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EXAMINING YOUTH ENGAGEMENT IN AGRIPRENEURSHIP OPPORTUNITIES 
AS A MEANS OF SUSTAINABLE LIVELIHOODS IN MINING COMMUNITIES IN 
THE BIRIM NORTH DISTRICT OF GHANA 
BY 
EMMANUEL OSEI OPOKU 
(10250136) 
 
THIS THESIS IS SUBMITTED TO THE UNIVERSITY OF GHANA, LEGON, IN 
PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE AWARD OF MASTER 
OF PHILOSOPHY DEGREE IN AGRICULTURAL ADMINISTRATION 
 
 
DEPARTMENT OF AGRICULTURAL ECONOMICS AND AGRIBUSINESS 
COLLEGE OF BASIC AND APPLIED SCIENCES 
UNIVERSITY OF GHANA, LEGON 
 
DECEMBER, 2022. 
 
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DEDICATION 
To Hon. John Osei Frimpong, Member of Parliament of the Fourth Republic of Ghana, Birim North 
Constituency, and my lovely mother the late Regina Asi Nuamah. May her soul rest in peace. 
 
 
 
 
 
  
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ACKNOWLEDGEMENTS 
 
My foremost thanks go to the Almighty God who has endowed me with this opportunity, knowledge, 
and ability to produce this thesis. I appreciate His protection throughout my education to this level. My 
deepest gratitude also goes to my supervisors, Prof. Irene S. Egyir, major supervisor and Dr. Abigail A. 
Adaku, co-supervisor in the Department of Agricultural Economics and Agribusiness, College of Basic 
and Applied Sciences in the University of Ghana for their corrective measures, valuable suggestions, 
constructive criticisms and encouragements at all levels of this work. Without their efforts, I could have 
never come out with this work. I am also greatly indebted to Ivan Akuttey, Director of Agriculture, Birim 
North District, Michael Okoampa-Agyemang, Officer, Ghana Cocoa Board Department of Cocoa Health 
and Extension, Birim North District, Hon. Francis Offei, Asomani Stephen, and my two siblings Paul 
Okyere and Elias Kwadwo Edoh, for their invaluable advice and support throughout my education. I am 
also grateful to all of the personnel at Royals Academy International School in Akyem Hweakwae for 
their dedication and successful management of the school while I was away. I would like to express my 
gratitude to Eric Nana Opoku, Michael Opoku, Francisca Opoku, and Mavis Agyemang for their 
invaluable support, and encouraging comments throughout this course. I would also like to express my 
sincere gratitude to Emmanuel Arkorful and Emmanuel Abenney, my course mates for their co-operation 
and teamwork throughout this course, and last but not least Miriam Ewurasi Forson for her relentless 
effort during the review and organization of this thesis. May the Good Lord bless them all.                                                                  
 
 
Emmanuel Osei Opoku  
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ABSTRACT 
This study examines youth engagement in agripreneurship opportunities (AOs) as a means of attaining 
sustainable livelihoods in the Birim North District of Ghana. The study's specific objectives are to identify 
AOs that may be exploited as sources of employment and income generation, as well as to evaluate the 
socio-economic factors that impact youth's decision to engage in AOs and identify the constraints facing 
agripreneurs in the study area. By adopting the multi-stage sampling technique, a total of 220 youth were 
sampled from six different communities. Both qualitative and quantitative methods were used for the 
analysis. By employing the Likert scale technique, the most important AOs were identified. The binary 
logistic regression analysis was used to identify the socio-economic factors that influence the youth's 
decision to engage in AOs. To identify and rank the constraints faced by agripreneurs in the research 
area, Kendall's Coefficient of Concordance was utilized. The Likert scale analysis found twelve AOs, 
with livestock and speculative agripreneurship businesses like palm oil and grains aggregation 
dominating. The binary logistic regression analysis revealed that the most important socio-economic 
characteristics that positively influenced the youth's decision to engage in AOs were sex, employment 
status, and educational level. The most pressing constraints facing agripreneurs in the study area include 
lack of financial support, high inputs prices, and inadequate storage facilities for palm oil and grains. The 
study concludes that males who are employed and highly educated are more likely to engage in 
agripreneurship opportunities than their counterparts, though venturing into them is constrained by lack 
of financial support, high inputs prices and inadequate storage facilities. Based on the findings, the study 
recommends that any programme aimed at encouraging the youth in the Birim North District (BND) to 
engage in AOs should target livestock production enterprises and trading in grains and palm oil 
aggregation. Female participation, employment, and education are key components that need to be 
encouraged by policymakers to stimulate the adoption of agripreneurship. The financial institutions 
should create more avenues that enable the youth agripreneurs to access funds to support their 
agripreneurship venture. This could be done by designing a financial package with flexible terms of 
repayment for the youth agripreneurs in the BND. The government should continue to provide subsidies 
on farm inputs (seeds, fertilizers, weedicides, pesticides, etc.) and extend them to reach the youth in the 
BND. This will reduce the burden of purchasing inputs at high prices by the youth and increase their 
participation in AOs.    
 
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TABLE OF CONTENTS 
CONTENTS                                                                                                              PAGE 
DECLARATION ........................................................................................................................................ i 
DEDICATION ........................................................................................................................................... ii 
ACKNOWLEDGEMENTS ......................................................................................................................iii 
ABSTRACT .............................................................................................................................................. iv 
TABLE OF CONTENTS ........................................................................................................................... v 
LIST OF TABLES ..................................................................................................................................viii 
LIST OF FIGURES ................................................................................................................................... x 
ACRONYMS ............................................................................................................................................ xi 
INTRODUCTION ..................................................................................................................................... 1 
1.1 Background of the Study .................................................................................................................. 1 
1.2 Problem Statement of the Study ....................................................................................................... 8 
1.3 Objectives of the Study .................................................................................................................. 10 
1.4 Justification of the Study ................................................................................................................ 10 
1.5 Organization of the Study .............................................................................................................. 13 
CHAPTER TWO ..................................................................................................................................... 14 
LITERATURE REVIEW ........................................................................................................................ 14 
2.1 Introduction .................................................................................................................................... 14 
2.2 Overview of Agripreneurship......................................................................................................... 14 
2.3 Agripreneurship Opportunities ....................................................................................................... 23 
2.4 Economic Importance of Agripreneurship ..................................................................................... 26 
2.5 Differences Between Agripreneurs and Non-Agripreneurs ........................................................... 27 
2.6 The Youth in Perspective ............................................................................................................... 32 
2.7 Sustainable Livelihoods Approach................................................................................................. 35 
2.8 The Five Livelihood Assets............................................................................................................ 36 
2.9 Livelihoods Strategies .................................................................................................................... 38 
2.10 Sustainable Livelihood Outcomes ................................................................................................ 39 
2.11 Empirical Reviews ....................................................................................................................... 40 
2.11.1 Binary Logistic Regression Model ........................................................................................ 40 
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2.11.2 Influence of Socio-Economic Characteristics on the Decision of the Youth to Engage in 
Agripreneurship Opportunities ......................................................................................................... 41 
2.11.3 The Constraints Facing Agripreneurs .................................................................................... 44 
2.12 Summary and Identification of Knowledge Gap.......................................................................... 46 
CHAPTER THREE ................................................................................................................................. 48 
METHODOLOGY .................................................................................................................................. 48 
3.1 Introduction .................................................................................................................................... 48 
3.2 Theoretical Framework .................................................................................................................. 48 
3.3 Conceptual Framework .................................................................................................................. 50 
3.4 Methods of Data Analysis .............................................................................................................. 53 
3.4.1 Identifying Agripreneurship Opportunities in the Study Area ................................................ 53 
3.4.2 Determining the Socio-Economic Characteristics Influencing the Youth’s Decision to Engage 
in Agripreneurship Opportunities ..................................................................................................... 53 
3.4.3 Testing the Model Fit .............................................................................................................. 55 
3.4.4 The Likelihood Ratio Test ....................................................................................................... 55 
3.4.5 The Wald Test ......................................................................................................................... 56 
3.4.6 Pseudo R2 (Nagelkerke’s R2 and Cox and Snell’s R2) ............................................................. 56 
3.4.7 Test for Econometric Problems ............................................................................................... 57 
3.4.8 Identifying the Constraints Facing Agripreneurs in the Study Area ....................................... 58 
3.5 Methods of Data Collection ........................................................................................................... 60 
3.5.1 Sources of Data, Instruments and Interview Procedures ......................................................... 60 
3.5.2 Description of Variables Used in the Logit model and Hypothesis ........................................ 62 
3.5.3 Sampling Procedure ................................................................................................................. 66 
3.5.4 The Study Area ........................................................................................................................ 68 
3.6 Scope and Limitations of the Study ............................................................................................... 73 
CHAPTER FOUR .................................................................................................................................... 75 
RESULTS AND DISCUSSIONS ............................................................................................................ 75 
4.1 Introduction .................................................................................................................................... 75 
4.2 Background of the Respondents ..................................................................................................... 75 
    4.2.1 Community-Level Characteristics........................................................................................... 75 
4.2.2 Socio-Economic Characteristics of the Respondents .............................................................. 77 
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4.2.3 Agripreneurship Activities of the Respondents ....................................................................... 86 
4.2.4 Speculative Agripreneurship Business ........................................................................................ 89 
4.3: Identification of the Agripreneurship Opportunities ..................................................................... 91 
4.4 Socio-Economic Characteristics Influencing the Decision to Engage in Agripreneurship 
Opportunities ........................................................................................................................................ 93 
4.5 Constraints Facing Agripreneurs in the Study Area....................................................................... 98 
CHAPTER FIVE ................................................................................................................................... 102 
SUMMARY, CONCLUSIONS AND RECOMMENDATIONS.......................................................... 102 
5.1 Introduction .................................................................................................................................. 102 
5.2 Summary of the Study .................................................................................................................. 102 
5.3 Conclusions .................................................................................................................................. 103 
5. 4 Policy Recommendations ............................................................................................................ 104 
REFERENCES ...................................................................................................................................... 106 
APPENDICES ....................................................................................................................................... 114 
Appendix i: Questionnaires ................................................................................................................ 114 
Appendix ii: Test for Multicollinearity .............................................................................................. 122 
Appendix iii: Output Files .................................................................................................................. 124 
 
  
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LIST OF TABLES 
Table 2. 1: Unique Terminologies used in the Definitions of Agripreneurship ................................... 17 
Table 2.2: Some Unique Characteristics of Entrepreneur Farmer (Agripreneur) ................................ 30 
Table 2.3: Global Youth Unemployment Outlook ............................................................................... 35 
Table 3.1: Description of variables, measurement and their theoretical a priori ................................. 65 
Table 3.2: Distribution of the Number of Respondents Sampled from each Community ................... 67 
Table 4.1: Infrastructure Base Resources in the Study Area................................................................ 76 
Table 4.2: Age Distribution of the Respondents .................................................................................. 77 
Table 4.3: Sex Distribution of the Respondents ................................................................................... 78 
Table 4.4: Educational Level of the Respondents ................................................................................ 79 
Table 4.5: Marital Status of the Respondents ...................................................................................... 80 
Table 4.6: Household Size of the Respondents .................................................................................... 81 
Table 4.7: Distribution of Employment Status of the Respondents ..................................................... 81 
Table 4. 8 Range of Average Annual Income of the Respondents ...................................................... 83 
Table 4.9: Agripreneurship Experience of the Respondents ................................................................ 84 
Table 4.10: Sources of Information on Awareness of Agripreneurship .............................................. 85 
Table 4.11: Crops Grown by the Respondents ..................................................................................... 86 
Table 4.12: Horticultural Activities Carried Out by the Respondents ................................................. 87 
Table 4.13: Farming System Practice by the Respondents .................................................................. 88 
Table 4.14: Farm Size of the Respondents ........................................................................................... 88 
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Table 4.15: Livestock Kept by the Respondents .................................................................................. 89 
Table 4.16: Trading Activities of the Respondents .............................................................................. 90 
Table 4.17: Expected Periods of Speculative Agripreneurship Business ............................................ 91 
Table 4.18: Agripreneurship Opportunities ......................................................................................... 92 
Table 4.19: Socio-Economic Factors that Influence the Decision of the Youth to Engage in 
Agripreneurship Opportunities ............................................................................................................. 98 
Table 4.20: Constraints Facing Agripreneurs in the Study Area ....................................................... 100 
 
 
 
 
 
 
 
 
 
 
 
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LIST OF FIGURES 
Figure 1.1: Proportion of the Total Labour Force by Sector .................................................................. 3 
Figure 1.2: Declining State of Agriculture Share of the GDP (%), 2016 – 2019 .................................. 4 
Figure 2.1: Sustainable Livelihoods Framework ................................................................................. 39 
Figure 3.1: Conceptual Framework ...................................................................................................... 52 
Figure 3.2: Sources of Farm Labour .................................................................................................... 72 
Figure 4.1: Availability and Accessibility to Portable Water in the Study Area ................................. 76 
Figure 4.2: Awareness Level of Agripreneurship Opportunities in the Study Area ............................ 85 
Figure 4.3: Decision of the Youth to Engage in Agripreneurship Opportunities ................................ 97 
 
 
 
 
 
 
 
 
 
 
 
 
 
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ACRONYMS 
AU    African Union 
BAC    Business Advisory Centre 
BNDA    Birim North District Assembly 
DFID    Department for International Development 
FAO    Food and Agriculture Organisation of the United Nations 
FASDEP   Food and Agricultural Sector Development Policy 
GDP    Gross Domestic Product 
GLSS    Ghana Living Standard Survey 
GSS    Ghana Statistical Service 
IFAD    International Fund for Agricultural Development 
ILO    International Labour Organization 
IMF    International Monetary Fund 
IRD    Integrated Rural Development 
IYF     International Youth Foundation  
METASIP              Medium Term Agricultural Sector Investment Plan 
MoFA    Ministry of Food and Agriculture 
OECD    Organization for Economic Co-operation and Development 
OLS    Ordinary Least Squares 
NEPAD   New Partnership for African Development 
SLA    Sustainable Livelihood Approach 
SSA    Sub-Saharan Africa 
SRID    Statistics, Research and Information Directorate 
UNCTAD   United Nations Conference on Trade and Development 
UNESCO   United Nations Educational, Scientific and Cultural Organization 
US    United States 
USDA    United States Department of Agriculture
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CHAPTER ONE 
INTRODUCTION 
1.1 Background of the Study   
In many countries, agriculture has long been seen as one of the most important drivers of economic 
growth. For the vast majority of the world's population, it is the primary source of labour, income, and 
sustenance. This is especially true in countries where the bulk of the population lives in rural areas. 
According to Rao and Kumar (2016), almost 70% of India's population lives in rural areas and earns a 
living through agriculture and related businesses. Agriculture also employs more than half of the overall 
labour-force, according to the International Monetary Fund (IMF), and provides livelihoods for a large 
number of small-scale producers who account for roughly 80% of all farms in Sub-Saharan Africa (SSA). 
Aside, the sector ensures sustainable linkages among agro-based industries all over the world through 
provisions of raw materials. The sector also contributes to economic growth by contributing significantly 
to Gross Domestic Product (GDP) both domestic and international level as well as generating revenues 
for the government through exports earnings. In 2018, the agricultural sector accounted for 18.27 percent 
of Ghana’s GDP and 4 percent of the global economy.  
Using the United States (US) economy as an example in a global perspective, the US Department 
of Agriculture (USDA) reported that agriculture, food, and related companies contributed $1.109 trillion 
to the US economy in 2019. This total accounts for 5.2 percent of GDP, according to the estimates. 
Agriculture and food employ 22.2 million full- and part-time workers, accounting for 10.9 percent of 
total US employment, according to the study. Direct on-farm employment accounted for roughly 2.6 
million jobs in the United States, or 1.3 percent of total employment (USDA, 2020). 
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According to the Statistics, Research and Information Directorate (SRID), agriculture is 
acknowledged as a sector for poverty reduction in Africa (2016). Furthermore, it represents a significant 
portion of all African economies, and as a sector, it has the potential to contribute to major continental 
priorities such as poverty and hunger eradication, intra-Africa trade and investment growth, rapid 
industrialization and economic diversification, sustainable resource and environmental management, job 
creation, human security, and shared prosperity (Blein, et al. 2013). In many SSA countries, agriculture 
is also a potential option for stimulating growth, relieving poverty, and enhancing food security (Byerlee 
et al. 2008). According to the Organisation for Economic Co-operation and Development-Food and 
Agriculture Organization (OECD-FAO), agriculture accounts for 15 percent of total GDP in African 
countries on average, but it varies from less than 3 percent in Botswana and South Africa to more than 
50 percent in Chad, implying a diversified range of economic systems in Africa. However, most African 
countries are developing countries that use low technology in production compared to some developed 
worlds that use high technology for agricultural activities. Meanwhile, by far, agriculture offers greater 
opportunities for growth, infrastructural development, and poverty alleviation in majorities of African 
countries (FAO, 2015). 
Ghana is indeed an agriculture-based economy since the majority of the population is engaged in 
it as their main source of economic activities and livelihoods. Be that as it may be in the global and 
African economies, in Ghana, agriculture serves as a source of employment, income, revenue, alleviation 
of poverty, food security, reduction of hunger, source of livelihoods as well as contributing to the 
country’s GDP despite its declined since 2016. According to the Ghana Statistical Service (GSS), 
agriculture accounts for 38.3 percent of overall employment in 2019, with service and industry 
accounting for 43.5 percent and 18.2 percent, respectively. This means, the agriculture sector is the 
second largest employer in Ghana’s economy, following after the service sector (see figure 1.1).  
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Industry
18%
Agriculture
38%
Service
45%
 
Figure 1.1: Proportion of the Total Labour Force by Sector  
Source: Author’s design based on GSS and GLSS, (2019) data. 
 
In line with these important roles of agriculture, the Food and Agriculture Organization of the US 
reported that the development of the sector has been one of the major priorities for the Government of 
Ghana (FAO, 2015).   By considering these facts and given the huge number of the youth cohort in which 
the majority (about 85%) are concentrated in SSA countries and their demographic characteristics, their 
engagement in agriculture activities becomes crucial in order to utilize the various opportunities available 
in the sector effectively. This calls for a paradigm shift. A shift that requires pro-activeness, 
innovativeness, creativity and a risk-taking mind-set among the youth. Thus, entrepreneurship in 
agriculture or agripreneurship becomes an indispensable tool and the need of the hour to curb the menace 
of unemployment, inadequate income, sources of livelihoods, obsolete agricultural practices, migration 
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to urban communities, especially in search of ‘white coloured jobs’, and the declining state of the 
country’s GDP (see figure 1.2). 
25
20.98%
19.7%
20 18.27%
17.31%
15
10
5
0
2016 2017 2018 2019
Years
 
Figure 1.2: Declining State of Agriculture Share of the GDP (%), 2016 – 2019  
Source: Author’s design based on MoF, (2019) data. 
 
