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UWAOMA, IJEOMA GRACE
PG/Ph.D/06/42153
ECONOMICS OF SMALL SCALE SOYBEAN PROCESSING FIRMS IN ANAMBRA STATE, NIGERIA
FACULTY OF AGRICULTURE SCIENCE
DEPARTMENT OF AGRICULTURAL ECONOMICS
Paul Okeke
Digitally Signed by: Content manager’s Name DN : CN = Webmaster’s name O= University of Nigeria, Nsukka OU = Innovation Centre
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TITLE PAGE
ECONOMICS OF SMALL SCALE SOYBEAN PROCESSING FIRMS IN ANAMBRA STATE, NIGERIA
BY
UWAOMA, IJEOMA GRACE PG/Ph.D/06/42153
A THESIS SUBMITTED TO THE DEPARTMENT OF AGRICULTURAL
ECONOMICS, UNIVERSITY OF NIGERIA, NSUKKA IN PARTIAL
FULFILLMENT OF THE REQUIREMENTS FOR THE AWARD OF
DOCTOR OF PHILOSOPHY DEGREE IN AGRICULTURAL
ECONOMICS
SUPERVISORS: PROF. E. C. OKORJI
PROF. E. C. NWAGBO
JANUARY 2015
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CERTIFICATION
UWAOMA, Grace Ijeoma a postgraduate student in the Department of Agricultural
Economics, with registration number PG/Ph.D/06/42153 has satisfactorily completed the
requirements for research work for the award of the degree of Doctor of Philosophy
(Ph.D) in Agricultural Economics. The work embodied in this thesis, except where duly
acknowledged, is the product of the student's original work and has not been previously
published in part or full for any other diploma or degree of this or any other University.
___________________________ ________________ UWAOMA, GRACE IJEOMA DATE (STUDENT) _____________________________ ________________ PROF. E. C. OKORJI DATE (SUPERVISOR)
_____________________________ ________________ PROF. E. C. NWAGBO DATE (SUPERVISOR)
_____________________________ ________________ PROF. S.A.N.D CHIDEBELU DATE (HEAD OF DEPARTMENT) _____________________________ ________________ EXTERNAL EXAMINER DATE
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DEDICATION
This work is dedicated to God the Father, Son and Holy Ghost, and to the lovely memory
of my late brother, Chinedu Collins Akanihu.
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ACKNOWLEDGEMENT
Words may not be enough to acknowledge and appreciate my supervisors, Prof. E. C.
Okorji and Late Prof. E. C. Nwagbo who provided great insight and guide for this work.
Their interest, commitment and support was paramount to the success recorded in the
study. They read through the work critically at each stage and their input went a long way
in shaping this work. Thank you very much for your patience and for allowing me tap
from your wealth of knowledge and experience. I further thank Prof. E. C. Okorji for his
fatherly advice and encouragement which enabled me move on with this work.
My thanks and appreciation goes to my lecturers who laid the theoretical foundation upon
which this work was carried out. They included Profs. Emea Arua (HRH), S.A.N.D.
Chidebelu, E. C. Okorji, N. J.Nweze (KSP), C. J. Arene, C. U. Okoye and A. I. Achike
(Mrs.). Others are Drs. A. E. Enete, N. A. Chukwuone, F. U. Agbo, B.C Okpukpara, E.
C. Amaechina and P. I. Opata. My special thanks goes to Mr. P. I. Njepuome and my
friends Mrs. Rosemary Arua, Mrs. Chinasa Onyenekwe, Mrs. Chinwoke Ike, Mrs. E.E.
Romain, Ifeanyi Ugwu and Blessing Onyishi.
I will ever remain grateful to my loving husband Sir I. O. Uwaoma for his moral support
and encouragement. He was always there for me and our children. I salute my loving
children who were always assisting me with internet information. They are Engr.
Franklin, Dr. Henry, Amaka, Kosy and Somto Uwaoma. May God continue to bless them
and keep them for us. I say thank you to my mother Mrs. F. Akanihu and my Late Father
Mr. L. C. Akanihu who never ceased prayinf for me.Thanks to my siblings - Doris,
Vivian, Charles - Bongo, Emeka, Ikenna, Chijioke, their spouses and my mother-in-law
(Mrs. R. O. Uwaoma) for their encouragement. My supervisor's wife Prof. Ij Okorji and
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her children (UC, Ada and Obiora) who spiced up my life during the period of this work.
There was never a dull moment with them. I thank them for their moral support and
encouragement, and pray that my good God will bless them.
The support I got from my friends and roommates helped to encourage me. I am therefore
grateful to Oti, G. O., Ibe Justina, Mfon Basil, Chukwudi Chinemerem, Mrs. A. Okpala,
Chidimma Abakwo, Akudo Osuji, Chinyere Ugwu, Ngozi, Pat Ofordum and Nsionu
Chidimma. This study would not have been completed without the grace and strength
supplied by God Almighty (My Loving Father). To Him be all Honour, Glory and Praise,
in Jesus name, Amen!
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ABSTRACT
The study investigated the economics of small scale soybean processing firms in Anambra State, Nigeria using primary data. It examined the technologies used for small scale soybean processing; the socioeconomic and institutional factors that influenced the choice of technologies used; the technical efficiency of small scale soybean processing firms; the value added by processing soybean; the profitability, factors that affected profitability; the constraints to small scale soybean processing firms; and the level of gender participation in small scale soybean processing. Using well-structured questionnaire, multistage random sampling technique was adopted to select 150 soymilk processing firms and 100 soyflour processing firms from three(3) agricultural zones (Aguata, Awka and Onitsha) in the state. However, only 142 and 95 respondents from soymilk and soyflour processing firms were used in the analyses. Data were analysed using descriptive and inferential statistics, such as multinomial logit model, stochastic frontier production function, gross margin and profit function analysis, analysis of variance and t-test. The study found that the locally fabricated machine was the most predominant technology used for soybean processing and accounted for about 30% in soymilk and 44% in soyflour. Other technologies used included 45TG x 160-Grain, 45TG x160-Galvanized, 45TG x160-Japan, 45TG x 160-Stainless and 60GX x 175- Galvanized. Age, income, level of education and household size of the processor, cost of processing technology, age of the processing firm, availability of spare parts, technicians, household employees and paid employees were the factors that significantly affected choice of processing technology at P<0.05. The results further showed that soybean processing into soyflour was technically efficient at 95% while that of soymilk was inefficient at 44%. Also, the value added to 1kg of soybean processed into soymilk was N680 while that of soyflour was N440. Soybean processed into soymilk and soyflour were profitable. The average gross margin per processor per annum for soymilk was N596,111.41 while that of soyflour was N450,737.76. Results further indicated that there were significant differences between the costs and returns to soybean processing into soymilk and soyflour, respectively at P<0.01. Cost of soybean seeds, cost of fuel, transportation and packaging were the factors that significantly affected the profitability of soybean processing at P<0.05. Lack of capital, lack of credit facilities, inadequate power and water supplies, high and multiple taxation and high cost of spare parts were the factors that the processors perceived to be greatly constraining soybean processing at an average rating of 3.29 for soymilk and 3.35 for soyflour on a 4-point Likert rating scale. The study found that females participated predominantly in the buying of soybean seeds (82%), washing and boiling of soybean (80%), winnowing/dehusking (90%), packaging (93%) and sales (87%); while the males played predominant role in shelling (75%) and grinding/milling (92%). Further analysis showed that there was significant difference in the participation of men and women in soybean processing into soyflour and soymilk at P<0.01. Provision of credit facilities, granting of tax incentives and provision of adequate power and water supply to soybean processors were some of the policy recommendations proffered.
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TABLE OF CONTENTS
Title Page i
Certification ii
Dedication iii
Acknowledgement iv
Abstract vi
Table of Contents vii
List of Tables ix
List of Figures x
1.0 Chapter One Introduction
1.1 Background Information 1
1.2 Problem Statement 5
1.3 Objectives of the Study 8
1.4 Research Hypotheses 8
1.5 Justification of the Study 9
1.6 limitation of the study 10
Chapter Two Review of Related Literature
2.1 Description of Soybean and its Origin in Nigeria 12
2.2 Importance of Soybean 13
2.3 Processing 16
2.3.1 Meaning of processing/General Overview of Processing 16
2.3.2 Handling Stages in processing 18
2.3.3 Products of Soybean Processing 18
2.4 Agro-Processing Activities 26
2.5 Gender Issues in Processing 27
2.6 Value Adding Process in Agri-Business 28
2.7 Small Scale Industries 29
2.7.1 Role of Small-Scale Industries in Developing Countries 31
2.7.2 Technology Use in Small Scale Industries 32
2.7.3 Constraints of Small Scale Industries 34
2.7.4 Sources of funding for small scale industries 34
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2.8 Theoretical Framework 35
2.8.1 Value Chain in Agricultural Processing 36
2.8.2 Scale of Production 38
2.9 Analytical Framework 39
2.9.1 The Stochastic Frontier Production Function 39
2.9.2 Profitability Analysis 42
2.9.3 Multinomial Logit Model 44
2.9.4 Likert Scale 45
3.0 Chapter Three Research Methodology
3.1 The Study Area 46
3.2 Sampling Technique 47
3.3 Method of Data Collection 49
3.4 Analytical Technique 49
4.0 Chapter Four Results and Discussion
4.1 Socioeconomic Characteristics of Soybean Processors 54
4.2 Technologies Being Used for Small Scale Soybean Processing 58
4.3 Determinants of Choice of Soybean Processing Technology 59
4.4 Technical Efficiency of Small Scale Soybean Processing Firms 62
4.5 Value Added to Soybean Processing into Soymilk and Soyflour 68
4.6 Profitability of Small Scale Soybean Processing into Soymilk and Soyflour 69
4.7 Perception of Processors on Constraints to Soybean Processing 74
4.8 Gender Participation in Small Scale Soybean Processing 76
5.0 Chapter Five Summary, Conclusion and Recommendations
5.1 Summary 78
5.2 Conclusion 80
5.3 Recommendations 80
5.4 Contribution to Knowledge 81
References 82
Appendices 92
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LIST OF TABLES
2.1 Analysis of Soyflours 21
4.1 Socioeconomic Characteristics of Soybean Processors 57
4.2 Multinomial Logit Result on Factors Affecting Choice of
Soybean Processing Technology 61
4.3 Maximum Likelihood Estimates of the Stochastic production
Frontier Function in Soyflour Processing 64
4.4 Frequency Distribution of Technical Efficiencies of Soyflour Processors 65
4.5 Maximum Likelihood Estimates of the Stochastic Production Frontier Function in Soymilk Processing 67
4.6 Frequency Distribution of Technical Efficiencies of Soymilk Processors 68
4.7 Value Added by processing Soybean into Soymilk 69
4.8 Value Added by processing Soybean into Soyflour 69
4.9 Gross Margin of Soybean Processing into Soymilk and Soyflour 70
4.10 Regression Result on the Factors Affecting the Profitability of Soybean Processing into Soymilk 73
4.11 Regression Result on the Factors Affecting the Profitability of Soybean Processing into Soyflour 74
4.12 Perception of Processors on Constraints to Soybean Processing into Soymilk 75
4.13 Perception of Processors on Constraints to Soybean Processing into Soyflour 76
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LIST OF FIGURES
2.1 Flow Diagrams for Processing Soybean into Full Fat Flour 20
2.2 Flow Diagram for Processing Soymilk 26
2.3 The Value Chain Model of Michael E. Poter 36
2.4 Critical Dimension of a Value Chain 38
3.1 Map of Anambra State Showing Sampled Areas 48
4.1 Technologies Used for Small Scale Soybean Processing 59
4.2 Cost and Returns of Soybean Processing into Soymilk/Annum 71
4.3 Cost and Returns of Soybean Processing into Soyflour/Annum 72
4.4 Gender Participation in Small Scale Soybean Processing Firms 77
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CHAPTER ONE
INTRODUCTION
1.1 Background Information
Industrialization in Nigeria like most developing countries is very crucial for rapid
economic and social development. The higher the level of industrialization the higher the
value of Gross Domestic Product (GDP). It is the main hope of most developing countries
to increase their per capita income. This can be achieved fast through the expansion of
small scale industries such as soybean processing firms (Sadiq, 2004). Generally, small-
scale firms form the bedrock of any nation’s industrial take-off especially in a developing
country like Nigeria (CBN, 2002). According to the Federal Government Small Scale
Industry Development Plan of 1980 small scale industry refers to any manufacturing
process or service industry, with a capital not exceeding N150, 000 in manufacturing and
equipment. Also, the Small Scale Industries Association of Nigeria in 1973, defined
small scale business as those having investment (i.e. capital, land, building and
equipment) of up to N60, 000 (Pre-SAP value) and employing not more than fifty
persons. (Umar, 2010)
The development of small-scale industries is imperative prerequisite for
sustaining a well- balanced industrial sector. In the absence of active and vital small firm
sectors, the economy would decay (Elsenhans, 2008). It is needful then to develop greater
interest in improving the small scale industries such as the small-scale soybean
processing firms.
Small scale industries are considered engine for economic growth all over the
world. They represent the largest proportion of the manufacturing sector in every country,
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and have always played a key role in the economies of all major industrial countries
(Singh, Garg and Deshmukh, 2008). In Nigeria, the small and medium enterprises,
(SMEs) account for about 70% percent of industrial employment and well over 50
percent of the Gross Domestic Product (Olutunla and Obamuyi, 2008). Among the
objectives of the industrial sector stipulated by the Nigerian government is local sourcing
of raw material to promote greater linkage and backward integration to raise the general
level of economic activities. The local sourcing of raw material will be achieved through
increased use of raw materials from agriculture (Igene, 2008). According to Federal
Ministry of Commerce and Industry, the low performance of Nigeria’s industrial sector
was as a result of over-reliance on large-scale capital intensive industries and inadequate
attention to the development of small-scale industries (Manyong, Ikpi and Olayemi,
2005).
The major tool through which government can enhance employment generation in
Nigeria is the promotion of small scale industries. In line with this, the Federal
Government of Nigeria set up a coordinating organization called Small-Scale Industrial
Corporation in 1971. The corporation accorded high priority to industries engaged in
manufacturing basic needs including food processing and other agro industries.
Furthermore, there was a renewed emphasis on the development of small-scale
agribusiness activities. Agribusiness activities involve individuals and institutions
engaged in the production, processing, transportation, storage, financing, marketing and
regulation of the world’s food and fiber products (Ike, 1999). Agro industries which form
part of agribusiness activities are generally enterprises that process agricultural raw
materials ranging from clearing, grading, cooking, mixing and chemical addition
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(Akwaeze, 2009). It is estimated that 60% of the labour force in sub-Saharan Africa are
gainfully employed in small-scale food processing enterprises and majority are women
(ITDG, 2005).
