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TECHNICAL EFFICIENCY IN THE PRODUCTION OF NSUKKA YELLOW PEPPER
AMONG RURAL FARMERS IN ENUGU NORTH AGRICULTURAL ZONE, ENUGU
STATE, NIGERIA
BY
UGWU STANLEY IFEANYI
REG. NO: PG/MSC/05/40132
A DISSERTATION SUBMITTED TO THE DEPARTMENT OF AGRICULTURAL
ECONOMICS IN THE PARTIAL FULLFILMENT
OF THE REQUIREMENT FOR THE AWARD OF
MASTER OF SCIENCE (M.Sc) DEGREE IN
AGRICULTURAL ECONOMICS
UNIVERSITY OF NIGERIA, NSUKKA
JUNE, 2010
2
TITLE PAGE
TECHNICAL EFFICIENCY IN THE PRODUCTION OF NSUKKA YELLOW PEPPER
AMONG RURAL FARMERS IN ENUGU NORTH AGRICULTURAL ZONE, ENUGU
STATE, NIGERIA
3
CERTIFICATION
This is to certify that this research work is an original work undertaken by UGWU STANLEY
IFEANYI , a Postgraduate Student of the Department of Agricultural Economics with
registration number, PG/M.Sc/05/40132, and has been prepared in accordance with the
regulations governing the preparation of project work in the University of Nigeria, Nsukka.
------------------------ ---------------------------
Prof. E. C. Okorji Prof. E. C. Nwagbo
Project Supervisor Head of Department
----------------------------
External Examiner
DEDICATION
4
This work is dedicated to the Almighty God the all-knowing, most merciful and Alpha and
Omega who continues to shower us with unmerited blessings and favour.
5
�������������
Glory, honour and adoration belongs to the almighty God for his love , protection,
providence, mercies, life which only he gives. My thanks goes to my mother for her
immeasurable encouragement and financial support when the going got too tough. I also
remember Arch. Odo Everestus odo and family for their encouragement. I cannot forget in a
hurry my guy, Agada Alfred (Gadosky) for sacrificing his comfort just for his friend to ‘tag
along’.
I acknowledge the concerted effort of my supervisor Prof. E.C Okorji in thumbing
through this work ensuring that it is concluded and rightly. I owe him heart felt gratitude.
Thanks also go to the current Head of Department of Agric. Economics University of
Nigeria, Nsukka, Prof. E.C. Nwagbo. My gratitude also goes to other Professors in the
Department, Prof. S. A. N. D. Chidebelu, Prof. E. O. Arua, Prof. C. J. Arene, Prof. N. J. Nweze
and Prof. E. C. Eboh. I have benefited immensely from your vast knowledge.
Worthy of mentioning are other lecturers in the department namely: Dr. (Mrs) A. I.
Achike, Dr. F. U. Agbo, Dr. A. A. Enete, Dr. C. U. Okoye, Dr. Ben C. Okpukpara, Mr P. B. I.
Njepuome, Dr. Chukwuone Nnaemeka and other lecturers who have contributed in numerous
ways to the development of this work.
My special thanks go to my dear friends and classmates: Ceejay, Ezea Emma, Ebere,
Ojogu, Taofeeq, Charso, Pipi, Mantu. Also to my cousin Onyinye who insisted I include her
name.
I acknowledge the scholars whose works I have cited in this thesis.
Ugwu, S.I.
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TABLE OF CONTENTS
Cover Page ………………………………………………………………………………i
Certification Page………………………………………………………………………..ii
Dedication………………………………………………………………………………..iii
Acknowledgement………………………………………………………………….…….iv
Abstract…………………………………………………………………………………...v
List of Tables………………..…………………………………………………………viii
Abstract…………………………………………………………………………………...ix
CHAPTER ONE
INTRODUCTION...........................................................................................................1
1.2 Backgroung of the study.......................................................................................1
1.3 Problem Statement................................................................................................2
1.4 Objectives of the Study.........................................................................................3
1.5 Hypotheses of the Study.......................................................................................4
1.6 Justification for the Study.....................................................................................4
CHAPTER TWO
2.0 LITERATURE REVIEW......................................................................................5
2.1 Agronomy of pepper..............................................................................................5
2.2 Sustainable Resource Use and food Security.........................................................6
2.3 Global Agricultural Resource Use Trend...............................................................7
7
2.4 Agricultural Research and Resource Productivity................................................8
2.5 Measures of Efficiency.........................................................................................9
2.5.1 Technical Efficiency.............................................................................................10
2.5.2 Allocative Efficiency.............................................................................................10
2.5.3 Pareto efficiency....................................................................................................11
2.6 Related Studies on Efficiency................................................................................11
2.7 Theoretical Framework…………..........................................................................12
CHAPTER THREE
3.0 METHOLOGY.....................................................................................................15
3.1 Study Area............................................................................................................15
3.2 Sampling procedure..............................................................................................15
3.3 Method of data Collection....................................................................................16
3.4 Data analysis.........................................................................................................16
3.5 Model Specification..............................................................................................16
3.5.2 Technical Efficiency Model..................................................................................17
3.6 Student’s T-test.....................................................................................................17
CHAPTER FOUR
4.0 RESULTS AND DISCUSSIONS.......................................................................19
4.1 Socio-economic Characteristics of the Respondents...........................................19
4.1.1 Gender of the Respondents..................................................................................19
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4.1.2 Age Distribution of the Respondents...................................................................20
4.1.3 Marital Status of the Respondents........................................................................20
4.1.4 Years of formal Education....................................................................................21
4.1.5 Family Size of the respondents............................................................................22
4.1.6 Major Occupation of the Respondents.................................................................22
4.1.7 Farm income level of the Respondents................................................................22
4.1.8 Ownership of Farmland........................................................................................23
4.2 Forms of Farming systems...................................................................................23
4.3 Production ofNsukka Yellow Pepper in the Study Area......................................24
4.4 Land Area Cultivated of Nsukka yellow Pepper by the Respondents…………..25
4.5 Influence of socio-economic charateristics of the Respondents on
their technical efficiency.......................................................................................26
4.6 The Efficeincy of Resource use............................................................................28
4.7 Distribution of the Technical Efficiency indices of the Respondents...................29
CHAPTER FIVE
5.0 SUMMARY, CONCLUSION AND RECOMMENDATION..............................30
5.1 Summary................................................................................................................30
5.2 Conclusion.............................................................................................................31
5.3 Recommendations..................................................................................................32
REFERENCES................................................................................................................33
9
LIST OF TABLES
4.1.1 Distribution of Respondents According to their Socio-economic
Characteristics……………..……………………………………………………..19
4.2 Frequency Distribution of Respondents According to Forms of farming
System…………………………………………………………………………..23
4.3 Schedule of Farming Activities of Respondents................................................24
4.4 Distribution of he respondents according to land area cultivated........................25
4.5 Maximum Likelihood Estimates of inefficiency Parameters
using Cobd-Douglas frontier function.............................. ..................................26
4.6 Maximum Likelihood Estimates of Stochastic Frontier Production
Function of Nsukka Yellow Pepper Production..................................................28
4.7 Distribution of Technical Effiency Indices of the Respodents in
the Study Area…………………………………………………………….…...29
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����� ���
This study determines the technical efficiency level and socioeconomic characteristics that
influence the technical efficiency of yellow pepper farmers. Stochastic Frontier function that
incorporated inefficiency factors was estimated using a Maximum Likelihood technique to
provide estimates of technical efficiency and its determinants using data obtained from 60
Nsukka Yellow pepper farmers in Nsukka, Enugu State, Nigeria. The result shows that the mean
technical efficiency was 70 percent indicating the need for improvement in efficiency level for
additional 30 percent. The result also indicates that the respondents are operating in the rational
stage (stage II) of production as indicated by the return to scale of 0.797. The empirical findings
also show that age, number of years spent in formal education, family size and farming
experience are important socio-economic determinants of the respondents’ level of efficiency.
Farming experience had positive influence on the respondents’ level of efficiency. It was
recommended that rural women should be effectively mobilized for full participation in the
production of Nsukka yellow pepper through the use of extension agents and community leaders,
provision of farm inputs through establishment of channels that will enable farmer access credit
facilities and review of land use act to give the rural farmers access to land.
