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LEVEL OF ADOPTION OF IMPROVED CASSAVA
VARIETIES AND THE PROFITABILITY OF
CASSAVA PRODUCTION IN ENUGU STATE,
NIGERIA
BY
OKORIE, OGUEJIOFOR JOSEPH
PG/M.Sc/06/40782
DEPARTMENT OF AGRICULTURAL ECONOMICS
UNIVERSITY OF NIGERIA, NSUKKA
SEPTEMBER, 2012
i
TITLE PAGE
LEVEL OF ADOPTION OF IMPROVED CASSAVA
VARIETIES AND THE PROFITABILITY OF
CASSAVA PRODUCTION IN ENUGU STATE,
NIGERIA
A DISSERTATION SUBMITTED TO THE DEPARTMENT
OF AGRICULTURAL ECONOMICS, UNIVERSITY OF
NIGERIA, NSUKKA
BY
OKORIE, OGUEJIOFOR JOSEPH
PG/M.Sc/06/40782
SUPERVISOR: DR. A. A. ENETE
SEPTEMBER, 2012
ii
CERTIFICATION
OKORIE, OGUEJIOFOR JOSEPH, a postgraduate student in the department of
Agricultural Economics with registration number PG/M.Sc/06/40782 has satisfactorily
completed the requirement for the course and research work for the award of the
degree of Masters of Science (M.Sc.) in Agricultural Economics. The work embodied
in this dissertation, except where duly acknowledged , is a product of my original
work and has not been published in part or full for any other diploma or degree of this
or any other University.
------------------------------ ----------------------------
Dr. A. A. ENETE Prof. E. C. Okorji
(Supervisor) (Head of Department,)
------------------------------- --------------------------
Date Date
---------------------------------
EXTERNAL EXAMINER
------------------------
Date
iii
DEDICATION
To my parents; Mr Adulphus N.Okorie, and Mrs. Theresa M Okorie and my siblings for
their love and care.
iv
ACKNOWLEDGEMENT
I here express my indebtedness and appreciation to all persons who in different ways
rendered moral, material and financial assistance that made this project work a success.
In a special way, I wish to acknowledge the contribution of my siblings; Mrs.
Eberechukwu M. Ireh, Mrs. Beatrice E. Oluah and Mr. Innocent Okorie who provided my
tuition fees. I equally appreciate their prayers and moral support. Without them, I wouldn’t
have started the programme in the first place.
I want to appreciate the contribution of Mr. Solomon M. Madukaife of the
Department of Statistics, University of Nigeria, Nsukka, who tried various ways to run my
data and even consulted his senior colleagues but all effort failed. I appreciate your effort
immensely.
I am most indebted to my supervisor, Dr. A. A. Enete who took extra pain to
encourage me through the frustrating period of data trials/analysis. Words are not enough to
appreciate you. Thank you.
Last and not the least, I appreciate the contribution of my wife, Mrs. Patience
Onyebuchi Okorie for keeping the dream alive and making sure that this project was given
much priority. My thanks go to numerous persons whose names might not be mentioned.
Your contributions are highly appreciated.
Okorie, O. J.
v
TABLE OF CONTENTS Tittles page…………………………………………………………………………. i
Certification……………………………………………………………………….. ii
Dedication………………………………………………………………………… iii
Acknowledgement………………………………………………………………… iv
Table of contents ……………………………………………………………… v
List of tables …………………………………………………………… vii
List of figures …………………………………………………………………… viii
Abstract…………………………………………………………………………… ix
CHAPTER ONE 1.0 Introduction…………………………………………………………………. 1
1.1 Background of study………………………………………………………… 1
1.2 Problem statement…………………………………………………………… 3
1.3 Objectives of the study………………………………………………………. 4
1.4 Research hypothesis…………………………………………………………. 5
1.5 Significance of the study…………………………………………………….. 5
1.6 Limitation of the study ……………………………………………………… 6
CHAPTER TWO 2.0 Literature review……………………………………………………………… 7
2.1 Forms of agricultural innovation… …………………………………………. 7
2.2 Stimuli of agricultural innovation ………………………………………… 7
2.3 Innovation adoption ………………………………………… 10
2.4 Some agronomic practices associated with improved cassava varieties…...…. 11
2.5.1 Socio-economic characteristics of small holder farmer in Nigeria ………… 12
2.5.3 Summary of socio economic characteristics of decision makers on
Adoption of innovation ………………………………………… 13
2.6.1 Cropping pattern of small holder farmer in Nigeria ………………………… 14
2.6.2 Reasons for cropping pattern ………………………………………… 15
2.7 Production of cassava and its market prospect for small holder farmers in
Nigeria ………………………………………………………………………… 15
2.8.1 Theoretical framework ………………………………………… 16
2.8.2 Productivity indices and conditions for efficiency ………………………… 18
2.8.3 Efficiency measurement ……………………………………………………… 19
2.8.4 Perfect allocative efficiency ………………………………………… 20
2.8.5 Cost and returns analysis ………………………………………… 20
2.8.6 Functional forms of production ………………………………………… 21
2.9 Analytical framework. ………………………………………… 22
2.9.1 Multiple regression analysis ………………………………………… 22
2.9.2 Stochastic profit frontier function ………………………………………… 22
2.9.3 Profit model ………………………………………… 24
CHAPTER THREE 3.0 Research methodology ………………………………………………………. 25
3.1 Study area ………………………………………………… 25
3.2 Sampling procedure ………………………………………………… 25
3.3 Method of data collection ………………………………………………… 26
vi
3.4 Data analysis ………………………………………………… 26
3.5 Model specification ………………………………………………… 26
3.5.1 Stochastic profit frontier model ………………………………………… 26
3.5.2 Heckman’s two-stage model ………………………………………… 27
CHAPTER FOUR 4.0 Result and discussion ………………………………………………………… 29
4.1.0 Socio economic characteristics………………………………………………… 29
4.1.1 Sex of respondents …………………………………………………. 29
4.1.2 Age of respondents ……………………………………………….… 29
4.1.3 Marital status of respondents …………………………………………. 30
4.1.4 Level of education of respondents ……………………………………….… 30
4.1.5 Primary occupation of respondents …………………………………………. 31
4.1.6 Method of land acquisition …………………………………………. 31
4.1.7 Method of capital acquisition …………………………………………. 31
4.1.8 Household size of respondents …………………………………………. 32
4.1.9 Cassava farm size (in hectares) of respondents …………………………. 32
4.1.10 Household income of respondents …………………………………………. 33
4.2.0 Cropping System of respondents …………………………………………. 34
4.2.1 Rate of production of production of cassava-based crops …………………. 34
4.2.2 Fallow practices of respondents …………………………………………. 34
4.2.3 Intercrop practices of respondents …………………………………………. 34
4.2.4 Cassava-based intercrop of respondents …………………………………. 35
4.2.5 Fallow years practices …………………………………………. 35
4.3.0 The effect of socio-economic variables on the profit efficiency of cassava-
based enterprise in the study area. …………………………………. 36
4.4.0 The effect of socioeconomic variables on adoption decision and the extent of adoption
of improved cassava varieties in the study area…………………....... 38
4.5.0 Constraints to the extent of improved cassava production ………………… 40
CHAPTER FIVE 5.0 Summary, conclusions and recommendations ………………………… 42
5.1 Summary ………………………………………………… 42
5.2 Conclusion ………………………………………………… 44
5.3 Recommendations ………………………………………………… 44
References ……………………………………………………………… 46 Appendix 1
Appendix 2
Appendix 3
vii
LIST OF TABLES
2.5.2 Distribution of farm size in Eastern Nigeria (1970/71) 12
2.6.1 Crop intercrops in five agro-ecological zones in Nigeria 14
4.1 Sex descriptions of respondents 29
4.2 Age description of respondents 29
4.3 Marital status of respondents 30
4.4 Educational level description of respondents 30
4.5 Primary occupation description of respondents 31
4.6 Description of acquisition methods 31
4.7 Distribution of respondents by method of capital acquisition 32
4.8 Description of households according to household size 32
4.9 Farm size distribution of respondents 33
4.10 Distribution of household income of respondents 33
4.11 Rate (years) distribution of cassava based crop cultivation 34
4.12 Fallow system distribution of respondents 34
4.13 Distribution of cropping practices of respondents 35
4.14 Distribution of cassava based intercrops in the study area 35
4.15 Fallow year description among respondents 36
4.16 Summary statistics of variables for the estimation of stochastic profit frontier
model 36
4.17 Maximum likelihood estimates of the stochastic profit frontier functions 37
4.18 Parameter estimates of the sample selection (Heckman two-stage) model 39
4.19 Constraints to extent of improved cassava production 41
viii
LIST OF FIGURES
Figure 1: Labour saving technical change 17
Figure 2: Capital saving technical change 17
Figure 3: Production function 18
ix
ABSTRACT
While much work has been done on the adoption and spread of agricultural
innovation (technologies), adoption has not been seen as a continuum. It became imperative
to analyze the socio-economic variables’ status and their effects on the farmers’ resource use
pattern as evidenced in their decision relative to cassava technologies. The main objective of
the study was to analyze adoption and profitability of cassava production in Enugu State.
Structured questionnaire and interview schedule were used to generate data which were
analyzed using appropriate descriptive and inferential statistics. The result of the analysis
underscores the poor farming resources of respondents, typical of subsistence agriculture.
The mean gross margin in cassava production was N19, 228.40 and standard deviation of
N7, 658.02per cropping season; average farm sizes of 0.31 hectare and standard deviation of
0.38 hectare; output price per barrow of N 1149.77 and standard deviation of N 423.91; the
average price of fertilizer was N4,038.81 with N738.77 standard deviation per bag; the price
of labour was comparatively stable across the sampled area with average of N977.63 and
standard deviation of N212.67per man-day. The estimated parameters of socio-economic
variables using the stochastic profit frontier analysis showed that farm sizes of households
was negative and significant in generating gross margin while the average price of fertilizer
was positive and significant with gross margin. For inefficiency factors, age was positive and
significant while years of formal education, years of experience and household size were
negatively and significantly related with gross margin. The discrete decision of whether or
not to adopt and the continuous decision of extent of adoption of improved cassava varieties
showed that farm size was negatively and significantly related with the discrete decision of
whether or not to adopt while the price of labour, price of fertilizer, household sizes and the
age of household heads were positively and significantly related with the discrete decision
too. These factors failed to affect the extent of adoption significantly. The constraints
militating against the extent of adoption of improved varieties of cassava were identified with
capital as the most critical followed by poor access to credit, low income of households, land
scarcity, poor price of finished cassava products, lack of processing facilities labour scarcity
and cost of planting materials respectively. It was recommended that government should
provide arable land, credit inputs and source of capital, among other things, to enhance
proper adoption of improved cassava varieties and hence increase in the level of cassava
production.
1
CHAPTER ONE
INTRODUCTION
1.1 Background
The importance of new technologies in agriculture is tremendous. Technology
is defined as the method of doing things that are based on the modern knowledge of
science and computers (Longman, 2007). Technologies that are very importance in
agriculture include: improvement in agronomic practices, production of high
yielding, disease resistant crops and animal hybrids with wide adaptability, the use of
agrochemicals like fertilizers, herbicides and insecticides, the development of
integrated pest management systems, development of irrigation methods and farm
machineries with enhanced efficiency. When some of these are combined in
production process, they improve productivity. For instance, the intensification of
improved crops like cassava with irrigation during drought and fertilization will
produce all time high level of cassava output per given land area. Productivity is the
increase in the average output per unit input. Productivity enhances increase in
income and food security (WTO, 2000)
However, a great deal of these agricultural activities in Nigeria is on small
land holdings. More than half of the Nigerian population are in farming (largely the
subsistence type) (World Bank, 2007). The major crops are sorghum, millet,
soybean, peanut, cottons, maize, yam, rice, palm products, coca, cassava and rubber,
in addition, poultry, goats, sheep, pigs, cattle, fisheries are raised (Wikipedia, 2007).
Farmers with limited resources are the mainstay of food supply for billions of people
and this situation is likely to continue for decades, perhaps centuries (Kaindaneh,
2007). The potential for increased food production therefore would rely on adoption
of improved (new) technologies by this group of farmers.
