i
EFFECTS OF THE BENUE ADP’s CASSAVA PRODUCTION TECHNOLOGIES
ON THE PRODUCTIVITY AND
INCOMES OF WOMEN FARMERS
IN BENUE STATE,
NIGERIA
BY
ATAGHER, MONICA MWUESE
PG/Ph.D/02/32649
DEPARTMENT OF AGRICULTURAL ECONOMICS,
FACULTY OF AGRICULTURE
UNIVERSITY OF NIGERIA,
NSUKKA.
JULY, 2013
ii
EFFECTS OF THE BENUE ADP’s CASSAVA PRODUCTION
TECHNOLOGIES ON THE PRODUCTIVITY AND
INCOMES OF WOMEN FARMERS
IN BENUE STATE,
NIGERIA
BY
ATAGHER, MONICA MWUESE
PG/Ph.D/02/32649
A THESIS SUBMITTED IN PARTIAL FULFILMENT OF THE REQUIREMENT FOR
THE AWARD OF DOCTOR OF PHILOSOPHY (Ph.D) DEGREE IN AGRICULTURAL
ECONOMICS WITH SPECAILIZATION IN
AGRICLTURAL FINANCE AND ROJECT
ANALYSIS, DEPARTMENT OF AGRICULTURAL
ECONOMICS, FACULTY OF AGRICULTURE,
UNIVERSITY OF NIGERIA,
NSUKKA
iii
CERTIFICATION
This is to certify that ATAGHER, MONICA MWUESE, a Postgraduate student of the
Department of Agricultural Economics with registration number PG/Ph.D/02/32649 has
satisfactorily completed this research work as part of the requirement for the award of the degree
of Doctor of Philosophy (Ph.D) in Agricultural Economics with specialization in Agricultural
Finance and Project Analysis. The work embodied in this research work is original and has not
been submitted in part or full to any other degree of this University or any University. This
research work has been approved for the Department of Agricultural Economics, University of
Nigeria, Nsukka.
.................................................... ………………………………
Prof. E.C. Nwagbo Prof. E.C. Okorji
Supervisor Supervisor
……………………………….. ………………………………
Prof. E.C. Okorji External Examiner
Head of Department
iv
DEDICATION
This work is dedicated to the Almighty God, and to the special people in my heart namely my
parents, spouse, children, siblings, destiny helpers and the helpless
masses of farmers toiling day and night to feed the world.
v
ACKNOWLEDGEMENT
I am grateful to the Almighty God who has given me life and has ordered it according to
His divine purpose. Lord receive all the glory, worship and adoration for all You have done for
me.
My sincere appreciation goes to the three giant academic luminaries that anchored this
work namely my two supervisors late Professor E C. Nwagbo and Professor E.C. Okorji (current
Head of Agricultural Economics Department, UNN), and my external examiner, Professor C.C.
Eze of the Federal University of Technology Owerri. Sirs, your painstaking efforts in carefully
guiding and directing this research work through constructive criticism and suggestions have
produced maximum results. I am equally grateful to all professors in the Agricultural Economics
Department, University of Nigeria, especially Professor SAND Chidebulu, Professor A.I.
Achike, Prof. E.C. Eboh, Prof Arene, Professors N.J. Nweze, Dr. Enete, Dr. Okpukpara, Dr E.
C. Amaechina and all staff of the department. Time and space may not permit me to mention you
all by name but I appreciate your individual and collective efforts for the success of this work.
I am forever grateful to my parents late Mr. Ajekwe Ibamke Benave and Mrs. Pam
Mbakeghen Ajekwe for their love, and care for me which made them to lay a very solid
foundation for me. I am also very grateful to my husband Mr. Ionguga Atagher who took off
where my parents allowed him, and has nurtured me up to this stage. Our children, Maanenge,
Terdoo, Seumbur, Afa and Verashe have had to suffer long periods of my absence even in their
formative years. I really love and appreciate you all. May God continue to bless and enlarge your
coast. I highly appreciate the love and support of my entire family especially my siblings
namely Mbanengen (Late), Mbakoron, Tagher, Nguvaan and Akpen who have always been there
for me.
vi
I wish to acknowledge the contribution of all my lecturers at the university of agriculture
Makurdi namely Prof. G. B. Ayoola, Prof C.P. Obinne, Prof. P. E. Erhabor, Prof. J. Umeh, Prof.
I. Verinumbe, Prof. Avav, Prof, Ejembi, Dr. Abu; Dr V. Oboh and numerous others for their
invaluable input into my academic life. I also wish to register my gratitude to all my lecturers
from Ahmadu Bello University Zaria, namely the late Dr. Apeji, Prof. Chieze, Prof. Ebenebe,
Prof. Ega, Prof Agbo, Prof. Abalu, Prof. Ogungbile and several others for their considerable
investment into my academic life from my primary school days up to this stage.
I deeply appreciate my friend and destiny helper Dr.(Mrs) Phidelia Ramatu Waziri Ugwu
for all the assistance with the analysis of this work. I am also highly indebted to my friend Mrs
L.M. Nor who bought the form and processed my Ph.D admission for me, and to all my friends
especially Mrs Anans Tsumba, late Mrs. Comfort Madaki, late Mrs Margaret Nyebe, Mrs
Charity Iorfa, Mrs Becky Akura and Becky Agishi, late Mrs Beatrice K. Bako, Ngukwase
Surma, Anita Adoba, Joy Ikoku, Rosemary Ahom (nee Asan), Fatima Mohammed (Fati), Vicky
Otigba who have impacted my life positively and enabled me to move forward, and my in-laws
Nguwasen Atagher (now Nguwasen Hange) and Dr (Mrs) Rebecca Ape who were in ABU and
helped take care of „Amina‟ (Barrister Seumbur Atagher) while I went to school.
I also wish to appreciate the Rector and staff of Akperan Orshi College of Agriculture
Yandev Gboko Benue State, Nigeria. I also wish to appreciate Mr Tersoo Murphy of Benue State
University E-library who was my computer teacher who together with Engr. Mgbadike
Chukwuka David assisted me on the net. My PG friends Chineme (Fattest) and Miss Kelechi
Anyawu, who facilitated this work by sharpening my computer skills, My sisters in the Lord
Sister Sikemi Bamigboye, C.U. Ajoma, Grace Eba and others too numerous to mention,
including the entire Graduate Students‟ Fellowship (GSF) of UNN who provided the moral and
vii
spiritual support that made it possible to complete this work. May God bless you all. Always
ensure that you walk worthy of the Lord.
Finally I wish thank all the extension workers in Benue State who assisted the work in
various ways especially members of the Women in Agriculture (WIA) in Benue State, and all the
cassava women farmers who provided the information used for this research work. I am
convinced that your efforts will not be in vain for I believe God will use this research work to
increase us all.
.
viii
Abstract
This research work was carried out to assess the effects of the Benue ADP’s cassava production
technologies on the production and incomes of women farmers in Benue State, Nigeria. The
specific objectives were to identify and describe the cassava production technologies available in
the study area; to describe the socio-economic characteristics o f cassava women farmers in the
study area and determine their effect correspondents productivity and incomes; to determine
and compare the productivities and incomes of ADP and non-ADP cassava women farmers in
Benue State; Nigeria, and to identify the constraints (and prospects) that affect the productivity
and income from cassava production among women farmers in the study area. Three hypotheses
guided the study namely (i) Socio-economic characteristics of ADP and non-ADP cassava
women farmers in the study area have no significant effect on their output; (ii) there is no
significant difference between the productivity of ADP and non-ADP women farmers in the study
area, and (iii) gross margin from cassava enterprises among ADP and non-ADP women farmers
in the study area does not differ significantly. A multi stage sampling technique was used to
randomly select a total of 120 ADP and 120 non-ADP respondents from six Local Government
Areas of Benue State namely Vandeikya and Ushongo in zone A, Gboko and Buruku in zone B,
Okpoku and Ohimini in zone C. Data was collected through well structured pretested
questionnaires in addition to focus group discussions and personal observations. Secondary data
sources relevant to the study were also used. Information on respondents’ socioeconomic
characteristics such as age, level of education, marital status, household size, costs and returns
in cassava production and marketing among others were collected. The data was analyzed using
descriptive statistics as well as chi-square, multiple regression, total factor productivity and
gross margin analyses. Chi-square results showed that except for age and membership of
ix
farmers associations, socio-economic characteristics of study farmers such as level of education,
marital status, farming experience, family size (household size) had no significant effect on their
output. There was a significant difference between the output of ADP and non-ADP respondents.
Data analysis showed that 90.8% of the ADP and 70.1% of the non-ADP respondents were
below 50 years of age. Thus about 70-90% of all women farmers studied were below the age of
50. Among the ADP group, 78.2% of the respondents were married while 70.1 of the non-ADP
group respondents were married. Overall, 96.4% of all the respondents were married; divorced
or widowed. Among the ADP respondents, 81.6% had some form of education while less than
20% (or 18.49%) did not have any formal education. In the non –ADP group 63.2% had some
form of education while less than 40% (or 36.8%) did not have any formal education. Thus, 60-
80% of all cassava women farmers sampled were educated to a level while 20-40% did not have
formal education. Cassava women farmers in the study area had moderate family sizes. Seventy-
seven percent (77%) of the ADP and about 75% of the non-ADP had family sizes below 10 while
about20% of ADP and 23% of non-ADP respondents had family sizes between 10-20 persons.
Among the ADP farmers 37.9% have never belonged to any farmers’ association, 17.2% were
once members while 44.4% were still members of farmers associations. About 82.8% of the non-
ADP respondents have never belonged to any farmers association, 5.7% were once members,
and 11.5% were currently members. Results of data analysis also showed that 71.3% and 58.6%
of the ADP and non-ADP cassava women farmers respectively had been farming for close to 10
years and must have acquired the necessary experience successful cassava production. Chow’s
F-test showed that there was a significant difference between the productivity of ADP (2.96) and
non-ADP women farmers (1.68). This was attributed to the effect of improved cassava
production technologies and extension contact. The major variables that explained variations in
ADP cassava women farmers’ productivity were use of improved cassava stem cuttings, farm
x
size and access to credit which together explained 40.2% of variation in ADP productivity. The
major factors that explained variation in non-ADP cassava women farmers’ productivity were
years of education, family size and access to credit, which explained 93.0% of variation in non-
ADP productivity. Comparison of the mean gross margins of ADP (₦16,523.87) and non-ADP
respondents (₦3,777.56) using t-test showed that there was a significant difference between ADP
and non-ADP gross margins. This significant difference was attributed to the use of improved
cassava production technologies and extension contact (provided by the Benue ADP) by the
ADP cassava women farmers. Provision of credit, production resources such as fertilizers,
improved ‘seeds’, tractor services, rural infrastructure and others were recommended for
increased cassava productivity in the study area.
xi
TABLE OF CONTENTS
Title page - - - - - - - - - - i
Certification - - - - - - - - - iii
Dedication - - - - - - - - - iv
Acknowledgements - - - - - - - - v
Abstract - - - - - - - - - viii
List of tables - - - - - - - - - xiv
List of figures - - - - - - - - - xv
Chapter one
1.1 Background information - - - - - - 1
1.2 Problem statement - - - - - - - - 6
1.3 Objectives of the Study - - - - - - - - 10
1.4 Statement of Hypotheses - - - - - - - - 10
1.5 Justification of the Study - - - - - - - - 11
1.6 Limitations of the Study - - - - - - - - 12
Chapter Two
2.0 Review of Related Literature - - - - - - - 13
xii
2.1 Technology and its importance in Productivity Improvement - - - 13
2.2 Agricultural Household Income - - - - - 16
2.3 Concept of Efficiency - - - - - 17
2.4 Agriculture, Economic Growth, and Economic Development - - 21
2.5 Agriculture, Economic Growth, and Poverty Reduction in Nigeria - - 24
2.6 Agricultural Growth and Food Security in Nigeria - - - - 29
2.7 Economics of Cassava Production - - - - - - 32
2.8 Agricultural Development Projects (ADPs) in Nigeria - - - 35
2.8.1 Benue Agricultural and Rural Development Authority (BNARDA) - - 36
2.9 Empirical Studies of the Effects of Improved Agricultural Technology
Use in Nigeria - - - - - - - - - - 37
2.10 Theoretical framework - - - - - - - - - 41
2.10.1Concepts in the Theory of Production: Production Function and
Productivity - - - - - - - - - - 42
2.10.1.1 Measurement of Productivity - - - - - 44
2.10.1.2 Determinants of Productivity - - - - - 45
2.10.1.3 Review of Selected Determinants of Productivity - - - 46
xiii
2.10.1.4 Importance of Productivity - - - - - 48
2.12 Analytical Framework - - - -- - - - - 49
2.12.1 Regression Analysis - - - - - - - - 49
2.12.2 Enterprises Analysis - - - - - - - - - 51
2.12.3 Tests of Significance - - - - - - - - 53
Chapter Three: Methodology - - - - - - 55
3.1 Study Area - - - - - - - - 55
3.2 Sampling Technique - - - - - - - - 56
3.3 Method of Data Collection - - - - - - - 57
3.4 Analytical Technique - - - - -- - - - 58
3.4.1 Model Specification - - - - - - - - 58
3.4.1.1 Output Model - - - - - - - - - 58
3.4.1.2 Total Factor Productivity - - - - - - 60
3.4.1.3 Gross Margin Analysis - - - - - - 60
3.5 A priori Expectations - - - - - - - 61
3.6 Tests of Significance - - - - - - - - 64
3.7 Hypotheses Testing - - - - - - - 65
xiv
3.7.1 Chi-square Analysis - - - - - - 65
Chapter Four - - - - - - - - 67
Results and Discussions - - - - - - - - 67
4.1 Socio-economic Characteristics of Respondents - - - - - 67
4.1.1 Age Distribution of Respondents - - - - - 68
4.1.2 Marital Status of Respondents - - - - - - - 69
4.1.3 Level of Education of Respondents - - - - - - 70
4.1.4 Family Size of Respondents - - - - - - - 72
4.1.5 Memberships of Farmers Associations - - - - - - 73
4.1.6 Farming Experience of Respondents - - - - - - 75
4.2 Costs and Return Analysis of Cassava Enterprises of Respondents - - 77
4.2.1 Comparison of ADP and non-ADP Output and Hypothesis Testing Two - 78
4.2.1.1 Determinants of the ADP Respondents Output - - - 80
4.2.1.2 Determinants of non-ADP Output - - - - - - 84
4.2.1.3 Determinants of Pooled Respondents‟ Output - - - - 87
4.3 Gross Margin Analysis and Testing of Hypothesis Two - - - - 90
4.3.1 Determinants of ADP Gross Margin - - - - - - 91
xv
4.3.2 Determinants of Non-ADP Gross Margin - - - 93
4.3.3 Combined or pooled Gross Margin of Respondents - - - - 95
4.4 Improved Cassava Technologies Available in Benue State - - -
97
4.5 Constraints to Cassava Production and Distribution in Benue State - 98
4.5.1 Perceived Problems of Respondents - - - - 98
4.5.2 Prospects for Cassava Production in Nigeria - - - - 101
4.5.3 Facilitators of Agricultural Production and Productivity - - 101
4.5.3.1 Rural Roads in the Study Area - - - - - 101
4.5.3.2 Rural Water Supply in the Study Area - - - - 102
4.5.3.3 Type of Cooking Fuel used by Respondents in the Study Area - - 104
4.5.3.4 Respondents‟ Sources of Fuel Cassava for Production - - 107
Chapter Five: Summary, Conclusion and Recommendations 109
5.1 Summary - - - - - - - - - - 109
5.2 Conclusion - - - - - - - - - 113
5.3 Recommendations - - - - - - - - 115
5.4 Contribution to Knowledge - - - - - - - 117
5.5 Areas for future research - - - - - - - - 117
xvi
References - - - - - - - - - - 118
Appendix - - - - - - - - - 137
xvii
LIST OF TABLES
TABLE PAGE
4.1 Age Distribution of Respondents - - - - - - - 69
4.2 Marital Status of Respondents - - - - -- 70
4.3 Level of Education of Respondents - - - - 71
4.4 Family Size of Respondents - - - - - 73
4.5 Membership of Farmers Association Among Respondents - - 74
4.6 Farming Experience of Respondents - - - - 76
4.7 Costs and Return Analysis of Cassava Enterprises of Respondents 77
4.8 Determinants of ADP Respondents Output - - - - 80
4.9 Determinant of non-ADP Respondents Output - - - 84
4.10 Determinants of Respondents‟ Pooled Output - - - 87
4.11 Determinants of ADP Respondents Gross Margin - - - 91
4.12 Determinant of non-ADP Respondents Gross Margin - - 94
4.13 Determinants of Respondents Pooled Gross Margin - - - 96
xviii
LIST OF FIGURES
FIGURES PAGE
4.1 Identical Constraints of Respondents 100
4.2 Road Network of the Respondents 102
4.3 Water-Supply System of the Respondents 103
4.3.1 Water-Supply System of the Respondents (Separated) 104
4.4 Sources of Cooking Fuel of Respondents 105
4.4.1 Separated Sources of Cooking Fuel of Respondents 106
4.5 Sources of Funds for Respondents among Respondents 107
1
CHAPTER ONE
INTRODUCTION
1.1 Background Information
Nigeria is a country with great natural and human resource endowments. According to
Ayoola (2009), Nigeria is a country that covers 98.3 million hectares with a largely rural
population of about 150million comprising 350 ethnic nationalities. The country measures 1200
kilometres (km) from East to West, and about 1500km from North to South. Nigeria is blessed
with other natural resources such as petroleum and solid mineral deposits. The water resources
consist of large water bodies of surface water (268 billion cubic metres), underground water (58
billion cubic metres) and an extensive coastline coupled with an annual rainfall in the range of
300- 4000milimetres per annum. These features imply that the country is endowed with vast
physical and human resources required for accelerated development of its agricultural economy.
However, despite Nigeria‟s great resource endowments, Nigerians are among the poorest
people in the world (UNDP, 2005; Nsikakabasi and Ukoha, 2010). In spite of oil wealth and
revenues amounting to over 300 billion US Dollars since 1970s, Nigeria is still a poor country
where per capita income averaged only $1075 in 2009 (Central Bank of Nigeria, 2009). Since
non-oil export receipts are small, export revenues are greatly influenced by oil and gas prices.
Government‟s fiscal policy that depends on oil and gas prices fluctuates in line with these prices.
The major challenges facing the country are stabilizing expenditures and ensuring the
government‟s ability to meet social and human development goals (UNDP,2008). The human
development report by the United Nations Development Programme, UNDP (2005) revealed that
Nigeria is one of the poorest among poor nations of the world. With a human poverty index HPI-
2
1 value of 38.8%, Nigeria is ranked 75th
among 103 developing countries. According to the
National Bureau of Statistics (2005), about 52% of Nigerians are living in poverty and about
70million people live on less than 1US dollar a day.
Dauda (2002) reported that poverty in Nigeria, like in other developing countries has a
predominantly female face and that women in the rural areas of the country suffer the harshest
deprivation and are extremely vulnerable to poverty. According to the author, of the one million
adults who have no access to basic education, 60% are women. Furthermore, women are
particularly disadvantaged since over 68% of female headed households are living in poverty.
The International Monetary Fund (2004) observed that Nigeria has significant gender
inequalities in women‟s labour market participation, remuneration, health and human capital,
with indicators for women being recorded as substantially lower than those for men are. Women
in Nigeria are likely to be poorer than men and have fewer options for escaping poverty. Widows
are more vulnerable to poverty than widowers as a result of patriarchal property rights and
inheritance practices. Furthermore, since women have less formal education than men, they tend
to be disproportionately confined to lower return, low productivity, and employment in the
informal economy with limited ability to escape poverty through employment. According to the
International Fund for Agricultural Development (2001), more than 50% of the population is
affected by HIV/AIDS, and 50million Nigerians majority of them women and children suffer
from a combination of protein energy malnutrition, vitamin A deficiency, iron deficiency
anaemia, and iodine deficiency diseases. The apparent improvement in growth indices since
2004 is yet to be translated into welfare improvement for Nigerians, a situation that Eboh (2011)
termed „jobless growth‟.
3
One of the major causes of poverty in Nigeria is the decline in agricultural productivity
consequent to over dependence on oil. Prior to the discovery of oil in the 1970s, agriculture was
the mainstay of the Nigerian economy, accounting for about two-thirds of the gross domestic
product (GDP) and 75% of export earnings (Ayoola, 2009). From the standpoint of occupational
distribution and contribution to GDP, agriculture was the leading sector. During this time,
Nigeria was the world‟s second largest producer of cocoa, largest exporter of palm kernel and
palm oil, Nigeria was also a leading exporter of other commodities such as cotton, groundnut,
rubber, and hides and skin (Ogen, 2007). With the oil boom, agriculture‟s contribution to GDP
declined to about 25% by 1980, later moved up to 41% by 2001-2004. Consequently, Nigeria
moved from being a large exporter to a major importer of agricultural products (CBN, 2005;
Yusuf and Adenegan, 2008). The low agricultural output has led to the poor performance of the
food subsector as food demand became higher than food supply. This has induced high increases
in the country‟s food imports from about N8billion in 1996 to over N183billion in 2005, and
increased the prices of major staple crops in the country (CBN, 2005). Other causes of poverty
include the insufficient and poorly distributed GDP growth in combination with high population
growth and inadequate job creation to absorb the growing labour force, volatile oil revenues,
weak governance and corruption which have continually hindered public sector initiatives to
reduce poverty(IMF, 2004; United States Agency for International Development, 2007).
In recognition of the critical role of agriculture to the country‟s economic development,
many Nigerian governments introduced various measures to boost agricultural production and
alleviate poverty in the country including the Agricultural Development Projects (ADPs). Most
of these programmes have failed to produce the desired results ( Idachaba, 1991; Nnadozie and
Nwanu, 2002, Ogwumike, 2009). According to Eboh (2011), in spite of successive
4
progammes, the economy remains undiversified and highly skewed, as crude oil still accounts
for more than 95% of total export revenues and up to 80-85% of government revenues, but
contributes less than 4% of total employment. Agriculture‟s contribution to GDP is presently
about 41%. Ogwumike (2009) explained that the major reasons for failure of poverty alleviation
efforts in Nigeria include programme inconsistency, poor implementation, corruption of
government officials and public servants, poor targeting mechanisms, and the inability to focus
directly on the poor ( in terms of identifying the poor and the nature of their poverty). He further
explained that sustainable poverty reduction in Nigeria would require the proper identification of
the poor (their characteristics and survival strategies) as well as a multi pronged approach in
tackling the poverty problem given its multidimensional nature.
