faculty of agriculture - university of nigeria
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ANWASIA ANTHONIA IFEOMA
PG/MSC/11/58393
PRODUCTON EFFICIENCY OF SMALL SCALE
BROILER FARMERS IN DELTA STATE, NIGERIA
Faculty of Agriculture
Agricultural Economics
Chukwueloka.O. Uzowulu
Digitally Signed by: Content manager’s Name
DN : CN = Webmaster’s name
O= University of Nigeria, Nsukka
OU = Innovation Centre
PRODUCTON EFFICIENCY OF SMALL SCALE BROILER FARMERS
IN DELTA STATE, NIGERIA
BY
ANWASIA ANTHONIA IFEOMA
PG/MSC/11/58393
A DESSERTATION SUBMITTED TO THE DEPARTMENT OF
AGRICULTURAL ECONOMICS, FACULTY OF AGRICULTURE,
UNIVERSITY OF NIGERIA, NSUKKA IN PARTIAL FULFILMENT OF
THE REQUIREMENT FOR THE AWARD OF MASTERS DEGREE
(M.Sc) IN AGRICULTURAL ECONOMICS
DECEMBER 2015
CERTIFICATION
Anwasia, Anthonia Ifeoma. A post graduate student in the Department of Agricultural
Economics with registration number PG/MSC/11/58393 has satisfactorily completed the
requirement for the Masters of Science in Agricultural Economics. The work in this
dissertation is original and has not been submitted in part or in full for any other diploma or
degree of this or any other university.
Dr. A.A Enete Prof. S.A.N.D Chidebelu
(supervisor) (supervisor)
Date: _________________
Date:__________________
Prof. J.S. Orebiyi
(External Examiner)
Date:___________________
DEDICATION
This work is dedicated to the Lord Almighty who guided me during this programme.
Also to my parents
Chief and Mrs Benjamin ANWASIA jp
ACKNOWLEDGEMENT
First and foremost, am particularly grateful to God Almighty, my source of strength
and help for his mercy and grace upon me throughout the course of this study. A sincere
appreciation to a laudable supervisor, Prof. E.C. Okorji for his special interest in my success,
support, contribution, guidiance, willingness, patience and fatherly encouragement during all
the stages of this work.
To the lecturers in my Department, Prof S.A.N.D Chidebelu, Prof. C.J. Arene, Prof.
C.U Okoye, Prof. (Mrs) A.I Achike, Prof. N.J. Nweze, Dr. A.A Enete, Dr. F.U Agbo, Dr.
B.C. Opkukpara and a host of others. I say thank you for your encouragement, suggestions
and constructive criticism during my proposal and thereafter.
To my family and friends for their financial support, encouragement and prayers
during my course of study. And also to Dr Chukwuji for his help and support during the
analysis of my research work.
ABSTRACT
This study examined the Production Efficiency of Small Scale Broiler Farmers in Delta State,
Nigeria. The specific objectives were to estimate the technical efficiency of small scale
broiler farmers and identify factors that influence technical efficiency among broiler farmers.
The Study used multistage random sampling technique. A structured questionnaire was used
to obtain information from a randomly selected sample of 120 small scale broiler farmers
from Delta State, Nigeria. Descriptive Statistics, stochastic frontier production function and
inefficiency effect model were used in analyzing the data. Among the findings were that 40%
were aged between 21 and 30 years. 58% were male head households. 68% were married
while 97% had formal education either at primary, secondary or tertiary level. The average
household size was 6. 52% had farming experience between 6 and 1o years, with a mean
experience of 8 years. 68% used the deep litter system. 33% had a stock size of between 41
and 60 broilers while 56% raised their birds for only 12 weeks. 60% of the respondents
acquired land by place of residence. 85% bought feeds from the feed millers, 77% used
personal savings while 78% used family labour. The stochastic frontier analysis showed that
stock of bird and value of feed had positive sign and highly significant at 1% level of
probability. The computed mean technical efficiency was 78% while the minimum and
maximum efficiencies were 56% and 99% respectively. The technical inefficiency model
showed that age and educational level had significant inverse relationship with technical
inefficiency while credit access had significant positive relationship with technical
inefficiency.
TABLE OF CONTENTS
Title page i
Certification ii
Dedication iii
Acknowledgment iv
Abstract v
Table of contents vi
CHAPTER ONE: INTRODUCTION
1.1 Background information 1
1.2 Statement of Problem 5
1.3 Objectives of the study 6
1.4 Research hypothesis 7
1.5 Justification of the study 7
CHAPTER TWO: REVIEW OF RELATED LITERATURE
2.1 Poultry development in Nigeria 9
2.2 Poultry Sub-sectors in Nigeria 10
2.3 Definition of Poultry 11
2.4 Small scale poultry production 11
2.5 Management of poultry Enterprise 12
2.6 Feeds/Nurition 13
2.7 Diseases and health control of poultry production 16
2.8 Productive efficiencies and their determinants: Empirical Evidence 17
2.9 Theoretical framework 20
2.10 Analytical framework 23
CHAPTER THREE: RESEARCH METHODOLOGY
3.1 The study Area 28
3.2 Sampling Procedure 29
3.3 Method of Data Collection 29
3.4 Data Analysis 30
3.5 Model Specification 30
3.5.1 The Stochastic Frontier Production Function 30
3.5.2 Inefficiency Model 31
CHAPTER FOUR: RESULT AND DISCUSSION
4.1 Socio-economic characteristics of the respondents 32
4.2 Management practices and acquisition of resources of the respondents 36
4.3 Efficiency Results 38
4.4 Determinants of Technical Efficiency in Broiler Production 39
4.5 Technical Efficiency Estimates 41
4.6 Constraints on Broiler Production 42
CHAPETER FIVE: CONCLUTION AND RECOMMENDATIONS
5.1 Summary of major findings 44
5.2 Conclusion 45
5.3 Recommendations 46
5.4 Contribution to Knowledge 46
5.5 Area of further Research 47
REFERENCES 48
CHAPTER ONE
INTRODUCTION
1.1 Background of Study
Food production in Nigeria has not kept pace with its population growth, because the
population is growing at about 3.2% per annum while food production is at about 2.0%
(National Bureau of Statistics, [NBS] 2011). The differences between the rate of food
production and population growth has led to a food demand supply gap thus leading to a
widening gap between domestic food production and total requirement, an increase resort to
food importation and high rate of increase in food prices and as a result, wide spread hunger
and malnutrition are evident in the country (Ojo, 2003).
Nigeria’s poultry industry has its root in the initiative of regional governments from
the 1960’s when the Western Regional Government entered into joint pilot poultry
production schemes with some foreign partners, notably the Israeli government (Adene &
Oguntade, 2006). The entry of private investors into poultry production in the late 1960s to
early 1970s marked the onset of indigenous commercial poultry industry. It then spread from
the west to the eastern region and parts of the Northern region. The first decade or so of this
period witnessed a tremendous growth in the industry, especially in the West (Adene &
Oguntade, 2006). The size of the industry grew from less than 1million in the mid-1960s to
over 40 million by the early parts of the 1980s. All along, the growth of the industry had been
propped on by government initiatives and incentives especially in terms of training,
technological support, input support services and others. Thus for example, many of the
poultry technical staff were products of government subsidizing training programmes, while
inputs like vaccines and diagnostic services were subsidized by government or even free
initially (Adene & Oguntade, 2006). Meanwhile the national economic climate was enjoying
a boost from the newly advancing petroleum sector and this visibly helped to propel national
investment sector, including poultry rapidly forward. As from this time, the poultry industry
had started to be self-supporting, viable and attractive to financial institutions.
However, few major glitches truncated the growth path of the industry, which was
transiting from small-scale hybrid broilers and layers and backyard poultry enterprises/semi-
commercial to medium scale commercial enterprises. First, was the very high input cost
especially feed of broilers, which was recorded to constitute over 51% of total cost of
production (Effiong & Onuekwusi, 2006). This partly resulted from policy inconsistencies of
the Government. During the Structural Adjustment Programme (SAP) between 1987-1994,
the industry almost collapsed due to the ban on raw materials for the poultry industry. This
was followed by guided deregulation in 1994, which resulted in a breakthrough and
subsequent increase in poultry meat production from 63,000MT in 1994 to 73,000MT in
1995, 1997. In 1998, the Federal budget threw open the importation of live chilled frozen
chicken and egg at a tariff of 150%, which was later reduced to 55% in 1999. This led to
reduction in local production which fell to 1.3% as compared with 2.7% in 1997. Similarly,
the shift in lending policies in favour of food crops as against livestock industry exacerbated
the situation. In this dispensation, banks were directed to increase lending to 50% for food
crops production and distribution, 15% to livestock and 35% to other agricultural crops
Onyeagocha, Ehirim, Emenyonu, Onyemauwa, & Nwosu (2010).
The importance of poultry to national economy cannot be over emphasized as it has
become popular for the small-holders that have contributed to the economy of the country. In
Nigeria, poultry contributes about 15 percent of the total annual protein intake with
approximately 1.3kg of poultry products consumed per head per annum Ologbon & Ambali
(2012). The poultry industry has assumed greater importance in improving employment
opportunities and animal food production in Nigeria. An earlier report by Mbanasor (2002)
showed that about 10 percent of the Nigerian population is engaged in poultry production,
mostly subsistence and small or medium sized farms.
Broiler production is carried out in all parts of the country, with no known religious,
social or cultural inhibitions associated with their consumption. Specifically, investment in
broiler enterprises is attractive because the production cost per unit is low relative to other
types of livestock, poultry meat is very tender and commonly used in ceremonies compared
to other birds and broiler enterprises have short production circle. Owing to these obvious
advantages of broiler enterprises, large number of farmers, men and women go into their
production, many of whom do so for income generation purposes (Nwajiuba and Nwoke,
2000), besides meeting the protein needs of the household. The evidence of this is the
preponderance of producers–hawkers of broiler products in the urban and rural markets
particularly during festive periods, when their demands are highest and selling prices
favorable.
Most of the small-scale broiler farmers in Delta State are rural dwellers and often rear
other livestock such as, turkey, goats, ducks and pigeons alongside broilers (Delta State
Ministry of Agriculture and Natural Resources, [DSMANR] 2010). Small scale broiler
farming in Delta State is constrained by various factors ranging from lack of technical
knowhow, disease outbreak, high cost of poultry equipment, day-old chicks, drugs, feeds and
capital take off Ike & Udeh, (2011) which leads to low output.
Broiler production like any other economic venture is dependent on resource inputs.
As noted by Etim and Udoh, (2007) maximum poultry production depends partly on the
environment, technical know-how and the quality of resources employed in the production
process. But to optimize production and ensure sustainability, there is need for judicious
management of the resources employed in the broiler enterprise. Inefficiency of resource use
and utilization can seriously jeopardize and hamper food production and availability.
The term efficiency can be described as a process that uses the lowest amount of
inputs to produce greatest amount of output. Thus efficiency simply means reducing the
amount of wasted inputs. The modern theory of efficiency dates back to the pioneering work
of Farell, (1977) who proposed that the efficiency of a farm consist of technical and
allocative component and the combination of these two components provide a measure of
total economic efficiency (overall efficiency). As noted by Yao & Liu (1998) technical
efficiency, which is the main focus of this study, is the ability to produce maximum output
from a given set of inputs, given the available technology, Economics Dictionary, (2012) also
defined Technical efficiency as the effectiveness with which a given set of inputs is used to
produce an output.
The crucial role of efficiency in increasing agricultural output has been widely
recognized by researchers and policy makers alike (Nwaru, 2005; Ike and Inoni, 2006 Ike,
2008; Okoye, 2006). Indeed, considerable efforts have been devoted to the analysis of farm
level efficiency in developing countries. An underlying premise behind most of the work of
efficiency is that if farmers are not making efficient use of existing technologies, then efforts
designed to improve efficiency would be more cost effective than introducing new
technologies as a means of increasing agricultural output (Effiong, 2005; Ike, 2008). The
focus of this study is on the assessment of technical efficiency of small scale broiler farms in
Delta State, Nigeria.
1.2 Statement of problem
Poultry production in Nigeria is largely in the hands of our local producers who
produce mainly for home consumption with little for sale to other consumers. In 2002, the
Federal Government banned the importation of poultry products into the country. This posed
a greater pressure and challenge to our local farmers to produce commercially so as to meet
the ever-increasing demand of poultry products in our diet. Protein obtained from poultry
products (meat and egg) is needed for the growth and development of the entire populace,
thus increases the standard of living and income of the poultry farmer.
