mphil thesis finalversion
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Francis Chilenga's Master dissertation focused on the assessment of the effectiveness of the Sasakawa Global 2000 Programme approach to agricultural technology delivery in northen MalawiTRANSCRIPT
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ii
DECLARATION
Candidate’s Declaration
I hereby declare that this thesis is the result of my own original work and that no
part of it has been presented for another degree in this university or elsewhere.
Candidate’s Signature: ………………………………….Date: …………………...
Name: ………………………………………………………………………………
Supervisors’ Declaration
We hereby declare that the preparation and presentation of the thesis were
supervised in accordance with the guidelines on supervision of thesis laid down
by the University of Cape Coast.
Principal Supervisor’s Signature: …………………………..Date: ………………..
Name: ………………………………………………………………………………
Co-Supervisor’s Signature: ………………………………… Date: ………………
Name: ………………………………………………………………………………
iii
ABSTRACT
Sufficient food production remains an important condition for alleviating
food insecurity in Malawi. However, achieving sustainable food security requires
that farmers continually adopt improved agricultural production technologies in
order to realize yield potentials from a decreasing land resource base. An effective
and efficient extension system is, thus, very essential to the dissemination and
adoption of improved agricultural technologies. This study was carried out to
assess the effectiveness of the Sasakawa Global 2000 approach to agricultural
technology delivery in Northern Malawi. Using a descriptive correlational survey
design, data were collected from 194 Sasakawa Global 2000 participant-farmers
using a proportionate stratified random sampling method from two purposively
sampled districts, namely Rumphi and Chitipa in Northern Malawi. The results
revealed that the Sasakawa Global 2000 approach attracted a high level of
participation by farmers in planning, monitoring and evaluation of programme
activities. The management training plot and access to farm credit were the two
important factors found to explain the effectiveness of the Sasakawa Global 2000
approach. Results also revealed a high level of adoption of the technologies
disseminated under the Sasakawa Global 2000 Programme.
Based on these key findings, it is recommended that the Ministry of
Agriculture and Food Security (MoAFS) should mainstream the management
training plot into public extension programmes. In addition, MoAFS should
promote the use of participatory extension approaches in agricultural services
iv
delivery. Improving smallholder farmers’ access to farm credit through
appropriate government interventions will also help smallholder farmers ensure
food security at household level.
v
ACKNOWLEDGEMENTS
I would like to express my sincere appreciation to my Principal
Supervisor, Dr. Ismail bin Yahya and Co-supervisor, Dr. Albert Obeng Mensah,
for their constant guidance and encouragement, without which this work would
not have been possible. For their unwavering support, I am truly grateful. I am
also grateful to all the lecturers in the School of Agriculture, Department of
Agricultural Economics and Extension in particular, especially Professor Joseph
Kwarteng and Dr. Festus Annor-Frempong for their support towards the
successful completion of my studies in Ghana.
Without the financial support of the Sasakawa Africa Fund for Extension
education (SAFE) which offered me a scholarship for graduate studies, this work
would not have been possible. Special thanks go to Dr. Deola Naibakelao, and
Mr. Nick Sichinga, National Coordinator for SG 2000 in Malawi for granting me
that rare opportunity. I also would like to express my heartfelt gratitudes to the
Ministry of Agriculture and Food Security in Malawi for granting me study leave
and for supporting me during the entire data collection period. Many thanks also
go to Messrs M. Lweya, M.T.W Hara, D. Nyirenda and N. Mwenibungu for their
assistance and dedication during the field work. I am really grateful to them.
I would also like to thank my friends, and colleagues at the University of
Cape Coast for their encouragement and moral support which made my stay and
studies in Ghana more enjoyable. To them I say “we meet to part, but more
importantly we part to meet.”
