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DETERMINANTS OF ADOPTION OF MODERN AGROFORESTRY TECHNOLOGIES
BY AGRICULTURAL DEVELOPMENT PROGRAMME CONTACT
FARMERS IN ENUGU STATE NIGERIA
BY
OCHIAKA, JOSEPH SUNDAY
PG/Ph.D/02/33715
DEPARTMENT OF VOCATIONAL TEACHER EDUCATION
(AGRICULTURAL EDUCATION)
UNIVERSITY OF NIGERIA, NSUKKA
DECEMBER, 2013
DETERMINANTS OF ADOPTION OF MODERN AGROFORESTRY TECHNOLOGIES BY
AGRICULTURAL DEVELOPMENT PROGRAMME CONTACT
FARMERS IN ENUGU STATE NIGERIA
BY
OCHIAKA, JOSEPH SUNDAY
PG/Ph.D/02/33715
A THESIS REPORT PRESENTED TO DEPARTMENT OF VOCATIONAL
TEACHER EDUCATION, UNIVERSITY OF NIGERIA, NSUKKA IN
PARTIAL FULIMENT OF THE REQUIREMENTS FOR THE
AWARD OF DEGREE OF DOCTOR OF PHILOSOPHY
(Ph.D) IN AGRICULTURAL EDUCATION
DECEMBER 2013
APPROVAL PAGE
DETERMINANTS OF ADOPTION OF MODERN AGROFORESTRY
TECHNOLOGIES BY AGRICULTURAL DEVELOPMENT PROGRAMME
CONTACT FARMERS IN ENUGU STATE NIGERIA
BY
OCHIAKA, JOSEPH SUNDAY
PG/Ph.D/02/33715
RESEARCH REPORT PRESENTED TO THE DEPARTMENT OF VOCATIONAL
TEACHER EDUCATION, UNIVERSITY OF NIGERIA, NSUKKA.
APPROVED
------------------------- -----------------------------------
PROF. C.S IFELUNNI PROF C A. IGBO.
DEAN OF FACULTY HEAD OF DEPARTMENT
--------------------------------- ---------------------------------
DR. R.O MAMA.
SUPERVISOR EXTERNAL EXAMINER
CERTIFICATION
Ochiaka, Joseph Sunday, a postgraduate student in the Department of
Vocational Teacher Education, with Registration Number PG/Ph.D/02/33715 has
satisfactorily completed the requirements for the research for the degree of Doctor of
Philosophy (Ph.D) in Agricultural Education. The work embodied in this thesis is
original and has not been submitted either in part or full for any other Diploma or
Degree of this University or any other University.
------------------------------------------ -----------------------------
Ochiaka, Joseph Sunday Dr. R. O. Mama
Student Supervisor
DEDICATION
This work is dedicated to my Late Father, Ozo Godwin Okwesilieze Ochiaka.
ACKNOWLEDGEMENTS
Firstly, I thank. Dr. R.O, Mama, my supervisor. Words are grossly inadequate to
express my indebtedness for his highly valued academic contributions to the work. Prof N.J.
Ogbazi, Dr (Mrs)T.C Ogbuanya , my Content and Design Readers respectfully at the
proposal stage, I thank you for your contributions to the work. Prof. Okwo F.A and Dr
Akogwu A.C (Late) my validators, I equally acknowledge your critical analysis of the
instrument and your contributions to the work. I equally thank Prof. E. C. Osinem, and the
Head of Department, Prof. C.A Igbo for the words of encouragements and advice.
I also in a special way thank Prof D.N Eze,the Panel chairman at the Faculty
seminar for the expert advice on the ideal statistical tools that produced better shape to
the study.I equally thank the Faculty Postgraduate Representative at the seminar, Prof S.C
Nwizu for the academic contributions .Drs F.M.Onuh and L.N Nworgu, content and Design
Readers at the Faculty seminar respectfully, I thank you for the corrections which had
been effected .Equally appreciated is Mr Ugwuoke Cajetan who tirelessly helped in the
arrangements for the Faculty Seminar.
I also acknowledge the financial and time sacrifices of my nuclear family, my lovely
wife - Priceless P.O and children, for being caring and supportive through prayers,
throughout the period of the study. My thanks also go to my elder brother Engr. B.M
Ochiaka and wife for their financial assistance. My younger brother Raph and wife, I thank
you for your prayers. Ezikeanyi Stella I also appreciate your financial assistance.
Thanks to my friends and well wishers Profs- I.A Madu and M.I Okwueze, Drs-
Ebe F.E, Ukwungwu, N.J, , and Charles U. Eze. I appreciate your kind words of
encouragement. I also thank Mr Ugwu E.B.I for being a confident friend.
Finally, thanks to Mrs. Akpa, Clara Chinenye of Business Education Unit of
Vocational Teacher Education, University of Nigeria, Nsukka, who typed and formatted
the work.
TABLE OF CONTENTS
TITTLE PAGE - - - - - - - - - i
APPROVAL PAGE- - - - - - - - - - ii
CERTIFICATION - - - - - - - - iii
DEDICATION - - - - - - - - - iv
ACKNOWLEDGEMENTS - - - - - - - - v
TABLE OF CONTENTS - -- - - - - - - vi
LIST OF TABLES - - - - - - - - - ix
LIST OF FIGURES - - - - - - - - - x
ABSTRACT - - - - - - - - - - xi
CHAPTER ONE: INTRODUCTION
Background of the Study -- -- -- -- -- -- -- - 1
Statement of the Problem-- -- -- -- -- -- -- - 6
Purpose of the Study-- -- -- -- -- -- -- -- 7
Significance of the Study-- - -- -- -- -- -- - 8
Research Questions-- -- -- -- -- -- -- -- - 9
Hypotheses-- - -- -- -- -- -- -- -- - 9
Scope of the Study --- -- -- -- -- -- -- - 10
CHAPTER TWO: REVIEW OF RELATED LITERATURE
Conceptual Framework of the Study -- -- -- -- -- 11
Theoretical Framework -- -- -- -- -- -- -- 27
Related Empirical Studies -- -- -- -- -- -- -- 35
Summary of Literature Reviewed-- -- --- -- -- -- -- 46
CHAPTER THREE: METHODOLOGY
Design of the Study --- --- --- --- -- -- -- -- 47
Area of the Study-- -- -- -- -- -- -- -- -- 47
Population for the Study-- -- -- --- -- -- -- -- 49
Sample and Sampling Technique -- -- -- -- -- -- -- 49
Instrument for Data Collection -- -- -- -- -- -- -- 50
Validation of the Instrument -- - -- -- -- -- -- 51
Reliability of the Instrument -- - -- -- -- -- -- 51
Method of Data Collection-- - -- -- -- -- -- 52
Method of Data Analysis -- -- -- -- -- -- -- 52
CHAPTER FOUR: PRESENTATION AND ANALYSIS OF DATA
Research Question One -- -- -- -- --- -- -- 54
Research Question Two -- -- -- -- --- -- -- 72
Research Question Three -- -- -- -- --- -- -- 75
Research Question Four -- -- -- -- --- -- -- 77
Research Question Five -- -- -- -- -- -- -- 78
Hypothesis One -- -- -- -- -- -- -- -- 79
Hypothesis Two -- -- -- -- -- -- -- -- 94
Hypothesis Three -- -- -- -- -- -- -- -- 95
Hypothesis Four -- -- -- -- -- -- -- -- 98
Hypothesis Five -- -- -- -- --- -- -- -- 100
Findings of the Study -- -- -- -- -- -- -- -- 101
Discussion of the Findings-- -- -- -- -- -- -- 116
CHAPTER FIVE – SUMMARY, CONCLUSION AND RECOMMENDATIONS
Restatement of the Problem -- -- -- --- --- -- -- 126
Summary of the Procedures Used -- -- -- -- -- -- 126
Major Findings of the Study -- -- -- -- -- ---- - 127
Conclusion -- -- -- -- -- -- -- -- -- -- 128
Implications of the Findings -- - -- -- -- -- -- 130
Recommendations -- -- -- -- -- -- -- -- 131
Suggestions for Further Studies -- -- -- -- -- -- -- 131
REFERENCES-- -- -- -- -- -- -- -- -- 133
APPENDICES -- -- -- -- -- -- -- -- -- 148
LIST OF TABLES
1. Distribution of Respondents according to stages of adoption of bees
baiting technologies -- -- -- -- -- -- -- -- 54
2. Distribution of Respondents according to stages of adoption of bees
management technologies -- -- -- -- -- -- -- 57
3. Distribution of Respondents according to stages of adoption of bees
feeding technologies -- -- -- -- -- -- -- -- 59
4. Distribution of Respondents according to stages of adoption of honey
harvesting technologies -- -- -- -- -- -- -- 61
5. Distribution of Respondents according to stages of adaptation of cassava/
Maize in alley technologies -- -- -- -- -- -- -- 63
6. Distribution of Respondents according to stages of adoption of
multipurpose tree establishment technologies -- -- -- -- 66
7. Distribution of Respondents according to stages of adoption of
browse plants establishment technologies for feeding livestock -- -- 68
8. Distribution of Respondents according to stages of adoption of vertiver
grasses planting for erosion/wind control technologies -- -- -- -- 70
9. Multiple Regression Results of the determinants of the Socio economic
Characteristics of the Respondents on adoption of modern agroforestry
Technologies -- -- -- -- -- -- -- -- -- 73
10. Mean ratings of ADP contact farmers on agricultural extension teaching
methods determinants of modern agroforestry technologies -- -- 76
11. Distribution of Respondents on mean ratings on agroforestry input
determinants -- -- -- -- -- -- -- -- -- 77
12. Distribution of Respondents on mean ratings on environmental
Determinants -- -- -- -- -- -- -- -- -- 78
13. Mean Ratings and t-test Analysis of the Responses of Male and Female
ADP contact farmers on stages of adoption of bees baiting technologies 80
14. Mean Ratings and t-test Analysis of the Responses of Male and Female ADP
contact farmers on stages of adoption on bees management technologies 81
15. Mean ratings and t-test analysis of male and female ADP contact farmers on
stages of adoption of bees feeding technologies -- -- -- -- -- 83
16. Mean ratings and t-test Analysis of the responses of male and female
ADP contact farmers on stages of adoption of honey harvesting
technologies -- -- -- -- -- -- -- -- 84
17. Mean ratings and t-test analysis of the responses of male and female ADP
contact farmers on stages of adoption of cassava//maize in alley
Technologies -- -- -- -- -- -- -- -- 86
18. Mean ratings and t-test analysis of male and female ADP contact farmers
on stages of adoption of multi-purpose trees establishment technologies. 88
19. Mean ratings and t-test analysis of male and female ADP contact
farmers on stages of adoption of browse tree establishment technologies 90
20. Mean ratings and t-test analysis of male and female ADP contact farmers
on stages of adoption of vertiver grasses for erosion/wind control
technologies -- -- -- -- -- -- -- -- -- 92
21. Mean ratings and t-test analysis of male and female ADP contact
farmers on gender as a determinant of adoption of bees management
technologies -- -- -- -- -- -- -- -- -- 94
22. Mean ratings and t-test analysis of literate and non-literate ADP
contact farmers on agricultural extension teaching methods as determinants
of adoption of modern agroforestry technologies 96
23. Mean ratings and t-test analysis of male and female ADP contact farmers
on agroforestry inputs as determinants of adoption of the technologies 98
24. Mean ratings and analysis of variance (ANOVA) of male and female
ADP contact farmers from Awgu , Enugu and Nsukka on environmental
facors as determinants of adoption of modern agroforestry technologies 100
LIST OF FIGURES
1. Variables Determining Rate of Adoption of Innovations. 18
2. Extension Sub-Programme of Enugu State Agricultural Development
Programme. 22
3. Interrelations of Determinants of Modern Agroforestry Technologies. 26
4. System Approach to Technology Transfer 28
5. Schematic Representation of Induced Innovation Theory of Adoption 30
6. Innovation-Decision Theory of Adoption 31
7. Adoption -Diffusion Model 34
Abstract
This study was carried to find the determinants of adoption of modern agroforestry
technologies among ADP contact farmers in Enugu State Nigeria. 360 ADP contact farmers
constituted the sample of the study. A-120 item structured questionnaire validated by three
experts was used to collect data for the study. The Cronbach Alpha method was used to test
the internal consistency of the instrument. The weighted mean, percentages, and multiple
regression were used to analyse data that answered the research questions, while t-test
statistic and Analysis of Variance (ANOVA ) were used to test the hypotheses. The
socioeconomic characteristics of contact farmers that were determinants of adoption of
the technologies were gender, experience of the farmers with agricultural extension
workers,educational qualification and frequency of visit of agricultural extension
workers. Agricultural extension teaching methods identified as determinants were –
farm/home visits, group discussion, circle meetings, exhibition and conducted tours
.agroforestry inputs identified as determinants were improved tree seedlings, modern
beehives, vertiver grasses and organic/inorganic fertilizers. Environmental factors identified
as determinants were rainfall, sunlight, temperature, fertility status of the soil, topography,
soil erosion drought, pests and diseases. The study equally revealed that there is no
significant difference between the mean responses of male and female ADP contact
farmers on stages of adoption of the technologies . The study found that there is no
significant difference in the mean responses of literate and non-literate contact farmers
on the adoption of modern agroforestry technologies. The study found that the
environmental factors such as rainfall, temperature, soil fertility determined the adoption of
modern agroforestry technologies in Awgu, Enugu and Nsukka agricultural zones of Enugu
state. Based on the findings, the following recommendations were made among which
include that; the educational qualifications of agricultural extension agents of the state ADP
should be raised to a minimum of a University degree in Agricultural Extension/Education
or related discipline. Contact farmers should be involved both in designing, planning and
execution of modern agroforestry technologies, so that their socio-economic characteristics
will be taken into consideration. There should be capacity building for agricultural extension
workers particularly in educational methodology. Government, non-governmental
organizations, donor agencies should subsidize agroforestry inputs, while communities,
schools and private individuals should establish tree nurseries. Governments and non
governmental organization should invest in agroforestry researches which will benefit the
society.
CHAPTER ONE
INTRODUCTION
Background of the Study
The government and farmers in Nigeria are faced with the challenge of increasing
agricultural production to cater for the increasing population. There is increasing
demand for food, fibre and wood from the limited land area which calls for
cooperation and integrated approach to agriculture and forestry through agroforestry.
Enugu State Agricultural Development Programme adopted unified agriculture extension
system. This is an extension system that operates with frontline extension agents teaching
farmers in the five components of the system. These components include:- crops, livestock,
fishery, women in agriculture and agroforestry (Okomoda and Ayanda 1996). It indicates
joining all the components in order to achieve improved food production and income
level of farmers to enhance livelihood.
Agroforestry is a sustainable land management system which combines production
of crops, forest trees and animals simultaneously on the same unit of land and applies
management practices that are compatible with local population (Kings, 1996 and Barrett,
2002). Agroforestry is equally a natural principle for resource management and most reliable
means of helping farmers to overcome soil problems and increase their harvests
(International Institute for Tropical Agriculture (IITA 1992). It is one of the innovations
being taught to Agricultural Development Programme contact farmers because; it offers
multiple agronomic, environmental and socio-economic benefits. Agroforestry can use only
5% of the farming land area and yet account for over 50% of the biodiversity, improving
wild life, harboring birds, beneficial insects, and moderating microclimates (Mercer, 2003).
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Another benefit is that, it can utilize solar energy more efficiently than monocultural
systems and can also reduce the incidence of pests and diseases in the farm. Technologies
refer to the techniques, devices, machines that improve efficiency of labour and the
comfort of human beings (Okeke, 1990). In the present study, technologies are those
techniques and devices that are applicable to modern agroforestry, which are taught to
Agricultural Development Programme contact farmers in the study area. Technologies are
therefore, techniques and devices planned and designed within the framework of scientific
discipline by research institutes to improve the practice of
technologies such as in agroforestry (Nwabueze, 2001). The modern agroforestry
technologies taught by Enugu State Agricultural Development Programme (ADP) are
Beekeeping for honey production, cassava/maize in alley, establishment of multipurpose
trees and control of flower/fruit abortion, establishment of browse plants for feeding
livestock and the use of Vertiver grasses for wind and erosion control.(Enugu State
Agricultural Development Programme Field Report, 2004)
The source of these technologies was the adaptive research which is a concept and
procedure for developing and verifying new techniques that are relevant and appropriate to
farmers needs and circumstances (Onyishi, 2001). The technologies are taught by resource
persons from research Institutes and Universities, during the monthly technology review
meetings (MTRM) usually held at the state headquarters. The monthly technology review
meeting is the monthly workshop designed to strengthen the linkages between research and
extension in order to provide relevant technologies to farmers. It also ensures that problems
in the field which are identified are relayed to relevant research Institutes and Universities
for solutions and classifications. Those that do attend the monthly technology review
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meeting included: Subject matter specialists, Zonal Extension officers, Zonal managers,
Directors of Engineering, Extension and Chief Technical officer.
Enugu State ADP agroforestry extension programme is headed by Chief
Agroforestry Officer at the state headquarters. There are three zonal subject matter
specialists in agroforestry that teach agroforestry technologies at the fortnightly training
sessions at the zones. The subject matter specialist in agroforestry teach agroforestry
technologies to agricultural extension workers made up of site extension agents, site
enumerators and site supervisors These agricultural extension workers in turn taught
modern agroforestry technologies to contact farmers.
The farmers taught the improved agroforestry technologies by agricultural extension
workers are called contact farmers. They are usually eighty (80) selected by extension
agents in a site for a farming season. The choice of eighty contact farmers is according to the
design of operating the Training and Visit Extension System (Benor, Harrison, and Baxter
2004). The agricultural extension agent used these criteria in selecting the farmers: being
full-time farmer, willingness to participate in demonstrations, accessible and ready to share
ideas with other farmers and resident in the site for that particular year. The
list of the contact farmers was annually updated to ensure that those who were not willing to
continue or moved out of the site for a particular farming year were not included (Enugu
State ADP Field Report, 2004).The farmers could be literate or non-literate, literate farmers
were those who completed primary schools, while the non-literate ones were those who did
not attend primary school nor did they attend evening adult classes. They could be males or
females and were located at the different sites in the state. The study sought to find out the
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influence of the literacy level and gender on the adoption of modern agroforestry
technologies by contact farmers.
A site in this study refers to a place where the activities of the National Programme
on Food Security are carried out in line with National Economic and Empowerment Strategy
[NEEDS] and State Economic and Empowerment Strategy (SEEDS). The main objective of
the programme was extending the application of innovative low-cost technologies to
improve the productivity and sustainability of agricultural systems of poor farmers. The state
has nine sites involved in rain fed crops, small scale irrigation, livestock, fisheries,
agroprocessing, agroforestry and community seed establishment (Omeje, 2008) The choice
of using sites for the study was because of the high level of activities going on there,
particularly as it concerned modern agroforestry technologies.
The agricultural extension worker taught the Agricultural Development Programme
contact farmers using the following agricultural extension teaching methods; individual,
groups and mass media. Individual extension teaching method anchored on the fact that
learning is an individual activity. The method was used in teaching modern agroforestry
technologies, in recognition of the fact that learning is an individual process and that the
personal influence of the extension worker was an important factor in the participation of
farmers in extension activities. The individual extension teaching methods studied included;
farm/home visits, office l/telephone calls, personal letters, and result demonstration. Group
extension teaching methods take into account the inclination of the individuals to respond to
the pressures and opinions of groups in which they participate and to listen to the views of
others before arriving at a decision about making changes in their farming operations. The
group extension teaching methods included group meetings, group discussions, exhibitions
conducted tours and method demonstrations. The mass media methods are useful in reaching
a wide audience at a very fast rate. They were important in stimulating farmer’s interest new
ideas and practices. They included newspapers, radios, televisions and publications which
include bulletins, pamphlets, and leaflets.
Adoption is defined as the mental process through which an individual passes from
first hearing about an innovation to final adoption.(Rogers, 1995) When innovations such as
modern agroforestry technologies were introduced and farmers fail to use them,it leads to
efforts in futility. However, if the innovations were accepted and put into use, the
technologies were said to be adopted and the efforts of research will not have been wasted.
The innovation-decision theory of adoption has five steps namelyknowledge (awareness),
persuasion (interest), decision/evaluation), implementation, (trial) and confirmation
(adoption or use). According to the theory the individual seeks knowledge of and skills
which will ultimately affect the adoption process. For a potential adopter, the process will
proceed through the various steps and lead to adoption, or alternately, lead to rejection, of
the innovation. Determinants of adoption refer to the factors that promote the adoption
process of an innovation and in the present study, modern agroforestry technologies.
A number of improved technologies of the unified extension system which is made
up of fishery, crops, livestock, Women in Agriculture had high adoption rate by Agricultural
Development Programme contact farmers. For example in crops component 75% of yam
farmers have adopted yam minisett technology. On livestock component 65% livestock
farmers have adopted sheep and goat production technology.Women in Agriculture, 96%
ADP contact female farmers have adopted demonstrations on processing cassava into
confectioneries. However the stages the contact farmer were in the adoption process on
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modern agroforestry technologies had not been ascertained (Adumike, 2003). The study
sought to identify the determinants of the adoption of modern agroforestry technologies by
contact farmers in Enugu State.
Statement of the Problem
The current government of Nigeria has been taking different measures to reduce
poverty,curb climate change problems and improve the standard of living of the
people.Agroforestry is one of the strategies designed to ensure conservation and mitigate the
effect of climate change.Agroforestry is a dynamic ecological based natural resource
management system that through integration of trees on farms and agricultural landscapes
,diversifies and sustains production for increased social,economic and environmental
benefits for farmers.
By recognizing the benefits of modern agroforestry, Enugu state Agricultural
Development Programme adopted unifield extension system made of crops, livestock, and
women in agriculture, fishery and modern agroforestry technologies. There has been
appreciable adoption of other components, but not so in modern agroforestry technologies.
This situation calls for the identification of the determinants of the adoption of the
technologies by contact farmers.
The main problem of the study was that the stage in the adoption process of
contact farmers adopting components of unifield extension system such as fishery
,crops,livestock and women in agriculture were known, but that of modern agroforestry
technologies were not ascertained and the study sought to find out the determinants of
adoption of modern agroforestry technologies among Agricultural Development
Programme contact farmers in Enugu State.
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In view of the above situation and in recognition of the benefits of agroforestry
adoption, and the efforts of teaching the technologies for some years by Agricultural
Development Programme in Enugu state, it was necessary that the study be conducted to
find out the determinants of adoption of the technologies among contact farmers in Enugu
state for possibly strengthening the programme.
Purpose of the Study
The purpose of the study was to analyze and find out why modern agroforestry
technologies were not being taken up by contact farmers by examining factors that
determine adoption of the technologies among Agricultural Development Programme
contact farmers in Enugu State. Specifically, the study sought to:
1. find out the specific stages on the adoption process of contact farmers adopting
modern agroforestry technologies in the study area
2. find out the influence of socio-economic characteristics of Agricultural
Development Programme contact farmers on adoption of modern agro forestry
technologies.
3. identify the agricultural extension teaching methods that were determinants of
adoption of modern agroforestry technologies among Agricultural Development
Programme contact farmers.
4. identify the modern agroforestry technologies inputs that were determinants of the
adoption of the technologies
5. identify the environmental factors that were determinants of the adoption of modern
agroforestry technologies.
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Significance of the Study
The findings of the study will be of immense help to a wide range of beneficiaries -,
Agricultural Development Programme contact farmers and Enugu State Agricultural
Development Programme staff and management.The other possible beneficiaries include-
Policy makers, governments, Researchers and stakeholders in Agricultural Education,
Extension and related fields.
The Agricultural Development Programme contact farmers, who reside in the rural
areas when they adopt the technologies, will help improve their economic base. The
information provided by the study could also be used to improve future activities of contact
farmers on other components of the unified agricultural extension system.
The result of the study will also enable the management of Enugu State Agricultural
Development Programme to evaluate their methods of technology transfer and utilization by
contact farmers. The information provided by the study could also be used by Enugu State
Agricultural Development Programme (ENADEP) management to improve their future
activities on agroforestry technology transfer. The findings of the study also provided
additional information to agricultural extension staff on the potential determinants of
technology transfer and adoption of other components of the unified extension system-crops,
livestock, fisheries and women in agriculture.
Stakeholders in agricultural education will find the aspect of the findings that
provided information on agroforestry very useful. It highlighted the need to incorporate
agroforestry themes into our Agricultural Education curriculum at the lower, upper basic and
tertiary education.
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Governments at the Federal, State and local levels could benefit from the study if
they harness the opportunities by investing in agroforestry which makes it sustainable,
renewable, economically feasible and highly profitable.
Research Questions
The study was guided by the following research questions;
1 What are the specific stages on the adoption process of Agricultural Development
Programme contact farmers adopting modern agroforestry technologies in Enugu
State?
2 What are the influences of socio-economic characteristics of Agricultural
Development Programme contact farmers on the adoption of modern agroforestry
technologies?
3 What are the agricultural extension teaching methods that were determinants of
adoption of modern agroforestry technologies by contact farmers?
4 What are the modern agroforestry inputs that were determinants of adoption of
modern agroforestry technologies?
5 What are the environmental factors that were determinants of adoption of modern
agroforestry technologies?
Hypotheses
The following null hypotheses were formulated for the studyand were tested at 0.05 level of
significance;.
H01. There is no significant difference between the means of males and females Agricultural
Development Programme contact farmers on the specific stages on the adoption process on
adoption of modern agroforestry technologies.
10
H02: There is no significant difference on the socio-economic characteristics of contact
farmersdeterminants on their adoption of modern agroforestry technologies.
H03: There is no significant difference between the mean ratings of Agricultural
Development Programme contact farmers on literacy level on agricultural extension
teaching methods determinants on adoption of modern agroforestry technologies.
H04: There is no significant difference between the mean ratings of male and female
Agricultural Development Programme contact farmers on agroforestry inputs
determinants on adoption of modern agroforestry technologies
H05: There is no significant difference among the means of Agricultural Development
Programme contact farmers in the three agricultural zones of Enugu state on the
determinants of environmental factors on adoption of modern agroforestry
technologies.
Scope of the Study
The study was delimited to Enugu State Agricultural Development Programme. The
state is one of the states of the Federal Government of Nigeria implementing the National
programme for food security. The state has nine sites of the programme. The study was also
delimited to modern agroforestry technologies taught to farmers by extension agents of
Agricultural Development Programme in the state. These modern agroforestry technologies
include; beekeeping for honey production, cassava/maize in alley, establishment of
multipurpose trees, establishment of browse plants for livestock feeding and planting
vertiver grasses for erosion control.
i
CHAPTER TWO
REVIEW OF RELATED LITERATURE
The review of related literature was presented under the following sub-headings: -
Conceptual Framework
� Concept and process of Adoption
� Socio-economic characteristics of Agricultural Development Project contact farmers
� Agricultural Extension Teaching methods
� Agroforestry technologies inputs
� Environmental factors
Theoretical Framework
� System Approach for Technology Transfer
� Induced Innovation Theory of Adoption
� Innovation-Decision Theory of Adoption
Related Empirical Studies
� Agroforestry adoption studies in Central America
� Agroforestry adoption studies in Sub-Saharan Africa
� Agroforestry adoption studies in Cameroun
� Agroforestry adoption studies in Atlantique, Republic of Benin
� Agroforestry adoption studies in Semi-Arid regions of India
� Agroforestry adoption studies in Kenya
� Empirical studies of Agroforestry adoption in Nigeria
Summary of Literature Reviewed
Conceptual Framework
A conceptual framework for research purposes is a schematic description and
illustration of the causative mechanisms and relationship deducible from the research
11
ii
problems (Eboh, 2009). It is used to express a relationship or set of relationships or
interactions between phenomena, as well as the process associated with the interaction.
It is also the meeting point of the theoretical argument of the research, the intellectual
hot-spot of research, the condensed picture of the research problem and the mental
imaginary of the orientation of the research (Eboh, 2009).Conceptual framework
performs these roles in empirical research such as giving research an identity and enables
the process of inquiry to move from vague and confusing ideas about what is to be studied
to the actual research, lends greater credibility to research process, facilitates verifiability,
validity of research findings, and it is a tool for identifying what the researcher would
observe, how the research would observe, and what interpretations the researcher would
place on various possible observations (Eboh, 1999a)
Concept on the other hand refers to ordered representation of abstract phenomena in
compartments that permit their operationalization (Anyakoha, 2009). The concepts in this
study included- gender and educational qualification of the contact farmers, adoption which
is the level of utilization, modern agroforestry technologies which are new agroforestry
technologies from Agricultural Development programme, inputs ,agricultural extension
teaching methods, and environmental elements.
