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Digitally Signed by: Content manager’s Name
DN : CN = Weabmaster’s name
O= University of Nigeria, Nsukka
Nwamarah Uche
Faculty of Agricultural
Department of Agricultural Economics
TRADITIONAL AND MODERN GROUNDNUT
PROCESSING AND MARKETING IN NORTH CENTRAL
NIGERIA
ABOKI, PETER MAISAJE
PG/Ph.D./06/42156
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TRADITIONAL AND MODERN GROUNDNUT PROCESSING
AND MARKETING IN NORTH CENTRAL NIGERIA
BY
ABOKI, PETER MAISAJE
PG/Ph.D./06/42156
DEPARTMENT OF AGRICULTURAL ECONOMICS,
FACULTY OF AGRICULTURE, UNIVERSITY OF NIGERIA,
NSUKKA
iii
JANUARY, 2015
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Title Page
TRADITIONAL AND MODERN GROUNDNUT PROCESSING AND
MARKETING IN NORTH CENTRAL NIGERIA
BY
ABOKI, PETER MAISAJE
B.Agric. Tech-Agricultural Economics and Extension (FUTO), M.Sc. Agricultural
Economics (ABU)
A Ph.D. Thesis Submitted to the
Department of Agricultural Economics,
Faculty of Agriculture, University of Nigeria, Nsukka
In partial Fulfillment of the Requirements for the Award of Doctor of
Philosophy (Ph.D) in Agricultural Economics of University of Nigeria, Nsukka
JANUARY, 2015
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Certification
This is to certify that ABOKI, PETER MAISAJE, a post graduate student of the Department of
Agricultural Economics, Faculty of Agriculture, University of Nigeria, Nsukka, with the
registration number PG/Ph.D./06/42156 has satisfactorily completed the requirement for the
award of degree of Doctor of Philosophy (Ph.D.) in Agricultural Economics (Agricultural
Marketing and Agribusiness Management). The work embodied in this thesis has not been
submitted in part or in full for any other degree or diploma of this or any other University.
..………………………………… …....……….
Aboki, Peter Maisaje Date
(Student)
--------------------------------- ----------------- ------------------------------- ---------------
Prof. S.A.N.D. Chidebelu Date Prof. C.J. Arene Date
(Supervisor) (Supervisor)
---------------------------------- -------------------------
Prof. S.A.N.D. Chidebelu Date
(Head of Department)
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Dedication
To Almighty God for His grace and mercy thus far, and which endure forever; to
the memory of my Father Mr. M R Aboki, and my Mother, Mrs. L. M. Aboki
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Acknowledgement
I am much gratitude to almighty God for His grace and enablement thus far. I express my
gratitude and appreciation to my supervisors, Prof. S.A.N.D. Chidebelu, the Head of department,
and Prof. C.J. Arene for their valuable contributions, prompt attention and encouragement in the
course of this thesis. The various contributions of the followings are highly noted- Prof. E.C.
Okorji, Dr. A. A. Enete (Post graduate seminar co-ordinator), Prof. Noble J. Nweze, Prof. (Mrs)
A.I. Achike, Prof. C.U. Okoye, Dr. F.U. Agbo, other lecturers, and my colleagues, the post
graduate students, Department of Agricultural Economics, University of Nigeria, Nsukka.
My appreciation also goes to Prof. D.O.A. Phillips who gave me the frontier software,
Prof. S. A. Rahaman for the initial backup. To my friends, Dr. T.A.K. Anzaku, Dr. M.M. Ari, Dr.
I. Joshua, and Dr. R. E. Barde for the encouragements. I gratefully appreciate my wife Mrs.
Felicia M. Aboki and my entire family for the support to complete this work; I cannot forget
Henry Ajuzie Dozie a Ph.D. student in the University, for always being available to run my
errands. Thank you all.
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ABSTRACT
The study evaluated the traditional and modern groundnut processing and marketing in North
Central Nigeria. The focus was on groundnut oil processing and marketing systems; input use
efficiency in production and factors that made for efficiency; profitability of the processing
activity and factors that determined profitability; examination of value added by processing;
integration of markets for the processed products and problems of the industry. A total of 175
traditional processors were selected and 17 small-Scale modern processors covered from
Nasarawa, Benue and Niger States. Pre-tested, structured questionnaires and observations were
used as instruments of data collection. Types of data collected were those on socio-economic
characteristics of processors, groundnut procurement, processing, and ground nut oil (GNO) and
groundnut cake (GNC) marketing. Weekly price series for GNO and GNC were also collected at
various markets within the region. Data analyses were attained by use of descriptive and
inferential statistics, stochastic frontier analysis (SFA), profit function analysis, t-test statistic and
Johansen test for co-integration. Hypotheses were also tested appropriately. The average age of
traditional processors in North Central Nigeria was 38 years and 41years for modern processors.
Ninety-four percent of the traditional processors were women while 88% of modern processors
were men. Majority of the processors did not participate in co-operative activities. Sixty percent
of groundnut processed by traditional processors came from farmers while 94% of groundnut
processed by modern processors was obtained from traders. The maximum likelihood result for
traditional processors indicated the presence of inefficiency. Raw groundnut variable was
significant at 1% level of significance (LOS) in Nasarawa and Niger States. Fuel-wood and salt
were both significant at 1% LOS in Nasarawa and Benue States. In the inefficiency aspects, age
and years of experience were significant at 1% LOS in all the states. For the zone, labour and salt
were significant at 1% LOS; fuel-wood 5% and raw groundnut 10% LOS. In the inefficiency
aspect for the zone, household size was significant at 5% LOS, while level of education was
significant at 10% level of probability. Raw groundnut and labour were significant in modern
processing, while education and experience at 10% in the inefficiency aspect. Most of the
traditional processors had their efficiency scores above 0.80 and modern processors were from
0.47. In the profit function results for traditional processors, fuel-wood and packaging variables
were significant at 1% LOS. Raw groundnut, procurement and maintenance were significant at
1% in modern processing. Value added was 41% for traditional processors and 44% for modern
processors. There was significant difference in the value of groundnut before and value after
processing. The Johansen trace test result indicated five co-integration vectors at 5% level of
probability for GNO and two co-integration equations for GNC. The markets for GNO and GNC
were not fully integrated. Administrative regulations affected market integration for GNO which
was significant at 5% LOS. Constraints identified included inadequate finance, inadequate
electricity, machine breakdown and transportation. Recommendations made included improved
packaging, finance, electricity supply and co-operative education.
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TABLE OF CONTENTS
Content Page
Cover page
Title page i
Certification ii
Dedication iii
Acknowledgement iv
Abstract v
Table of contents vi
List of tables x
List of figures xii
CHAPTER ONE: INTRODUCTION
1.1 Background of the study 1
1.2 Statement of the problem 6
1.3 Objectives of the study 9
1.4 Hypotheses 10
1.5 Justification 10
1.6 Limitation of the Study 12
CHAPTER TWO: LITERATURE REVIEW
2.1 Groundnut Processing Technologies and Systems 13
2.1.1 The traditional and modern methods of groundnut oil extraction in Nigeria 15
2.1.2 Capital ownership and organizational structures of agricultural processing 17
2.2 Marketing of Finished Products 18
2.2.1 Marketing strategies for agro-industrial products 21
2.2.2 Market demand for agro-industrial products 22
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2.3 Profitability Measures and Value addition 24
2.3.1 Profitability analysis 26
2.3.2 The value adding process in agriculture 27
2.4 Input Use and Efficiency 29
2.4.1 Efficiency measurement 30
2.5 Market Integration 34
2.5.1 Market integration and the law of one price (LOP) 35
2.5.1.1 Stochastic Process and the Unit Root Problem 36
2.5.1.2 Co-integration 38
2.5.1.3 Co-integration and Error correction Mechanism (ECM) 39
2.5.1.4 The Johansen Trace test 39
2.6 Problems of Agricultural Processing Industry 40
2.7Theoretical Framework 41
2.7.1 Value chain in Agricultural Processing and marketing 44
2.8 Analytical Framework 47
2.8.1 Stochastic frontier production function 47
2.8.2 Profitability analysis 51
2.8.3 Measurement of co-integration and the law of one price (LOP) 52
2.8.3.1 The unit root problem 53
2.8.3.2 Unit root test 54
2.8.3.3 Co-integration: The Johansen test 56
2.8.3.4 Determinants of co-integration 58
CHAPTER THREE: METHODOLOGY
3.1 Study area 59
3.2 Sampling technique 60
3.3 Data collection 61
3.4 Data Analysis 62
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3.4.1. Stochastic Frontier Model 62
3.4.2 Profit Function Analysis 65
3.4.3 Value addition model 66
3.4.3 Johansen trace test 67
3.4.3.1Determinants of co-integration 67
CHAPTER FOUR: RESULTS AND DISCUSSION
4.1 Socio-economic Characteristics of Small-scale traditional and modern Groundnut
Processors in Northern Central Nigeria 69
4.1.1 Age distribution of groundnut oil processors 69
4.1.2 Gender distribution of the processors 70
4.1.3 Marital status 71
4.1.4 Household size 71
4.1.5 Educational level of processors 72
4.1.6 Cooperative participation 72
4.1.7 Years of experience 73
4.2 Groundnut Oil Production, Marketing, and the Value Chain in the Study Area 75
4.2.1 Procurement 77
4.2.2Traditional groundnut oil production method 79
4.2.3 Modern groundnut oil production method 80
4.2.4 Marketing 82
4.3. Input Use Efficiency in Traditional and Modern Groundnut Oil Production in North
Central Nigeria 86
4.3.1Technical efficiency estimates for groundnut oil producers in North Central Nigeria 89
4. 4. The Profitability Analysis of Traditional and Small-scale Modern Processing and
Marketing of GNO and GNC 94
4.4.1 Gross margin results of groundnut processing 94
4.4.2 Determinants of profitability of groundnut processing in North Central Nigeria 97
4.5 Value Added by Processing Groundnut into GNC and GNC 100
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4.5.1Test of significance of value added 102
4.6 Level of Integration of Markets Groundnut oil (GNO) and Groundnut cake (GNC) 103
4.6.1 Result of the unit root test 104
4.6.2 Result of the Johansen test for co-integration 105
4.6.3 Determinants of market integration 107
4. 7 Constraints Facing the Groundnut Processing Industry 109
4.7.1 Identified constraints 109
CHAPTER FIVE: SUMMARY, CONCLUSION AND RECOMMENDATIONS
5.1 Summary 113
5.2 Conclusion 118
5.3 Recommendations 119
5.4 Addition to knowledge 120
5.5 Areas needing further research 121
REFERENCES 123
APPENDIX A 131
APPENDIX B 141
LIST OF TABLES
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Table Page
1.1 Categories of Agro-processing by level of transformation of raw materials 3
1.2. Top ten world producers of peanuts - 2008/2009 5
3.1: Population and sample selection for the study 61
4.1 Socio-economic characteristics of traditional small-scale modern processors in North
Central Nigeria 73
4.2 Statistical summary of selected activities of traditional and small-scale modern GNO
processors in North Central Nigeria 77
4.3 Marketing activities of processors in the States and North Central Nigeria 84
4.4 Generalized log likelihood-ratio tests of the complete technical efficiency of
groundnut oil processors in North Central Nigeria 86
4.5 Maximum likelihood estimates (MLE) of the stochastic frontier production
(processing) function for traditional GNO processors in Nasarawa and Benue States 90
4.6 Maximum likelihood estimates (MLE) of the stochastic frontier production
(processing) function for GNO processing in Niger state and North Central Nigeria 91
4.7 Maximum likelihood estimation (MLE) of the stochastic frontier production
(processing) function for modern GNO processors in North Central Nigeria 92
4.8 Distribution of technical efficiency estimates for traditional (small – scale) and modern
GNO processors in the states and the North Central 93
4.9 Gross Margin for Traditional and modern GNO processing the States and the Region 95
4.10 Regression results of the determinants of profitability of traditional GNO processing in
Nasrawa, Benue and Niger states 98
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4.11 Regression results of the profit function of determinants of profitability of traditional
and small-Scale modern GNO processing in North Central Nigeria 99
4.12 Value added by processing groundnut into oil and cake in North Central Nigeria 100
4.13 Result of test of differences in value of groundnut seed before and after processing 102
4.14 Augmented Dickey -Fuller (ADF) Unit root test for price series at level and at first
difference 103
4.15 Result of the multivariate Johansen test for Co-integration for GNO price series 105
4.16 Result of the multivariate Johansen test for Co-integration for GNC price series 105
4.17 Result of factors that determine the level of integration of groundnut oil markets in
North Central Nigeria 107
4.18 Result of factors that determine the level of integration of groundnut cake market in
North Central Nigeria 107
4.19 Constraints to groundnut oil processing in the selected states in North Central Zone 111
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LIST OF FIGURES
Figure Page
1.1 Peanut (Arachis hypogea) plant 4
2.1 Illustration of production efficiency 33
2.2: The generic value chain of Michael E Porter 44
2.3 Flow chart of Agro-processing value chain 46
4.1 The Groundnut oil processing chain in North Central Nigeria 75
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CHAPTER ONE
INTRODUCTION
1.1 Background of the Study
Processing, storage and marketing of agricultural produce have become increasingly
important to the economies of most developing countries, as they have been to industrialized
nations at various stages of their development. Due to technical progress, marketable surpluses
from agricultural production have grown significantly; while rapid growth in urban populations
and rising per capita incomes have enlarged and diversified the demand for processed
agricultural products, whether food or raw materials for industries. Perhaps Processing is one of
the most important physical functions of agricultural marketing. Olayide & Heady (1982) opined
that processing was an important component of agribusiness development, because a large
portion of farm production underwent some degree of change between harvesting and final use.
More so agro-processing is capable of strongly shaping farm production decisions. It enables
quality enhancement, preservation and differentiation of farm production thereby enhancing its
marketability. It has also been noted that Agricultural processing activities are small-scale and
require low investment capital, hence can easily be undertaken by women (Fellows & Hampton,
1997; RMRDC, 2004; Kadurumba, Kadurumba & Umeh, 2009; FAO, 2011).
Farm products’ processing play a significant role in the economies of developing
countries, where it accounts for between 51% and 60% of value added by manufacturing and
between 60% and 70% of total industrial development. Over half of the manufacturing activities
in the developing countries of the world consist of agro-industries preserving and transforming
agricultural raw materials (Olayide & Heady, 1982; Brown, 1986). FAO (2012) observed that
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increased urbanization, distance between home and work-place, working women and changes in
family cohesion has increased demand for shelf-stable, convenience and value added food.
Agricultural processing facilities have a strong impact of stimulating consumer demands
backward to the farm sector, to keep pace with demand for raw materials supply for processing.
Based on farm products, agricultural processing schemes can be sited in areas where other
industries will not be viable, as they are more intensive users of domestic rather than imported
raw materials due to their local availability (Brown, 1986; Austin, 1992; Brown, Deloitte &
Touche, 1994). More importantly, the gains of increased agricultural production through
technical progress will be lost if it is not consolidated through the development of economically
viable processing sector. So also the skills developed through planning and implementation of
agricultural processing and preservation will strengthen stakeholders’ entrepreneurial attributes,
thereby enhancing their economic empowerment (FAO, 2011). As a means of mitigating
problem of food shortage, FAO (2012) among other issues emphasized adding value or
improving the food agro- processing for consumption and the market.
An efficient marketing system connects producers and consumers, directs efficient
allocation of resources in production and distribution of output, while ensuring maximum
economic benefits to participants. Conceptually, agricultural processing which is a segment in
agricultural marketing, involves the transformation of raw materials to the forms required by the
consumer or for the next stage in a manufacturing and distribution chain (Olukosi & Isitor, 1990;
Boland, 2009).This entails transforming and preserving agricultural output, through physical
and/ or chemical alteration. FAO (2011) defined food processing and preservation as a set of
physical, chemical and biological processes that are performed to prolong shelf-life of foods, and
at the same time retain the features that determine the quality, such as colour, texture, flavour
3
and especially its nutritional value. Austin (1992) also viewed agricultural processing industry as
any enterprise that is involved in the processing of materials of plant or animal origin, which he
also described as agro-industry. In the World Bank development activities, the term “agro-
industry” covered agro-industrial processes such as grain milling, fruit and vegetable canning, oil
seeds crushing, and meat packaging as well as the function of marketing(Brown,1986). Hence it
was touted that starting a small rice mill or an oil press marked an early stage in the first steps on
the road to industrialization. The nature of processing and level of transformation can vary
tremendously ranging from cleaning, grading and boxing fruits and milling to oil extraction,
mixing and chemical alteration(Austin,1992), (Table 1.1).
Table 1.1: Categories of Agro-processing by level of transformation of raw materials
Level Activities Illustrative Product
L1 Cleaning, grading, storage Fresh fruits, eggs, fresh vegetables.
L2 Ginning, milling, cutting, mixing Cereals (grains), meat, spices, animal
feeds, jute, cotton, rubber, lumber and
flour
L3 Cooking, pasteurization, dehydration Dairy products, canned or frozen fruits,
refined vegetable oils, furniture, sugar
and beverages.
L4 Chemical alteration and texturization Instant foods, texture vegetable,
Products, tires
Source: Adapted from Austin (1992)
Groundnut (Arachis hypogea) is known to the Hausas as ‘Gyadda’, to the Ibos as
‘Opapa’, the Yorubas as ‘Epa,’ the Americans as peanuts, and the French as arachides. It is a
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leguminous crop grown all over the world as an important oil seed crop native to South America.
Groundnut is thought to have been introduced to West Africa early in the slave trade by the
Portuguese, mainly to supplement the diet of slaves in transit. Its spread into the interior of West
Africa was rapid in the eighteenth century. By 1850s it was common in parts of Hausa land of
Nigeria and thought to be as important as potatoes in Europe by a British traveler (Hogendorn,
1978). Groundnut is a short herbaceous annual crop that produces its pods inside the soil, (figure
1.1).
Plate 1.1 : Peanut (Arachis hypogea) plant
Source : Wikipedia (2010)
Historically, the Sudan and northern guinea savanna of Nigeria have been the high
producing zones. However, the development of several varieties by the Institute for Agricultural
Research (IAR), Ahmadu Bello University, Zaria, has led to even higher output in the southern
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guinea savannah zone, covering the North Central States of Nigeria (RMRDC, 2004). Nigeria
was third among the world ten highest producers of groundnut with 3, 835,600 tonnes
(unshelled) after China and India in 2007/2008 output year (USDA, 2010), but now fourth with
1.55million metric tonnes (shelled) in the 2008/2009 output season (USDA, 2010), ( Table 1. 2).
Table 1. 2: Top ten world producers of peanuts - 2008/2009
Country
Production (Million Metric Tonnes)
People's Republic of China
14.30
India 6.25
United States 2.34
Nigeria 1.55
Indonesia 1.25
Myanmar 1.00
Sudan 0.85
Senegal 0.71
Argentina 0.58
Vietnam 0.50
World 34.43
Source: USDA Foreign Agricultural Service( 2010)
A mature groundnut pod contains 2-4 kernels (nuts) per pod depending on the variety and
is traded decorticated and unshelled. In Nigeria, it is eaten as whole nut, raw, boiled or roasted
and also crushed to get the oil and the cake. The oil is known as groundnut oil (GNO) and the
residue known as groundnut cake (GNC). Groundnut is rated the third major oil seed of the
world after soya bean and cotton (USDA, 2010). Groundnut oil is used for cooking, as salad oil,
for canning sardines, and margarine manufacturing (Sharma & Caralli, 2004).The residue after
oil extraction is a source of protein for animal feed. In traditional oil extraction method, this
residue is fried into a local delicacy known as groundnut cake (GNC) or ‘kulikuli’in Hausa. This
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is ground and consumed in composite with several local dishes. Elsewhere, groundnut is
processed into peanut butter, peanut flour, peanut flakes and many other products.
Bulk export of groundnuts from Nigeria started to decline in the 1960s in favour of local
crushing by mills in and around Kano and else-where. In 1973/74 cropping season, only 35% of
the 559,000 metric tonnes purchased by the marketing board was exported. By 1973/74 also a
policy decision to discontinue export of groundnuts entirely was put in place to allow for local
processing. Any export of groundnut after then was in form of groundnut oil (GNO) or cake
(GNC) (Hogendorn, 1978).
The petroleum oil boom and its consequence upon the agricultural sector saw Nigeria
importing groundnut oil. In 1980, about 200,000 tonnes of groundnut products were imported in
form of vegetable oil. The 1.95million tonnes output in 1974 dropped to 0.4million tonnes in
1983. Consequently many groundnut processing mills had to close down because of
unavailability of the raw material (RMRDC, 2004). However, with the abolition of organized
marketing of agricultural products in 1986, the processing and marketing of groundnuts and its
products have been left to the private sector (Ingawa, 2004). A survey by RMRDC (2004)
showed groundnut output to be 1.98million tonnes for 2003, with greater portion coming from
Bauchi and Nasarawa States with 72,000 tonnes and 70,420 tonnes, respectively, and higher
estimates for 2004. The rain fed output for Nasarawa State in 2008 was put at 92,450 metric
tonnes (NADP, 2009). The soaring demand for groundnut oil in manufacturing and domestic
need has kept the pressure on the groundnut crushing industry.
1.2 Statement of the Problem
Agricultural development policies and programmes have tended to lay emphasis on
improving farm productivity, but with less attention on the processing and storage of the
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resultant output. For instance, 95% of funding of the Consultative Group on International
Agricultural Research (CGIAR) in about 20 to 30 years was devoted to production related
research activities (Ferris, 1999). Agricultural credit disbursement in Nigeria has also been in
favour of crop production with grain alone taking 67% (CBN, 1998). Consequently, the gains of
increased agricultural productivity will not be fully realized if not sustained through the
development of a viable processing and marketing sector to support the technical progress
attained in production. Market forces have instigated greater opportunities for product
differentiation and value addition in some respects (Boland, 2009). These include i) increased
consumer demand regarding health, nutrition, and convenience food; ii) efforts by food
processors to improve their productivity; and iii) technological advances that enable producers to
produce what consumers and processors/manufacturers desire. Importantly, improvement of
efficiency in the value chain fosters more equitable, transparent and sustainable distribution of
benefits to the various stakeholders (FAO, 2011)
Local processing of groundnut and other sources of oil have still not met the domestic
demand for vegetable oil. This is shown in the importation of vegetable oil to supplement local
production, with its attendant drain on foreign exchange. The short fall in demand has been
estimated at between 300,000 tonnes and 400,000 tonnes per annum. Hence the Presidential
Initiative on Vegetable Oil was put in place, to obtain three million tonnes of vegetable oil per
annum from five million tonnes of groundnut and to start exportation by the year 2010 (Ojowu,
2004). Consequent upon the above, the challenge of achieving this target was on the groundnut
processing industry. Hence this study focused on critical areas in groundnut processing and
products marketing chain for appropriate intervention measures to achieve efficiency and
increase products availability.
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In agricultural processing schemes as in production, several inputs are involved. Raw
material that is the farm produce can constitute 90% of the entire inputs needed depending on the
level of processing (Austin, 1992). The efficiencies involved in transforming inputs into desired
output need be known (Olayide & Heady, 1982). One of the problems responsible for poor
performance of developing countries especially in sub- Sahara Africa in international trade is
attributed to low value addition. Consequently, products do not meet international standards, and
do not compete favourably in the international trade. Optimization of groundnut oil (GNO) and
groundnut cake) GNC processing and marketing, is therefore an ultimate desire.
It is also understood from the foregoing that there are information and product gaps in the
value chain with respect to groundnut oil, all pointing to inefficiency along the value chain. Most
technical and economic efficiency studies have concentrated on primary production of crops and
livestock with few on processing, for example Okoh, (1999) worked on cassava roots and its
processed products. Kadurumba, Kadurumba & Umeh, (2009) also worked on allocative
efficiency of traditional palm oil processing in Imo State. Analysis of technical and economic
efficiency data from processing through marketing, with its positive effects in the chain, and
integration of markets for processed products is crucial, but unavailable. Consequently, this
research has addressed the inefficiencies in the value chain, as depicted in capacity under
utilization of plants, poor quality products, low quantity of output from given level of raw
material, inadequate price and output information, unattractive profit incentives, and income
fluctuations.
It has been established that initiating activities from the market - end of the commodity
value chain, using improvements in processing and market expansion to provide “demand pull”
that benefits raw material producers, especially small- holder farmers, is necessary for
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sustainable agricultural development (Ojowu, 2006). A survey by RMRDC (2004) revealed
Nigerian’s groundnut output of 1.976million tonnes. With the entire crop consumed in Nigeria,
examining the performance of the processing segment and hence the downstream segment of
groundnut industry will improve efficiency in operations for processors, and entire value chain.