vi
DEDICATION
To my parents, Kingsley Wakisa Chilenga and Rozalia Nandeka
vii
LIST OF ACRONYMS AND ABBREVIATIONS
ADD Agricultural Development Division
AEDC Agricultural Extension Development Coordinator
AEDO Agricultural Extension Development Officer
ASP Agricultural Services Project
BES Block Extension System
DADO District Agricultural Development Office
DAES Department of Agricultural Extension Services
EPA Extension Planning Area
FAO Food and Agriculture Organisation of the United Nations
GoM Government of Malawi
IPM Integrated Pest Management
MDGS Malawi Development and Growth Strategy
MoAFS Ministry of Agriculture and Food Security
NGO Non-Governmental Organisation
NRIA National Research Institute for Agriculture
SAA Sasakawa Africa Association
SG 2000 Sasakawa Global 2000
T & V Training and Visit
ToT Transfer of Technology
USAID United States Agency for International Development
WB World Bank
viii
TABLE OF CONTENTS
Content Page
DECLARATION .................................................................................................... ii
ABSTRACT........................................................................................................... iii
ACKNOWLEDGEMENTS.................................................................................... v
DEDICATION....................................................................................................... vi
LIST OF ACRONYMS AND ABBREVIATIONS ............................................. vii
TABLE OF CONTENTS.....................................................................................viii
LIST OF TABLES............................................................................................... xiv
LIST OF FIGURES ............................................................................................ xvii
CHAPTER 1: INTRODUCTION1
Background to the Study......................................................................................... 1
Statement of the Problem........................................................................................ 6
Objectives of the Study........................................................................................... 8
General Objective ................................................................................................... 8
Specific Objectives ................................................................................................. 8
Research Hypotheses .............................................................................................. 9
Variables in the Study........................................................................................... 11
Rationale for the Study ......................................................................................... 12
Delimitations......................................................................................................... 13
Definition of Key Terms....................................................................................... 14
Description of Study Area .................................................................................... 15
Country Profile...................................................................................................... 15
ix
Sampled Districts .................................................................................................. 16
Chitipa District: A Brief Profile............................................................................ 17
Rumphi District: A Brief Profile........................................................................... 18
CHAPTER 2: LITERATURE REVIEW
Introduction........................................................................................................... 21
Agricultural extension: Meaning and its significance .......................................... 21
Agricultural Extension in Malawi: An Overview................................................. 24
SG 2000 and Agriculture Development in Malawi .............................................. 25
Agricultural Extension Models: A Comparative Overview.................................. 27
The Technology Transfer Model .......................................................................... 28
Farmer First Model ............................................................................................... 29
Participatory Model .............................................................................................. 30
Sustainable development extension model ........................................................... 31
Extension Communication Methods..................................................................... 32
A Comparison of Individual and Group Methods ................................................ 33
Farmer Participation in Extension Programmes ................................................... 35
Definition of Participation .................................................................................... 35
Types and Levels of Participation......................................................................... 35
Benefits of Participation ....................................................................................... 37
Costs of Participation............................................................................................ 38
Key Elements in Promoting Participation............................................................. 39
Adoption and Diffusion of Innovations ................................................................ 40
Stages in the Adoption Process............................................................................. 40
x
Adopter Categories and their Characteristics ....................................................... 42
Determinants of Adoption..................................................................................... 42
Economic Factors.................................................................................................. 44
Farm Size .............................................................................................................. 44
Cost of Technology............................................................................................... 46
Level of Expected benefits.................................................................................... 46
Off-farm hours ...................................................................................................... 47
Social Factors........................................................................................................ 47
Age of Adopter ..................................................................................................... 47
Education .............................................................................................................. 49
Gender Issues and Concerns ................................................................................. 49
Institutional Factors .............................................................................................. 50
Extension Contacts................................................................................................ 50
The Combined Effect............................................................................................ 51
Adoption of Maize Production Technologies in Sub-Saharan Africa .................. 53
Use of Inorganic fertilizer and Improved Varieties .............................................. 53
Adoption of Other Crop Management Practices................................................... 54
Conservation Tillage............................................................................................. 55
Definition of Conservation Tillage ....................................................................... 55
Impact of Conservation Tillage on Yield.............................................................. 55
Adoption of Conservation Tillage ........................................................................ 56
Conceptual framework.......................................................................................... 57
Introduction........................................................................................................... 57
xi
CHAPTER 3: RESEARCH METHODOLOGY
Introduction........................................................................................................... 63
Research Design.................................................................................................... 63
Population of Study............................................................................................... 64
Sampling and Sample Size.................................................................................... 64
Instrumentation ..................................................................................................... 65
Validation of Instrument ....................................................................................... 67
Pilot-testing the Instrument................................................................................... 67
Training of Interviewers ....................................................................................... 68
Data Collection ..................................................................................................... 69
Data Management and Analysis ........................................................................... 69
Hypotheses Testing............................................................................................... 70