11
CHAPTER ���
INTRODUCTION
1.1 Background of the Study
Small-scale farmers occupy a central position in Nigeria’s agricultural policy (Ajibefun
and Abdukadri, 2004). Agricultural activities in Nigeria seem to be shouldered by small-scale
farmers who mostly live in rural communities. These farmers are poor and tend to practice
production system that may not utilize resources efficiently.
Small-scale farmers are constrained by lack of credit, which translates to inadequacy of
working capital (Kibaara, 2005). This in part brings about the vicious cycle of poverty. The
theory of vicious cycle of poverty tries to explain the reason for poor investment of resources
(agricultural), which leads to poor output and the consequent low income. However, increment in
agricultural output does not depend on heavy investment of agricultural resources. To achieve
possible optimum agricultural output from a given set of resources, farmers are confronted with
the challenges of increasing the agricultural productivity in an attempt to feed the ever growing
population by using resources efficiently.
Resources are of paramount importance in agriculture and these include land, labour,
capital and management. The profitability of an agricultural enterprise hinges on efficient
utilization of resources needed and available to such enterprise. Clear understanding of resource
use patterns by farmers and their adjustment to external factors are necessary for increase in the
productivity of agricultural resources (Amaza and Olayemi, 2002). Efficiency of resource use
may be defined as the extent to which a given set of resources are being allocated across uses or
activities in a manner that maximizes whatever value they tend to produce such as output, market
value or utility . Therefore, the efficiency of a farm as a production unit is how effectively it uses
resources for the purpose of profit or output maximization.
Rural farmers in Enugu North agricultural zone (Nsukka area) of Enugu State are small-
scale farmers with variety of constraints. These constraints include resource availability, resource
allocation and difficulties in controlling resources in production process (Awoke and Okorji,
2004). It is expected that these constraints do affect the production of Nsukka yellow pepper in
the area.
12
Pepper is an important vegetable in most societies and plays a significant role in our
everyday food. Today, peppers are grown (Capsicum spp) widely in many countries, Nigeria
inclusive and it forms an integral part of local cuisine. It adds flavour, colour and pungency to
several delicacies. The interest in pepper extends to its nutritional and medicinal values in that
peppers are recognized sources of vitamins C and E and are high in antioxidants. These
compounds are associated with prevention of cardiovascular disorders, cancers and cataracts. In
addition, it can be used for preservation of cowpea against weevil attack (Echezona, 2006).
The varieties of pepper prevalently grown in Nigeria include:
• Cayenne pepper or red pepper – ‘Sombo’ ( Capsicum frutescence)
• ‘Atarodo’ ( Capsicum annum )
• ‘Tatase’ ( Capsicum annum )
• Nsukka Yellow pepper (Capsicum annum)
Nsukka yellow pepper popularly referred to as “Ose Nsukka” owes its name to its
characteristic yellow colour and the area it is popularly grown. Nsukka yellow pepper is an
important commercial fruit vegetable. Its cultivation forms a major and sometimes the only
agricultural activity of rural women in Enugu state (Onwubuya et al, 2008).
�
1.2 Problem Statement
Pepper production in Nigeria has not attracted the same research patronage like the
following crop: cassava, cocoa, rice, to mention a few. The area, the production and yield data
are difficult to come by. Regrettably, Nsukka yellow pepper production is towing the same line.
Few or no medium and large scale farmer has invested in it. Research interest in it is
insignificant. This obviously can be explained by the dearth of records and results from research
findings to convince the medium and large scale farmers on the need to engage in Nsukka yellow
pepper production by a way of elucidating to them the economic viability of such enterprise.
This development has culminated in the bulk of its production to be in the hands of small-holder
farmers. Small-holder farmers according to Awoke and Okorji, (2004) are farmers whose
production capacity falls between 2.5 and 5 hectares per cropping season.
The production of Nsukka yellow pepper could be said to have remained in the hands of
small-holder rural farmers. These farmers like most rural farmers in Nigeria are resource poor
and operate on small-scale. They hardly use mechanized and other improved agricultural
13
implements and so it can be asserted that they still carry out their agricultural activities the
‘traditional ways’. This is in line with Olayemi, (1980) who observed that the kinds and qualities
of resources used in primary production activities in tropical countries are characterized by
forms, which tend to give rise to low output. This also tends to hide the importance and
popularity of Nsukka yellow pepper production. It is one of the major agricultural activities of
rural women in Enugu State (Onwubuya et al, 2008). Again the markets and industrial
requirements are hardly met (NISPRI, 2000). Supply is constrained, thus there is every need to
raise productivity of the pepper farmers. This can be done through the farmers’ adoption of
improved technologies or improvement in the efficiency of use agricultural resources or both.
However, they are often times left with the choice of improving the level of efficiency because
of their low rate of adoption of improved technologies due to resource poverty (Idiong, 2007).
This foregoing informs for the appropriateness of efficient utilization of resources in the
production of Nsukka yellow pepper as a strategy for improvement of productivity in the
enterprise. Therefore, this study seeks to provide answers to the following research questions:
i. What are the socio-economic characteristics of yellow pepper farmers in the study
area?
ii. What forms of production systems do the farmers use in the production of Nsukka
yellow pepper?
iii. Are farmers efficient in their use of labour, land and capital?
iv. What influences do socio-economic characteristics of the farmers have on their
production efficiency?
1.3 Objectives of the Study
The general objective of the study is to determine the efficiency of resource use in the
production of yellow pepper among rural farmers in Nsukka area. The specific objectives are to:
i. assess the socio-economic characteristics of the yellow pepper farmers in the study area.
ii. assess the production systems employed by the farmers in the production of Nsukka
yellow pepper,
iii. determine the efficiency of labour, land and capital use in producing Nsukka yellow
pepper through the estimation of the responsiveness of the yield to land, labour and
capital,
14
iv. estimate the influence of the farmers’ socio-economic characteristics on the efficiency
of their production of Nsukka yellow pepper and
v. suggest appropriate policies from the empirical results.
1.4 Hypotheses of the Study
The following null hypotheses will be tested.
1. Ho: the socio-economic characteristics of the yellow pepper farmers have no significant
influence on the technical efficiency.
2. Ho: the use of labour, land and capital have no significant influence on the yield of
yellow pepper.
1.5 Justification for the Study
The current situation of Nigeria’s inability to key in on producing pepper for the world
market even with the country’s advantage of being able to produce high quality pepper – Nsukka
yellow pepper, needs urgent attention. The farmers of Nsukka yellow pepper like most farmers in
Nigeria are rural dwellers and appropriate knowledge of how different resources they employ in
farms are utilized becomes imperative. This will lead to the farmers’ adjustment of their input
use and embrace efficient methods of production, which will consequently lead to reduction in
amount resources expended during production and improvement in their farm profit will be
guaranteed.
Evaluation of yellow pepper production will necessitate the understanding of how the
farmers will allocate and use their resources and improve their productivity, consequently their
income. This is important as the country’s agricultural policy objectives include: the increment
of food production, increment in the production of agricultural raw materials as inputs for the
economy, enhancement of incomes of small farmers and households with a view to alleviating
poverty and promoting rural development/employment (WTO, 2005).
Sound knowledge of utilization of agricultural resources in a manner that will enhance
the output and minimize waste and it will lead to designing of policies that will increase the
farmers’ chances of using resources efficiently. The essence of this study is to present empirical
findings on resource use efficiency of yellow pepper production in Nsukka area and fill the gap
in resource-use literature.
15
CHAPTER TWO
2.0 LITERATURE REVIEW
Literature for this study is presented as follows:
2.1 Agronomy of Pepper.
Peppers are warm season crops originally native of Central and South America.
Portuguese traders introduced it to India, Africa and other parts of Asia around 450-500 years
ago (Berke, 2002). They quickly adapted to wide range ecological zones and today, they form
integral part of local delicacies across countries. For instance, the Indonesian sambal, the Thai
hot and sour soup, the Korean kimchi and even the Indian curry.
Botanically, hot peppers mostly referred to as chilies belong to the family, Solanaceae,
and are close to the tomato, nightshade, and potato. They belong to the genius, Capsicum, which
probably comes from the latin word, caspa, meaning chest or box, because of its shape (the fruit
encloses the seeds very neatly, as the box) (Garzon-Tiznado and Carrillo, 2002).