The synchronous movement over time among prices in different markets has become an
important index of efficiency in the markets. For a market system, domestic or foreign, efficient
performances of its developmental functions depend on the ease with which price changes and
responses are transmitted spatially and temporally within the system. Market integration modeled
within the framework of the spatial price equilibrium (SPE) model of inter market linkages in the
point space tradition, that is subject to production shocks and general price information is crucial
for attainment of efficiency of the markets. The poor infrastructural development in developing
countries as Nigeria leaves lots of doubts in the attainment of integration of the markets for agro-
industrial products, such as groundnut oil and groundnut cake and hence the much desired
efficiency in their marketing systems. Acquah & Owusu (2012) suggested further investigation
into influence of external factors such as market infrastructure, government policy and self
sufficient production, product characteristics and utilization towards market integration.
1.3 Objectives of the Study.
The broad objective of the study was to examine the performance of traditional and
modern groundnut processing and marketing in North Central Nigeria. The specific objectives
were to:
(i) examine the socio- economic characteristics of traditional and modern groundnut oil
processors;
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(ii) describe the traditional and modern groundnut oil processing and marketing systems in
the area and hence the processing value chain;
(iii) examine the technical efficiency in traditional and modern groundnut oil production and
identify the factors that determine efficiency;
(iv) estimate the profitability of groundnut oil (GNO) processing and identify the factors that
make for their profit;
(v) examine value added by processing groundnut into GNO and GNC in the area;
(vi) estimate the level of integration of GNO and GNC markets and identify the factors that
influence their integration; and
(vii) examine the problems of groundnut oil processing and marketing.
1.4 Hypotheses
The null hypotheses tested were:
H01: Groundnut processing into GNO and GNC is not efficient;
H02: Variable input and output prices of GNO and GNC do not affect their variable profit;
H03: There is no significant difference in value of groundnut before and after processing; and
H04: Markets for GNO and GNC are not integrated.
1.5 Justification of the Study
Positive net returns in any business is an incentive to continue the business. The
continuity of investment in crop processing will largely depend on its profitability. Ojowu (2006)
noted that “demand- pull” and profit incentive make the changes achieved in developing
agricultural processing sustainable. More so that participants in groundnut processing are the low
income group who depend largely on what is generated for their sustenance. Therefore the
11
findings of this study will be of immense importance to the participants in the attainment of their
business goal.
This study will particularly benefit groundnut processors in increasing the value (quality
and quantity) of products at lower cost and hence increase their income. For the marketing
agencies, efficiency in the marketing system will increase their returns and lower cost to
consumers and manufacturers that use the products as raw materials. Households will be able to
acquire GNO and GNC at lower cost for domestic needs. On the whole, the entire value chain
from raw material supply (groundnut farmers) through traders to consumers and manufacturers
(Bakers, confectioneries, margarine manufacturers, etc) will be improved.
An inverse relationship between increased mechanization of crop processing and women
participation was observed by Bruinsma (1999); and that some technologies targeted at
alleviating poverty among women did not actually benefit them. The findings of this study will
provide technology related information for purpose of intervention and development of
appropriate technologies to alleviate poverty among the rural poor, particularly in the study area
where religion and other cultural values restrict women from some outdoor income generating
activities. It is understood that spatial market integration ensures that a regional balance is
attained among food-deficit, food-surplus and non-food producing areas through effective
transmission of price signals (Chirwa, 2000).
The findings of this study will set the pace for further research into the groundnut
industry. It will also generally provide base-line information to the private sector, government
agencies and the non- governmental organizations (NGOs) in their businesses and development
activities which will also increase the supply of GNO needed for both domestic and industrial
purposes.
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1.6 Limitations of the Study
The major limitations in this study were those inherent in social and economic research.
Foremost of which was poor record keeping especially among the traditional processors, with
consequent dependency on memory recall for processing and marketing activities with inherent
unreliability. Probing and leading questions were however used during data collection to assist
respondents affirm their responses. Secondly, the study had to contend with the mood and the
on-going activities of the processors during data collection. Because processors could not just
stop their processing work or selling products to respond to questions, it was difficult getting
them to do that. To enhance the quality of information collected, interviews and observations
were used during data collection. This however extended data collection period with its attendant
cost.
This study covered only groundnut oil processors in the selected states. Only those
actively involved in the groundnut oil processing and products marketing activities comprised
the elements in the study, hence other groundnut products were not covered.
13
CHAPTER TWO
LITERATURE REVIEW
2.1 Groundnut Processing Technologies and Systems
Technology is seen as the body of know-how about materials, techniques of production,
and operation of equipment based on the application of scientific knowledge (Black,
2002).Modern technology enables the achievement of objectives at a faster rate. Therefore, a
sound understanding of current technology, their technical, social and economic environment, is
necessary to promote processing enterprises (Bruinsma, 1999). Every technology must be
assessed to determine its adaptability and attendant social, economic and environmental
consequence. Arene, Nwagbo & Okoye (2004) stressed that an existing technology can be
assessed to determine the extent it contributes to satisfying basic human needs; its promotion of
self reliance through the use of local human and material resources; and how it affects social and
cultural structures, norms and attitudes of the society.
Technology selection is often the most crucial decision in designing and undertaking an
agricultural processing project (Austin, 1992). Consequent upon that, Austin (1992) noted that
before choosing an agro-processing technology, the market requirement, technical processing
requirement, cost and availability of labour, capital, energy, raw materials, capacity utilization,
skill capabilities and nutritional consequence must be taken into account. It has been observed
that most technologies for food processing are technology driven rather than product or market
driven hence have had problems of adaptation (Bruinsma and Nont, 1991; Connry et al, 1995;
and Zommers, 1995). In agricultural processing, Orji (2008) suggested a vibrant linkage among
processors, industrial users of products and agro- equipment manufacturers. This will enable the
14
fabrication of appropriate machines for agro- processing activities and optimization of the
processing segment in the commodity chain.
Bruinsma (1999) compiled technology selection guidelines from Fellows and Hampton
(1997) as follows; (i) technological effectiveness (whether the equipment will do the job required
at the intended scale of production); (ii) relative cost of the equipment and its maintenance; (iii)
operating cost and overall profitability; (iv) conformity with existing administrative and
production conditions; (v) conformity with the existing supplies of raw material and the
marketing opportunities; (vi) social effects, such as displacement of the work force; (vii) training
and skill levels required for equipment operation, maintenance and repair; (Viii) health and
safety features and environmental impact; (ix) flexibility to perform more than one function; and
(x) compatibility with other parts of a process and stages in the food chain.
In groundnut oil extraction, the basic principle is the same worldwide. These are: (i) pre-
treatment, which involves cleaning, scorching and crushing the groundnut; and (ii) extraction,
which is the separation of the raw material into oil and residue (cake) (Sharma & Carilli, 1999;
RMRDC, 2004). The cells of groundnut embryo contain oil in an extremely fine emulsion
(Woodroof & Leahy, 1940). When nuts or kernels are broken or bruised, a sufficient number of
cells are injured, hence cause tiny drops of free oil to ooze out and collect on the surface (Asiedu,
1989). In Nigeria and elsewhere, oil extraction from groundnut kernel is done by mechanical
pressing or by use of solvent (RMRDC, 2004; Asiedu, 1989). Solvent extraction is capable of
bringing out nearly all the oil from the groundnut material, a high yield of oil of better quality
and high protein meal (Khan & Hanna, 1983). In the mechanical extraction, the efficiency of oil
expression rarely exceeds 90% but it has the advantage of yielding end-products free of
dissolved chemicals, and is safer and less expensive to undertake (Asiedu, 1989). The residue,
15
the groundnut cake is further fried dry and is used for human consumption popularly known as
‘kulikuli’ in Hausa dialect of Northern Nigeria (RMRDC, 2004).
In Nigeria and West Africa, mechanical and other methods of extraction co-exist. They
all follow the sequence thus: (i) shelling/decortications and drying of groundnut kernels; (ii)
roasting/scorching, the process where the kernels are fried to enable quick polishing and easier
oil extraction; (iii) polishing, which is the removal of the redskin or testa from the groundnut
kernel; (iv) grinding/crushing or pasting, a process that turns the roasted polished and cleaned
groundnut seeds into paste; (v) oil extraction, a process which removes the oil from the
groundnut paste by means of some mechanical effort or solvent and; (vi) refining and quality
control(Asiedu, 1989).
2.1.1 The traditional and modern methods of groundnut oil extraction in Nigeria
In the traditional method, groundnut is shelled, cleaned and roasted lightly. Next, the testa or
redskins are removed by rubbing kernels against each other by hand. The de-skinned kernels are
pounded with mortar and pestle or ground with stones. The hammer mills used to mill grains in
the villages is also used to mill the groundnut kernel in some of the communities. The oil-rich
paste is kneaded and pressed by hand or with pestle and mortar to extract the oil. Small quantity
of warm water is usually added following each pressing operation until as much of the oil-water
mixture as possible has been extracted. The oil-water mixture is fried or ‘fire dried’ over a low
fire to remove the water. Lastly, the oil is decanted into bottles or cans, for home use or the
market. Alternatively, the paste is boiled and the oil rising to the surface is skimmed off. This
may continue until no sign of oil is seen. The traditional method has low oil extraction rate, but
the resulting press cake is used for human consumption (Asiedu, 1989; RMRDC, 2004;
NAERLS, 2006).
16
The modern methods used are the expeller methods and solvent extraction process. For
the expeller pressing method, the pre-treated, crushed groundnut is continuously fed into the
expeller, consisting of a screw which rotates within a sturdily built cylinder. The groundnut mass
is fed from the top larger end of the expeller chamber and pressure exerted as the screw turns
forcing the mass towards the discharge end. Friction and pressure cause the mass to heat, which
facilitates oil extraction. The groundnut oil passes through the perforated screen walls and is
collected beneath the expeller chamber, while the press cake is extruded from the discharge end.
The resulting press cake contains 5% oil and can be made into ‘kulikuli’.
The solvent extraction of groundnut oil is similar to solvent extraction of soya bean oil.
Groundnut destined for oil is not fried, but the red skin is removed. Next, the nuts are cracked
into pieces and conditioned to 10-11 percent moisture content at 70oc or more, and then flaked
by passing through rolls. Sometimes the flakes are cooked before being conveyed into the
extractor. In the extractor, the oil is removed by means of a solvent. The solvent laden flakes
are then passed through a desolvenizer which recovers the solvent. The defatted and de-
solvenized cake may undergo further treatment before it is used as feed. The crude oil is
clarified by passing it through a filter press. After which it may be dehydrated and sent for
refining. Solvent used in oil extractor include ethanol and hexane (Asiedu, 1989; RMRDC,
2004).
This process is basically the same for most vegetable oils. It can also be applied to oil
extracted using hydraulic press or screw expeller. Refining consists of alkali refining which
neutralizes the free fatty acids using sodium hydroxide (NaOH), bleaching to improve flavor
stability using natural bleaching earth, and deodorizing to remove odour using steam distillation
under vacuum. If GNO is to be used as salad oil, it undergoes a process of winterization prior to
17
deodorization. The alkaline refined and bleached oil, if necessary, is hydrogenated to an iodine
value of about 105 before it is winterized to remove fats that crystallize out at about 0oC (Asiedu,
1989).
2.1.2 Capital ownership and organizational structures of agricultural processing
Capital has been viewed by Erikson, Akridge, Bernard & Downey (2002) as financial
resources of a business, comprising in the broadest sense, all assets of the business and
representing both owned and borrowed funds. Black (2002) sees capital as man- made means of
production. It is any good that is designed for production of other goods and services, example
machinery. These include financial assets which will be used to provide income. Olayide &
Heady (1982) explained that capital resources came into agricultural production in form of farm
machinery, as tractors, harvesters, tools and equipment for threshing, shelling, grinding, milking;
biological capital, in form of fertilizers, herbicides, insecticides, and certified seeds; and also in
form of livestock feeds, feed mixes and additives, drugs and improved breeds.
In agricultural processing, capital items will include the raw materials (farm produce),
processing machines and equipment, packaging items, additives, and money capital among
others required for running of the outfit (Brown, 1986; Austin, 1992). Capital in form of
machines, tools and equipment are of critical importance to an agricultural processing activity.
Many processors have encountered problems of capacity under-utilization due to improper
estimation of working capital needs, such that they are not able to acquire adequate raw materials
during harvest, hence leaving the plant short of raw materials for the processing activity.
Consequently, enterprise development and the secondary processing sector have sometimes
failed because the approach taken was technology driven, rather than market oriented (Bruinsma,
18
1999). Therefore, decision to purchase a machine must be based on market demand for the
products to be processed (Austin, 1992).
Technologies for food processing are normally small-scale as they are accessible and
affordable (Clarke, 1987). They can be sited near raw material or where other industries cannot
be located, as was the case with small-scale rice processing in Java (FAO, 1982). In small-scale
agricultural processing in sub-Sahara Africa (SSA), most machines and equipment operate on
contract providing services to owners of the crops or farm output (Clarke, 1987; Bruinsma,
1999). This was shown in cassava processing in Benue State, Nigeria. Aboki & Saingbe (2007)
also reported that all the small-scale rice hulling machines in Lafia, Nasarawa State, operated as
contract mills, providing milling services to paddy owners on hire.
Bruisma (1999) compiled the various forms of organization and ownership arrangement
for processing equipment as follows:
i the village cooperative or interest group can own and run the processing plant and pay a salary
to the machine operator(s) and labourers;
ii. the village cooperative or interest group leases equipment from a local workshop with
sufficient experience in equipment manufacture and maintenance;
iii. a private enterprise functions as a service mill and processes small batches for individual
families, or larger batches on contract for the village cooperative or individual business persons;
and, (iv) a private processing enterprise operating on a fully commercial basis where it buys raw
materials from the village and the surrounding area for further processing.
2.2 Marketing of Finished Products
One of the challenges of agricultural processing industry is the identification of the target
markets for the products. This can be based on consumer needs, market segmentation and buying
19
pattern (Austin, 1992). For processed foods, which form bulk of the agro-industrial products,
consumers’ needs are expressed in preference for products’ taste, smell, colour, texture,
appearance and convenience for users, and, most importantly, nutritional requirement (Austin,
1992; Brown et al, 1994). For other agro-industrial products such as cotton, jute and wood,
whose consumers are industrial users; the emphasis is on price and physical quality (Austin,
1992).
Products are purchased based on the various segments of the markets. These may include
geographical location of consumers which reveals ethnic or regional taste, or other socio –
cultural attributes of the consumers. Income levels of consumers are also strong determinants of
purchases hence an important segment. Also, effective demand and food preferences change as
income levels rise, which also reflect product pricing and product characteristics (Gould &
Villarreal, 2006; Abdullai & Aubert, 2004). For instance, packaging of products is made in
various sizes to cater for the low and high income consumers (Austin, 1992; Ganewatta,
Waschik, Jayasuriya & Edwards, 2005).
Another market for agro – industrial products is the export market. This market requires
high quality products with good packaging. There is increasing difficulty in export of agro –
industrial products to other countries because of increasing protection of domestic agro-
industries in the traditional export markets (Brown, 1986 and Austin, 1992). Access to such
markets may require a thorough knowledge of regulations and trade structure, changes in
packaging or distributional channel, or even new product development (Brown, 1986).
Domestic markets appear to offer growing opportunities for agro-industrial products for
both product diversification and import substitution. With increasing income and status, demand
for high value products is on the rise (Gould & Villarreal, 2006 and Brown et al, 1994). Agro-
20
industrial product user type such as industrial consumers, institutional consumers, wholesale and
retail businessmen, and end consumers need to be considered. Product type- necessities, status
items, convenience goods or specialty products, health foods, food away from home (FAH)
require attention (Austin, 1992; Gould & Villarreal, 2006). In India, 50% of the value of
agricultural production is now high value, but in Africa it is only 5% (Morris, 1994). The buying
process of agro-industrial products can also be examined based on who decides the purchase of
the product, how they decide, and where to make the purchase (Austin, 1992; Fousekis &
Lazaradis 2005). Market information helps in marketing decision for agro – products to be taken
adequately. Potential buyers, producers and marketers can be identified; while location and
prices of products can be revealed via market information research. Costs can be tremendously
reduced if reliable information on a product is available to the producer, marketer or even the
consumer (Austin, 1992).
The market place is normally crowded with agro- industrial firms and products,
especially in developed countries, each aiming at controlling or accessing the market, hence the
competitive environment. The five sources of competition of Michael Porter- rivals, potential
entrants, substitutes, suppliers and buyers- need be considered in marketing process of agro-
industrial products (Austin, 1992). Agro-industries present a typical representation of
monopolistic competition with several firms. Market place will be explored by providing buyers
with greater values, which are attainable through cost advantage or product differentiation
(Bruinsma, 1999). Cost advantage allows more effective price competition and product
differentiation enables more effective quality competition, so that the price-quality interplay
yields the ultimate consumer value. In the USA, major meat packers reduced transportation and
labor costs by relocating to rural livestock production areas. Differentiation of product can be in
21
form of, for instance, vegetable oil, with varying oleic acid, distinct flavor and special consumer
preferences (Sharma & Carralli, 2002). Packaging is also a differentiating factor. South
American banana companies’ use of cartons created value for distribution and retailers through
more efficient handling, quality control and branding. There is a difference between intrinsic and
perceived quality of products. Brand and image creation is a strategy for perceived quality
competition, but packaging, product content and services are means of intrinsic quality
differentiation; value is therefore in the eyes of the buyer (Austin, 1992).
2.2.1 Marketing strategies for agro-industrial products
The basic consideration in product marketing includes design, pricing, promotion and
distribution referred to as the marketing mix. Several design options are acceptable for agro –
industrial products. These include taste, texture, cooking ease, colour, odour, nutritive value,
convenience, size and packaging. For furniture, leather or wood products, consideration will
include durability and fashion (Austin, 1992). In Nigeria the iodization of salt and fortification of
vegetable oil, and sugar with vitamins is an example. Cost of product designs and final price of
the product must be evaluated to avoid prohibitive cost. Demand and supply forces in the
markets set prices for most agro – industrial commodities. Internationally, most developing
countries are price takers, while the exporting countries set the prices (Austin, 1992). For locally
processed products, pricing vary from market to market within regions and between regions,
attributable to such imperfections as lack of information, poor infrastructure and so on (Damisa
& Rahaman, 2004).
Sales promotion creates awareness and stimulates demand for products in a consumer. A
superior product will not reach its sales potential unless consumers are aware of its advantages.
In the USA food processors spend about 3% of their revenue on advertising, which amounts to
22
about 1.5 percent of consumers’ at home food expenditure (Kolhs & Uhls, 2002). Promotion is
much less for staples and undifferentiated products and more for processed products.
Promotional activities are commonly seen in the print and electronic media in Nigeria, and road
shows among others; they all stress real and imaginary advantages of products.
The distribution system is important because it links the processors to the market place.
The structure of the marketing and distribution system describes the length of channel and the
number of intermediaries between the manufacturers and the consumer. These include the
wholesalers, commission agents, brokers and retailers operating at different levels of the
product’s chain (Austin, 1992). Several marketing functions carried out include logistics
operations (transportation, storage, repackaging), financing, and promotion. In an efficient
marketing system of developed countries like USA, 28 percent of the consumers food dollar
went to farmers, 72 percent went for processing and marketing functions (Kolhs & Uhls, 2000).
The reverse was the case in Ghana where farmers received 71 percent of the retail price of rice,
processor 12 percent, assemblers / wholesalers 12 percent and retailers 5 percent (Timmer,
Walter & Scott, 1983).The nature of agricultural produce requires specialized and speedy
transportation, storage arrangement for both procured raw materials and processed products,
(Austin, 1992). Food wholesaling in developing countries tends to be highly fragmented and
relatively small-scale though effort to improve the system is being stepped up (Brown, 1986).
2.2.2 Market demand for agro-industrial products
Demand as defined by Erikson, Akridge, Bernard & Downey (2004) is the quantity of a
commodity that consumers are willing and able to buy in the market at various prices. Arising
from the theory of consumers’ behaviour, in which consumers are rational and thrive towards
maximizing utility given their income (Nellis & Parker, 2000).The demand situation for agro-
23
industrial products set the pace for its marketing plans, and because it makes no economic sense
producing a product that cannot be sold (Kotler & Keller, 2006). Therefore, consumer demand
analysis becomes critical in the processing and marketing of a product. To grapple with this,
Austin (1992) suggested consumer analysis to include consumer needs, market segmentation, the
processing process and market research ; competitive environment to include market structure,
the basis for competition and government influence; the marketing plan analysis to include
product design, pricing, promotion and distribution, and demand forecasting which include
examining data needs for forecasting technique and projecting sales.
A cross-section analysis of household demand for food and nutrients in Tanzania
(Abdullai & Aubert, 2004) revealed that income and other socio- economic variables exerted
significant effects on the demand for food and nutrients. The other socio- economic variables
included women’s schooling, size of household and urban residence. The study also showed that
households in urban areas consumed more fats and oils than their rural counterparts. In a study of
Greek households, Fousekis & Lazaradis (2005) concluded that the age of the household head,
the degree of urbanization, the percentage of expenditure devoted to food away-from home
(FAH), and per capita consumption expenditure affected the intake of nutrients; while household
head gender and educational achievements were effective only in certain locations in the study.
Gould & Villarreal (2006) in a study in China reported that 26% of household income was spent
on food at home (FAH); with household in the lowest income group spending 40% of household
income on FAH, while the highest income bracket household spent 15%. It was revealed that
percentage of food expenditure on food away from home (FAFH) was higher than on FAH.
Also, noted was decrease in percentage of income devoted to food with increase in income of
household.
24
Sabates et al (2001) noted that educational attainment of the main meal planner had
impact on food choice and nutritional quality of the resulting diet. Gould & Villarreal (2006)
observed that 40% of the household meal planners did not have a high school education. They,
also, hypothesized that household food choice patterns were influenced by the age structure of
the household members. Sub-Saharan Africa (SSA) is expected to experience growth in food
demand largely driven by rapid population growth. Demand for roots and tubers has also
increased over the years due to increased consumer and industrial needs emanating from
population pressure, favourable markets, government support and price interactions (
Nwachukwu, Agwu, Onyewaku & Egeonu, 2009). Agro – industries enhance product
diversification and improvement necessary to change the demand pattern for farm producers
(Brown, 1986).
2.3 Profitability Measures and Value addition
Profit is defined as total revenue minus total cost (Erikson, et al 2004). They outlined
four perspectives of profit; (i) profit is a reward for taking risks in business; (ii) profit results
from the control of scarce resources; when a citizen owns a resource that others want, the others
will bid up the price which will then generate profit for the owner; (iii) profits exist because
some people have access to information others do not have. This special knowledge include
secret formulas or processes, exclusive right to inventions, property rights and patents, e.t.c.,
ensuring profit for the creator; and (iv)profits could exist simply because some businesses are
managed better than others; their managers are often creative planners and thinkers with efficient
organizational abilities.
The accountant looks at profit as the income that remains after all contractual, measurable
costs are subtracted. The economists however determine profits by examining alternative uses of
25
resources within the firm. Hence, economic profit is defined as accounting profit less opportunity
cost. It forces an examination of alternative uses of resources and helps in analyzing alternative
courses of action by the firm (Kay, 2000).
It is contended that the entrepreneur’s motive for producing any given product is that of
the attainment of maximum profit, while consumers or buyers’ motive is that of utility
maximization (Olayide & Heady, 1982). The profit motive is the ‘spark plug’ of a market
oriented capitalist economy. The prospect of earning and keeping a profit serves as the incentive
for creativity and efficiency among people. It stimulates risky ventures and drives people to
develop ways of cutting costs and improving techniques, always in an effort to satisfy consumers
desires (Erikson et al, 2002).
Kotler & Keller (2006) suggested that firms should be able to measure the profitability
of their products, territories, customer groups, segments, trade channels and other sizes;
emphasizing that this will help the management to determine whether any products or marketing
activity should be expanded, reduced or eliminated. Marketing profitability analysis generally
indicates the relative profitability of different channels, products, territories or other marketing
entities. More so, companies are showing interest in using market profitability analysis or broad
version activity based cost accounting (ABC) to quantify the true profitability of different
activities (Cooper & Kaplan, 1991). Profitability can be improved by managers if there is
reduction in resources needed to perform various activities or make resources more productive or
acquire them at least cost; or alternatively raise prices on products that consume heavy amount of
support services (Kotler & Keller, 2006).