The socio-economic variables examined in the study were gender and literacy level
of Agricultural Development Programme contact farmers, size of farm, source of farm
labour, experience, and frequency of agricultural extension contacts.
• Agricultural extension teaching methods in the study were-farm/home visits, group
discussions, circle meetings, field days, office calls, personal letters, result
demonstrations, excursions, method demonstrations and the use of radio.
iii
• The agroforestry inputs viewed in terms of cost and availability determinants related
to the study were improved tree seedlings, cassava cuttings, modern beehives,
baiting materials, honey harvesting materials, honey processing materials, vertivar
grasses, seed dressing chemicals, organic and inorganic manures, liming materials,
Herbicides, lands, and insecticides
• The environmental factors regarded as determinants with regards to this study
included rainfall, sunlight, temperature, fertility status, topography, soil erosion,
drought, pests, diseases and cloud cover.
The conceptual framework helped the researcher identify what to observe and the
interpretations given to the various observations (variables). In view of diminishing arable
land, coupled with diminishing soil fertility of agricultural lands, agroforestry which is a
system whereby woody plants are raised with agricultural crops is very essential in meeting
the production of the required foods crops and woody products (Okafor, 2001). Agroforestry
is the purposeful growing of trees and crops in interacting combination for a variety of
objectives. Acting as an interphase between agriculture and forestry, it is a promising
approach to land use especially in the tropics. It is also a land use system that combines wood
production or tree planting with agricultural crops and/or animals so as to get higher
productivity, more economic returns, and social benefits on a sustained basis than obtainable
from monoculture on the same unit of land (Nair, 1985). The United Nations Environmental
programme on it’s part defined agroforestry as a broad term for any land use system that
combines trees ,crops and animals in an interactive manner either simultaneously or
sequentially on the same unit of land (UNEP, 1986). Maydell (1985) who noted that
agroforestry is a new term for the old practice of growing trees and shrubs together with
iv
agricultural crops and animals on the same piece of land. The researcher observed that it
accelerated the general trend from monoculture tree plantation to ecologically more stable
multi-species stands which correspond more to the demands of rural people.
There are traditional and modern agroforestry practices. Traditional agroforestry is
developed in the traditional settings/villages and have been transmitted unmodified from
one generation to another. These traditional practices include shifting cultivation/bush
fallowing, scattered farm tree system, live tree fencing, compound farm systems (National
Agricultural Extension liaison services, Zaria, 1991). These traditional agroforestry practices
do not follow any planting pattern. They are planted as the rural farmers’ desire without any
proper planting arrangements.
The concept of modern agroforestry technologies on the hand arose from the
desire and efforts of research to improve the already existing traditional agroforestry
practices(Bruisna,1998). Modern agroforestry technologies are those agroforestry
technologies conceived, planned and designed within the scientific disciplines by research
institutes and extended to Agricultural Development Programmes for onward transfer to
ADP contact farmers. These technologies include;
� Beekeeping for honey production.
� Cassava/maize in alley
� Establishment of multipurpose trees
� Establishment of browse plants for livestock feeding
� Planting of vertiver grasses for erosion control
These modern agroforestry technologies are being taught to ADP contact farmers by
agricultural extension workers either in their homes or during circle meetings.
v
Agroforestry as a System
An agroforestry system is a type of agroforestry land – use that is specific and
described according to its biological composition, arrangement, and level of technical
management, and socio-economic features (Okafor, 2002). The author stated that a system is
composed of various sub-systems or compartments, each with definable boundaries, though
seldom existing independently.
An agroforestry practice on the other hand, is a specific land management unit
operation of agroforestry components. Agroforestry systems have been classified as follows.
1. Agro-silvicultural systems. This is the agroforestry system that involves raising
of agricultural crops with forestry tree crops. A typical examples of this system
are taungya system and alley farming.
2. Agro-silvi – pastoral system. This involves raising of food crops, forest trees, and
grazing animals.
3. Silvi-pastoral system. This involves the raising of trees and livestock.
4. Silvi-fishery (pisiculture). This involves the growing of some trees species,growing
among fish in the coastal areas (Adegbehin, 1999). The various agroforestry
practices commonly in use include the following- taungya practice, alley
cropping/farming, traditional homestead farms/gardens, scattered farm tree system
and live fences. The taungya practice as a system according to Adegbehin, (1999),
system was first used in 1862 at Burma, from where it was introduced as a
silvicultural experiment at Sapele in Edo State, Nigeria in 1982. Taungya practice is
essentially an adoption of the traditional shifting cultivation, whereby the farmers
are able to raise food crops, for at least one year in a forestland. The successive
vi
areas are then converted into plantations, as the farmers shift their farming activities
to new forest areas.
Alleycropping farming system: (when livestock production is incorporated). This is a
form of agroforestry practice developed and popularized by scientists at the International
Institute for Tropical Agriculture (IITA), Ibadan, in 1971. It is a cropping method in which
food crops, such as maize, yams, grew among fast growing leguminous trees (Nitrogen –
fixing trees) such as gliricidia sepium, leucaena leucocephala and cajanus cajan. These trees
should have the qualities of being- easy to establish, have deep root system, have heavy
foliage and have rapid regeneration after pruning. The leaves of the trees act as fodder for
feeding livestock (Adegbehin, 1999).
The basic concept underlying the system is derived from the widespread recognition
by farmers of the soil restorative value of some tree species .The alley cropping system has
the merit that it does not require frequent application of multi-element fertilizers and
liming .It has been recommended as a suitable alternative to shifting cultivation and the
traditional bush fallow system to the humid tropics (Kang etal 1984).
Concept and Processes of Adoption
Adoption is a decision to continue full use of an innovation, while an innovation
is an idea perceived as new by the individual or groups (Rogers, 1971). In the present
study, the innovations are those modern agroforestry technologies, taught to contact farmers,
by agricultural extension workers of Enugu State Agricultural Development Programme.
When the modern agroforestry technologies, were introduced to the contact farmers, they
were curious and suspicious. They were curious to know how these modern agroforestry
technologies, will perform vis–a–vis, the traditional agroforestry technologies/practices
vii
which they have been practicing. Adoption of these modern agroforestry technologies
therefore is a function of the ability of agricultural extension workers to guide the farmers
from an awareness stage to adoption, which very much depends on their trainings in
methodology (Obibuaku 1983, Alao 1971, Mijindadi, 1986 and Njoku, 1992).
Farmers, when presented with innovations do not adopt them immediately. The rate
of adoption is the relative speed with which the innovation such as the modern agroforestry
technologies is adopted by a good number of the contact farmers. It is generally measured by
the number of contact farmers, who adopt the technologies in their farms.
viii
Perceived Attributes of innovations
Relative advantage
compatibility
complexity
trialability
observability
Communication channels
(e.g. mass media or interpersonal
Nature of social system
e.g. modern or traditional
The above attributes of innovations such as in modern agroforestry technologies,
affect the rate of adoption of the technologies. The attributes of modern agroforestry
technologies are briefly discussed.
(a) The relative advantage is the degree to which the modern agroforestry technologies
are perceived as being better than the traditional agroforestry practices.
(b) The compatibility of the modern agroforestry technologies refers, to the extent to
which they are consistent, with existing values, norms and past experiences, of
the contact farmers.
(c) The complexity refers, to the degree to which, the modern agroforestry technologies
are relative to understand or use. Innovations that are relatively simple to understand
will tend, to be more readily adopted, than those that are complex.
Adoption of modern
agroforestry
technologies
RATE OF
ADOPTION OF
INNOVATION
Figure 2. Variables determining rate of Adoption of Innovations such as Modern Agroforestry
ix
(d) Trialability refers to the degree, to which modern agroforestry technologies may be
experimented on a limited basis.
(e) Observability, refers to the degree to which the results of an innovation, such as
modern agroforestry technologies are visible to the contact farmers.
The length of time required between the awareness stage of an innovation such, as
modern agroforestry technologies, and final adoption stage is known as the adoption
period, while the stages before the final adoption is referred to as the adoption
process. Adoption is regarded as the full scale integration of a new idea or practice
into an on-going farm practice .In the present study, the integration and continuous use
of modern agroforestry technologies into the already existing traditional agroforestry
practices could be regarded as adoption. Obibuaku,, in Eze (2009), stated that the first step
towards the adoption of a new farm practice is the knowledge that the farm practice
exists (Awareness). He asserted that such knowledge is useless, unless the farmer uses it
adoption, therefore occurs over a period of time.
Contact farmers adopt innovations such as modern agroforestry technologies at
different rates. Rogers (1995), categorized farmers depending on when, how they receive and
adopt new ideas into six adopter categories-innovators, 2.5% early adopters 13.5% Early
majority 34% late majority 34%, late adopters/laggards 13.5% and never adopters 2.5% . The
characteristic of each category is briefly discussed below according to Rogers (1995).
1. Innovators (2.5%). These are contact farmers that are venturesome, educated, and
have multiple information sources. They adopt new ideas such as modern
agroforestry technologies immediately they come out for adoption.
x
2. Early adopters 13.5%. These groups of farmers are social leaders, popular and
educated. They also belong to many communities and farmers association.
3. Early majority 34. This is the third category of adopters. They are slow in taking
decisions, and wait to see result first. They are deliberate, had many informal social
contacts.
4. Late majority (34. %). These groups of farmers take extra security before adopting
any innovation such as modern agroforestry technologies. They are skeptical,
traditional and have lower socio-economic status.
5. Late adopters/laggards (13.5%). These groups of farmers prefer to stick to traditional
methods of farming such as honey production using traditional practices. They do not
attend extension meetings nor do they belong to farmers groups. Neighbours and
friends are their main information sources.
6. Never adopters. These are the last group of the adopter categories. They are the
lowest members of the community in terms of socio-economic status. The group
includes; drunkards and the b ad elements in the community and may not
even be farmers. Agricultural extension personnel should not neglect them
because they may give valuable information about the community to the
extension agents.
Social-economic characteristics of Agricultural Development Programme Contact
Farmers
The demographic variables of Agricultural Development Programme contact farmers
with regards to this study included gender,, educational qualifications, size of farm,
experience, source of farm labour, source of information on modern agroforestry
technologies, regularity of trainings by extension workers. Aside from these ADP contact
xi
farmers have special qualities that extension workers should note while dealing with them.
These include:
• Have more experience
• Tend to be more autonomous
• Interested in the immediate usefulness of the knowledge gained
• Learn different things because they face different tasks such as parenthood (Enugu
State ADP Implementation completion Report, 2006).
Extension sub-programme of Enugu State Agricultural Development Programme
An extension system indicates extension practices with identifiable independent staff
linked to continous sources of improved technologies and communication patterns for
reaching the end- users (Madukwe, 1995).Within the programme, the bulk of extension work
is under the extension sub-programme. Extension service sub-programme has the following
objectives among others.
• To assist farmers adopt recommendations of agricultural technologies using fixed
visit schedule.
• Organize farmers into groups
• Locate and process relevant technologies to farmers.
• Report farmers problems and
• Evaluate farmers practices.(Enugu State ADP field Report ,2006)
With set extension objectives, the agricultural extension services of Enugu State
Agricultural Development Programme operate the unified agricultural extension system
(UAES) using the training and visit (T&V) system. The unified agricultural extension system
xii
Director of
Extension
Extension
Component
(Head, Director)
Media Support
Component
(Head Media
Agroforestry
Component (Chief
Agroforestry Officer
Women in
Agriculture (Head
WIA)
Zeo
Nsukka Assistant
Head of Media Asst.
Head
WIA
SMS SMS SMS
Agro Agro Agro
Forestry Forestry Forestry
Zeo
Awgu Zeo
Enugu
Bes Bes Bes Bes Bes Bes Bes Bes Cameraman/
Photographer Sms Sms Sms
Wia Wia Wia
Bea and Ea
is made up of crops, livestock, fisheries, women in agricultural and agroforestry. See page 26
for illustration.
Source: Enugu State Agricultural Development Programme (ENADEP)
Implementation Completion Report, (2006)
The extension sub–programme is charged with the responsibility of teaching
improved technologies such as modern agroforestry technologies to ADP contact farmers.
Environmental Factors
Environmental factors greatly after agricultural production. They impose limitation our
agricultural production. For example in places where the rainfall is low, the cropping season
Figure 3. Extension Sub-programme of Enugu State Agricultural Development Programme
xiii
lasts very short. In Enugu state, farmers rely on rain-fed agriculture since there are very few
irrigation methods. Farmers in different agricultural zones of the state operate under different
environmental conditions. Environmental factors such as rainfall, sunlight, temperature,
relative humidity, wind and edaphic factors such as soil texture, soil structure, soil erosion,
topography of farmlands, cloud cover, pests, diseases, nature of farm roads, drought
influence adoption since majority of the modern agroforestry technologies are crop- based, it
might be necessary to investigate on how the environmental factors may likely affect
adoption of modern agroforestry technologies among ADP contact farmers in Enugu state.
Stages on the Adoption process and number of ADP contact farmers adopting modern
Agroforestry Technologies
The ultimate users of technologies from agricultural extension are the farmers made
up of contact and non-contact farmers. Ikeorgu, (1989), stated that research in itself is
useless to development of context, if it is not properly extended to farmers and they
make use of it. The ADP contact farmers are supposed to be aware of the modern
agroforestry and the technologies, adopt the technologies and benefit from the adoption of
the technologies.
The specific stages on adoption of agroforestry technologies refers to the stage
in the adoption process which a particular farmer is on the adoption process which of
the Agricultural Development Programme contact farmers that are at awareness, interest,
evaluation, trial and adoption stage in the adoption process.Isife (1996), quoting Enugu
State Agricultural Development Programme noted that while majority of the farmers were
on awareness stage in some technologies, some were on adoption in some technologies.
Agricultural Extension Teaching Methods
xiv
The agricultural extension teaching methods employed by the agricultural extension
workers directly affected the effectiveness of their efforts. Agricultural extension work
requires many methods and tools for teaching contact farmers. A good extension worker
must not only have in his command avariety of teaching methods,but must also know where
to use them to achieve best results.The teaching methods used by the agricultural extension
workers vary according to farmers circumstances and their stance in various stages of the
adoption process.
Agricultural extension teaching methods can be defined as devices used to create
situations in which new information can pass freely between the extension workers and
the farming communities (Obibuaku, 1983). For the agricultural extension worker to be
effective the following conditions must be met-learning situation,learning objectives,
learning experiences and a variety of teaching methods. The learning situation included
the extension site worker who has clear objectives knows the subject matter to be taught,and
the Agricultural Development Programme contact farmer who are capable of and
interested in learning and the subject matter which in the present study involves
modern agroforestry technologies. The objectives concern the learning outcomes which
must be stated in clear terms. The learning experience deals with the mental or physical
reactions one makes in a learning situation through seeing, hearing or performing
activities.Obibuaku (1983) stated that agricultural extension workers in Nigeria do not make
use of adequate extension teaching methods
An effective use of extension teaching methods will definitely help to achieve one
of the millennium development goals set by United Nations to achieve by 2015, which
is to halve the proportion of people whose income is less than one dollar a day and
xv
who suffer from hunger. This objective it hopes to achieve mainly through empowering
people,majority of whom are poverty stricken through education and provision of
vocational training (Ikeoji, 2010). If the agricultural extension workers used effective
agricultural extension teaching methods, there was the likelihood that majority of the farmers
will be on adoption stages, indicating high number of the farmers adopting the technologies.
This is more important when agroforestry is one of the mitigations of the current climate
change.
Modern Agroforestry Inputs
Inputs are agricultural materials used in producing farm produce. Modern agroforestry
technologies require inputs such as improved tree seedlings, improved maize seeds (Oba
super98), improved cassava cuttings (IITA series), tropical manihot selection (TMS 3055 and
TMS 30572). The other needed inputs include modern beehive (lanstroth) improved bee
combs, harvesting tools such as knife, bucket, bee suit. There is also the need for the
production of vertivar grasses, boronated superphosphate or vine for the treatment and
management of flower/fruit abortion. Inputs such as organic and inorganic fertilizers are also
needed. The organic manure includes poultry and pig manners while inorganic fertilizer
includes NPK 15:15:15, 20:10:10. There is also the need for seed dressing chemicals
such as the miral and furadan for the treatment of seedlings to ensure that termites
and other soil pests do not destroy the seedlings in the soil. The availability and cost of
modern agroforestry inputs are determinants of adoption of modern agroforestry technologies
because when they are available at considerate prices, the farmers should be in position
to either try or completely adopt the technologies otherwise, the innovations will be
wasted and efforts will be in futility.
xvi
The conceptual framework of this work is therefore a manifest representation of these
concepts in the interactive modes or linking of the elements or variables or factors that are
related to the problem being studied.
Source: Adapted from Eboh E.C. (2009) the household and farmer level factors
affecting tree integration on farms and their interaction effects on tree
management in Eastern Nigeria.
Above is a conceptual framework illustrating, a vicious cycle model that defines the
interralations between socio economic characteristics (gender and educational qualifications),
agricultural extension teaching methods, inputs, environmental factors that were
determinants of adoption of modern agroforestry technologies.
Theoretical Framework
Environmental
factors
Agroforestry inputs
Social-economic characteristics Gender
and Educational qualification
Size of the farm, Source of labour,
Experience agric,extension visits visits
Figure 1: Interrelations of determinants of modern Agroforestry Technologies
Agricultural extension
teaching methods
Different stages on adoption
process on modern
agroforestry technologies
(awareness,interest,evaluation,trial
and adoption
xvii
The theories upon which this study is anchored are System Approach for Technology
Transfer, Induced Innovation theory of Adoption, Innovation-Decision Theory of Adoption
and Adoption-Diffusion model.The theories are discussed below.
Theory as defined by Olaitan in Eze (2009), is a set of related statements that are
arranged so as to give functional meaning to a set or series of events. These sets or related
statements may take the form of descriptive or functional definitions. A framework on the
other side is simply the structure of the idea or concepts and how they are put together.
According to Coakly (1990) theoretical framework in educational inquires helps to ask
questions, interpret information, set goals and select strategies for achieving the goals.
Finally, theoretical framework relates to concepts and these guide research.
System Approach for Technology Transfer
This model was introduced by Nagel (1980) but later amplified by Swanson and
Clear (1993). The system approach for technology transfer is made up of three sub systems-
Technology generation, Technology transfer and Technology utilization. The technology
generation sub system involves research centers and universities where new technologies
emerge .In the present study, it involves multi- locational trials, validation of indigenous
technologies, carries out diagnostic surveys and receives feedback from extension agency.
The second sub- system is the technology transfer sub-system which is involved in
teaching the new technologies. In the present study Agricultural Development Prpgramme is
the technology transfer sub system.The agricultural extension workers of the programme
teach the ADP contact farmers. The last sub-system is the technology utilization subsystems
which are the ADP contact farmers. They are expected to use the technologies and benefit
from adopting the technologies.
xviii
Figure 4. System Approach to Technology Transfer
System Approach to Technology Generation – utilization as adapted from Eze S.O.
(1997)
When the technology generation subsystem such as research centers and universities
developed technologies, the Agricultural Development Programme extension workers taught
Agricultural Development Programme contact farmers the technologies such as the modern
agroforestry technologies. There waslinkages between research centers/Universities
Agricultural Development programme and Agricultural Development Programme contact
farmers.This was the theory used for the study .The technology generation subsystem was the
monthly Technology Review meetings that generate technologies such as modern
agroforestry Technologies. The technology transfer was the Enugu State Agricultural
Development Programme that transferred the modern agroforestry technologies. Finally, the
TECHNOLOGY
GENERATION
SUB-SYSTEM
- evolution of new
technologies
- conducts multi-
locational trails
- validates and
indigenized
technologies
- carried diagnostic
survey
- receives feedback
from extension
agency
LINKAGE
BETWEEN
ADPs AND
RESEARCH
INSTITUTES
TECHNOLOGY
TRANSFER SUB-
SYSTEM
- Staff training
- Teaches farmers
- Conducts
demonstrations
using field days
- Visits farmers and
identifies field
problems
LINKAGE
BETWEEN
TECNOLOGY
TRANSFER
AND
TECHNOLOGY
UTILIZATIONS
TECHNOLOGY
UTILIZATION SUB-
SYSTEM
- Aware of modern
technologies
- Adopts modern
technologies
- Discusses field
problem with agric
extension workers
- Attends circle
regular meetings
- Attends workshops
seminars, organised
by the extension
agency (technology
transfer sub-
xix
technology utilization subsystem was the contact farmers who underwent different adoption
stages before adopting the technologies.
Induced Innovation Theory of Adoption
The induced innovation theory is used to explain the effects of situations to adoption
of modern agroforestry technologies. The protagonist of the theory was Boseup (1965). He
showed that as population densities rose, the demand for agricultural produce increased. The
resulting land pressure as a result of population induced the adoption of technologies that will
improve land use. The limited nature of land, increasing population and inability of farmers
to use inorganic fertilizers due to high cost, made the land to loose its fertility. These
conditions induced adoption of natural resources base technologies, such as the modern
agroforestry technologies.
The theory is made up of three subsystems-innovative technologies, induced
conditions and merits of innovation adoption. The different subsystems are briefly discussed
(1) Innovative technologies. These include modern agroforestry technologies extended to
ADP contact farmers-Beekeeping for honey production, cassava/maize in alley,
establishment of multipurpose trees and control of flower/fruit abortion, establishment of
browse plants, use of vertivar grasses for wind and erosion control, (2) The second sub-
system is the induced conditions, these include land areas that are fragmented, high
population, high demand for agricultural products, the need to protect the ecosystem and the
need to conserve the soil (3) The third sub-system concerns the merits that will accrue as a
result of adopting the modern agroforestry technologies. These include maintains soil
fertility, improves water conservation, provides favourable micro-climatic conditions,
increase farm income, improves the standard of living of contact farmers.
xx
A B C
The induced innovation theory of adoption is fitted into the study because the
innovative technologies which are modern agroforestry are new to the farmers .The induced
conditions are the factors that will compel the farmers to adopt the technologies .and the
merits of adopting the technologies.
Innovation-Decision Theory of Adoption
This theory was propounded by Rogers (1995). In spite of criticisms of Rogers
adoption theory many readers and researchers have found the adoption theory insightful
Innovative technologies
- Bee keeping for
honey production
- Cassava/maize in
alley
- Establishment of
multipurpose trees
- Establishment of
browse plant for
feeding livestock
- Planting vertivar
grasses for erosion
control
- Treatment of
fruit/flower
abortion in trees.
Induced conditions
- Limited land area
- High population
densities
- High demand for
agricultural
products
- Need to protect the
ecosystem
- Need to conserve
the soil
- Merits of innovation
- Maintains soil fertility
- Improves water
conservation
- Provides favourable
micro-climatic
conditions
- Increases the farm
income of farmers
- Improves the standard
of living of farmers.
Figure 5: Schematic representation of induced innovation theory as adapted from
Boserup’s conditions for agricultural growth (1965)
xxi
specially in relation to the organization of the mental decision making process undergone by
various farmers before taking a final stand in an innovation such as modern agroforestry
technologies.
Rogers (1995), defined adoption process as “the mental process through which an
individual passes through from first hearing about an innovation to final adoption. The
innovation-decision theory of adoption has five steps”. These steps include knowledge
(awareness), persuasion (interest), decision/evaluation), implementation, (trial) and
confirmation (adoption or use). According to the theory the individual seeks knowledge of
and skills which will ultimately affect the adoption process. For a potential adopter, the
process will proceed through the various steps and lead to adoptions, or alternately, lead to
rejection, of the innovation (Rogers, 1995). The various steps in the adoption theory are
briefly discussed below.
Figure 6. Innovation –Decision Theory of Adoption
Source: Innovation-Decision theory of Adoption (Rogers, 1995)
1. Awareness (knowledge): This is the stage where the contact farmers hear about the
existence of a new innovation such as modern agroforestry technologies. They know
little about it.
Trial
Evaluation
Interest
Awareness
Adoption
xxii
2. Interest (persuasion): If at this stage, the innovation in step one above relates to the
farmers conditions, problems or need, he/she will ask or seek for additional
information about it. This implies that they have developed interest. They may ask
questions such as why is honey produced in traditional system not as good as the ones
produced, through modern method. The agricultural extension agent should at this
stage intensify efforts to give the farmers more information.
3. Evaluation (Decision): The contact farmers having collected all the information about
the innovation shall mentally assess the new technologies. He/she considers the
merits of the innovation to find out, if the new thing will “pay off”. The farmers make
mental trial of the idea and begin to appreciate it in relation to his values.
4. Trial (implementation): At this stage, the farmers exercise cautions. The contact
farmers apply the new technology in a small plot to guarantee security of the crops.
At harvest he/she examines the yield, quality, quantity and even the market situation
.and takes decision whether to adopt the technology or not..
5. Adoption (confirmation): If the farmer is convinced by the trial he/she will
undertake large scale practices and continues the use of the idea.
The innovation –decision theory of adoption describes vividly the processes through
which ADP contact farmers undergo before finally adopting any technology. The concept of
individual differences makes it possible that individual contact farmers do not adopt
technologies at the same time.
Adoption-Diffusion Model
This model is a macro framework for examining a large social group as a diffusion
system. This model states that technology adoption takes off from innovative to
xxiii
communicative and practitioner sub-systems. This model was proposed by a sociologist
Milton coughenour (1991). The model has three sub-systems: innovative, practitioner and
communicative.The following subsystems are briefly discussed- (1) innovative subsystems.
The essential question to ask at this point is, from where do new ideas emerge? The sources
of new ideas/innovations include universities, ‘think tanks’ research institutes, and industrial
and governmental research laboratories. The cardinal duty of the innovative sub-system is to
invent/discover innovations. The modern agroforestry technologies were the efforts of the
innovative subsystem (2) The second subsystem within the model is the communicative
subsystem. This subsystem deals with the ways the technologies are extended to the final
users. The communicative subsystem includes mass media, churches, sales organizations of
commercial firms, governmental and private agencies charged with information spread,
university extension service. Extension services have served as the model for most other
change agencies. These change agencies extend innovation/technologies using different
media channels (3) The third subsystem in the model is the practitioner subsystem. These are
those individuals, farmers, social organizations, engaged in the use of the innovations.
xxiv
Figure 7. Adoption – diffusion model
A model of the diffusion system showing, the major elements involved in the
diffusion, of modern agroforestry technologies, adapted from, a model of the diffusion
systems, showing the major elements, involved in diffusion of hybrid seed corn, in the
United States, Mendel (1856).
The adoption – diffusion theory of adoption states that adoption passes through
innovative,communication,and practitioner sub-systems before users make use of it.In the
present study the innovative sub-systems concerns research institutes and Universities whose
cardinal duty is to invent or discover new ideas such as the modern agroforestry
technologies.The second sub-system is the communicative sub-system which are involved in
Innovative Subsystem Communication
Subsystem
Practitioner subsystem
Government
Research
Laboratories
Universities
Research
Institutes Extension
Change
Agents
Mass Media
Farmers
xxv
extending the technologies to the final users which in the study are Agricultural Development
Programme. The practitioner sub-system are those individual that use the new ideas which in
the study are ADP contact farmers.
Related Empirical Studies
The empirical studies on agroforetry technologies adoption in different parts of the
world and Nigeria were briefly discussed below.
Franzel and Scherr (2002), who developed a frame work for assessing agroforestry
adoption in central America. They collected eight ex-ante evaluations of 21 agroforestry
projects in Central America (Costa Rica, El Salvador, Guatemala, Honduras, Nicaragua and
Panama) and the Caribbeans (Dominican Republic and Haiti). The basic approach used in
the framework was a variety of participatory appraisal and surveys to identify farmers
problems and needs, which were then, used by researchers, to design systems for On-farm
experimentation. The researchers used one hundred and fifty farmers as sample and
distributed one hundred and fifty questionnaire to the sample. The data were analyzed using
mean and standard deviation.The result of the study found that preferences, resource
endowment markets incentives, and uncertainty were crucial factors in adopting agroforestry
technologies. The study equally stated that the relationship between the new technology
and the total farm enterprise, and the existing capital, labour and land constraints, were
crucial to the adoptability of agroforestry technologies.Local scarcities as reflected in the
prices of wood products, appeared to be the key factor to profitability, and adoption of
agroforestry in Central America and the Caribbeans. Based on these studies, current,Lutz
and Scherr (1995), who concluded that including trees in agricultural systems reduces,
sensitivity to annual crop yield, and price variability.
xxvi
Pattanayak, Mercer and Sills (2003), also reviewed 120 articles on adoption of agricultural,
and forestry technology, by small holders and concluded, that the following five categories of
factors, explain technology adoption, these include: preferences, resources, endowments,
market incentives/biophysical factors, and risk and uncertainty.