Entrepreneurship in agriculture produces a ‘new farmer’. A farmer who takes calculated risk to establish 
his/her own business, produce goods and/or services in the agricultural sector to meet market demand 
ultimately for a profit is called an agripreneur. Similarly, the application of entrepreneurial activities and 
principles in the agricultural sector is called agripreneurship. Thus, agricultural entrepreneurship and 
agripreneurship are synonymous to each other (Bairwa et al. 2014), and can be used interchangeably. 
Simply put, agripreneurship is a combination of agriculture and entrepreneurship. In other words, it is 
entrepreneurship in agriculture. With this regard, studies by Uneze (2013) and Lans et al. (2013) revealed 
that agripreneurship stems from general entrepreneurship. According to Dollinger (2003), 
entrepreneurship in agriculture or agripreneurship is a process of creating and innovating economic 
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structure for development or gain under risk and uncertainty in agriculture. Furthermore, agripreneurship, 
according to Volkmann et al. (2010), comprises taking the risks and embracing uncertainty in order to 
develop a commercial firm with the goal of producing a profit or receiving a return on investment. Ahmed 
et al. (2011) on the other hand, saw agripreneurship as an employment strategy that can lead to economic 
self-sufficiency for rural people but Nwibo et al. (2016) put it as a process of making agriculture a more 
appealing and profitable endeavour. According to Mujuru (2014), the absorption of strategic 
entrepreneurial skills is said to improve agricultural efficiency. Furthermore, an increase in agricultural 
productivity through agripreneurship can help to advance human development. As a result, having an 
entrepreneurial mind-set and smart resource management allow farmers to earn more money, and in 
emerging nations, successful entrepreneurial agriculture decreases poverty, creates jobs, and provides a 
steady source of income for the poor. Hence, a well-established agricultural entrepreneurship can bring 
sustainable development in any country (Mujuru, 2014). Furthermore, as Rezai, Mohamed, & Shamsudin 
(2011) point out, if a country's youth is equipped with agripreneurship skills, the country's agriculture 
will thrive, boosting the chances of food security for the country's rising population.  
Agripreneurship generates a wide range of economic benefits, according to Nachimuthu et al. 
(2018), including increased agricultural productivity, the formation of new businesses, the development 
of innovative products and services, and the development of rural communities. Agripreneurship is an 
employment method that ensures independence and economic self-sufficiency, according to Uche & 
Familusi (2018). They also noted that agripreneurship may aid social and economic development by 
lowering poverty, boosting nutrition, and assuring economic food security. It will also result in the 
diversification of the economy and sources of income, as well as the development of jobs and 
entrepreneurial opportunities. As a result, agricultural entrepreneurship must be fostered in order to create 
jobs, enhance productivity, and strengthen the economy, as agriculture currently employs the country's 
largest workforce (Ibid), after the service sector. Furthermore, sustainable alternatives outside of 
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agriculture are limited; thus, a country's agripreneurship development is critical (Uche et al. 2018). 
Additionally, according to Bairwa et al. (2014), increased productivity, the development of new 
businesses that will result in job creation, product and service delivery improvements, and an increase in 
wealth will all benefit from the application and practice of agripreneurship. A study by Mujuru, (2014), 
has highlighted the importance of engaging the youth in agripreneurship opportunities, emphasizing that 
agripreneurship has the ability to contribute to the development of an economy by providing work options 
for the youth who live in the remote locations both directly and indirectly, helps to improve nutrition, 
food independence and security. 
Ghana being one of the developing economies with more than 38 percent of the population 
involved in agriculture, the spirit of entrepreneurship among farmers needs to be disseminated throughout 
the country. This is because entrepreneurship is seen as one of the key engines for growth and 
development in many nations. It should be a matter of urgency and strategically positioned in Ghana to 
transform traditional agriculture to agripreneurship and this is believed to be possible through the 
engagement of the youth cohort. Additionally, data from the United Nations-Department of Economic 
and Social Affairs (UN-DESA) indicates that youth make up a large portion of the world's population, 
particularly in developing nations like Ghana. Furthermore, according to the International Labour 
Organization (ILO), 70.3 million youth aged 15 to 24 were unemployed in 2017, with that number 
predicted to surge up to 71.1 million. This raises a number of important concerns including how to 
produce enough food to meet the expected increase in food demands, and how to find decent and 
sustainable jobs to meet this large youth population. These concerns are important for the economy of 
Ghana because the country experiences a high youthful population (GSS, 2019) hence agripreneurship is 
the way to go. Youth in rural regions is a great resource, according to Allajabou & Bello (2014), World 
Bank (2013), and United Nations Educational, Scientific, and Cultural Organisation (UNESCO) (2002), 
as reported in Barau & Afrad (2017). They are not only more numerous, but they are also more 
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enthusiastic, bold, and often have innovative ideas that can contribute significantly to socio-economic 
growth if they are adequately handled and given opportunities to participate in development concerns in 
all sectors of the country. They also bring vigour, zeal, and innovation to the workplace, and when their 
willingness to contribute is paired with opportunity, they have the potential to revolutionize economic 
and social progress. Given these significant roles, it is believed that agripreneurship can convert an ‘idle’ 
youth into a productive youth in such a way that their stamina can be harnessed to boost productivity in 
all corners of the agri-sector. However, the utilization of agripreneurship opportunities by the youth is 
impeded by inadequate access to agricultural raw materials, financial assistance, marketing 
arrangements, information symmetry, new technology, or consultation services for a successful 
commercial farming development. Hence, a study that examines the youth engagement in agripreneurship 
opportunities as a means of sustainable livelihoods is a critical matter for economic analysts and policy 
makers. Such study will serve as a precursor for employment, self-reliance, wealth creation and economic 
development for both developing and developed countries. Therefore, with a sound understanding of 
agripreneurship, the youth in the Birim North District (BND) in particular are expected to perform the 
responsibility of managing their own businesses as agripreneurs despite the constraints that may be 
encountered. Further, it is also of great importance to identify those agripreneurship opportunities 
available in the district so as to bring them to the door step of the youth and examine the factors that 
influence their decision of engagement. This is not only necessary to release the untapped potentials of 
the youth but also a critical tool for developing a workforce for all the youth who are agripreneurs to take 
care of the agricultural sector and replace the aging population of farmers all over the world.  
Accordingly, the main agripreneurship activities in the BND include crop production (both food 
and cash crops) which are done on subsistence and market-oriented levels. Trading of agricultural inputs 
and outputs are also rampant in the district. Due to an influx of many youth in search of jobs in the district 
especially in the mining company, there has been an increase in demand for food and agricultural products 
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in general. Thus, with little motivation the youth can take an advantage and enter into the sector so as to 
become agripreneurs. Agripreneurs, according to Nwibo et al. (2016), are economic innovators who 
initiate new ideas, create and exploit possible opportunities, develop innovative ways of carrying out 
diverse processes in agricultural input supply, production, and marketing. Thus, with limited access to 
land for farming in mining communities, it becomes necessary for the youth living in such communities 
to be innovators and adapt to new ways of doing things in agriculture which in this case is agripreneurship 
so that even the small land left can be used economically and sustainably to improve livelihoods. 
1.2 Problem Statement of the Study  
The youth in the Birim North District (BND) originally carry out agricultural activities as the main source 
of employment, income and livelihoods with the majority of them cultivating oil palm under the out-
grower scheme of Ghana Oil Palm Development Company, an agro-processing company located at Kwae 
in the Kwaebibirem District. In 2002, Newmont Ghana Company Limited started exploratory studies and 
discovered gold deposits in the land used for agricultural activities in the district. In 2010, the company 
started acquiring the land from the youth for mining by following the legalities involved. A preliminary 
investigation conducted by the researcher in the communities understudy revealed that about 95 percent 
of the youth living in the study area recently have developed an interest in working in the Newmont 
Mining Company. At the same time, about 30 percent engaged in motor and tricycle riding in quest of 
‘quick money’, neglecting the field of agriculture. This is a threat to the future of agricultural 
development, source of labour, and food security in the district and the nation in general as well as agro-
based local industries that depend on agriculture for their raw materials if appropriate measures are not 
put in place. The situation was alarming and has led to over-reliance on job search in the mining company 
by almost all the youth in the study area. However, a common notion is that the company cannot employ 
all the youth in the district simultaneously coupled with employment requirements such as skills, 
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experience, and qualification necessities. Therefore, two main questions one would like to know the 
answers are; how long should a youth wait until he/she gets employment in the mining company, and 
what if he or she does not have the right qualifications? The youth must therefore do something of 
economic value on their own in order to sustain their livelihoods. In a situation like this, agripreneurship 
has been identified as the need of the hour (Veni & Lakshmi, 2018). At the same time, Barau & Afrad 
(2017) have already reported that in order to identify and meet the need of the present-day youth, market 
demand and establish long-term sustainability in the fight to end hunger, agripreneurship is required. A 
study by Dzanku & Aidam, (2013) has also reported that although there has been growth in other sectors 
such as service and industry, such growth has not been adequately backed by job creation; therefore, to 
create more jobs for the youth, agripreneurship is critical. In a similar vein, as according to the 
International Youth Foundation (IYF), a large percentage of the world's weakest youth live in rural areas 
with low economies, which provides the youth with limited opportunities for meaningful employment 
because of lack of knowledge in agricultural entrepreneurship. However, their numbers are increasing, 
which should be a big source of concern for everyone. On this point, Mukembo and Edwards (2016) 
asserted that applying agripreneurship concepts will allow one to find, develop, and manage viable 
agricultural enterprises/projects in the most efficient and sustainable manner for profit and improved 
livelihoods. Additionally, agripreneurship, according to Uche & Familusi (2018), is an employment 
approach that promotes independence and economic self-sufficiency. 
Furthermore, given the importance of entrepreneurship in jobs and wealth creation in other 
businesses and industries such as banking, information and communication technology, and insurance, 
there is a worldwide recognition that applications of entrepreneurship in agriculture is important but has 
been overlooked for a long time (Fitz-Koch et al. 2017; Alsos, Ljunggren, & Welter, 2003). Therefore, a 
study that identifies the various agripreneurship opportunities and analyzes the variations in socio-
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economic characteristics as factors influencing the decision of the youth to engage in those opportunities 
is very crucial. To this effect, this study has been set forth to address these three research questions: 
1. What are the agripreneurship opportunities in the study area that can be used by the youth as 
sources of employment and income generation? 
2. What socio-economic characteristics influence the decision of the youth to engage in 
agripreneurship opportunities? 
3. What are the constraints do agripreneurs in the study area face? 
1.3 Objectives of the Study   
The major objective of the study is to examine the youth engagement in agripreneurship opportunities as 
a means of sustainable livelihoods in the Birim North District of Ghana. 
The specific objectives are to: 
1. Identify the agripreneurship opportunities in the study area that can be used by the youth as 
sources of employment and income generation, 
2. Determine the socio-economic characteristics that influence the decision of the youth to engage 
in agripreneurship opportunities and 
3. Identify the constraints agripreneurs in the study area face. 
1.4 Justification of the Study  
The Ghanaian government frequently formulates and implements policies targeted at boosting youth 
participation in agriculture in order to promote and increase economic growth and development and 
provide jobs for the youth. Despite government efforts to encourage the youth to work in agriculture, in 
the literature, there is evidence that youth show negative attitudes toward the industry, describing it as 
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having low economic returns and farmers having a low social status when compared to other workers in 
different sectors (Sandeepa & Salara, 2020). The situation is very prevalent in the district where the youth 
are disregarding agriculture as a career and searching for jobs in the mining company, engaging in motor 
and tricycle riding that will earn them ‘quick money’. This is probably because the youth have not 
realized the several opportunities that are available in the agriculture sector.  
  In addition, increasing farm income has also been identified as one of the means through which 
the Government of Ghana seeks to revamp the agricultural sector as incorporated in Food and 
Agricultural Sector Development Policy (FASDEP II) objectives. In this way, agripreneurship has been 
identified as having the potentials to enable the youth to increase farm incomes. This has been affirmed 
by studies such as Veni & Lakshmi (2018) who reported that agripreneurship enhances farm incomes 
and increases the number of career prospects for persons living in remote areas in the economy. 
According to Bairwa et al. (2014), agripreneurship plays a variety of roles in the national economy's 
growth and development, including entrepreneurship development, which boosts income levels and job 
prospects in both rural and urban areas. Uche & Familusi (2018) recognized this and stated that 
a considerable portion of the population will get direct work and income as a result of agripreneurship, 
including the youth. And as it has been noted, agripreneurship provides job opportunities, income 
generation, poverty alleviation, food security, and rural livelihoods for the benefit of the youth living in 
a country when it comes to development (Nachimuthu, 2018). Furthermore, according to the World Bank 
(2013), youth bring energy, vitality, and innovation to the workplace, and their enthusiasm to contribute, 
when paired with opportunity, can have a significant effect on economic growth and social development. 
Also, former UN Secretary-General Kofi Annan stated “Any society that does not succeed in tapping 
into its energy and creativity of its youth will be left behind” (Kofi Annan Foundation, 2013). As a result, 
youth must be seen as performers, partners, and players. They are, in reality, strategic catalysts for new 
ideas that contribute to peace, human growth, and human society rejuvenation, especially in a globalizing 
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world (UNESCO, 2002). In addition, Heinert & Roberts (2016) argue that incorporating youth is critical, 
particularly in major industries such as agriculture, because they represent a growing segment of society 
and are, quite literally, the future decision makers. Furthermore, the UN-DESA stated that youth must be 
actively involved in sustainable development in order to achieve sustainable, inclusive, and stable 
societies, and that in order to avoid unemployment, poverty, gender inequality, conflict, and migration 
are all obstacles and difficulties to long-term development which are more prevalent in developing 
countries (UN-DESA, 2018). All these assertions make it relevant to research and bring to the attention 
of the youth the numerous agricultural opportunities that can be invested in as a source of employment 
and income generation in the quest for sustainable livelihoods in BND. This new mechanism, known as 
agripreneurship, will not only create a new source of employment, but can contribute to the radicalization 
of farming operations and the introduction of innovation in order to boost production per hectare 
(Sandeepa & Salara, 2020).  
  Moreover, many of the existing literature such as Uche & Familusi (2018), Nachimutu et al. 
(2018), Verma & Sahoo (2018) and Nib et al. (2016) mentioned a lot of constraints facing agripreneurs 
but the most pressing ones were not indicated in the studies. As a result, policymakers and economists 
will find it a tough time in determining which specific constraints need to be addressed in a given 
community and which socio-economic factors have a substantial impact on youth's decision to engage 
in agripreneurship opportunities. Thus, this study will bring to life the type of relationship which exists 
between the youth’s decision to engage in agripreneurship opportunities and socio-economic 
characteristics, and further informs policymakers of the main constraints agripreneurs in the study area 
faced. In this way, identification of a particular area by the policymakers and economic analysts that need 
a conducive environment becomes straightforward. It is also believed that with a sound understanding 
and a deep knowledge in agripreneurship where the principles of entrepreneurship are applied in 
agriculture, more agripreneurs would be created to assist in curbing the challenges of youth 
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unemployment, low productivity, the recent decline of agriculture share of the GDP, youth migration, 
and food insecurity among others.  The findings of this study could also be adopted and implemented by 
the institutions such as the Ministry of Food and Agriculture (MoFA), Rural Enterprise Project (REP) 
and Business Advisory Centres (BACs), agri-business companies, agri-business consultants, 
entrepreneurs for the benefit of their clients and the people living in their locality. It is also hoped that 
the study will contribute to academic knowledge and serve as a source of reference for future studies. 
1.5 Organization of the Study   
This research is divided into five sections. The first chapter is the introduction, which covers the study's 
background, problem statement, objectives, and justifications. The literature review is presented in 
Chapter 2, and Chapter 3 describes the methodology used to achieve the specific objectives, including 
theoretical and conceptual frameworks, data collection methods, including data sources, instruments, 
interview and sampling procedures, a description of the study area, and the scope and limitations of the 
study. The findings are presented and discussed in Chapter 4. The study's summary, conclusions, and 
recommendations are presented in Chapter 5, which is the last chapter.  
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CHAPTER TWO 
 
LITERATURE REVIEW  
 
2.1 Introduction  
This chapter reviews and organizes the relevant theoretical and empirical literature on the issues under 
study into the following sections: The first section provides an overview of agripreneurship and its link 
in entrepreneurship, agripreneurship opportunities, economic importance of agripreneurship, and the 
differences between agripreneurs and non-agripreneurs. The youth in perspective and the sustainable 
livelihoods approach are presented in the second section. The third portion, on the other hand, examines 
the factors that influence youth's decision to engage in agripreneurship opportunities and the constraints 
that agripreneurs confront in pursuing agripreneurship activities. The fourth section, which is the final 
section, presents empirical reviews on the methods and models for estimating socio-economic 
characteristics affecting the decisions of the youth to engage in agripreneurship opportunities and 
conclude the chapter by summarizing the knowledge gaps identified in the literature.  
2.2 Overview of Agripreneurship  
Agripreneurship is a derivative terminology from the combination of agriculture and entrepreneurship. 
The whole idea is to introduce entrepreneurial activities and principles into agriculture and its allied 
sectors for a better and more economic gains. This is undoubtedly due to the observations of the critical 
roles entrepreneurship plays in other sectors such as banking, insurance, marketing and information 
technology. These roles include for instance job creation, self-employment, opportunity identification 
and utilization and income generation. As such, the origin and the meaning of entrepreneurship have been 
linked to agriculture called agripreneurship.  Despite these, there are a number of accumulated evidences 
that show that research on entrepreneurship is often attributed to non-agricultural activities as observed 
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by researchers such as Alsos et al. (2012) who mentioned that the evidence demonstrating the exclusion 
of agriculture from entrepreneurship research is overwhelming. In addition, this is because agriculture is 
seen as a ‘special case industry’ as a result of the political and strategic nature of food security and the 
sector receiving public support such as subsidies. Consequently, the sector was seen, as being non-
entrepreneurial as a result of such obscuring public policies (Hilmi, 2018). However, evidence was found 
in literature to support the proposition that farmers undertaking agriculture as economic activities are 
entrepreneurs. For instance, farmers take the risk and make decisions to apply inputs under erratic rainfall 
and even a period of uncertainties such as adverse weather conditions, diseases and pest infestations to 
produce an output as entrepreneurs do. For example, the quantity and quality of output derived from a 
bundle of inputs cannot be known with certainty (Moschini & Hennessy, 2001). There are also many 
risks and uncertainties that range from marketing, technological, financial, policy and human matters.  
This should entail that those farmers as operators in the agricultural sector are risk-takers and managers, 
regardless of whether they commercialize or do not commercialize their production outputs (FAO, 2015).  
Small-scale farmers can also be called entrepreneurs, according to the report, because they raise 
funds from a variety of sources and invest it in productive assets, which may include a shovel or a bicycle. 
They make decisions and are willing to embrace both risks and benefits. And agriculture requires multiple 
decisions, including what to plant, which inputs to use and how to use them, when to plough, sow, and 
harvest; how much to preserve for household consumption versus how much to sell for profit; and how 
much to store. All of these decisions are frequently made in an economic environment in which markets 
are inefficient, if at all, and in which there is information asymmetry and other hazards such as poor 
weather and price swings, are present (FAO, 2015).Thus, seeing the context of operations in the 
agricultural sector, farmers are entrepreneurs, who may be called agripreneurs, which entail 
agripreneurship (Hilmi, 2018). One important question raised during a debate about agricultural 
entrepreneurship was whether agricultural entrepreneurship differed from entrepreneurship in non-
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agricultural sectors, and as Lans et al. (2013) pointed out, the answer could be yes or no, depending on 
the type of research paradigm used by a researcher, while Fitz-Koch et al (2017) made it specific that 
agripreneurship is different from non-agricultural entrepreneurship and should be considered as a class 
of its own. This is further reinforced historically as several of the founding figures of entrepreneurship 
theory, such as Cantillon, Say, and Mill, wrote during a period when agriculture was the dominant 
industry, and many of the examples they used to illustrate entrepreneurship came from agricultural 
production (Alsos et al. 2012).  
 Agripreneurship, according to Mukembo & Edwards (2016), is the use of entrepreneurial 
principles to find, develop, and manage successful agricultural enterprises/projects for profit and 
improved livelihoods. Agripreneurship, according to Rao and Kumar (2016), is the entrepreneurial 
process used in agricultural or related industries. Furthermore, agripreneurship is the process of adopting 
new methods, procedures, and techniques in agriculture or related industries in order to improve output 
and earnings: agripreneurship therefore transforms agricultural activity into an entrepreneurial activity. 
Agripreneurship, according to Yousoff et al. (2015), is defined as agricultural entrepreneurial activities 
that are regulated to produce, distribute, market, or transport agricultural products for profit. 
Generally, agripreneurship is defined as agriculture that is sustainable, community-oriented, and 
directly-marketed (Narendran & Ranganathan, 2015). Agripreneurship, according to Uneze (2013), is a 
notion unique to agriculture and derived from a broader perspective of entrepreneurship. Dollinger (2003) 
described agriculture entrepreneurship as the formation of creative economic organizations for the 
purpose of growth or profit in the face of risk and uncertainty. Estahbanaty (2013) defined agricultural 
entrepreneurship as a value creation process composed of a unique set of resources to exploit and take 
advantage of opportunities in rural areas. Agripreneurship, according to Bairwa et al. (2014), is a 
productive marriage of agriculture and entrepreneurship. Agripreneurship, according to Volkmann et al. 
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(2010), entails taking risks and accepting uncertainties in order to build a commercial enterprise with the 
goal of profit or return on investment. Agripreneurship, according to Macher (1999), is a lucrative 
marriage of agriculture and entrepreneurship, in which agriculture is viewed as a business venture. 
Kumari & Prasad (2017) called it agri-entrepreneurship and defined it as a way towards self-
sustainability. Succinctly, agripreneurship is the discovery of new products, services, or production 
techniques, as well as new markets for new products and inputs that were not previously available 
(Needy, 2011).  
This study, on the other hand, defined agripreneurship as a practice of taking the risk in an 
innovative way to create or develop a product or service in the agricultural sector in return for profit. It 
should be emphasized that in all these definitions, entrepreneurship and agriculture converged holistically 
in an attempt to provide a specific definition of what entrepreneurship in agriculture is, in other words, 
what agripreneurship is. The key terminologies used by the various authors in the process of propounding 
definitions for agripreneurship are summarized in the table 2.1 as an extension of what has been published 
by Hilmi (2018). 
Table 2.1: Unique Terminologies Used in the Definitions of Agripreneurship 
No. Terminologies Publication authors 
1.  Value creation process using a unique set of resources. Estahbanaty, (2019) 
2.  Rural  Estahbanaty, (2019) 
3.  New methods, processes and techniques in agriculture. Rao & Kumar, (2016) 
4.  Converts agricultural activity into an entrepreneurial activity.  Rao & Kumar, (2016) 
5.  Principles, sustainable and improved livelihoods. Mukembo & Edwards, (2016) 
6.  Activities regulated to generate income. Yousoff et al. (2015) 
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7.  Sustainable, community-oriented and directly-marketed Narendran & Ranganathan, (2015) 
agriculture. 
8.  Profitable marriage of agriculture and entrepreneurship. Bairwa et al. (2014) 
9.  Concept widely drawn from entrepreneurship. Uneze, (2013) 
10.  Identification of new products/services, inputs and strategies Needy, (2011)   
that did not previously exist.  
11.  Taking risks and accepting uncertainties to get profit or Volkmann et al. (2010) 
returns on the investment. 
12.  Innovative economic organization under risk and Dollinger, (2003) 
uncertainties. 
13.  A combination of agriculture and entrepreneurship for a Macher, (1999) 
profit whereby agriculture is seen as a business venture. 
14.  A practice of taking the risk in an innovative way to create or Author’s, (2022) 
develop a product or service in agricultural sector in return 
for profit. 
Source: Hilmi, (2018). 
 
From table 2.1, it is seen that agripreneurship differs from other non-agricultural entrepreneurship. This 
agrees with Fitz-Koch et al. (2017) who posited that agripreneurship is different from non-agricultural 
entrepreneurship and should be considered as a class of its own. Despite this, the definitions did not 
reveal whether agripreneurship entails traditional agriculture or not and whether subsistence farmers are 
also agripreneurs or not. This distinction is important because it is noted that most of the definitions 
emphasized profit generation and returns obtained from the investment and these are notably not 
characteristics of traditional and subsistence farmers. In these types of farming, the main concern of the 
farmers is how much to produce and how to feed the family instead of the marketing and profit aspect of 
production. Thus, the existing literature has attempted in one way or the other to separate 
entrepreneurship in agriculture from entrepreneurship in other non-agriculture businesses but did not give 
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the difference between agripreneurship and traditional agriculture and those practicing subsistence 
farming. As a result, this study argues that agripreneurship is not the same as traditional agriculture which 
involves using tools such as cutlasses and hoes to till and cultivate a piece of land and rearing of farm 
animals. In this case, the report which has been provided by FAO (2015) that all farmers, regardless of 
whether they commercialize or not, are agripreneurs is contentious and therefore must be reconsidered. 
This is because one of the basic ideas behind entrepreneurship is the ability to take the risk and enter into 
a business venture for a profit. Not all farmers are taking the risk and or doing the agribusiness. Also, it 
is difficult to tell if any whether subsistence farmers who produced for domestic consumption receive 
profit. In addition, the original entrepreneurship orientation construct emphasizes three important features 
of entrepreneurial behaviour: which are creativity, risk-taking, and pro-activeness (Lans et al. 2013) and 
where these three elements are lacking, there would be no entrepreneurship. In effect, the innovativeness 
and risk-taking aspect cannot be completely taken out so as agripreneurship is concerned. With this view, 
farmers who are risk averse, unable to adopt new methods of farming including adapting to new 
technology, unable to enter into the business world and cultivate what could be used only to feed the 
family are not agripreneurs. In practice, they are not taking any risk and therefore see agriculture as a 
way of life; something laid down by their forefathers and hence must participate. Therefore, it is not 
surprising that other researchers such as Faria & Mixon (2016) have described agripreneurs as ‘new type 
farmers’ whiles others such as Rao & Kumar (2016) described them as ‘innovators’. It should also be 
mentioned here that so far as entrepreneurship or agripreneurship is concerned, the business aspect is a 
necessity where the business is translated to mean profit-making from a venture.   
Because agripreneurship originated from entrepreneurship, a detail understanding of entrepreneurship is 
required. In this way, the link between agripreneurship and entrepreneurship becomes explicit and easy 
to apply. The word entrepreneurship originated from the French word ‘entreprendre’ which can be broken 
down into two words ‘entre’ and ‘prendre’ where ‘entre’ means between and ‘pendre’ means to take 
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(Barringer & Duane, 2016). Thus, according to Coulter (2003), the word refers to ‘go between’ or 
‘between-taker’. In short, it means to ‘undertake’.  According to Merriam Webster Dictionary, the phrase 
was first used in 1762 to describe a person who organizes, manages, and assumes the risk of a business 
or venture in order to gain money. And as according to Hilmi (2018), the term was first used in the 
seventeenth century by Richard Cantillon, an economist, to describe a person who takes the risks in 
pursuing opportunities cited in Casson et al. (2008), and who acted as a bridge between someone with 
capital, money, and he or she or a group of people would take on business opportunities. This later 
evolved in the eighteenth century to include someone (entrepreneur) who was taking a risk in terms of 
planning, supervising, organizing, and owning factors of production, while economist Say would go even 
further in the nineteenth century by proposing that the profit derived from capital ownership and that 
derived from entrepreneurial gains were distinct and separate (Coulter, 2003). Entrepreneurship, 
according to Onuoha (2007), is the process of creating new businesses or renewing existing ones, 
particularly new firms, in response to possibilities. Drucker (1985) mentioned that entrepreneurship is 
the act of making something new by using the existing resources with new wealth-producing capacity. 
Entrepreneurship is therefore about taking risks (Drucker, 1970 and Knight, 1920). A new entry is a vital 
act of entrepreneurship. Entering new or current markets with new or existing goods or services is one 
way to make a successful entry. The act of initiating a new endeavour, whether by a start-up, an existing 
corporation, or through internal corporate venturing, is known as a new entrance (Lumpkin & Dess, 
1996). Entrepreneurship encompasses the study of sources of opportunity, the processes of discovery, 
evaluation, and exploitation of opportunities, as well as the people who discover, evaluate, and exploit 
them (Shane & Venkataraman, 2000). Entrepreneurship is a context-dependent on social process in which 
individuals and organizations create wealth by combining unique bundles of resources to produce new 
products and services and take advantage of market possibilities. (Hitt & Sirmon, Ireland, 2003). Within 
a new or existing organization, entrepreneurship is a mindset and a process for starting and growing a 
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business by combining risk-taking, creativity, and/or invention with sound management (Commission of 
the European Communities, 2003). As entrepreneurs working in non-agricultural firms such as banking, 
information technology, marketing and insurance are notably taking risks to come out with new ideas 
and manage businesses for profits so as farmers. 
Entrepreneurship is different from entrepreneur. Entrepreneurs are those who pursue 
entrepreneurship. The traditional definition of entrepreneur is often attributed to Richard Cantillon, over 
three centuries ago, as a risk-taker who consciously commits resources to a commercial endeavour so as 
to seize opportunities in exchange for a return (Casson, 2002; Ahmad & Seymour, 2008). Entrepreneurs, 
according to Cantillon, are persons who acquire goods at a known current prices at a market and 
determine to sell them in the near future based on price differences. As a result, the entrepreneurs are 
risk-takers and carriers of uncertainties. They, according to Bolton & Thompson (2000), are individuals 
who frequently develop and innovate in order to build something of recognized economic value around 
observed prospects. Entrepreneurs, according to Hisrich (1990), are people who can show initiative, 
innovative thinking, coordinate social and economic systems to turn resources and situations into 
practical account, and accept risks and setbacks. They are the ultimate business's decision-makers and 
are in charge for its success or failure (FAO, 2019). They are distinguished by attributes such as creativity 
and ingenuity, and can transform ideas into reality through meticulous preparation and excellent 
administration (European Commission, 2012). According to Defoe (2001), entrepreneurs are “pro-
jectors” implying that at least they are responsible for the management of a business. According to Joseph 
Schumpeter, the entrepreneurs are the innovators who put into action the changes within markets through 
the carrying out of new combinations. Schumpeter noted that these combinations can take many forms 
including; the introduction of a new good or quality thereof, the introduction of a new method of 
production, the opening of a new market, the conquest of a new source of supply of new materials or 
parts, and the carrying out of the new organization of any industry (Schumpeter, 1934). By implementing 
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change within markets and carrying out new combinations, Knight (1921) opined that entrepreneurs are 
those individuals who attempt to forecast and act upon change within markets and bear the uncertainty 
of market dynamics. They are always speculators and deal with the uncertain conditions in the future.  
Their success or failure depends on the correctness of their anticipation of uncertain events.  If they fail 
in their understanding of things to come, they are doomed. They recognize and act based on the profit 
opportunities, more importantly arbitrageurs (Kirzner, 1973). Butler (2006) noted that entrepreneurs are 
complex combination of some interacting factors and these factors include possessing resilience, tenacity, 
opportunity spotting, and risk-taking. According to the study, these factors are called personality factors. 
Others include attitude, skills and motivation. The ability to network, think strategically, gain access to 
resources, business knowledge and acumen, interpersonal skills, and people management capabilities 
were called skills, while the attitude consists of having awareness of the importance of customer focus, 
the application of creativity and imagination, defined personal standards and values, and the perception 
of enterprise as a positive activity. On the other hand, motivation was defined as personal drive and 
ambition, the desire to make an effect, the need for achievement or self-satisfaction, a desire for prestige, 
the need to generate and amass wealth, and perform social duty. The study further reported that the 
presence, combination and interaction of these factors determine both how an entrepreneur engages in 
enterprising activities, and ultimately the degree of success that is achieved. The entrepreneurs are 
opportunity-driven operating beyond the subsistence level–not small family farmers. They are often seen 
as innovators and designers of new ideas and business processes. Entrepreneurs are usually creative, take 
opportunities and accept risks, and can quickly change business strategies to adapt to changing 
environments. They are often innovators (Kahan, 2012). They need not necessarily be the financier of 
the business venture, or the owner of the physical assets, but is the main or one of the main risk-takers 
and net profit beneficiaries (FAO, 2019). They exhibit a number of characteristics that distinguish them 
from conventional business owners. These characteristics according to Zhang & Cueto (2015) include 
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initiative, risk-taking, leadership, business and profit orientation, unconventional or out of the box 
thinking, never say die attitude, willingness and the ability to follow the new technology. They have the 
ability to turn their ideas into business opportunities. Many of them do not compromise on traditional 
jobs but completely enter into a new field and make a success of it through their own ingenuity or with 
some institutional or state help. They are very good in perceiving new business opportunities and usually 
exhibit positive biases in their perception that is, a bias towards identifying new business possibilities to 
meet the market needs. In this case, they demonstrate a pro-risk-taking attitude that makes them more 
likely to exploit the identified opportunities. Entrepreneurs are bold enough to go out there to bring out 
what has never existed there before or modify a system to a level that satisfies the needs of customers.   
 