Foods are processed to improve their digestibility and to enhance their appeal to
the consumer. Processing also serves to extend the availability of foods beyond the area
and season of production, thus stabilizing supplies and increasing food security at
national and household levels (FAO, 2001). A particular important aspect of food
processing is that it permits great diet diversity, giving consumers access to a wide choice
of products and hence to a better range of vitamins and minerals than they would
otherwise consume. The most basic level of processing is food preservation, which in a
variety of forms has been practiced by families in traditional societies for generations to
provide food when sources of fresh food are scarce (FAO, 2001).The soybean processing
firms as well as other agro-firms contribute significantly to a nation’s economic
development; also they provide the nation with nutrients critical to the well-being of an
expanding population.
To attain good health in Nigeria, the importance of protein in the daily meal of
every citizen cannot be overlooked. The Food and Agriculture Organization (FAO)
stipulated that every individual is expected to consume 71gram of protein every day
(FAO, 2009). A cheap protein source is then a step forward towards promoting good
health (USDA, 2000). Animal protein sources which include fish, beef, mutton, pork,
chevon etc. are very expensive and in most cases beyond the reach of average Nigerian
household. The tendency is to fallback to plant protein which is relatively cheap and got
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from cowpea, pigeon pea, bambara groundnut, soybean and so on, with soybean having
the highest percent of protein.
Soybean (Glycine max) is a legume that grows in tropical, subtropical and
temperate climates. It has 40 chromosomes and is self-fertile species with less than one
percent out crossing. Soybean was introduced to Africa in the 19th century by Chinese
traders along the east coast of Africa (IITA, 2007).
Soybean is an important source of high quality but inexpensive protein and oil.
According to International Institute of Tropical Agriculture (IITA, 2007), it has average
protein content of 40% and oil content of 20%. It is the only plant source that contains all
the Essential Amino Acid (EAA) (Tiwo, 2004). The oil produced from soybean is highly
digestible and contains no cholesterol. A “by-product” from the oil production (soybean
cake) is used as a high protein animal feed in many countries. Soybean also improves soil
fertility by adding nitrogen from the atmosphere. This is a major benefit in African
farming systems, where soils have become exhausted by the need to produce more food
for increasing populations and where fertilizers are hardly available and are expensive for
farmers.
Soybean consumption according to IITA (2003), has increased dramatically,
improving nutrition particularly among the urban, poor and middle income groups.
Soybean fortified products not only have more protein and minerals than their non-
fortified counterparts, they are considerably cheaper than other sources of high-protein
such as fish, meats, milk and other protein-rich legumes. The cost of protein when
purchased as soybeans, is only about 10-20% of the protein from fish, meat, eggs or milk.
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Many Nigerians now incorporate soybean into their diets and the Nigerian Government
has declared its production and utilization a national priority (IITA, 2003).
Soybean is processed into various forms such as soymilk, soyflour, soy meat, soy
spice, tofu and so on. The International Development Research Center (IDRC) has
sponsored projects which have been instrumental to encouraging the development of
more than forty soybean-based foods including soymilk, yogurt, soyflour, biscuits, baby
food, condiments and breakfast cereals (IDRC, 2006). These products are highly
patronized because they are inexpensive, have acceptable tastes and some are
conveniently sold where people congregate. They have become major sources of the daily
protein intake of children and adults (Okoli, 1998).
In Nigeria, so many households have started eating soybean foods (CGIAR,
2007). A study by IITA carried out in Nigeria showed that the nutritional status of
children is significantly better in soybean producing/using households than in those
households that did not use soybean. The study also provided evidence that soybean
processing had a positive impact on the producer’s income (IITA, 2006).
The thinking of many concerned Nigerians today is how the generality of the poor masses
can be empowered to be self-reliant. The government and some non-governmental bodies
have been grappling with some strategies put in place to combat poverty, so as to reduce
it to the barest minimum (Fayam, 2004).
The problems of mass poverty arising from the production and consumption
pattern of Nigerians need to be addressed. National Economic Empowerment and
Development Strategies (NEEDS), which is a programme of the Federal Government of
Nigeria, spelt out in clear terms the need to assist farmers in provision of agricultural
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inputs in order to tackle poverty head on, since half of Nigerian’s poor people work in
that sector (Onwualu, 2007). Onwualu (2007), reported that, there is need therefore to
support small and medium-scale enterprises in the agricultural sector, to help generate
employment and create wealth and by so doing alleviate poverty in the land.
1.2 Problem Statement
Soybean though still regarded as a relatively new crop, has made a successful
incursion into the diet of many Nigerians, particularly children and nursing mothers.
Soybean derivatives such as Soymilk, Soyflour, Soy-Ogi and so on, have been developed
and found to be highly nutritious and good substitutes for more conventional food
ingredients like melon, cow milk and cowpea (Osho, 2003). Despite the high nutritional
value of soybean in relation to other legumes, lack of knowledge of its uses has limited its
adoption, production and processing in non-traditional areas of cultivation (Osho,
Akinleye and Akanni, 2009). To bridge the gap, efforts are being made by research
institutes, Non-Governmental Organizations (NGOs) and industries to promote the
production, processing and utilization of soybean in Nigeria (Osho, 2003).
Vast resources in Nigeria (and other developing economies) are either unutilized
or underutilized. A major section of its manpower is lying idle. Capital is scarce and
investment is lean. Production is traditional and the technique outdated. The output is
insufficient and basic needs of people remain unfulfilled (Kalchi, 2008). Industrialization
through developing small scale firms is the answer to this present state of the economy. A
major challenge for small scale firms is to continuously provide innovative and
customized products using the best available process technologies.
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The small scale sector occupies an important position in the economy of
developing nations. The sector employs the largest manpower next to agriculture. Its
growth largely depend on its ability to innovate, improve operational efficiency and
increase productivity. Small scale firms are generally less efficient in process and utility
energy use. There is the problem of lack of technical capacity in these firms to identify,
access, adapt and adopt better technologies and operating practices (Ashok, 2000).
Inadequate information about the improved technologies is one of the problems in
agricultural production (Fabiyi, Danladi and Mahmood, 2007). Leisinger (2000), asserts
that it is not conceivable that agriculture in developing countries, can deliver the
expected without modern technologies. Research emphasis has been on appropriate
technology for increasing food availability. However, enough research has not been
conducted to study the various technologies employed by existing processors,
profitability of such technologies and how they influence quality and quantity of the
processed product.
Over the past two decades, issues relating to the recognition of women’s role in
economic and social development and of equality between men and women have fostered
increasing interest among policy makers and development practitioners. Despite a
noticeable improvement in gender awareness worldwide, data on women’s work and
economic contribution have remained far from being comprehensive (Odebode, 2011).
Their economic role have been undervalued, underestimated and seldom acknowledged
for proper articulation in development plans and policy information (Odebode, 2011).
However, the influence of gender on processing activities has not been properly analyzed
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to assess the involvement of men and women in soybean processing activities as it affects
the study area.
Research works on soybean have been carried out on; soybean production,
Egbujie (1996); Utilization, Ugwuoke (2005) and Yahaya (1991); Kokoiwen (2002),
focused on soybeans as protein supplement for nursing mothers. Research on the
economics of processing of soybean in the study area has been neglected. It is therefore
hoped that this study will fill up the gap. The study will look at the edible forms soybean
can be processed into by small-scale firms, costs involved in processing, returns made
and access to financial services by the processing firms.
1.3 Objectives of the Study
The broad objective of this study was to examine the economics of small-scale
soybean processing firms in Anambra State, Nigeria.
The specific objectives were to:
i. identify the technologies being used for small scale soybean processing;
ii. describe the socio-economic and institutional factors that influenced the choice of
technology used for small scale soybean processing;
iii. determine the technical efficiency of small scale soybean processing firms;
iv. examine the value added by processing soybean into soymilk and soyflour
respectively;
v. assess the profitability of small scale soybean processing firms and the factors that
affect it;
vi. identify the constraints of small scale soybean processing firms; and
vii. assess the level of gender participation in small scale soybean processing.
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1.4 Research Hypotheses
In line with the stated objectives of the study, the following null hypotheses were
tested:
H01: Socioeconomic characteristics do not significantly affect choice of
technology used for small scale soybean processing.
H02: There are no significant difference between the costs and returns to soybean
processing.
H03: There is no significant difference in the level of gender participation in small
scale soybean processing.
1.5 Justification of the Study
In a world influenced by globalization, diversification is strength and a resource.
People have to fight poverty and to further social and financial equality (Ms Danis
Association for International Co-operation, 2006). Small scale soybean processing
industry is of interest as an enterprise that will increase income and employment in rural
and urban areas (Bachman, 2001).
The increasing importance of soybean as food crop in Nigeria calls for effort to
increase its enterprise. The study on the economics of small-scale soybean processing
firms, will give insight to the reason behind the choice of processing techniques used by
soybean processors, it will provide information on the returns made and costs accrued to
the processors. The study will also identify the problems encountered by these
processors.
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The Federal Government of Nigeria will benefit from the study since increased
utilization of soybeans will help to have a balance between the diet of the people and
their protein intake. IDRC (2001) reported that soybean consumption has been
acknowledged as the major diet for increasing the protein requirement of the urban poor
and middle income group. This will reduce susceptibility to malnutrition thereby
promoting good health for members of the society. Good health will in turn increase the
work force or human capital of the nation for a productive economy for sustainable
development.
Hopefully, findings of the study will assist both processors and government by
providing information that will be used in increasing income and welfare of the populace,
consequently enhancing agricultural productivity.
In the face of increased threats on agricultural sustainability and productivity as
well as lack of alternative employment opportunities, there is need for policies on
agricultural development, poverty alleviation and livelihood improvement. This study
will help the policy makers in these areas.
The study will be of immense benefit to prospective investors in soybean
processing industry, policy makers and the government, especially as a reference
material in its interest in developing agro industries. As such research scholars interested
in this and related topics will find the work very useful.
1.6 Limitations
Soybean is processed into different forms, but this study is limited to processing
soybean into soymilk and soyflour only. The researcher found it difficult to get
11
information on the socio economic characteristics and revenue of some respondents. With
some persuasions and explanations that, they were purely for an academic exercise and
she was not asking to harm them or learn their techniques; they opened up and answered
the questions. However, despite these limitations, the goals for the research were
achieved.
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CHAPTER TWO
REVIEW OF RELATED LITERATURE
Literature relevant to this study were reviewed under the following headings;
2.1 Description of soybean and its origin in Nigeria
2.2 Importance of soybean
2.3 Processing
2.4 Agro-Processing activities
2.5 Gender issues in processing
2.6 Value adding process in Agri-business
2.7 Small scale industries
2.8 Theoretical framework
2.9 Analytical framework
2.1 Description of soybean and its origin in Nigeria
Soybean (Glycine max L.) is an annual herbaceous legume plant of the pea family
Leguminosae and subfamily Papilionnidea (Pampluna and Roger, 2004; Zeki, 1997;
Encyclopaedia Britannica, 2006). Soybean was first introduced to Nigeria in 1908.
Attempts to grow the crop at Moor plantation, Ibadan at that time failed. Later,
introduction of the crop to the savanna ecology (Samaru and Yandev) in 1928 proved
successful. It then spread into other parts of northern Nigeria, and soon became a cash
crop in the Tiv division of Benue Province (now Benue State), which thereafter became
the leading production centre. Most of these crops were exported as cash crop to Europe,
with just a little amount fed locally to animals. A small portion was however used as food
in the northern states (Iwe, 2003).
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Nigeria is the largest producer of soybean for food in the west and central of
Africa (Iwe, 2003). It ranked third in the 1982-84 period only to Egypt and Zimbabwe.
The production figures for the three west, north and South African countries were 67000t,
90000t and 160000t, respectively (Kolavillis, Williams and Kauffman, 1987).
2.2 Importance of soybean
Soybean for several reasons is a valuable and economical agricultural commodity.
It has favourable agronomic characteristics including good adaptability to wide range of
soil and climate and ability to improve soil fertility by fixing atmospheric nitrogen
through the root nodules and also through leaf fall on the ground at maturity. This makes
it a good rotational crop for use with high nitrogen-consuming crops such as maize and
rice (Iwe, 2003). This is a major benefit in African farming system, where soils have
become exhausted by the need to produce more food, for increasing populations and
where fertilizers are not available or are too expensive for farmers to buy (CGIAR, 2007).
Soybean has several domestic and industrial uses. The domestic utilization
account for about 25 percent of the total production (Omotayo, Olowe, Babajide, and
Ojo, 2007). It assumes an important position as a world crop because of its high quality
protein content and rich oil, and because of its multiple uses of all other legume crops
(Dashiel, 2008). The crop has a unique chemical composition of an average dry matter
basis; it contains about 40% protein and 20% oil. With this, it ranks highest in terms of
protein content (both in quality and quantity), among all food crops and second highest
oil content among all food legumes. The oil is highest in terms of quality. It contains a
high proportion of unsaturated fatty acids such as linolenic and lineic acids hence; it is
14
healthful oil (Iwe, 2003). Soy protein contains all the essential amino acids most of which
are present in amounts that closely match those required from humans or animals. 1kg of
soybean contains as much protein as, 2kg of boneless meat or 45cups of cow’ milk or 5
dozens of eggs (Dashiel, 2008). It has a protein-digestibility corrected amino acid score,
very close to 1, the highest rating possible and the same rating for animal proteins such as
egg white and casein (Liu, 2000).
There are numerous uses of soy protein into human food. It is used to supplement
animal protein products at a lower cost per unit of protein. For example, isolated soy
proteins can be used in combination with meat, fish or milk to produce processed
products like sausages, canned meat, and so on. Soybean is used to fortify cereal products
such as bread, cookies, sandwich spread, etc. (Naik and Gleason, 2010; Schwarz and
Allwood, 2007).
Soybean is used for making high protein food for children. It is also used to
fortify local foods so as to increase the protein content/quality of such foods. This
includes mixing soybean with maize flour, cassava flour, wheat flour etc, to make fufu.
There are drink mixes with soybean to boast energy as well as supply protein. Soybean
contains a good amount of minerals, salt and vitamins (Naik and Gleason, 2010).
Soybean is eaten by man in various forms which include mature whole beans, or
immature green soybean cooked as vegetable. It can be processed into soyflour, soymilk,
soy yoghurt, etc. With more than 80% of all black Africans having lactose intolerance
(which makes it difficult for them to digest dairy milk easily), soymilk can fill the milk
gap (Shurtlef and Aoyagi, 2007).
15
Soybean is used as fodder to feed animals. This forage can be made into hay or
silage. Soybean cake is also an excellent nutritive food for livestock and poultry.