Adoption of a technology is the application of knowledge that is new within a
specific context like agriculture. When there is a change in the production process of
goods and services, technological change is said to occur. Adoption of improved
cassava varieties begins with the decision of farmers to replace old inferior varieties
or to supplement their stock of planting materials with new improved varieties. The
most important step in the application of the new technology is the awareness of the
economic incentives accruable from it. The level of adoption entails the actual
2
hectarage cultivation of improved cassava varieties versus the local/ traditional
varieties.
The benefits of adopting improved cassava varieties are much for Nigerian
farmers. Though, the hybrids have better sequestration power for soil nutrients than
the local/traditional varieties, they need fertilizer and irrigation in case of drought for
optimum yield. Nevertheless, improved cassava varieties can survive and perform
without those accompanying inputs and yet gives higher yield than the local varieties
when grown under the same circumstance. Therefore, it guarantees the households
with limited resources to still realize better livelihood from cassava production. In
the rural households, the spread of improved cassava varieties does not usually
follow commercial pathways. Family relations and neighborhood friends first receive
gift of cuttings from primary recipients. Though, accidental sales of propagules only
do occur where buyers appreciate the benefits which they derive from growing such
new varieties. These improved varieties differ in their resistance to cassava diseases
and pests such as cassava mosaic virus (CMV), cassava anthracnose diseases (CAD),
cassava mealy bug (CMB) and cassava green spider mite (CGM). They also produce
tubers with varying quality of roots at differing maturity duration and storage in the
ground (Okigbo, 1978; Hahn, 1983; Herran and Bennett, 1984; IITA, 1984).
Normally, a field of cassava in south-eastern Nigeria may contain different
combination of improved and local varieties of cassava. A particular cultivar may be
grown in a locality depending on the perceived quality it possesses. A wide variety
of cassava including both improved and local varieties can be observed in a farmer’s
field but one or two can be seen more frequently in a given zone.
Technological change has been a major factor shaping U.S.A’s agriculture in
the last 100 years (Schultz, 1964; Cochrane, 1979). For instance, a comparison of
agriculture pattern in the United States at the beginning (1920) and end of the
century (1995) shows that harvested cropland has declined from 350 to 320 million
acres, the share of agricultural labour to total labour force has decreased substantially
(from 26 to 2.6 percent) and the number of people now employed in agriculture has
declined (9.5 million in 1920 Vs 3.5 million in 1995), yet agricultural production in
1995 was 3.3 times greater than in 1920 (United states Bureau of Census, 1998).
3
This achievement in the U.S could spin from consistently high rate of return
on public investment in agricultural research and extension indicating
underinvestment in these activities. Public spending for research and development in
agriculture shows that federal monies tend to emphasize research on sciences and
commodities which are produced in different states while individual states provide
much of the public support for innovation inducing activities are specialties of the
state. These have resulted to decline in federal share in public research to the state
share. This has promoted the tendency to move from one line of research to another
and, thus, both dynamic and risk consideration tends to diversify innovative effort.
Technological change has been the product of innovative activities while innovation
is the development, adaptation or imitation and subsequent adoption of these
technologies within a specific context like agriculture.
However, the innovative effort in Nigeria is significantly shouldered by
federal government agencies and institutes for agricultural development. Some of the
institutions charged with these responsibilities are; International Institute for
Tropical Agriculture (I.I.T.A), National Root Crop Research Institute (NRCRI)
National Institute for Oil palm Research (NIFOR), Agricultural Development
Projects (ADPs), River Basin Development Authorities (RBDAs), National Fertilizer
Company of Nigeria (NAFCON) etc (CBN, 2003).
For instance, IITA in collaboration with national agricultural research system
developed a number of improved varieties, practices, systems, and processes and
these products were disseminated widely in Sub-saharan Africa. Between 1970 and
1998, 206 varieties of cassava with over 50 percent average yield advantage over
traditional varieties were released in 20 countries in Sub-saharan Africa and planted
on over 22 percent of the cassava area (Manyong, Alene, Sango et al 2006).
1.2 Problem Statement
The roles of agriculture and the benefits of agricultural innovations like
improved cassava varieties with its numerous advantages over the local varieties
seem to be eluding Nigeria as a country that depends on agriculture. This is because
the impact of the new technologies is conditional on adoption by farmers who do not
adopt technologies properly resulting to inadequate level of cassava production and
low income generation to farm households. DFID (2006) maintained that the issue of
4
the level and determinants of adoption of technologies, which is lacking among
agrarian communities, has been as important as their impact on their livelihood.
Adoption of innovations in general is the corner stone to economic
empowerment. In Nigeria and else where in the world, the adoption of agricultural
innovation has attracted much scholarly works. Scholars generally agree that socio
economic and institutional factors affect agricultural innovation adoption. Arene,
(1994) reported a positive and significant relationship between family size and
adoption. Education, size of holding and cosmopoliteness according to Oladele
(2005) accounted for significant variation in adoption behaviour of farmers.
Manyong, Alene, and Sango (2006) reported that access to credit and household
income was positively significant with adoption.
Available information shows that much work has not been done to establish
the factor (s) determining or affecting the level of adoption of cassava varieties in
Enugu state. Specifically, past studies failed to discuss adoption decisions as a two-
stage process. However, adoption involves a two- stage decision problem for a
household. The first is a discrete decision of whether or not to adopt improved
cassava varieties while the second is a continuous decision of how much of the
improved cassava varieties that will be adopted conditional on the first decision of
whether or not to adopt the innovation. Moreover, the variables affecting the two
decisions may not be exactly the same. There could be fixed type variables affecting
the first decision to adopt the improved cassava varieties but not the level of
adoption. So that when the first decision is made, they do not affect the second
decision (Enete, 2003). This study therefore hopes to explore the smallholder’s
adoption decision in this context. Morover, above subsistence level of production,
the household hopes to reduce their choice constraints and would use available
resources to achieve this objective(s). The increase in income of a household from an
enterprise would result to the consideration of a more efficient means by the
households. However, little attention has been made to measuring profit efficiency of
cassava farmers in Enugu State even when the prices of inputs and output are known
in attempt to examine the profit efficiency of inputs and of farmers. The profit of a
farm enterprise in monetary terms could be in terms of gross margin or net profit.
Past studies tended to concentrate on the determination of gross margin without
5
ascertaining the individual contribution of the inputs to the gross margin in cassava
production (Onwuchekwa and Nwagbo, 1986; Olagoke, 1990; Nwakpu, 2007). This
study also aims to address these issues.
1.3 Objectives of study
The broad objective of the study was to analyze the level of adoption of
improved cassava varieties and profitability of cassava in Enugu state
The specific objectives were to:
(i) describe the socio-economic characteristics of respondents
(ii) describe the cassava- based cropping system of the farmers
(iii) estimate the profit efficiencies of direct factor inputs in cassava
production.
(iv) estimate the effects of inefficiency factors in cassava production
(v) estimate the factors affecting adoption and the level of adoption of
improved cassava varieties.
(vi) identify the constraints militating against the level of adoption of the
improved cassava varieties.
1.4 Research hypotheses
(i) all factor inputs do not contribute significantly to profit in cassava
production.
(ii) all farmers are not efficient in generating profit ( gross margin )
(iii) the same factors that significantly affect adoption do not affect the level
of adoption of improved cassava varieties significantly and in the same
direction.
1.5 Significance of the study.
Productivity in agricultural activities needs to be enhanced to meet up with
energy needs of the teeming population and combat the ravages of hunger and
poverty. Since the foreseeable future of smallholder farmers is tied to agriculture
(Marinos and Ehui, 2006), the study becomes worthwhile. This calls for the re-
assessment of the position of agricultural innovation adoption by way of the extent
of adoption by smallholder farmers who are production engine in developing
6
countries like Nigeria. Equally, agricultural innovation is the engine that could drive
the productivity of this vulnerable group. It is obvious that proper adoption of
agricultural innovation will improve the productivity of labour among other factor
inputs. This will result in increased agricultural output of the farmer.
In addition, much of the smallholder farmers are not literate enough to avail
themselves of the implication of technical efficiency indicators hence the conversion
of the efficiencies of factor inputs to monetary terms. This will improve their
understanding that will in turn translate to proper resource allocation decision for
profit maximization. All these put together will result to income generation ability of
the smallholder farmers thereby reducing their choice constraints.
Moreover, low productivity in agriculture is blamed on poor adoption of
agricultural innovations and much work has been based on them but with less result.
It is expected that this study will result in increased input productivity thereby
shifting the resources of researchers beyond primary output level. It is equally
expected that the work will provide empirical information base for further research
in other related fields.
Finally, this work is expected to change the perception of policy makers in
agricultural development by seeing adoption as a continuum. This will encourage a
redesign of program towards increasing farmers’ output from agriculture, which is
the main occupation of farmers that constitute 70 percent of the labour force. In this
way, export base of Nigeria vis a vis primary agricultural commodities will receive a
boost. This in turn will generate enough foreign exchange that affects national
income positively. These create the enablement for realizing Millennium
Development Goals.
1.6 Limitations of the study.
This study was limited to the rural areas of Enugu State. It was in the plan of
the work to study equally the effect of the cost of cassava stem and average credit
accessed on the gross margin in cassava production but rural areas did not provide
much information on them. First, cassava stems are not sold but farmers could get
them from their neighbours during harvest. Credit facilities are not in existence in the
communities sampled.
7
Credit in this work is limited to all goods and services supplied to the farmers
without down cash payment so that the cost of such goods and services and the
interest accruing to them is paid at the end of the farming season. These goods and
services could be supplied by Government or NGOs. The goods and services might
include cassava stem, agro chemicals or pesticide application services.
In addition, main sources of information provided by cassava farmers were
memory recalls. The respondents lacked the ability to keep comprehensive farm
records hence much persuasion was used to obtain as much information as possible.
Information on the average annual income of respondents was based on the proxy –
expenditure on various items as contained in the questionnaire. However, some
respondents were reluctant at given information on their expenditures. Most of the
data generated were on the subjective bases. It is hoped this would not impair much
on the reliability of the result. Finally, the work is a cross sectional data.
8
CHAPTER TWO
2.0 LITERATURE REVIEW
Literature for this study were reviewed on the following:-
1. Forms of agricultural innovation.
2. Stimuli of agricultural innovation.
3. Innovation adoption and diffusion.
4. Agronomic practices for improved varieties of crops.
5. Socio economic characteristics of smallholder farmer in Nigeria.
6. Cropping patterns of smallholder farmer in Nigeria and the reasons.
7. Productivity and market prospects for smallholder farmer in Nigeria for
improved varieties of cassava.
8. Theoretical framework.
9. Analytical framework.
2.1 Forms of Agricultural Innovations.
Innovations could be categorized according to their sources of generation.
Zilberman and Sunding (2000) made distinction between innovations that are
embodied and ones that are disembodied. Embodied innovations include capital
goods or products such as tractors, combines, seeds and fertilizers. Disembodied
innovations include pest management system. Private bodies invest in embodied
ones while the public invest in disembodied innovation. They added that private
investment is less likely to generate disembodied innovations because of the
difficulty in selling their products, that is however, the area of public activity while
the generation of embodied innovation requires appropriate institution for
intellectual property right protection.
2.2 Stimuli of Agricultural Innovation
Agricultural innovation is mainly induced by the need at a particular point in
time. According to Alston, Norton, and Pardey (1995), new discoveries are not the
result of inspiration occurring randomly without strong link to physical reality.
Hayam and Ruttan (1985) formalized and empirically verified their theory of
induced innovation that closely linked the emergence of innovation with economic
9
conditions. They argue that the search for new innovation is an economic activity
that is significantly affected by economic conditions. New innovations are more
likely to emerge in response to scarcity and economic opportunities. For example,
labour shortage will include labour saving technologies. Environmentally friendly
techniques are likely to be linked to the impositions of strict environment regulation.
Similarly, food shortages or high prices of agricultural commodities will likely lead
to the introduction of a new high yielding varieties, and perceived changes in
consumer preferences may provide the background for new innovations that modify
product quality.