Presently, the industry has been adversely affected by major problems associated with
the raising of broilers such as their susceptibility to diseases and sensitivity to feeding and
other environmental factors such as temperature, ventilation, light and sound (Adebayo &
Adeola 2005). Study by Ojo (2003) revealed that, the industry falls short of its aim of self-
sufficiency in animal protein production in the country. Annual protein consumption is put
at 5gm/capita per day which is a far cry from FAO recommended level of 35gm/capita per
day. Also, in the past years, many small-scale operators in the poultry industry have been
forced out of business due to problems ranging from shortage and high cost of feed, high
cost and inadequate veterinary services and drugs, poor quality of equipment and other
inputs. Lack of proper management in terms of feeding, housing, health care and traditional
methods used by poultry farmers among other factors are responsible for the low
productivity. Ajibefun, (2006) says that inefficiency is a problem in raising production and
productivity in Nigerian Agriculture. The issue of efficient choice and use of
resources/technology has received less attention in developing country like Nigeria. Feder,
Just., & Zilberman (1985) says that unless an existing technology is fully utilized, benefits
from new technology may not be realized thus it is possible to raise output of broiler farmers
if new technologies are the targets of farmers.
Other problems include rising cost of the major inputs such as feeds, drugs, and
equipment (Sekoni, 2002) which is a constant set back in poultry industry. Also, the storage
of poultry products is another problem, which is largely due to epileptic power supply and as
such farmers incur extra cost of hiring generators in order to avoid the spoilage of these
products.
Although available literature shows that many studies have been done on poultry
production, but the attention was more on the economic analysis of poultry broiler farming
(e.g Ugbome, 2006; Amos, 2006, Bamiro, 2008; Adebiyi, 2000; Ojo, 2003; Adebayo and
Adeola, 2005). Some others looked at the Profit Efficiency in Broiler Production (Effiong
and Onyenweaku. 2006; Oladeebo and Ambe-Lamidi 2007; Okafor, Odii, Emeyonu & Obih
2006). Little or nothing has been done to look at the technical efficiency and factors
influencing technical efficiency among small scale broiler farmers especially in Delta State.
Therefore, this study seeks to estimate the technical efficiency of small scale broiler farmers
and identify those factors that determine their level of technical efficiency in Delta State,
Nigeria.
1.3 Objectives of the study
The broad objective of the study is to assess the technical efficiency of small scale
poultry (broiler) farmers in Delta State.
The specific objectives are to:-
i. describe the socio-economic characteristics of small-scale broiler farmers;
ii. describe the management practices and identify method of acquisition of
production resources of small scale broiler farmers;
iii. estimate the technical efficiency of small scale broiler farmers;
iv. identify factors that influence technical efficiency among broiler farmers;
v. identify the constraints associated with broiler production;
1.4 Research Hypotheses
The following null hypotheses was tested:
i. Socio-economic characteristics of small scale broiler farmers do not significantly
affect their technical efficiency in the study area.
ii. Institutional variables of small-scale broiler farmers have no significant influence
on the farmer’s technical efficiency.
1.5 Justification of the study
Small-scale broiler farmers with low literacy level, lack of credit, capital and
infrastructure and poor extension services have difficulties in understanding and adopting
new technologies which often require good and relevant education and good extension
services Kebede, (2001). To turn the situation around, there is need for small-scale broiler
farmers to understand and adopt new technologies so as to enhance farm output and income.
Output growths are however not only determined by technological innovations but also by
the efficiency with which available technologies are used (Bravo-Ureta & Evenson, 1994).
Therefore the study of the present level of efficiency and the identification of factors
influencing technical efficiency among small-scale broiler farmers is necessary.
This research is directed, at providing information that would assist small scale broiler
farmers on how best to attain efficiency in broiler production and furnish them with
information on the factors that influence technical efficiency in production, this will enable
them to be well armed before going into the business. The issue that white meat provides a
more healthful alternative to red meat because of the higher proportion of unsaturated fatty
acids than saturated fatty acids contained in white meat and the fact that white meat does not
contain the Trans fats that contribute to coronary heart disease, which can be found in high
quantity in red meat also justifies the need for this study.
In addition, this study will enable Government bodies to identify problems faced by
small scale broiler farmers on the efficiency of input use and to be able to come up with
interventions on how the situation can be turned around. Also, measurement of the extent and
determinants of technical efficiency differentials indicate which aspect of farm and farmers
characteristics can be addressed by public and private investor to improve efficiency and
hence will improve their standard of living.
In order to design appropriate policies that will bring about an efficient broiler
production, there is need to carry out a study on efficiency in broiler production in Nigeria.
This will greatly enable policy makers to identify constrains and potential areas for its
improvement considering the need to enhance protein intake.
Furthermore, this study differs from the previous ones conducted in the country as it
will assess the technical efficiency and the determinants of technical efficiency of small
scale broiler farmers in Delta State. Hence, the findings of the study will be a reliable
quantitative result and source of reference to policy makers to adequately make relevant
policies that would promote broiler production in Delta State. It will equally contribute to
the general body of knowledge in the study area.
CHAPTER TWO
REVIEW OF RELATED LITERATURE
The related literature of this study will be reviewed under the following subheadings:
2.1 Poultry development in Nigeria
2.2 Poultry sub-sectors in Nigeria.
2.3 Definition of poultry
2.4 Small scale poultry production
2.5 Management of poultry enterprise
2.6 Feeds/Nutrition
2.7 Disease and health control of poultry production
2.8 productive efficiencies and their determinants
2.9 Theoretical framework
2.10 Analytical framework
2.1 Poultry development in Nigeria
In Nigeria, poultry industry is fast growing as the demand for chicken products is
increasing. A report of UNISPAR/UNESCO-sponsored projects carried out at the National
Centre for Energy Research and Development, Nsukka, Nigeria on raising healthier poultry
(NCERD, 2000) stated that about 10% of Nigerian population is engaged in poultry
production of varying sizes and it is one of the avenues that can be explored for poverty
alleviation and eradication. In recent decades, there is significant progress in genetic selection
of fast-growing meat-type chickens (Abioja, 2010) which has led to the production of broiler
chickens that will weigh over 2kg at six weeks of age with 3.5kg of a balanced diet compared
with 2kg in fourteen weeks with 10kg feed in the 1930s (Smith, 2001). Most of the present
day improved strains of chickens were introduced from the temperate regions to the tropics.
2.2 Poultry sub-sectors in Nigeria.
There are two distinct poultry production systems in Nigeria, as in most developing
countries of Africa and Asia. Each of these two systems is associated with features of scale,
stock, husbandry and productivity that therefore define the two distinct production systems.
The two systems are conventionally referred to as the commercial poultry and the rural
poultry, respectively (Adene and Oguntade, 2006). The Commercial Production System as
the name implies is industrial in its prototype and therefore based on large, dense and uniform
stocks of modern poultry hybrids. It is capital and labour intensive; as well as inputs and
technology demanding. On the other hand, the Rural Poultry is by convention a subsistence
system which comprises stocks of non-standard breeds or mixed strain, types and ages. It is
generally of small scale, associated with household and little or no veterinary inputs. The
rural poultry sector is therefore in its original sense, a village-based, household or individual
holding and occupation which has however been extended to non-village settings in peri-
urban localities, mainly by the middle class dwellers.
Apart from a classification based on the housing scale, biosecurity level has become
the key criterion in recent literature probably due to increasing emergence and spread of
Trans‐boundary Animal Diseases (TADs) across continents. FAO (2006) defined four poultry
production sectors based on experiences in Asia as follows:
Sector 1: Industrial Integrated System with high bio-security systems.
Sector 2: Commercial Poultry Production System with moderate to high biosecurity systems
Sector 3: Commercial Poultry Production System with low to minimal bio-security systems
Sector 4: Village or backyard Production with minimal bio-security.
The main focus of this study is on the forth sector above.
2.3 Definition of Poultry
Poultry are chickens, ducks, geese, guinea fowls, turkeys and other related birds kept
for meat and egg. In Nigeria, the poultry population is estimated to be 140 million (Ocholi et
al; 2006). They are the most commonly kept livestock and over 70% of those keeping
livestock are reported to keep chickens (Amar-Klemesu and Maxwell, 2000). Chickens have
its scientific name to be Gallus domestics and it is one type of poultry. It belongs to the
family phasiendae and it is estimated to be about 69% of the total number of birds kept in
Nigeria (Sonaiya, 1990). Broilers are a type of chicken (apart from cockerels and layers) kept
for meat production and by implication a source of protein (FOA 2006). They are young
chickens suitable for boiling or roosting, at about 10 weeks old.
2.4 Small Scale poultry production
For industrial poultry production to express their full genetic potential, certain basic
requirements must be provided. These include environment, good management, balanced
rations and adequate housing (Akinwumi and Ikpi, 1979). These facilities can be provided
through adequate capital base, which is lacking in Nigeria. High cost of feeds, poor quality of
day old chick (DOC), inadequate extension and training agents has been the bane to industrial
poultry production making family poultry production in Nigeria popular.
Family poultry at 104million out-number all other livestock in Nigeria. Commercial
chicken holdings account for only 10million chickens or 11percent of the total chicken
population of 82.4million (Sonaiya, 2000). Families maintain the bulk of poultry in Nigeria
under low input, extensive system (Sonaiya, 1995). Family poultry are important as provider
of meat and egg. It is generally assumed that family poultry production systems are
economically efficient because, although the output from the individual bird is low, the inputs
are usually lower (Sonaiya, 2001). This assumption has not been properly investigated using
econometric model. The econometric investigation is very important in transforming family
poultry production system. According to (Kitalyi,1998), the transformation of family poultry
into economically viable enterprise would require better understanding of the socio-economic
aspects of the production system. This is consistent with the view of Sonaiya, 2000 who said
that as the socio-economic importance of family poultry is being recognized, economic
analysis is required to identify and evaluate problems and plan appropriate intervention
2.5 Management of poultry enterprise
Management may be regarded as the art of utilizing all the available resources at the
disposal of the entrepreneur for effective production. The most pressing goal of any
enterprise is profit maximization in which poultry production is not an exception. This cannot
be possible without effective decision making, supervision and coordinating ability of the
entrepreneur. Ngoka et al (1983) noted that the amount of profit made in poultry production
depends primarily on good management. They further observed that people were increasingly
becoming aware of the need to have skilled manpower to run poultry production operations.
Would-be poultry farmers now accord high priority to training their own poultry operators
before actually beginning production.
On the issue of management system, Kekeocha (1984) observed that the type, the area
and the location of the farm, the economic status and understanding of the farmer help to
determine which system is used. According to him, extensive system is suitable where large
area of land is available and requires minimum capital investment but are usually associated
with high mortality rate due to prey by wild animals. Intensive system involves high capital
investment and labour but have lower mortality rates if diseases are controlled and makes for
easy record keeping. The semi-intensive system according to him combines both the
advantages and disadvantages of extensive and intensive systems.
On account of cleanliness, Ikeme, (1990) noted that good sanitary measures prevent
infection and spread of diseases. Where portable water does not come in automatic water, or
when there is non-continuous flow of water, water containers should be cleaned at least twice
a day. The feed troughs should be cleaned at least twice a week. The birds are allocated to eat
up the feed in the trough to avoid wastage. Clean surroundings, disinfections and
disinfestations of the whole poultry house are necessary periodically.
Another important aspect of management is record keeping which many poultry
farmers in Nigeria handle with levity. Most of them may keep records but such records may
be inappropriate and inaccurate. In line with this, Dovel (1996) concluded that,
inappropriateness is a major factor in explaining the poor adaption or level of record keeping
and certain success parameters but especially in perceived appropriateness and aspiration of
poultry farmers who in general regard the recommended level of record keeping to be
unnecessarily sophisticated and detailed.
Omotosho and Ladele (1986) observed that government and individuals pump money
into poultry but receive poor returns. They further ascertained that many producers lack the
technical know-how of the business and as such perform below expectation.
2.6 Feeds/Nutrition
Obioha (1992) noted that since poultry was kept by man for the purpose of providing
edible animal products which could be exchanged for cash. It was therefore worthwhile to
remember and furnish both the maintenance and production requirements of the animal,
which ensured that the animal stayed alive, grew and reproduced. According to him, balanced
diet or good nutrition was primarily used:
- to maintain and perform such normal physiological functions of life such as mobility,
respiration, metabolism and muscular activities;
- to store up excess materials as meat, egg and energy which may be used for
production work; and
- to increase the resistance of poultry to diseases.
On the account of feed costs, Oluyemi and Roberts (1979) observed that the most
important limiting factor in the expansion of the poultry industry in Nigeria was high cost of
feed ingredients particularly grains. According to them, the importation of feeds into a
country usually increases the cost of feeds. They went further to say that because there were
no feed quality controls in Nigeria, the quality of feeds commercially available could not be
guaranteed. The low quality feeds undoubtedly contributed to the low performance of poultry
in Nigeria, which in turn was a factor in the high cost of poultry products (Kekeocha, 1984).
Abdulrahim and Salem (1996) ranked the cost of feed as the highest in terms of production
cost while chick cost, medicine and vaccine were ranked second and third respectively.
