy as evidenced in literature is a fundamental platform of 
facilitating knowledge transfer. 
5.3 Knowledge Gaps and Knowledge Transfer· 
The first objective that motivated this study was to determine the knowledge and skills gap 
among farmers in Kenya while the second research objective sought to determine the 
relationship between the knowledge gaps and knowledge transfer. Literature reviewed revealed 
numerous sources of knowledge and skill gaps among farmers. The sources identified as 
potential determinants of knowledge and skill transfer were structured in questionnaires and the 
resulting data analyzed. By testing hypothesis one (H 1) the study validated objective one and 
two. The study failed to reject H 1 and concluded that there were knowledge gaps in farming and 
that a statistically significant relationship exists between knowledge gaps and successful 
knowledge transfer. 
Five variables were identified as having significant effect p value::; 0.05 including. These were: 
What is your highest level of education (p=0.041 ), what did you study in college (p=0.050), what 
skills or knowledge can SSFs borrow from LSFs (p=0 .042), do you listen to radio I TV programs 
52 
in agriculture (p=O.OO 1 ), have you heard about KARl, coffee research foundation or any other 
research institute and what they do (p=O.OOl). The five are discussed below. 
The level of education of the entrepreneurial farmer significantly affected their ability to transfer 
or acquire knowledge. As mentioned in discussion on the farmers demography above, most of 
the farmers interviewed had up to college level of education and literate was touted as a critical 
ingredient of successful knowledge transfer. It is easier to train educated farmers on new 
techniques of commercial farming, efficient production methods, use of agrochemicals to boost 
profits from farming proceeds amongst others. Lack of formal education impedes knowledge 
transfer and ends up stifling the farming enterprise. Education is critical as expounded in the UK 
educational policy, which asset1ed that the economic future depended on productivity as a nation 
and that required a labour force with the skills to match the best in the world (Department for 
Education and Skills, 2006). 
The study established a positive relationship between what the farmers had studied in college and 
knowledge transfer. The descriptive frequencies had indicated that a majority of those 
interviewed had studied agriculture in college. This relationship indicates a possible existence of 
a significant correlation between the skills acquired at college level and knowledge transfer. 
College graduate with agricultural orientation are more likely to seek employment in agriculture 
and have a higher potential of appreciating knowledge transferred to them in agriculture. 
A positive significant relationship was observed between ' what skills or knowledge can SSFs 
borrow from LSFs' and knowledge transfer. Most of the LSFs are either well established 
organizations with formal structures and technically trained employees with vast skills in 
agriculture. It was observed that interaction between the small scale farmers and the LSFs, 
facilitated knowledge transfer to a large extent. From the findings of the study, the productivity 
ofthe SSFs can be boosted through knowledge and skills transfer. The respondents, who operate 
SSFs, asset1ed that their key requirements are access to agrochemicals and knowledge on 
application of the same. This could be attributed to the fact that the agrochemicals are expensive 
and rate of crop failure is high due to pests and diseases and requires specific training on 
application of agrochemicals . Interaction of the SSFs and well established LSFs is therefore 
53 
more likely to increase knowledge transfer resulting in a more entrepreneurial farmer, especially 
the SSF. 
Modern farmers who operate successful business use modern techniques. The study confirmed 
that the most sought after knowledge transfer from LSFs to SSFs was how to use agrochemicals 
for various crops. Proper use of agrochemicals results in increased productivity of the farming 
activity. SSFs also sought market information . Successful farmers are those able to sell their 
output to not only the local market but even foreign markets. This is confirmed in a repmt 
documented in the Economic Survey (2009) that exhibits an increasing trend of expmting farm 
produce. The study established that SSFs also seek a symbiotic relationship with LSFs in order to 
acquire knowledge about contemporary research indicating new crops or new seedlings in the 
market. An appreciation of such knowledge is likely to positively influence farm performance 
making the farming activity entrepreneurial in scope. 
A strong positive relationship between ' do you listen to radio and TV programs in agriculture 
and knowledge transfer was observed to exist in this study. This meant that farmers sought 
information on contemporary developments in agriculture, because such information would 
boost their farm performance to a greater extent. The exploratory interviews had given a strong 
indication that the most sought after information was on the use of agrochemicals to boost farm 
performance. They also seek information on the availability of markets for their produce and 
most prevalent prices in the market. Farmers got this information by listening to radio and TV . 
A Chi-Square test for independence of association established the existence of a statistical 
significant association between listening to radio I TV programs in agriculture and the Radio I 
TV programs in agriculture listened to . This observation means that fanners who listen to 
radio/TV may end up getting more information than those who don ' t. Knowledge access and 
transfer is more likely when such information is relayed via Radio/TV than other modes of 
communication. 
Not many of the small-scale farmers have read books on agriculture or received post tettiary 
education. Their counterpatts in the large-scale farms have advanced training and the knowledge 
and skills gap could be a limiting factor on agricultural productivity and commercialization 
54 
especially of small scale farms . The mediums for knowledge transfer were determined as 
schools, the media, the government, extension officers and individual effmt. Bearing this in 
mind, a likely appropriate model for knowledge transfer needs to encompass 'advisers' of 
agricultural knowledge or extension officers. 
