CHALLENGES AND OPPORTUNITIES FOR IN FARMING IN RUAI AREA- (NAIROBI COUNTY)

BY:

MILCAH WAMBUI WAMUGUNDA

UNITED STATES INTERNATIONAL UNIVERSITY – AFRICA

SUMMER 2018 CHALLENGES AND OPPORTUNITIES FOR FARMERS IN DAIRY FARMING AGRIBUSINESS IN RUAI AREA- (NAIROBI COUNTY)

BY

MILCAH WAMBUI WAMUGUNDA

A Research Report Submitted to the Chandaria School of Business in Partial Fulfillment of the Requirement for the Degree of Masters in Business Administration (MBA) Leadership & Management

UNITED STATES INTERNATIONAL UNIVERSITY

SUMMER 2018 STUDENT DECLARATION

I hereby declare that this research project is the product of my own work and it has not been submitted or presented for examination in any other university.

Sign ………………………………….. Date ……………………………

WAMUGUNDA MILCAH WAMBUI

ID - 654114

This project has been submitted for examination purposes for my approval to the project supervisor.

Sign ……………………………….. Date ………………………………

PROFESSOR F. NEWA University Supervisor.

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COPYRIGHT

This research project is my original work and it has not been submitted or presented for examination in any other university.

ABSTRACT

The purpose of the study was to determine the challenges, opportunities and strategies for farmers engaged in dairy farming, with a focus on small scale dairy farmers in Ruai Area, Nairobi County. The study aimed at exploring opportunities available for farmers who venture in dairy farming, determining challenges affecting dairy farmers and determining strategies to encourage more farmers to venture in dairy farming enterprise.

A descriptive research design was used to provide for observation and description of the behavior of the subjects in an objective manner. The target population were small scale dairy farmers from Ruai, Nairobi County. The research population consisted of 80 small scale dairy farmers at Ruai Nairobi County and through the use of simple random sampling technique, a selection of 67 respondents was determined. Through the use of a structured questionnaire, the study adopted a descriptive and inferential statistics in data analysis and presentation. For descriptive statistics, the study adopted percentage and frequency distribution, cross-tabulation, mean and standard deviation. While for inferential statistics, the study used factor analysis, correlation and regression analysis. Data was presented by way of Tables and Figures.

The study in the first objective explored how opportunities available for farmers who venture in dairy farming influenced dairy farming enterprise in Ruai, Nairobi County. The study found that farmers who are more innovative enhance their level of income. The study also revealed that effective extension services provide farmers with information that helps them to

iii optimize their use of scarce resources. Technology in provides important information to famers to enhance agribusiness. From the study, it was illustrated that dairy farmers go for trainings on how to enhance production. The study also found that technologies assist farmers to balance and manage food and herds.

The study in the second objective established the challenges affecting farmers who venture in dairy farming. The research found that prevalence of various animal diseases is a challenge to milk production. From the study, it was revealed that increasing feed prices has cause constraints to increase milk production. The study established that low fertility reduces the profit by decreasing the average milk production and the number of per cow per year. The study also illustrates that most of the poor farmers do not have financial means required to make the initial investment and acquire the associated technological inputs. Small scale dairy farmers were found to experience inadequate animal feed resources and this problem of inadequate feed is as a result of the limited land available for pasture establishment.

The study in the third objective determined strategies to encourage more farmers to venture in dairy farming enterprise. The study found that farmers should encourage the use of (AI) to enhance the quality of breeds. Dairy must be provided with water throughout. The study reveals that improving market access to creates an opportunity for enhanced dairy production. The study demonstrated that dairy farmers should focus on prevention and control of risk factors in cattle and be trained on milk production systems by qualified individuals who understand the whole dairy farming system. To enhance dairy farming, the study found that dairy animals should be given certain foods for body maintenance and for production. The study found that small scale dairy farmers can get high producers cattle through genetic improvement of their existing herd. The study revealed that developing credit facilities for small scale dairy farmers enhances productivity.

The study concludes that technology in agriculture provides important information to farmers to enhance agribusiness hence dairy farmers who embrace technology are more innovative and this enhances their level of income. The study also concludes that prevalence of animal diseases is the greatest challenge dairy farmers experience as far as milk production is iv concerned. Dairy farmers should enhance the quality and quantity of dairy products; they should encourage the use of artificial insemination (AI).

The study recommends the small scale dairy farmers to embrace technology in farming as it has been approved to be of more beneficial to farmers by enhancing the quick access to information about dairy farming, facilitating accessibility to the markets, and enhancing the quality and quantity of dairy products. The study also recommends the access to pertinent information about animal diseases and preventing them through the utilization of veterinary services. Dairy farmers should embrace the use of artificial insemination (AL) to enhance the quality and quantity of dairy products.

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ACKNOWLEGDEMENT

First and foremost, I would like to thank GOD for giving me the strength and patience needed to complete this project. Also I would like to extend my deepest gratitude to my family, my university supervisor and my classmates for giving me the support and prayers I needed to complete this project. Last but not least I thank all my farmers in Ruai area, Nairobi county for their great cooperation. Thank you again and may GOD bless you all.

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DEDICATION

This study is dedicated to my entire family who lovingly endured the long hours of sleepless nights while compiling the document. Thank you all for the patience.

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TABLE OF CONTENTS

STUDENT DECLARATION ...... ii COPYRIGHT ...... iii ABSTRACT ...... iii ACKNOWLEGDEMENT ...... vi DEDICATION ...... vii TABLE OF CONTENTS ...... viii LIST OF TABLES ...... x CHAPTER ONE ...... 1 1.0 INTRODUCTION ...... 1 1.1 Background of the Study ...... 1 1.2 Statement of the Problem ...... 5 1.3 General Objective ...... 5 1.4 Specific Objective ...... 5 1.5 Significance of the Study ...... 6 1.6 Scope of the study ...... 7 1.7 Definition of Terms...... 7 1.8 Chapter Summary ...... 9 CHAPTER TWO ...... 10 2.0 LITERATURE REVIEW ...... 10 2.1 Introduction ...... 10 2.2 Opportunities Available in Dairy Farming ...... 10 2.3 Challenges in Dairy Farming ...... 16 2.4 Strategies to Encourage Dairy Farming ...... 20 2.5 Chapter Summary ...... 26 CHAPTER THREE ...... 27 3.0 RESEARCH METHODOLOGY...... 27 3.1 Introduction ...... 27 3.2 Research Design...... 27 3.3 Population and Sampling Design ...... 27 3.4 Data Collection Methods ...... 29 3.5 Research Procedure ...... 29 3.6 Data Analysis Methods ...... 30 3.7 Chapter Summary ...... 32 CHAPTER FOUR ...... 33 4.0 RESULTS AND FINDINGS ...... 33 4.1 Introduction ...... 33 4.2 General Information ...... 33 4.3 Response Rate ...... 33 4.4 Demographic Statistics ...... 33 4.5.1 Multicollinearity Test...... 40 4.5.2 Test for Normality...... 40

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4.6 Opportunities for Dairy Farming Agribusiness ...... 41 4.7 Challenges in Dairy Farming Agribusiness ...... 52 4.8 Strategies to Encourage Dairy Farming Agribusiness ...... 64 4.9 Chapter Summary ...... 75 CHAPTER FIVE ...... 76 5.0 DISCUSSION, CONCLUSIONS AND RECOMMENDATIONS ...... 76 5.1 Introduction ...... 76 5.2 Summary ...... 76 5.3 Discussion ...... 77 5.4 Conclusions ...... 83 5.5 Recommendations ...... 84 REFERENCES ...... 87 APPENDICES ...... 91 Appendix 1: Letter of Introduction ...... 91 Appendix 11: Study Questionnaire ...... 92

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LIST OF TABLES Table 4.1: Response Rate ...... 33 Table 4.2: Gender of Respondents ...... 34 Table 4.3: Age Group of Respondents ...... 34 Table 4.4: Highest Level of of Respondents ...... 35 Table 4.5: Gender and Number of Cows Owned ...... 36 Table 4.6: Number of Cows Owned and Litres of Milk Produced ...... 37 Table 4.7: Level of Education and Selling Milk ...... 38 Table 4.8: Selling Milk and Price ...... 39 Table 4.9: MulticollinearityTest ...... 40 Table 4.10: Opportunities for Dairy Farming Agribusiness ...... 42 Table 4.11: KMO and Bartlett’s Test for Opportunities for Dairy Farming Agribusiness ..... 44 Table 4.12: Total Variance Explained for Opportunities for Dairy Farming Agribusiness ... 45 Table 4.13: Pattern Matrix of Idealized Influence ...... 46 Table 4.14: Correlations between Demographics and Opportunities in Dairy Farming ...... 48 Table 4.15: Model Summary for Opportunities for Dairy Farming ...... 51 Table 4.16: ANOVA for Opportunities for Dairy Farming ...... 51 Table 4.17: Coefficients of Opportunities for Dairy Farming and Dairy Farming Enterprise 52 Table 4.18: Challenges in Dairy Farming Agribusiness ...... 54 Table 4.19: KMO and Bartlett’s Test for Challenges in Dairy Farming Agribusiness ...... 55 Table 4.20: Total Variance Explained for Challenges in Dairy Farming Agribusiness ...... 56 Table 4.21: Pattern Matrix for Challenges in Dairy farming Agribusiness ...... 58 Table 4.23: Model Summary of Challenges in Dairy Farming Agribusiness...... 63 Table 4.24: ANOVA for Challenges in Dairy Farming Agribusiness ...... 63 Table 4.25: Coefficients Variation of Challenges in Dairy Farming Enterprise ...... 64 Table 4.26: Strategies to Encourage Dairy Farming Agribusiness ...... 65 Table 4.27: KMO and Bartlett’s Test for Dairy Farming Strategies ...... 67 Table 4.29: Pattern Matrix of Dairy Farming Strategies ...... 69 Table 4.31: Model Summary for Dairy Farming Strategies ...... 74 Table 4.32: ANOVA for Mentorship and Coaching ...... 74 x

Table 4.33: Coefficients Variation of Dairy Farming Strategies ...... 75

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CHAPTER ONE

1.0 INTRODUCTION 1.1 Background of the Study

Dairy production is a biologically efficient system that converts large quantities of roughage, the most abundant feed in the tropics, to milk, the most nutritious food known to man. It is also more labor intensive and supports substantial employment in production, processing and marketing (De Leeuw, Omore, Staal, & Thorpe, 2010). A dairy product is food produced from the milk of . Dairy products are usually high energy-yielding food products. A production plant for the processing of milk is called a dairy or a dairy factory. Apart from breastfed infants, the consumption of dairy products is sourced primarily from the milk of cows, yet goats, , yaks, horses, camels, and other mammals are other sources of dairy products consumed by (De Leeuw, et, al. 2010).

An estimated 75 percent of the world’s poor live in rural areas, and at least 600 million of these people possess to produce food, generate income, manage risks and increase assets (FAO, 2010). The development of small-scale livestock enterprises must be seen as a key element of any efforts to eliminate extreme poverty and hunger, and it is the important contribution livestock makes to sustaining livelihoods, especially in rural areas (FAO, 2011).

In sharp contrast to the industrially advanced nations of the world, dairying in differentiates itself in several socio-economic features. It ranks first in the bovine population with 196 million cattle and 80 million buffaloes (a total 276 million animals), and accounts for about 51 percent of Asian and about 19 percent of the world bovine population (Kurup, 2012). With an annual increase of 4.7% in milk production since 1971, dairying has played a prominent role towards household nutrition security and also in strengthening the rural economy. It has also been recognized as an instrument to bring about socio-economic transformations in the rural sector. The dairy sector has helped the national economy by emerging as the highest milk producing country in the world. According to FAO’s Economic and Social Development estimate (FAO , 2011), India’s milk production has increased from a mere 17 million tons produced in 1951 to 74 million tons in 1998. This is now 13.5 percent

1 of the world’s milk production. This progress made by India in the field of dairying may be attributed to the concerted efforts of a large number of milk producing farmers, scientists, planners, NGOS, dairy co-operatives and the (Mathur, 2011).

The majority of African countries economy is dependent on agriculture, it helps to create job opportunities, income generation, and involve in exports. As money received by the most family increases, they are more excited to save and expense money, stimulating to growth and investment in other sectors. Agricultural sector is an essential to rise continuously to deal with famine, poverty and unfairness. Because, having a strong agriculture sector helps to creates more job opportunities, earn more money and more food for the poor (FAO, 2003).

The sub-Saharan Africa livestock sector performance in the last twenty years has not been sufficient; in most African countries, development in livestock production has been inadequate even to keep levels of consumption (Addis, 1989). According to Massow, (1989), it is hard to increase production and marketing systems which can efficiently provide the rising urban demand where traditional pastoral systems make milk mostly for survival in the infertile zone and parts of the partially fertile zone. Furthermore, African governments have often got involved on behalf of urban interests to the damage of producer price incentives. Like other African countries, agriculture is the main determinant for Ethiopian economy, and livestock is a crucial component of the rural economy and the livelihood of the survival farmers. The livestock subsector plays an essential role as basis of food, income, services and foreign exchange to the Ethiopian economy, and gives to 12% and 33% of the total and agricultural GDP, respectively, and accounts for 12–15% of the total export earnings (Ayele, Assegid, Jabbar, Ahmed, & Belachew, 2003). Due to having a large livestock population, a favorable climate to get a better production, and comparatively the environment is free from disease for livestock; Ethiopia holds huge possibility for dairy development. Ethiopia obtain milk production largely from cow however, it also get a little bit quantities from goat and camel in some regions specifically in pastoralist areas like Afar and Somali region (Kaitibie, Omore, Rich, & Kristjanson, 2010). Although the livestock population is large, the available livestock subsector in Ethiopia is small in production in general, and compared to its potential, the direct contribution it makes to the national economy is insufficient. Ethiopia is

2 currently unable to meet the increasing demand for milk and other dairy products for its increasing population, especially in the urban areas (Getachew, 2014).

Kenya is one of Africa’s largest dairy producers and the country relies heavily on its dairy sector (Atieno & Kanyinga, 2014). In Kenya, smallholder dairy production is estimated to constitute 56% of the total milk production and Kenya has approximately 2.5 million spread out on 625,000 smaller (Omore, Muriuki, Kenyanjiu, Owango, & Staal, 2009). The sector employs many Kenyans and functions both as a source of economic income and as a part of their household diet. Smallholder dairy farming is therefore vital to the livelihoods of these farmers. Around 60% of the milk produced in Kenya comes from the fertile lands in the Rift Valley and Central Province.

The dairy sector in Kenya has undergone different political courses, ranging back before the independence of the country in 1963. In the pre-independence period, European settlers brought with them the practice of dairy cattle breeding and took over most of the agricultural land in Kenya in order to perform large-scale farming for export, supported by the colonial administration (Muriuki, 2011). The relationship between state and settle farmers had importance in relation to political regulation. As a result of reductions in dairy prices, following the First World War depression, many dairy operators merged into bigger corporations like the Kenya Co-operative Creameries (KCC), which was established in 1925 (Atieno & Kanyinga, 2014). The KCC was created to market and process the products, thereby supporting the settlers’ dairy production (Atieno & Kanyinga, 2014). In 1954, the Swynnerton Plan was released, giving indigenous Kenyans the right to obtain commercial farming (Muriuki, et al., 2013). Consequently, the indigenous people started participating in small-scale commercial farming and the farmers gathered in co-operatives in order to strengthen the marketing. In 1958, the KCC became the dominant agent in marketing for the dairy farmers and later the Kenya Dairy Board (KDB) was founded to regulate the market (Atieno & Kanyinga, 2014).