Presently, agricultural development forms an important component of Nigeria‟s overall
national prosperity ambition to become one of the top 20 economies in the world by the year
2020 (vision 20-20-20). To achieve this, the Human Development Report of the UNDP (2008)
estimated that Nigeria would require overall growth of above 10% on a consistent basis to attain
this vision. As a result, Nigeria has set targets for year 2020 namely a GDP of US$900 billion,
out of which 15% (or US$135billion) is to come from agriculture, and a per capita income of
US$4000. These impressive targets are set based on the expectation to make optimal use of non-
oil sources of economic growth such as agriculture and others. Nigeria wants to achieve in the
medium term an average annual GDP growth rate of 11% from the 7% growth during 2004-
2009. It is expected that the country‟s GDP would increase from US$212billion in 2008 to
US$333 billion in 2013 while annual per capita GDP is expected to increase from US$1075 in
2009 to US$2008 in 2013. Similarly, non-oil exports (mainly from agriculture) are targeted to
5
grow at an average annual rate of 30.0% from 2010-2013. A major component of these exports is
to be cassava (Eboh, 2011).
According to the Food and Agriculture Organisation (2004) and the Federal Ministry of
Agriculture and Water Resources (2008), Nigeria produces about 49million metric tonnes (MT)
of cassava annually, an increase of 44% from the previous annual output of 34millionMT. The
Federal Government has targeted a 100% increase in the annual yield of cassava to 100million
MT by 2011. FAO(2004) observed that past increases in cassava yield have been due to
increases in land area cultivated rather than increases in yield per hectare. This trend, Erhabor
and Omokaro(2007) warned is not sustainable because of competing demand for land from other
uses. Hence the urgent need to raise cassava yields through productivity increase rather than land
area expansion.
To increase the current level of production, there is need to examine other ways through
which productivity increases could come. One possible way is through the improvement of the
productivity of cassava women farmers. Women in Nigeria play a central role in cassava
production, processing and marketing, contributing about 58% of the total agricultural labour in
the Southwest, 67% in the Southeast, and 58% in the central zones. To cope with the severe
economic crisis now being experienced by many sub-Saharan African countries (including
Nigeria), human resources, especially women, have become a component of many national
development plans (for instance Nigerian women form part and parcel of the national efforts to
attain the millennium development goals and vision 2020 among others). Cloud and Knowles
(1988), Rahman (2008), Alufohai and Ojogho (2010) that women of many countries are the
principal producers and sellers of food, who in addition to their domestic activities spend lengthy
hours in these efforts. Studies show that the role women play and their position in meeting the
6
challenges of agricultural production and development are both dominant and prominent.
Therefore, they should receive assistance (Cloud and Knowles, 1988; Rahman, 2008, Alufohai
and Ojogho, 2010). According to World Bank (1994) development and growth are best served
where scarce public resources are invested where they yield the highest economic and social
returns, and that indeed social returns, are on the whole greater for women than for men and that
investing in the women helps achieve these goals since the economic and social returns to such
investments are higher.
1.2 Problem Statement
Several studies have demonstrated the significant contribution of Nigerian women to
agriculture ( Arene and Omoregie, 1991; Ijere, 1991; Ogbimi and Williams, 2001). In Nigeria,
women make up to 60- 80% of agricultural labour force producing two-third of food crops
(World Bank, 2003) and 80% of the food, and are involved in food production, food processing
and marketing ( World Bank, 1994; CTA, 2002). As much as 73% of women are involved in
cash crops, arable crops and vegetable gardening, post-harvest activities(16%), agro-
forestry(15%) while in some states, rural women have virtually taken over the production and
processing of arable crops (Afolabi, 2008).
In spite of the fact that women make numerous contributions to agricultural production,
widespread assumption that men and not women make key farm management decisions has
prevailed. So when resources are released for agricultural development, women are often
marginalized or even excluded (Ijere,1991; Oluwasola, 1998; IFAD, 2001). As a result, 70% of
the world‟s poorest people (including Nigeria) are women (IFAD, 1992; United Nation
Development Programme, 2004, Nsikakabasi and Ukoha, 2010). Reasons for the neglect of
7
women‟s contribution to agricultural development include the small and fragmented nature of
their farms, their lack of education, information and technical skills, their numerous domestic
chores, lack of interest among planners on the role of women, societal attitude and traditions in
the African society among others. According to Ogbimi and Williams (2001), the reason for
women‟s limited access to income and economic opportunities is that women work at the margin
of development efforts and programmes.
The establishment of the agricultural development programmes in Nigeria ushered in a
new era in the history of Nigerian agriculture because for the first time an agricultural
development programme focused attention on women farmers as an important component of
agricultural development. A special component called the women in agriculture (WIA) was
incorporated for women in 1990. While the general aim of the ADP was to raise farm
productivity and standard of living of farm families, WIA was to address the peculiar needs of
women farmers especially in gender specific issues, with emphasis on 70% production and 30%
post harvest technologies. This was to harness the total farm agricultural capabilities of farm
women, so as to build better lives for them, their families, communities, and the nation at large.
The specific objectives of WIA are to improve extension outreach to rural women; to train and
encourage women farmers to adopt and use improved technologies in agricultural production,
processing and utilization; to source and develop through research sustainable recommendation
of technologies for activities solely performed by women; to train women in income generating
activities by facilitating and motivating women farmers to form cooperative groups to strengthen
and enable them acquire productive skills; to liaise and collaborate with national, and
international organizations that have programmes for women (World Bank, 1996). The ultimate
goal was to raise the income of women from agricultural enterprises.
8
Since 1990, the WIA in Benue ADP has disseminated different technologies to women
farmers in the state such as: (a) Crop varieties (maize, soya bean, rice, groundnuts, cassava,
beniseed, sweet potatoes, and cowpea). (b) Yam minisett technique. (c) Crop mixtures
(yam/cassava/maize/egusi alternate row, soya bean/maize, soyabean/sorghum,
groundnut/cassava, groundnut/maize, groundnut/sorghum, and rice/maize). (d) Livestock
production (piggery, rabittary and poultry). (e) Fishery (homestead fish production, pond
construction, stocking and feeding, cultural practices, checking of overflow, checking of weeds,
fish feed formulation etc). (f) Agro forestry (bee keeping, management of beehive, honey
harvesting, snail farming and mushroom production). (g) Fadama (vegetable production,
management and use of tube wells, wash bores, and water pumps). (h) Post harvest innovations
(processing, packaging, storage and marketing strategies) to women farmers in Benue State
(BNARDA,1997).
Among the crop varieties, Cassava production technology was selected because it has
many advantages over the other technologies namely :(i) cassava is one of the dominant crops in
the study area, (ii) there is no cultural restriction on cassava production by women in the
area,(iii) the renewed international interest in the cassava crop as a source of biofuels (ethanol)
has raised the importance of the crop. Other reasons are: (iv) Nigeria has potential comparative
advantage(ability to produce at lower opportunity cost than others) in cassava production
(Ayoola, 2009) in terms of a conducive climatic environment, abundant human and material
resources, and favourable government policies (Fakayode et al, 2008) and others making it most
suitable for this study. The cassava crop itself has some desirable qualities; it can be produced
profitably because of its comparative low labour input (Erhabor and Omokaro, 2008). The crop
can produce a reasonable crop on marginal soils too poor for other crops (FAO, 2000). This is a
9
major production advantage because in most cases women are allocated marginal lands to
cultivate while men usually get the fertile ones. Besides, cassava is easy to process and responds
readily to improvement. As a cash crop, cassava generates more cash income for the largest
number of households than other staples, contributing positively to poverty alleviation and rural
welfare (Enete, 2007) . These and other features have endowed cassava with a special capacity to
contribute to food security, equity, poverty alleviation, and environmental protection (Clair, et al,
2000), making it very suitable for studying.
Casley and Kumar (1987) observed that in development projects as well as other areas of
human endeavour, well planned and sincerely executed efforts do not necessarily produce the
desired results. About 50% of Benue state and its environs are involved in cassava production on
a land area of about 164,550 hectares with individuals cultivating an average of about 1.36
hectares (Erhabor and Obiagwu, 1995; Daudu, et al, 2008 ). It is important to find out what
really happened especially in the Benue ADP‟s WIA programme in order to incorporate the
lessons into further planning for agricultural development in Nigeria. Also there is a gap in
knowledge about the effect of the WIA programme on women farmers‟ productivity and
incomes in Benue State generally. This study‟s aim is to fill this gap. The study seeks to answer
the following questions: how have the women farmers in the study area fared since the
introduction of the project? Are Benue state women farmers better off economically, socially as
a result of their participation in the project? Specifically do these women farm better and earn
more incomes? What are the constraints, opportunities and lessons learnt from the project? How
can all these be incorporated into Nigeria‟s current efforts to improve agricultural productivity,
provide food security, reduce poverty, and to become one of the 20 most industrialized nations in
the world in 2020?
10
1.3 Objectives of the study
The broad objective of this study is to determine the effect of the Benue ADP‟s cassava
production technology on the productivity and incomes of women farmers in Benue State,
Nigeria.
The specific objectives were to:
(i) identify and describe the cassava production technologies available in the study area.
(ii) describe the socio-economic characteristics of cassava women farmers in the study area
and determine their effect on respondents‟ productivity and income;
(iii) determine and compare the productivities and incomes of ADP and non-ADP cassava
women farmers in Benue State, Nigeria;
(iv) determine whether there are any constraints or prospects for increased cassava
production in the study area.
(v) make recommendations based on research findings.
1.4 Hypotheses
The following null hypotheses were tested:
(i) Socio-economic characteristics of ADP and non-ADP cassava women farmers in the study
area have no significant effect on their output.
(ii) There is no significant difference between the productivity of ADP and non-ADP women
farmers in the study area.
11
(iii) The gross margin from cassava enterprises among ADP and non-ADP women farmers in
the study area does not differ significantly.
1.5 Justification for the study
In a historic move, the agricultural development projects introduced to promote
agricultural and rural development in Nigeria incorporated a sub-component, the women in
Agriculture (WIA) for the first time to take care of the needs of women farmers. Other
components such as the provision of rural credit, rural infrastructure among others are also of
direct and indirect importance to the women farmers. An examination of how the rural areas and
the women farmers have fared since is necessary to enhance the understanding of the way
forward for agriculture and rural development in Nigeria. This study will be of benefit to
stakeholders in agriculture and rural development in the study area and Nigeria as a whole
namely-government policy makers, international donor organizations, non-governmental
organizations, researchers, women farmers, and farm families.
This study was undertaken to provide an independent and impartial revelation of the effect
(positive and negative) of this landmark policy and also reveal its weaknesses and strengths. This
was to help all concerned to understand how the project was received, and give an insight on
how the women farmers view the project, and how they, their families and the rural areas have
fared. The results of this work have provided a feedback which will enable agricultural planners
to modify and refine on – going projects, evaluate successes and failures. This could provide
lessons for future planning and investment in agriculture in Nigeria to the benefit of all
concerned with agriculture, rural development and poverty reduction.
12
1.6 Limitations of the Study
Time and financial constraints limited the sample size, duration and extent of data
collection. The study was also constrained by the accuracy of respondents‟ records. They hardly
kept records of their farm operations and in most cases depended on memory recall. Again most
of the values they gave were estimates. Also in spite of the explanation that we were not
government agents out to assess their incomes for tax purposes, some were still suspicious. So
they could have downplayed their benefits and exaggerated their expenses here and there.
However, irrespective of these shortfalls, the results of the field data were taken as the true
situation of the study area, and therefore did not diminish the value of research outcome hence its
reliability.
13
CHAPTER TWO
REVIEW OF RELATED LITERATURE
2.1 Technology and its Importance in Productivity Improvement.
Technology is the making, usage and knowledge of tools, machinery, techniques, crafts,
systems or methods of organization in order to solve a problem or perform a specific function. It
can also refer to a collection of such tools and machinery. The word technology comes from the
Greek word “technologia” which is coined from two Greek words: te‟chne‟ meaning “art, skill,
craft” and “logia” which means study of. The term technology can be applied generally to
specific areas, for example construction technology, medical technology, agricultural technology
among others. According to Eiyedum (2003) improved productivity in Nigeria and other
countries require continuous inflow of information of innovations on how farm operations could
be effectively and efficiently carried out. This is agricultural technology.
Agricultural technology consists of equipment, machines, and implements, (available
inputs such as seeds, agrochemicals, fertilizers, and irrigation) methods, procedures or
techniques which are put into crop (and/ animal production) to reduce manual labour and
maximize output (Odii, 2003). Agricultural technology has been a primary factor contributing to
increases in farm productivity in developing countries over the past half century. Although there
is still widespread food insecurity, the situation without the current technology development
would have been unimaginable. Food prices are lower because of technology. Improvements in
technology are the principal means of improving the efficiency of agricultural production, which
is the key to pro-poor growth. Agricultural technology can affect smallholder income, labour
opportunities for the poor, food prices, environmental sustainability and linkages with rest of the
14
rural economy. More efficient farming brought about by new technology could lower cost of
production, and thus enhance the competitive position of agricultural products on the
international market (Yao-chi, 1985; Maduekwe, 2008).
In many parts of the world especially in South and East Asia, growth in agricultural
productivity has been rapid largely because of the adoption of new agricultural technologies. For
instance, for millions of poor people in Asia, the technological advances of the green revolution
(complemented by massive increase in irrigation ) provided a route out of poverty through
directly increasing producer incomes and wages, lowering the price of food, and generating new
livelihood opportunities as success in agriculture provided the basis for economic diversification.
Thus agriculture led the Asian industrial revolution(Timmer, 1994).
However, to be effective an agricultural technology must be appropriate. An appropriate
technology must relate first to the developmental objectives of the sector and the production
problems of the farmers. The criteria used to test the appropriateness of a technology are
ecological considerations, socio-cultural considerations, simplicity, labour intensity, divisibility,
and riskyness (Ayoola, 1987; Galan, 1987; Odii, 2003). Maduekwe (2008) citing James (2005)
stated that advances in technology come through discovery of new products and introduction of
new techniques. New technologies come in two different forms (i) new products such as
chemicals to control pest, drugs to control diseases, or sensors and computers that automatically
control moisture conditions and (ii) new processes such as the ability to make better economic
decisions or apply the best combination of cultural practices.
The adoption of technology requires adequate incentives for producers, because
investment in labour and cash will not be made unless there are adequate returns. According to
Maduekwe (2008), one of the most important supporting factors for technology adoption is the
15
adequacy of markets for output and inputs. Idisi (2005) observed that Nigeria has entered the
advanced agricultural and information technology era with (a) falling farmland values (b) a
growing lack of comparative advantage in producing traditional Nigerian export commodities.
This decline in agricultural export he observed could significantly account for the present low
income of Nigerian farmers. According to Ike (2008) the components of improved crop
production technology in Nigeria include (i) high yielding varieties (ii) timely planting (iii)
fertilization (iv) improved cultural practices (v) minimum tillage and (vi) use of pesticides. The
author is however, of the opinion that small scale farmers have low access to such technologies.
Hite (1985) observed that generally new technologies have beneficial effect in the
environment and natural resources in two ways (i) most of the technologies are expected to
increase productivity and thus reduce the land and water requirements for meeting future
agricultural needs. As a result these technologies are expected to reduce environmental problems
associated with the use of land and water namely, soil erosion, threats to wild life habitat, and
contamination of the environment. (ii) Most of the new technologies are biological and
informational rather than mechanical and chemical which prevailed in the past. For instance, new
vaccine produced using recombinant Deoxyribo Nucleic Acid (D NA) techniques are safer than
transitional vaccines, and genetically altered diseases and insect resistant crops could reduce or
eliminate the use of chemicals which contaminate the environment.
However, it should be noted that not all technologies are environmentally friendly (or
even beneficial) new tillage technologies that reduce soil erosion could threaten wild life because
of increasing damage from the use of agricultural chemicals. The recombinant DNA techniques
which reduce resource needs and threat to the environment can also degrade the environment.
For example genetically engineered new herbicide resistant varieties of crops could allow
16
farmers to use much higher levels of herbicide than presently used in weed control. For these
reasons Maduekwe (2008) maintained that in making decisions about new technology, policy
makers should consider international competition, benefits to agriculture and other industries, as
well as the ethical, social, environmental, and public health impacts of the new technology.
2.2 Agricultural Household Income
According to Korie (2010), total household income is the sum of the net income from
agricultural and non-agricultural self-employment and wage labour activities. The net income
from an activity is obtained by subtracting the cash expenses incurred in production from the
gross income. Gross farm income is the sum of all receipts from the sale of crops, livestock, and
all farm related goods services as well as all forms of direct payments from government. Income
and wealth are only partial indicators of wellbeing. In industrialized nations, other measures
include the ability to control one‟s own environment, quality of working conditions,
independence among others. In developing countries, measures of welfare include the
fundamental issue of life expectancy, food security and health.
Two of the most obvious income measures are total income and disposable income. Total
income refers to the composition of resources flowing towards the household from their
engagement in agriculture and from a range of other sources and how these sources differ over
time, place and among different households. These comprise income in money terms (profits,
cash wages, interest received, social benefits) and in kind. Disposable income has a more direct
relationship to economic welfare as it relates to command in the market over goods and services,
what is left over being saved.
17
Farmers may receive income from many sources but the most common source is the sale of
crops, livestock and other produce raised or bought for resale. The entire amount a farmer
receives including money and the fair market value of property or service minus farm expenses
constitute profit or loss from the farm. Another source of farm income is bartering income which
occurs when farm products are traded for other farm goods and services. For instance a farmer
helps build a barn for another and receives a goat for his services, or another farmer exchanges a
basket of mangoes for some tubers of yam among others. Other sourcses of farm income include
cooperative distributions, agricultural programme payments, commodity credit loans, crop
insurance proceeds and federal disaster payments and custom (machine hire) income. Farm
expenses include amounts paid for farm labour, purchase of farm inputs, depreciation on farm
property such as buildings, machinery, equipment and others.
2.3 Concept of Efficiency
Efficiency has to do with the relative performance of the processes that turn inputs into
outputs. Olayide and Heady (1982) defined efficiency as the index of the ratio of the value of
total farm output to the value of total inputs used in farm production. They also noted that
increases in productivity will contribute to the well being of the society as a whole. According to
Olukosi and Erhabor (1982) efficiency is the quantity of output(y) per unit of input and used in
the production process. That is the average physical product (APP) =Y/X, where APP = average
physical product (APP), Y = output, X = input. Onojah (2004) maintained that efficiency is
related to the use of farm resources without waste. The efficiency of a firm consists of three
components: technical, allocative and economic efficiencies.
Technical efficiency is the ability to produce a given level of output with minimum
quantity of inputs under a certain technology. Technical efficiency is a major component of
18
productivity which itself is a measure of performance (Fare et al, 1985; Farrell, 1957; Yusuf and
Adenegan, 2007). According to Coelli et al (1998) technical efficiency indicates whether a firm
uses the best available technology. It reflects the ability of a farm to obtain maximum output
from a given level of inputs. According to Omotesho et al (2008), technical inefficiency arises
when actual or observed output from a given input mix is less than the maximum possible.
Okoruwa and Ogundele (2008) maintained that the technical efficiency of a farm is characterized
by the relationship between observed (actual) production and some ideal or potential production.
They further noted that measurement of firm specific technical efficiency is based upon
deviation of observed output from the best production or efficient production frontier. If a firm‟s
production point lies on the frontier, the firm is perfectly efficient. If it lies below the frontier, it
is technically inefficient, with ratio of the actual to the potential defining the level of efficiency
of the firm or farm firm. Ogundari and Ojo (2006) defined technical efficiency as the ability of
the firm to produce a given level of output with a minimum quantity of inputs under a certain
technology.
In an economy, technical efficiency occurs when the economy is utilizing all of its
resources and operating at its production possibility frontier (PPF). This happens when all the
firms operate using their best practice technological and managerial processes. By improving
these processes a farm (or firm) can extend its production possibility frontier outwards and
increase efficiency further. Technical efficiency is a situation where it is possible for a firm to
produce with the given know-how: (i) a large amount from the same inputs. (ii) the same output
with less of one or more inputs without increasing the amounts of other inputs. Yusuf and
Adenegan (2007) maintained that a technically efficient farm operates on the production frontier
while an inefficient farm operates below the frontier. The inefficient farm could operate on the
19
frontier either by increasing output with the same input bundle or by using less inputs to produce
the same output. They further observed that the more a farm gets to the frontier, the more
efficient it becomes and vice versa. According to Yusuf and Adenegan (2007), methods used in
the study of farm technical efficiency fall under two board groups of parametric and non-
parametric groups. They further noted that of the parametric, the stochastic frontier production
function approach and the non-parametric mathematical programming approach referred to as
data envelopment analysis are the most popular techniques.
Omotesho et al (2008) observed that efficiency improvement is an important component
of production growth in any economy. Production efficiency is the pre-requisite for allocative or
economic efficiency. Technical efficiency is defined as the ratio of actual yield to potential yield
is obtained by multiplying the firm‟s resource level by the corresponding estimated frontier
coefficient and summing up over the number of coefficients. Formally, the level of technical
efficiency is measured by the distance a particular firm is from the production frontier. Thus a
firm that sits on the production from is said to be technically efficient. This concept is important
to firms because their profit depends highly on their value of technical efficiency. Two firms,
which have identical technologies and same inputs, but different levels of technical efficiency
will have different levels of output leading to higher revenue for one firm although both have the
same costs, thus, obviously generating higher profit for the more efficient firm (Chukwuji et al,
2007).
Allocative efficiency refers to the ability of farm (or firm) to choose optimum input levels
for given factor prices (Ogundari and Ojo, 2006; Okoruwa and Ogundele, 2008). Allocative or
price efficiency is the ability to choose the level of inputs that maximizes profits given factor
prices (Dillion and Anderson, 1971; Mock, 1981; Abagi, 2004). According to Olukosi and
20
Ogungbile (1989), Haruna et al (2008), allocative efficiency is the extent to which farmers (for
instance) make efficient decisions by using inputs up to the level at which their marginal
contribution to production value is equal to the cost of the factor or input. Allocative efficiency is
achieved at the point when the Marginal Value Product (MVP) equals the Marginal Factor Cost
(MFC) (Olukosi and Ogungbile, 1989;Haruna et al, 2008). The MVP is calculated from the
respective regression coefficients using appropriate optimum level of output price depending on
the lead equation of the functional form. The MFC is the market price of one unit of input. An
efficiency ratio of unity (1.0) means economically optimum allocation efficiency. A ratio of less
than one the implies that input is being over utilized, while a ratio greater than one means the
input is under utilized (Onojah, 2004; Haruna et al, 2008).