A significant relationship was established between 'have you heard about KARl, coffee research 
foundation or any other research institute and what they do and knowledge transfer. According to 
Kiplang'at (1999), the key to increased agricultural production ultimately lies with the Nation ' s 
ability to disseminate relevant information to the farming community to facilitate the effective 
adoption of new production techniques and knowledge transfer from large-scale farmers. In 
Kenya KARl is tasked with such responsibility and while the study established that most (90%) 
of the farmers had heard about KARl, the interviews indicated that they complained of not being 
able to access KARl. This was a strong indicator of a knowledge gap, that while KARl could be 
holding crucial knowledge on new skills on crop production, such information never reached 
farmers to help revitalize their farming into an entrepreneurial activity. 
The study noted that the most sought after knowledge from KARl by LSFs and SSFs was 
information on: Information on crops that are more weather resistant, planting methods, crop 
diseases control, market information, quality information and pricing information. The study 
observed that such information from KARl would boost production and increase the volume for 
local and international sales making farmers gain from the entrepreneurial ventures of agri-
business. Hence if the transfer of knowledge and skills from KARl to farmers is enhanced, the 
volume of trade, patticularly expmt earnings from agriculture could be increased leading to 
economic growth as shown in Table 2.4. 
5.4 Moderating Effect ofHindt·ance (Integrated model) 
Third research objective was to determine the moderating effect of hindrances on the relationship 
between knowledge gaps and successful knowledge transfer in the agricultural sector in Kenya. 
This was restated as hypotheses three (H3) and tested. To test for the moderating effect, the study 
developed three steps. First an examination of the existence of a significant relationship between 
knowledge gaps and successful knowledge transfer (Ht), followed by an examination of the 
55 
existence of a significant relationship between hindrance and successful knowledge transfer (H2) 
and last an examination of the existence of a significant relationship between knowledge gaps 
and successful knowledge transfer after integrating hindrance as a moderating variable. 
There was a significant relationship between knowledge gaps and successful knowledge transfer 
and H1 was validated . There was a significant relationship between hindrance and successful 
knowledge transfer and H2 was validated. There was no statistically significant relationship 
between hindrance and successful knowledge transfer and H3 was not accepted. The study 
however noted that while hindrance played no significant role in moderating the relationship 
between knowledge and knowledge transfer, hindrance significantly related with knowledge 
transfer. 
The concept of hindrance was coined in the study to refer to parameters that had a strong 
influence on knowledge transfer but which the farmers/researcher had no control over. The 
variables that defined hindrance included: risk associated with weather, cultural association and 
farmers' unmet need. 
Weather was identified as the most critical challenge in adopting knowledge, from knowledge 
transfer, with up to 70% of the respondents casting blame on the unpredictability of weather for 
their crop failure . A further 57.5% of the farmers mentioned that drought related to prolonged 
dry spells negatively influenced their eff01t of adopting new knowledge. This variable was 
identified in Economic Survey (2009) as a major challenge to growth in the agricultural sector. 
Cultural association was a conceptualized as encompassing inherent cultural practices that define 
the behavior of a people. In this study culture was examined from the perspective of: land 
ownership, gender roles, primary economic activity ofthe respondent, household heads, number 
of household members involved in farming and key agricultural activity. The study confirmed 
that because of African cultural practices, land ownership was to a great extent under control of 
the male gender. That because of the nature of farming the female gender played a less 
significant role in farming activities . The primary economic activity of those surveyed was 
agriculture, meaning there was need to transfer knowledge for their farming activities to prosper 
and become entrepreneurial. 
56 
The study observed that the farmers had numerous unmet needs. The major unmet needs were 
the unaffordable prices of inputs and implements, market accessibility, poverty and land size. 
Pove1ty lead to lack of equipment, insufficient resources to purchase fertilizers and inputs, and 
illiteracy among the small-scale farmers were determined as key hindrances to knowledge and 
skills transfer. While the large-scale farmers invest heavily on agricultural inputs and 
equipments, a different trend is witnessed among the small-scale farmers. The sizes of the farms 
of the small-scale farmers were on average between 2 to I 0 acres whilst the LSFs possess on 
average between 100 to 200 acres of land. The existence of these unmet needs reduced the 
farmer's potentiality to gain from knowledge transfer. 
5.5 Chapter Summary 
This chapter broadly discussed that made significant contribution to the study. The chapters 
explained how the three research objectives were met and the extent of attainment. From the 
discussion, the study deduced that knowledge gaps exist and they influence knowledge transfer 
and entrepreneurship in Kenya's agricultural sector, especially among the small scale farmers . 
57 
CHAPTER SIX: CONCLUSION AND RECOMMENDATION 
6.1 Conclusion 
This study concludes that there is a significant relationship between the independent (knowledge 
gaps) and dependent variable (knowledge transfer). Knowledge gaps affect knowledge transfer to 
a great extent. The most significant predictors of knowledge transfer according to their impact 
factor were the following five : skills transfer from the large scale farmers to small scale farmers 
(0.40 1 ), access to research centers (KARl) and extension service providers like Syngeta EA, 
Osha chemicals, Amiran, Bayer EA, Twiga Chemicals, Kenya Seeds Company and others 
(0.338), access to information on agriculture relayed via TV and radio (0.306), education of the 
farmers (0.266) and the courses pursued at college level (0.241 ). 
From the findings , it was deduced that the likely appropriate model for knowledge transfer will 
involve the use of both formal and informal learning institutions as mediums for knowledge 
transfer. The likely appropriate model will entail having the actors interact and share best 
practice 
Agriculture was prescribed in Kenya's MDGs as the avenue to improve the economy of the 
country and from the findings of the study, more than half of the respondents had heard and read 
of the Vision 2030 plan and indeed strongly agreed that with knowledge transfer, the farmers ' 
productivity would increase thereby inching closer to meeting the MDG goals on agricultural 
productivity and narrowing the gap between the large-scale farmers and small-scale farmers. The 
findings were also similar to the affirmation of the hypothesis that was formulated to state that 
with the implementation of a robust model for knowledge and skills transfer, there will be 
increased knowledge transfer among the small and large-scale farmers leading to higher 
productivity and commercialization of farming especially of the small scale farms . 