The work of Boserup (1965), Binswanger and Malntire (1987) on the
evolution of agricultural system supports the induced innovation hypothesis. Early
human groups, consisting of relatively small number of members who could roam
large areas of land, were hunters and gatherers. An increase in population led to the
evolution of agricultural systems. In tropical region where population density was
still relatively small, farmers relied on slash and burn systems. The transition to more
intensive farming system that used crop rotation and fertilization occurred as
population density increased. The need to overcome disease and improved yields led
to the development of innovations in pest control and breeding. In addition, the work
of Berak and Perlaff (1985) suggests that the same phenomena occur with seafood.
An increased demand for fish and expanded harvesting may lead to the depletion and
a rise in harvesting cost, and thus trigger economic incentive to develop alternative
aquaculture and Mari culture for the provision of sea food.
While scarcity and economic opportunities represent potential demand that is
in most cases, necessary for the emergence of new innovations a potential demand is
not sufficient for inducing innovations. In addition to demand, the emergence of
innovation requires technical feasibility and new scientific knowledge that will
provide the technical base for the new technology. Thus, in many cases,
breakthrough knowledge gives rise to new technologies, (Alston, Norton, and
Pardey, 1995).
Finally, the potential demand and appropriate knowledge base are integrated
with the right institutional setup and together they provide the background for
innovation activities. These ideas can be demonstrated by an over view of some of
10
the major waves of innovation that have affected lower income countries’ agriculture
like Nigeria, for some centuries now.
The dramatic cassava transformation that is under way in Nigeria and Ghana
is Africa’s best-kept secret (Nweke, 2004). The transformation describes how the
new TMS varieties have transformed cassava from a low yielding, famine reserve
crop to high yielding cash crop that is prepared and consumed as garri, a dry cereal
(Nweke, 2004).
In 1891, Warbug reported that the Mosaic (cassava mosaic) virus was
prevalent in East Africa and adjacent islands. Soon after, the mosaic disease was
reported in most countries in central and West Africa. The widespread occurrence of
the mosaic disease motivated the British Colonial government to launch a cassava-
breeding program at the Amani research station in Tanzania in the mid 1930s. The
goal of the research was to develop varieties that were tolerant to the mosaic disease,
(Hahn, Howland and Terry, 1980).
The research chronology went on until 1958, at Moor Plantation Research
Station, Ibadan, Nigeria. The ceara rubber was selected and crossed with cassava
hybrid 58308 from the seed derived from the Jennings’ series 5318/34. The ceara
rubber x cassava hybrid 58308, though resistant to mosaic disease gave low yield
and poor root quality. Then the ceara rubber x cassava hybrid 58308 with high
yielding West African selection to combine the mosaic disease resistant genes of the
ceara rubber x cassava hybrid 58308 gave the gene for high yield from West African
varieties, (Hahn, Howland and terry, 1980).
At Nigeria’s independence, in 1960, the cassava breeding program at Moor
Plantation Research Station, Ibadan was moved to the Federal Root Crop Research
(Now National Crop Research) Institute, Umudike in Eastern Nigeria and breeding
work continued by Ekandem. Unfortunately, almost all the progenies developed
from the ceara rubber x cassava hybrid 58308, and the records of the research
program at Umudike along with records transferred from the Moor Plantations
Research Station in 1960 were lost during the Nigerian civil war, (Hahn, 1998).
Cassava breeding at 11TA’s Ibadan headquarters commenced in 1971 when
S.K Hahn was appointed as the leader of the institutes of Root and Tuber
Programme, (Nweke, 2004). Hahn’s strategy for developing Tropical Manihot
11
Series varieties was a collaborative undertaking involving National Cassava
Research Programmes, training national scientists, developing partnerships with
privates companies, and investing in germ plasm exploration and conservation. The
11TA’s cassava breeding programme was carried out by multi disciplinary team
including a plant Pathologist, Entomologist, Nematologist, Virologist, Agronomist,
Tissue culture specialists, Biochemist and Food Technologist, (Dixon, Asiedu and
Hahn, 1992).
After six years (1971-1977) of research, Hahn and his staff achieved the goal
of developing high-yielding mosaic resistant TMS (Tropical Manioc selection)
varieties. These new high yielding mosaic-resistant varieties include TMs 50395,
63397, 30555, 4 (2) 1425, and 30572, (Nweke, 2004). The Collaborated Study on
Cassava in Africa (COSCA) researchers discovered that the farm level yield in the
TMS varieties in Nigeria was fourty percent (40%) higher than that of local varieties
even when grown without fertilizer.
2.3 Innovation Adoption.
Oladele (2005) defines adoptions of innovation as the decision to apply an
innovation and continue to use it. A wide range of economic, social, physical and
technical aspect of farming influence adoption of agricultural production technology.
Recent studies in Europe, Asia and Africa have identified farm and
technology specific factors - institutional, policy variables and environmental factors
to explain the pattern and intensity of adoption (Charmala and Hossain (1996), Frank
(1997), Abdelmagid and Hassan (1996), Rao and Rao (1996) found a positive and
significant association between age, farming experience, training received, socio-
economic status, cropping intensity, aspiration, economic motivation,
innovativeness, information source utilization, information source, agent credibility
and adoption. Agbamu (1993) found only knowledge of a practice to be significantly
related to its adoptions. Ikpi, Stanton and Tyler, (1992) showed that where farmers
have to adopt a new crop technology that shift time from their home to production
activity sector, the probability and rate of adoption of such technology is higher.
12
Also a family time is shifted away from the farming sector to home production
sector, the economic impact index increases.
Arene (1994) reported a positive and significant relationship between family
size and adoption. On the other hand Voh (1982) established that household size is
not significantly related to adoptions. Abdul, Ashfag and Sultan (1993), reported a
significant relationship between landholding (farm size) and adoption. Voh (1982)
also reported that socio-economic status of farmers is positively and strongly related
to adoption. This repot implied that the higher the socio-economic status, the higher
the tendency to adopt innovations. Igodan, Oheji, and Ekpere (1988) reported that
farmers who are more exposed to formal extension information have a high
propensity towards adoption than those with less exposure.
However, Abdul, Ashfag and Sultan (1993) did not establish any relationship
between education and adoption. Education, size of holdings and cosmopolitans
accounted for significant variation in communication behaviour of farmers.
Goswami and Sagar (1994) identified some factors associated with knowledge level
of an innovation. They found educational level, family educational status, innovation
proneness and utilizations of mass media to be positively and significantly
correllated with knowledge level. Earlier evidences of Rogers (1962), Ryan and
Gross (1943) led to the categorization of adoption behaviour into innovators, early
adopters, early majority, late majority and laggards.
2.4 Some agronomic practices associated with improved cassava varieties.
These include:
(i) Stem storage: keep the bundles of stems stacked vertically on the soil under a
shade. The distal end of the stem should touch the soil. Moisten the soil
regularly and keep the surrounding weed free-this way the stems can be started
for 3 months.
(ii) Time of planting: planting should be done as soon rain becomes ready in the
area
(iii) Plant population: the optimum plant population for a high root yield is 10,000
plants per hectare obtainable when plants are spaced at 1x1m. This population
is seldom achieved at harvest due to losses caused by genetic and environment
13
factors. In order to harvest a plant population near the optimum, an initial plant
population per hectare of 12,300 at 0.9m x 0.9m will vary depending on
whether cassava is planted sole or in association with other crops.
(iv) Intercropping
Cassava is compatible with many crops when intercropped. The best intercrops
of cassava in Nigeria include maize, melon, groundnut cowpea and vegetables.
Other less important intercrops of particularly in south-south and southeastern
Nigeria include yam, cocoyam, sweet potato, plantain and banana. High
branching varieties of cassava are best for intercropping, profuse and low
branching varieties will shade light off the intercrops.
(v) Weed control: this could be by cultural, mechanical or chemical methods.
Integrated used control (cultural, chemical and mechanical) is recommended.
The ideal combination will depend on the agro-ecology, weed spectrum and
level of infestations, soil type and cropping system.
(vi) Fertilizer Rate and Time of Application
Ideally, fertilizer recommendation is based on soil analysis but when this is
not done, then land history and vegetation is used as a guide. Lands naturally
inundated with Chromolaena odorato as weed can support a good cassava
crop without fertilizer while the presence of spear grass or poorly established
vegetation is a symbol for fertilization under continuous cultivation in the
forest zone. Apply a first dose of 200kg (4 bags) of N.P.K 15:15:15 per
hectare or a full small matchbox per plant at 4-6 weeks after planting (June/
July). Second dose of 100kg of muriate of potash or a half-full small
matchbox per plant at 14-16 weeks after planting (September) should be
applied. In the savanna zone, apply as in the first stage of forest zone but a
second dose of 50kg murate of potash per hectare. Apply fertilizer in holes
5cm deep and 10cm radius from the plant; do not apply if the soil is dry.
Harvesting is made as the need be.
Source; Information and communication system (ICS Nigeria, Guide)
14
2.5.1 Social-economic characteristics of smallholder farmer in Nigeria.
In Nigeria, about 15% of the population is peasant farmers living in the rural
areas that are the main stay of agricultural production (Fawole and Oladele, 2007).
These farmers operate on a small-scale farm holding of 1-2 hecters, which are
usually scattered over a wide area. According to Olayide, Olagemi and Eweka
(1981) about 75% of Nigeria arable land is under cultivation with land-human ratio
of 58 persons per square Kilometer in south western Nigeria. Ndubizu (1990)
reported that a survey carried out in 1970 showed that in four years the number of
small crop growers as a percentage of total crops producers’ population has risen to
99.7%. The survey showed that by 1970, nearly half of all the crop producers 42.6
percent grow crops on land holdings less than one quarter of an acre or less than 0.10
ha
2.5.2 Distributions of farm size in Eastern Nigeria 1970/71
Size of farm in (acres) Percentage
(Excluding the upper limit)
Under 0.25 42.60
0.25 - 0.5 21.89
0.5 - 1.0 20.52
1.0 - 2.5 12.31
2.5 - 5.0 2.16
5.0 - 10.0 0.31
Over - 10.0 0.21
Source: Okongwu,(1972) .
This shows that the average sizes of farmlands are very small. The
production practice of small-scale farmer is synonymous with their production small
hectare due to tenurial rights (Fawole and Oladele (2007). Other factors include poor
access to credit and other production inputs, poor managerial ability and enterprise
combination are informed by ecological considerations, available resources, taste
and preference of farm families. Olayide, Olayemi and Eweka (1981) stated that
truly diversified enterprise-oriented economy is typical of most rural economy.
15
Ndubizu (1990) posited that outside land, labour (quantity and quality) is the
next very important input requirement for successful crop production. Estimates of
the labour requirement for each farm enterprises as the total amount of labour each
month would give an idea in comparative terms the degree of engagement of the
average small holder farmer in business of farming. Ogunsumi (2005) added that
there are two sources of labour for a smallholder farmer namely household source
and hired labour. Ogunsumi (2005) found out in his study that labour exists but is
used sparingly. Labour is required mainly in the area of clearing, ridging, weeding,
planting and harvesting. Agricultural labour had been at ever increasing higher cost.
This has implication on cost of production and farmers hardly break even if all cost
had to be actually considered in farm budget analysis.
2.5.3 A Summary of Socio-Economic Characteristics of Decision Makers on
Adoption of Innovation.
Education is positively related to innovativeness.
Literacy is positively related to innovativeness.
Income is positively related to innovativeness.
Level of living is positively related to innovativeness.
There is no consistent relationship between age and innovativeness.
Knowledge ability is positively related to innovativeness.
Attitude towards change is positively related to innovativeness.
Achievement motivation is positively related to innovativeness.
Education aspirations are positively related to innovativeness.
There is not yet adequate evidence about the relationship of such attitudinal
variables as business orientation, satisfaction with life, empathy, and rigidity
to innovation.
Cosmopolateness is positively related to innovativeness.
Mass media exposure is positively related to innovativeness.
Contacts with change agencies are positively related to innovativeness.
Deviancy from norms (of social system) is positively related to
innovativeness.
Group participations is positively related to innovativeness.