Soybean are used as raw materials for industrial products such as oil, soap, cream,
inks, crayons, plastics, textiles, bio-diesels, etc. It has been found effective as an insect
repellant in some studies (Mc Graw-Hill Encyclopedia of Science and Technology,
2005).
Soybean has dietary and therapeutic indications. Medical personnel therefore use
and or recommend it because of its healing properties (Pampluna and Roger, 2004). They
attest that:
a. Eating soy and its derivatives help women maintain hormonal balance because of
its isoflavones (vegetable estrogens). The benefits derived are as follows:
regulation of the menstrual cycle in pre-menopausal women, relief from the
symptoms of menopause, reduced breast and uterus cancer risk.
b. Men who regularly eat, soybean enjoy the following: lower risk of prostate
cancer, lower risk of heart attack.
c. Soy products reduce the urinary loss of calcium and increase mineralization and
bone density, and so, prevents osteoporosis.
d. Regular consumption of soy and its derivatives reduces total blood cholesterol
level.
e. The effect of soy make the arteries becomes less rigid and narrow thereby taking
care of arteriosclerosis, and consequently reducing heart attack and its resultant
stroke.
16
The National Cancer Institute of the United States, according to Pampluna et al
(2004), is dedicating a great deal of attention to the anti-cancinogeni effects of soy and its
derivation, that daily consumption of soy products reduce the risk of breast, colon, rectal,
stomach, prostrate and lungs cancers.
The dramatic increases in soy food sales is largely credited to the Food and Drug
Administration’s (FDA) approval of health claims for soy as regards their cholesterol
lowering ability (University of Kentucky in New England Journal of Medicine). Old
Chinese herbal institution suggests that, soybean was a specific remedy for proper
functioning of the liver, kidney, heart and stomach (Duke, 2000).
Soybeans although highly nutritious and possesses extraordinary healing
properties, does have some drawbacks, it has very low content of pro-Vitamin A and
vitamin C, and lacks vitamin B12. Consumption should therefore be accompanied by fresh
fruits and vegetable. Like all raw legumes, soybean, contains toxic substances known as
anti-nutritive factors. Fortunately, soy’s anti-nutritive factors disappear partially or
completely when it is soaked in water and cooked, fermented, industrially processed or
allowed to sprout (Pamplune and Roger, 2004).
2.3 Processing
2.3.1 Meaning of Processing/General Overview of Processing
Processing means to treat raw materials and foods in other to change and preserve
it (Oxford Advance Learner’s Dictionary, 2000). Village-based processing include basic
transformation activities such as milling, as well as processing of products for which
there is a potential market, Such processing which can be done on an individual or group
17
basis provides employment for millions of rural people and is often one of the sources of
income for rural women. The preparation of gari ( a dried fermented cassava product),
and smoking of fish are examples of common s processes which transform highly
perishable commodities into products that can be transported long distances and stored
(FAO, 2000).
FAO (2000), asserted that, agro-industries convert commodities into processed
foods which are usually more stable and more marketable than the raw, untreated
commodities. They can thus make available certain types of food e.g. animal and soy
protein, often at low prices to consumers who would otherwise not have access to them.
They can also ensure all year-round availability of seasonal, perishable products and
provide food in a more convenient form than the raw material. Where urban populations
require processed foods in large quantities, mechanized processes with high output
capacities are generally efficient and economic. Widely dispersed populations on the
other hand may be better served by smaller-scale technologies. Food processing
industries may be concentrated in urban centers or spread among rural communities
where they offer the twin advantages of processing perishable crops and animal products
close to their source and providing income for rural people.
The processing industry plays a major role in the provision of adequate nutrition
for the teeming populace due to its unique position in meeting the daily food need of
different cadres of people in the society living in urban and semi-urban areas and in
villages (Ogunsumi, 2007).
18
2.3.2 Handling Stages in Processing
Primary processing according to FAO (2010), refers to the immediate post-harvest
handling activities. For cereals and legume grains, such activities include drying,
threshing or shelling. Such operations reduce the fiber content and may extend the
storage life of the foodstuff.
Secondary processing or transformation usually involves some alteration in the
form of the foodstuff to facilitate its subsequent use. Cereal and legume grains may be
cleaned, graded, tempered or parboiled, dehulled and polished or split into halves. Tubers
may be peeled, sliced and sun dried. Many grains are ground, pounded or milled and
sieved to give various grades of meal or flour (FAO, 2010).
Tertiary processing involves the conversion of uncooked materials into products
and food for human consumption. The processing may take place at a commercial level
as in the extrusion cooking of cereal-legumes mixes or production of commercial
weaning foods, or at the domestic level in the preparation of family meals (FAO, 2010).
2.3.3 Products from Soybean Processing
The soybean is so versatile that it can be processed into a wide variety of food
products. Processing into these forms (food products) is done to enhance consumption
(Liu, 2000). Processing is necessary due to some reasons which include to:
• remove the beany flavour in whole beans.
• make it tasteful (palatable) to meet consumers taste.
• remove the trypsin inhibitor which is harmful to man.
19
The first processing steps for the production of soybean products are: cleaning
(i.e. removing foreign materials, splits and damaged beans), and dehulling of the beans to
remove the cotyledon (Oyekunle, 2004).
Dehulling is done either by dry or wet method depending on the form (food) it is
to be processed into (INTSOY- University of Illinois, 2008).
Dry method involves the following steps:
i. cleaning whole soybean by removing damaged grains dirt and stones.
ii. heat the beans in oven or under the hot sun or place the bean with little
amount of corn meal in a pot/saucepan and heat on a stove (the corn meal is
used to prevent the beans from being over heated). Heating on stove requires
regular stirring.
iii. use a stone mill or milling machine to split the beans and remove the hulls.
iv. winnow the hulls from the cotyledons.
The wet method involves the following steps:
i. drop the cleaned beans into boiling water. Simmer for 25 – 30 minutes.
ii. drain the blanch water and rinse the blanched beans well. Keep the beans in a
bowl with cold water. Scrub the beans between two hands to force the hulls
from the cotyledons. Drain the water with the hulls and repeat operation until
most of the hulls are removed from the cotyledons.
iii. these cotyledons can be directly used for preparing many soy foods. If not
used immediately, they must be dried.
20
2.3.3.1 Soyflours
Soyflour and grits are the simplest of all edible soybean protein products (Iwe,
2003). The extent of processing which goes into their production starts thus: pick out
unwanted materials, wash properly, and drain the water. Boil for 25 – 30 minutes at
boiling point, or soak overnight and boil slightly after. Drain water then add cold water,
remove the cotyledon (dehusk) while still warm. Wash again with clean water, sundry or
oven dry (sun drying will give you the milk colour). Mill in a milling machine or any
other grinding device available. Pack in airtight container (it can last upward of 6months
without decaying) (ADP, 2008).
An alternative method involves, soaking the cleaned beans in water, drain and
boil for about 25 minutes, drain the water, air dry or fry in a saucepan or pot, crack by
milling, winnow to separate the hulls, and then ground/mill in a milling machine. Pack in
air-tight containers. The Flow diagram for processing soybean into full fat flour is shown
in figure 2.1.
Soybean Soybean
Cleaning Cleaning
Wash Soak in water
Boil for 25 or 30 minutes or Boil for 25 minutes
Soak overnight and warm slightly Air-dry or fry in pot
Drain Shelling
Allow to cool a little in cold water Winnowing
Dehusk while still warm Grinding/milling
Wash with clean water Full fat flour
(pack in air tight container)
Sun-dry or oven-dry
Grinding/milling
Full fat flour (pack in container)
Figure 2.1: Flow diagrams for processing soybean into full fat flour Source: ADP (2008).
21
Processing of soyflour for large-scale production involves the use of
expensive oil milling machines. It consists principally of three steps (dehulling, heat
treatment and milling) which are all done by the machine.
Soyflour or grits according to Iwe (2003); Zeki (1997) are classified according to
their lipid contents into the following:
a. full fat soyflour: made from unextracted dehulled beans; contains
about 18 -20% oil.
b. defatted soyflour: obtained from solvent extracted flakes and contains
less than 1% oil.
c. Low fat soyflour: made by adding back some oil to defatted soyflour.
Lipid content varies according to specific actions, usually between 4.5
– 9%. The most common range is between 5 – 6%.
d. High fat soyflour: produced by adding back soybean oil to defatted
flour, usually at the level of 15%.
Typical analysis of the classes of soyflours is shown in table 2.1.
Table 2.1: Analysis of soyflours
Material (%) Protein (%) Moisture (%) Fat(%) Fiber(%) Ash
Soybean 42.6 11.0 20.0 5.3 5.0
Full fat soyflour 46.6 5.0 22.1 2.1 5.2
Defatted soyflour 59.0 7.0 0.9 2.6 6.4
Source: Iwe (2003).
22
There are few differences however between soyflour and soygrits. These include:
Soyflour: refers to products obtained by finely grinding full fat dehulled soybeans
or defatted flakes made from dehulled soybeans. To be regarded as soyflour, at least 79%
of the product must pass through a standard sieve of 100 mesh (FAO, 2002; Iwe, 2003).
Soy grits: almost of same composition as the flour, but of coarser granulation.
They are usually classified into three groups according to particle size: coarse – 10 to 20
mesh; medium – 20 to 40 mesh; fine – 40 to 80 mesh (FAO, 2002; Iwe, 2003). This work
would however be on full fat soyflour.
2.3.3.2 Soymilk
Soymilk is a water extract of whole soybean (FAO, 2002). It is milk-like product
obtained from soybeans (Oyekunle, 2004).
The traditional method of soymilk preparation follows this sequence: clean
soybeans, soak beans in water for 12 – 24 hours at room temperature (the water will be 3
– 4 times the dry soybean weight). Water is changed frequently. Remove cotyledon then
grind the beans into fine paste. Water is added to the mass during grinding at a ratio of
1:3. The slurry is boiled to foaming for about one hour with continuous stirring. Filter
through cheese cloth to remove fibrous materials (called okra). Collect the liquid portion
and store in a container. The milk stores for a few hours under ambient conditions and a
few days in the refrigerator (Iwe, 1991; Carison, 2007).
The traditional soymilk has a distinctive beany and chalky mouth feel flavour
which is unacceptable to most consumers (FAO, 2002; Iwe, 2003). Food Technologists
have made considerable improvement on this extraction process. Such improvements
23
include homogenization, pasteurization, careful control over temperatures and use of
additional ingredients (Carison, 2007).
Several approaches have been adopted to overcome the problem of off flavours in
soymilk. The INTSOY home processing method is an improvement on the traditional
processing method.
The University of Illionis at Urbana developed the INTSOY process which was
reported to be free of off flavour, and had excellent suspension stability (Carison, 2007).
The INTSOY processing steps include the following: soak the cleaned bean in boiling
water containing a small amount of sodium bicarbonate (baking soda – 0.5% of the water
by weight). This softens the beans and help get rid of oligosaccharides. Drain the water,
add the partially blanched beans to fresh boiling water containing sodium bicarbonate and
cook for five additional minutes. Drain the water and grind the beans along with boiling
water, to make slurry. Stir the slurry well and filter with cheese cloth. Squeeze out as
much milk as possible. Simmer the filtrate (milk) for 29 minutes. The filtrate is
homogenized (pumped through a homogenizer) and pasteurized. Add salt, sugar and
flavouring as desired and pour into containers/bottled.
The concentration of solids easily can be adjusted according to the final use of the
soymilk (National Soybean Research laboratory (NSRL), 2010; FAO, 2002). The
INTSOY process has been designed for small-scale plants. Another process known as the
soy technology system (STS) process is a complete technology package for large scale
production (INTSOY-University of Illinois, 2008; FAO, 2002).
Heat treatment is a very important step in soymilk processing. It is necessary to
hydrate thoroughly, heat the raw soybean before grinding into slurry, to prevent
24
development of typical beany flavour. It also destroys the anti-nutritional trypsin
inhibitor. However, boiling times longer than recommended will reduce the amount of
protein in the final product (INTSOY-University of Illinois, 2008). Some critical
variables in the processing of soymilk may include length of heating whole beans or
generally the handling of whole beans prior to milk extraction, amount of water used in
soaking and length of soaking, amount of water added to soy-slurry or flour and the
extent to which the resulting emulsion is handled or heat treated. Manipulation of those
variables leads to obtaining soymilk with varied characteristics (Iwe, 2003).
The fact that soymilk when properly formulated, closely resembles cow milk
make it an attractive alternative to conventional milk (Iwe, 1991; Ahmed, 2009). Both
milks have approximately the same protein content (3.5 – 4%) and their amino acid
profile show a very close relationship. The difference is that soymilk lacks sulphur
containing amino acid like methionine. As an advantage, it does not contain lactose,
which constitutes a problem (lactose intolerance) in human infants consuming cow milk.
It is less costly to produce and therefore offers an attractive alternative to cow milk for
hundreds of millions of people in the developing countries. More importantly, soymilk is
completely free from cholesterol – a fat component implication in cardiovascular disease
such as hypertension.
Following these advantages, soymilk has been recommended by physicians for
years to patients who are allergic to cow milk; to those who have suffered or are at high
risk of degenerative heart diseases who need milk with unsaturated fat as a replacement
for dairy milk (Pampluna and Roger, 2004; Ahmed, 2009).
25
There are varieties of soymilk products like plain soymilk, flavoured soymilk and
fortified soymilk (calcium enriched). Some ways to use soymilk are:
• Soymilk served as nutritious and refreshing drinks. It is served hot or cold. It can
be taken plain or add sweeteners or other flavours.
• Soymilk may be used in cooking to make creamy sauces or soup.
• Soymilk is used as the liquid in baking breads, cakes, etc.
• Soymilk is the base for the preparation of many other products such as Tofu, soy
yoghurt, soy cheese (INTSOY-University of Illinois, 2008).
The solid residue that is left after extracting soymilk is called okara. For each cup
of dry soybean used to make soymilk, you will get a little less than two cups of okara.
The okara contains high quality protein and fiber and can be used in many different
recipes. It should be used the same day the soymilk is made or refrigerated or frozen for
later use (INTSOY-University of Illinois, 2008).
Ways of using okara include the following:
i. add okara to soaps, stew or sauce as thickener.
ii. mix okara with ground meat before preparing meatballs, sausages or burgers.
iii. mix okara with cheese and chopped vegetables and seasonings to make a
spread for bread.
iv. stir a little okotiara into porridge and weaning foods.
The flow diagram for processing soybean into soy milk, is shown in figure 2.2.
26
Figure. 2.2: Flow diagram for processing soymilk Source: FAO, 2002.
2.4 Agro-Processing Activities
Agro-processing is defined by Igene (2008), as the process or actions taken by
manufacturers, entrepreneurs, farmers, individuals or groups to convert primary (raw)
agricultural products into consumable and stable commodities for the market. Processing
involves cutting, milling, molding, fermenting, blending, etc.