58 
6.2 Recommendation 
Investment in knowledge transfer may not necessarily guarantee a return on the investment and 
productivity. A key recommendation to other scholars and academicians is to unde1take studies 
that determine the return on investment in knowledge transfer for the farmers, especially the 
small scale farmers, but also for other actors. 
A key recommendation to the Government is to suppmt the farmers by suppmting the purchase 
of inputs and equipment as well as revive the services of agricultural extension workers to 
continuously advise the farmers on the various farming challenges. The new devolved 
government and the Counties provide a new oppmtunity for the ministry of agriculture to 
establish agricultural extension service depa1tments at County levels. This will ensure that SSFs 
in all the Counties can easily access the services of agricultural extension officers. Eventually 
this will ensure smooth and localized knowledge and skills transfer to the fanners, increased 
farm production and commercialization of farms especially the small scale farms. 
The fanners continue to struggle to purchase farm inputs and equipments and thus, there is 
urgent need for the government to formulate favorable policies that will lead to enhanced 
agricultural productivity. Fertilizer for example can be manufactured locally and the government 
can also invest more in the metrological depa1tment for accurate weather forecasts. 
A key recommendation to the stakeholders like multinationals and KARl is to continue 
researching and promoting new skills in farming. Agriculture remains the backbone of our 
economy and therefore it is impmtant to research and appropriately communicate the research 
outcome to the fanners in a way they can understand so as to increase farm productivity. 
One recommendation to the media is to increase agricultural educative programs and 
documentaries that will facilitate knowledge transfer. And to the schools, institutions, colleges 
and universities is to continue in their efforts of research and development, pa1tnership with 
other institutions for impact and continually train and educate the small-scale farmers in order to 
enhance their agricultural knowledge and skills levels . 
59 
As an area for fllliher research, one of the applicable issues that arise is measuring the return on 
investment (ROI) in knowledge transfer. There is need for other scholars to fllliher study this 
area to determine and quantify the large and small-scale farmers' returns on investing in 
knowledge transfer. When you attend the field days, when you read agricultural books, when you 
seek the assistance of the extension officers, when you listen to radio programmes and interact 
with the large scale or other small scale farmers, what returns would the farmer expect from this, 
by how much should the farmer expect his production to increase?. 
60 
References 
Atchoarena, D. & Holmes, K. (2004). The Role of Agricultural Colleges and Universities in Rural 
Development and Lifelong Learning. Journal of Agriculture and Development, Vol. 2, Nos. 1&2 
Bailey, KD (1987). Methods of Social Science Research. New York: The Free Press. 
Blood, D. C. & Studdert, V.P. (2007) . Saunders Comprehensive Veterinary Dictionary, 3 ed 
Bou-Llusar, J.C & Segarra-Cipre's, M . (2006). Strategic knowledge transfer and its implications for 
competitive advantage: An Integrative Conceptual Framework. 
Butler, A. Le Grice, P. Reed, M . (2006). Delimiting knowledge transfer from training. Education + 
Training, Vol. 48 Iss: 8/9, pp.627- 641 
CAADP Africa Forum, (2009) . The Bottom ofthe Pyramid : Agricultural Development for the 
Vulnerable . 
Clegg, S., Carter, C. and Kornberger, M. (2004). Get up, I feel like being a strategy machine. European 
Management Review, Vol. 1 No.1 , pp. 21-8 . 
Cooper, D. R., & Schindler, P. S . (2008). Business Research Methods, International Edition, I Oth ed . 
Daily Nation (20 1 0) . Daily Nation. A vail able at: 
http ://www .humanflowerproject.com/index. php/weblog/comments/riots _ in_naivasha/ [Accessed 
10 December 2012] 
Department for Education and Skills (2006) . Further Education: Raising SkWs, Improving Life 
Chances, The Stationary Office, London. 
Derpsch, R . 2005 . The extent of Conservation Agriculture adoption worldwide: Implications and impact. 
Proceedings of the Third International Congress of Conservation Agriculture. Nairobi , Kenya. 
61 
Diederen, P. van Meiji, H. and Wolters, A. (1999). Innovation in agriculture: innovators, early adopters 
and laggards. Paper for the 3rd International Conference on Technology Policy and Innovation, 
LEI-DLO, Den Haag. 
Dumanski, J ., R. Peiretti , J . Benetis, D. McGarry, & C . Pieri. (2006). The paradigm of conservation 
tillage. Proc. World Assoc. Soil and Water Conserv., Pl: 58-64. 
Economic Survey (2009). Kenya Institute for Public Policy Research and Analysis, Republic of Kenya, 
Nairobi. 