Economic efficiency, total or overall efficiency is the product of technical and allocation
efficiencies. An economically efficient input/output combination would be both on the frontier
and the expansion path (Ogundari and Ojo, 2006). Economic efficiency exists when the MVP is
not significantly different from MC (MVP = MC). To achieve economic efficiency, the ratio of
MVP to MFC must be equal to one. Economic efficiency is concerned with maximum profit.
That is when a firm chooses resources and enterprises in such a way as to attain an economic
optimum. This means a firm uses a given resource in such a way that its value marginal product
is just sufficient to offset its marginal cost (Adegeye and Dittoh, 1985).
21
2.4 Agriculture, Economic Growth and Economic Development
According to Udoh (2007) economic growth represents the expansion of a country‟s
potential gross domestic product (GDP) or national output. This happens when the production
possibility frontier (PPF) shifts upwards indicating an increase in production. This means
economic growth. This is achieved by increase in input factors and improvement in technology.
A successive expansion in the process of shifting the production frontier upwards accompanied
by certain institutional and structural changes can lead to economic development. Economic
growth means more output. While economic development implies both more output and changes
in the technical and institutional arrangement by which output is produced and distributed, and
imply changes in the composition of output and in the allocation of inputs by sectors.
Udoh (2007) explained that economic growth is related to quantitative sustained increase
in the country‟s per capita income (output) accompanied by expansion in the labour force,
consumption, capital and volume of trade. Economic development on the other hand is a wider
concept than economic growth. It means economic growth plus changes. Economic development
he further explained is related to quantitative changes in economic want, productivity and
knowledge, or the upward movement of the social system. Economic development describes the
underlying determinants of growth such as technological and structural changes. In his opinion,
an economy can grow but it may not develop because poverty, unemployment and inequalities
may continue to persist due to the absence of technological and structural changes. This is
exactly what has been happening in Nigeria since 2004 where the so called over 7% growth in
national output has not translated into improved welfare for the majority of Nigerians leading to
what Eboh (2011) has described as „jobless growth‟. Development has been described as a
process by which man increases or maximizes his control and use of material resources nature
22
has endowed him and his environment. Development generally consists of (i) increased material
wealth for individuals and the nation (ii) eliminating unemployment, (iii) eliminating poverty
and want (iv) eliminating inequality (v) increasing the general availability of labour saving
devises, hence the modern emphasis on technology and technology growth. The main purpose of
economic development is to build capital equipment of a sufficient scale, to increase productivity
in agriculture, mining, plantation and industry. The capital is needed to construct schools,
hospitals, roads, railways among others in the creation of economic and social overhead capital
Udoh, 2007 .
Economic growth and the improvement of agriculture are two drivers of poverty reduction,
along with efforts to secure assets and enhance capital in marginal groups. The agricultural
sector is the largest single employer and contributor to the gross domestic product (GDP) in most
Africa Countries including Nigeria (IFAD, 2001). The sector accounts for 30% of Africa‟s GDP
and over 50% of the value of Africa‟s non-oil exports, and 70% of the population depend on
agriculture for their livelihoods (Jones, 2004). Yet, Hamilton (1994) pointed out that the
performance of the sector has been far from satisfactory and there is widespread poverty in the
region. Jones (2004) maintained that though Africa has more than enough resources to feed its
807 million people, food production per capita has failed to keep up with population growth rate.
With African‟s rural population trebling between 1950 and 2001, and set to grow by another
50% to 600 million by 2030, Africa‟s growth rate far outstrips elsewhere in the world. According
to Ogen (2007) a strong and efficient agricultural sector would enable a country (or region) to
feed its growing population, generate employment, earn foreign exchange and provide raw
materials for industries. This he further explained is because the agricultural sector has a
multiplier effect on any nation‟s socio- economic and industrial fabric because of the sector‟s
23
multifunctional nature which has made it the “engine” of growth in virtually all developed
economies.
Eicher and Wilt (1964) observed that there are no cases of successful development of a
major country in which a rise in agricultural productivity did not precede or accompany
industrial development. They concluded that development over the long run will not likely occur
if it is not tied to either an agricultural or industrial development. Nicholls (1964) maintained that
until underdeveloped countries succeed in achieving and sustaining (either through production or
imports) a reliable food surplus, they have not fulfilled fundamentally the condition for economic
development. He further noted that even England and Western Europe were able to initiate
industrial revolution because agricultural revolution had already provided a food surplus which
sufficed until they could supplement their rapidly increasing food needs by imports. According
to Timmer(1994), the industrial revolution in Asia was agriculturally led. In South America,
Ogen (2007) reported that Brazil‟s stupendous economic growth and current status as a newly
industrialized nation (NIN) has been facilitated by a sustained agricultural revolution. Senauer
(2002) observed that agricultural growth was necessary for poverty reduction in India, and
attributed the slowing rate of poverty reduction in Asia and the worsening poverty situation in
sub-Saharan Africa (SSA) to the neglect of agriculture by governments and international aid
agencies.
The role of agriculture in economic development includes: (i) to provide additional food
supplies to match growing population and incomes in an increasingly urbanized economy; (ii) to
provide additional source of foreign exchange earnings to ease the structural problems associated
with growth and industrialization; (iii) to provide the labour force necessary for the growing
industrial sector; (iv) to provide the source of capital formation for development outside
24
agriculture through the transfer of agricultural surpluses to other sectors and, (v) to provide raw
materials and more importantly the expanded markets necessary to stimulate industrial growth
(Johnson and Mellor, 1964; Eboh, 2011).
2.5 Agriculture, Economic Growth and Poverty Reduction in Nigeria
Joachim et al (2004) observed that there is a strong relationship between agricultural
productivity, hunger and poverty, and that three quarters of the world‟s poor people live in the
rural areas and make their living from agriculture. In addition, they noted that rates of poverty
reduction have been very closely related to agricultural performance particularly the rate of
growth of productivity. According to them, this indicates that countries that have increased their
agricultural productivity the most have also achieved the greatest reductions in poverty.
According to Fan and Rosegrant, (2008), Etim and Ukoha (2010), investing in agriculture is a
key to reducing poverty and hunger in developing countries (such as Nigeria) and is an essential
element in addressing the current food price crisis.
Eboh et al (2006) showed that agricultural sector performance and poverty trend are
somehow associated. They showed that negative annual agricultural growth from1981-85 was
accompanied by increase in poverty from 28% in 1980 to 43% in 1985. When, from1986-90, the
country recorded higher annual average agricultural growth of 6.7%, poverty reduced from 43%
in1985 to34% in1992. Later, a decline in annual average agricultural growth of 2.4% per annum
in1990-1996 was followed by increase in poverty from 34% to 65.6% in 1996. The CBN (2005)
has observed that Nigeria has lost decades of growth as a result of slow economic growth. Per
capita GDP has remained stagnant in the 1990s, growing just at 2.2% between 1999 and 2003 far
lower than the 4.2 percent per capita growth needed to reduce poverty. Among the reasons for
this poor performance has been failure of the agricultural sector (Idachaba, 1991). After
25
independence in the 1960s the Nigerian agriculture was expected to provide the food
requirements of the rapidly expanding population, the agricultural raw materials for Nigeria‟s
developing industries, the volume of exports needed to pay for the imports of capital goods,
employment for the additional agricultural working population and substantial capital to finance
the development of the whole economy. At that time, FAO (1966) warned that without a net flow
of funds from agriculture, the growth of the economy as a whole would remain limited and
Nigeria‟s overall development will suffer from the inadequate growth in the sector. This is
because only increases in agricultural productivity can release an increasing proportion of the
population for producing other goods and services, raise the per capita incomes of the
agricultural population to keep pace with growth in the living standards of the rest of the
economy, and lower the production costs of agricultural products. This would benefit domestic
consumers and at least maintain the competitive position of Nigeria‟s exports on world markets.
The report concluded that productivity can only be raised if there is substantial capital formation
within agriculture along with institutional changes, improved techniques of production, a rapid
growth in private and government services, and facilities available to the rural population. What
happened is now history as Nigeria has lost competitive advantage even in her traditional export
crops. According to Idisi, (2005), this loss of competitive advantage could significantly account
for the falling agricultural incomes and overwhelming rural poverty. Competitive advantage is an
advantage over competitors gained by offering consumers greater value either by means of lower
prices or by providing greater benefits and services that justify higher prices.
According to Njoku (2001), poverty refers to a situation whereby a person or persons‟
resources (material, cultural and social) are so limited as to exclude them from the minimum
acceptable way of life in the environment in which they live. Poverty can be defined in terms of
26
the minimum level of income below which people cannot participate in the normal way of life in
a particular society. Poverty can be conceptualized in terms of absolute and relative poverty.
According to Nnadozie and Nwanu (2002) absolute poverty refers to a situation where
individuals or farmers do not have access to biologically necessary nutrition, clothing, shelter,
and other welfare necessities like education, medical care, transportation, freedom of expression
and security against antisocial people. Absolute poverty can be defined in terms of the
appropriate maximum proportion of income that family spends on subsistence. Any family that
spends more than the specified maximum share of their income on basic needs such as food,
housing, healthcare etc is considered poor (Watts, 1967; Ruggles, 1990; Nnadozie and Nwanu,
2002). Relative poverty is defined in terms of the living standards that prevail in a particular
society. Presently relative poverty is measured by defining a poverty line. This gives a cut off
based on measurable indices, below which people are said to be poor. Thus, the number of
people below the poverty line is a measure of poverty. This number is known as head count.
Another index, the poverty gap is the average deviation of the incomes of the poor from the
poverty line.
Poverty reduction is the most difficult challenge facing Nigeria and its people, and the
greatest obstacle to the pursuit of sustainable socio- economic growth. Though the agricultural
sector has the highest potential for reducing poverty and inequality ( since the sector accounts for
about 41% of GDP and 60% of total employment), that potential has not been effectively
utilized. That is why despite being the largest contributor to economic growth; the agricultural
sector has the highest incidence of poverty (70%) among all economic sectors in Nigeria. This is
partly because though the agricultural sector is correctly targeted as the engine of growth for the
Nigerian economy, sectoral actors that will move that engine are not properly targeted.
27
According to Eboh(2011), another reason for the poverty in the agricultural sector is the
unfavourable terms of trade between agriculture and other economic sectors ( in terms of return
to labour, purchasing power parity) caused by government policies that aim at ensuring food
availability and affordability. USAID (2007) observed that increases in traded agricultural
products should provide cheaper foodstuff in urban areas, but the decline of agricultural prices
and the removal of subsidies can be accompanied by a decline in agricultural incomes, which
have far reaching distributional implications.
Poverty in Nigeria has many dimensions and manifestations including
joblessness, over-indebtedness, economic dependence, lack of freedom, inability to provide the
basic needs or own assets. Poor people in Nigeria tend to live in dirty localities that put
significant pressure on the physical environment contributing to environmental degradation. The
poor especially farmers, perceive their economic circumstances to be fraught with uncertainty
affected by events over which they have no control, such as primary commodity prices, the
volume of rainfall, pest attacks, fire out breaks , changes in soil condition, and social conflicts.
Of all these, lack of food is the most critical dimension of poverty (CBN, 2005). Lele and Adu-
Nyako (1991), listed other characteristics of poverty as low income and investment racket,
underutilized and/or unutilized natural resources, rapidly increasing populations, near absence of
social infrastructures such as portable water, schools and access roads, pervasive gullibility,
powerlessness, disease, insecurity and ignorance, and a high level of general vulnerability.
The Federal Office of Statistics(FOS,1996) enumerated the basic causes of poverty in
Nigeria which basically relate to the problems of access and endowments to include inadequate
employment opportunities for the poor (as a result of stunted growth of the economy);
inadequate access to assets such as land and capital by the poor(attributed to absence of land
28
reform); minimal opportunities for small scale credit facilities; inadequate access to the means of
fostering rural development in poor regions caused by the high preference for high potential
areas, and the strong urban bias in the design of development programmes; inadequate access to
markets for the goods and services that the poor can sell. Others include inadequate access to
education, health care, sanitation and water resulting from inequitable social service delivery
which prevents poor people from living healthy active lives and taking full advantage of
employment opportunities; the destruction of natural resource endowments which has led to
reduced productivity in agriculture, forestry and fisheries; inadequate access to assistance by
victims of transitory poverty such as drought, floods, pests and war; and an inadequate
involvement of the poor in the design and implementation of development programmes.
Although by its nature and structure, the Nigerian agricultural sector has the highest
potential to reduce poverty, in reality the reverse has been the case as the agricultural sector has
the highest incidence of poverty among all economic sectors in the country. According to Eboh
(2011), 7 out of every 10 farming households in Nigeria are living below the national poverty
line, and 6 out of every 10 poor households are in agriculture. Abalu (2008) observed that past
patterns of agriculture in Nigeria have not only been insufficient but more importantly have
failed to serve as the engine of growth of the overall economy. According to the author, the
faster agriculture grows the faster its relative size declines. This process by which agriculture
becomes less dominant, but paves the way for overall development is the foundation for
achieving agricultural self sufficiency. But for this foundation to be successfully laid, a dynamic
agricultural growth process that takes adequate account of the critical inter linkages between the
agricultural and non-agricultural sectors must first evolve. Such a dynamic agricultural sector
properly linked to the other sectors can make the following contributions to structural
29
transformation (i) labour released by the increasing productivity in agriculture is available to
other sectors (ii) capital (both physical and human) generated in agriculture can flow to other
sectors. (iii) Increased agricultural productivity that will provide the food needed by a growing
non-agricultural labour force (iv) agriculture can generate foreign exchange earnings by
producing both export crops and import substitutes (v) agriculture can provide a market for the
non-agricultural sectors. According to Eboh(2003) and Eboh and Lemchi (2010), this kind of
agriculture can adequately address the poverty issue. This is yet to happen in Nigeria.
2.6 Agricultural Growth and Food Security in Nigeria
According to Senauer (2002), the essential components of an agricultural growth strategy
are the technical change that increases output and cuts production costs, investment in rural
infrastructure such as roads, irrigation, and widespread participation by small peasant producers
including women farmers. Food security on the other hand, means assuring availability and
access to sufficient quantities of food for all including the poor. Food security is the ability of a
country or region to assure on long term basis that its food system provides the total population
access to a timely, reliable and nutritionally adequate supply of food. According to Erhabor and
Emokaro (2008) food security exists when all people, at all times have physical and economic
access to sufficient, safe and nutritious food to meet their dietary needs and food preferences for
an active and healthy life. Food security encompasses a wide range of issues associated with
food and nutrition at national, regional, household and individual levels. It covers a wide range
of issues on food production, food supply, food demand, food consumption and food nutrient
uptake. In fact food security status has become an important component of human development
indicators. Specifically, in the context of households and individuals within households, Olayemi
(2008) defined food security as a situation where a household for instance has both the physical
30
access (supply) and economic access (effective demand) to adequate food for its members and
without undue risk of losing such access. He named the three essential components of food
security as (i) adequate availability of food (adequate supply of food), (ii) adequate purchasing
power for food (adequate capacity to demand food) and (iii) sustainability or stability of both
physical and economic access to food. In summary, factors affecting food security can be
grouped into supply-side factors and demand-side factors and sustainability of food access
factors.
According to Olayemi (2008) factors affecting household food security in Nigeria can be
grouped into supply-side and demand side factors. The supply-side factors include: quality and
quantity of production resources (i) available food production technologies (ii) quantity of
households own food production (iii) seasonal and annual variability in food production (iv) food
processing, storage and preservation technologies and practices (v) food and non-food prices (vi)
available market supply of food formal sources at prices that are within a household‟s means.
Olayemi (2008) enumerated the demand-side factors as; (i) household income and variability of
income over time (ii) value of household‟s economic assets (iii) quality of household‟s human
capital (iv) rate of consumer price inflation (v) households demographic factors such number,
gender and age composition (vi) socio-cultural factors such as health and sanitation conditions,
education, cultural norms, and food consumption habits.
Eicher and Staaz (1980) observed that one of the reasons for widespread hunger in
Africa, a continent in which some areas have high potential is the failure of African countries to
develop economic systems that generate sufficient real income for the poor to ensure access to
adequate food produced at home or purchased in the market. On a similar note, Wheeler (1990)
observed that most people are hungry because they lack incomes to buy food, and suggested that
31
to reduce significantly the number of hungry people, the strategy adopted should focus on
agricultural growth and employment creation. He argued that since a large proportion of national
income is generated by agriculture related employment, any strategy to increase growth rate must
be supported by accelerated growth in agriculture. Similarly, Timmer (1994) gave two reasons
why the rice based economics of South East Asia have out-spaced the coarse grain and root crop
based economies of sub-Saharan Africa, although the latter had higher potential than the former
at their independence in the 1960s as: while the governments of sub-Saharan Africa neglected
agriculture, government policy makers in South East Asia invested heavily in building a
comparative advantage in a wide range of agricultural exports; and governments in South East-
Asia actively sought to provide food security to domestic consumers both urban and rural.
The Federal Government of Nigeria has taken a number of policy initiatives with a view to
boost agricultural production, provide food security and reduce poverty. These include the
liberalization of different agricultural input delivery systems, introduction of measures to involve
the private sector in the agricultural sector, launching of special programmes on food security
(SPFS), Fadama I, II, III, strategic grain reserves, Small and Medium Enterprises Development
Agency of Nigeria (SMEDAN), and increased budgetary allocation to agriculture (Olayemi,
2008). However, to be effective, investments to reduce rural poverty and increase food security
must of necessity involve women because according FAO (1999) out of the 1.3 billion people
living in poverty in the world (including Nigeria), 70% are women. Therefore women must be
sufficiently involved if these efforts are to succeed.
32
2.7 Economics of Cassava Production
Cassava (Manihot esculenta Crantz) also known as rogo, paki, gbaguda, akpu, alogo, ege,
itakom, olomgwo in several Nigerian languages is a perennial shrub with an edible root, which
grows in tropical and subtropical areas. The crop originated from Brazil where it is a major
staple food for the people. It is drought resistant and can grow in low-nutrient soils. According to
Ano (2003), cassava is grown throughout the tropics and can be regarded as the most important
root crop, in terms of area of cultivation and total production. Clair et al (2000) stated that
cassava is a crop of the poor and occupies mainly agriculturally marginal environments. Also
cassava as a women crop is becoming increasingly more evident. According to Chukwuji et al
(2007), cassava is reputed for being a hardy crop, producing economic yields under conditions of
drought, low soil fertility, locust attack, poor husbandry, and other adverse production conditions
where other crops cannot survive. As a result of its tolerance to extreme ecological stress
conditions and poor soils, cassava plays a major role in reducing food insecurity and rural
poverty.
Nigeria is the largest cassava producer in the world followed by Brazil, Indonesia and
Thailand. In Africa, other major producers include the Democratic Republic of Congo, Ghana,
Madagascar, Mozambique, Tanzania and Uganda. In Nigeria, Benue State, followed by Kogi
State in the North Central Zone are the highest producers. Others include Cross River, Akwa
Ibom, Rivers and Delta States (IITA, 2004). Cassava in Nigeria is grown either as a sole crop or
in association with other cops (Chukwuji, 2008). In a study of 494 fields where cassava was
grown, Nweke (1997) found that 36%, 38, and 26% grew the crop as sole, major and minor crop
respectively. Fakayode et al (2008) explained that though the cassava when grown as a sole crop
results in high output, the greatest disadvantage of the sole crop is that in instances of pests and
33
disease attack the farmers usually loses a significant part of their output. So most farmers prefer
to grow the crop in mixtures to ensure against total crop loses.
Cassava is a basic staple and a major source of farm income for the people of sub-Saharan
Africa. The crop contributes about 40% of the Calories consumed in Africa (IITA, 1990), and
both rich and poor farmers often derive more income from cassava than from any other crop or
income earning activity ( Enete, 2007). In addition, cassava is the most widely cultivated tuber in
sub-Saharan Africa and the second most important staple in terms of per capita food energy
consumed. The crop has become the paramount staple food security crop in sub-Saharan Africa,
and a mainstay of the rural and increasing the urban economy. Cassava is a very important crop
to Nigeria. Its comparative production advantage over other staples serves to encourage its
cultivation even by the resource-poor farmers. The crop‟s production is thought to require les
labour than other staples and can give reasonable yields on low fertile soils. According to
FAO(2000) and Olasunkanmi (2012), cassava is a good staple whose cultivation can provide the
nationally required foods security minimum of 2400 calories per person per day (FAO, 2000).
Studies revealed that in the southern part of Nigeria, farmers grow cassava in mixtures with
maize, cocoyam, yam, and vegetables but some grow it sole while in the northern areas, sole
cropping is more common(Mahungu,1994; Haruna, 2008). According to Nweke (1997) and
Chukwuji et al (2007) cassava generates about 25% of cash income from all crops grown,
constituting the most important single source of cash income. Studies have reported that the
prospects for enhanced foreign exchange earnings from cassava exports is becoming
significantly high following recent interest of foreign nations to buy a cassava products from
Nigeria (Chukwuji, et al, 2007; Ater, Lawal and Ortese, 2008).
34
As a food crop, cassava has some inherent characteristics which make it attractive,
especially to small holder farmers in Nigeria. First, it is rich in carbohydrate especially starch
and consequently has multiple end uses. Secondly, it is available all year round, making it
preferable to other more seasonal crops such as grains, peas, beans and other crops for food
security. Compared to grains, cassava is more tolerant of low soil fertility and more resistant to
drought, pests and diseases. Furthermore, cassava roots can be stored in the ground for months
after they mature, and are harvested continuously throughout the year thus tidying farmers over
the hungry seasons after other crops have been planted but are not yet mature(Chukwuji, et al,
2007). For human consumption, cassava can be processed into granules (gari), paste (akpu) and
chips or consumed freshly boiled or raw especially the sweet varieties with minimal hydrogen
cyanide content (Muhammad-Lawal, et al, 2012). The leaves are used as a green vegetable
providing among others vitamins A and B. Cassava can also be used as a binding agent in the
production of paper and textiles, and as monosodium glutamate(a flavouring agent in cooking),
as a part substitute for wheat flour, in fuel (gasohol), and alcohol (ethanol) for energy, chips and
pellets for animal feed and starch based products. As a food, cassava is used for baking cereals
and snacks, soups beverage emulsifiers, powdered non-dairy creamers and confectionaries.