Interpersonal communication exposure is positively related to innovativeness.
Opinion leadership is positively related to innovativeness.
16
Relative advantage of the innovation is positively related to the rate of
adoption.
Compatibility of the innovation is positively related to rate of adoption.
Fulfillment of felt needs by the innovation is positively related to rate of
adoption.
There is not adequate evidence as to the relationship of various change agency
strategies and the rate of adoption of innovations.
Innovativeness is defined as the degree to which individuals is relatively
earlier than other member of his social system to adopt new ideas.
Source: Rogers and Everett (1992).
2.6.1 Cropping pattern of smallholder farmer in Nigeria
In Nigeria, the predominant arable cropping system as described by National
Agricultural Research Programme (NARP) (1997) are cassava based, yam based,
maize based, rice based and vegetable based systems. Cassava is grown in mixture
with maize, cocoyam, okra, and tomatoes or relayed with yam. Yam is planted as
sole crop but unusually intercropped with melon, pepper, okra, and amaranths.
Maize may be grown solely or intercropped with cassava in particular. Upland rice is
usually cultivated sole, but may carry few rows of maize. Cowpea, pigeon pea and
soybeans are the main legumes that are either intercropped with maize and cassava
or grown as sole crops. Pigeon pea is usually intercropped with maize or cassava or
relayed with yam. On the harvest of companion crops, it becomes a sole pigeon pea
crop. In most cases, the fruit vegetable are planted as companion crops, however,
tomatoes, pepper and okra are in recent times grown as sole crop or pepper as avenue
crop in a cassava/ pepper intercrops.
Crop intercrops in five agro-ecological zones in Nigeria.
Agro-ecological zones
Crop mixture North
west
North
East
Central South
West
South
East
Yam/maize x x x
Maize/Rice x x x x x
Cassava/Maize x x
Sorghum/Cowpea x x x
Maize/Cowpea x x x
Maize/Cocoyam x
Maize/Sorghum x x x
Maize/Groundnut x x x
Sorghum/Millet/Cowpea x x
Maize/Yam/Cassava x x x
17
Maize/Sorghum/Groundnut x x
Maize/Yam/vegetable x x x
Maize/Yam/Cassava/Melon x x x
Millet/Cowpea x x
Millet/Sorghum/Cowpea x x
Millet/Maize x x
Maize/Soybean x x x
Maize/Cocoyam/Cassava x
Maize/Melon/Cassava x x x
Maize/Cassava/Cocoyam/vegetable x x x
Maize/Yam/Cassava/Cocoyam x x
Maize/Irish potatoes x x
Maize/Cotton x
Source: National Agricultural Research Project (1997)
2.6.2 Reasons for Cropping Pattern.
Factors that informed the combination of enterprises is a great deal of
uncertainty under which farmers operate. It could be inferred that the proximate risks
experienced by small-scale farmers were sufficient to completely mask any
difference in the household managerial ability, (Fawole and Oladele, (2007). The
risk of production and reliance on the market virtually force poorer producers to
adopt subsistence-oriented strategies. It therefore, implies that a farming system had
been evolved which emphasizes multiple cropping system in order to hold forth for
the risky nature, then subsistence become more pronounced.
Another reason for the mixed cropping pattern is to ensure food security for
the farm families. Household food security is implied this way as having food
available round the year (off season and during the season). Soil conservation is the
next in the order of importance. This reason may be inferred from the crop rotation
principles that tend to allow for soil rejuvenation when crops with different demand
on the soil are grown in sequence. The fact that cassava is used as fallow crop may
justify its inclusion in the cropping system.
2.7 Production of Cassava and its Market prospect for Smallholder Farmers
in Nigeria
Agricultural technologies have been selected on the basis that they will lead to
agricultures commercialization thereby enhancing rapid income generation for
farmers and private practitioner. In 1954, the average cassava yield in Africa was
between 5 and 10 tons per ha (Jones, 1959). In early 1991, the Collaborated Study of
18
Cassava in Africa yield measurements revealed that the average on-farm cassava
fresh root yield for the six COSCA study countries was 11.9 tons per hectare. For
(Nweke, 2004) cassava yield was increasing in Africa in the early 1990s because of
the planting of high yielding varieties and the adoption of better agronomic practices
– the average farm level yield was highest in Nigeria where the means was 13.1 tons
per ha (Nweke, 2004).
In the early 1960s, Africa accounted for 42 percent of the world production.
Thirty years later, in the early 1990s, Africa produced half of world cassava output
spearheaded by Nigeria; four fold increase in production and replacement of Brazil
as the world’s leading cassava producer (Nweke, 2004). While Brazil produced
nearly three times as much as Nigeria in the early 1960s, 21.9 million tons compare
to only 7.8 million tones in Nigeria the standing has changed. By 1990s Ghana
produced 7.2 million annually and advanced to the position of the third largest
producer in Africa after Nigeria and Congo (Nweke, 2004).
Cassava’s low input requirement, a trait that is compatible with Africa’s
resource endowment (weak rural credit market, relatively abundant and seasonal
labour scarcity) and the cassava’s resistance to pest and diseases explains the
expansion in cassava production. Moreover, as the average farm size shrink under
population pressure, farmers are searching for crops with a higher output of energy
per hectare as a strategy for overcoming hunger. Food shortages precipitated by a
combination of political and civil unrest, economic stagnation, erratic rainfall
patterns and rapid population growth have had a greater influence on cassava
production in Africa than anywhere in the world (Nweke, 2004).
Marketing of cassava as a cash crop plays a key role in the expansion of
cassava production. Farmers in most of the COSCA villages in Ghana and Nigeria
cited market access as the principal reason for their expansion of cassava area. While
in some other villages farmers cited difficult road access to market centers as the
reason for reducing the area planted with cassava. According to Nweke (2004) a
closely related critical variable in the expansion of the cassava area in Nigeria and
Ghana is the availability of improved processing equipment to remove water from
the roots (the roots are 70 percent water) and thereby reduce the cost of
transportation. He also added that improved processing and good preparation
19
methods reduce bulk and make it possible for cassava products to be transported at
reduced costs over poor roads to distant urban market centers (Information and
Communication Support for Agricultural Growth in Nigeria).
All parts of the crop (stem, leaves and tuberous roots) can be harvested for
specific market. In Nigeria, there is usually high demand for planting material of
improved varieties at the beginning of the planting season. Harvesting, packaging
and sale of stems can be made to increase the farmers’ profit margin from the farm.
2.8 Theoretical Framework
Innovation or technical change plays vital role in many areas or fields of
economics. Environmental economist are concerned with how new innovation
affects the environment, Natural Resource Economists are interested in new
innovation that improve the efficiency with which non renewable resources are used.
Many macro economists point to technological changes as the primary impetus for
economic growth.
Jhingan (2000) posited that a technical change or innovation consist of
discovering new methods of production, developing new products and introducing
new techniques. Technical change is synonymous with a change in the production
function, when there is a technical change; it leads to an increase in productivity of
labour and capital (inputs). This is represented diagrammatically by a shift towards
the origin and even a change in the slope of the isoquant. This signifies that more
output can be produced either with the same inputs or with fewer inputs.
20
t1
t
R
Capital
Figure 1
Lab
ou
r
0
labour saving technical
change.
A
B Q = f (K, A(t)L)
t1
t
R
Capital
Figure 2
Lab
ou
r
0
Capital saving technical change
Q =F (L, A (t) K)
21
Technical change could be input neutral or specific input saving as in fig 1 and 2.
Under input neutrality, the input ratio is constant but when input is specific, the input
ratio changes (marginal rate of technical substitution) In summary, technical change
results in increase in productivity of inputs. In agricultural production, the physical
inputs, that is, land, labour, capital are transformed by the farm firm under a good
management with the ultimate goal of maximization of profit, minimization of cost
and maximization of satisfaction or the combination of these, (Olayide and Heady,
1982).
2.8.2: Productivity indices and conditions for efficiency.
Total Physical Product (TPP) is the overall quantity of output resulting from
the transformation process of a given quantity of input (s)
TPP = Y = f (xi)
i – 1, 2, 3, .. .n.
Average Physical Product, APP
this is the ratio of the total product and total input used in the production process
APP = x
TPP
x
Y
t2 (technical
change)
t1 (technique 1)
Figure 3.
Input 0X
Ou
tpu
t
Production function.
22
Where Y and X are output and input respectively
Marginal Physical Product (MPP): This is the output response to a unit increase in
input during production process. It is the first derivative if the total physical product
functions with respect to the input.
MPP = DX
DY
Under a short run condition, that is, where one factor input varies and other inputs
are limiting in the production process Y = f(X1/ X2, X3 X4 …. Xn)
The marginal physical product increases the TPP at a decreasing rate,
increases the TPP at an increasing rate and increases TPP at a decreasing rate and
after this point the TPP decreases as depicted in the classical production function.
When the MPP = 0, TPP is maximized. A system is efficient when output is
maximized at a given cost or a given output is achieved at a minimum cost possible.
Innovation or technical change brings about an upward change in the
efficiency of the inputs in the production process.
2.8.3 Efficiency measurement
Various approaches to the estimation of the efficiency of factor inputs used in
production as used by scholars abound in literature.
Olukosi and Erhabor (1998) defined efficiency as the quantity of output (y) per unit
of input (x) used in a production process. They likened efficiency to Average
Physical Product (APP) measured in terms of
APP = y/x --------------------- (i)
Where:
APP = Average Physical Product (= efficiency)
y = output
x = input
Using equation (i) above, labour efficiency in the production of say 200kg of cassava
tuber is equal to the 200kg of cassava divided by the total amount of labour say 300
man-hours used in producing the 200kg of cassava tuber during the production
period. Arene (1995) used Kay’s approach to measure labour efficiency in rice
production in Nigeria
23
Ehui and Spencer (1990) stated that efficiency is synonymous with productivity,
which in turn is defined in terms of the efficiency with which factor inputs are
converted to outputs within the production process. They added that approaches to
the estimation of factor efficiency or productivity include:
i Partial Productivity
ii Total Factor Productivity (TFP)
Partial productivity of an input like Olukosi and Erhabor’s (1987) approach to the
estimation of factor efficiency; shows the ratio of output to a single input. On the
other hand, total factor productivity (TFP) measures the ratio of output to all input
combined. Partial productivity index number approach is considered to be
computationally simple and feasible especially as it provides insight into the
efficiency of an input in the production process. Yet its use is condemned on the
ground that it masks many of the factors that account for observed efficiency growth
(Ehui and Spencer 1990). Therefore total factor productivity based on
comprehensive aggregate of outputs and inputs that clarifies the issues of changes in
the quantity and quality of inputs as well as efficiency growth is often recommended.
(Cowing and Stevension,1981; Antle and Capalbo,1988). Two forms of the
estimation of total factor productivity include:
i. The growth accounting (or index number) approach.
ii. The econometric or parametric approach involving the estimation of the
production function.
2.8.4 Perfect Allocative Efficiency (PAE)
PAE =MVPXI = 1
MFCXI
At such point of economic efficiency, every naira spent in acquiring an
additional unit of the given input (xI) into the production process adds exactly one
naira to the total revenue (Olukosi and Erhabor 1998). The above framework is often
criticized on the ground that it heavily relies on average values, (Olagoke 1990).
However, several researchers (Ogunfowora et al,1986; Olagoke 1990), used the
method and reported various levels of allocative efficiencies of farmers under
various input levels under smallholder farming systems in Nigeria
24
2.8.5 Cost and return analysis
This analytical technique is otherwise referred to as enterprise budget. It
provides information on the financial and physical transactions or plan for the farm
enterprise for a given production period. Costs and returns analysis is often
composed of two major components: the cost or expenditure component and the
returns, revenue or income component. The costs component is further sub-divided
into variable cost sub-section for listing out the quantity and value of all variable
items, and fixed cost sub-section for the cost of fixed items such as tractor, storage
houses which do not change at least in the short run. The revenue component shows
the output or returns both in physical terms and the corresponding monetary
equivalent or gross revenue. The data from both components are further subjected to
computational analysis so as to ascertain the profitability situations of the farm
enterprise.