In sub-Saharan Africa case studies Vosti, Witcover, Oliveria and Faminow (1998),
used sixty farmers as sample from four hundred farmers using questionnaire to collect the
data which were analysed using mean,and standard deviation. The study found that
agroforestry is shown to have potential to increase farm income and solve difficult
environmental problems. In addition to the products and services provided, African farmers
in Kenya and Zambia value the experimental agroforestry systems for their risk reducing
impacts.
Agroforestry study conducted in western Brazilian Amazon by Vosti, Witcover,
Olivera and Faminow (1998), who examined the adoption potential and related policy issues
for adoption of five simple agroforestry systems including cacao and/or coffee combined,
with rubber and black pepper combinations in the western Brazilian Amazon.Two
hundred and forty farmers constituted the sample, using questionnaire to collect the data
from one thousand farmers.The data were analyzed using mean, standard deviation and
multiple regression as statistical tools. The study found that high investment requirements,
negative cash flows in early years, and uncertain local demand reduce adoption potential of
modern agroforestry technologies by farmers in the area. The study concluded that
evaluating agroforestry adoption potential requires a thorough understanding of the physical
and financial returns to all factors of production, process including establishment,
maintenance, harvesting , processing, marketing and distribution of products. Other factors
xxvii
in determining adoption potential are scale of production: Profitability and returns to factors
of production change with scale of production particularly with crop mixes. Timing/size of
investment: Cost of under-investing or delaying investment can be quite high for
agroforestry compared to annual cropping or pasturing.
In a related study on adoption of agroforestry technology in Cameroon by Kang
(1984) one hundred and eighty farmers were used as sample out of five hundred and forty
farmers.The instrument used for data collection was questionnaire and the data were
analyzed using mean and standard deviation. The study found that farmers adopted
agroforestry technologies because the farmers could not afford to purchase the expensive
inorganic fertilizers. The farmers equally adopted agroforestry because it added nitrogen to
soil by the woody legumionous trees. Another reason for the adoption of agroforestry was
that it helped in recycling of nutrient, helps in soil conservation and suppression of weeds.
Agroforestry can sustain crop production, in the high rainfall forest zone of Cameroon
because of the following reasons: low soil fertility, high cost of fertilizer and high population
density According to Kang (1984), the adoption of alley farming, an agroforestry system has
been encouraged because farmers cannot afford to purchase the expensive chemical
fertilizers. The addition of Nitrogen to the soil by woody, leguminous species is one of the
major advantages of agroforestry farming. Kang (1984), who reported that 15 tons of fresh
leucaena prunings annual provided 160kg/ha Nitrogen, 15kg/ha potassium, 150kg/ha
phosphorous to sandy entisol soils of Nigeria. Rachie (1983), who equally reported a
nitrogen yield of 127kg/ha for 4 months old leucaena plants grown in the valley of
Colombiathe trials at Yaounde, were intercropped with maize, groundnut and cassava. There
was a high yield differential when compared with sole cropsAlley farming agroforestry
xxviii
system is still, a very new technology in Cameroon, it retains the basic component of
traditional bush fallow agriculture, and introduces important improvement, biologically,
recycling of nutrients, soil conservation, suppression of weeds and reduced work load are
major benefits of alley farming agroforestry system in Cameroon.
Similarly in a study on agroforestry adoption conducted in the province of
Atlantique, Benin Republic by Kings (1984), who in collaboration with researchers under
the umbrella of Centre of Regional Action for Rural Development, (RARD) whose research
objective was to; examine the adoption possibilities of potential cropping patterns developed
by International and national research institutes. It’s main aim was to promote agricultural
development through innovations that will overcome the obstacles to agricultural
improvement in the province of Atlantique in the southeastern region of Benin. Sixty farmers
were randomly selected out of one hundred and seventy three farmers.The instrument used
for data collection was questionnaire and the result was analysed by the use of mean and
standard deviation. The findings of the study show that inclusion of agroforestry
technologies promotes yields, reduces weeds, adds nitrogen to the soil, and concluded that
agroforestry is good for extending to farmers as better alternative to natural fallow. The
subsequent intensive land-use has caused a rapid deterioration of the soil structure, a decrease
in fertility and a reduction in the fallow period. The researcher recommended food self-
sufficiency through research and utilization of agroforestry technologies that will allow
intensive land-use with minimal inputs as a substitute to natural fallow. The study adopted
the alley cropping system developed at the International institute of tropical agriculture,
Ibadan, Nigeria. In another agroforestry adoption study conducted in the semi-arid
region of India Franzel (1999), sixty five farmers were randomly selected for the study out
xxix
of two hundred and forty farmers. The instrument used for data collection was
questionnaire and the data were analyzed using mean, frequencies ,standard deviation and
multiple regression analysis .The findings of the study stated that agroforestry practices
provide fodder for livestock feeding, increased gross cash returns and farmers made use of
leucana for fuelwood. Researchers in the Central Rresearch Institute for Dry land
Agriculture (CRIDA), identifield what the biggest constraints to farming was for residents of
the Telangana region, Andhra Pradesh .The researchers of the research institute identified
that lack of water and fodder as the main constraints in adopting agroforestry
technologies in the area. The farmers in the Indian dry lands were often forced to sell
bullock in the dry season at just 10% of their real price, because of fodder scarcity. The
Indian Grasslands and fodder Research (IGFR), in Jhansi, India has demonstrated that
increased fodder production is feasible through the adoption of establishment of browse
plants. According to the farmers, it has the potential of allowing the farmers to produce
fodder, without seriously disrupting existing farm practices. In the semi-arid regions of India,
the primary benefits, of browse tree farming is viewed, as more or less permanent fixture of
farmers with alleys spaced at wide enough intervals to less their negative impact on crops.
Gross cash returns are higher in all cases for alley-farmed systems. Alley-farmed sorghum
yielded nearly twice the income of sole sorghum. The alley-farmed pigeon pea system
yielded almost seven times the income of sole pigeon pea. This was due to the value of
leucaena by products Most of leucaena by-products value is derived from it’s sale as fodder,
which appears to be of greater economic benefit than its application to the crops as a green
mulch. Studies by the All-India coordinated research project for Dry land Agriculture and
Agroforestry concluded that alley farming is suitable for the semi-arid areas of India. The
xxx
use of leucaena for fuelwood and fodder made the gross economic returns in alley farming
almost twice those in sole cropping.
Agroforestry adoption study conducted in Kenya by Scherr (1995), the study was
designed to test several agroforestry interventions, aimed at reversing the constraints that
have appeared in the semi-arid lands of Kenya, as a result of population pressure three
hundred farmers sampled from one thousand nine hundred farmers.The data for the study
was collected by the use of questionnaire and analysed using percentages,means and
standard deviation .The finding of the study which was in line with design methodology of
diagnosis and design methodology of the International council for Research in Agroforestry
(ICRAF), identified research needs of the semi-arid areas of Kenya to include: Low
productivity of crops, as a result of low soil fertility (low organic matter and nitrogen levels)
high rate of soil erosion and runoff shortage of animal fodder, especially during the dry
season shortage of fuel wood and building poles and shortage of cash (no cash crops)The
findings of the study was in line with the above research needs suggested developing
agroforestry practices/technologies that will take adequate care of the above mentioned
problems. The project objectives of International council for research in Agroforestry
(ICRAF), were to develop agroforestry technologies for the semi-arid areas of Kenya, and
other East African countries, with a view to improving the quality of lives of the
inhabitants. Following the identification of the constraints, the following objectives were
stated:to examine the possibilities of maintaining or increasing productivity by establishing
alley cropping to examine the possibilities of improving the quality, quantity and seasonal
distribution of forage crops on the farm by planting fodder trees or such species in the
grazing areas and by developing cut-and-carry forage systems to examine the possibilities
xxxi
of reducing the labour requirements of free-grazing systems to examine the possibilities of
increasing the cash income of the farmers by the introduction of trees.Result from trials in
Machakos districts gave green matter yields for leucaena about 1.5kg/tree per season at
average intra row spacing of 0.62m and between row spacing of 3.5m. The relative yield of
maize per unit or crop area increased by 22%. Due to the high population density of Kenya,
the intervention technologies designed to counteract the constraints as a result of the issues of
over-population is alley cropping which is a modern agroforestry technologies.
Studies on agroforestry adoption in Western Kenya by Scherr (1995), on the
economic factors in farmer adoption of agroforestry in western Kenya revealed that
agroforestry practices in western Kenya evolved historically, along with land-use
intensification, to meet new needs for tree products and services. The choice of agroforestry
practices on particular farms varied considerably, reflecting resource constraints and
differing livelihood strategies. Farmers consistently adopted agroforestry technologies to
reduce associated risks.
In Nigeria a number of agroforestry adoption studies were undertaken by researchers
from corporate and private organizations because of the benefits of agroforestry practices.
Cobbina and Atta-krah (1989), conducted research on agroforestry practices. The study was
conducted in Oyo State of Nigeria and the villages used for the study were Owu-Ile and Iwo
–Ale. The sample for the study was twenty from a population of one hundred and eighty.The
data were collected by the use of questionnaire. The analysis was done using mean and
standard deviation .The findings of the study found that planting of two leguminous trees
glirricida sepieum and lucaena which are leguminous trees increased foliage, increased soil
fertility and the crops yielded double. The nitrogen-rich foliage from the recommended trees
xxxii
of leucaena and gliricida can be applied as green manure for the maintenance of soil fertility
or can be fed to livestock as a high protein feed supplement.
In another study conducted by National Research council (NRC,1984), on
agroforestry technologies, the council found out that, Agroforestry adoption is likely to
sustain economic productivity, without causing severe degradation of the environment. NRC
(1993), further, noted that because of the low fertility of most upland tropical soils, some
degradation is inevitable with any cultivation systems. Lai (1989), stated that soil organic
matter, Ph, soil structure, infiltration rate, cation exchange capacity, and the base saturation
percentages are maintained at more favourable levels in agroforestry systems due to reduced
losses to run-off, and soil erosion, efficient nutrient recycling, biological nitrogen fixation by
leguminous trees, favourable soil temperature caused by drying, and improved drainage
because of roots and other biomass. The International institute for Tropical agriculture
(IITA) Ibadan, Nigeria has also been conducting field trials of agroforestry systems
especially in alley cropping/ farming. IITA (1992), stated that on-farm testing of alley
cropping/ farming methodology had been developed between 1989 to 1992 through
International research coordinated by the Alley farming Network for tropical Africa
(AFNETA). According to IITA (1992), alleycropping/ farming research evolved during the
1970s at the institute, as a land use system for managing the fragile uplands in the humid and
sub-humid (forest and savannah) zones of sub-savannah Africa. The aim was to develop a
substitute for the traditional bush-fallow, slash and burn system of land renewal.The
empirical study has relationship with the study because both studies advocated for the
replacement of bush fallow with either agroforestry in general or modern agroforestry
technologies.
xxxiii
In another study on agroforestry conducted in Nigeria by Bifex (1997), method
adopted was the field research method supported by Diagnosis and Design developed by
the International council for Research in Agroforestry (ICRAF) Steppler (1987) .A total of 50
farmers were randomly selected from six hundred farmers from Anambra,Ebonyi and Enugu
states.The data were collected using diagnosis and design while secondary data were from
various publications of research Institutions such as International Institute for tropical
Agriculture (IITA).Local enumerators were used to collect data for the study. Data were
analyzed in stages. The first stage was diagnostic activities involving observations and
inventory listing. The second stage involved mapping of zones with equal agroforestry
potentials and design of appropriate strategies.Sensitivity analysis was used to determine the
range of conditions such as inputs or outputs, prices and productivity levels under which an
agroforestry technology was likely to be profitable. The analysis also involved qualitative
assessment and anecdotal information on social and environmental costs and benefits of
agroforestry in the communities. Furthermore correlation coefficient was used to ascertain
the relationships between the various relevant factors comprising economic,social and
environmental, water,nutrients, pests crops biodiversity, livestock and people and how they
act as inter-related components of agro-ecosystems. The study found that agroforestry
approach was well suited to small farming units in the wet forest zone of the south, as well as
to the great silvo-pastoral expanses of the drier savannah zone in the north alike because; the
planting of trees in agroforestry singly or in small groups in suitable micro-climates is more
suitable to the environment than large concentration of trees and It is well suited to tackling
the problems of desertification. The study also found that since most farmers in Nigeria
cannot afford the costly inputs, it has become necessary to develop; A low-input soil
xxxiv
management technology that can sustain crop production and at the same time protect the
environment. In essence, sustainable land management provided by agroforestry contributes
to food security .Against this backdrop, it becomes imperative that in order to break the cycle
of environmental degradation and continued decline in agricultural production, farming
population in Nigeria must be able to meet their needs in ways that are socially,
economically, environmentally friendly and viable in on a long term basis .Such a viable and
environmentally friendly and sustainable option is agroforestry (Nair,1980; Beets,1989;
Baumer,1990 and Current, 1995). This empirical study has relationship with the study
because both studies suggested the adoption of agroforestry and modern agroforestry
technologies as strategies to overcome environmental problems and equally helping to
mitigate the ravaging climate change.
Another study on economic and environmental impact of agroforestry in Anambra
State was by Amechina, (1998). A sample of 60 farmers was randomly selected for the study
out of one hundred farmers practicing agroforestry in the communities.The data were
collected by the use of questionnaire by agricultural extension workers of Anambra state
Agricultural Development programme.The data were analyzed using different statistical tools
like descriptive statistics,farm budget analysis,gross margin analysis and benefit cost model
was used to evaluate the long –term benefits and costs of agroforestry. The study found that
the predominant farming systems in the south Eastern Nigeria were based on the bush fallow
strategy in combination with complex mixtures of crops.The population pressure has caused
a reduction in fallow periods and led to expansion of cultivation into newly cleared lands
thereby increasing the tendency towards deforestation, ,soil degradation and productivity
decline and therefore heightened agroecological risks (Ehui and Spencer, 1990). The study
xxxv
concluded that the worsening environmental conditions calls for timely and vigorous
research and policy intervention to promote widespread adoption of environment-enhancing
agricultural production system such as agroforestry (Eboh, 1994). This empirical study was
related with the study by identifying that the predominant farming systems in the south
Eastern Nigeria were bush fallow in combinations with complex mixtures of crops and the
need to introduce a more environmentally friendly system such as agroforestry and modern
agroforestry technologies.
Another study on the constraints to adoption of recommended multiple cropping
systems and the implications of their non-adoption to rural poverty in Ebonyi and Enugu
States by Ochiaka, (1998). The population of the study was 960 contact farmers from 12
blocks from Nsukka and Abakaliki agricultural zones of the states.The sample was one
hundred and twenty contact farmers.The data were collected by the use of questionnaire and
the data were collected by enumerators trained by the researcher and the data were
analyzed using frequency, percentages and mean, The study found that the following
factors were constraints to the adoption of the technologies-planting materials,agrochemicals
and, fund the following environmental factors were constraints to adoption of the
technologies-irregular rainfall,pests,diseases,soil erosion and fire. This empirical study has
relationship with the study because both studies idenfield planting materials, agrochemicals,
fund, irregular rainfall, pests, diseases, soil erosion and fire as constrains to adoption of both
studies.
Summary of Literature Reviewed
xxxvi
Relevant concepts, theories related to the study and review of empirical studies were
presented. Different variables such as stages of adoption in the adoption process, educational
qualifications of Agricultural Development Programme contact farmers, agricultural
extension teaching methods, modern agroforestry inputs and environmental factors were
reviewed.
The literature reviewed different factors, situations, merits, and conditions for the
introduction of agroforestry practices in different parts of the world in general and Nigeria in
particular. Most of the studies reviewed concentrated on the economic studies of agroforestry
in general. The literature reviewed also treated agroforestry generally and most of the
literature was on traditional agroforestry practices. Modern agroforestry technologies as
innovations from the traditional agroforestry have not been extensively studied and
hence this study which concentrated on modern agroforestry technologies.There is therefore
a gap between the adoption of traditional agroforestry practices and modern agroforestry
technologies in terms of content, adoptability, constraints and design by both farmers on
the traditional agroforestry and contact farmers of Agricultural Development Programme.
This study was therefore to fill the gap by empirically studying the determinants of
adoption of modern agroforestry technologies by Agricultural development Programme
contact farmers in Enugu State.
xxxvii
CHAPTER THREE
METHODOLOGY
This chapter was presented under the following headings –design of the study, area
of the study, population for the study, sample and sampling technique, instrument for
data collection, validation of the instrument, reliability of the instrument, method of data
collection, and method of data analysis.
Design of the Study
The study adopted the descriptive survey research design. Survey research design
according to Izundu in Nkata (2006) involved assessing attitudes or opinions of respondents.
The design was used to collect detailed factual information that described existing
phenomena. It was interested in the accurate assessment of the characteristics of the
whole population of people by studying samples, drawn from the population. It focuses on
people, the vital facts of people, their beliefs, opinions, attitudes and motivation. It also
interprets, synthesizes, and integrates data, and points to implications, and interrelationships.
Survey research design is also practical, as it identifies present condition, and points to
present needs (Nworgu, 2006). The choice of the design was because it is concerned with
people and focused on contact farmers, it found out the characteristics of a sample which
was used to generalize on the entire population pointing to implications and
interrelationships.
Area of the Study
The study was conducted in Enugu State of Nigeria. The state shares borders
with Abia, and Imo States to the south, Anambra State to the West, Ebonyi State to the East,
Benue state to the NorthEast and Kogi State to the Northwest. The state covers an
47
xxxviii
estimated land area of 12,727 square kilometers. It has an average rainfall of about 1500mm
to 2000mm annually, and a mean temperature of about 30oc
. There is an estimated 4.3
million persons in the state with about 498,621 farm families (Abonyi, 2001).
The state is subdivided into three Agricultural Zones of Awgu, Enugu and Nsukka
covering nine sites under the National Programme on food security. A site in this study refers
to a place where the activities of the National Programme on Food Security are
carried out in line with National Economic and Empowerment Strategy [NEEDS] and
State Economic and Empowerment Strategy (SEEDS). The main objective of the
programme was extending the application of innovative low-cost technologies to
improve the productivity and sustainability of agricultural systems of poor farmers
Awgu zone has two sites namely Nenwe and Inyi, Enugu zone has four sites namely
Amagunze,Obeagu,Obinofia and Ehamufu while Nsukka zone has three sites made up of
Adani, Agu -Ukehe and Obollo-Etiti (Enugu State ADP Field Report, 2008).
Enugu state has a good fertile land, and a good climatic condition all year round, and
the soil is well drained during its rainy seasons. The mean temperature in Enugu state in the
hottest month of February is about 87.16 (30.64), while the lowest temperatures occur in the
month of November, reaching 60.54 (15.86) (Ogundipe, 2011).The major occupation of the
people is farming participating in different World Bank sponsored agricultural projects such
as Agricultural Development programmes, FADAMA, Commercial Agriculture
Development project and National programme on food security. Crop production in the state
is seasonal following the rainfall pattern, food crops grown include: yam, cassava, maize,
cocoyam, vegetables, groundnut, pepperfruit, while tree crops grown include: citrus, mango,
cashew, oil palm, coconut, and kola. Traditionally agroforestry practices such as scattered
xxxix
farm tree- system; live-tree fencing practices exist in most farms in the state. The choice of
the state for the study was because the climate of the area is favourable for the practice of
modern agroforestry technologiesLand is increasingly becoming a scarce resource. Moreover
Agricultural Development Programme contact farmers were taught these technologies for
quite some time and the contact farmers were also actively involved in the National
Programme for Food Security (NPFS) of the Federal government of Nigeria. Enugu State
Agricultural Development Programme (ENADEP), (Enugu State ADP implementation
completion Report 2006).
Population for the Study
The population for the study was 720 Agricultural Development Programme contact
farmers randomly selected from the three agricultural zones of the state where the sites for
National Programme for Food Security are located. Based on the number of sites per an
agricultural zone,the contact farmers were distributed as follows; Awgu zone with two sites
had 160 contact farmers,Enugu zone with four sites had 160 contact farmers,while Nsukka
zone with three sites had 120 contact farmers.
Sample and Sampling Technique
The sample for the study was 360 Agricultural Development Project contact farmers.
Forty Agricultural Development Project contact farmers were randomly selected from
each of the nine sites. The choice of selecting 40 contact farmers was in line with Nwanna
in Alio (2008) which stated that if the population is a few hundreds a 50% or less sample is
accepted . Random sampling technique was used to draw the sample because it gave no
chance of the sample being biased and was considered representative of the population
and therefore deemed generalizable to the population (Nworgu, 2006). In the
xl
randomization process, the name of each contact farmer was written on a slip of paper
and the slips were folded and put in a container. After a thorough reshuffling, the
researcher and the site extension agents dipped hands into the container and picked one slip.
The name in the slip was recorded folded again and put back into the container. This process
was repeated until the researcher selected the required number of farmers to constitute
the sample size.
Instrument for Data Collection
The data for the study was collected by the use of a 120-item structured questionnaire
generated from literature reviewed for the study. The choice of questionnaire was that, it was
suited to the design and nature of the required data .They were also used to obtain facts about
past, present, anticipated events, conditions ,practices and made inquiries concerning
attitudes and opinions (Alio, 2008). The questionnaire was divided into 1-5 parts.
Part I sought data on the demographic information of the contact farmers which
contained 10 items while Part II also sought data on the specific stages on adoption process
of contact farmers on the five modern agroforestry technologies which contained 75 items
and Part III sought data on the agricultural extension teaching methods and contained
11 items. Part IV sought information on agroforestry inputs and contained 14 items and Part
V sought information on the environmental factors and contained 10 items.A five-scale
Rogers stages of adoption process was used to assess stages of adoption of modern
agroforestry technologies in the adoption process.The rating scales were Adoption-5,Trial-
4,Evaluation-3,Interst-2,and Awareness-1 (See appendix C). A four- point rating scale was
used to identify the determinants of the technologies. The rating scale were Very Great
extent-4, Great extent-3, little extent-2 and Very little extent-1.
xli
Validation of the Instrument
The questionnaire was face-validated by three experts, one from Department of
Science Education, University of Nigeria, Nsukka, one from Department of Technology and
Vocational Education, Enugu State University of Science and Technology, Enugu and one
from Agroforestry Sub-programme, Enugu state Agricultural Development Programme.
They checked the tenses, whether they were framed positively or negatively of the
items in the instrument and the contents closeness to the research questions. Out of
201 items sent for validation, only 120 were restructured and survived as useable for the
study. The experts restructured some items, added and removed some items as was deemed
appropriate by them. The improved copies of the questionnaire were used for the study.
Reliability of the Instrument
In order to ascertain the reliability of the instrument 40 copies of the questionnaire
were administered to 40 ADP contact farmers made up of 20 males and 20 females from
the circles not used for the study. After administering the instrument to the contact
farmers, two weeks later the instrument was re-administered for trial testing to contact
farmers who were not used for the study. To obtain an estimate of internal consistency, the
two sets of scores from the two groups of contact farmers were analyzed using cronbach
alpha method. The following clusters of the instrument had these co-efficient values, stages
on adoption process 0.88, agricultural extension teaching methods 0.78, modern agroforestry,
input factors 0.88-environmental factors had 0.78. The overall reliability result was 0.75. The
reliability result is attached at the appendices (see appendix G).
Method of Data Collection
xlii
The data were collected by the researcher and nine site extension agents who
were in charge of the sites used for the study. The researcher trained the site extension
agents on the objectives and procedures of the study, and how to collect reliable data
from the Agricultural Development Programme contact farmers, during the fortnightly
training sessions of the three agricultural zones at Nkwo-Nike for Enugu East, Ugwuoba,
Oji-River for Enugu West and Onuiyi Nsukka for Enugu North .The extension workers
distributed 292 copies of the questionnaire to literate Agricultural Development Programme
contact farmers and recorded the responses of 68 Agrcultural Development programme
non-literate contact farmers. The non-literate farmers were those farmers that could neither
write nor read the official language (English language). The researcher retrieved the 360
duly completed questionnaires giving a (100%) retrieval.
Method of Data Analysis
The data were analyzed using frequency tables, percentages, means which were used
to answer research questions 1,3,4 and 5, while multiple regression analysis was specifically
used to answer research question 2, t-test statistic was used to test hypotheses 1-4 while
Analysis of Variance (ANOVA) was specifically used to test hypothesis five at probability
of 0.05 level of significance. On a five Rogers scale in adoption process ; awareness-
1.interest-2,evaluation-3 trial-4and adoption-5Any item with a weighted mean value of 3.0
and above was regarded as adopted, while any item with a weighted mean value of less than
3.0 was regarded as not adopted. On the extent to which the factors of extension teaching
methods, inputs ,and environmental element were determinants of adoption of modern
agroforestry technologies, a four point scale was used, any item with a weighted mean value
of 2.50 and above was identified as determinants while, any item with a mean value of less
xliii
than 2.50 was identified as non determinants of adoption of modern agroforestry
technologies. The values attached to the response options of the questionnaires were very
great extent -4,great extent -3 little extent -2 and very little extent- 1,the arithematic mean of
the values were computed to be 2.50. The Statistical Package for the Social Science (SPSS)
15th
version was used for data analysis.
xliv
CHAPTER FOUR
PRESENTATION AND ANALYSIS OF DATA
This chapter presents the analysis of data collected for the study.
The presentation and analysis of data were arranged according to the research
questions and the hypotheses.
Research Question I. What are the stages of adoption of modern agroforestry technologies
among Agricultural Development Programme contact farmers in Enugu State?
The data used to answer research question 1 are presented in Tables 1-8.
Table 1
Distribution of Respondents According to Stages of Adoption of Bees Baiting
Technologies (N = 360)
S/N Item Statement Response Frequencies and Percentages
A I E T Use X
Remarks
1 Decide on the type of wood for beehive
construction
199
(55.5)
76 (21.1) 27 (7.5) 46
(2.8)
12
(3.3)
1.88 Not adopted
2 Decide on the design of the beehive 173
(48.0)
76
(21.1)
45
(12.5)
42
(11.7)
24
(6.7)
2.07 Not adopted
3 Decide on the species of bees to use 189
(52.5)
24
(26.1)
27
(7.5)
31
(6.6)
19
(5.3)
1.88 Not adopted
4 Allow sufficient space between the frames 188
(52.2)
61
(16.9)
55
(12.2)
45
(12.5)
22
(6.1)
2.03 Not adopted
5 Reject frames with too much space 193
(53.6)
73
(20.3)
32
(8.9)
39
(10.8)
23
(6.4)
1.96 Not adopted
6 Identify proper periods to bait 160
(44.4)
64
(17.8)
38
(10.6)
51
(14.2)
47
(3.0)
2.34 Not adopted
7 Identify good sites for baiting 148
(41.1)
81
(22.5)
27
(7.5)
53
(14.7)
50
(3.9)
2.36 Not adopted
8 Identify sources for baiting materials 154
(42.8)
69
(19.2)
31
(8.6)
51
(14.2)
55
(15.3)
2.40 Not adopted
9 Inspect when the bees have colonized 165
(45.8)
60
(16.7)
30
(8.3)
59
(16.4)
46
(12.8)
1.49 Not adopted
10 Provid supplementary feeding 180
(50)
59
(16.4)
20
(5.6)
61
(16.9)
40
(11.1)
2.20 Not adopted
11 Provide shades to the beehives 162
(4.5)
54
(15.0)
26
(7.2)
67
(18.6)
51
(14.2)
2.41 Not adopted
12 Identify groups of bees in the hives 184
(51.1)
60
(16.7)
24
(6.71)
53
(14.7)
39
(10.8)
2.18 Not adopted
13 Practise routine checks on the bees in the
hives
182
(50.6)
56
(15.6)
19
(5.3)
57
(15.8)
4
(12.8)
2.25 Not adopted
A = Awareness I = Interest, E = Evaluation, T = Trial, A = Adoption/use, N = Number of respondents, X
= Mean of the item.