Almost all the food, fiber, feed and fuel commodities undergo series of post-
harvest operations which include clearing, grading, separation, drying, storage, milling,
food processing, packaging, transport and marketing. Agricultural processing is thus
directed towards conservation of produce and value-adding to make the material more
readily usable and economically more remunerative (Ebene, 2008).
Agro-processing projects aim to increase income and access to food for the poor
through the establishment of small-scale appropriate and sustainable processing firms that
are flexible, require little capital investment and can be carried out in the home without
Filtered
Processing of soymilk
Whole soybean
Soaked and grind with water
Soybean slurry
Heated at 95-1000C
Soymilk
27
the need for sophisticated or expensive equipment. It is also the basis for a range of
productive industries’ processed, value-added products (Igene, 2008).
Food processing according to him empowers women who are most often involved
in agro-processing. About 60% of women he asserts form the labour force of small-scale
food processing in Africa. It enhances food security, improvement of health, and help
overcome seasonality and perish-ability constraints.
2.5 Gender Issues in Processing
Gender is the culturally specific set of characteristics that identifies the social
behaviour of women and men and the relationship between them (Odebode, 2011).
Gender is not just the differentiation between male and female but, involves socially
ascribed roles, responsibilities and opportunities associated with women and men as well
as the hidden power structures that govern relationships between them (UNDP, 2005).
Gender however, does not refer to women alone because the activities of women can only
be understood fully in relation to the gender division of responsibilities in the household,
communities or nation (Oyewole, 2002).
Sex role differences in the agricultural household resource has begun to receive
increased attention on a theoretical level (Adegbe, 2000). She asserts that, women play a
vital role in agro-processing in developing economies (including Nigeria). Their efforts
however, have been largely taken for granted, their needs ignored and their works remain
statically invisible. Women play important roles in food processing and preservation
(Kolawole, Williams and Awujola, 2010).
28
Generally, with the rapid socio-economic growth now being experienced all over
the world, women are found to be playing significant roles wherever they are found. The
significant role which they play in eradication of malnutrition due to their role in
production as well as preparation of food consumed by their families, has been
recognized. They take part actively in farming activities and in processing farm products
in addition to their domestic and reproductive responsibilities (Fabiyi, Danladi and
Mahmood, 2007). The agricultural activities of women go beyond crop production to
other agricultural aspects like fisheries, poultry, sheep and goat rearing, processing, and
so on. Even women in seclusion (purdan) generate substantial income through food
processing (Yahaya, 2002).
Women in Africa (including Nigeria) generally play an important role in small-
scale traditional agricultural production. They are the principal labour force on small
firms and perform large share in processing and marketing of agricultural products
(Odebode, 2011). Today, it is widely accepted that women’s as well as men’s views and
the understanding of gender differences are important in helping science shape improved
technologies for agricultural development and to meet the needs and fit the circumstances
of small firm operators (Odurukwe, Mathews-Njoku and Okereke, 2006).
2.6 Value adding process in Agri-Business
The development of food industries has been based on concept of added value,
which foster processing of primary commodities that are traded locally and
internationally (FAO, 2000). The value added of any enterprise is the market price of the
goods and services produced less the cost of materials and services purchased from others
29
(Gittinger, 1984). It is the difference between gross output and the value of intermediate
consumption.
Black (2002), defined value adding as the total value of a firm’s output less the
value of inputs purchased from other firms. The value added then becomes what is left to
be shared between wages of the employees and profits for owners of the business.
Gittinger (1984), noted however, that there could be gross value added, in which case the
value of inputs is not subtracted, and net value added where deductions are made for
inputs including depreciation, labour and management among others. In this case value
added could be positive or negative. Brown and Touch (2009), explained that the
difference between cost of ingredients (including farm produce), and the post processing
price of the finished products is the value added through processing. This will be the
working definition for this research study.
In the modern economy, there is generally more value added in the processing,
manufacture and marketing parts of the business chain than in the raw material
production at the farmers’ level (Ministry of Agriculture, Animal Husbandary and Food
(MAAHF), 2010). The food processing sector occupies a relevant place in overall
turnover and value adding of products (UNIDO), 2005). The value chain development is
the market (Herr, 2007).
2.7 Small Scale Industries
A firm whether it is small or big, simple or complex, private or public, etc. is
established to provide goods and services at competitive prices. In Nigeria, business has
been classified as small, medium or large scale (Ayozie, 2006). Small scale business,
30
small scale industry and small scale entrepreneur are often used interchangeably to mean
a small scale firm. However, a small scale industry can be defined by the criteria of
project cost, invested capital, value of annual turnover by the employees and number of
paid employees.
In Nigeria and worldwide, there seem to be no specific definition of small scale
industry. Different authors, scholars, organizations and countries have different ideas as
to the differences in capital outlay, number of employees, sale turnover, fixed capital
investment, available plant and machinery, market share and the level of development.
The third National Development Plan of Nigeria, defined a small scale firm as a
manufacturing establishment employing less than ten people, or whose investment in
machinery and equipment does not exceed six hundred thousand naira. The Federal
ministry of Industries defined it as those enterprises that cost not more than N500, 000
including working capital to set up.
The Federal Government Small Scale Industry Development Plan of 1980 defined
a small scale industry in Nigeria as any manufacturing process or service industry, with a
capital not exceeding N150, 000 in manufacturing and equipment. The small scale
industries association of Nigeria, defined small scale business as those having investment
(i.e. capital, land, building and equipment) of up to Sixty thousand Naira (Pre-SAP value)
and employing not more than fifty persons.
The Centre for Management Development (CMD) in the policy proposal
submitted to the Federal Government in 1982 defined small scale industry as, “a
manufacturing process or servicing industry involved in a factory of production type of
operation, employing up to 50 full-time workers. In the United Kingdom, the committee
31
of inquiry on small firms (Bolton Committee) sees a small firm as one with a relatively
small share of its market. It is managed by its owner or part-owners in a personalized way
and not through the medium of a formalized management structure. It is independent in
that it does not form part of a large enterprise nor one of its members subject to outside
control when taking major decisions (FAO, 2009).
For the purpose of this study, the definition of small scale industry will be in line
with the Federal Government Small Scale Industry Development Plan of 1980 which
stipulates assets in capital equipment, plant and working capital not exceeding N150,000
with not more than 50 employees.
2.7.1 Role of Small-Scale Industries in a Developing Economy
Small scale industries have a lot of important contributions to make towards the
economic development of a country (Shoken, 2000). They are useful in creating
improvement in the economy (Aderanti, 2010).
Some of the importance of small scale industries according to Shoken (2000) and
Aderanti (2010) are as follows:
i. Provision of employment. Small scale industries have given jobs to a lot of
youths, retired workers and out of school graduates, thereby, reducing the
unemployment rate and its attendant social complication of armed robbery and
white collar crimes.
ii. It helps to bring about new goods and services and supply the needs of larger
industries. They have contributed immensely to the production of raw
materials in form of semi-processed goods for use by larger industries.
32
iii. It provides and introduces major source of new ideas and inventions which
contribute to the sustenance of the nation’s economy.
iv. They assist in increasing revenue being generated by the government through
payment of tax and other charges.
v. They promote the development of indigenous manpower/entrepreneurship as
well as increasing local participation in the manufacturing sector. This also
helps to reduce urban migration.
vi. The activities of small scale industries have resulted in the mobilization of the
resources (raw materials) of the community, environment thereby improving
on the standard of living of the population. This also helps in solving the
problem of importing raw materials and conserves our foreign reserve.
vii. It is a base for the development of appropriate technology and provides a
veritable ground for skilled, unskilled and semi-skilled workers.
Ayozie (2006) asserts that it has uplifted the dignity of labour and has upgraded
the social status of Nigerian youths, by showcasing them as very successful
entrepreneurs.
2.7.2 Technology use in Small Scale Industries
Industries in developing nations are characterized by small firms which, vary
drastically depending on whether the technologies they use are, traditional, non-
traditional (low technology or high technology (UNIDO, 2009). Traditional technology
means that labour processes are essentially manual and less dependent upon machine
power (Bhavani, 2002). In these developing economies, modern technology is in
competition with backward local technology the employment of modern technology
33
which provides increase in productivity (though initially limited to some sectors of
production) is hindered by the scarcity of appropriately skilled labour (Elsenhans, 2005).
The processing firms need to improve productivity through upgrading of its
technologies (Anyanwu, 2011). According to him, technology help to improve
productivity in some ways which include;
- providing better machinery that can reduce production time and costs
- better methods and process controls
- breakthrough into completely new ways of doing things, and
- product designs that can improve completive edge and reduce cost.
Elsenhans (2005) and Anyanwu (2011), opined that most machines now in use by
small scale firms are obsolete and the cost of maintaining them is high. They should be
replaced with modern ones that have better product designs and faster in processes. The
modernization of poor economies under the condition of the availability of modern
technologies (which in comparison to the performance cost of locally available
technologies as cheaper), allows for rapid increases in productivity (Elsenhans, 2005).
Good technology creates opportunities for product innovations. Product innovations
provide economies of scale and further incentives to mechanization and modernization
(Kamath, 2009). Propelled by technological development and capacity building, small
scale firms will flourish all over the nation. The proliferation of these firms will provide
many opportunities for entrepreneurship and employment, leading to a significant rise in
income for the people (Kamath, 2009).
34
2.7.3 Constraints of Small Scale Industries
The challenges facing small scale enterprises in many developing countries are
monumental. The most worrying among them is funding (Akogu, 2006). There is
inadequate capital to buy the stock and equipment (Ayozie, 2006). Most small scale firms
are not very attractive for banks as they want to minimize their risk profile. There is also
the use of obsolete production methods, and equipment as a means of maintaining stocks
and inventory. Most production ideas are things inherited from parents, and most of the
ideas die with the originators (Ayozie, 2006; Sadiq, 2004).
Small scale industries lack good quality control of their operations. In this respect,
they rely mainly on replacing faulty products instead of developing good quality control
system. They lack understanding and the application of marketing concept. They lack the
knowledge and skill of basic marketing ingredients – marketing research, market
segmentation and marketing planning and control. This can lead to poor quality products,
poor promotion, poor distribution, unawareness of competition and poor pricing methods
(Ayozie, 2006).
2.7.4 Sources of Funding for small scale industries
The available sources for funding small scale enterprises in Nigeria include the
Small and Medium Industries Equity Investment Scheme (SMIEIS), SME Manager
Limited (SML), Bank of Industry, the New Partnership for African Development
(NEPAD). Other sources are; personal savings, borrowing from spouses, friends,
informal financial institutions.
35
2.8 Theoretical Framework
The theoretical framework of this study was mainly on value addition (value
chain analysis) and scale of production concepts. The concept of value chain analysis is
based on the economic value of a product to the consumer. A value chain is a sequence of
target combinations of production factors that create a marketable product or service from
its conception to the final consumption (International Labour organization (ILO), 2008).
Value chain development is all about making the consumer at the end of the chain
satisfied. If enterprises cannot satisfy the needs (or requirements, preferences, desires) of
their buyers, the buyers will sooner or later turn to other suppliers (Herr, 2007). All
activities of a particular chain are directed towards the market. Value is added to a
product as it passes through different stages of activities in the value chain which
involves moving from producer to the ultimate consumer. Micheal Porter of Harvard
University sees the value chain as a tool for identifying ways to create more customer
satisfaction (Kotler and Keller, 2006). According to his model, every firm is a synthesis
of activities performed to design, produce, market and support its products (Kotler and
Keller, 2006). The goal is to maximize value creation. This includes minimizing cost and
maximizing profit. In Michael Porter’s value chain model shown in fig 2.3, he identified
nine activities that create value and cost in a specific business. The primary activities
include, the sequence of brining materials into the business (in-bound logistics),
converting them into products (operations), shipping out final products (out-bound
logistics), marketing them (marketing and sales), and servicing them (service); while the
supportive activities include, firm infrastructure, human resources management,
technology development and procurement.
36
2.8.1 Value Chain in Agricultural Processing
Value chain in agriculture refers to the fact that value is added to farm products
through the combination with other resources (example tools, manpower, knowledge,
skills and other raw materials). As the product passes through several stages of the value
chain, the value of the product increases (Herr, 2007). This includes activities such as
design, production, marketing distribution and support services up to the final consumer
(ILO, 2008). Value chain optimization is an efficient tool in the development of food
processing (Hagelaar, 1994).
The agricultural processing sector is increasingly characterized by more tightly
aligned value chains from genetics through producers and processors to end users and
consumers (Boehije, Hofing and Schroeder, 2000). The improvement of each of these
Firm infrastructure
Technology Development
Procurement
Inb
ound
lo
gis
tics
Operations Outbound logistics
Marketing & Sales
Service
Human resources management
Margin
Margin
Su
ppor
t se
rvic
es
Figure 2.3: The value chain model of Michael E Porter. (Kotler and Keller, 2006)
37
stages needs to be analyzed and monitored in relation to other links in the chain. This
calls for good cooperation between the different participants. The activities of a value
chain can be contained within a single firm, as well as within a single geographical
location or spread over wider areas (ILO 2008). To analyse value chain in agricultural
processing, Boehije and Lee (1999), noted that the pressures for chain formation appears
in three sequences: capturing efficiencies and controlling costs; reducing risk (quality,
quantity and food safety); and responding to consumer demands for attributes. This is
very necessary because it will make no economic sense to invest in processing a product
that there is no demand for.
Boehije and Lee (1999), noted that understanding the six critical dimensions of a
value chain will help the participants understand the system. The dimensions include the
following; (a) to explicitly specify the value creating activities in the production –
distribution process, and to provide an explicit structure for the linkages among these
activities or processes. This is the fundamental concept of a value chain. (b) the
specification of the product flow features of the chain (these features include
transportation, quality control attributes, full utilization of plant and equipment in all
stages, inventory management and logistics management), (c) the financial or cash flow
across the participants and processes, (d) information flow across the chain (accuracy,
strength and cost of messages and openness to shearing information, (e) available
incentives (f) the chain governance or coordination system.
38
A flow chart of the critical dimensions of a value chain is shown in fig 2.4
Fig 2.4: Critical dimensions of a value chain
Source: Boehije and Lee (1999)
2.8.2 Scale of Production
Scale of production refers to the amount of factors used, the quantities of products
produced and the techniques of production adopted by the producer (Jhingan, 1999).
Production may be carried out on a small scale or large scale by a firm. When a firm
operates by using less capital and small quantities of other factors of production, the scale
of production is said to be small. A firm using more capital and larger quantities of other
factors is operating on a large scale. The scale of production expands as production
increases with the increase in the quantities of land, labour and capital.