Cassava starch is used in various industrial sectors such as paper manufacturing, cosmetics, and
pharmaceutics (Balogun, 2003). According to Clair et al (2000), these and other features have
endowed cassava with special capacity to contribute to food security, equity, poverty alleviation
and environmental protection. According to Erhabor and Omokaro (2008), cassava serves as a
bench mark for food security in Africa and other parts of the world, as it remains a staple in
Africa, Latin America, Asia, and the Caribbean. In Nigeria, they maintained that the cassava
crop is a viable option as a mitigating measure against food crisis and as a poverty reduction
strategy.
35
2.8 Agricultural Development Projects (ADP) in Nigeria
According to Gittinger (1982), an agricultural project is an investment activity in which financial
resources are expended to create capital assets that produce benefits over an extended period of
time. Agricultural projects usually take different forms namely, irrigation, livestock, rural credit,
land settlement, tree crops, agricultural machinery, and agricultural education, as well as multi
sectoral rural development projects with major agricultural components.
The agricultural development projects in Nigeria were designed in response to the fall in
agricultural productivity, and the concern to sustain domestic food supplies, as labour had moved
out of agriculture into more remunerative activities that were befitting from the oil boom.
Conversely, domestic recycling of oil income provided the opportunity for the government, with
World Bank support, to develop the ADPs. The projects provide agricultural investment and
services, rural roads and village water supplies. The government‟s adoption of the ADP concept
put the smallholder sector at the centre of the agricultural development strategy, and marked a
clear shift away from capital-intensive investment projects for selected areas of high agricultural
potential. The first ADPs were enclave projects each covering specific region within a given
state. According to the National Agricultural Extension and Research Liaison Services
(NAERLS, 1997), the ADPs were initiated in Nigeria in the 1970s to correct the weakness of the
earlier extension approaches in Nigeria. The first three enclave projects namely Sokoto(1974),
Gusua (1974) and Gombe (1975) blossomed into Ayangba (1977), Lafia (1977), Bida (1979),
Ilorin (1980), Ekiti- Akoko (1981) and Oyo-North (1982). Their early results impressed both the
Federal and State Governments, and there was pressure to replicate the approach across all the
states. Following successful negotiations for multi-state agricultural development projects with
36
the World Bank, the country now has one agricultural development project in each state of the
country including the Federal Capital Territory Abuja.
The central features of the ADP strategy is the reliance on the small scale farmer as a
pivot of an incremental production technology. It must also be emphasized that the ADP strategy
is based on the premise that a combination of factors comprising the right technology, effective
extension, access to agricultural inputs, adequate market, and infrastructural facilities are
essential to raise agricultural land productivity and improve the living standards of the rural
dwellers. The ADPs sought to increase food production and farm incomes. It was assumed that
productivity increases would come from the use of improved technology, especially planting
materials and fertilizers. The agricultural components of the projects were designed around
systems for developing technology and transferring it to farmers, distributing modern inputs and
land development including small scale irrigation and land clearing. From literature,
technologies available on cassava production include improved cassava cuttings, pesticides,
herbicides, fertilizers, planting angle, alternate row/crop geometry in cassava/maize/yam
intercrop, planting distance, use of high yielding varieties, use of machinery, improved
processing/storage (Eze et al, 2006).
2.8.1. Benue Agricultural and Rural Development Authority (BNARDA)
The Benue Agricultural and Rural Development Authority (BNARDA) is one of first
generation multi-state agricultural development projects in the country (Adagba, 2002;
NAERLS, 1997). The Benue Agricultural and Rural Development Authority (BNARDA) was
established in 1986 but became loan effective in 1987. It was inaugurated as a Parastatal by
Eddict No. 7 of 1985 and was co-financed by the World Bank, Federal and Benue State
37
Governments. The overall objectives of the authority are to promote, encourage agricultural
production in Benue State and raise the incomes and standard of living of the rural farmers.
To achieve these objectives, the operations of BNARDA were organized into three core
sub-programmes: agricultural, engineering and commercial services. Four support sub-
programmes namely administration, finance, human resource development, and planning,
monitoring and evaluation were created to complete and support the core-sub programmes. To
effectively cover the state and achieve greater impact, BNARDA is operated on the basis of three
agro-development zones namely Northern, Central and Eastern zones with headquarters at
Adikpo, Otukpo and Gboko respectively. The goal of BNARDA is to promote sustained crop
and food production, and raise the income and standard of living of farmers in Benue State. The
overall objectives are to assist the small-scale farmers increase food production, develop an
effective agricultural extension system, focusing on agricultural potentials in effectively utilizing
intensive programme of on-farm adaptive research, establish and operate a workable input
procurement and distribution system capable of serving the farmers at the right time, and to
provide rural infrastructure for sustained development (Adagba, 2002).
2.9 Empirical Studies of the Effect of Improved Agricultural Technology Use in Nigeria
Ater (2003) did a comparative analysis of productivity response and poverty alleviation
among beneficiaries and non-beneficiaries of World Bank assisted dry season Fadama
Development Project in Benue State. The study used multiple regression analysis, discriminant
analysis and stochastic frontier production function to evaluate productivity and efficiency
among targeted beneficiaries. Poverty alleviation indicators, valued productivity and production
efficiencies of the two groups were estimated. The study showed among other things that
beneficiaries had higher incomes and higher output values than non-beneficiaries as a result of
38
better decisions. Results showed that a positive impact was made on beneficiaries selected
indicators. The stochastic frontier production analysis used indicated difference in production
efficiencies of 94% for beneficiaries and 57% for non-beneficiaries. The study identified
inefficiency indicators for Benue State dry season farmers as: farmer‟s age, number of man-days
loss to ill health, extension contact score, literacy rating. The study concluded that increasing
social overhead capital endowment among resources poor small scale producers in the Fadamas
was a pre-requisite for achieving poverty alleviation, and this can be accelerated by improving
farmer based productive inefficiency indicators through credit and enlightenment campaigns.
Asogwa (2005) did an evaluation of government agricultural policies and effects of
resources use in cassava production in Benue State, Nigeria. Data from 360 cassava farmers was
analysed using descriptive as well as inferential statistics such as stochastic frontier production
function. Results of the field data analysis indicated technical inefficiency decline among
cassava farmers in Benue State with the coefficients of improved cassava varieties and improved
cassava processing technology as -0.18 and -0.1 respectively. The study concluded that inputs
supplied to the farmers through the policy intervention of the government were efficiently used,
hence the increased cassava output among the cassava farmers. Furthermore, the policy package
in form of improved cassava varieties, improved cassava processing technologies made available
to farmers increased the efficiency of their resources-use, hence maximization of their profit. He
therefore recommended that policies that encourage input expansion in cassava industry should
be sufficiently reinforced to bring about much larger increases in cassava production in Nigeria
with consequent maximization of the profit of cassava farmers.
Bello (2005) carried out a study to determine the effectiveness of contact farmers in the T
and V extension system in Nasarawa State ADP programme. Chi-Square and multiple regression
39
analysis were used to analyse the data collected. Analysis of data showed that level of education,
farmer‟s age and farm size had a significant effect on farmers income while extension contact,
particularly had not. He inferred that land tenure influenced visits of non-contact farmers for the
purpose of getting extension messages from contact farmers.
Eze, Ibekwe, Onoh and Nwajiuba (2006) undertook a research to isolate the
determinants of improved cassava production technologies among farmers in Enugu State,
Nigeria. Data collected from a sample of 250 farmers from 10 local government areas of Enugu
State were analysed using descriptive statistics and multiple regression analysis. Results showed
that cassava production technologies that were at various stages of adoption were use of
improved cassava stem cuttings, use of herbicides/pesticides, alternate row/crop geometry in
cassava maize intercrop, planting distance, use of fertilizers, machinery, improved storage and
processing, and planting angle. The study found the overall mean adoption score and index of
0.96 and 0.191 respectively. The low level of adoption was attributed to the cost of the
technologies, their appropriateness, scarcity or non availability of the extension agents in the
study area. The study showed that level of education, age of farmers, farm size, farm income and
extension visits were the major determinants of adoption of improved cassava production
technologies in the study area.
Costs and return analysis of improved and alternate cassava production technologies in
Enugu State was the focus of another research by Eze, Onoh and Ibekwe (2006). Data collected
from a random sample of 250 farmers and 30 extension staff in the three (3) agricultural zones of
Enugu State were analysed using descriptive statistics, and the costs and return principle. The
results showed the improved technologies more profitable compared to the alternate technology,
with the ratio of improved technology to the alternate technology of 3:1. These findings imply
40
that the improved technologies are three times better than the alternate technology. They
concluded that the results hold promise for improved technologies and has implication for farm
level investment by farmers in the study area.
Productivity analysis of prevalent cassava-based production systems in the large Guinea
Savannah using Kwara State as a case study was undertaken by Fakayode, Babatunde and Ajao
(2008). Data from 160 cassava producing households were analysed using Total Farm
Productivity (TFP) and Ordinary Least Square (OLS) methods. Results showed the
cassava/maize enterprise with a TFP of 4.4 level as the most popular and most productive
cassava-based enterprise. This was followed by cassava/cowpea, cassava/maize/ guinea corn and
cassava/melon systems. To achieve increased yields per cost outlay, the study recommended the
enhancement of farmers access to education and the encouragement of farmers in the cassava
copping systems on the use of land and labour saving technologies.
Ndanitsa (2008) examined the impact of small scale irrigation technology on crop
production under Fadama areas of Niger State. He used farm budgeting model, farm production
model and linear programming to analyse the data collected from farmers engaged in various
crop enterprises under the Fadama programme. The farm budgeting model showed that Fadama
cultivation was profitable. The farm production model revealed that land, labour and purchased
inputs had a significant positive relationship with output of the enterprises. The linear
programming analysis revealed that opportunities exist for increasing profits through resources
reorganization under the programme.
Economics of improved and local cassava varieties and its welfare effects on producing
farmers in Oyo State, Nigeria was the focus of a study by Muhammad-Lawal, Salau and Ajayi
(2012). Data from 120 respondents obtained through a three -stage sampling procedure was
41
analysed using descriptive statistics, gross margin and multiple regression analysis. Results of
the data analysed showed a gross margin of ₦167,733 and ₦114,569 for farmers using improved
and local varieties of cassava respectively. The multiple regression model used showed that farm
size, age of farmers, and household size were the variables that explained variation in the output
of cassava farmers in the study area. To increase cassava production, the study recommended
that policies that ensure farmers access to land and reduction in family size should be adopted.
A study to assess the impact of extension services on cassava farming in Benue State was
carried out by Okwoche and Asogwa (2012). Data collected from 180 randomly sampled farmers
were analysed using descriptive statistics such as frequency distribution, percentages, and
inferential statistics such as correlation analysis. Results showed that there was a significant
relationship between farmers‟ access to extension services and profitability of farming since
farmers who had access to extension services had higher profits. Access to extension services
had a significant impact on cassava farming in the study area. The study recommended that
extension agents should put in more efforts in reaching farmers that have not had contact with
them so as to pass on useful information them in order to increase their profitability. Also
cassava farmers should be encouraged to form cooperatives and cassava farmer groups.
2.10 Theoretical Framework
This work is anchored on the theory of production and the related theory of the firm. In
agricultural production, the farm firm combines resources such as land, labour, capital and
management to produce output. The main goal of the farm-firm may be to maximize profit,
minimize cost, and maximize utility (satisfaction) or a combination of all these (Olayide and
Heady ,1982). Resource use is a concept used to describe the allocation of farm resources such as
land, labour, capital and management in their various forms between competing alternative. In
42
doing this the farm firm aims to derive maximum benefit from these resources. Since resources
are scarce and have competing uses, they can limit production of output. For this reason
resources use or allocation is one of the basic functions of an economic system (Nwosu, 2005).
According to Nwosu (2005), maximum resource productivity would imply obtaining the
maximum possible output from the minimum possible set of inputs; hence optimal productivity
of resources implies an efficient utilization of resources in the production process subject to
constraints. For the purpose of this study, the following concepts relating to the theory of
production were reviewed:
2.10.1 Concepts in the Theory of Production: Production, Production Function and
productivity
Production and productivity are related concepts in economic theory. However,
production is not the same as productivity. Production is the same as output. It is physical
produce and can be reported in units of volume or weight. Productivity is defined as the output
per unit of input where „input‟ can be land, labour/ or capital, and output is agricultural
produce(in this case). Kamajou (1991) defined production as the output of goods and services
coming from the production(manufacturing) process and productivity as output or production per
unit of inputs used. Production has also been defined as the rate at which resources (inputs) are
transformed into products, and the production function as the technical relationship between
inputs and output in a given period of time (Mijindadi, 1980: Olayide and Heady, 1982).
According to Adewusi (2006), production is the ratio of output to inputs used to produce goods
and services while productivity is the index which allows assessment of efficiency and effective
utilization of resources to obtain a certain output. Mathematically, the production function is
continuous and differentiable and this property of differentiability enables its use to estimate the
43
rate of return (Olayide and Heady, 1982: Olukosi and Ogungbile, 1989: Adewusi, 2006: Haruna
et al, 2008).
The purpose of the production function is to identify and measure how variable inputs are
able to explain the variability in outputs. The greater the extent to which variable inputs explain
variability in output, the greater their influence (or explanatory power). For any production
function, the correct functional form can be determined by fitting various feasible functional
forms to obtain the best fit which is normally selected on the basis of economic, statistical and
econometric soundness (Olagoke,1990; Ugwu, 1990; Omolola, 1998; Nwosu, 2005). The
simplest mathematical form of the production function can be stated as Q= f(X), where
Q=output, X= inputs and F=function of indicating casual relationship between Q and X. Heady
and Dillion (1972) and Nwosu (2005) explained that various functional forms can be used to
describe production relationships, but in practice the most commonly used forms include the
linear, quadratic and Cobb Douglass functional forms.
According to Adewusi (2006), increase in productivity could be due to technological
advances, improvement in managerial advances, and techniques of efficient use of inputs used in
the production process. He identified the following ways of productivity increase : (i) increase in
output and inputs, with output increasing more proportionately than inputs, (ii)increase in output
while inputs remain the same,(iii) decrease in both output and inputs with inputs decreasing
proportionately more than outputs, and(iv) decrease in inputs while output remain the same.
FAO (2007) observed that increase in productivity can contribute to economic growth by
providing more food, increasing the prospects for growth and competiveness on the agricultural
market, income distribution and savings, and labour migration to other sectors.
44
An increase in a nation‟s agricultural productivity implies a more efficient distribution of
scarce resources. It is believed that farm productivity is functionally dependent on quantifiable
parameters which individually and collectively as regressors exhibit a causal relationship being
best represented by a regression model (Olayemi and Olayide, 1981; Koutsoyiannis, 2001).
Kamajou (1991) identified two ways by which productivity increases can come as improvement
in technology and improvement in the quality and organization of resources used in production.
Ajibefun (2002) and Asogwa (2005) have observed that this is important for Nigeria where
raising the production per unit of land is the key to effectively addressing the challenges of
achieving food security since most cultivable land has already been brought under cultivation,
and even in areas where wide expanse of land is still available, physical and technological
constraints prevent large scale conversion of potentially cultivable land. Proper use of available
resources such as land is thus very essential.
2.10.1.1 Measurement of Productivity
Productivity measures are subdivided into partial and total measures. According to
Ohajiana(2000), partial productivity measures are the amount of output per unit of a particular
input such as labour, land and capital. Commonly used measures are yield (output per units of
land), labour productivity (output per economically active person (EAP) or per agricultural
person-hour. Yield is commonly used to assess the success of new production practices or
technology. Labour productivity is often used to compare productivity of sectors within or across
economies. It is also used as an indicator of rural welfare or living standards. It reflects the
ability to acquire income through sale of agricultural goods or agricultural production. According
to Fakayode et al (2008) total factor productivity (TFP) or total productivity is the ratio of the
output to the total variable costs of production as shown:
45
TFP = Y/TVC, Where TFP = total factor productivity, Y = total output, TVC = total variable
costs of production. Alternatively put, TFP = Y/∑PiXi, where Pi = unit price of the ith
variable
input, Xi = quantity of the ith
variable input. ∑ = summation sign. This methodology ignores the
role of total fixed costs as this does not affect both the profit maximization and resource-use
efficiency condition. Besides, TFC is fixed and as such a constant( Fakayode et al,2008). Using
the cost approach, Fakayode et al (2008) established that TFP can be measured as the inverse of
unit variable costs. From cost theory, AVC=TVC/Y, where AVC= average variable cost (₦),
therefore, TFP = Y/TVC = 1/AVC. As such, TFP is the inverse of AVC. The partial productivity
estimates are the marginal products (MP) given as: MP=δTFP/δX, where X = variable factor
(Fakayode et al, 2008).
2.10.1.2 Determinants of Productivity
According to FAO (2001) productivity is affected by the level of investment. Investment
refers to the change in fixed inputs used in the production process. Narrowly defined investment
means change in physical capital that has a useful life of one year or longer (land, equipment,
machinery, storage facilities, livestock). Agricultural investment should include improvements
on land, development of natural resources, and development of human and social capital in
addition to physical capital formation. Beets (1990) grouped factors that affect agricultural
technology as follows: (i) Physical factors such as land area, soils and climate. (ii) Technological
factors such as availability of technical know-how input (iii) Human factors such as the way
society makes use of the physical and technological factors. One of the most important ways of
increasing agricultural productivity is through improved crop husbandry practices such as better
weeding, use of improved planting materials, recommended plant configuration, good soil
management, appropriate timing of cultural operations, optimal use of labour, and use of external
46
inputs such as fertilizers, machines and pesticides. According to Fakayode (2008) other factors
include technology, labour employment, education and training of farm operators, agro-
environmental conditions, security of land ownership rights, and funding which determines the
maximal physical quantity of output that can be reached as well as the number and quantity of
inputs required. Okuneye (1986) observed that one of the causes of the decline in agricultural
productivity is the inefficient allocation of resources in the agricultural production potentials of
the economy. He noted that land, labour, capital and water resources are inefficiently allocated
thereby leading to decrease in productivity.
2.10.1.2.1 Review of Selected Determinants of Agricultural Productivity
As a result of their critical importance in productivity improvement, this study paid special
attention to and examined the effect of selected factors on productivity improvement namely
rural roads, rural water supply, rural cooking fuel, and agricultural credit.
The importance of infrastructural facilities such as roads, rural water supply, rural
electricity and others for agricultural growth and development is well established (Munonge,
2008). Inoni (2008) observed that rural infrastructure investments do not only affect total factor
productivity, but also contribute directly to substantial reduction in rural poverty. According to
Jacoby (2000) and Inoni (2008) good road network and marketing facilities accelerate efficient
delivery of farm inputs, reduce transport costs, and enhance spatial agricultural production and
distribution. The importance of rural transport infrastructure has also been well established.
Ahmed and Hossain (1990) found that in villages with better access roads, fertilizer costs were
14% lower, wages were 12% higher and crop output was 32% higher in Bangladesh. In India,
Fan et al (2000) found that public investment in rural roads and marketing infrastructure had a
significant impact on agricultural output and farm income. It has been found that public
47
infrastructure such as good roads, transportation, reliable communication; irrigation services,
agricultural research, education, and health have a significant impact on productivity. (Antle,
1983; Binswanger, 1989; World Bank, 1994; Nwosu, 2005).
Inoni (2008) observed that investment in rural infrastructure lowered transportation costs,
increased farmers‟ access to markets, and led to substantial expansion in agricultural output.
They also found that better roads reduced transaction costs of credit services resulting to increase
lending to farmers, higher demands for agricultural inputs, and higher crop agricultural output.
They also found that better roads reduced transaction costs of credit services resulting to increase
lending to farmers, higher demands for agricultural inputs, and higher crop yields. According to
the Human Development Report (2008) efficient economic infrastructure is central to raising
productivity and increasing growth in Nigeria.
Apart from infrastructure, cooking fuel is another factor that engages women‟s time and
energy. Empirical evidence show that wood fuel is the predominant form of cooking fuel even in
the urban areas of Nigeria. Onyekuru and Eboh (2008)observed that kerosene has become a
luxury item only a few can afford its use now for cooking. Studies have found that agricultural
credit has positive output and employment effects (Olomola, 1988).
Many farmers are poor and trapped in a vicious cycle of poverty because they
cultivate small areas of land from which they produce little output, and hence sell only a very
small amount, which can not help in expanding their farms, acquiring new technologies so the
cycle continues (Adegeye and Dittoh, 1985; Awotide et al, 2008). To break out of the vicious
cycle of poverty, credit is essential since it determines access to all the resources the farmers
depend on. Awotide et al (2008), argued that agricultural credit is required not just for
agricultural production alone, but also for consumption. This they explained is necessary to make
48
farmers more productive in their labour input and to help them during the lean periods before and
after planting season, during which farmers hardly have enough to eat which affects their
productivity.
2.10.1.3 Importance of Productivity
The importance of productivity is that it gives a measure of efficiency. It tells in one figure
how much input was used to produce a unit of output. For instance labour productivity in cassava
production in Nigeria in 2010 is a number that gives the amount of cassava one Nigerian
agricultural worker produced in 2010. Productivity increases translate to lower cost per unit
which typically results in lower prices for consumers especially where industry is in competition.
Productivity increase improves the standard of living as lower prices allow consumers to
purchase more per unit of income other factors remaining constant. Productivity increase can
mean higher profits for management, transport, more revenue for government. When agricultural
producers‟ income rises, they spend the money on non-agricultural items, thereby creating jobs
for others throughout the economy. Ayoola (2008) citing IFPRI (1997) stated that for every US $
1.0 increase in agricultural output in developing countries, the overall economy grows by US $
2.3. Furthermore, productive agriculture helps to alleviate rural poverty and helps prevent natural
resource degradation. More than 75 percent of the poor in many sub-Sahara and Asian countries
are made up of the rural poor who depend directly or indirectly on agriculture. These same rural
poor are forced to overuse or misuse the natural resource base in order to meet their basic needs.
Therefore a situation of low agricultural productivity would tend to retard the national economy,
increase rural poverty and deplete the natural resource base thereby undermining sustainable
development.
49
This work also benefited from other theories of agricultural development such as the
diffusion theory and the high input pay-off model and the diffusion model as highlighted below
(Ayoola, 2009):
(i) High input pay-off model. This assumes that farmers are efficient allocators of resources and
also respond to economic stimuli, but operate under immense technical and economic
constraints. So they need support in form of improved seeds and other technical inputs, a well as
optimal output prices. This implies that there should be price intervention policies to help
farmers.
(ii) Diffusion model. According to this model productivity differences among farmers arise due
to differential access to information and critical knowledge. The need for agricultural extension
therefore, arises. Effective extension would improve profitability of the farm business, thereby
providing new points of growth of the agricultural economy.