CHAPTER 4: RESULTS AND DISCUSSION
Introduction........................................................................................................... 72
Demographic and Socio economic Characteristics of Farmers -.......................... 72
Sex......................................................................................................................... 72
Age........................................................................................................................ 73
Formal Education.................................................................................................. 74
Household size...................................................................................................... 76
Farm Labour.......................................................................................................... 77
Land holding size.................................................................................................. 78
Years of Farming Experience ............................................................................... 79
Income level.......................................................................................................... 80
xii
Major crops grown................................................................................................ 81
Utilisation of cultivated crops............................................................................... 82
Access to credit ..................................................................................................... 84
Use of credit .......................................................................................................... 84
Reasons for not accessing credit ........................................................................... 85
Sources of credit ................................................................................................... 86
Sources of agricultural extension services............................................................87
Extension teaching methods experienced by farmers........................................... 88
Farmers’ Perceptions of the Level of Participation in SG 2000 Programme ....... 90
Farmers’ Perceptions of the Effectiveness of the Management Training Plot as
used under SG 2000 Programme Approach.............................................. 92
Farmers’ Perceptions of the Level of Satisfaction with Technologies
Disseminated under SG 2000 Programme................................................ 94
Farmers’ Perceptions of the Level of Adoption of Technologies Disseminated
under SG 2000 Programme....................................................................... 95
Constraints to adoption of agricultural technologies disseminated under SG 2000
Programme................................................................................................ 96
Independent sampled t-test –comparison of means of level of participation,
perception on management training plot effectiveness, level of satisfaction
with technologies and level of technology adoption by districts.............. 98
Independent sampled t-test –comparison of means of perception on level of
participation, perception on management training plot effectiveness, level
xiii
of satisfaction with technologies and level of technology adoption by sex
of respondents ......................................................................................... 100
Relationship between overall effectiveness of SG 2000 Programme Approach to
agricultural technology delivery and selected variables ......................... 102
Relationship between level of participation and farmers’ demographic and socio-
economic characteristics ......................................................................... 105
Relationship between level of technology adoption and selected farmers’
demographic and socio-economic characteristics................................... 106
Predictors of the overall effectiveness of the SG 2000 Programme Approach to
agricultural technology delivery ............................................................. 112
CHAPTER 5: SUMMARY, CONCLUSIONS AND RECOMMENDATIONS
Introduction......................................................................................................... 114
Summary of Thesis ............................................................................................. 114
Conclusions......................................................................................................... 123
Recommendations............................................................................................... 126
Future Research Direction .................................................................................. 128
REFERENCES ................................................................................................... 130
APPENDIX I: FARMERS’ INTERVIEW SCHEDULE ................................... 145
xiv
LIST OF TABLES
Table Page
1: Reliability Coefficients ..................................................................................... 68
2: Davis Conversion for correlations .................................................................... 71
3: Sex distribution of respondent-farmers in the study area ................................. 73
4: Age distribution of respondent-farmers in the study area................................. 73
5: Formal education level of respondent-farmers in the study area...................... 75
6: Household size distribution of respondent-farmers in the study area............... 76
7: Frequency distribution of farm labour sources as reported by farmers ............ 77
8: Frequency distribution of landholding size as reported by respondent-farmers
................................................................................................................... 78
9: Frequency distribution of years of farming experience as reported by
respondent-farmers ................................................................................... 80
10: Frequency distribution of income levels of respondent- farmers ................... 81
11: Summary statistics of major crops grown as reported by respondent-farmers82
12: Utilization of major crops grown as reported by respondent-farmers ............ 83
13: Distribution of respondent-farmers who have ever accessed credit in the study
area............................................................................................................ 85
14: Use of credit as reported by respondent-farmers............................................ 85
15: Frequency distribution of respondent-farmers’ reasons for not accessing credit
................................................................................................................... 86
16: Sources of credit by respondent-farmers ........................................................ 86
xv
17: Respondent-farmers’ sources of agricultural extension services in the study
area............................................................................................................ 87
18: Extension teaching methods as experienced by respondent-farmers in the
study area .................................................................................................. 89
19: Respondent-farmers perceptions of level of participation in SG 2000
Programme................................................................................................ 91
20: Respondent-farmers perceptions of effectiveness of management training plot
as used under SG 2000 Programme Approach ...................................... 93
21: Respondent-farmers’ perceptions on level of satisfaction with technologies
disseminated under SG 2000 Programme.............................................. 94
22: Respondent-farmers’ perceptions of level of adoption of technologies
disseminated under SG 2000 Programme.............................................. 95
23: Frequency distribution of the constraints to adoption of technologies
disseminated under SG 2000 Programme as reported by farmers......... 97
24: An independent samples t-test analysis by selected district ........................... 99
25: An independent samples t-test analysis by sex of respondent-farmers ........ 101
26: Correlation matrix showing the relationship between overall effectiveness of
the SG 2000 approach and related variables........................................ 104
27: Relationship between respondent-farmers’ level of participation in the
programme and related selected demographic and socio-economic
characteristics.......................................................................................106
28: Relationship between level of technology adoption and selected respondent-
farmers’ demographic and socio-economic characteristics. ................ 108
xvi
29: Regression coefficients ................................................................................. 112
xvii
LIST OF FIGURES
Figure Page
1: Map of Malawi Showing Location of the Sampled Districts ........................... 19
2: Location of Focal Study Areas in the Districts Sampled.................................. 20
3: The Sustainable Development Extension Model.............................................. 32
4: A Conceptual Framework of the Perceived effectiveness of SG2000
Programme Approach to agricultural technology delivery....................... 60
1
CHAPTER 1: INTRODUCTION
Background to the Study
Agriculture is the single most important sector of Malawi’s economy.