Encyclopedia ofthe Nations (2010). Encyclopedia of the Nations, available at: 
www .nationsencyclopedia.com, [Accessed 27 August 20 12] 
EPZA (2005). Export Processing Zones Authority, Republic of Kenya 
FAO & World Bank (2000). Agricultural Knowledge and Information Systems for Rural Development 
(AKIS/RD): Strategic Vision and Guiding Principles, Food and Agriculture Organization of the 
United Nations (FAO) and the World Bank, Rome, available at: www. 
fao.org/sd/EXdirect/EXre0027 .htm 
Fujita, (n.d.) . cited in Zakaria, S. & Nagata, H. (2009). Knowledge creation and flow in agriculture: The 
experience and role of the Japanese extension advisors. Vol. 31 No. 1/2, 2010, pp. 27-35 
Haggblade, S. (2011). Modernizing African agribusiness: reflections for the future. Journal of 
Agribusiness in Developing and Emerging Economies, Vol. 1 No . I, pp. I 0-30 
Haggblade, S., Hazell, P.B.R. and Reardon, T . (1989). Strategies for stimulating equitable growth in the 
rural nonfarm economy, in Haggblade, S. and Hazell, P.B.R. and Reardon, T. (Eds), 
Transforming the Rural Nonfarm Economy: Opportunities and Threads in the Developing 
World, Johns Hopkins University Press, Baltimore, MD, pp. 396-416. 
62 
Johnson G, Scholes K, Whittington R. (2006). Introducing Strategy; Exploring Corporate Strategy, 7th 
Edition 9-10 
Jones, G .E. , Rolls, M.J . and Tranter, R.B. (1987). Information Management in Agriculture. British 
Library R&D Rep011 No. 5931, Agricultural Extension and Rural Development Centre, Reading. 
Kenanoglu, Z. & Karahan, 0 . (2002) . Policy Implementations for Organic Agriculture in Turkey. Vol. 
104, No.3/4/5 2002 , pp. 300-318 . 
Kenya Bureau of Statistics (2009). Kenya Bureau of Statistics, Republic of Kenya, Nairobi 
Khamseh, H . M & Jolly, D . R (2008) . Knowledge transfer in alliances: determinant factors. VOL. 12 
NO. 1 2008, pp. 37-50 
Kilpatrick, S. (1998a) . Education and training: impacts on farm management practice. CRLRA 
discussion paper, Launceston, Tasmania. 
Kilpatrick, S. (1998b) . How does education and training impact on management practice . CRLRA 
discussion paper, Launceston, Tasmania. 
Kilpatrick, S. (2002). Learning and building social capital in a community of family farm businesses. 
International Journal of Lifelong Education, Vol. 21, pp . 446-61. 
Kip lang' at, J. (1999) . An analysis of the opportunities for information technology in improving access, 
transfer and the use of agricultural information in the rural areas of Kenya. Vol. 20, No . 2, pp. 
115-127 
Kumar, J. A & Ganesh, L. A (2008) . Research on knowledge transfer in organizations: a morphology, 
VOL. 13 NO.4 2009, pp. 161-174. 
63 
Lanjouw, P. (2007). Does the rural nonfarm economy contribute to poverty reduction?, in Haggblade, 
S., Hazell, P.B.R. and Reardon, T. (Eds), Transforming the Rural Nonfarm Economy: 
Opportunities and Threats in the Developing World, Johns Hopkins University Press, Baltimore, 
MD, pp. 55-82. 
Learning and Skills Development Agency (2002). Attracting New Learners: International Evidence and 
Practice, Learning and Skills Development Agency. London. 
Lucas, N. (2006). Global retail chains and poor farmers : evidence from Madagascar, World 
Development, Vol. 37 No. 11 , pp. 1728-41. 
Maguire, C. (2002). Changing Expectations for Higher Agricultural Education . IIEP Newsletter, 20 (4) 
Malhotra, NK (2002) . Marketing Research: An Applied Orientation. 3rd edition , Upper Saddle River, 
New Jersey, Prentice Hall. 
Matlay, H. (1999). Vocational education and training in Britain: a small business perspective. 
Education and Training, Vol. 41 No. 1, pp. 6-13. 
Matlay, H. and Hyland, T. (1999) . Small Firms and the University for Industry: an appraisal. 
Educational Studies, Vol. 25 No.3, pp . 253-67. 
Mbithi, P.M (2004). Issues in the transfer of useful knowledge in agricultural development in Kenya. 
Department of Sociology, University ofNairobi, Nairobi, Kenya. 
Mellenbergh, G. J. (2008) . Chapter 10: Tests and Questionnaires: Construction and administration. In 
Ader, H.J ., & Mellenbergh, G. J. (Eds.) Advising on Research Methods: a consultant's opinion 
(pp. 211--236). Huizen: Johannes van Kessel Publishing. 
64 
Miesing, Kriger & Slough (2006). Towards a model of effective knowledge transfer within 
transnationals: The case ofChineseforeign invested enterprises. Springer Science, Business 
Media, 32:109-122 
Ministry of Agriculture (20 12). Minislly of Agriculture, Republic of Kenya 
Ministry of Agriculture of Kenya, (2009). Ministry of Agriculture, Republic of Kenya 
Ministry of Agriculture, Fisheries and Food, (2001) . Ministry of Agriculture, Fisheries and Food, 
Republic of Kenya 
Mintzberg, H. (1990). The design school: reconsidering the basic premises of strategic management. 
Strategic Management Journal , Vol. XI, pp. 171-95. 
Mugenda, A.G. (2008) . Social Science Research. Nairobi: Acts Press. 
Mugenda, O.M., & Mugenda, A.G. (2003) . Research Methods: Qualitative and Quantitative 
Approaches. Nairobi: Acts Press. 
National Development Plan (2010). Ministry ofStatefor Planning, Republic of Kenya, Nairobi. 
Nieuwenhuis, L. F. M (2002) . lnnovation and learning in Agriculture. Stoas research, Wageningen, 
Netherlands, pp. 283-291 
Njaramba, J . G (20 11). Growth and structure of organic agriculture in Kenya since early 1980s. 