2.11 Analytical Framework
According to Eboh (1998) the type of study to be undertaken determines the type
of analysis and analytical technique. He further explained that for exploratory studies,
rates, means, percentages, frequency distribution may be adequate, but a more detailed
and higher level analysis is needed for case studies and sample surveys especially those
dealing with quantitative data. Analytical framework for this study was as follows:
2.12.1 Regression Analysis
Koutsoyiannis (2001) observed that regression analysis is one of the most commonly
used techniques in analyzing dependence among variables. According to Eboh (1998) and
50
Onojah (2008) the aim of regression analysis is to establish and prove how one variable is related
to another variable. It is based on the statement of causal or functional relationship between the
variables. They noted that the key relationship in a regression is the regression equation, which
contains the regression parameters whose values are to be estimated using the data. The
parameters measure the relationship between the dependent (or response, variable or regressand)
and the explanatory (independent, exogenous) variable(s) also called repressor(s). Onojah (2008)
pointed out that regression analysis uses different functional forms such as linear, semi log,
double log, polynomial, reciprocal and curvilinear to mention but a few forms. Two types of
regressions are available depending on the number of explanatory variables namely simple and
multiple regressions.
A simple regression is a functional statement of how one variable called dependent
variable varies or depends on changes in another variable called the independent variable. The
main relationship in simple regression is the regression line which for a linear regression
equation is given as Y = α + β X + e. This is the best straight line or linear approximation of the
relationship between the dependent and the independent variables. Y = the response or dependent
variable, X = the independent variable, β = the slope of the regression line, e is the disturbance
(stochastic term or error term) while α and β are the regression parameters whose values are to be
estimated using the data. This can be done using the ordinary least square method, so called
because its estimation of α and β minimizes the sum of squared error estimates for the given data
(Kmenta, 1977; Koutsoyiannis, 2001). Once a regression has been fitted, the task of the
researcher is to estimate the functional or causal relationship and test its validity (Eboh, 2009).
Koutsoyiannis (2001), Gujarati (2006) and Onoja et al (2008) have enumerated some of the
criteria for selecting or judging a good econometric model as (i) parsimony or simplicity, (ii)
51
identifiability which means that estimated parameters must have unique values, (iii) goodness of
fit (high R2), (iv) theoretical consistency (signs of estimated parameters must be consistent with a
priori expectations about them) and (v) possession of predictive power. The best regression fit is
determined by the level of the coefficient of determination (R2), the level of significance of the
overall equation (F = statistic), the level of significance of each coefficients (t = statistic), the
correct signs and magnitude of the coefficients relative to a priori expectations about them
(Heady and Dillion, 1972; Herbet, 2003; Erhabor and Omokaro, 2008).
Multiple regression analysis is an econometric method used to study relationships
involving more than two variables. The variation in the dependent variable is explained by more
than one independent variable. Gujarati (2006) and Onoja et al (2008) pointed out that most
regression models are multiple regression models because few economic phenomena can be
explained by only one variable. The form of the multiple regression can be explicitly given as Y
= b0 + b1X1 + b2X2 + … bnXn + e where Y = dependent variable, bo = intercept, X1 – Xn =
independent variables b1 – bn = regression parameters and e = random disturbance or error term.
Each (2003) explained that most recent works involving multiple regressions prefer the
use of the ordinary least square estimation since the parameter estimates from the ordinary least
square technique have some optimal properties such as linearity, unbiasedness and minimum
variance. Multiple regression analysis was used in this research to determine the factors that
affect output and gross margin of cassava women farmers in Benue state, Nigeria.
2.11.2 Enterprises Analysis
Gross margin analysis is a kind of enterprise analysis which involves evaluating the
efficiency of an individual enterprise or farm plan so that comparisons can be made between
52
enterprises or different farm plans. Gross margin (GM) is the difference between total value
product (TVP) their total variable costs (TVC). Gross margin minus the total fixed costs (TFC)
gives the net return (NR) which is a measure of profitability of small scale cropping enterprises
(Olucosi, Isitor and Ode, 2006). Simply put TVP-TVC=GM, GM-TFC=NR. Since fixed costs
are usually negligible in Nigerian (peasant) agriculture, gross margin is just total value product
(TVP) or total revenue (TR) minus total variable costs (TVC) (Olukosi and Erhabor, 1988).
Nwosu (2005) observed that enterprise analysis is used to identify unprofitable enterprises, and
involves dividing and apportioning the fixed cost among various farm enterprises that utilize the
fixed cost items of the farm. Variable costs on the farm include seeds or planting materials,
chemicals, labour, fertilizer, transportation and others. Fixed cost items include capital
depreciation, insurance premium among others.
According to Olukosi et al (2006) a farm budget (a type of enterprise analysis) is a detailed
physical and financial plan for operation of a farm for a certain period. Farm budget model
enables the estimation of total expenses (costs) as well as various receipts/revenue (or returns)
within a production period (Olukosi and Erhabor, 1988). Musa et al (2006) stated that the farm
budget model gives a measure of profitability. The farm budget model used in this study was
according to the procedure adopted by Alabi and Adebayo(2008) and it is specified as:
GM = GFI – TVC
NFI = TGM – TFC
Where GM = Gross Margin, GFI = Gross Farm Income, TVC = Total Variable Costs,
NFI = Net Farm Income, TGM = Total Gross Margin, TFC = Total Fixed Costs. Since
53
total fixed costs (TFC) are assumed negligible in this case (subsistence agriculture), net
farm income equals farm profits.
The Gross margin of a farm Business can be calculated using the following formula:
(GM = Σ(Pij Qij – Rij Xij)
Where GM = Gross margin (N/ Ton)
Pij = Price of the 1th
output for the j respondent
Qij = quantity of the ith
output for the jth
respondent.
Rij = Price of the ith
variable input for the jth
respondent
Xi j= quantity of the ith
variable input for the jth
respondent
i = 1,…, m; j=1,…,n; m= types of variables, n = total number of respondents
P = average price of output (N/unit; Q = quantity of cassava crop (kg)
The study used the gross margin model to compare the profitability of farm enterprises
operated by the ADP and non-ADP women farmers in the study area.
2.12.3 Tests of Significance
Chow‟s test (F – test) was used to test the overall significance of the regression. The
model is specified as:
FChow = [Σep2 – (Σe1
2 + Σe2
2)]/k
( Σe12 + Σe2
2)/(n1 + n2 – 2k)
54
Where Σep2 = sum of pooled unexplained variations from multiple regression of
observations from ADP and non ADP woman farmers.
Σe12 = sum of residual variations from multiple regression of ADP respondents‟data.
Σe22 = Sum of residual variations from multiple regression of non-ADP respondents‟
data,
n1 = sample size of ADP respondents
n2 = sample size non ADP respondents
k = number of estimated parameters including the intercept.
Decision Rule: Accept Ho if calculated F-value is greater than critical F-value at 5% level of
significance otherwise reject Ho and accept the alternative hypothesis.
55
CHAPTERTHREE
RESEARCH METHODOLOGY
3.1 The Study Area
The study area Benue State derives its name from river Benue, the second largest river in
Nigeria after the Niger. The state was created out of the Benue Plateau state in 1976. Benue state
has a population of 4,219,244 people (NPC, 2006) and a total land mass of 34,095Km2. It is
located between longitude 80E and 10
0E, latitude 6
03
0N and 8
1/20N (BNARDA, 1998).
The state is in North central Nigeria and shares boundaries with Cross River, to the south,
Enugu to the south West, Ebonyi to the south, Kogi to the west, Taraba and Nasarawa State to
the East and North respectively. It shares an international boundary with the republic of
Cameroon to the South East (BNARDA, 1998). The state is bordered on the North by 280km
River Benue, and is transverse by 202km of River Katsina-Ala in the inland areas (Asogwa et al,
undated).
Benue State has abundant human and material resources. The state is in the rich
agriculture land of the Guinea Savannah zone of the Nigeria. The state has two major rivers – the
Benue and Katsina- Ala Rivers and several lakes, ponds and streams which are suitable for both
upland and fadama crop production. The state has two main seasons, the rainy season which
usually starts from April and ends in October with an average precipitation of 1500mm, the daily
mean temperature during rainy season is 280C. The dry season normally is from November to
March. It is characterized by harmatan winds for the most part. The soil and climate of Benue
state support the production of crops such as yam, cassava, cocoyam, sweet potatoes. Other crops
produced in the state include rice, maize, millet, sorghum, soyabeans, beniseed, groundnut,
56
beans, ginger and sugar cane among others. The state also produces a great deal of livestock,
forest products and fishes (Ater, 2002). Benue State was chosen for this study because of its
suitability to the study since the State is the largest producer of cassava in Nigeria (Olasunkanmi
et al, 2012; Asogwa, et al). Women farmers in Benue State have participated and received
extension teaching on the following ADP programmes: (a) Crop varieties such as maize, soya
bean, rice, groundnuts, cassava, beniseed, sweet potatoes, and cowpea. (b) Yam minisett
technique. (c) Crop mixtures such as yam/cassava/maize/egusi alternate row, soyabean/maize,
soyabean/sorghum, groundnut/cassava, groundnut/maize, groundnut/sorghum, and rice/maize.
(d) Livestock production such as piggery, rabittary and poultry. (e) Fisheries production such as
homestead fish production, pond construction, stocking and feeding, cultural practices, checking
of overflow, checking of weeds, fish feed formulation etc. (f) Agro forestry such as bee keeping,
management of beehive, honey harvesting, snail farming and mushroom production. (g) Fadama
activities such as vegetable production, management and use of tube wells, wash bores, and
water pumps. (h) Post harvest innovations such as processing, packaging, storage and marketing
strategies (BNARDA, 1998).
3.2 Sampling Techniques
The sampling frame for this study consists of all the cassava women farmers in Benue
State. Asogwa, Umeh and Penda (undated) citing BNARDA sources stated that there are 4,013
cassava farmers in Benue State. A multi stage sampling technique was used to select respondents
for the study. Stage one was the purposive selection of two cassava producing local governments
from each of the three zones in the state, making a total of six Local Governments, stage two was
the random selection of two blocks from each of the selected local government areas, making a
total twelve blocks. Stage three was random selection of ten women farmers that participated in
57
the ADP‟ WIA cassava production programme (ADP women farmers) alongside ten other
women that did not participate in the ADP programme (non-ADP women farmers) from each
block. This gave a total of 120 women ADP women farmers and 120 non- ADP women farmers
giving a sample size of 240 respondents. Cassava was grown in several mixtures in the study
area namely cassava/yam, cassava/maize, groundnut/guineacorn/cassava, cassava/rice,
groundnut/cassava among others. However, there are other farmers that plants hectares of sole
cassava in the area though in terms of numbers, those that intercropped cassava were
predominant. However, Olasunkanmi et al (2012) found in Ogun State that sole cropping of
cassava wass more profitable ( produced higher net farm income) than some of the enterprise
combinations. Improved cassava varieties in the study area include Coutonou, the Tropical
Manioc Selections (TMS) of which TMS 30572 which is highly favoured according to
BNARDA sources for its high yield(27Tonnes/ha), high quality akpu and garri, and TMS 3419 is
being introduced for its high yield ( potential yield of 44Tonnes/ha). Local varieties of cassava in
parts of the study area go by the names Panya, Peter, John, Wari (sweet type), Congo, Akpu and
others. The duration of sampling was three months from July to September, 2010.
3.3 Method of Data Collection
Data for this study came from both primary and secondary sources. Primary data were
collected by the use of two sets of structured pretested questionnaires. Information on
respondents‟ socioeconomic characteristics such as age, level of education, marital status,
household size, costs and returns in cassava production and marketing, among others were
collected. Secondary data for this study were obtained from the Benue State Agriculture and
Rural Development Authority, Benue State and Federal Ministry of Agriculture and Natural
58
Resources, the National Bureau of Statistics (NBS), the Central Bank of Nigeria (CBN),
published and unpublished materials relevant to the study.
3.4 Analytical Technique
Objectives i, ii, iii and iv were partly realized using descriptive statistics such as
percentage, graph, mean, frequency distribution. In addition, chi-square analysis was used to
realize part of objective ii. Part of objective iii was realized using total factor productivity
analysis and farm budgeting model respectively, Objective iv was partly realized using the
ordinary least square multiple regression analysis.
3.4.1 Model Specification
The models were specified for this study as follows:
3.4.1.1 Output Model
The ordinary least square multiple regression model was used to estimate the
determinants of respondents‟ output. This was similar to the procedure adopted by Alabi and
Adebayo (2008). The implicit form of the model is as:
Y = F(X1, X2, X3, X4, X5, X6, X7, X8, X9, X10) e
Where:
Y = output of cassava in kg
X1 = farming experience of farmers (years)
X2 = years of education (years)
59
X3 = family size of farmer (number)
X4 = quantity of fertilizer used (kg)
X5 = use of improved cassava cuttings (dummy, 1 = improved, 0 otherwise).
X6 = amount of agrochemicals used (Litres)
X7 = farm size in (hectares)
X8 = total amount of labour used (man-days)
X9 = credit use (dummy, 1 = credit use and 0 otherwise)
X10 = access to extension advice (dummy, access=1, 0 otherwise)
e = error term
Two functional forms semi-log and double log were tried using the ordinary least square
(OLS) estimation for each of the ADP and non-ADP women farmers groups. Initially four
functional forms linear, semi-log, double-log and exponential forms were proposed but the others
were dropped for reason of their being unstable. Besides, in production studies, the Cobb-
Douglas production function seems to be the most preferred. The explicit forms of the functions
were as follows:
Semi-log:
Yc = b0+ b11nx1 + b21nx2 + b31nx3 + b41nx4 + b51nx5 + b61nx6 + b71nx7 + b81nx8 + b91nx9+ e
Double log:
1nYc = b0 + b11nx1 + b21nx2 + b31nx3 + b41nx4 + b51nx5 + b61nx6 + b71nx7 + b81nx8 + e
60
The functional form producing the best fit was selected on the basis of the number of
statistically significant variables, the value of F – statistic, the magnitude of the coefficient of
multiple determination R2 (the explanatory power of the model) and the statistical significant of
the magnitude of the coefficients and the signs of the estimated parameters. The value of R2 (the
coefficient of determination). It is a measure of how useful the explanatory variables are in
predicting the response (dependent) variable and is referred to as measures of effect size.
t- Statistic gives the individual influence of each explanatory variable on explaining variations in
the dependent variable. The t-statistic is the value of coefficient divided by its standard error.
The standard error is an estimate of the standard deviation of the coefficient that is the amount it
varies across cases. It is a measure of the precision with which the regression coefficient is
measured. If a coefficient in large relative to its standard error, then it is probably different from
zero.
(i) F-value which gives the overall (collective) significant influence of the independent
variables on the dependent variable.
3.4.1.2 Total Factor productivity Analysis
According to Fakayode et al (2008), total factor (TFP) analysis can be used to estimate
productivity while ordinary least square regression method can be used to analyse the effect of
various factors (variables) on productivity. According to them, TFP = Y/TVCCp, Where TFP =
total factor productivity, Y = total output, TVCCp = total variable costs of cassava production.
Alternatively put, TFP = Y/∑PiXi, where Pi = unit price of the ith
variable input, Xi = quantity
of the ith
variable input, ∑ = summation sign. Total Factor Productivity analysis was used in this
study to estimate the productivities of ADP and non-ADP women farmers in this study, and the
61
ordinary least square regression method was used to analyse the effect of various factors on their
productivity.
3.4.1.3 Gross Margin Analysis
Gross margin analysis was used to realize part of objective v. It is specified as:
GMCE = GFICE – TVCCp
Where
GMCE = Gross Margin from Cassava Enterprise
GFICE = Gross Farm Income from Cassava Enterprise
TVCCp = Total Variable Costs of Cassava production.
Factors influencing respondents‟ gross margin from cassava enterprise were determined
using the ordinary least square multiple regression model specified as:
GMCE = f (Q,Pq,TVCCp, Cp, Ct) e
The implicit form is:
GMCE = (b0 + b1Q + b2Pq + b3TVCCp + b4Cp + b5Ct) e
Where:
GMCE =Gross margin from Cassava Enterprise (N)
Q = out put in(N)
Pq = price of output (N)
62
TVCCp = total variable costs of cassava enterprise (N)
Cp = processing costs (N)
Ct = transport costs (N)
e = error term
Two functional forms namely semi-log and double log were tried:
Semi-log: GMCE = b0 + b11nQ + b21nPq + b31nTVCCp + b41nCp + b51nCt + e
Double-log: ln GMCE = b0 + b11nQ + b21nPq + b31nTVCCp + b41nCp + b51nCt + e
The lead equation was chosen on the basis of the level of the coefficient of multiple
determination (R2), the level of significance of the F and t – statistic(s), the correct signs and
magnitudes of the coefficients relative to a priori expectations about them among others
3.5 A Priori Expectations about the Variables
The magnitudes of the output, gross margin and all the explanatory variables are expected
to be positive since negative values would render the functions infeasible (Koutsoyiannis, 2001).
A priori expectations on the regression coefficients of the following explanatory variables in
both the output and gross margin models as follows:
Farming experience: This is expected to have an initial positive influence on output. Output
may increase as farmer gets older and acquires farming experience up to a maximum point. After
this age may begin to reduce output as the farmer gets too old to farm effectively (Damisa and
Igonoh, 2007). The a priori expectation about the coefficient of this variable was that it would be
positive.
63
Years of Education: This was expected to have positive impact on output since an educated
farmer may better understand the importance and proper use of the improved technology than the
uneducated and hence produce more under given conditions. Onyeweaku et al (2004) education
increases productivity and enhances farmers‟ ability to understand and evaluate new production
techniques. According to Nsikakabasi et al (2010), and Ajibefun and Aderinola (2004), educated
farmers are better adopters of agricultural innovations and tend to have higher yields and
incomes from cultivated areas. Hence the coefficient of years of education was expected to be
positively related to output.
Family Size: The influence of family size on output could be positive or negative depending on
whether or not the family members are of age and do help on the farm. A large family size may
have a positive influence on output if the members can help on the farm. However, if the
members are mostly children of school age and aged dependants whom the family spends
money on their education (money that can be used for farm production) the influence of family
size may be negative. Rahman, Ogungbile, and Tabo (2002) reported that adoption index might
either be positively or negatively related to family size depending on the nature of age structure
and labour contribution by members. Coefficient of this variable might be positive or negative
as explained above. Inoni(2008) observed that house hold size showing the number of people in
the work force had a strong effect on output. According to Ogungbilel et al (2002), the
household size is the total number of individuals that live and feed in the household. A
household is made up of the head, wives, children and extended family relatives. The larger
the sizes of the work force in a farming household, the higher the output. Inoni(2008) found that
a 10% increase in work force will raise output by 4%. Family or household size was
hypothesized to be positively related to output in this study.
64
Labour, Credit, Agrochemicals, Fertilizers and Price of output
Omonona (2003), showed that small holder cassava farmers in Cross River and Kogi
States respectively were not efficient in the use of production inputs. However, Spencer (2002)
maintained that small holder farmers who are responsible for producing over 90% of agricultural
output in Sub-Saharan Africa can be efficient. Therefore, coefficients of these variables were
expected to be positive or negative depending on how they are used. The coefficient of the price
of output was expected to have positive effect.
Cost of Processing(Cp), Cost of Transportation(Ct), Total Variable Costs of Cassava
production(TVCCp)
According to Inoni(2008), reduced transportation costs lead to substantial expansion in
output and therefore gross margin. Gittinger (1994) described costs as anything that reduces an
objective. The objective in this case is increased productivity and income from cassava
enterprises of women farmers. Therefore coefficients of the above variables are expected to be
negative since they are costs and are expected to have a reducing or negative effect on the
dependent variable (gross margin).
65
Improved Variety use and extension contact.
These are dummy variables and they were expected to contribute positively to the output.
According to Damisa and Yohanna(2005), extension contact refers to the number of contacts
farmers have with extension agents and number of training farmers received through attendance
of workshops, seminars and film shows. It is expected that the higher the contact of the farmers
with the extension agents, the faster and wider the technology will be accepted. Hence more
output and higher gross margin. Therefore, contact with extension agent (to get extension
message) was a priori expected to contribute positively to the dependent variables (output and
income). Their coefficients were expected to be positive.
3.6 Tests of Significance
The estimated regression parameters were subjected to the Chow‟s-test to determine the
overall significance of the regression and the t-test to test the individual impacts of the various
explanatory variables in explaining variation in the dependence variable. Chow‟s F-test or F-
ratio was used to test whether the output and income of ADP and non-ADP were the same or if
they differed significantly. It is specified as:
FChow = [Σep2 – (Σe1
2 + Σe2
2)]/k
(Σe12 + Σe2
2)/(n1 + n2 – 2k)
Where
Σep2 = sum of pooled unexplained variations from multiple regression of observations
from ADP and non ADP woman farmers.
66
Σe12 = sum of residual variations from multiple regression of observations from
ADP woman farmers.
Σe22 = Sum of residual variations from multiple regression of observation from non-ADP
women farmers
n1 = Sample size of ADP women farmers.
n2 = Sample size of ADP women farmers
k = number of estimated parameters including the intercept
Decision Rule: Accept Ho if calculated F-value is greater than critical F-value at 5% level of
significance otherwise reject Ho and accept the alternative hypothesis.
3.7 Testing of Hypotheses
Hypothesis (i) was tested using Chi-square analysis. Hypotheses (ii) and (iii) were tested
using Chow‟s test. Three multiple regressions using output and income data from ADP, non-
ADP and pooled (ADP and non-ADP combined) cassava women farmers were ran to generate
the residual sum of squares used for the Chow‟s test.
3.7.1 Chi-Square Analysis
Chi-square was used to test hypothesis one. It carried out to determine whether socio-
economic characteristics of respondents had any relationship with their output. This was tested at
5% level of significance. This was used according to the procedure adopted by Umar (2008). It is
shown thus:
X2
= Σ(fo-fe)2
fe
67
Where
X2 = Chi-square
Fo = Frequency observed
Fe = Expected frequency
Decision Rule: Accept null hypothesis if chi-square calculated, X2(cal) > X
2 (tab) at 5% level of
significance otherwise reject null hypothesis and accept alternative hypothesis.
68
CHAPTER FOUR
RESULTS AND DISCUSSION
4.1 Socio-economic Characteristics of Respondents and Test of Null Hypothesis One
Socio-economic variables have been found to have an effect on farmers output. Socio-
economic characteristics (or variables) like education and farming experience for instance are
measures of human capital. Education reflects the ability to implement technology, and together
with experience can increase the value of human resources, and is thus expected to increase the
output of farmers. Therefore, it was necessary to show that differences in output among
respondents were not due to differences in their socio-economic characteristics. Chi-square
analysis was employed to achieve this purpose.