54
xlv
For item 1 Table 1, 199 respondents were at awareness stage, 76 respondents were on
interest stage, 27 respondents were on evaluation stage, 46 respondents were on trial stage
while 12 respondents were at adoption stage. The mean value of 1.88 which was below the
cut off point indicated that majority of the respondents were yet to reach adoption stage on
the item.For item 2, 173 were on awareness, 76 on interest, 45 on evaluation, 42 on trial and
24 on adoption stage. The mean value of 2.07 which is below the cut off point indicated that
majority of the respondents had not reached the adoption stage for the item. For item 3,189
were on awareness, 24 were on interest, 27 were on evaluation, 31 were on trial while 19
were on adoption stage. The mean value of 1.88 which was below the cut off point showed
that majority of the respondents had not adopted the item.For item 4,188 were on
awareness,61 were on interest, 55 were on evaluation,45 were on trial whereas 22 were on
adoption. The mean value of 2.03 which was below the cut off point indicated that majority
of the respondents did not adopt the item.For item 5,193 were on awareness, 73 were on
interest, 32 were on evaluation, 39 were on trial whereas 23 were on adoption.The mean
value of 1.96 which was below the cut off point indicated that majority of the respondents
did not adopt the item.For item 6,160 were on awareness,64 were on interest, 38 were on
evaluation, 51 were on trial whereas 47 were on adoption stage .The mean value of 2.34
which was below the cut off point indicated that majority of the respondents did not adopt the
item.For item 7,148 were on awareness, 81 were on interest, 27 were on evaluation, 53 were
on trial whereas 50 were on adoption.The mean value of 2.36 which was below the cut off
point indicated that majority of the respondents did not adopt the item.
For item 8,154 were on awareness, 69 were on interest, 31 were on evaluation, 51 were on
trial whereas 55 were on adoption. The mean value of 2.40 which was below the cut off
xlvi
point indicated that majority of the respondents did not adopt the item.For item 9,165
were on awareness, 60 were on interest, 30 were on evaluation ,59 were on trial whereas
46 were on adoption. The mean value of 1.49 which was below the cut off point showed that
majority of the respondents did not adopt the item. For item 10,180 were on awareness, 59
were on interest, 20 were on evaluation, 61 were on trial whereas 40 were on adoption. The
mean value of 2.20 which was below the cut off point showed that majority of the
respondents did not adopt the item.
For item 11,162 were on awareness, 54 were on interest, 26 were on evaluation,67
were on trial whereas 51 were on adoption.The mean value of 2.41 which was below the cut
off point indicated that majority of the respondents did not adopt the item. For item
12,184 were on awareness, 60 were on interest, 24 were on evaluation, 53 were on trial
whereas 39 were on adoption. The mean value of 2.18 which was below the cut off point
indicated that majority of the respondents did not adopt the item.For item 13,182 were on
awareness, 56 were on interest, 19 were on evaluation, 57 were on trial whereas 04 were on
adoption stage. The mean value of 2.25 which was below the cut off point indicated that
majority of the respondents did not adopt the item.
xlvii
Table 2
Distribution of Respondents according to Stages of Adoption of Bees Management
Technologies N= (360 ADP Contact Farmers)
S/N Item Statement Response Frequencies and Percentages
A I E T (use) X Remarks
14 Identify the queen 178
(49.4)
77
(21.4)
32
(8.9)
49
(13.6)
24
(6.7)
2.07 Not adopted
15 Identify signs of swarming of bees 155
(43.1)
79
(21.9)
40
(11.1)
56
(15.6)
30
(8.3)
2.24 Not adopted
16 Avoid wearing red coloured dresses 184
(51.1)
62
(17.2)
42
(11.7)
37
(10.3)
35
(9.7)
2.10 Not adopted
17 Avoid wearing yellow dresses 200
(55.6)
42
(11.7)
35
(9.7)
39
(10.8)
44
(12.2)
2.12 Not adopted
18 Avoid perfume soap 184
(51.1)
59
(16.4)
32
(8.9)
38
(10.6)
47
(13.1)
1.88 Not adopted
19 Wear protective clothing/covering 145
(40.3)
44
(12.2)
32
(8.9)
69
(19.2)
70
(19.4)
2.65 Not adopted
20 Avoid perfumes 176
(48.9
52
(14.4)
41
(11.4)
34
(9.4)
57
(15.8)
2.29 Not adopted
21 Identify pest attack on beehives 162
(45)
48
(13.3)
28
(7.8)
55
(15.3)
67
(18.6)
2.49 Not adopted
A = Awareness, I = Interest, E = Evaluation, T = Trial, AD = Adoption/use.
N = Number of respondent, X = Mean of the item,
Values in parentheses ( ) represented the percentages of the frequencies.
NA=Not adopted
For item 14 Table 2, 178 respondents were on awareness 77 were on interest stage, 32
were on evaluation, 49 were on trial stage and 24 were on adoption. The mean value of 2.07
which was below the cut off point indicated that majority of the respondents did not adopt the
item. For item 15, 155 were on awareness, 79 were on interest, 40 were on evaluation, 56
were on trial and 30 were on adoption. The mean value of 2.24 indicated that majority of
the respondents did not adopt/use item 15.
For item 16, 184 were on awareness, 62 were on interest stage, 42 were on
evaluation, 37 were on trial stage and 35 were on adoption stage . The mean value of
xlviii
2.10 indicated that majority of the respondents did not adopt/use item 16. For item 17, 200
were on awareness stage, 42 were on interest stage, 35 were on evaluation, 39 were on trial
stage and 44 were on adoption. This indicated that majority of the respondents did not
adopt/use item 17. For item 18, 184 were on awareness 32 were on evaluation .the mean
value of 1.88 indicated that majority of the respondents did not adopt item 18.
For item 19, 145 respondents were on awareness stage, 44 were on evaluation stage
69, were on trial stage, while 70 were on adoption stage. The mean value of 2.65 showed that
majority of the respondents did not adopt/use item 19. For item 20, 176 respondents were on
awareness stage, 52 were on interest stage, 52 were on interest stage , 41 were on evaluation
34, were on trial stage while 57 were on adoption stage. The mean value of 2.29 showed
that majority of the respondents did not adopt item 20. For item 21, 162 respondents were on
awareness stage, 48 were on interest stage, 28 were on evaluation, 55 were on trial stage and
67 were on adoption. The mean value of 2.49 showed that majority of the respondents did not
use/adopt item 21.
xlix
Table 3
Distribution of Respondents According to Stages of Adoption of Bees Feeding
Technologies
(N= 360 ADP contact farmers) S/N Item Statement Response Frequencies and Percentages
A I E T (use) X Remarks
22 Provide open feeding 195
(54.2)
68
(18.9)
36
(10.0)
44
(12.2)
17
(4.7)
1.94 Not adopted
23 Avoid open feeding 192
(53.3)
66
(18.3)
34
(9.4)
49
(13.6)
19
(5.3)
1.99 Not adopted
24 Bait in area with abundant flowers 175
(48.6)
49
(13.6)
39
(10.8)
44
(12.2)
53
(14.7)
2.31 Not adopted
25 Provide water during dry season 203
(56.4)
51
(14.2)
44
(12.2)
35
(9.7)
27
(7.5)
1.98 Not adopted
26 Decide on the types of comb to use 188
(52.2)
59
(16.4)
33
(9.2)
10
(11.1)
40
(11.1)
2.98 Not adopted
A = Awareness, I = interest, E = Evaluation
T = Trial, A = Adoption
N = Number of Respondents
X = Mean of the items
Values in parentheses ( ) represented the percentages of the frequencies
For item 22 Table 3, 195 respondents were on awareness stage,68 were on
interest,36 were onevaluation,44 were on trial while17 were on adoption stage.The mean
value of 1.94 which was below the cut off point indicated that majority of the
respondents did not adopt the item .For item 23,192 respondents were on awareness
stage ,66 were on interest, 34 were on evaluation ,49 were on trial while 19 were on
l
adoption stage.The mean value of 1.99 which was below the cut off point indicated
that majority of the respondents did not adopt the item.
For item 24, 175 respondents were on awareness stage, 49 were on interest ,39
were on evaluation, 44 were on trial while 53 were on adoption stage .The mean value of
2.31 which was below the cut off point indicated that majority of the respondents did
not adopt the item. For item 25, 203 respondents were on awareness stage, 51 were on
interest, 44 were on evaluation, 35 were on trial while 27 were on adoption stage .The
mean value of 2.31 which was below the cut off point indicated that majority of the
respondents did not adopt the item For item 26, 188 respondents were on awareness
stage, 59 were on interest, 33 were on evaluation ,10 were on trial while 40 were on
adoption stage. The mean value of 2.98 which was below the cut off point indicated
that majority of the respondents did not adopt the item.
li
Table 4
Distribution of Respondents According to Stages of Adoption of Honey
Harvesting Technologies
(N = 360 ADP Contact Farmers)
S/N Item Statement Response Frequencies and Percentages
A I E T (use) X Remarks
27 Put on bees harvesting suit 135
(37.5)
39
(10.8)
34
(9.4)
93
(25.8)
59
(16.4)
2.73 Not adopted
28 Place and assemble harvesting tools 132
(36.7)
66
(18.3)
40
(11.1)
58
(16.1)
64
(17.8)
2.60 Not adopted
29 Light the smoker 129
(35.8)
57
(15.8)
37
(10.3)
55
(15.3)
82
(22.8)
2.73 Not adopted
30 Puff off the smoker into the entrance
to weaken the bees
144
(40)
62
(17.2)
28
(7.8)
54
(15.0)
72
(20.0)
2.58 Not adopted
31 Lift the top bar and send in puffs 182
(50.6)
50
(13.9)
24
(6.7)
56
(15.6)
48
(13.3)
2.27 Not adopted
32 Check for any leakages in the
beehives
165
(45.8)
59
(16.4)
24
(6.7)
47
(13.1)
65
(18.1)
2.41 Not adopted
33 Check for signs of swarming 154
(42.8)
66
(18.3)
29
(8.1)
45
(12.5)
66
(18.3)
2.73 Not adopted
34 Harvest honey timely 141
(39.2)
27
(7.5)
22
(6.1)
71
(19.7)
99
(27.5)
2.90 Not adopted
A = Awareness, I = interest, E = Evaluation
T = Trial, A = Adoption/use
N = Number of Respondents
X = Mean of the items
Values in parentheses ( ) represented the percentages of the frequencies
For item 27 Table 4,135 respondents were at awareness stage, 39 were on interest,34
were on evaluation, 93 were on trial, while 59 were on adoption stage. The mean value of
2.73 which was below the cut off point indicated that majority of the respondents did not
adopt the item. For item 28,132 respondents were at awareness stage, 66 were on interest, 40
were on evaluation, 58 were on trial, while 64 were on adoption stage. The mean value of
lii
2.60 which was below the cut off point which indicated that majority of the respondents did
not adopt the item. For item 29,129 respondents were at awareness stage, 57 were on
interest,37 were on evaluation, 55 were on trial,while 82 were on adoption stage.The mean
value of 2.73 which was below the cut off point which indicated that majority of the
respondents did not adopt the item.For item 30,144 respondents were at awareness stage, 62
were on interest, 28 were on evaluation, 54 were on trial,while 72 were on adoption
stage.The mean value of 2.58 which was below the cut off point indicated that majority of
the respondents did not adopt the item .For item 31,182 respondents were at awareness
stage, 50 were on interest, 24 were evaluation, 56 were on trial,while 48 were on adoption
stage.The mean value of 2.27 which was below the cut off point indicated that majority
of the respondents did not adopt the item .For item 32,165 respondents were at awareness
stage, 59 were on interest, 24 were on evaluation, 47 were on trial,while 65 were on
adoption stage. The mean value of 2.41 which was below the cut off point indicated
that majority of the respondents did not adopt the item. For item 33,154 respondents were
at awareness stage, 66 were on interest, 29 were on evaluation, 45 were on trial,while 66
were on adoption stage. The mean value of 2.73 which was below the cut off point indicated
that majority of the respondents did not adopt the item.For item 34,141 respondents were at
awareness stage, 27 were on interest, 22 were on evaluation, 71 were on trial,while 99 were
on adoption stage.The mean value of 2.90 which was below the cut off point indicated
that majority of the respondents did not adopt the item.
liii
Table 5
Distribution of Respondents According to Stages of Adoption of Cassava/Maize in Alley Technologies ( N = 360)
S/N Item Statement Response Frequencies and Percentages
A I E T A (use) X Remarks
35 Identify improved maize seeds 68
(18.9)
13
(3.6)
19
(5.3)
87
(24.2)
173
(48.1)
3.79 Adopted
36 Identify improved cassava varieties 77
(21.4)
17
(4.6)
24
(6.7)
85
(23.6)
157
(43.6)
3.63 Adopted
37 Identify tree species 106
(29.4)
51
(14.2)
34
(9.4)
81
(22.5)
88
(24.4)
2.98 Not
Adopted
38 Identify suitable soils 91
(25.3)
38
(10.6)
34
(9.4)
88
(24.4)
109
(30.3)
3.24 Adopted
39 Practice tree management technologies 87
(24.2)
35
(9.7)
31
(8.6)
79
(21.9)
128
(35.6)
3.35 Adopted
40 Know when to prune 84
(23.3)
48
(13.3)
25
(6.9)
65
(18.1)
138
(38.3)
3.30 Adopted
41 Identify proper spacing 115
(31.9)
50
(13.9)
28
(7.8)
68
(18.9)
99
(27.5)
2.96 Not
Adopted
42 Identify proper time to apply
organic/inorganic manures
81
(22.5)
40
(11.1)
28
(7.8)
74
(20.6)
137
(38.1)
3.17 Adopted
43 Identify when to weed the farm 79
(21.9)
27
(7.5)
30
(8.3)
60
(16.7)
164
(45.6)
3.56 Adopted
44 Identify how to manage the trees 98
(27.2)
38
(10.6)
30
(8.3)
59
(16.4)
135
(37.5)
3.26 Adopted
45 Identify when there was pest attack 93
(25.8)
29
(8.1)
31
(8.6)
74
(21.9)
128
(35.6)
3.33 Adopted
46 Identify when there was disease
outbreak
95
(26.4)
25
(6.9)
30
(8.3)
69
(19.2)
141
(39.2)
3.38 Adopted
47 Develop weeding time schedules 77
(21.4)
13
(3.6)
26
(7.2)
51
(14.2)
193
(53.6)
3.75 Adopted
A = Awareness, I = interest, E = Evaluation
T = Trial, A= Adoption/use
N = Number of Respondents
X = Mean of the items
Values in parentheses ( ) represented the percentages of the frequencies
For item 35 Table 5, 68 of the respondents were on awareness stage, 13 were on
interest,19 were on evaluation,87 were on trial while 173 were on adoption stage.The mean
value of 3.79 which is above the cut off point indicated that majority of the
liv
respondents adopted the item. For item 36, 77 of the respondents were on awareness
stage, 17 were on interest, 24 were on evaluation, 85 were on trial while 157 were on
adoption stage.The mean value of 3.63 which is above the cut off point indicated that
majority of the respondents adopted the item. For item 37, 106 of the respondents were on
awareness stage, 51 were on interest, 34 were on evaluation, 81 were on trial while 88 were
on adoption stage.The mean value of 2.98 which is below the cut off point indicated that
majority of the respondents did not adopted the item.For item 38, 91 of the respondents were
on awareness stage, 38 were on interest, 34 were on evaluation, 88 were on trial while 109
were on adoption stage.The mean value of 3.24 which is above the cut off point indicated
that majority of the respondents adopted the item. 40,87 of the respondents were on
awareness stage, 35 were on interest, 31 were on evaluation, 79 were on trial while 128 were
on adoption stage. The mean value of 3.35 which is above the cut off point indicated that
majority of the respondents adopted the item
For item 41, 84 of the respondents were on awareness stage, 48 were on interest, 25
were on evaluation, 65 were on trial while 138 were on adoption stage. The mean value of
3.30 which is above the cut off point indicated that majority of the respondents adopted the
item.For item. 42, 115 of the respondents were on awareness stage, 50 were on interest, 28
were on evaluation,68 were on trial while 99 were on adoption stage.The mean value of 2.96
which is below the cut off point indicated that majority of the respondents did not
adopted the item.For item. 43,81 of the respondents were on awareness stage, 40 were on
interest,28 were on evaluation, 74 were on trial while 137 were on adoption stage. The mean
value of 3.17 which is below the cut off point indicated that majority of the respondents
adopted the item.
lv
For item 44, 79 of the respondents were on awareness stage, 27 were on interest, 30
were on evaluation, 60 were on trial while 135 were on adoption stage. The mean value of
3.26 which is below the cut off point indicated that majority of the respondents adopted the
item.For item.45, 98 of the respondents were on awareness stage, 38 were on interest, 30
were on evaluation, 59 were on trial while 135 were on adoption stage. The mean value of
3.26 which is below the cut off point indicated that majority of the respondents adopted the
item.For item. 46, 98 of the respondents were on awareness stage, 38 were on interest, 30
were on evaluation, 59 were on trial while 135 were on adoption stage.The mean value of
3.26 which is below the cut off point indicated that majority of the respondents adopted the
item.For item.E11, 93 of the respondents were on awareness stage, 29 were on interest, 31
were on evaluation, 74 were on trial while 128 were on adoption stage. The mean value of
3.33 which is below the cut off point indicated that majority of the respondents adopted the
item.
For item.47, 95 of the respondents were on awareness stage, 25 were on interest, 30
were on evaluation, 69 were on trial while 141 were on adoption stage. The mean value of
3.38 which is below the cut off point indicated that majority of the respondents adopted the
item.For item.E13, 77 of the respondents were on awareness stage, 13 were on interest,26
were on evaluation, 51 were on trial while 193 were on adoption stage.The mean value of
3.75 which is below the cut off point indicated that majority of the respondents adopted the
item.
lvi
Table 6
Distribution of Respondents According to Stages of Adoption of Establishing
Multipurpose Trees Technologies
(N = 360)
S/N Item Statement Response Frequencies and Percentages
A I E T Use X Remarks
48 Identify suitable sites 116
(32.2)
48
(13.3)
32
(8.9)
90
(25.0)
74
(20.6)
2.88 Not
Adopted
49 Identify improved tree seedlings 117
(32.5)
56
(15.6)
54
(15.0)
75
(20.8)
58
(16.1)
2.73 Not
Adopted
50 Use correct spacing 147
(40.8)
56
(15.6)
40
(11.1)
57
(15.8)
60
(16.7)
2.52 Not
Adopted
51 Carry out routine management of
trees
143
(39.7)
75
(20.8)
33
(9.2)
51
(14.2)
58
(16.1)
2.46 Not
Adopted
52 Apply tree treatment chemicals 174
(48.3)
44
(12.2)
29
(8.1)
59
(16.4)
54
(15.0)
2.40 Not
Adopted
53 Prune trees regularly 119
(33.1)
34
(9.4)
39
(10.8)
51
(14.2)
117
(32.5)
3.0 Adopted
54 Practice propagation of herbaceous
plants
156
(43.3)
64
(17.8)
36
(10.0)
55
(15.3)
49
(13.6)
2.38 Not
Adopted
55 Add organic/inorganic manures 116
(32.2
51
(14.2)
36
(10.0)
49
(13.6)
108
(30.0)
2.95 Not
Adopted
A = Awareness, I = interest, E = Evaluation
T = Trial, AD = Adoption/use
N = Number of Respondents
X = Meanof the items
Values in parentheses ( ) represented the percentages of the frequencies
For item 48 Table 6, 116 of the respondents were on awareness stage ,48
were on interest, 32 were on evaluation, 90 were on trial stage while 74 were on adoption
stage.The mean value of 2.88 which was below the cut off point indicated that majority
of the respondents did not adopt the item. For item 49,117 of the respondents were on
lvii
awareness stage, 56 were on interest, 54 were on evaluation, 75 were on trial stage while 58
were on adoption stage.The mean value of 2.73 which was below the cut off point indicated
that majority of the respondents did not adopt the item. For item 50,147 of the respondents
were on awareness stage, 56 were on interest, 40 were on evaluation, 57 were on trial stage
while 60 were on adoption stage. The mean value of 2.52 which was below the cut off point
indicated that majority of the respondents did not adopt the item.
For item 51,143 of the respondents were on awareness stage, 75 were on interest, 33
were on evaluation, 51 were on trial stage while 58 were on adoption stage. The mean value
of 2.46 which was below the cut off point indicated that majority of the respondents did not
adopt the item.For item 52,174 of the respondents were on awareness stage, 44 were on
interest, 29 were on evaluation, 59 were on trial stage while 54 were on adoption stage. The
mean value of 2.40 which was below the cut off point indicated that majority of the
respondents did not adopt the item.For item 53,119 of the respondents were on awareness
stage, 34 were on interest, 39 were on evaluation, 51 were on trial stage while 54 were on
adoption stage. The mean value of 2.40 which was below the cut off point indicated that
majority of the respondents did not adopt the item.
For item 54, 156 of the respondents were on awareness stage, 64 were on interest,
36 were on evaluation, 55 were on trial stage while 49 were on adoption stage.The mean
value of 2.38 which was below the cut off point indicated that majority of the respondents
did not adopt the item. For item 55,116 of the respondents were on awareness stage, 51 were
on interest, 36 were on evaluation, 49 were on trial stage while 108 were on adoption stage.
The mean value of 2.95 which was below the cut off point indicated that majority of the
respondents did not adopt the item.
lviii
Table 7
Distribution of Respondents According to Stages of Adoption on Establishing
Browse Plants Technologies.
( N = 360)
S/N Item Statement Response Frequencies and Percentages
A I E T (use) X Remarks
56 Identify trees to be used 125
(34.7)
42
(11.7)
26
(7.2)
66
(18.3)
101
(28.1)
2.70 Not
Adopted
57 Identify trees that have deep root
system
142
(39.4)
47
(13.1)
32
(8.9)
61
(16.9)
78
(21.7)
2.68 Not
Adopted
58 Identify trees that undergo rapid
regeneration
143
(39.7)
50
(13.9)
29
(81)
67
(18.6)
71
(19.7)
2.64 Not
Adopted
59 Carry routine browse tree
maintenance
112
(31.1)
42
(11.7)
33
(9.2)
71
(19.7)
102
(28.3)
3.03 Adopted
60 Determine when to use browse trees
to feed livestock.
116
(32.2)
29
(8.1)
30
(8.3)
61
(16.9)
124
(34.4)
3.13 Adopted
61 Identify what parts of the browse
trees to use
95
(26.4)
51
(14.2)
30
(8.3)
69
(19.2)
115
(31.9)
3.16 Adopted
62 Identify how to use the browse
trees
120
(33.3)
31
(86)
23
(6.4)
69
(19.2)
117
(32.5)
3.0 Adopted
63 Identify how to replace browse
trees
118
(32.8)
42
(11.7)
27
(7.5)
70
(19.4)
103
(28.6)
2.99 Not
Adopted
A = Awareness, I = interest, E = Evaluation
T = Trial, A = Adoption/use
N = Number of Respondents
X = Mean of the items
Values in parentheses ( ) represented the percentages of the frequencies
For item 56 in Table 7, 125 of the respondents were at awareness stage, 42 were on
interest, 26 on evaluation, 66 were on trial stage while 101 were on adoption stage. The mean
value of 2.70 which is below the cut off point was an indication that majority of the
respondents did not adopt the item.For item 57,142 of the respondents were at awareness
stage, 47 were on interest, 32 on evaluation,61 were on trial stage while 101 were on
adoption stage. The mean value of 2.70 which is below the cut off point was an indication
that majority of the respondents did not adopt the item. For item 58,143 of the respondents
lix
were at awareness stage, 50 were on interest, 29 on evaluation, 67 were on trial stage while
71 were on adoption stage. The mean value of 2.64 which is below the cut off point was an
indication that majority of the respondents did not adopt the item .For item 59,112 of the
respondents were at awareness stage, 42 were on interest, 33 on evaluation, 71 were on trial
stage while 102 were on adoption stage. The mean value of 3.03 which is above the cut off
point was an indication that majority of the respondents adopted the item .For item
60,116 of the respondents were at awareness stage, 29 were on interest, 30 on evaluation,61
were on trial stage while 124 were on adoption stage. The mean value of 3.13 which is
above the cut off point was an indication that majority of the respondents adopted the
item.
For item 61, 95 of the respondents were at awareness stage, 51 were on interest, 30 on
evaluation, 69 were on trial stage while 115 were on adoption stage. The mean value of 3.16
which is above the cut off point was an indication that majority of the respondents
adopted the item.For item 62,120 of the respondents were at awareness stage, 31 were on
interest, 23 on evaluation,69 were on trial stage while 117 were on adoption stage .The mean
value of 3.0 which is above the cut off point was an indication that majority of the
respondents adopted the item.For item 63,118 of the respondents were at awareness stage,
42 were on interest, 27 on evaluation,70 were on trial stage while 103 were on adoption
stage.The mean value of 2.99 which was below the cut off point was an indication that
majority of the respondents did not adopted the item.
lx
Table 8
Distribution of Respondents According to Stages of Adoption of Planting Vertiver Grasses
Technologies
(N=360)
S/N Item Statement Response Frequencies and Percentages
A I E T (use) X Remarks
64 Identify vertiver grasses 186
(51.7)
55
(15.3)
21
(5.8)
62
(17.2)
35
(10.0)
2.17 Not Adopted
65 Identify where to plant the grasses 184
(51.1)
50
(13.9)
27
(7.5)
60
(16.7)
39
(10.8)
2.22 Not Adopted
66 Identify when to practice the
technology
183
(50.8)
56
(15.6)
33
(9.2)
53
(14.7)
35
(9.7)
2.17 Not Adopted
67 Identify the planting requirements 194
(53.9)
56
(15.6)
27
(7.5)
40
(11.1)
43
(11.9)
2.12 Not Adopted
68 Identify the recommended spacing 197
(54.7)
56
(15.6)
27
(7.5)
46
(12.8)
34
(9.4)
2.07 Not Adopted
69 Practice the techniques of planting
the grasses
174
(48.3)
60
(16.7)
19
(5.3)
54
(15.0)
53
(14.7)
2.31 Not Adopted
70 Apply cultural practices 130
(36.1)
51
(14.2)
23
(6.4)
48
(13.3)
208
(30.0)
2.87 Not Adopted
71 Identify how to apply manures 67
(18.6)
21
(5.8)
37
(10.3)
37
(10.3)
198
(60.3)
2.75 Not Adopted
72 Identify when to trim the grasses 59
(16.4)
15
(4.2)
38
(10.6)
31
(8.6)
517
(60.3)
3.01 Adopted
73 Identify pests on the grasses 62
(17.2)
15
(4.2)
41
(11.4)
25
(6.9)
517
(60.3)
3.89 Adopted
74 Maintain the trees during wet
seasons.
63
(1.5)
16
(4.4)
32
(8.9)
26
(7.2)
223
)7.2
3.92 Adopted
75 Maintain the trees during dry
seasons
54
(150
17
(4.7)
36
10.0)
25
(6.9)
228
(63.3)
3.99 Adopted
A = Awareness, I = interest, E = Evaluation
T = Trial, A = Adoption/use
N = Number of Respondents
X = Mean of the items
Values in parentheses ( ) represented the percentages of the frequencies
For item 64 Table 8, 186 respondents were on awareness stage, 55 were on interest,
21 were on evaluation stage, 62 were on trial stage, while 35 were on adoption stage. The
mean value of 2.17 which was below the cut off point indicated that majority of the
lxi
respondents did not adopt the item.For item 65, 184 respondents were on awareness stage, 50
were on interest, 27 were on evaluation stage, 60 were on trial stage, while 39 were on
adoption stage.The mean value of 2.22 which was below the cut off point indicated that
majority of the respondents did not adopt the item.For item 66,183 respondents were on
awareness stage, 56 were on interest, 33 were on evaluation stage, 53 were on trial stage,
while 35 were on adoption stage. The mean value of 2.17 which was below the cut off point
indicated that majority of the respondents did not adopt the item.For item 67 ,194
respondents were on awareness stage, 56 were on interest, 27 were on evaluation stage, 40
were on trial stage, while 43 were on adoption stage. The mean value of 2.12 which was
below the cut off point indicated that majority of the respondents did not adopt the item.For
item 68,197 respondents were on awareness stage, 56 were on interest, 27 were on
evaluation stage, 46 were on trial stage, while 34 were on adoption stage. The mean value of
2.07 which was below the cut off point indicated that majority of the respondents did not
adopt the item.