Jawaharlal Nehru University India 
Nweke, F.I. , Spencer, D.C. and Lynam, J .K. (2002) . The Cassava Transformation: Aji-ica 's Best-Kept 
Secret, Michigan State University Press, East Lansing, MI. 
65 
Ojiambo, J.B. (1989). Communication of Agricultural Information between Research Scientists, 
Extension Personnel and Farmers in Kenya, phD Thesis, University of Pittsburgh, Pittsburgh. 
Onyekwere, 0.0., Akinrele, LA. Koleoso, O.A. and Heys, G. (1989) . /ndustrialization ofgari 
fermentation, in Steinkraus, K.H. (Ed), Industrialization oflndigenous Fermented Foods, Marcel 
Dekker, New York, NY, pp . 363-410. 
Orodho, A. J., & Kombo, D. K. (2002). Research methods. Nairobi: Kenyatta University, Institute of 
Open Learning 
Pannell, D. J ( 1999). Economics, extension and the adoption of land conservation innovations in 
agriculture. 
P01ter, M. E. (1996). What is strategy?, Harvard Business Review, November-December, 61-78.The 
value chain 
Rees, D.J ., Momanyi, M ., Wekundah, J., Ndungu, F., Odondi, J., Oyure, A.O., Andima, D., Kamau, M ., 
Ndubi , J., Musembi, F., Mwaura, L. and Joldersma, R. (2000). Agricultural knowledge and 
information systems in Kenya: implications for technology dissemination and development. 
Agricultural Research and Extension Network (AgREN) Network Paper No. 107, available at: 
www .odi .org. uk/agren/papers/agrenpaper_1 07. pdf 
Republic of Kenya (2006). Economic Survey 2006, Republic of Kenya, Nairobi 
Republic of Kenya (2008-2012), Ministry of State for Planning, Vision 2030, Republic of Kenya, 
Nairobi 
Roling, N. ( 1990). The agricultural research-technology transfer interface: A knowledge systems 
perspective, in Kaimowitz, D. (Ed.), Making the Link: Agricultural Research and Technology 
Transfer in Developing Countries, Westview, London, pp. 1-42. 
66 
Rousson, V ., T. Gasser, B. Seifett (2002). Assessing intra rater, inter rater and test- retest reliability of 
continuous measurements. 
Sarantakos, S. (1993). Social Research. Australia: Macmillan Education 
Smulders, H. and Verhaar, C. (1998). Sustainable Knowledge, Stoas research, Wageningen 
Spender, J .C. (1994). Knowing, managing and learning: a dynamic managerial epistemology. 
Management Learning, Vol. 25, pp. 387-412 . 
Tatchell, M. (2005). Scientific Skills for Knowledge Transfer in Arable Agriculture in England: Mark 
Tatchell Consulting 
Taylor, M. and Plummer, P. (2003). Promoting local economic growth: the role of Entrepreneurship 
and human capital. Education+ Training, Vol. 45 Nos 8/9, pp. 558-63. 
UNECA (2009). Cited in Biekpe (2009). African Capital Markets : Legal and Governance Framework. A 
Training Manual for Africa, Centre for Investment Analysis University of Stellenbosch, South 
Africa, available at www.uneca.org 
Warren, M. (2003). SWARD: patticipatory knowledge transfer at work. Journal of Agricultural 
Education and Extension , Vol. 9, pp. 43-50. 
Zakaria, S. & Nagata, H. (2009). Knowledge creation and flow in agriculture: The experience and role 
oftheJapanese extension advisors. Vol. 31 No. 1/2,2010, pp. 27-35 
Zijp, W. ( 1994 ). Improving the Transfer and Use of Agricultural Information: a Guide to InformaNon 
Technology World Bank, Washington, DC. 
67 
APPENDICES 
APPENDIX I: Tables 
Table 2.1: Gross Domestic Product by Activity (Per cent contributions to GOP) 
INDUSTRY 2004 2005 2006 2007 2008 
Agriculture and forestry 24.4 23.8 23.4 2 1.6 23.4 
Fishing 0.5 0.4 0.4 0.4 0.4 
Manufacturing 10.0 10.5 10.3 I 0.4 10.6 
Electricity and water supply 1.9 2.0 1.8 1.5 1.5 
Construction 3.8 4.0 3.9 3.8 3.8 
Wholesale and retail trade, repairs 9.2 9.2 9.3 9.7 10.0 
Hotels and restaurants 1.3 1.4 1.5 1.6 1.1 
Transpmt and communication 9.9 10.3 10.6 10.6 10.2 
Financial intermediation 3.5 3.5 4.0 4.7 4.7 
Real estate, renting and business services 5.7 5.6 5.4 5.3 5.1 
Public administration and defense 4.2 4.5 5.4 5.8 5.0 
Education 7.9 7.4 7.0 6.8 6.3 
Health and social work 2.6 2.6 2.5 2.5 2.4 
Other community, social and personal services 3.9 3.8 3.6 3.5 3.4 
Private households with employed persons 0.4 0.4 0.4 0.4 0.4 
Less : Financial services indirectly measured -0.7 -0.8 -0.9 -1.1 -0.9 
All industries at basic prices 88.9 89.1 89 .0 88 .2 88.2 
Taxes less subsidies on products 11.1 10.9 11.0 11.8 11.8 