Therefore, the cost of feed accounts for 70-80 percent of the total production cost.
Feed is said to be the most important input for profitable poultry production, however it
has continued to be a problem to most poultry farmers. The main obstacle to livestock
improvement apart from the incidence of ectoparasite and disease in the country is that of
inadequate and unbalanced feeding (oyenuga 1996 and Eruvbetine, Aiyedum, & Kusumo,
1999).
It is found that most poultry farmers in Nigeria compound poultry feed themselves but
according to Saleh (1995), domestic production of feed resources do not still meet
consumption needs. Therefore, the high percentage of feed in the cost of production as earlier
mentioned shows that, the importance of feed in poultry production cannot be over-
emphasized.
2.7 Disease and health control of poultry production
Major disease of poultry in Nigeria that have been predominantly identified in
commercial poultry are Newcastle disease (ND). Infectious bursal disease (IBD) or
Gumboro, Marek disease (MD), fowl typhoid, cholera, mycoplasmosis and coccidiosis
(Adene, 2006). Other health problems in poultry production are external and internal
parasites. A study on ectoparasites of domestic fowls in Nigeria showed that lice,
Menacanthus straminen, was the major problem in rural poultry (Zaria, Sinha, Natiti, &
Nawathe, 1993). In this Nigerian study, the external parasite problem was associated with
season – higher rates of infestation occurred during the rainy season. A study on the
incidence of worms in chicken farms in Nigeria found that the most common species were
Ascardia galli, Prosthgonium spp, Strongyloids avium and Heterakis gallinarum Tano,
(1995).
In view of the above, it is not surprising that Newcastle disease is the most researched
disease in poultry production. There is a literature on the epidemiology and control on ND as
reviewed by Alexander (1991) and Awan (1993). In 1991, FAO sponsored an international
workshop on production and quality control on ND vaccines for rural Africa (Rweyemamu et
al, (1991). Recently, there has been increasing concern on control of ND in poultry
production, stimulated by the introduction of a thermostable orally administered vaccine
(V4) in southeast Asia, mainly supported by ACIAR (Copland, 1987).
Alexander (1991) noted that global regulation and control of ND is influenced by the
growing multinational poultry trading industry involving poultry products and genetic stock.
Furthermore, uncertainties associated with different countries making an open declaration of
ND to international agencies such as the International Office of Epizootics (IOE) has limited
worldwide control of the disease. Major factors associated with the transmission of ND in
poultry production are exposure to the natural environment, including wild fauna; flocks of
various ages and susceptible new hatches (Chabeuf, 1990; Olabode et al., 1992); and contact
through either exchange of live chickens and products or movement between households and
villages. In an experiment to study transmission of ND in poultry production, Huchzermeyer
(1993) ruled out airborne spread of ND in poultry production in the tropics, and asserted that
transmission is mainly through contact. Similarly, Martin and Spradbrow (1992) noted that
transmission by air is unlikely, because a larger number of birds are necessary to generate
sufficiently dense aerosol for such transmission. Therefore, bird-to-bird contact would seem
to be the mode of transmission in tropical and subtropical production system.
The recent development and use of thermostable vaccine (NDV4) has created fresh
interest for the control of ND in poultry production (Copland, 1987; Spradbrow, 1990;
Spradbrow and Samuel, 1991). In Africa, a number of countries have introduced the vaccine
on a trial basis. A major concern has been the identification of appropriate food carriers to
introduce the vaccine. Virucidal activities of some grains that reduce the effectiveness of the
vaccine have been reported by Rehmani, Spradbrow and Wes (1995). The development of
poultry health programmes requires reliable information on the epidemiology of diseases,
which is lacking in poultry production systems (Pandey, 1993). Disease surveillance is
further limited by poor infrastructure and comminucation, as well as inadequate diagnostic
facilities.
2.8 Productive efficiencies and their determinants
Several studies have identified several factors influencing the productive efficiency of
either livestock or food crop farmers. Thus some of these studies are hereby reviewed in this
section.
Ajibefun, (2006) used the translog stochastic frontier production function to analyze
and link the level of technical efficiency of Nigeria small scale farmers to specific farmer’s
socio economic and policy variables. The result showed that while farmers socio-economic
and policy variables significantly influenced the level of technical efficiency, education has
the highest marginal effect. The highest mean technical efficiency of 0.77 occurs among
group of farmers within 7-12 years of schooling (secondary school education group) while
the least mean technical efficiency (0.54) occurs within the category of farmers with years of
schooling within 1-6years. It implies that technical efficiency has a direct relationship with
years of schooling.
Battese and coelli (1995) defined a stochastic frontier production function (SFPF) for
panel data for india farmers and the technical inefficiency were assumed to be a function of
firm- specific variables and time. The hypothesis that inefficiency effects are not a linear
function of age and schooling of farmers as well as years of observation was rejected.
Yusuf and Malomo (2007) applied a two stage estimation approach (Data
Envelopment Analysis and OLS regression) to determine the TE of small, medium and large
scale poultry farmers in Ogun state Nigeria. They reported mean TEs of 0.8877, 0.8687 and
0.8638 for farmers with large, medium and small scale farmers respectively. Years of
experience and educational level have positive effect on technical efficiency at 1 percent.
They concluded that egg production is profitable in the study are with net returns of N589,
N464.46 and N 739.56 per bird for small, medium and large scale farmers respectively.
Amata and Olayemi, (1998) investigated production efficiency in food crop
enterprises in Gombe state, Nigeria. The sample size was 123 food crop farmers and the data
was obtained through the use of multi-stage sampling technique, a stochastic frontier
production function, using the maximum likelihood estimation (MLE) was used as analytical
tool. The MLE result revealed that land, family labour, hired labour and fertilizers are the
major factors that influence the output of food crops. The effect of land area on output is
positive and coefficient found to be statistically significant at 1 percent level. The coefficient
of family labour is found to be negative but significant at 1 percent level, thus suggesting an
excessive use of family labour in food production. Hired labour and fertilizer have positive
effects on output and their coefficients are statistically significant at 5 percent level. Maize-
based enterprises are the most efficient in terms of Technical Efficiency (TE), followed by
cowpea-based enterprise with mean TE indices of 0.73 and 0.72 respectively. In terms of
Economic Efficiency (EE), cowpea-based enterprise is the most efficient with mean EE of
0.59.
Seyoum et al (1998) investigated the technical efficiency of two samples of maize
producers in eastern Ethiopia, one involving farmers within the Sasakawa-Global 2000
project and others involving farmers outside this program. The study uses stochastic
production frontier in which the technical inefficiency effects are assumed to be the farmers
in their agricultural production operation. For the cross-sectional data obtained for the
1995/96 agricultural year, Cobb- Douglas stochastic production frontiers were found to be
adequate representation of the Translog stochastic frontiers for farmers within and outside the
project. The empirical results indicate that farmers within the SG 2000 project are more
technically efficient than farmers outside the project relative to their respective technologies.
Ajibefun, et. al. (1999) investigated a stochastic frontier production in Ondo State and
the technical inefficiency effects are assumed to be a function of some farm – specific and
farmer – specific variables. For the study, data set covers 120 poultry farms and information
was collected on production inputs and outputs, with special interest in egg production. Data
was also collected on other variable which could influence technical efficiency of egg
production. Result of analysis indicates that the level of technical efficiencies varies widely
across farms, ranging between 49 percent and 85 percent, with a mean technical efficiency of
65 percent. The analysis also indicates that variables such as age and years of experience of
the primary decision maker of the poultry egg producer have significant influence on the
level of technical efficiency.
Etim, (2001) analyzed the technical inefficiency of urban farming among households
in Akwa Ibom State. Ramdom sampling technique was used to collect primary data from 70
urban farmers in Uyo and Ikot Ekpene Local Government Areas of Akwa Ibom State through
structured questionnaire. A stochastic production frontier based on Cobb- Douglass
production function was developed to capture inefficiency variables. The maximum
likelihood estimation of the stochastic production frontier revealed the presence of decreasing
returns to scale in all the physical inputs (farmland, fertilizer plantings) except labour. The
analysis also indicates that the mean technical efficiency was 69.47% with the 99.43% for the
most efficient urban farmers (27 percent) and 11.86% for the least efficient urban farmers (7
percent).
A lot more studies have been done on TE and its determinants in the field of crop
production than in livestock and fisheries in Nigeria and other countries of the world. For
instance, studies by Adesina and Djato (1996), Seyoum, Battese and Fleming (1998), Wadud
and White (2000), Weir and Night (2000), Owens, Hoddinott and Kinsey (2001), Sherlund,
Barrett and Adesina (2002), Ogundele and Okoruwa (2004), Chukwuji (2006), Ike and Inoni
(2006), Ike (2008) and many more investigated technical efficiency (TE) and determinants of
TE on various crop farmers using different farm and farmer characteristics. Their general
conclusions are that there exist reasonable degrees of inefficiencies among farmers in
developing countries (Nigeria inclusive). A summary of the factors that gave rise to the
degree of inefficiencies according to the various studies include farm characteristics such as
farm size, labour, capital, soil and weather characteristics and nature of farm tenancy. Farmer
characteristics like education, age, farming experience, extension services, off-farm
employment and household size were also identified. Thus this study will explore the
relevance of some of these factors in the determination of technical efficiency level in poultry
(broiler) production.
2.8 Theoretical framework
The theoretical frame work of this study hinges on the theory of production.
Production economics is concerned with optimization of resources and optimization implies
efficiency (Baumol, 1977). Production is concerned with the relative performance of the
process used in transforming input into output. The analysis of efficiency is generally
associated with the possibility of farms producing a certain optimal level of output from a
given bundle of resources or certain level of output at “least cost”. Farrel (1977) distinguishes
between three types of efficiency: (a) technical efficiency (TE), (b) allocative or price
efficiency (AE), and (c) economic efficiency (EE).
2.9.1 Technical efficiency
Yao and Liu (1998) defined technical efficiency as the ability to produce maximum
output from a given set of inputs, given the available technology. Technical efficiency
according to (Nwaru, 2003) refers to the ability of a given set of entrepreneurs to employ the
best practice in any industry so that not more than the necessary amount of a given set of
resources is used in producing the best level of output.
Technical efficiency (TE) in production is a measure of how close a firm is to the
maximum output level as defined by the frontier given its input level. Technical efficiency
relates actual output to the expected output based on factor endowment. The greater the ratio,
the greater is the technical efficiency of the firm. This definition of technical efficiency
implies that difference in technical efficiency between firms exists. The production function
pre-supposes technical efficiency, whereby maximum output is obtained from a given level
of input combination. Therefore, it is a factor-product relationship. An important assumption
relating to efficiency is that firms operate on the outer bound production function, that is, on
their efficiency frontier. When firms fail to operate on the outer bound production function,
they are said to be technically inefficient. For such firms an improvement in technical
efficiency maybe achieved in three ways (Heady, 1960). Firstly, technical efficiency can be
enhanced through improved production techniques. This may imply a change in factor
proportions through factor substitution under a given technology. Hence, it may represent a
change along the given production function. Secondly, technical efficiency can also be
improved through an improvement in the production technology. This represents a change in
the production itself such that the same amount of resources produce more output, or
alternatively, the same amount of output is derived from smaller quantities of resources than
before. Thirdly, technical efficiency can be improved through an improvement in both
production technique and technology.
2.8.2 Allocative efficiency
While technical efficiency is only concerned with the physical relationship between
input and output, allocative efficiency takes into account price relationship in addition to the
physical relationship. Thus, allocative efficiency is concerned with choosing optimal sets of
inputs. In this regard, a firm is allocatively efficient when production occurs at a point where
the marginal value product is equal to the marginal factor cost. [MVP = MFC]
Allocative efficiency can also be seen as the ability to combine inputs and outputs in
optimal proportions in light of prevailing prices Lovell, (1993). Ferrell (as cited in Olayide
and Heady, 1982) defined allocative efficiency as the measure of a firm’s success in choosing
an optimal set of inputs. This is an indication of the gains that can be obtained by varying the
input ratios on certain assumptions about the future price structure.
2.8.3 Economic efficiency
Economic efficiency is a situation where there are both technical efficiency and
allocative efficiency. Therefore, the achievement of either of technical efficiency or
allocative efficiency is a necessary but not a sufficient condition to ensure economic
efficiency (Ellis, 1988). The simultaneous achievement of both efficiencies however,
provides the sufficient condition for economic efficiency. The sufficient condition according
to (Heady, 1960), occurs when price relationship are employed to denote maximum profits
for the firm or when choice indicators are employed to denote the maximization of other
economic objectives. Thus, economic efficiency refers to the choice of the best combination
for a particular level of output, which is determined by both input and output prices.