For item 69, 174 respondents were on awareness stage, 60 were on interest, 19 were
on evaluation stage, 54 were on trial stage, while 53 were on adoption stage. The mean value
of 2.31 which was below the cut off point indicated that majority of the respondents did not
adopt the item.For item 70 ,130 respondents were on awareness stage, 51 were on interest,
23 were on evaluation stage, 48 were on trial stage, while 208 were on adoption stage. The
mean value of 2.87 which was below the cut off point indicated that majority of the
respondents did not adopt the item.For item 71 , 67 respondents were on awareness stage, 21
were on interest, 37 were on evaluation stage, 37 were on trial stage, while 198 were on
adoption stage. The mean value of 2.75 which was below the cut off point indicated that
lxii
majority of the respondents did not adopt the item.For item 72,59 respondents were on
awareness stage, 15 were on interest, 38 were on evaluation stage, 31 were on trial stage,
while 217 were on adoption stage. The mean value of 3.01 which was above the cut off point
indicated that majority of the respondents adopted the item.For item 73,62 respondents were
on awareness stage, 15 were on interest, 41 were on evaluation stage, 25 were on trial stage,
while 217 were on adoption stage. The mean value of 3.89 which was above the cut off point
indicated that majority of the respondents adopted the item.For item 74,63 respondents were
on awareness stage, 16 were on interest, 32 were on evaluation stage, 26 were on trial stage,
while 223 were on adoption stage. The mean value of 3.92 which was above the cut off point
indicated that majority of the respondents adopted the item.For item 75,54 respondents were
on awareness stage, 17 were on interest, 36 were on evaluation stage, 25 were on trial stage,
while 228 were on adoption stage. The mean value of 3.99 which was above the cut off point
indicated that majority of the respondents adopted the item.
Research Question 2
What were the determinants of socio-economic characteristics of ADP contact
farmers on the adoption of modern agroforestry technologies in Enugu State?
Data used to answer research question 2 are presented in Table 9
lxiii
Table 9
Multiple Regression Result of the influence of Socioeconomic Characteristics of the
Respondents on their Adoption of Modern Agroforestry Technologies in Enugu State
Variables Coefficients Standard T Sig
(constant) 3.396 0.218 15.577 .000***
76 Gender 1.087 0.112 0.98 .003**
77 Years with Enugu State Agric.
78Development Programme (ENADEP)
0.201 0.511 0.105 .017**
79 Education 1.018 0.032 1.057 .000***
80 Experience 0.086 0.012 0.703 .204
81 Farm size -2.012 0.032 -2.380 .003**
82 Extension Contacts 0.186 0.043 0.436 .003**
R2 = 795 Adjusted R
2 = .792
Standard Error = .37373
Durbin Watson = 1.968
F = 13.266
a!=How long the farmers have been with Agricultural Development Project
a2=How long the farmers have been practicing modern agroforestry within the ADP.
*** Significant at 1 %
** Significant at 5%
Linear multiple regression analysis was employed to estimate the influence of the
socioeconomic characteristics of the contact farmers on their adoption of modern
agroforestry technologies in the study area. The linear multiple regression model was fit,
based on the R2 value of 0.795, Adjusted R
2 value of 0.792, levels of significance of the
variables and signs.
lxiv
The result presented in table 9 showed that out of the six explanatory variables
estimated, five variables were statistically significant at 1% and 5% levels of significance.
These variables included: gender, years with Enugu State Agricultural Development
Programme (ENADEP), education, experience and extension contacts.
The gender of the respondents (males =1 female = 0) significantly and positively
influenced the adoption of modern agroforestry technologies by the contact farmers. The
positive relationship indicated the extent to which male farmers adopted the technologies
more than their female counterparts. The coefficient of the number of years of the contact
farmers with Enugu State Agricultural Development Programme (ENADEP) was positive
and significantly influenced adoption of modern agroforestry technologies. The implication
of this finding was that as the number of years with ENADEP increased, the tendency of the
contact farmers to adopt modern agroforestry technologies also increased.
The level of education attained by the respondents was significant and positively
influenced adoption of modern agroforestry technologies by the contact farmers. This
showed that as level of education of the contact farmers increased, the rate of adoption of
modern agroforestry technologies also increased. The years of experience of the contact
farmers in modern agroforestry technologies enterprise was positive and significant. This
conform with aprori expectation which suggested that as years of experience of the contact
farmers in modern agroforestry farming practice increased, their level of awareness and
readiness to adopt modern agroforestry technologies increased. The coefficient of extension
contacts was also positive and significantly influenced adoption of modern agroforestry
technologies by the contact farmers. This indicated that increase in number of extension
visits had an increasing effect on the farmers level of adoption of modern agroforestry
lxv
technologies. On the other hand, the coefficient of farm size was negative but not statistically
significant which indicated that the independent variable “farm size”, was not important in
adoption of modern agroforestry technologies among the contact farmers in Enugu State.
Research Question 3
What are the agricultural extension teaching methods that were determinants of
adoption of modern agroforestry technologies?
Data used to answer research question 3 are presented in Table 10 .
lxvi
Table 10
Mean Ratings of ADP Contact Farmers on the Agricultural Extension Teaching
Methods as Determinants of Adoption of Modern Agroforestry Technologies
(N = 360)
S/N Agricultural Extension Teaching Methods X SD Remarks
83 Farm/home visits 3.55 0.72 Determinant
84 Group discussion 3.35 0.75 Determinant
85 Circle meetings 3.11 0.97 Determinant
86 Field days 3.40 0.88 Determinant
87 Office calls 2.04 0.12 Not determinant
88 Personal letters 2.17 1.11 Not determinant
89 Result demonstration 3.34 0.84 Determinant
90 Exhibition 3.27 0.87 Determinant
91 Conducted tours (excursion) 3.01 0.99 Determinant
92 Method demonstration 3.29 0.93 Determinant
93 Use of radio 2.09 1.16 Not determinant
Note X = Mean
SD = Standard deviation
N = Number of respondents
D = Determinant
ND = Not determinant
Data presented Table 10, showed that the mean ratings of the respondents on the
eleven (11) identified agricultural extension teaching methods in modern agroforestry
technologies range from 2.09 – 3.33. Eight (8) items had mean 3.01 – 3.55 which were above
the cut-off point of 2.50 on a 4-point rating scale, while three (3) items had mean ratings of
2.04 - 2.17. This indicated that eight out of all agricultural extension teaching methods
influenced positively the adoption of modern agroforestry technologies among contact
farmers in Enugu State. The standard deviation values ranged from 0.72-0.99 for the items
lxvii
that were influential showing that the respondents were not far from one another in their
responses.
Research Question 4
What are the agroforestry inputs that determined the adoption of modern agroforestry
technologies?
Data used to answer research question 4 are presented in Table 11
Table 11
Mean Ratings of ADP Contact Farmers on the Agroforestry Inputs as Determinants
of Adoption of Modern Agroforestry Technologies
(N = 360)
S/N Input Factors X SD Remarks
94 Improved tree seedlings 3.58 0.67 Determinant
95 Improved cassava cuttings 3.57 0.62 Determinant
96 Modern beehives 3.28 0.83 Determinant
97 Baiting materials 3.18 0.85 Determinant
98 Honey harvesting materials 3.26 0.83 Determinant
99 Honey processing materials 2.97 0.98 Determinant
100 Vertiver grasses 2.92 1.11 Determinant
101 Seed dressing chemicals 3.01 1.08 Determinant
102 Organic and inorganic manures 3.31 0.95 Determinant
103 Liming materials 2.81 1.14 Determinant
104 Herbicides 3.01 1.07 Determinant
105 Lands 2.56 1.27 Determinant
106 Insecticides 3.09 1.05 Determinant
107 Vine and boronated superphosphate 2.74 1.16 Determinant
Data presented Table 11 showed that the mean ratings of the respondents on the
fourteen (14) identified input factors items in modern agroforestry technologies received
mean ratings from 2.56 -3.58, which were all above the cut-off point of 2.50 on a 4-point
lxviii
rating scale. This indicated that all the identified fourteen input factors items in modern
agroforestry technologies adoption were determinants in the adoption of modern agroforestry
technologies. The standard deviation values ranged from 0.62-1.27 which showed that the
respondents were not far from one another in their responses and that they were not too far
from the mean.
Research Question 5
What are the environmental factors that were determinants of the adoption of modern
agroforestry technologies?
Data used to answer research question 5 are presented in Table 12
Table 12
Mean Ratings of the Responses of ADP Contact Farmers on the Environmental
Factors as Determinants of Adoption of Modern Agroforestry Technologies
N = 360
S/N Environmental X SD Remarks
108 Rainfall 3.69 0.060 Determinant
109 Sunlight 2.16 0.934 Determinant
110 Temperature 2.01 1.047 Determinant
111 Fertility status of the soil 3.52 0.779 Determinant
112 Topography 2.74 1.075 Determinant
113 Soil erosion 3.02 1.043 Determinant
114 Drought 2.95 1.207 Determinant
115 Pests 3.47 0.806 Determinant
116 Diseases 3.42 0.823 Determinant
117 Cloud cover 1.85 1.052 Not a determinant
Data presented Table 12, above showed that the mean ratings of the respondents
on the ten (10) identified environmental factors items in adoption of modern agroforestry
lxix
technologies received mean ratings from 1.85-3.69. Nine (9) of the items had mean ratings
from 2.74 -3.69 which were above the cut-off point of 2.50 on a 4-point rating scale. This
indicated that the nine (9) identified environmental factors determined the adoption of
modern agroforestry technologies. The standard deviation values ranged from 0.61-1.08
which showed that the respondents were not far from one another in their responses and they
were not too far from the mean.
Results of Hypotheses of the Study
The result of the hypotheses for the study are presented below:
Hypothesis 1
H01 There was no significant difference in the mean ratings of male and female ADP
contact farmers on stages of adoption of modern agroforestry technologies.
Data used to answer hypothesis 1 are presented in Tables 13-20.
lxx
Table 13
t-test Analysis of the Mean Ratings on the Stages of Adoption of Bees Baiting Skills
by Male and Female ADP Contact Farmers
N1=225; N2=135.
Data used to answer hypothesis 1 (A) are presented in Table 13
S/N Item Statement X1
S12
X2
S22
t-cal t-tab RMK
118 Decide on type of wood 4.14 1.414 4.09 1.461 0.317 1.96 NS
119 Decide on the design the beehive 3.92 1.685 393 1.660 -0.126 1.96 NS
120 Decide on the species of bees 4.18 1.308 4.02 1.574 1.191 1.96 NS
121 Allow sufficient space 3.95 1.658 4.00 1.776 -374 1.96 NS
122 Reject farmers with too much space 4.09 1.555 396 1.774 0.949 1.96 NS
123 Identify proper periods to bait 3.65 2.327 3.96 1.962 -0.251 1.96 NS
124 Identify good sites for baiting 3.59 2.431 3.71 1.879 -0.778 1.96 NS
125 Identify sources of baiting materials 3.55 2.490 3.68 1.995 -0.80 1.96 NS
126 Inspect when the bees have colonized 3.74 2.219 3.53 2.266 1.282 1.96 NS
127 Provide supplementary feeds 3.85 2.165 3.64 2.216 1.269 1.96 NS
128 Provide shade to the beehives 3.50 2.617 3.71 1.953 -0.127 1.96 NS
129 Identify the groups of bees in the hives 3.78 2.189 3.89 2.004 0.724 1.96 NS
130 Practice routinue checks on the bees in the
hives
3.78 2.290 3.80 2.296 0.458 1.96 NS
Key: X X1 = Mean of male ADP contact farmers
X2 = Mean of female ADP contact farmers
S12 = Variance of male farmers
S22 = Variance of female farmers
NS = Not significant
RMK=Remarks
Data presented Table 13, showed that all the thirteen (13) baiting technologies items
in modern agroforestry technologies adoption had their calculated t-values ranged from -
0.374-1.191, which were all less than t-table value of 1.96 (two- tailed test) at p< 0.05 level
lxxi
of significant and at 358 degree of freedom (df). This showed that there were no significant
differences in the mean ratings of the responses of the two groups of respondents (male and
female ADP contact farmers) on the thirteen baiting skills needed for the adoption of modern
agroforestry technologies (Beekeeping) by ADP contact farmers in Enugu State. Therefore,
the null hypothesis of no significant difference on the mean ratings on the stages of adoption
of beekeeping technologiess by the respondents on these items was upheld.
Table 14
t-test Analysis of the Mean Ratings of Male and Female Respondents on Gender
Determinant on Bees Management Skills
N1= N225:N2=135
Data used to answer hypothesis 1 (B) are presented in Table 14
N1 N2
S/N Item statement X 1 S1
2 X 2
S22
t-cal t-tab
131 Identify the queen in the beehive 4.01 1.69 3.80 1.77 1.49 1.96 NS
132 Identify signs of swarming of bees 3.71 1.99 3.84 1.64 0.93 1.96 NS
133 Avoid wearing red coloured dresses
while visiting beehives
3.89 1.99 3.89 1.79 0.10 1.96 NS
134 Avoid wearing yellow dresses 3.89 2.16 3.84 2.21 0.38 1.96 NS
135 Avoid using perfumed soaps 3.86 2.15 3.76 2.22 0.64 1.96 NS
136 Wear protective clothing 3.36 2.59 3.33 2.59 0.13 1.96 NS
137 Avoid spraying perfumes before
going to the beehive
3.75 2.29 3.65 2.39 0.57 1.96 NS
138 Identify pest attack on beehives 3.51 2.54 3.50 2.67 0.42 1.96 NS
Key: X X1 = Mean of male ADP contact farmers X2 = Mean of female ADP contact farmers
S12 = Variance of male farmers
S22 = Variance of female farmers
NS = Not significant
RMK=Remarks
lxxii
Data presented Table 14, revealed that all the eight (8) bees management technologies
on bees management technologies aspect of modern agroforestry technologies has their
calculated t-values ranged from 0.10-1.49 which were all less than t-table value of 1.96 (two
tailed test) at p< 0.05 level of significance and at 358 degree of freedom (df). This indicated
that there were no significant differences in the mean ratings of the two groups of
respondents (male and female ADP contact farmers) on gender determinants on adoption of
the 8 bees management skills. Therefore, the null hypothesis of no significant difference was
accepted for the items.
lxxiii
Table 15
t-test Analysis of the Mean Ratings of Male and Female Respondents on Gender
Determinants on Adoption of Bees Feeding Technologies
N1=225; N2=135
Data used to answer hypothesis 1(C) are presented in Table 15
Item statement X1 S12
X2 S22
t-cal t-tab RMK
139 Provide open feeding 4.14 1.360 3.92 1.86 1.62 1.96 NS
140 Avoid open feeding 3.99 1.692 4.03 1.59 0.24 1.96 NS
141 Bait in area with abundant flowers 3.63 2.475 3.79 2.06 0.97 1.96 NS
142 Provide water during dry season 4.05 1.671 3.96 1.90 0.65 1.96 NS
143 Decide on the types of combs to use 3.88 1.954 3.867 2.22 0.85 1.96 NS
Key: X X1 = Mean of male ADP contact farmers
X2 = Mean of female ADP contact farmers
S12 = Variance of male farmers
S22 = Variance of female farmers
NS = Not significant
RMK= Remarks
Data presented Table 16, revealed that all the five (5) bee feeding technologies in
modern beekeeping technologies had their calculated t-values ranged from 0.24 -1.62 which
were all less than the t-table (critical) value of 1.96 (two tailed test) at p< 0.05 level of
significance and at 358 degree of freedom (df). This showed that there were no significant
differences in the mean ratings of the two groups of respondents (male and female ADP
contact farmers) on the five (5) bees feeding skills needed for the adoption of beekeeping
(modern agroforestry technology) by ADP contact farmers in Enugu State.
Therefore the null hypothesis of no significant difference in the mean ratings of the
two groups of respondents on these items was upheld.
lxxiv
Table 16
t-test Analysis of the Mean Ratings of the Male and Female Respondents on Gender
Determinant on Adoption of Honey Harvesting Skills
N1=225; N2=135
Data used to answer hypothesis 1(D) are presented in Table 16
Item statement X1 S12
X2 S22
t-cal t-tab RMK
144 Put on bees harvesting suit 3.35 2.48 3.15 2.411 1.16 1.96 NS
145 Place and assemble harvesting tools 3.38 2.42 3.44 2.29 -0.35 1.96 NS
146 Light smoker 3.19 2.58 3.39 2.59 -1.15 1.96 NS
147 Puff off the smoke 3.37 2.49 3.51 2.66 -0.818 1.96 NS
148 Lift the top bar and send in puffs 3.81 2.22 3.59 2.49 1.30 1.96 NS
149 Check for any leakages in the beehives 3.62 2.42 3.54 2.69 0.44 1.96 NS
150 Check for signs of swarming 3.58 2.34 3.49 2.67 0.48 1.96 NS
151 Harvest honey timely – January or
June, July or December
3.12 3.05 3.09 2.76 0.13 1.96 NS
Key: X 1 = Mean of male ADP contact farmers
X 2 = Mean of female ADP contact farmers
S12 = Variance of male farmers
S22 = Variance of female farmers
NS = Not significant
RMK =Remarks
Data presented Table 16, revealed that all the eight (8) honey harvesting technologies in
modern beekeeping had their calculated t-values ranged from -1.15-01.30 which were all less
than the t-tab (critical) value of 1.96 (two tailed test) at p< 0.05 level of significant and at
358 degree of freedom (df). This showed that there were no significant differences in the
mean ratings of the responses of the two groups of the respondents (male and female ADP
contact farmers) on the eight (8) honey harvesting skills needed for adopting of beekeeping
lxxv
(modern agroforestry technology) by ADP contact farmers in Enugu State. Therefore the null
hypothesis of no significant difference in the mean ratings of the two gender groups of
respondents on these items was accepted for each of the 8 items.
lxxvi
Table 17
t-test Analysis of the Mean Ratings of Male and Female Respondents Social
Economic Characteristics on Adoption of Cassava/Maize in Alley Technologies
N1=225; N2=135
Data used to answer hypothesis 1 (E) are presentd in Table 17
Item Statement X 1 S1
2 X 2
S22
t-cal t-tab Rema
rks
152 Identify improved maize seeds 2.27 2.47 2.12 2.12 0.889 1.96 NS
153 Identify improved cassava varieties 2.47 2.65 2.20 2.19 1.56 1.96 NS
154 Identify tree species 3.14 2.56 2.81 2.43 1.94 1.96 NS
155 Identify suitable soils 2.85 2.66 2.61 2.27 1.42 1.96 NS
156 Practice tree management
technologies
2.66 2.64 2.64 2.53 0.118 1.96 NS
157 Determine when to prune 2.41 2.63 2.41 2.66 2.15 1.96 NS
158 Identify proper spacing 2.99 2.74 3.11 2.68 -0.64 1.96 NS
159 Determine proper time to apply
organic and inorganic manures
2.56 2.57 2.64 2.62 -0.46 1.96 NS
160 Find out how to weed (manure) 2.58 2.76 2.20 2.34 2.15 1.96 NS
161 Determine how to manage the trees 2.88 2.85 2.49 2.64 2.12 1.96 NS
162 Identify when there is pests attack 2.78 2.81 2.48 2.36 1.68 1.96 NS
163 Identify when there is diseases
outbreak
2.69 2.89 2.49 2.51 1.12 1.96 NS
164 Develop weeding time schedule 2.32 2.73 2.14 2.45 0.992 1.96 NS
Key: X X1 = Mean of male ADP contact farmers
X2 = Mean of female ADP contact farmers
S12 = Variance of male farmers
S22 = Variance of female farmers
NS = Not significant
RMK=Remarks
lxxvii
Data presented Table 17, revealed that 10 out of 13 cassava/maize in alley
technologies items for adoption had their calculated t-values ranged from -0.46 -1.94 which
were less than t-table value of 1.96 (two tailed test) at p < 0.05 level of significance and at
358 degree of freedom (df). This indicated that there were no significant differences in the
mean ratings of the two groups of respondents (male and female ADP contact farmers) on the
(10) cassava/maize in alley technologies needed for their adoption in Enugu State. Therefore,
the null hypothesis of no significant difference in the mean ratings of the responses of the
two groups of respondents on the 10 items was accepted for each of the 10 items.
Table 18
lxxviii
t –test Analysis of the Mean Ratings of Male and Female ADP Contact Farmers on
the Stages of Adoption of Multipurpose Tree Establishment Technologies
N1=225; N2=135
Data used to answer hypothesis 1 (F) are presented in Table 18
S/N Item statement X1 S12
X2 S22
t-cal t-tab RMK
164 Identify suitable sites for tree planting 3.19 2.55 3.00 2.37 1.09 1.96 NS
165 Identify improved tree seedlings 3.29 2.25 3.24 2.23 0.300 1.96 NS
166 Use correct spacing 3.42 2.46 3.58 2.28 0.924 1.96 NS
167 Carry out routine management of the
trees
3.42 2.47 3.74 1.97 0-1.96 1.96 NS
168 Apply tree treatment chemicals 3.57 2.51 3.71 2.33 0-.81 1.96 NS
169 Prune the trees regularly 2.98 2.94 3.93 2.76 0.27 1.96 NS
170 Practice propagation of herbaceous
plants
3.57 2.37 3.70 2.00 -0.83 1.96 NS
171 Add required organic/inorganic
manures
3.08 2.78 3.00 2.78 0.83 1.96 NS
Key: X1 = Mean of male ADP contact farmers
X2 = Mean of female ADP contact farmers
S12 = Variance of male farmers
S22 = Variance of female farmers
NS = Not significant
RMK=Remarks
Data presented Table 18, revealed that all the eight (8) multipurpose trees
technologies had their calculated t-values ranged from -1.96-1.09 which were all less than the
t-tabulated (critical) value of 1.96 (two tailed test) at p< 0.05 level of significance and at 358
degree of freedom (df). This indicated that there were no significant difference in the mean
ratings of the responses of the two groups of respondents (male and female ADP contact
farmers) on the eight (8) multipurpose trees technologies needed for adoption of modern
lxxix
agroforestry technologies by ADP contact farmers in Enugu State. Therefore the null
hypothesis of no significant difference in the mean ratings of the two groups of respondents
on the level of adoption of trees establishment skills was accepted for each of the eight (8)
items.
lxxx
Table 19
t-test Analysis of the Mean Ratings of Male and Female ADP Contact Farmers on
Adoption of Browse Tree Establishment Technologies
N1=225; N2=135
Data used to answer hypothesis 1(G) are presented in Table 19
S/N Item Statement X1 S12
X2 S22
t-cal t-tab RMK
172 Identify trees to be used 3.15 2.85 2.93 2.74 1.23 1.96 NS
173 Identify trees that have deep roots 3.28 2.69 3.38 2.59 -0/55 1.96 NS
174 Identify trees that usually regenerate
fast after pruning
3.32 2.67 3.40 2.45 -0.43 1.96 NS
175 Carry out routine browse tree
maintenance
3.05 2.76 2.85 2.59 1.10 1.96 NS
176 Determine when to use browse trees 2.96 2.88 2.69 2.92 1.47 1.96 NS
177 Identify what part of the browse
trees to use
2.98 2.54 2.61 2.76 2.10 1.96 NS
178 Identify how to use browse trees 3.00 2.95 2.76 2.83 1.34 1.96 NS
179 Determine how to replace browse
trees
3.12 2.78 2.91 2.73 1.69 1.96 NS
Key: X1 = Mean of male ADP contact farmers
X2 = Mean of female ADP contact farmers
S12 = Variance of male farmers
S22 = Variance of female farmers
NS = Not significant
RMK=Remarks
Data presented Table 19, above revealed that, 7 out of 8 browse tree establishment
technologies items had their calculated t-values ranged from -0.43-1.69 which were less than
t-table value of 1.96 (two tailed test) at p< 0.05 level of significance and at 358 degree of
freedom (df). This indicated that there were no significant differences in the mean ratings of
lxxxi
the responses of the two groups of respondents (male and female ADP contact farmers) on
the seven (7) browse tree establishment technologies items of modern agroforestry
technologies adoption among ADP contact farmers in Enugu State. Therefore, the null
hypothesis of no significant difference in the mean ratings of the responses of the two groups
of respondents on the seven items was upheld.
The data further showed that item 6 had t-calculated value of 2.10, which was greater
than t-table value of 1.96 (two failed test) at p < 0.05 level of significance and at 358 degree
of freedom (df). This indicated that there was significant difference in the mean ratings of the
responses of the two groups of respondents on the item. Based on this result null hypothesis
of no significant difference in the mean ratings of the responses of the two groups on item 7
was rejected.
The data also showed further that items 6, 9, and 10 had t-calculated values of 1.15,
2.15 and 2.12 which were greater than t-table value of 1.96 (two tailed test) at P< 0.05 level
of significance and 358 degree of freedom (df). This indicated that there was significant
difference in the mean ratings of the responses of the two groups of respondents on the items.
Based on this result, the null hypothesis of no significant difference in the mean ratings of the
responses of the two groups of respondents on items 6, 9 and 10 were rejected.
lxxxii
Table 20
t-test Analysis of the Mean Ratings of Male and Female ADP Contact Farmers on
Stages of Adoption of Planting Vertiver Grasses for Erosion/wind Control Technologies
N1=225; N2=135
Data used to answer hypothesis 1(h) are presented in Table 20
S/N Item statement X1 S12
X2 S22
t-cal t-tab RMK
180 Ability to identify vertivar grasses 3.84 2.12 3.78 2.17 0.36 1.96 NS
181 Ability to identify where to plant the
grasses
3.78 2.22 3.78 2.14 0.00 1.96 NS
182 Ability to identify when to practice the
technology
3.80 2.06 3.87 2.02 -.45 1.96 NS
183 Ability to identify the planting
materials
3.84 2.23 3.96 1.96 -.802 1.96 NS
184 Ability to identify the recommended
spacing
3.92 2.03 3.69 1.92 -.31 1.96 NS
185 Ability to practice the techniques of
planting the grasses
3.69 2.41 3.69 2.32 0.00 1.96 NS
186 Ability to apply cultural practices-
weeding, fertilizer application etc
3.12 2.92 3.15 2.92 -.15 1.96 NS
187 Ability to identify how to apply the
manures.
2.35 2.69 2.02 2.17 1.91 1.96 NS
188 Ability to identify when to trim the
grasses.
2.16 2.51 1.94 2.07 1.31 1.96 NS
189 Ability to identify pests on the grasses 2.19 2.58 1.94 2.16 1.26 1.96 NS
190 Identify diseases on the grasses 2.14 2.59 1.98 2.25 0.92 1.96 NS
191 Ability to maintain the trees during dry
seasons.
2.07 2.38 1.91 2.11 0.97 1.96 NS
Key: X1 = Mean of male ADP contact farmers
X2 = Mean of female ADP contact farmers
S12 = Variance of male farmers
S22 = Variance of female farmers
NS = Not significant
RMK=Remarks
lxxxiii
Data presented Table 20, revealed that all the twelve (12) vertiver grasses planting
technologies had their calculated t-values ranged from -.15-1.91 which were all less than t-
table value of 1.96 (two tailed test) at p < 0.05 level of significance and at 358 degree of
freedom (df). This indicated that there were no significant differences in the mean ratings of
the responses of the two groups of respondents (male and female ADP contact farmer) on the
adopted twelve vertivar grasses planting technologies on aspect of modern agroforestry
technologies by ADP contact farmers in Enugu State. Therefore, the null hypothesis of no
significant difference was accepted for all the items.
lxxxiv
Hypothesis 2
Ho2: There was no significant difference in the mean ratings of male and female ADP
contact farmers on the determinants of socio-economic characteristics on adoption of
modern agroforestry technologies.
Table 21
t-test Analysis of the Mean Ratings of Male and Female Respondents on the
Determinants of Socio-Economic Characteristics on Bees Management Skills
N1=225; N2=135
Data used to answer hypothesis 2 are presented in Table 21.
S/N Item statement X1 S12
X2 S22
t-cal t-tab RMK
192 Identify the queen 4.01 1.692 3.80 1.773 1.49 1.96 NS
193 Identify signs of swarming 3.71 1.994 3.84 1.640 0.928 1.96 NS
194 Avoid wearing red dresses 3.89 1.985 3.89 1.795 0.10 1.96 NS
195 Avoid yellow dresses 3.89 2.164 3.84 2.212 0.38 1.96 NS
196 Avoid using perfumed soaps 3.85 2.149 3.75 2.216 0.637 1.96 NS
197 Wear protective clothing 3.35 2.596 3.33 2.597 0.127 1.96 NS
198 Avoid perfumes 3.75 2.297 3.65 2.393 0.570 1.96 NS
199 Identify pest attack on beehives 3.51 2.537 3.50 2.670 0.42 1.96 NS
Key: X X1 = Mean of male ADP contact farmers
X2 = Mean of female ADP contact farmers
S12 = Variance of male farmers
S22 = Variance of female farmers
NS = Not significant
RMK=Remarks
Data presented Table 21, revealed that all the eight (8) bee management skills in
modern bee keeping technologies had their calculated t-values ranged from 0.10-1.49, which
lxxxv
were all less than the t-table value of 1.96 (two tailed test) at p < 0.05 level of significance
and at 358 degree of freedom (df). This showed that there were no significant difference in
the mean ratings of the responses of the two groups of respondents (male and female ADP
contact farmers) on the eight (8) bee management skills needed for the adoption of
beekeeping (agroforestry technology) by ADP contact farmers in Enugu State.