After independence, new policies entailed full inclusion of the indigenous Kenyans into the dairy production (Muriuki, 2011). Large-scale farms were split up and sold, resulting in a high increase of smallholder dairy farms (Omore, Muriuki, Kenyanjiu, Owango, & Staal,

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2009). The dairy sector was prioritized as a key factor for the country’s development and was highly subsidized by the government up until the 1980’s (Atieno & Kanyinga, 2014). In 1992, the dairy industry was liberalized and different players now entered the market to compete with KCC. One of the new operators were informal buyers, made up by small-scale milk traders, also known as hawkers, buying raw milk from the farmers to sell to consumers (Atieno & Kanyinga, 2014). Liberalization thus led to a growth in the informal market, responding to the breakdown of many marketing co-operatives in the 1990’s. Additionally, the liberalization led to an increase of formal private processors, such as Brookside. However, the formal actors struggled to compete with the informal market which is estimated to now provide around 86 % of the marketed milk to the consumers in Kenya (Kaitibie, Omore, Rich, & Kristjanson, 2010).

To counter this development, the informal milk market has been widely suppressed by policy makers, who mainly answer to well-resourced representatives from the formal market. Policies have therefore often failed to accommodate for the interests of the informal market players, despite the fact that these smallholder dairy producers and traders constitute the vast majority of stakeholders in the Kenyan dairy industry (Leskmono, Young, Hooton, Muriuki, & Romney, 2006). The policy makers and formal market players especially criticize the informal market for the health risks and quality concerns associated with poor hygienic practices surrounding the production and selling of raw milk (Kaitibie, Omore, Rich, & Kristjanson, 2010).

The Dairy Master Plan of 2010 aims to transform milk production in Kenya into a globally competitive dairy sector with high quality standards with regards to environmental and public health ( (Ogionwo, 2011), 2010). Policy makers are therefore currently emphasizing on promoting higher production and a shift to the formal dairy market. When formulating and promoting these policy goals it is however important to consider the main stakeholders in the industry and take their challenges regarding milk production and marketing into account. In order to do so, a case study was carried out in Bathi, Kiambu County, where the challenges that the smallholder dairy farmers encounter, concerning milk production and marketing, were investigated. The Sustainable Livelihoods Framework was applied as a theoretical tool to better understand and assess the farmers challenges and associated livelihood strategies.

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1.2 Statement of the Problem

By the year 2030, the present proportion of urban population will enlarge from 75% to 83% in Latin America and Caribbean, from 37% to 53% in Asia and Pacific and from 38% to 55% in Africa (FAO, 2003). As the population density expected to increase, the consumption for milk increased during the last twenty years are estimated to keep on in the coming years, and creating an actual livestock change due to there is a rapid population growth, more people leave countryside to live in urban and expected income growth. This change gives new and increasing market opportunities for small holder livestock producers (Delgado, 2009).

The fast movement of people from countryside to live in cities, following increase in human inhabitants and standard of living of the urban population can be thought as a good opportunity for the expansion of dairy in the area (Sintayehu, Fekadu, Azage, & Berhanu, 2015).

Countries that are presently taking pleasure in the maximum standard of living are those that have a well developed animal agriculture as demand for animal products enhance with economic growth. In Kenya context, in spite of the large capacity for development the country has to make milk, there is a constant shortage of the product in most part of the country. This is reflected where only 5% of milk produced in rural areas is marketed as liquid milk (Getachew, 2014). This occurs largely from inadequate production joined with inhibitive cultural undesirable related to consumption and lack of correct processing and marketing (Zegeye, 2013).

1.3 General Objective

The main objective of the study is to determine the challenges, opportunities and strategies for farmers engaged in dairy farming in Ruai Area in Nairobi County.

1.4 Specific Objective

The objective of the study is to:

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1.4.1 Explore opportunities available for farmers who venture in dairy farming in Ruai area. 1.4.2 Find out challenges affecting farmers who venture in dairy farming 1.4.3 Determine strategies to encourage more farmers to venture in dairy farming enterprise

1.5 Significance of the Study

1.5.1 Ruai Dairy Farmers

The information from this study will benefit dairy farmers from Ruai, Nairobi County. The farmers will use the information from this study to understand the challenges and opportunities of dairy farming and make on time decisions on how to increase milk production and enhance marketing.

1.5.2 Dairy Board of Kenya

The study will benefit the Dairy Board of Kenya by enhancing it with more relevant information about dairy farming. The study will provide information on the opportunities and challenges available in the dairy farming. The study will also offer best strategies to enhance dairy farming.

1.5.3 Other Dairy Farmers in Kenya

This study is intended to benefit other dairy farmers in the industry in helping them to understand and react proactively to the challenges and opportunities arising from this environment. This will make them develop strategies to minimize the impact of the challenges and utilize the opportunities to grow their business.

1.5.4 Scholars and Future Researchers in Colleges and Universities

For scholars, it will give full proof that understanding challenges and opportunities in dairy farming is essential for the better performance of the industry. The study will also be useful

6 to researchers in providing an in-depth understanding of challenges and opportunities in dairy farming.

1.5.5 Policy Makers

This study will inform policy makers on the need to make and implement good policies that would help different dairy farmers in managing the challenges and utilize on the opportunities arising from the industry.

1.6 Scope of the study

The focus of this study is on dairy farmers from Ruai, Nairobi County. The study will be assessing the challenges and opportunities for farmers engaged in dairy farming in Ruai Area in Nairobi County. The study will target the dairy farmers from Ruail area to obtain information pertaining to challenges and opportunities arising from dairy farming. The data collection tool will be a questionnaire hence the study will divide the respondents into different stratum. The study will take place in Nairobi County at Ruai area as it is the main focus of the study. The research will go for a period of six months starting from January 2018. The study is expected to experience a limitation of obtaining relevant data from the respondents, as respondents are deemed not to be sure of what the researcher needs data for. Also cost for the study will be a hindrance as the researcher will be needed to print research materials, employ research assistance and do phone follow ups.

1.7 Definition of Terms 1.7.1 Dairy Farming

Dairy farming is a class of agriculture for long-term production of milk, which is processed (either on the or at a dairy plant, either of which may be called a dairy) for eventual sale of a dairy product (Delgado, 2009).

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1.7.2 Challenges

A challenge is a situation of being faced with something that needs great mental or physical effort in order to be done successfully and therefore tests a person's ability (Ayele, Assegid, Jabbar, Ahmed, & Belachew, 2003).

1.7.3 Opportunities

Opportunity is defined as an exploitable set of circumstances with uncertain outcome, requiring commitment of resources and involving exposure to risk (Zegeye, 2013).

1.7.4 Strategy

Strategy is an action that managers take to attain one or more of the organization’s goals (Okereke, 2016). Strategy can also be defined as a general direction set for the company and its various components to achieve a desired state in the future. Strategy results from the detailed strategic planning process (Kedija, 2013).

1.7.5 Dairy Products

Dairy products or milk products are a type of food produced from or containing the milk of mammals, primarily cattle, water buffaloes, goats, sheep, camels and many more (Kurup, 2012).

1.7.6 Agribusiness

Agribusiness is the business sector encompassing farming and farming-related commercial activities. The business involves all the steps required to send an agricultural good to market: production, processing, and distribution. It is an important component of the economy in countries with arable land, since agricultural products can be exported (Anderson & Feder, 2013).

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1.8 Chapter Summary

Chapter one presents the background information about challenges and opportunity for farmers in dairy farming. This section also outlines the objectives of the research, the significance of the study, importance and the scope of the study as well as the definitions of specific terms used in the research. Chapter two will review literature which is guided by the research objectives identified in chapter one.

Chapter three will identify the research methodology that highlights the various procedures and methods that will be used by the researcher while conducting the research. Chapter four will present the results and findings while chapter five will provide a discussion on the findings of the research guided by the specific research objectives then a conclusion and recommendation of the study given.

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CHAPTER TWO

2.0 LITERATURE REVIEW

2.1 Introduction

This chapter reviews and presents the literature on challenges and opportunities for farmers engaged in dairy farming. The study is divided into different sections. The first section is on opportunities available in dairy farming, the second section is on challenges in dairy farming and the third section is on the strategies to encourage dairy farming. The last section of this chapter is a summary of the whole chapter.

2.2 Opportunities Available in Dairy Farming 2.2.1 Opportunities

An opportunity is an occasion or situation that makes it possible to do something that you want to do or have to do, or the possibility of doing something. There are numerous opportunities in the dairy farming that farmers take advantage of. These opportunities are; new technologies, interlink between sectors, marketing through rural milk traders, and agriculture extension services. The opportunities in dairy farming are discussed as in the below sub-section.

2.2.1.1 Take Advantage of New Technologies

At a technical level, emerging technologies and ‘datafication’ of agriculture are providing farmers with quantifiable information to continuously measure, react, and monitor farm operations. This can provide farmers with the insight to ensure that all decisions contribute positively to farm efficiency and profitability. Relatively simple technologies to assist with grass and herd management are becoming more widespread, and may signal the beginning of a wider trend. Various smartphone data applications, robotics (such as automated milking parlours), drones, micro-sensor technology and satellite systems are all likely to become more commonplace on the farms of the future.

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Rogers (1968) defines as an idea, practice or object perceived as new by an individual, while diffusion is the process through which the new idea spreads from a source – its original invention by a creative individual to its adoption by users. Adoption implies a decision to continue full use of the idea as distinct from a decision merely to try it, because of the benefits/advantages accruing from adopting technology. Ogionwo (2011) argues that the more innovative the farmers are the better off the they become in terms of farm income and high level of living, implying that farmers with great resources are likely to take the risks involved in going over to a new practice. Rogers (1968) indicate that the relative advantage of innovation, that is positive related to adoption of the practice, could be economically profitable or the new idea minimizes the costs. Rostow (2012) argues that revolutionary changes in agricultural productivity are essential conditions for successful take-off of economic growth of society. Chitere (2011) concurs with this argument and indicates that the adoption of technology of the community members will definitely bring social change in a given community.

According to Chitere (2011) could be introduced to a few members of a social unit, for example a rural village, then from these few members the innovations could diffuse, trickle down or be communicated to other members of the social unit. Chitere (2011) explains four factors which influence the diffusion process of innovations. First, innovation- decision process is a series of mental stages where an individual becomes aware of new ideas to the time the idea is adopted. Hence, the stages, according to Chitere, are: ‘awareness’’ where an individual has heard of the new ideas; “interest” stage where he/she seeks more information about the new ideas; “persuasion” stage during which the individual compares the pros and cons of the idea; “trial” stage he/she tries out the idea on a small scale and, finally, “adoption” where the individual opts to use the new ideas as part and parcel of his/her ongoing operations.

The second factor is personal characteristics of adopters. Some individuals adopt innovations faster than others. Such individuals tend to take risks and are more open to new ideas. Rogers and Shoemaker categorise the adopters: “anxious innovators” who comprise about 2.5%, they try new ideas, take risks and have resources that enable them to adopt new ideas; “early adopters” about 12.5% who usually have more education and resources to enable them adopt

11 new ideas, “laggards”(13%) who the last members of a community to adopt new ideas. They are usually less educated and with fewer resources for adoption of new ideas (Belete, 2010).

The third factor, on relative advantage, refers to the attributes of new ideas perceived as being better than the old idea that is replaced. An example can be seen in terms of economic profitability of savings in labour. The fourth factor the Chitere (2011) explains relates to the communication process of innovation, which refers to the transmission of information or messages from a source, for example agricultural agents, to a receiver /adopter, for example a .

Okereke (2016) argues that adoption of technology involves application of mental and physical efforts directed to achieving a better value. Technology is a tool that provides better living conditions and enhances the capacity of the people concerned. It is a systematic application of scientific knowledge to practical purposes and includes inventions, innovations, techniques, practices and materials.

Farmers implement new ideas, improve practice and use research findings in order to boost their productivity in livestock. Dairy cattle farming in Kenya were introduced by European white colonial settlers who imported the exotic breeds, mainly the Ayrshires, Freisians, Guernsey and Jersey. These breeds were later crossed with the indigenous cattle and over the years produced the national dairy cattle herd (Getachew, 2014). According to Peeler and Omare (1997), the dairy cattle population is estimated to about 3 million. In dairy sector, the milk produced in Kenya is primarily from cattle, which contribute about 84%, with rest from 12% camel, and goats 4%. The major types of cattle kept are improved exotic breeds and their crosses (60%) and indigenous zebu (24%) from the communities in drier parts of the country (GOK 1989).

2.2.1.2 Interlinkages between both Sectors

Farm production of the EU dairy sector and of the EU bovine meat sector are closely interlinked with each other as both keep cattle as the basis of their production. The differentiation consists in the cattle breed that is used for production. Cattle breeds are commonly categorized according to whether they produce mainly milk, mainly bovine meat

12 or meat and milk. Consequently, they are referred to as milk, beef or dual-purpose breeds (Muriuki, 2011). While milk breeds have a high milk productivity but only produce low quantities of meat, beef breeds grow quickly, show large daily weight gains but have lower milk yields. Dual purpose-breeds are in-between, producing milk amounts smaller than the ones of pure milk breeds but substantially above the milk yield of beef breeds. On the other hand, they show a faster growth and develop more meat than pure milk breeds but less than pure beef breeds. Hence, milk breeds are predominantly used for milk production, while beef breeds for bovine meat production. Cows of dual-purpose breeds are usually milked, but they and their off-spring also provide good meat yields. Finally, the breeds of each cattle type can be freely crossed with breeds of other types so that the off-spring is crossbred.

The cows are central to the reproduction of cattle herd. For the reproduction of the dairy herd, female calves of the milk-type are needed, while for the reproduction of the beef cattle herd, female calves of the meat-type are needed. However, for meat production male calves of the meat-type are preferred, as they show larger daily weight gains than female calves. The choice of the sperm used for insemination of the cow in combination with the breed of the cow determines to which cattle type the resulting will belong to (Kaitibie, Omore, Rich, & Kristjanson, 2010).

2.2.1.3 Marketing through Rural Milk Traders

Traditionally, the most important middlemen are the numerous rural milk traders, in Ethiopia commonly called katcha dodhis. Equipped with a bicycle or horse cart, or in some cases now with a motorcycle and two to four milk cans, they make daily visits on average to 15–20 small milk producers, collecting some 75–90 litres of raw milk. This may take three hours and the distance cycled can easily be 20 km. Most of the katcha dodhis are independent, only a few are employed by larger highway collectors. Under the traditional system, women sell the milk (Atieno & Kanyinga, 2014).

Where competition is strong, usually in production areas with good access, the katcha dodhis often have contracts with the producers to secure milk supply for a certain period. Then the purchasing price may be fixed, interest free advances may be given or both ties will be used.

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The value of advances usually corresponds to the value of milk supplied within two to four weeks. As most katcha dodhis do not have sufficient resources to finance their suppliers, they in turn get advances from the larger collectors and sometimes from rural shopkeepers. If no advances are granted, payment is normally effected within one week after milk collection (Kurup, 2012).

With a few exceptions, milk is collected only in the morning, the evening milk being used mainly for home consumption. Milk is always collected by volume, never by weight, using measures of varying types and sizes. Milk producers normally supply pure, unadulterated milk; however, to prevent deterioration of the milk during their collecting tour, especially in the hot season, the katcha dodhis add certain quantities of ice to lower the temperature. The ensuing dilution may well result in a 10–20% increase of the milk volume (FAO , 2011).

2.2.1.4 Agriculture Extension Services

Dahama and Bhatnagar (1987) define extension as education applied on behavioural science, the knowledge of which is applied to bring about desirable changes in the behavioural complex of human beings, usually through various strategies and programmes of change and applying the latest scientific and technological innovations. Extension education aims at dissemination of useful and practical information relating to a sector of development such as agricultural extension and livestock extension aimed at improving productivity. Agricultural extension is a program geared towards learning rather than teaching paradigm. Morris (1999) indicates that agricultural extensions promote agricultural technologies to meet farmers’ needs. The extension education brings desirable changes in the quality of life of the target group that it serves by helping them to change their attitudes, knowledge, skills and resources such as land, pasture, water and livestock. According to Okereke (2016), extension services involve teaching, research and transfer of new technologies and information to farmers using different media like radio, television, or newspapers.