Chi-square analysis was used to explore the statistical significance of the difference in the
socio-economic variables of ADP and non-ADP respondents. Chi-square was used because the
variables (ADP and non-ADP) were categorical and their socio-economic attributes for instance
education were also categorical (no formal education, primary, secondary and tertiary education).
The objective was to test the null hypothesis (Null Hypothesis One) that socio-economic
variables of ADP and non-ADP cassava women farmers did not differ significantly and therefore
have no significant effect on their output against the alternative hypothesis that socio-economic
variables of ADP and non-ADP women farmers differed significantly, and have a significant
effect on their output. For each of the socio-economic variables (such as age, marital status,
family size, level of education, membership of farmers associations, and farming experience) this
hypothesis was tested using chi-square at the relevant degrees of freedom and 5 percent level of
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significance. The results are presented alongside the analysis of each socioeconomic variable
starting from table 4.1.
4.1.1 Age Distribution of Respondents
The age of farmers is one of the major determinants of how active they are. All things
being equal, the productivity of a farmer is expected to rise with age as the farmer becomes older
and acquires more experience in farming. After some years when old age sets in, the productivity
of the farmer will begin to diminish until she/he is no longer able to farm. Table 4.1 shows the
age distribution of women farmers in the study area.
Among the ADP women farmers, 3.4% was in the age range of less than 20years, 26.5%
were in the age bracket of 20-29years, 43.7% aged between 30-39 years, 6.9% were in the age
range of 50-59 years. Thus 90.8% of the ADP respondents were below 50 years of age. This
means most the ADP respondents were in the young and active age for production. In the non-
ADP group 70.1% of the respondents were below 50 years of age. From the table below the non-
ADP women below the age of 20 years were 3.4%, 16.1% were between 20-29 years of age,
26.4% aged between 30-39 years of age, 17.2% were between the ages of 50-59 years, and only
2.3% were 60 years and above. Though the ADP group had younger women in cassava farming
than the non-ADP group, one can generalize that the 70-90% of all women farmers studied were
below the age of 50. Moreover, less than 5% of all the women studied were aged 60 years and
above. The importance of age distribution of farmers has is because agriculture especially in the
rural areas relies heavily on the use of human power and younger stronger people are better able
to cope.
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Table 4.1: Age Distribution of Respondents
AGE (YEARS) ADP Non-ADP POOLED
Freq % C. % Freq % C.% Freq % C.%
<20 3 3.4 3.4 3 3.4 3.4 6 3.4 3.4
20-29 23 26.4 29.9 14 16.1 19.5 37 21.3 24.7
30-39 38 43.7 73.6 23 26.4 46.0 61 35.1 59.8
40-49 15 17.2 90.8 21 24.1 70.1 36 20.7 80.5
50-59 6 6.9 97.7 20 23.0 93.1 26 14.9 95.4
60 &
above
2 2.3 100.0 6 6.9 100.0 8 4.6 100.0
Total 87 100.0 87 100.0 174 100.0
Source: Field Survey, 2010
There was a significant difference in age distribution between ADP and non-ADP
cassava women farmers. This is because calculated Chi-square value obtained (16.416) is greater
than table value (11.07) at 5% level of significance and 5 degrees of freedom (besides P < 0.05).
However, in the regression results, the coefficient for farming experience which was used as a
proxy for age was not significant implying that age did not make a significant effect on output.
Therefore, the differences in respondents‟ productivity and gross margins cannot be attributed to
the differences in their ages.
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4.1.2 Marital Status of Respondents
Among the ADP group, 78.2% of the respondents were married while 70.1 of the non-
ADP group respondents were married. Furthermore 4.6% were divorced, 13.8% widowed and
3.4% were single among the ADP women farmers. The non-ADP women farmers 70.1% married
women, 13.8% divorced, 12.6% widowed and 3.4% single women. Overall, 96.4% of all the
respondents were married, divorced or widowed only 3.4% of all women sampled were single as
shown in Table 4.2:
Table 4.2 Marital Status of Respondents
STATUS ADP Non-ADP TOTAL (ADP + Non-
ADP)
Freq % C. % Freq % C.% Freq % C.%
Married 68 78.2 78.2 61 70.1 70.1 129 74.1 74.1
Divorced 4 4.6 82.8 12 13.8 83.9 16 9.2 83.1
Widowed 12 13.8 96.6 11 12.6 96.6 23 13.3 96.4
Single 3 3.4 100.0 3 3.4 100.0 6 3.4 100.0
Total 87 100.00 87 100.00 174 100.0
Source: Field survey, 2010
There was no significant difference in marital status of ADP and non-ADP respondents
since calculated chi-square value (4.423) is less than critical value (7.82) at 5 percent level of
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significance and 3 degree of freedom (P > 0.05). Therefore, the differences in respondents‟
productivity and gross margins cannot be attributed to the differences in their marital status.
4.1.3 Level of Educational of Respondents
Education is one of the major factors in the adoption of a new technology. Education is
investment in human capital which helps to raise the quality of farmers‟ farming skills, increase
their information and farming efficiency. This helps the farmers to improve their productivity
and production efficiency which eventually translates into high standard of living or welfare.
This is because education helps unlock the natural, latent or inherent enterprising abilities of
farmers.
Analysis of the educational levels of respondents in the study area showed that
18.4% of the ADP women farmers had no formal education, 40.2% attended primary school, and
25.3% had secondary education while 16.1% had higher education. Among the non-ADP women
farmers 36.9% had no formal education, 33.3% had primary education, 17.2% had secondary
education and 12.6% had tertiary education. All in all over 80% (or 81.6%) of the women in the
ADP group had some form of education while less than 20% (or 18.49%) did not have any
formal education. In the non –ADP group over 60% (or 63.2%) had some form of education
while less than 40% (or 36.8%) did not have any formal education. One can conclude that 60-
80% of all women sampled had some form of education while 20-40% did not benefit from
formal education. Thus one can also conclude that majority of the women farmers studied were
educated enough to enable them successfully adopt innovations to improve their productivity
incomes and welfare. Table 4.3 shows the level of education of respondents:
Table 4.3 Level of Education of Respondents
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Educational
Level
ADP NADP TOTAL (ADP +
NADP)
Freq % C. % Freq % C.% Freq % C.%
No formal
education
16 18.4 18.4 32 36.8 36.8 48 27.6 27.6
Primary
education
35 40.2 58.6 29 33.3 70.1 64 36.8 64.4
Secondary
education
22 25.3 83.9 15 17.2 87.4 37 21.3 85.7
Tertiary
education
14 16.1 100.0 11 12.6 100.0 25 14.3 100.0
Total 87 100.00 87 100.00 174
Source: Field survey, 2010.
According to Ogwumike (2000), lower levels of education are associated with higher rates
of poverty. Nsikakabasi et al (2010) also observed that educated farmers are better adopters of
agricultural innovations and tend to have higher yields and incomes from cultivated areas. For
these reasons, Chi-square was used to test whether the observed differences in productivity and
gross margins among ADP and non-ADP respondents were due to differences in their
educational levels or not. However, results showed that there was no significant difference in the
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level of education of ADP and non-ADP respondents. This is because calculated Chi-Square
value (7.580) was less than critical value (7.693) at 3 degrees of freedom and 5 percent level of
significance (P > 0.05). Therefore, the observed differences in respondents‟ productivity and
gross margins cannot be attributed to the differences in their educational status
4.1.4 Family Size of Respondents
A large family size is important in the supply of farm labor in farming communities
especially in Nigeria where farmers depend on human manual labour. Although, in a situation
where the members of a family are mainly young children of school age and old family
members, a large family size may not contribute much to farm production because they may be
unable to contribute the much needed labour for farm operations. Table 4.4 shows the family size
of the respondents in the study area:
Table 4.4: Family size of Respondents in the Study Area
Family ADP NADP POOLED
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Size
Freq % C. % Freq % C.% Freq % C.%
< 10 67 77.0 77.0 65 74.7 74.7 132 75.90 75.9
10-20 17 19.6 96.6 20 23.0 97.7 37 21.20 97.2
21-31 2 2.3 98.9 2 2.3 100 4 2.30
> 31 1 0.1 100.0 - - - 1 0.50 100.0
Total 87 100.00 87 100.00 174 100.0
Source: Field survey, 2010.
Data analysis showed that 77.0% of the ADP respondents had family sizes below 10 while
among the non ADP respondents, 74. % had family sizes between less than 10 persons, while
23.0% had family sizes between 10-20 persons. Since family sizes were not the same, there was
need to test whether the differences were significant or not. Chi-square analysis was used to
achieve this. However, Chi-square analysis showed that there was no significant difference in the
distribution of family sizes of ADP and non-ADP respondents. This is because calculated Chi-
Square value (24.924) is less than critical value (33.93) at 22 degrees of freedom and 5 percent
level of significance (P >0.05). Therefore, the differences in respondents‟ productivity and gross
margins cannot be attributed to the differences in their family sizes.
4.1.5 Membership of Farmers Associations
Membership of farmers‟ associations give an indication of the social networks available
to the farmer. These net works of social relationships determine farmers‟ access to credit and
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other resources needed for agricultural production. In most situations, farmers‟ resort to these
social net works for cash and other needs. They provide a source of ready cash and other input
needs. They may also represent a source of transfer of information about production and current
affairs relevant to farmer‟s needs. Membership of farmers‟ associations shows the level of
organization among farmers. The more farmers are organized into associations, the better they
are positioned to take advantages of opportunities that come their way. For example government,
NGO and international aid to farmers‟ is channeled through farmers associations rather than to
individuals.
Data analysis showed that among the ADP farmers, 37.9% had never belonged to any
farmers‟ association, 17.2% were once members while 44.4% are still members of farmers
associations. About 82.8% of the non-ADP respondents have never belonged to any farmers
association, 5.7% were once members, while 11.5% are currently members. From table 4.5
below, it can be clearly seen that the ADP group are better organized as many of them (44.4%)
belong to farmer‟s associations. They are therefore better positioned to get assistance from
government and other organizations including banks for their farm operations. They have
relatively more access to banks and other organizational credit sources than the non-ADP women
farmers. Membership of farmers‟ associations among respondents is shown in Table4.5:
Table 4. 5: Membership of Farmers’ Associations among Respondents
Status ADP Non-ADP POOLED
Freq % C. % Freq % C.% Freq % C.%
Never been a 33 37.9 37.9 72 82.8 82.8 105 60.3 60.3
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member
Once a member 15 17.2 55.2 5 5.7 88.5 20 11.5 71.8
Still a member 39 44.8 100.0 10 11.5 100.0 49 28.2 100.0
Total 87 100.00 87 100.00 174 100.0
Source: Field survey, 2010.
Since there were differences between respondents on account of their membership of farmers‟
associations, there was need to test whether this would have a significant effect on their
productivity and gross margin. Chi-square was used to find out. Chi-square results showed a
significant difference between ADP and non-ADP respondents with respect to membership of
farmers associations. This is because chi-square calculated value (36.649) was greater than
tabulated value (5.99) at 5 percent level of significance and 2 degrees of freedom (P > 0.05).
However, the coefficient of their membership of farmers‟ associations (which was a proxy for
access to credit) was not significant. Therefore, the observed differences in respondents‟
productivity and gross margins cannot be attributed to the differences in their farming
experiences.
4.1.6 Farming Experience of Respondents
Experience is knowledge and skills gained by contact with facts and events. The number
of years a farmer has spent in farming gives an indication of the practical knowledge she has
gained on how to cope in production, since well experienced farmers are better risk managers
than the inexperienced ones (Onyekuru, 2008). When properly channeled, experience can lead
to higher efficiency, higher productivity, higher incomes and higher standard of living for the
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farmer, her family, community and the nation. However, experience can sometimes become a
limiting factor to production improvement as farmers become set in their ways and refuse to
change and take advantage of new ideas on production. In conclusion while experience is a
necessary condition for productivity improvement, it is however not a sufficient condition.
Farmers with years of experience in farming should also watch out for new innovations that can
improve their productivity. Table 4.6 gives the farming experience of respondents.
Data analysis on the distribution of respondents according to farming experience showed
that 34.5% of the ADP farmers have been farming for 1-5 years, another 17.2% have been
farming for 11-15 years, 8.0% have been farming for 16-20 year, and 3.4% have been farming
for over 20 years. Among the non-ADP women farmers, 24.1% have been farming for 1-5 years,
34.5% have been farming for 6-10 years, furthermore 18.4% have been farming for 11-15 years,
9.2% have been farming for 16-20 years while 13.8% have been farming for over 20 year.
Table 4.6: Farming Experience of Respondents
Experience ADP NADP POOLED
(Years) Freq % C. % Freq % C.% Freq % C.%
1-5 30 34.5 34.5 21 24.1 24.1 51 29.3 29.3
6-10 32 36.8 71.3 30 34.5 58.6 62 35.7 65.0
11-15 15 17.2 88.5 16 18.4 77.0 31 17.8 82.8
16-20 7 8.0 96.6 8 9.2 86.2 15 8.6 91.4
79
>20 3 3.4 100.0 12 13.8 100.0 15 8.6 100.0
Total 87 100.0 87 100.0 174 100.0
Source: Field survey, 2010.
Chi-square analysis showed no significant difference in farming experiences of ADP and
non-ADP respondents. This is because calculated Chi-square value (7.152) was less than
tabulated value (9.49) at 5 percent level of significance and 4 degrees of freedom. This implies
that there is no significant difference between ADP and non-ADP respondents with respect to
their farming experiences, so observed differences in respondents‟ productivity and gross
margins cannot be attributed to the differences in their farming experiences.
4.2 Mean Costs and Return Analysis of Cassava Enterprises of Respondents
Table 4.7 shows the costs and return analysis of cassava enterprises of respondents
Table 4.7 Costs and Return Analysis of Cassava Enterprises of Respondents
A D P Respondents non-A D P Respondents
Total Output(kg/ha) 21,297.97 14,270.59
Quantity(kg) price(₦/kg) Amount(₦) Quantity(kg) price(₦/kg) Amount(₦)
Akpu 255.27 20 5,105.40 144.45 20 2,889.00
Chips 486.98 23 11,200.54 205.39 23 4,724.16
Garri 189.78 45 8,540.16 168.95 45 7,602.75
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GFI 24,846.10 15,205.91
Production and other Costs (₦)
Planting materials 2,905.92 3,059.25
Fertilizer 1,900.01 1,850.10
Labour 2,000.30 3,000.00
Agrochemicals 400.00 570.00
Cost of processing 516.00 1,448.80
Cost of transportation 600.00 1,500.20
TVCCp 8,206.23 9,479.35
Rent on land 1,000.00 1,000.00
Total costs (TCCE) 9,322.23 12,428.15
Productivity (Y/TVCCp) 2.96 1.68
GMCE(GFICE - TVCCp) 16,523.87 3,777.56
NFI (GM- TFC) 15,523.87 2,777.76
Source: Computed from Field Survey, 2010
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To determine the total factor productivities (TFP), gross margins (GMCE), net farm income
(NFICE) of ADP and non-ADP women farmers, a cost and return analysis of respondents‟
cassava enterprises were carried out and the various indices are presented in table 4.7 above.
4.2.3 Comparison of ADP and non-ADP Productivity and Testing of Hypothesis Two:
The mean output per hectare of ADP respondents was 21,297.97 kilogrammes while that of
the non-ADP respondents was 14270.59kilogrammes as shown in table 4.7. The total variable
costs of production (TVCCp ) for ADP and non-ADP cassava women farmers were ₦8,206.23
and ₦9,479.35 respectively, and the computed productivities for ADP and non-ADP cassava
women farmers were 2.96 and 1.68 respectively.Chow‟s F-test was used as to determine whether
there is a significant difference between ADP and non-ADP cassava women farmers
productivity. This was to test the second hypothesis of the study. To test this hypothesis the
unexplained variations (residuals) from the multiple regressions of observations from ADP
respondents (e2
1), the non-ADP respondents (e22) and the pooled regression (e
2p) were used to
compute Chow‟s F-test used in testing the null hypothesis two. Results showed that calculated
Chow‟s F (27.56) was greater than table F (1.93) at 9 degrees of freedom numerator and 142
degrees of freedom denominator, and at 5% level of significance. This implies that there is a
significant difference between ADP and non-ADP output. This difference has been attributed to
the superior performance of the ADP cassava production technologies. The null hypothesis was
therefore rejected and the alternative hypothesis accepted. This implies that there is a significant
difference between ADP and non-ADP output. This difference has been attributed to the superior
performance of the ADP cassava production technologies. The implication of this finding is that
use of ADP cassava production technology can greatly enhance farmers‟ productivity in Benue
State, Nigeria. This findings agree with Asogwa, Umeh and Penda (undated) who concluded that
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planting of improved cassava varieties, the use of improved processing technology, access to
cassava markets, access to extension services, and access to agricultural credit are important for
achieving effective utilization of inputs in Benue State, Nigeria.
4.2 Determination of the Factors Affecting the Productivity of Respondents
From Chi- square analysis of socio-economic characteristics of cassava women farmers
(ADP and non-ADP), the null hypothesis was accepted for marital status, level of education,
family size and farming experience since they had no significant effect on output, and could not
be responsible for the observed differences in output. However, the analysis showed a significant
relationship between age, membership of farmers‟ associations and output among cassava
women farmers studied. For age, least square multiple regression analysis showed that age was
not a significant variable in explaining output variations. The coefficient for membership of
farmers‟ associations (proxy for credit) was negative but significant indicating the variable‟s
tendency to reduce output. The possible explanation could be non accessibility of credit by the
associations and the inability to properly utilize the little credit obtained agricultural purposes.
To get the determinants of productivity of respondents, two linearised functional forms of
the ordinary least square multiple regression were fitted: semi-log and double-log using data
collected from field survey. Three regressions were fitted, one for ADP women farmers, non
ADP women farmers and the pooled data of the two. Two functional forms of the regression-
semi log and double-log were fitted and the best fit chosen. Two functions were used because the
researcher was mandated to use two instead of one
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4.2.1 Determinants of ADP Respondents’ Output
As a result of suspected multicollinearity (coupled with the fact that a constraint was imposed
on the research to use only semi-log and double-log functions), the stepwise double-log model
was selected as the lead equation for the output of ADP respondents as shown in table 4.7
Table 4.7: Determinants of ADP Respondents’ output
Variable B. coeff. Std error t-value Sig.
Constant
Farming Experience (X1)+
8.747
0.099
0.461
18.982
0.681
0.000
0.501
Years of Education(X2)+ 0.140 0.973 0.338
Family Size (X3)+ 0.134 0.998 0.330
Amount of Fertilizer (X4)+ 0.287 1.558 0.129
Improved Cassava use(X5)* 0.168 0.091 1.884 0.074
Amount of Agrochemicals (X6)** 0.326 0.131 2.478 0.018
Farm Size (X7)* + 0.247 1.917 0.064
Amount of Labour used (X8)+ 0.222 1.123 0.270
Credit Access (dummy) (X9)** -0.312 0.149 -2.093 0.044
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Extension Access (dummy)(X10)+ 0.012 0.085 0.933
R2
=0.402
Adjusted R2
= 0.349
F- Ratio
= 7.619
Source: Field Survey, 2010.
The value of R2 was 0.402 indicating that 40.2% of the variation in output was due to the
variables included in the model. The adjusted R2 was 0.349 which means that 34.9% of the
model was successful. The F-ratio (7.619) was significant beyond 1% (P<0.01).
The coefficient of farming experience (X1) was positive but not significant implying that
farming experience had a positive insignificant effect on output. This agrees with a priori
expectation of a positive effect, also agrees with Damisa and Igonoh (2007) who posited that
farming experience (a proxy of age) usually increases output until farmers get too old to farm
effectively.
The coefficient of years of education (X2) was positive but not significant implying the
positive and insignificant contribution of the variable to output. This is in agreement Ajibefun
and Aderinola (2004) that enhances farmers‟ productivity and ability to evaluate technologies.
The positive relationship but insignificant relationship could be due to weak extension linkage to
deliver information on technologies to respondents. This re-emphasizes the need to reposition the
extension system to optimally perform their role in the study area.
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The coefficient of family size (X3) was positive but insignificant implying that family size
had a positive insignificant contribution of family size to output. This contrasts with Inoni (2008)
who posited that household size had a strong positive effect on output.
The coefficient of amount of fertilizer applied (X4) was positive but insignificant implying
the positive insignificant contribution of fertilizer applied to output. This is contrary to a priori
expectation of a positive significant relationship but agrees with Haruna et al (2008). This
could be due to suboptimal levels of fertilizer applied. Improved cassava varieties perform
optimally under optimum production environments such as adequate amounts of the correct
nutrients among others. This result is not surprising since, since fertilizer was used in the study
area to reward party stalwarts and little was available to the actual producers such as the
cassava women in this study, especially in 2010. This resulted in high price of the product and
inability of most farmers to buy it because of poverty
The coefficient for use of improved cassava variety (X5) was positive and significant at
10%. This implies that use of improved cassava stem cuttings has a positive significant effect
on output and productivity. This is according to a priori expectation since improved varieties
are developed and distributed to farmers to raise their farm yields. This agrees with Odii (2003)
that improvement in agricultural technology leads to improvement in farm productivity. This
implies that policies to develop and distribute improved technologies to farmers should be
encouraged in order to increase productivity, income and reduce poverty in the rural areas.
The coefficient of amount of agrochemicals used (X6) is positive and significant implying
that sagrochemicals make positive significant contribution to output. This also agrees with Odii
(2003) observation that use of agrochemicals can lead to output maximization.
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The coefficient of farm size(X7) was positive and significant implying the positive
significant contribution of the variable to output. This result conforms to Morris (2001), Herath
and Takeya, (2003), Damisa and Igonoh(2007) and it underscores the need to provide funds for
farmers especially women farmers to increase their farm sizes to enable them increase their
production and enjoy higher incomes.
The coefficient of amount of labour used (X8) was positive but not significant implying
the positive non-significant contribution of labour to output. This is not surprising since it
reinforces the observation by Erhabor and Omokaro (2008) that cassava is a low- labour crop.
Since that crop is weed tolerant, the cassava women farmers (usually overburdened) paid more
attention to weeding other crops, which may have reduced the labour for weeding, significantly
impacting the labour input.
The coefficient of use of credit(X9) was negative and insignificant implying the variable‟s
non-significant contribution to output. This is not surprising because it is an open secret that
women farmers hardly access credit. This has been attested to by Chukwuone (1995) who found
that women are discriminated against in terms of accessibility to loans, when in a study he
discovered that only 9.12% of the females who applied for loans were granted as against 90.9%
of the males. This also reinforces the view of Mbah(2008) that women benefit little from
agricultural services such as agricultural extension, credit schemes, land acquisition, and
technology that would improve their productivity. The implication of this findings is that policies
should be made to overcome the problem of collateral and other issues that limit women‟s access
to credit.