2.10 Analytical framework
The level of technical efficiency of a particular firm is characterized by the
relationship between observed production and some ideal or potential production Greene,
(1993). The measurement of firm specific technical efficiency is based upon deviation of
observed output from the best production or efficient production frontier. If a firm’s actual
production point lies on the frontier, then the firm is said to be perfectly efficient in the use of
the production inputs; Sean & Ines (2002).
Farrell’s (1957) definition of technical efficiency led to the development of methods
for estimating the relative technical efficiencies of firms. The common feature of these
estimation techniques is that information is extracted from extreme observations from a body
of data to determine the best practice production frontier (Lewis and Lovell, 1990). From
this, the relative measure of technical efficiency from the individual firm can be derived.
Despite this similarity, the approaches for estimating technical efficiency can be generally
categorized under the distinctly opposing techniques of parametric and non-parametric
methods (Seiford and Thrall, 1990).
Stochastic frontier incorporates a measure of random error. This involves the estimation of a
stochastic production frontier, where the output of a firm is a function of a set of inputs,
inefficiency and error term. An often quoted disadvantage of the technique, however, is that
they impose an explicit functional form and distribution assumption on the data. In contrast,
the linear programming technique of data envelopment analysis (DEA) does not impose any
assumption about functional forms; hence it is less prone to misspecification. Further, DEA is
a non-parametric approach that does not take into account random error. Hence, it is not
subsequently subjected to the problems of assuming an underlying distribution about the error
term. However, since DEA cannot take account of such statistical noise, the efficiency
estimates may be biased if the production process is largely characterized by stochastic
elements Sean & Ines (2002). The most commonly used packages for estimating stochastic
production frontier and inefficiency are FRONTIER 4.1 (Coelli, 1996).
FRONTIER 4.1 is a single purpose package specifically designed for the estimation
of stochastic production frontier (and nothing else), while LIMDEP is a more general
package designed for a range of non-standard (i.e. Non-OLS) econometric estimation. An
advantage of the FRONTIER 4.1 is that estimates of efficiency are produced as a direct
output from the package and also able to accommodate a wider range of assumptions about
the error distribution term than LIMDEP, although it is unable to model exponential
distributions, neither can it include gamma distributions. Only FRONTIER is able to estimate
an inefficiency model as a one-step process. Sean and Ines, (2002).
This study employed the stochastic frontier model which was first proposed
simultaneously by Aigner, Lovell and Schmidt (1977); Battesse and Corra (1977); and
popularized by Forsund, Lovell and Schmidt (1980); Battese (1992), Coelli, Prasada and
Battese (1998); Kumbakar and Lovell (2000). And more recently, empirical applications of
the technique in efficiency analysis have been reported by Ojo and Ajibefun (2000); Iwala
(2006); Chukwuji (2006); Ike and Inoni (2006); Akinleye (2007); Al Hassan (2008).
A stochastic frontier production function is defined by:
Yi = f(Xi; Bi) exp Vi –Ui = 1, 2 - - - - - - - - - - n
OR Yi = o i Xi + (Vi – Ui) - - - - - - (1)
Where:
Yi is output of the ith farm; Xi is the vector of input quantities used by the ith; Bi is the vector
of production function (unknown) parameters to be estimated, f represents an appropriate
function (eg. Cobb–Douglas, Translog etc). The cobb-Douglas frontier production function
can be specified in logarithm form as:
In Yi = 0 +∑iInXij+Vi−Ui
Where: Yi, 0 i and Xi are as defined above. While a typical translog production function
in its logarithm form is denoted as:
In Yi=0 ∑iInXij ½∑∑Bij InXij (Vi –Ui).
Where: Yi, 0 i and Xi are as defined earlier, while the subscripts i and j represent ith
farmer and jth observation of the farmer.
The cobb-Douglas production function was used for this study. This is because it has
been the most commonly used function in the specification and estimation of production
frontier in empirical studies (Seyoum , Battese & Fleming (1998), Ajibefun & Abdulkadri,
(1999). It is attractive due to its simplicity and because of the logarithmic nature of the
production function that makes econometric estimation of the parameters a very simple
matter.
The term Vi is a symmetric error (ie the systemic component), which accounts for
random variation in input due to factors beyond the control of the farmer, eg. weather, disease
outbreaks etc. While the term Ui is a non-negative random variable representing inefficiency
in production relative to the stochastic frontier. The random error, Vi is assumed to be
independently and identically distributed with zero mean and constant variance N (0, δν²) and
independent of the Ui. The Ui assumed to be non-negative terms represents the deviations
from the frontier production function which is attributed to controllable factors (technical
inefficiency). It is half normal, identically and independently distributed with zero mean and
constant variance, N (0 δᴜ ²). It is further assumed that the average level of technical
efficiency measured by the mode of the truncated normal distribution (ie. Ui) is a function of
factors believed to affect technical inefficiency as shown in equation (2)
Ui = δ0 + δi Ζi -------------- (2)
Where, Zi is a column vector of hypothesized efficiency determinants (ie. the socio-economic
factors such as age, farming experience etc) and δ is the intercept and δi are unknown
parameters to be estimated. It is clear that if Ui does not exist in equation (1) or Ui = δs = 0;
the stochastic frontier production reduces to a traditional function. In that case, the observed
units are equally efficient and residual output is solely explained by unsystematic influences.
The distribution parameters, Ui and δᴜ ² are hence the inefficiency indicators of the farmer,
indicating the average level of technical inefficiency level across observational units given
functional and distributional assumptions, the values of unknown coefficients in equation (1)
and (2), ie i, δs, δᴜ ² and δν ² will be jointly estimated by the method of maximum likelihood
estimation (MLE) using the computer programme FRONTIER version 4.1 developed by
Coelli (1996). The technical efficiency is empirically measured by decomposing the deviation
into a random component (Vi) and an inefficiency component (Ui). The technical efficiency
of an individual farm-firm is defined in terms of the observed output (Yi) to the
corresponding frontier output (Y*) given available technology, that is,
TE = Yi/Yi*
= f(Xi; Bi) exp (Vi –Ui)
f(Xi; Bi) exp (Vi)
TE = Exp (-Ui) - - - - - - - - - (3)
So that, 0≤ TE≤ 1. An estimated value of technical efficiency for each observation can
be calculated as in equation (3). If TE = 1, the firm is said to be technically efficient and its
output level is on the frontier. Otherwise, ie, if TE < 1, the firm is technically inefficient
because it could have produced more outputs with the given level of inputs irrespective on
input prices.
CHAPTER THREE
RESEARCH METHODOLOGY
3.1 The Study Area
The study area is Delta State. Delta State lies approximately between longitude 50 00΄
E and 60 45΄E of the Greenwich Meridian, and latitude 50 00΄N and 60 30΄ N of the Equator.
The State has a total land area of 17,440 km2, about one third of this is swampy and water
logged, (Delta State Dairy, 2003).
Delta State is bounded in the north by Edo State, in the East by Anambra and Rivers
State and in the south by Bayelsa State. The Atlantic Ocean forms the western boundary
while the North-West boundary is Ondo State. The State is made up of 25 Local Government
Areas and has a population of 4.1 million (NPC, 2006). Delta State has a tropical climate
with distinct dry and rainy seasons. The rainy season is mainly from April to October while
the dry season is from November to March. The temperature ranges from 29 to 34ºC with an
average of about 30ºC (Delta State Ministry of Agriculture and Natural Resources, 2000).
The state is divided into 3 Agricultural zones with 25 Local Government Areas
(LGAs). The 3 Agricultural Zones include Delta North (9 LGAs), Delta Central (8 LGAs)
and Delta South (8LGAs). Delta North consist of Oshimili South, Oshimili North, Aniocha
North, Aniocha South, Ika South, Ika North-East, Ukwani, Ndokwa East and Ndokwa West.
Delta Cental consist of Warri North, Warri South-West, Warri South, Ughelli North, Ughelli
South, Sapele, Ethiope West and Ethiope East. Delta South consist of Isoko North, Isoko
South, Patani, Bomadi, Okpe, Uvwie, Udu. and Burutu.
3.2 Sampling Procedure
Multistage sampling technique was used to select respondents for study. The
respondents are farmers who rear broiler on small-scale (100 birds and below). Delta State
consists of three (3) agricultural zones. These are the Delta North; Delta Central and Delta
South agricultural zones. The three zones were used for the study.
First from each of the three agricultural zones, four Local Government Areas were
randomly selected to give a total of 12 Local Government Areas. Secondly, two communities
were randomly selected from each of the twelve Local Government Areas to give a total of
24 communities. Finally, in each community, with the assistant of a local extension
personnel, a list of small scale broiler farmers was compiled and then five (5) respondents
(broiler farmers) were randomly selected from each of the 24 communities to make a total of
120 respondents that was the sample size for this study.
3.3 Data Collection
Primary data was used for the study. This data was collected using a set of detailed
and well-structured questionnaire which consist of socio-economic factors, method of land
acquisition, management system adopted, input-output data such as data on output of broiler
production in number, source of labour, as well as production constraint. Two well-trained
and resident enumerators from each of the selected communities assisted in the
administration of the questionnaire. The questionnaires were administered to small scale
broiler farmers in the selected communities.
3.4 Data Analysis
Objectives (i), (ii) and (v) were achieved using descriptive statistics such as mean,
frequency distribution and percentages. Objective (iii) was achieved using stochastic frontier
production model and objective (iv) was achieved using the inefficiency effects model.
3.4.1 Model Specification
3.4.1.1 The Stochastic Frontier Production Function
This model was used to achieve objective iii of the study.
The Cobb-Douglas stochastic frontier production function was used for this study
because it has been widely used in agricultural studies and because of its mathematical
simplicity. It is specified thus:
In Yi = +∑iInXij+Vi−Ui - - - - - - - - (1)
Where:
Yi = Output of broiler (naira)
X1 = herd size (number of birds)
X2 = value of feeds (Naira)
X3= value of drugs and vaccines (Naira)
X4= labour (mandays)
Vi = random or statistical disturbance term which captures the effect of weather and other
factors outside the control of the farmer.
Ui = farmer and farm specific characteristics related to production efficiency (technical
inefficiency effects).
Transportation cost was not included as part of the variable for this study. The reason
is that transportation cost was not a significant factor that affects broiler production in the
study area.
3.4.1.2 Inefficiency Model
This model was used to achieve objective iv of the study.
Uij = δ0 + δ1Z1ij + δ2Z2ij + δ3Z3ij + δ4Z4ij + δ5Z5ij + δ6Z6ij + δ7Z7ij + δ8Z8ij - - - - - -
- (2)
Where:
Uij is the technical inefficiency of the ith farmer and jth observation of the farmer.
δ0 is the constant
δs are the parameters to be estimated
Z1= membership of Cooperatives Societies (number)
Z2= farmers age (years)
Z3= farmers educational level (years)
Z4= family size (number of persons in the family)
Z5= farmers farming experience measured (number of years spent in broiler farming)
Z6= access to credit by farmer (number of credit)
Z7= contact with extension service (Number of visits)
3.4.2 Hypotheses Testing
Hypotheses (i) and (ii) were tested using t-test embedded in the inefficiency model
component of the stochastic frontier production function.
CHAPTER FOUR
4.0 RESULT AND DISCUSSION
4.1 SOCIO-ECONOMIC CHARACTERISTICS OF SMALL SCALE BROILER
FARMERS
Some socio-economic characteristics of the respondents were ascertained. These includes
age, gender, marital status, level of education, household size and farming experience.
4.1.2: Age of the respondents
The frequency distribution of respondents according to socio economic characteristics
is shown in table 4.1 shows that most of the small scale broiler farmers 69% fell within the
productive age range of 21-40years. The average age of the broiler farmers was estimated to
be 36years. Therefore, for small scale broiler farmers, there is a strong tendency that
productivity will continue to rise in the mean time. The average age was about 36years which
means that small scale broiler farmers are in their prime and active age of producton. They
are likely to be productive in the next decade and broiler production in the country will likely
increase. This result disagrees with the findings of Chavanapoonphol et al. (2005) that
Thailand rice farmers were quite old of average of 51years. But this study agrees with the
findings of Otitoju (2008) which found out that small and medium scale soybean farmers in
Benue State, Nigeria have average age of about 33 and 39years respectively.
4.1.2: Sex of the respondents
Table 4.1 showed that both men and women were actively involved in broiler
production but the percentage of men were more. Men accounted for 58% while female were
about 43%. The high number of males might be attributed to hard task (such as, building of
the poultry house, changing of poultry litters, processing of fish meal/blood meal ecetera) out
in broiler production process.
4.1.3: Marital Status of the Respondents
Result from table 4.1 showed that about 68% of the respondents were married. About
29% were single while 1.7% were divorced and 1.7% were widowed. The high number of
married people in the business was to reduce labour cost as most married persons have
children that constitute the labour force in broiler production.