Therefore the null hypothesis of no significant difference in the mean ratings of the
two groups of respondents on these items was upheld.
Hypothesis 3
HO3: There was no significant difference in the mean ratings of literate and non literate
respondents on agricultural extension teaching methods as determinants on adoption
of modern agroforestry technologies.
Data used to answer hypothesis 3 are presented in Table 22.
lxxxvi
Table 22
t-test Analysis of litereate and non-literate ADP Contact Farmers on Agricultural
Extension Teaching Methods as Determinants of Adoption of Modern Agroforestry
Technologies N1=195; N2=165
S/N Item statement X1 S12
X2 S22
t-cal t-tab RMK
200 Farm/home visit 3.51 0.55 3.56 0.51 0.47 1.96 NS
201 Group discussion 3.19 0.64 3.39 0.54 1.96 1.96 NS
202 Circle meetings 2.66 1.24 3.22 0.79 4.67 1.96 NS
203 Field days 3.53 0.68 3.37 0.79 -1.38 1.96 NS
204 Office calls 1.86 1.09 2.09 1.29 1.66 1.96 NS
205 Personal letters 2.01 1.05 2.21 1.29 1.35 1.96 NS
206 Result demonstration 3.51 0.76 3.36 0.70 -1.44 1.96 NS
207 Method demonstration 3.55 0.46 3.20 0.83 -3.101 1.96 NS
208 Conducted tours (excursion) 3.14 1.17 3.03 0.96 -0.85 1.96 NS
209 Exhibition 3.35 0.79 3.28 0.89 -0.64 1.96 NS
210 Use of radio 1.82 1.06 2.16 0.42 -.66 1.96 NS
Key: X1 = Mean of male ADP contact farmers
X2 = Mean of female ADP contact farmers
S12 = Variance of male farmers
S22 = Variance of female farmers
NS = Not significant
RMK=Remarks
Data presented Table 22, revealed that 9 out of 11 agricultural extension teaching
items had their calculated t-value ranged from -3.10-1.66 which were less than t-table value
of 1.96 (two tailed test) at p < 0.05 level of significance and at 358 degree of freedom (df).
This indicated that there were no significant differences in the mean ratings of the responses
lxxxvii
of the two groups of respondents (non literate and literate ADP contact farmers) on the nine
agricultural extension teaching methods determinants on adoption of modern agroforestry
technologies among ADP contact farmer in Enugu State. Therefore, the null hypothesis of no
significant difference in the meant ratings of the responses of the two groups of respondents
on the 9 items was accepted.
The data also showed that items 2 and 3 had t-calculated values of 1.96 and 4.67
which were greater than t-table value of 1.96 (two tailed test) of p < 0.05 level of significance
and at 358 degree of freedom (df). This indicated that there was significant difference in the
mean ratings of the responses of the two groups of respondents on the items. Based on this
result the null hypothesis of no significant difference in the mean ratings of the responses of
the two groups of respondents on the items 2 and 3 was rejected.
lxxxviii
Hypothesis 4
Ho4: There was no significant difference in the mean ratings of male and female ADP
contact farmers on agroforestry inputs as determinants of adoption of modern
agroforestry technologies.
Data used to answer hypothesis 4 are presented in Table 23
Table 23
t-test Analysis of Male and Female ADP Contact Farmers on Agroforestry Inputs as
determinants of Adoption of Modern Agroforestry Technologies.
N1=225; N2=135
S/N Item statement X1 S12
X2 S22
t-cal t-tab RMK
212 Improved tree seedlings 3.58 0.53 3.58 0.35 0.60 1.96 NS
213 Improved cassava cuttings 3.59 0.40 3.53 0.37 0.92 1.96 NS
214 Modern bee hives 3.33 0.69 3.19 0.67 1.64 1.96 NS
215 Baiting materials 3.10 0.83 3.32 0.50 -2.36 1.96 NS
216 Honey harvesting materials 3.28 0.76 3.25 0.59 0.31 1.96 NS
217 Honey processing materials 2.87 0.98 3.13 0.90 -2.40 1.96 NS
218 Vertiver grasses 3.09 1.08 2.64 1.38 3.74 1.96 NS
219 Seed dressing chemicals 3.08 1.17 2.89 1.14 1.59 1.96 NS
220 Organic and inorganic manures 3.39 0.81 3.17 1.04 2.14 1.96 NS
221 Herbicides for weed control 2.94 1.16 2.58 1.49 2.85 1.96 NS
222 Weed control 3.11 1.02 2.85 1.34 2.19 1.96 NS
223 Lands 2.60 1.64 2.51 1.59 0.64 1.96 NS
224 Insecticides 3.24 0.96 2.84 1.27 3.54 1.96 NS
225 Vines and boronated superphosphate 2.79 1.30 2.65 1.47 1.00 1.96 NS
Key: X 1 = Mean of male ADP contact farmers X 2 = Mean of female ADP contact farmers
S12 = Variance of male farmers
S22 = Variance of female farmers
NS = Not significant
RMK =Remarks
lxxxix
Data presented Table 23, showed that 9 out of 14 input factors items as determinants
of adoption of modern agroforestry technologies had their calculated t-values ranged from -
2.36 to 1.64 which were less than t-table value of 1.96 (two tailed test) at probability level
(p<) 0.05 level of significance and at 358 degree of freedom (df). This indicated that there
were no significant differences in the mean ratings of the responses of the two groups of
respondents (male and female ADP contact farmers, on the 9 (nine) input factors as
determinants of adoption of modern agroforestry technologies among ADP contact farmers
in Enugu State. Therefore, the null hypothesis of no significant difference in the mean ratings
of the responses of the two groups of respondents on the 9 items was accepted.
The data also showed that, items 7, 9, 10, 11 and 13 had t-calculated values of 3.74,
2.14, 2.85, 2.19 and 3.54, which were greater than t-table (critical) value of 1.96 (two- tailed
tests) at p < 0.05 level of significance and at 358 degree of freedom (df). This indicated that
there was significant difference in the mean ratings of the responses of the two groups of
respondents on the items. Based on this result, the null hypothesis of no significant difference
in the mean ratings of the responses of the two groups of respondents on items 7, 9, 10, 11
and 13 was rejected.
xc
Hypothesis 5
HO5: There was no significant difference in the mean of ADP contact farmers in the three
agricultural zones on environmental factors as determinants of adoption of modern
agroforestry technologies. Data presented were used to answer hypothesis 5
Table 24
Analysis of Variance (ANOVA) of the Mean Ratings of the Responses of Contact farmers
from Awgu, Enugu and Nsukka Agricultural Zones of Enugu State on Environmental
Factors as Determinants of the Adoption of Modern Agroforestry Technologies
S/N Environmental elements
Sum of square Df Mean square
F-cal F-tab Rmk
226 Rainfall Between group 3.30 Within groups 128.69 Total 131.99
2 357 359
1.651 0.360
4.581 3.00 S
227 Sunlight Between groups 2.64 Within groups 310.65 Total 313.29
2 357 359
0.319 0.870
1.516 3.00 S
228 Temperature Between groups 20.99 Within groups 372.99 Total 393.98
2 357 359
10.498 1.045
10.048 3.00 S
229 Fertility status of the soil
Between groups 3.63 Within groups 214.19 Total 217.82
2 357 359
1.817 0.600
3.028 3.00 S
230 Topography of the soil
Between groups 31.49 Within groups 383.49 Total 414.98
2 357 359
15.743 1.074
14.656 3.00 S
231 Soil erosion Between groups 10.37 Within groups 38.47 Total 390.84
2 357 359
5.186 1.066
2.87 3.00 S
232 Drought Between groups 65.87 Within groups 457.23 Total 523.10
2 357 359
32.937 1.281
25.717 3.00 S
233 Pests Between groups 5.66 Within groups 228.00 Total 233.66
2 357 359
2.830 0.639
4.431 3.00 S
234 Diseases Between groups 5.65 Within groups 237.84
2 357 359
2.829 0.666
4.246 3.00 S
235 Cloud cover Between groups 14.59 Within groups 282.60 Total 397.19
2 357 359
7.298 1.072
6.810 3.00 S
Significant at p < 0.05
Key: SS = Sum of square, Df = Degree of freedom, Ms = Mean square, F-cal = F-calculated
F-tab (critical) = F-tabulated, Rmk = Remarks, S = significance
xci
Table 24 showed that the f-cal value of 1.516 for item 2 were less than f-tab of
3.00 at 0.05 level of significance and at 2 and 357 degree of freedom (df). This indicated that
there was no significant difference in the mean ratings of the responses of the contact
farmers from Awgu, Enugu and Nsukka Agricultural Zones on the environmental elements
as determinants of adoption of modern agroforestry technologies by ADP contact
farmers in Enugu State. Therefore, the null hypothesis of no significant difference was
accepted for item 227 in the Table.
The F-cal values of the remaining 9 (nine) items in the Table ranged from 3.028 to
25.717 which were all greater than the f-tab of 3.00 at 0.05 level of significant and at 2 and
357 degrees of freedom (df). This indicated that there were significant differences in the
mean ratings of the responses of the contact farmers from Awgu, Enugu and Nsukka
Agricultural Zones on these environmental determinants -rainfall, temperature, soil fertility,
topography, soil erosion, drought, pests, diseases and cloud cover.Therefore, the null
hypothesis of no significant difference was rejected for the 9 (nine) items in the Table. The
post-hoc (multiple comparison) analysis of the 9 (nine) items revealed that there was
significance difference in the mean ratings among the three groups of respondents: that was
contact farmers from Awgu, Enugu and Nsukka agricultural zones of Enugu State.
Findings of the Study
The findings of the study are presented according to the sub-headings below;
Bees Baiting Technologies
The following bees baiting technologies skills were not adopted by Agricultural
Development Project contact farmers;
xcii
(1) Type of wood for beehive construction
(2) Design of the beehive
(3) Species of bees to be used
(4) Sufficient space between the frames in the beehives
(5) Rejection of frames with too much space
(6) Identify good sites for baiting
(7) Identify proper periods to bait
(8) Identify sources of baiting materials
(9) Inspect when the bees have colonized.
(10) Provide supplementary feeding
(11) Provide shades to the beehives
(12) Identify groups of bees in the hives and
(13) Practice routinue checks on the bees in the hives.
Majority of the contact farmers of Agricultural Development Programme were at
awareness, interest , evaluation, and trial stages in the adoption process and were yet to
adopt the above bees baiting technologies.
Bees Management Technologies
The study found that Agricultural Development Programme contact farmers did not
adopt the following bees management technologies;
(1) Identify the queen
(2) Identify signs of swarming of bees
(3) Avoid wearing red coloured dresses
(4) Avoid wearing yellow dresses
xciii
(5) Avoid perfumed soap
(6) Wear protective clothing/coverings
(7) Avoid perfumes and
(8) Identify pest attack on beehives
The Agricultural Development Programme contact farmers were at awareness
interest,evaluation .and trial stages on the adoption process showing that the farmers were
yet to adopt the bees management technologies skills listed above.
Bees Feeding Technologies
The following bees feeding technologies skills were not adopted by Agricultural
Development programme contact farmers;
(1) Provide space feeding
(2) Avoid open feeding
(3) Bait in an area with abundant flowers
(4) Provide water during dry season and
(5) Decide on type of comb to use.
Agricultural Development Programme contact farmers were also at awareness
interest, evaluation and trial stages on the adoption process yet to adopt the bees feeding
technologies skills.
Honey Harvesting Technologies
The following honey harvesting technologies were not adopted by Agricultural
Development Programme contact farmers.
(1) Put on bees harvesting suit
(2) Place and assemble harvesting tools
(3) Light the smoker
xciv
(4) Puff off the smoker into the entrance to weaken the bees.
(5) Lift the top bar and send in the puffs
(6) Check for any linkage in the beehive
(7) Check for signs of swarming and
(8) Harvest honey timely
Similarly, Agricultural Development Programme contact farmers were at awareness,
interest, evaluation and trial stages but yet to reach adoption stage on honey harvesting
technologies.
Cassava/Maize in Alley Technologies
The following cassava/maize technologies were found to be adopted by Agricultural
Development programme contact farmers;
(1) Identify improved maize seeds
(2) Identify improved cassava varieties
(3) Identify suitable soils
(4) Practice tree management technologies skills
(5) Identify time to apply organic/inorganic manures
(6) Identify when to weed the farm
(7) Identify how to manage the trees
(8) Identify when there is pest attack
(9) Identify when there is disease outbreak
(10) Develop weeding time schedules
The study equally found that Agricultural Development programme contact farmers
did not adopted two items namely- identify tree species and proper spacing technologies.
xcv
Agricultural Development Programme contact farmers had adopted eleven items in cassava/maize in
alley technologies but did not adopt two items of cassava/maize in alley technologies.
Multipurpose Tree Technologies
It was also found that Agricultural Development programme contact farmers did
not adopt the following multipurpose trees technologies.
(1) Identify suitable sites
(2) Identify improved tree seedlings
(3) Use correct spacing
(4) Carry out routinue management of trees
(5) Apply tree treatment chemicals on the trees
(6) Prune trees regularly
(7) Practice propagation of herbaceous plants
(8) Add organic/inorganic manures
Majority of Agricultural Development Programme contact farmers were at awareness,
interest, evaluation and trial stages on the adoption process yet to adopt multipurpose
tree establishment technologies.
Browse Plant Technologies
The following browse plants technologies were not adopted by Agricultural
Development programme contact farmers;
(1) Identify trees to be used
(2) Identify trees that have deep root system
(3) Identify trees that undergo rapid regeneration
(4) Carry routinue browse tree maintenance
xcvi
(5) Determine when to use browse trees to feed livestock
(6) Identify what part of the browse trees to use
(7) Identify how to use browse trees
(8) Identify how to replace browse trees
Majority of Agricultural Development Programme contact farmers did not adopt
browse plant establishment technologies.
Vertiver Grass Technologies
The following vertiver grass planting technologies skills were not adopted by
Agricultural Development programme contact farmers.
(1) Identify vertiver grasses
(2) Identify where to plant the grasses
(3) Identify when to practice the technologies
(4) Identify the planting requirements
(5) Identify the recommended spacing
(6) Practice the technologies of planting the grasses
(7) Apply cultural practices
(8) Identify how to apply manures
(9) Identify when to trim the grasses
(10) Identify pests on the grasses
(11) Maintain the trees during wet season
(12) Maintain the trees during dry season.
Majority of Agricultural Development Programme contact farmers did not adopt
vertiver grasses planting technologies.
xcvii
Socioeconomic characteristics of Agricultural Development Programme contact Farmers
(1) Male Agricultural Development Programme contact farmers adopted modern agroforestry
technologies more than their female counterparts.
(2) Agricultural Development Programme contact farmers who have been with Enugu state
Agriculture Development programme for some years adopted the technologies more
than those farmers who had few years with the programme.
(3) The contact farmers who were aware of modern agroforestry technologies adopted the
technologies better than those who never practiced modern agroforestry technologies.
(4) Agricultural Development Programme contact farmers who regularly attended the
training of agricultural extension agents adopted the technologies more than those that
had no contact with agricultural extension agents.
(5) Literate ADP contact farmers adopted modern agroforestry technologies more than
non-literate contact farmers.
Agricultural Extension Teaching Methods
The following agricultural extension teaching methods were found to be determinants
of adoption of modern agroforestry technologies;
(1) Farm/home visit
(2) Group discussion
(3) Circle meetings
(4) Field days
(5) Result demonstration
(6) Method demonstration
(7) Exhibition and
(8) Conducted tours
xcviii
However,the revealed that the following agricultural extension teaching methods
were non-determinant of adoption of modern agroforestry technologies (IX) Office calls (X)
Personal letters and Use of radio.
Modern Agroforestry Inputs
The following agroforestry inputs were identified as determinants of adoption of
modern agroforestry technologies;
(1) Improved tree seedlings
(2) Improved cassava cuttings
(3) Modern beehives
(4) Baiting materials
(5) Honey harvesting materials
(6) Honey processing materials
(7) Vertiver grasses
(8) Seed dressing chemicals
(9) Organic/inorganic manures
(10) Liming materials
(11) Herbicides
(12) Lands
(13) Insecticides and
(14) Vine and boronated super phosphate
Environmental Factors
The following environmental factors were identifield as determinants of modern
agroforestry technologies;
xcix
(1) Rainfall
(2) Sunlight
(3) Temperature
(4) Fertility status of the soil
(5) Topography
(6) Soil erosion
(7) Drought
(8) Pests and
(9) Diseases.
Hypotheses Results
The hypotheses result are presented below according to the following sub-headings
Bees Baiting Technologies
The hypothesis result indicated that there were no significant differences between
male and female contact farmers on the following bees baiting technologies –
(1) type of wood,
(2) design,
(3) specie of bees,
(4) allow sufficient space,
(5) reject frames with too much space,
(6) identify proper periods to bait,
(7) identify good sites for baiting,
(8) identify sources of baiting materials,
(9) inspect when the bees have colonized
c
(10) provide supplementary feeds,
(11) provide shade to the beehives,
(12) identify groups of bees and
(13) practice routine checks of bees in the hives.
Bees Management Technologies
The result of the above hypothesis showed that there was no significant
differences of male and female contact farmers on bees management skills –
(1) identify the queen,
(2) identify signs of warming
(3) avoid red coloured dresses,
(4) avoid yellow dresses,
(5) avoid using perfumed soaps,
(6) wear protective clothing,
(7) avoid perfumes and
(8) identify pest attack on beehives.
Bees Feeding Technologies
The finding of the hypothesis on bees feeding technologies indicated that there was
no significant difference between the male and female Agriculture Development Programme
contact farmers on the following bees feeding technologies;
(1) Provide open feeding
(2) Avoid open feeding
(3) Bait in area with abundant flowers
(4) Provide water during dry season and
ci
(5) Decide on type of combs to use
Honey Harvesting Technologies
The finding of the hypothesis on honey harvesting technologies indicated that that
there was no significant difference between male and female ADP contact farmers of male
and female contact farmers on honey harvesting technologies;
(1) put on bees harvesting suit
(2) place and assemble harvesting tools
(3) light the smoke
(4) puff off the smoke,
(5) lift the top bar and send in puffs,
(6) check for any leakages in the beehives,
(7) check for signs of swarming and
(8) harvest honey timely.
Cassava/Maize in Alley Technologies
The result of hypothesis testing indicated that there were no significant differences
between the male and female ADP contact farmers on the following cassava/Maize in alley
technologies;
(1) identify improved maize seeds,
(2) (II) improved cassava varieties,
(3) tree species
(4) identify suitable soils,
(5) practice tree management techniques,
(6) when pests attacked
cii
(7) identify proper spacing,
(8) proper time to apply organic/inorganic manures,
(9) disease outbreak and
(10) develop weeding time schedule.
Conversely, the findings of the study showed that 3 (three) cassava/maize is alley
technologies – determine when to prune, find how to weed and determine how to manage the
trees were significant. Therefore, the null hypothesis of no significant differences for the
three items was rejected.
Multipurpose Tree establishment Technologies
The result of the hypothesis on multipurpose tree establishment technologies
indicated that theree was no significant differences of male and female contact farmers on the
following establishing multipurpose trees technologies
(1) identify suitable sites for tree planting,
(2) identify improved tree seedlings,
(3) use correct spacing,
(4) carry out routine management of the trees,
(5) apply tree treatment chemicals,
(6) prune the tree regularly,
(7) practice propagation of herbaceous plants and
(8) adding required quantity of organic/inorganic manures.
ciii
Browse tree establishment Technologies
The result of the hypothesis testing on browse grasses establishment technologies
indicated that there was no significant differences of male and female contact farmers browse
trees establishment technologies;
(1) identify trees to be used,
(2) identify trees with deep roots,
(3) trees that usually regenerate,
(4) carry out routine browse trees maintenance,
(5) determine when to use browse trees,
(6) identify how to use browse trees and
(7) determine how to use browse trees. Therefore, the null hypothesis of no significant
differences in the mean ratings of the male and female contact farmers on the seven
items was accepted.
Planting Vertiver Grasses for Erosion/Wind Control Technologies
The result of the hypothesis on male female contact farmers showed that there was
no significant differences of male and female contact farmers on planting of vertiver
grasses for erosion/wind control technologies
( 1) ability to identify vertiver grasses,
(2) ability to identify when to practice the technologies,
(3) ability to identify the recommended spacing,
(4) ability to practice the techniques of planting the grasses,
(5) ability to apply cultural practices,
(6) ability to identify how to apply the manures,
civ
(7) ability to identify pests on the grasses,
(8) ability to identify diseases on the grasses and
(9) ability to maintain the trees during dry season.
Socioeconomic characteristics of Male and Female Agricultural Development
Programme contact Farmers on bee Management Technologies
The result of the hypothesis on socioeconomic characteristics of the respondents on
bees management technologies showed that there was no significant difference between male
and female Agricultural Development Programme contact farmers on bees management
technologies listed below;
(1) Identify the queen
(2) Identify signs of swarming
(3) Avoid wearing red dresses
(4) Avoid yellow dresses
(5) Avoid using perfumed soaps
(6) Wear protective clothings
(7) Avoid perfumes
(8) Identify pest attack on beehives
Literate and Non-literate respondents on Agricultural Extension Teaching
The result of hypothesis tested on the respondents indicated that there was no
significant difference between the literate and non-literate Agricultural Development
Programme contact farmers on the following agricultural extension teaching methods;
(1) Farm/home visit
(2) Group discussion
(3) Circle meetings
cv
(4) Field days
(5) Office calls
(6) Personal letters
(7) Result demonstration
(8) Method demonstration
(9) Conducted tours
(10) Exhibition
(11) Use of Radio
Male and Female Agricultural Development Programme contact Farmers on
Agroforestry Inputs
The result of the hypothesis on male and female Agricultural Development
Programme and agroforestry inputs showed that there was no significant difference between
the respondents on the following agroforestry inputs;
(1) Improved tree seedlings
(2) Improved cassava cuttings
(3) Modern beehives
(4) Baiting materials
(5) Honey harvesting materials
(6) Honey processing materials
(7) Vertiver grasses
(8) Seed dressing chemicals
(9) Organic and inorganic manures
(10) Herbicides for weed control
(11) Weed control
cvi
(12) Lands
(13) Insecticides
(14) Vines and boronated superphosphate.
Agricultural Development Programme contact Farmers in the three Agricultural Zones
of Enugu State and Environmental Factors
The result of the hypothesis on the environmental factors indicated that there was
significant difference on the respondents from Awgu, Enugu and Nsukka zones on the
following environmental factors;
(1) Rainfall
(2) Sunlight
(3) Temperature
(4) Fertility status of the soil
(5) Soil erosion
(6) Drought
(7) Pests
(8) Diseases
(9) Cloud cover
Discussion of the Findings
The modern agroforestry technologies taught to Agricultural Development
Project contact farmers in Enugu state was taught to them as package consisting of
distinct components.As a result there were differential adoption rates of the components
of modern agroforestry technologies investigated .Below are the discussion of the
findings according to the sub-headings.
cvii
Bee Baiting Technologies
The finding of the study revealed that the contact farmers were yet to reach
adoption stage on bee baiting technologies. This finding was in agreement with
Springer (1997), who reported that even though modern agroforestry technologies had
potentials for economic growth, poverty alleviation and environmental sustainability,the
technologies are yet to be widely adopted. In order to encourage farmers to adopt bee
baiting technologies which will pave way for high quality honey production, Stoner,
Wilson and Harvey (1985), who reported that the use of acephate-treated (orthene 75)
sucrose as bee baits increased the chance of bees baiting. The bait was also found to
be very effective for Africanized honey bee populations and protects visitors from
potentially serious stinging encounters.
Bees Management Technologies
On bees management technologies, it was also found that the farmers were yet
to reach the adoption stage on the technologies. There is need for the bees to be
properly managed for optimal honey production. The low adoption of bees
management technologies revealed by the study was in consonance with Peterson
(2013), who reported that a few species of bees are now under management to enable
them pollinate crops particularly megachile rotundata alfafa specie. These bees are
highly efficient pollinators providing much needed diversity in apiculture pollination.
Bees Feeding Technologies
The study revealed that the farmers were also not adopting bees feeding
technologies. If the bees were not fed properly they will not pollinate flowers, while
seeds and fruits will not be produced. Bees feeding technologies adoption is very
cviii
crucial as according to Godoy (1992), bees colonies are dying all over the world due
to uncared attitude and that bees like all living entities require a healthy environment
and food to flourish.
Bees Harvesting Technologies
On bees harvesting technologies, the study equally indicated that majority of
the contact farmers were yet to advance to adoption stage. This finding agreed with
Feder and Feder (1985), and Umali (1993), who stated that a number of features of
agroforestry make its adoption unique and deserving its review. Firstly adoption of
agroforestry is considered more complex than traditional agriculture because it requires
establishing a new input-output mix of animals, perennials and fodder. Secondly,
there are few packages of agroforestry natural resources management practices to
deliver to farmers (Barret, 2002). The finding was equally in agreement with Mercer
and Miller (1997) who stated that there have been few major advances in the
study of agroforestry adoption. Pannel (1999), listed four conditions necessary for
farmers adoption of agroforestry innovations to include; Awareness of the agroforestry
practice, perception that it is feasible to try the agroforestry technologies, perception that the
agroforestry technologies are worthy trialing and perception that the agroforestry
technologies promote the farmers objectives.
The findings of the study was also in agreement with Mercer and Miller (1997), that
stated that there have been few major advances in the study of agroforestry adoption.
Pannell (1999), stated four conditions necessary for farmers adoption of agroforestry
innovations to include, awareness of the agroforestry practice, perception that it is feasible to
try the agroforestry technologies, perception that the agroforestry technologies are worthy
cix
trialing and perception that the agroforestry technologies promote the farmers objectives.
This is in agreement with the findings of the study that showed that these harvesting
technologies were not adopted by contact farmers. Agricultural extension agents should use
different teaching methodologies to promote the adoption of bees havesting technology.
Cassava/Maize in Alley Technologies
On cassava/maize adoption, the findings show that Agricultural Development
Programme contact farmers adopted the technology. The finding agrees with aprioir
expectation because Nigeria is the highest producer of cassava in the world. Besides
leguminous trees integrated into intensive cassava/maize can provide additional food
and cash and substitute in part for nitrogeneous fertilizer (Okon, 2004). The finding was in
line with Mulongoy (1986), who observed that alley cropping system has retained the
basic features of bush fallow but has the merit of combining the cropping and fallow
phases and utilizing tree species that are easier to manage than natural fallow.
Establishment of Multipurpose Trees Technologies
The finding of the study on the establishment of multi-purpose trees technologies
indicated that majority of Agricultural Development project contact farmers did not
adopt establishment of multiple trees technologies (Gliricidia Sepium and Leucaena
Leucocephala). The finding was not in aggrement with that of Pezo et al (1991) who found
that the trials in the Humid zone of west Africa (HZWA) conducted by International
Institute for Tropical Agriculture (IITA), Ibadan and International Livestock centre for Africa
(ILCA) involving the use of Gliricidia sepium and leucaenae leucocephala have been
adopted due to the benefits on crop production and animal improvement through alley
farming. However,the finding was in agreement with Attah-Krah et al (1989) and;
cx
Akinbamijo et al 2006), who found out that the species have low adoption in terms of
productivity,palatability, presence of toxic substances and adaptability. The finding of
the study was also in line with Attah-Krah and Reynolds (1989), who found out that the
reluctance of smallholder farmers to adopt these tree species as supplements for small
ruminant nutrition has necessitated the search for other tree species,that may offer other
additional benefits. That was why few smallholder farmers do not adopt the technologies.
Equally, the finding of the study disagrees with the finding of Bennison et al (1993) who
identified Gliricidia sepium as a legume tree producing high quality fodder a s a potential
substitute fodder than other grasses.