GDP at market prices 100.0 100.0 100.0 100.0 100.0 
(Economic Survey, 2009) 
Table 2.2: Domestic Exports by Broad Economic Category, 2004 -2008 
DESCRIPTION 2004 2005 2006 2007 2008 
I. Food and Beverage 75,071 91 ,127 97,801 105,549 130,273 
2. Industrial supplies (non-food) 39,479 51 ,219 53,715 66,678 92,500 
3. Fuel and lubricants 1,182 6,894 6,882 8,931 4,589 
4. Machinery & other capital equipment 1,904 2,252 3,529 4,426 5,566 
5. Transport equipment 1,716 2,206 2,762 3,475 3,672 
6. Consumer goods not elsewhere specified 33,648 56,072 63 ,436 72,623 86,036 
7. Goods not elsewhere specified 6,061 148 56 .., 24 .J 
TOTAL 159,061 209,918 228,181 261,685 322,660 
PERCENT AGE SHARES 
I. Food and Beverage 47.2 43.4 42.9 40.3 40.4 
2. Industrial Supplies (Non-Food) 24.8 24.4 23 .5 25.5 28.7 
3. Fuel and Lubricants 0.7 3.3 3.0 3.4 1.4 
4. Machinery and other Capital 1.2 1.1 1.5 1.7 1.7 
Equipment 
5. Transpmt Equipment 1.1 1.1 1.2 1.3 1.1 
6. Consumer Goods not elsewhere 21.2 26.7 27.8 27 .8 26.7 
68 
Specified 
7. Goods not elsewhere specified 3.8 0.1 0.0 0.0 0.0 
TOTAL 100.00 100.00 100.00 100.00 100.00 
(Economic Survey, 2009) 
Table 2.3: Wage Employment by Industry and Sector, 2004-2008 
2004 2005 2006 2007 2008 %Change 
PRlV ATE SECTOR 
Agriculture and Forestry 264.8 272.4 280.3 289.0 289.7 0.2 
Mining and Quarrying 4.9 5.1 5.3 5.6 5.9 5.4 
Manufacturing 212.0 221.7 228.5 237 .9 237.0 -0.4 
Electricity and Water 1.9 1.9 1.9 2 . .2 2.3 4.5 
Building and Construction 54.4 55.7 57.9 61.2 64.9 6.0 
Wholesale and Retail Trade, 161.7 169.2 179.6 189.8 196.4 3.5 
Restaurants and Hotels 
Transport and 60.7 75 .1 90.9 117.8 120.3 2.1 
Communication 
Finance, Insurance, Real 70.2 72.4 75.5 79 .0 81.9 3.7 
Estate and Business Services 
Community, Social and 276.7 283.9 287 .8 299 .2 307.1 2.6 
Personal Services 
TOTAL PRIVATE SECTOR 1,107.3 1,157.4 1,207.7 1,281.7 1,305.5 1.9 
PUBLIC SECTOR 
Agriculture and Forestry 55.8 54.8 54.3 50.9 51.0 0.2 
Mining and Quarrying 0.7 0.7 0.7 0.7 0.7 0.0 
Manufacturing 31.6 31.1 30.8 26 .9 27.1 0.7 
Electricity and Water 18.9 18.3 17.7 16.8 17.0 1.2 
Building and Construction 23 .0 22.5 22.0 20.1 19.9 -1.0 
Wholesale and Retail Trade, 6.3 6.2 6.3 6.0 6.0 0.0 
Restaurants and Hotels 
Transp01t and 37 .9 38.9 40.2 36.1 37.4 3.6 
Communication 
Finance, Insurance, Real 15 .0 16.5 16.8 14.3 14.2 -0 .7 
Estate and Business Services 
Community, Social and 468.2 465 .2 461 .1 456.3 464.7 1.8 
Personal Services 
TOTAL PRIVATE SECTOR 657.4 654.2 628.1 638.0 1.6 
(Economic Survey, 2009) 
Table 2.4 : Recorded Marketed Production at Current Prices, 2004-2008 (Ksh Million) 
2004 2005 2006 2007 2008 
CEREALS 
Maize 6,880.5 6,342.4 7,170.2 7,969.2 8,326.6 
Wheat 1,864.0 2,232.3 2,073.4 3,074.1 2,613.8 
69 
Others 2,055.3 3,329.5 3,843.2 3,574.3 2,458.1 
Total 10,799.8 11,904.2 13,086.8 14,617.6 13,398.4 
HORTICULTURE 
Cut flowers 18,720.0 22,896.8 23,560.6 43,101.5 39,765.9 
Vegetables 12,068.0 13,891.4 17,822.9 22,354.3 16,128.7 
Fruits 1,803 .0 2,049.9 1,737.3 1,797.9 2,071.2 
Total 32,591.0 38,838.1 43,1208 67,253.7 57,965.8 
TEMPORARY TNDUSTRIAL 
CROPS 
Sugar-cane 8,389.8 9,169.6 9,998.9 11 ,704.3 12,291.1 
Pyrethrum 305.7 158.1 74.1 98.6 91.9 
Others 644.6 859.0 602.5 808.5 786 .5 
Total 9,340.1 10,186.7 10,675.5 12,611.4 13,169.5 
PERMANENT CROPS 
Coffee 7,284.5 8,999.1 10,023.6 9,089.9 6,859.3 
Tea 41,212.2 38,829.9 45,162.0 43,887 .0 55,383 .1 
Sisal 1,275.4 1,289.2 1,228.2 1,492.9 1,451.4 
Total 49,772.0 47,948.2 56,413.8 54,469.8 63,693.8 
TOTAL CROPS 102,502.9 108,877.2 123,296.9 148,952.5 148.227.5 
LIVESTOCK AND PRODUCTS 
Cattle and Calves 11 ,284.8 13,063 .5 13,403.2 13,451.6 13,494.1 
Dairy Produce 4,385.0 5,313.2 6,494.4 8,462.2 8,368.7 
Chicken and eggs 1,705.7 1,901.5 2,186.7 2,575.5 2,788.8 
Others 3,391.2 3,980 .7 4,291.5 5,202.1 5,977.6 
Total 20,766.7 24,258.9 26,375.8 29,691.4 30,629.1 
FISH 
Freshwater fish 7,182,2 13 7,207,619 8,070,557 7,983 ,627 8,382,808 
Marine fish 327,592 305,871 334,624 422,046 443 ,148 
Crustaceans fish 221,106 99,278 123, 105 145,437 152,709 
Other marine products 29,895 39,098 38,485 43 ,382 45,551 
TOTAL 7,760,806 7,651,866 8,566,771 8,594,492 9,024,216 
GRAND TOTAL 131,030.4 140,788.0 158,239.5 187,238.4 187,780.1 
(Economic Survey, 2009) 