Various models of profitability have been used in production and marketing researches.
Onuoha, Okereke & Asumugha (2009) applied the gross margin and net income analysis in
26
determining profitability of feed-mills in Umudike, Abia State, Nigeria; in which profitability
was reported at 1.06 for every naira spent. In similar study on paddy enterprises, Okoye &
Anuebunwa (2009) reported gross margin of 33% and 27% for the two enterprises. Ezedinma
(2007) noted that the profitability of a market is a direct indicator of degree of efficiency of the
marketing system.
2.3.1Profitability analysis
In economic theory, profit is maximized at output level where marginal cost equals
marginal revenue (Koutsiyianis, 1979). Thus, one can determine profit by comparing total
revenue with total cost, or by comparing average price and average total cost. Multiplying the
difference by the total output gives the total profit or loss (Nellis & Parker, 2000).
Olayide & Heady (1982) derived unconstrained profit maximization given two factors and one
product production function as follows;
Q = f (x1, x2) ……………………………………. 2.1
C = r1 x1 + r2 x2 + b……………………………. 2.2
Where,
Q = output, C= cost, x1, x2 are inputs;, r= price of input, b= fixed cost.
Understanding that profit (π) is given as revenue (price multiplied by quantity) less cost,
and then one has a function of the form:
π = Pf (X1, X2) – r1 X1 – r2 X2 – b) ……………………. 2.3
To maximize profits, we set the partial derivatives of π with respect to Xs and equate to
zero and solve. Hence profit is maximized at output level where marginal cost (MC)
equals marginal revenue (MR), given p= price of output. This is given as
δπ/δx1= pf1-r1 = 0 …………………..2.4
27
δπ/δx2 = pf2 – r2 = 0 …………………… 2.5
Solving 1and 2 then
Pf1 = r1; pf2=r2 ……………………… 2.6
If the price of a commodity exceeds the average total cost (ATC) of production,
supernormal (pure) profits are made as opposed to normal profits; and if the price is below
average variable cost (AVC), the firm is at shut down point, in the short run (Nellis & Parker,
2000). Normal profit is the minimum rate of profit which must be earned to ensure that
sufficient number of people are prepared to invest, organize production and undertake risk in an
industry (Nellis & Parker, 2000; Frank & Bernanke, 2001).
2.3.2 The value adding process in agriculture
The difference in values of raw agricultural product before processing and after
processing is the added value. Black (2002) defined value adding as the total value of a firm’s
output less the value of inputs purchased from other firms. Value added is thus what is left to be
shared between wages of the employees and profits for owners of the business. Gittinger (1972)
noted that one could have gross value added, in which case the value of inputs is not subtracted,
and the net value added where deductions are made for inputs including depreciation, labor,
management, and cost, among others. In this case value added could be positive or negative as
the case may be. In agricultural processing, Austin (1992) and Brown et al, (1994) explained that
the difference between cost of ingredients (including farm produce), and the ex-factory or post-
processing price of the finished products is the value added through processing. Without
prejudice to other opinions, this will be the working definition for this study.
Agricultural marketing provides form, place, time and possession utilities to consumers
(Kohls & Uhls, 2000). Agricultural processing changes the form of the farm produce to a state
28
required by consumers or next stage in a manufacturing scheme, hence creating form utility.
Olukosi & Isitor (1990) described processors and manufacturers’ activities as increasing the
quality and value of farm produce.
The value adding process however runs in the entire food marketing channel from
production through processors, the traders to the final consumer. The optimization of the food
marketing chain is now attracting attention from agencies involved in agricultural research and
rural development (Fellows & Hampton, 1997; Bruinsma, 1999). The technical advisory
committee to CGIAR also takes the view that research on agricultural productivity needs to be
complemented by more research on product utilization and post-production activities, storage,
processing and marketing as part of a coherent approach (Bruinsman, 1999). Ezedinma (2007)
also advocated commodity chain approach to agricultural development. Ganewatta, Waschik,
Jayasuriya & Edward (2005) observed that the East Asian industrialized countries adapted
policies to enhance domestic processing of primary commodities as a tool for accelerating
employment, growth, export revenues and development.
Agricultural processing industry or Agro-industry has grown in size because of its
integration of agricultural and industrial activities (Austin, 1992). It is seen as probably the most
important component of agribusiness (Olayide & Heady, 1982). In the United States of America,
food processing constitutes the bulk of value adding activities and small businesses (Torok, et al,
1990). Agro- industry has also been defined as any activity that deals with the processing of
material of plant or animal origin, which is why agro-processing industries dominate
manufacturing activities in less developed countries where agriculture is the main stay of the
economies (Brown, Deloitte & Touch, 1994). Brown (1986) & Austin (1992) explained agro-
29
industries to include such activities as oil seed crushing, grain milling, fruit and vegetable
canning, meat packaging, the textile industry, and function of marketing.
It is said that one of the first steps on the road to industrialization is the processing
industry, so that starting a small rice mill or an oil press marks an early stage in industrialization
(Austin, 1992). More so a nation may not fully use its agronomic resources without a viable
agricultural processing sector, because most agricultural products including subsistence products
are processed to some extent (Brown, 1986). Brown, Deloitte & Touche (1994) pointed out that a
post-harvest enterprise can influence the volume and disposition of agricultural production,
likewise the degree of food self-sufficiency, it induces changes in infrastructure; enhance
employment and contribution to foreign exchange earnings. The establishment of agro allied
industry can in addition to immediate and direct benefits to farmers bring about development of
other infrastructural amenities such as improved transportation facilities, water and electricity
supply, schools and health services (Barau, 1979; Orewa, 1978).
2.4 Input Use and Efficiency Measurement
Agricultural productivity is an index of the ratio of the value of total farm input to the
value of output. The attainment of social welfare ‘parieto optimality’ of every society hinges on
the maximum use of available resources, which with every re-allocation of resources, everyone
is made better and no one is made worst off (Kutsoyianis, 1979; Black, 2000). The input- output
relationship in production process becomes important in four areas; (i) distribution of income,
(ii) allocation of resources, (iii) the relationship between stock and flow resources, and (iv)the
measurement of efficiency or productivity (Olayide & Heady, 1982). Kutsoyianis (1979) also
emphasized the principle of efficiency in the overall equilibrium of the consumers, producers
and the markets. Maximum resource productivity then means obtaining the maximum possible
30
output from the minimum possible set of inputs. In this perspective, optimal resource
productivity implies an efficient utilization of resources in the production process; thereby
expressing synonymy of productivity and efficiency (Olayide & Heady, 1982). Lassita &
Odening (2003) noted that maximum productivity also called ‘best practice’ is revealed in the
production frontier and, hence, efficiency involves the distance to this frontier.
2.4.1 Efficiency measurement
Efficiency is a term that applies in several aspects of economic life, defined by Black
(2000) as getting any given results with the smallest possible inputs, or getting the maximum
possible output from given resources. This is applied both in agricultural production and
marketing. In agricultural marketing, the term market efficiency is used to describe the
performance of the marketing system, encompassed in performance of marketing factors in
which efficiency is defined as increasing the output – input ratio (Erikson et al, (2002). Olukosi
& Isitor (1990) observed that efficiency was an engineering terminology commonly used in
machines to measure the ratio of output to input. Hence, marketing efficiency can be defined as
the maximization of the ratio of output to input of marketing (Kotler & Keller, 2006). The inputs
of marketing include the resources expended in providing marketing services such as capital,
labour and management.
Meanwhile, marketing output includes time, form, place and possession utilities which
consumers derive from the marketing of products. Therefore, marketing inputs are the cost of
providing marketing services whereas marketing outputs are the benefits or satisfaction created,
or the value added to the commodity when it passes through the marketing system (Olukosi &
Isitor, 1990; Kotler & Keller, 2006). Efficiency ratios can be measured in physical terms or in
monetary terms (Bamire et al, 2007). If in monetary terms, the concept becomes a ratio of
31
benefits to cost (Olukosi & Isitor, 1990).The higher the ratio the higher the marketing efficiency,
and the better the marketing system.
Estimating the inputs of marketing is much easier than the outputs of marketing. The
input cost of marketing is the value of all resources used in the marketing process. The best
measure of marketing output (consumers’ satisfaction) is the price consumers are willing to pay
for the farm products with different levels of marketing utilities (Shepherd & Futrell, 1970;
Downey and Trocke, 1981; Olukosi & Isitor, 1990).
Marketing efficiency, according to Kohls & Uhls (2000), Erikson et al, (2002), and
Kotler & Keller (2006) could be attained in any of the following situations:
(a) output remains constant while input decreases;
(b) output increases while input remains constant;
(c) output increase more than input increase; and
(d) output decreases more slowly than decreases in input
Marketing efficiency can be looked upon in two perspectives, operational efficiency or
technical efficiency and pricing efficiency or economic efficiency. Operational or technical
efficiency measures the productivity of performing marketing services within a firm, with
emphasis on the cost of providing marketing services. This assumes that the essential nature of
output of goods and services remain unchanged, hence the focus is on reducing the cost of inputs
used in doing the job. For instance, an innovative method of crating eggs or tomatoes may not
only increase the quantity handled in a given space, but also reduce damages during
transportation. In this, one has improved operation efficiency.
Olukosi & Isitor (1990) posit that pricing efficiency assumes a physical input-output
relationship that remains constant; hence, pricing efficiency is concerned with how effectively
32
prices reflect the costs of moving the output through the marketing system. In this case, prices
that consumers pay for goods delivered by the marketing system should adequately reflect all
marketing and production costs, therefore bringing about improvements in the operations of
buying and selling and the pricing aspect reflecting consumers’ wishes. In a perfectly
competitive economic environment, prices will adequately serve this purpose by reflecting all
costs of marketing. Marketing imperfections such as dominance of few firms or inadequate price
information will give rise to pricing inefficiency (Truet & Truet, 1990; Olukosi & Isitor, 1990;
Frank & Bernanke, 2002).
In the light of the above, Bressler & King (1970) noted that efficiency models were
closely related to and sometimes identical with competitive models. Here, the theoretical
construct must come largely from the theory of perfect market in which efficient market will
establish prices that are interrelated through space by transportation costs, through form by costs
of processing and through time as a consequence of the costs of storage. Therefore, only a
normal profit is earned by participants in the marketing system (Kutsoyianis; 1979; Nellis &
Parker, 1999).
In production, efficiency is concerned with the relative performance of the process in
transforming inputs into output (Arene, 2003). Efficiency of a production system then compares
between observed and optimal values of its output and the inputs used in the production process.
This is in the form of ratio of observed output to the maximum potential of observed inputs
required to produce a given level of output or some combinations of the two scenarios (Olayide
& Heady, 1982)
Arising from the initial definitions of efficiency by Farell (1957), Alvarez & Arias (2004)
said that a firm is considered to be technically efficient if it obtains the maximum attainable
33
output given a level of inputs and the technology used. Also from Farell’s work, Osborne &
Trueblood (2006) made a distinction among technical efficiency (TE), allocation efficiency (AE)
and economic efficiency (EE). Furtherance to that, with an input orientation, TE refers to the
ability to minimize physical input use for a given level of output; AE refers to the ability to
achieve cost minimization for a given output level, while EE refers to the combined effect of
achieving both TE and AE. Most empirical studies of efficiency are on technical efficiency
rather than economic efficiency because data on price, input and output for economic efficiency
analysis are difficult to gather due to price instability, (Osborne & Trueblood, 2006). Efficiency
is illustrated in figure 2.1.
Figure 2.1: Illustration of efficiency adopted from Osborne & Trueblood (2006)
In Figure 2.1, given an efficient isoquant Y and the Iso- cost line, point A is technically
inefficient since it is located away from the production isoquant for output level Y. Point B is
technically efficient because it lies on the isoquant for the output level Y, however this point is
not allocatively efficient because it does not lie on the iso-cost line, that is no tangency between
Y Isoquant
Iso-cost line
B
A
C
y
x
34
the isoquant and the iso-cost line. Point C lies on both the isoquant and the iso- cost line, where it
is both technically and allocatively efficient, that is economically efficient (Osborne &
Trueblood, 2006). Parametric and non- parametric approaches have been used to measure
efficiency (Alvarez and Arias, 2004; Latruffe, Belcombe, Davidora & Zawalinska, 2005;
Osborne & Trueblood, 2006; Amaefula, Onyenweaku & Asumugha, 2009).
2.5 Market Integration
Market integration has been studied by several authors, with several approaches to testing
spatial market integration using market price to examine the concepts of spatial arbitrage of food
marketing systems in developing countries. Jones (1972) and Dadi et al (1992) applied
correlation analysis in the study of food market integration in Nigeria and Ethiopia, respectively.
Damisa & Rahaman (2004) also used static regression analysis to study market integration of
cowpea, ground nut, sorghum and millet in Kano in which prices in some markets affected the
others, while some others did not.
Markets are said to be integrated or efficient if the correlation coefficient (R) or
regression (β) attain values greater than zero but not greater than one. If R> 0.9, markets are said
to be highly integrated; if R<0.8, the markets are said to be moderately integrated. But if R<0.5,
then there is no integration and prices move independently of each other (Adeleye, 1988, Damisa
& Rahaman 2004).
Okoh (1999) and Akintola (1999) also adopted the Mendoza & Rosegrant (1995)
approach to study market co-integration, but avoided the problem of non stationarity by
undertaking unit root test and differencing the series; they observed that cassava root and gari
markets in the study area were weakly associated, and had some form of price leadership in the
system. Kindie et al (2005) applied the auto regressive distributed lagged (ARDL) via OLS in
35
the analysis of markets integration for white teff in Ethiopia. Dittoh (1994) applied the Ravallion
model using ARDL to study market efficiency in vegetable markets in Nigeria. Static regression
models and some others have been found to be inadequate for analysis of LOP and co-integration
due to possible non-stationarity of the series which may lead to spurious regression (Chirwa
2000, Okoh & Egbon 2005, Asche et al, 1999). The Johansen trace test has been found to be
more suitable. The price series used in various studies were collected weekly or fortnightly
(Damisa & Rahaman, 2004; Ali & Rahaman 2009). Those that used monthly data include Okoh
(1999), Okoh & Akintola 1999; Chirwa, 2000; Kindie et al, 2005; Asche et al, 2005).
Several studies on the integration of Nigerian markets and elsewhere point to some major
sources of poor integration and inefficiency to include poor price information transmission, too
many intermediaries and high cost of transportation as well as the sources and validity of price
data (Okoh & Egbon, 2005). Chirwa (2000) also noticed factors influencing market integration
to include infrastructure, consisting of transport costs, extent of the transport network,
communication facilities and availability and access to credit facilities.
2.5.1 Market integration and the law of one price (LOP)
Two markets are said to be spatially integrated if when trade takes place between
them; price in the importing market equals price in the exporting market plus the transportation
cost and the other transfer costs involved in moving the commodity (Chirwa, 2000); rural prices
and urban prices plus transportation or other transfer costs (Okoh & Egbon, 2005); producer
price and wholesale price plus transportation and other cost (Kindie et al, 2005); wild Salmon
price and Farmed Salmon prices (Asche et al, 2005).
The perfectly competitive market conditions represent an ideal market structure for
market integration, given the attributes that prices adjust instantaneously to any new information.
36
The principle of market integration is itself hinged on the ‘law of one price’ (LOP), which is
analysed within the framework of perfect market model. By the Marshallian propositions on
economic market, two regions are in the same economic markets for a homogeneous good if the
price for that good differs by exactly the inter – regional transportation cost (Okoh & Egbon,
2005). The expression for the LOP can be given as
Pti + Kt
ij = Pt
j …………… 2.7
Where,
Pti = Price of product in the exporting market in the period t.
Ptj = the contemporaneous price of the product in the importing market.
Ktij = the transfer cost in the same period
a strict version of the LOP; trade exist between the markets. But if
Pti + Kt
ij > Pt
i ………………… 2.8
then there is no incentives to trade; or if Ktij ≠ 0 , then the prices have a proportional
relationship, but their levels would differ due to factors such as transportation cost , processing
cost, market fees, quality differences e.t.c. This is a weak version of LOP. If no barriers to trade
exist between markets, trade will cause prices in the two markets to move on a one-for-one basis
and the spatial arbitrage conditions are holding (Asche et al, 1999; Okoh & Egbon, 2005).
2.5.1.1 Stochastic Process and the Unit Root Problem
The unit root analysis and test are the starting point in the analysis of market integration.
Consequently stationary stochastic process is of great interest to the price series analyst with
respect to market integration. This implies stationarity (weak stationarity) in a random or
stochastic process in a collection of random variables ordered in time (Gujarathi, 2007). This is
expressed as
37
Mean: E (Yt) = µ
Variance: Var (Yt) = E (Yt – µ) 2
= σ2
Covariance: Yt = E [(Yt – µ) (Yt+k - µ)]
A time series is stationary if its mean, variance and auto covariance remain the same at various
lags or points of measurement that is they are time invariant. Hence non-stationary time series
will have a time varying mean or time varying variance or both. A stochastic process (time
series) is purely random or white noise if it has zero mean, constant variance and is serially
uncorrelated, denoted as Ut iidN (0, σ2) (Gujarathi, 2007).
Although the interest lies in stationarity of series, economic series are seldom stationary (non
stationary). A classical example is in the random walk model (RWM). This is distinguished into
random work without drift given as:
Yt = Yt-t + Ut ……………………………… 2.9
Yt = Value of y at time t
Yt-1 = value of Yt lag 1 period
Equation (1) can be written as (Yt-Yt-1) = Yt = Ut ……………….. 2.10
Random walk with drift given as
Yt = δ + Yt-1 + Ut …………….. 2.11
where δ is the drift parameter. Equation (2.11) can also be written as,
Yt - Yt-1= Yt = δ + Ut …………… 2.12
Which shows that Yt drifts upward or downward depending whether δ is positive or negative.
Equation (4) is an AR (1) model. The random walk model is an example of a unit root process. If
we rewrite equation (1) as
Yt = ρYt-1 + Ut -1 ≤ ρ ≤ 1 ………….. 2.13
38
If ρ =1, equation (5) becomes a RWM without drift and we face a unit root problem, situation of
non-stationarity where the variance of Yt is non-stationary. If however the absolute value /ρ/ ≤ 1
the time series Yt is stationary.
To achieve stationarity and avoid the phenomenon of spurious and nonsensical regression in the
case of the unit root or RWM model i e the series is integrated of the order one I (1), differencing
the series of I (0) has to take place. Most economic time series are I (1) and generally become
stationary only after taking their first difference. If a series is differenced d times to become
stationary, then it is integrated of order d, I (d).
2.5.1.2 Co-integration
A time series is stationery if it is I (0), and non stationary if I(1), that is they have
stochastic trend. Hence a linear combination can cancel out the stochastic trends in the two series
that are I (1), and we do not have a spurious regression. In which case we say the two variables
are co-integrated. From the economic point of view two series are co- integrated if they have a
long-term, or equilibrium relationship between them. Therefore the traditional regression
methodology applies including the t and F tests. As Granger puts it “a test for co- integration”
can be thought of as a pre-test to avoid nonsensical regression situations. Some sample tests for
certification are the Engle-Granger (EG) or Augmented Engle-Granger (AEG) test which has its
roots in ADF (Ali & Rahaman, 2009). The co-integrating Regression Durbin–Watson (CRDW)
test, whose critical values were provided by Sargen and Bhargana, used the Durbin – Watson d-
statistic obtained from the co-integrating regression (Gujarathi, 2007).
2.5.1.3 Co-integration and Error correction Mechanism (ECM)
Co-integration implies a long term or equilibrium relationship between two variables. In the
short run, there may be disequilibrium. Here we can treat the error term in the co integrated
39
variables regression as equilibrium error. This error can be tied to the short run behaviour of the
series (or variable) to its long term value. This was used by Engle and Granger to correct for
disequilibrium. As Granger puts it, if two variables Y and X are co-integrated, the relationship
between the two can be expressed as ECM given as
Yt = a0 + α1tXt + α2µt-1 + εt …………….. 2.14
2.5.1.4 The Johansen Trace test
The Johansen test (Bivariate and multivariate) are based on vector auto regressive (VAR)
model; a reduced form which avoids the problem of simultaneity (Asche et al, 1999; 2005). The
Johansen trace test detects the number of co integration vectors that exists between two or more
integrated series. The test follows the maximum likelihood estimation procedure that provides
estimates of all co-integration vectors existing among a group of variables. The presence of co-
integration among for instance pairs of price series shows the existence of integration among the
series. A further test of residuals and test for variable exclusion confirms the existence of market
integration between spatially differentiated markets. The Johansen bi-variate and multivariate
tests (Johansen 1998; Johansen & Juselius 1992; Asche et al, 1999; Asche , 2005) has gained
popularity; and is widely used for the test of the Law of one price (LOP) in agriculture and food
markets and is preferred to the Engel–Granger procedure(Okoh & Egbon 2005; Asche et al,
2005)
2.6 Problems of Agricultural Processing Industry
There are peculiar problems associated with agricultural processing which make it
different from other forms of businesses. Most raw materials are perishable and will quickly
deteriorate if not processed immediately. Special and speedy transportation and processing
facilities are required to maintain the quality of both the raw materials and the processed
40
products (Fellows & Hampton, 1997). The cost of these facilities could be prohibitive for small-
scale processors.
For crop and animal products, seasonality is a critical factor in the capacity utilization of
processing machines and increases in unit cost of production (Brown, 1986). Seasonality of raw
materials means that they can only be processed for part of the year, while the plants may be idle
at some other periods. During the peak season, supplies waiting to be processed can even
deteriorate, and high risk of plant breakdown due to intensive operations (Browm, 1986; Austin,
1992). Plants that can handle a variety of crops can be installed to reduce risk.
Technological incompatibility and technical incompetence have brought so much to bear
on ago- processing industry. Most processing machines are operated by unskilled personnel,
which reduce the efficiencies of these machines and consequent poor quality of the products.
Traditions can simply not allow some technologies to flourish (Barau, 1979; Brown, 1986). In
addition to operating frequently in economically hostile environment, majority of food
processing enterprises are small-scale and within the informal sector hence have little economic
powers. These include poor access to credit and infrastructure (Fellows and Hampton, 1997).
Brown (1986) outlined two types of labour related problems in the World Bank assisted
agro-industries; (i) for public enterprises there is pressure to absorb excess labour, though
economically beneficial, can inflate labour cost to the financial detriment of the enterprises; and
(ii) low labour turn over. In the same study he observed that in their locations (rural areas),
agricultural industries suffered from lack of access to rail system, water supply, electricity and
facilities for waste disposal. Financial management and process management are closely related
and in the World Bank lending to agro-industries have been serious problems; in which Brown
41
(1986) and Austin (1992) suggested adequate financial planning that actually takes care of
inventory management as it affects raw material supplies and liquidity management.
It was specifically observed by Brown (1986) that high level of optimism regarding the
supply of raw material markets led to wide spread under capacity utilization in agro-industrial
projects. Several agro- allied industries have collapsed due to irregular supplies and shortage of
raw material. A typical case is that of Cadbury tomato processing plant located in Zaria that had
to close down on the 25th
May, 1978 due to lack of raw material for processing(Orewa, 1978).
Crop processing is an activity traditionally undertaken by women (Clarke, 1987; Paris,
1988). The techniques are quite arduous, involving large investment of time for a little result.
Two categories of women are involved. The farm women who process their own crops for family
consumption, and the landless women or wives of marginal farmers who process other people’s
crops as a means of supplementing family income (Clarke, 1987). Even then the development of
modern processing and its wide spread had destroyed millions of part- time jobs for the poorest
section of the society. Some 7 to 8 million women were estimated to have lost their jobs
following mechanization of rice in Java. In Bangladesh, each new rice mill is said to put about
350 women out of part-time employment (Clarke, 1987; Austin, 1992).
2.7 Theoretical Framework
Theoretical perspective for this research is the value chain analysis or value addition
concept. The value chain analysis is a concept based on the economic value of a product to the
consumer. It is a business concept concerned with creating and sustaining superior performance.
A product gains value as it passes through stages of activities in the value chain, as it moves
from producer to the ultimate consumer. Michael Porter of Harvard University proposed the
value chain as a tool for identifying ways to create more customer value. According to this
42
model, every firm is a synthesis of activities performed to design, produce, market, and support
its product (Kotler & Keller, 2006). The ultimate goal is maximization of value creation, which
culminates into cost minimization and profit maximization.