Inputs Production Processing Marketing
Product
Financial
Information
Incentive
Governance
39
Jhingan (1999) noted that an industry’s scale of production expands with the
increase in the number of firms in the industry, and/or with the increase in the size of the
firms in it. The scale of productions of a firm has great impact on its profitability. In
soybean processing, the types of technology and inputs used by the firms will determine
the quality and quantity of products (eg soymilk and soyflour). If the scale of production
increases, it results in the production of higher quality products (like soymilk and flour).
This would have higher value to the consumers and would therefore, attract higher prices
and ultimately increase the firms’ profitability.
2.9 Analytical Framework
The analytical framework of this study included; the Stochastic Frontier
Production Function (SFPF), Profitability analysis, the Multinomial Logit (MNL) model
and likert scale
2.9.1 The Stochastic Frontier Production Function
The production function is a technical relationship between resource (factor)
inputs and production (commodity) outputs. It describes the transformation of factor at
any particular time period (Arene, 2008; Koutsoyiannis, 2003). The stochastic frontier
model is an economic model that is widely used in the determination of efficiency
especially the technical efficiency of production techniques. The model was first
propounded by Ainger, Lovell and Schmit (1977), and Meeusen and Van den Broeck
(1977).
It is an improvement on the traditional production function using mathematical
programming to construct production frontiers. Applications of the technique in
40
efficiency analysis have been reported by Ojo and Ajibefun (2000); Ike and Inoni (2006);
Akinleye (2007); Al-hassan (2008); Sharma and Leung (2008); Otitoju and Arene (2010).
A stochastic Frontier Production is given as:
Yi = f (Xi; β). exp Vi – Ui i.e. i = 1, 2, …, N (2.1)
Where:
Yi = Observed output of the ith firm
Xi = Vectors of N inputs used by the ith firm
β = vectors of unknown parameters to be estimated
Vi = random variables (Shocks)
Ui = non negative random variables that are assumed to account for technical
inefficiency in production.
The Vi are independent of the Ui (Kumbhakar and Lovell, 2000). Equation (2.1) above
can also be approximated in a translog form (Amaefula, Onyewaku and Asumugha,
2009), as follows:
Yit = β0 + βyt + ½ βyyt2 + ∑ βjX jit + ½ ∑∑ βjkXkit + ∑ βjtX jitt + eit (2.2)
Where:
Y it is the logarithm of the observed output by the ith firm at point t, Xjit is the logarithm
of the quantity of the jth input used by the firm at period t,β is a vector of parameters to
be estimated. Symmetrically imposed, β ij= β kj and eit = vit – uit being a composite error
term. The vit and the uit are assumed to be independently distributed from each other. It is
further assumed that the average level of technical inefficiency measured by the mode of
truncated normal distribution (the ui) is a function of factors believed to affect technical
inefficiency as shown in equation (2.3)
41
Ui = δ0 + δiZi (2.3)
Where, Zi is a column vector of hypothesized efficient determinants (that is, the socio-
economic factors such as age, processing experience, etc) and δ0 is the intercept and δ1
are unknown parameters to be estimated. It is clear that if ui does not exist in equation
(2.1) or ui = δs =o, the stochastic frontier production reduces to a traditional function. In
that case, the observed units are equally efficient and residual output is solely explained
by unsystematic influences. The distribution parameters ui and δu2 are hence the
inefficiency indicators of the processors indicating the average level of technical
inefficiency level across observational units given functional and distributional
assumptions, the value of unknown coefficients in equation (2.1) and (2.3) (that is β i, δs
δu2 and δv
2 will be jointly estimated by the method of maximum likelihood estimation
(MLE) using the computer programme FRONTIER version 4.1 developed by Coelli
(1994). The technical efficiency of an individual processing firm is defined in terms of
the observed output (Yi) to the corresponding frontier output (Yi*) given available
technology that is,
TE = Yi/Yi*
f(Xi,βi) exp (vi – ui)
f(Xi,βi) exp (vi)
TE = Exp (-Ui) (2.4)
So that, .1≤≤ TEo An estimated value of technical efficiency for each observation can
be calculated as in equation (2.4). If TE = 1 the firm is said to be technically efficient and
its output level on the frontier otherwise, i.e if TE < 1, the firm is technically inefficient
42
because it could have produced more outputs with the given level of inputs irrespective of
input prices.
Using the MLE regression to analyze production function model of various
functional forms is preferred to the ordinary least squares (OLS), Seemingly unrelated
regression estimation (SURE) and many others. This is because the MLE chooses among
all possible estimates of the parameters, those values which make the probability of
obtaining the observed sample as large as possible (Koutsoyannis, 2003; Gujarati, 2004).
2.9.2 Profitability Analysis
To estimate the profitability of resource inputs in small-scale soybean processing
firms, the profit function is employed. The inputs include variable inputs (soybean seeds,
water, fire wood, kerosene, flavours, labour) and fixed inputs (Milling machine, cooking
equipment, winnowing equipment). The profit function is important in analysis reflecting
marginal resource profitability at mean level on input price.
In line with Olayide and Heady (1982), the linear profit function analytical model
is stated thus: Given a production function in which m variable inputs, Xi X2 … Xm; Z1,
Z2,.. Zn, are related to Y as follows,
Y = f (X1, X2, Xm; Z1, Z2 … Zn) (2.5)
The opportunity cost of fixed inputs is zero in the short run. The processor
therefore only needs to maximize the returns to variable inputs. That is the sales value of
output less the cost of variable inputs, called the variable cost. The resulting returns also
called the variable profit (π ), to variable inputs in respect to the production function
given in (2.5) above can be written as:
43
π = Pyf(X1, X2,..., X; Z1, Z2, …, Zn) -∑ PiXx (2.6)
Where, Py is the price of output and Pi is the output per unit price of the ith variable
inputs, 1 = 1, 2,… m.
For profit maximization of π in the short run, the first order partial derivatives
with respect to the variable inputs equated to zero are each taken. Hence the partial
derivative from (6) with respect to Xi, i = 1, 2 …..m equated to zero is given by
δy/δx1 = Pyfi = Pi (2.7)
Where fi shows the first order partial derivative with respect to the ith input. Since from
(5), f(X1, X2 …. Xm; Z1, Z2 …. Zn) is equal to Y, (2.7) can also be written as
Py δy/δx1 = Pi or δy/δx1 = Pi/Py, i= 1, 2 … m (2.8)
There will thus be m simultaneous equations in m unknowns, which can be solved to
obtain the optimum input quantities Xi*, 1 = 1, 2 ….. m, given by
X i* = (Py, P1, P2, …… Pm, Z1, Z2 …… Zn), i = 1, 2, … m (2.9)
Equation (2.9) gives the demand function for the ith variable input
Substituting the demand functions in (2.9) and (2.7) what is obtained is given as
π* = Pyf(X1*, X2
*, … Xm*; Z1, Z2, …, Zn) - ∑PiX i
* (2.10)
Where, xi* (i= 1, 2 ….. m) is the optimum quantity of the ith variable input and π *
corresponds to the amount of maximum variable profits. Obviously however, π * with a
harsh in (2.10) is express as a function of the price of inputs and the fixed inputs
quantities. Given that the alternative use of fixed input is zero in the short run, that is
profit horizon, the interest is on the analysis of variable inputs to be used in soymilk/flour
processing.
Thus π * = (Py, P1, P2 …. Pm; Z1, Z2…,Zn) (2.11)
44
2.9.3 Multinomial Logit Model
The multinomial Logit (MNL) model is used in studies involving multiple choices
(Apata, Ogunyinka, Sanusi and Ogunwande, 2010). It determines the effect of the
explanatory variables on a choice of a discrete set of options, for example, the choice of
using grinder or milling machine. This model will be used in identifying determinants of
processors choice of technology. The technique is widely used in studies involving
multiple choices compared to other alternative techniques such as the multinomial Probit
(MNP) model, because it is easier to compute (Apata et al, 2010). The MNL model for
choices of technology is specified in equation (2.12).
Pr (Ai = j) = exp(Xiβi)
∑jk = 0 exp(Xiβi) (i = 0, 1, …, J) (2.12)
Where:
A i = random variable representing the processing technology chosen.
X = explanatory variables such as socio economic and institutional factors.
The MNL model provides a convenient closed form for underlying choice
probabilities, without having multivariate integration, making it simple to compute choice
situations characterized by many alternatives. The major limitation of this model is the
independence or irrelevant alternatives (IIA) property, which states that the ratios of the
probabilities of choosing any two alternatives remain the same irrespective of the number
of alternatives available (Hausman and Mcfadden, 1984).
45
2.9.4 Likert Scale
The Likert scale named after Rensis Likert who developed it in 1932, is one of
the most widely used techniques to measure attitudes (Ary et al., 2006). They inferred
that Likert scale assesses attitude toward an issue by presenting a set of statements about
the issue and requesting respondents to indicate for each whether they strongly agree,
agree, disagree, or strongly disagree. These various agree-disagree responses are assigned
a numeric value, and the total scale score is found by summing the numeric value, and
the numeric responses given to each item, which represents the individual’s attitude
toward the issue. This is why the scale is also called Summated-rating scale (Anaekwe,
2007).
46
CHAPTER THREE
RESEARCH METHODOLOGY
3.1 The Study Area
The study area is Anambra State of Nigeria (Figure 3.1). It derives its name from
the Anambra river (Omambala River) which is a tributary to River Niger, and lies
approximately between latitudes 5040’N and 604’N of the equator and longitudes 6031’E
and 7050’E of the Greenwich meridian (SEEDS, 2009). The state occupies an area of
about 4,887km and has a population of 4,182,032(NPC, 2007). It has twenty one (21)
local government areas (LGAs) and bounded to the east by Enugu State, to the west by
Delta state, to the north by Kogi state and to the south by Imo and Abia states (ANADEP,
2007).
The state is situated on a fairly flat land with tropical vegetation. The climate is
humid with mean annual rainfall of 2010mm and average temperature of 870C. It has a
weak soil that is easily eroded (SEEDS, 2009). According to ANADEP (2007) Anambra
state has four (4) agricultural zones, namely;
� Aguata zone comprising of Aguata, Nnewi North, Nnewi South, Orumba North,
and Orumba South LGAs
� Anambra zone comprising Anambra east, Anambra west, Ayamelum and Oyi
LGAs
� Awka zone comprising Aniocha, Awka South, Awka North, Dunukofia and
Njikoka LGAs
� Onitsha zone comprising Ekwusigo, Idemili South, Idemili North, Ihiala, Ogbaru,
Onitsha South and Onitsha North LGAs
47
The people of Anambra State are known to be resourceful and very enterprising
especially in the area of commerce. However their primary production activities are
agriculture and industry (SEEDS, 2009). Food crops grown in the state include cassava,
yam, rice, cocoyam, maize, melon, cowpea and sweet potato. Most parts of the state are
concentrated with small to medium-scale firms with few large scale ones. Soybean
processing activities are being carried out by small scale firms and this has to some extent
mitigated the high rate of unemployment especially among women and youths in the
area.
3.2 Sampling Technique
A multi–stage sampling technique was used in the selection of respondents for the
study. First, the agricultural zones in the State were stratified into high and low
commercial soybean processing zones. The stratification was based on the concentration
of soybean processing firms in the area. Three highest zones – Aguata, Awka and
Onitsha- were selected. Stage two involved getting a pool of the names of soybean
processing firms (through the help of Ministry of Commerce and Industry, Anambra
ADP, business premises registration Department of Local Government Areas of selected
zones and soybean dealers in some major markets such as Oye Ekwulobia, Nkwo Nnewi,
Eke Awka, Ose Okwodu, Ochanja, Ogbaru main market, Nkwo Ogbe). These dealers
know their customers that buy in bulk for processing. Their names and locations were
collected from this source because, some of them were not registered, so their names did
not appear in the records of the Ministry. From these names, 150 soymilk processing
firms and 100 soyflour processing firms were randomly selected. Out of these, 142 and
48
95 firms were used respectively for the analysis, making it 237 respondents. This was due
to the fact that, some questionnaire were either not retrieved from the respondents or were
invalid due to improper completion and filling.
Figure 3.1: Map of Anambra State showing sampled areas
49
3.3 Method of Data Collection
Data for this study were collected mainly from primary sources. This was through
the use of structured pre-tested questionnaire which were administered to the soybean
processing firms. The questionnaire included such questions as; sources of whole
soybean seed supply, the cost and quantity of whole soybean seed, selling prices and
amount realized after the sales of products, types and number of machine used, cost of
machine, availability of spare parts, taxes and levies paid and problems encountered in
processing soybean. Oral interviews were also used to elicit information from the
respondents. Some observational techniques were also applied. The questionnaire were
administered by the researcher and some trained research assistants from the area.
3.4 Analytical Techniques
The descriptive tools of statistics such as means and percentages were used to
achieve objectives (i) and (vii). The Multinomial Logit (MNL) model was used to
determine objective (ii). The model specification is shown as in equation 3.1.
Pr (Ai = 9) = exp(X1β1 + X2β2 + X3β3 + ... + X9β9)
∑jk = 0 exp(X1β1 + X2β2 + X3β3 + ... + X9β9) (3.1)
Where:
Ai = a random variable representing the choice of processing technology. The small scale
soybean processing firms are assumed to face a set of discrete, mutually exclusive
choices of technology. These choices are also assumed to depend on a number of socio
economic and institutional factors: X:
50
X1 = sex (male=0, female=1)
X2 = income (N)
X3 = age (years)
X4 = level of education (years of formal education)
X5 = availability of land/shop
X6 = amount paid as taxes/levies (N)
X7 = No. of hours of electricity consumed /day
X8 = availability of spare parts (Yes/No)
X9 = availability of technicians (Yes/No)
The stochastic frontier production function was used to achieve objective (iii).
The model is specified as follows;
lnY = β0 + β1lnX1ij + β2lnX2ij + β3lnX3ij + β4lnX4ij + (Vij – Uij) (3.2)
Where:
ln = represents logarithm to base e subscripts refers to jth observation.
Y = Value of output of processors (Soymilk/Soyflour) (N)
X1 = whole soybean seed (kg)
X2 = Labour (Man days)
X3 = Capital (fire wood/kerosene, fuel, transportation, packaging, flavors) (N)
X4 = water (litres)
V i = random variables (shocks)
Uij = technical inefficiency effect, which is assumed to be independent of Vij. If Uij = 0,
then there is no technical inefficiency occurring, therefore the production lies on the
stochastic frontier. If Uij >0, then the production lies below the frontier and is
inefficient.