Madukwe (2006) describes three approaches used by extension agents in passing of agricultural technologies. These are, first, extension-farmer contact, which refers to a situation where an extension officer contacts a farmer on a one to one basis passing on

14 agricultural information. Although method is very effective, it is expensive and has narrow spectrum. The second approach is the farmers’ group, which refers to passing of agricultural technologies to farmers in organized groups who are interacting together towards achieving a common goal. The farmers form a group supporting one another to learn and adopt technology, hence amplifying extension process. In this method, extension agents not only impose outside technologies but also act as catalysts and mobilize of farmers in recognizing local innovations, helping to assess and encourage adoption of technologies. The approach enhances the dissemination of information to a wider spectrum of users.

The third one is called the Farmer field School Approach, where farmers meet periodically with facilitators. It is a participatory method of technology development and dissemination based on adult learning principles and experimental learning, hence facilitates farmers’ demand for knowledge and offers an opportunity for the end users to choose, test and adapt technologies according to their needs. The approach reflects the four elements of experiential learning cycle; concrete experience, observation and reflection, generalization and abstract conceptualization, and active experimentation.

Anderson and Feder (2013) argue that investments in extension services have the potential to improve agricultural productivity and increase farmers’ incomes especially developing countries where more than 90% of the world’s nearly one million extension personnel are located. According to Muyanga and Jayne (2006), a consensus exists that extension services, if functioning effectively, improve agricultural productivity through providing farmers with information that helps them to optimize their use of limited resources.

The ultimate objective of livestock extension education is the development of livestock farmers by improving their living standards through bringing desirable changes in attitudes, skills and knowledge about recent technologies and their applications. The livestock extension education plays an important role in empowering the farmers with appropriate technological knowledge and skills through various forms of extension education and training programmes (ILCA, 2011).

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In dairy farming, the extension personnel educate dairy farmers/producers on the best way to use to improve livestock productivity. The extension agents demonstrate new technology and teach better management practices to dairy farmers through farm visits, newsletters, meetings, seminars and field days (Land O’Lakes, 2008). The extension agents include the veterinarians who advise farmers about general animal health problems provide health services and care. They also offer reproductive and health programme and animal feed consultants who advise farmers on animal nutrition and feeding programmes. Dairy technologists educate farmers on dairy products processing and value addition.

2.3 Challenges in Dairy Farming 2.3.1 Challenges A challenge is a situation of being faced with something that needs great mental or physical effort in order to be done successfully and therefore tests a person's ability (Ayele, Assegid, Jabbar, Ahmed, & Belachew, 2003). Dairy farming has been found to face many challenges as it tries to get through the agribusiness industry. The study found that the challenges that dairy farmers face are; genetic limitation, inadquate animal feed resources, animal health problems, reproductive problems, inadequate extension and training services, lack of research and information exchange system, lack of education and consultation, policy and socio-economic challenges, milk market linkage challenges, and limited availability of credit to the dairy farmer.

2.3.1.1 Genetic Limitation

About 99% of the cattle populations in Africa especially Kenya are indigenous that are adapted to feed and water shortages, diseases challenges and harsh climates. The productivity of indigenous cattle believed to be poor even if no practical recording scheme has been used to judge their merit (Mathur, 2011). The main problem of milk production in the country is that of the poor genetic potential of the indigenous cattle, which gives to low milk output. Crossbreeding has been practiced with encouraging results. However, a strictly controlled breeding program has not been practiced. Milk production is as low as 0.5 to 2 litres per day over a period of 160 to 200 days. Improving the feeding water availability and health care of indigenous cattle did not increase the quantity of milk per day to allow the

16 animals to be used for commercial market-oriented milk production (Muriuki, 2011). If improvement of the local Ethiopian breeds for milk production is targeted, then it is important to have a designed selection program in place for a few selected promising breeds (Zegeye, 2013).

2.3.1.2 Inadequate Animal Feed Resources

The primary constraints to increased milk production under all production systems are inadequate feed resources, poor pasture development and the ever increasing feed prices. Farmers tend to be keep cattle at stoking rates that for exceeds the carrying capacity of their lands. This has resulted in degraded pastures and eroded soils. Stock numbers are not normally reduced in the dry season leading to grazing lands becoming progressively over grazed. In the dominating crop/livestock production system, producers supplement the feeding of their cows with crop residues and farm by products from their farms (Solomon- Bekure, Grahdin, & Nate, 2011).

2.3.1.3 Animal Health Problems

The prevalence of various animal diseases, tick borne diseases, internal and external parasite and infectious diseases affect dairy development programs in various scale, depends on ecological zones and management levels. A number of parasite, bacterial, fungal and viral diseases and nutritional deficiency which are prevalent in the country affect the productivity and reproductive efficiency of dairy cattle and make individuals insecure to be involved in and invest on dairy production specially cows used with exotic blood (Rostow, 2012). Among these diseases, venereal diseases have a direct effect while nutritional deficiencies and other infectious diseases play an indirect role in hampering the reproductive efficiency of dairy cows. The animal health services provided are inadequate, the costs of drugs is very high, while the diagnostic services are not readily available to the dairy farmer. This is partly attributed to the insufficient budget allocated to veterinary services. As it has been reported; an overall diseases occurrence 46.8% and 33.6% in urban and per-urban in the central highlands, respectively (Ibrahim & Olaloku, 2009).

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2.3.1 Reproductive Problems

Reproductive efficiency is a critical component of successful dairy operation and act as an important component of a profitable dairy farm, whereas reproductive inefficiency is one of the most costly problems facing the dairy industry today. Reproductive problems occur frequently in lactating dairy cows and dramatically affect reproductive efficiency in dairy herd. Some of the most common problems include: twining, dystocia, abortion, stillbirth, retained placenta, pyometra and repeat breeder (DAGRIS, 2014). These are diverse disorders that are similar in that they all can result in impaired reproductive function. Deciding whether to breed, treat, or cull dairy cows exhibiting one or more of these reproductive problems is a challenges for both veterinarians and dairy producers. In addition, there is a considerable controversy among dairy scientists and bovine practitioners regarding the economic impacts of these problems in dairy operation and the most effective management or therapeutic intervention for treating them (Delgado, 2009).

Dairy farming should focus on prevention and control of risk factors associated with each problem rather than on prescriptive therapeutic interventions. Low fertility reduces the profit by decreasing the average milk production and the number of calves per cow per year. Poor reproductive performance is a major cause of involuntary culling and therefore reduces the opportunity for voluntary culling and has a negative effect on the productivity of a dairy herd. Reproductive performance is influenced by the interactive effect of environment, management, health and genetic factors (FAO, 2003).

2.3.2 Inadequate Extension and Training Services:

Effective and adequate extension services, advice on animal nutrition and feeding management, reproduction, hygiene, extension works to transfer new technologies, training in milk handling and marketing, farm management and dairy production efficiency are not always available to the dairy farmer. There is no extension to supply information about technologies to improve production and marketing to estimate certain development. A shift towards a developed dairy industry requires more support from advisory services and more effective links with research services (Kedija, 2013).

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2.3.3 Lack of Research and Information Exchange System

Considering the importance of central institutions to guide and coordinate agricultural researches and unorganized information system to publicize results of research works, new technologies and policy, weak leakages between research, extension and technology users are one of the critical factors that hinder dairy development in the country. This weakness stems partially from the absence of sound linkage polices in the agricultural knowledge generation and transfer systems (Ogionwo, 2011).

2.3.4 Lack of Education and Consultation

There are shortages of qualified personnel, poor education and management expertise of farmers, miss understanding of production systems and lack of knowledge gained through researches to farmers, ignorance the experience and knowledge of local farmers and absence of forums for consultation and discussion with the farmers (Anderson & Feder, 2013).

2.3.5 Policy and Socio-Economic Challenges

Unavailability of land: The problem of inadequate feed is as a result of the limited land available for pasture establishment, especially in the productive highland zones that have a potential for dairy development. In Amhara region, for instance, nearly all the land suitable for cultivation is already in use, while in Oromia land is scarce in many areas (Chitere, 2011).

The scarcity of the land is becoming a critical problem in many parts of Ethiopia, in certain localities are estimated 50% of the population have a problem of land scarcity. If land degradation is not halted and reversed in some areas of the country, it would become extremely difficult to expand dairy production. In the traditional sector, land becomes a challenge to milk production as a result of overstocking, in urban and per-urban dairying, lack of grazing land is often a limited factors. The intensification of the dairy industry by using fewer numbers of improved dairy cows with increased productivity per cow should be a strategy to be followed (De Leeuw, Omore, Staal, & Thorpe, 2010).

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2.3.6 Milk Market Linkage Challenges

There are no promotional activities being carried out by various government offices to portray milk as a highly nutritious and essential food for the health of nation. There are also no price regulatory mechanisms in place that can much such an important food item easily available and affordable to a large segments of the population. As earlier mentioned, there are no functional quality controls and payment systems in the country (Kaitibie, Omore, Rich, & Kristjanson, 2010).

Improving market access to dairy product creates an opportunity for enhanced dairy production. However, marketing and access to market have been reported to be the major problems (Sintayehu, Fekadu, Azage, & Berhanu, 2015). Distance to market, shortage of milk and seasonal fluctuation in milk supply has been reported to be the major determinant across all the production systems. Besides, lack of access to market (21.2%), cultural taboo to sell milk (20.8%), spoilage of milk (1.9%) and high transport cost have been identified to be the major reasons for weak market access (Rostow, 2012).

2.3.7 Limited Availability of Credit to the Dairy Farmer

Many farmers are aware of the existence of the improved technologies that can offer them higher returns as compared with their conventional practices. However, most of the poor farmers do not have financial means required to make the initial investment and acquire the associated technological inputs, financial supports or credit facilities to smallholder farmers who intend to enter in to commercial dairy farming are very much limited. The importance of establishing credit facilities is crucial step to the country’s dairy sector as indicated in the livestock development master plan (Belete, 2010).

2.4 Strategies to Encourage Dairy Farming 2.4.1 Genetic Improvement

Every dairy farmer desires to have a high producer cow in terms of yielding milk enough for his family and for commercial purposes, besides high conception rates (Wattiux, 1992). Farmers acquire such cows by buying a genetically developed cow or genetically improving

20 their existing cows with the aim of getting a cow that will produce more milk. However, according to Baltenweck (2012), buying a high dairy producer is very expensive and most smallholders cannot afford. However, small dairy farmers can still get high producers cattle through genetic improvement of their existing herd.

Wattiux (1996) defines genetic improvement in cattle as utilization of exotic breed and their crosses genotypes (genetic makeup) or alleles (genes) present in an individual cow that are responsible for high milk production. The production of milk requires the action of numerous genes, each responsible for a specific aspect of milk synthesis. These include: Genes responsible for the synthesis of the secretory tissues in the udder; Genes responsible for the blood supply to the udder; Genes involved in the capacity of the cow to digest and metabolize food.

In addition to the action of the genes, synthesis of milk requires availability of the building blocks of milk components (protein, glucose, minerals, fat and vitamins) which come from the digestion and metabolism of the feeds, thus feeding influences milk production. According to Mendel (2014), these alleles are located in sex cells, which are transmitted during fertilization. Cattle with superior genes of high milk potential production should therefore, be used for upgrading the existing dairy herd. The dairy cattle (both exotic breeds and their crosses) population in Kenya has now grown to an estimate of 3 million (Peeler & Omare, 1997).

2.4.2 Artificial insemination (A.I)

This is a technique by which semen is introduced artificially by a technician into the genital tract of the female at the time of sexual receptivity in attempt to cause a pregnancy (Wattiaux, 1996). AI was pioneered by a Russian scientist working with horses and was first used by Danish breeders on large scale in dairy cows. The method is currently practiced in Kenya and with liberalization of the dairy industry in 1992; the AI service was fully privatized. The semen is packed in plastic straws and stored in a liquid nitrogen refrigerator maintained at – (1960c). This technique is performed by a technician who has special

21 training and understands the steps involved in the procedure. All these costs are now incurred by the farmers, so it has become very expensive for an ordinary small dairy farmer.

According Okereke (2016), improvement through breeding aimed at increasing milk yields has been very low in developing countries due to poor implementation of government policy in breeding, lack of proper national herd recording system and local breeds, which are genetically poor for milk production.

AI provides opportunities to choose sires that are proven to transmit desirable traits in a dairy cow population. AI eliminates the costs and the risks of maintaining on the farm. It minimizes the risk of getting offspring with undesirable traits. It also provides the opportunity for providing sires at a good age. Thus, the genetic make-up of a proven sire is known with a certain degree of confidence, but that of a on the farm is usually unknown. AI further minimises the risk of spreading STDs. The benefits of AI are offered cumulatively over generations of cows. The genetic value of cows increases rapidly over time as a result of intensive selection from one generation to the next (Rogers, 1968).

Artificial insemination requires a large degree of cooperation between the breeders, the technicians, the insemination countries and the breeding associations. Although a number of farmers are using AI especially in high potentials areas, a big number farmers do not know of the existence and importance of AI service, which is advantageous over natural method. Some people unfortunately believes that the AI conception rates are low, and that the calves resulting from AI are physically weak and cannot withstand the harsh conditions. Radostits, et al (1983) indicates low conception rates are mainly contributed to by ignorant farmers who are unaware of details, or failure to know the signs of a cow on heat and poor timing of AI service. Therefore to increase the conception rate, it is necessary to educate farmers on heat detection so that cows can be served at the right time.

In Kenya, dairy breeding started in 1920 with formation of Kenya Stud Book that kept the upgrading register. In 1946, CAIS was established with the objectives of semen production and catering for formation dairy recording services of Kenya. In 1969 the national artificial scheme was launched, which covered three quarters of all high potential small holders areas

22 with the main objective of supplying dairy farmers with better quality breeding stock through AI. In 1992 the government, under external pressure and budgetary constraints, liberalized the dairy industry including privatisations of the AI services. Since then, AI has become expensive and unaffordable to majority of smallholders. This has led to continuation of use of natural methods, whose disadvantages by far outweigh the advantages. It is expensive to keep a bull; there is likelihood of STDs transmission and high chances of physical injuries to the animal and to the farmer by a bull (Wattiux, 1996).

2.4.3 Nutrition

The state of feeding technology of dairy cattle, especially in developing countries, is wanting. According to Belete (2010), whose case study on feeding management of dairy cows cited inadequate nutrition as a major constraint that negatively affects the growth and viability of dairy farming. A well-fed animal will grow faster, reach reproduction stage early and produce more milk, remain in good health status and maintain good body condition. Delgado (2009) suggests that dairy animals require certain foods for body maintenance and for production. The maintenance ration varies with breed and size of the animal, whereas production ration is required by dairy cattle for milk production.

Dairy cows that produce more milk will therefore require more and richer amounts of food. These are in form of fodder (grass), legumes and other edible plants. In pastoral areas, grass is the most available, cheap and best to feed cattle. To enhance milk production, dairy cattle are also supplemented with concentrates and mineral salts. These are important in correcting certain nutrients deficiencies in and low feed (fodder) intake. The availability and prices of concentrates are variable especially in developing countries where animals are competing with man for food. The emphasis should be placed on home-made or village- produced processing by-products rather than on commercially compounded feeds (Ayele, Assegid, Jabbar, Ahmed, & Belachew, 2003).

In Kenya, a majority of smallholder’s farmers keep more animals than they can feed from their own land. Estimates by Reynolds et al (1999) show that smallholder farmers produce about 70% of the feed from their own resources.