2.3 Agripreneurship Opportunities 
 Hills et al. (2004), Shane & Venkataraman, (2000) have opined that opportunities are the main focus of 
entrepreneurship and as mentioned by Busenitz et al. (2014) to its legitimacy should be a different field 
of study. According to Short et al. (2010), there could be no entrepreneurship without opportunities. 
Therefore, before exploring what constitute agripreneurship opportunities, it is important first of all to 
clarify what exactly constitute opportunities. Based on this, Oyson & Whittaker (2015) defined 
opportunities as entrepreneurially discovered or created situations in which new goods, services, raw 
materials, markets and organizing methods are conceived as having a potential for exploitation. 
According to Stevenson & Jarillo (1990), opportunities are future situations which deemed desirable and 
feasible. Hills et al. (1997) referred to opportunities as either the possibility to create new businesses or 
significantly improve the position of an existing business, in both cases resulting in new profit potential. 
Likewise, Kaish & Gilad (1991) defined opportunities as market gaps resulting from disequilibrium.  In 
definition like these, opportunities are defined as unmet demand that currently exist in a particular market, 
although the potential for economic profit is not made explicit. Karlesky (2015) based on his research 
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findings also defined opportunities as new combinations of technologies and markets. He further opined 
that based on subjective beliefs new technology-market combinations can be created through (a) the 
development and application of new technologies, or (b) the application of existing technologies in new 
markets.  
Choi & Shepherd (2004) for example, suggested that opportunities exist when there is customer 
demand for a new product. In addition, Joseph Schumpeter over a century ago noted five different types 
of entrepreneurial opportunities which are also crucial in agripreneurship. To mention them here are; the 
introduction of new goods, the introduction of a new method of production, the opening of a new market, 
the control of a new source of raw materials or half-manufactured goods, and the creation of a new type 
of industrial organization. Other scholars have emphasized the role market plays in their definition of 
opportunities. With this understanding and based on Stevenson & Jarillo’s (1990) definition of 
opportunities, this study defines agripreneurship opportunities as those desirable activities that can either 
be created or discovered in the agriculture sector based on the alertness and capabilities of the individual 
to meet the unmet conditions of market demand.  In the agricultural sector, these opportunities may be 
categorized into off-farm and on-farm activities. The majority of the off-farm agripreneurship 
opportunities have been noted to concentrate on the value chain. They include input procurement, 
distribution, and the hiring of implements and equipment such as tractors, seed drills, sprayers, harvesters, 
threshers, and dryers, as well as technical services like irrigation installation, weed control, plant 
protection, harvesting, threshing, transportation, and storage (Veni & Lakshmi, 2018). Aside from the 
provision of cattle feed, mineral combination, and forage seeds, similar off-farm opportunities exist in 
the animal husbandry sector for breeding, vaccination, disease diagnosis, and treatment (Veni & 
Lakshmi, 2018). Mukhopadhyay & Mukhopadhyay (2020) also emphasized four key areas where 
agripreneurship opportunities abound. Crop guidance, agricultural input sales, market linkages, and loan 
facilitation are among them. They also stated that agripreneurship potential existst in dairy, sericulture, 
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goat, rabbit, floriculture, fishery, shrimp farming, sheep farming, vegetable cultivation, and nursery 
farming. All of them are off-farm agribusiness options that can be profitably utilized by the youth. 
Other researchers, such as Uneze (2013), included agric-tourism entrepreneurship and other 
service areas in the agriculture value chain, such as transportation, storage, and packaging, workshops 
and service center training, and agricultural implement repair. Food production systems will be stabilized 
by post-harvest technologies likeprocessing, packing, and storage, which will assure long-term food 
availability in the country and eliminate excessive reliance on food imports. Furthermore, the 
manufacturing and selling of farm machinery, tools, and equipment will open up new avenues for private 
sector participation and initiatives. Agro-industries can also play a role in agricultural operations by 
producing, distributing, and supplying inputs such as seeds, fertilizers, and other agro-chemicals. Food 
processing and marketing are two other areas where agripreneurship opportunities abound but are not yet 
fully explored (Ado, 2017). Within the extended food system, the agripreneur may also investigate the 
complete range of 'agribusiness options,' which can encompass logistics, services, cooking, and waste 
recycling. As the agricultural sector continues to struggle with waste and losses as a result of insufficient 
processing and storage, there are always opportunities to be taken advantage of. 
On-farm opportunities on the other hand, include arable and tree crops production, vegetables 
production, weeds control, harvesting, application of pesticides and fertilizers. In addition, most farmers 
in the rural communities are unaware of the right planting distances and correct dosage of fertilizer 
application per plant per hectare. By organizing workshops and demonstrations for farmers under this 
circumstance, an opportunity is created. In brief, Otache (2017) outlined these agribusiness opportunities 
in which the youth in particular can venture into. They are apple, cashew, orange orchard, mango, palm 
tree (fruits), rubber, cotton wool and cocoa plantation farming businesses. The study further mentioned 
beans, carrot, cassava, rice, maize, yam, sweet and Irish potatoes, pineapple, sugarcane, flower, 
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vegetable, groundnut and onion farming businesses as another areas of opportunities. In animal 
production, he noted that cattle, goat, sheep, poultry and pig rearing are all lucrative areas of investment 
for the bulging youth. Others include grass cutter, bees, fish farming, mushroom production, rabbit and 
snail farming business. Other opportunities which need to be added to the above list are the taro 
(Colocassia esculenta) farming business, plantain and banana farming business, tiger nut (Cyperus 
esculentus) farming business and water-melon farming business. With these numerous agripreneurship 
opportunities, there is no doubt that Nwibo et al. (2016) mentioned that many business opportunities can 
be found in agriculture. Hence, the youth can take the risk and venture into any of them depending upon 
appropriate environment for an income and consequently a source of livelihoods. 
2.4 Economic Importance of Agripreneurship  
According to Kumar & Kumar (2019), the agriculture industry has the ability to contribute to a nation's 
revenue while also providing direct jobs and income to both the wealthy and the poor. At the same time, 
Mukembo and Edwards (2016) point out that agripreneurship invariably generates foreign exchange and 
aids in the resolution of some balance of payment issues through value addition and product exportation. 
This is especially true for countries whose primary source of international commerce is agriculture.  Thus, 
to achieve economic growth through employment, more income and revenue to the economy of Ghana 
as a whole, agripreneurship is undoubtedly one of the key options to adopt realizing that, a significant 
number of the country’s population depend on agriculture. In addition, Bairwa et al. (2012) stated that 
entrepreneurship in agriculture will lead to an increase in wealth while Casson et al. (2008) opined that 
entrepreneurship make a vital contribution to economic growth. This is still reinforced for example by 
Bairwa (2012) who opined that if entrepreneurship in agriculture would be applied and practiced 
accordingly, it will lead to a wide range of economic benefits including increase productivity, innovations 
in products/services delivery and development of a new agribusiness ventures leading to job creations 
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for the youth. Further, Uche & Familusi, (2018) have also posited that there are a lot of economic gains 
in agripreneurship comprising of reduction of poverty index, improved nutrition and food security for an 
economy. In addition, they noted that agripreneurship development will lead to diversifying the economy 
especially in an areas of income generation, providing employment and other entrepreneurial 
opportunities all of which has an economic benefit for a country. According to Hisrich et al. (2010), 
entrepreneurship has a role in the development of an economy by increasing the per capita output and 
income bases. These, they noted lead to a change in the structure of a society and businesses through an 
innovation systems. In addition, Ahmed et al. (2011) consider agripreneurship in terms of job creation 
strategy that has a potential of making people living in the rural areas self-sufficiency. Therefore, 
according to Kumari et al. (2020), this concept called agripreneurship will be one of the driving force to 
move agriculture from production to business and from subsistence to commercial in order to create more 
employment opportunities and income realizing that, normally, there are limited sources of livelihood 
options in other sectors such as industry and service. In this direction, Metcalfe (2008) stated that 
entrepreneurship is the motive force of internal economic change. Therefore, agripreneurship acts as 
catalyst for creating more jobs, raising productivity and growing the economy since for now, agriculture 
is the second highest employer of the labour-force in the country and reliable opportunities available for 
the majority of the youth outside agriculture are limited (Uche & Familusi, 2018). In line with this, 
agripreneurship is not only an opportunity for earning income and develop a country that require a greater 
emphasis, but also inevitability for improving the productivity and profitability in the agricultural sector.   
2.5 Differences Between Agripreneurs and Non-Agripreneurs  
It is essential to distinguish here who are agripreneurs and those who are not. According to FAO (2019), 
agripreneurs are those people who deliberately take the risk in order to allocate resources to an 
agribusiness venture so as to exploit the available opportunities in return for profit. Because they allocate 
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resources to a business, they are often considered as the primary decision-makers in which the success 
and failure of a business depend. They make differences in their businesses through growth at a scale in 
terms of how the job would be done, marketing and profit making, and enter the formal economy at least 
at some stages. They do not remain in the primary level of production for a very long period but also 
create and develop businesses all along agriculture value chain, comprising of processing, packaging, 
logistics, marketing, wholesaling, service provision or input supply activities (FAO, 2019).  
Agripreneurs are those innovators who are capable of making change in the economy by serving 
new markets or creating new methods of doing things in the agriculture sector. Thus, agripreneurs may 
be individuals who undertake a variety of activities in the agricultural sector in order to become 
entrepreneurs (Ndedi, 2017). According to Rao & Kumar (2016), agripreneurs are innovators, who drives 
change in a rural economy, by adopting innovative ideas in agriculture and its allied sectors. They take 
the risk, adopt innovation, create new ways of doing things, and tap new markets. Therefore, agripreneurs 
are those who undertake a variety of activities in agriculture and its allied sectors to be entrepreneur: 
explicitly agripreneurs are risk-takers, opportunists, and initiators who deal with the uncertain 
agricultural business environment of the farm (Tripathi & Agarwal, 2015). Bairwa et al. (2014) put it in 
this way; agripreneurs are people who carry out the various activities in the agricultural sector in order 
to become entrepreneurs. Agripreneurs, according to Nagalakshmi & Sudhakar (2013), entrepreneurs 
whose primary business is agriculture or agriculture-related. Agripreneurs are self-employed business 
persons who make money in the agricultural industry by pursuing a business idea (Aleke, Ojiako, & 
Wainwright, 2011). Agripreneurs, according to Macher (1999), are people who operate an 
agripreneurship venture: they are people who perform agriculture (farming activities) at their own risk. 
Agripreneurs are those people who create new ways to invent, convert, or establish a product or service 
within the agricultural value chain, including value addition to current products, while also taking the 
risks (Tripathi & Agarwal, 2015; Bairwa et al. 2014; Nagalakshmi & Sudhakar, 2013; Singh & Sharma, 
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2012). It is now sufficient to see why agripreneurs are different from other farmers. For instance, it is 
seen in the definition of Rao & Kumar, (2016) that agripreneurs are innovators who are capable to drive 
change in a rural economy. They attempt to be entrepreneurs (Nagalakshmi & Sudhakar, 2013; Bairwa 
et al. 2014). They manage agripreneurship venture (farm activities) under their own risk (Macher, 1999). 
In addition, Ndedi, (2017) pointed out some attributes that in general describe agripreneurs. They must 
be proactive, curious, determined, tenacious, visionary, industrious, honest, innovative, and creative, as 
well as possess good management and organizational abilities. According to modern and indigenous 
knowledge, the farmer entrepreneurs are also required to have exceptional foresight in terms of resources 
and environmental restrictions, as well as the ability to learn from others and their own experience 
(Estahbanaty, 2013). Farmer-entrepreneurs are market-focused, forward-thinking, and willing to take 
measured risks, develop new goods, adopt new technology, and innovate in their application. They are 
willing to make their firms more sustainable as they give greater attention to their long-term existence. 
These new breed of farmers are continuously looking for new methods to grow, improve, extend their 
business, and incorporate new farming technologies (Faria & Mixon, 2016). According to Damarla & 
Kumar (2015), entrepreneurial farmer are one who seize possibilities. Farmers with an entrepreneurial 
spirit take many different decisions regarding their farm activities in the framework of the value chain, 
which affect the profits of the farm business until the product reaches the final user, passionately, 
excitedly, and attentively. Agripreneurs, according to Carr & Roulin (2016), are individual who work in 
the food system, preferably in a rural setting but not always. They see a market potential to produce food 
directly such as vegetables, fruit, dairy, meat, fish, and grains, using new and sustainable farming 
methods. The distinctive features that distinguish agripreneurs from other farmers are presented in table 
2.2. 
 
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Table 2.2: Some Unique Characteristics of Entrepreneur Farmer (Agripreneur)  
 Descriptions of who are agripreneurs Publication Author’s 
1.  Risk takers and key decision makers who determine whether a company FAO, (2019). 
succeeds or fails. 
2.  Entrepreneurs are innovators who promote economic development and (Ndedi, 2017; Bairwa et al. 
engage in a wide range of agricultural activities. 2014; Rao & Kumar, 2016) 
3.  By embracing creative ideas in agriculture and related industries, an  
agripreneurs tap new markets, take risks, and promote change in a rural Rao & Kumar, (2016) 
economy. 
4.  Work in a food system that is based in, but not restricted to, a rural  
setting. Carr & Roulin, (2016) 
5.  A new kind of farmers who embrace new farming technologies and are  
committed to making the farm business more sustainable (in the long Faria & Mixon, (2016) 
term). 
6.  Innovative and environmentally friendly production processes are used. Carr & Roulin, (2016) 
7.  Make the most of opportunities. Damarla & Kumar, (2015) 
8.  In the agricultural industry, invent new ways of doing things  
(innovators). Bairwa et al. (2014) 
9.  Have exceptional foresight in terms of resources and environmental  
limits, as well as the ability to learn from others and their own past, Estahbanaty, (2013) 
based on modern and indigenous knowledge. 
10.  Self-employed business owners. Aleke, Ojiako, & Wainwright, 
(2011) 
11.  Manage an agripreneurship venture under their own risk. Macher, (1999) 
12.  Persons or a group with the legal authority to use or exploit land or other  
associated elements for agricultural, forestry, or mixed-use purposes. Suarez, (1972). 
13.  Innovators who take risks to create or develop a product/service of Author’s, (2022) 
economic value for profit. 
  
Source: Hilmi, (2018). 
 
From table 2.2 above, the characteristics of agripreneurs have been revealed. These characteristics 
typically distinguish agripreneurs from other farmers who are not agripreneurs. This study summed these 
characteristics into three main points; that all agripreneurs are risk-takers, that all agripreneurs are 
innovators and that all agripreneurs are developers. With these characteristics, a classic question that can 
be posed is, are all farmers agripreneurs?  It is therefore argue that not all farmers are agripreneurs except 
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those who possessed the three characteristics given above. It is a necessity for agripreneurs to take risks 
irrespective of the scale of their business. That is, whether the scale of production is commercialized or 
not. At this point, it is not surprising that Drucker and Knight opined that entrepreneurship is about taking 
the risk. However, taking risks alone cannot be translated into tangible or physical object with economic 
value and this explains why it is also a necessity for agripreneurs to be innovators or developers or both. 
The innovators and developers seem synonymous; however, they are not. Being an innovator refers to 
the ability to create or discover something new that never existed in the market place before, based on 
the risk taken in return for profit. Being a developer on the other hand simply means the ability to develop 
or expand an already existing business based on the risk taken in return for profit. Individuals who apply 
these principles become ‘new farmers’ and they are those specifically called agripreneurs. Therefore , it 
is posited here that whenever these three key attributes are lacking, a farmer cannot be described as 
agripreneur and therefore all farmers are not agripreneurs. The definition of ‘farmer entrepreneur’ devised 
by Faria & Franklin (2016) concurs with this proposition that not all farmers are agripreneurs. Farmer 
entrepreneurs, according to their definition, are individuals who are market-oriented, forward-thinking, 
and willing to take measured chances in developing new products, embracing new technology, and 
modernizing their use. They went on to say that, as a result of globalization and greater market 
integration, farmer entrepreneurs are held more accountable for shifts in market demand related to 
changes in preferences, trends, and fashions, and that, farmer-entrepreneurs are more connected to supply 
chains, integrated in industry, and active in the creation of new networks than traditional farmers.  
In addition, Sandeepa & Sarala, (2020) mentioned that for an individual to be a right agripreneur, 
he or she must possess the risk-bearing capacity and quest for knowledge in the agriculture sector. In a 
similar view, Macher (1999) noted that agripreneur operates agripreneurship venture (farm activities) at 
his/her own risk. Furthermore, according to FAO (2013), a farmer-entrepreneur creates a clear mental 
picture of what is feasible and the future he (or she) desires. He understands that the market determines 
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what is achievable. The farmer-entrepreneur is constantly on the lookout for new business prospects. He 
(she) is aware that the market offers fresh chances. Profits are important to the farmer-entrepreneur. He 
(she) understands that the market is where profit is made.  
2.6 The Youth in Perspective  
The literature's notion of youth varies from country to country. As a result, there have been numerous 
definitions employed in the literature. Individuals between the ages of 15 and 24 are defined as a youth 
by the United Nations General Assembly, without regard to other classifications used by member nations. 
This concept was proposed during the International Youth Year in 1985, and it was primarily for statistics 
purposes in order to improve the design of youth development programs. The World Development Report 
(2007) broadened the definition of youth to encompass all people aged 12 to 24. Food and Agriculture 
Organization of United States based on its global programmes for the youth development in rural areas 
defined youth as individuals that fall within the age bracket of 10 to 25 years. These definitions of the 
youth, stating age range of 10 to 25 years seemingly contains ‘in-school category’ and it will therefore 
be difficult in practice to be used to develop a framework that deals with farmers in general. National 
Youth Policy of Ghana (2010) defines “youth” as “persons who are within the age bracket of fifteen (15) 
and thirty-five (35) years”. Youth is also a time of transition from a family-dependent childhood to 
adulthood and absorption into society as a responsible citizen. As a result, the term "youth" is frequently 
used to refer to people who are in the process of transitioning from childhood to adulthood, which 
includes processes such as sexual maturation and increasing social and economic independence from 
parents, as well as others who care for them (Bennell, 2007).  
According to the Ghana Statistical Service-Population and Housing Census (2010) the youth 
constitutes about 35.1percent of the population.  Because this study is about entrepreneurship in 
agriculture, it is appropriate to adopt the definition given by the National Youth Policy of Ghana in order 
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to increase the chance of interviewing the youth who are farmers, in other words, those who are 
agripreneurs. This definition is also consistent with Kenya, Tanzania and the African Union (AU) 
definition of youth. According to the Food and Agriculture Organization of the United Nations (FAO), 
youth can make major positive contributions to economic growth, social and environmental benefits. 
This is believed to be possible through agripreneurship. Notwithstanding, the youth are mostly faced with 
the challenges of unemployment and underemployment in the most African continent which has been 
noted to have the youngest population. To address this, FAO (2019) stated that fostering 
entrepreneurship, particularly agripreneurship, is an important element of the solution for producing jobs 
for the world's rapidly growing youth population. Furthermore, ambitious young entrepreneurs require 
increased skills and education, as well as financial and capital resources, mentorship, and connections to 
professional networks.  
Between 2005 and 2035, the global youth population will grow by 26 percent in Sub-Saharan 
Africa (SSA) and 20 percent in Southeast Asia (Bennell, 2010). According to the FAO, roughly 55 
percent of youth live in rural regions, although this ratio can reach 70 percent in SSA and South Asia. 
Youth aged 15-24 account for 36 percent of the total labour force in SSA, 33 percent in the Near East 
and North Africa (NENA), and 29 percent in South Asia. Developing countries will account for roughly 
85 percent of the additional 500 million young people who will reach working age during the next decade. 
As a result, the rapid growth of the global youth population, particularly in Africa, should cause alarm 
among all, particularly governments and other responsible authorities. However, because the agricultural 
sector is so vital to life's sustainability and survival in terms of ending hunger, achieving food security, 
and improving nutrition, efforts to modernize agriculture into entrepreneurial ventures should be at the 
forefront of all efforts to attract the youth into agripreneurship (Bennell, 2007). 
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The ability to engage youth in agripreneurship has the potential to alleviate the problems of an 
aging farm population while also increasing youth employment. This necessitates securing youth interest 
and participation in agriculture through a deliberate shift in policy, training, and promotion that 
specifically targets youth. These people constitute not just a society's productive backbone, a key source 
of ideas and innovations, but also the primary market for food consumption and, in many cases, the 
leaders and drivers of public opinion, public policy, and action (Naamwintome & Bagson, 2013). Being 
the drivers of public opinion and public policy, it is evident that with appropriate motivation the youth 
can help to transform Ghana agriculture sector to meet the aching challenges in food security, 
unemployment, poverty, social vices and rural livelihoods. According to the International Labour 
Organization (ILO), youth suffer a rising shortage of adequate jobs and high levels of economic and 
social insecurity, with worldwide youth unemployment standing at 74.5 million in 2013 and expected to 
rise further. The youth especially those living in the rural areas are normally hitched by inadequate access 
to infrastructure, quality education, decent jobs compared to their counterpart youth living in the urban 
communities. Youth must first be "ready for economic opportunity," according to World Vision 
International (2014), in order to have sufficient incomes and resources to sustain productive and 
satisfying lives. They recognized that so as sustainable livelihood is concerned, readiness alone is not 
sufficient. As a result, they proposed six further preconditions, which the report termed as "pathways" by 
which a "ready" youth might achieve sustainable livelihoods, and the more of them there are, the better 
the chances of success. In this way, the youth are sufficiently employed or self-employed, they receive 
fair and inclusive treatment at work, they work in a safe and healthy workplace, they engage in continual 
learning and development, and they are socially responsible and play an active role in their communities 
(e.g. social justice, responsibility, and environment). The report further mentioned that the global youth 
population in the year 2014 was 1.8 billion of which 85 percent constituting 1.53 live in developing 
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countries. With this, 40 percent were unemployed (Table 2.3) and this calls for appropriate remediation 
(World Vision International, 2014). Hence, agripreneurship is the way to go. 
Table 2.3: Global Youth Unemployment Outlook 
Global youth population 1.8 billion  
Those living in the developing countries 1.53 billion (85%) 
Unemployed 0.72 billion (40%) 
Source: World Vision International, (2014).  
 