Table 2.5: Sale to Marketing Boards from Large and Small Farms, 2004-2008 
LARGE FARMS SMALL FARMS TOTAL 
KSHmn. Annual KSHmn. Annual KSHmn. Annual 
Percentage Percentage Percentage 
Change Change Change 
2004 33,086 21.59 90,184 16.20 123,270 17.60 73.16 
2005 34,828 5.27 98,308 9.00 133,136 8.00 73 .84 
2006 38,860 11.58 110,813 12.72 149,673 12.42 74.04 
2007 43 ,053 10.79 135,591 22.36 178,644 19.36 75.90 
2008 48,148 11 .83 130,709 -3 .60 178,857 0.12 73.08 
(Economtc Survey, 2009) 
70 
APPENDIX II: Timeline of Activities 
WORK PLAN 
Progress 
Stage Stage Description Pr·oposed dates 
1 Scoping of the Research study lOth Feb. 2011 
2 Choice of Research Topic 25th Feb to 15th Mar 2011 
3 Research Problem clarification, Research 20th to 30th Mar 2011 
objectives, Purpose and Significance 
4 Foundation Literature survey 1st 15th April 2011 
5 Proposal of Research Methodology April 2011 
6 Advanced Literature Review (Chapter By April 2011 to April 2012 Chapter) 
7 Detailed Proposal of Research Methodology May 2011 Dec 2011 
8 Data Collection June to Aug 2011 
9 Data analysis and Interpretation Sept 2011 to Jan 2012 
10 Thesis Rep01t writing June 2011 to April2012 
1 1 Thesis Repott Assessment By Supervisors Sept 2011, Dec 2011, May 2012, April2013 
12 Thesis Correction Upon Assessment By Dec 2011, Sept 2012, May Supervisors 2013 
Any remarks: God is good!! 
71 
APPENDIX III: Letter of Introduction 
161" March, 20 1 1 
Strathmore Business School 
Nairobi. 
Dear Respondent, 
RE: REQUEST TO FILL THE QUESTIONNAIRE 
I am an MBA student at Strathmore University, and I wish to undertake the study whose topic is 
"Investigating Knowledge and Knowledge Transfer for Entrept·eneut·ship in Kenya's 
Agricultural Sectot·" 
I therefore kindly request that you take 10 minutes to respond to the questionnaire. This is an 
academic research and the information you provide will be used for academic purposes only and 
treated with confidence. Strict ethical principles will be observed to ensure confidentiality. 
There will be no reference to your name or shop in the analysis; however I have provided my 
contacts and email address for any clarification . The response you provide will help in 
accomplishing the research objectives and will enable me complete my studies . The results will 
help in formulating policies that will supp01t you . 
In case of any questions, please do not hesitate to contact the undersigned on 
Thank you very much for your invaluable contribution and we appreciate the time you have 
spared to answer the questions. 
Sincerely, 
William W. Ngugi 
Tel. 0720632 464 
72 
APPENDIX IV: Questionnaire 
Q 1. Identification Particulars 
Please provide the following identification information 
1. Date of interview 
2. Name of enumerator 
3. District or county 
4. Name of respondent 
5. Gender of respondent 
6. Age of respondent 
7. Division 
8. Location 
9. Sub-Location 
Q2. Household Pat·ticulars 
Provide the following detail about the household head 
Gender Age Primary activity Fanning Highest 
1 =Male, 2 =Female (years) experience Educatio 
(years) n level 
Q3. Is the household head the farm ownet·? 
( ) l.Yes ( ) No 
If not, who is the fat·m owner? 
--------------------------------------
73 
Q4. Number of Household members (including HH head) living permanently on the compound 
Age Categories Males Female Total Number actually 
working on the farm 
at least once a week 
< 20 years 
21-50 
> 50 years 
Number of children attending 
school 
Number of siblings opting to do 
farming 
Number of siblings opting for the 
city 
Q5. Number ofNon-Resident Household members, living away but who occasionally benefit 
or assist the farm activities 
Age Categories Males Female Total 
< 20 years 
21-50 
> 50 years 
Q6 . Are any of the non-residents (in 5 above) involved in any agricultural activities 
Agl"icultur·al activities Yes No 
selling animal feeds I products 
selling agro-chemicals 
selling agricultural machinery 
agricultural education 
others 
74 
Q7. Can you identify the main reasons why some choose to go to the city? 
I. Seek for a job 
II. For education 
III. Business 
IV. Married 
V. Do not know 
VI. Others, please name 
Q8. What is your highest level of education? Just tell me to stop when I reach the appropriate 
category. 