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Dairy cattle must be provided with water throughout. Water comprises 70% of the lean animal body and is an active structural constituent. It is important in body metabolism, digestion and secretion. According to Zegeye (2013), dairy cattle suffer more quickly from an inadequate water intake than from deficiencies of any other nutrient. Milk production and feed intake will be depressed if free access to water is not allowed. However, most smallholders do not have a reservoir for water, others fetch the water from river causing extra labour cost; this may be straining in providing enough water to dairy cattle. This can be improved by enhancing rainwater harvesting into roof water catchment tanks.

Henderson et al, (1983) indicates that fodder is the major component of the feed of the dairy cattle. It is cut from the growing areas and sun-dried and then fed to the cows as dry matter. It provides the cow with energy, proteins, minerals and vitamins. This includes (dried grass), stocks, stock, , Napier grass, legumes, Lucerne and kales. Grass can also be grown and grazing management systems applied to ensure maximum utilization of the grown grass. Small scale holders will therefore be required to grow fodder in their available farms to feed their cows. However, farmers in urban areas, because of their limited size of land, will be required to source the fodder elsewhere – most likely will buy in the surrounding areas.

In order to maintain productivity for the dairy cattle, especially the dry seasons, smallholder dairy farmers need to improve feed availability. However, it is obvious that no Kenya farm can be correctly stocked for all times of the year (Mugivane, 1999). Smallholders, due to limitations of land, finances and increased population pressure, do not grow enough for their cattle through the year. However, extension officers advise and show farmers method to preserve the feed during the time it is in excess so that there is adequate feed during dry seasons.

The most common method of fodder conservation is making. By this method green food is preserved with relatively slight losses, it is the process that if carefully carried out, will provide a succulent feed for stock when dry conditions prevail and little or no succulent food is available. Silage making is a process within the capacity of any farmer no matter how limited their facilities may be (Henderson et al, 1983).

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Hay making is another way of preserving feed. It involves a reduction in the moisture content of a cut green crop/grass by natural means, until it can be stored in bulk without the risk of spoilage by fermentation mould growth (Belete, 2010). The making of hay and storing is a very valuable means of preserving dry weather feed, and the quality of hay depends on nutritive value of the original material use. Due to increasing population pressure on the arable land for dairy farming, cattle are being confined in a stall and fed there all year. There is minimal movement of cattle because they are not allowed grazing in the fields. The model is useful in areas where there is shortage of grazing land, low productivity of dairy cows and high prevalence of diseases. However, this requires an increased level of labour needed in cutting the fodder and cleaning the stall (Mugivane, 1999).

2.4.4 Animal Health Care

In both developing and developed countries, animals’ diseases, parasite infestation and public health problems constitute a major problem to livestock production and safe utilization of animals’ products. Disease outbreaks, especially the contagious and zoonotic types, lead to serious socio-economic consequences such as production losses, loss of livelihood, food insecurity, poverty, restriction of marketing opportunities and public health risks. Globally, the OIE (World Organisation for animal health) insist that animals for trade must be in good health and free from contagious diseases to the people or to other animals.OIE ensures safety of international trade of animals and their related products by issuing harmonised sanitary guidelines on international certification and disease control methods to minimize adverse economic losses and human deaths (DAGRIS, 2014). This promotes international trade in animals and animal products by ensuring scientific based standards are met. However, most developing countries have social and economic pressing problems which mean that animal’s diseases control policies can only be implemented when the diseases cause serious losses and threaten the lives of the people (Kurup, 2012).

The diseases affecting dairy cattle can be classified as metabolic diseases, infectious, chemical conditions and parasitic infestation (both external and internal parasites) (Radostits, 1983). Prevention and progressive control of the disease is very important, especially to those that occur as outbreaks such as the FMD, LSD, Anthrax and Rift valley fever. Such diseases

25 cause major food shortages, destabilize markets and trigger trade measures. Massive vaccinations of cattle against preventable diseases and imposing quarantine in case of outbreaks, and bio-security control measures are used to contain the zoonotic and spread of diseases that can result to heavy economic losses.

2.5 Chapter Summary

This chapter determined the challenges and opportunities for farmers engaged in dairy farming in Ruai Area in Nairobi County. It has discussed the opportunities available in dairy farming, challenges in dairy farming and strategies to be adopted to encourage dairy farming. The next chapter, research methodology, will explore the best methodology the research adopted to reach to the solution of the problem.

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CHAPTER THREE

3.0 RESEARCH METHODOLOGY 3.1 Introduction

The chapter describes the research methodology and procedures that will be used to carry out the study. In this chapter, the population and sampling design are also described. Below sampling design the: sampling frame, sampling technique, sample size and research procedures are defined. Described also in the chapter are data collection and analysis techniques to be employed. Finally the chapter is summarized.

3.2 Research Design

Research design provides the framework to be used as a guide in collecting and analyzing data (Coopers & Schindler, 2014). The research design that will be employed in this study will be descriptive research design. Descriptive research is a study designed to depict the participants in an accurate way. More simply put, descriptive research is all about describing people who take part in the study. Descriptive studies describe features associated with the subject population Schindler (2011). Descriptive design will be useful for this study as it aids in measuring and finding out the relationships among variables. From the study, the descriptive research design will be used to collect in depth information about the population under study and thus provide recommendations that are specific and relevant.

3.3 Population and Sampling Design 3.3.1 Population

Population according to Schindler (2008) is total collection of elements upon which deductions can be made. The larger set of observation is the population while the smaller set is called the sample. According to Mutembei, Mulei, and Mbithi (2015), there are 80 dairy farmers in Ruai area, Nairobi County. This study therefore will focus on 80 small scale dairy farmers of Ruai area, Nairobi County.

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3.3.2 Sampling Design

A group from the population that represents the population is referred to as a sample (Coopers & Schindler, 2014). Sampling design is a procedure used in selecting a balanced representation from the total sample size which is the population under study. Sampling enables: increased speed of data collection, lower cost, and accuracy of results and availability of population elements. The study will use a simple random sampling procedure to get a representative sample size.

3.3.3 Sampling Frame

According to Cooper and Schindler (2008) sampling frame is a record of elements from which the sample is in fact drawn and it is closely interrelated to the population under study. The list could be of institutions, individuals, geographical areas, or other units (Churchill & Brown, 2007). In this study the list of small scale dairy farmers will come from Agricultural Department, Nairobi County.

3.3.4 Sampling Technique

According to Collins and Hussey (2006) sampling technique is process used in selecting elements from the population that stands for the population. Simple random sampling method was used to pick a representative sample of the target population. The sample was picked at random from the 80 farmers. This sampling technique was adopted because, as postulated by Bienstock (2006) everyone in the target population had an equal chance of being picked and contacted. As such, there was no bias in the sample selected. Simple random sampling ensured that all the farmers at Ruai were well represented and none was left out.

3.3.5 Sample Size

A sample size is a number of units (persons, animals, patients, specified circumstances, etc.) in a population to be studied (Schindler & Cooper, 2014). The study used a significant level of 0.05 and confidence level of 95%. The farmers sample was determined as the maximum sample size using the widely used and simplified Yamane (1967) formula (as cited by Singh

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& Masuku, 2014) given as n= N/ (1+N*e2), where n = sample size, N= population in this case 80 farmers, and e = precision level of +/-5% (e2=0.0025), which yields a sample size of 67. The sample sizes computed on a prorated basis are summarized below.

3.4 Data Collection Methods

Data observed or collected from firsthand experience is referred to as primary data (Cooper & Schindler, 2014). The study will obtain secondary data from agricultural department of Nairobi County. The primary data will be collected using questionnaires. The questionnaire will be designed to capture the essential information needed for analysis. It will capture respondents’ general information and specific information arising from the various objectives.

The questionnaire will contain structured questions. The researcher will assist the respondents, where required to understand the implication of the study and make sure that the response is compatible with the objective of the study. The questionnaire will be attached with a letter to the respondents informing them of confidentiality and use of the information they would disseminate.

3.5 Research Procedure

A pilot study will be conducted using five respondents to ascertain the suitability of the tool (questionnaire) before administering it in the study. Pre-testing will enable the researcher to modify the questionnaire and improve the objectivity and effectiveness.

The pilot study will be conducted to test the reliability of the research instrument in collecting the required study data. Reliability refers to the degree to which a measure produces consistent results, free of errors, and comprises three measures; stability being the consistent results from repeated measuring of the same unit of study, equivalence being the extent of variations arising from different observers or samples and internal consistency being the similarity among the measured items (Cooper & Schindler, 2014).

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Thabane et al. (2010) summarized the need for a pilot study as giving the researcher an opportunity to assess the feasibility of the study before beginning the actual research. Whereas carrying out research without a comprehensive pilot may result in a waste of time and resources, a more disastrous outcome of such as study may be findings that are statistically inconsequential due to collection of non-useful data.

The questionnaire will take approximately 15-20 minutes to complete. Accompanying the questionnaire will be a letter of introduction that assures confidentiality to the respondent. The researcher will seek the help of research assistant who will be trained and administering the questionnaire. The administration of the questionnaire will be face-to-face hence the researcher and research assistant will assist the respondent where necessary to understand the significance of the study so as to ensure that the response is compatible with the objective of the study. This will encourage the respondent’s cooperation leading to timely return of the questionnaires as well as attaining the targeted response rate.

3.6 Data Analysis Methods

Data analysis is a research method for the objective, systematic and qualitative description of the noticeable content of a communication. In order for research quality in this study, quantitative technique of data analysis was used (Cooper & Schneider, 2014). According to Denscombe (2006) descriptive statistics entails a process of converting a mass of raw data into charts, tables, with frequency distribution and percentages, which are very important part of making sense of the data. The research data will be analyzed using Statistical Package for Social Sciences (SPSS) program and presented using tables to give a clear image of the research findings at a glance.

The collected data will be prepared for analysis, which include editing, coding, and data processing (Cooper & Schindler, 2014), using appropriate research analysis software, such as Statistical Package for Social Sciences (SPSS) program and Excel. Editing will sought to detect errors in the collected data, omissions and missing data that can affect the results. Coding of the data will be done to convert the responses into categories in line with the test variables, and where necessary, the data will be transformed to facilitate analysis.

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Transformation include reverse scoring on some of the scale scores in questions that are asked indirectly to ensure consistency of scoring; and where it will be necessary to group responses to different items for the same concept to derive its combined score (Sekaran & Bougie, 2013).

The data will be analyzed to generate descriptive statistics, which include frequency, mode, mean, median, and percentage that define the characteristics of the studied population. These provides the basis for exploratory data analysis using data displays of tables, graphs, histograms to reveal patterns, in preparation for the confirmatory data analysis.

In addition to the descriptive statistics, and ANOVA, the study data will be analyzed to generate inferential statistics for the purpose of explaining the relationship between the independent and dependent variables. This includes bivariate correlation to determine the direction, strength and significance of the association of the variables, and reviewed through the SPSS generated measure of the linkage, the correlation coefficient with values (-1

The predictive and forecasting ability of the data, as well as the strength of the relationship, will be determined using multiple regression analysis, which shows the effects on the firm value as the dependent variable by variations in the independent shareholder engagement variables, represented by the β1-ias the coefficients or slope of the regression line. The strength of the relationship will be provided by the coefficient of determination (R2), which is the proportion of the variation in the dependent variable explained by each of the independent variables (Cooper & Schindler, 2014; Sekaran & Bougie, 2013). The regression involves different steps; the first step is to test for violation of the assumptions of linearity of the data using the scatter plot, and the normality of the variable using probability plots and histograms (Cooper & Schindler, 2014), tests for heteroscedasticity and multicollinearity and auto-correlation. The next step will be estimating of the model and testing its fit followed by an evaluation of its validity and usefulness.

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3.7 Chapter Summary

Chapter three has described the methodology and procedures that will be used to carry out the study. It started with a brief introduction highlighting the general methodology and structure of the chapter. The chapter also highlighted the method that was used to conduct the research and its use justified. The population was defined and the sampling technique, technique, and sample size described. Finally, the data collection techniques and research procedures to be used have been discussed. The next chapter to follow is Chapter four which discusses the Results and Findings of the study.

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CHAPTER FOUR

4.0 RESULTS AND FINDINGS 4.1 Introduction

This chapter provides details of this study’s discoveries and detections. Descriptive statistics were generated and multiple linear regression models fitted to determine the impact of the four independent variables constructs: leadership style, structure, and resources, culture and the intervening variable donor policies on the dependent variable, organizational effectiveness. Inferential statistics undertaken are correlation and multiple regressions. The final section of the study is the summary of the whole chapter.

4.2 General Information 4.3 Response Rate

The response rate of the survey calculation is by getting the number of completed questionnaires divided by the total number of questionnaires issued. Eighty eight questionnaires were distributed to small scale dairy farmers in Ruai Nairobi County. Out of the 88 questionnaires distributed, 66 questionnaires were fully completed. This represented a response rate of 75 percent. This is represented in Table 4.1.

Table 4.1: Response Rate

DISTRIBUTION Sample Size Frequency Percentage Filled and collected 66 75 Unfilled 22 25 Total Sample 88 100

4.4 Demographic Statistics 4.4.1 Gender of Respondents

The analysis in Table 4.2 illustrates the gender representation of the study. From the table, it is well indicated that 56.1% of the population practicing small scale dairy farming in Ruai is

33 male while 43.9% is female. The study implies that majority of the population at practicing small scale dairy farming at Ruai are males.

Table 4.2: Gender of Respondents Frequency Percentage Male 37 56.1 Female 29 43.9 Total 66 100.0 4.4.2 Age Group of Respondents

The study used Table 4.3 to demonstrate the age representation of the population practicing small scale dairy farming at Ruai. From the table, it is displayed that 18.2% of respondents were between 20 to 30 years of age, 24.2% of respondents were between 31 to 45 years of age, 37.9% of respondents were between 46 to 60 years of age. The study also depicted that 19.7% of respondents were offer 60 years of age. The implication of the study is that majority of the population practicing small scale dairy farming at Ruai are between 46 to 60 years of age. The assumption from the study is that small scale dairy farming is more practiced by older people than younger ones as the youths are busy looking for white collar jobs.

Table 4.3: Age Group of Respondents Frequency Percentage 20 – 30 years 12 18.2

31 – 45 years 16 24.2

46 - 60 years 25 37.9

Over 60 years 13 19.7

Total 66 100.0

4.4.3 Level of Education of Respondents

About sixty five percent (65.2%) of respondents were holding certificate level of education and other levels below the certificate level, 25.8% of respondents were holding diploma level 34 of education and 9.1% of respondents were hold bachelor’s degree as their highest level of education. The assumption from the study is that those who attain the highest level of education believe that small scale dairy farming is for less educated individuals. This is indicated in Table 4.4.

Table 4.4: Highest Level of Education of Respondents Frequency Percent Certificate and below 43 65.2

Diploma 17 25.8 Bachelor's Degree 6 9.1

Total 66 100.0

4.4.4 Gender and Number of Cows Owned

Table 4.5 depicts the relationship between gender and number of cows owned by respondents. From the study, it is well indicated that respondents who owned less than two cows, 50% were male respondents and 50% were female respondents. For respondents who owned two to five cows, 60% were male respondents and 40% were female respondents. The study also demonstrates 51.7% of respondents who owned between 6 to 10 cows were male respondents and 48.3% were female respondents. About sixty percent (57.1%) of respondents who owned between 11 to 15 cows were male respondents and 42.9% were female respondents. The study demonstrates that respondents who owned between 16 to 20 cows, 66.7% were of male gender and 33.3% were of female gender. The study implies that male respondents own more cows that their female counterparts.