4.1.4: Educational level of Respondents
The result shows that about 97% of small scale broiler farmers had formal education
at primary, secondary and tertiary level at 6%, 35% and 56% respectively. On the other hand,
three percent had no formal education. The average years of schooling of the respondents as
estimated by this study was about 14years. This implies that there were more educated people
in small scale broiler production. However, this does not suggest that in broiler production
education was a barrier but rather an added advantage for efficient management. With this
level of education, there is tendency of the farmers being able to increase the level of
technology adopted and skill acquisition. This study agrees with the findings of (Ologbon,
Olugbenga and Ambali, & Omotuyole 2012) that found out that greater percentage of small
scale poultry farmers in Ogun State had formal Education. The findings disagrees with the
findings of Gbigbi (2012) that found out that greater percentage of Artisanal fishing
households in Niger Delta had no formal education.
4.1.5: Household size of the Respondents
Family size is recognized as a major source of labour supply in small holder
agricultural production in most African country like Nigeria. This comprises the labour of all
males, females and children in a household, who participate agricultural production. Table
4.1 shows the distribution of respondents according to their household size. Majority of the
respondents (58%) fell within the household size of 4-6 persons, (2%) fell within the
household size of 10 and above persons. The average family size of the respondents was
about 6 persons per household. This result agrees with the findings of Ugbome (2006) who
found out that majority of the respondents (small scale broiler farmers in Delta State) had an
average family size of 6 people and also agrees with the finding of Ezeh, Anyiro and
Chukwu, (2012) that Poultry Broiler farmers in Umuahia Capital Territory of Abia State,
Nigeria had the average household size of 6.
4.1.6: Farming Experience of the Respondents
The distribution of respondents by farming experience as shown in table 4.1 indicates
that there was influx of new entrants into broiler production in recent times. This could be
due to the ban on importation of frozen broiler product by the Federal Government. This is
represented by about 86% who had from 1-10years of experience. The result shows that
majority 52% had farming experience of 6-10years, followed by about 34% who had farming
experience of 1-5years, 8% had farming experience of 11-15years and 6% had farming
experience of 15years and above. Table 4.1 showed that the average farming experience of
the respondents was about 8years which means that they were still new in the business and
had no experience in broiler production. However, the more experience the broiler farmers
have, the more technically efficient they will be in production.
Table 4.1: Frequency Distribution of Respondents according to their Socio-Economic
Characteristics
Socio-economic Categories Frequency Percentage
Mean
Characteristics
Age (years) 11-20 9 7.50
21-30 48 40.00
31-40 35 29.16
36years
41-50 13 10.83
51-60 8 6.70
Above 60 7 5.83
120 100
Gender Male 69 57.50
Female 51 42.50
120 100
Marital Status Married 81 67.50
Single 35 29.16
Divorced 2 1.67
Widow(er) 2 1.67
120 100
Educational level No formal (0) 4 3.33
Primary (6) 7 5.83
Secondary (12) 42 35.0
14years
Tertiary (above 12) 67 55.83
120 100
Household size 1-3persons 14 11.70
4-6persons 69 57.50
6persons
7-9persons 35 29.20
Above 10 2 1.70
120 100
Farming Experience 1-5years 41 34.20
6-10years 62 51.70 8years
11.-15years 10 8.33
Above 15 7 5.83
120 100
Source: Field survey data, 2013
4.2 Management Practices and Acquisition of Resources of the Respondents.
The management practices and acquisition of resources by the respondents were
presented in Table 4.2. The table shows that majority (68%) adopted deep litter system, about
23% adopted free range while the least (9%) adopted battery cage system.
Majority (33%) raised 41-60 broilers, followed by about 28% who raised between 21-
40broilers. About 19% raised 1-20 broilers, 12% raised between 61-80 broilers while the
least (8%) raised between 81-100broilers. Majority about (98%) raised broilers for 10 and 12
weeks, two percent raised broilers for more than 12 weeks.
Majority of the farmers 66% acquired land by place of residence; about 26% acquired
land by gift/inheritance. This might limit large scale production that require large area of land
because place of residence and inherited lands might be too small and fragmented into
smaller portions in different areas. About six percent acquired land by purchase/rent while the
least (3%) acquired land by lease. On feed source, table 4.2 shows that majority (85%)
bought feed from the feed miller while the remaining 15% mill feed themselves.
On sources of capital, Table 4.2 shows that most of the respondents (77%) used
personal savings, about 17% obtained Capital as gifts from relatives while 4% and 3%
sourced their capital from Cooperative Societies and Banks respectively. On source of labour,
Table 4.2 shows that majority (78%) used only family labour, 16% used both family and
hired labour while about 6% used hired labour only.
Table 4.2: Management Practices and Acquisition of Resources of the Respondents.
Category Frequency Percentage
Management System Adopted
Deep Litter 82 68.33
Battery cage 11 9.17
Free Range 27 22.50
120 100
Number of birds
1-20 23 19.20
21-40 33 27.50
41-60 40 33.33
61-80 14 11.70
81-100 10 8.33
120 100
Duration
8 weeks 0 0.00
10 weeks 50 41.70
12 weeks 67 55.83
Above 12 weeks 3 2.50
120 100
Land Acquisition
Gift/ Inheritance 31 25.83
Purchased/ Rent 7 5.83
Lease 3 2.50
Place of Residence 79 65.83
120 100
Sources of feed
Milled by self 18 15.00
Bought from feed miller 102 85.00
120 100
Sources of Capital
Personal savings 92 76.70
Cooperative societies 5 4.20
Money lender/bank loan 3 2.50
Gift from relatives 20 16.70
120 100
Sources of Labour
Family 94 78.33
Hired 7 5.83
Both family and hired 19 15.83
120 100
Source: Field survey data, 2013
4.3 Efficiency Results
4.3.1 Estimated Production Function
The maximum likelihood (ML) estimates of the Cobb-Douglas stochastic frontier
production function for small scale broiler farmers are presented in table 4.3. The
coefficients of herd size and value of feed have the a priori expected positive signs and are
significant at 1% showing direct relationship with output. This implies that a 1% increase in
herd size and value of feed will increase the quality of small scale broiler farmers by 0.3152
and 0.1850 respectively.
The estimated variance (δ²s=0.0023) is statistically significant at 1% level of probability. This
value indicates that technical inefficiency is highly significant in the small scale broiler
farmers’ production activities.
The γ parameter shows the relative magnitude of the variance in output associated
with technical efficiency. The coefficients of the variables derived from the Maximum
Likelihood Estimation (MLE) are very important for discussing results of the analysis of the
data. This coefficient represents percentage change in the dependent variables as a result of
percentage change in the independent (or explanatory) variables. Gamma (γ) is estimated at
0.5238 and is statistically significant at 1% indicating that 52% of the total variation in broiler
output is due to technical inefficiency.
Table 4.3: Estimated Dual Purpose Cobb-Douglas Stochastic Frontier Production
Function for Small Scale Broiler Households in Delta State, Nigeria
Variables parameters coefficients standard error t-
value Production factors
Constant term β0 3.6699 0.2618
14.0166***
Stock of birds β1 0.3152 0.0780
4.0406***
Value of feed β2 0.1850 0.0715
2.5888***
Drugs and vaccines β3 0.0295 0.0277 1.0670
Labour β4 0.0015 0.0027 0.5388
Inefficiency factors
Constant term Z0 0.7874 0.0561
14.0338***
Cooperative society Z1 0.0043 0.0096 0.4484
Age Z2 -0.0112 0.0011 -
10.6000***
Educational level Z3 -0.0436 0.0087 -
5.0174***
Family size Z4 -0.0002 0.0021 -0.1072
Farming experience Z5 -0.0006 0.0012 -0.4777
Credit access Z6 0.0301 0.0134
2.2529**
Extension visits Z7 0.0023 0.0033 1.1198
Diagnostic statistics
Total variance (sigma δ²s 0.0023 0.0003
8.0951***
Squared)
Variance ratio (Gamma) γ 0.5238 0.0825
6.3480***
LR. Test 161.4280
Log-likelihood Function 337.0780
***, ** and * are significant levels at 1%, 5% and 10% respectively
Source: Field survey data, 2013
4.4 Determinants of Technical Efficiency in Small Scale Broiler Production
This section presents the results of the analysis of the factors that determine or
influence technical efficiency in small scale broiler production in Delta State. These
explanatory variables (or factors) are of interest in this study because they have important
policy implications.
Table 4.3 Estimated Cobb-Douglas Stochastic Frontier Production Function for Small Scale
Broiler Households in Delta State, Nigeria above presents the results of the inefficiency
model for small scale broiler farmers. Age, Educational level and credit access have
significant effect on the level of technical inefficiency with a coefficient of -0.0112 and -
0.0436 respectively. Credit access has positive relationship with technical inefficiency with a
coefficient of 0.0301. The positive coefficient implies that any increase in the value of the
variable would lead to an increase in the technical inefficiency level of the farmer. Age and
Educational level have negative relationship with technical inefficiency. The negative
coefficients imply that any increase in the value of the variable would lead to a decrease in
the technical inefficiency level of the farmer.
Age
The coefficient of age has a negative sign and is statistically significant at 1% level of
probability as shown in table 4.3. This implies that as the age of broiler farmers increases,
their level of technical inefficiency reduces (or technical efficiency increases). This finding
disagrees with the findings of Mbanasor and Kalu (2008) who reported that the older the
household head becomes, the more he or she is unable to combine the available technology.
However, the findings tends to agree with the findings of Chavanapoonphol et al (2005) and
Ogundari (2006) in which they found out that technical efficiency and profit efficiency,
increase with age respectively.
Educational Level
The coefficient of educational level has a negative sign and is statistically significant
at 1% as shown in table 4.3. This suggests that as the level of education of the farmers
increase, their level of technical inefficiency reduces. This finding agrees with that of Ezeh,
Anyiro and Chukwu (2012) whose result showed the coefficient of educational level to be
negative and statistically significant at 1% level of probability. The findings disagree with
Onyenweaku and Nwaru (2005), Onyenweaku, Igwe and Mbanasor (2004) whose results
showed the coefficient of educational level to be positive, implying that the more educated
the farmers become, their level of technical inefficiency increases.
Credit Access
The estimated coefficient for small scale broiler farmers, credit accessibility was
positive and significant at 5% level of probability. The finding shows that the technical
inefficiencies tend to increase for farmers that had access to credit. This maybe as a result of
the fact that credit received by the farmers were not properly managed or diverted to other
uses. The result disagrees with the findings of Solios, Bravo-Ureta and Quiroga (2006) which
indicated that inefficiency decreases with credit accessibility. However, it agrees with the
findings of Obwona (2000; 2006). It implies that a well structured and supervised credit
programme or facilities have to be put in place with easy access to monitor and ensure that
credit received by the farmers are properly managed and not diverted to other unproductive
ventures.
4.4 TECHNICAL EFFICIENCY ESTIMATES FOR SMALL SCALE BROILER
FARMERS IN DELTA STATE, NIGERIA.
The technical efficiency shows the ability of farmers to derive maximum output from
the inputs used in broiler production. Given the results of the Cobb-Douglas stochastic
frontier model, the technical estimates are presented and discussed in table 4.4 below.
The technical efficiency of the sampled households is less than 1 (or 100%),
indicating that all the households are producing below the maximum efficiency frontier. A
range of technical efficiency is observed across the sampled households where the spread is
large. The best broiler household had a technical efficiency of 99.45%, while the worst
household had a technical efficiency of 56%. The mean technical efficiency was 78%. This
implies that on the average, the respondents were able to obtain just over 78% of optimal
output from a given set of inputs. This shows that small scale broiler farmers households
technical efficiency can be improved by 22% in order to raise the level of broiler output in
the study area. The distribution of technical efficiency of the small scale broiler households
show that none of the household heads had a technical efficiency of less than 50, while 51
household heads representing 42.50% had a technical efficiency of above 80%.
Table 4.4: Frequency Distribution of Technical Efficiency among Small Scale Broiler
Households in Delta State, Nigeria
Technical Efficiency Range % Frequency Relative Frequency
≤50 0 0.00
51-60 4 3.33
61-70 36 30.00
71-80 29 24.17
81-90 27 22.50
91-100 24 20.00
Total 120 100
Mean technical efficiency 78%
Minimum technical efficiency 56%
Maximum technical efficiency 99.45%
Source: Field survey data, 2013
Table 4.5: Constraints on Broiler Production as Perceived by the Farmers.
Some constraints were identified as hindrances to increased broiler production
amongst the farmers. Table 4.5 below presents the identified constraints on small scale
broiler production as perceived by the farmers. The major constraint encountered by small
scale broiler farmers is feed cost (50.00%). This finding agrees with the findings of Ugbome
(2006) whose findings showed that feed cost was the major constraint on poultry production.