The coefficient of access to extension(X10) was positive but not significant implying that
access to extension made positive insignificant contribution to output. This is contrary to a priori
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expectation but it agrees with Yusuf and Adenegan (2008) that extension services have generally
been poor in Nigeria since withdrawal of World Bank funding from the Agricultural
Development Projects. This has constrained dissemination of agricultural innovations to farmers
who do not have timely access to yield improving inputs. Hence, access to extension might not
have the expected effect. In the course of data collection, it was discovered that extension system
in Benue State especially the Women in Agriculture subcomponent was ont in the4 best shape.
Members that have been lost to other programmes, retirement, death, or any reason were not
replaced, as such there were manpower issues, in addition tothe issues of staff welfare and proper
funding.
4.2.2 Determinants of non-ADP Output
The double- log model was chosen as the lead equation for modeling non-ADP output.
The value of R2 was 0.930 showing that 93.0% of the variation in the output was explained by
the explanatory variables included in the model. The value of adjusted R2 was 0.757 implying
that the model was 75.7% successful. The F-ratio was 5.352 was significant at 10% level
showing that the combined influence of the explanatory variables was strong. Table 4.8 below
shows the determinants of non-ADP farmers‟ output.
Table 4.8: Determinants of Non-ADP Respondents’ Output.
Variable B Std. error t sig t & sig
Constant
**Farming experience (X1)
7.648
-0.675
2.990
0.225
2.558
-3.002
0.063
0.65
-3.002,.065
*Years of education (X2) 1.236 0.325 3.803 0.019 3.803,0.019
***Family size (X3) -0.079 0.144 -0.547 0.013 -0.547,.013
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Amount of fertilizer applied (X4) 0.189 0.255 0.741 0.500 0.741,.500
Improved cassava stem cuttings (X5) 0.492 0.187 2.633 0.058 2.633,.058
Agrochemical and planting materials (X6) 0.110 0.206 0.535 0.621 0.535,.621
Farm size (X7) 0.523 0.461 1.134 0.320 1.134,.320
Amount of labour used (X8) -0.366 0.612 -0.598 0.582 -0.598,
**Access to Credit (dummy) (X9) -0.681 0.209 -3.258 0.031 -3.258,.031
Extension(dummy) (X10)
R2
=0.930
Adjusted R2
= 0.757
F- Ratio
= 5.352
0.030 0.145 0.207 0.846
0.207,.8
Source: Field Survey, 2010
The coefficient of farming experience (X1) was negative and significant at 10% level
implying its negative contribution to output of non-ADP women farmers. The coefficient‟s
value of 0.675 implies that one percent change in farming experience will tend to reduce output
by 0.675%. This could be because farmers as they grow older and become more conservative,
and less efficient. This is contrary to Inoni (2008) who found that the age of farmers play a
significant role in the income they generate in a year. As the farmer gets older and more
experienced the more resources such as labour and land he posses to generate more income.
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The coefficient of years of education (X2) was positive and significant at 5% implying the
significant contribution of the variable to output. The coefficients value of 1.236 implies that one
percent increase in years of education will tend to increase output by 1.236%. This conforms to a
priori expectation and Onyeweaku et al (2004) education increases farmers‟ productivity.
The coefficient of family size (X3) was negative and significant implying that family size
had a negative significant contribution to output. This implies that family size tends to reduce
output and productivity. This is contrary to Inoni (2008) posited that household (or family) size
can contribute positively to output because of potential supply of labour for farming. The
possible explanation could be that money which could be used to expand the farm and produce
more is been used meet the needs of large family.
The coefficient for amount of fertilizer used (X4) was positive but not significant implying
that fertilizer had a positive insignificant contribution to output. This is contrary to a priori
expectation.This could be due to the inability of the farmers to buy it because of poverty and the
distribution of fertilizer along party lines as was the case in the study area in 2010.
The coefficient of use of improved cassava stem cuttings (X5) was positive and significant at
10% implying the positive significant contribution of the use of improved cassava to output. The
coefficient‟s value of 0.492 implies that 1% change in use of improved cassava stem cuttings
would tend to change output by 0.492%. This conforms with a priori expectation and agrees with
Odii (2003) that use of improved technologies such as improved cassava varieties has positive
effect on output and productivity.
90
The coefficient of agrochemical (X6) was positive but insignificant implying the use of
agrochemicals did not make a significant contribution to output. Empirical evidence show that
farmers use low levels of production inputs such as agrochemicals.
The coefficient of farm size (X7) was positive but insignificant implying the positive
insignificant contribution of farm size to output. This contrary to a priori expectation of a
positive relationship and may be attributed low input usage and the use of tradition implements.
The coefficient of labour used (X8) was negative but not significant implying the positive
insignificant contribution of labour to output. This conforms to Erhabor and Omokaro (2008)
that cassava is low-labour crop.
The coefficient for access to credit(X9) was negative and significant at 5% level implying
the significant negative contribution of access to credit to output. This is contrary to a priori
expectation of a positive relationship. The result is also contrary to Yusuf and Adenegan
(2008) who reported a positive insignificant coefficient of access to credit in their study of
technical efficiency among women farmers in Kogi State, Nigeria. This negative result could
be because credit obtained for farm production is being diverted to other uses.
The coefficient of access to extension (X10) was positive but not significant implying the
positive insignificant contribution of extension to output. This was contrary to a priori
expectation and was attributed to the internal problems of the extension system in Benue State.
4.2.3: Determinants of Respondents’ Pooled Output
The stepwise semi-log model was selected as the lead equation for the pooled output of
the respondents (ADP and non-ADP) as shown in table 4.9 .
91
Table 4.9: Determinants of Respondents’ Pooled Output
Variable B Coeff Std Error t- value Signif.
Constant
Farming Eperience (X1)
2498.435 8648.728 0.289 0.774
Years of Education(X2)** 7852.856 3767.715 2.084 0.045
Family Size (X3) -0.128 -0.910 0.370
Amount of Fertilizer (X4) 0.069 0.544 0.590
Improved Cassava use(X5)*** 4540.118 1338.619 3.392 0.002
Amount of Agrochem. (X6) 0.126 0.842 0.406
Farm Size (X7) *** 12952.010 2999.968 4.317 0.000
Amount of Labour used (X8) 0.151 1.139 0.263
Credit Access (dummy) (X9) -0.085 -0.674 0.505
Extension(dummy) (X10)
R2
=0.489
Adjusted R2
= 0.444
F- Ratio
= 10.837
Extension (dummy) (X10)
0.155 1.157 0.256
. Source: Field Survey, 2010
92
The value of R2 was 0.489 showing that 48.9.0% of the variation in the output was explained by
the explanatory variables included in the model. The value of adjusted R2 was 0.444 implying
that the model was 44.4% successful. The value of F-ratio (10.837) was significant at 10% level
showing that the combined influence of the explanatory variables was strong. Table 4.2.3 shows
the determinants of the pooled output.
Major variables that explained variations in the pooled respondents‟ output were years
of education, use of improved cassava stem cuttings and farm size.
The coefficient of farming experience (X1) was negative and not significant implying the
negative non-significant contribution of farming experience to non-ADP output.
The coefficient of years of education (X2) was positive and significant at 5% implying the
positive significant contribution of the variable to output. The coefficient‟s value of 7852.856
implies that one percent increase in years of education will tend to increase output by 7852.856
units. This conforms to a priori expectation.
The coefficient of family size (X3) was negative and insignificant implying that family
size had a negative insignificant contribute ion to output.
The coefficient for amount of fertilizer used (X4) was positive but not significant
implying that fertilizer had a positive insignificant contribution to output. This is contrary to a
priori expectation. This could be due to the use of inadequate amounts of fertilizers- a
consequence of the high price of the product and inability of the farmers to buy it because of
poverty.
93
The coefficient of use of improved cassava stem cuttings (X5) was positive and significant
beyond 1% (P<0.01) implying the positive highly significant contribution of the use of improved
cassava to output. The coefficient‟s value of 4540.118 implies that 1% change in use of
improved cassava stem cuttings would tend to change output by 4540.118 units.
The coefficient of agrochemical (X6) was positive but insignificant implying the positive
insignificant contribution of agrochemicals to output.
The coefficient of farm size (X7) was positive and highly significant (P=0.000) implying the
positive highly significant contribution of farm size to output. The coefficient‟s value of
12952.010 implies that 1% change in farm size would tend to change output by12952 .010 units.
The implication is that increasing their farm holdings would tend to enhance cassava women
farmers‟ productivity and incomes. Land issues are among the perceived constraints of the
cassava women farmers in this study (figure 4.1). There is need for policies to enable women
farmers access more and better land for agricultural production particularly in the study area.
The coefficient of labour used (X8) was positive but not significant implying the positive
insignificant contribution of labour to the pooled output.
The coefficient for access to credit was negative and insignificant implying the negative
insignificant contribution of access to credit to output.
The coefficient of access to extension (X10) was positive but not significant implying the
positive insignificant contribution of extension to the pooled output. This is contrary to a priori
expectation and implies that there is strengthen extension to produce the desired impact.
94
4.3 Gross Margin Analysis and Testing of Hypothesis Three
The gross mean margin of ADP and non-ADP cassava women farmers was determined as
shown in table 4.7. The gross mean margin of ADP cassava women farmers was ₦16,523.87
while that of non-ADP cassava women farmers was ₦3,777.56 respectively. This compares
favourably with Bello and Salau (2008) who reported that 49.7%, 26.0% and 16.4% of cassava
women processors earned annual incomes between 15,000 naira and above, 5,000-10,000 naira,
and 10,000 -15,000naira respectively.
The test of hypothesis three which is the test of significance between the two means ( t-test
statistic for the mean gross margin of ADP and non-ADP cassava women farmers) was done
using t-test. Results showed that calculated t-value (108.4) was greater than critical value (1.645)
at 5% level of significance. Therefore, the null hypothesis which stated that there was no
significant difference between the gross margin of ADP and non-ADP cassava women farmers in
Benue State was rejected and the alternative hypothesis which stated that there is a significant
difference between the gross margin of ADP and non-ADP women farmers in Benue State was
accepted. The significant difference in gross margin was attributed to the use of improved
cassava production technologies and extension contact (provided by the Benue ADP) by the
ADP cassava women farmers. This implies that the use of improved agricultural technologies
such as improved planting materials like cassava stems, extension teachings can improve farmers
gross margins and incomes(since empirical evidence exists that fixed costs are negligible in
small scale agriculture). To estimate the factors affecting the gross margin of cassava women
farmers in Benue State, multiple regression of data collected from ADP women farmers and
multiple regression of the data collected from non-ADP women farmers were fitted and the
determinants are as shown:
95
4.3.1 Determinants of ADP Women Farmers’ Gross Margin
As shown in table 4.10, the major variables that explained variation in gross margin
among ADP respondents were output (Q), price of output (Pq), cost of processing (Cp) and cost
of transportation (Ct). The value of R2 was 0.478 implying that 47.8% of the variation in gross
margin was explained by variables included in the model. The adjusted R2 value of 0.446 shows
that 44.6% of the model was successful. The F-ratio of 14.810 which is significant beyond 1%
(P<0.01) means the overall impact of the explanatory variables on the dependent variable is high.
Table4.10: Determinants of ADP Women Farmers’ Gross Margin
Variable Coefficient Standard error t-value Significance
Constant 2.512 1.430 1.757 0.083
Output(Q) 0.573 0.101 5.647 0.000***
Price of Q(Pq) 0.061 0.016 3.855 0.000***
TVCCp -0.119 0.092 -1.299 0.196
Proc Cost(Cp) 0.194 0.093 2.083 0.040**
Transpt Cost(Ct)
R2
= 0.478
AdjustedR2=0.446
F- Ratio = 14.810
0.150 0.087 1.715 0.090
Source: Computed from Field Survey Data, 2010
96
The coefficient of output, Q (0.573) is positive and significant beyond 1% (P = 0.000).
It implies the positive significant contribution of output to gross margin of ADP cassava women
farmers. This is according to a priori expectation because the more the output the more the gross
margin and it makes economic sense since a negative output would be meaningless. Since the
function is a double log, the coefficients are direct elasticities. This implies that 1% increase in
output will tend to produce an increase in gross margin of 0.573% and vice versa. This result
implies that any measure that would change output would tend to change gross margin in a
similar direction. It means any policy to increase output would tend to increase gross margin and
therefore farmers‟ incomes. This will tend to reduce poverty and improve welfare.
The coefficient of price of output (Pq) was positive (0.061) and significant beyond 1%
(P<0.01. This implies that price of output made a positive highly significant contribution to gross
margin. The implication of this result is that appropriate pricing of cassava output is necessary to
improve farmers‟ gross margin, incomes and farmers‟ welfare through poverty reduction.
Optimal pricing of output will produce optimal results in gross margin and serve as an incentive
to produce more. Poor pricing of cassava will tend to reduce gross margin and serve as a
disincentive for further production ceteris paribus.
The coefficient of total variable costs of cassava production (TVCCp) was negative (-0.119)
but not significant implying the negative insignificant contribution of total cots of production to
gross margin of ADP cassava women farmers in the study area. This is contrary to a priori
expectation of a negative significant relationship. From theory, cassava is weed tolerant and can
produce a reasonable (but not optimal) crop with minimum expenditure of labour and other
resources, so this is not surprising. Another reason could be the use of inadequate amounts
fertilizer (in some cases complete absence of fertilizer), and other relevant production resources
97
on these farms under study, It is therefore not surprising that total variable costs have no
significant impact gross margin.
The coefficient of cost of processing (Cp) was positive and significant at 5% (P=0.040)
implying the positive significant contribution of the variable to gross margin. This implies that
1% change in cost of processing would tend to change gross margin by 0.19%. The policy
implication here is that provision of more and better processing facilities would positively
facilitate processing. Since a positive significant relationship exist between cost of processing
and gross margin, the more the processing, the more gross margin and the higher the women
farmers‟ standard of living. This implies that provision of more processing opportunities for
cassava women farmers will tend to reduce poverty and increase their standard of living.
The coefficient of cost of transportation (Ct) was positive (0.150) but insignificant at 5%
implying the positive insignificant effect of cost of transportation to gross margin.
The regression equation for the gross margin of ADP cassava women farmers in the study
area can be written as follows:
GM= 2.512*+ 0.573Q***+ 0.061Pq*** + 0.194Cp** + 0.150Ct*
(1.430) (0.101) (0.016) (0.093) (0.087)
The values in brackets are the standard errors of the coefficients while the symbols **, ***
show that the coefficients are significant at the 5% and 1% levels respectively.
4.3.2 Determinants of non- ADP Cassava Women Farmers’ Gross Margin
98
The double –log function was selected as the lead equation for modeling the non-ADP
respondents‟ gross margin. The major variables that explained variation in the non-ADP
respondents‟ gross margin were output (Q), price of output (Pq) and cost of transportation. The
value of the coefficient of determination (R2) was 0.483 which means 48.3% of the variation in
gross margin among non-ADP respondents was explained by variables included in the model.
The value of F (13.282) was high and highly significant (P=0.000) showing that though the
individual effect of the explanatory variables might be slightly weak, their combined effect is
very strong. The Table 4.11 shows the results of non-ADP gross margin regression:
99
Table 4.11: Determinants of non-ADP Respondents’ Gross Margin
Variable Coefficient Std error t-value Sig.
Constant 2.627 1.389 1.891 0.063
Output(Q) 0.612 0.114 5.388 0.000
Price of Q(Pq) 0.060 0.016 3.838 0.000
TVCCp -0.115 0.95 -1.215 0.228
Proc.Cost(Cp) 0.132 0.092 1.428 0.158
Transpt Cost(Ct) 0.148 0.083 1.782 0.079
R2
=0.483
Adjusted R2
= 0.444
F- Ratio
= 13.282
Source: Field Survey, 2010.
The coefficient of output (Q) was positive (0.612) and highly significant (P<0.01) implying
the positive significant contribution of output to gross margin of non-ADP respondents.
The coefficient of price of output (Pq) was positive (0.060) and highly significant (P<0.01)
implying the positive significant effect of price of output to gross margin. This underscores the
importance of output price in income generation and poverty reduction. The policy implied here
is that paying farmers good prices for their cassava products will tend to increase the income
100
available to them which they can use to expand their farm holdings among other uses. This will
provide a route out of poverty for the farmers all other things being equal.
The coefficient of total variable costs of cassava production (TVCCp) was negative (-0.115)
and insignificant (P>0.05) implying the negative insignificant contribution of the price of output
to gross margin. This is contrary to a priori expectation of a significant relationship. This could
be because the cassava women farmers use very low levels of production inputs such as
fertilizers, agrochemicals and even labour for cassava production because of poverty which make
it difficult to access production resources.
The coefficient of cost of processing (Cp) was positive (0.132) but not significant (P>0.05)
implying the positive insignificant contribution of the cost of processing to gross margin. This
could be because unlike the ADP respondents‟, the non-ADP group do not have access to
processing facilities since they are well organized into cooperatives as the ADP respondents.
The coefficient of cost of transportation (Ct) was positive (0.150) but not significant (P>0.05)
at 5%. This implies the positive insignificant contribution of cost of transportation to gross
margin. Probably as a result of poverty and the poor road network, and the consequent high cost
of transportation, most respondent transport their farm produce by head to the markets so they do
not incure costs to transport their produce hence the insignificant transport cost coefficient.
The regression equation for non-ADP respondents‟ gross margin is as follows:
GM = 2.627 + 0.612Q + 0.060Pq + 0.150Ct
(1.389) (0.114) (0.016) (0.083)
The values in brackets are the standard errors of the coefficients.
101
4.3.3 Combined (Pooled) Gross Margin of Respondents
The double-log model was selected for the combined (pooled) gross margin. Major variables
that contributed pooled gross margin are output and cost of processing as shown in Table 4.12.
The value of the coefficient of determination (R2) is 0.964 which means 96.4% of the
variation in gross margin of the respondents was explained by variables included in the model.
The adjusted (R2) was 0.693 which implies that 69.3% of the model was successful. The value
of F was 3.561, and it is significant beyond 1% (P=0.004) showing that the combined impact of
the explanatory variables was very strong on the dependent variable.
The coefficient of output (Q) was positive and significant at 5% (P<0.05).This implies
the significant contribution of output to gross margin. The coefficient‟s value implies that 1%
change in output would tend to produce a change of 7387.713 units in gross margin.
Table 4.12: Determinants of Respondents’ Pooled Gross Margin.
Variable Coefficient Std. Error t-value Significance
Constant -147031.556 56827.666 -2.587 0.011
Output(Q) 7387.713 3660.181 2.018⃰⃰⃰⃰ 0.045
Price of Q (Pq) 610.436 727.830 0.839 0.403
TVCCp -1291.893 4643.881 0.278 0.781
Proc. Cost(Cp) 10768.209 4596.092 2.343⃰ 0.020
Transpt Cost(Ct 1638.339 1684.789 0.972 0.332
102
R2
=0.964
Adjusted R2
= 0.693
F- Ratio
= 3.561
Source: Field Survey Data, 2010
It is important to note that in the combined gross margin regression; only output and cost of
processing are significant. The coefficient of cost of processing is positive contrary to a priori
expectation, but it implies that processing tends to increase gross margin. The policy implication
is that providing more processing opportunities for cassava women farmers will tend to increase
their gross margin, income and reduce rural poverty.
The coefficient of price of output (Pq) was positive and significant (p<0.05) implying the
positive significant impact of the price of output to the pooled gross margin. The policy
implication her is that appropriate pricing of cassava products can lead to high incomes to
farmers. This could induce a positive supply response since according to the law of supply, the
higher the price the higher the supply and vice versa.
The coefficient of total variable costs of cassava production (TVCCp) was negative and
insignificant (P>0.05) implying the negative insignificant contribution of TVCCp to the
combined gross margin. This is not surprising since according to Erhabor and Omokaro(2008),
cassava can produced profitably due to its comparative low labour requirements and tolerance of
low nutrient levels among others. This can explain why the total variable costs of production are
low and insignificant. The crop can be low on labour and other costs of production.
The coefficient of cost of processing (Cp) was positive and significant at 5% (P<0.05)
implying the positive significant contribution of the cost of processing to gross margin. The
103
coefficient‟s value of 10768.209 implies that 1% change in cost of processing would tend to
produce a change of 10768.209 units in gross margin. Implied policy here is policy measures to
increase processing of cassava in the study area would tend to increase gross margin, income and
reduce rural poverty.
The coefficient of cost of transportation (Ct) was positive but not significant (P>0.05)
implying the positive insignificant contribution of the variable to gross margin.
4.4 Improved Cassava Production Technologies Available in Benue State
Cassava production technologies identified in Benue State include improved cassava varieties
such as the varieties released by the International Institute for Tropical Agriculture known as the
Tropical Manioc Selection (TMS) among other varieties. Those in use in Benue State include of
TMS 4(2)142, TMS 30555, TMS 30572, and TMS 3419. Others were NR7221 and NR7706.
Cassava women farmers in the study area favoured TMS 30572 for high quality garri and akpu
(rated paste) and the variety was found growing throughout the study area. Other cassava
production technologies available include use of herbicides/pesticides, cassava-based mixtures,
planting distance, use of fertilizers, machinery, and improved storage, processing, and planting
angle. According to Daudu et al (2008) mechanized cassava processing farm assets
(technologies) in the state (technologies) include fryers, pressers, graters, and dryers among
others.
4.5 Constraints to increased Cassava Production and Distribution in Benue State
In this section, efforts were made to identify the constraints or limitation to increased
productivity among respondents:
104
4.5.1 Perceived Problems of Respondents
Respondents were asked to identify their perceived constraints to increased cassava
production and distribution in the study area that is factors they feel when removed would enable
them to produce and sell more cassava products. Their identified constraints are shown in figure
4.1. The data is presented in bar graphs with their heights in percentages representing the
strengths of the problems.
The ADP cassava women farmers in the study identified the problem of processing
(46.0%), poor pricing of output (37.9%), lack of credit (34.5%), soil fertility (31.0%), labour
(28.7%), transportation (27.6%) and poor market infrastructure (26.4%) among others as factors
limiting their productivity.
The non-ADP women farmers on the other hand identified poor pricing of output (50.6%), lack
of credit (47.1%), soil fertility problems (46.0%), processing problems (40.2%), lack of good
market infrastructure (34.5%), labour problems (31.0%), transportation problem (27.6%) among
others on constraints to their productivity improvement.