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Table 4.5: Gender and Number of Cows Owned

Gender Male Female Total How many cows Less than 2 2 2 4 do you have? cows 50.0% 50.0% 100.0% 2 - 5 cows 12 8 20 60.0% 40.0% 100.0% 6 - 10 cows 15 14 29 51.7% 48.3% 100.0% 11 - 15 cows 4 3 7 57.1% 42.9% 100.0% 16 - 20 cows 2 1 3 66.7% 33.3% 100.0% More than 20 2 1 3 cows 66.7% 33.3% 100.0% Total 37 29 66 56.1% 43.9% 100.0%

4.4.5 Number of Cows Owned and Litres of Milk Produced

The study in Table 4.6 shows the cross-tabulation between the number of cows owned and amount of milk produced. The study reveals that for respondents with less than two cows, 25% of them get less than five litres of milk, 25% get between five to fifteen litres of milk, and 50% receive between sixteen to twenty five litres of milk. For respondents with two to five cows, 5% receive less than five litres of milk, 55% receive between five to fifteen litres of milk, 20% receive between sixteen to twenty five litres of milk and another 20% get between twenty six to thirty five litres of milk.

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Table 4.6: Number of Cows Owned and Litres of Milk Produced

Many much milk do your cows produce per day? Less More than 5 5 -15 16 - 25 26 - 35 36 - 45 than 45 litres litres litres litres litres litres Total How Less 1 1 2 0 0 0 4 many than 2 25.0% 25.0% 50.0% 0.0% 0.0% 0.0% 100.0% cows cows do you 2 - 5 1 11 4 4 0 0 20 have? cows 5.0% 55.0% 20.0% 20.0% 0.0% 0.0% 100.0% 6 - 10 1 3 8 7 8 2 29 cows 3.4% 10.3% 27.6% 24.1% 27.6% 6.9% 100.0% 11 - 15 0 0 2 0 3 2 7 cows 0.0% 0.0% 28.6% 0.0% 42.9% 28.6% 100.0% 16 - 20 0 0 2 0 1 0 3 cows 0.0% 0.0% 66.7% 0.0% 33.3% 0.0% 100.0% More 0 0 0 1 0 2 3 than 20 0.0% 0.0% 0.0% 33.3% 0.0% 66.7% 100.0% cows Total 3 15 18 12 12 6 66 4.5% 22.7% 27.3% 18.2% 18.2% 9.1% 100.0%

The study also shows that respondents with six to ten cows, 3.4% receive less than five litres of milk, 10.3% receive between five to fifteen litres, 27.6% get between sixteen to twenty litres of milk, 24.1% get between twenty six to thirty five litres, 27.6% receive between thirty six to forty five litres of milk, and 6.9% get more than 45 litres of milk. About thirty percent (28.6%) of respondents with eleven to fifteen cows receive between sixteen to twenty five litres of milk, 42.9% get between 36 to 45 litres of milk, and 28.6% receive more than forty five litres. For respondents who own sixteen to twenty cows, 66.7% receive between sixteen

37 to twenty litres of milk, and 33.3% receive between thirty six to forty five litres of milk. Finally, respondents with more than twenty cows, 33.3% get twenty six to thirty five litres of milk and 66.7% get more than forty five litres of milk.

4.4.6 Level of Education and Selling Milk

The study in Table 4.7 illustrates the relationship between level of education and how respondents sell their milk. The study found that 39.5% of respondents with certificate and below as their highest level of education sell their milk through cooperatives, 58.1% sell through vendors and 2.3% sell through other means. For respondents with diploma level of education, 70.6% sell their milk through cooperatives, and 29.4% sell their milk through vendors. About sixty six (66.7%) of respondents with degree level of education sell their milk through cooperatives, 16.7% sell through vendors and 16.7% of respondents sell their milk through other means.

Table 4.7: Level of Education and Selling Milk How do you sell your milk? Through Through Cooperatives Vendors Others Total What is your Certificate and 17 25 1 43 highest level below 39.5% 58.1% 2.3% 100.0% of education? Diploma 12 5 0 17 70.6% 29.4% 0.0% 100.0% Bachelor's 4 1 1 6 Degree 66.7% 16.7% 16.7% 100.0% Total 33 31 2 66 50.0% 47.0% 3.0% 100.0%

4.4.7 Selling Milk and Price The study reveals the cross-tabulation between means through which small scale dairy farmers sell their mil and the price they sell their milk per litre. For respondents who sell their milk for 30 to 40 shillings per litre, 35.3% sell it through cooperatives, and 64.7% sell it

38 through vendors. Respondents who sell their milk for between 41 to 50 shillings per litre, 72.4% do it through cooperatives, 24.1% through vendors and 3.4% through other means. Exactly 50% of respondents who sell their milk for between 51 to 60 shillings per litre do it through vendors and 50% do it through other means. The study finally states that 100% of respondents who sell their milk for 61 to 70 shillings per litre sell it through vendors.

Table 4.8: Selling Milk and Price How do you sell your milk? Through Through Cooperatives Vendors Others Total How 30 to 40 12 22 0 34 much do shillings 35.3% 64.7% 0.0% 100.0% you sell 41 to 50 21 7 1 29 your shillings 72.4% 24.1% 3.4% 100.0% milk per 51 to 60 0 1 1 2 litre? shillings 0.0% 50.0% 50.0% 100.0% 61 to 70 0 1 0 1 shillings 0.0% 100.0% 0.0% 100.0% Total 33 31 2 66 50.0% 47.0% 3.0% 100.0%

4.5 Diagnostic Tests The diagnostic tests that were run on the data were in line with the assumptions of a multiple regression model. The mean score for each of the variables in the questionnaire was calculated before carrying out the analysis. Before testing out the assumptions, dimension reduction using factor analysis was conducted on the mean score of the variables measuring the dependent variable of firm value creating outcomes as well as the independent variables. The diagnostics ran on the data were to test for existence of multicollinearity, normal distribution of residuals.

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4.5.1 Multicollinearity Test The assumption of the multiple linear regression is that there is no multicollinearity in the data which is brought about when the independent variables are highly correlated with each other. To test for the fulfilment of this condition, the Variance Inflation Factors (VIF) were inspected. High VIF of more than 10 would indicate the existence of multicollinearity, (Dormann et.al. 2013). This is because the VIF’s are inflated when there exits high correlation amongst the independent variables. Table 3.6 below shows that the VIF factors for all the independent variables are below 10 hence the assumption of non-multicollinearity was met.

Table 4.9: MulticollinearityTest Collinearity Statistics Model Tolerance VIF 1 (Constant) Opportunities for dairy farming agribusiness .594 1.683 Challenges in dairy farming agribusiness .937 1.067 Strategies to encourage dairy farming agribusiness .604 1.657 a. Dependent Variable: Dairy farming enterprise

4.5.2 Test for Normality The assumption of normality for multiple regression assumes that the residuals of the model are normally distributed. To test for the assumption, a histogram of the distribution of the residuals was plotted and inspected as per figure 4.1 below. The plot shows that the residuals assume a bell shape curve indicating that they are normally distributed.

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Figure 3.1: Test for Normality

4.6 Opportunities for Dairy Farming Agribusiness

The objective of the study was to explore opportunities available for farmers who venture in dairy farming in Ruai area. The study sought information from technology, innovation, dairy farming, agricultural extensions, and agribusiness. The study analyzed descriptive statistics in terms of mean, mode, median and standard deviation, factor analysis, correlation and regression analysis.

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4.6.1 Descriptive Statistics of Opportunities for Dairy Farming

Table 4.10: Opportunities for Dairy Farming Agribusiness N Mean Median Mode Std. Constructs Valid Missing Deviation Technology in agriculture provides 66 0 3.97 4.00 4 .784 important information to famers to enhance agribusiness Technologies assist farmers to 66 0 3.83 4.00 4 .986 balance and manage food and herds Farmers who are more innovative 66 0 4.29 4.00 4 .674 enhance their level of income Dairy farmers take risks to adopt a 65 1 3.51 4.00 4 1.033 technology that will benefit them in future To enhance production, dairy farmers 66 0 3.17 3.00 4 1.210 like crossbreeding among cattle of milk, beef or dual-purpose breed It is more effective for rural farmers 66 0 1.77 1.00 1 1.107 to market their milk through middlemen Dairy farmers go for trainings on 66 0 3.95 4.00 4 .849 how to enhance milk production Agricultural extensions promote 66 0 3.68 4.00 4 .914 agricultural technologies to meet farmers’ needs Middlemen milk collectors 66 0 1.94 2.00 1 1.201 understand better small scale dairy farmers that large highway collectors Effective extension services provide 66 0 3.98 4.00 4 .868 farmers with information that helps them to optimize their use of scarce resources

The study in Table 4.10 shows that the means for opportunities for dairy farming agribusiness ranged from 1.77 “It is more effective for rural farmers to market their milk through middlemen” to 4.29 “Farmers who are more innovative enhance their level of income”. This means that most of respondents did not believe that it is more effective to

42 market their milk through middlemen but they highly agreed that farmers who are more innovative enhance their level of income. The median was 4.00 and mode was 4. The standard deviation range is from 1.210 to 0.674 and this shows little to no variation from the mean.

4.6.2 Exploratory Factor Analysis (EFA)

In this section, the study used exploratory factor analysis to purify and refine the variables into the most effective number of factors.

The study’s construct measures were initially purified using exploratory factor analysis (EFA) and tested for reliability analysis. The raw measures were purified and tested for validity and reliability by running a series of tests. Exploratory factor analysis was performed to attain measure purification and refine the variables into the most effective number of factors. Reliability analysis was then conducted.

Each of the elements or rather constructs was refined by utilizing principal component analysis. Principal component analysis extracted factors, and factor loadings greater than 0.5 were retained for each principal component extracted (Hair et al., 2010). To assess the factorability of items, the study observed two indicators, these are, Kaiser Meyer-Olin Measure of Sampling Adequacy (KMO), and Barlett’s Test of Sphericity. From the analysis in Table 4.11, the study found that opportunities for dairy farming agribusiness had KMO Measures of Sampling Adequacy of 0.696 which is above the threshold of 0.6 (Kaiser, 1974), as well as p-values of 0.000 for Barlett’s test of Sphericity (Barlett, 1954) below 0.05. When applying EFA, the results showed a clear factor structure with an acceptable level of cross loadings.

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Table 4.11: KMO and Bartlett’s Test for Opportunities for Dairy Farming Agribusiness

KMO and Bartlett's Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .696 Bartlett's Test of Sphericity Approx. Chi-Square 179.664 df 45 Sig. .000

Based on Kaiser’s criterion, four (4) factors out of ten (10) factors were imputed. In this case, four (4) factors in the initial solution had Eigen values greater than 1.00 and together, they accounted for 70.365% of the variability in the original variables with one variable emerging dominant and accounted for 34.111% of the variance in the original variables data as indicated in Table 4.12.

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Table 4.12: Total Variance Explained for Opportunities for Dairy Farming Agribusiness

Total Variance Explained Rotation Sums of Extraction Sums of Squared Squared Initial Eigenvalues Loadings Loadingsa % of Cumulative % of Cumulative Component Total Variance % Total Variance % Total 1 3.411 34.111 34.111 3.411 34.111 34.111 2.679 2 1.410 14.103 48.214 1.410 14.103 48.214 1.727 3 1.118 11.180 59.394 1.118 11.180 59.394 1.241 4 1.097 10.971 70.365 1.097 10.971 70.365 2.505 5 .838 8.376 78.741 6 .650 6.501 85.242 7 .540 5.404 90.645 8 .400 4.001 94.647 9 .281 2.808 97.455 10 .255 2.545 100.000 Extraction Method: Principal Component Analysis. a. When components are correlated, sums of squared loadings cannot be added to obtain a total variance.

Table 4.12 shows the loading for the measurement model. The coefficients ranged between 0.522 and 0.732, indicating that the variables are almost perfectly related to factor pattern and clear factor structure with an acceptable level of cross loadings.

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Table 4.13: Pattern Matrix of Idealized Influence

Component Matrixa Component 1 2 3 4 Technology in agriculture provides important information to .732 famers to enhance agribusiness Technologies assist farmers to balance and manage food and .697 herds Farmers who are more innovative enhance their level of .531 income Dairy farmers take risks to adopt a technology that will .581 benefit them in future To enhance production, dairy farmers like crossbreeding .710 among cattle of milk, beef or dual-purpose breed It is more effective for rural farmers to market their milk .642 through middlemen Dairy farmers go for trainings on how to enhance milk .522 production Agricultural extensions promote agricultural technologies to .645 meet farmers’ needs Middlemen milk collectors understand better small scale .670 dairy farmers that large highway collectors Effective extension services provide farmers with .689 information that helps them to optimize their use of scarce resources Extraction Method: Principal Component Analysis. a. 4 components extracted.

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4.6.3 Correlations between Demographics and Opportunities in Dairy Farming Table 4.14 reveals the correlations between demographic profiles of respondents and opportunities for dairy farming agribusiness. From the study, it is clear that the statement “Technologies assist farmers to balance and manage food and herds” significantly correlates with “Many much milk do your cows produce per day?” (r= -0.355**, p<0.01, N=66). The statement “To enhance production, dairy farmers like crossbreeding among cattle of milk, beef or dual-purpose breed” significantly correlates with “Many much milk do your cows produce per day?” (-0.390**, p<0.01, N= 66). The statement “Middlemen milk collectors understand better small scale dairy farmers that large highway collectors” significantly correlates with “How much do you sell your milk per litre?” (r= -0.338**, p<0.01, N= 66). Effective extension services provide farmers with information that helps them to optimize their use of scarce resources hence they understand how much to price their milk per litre (r= -0.319, p<0.01, N=66).

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Table 4.14: Correlations between Demographics and Opportunities in Dairy Farming

Gender What How is long have your bracket? age you practicing dairy How been farming? cows Many many do you have? milk much do your cows produce day? per How do you sell milk?your How much do you sell your litre? milk per Technologies Pearson -.099 -.209 -.136 .042 -.355** - .000 assist farmers to Correlatio .14 balance and n 4 manage food and Sig. (2- .430 .092 .276 .739 .003 .24 1.000 herds tailed) 9 N 66 66 66 66 66 66 66 Farmers who are Pearson -.016 -.254* -.284* - -.157 .28 -.121 more innovative Correlatio .209 2* enhance their n level of income Sig. (2- .899 .039 .021 .091 .209 .02 .335 tailed) 2 N 66 66 66 66 66 66 66 To enhance Pearson -.377** -.322** -.355** - -.390** - -.479** production, dairy Correlatio .250 .08 farmers like n * 7 crossbreeding Sig. (2- .002 .008 .003 .043 .001 .48 .000 among cattle of tailed) 8 milk, beef or N 66 66 66 66 66 66 66 dual-purpose breed Dairy farmers go Pearson -.025 -.076 .138 .125 -.255* .11 .018 for trainings on Correlatio 6 how to enhance n milk production Sig. (2- .844 .544 .269 .317 .039 .35 .885 tailed) 4

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N 66 66 66 66 66 66 66 Agricultural Pearson -.093 -.110 -.131 - -.152 - -.305* extensions Correlatio .119 .20 promote n 6 agricultural Sig. (2- .456 .379 .295 .342 .223 .09 .013 technologies to tailed) 7 meet farmers’ N 66 66 66 66 66 66 66 needs Middlemen milk Pearson -.237 -.161 -.216 - -.222 .00 -.338** collectors Correlatio .050 3 understand better n small scale dairy Sig. (2- .056 .197 .081 .691 .073 .98 .005 farmers that large tailed) 2 highway N 66 66 66 66 66 66 66 collectors Effective Pearson -.055 -.218 -.171 - -.237 .04 -.319** extension Correlatio .160 8 services provide n farmers with Sig. (2- .659 .078 .169 .200 .055 .70 .009 information that tailed) 0 helps them to N 66 66 66 66 66 66 66 optimize their use of scarce resources **. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed).