Browse Grasses Technologies
The finding of the study equally revealed that majority of Agricultural
Development Project contact farmers were yet to adopt browse plant establishment
technologies. The finding of the study was in line with Mecha and Adegbola (1985),
who found out that available information on browse plants technologies and adoption
in SouthEast of Nigeria was scanty. This finding was also corroborated by Okigbo
(1980), who stated that most of the information were unpublished. Equally, Okoli et al
(2003), disagreed with the finding when he noted that, there were much more browse
plants technologies adoption in the area. The finding was in disagreement with Ahamefule
(2006), who noted that identified that distribution, diversity and adoption of browse plants in
Abia state of Nigeria was lacking. This finding completely agreed with the finding of the
study which showed that the adoption rate of the technologies was low. Browse plants
provide vitamins, and mineral elements, which are mostly lacking in grassland pasture. Their
cxi
year round evergreen presentation and nutritional abundance provides for year round
provision of fodder (Opara, 1996, Oji and Isilebo, 2000).
Vertiver Grasses Technologies
The study on vertiver grasses technologies revealed that majority of the
Agricultural Development Project contact farmers were yet to adopt vertiver grasses
establishment technologies.Vertiver grass cultivation is a vegetative system of soil and
moisture conservation which have proved cheaper, and more effective than the other soil
erosion control measures. The finding was at variance with Grimshaw (1993), who
found out that the technologies had been adopted in different countries including India,
China ,Philippines and Indonesia. Accordingly the finding show that vertiver grasses are
grown as hedges on low Ph red soils in China. In India, it is being used successfully
on black cotton soils (severely cracking vertisols) on slopes of 2 % or less .In other
countries such as Trinidad, it has been used for years to stabilize the rock-based road
sides.The finding revealed that in every case this unique grass has displayed extraordinary
characteristics that make it an ideal, low-cost, non-site-specific system for controlling soil
loss and improving soil moisture. Finally, the finding of the study was in agreement
with Grimshaw (1993), who stated that inspite of the efforts of Agricultural Development
Programme, some farmers are still reluctant to adopt browse plants. This has been
because the cultivation of vertiver plants in agricultural areas to conserve soil and water does
not produce tangible benefits in terms of revenue.
Socio-Economic Characteristics of ADP Contact Farmers
One of the findings of the study on socio-economic characteristics of ADP contact
farmers was that male ADP contact farmers were more aware of the modern agroforestry
cxii
technologies than the females. This finding was in consonance with Scherr (1995), who
concluded that there was a significant gender difference in agroforestry technologies
adoption with males planting 50% more trees per hectare than the females.Men also tended
to plant trees on crop land while women planted trees specifically for fuelwood. However,
the finding was at variance with Okoye and Onyenwaku (2007), who in a study on Economic
efficiency of small holder cocoyam farmers in Anambra State found that majority (82%) of
the respondents were females while only (18%) were males.It was possible that women
domination of cocoyam production could be because cocoyam farming is less tedious
compared to other crops and particularly agroforestry technologies.Equally, the finding was
at variance with FAO’s (1995), who reported that in Zimbabwe, women are playing
important roles in tropical forest preservation as adaptation strategy for climate change.
Women are managing forest resources and development projects through woodlot ownership,
tree planting and nursery development.
The finding of the study also showed that Agricultural Development Programme
contact farmers educational attainment helped in the adoption of modern agroforestry
technologies. The findings was in line with studies on adoption of new technologies (Asfaw
and Admassie, 2004; Laper and Ehui, 2004, Caswel et al (2001), who stated that education
creates a favourable mental attitude for acceptance of information which positively influence
adoption of technologies such as modern agroforestry technologies.
Agricultural Extension Teaching Methods
The study also showed that farm/Home visit, group discussion, circle meetings, field
days, method and result demonstration and excursion were determinant of adoption of the
technologies. These traditional extension teaching methods used by the agricultural extension
cxiii
agents had positive impact on the adoption of modern agroforestry technonogies. The use of
these methods of teaching and the low adoption of some of the technologies could
suggest that farmers were either not understanding the teaching methods being used, or that the
extension agents failed to use other extension teaching methods that could promote better
understanding and adoption of modern agroforestry technologies. It is possible on the
account of the observed lapses above that FAO (1995) came up with two alternatives to
traditional extension methods-the Farmer Field Schools and the Farmer Forest Management
schools (FFMS). Omenesa (2013) stated that Farmer Forest management school was to allow
forest users gain knowledge, critical skills and self-confidence to make decisions about forest
management based on their own experiments, so that forest can sustainably provide them
benefits. It is an approach of forefront extension aimed at promoting farmer led extension.
Agroforestry Inputs
Agricultural inputs relate to those vital elements used to make agriculture both
possible and profitable (Faborode, 2005). The study found that modern agroforestry inputs
such as improved tree seedlings, improved cassava cuttings, modern beehives, baiting
materials, honey harvesting materials, honey processing materials, vertiver grasses, seed
dressing chemicals, organic and inorganic manures, liming materials, herbicides, lands,
insecticides and vine and boronated superphosphate availability are determinants of the
adoption of the technologies. The findings of the study was in agreement with Ubaka, (2002),
who stated that farm inputs availability and cost constituted primary hindrance to
technologies adoption. The unavailability and high cost of farm inputs such as inorganic
fertilizers greatly affected the production and adoption of technologies. This is usually
worsened by the fertilizer companies which usually produced below installed capacities and
cxiv
most often slowed down thereby encouraged increased importation with its accompanying
high unit costs (Ubaka, 2002). Most of the farm input dealers closer to the farmers have little
or no basic training on the inputs they stock, no monitoring procedure on performance, no
professional guide and advice to their clients. Another finding of the study showed that
agroforestry technologies inputs such as tree seedlings, modern beehives, baiting materials,
honey harvesting materials, vertiver grasses, seed dressing chemicals, organic and inorganic
manures and herbicides were needed for the adoption of modern agroforestry technologies.
The assets and resources available to farmers for investing in new technologies such as tree
seedlings, manures etc are critical to adoption decisions. This is in agreement with Patel et al
(1995), and and Scherr (1993), who found that early adopters tend to be better –off
households who are better situated to take advantage of new innovations. According to the
finding, these households are more likely to have capital, more land and labour to facilitate
risky investment in proven technologies as modern agroforestry technologies. The finding
was also in line with Kwesiga etal (2003), who identified lack of planting materials (Tree
seedlings, organic/inorganic manures) and general lack of planting materials as a limitation
to agroforestry adoption. Fagbemi and Nwoboshi (1992), stated that inability of local
farmers to use manure on their land often makes land to loose its fertility and ability to
sustain crop yield after few years of cultivation. They emphasized that adoption and
intensification of agroforestry technologies in most farming system would be a veritable
substitute in raising the agricultural production in the tropics.
Environmental Factors
The finding of the study also showed that environmental factors such as rainfall,
temperature, fertility status of the soil, topography of soil, soil erosion, drought, pests and
cxv
diseases were found to be determinants of adoption of modern agroforestry technologies. The
effect of climate on agriculture/modern agroforestry technologies in parts of Enugu State is
related to variations in local climates. This is so because all crops including modern
agroforestry technologies are dependent on the amount of rainfall. Hence most of the crops
are cultivated during rainy season in the state as there are little water ways that could be used
for irrigation and dry season cultivation. Mba (2010) stated that this truncates all efforts by
individuals, governments to ensure agricultural viability in the State. Environmental factors
such as topography, soil quality, rainfall, temperature have rarely been included in
agroforestry adoption studies. However, Pattanayak et al (2003) found that if included in
agroforestry technologies, they will be important predictors of adoption of agroforestry.
Furthermore, the finding is in consonance with IPCC (2007) and BNRCC (2008), who stated
that changing of temperature will lead to low/poor yields particularly in low-income
countries as Nigeria where climate is the primary determinants of agricultural productivity.
Similarly, the study identified the following environmental factors as determinants of
the adoption of the technologies-regularity of rainfalls, sunlight variations, unstable
temperature, fertility status of the soil, topography, pests and diseases. This was in line with
the finding on constraints to adoption of recommended multiple cropping systems and the
implications of their non- adoption to rural poverty in Enugu State (Ochiaka, 1998).
cxvi
CHAPTER FIVE
SUMMARY CONCLUSION AND RECOMMENDATIONS
Re-statement of the Problem
Agricultural Development Programme in Enugu state adopted unified agriculture
extension system. This is an extension system that operates with frontline extension agents
teaching farmers in the five components of the system. These components include:- crops,
livestock, fishery, women in agriculture and agroforestry. These components have been
taught to contact farmers in the state for quite a long period. While four of the above
components have been adopted ,modern agroforestry technologies have not been massively
adopted and the stages the farmers were on adoption of agroforestry technologies were not
ascertained
In view of the above situation, it was necessary that this study was conducted to
identify the determinants of adoption of modern agroforestry technologies among contact
farmers in Enugu State.
Summary of the Procedures Used
Five research questions were developed and answered, while five null hypotheses
were formulated and tested at 0.05 level of significance.Survey research design was adopted
for the study. A 120 item- structured questionnaire was developed from the literature
reviewed to obtain data for the study. The scales for the questionnaire were: aware, interest,
evaluation, trial and adoption for the stages of adoption and very great extent, great extent,
little extent and very little extent for the factors that determined adoption of the technologies.
Three experts face validated the questionnaire while cronbach alpha method was used to
determine the internal consistency of the instrument and an overall reliability index of 0.75
126
cxvii
was obtained. The questionnaire was administered on 360 respondents and a total of 360
copies (100%) were retrieved and analysed.
The mean, percentages, standard deviation and multiple regression analysis were
used to answer the research questions. t-test statistic were used to test hypotheses 1 – 4 at
0.05 level of significant, while analysis of variance (ANOVA) was used to test hypothesis 5
also at 0.05 level of probability. A cut off point of 2.50 was used to identify the items that
were regarded as determinants. A cut off point of 3.00 was also used to identify the adoption
stages of the respondents. Multiple regression analysis was used to analyse research question
2.
The null hypothesis of no significant difference was accepted for any item whose t-
cal was less than t-tab value of 1.96. The null hypothesis of no significant difference was
rejected if the t-cal is greater than t-tab of 1.96. For hypothesis 5, the null hypothesis of no
significant difference was accepted for any item if the f-cal was less than F-tab value of 3.00.
The null hypothesis of no significance difference was rejected if the F-cal is greater than the
F-tab value of 3.00.
Major Findings of the Study
Based on the specific purposes of the study , the following principal findings
are presented.
1. Majority of the Agricultural Development Programme contact farmers were at
awareness, interest, evaluation and trial stages on the adoption process on bees baiting
technologies, bees management technologies, bees feeding technologies,
establishment of multipurpose tree, and vertiver grasses establishment technologies.
cxviii
However, the contact farmers were at adoption stage on cassava/maize in alley
technologies of modern agroforestry technologies.
2. The following socio –economic characteristic were identified as determinants of the
adoption of modern technologies: Gender, educational qualification, experience in
the number of years the farmer have been with Enugu State Agricultural
Development Programme and the number of years the contact farmers have practiced
modern agroforestry technologies and agricultural extension contact.
3. The agricultural extension teaching methods that were determinants of adoption of
the modern technologies were farm/home visit, group discussion, circle meeting, field
days, result demonstration, conducted tours and method demonstration.
4. The following agroforestry inputs were determinants of adoption of modern
agroforestry technologies –tree seedlings, cassava cuttings, modern beehives, honey
harvesting materials, honey processing materials, vertivar grasses, organic/inorganic
fertilizers, liming materials, herbicides, lands, and insecticides..
5. The following environmental factors were found to be determinants of adoption of
modern agroforestry technologies –rainfall, sunlight, temperature, fertility status of
soil, topography of soil, soil erosion, drought, pests, diseases and cloud cover.
Conclusion
The investigation of the determinants of adoption of modern agroforestry
technologies by Agricultural Development Programme contact farmers in Enugu State is
timely and for great importance. This is because while other components of unifield
agricultural extension system namely crops, livestock, fishery and women in agriculture have
considerable adoption rate, while modern agroforestry technologies which is part of the
cxix
unifield extension system has low adoption rate. This is more disturbing because modern
agroforestry technologies have the potential for economic growth, poverty alleviation and
environmental sustainability.
The observed lapses in the adoption of different components of modern agroforestry
contact dagricultural extension agents that teach the technologies, and the contact farmers.
Male ADP contact farmers who had initial contact with Agricultural Development
Programme, those who had tried modern agroforestry technologies and literate contact
farmers adopted modern technologies.More efforts should be put by agricultural extension
agents to involve female contact farmers and non-literate contact farmers during teaching
modern agrofrestry technologies.
The use of convectional agricultural extension teaching methods such as circle
meetings,field days and exhibitions has not been useful in promoting the adoption of modern
agroforestry technologies. There is need to integrate new agroforestry teaching methods
such as forest management schools and farmers field schools. This combination is likely to
promote the adoption of modern agroforestry technologies.
The use of inputs is very crucial and critical in adoption of modern agroforestry
technologies.The availability and affordability of the inputs will go a long way in
encouraging contact farmers to adopt the technologies.
The differential influence of environmental factors in three agricultural zones of the
state showed that modern agroforestry technologies being crop based has environment
factors as crucial in adoption of modern technologies. If the identifeld determinants to the
adoption of the technologies are fully addressed, there will be high adoption of the
cxx
technologies which will lead to improved standard of living for Agricultural Development
Programme contact farmers in Enugu State.
Implications of the Study
The findings of this study have some far reaching implications for contact farmers,
extension agents, government and all stakeholders in agricultural enterprise. The principal
implications of the finding is that government and other stakeholders are aware that there
were low adoption of agroforestry component of the unified extension system in the state and
be in a position to help improve the adoption of the technologies by contact farmers of Enugu
State Agricultural Development Programme (ENADEP). The other implication is that
sponsors and donor agencies of this programme are aware and conversant with the
determinants of adoption, so that policy directives on ways to improve adoption will be
properly designed and applied.
The determinants of adoption of modern agroforestry technologies which was the
focus of this study will act as a guiding instrument to be used in determining the level of
adoption and determinants of adoption of other components of the unified extension system
of Enugu state Agricultural Development Programme and other Agricultural Development
Programmes in the country.
The findings also have implication for contact farmers, since they are exposed to the
benefits they will derive from adopting agroforestry technologies. It calls for proper delivery,
supervision of extension agents to ensure that hindrances to mass adoption are properly
handled. Contact farmers who operate as individual farmers are advised to form
group/cooperative to access the benefits of commercial farming.
cxxi
The study also has implications to the monthly technology review meetings
(MTRMs) whose mandate is to design and to review the technologies monthly. The meeting
were now aware of identified problems and should be able to redesign apiculture,
multipurpose and ventivar grasses technologies for better adoption by the contact farmers.
Recommendations
1. The educational qualifications of agricultural extension officers of the state
Agricultural Development Programme should be raised to a minimum of a University
degree in Agricultural Extension/Education or related discipline.
2. Contact farmers should be involved both in designing, planning and execution of
modern agroforestry technologies, so that their socio-economic characteristics will be
taken into consideration.
3. There should be capacity trainings for agricultural extension workers particularly in
educational methodology.
4. Government, non-governmental organizations and donor agencies should subsidize
agroforestry inputs while, communities, schools and private individual should
establish tree nurseries
5. Irrigation agriculture should complement rain-fed agriculture so that agroforestry
trees should have enough conducive climates for growth and development.
Suggestions for Further Studies
Based on the findings of the study the following suggestions are made for further
studies.
1. Training needs of agricultural extension workers on different teaching strategies for
effective agricultural technology adoption.
cxxii
2. Involvement of farmers in planning, execution of agricultural innovations for easy
adoption of Agroforestry technologies.
3. Determinants of adoption of women in agriculture component of Enugu State
Agricultural Development Programme by literate female farmers in Enugu State.
cxxiii
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APPENDICES
A - Request for validation of Research Instrument
B - Validators Comments
C - Request for responses to a questionnaire
D - Organisational structure of Enugu State Agricultural Development
Programme
E - Farming enterprises/Technologies taught to contact farmers by Enugu state
Agricultural Development Programme
F - National Programme for food security in Enugu State
G - SPSS Computer Analysis Results
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APPENDIX A
Department of Vocational Teacher Education
University of Nigeria, Nsukka
20/5/2010
Dear Sir/Madam,
REQUEST FOR VALIDATION OF RESEARCH INSTRUMENT
The attached research instrument is designed to collect necessary data for the study
titled; Determinants of Adoption of Modern Agroforestry Technologies by Agricultural
Development Programme contact farmers in Enugu State .I humbly request you to please
validate this instrument based on the specific purposes of the study, research questions and
hypotheses. You are specifically required to assess and review the items in terms of their
relevance to the purpose of the study.
Please assess and validate this instrument stating your critics corrections or
recommendations at the end of each section of the instrument. Thanks for your anticipated
cooperation.
Ochiaka, Joseph .S
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APPENDIX B
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APPENDIX C
Department of Vocational Teacher Education,
(Agricultural Education).
University of Nigeria,
Nsukka.
23rd
March, 2011.
QUESTIONNAIRE FOR AGRICULTURAL DEVELOPMENT PROGRAMME
CONTACT FARMERS IN ENUGU STATE
Dear Sir/Madam,
I am a postgraduate student in the above Department, currently undertaking a
research work on- Determinants of Adoption of modern Agroforestry Technologies by
Agricultural Development Programme contact farmers in Enugu State.
You have been selected as one of the Agricultural Development Programme contact
farmers to supply the information needed, for the research work. All information to be
given will be treated as confidential, and will be used strictly for this work.
Thank you.
Yours faithfully,
Ochiaka J .S
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PART ONE
STAGES OF ADOPTION OF MODERN AGROFORESTRY TECHNOLOGIES
INSTRUCTION: For each item, you are please requested to check (�) the stage you are in
the adoption process with regards to modern agroforestry technologies taught to you by
Agricultural Development Programme workers. Use the following response categories
below A-Aware I-Interest E-Evaluation
T-Trial A-Adoption (use).
A-Beekeeping for Honey Production
S/N Bees Baiting Technologies A I E T USE
1 Decide on the type of wood to be used for beehive construction
2 Decide on the design of the beehive
3 Decide on the species to use e.g A .mellifera
4 Allow sufficient space between the frames
5 Reject frames with too much space
6 Identify proper periods to bait
7 Identify good sites for baiting
8 Identify sources for baiting materials
9 Inspect when the bees have colonized
10 Provide supplementary feeding to the bees
11 Provide shade to the beehives
12
Identify groups of bees in the hives-workers ,queen and drones
13 Practice routinue checks on the bees in the hives
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Bees Management Technologies A I E T USE
14 Identify the Queen
15 Identify signs of swarming of the bees
16 Avoid red coloured dresses when going to the beehive
17 Avoid yellow dresses when going to the beehive
18 Avoid using perfumed soaps to bath before going to the hive
19 Wear protective covering before going to the hive
20 Avoid perfumes as the scents provoke the bees
21 Identify pests attack on beehives
Bees Feeding Technologies A I E T USE
22 Provide open feeding in apiary
23 Avoid open feeding in apiary
24 Bait in area with abundant flowers
25 Provide water during dry season to avoid severing
26 Decide on the types of comb to use
Honey Harvesting Technologies A I E T USE
27 Put on bee harvesting suit
28 Place and assemble harvesting tools within reach
29 Light the smoker
30 Puff off the smoker into the entrance to weaken the bees
31 Lift the top bar and send in puffs
32 Check for any leakages in the beehive
33 Check for signs of swarming
34 Harvest honey timely- Jan-June or July-December
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Cassava/Maize in Alley Technologies A I E T USE
35 Improved maize seeds
36 Improved cassava varieties
37 Tree species for the technologies.
38 Suitable soils for the practice of the alley
39
Practice tree management techniques such as pruning, trimming
40 Ability to know when to prune to avoid overcrowding
41 Ability to identify proper spacing for the arable crops.
42 Ability to identify proper time to apply apply organic (pig,
poultry) and inorganic manures
43 Ability to identify when to weed the farm
44 Ability to identify how to manage the trees during dry season
45 Ability to identify when there is pest attack on the trees and
arable crops.
46 Ability to identify when there is disease outbreak in the farms
47 Ability to weed the farm as and when due
Establishment of Multipurpose Trees Technologies A I E T USE
48 Ability to identify suitable sites for planting the trees.
49 Identify improved trees seedlings
50 Ability to use correct spacing in the tree planting
51 Ability to Carry out routine management of the trees
52 Ability to apply tree treatment chemicals
53 Ability to prune the trees regularly
54 Ability to practice propagation of herbaceous plants used as
multipurpose trees
55 Ability to add the required organic and inorganic manures to the
trees.
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Establishment of Browse Plants Technologies A I E T USE
56 Ability to identify the trees to be used, which should be fast
growing.
57 Identify trees that have deep root system
58 Identify trees that usually undergo rapid regeneration after
prunnings
59 Ability to Carry out routine browse tree maintenance practices
such as trimming
60 Ability to determine when to use browse trees to feed livestock.
61 Ability to identify what part of the browse trees to use in
feeding animals
62 Ability to identify how to use the browse trees for feeding
livestock
63 Ability to identify how to replace browse trees
Planting of Vertiver Grasses Technologies A I E T USE
64 Ability to identify vertivar grasses
65 Ability to identify where to plant the grasses
66 Ability to identify when to practice the technology
67 Ability to identify the planting requirements such
as manures, water
68 Ability to identify the recommended spacing.
69 Ability to practice the techniques of planting the grasses
70 Ability to apply cultural practices such as weeding, fertilizer
application.
Ability to identify how to apply the manures
71 Ability to identify when to trim the grasses
72 Ability to identify pests on the grasses
73 Identify disease on the grasses
74 Ability to maintain the trees during dry seasons.
cxlvi
PART TWO
GENERAL INFORMATION ON AGRICULTURAL DEVELOPMENT
PROGRAMME CONTACT FARMERS IN ENUGU STATE
INSTRUCTION: Kindly fill the blank spaces provided or check (�) in the boxes as it
appeals to you.
75. Your agricultural zone is (a) Awgu ( ) (b)Enugu ( ) (c) Nsukka ( )
76 Your gender (a) Male (b) female
77 How long have you been with ENADEP as a contact farmer----------------------------
-----------------------------------------------------------------------------------------------------
78 Marital Status
(a) Married (b) Single
(c) Divorced (d) Widowed
79 Your highest level of formal education as a contact farmer
(a) No primary school at all (b) Incomplete primary school
(c) Complete primary school (d) incomplete secondary school
(e) Complete secondary school
(f) Others please specify ----------------------------------------------------------------------
80 Your major source of farm labour
(a) Family labour (b) Hired labour (c) Work group
(d) Personal labour (e) Mechanized
(vi) Others (please specify) --------------------------------------------------------------------
81 Your experience ( in years ) in modern agroforestry practices-----------------------------
----------------------------------------------------------------------------------------------------
cxlvii
82 Farm size (in hectares) owned by the respondents
(a) 0-2ha (b) 3-5ha (c) 6-8 ha
(d) 9-11ha
(e) 12ha and above
. (f) Others please specify -----------------------------------------------------
83 How often does an agric extension agent of Enugu state Agricultural Development
Programme visit you in a month?
(a) Once (b) twice (c) Daily
(d) Three times
(e) Others please specify --------------------------------------------------------
84. What is your major source of fund for agricultural activities
(a) Personal savings (b) Money lenders (c) Thrifts
(d) Commercial banks (e) Microfinance banks
(f) Others please specify-------------------------------------------------------------
cxlviii
PART THREE
AGRICULTURAL EXTENSION TEACHING METHODS
Instruction: Indicate the extent to which you agree with the items listed below on the extent,
the items determine adoption of the modern agroforestry technologies. Using the following
rating scales which will also guide you in answering agricultural extension teaching methods,
.Modern agroforestry inputs and environmental factors.
Very great extent-4 great extent-3Little extent -2 Very little extent -1
S/N Items VGE GE LE VLE
85 Farm /Home visit
86 Group discussion
87 Circle meeting
88 Field days
89 Office calls.
90 Personal letters
91 Result demonstration
92 Exhibition
93 Conducted tours (excursion, field days).
94 Method demonstration
95 Use of radio
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PART FOUR: MODERN AGROFORESTRY INPUTS
Items VGE GE LE VLE
96 Improved tree seedlings to practices the system
97 Improved cassava cuttings to practice the technologies.
98 Modern beehives to practice the technologies
99 Baiting materials such as sugar and upwine.
100 Honey harvesting materials
101 Honey processing materials
102 vertivar grasses for erosion control
103 Seed dressing chemicals such as miral and furadan
104 Organic and inorganic manures
105 Liming materials.
106 Herbicides for weed control
107 Lands to practice the technologies
108 Insecticides to control insects in the farms
109 Vine and boronated super phosphate for the treatment of flower/fruit
abortion
cl
PART FIVE
ENVIRONMENTAL FACTORS
Items VGE GE LE VLE
110 Rainfall
111 Sunlight
112 Temperature
113 Fertility status of soil
114 Topography of soil
115 Soil erosion
116 Drought
117 Pests
118 Diseases
119 Cloud cover
cli
APPENDIX D
THE ORGANIZATIONAL STRUCTURE OF ENUGU STATE AGRICULTURAL
DEVELOPMENT PROGRAMME (ENADEP)
Technical
services Engineering
services
Manpower
Development
andtraining
Planning
monitoring and
evaluation
Finance Administration
Zonal
managers
Zonal
Extension
Officers
Subject matter
specialists
Other zonal
officers
Block
Extension
supervisors
Block Extension
agents
Extension agents
Contact farmers
Source: Management
information center,
EnuguState Agricultural
development Programme
Headquarters-Enugu (1992)
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APPENDIX E
Farming enterprises /technologies/messages taught to contact farmers, by agricultural agents,
of Enugu State Agricultural Development Programme, by the use of small plot adaptive
Techniques (SPATS), and Management Training Plots (MTPS) in 2002.
Farming Enterprises
Activities Target Achievement % achievement
Crops
Yam minsett techniques, for seed yam
production.
180 135 75
Improved cassava/maize. 410 210 51
Upland production 180 125 80
kitchen garden/dry season vegetable 228 147 58
Use of planophils (Akidi) to control weed in
Y/C/M/C/ intercrop
180 95 52
Sole soyabean 180 75 41
Livestock
Goat and sheep production 135 65 40.09
Pig production 135 75 55
Brooding of local chicks 135 50 37
Agroforestry
Bee-keeping for honey production 135 35 33
Use of vertivar grass for erosion control 90 15 15
Plantain/cocoyam intercrop 90 30 34
Cassava/maize in alley 90 15 14
Plantain/banana 50 5 10
Establishment of browse plant 50 10 20
Planting of multipurpose trees 50 5 10
Fencing/boundary planting 50 20 40
Enugu state ADP Annual Report (1992).
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APPENDIX F
NATIONAL PROGRAMME FOR FOOD SECURITY IN ENUGU STATE
Background
Food security has generated a lot of concern all over the world. The Federal and State
Governments in Nigeria in the NEEDS (National economic and empowerment development
strategy) and SEEDS (State economic and empowerment development strategy) made food
security one of its most important programme objectives. It is in this context that between
2002 and 2006 the Federal Government of Nigeria introduced the National Special
Programme for Food Security (NSPFS) to cover all the 36 states in the Nigeria and Abuja.
The SPFS offers a practical vehicle for piloting and eventually extending the application of
innovative low-cost approaches both technically and institutional to improving the
productivity and sustainability of agricultural systems with the ultimate objective of
contributing to better livelihoods for food farmers. Enugu state implemented the programme
between 2002 and 2006. The implementation report showed that it made significant impact
on the standards of rural farmers in the state.
cliv
IMPACT OF NPFS IN ENUGU STATE
During the prevail of implementation at 3 pilot sites in Enugu State namely: Adani in
Uzouwani LGA, Amagunze in Nkanu East LGA and Nenwe in Awgu LGA farmers groups
were given free interest loan of N8.0m in each site yearly for the following activities (a)
Rainfed crops (b) Small scale irrigation (c) Livestock including fisheries (d) Agroprocessing
(e) Agroforestry (f) Community seed establishment.