Peppers are warm season crop and require about the same growing conditions as tomato
and eggplant. They are very sensitive to light and grow poorly when the temperatures are
between 4.4 to 15.60C range. Very little fruit set occurs when temperatures are above 320C
during the day and 150C in the night. Fruits that do set when temperature is above 270C are
usually small and poorly shaped (Motes, Criswell and Damicone, 2004). Some of the small-
fruited pungent peppers are more tolerant to high temperature fruit set problems than typical
American bell type peppers. However, such conditions are mostly applicable to the peppers
grown in Americas. Pepper plants require continuous growth for satisfactory results. They are
very sensitive to unfavorable weather though, of course, the farmer has little control over the
weather. Peppers will often drop their blossoms when temperatures are high and humidity is low.
Cool weather can also keep the plant from flowering. Deep cultivation that cuts the roots causes
water stress on the plant that makes blossoms drop. Even a short dry period can cause the same
conditions (Hardin and Baniecki, 1984).
In Nigeria, production of pepper starts with the preparation of the nursery, which requires
good soil medium (USAID, 2003). The soil should be rich, well drained and free of diseases and
insect pests. Its nursery medium must consist of organic manure and topsoil in equal proportions.
In the field, the beds where the seedlings will be transplanted require the application of
fumigants to kill pests, fungi, weed, etc. According USAID (2004), VAPAM is the
16
recommended fumigant that should be applied at the rate of 1 liter to 20 liters of water per bed of
1 m x 10 m. When used, the soil should be heavily wetted to the depth of 15 cm and covered
with palm fronds. It is required that the seedling be shaded during the nursery stage to harden
them.
During harvesting period, the colour is important item of quality therefore fruits should
be harvested when they start to turn yellow or red depending on the variety. With good care,
‘tatase’ and ‘atarodo’ can remain productive for two years but ‘sombo’ can last for four years
and the fruit yield is of 3 – 6 tonnes per hectare.
2.2. Sustainable Resource Use and Food Security
Sustainable agriculture is a long term approach to agriculture that combines efficient
production with wise stewardship of the earth’s resources (Ogunsunmi, 2005). For agriculture to
be sustainable, cultural practices should be designed to meet the needs of the present generation
without compromising those of the future generations (Bossel, 1999). According to him,
sustainability is quite complex and has linkages with the economy, environment, demography
and others.
Population growth has increased the pressure on naturally endowed resources bringing
about reduction in fallow period (Ajibefun and Abdukadri, 2004). Therefore, there is the need to
adopt intensive production practices for increased food production (Van Keulen and Bremen,
1990). This is based on suggestion of Hayami and Rutan, (1985) that adjustment of farming
systems and adoption of new technologies based on more intensive use of land, labour or capital
will only take place when factor proportions are constrained. Maxwell and Wiebe, (1999),
reported that resources are critical to food security because they tend to determine the ways in
which individuals, households and countries gain access to food through production and
exchange.
Food security according to Madely, (2002) is defined as the availability of food at all
times, to which people have means of access, that is nutritionally adequate in terms of quantity,
quality and is acceptable within the given culture. Sen, (1981) is of the opinion that individuals’
access to food may come from trade or other means in addition to food production. He further
reported that shortfalls in food production are not sufficient cause of hunger in a country. Hunger
results from variety of factors which may include changes in income, food prices and inability of
17
the country to import the needed food. This was also noted by Shapouri and Rosen, (2001) that
domestic food production is less critical to food security if a country can import required foods.
Resources and food security are related in a significant way. The engagement of
individuals in food production leads to their allocation of resulting income along with their
remaining stock of resources to consumption and investment (Maxwell and Wiebe, 1999). In
Sub-Saharan Africa, consumption is prioritized over investment (World Bank, 1997), which
reflects low saving rates. This however may conceal the depletion of natural resources and other
resources important for food security over time. Therefore there is a great tendency that pepper
farmers could also be faced with the issue of continued depletion of their farm resources while
less and less are provided for reinvestment into their farms.
2.3 Global Agricultural Resource Use Trend
Resource priority changes as economies evolve, in low-income economies; priority is
typically given to issues related to the management of natural resources for poverty alleviation
and food security (UNEP, 1997). The growth of economies shifts priority to include resource
problems associated with industrialization, such as air and water quality, and treatment and
disposal of waste.
About 11 percent of global land area is considered arable land, ranging from 5 percent in
the Middle East and Northern Africa to 43 percent in South Asia. According to Alexandratos and
Bruisma (1999), agricultural land has increased in recent times at an annual rate of 0.3 percent.
This increment comes from expansion of cultivation into marginal lands. They further reported
that the projected increase in agricultural land is only a small portion of total unused land with
rain-fed crop potential. Houghton, (1994), cautions that land conversion continues at high levels
in some regions and raises concerns about future constraints in those areas.
Water is abundant globally but scarce in many regions (UNEP, 1997). Only 7 percent
renewable freshwater is withdrawn from rivers, aquifers world wide each year (World Bank,
1992). According to (Rosegrant et al, 1999), rapid growth in water demand in combination with
high cost of developing new water resources could threaten future growth in food production. In
Sub-Saharan Africa, public sector irrigation schemes have been generally expensive to construct
and maintain and their performance disappointing (FAO and World Bank, 2001).
18
The earth’s atmosphere is a critical component of global resources being modified by
human activities. The activities could be in form of emission from burning of fossil fuel, which
leads to global warming and its attendant effects on agricultural productivity. However, Darwin,
et al, (1995) is of the opinion that global warming is not expected to constitute a threat to food
production on global scale, though some resource poor regions, particularly those in the tropics
may suffer reduction in food availability and access.
The world population reached 6 billion with the highest growth rate in Sub-Saharan
Africa (United Nations, 1999). Conversely, the growth rate has been slowed down by increased
mortality from HIV/AIDS. Nevertheless, the bulk of labour force in this region remains in
agriculture. Poverty and malnutrition are high coupled with low adult literacy (Dasguta, 1993).
All these have negative influence on the productivity and tend to cause continuing pressure on
natural resources.
2. 4. Agricultural Research and Resource Productivity
Agriculture is the principal source of food, livelihood and foreign exchange earner in
Sub-Saharan Africa (Bandianne and Delgado, 1995). Agricultural production is particularly
important component of food security. Consequently, agricultural productivity is critical to the
ability to meet food security and economic development objectives in the face of rapid
population growth (Wiebe, 2001).
Presently, the objectives of research in agriculture include, increasing income, improving
food and nutritional security and protecting the environment of farmers (Philip and Wafula,
1997). According to Sys Van Ranset and Debaveye, (1991), these objectives are elusive to
achieve because of variation in principal resources of agricultural production which include:
• Natural (climate, vegetation, water, hydrology, land form and soil).
• Human (farm labour, social structure that affect land use patterns and allotment).
• Capital (funds availability both private and national)
Multidisciplinary research by natural and social scientists is necessary in order to
understand how natural, human and capital resources interact (Philip and Wafula, 1997) in order
to achieve improved resource productivity. Climatic factors such as rainfall, temperature and
radiation interact with crop characteristics (Dewit, 1965). Natural science research can determine
the type and amount of farm inputs required for improved agricultural productivity. The
19
utilization of the inputs will depend on the socio-cultural and economic situations of the farmers,
prevailing agricultural policies and institutional structures that require research by social
scientists (Evan, 1991).
Technologies have been developed that increase the agricultural productivity especially
in the area of using the benefits of vegetation such as Biological Nitrogen Fixation (BNF),
application of green manure, needs for short fallows and biomass transfer (Giller, et al, 1997).
The overall knowledge by farmers, of the contributions of vegetation in agriculture and
subsequent adoption or not of these soil fertility improvement strategies are areas of social
science research (Evan, 1991).
In Africa, research has targeted production of high yielding varieties of exotic crops at
the expense of native varieties (Juma, 1991). The production of such high yielding varieties
depends on the availability of a whole package of inputs such as water, fertilizer, pesticides, etc.
This in actual sense is not suitable for poor farmers who cannot afford them.