A combined (pooled) analysis of respondents‟ constraints (ADP and non-ADP)
showed that poor pricing of output (44.3%), processing problems (43.1%), lack of credit
(40.8%), soil fertility problems (38.5%), poor market infrastructure (30.5%), labour problems
(29.9%), transportation problem (27.6%) are the major problems militating against the increased
productivity of respondents. Respondents perceived constraints are shown in figure 4.1. The
data is presented in bar graphs with their heights in percentages representing the strengths of the
problems.
105
Figure 4.1 Identified Constraints of Respondents
Source: Field Survey, 201
0
10
20
30
40
50
60
ADP
NADP
POOLED
106
4.5.2 Prospects for Cassava Production in Nigeria
From empirical studies, empirical observations and review of literature on cassava production in
Nigeria, the study discovered that cassava is a high potential and viable crop for food security
and poverty reduction in the Nigeria. This is because of the international interest to purchase
cassava products from Nigeria (Chukwuji, 2007), the various uses of cassava (domestic and
industrial, local and international), all of which could create potential high demand for cassava
products in Nigeria. On the other hand, the Presidential initiative on cassava production, the
various research works and research institutions working on the crop in Nigeria, and the
significant difference improved varieties of cassava are making to farmers‟ yields and incomes
(as in this study) among other factors, indicate the potential for an adequate supply response. The
implication of this is for Nigeria to put in place an enabling environment to take maximum
advantage of these opportunities.
4.5.3 Facilitators of Agricultural Production and Productivity
As a result of their critical importance to productivity improvement in the study area, this
research paid special attention to certain constraining factors which are hereby referred to as
facilitators of agricultural production and productivity improvement.
4.5.1.1 Rural Roads in the Study Area
Rural roads are important for input procurement and produce evacuation, and distribution to
areas of need. Inoni (2008) observed that roads are a significant determinant of transportation
costs in sub-Saharan Africa, and that in land locked regions, transport costs can be as high as
107
50%. He further noted that without a good road net work, rural farmers sell their produce at very
low prices leading to low incomes and low standard of living. The ADP planners in recognition
of this incorporated a rural road net work development into the programme. Provision of rural
infrastructure ranked as the number one and two problems among the ADP and non-ADP women
farmers respectively. The study made efforts to closely examine the women farmers‟ access to
some of these infrastructure especially roads and the results are shown in figure 4.2:
scale:1cm=10(y-axis)
Figure 4.2: Road Network of the Respondents
Source: Field Survey, 2010
From figure 4.2 above, only 8% of the ADP women farmers and 23% of the non-ADP
women farmers have access roads which are all season (passable all year round), 92% of the
ADP women farmers and 77% of the non -ADP women farmers use roads which have one
problem or another during part of the year which make them impassable. No wander
transportation costs are high and their coefficients are significant in both ADP and non-ADP
gross margin regressions.
0
20
40
60
80
100
120
it is all season road
It is all fairly good most of the year
not passable in rainy season
Very bad most of the
time
8
64.4
26.41.1
23
43.7
21.8
11.5
NADP
ADP
108
4.5.1.2 Rural Water Supply in the Study Area
Water is one of the basic needs of life (Ogwumike, 2000). Women have the gendered
responsibility to source for water in the African context (including the study area). This is time
consuming and can prevent the women farmers from applying the needed labour for agriculture
production. Hence the ADP planners also incorporated a water provision component to ease the
burden of sourcing water so that the women farmers can effectively carry out their production
and other activities. The study assessed the sources of water supply available to the respondents.
The result is presented in figure 4.3:
Scale:1cm=10% ( on
x-axis)
Figure 4.3: Water Supply System of the Respondents
Source: Field Survey, 2010
Among the ADP respondents, 18.4% get their water from hand dug wells while 33.3% get
their drinking water supply from streams (far or near). About 6% of the non-ADP women
farmers get their drinking water from borehole or pipe borne water supply, 65.5% get theirs from
0 20 40 60 80
Borehole or pipeborne water
Hand dug well
Nearby stream
Faraway stream
NADP
ADP
109
hand dug wells while 28.7% obtain their drinking water from streams. Getting drinking water
from source other than recommended hygienic sources has implication for the health of the
women, their families and communities. An impure water supply not only constrains the family‟s
productivity and incomes but also national output and income as well. According to Ogwumike
(2000) time wasted in searching for water by the women and children can be saved and devoted
to other uses. The above chart is separated into ADP and non-ADP water supply sources as
shown:
4.5.2 Types of Cooking Fuel used by Respondents in the Study Area
Closely related to the problem of sourcing water for drinking and other domestic purposes
is the problem of sourcing fuel for cooking. In the study area, it is also the gendered
responsibility of the women folk to source fuel for cooking. The task is becoming increasingly
difficult, as the forests through which fuel wood was obtained are now receding due to
desertification and population pressure on land. Women now have to work harder and further to
get fuel wood. This takes both energy and time, and interferes with agricultural production and
0 20 40 60
Borehole or …
Hand dug well
Nearby stream
Faraway stream
16.1
48.3
29.9
5.7
Source of Drinking Water for ADP
ADP
0 50 100
Borehole or …
Hand dug well
Nearby stream
Faraway stream
5.7
65.5
27.6
1.1
Source of Drinking Water for NADP
NADP
110
other activities women do. The ease with which women can acquire fuel for cooking will
determine the time, energy and other resources available for agricultural production. Figure 4.4
below gives respondents‟ sources of fuel for cooking:
Figure 4.4 Types of Cooking Fuel of the Respondents
Source: Field Survey, 2010
From the table above, 94.3% of the non-ADP and 86.3% of ADP respondents cook with
firewood only. The implication here is depletion of forests for fuel. The situation is worse
among NADP women farmers where 94.3% use only firewood for cooking, and the rest 5.7%
used kerosene and firewood. None of the non-ADP women farmers used either kerosene or gas
alone. This has implication for environmental degradation, sustainable agriculture, poverty
reduction and global warming. This is because in most cases the fire wood is obtained by felling
trees which are sometimes burnt to get charcoal which is increasingly being preferred because it
does not produce smoke and dirty their cooking utensils. This can degrade the environment and
undermine agricultural productivity in the area. The above figure is separated into the ADP and
non-ADP components as shown:
86.3
11.51.1 1.1
94.3
5.70 0
0102030405060708090
100
Fire wood
Kerosine +
firewood
kerosine only
Gas
ADP
NADP
111
This result reinforces the observation by Van-Keulen (2007) that degradation of ecosystem
services is exacerbating the problems of poverty and food insecurity in the developing world,
particularly in the poorest countries (like Nigeria). Global climate change is taking place against
a natural environment that is already stressed by resource degradation as result of various factors,
including certain forms of agricultural technology and input use. In Nigeria, Onyekuru and Eboh
(2008) observed that acute deforestation driven by uncontrolled demand for wood, mostly for
fuel wood, and also for export, has within a century reduced the country‟s forest cover to less
than 38,620 square kilometers, less than five percent of its original size. This study has proposed
that cassava- based environmentally friendly cooking fuels such as ethanol gelfuel can used to
solve this problem. Implied policy measure is to step up action on ethanol production from
cassava for use as cooking fuel (gelfuel) instead of fuel wood or charcoal. A viable ethanol
industry (from cassava / or sugarcane sources) in Nigeria will have immense economic benefits
namely generation of additional revenue for government, provision of energy fuels (gel fuel for
cooking, gasohol for vehicles), job creation, increased income for producers and others.
86.3
11.5 1.1 1.1
Type of Cooking Fuel for ADP
Fire wood
Kerosine + firewood
kerosine only
Gas
94.3
5.7 00
Type of Cooking Fuel for NADP
Fire wood
Kerosine + firewood
kerosine only
Gas
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4.5.3 Respondents’ Sources of Fund for Cassava Production
Provision of funds, for cassava production, ranked second and third, among ADP and non-
ADP respondents‟ problems respectively. Studies have found that agricultural credit has positive
output and employment effects (Olomola, 1988). Many farmers are poor and trapped in a vicious
cycle of poverty because they cultivate small areas of land from which they produce little output,
and hence sell only a very small amount, which can not help in expanding their farms, acquiring
new technologies so the cycle continues (Adegeye and Dittoh, 1985; Awotide et al, 2008). To
break out of the vicious cycle of poverty, credit is essential since it determines access to all the
resources the farmers depend on. Awotide et al (2008), argued that agricultural credit is required
not just for agricultural production alone, but also for consumption. This they explained is
necessary to make farmers more productive in their labour input and to help them during the lean
periods before and after planting season, during which farmers hardly have enough to eat which
affects their productivity. Figure 4.5 shows respondents‟ access to funds for farm production:
Figure 4.5: Sources of Funds for Production among Respondents
Source: Field Survey, 2010
0102030405060708090 77
13.82.3 0
6.9
80.5
14.92.3 1.1 1.1 ADP in percentage
NADP in percentage
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Results show that 78.7% of all sampled women farmers funded their farm operations
from their personal savings. The Bank of Agriculture (formally Nigerian Agricultural
Cooperative and Rural Development Bank) which is the main institution established to provide
agricultural credit to farmers contributed only 6% of the funds while commercial banks
collectively contributed less than 1%. All together, 95.4% of the funds for cassava production by
sampled women farmers came from informal sources. The implication of this finding is that
women farmers such as the cassava women farmers in this do not have access to former sources
of agricultural credit despite the existence of credit institutions with mandate to lend to
agriculture. This findings agree with Eboh (2012) who lamented that the flow of credit to
agriculture is meagre, and further noted that from 1978-2010, the Nigeria Agricultural
Cooperative and Rural Development Bank (the major institution responsible for provision of
credit to agriculture in Nigeria) has extended only a total of 593,712 loans valued at ₦26.1
billion. This is grossly inadequate and there is urgent need to increase credit flow for agricultural
production, especially to women farmers such as the cassava women farmers in Benue State.
The implication is for policy to identify and deliver agricultural credit in places of urgent need
such as the cassava women farmers for increased agricultural productivity in Nigeria.
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CHAPTER FIVE
SUMMARY, CONCLUSION AND RECOMMENDATIONS
5.1 SUMMARY
This study was undertaken to assess the effect of the cassava production technologies
promoted by the Benue Agricultural Development project (ADP) on the productivity and
incomes of women farmers in Benue state, Nigeria. The specific objectives were to identify and
describe the cassava production technologies available in the study area, to describe the socio-
economic characteristics of cassava women farmers in the study area and determine their effect
on respondents productivity and incomes, to determine and compare the productivities and
incomes of ADP and non-ADP cassava women farmers in Benue State, Nigeria, and to identify
the constraints (and prospects) that affect the productivity and income from cassava production
among women farmers in the study area, and how to use all these to improve productivity and
rural welfare in the study area , and the nation at large. Three hypotheses guided the study
namely (i) Socio-economic characteristics of ADP and non-ADP cassava women farmers in the
study area have no significant effect on their output; (ii) there is no significant difference
between the productivity of ADP and non-ADP women farmers in the study area, and (iii) gross
margin from cassava enterprises among ADP and non-ADP women farmers in the study area
does not differ significantly.
A multi stage sampling technique was used to randomly select a total of 240 (120
ADP and 120 non-ADP) respondents from six Local Government Areas of Benue State namely
Vandeikya and Ushongo in zone A, Gboko and Buruku in zone B, Okpoku and Ohimini in zone
C. Instruments for data collection were well structured set of pretested questionnaires, in
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addition, focus group discussions and personal observations as well as secondary data sources
relevant to the study were used. Information on respondents‟ socioeconomic characteristics such
as age, level of education, marital status, household size, as well as costs and returns in cassava
production and marketing, production constraints and others were collected. The data was
analyzed using descriptive statistics such as mean, frequency tables, percentage, as well as chi-
square, multiple regression, total factor productivity and gross margin analyses.
Data analysis of respondents‟ socio-economic characteristics showed that 90.8% of the
ADP and 70.1% of the non-ADP respondents were below 50 years of age. Thus about 70-90%
of all women farmers studied were below the age of 50. Moreover, less than 5% of all the
women studied were aged 60 years and above. This implies that respondents were young and
energetic enough to farm. The importance of age distribution of farmers is because agriculture
especially in the rural areas (including the study area) relies heavily on the use of human power
and younger stronger people are better able to cope. Among the ADP group, 78.2% of the
respondents were married while 70.1 of the non-ADP group respondents were married. Overall,
96.4% of all the respondents were married; divorced or widowed. Only 3.4% of all cassava
women farmers sampled were single. Among the ADP respondents, 81.6% had some form of
education while less than 20% (or 18.49%) did not have any formal education. In the non –ADP
group 63.2% had some form of education while less than 40% (or 36.8%) did not have any
formal education. One can conclude that 60-80% of all cassava women farmers sampled were
educated to a level while 20-40% did not benefit from any formal education. Therefore, majority
of the cassava women farmers studied were educated enough to enable them successfully adopt
innovations that would improve their productivity and welfare.
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Cassava women farmers in the study area had moderate family sizes. Seventy-seven percent
(77%) of the ADP and about 75% of the non-ADP had family sizes below 10 while about20% of
ADP and 23% of non-ADP respondents had family sizes between 10-20 persons. A large family
size is important in the supply of farm labor in farming communities especially in Nigeria where
farmers depend on human manual labour. Hence, respondents had a potential reservoir of labour
for farm work.
Among the ADP farmers 37.9% have never belonged to any farmers‟ association, 17.2% were
once members while 44.4% are still members of farmers associations. About 82.8% of the non-
ADP respondents have never belonged to any farmers association, 5.7% were once members,
11.5% were currently members. From table 4.5, it can be clearly seen that the ADP group are
better organized as many of them (44.4%) belong to farmer‟s associations. They were therefore
better positioned to get assistance from government and other organizations including banks for
their farm operations. Hence they were able to get some loans from banks while the non-ADP
did not get any at all. This could probably be as a result of their inability to organize themselves
properly into farmers‟ associations. Results of data analysis also showed that 71.3% and 58.6%
of the ADP and non-ADP cassava women farmers respectively had been farming for close to 10
years and must have acquired the necessary experience successful cassava production.
Chi-square analysis (used to test hypothesis one) showed that except for age and
membership of farmers‟ associations (which were significant), there was no significant
difference between socio economic variables of ADP and non-ADP respondents. This implies
that socio-economic characteristics of respondents such as level of education, marital status,
farming experience of ADP and non-ADP respondents had no significant effect on their output at
5% level of significance. Chow‟s F-test, showed that there was a significant difference between
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the productivity of ADP (2.96) and that of non-ADP women farmers (1.68). This was attributed
to the use of ADP improved cassava production technologies and extension contact. The
implication of this finding is that the use of improved cassava production technologies of the
Benue State ADP (BNARDA) would tend to improve farmers‟ productivity. The major variables
that explained variations in ADP cassava women farmers‟ productivity were use of improved
cassava stem cuttings, farm size and access to credit which together explained 40.2% of variation
in ADP productivity. The major factors that explained variation in non-ADP cassava women
farmers‟ productivity were years of education, family size and access to credit, which explained
93.0% of variation in non-ADP productivity.
Hypothesis three was tested by use of t-test to compare the mean gross margins of ADP
(₦16,523.87) and that of non-ADP (₦3,777.56) respondents. Results showed that there was a
significant difference between ADP and non-ADP gross margins. The significant difference in
gross margin was attributed to the use of improved cassava production technologies and
extension contact (provided by the Benue ADP) by the ADP cassava women farmers. This
implies that the use of improved agricultural technologies such as improved planting materials
like cassava stems, extension teachings would tend to improve farmers‟ gross margins and
incomes. Major variables that explained variation in gross margin among ADP respondents were
output (Q), price of output (Pq), cost of processing (Cp) and cost of transportation (Ct). Major
variables that explained variation in the non-ADP respondents‟ gross margin were output (Q),
price of output (Pq) and cost of transportation (Ct).
According to the ADP women farmers, major constraints to their improved productivity
include processing problem (46.0%), poor pricing of output (37.9%), lack of credit (34.5%), soil
fertility problems (31.0%), labour problems (28.7%), transportation problems (27.6%), poor
118
market infrastructure (26.4%). The non-ADP women farmers identified their major constraints as
poor pricing of output (50.6%), lack of credit (47.1%), soil fertilityproblems (46.0%), processing
problems (40.2%). Poor market infrastructure (34.5%), labour problems (31.0%), transportation
problems (27.6%). A pooled analysis of all the women farmers studied (both ADP and non-
ADP) showed that major impediments to increased productivity include poor pricing of output
(44.3%), processing problems (43.1%), lack of credit (40.8%), soil fertility problems (38.5%),
poor market infrastructure (30.5%), labour problem (28.5%) and transportation problem (27.6%).
A combined analysis of respondents‟ constraints (ADP and non-ADP) showed that
poor pricing of output (44.3%), processing problems (43.1%), lack of credit (40.8%), soil
fertility problems (38.5%), poor market infrastructure (30.5%), labour problems (29.9%),
transportation problem (27.6%) are the major problems militating against the increased
productivity of respondents.
5.2 CONCLUSION
This research was carried out to find out the effect of Benue ADP cassava production
technologies on the productivity and incomes of women farmers in Benue State. Comparisons
were made between the output and gross margin of ADP women farmers who had access to this
technology and extension services, and non-ADP women farmers who did not. The following
conclusions were drawn:
(i) Except for age and membership of farmers‟ associations, there was no significant difference
between the cassava output and socio-economic factors of ADP and non-ADP respondents;
119
(ii) There is a significant difference between the output of ADP women farmers and non-ADP
women farmers in Benue State. This was attributed to the significant influence of improved
variety of cassava on ADP output and extension contact;
(iii) The study found that there was no significant difference in gross margin among the ADP
and non-ADP respondents in the study area. This was attributed to poor pricing and poor value
addition consequent to inadequate processing facilities among other factors;
(iv) Certain factors such as poor pricing of cassava products, inadequate processing facilities,
lack of credit, fertilizers and other production resources, inadequate rural and market
infrastructure among others have constrained successful production and marketing of cassava in
the study area; and
(v) The study found that certain factors such as rural water supply, cooking fuel, funding of
production had an indirect constraining effect on productivity and sustainable agriculture. These
constraints must be addressed in order to raise productivity and welfare of rural farmers
especially women.
(vi) the study also found that cassava is a high potential and viable crop for food security and
poverty reduction in Nigeria. From empirical studies, empirical observations and review of
literature on cassava production in Nigeria, the study discovered that cassava is a high potential
and viable crop for food security and poverty reduction in the Nigeria. This is because of the
international interest to purchase cassava products from Nigeria, the various uses of cassava
(domestic and industrial, local and international), all of which could create potential high
demand for cassava products in Nigeria. On the other hand, the Presidential initiative on cassava
production, the various research works and research institutions working on the crop in Nigeria,
120
and the significant difference improved varieties of cassava are making to farmers‟ yields and
incomes (as in this study) among other factors, indicate the potential for an adequate supply
response. The implication of this is for Nigeria to put in place an enabling environment to take
maximum advantage of these opportunities.
5.3 RECOMMENDATIONS
Based on research findings the following recommendations are made;
(i) Production resources such as fertilizers, agrochemicals, tractor hiring services, processing
machines, improved seeds and planting materials, agricultural credit should be given to farmers
particularly women farmers. There should be targeted delivery to identified women farmer
groups to ensure that the materials do not get diverted to non-targets. These should be highly
subsidized, affordable, provided on time and in enough quantities.
(ii) Genuine efforts should be made to avoid playing politics with agriculture in Nigeria
especially in the study area. The practice of giving fertilizer and other production resources to
politicians and other interest groups instead of genuine farmers as was the case in the study area
in the year under study (2010) is counterproductive in a country seeking to make agriculture the
engine of development.
(iii) Credit is recommended for the women farmers (especially in Benue state) because of their
peculiar financial vulnerability to enable them purchase production inputs and meet other farm
needs.
121
(iv) Women farmers should be encouraged to form farmers‟ cooperatives or join existing ones to
pool their resources together and to take advantage of development assistance. Strict supervision
to ensure that farmers do not divert development assistance to non-farm uses should be ensured.
(v) The government and other stakeholders should rise to the challenge of providing facilities
for processing to increase value addition in cassava production to avoid waste, and to serve as an
incentive for more production. The processing facilities should be women friendly and must be
sited in the rural areas if they are to solve the problems of agricultural processing, rural
unemployment, rural-urban migration and others.
(vi) Arrangements should be made for farmers (especially women farmers) to readily depose of
their output at reasonable prices to encourage more production. Alternatively government and
interested investors should buy cassava and process it according to specification. This implies
the urgent need to provide in a conducive operating environment for investors (foreign or local).
This calls for an urgent need to maintain law and order to ensure security of lives and property,
and serious sincere efforts at fighting corruption.
(vii) Rural infrastructure such as roads, hospitals, clean water supply, electricity, schools,
communication facilities, market infrastructure and others should be provided to make rural life
more comfortable in order to improve farm productivity and to check rural-urban migration
especially in the study area.
(viii) Relevant laws and institutional adjustments should be made to address land tenurial issues
to enable women access more and better land for agricultural production particularly in the study
area.
122
(ix) The extension system in Benue State should be enabled to function better. Members that
have been lost to other programmes, retirement, death, or any reason should be replaced. In
addition, the issues of staff welfare and proper funding of the programme (or a similar one)
should be looked into.
(x) The production and use of more environmentally friendly sources of cooking fuel such as the
cassava based ethanol cooking fuels (ethanol gelfuel) should be encouraged to ease the burden of
sourcing for fuel, and to reduce environmental degradation of land because of fuel wood. This is
because ethanol is environmental friendly with lower air pollution and reduced global warming.
This will in addition provide the much needed economic diversification and government revenue
(from ethanol exports), jobs for the teeming unemployed Nigerians and income for producers
among others.
5.4 Contribution to Knowledge
This research has developed models that could be used to study cassava production among
women farmers in the study area or anywhere else. With little or no modification, these models
could also be applied on other crops or other related research.
5.5 Areas for Future Research
(i) This analysis should be done with data collected for a number of years to further confirm
the results.
(ii) Other crops within the ADP (WIA) need to be evaluated alongside the cassava progamme
to have an overall effect since evaluating one crop in a project of several components may not
provide sufficient basis to make value judgements about it. This is because success or failure in
123
one area can easily be „swallowed up‟ by success or failure in other areas of the project or
programme.
(iii) Future researchers could carry out this analysis on cassava, (or other crops) of the ADP
project or any other project using other methods of analysis such as Stochastic Frontier
Production analysis, linear programming and others.
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