Lack of capital was estimated to be 28%. Other problems included pest/disease outbreak
10%, pilfering 4%, high mortality rate7% and shortage of water 1%. One could then say that
the constraints to broiler production were mainly due to input factors than management
factors.
Table 4.5: Frequency Distribution of Constraints associated with Small Scale Broiler
Production in Delta State, Nigeria
Constraints frequency percentage%
Pilfering 5 4.20
Pest/disease outbreak 13 10.80
Feed cost 60 50.00
Lack of capital/fund 33 27.50
High mortality rate 8 6.70
Shortage of water 1 0.83
Total 120 100
Source: Field survey data, 2013
CHAPTER FIVE
5.0 SUMMARY, CONCLUSION AND RECOMMENDATIONS
5.1 Summary of Major Findings
This study was carried out with the view to examine the production efficiency of
small scale broiler farmers in Delta State, Nigeria. A sample size of 120 broiler farming
households were randomly sampled using a set of detailed and well structured questionnaire.
Objectives (i), (ii) and (v) were realized using descriptive statistics such as mean, frequency
distribution and percentages. Objective (iii) was achieved using a stochastic frontier
production model, objective (iv) was achieved using inefficiency effects model
simultaneously to realize with stochastic frontier model through maximum likelihood
estimation (MLE) program available in frontier version 4.1. The study made the following
major findings:
Considering the socioeconomic characteristics of small scale broiler farming households in
the study area, greater percentage of about 40%of them fell between age range of 21-30years
while their computed average age was about 36years in the study area. Male dominated
broiler production in the study area, about 58% were male; Majority of the respondents about
68% were married.
Greater percentage of about 56% of the broiler farmers had tertiary education with
average of about 14years of formal education in the study area. Greater percentage of about
58% of the broiler farmer household fell within the household size of 4-6 with computed
average of about 6 people. Greater percentage of about 52% was found to have farming
experience of 6-10years and the computed average farming experience was about 8years.
Investigation into the management practices showed that the household heads were involved
in different management practices, ranging from the use of deep litter, battery cage and free
range systems. Greater percentage of about 68% of the farm household heads used the deep
litter system; about 33% raised between 41-60 broilers, about 56% raised broilers for 12
weeks. Majority of about 66% of the respondents acquired land by place of residence, 85% of
the respondents bought feed from the feed miller, 77% used personal savings as source of
capital while greater percentage of about 78% used family labour. The maximum likelihood
(ML) estimates of the Cobb-Douglas stochastic frontier production parameters for small scale
broiler production showed that the coefficients of stock of birds and feed have positive signs
and are statistically significant at 1% level of probability. The factors affecting technical
efficiency is small scale broiler production by households showed that age and educational
level decreased the household’s technical inefficiency and invariably increased their technical
efficiencies, while credit access increased their technical inefficiencies at 5% level of
probability. On the other hand, cooperative membership, household size, farm experience and
extension visits showed no significant relationship with output. The study showed that the
major constraints against small scale broiler production in the study area are feed cost, lack of
capital/fund and disease outbreak.
5.2 Conclusion
The study investigated the production efficiency of small scale broiler farmers in
Delta State, Nigeria. The results of this study showed that small scale broiler farmer’s
households in Delta State were technically inefficient presumably as a result high cost and
miss-management of inputs like feed leading to low output and income. The results revealed
that age, level of education and credit accessibility influenced the technical efficiency of
small scale broiler production. Individual levels of technical efficiency ranged between 56%
and 99% with a mean of 78%, suggesting that opportunities still exists for increasing
productivity and income of broiler farmers in the study area. This can be achieved by
increasing the efficiency of resources used at the farm level up to 22%.
5.3 Recommendations
The results of this study have some vital policy implications for enhancing the technical
efficiency of broiler farmers at the present level of technology in the area. The following
policy implications are presented;
i. Government should develop and implement policies aimed at subsidizing cost of
production inputs like feed, drugs/medicine, day old chicks and target such
policies at experienced broiler farmers to help increase production and efficiency.
ii. The State Ministry of Agriculture should implement already existing laws that
will prevent hatchery operators from pushing or selling day-old chicks with
hatcheries related problems to the rearers. Also, more hatcheries should be
provided by the ministry to make day-old chicks readily available and affordable
at reduced price.
iii. Farmers should make effort to keep their surroundings clean and ensure that litter
materials are disposed off as at when due. This will help to reduce the incidence of
disease outbreak and improve conditions for poultry rearing which in turn will
lead to increase in production output.
5.4 Contribution to Knowledge
Despite the proliferation of research in this area, few studies have joint analyzed technical
efficiency and factors that influence technical efficiency on small scale broiler farming.
Efficiency study had been undertaken in the past but this study has integrated the technical
efficiency and the determinants of technical efficiency. It was shown that small scale broiler
farmers are technically inefficient. This shows that they are presently underutilizing their
productive resources. Broiler production efficiency level can be improved as farmers
combined more resources at the present technology level in the study area.
5.5 Areas of further research:
There is need to embark on further research in the following areas;
i. Technical efficiency of each poultry animal (layers, turkey, pigeon, ducks etc) in
the riverine area of Delta State
ii. Production efficiency of large scale poultry broiler farmers in Delta State
iii. Effect of flood on the efficiency level of small scale broiler farmers in the riverine
areas of Delta State.
REFERENCES
Abdulrahim, S.M. and Salem, M.A. (1996). Cost of Production and Inputs of Broiler Chick
for the four Federations in Jordan, Dirasat. Agricultural Science 23(2),
151-158.
Abioja, M.O. (2010). Monthly fertility and hatchability of breeder hens and effects of vitamin
C and chilled water on broiler growth, panting and rectal temperature. Unpublished
Thesis. University of Agriculture, Abeokuta. pp 145.
Adebayo, O. O and Adeola, R. G. (2005). Socioeconomic Factors Affecting Poultry Farmers
in Ejigbo Local government Area of Osun State. Journal of Human Ecology. 18:39-41.
Adebiyi, M. A. (2000). ‘Economic Analysis of Egg Production in Ondo State’. An
Unpublished B.Tech Project. Department of Agricultural Economics and Extension,
Federal University of Technology, Akure. Pp 10-11.
Adene, D. F. (2006). Avian Influenza: The Trans-continental Plague. (WPSAS-Ghana ,
Poultry Seminar) Accra, 11 -14 April, 2006.
Adene, D.F. and Oguntade, A.E. (2006). The structure and importance of the commercial and
rural based poultry industry in Nigeria. Nigerian Poultry Sector Report, FAO (Rome)
study. http://www.fao.org/docs/ eims/upload//214281/Review Nigeria.
Adesina, A.A. and Djato, K.K. (1996). Farm size, Relative Efficiency and Agrarian Policy in
Cote d’Ivore: Profit Function Analysis of Rice Farmers. Agricultural Economics, 14:
93-102.
Aigner, D.J, Lovell, C.A, and Schmidt, P.(1977). Formulation and Estimation of Stochastic
Production Function Model. Journal of Econometrics. 6 (1): 21-37.
Ajibefun, I.A. & Abdulkadri, A.O. (1999). An investigation of Technical Inefficiency of
Production Of farmers Under the National Directorate of Emplyment in Ondo State,
Nigeria”. Applied Economic letters, 6, 111-114.
Ajibefun, I.A. (2006). Linking Socio-economic and Policy Variables to Technical Efficiency
of Traditional Agricultural Production: Empirical Evidence from Nigeria. Poster paper
prepared for presentation at the International Association of Agricultural Economists
Conference, Gold Coast, Australia, August 12-18.
Akinleye, S.O. (2007). Technical Efficiency of Arable Crop Farming in Ijebu Division of
Ogun State. Farm Management Association of Nigeria (FAMAN). Journal of
Agricultural Science, 8(2):27-33.
Akinwumi, J.A, Ikpi A.E and Adeyeye, E.A. (1979). “Economic Analysis of Nigerian poultry
Industry”. Report Submitted to Federal Livestock Department, Lagos.
Al- Hassan, S. (2008). Technical Efficiency of Rice Farmers in Northern Ghana. African
Economic Research Consortium (AERC) Research Paper 178.
Alexander, D.J. (1991). Newcastle disease. In Rweyemamu, M.M., Palya, V., WIN, T. &
Sylla. D., eds. Newcastle disease vaccines for rural Africa. Proceedings of a Workshop
held at the Pan African Veterinary Vaccine Center (PANVAC), Debre Zeit, Addis
Ababa, Ethiopia, 22-26 April 1991, P. 7-45.
Amar-Klemesu, M. and Maxwell, D. (2000). Accra: Urban Agriculture as an asset Strategy.
In: Supplementary Income and Diets, Bakker, N., Dubbeling,M., Gundel, S.,
Koschella, U.S. and dezeeuw, H. (Eds.). Growing Cities, Growing Food, Havana,
Cuba, pp: 234-236.
Amata, P.S. & Olayemi, J.K. (1998). An Investigation of Production Efficiency in Food Crop
Enterprises, Gombe State, Nigeria. Journal of Rural Economics and Development. Vol.
13, 1998/1999 pp. 111-122.
Amos, T.T (2006): Analysis of Backyard Poultry Production in Ondo State, Nigeria.
International Journal of Poultry Science. 5 (3): 247-250.
Awan, M.A. (1993). The Epidemiology of Newcastle Disease in Rural Poultry. Unpublished
M.Sc. Dissertation. University of reading, UK.
Awobajo,O.K, Akinrolabu, .R.T, Mako, A.A,Igbosanu, A.O & Olatokunbo, O.T, (2007). The
Mortality Rate of Two Different Breeds of Broilers after Brooding Stage to Maturity.
Middle-East Journal of Scientific Research 2 (1): 37-42, 2007
Awobajo, O.K, Grace, O, Mako,A.A, Igbosanun A.O, & Ettu, R.O. (2009). Mortality
Rate at the Brooding Stage of Various Broiler Breeds Reared in Sub Sahara Region of
West Africa. International Journal of Poultry Science 8 (8): 792-797, 2009
Bamiro, O.M. (2008). Economic Performance of Commercial Poultry Farms in Oyo State
Nigeria. International Journal of Poultry Science. 7(11); 1117-1121
Battese, G.E. (1992). Frontier Production Functions and Technical Efficiency. A Survey of
Emperical Applications in Agricultural Economics. Agricultural Economics, 20:185-
208.
Battese, G.E. & Corra, G.S. (1977). Estimation of a Production Frontier Model: With
Application to Pastoral Zones of Easthern Australia. Australian Journal of Agricultural
Economics, 21: 169-179.
Battese, G.E. & T.J. Coelli (1995). “ A Model for Technical Inefficiency Effect in Stochastic
Frontier Production Functions for Panel Data”. Empirical Economics, 20:325-332.
Baumol, W.J. (1977). Economic theory and operation analysis, New Jersey, prentice-hall,
Englewood, Cliffs.
Binam, J.N. Tonye, J., Wandji, N. and Akoa, M. (2004). Factors Affecting the Technical
Efficiency Among Smallholder Farmers in the Slash and Burn Agriculture Zone of
Cameroun. Food Policy, 29(5): 531-45.
Bravo –Ureta, B.E. & Evenson, R. (1994). “Efficiency in Agricultural Production: The Case
of Peasant Farmers in Eastern Paraguay”. Agricultural Economics, 10,27-37.
Brian, D.F. (2012). Environmental Factors to Control when Brooding Chicks. Collage of
agricultural and environmental science, University of Georgia.
http://www.caes.uga.edu/publications
Chabeuf, N. (1990). Disease Prevention in Small Holder Village Poultry Production in
Africa. In Proceedings, CTA Seminar on Small Holder Rural Poultry Production.
Thessaloniki, Greece, 9-13 October 1990, Vol. 1, P. 129-137.
Chavanapoonphol, Y., Battese, G.E. & Chang, H.C. (2005, February 9-11). The impact of
rural financial services on the technical efficiency of rice farmers in the upper North of
Thailand. Paper presented at the Annual Conference of the Australian Agricultural and
Resource Economics Society at Coffs Harbour.
Chukwuji, C.O. (2006). Resource use Efficiency in Cassava-Based Food Crop Production
Systems in Delta State, Nigeria. Ph.D. Dissertation. Delta State University, Asaba.
Coelli, T. & Battese, G. (1996). Identification of Factors which Influence Technical
Inefficiency of Indian Farmers. Australian Journal of Agricultural Economics, 40 (2),
103-128.
Coelli, T.J. (1996). A Guide to FRONTIER 4.1: “A Computer Programme for Stochastic
Frontier Production and Cost Function Estimation”. Working Paper in Center for
Efficiency and Productivity Analysis. Department of Econometrics, University of New
England, Armidale. 32-36.
Coelli, T.J. Prasada, R. & Battese, G. (1998). An Introduction to Efficiency and Productivity
Analysis. Boston: Kluwer Academic Press.