I. No formal education 
II. Primary school 
III. Secondary school 
IV. Tertiary I college 
v. University and above 
VI. Dropped out 
VII . Do not read 
Q9. What did you study in college (accounting, agriculture, electrical, mason, animal 
husbandry?) 
Q I 0. What is your job occupation? 
Q II. If in agriculture related, what is your main role? 
Q 12. What category do you fall? 
I. Small Scale Farmer 
II. Large Scale Farmer 
III. Other- Government official I Stakeholder 
IV. Others 
-----------------
75 
----------------------
Q 13 . What is the approximate size of cultivated land? 
Q14. How long have you worked on the farm ______ (In years) 
(In months) 
------
_______ (any other response) 
Housing and Food Security (to check income) 
Q15. Type of wall for the house:O mud only0 plastered0 woodenO bricks0 stone 
Q 16. Type of roof for the house: O grass O iron-sheet O tiles 
Q17 . What% ofyour income is from agriculture _____ ? 
Q 18. Where do you sell your crops? 
0 Local markets' 0 Marigiti in Nairobi 0 Exports market 0 Others 
Q19. Do you keep animals I pets (cows, goats, chicken, pigs, horses, donkeys, dogs, cats)? 
0 Yes 0 No 
Q20. Do you experience any food shmiage? 
I. If yes, what have you done to reduce the problem (use offeitilizer, irrigation, green 
houses, attend field days, sought assistance of extension officers) 
Q21. What would you say are the major challenges in your farming operations, in terms of 
priority? 
D 
D 
D 
D 
D 
D 
D 
D 
Weather 
Knowledge in agriculture 
Lack of agriculture inputs (seeds, chemicals etc) 
Lack of agriculture equipments 
Prices for both inputs and equipments 
Labor 
Market access 
Others, name them ______ _ 
76 
Q22. What is the most important unmet need in your farming activities? 
D Lack of knowledge 
D Lack of inputs (chemicals, seeds, etc) 
D Lack of equipments 
D Prices for inputs and equipments 
D Market inaccessibility 
D Others, name them ______ _ 
RISKS AND RISK MANAGEMENT 
Q23 . If you have experienced any risks related to weather, pests or diseases please indicate 
which ones. 
Risks What do you do to reduce the risk? 
Droughts, dry spells 
Flooding 
Pests 
Diseases 
Weeds 
Other, specify 
Q24. Do you have any crop insurance (e.g. Weather index insurance)? 
DYes 
DNa 
Q25. What else would you like to have insured?-----------------
77 
Knowledge Transfer 
Q26. On a scale of 1 to 5 where 1- is Strongly Disagree and 5- is Strongly Agree, how would 
you rate the following attributes ... 
Attributes 1 - 2 '"' 4 5 -.) 
Strongly Strongly 
Disagree Agree 
Effective knowledge transfer 111 agriculture 
would increase productivity of small and LSFs 
0 0 0 0 0 
in Kenya, 
You have heard of I read vision 2030 
0 0 0 0 0 
If skills or knowledge are transferred from LSFs 
to small scale farmers, the gap in the knowledge 
will reduce. 0 0 0 0 0 
LSFs have the knowledge they need for their 
farming activities- chemicals they need for 
their crops and the application, weather, 
equipments, markets 0 0 0 0 0 
SSFs have the knowledge they need for their 
farming activities- chemicals they need for 
0 0 0 0 0 
their crops and the application, weather, 
equipments, markets 
78 
Q27. Thinking specifically on Agriculture. How would you AGREE with the following 
statements? 
RANDOMIZE Strongly Disagree 
Disagree 
Small-Scale farmers need new Skills, 
Research & Development (e.g. from 0 0 
KARl) 
Small-Scale farmers have the potential to 
transfer their skills and knowledge to the 0 0 
large-scale farmers 
Q28 What skills or knowledge can SSFs borrow from LSFs? 
D Required agrochemicals for various crops 
D Application of agrochemicals 
D Weather knowledge 
D Market information 
D Research and development in new crops 
D Agricultural management skills 
D Others 
------
Neither 
Disagree 
Nor 
Agree 
0 
0 
Q29. Have you ever bought I read a book in agriculture? If yes, which one? 
Agree Strongly 
Agree 
0 0 
0 0 
-----
Q30. Do you listen to radio /TV programs in agriculture? _____________ _ 
If yes, which ones? ____________________________ _ 
Don't 
Know 
0 
0 
Q31. Have you heard of KARl, Coffee Research Foundation or any other research institution and 
what they do? What do you know about them? 
79 
Q32. Have you heard of the following companies (Place a tick against the one you know) 
Syngenta East Africa Limited, Monsanto, DuPont, Osho Chemicals, Am iran, Bayer East Africa 
Limited, SeedCo, Pioneer Seed Company, Twiga Chemicals, Murphy Chemicals, Kenya Seeds 
Company? 
Q33. What, in your opinion, facilitates knowledge transfer in Kenya? 
0 Government 0 schools and colleges, 0 media 0 individual eff011 
0Money 0 Universities, 0 Others' ________ _ 
Q34. Why do you think some farmers remain poor? 
Q35 . Any other comment ___________________________ _ 
80 
APPENDIXV: Photo Album 
Photo 1: small-scale's farmer greenhouse 
Photo 2: Small-scale farmer attending to greenhouse 
81 
Photo 3: Crop (tomatoes) in greenhouse 
Photo 4: Small Scale Farms 
82