The use of costs and returns enterprises budget as an analytical tool is often
condemned on the ground that it does not provide satisfactory information on the
relative importance of the various inputs in contributing to output. Besides, the use
of data obtained can only be applied in the area from which the data were generated
since it uses only money as the unit of measurement. Its ease of computation and
simplicity once appropriate data have been generated, have so endeared the tool
among production economist and farm managers several of whom have used the
model in the profitability analysis of farm enterprises.
2.8.6 Functional forms of production
1. Linear function Y = a + bX1 + e
2. Cobb-Douglas function Y = eLAK B1
3. Double log: log Y = log a + b log X1 + e
4. Quadratic Y = a + b1X1 + b2X2 + e
The decision of where in the production function to produce hinges on the
regions where the marginal physical product curve first cuts the average physical
product curve and where the marginal physical product curve later cuts the input
axis. Invariably, it is the region in the cost function where the marginal cost is above
the average cost.
25
Profit is maximized where marginal value product equals marginal cost (Economic
efficiency condition).
Total profit is realized by subtracting total cost from total revenue
= TR – TC
Where TC = TFC + TVC
TR = Py.f (x1/x2 x3 .. xn.)
Where Py = Price of output
f(X1/X2x3 … Xn) = output
TFC = total fixed cost
TVC = total variable cost
Px1X1
Where the fixed costs are minimal, that is, the asset is insignificant; the gross margin
analysis is employed.
GM = TR – TVC
When there are two techniques of production that use the same inputs but different
levels of output, the technique that gives higher output hence more profit is
preferred.
2.9 Analytical framework
The analytical tools that will be examined are;
1. Multiple regression analysis
2. Stochastic Frontier Analysis
3. Probit model
2.9.1 Multiple regression analysis
Regression is one of the various econometric methods that can be used to
derive estimates of the parameters of economic relationship from a statistical
observation, (Koutsoyianis, 1977). Regression analysis is concerned with the study
of the dependence of one variable, the dependent variable, on one or more other
variables, the explanatory variables with the view of estimating and/or predicting the
(population) mean or average value of the former in terms of the known or fixed (in
repeated sampling) values of the latter (Gujarati, 2004) when many explanatory
26
variables are used to explain the behaviour of the dependent variable, it is a multiple
regression case.
Ordinary least method is used in parametric estimations from the samples of
the population because of the following; it results in unbiased estimators, efficient
estimators, linear estimators, best, linear unbiased estimators (BLUE), minimum
mean square error (MSE) estimator and sufficient estimators. Structurally multiple
regression function is specified implicitly as
Y = f(x1 x2 x3 … xn, u)
Explicitly;
Y = b0 + b1x1 + b2x2 … bnxn + u)
Y is the dependent variable while X1 are the explanatory variables and u is the
surrogate variable or error term. The error term may include omitted variables, error
from measurement, erratic behaviour of human being etc.
2.9.2 Stochastic profit frontier function
The stochastic frontier model was simultaneously proposed by Aigner, Lovell
and Schmidt (1992) and Meeusen and Van den Broeck (1997) who drew their works
upon the Farrel (1957) seminar paper on efficiency measurement in which he defined
production efficiency as the ability to a firm (farmer) to produce a given level of
output at lowest cost.
Broadly, three quantitative approaches are developed for measuring
production efficiency; parametric (deterministic and stochastic), non parametric
based on Data Envelopment Analysis (DEA) and productivity indices based on
growth accounting and indices theory principles, (Coelli, 1988). Stochastic frontier
analysis (SFA) and DEA are the most commonly used methods. (Ogundari, 2006).
Both methods estimate the efficiency frontier and calculate the firm’s
technical cost and profit efficiency relative to it. The frontier shows the best
performance observed among the firms and it is considered as the efficient frontier.
The SFA approach inquires that a functional firm be specified for the frontier
production function while DEA approach uses linear programming to construct a
piecewise frontier that envelops the observation of all firms (farmers). An advantage
of the DEA method is that multiple inputs and outputs can be considered
27
simultaneously and inputs and outputs can be quantified using different units of
measurements.
However, a strong point of SFA when compared to DEA is that it takes into
account measurement errors and other noise (errors) in the data. This point is very
important for studies of farm level data in developing economy like Nigeria as data
generally include measurement errors, (Ogundari, 2006).
Economic applications of Stochastic Profit Frontier Model for Productions
efficiency Analysis include: Adesina and Djato (1996) who applied the techniques in
the study of efficiency of rice farmers in Cote d’ Ivoire. Berger and Mester (1997)
applied the techniques to U.S. Banking Institute and Maudos (2003) applied the
technique to European banks.
Farm profit is measured in terms of Gross margin (GM) that equals the
difference between the total revenue TR and Total Variable Cost TVC
GM (II) = (TR – TVC) = PQ - WXi
To normalize the profit function GM is divided on both sides of the equation by P
which is the market price of the output.(cassava)
That is, P
PZ )(=
P
WXiPQ )( =
P
WXiQ
= f(XiZi) - Pi Xi
Where TR represent total revenue, TVC – Total Variable Cost, P represents Price of
Output (Q), X represent the quantity of Optimized Input used, Z represents Price of
Fixed Inputs used. Pi = W/P which represent normalized price of input Xi while f(Xi,
Z) represent production function.
Cobb-Douglas profit function in implicit form that specifies production
efficiency of the farmers is expressed as follows.
= f(PiZ) exp (Vi –Ui) i = 1, 2 ... n where
, Pi and Z as defined above. The V is the Independent, identically
distributed randomly errors and Ui is profit inefficiency effect.
The inefficiency model Ui is defined
Ui = o + iMi
Where Mi is the socio-economic variables to indicate their possible influence
on the profit efficiencies.
28
Standard Profit function assumes that markets for input and output are perfectly
competitive.
.2.9.3: Probit model.
Probit Model is one of the qualitative response models. Among the qualitative
response models are Logit, Linear Probability Model, for dichotomous models.
Linear Probability Model is inefficient due to the fact that the possibilities of
the responses are untruncated, that is, the values lie beyond 0 and 1 in violation of
probability concept. (0 (i/X) 1). Logit and Probit Model take the graphical
form of cumulative distribution functions. The Logit model uses (OLS) Ordinary
Least Square or the Weighted Least Square (WLS) for group data but it is difficult to
apply in individual data. It is preferred to probit when the sample size is as large as
the application of probit model for analysis involves complex integration. Logit
model is difficult to apply in individual data except with computer programmes that
uses (ML) a non-linear maximum likelihood estimation,(Gujarati, 2004). The Probit
and Logit Models are interchangeable because the result of analysis using them meet
the requirements of the test-statistic (likelihood ratio), Macfaden R2 , denoted by R2
Mcf.
29
CHAPTER THREE
RESEARCH METHODOLOGY
3.1 Study Area
The study area is Enugu State, of Nigeria. Enugu State was created out of the
old Anambra state. According to Ezike (1998), the state is located between
longitudes 6053′ and 7
055′E, latitudes 5
056′ and 7
006′N. Enugu State is
bounded in the east by Ebonyi State, in the north by Benue and Kogi states, in the
south by Abia State and west by Anambra State. It occupies an area of about
8,022.95 km2 (Ezike, 1998) and has a population of 3,257,298 people (NPC, 2006)
Enugu state is made up of seventeen local Government areas and is divided
into three agricultural zones namely;
Enugu Zone comprising Enugu East, Enugu North, Ezeagu, Igbo-Etiti and
Udi local government areas.
Awgu Zone comprising Awgu, Aninri, Enugu south, Nkanu East, Nkanu
west and Oji-River local government areas.
Nsukka Zone comprising Igbo-Eze South, Isi-Uzo, Nsukka, Udenu, Uzo-
Uwani, Igbo-Etiti local government areas (ENADEP, 1997)
Enugu state has a tropical climate with its characteristic high temperature all
year round. The State enjoys two distinct seasons. These are rainy (April to October)
and dry (November to March) seasons.
Enugu State’s climate supports the growing of the following crops; yam, cassava, oil
palm, cashew, cocoa, vegetables, maize, rice etc
3.2 Sampling procedure
The survey was carried out in Enugu State. Multi-stage random sampling technique was used to
select smallholder farming households from a list of 44,200 registered farming households in the
Enugu State Fadama Co-ordinating Office (ESFCO as at 2009). Firstly, two political zones were
randomly selected out of the three agricultural zones in the state. Three local government areas were
randomly selected from each of the two selected agricultural zones, making a total of six local
government areas. Four rural communities were then randomly selected from the list of ten
communities in each local government fadama desk office, making a total of twenty-four rural
communities for the study. Nine households were randomly selected from a list of households in the
selected rural communities. On the whole, a total of two hundred and nineteen (219) farm
households were sampled. Structured questionnaire and interview schedule were used to collect
30
data. Data were analyzed using descriptive statistics, stochastic profit frontier analysis and
Heckman’s two-stage model.
3.3 Method of data collection
Data collection was essentially from primary source. The primary data were
derived from a set of structured questionnaire administered to the respondents.
Interview schedule was used to supplement the structured questionnaire in the case
of illiterate respondents. The data collection was focused on the following; age
(years), educational level (years), farming experience (years), household size
(numbers), farm size (ha), prize per man day of labour (N), price per 50kg of
fertilizer (N), price per a bundle of cassava stem (N), average price of farm tools (N),
income of household head (N), average credit accessed (N), previous year’s profit
(gain=1, 0 otherwise), future year’s expectation ( gain = 1, 0 otherwise) etc.
3.4 Data analysis
Descriptive and inferential tools were employed to achieve the objectives of
the study. Specifically:
i) Objectives one and two were realized using tables, percentages and frequencies.
ii) Objectives three and four were realized using stochastic profit frontier analysis.
iii) Objective five was realized using Heckman’s two-stage model.
iv) Objective six was realized using a four point Likert Scale.
3.5 Model specification.
3.5.1 Stochastic Profit Frontier Analysis
The implicit Cobb-Douglas profit function that specifies production efficiency
of the farmer is expressed as follows;
i = f (Pi, Z) exp (Vi – ui)
Where i = 1, 2, 3, ….. n
Where (Gross Margin) = (TR – TVC) = (PQ – WX)
To normalize the profit function, p which is the market price of output (unit measure
of cassava) divides the gross margin on both sides of the equation above. That is /P
(p,z ) = ( PqQ – Wxi )/Pq = Q – WXi/Pq =f(X, Z) where TR = total revenue, TVC-
total variable cost – ( (PxXi ). Px represents the cost of output (Q), X represents the
31
quantity of the optimized input used , Z represents the average price of the fixed
inputs used, pi = W/P which normalized price of input X while f(Xi, Z ) is the
production function.
The extension of Cobb-Douglas work by Coelli (1996) to specify the stochastic
frontier function with behaviour inefficiency components and to estimate all
parameters together in one-step maximum likelihood estimation in the study area is
specified as follows;
Explicit function
In = Ln B0 + B1Ln Z1i + B2 1n P1i + B3 1n P2i + B4 1n Z2i + (Vi –Uii)
Where: represent normalized gross margin computed as total revenue less variable
cost divided by farm specific unit price (barrow or basin full)
Z1 represents average number of hectare (ha) put to use.
P1 represents average price per man-day of labour
P2 represent average price per 50 kg of fertilizer
Z2 represent average output price (barrow)
The inefficiency model Ui is defined by:
Ui = 0 + 1 M1i + 2 M2i + 3 M3i + 4 M4i
Where M1, M2, M3, M4 represent age (years), educational level (years), farming
experience (years) and household size (number). The socio-economic variables are
included in the model to indicate their influence on the profit efficiencies
If U1 > 0, the farmer is inefficient and losses profit as a result of inefficiency.
The estimates for all the parameters of the stochastic profit function and the
inefficiency model are simultaneously obtained using the program Frontier Version
4.1c (Coelli, 1996)
3.5.2 Heckmans Two -Stage Model
Heckman’s (1976) two-stage model will be used to realize objective five.