2.7 Sustainable Livelihoods Approach  
The Sustainable Livelihoods Approach (SLA) was initially mentioned in research papers in the 1980s, 
and it was based on the Integrated Rural Development (IRD) model, participatory development, basic 
requirements, food security studies, and other sector-wide approaches (DFID, 2003). To arrive at a more 
holistic view of poverty, the framework combines different sorts of analyses linked to households, 
gender, governance, and farming systems (Buechler, 2004). The framework depicts that livelihood is a 
dynamic concept that depends on many factors such as natural, economic/financial, and a number of 
capitals known as livelihood assets (Carney, 1998). Other factors include politics, terms of trade, 
agricultural intensification and extensification, organizational and institutional factors that combined to 
produce sustainable livelihood outcomes. Chambers & Conway (1992) define livelihoods as the 
capabilities, assets (including both material and social resources), and activities required to create a 
living. In simple terms as explained by Chambers (1995) livelihood is a means of gaining a living. 
Chambers & Conway went on to say that a way of life is sustainable if it can cope with and recover from 
stresses and shocks while maintaining or improving capabilities and assets without jeopardizing the 
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natural resource base. To put it another way, sustainable livelihoods are resilient and capable of meeting 
both current and future generations' requirements (Chambers & Conway, 1992). In this way, there is a 
strong expectation that the application of agripreneurship opportunities will enable the households to 
attain sustainable livelihoods as has been mentioned by Adesope et al. (2011), Ifeanyi-Obi et al. (2011), 
and Nzeh & Eboh (2010) who discovered that farming and commerce are the most important sustainable 
livelihood activities carried out by Nigerian rural residents.  
2.8 The Five Livelihood Assets  
Carney, (1998) termed the five different types of capitals found in the conceptual framework of the 
sustainable livelihood as livelihood assets. These assets consist of both tangible and intangible resources 
that the households combine to enhance their well-being. Human capital, natural capital, financial capital, 
social capital, and physical capital are the five types of capital. Based on the DFID, (2001) approach, the 
various capitals are explained below. 
Human Capital 
Human capital refers to several qualities of people's personalities such as skills, knowledge, job capacity, 
and good health that, when combined, enable persons or individuals to pursue various livelihood 
strategies that improve livelihood results (DFID, 2001). These sort of capitals, according to DFID, are 
factors of the amount and quality of labour available at the household level. Human capital varied 
depending on household size, educational and skill levels, health status, and leadership potential, 
according to the study. Human capital is also a prerequisite for the other four types of capital. 
Financial Capital 
Financial capital refers to the financial resources that individuals or households employ to attain their 
livelihood goals. They include liquid assets such as livestock and jewelry that can be converted to cash 
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at any time, as well as available stocks such as savings that can be stored in a variety of formats; cash, 
deposit, or liquid assets that can be converted to cash at any time (DFID, 2001). Financial capital can 
also be obtained or provided by financial institutions in a form of credits and loans in which case liabilities 
may be attached. Financial capital is the most versatile of the five forms of assets in the sustainable 
livelihoods framework, since it can be easily transferred to other types of capital or utilized directly to 
accomplish livelihoods objectives. Other common financial resources are credit, remittances and 
businesses.  Despite these, financial resources have been observed to be the least asset normally available 
to the poor, making it difficult for them to realize better livelihoods outcomes. Moreover, in terms of 
access to financial capital by women, according to DFID (2001), globally, over 70 percent of women 
living in the rural regions are poor. 
Natural Capital 
It represents the natural resource stocks that people rely on when pursuing their sustainable livelihoods 
outcomes. Trees, land, forest, rivers, clean air, coastal resources, and other natural resource stocks 
provide direct and indirect benefits to people. The relationship between natural capital and livelihoods is 
particularly essential in rural settings, according to Marchetta (2011), because most shocks in rural areas 
are caused by damage to natural assets. 
Physical Capital 
Physical capital consists of fundamental infrastructure and physical goods that can be used to support 
people's or households' livelihoods. For instance, improved and affordable seeds, reliable and affordable 
irrigation systems, affordable transport systems, energy/electricity that is affordable, the shelter of 
sufficient quality, and durability in order to be more productive and meet their fundamental necessities 
in order to achieve their livelihood goals. 
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Social Capital  
Social capital refers to both official and informal social interactions, as well as social resources, from 
which people can draw a variety of chances and benefits to improve their lives. People can increase their 
ability, skills, and knowledge to work with others by engaging in interactions with others through work 
or shared interests that increase their ability, skills, and knowledge to work with others, membership in 
more formal groups in which relationships are governed by accepted rules and norms, and building 
trusting relationships that facilitate co-operation, reduce transaction costs, and in most cases help in the 
development of safety networks among the vulnerable to achieve better livelihoods outcomes.  
 Succinctly, the sustainable livelihoods approach can be said to be the function of human, natural, 
financial, social and physical capital. Theoretically, this can be formulated as; SLA = f(HC, NC, FC, SC, 
PC, β). Where the letters are the initials and the subscript c represents capital such that HC = Human capital, 
NC = Natural capital, FC = Financial capital in that order and β accounts for other factors such as 
transforming and institutional structures. It must also be noted that each capital has its own specific 
components. Education, skills, knowledge, health, and labour, for example, are all components of human 
capital. Human capital, according to DFID (1999), is defined as "the skills, knowledge, ability to work, 
and good health that enable people to pursue various livelihood strategies and attain their livelihood 
objectives." Human capital is a measure of the quantity and quality of labour available in a household; it 
fluctuates depending on household size, skill level, leadership potential, and health status (DFID, 1999).  
2.9 Livelihoods Strategies  
People's livelihood strategies are made up of a variety of choices or possibilities that they apply to make 
the most use of the resources available to them in order to attain their livelihoods goals (Carney, 1998). 
These according to the framework, consists of natural and non-natural resources based. Migration is also 
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one of the strategies that people employ during the periods of shocks and crises. For instance, farmers 
are more likely to migrate during persistent drought, disease and pest infestation that affect their 
economic activities. Lack of employment in a particular area, on the other hand, may also cause people 
to migrate in search of jobs that better enhance their livelihoods.  
2.10 Sustainable Livelihood Outcomes  
According to DFID (2001) sustainable livelihoods framework, livelihoods outcomes consist of more 
income, enhanced well-being, quality education, improved food security, sustainable use of natural 
resources and reduced vulnerability. Further, within the framework, the vulnerability context constitutes 
trends, shocks, culture and environment while the transforming factor processes constitute the level of 
government, private sector and institutional factors such as laws and policies. In this case, whenever there 
are better interactions of livelihoods capitals, there is a higher chance of attaining better livelihoods 
outcomes. The framework for sustainable livelihoods, as used by Mazibuko (2018) is shown in figure 
2.1. 
 
Figure 2.1: Sustainable Livelihoods Framework  
Source: Carney, (1998). 
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Figure 2.1 depicts that livelihoods activities are diverse and do not depend on one particular activity. 
Thus, in agriculture, people may adopt the strategies of livestock and vegetable production, trading as in 
retailing of food crops, processed products and inputs, supply of raw materials to local industries, and 
services as in selling of labour for cash or in kind. The application of any of these multiple opportunities 
though not exhaustive coupled with taking some measured risk is termed as engagement of 
agripreneurship opportunities as a means of sustainable livelihoods with the idea that, the livelihood 
approach puts people first at the centre of development.  
2.11 Empirical Reviews    
2.11.1 Binary Logistic Regression Model 
The Logit model, also known as the binary logistic regression model, is a closed form model that has 
been used to quantify the effects of factors that influence people's decision to engage in an activity 
(Twumasi et al. 2019). When the dependent variable has two possible outcomes or is dichotomous, this 
model is acceptable. According to Train (2009), the model has some flaws, including the inability to 
represent arbitrary taste diversity, inability to be utilized in conjunction with multi-dimensional data when 
different components are related throughout time, and the capacity to allow for prohibitive replacement 
patterns. Despite this, Train noted that the model's parameter estimates are asymptotically consistent and 
efficient hence can be applied for a dichotomous response.  
The goodness of model fit is determined by using, Wald test both of which follow X2 distribution 
with 1 degree of freedom and Pseudo R2s consisting of Nagelkerke and Cox and Snell. These test statistics 
have values ranging from zero to 1. The higher the value i.e. value close to 1, the better the fit and vice 
versa. 
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2.11.2 Influence of Socio-Economic Characteristics on the Decision of the Youth to Engage in 
Agripreneurship Opportunities  
According to Mukembo & Edwards (2016), a variety of variables encourage people to become 
entrepreneurs, and the same factors may motivate youth to seek agripreneurship. These are known as 
push and pull factors, according to Alsos et al. (2011) and Vyavahare & Bendal (2012). The pull factors, 
emerge from the individual's inner self or desire; in this way, their motives are intrinsic. Whereas the 
push factors primarily arise from situations and circumstances surrounding an individual, i.e., extrinsic 
forces. As a result, both extrinsic and intrinsic variables influence the youth's decision to pursue 
agripreneurship opportunities.  
Twumasi et al. (2019) used a binary logit model to identify significant factors influencing youth 
decision to engage in agriculture on the one hand, and a truncated Tobit model to determine factors 
affecting the actual amount of capital used for agriculture by a youth who decided to participate in 
farming on the other. By using the price of farm inputs, income perception, access to credit and land, 
parent occupation, course of study, and other socio-economic characteristics of the respondents such as 
education, age, and gender as predictors, they discovered that 65 percent, 39 percent, 40 percent, 39 
percent, and 48 percent of the respondents had access to credit, perceived that farm inputs (fertilizer, 
seed, etc.) prices were high, parents engaged in farming as their occupation, had basic dietary needs, and 
perceived that one can generate high income in farming respectively. They concluded that the intensity 
of youth participation in farm activities at postsecondary institutions was not encouraging. Furthermore, 
just 38 percent of the people polled had access to agricultural land. At 1 percent, perceptions of farm 
input prices, finance availability, fundamental farming knowledge, land availability, and income 
perceptions were all significant. Education was also shown to be substantial, accounting for a 5 percent 
significant level of the total. This suggests that there are considerable disparities in perceived agricultural 
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input prices, availability to credit, fundamental farming expertise, access to land, education, and income 
perceptions between participants and non-participants.  
The study also indicated that, while farming provides many benefits to a nation, the majority of 
tertiary institution youth, 315 (70 percent) against their counterparts, 135 (30 percent), chose not to 
engage in farming. The findings also revealed that youth perceptions of farm input prices, youth level of 
education, youth access to credit, youth access to land, youth course of study at tertiary institutions, youth 
gender composition, and youth perceptions of farm income all have a significant impact on youth decision 
and intensity to engage in farming. As a result, they came to the conclusion that youth participation in 
agriculture is an important strategy to reduce unemployment, food insecurity, immoral social behaviours 
such as crime and drug misuse, and extreme poverty in developing nations like Ghana. 
  Another study by Nwibo et al. (2016) used multi-logit regression analysis on socio-economic 
characteristics such as age, gender, educational level, household size, marital status, total annual income, 
head of household, agripreneurship experience, farm size, and membership to farmers' associations to 
investigate the determinants of households' decision to become agripreneurs in Ishielu Local Government 
Area of Ebonyi State, Nigeria. The findings demonstrated that the coefficient of age was inversely 
associated to rural families' agripreneurship drive and was statistically significant at a 1 percent (P = 
0.01) level of significance. As a result, they believe that the desire to become an agripreneur diminishes 
as people get older. As a result, younger household heads are keener to become agripreneurs, as such 
investment works as a buffer against the negative consequences of retirement, but such aspirations tend 
to decline as people get older. At a 5 percent threshold of significance (P = 0.05), the coefficient of 
household size was positive and significant. According to the study, this was in line with the initial a 
priori expectation because a big household size can provide relatively a low-cost source of labour. Again, 
as the size of a family grows, the home head must consider how to meet the family's consumption 
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demands. Making agriculture a business comes to mind as a way to generate enough cash to meet the 
expanding family demand. 
The coefficient of education was also positive and significant at a 1 percent (P = 0.01) level, 
indicating that education is a driving force in agribusiness performance (Nwibo & Okorie, 2013). As a 
result, education is required to comprehend the nitty-gritty of entering any business for profit 
maximization. At a 10 percent level of probability, the yearly income coefficient was positive and 
significant. As a result, they indicated that as a household's income profile rises, so does the desire to 
expand into an agripreneurship enterprise. This was discovered to be in line with the a priori expectation, 
as increased income has been shown to motivate higher investment in various projects. At a 1 percent (P 
= 0.01) level, the coefficient of years of agricultural experience was also positive and significant. This 
means that a farmer's agribusiness experience has a significant impact on his or her decision to start a 
comparable business in the same location or somewhere when a similar opportunity exists. According to 
the findings, effective availability of important investment facilities such as electricity, good roads, and 
organized and proper information dissemination to rural households on how to discover feasible 
agripreneurship opportunities should be taken into account by the policymakers.  
Another study by Levent & Catherine (2011) discovered that the entrepreneur's prior company 
experience has a beneficial impact on the firm's entrepreneurial orientation.  
 Another study by Ifeanyi-Obi & Matthews-Njoku (2014) collected data from respondents using a 
Likert-type scale of not significant, less significant, significant, and very significant with weights of 1, 2, 
3, and 4 correspondingly. With mean values of 3.7 and 2.9, they discovered that farming and trading 
accordingly were the most important livelihood activities for rural people in the study area. According to 
the study's conclusions, the government should improve farming in terms of mechanization of farming 
activities in order to attract young graduates to the industry. 
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Adi (2007) conducted another study in Eastern Nigeria, identifying education as one of the 
primary predictors of livelihood strategy in the region. Levent & Catherine (2011) also discovered a 
significant positive relationship between entrepreneurs' educational attainment and a firm's 
entrepreneurship orientation, claiming that education equips business owners with the skills and 
reflective mindsets needed to understand customers and respond to their needs. As a result, a greater 
degree of education improves the entrepreneur's analytical and computational talents, as well as his or 
her communication skills.  
Turkson & Naandam (2006) discovered that, in comparison to their female counterparts, males 
are better capable of managing family productive assets and are more likely to be decision makers in 
African societies. 
Another study conducted in Latin America, revealed that the main impediment to continuing 
businesses by women in Latin America is the difficulty of obtaining financing Camelia et al. (n. d). 
Magagula & Tsvakirai (2020) also examined the factors that influence the intention of youth to participate 
in agripreneurship in South Africa. The findings of the study revealed that parental financial support, 
pursuing agricultural studies in school and perceived economic benefits accrued to agriculture positively 
influence a youth's intention to pursue agripreneurship but marital status were found negatively related 
to a youth's intention to pursue agriculture.  
2.11.3 The Constraints Facing Agripreneurs 
Constraints impose restrictions on a system and make it difficult to optimize resources efficiently. They 
act as barriers and make it difficult to attain fully the objectives of an organization. The constraints which 
may be used interchangeably with the words such as challenges, obstacles, limitations and barriers 
prevent the youth from achieving their goals of becoming successful agripreneurs. The constraints that 
rural households face, according to Nib et al. (2016), can be divided into three categories: socio-cultural, 
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knowledge-base, and economic issues. According to the study, socio-cultural issues include land tenure 
and a lack of investment infrastructure, while knowledge-based factors include a lack of technical know-
how, insufficient extension agent training, and a lack of access to research data. Furthermore, high 
interest rates on loans, a highly competitive market, poor returns on agricultural investment, and a lack 
of access to financing are also economic considerations. The study found that these elements had an 
impact on agripreneurship. In other words, they operate as constraints to agripreneurship. 
Despite the fact that most rural youth are already involved in informal agriculture in some way, 
according to another study by IYF (2014), they may not see it as an attractive or viable career option 
because of obstacles such as geographic isolation, unfriendly land-use policies, poor infrastructure, high 
transportation costs, and/or unavailable agricultural inputs. Inadequate information, limited skills, market 
instability, inadequate resources, and infrastructure, among other things, are impediments to the 
establishment of agripreneurial culture in poor countries. According to Carr & Rollin (2016) in UNCTAD 
(2015). It is difficult to build effective commercial agripreneurship firms given these constraints. 
Furthermore, the hand-operated hoe has remained the primary farm implement for working the soil, 
making agriculture tough and unappealing to rural youth for a long time. Despite agriculture being a 
major livelihood activities for the rural people, poor farm implements and other farm inputs, lack of 
technology in food processing and preservation, lack of a solid and reliable market for farm products, 
and bad infrastructure cause the youth to lose interest (Allajabou & Bello, 2014). Youth, on the other 
hand, are not necessarily hostile to agriculture or rural areas per se, according to a 2014 report by the 
International Fund for Agricultural Development (IFAD). Instead, they desire activities that may provide 
them with adequate livelihoods. As a result, they migrate to urban cities because of opportunities that 
may be difficult to discover in the rural communities.  
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Nachimuthu et al. (2018) also reported that most farmers lack access to agricultural raw materials, 
financial aid, marketing arrangements, new technology access, and advisory services for commercial 
farming. Another study conducted in India by Verma & Sahoo (2018) found that, while India has the 
greatest potential for growth in agripreneurship, with a wide range of opportunities in backward and 
forward linkages with secondary and tertiary sectors, it faces challenges such as inadequate infrastructural 
facilities, a lack of entrepreneurial culture among the population, and migration of workers to urban areas, 
among others, indicating that these constraints must be addressed through appropriate mechanisms.  
2.12 Summary and Identification of Knowledge Gap   
It was noted that literature has provided a lot of empirical definitions of agripreneurship. However, it was 
found that each definition has its own unique terminologies that distinguish it from the other and this 
could be attributed to differences in geographical, political, socio-economic, cultural background, 
framework, and the context to which the researchers want to establish. Despite these, all the definitions 
identified were in consensus that agripreneurship refers to entrepreneurship in agriculture, and that 
agripreneurs are those whose business is agriculture or agriculture-related ultimately for a profit. They 
exhibit characteristics of risk-taking, initiatives and essentially innovativeness and pro-activeness. Also, 
while the literature mentioned several agripreneurship opportunities, there was no operational definition 
for agripreneurship opportunities. As a result, the study fills this gap by defining agripreneurship 
opportunities based on Stevenson & Jarillo (1990) and Kaish & Gilad (1991) definition of opportunity. 
Thus, the study adds to academic discourse by defining agripreneurship opportunities as those desirable 
activities that can either be created or discovered in the agriculture sector based on the individual’s 
alertness and capabilities to meet the unmet conditions of market demand. It was also unclear if traditional 
farmers producing for domestic use are agripreneurs in the literature studied. As a result, an attempt has 
been made to close this gap by arguing that such farmers cannot be defined as agripreneurs because they 
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do not make a profit from their produce or receive returns on their investments. Their major concern is 
how they will feed their family instead of seeing agriculture as a business. 
The study also demonstrated the links between youth's desire to pursue agripreneurship 
opportunities and the socio-economic features of agripreneurs. Furthermore, despite the fact that many 
constraints have been mentioned in the literatures, no empirical analysis of constraints facing 
agripreneurs was identified in order to determine whether there is some degree of agreement among the 
respondents as to which constraint has the least or greatest influence on agripreneurship activity, given a 
specific locality. As a result, this study employs Kendall's Co-efficient of Concordance analysis to rank 
and investigate the degree of agreement or disagreement among the respondents in the ranking of 
constraints, which, as previously stated, will make it easier for stakeholders, economists, and 
policymakers in the study area to identify the most pressing constraints so that appropriate remedies can 
be suggested.  
  
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CHAPTER THREE 
METHODOLOGY  
3.1 Introduction   
This chapter provides the theoretical and conceptual framework of the study and deals with the 
methodologies adopted to achieve the specific objectives. Both qualitative and quantitative methods of 
analysis were used. The qualitative method focuses on the use of descriptive statistics to describe the 
findings while the quantitative method used econometric models to estimate the parameters and 
relationships among variables used in the study. Statistical packages such as SPSS version 20 and Excel 
were also used for the analysis. The final part explains methods of data collection and analysis, sampling 
techniques, interview procedures, sources of data, geographical area and the limitations of the study. 
3.2 Theoretical Framework  
Agripreneurship, according to Mukembo & Edwards (2016), is the use of entrepreneurial principles to 
identify, develop, and manage successful agricultural enterprises/projects for profit and improved 
livelihoods. Based on the rational choice perspective on the behaviour of an individual, a rational 
individual will be willing to engage in agripreneurship opportunities and use them to improve his/her 
livelihoods if and only if he/she derives the maximum benefits or satisfaction from them compared to the 
other available alternatives. As a result, given their limited resources, the youth in the study area will 
choose an opportunity that will provide them with the greatest level of satisfaction. The theoretical 
foundation of this study is therefore based on this assumption: Hence, if agripreneurship opportunities 
will enhance the livelihoods of the youth, then such opportunities may be represented as j = 1, and if they 
will not, then such opportunities can be represented as j = 0. Let Փ be the resources available to the youth 
in order to convert such opportunity into a profitable business venture and α be other factors such as 
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individual’s socio-economic characteristics. Then, the livelihoods of the youth can be expressed as: L1 = 
L(1, Փ, α) if an opportunity will improve livelihoods and L0 = L(0, Փ, α) if it will not. Hence, L(1, Փ, α) 
and L0 = L(0, Փ, α) are the benefits derived by the youth should they decide to engage in agripreneurship 
opportunities. However, in the deterministic approach where a stochastic term is introduced, the equation 
can be specified as: L1 = L(1, Փ, α) + ε1 and L0 = L(0, Փ, α) + ε0 where ε is the stochastic component 
representing an aspect of the benefits known by the youth but unobserved or latent to the researcher. The 
youth is assumed to be rational and knows the resources (Փ) to be used, the cost (C) to be incurred and 
has the ability (Λ) to become agripreneur. Therefore, the equation can be written as; L1= Λ(1, Փ – C, α) 
+ ε1 and L0 = Λ(0, Փ – C, α) + ε0. The presence of the stochastic term, ε makes it possible to make 
probability statement about the decision of the behaviour of the youth. Hence, P = P[Λ(1, Փ - C, α) + ε1] 
and P = P[Λ(0, Փ - C, α) + ε0]. All other things being equal, the youth will be willing to engage and use 
agripreneurship opportunities as sources of employment and income generation to improve their 
livelihoods if and only if the satisfaction derived from utilizing the opportunity is greater than other 
existing alternative opportunities. Therefore, the equation can be explicitly be written as: P = P[Λ(1, Փ 
– C, α) + ε1] > P[Λ(0, Փ – C, α) + ε0]. Let Λ(1, Փ – C ) = β1 and Λ(0, Փ – C ) = β0, then, the equation can 
be further expressed as: P = P(β1 αi + ε1 > β0 αi + ε0). By rearranging the common terms on one side, the 
equation becomes; P(β1 αi - β0 αi > ε0 - ε1) which is appropriate and suitable for the study as it makes it 
possible to determine the benefits/satisfaction the youth would derive should they decide to engage and 
use agripreneurship opportunities as sources of income generation. The other two assumptions made were 
that the satisfaction theory employed is additive and separable and that the benefits derived by the 
individual vary as a random variable across different opportunities, enterprises, projects, investments, 
jobs, products, or services.  
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3.3 Conceptual Framework   
With limited access to land for agricultural activities in mining-affected communities, it becomes 
necessary for the youth living in such communities to be more innovative and diversify into a new 
approach of agriculture in such a way that even the small land left can be used more economically and 
sustainably to improve livelihoods. Thus, engagement in agripreneurship or entrepreneurship in 
agriculture becomes crucial in such communities. The conceptual framework of the study describes step-
by-step the factors that determine the youth’s decision to engage in agripreneurship opportunities 
available in the agricultural sector. The centre of the figure depicts the general opportunities available in 
agriculture which can be relied on as sources of employment, income generation, and ultimately 
improvement of livelihoods. However, the possibility of this to some large extent depends firstly on the 
identification of the sub-opportunities under each broad activity such as farming, livestock or trade and 
or service. In this direction, Nwibo et al. (2016) have mentioned farm inputs supply, farm production and 
farm processing/distribution as various areas in which agripreneurship opportunities abound while trade, 
livestock and non-traditional agricultural activities have also been considered in this study. In addition, 
Otache (2017) listed thirty-one agripreneurial businesses that the youth can venture into. However, the 
decision of the youth to venture into any of the opportunities is affected by institutional factors which 
include access to financial support, market and membership of an organization and socio-economic 
characteristics comprising of age, level of education, household size, experience, and average annual 
income among others. These factors ceteris paribus determine whether the youth would decide to engage 
and use the opportunities as a means of sustainable livelihoods or not. Besides, the utilization of the 
opportunities as means of attaining sustainable livelihoods are associated with many constraints which 
may affect their decisions and discourage them from using the opportunities. The constraints would not 
only affect their decisions to use the available opportunities but also their willingness to engage. Some 
of the constraints according to Kumar & Kumar (2019) are low level of education and entrepreneurship 
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training, inadequate market facilities and poor transportation systems. In addition, Muchara & Batha 
(2016) mentioned difficulty in accessing resources such as land, water, credit, information, and 
inadequate property right. Focusing on the individual youth, Wolf (2004) also mentioned unawareness 
of the individual’s strengths, weaknesses and competencies as constraints facing agripreneurs. Therefore, 
the combination of institutional factors, socio-economic factors and constraints determine the decision 
of the youth to engage in agripreneurship opportunities. Furthermore, it is anticipated that if the youth 
will decide to engage in agripreneurship opportunities, they will achieve long-term livelihood outcomes 
such as increased income, enhanced well-being, excellent education, food security, sustainable use of 
natural resources, and reduced vulnerability. Based on this notion, the study's conceptual framework is 
depicted in figure 3.1 below. 
 