Of course marketing involves satisfying consumers’ needs and wants; therefore the task
of any business is to deliver customers’ value at a profit (Kotler & Keller, 2006). In that
perspective, Kotler & Armstrong (2008) emphasized the following: (i ) creating values for
customers to capture values from customers in return;(ii) building and managing strong value
creating brands; (iii) managing returns from marketing to recapture value; (iv) harnessing new
marketing technologies and; and (v) marketing in a socially responsible way around the globe.
In analyzing the value delivery (value chain) process, Kotler & Keller (2006) made two
observations. Firstly, the traditional view of marketing in which the firm makes a product and
sells it. In this view, marketing takes place in the second half of the process, that is, after the
product had been made. Here, the company knew what to produce and the market would buy
enough units for the firm to make profit. Market institutions or agencies that subscribe to this
approach can best succeed in economies marked by goods shortages, where consumers are not
fussy about quality, features, or style. Examples are the markets for basic staple goods in
developing countries or evolving markets. Secondly, in developed economies, where people face
abundant choices, the traditional views of the business process will not work. Here, the “mass
market” is actually splintering into numerous micro-markets, each with its own wants,
perceptions, preferences, and buying criteria. Therefore, the belief at the core of the new view of
business process is proper definition of target markets. This places marketing at the beginning of
planning. In the value delivery process, firms see themselves as part of the chain instead of
emphasizing making and selling. Nimalya Kumar (2004) put forth ‘3Vs’ approach to value
43
delivery in marketing; (i) define the value segment or customers; (ii) define the value
proposition; and (iii) the value network that will deliver the promised service. Other similar
views expressed by Webster (1997) are; (i) value defining process, e g market research and
company self-analysis; (ii) value developing process, example new product development,
sourcing strategy and vendor selection; and (iii) value delivery process such as advertising and
managing distribution.
The value chain as proposed by Michael Porter (1985) is shown in figure 2.2. In this
model, the value chain identifies nine strategically relevant activities that create value and cost in
a specific business. These value creating activities consist of five primary activities and four
supportive activities (Kotler & Keller, 2006). The primary activities cover the sequence of
bringing 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
procurement, technology development, human resources management, and firm infrastructure.
These are handled by specialized departments or elsewhere. Note the firm’s infrastructure covers
general management such as planning, finance, accounting, legal, and government affairs.
44
Figure 2.2: The generic value chain of Michael E Porter adapted from Kotler & Keller (2006)
2.7.1 Value chain in agricultural processing and marketing
Value chain optimization can be an efficient tool in the development of food processing
and marketing (Hagelaar, 1994). The improvement of each step in the food marketing chain
needs to be analyzed and monitored in relation to other links in the chain. This calls for good
cooperation between the different actors in the chain. An important aspect of chain optimization
is chain marketing, defined as cooperation in marketing through the whole chain. The
management of improvement in the value delivery network is called chain management
(Bruinsma, 1999). This will develop as agricultural production becomes more market driven than
production driven. A flow chart of a typical agricultural value chain is shown in figure 2.3.
In an analysis of development in food chain in Europe, Bruinsma (1999) in quoting
Zuurbiov (1981) noted some reasons for greater emphasis on cooperation with other actors in the
food marketing chain most of which are applicable in Africa. They include: (a) reduction of
Margin Firm infrastructure
Human resources management
Technology development
Procurement
Inbound
logistics Operations
Outbound logistics
Marketing
& Sales Services
Margin
Margin
Support
45
transaction and coordination costs through better organization; (b) greater access to information
about new technologies as well as the technologies themselves; (c) reduction in uncertainty about
actions that other individuals or groups in the chain may take; (d) possible increased
competitiveness; and (e) reduction in logistical problems regarding the handling of perishables
which otherwise may lead to loss of product quality. Another concept that is of great importance
to the optimization of the food marketing chain is quality assurance. Instituting quality assurance
measures at all stages (production, processing and trade) of the marketing chain will ensure that
the marketed product is demanded by consumers and is also safe for consumption.
Critical factors or approaches that underline the understanding of value delivery network
and eventual successful operation of agro-processing enterprises as set out by Brown (1986),
Austin (1992); Brown, Deloitte, and Touche (1994) include; (i) raw material supply and
procurement, (ii) processing component, and (iii) marketing. Although this is the operational
order of the material flow in the production chain, the marketing factor is the logical starting
point, because it will make no economic sense to invest in processing a product that there is no
demand for it.
46
Figure 2.3: Flow chart of Agro-processing value chain
Source: Adapted from Austin (1992)
Breed stock Extension
& Research Others Agro-
chemicalSeeds Equipment
Transport
Farm Storage
Produce
Land&
Irrigation
On-farm/Home
use Transportation
Agro-
industry
Products
Transportation
Export Storage Distributor Storag
Products
Transportation
Retailer Storage
Products
Consumer
47
2.8 Analytical Framework
The analytical framework for this research is based on the optimization of value chain
from Groundnut processing through products marketing. Production efficiency and profitability
analysis models of the firms in groundnut oil processing and product marketing, market co-
integration analysis and the law of one price (LOP) as a measure of market efficiency were also
adopted. Hypotheses were tested appropriately as required.
2.8.1 Stochastic frontier production function
This was used to evaluate the production efficiency of groundnut oil and groundnut cake
together. Production has been defined by Olayide & Heady (1982) as a process whereby some
goods and services referred to as inputs are transformed into other goods and services called
output. In agricultural processing this changing of input form into output involves changing of
farm produce into another form desired by consumers or manufacturers (Austin, 1992; Olukosi
& Isitor, 1990). Brown et al (1994) observed this activity adds more value to the raw farm
produce. The technical efficiency involved in changing the input into output as well as factors
that determine the inefficiency were analysed using the stochastic frontier production function.
The stochastic frontier analysis (SFA) is based on the premise which represents an
improvement over the traditional production function and deterministic functions using
mathematical programming to construct production frontiers. It recognizes the possibility that a
firm’s performance may be affected by factors completely outside its control, such as bad
weather and input supply breakdowns as well as factors under its control. To lump the effects of
exogenous shocks, both favourable and unfavourable to the firm together with the effects of
measurement errors and efficiency into a single one- sided error term is incorrect, as is the case
with deterministic frontiers. Again, in the statistical noise that every econometric empirical
48
relationship carries, the standard interpretation is that, first, there may be measurement errors on
dependent variables; secondly, the equation may not be completely specified due to omitted
variables. This argument holds for production functions as it is for any other equations assuming
a one - sided noise. The essential idea in stochastic frontier model as put forward by Aigner,
Lovell & Schmit (1977) and Meuesen & van den Brook (1977) is that the error term is composed
of two parts, the effects of measurement error, other statistical noise, and random shocks outside
the firm’s control. The original specification which involved a production function for cross-
sectional data had an error term with two components, one to account for random effects and
another to account for technical inefficiency. Several specifications of the frontier production
function have been developed by Battese & Coelli (1992, 1995). For this research the model is
given as,
Yi = Xi β+ (vi-ui) …………………. 2.15
i =1, ……, N
Where,
Yi = the production (or logarithm of production) of the ith firm;
Xi = kx1 vector of (transformation of the) input quantities of the ith firm;
β = vector of unknown parameters;
Vi = random variables which are assumed to be iidN (0, σ v2) and independent of the
Ui which are non-negative random variables that are assumed to account for technical
inefficiency in production and are said to be iid, N (0, σv2).
Equation (1) above can also be approximated in a translog form (Karagiannis & Sarris 2005;
Amefula et al, 2009) as follows
Yit = β0 + βγt +1/2βγγt2
+∑βjxjit + 1/2∑∑βjkxkit +∑βjtxjitt +eit …………………….. 2.16
49
Where Yit is the logarithm of the observed output by the ith firm at period 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, βij = βkj and eit = vit- uit being a composite error term. The vit
term corresponds to white noise and represents those factors that cannot be controlled by
processors, such as weather conditions, labour market conflicts, access to credit as well as
measurement error and omitted explanatory variables. On the other hand Uit term is a non
negative random variable associated with technical inefficiency. The vit and the uit are assumed
to be independently distributed from each other. The technical inefficiency effects uit, can be
replaced with a linear function of explanatory variables (Battese & coelli, 1995). The technical
inefficiency effects are assumed to be independent and non negative truncations (at zero) of
normal distribution with unknown mean and variance. Specifically,
Uit = δ0 + ∑δmzmi + wit…………………… 2.17
Where zmi a column vector of hypothesized explanatory variables associated with technical
inefficiency; δ0 and δm (m=1...h) are parameters to be estimated, and uit is independently and
identically distributed with N (0, σu2) random variable truncated at – (δ0 + ∑δmzmi) from below.
Equation (3), an average level technical efficiency measured by mode of truncated normal
distribution (i. e, Uit) has been assumed to be function of socio-economic factors (Kumbhakar &
Heshmati, 1995; Yau and Liu, 1998; Ogundele & Okoruwa, 2006). If uit does not exist or uit =0,
the stochastic frontier production function reduces to the traditional production function of Cobb-
Douglas. The distributional parameters Uit and δu2 are hence inefficiency indicators of the
processor, indicating the level of technical inefficiency and later dispersion of inefficiency level
across observable units (Battese & Coelli, 1995; Bamire, Oluwasola & Adesanya, 2007).
Together, equations (2) and (3) can be estimated by means of the computer programme
50
FRONTIER version 4.1c developed to obtain the maximum likelihood estimates of the Frontier
production model detailed in Battese & Coelli (1988,1992, 1995) and Coelli (1996), Battese,
Coelli & Colby (1989) which are special cases of Coelli(1992). The programme can
accommodate panel data, time-varying and invariant efficiencies; cost and production functions;
half normal and truncated normal distribution; and functional forms which have a dependant
variable in logged or original units.
Hypotheses can be tested using the generalized likelihood ratio test statistic λ = - 2lnL
(H0) – lnL(H1), where L(H0) and L(H1) are values of the likelihood function under the null H0
and the alternative H1 hypotheses respectively. If γ= δ0 = δm (m=1 …….h), then inefficiency
effects are not present and consequently each firm in the sample operates on the frontier. It
should be noted that γ=0 if there is no difference between the null and alternative hypotheses,
and if not the likelihood function (LF) will diverge. Asymptotically the λ follows the χ2 (mixed
χ2) distribution hence the statistical significance can be tested at a chosen α with degree of
freedom equal number of restrictions.
Coelli (1996) also observed that this model has been used in a vast number of
applications over two decades. That the original version has been altered and extended in many
ways which include more general specification of distributional assumptions for the non-
negative random variables which account for technical inefficiency; consideration of panel data
and time varying technical efficiency, and the extension of the method to cost functions and
estimation of systems of equations among many. A comprehensive review of literature on the
model can be found in Forsund, Lovell & Schmit(1980), Baur(1990) and Greene(1993).
51
2.8.2 Profitability analysis
The profit function was employed to estimate the profitability of resource input in
groundnut processing enterprise. These inputs include variable (groundnut, firewood, water, salt,
grinding) and fixed input (frying equipment, pressing equipment, kneading surface) and labour.
The profit function was used because of its importance in diagnostic analysis reflecting marginal
resource profitability at mean level on input price.
Following Sankhayan (1981), Olayide & Heady (1982) and Arene (2002), the linear profit
function analytical model is stated thus: Given a production function in which m variable inputs,
x1, x2 ….xm; Z1, Z2 …Zn, are related to Y as follows,
Y= f(x1, x2, xm; Z1, Z2…Zn) ………………………….. 2.18
In the short run, the opportunity cost of fixed inputs is zero. Therefore the processor needs only
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),
the variable inputs in respect to the production function given in (2.18) above can be written as:
π = Py f(x1, x2… xm; Z1, Z2….Zn) - ∑ Pixi ………….. 2.19
Where Py is the price of output and Pi is the output per unit price of the ith variable inputs, i = 1,
2…m.
For profit maximization of π in the short run, the first order partial derivative with respect to the
variable inputs equated to zero are each taken (Olayide & Heady, 1982). Hence the partial
derivative from (2.19) with respect to Xi, i=1, 2 …., m, equated to zero is given by
δy/δx1 = Py fi = pi ……….. 2.20,
where fi denotes the first order partial derivative with respect to the ith input. Since from (2.18),
f(x1, x2… xm; z1, z2 …Zn) is equal to Y, (2.20) can also be written as
52
pyδy/δx1 = pi or δy/δx1 = pi/py, i=1, 2 ….m …………… 2.21.
There will thus be m simultaneous equations in m unknowns, which can be solved to obtain the
optimum input quantities X*i, I = 1, 2 ….m, given by
Xi*= Xi*(py,p1, p2, ….pm,z1 ,z2, ….zn), i = 1, 2,……m ……….2.22
Equation (5) gives the demand function for the ith variable input.
Substituting the demand functions given by (2.22) and (2.20), what is obtained is given as
π* = P f(xi*x2*,…x*m; z1,z2,…...,zn) - ∑pixi* …………..2.23,
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 or gross margin (GM). Obviously however, π* with a
harsh in (2.23) is expressed 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 input to be used in groundnut oil processing. Thus
π* = π*(py, p1, p2… pm; z1, z2, …….,zn) …………….2.24.
2.8.3 Measurement of co-integration and the law of one price (LOP)
The law of one price (LOP) captures the existence of equilibrium due to efficient
commodity arbitrage between two or more trading markets. It assumes that if markets are
integrated, price change in one market will be transmitted on a one-for-one basis to other markets
instantaneously (Chirwa, 2000).
This can be written as
Pti = α +βPt
j …………………… 2.25
Where,
Ptj and Pt
j are the natural logarithm of prices of homogeneous goods in markets i and j
respectively. In empirical work, evidence of how price change in one market generate price
53
changes in another market so as to bring about long run equilibrium relationship (Asche et al
2005), can be written as
Pt 1 – β0- B1 Pt
2 = et ……………………… 2.26
where,
Pt is the logarithm of the price observed in market i at time t, β0 is a constant term that captures
transportation, or transaction cost and other quality differences, and β1 gives the relationship
between the processes. If β = 0 there is no relationship between the two price series, if β1 =1, the
LOP holds and the relative price is constant. If β1 is different from 0 but not equal to 1, there is
relationship between the prices, the relative price is not constant and the markets are not fully
integrated. If Pt1 and Pt
2 are co-integrated, the error term, et, will be stationary. This forms the
basis of Engle and Granger test for co-integration and the unit root test by performing the
Augmented Dickey-Fuller (ADF) unit root test (Asche et al, 2005). Due to the weakness inherent
in the Engle–Granger procedure, it is replaced by the more powerful Johansen trace test in the
analysis of co-integrated markets (Chirwa, 2000; Okoh & Egbon, 2005; Gujarathi, 2007).
2.8.3.1The unit root problem
Random walk process may have no drift, may have drift or may have both deterministic
and stochastic trends. For these reasons, the actual procedure of ADF test for unit root on the
price series will require estimation of the three models for the three possibilities (Gujarathi,
2007; Ali & Rahaman, 2009). These are:
the random walk (RMW) without drift,
Yt = δyt-1 + αt∑Yt-1 + e………………… 2.27;
the random walk with drift,
Yt = β1+ δYt-1 + α1∑Yt-1 +e ………… 2.28; and
54
the random walk with drift around a deterministic trend,
Yt = βt + β2t + δYt-1 + α∑Yt-1 + e ………… 2.29;
where,
Yt = price series in market Y during period t,
Yt = first difference of series Y, ie Yt-Yt-1,
t = trend variable (1, 2, 3… n) n being the length of data series in years; m = no of lagged
difference; and
e = Error.
Β1, β2, δ, α = parameters to be estimated.
2.8.3.2 Unit root test
Unit root test is a test of stationarity (non stationarity) required to avoid spurious
regression. Given that equation (2.29)
Yt = ρYt- 1 + Ut …….. 2.30 -1 ≤ ρ ≤ 1
Where Ut is a white noise error term; recall that if ρ= 1, a case of unit root, equation (2.30) an
RWM is I (1). We regress Yt on its lagged value Yt-1 and see if estimated ρ is statistically equal
to 1. If so, then Yt is non-stationary. If we subtract Yt-1 from both sides of equation (2.30), we
obtain
Yt – Yt–1 = ρYt-1- Yt-1 + Ut …………….. 2.31
= (ρ-1) Yt-1 + Ut
Which can be written alternatively as
Yt = δYt-1 + Ut ……………… (2.32)
Where δ = (ρ-1) and is the first difference operator. In practice equation (2.32) is estimated to
test the (null) hypothesis that δ = 0. If δ = 0 then ρ = 1, hence we have a unit root which means
55
the time series under consideration is non-stationary. If negative, it is stationary. Note also that if
δ = 0, equation (2.32) becomes
Yt = (Yt-Yt-1) = Ut ……………………. 2.33.
Here Ut is white noise error term and stationary, which implies that the first difference of a
RWM time series is stationary.
Testing the null hypothesis of non stationary against the alternative of stationary, the Augmented
Dickey - Fuller (ADF) and Phillips Perron (PP) tests are applied. The ADF is a parametric tests,
whereas the PP test statistics uses a non parametric modification of the Dickey – Fuller test.
Under the null hypothesis that δ = 0 (ie ρ = 1), the t-value of the estimated coefficient of yt-1 does
not follow the t-distribution even in large samples, that is it does not have asymptotic normal
distribution. The Dickey- Fuller statistic has shown under the null hypothesis that δ = 0 and the
estimated coefficient of yt-1 in equation (2.33) follows the τ (tau) statistic. If the hypothesis δ = 0
is rejected, the series is stationary. The Augmented Dickey- Fuller (ADF) is based on statistics
obtained from applying the OLS method to the following equation (Gujarathi, 2007; Ali &
Rahaman 2009)
Pt = µ + βt + ΦPt-1 + ∑dPt-1 + εt ………………… 2.34
Where Pt = price series, t = time trend; Pt-1 = Pt-1- Pt-1+1; εt iid(0, σ2)
To determine whether Pt is non- stationary, the unit root test statistic is calculated and tested as
above.
In addition, the Dickey-Fuller (DF) test assumption is the error terms, Ut, being white
noise. The ADF adjusts it to take care of possible serial correlation in the error terms by adding
the lagged difference terms of the regressand. Phillips and Perron (Gujarathi, 2007) used non-
parametric statistical methods to take care of the serial correlation in the error term without
56
adding lagged difference terms. Asymptotically, the Phillips – Perron test is the same as ADF
test statistic.
2.8.3.3 Co-integration: The Johansen test
The multivariate Johansen model can be expressed as follows. Let Xt denote an nx1 vector,
where the maintained hypothesis is that Xt follows an unrestricted vector auto regression (VAR)
in the levels of the variables (Asche et al, 2005).
Xt = П1Xt-1 + …..+ ПkXt-k + ФDt + µ +εt ……….. 2.35
where each Пt is an nxn matrix of parameters, µ a constant term, and εt iid (0,σ2) matrix Ω. In
(ECM) or difference equation (10) can be written as
Xt = Ґt Xt-1 + …….. + Ґk-1 Xt-1+1 +ПXt-k + ψDXt + εt ………… 2.36
With Ґi = -1 +П1 + ……. + Пi, i =1 ……k-1
Пi = -1 + П1 + …..+ Пk. Hence Π is the long run “level” solution to equation (2.35).
Given that Xt is a vector I(1) variables, the left hand side and the first (k-1) elements of equation
(2.36) are I(0), and the kth element of equation (2.36) is linear combination of I(1) variables. If
the assumptions on error term holds, the kth element must also be I (0); Пt-k I(0). Thus either Xt
contains a number of co-integration vectors, or П be matrix of zeros.
A 2 variable system model of the Johansen VAR procedure (Chirwa, 2000) is given as ECM
Xt = µ + ∑Ґt-1 + ПXt-1 + ε ………………. 2.37
Where Xt is nx1 vector containing the series of interest (spatial prices); Ґ and П are the matrices
of parameters, k is asymptotic to capture the short run dynamics of the underlying VAR and to
produce normally distributed white noise residuals, and εt is the vector of the white noise error.
Given that rank (П) = r; П, r, indicates the number of linear combinations of Xt that are
stationary. If r = n, the variables in levels are stationary; if r = 0 so that П = 0, none of the linear
57
combinations are stationary. When 0<r<n, there exist r co-integration vectors, or r stationary
combinations of Xt. П = αβ, where both α and β are nxr matrices, and β contains the co-
integration vectors (the ECM in the system) and α adjustment parameters.
The Johansen procedures involves two tests for the number of co-integration vectors in the
system, that is whether the П matrices in equation (2.35) has less than full rank using the
maximal eigenvalue (2.36) and the trace test (2.37). These are given (Hjalmarsson &Osterholm,
2007) as
Jmax = - Tln (1- λr+1) …………………………. 2.38
Jtrace = -T∑ni-r+1 ln (1-λi)…………………………. 2.39
T is sample size, and λ is the i:th largest canonical correlation (largest eigenvalue). The trace test
tests the null hypothesis of r co-integrating vectors against the alternative hypothesis of n co-
integrating vectors. The maximum eigenvalues test on the other hand tests the null hypothesis of
r co-integrating vectors against the alternative hypothesis of r + 1 co-integrating vectors
A wide range of hypothesis testing on the coefficients α and β, is allowed by the Johansen
procedure, using the likelihood ratio test (Johansen and Juselius 1990). If the LOP hypothesis is
of interest, it is the restrictions on the parameters in the co-integration vector β that is to be
tested. Where there are two price series in Xt vector, and provided that these series co-integrate,
the rank of П = αβ is equal to 1 and α and β are 2x1 vectors. A test of LOP is actually a test of
whether β = (1,-1). If a group of goods are to be in the same market, all prices must be pair –
wise co-integrated. This allows a multivariate test of LOP, since it implies only one common
stochastic trend in the system, and therefore with n prices in the system, there must be n-1 co-
integration vectors (Asche et al, 1999; Gonzalez- Rivera & Helfand 2001; Asche at al, 2005).
58
Generally, in a system with n data series and r co-integration vectors, there will be n-1 different
stochastic trends.
2.8.3.4 Determinants of co-integration
Market integration is consequent of activities of participants and the operating environment
defined by government policies, and infrastructural development among others. The cost of
carrying out marketing functions of transportation, processing and storage as well as profit
margin, of traders is central to the concept of spatial arbitrage. Several factors influencing the
extent of market integration have been identified in literature to include market infrastructure,
production differentials and shocks, and the policy environment (Golletti et al, 1995, Felchamps
& Gavien 1996; Chirwa, 2000; Kindie, Verbeke & Viaene, 2005; Asche, Gutternsen, Sebulonsen
and Sissener, 2005; Ali & Rahaman, 2009).
59
CHAPTER THREE
METHODOLOGY
3.1 Study Area
The location for this research is the geo-political area described as North Central Nigeria.
North Central Nigeria politically comprises Benue, Kogi, Kwara, Nasarawa, Niger, Plateau
States and the Federal Capital Territory (FCT). This area is located between longitudes 4o35’E
and 9o4’E and latitudes 7
o 09’ N and 9
o53’N (Phillips, 1996). This area lies within the guinea
savanna zone of Nigeria. The vegetation is characteristic of the tropical, deciduous forests that
existed centuries ago, with interspersion of thicket, grassland, fringing forests and woodland or
gallery forest along the river valleys (Iloeje, 1985). Some areas in some of the seven states such
as Kogi, Benue and Kwara fall within the tropical rain forest zone of Nigeria.
The north central Nigeria (NCN) has an estimated population of 21, 682, 776 people as
estimated by 2006 population census, with land area of 242, 425 km2. The selected states of
Nasarawa, Benue and Niger have a combined population of 11, 121, 989 people with a land mass
of 137, 536km2 (Wikipedia, 2013). The major regional markets include Gboko, Makurdi, Otukpo
and Zaki Biam in Benue State; Lafia. Keffi, Nasarawa Eggon and Mararaba in Nasarawa state;
Suleija, Bida, Minna and Kontagora markets in Niger State; Lokoja, Okenne and Anyangba in
Kogi State; Jos and Shendam markets in Plateau State; Offa and Ilorin in Kwara State; and Wuse
and Gwagwalada markets in the Federal Capital Territory (FCT). These markets are noted for
trade in agricultural commodities. The zone links the north and the southern parts of the country.
Major roads and railway lines pass through these states from north to southern Nigeria. The two
60
rivers, Niger and Benue and their tributaries run through the zone and provide potentials for in-
land water ways and ports for trading activities.