Total resource productivity in agriculture is estimated to have grown by an average of 1.3
percent annually between 1961 and 1991 for Africa as whole (Lusigi and Thirtle, 1997). Land
productivity rose by an average of 1.9 percent per year between 1980 and mid 1990s while food
production grew by 2.4 percent per year (World Bank, 1998). By contrast, labour productivity
fell by an average of one percent per year. Meanwhile, growth in agricultural productivity
appears to be slowing and land degradation has been blamed as contributing factor (Wiebe
2006). The interaction between biophysical processes and economic choices are complex, and
data necessary to measure these processes are scarce so estimates of land degradation impact on
productivity vary widely.
2.5. Measures of efficiency
Efficiency of a production system is a representation of comparison between observed
and optimal values of its output and the inputs used in the production process. This takes the
form of ratio of observed to the maximum potential output obtainable from a given level of input
or the ratio of minimum potential of observed input required to produce a given level of output
or some combination of the two (www.ojp.usdoj.gov/BJA., 2006). Since the economic rational
behind farmers’ engagement in crop production is for the maximization of his profit or output
then it is the wish of the farmers to use methods of production that do not waste resources. The
20
first step in the analysis of farm in production is to understand the concept of technical and
allocative efficiencies (Bronfrenbrenner, et al, 1990).
2.5.1 Technical Efficiency
Technical efficiency measures are the comparisons of maximum observed values of
output and its optimal values with a given level of input use. In other words, technical inefficient
farm is the farm that produces too little output from a given bundle of inputs. According to
Nwakalobo, (2000), a farmer who is said to be technically efficient produces as much output as
possible from a given set of inputs or if he uses the smallest possible amount of inputs for a
given level output. Technical efficiency is thus calculated as follows:
However, several other methods for measuring technical efficiency exist. The choice of
method depends on the data and the researcher’s philosophical view of importance of
measurement error (Forsund,et al, 1980). The various methods calculate technical efficiency
index (TE score), which measures the distance of the observed firm from a point on the
production frontier (Brock, et al, 2006). Firms lying on the production frontier are 100 percent
technically efficient (with TE= 1) and technical inefficiency of the firm increases with the
distance from the production frontier.
2.5.2 Allocative Efficiency
Analyses of allocative efficiency usually assume that firms seek to optimize a profit
maximization objective function subject to resource constraints (Ogunsunmi, 2005). Resources
are said to be efficiently allocated when the marginal product of each resource equals its price.
Therefore, profit maximizing entrepreneur will not use a resource beyond the point where the
resource adds just as much to his revenue as it adds to the cost. This will only mean exchange of
money or the same value. Allocative efficiency hence, gives direction and magnitude of resource
adjustment.
TE =
Actual output
Potential output
21
2.5.3 Pareto Efficiency
Pareto efficiency, which most often is referred to as pareto optimality is a concept
developed by an Italian economist and mathematician by name Vilfredo Fedrerico Damaso
Pareto (1848 – 1923). This came as result of his analysis of welfare economics. It is a useful
criterion for comparing the outcomes of different economic institutions (Varian, 1996). An
institution is said to be pareto-efficient in the way it allocates its resources when such allocations
leaves nobody better –off and or at least nobody worse-off. Conversely, it can be said that an
economic system is inefficient in pareto terms when some people are better-off and others at
least some well-off. The illustration of pareto-efficiency can be done with consumers A and B.
This argument follows that in a pareto-efficient allocation, the Marginal Rate of Substitution
(MRS) of consumer A has to be equal to the MRS of consumer B, that is, the rate at which
consumer A is willing to exchange one good for the other with, should be equal to the rate at
which consumer B would be just willing to trade one good for the other. The conditions
according to Stiegeler and Thomas (1976) are that for the goods, the MRS should be equal to the
ratio of the prices for the factors of production. In determining the pareto optimal conditions, the
points equivalent to efficiency points on the Edgeworth-Bowly box curve are called the pareto
optimal points.
2.6. Related Studies on Efficiency of Agricultural Production
Farmers’ adoption of practices to ensure better crop production is dependent on the
efficient use of production resources (Amara, et al, 1999). There have been variations in
achieving efficient use of resources by farmers owing to various factors. Technical factors in
empirical studies include the knowledge of farmers on agronomic practices and timeliness of
operations (Kalirajan, 1990), location factors (Abdulai and Eberlin, 2001), farm type as crop or
mixed enterprise and access to irrigation, (Battese, 1992).
Studies in Tigray, northern region of Ethiopia show that short-term land contracting
could be source long term inefficiency of production on continually share cropped plots even if
the contracts are extended on a seasonal basis (Tesfay, et al, 2005). A study by Ajibefun and
Abdukadri, (2004) on impact of size on farm operation resource use efficiency in small-scale
farming reveals that resource availability does not translate into efficiency. In the study, it was
22
discovered that farmers with less intensive use of land, labour and capital resources are more
efficient in the use of these resources than farmers that use resources intensively.
Ogundari,et al, (2004) in the study of impact of economies of scale and cost efficiency in
small-scale maize production in Nigeria, indicated that the small scale resource poor farmers are
efficient in their use of resources and the expansion of their level of production will reduce cost
per output. This is in accordance with results from earlier study that indicate higher relative
efficiency for small farms, (Yotopolous and Lau, 1973)
Alabi and Aruna, (2006) studied the technical efficiency of family poultry production in
Niger-Delta, Nigeria. It was discovered that inefficiency parameters show age as being
negatively related to poultry production. It further showed that family size, gender, innovation
adoption have negative relationship with efficiency of poultry production.
A study by Awudu and Huffman, (2000) on economic efficiency of rice farmers in
northern Ghana showed that 29 percent of potential maximum profit was lost due to inefficiency.
The study further indicated that higher level of education reduced profit inefficiency.
2.7 Theoretical Framework
Farrell, (1957) developed the basis of standard efficiency methodology.
The input saving efficiency consists of two components, (i) technical efficiency which reflects
the ability of firm contracting inputs for a given set of output, (ii) allocative efficiency which
reflects the ability of a firm to use inputs in the optimal proportion, given their respective prices,
(Kumar and Gupta, 2004). Economic efficiency (EE) is the overall performance measure and it is
equal to the product of technical efficiency and allocative efficiency ( Bronfenbrenner, 1990).
Over the years, Farrell’s methodology has undergone some refinements and
improvements, which consequently resulted in the stochastic frontier model. The model has wide
acceptance in agricultural economic literature because of its consistency with theory, versatility
and relative ease of estimation (Kibaara, 2005).
The stochastic frontier production function was independently proposed by Aigner,
Lovell and Schmidt (1977) and Meeusen and Van den Broeck (1977). The stochastic frontier
production function often assumes Cobb-Douglas production function with constant returns to
scale and constructs a linear production frontier in the input/ output space. However, it assumes
that deviation from the frontier (error term) can be split into two components : a symmetrical
23
random error with mean zero and efficiency component that takes only positive values from a
truncated normal distribution with positive mean (Brock, et al, n.d). The original specification
involved a production function specified for cross-sectional data which had an error term that
had two components, one to account for random effects and the other to account for technical
inefficiency (Coelli, 1993).
The frontier function for cross-sectional data can be illustrated with a firm using inputs
(x1, x2, …, xn ) to produce Y. Efficiency transformation of inputs into output is characterized by
the production function F(x), which shows the maximum output obtainable from various input
vectors. The stochastic frontier production function assumes the presence of inefficiency in
production; hence the function is defined by
Yi = Xi �+ (Vi – Ui) …(1)
Where:
Yi is the production (or logarithm of production) of the i - th firm;
Xi is a K × 1 vector of (transformations of the) input quantities of the i-th firm ;
� is a vector of unknown parameters;
the Vi are random variables which are assumed to be independently identically distributed (iid).
N(0,óv2), and independent of the Ui , which are non-negative random variables which are
assumed to account for technical inefficiency in production and are often assumed to be iid.
N(0,óu2).