 
 
 
 
 
 
 
 
 
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Socio-economic factors  
  Age YOUTH ENGAGEMENT IN Constraints 
 Sex AGRIPRENEURSHIP 
 Educational OPPORTUNITIES  Low demand 
 level for 
 Employment   FARMING agricultural 
  Experience   TRADING/SERVICE outputs 
 Poor 
 NON-TRADITIONAL 
Institutional factors transportation 
 AGRICULTURE 
systems 
 Training 
 High inputs 
 Financial support  
 prices 
 Access to market  Inadequate 
 Membership of storage 
 an organization facilities 
 Lack of 
 financial 
YES 
support 
NO 
   
 
Attainment of sustainable Unattainable of sustainable 
 livelihood outcomes livelihood outcmes 
Figure 3.1: Conceptual Framework  
Source: Author’s Design, 2022 
 
 
 
 
 
 
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3.4 Methods of Data Analysis  
3.4.1 Identifying Agripreneurship Opportunities in the Study Area  
This objective was achieved by presenting the various agripreneurship opportunities identified in the 
literature to the respondents in a form of structured questionnaires. The respondents were then asked to 
rank them on a four-point Likert scale of 1, 2, 3, and 4, with 1 being not significant, 2 less significant, 3 
significant, and 4 most significant. After that, the mid-point value of 2.5 was computed. The data was 
analyzed using a qualitative method based on descriptive statistics, and the mean score of each outcome 
calculated. The calculated mean score was then compared to the mid-point value (2.5) of the Likert scale 
and based on this the decision was established. Thus, any agripreneurship opportunity with a mean score 
greater than or equal to 2.5 is considered significant by the youth and thus can be used as a source of 
employment and income generation, whereas any agripreneurship opportunity with a mean score less 
than 2.5 is considered insignificant by the youth and thus cannot be used as a source of employment and 
income generation. In that case, the youth in the study area cannot rely on such opportunities as a method 
of securing long-term employment. The data was then organized into a table with frequencies and 
percentages.  
3.4.2 Determining the Socio-Economic Characteristics Influencing the Youth’s Decision to 
Engage in Agripreneurship Opportunities 
The respondents' socio-economic characteristics were elicited using questionnaires. The data was 
analyzed using a binary logistic regression model, with the youth's decision to engage in agripreneurship 
opportunities as the dependent variable and socio-economic factors as the independent variables. The 
model was found to be appropriate for the data set because the dependent variable; youth decision to 
engage in agripreneurship opportunities was a dichotomous response, that is, contains two decision 
responses only; whether the respondents will engage in agripreneurship opportunities or not. Thus, the 
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dependent variable was a binary (dummy) measure and was 1 if they are willing to engage and 0 if 
otherwise. By using a binary choice, the model as used by Nwibo et al. (2016) is specified as follows:  
DYE = β0 + β1AGE + β2SEX + β3MASTATUS + β4HHS+ β5AGRIEXP + β6EDU + β7EMPLOY + 
β8AWAREAGRIP + β9FARMSIZE + β10FBO + ε 
Where DYE is the dependent variable = Decision of the youth to engage in agripreneurship opportunities 
 β0 = the constant term 
β = coefficients to be estimated 
ε = the random error term. 
The odds ratio values (Exp(B)) obtained from the analysis were then converted to probabilities using the 
odds
formula: P =  .  This was necessary so that predictions can be made in terms of probabilities. The 
1+odds
other models indicating the inclusion and exclusion of lack of financial support and high farm inputs 
price variables as ranked by the respondents as the most pressing constraints are specified as follows: 
DYE = β0 + β1AGE + β2SEX + β3MASTATUS + β4HHS+ β5AGRIEXP + β6EDU + β7EMPLOY + 
β8AWAREAGRIP + β9FARMSIZE + β10FBO + β11LFINASUPPORT + β12FAMINPUTP + ε 
When the variable, lack of financial support was dropped from the model, it becomes:  
DYE = β0 + β1AGE + β2SEX + β3MASTATUS + β4HHS+ β5AGRIEXP + β6EDU + β7EMPLOY + 
β8AWAREAGRIP + β9FARMSIZE + β10FBO + β11FAMINPUTC + ε 
Also, when the variable high farm input price was dropped from the model, it becomes:  
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DYE = β0 + β1AGE + β2SEX + β3MASTATUS + β4HHS+ β5AGRIEXP + β6EDU + β7EMPLOY + 
β8AWAREAGRIP + β9FARMSIZE + β10FBO + β11LFINASUPPORT + ε.  
Where the variables and symbols have their usual meanings. These model specifications were also 
necessary in order to verify and determine whether lack of financial support, high farm inputs price or 
both as ranked by the youth agripreneurs in the study area are significant and to what extent. 
3.4.3 Testing the Model Fit  
The goodness-of-fit of the model selected for the analysis was tested using the likelihood ratio test. This 
serves as an indicator to assess the appropriateness of the model fit. The other statistics such as Wald 
statistic, Pseudo R2 consisting of Nagelkerke and Cox and Snell were also used to test for the significance 
of independent variables with the use of chi-square (X2) distribution with 1 as a degree of freedom. The 
values of these test statistics range from 0 to 1. The higher the value, (that is, a value near to 1), the better 
the fit and vice versa; again, utilizing the binary logistic regression model for this goal was appropriate 
because these indicators were available to measure the model fit.   
3.4.4 The Likelihood Ratio Test 
This test specifically tests the significance of coefficients in the model used with or without predictors to 
compare one model with the other called the restricted model using the log likelihoods ratios. This helps 
to determine whether a specific coefficient in one or another model is statistically significant. The test 
statistic as used by Altaş & Öztunç (2013) is specified below: 
𝑀𝑎𝑥𝑖𝑚𝑢𝑚 𝑙𝑖𝑘𝑒𝑙𝑖ℎ𝑜𝑜𝑑 𝑜𝑓 𝑡ℎ𝑒 𝑟𝑒𝑠𝑡𝑟𝑖𝑐𝑡𝑒𝑑 𝑚𝑜𝑑𝑒𝑙
The Likelihood Ratio Test = -2ln[ ].  
𝑀𝑎𝑥𝑖𝑚𝑢𝑚 𝑙𝑖𝑘𝑒𝑙𝑖ℎ𝑜𝑜𝑑 𝑜𝑓 𝑡ℎ𝑒 𝑢𝑛𝑟𝑒𝑠𝑡𝑟𝑖𝑐𝑡𝑒𝑑 𝑚𝑜𝑑𝑒𝑙
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3.4.5 The Wald Test 
This test, unlike the likelihood ratio test, requires the estimation of only one model. It is based on the 
hypothesis that some coefficients, βi in the model fit are equal to zero. If the coefficient is significantly 
different from zero, the predictor is statistically significant and contributes significantly to the total 
outcome prediction. The test statistic as employed by Altaş & Öztunç (2013) is as follows:  
𝐶𝑜𝑒𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑡 𝑒𝑠𝑡𝑖𝑚𝑎𝑡𝑒𝑠 ̂𝛽𝑗
Wald = [  ]2  =   [ ]2. 
𝑆𝑡𝑎𝑛𝑑𝑎𝑟𝑑 𝑒𝑟𝑟𝑜𝑟 𝑜𝑓 𝑡ℎ𝑒 𝑒𝑠𝑡𝑖𝑚𝑎𝑡𝑒𝑑 𝑐𝑜𝑒𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑡 𝑆𝐸𝛽?̂?
3.4.6 Pseudo R2 (Nagelkerke’s R2 and Cox and Snell’s R2) 
 The two most R2s used in binary logistic regression analysis are Nagelkerke’s R2 and Cox & Snell’s R2. 
These R2s are in practice similar in meaning to that of R2 in the linear regression model. They are 
necessary to determine the extent to which the explanatory variables explained the overall outcome. In 
these statistics, zero denotes no prediction at all, showing that the explanatory factors have no influence 
on the outcome variable's prediction, and 1 denotes perfect prediction, suggesting that the explanatory 
variables have a considerable impact on the outcome variable's prediction. The following are the two 
statistics: 
2
 𝑹𝟐
[ 𝐿𝐿(𝑁𝑒𝑤)− 𝐿𝐿(𝐵𝑎𝑠𝑒𝑙𝑖𝑛𝑒)]
Cox and Snell, 𝑪𝑺 =  1 − 𝑒
−
𝑛  and  
𝑅2𝟐 𝐶𝑆
Nagelkerke, 𝑹𝑵 =  2𝐿𝐿(𝐵𝑎𝑠𝑒𝑙𝑖𝑛𝑒  )
1−𝑒[ ]
𝑛
The two LLs reflect log likelihood in the new model and log likelihood in the prior model, and n 
represents the sample size. 
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3.4.7 Test for Econometric Problems 
The three main econometric problems commonly encountered in econometric models are autocorrelation, 
heteroscedasticity, and multicollinearity. If their existence in a regression model is not checked and 
corrected, decisions and inferences made based on the coefficient estimates would be misleading.    
Autocorrelation occurs when the disturbance terms in the regression model are correlated. In other 
words, the covariance between the disturbance terms is not equal to zero, that is, cov(ui, uj) ≠ 0 and i ≠ j 
where i and j are two different observations. Because autocorrelation is noted to be a typical characteristic 
of time series data, its test was relaxed in this study because the study primarily relied on cross-sectional 
data.  
When the variance of the disturbance term in regression analysis is not constant, 
heteroscedasticity arises. In other words, the disturbance term's variance is not homoscedastic; that is, 
var(ui) = δ
2
i, where i = 1, 2, 3,….. showing that the variance of the residuals varies. The disturbance term 
in the binary logistic regression model employed in the analysis was assumed to be homoscedastic. This 
is because, in the presence of heteroscedasticity, the parameter estimates remain consistent and retain 
their BLUE features, that is, they are the best linear unbiased estimators (Gujarati & Porter, 2009; P: 371) 
and are asymptotically efficient.  
Multicollinearity on the other hand, occurs when there is a linear relationship among explanatory 
variables in multiple regression models. For perfect multicollinearity, the coefficients of the explanatory 
variables cannot be determined. The coefficients can only be estimated if multicollinearity is less or near 
perfect. In this case, the coefficients estimates are inaccurate, have high standard errors, unstable 
(sensitive to a small change in data) and have large variances and confident intervals leading to 
insignificant test statistics such as t and F ratios. The existence or lack of collinearity among the variables 
was evaluated using graphical and variance inflation factor (VIF) methods with SPSS version 20 software 
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because the logistic regression model used for the analysis included several explanatory variables. The 
scatterplot revealed that the little dots follow the normalcy line, indicating that there is no 
multicollinearity.  
The VIF shows how the variance of a coefficient is inflated due to the presence of 
multicollinearity among explanatory variables. The decision was based on the value or range of the VIF. 
If the VIF = 1, it indicates that the explanatory variables are not correlated to one another. On the other 
hand, if the VIF value occurs in the range 1< VIF < 5, it indicates that the collinearity exists in the model 
but moderately. Also, if VIF ≥ 5 to 10, it indicates that collinearity among the explanatory variables in 
the model is high especially when VIF > 10. The range of values of the VIF obtained during the analysis 
of this study was 1 to 2.8, indicating that multicollinearity among the explanatory variables used in the 
model was absent. Hence, reliable decisions and inferences can be made based on the coefficients 
estimated from the model used for the analysis. See Appendix ii for the results of the multicollinearity 
test.  
 3.4.8 Identifying the Constraints Facing Agripreneurs in the Study Area  
The constraints facing agripreneurs were identified through the literature review. The questionnaires were 
then structured in two sections to elicit the needed data from the respondents for the analysis. The first 
section consists of the list of all the constraints identified from the literature and the second section gives 
room for the respondents to specify their own constraints which were not found in the list. The 
questionnaires were then administered in a form of interviews scheduled with the purposively selected 
agripreneurs households. The respondents were asked to rank all of the constraints on a numeric scale 
from least to most pressing, using the numbers 1, 2, 3, 4, 5,......n. Data collected were analyzed using 
Kendall’s Coefficient of Concordance also called Kendall’s W. This technique helped to ascertain the 
degree of agreement or disagreement among the rankings by the respondents. In determining the total 
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rank score for the identified constraints, the constraint with the least mean score was ranked as the most 
pressing whilst the constraints with the highest mean score was ranked as the least pressing constraints 
(Denkyirah et al. 2016). The weighted average formula is specified as: 
∑ 𝑇
Weighted average =       
∑ 𝑛
Where 𝚺T = sum of ranks for each constraint 
𝚺n = number of respondents. 
 Mathematically, the Kendall’s Coefficient of Concordance is expressed as: 
12s−3m2n(n+1)2
W =    
m2(n2−n)−mT
Where s = sum of squared deviations 
m = number of respondents 
n = total number of constraints being ranked 
T = correction factor for tied ranks 
The Kendall’s Coefficient of Concordance or Kendall’s W gives a value that ranges between zero and 
one i. e. 0 ≤ W ≤ 1. A value greater than zero indicates some level of agreement while a value equal to 
zero indicates that there is no agreement among the items ranked. 
Hypothesis and Significant Test for the Kendall’s Coefficient of Concordance 
Null hypothesis, H0 = There is disagreement among the respondents in the ranking of the constraints.  
Alternate hypothesis, H1 = There is an agreement among the respondents in the ranking of the constraints. 
Using the formula X2 = k(n-1)W, the chi-square, X2 test was calculated. The X2 critical is read from a chi-
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square table with n - 1 degrees of freedom, where k is the number of raters. If the calculated X2 is greater 
than the X2 critical at the P = 0. 05 level of significance, the null hypothesis, H0, is rejected, and the 
conclusion is that there is agreement among the respondents in ranking the constraints; otherwise, the H0 
is not rejected, and the conclusion is that there is disagreement among the respondents in ranking the 
constraints. 
3.5 Methods of Data Collection  
 
3.5.1 Sources of Data, Instruments and Interview Procedures 
For the analysis, the study used both primary and secondary data. The primary data was acquired solely 
through a survey, with questionnaires serving as the major instruments. The other techniques such as 
personal interviews and focus group discussions with the key informants in the six randomly selected 
communities were also conducted. Heads of institutions such as the department of agriculture, 
Agribusiness Advisory Centres, owners of agribusiness shops and human resources managers that are 
always in direct contact with the youth in the six communities were also consulted to discuss issues 
concerning the attitudes of the youth participation in agriculture. The questionnaires were therefore 
designed consisting of both open and closed questions to elicit data about the respondents’ socio-
economic characteristics, nature of farms, community level characteristics, and their opinions relevant to 
the attainment of the specific objectives. The open questions allowed the respondents to provide the 
necessary answers to the questions asked without any special restrictions. The closed questions on the 
other hand, asked for specific answers. The questionnaires were designed to consist of four sections. The 
first part focused on the respondents' socioeconomic factors, such as age, sex, marital status, family size, 
income, and educational attainment. The second component is based on the respondents' agripreneurship 
activities being identified. The questions asked include decision to engage in agripreneurship 
opportunity, type of agricultural business/enterprise owned or managed, the number of such business, 
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experience, scale, and type of output produced, services offered and expected average annual income. 
The third part of the questionnaires concentrated on the decision of the youth to engage in agripreneurship 
opportunities. In this case, if the response is yes, it is recorded as 1. On the other hand, if the response is 
no, it is recorded as 0 and the respondents are further asked based on their best of knowledge to give their 
reasons. A follow-up question is asked using the Likert scale of 1, 2, 3 and 4 for not significant, less 
significant, significant and most significant respectively. The fourth part captures questions on the 
specifications and ranking of the constraints facing agripreneurs in the six selected communities. By 
outlining all the constraints identified, the questionnaires asked the respondents to rank them using the 
numeric scale 1, 2, 3, 4… n. See Appendix i for the questionnaires used for the survey. 
Secondary data needed for the accomplishment of the specific objectives were obtained by 
reading literature such as articles, journals, books relevant to the subject matter and the internet using 
specific keywords and website addresses. Key-words such as the meaning of entrepreneurship, meaning 
of agripreneurship, agripreneurship opportunities, challenges, obstacles and constraints of 
agripreneurship were input into the search engines. Information that popped up was thoroughly read and 
the needed data ascertained. These pieces of information obtained from the literature and the internet 
helped the researcher gain more insight into the various definitions of entrepreneurship, agripreneurship, 
agripreneurship opportunities and the constraints agripreneurs faced. The other secondary data such as 
the total population of people living in the study area, land area, the area under cultivation, mining, the 
existence of agribusinesses and the district map were obtained from departments and institutions such as 
the Ministry of Food and Agriculture (MoFA), Ghana Statistical Service (GSS), and the District 
Assembly (DA). In particular, information from the Ghana Statistical Service Population and Housing 
Census and the District Assembly gave the researcher more insight into the description of the study area. 
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3.5.2 Description of Variables Used in the Logit model and Hypothesis 
1. AGE (AGE):  According to FAO (2008), the working age of the agricultural labour-force is between 
31 and 40 years. As a result, it is believed that youth's age will have a positive or negative impact on their 
decision to pursue agripreneurship opportunities. As the youth is aging (from 15 to 40 years), the more 
the experience he or she gathers. Their experience guides them to undertake a business that is more viable 
at the right time and in the right environment. They may also be capable of switching to different 
alternatives when there is economic shock because of the experience they have gathered throughout their 
working life. However, the ages below 15 and above 40 years are predicted to have a detrimental impact 
on their decision to pursue agripreneurship opportunities. During the survey, age was measured in years 
in a particular range categorizations. 
2. Sex (SEX): It was expected that being a male will play a significant role in the decision of the youth 
to engage in agripreneurship opportunities than in female counterparts with a positive correlation. This 
is because males are normally endowed with resources and are capable to take risks compared to females 
in the same environment. In addition, Mbam & Nwibo (2013) have found that the males participate more 
in agricultural activities than the females in a similar environment. In the questionnaire, sex was measured 
as a dummy with a value of 1 for males and 0 for females.  
3. Marital status (MASTATUS): It was hypothesized that the youth marital status will have a positive 
or negative impact on their decision to pursue agripreneurship opportunities. This is because those who 
have married would like to generate more income to cater for the family even if they bear no children 
and in doing this, engagement in agripreneurship opportunities become option. However, the drive to 
engage in agripreneurship is expected to decline among the youth who are single. During the survey, 
marital status was measured as numeric using 0, 1, 2, and 3 for single, married, divorced and separated 
respectively.    
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4. Household Size (HHS): It was hypothesized that the number of people who live or may not live under 
the same roof but eat from the same pot with a recognized leader will affect the decision of the youth to 
engage in agripreneurship opportunities positively. This is because as the size of the family continue to 
increase, the head of the household begins to identify other sources of income to meet the consumption 
and utility demands of the family. In doing this, engaging in agripreneurship opportunities comes to mind 
to generate enough income to cater for the household needs. Thus, the household will be willing to invest 
in new opportunities to increase their income and hence improve their livelihoods. Aside, a family with 
a large household size will have an advantage of a source of labour. During the survey, household size 
was recorded by the researcher as a number given by the respondents typically the head of the household. 
5. Previous Agripreneurship Experience (AGRIEXP): It was hypothesized that agripreneurship 
experience will have a positive influence on the decision of the youth engagement. This is because the 
more experienced an agripreneur has gained, the more practical skills and knowledge he or she has 
gathered and the higher the probability to manage and deal with risk about entrepreneurship in 
agriculture. As noted by Mbam et al. (2013) the more experience one obtains, the more he or she is 
exposed to strategies for effective management and marketing. Agripreneurship experience was 
measured as the number of years one has involved himself/herself in agricultural businesses. 
6. Education (EDU): It was expected that education will have a substantial favourable impact on youth's 
decision to engage in agripreneurship opportunities. This is because the better the youth’s educational 
attainment, the more likely he or she acquires the necessary skills, information, and capacity to recognize 
the different opportunities in agriculture and related industries that match their interests and aspirations. 
During the survey, education was measured as levels of formal education attained using numeric such as 
1, 2, 3 ...... starting from basic school to tertiary. 
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7. Employment Status (EMPLOY): It was hypothesized that employment will either have negative or 
positive relationship with the decision of the youth to engage in agripreneurship opportunities because 
those who have been engaged may already have sources of income to sustain their livelihoods. On the 
other hand, those who are unemployed may seek a job in order to generate income to sustain their 
livelihoods and to take care of the households. In doing this, agripreneurship becomes an option. It was 
measured using numeric. 
8. Awareness of Agripreneurship Opportunities (AWAREAGRIP): Awareness was hypothesized to 
have either negative or positive effect on the decision of the youth to engage in agripreneurship 
opportunities. This is because if they are aware and know the benefits, they are more likely to engage 
relative to if they are not aware. It was measured as a dummy with 1 as ‘yes, I am aware of agripreneurship 
opportunities’ and 0 for ‘no, I am not aware of agripreneurship opportunities.  
9. Farm Size (FARMSIZE): The bigger the farm size (large scale production) the higher the farm 
income and vice versa. Therefore, it was hypothesized that farm size will have a positive effect on the 
decision of the youth to engage in agripreneurship opportunities of the youth. Farm size was measured 
in acres during the survey and then converted to hectares using the proportion 2.471 acres = 1 hectare 
during the analysis. 
10. Membership of Farmers’ Based Organization (FBO): It was assumed that belonging to an 
association or organization has a positive effect on the decision of the youth to engage in agripreneurship 
opportunities because members receive training, acquire knowledge and skills that increase their chances 
of engagement to utilize the opportunities more effectively. It was measured as dummy such that yes = 1 
and no = 0.   
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11. Financial Support (FINASUPPORT): It was hypothesized that financial support will either have a 
positive or negative influence on the youth’s decision to engage in agripreneurship opportunities because 
the youth may have a certain level of financial ease to develop or expand their business or create a new 
one if they have access to financial support. This will lead to more jobs, employment and income 
generation thereby enhancing their livelihoods compared to if they have no access or lack financial 
support. During the survey, financial support was measured as a dummy with 1 = having access to 
financial support when required and 0 = having no access to financial support when required.  
12. Farm Input Price (FAMINPUTP): It was hypothesized that farm input price will either have 
positive or negative influence on the decision of the youth to engage in agripreneurship opportunities. It 
will demotivate the youth and have a negative influence on their decision to engage if they perceive that 
the prices of inputs are high and vice versa. It was measured using numeric such that 1 = No, I perceived 
that input prices are low and 2 = Yes, I perceived that inputs prices are high. The table 3.1 shows the 
variables used in the model, how they were measured, and their corresponding theoretical a priori. 
Table 3.1: Description of Variables, Measurement and their Theoretical a Priori 
Description of variables Variables codes Measurement A priori 
Age AGE Years (Range) -/+ 
Sex SEX Male = 1, Female = 0 + 
Marital status MASTATUS Numeric -/+ 
Household size HHS Numeric + 
Previous agripreneurship experience AGRIEXP Years (Range) + 
Education EDU Numeric + 
Employment EMPLOY Numeric -/+ 
Awareness of agripreneurship AWAREAGRIP No = 0, Yes = 1 -/+ 
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Farm size FARMSIZE Acres (Range) -/+ 
Membership of Farmers’ Based FBO No = 0, Yes = 1 + 
Organization 
Financial support FINASUPPORT  No = 0, Yes = 1 -/+ 
Farm input price FAMINPUTP Numeric  -/+ 
Source: Author’s design, 2022   
 