The North Central States are known for the production of crops including yam, rice,
groundnut, cassava, beans, maize, citrus, cashew, cocoa and variety of other fruits. These crops
form the basis of trade within the region and with other parts of the country. Livestock
production is also vibrant here (Agboola, 1979; Iloeje, 1985; Olam, 2006). Groundnut processing
and processed products marketing are very vibrant business activity involving men, women and
youths; both in traditional and modern processing in this area. Generally small-scale industries,
especially agro-based provide impetus for economic growth and development of the area.
3.2 Sampling Technique
The population for this study comprised traditional and modern groundnut oil processors
in North Central Nigeria. Four groundnut producing states (Nasarawa, Plateau, Benue and Niger)
and the FCT were identified as reported by RMRDC (2004). Three groundnut producing states
were randomly selected for the study. The LGAs were purposively selected based on groundnut
processing and marketing activities, while the respondents were taken randomly. The formula
applied is as given below
SS = SP / ZP x TS
Where,
SS = state sample size selected,
SP = state total sampling frame, and
ZP = zonal total sampling frame (selected states).
TS = Total sample size.
Sampling from the two producing LGAs of each state, given as
61
LS = LT/ST x TL,
Where
LS = LGA sample size selected,
LT = total LGA sampling frame,
ST =state total sampling frame (selected LGAs), and
TL = total sample size.
The samples were randomly and proportionally taken based on the estimated population of
traditional processors in the selected LGAs of the States. The estimated populations (sampling
frame) of processors were Nasarawa State 350; Benue State, 225; and Niger State, 300; obtained
from Agricultural Development Programmes (ADPs), Ministry of Commerce and Industries and
groundnut oil (GNO) processors in the states. The distribution of the proportionately selected
samples for the states was Nasarawa state 70; Benue State 45 state; Niger State 60; and the total
sample for North Central Nigeria was 175, (Table 3.1). The active population of modern
processors in the selected states within the zone was 17 and all were taken for the study.
Table 3.1: Population and sample selection for the study
State Population Sample LGA LGA Sample
Nasarawa 350 70 Lafia 30
N/Eggon 40
Niger 300 60 Bida 36
Chachaga(Minna) 24
Benue 225 45 Gboko 20
Makurdi 25
Total 875 175 175
3.3 Data Collection
Primary data were used for this study. These were collected through survey by means of
structured and pre-tested questionnaires. Personal interview was used to administer the
62
questionnaires. Observations were employed for on the spot assessment of processing and
marketing activities at various processing sites and markets where possible (Alamu & Olukosi,
2008). Price data chart was used to collect price data.
Socio-economic characteristics such as age, sex, experience, cooperatives participation,
and educational attainment were covered. Data on procurement of groundnut for processing,
processes involved in processing of the groundnut into oil and cake and costs involved at each
stage were obtained as well as other inputs used, their respective quantities and prices. Quantities
and values of oil and cake sold by processors were also collected. Processing cost and other
charges involved also obtained from the respondents, and many others. Data collection took
place between December 2010 and November 2011. Weekly price data on market prices of
groundnut oil (GNO) and groundnut cake (GNC) were collected for 52 weeks from December
2010 t0 May 2011 in six strategic markets. These were Lafia, Nasarawa –Eggon in Narawa state;
Minna (Chachaga) and Bida in Niger state, Makurdi in Benue State and Wuse in the Federal
Capital Territory (FCT).
3.4 Data Analysis
Data were analyzed by means of descriptive and inferential statistics. Descriptive statistics was
used to achieve objectives 1, and 6. Objective 2 was attained with stochastic frontier analysis.
Gross margin and profit function were used to achieve objective 3 and t-test was used to achieve
objective 4. Objective 5 was realized with the Johansen test for co-integration analysis.
3.4.1. Stochastic frontier model
The Stochastic frontier production function was used to evaluate the processing efficiency
involved in traditional and modern groundnut oil processing. This enabled the attainment of
objective two of the study. The model for traditional processing is specified as follows:
63
LnY =β0+ β1lnX1 + β2lnX2+ β3lnX3 +β4lnX4 + (vij – Uij)………….. 3.1
Where,
Y = output of processors (GNO +GNC) (kg)
X1 = Raw groundnut seeds (kg)
X2 = Labour (hours)
X3 =Fuel-wood (N)
X4 =salt (kg)
Vij = random effect which are assumed to be iid N (0, σ)
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.
The absolute value of Uij is expressed as follows:
Uij=δ0 +δ1Z1 +δ2Z2 +δ3Z3 +δ4Z4 + δ5Z5+ δ6Z6+ δ7Z7 …………….3.2
where,
Uij= technical inefficiency or characteristics related to inefficiency;
Z1 =age of processors in years
Z2 = level of education (years of formal education)
Z3 = years of experience in GNO processing;
Z 4 = gender (1 male, 0 female);
Z5 = household size (actual number of members);
Z6 = marital status (1 married, 0 otherwise)
Z7 = Cooperative participation (1menber, 0 otherwise)
The model used to evaluate the efficiency in modern processing is presented below.
64
LnY =β0+ β1lnX1 + β2lnX2+ β3lnX3 +β4lnX4 + (vij – Uij)………….. 3.3
Where,
Y = output of processors (GNO +GNC) (kg)
X1 = Raw groundnut seeds (kg)
X2 = Labour (hours)
X3 =Maintenance of equipment/machines (N)
X4 = Price of raw groundnut (N)
The inefficiency model was given as follows:
Uij=δ0 +δ1Z1 +δ2Z2 +δ3Z3 …………………….3.4
where,
Uij= technical inefficiency or characteristics related to inefficiency;
Z1 = level of education (years of formal education)
Z2 = years of experience in GNO processing;
Z 3 = gender (1 male, 0 female);
The maximum likelihood estimation of the βs and δs coefficients above was done simultaneously
using the Frontier 4.1c computer programme by Coelli (1996). Hypotheses were tested using the
generalized likelihood ratio test statistic λ = - 2lnL (H0) – lnL(H1), where L(H0) and L(H1) are
values of the likelihood function under the null (H0:) and the alternative (H1:) hypotheses
respectively. If γ= δ0 = δm (m=1 …….h), then inefficiency effects are not present and
consequently each firm in the sample operates on the frontier. It should be noted that γ=0 if there
is no difference between the null and alternative hypotheses, and if not the likelihood function
(LF) will diverge. Asymptotically the λ follows the χ2 (mixed χ2
) distribution hence the statistical
significance can be tested at a chosen α with degree of freedom equal number of restrictions.
65
3.4.2 Profit Function Analysis
As a prelude to the estimation of the profit function, gross profit margin was adopted to estimate
the average costs and returns per week to the processors who processed groundnut oil for the
achievement of part of objective three. The model is given as;
GM = TR – TVC ………….. .. 3.5
Where,
GM= Gross margin (in Naira); TR = Total Revenue (in Naira); TVC = Total Variable Cost (in
Naira).
The generalized profit function model is expressed thus:
π* =π*(py1py2, p1, p2,p3;z1,z2) ……………….3.6
Where;
π* = amount of maximum variable profits (GM) from sales of GNO and GNC per week
py = price of output GNO and GNC (N)
p1= per unit price of groundnut (N)
p2= per unit price of labour (N/man hour)
p3=per unit price of fuel wood (N)
p4=per unit price of packaging (N)
p5= per unit price of transportation cost (N)
P6 = per unit price of salt (N)
Zs are fixed cost items and so were not anaysed because the analysis is based on the short run
effects of input costs, Arene (2002). The result of the regression analysis was evaluated on the
basis of the coefficient of multiple determination (R2), t-values and the F-values for the
respective states studied.
66
3.4.3 Value addition model
Value addition is the difference in value of agricultural product before and after processing
(Gitinger 1972; Brown, 1986). Brown et al, (1994) explained further that the difference between
the cost of ingredients and the ex-factory or post processing price of the finished product is the
value added through processing. This could be gross value added or net value added. It was
applied to achieve part of objective 4.
In this study the gross value added was determined as follows:
Va = Vp – Vb ……………….. 3.7
Where
Va = Value added to raw groundnut after processing (N/tonne)
Vp = value of processed groundnut products (GNO and GNC) from one tonne of
groundnut (N)
Vb = value of unprocessed groundnut per tonne (N)
This can also be presented in percentage as follows
Va% = Vp – Vb/Vp x100 …………………. 3.8
A test of significance was done to verify the null hypothesis (H0: X1 = X2), given as
t = X1 – X2/√σ21 +σ2
2/N1 +N2 -2 …………………. 3.9
Where
t = test statistic
X1 = mean value of groundnut before processing
X2 = mean value of processed groundnut products (GNO&GNC).
σ1 = variance of value of groundnut before processing
σ2= variance of Value of groundnut products (GNO&GNC)
N1 and N2 are equal sample sizes
67
When the value of input used in processing is subtracted from va in equation (3.7) 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.
3.4.3 Johansen trace test (a measure of market integration)
This is based on the maximum likelihood estimation of the error correction model. This is for the
attainment of part of objective 5 of the study, (Chirwa, 2000; Asche et al, 2005). The model
underlining the co-integrating VAR option is given by the VECM thus:
Xt = a0t + αβXt-1 + ∑ГiXt-1 + Ut ………………….. 3.10
Where,
Xt = wholesale (processors) prices in market i) with respect to GNO, GNC per week;
mt x1 vector of jointly determined I (0) variable.
a0t = intercepts, an mt x1 vector
αβ = the long run multiplier matrix of order mx1(ECM)
Гi = mx1 (n=number of lagged difference of Xt) coefficients of lagged Xt variables
= change operator
Ut = error term
The ECM model estimation was preceded by Augmented Dickey-Fuller (ADF) unit root test, to
test the non-stationarity of the series or otherwise.
The Johansen trace statistic for testing the null hypothesis of r co-integration relationships was
given as LRtrace(r/k) = -T∑r+i log (1-λi), where λ is the largest eigenvalue of the П matrix, and T is
the sample size.
3.4.4 Determinants of market integration
An empirical model for analysing the determinants of market integration for the attainment of
part of objective 5 in this study is expressed as follows:
Yij = ao + β1x1 + β2X2 + β3X3 +β4X4 + ε …………………. 3.11
68
Where
Yij = the Johansen bi-variate trace statistics for paired markets as a measure of market
integration for GNO and GNC
X1 = the shortest distance between market i and market j (km) proxy for transport cost,
X2 = the number of telephones owned by processors in markets i and j,
X3 = average no of groundnut processing facilities in markets i and j,
X4 = membership of groundnut processing associations in districts of markets i and j,
X5 = administrative regulations on groundnut oil (dummy) in markets i and j, and
ε = error term.
69
CHAPTER FOUR
RESULTS AND DISCUSSION
This chapter presents the results and discussion of the study on traditional and modern
groundnut processing and marketing in North Central Nigeria. Firstly, it examined the socio-
economic characteristics of groundnut oil processors; then described the groundnut processing
and marketing systems and hence the processing value chain; examined the input use efficiency
in groundnut processing and the factors that made for efficiency. It also dealt with the
profitability of processing and the marketing of groundnut oil (GNO) and groundnut cake
(GNC), and the factors that determined profitability. It also assessed the value added by
processing groundnut into oil and cake; and examined the integration of GNO and GNC markets
and the factors that influenced market integration. Finally it examined the problems of GNO
processing industry in the study area.
4.1 Socio-economic Characteristics of Traditional and Modern Groundnut Oil Processors
in North Central Nigeria
The socio-economic characteristics of groundnut oil processors in North Central Nigeria
(NCN) are shown in Table 4.1. This is presented for traditional processors and modern
processors.
4.1.1 Age distribution of groundnut oil processors.
The age distribution of the processors in the selected states (Table 4.1) shows that 45% of
the traditional processors in Nasarawa State and 56% in Niger state were aged between 31 and
40 years, constituting the largest group of respondents. In Benue State, the age group 21 to 30
years was the highest with 33%. The lowest group was that of those aged 51 years and above
having 17% in Benue State; 7% in Nasarawa State and 1.67% in Niger State.
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In the pooled result for the North Central Nigeria, the age group, 31 to 40 years, was the
highest with 42% and the lowest was 51 years and above with 8%. Among the modern
processors, this age group was also the highest with 47%. The average age of traditional
processors in the entire North Central Nigeria was 38years. The minimum age of 20 years was
found in Benue State and the maximum age of 65 years was in Niger State. For modern
processors the average age was 41 years with a minimum of 28 years and a maximum of 58
years.
4.1.2 Gender distribution of the processors
The gender distribution of the processors also presented in Table 4.1 shows that 97%,
96% and 94% of the traditional processors in Benue, Niger and Nasarawa States, respectively,
were female. The males were 3%, 4% and 6% for the respective states. This showed that women
were more involved in small-scale traditional GNO processing than men, probably because of
their involvement in processing food for home consumption. This agrees with Bruinsma & Nout
(1991) that more women are involved in food processing because of their domestic role in
processing food for home consumption.
For the North Central Zone, the females accounted for 94.86% of traditional processors
and males, 5.14%. In modern processing, 88.2% were males and 11.76%, females. This drastic
switch in gender participation might be attributed to the fact that female processors did not have
the needed capital to invest in modern processing requiring modern machines and processing
technologies. This agreed with Bruinsvma & Nout, (1991) and Bruinsma (1999) that an inverse
relationship existed between investment in processing and women participation in agro-
processing. They also reported a direct relationship for men; which is also shown in their
participation in modern processing.
71
4.1.3 Marital status
The results showed that most of the traditional processors in the states were married.
Ninety eight percent of them were married in Niger State while Nasarawa and Benue States had
96% each. The result for the entire North central zone showed that 96.6% of the traditional
processors were married. For modern processors, all (100%) of them were married. The result
implied that most of the processors were married and needed to work so as to earn some income
to support their families, hence their involvement in small-scale groundnut oil processing and
marketing.
4.1.4 Household size
The household sizes for the states, the zone and modern processors are also shown in
Table 4.1. The household size of 6 to10 persons was the highest in all the states, with the highest
percentage in Niger State (70%) and lowest in Nasarawa State (54%). This was followed by
household size of one to five persons with the highest percentage in Nasarawa State (25%) and
lowest in Niger State (11%). Other details are as shown in the Table. For both the traditional
processors and small-scale modern processors in the zone, processors with six to ten persons per
household were still dominant with 61.71% and 58.83%, respectively. The average household
sizes for the states were eight persons for Nasarawa State, seven persons for Benue State and
eight persons per household for Niger State. The maximum household size for traditional
processors in the zone was 25 persons per household and the minimum was one, with a mean of
eight persons per household.
For modern processors, household size of six to ten persons was the highest with 58%.
Their maximum household size was nineteen persons per household, and a minimum of three
persons with a mean of 7.5 ≈ 8 persons. Household size is important because it provides cheap
72
and available labour in traditional processing especially in groundnut roasting, polishing, and
cake moulding and frying. This result agrees with findings of Otitoju & Arene (2010), that
households of farmers in Benue State in North Central Nigeria had an average household size of
seven persons.
4.1.5 Educational level of processors
For the traditional processors (Table 4.1) those with less than three years of formal
education were more, with 72% in Nasarawa State and 53% in Benue. This was followed by
those with 4 – 6 years (primary education) with 51% in Niger State. The highest average years of
education was in Niger State with 6.4 years and the lowest in Nasarawa State with two years.
The average for North Central Nigeria was four years of formal education. This showed that
most traditional processors only attempted primary education which may explain why they have
remained at that level of processing.
For the modern processors, those in the range of 13years and above of formal education
were the highest with 47%, followed by those with 7-9 years of formal education (41%). The
average years of education for modern processors were thirteen years; the maximum was
eighteen years and the minimum was four years. This implied that modern processors of
groundnut oil were more educated than the traditional processors. This implied too that education
had positive effect on the level of investment in processing, consequent upon access to modern
processing technology, information and risk bearing abilities.
4.1.6 Cooperative participation
The result in Table 4.1 indicated that majority of traditional processors did not belong to
any processing co-operative organization. They were 84% in Nasarawa State, 100% in Benue
73
State and 93% in Niger State. For the zone, 91% did not belong to any co-operative organization.
All (100%) of modern processors were not members of any processing and marketing co-
operative. The implication is that the derivable benefits in co-operative organization are missed
by these processors. Processing and product marketing experiences and information could not be
shared. So also information concerning raw materials availability and processing technologies
were not readily accessed by the processors. This in another perspective portrays the competitive
nature of the industry in which processors acted independently in their processing and marketing
decisions. However the processors requested to be assisted to form cooperatives and self-help
organizations in their communities.
4.1.7 Years of experience
Table 4.1 also shows the processors’ years of experience in groundnut oil processing.
Sixty percent were those with 11- 20 years experience and were found in Niger State; 45% in
Nasarawa Sate and 28% in Benue State. They were the highest in the North Central zone with
45%. Those with 21 – 30 years and above of experience were fewer.
The average years of experience were 15 years in Nasarawa State, 13 years in Benue
State and 12 years in Niger State. The average year of experience for the zone was 14 years, with
the maximum being 35 and minimum one year. For the modern processors, those with 1 -10
years experience were 88.24% while those with 11 – 20 years had 11.96%. The average years of
experience were 6.5 years. The maximum was 15 years and the minimum was two years. This
indicated that years of experience were high enough for the processors to have acquired wealth
of experiences to enable them carry on their processing and marketing activities effectively.
Most of them actually learned the trade through experiences gathered from apprenticeship with
their parents, neighbours or established processors
74
Table 4.1 Distribution of traditional and modern groundnut oil processors by socio-
economic characteristics Variable Nasarawa
N = 70
Benue
N = 45
Niger
N = 60
North Central
N = 175
Modern
N=17
Freq (%) Freq (%) Freq (%) Freq (%) Freq (%)
Age in years
21 – 30 16 (22.86) 17 (33.33) 6 (10) 39 (22.28) 1 (5.88)
31 – 40 32 (45.71) 9 (20.00) 34 (56.67) 75 (42.86) 8 (47.06)
41 – 50 17 (24.29) 11 (24.44) 19 (31.67) 47 (26.86) 7 (41.18)
51 – and above 5 (7.14) 8 (17.78) 1 (1.67) 14 (7.99) 1 (5.88)
Mean 38.34years 38.6years 37.9years 38.3years 41.2years
Max 60 65 55 65 58
Min 25 20 26 22 28
Gender
Male 6 (8.58) 1 (2.22) 2 (3.33) 9 (5.14) 15 (88.24)
Female 64 (91.42) 44 (97.78) 58 (96.67) 166 (94.86) 2 (11.76)
Marital Status
Married 67 (95.71) 43 (93.55) 58 (96.67) 169 (96.57) 17 (100)
Single 2 (2.86) 2 (4.44) 1 (1.67) 5 (2.86) 0 (0.00)
Widow 1 (1.43) 0 (22) 11 (1.0) 1 (0.57) 0 (0.00)
Household Size (No. of persons)
1 – 5 18 (25.71) 10 (22.22) 7 (11.67) 34 (19.43) 5 (29.41)
6 – 10 38 (54.29) 28 (62.22) 42 (70.00) 108 (61.71) 10 (58.83)
11 – 15 7 (10.00) 7 (15.55) 9 (15.0) 24 (13.7) 1 (5.88)
15 – and above 7 (10.00) 0 (00) 2 (3.33) 9 (5.14) 1 (5.88)
Mean(persons) 8.6 7.3 8.4 8 7.5
Max 25 12 20 25 19
Min 3 1 2 1 3
Education level (years)
1 – 3 51 (72.86) 24 (53.33) 11 (18.34) 86 (49.14) 1 (5.88)
4 – 6 16 (22.86) 8 (17.78) 31 (51.68) 53 (30.29) 1 (5.88)
7 – 9 2 (2.86) 5 (11.11) 4 (6.68) 11 (6.29) 7 (41.18)
10 – 12 2 (2.86) 7 (15.55) 14 (23.34) 23 (13.14) -
13 and above 1 (1.49) 1 (2.22) 0 2 (1.14) 8 (47.06)
Mean (years) 2.02 3.69 6.4 4 13
Max 12 14 12 14 18
Min 0 0 0 0 4
Co-operative participation
Yes 11 (15.171 0 (0.00) 4 (6.67) 15 (8.57) 0 (00)
No 59 (84.26) 45 (100) 56 (93.33) 160 (91.43) 17 (100)
Years of Experience
1 – 10 24 (34.29) 25 (55.55) 22 (36.67) 71 (40.57) 15 (88.24)
11 – 20 32 (45.71) 12 (26.66) 36 (60) 80 (45.71) 2 (11.76)
21 – 30 12 (17.14) 5 (11.11) 2 (3.33) 19 (10.86) - (0.00)
31 and above 2 (2.86) 3 (6.67) 0 (0.00) 5 (2.86) - (0.00)
Mean (years) 15.67 13.58 12.73 14.13 6.45
Max 35 35 25 35 15
Min 2 1 5 1 2
Source: Computed from field survey data, 2010/2011
75
4.2 Groundnut Oil Processing, Marketing systems in the Study Area
In this study, the traditional and the small-scale modern processing methods were
practiced. The traditional method applied some mechanical and modern methods in its activities;
therefore, there may not be a purely traditional method of groundnut oil processing. Hence, a
description of traditional and small-scale modern processing seemed more appropriate because
all the operations were small-scale; though most modern groundnut oil processing activities were
automated and processed larger quantities than the traditional methods. The major activities in
groundnut oil processing industry, as in other agro-processing schemes, began with the
procurement of raw groundnut from rural and urban markets and farmsteads. The raw groundnut
was then transported to the processing sites for processing. The final stage was the marketing of
the processed products. In this study the products marketed were groundnut oil (GNO) and
groundnut cake (GNC). The groundnut oil processing chain is shown figure 4.1
The major buyers of GNO included traders, consumers, food processors,
bakeries/catering firms, and oil processing and packaging companies. The major buyers of GNC
from traditional processors were consumers, retailers and other traders from far north and eastern
states; and food/meat processors such as the ‘suya’ steak meat roasters. The GNC from modern
processors was sold entirely to the feed mills and poultry farms, and used for animal feeds only.
76
Fig 4.1: Groundnut oil processing chain in North Central Nigeria
Source: Computed from field survey data, 2010/2011
Farms
Raw groundnut
Transportation
Processing Pre - treatment
Cake frying
Oil expelling
Products
Groundnut cake Consumers
Feed mill
Groundnut oil Consumers
Distribution
Canning/Bakery
Food/manufacturers
Traders
Consumers
Oil Packaging
Consumers
Consumers
Poultry feeds
Food processors
Farm inputs
77
4.2.1 Procurement (traditional and modern)
This activity involved organizing the purchase and transportation of required raw
groundnut from rural markets, urban markets, farm stead and other sources to the processing
sites or mills. These sources were scattered in markets and locations within the local government
areas, states and even outside the states. Hence, organizing and buying the needed quantity of
raw groundnut for all year processing was very challenging especially for modern processors.
In this study, 81% of raw groundnuts procured by traditional processors in Nasarawa
State was from farmers while 19% was from traders (Table 4.2). In Benue State, it was 51%
from traders and 49% from farmers. Niger State was 55% from traders and 45% from farmers.
For the North Central zone, 60% was obtained from farmers and 40% from traders, for
traditional processing. For the modern mills, 94% of their entire groundnut came from traders
and agents, while only 6% came from farmers directly.
The respective quantities and value of the groundnuts procured per state and the zone are
also shown in Table 4.2. The highest quantity procured and processed by traditional processors
was in Nasarawa State with 263kg/week valued at N36, 632. This was followed by Benue State
with 100.5kg worth N16, 417. The least was in Niger State with 46.9kg valued at N5, 878. The
maximum and minimum quantities procured are also shown in Table 4.2. The average quantity
of groundnut procured by the traditional processors in the zone was 147kg worth N20, 889,
while the modern processors procured an average of 6, 222kg per week valued at N897, 906. The
maximum and minimum quantities procured by modern processors are also shown in the table.