Furthermore, Battese and Coelli (1992) proposed a stochastic production function for
(unbalanced) panel data which has firm effects that are assumed to be distributed as truncated
normal random variables, which are also permitted to vary systematically with time. The model
may be expressed as:
Yit = Xit� + (Vit – Uit) ,i…,N t=1,.., T, …(2)
Where
Yit is (the logarithm of) the production of the I-th firm in the t-th time
period;
Xit is a vector of (transformations of the) input quantities of the i-th firm in t-th time
period;
� is as defined earlier;
24
the Vit are random variables which are assumed to be iid N(0,óv2), and independent of the
Uit = (U exp(-�(t-T)), where:
The Ui are non-negative random variables which are assumed to account for technical
inefficiency in production and are assumed to be iid as truncation at zero of the N(ì,óu2)
distribution; � is a parameter to be estimated; And the panel of data needs not to be complete (i.e.
unbalanced panel data).
25
CHAPTER THREE
3.0 METHODOLOGY
3.1 Study Area
The study area is Enugu North agricultural zone of Enugu state, Nigeria. The area was
formally referred to as Nsukka Agricultural zone but was changed following the change of
nomenclature by Enugu State Agricultural Development Programme (ENADEP) from its former
name (ENADEP memo, 2004). The study area comprises of six local government areas out of
the 17 local government areas of the state. The State lies between latitudes 5053’ north and 7025’
north and also between longitudes 7053’ east and 7055’ east (Balogun, 2000). The local
government areas that make up Enugu North Agricultural zone include: Igbo-Etiti, Igbo-Eze
North, Igbo-Eze South, Nsukka, Udenu, and Uzo-Uwani. The study area shares boundaries with
Benue and Kogi States in the North and Anambra State in the west. It has a total population of
1,228,586 and land area of 2363.461 square kilometers (Federal Republic of Nigeria Official
Gazette, 2007)
The study area is covered by open grassland, with occasional woodlands and clusters of
oil palm trees with seasonal variation of hot and mild cold weather. There are two marked
seasons, the rainy season and the dry season. Rainy season occurs from April to October, while
the dry season starts from November and ends in March. According to, 80 percent of the
inhabitants are farmers, yams, oil palm products, taro, corn (maize), rice, and cassava are the
main crops with varieties of fruits, vegetables and legumes Ofomata, (1978).
3.2 Sampling Procedure
For the purpose of this study, multi-stage sampling method was employed in selecting the
respondents. Thus the first stage was to choose three (3) local government areas randomly out of
the six local government areas that make up Enugu North agricultural zone. The second involved
selection of two (2) communities randomly out of each of the three local government areas. It
should be noted that each of the local government areas has no equal number of communities.
The third stage was purposive selection of two (2) villages from each of the earlier selected
communities. The villages were selected based their popularity in the production of Nsukka
yellow pepper. The list of names of yellow pepper farmers was prepared in each of the twelve
villages through consultation of key informants in the communities. Finally, five (5) respondents
26
(Nsukka yellow pepper farmers) were randomly selected from each of the villages. This brings
the total number of respondents to sixty (60) which constitutes the sample size. Enugu North
Agricultural zone has estimated number of Nsukka yellow pepper farmers to be 400 thus the
sample size represents 15% of Nsukka yellow pepper farmers.
3.3 Method of Data collection
The information supplied by the farmers provided the bulk of the primary data. It is
important to note that the data provide useful information as regards the socio-economic status of
the rural yellow pepper farmers in the study area, the amount of resources used in the production
and the corresponding output. This information was collected with the help of two enumerators,
who were trained for the administration of the questionnaire.
3.4 Analytical Techniques Used
Econometric techniques and descriptive statistics were used in analyzing the data
collected. Descriptive statistics were employed in achieving objectives (i) and (ii) which are
socio-economic characteristics of the respondents and the production systems embarked upon by
the farmers respectively. Econometric techniques were used in realizing objectives (iii) and (iv).
Descriptive statistics used include frequency distribution, percentages and students t-test.
The econometric techniques used include: stochastic frontier production function. The stochastic
frontier production function and technical efficiency models were jointly estimated using soft-
ware package, FRONTIER 4.1.
3.5 Model Specification
Production function approach is used to estimate the objective (iii) and (iv). The general
form of the function is specified as:
LnYi = �o + �1InX1 + �2InX2 + �3InX3 + … + �5 InX5 + (Vi +Ui)…..(3)
Where:
Y = Output (Kg)
X1 = labour (mandays)
X2 = farm size (hectares)
X3 = fertilizer (Kg)
27
X4 = Seeds (gram)
X5 = Manure (50 kg bag)
In traditional theory of production function the value of coefficients for regression can be
used to estimate how efficient farmers are in their resource - use (Nwakalobo, 2000). The same
principle could be applied to the coefficients of stochastic production function which has the
same causal relationship with the output. In this study, fertilizer, seeds and manure are proxies
for capital inputs.
3.5.1. Technical Efficiency Model
In the estimation of the influence of socio-economic variables on the technical efficiency
of the farmers, technical efficiency model was specified as follows:
Ui = �o + �1Z1 + �2Z2 + �3Z3 + �4Z4 + …… 4
Where:
Ui = technical inefficiency
Z1 = age of the farmer (years)
Z2 = number of years spent in formal education (years)
Z3 = family size (number of individuals in a household)
Z4 = farming experience (years)
�i = parameters to be estimated.
The parameters for the stochastic frontier production function model are obtained by
maximum likelihood estimation method using the computer programme, FRONTIER 4.1 where
equations 3 and 4 were jointly estimated.
3.6 Student T-test
Student t-test was used to test the influence of socio-economic characteristics of the respondents
on their technical efficiency. It was also used in testing the influence of land, labour, land capital
on the yield of Nsukka yellow pepper.
The formula for t-statistics:
Where:
(n-1) degree of freedom t � x − u
s.d / �n
-
-
28
x = Sample mean
u = Population mean
s.d = Standard deviation
n = sample size
29
CHAPTER FOUR
4.0 Results and Discussion
4.1 Socio-Economic Characteristics of the Respondents
Socio-economic characteristics of the farmers considered in the study are: gender, age,
marital status, years spent in formal education, family size, major occupation, farm income level
and ownership of the farm.
Table 4.1: Distribution of the respondents according to their socio-economics characteristics
Socio-economic variables Number of respondents Percentage
Male 0 0
Female 60 100
Age
<20 1 1.7
21-30 12 20
31-40 22 36.7
41-50 17 28.3
51-above 8 13.3
Marital status
Single 11 18.3
Married 32 53.3
Widowed 12 20
Separated 5 8.3
Years spent in formal education
Zero 9 15
1-6 years 16 26.7
7-12 years 25 46.7
>13 years 7 11.6
Family size
1 -5. 20 33.3
30
4.1.1 Gender of the Respondents
Certain businesses are gender biased; therefore it is important to determine the gender of
the respondents as it will further show the extent of participation of the respondents in the
production of Nsukka yellow pepper. Table 4.1 shows the frequency distribution of the
respondents with respect to their gender.
The Table shows that all the respondents (100%) were women. This indicates a complete
dominance of Nsukka yellow pepper production by the females in the study area. This could be
attributed to the male’s seeming negative attitude in engaging in production of vegetable and
other related crops.
4.1.2 The Age Distribution of the Respondents
Age is an important factor in any agricultural activity. According to Agbo (2006), age is
inversely related to performance. Pepper production, just like any other agricultural enterprise
6 -10 29 48.3
11 -15. 11 18.4
16 –above - -
Major occupation Artisan 15 25 Civil service 8 13.3 Farming 15 25 Trading 22 36.7 Farm income (N) < 50,000 6 10 50,000 - 100,000 11 18.3 100,001-150,000 20 33.3 150,001 -200,000 16 26.7 >200,000 7 11.7 Ownership of farm land Family 36 60 Community 9 15 Lease 15 25
31
requires intensive labour. Such labour can only be sourced from the young and strong individuals
in the populace.
Majority of the respondents (36.7%) are within the age range of 31-40 years. This is
closely followed by those in the age bracket of 41-50 years, which constitutes 28.3% of the
respondents. The least in this group are those respondents below the age of 20 years that only
make up 1.7% of the respondents. The implication of the results in Table 4.1 is that the Nsukka
yellow farmers are relatively young as the majority have not passed middle age and thus can be
considered very active in economic sense.