Copland, J.W. (1987). Newcastle disease in Poultry. A new food pellet Vaccine. ACIAR
Monograph No.5. Canberra, Australia, ACIAR.
Delta State Dairy, (2003). History of Delta State. Delta State Printing Press, pp: 1.
Delta State Ministry of Agriculture and Natural Resources [DSMANR] (2010). Poultry
Production in Delta State. Delta State Printing Press, pp: 20-21.
Delta State Ministry of Agriculture and Natural Resources [DSMANR] (2000). Statistical
Information. Delta State Printing Press, pp: 1-3.
Dovel, G.H., (1996). Time- lag Phenomenon in Record keeping: Resistance or
Inappropriateness. Journal of Extension Systems. 12(2) 16-32.
Economics Dictionary (2012). http://www.economicshelp.org/blog/glossary/technical-
efficiency/
Effiong, E.O. (2005). Efficiency of Production in Selected Livestock Enterprises in Akwa
Ibom State, Nigeria. A PhD Dissertation Submitted to the Department of Agricultural
Economics, Michael Okpara University of Agriculture, Umudike.
Effiong, E.O. and Onyenweaku. C.E. (2006). Profit Efficiency in Broiler Production in
Akwa Ibom State. International Journal of Agriculture and Development 7(1)
Eruvbetine, D. Aiyedum, M.D. & Kusumo, B.C., (1999). The Effect of Cassava-Soya
Inclusion in Diet for Broiler Chicken. Book of proceeding NSAP 26th Annual
Conference. 105-109.
Ellis, F. (1988). ‘Survey Article: Household Strategies and Rural Livelihood Diversification’,
the Journal of Development Studies 35 (1): 1-38.
Etim, N.A. (2001). Analysis of urban Farming Among Households in Akwa-Ibom State.
Unpublished M.Sc Project, Dept. of Agricultural Economics, University of Ibadan,
Nigeria.
Etim, N.A & Udoh, E.J. (2007). Measuring Technical Efficiency of Broiler Production
among Rural Farmers in Akwa-Ibom State, Proceeding of the 32nd annual Conference
of the Nigeria Society for Animal Prodection, held at the University of Calaber,
Calabar, Cross River State, March 18-21, 2007: 412-414
Ezeh, C.O. Anyiro & Chukwu, J.A. (2012). Technical Efficiency in Poultry Broiler
Production in
Umuahia Capital Territory of Abia State, Nigeria. Greener Journal of Agricultural
Sciences 2 (1), pp. 001-007, January 2012.
Feder, G.S., Just., R.E. & Zilberman. D. (1985). “Adoption of Agricultural Innovation in
Developing Countries: A Survey”. Economic Development and Cultural Changes, 33,
355-398.
Federal Office of Statistics, [FOS] (2006). Poverty alleviation in Nigeria Federal Office of
Statistics, Lagos, Nigeria.
Ferrel, M.J. (1977). The measurement of Efficiency. Journal of the Royal Statistical Society
Series A 20, pp 253-281.
Ferrel, M.J. (1957). The Measurement of Productive Efficiency. Journal of the Royal
Statistical Society Series A General, pp 1948-1987.
Fisseha, M., Abera, M. & Tadelle, D. (2010): Assessment of village chicken production
system and evaluation of the productive and reproductive performance of local chicken
ecotype in Bure district, North West Ethiopia, African. Journal of Agriculture Research,
5: 1739-1748.
Food and Agricultural Organisation of the United Nations (FAO), (2010). Poultry Meat and
Eggs: Agribusiness Handbook. Director of Investment Centre Division, FAO., Rome,
Italy, pp: 77.
Food and Agricultural Organisation of the United Nations [FAO], (2006). Poultry review –
Ghana: The structure and importance of the commercial and village based poultry in
Ghana, by K.G. Aning. Final review report. Rome.
Forsund, F.R. C.A.K. Lovell and Schmidt.P. (1980). A Survey of Frontier Production
Function and their Relationship to Efficiency Measurement. Journal of Econometrics,
13,5-25.
Gbigbi, M.T. (2012). Effects of Oil Exploitation on the Efficiency of Artisanal Fishing
Households in the Niger Delta. Unpublished Ph.D Thesis submitted to the Dept. of
Agricultural Economics, University of Nigeria Nsukka.
Greene, W.H (1993). The Econometrics Approach to Efficiency Analysis” in Fried; Haddad,
Lawrence, John Hoddinott, and Harold Alderman, eds. 1997.“Intrahousehold Resource
Allocation in Developing Countries: Methods, Models and Policy. Baltimore: Johns
Hopkins University Press for the International Food Policy Research Institute.
Heady, E.O. (1960). Economic of Agricultural Production and Resource-use, Prentice-hall
New Jersey.
Huchzermeyer, F.W. (1993). Why is Velogenic Newcastle Disease Endemic in some
Countries and not in others? Zimbabwe Veterinary Journal, 24(3), 111-113.ssss
Ike, P.C. (2008). Analysis of Rural Women Efficiency in Irvingia Seed (Ogbono) Species
Processing in Udenu Local Government Area of Enugu State, Nigeria, In Ume, Obinne
and Lawal (eds), Prospects and Challenges of Adding Value to Agricultural Products.
Proceedings of FAMAN 22nd Annual Conference, Makurdi. Pp 124-132.
Ike, P.C. & O.E. Inoni (2006). Determinants of Yam Production and Economic Efficiency
Among Smallholder Farmers in South Eastern Nigeria. Journal of Central European
Agriculture, 7(2): 337-342.
Ike, P.C. & Inoni, O.E. (2006). Determinants of yam production and economic efficiency
among small holder farmers in Southeastern Nigeria. Journal of Central European
Agriculture, 7: 337-342.
Ike, P.C., (2008). Estimating production technical efficiency of Irvingia seed (ogbono)
species farmers in Nsukka Agricultural zone of Enugu State, Nigeria. Journal of
Sustainable Agricultural. Resources., 28: 1-7.
Ike, P.C & Udeh, I (2011). Comparative Analysis of Allocative Efficiency in Input use by
Credit and Non Credit User Small Scale Poulty Farmers in Delta State, Nigeria. Asian
Journal of Agricultural Sciences 3(6): 481-486.
Ikeme, A.I, (1990). The Challenges of Agriculture in National Development. Enugu Optimal
Publisher. Pp. 296-303.
Iwala, S.O.; Okunlola., J.O. and Imoudu, P.B. (2006). Productivity and Technical Efficiency
of Oil Palm Production in Nigeria. Journal of Food, Agriculture and Environment, 4
(3&4): 181-185.
Kebede. T.A. (2001). Farm Household Technical Efficiency: A Stochastic Frontier Analysis:
A case of Rice Producers in Mardi Watershed in the Western Development Region of
Nepal)”. Unpublished Masters Thesis, Dept. of Economics and Social Sciences,
Agricultural University of Norway.
Kekeocha, C.C, (1984). Poultry Production Handbook. Nairobi, Pfizer Corporation. Pp. 92-
110.
Kitalyi, A.J. (1998). Village Chicken production System in rural Africa household food
security and gender issues. FAO Animal production and Health Paper 142. FAO of
United Nations, Rome. Pp23-34.
Kumbhaker, S.C. & Lovell., C.A.K. (2000). Stochastic Frontier Analysis. Cambridge:
Cambridge University Press.
Lewis, G.D and Lovell, C.A.K. (1990). Techical Efficiency Estimates. Journal of
Agricultural Economics, 20 (2), 60-67.
Lovell, C.A.K. (1993). “Production Frontiers and Productive Efficiency”. In: Fried, C.A.K.
Lovell and S.S. Schmidt (Eds) The Measurement of Productive Efficiency. New York.
Oxford University Press.
Matin, J.C and Spradbrow P.B. (1992). Newcastle Disease in Village Chickens. Poultry
Science Review 5: 57-59.
Mbanasor, J.A. (2002): “Resource use pattern among poultry enterprises in Abia State
Nigeria”. Nigeria Journal of Animal Production. 29: 64- 70.
Mbanasor, J.A and Kalu, K.C. (2008). “Economic Efficiency of Commercial Vegetable
Production System in Akwa Ibom State, Nigeria: A Translog Stochastic Cost Function
Approach”. Tropical and Subtropical Agrosystems 8(3): 313-318.
Mohammed, J. & Erandi, P. (2003). Sensitivity of Technical Efficiency Estimates to
Estimation Approaches: An Investigation using New Zealand dairy Industry data.
Economic Discussion Papers. No. 0306, University of Otago.
National Bureau of Statistics (NBS) official website http://www.nigerianstat.gov.ng
National Population Commission (2006): Provisional Census Figures, 2006 National
Population Census. NPC, Abuja, Nigeria.
National Centre for Energy Research and Developmen [NCERD]. (2000). Raising healthier:
Nigeria. National Centre for Energy Research and Development, University of Nigeria,
Nsukka, Nigeria. Reports of UNISPAR/UNESCO sponsored projects 9: 70-76.
Nchare, A. (2007). Analysis of factors affecting technical efficiency of Arabica coffee
producers in Cameroun. (AERC Research paper 163). Nairobi, Kenya: African
Economic Research Consortium.
Ngoka, D.A, Froning G.W and Babji A.S. (1983). Effect of temperature on egg yolk
characteristics of egg from young and old laying hens. Poult Sci. 62(4): 718-720.
Nwajiuba, C, and Nwoke E. (2000). Inflation and the poultry industry in Nigeria:
implications for profitability. In: Oji U.I, Mgbere O.O. (eds) Proceedings of the 5th
Annual Conference of the Animal Science Society of Nigeria. Port Harcourt, Nigeria,
pp 206-208.
Nwaru, J.C., (2005). Application of a stochastic frontier production function to the
measurement of technical efficiency in food crop production in Imo State Nigeria. The
Nigeria Agricultural Journal., 3: 1-12.
Nwaru, J.C. (2003). “Gender and Relative Production Efficiency in Food Crop Farming in
Abia State of Nigeria”. Nigerian Agricultural Journal 34:1-10.
Obioha, F.C. (1992). A Guide to Commercial Poultry Production in the Tropics. Enugu,
Acena Publishers. Pp. 26-30.
Obwona, M. (2006). Determinants of technical efficiency differentials amongst small and
medium scale farmers in Uganda: A case of Tobacco Grower. AERC Research Paper
152, Nairobi, Kenya. Retrieved October 10, 2006, from
http://www.aercafrica.org/documents/rp152.
Obwona, M. (2000). Determinants of technical efficiency differentials amongst small and
medium scale farmers in Uganda: A case of Tobacco Grower.A final report presented
at AERCbi-annual workshop at Grand Requency Hotel, Nairobi, Kenya.
Ocholi, R.A., Oyetunde, I.L., Kumblish P., Odugbo M.O. & Taama et al., (2006).
Epidemiology of an outbreak of highly pathogenic avian inflernza caused by the virus
subtype H5N1 in Nigeria in 2006. VOM J. Vet. Sci., Nat. Vet. Res. Inst. Jos, pp:1-8.
Ogundarin, K. (2006). “Determiants of Profit Efficiency Among Small Scale Rice Farmers in
Nigeria: A Profit Function Approach”, 2006 Annual meeting, August 12-18, 2006.
Ogundele, O.O. & V.O. Okoruwa (2006). Technical efficiency differentials in rice production
technologies in Nigeria. AERC Research Paper 154, Nairobi, Kenya. April 2006.
Retrieved October 10, 2006, from http://www.aercafrica.org/documents/rp154.pdfs.
Ogundele, F.O. & V.O. Okoruwa (2004). A Comparative Analysis of Technical Efficiency
between traditional and improved rice variety farmers in Nigeria. African Journal of
Economic Policy. University of Ibadan. 11(1): 91-108.
Ojo, S.O. (2003). Productivity and Technical Efficiency of Poultry Egg production in
Nigeria. International Journal of Poultry Science, 2(6): 459-464.
Okafor, R.M.,.Odii, M.A.C.A., Emeyonu., C.A. & Obih,U. (2006). Profitability Analysis of
Poultry Production in Imo State, Nigeria. In S.O. Adepoju and P. B. Okuneye (eds).
2006. Proceedings of the Farm Management Association of Nigeria
Okoye, B.C. (2006). Efficiency of Small holder Cocoyam Production in Anambra State. An
M.Sc Thesis Submitted to the Department of Agricultural Economics, Michael Okpara
University of Agriculture, Umudike.
Olabode, A.O., Lamorde, A.G., Shiidali, N.N., Chukweudo, A.A. and Spradbrow, P.B.
(1992). Village chickens and Newcastle disease in Nigeria. In Spradbrow, P.D., ed.
Newcastle disease in village chichens: Control with Thermostable Oral Vaccines.
ACIAR Proceedings, No. 39. Canberra, Australia, ACIAR. P. 159-160.