Due to the success recorded in this pilot phase, the Federal Government of Nigeria
has decided to expand the programme to 9 sites in each state of the federation. The sites of
National programme for food security in Enugu state are:
1. Adani, Uzo-Uwani LGA
2. Amagunze, Nkanu East LGA
3. Nenwe, Aninri LGA
4. Agu-Ukehe, Igbo-Etiti LGA
5. Obollo-Etiti, Udenu LGA
6. Eha-Amufu, Isi Uzo LGA
7. Inyi, Oji River LGA
8. Obeagu, Enugu South LGA and
9. Obinofia Ndiagu, Ezeagu LGA
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APPENDIX G
RESEARCH QUESTION 1
Cluster 1 Baiting Technologies
N Minimum Maximum Mean
Std.
Deviation
Baiting Tech 1 360 1.00 5.00 1.8778 1.19495
Baiting Tech 2 360 1.00 5.00 2.0778 1.29260
Baiting Tech 3 360 1.00 5.00 1.8806 1.18705
Baiting Tech 4 360 1.00 5.00 2.0333 1.30309
Baiting Tech 5 360 1.00 5.00 1.9611 1.27921
Baiting Tech 6 360 1.00 5.00 2.3361 1.47809
Baiting Tech 7 360 1.00 5.00 2.3722 1.48166
Baiting Tech 8 360 1.00 5.00 3.6000 1.51722
Baiting Tech 9 360 1.00 5.00 2.3361 1.49682
Baiting Tech 10 360 1.00 5.00 2.2278 1.47903
Baiting Tech 11 360 1.00 5.00 2.4194 1.54023
Baiting Tech 12 360 1.00 5.00 2.1750 1.45496
Baiting Tech 13 360 1.00 5.00 2.2472 1.51225
Valid N
(listwise) 360
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Cluster 2 Bees Management Technologies
N Minimum Maximum Mean
Std.
Deviation
Bees Mgt Tech 1 360 1.00 5.00 2.0667 1.31458
Bees Mgt Tech 2 360 1.00 5.00 2.2417 1.36406
Bees Mgt Tech 3 360 1.00 5.00 2.1028 1.38153
Bees Mgt Tech 4 360 1.00 5.00 2.1250 1.47531
Bees Mgt Tech 5 360 1.00 5.00 2.1806 1.47331
Bees Mgt Tech 6 360 1.00 5.00 2.6528 1.60916
Bees Mgt Tech 7 360 1.00 5.00 2.2889 1.52596
Bees Mgt Tech 8 360 1.00 5.00 2.4917 1.60603
Valid N (listwise) 360
CLUSTER 3 BEES FEEDING TECHNOLOGIES
N Minimum Maximum Mean
Std.
Deviation
Bees Feeding 1 360 1.00 5.00 1.9444 1.24772
Bees Feeding 2 360 1.00 5.00 1.9917 1.28520
Bees Feeding 3 360 1.00 5.00 2.3083 1.52306
Bees Feeding 4 360 1.00 5.00 1.9778 1.32453
Bees Feeding 5 360 1.00 5.00 2.1250 1.43122
Valid N
(listwise) 360
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Cluster 4 Honey Harvesting Technologies
N Minimum Maximum Mean
Std.
Deviation
Honey Hav 1 360 1.00 5.00 2.7278 1.56682
Honey Hav 2 360 1.00 5.00 2.6000 1.53909
Honey Hav 3 360 1.00 5.00 2.7333 1.60986
Honey Hav 4 360 1.00 5.00 2.5778 1.59720
Honey Hav 5 360 1.00 5.00 2.2722 1.52539
Honey Hav 6 360 1.00 5.00 2.4111 1.58436
Honey Hav 7 360 1.00 5.00 2.4528 1.56849
Honey Hav 8 360 1.00 5.00 2.8889 1.71310
Valid N
(listwise) 360
Cluster 5 Cassava/Maize in Alley Technologies
N Minimum Maximum Mean
Std.
Deviation
Cass Maize 1 360 1.00 5.00 3.7889 1.52961
Cass Maize 2 360 1.00 5.00 3.6333 1.57770
Cass Maize 3 360 1.00 5.00 2.9833 1.59027
Cass Maize 4 360 1.00 5.00 3.2389 1.58813
Cass Maize 5 360 1.00 5.00 3.3500 1.60943
Cass Maize 6 360 1.00 5.00 3.3472 1.63322
Cass Maize 7 360 1.00 5.00 2.9611 1.64669
Cass Maize 8 360 1.00 5.00 3.4056 1.60673
Cass Maize 9 360 1.00 5.00 3.5639 1.62204
Cass Maize 10 360 1.00 5.00 3.2639 1.67226
Cass Maize 11 360 1.00 5.00 3.3333 1.62844
Cass Maize 12 360 1.00 5.00 3.3778 1.65779
Cass Maize 13 360 1.00 5.00 3.7500 1.61943
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Cluster 5 Cassava/Maize in Alley Technologies
N Minimum Maximum Mean
Std.
Deviation
Cass Maize 1 360 1.00 5.00 3.7889 1.52961
Cass Maize 2 360 1.00 5.00 3.6333 1.57770
Cass Maize 3 360 1.00 5.00 2.9833 1.59027
Cass Maize 4 360 1.00 5.00 3.2389 1.58813
Cass Maize 5 360 1.00 5.00 3.3500 1.60943
Cass Maize 6 360 1.00 5.00 3.3472 1.63322
Cass Maize 7 360 1.00 5.00 2.9611 1.64669
Cass Maize 8 360 1.00 5.00 3.4056 1.60673
Cass Maize 9 360 1.00 5.00 3.5639 1.62204
Cass Maize 10 360 1.00 5.00 3.2639 1.67226
Cass Maize 11 360 1.00 5.00 3.3333 1.62844
Cass Maize 12 360 1.00 5.00 3.3778 1.65779
Cass Maize 13 360 1.00 5.00 3.7500 1.61943
Valid N
(listwise) 360
Cluster 6 Establishment of Multipurpose Trees Technologies
N Minimum Maximum Mean
Std.
Deviation
Multipurpose 1 360 1.00 5.00 2.8833 1.57726
Multipurpose 2 360 1.00 5.00 2.7250 1.49630
Multipurpose 3 360 1.00 5.00 2.5194 1.54583
Multipurpose 4 360 1.00 5.00 2.4722 1.52026
Multipurpose 5 360 1.00 5.00 2.3750 1.56064
Multipurpose 6 360 1.00 5.00 3.0361 1.69263
Multipurpose 7 360 1.00 5.00 2.3806 1.49359
Multipurpose 8 360 1.00 5.00 2.9500 1.66489
Valid N
(listwise) 360
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Cluster 7 Establishment of Browse Plants for Livestock Feeding
Technologies
N Minimum Maximum Mean
Std.
Deviation
Brouse Plant 1 360 1.00 5.00 2.9333 1.67764
Brouse Plant 2 360 1.00 5.00 2.6833 1.62835
Brouse Plant 3 360 1.00 5.00 2.6472 1.60795
Brouse Plant 4 360 1.00 5.00 3.0250 1.64272
Brouse Plant 5 360 1.00 5.00 3.1333 1.70498
Brouse Plant 6 360 1.00 5.00 3.1611 1.62729
Brouse Plant 7 360 1.00 5.00 3.0889 1.70625
Brouse Plant 8 360 1.00 5.00 2.9944 1.66564
Valid N
(listwise) 360
Cluster 8 planting of Vertiver Grasses for Erosion/wind Control
Technologies
N Minimum Maximum Mean
Std.
Deviation
Vertiver Grass 1 360 1.00 5.00 2.1861 1.46123
Vertiver Grass 2 360 1.00 5.00 2.2222 1.47799
Vertiver Grass 3 360 1.00 5.00 2.1694 1.42858
Vertiver Grass 4 360 1.00 5.00 2.1167 1.45795
Vertiver Grass 5 360 1.00 5.00 2.0667 1.40869
Vertiver Grass 6 360 1.00 5.00 2.3111 1.53977
Vertiver Grass 7 360 1.00 5.00 2.8694 1.70601
Vertiver Grass 8 360 1.00 5.00 3.7722 1.58626
Vertiver Grass 9 360 1.00 5.00 3.9222 1.53313
Vertiver Grass 10 360 1.00 5.00 3.8889 1.55812
Vertiver Grass 11 360 1.00 5.00 3.9167 1.56876
Vertiver Grass 12 360 1.00 5.00 3.9889 1.50945
Valid N (listwise) 360
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Frequencies and Percentages of Responses on the Items
Baiting Bees Technologies (Cluster One)
Bees Baiting 1
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 1 199 55.3 55.3 55.3
2 76 21.1 21.1 76.4
3 27 7.5 7.5 83.9
4 46 12.8 12.8 96.7
5 12 3.3 3.3 100.0
Total 360 100.0 100.0
Bees Baiting 2
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 1 173 48.1 48.1 48.1
2 76 21.1 21.1 69.2
3 45 12.5 12.5 81.7
4 42 11.7 11.7 93.3
5 24 6.7 6.7 100.0
Total 360 100.0 100.0
Bees Baiting 3
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 1 189 52.5 52.5 52.5
2 94 26.1 26.1 78.6
3 27 7.5 7.5 86.1
4 31 8.6 8.6 94.7
5 19 5.3 5.3 100.0
Total 360 100.0 100.0
Bees Baiting 4
Frequency Percent
Valid
Percent
Cumulative
Percent
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Valid 1 188 52.2 52.2 52.2
2 61 16.9 16.9 69.2
3 44 12.2 12.2 81.4
4 45 12.5 12.5 93.9
5 22 6.1 6.1 100.0
Total 360 100.0 100.0
Bees Baiting 5
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 1 193 53.6 53.6 53.6
2 73 20.3 20.3 73.9
3 32 8.9 8.9 82.8
4 39 10.8 10.8 93.6
5 23 6.4 6.4 100.0
Total 360 100.0 100.0
Bees Baiting 6
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 1 160 44.4 44.4 44.4
2 64 17.8 17.8 62.2
3 38 10.6 10.6 72.8
4 51 14.2 14.2 86.9
5 47 13.1 13.1 100.0
Total 360 100.0 100.0
Bees Baiting 7
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 1 149 41.4 41.4 41.4
2 81 22.5 22.5 63.9
3 27 7.5 7.5 71.4
4 53 14.7 14.7 86.1
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5 50 13.9 13.9 100.0
Total 360 100.0 100.0
Bees Baiting 8
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 1 55 15.3 15.3 15.3
2 51 14.2 14.2 29.4
3 31 8.6 8.6 38.1
4 69 19.2 19.2 57.2
5 154 42.8 42.8 100.0
Total 360 100.0 100.0
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Bees Baiting 9
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 1 165 45.8 45.8 45.8
2 60 16.7 16.7 62.5
3 30 8.3 8.3 70.8
4 59 16.4 16.4 87.2
5 46 12.8 12.8 100.0
Total 360 100.0 100.0
Bees Baiting 10
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 1 180 50.0 50.0 50.0
2 59 16.4 16.4 66.4
3 20 5.6 5.6 71.9
4 61 16.9 16.9 88.9
5 40 11.1 11.1 100.0
Total 360 100.0 100.0
Bees Baiting 11
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 1 162 45.0 45.0 45.0
2 54 15.0 15.0 60.0
3 26 7.2 7.2 67.2
4 67 18.6 18.6 85.8
5 51 14.2 14.2 100.0
Total 360 100.0 100.0
Bees Baiting 12
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 1 184 51.1 51.1 51.1
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2 60 16.7 16.7 67.8
3 24 6.7 6.7 74.4
4 53 14.7 14.7 89.2
5 39 10.8 10.8 100.0
Total 360 100.0 100.0
Bees Baiting 13
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 1 182 50.6 50.6 50.6
2 56 15.6 15.6 66.1
3 19 5.3 5.3 71.4
4 57 15.8 15.8 87.2
5 46 12.8 12.8 100.0
Total 360 100.0 100.0
Bees Management Technologies
Bees Management Technologies 1
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 1 178 49.4 49.4 49.4
2 77 21.4 21.4 70.8
3 32 8.9 8.9 79.7
4 49 13.6 13.6 93.3
5 24 6.7 6.7 100.0
Total 360 100.0 100.0
Bees Management Technologies 2
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 1 155 43.1 43.1 43.1
2 79 21.9 21.9 65.0
3 40 11.1 11.1 76.1
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Bees Management Technologies 4
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 1 200 55.6 55.6 55.6
2 42 11.7 11.7 67.2
3 35 9.7 9.7 76.9
4 39 10.8 10.8 87.8
5 44 12.2 12.2 100.0
Total 360 100.0 100.0
4 56 15.6 15.6 91.7
5 30 8.3 8.3 100.0
Total 360 100.0 100.0
Bees Management Technologies 3
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 1 184 51.1 51.1 51.1
2 62 17.2 17.2 68.3
3 42 11.7 11.7 80.0
4 37 10.3 10.3 90.3
5 35 9.7 9.7 100.0
Total 360 100.0 100.0
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Bees Management Technologies 5
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 1 184 51.1 51.1 51.1
2 59 16.4 16.4 67.5
3 32 8.9 8.9 76.4
4 38 10.6 10.6 86.9
5 47 13.1 13.1 100.0
Total 360 100.0 100.0
Bees Management Technologies 6
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 1 145 40.3 40.3 40.3
2 44 12.2 12.2 52.5
3 32 8.9 8.9 61.4
4 69 19.2 19.2 80.6
5 70 19.4 19.4 100.0
Total 360 100.0 100.0
Bees Management Technologies 7
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 1 176 48.9 48.9 48.9
2 52 14.4 14.4 63.3
3 41 11.4 11.4 74.7
4 34 9.4 9.4 84.2
5 57 15.8 15.8 100.0
Total 360 100.0 100.0
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Bees Management Technologies 8
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 1 162 45.0 45.0 45.0
2 48 13.3 13.3 58.3
3 28 7.8 7.8 66.1
4 55 15.3 15.3 81.4
5 67 18.6 18.6 100.0
Total 360 100.0 100.0
Bees Feeding Technologies
Bees Feeding Technologies 1
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 1 195 54.2 54.2 54.2
2 68 18.9 18.9 73.1
3 36 10.0 10.0 83.1
4 44 12.2 12.2 95.3
5 17 4.7 4.7 100.0
Total 360 100.0 100.0
Bees Feeding Technologies 2
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 1 192 53.3 53.3 53.3
2 66 18.3 18.3 71.7
3 34 9.4 9.4 81.1
4 49 13.6 13.6 94.7
5 19 5.3 5.3 100.0
Total 360 100.0 100.0
clxviii
Bees Feeding Technologies 3
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 1 175 48.6 48.6 48.6
2 49 13.6 13.6 62.2
3 39 10.8 10.8 73.1
4 44 12.2 12.2 85.3
5 53 14.7 14.7 100.0
Total 360 100.0 100.0
Bees Feeding Technologies 4
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 1 203 56.4 56.4 56.4
2 51 14.2 14.2 70.6
3 44 12.2 12.2 82.8
4 35 9.7 9.7 92.5
5 27 7.5 7.5 100.0
Total 360 100.0 100.0
Bees Feeding Technologies 5
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 1 188 52.2 52.2 52.2
2 59 16.4 16.4 68.6
3 33 9.2 9.2 77.8
4 40 11.1 11.1 88.9
5 40 11.1 11.1 100.0
Total 360 100.0 100.0
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Honey Harvesting Technologies
HoneyHarvesting Technologies 1
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 1 135 37.5 37.5 37.5
2 39 10.8 10.8 48.3
3 34 9.4 9.4 57.8
4 93 25.8 25.8 83.6
5 59 16.4 16.4 100.0
Total 360 100.0 100.0
Honey Harvesting Technologies 2
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 1 132 36.7 36.7 36.7
2 66 18.3 18.3 55.0
3 40 11.1 11.1 66.1
4 58 16.1 16.1 82.2
5 64 17.8 17.8 100.0
Total 360 100.0 100.0
Honey Harvesting Technologies 3
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 1 129 35.8 35.8 35.8
2 57 15.8 15.8 51.7
3 37 10.3 10.3 61.9
4 55 15.3 15.3 77.2
5 82 22.8 22.8 100.0
Total 360 100.0 100.0
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Honey Harvesting Technologies 4
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 1 144 40.0 40.0 40.0
2 62 17.2 17.2 57.2
3 28 7.8 7.8 65.0
4 54 15.0 15.0 80.0
5 72 20.0 20.0 100.0
Total 360 100.0 100.0
Honey Harvesting Technologies 5
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 1 182 50.6 50.6 50.6
2 50 13.9 13.9 64.4
3 24 6.7 6.7 71.1
4 56 15.6 15.6 86.7
5 48 13.3 13.3 100.0
Total 360 100.0 100.0
Honey Harvesting Technologies 6
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 1 165 45.8 45.8 45.8
2 59 16.4 16.4 62.2
3 24 6.7 6.7 68.9
4 47 13.1 13.1 81.9
5 65 18.1 18.1 100.0
Total 360 100.0 100.0
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Honey Harvesting Technologies 7
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 1 154 42.8 42.8 42.8
2 66 18.3 18.3 61.1
3 29 8.1 8.1 69.2
4 45 12.5 12.5 81.7
5 66 18.3 18.3 100.0
Total 360 100.0 100.0
Honey Harvesting Technologies 8
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 1 141 39.2 39.2 39.2
2 27 7.5 7.5 46.7
3 22 6.1 6.1 52.8
4 71 19.7 19.7 72.5
5 99 27.5 27.5 100.0
Total 360 100.0 100.0
Cassava and Maize Alley Technologies
Cassava/Maize in Alley Technologies 1
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 1 68 18.9 18.9 18.9
2 13 3.6 3.6 22.5
3 19 5.3 5.3 27.8
4 87 24.2 24.2 51.9
5 173 48.1 48.1 100.0
Total 360 100.0 100.0
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Cassava/Maize in Alley Technologies 2
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 1 77 21.4 21.4 21.4
2 17 4.7 4.7 26.1
3 24 6.7 6.7 32.8
4 85 23.6 23.6 56.4
5 157 43.6 43.6 100.0
Total 360 100.0 100.0
Cassava/Maize in Alley Technologies 3
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 1 106 29.4 29.4 29.4
2 51 14.2 14.2 43.6
3 34 9.4 9.4 53.1
4 81 22.5 22.5 75.6
5 88 24.4 24.4 100.0
Total 360 100.0 100.0
Cassava/Maize in Alley Technologies 4
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 1 91 25.3 25.3 25.3
2 38 10.6 10.6 35.8
3 34 9.4 9.4 45.3
4 88 24.4 24.4 69.7
5 109 30.3 30.3 100.0
Total 360 100.0 100.0
Cassava/Maize in Alley Technologies 5
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 1 87 24.2 24.2 24.2
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2 35 9.7 9.7 33.9
3 31 8.6 8.6 42.5
4 79 21.9 21.9 64.4
5 128 35.6 35.6 100.0
Total 360 100.0 100.0
Cassava/Maize in Alley Technologies 6
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 1 84 23.3 23.3 23.3
2 48 13.3 13.3 36.7
3 25 6.9 6.9 43.6
4 65 18.1 18.1 61.7
5 138 38.3 38.3 100.0
Total 360 100.0 100.0
Cassava/Maize in Alley Technologies 7
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 1 115 31.9 31.9 31.9
2 50 13.9 13.9 45.8
3 28 7.8 7.8 53.6
4 68 18.9 18.9 72.5
5 99 27.5 27.5 100.0
Total 360 100.0 100.0
Cassava/Maize in Alley Technologies 8
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 1 81 22.5 22.5 22.5
2 40 11.1 11.1 33.6
3 28 7.8 7.8 41.4
4 74 20.6 20.6 61.9
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5 137 38.1
38.1
100.0
Total 360 100.0 100.0
Cassava /Maize in Alley Technologies 9
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 1 79 21.9 21.9 21.9
2 27 7.5 7.5 29.4
3 30 8.3 8.3 37.8
4 60 16.7 16.7 54.4
5 164 45.6 45.6 100.0
Total 360 100.0 100.0
Cassava/Maize in Alley Technologies 10
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 1 98 27.2 27.2 27.2
2 38 10.6 10.6 37.8
3 30 8.3 8.3 46.1
4 59 16.4 16.4 62.5
5 135 37.5 37.5 100.0
Total 360 100.0 100.0
Cassava Maize in Alley Technologies 10
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 1 93 25.8 25.8 25.8
2 29 8.1 8.1 33.9
3 31 8.6 8.6 42.5
4 79 21.9 21.9 64.4
5 128 35.6 35.6 100.0
Total 360 100.0 100.0
CassavaMaize in Alley Technologies 11
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Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 1 95 26.4 26.4 26.4
2 25 6.9 6.9 33.3
3 30 8.3 8.3 41.7
4 69 19.2 19.2 60.8
5 141 39.2 39.2 100.0
Total 360 100.0 100.0
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CassavaMaize in Alley Technologies 12
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 1 77 21.4 21.4 21.4
2 13 3.6 3.6 25.0
3 26 7.2 7.2 32.2
4 51 14.2 14.2 46.4
5 193 53.6 53.6 100.0
Total 360 100.0 100.0
Multipurpose Trees Establishment Technologies
Multipurpose Trees 1
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 1 116 32.2 32.2 32.2
2 48 13.3 13.3 45.6
3 32 8.9 8.9 54.4
4 90 25.0 25.0 79.4
5 74 20.6 20.6 100.0
Total 360 100.0 100.0
Multipurpose Trees 2
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 1 117 32.5 32.5 32.5
2 56 15.6 15.6 48.1
3 54 15.0 15.0 63.1
4 75 20.8 20.8 83.9
5 58 16.1 16.1 100.0
Total 360 100.0 100.0
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Multipurpose Trees Technologies 3
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 1 147 40.8 40.8 40.8
2 56 15.6 15.6 56.4
3 40 11.1 11.1 67.5
4 57 15.8 15.8 83.3
5 60 16.7 16.7 100.0
Total 360 100.0 100.0
Multipurpose Trees Technologies 4
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 1 142 39.4 39.4 39.4
2 75 20.8 20.8 60.3
3 33 9.2 9.2 69.4
4 51 14.2 14.2 83.6
5 59 16.4 16.4 100.0
Total 360 100.0 100.0
Multipurpose Trees Technologies 5
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 1 174 48.3 48.3 48.3
2 44 12.2 12.2 60.6
3 29 8.1 8.1 68.6
4 59 16.4 16.4 85.0
5 54 15.0 15.0 100.0
Total 360 100.0 100.0
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Multipurpose Trees Technologies 6
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 1 119 33.1 33.1 33.1
2 34 9.4 9.4 42.5
3 39 10.8 10.8 53.3
4 51 14.2 14.2 67.5
5 117 32.5 32.5 100.0
Total 360 100.0 100.0
Multipurpose Trees Technologies 7
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 1 156 43.3 43.3 43.3
2 64 17.8 17.8 61.1
3 36 10.0 10.0 71.1
4 55 15.3 15.3 86.4
5 49 13.6 13.6 100.0
Total 360 100.0 100.0
Multipurpose Trees Technologies 8
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 1 116 32.2 32.2 32.2
2 51 14.2 14.2 46.4
3 36 10.0 10.0 56.4
4 49 13.6 13.6 70.0
5 108 30.0 30.0 100.0
Total 360 100.0 100.0
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Establishing Browse Plants Technologies
Browse Plants Technologies 1
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 1 125 34.7 34.7 34.7
2 42 11.7 11.7 46.4
3 26 7.2 7.2 53.6
4 66 18.3 18.3 71.9
5 101 28.1 28.1 100.0
Total 360 100.0 100.0
Browse Plant Technologies 2
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 1 142 39.4 39.4 39.4
2 47 13.1 13.1 52.5
3 32 8.9 8.9 61.4
4 61 16.9 16.9 78.3
5 78 21.7 21.7 100.0
Total 360 100.0 100.0
Browse Plant Technologies 3
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 1 143 39.7 39.7 39.7
2 50 13.9 13.9 53.6
3 29 8.1 8.1 61.7
4 67 18.6 18.6 80.3
5 71 19.7 19.7 100.0
Total 360 100.0 100.0
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Browse Plant Technologies 4
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 1 112 31.1 31.1 31.1
2 42 11.7 11.7 42.8
3 33 9.2 9.2 51.9
4 71 19.7 19.7 71.7
5 102 28.3 28.3 100.0
Total 360 100.0 100.0
Browse Plants Technologies 5
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 1 116 32.2 32.2 32.2
2 29 8.1 8.1 40.3
3 30 8.3 8.3 48.6
4 61 16.9 16.9 65.6
5 124 34.4 34.4 100.0
Total 360 100.0 100.0
Browse Plants Technologies 6
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 1 95 26.4 26.4 26.4
2 51 14.2 14.2 40.6
3 30 8.3 8.3 48.9
4 69 19.2 19.2 68.1
5 115 31.9 31.9 100.0
Total 360 100.0 100.0
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Browse Plants Technologies 7
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 1 120 33.3 33.3 33.3
2 31 8.6 8.6 41.9
3 23 6.4 6.4 48.3
4 69 19.2 19.2 67.5
5 117 32.5 32.5 100.0
Total 360 100.0 100.0
Browse Plants Technologies 8
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 1 118 32.8 32.8 32.8
2 42 11.7 11.7 44.4
3 27 7.5 7.5 51.9
4 70 19.4 19.4 71.4
5 103 28.6 28.6 100.0
Total 360 100.0 100.0
Vertiver Grasses Establishment Technologies
Vertiver Grass Technologies 1
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 1 186 51.7 51.7 51.7
2 55 15.3 15.3 66.9
3 21 5.8 5.8 72.8
4 62 17.2 17.2 90.0
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5 36 10.0 10.0 100.0
Total 360 100.0 100.0
Vertiver Grass Technologies 2
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 1 184 51.1 51.1 51.1
2 50 13.9 13.9 65.0
3 27 7.5 7.5 72.5
4 60 16.7 16.7 89.2
5 39 10.8 10.8 100.0
Total 360 100.0 100.0
Vertiver Grass Technologies 3
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 1 183 50.8 50.8 50.8
2 56 15.6 15.6 66.4
3 33 9.2 9.2 75.6
4 53 14.7 14.7 90.3
5 35 9.7 9.7 100.0
Total 360 100.0 100.0
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Vertiver Grass Technologies 4
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 1 194 53.9 53.9 53.9
2 56 15.6 15.6 69.4
3 27 7.5 7.5 76.9
4 40 11.1 11.1 88.1
5 43 11.9 11.9 100.0
Total 360 100.0 100.0
Vertiver Grass Technologies 5
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 1 197 54.7 54.7 54.7
2 56 15.6 15.6 70.3
3 27 7.5 7.5 77.8
4 46 12.8 12.8 90.6
5 34 9.4 9.4 100.0
Total 360 100.0 100.0
Vertiver Grass Technologies 6
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 1 174 48.3 48.3 48.3
2 60 16.7 16.7 65.0
3 19 5.3 5.3 70.3
4 54 15.0 15.0 85.3
5 53 14.7 14.7 100.0
Total 360 100.0 100.0
Vertiver Grass Technologies 7
Frequency Percent
Valid
Percent
Cumulative
Percent
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Valid 1 130 36.1 36.1 36.1
2 51 14.2 14.2 50.3
3 23 6.4 6.4 56.7
4 48 13.3 13.3 70.0
5 108 30.0 30.0 100.0
Total 360 100.0 100.0
Vertiver Grass Technologies 8
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 1 67 18.6 18.6 18.6
2 21 5.8 5.8 24.4
3 37 10.3 10.3 34.7
4 37 10.3 10.3 45.0
5 198 55.0 55.0 100.0
Total 360 100.0 100.0
Vertiver Grass Technologies 9
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 1 59 16.4 16.4 16.4
2 15 4.2 4.2 20.6
3 38 10.6 10.6 31.1
4 31 8.6 8.6 39.7
5 217 60.3 60.3 100.0
Total 360 100.0 100.0
Vertiver Grass Technologies 10
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 1 62 17.2 17.2 17.2
2 15 4.2 4.2 21.4
3 41 11.4 11.4 32.8
4 25 6.9 6.9 39.7
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5 217 60.3 60.3 100.0
Total 360 100.0 100.0
Vertiver Grass Technologies 11
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 1 63 17.5 17.5 17.5
2 16 4.4 4.4 21.9
3 32 8.9 8.9 30.8
4 26 7.2 7.2 38.1
5 223 61.9 61.9 100.0
Total 360 100.0 100.0
Vertiver Grass Technologies 12
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 1 54 15.0 15.0 15.0
2 17 4.7 4.7 19.7
3 36 10.0 10.0 29.7
4 25 6.9 6.9 36.7
5 228 63.3 63.3 100.0
Total 360 100.0 100.0
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