3.5.3 Sampling Procedure 
The multi-stage sampling technique was employed. This procedure helped the investigator to stratify the 
population into clusters and allows for flexibility to select the sample size carefully. 
Birim North District was purposively chosen for the study because it is one of the districts in 
which one of the worlds’ leading gold mining companies, Newmont is operating leading to an influx of 
many youth in the district in search of job in the mining company. Within the district, six communities 
were randomly selected namely New Abirem, Adausena, Hweakwae, Abodom, Praso, and Noyem. The 
selected communities were found to be suitable for the study because they are all located within the 
outskirts of the mining activities. After chosen the communities, clusters were formed in each community 
and the various houses within each cluster were entered step-by-step and the number of households within 
a particular house were interviewed using the questionnaire designed. The youth respondents were then 
carefully selected from each household based on their age categorization that is from 15 to 35 years 
according to the definition of Youth Policy of Ghana (2010). Hence, if the youth in these communities 
are well-informed to take risk through research to come out with innovative ideas more agripreneurs will 
be created and the willingness of the youth to participate in agricultural activities will be increased.  
The sample size for the study was determined by following the random probability formula which 
is given as: 
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 z
2(p∗q) z2(p∗q)
 n = 2  ÷ 1 + [ ] e  e2N
Where 
n = sample size 
z = z-score at a chosen confident level 
e = margin of error 
N = population 
p = probability of the presence of the key attribute of the population 
 q = the probability of the absence of the key attribute so that the summation of p and q = 1 such that if p 
= x then q = 1 – x. At confident level of 95%, z-score = 1.96 and the margin of error, e which is the 
confident interval = 5%. If p = 0.80 then q = 0.20 and the population (N) considered in the six 
communities was fifteen thousand (N = 15,000). By substituting this data into the above formula, a total 
of 242 respondents were sampled for the study. However, 220 responses were received vis-à-vis the 
questionnaires distributed and this was used for the analysis. Table 3.2 shows the six randomly selected 
communities and the percentage of actual responses received from each community. 
Table 3.2: Distribution of the Number of Respondents Sampled from each Community 
Communities Targeted sample  Actual responses received  Percentage of the actual responses 
received (%)  
New Abirem 65 60 92.3 
Adausena 35 30 85.7 
Hweakwae 30 30 100.0 
Abodom 32 30 93.8 
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Praso 45 40 88.9 
Noyem  35 30 85.7 
Total 242 220 90.9 
Source: Author’s computation, 2022 
3.5.4 The Study Area 
Birim North District (BND) is one of the Ghana's 260 Metropolitan, Municipal and District Assemblies 
(MMDAs), which are part of the 33 Municipalities and Districts in the Eastern Region, which are 
overseen by the Ministry of Local Government, Rural Development, and Environment. The district, with 
New Abirem as its capital, was established in 1987 under Legislative Act 462 (Legislative Instrument 
1993) in accordance with the Government Decentralization and Local Reform Policy to promote effective 
decentralized governance and expedite the development of the area.  
BND is located in Ghana's eastern region, between the latitude of 6018N and the longitude of 
0059W on the equator. The district is bordered on the north by Kwahu West Municipal, on the west by 
Asante Akyem South and Adansi South Districts, on the south by the newly formed Akyemansa District, 
and on the east by the Atiwa Kwaebibirem District. The District Assembly which is responsible for day-
to-day administration and implementation of government policies is situated in the south-west part of 
Ntronang to Oda road. There are three police stations in the district located at Afosu, New Abirem and 
Ntronang that ensures compliance of law and order in the district. There are also three SHSs called New 
Abirem-Afosu Senior High School located in the district capital, St. Michael Senior High School located 
at Akoasi, and Amuana Praso Senior High School located at Amuana Praso. There is also one radio 
station called ‘Ananse Fm’104.9MHz that serves as a medium of communication, information 
dissemination and announcement for communities in the district. The district government hospital is also 
found at New Abirem responsible for delivery of health care services though other private hospitals such 
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as I and I also exist. The market day occurs every Tuesday and Friday at New Abirem where farmers 
take advantage to sell their farm produce. All these help in the development and growth of the district. 
The total population in the district as of 2010 according to National Population and Housing 
Census is 78,907 of which 71,016 (90 percent) live in rural communities. Of this population, 39,572 (50.2 
percent) are males and 39,335 (49.8 percent) are females. This indicates that males are slightly (237) 
more than females about 0.4 percent in the district. The district's population is mostly young (51.5 
percent), with a large base population pyramid that tapers off with a limited number of elderly people 
(6.5 percent). The District's total age dependency ratio is 81.7, with males having a greater age 
dependency ratio (83.2) than females (80.3). With a total surface area of 550 km2, the district has a 
population density of 143.5 people per square kilometer. This suggests a significant population demand 
on land and infrastructure in the district, particularly in the district capital, such as water, electricity, 
roads, markets, housing, social services, and other resources.  
Within the district's jurisdiction are 78 settlements. New Abirem, the district capital, is roughly 
241 kilometers north-west of Accra and has a population of about 6,123 people. After Akoasi and Afosu, 
it is the district's third largest urban center (GSS, 2010 PHP). According to GSS data from 2010, the 
average household size in the district was 4.2 people per household. The urban score is 4.0, whereas the 
rural score is 4.3. Akoase, Amuana Praso, Afosu, New Abirem, Old Abirem, Ntronang, and Nkwarteng 
are among the district's urban towns, while Nyafoman, Noyem, Adadekrom, Tweapease, Adausena, 
Hweakwae, and Abohema are among the district's rural towns. Agriculture, which includes both food 
and tree crops, and livestock production, is the district's main economic activity.  
The major tree crops cultivated in the district are oil palm, cocoa and citrus comprising of about 
77,546 hectares and the dominant food crops include maize, cassava, plantain, rice and cocoyam 
comprising about 13,491 hectares including vegetable cultivation. This gives 91,037 hectares total area 
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of land under cultivation in the district. Manufacturing and processing of palm oil are also carried out in 
the district but farming is the leading economic activity and serves as a significant source of livelihoods 
for dwellers in the district (GSS, 2010 PHP).  
Farmers use cutlass and hoe as the main tools for farming accounting for subsistence and small-
scale production with an average farm size of about 1.2 hectares, a typical characteristic of rural 
households except few farmers undertaking commercial production. The accessibility of land for 
agricultural activities is through a land tenure system consisting of leasing, renting, sharing locally called 
‘abunu’ and ‘abusa’ and through inheritance (MoFA, 2018). Three main farming systems are very 
common in the district namely mixed cropping, intercropping and mixed farming. As seen in figure 3.2, 
the primary sources of farm labour are homes or families, as well as hired labour. Land-availability and 
acquisition, farm sizes and holdings, farming systems, farm labour, area under cultivation and types of 
crops grown, as well as their estimated yields, sources of finance, extension services, farm tools and 
equipment, storage and marketing systems are all considered under agricultural sector. 
The district is distinguished by topographical elements such as drainage, tributaries, relief, and 
climatic factors such as rainfall, temperature, and humidity. The Pra River, which also acts as a border 
between the district and two other districts in Ashanti Region, is the main source of drainage. The district 
has tributaries such as the Maamang, Nwi, Sukrang, Afotosu, and Adechensu Rivers. All of these 
tributaries form confluence with the Pra River, which also runs south and enters into the sea near Shama, 
Western Region. The district's terrain is mountainous, especially in the forest belt, and undulating, rising 
to 61 metres above sea level in certain places. The rainfall pattern consists of two main seasons in the 
district. The first season occurs in late March to mid-July and the second season also occurs in late August 
to late October with an amount of rainfall between 1500mm and 2000mm during the two peaks periods 
of May to July and August to October annually. 
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The temperature is relatively stable throughout the year and ranges between 250C to 320C and 
relative humidity of about 55 percent to 59 percent per year. The climatic conditions are also favourable 
for crops production. The economic implications of these unique features especially the rivers are that 
they can be used in the form of irrigation to boost agricultural production to generate employment, 
increase agricultural productivity, and ensure sustainable livelihoods for the people living in the district.  
The district is located within the semi-deciduous forest belt of the vegetation of Ghana consisting of tall 
trees with evergreen undergrowth.  
BND was chosen for the study because it is one of the districts endowed with valuable natural 
resources such as gold and timber. The forest contains a large species of economic trees such as 
Triplochiton scleroxylon, Ceiba pentandra, Cedrela odorata and Khaya ivorensis known in Ghana as 
Wawa, Onyina, Cedrela and Mahogany respectively that are important for the timber industry. The forest 
also contains gold deposit which is important for the mining industry, both domestic and abroad and it is 
the reason why one of the world-leading gold mining companies, Newmont is operating leading to an 
influx of many youth in search of jobs in the mining company.  
In addition to the public infrastructure, the youth in the district have access to improved 
infrastructure such as water, clinics, roads, schools and scholarship from the mining company as part of 
fulfilling their corporate social responsibilities, which was the case in the four of the six communities 
studied. Those already employed in the mining company in addition to the influx of newcomers will also 
increase demand for food crops, animal products and services, thus improving opportunities for the youth 
in the area who venture into agripreneurship. This makes the study area (Birim North District) a very 
unique one. However, in this study, the youth living in the mining communities are not different from 
those living in the non-mining communities.  
Two main challenges were identified as facing the district in general. The first is a bigger market 
that can offer better prices for agricultural produce in order to reduce difficulties in haggling prices with 
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farmers. The other challenge was inadequate storage and processing facilities for storing vegetables and 
crops such as tomatoes, citrus and rice (MoFA, 2018). Figure 3.3 is the map of Birim North District 
showing the various communities where the study was conducted. 
 
100
90
80
70
60 51.3 %
50
37.0%
40
30
20
4.7% 7.0%10
0
Hired labour Family Nnoboa More than one
type
Types of farm labour
 
Figure 3.2: Sources of Farm Labour  
Source: MoFA (2016, BNDA). 
 
From figure 3.2, it is seen that 51.3 percent of farmers use hired labour. Farmers who rely on only 
family/household labour constitute 37 percent, 4.7percent rely on ‘nnoboa’ whilst 7 percent of the farmers 
depend on more than one type of labour for their farming activities in the BND. 
 
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Figure 3.3: Map of the Study Area 
Source: GSS (2010, PHC) 
 
3.6 Scope and Limitations of the Study  
The study was set forth to examine the youth engagement in agripreneurship opportunities as a means of 
attaining sustainable livelihoods in mining communities in the Birim North District of Ghana. The 
achievement of the specific objectives requires resources in terms of financing and time. This resulted in 
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only 220 respondents being sampled as against the calculated sample of 242 sample size from the six 
communities instead of the entire district. Thus, a study taking into account the entire communities within 
the district with a larger sample size can also be carried out by future researchers. This is because it is 
well-known that as the sample size increases, the population parameter approaches its true mean value. 
Travelling up and down during the survey amidst COVID-19 pandemic was also not an easy task. The 
unwillingness of some of the respondents to answer the questionnaires was also encountered. This was 
emphatically due to inadequate and lack of awareness of entrepreneurship and agripreneurship 
knowledge among some of the respondents. Therefore, it was necessary to do enormous clarifications, 
definitions and continuous follow-up until the required number of responses were obtained. In addition, 
simple descriptive statistics were used to identify the most significant agripreneurship opportunities. 
However, this could have also been done using econometrics models to ascertain the main relationships 
among those opportunities. This could have helped to quantify the significance level of each opportunity 
identified in the study area. Moreover, several agripreneurship opportunities exist in agriculture, the study 
focused on those that commonly prevail in the study area. Several constraints were also identified as 
facing agripreneurs in the study area. However, only those relevant to this study were used for the 
analysis. Last but not the least, a lack of communication platforms for agripreneurs in the study area was 
noted as limiting accessibility of agripreneurship information. As a result, future research can be done to 
determine the impact of communication platforms on the utilization of agripreneurship opportunities. 
  
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CHAPTER FOUR 
RESULTS AND DISCUSSIONS 
4.1 Introduction  
The results of the study are presented in this chapter. The first section describes the background and the 
socio-economic characteristics of the respondents. The second section is on the presentation and 
description of the agripreneurship opportunities identified and the analysis of the factors that influence 
the decision of the youth to engage in agripreneurship opportunities. The third section, which is the final 
part describes the analysis of the constraints agripreneurs in the BND face. 
4.2 Background of the Respondents  
4.2.1 Community-Level Characteristics   
Data were collected on the infrastructural status from the key informants in the various communities 
under study. This information helped to determine the position of the developmental progress of the 
communities studied and hence will help investors to decide on investment portfolios that would be 
appropriately fit a particular community in the study area. The findings revealed that the majority of the 
respondents constituting 33.2 percent were with the view that communication networks are good whiles 
19.5 percent were with the view that the communication networks are very poor. On the communities’ 
road networks, 65.0 percent responded that the communities’ road networks are very good whiles only 
2.7 percent mentioned that they are very poor. In a similar view, the majority of the respondents 
consisting of 75.0 percent gave positive response that education facilities in the study area are in a good 
state whiles only 5.9 percent were with the view that the education facilities are in a poor state. However, 
none of the respondents indicated that the educational facilities in the study area are in a very poor state.  
The findings are presented in table 4.1 below.   
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Table 4.1: Infrastructure Base Resources in the Study Area  
Infrastructure Communication network Community road network Education facility 
availability and access 
Indicators Freq. Percentage Freq. Percentage Freq. Percentage 
Very good 68 30.9 143 65.0 42 19.1 
Good  73 33.2 51 23.2 165 75.0 
Poor  36 16.4 20 9.1 13 5.9 
Very poor  43 19.5 6 2.7 0 0.0 
Total 220 100.0 220 100.0 220 100.0 
Source: Field Survey, 2022. 
On the other hand, 58.0 percent indicated that the availability and accessibility of portable water for 
domestic consumption was very good and 42.0 percent indicated that it was good. This is illustrated in 
figure 4.1. These results were consistent with the initial expectation because it was initially anticipated 
that one of the departments of the Newmont Mining Company called Water and Sanitation Management 
in collaboration with Ghana Water Company and the District Assembly will ensure quality water 
distribution to the various communities under study.   
100
80
58.0%
60 42.0%
40
20
0
Yes No
Number of Respondents
 
Figure 4.1: Availability and Accessibility to Portable Water in the Study Area  
Source: Field Survey, 2022 
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4.2.2 Socio-Economic Characteristics of the Respondents 
The socio-economic characteristics of the respondents are discussed and presented in a form of tables 
using frequencies and percentages as follows: 
Age: The age distribution of the respondents is shown in Table 4.2. Some of the respondents in the 
research area were found to be 35 percent between the ages of 25 and 30, and 65 percent between the 
ages of 31 and 35. This result revealed that the respondents for the interview were chosen based on the 
National Youth Policy of Ghana (2010) definition of youth as people between the ages of fifteen and 
thirty-five. However, those within the age bracket of fifteen to twenty-four years were not sampled. This 
is because they were found to belong to the ‘in-school group’ and therefore do not play a significant role 
in agricultural activities in the study area. 
Table 4.2: Age Distribution of the Respondents 
Age Frequency Percentage 
25 to 30  77 35. 0 
31 to 35 143 65.0 
Total  220 100.0 
Source: Field Survey, 2022 
 
Sex: 122 out of the 220 respondents interviewed for the study were males, accounting for 55.45 percent 
of the total, while 98 were females, accounting for 44.55 percent. In comparison to their female 
counterparts in the research area, males dominate the use of agripreneurship opportunities as a means of 
sustainable livelihoods. This could be because males are more competent of taking the risks and venturing 
into agriculture activities than females in most African countries, particularly in rural areas. This 
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conclusion supports the findings of Mbam & Nwibo (2013), who found that males are more involved in 
agricultural activities than females. In most African communities, males are also described as decision-
makers and are capable to manage resources well than female counterpart in the same environment 
(Turkson & Naandam, 2006).The findings are shown in the table 4.3. 
Table 4.3: Sex Distribution of the Respondents 
Sex Frequency Percentage 
Males 122 55.5 
Females  98 44.6 
Total 220 100.0 
Source: Field Survey, 2022 
Educational Level: In the sustainable livelihoods framework, education is one of the human capital that 
needs to be developed. It is one of the vital tools necessary for economic development. As a result, the 
educational background of the youth under the study was assessed during the field survey. The results of 
the educational level of the respondents interviewed are presented in table 4.4 below. It was revealed that 
5.9 percent of the respondents had primary education only, 26.8 percent had JHS/JSS education, 30.9 
percent had SSS/SHS education, 13.2 percent had technical/vocational education, 15.5 percent were 
undergraduates, 7.3 percent have had first-degree qualification and 0.5 percent constituting one 
respondent had received post graduate diploma education. These results showed that the literacy level of 
the respondents was very high with the majority completing secondary education. This finding may be 
attributed to the availability of the scholarship grant that is provided by the Newmont Mining Company, 
a department called Newmont-Akyem Development Foundation (NAkDeF) in the study area that is used 
to sponsor any student who passes his or her basic school examination and willing to further his or her 
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education up to first-degree level. However, of all the 220-youth interviewed, none of them was found to 
have had or reached masters or PhD level of education.  
Table 4.4: Educational Level of the Respondents 
 
Educational level Frequency Percentage 
Primary  13 5.9  
JHS/JSS 59 26.8 
 
SSS/SHS 68 30.9 
Technical/Vocational 29 13.2  
Undergraduate 34 15.5 
 
First degree 16 7.3 
Post Graduate Diploma 1 0.5  
Total 220 100.0 
 
Source: Field Survey, 2022 
 
The results of the other socio-economic characteristics such as marital status, household size, average 
annual income, farm activities, farm size, crop production, awareness of agripreneurship opportunities, 
membership of farmers’ base organization, agripreneurship experience and off-farm agricultural 
activities are also discussed and presented in tables/figures as follows: 
Marital Status: Marriage is a legal relationship between a man and woman who have agreed to live 
together as one in a form of companionship. In most cases, it is one of the important social relationships 
which is very difficult to avoid if not impossible in one’s life. In some households, the married couple 
serves as a source of labour contributing to family income. This necessitated the study to consider the 
marital status of the respondents. The findings are presented in table 4.5. From the table, it is seen that 
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out of the total of 220 respondents interviewed in the study area, 45.9 percent consisting of 101 
respondents were married whiles 33.2 percent consisting of 73 respondents were singled. In addition, it 
is seen from the table that 13.6, 1.4 and 5.9 percent of the respondents were divorced, widowed and 
separated respectively. 
Table 4.5: Marital Status of the Respondents 
Marital status Frequency  Percentage  
Single 73 33.2  
Married  101 45.9  
Divorced  30 13.6 
 
Widowed  3 1.4 
 
Separated  13 5.9 
 
Total 220 100.0 
Source: Field Survey, 2022  
Household Size: A household is a group of people who may or may not live in the same dwelling unit 
but eat from a "shared pot" with a recognized leader. Households are the primary source of farm labour 
in most rural areas where labour and financial resources are scarce. As a result, the larger the household, 
the more labour is available for agricultural tasks. This ostensibly assists such households in saving 
money that would otherwise be spent on hiring labour. Furthermore, compared to a home with 
disorganized and poorly educated individuals, a household with well-organized and highly educated 
people is more likely to make more revenue and thus be able to sustain their livelihoods. The results of 
the household size obtained by interviewing the heads of households during the survey are shown in table 
4.6. According to the data, the majority of the youth constituting 58.2 percent live in households with 4 
to 6 members, while 35.5 percent live in households with 1 to 3 members. Only 6.4 percent of households 
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have more than 6 individuals. The standard deviation obtained was 0.579, and the average household size 
was 1.71 members, with 1 and 3 representing the least and maximum members respectively. 
Table 4.6: Household Size of the Respondents 
Household size Frequency  Percentage 
1 to 3 78 35.5 
4 to 6 128 58.2 
Above 6 14 6.4 
Average 1.71 Minimum = 1 
Std. Deviation 0.579 Maximum = 3 
Source: Field Survey, 2022 
Employment Status of the Respondents: Employment is one of the major sources of income to a 
person, family and household. It helps one to earn income to pay bills, fees, consumption and acquisition 
of wealth. As a result, it was necessary to find out the employment status of all the respondents in the 
study area. The results of the distribution of employment status among the youth are presented in table 
4.7 below. 
Table 4.7: Distribution of Employment Status of the Respondents 
 
Employment status Frequency Percentage 
 
Employed full-time 98 44.5 
 
Part-time 65 29.5 
 
Self-employed 16 7.3 
 
Unemployed 41 18.6 
 
Total  220 100.0 
Source: Field Survey, 2022 
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 These findings are encouraging because they indicated that the majority of the youth in the study area 
are working. The results revealed that out of the total number of 220 youth interviewed, 44.5 percent 
were in full-time and 29.5 percent were in part-time employment. Only 7.3 percent were self-employed 
and 18.6 percent were unemployed. The finding of the low proportion of self-employed among the youth 
in the study area may be attributed to inadequate capital, infrastructures, and well-organized institutional 
factors that support the creation of own businesses. The low percentage of self-employed among the 
youth is a clear indication that there is a lack of entrepreneurial spirit among the youth in the study area 
since self-employed has been recognized as one of the major characteristics of all entrepreneurs. For 
instance, FAO (2016) at one time posited that entrepreneurs are self-employed persons who run their 
own business. An enabling environment that enhances the creation of businesses needs to be created in 
the study area so that the youth could develop an interest in establishing their own businesses. On the 
other hand, those found to be unemployed are encouraged to consider participating in agriculture as a 
source of employment. In line with this Bernell (2018) reported that there are numerous opportunities 
available in agriculture and by far it is one of the areas that requires low start-up capital.  
Average Annual Income: The respondents' average annual income from all agripreneurship businesses, 
both on-farm and off-farm, was evaluated. It was found that the average annual income of the majority 
was very low. The findings are presented in table 4.8. Out of the 220 youth interviewed, 47.7 percent 
earn an average annual income of three thousand, one hundred Ghana cedis to five thousand Ghana cedis 
(GHS3,100 to GHS5,000) while 17.7 percent earn an average annual income ranging from one thousand 
Ghana cedis to two thousand Ghana cedis  (GHS 1,000 to GHS2,000), 19.1 percent also earn an average 
annual income of two thousand, one hundred to three-thousand Ghana cedis (GHS2,100 to GHS3,000). 
On the other hand, only 4.5 percent earn an average annual income of less than one thousand Ghana cedis 
(less than GHS1,000) while 10.9 percent did not reveal the level of their average annual income to the 
researcher and this was titled as not available in the table.  
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Table 4. 8 Range of Average Annual Income of the Respondents  
Average Annual Income (GHS)  Frequency Percentage 
Below 1,000 10 4.5 
1,000 – 2,000 39 17.7 
2,000 – 3,000 42 19.1 
3,100 – 5,000 105 47.7 
Not available 24 10.9 
Total 220 100.0 
Source: Field Survey, 2022 
 Agripreneurship Experience: Experience leads to the acquisition of knowledge and skills by an 
individual. It is gained in most cases as a result of continuous performance of an activity or regular 
observations of situations. It enables one to better understand things and be able to deal with situations 
more appropriately. Due to its indispensability in one daily job activity, the experience level of the 
respondents was assessed. Table 4.9 shows the findings obtained. It is seen from the table that the 
majority of the respondents consisting of 45.8 percent have had experience from 1 to 3 years and 17.9 
percent have had agripreneurship experience ranging from 4 to 6 years. On the other hand, 25.4 percent 
had experience below 1 year and only 10.9 had experience that was above 6 years. 
 