78
Table 4.2 Statistical summary of activities of traditional and modern processors in North
Central Nigeria
Variable Nasarawa Benue Niger NCN Modern
Major source of G/nut
Farmers 57 (81%) 22 (49%) 27 (45%) 106 (60%) 1 (6%)
Traders 13 (19%) 23 (51%) 33 (55%) 69 (40%) 16 (94%)
Ave. quantity purchased (kg)/ wk
Mean 263.68 100.25 46.9 147.33 6222.363
Max 1680 350 7 1680 60000
Min 35 16.8 7 7 560
Value of G/nut per wk (N)
Mean 36, 632.14 16, 417.67 5, 878.17 20, 889.91 897, 906
Max 240, 000 65, 000 32, 400 240, 000 10,
285,716
Min 3, 500 3, 000 750 750 64, 000
Price of G/nut/kg per wk(N)
Mean 197.6 241 160 199 15, 197
Max 265 314 180 314 17, 000
Min 140 158 150 140 8, 000
Qty of GNO obtained/wk (251)
Mean 5.02 1.73 0.67 2.94 2436.28kg
Max 3.6 4.8 3.4 36 27000kg
Min 1.5 0.3 0.06 0.06 201.6
Qty of GNC obtained (basin)
Mean 9 3.36 2.34 4.39 2756.29kg
Max 38 11 7.3 38 30000kg
Min 2 1 0.3 0.3 300kg
Filtering oil
Yes 39 (56%) 14 (33%) 18 (30%) 71 (40%) 5 (30%)
No 31 (44%) 31 (67%) 42 (70%) 104 (60%) 12 (70%)
Additives/fortification
Yes 2 (3%) 0 (00%) 10 (17%) 12 (7%) 3 (18%)
No 68 (97%) 45 (100%) 50 (83%) 163 (93%) 14 (82%)
Adequate Electricity
Yes 15 (21%) 10 (22%) 18 (30%) 43 (25%) 0 (00%)
No 55 (79%) 35 (78%) 42 (70%) 132 (75%) 17 (100%)
GSM ownership
Yes 44 (63%) 40 (88%) 54 (90%) 138 (79%) 17 (100%)
No 26 (37%) 5 (12%) 6 (10%) 37 (21%) 0 (00%)
25litre GNO = 25.2kg; 1basin of traditionally processed GNC = 22.6kg Source: Computed from field survey data, 2010/2011
79
4.2.2 Traditional processing method
Pre-treatment of raw groundnut seed:-These were the activities carried out on the groundnut
itself before oil extraction took place. These involved decorticating (shelling) the groundnut, if
bought unshelled, cleaning, drying the groundnut, scorching/frying, polishing and then crushing
into paste before feeding it into the oil expelling machine. Decorticators of various throughputs
were used for shelling. Hand shelling was also done in some rural communities. The groundnut
was further cleaned to remove any leftover shell, stones and other impurities. It was further dried
to enhance scorching/roasting process.
The frying/scorching process in the traditional method was done manually. This required
the following equipment: local oven made of clay or a tripod of stones, frying pan, trays, stirrers
and pebbles to mix with the groundnut, and firewood to provide the heat. Frying was done lightly
to enhance polishing. This process brought out the desired aroma in the oil and cake which was
preferred and consumed by people, commonly referred to as the aromatic roasted peanut oil and
cake. The Institute of Agricultural Research (IAR) Ahmadu Bello University has designed some
simple manually operated roasters and expellers, that are also commonly used (IAR, undated).
Polishing is the removal of the testa (skin of the groundnut) from the scorched groundnut.
In this study, all the traditional processors used the polishing machine for this purpose, which
were just the normal hammer mills. The machines were reset not to crush or grind the groundnut,
but to loosen the testa for easy winnowing. This process was done at cost per quantity polished.
The processors paid the owner of the mill for the service an average of N67 per bag (Table 4.9).
This machine can be acquired at a cost of between N30, 000 and N50, 000; most of which were
diesel/petrol engine driven due to unavailable electricity, even though the electric motor driven
machines were more efficient and did cleaner jobs than the diesel and petrol engine driven
machines.
80
Crushing/Pasting: This involved crushing the groundnut into paste to enhance oil removal. The
roasted and polished groundnut was blended in the milling machines which were similar to those
used in polishing, except for the adjustment in the grinding unit, this time to mill the groundnut
into paste.
Oil extraction: For extraction of the oil, the paste was fed into drum-like machine with a central
shaft that spun. As the machine spun, oil gathered and was collected. In some designs, there were
grooves through which the oil was collected. The oil was collected into containers while the cake
was also removed preparatory to frying.
Though electric driven machines were preferred because of more efficiency and better
jobs, diesel/petrol machines were prevalent due to inadequate and irregular electricity supply.
Most of these machines ran as contract mills providing polishing, crushing and expelling services
to processors and not getting involved in other aspects of the processing. These machines could
cost up to N70, 000 on the average. The oil obtained was normally used to fry the cake. This also
reduced the moisture content of the oil after which it was packaged for sale.
Cake moulding/frying: The cake in this method was moulded into different shapes and sizes for
frying. Additional labour was always required for moulding cake. The smaller the size of the
cake the better the fry level and this stored best. After moulding the cake, it was fried-dry in
frying pans over the oven for some minutes in the oil, after which it was cooled off and packaged
for the market.
4.2.3 Modern processing method
In this method, large capacity expellers and oil press machines were used, though the
activities were automated and large quantities of groundnut could be processed within a short
time period. The principle was similar to the traditional method except for the scale and level of
automation which also varied depending on the machine type and capacity. In this study, two
81
major levels of modern processing existed. These were the single unit machines and the large
systems of machines. The single unit machines had capacities ranging from 0.5 tonnes of
groundnut (GN) per day to 10 tonnes per day. In this set up, some pre – treatment such as heating
and steaming were done before feeding the groundnut into the expeller. Groundnut was crushed
and oil expelled through a unit and the cake through the other opening in the machine. The oil
was collected into drums and the cake was also packed for sale. In some types, no frying or
steaming of the groundnut was done, hence the oil and cake came out raw. In this method, the oil
is referred to as cold press peanut oil. The cake from this unit was only used for animal feed. The
oil was further heated for 30minutes and salted before packing into 25 litre containers. The
machines and equipment required were electric motor or diesel generators, oil expeller, drums,
shovel, funnels, and plastic rubber containers. The common expeller model is the 105 Golden
Star of 5 – 10 horse power.
In the large, fully automated processing mills, all operations were mechanized. Some had
capacities to process groundnut and soya beans, and could process up to 10 tonnes or more of the
raw materials per day. In this method, the groundnut with shell or seed/shell was fried in the
cooker, moved to steamer (smaller cookers), then through the conveyors to the expellers, where
the oil was extracted. The oil dropped on another conveyor and was conveyed to the filter tank. It
was then moved to the batch refiner and finally to the storage tanks and sold. The cake was
conveyed via the cake elevator to the hopper from where it was bagged.
The GNO in this method was further checked for free fatty acid (FFA), and moisture
content (MC). If high, further refining was done to bring the MC to 0.02. If the free fatty acid
(FFA) was high it was also refined to the required level. Vitamin A and iodine were also added
to meet market standard. In this process the refined groundnut oil was gotten. The oil was then
sold to buyers who were GNO packaging firms or manufacturers or traders. The cake was also
82
sold to animal feed firms or animal farms. The price per kg of the cake depended on the crude
protein content.
Equipment used in this type of processing included boilers to supply the heating, big
cookers, small cookers, expellers, conveyors, several motors, storage tanks, refiners, flakers,
softeners, crushers, and dis-stoner. Processing mills bought and stored their raw materials
(groundnut) to last till the next harvest season.
4.2.4 Marketing
Product marketing commenced immediately after processing activities. The products sold
were the groundnut oil (GNO) and groundnut cake (GNC). For traditional processors in
Nasarawa State, 57% of them sold their products both at the processing sites and the markets,
30% sold in the markets and 13% sold only at the sites, (Table 4.3). In Benue State, 33% sold at
the sites and 42% sold both in the markets and the sites while 25% sold only at the sites. In
Niger State, 30% sold in the markets and 32% at the processing sites. For the North Central zone,
47% sold at sites and the markets, 29% sold in the markets only, while 24% sold at the
processing sites. For modern processors, 76% of the firms supplied to buyers at their respective
locations, and only 12% sold at the sites.
Pertaining to unit of sales, Table 4.3 also shows that traditional processors sold their
products in small quantities probably to meet the needs of small buyers. Ninety percent in Niger
State, 66% in Nasarawa State, 34% in Benue State sold in small units. In the entire North Central
Zone 60% sold in small units. Some traditional processors also sold in larger units of 25litres
containers; 65% of them were in Nasarawa State; 44% in Benue State and only 10% in Niger
State. All the modern processors (100%) sold their products in larger quantities in 25litres cans,
drums and tankers.
83
Concerning availability of markets for processed products in the study area (Table 4.3),
98% of the traditional processors in Niger State, 94% in Nasarawa State and 58% in Benue State
agreed that there was adequate market for groundnut oil. In the North central zone it was 86%.
All modern processors (100%) agreed that there was good market for GNO, and they received
bookings for products ahead of production. The problem of low market for GNO faced by
traditional processors in Benue State, particularly Makurdi, was attributed to inadequate market
information resulting in large inventory of unsold products. This resulted in alternate day
marketing of products. That is processors, sold only on market days allocated to them by the
market association; a kind of quota sales. In the market for groundnut cake, 98% in Niger State;
96% in Nasarawa State and 76% in Benue State agreed that there was good market for groundnut
cake. In the North Central Zone, 91% said the market was good. For modern processors, the
market was favourable because all their cake was sold even ahead of production. Only a few
traditional processors, 4% in Nasarawa State, 24% in Benue State and 2% in Niger State said
there was no market. Also a few, 9% for North Central Zone said there was no market.
Customers’ patronage of the products of traditional and modern processed products is
also shown in Table 4.3. It was observed that purchases of GNO by consumers, from traditional
processors were highest in Benue State with 96%; 93% in Niger State and lowest in Nasarawa
State with 27%. It was 67% in the entire zone. Only 18% of the consumers bought GNO from
modern processors. Wholesalers’ purchase of GNO produced by traditional processors in
Nasarawa State was 71%; 27% and 20% in Benue and Niger States, respectively. The result for
the central zone showed that wholesalers’ purchase was 42%. For the modern processors, 94% of
their sales were to wholesalers. Retailers were the highest (94%) customers of GNO from
traditional processors in Nasarawa State; 100% in Benue State and 95% in Niger State. In the
entire zone, it was 96%, and 88% from modern processors. Fewer manufacturers and processors
84
(packaging firms) bought GNO in the study area, but usually in larger quantities, see also table
4.3. These processors included an oil processing and packaging company with a factory in Jos.
It was observed here that major buyers of GNO from traditional processors were the
retailers, consumers and followed by wholesalers (Table 4.3). The retailers bought and sold
within their communities or nearby markets; while the wholesalers came from distant markets in
the eastern and northern states to buy the products for sell in their home markets. With respect to
groundnut cake, consumers were the highest buyers of GNC produced by traditional processors.
This was seen in 66% of them in Nasarawa State; 100% in Benue State and 94% in Niger State
and 86% for the North Central Zone. No consumer bought GNC from modern processors. It is
also noted that wholesalers patronized 76% of the processors in Nasarawa State, and less in other
states. No wholesalers patronized modern processors’ cake. Ninety seven percent (97%) of the
retailers in Nasarawa State; 96% in Benue State and 95% in Niger State bought cake from
traditional processors. For the zone it was 96% of the retailers that bought GNC from traditional
processors and no retailer bought GNC from modern processors. Very few manufacturers and
processors bought GNC from traditional processors. It was noted that retailers and consumers
were the major buyers of GNC from traditional processors, followed by wholesalers. Retailers
bought and sold within their communities. Consumers bought to eat, drink with ‘gari,’ make
local salad, while manufacturers used it in food processing e.g. steak meat and/or mixed with
vegetables among others. All the cake (100%) processed by modern processors were sold to
manufacturers; animal feeds makers in farms and feed companies, who normally placed order
ahead of production. The cake from modern processors was not consumed by humans because of
the method of processing, but used for animal feeds.
85
Table 4.3 Marketing activities of processors in the States and North Central Nigeria
Source: Computed from field survey data, 2010/2011
Variable Nasarawa
(70) (%)
Benue
(45) (%)
Niger
(60) (%)
NCN
(175) (%)
Modern
(17) (%)
Available market GNO
Yes 66 (94) 26 (58) 59 (98) 151 (86) 17 (100)
No 4 (6) 19 (42) 1 (2) 24 (14) 0 (00)
Available market GNC
Yes 67 (96) 34 (58) 59 (98) 160 (91) 17 (100)
No 3 (4) 11 (24) 1 (2) 15 (9) 0 (00)
Distribution of GNO customers
Consumers 19 (27) 43 (96) 56 (93) 118 (67) 3 (18)
Wholesalers 50 (71) 12 (27) 12 (20) 74 (42) 16 (94)
Retailers 67 (96) 45 (100) 57 (95) 169 (96) 15 (88)
Manufacturers 5 (7.14) 15 (33) 7 (12) 27 (15) 6 (29)
Processors 10 (14) 0 (00) 0 (00) 10 (6) 1 (3)
Distribution of GNC customers
Consumers 46 (66) 45 (100) 59 (93) 150 (86) 0 (00)
Wholesalers 53 (75) 7 (16) 11 (18) 71 (41) 0 (00)
Retailers 68 (33) 43 (96) 57 (95) 168 (96) 0 (00)
Manufacturers 6 (8.57) 31 (69) 12 (20) 49 (28) 17(100) poultry
Processors 0 (00) 0 (00) 1 (2) 1 (0.5) 0 (00)
Sales location
Processing sites 9 (13) 15 (33) 19 (32) 43 (24) 2 (12)
Markets 21 (30) 11 (25) 18 (30) 50 (29) 13 (76)
Both 40 (57) 19 (42) 23 (38) 82 (47) 2 (12)
Sales in units
Smaller units 22 (32) 30 (67) 54 (90) 106 (60) 0 (00)
Both large/small 48 (68) 15 (33) 6 (10) 69 (40) 17 (100)
86
4.3. Input Use Efficiency in Traditional and Modern Groundnut Oil Processing in North
Central Nigeria
The result of the analysis of technical efficiencies of traditional and modern groundnut oil
processors is presented in this section. The parameters of maximum likelihood estimation (MLE)
of the stochastic frontier function adopted is discussed and presented in Tables 4.5, 4.6 and 4.7.
This is for traditional processors in Nasarwa, Benue and Niger States; as well as for the zone and
modern processors in the selected states. To validate the results from the stochastic frontier
analysis (SFA) models for purpose of analysis, test of hypotheses was done to show the presence
of inefficiency in the models, else the model could be analysed with the ordinary least square
(OLS) method (Coelli 1996; Saris & Kariagianis, 2006). This was achieved with the likelihood
ratio test and the result is presented in Table 4.4. The null hypothesis (H0 :) that γ=δ0=δm=0
indicating that technical inefficiency was not present in the models was rejected at various levels
of significance of alpha. This implied that there existed some level of inefficiency in the
processing activities of processors, hence the models were appropriate for analysis (Ajibefun &
Daramola, 2003; Osborne & Trueblood, 2006; Bamire, Oluwasola & Adesiyan, 2007).
87
Table 4.4 Generalized log likelihood-ratio tests of the complete technical efficiency of
groundnut oil processors (γ=0) in North Central Nigeria
Processors Log likelihood
function
λ Critical value
(χ2)*
Decision
Nasarawa State -678.61 15.00 13.36 (α = .100) Reject
Benue State -441.84 38.83 20.09 (α = .010) Reject
Niger State -523.49 6.34 5.07 (α = .750) Reject
North Central -1630.49 3.97 3.49 (α = .900) Reject
Modern -116.90 13.16 NA -
* Critical values (8 degrees of freedom) obtained from table D.4 pp 988-989 in Gujarathi (2007), the
abridged table from table of percentage points χ2 (at α) by E. S. Pearson & H. O. Hartley eds. Biometrika
table for statisticians Vol. 1,3d., table 8, Cambridge University Press, New York, 1966
Source: Computed from field survey data, 2010/2011
The maximum likelihood estimates of the parameters for the models estimated for
traditional processors in the States and the zone are presented in Tables 4.5, 4.6 and 4.7. The
results from technical efficiency aspect indicated that raw groundnut (X1), was significant at 1%
level of probability in Nasarawa and Niger States, but not significant in Benue State (Tables 4.5
and 4.7). It had positive coefficient in all the States. This explains the fact that quantity or quality
of raw groundnuts for processing determined, to a very large extent the yield of groundnut oil
(GNO) and groundnut cake (GNC) obtained. Labour (X2) was also significant in Nasarawa State
at 1% level of significance (LOS), but not significant in Benue and Niger States. Labour
coefficient was negative in all the States, except in Benue State. This suggests caution in labour
use, so as not to exceed its marginal productivity. Fuel-wood (X3) was significant at 5% in Benue
State with positive coefficient. It was not significant in Nasarawa and Niger States. Salt (X4) was
significant in Nasarawa State at 1% LOS and positive.
88
In the inefficiency model, age (Z1) was significant in Nasarawa and Niger States at 1%
LOS. Age coefficient was negatively signed in all the States, implying that increase in age
reduced inefficiency of the processors. This is plausible because age goes with accumulation of
experiences, knowledge and human capital development capable of reducing inefficiencies.
Level of education (Z2) was significant only in Nasarawa State at 10% LOS with negative
coefficients in all the States. This also implied that the level of education reduced inefficiencies
in traditional processing. Years of experience (Z3) was significant at 1% LOS in Benue and
Niger States, and significant at 5% LOS in Nasarawa State. The years of experience coefficient
in all the States except Niger State were negative. Gender (Z4) was significant at 1% in Niger
State, and 5% in Nasarawa State. Marital status (Z5) was only significant in Nasarawa State at
5% LOS and negative. Household size (Z6) was also significant at 5% LOS and negative only in
Nasarawa State. Cooperative participation (Z7) was significant at 1% LOS in Benue and Niger
States.
The results for the North Central Zone for both traditional and small-scale modern
processors are presented in Tables 4.6 and 4.7. In the pooled data for the zone, labour and salt
were significant at 1% level of significance. Fuel-wood was significant at 5% while raw
groundnut was significant at 10% LOS. In the inefficiency model, household size was significant
at 5% LOS while level of education and years of experience were significant at 10% level of
probability. In small-scale modern processing, raw groundnut and labour were significant at 1%
level of probability (Table 4.7). This underscores the critical nature of these variables. This is in
line with the fact that raw groundnut constitute over 80% of variable input in groundnut oil
processing. Labour marks its importance as procurement, processing and marketing of products
required labour. Maintenance and quality of groundnut were not significant but contributed
89
positively to efficiency attainment in the processing. Level of education and years of experience
were significant at 1%. Gender had little or no effect in efficiency of modern groundnut
processing.
Efficiency estimates from the model in the various states for traditional processors
indicated that the gamma, γ, statistic was 0.5501 and significant at 1% level of probability in
Nasarawa State, but not significant in Benue State. In Niger State γ was 000.184 and significant
at 1%. For modern processors, the γ statistic was estimated at 0.89 and significant at 5% level of
probability. It is to be noted that γ (0, 1), therefore the closer γ is to one, and the more error
variance is attributable
to inefficiency. If γ is 0 and statistically insignificant then the ordinary least squares (OLS)
method becomes more appropriate tool for this analysis. In this study, inefficiency in the
production (processing) activities has been identified, which is akin to findings of Karagianis &
Saris (2006); Ogundele & Okuruwa (2006) and Bamire et al (2007).
4.3.1Technical efficiency estimates for groundnut oil processors in North Central Nigeria
Frequency distributions of technical efficiency scores as well as the means are reported in
Table 4.8 for traditional processors in the states and the region as well as for small-scale modern
processors. Majority of traditional processors had efficiency scores above 0.80 in all the states
and the zone, except for the modern processors whose efficiency scores were fairly distributed
from 0.56 – 1.0. The mean efficiency in Nasarawa State was 0.880; Benue State, 0.851; Niger
State, 0.979 and for the zone 0.907. For modern processors the mean efficiency was 0.741. The
minimum efficiency score for traditional processors in Benue State was 0.32 and a maximum of
one. In Nasarawa State, the minimum was 0.461and a maximum of 0.999; a minimum of 0.913
and a maximum of 0.998 for Niger State. For North Central zone, the minimum score was 0.32
90
and a maximum of one; in the small-scale modern processing the minimum efficiency score was
0.473 and the maximum of 0.99 with a mean of 0.804.
The high technical efficiency recorded in this study implied that processors attained
efficiency even though some level of inefficiency was still present. That is, there existed little
chance to improve technical efficiency of the processors given their present state of technology,
if they were operating with such high efficiencies. Ogundelele & Okoruwa (2006) and Amaza et
al (2007) also reported such high efficiencies among rice farmers. Arising from this result, it is
implied that any desire to increase output required change in technology type and hence the
production function of the processors. On the structure of technical efficiency, some firms were
distributed below 0.8 (80%) implying that there was still room for some improvements based on
the technology currently practiced by the processors. Also agro-processing best approximates to
the industrial production than crop and animal production, hence the precision in measurement
and the resultant high efficiency scores.
91
Table 4.5 Maximum likelihood estimates (MLE) of the stochastic frontier production
(processing) function for traditional GNO processors in Nasarawa and Benue States
Variable Nasarawa Benue
Production model Parameter Coefficient t-ratio Coefficient t-ratio
Constant βo 5427.50 557.81*** -727.22 -40.79***
(9.730) (17.83)
Raw g/nut seeds (X1) β1 258.17 52.65*** 47.64 1.02
(4.903) (46.87)
Labour (X2) β2 -12.76 -3.67*** 5.38 0.42
(3.451) (12.95)
Fuel Wood (X3) β3 -1.975 -1.27 0.310 3.70***
(1.551) (8.40)
Salt (X4) β4 33.45 2.60*** -5.656 -0.18
(12.824) (31.69)
Technical inefficiency model
Age in years (Z1) 76.73 0.350 -1.25 -4.46***
(2.19) (0.28)
Level of education (Z2) -923.25 -1.890* -62.93 -0.48
(488.46) (132.37)
Years of experience (Z3) -842.07 -2.11** -24.80 -5.99***
(399.35) (413.470)
Gender (Z4) 687.73 2.25** 50.694375 0.21
(305.36) (244.21)
Marital status (Z5) 43.29 2.11** -0.5086 -0.14
(20.55) (3.60)
Household size (Z6) -96.89 -2.14** 0.63 0.73
(46.85) (0.86)
Cooperative (Z7) 1906.79 -2.139** 7.49 7.02***
(389.44) (106.61)
Variances
σ2 2750.94E+4 2750.72E+4* 4838E+8 4838E+4***
(1.00007) (1.00000069)
γ 0.5501 4.92*** 00000.30E-4 0.047
(0.1118) (0.6423)
Log likelihood function -678.61 -441.84
***, **, * = 1%, 5% and 10% levels of significance respectively. Values in parenthesis are
standard errors
Source: Computed from field survey data 2010/2011
92
Table 4.6 Maximum likelihood estimates (MLE) of the stochastic frontier production
(processing) function for GNO processing in Niger state and North Central Nigeria
Variable Niger State (60) North central Nigeria
(175)
Production model Parameter Coefficient t-ratio Coefficient t-ratio
Constant βo 20.97 20.52*** 3284.18 3047.60***
(1.02) (1.077)
Raw g/nut seeds (X1) β1 289.49 98.52*** -14.328 -1.89*
(2.93) (7.624)
Labour(X2) β2 -1.244 -0.99 -2.641 -2.641***
(1.248) (1.073)
Fuel Wood (X3) β3 -0.62 -0.39 1.352 2.11**
(1.59) (0.640)
Salt (X4) β4 -2.81 -0.600 1.335 31.53***
(4.67) (0.0423)
Technical inefficiency model
Age in years (Z1) -0.138 -4.13*** 10.262 1.48
(3.33) (6.939)
Level of education (Z2) -1.78 -1.05 -24.806 -1.88*
(1.69) (13.28)
Years of experience (Z3) 32.78 3.61*** -34.46 -1.78*
(9.06) (19.40)
Gender (Z4) -11.14 -4.57*** 1.160 0.987
(2.42) (1.176)
Marital status (Z5) 0.711 0.699 -3.82 -1.62
(1.01) (2.36)
Household size (Z6) 0.433 0.429 73.29 2.00**
(1.01) (36.56)
Cooperative (Z7) 40.60 3.81*** -1.55 -1.211
(10.64) (1.282)
Variances
σ2 2545.04E+3 2545. 04E+3*** 7658.71E+3 7658.71E+3***
(1.0000) (1.0000)
γ -000.184 5.503*** 0.0000E+3 0.001926
(000.333) (0.00000519)
Log likelihood function -523.49 -1630.49
***, **, * = 1%, 5% and 10% level of significance in that order. Values in parenthesis are
standard errors.