The previous section looked at the correlations between demographic information of respondents and opportunities for dairy farming agribusiness. This section shows in a curve the direction of relationship between the parameters of demographic information and opportunities for dairy farming agribusiness. The results in Figure 4.2 demonstrate that there

49 is a positive relationship between demographic profiles of respondents and opportunities for dairy farming agribusiness.

Figure 4.2: Estimate Curve of demographic profiles and opportunities for dairy farming

4.6.4 Regression Analysis and Hypothesis Testing for Opportunities for Dairy Farming and Dairy farming Enterprise

The model analysis of regression is shown in the Table 4.15. Regression indicates the strength of the relationship between the independent variables (opportunities for dairy farming) and the dependent variable (dairy farming enterprise). The R square value in this case is 0.732 which clearly suggests that there is a significant relationship between

50 opportunities for dairy farming and dairy farming enterprise. This indicates that opportunities for dairy farming agribusiness cause a variation of 73.2% in dairy farming enterprise.

Table 4.15: Model Summary for Opportunities for Dairy Farming

Model Summary Adjusted R Std. Error of Model R R Square Square the Estimate 1 .856a .732 .728 .193 a. Predictors: (Constant), Opportunities for dairy farming agribusiness

The analysis of variation is illustrated in Table 4.16. The ANOVA table indicates that the overall model was a good fit since F (1, 64) = 174.739, p-value =0.000<0.05.

Table 4.16: ANOVA for Opportunities for Dairy Farming

ANOVAa Sum of Mean Model Squares df Square F Sig. 1 Regression 6.513 1 6.513 174.739 .000b Residual 2.385 64 .037 Total 8.898 65 a. Dependent Variable: Dairy farming enterprise b. Predictors: (Constant), Opportunities for dairy farming agribusiness

The coefficient of the study is presented in table 4.17 below.

The model for regression analysis is presented as:

Dairy farming enterprise = 1.677 + 0.5811 + 휀

Where 1 is the parameters in the model.

Opportunities for dairy farming agribusiness were found to have a positive linearly significant influence on dairy business enterprise(훽 = 0.581, 푝 = 0.000 < 0.05 ). Here

51 one unit change in opportunities in the dairy farming agribusiness results in 0.581 unit increase in dairy farming enterprise. This is demonstrated in Table 4.17.

Table 4.17: Coefficients of Opportunities for Dairy Farming and Dairy Farming Enterprise

Coefficientsa Unstandardized Standardized Coefficients Coefficients Std. Model B Error Beta t Sig. 1 (Constant) 1.677 .152 11.049 .000 Opportunities for dairy farming .581 .044 .856 13.219 .000 agribusiness a. Dependent Variable: Dairy farming enterprise

4.7 Challenges in Dairy Farming Agribusiness

The second objective of the study was to examine the challenges in dairy farming agribusiness. The study sought information from indigenous breeds, animal feed resources, feed prices, animal diseases, low fertility, limited land, and financial means required. The challenges in dairy farming agribusiness were analyzed in terms of descriptive statistics, factor analysis, correlation analysis and finally regression analysis.

4.7.1 Descriptive of Challenges in Dairy Farming Agribusiness

Tests for descriptive statistics were performed using statistical software called SPSS. The descriptive results for variable of challenges in dairy farming agribusiness were provided in terms of the mean, median, mode, and standard deviation. The total number of respondents analyzed in each measure ranged from 64 to 65. This was determined by the number of valid complete questionnaires in each case. This is demonstrated in Table 4.18.

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From the study, the mean ranged from 2.77 “There are no promotional activities being carried out by various government offices to portray milk as a highly nutritious and essential food for the health of nation” to 4.29 “The prevalence of various animal diseases is a challenge to milk production.” The study findings means that more small scale farmers feel that prevalence of various animal diseases is a great challenge to their activity than they feel for the lack of promotional activities by the various government agencies. The median ranged from 2.00 to 4.00 while the mode was 4. The standard deviation ranged from 0.576 “Increasing feed prices has cause constraints to increase milk production” to 1.245 “Most of the poor farmers do not have financial means required to make the initial investment and acquire the associated technological inputs.” This means that small scale dairy farmers feel more weight on increasing feed prices than they feel on accessing financial means to invest in technology.

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Table 4.18: Challenges in Dairy Farming Agribusiness N Mean Median Mode Std. Valid Missing Deviation Small scale farmers rely on 65 1 2.91 3.00 4 1.142 indigenous breeds that give low milk output Small scale dairy farmers experience 65 1 3.48 4.00 4 1.147 inadequate animal feed resources Increasing feed prices has cause 64 2 4.28 4.00 4 .576 constraints to increase milk production The prevalence of various animal 65 1 4.29 4.00 4 .701 diseases is a challenge to milk production The animal health services provided 65 1 2.85 2.00 2 1.215 to rural farmers are inadequate There are no promotional activities 65 1 2.77 3.00 3 .915 being carried out by various government offices to portray milk as a highly nutritious and essential food for the health of nation Low fertility reduces the profit by 64 2 4.23 4.00 4 .584 decreasing the average milk production and the number of calves per cow per year There is no extension to supply 65 1 2.92 3.00 3 .989 information about technologies to improve production and marketing to estimate certain development The problem of inadequate feed is as 65 1 3.03 3.00 4 1.104 a result of the limited land available for pasture establishment Most of the poor farmers do not have 65 1 3.83 4.00 4 1.245 financial means required to make the initial investment and acquire the associated technological inputs

4.7.2 Factor Analysis of Challenges in Dairy Farming Agribusiness

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The analysis in Table 4.19 shows that inspirational motivation had KMO Measures of Sampling Adequacy of 0.665 which is above the threshold of 0.6. The study also shows the p-values of 0.000 for Barlett’s test of Sphericity that is less than 0.05. When applying EFA, the results showed a clear factor structure with an acceptable level of cross loadings.

Table 4.19: KMO and Bartlett’s Test for Challenges in Dairy Farming Agribusiness

KMO and Bartlett's Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .560 Bartlett's Test of Sphericity Approx. Chi-Square 132.654 df 45 Sig. .000

Based on Kaiser’s criterion, four factors out of ten factors were imputed. In this case, four factors in the initial solution had Eigen values greater than 1.00 and together, they accounted for 66.386 percent of the variability in the original variables with one variable emerging dominant and accounted for 28.342 percent of the variations in the original variables data as illustrated in Table 4.20.

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Table 4.20: Total Variance Explained for Challenges in Dairy Farming Agribusiness

Total Variance Explained Extraction Sums of Squared Initial Eigenvalues Loadings % of Cumulative % of Cumulative Component Total Variance % Total Variance % 1 2.834 28.342 28.342 2.834 28.342 28.342 2 1.526 15.264 43.605 1.526 15.264 43.605 3 1.171 11.709 55.314 1.171 11.709 55.314 4 1.107 11.072 66.386 1.107 11.072 66.386 5 .905 9.053 75.439 6 .744 7.443 82.882 7 .605 6.050 88.932 8 .512 5.125 94.056 9 .344 3.441 97.497 10 .250 2.503 100.000 Extraction Method: Principal Component Analysis.

From Figure 4.4, the scree plot displays that four factors had eigenvalue greater than one hence confirming the findings of the total variance explained for challenges in dairy farming.

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Figure 4.4: Scree Plot for Challenges in Dairy Farming Agribusiness

Table 4.21 shows the loading for the measurement model. The coefficients ranged between 0.526 and 0.901, indicating that the variables are almost perfectly related to factor pattern and clear factor structure with an acceptable level of cross loadings.

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Table 4.21: Pattern Matrix for Challenges in Dairy farming Agribusiness

Component Matrixa Component 1 2 3 4 Small scale farmers rely on indigenous breeds that give low milk .489 output Small scale dairy farmers experience inadequate animal feed .765 resources Increasing feed prices has cause constraints to increase milk .639 production The prevalence of various animal diseases is a challenge to milk .722 production The animal health services provided to rural farmers are .623 inadequate There are no promotional activities being carried out by various .901 government offices to portray milk as a highly nutritious and essential food for the health of nation Low fertility reduces the profit by decreasing the average milk .633 production and the number of calves per cow per year There is no extension to supply information about technologies to .723 improve production and marketing to estimate certain development The problem of inadequate feed is as a result of the limited land .526 available for pasture establishment most of the poor farmers do not have financial means required to .753 make the initial investment and acquire the associated technological inputs Extraction Method: Principal Component Analysis. a. 4 components extracted.

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4.7.3 Correlations of Challenges in Dairy Farming Agribusiness

The study in Table 4.22 shows in a correlation table the relationship between parameters of challenges in dairy farming agribusiness and demographic information of respondents. The study found that “Small scale dairy farmers experience inadequate animal feed resources” significantly correlate to “How much do you sell your milk per litre?” (r= -0.366**, p< 0.01, N= 65). The statement “The prevalence of various animal diseases is a challenge to milk production” significantly correlates with “Many much milk do your cows produce per day?” (r= 0.388**, p< 0.01, N= 65). The study also reveals that “Small scale farmers rely on indigenous breeds that give low milk output” correlates with gender of respondents (r= - 0.255*, p< 0.05, N= 65) and age bracket of respondents (r= -0.290*, p< 0.05, N= 65). The statement “The animal health services provided to rural farmers are inadequate” correlates with the number of cows a small scale farmer has (r= 0.297, p< 0.05, N= 65).

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Table 4.22: Correlations between Demographic Information and Challenges in Dairy Farming

Correlations How Many much How much How do How many milk do do you What is long have cows your you sell your you been do cows sell your age practicin you produc your milk Gende bracket g dairy have e per milk per r ? farming? ? day? ? litre? Small scale Pearson -.255* -.290* -.137 -.092 -.246* .052 -.036 farmers rely Correlatio on n indigenous Sig. (2- .041 .019 .277 .468 .048 .680 .776 breeds that tailed) give low N 65 65 65 65 65 65 65 milk output Small scale Pearson -.267* -.176 -.155 -.134 -.170 -.005 - dairy farmers Correlatio .366* experience n * inadequate Sig. (2- .031 .160 .218 .285 .177 .967 .003 animal feed tailed) resources N 65 65 65 65 65 65 65 Increasing Pearson -.009 .259* .033 -.008 .243 .182 -.178 feed prices Correlatio has cause n constraints to Sig. (2- .946 .039 .793 .953 .053 .151 .160 increase milk tailed)

60 production N 64 64 64 64 64 64 64 The Pearson -.021 .283* .169 .054 .388** .240 .017 prevalence of Correlatio various n animal Sig. (2- .867 .022 .179 .667 .001 .054 .895 diseases is a tailed) challenge to N 65 65 65 65 65 65 65 milk production The animal Pearson .243 .188 .223 .297* .235 - .414* health Correlatio .292* * services n provided to Sig. (2- .051 .133 .074 .016 .060 .018 .001 rural farmers tailed) are N 65 65 65 65 65 65 65 inadequate Low fertility Pearson -.031 .169 .080 .034 .049 .302* -.019 reduces the Correlatio profit by n decreasing Sig. (2- .810 .183 .532 .792 .699 .015 .884 the average tailed) milk N 64 64 64 64 64 64 64 production and the number of calves per cow per year There is no Pearson .070 .092 -.061 .064 .156 .074 -.105 extension to Correlatio supply n information Sig. (2- .578 .465 .629 .615 .216 .561 .406

61 about tailed) technologies N 65 65 65 65 65 65 65 to improve production and marketing to estimate certain development The problem Pearson -.053 -.198 -.034 -.010 -.134 -.026 -.224 of inadequate Correlatio feed is as a n result of the Sig. (2- .672 .114 .785 .936 .288 .835 .072 limited land tailed) available for N 65 65 65 65 65 65 65 pasture establishmen t most of the Pearson -.278* .080 .141 .100 .043 .173 - poor farmers Correlatio .313* do not have n financial Sig. (2- .025 .528 .262 .428 .733 .168 .011 means tailed) required to N 65 65 65 65 65 65 65 make the initial investment and acquire the associated technological

62 inputs 4.7.4 Regression Analysis of Challenges in Dairy Farming Agribusiness

The R square value in the model summary is 0.356 and this indicates that challenges in dairy farming agribusiness causes a variation of 35.6% in dairy farming enterprise.

Table 4.23: Model Summary of Challenges in Dairy Farming Agribusiness

Model Summary Adjusted R Std. Error of the Model R R Square Square Estimate 1 .597a .356 .346 .29742 a. Predictors: (Constant), Challenges in dairy farming agribusiness The ANOVA table points out that the overall model was a good fit since F (5, 159) = 2.082, p-value =0.000<0.05. This is illustrated in Table 4.24.

Table 4.24: ANOVA for Challenges in Dairy Farming Agribusiness

ANOVAa Sum of Mean Model Squares df Square F Sig. 1 Regression 3.081 1 3.081 34.829 .000b Residual 5.573 63 .088 Total 8.654 64 a. Dependent Variable: Dairy farming enterprise b. Predictors: (Constant), Challenges in dairy farming agribusiness

The coefficient is 0.510 and p value is 0.000. The study used linear regression model to test the relationship between challenges in dairy farming agribusiness and dairy farming enterprise. The linear equation model is stated as; Y = α0+ α1X1 + €: Where Y= Dairy farming Enterprise, α = Constant value, X1 = Challenges in dairy farming agribusiness and € = error term

The following were the results of the model in Table 4.25,

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Table 4.25: Coefficients Variation of Challenges in Dairy Farming Enterprise

Coefficientsa Unstandardized Standardized Coefficients Coefficients Model B Std. Error Beta t Sig. 1 (Constant) 1.890 .301 6.281 .000 Challenges in dairy farming .510 .086 .597 5.902 .000 agribusiness a. Dependent Variable: Dairy farming enterprise

4.8 Strategies to Encourage Dairy Farming Agribusiness

The third objective of the study was to determine strategies to encourage more farmers to venture in dairy farming enterprise. The study sought information from genetic improvement, artificial insemination, prevention and control of risk, credit facilities, improving market access, training and policies. The strategies to encourage dairy farming agribusiness were analyzed in terms of descriptive statistics, factor analysis, correlation analysis and finally regression analysis.

4.8.1 Descriptive of Strategies for Encouraging Dairy Farming Agribusiness The study adopted mean, mode, median and standard deviation (S.D) as statistical tools that were used to analyzed the significance of the variables. The total number of respondents analyzed in each measure was 66. This is demonstrated in Table 4.26.

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Table 4.26: Strategies to Encourage Dairy Farming Agribusiness

N Mean Median Mode Std. Valid Missing Deviation Small dairy farmers can get high 66 0 3.94 4.00 4 .802 producers cattle through genetic improvement of their existing herd Small scale farmers should 66 0 3.80 4.00 4 .915 encourage genetic improvement in cattle to utilize their exotic breed and their crosses genotypes Farmers should encourage the use of 66 0 4.56 5.00 5 .659 artificial insemination (AI) to enhance the quality of breeds Dairy animals should be given 66 0 4.03 4.00 4 .701 certain foods for body maintenance and for production Dairy cattle must be provided with 66 0 4.50 5.00 5 .881 water throughout Dairy farming should focus on 66 0 4.15 4.00 4 .614 prevention and control of risk factors in cattle Development of credit facilities for 66 0 3.85 4.00 4 1.140 small scale dairy farmers will enhance productivity Improving market access to dairy 66 0 4.44 4.00 5 .585 product creates an opportunity for enhanced dairy production Farmers should be trained on milk 66 0 4.05 4.00 5 .999 production systems by qualified individuals who understands the

65 whole dairy farming system

There should be a sound linkage 66 0 3.70 4.00 4 1.136 polices in the agricultural knowledge generation and transfer systems

From the table, the mean ranged from 3.70 “There should be a sound linkage policies in the agricultural knowledge generation and transfer systems” to 4.56 “Farmers should encourage the use of artificial insemination (AI) to enhance the quality of breeds.” This means that more small scale farmers believe that artificial insemination can highly enhance the productivity of their dairy products than instituting policies to enhance agricultural knowledge. The median and mode were 4.00 and 4 respectively. The standard deviation of the study ranged from 0.585 “Improving market access to dairy product creates an opportunity for enhanced dairy production” to 1.140 “Development of credit facilities for small scale dairy farmers will enhance productivity.” Here the study found that it is more critical to improve market accessibility for small scale farmers than developing credit facilities.