Thus, the performance of the economy depends critically on the performance of
the agricultural sector. The agricultural sector accounts for about 90 per cent of
export earnings, provides 85 per cent of total employment and contributes about
39 per cent of the country’s gross domestic product (FAO, 2005).
Malawi’s development policy for the medium term continues to
recognize the agricultural sector as the pillar of the economy, with priority
centered on ensuring food security, increasing export earnings and providing of
employment, incomes and livelihood for the population (GoM, 2006). For the
agricultural sector to play this crucial role in the economy in a sustainable way,
rapid growth in output and productivity within the sector is critical. It is widely
recognized that the sustained flow of and utilization of improved technologies is
the key to increased growth and productivity (Maunder, 1973; Swanson & Claar,
1984; Frank & Chamala, 1992).
According to Ministry of Agriculture and Food Security (GoM, 2000)
agriculture occupies about 56 per cent of the total land area covering 5.3 million
hectares of the country’s 9.4 million hectares. The agriculture sector is dualistic,
2
consisting of smallholder farmers and an estate sub-sector. The smallholder sub-
sector is based on a customary land-tenure system and is primarily subsistence,
providing the bulk of food production. The smallholder sub-sector occupies about
80 per cent while the estate sub-sector occupies the remaining 20 per cent of the
agricultural land. Due to high population pressure on land, some 2.6 million
smallholder farmers cultivate less than a hectare of land of which half cultivate
less than 0.5 a hectare (GoM, 2000).
Agriculture in Malawi is mainly rainfed, of single season with low input
investment and low output. Moreover, it is vulnerable to changing climatic and
policy conditions. Small farms, low yields and unpredictable policies result in
chronic food shortages. Declining staple food production has moved Malawi from
being a net exporter in the 1980s to being a net importer in recent years (GoM,
2007). Nationally, about 40 percent of the rural households are not able to
produce enough food to meet the household food consumption needs.
Sufficient food production remains an important condition for alleviating
food insecurity in the country. Moreover the demand for food is likely to increase
in the near future with ever-increasing population growth. Malawi’s population is
estimated at around 12.5 million as compared to 8 million in 1987 representing an
annual growth rate of 3.2 percent (GoM, 2007). This means that much of the
increased food production will have to be realized on land that is already under
cultivation. The availability of new land suitable for agriculture is limited.
Therefore, agricultural production has to be intensified in diverse and risk prone
rainfed areas.
3
The Agricultural Services Project (ASP) spearheaded the main agricultural
technology development and dissemination efforts in Malawi in the late 1980s
and 1990s (Esser, Øygard, Chibwana and Blakie (2005). Under this project
farming systems methodologies were introduced with technical assistance from
United States Agency for International Development (USAID). The extension
efforts were based on the Block Extension System (BES), a modified form of the
Training and Visit (T&V) system. The BES entailed the establishment of
systematic message-based extension management system (MoAFS, 2000).
Embodied in this approach was a regular training programme intended to improve
the professional skills of staff and enhance their knowledge across disciplines. In
addition the approach emphasized use of contact farmers for technology
dissemination. But the hierarchical nature of technology development and
dissemination made it very difficult to create a farmer responsive system. A more
recent reorientation of agricultural extension emphasizes on a pluralistic, demand-
driven and decentralized participatory extension approach (MoAFS, 2000).