Source: Computed from field data, 2010 – 2011
93
Table 4.7 Maximum likelihood estimation (MLE) of the stochastic frontier production
(processing) function for modern GNO processors in North Central Nigeria
Variable
Production model Coefficient Standard error t-ratio
Constant βo 522.88 1.003 550.99***
Raw G/N (X1) β1 0.587 0.147 3.991***
Labour (X2) β2 1.763 0.396 4.455***
Maintenance cost (X3) β3 0.0758 0.089 0.848
Quality of g/nut seed(N) (X4) β4 0.0023 0.0017 1.288
Technical inefficiency model
Constant δ0 0.1356 1.003 0.1351
Level of education (Z1) δ1 -14.69 7.596 -1.933*
Years of experience (Z2) δ2 42.87 22.84 1.88*
Gender (Z3) δ3 0.0033 1.000 0.0033
Variances
σ2 148628.1 1.000 148628.4***
γ 0.98 0.01125 89.17**
Log likelihood function -116.90
***, **,* = 1%, 5% and 10% levels of significance respectively. N=17
Source: Computed from field survey data, 2010/2011
94
Table 4.8 Distribution of technical efficiency estimates for traditional and modern small -
scale GNO processors in the states and the North Central zone
Efficiency Estimates Nasarawa (70) Benue (45) Niger (60) North Central
(175)
Modern (17)
≤ 0.50 1(1.43%) 1(2.22%) 0(00%) 2(1.14%) 2(11.78%)
0.51 – 0.55 0(00%) 1(2.22%) 0(00%) 1(0.57%) 0(00%)
0.56 – 0.60 0(00%) 5(11.11%) 0(00%) 5(2.86%) 1(5.88%)
0.61 – 0.65 0(00%) 4(8.88%) 0(00%) 4(2.29%) 0(00%)
0.66 – 0.70 1(1.43%) 2(4.44%) 0(00%) 3(1.71%) 1(5.88%)
0.71 -0.75 1(1.43%) 0(00%) 0(00%) 1(0.57%) 0(00%)
0.76 – 0.80 6(8.57%) 1(2.22%) 0(00%) 7(3.99%) 3(17.65%)
0.81 – 0.85 21(30%) 3(6.66%) 0(00%) 24(13.71%) 2(11.78%)
0.86 – 0.90 11(15.7%) 1(2.22%) 0(00%) 12(6.86%) 3(17.65%)
0.91 – 0.95 12(17.14%) 6(13.33%) 7(11.67%) 25(14.29%) 2(11.78%)
0.96 – 1.00 17(24.28%) 21(16.66%) 53(88.33%) 91(51.99%) 3(17.65%)
Mean 0.880 0.851 0.97 0.91 0.804
Max 0.999 1 0.998 1 0.998
Min 0.461 0.320 0.913 0.320 0.473
Source: Computed from field survey data, 2010/2011
4. 4. Profitability Analysis of Traditional and Modern Processing and Marketing of GNO
and GNC
4.4.1 Gross margin results of groundnut oil processing
The gross margin associated with processing groundnut oil both in the traditional and
small-scale modern methods per week for the States and the region is presented in Table 4.9. The
procurement, processing and marketing cost items for the respective quantities of raw groundnut
processed are also shown.
95
The table presents, on state basis, the costs and returns of traditional and modern GNO
processing and hence the gross margin in North Central Nigeria (NCN). For an average of
263.68kg of groundnuts processed in Nasarawa State, the total revenue obtained was N49, 105
(N186, 229/tonne) while the total variable costs (TVC) of N39, 691 (N150, 527/tonne) was
realized. A gross margin (GM) of N9, 414 or N35, 702/tonne representing 23.71% was obtained.
In Benue State, for a quantity of 100.5kg, a total revenue of N25, 659 (N255, 950/tonne) was
realized. The TVC of N21, 230 (N211, 770/tonne) and gross margin of N4, 429 (N44, 179/ton)
equal to 20.86% was obtained. For a given quantity of 46kg in Niger State, the total revenue
gotten was N9, 546 (N20, 752/tonne), its TVC was N7, 807 (N169, 717/tonne) with a gross
margin of N1, 739 (N37, 804/tonne) representing 22.27%.
For the entire North Central, TVC was N25, 314 (N172, 204/tonne) with total revenue of
N29, 513 (N200, 768/tonne) per week, and GM of N4, 199 (N28, 564/tonne) representing
16.58%. For the modern processors, the total variable expenses were N937, 966 for 6222kg
(6.222 tonnes) of groundnut processed per week with total revenue of N1, 294, 609. The gross
margin was calculated at N356, 643 that is N57, 319/tonne and 38% of the TR. The modern mills
had a high GM of 38%. This is attributable to the economies of size production enjoyed by them.
It is noted from the results that GNO processing in the States in the North Central Nigeria
was profitable, given that activities from procurement through processing to marketing were
quantifiable and monetized. Fixed cost items were not estimated in line with the proposed tools
of analysis, and because most services of the fixed cost items in traditional processing could be
accessed and paid for. This explains the adherence to GM as a measure of profitability in this
study (Arene, 2002)
96
Table 4.9: Gross Margin for Traditional and modern GNO processing the States and the
Region
Operation/ States Nasarawa
263.68kg
Benue
100.25kg
Niger
46.01kg
N C N
147kg
Modern
6222kg
(A) Revenue (N)
(a) GNO 34447 16193 4817 19594 1127829
(b) Cake 14658 9466 4729 9919 166780
Total Revenue 49105 25659 9546 29513 1294609
186229/ton 255950/ton 207521/ton 200768/ton
(B)Variable cost (N)
(a) Procurement
(i) Value of raw GN 33, 632 16, 417 5, 878 20, 889.91 897, 906
(ii)Transportation 240 356 91 207 103
(i) Loading/off loading 131 146 39 118 832
(ii) Market charges 91 177 29 85 641
(iii)Commission agents 108 113 22 69 675
(iv) Other charges 70 147 95 103 1, 040
Total 34, 272 17, 356 6, 154 21, 471 918, 264
(b) Processing
(i) Decortications 504 372 00 420 maint 1970/wk
(ii) Scorching/roasting 514 300 187 350 -
(iii)Polishing 393 217 74 238 electricity 1265
(iv) Crushing / pasting 581 411 121 380 -
(v) Oil expelling 577 374 113 365 -
(vi) Cake frying 343 184 135 231 -
(vii) Fuel wood 944 710 201 631 263
(viii) Salt 91 89 45 75 -
(ix) Other cost 269 185 266 247 1145
Total 4, 216 2, 850 1, 142 2, 937 16, 169
( c) Marketing
(i) Transportation 205 206 107 193 -
(ii) Packing 41 42 52 45 -
(iii)Other marketing cost 957 776 352 668 3, 533
Total 1, 203 1, 024 511 906 3, 532
Total variable cost 39, 691 21, 230 7, 807 25, 314 937966
150,527/ton 211, 701/ton 169, 717/ton 172, 204/ton -
(C)Total GM(A-B) 9414 4429 1, 739 4, 199 356, 643
35702/ton 44179/ton 37804/ton 28564/ton 57319/ton
% GM 23.71% 20.86% 22.27% 16.58% 38%
Source: Computed from field survey data, 2010/2011
97
4.4.2 Determinants of profit of groundnut oil processing in North Central Nigeria
This section discusses the results of regression analysis of selected economic variables on
the gross margin of the traditional and modern processors in the study area. The results for the
selected states are presented in Tables 4.10 and 4.11. The task here is to test the null hypothesis
that β=0 or that the β’s coefficients were not statistically different from zero.
The results for the states in the zone (Table 4.10) indicated that fuel-wood (X3) and
packaging (X4) were significant at 1% level of probability in Nasarawa, Benue and Niger States,
and all with positive coefficients. This brought to the forefront the importance of these two
variables in the gross margin of the traditional processors. Fuel wood, as a source of energy for
processing, is critical due to inadequate electricity, to enable the use of simple, locally fabricated,
and electrically operated machines for processing. The extended effect is the continuous
deforestation and environmental problems. Packaging, a convenient way of presenting a product
to a consumer, has been seen to be very critical, therefore better packaging will increase the
gross margin of processors and hence their profit. This also brings the packaging enterprises into
the value chain. Transportation (X5) was significant in Niger State at 1% LOS, and 10% level of
significance in Nasarawa State, and not significant in Benue State; but positive in all the three
States implying too that transportation contributed positively to the gross margin of processors.
The salt (X6) variable was significant at 5% level of probability in Nasarawa State and negative
in all the States. This implied that increased use would reduce the gross margin of processors. Of
course salt is a necessity with inelastic demand hence its use in GNC has to be with caution, also
given the fact that in Niger State, pepper is used instead of salt. It is not needed in GNC used in
steak meat (suya) making. Price of raw groundnut (X1) which included procurement cost and
labour (X2) were not significant. These variables had negative coefficients calling for caution in
98
their use. The adjusted R2 was 0.94 for Nasarawa State, 0.96 for Benue State and 0.88 for
Niger State. This implied that the selected independent variables explained 88%, 94%, and 96%
variation in the gross margin of processors in Niger, Nasarawa and Benue States, respectively.
Table 4.11 presents the results for pooled data for traditional and small-scale modern processors
within North Central Nigeria. Fuel wood and packaging were significant for traditional
processors at 1% level of probability, while transportation was significant at 10% level. This
implied that fuel wood, packaging and transportation were the critical factors that determined
gross margin and hence profitability of GNO Processors. The coefficient of labour and salt were
however negative, calling for caution in their use. The price of groundnut was positive but not
significant, meaning it was not very critical in the level of profit of processors. The adjusted
coefficient of multiple determination (R2) was 0.944 for the traditional processors. The F values
and the standard errors are as presented in the table.
For modern processors, the independent variables were selected based on their
importance in the processing activity. The critical variables were raw groundnut (x1),
procurement cost (x2) and maintenance cost (x3) which were all significant at 1% level of
probability. Electricity, labour and fuel wood however negative implying that their increase use
could deplete the profit of modern processors. The adjusted coefficient of multiple determination
(R2) was 0.97. The F value, Durbin-Watson (DW) and the standard errors were also as
presented in the table 4.11
99
Table 4.10 Regression results of the determinants of profitability of traditional GNO
processing in Nasrawa, Benue and Niger states
Nasarawa (70) Benue (45) Niger (60)
Variables Coefficients t-value Coefficients t-value Coefficients t-value
Constant -2110.84 -0.627 922.58 1.22 -519.83 -0.66
(3368.47) (753.73) (785.73)
Price of raw GN (N) (X1) 0.018 0.597 -0.045 -1.588 -6.34 -1.36
(16.76) (3.17) (4.67)
Labour (N) (X2) -0.072 -1.488 -0.034 -0.92 -0.892 -1.23
(2.205) (1.31) (0.724)
Fuel wood (N) (X3) 0.491 4.48 *** 0.363 5.04*** 6.36 4.81***
(1.480) (0.65) (1.32)
Packaging (N) (X4) 0.533 5.25 *** 0.596 8.94*** 53.23 5.66***
(10.27) (7.19) (9.41)
Transport (N) (X5) 0.100 1.77* 0.076 1.64 8.95 3.06***
(1.85) (1.16) (2.93)
Salt (N) (X6) -0.107 -2.231** 0.023 0.66 -3.33 -0.96
(9.49) (2.60) (3.478)
R2= 0.94 R2
= 0.96 R2 = 0.88
DW = 1.54 DW = 1.705 DW = 1.73
F = 180.61 F = 205.45 F = 75.35
***, **,* = 1%, 5% and 10% levels of significance respectively.
Source: Computed from field survey data, 2010/2011
100
Table 4.11: Regression results of the profit function of determinants of profitability of
traditional and small-Scale modern GNO processing in North Central Nigeria
Traditional (175) Modern (17)
Variables Coefficients t-value variable coefficients t-value
Constant -1293.65 -1.42 Constant 19063.48 1.53
(910.21) (25000.29)
Price of raw g/nut (N) (X1) 2.806 0.640 Price of raw g/nut (X1) 18.02 3.497***
(4.383) (5.155)
Labour (N) (X2) -1.290 -1.35 Procure ment (N) (X2) -33.19 -4.002***
(0.954) (8.288)
Fuel wood (N) (X3) 5.040 7.53 *** Maintenance (N) (X3) 10.381 2.806***
(0.670) (3.699)
Packaging (N) (X4) 59.881 11.64 *** Labour (N) (X4) -1.031 -.289
(5.15) (3.564)
Transport (N) (X5) 1.80 1.71* Fuel wood (N) (X5) -.583 -.094
(1.050) (6.232)
Salt (N) (X6) -5.74 -1.49 Electricity/diesel (N) (X6) -10.70 0.78
(3.85) (13.67)
R2 = 0.94 R2
= 0.97
DW = 1.53 DW = 2.02
F = 484.53 F = 126.41
***, **,* = 1%, 5% and 10% levels of significance respectively.
Source: Computed from field survey data, 2010/2011
4.5 Value Added by Processing Groundnut into GNO and GNC in North Central Nigeria
Processors and manufacturers are described as those who undertake some activities on
the farm products to change their form (Olukosi & Isitor, 1990). This change in form implies
increase in quality and value – form utility. The value added to a tonne of raw groundnut is
simply the change in its value before and after processing (Gittinger, 1972; Brown, 86; Brown et
al, 94). For this study, value added to groundnut per week is shown in Table 4. 12.
On state basis, the highest quantity of 263kg processed per week was in Nasarawa State worth
N33, 632 with final market value of products (GNO and GNC) at N49, 105. This was with added
101
value of N15, 473 (N58, 680 per tonne) which equaled 46% value added. In Benue State, 100kg
of groundnuts worth N16, 417 yielded products valued at N25, 659. The value added was N9,
242 (N92, 420 per tonne) representing 56%. The least quantity was in Niger State at 46.9kg per
week worth (N5, 878) yielding products worth (N9, 546) with the highest value added of N3,
668 (N92, 420 per tonne) of groundnut processed amounting to 62.40% value addition.
For the North Central Zone, an average of 147kg of groundnuts was processed per week
valued at N20, 889. The value of GNO obtained was N19, 594 and GNC N9, 919 all valued at
N29, 513, with added value of N8, 624 or N58, 666 per tonne representing 41.28% value
addition. Table 4.12 also shows the value addition for modern processing in which the quantity
processed was 6, 222kg per week on the average valued at N897, 906. The GNO obtained was
valued at N1, 127, 829, and cake N166780, totaling N1, 294, 609. The valued added in modern
processing was calculated at N396, 703 (44.15%) or N63, 758 per tonne.
Table 4.12: Value added by processing groundnut into oil and cake in North Central
Nigeria
Source: Computed from field survey data, 2010/2011
Location/Quantity Value of
raw g/nut
(N)
Value of product Value added Naira per
tone GNO
(N)
GNC
(N)
Total Value
(N)
(GNO+GN
C)
Nasarawa (263.68kg) 33632 34447 14658 49105 15473 (46%) 58680
Benue (100kg) 16417 16193 9466 25659 9242 (56.30%) 92420
Niger (46.9kg) 5878 4817 4729 9546 3668 (62.40%) 78208
North Central
(147kg)
20889 19594 9919 29513 8624 (41.28%) 58666
Modern (6222kg) 897906 1127829 166780 1294609 396703 (44.2%) 63758
102
4.5.1 Test of significance of value added
The result of test comparing the means of value added through processing in the selected
States and the zone, for traditional and modern processing is presented in Table 4.13. The
student’s t- statistic was used to compare the mean value of raw groundnut seeds before
processing and its value after processing both in traditional and modern processing. The result
shows that there was significant difference in means of value of groundnut before processing and
its value after processing at 5% LOS in Nasarawa, Benue and Niger States. The result for the
modern processors in the zone was not significant. Therefore, the null hypothesis (H0: µ=0) that
there was no significant difference between the value of groundnut before and after processing
was rejected at 5% level of probability for traditional processors. This was however not rejected
for modern processors. This implied that traditional processors were more efficient and added
more value to raw groundnut than modern processors. This could be due to inadequate
infrastructural facilities such as electricity, and good roads that enhance large scale processing
activities
103
Table 4.13: Result of test of differences in value of groundnut seed before and after
processing
Location Mean X Stand.dev Stand. Error tα.05 Sig
Nasarawa Before 36632.14 70 42802.29 5115.85 -9.118 .000
After 49105.88 70 53964.94 6450.04
Benue Before 16417.67 45 12883 1920.55 -11.45 .000
After 25660.11 45 17975.56 2679.64
Niger Before 5878.16 60 5297.53 683.91 -7.76 .000
After 9559.13 60 8788.93 1134.65
NCN Before 20889.91 175 30992.95 2342.85 -12.89 .000
After 29518.09 175 39475.49 2984.07
Modern Before 897906.71 17 2.42569E6 5.8817E5 -1.329 .202
After 1.2946E6 17 3.6554E6 8.8657E5
Source: Computed from field survey data, 2010/2011
4.6 Level of Integration of Markets of Groundnut Oil (GNO) and Groundnut Cake (GNC)
The procedure started with a test for non – stationarity of the series. This was then
followed by the Johansen multivariate and bivariate tests for co - integration carried out on the
price series obtained in Wuse, Bida, Minna, Nasarawa Eggon, Lafia and Makurdi markets within
the North Central Nigeria.
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4.6.1 Result of the unit root test
To examine the time series properties of the price series, the augmented Dickey – Fuller
(ADF) unit root test approach was used. At the level form of each series, the null hypothesis was
that each data series was non stationary. If the hypothesis was not rejected, the test was repeated
using the first difference of each price series. The results of the ADF test for individual price
series (GNO & GNC) in each of the markets are reported both for the prices at levels and for the
prices in the first difference in Table 4. 14. For GNO prices in Lafia, Wuse and Nasarawa Eggon,
the null hypothesis was not rejected at level but rejected for the other three markets. However, at
first difference the null hypothesis for prices in all the markets for GNO and GNC were rejected
at 5% LOS, and the series were I (0). That is, all the respective price series for GNO and GNC in
all the locations were integrated of the same other. Therefore, they were set for the conduct of
the Johansen test for co-integration.
Table 4.14: Augmented Dickey -Fuller (ADF) Unit root test for price series at level and at
first difference
Markets/price series Level First difference
GNO GNC GNO GNC
Lafia (ser 01) -5.221041 -2.976499 -7.387304 -6.508664
Wuse (ser 02) -5.929753 -3.127987 -8.063058 -5.913575
Nasarawa Eggon (ser 03) -4.933190 -2.132298 -7.668375 -7.890013
Minna (ser 04) -2.707102 -2.531061 -6.875167 -6.329373
Bida (ser 05) -2.421234 -2.7487 -5.107408 -5.815462
Makurdi (ser 06) -2.254686 -1.667922 -7.242875 -4.301228
Mackinnon critical values for ADF test at 1% LOS = -3.5682; 5% =-2.9215; 10%=-2.5983
Source: Computed from field survey data, 2010/2011
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4.6.2 Result of the Johansen test for co-integration
The Johansen multivariate and bi-variate tests were conducted for groundnut oil (GNO)
and groundnut cake (GNC) price series that were integrated of the same order. The result of the
Johansen multivariate test is presented in Tables 4.15 for GNO and 4.16 for GNC. For GNO
(Table 4.15), there were five co-integration vectors at 5% significance level, and accordingly one
common stochastic trend in the system. Hence, their locations seemed to be within the same
market, the North Central Zone of Nigeria. All the bi-variate tests showed at least one co -
integration vector and hence one common stochastic trend which also aligned with the result
from multivariate test. The result shows that the markets were integrated as the relative prices
were stable signifying long run equilibrium relationship over time and the law of one price
(LOP) held. This implied that the commodity was a tradable item within the zone, though there
was room for improvement in integration of the markets given the Eigen values.
Table 4.16 presents the multivariate results for GNC. The result showed two co-integrating
vectors at 5% significance level. This implied that the market for groundnut cake was not highly
integrated. This was attributable to the fact that bulk of the GNC processed was consumed within
markets of the processors. GNC was also treated as a by-product from processing so that price
for GNC was not of paramount trade importance.
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Table 4.15: Result of the multivariate Johansen test for Co-integration for GNO price
series
Eigen value Likelihood ratio 5 percent
critical level
1 percent critical
level
Hypothesized No
of CE(S)
0.636192 142.7042 94.15 103.18 None**
0.453109 92.14776 68.52 76.07 At most 1**
0.401933 61.97245 47.21 54.46 At most 2**
0.328333 36.26979 29.68 35.65 At most 3**
0.231063 16.37017 15.41 20.04 At most 4*
0.062612 3.232881 3.76 6.65 At most 5
*(**) denotes rejection of the hypothesis at 5% (1%) significance level.
L.R. test indicates 5 co – integrating equations at 5% significance level.
Source: Computed from field survey data, 2010/2011
Table 4.16: Result of the multivariate Johansen test for Co-integration for GNC price
series (GNC)
Eigen Value Likelihood ratio 5 percent 1 percent Hypothesized No
of CE(S)
0.528014 111.2450 94.15 103.18 None**
0.489100 80.70469 68.52 76.07 At most 1**
0.371874 47.12563 47.21 54.46 At most 2
0.251224 23.87487 29.68 35.65 At most 3
0.141876 9.409106 15.41 20.04 At most 4
0.034564 1.758793 3.76 6.65 At most 5
*(**) denotes rejection of the hypothesis at 5% (1%) significance level.
LR test indicates 2 co – integrating equations at 5% significance level.
Source: Computed from field survey data, 2010/2011
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4.6.3 Determinants of market integration
The regression results of the factors that influenced the level of market integration for
groundnut oil (GNO) and groundnut cake GNC) are presented in Tables 4.17 and 4.18. For the
factors that affect the integration of GNO markets (Table 4.17), the coefficient of multiple
determination, adjusted R2 was 0.82, implying that 82% of variation in the market integration of
GNO market was explained by the independent variables. In the result, the number of processing
facilities and administrative regulations in the markets significantly influenced the integration of
the markets at 1% significance level. This also meant that more administrative regulations stifled
or reduced market integration. This agrees with Chirwa (2000) in his findings in the integration
of markets for maize in Malawi where government still maintained some restrictions. The
shortest distance between the markets and membership of co-operatives were positively signed.
Telephone (GSM) ownership was negatively signed. This meant that the phones were not
necessarily used for marketing activities and, for reasons not identified reduced market
integration for GNO.
Table 4.18 presents the regression results for groundnut cake (GNC). The number of processing
facilities in paired markets was significant at 1%level of significance, meaning that the higher
the number of processing facilities in the locations where the markets were, the more the
marketing activities and hence integration of the GNC markets. Shortest distance between the
markets and membership of co-operative had positive effects, while GSM ownership and
administrative regulations had negative effects but were not significant. The adjusted R2 was
0.513. This meant that some factors that influenced the integration of GNC market might not
have been identified.
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4.17: Result of factors that determined the level of integration of groundnut oil markets in
North Central Nigeria (NCN)
Variable Coefficient Standard error t-value LOS
Constant - 21.209 12.782 -1.659 .131
Shortest distance (X1) 0.012 0.010 -1.263 0.238
No of GSM (X2) - .289 0.238 -1.213 0.256
No of processing facilities (X3) 1.188 0.198 6.004*** 0.000
Membership of co-operative (X4) 2.243 2.881 0.779 0.456
Administrative regulations (X5) 9.463 2.903 -3.260*** 0.01
R2 = 0.82, F = 13.656, *** = sig at 1%, ** = sig at 5%, *=sig at 10%
Source: Computed from field survey data, 2010/2011
4.18: Result of factors that determined the level of integration of groundnut cake market in
NCN
Variable Coefficient Standard error t-value LOS
Constant - 3.06 9.63 -0.318 0.758
Shortest distance (X1) 0.10 0.007 1.282 0.232
No of GSM (X2) -0 .186 0.179 -1.041 0.325
No of processing facilities (X3) 0.431 0.149 2.890*** 0.018
Membership of co- operative (X4) 0.082 2.170 0.038 0.971
Administrative regulations (X5) -1.66 2.187 0.759 0.467
R2= 0.213, SE: 3.04 F = 1.898 ***=sig at1%, **= sig at 5%,*=sig at 10%
Source: Computed from field survey data, 2010/2011
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4. 7 Constraints Facing Groundnut Oil Processing Industry in North Central Nigeria.
Groundnut oil processing industry in the North Central Nigeria is fraught with some problems as
revealed in this study.
4.7.1 Identified constraints
The identified constraints as presented in Tables 4.19 include:
i) Inadequate finance: The responses from the states indicated that 86%, 75% and 50% in
Benue, Niger and Nasarawa States respectively responded that inadequate finance
was a major problem that hampered progress in the processing business.