Oladeebo J.O., & Ambe-Lamidi A.I. (2007). Profitability, Input Elasticities and Economic
Efficiency of Poultry Production among Youth Farmers in Osun State, Nigeria.
International Journal of Poultry Science 6 (12): 994-998, 2007.
Olayide, S.O. & E. Heady (1982). Introduction to Agricultural Production Economics.
Ibadan: Ibadan University Press.
Ologbon, A. C & Ambali, I. (2012). Poultry Enterprise Combination among Sall-Scale
Farmers in Ogun State, Nigeria: A Technical Efficiency Approach. Journal of
Agriculture and Veterinary Sciences. Pp 8.
Oluyemi, J.A. & Roberts F.A. (1979). Agricultural Policy of the Military Era, 1966-1979.
Annual Conference of the Nigeria Economic Society, 1980. Pp. 96-100.
Oluyemi, J.A. & Roberts F.A., 1979. Poultry Production in the Tropics. Enugu , Acena
Publishers. 47-51.
Omotosho, O.A. & Ladele. A.A., (1986)., Management Problems in Large Scale Poultry
Business in Nigeria. A paper presented at the 3rd National Conference of Farm
Management Association of Nigeria. (FAMAN), P. 221.
Onyeagocha, S.U.O., Ehirim, N.C., Emenyonu, C.A, Onyemauwa, C.S & Nwaosu, F.O
(2010). Post-Avian Flu Profitability and Resource Use Efficiency of Broiler Farmers in
Akwa Ibom State, South-South, Nigeria. Agricultural Journal 5(2): 37-44, 2010.
Onyenweaku, C. E. & J. C. Nwaru (2005). Application of the Stochastic Frontier Production
Function to the Measurement of Technical Efficiency in food production in Imo State,
Nigeria. Nigeria Agricultural Journal.
Onyenweaku, C. E., K. C. Igwe and J. A. Mbanasor (2004). “Application of the Stochastic
Frontier Production Function to the Measurement of Technical Efficiency in Yam
Production in Nasarawa State, Nigeria”. Journal of Sustainable Tropical agricultural
Research, Nigeria 13: 20-25.
Otutoju, M.A. (2008). Determinants of technical efficiency in small and medium-scale
soybean production in Benue State, Nigeria. Unpublished M.SC dissertation submitted
to the Dept. of Agricultural Economics, University of Nigeria, Nsukka.
Owens, T., Hoddinott., T, and Kinsey., B. (2001). The impact of Agricultural Extension on
Farm Production in Resettlement Areas of Zimbabwe: Working Paper CSAE. WPS/
2001-6. Center for the Study of African Economics, University of Oxford, UK.
Oyenuga, V.A., (1996). The Level of the Nigeria Livestock Industry. World review of Animal
Production. (1):57.
Pandey, V.S. (1993), Epidermiology and Economics of Village Poultry Production in Africa:
Overview. In Pandey, V.S. &Demey. F., eds. Village Poultry Production in Africa,
Proceedings of an International Workshop. 124-128.
Rweyemamu, M.M., Palya, V., Win, T. & Sylla, D. (1991). Newcastle Disease Vaccines for
Rural Africa. Proceedings of a Workshop held at the Pan African Veterinary Vaccine
Center (PANVVC), Debre Zeit, Addis Ababa, Ethiopia, 22-26 April 1991.
Saleh, S.A. (1995). Economic Analysis of Stock and Poultry Feed Resource in Egypt.
Journal of Agricultural Science. 26 (1): 225-273.
Sean, P. & Ines, H. (2002). Estimation of Technical Efficiency: A Review of some of
Stochastic Frontier and DEA software. Virtual Edition Volb, Issue1.
Seiford, L.M. & Thrall, R.M. (1990). Recent Development in DEA: The Mathematical
Programming Approach to Frontier Analysis, Journal of Econometics, 46, 7-38.
Sekoni, A. A. (2002). Management of layers and breeders. In: Gefu, J. O., Adeyinka, I. A.
and Sekoni, A. A (eds.) Poultry production in Nigeria: a training manual on national
training workshop. NAPRI, ABU, Nigeria. 88-94.
Seyoum, E.T., Battese, G.E. & Fleming E.M. (1998). Technical Efficiency and Productivity
of Maize Production in Eastern Ethiopia. A Study of Farmers within and outside the
Sasakawa- Global 2000 project, Agricultural Economics, 19, 341-348.
Sherlund, S.M.,. Barrett., C.B & Adesina., A.A.(2002). Small holder Technical Efficiency
Controlling for Environmental Production Conditions. Journal of Development
Economics, 68 (10): 85-101.
Smith, A. J. (2001). Poultry in context. The Tropical Agriculturist CTA. 1-63.
Solis, D, Bravo-Ureta, R.E. and Quiroga (2006). Technical efficiency and adaptation of soil
conservation in Elsalvador and Honduras. A combined paper prepared for presentation
at the International Association of Agricultural Economists Conference, Gold Coast,
Australia, August 12-18, 2006.
Sonaiya, E.B. (1995). Feed resources for smallholder poultry production in Nigeria. World
Animal Review 82 (1): 25-33.
Sonaiya, E.B. (2000). Backyard poultry production for socio-economic Advancement of the
Nigeria Family: Requirement for Research and Development. Nigerian Poultry Science
Journal 1: 88-107.
Sonaiya, E.B. (2001). Small poultry holdings, the family and community development-
ethology, ethics and self-interest, Livestock Community and Environment. Proceedings
of the 10th Conference of the Association of Institutions for Tropical Vererinary
Medicine, Copenhagen, Denmark, 2001.
Sonaiya, E.B. (1990). The context and prospects for development of smallholder rural poultry
production in Africa. Proceedings of the CTA International Seminar on Smallholder
Rural Poultry Production, Oct. 9-13, Thessaloniki, Greece, 35-52.
Spradbrow, P.B. & Samuel, J.L. (1991). Oral Newcastle disease Vaccination with V4 virus in
chickens: Comparison with other routes. Australian Veterinary Journal, 68 (3):114-115.
Spradbrow, P.B. (1990). Village poultry and preventive veterinary medicine. Preventive
Veterinary Medicine, 8:305-307.
Tona, G.O. (1995). Incidence of worms in chickens on farms in Ikorodu Local Government
Area of Lagos State, Nigeria. African Network of Rural Poultry Development
Newaletter, 5 (1).
Ugbome, L. (2006). Economic analysis of poultry broiler farmers in Delta State.
Unpublished M.SC dissertation submitted to the Dept. of Agricultural Economics, Delta
State University Abraka, Nigeria.
Wadud, A. & White, B. (2000). Farm Household Efficiency in Bangladesh: A Comparison of
Stochastic Frontier and DEA Methods. Applied Economics, 32: 1665-73.
Weir, S. & Knight. (2000). Education Externalities in Rural Ethiopia: Evidence from
Average and Stochastic Frontier Production Functions. Working Paper CSAE.
WPS/2000-4. Center for the Study of African Economics, University of Oxford. UK.
With, L., 2007. Problems associated with hatching Coab 500 broiler chicks in New Zealand
conditions.http://www.jcu.edu.au/school/bms/avpa/avpa_NZ_ 2007/.
Yao, S. and Liu, Z. (1998). Determinants of Grains Production and Technical Efficiency in
China. Agricultural Economics 49(2):171-184.
Yusuf, S.A. and Malomo (2007). Technical Efficiency of Egg Production in Ogun State: A
Data Envelopment Analysis (DEA) Approach. International journal of poultry science 6
(9):627-629.
Zaria, T., Sinha, P.K, Natiti, L.S, and Nawathe, D.r. (1993). Ectoparasites of domestic fowl
in an Arid Zone. African Network of Rural Poultry Development Newsletter. 3 (2):7.
Dept of Agricultural
Economics,
University of Nigeria.
Nsukka
Date:
Dear respondent,
A REQUEST TO RESPOND TO A QUESTIONNAIRE
I am a post-graduate student of the above named Department currently
undertaking a research work entitled “Production Efficiency of small scale
broiler farmers in Delta State, Nigeria”.
This questionnaire is a part of the research procedure that will enable me
to gather adequate information to give my work authenticity. I therefore, urge
you to kindly respond to the following question as objectively as possible.
Every information supplied will be treated strictly confidential. Thank you.
Yours faithfully,
Anwasia Anthonia
APPENDIX
QUESTIONNAIRE SCHEDULE
A. LOCATION
1. Zone :
2. Local Government Area :
3. Village :
B. SOCIO- ECONOMIC CHARACTERISTICS OF THE FARMERS
4. Name:
5. Age:
6. Sex: Male ( ) Female ( )
7. Marital status: married ( ) single ( ) divorced ( ) widow(er) ( )
8. Level of education:
a) No formal education ( )
b) Primary education ( )
c) Secondary education ( )
d) Tertiary education (OND, NCE, HND,/B.Sc, M.Sc, P.hd) ( )
9. Family size: (a) 1-3 ( ) (b) 4-6 ( ) (c) 7-9 ( ) (d) 10 and above ( )
10 Farming experience: (a) 1-5years ( ) (b) 6-10 years ( ) (c) 11-15 years ( )
(d) Above 15 years ( )
B. MANAGEMENT PRACTICES AND ACQUISITION OF
RESOURCES
11. What system of management did you adopt?
(a) Deep litter system ( )
(b) Battery cage ( )
(c) Free range system ( )
12. Why did you adopt this system?
(a) It is cheaper ( )
(b) Requires less labour ( )
(c) Others ( specify)
13. How many broilers did you raise in 2012?
(a) 1 – 20 ( ) (b) 21 – 40 ( ) (c) 41 – 60 ( ) (d) 61 – 80 ( )
(e) 81 – 100 ( )
14. How many birds were lost?
(a) None ( )
(b) 1 - 20 ( )
(c) 21 – 30 ( )
(d) 31 – 40 ( )
(e) 40 and above ( )
15. What were the major causes of death?
(a) Pest/Disease outbreak ( )
(b) Poor nutrition ( )
(c) Environmental condition ( )
(d) Poor hygiene ( )
(e) Others (specify)
16. How long did you raise the broilers?
(a) 4 weeks ( ) (b) 6 weeks ( ) (C) 8 weeks ( ) (d) 10 weeks ( )
(e) Above 10 weeks
17. How did you acquire the land on which you operate?
(a) Inheritance ( )
(b) Purchased ( )
(c) Lease ( )
(d) Gift ( )
(e) Place of residence ( )
(f) Rent ( )
If rented, how much do you pay for the use per annum ₦
18. What other livestock do you rear alongside broiler?
(a) Pigeon ( )
(b) Goat ( )
(c) Turkey ( )
(d) Duck ( )
(e) Others (specify)
19. How do you get your feeds?
(a) Milled by self ( )
(b) Bought from feed miller ( )
20. How many times a day do you feed the broilers?
(a) Once ( ) (b) twice ( ) (c) 3 times ( ) (d) 4 times ( )
(e) More than 4 times ( )
21. Do you buy drugs/vaccines?
(a) Yes ( ) (b) No ( )
If yes, how much was spent on drugs and vaccines? ₦
22. How many birds were sold?
23. At what cost was each bird sold?
24. How many bags of feed were used on the whole?
25. What was the cost of feed per bag of feed? ₦
D. FARM RESOURCES
26. What were the sources of your financial capital?
(a) Personal savings ( )
(b) Cooperative society ( )
(c) Money lender ( )
(d) Bank loan ( )
(e) Gift from relatives ( )
27. If financial capital is borrowed, how much was borrowed?
₦ and what was the interest charged per annum? ₦
28. Did you receive any credit during the broiler production period?
Yes ( ) No ( )
29. If yes, what were the amount and the sources?
Sources amount ₦
……………….. ……………………
…………….... …………………….
TOTAL
30. What did you use the loan for?
.................................................................................................
E. AGRICULTURAL EXTENSION SERVICES
31. Do you have agricultural extension workers in your area?
Yes ( ) No ( )
32. Do you get advice from them?
Yes ( ) No ( )
If yes, how many visits do they usually make in a year? Visits.
F. LABOUR ASSESSMENT
33. What are your sources of labour?
(a) Family ( )
(b) hired ( )
(c) both ( )
If hired, at what cost? ₦
34. What was the number of people that worked in the farm?
G. CONSTRAINTS ON BROILER PRODUCTION AS PERCIEVED BY
THE FARMERS
35. What are the problems that militated against effective broiler production in 2012?
a. Pilfering ( )
b. Pest/Disease outbreak ( )
c. Feed cost ( )
d. Lack of Fund ( )
e. High motility rate ( )
f. Unavailability of feed ( )
g. Shortage of water ( )
h. Lack of capital ( )
i. Others (specify)
36. As an experienced broiler farmer, what are your suggestions for improvement in the
poultry industry?
s