First the equation on the discrete decision of whether to adopt innovation or not is
estimated, and second, the equation on the extent of adoption of the innovation is
estimated with the inverse Mill’s ratio obtained from the first estimation included as
independent variable. The procedure is as follows: whether to adopt innovation or
not is modeled as
32
XZ ……………..(a)
Where Z= 1 if a household adopts innovation, Z=0 otherwise
Extent of innovation adoption
bUXY ......................
Where X is a vector of exogenous variables. Y>0 if Z = 1, and Y = 0 if Z = 0,
e, u – N(0, i ) with correlation . Equation (b)can be estimated as
euXZYE 1/
Where andandXXe ,/ are standard normal pdf and cdf respectively
of the first decision. So that equation (b) is estimated including as an explanatory
variable (Enete, 2003).
The implicit function(s)
E (Y /Z=1) = f (FS, APMDL, HHL, ACA, PYP, FPE, EdF, AF, ACCS,
ACFERT,U)
Explicit function;
E(Y/Z =1)= B0 + B1 FS + B2 APMDL + B3 HHI + B4 ACA + B5 PYP +
B6FPE + B7EDF +B8AF + B9AC FERT + ρσUλè +ε
Where Y represent the average price of a basin or barrow
FS represents farm size (ha)
APMD: represents average price/man day labour
HHI represents household income (N)
ACA represents average credit accessed. (N)
PYP represent previous year profit (gain or loss)
FYE represents future year expectation (gain or loss)
EDF represent level of education of the farmer (years)
AF represents the years of experience of the farmer
ACFERT represents average cost of fertilizer (50kg)
e =error term
Bs = coefficients
Bo = constant
33
CHAPTER FOUR
RESULTS AND DISCUSSIONS
4.1.0 Socio Economic Characteristics:
The socio economic variables of farmers may influence their decisions
regarding scale, enterprise diversification, and production processes. The socio-
economic variables considered in this study were age, sex, marital status, household
size, level of education, farming experience, income level of the farmer, farm size in
hectares and they are described below.
4.1.1 Sex of Respondents
Table 4.1 showed that the male constituted 73.5 percent of respondents while female
accounted for 26.5 percent of the sample. This is in contradiction with the traditional
belief that females grow more of cassava while males grow yam. It could be that
cassava production was becoming more economically viable.
Table 4.1: Sex description of respondents
Sex Frequency Percent Cum. Freq.
Male 161 73.5 73.5
Female 58 26.5 100.0
Total 219 100.0 100.0
Source: Field survey data 2009/10
4.1.2 Age of Respondents
The age distribution of respondents as shown in table 4.2 revealed that 56.2
percent of them fall within the age category of less than or 50 years, while 43.9
percent fall within the age category of greater than 50 yrs. This implies that young
and vibrant people are still involved in cassava production in the study area.
34
4.2 Age Description of Household heads
Age Frequency Percent Cum. Freq.
≤20 2 0.9 0.9
21-30 23 10.5 11.4
31-40 49 22.4 33.8
41-50 49 22.4 56.2
>50 96 43.9 100.0
219 100.00 100.00
Source: Field survey data 2009/10
4.1.3 Marital Status of Respondent
Table 4.3 showed that 79.5 percent of respondents were married, 5.0 percent
single, 3.7 percent widowed, and 11.9 percent divorced. Married heads of
households are most likely to have available labour for cassava production.
4.3 Marital Status of Respondents
Marital status Frequency Percent Cum. Freq.
Married 174 79.5 79.5
Single 11 5.0 84.5
Divorced 8 3.7 88.1
Widowed 26 11.9 100.0
Total 219 100 100
Source: Field survey data 2009/10
4.1.4 Level of Education of Respondents
Table 4.4 showed that 17.4% of the respondents had no formal education
while 82.7% had one form of education or the other; primary education accounted
for 34.7 percent, secondary education, 41.1percent while tertiary education was 6.9
percent. This is in contradiction with Kaindaneh (2007) that farmers cultivating
small farms are illiterate or uneducated. This implies that farmers in the area are
relatively educated and hence likely to be receptive to new innovations, and will
easily adopt them for greater productivity.
35
4.4 Educational Level Description of Respondents
Education Level Frequency Percent Cum. Freq.
No formal
education.
38 17.4 17.4
Primary 76 34.7 52.1
Junior secondary 9 8.7 60.7
Senior secondary 71 32.4 93.2
Tertiary 15 6.9 100.00
Total 219 100.0 100.0
Source: Field survey data 2009/10
4.1.5 Primary Occupation of Respondents
Table 4.5 revealed that 68.0 percent of the respondents had farming as
primary occupation while 10.5% were traders, 15.1% were civil servants and 6.4%
were artisan. This showed that majority of the respondents engaged in farming for
livelihood.
4.5 Primary Occupation Description of Respondents
Primary
Occupation.
Frequency Percent Cum. Freq.
Farming 149 68.0 68.0
Trading 23 10.5 78.5
Civil servant 33 15.1 93.6
Artisan 14 6.4 100.0
Total 219 100.0 100.0
Source: Field survey 2009/10.
4.1.6 Method of Land Acquisition
Table 4.6 below shows that 3.7% of respondents acquired land through leasehold,
31.5% rented land for farming activities, and 57.1% acquired land through inheritance while
0.5% and 7.3% acquired land through exchange and communal method respectively.
36
4.6 Distribution of Land Acquisition Methods
Land acquisition Frequency Percent Cum. Freq.
Leasehold 8 3.7 3.7
Rent 69 31.5 35.2
Exchange 1 0.5 35.6
Inheritance 125 57.1 92.7
Communal 16 7.3 100.0
Total 219 100.0 100.0
Source: Field survey 2009/10
4.1.7 Method of Capital Acquisition
Table 4.7 showed the four means of capital acquisitions, namely; government
subsidy or projects grants, personal savings, Bank loans and informal lenders. 83.1%
of the respondents’ acquired capital through personal savings, 15.1% acquired
through informal lenders, while those who acquired through government (0.9) and
banks (0.9) were very low. This could mean that the respondent had no collateral
securities to borrow from banks or that the banks interest rate was high. This
suggests that the respondents may be under credit constraints.
Table 4.7 Distribution of Respondents by Method of Capital Acquisition
Capital
acquisition.
Frequency Percent Cum. Freq.
Government 2 0.9 0.9
Personal saving 182 83.1 84.0
Banks 2 0.9 84.9
Informal lenders 33 15.1 100.0
Total 219 100.0 100.0
Source: Field survey 2009/10.
4.1.8 Household Size of Respondents
A household comprised of all persons who live under the same roof and eat
from the same pot (F.O.S 1985). Lipsey (1986) defined household as all people who
live under one roof and make joint financial decision. For the purpose of this study, a
37
household implies the head, wife or wives, children and other dependents that live
under the same roof. From the survey (table 4.8) households with sizes ranging from
4-6 accounted for 46.12% of respondents. Those whose sizes ranged from7-9 in
number accounted for 36.53%, the range of1-3 persons accounted for 9.59%, and the
household range of 10-12 and 13-15 accounted for 6.9% and 1.37% of the
respondents respectively.
Table 4.8 Distribution of Respondent according to Household Size
Household size Frequency Percent Cum. Freq.
1-3 21 9.59 9.95
4-6 101 46.12 55.71
7-9 80 36.53 92.24
10-12 14 6.39 98.63
13-15 3 1.37 100.0
Total 219 100.0 100.0
Source: Field survey data 2009/10
4.1.9 Cassava Farm Size (in Hectares) of Respondents
Farm size is affected by many factors including household size, available
arable land, level of capital of the farmer among others (Kaindaneh, 2007). Table 4.9
showed that on the average, respondents had farm size of 0.3125 hectares. Majority
(79.9 percent) of the respondents had farms whose sizes ranged from size range of
0.01 to 0.39 ha. This was followed by that of 0.42ha-0.74ha representing 9.58
percent. Farm size of range 1.5ha-2.0ha was rare accounting for 1.85 percent. This
result agrees with Ndubuizu (1990) that arable land per farmer was small. The farm
size distribution also agrees with Brundtland commission categorization of
agricultural system (WCED 1987), which suggested that resource poor agriculture
generally had small farm units, fragile soil and rain dependent and minimum inputs.
38
Table 4.9 Farm Size Distributions of Respondents
Farm size (Ha) Frequency Percent Cum. Freq.
0.01-0.39 1.75 79.9 79.9
0.40-0.74 21 9.58 89.48
0.75-1.17 8 3.65 93.13
1.18-1.50 11 5.02 98.15
>1.5 ≤ 2.0 4 1.83 100.0
Total 219 100.0 100.0
Source: Field survey data 2009/10
4.1.10 Household Income of Respondents
The household income is a source of capital for farm operation. Family
income may be channeled to any enterprise depending on the utility it provides to the
household. Table 4.10 showed that of the 219 respondents, 35.16 percent generated
income within the range of N98, 000.00 to N255, 000.00 annually. While the income
ranges of N260, 000.00 to N415, 500.00 accounted for 31.5 percent. The
respondents’ information did not show the proportion of income accruing from off-
farm activities
Table 4.10 Distribution of Household Income of Respondents
Household income
(N,000)
Freq. Percent Cum. Freq.
98,000.00-255,000 77 35.16 35.16
260-415 68 31.05 66.21
420-575 45 20.55 86.76
580-727 17 7.76 94.52
738-890 12 5.48 100.0
Total 219 100.0 100.0
Source: Field survey data 2009/10
39
4.2.0 Cropping System of Respondents
The cropping pattern covered in this study includes; rate/intensity (years) of
production of cassava based crops, status of fertilizer availability/use, land use
culture, intercrops, and fallow years.
4.2.1 Rate of Production of Cassava- based Cropping system
Table 4.11showed that 58.0 percent of the farmers grew cassava based crop
yearly while 42.0 percent did not. This could be that 42.0 percent of farmers have
limited supply of arable land as evident in the farm size distribution of farmers in the
study.
Table 4.11: Rate (years) Distribution of Cassava based Crop Cultivation
Rate Freq. Percent Cum. Freq.
Yearly 127 58.0 58.0
Not yearly 92 42.0 100.0
Total 219 100.0 100.0
Source: Field survey data 2009/10
4.2.2 Fallow Practices by Respondents
Table 4.12 showed that 17.8 percent of respondents grew cassava continually
on a given plot of land while 82.2 percent grew it for a year or two and left the land
to fallow for 1-4 years before coming back to it again.
Table 4.12 Fallow System of Respondents
Fallow practice Freq. Percent Cum. Freq.
Continuous cropping
39 17.8 17.8
Fallow practice (1-4 years) 180 82.2 100.0
Total 219 100.0 100.0
Source: Field survey data 2009/10
4.2.3 Intercrop Practices of Respondents
Table 4.13 showed that 74.9 percent of respondents intercrop cassava with
other crops in a season while 25.1 percent grow cassava as a sole crop.
40
Table 4.13: Distribution of Cropping Practices of Respondents
No of Respondent Percent
Intercrop
cassava with
other crops
164 74.9
Grow cassava
as Sole crops
55 25.1
Total 219 100.0
Source: Field survey data 2009/10
4.1.4: Cassava based intercrops in the study area
Table 4.14 presents the different crops interplanted with cassava in the area.
The table shows that some (38 % of respndents) interplant cassava with maize and
yam while some other farmers include melon and cocoyam to these two crops in
cassava basedintercrop. About 17 % of the respondents combine cassava with maize
and melon in an intercrop.
Table 4.14: Distribution of cassava-based intercrop in the study area
Cassava Base No of Respondent Percent
+ maize + yam 84 38.3
+ yam + maize + melon 64 29.2
maize + yam + cocoyam 34 15.5
+ melon + maize 37 16.9
Total 219 100.0
Source: Field survey data 2009/10
4.2.5 Fallow Years practices.
Fallow year is a period during which farmland is allowed to regenerate by
natural means the nutrient level that can support future agricultural production. The
fallow period could be affected by urbanization, population growth, availability of
fertilizer and other agrochemicals, and the incidence of pest on the farm. Table 4.15
revealed that 49.8 percent of the farmers practiced two years fallow period. This was
followed by periods of three, one, and four year’s fallow with 30.6, 11.4 and 8.2
41
percent of respondents respectively. This suggests that the fallow years were short
such that fertilizer addition maybe required in keeping farmers in production.