Comparatively, 68% of traditional processors for zone and 94% of modern processors
believed the inadequacy of finance was a major constraint to their processing
activities. Their inability to acquire crushing and expelling machines, which could be
locally fabricated, was due to inadequate finance. More so that majority of the
traditional processors were women who are traditionally known to control little
capital;
ii) Machine breakdown: Fewer respondents had problem of incessant machine breakdown.
The results for the states showed Nasarawa State with the highest percentage (28%)
of this problem and 8% each for Benue and Niger States. For the North Central Zone,
it was 16%. However, 100% of the modern processors agreed that regular breakdown
of machines affected their production, though maintenance service and spare parts
were available;
iii) Inadequate processing technology: This constraint was emphasized by 73% of the
processors in Benue State, 55% in Nasarawa State, and 36% in Niger state. The
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result for the North Central Zone showed 53% of traditional processors with this
problem. Only 23% of the modern processors complained of the problem. The feeling
among traditional processors was that the state of the processing technology was very
low and needed improvement. There was need for frying machine, cake moulding
machines and improved expellers.
iv) Inadequate electricity: Most labour/energy saving processing machines were
electrically operated. Their being put to use was hampered by unavailable electricity.
The result for the states showed 76% of traditional processors in Benue state
complaining of this problem; Nasarawa State, 85%; Niger State, 83% and the North
Central Zone, 82%. All (100%) of the modern processors had this problem. It was
also noted that inadequate electricity increased operation cost in diesel and petrol and
also led to capacity underutilization of machines. Electric operated machines did
more jobs and cleaner products than diesel operated machines;
v) Inadequate quantity/high cost of groundnut: This has to do with seasonal harvest of
groundnut. The raw groundnut becomes scarce and costly during the off season, so
that processors could not maintain processing activities throughout the year. In
Nasarawa State, 84% of traditional processors, 88% in Benue State and 20% in Niger
State shared this constraint. They comprised 80% in the North Central Zone, while
88% of modern processors had this problem. Some of the modern processors made
adequate arrangement to procure groundnut for all year round processing. The largest
among them had stores and procured enough for all season processing;
vi) Poor sales: This was more critical in Nasarawa State, 58%, Benue State, 55% and Niger
State, 38%. For the pooled data for the zone, 50% of traditional processors had this
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constraint. This was an issue in Makurdi, Benue State where it was attributed to
inadequate market information on products’ availability, place and price for buyers.
This was so critical that the market adopted a kind of quota system sales, that is,
alternative day sales. For modern processors, it was only 29%. This was because their
products were easily sold. Orders were placed from feed mills for cake and oil even
before processing;
vii) Inadequate transport facilities: This constraint constituted a major problem in Niger
State with 75% of the processors, 85% in Nasarawa and 77% Benue States. North
Central Zone had 80%, while in modern processing it was 76%. In the areas with
fewer processors, the farmers and traders brought their groundnuts to the markets
where the processors went to buy. Some even brought theirs to the processing sites;
viii) No work shade/Stores: In the States, 57% of processors in Nasarawa State, 45% in
Niger State and 13% in Benue State complained of this problem. This problem was
critical especially during the raining season when processors could not fry, polish
groundnut or run expelling machines outside. In those areas where this was not much
a problem, most of the processing activities were done indoors. For the North Central
Zone, 41% of the traditional processors faced this problem. None of the modern
processors complained of this problem because they all operated within established
buildings.
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Table 4.19 Constraints to groundnut oil processing in the selected states in North Central
Zone
Constraints*/
Frequency
Nasarawa
70 (%)
Benue
45 (%)
Niger
60 (%)
North Central
175 (%)
Modern
17 (%)
Inadequate finance 35 (50) 39 (86.66) 45 (75.00) 119 (68) 16 (94.11)
Machine breakdown 20 (28.57) 4 (8.88) 05 (8.33) 29 (16.57) 17 (100)
No improved tech 39 (55.71) 33 (73.33) 22 (36.67) 94 (53.71) 4 (23.52)
Inadequate electricity 60 (85.71) 34 (76.55) 50 (83) 144 (82.29) 17 (100)
Lack/high cost of G/nut 59 (84) 40 (88.89) 41 (63.55) 140 (80.00) 15 (88.24)
Poor sales 41 (58.57) 25 (55.55) 23 (38.33) 89 (50.86) 5 (29.41)
Inadequate transportation 60 (85.71) 35(77.78) 45 (75.00) 140 (80.00) 13 (76.47)
No work shade 40 (57.14) 6 (13.33) 27 (45.00) 73 (41.71) 0 (00)
*= Multiple responses were recorded
Source: Computed from field survey data, 2010/2011
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CHAPTER FIVE
5.0 SUMMARY, CONCLUSIONS AND RECOMMENDATIONS
5.1 Summary
The study examined the traditional and modern groundnut oil processing (production)
and marketing in North Central Nigeria. Special attention was paid to the socio-economic
characteristics of the processors, traditional and modern groundnut oil processing and marketing
systems, input use efficiency in traditional and modern groundnut oil processing, and factors that
determine efficiency, profitability of processing of groundnut oil (GNO) and groundnut cake
(GNC), factors affecting profitability; value added by processing; level of integration of the
products (GNC and GNO) markets, and the problems of the industry in the study area.
For this study, three states were randomly selected while six LGAs were purposively
sampled based on groundnut production and processing activities. Random sampling was used to
select the required samples of traditional processors from the selected LGAs. Seventy
respondents were taken proportionately from Nasarawa State, 45 from Benue State and 60 from
Niger State. All modern processors in the selected states were covered. A total of 175 traditional
processors and 17 small-scale modern processors were selected, bringing the number of
processors studied to192. Data were collected using structured questionnaire and observations.
Data collected included those on socio-economic characteristics of the respondents, groundnut
procurement, and processing and marketing information. Price series data were obtained on
weekly basis for groundnut oil and groundnut cake from strategic markets in the purposively
selected states and the FCT. The selected markets were Lafia, Nasarawa Eggon, Makurdi,
Minna, Bida and Wuse (Abuja) markets all in North Central Nigeria.
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Data were analysed using descriptive and inferential statistics, stochastic frontier
production function analysis, gross processing margin, and profit function, test of difference in
means and Johansen test for co-integration. Hypotheses were tested appropriately
The results on socio-economic characteristics indicated that majority (56%) of the
traditional processors were between the ages of 31 and 40 years and found in Niger State. They
were 45% in Nasarawa State and 20% in Niger State. This was also the highest group in the
North Central Zone with 42%, and 47% for modern processors. The average age for traditional
processors in the North Central Zone was 38 years and 41 years for modern processors. Almost
all the traditional processors (94%) were female and only 6% were male. Likewise 88% of small-
scale modern processors were male and only12% were female. Ninety-six percent of the
traditional processors and 100% of modern processors were married. Household size of 6-10
persons was the highest in all the states, with 70% in Niger State and lowest, 54%, in Nasarawa
state. This household size made up 61% for traditional processors in North Central Zone and
58% for modern processors. This was followed by household size ranging from 1-5 persons
(25%) in Nasarawa State. The average household size was eight persons per household for
traditional and modern processors in the North Central Zone.
Seventy – two percent of the traditional processors had less than three years of formal
education in Nasarawa State, 53% in Benue State and 49% for North Central Zone. Modern
processors had 88% with over 10 years of formal education. The average years of education for
traditional processors were four years and 13 years for modern processors. Eighty four percent of
traditional processors in Nasarawa State; 100% in Benue State and 94% in Niger State did not
participate in co-operative associations. For the zone, 91% of the traditional processors and
100% of modern processors did not participate in co-operative activities. Traditional processors
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with 1 – 10 years of experience were 55% in Benue and 40% for the zone. Those with 11-20
years of experience were 60% in Niger State and 45% in Nasarawa State, as well as for the zone;
while modern processors in the same range of years of experience were 88%. The processing set
up began with procurement of raw groundnut from farmers or traders, transporting it to the
processing sites where the processing activities of cleaning, roasting, polishing and crushing, and
then expelling the oil via expellers took place. The groundnut oil (GNO) and groundnut cake
(GNC) were then prepared for market. The cake in traditional processing was moulded into
different sizes and shapes, fried and also marketed.
On the state basis 81% of groundnut procured for processing from Nasarawa state came
from farmers and 19% from traders. In Benue state 51% came from farmers and 49% from
traders. In Niger State, it was 55% from farmers and 45% from traders. For the zone, 60% was
obtained from farmers and 40% from traders. For the modern mills 94% of their groundnut
processed came from traders. The highest quantity traditionally processed, 263kg/week was in
Nasarawa State valued at N36, 632, and lowest in Niger State, 46.9kg valued at N5, 878; while
modern processors processed 6, 222kg per week. For the traditional processors, their GNO and
GNC were patronized by retailers and consumers. Modern processors sold more of their GNO to
wholesalers and retailers, and all their cake was sold to livestock feeds manufacturing companies
and animal farms.
The major activities carried out in GNO processing were pre- treatment, which include
decortications for unshelled groundnut, cleaning, drying, frying/roasting, polishing and crushing
the groundnut into paste. The next activity was feeding the paste into the oil expelling machine
where the oil was expelled. The principle was similar for both traditional and modern small-scale
modern processing, except for some differences in automation and application of certain
116
techniques. The cake processed in the traditional method was consumed by humans but that from
modern processing was only used for animal feeds.
The result for input use efficiency from the maximum likelihood estimation (MLE)
rejected the null hypothesis that γ = δm = 0 indicating the absence of technical inefficiencies of
the processors in all the locations at various significance levels. Hence the model was used to
analyze efficiency of traditional and modern GNO processing in the states, and the zone. In the
results for the states, raw groundnut was significant at 1% in Nasarawa and Niger States, labour
was significant in Nasarawa state also at 1% level of probability but negative. Fuel wood and salt
were both significant at 1% level of significance in Nasarawa and Benue States. In the
inefficiency aspects age and years of experience were significant at 1% level of probability in all
the States, while gender, marital status, household size and co-operative participation were
significant in Nasarawa State at 5% LOS. In the zone, raw groundnut was significant at 1% LOS
for traditional processors and modern processors. Salt and fuel wood were also significant at 1%
and 5% level of significance in that order. In the inefficiency model, household size was
significant at 5%, level of education and years of experience were significant at 10% LOS, but
negative. In modern processing only groundnut seed and labour were significant at 1% in the
technical efficiency, while level of experience and years of experience were significant at 10%.
The γ statistic was estimated at 0.5501 and significant at 1% in Nasarawa State, in Niger State
the γ was 0.184 and significant at 1%. For the North Central Nigeria γ was 0.0003 and not
significant. For modern processors the γ statistic was significant at 5% LOS.
In the technical efficiency distribution, majority of the traditional processors had their
efficiency scores above 0.80 in all the states and the region. The mean efficiencies scores were
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0.851 for Benue State, 0.889 for Nasarawa State and 0.979 for Niger State. It was 0.907 and
0.804 for the region and modern processors in that order.
The gross processing margin (GM) for the States showed that Nasarawa State had 23%
gross margin, Benue State 20% and Niger State 22%. The regional results showed that the total
variable cost (TVC) was N172, 204/tonne of groundnut with Total revenue (TR) of N200,
768/tonne with a GM of N28, 564/tonne representing 16.58% GM in traditional processing. In
modern processing the TVC was N937, 966 for 6222kg per week with gross revenue of N1,
294,609. The gross margin was put at N356, 643 that is N57, 319/tonne or 38% GM.
The regression results of the factors that determined the profitability of traditional
processors indicated that fuel wood and packaging variables were significant at 1% level of
probability in all the States. While transportation was significant at 1% level of significance
(LOS) in Niger State and 10% LOS in Nasarawa State. Salt was also significant at 1% LOS in
Nasarawa State. For the zone, fuel-wood and packaging were significant at 1% while
transportation was significant at 10%. In the modern processing price of groundnut, procurement
and maintenance were significant at 1%. The adjusted R2 for traditional processors in the zone
was 0.944 and 0.97 for modern processors.
On the State basis, the highest value added was in Niger State with 62.40%. For the
North Central zone the value added was 41.28%. For modern processors the value added was put
at 44.15%. For differences in means of value of groundnut before and after processing, the result
of the student t-test indicated that the null hypothesis of no significant difference between the
means was rejected at 5% LOS for all the states and the region. This was not rejected for small-
scale modern processors. This means there were significant differences between the means of
value of groundnut traditionally processed before and after processing.
118
The result of the unit root tests on the price series showed that all the series were
stationary at first difference, hence integrated of the same order at 5% level of significance,
given the Augmented Dickey Fuller (ADF) test. The result of the Johansen Multivariate test for
co-integration indicated 5 co-integrating vectors at 5% significance level. All series were in the
same market and the law of one price (LOP) held for groundnut oil in North Central Nigeria. For
the GNC series, the result of the Johansen multivariate test indicated 2 co-integrating equations
given the 6 price series, which implied that the market for GNC was poorly integrated in the
region. The result of analysis of determinants of the co-integration in these markets indicated an
Adjusted R2 = 0.82 for GNO. The number of processing facilities and administrative regulations
variables were significant at 5% level of significance. The number of processing facilities was
positively signed but administrative regulations coefficient was negative indicating its negative
effects on market integration. In the groundnut cake market, the adjusted R2 was 0.213, and the
hypothesized variables accounted for 21% of the variation in the integration of the GNC market.
Several constraints were identified which included inadequate finance, machine breakdown
inadequate processing technology for traditional processors, inadequate electricity and
transportation.
5.2 Conclusion
Majority of the traditional groundnut oil processors were women processing GNO to
increase family income, alongside modern processors. Processing value chain offers opportunity
for farm products diversification and preservation as well as business opportunities particularly
in groundnut oil and cake. Processors in the GNO value chain operated efficiently despite some
identified challenges. They also added significant value to raw groundnut and in doing so made
profit for themselves. Improved transportation and packaging would enhance the profit of the
119
processors. The markets for the processed products (GNO and GNC) were integrated to a certain
level and price transmission within the region enhanced marketing activities. Major concerns of
processors included inadequate credit, work shade, electricity, improved processing technologies
and co-operative participation, and if addressed will enhance performance in the industry in the
study area.
5.3 Recommendations
The following recommendations were drawn from this study:
(i) Processing value chain still remains the major point to diversify primary products as
shown by positive gross margin and value addition attained. Consequently heavy and
sustained investment in the agricultural processing sector is recommended if
agriculture is to become a business in Nigeria, away from its present subsistence
state;
(ii) High efficiency scores recorded from this study indicates the resilience of processors in
processing groundnut oil despite challenges; therefore they need to be supported with
energy saving equipment and technologies for increased productivity.
(iii)Modern processors are have recorded more value added in groundnut processing
therefore, should to be encouraged by the provision of infrastructure such as
electricity, credit, good transportation among others;
(iv) The significance of fuel-wood use signals negative environmental impact, therefore it is
recommended that alternative sources of energy for processing need be provided
instead of fuel-wood, for sustainable environment;
120
(v) Packaging significantly increased profit, therefore packaging firms should explore the
opportunities of providing special packaging for oil to meet the needs of spectrum of
oil consumers/buyers;
(vi) Scarcity of raw groundnut for processing means that the sustenance and growth of the
value chain is in jeopardy, therefore production of the crop must be encouraged
beyond the present subsistence level. Improved seeds, and harvesting methods need
be taught to producers as an extension service agenda;
(vii) Finance inadequacy was felt by processors; hence an enabling environment for
functional credit facilities provision that can be accessed by the processors is needed.
This arrangement should also be attractive to private players and NGOs to support the
processing industry; and
(viii) Co-operative participation was very low among processors, therefore co-operatives
formation by the processors is recommended. This will create opportunity for shared
experiences and information on prices of both input and output, processing and
marketing information among the processors for better business.
5.4 Addition to Knowledge
(1) This study has approached research both in basic and adaptive perspective, academic and
business orientation. This is shown in the models and simple tools applied in the analysis
which has added information for both research and business in the study area, which
hitherto have not been available.
(2) The application of technical efficiency has been concentrated on the farm level
production and but very rare in agro-industrial production (agro-processing). This
research has applied the stochastic production function approach to agro-processing
121
scenario in North Central Nigeria. With the technical efficiency scores of individual firms
shown, processors can be advised individually.
(3) Having applied the Johasen test for market integration, which is a new approach to
measuring market integration particularly in the study area, a new frontier has been
reached.
(4) The study has revealed that GNO processing could be used by government and
development agencies to reduce poverty and redundancy among women especially
women in Purdah in the study area.
(5) This research has also brought to the forefront agricultural processing, not only as a tool
for product diversification, and preservation of farm produce but also a profit making
business activity.
5.5 Areas Needing Further Research
(i) There is need to do research on the two ends of the value chain, that is, upstream
(groundnut production) and downstream (groundnut oil and groundnut cake
consumption) to have a complete view of the value chain.
(ii) Studies in participatory technology development (PTD) to find appropriate and affordable
processing technologies for the processors are needed to improve productivity. This is
necessary given the fact that many of these small-scale processing technologies can
be fabricated locally hence can easily be accessible and affordable.
(iii)A study on improved packaging of processed products is needed, because packaging
provides a convenient way to present a product to a consumer. The research will help
determine the appropriate packaging for the products, considering the attendant cost
and returns connected to it.
122
(iv) Price still remains the central factor in allocating resources in production, distribution and
consumption of goods and services. Further research in price movement of groundnut
and its processed products and indeed similar products within the same market will
enhance pricing efficiency, as informed economic decisions will be made. The end
result will be increased efficiency in the entire marketing system.
123
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APPENDIX A
University of Nigeria, Nsukka
Faculty of Agriculture
Department of Agricultural Economics
Questionnaire
Dear respondent,
I am a PhD research student of the University of Nigeria Nsukka. I am doing a research titled
‘Economics of groundnut processing and marketing in north central Nigeria’. Please kindly assist
in the completion of this interview schedule. Your responses will be of immense importance in
the achievement of the objectives of this research. The information thus given will be used for
the research only and shall be treated with utmost confidentiality. Your identity will not be
disclosed to anybody for any reason which shall be detrimental to you.
Thank you very much.
Yours faithfully
Aboki, Peter Maisaje
135
PROCESSORS’ QUESTIONNAIRE
Section A: Socio-Economic characteristics of respondents.
State………………LGA…………………… Location……………………..
1. (i)Name………………………………. (ii)Age……………….. (ii) Gender:
Male…………… female………….
2. Marital status: married………………single…………………….divorced.
3. Household size: No of
wives…………………Children……………..dependants………………..
4. Years of experience in groundnut processing (oil and cake)…………………
5. Occupation: (i) major………………..minor………………………
6. Education: Years of formal schooling…………….
7. Membership of Groundnut processing association (yes or no)…………. Any other ether
co-operative yes……….. No………….
Section B: Groundnut oil processing activities.
1 (a) what quantity of groundnut do you process in a week? ………………………..
(b)
Price/quantity…………………………………………………………………………………
…………………………….
(mudus, basins, bags or kg).
2. The quantity of oil obtained from the quantity (bottle, 4litre, 10litre,
25litre)………………….. and, cake (mudus, basin, bag)…………………
3. Method of processing you are involved in (tick): traditional
processing…………………modern processing……………....
4 Sources of groundnut for processing: Farmers……………….. Traders……………….
5 Procurement of groundnut for processing per bag
Activity Price/unit Total cost
I Raw groundnut seed
Ii Transportation
iii Loading/off-loading
136
Iv Market charges
V Commission agents
vi Others(specify)
6 For various activities in GNO processing cost items per qty (mudus, basin or bag) of groundnut
Activity Equipment Labour
cost
Equipment
cost
Quantity of
G/nut
Shelling, decortications, drying &
storage
Scorching/roasting
Polishing
Grinding, pasting or crushing
Oil expelling or extraction
Oil refining/treatment
Cake molding/frying
Oil/Cake packaging
Marketing
Others(Specify)
8. Equipment ownership/ maintenance
equipment Own(1)/hire(2) Maint cost/wk Age
7. Do you have any training in the operations of the equipment/ machine?
Yes……..No……….
8. If no to 8 give reasons ……………………………………
137
………………………………………………………………………………………………
………………
………………………………………………………………………………………………
…………………..
………………………………………………………………………………………………
…………………….
9. Do the operators (if hired) have technical training in the machines? Yes…….. No………
12 (a) Do you get spare parts: yes………no………. (b) Do you get Maintenance/technician:
yes………..No………..
13 Labour use
Equipt/activity Family labour
(no of person)
Hrs spent Hired labour
No of persons
Time spent (hrs) Cost
14 Material inputs (additives)
Material/additives qty Cost Material/additives qty Cost
Fuel/wood Fortification
Electricity Water
Diesel/petrol Salt
Packaging Others(specify)
15 Do you sell at (i) processing site ………(ii) markets ………………(iii) through co-
operatives
16 If in markets complete the followings (i)
market qty Transportation +self Total cost
138
Makt1
Mkt2
(ii) Revenue obtained from groundnut oil
Unit Unit price Qty/mkt Total/ sales
Cup( peak milk tin)
Bottle
5litres
10ltres
25ltres
Others (specify)
(iii) Revenue obtained from groundnut cake
Unit Unit/price kg
Counting
Mudu
Bag
Others(specify)
17. Do you package into units? Yes…………No………..
18. If yes tick appropriately (a) less than 1/2 litre (b) 1/2litre or more (c) 1litre or more (d)2litre
or more (e) 4litres or more(f) 10litres or more(g)25 litres (h) others (specify)………...
19. Do you Clean (filter) the oil before sale? Yes………… No…………..
20. Do you add any additives? Yes…………No………..
21. If yes tick (i) vitamin A (b) iodine (c) others
(specify)…………………………………………………….
22. What qty of the products do you sell per week? (a)Oil ………………………… (b)
Cake………………………
23. Do you package your cake? Yes……………..No………………..
139
24. If yes, give example………………….price………………
25. Fill in the table qty of cake from given groundnut.
Qty Qty(oil) Qty(cake)
1 mudu
1 basin
1 bag
26. Distribution of oil and cake produced
Qty Groundnut oil Groundnut Cake
Obtained
Consumed
Gift
Stored
Sold
Others(specify)
27. Utilities used
Utilities Quantity used Charge/month
Water
Electricity
Telephone
Others (specify)
28. (a) Do you have extension visits? Yes…………..No…………. (b) If yes, from which
organization(s) ………………………………….
29. What do they tell you?
............................................................................................................................................................
............................................................................................................................................................
................
140
30. Did you have any business training before starting?
Yes……………………No…………………………
31. If yes, state
type…………………………………………………………………………………………………
……………………………, and from which organization
………………………………………………………………………………………………………
……………………………….
32. Do you have ready markets for your product? (i)Cake (a) yes ……….. (b) No……….. (ii)
Oil (a) Yes………… (b) No……...
33. If no state reasons
………………………………………………………………………………………………………
………………………………………………………………………………………………………
………………………………………………………………………………………………………
………………………………………………………………………………………………………
…………………………………………………………….
34. Who are your customers: (a) Oil- (i) consumers (ii) wholesalers (iii) retailers (iv)
manufacturers (v) processors. (b) cake-consumers (ii) wholesalers (iii) retailers (iv)
manufacturers (v) processors
35. What are the constraints to your processing business?
………………………………………………………………………………………………………
………………………………………………………………………………………………………
………………………………………………………………………………………………………
………………………………………………………………………………………………………
………………………………………………………………………………………………………
………………………………………………………………………………………………………
………………………………………………………………………………………………………
………………………………………………………………………
36. What are the possible solutions to these constraints?
………………………………………………………………………………………………………
………………………………………………………………………………………………………
………………………………………………………………………………………………………
………………………………………………………………………………………………………
………………………………………………………………………………………………………
………………………………………………………………………………………………………
…………
141
APPENDIX B
University of Nigeria, Nsukka
Faculty of Agriculture/Department of Agricultural Economics
Weekly Price Data chart
State--------------------LGA-------------Market location----------------District-------------------
Category (rural/urban) ---------------- supervisor----------------------------------
Weekly price chart for groundnut oil and groundnut cake/wholesale and retail
S/No Date Processors’
price
oil/unit
Wholesale
price oil
/unit
Retail
Price
oil/unit
Processors’
price
cake/unit
Wholesale
price
cake/unit
Retail
Price
cake/unit
Remark
G/oil units: cups, beer-bottle, 1litre, 2litre, 4litre, 10litre &25litre
Cake units: units (counting), mudus and bags, kg