4.1.3 Marital status of the Respondents
Marital status of the respondents is necessary in the study because access to farmland can
be constrained by marital status. Access to farmland is an important socio-economic variable to
be considered before engaging in any farm enterprise. Again marital status of individuals tends
to determine the family size and also the availability of labour from the household for farming
activities. Frequency distribution of respondents according to their marital status is presented in
Table 4.1. Over fifty percent of the respondents are married while 20%, 18.3% and 8.3% are
widowed, single and separated respectively. The import of this is that as majority of the
respondents are married and widowed, they tend to have easy access to use land inherited or
owned by their husbands. This has an impact on the profitability of their pepper enterprises as
the cost of renting or leasing land is minimized. It also leads to better management of the
farmland.
4.1.4 Distribution According to Years of Formal Education
It is a popular notion that education has influence on productivity as it enhances better
utilization of resources hence the number of years spent in formal education is an important
variable in this study. Table 4.1 details the frequency distribution of the respondents according to
number of years spent in formal education (school years).
The result shows that the majority of the respondents (46.7%) spent 7-12 years in formal
education. This is followed by 26.7% of the respondents who agreed to have spent 1-6 years in
formal education. Fifteen percent of the respondents were of the opinion that they never went for
formal education. In other words, they never went to school. However, the least in this category
32
are the respondents that spent above 13 years in formal education. This group of respondents
constitutes 11.6% of the total respondents.
This implies that about 47% of the respondents attended secondary school, about 27% obtained
primary school education, and 15% accounted for those that got no formal education while about
11% went beyond secondary school education.
4.1.5 Family Size of the Respondents
Family size is a pertinent variable in farm business. According to Ebe (2006), it is
assumed that a business which is labour intensive requires big household size that could provide
the labour needed at a least cost. Table 4.1 shows that about 48% of the respondents have family
size of between 6 – 10 persons. About 33% of the respondents have family size of between 1 – 5
individuals while about 18% of the respondents have family size of 11 – 15 persons. It could be
seen from the table 4.1 that the bulk of the families engaged in the production of Nsukka yellow
pepper have comparatively large family size which could ease labour cost in their farm practices.
4.1.6 Major Occupation of the Respondents
It is expected that the occupation of respondent should have a positive relationship with
their farming activities. The assumption is that the respondents in farming related occupation
should be more involved in the production of Nsukka yellow pepper. The distribution of
respondents according to their major occupations is presented in Table 4.1.
The Table indicates that about 37% of the respondents have trading as their major
occupation. However, this was not expected owing to the fact that the respondents are supposed
to be much more involved in agricultural activities than those in other businesses. The proportion
of respondents who are farmers and artisans constituted 25% each for the two occupations out of
the total respondents. Thus the two occupations have a combined proportion of 50%. About
13.3% of the respondents are civil servants.
4.1.7 Farm Income Level of the Respondents
It was established from the result in Table 4.1 that about 33% of the respondents are at
the farm income level of between N100,000 – N150,000. This is followed by the respondents
who are at the farm income level of N150,001 – N200,000. About 18% of the respondents do get
33
farm income of between N50,000 – N100,000. This however, is followed by about 12% of the
respondents who are at the farm income level above N200,000. The least proportion of the
respondents has farm income below N50,000. This group makes up 10% of the respondents.
4.1.8 Ownership of Farmland
Ownership of farmland is an important factor in farm enterprise and as such, has great
influence on the profitability of the enterprise and management of the farmland. Table 4.1 shows
that 60% of the respondents use family land in the production of Nsukka yellow pepper. Twenty
five percent of the respondents use land they got on lease while 15% of the respondents produce
on land owned by the community.
4.2 Forms of farming systems
In the process of producing Nsukka yellow pepper, the farmers employ various cultural
and crop management strategies to either boost their output or income level. The frequency
distribution of respondents according to the various cultural and crop management strategies in
producing Nsukka yellow pepper in the study area is presented in Table 4.2
Table 4.2: Frequency Distribution of the Respondents According to Forms of Farming
Systems Frequency Percentage Multiple cropping Yes 20 33.3 No 42 67.7 Total 62* 100 Mixed farming yes 32 52.5 No 29 47.5 Total 61* 100 Irrigation Yes 42 80 No 12 20 Total 60 100 Intensity of production All year round 17 28.3 Rainy season 43 71.7 Total 60 100 Multiple responses were obtained in some cases. Source: Field survey data, 2007/2008
34
According to the result in Table 4.2, about 33% of the respondents agreed to have
practiced multiple cropping in producing Nsukka yellow pepper. However, the majority about
68% did not practice multiple cropping. This implies that for the few respondents about 33%
who were into multiple cropping did so probably as insurance against crop failure and ensuring
household food security.
Furthermore, the respondents did engage in mixed farming for some reasons. These
reasons could be as cover against crop failure, and for supply of farm inputs like manure, etc.
Table 4.9 also shows that about 53% of the respondents accepted to have practice mixed
farming, while 48% did not. The engagement of the majority of the respondents in mixed
farming could be for the reason mentioned earlier.
Eighty percent of the respondents agreed to have been practicing some form of irrigation,
while the remaining 20% depended on rainfall for their farming. About 72% of the respondents
disclosed they always grow the crop in rainy seasons. About 17% of the respondents grow
Nsukka yellow pepper all year round.
4.3 Production of Nsukka yellow pepper in the study area
Production of Nsukka yellow pepper like most vegetable production begins from the
nursery. However this is followed by series of other cultural practices as presented in table 4.3
Table 4.3: Schedule of farming activities of the respondents Month Activity Input Output (Jute Bags) Dec - January 1. Nursery 1. Seeds 2.Tillage 2. Baskets 3. Cellophane
4. Farm labour
March - April 1. Bed making 1. Farm labour
2. Transplanting 2. Manure (11 Jute bags)
May – July 1. Weeding 1. Farm labour
2. Fertilizer/ manure application August – November 1. Harvesting
1. Farm labour
2.Ferilizer application ( 100Kg)
2. Farm labour Yellow pepper (14 jute bags)
*Fertilizer application is done at the second stage of Harvest N.B: average Farm size: 2.5 hectares
35
The farming activities for the production Nsukka Yellow pepper starts from December
and ends November. According to table 4.3 above, December and January are usually the time
for the preparation of the nursery. However, primary tillage is carried out within the same period.
The major inputs in during the stage of nursery preparation are Yellow Pepper seeds, poultry
manure, baskets, cellophane bags and farm labour.
March and April are the time for bed making and transplanting. The major inputs are
farm labour and poultry manure. For land area of about 2.5 hectares, about 11 jute bags of
poultry manure is incorporated into the soil during the bed making.
Weeding operation and fertilizer application are carried out mainly between the months
of August and September. Fertilizer is added after the first harvest stage. The weeding operation
takes an average of 18 man-days though; herbicides are used on rare cases.
Harvesting is done in three phases and it normally from the month of August with peak of
harvest at the middle of September. The harvest wanes in November which is left for production
of seeds for subsequent nursery preparation. Average yield of Nsukka yellow pepper is about
0.24 metric tonne per hectare.
4.4 Land area Cultivated of Nsukka Yellow pepper by the respondents
Output and profitability of a farm enterprise is expected to increase as result of increment
in wise and prudent use of resources. Farmers are expected to reap economy of scale in
increasing the area brought under cultivation given that other variables are constant and
favourable. Table 4.4 is the frequency distribution of the respondents according to land area
cultivated.
Table 4.4: Frequency Distribution of the Respondents According to land Area Cultivated Land Area Farmers Percentage
<1ha 12 20
1.1-1.5 10 16.7
1.5-2ha 18 30
2.1-2.5 11 18.3
2.6-3ha 9 15
>3ha 0 0
60 100
Source: Field Survey 2007/2008
36
Twenty percent of the respondents have farm size less than 1 hectare where they grow the
pepper. This is followed by about 18% of the respondents that grow Nsukka yellow pepper in
farm size of between 2.1 and 2.5 ha. Furthermore, 15% of the respondents grow Nsukka yellow
pepper in the land area of between 2.6 and 3 hectares, while 30% of the respondents cultivated
pepper on farm size of 1.5 – 2 ha. These indicate that respondents are small holder farmers as
pointed out by Awoke and Okorji (2004), that small holder farmers have production capacity that
fall between 2.5 and 5 hectares per season.