Table 4.15: Fallow year description among respondents
Fallow years Frequency Percentage
One year 25 11.4
Two years 109 49.8`
Three years 67 30.6
Four years 18 8.2
Total 219 100.0
Source: Field survey data 2009/10.
4.3.0: The effect of socio-economic variables on the Profit Efficiency of cassava-
based
enterprise in the study area.
In this section, profit efficiency of factor inputs as cost of labour, farm size, cost of
fertilizer, age of the farmer, educational level of the farmer, years of experience, and
household size were estimated.
Table 4.19: Summary Statistics of Variables for the estimation of the stochastic
profit
frontier model
Variable Min Max Mean Standard Dev.
1 Gross margin (N) 100.0 182,200.00 19,228.40 7,658.02
2 Average price of labour
(N)
600.00 1,800.00 977.63 212.67
3 Farm size (Ha) 0.01 2.0 0.31 0.38
4 Output price (N) 300.00 3,000.00 1149.77 423.91
5 Average price of fertilizer 3,750.00 5,200.00 4,038.81 738.77
6 Age (years) 20.0 50.0 43.0 10.70
7 Education level (years) 0.0 16.0 8.0 4.95
8 Year of experience (years) 1.0 56 18.70 10.99
9 Household size (No) 1.0 15.0 6.24 2.34
Source: Field survey data 2009/10
Table 4.16 gives the summary statistics of variables for the estimation of
stochastic profit frontier model. The mean gross margin of N19, 228.4 and standard
deviation of N7, 658.02 were recorded. The average farm size was 0.31ha with a
standard deviation of 0.38 ha. In addition, the average output price of N1149.77 with
N423.91 standard deviation per barrow load measure was recorded. The price of
fertilizer had an average price of N4,038.81and standard deviation of N738.77 per
42
bag. The price of labour was comparatively stable with average of N977.63 and
standard deviation of N212.67
The maximum likelihood estimations of the parameters of the stochastic profit
frontier model are presented in Table 4.17. The estimated coefficient of the
parameters of the normalized profit function based on the assumption of competitive
market and a rational producer were negative except for fertilizer that was positive.
The study also revealed that there was presence of profit inefficiency effects among
cassava farmers in the study area. It is confirmed by a test of hypothesis for the
presence of inefficiency effects using the generalized likelihood ratio test and
significance of gamma () estimate. The generalized likelihood ratio test which is
defined by the chi2 (2) distribution shows that the computed chi square of 32.13
was significant at P<5%. The null hypothesis was strongly rejected leading to the
preference of model 2.
Furthermore, the estimated gamma () of model 2 (0.99) was highly
significant at p < 1%. This implies that one sided random inefficiency component
strongly dominates the measurement error (and other random disturbance) indicating
that about 99 percent of the variation in actual profit arose from the difference in
farmers’ practices rather than random variability.
43
Table 4.17: Maximum Likelihood Estimates of the Stochastic Profit Frontier function
Variable Parameters Model 1 Model 2
General model
Constant B0 3.3853***
(7.2052)
3.212***
(10.37951)
Farm size B1 -0.005530
(0.189279)
-0.2856**
(2.704912)
Average price of labour B2 0.0974469
(0.80722)
-0.003110789
(0.119733)
Average price of
fertilizer
B3 -0.003268
(0.086741)
0.04296***
(4.4423)
Inefficiency
Constant 0 0 -0.3485850
(0.37534)
Age (years) 1 0 0.0526978**
(2.27415)
Educational level (yrs) 2 0 -0.268489*
(2.15022)
Yrs of experience (yrs) 3 0 -0.6975**
(3.18980)
Household size (No) 4 0 -0.0179547
(0.28980)
Variance
Sigma square 2 2 2
u v 0.238 0.2253***
(3.8672)
Gamma 2 2 2
u u v 0 .99**
(45.6727)
Log likelihood LLF 47.653923 65.7198
Source: Field survey data 2009/10 Figures in parenthesis are t-ratio
* Estimate is significant at P < 10%
** Estimate is significant at P < 5%
*** Estimate is significant at P < 1%
The positive coefficient of fertilizer was expected because the farmland in the
sampled area was generally under continuous cultivation with few cases of short
fallow years hence the fertility status is expected to be poor. The effect is significant
at p< 1 percent. The coefficients suggest that for every one naira spent on fertilizer, it
generates additional four (4) kobo to gross margin. The average price of labour was
negative as expected but was not significant. For farm size, a unit increase in farm
size reduces gross margin by 28 percent and is significant at p< 5 percent. This may
44
be because of the level of poverty among the farmers. Poor farm households are
usually undercapitalized (Enete and Achike 2008) and hence may not
proportionately increase other inputs as farm size increased. This result of the
estimates leads to the rejection of the hypothesis that all inputs have significant
effect on the gross margin.
The parameters for the determinants of profit inefficiency were reported in the
lower part of Table 4.20. The analysis of inefficiency models shows that the signs
and significance of the estimated coefficient in inefficiency models have important
implication on the profit efficiency of farmers. Based on this, all variables in the
inefficiency model have negative coefficients excerpt for age which was positive.
This implies that educational level; farm experience and household size decrease
with increased inefficiency. In other words, increase in these factors except age
increases the efficiency of the farmer. For age, inefficiency increases with aging.
This result is expected due to degenerating effect of age (senescence).
The positive effect of age is in agreement with the work of Abdulail and
Huffman
(1998). While the negative coefficients of educational level, years of experience and
household size agree with the work of Kumbhakar and Bhatta Charya (1992b) and
Ogundari (2006).
4.4.0: The effect of socio-economic variables of farmers on their adoption,
decision and the extent of adoption of improved cassava varieties in the
study area.
This section is devoted to evaluating the effects of the socio-economic variables as
cost of labour, farm size, cost of fertilizer, output price, age of the farmer, educational level
of the farmer, years of experience, and household size on the farmers’adoption and extent of
adoption decision regarding improved cassava varieties.
45
Table 4.18: Parameter estimates of the sample selection (Heckman two- stage) model
(Stata version 11.1 analysis result)
Variable Selection equation
Result (probability of
adoption)
Outcome equation
Result (extent of
adoption)
Price/barrow 0.0002771
(1.08)
0.0000327
(0.76)
Farm size -0.35961***
(-4.82)
-0.0469508
(0.31)
Average price of labour 0.0009702**
(2.16)
-0.0000195
(0.18)
Household income 1.07 e -07
(0.19)
3.65 e -8
(0.51)
Future year’s profit
(expectation)
0.2745696
(1.31)
0.01515
(0.39)
logeducational level 0.130408
(1.13)
0.0114568
(0.53)
Years of experience -0.0086576
(-0.89)
0.0023604
(1.49)
logaverage price of
fertilizer
0.0884329***
(3.50)
-.0006042
(-0.07)
Household size 0.1512229**
(3.13)
-0.0013902
(-0.10)
Age of respondent 0.0178763*
(1.69)
0.0008087
(0.46)
Constant -3.083594***
(-3.73)
0.354707
(0.86)
Mills
rho
-0.39800
Sigma 0.23608918
Source: Field survey data 2009/10
No of observation = 219
Censored observations = 91
Uncensored observations = 128
Wald Ch2 (10) = 5.96
Prob>Ch2 = 0.8745
Variables in parenthesis are t-ratios
*,**, ***, indicate significance at p<10%, p<5% and p<1% levels respectively.
46
Table 4.18 showed that the farm size was negatively related with the discrete
decision of whether or not to adopt and the continuous decision of extent of adoption
of improved cassava varieties. While the relationship with the extent of adoption was
not statistically significant, that of the discrete decision was highly significant (p<
0.01). This agrees with the previous observation on farm size and profitability, which
was explained with the level of poverty among farm households.
Price of labour was positively and highly significantly related with the
descrete decision of whether or not to adopt, but negatively, though not significantly
related with continuous decision of extent of adoption. The positive relationship is
surprising because high price of labour is supposed to act as a disincentive for
engaing in new cassava varieties. However, the data collected for this study was a
cross-sectional data. It is therefore possible that those farmers who paid higher
wages were also those who hired in more labourers and hence those who have the
greater capacity to adopt imprived varieties.
The price of fertilizer was positively and significantly related with the first
decision of whether or not to adopt but negatively, though not significantly, related
with the second decision of the extent of adoption of improved cassava varieties. Its
negative relationship with the extent of adoption is to be expected, as higher price of
fertilizer will curtail the quantity of fertilizer to be bought for use in cassava field
and hence also reduce the level of adoption. However, the positive and significant
relationship between price of fertilizer and whether or not to adopt is surprising. A
plausible explanation for this phenomenon is that higher fertilizer prices are
interpreted by some cassava farmers as signal of impending fertilizer scarcity,
motivating them to stocking fertilizer (Enete and Igbokwe 2009)
Household size was positively and significantly related with the descrete
decision of whether or not to adopt but negatively, though not significantly related
with the extent of adoption of improved cassava varieties. The positive and
important relationship with the first decision is to be expected as farmers with large
household size would expectedly be endowed with available household labour for
use in the farm. However, the negative relationship with the extent of adoption
suggests that this expectation may not have been met because of rural-urban
migration.
47
The age of the farmer was positively related with both the first decision of
whether or not to adopt and the second decision of the extent of adoption, with the
former’s reltionship being statistically significant. Experience, which comes with
age, may endow the farmer with the ability to take healthier production decision than
younger ones (Enete et al, 2002).
4.5.0 Constraints to the level of adoption of improved Cassava Production.
During the fieldwork component of the study, the responents were asked to
indicate the extent to which some hypothesized constraints were binding on them.
This was done using a four point Likert scale namely Strong =4, Mild =3, Not at all
=2, Do not know =1.
Table 4.19 represents the result of the analysis. Constraints whose average rank was
equal or above the average of 2.5 were considered as binding, while those below 2.5
were considered not binding. The table shows that all constraints listed were binding
on the respondents with the most critical of them being lack of capital. Enete and
Achike (2008), reported undercapitalization as a mjor factor inhibiting smallholder
farmers from adopting modern inputs. This also explains the second and third most
critical constraints- poor access to credit and low income of farmers respectively.
The respondents reported land scarcity as a binding constraint (mean = 3.3). This
may be due to the problem of land tenure which leads to unnecessary fragmentation
of farm lands and generally prevents farmers from having complete ownership of
farm lands (Nweke and Enete 1999). There were also constraints such as poor price
of farm products (mean = 2.9), lack of processing facilities (mean = 3.2) labour
scarcity (mean = 3.1) and cost of planting materials (mean = 2.9)
48
Table 4.19: Constraints to extent improved Cassava Production; descriptive
table
Constraints Min Max Mean Standard error Rank
Land scarcity 1 4 3.3 .87 4th
Cost of playing material 1 4 2.9 .85 9th
Poor price of cassava root 1 4 3.1 .81 8th
Lack of processing facilities 1 4 3.2 .74 6th
Lack of capital 1 4 3.7 .65 1st
Labour scarcity 1 4 3.1 .79 7th
Poor access of credit 1 4 3.3 1.15 2nd
Low income of farmers 1 4 3.4 0.81 3rd
Poor price of finished products 1 4 2.9 .79 5th
Source: Field survey data 2009/10
.
49
CHAPTER FIVE
5.0 SUMMARY, CONCLUSIONS AND RECOMMENDATIONS
5.1 Summary
Economic empowerment in developing countries is generally hinged on agriculture
and the wheel could grind to a halt in the absence of innovation in the sector. Agricultural
innovation increases the productivity of factor inputs and hence higher interest on factors of
production which affects the income and general welfare of the households. The inadequate
level production of cassava is often blamed on poor adoption of improved cassava varieties
among smallholder farmers in Nigeria. Available literature show that much work has been
done on the spread and adoption of improved cassava varieties, but not on the estimation of
the level of adoption of improved cassava varieties (hectare of land allocated to the
cultivation of improved cassava) and the profitability of factor inputs remain limited. This
study therefore analyzed adoption of improved cassava varieties and the profitability of
cassava in this regard The study sought to; (i) describe the socio-economic characteristics of
smallholder farmers; (ii) describe the cropping pattern among them; (iii) estimate the profit
efficiency of factor inputs; (iv) estimate the effect of inefficiency factors on profitability; (v)
estimate the factors affecting adoption and the level of adoption of improved cassava
varieties and (vi) identify the constraints militating against the adoption of improved
varieties of cassava.