4.8.2 Exploratory Factor Analysis (EFA) for Strategies to encourage more Dairy Farming

From the analysis in Table 4.27, the study found that dairy farming strategies had KMO Measures of Sampling Adequacy of 0.737 which is above the threshold of 0.6 (Kaiser, 1974), as well as p-values of 0.000 for Barlett’s test of Sphericity (Barlett, 1954) below 0.05. When applying EFA, the results showed a clear factor structure with an acceptable level of cross loadings.

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Table 4.27: KMO and Bartlett’s Test for Dairy Farming Strategies

KMO and Bartlett's Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .737 Bartlett's Test of Sphericity Approx. Chi-Square 157.739 df 45 Sig. .000

Based on Kaiser’s criterion, four (4) factors out of ten (10) factors were imputed. In this case, four (4) factors in the initial solution had Eigen values greater than 1.00 and together, they accounted for 69.387% of the variability in the original variables with one variable emerging dominant and accounted for 32.769% of the variance in the original variables data as indicated in table 4.28.

Table 4.28: Total Variance Explained for Dairy Farming Strategies

Total Variance Explained Extraction Sums of Squared Initial Eigenvalues Loadings % of Cumulative % of Cumulative Component Total Variance % Total Variance % 1 3.277 32.769 32.769 3.277 32.769 32.769 2 1.396 13.957 46.726 1.396 13.957 46.726 3 1.189 11.885 58.611 1.189 11.885 58.611 4 1.078 10.775 69.387 1.078 10.775 69.387 5 .726 7.260 76.646 6 .648 6.483 83.129 7 .562 5.624 88.753 8 .467 4.670 93.422 9 .398 3.984 97.407 10 .259 2.593 100.000 Extraction Method: Principal Component Analysis.

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Scree plot in Figure 4.9 demonstrates that four (4) factors had eigenvalue greater than one hence confirming the findings of the total variance explained for intellectual stimulation.

Figure 4.5: Scree Plot of Dairy Farming Strategies

Table 4.29 shows the loading for the measurement model. The coefficients ranged between 0.504 and 0.834, indicating that the variables are almost perfectly related to factor pattern and clear factor structure with an acceptable level of cross loadings.

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Table 4.29: Pattern Matrix of Dairy Farming Strategies

Component Matrixa Component 1 2 3 4 Small dairy farmers can get high producers cattle through .670 genetic improvement of their existing herd Small scale farmers should encourage genetic .818 improvement in cattle to utilize their exotic breed and their crosses genotypes Farmers should encourage the use of artificial .691 insemination (AI) to enhance the quality of breeds Dairy animals should be given certain foods for body .917 maintenance and for production Dairy cattle must be provided with water throughout .532 Dairy farming should focus on prevention and control of .758 risk factors in cattle Development of credit facilities for small scale dairy .576 farmers will enhance productivity Improving market access to dairy product creates an .504 opportunity for enhanced dairy production Farmers should be trained on milk production systems by .737 qualified individuals who understands the whole dairy farming system There should be a sound linkage polices in the agricultural .834 knowledge generation and transfer systems Extraction Method: Principal Component Analysis. a. 4 components extracted.

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4.8.3 Correlations of Challenges in Dairy Farming Agribusiness

The study in Table 4.30 shows in a correlation table the relationship between parameters of dairy farming strategies and demographic information of respondents. The study found that “Small dairy farmers can get high producers cattle through genetic improvement of their existing herd” significantly correlate to “How much milk do your cows produce per day?” (r= -0.319**, p< 0.01, N= 66). The statement “Dairy animals should be given certain foods for body maintenance and for production” significantly correlates with “How do you sell your milk?” (r= -0.315**, p< 0.01, N= 66). The study also reveals that “Dairy farming should focus on prevention and control of risk factors in cattle” correlates with “How long you have been practicing dairy farming?” (r= -0.242*, p< 0.05, N= 66) and age bracket of respondents (r= -0.247*, p< 0.05, N= 66). The statement “There should be a sound linkage polices in the agricultural knowledge generation and transfer systems” correlates with “How much do you sell your milk per litre?” (r= 0.278, p< 0.05, N= 66).

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Table 4.30: Correlations between Demographic Information and Dairy Farming Strategies

Correlations How How muc How much h do How long many milk do How you What is have you cows your do sell your been do cows you your age practicin you produc sell milk Gende bracket g dairy have e per your per r ? farming? ? day? milk? litre? Small dairy Pearson .029 -.107 -.123 -.041 -.319** .073 -.115 farmers can Correlatio get high n producers Sig. (2- .817 .391 .324 .747 .009 .562 .358 cattle tailed) through N 66 66 66 66 66 66 66 genetic improvemen t of their existing herd Small scale Pearson -.043 -.222 -.252* -.153 -.358** .147 -.182 farmers Correlatio should n encourage Sig. (2- .730 .073 .041 .220 .003 .240 .143 genetic tailed) improvemen N 66 66 66 66 66 66 66

71 t in cattle to utilize their exotic breed and their crosses genotypes Farmers Pearson .128 .188 .048 .091 .177 .099 -.080 should Correlatio encourage n the use of Sig. (2- .306 .130 .701 .468 .155 .429 .523 artificial tailed) insemination N 66 66 66 66 66 66 66 (AI) to enhance the quality of breeds Dairy Pearson .049 -.091 .080 .062 -.254* - .204 animals Correlatio .315* should be n * given certain Sig. (2- .695 .467 .523 .619 .040 .010 .101 foods for tailed) body N 66 66 66 66 66 66 66 maintenance and for production Dairy Pearson .080 -.247* -.242* -.181 -.308* .031 -.018 farming Correlatio should focus n on Sig. (2- .521 .046 .050 .145 .012 .804 .887 prevention tailed) and control N 66 66 66 66 66 66 66

72 of risk factors in cattle Farmers Pearson -.071 -.241 -.265* -.148 -.184 .149 -.209 should be Correlatio trained on n milk Sig. (2- .569 .051 .032 .237 .140 .234 .092 production tailed) systems by N 66 66 66 66 66 66 66 qualified individuals who understands the whole dairy farming system There should Pearson -.033 -.285* -.328** -.240 -.205 .015 - be a sound Correlatio .278* linkage n polices in Sig. (2- .794 .021 .007 .053 .098 .907 .024 the tailed) agricultural N 66 66 66 66 66 66 66 knowledge generation and transfer systems

4.8.4 Regression Analysis and Hypothesis Testing for Dairy Farming Strategies

The R square value in the model summary is 0.643 and this indicates that dairy farming strategies causes a variation of 64.3% in dairy farming enterprise.

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Table 4.31: Model Summary for Dairy Farming Strategies

Model Summary Adjusted R Std. Error of the Model R R Square Square Estimate 1 .802a .643 .637 .22292 a. Predictors: (Constant), Strategies to encourage dairy farming agribusiness

The ANOVA table points out that the overall model was a good fit since F (1, 64) = 115.054, p-value =0.000<0.05. This is illustrated in Table 4.32.

Table 4.32: ANOVA for Mentorship and Coaching

ANOVAa Sum of Mean Model Squares df Square F Sig. 1 Regression 5.718 1 5.718 115.054 .000b Residual 3.180 64 .050 Total 8.898 65 a. Dependent Variable: Dairy farming enterprise b. Predictors: (Constant), Strategies to encourage dairy farming agribusiness

The coefficient of the study is presented in table 4.33 below.

The model for regression analysis is presented as:

Dairy Farming Enterprise = 1.085 + 0.6281 + 휀

From the model, it is demonstrated that Strategies to encourage dairy farming agribusiness positively significantly influence dairy farming enterprise(훽 = 0.628, 푝 = 0.000 < 0.05 ).

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Here one unit change in dairy farming strategies results in 0.628 unit increase in dairy farming enterprise.

Table 4.33: Coefficients Variation of Dairy Farming Strategies

Coefficientsa Unstandardized Standardized Coefficients Coefficients Std. Model B Error Beta t Sig. 1 (Constant) 1.085 .242 4.490 .000 Strategies to encourage .628 .059 .802 10.726 .000 dairy farming agribusiness a. Dependent Variable: Dairy farming enterprise

4.9 Chapter Summary

This chapter has provided the results and findings with respect to the data given out by the respondents who were small scale dairy farmers from Ruai. The chapter provided analysis on the response rate, background information, opportunities available for farmers who venture in dairy farming, challenges affecting farmers who venture in dairy farming, and strategies to encourage more farmers to venture in dairy farming agribusiness. The next chapter provides the summary, discussions, conclusions and recommendations.

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CHAPTER FIVE

5.0 DISCUSSION, CONCLUSIONS AND RECOMMENDATIONS 5.1 Introduction

This chapter provides the discussion, conclusions and recommendations of the study. The chapter is structured in a way that summary of the study is the first element, followed by discussions, conclusion, and finally recommendations.

5.2 Summary

The purpose of the study was to determine the challenges, opportunities and strategies for farmers engaged in dairy farming, with a focus on small scale dairy farmers in Ruai Area, Nairobi County. The study aimed at exploring opportunities available for farmers who venture in dairy farming, determining challenges affecting dairy farmers and determining strategies to encourage more farmers to venture in dairy farming agribusiness.

A descriptive research design was used to provide for observation and description of the behavior of the subjects in an objective manner. The target population were small scale dairy farmers from Ruai, Nairobi County. The research population consisted of 80 small scale dairy farmers at Ruai Nairobi County and through the use of simple random sampling technique, a selection of 67 respondents was determined. Through the use of a structured questionnaire, the study adopted a descriptive and inferential statistics in data analysis and presentation. For descriptive statistics, the study adopted percentage and frequency distribution, cross-tabulation, mean and standard deviation. While for inferential statistics, the study used factor analysis, correlation and regression analysis. Data was presented by way of Tables and Figures.

The study in the first objective explored how opportunities available for farmers who venture in dairy farming influenced dairy farming agribusiness in Ruai, Nairobi County. The study found that farmers who are more innovative enhance their level of income. The study also revealed that effective extension services provide farmers with information that helps them to optimize their use of scarce resources. Technology in agriculture provides important

76 information to famers to enhance agribusiness. From the study, it was illustrated that dairy farmers go for trainings on how to enhance milk production. The study also found that technologies assist farmers to balance and manage food and herds.

The study in the second objective established the challenges affecting farmers who venture in dairy farming. The research found that prevalence of various animal diseases is a challenge to milk production. From the study, it was revealed that increasing feed prices has cause constraints to increase milk production. The study established that low fertility reduces the profit by decreasing the average milk production and the number of calves per cow per year. The study also illustrates that most of the poor farmers do not have financial means required to make the initial investment and acquire the associated technological inputs. Small scale dairy farmers were found to experience inadequate animal feed resources and this problem of inadequate feed is as a result of the limited land available for pasture establishment.

The study in the third objective determined strategies to encourage more farmers to venture in dairy farming agribusiness. The study found that farmers should encourage the use of artificial insemination (AI) to enhance the quality of breeds. Dairy cattle must be provided with water throughout. The study reveals that improving market access to dairy product creates an opportunity for enhanced dairy production. The study demonstrated that dairy farmers should focus on prevention and control of risk factors in cattle and be trained on milk production systems by qualified individuals who understand the whole dairy farming system. To enhance dairy farming, the study found that dairy animals should be given certain foods for body maintenance and for production. The study found that small scale dairy farmers can get high producers cattle through genetic improvement of their existing herd. The study revealed that developing credit facilities for small scale dairy farmers enhances productivity.

5.3 Discussion 5.3.1 Opportunities Available in Dairy Farming

Under this objective, the study aimed at exploring the opportunities available for farmers who ventured in dairy farming. The findings of the study revealed that farmers who are more innovative enhance their level of income. This finding is echoed in the findings of Ogionwo

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(2011) who found that innovative dairy farmers are better off than their counterparts who are not innovative and that they are in a very good capacity to enhance their income. The author added that highly innovative farmers have better and high living standards than their counterparts and that the farmers with great resources are likely to take the risks involved in going over to a new practice. Rostow (2012) argues that revolutionary changes in agricultural productivity are essential conditions for successful take-off of economic growth of society. Chitere (2011) concurs with this argument and indicates that the adoption of technology of the community members will definitely bring social change in a given community.

The study found that effective extension services provide farmers with information that helps them to optimize their use of scarce resources. The findings of the study support the findings of Morris (1999) who indicates that agricultural extensions promote agricultural technologies to meet farmers’ needs. The extension education brings desirable changes in the quality of life of the target group that it serves by helping them to change their attitudes, knowledge, skills and resources such as land, pasture, water and livestock. Okereke (2016) on the other hand found extension services involve teaching, research and transfer of new technologies and information to farmers using different media like radio, television, or newspapers. Anderson and Feder (2013) argue that investments in extension services have the potential to improve agricultural productivity and increase farmers’ incomes especially developing countries where more than 90% of the world’s nearly one million extension personnel are located. According to Muyanga and Jayne (2006), a consensus exists that extension services, if functioning effectively, improve agricultural productivity through providing farmers with information that helps them to optimize their use of limited resources.

The study reveals that technology in agriculture provides important information to famers to enhance agribusiness. This finding is mirrored in the finding of Belete (2010) who asserts that technology in agriculture provide farmers with the insight to ensure that all decisions contribute positively to farm efficiency and profitability. Okereke (2016) argues that adoption of technology involves application of mental and physical efforts directed to achieving a better value. Technology is a tool that provides better living conditions and enhances the capacity of the people concerned. It is a systematic application of scientific knowledge to practical purposes and includes inventions, innovations, techniques, practices

78 and materials. In addition, Getachew (2014) affirms that farmers implement new ideas, improve practice and use research findings in order to boost their productivity in livestock.

From the study, it is well noted that dairy farmers go for trainings on how to enhance milk production. The finding of the study supports the findings of Pomeory (2013) who found that the ultimate objective of livestock extension education is the development of livestock farmers by improving their living standards through bringing desirable changes in attitudes, skills and knowledge about recent technologies and their applications. The livestock extension education plays an important role in empowering the farmers with appropriate technological knowledge and skills through various forms of extension education and training programs. Madukwe (2006) in his study argues that In dairy farming, the extension personnel educate dairy farmers/producers on the best way to use to improve livestock productivity. The extension agents demonstrate new technology and teach better management practices to dairy farmers through farm visits, newsletters, meetings, seminars and field days.

The study reveals that dairy farmers take risks to adopt a technology that will benefit them in future. The study findings are echoed in the study of Chitere (2011) who revealed that farmers with greater resources are likely to take the risks involved in going over to a new practice. Rogers (1968) indicate that the relative advantage of innovation, that is positive related to adoption of the practice, could be economically profitable or the new idea minimizes the costs. On the other hand, Belete (2010) found that some individuals adopt innovations faster than others. Such individuals tend to take risks and are more open to new ideas.

5.3.2 Challenges in Dairy Farming

The study in the second objective aimed at examining the challenges affecting farmers who venture in dairy farming. The study established that the prevalence of various animal diseases is a challenge to milk production. The study confirms the findings of Rostow (2012) who found that tick borne diseases, internal and external parasite and infectious diseases affect dairy development programs in various scale, depends on ecological zones and management

79 levels. The study found that a number of parasite, bacterial, fungal and viral diseases and nutritional deficiency which are prevalent in the country affect the productivity and reproductive efficiency of dairy cattle and make individuals insecure to be involved in and invest on dairy production specially cows used with exotic blood. On the other hand, Ibrahim and Olaloku (2009) found that venereal diseases have a direct effect while nutritional deficiencies and other infectious diseases play an indirect role in hampering the reproductive efficiency of dairy cows.