51
The absolute value of Uij is expressed as follows:
Uij = δ0 + δ 1Z1 + δ 2Z2 + δ3Z3 + δ 4Z4 + δ 5Z5 + δ6Z6 + δ7Z7 + δ8Z8 (3.3)
Where:
Uji = technical inefficiency or characteristics related to inefficiency
Z1 = Age of processors (years)
Z2 = Level of education (years of formal education)
Z3 = Years of experience in soybean processing
Z4 = Gender (dummy: male = 0, female = 1)
Z5 = Household size (actual number of members)
Z6 = Marital status (I married, 0 otherwise)
Z7 = Level of income (N)
Z8 = Access to credit (1 accessed credit, 0 otherwise).
The maximum likelihood estimation of the β s and + δs coefficients above shall be done
simultaneously using the frontier 4./c computer programme by Coelli (1996).
Objective (v) was determined using gross margin and profit function analysis. It
was used to estimate profitability levels of individual resource inputs on the soybean
processing enterprises. These inputs include variable and fixed capital.
The profit function model is specified thus:
π* = (Py, P1, P2, P3, Z1, Z2) (3.4)
Where:
π* = amount of maximum variable profits (GM) from sales of soymilk and soyflour
respectively, per annum
Py = Price of output soymilk / soyflour (N)
52
P1 = per unit price of whole soybean seed (N
P2 = per unit price of labour (N / man day)
P3 = per unit price of fuel wood /kerosene (N)
P4 = per unit price of transportation cost (N)
P5 = per unit price of condiments / flavor (N)
P6 = per unit price of packaging(N)
P7 = per unit price of water (N)
P8 = per unit price of diesel/petrol (N)
Objective (vi) was attained using the Likert Scale Rating Technique on 4-point basis.
The 4-point rating will normally force a choice on the respondents since there is no mid-
point to make them indifferent. The grading was in this order: strongly agreed (SA) = 4,
agreed (A) = 3, disagree (D) = 2, and strongly disagree (SD) = 1.
The mean score of the respondents based on the 4-point scale was 4 + 3 + 2 + 1 = 10,
10/4 = 2.50. Using the interval scale of 0.05, the upper limit cut-off point was 2.50 + 0.05
= 2.55. The lower limit was 2.50 – 0.05 = 2.45. Based on these, any mean score below
2.45 (i.e. MS < 2.45) was regarded as not important. Those between 2.45 and 2.55 were
considered as important (i.e. 2.45 < MS < 2.55). Mean score greater than 2.55 (MS >
2.55) will however be considered very important.
The value addition model was used to achieve objective (iv). The gross value
added was determined as follows:
Va = Vp – Vb (3.5)
Where:
Va = Value added to whole soybean seed after processing (N/kg)
53
Vp = Value of processed soybean products (soymilk/soyflour) from 1kg of soybean seed (N)
Vb = Value of unprocessed soybean per kg (N)
This can also be presented in percentage as follows:
Va% = Vp-Vb/Vp x 100% (3.6)
When the value of input used in processing is subtracted from Va in equation
(3.6) above, we obtain net value added. This principle can be applied at any point in the
value chain to determine value added at that point.
Research hypothesis were analyzed using t – test. The t-test model is specified
below:
t* = b1* (3.7)
σ (b1*)
Where:
t* = t calculated
b1*= estimated value of b1
σ (b1*) = standard error of b1
54
CHAPTER FOUR
RESULTS AND DISCUSSION
4.1 Socioeconomic Characteristics of Soybean Processors
The socioeconomic characteristics of soybean soyflour and soymilk processors
are shown in table 4.1.
4.1.1 Sex distribution among processors
Table 4.1 shows that 31% of the heads/owners of small scale soybean processing
firms(processors), that process into soyflour were males while 69% were females;
whereas in soymilk processing, 26% of them were males while 74% were females. These
imply that the firms processing soybean into soyflour and soymilk are owned mostly by
females.
4.1.2: Marital status of processors
The result shows that majority of the heads/owners of soybean processing firms in
the study area, were married (table 4.1). From the table, 91% of the soyflour processors
were married while for soymilk processors, 89% were married. There were no divorcees
for both soyflour and soymilk processors, while the number of singles were 5% for
soyflour and 4% for soymilk. Furthermore, 4% of the soyflour processors were widowed
whereas in soymilk processing they were 7%.
55
Table 4.1: Socioeconomic characteristics of soybean processors
Socioeconomic characteristics Soyflour Soymilk
Freq. (%) Freq. (%)
Sex: Male 29 31 37 26 Female 66 69 105 74
Marital status: Single 5 5 6 4 Married 86 91 127 89 Divorced 0 0 0 0 Widowed 4 4 9 7
Age FO: 20 -- 30 years 14 15 32 23 31 – 40 years 33 35 49 34 41 – 50 years 48 50 51 36 Above 50 years 0 0 10 7 Mean 43 40
Household size: 1 – 3 4 4 8 6 4 – 6 53 56 75 53 7 – 9 38 40 56 39 > 9 0 0 3 2 Mean 6 6
Education level: Primary education 26 27 44 31 Secondary education 52 55 72 51 Tertiary education 17 18 26 18
Primary occup.: Soybean processing 93 98 136 96 Civil servants 1 1 4 3 Tailoring 1 1 2 1 Farming 0 0 0 0
Age of firm: < 1 1 1 3 2 1 – 5 years 36 38 44 31 6 – 10 years 42 45 78 55 Above 10 years 16 17 17 12 Mean 7 9
HH employees: None 20 21 0 0 1 - 2 66 69 108 76 3 - 4 9 10 34 24 Mean 2 2
Paid employees: 1 - 2 16 17 29 20 3 - 4 55 58 61 43 5 - 6 24 25 52 37 Mean 4 3 Total Frequency (N) 95 142 Key: FO - firm owners; occup – occupation; HH – household; Source: Field survey, 2012.
56
4.1.3: Age of processors
The result showed that the average age of soyflour processors was 43years, while
that of soymilk processors was 40years. The predominant age range of the
processors/owners of small scale firms in the study area was 41 – 50years: 50% for
soyflour processors and 36% for soymilk processors. In soyflour processing, 35% of the
processors were aged between 31 – 40years and it was 34% for soymilk processors.
More so, 15% and 23% of them were 21 – 30years for soyflour and soymilk processing,
respectively. None of the soyflour processors were above 50years but 7% of those
involved in soymilk processing were above 50years.
4.1.4: Household size
The result shows that the average household size for both soyflour and soymilk
processors was 6 members, respectively. Further results showed that 4% and 6% heads of
small scale soybean processing firms for soyflour and soymilk respectively had between
one and three persons in their household, 56% and 53% of them had between four and six
persons, while 40% and 9% of them had between seven and nine persons respectively.
Table 4.1 further showed that none of the soyflour processors had more than nine persons
in their household whereas 2% of soymilk processors had.
4.1.5: Level of education of processors
Table 4.1 shows that all the soyflour and soybean processors in the study area,
had one form of education or the other. The table showed that 27% and 31% of the
soyflour and soymilk processors respectively had primary education; while 55% and 51%
57
of them had secondary education. Furthermore, 18% of the soyflour and soymilk
processors had tertiary education, respectively.
4.1.6: Primary occupation of the processors:
From the result as shown in table 4.1, the processing of soybean is the primary
occupation of the processors, although very negligible proportion of them do other
activities like working for government/civil servants (1% for soyflour and 3% for
soymilk) and tailoring (1% for soyflour and soymilk, respectively).
4.1.7: Age of the firms
The result showed that the average age of soyflour processing firms was 2years
while that of soymilk processing firms was 9years. Majority of the small scale soybean
processing firms in the study area, are between 6 and 10 years of age as shown in table
4.1 (45% for firms processing into soyflour and 55% for soymilk firms). More so, 38%
and 31% of the firms are between the ages of one and five years for soyflour and
soymilk, respectively; while 17% and 12% of them are above 10years of age. There are
very few processors whose firms were less than one year of age as shown in their
proportion of 1% for soyflour and 2% for soymilk.
4.1.8: Household employee
The result showed that the small soyflour and soymilk processors had an average
of two household employees, respectively. Also, 69% of the small scale soybean firms
that process soybean seeds into soyflour and 76% that process into soymilk, had one to
two household employees in their firms, whereas 10% and 24% of them, respectively had
three to four household employees.
58
4.1.9: Paid employee
The result showed that small scale soyflour and soymilk processors had an
average of four and three paid employees, respectively. Table 4.1 shows that 17% and
20% of soyflour and soymilk processing firms, respectively had one to two paid
employees, 58% and 43% of them had three to four paid employees, while 25% and 37%
of them had five to six paid employees with an average of four paid employees for
soyflour and three paid employees for soymilk. This portends good development for the
country as the small scale soybean processing firms are contributing towards employment
generation in the country. It also means that if the processors are supported and
encouraged, and their businesses grows, they will employ more Nigerians, thereby
contribute immensely to employment creation and poverty reduction in the country.
4.2 Technologies Being Used for Small Scale Soybean Processing
The technologies used for small scale soybean processing into soymilk and
soyflour as shown in figure 4.1 include, the local machine, 45TG x 160-Grain, 45TG
x160-Galvanized, 45TG x160-Japan, 45TG x 160-Stainless and 60GX x 175- Galvanized
The results showed that local machine is the predominant technology for processing
soybean as it accounted for 30 per cent and 44 per cent of all the technologies for soymilk
and soyflour, respectively. The local machine is cheaper to acquire, easier to operate,
requires less space and generally requires little or no technical know-how to operate
which makes its technicians and spare-parts more accessible compared to other
processing technologies (machines) which are more costly and complicated to operate.
59
These therefore could explain the rationale for the predominance of the locally fabricated
machines as soybean processing technology in the study area.
Furthermore, in line with the foregoing, 60GX x 175-Galvanized technology
which is the biggest, costliest and most advanced soybean processing technology in use
by the respondents had the least frequency for both soy milk and soy flour(figure 4.1).
Most of the businesses are small scale with self and family employment, and the
respondents generally are low income earners with poor educational background and
skills, and large family sizes. These factors combine to limit the use of this processing
technology in the area. In addition, the 60GX x 175 is bigger than other technologies and
as such not all processors have the space-requirement for this type of technology. It is in
consonance with earlier research findings that small scale businesses are highly
constrained by finance and as such cannot afford the high cost of modern machines and
technologies (Olutunla and Obamuyi, 2008).
Figure 4.1: Technologies used for small scale soybean processing Source: Field survey, 2012.
Fre
quen
cy (
%)
Technologies
60
4.3 Determinants of Choice of Soybean Processing Technology
The multinomial regression results on the choice of soybean processing
technology are shown in Table 4.2.
Table 4.2: Multinomial logit result on factors affecting choice of soybean processing technology Variables A B C D E
Intercept -404.318 -429.319 -816.265 247.444 247.44 (1.375E5) (1.964E5) (6.077E5) (1.329E5) (6.689E6)
Age -2.196 -0.786 -2.583 -1.610 -1.826 (913.834)* (812.756)** (679.934)** (636.181)* (2.163E4)*
Sex of firm owners 198.787 226.762 217.268 -11.003 24.923 (6.279E4) (5.902E4) (1.993E4) (1.724E4) (8.119E5)
Income 0.000 0.000 0.000 0.000 0.000 (0.005) *** (0.008) * (0.007) *** (0.007)** (0.107)**
Level of education 16.509 13.890 25.554 6.102 9.362 (3155.917) (3132.642) (2302.450) (2105.062) (7.353E4)*
Cost of machine -63.535 -67.047 -48.673 -29.184 -96.984 (3.252E4)*** (2.055E4)** (3.734E4)** (2.588E4)** (3.989E5)*
Household size -16.061 -25.213 -45.421 -0.190 -13.926 (7235.499) (1.019E4) (6935.556) (6745.408) (1.176E5)*
Firm's age 5.533 7.924 3.272 2.424 13.864 (3897.049)** (3344.222)* (2217.180)** (1852.089)** (2.069E4)*
Land 80.172 130.772 235.374 -55.484 161.52 (8.867E4) (8.326E4) (5.999E5) (9.531E4) (1.433E6)
Tax 0.10 0.001 -0.014 0.035 0.005 (25.937) (14.669) (10.738) (8.594) (68.561)
Power 0.044 0.028 0.064 -0.007 0.041 (5.191) (7.128) (7.037) (5.573) (288.097)
Spareparts 1.567 0.154 0.886 0.196 0.607 (6587.879)*** (5244.445)** (4865.311) (4770.495) (5985.214)
Techncians -0.963 -0.668 -0.388 -0.013 -0.123 (6824.813)** (5566.268)* (5335.586) (5235.574) (6596.456)
Household Employees 43.834 28.161 42.865 0.440 -9.906 (9790.680)* (1.144E4)** (8280.659) (7292.571) (4.900E5)
Paid Employees -35.689 -29.353 18.470 -52.250 -6.255 (1.318E4) (1.904E4) (1.121E4) (9310.606)** (4.349E5)*
R2 0.684 0.875 0.922 0.721 0.625 F 32.01*** 23.25** 20.73*** 18.12*** 20.02**
N 237
A, B, C, D, & E are choices of processing technology representing 45TGx160-Grain, Galvanized, Japan & Stainless & 60GXx175-Galvanized technologies, respectively. L. edu - Level of education, HH - Household. Source: Field survey, 2012.
61
The results showed that age, income, level of education and household size were
the socioeconomic factors that significantly affected the choice of other processing
technologies over the local technology by the respondents. The age and income of the
processors were significant in all the processing technologies, while level of education
and household size significantly accounted for the choice of 60GXx175 processing
technology. While age is negative, income is positive. This implies that as the
respondents get older, there is the tendency to leave processing activities as they may not
be able to compete with the younger ones considering the different stages of processing
activities carried out directly in the enterprise. Income on the other hand is a very
important positive factor as it provides the resources for the acquisition and running of
the processing technologies (machines). However, both level of education and household
size were positively and negatively significant factors in determining the choice of
processing technologies, respectively. The results showed that the respondents with
higher educational levels prefer the more advanced technologies, such as, 45GTG x160
and 60GX x 175 and most of them also have smaller household sizes compared to others.
Higher educational level and low family sizes helped to equip the respondents with the
technical and managerial know-how and resources needed to acquire and operate the
machines.
The study further showed that the cost of purchasing the processing technology
(machine) and the age of the firm or the processor in the business were significant in
explaining the choice of other processing technologies over the local technology. While
the availability of spare-parts and technicians as well as the use of household employees
were significant factors that determined the choice of 45TG x 160Grain technology, use
62
of paid employees was positively significant in determining the choice of the use of most
advanced technologies 45TG x160Stainless and 60GX x 175Galvanized in processing.
The effect of the cost of purchasing the processing technology is negative while
the firm age is positive. This implies that as the cost of the machines decreases, the
processors are able to purchase more and are also likely to go for improved and more
advanced machines, and also, as the firm advances in age the more its ability to
accumulate wealth and hence its ability to acquire more expensive and advanced machine
increases.