4.5 Influence of Socio-economic Characteristics of the Respondents on their Efficiency
Technical inefficiency model was used to estimate the influence of socio-economic
variables of the respondents on their level of efficiency. The explanatory variables include age,
number of years spent in formal education, family size and years of farming experience.
The maximum likelihood estimates (MLE) of the inefficiency parameter estimates is
shown in table 4.5.
Table 4.5: Maximum Likelihood estimates of Inefficiency Parameters using Cobb-Douglas Frontier Function.
Variables Parameters Estimates Constant �0 0.199(0.227)** Age (years) �2 -0.552(-0.322)** School Years(years) �3 -0.365(0.236)** Family size �4 -0.585(-0.109)** Experience �5 0.251(0.242)** Variance parameters Sigma-square �2 0.157(0.095)** Gamma � 0.837(0.149)* Ln (likelihood) Llf 0.17
** Significant at the .005 level; Figures in parenthesis are standard error (SE). Source: Computed MLE result
The above Table indicates that not all the parameter estimates conform to the a priori
expectations. The estimated coefficients of the inefficiency function provide some explanations
for the efficiency levels among individual respondents. Since the dependent variable of the
inefficiency model represents the mode of inefficiency, a positive sign of an estimated parameter
implies that the associated variable has a negative sign influence on efficiency and a negative
coefficient indicates the reverse.
37
The coefficient for age of the respondents was negative, therefore, signifying that age of
the respondents has positive relationship with their level of efficiency. One percent increase in
the age of the respondents resulted in about 55% increase in the level of technical inefficiency
and significant at 5%. This means that the age variable is an important factor in determining the
level of technical efficiency of producing Nsukka yellow pepper in the study area.
The number of years spent in formal education (school years) has positive causality on
the farmers’ level of efficiency. Table 4.5 shows that 1% increase in years of formal education
brought an increase in the level of efficiency by about 37%. This is in line the a priori
expectation that the number of years spent in formal education will reduce the level of technical
inefficiency. This is also in conformity with the works of Fasasi (2007); Parikh, Ali and Shah
(1995). Again, number of years spent in formal education was significant at 5%, thus could be
said to have substantial influence on the farmers’ level of efficiency.
Family size of the respondents was another important variable for the determination of
the level of efficiency of the respondents. The coefficient for family size is negative thus
influencing the level of technical efficiency of the respondents positively. One percent increase
in the family size resulted in about 58% increase in the respondents’ level of technical efficiency.
This coefficient was significantly different from zero at 5% and conforms to the a priori
expectation that increase in the size of the family reduces the level of technical inefficiency.
The farming experience of the respondents also accounted for the farmers’ level of
technical efficiency. According to table 4.1, the coefficient for farming experience has positive
causality on the technical efficiency. The result shows that a 1% increase in the years of farming
experience brought about increment in the level of technical efficiency by 25%. This is in
conformity with the a priori expectation. It also conforms to the findings of Fasasi (2007). The
coefficient was significant at 5% and as such, is an important factor in deciding the level of
technical efficiency of the respondents.
The sigma square was statistically significant at 5%. This is an indication of goodness of
fit and correctness of the specified distributed assumption of the composite error term. Gamma,
�, which is a variance error ratio (8.37%), suggests that unexplained systematic influences by the
production function are dominant source of random error, that is the existence of technical
inefficiency among the respondents, accounts for about 8.4% of the variation in output level of
the Nsukka yellow pepper and that the Gamma was significant at 10%. This confirms that in the
38
specified model, there is presence of one sided error component and that a classical regression
model of the production function based on the Ordinary Least Square (OLS) estimate would not
be adequate for representation of data. This confirms the relevance of the stochastic parameters
of the production frontier and maximum likelihood estimation.
4.6 The Efficiency of Use of Resources
In the production of Nsukka yellow pepper in the study area, table 4.6 shows the
maximum likelihood estimates of the data collected.
Table 4.6: Maximum Likelihood Estimates of Stochastic Frontier Production Function
of Nsukka yellow pepper production
Variables Parameters Estimates Constant �0 0.443(0.312)** Labour � 1 0.274(0.160)* Farm size �2 0.445(0.250)* Fertilizer �3 0.054(0.081) Seeds �4 0.298(0.245) Manure �5 0.445(0.189)** Sigma-square �2 0.157(0.095)** Gamma � 0.0837(0.149)* Ln (likelihood) 0.17 Mean Technical Efficiency 0.70
Figures in parenthesis are standard error (SE). ***Significant at 0.01 level; and *significant at 0.1 level. Source: Computed MLE result from frontier 4.1
The positive estimates of coefficients of labour, farm size, fertilizer and manure in the
table 4.6 indicate that all the parameter estimates have the expected positive signs with the
coefficient for seeds and fertilizer not being significantly different from zero and so not very
important in the study area. However, labour and farm size were significant at 10%. According
to Table 4.6, 1% increase in labour input resulted in approximately 27% increase in the output.
One percent increase in farm size brought about approximately 45% increase in the output.
Increased use of fertilizer by 1% resulted in approximately 5% increase in the output of yellow
pepper. For manure input, its coefficient indicates that 1% increment brought about 45%
increments in the output. The elasticity of manure use to output is statistically significant at 5%.
39
This implies that manure is an important capital input in the production of Nsukka yellow
pepper.
Furthermore, in determining the efficiency of use of different inputs in the production of
Nsukka yellow pepper by the selected rural farmers, the responsiveness of the outputs to the
input use (elasticity) was used. According to Table 4.6, labour, farm size, fertilizer, seeds and
manure were the inputs used. However, fertilizer, seeds and manure serve as proxies for capital.
Therefore the sum of the elasticities for capital inputs is 0.79. The economic implication of this
value is that the respondents have return to scale on capital of 0.79, which could be interpreted to
be that the respondents were 79% efficient in their use of capital resources.
4.7 Distribution of the technical Efficiency Indices of the Respondents
The technical efficiency Indices were derived from the Maximum likelihood estimate results
of the stochastic frontier production function. The indices in table 4.7 show that the best farmers
operate at technical efficiency of between 0.91 – 1.00. These respondents make up 25% of the
total respondents. The respondents that have their efficiency range between 0.71 – 0.80
constitute 18% of the sample size. The least technically efficient respondents (5%) are at the
efficiency range of 0.00 – 0.10.
Table 4.7 Distribution of technical Efficiency indices of the respondents in the study area
Efficiency class index Frequency Percentage 0.0 - 0.10 3 5
0.11 - 0.20 1 1.7 0.21 - 0.30 2 3.3 0.31 - 0.40 2 3.3 0.41 - 0.50 4 6.7 0.51 - 0.60 7 11.7 0.61 – 0.70 7 11.7 0.81 – 0.80 11 18.3 0.81 – 0.90 8 13.3 0.91 – 1.00 15 25
Total 60 100 Minimum Technical Efficiency: 0.10 Maximum technical Efficiency: 0.91 Mean technical Efficiency: 0.70 Source: MLE result from frontier 4.1
40
CHAPTER FIVE
SUMMARY, CONCLUSION AND RECOMMENDATIONS
5.1 Summary
This study was conducted to determine the efficiency of use of resources in the
production of Nsukka yellow pepper among the rural farmers in Enugu North agricultural zone.
Researches on the efficiency of utilization of resources by rural farmers in their agricultural
enterprises are expected to alleviate the problem of resource allocation in their farm enterprises.
It is expected that this will improve the standard of living of the rural farmers through
maximization of their output, which will translate to enhanced income level and subsequent
poverty reduction.
The study was guided by the following specific objectives: to assess the socio-economic
characteristics of yellow pepper farmers in the study area; to assess the production systems
employed by the farmers in the production of Nsukka yellow pepper; to determine labour, land
and capital use in the production of Nsukka yellow pepper; to estimate the influence of the
farmers’ socio-economic characteristics on their technical efficiency of resources used in
producing Nsukka yellow pepper.
Multi-stage random and purposive sampling techniques were adopted in the selection of
the respondents. Sixty respondents were selected and successfully administered questionnaires.