The study found that increasing feed prices has cause constraints to increase milk production. To support the point, Solomon-Bekure, Grahdin, and Nate (2011) found that the main constraints to increased milk production under all production systems are inadequate feed resources, poor pasture development and the ever increasing feed prices. Farmers tend to be keep cattle at stoking rates that for exceeds the carrying capacity of their grazing lands. This has resulted in degraded pastures and eroded soils. Zegeye (2013) found that stock numbers are not normally reduced in the dry season leading to grazing lands becoming progressively over grazed. In the dominating crop/livestock production system, producers supplement the feeding of their cows with crop residues and farm by products from their farms.

From the review, it was found that low fertility reduces the profit by decreasing the average milk production and the number of calves per cow per year. The discoveries of the review concur with the discoveries by Delgado (2009) who affirms that poor reproductive performance is a major cause of involuntary culling and therefore reduces the opportunity for voluntary culling and has a negative effect on the productivity of a dairy herd. The study found that reproductive performance is influenced by the interactive effect of environment, management, health and genetic factors. The study by Kedija (2013) confirms that reproductive efficiency is a critical component of successful dairy operation and act as an important component of a profitable dairy farm, whereas reproductive inefficiency is one of the most costly problems facing the dairy industry today. Rostow (2012) found that reproductive problems occur frequently in lactating dairy cows and dramatically affect reproductive efficiency in dairy herd.

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From the study, it is found that most of the poor farmers do not have financial means required to make the initial investment and acquire the associated technological inputs. Julien, et, al., (2011) confirmed that many farmers are aware of the existence of the improved technologies that can offer them higher returns as compared with their conventional practices. However, Belete (2010) found that most of the poor farmers do not have financial means required to make the initial investment and acquire the associated technological inputs, financial supports or credit facilities to smallholder farmers who intend to enter in to commercial dairy farming are very much limited. Rostow (2012) depicted that the importance of establishing credit facilities is crucial step to the country’s dairy sector as indicated in the livestock development master plan.

The study revealed that small scale dairy farmers experience inadequate animal feed resources and that the problem of inadequate feed is as a result of the limited land available for pasture establishment. Chitere (2011) indicated that the problem of inadequate feed is as a result of the limited land available for pasture establishment, especially in the productive highland zones that have a potential for dairy development. De Leeuw, et, al., (2010) identified that in the traditional sector, land becomes a challenge to milk production as a result of overstocking, in urban and per-urban dairying, lack of grazing land is often a limited factors. The intensification of the dairy industry by using fewer numbers of improved dairy cows with increased productivity per cow should be a strategy to be followed. The authors found that the scarcity of the land is becoming a critical problem in many parts of Ethiopia, in certain localities are estimated 50% of the population have a problem of land scarcity. If land degradation is not halted and reversed in some areas of the country, it would become extremely difficult to expand dairy production.

The study revealed that there are no promotional activities being carried out by various government offices to portray milk as a highly nutritious and essential food for the health of nation. The study agrees with the findings of Kaitibie, et, al., (2010) who found that there are no price regulatory mechanisms in place that can much such an important food item easily available and affordable to a large segments of the population. Sintayehu, et, al., (2015) found that improving market access to dairy product creates an opportunity for enhanced dairy production but marketing and access to market have been reported to be the major

81 problems. The study revealed that distance to market, shortage of milk and seasonal fluctuation in milk supply are the major determinant across all the production systems.

5.3.3 Strategies to Encourage Dairy Farming

The study aimed at determining the strategies to encourage dairy farming agribusiness. The study found that farmers should encourage the use of artificial insemination (AI) to enhance the quality of breeds. The survey agrees with the disclosures of Okereke (2016) who found that improvement through breeding aimed at increasing milk yields has been very low in developing countries due to poor implementation of government policy in breeding, lack of proper national herd recording system and local breeds, which are genetically poor for milk production. Rogers (1968) asserted that AI provides opportunities to choose sires that are proven to transmit desirable traits in a dairy cow population. The technique eliminates the costs and the risks of maintaining bulls on the farm and also minimizes the risk of getting offspring with undesirable traits. Baltenweck (2012) affirms that artificial insemination requires a large degree of cooperation between the breeders, the technicians, the insemination countries and the breeding associations. Although a number of farmers are using AI especially in high potentials areas, big number farmers do not know of the existence and importance of AI service, which is advantageous over natural method.

From the research, it is revealed that improving market access to dairy product creates an opportunity for enhanced dairy production. Sintayehu, et, al., (2011) revealed that improving market access to dairy product creates an opportunity for enhanced dairy production. Rostow (2012) confirms that lack of access to market, cultural taboo to sell milk, spoilage of milk and high transport cost have been identified to be the major reasons for weak market access. The study confirmed that limited access to markets was caused by lack of promotional activities being carried out by various government offices to portray milk as a highly nutritious and essential food for the health of nation.

The review also found that dairy cattle must be provided with water throughout. The findings of the study supports the findings of Zegeye (2013) who revealed that dairy cattle suffer more quickly from an inadequate water intake than from deficiencies of any other nutrient.

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Milk production and feed intake will be depressed if free access to water is not allowed. However, Belete (2010) found that most smallholders do not have a reservoir for water, others fetch the water from river causing extra labour cost; this may be straining in providing enough water to dairy cattle. This can be improved by enhancing rainwater harvesting into roof water catchment tanks.

From the study, it is well demonstrated that dairy farming should focus on prevention and control of risk factors in cattle. The study confirmed that supportive organizational culture enhances career development choices among employees. Kurup (2012) confirmed that prevention and progressive control of the disease is very important, especially to those that occur as outbreaks such as the FMD, LSD, Anthrax and Rift valley fever. Such diseases cause major food shortages, destabilize markets and trigger trade measures. Delgado (2009) found that massive vaccinations of cattle against preventable diseases and imposing quarantine in case of outbreaks, and bio-security control measures are used to contain the zoonotic and spread of diseases that can result to heavy economic losses. The study by FAO (2003) found that dairy farming should focus on prevention and control of risk factors associated with each problem rather than on prescriptive therapeutic interventions.

The study found that dairy animals should be given certain foods for body maintenance and for production. Belete (2010) found that a well-fed animal grows faster, reach reproduction stage early and produce more milk, remain in good health status and maintain good body condition. Delgado (2009) found that the maintenance ration varies with breed and size of the animal, whereas production ration is required by dairy cattle for milk production. Ayele, et, al., (2012) revealed that dairy cows that produce more milk will therefore require more and richer amounts of food and these feed are in form of fodder (grass), legumes and other edible plants. In pastoral areas, grass is the most available, cheap and best to feed cattle.

5.4 Conclusions 5.4.1 Opportunities Available in Dairy Farming

The study concludes that technology in agriculture provides important information to farmers to enhance agribusiness hence dairy farmers who embrace technology are more innovative

83 and this enhances their level of income. In contemporary world, technology has been proven to help many easily and fast access the markets. Many farmers can easily access relevant information concerning their dairy farming businesses at a click of a button. This information adds value to the production of dairy products. The study hence concludes that technology in dairy farming is an integral part in enhancing the quality and quantity of dairy products.

5.4.2 Challenges in Dairy Farming

The study concludes that prevalence of animal diseases is the greatest challenge dairy farmers experience as far as milk production is concerned. There are so many diseases that affect and these cause low milk production. Due to the diseases, the animals’ fertility is affected resulting in the decrease of average milk production and the number of calves per cow. This affects the profitability of dairy farmers making it hard for the farmers to maintain their animals in terms of feeding and providing health services.

5.4.3 Strategies to Encourage Dairy Farming

The study concludes that for dairy farmers to enhance the quality and quantity of dairy products, they should encourage the use of artificial insemination (AI). This technique, according to the study is the technique that is very much advantageous to the dairy farmers. The technique first of all provides the best breed that offers the best quality and quantity of dairy products. It eliminates the cost of maintaining a bull and minimizes the risk of getting offspring with undesirable traits. The technique minimizes the risk of infections that a cow would have contracted in case a bull would be used.

5.5 Recommendations 5.5.1 Recommendations for Improvement 5.5.1.1 Opportunities Available in Dairy Farming

The study recommends the small scale dairy farmers to embrace technology in farming as it has been approved to be of more beneficial to farmers by enhancing the quick access to information about dairy farming, facilitating accessibility to the markets, and enhancing the

84 quality and quantity of dairy products. The study also recommends to government agencies and all other policy makers who deal with agriculture to formulate and implement policies that would facilitate small scale dairy farmers’ access to relevant information about dairy farming, easy access to the markets and protect them from middle men and other cooperatives that would like to get their dairy products at very poor prices.

5.5.1.2 Challenges in Dairy Farming

The study recommends dairy farmers to take precaution and preventive measures to avoid their animals from contracting the various prevalent animal diseases that affects the production of dairy products as a result of low fertility. This can be done by accessing to pertinent information about animal diseases and preventing them through the utilization of veterinary services. The study also recommends the policy makers to implement policies that would enhance the accessibility of animal health services to all parts of the country. The study found treatment of animals to be very expensive hence it recommends the government to subsidize on the cost of animal treatment for it to be affordable even to small scale farmers.

5.5.1.3 Strategies to Encourage Dairy Farming

The study recommends dairy farmers to embrace the use of artificial insemination (AL) to enhance the quality and quantity of dairy products. As it was found from the study that artificial insemination is very advantageous to dairy farmers by preventing animal infections, the study recommends dairy farmers to encourage genetic improvement in cattle to enhance productivity. The study also recommends the government and policy makers to create awareness to all dairy farmers on the importance of enhancing the quality of breeds and make the services of artificial insemination accessible to the farmers.

5.5.2 Recommendations for Further Research

The purpose of the study was to determine the challenges, opportunities and strategies for farmers engaged in dairy farming in Ruai Area in Nairobi County. The study recommends future researchers and scholars to study the challenges, opportunities and strategies for

85 farmers engaged in dairy farming in other counties. The study also encourages scholars to determine the best strategies to enhance dairy farming.

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APPENDICES

Appendix 1: Letter of Introduction

Dear Sir/Madam; RE: RESEARCH QUESTIONNAIRE

I am a student at United States International University-Africa taking a Master’s degree in MBA (Global Health). As partial fulfillment of my MBA degree, I am conducting a research on “Challenges and Opportunities for Farmers in Dairy Farming Enterprises in Ruai- Nairobi County”.

The results of this study will provide dairy farmers in Kenya with information on the challenges and opportunities in dairy farming enterprises.

I request your involvement in answering the questionnaire to the best of your knowledge. Kindly note that any information given through this questionnaire is confidential and will only be used for the purpose of this study. Your assistance and response is much appreciated.

Regards;

Milcah Wambui Wamugunda.

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Appendix 11: Study Questionnaire Kindly attend to all questions. Section I: General information Section one is about the general information of the respondents. Kindly tick (√) where applicable

1. Gender Male [ ] Female [ ]

2. Age bracket (years) Below 20 [ ] 20 – 30 [ ] 31 – 45 [ ] 46 – 60 [ ] above 61 [ ]

3. Your level of education:

Diploma [ ] Bachelor’s Degree [ ] Master’s Degree [ ] Doctorate Degree [ ]

4. How long have you been practicing dairy farming? Less than 1 year [ ] 2 – 5 years [ ] 6 – 10 years [ ] 11 – 15years [ ] 16– 20 years [ ] more than 20 years [ ]

5. How many animals do you have? Less than 2 cows [ ] 2 – 5 cows [ ] 6 – 10 cows [ ] 11 – 15 cows [ ] 16– 20 cows [ ] more than 20 cows [ ]

6. Of the animals listed in number 5 above, how many are pedigree and local?

Animal No. Of Pedigree No. Of Local Cows ______

7. How much milk do your cows produce per day?

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Less than 5 litres [ ] 5 – 15 litres [ ] 16 – 25 litres [ ] 26 – 35 litres [ ] 36 – 45 cows [ ] more than 45 litres [ ]

8. How do you sell your milk? Through cooperatives [ ] Through vendors [ ] Others (please state) ______

9. How much do you sell your milk per litre?

Less than 30 shillings [ ] 30 to 40 shillings [ ] 41 to 50 shillings [ ] 51 to 60 shillings [ ] 61 to 70 shillings [ ] more than 70 shillings [ ]

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Section II: Opportunities for Dairy Farming Agribusiness

Kindly tick the extent to which you agree with the following statements on opportunities for dairy farming agribusiness by using a scale of 1 to 5. (1) Strongly Disagree, (2) Disagree, (3) Do Not Know, (4) Agree, (5) Strongly Agree

Statement 1 2 3 4 5

1. Technology in agriculture provides important information to famers to enhance agribusiness 2. Technologies assist farmers to balance and manage food and herds 3. Farmers who are more innovative enhance their level of income 4. Dairy farmers take risks to adopt a technology that will benefit them in future 5. To enhance production, dairy farmers like crossbreeding among cattle of milk, beef or dual- purpose breed 6. It is more effective for rural farmers to market their milk through middlemen 7. Dairy farmers go for trainings on how to enhance milk production 8. Agricultural extensions promote agricultural technologies to meet farmers’ needs 9. Middlemen milk collectors understand better small scale dairy farmers that large highway collectors 10. Effective extension services provide farmers with information that helps them to optimize their use of scarce resources

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Kindly state other opportunities in dairy farming ______

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Section III: Challenges in Dairy Farming Agribusiness Please indicate the extent to which you agree with the following statements on challenges in dairy farming agribusiness by using a scale of 1 to 5. (1) Strongly Disagree, (2) Disagree, (3) Do Not Know, (4) Agree, (5) Strongly Agree

Statement 1 2 3 4 5

1. Small scale farmers rely on indigenous breeds that give low milk output 2. Small scale dairy farmers experience inadequate animal feed resources 3. Increasing feed prices has cause constraints to increase milk production 4. The prevalence of various animal diseases is a challenge to milk production 5. The animal health services provided to rural farmers are inadequate 6. There are no promotional activities being carried out by various government offices to portray milk as a highly nutritious and essential food for the health of nation 7. Low fertility reduces the profit by decreasing the average milk production and the number of calves per cow per year 8. There is no extension to supply information about technologies to improve production and marketing to estimate certain development 9. The problem of inadequate feed is as a result of the limited land available for pasture establishment 10. most of the poor farmers do not have financial means required to make the initial investment and acquire the associated technological inputs

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According to you, what other challenges dairy farmers face? ______

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Section IV: Strategies to Encourage Dairy Farming Agribusiness Please indicate the extent to which you agree with the following statements on strategies to encourage dairy farming agribusiness using a scale of 1 to 5. (1) Strongly Disagree, (2) Disagree, (3) Do Not Know, (4) Agree, (5) Strongly Agree

Statement 1 2 3 4 5

1. Small dairy farmers can get high producers cattle through genetic improvement of their existing herd 2. Small scale farmers should encourage genetic improvement in cattle to utilize their exotic breed and their crosses genotypes 3. Farmers should encourage the use of artificial insemination (AI) to enhance the quality of breeds 4. Dairy animals should be given certain foods for body maintenance and for production 5. Dairy cattle must be provided with water throughout 6. Dairy farming should focus on prevention and control of risk factors in cattle 7. Development of credit facilities for small scale dairy farmers will enhance productivity 8. Improving market access to dairy product creates an opportunity for enhanced dairy production 9. Farmers should be trained on milk production systems by qualified individuals who understands the whole dairy farming system 10. There should be a sound linkage polices in the agricultural knowledge generation and transfer systems

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Kindly state other strategies to improve dairy farming ______

THANK YOU FOR YOU

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