 
 
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Table 4.9: Agripreneurship Experience of the Respondents 
Agripreneurship experience Frequency Percentage 
Below 1 year  51 25.4 
1 to 3 years  92 45.8 
4 to 6 years 36 17.9 
Above 6 years 22 10.9 
Total 201 100.0 
Source: Field Survey, 2022 
Awareness and Sources of Agripreneurship Information: Awareness creates sensitivity and makes 
individuals conscious about the existence or non-existence of an activity. Thus, the study investigated 
among the youth to ascertain whether they are aware of the existence of agripreneurship opportunities. If 
the majority of the respondents are aware of them, it is an indication of obtaining reliable data and vice 
versa. The result of the level of awareness of agripreneurship is summarized in figure 4.2. The figure 
depicts that the majority 74.5 percent (164) of the respondents were aware and understood what 
agripreneurship is about, indicating that they know agripreneurship opportunities. However, only 25.5 
percent (56) mentioned that they are unaware and therefore do not have knowledge of agripreneurship.  
Proportionally, the awareness level of agripreneurship among the youth in the study area was high as 
depicted in figure 4.2. 
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100
90
80 74.5%
70
60
50
40
30 25.5%
20
10
0
Yes No
Agripreneurship Awareness
 
Figure 4.2: Awareness Level of Agripreneurship Opportunities in the Study Area   
Source: Field Survey, 2022 
 
Sources of Agripreneurship Information: The results of sources of information on the awareness of 
agripreneurship are presented in table 4.10. The results revealed that the majority (64.0 percent) received 
their information from the media whiles 13.4, 4.9 and 17.7 percent received their information from 
Agriculture Extension Agent, Farmers’ Based Organisations and seminar/training/workshop 
respectively. 
Table 4.10: Sources of Information on Awareness of Agripreneurship 
Sources of awareness Frequency Percentage 
Agricultural Extension Agents  22 13.4 
Farmers’ Based Organisations 8 4.9 
Seminar/Training/Workshop 29 17.7 
Media 105 64.0 
Total 164 100.0 
Source: Field Survey, 2022 
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The results further revealed that the effort of the media in disseminating agripreneurship information was 
high while that of Agriculture Extension Agents in the study area was low. Therefore, they need to be 
encouraged to work harder in that direction. On the other hand, if the number of extension agent in the 
district is low, it should be channelled to the appropriate authorities so that more extension agents would 
be posted to the study area to assist in the dissemination of agripreneurship information. Since, for now, 
agripreneurship is the need of the hour. 
4.2.3 Agripreneurship Activities of the Respondents 
Farming activities: Out of the total, 201 respondents representing 91.4 percent grow crops, carry out 
horticulture, raise livestock and/or undertake non-traditional agriculture activities as sources of income.  
Crops Production: Crops grown by the respondents were grouped under food (arable) and tree crops. 
The arable crops consist of maize, rice, cassava, yam and plantain whiles the tree crops consist of oil 
palm and cocoa. Others were also found to involve in horticultural activities consisting of vegetables and 
fruits production. The findings are presented in table 4.11. The results indicated that 40.3 percent grow 
maize, 1.5 percent grows rice, 24.9 percent grow cassava, 2.5 percent grow yam and 4.5 percent grow 
plantain. Also, 20.9 percent were into oil palm production while 5.5 percent were into cocoa production.  
Table 4.11: Crops Grown by the Respondents 
Crops Frequency Percentage 
Maize 81 40.3 
Rice 3 1.5 
Cassava 50 24.9 
Yam  5 2.5 
Plantain  9 4.5 
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Oil palm 42 20.9 
Cocoa 11 5.5 
Total 201 100 
Source: Field Survey, 2022 
 
Horticultural Activities: It was also found that out of the total number of 201 respondents interviewed 
on agricultural activities, 55 of them were found to be in horticulture activities. With this, 94.5 percent 
produced vegetables such as aubergine, cabbage, okra, and tomatoes whiles only 5.5 percent produced 
fruits such as pineapple. The result is shown in table 4.12. 
Table 4.12: Horticultural Activities Carried Out by the Respondents 
Horticulture activities  Frequency Percentage 
Vegetables 52 94.5 
Fruits 3 5.5 
Total 55 100.0 
Source: Field Survey, 2022   
 
Farming Systems: The results of farming systems are shown in table 4.13. The majority of the youth 
constituting 79.6 percent practice mixed cropping, 10.4 percent crop rotation, 3.5 percent mixed farming 
and 6.5 percent practice other types of farming systems such as monocropping and intercropping. 
 
 
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Table 4.13: Farming System Practice by the Respondents  
   
Farming system Frequency  Percentage  
Mixed farming  7 3.5 
Mixed cropping  160 79.6 
Crop rotation 21 10.4 
Others  13 6.5 
Total  201 100.0 
Source: Field Survey, 2022 
Farm Size: The findings showed that 79.6 percent have a farm size between 0.40 to 2.02 hectares and 
14.9 percent have a farm size that is less than 0.40 hectares. Only 7.5 percent worked on a farm land 
which was between 2.43 to 4.05 hectares. The minimum size of their farms was found to be 0.40 hectares 
and the maximum was 1.21 hectares. The average hectarage was 0.88 which indicated that the farm size 
of the respondents was low. This could be attributed to the use of manpower and simple farm tools such 
as cutlass and hoe for farming in the study area accounting for small and medium scale production. The 
results are presented in table 4.14 below. 
Table 4.14: Farm Size of the Respondents 
Farm size  Frequency Percentage 
Less than 0.40 hectares 30 14.9 
0.40 to 2.02 hectares 156 79.6 
2.43 to 4.05 hectares 15 7.5 
Minimum  0.40  – 
Maximum  1.21 – 
Average 0.88 – 
Source: Field Survey, 2022 
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Farm Animals Kept by the Youth: Apart from farming activities carried out by the youth in the study 
area, 27 of them represent constituting 12.3 percent raised livestock in addition to farming activities. The 
results are shown in table 4.15. From the table, it is seen that 0.5 percent keeps poultry farm, 5.9 percent 
raised sheep, 2.3 percent raised goats and 3.6 percent raised pigs. 
Table 4.15: Livestock Kept by the Respondents 
Livestock  Frequency Percentages  
Sheep  13 5.9 
Goats  5 2.3 
Pigs  8 3.6 
Poultry 1 0.5 
Total 27 12.3 
Source: Field Survey, 2022 
Trading Activities: Table 4.16 shows the results of the trading activities of the respondents. A total of 
74 respondents representing 33.6 percent were found to conduct trade activities. With this, the majority 
consisting of 13.2 percent sold vegetables, 7.7 percent sold fruits and, 4.5 percent sold food stuff. Only 
few consisting of 1.4 were into agro-chemicals sales and 6.8 percent were into what this study termed it 
as ‘speculative agripreneurship business involving aggregation of palm oil and grains.’ 
 
4.2.4 Speculative Agripreneurship Business 
Speculative agripreneurship business is defined in this study as a type of agricultural trade whereby 
agripreneurs buy agricultural commodities at certain prices in the present and sell them at an uncertain 
price in the future. The focus group interview conducted with the agripreneurs involved in this type of 
business on the risky level revealed that it is riskier compared to other businesses due to future price 
uncertainties associated with it. Based on this, the definition was also devised as a process of undertaking 
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a risky trade (business) with the hope of making a profit in future in the agricultural sector. This brings 
to life what has been documented by Drucker and Cantillon over a century ago that entrepreneurs buy at 
a certain price in the present and sell at an uncertain price in future. In this way, agripreneurs involved in 
the business take the risk and buy the commodity with the anticipation of future price increase. Thus, the 
agripreneurs need to store the purchased commodity for some months before selling them. In this case, 
good storage leads to a high selling price and more profits.  
The respondents further explained that the business is very sensitive to time in terms of buying 
and selling periods. They revealed that buying and storing of the commodity should occur during bumper 
harvest while selling should occur during the period of scarcity. They further mentioned that this is where 
the profit level is highest. The three main commodities found to be traded by the agripreneurs were palm 
Table 4.16: Trading Activities of the Respondents 
Trading activities Frequency Percentage 
Food stuff  10 4.5 
Fruits vendors  17 7.7 
Vegetable vendors  29 13.2 
Agro-chemicals  3 1.4 
Speculative agripreneurship business 15 6.8 
Total 74 33.6 
Source: Field Survey, 2022   
oil, rice and maize. The results are summarized in table 4.17. From the table, two periods are indicated, 
Period I and Period II. Period I shows the range of months in which all the agripreneurs are expected to 
purchase the commodities (palm oil, rice or maze) for storage. It is also the period in which the 
commodities are in abundance and the prices are at their possible lowest level while Period II shows the 
range of months in which the agripreneurs are expected to release the commodity to the market for sale. 
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It also shows the periods of months in which the commodities are relatively scarce and their prices are at 
their highest possible level.  
 
Table 4.17: Expected Periods of Speculative Agripreneurship Business 
Commodities Palm oil  Rice  Maize  
 
Period I March – August  August – November  August – February  
Period II September – February  December – July  March – July  
Source: Author’s design based on the field data, 2022 
 
4.3: Identification of the Agripreneurship Opportunities  
Table 4.18 presents the agripreneurship opportunities identified as not significant, less significant, 
significant and most significant that can be used by the youth in the study area as sources of employment 
and income generation. From the table, it is seen that all the opportunities identified were significant 
except farming activities (planting, weeding and harvesting) for cash, horticulture involving vegetables 
and fruits production and being an artisan as in mechanic, welder and carpenter who contributes to the 
maintenance and services of agricultural implements. Thus, opportunities such as food crops production 
(maize, cassava and plantain), ownership of tree crops (oil palm, cocoa and citrus), trading (inputs: seeds, 
agrochemical, fertilizer), livestock enterprises (ruminant and non-ruminant production), offering of 
veterinary services, ownership of assets (for: processing, packaging and storage of agricultural produce), 
nursery, (seedlings production), non-traditional agriculture (honey, mushroom, snail, grasscutter 
production), and speculative agripreneurship businesses are the major sources of  livelihoods in the study 
area. Again, the mean values of each opportunity revealed that livestock enterprise was the most 
significant with a mean score of 3.25. This was followed by the pursuance of speculative agripreneurship 
business (palm oil and grains aggregation), ownership of tree crops, and non-traditional agriculture 
production with mean values of 3.19, 3.11 and 3.00 respectively.  
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Table 4.18: Agripreneurship Opportunities 
Opportunities Not Less Significant Most Mean 
significant significant significant values 
Farming activities (Planting, Weeding, 53 (24.1) 32 (14.5) 78 (35.5) 57 (25.9) 2.3 
Harvesting) 
Food crops production (Maize, 39(17.7) 29(13.2) 60(27.3) 92(41.8) 2.4 
Cassava, Plantain) 
Ownership of tree crops (Oil palm, 20 (9.1) 11 (5.0) 89 (40.5) 100 (45.5) 3.1 
Cocoa and Citrus) 
Horticulture/Vegetables/Fruits 124 (56.4) 52 (23.6) 21 (9.5) 23 (10.5) 1.3 
Production 
Trading/Seeds/Agrochemicals 39 (17.7) 22 (10.0) 96 (43.6) 63 (28.6) 2.8 
Livestock enterprise (Ruminant and 0 (0.0) 13 (5.9) 138 (62.7) 69 (31.4) 3.3 
N0n-ruminant production  
Veterinary services 2 (0.9) 57 (25.9) 115 (52.3) 46 (20.9) 2.9 
Ownership of assets (For processing, 22 (10.0) 84 (38.2) 56 (25.5) 58 (26.4) 2.7 
packaging and storage) 
Artisans/Mechanics/Welders/Carpenters 32 (14.5) 105 (47.7) 47 (21.4) 36 (16.4) 2.4 
Nursery/Seedlings production (Cocoa, 7 (3.2) 75 (34.1) 108 (49.1) 30 (13.6) 2.7 
Teaks, Cedrela) 
Non-traditional agriculture (Honey, 12 (5.5) 19 (8.6) 145 (65.9) 44 (20.0) 3.0 
Mushroom, Snail, Grasscutter 
production) 
Speculative Agripreneurship Business 7 (3.2) 13 (5.9) 132 (60.0) 68 (30.9) 3.2 
(SAB) 
 Mean score ≥ 2.50 implies that the agripreneurship opportunity is significant 
 Values in the parentheses are percentages 
 Number of opportunities = 12 
 N = 220 
Source: Field Survey, 2022 
 
 
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4.4 Socio-Economic Characteristics Influencing the Decision to Engage in Agripreneurship 
 
TOhpep orerstulntsit ioefs  the binary logistic regression analysis of the socio-economic characteristics which 
influence the decision of the youth to engage in agripreneurship opportunities are presented in table 4.19. 
The relationship and the significant levels among the variables used in the model are discussed as follows: 
Sex 
The sex of the respondents had a positive relationship with the decision of the youth to engage in 
agripreneurship opportunities and was statistically significant at both 5 percent (P = 0.05) and 10 percent 
(P = 0.10) levels of significant. The result was consistent with the initial a priori because it was expected 
that the male gender will play a very significant role in the decision of the youth to engage in 
agripreneurship opportunities than in female counterparts with a positive correlation. This is because 
males are normally endowed with resources and are capable to take risks compared to females in the 
same environment. This finding agrees with Mbam & Nwibo (2013) who posited that the male gender is 
more involved in agricultural activities than the female gender. It is also consistent with the finding of 
Turkson & Naandam (2006) who indicated that males are more capable to manage household productive 
assets and are mostly decision makers in African societies as compared to their female counterparts. The 
result is also similar to that of Nwibo et al. (2016) who found that the gender composition of the youth, 
significantly affect the decision and the intensity to engage in farming. In terms of probability, the model 
predicted 99.9 percent likelihood of the male gender deciding to engage in agripreneurship opportunities 
than the female counterpart ceteris paribus.   
Marital Status 
Marital status also had a positive influence on the youth decision to engage in agripreneurship 
opportunities and was significant at 10 percent (P = 0.10) level only. This was also in line with the initial 
a priori expectation since it was hypothesized that marital status will have either a positive or negative 
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effect on the decision of the youth in the study areas to engage in agripreneurship opportunities. This 
positive relationship suggests that, ceteris paribus, as the majority of the youth in the study area, get 
married, their drive to engage in agripreneurship opportunities increases. The model predicted 94.7 
percent chance of those who have married to decide to engage in agripreneurship opportunities as 
compared to those who have not.     
Previous Agripreneurship Experience 
Experience had a positive and significant relationship with the decision of the youth to engage in 
agripreneurship opportunities at significant levels of both 5 percent and 10 percent. This implies that the 
more experienced the youth have gathered by engaging in previous agripreneurship opportunities, the 
more they would be willing to embrace similar opportunities because as the level of experience increases, 
their knowledge level also increases. This result concurs with Nwibo & Okorie (2013) who found positive 
relationship between the years of agripreneurial experience and the youth decision to be agripreneur. 
Contrary to this was that, their agripreneurial experience was significant at 1 percent (P = 0.01) level. 
The finding also concurs with the study by Levent & Catherine (2011) who reported that past experience 
in business of the entrepreneur has positive impact on the entrepreneurial orientation of the firm. This 
implies that the agripreneurial experience gathered by a farmer has strong association in making him/her 
to establish similar business within the area or elsewhere where similar opportunity exists. The model 
predicted 93.9 percent probability of the youth having previous agripreneurship experience to decide to 
engage in agripreneurship opportunities ceteris paribus than those who do not have any agripreneurship 
experience.        
Education 
Education had positive influence on the decision of the youth to engage in agripreneurship opportunities 
and it was significant at both 5 percent and 10 percent level of significant respectively. This implies that, 
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ceteris paribus as the educational level of the youth in the study area increases the decision to engage in 
agripreneurship opportunities also increases. The finding was also consistent with the initial a priori 
expectation that education will have positive impacts on the decision of the youth’s engagement. The 
result was also consistent with Nwibo & Okorie, (2013) who mentioned that education has been noted to 
be a driving force in agripreneurial success. Hence, education is a prerequisite to understanding the nitty-
gritty of entering any venture for profit maximization. Again, the result concurs with Levent & Catherine, 
(2011) who found that educational attainment of an entrepreneur makes a positive impact on a firm’s 
entrepreneurial orientation.    
Employment Status 
Employment status also had positive influence on the decision of the youth to engage in the opportunities 
at a significant level of 5 percent and 10 percent. This was also consistent with the initial a priori 
expectation because it was noted that employment is a source of income to the youth. Thus, as the income 
from employment increases, the purchasing power of the youth also increases and this increases their 
chance to continue engaging in the opportunities so as to generate more income. The model predicted 
99.9 percent chance of those who were employed to decide to engage in agripreneurship opportunities 
than those who were not. 
Awareness 
Creating awareness on the adoption of entrepreneurship in agriculture among the youth as a means of 
achieving sustainable sources of employment and income generation had a positive effect on the decision 
to engage in agripreneurship and was significant at 10 percent level, indicating that with other factors 
being equal, as awareness level increases, the decision to engage in agripreneurship opportunities also 
increases. In other words, the more the youth are informed on the importance and the benefits of engaging 
in agripreneurship, the more their decision to participate increases. Awareness of agripreneurship was 
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also consistent with the initial positive a priori. The model predicted 99.3 percent chance of those who 
are aware of agripreneurship to decide to engage in agripreneurship opportunities compared to those who 
were not aware ceteris paribus.       
Farm Size 
Farm size had a positive influence on the youth decision to engage in agripreneurship opportunities and 
significant at both 5 percent and 10 percent level. This result is also consistent with the initial positive a 
priori expectation. Thus, the bigger the farm size, the higher the source of farm income. This implies that 
as farm size approaches large scale or becomes commercial, the more the decision of the youth to engage 
in agripreneurship opportunities increases. The model predicted 99.9 percent likelihood of those youth 
who have large farm size to decide to engage in agripreneurship opportunities than those who do not 
ceteris paribus. 
Farmers’ Based Organization (FBOs) 
Being a member of FBOs was significant at both 5 percent and 10 percent level and positively influence 
the decision of the youth to engage in agripreneurship opportunities. This was also in line with the a 
priori expectation because members belonging to an organization receive training; acquire knowledge 
and skills that increases their chances of engagement to utilize the opportunities more effectively to 
improve their livelihoods. Thus, as the youth join an organization, all things being equal, their decision 
to engage in agripreneurship opportunities also increases. The model predicted 99.9 percent probability 
of those youth who belong to an organization to decide to engage in agripreneurship opportunities 
compared to those who do not belong to any organization ceteris paribus.  
The findings further revealed that only a few consisting of 9 percent (19 respondents) of the youth 
compared to their counterparts consisting 91 percent (201 respondents) decided not to engage in 
agripreneurship opportunities. This is illustrated in figure 4.3 below. 
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No, 9%
Yes, 91%
  
Figure 4.3: Decision of the Youth to Engage in Agripreneurship Opportunities      
Source: Field Survey, 2022 
 However, other variables such as age and household size were not statistically significant at any level. 
This means that both variables, age and household size have no effect on the decision of the youth to 
engage in agripreneurship opportunities in the study area. These results however did not conform to the 
hypothesized a priori because it was initially expected that those variables would have a significant 
influence on their decision of engagement.  
The estimated R2 statistics consisting of Cox and Snell and Nagelkerke had values 0.394 and 
0.886 respectively. Based on these values, it can be seen that the Nagelkerke’s R2 predicts the model fit 
very well than Cox and Snell implying that the independent variables in the model explained 88.6 percent 
of the total variations of the dependent variable. In addition, the model was statistically significant at 1 
percent level based on the Chi-square test. The Log likelihood value of 19.268 indicates that the 
coefficients in the model in the presence of the explanatory variables were also statistically significant. 
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Table 4.19: Socio-Economic Factors that Influence the Decision of the Youth to Engage in 
Agripreneurship Opportunities 
Description of variables Coeff. estimates S. E. Wald df. Sig. Exp(B) P - value 
Constant term -57.707** 23.616 5.972 1 0.015 .000 . 000 
Age -2.676 1.678 2.543 1 0.111 0.069 0.065 
Sex  16.991** 6.720 6.392 1 0.011 23928717.736 0.999 
Marital status 2.891*** 1.486 3.784 1 0.052 18.008 0.947 
Household size -3.813 3.136 1.479 1 0.224 .022 0.022 
Agripreneurship experience 2.736** 1.348 4.121 1 0.042 15.429 0.939 
Education 3.983** 1.705 5.453 1 0.020 53.663 0.982 
Employment 8.744** 3.341 6.497 1 0.011 6275.597 0.999 
Awareness 4.901*** 2.677 3.352 1 0.067 134.361 0.993 
Farm size 14.432** 5.715 6.376 1 0.012 1852263.994 0.999 
Farmers’ Based Organization 6.842** 3.349 4.174 1 0.041 936.472 0.999 
Number of obs. = 220 **P = 0.05 and ***P = 0.10 are significant at 5% and 10% respectively. 
    Pseudo R2   
    Cox and Snell = 0.394 
    Nagelkerke = 0.886 
    -2Log likelihood = 19.268 
    Chi-square = 110.111 
    Prob. > chi-square = 0.000 
Source: Field Survey, 2022 
4.5 Constraints Facing Agripreneurs in the Study Area  
Table 4.20 presents the identified constraints facing the agripreneurs in the study area. It is seen from the 
table that lack of financial support received the least mean value of 2.07, followed by high inputs prices 
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which received a mean value of 2.48. On the other hand, inadequate storage facilities received a mean 
value of 3.71, a low level of agripreneurship education also received a mean value of 4.20, poor 
transportation systems received a mean value of 4.15 and that of inadequate demand for the output was 
4.40. This results revealed that the majority of the youth ranked lack of financial support first indicating 
that, lack of financial support was the most pressing constraint facing agripreneurs in the study area. 
High inputs prices were ranked as the second most pressing constraint that is the second most 
pressing constraints. Inadequate storage facilities were ranked third indicating that it is the third most 
pressing constraint. On the other hand, poor transportation systems and low demand for agricultural 
output were ranked fifth and sixth respectively, implying that they are the least constraints facing 
agripreneurs in the study area.  
In order to determine whether there was some degree of agreements among the respondents in 
ranking the constraints, Kendall’s Coefficient of Concordance also called Kendall’s W was applied, and 
the test statistics obtained are shown under table 4.20. The estimated Kendall’s W for the ranking of the 
constraints was 0.315. Based on the estimated chi-square value of 298.787 with 5 df, the null hypothesis 
as stated in the methodology is rejected and the conclusion is that there is some degree of agreement 
among the respondents in ranking the constraints, at an asymptotic significant level of 0.000. In this case, 
the youth in the study area fairly agree that the constraints facing agripreneurs in the study area are more 
related to first and foremost lack of financial support, high inputs prices and then inadequate storage 
facilities, while poor transportation systems and low demand for agricultural output being the least 
constraints in the BND.  
The finding of lack of financial support agrees with Nachimutu et al. (2018) who reported that 
most farmers are constraint with financial support while Nib et al. (2016) mentioned that it was due to 
lack of loan. The finding on high inputs prices as ranked by the respondents also concurs with what has 
been published by Twumasi et al. (2019) who found that 39 percent of the respondents perceived that 
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prices of farm inputs (fertilizer, seeds, etc.) were high and that of inadequate storage facilities also concurs 
with the findings of GSS (2010 PHC) who published that one of the main challenges farmers in Birim 
North faced is inadequate facilities for storing agricultural produce. 
Table 4.20: Constraints Facing Agripreneurs in the Study Area 
 
Constraints Mean Rank Rank 
Lack of financial support  2.07 1st 
High inputs prices  2.48 2nd  
Inadequate storage facilities 3.71 3rd 
Low level of agripreneurship education   4.20  4th 
Poor transportation systems 4.15 5th 
Low demand for agricultural output 4.40 6th  
N 190 
Kendall’s W 0.315 
Chi-square  298.787 
df. 5 
Asymp. Sig.  0.000 
Source: Field Survey, 2022 
 
By ranking lack of financial support and high input prices as the most pressing constraints, it becomes 
necessary to investigate whether they are statistically significant and have some degree of influences on 
their decision to engage in agripreneurship opportunities. Therefore, the two variables were input in the 
binary logistic model and analyzed. The results showed that none of the coefficients of the two variables 
was statistically significant at any level. 
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When the variable, lack of financial support was dropped from the model, all the variables were 
statistically significant at both P = 0.05 and P = 0. 10 level except age, household size and high input 
price which was not statistically significant at any level. 
In the same direction, when the variable lack of financial support was included while high input 
price was dropped from the model, none of the coefficients estimated was statistically significant at any 
level. In effect, both variables were not statistically significant at any level and therefore do not influence 
the youth decision to engage in agripreneurship opportunities despite ranking them as the most pressing 
constraints facing them in the study area. The results are presented in Appendix iii. 
  