The survey was carried out in Enugu State. Multi-stage random sampling technique
was used to select smallholder farming households from a list of 44,200 registered farming
households in the State Fadama Development Office (SFCO as at 2009). Firstly, two
agricultural zones were randomly selected out of the three agricultural zones in the state.
Three local government areas were randomly selected from each of the two selected
agricultural zones, making a total of six local government areas. Four rural communities
were then randomly selected from the list of ten communities in each local government
fadama desk office, making a total of twenty-four rural communities for the study. Nine
households were randomly selected from a list of 260 households in the selected rural
communities. On the whole, a total of two hundred and nineteen farm households were
sampled. Structured questionnaire and interview schedule were used to collect data. Data
were analyzed using descriptive statistics, stochastic profit frontier analysis and Heckman’s
two-stage model.
50
The result on the analysis showed that 73.5 % of farm household heads were male
while 26.5% were female. While 56.2% of the household heads fell within the age category
of less than 50years, 43.9% were above the age range showing that more vibrant people
were still involved in farming. Majority (79.5%) of household heads were married while
5%, 3.7%, 11.9% were single, widowed or divorced respectively. About 82.7% had formal
education, 17.4% had no formal education suggesting that on the average respondents were
educated. Farming was the primary occupation of 68.0% of the respondents while 32.0%
had farming as secondary occupation. While 57.1% of household head acquired farm land
through inheritance, 31.5%, 3.7%, 7.3% and 0.5% acquired theirs through rent, leasehold,
communal and exchange respectively. Capital was acquired mainly through personal
savings (83.1%) followed by informal lending (15.1%). Bank and government sources were
rare, accounting for 0.9% each. Farm household size ranged from 1-15. Sizes of 4-6 and 7-
9, accounted for 46.12%, 36.53% of respondents’ respectively. While 35.16% of
households generated income within the range of N98, 000.00 to N255, 000.00, 31.55% had
income range of N260, 000.00 to N415, 500.00 annually. There was no information to show
the proportion from off-farm activities.
Under cropping system, 58.0% grew cassava-based crops yearly and 42.0% did not.
Due to scarcity of arable land, 82.2% allowed the previously cultivated land to fallow for 1-
4 years while 17.8% grew cassava continually on a given plot of land. About 74.9% of
households intercrop cassava with other crops in a season and 25.1% grew cassava as a sole
crop.The popular intercrops were cassava, maize and yam accounted for 38.3% of
respondents while cassava, yam, maize, melon intercrops accounting for 29.2% of them.
The summary statistics of stochastic profit function analysis showed the mean gross
margin was N19, 228.40 and standard deviation of N7, 658.02; average farm sizes of 0.31
hectare and standard deviation of 0.38 hectare; output price per barrow of N1149.77 and
standard deviation of N423.91; the average price of fertilizer was N4,038.81 with N 738.77
standard deviation per bag; the price of labour was comparatively stable across the sampled
area with average of N977.63 per man-day and standard deviation of N212.67. the
estimated parameters of socio-economic variables using the stochastic profit frontier
analysis showed that farm sizes of households was negative and significant in generating
gross margin while the average price of fertilizer was positively and significantly related
with gross margin. For inefficiency factors, age was positively and significantly related with
gross margin while years of formal education, years of experience and household size were
negatively and significantly related with gross margin.
51
The parameter estimation of socio-economic variables on discrete decision of
whether or not to adopt and the continuous decision of extent of adoption of improved
cassava varieties showed that farm sizes were negatively and significantly related with the
discrete decision of whether or not to adopt while the price of labour in man-day, price of
fertilizer per bag, household sizes and the age of household heads were positively and
significantly related with the discrete decision too. These factors failed to affect the extent
of adoption significantly. The constraints militating against the extent of adoption of
improved varieties of cassava were identified. The constraints binding on the households
had capital as the most critical. This was followed by poor access to credit, low income of
households, land scarcity, poor price of finished cassava products, lack of processing
facilities labour scarcity and cost of planting materials respectively.
5.2: CONCLUSION
This study analyzed the adoption and the profitability of cassava production
in Enugu State. The study found out that socio-economic variables of the rural
households were limiting, typical of subsistence production. However, households
were willing to adopt improved cassava varieties but the business environment
would not help them do so in large scale. This was evident in the profit indices of
factors of production where increase in land allocated to cassava was associated with
great losses. This is a justification for intercropping cassava with other crops.
5.3: RECOMMENDATIONS
Based on the findings of the study, the following recommendations were
made towards achieving increased production of cassava by properly adopting
improved cassava varieties in Enugu State;
Government should investment in rural education through effective extension
delivery programme in the current political and economic environment in the
State. This will provide farmers with skills necessary for increased efficiency.
The government should secure enough arable land from communities that have
enough and make it available to individuals from the same communities for
agricultural production.
The Government should invest in agricultural sector to encourage diversification
of cassava products, that is- value addition, with effective demand to favour the
purchase of cassava roots at a profitable price.
52
Government should establish agricultural banks in the rural areas of the State to
provide soft loan and for easy accessibility to rural dwellers.
There should be subsidization of fertilizers and other agro-chemicals to enhance
their affordability by rural farmers. In addition, there should be provision of
credit input materials to farmers. These will encourage undercapitalized farmers
to adopt improved cassava varieties for better production
Generally, the Government should encourage the youths who are sources of
labour and more active in cassava production by giving them financial grants
and/credit. This will discourage rural-urban migration for white-collar jobs.
53
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60
APPENDIX 1
Department of Agricultural Economics
University of Nigeria
Nsukka
February 23rd
, 2009
Dear Sir/ Madam
I write to request for some information from you. The information required in the
questionnaire is very necessary to help me in a project work. Whatever information
supplied shall be handled in confidence and is strictly for research work.
Thanks for your understanding.
Yours sincerely,
Okorie, Oguejiofor Joseph
61
QUESTIONNAIRE
Section A: Socio-economic characteristics of respondent (cassava farmers)
Please tick a good ( ) or fill where applied
1. Name of respondent’s community ------------------
2. Head of household (tick) male ( ) female ( )
3. Age of household head (in years); (a) less than 20 (b) 21-30 (c) 31-40 (d) 41-50 (e)>50
4. Level of education; (a) Primary school, (b) Junior secondary school, (c) Senior
secondary school, (d) Tertiary school level, (e) No formal education.
5. Marital status: (a) Married --(b) Single--, (c) Divorced-(d) Widowed-
6. What is your major occupation? (a) Farming (b) Trading (c) Civil servant (d) Artisan
(e)others (specify)----
7. What is your minor occupation? (a) Farming (b) Trading (c) Civil servant (d) Artisan (e)
other (specify)-----
8. What is your Household size? (Nos)
9. How much is your annual household expenditure on (a) health.............. (b)
Education……………..(c) Property acquisition…………..(d) feeding…………. (e) farming….?
10. How many years have you been growing cassava?
Section B: Cropping Pattern in Cassava Production
11. Do you plant cassava on your plot yearly? (a) Yes ------- (b) No.------
12. If yes do you have access to adequate fertilizer supply? (a) Yes ------- (b) No. ------
13. Do you plant on different plots at different years (a) Yes ------ (b) No.------
14. Do you plant cassava as a sole crop? Yes ( ) No. ( )
15. If no, what crop(s) do you intercrop with cassava (a) Maize and yam (b) Yam, maize and
melon (c) Maize, yam and cocoyam (d) Others specify?
16. For your chosen intercrop, how many years does it take you to come back to the
previous farmed plot (a) 1 year (b) 2 years (c) 3 years (d) 4 years (e) others (specify)
17. What factor, if any, affect the time to come back to a piece to land previously
cultivated (a) Population Pressure (b) Fertilizer availability (c) access to credit (d) other
specify.
62
18. Why do you intercrop cassava with other crops (a) Increased Income (b) Food security
(c) Risk of crop failure (d) Fertility maintenance (e) all of the above.
Section C: Production Inputs per Plot of Cassava Farm
Labour:
19. What is the source of your farm labour? (a) Family labour (b) Hired labour (c) Family
and hired labour (d) Others specify.
20. What type of labour provider do you have? (a) Child (b) Women (c) Man
21. What is the maximum number of man-day used in your cassava farm fields for (a)
clearing------- (b) Cultivation --- (c) Planting ------ (d) Weeding ----- (e) Harvesting -------
(f) Fertilization -------
22. What is the average man-day cost in your cassava farm fields for (a) Clearing ------- (b)
Cultivation ------- (c) Planting -------- (d) Weeding------- (e) Harvesting ------- (f)
Fertilization -------.
23. What is the average child-day cost used in your cassava farm fields for (a) Clearing -----
-- (b) Cultivation ------------(c)Planting ------- (d)Weeding ------- (e) Harvesting ------- (f)
Fertilization -------
24. What is the average women-day cost in your cassava farm fields for (a) Clearing -------
(b) Cultivation ----------------(c) Planting ------- (d)Weeding ------- (e) Harvesting ------- (f)
Fertilization -------
25. How many times is weeding done in your cassava farm fields per season (a) 1 (b) 2 (c) 3
(d) 4
26. Do you own a tractor? Yes ( ) No( )
27. Do you hire the service of a tractor? Yes ( ) No ( )
28. If yes, indicate the average annual cost of hiring a tractor
Land:
29. Do you own a land? (a) Yes (b) No.
30. If no, how do you acquire land? please indicate (a) leasehold (b) rent (c) exchange (d)
inheritance (e) communal land.
31. How do you measure a plot in you area? (a) 50ft by 100ft (b) 60ft by 100ft (c) 100ft to
100ft (d) others specify.
63
32. How many farm fields do you have? (nos)
33. What is the size of your farm fields in plots? (a) 1 (b) 2 (c) 3 (d) 4 (e) others specify.
34. Please indicate the average cost (rent) of land per plot per annum if your farm is on
rent
Capital
35. What are the sources of your farm capital? (a) Government (b) personal savings (c)
bank (d) informal lenders (e) others specify.
36. What is the average credit on farm inputs accessed per annum ………….
37. Indicate the interest paid per annum on it………..
Cassava
38. Do you plant agric or local varieties of cassava?
39. If agric, how many bundles of the agric varieties of cassava did you use in your cassava
farm fields?
40. What is the average cost per bundle?
41. Did you use fertilizer in your cassava farm fields? (a) Yes (b) No.
42. If yes, how many 50kg bags of fertilizer did you use in your cassava farm fields?
43. What is the cost of 50kg bag in your area?
44. What proportion of your farm fields did you allocate to cassava? (a) 12 (b) whole (c)
quarter (d) other (specify) ---------------
45. What proportion of your cassava farm fields did you allocate to agric cassava? (a) 12
(b) whole (c) quarter (d) other (specify) ---------------
46. Did you make profit from your previous year harvest? (a) yes ( ) (b) No ( )
47. Did you plant agric varieties because of expected profit?
Sales
48. Did you sell your cassava in the market place or on the farm?
49. Did you sell some of your cassava stem?
50. How many bundles did you sell?
51. How much did you sell each bundle?
52. How did you sell your cassava root? (a) in barrow (b) basin
53. How many barrows or basin of cassava root did you harvest from your cassava farm
fields?
54. What was the average price per barrow or basin?
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Section D: Constraint
55.To what extent do you consider any of these constraints to your cassava
production? Please rate them as follows (strongly constraint; 4, mildly constraints; 3
No constraint, 2 Do not know; 1)
CONSTRAINTS 4 3 2 1
Scarcity of land
High cost of planting material
Poor price for unprocessed cassava
Lack of processing facilities
Lack of capital
Scarcity of labour
Poor access to credit
Low income of farmers
Poor price of finished cassava
product
APPENDIX 2
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67
68
69
70
71
72
APPENDIX 3
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