Primary data were collected and used. The analytical tools used include descriptive statistics,
stochastic frontier production function
Result showed that the females have complete dominance in the production of Nsukka
yellow pepper. The farmers of Nsukka yellow pepper are 100% female. About 37 percent of
respondents were in the age bracket of 31 – 40 years and 53.3% of the respondents were married.
Majority of the respondents (85%) attained appreciable level of formal education. The modal
family size was 6 – 10 individuals, which constitutes 48.3% of the respondents. Majority of the
respondents were artisans, civil servants and traders while 25% of the respondents were full time
farmers. The modal farm income per cropping season income level of the respondents (33.3%)
was N100,001 – N150,000, while 60% of the farmers cultivate on their family lands. The
average farm size of the respondents was 2 hectares.
Monocropping is a form farming system employed by 67.7% of the respondents, however
52.5% were engaged in mixed farming. Majority of the respondents practiced a form of irrigation
41
but the bulk of production 71.7% was done during rainy season. Furthermore the major inputs
used by the farmers are: land; labour; manure; fertilizer and seeds of Nsukka yellow pepper. The
average output of Nsukka yellow pepper is 0.24 tonne per hectare.
The estimates of influence of socio-economic characteristics of the farmers on their
production efficiency show that age, years spent in formal education, family size and experience
of the farmers affected the farmers’ technical efficiency significantly. The number of years spent
in formal education to had positive causality on the farmers’ technical efficiency.
The estimates of the stochastic frontier production function show that input variables that
influenced the output of Nsukka yellow pepper significantly are labour, farm size, and manure.
The estimates of the stochastic frontier function show labor, farm size, fertilizer, seeds and
manure had 0.27, 0.45, 0.05, 0.30 and 0.45 levels of efficiency respectively, indicating the need
to increase the use of these various inputs to increase the output of Nsukka yellow pepper. The
sum of the elasticities of capital inputs (fertilizer, seeds and manure) is 0.79. The distribution of
technical efficiency indices of the respondents show that the maximum technical efficiency index
is 0.91, the minimum is 0.10 while the mean technical efficiency is 0.70.
5.2 Conclusion
This study was centered on the estimation technical efficiency of the rural farmers
engaged in the production of Nsukka yellow pepper, using the stochastic parametric estimation
methods. A Cobb-Douglas functional form was employed to estimate the Maximum Likelihood
Estimates.
The study has shown that the rural yellow pepper farmers are women basically because
100% of the respondents were and were also relatively below middle age with most obtaining at
least secondary school education. Most had family size of between 6 – 10 persons. The bulk of
the respondents were artisans. The distribution of the technical efficiency indices show wide
variation in the level of technical efficiency, which indicates low level of technology adopted by
some of the respondents. It was observed that on the average, the level of technical efficiency is
high there is need to improve. The results also show that labour, farm size fertilizer, seeds and
manure had positive causality on the output. The coefficients for fertilizer and seeds had no
significant influence on the output. There coefficients were 0.27, 0.45, 0.05, 0.30 and 0.45
respectively. The farmers’ level of technical efficiency has also been shown to be influenced
42
significantly by age, number of years spent in formal education, family size and farming
experience. The technical efficiency of the farmers could be increased by 30%.
5.3 Recommendations
The findings of this study have important policy implications for improving the
economic status of the rural dwellers engaged in the production of Nsukka yellow pepper. The
following are the recommendations arising from the findings of this study:
i. Effective mobilization of rural women for full participation in the production of Nsukka
yellow pepper through the use of extension agents and community leaders. The extension
agents will disseminate improved farm practices and the adoption of the disseminated
information will be easy as most of the farmers are moderately educated.
ii. Creation of enlightenment campaigns that would encourage educated persons to embark
on production of Nsukka Yellow pepper.
iii. More capital inputs should be made available through provision of more organic manure
to increase the output of Nsukka yellow pepper. This could be facilitated through
establishment of channels that will enable the farmers, access credit facilities for
procurement of the capital inputs and also hiring of labour.
iv. A review of the land use act is needed to give the rural dwellers (women) better access to
farm land and thus help in the expansion of their farm sizes allocated for producing
Nsukka yellow pepper production.
43
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49
Department of Agric. Economics,
University of Nigeria, Nsukka
Date ----------------------------
Dear Respondent,
Request for Response to Questionnaire
I am an M.Sc postgraduate student of the above named department and the University currently
undertaking a research work titled “Technical Efficiency in the production of Nsukka Yellow
among Rural Farmers in Enugu North Agricultural Zone, Enugu State, Nigeria.”
You have been chosen as one of the respondents to supply the required information for this
study. I therefore solicit for your cooperation to respond as objective as possible to the questions.
It is pure academic work and all information supplied by you will be strictly treated in
confidence.
Thank you for your patient and cooperation.
Yours faithfully,
Ugwu Stanley I.
Questionnaire/ Interview Schedule
A. Location
1. Local Government Area………………………………………………
2. Village /Town……………………………………………………………
B. Socio-economic Characteristics of the Nsukka yellow pepper farmers
1. Gender: Male Female
2. Age in years……………………………………………………………………
3. Marital Status: Single Married Widow Divorced
4. Level of Education:
i. Never attended school
ii. Attended primary school
iii. Attended secondary school
iv. Attended any higher institution
50
5. How many years did you totally spend in school…………………………
6. Number of people in your household……………………………
7. For how many years have been involved in the production of Nsukka Yellow
pepper?…………………
8. What is your major occupation? Artisan Civil service Farming
Trading
9. How many hours on the average could you say you spend on the farm per day?.............
10. What is the farm size devoted to yellow pepper production? ……………………….
11. Who owns the farm you use? Family Community On lease
12. Please tick the appropriately the range of your farm income.
<N50,000.00 N50,001-N100,00.00 N100,001-N150,000
N150,001-N200,000 >N200,000.00
13. What forms of farming system do you employ? Please tick correctly.
i. Multiple cropping. Yes No
ii. Mixed farming. Yes No
iii. Irrigation. Yes No
iv. Intensity of production
All year round
Rainy season only
v. Others, specify……………………………………
51
14 What period of the year did you carry out the following operations and indicate the inputs
used. Please tick accordingly.
OPERATION INPUT Jan. Feb. Mar. Aprl May Jun Jul Aug. Sep Oct. Nov. Dec.
Nursery
land Clearing/Tillage
Bed making
Transplanting
Weeding
Fertilizer Application/ Manuring
Harvesting
52
PRODUCTION VARIABLES
�� ��� � �� � � � � � ���� ��� ��� � �� � ��� � � � � ��� � � ����� ���� � ��� ���� ��� ���� � �� �
�� � �� ���� �� ����� ��� ��� � � � � ��
LABOUR
Source of labour
Family
Hired
Hired and Family
Exchange
Others please specify
53
�� ��� � �� � � � � � ���� ��� ��� � �� � ��� � � � � ��� � � ����� ���� � ��� ���� ��� ���� � �� �
�� � �� ���� �� ����� ��� ��� � � � � ��
�
����������������
�� ������ � ���� ��������� ����������� � � ��������
�� ������
� ��������� ��� ��� � ����
������ ��� � ���� � ���� �
� ��� ��� � ����
������ ��� � ���� � ���� �
� ��� ��� � ����
������ ��� � ���� � ���� �
�� �� �� �� � �� � �� �� �� �� �� �� �� �� ��� ��� � � � �� �� �� �� �� �� �� �� �� � � �� � �� �� �� �� �� �� �� �� ��! � � � �� �����������������"�#� �� �� �� �� �� �� �� �� ��
"$#� �� �� �� �� �� �� �� �� ��"%#� �� �� �� �� �� �� �� �� ��
&� � � '� ����������������������"�#� �� �� �� �� �� �� �� �� ��
"$#� �� �� �� �� �� �� �� �� ��"%#� �� �� �� �� �� �� �� �� ��
( � �� �� �� � �� �� �� �� �� �� �� �� ����
�
�) ��� � �� �� ��� � � � � � ������� � ��� � � � � ��� ����� ��� ��� � �� � � ������� � �� � �� ���� ��� ���� � �� ��� � �� ��� �� �� � � � � �� �� �� �� ����
INPUT Quantity (Kg)
Fertilizer
Manure
Yellow pepper Seeds
Agrochemical:
Herbicide (1)
54
Pesticide (2)