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CHAPTER FIVE 
SUMMARY, CONCLUSIONS AND RECOMMENDATIONS 
5.1 Introduction  
This chapter presents the summary and conclusions of the study based on the results. This is followed by 
policy recommendations for further actions. 
5.2 Summary of the Study  
The study was set forth to identify the various agripreneurship opportunities that can be used by the youth 
as sources of employment and income generation in the quest of sustainable livelihoods. It also examined 
the socio-economic characteristics that would significantly influence the decision of the youth to engage 
in those agripreneurship opportunities. The study also identified and analyzed the constraints 
agripreneurs in the study area faced. The analytical approaches and models used for the achievements of 
the specific objectives were the Likert scale, binary logistic regression using SPSS version 20 and 
Kendall’s Coefficient of Concordance.  
By applying the multi-stage sample technique, pretest questionnaires were used through a survey 
to sample two hundred and twenty (220) youth from six randomly selected communities within Birim 
North District of Ghana. Specifically, the Likert scale was used to identify the most significant 
agripreneurship opportunity in the study area. The binary logistic regression model was used to analyze 
the socio-economic characteristics that significantly influence the decision of the youth to engage in 
agripreneurship opportunities. The Kendall’s Coefficient of Concordance was used to identify and rank 
the constraints facing agripreneurs in the study area as well as analyze the degree of agreement among 
the respondents in ranking the constraints.  
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The results of the Likert scale analysis identified twelve agripreneurship opportunities with 
livestock production enterprise and speculative agripreneurship business consisting of palm oil and grains 
aggregation dominating. The results of the binary logistic regression analysis of the socio-economic 
characteristics influencing the decision of the youth to engage in agripreneurship opportunities also 
showed that sex, employment status and farm size were the three most statistically significant at 5 percent 
(P = 0.05) and 10 percent (P = 0.10) level and positively correlated with the youth decision of 
engagement. The other socio-economic characteristics such as marital status, previous agripreneurship 
experience, education, awareness of agripreneurship, and membership of Farmers’ Based Organizations 
(FBOs) were also statistically significant at both 5 percent and 10 percent level of significance while age 
and household size were not significant at any level. 
The Kendall’s Coefficient of Concordance analysis revealed that lack of financial support was 
the most pressing constraint facing agripreneurs in the study area, followed by high inputs (seeds, 
fertilizers, pesticides, weedicides etc.) prices, and inadequate storage facilities for palm oil and grains 
aggregation. The results further revealed that there was some degree of agreement among the respondents 
in ranking of the constraints.   
5.3 Conclusions  
 Based on the findings, it can be concluded that livestock production enterprises, speculative 
agripreneurship business, non-traditional agriculture (honey production, grasscutter rearing, 
mushroom production, etc.), nursery (seedlings production) and ownership of assets (for 
processing and packaging of agricultural produce) are agripreneurship opportunities that can be 
engaged in by the youth in the study area as a means of employment and income generation for 
an improvement of their livelihoods.  
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 The major socio-economic characteristics that will influence the youth’s decision, should they 
decide to engage in agripreneurship opportunities in the study area are sex, marital status, previous 
experience, education, employment status, awareness of agripreneurship, farm size, and being a 
member of Farmers Based Organizations (FBOs).  
 Low demand for agricultural output, poor transportation system, inadequate storage facilities, low 
level of agripreneurship education, high inputs prices and lack of financial support were the 
constraints found facing agripreneurs in the study area. However, the respondents ranked lack of 
financial support, high farm inputs prices, and inadequate storage facilities as the most pressing 
constraints while poor transportation systems and low demand for agricultural produce were 
ranked as the least constraints. Thus, given the agripreneurship opportunities such as livestock 
production enterprise, speculative agripreneurship business, non-traditional agricultural activities 
and the significant socio-economic characteristics, males with high levels of education who are 
aware, more experienced and belong to Farmers Based Organization are more likely to decide to 
engage in agripreneurship opportunities than their counterpart. In general, the majority of the 
youth were willing to engage in the agripreneurship opportunities, in particular if they are 
provided with financial support and subsidies on farm inputs.  
5. 4 Policy Recommendations  
Based on the conclusions presented above, the following recommendations are put forward for policy 
considerations.  
 Agricultural programmes that aimed at providing employment and income generation to enhance 
the livelihoods of the youth living in the study area should target on livestock production 
enterprises, speculative agripreneurship business, non-traditional agriculture or nursery of tree 
crops as they were found significant.  
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 Education, awareness creation, and female participation are key components that need to be 
considered by the policymakers to stimulate the adoption of agripreneurship as avenues of 
employment and income generation. This could be achieved through designing a sensitization 
programmes such as workshops and trainings that include all the youth and the stakeholders to 
increase the awareness level and educate the youth on the benefits derived from participating in 
agripreneurship ventures. This is necessary because it was found that currently, 35 percent of the 
total respondents sampled for the study constituting 77 were into primary businesses unrelated to 
agriculture. Of this, 20 (9.1 percent) was found to engage in illegal mining activities, popularly 
known as ‘galamsey’, 46 (20.9 percent) were into a motorcycle and 11 (5.0 percent) were into 
tricycle riding. As indicated by some directors of Business Advisory Centres, their main focus is 
on how to get ‘quick money’ thus neglecting agriculture. 
 There should be a wakeup call on the financial institutions, NGOs and corporate bodies to devise 
mechanisms that facilitate easy accessibility of financial support for the youth who venture into 
agripreneurship projects. Government should continue to provide subsidies on farm inputs (seeds, 
fertilizers, weedicides, pesticides, etc.) and extend them to reach the youth in BND. This will 
reduce the burden of purchasing inputs at high prices by the youth and increase their participation 
in agripreneurship opportunities.     
 
  
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APPENDICES 
Appendix i: Questionnaires  
 
EXAMINING YOUTH ENGAGEMENT IN AGRIPRENEURSHIP OPPORTUNITIES AS A 
MEANS OF SUSTAINABLE LIVELIHOODS IN MINING COMMUNITIES IN THE BIRIM 
NORTH DISTRICT OF GHANA 
My name is Emmanuel Osei Opoku a graduate student at the above-mentioned university carrying a study 
on the topic: Examining Youth Engagement in Agripreneurship Opportunities as a means of Sustainable 
Livelihoods in Mining Communities in the Birim North District of Ghana with the specific objectives; 
1. What are the agripreneurship opportunities in the study area are sustainable that can be used by 
the youth as sources of employment and income generation? 
2. What socio-economic characteristics influence the decision of the youth to engage in 
agripreneurship opportunities? 
3. What are the constraints do agripreneurs in the study area face? 
It is an academic exercise and all the information given shall be used solely for this purpose. I would 
therefore be very grateful if you could provide the necessary answers to the questions stated below. All 
information provided would be treated anonymous and kept confidential. Thank you.  
You may contact me on 0204564849 for any enquiries.  
 
 
 
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BACKGROUND INFORMATION   
Name of the community ………………………………….…… Date ….... …../ …...…. / …...….                
 Please tick the appropriate box for the options provided for each question and specify where 
necessary. 
SOCIO-ECONOMIC CHARACTERISTICS 
1. What is your age (in completed years)? 1. 25 – 30  [    ] 2. 31 – 35 [    ] 3. Others (Please specify) 
............................................................................................................................................................ 
2. The sex of the respondent?  1. Male [     ]  0. Female [     ]    
3. What is your marital status? 1. Single [      ] 2. Married [      ] 3. Divorced [       ]  
4.  Widowed [     ]  5. Separated [      ]  
4. How many children do you have? 0 None [     ]  1 [     ] 2 [    ] 3 [    ] 4 [   ] 5 [   ] Above 5 [    ] 
5. What is your household size? 1. 1 – 3 [    ]  2. 4 – 6  [     ] 3. Above 6 [     ] 
6. What is your residential status? 0. Non – native [     ] 1. Native [      ] 
7. What is your highest level of formal education? 0. None [      ] 1.  Primary/Basic school [     ]   
2. JSS/JHS [     ] 3. SSS/SHS [     ] 4. Technical/vocational [     ] 5. Undergraduate [      ] 
6. First degree [   ] 7. Post graduate diploma [    ] 8. Masters degree [   ] 9. Doctorate degree [    ]   
8. What is your employment status? 0. Unemployed [      ]  1.Employed full-time [     ]  
2. Contract [       ]  3. Part time [     ]   4. Self-employed [      ] 
9. What is your major occupation? 1. Farming  [     ] 2. Trading [      ] 3. Teaching [    ]  
4. Miner [       ] 5. Civil servant [    ] 6. Others (Please specify) ……….……..…………… 
…………………………………………………………………………........……………. 
10. What is the range of your annual net income status? 1. Below GHS1,000 [   ] 2. GHS1,000 – 2,000 
[    ] 3.  GHS 2,100 – 3,000 [      ] 4. GHS 3,1000 – 5,000  [     ] 5. Others (Please specify) ………. 
 
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FARM AND MARKET – LEVEL CHARACTERISTICS 
11. What is the range of your farm size? (measured in acres) 1. Less than 1 [   ]  2. 1 – 5 [  ] 3. 6-10 [  ] 
4. 11 – 15 [    ] 5. Above 15 [    ] 
12. What type of land tenure system do you practice? 1. Freehold [     ] 2. Leasehold [     ]  
3. Customary [     ] 4. Government [     ] 
13. What type of farming system do you practice? 1. Mixed Farming [     ] 2. Mixed Cropping [      ]  
3. Crop Rotation [     ] 4.  Others (Please specify) …………….............................…...…… 
…………………………………………....................……………………………………… 
14. What major food crops do you normally produce?  1. Maize  [     ] 2. Rice [      ]  
3. Cassava [    ] 4. Yam [    ] 5. Plantain [     ] 6. Others (Please specify) ……................… 
……………………………………………………………................……….…….……… 
15. Could you please give reason for your selected crops …………………............………… 
………………………………………………………………................………..………… 
16. Do you produce them commercially, subsistence or on both basis?  
1. Yes, commercial only [      ] 2. Yes, subsistence [   ] 3. Both commercial and subsistence [    ]  
4. None of them [     ] 
17. How many distances (in kilometres) do you cover before reaching your farm?  
1Km [      ] 2Km [      ] 3Km [     ] Above 3km [    ] 
18. In terms of sales, do you produce them for sale with minor consumptions?  
1. Yes [      ] 2. No [     ]   3. Others (Please specify) …………............……………………… 
………………………………………………………...…..................………………….……. 
19. Do you own or take care of any of these tree crops; cocoa, oil palm or citrus? 1. Yes [   ] 2. No [    ] 
20. If yes select the one applicable to you 1. Cocoa only [     ] 2. Oil palm only [    ] 3. Citrus only [   ] 
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4. Cocoa and palm oil [     ]  5. Oil palm and citrus [    ] 6. Cocoa and citrus [    ] 7. All the three 
crops 
21. If yes, how many acres?  Please specify ………………………………………………… 
22. Where do you sell your agricultural produce?  1. Market [    ] 2. Farm gate [      ]  
3. Shop [      ] 4.Road side [     ] 5. Others (Please specify)……………....……………...  
COMMUNITY – LEVEL CHARACTERISTICS  
23. How long have you stayed in this community? 1. 1 – 10  [    ]  2. 11 – 20 [     ]  
3. 21 – 30 [    ] 4. Above 30 [    ] 
24. Do you belong to any farmer association or organization in this community?  
1. Yes [     ] 0.  No [     ]  
25. Do you have market in this community? 1. Yes [     ] 0. No [      ] 
26. What is the status of road networks in your community? 1. Good [    ] 2.  Very good [     ]  
3. Moderately good [     ] 4. Excellent [     ] 5. Poor [      ] 6. [      ] Very poor 
27. What is the status of communication networks in your community? 1. Good [      ]  
2. Very good [   ] 3. Moderately good [    ] 4. Excellent [    ] 5. Poor [   ] 6. Very poor [    ]  
28. Under what medium do community members make announcement? Through; 1. Community 
information centre [    ] 2. The uses of horns [       ] 3. House-to-house [    ] 
29. Do you have social centre in your community for gathering purposes? 1. Yes [    ] 0. No[    ] 
30.  What is the main economic activity in your community? 1. Farming [    ] 2. Trading [    ]  
3.  Mining [  ] 4. Others (specify) ……………………….....................…………………….. 
THE YOUTH DECISIONS TO ENGAGE IN AGRIPRENEURSHIP DIMENSIONS 
31. Are you aware of what agripreneurship is about?  1. Yes, I am aware [   ]  0. No, I am not aware [ ] 
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32. If yes, please indicate the source of your awareness? Through; 1. Extension agent [     ] 2. Farmers’ 
based association [      ] 3. Seminar/Training/Workshop [      ] 4. Media [     ] 5. Others (Please 
specify) ................................................................................................................................................. 
33. Are you currently engaged in any agripreneurship activity? 1. Yes [      ] 0. No [      ] 
34. If yes indicate the type of agripreneurship activity you are into (You may tick more than one) 1.  
Crop cultivation [    ] 2. Livestock [   ] 3. Service/Trading [   ] 4. Crop and Livestock [    ]  5. Crop 
and service/trade  [    ] 6. Livestock and Service/trade [    ]  7. Others (Please specify) 
…......................................................................................................................................................…. 
35. If crops, indicate the main type of crop grown ………...........................………….................……… 
36. Indicate the number of the crops enterprises ………………......................................……….……… 
37. If animals, indicate the main type of animal reared ……………......................……………...……… 
38. Indicate the number of the animal enterprises ……………………............….……….…………...… 
39. If services, indicate the main type of service you offer …………...........................………………… 
40. If trade, indicate the main type of trade ............................................................................................... 
41. Others (Please specify) …………………………….....................................................................…... 
42. Could you please specify the range of monthly income (in Ghana cedis) earned from each of them? 
………………………………......................……………......………………………...……………… 
43. If no to 36, please mention your current occupation/job:……………...………..…...........………….  
44. Could you please explain or give reasons why you changed your occupation? ..................................  
......................….....…......……….……………..………………..................................................…
…… ……………………………...….………………………………………...............….………   
45. Have you ever decided or planned to engage in any agriculture venture to the best of your 
knowledge? 1. Yes [     ]  0. No [     ] 
46. If yes, what venture? …………………………………….......………………………………………. 
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47. If no, please give reasons …………………………............…………………………………………. 
.......................................................................................................................................................... 
AGRIPRENEURSHIP AND LIVELIHOODS DIMENSION 
48. Would you say that agripreneurship activity has enhanced your livelihoods as a youth in this 
community?    1. Yes [     ]    0.   No [     ]  
49. Do you have access to financial support when required? 0. No [     ]  1. Yes [     ]  
50. If yes from which source? 1. Formal [     ]    2. Informal [      ] 3. Both [      ] 
51. If formal, which of the following is the main source? 1. Commercial bank [    ] 2. Rural bank [    ] 
3. Microfinance [    ] 4. Others (Please specify)……………....…………………………………... 
52. If no, which of the following is/are preventing you from getting access to financial support? 
53. 1. Interest rate is high [      ] 2. Cumbersome procedures [     ]  3. Do not have collateral [     ]  
4. Do not have account [     ]  
54. How do you perceive farm inputs prices at market? 1. Low [     ] 2. Average [   ] 3. High [    ] 
55. What are some of your expectations from the mining company, the government, NGOs and other 
institutions with respect to sustainable livelihoods and opportunities? Please specify ….........…….. 
56. On a scale of 1 to 4, 1 being not significant, 2 less significant, 3 significant and 4 being the most 
significant, how would you rank the following livelihoods activities in the table below? 
Livelihood activities Not Less Significant Most 
significant significant significant 
1. Farming activities (Planting,     
Weeding, Harvesting) 
2. Food crops production (Maize,     
Cassava, Plantain) 
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3. Trading/Seeds/Fertilizers/Agrochemi     
-cal/Processed products/Food stuff 
4. Ownership of tree crops (Cocoa, Oil     
palm, Citrus 
5. Horticulture/Vegetable/Fruits     
production 
6. Veterinary services     
7. Artisans/Mechanic/Carpenter/Welder     
8. Nursery     
9. Animal production enterprises     
(Poultry farm, Ruminant, Non-
ruminant production) 
10. Ownership of asset (Processing,     
Packaging and storage devices 
11. Non-traditional agriculture (Honey,     
Mushroom, Snail, Grasscutter 
production) 
 
CONSTRAINTS – DIMENSION   
57. Please rank the following constraints in order of significance. 1 being the least significant to 3 
being the most significant. 
 
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CONSTRAINTS RANK 
 
1. Low level of education/training in entrepreneurship  
 
2. Lack of financial support   
 
3. Inadequate market for output  
 
4. Poor transportation system    
 
5. High prices of farm inputs   
 
6. Information asymmetry  
 
7. Others (Please specify)   
 
 
  
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Appendix ii: Test for Multicollinearity 
 
Scatterplot Test for Multicollinearity  
 
 
 
 
 
 
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VIF Test for Multicollinearity  
 Unstandardized Standardize  95.0% C. I. for B Collinearity Statistics 
Coeff. d Coeff.  
 
B Std. Beta t Sig. Lower Upper Tolerance VIF 
Error Bound Bound 
(Constant) 303 .174  1.740 .083 -.040 .646   
AGE .068 .038 .115 1.778 .077 -.007 .143 .775 1.291 
SEX .098  .039 .173 2.486 .014 .020 .175 .676 1.479 
MARITAL .060 .023 .218 2.628 .009 .015 .105 .472 2.118 
STATUS 
HOUSEHOLD -.043 .037 -.089 -.1.182 .239 -116 .029 .574 1.742 
SIZE 
EXPERIENCE -.002 .018 -.006 -.093 .926 -.036 .033 .908 1.101 
EDUCATION .042 .014 .208 3.060 .003 .015 .070 .704 1.420 
LEVEL 
EMPLOYMENT .063 .017 .247 3.678 .000 .029 .097 .721 1.387 
STATUS 
AWARENESS .072 .038 .112 1.908 .058 -.002 .146 .951 1.051 
FARM SIZE .120 .038 .218 3.196 .002 .046 .195 .704 1.421 
FBOs .027 .035 .045 .758 .449 -.042 .096 .930 1.075 
FINANCIAL -.030 .019 -.152 -.1.595 .112 -.066 .007 .359 2.785 
SUPPORT 
FARM INPUT -.032 .016 -171 -2.032 .043 -.063 .001 .460 2.172 
COST 
 
a. Dependent variable: The decision of the youth to engage in agripreneurship opportunities 
 
 
 
 
 
 
 
 
 
 
 
 
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Appendix iii: Output Files  
The Binary Logistic Regression Analysis 
Variables in the Equation 
 B S.E. Wald df Sig. Exp(B) 95% C.I.for EXP(B) 
Lower Upper 
AGE -2.676 1.678 2.543 1 .111 .069 .003 1.846 
23928717.73 12569316613980
GENDER 16.991 6.720 6.392 1 .011 45.554 
6 .383 
MASTATUS 2.891 1.486 3.784 1 .052 18.008 .978 331.422 
HHS -3.813 3.136 1.479 1 .224 .022 .000 10.303 
AGRIEXP 2.736 1.348 4.121 1 .042 15.429 1.099 216.611 
a EDU 3.983 1.705 5.453 1 .020 53.663 1.897 1518.386 Step 1  
EMPLOY 8.744 3.431 6.497 1 .011 6275.597 7.541 5222442.111 
AWAREAGRIP 4.901 2.677 3.352 1 .067 134.361 .708 25511.563 
135699983651.2
FARMSIZE 14.432 5.715 6.376 1 .012 1852263.994 25.283 
35 
FBO 6.842 3.349 4.174 1 .041 936.472 1.321 664125.713 
-   
Constant 23.616 5.972 1 .015 .000 
57.709 
a. Variable(s) entered on step 1: AGE, GENDER, MASTATUS, HHS, AGRIEXP, EDU, EMPLOY, AWAREAGRIP, 
FARMSIZE, FBO. 
 
                 Model Summary 
Step -2 Log Cox & Snell R Nagelkerke R 
likelihood Square Square 
1 19.268a .394 .886 
a. Estimation terminated at iteration number 12 
because parameter estimates changed by less than 
.001. 
  
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The Binary Logistic Regression Analysis when Lack of Financial Support and High Input Prices were 
Included in the Model  
Variables in the Equation 
 B S.E. Wald df Sig. Exp(B) 95% C.I.for 
EXP(B) 
Lower Upper 
AGE -199.804 1864.008 .011 1 .915 .000 .000 . 
SEX 308.378 3089.936 .010 1 .921 8.447E+133 .000 . 
MASTATUS 153.635 1394.817 .012 1 .912 5.281E+066 .000 . 
HHS 114.367 1436.615 .006 1 .937 4.666E+049 .000 . 
AGRIEXP 87.591 808.388 .012 1 .914 1.097E+038 .000 . 
1004648956163446
EDU 43.754 477.768 .008 1 .927 .000 . 
6000.000 
EMPLOY 445.979 4480.753 .010 1 .921 4.855E+193 .000 . 
Step 1a 
AWAREAGRIP 85.444 1225.083 .005 1 .944 1.282E+037 .000 . 
FARMSIZE 576.916 5692.592 .010 1 .919 3.561E+250 .000 . 
FBO 87.552 888.792 .010 1 .922 1.055E+038 .000 . 
LFINANSUPPORT -222.752 2249.620 .010 1 .921 .000 .000 . 
5038693034183488
FAMINPUTP 66.090 1197.501 .003 1 .956 0000000000000.00 .000 . 
0 
Constant -1687.164 17045.228 .010 1 .921 .000   
 
a. Variable(s) entered on step 1: AGE, SEX, MASTATUS, HHS, AGRIEXP, EDU, EMPLOY, AWAREAGRIP, FARMSIZE, 
FBO, FINANCESUP, FAMINPUTP. 
 
Model Summary 
Step -2 Log Cox & Snell R Nagelkerke R 
likelihood Square Square 
1 .000a .445 1.000 
a. Estimation terminated at iteration number 20 
because maximum iterations has been reached. Final 
solution cannot be found. 
 
 
  
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The Binary Logistic Regression Analysis when Lack of Financial Support was Dropped from the 
Model and High Input Price Included  
Variables in the Equation 
 B S.E. Wald df Sig. Exp(B) 95% C.I. Exp(B) 
Lower Upper 
 AGE -2.281 1.722 1.754 1 .815 .102 .003 2.988 
 SEX 15.498 7.046 4.838 1 .028 5377100.986 5.410 5344014885384.726 
Step 1a MASTATUS 3.428 1.931 3.152 1 .076 30.805 .700 1355.500 
 HHS -4.231 3.311 1.633 1 .201 .015 .000 9.559 
 AGRIEXP 3.176 1.726 3.386 1 .066 23.943 .813 704.936 
 EDU 3.827 1.754 4.759 1 .029 45.934 1.475 1430.653 
 EMPLOY 9.316 3.891 5.733 1 .017 11111.442 5.420 22780931.647 
 AWAREAGRIP 5.413 3.098 3.053 1 .081 224.380 .517 97346.578 
 FARMSIZE 15.525 6.673 5.414 1 .020 5527920.475 11.554 2644680304413.799 
 FBO 7.285 3.516 4.292 1 .038 1458.072 1.481 1435360.804 
 FAMINPUTC .720 1.332 .293 1 .588 .487 .036 6.616 
 Constant -59.391 25.798 5.300 1 .021 .000   
a. Variable(s) entered on step 1: AGE, SEX, MASTATUS, HHS, AGRIEXP, EDU, EMPLOY, 
AWAREAGRIP, FARMSIZE, FBO, FAMINPUTC. 
Model Summary 
Step  -2 Log likelihood Cox & Snell R Nagelkerke R 
Square Square 
1 18.996a .395 .887 
a. Estimation terminated at iteration number 12 because 
parameter estimates changed by less than .001. 
  
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The Binary Logistic Regression Analysis when Lack of Financial Support was Included in the 
Model and High Input Cost Dropped 
 Variables in the Equation 
 B S.E. Wald df Sig. Exp(B) 95% C.I. Exp(B) 
Lower Upper 
 AGE -233.449 1771.843 .017 1 .895 .000 .000 . 
 SEX 315.671 2314.302 .019 1 .892 1.243E+137 .000 . 
Step 1a MASTATUS 189.776 1384.279 .019 1 .891 2.622E+082 .000 . 
 HHS 106.090 868.036 .015 1 .903 1.187E+046 .000 . 
 AGRIEXP 105.461 776.094 .018 1 .892 6.326E+04577025 .000 . 
9363529872 
 EDU 64.211 495.605 .017 1 .897 9000000000000.0 .000 . 
0 
 EMPLOY 566.464 4829.427 .014 1 .907 1.028E+246 .000 . 
 AWAREAGRIP 126.270 1001.840 .016 1 .900 6.891E+054 .000 . 
 FARMSIZE 768.060 7007.101 .012 1 .913 . .000 . 
 FBO 127.600 989.495 .017 1 .897 2.606E+055 .000 . 
 LFINANSUPPORT -189.692 1370.866 .019 1 .890 .000 .000 . 
 Constant -2221.000 19516.180 .013 1 909 .000   
a. Variable(s) entered on step 1: AGE, SEX, MASTATUS, HHS, AGRIEXP, EDU, EMPLOY, 
AWAREAGRIP, FARMSIZE, FBO, LFINANSUPPORT . 
Model Summary 
Step  -2 Log likelihood Cox & Snell R Nagelkerke R 
Square Square 
1 .000a .445 1.000 
a. Estimation terminated at iteration number 20 because 
maximum iteration has been reached. Final solution cannot be 
found. 
 
 
 
 
 
 
 
 
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The Kendall's W Test 
 Ranks 
 Mean Rank 
Lack of financial support 2.07 
High prices of farm inputs 2.48 
Inadequate storage facilities 3.71 
Low level of education 4.20 
Poor transportation 4.15 
Inadequate demand for 
4.40 
output 
 
Test Statistics 
N 190 
Kendall's Wa .315 
Chi-Square 298.787 
df 5 
Asymp. Sig. .000 
a. Kendall's Coefficient of 
Concordance 
 
 
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