Furthermore, the result showed that socio-economic characteristics accounted for
68.4 per cent, 87.5 per cent, 92.2 per cent, 72.1 per cent and 62.5 per cent of the choice of
processing technology for 45TGx160-Grain, Galvanized, Japan & Stainless, &
60GXx175, respectively. The test of the overall significance further showed that socio-
economic characteristics significantly affected the choice of technology used for small
scale soybean processing at 1 per cent with the exception of 45TGx160-Grain and
60GXx175 which were significant at 5 per cent.
4.4 Technical Efficiency of Small Scale Soybean Processing Firms.
4.4.1 Maximum likelihood estimates for the parameters of the stochastic frontier
production function in Soyflour Processing.
Maximum likelihood estimates for the parameters of the stochastic frontier
production function of soyflour processing are presented in Table 4.3.
63
Table 4.3: Maximum Likelihood Estimates of the Stochastic Production frontier function in Soyflour processing Variable Parameter Coefficient Standard
error t-ratio
Constant β0 6.128 1.142 5.365***
Soybean seed β1 0.489 0.415 1.178
Labour β2 -1.160 0.249 -4.660***
Capital β3 1.114 0.407 2.822***
Water β4 0.0045 0.0063 0.707
Inefficiency function
Intercept Z0 0.337 0.299 1.126
Age of processor Z1 0.0034 0.0059 0.568
Education Z2 -0.0188 0.0099 -1.906*
Years of experience Z3 0.0079 0.0011 -0.703
Household size Z4 0.0304 0.0260 1.172
Access to credit Z5 0.0171 0.0445 0.385
Non-processing
income
Z6 -0.118 0.0516 -2.289**
Diagnostic statistics
Sigma-squared (σ2 = σ2v + σ2
µ ) 0.0801 0.0145 5.554***
Gamma (γ = σ2µ / σ
2) 0.000000010 0.000025 0.000406
Log Likelihood -15.303
LR test
N
21.274
95
Source: Computed from field data, 2012.
Labour was inversely related to soyflour output but capital was positively related
to soyflour output, however, both were statistically significant at 1% level of probability.
This means that as labour increases the output of soyflour decreases, this could be as a
result of over use of family labour in soyflour processing in the study area. It is suggested
that output can still be maximized even when this family labour is reduced. This finding
is related to the work of Baruwa and Oke (2012). But for capital, a unit increase in
64
capital will cause an increase in soyflour output by 11.1%. This means that more output
can be derived from use of capital among the soyflour processors in the study area.
Education and non-processing income were the factors that influenced technical
efficiency in soyflour processing in the study area. Education (measured in years of
schooling) negatively and statistically influenced technical inefficiency at 10% level of
significance. This shows that as education increases technical inefficiency decreases (or
technical efficiency increases). A unit increase in years of schooling reduces technical
inefficiency by 1.9% as seen in Table 4.3. It means that, education has immense influence
on technical efficiency level of soyflour processors and it is an important factor in
processing technology adoption. Educated processors are expected to be more receptive
to improved processing techniques and hence make more profitable use of improved
processing innovations than uneducated processors. As such, they are expected to have
higher level of technical efficiency than processors with less education or no education.
This result agrees with the work of Baruwa and Oke (2012) that found a positive
relationship between education and technical efficiency in small-holder Cocoyam farms
in Ondo State, Nigeria. Ojo, Mohammed, Ojo, Yisa and Tsado (2009) also found out that
a positive relationship exit between the educational level of small-scale cowpea farmers
and their profit efficiency in Niger state, Nigeria. Furthermore, Ajibefun and Daramola
(2003) found out that education was negatively related to technical inefficiency of micro
entrepreneurs in the Nigerian economy and also Ajibefun, Daramola and Falusi (2006)
found that education had a negative effect on technical inefficiency of small scale rural
and urban farmers in Ondo State, Nigeria.
65
Non-processing income also had an inverse relationship with technical
inefficiency at 5% level of significance. This implies that as non-processing income of
soyflour processors increases their level of technical inefficiency decreases. As non-
processing income increases by one unit, technical inefficiency decreases by 11.8% as
shown in Table 4.3. This implies that income from non-processing activities are useful in
soyflour processing may be in buying the necessary inputs. As such it is expected that
soyflour processors with non-processing income are more technically efficient than
processors without non-processing income in the study area.
4.4.2 Technical efficiency in Soyflour processing:
A very important characteristic of production frontier model is its ability to
estimate individual firm's specific technical efficiencies. Table 4.4 shows the deciles
range of the frequency distribution of estimated technical efficiencies in Soyflour
processing. There is a variation in the levels of efficiency. Predicted technical efficiencies
ranged between 0.69 and 1.00 with the mean technical efficiency of 0.95. This implies
that soybean processing into soyflour is technically efficient.
Table 4.4 showed that 2.0% of the sampled soyflour processors fell within range
of technical efficiency of 0.61 and 0.70; 22.0% of the sampled respondents fell within the
range of 0.81 and 0.90, while 76.0% percent fell within the technical efficiency range of
0.91 and 1.00. With the mean technical efficiency of 0.95, this implies that the efficiency
level of Soyflour processors can still be increased by 5% at the present level of
processing technology in the study area.
66
Table 4.4: Frequency Distribution of technical efficiencies of soyflour Processors Efficiency index Frequency Percentage
≤ 0.60 - -
0.61 ≤ 0.70 2 2.0
0.71 ≤ 0.80 - -
0.81 ≤ 0.90 20 22.0
0.91 ≤ 1.00 73 76.0
Total (N) 95 100.0
Minimum technical efficiency 0.69
Maximum technical efficiency 1.00
Mean technical efficiency 0.95
Source: Computed from field data, 2012.
4.4.3 Maximum likelihood estimates for the parameters of the stochastic frontier
production function in Soymilk Processing
Maximum likelihood estimates for the parameters of the stochastic frontier
production function of Soymilk processing are presented in Table 4.5. Labour was
inversely related to soymilk output but water was positively related to soymilk output, but
both were statistically significant at 1% level of probability. This means that as labour
increases the output of soymilk decreases, this could be as a result of over use of family
labour in soymilk processing as it was applicable to soyflour processing in the study area.
This means soybean processors use more of family labour in the study area. It is
suggested that output can still be maximized when labour especially family labour is
reduced. But for water, a unit increase will cause an increase in soymilk output by 35.7
%. This means that more soymilk output can be derived from use of water among the
soymilk processors in the study area. The variance ratio, defined by γ = σ2µ / (σ
2v + σ2
µ )
is estimated to be 0.98, implying that 98 per cent of the discrepancies between observed
67
output and frontier output is primarily due to factors, which are within the control of the
soymilk processors in the sample under consideration.
The only factor among the hypothesized factors that actually influenced technical
efficiency level of soymilk processors is access to credit. It is statistically significant at
5% level of probability. As the soymilk processors have more access to credit the more
technically efficient they are in the study area.
Table 4.6 shows the deciles range of the frequency distribution of estimated
technical efficiencies among Soymilk processors. There is a variation in the levels of
efficiency. Predicted technical efficiencies ranged between 0.17 and 0.96 with the mean
technical efficiency of 0.44.
Table 4.5: Maximum Likelihood Estimates of the Stochastic Production frontier function in Soymilk processing Variable Parameter Coefficient Standard
error t-ratio
Constant β0 17.951 2.549 7.040*** Soybean seed β1 -0.132 0.191 -0.692 Labour β2 -0.192 0.107 -1.804* Capital β3 -0.099 0.077 -1.276 Water β4 0.357 0.133 2.688*** Inefficiency function Intercept Z0 0.901 0.389 2.311** Age of processor Z1 0.00814 0.0075 1.089 Education Z2 -0.0108 -0.0153 -0.704 Years of experience Z3 -0.0097 0.0157 0.617 Household size Z4 -0.0228 0.0400 -0.571 Access to credit Z5 -0.237 0.106 -2.231** Non-processing income Z6 -0.0072 0.044 -0.163 Diagnostic statistics Sigma- squared (σ2 = σ2
v + σ2µ ) 0.289 0.0289 9.955***
Gamma (γ = σ2µ / σ
2) 0.983 0.0311 31.613*** Log Likelihood -112.791 LR test
N 13.366
142
Source: Computed from field data, 2012.
68
4.4.4 Technical efficiency in soymilk processing:
From table 4.6, 39.3% of the sampled soymilk processors had technical
efficiencies of 0.30 and below; 16.7% of them had technical efficiencies within the range
of 0.31 and 0.40; 14.7% fell within 0.61 and 0.70; 10.7% of them fell within technical
efficiencies range of 0.71 and 0.80 7.3% fell the range of 0.51 and 0.60. Among the
sampled soymilk processors, 6.0% of them fell within technical efficiency range of 0.81
and 0.90; 4.0% fell the range of 0.41 and 0.50, while 1.3% fell within technical
efficiencies range of 0.91 and 1.00. The mean technical efficiency of soymilk processing
was 0.44; this implies that the efficiency level of Soymilk processors can still be
increased by 56% at the present level of processing technology in the study area. This
shows that the soymilk processors are immensely technically inefficient.
Table 4.6: Frequency Distribution of technical efficiencies of soymilk Processors Efficiency index Frequency Percentage
≤ 0.309 57 39.3
0.31 ≤ 0.40 24 16.7
0.41 ≤ 0.50 5 4.0
0.51 ≤ 0.60 10 7.3
0.61 ≤ 0.70 21 14.7
0.71 ≤ 0.80 15 10.7
0.81 ≤ 0.90 8 6.0
0.91 ≤ 1.00 2 1.3
Total (N) 142 100
Minimum technical
efficiency
0.17
Maximum technical
efficiency
0.96
Mean technical efficiency 0.44
Source: Computed from field data, 2012.
69
4.5 Value Addition to Soybean Processing
Tables 4.7 and 4.8 showed the value added by processing soybean into soymilk
and soyflour, respectively. From Table 4.7, the processing of 1kg of soybean into soymilk
raised the value of soybeans by N680 from N120 to N800, while in soyflour the value of
soybeans was raised by N440 from N120 to N560 (Table 4.8). These showed that there
were value added in processing soybeans into soymilk and soyflour respectively.
Although there were value addition in processing soybean to both soymilk and soyflour,
respectively that of soymilk was higher. This could be responsible for the prevalence of
soymilk processors compared to soyflour processors. Also, the high value of processed
soybean could explain the profitability of soybean processing and as well accounted for
the involvement in the business by many small scale rural and urban dwellers.
Table 4.7: Value added by processing soybean into soymilk
S/n Items Value
1. Value of 1kg soybean N120
2. Value of 1kg soybean processed into soymilk N800
3. Value added N680
Source: Field survey, 2012.
Table 4.8: Value added by processing soybean into soyflour
S/n Items Value
1. Value of 1kg soybean N120
2. Value of 1kg soybean processed into soyflour N560
3. Value added N440
Source: Field survey, 2012.
70
4.6 Profitability of Small Scale Soybean Processing
The results of the profitability of soybean processing into soymilk and soyflour
are shown in Table 4.9 and also in figures 4.2 and 4.3. Table 4.9 shows that processing
soybean into soymilk and soyflour were profitable, respectively. The gross margin for
firms processing soybean into soymilk per annum was N84,647,820 with an average
margin per firm of N596,111.41 per annum, and that of soyflour was N42,820,087 with
an average margin of N450,737.76 per processing firm, per annum. These results were
further reinforced by the t-test analysis between the means of the costs and returns of
soybean processing into soymilk and soyflour which show that they are significantly
(p<0.01) different (Appendices 2 & 3).
71
Table 4.9: Gross margin of soybean processing into soymilk and soyflour
Revenue (N) Soymilk Revenue (N) Soyflour
Items (5kg = N2500) Cost (N) % cost (5kg = N2450) Cost (N) % cost
Revenue 273,016,220 - 157,046,802 - -
Costs
Soybean seeds 73,347,360 40 38,229,350.25 35
Labour 5,501,052 3 5,461,335.75 5
Wood fuel/Kero 3,667,368 2 5,461,335.75 5
Flavour 9,168,420 5 2,184,534.30 2
Transport 18,336,840 10 12,014,938.65 11
Water 3,667,368 2 2,184,534.30 2
Packaging 14,669,472 8 16,384,007.25 15
PHCN bill 9,168,420 5 3,276,801.45 3
Fuel/Diesel 45,842,100 25 24029877.3 22
Depreciation on fixed assets 5,000 5,000
Total 273,016,220 188,368,400 100 157,046,802 114,226,715 100
Gross Margin/annum 84,647,820 42,820,087
Av. Gross Margin/annum 596,111.41 450,737.76
Source: Field survey, 2012.
72
In addition, the results showed that 50 per cent of the total money transaction in soybean
processing came as revenue for both soymilk and soyflour as shown in figures 4.2 and 4.3
respectively. While 16 per cent of which is profit in soymilk, 14 per cent is profit in
soyflour, and 34 per cent and 36 per cent are costs associated in processing soybean into
soymilk and soyflour, respectively. Of these costs, that of soybean seeds and purchase of
petrol were highest accounting for about 40 and 25 per cent respectively in soymilk, and
35 and 22 per cent respectively in soyflour. Also, costs of transportation and packaging
were other variables that constituted cost acrued to, soybean processing. The cost of
transport and packaging was 10 and 8 per cent respectively in soymilk and 11 and 15 per
cent respectively in soyflour.
Figure 4.2: Cost and Return of Soybean processing into soymilk/annum
73
Figure 4.3: Cost and Return of Soybean processing into soyflour/annum
4.6.1 Determinants of the Profitability of Soybean Processing into Soymilk and
Soyflour The determinants of the profitability of soybean processing into soymilk and
soyflour are shown in Tables 4.10 and 4.11, respectively. The results showed that costs of
soybean seed, petrol, transportation and packaging were the significant factors that
influenced the profitability or otherwise of processing soybean into soymilk and
soyflour, respectively. The direction of their impacts on profitability is negative
implying that, reductions in the costs of these variables would enhance profitability and
74
thus encourage more entrants to the business. This would create employment, wealth and
increase government receipts and ability to provide social goods and services, and
thereby enhancing national productivity and development in the country.
In processing soybean into soymilk and soyflour, cost of soybean seed and
purchase of petrol are significant at 1 per cent level respectively. While transportation
and packaging are significant at 5 per cent level respectively in both soymilk and
soyflour; flavour and PHCN power are significant at 10 per cent in soymilk while labour
and wood fuel are significant at 10 per cent in soyflour.
Table 4.10: Regression result of the factors affecting the profitability of soybean processing into soymilk Variables (N) Coefficients t – value
Constant -198269.854 -0.84**
Seed -1.002 -0.728***
Labour -3.326 -1.235
Wood 39.264 -1.236
Flavour 0.373 1.177*
Transport 22.091 0.258**
Water 99.464 1.136
Packaging -0.102 -0.481**
Power supply -11.567 -0.511*
Cost of petrol 2.459 -0.275***
R2 0.732
F 52.809** N 142 Note: * , ** and *** represent 10%, 5% and 1% levels of significant, respectively. Source: Field survey, 2012.
75
Table 4.11: Regression result on the factors affecting the profitability of soybean processing into soyflour Variables (N) Coefficients t – value
Constant 2.240E6 -0.951***
Seed 2.683 -0.566***
Labour -18.266 -1.391*
Wood fuel -119.347 -1.353*
Flavour -2.121 -1.150
Transport -20.602 -0.800**
Water -15.916 -0.303
Packaging 2.309 -0.554**
Power supply 68.627 -1.663
Cost of petrol 66.621 -2.954***
R2 0.653
F 21.811** N 95 Note: * , ** and *** represent 10%, 5% and 1% levels of significant, respectively. Source: Field survey, 2012. 4.7 Perception of processors on constraints to soybean processing
The perception of the processors on constraints to soybean processing are shown
in tables 4.12 (soymilk) and 4.13 (soyflour). The results of the Likert ratings indicated
strong perceptions of the processors on the constraints to soybean processing. The
average ratings of the processors on constraints to soybean processing into soymilk was
3.29, while it was 3.35 for soyflour. These imply that the performance and productivity of
soybean processing in the view of the processors are been seriously impeded. Tables
4.12 and 4.13 showed that insufficient capital, inadequate power and water supplies, lack
76
of credit facilities, high and multiple government taxes and levies (such as business
premises registration, Local Government fees, Association dues etc), and high cost of
spare-parts were the factors that seriously constrained soybean processing in the view of
the processors. In this light, an increase in the capital base of the processors through the
establishment of microcredit scheme for the processors will boast their productivity.
Also, efforts to stabilize power and water supplies in the country especially in the
operational areas of these processors will revamp the sector. Furthermore, the streamline
of government taxes and levies to remove multiple taxation of the processors will be an
added advantage to the sector. Also, government should consider giving the processors
some tax holidays to serve as incentives for the development of small scale soybean
processing industry.
Table 4.12: Likert scale rating of the perception of processors on constraints to soybean processing into soymilk Constraints Average ratings/points
Insufficient capital 4.70
Lack of credit facilities 3.38
Lack of required machine 2.78
Inadequate power supply 4.67
Inadequate water supply 3.86
Low patronage 2.15
High government levies/taxes 3.98
Poor accessibility 3.40
Low supply of soybean seed 1.35
Lack of experienced technicians 2.88
Lack of/ high cost of spare parts 3.07
Average rating 3.29 Source: Field survey, 2012
77
Table 4.13: Likert scale rating of the perception of processors on constraints to soybean processing into soyflour Constraints Average ratings/points
Insufficient capital 4.90
Lack of credit facilities 4.83
Lack of required machine 3.63
Inadequate power supply 4.15
Inadequate water supply 4.05
Low patronage 2.10
High government levies/taxes 4.07
Poor accessibility 3.25
Low supply of soybean seed 1.30
Lack of experienced technicians 1.45
Lack of/ high cost of spare parts 3.20
Average rating 3.35 Source: Field survey, 2012
4.8 Gender participation in small scale soybean processing
The results of the study on gender participation in soybean processing is shown in
figure 4.4. The study showed that the process of processing of soybean into soymilk and
soyflour flows almost in the same processes with the exception of shelling which is
carried out only in soyflour production. The result indicated that of the seven (7) distinct
activities involved in soybean processing, females predominantly performed five (5) of
such activities which includes buying of soybean seeds (82%), washing and boiling of
soybean (80%) and winnowing/dehusking (90%). The rest are packaging (93%) and sales
(87%). While two (2) of the soybean processing activities that males play predominant
role includes shelling (75%) in soyflour production and grinding/milling (92%). The
78
dominance of males in these two activities could be attributed to the energy-exerting
nature of the activities of which males generally have more physical energy.
Further analysis from the t-test showed that there were significant differences in
the participation of men and women in soybean processing (buying of soybean seeds,
washing and boiling of soybean, winnowing/dehusking, packaging, sales, shelling
production and grinding/milling) into soyflour and soymilk respectively at P<0.05. This
is in line with earlier research findings on the dominance of women in agricultural
production and processing activities (ITDG, 2005).
Figure 4.4: Gender participation in small scale soybean processing firms
Key: BSYS – Buying of soybean seed; WSYS – Washing & boiling of soybean seed; WN - Winnowing of soybean seeds; SH – Shelling of soybean for soyflour processing Source: Field survey, 2012.
Fre
que
ncy
(%
) o
f gen
der
par
ticip
atio
n in
so
ybea
n p
ro
cess
ing
79
CHAPTER FIVE
SUMMARY, CONCLUSION AND RECOMMENDATIONS
5.1 Summary
This study examined the economics of small-scale soybean processing firms in
Anambra State, Nigeria. Specifically, the study sought to; identify the technologies used
for small-scale soybean processing in Anambra state; describe the socio-economic and
institutional factors that influence the choice of technology used for small scale soybean
processing; determine the technical efficiency of these small scale processing firms;
determine the value added by processing soybean into soymilk and soyflour respectively;
assess the profitability of small scale soybean processing firms; identify the constraints of
the small scale soybean processing firms; and assess the level of gender participation in
small scale soybean processing in the study area.
Data for the study were collected from a sample of 142 soymilk firms and 95
firms that process soyflour, making it a total of 237 firms. The study used primary data.
Descriptive statistics, multinomial logit, Stochastic Frontier production function, profit
function, likert scale, and value addition model were used in analyzing the data.
Results obtained indicated that, local machines were the predominant technology
used in processing soymilk and soyflour, while the 60GX x 175Galvanized machine,
which is the biggest and most advanced technology, was used the least. The
socioeconomic factors that significantly affected the choices of processing technology
being used by the firms for both soymilk and soyflour respectively were age, income,
level of education and household size. Age however, had a negative effect. Cost of
purchasing the processing machine (technology), age of the firm, availability of spear
80
parts and technicians were significant institutional factors in choosing the technology for
use.
Labour was inversely related to the output of both, soymilk and soyflour but water
and capital were positively related to output of soymilk and soyflour respectively. Access
to capital influenced technical efficiency in soymilk processing while education and non-
processing income also influenced technical efficiency in soyflour processing in the study
area. Processing 1 kg of soybean into soymilk and soyflour added gross value of N680.00
and N440.00 respectively.
Processing soybean into soymilk and soyflour were profitable respectively. The
gross margin for soymilk per annum was N84,647,820 with an average margin of
N596,111.41 per processing firm, per annum; while that of soyflour was N42,820,087 per
annum with an average margin of N 450,737.76 per firm per annum.
The study also indicated that, costs of soybean seed, labour, packaging, power
supply and cost of petrol/diesel significantly affected the profitability of soybean
processing into soymilk and soyflour respectively. Insufficient capital, lack of credit
facilities, inadequate power and water supplies, high and multiple government levies and
taxes, high cost of spear parts were constraints faced by small scale soybean processing
firms in the study area. It was observed that males and females were involved in
processing of soybean, but females were more predominant as they account for 74% and
69% for soymilk and soyflour processing respectively.
81
5.2 Conclusion
The study showed that small scale soybean processing firms in Anambra state, use
predominantly the local technologies (machines) and are still technically inefficient.
Access to credit, education and non-processing income had effect in reducing their level
of inefficiency. At this present level of processing, technology efficiency level can still be
increased. The processing enterprise is profitable, creates job opportunities and helps add
value to soybean seed. The processing of soymilk and soyflour would thrive well if the
numerous constraints were reduced to the barest minimum with the government’s
assistance.
5.3 Recommendations
We made Recommendations based on the findings of this study as follows;
i. Capacity building programmes should be provided to operators/managers of small
scale soybean processing firms. This will enable them appreciate and adopt the
need to use modern machines and also, learn the mechanisms in their operations.
It will also improve their record keeping, savings, reinvestment of accumulated
profits and business planning, which would enhance the economic viability of
these firms
ii. Readily available and affordable sources of water supply should be provided by
the government to increase productivity of the processors. Government should re-
visit the small and medium enterprise policies, and ensure that credit facilities are
provided to small scale firms in the state. Efforts should be made to ensure that
the procedures for obtaining loans and other credit facilities (especially from
82
formal sources of credit), are less cumbersome. There should also be proper
monitoring to check the diversion of such funds collected by absentee processors
to other uses.
iii. Public formal financial institutions geared at alleviating poverty (such as Micro-
finance banks and family Economic Advancement Program (FEAP)), should
provide loans for processing firms and such loans should be made gender specific.
If the interest rate paid by women is reduced, more women would be encouraged
to go into processing business.
iv. The Power Holding Company of Nigeria (PHCN) should ensure regular power
supply and provide transformers for different communities in the state, especially
areas with industrial clusters. This will enable the firms reduce costs on petrol and
diesel, then plough the money back into the business.
5.4 Contribution to Knowledge
This research has shown that agricultural processing, particularly soybean
processing, is not only a tool for product diversification, and preservation of farm
produce but also a profit making business venture. The study also showed that, in the
study area, soybean is being processed into soymilk and soyflour, and the industry
created employment for some citizens.
83
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APPENDICES
Appendix 1: Questionnaire
Section A: Socio – economic characteristics of soybean processors
(Please tick √ against your choices in the boxes provided)
1. Name of Soybean processor _____________
2. Location of firm ___________________________________________
3. What is your primary Occupation? ___________________
4. Do you have any other occupation outside processing? Yes No
5. If yes, what is /are your other occupation(s)?
(a) Civil service (b) Tailoring (c) Farming
(d) others, please specify -----------------------------------------------------------------
6. Age of Processor : Less than 20 years 21-30 years 31-40 years
41-50 years more than 50 years
7. What is your sex? Male Female
8. Marital status: Married Single Widow/ Widower Divorced
9. Age of Firm ________ (years)
10. What is your household size? ________
11. How many of the people in your household help in processing? _____________
12. Number of paid employees : Number male Number Female
13. How many years have you spent in Formal Education?
14. What is your highest educational qualification?
(a) FSLC (b) O Level (c) NCE/HND/Degree
(d) Others, please specify ______
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SECTION B: Please fill in the spaces below, about your firm’s machine DETAILS RESPONSE 1. What level /type of technology do you use? (a) Small scale (b) Medium scale (c) Large scale 2. Number of machines 3. Name / type of machine 4. Cost of machine 5. Rated output of machine (kg/hr) 6. Actual output of machine (kg) Per hour Milk Flour Per day Milk Flour Per week Milk Flour Per month Milk Flour 7. Value of actual output of Machine (N) Per hour Milk Flour Per day Milk Flour Per week Milk Flour Per month Milk Flour 8. Are the spare parts for servicing your machine(s) available within your Local market?
Yes No
9. Do you have enough technicians to service your machine from your locality?
Yes No
10. How many hours of electricity do you have per day? -------
11. Do you use Generator set in your firm? Yes No
12. If yes give reason …………………………....................................................................
13. If No give reason ……………………………………………………………………….
14. What is the cost of fuelling the generator per week? #.................................................
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SECTION C: Resource Input and Output
1. Which of these equipment do you have and use in processing?
Equipment
Sieves
Pot(s)
Basin
Spoon
Plastic
drum
Others,
specify
No of units
Cost Per unit (N)
Total Value (N)
Year of Purchase
Expected Life span (Years)
Depreciation value
2. Cost of running the business Amount(N)
(i) Petrol/diesel and Lubricant per week
(ii) Cost of repairs and maintenance per month
(iii)Salary of operators
(iv) Salary of the owner or manager
(v) Electricity bills per month
(vi) Income tax or levies per month
(vii) Whole soybean seeds #/kg
(viii) Fuel wood / kerosene
(ix) Condiments/ flavours
(x) Bottling/Packaging
(xi) Transportation for (a) procuring inputs
(b) Supplying / Selling Products
(xii) Others, please specify --------------------------------------
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3. Processing of soybean Qty/ day selling price per litre/kg value /day (N)
(i) Soy milk
(ii) Soy flour
4. What is the source of Whole Soybean seed used? Personal farm , Market
others, please specify -----------
5. What proportion do you get from Farm……………………,
Market…………………others, …………………………….
6. If from the market, what is the price per kg?____________
7. What is the size of your shop/workshop? ---------
8. Is the shop/workshop: (a) rented (b) your own ?
9. What is the: (a) rent (b) Cost of the land ?
10. How much fund did you get from different sources for your business?
(a) Personal savings ---------------(b) spouse ------------- (c) relatives----------
- (d) banks---------------- (e) others, specify ------------------
SECTION D: Perception of processors on factors affecting small scale soybean processing.
KEY SA = Strongly Agree A = Agree D = Disagree SD = Strongly Disagree
Indicate (√ ) the factors affecting your firms SA A D SD i Lack of sufficient capital
ii Lack of credit facilities
iii Lack of required technology / machine for processing
iv inadequate power supply
v irregular water supply
vi Low Patronage
vii High Government levies / Taxations
viii Lack of accessibility
ix unavailability of adequate quantity of soybean seeds
x Lack of experienced technicians
xi Lack of / high cost of spare parts of machines
xii others specify
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SECTION E:
1. Do you keep record? Yes No
2. If yes which records do you keep? …………………………………………………
...................................................................................................................................
3. Which areas of your business do you think needs improvement? ---------------------
---------------------------------------------------------------------------------------------------
---------------------------------------------------------------------------------------------------
4. Have you obtained any form of assistance from the Government? Yes No
5. If yes, in which area or form have you been assisted? ----------------------------------
---------------------------------------------------------------------------------------------------
---------------------------------------------------------------------------------------------------
6. If no, what forms of assistance or services do you expect the government to
provide for soybean processing firms in your area? ------------------------------------
---------------------------------------------------------------------------------------------------
---------------------------------------------------------------------------------------------------
7. In your own opinion, what are the prospects of small scale soybean processing
firms and what proposals awaits new people that want to join the enterprise?
………………………………………………………………………………………
….……………………………………………………………………………….…
……….……………………………….....................................................................
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Appendix 3: t-test result on statistical difference in costs and returns to soybean
processing into soyflour
Sum of Squares df Mean Square t Sig
Between Groups 1287.56 2 643.78 62.54 .000
Within Groups 947.04 92 10.29
Total 2234.6 94
Appendix 4: t-test result on statistical difference in the participation of men and
women in soybean processing activities
Sum of Squares df Mean Square t Sig
Between Groups 96.79 2 48.39 2.13 0.006
Within Groups 90.88 4 22.72
Total 187.67 6