Small scale food producers in Malawi urgently need to improve total
factor productivity which can raise output to meet the country’s food consumption
needs. Existing low levels of productivity and low use of modern farming
practices hinder efforts to achieve progress in this direction. Various efforts by
non-governmental organisations (NGOs) have been made to raise agricultural
productivity by helping farmers to reduce technical inefficiency and fostering the
adoption of improved production technologies. A prominent example has been the
Sasakawa Global 2000 (SG 2000) agricultural programme which featured a strong
4
extension component directed at the dissemination of improved technology to
small scale producers and the improvement of farmers’ practices (Langyintuo,
2004).
SG 2000 is a non-profit organization established to develop programmes
for technology demonstration in various African countries in cooperation with
national extension services (Dowswell and Russel, 1991). Since 1986, SG 2000
has helped African farmers to improve their livelihoods through better farming
practices. It is an agricultural initiative of two non-governmental organizations
namely; Sasakawa Africa Association (SAA) and the Global 2000 Programme of
the Carter Centre in the USA. The SG 2000 programme is based on the principle
that “agricultural development cannot be achieved unless farmers have greater
access to science-based knowledge and technology, namely, improved varieties,
chemical fertilizers, and crop protection products, and improved crop
management practices” (Dowswell and Russel, 1991). The main features of SG
2000 programme are as follows;
• Close collaboration with partner country’s Ministry of Agriculture,
• Direct farmer participation in technology transfer, and
• Promotion of agricultural intensification with appropriate, financially
viable technology (Nubukpo and Galiba, 1999).
SG 2000 has adopted seven (7) important principles of best practice through its
experiences. The working principles are that:
• extension messages should be delivered to farmers as a package rather
than as isolated individual interventions;
5
• focus should be on single enterprise (main staple crop) first then on the
farming system;
• improved production technology should demonstrably and significantly
increase yield and productivity on the farm so that its monetary benefits to
the farmers are measurable in farmers’ terms (bags);
• demonstration plots should give farmers a first hand opportunity to test
improved production technologies on a commercial scale in their own
fields;
• inputs required for adoption of improved technologies should be pitched at
levels that are accessible through the private sector in rural areas, and
• farmers’ participation in testing improved technologies should be based on
their own conviction rather than on the promise of credit for inputs or
coercion; and
• farmers should therefore be encouraged to use their own resources for
demonstrations from the outset (Breth, 1998).
The SG 2000 Programme in Malawi was implemented in 1998 (SAA,
2006) and operated in partnership with the regional agricultural development
divisions of MoAFS and the National Research Institute for Agriculture (NRIA).
The focus of partnership was on disseminating improved maize production
technologies to resource-deficit farmers. Activities of SG 2000 Programme in
Malawi included:
6
• demonstration of on-shelf and ‘best bet’ maize production practices
(timely planting, correct plant spacing, correct ridge spacing, timely
harvesting, correct fertilizer application, use of improved maize varieties);
• demonstration of conservation farming in maize production (use of pre-
emergence and post emergence herbicides); and
• demonstration of improved post-harvest practices that reduce grain losses
(use of drying cribs and grain storage cribs) (Breth, 1998).
It is clear that sustainable agricultural development is the key to the future
for sub-Saharan African countries including Malawi. Throughout its years of
operation in Malawi, SG 2000 has been able to demonstrate that, given access to
available inputs and using them more efficiently with better farming practices,
small-scale farmers can easily double or triple their yields of staple food crops.
For example, farmers who have practiced conservation tillage as recommended by
extension workers have profited from the practice through significant increases in
yields obtained from 0.1 hectares mini plots (Ito, Matsumoto and Quinones,
2007).
Statement of the Problem
Achieving sustainable food security in Malawi requires that farmers
continually adopt improved agricultural production technologies in order to
realize yield potentials from a decreasing land resource base. An effective
extension system is central to the dissemination of any improved technologies.
Several NGOs have intervened in agricultural services delivery using diverse
7
approaches (Farrington, 1997). SG 2000 is one of the organizations that have
worked actively to alleviate food security by demonstrating to farmers how yield
potentials can be obtained by following recommended practices.
Although some programme reviews have been conducted about SG 2000
Programme activities in Malawi, they focused specifically on SG2000
contributions to increased crop yields; the government’s commitment to taking up
SG 2000 technology transfer activities; and recommendations for improving on-
going country programme activities (SAA Report, 2001-2002; Plucknett,
Matsumoto and Takase, 2002). After nine years of SG 2000 Programme
interventions in Malawi (1998-2006), it is logical and important to conduct an
assessment of the effectiveness of the SG 2000 Programme approach in
agricultural technology transfer focusing primarily on the perceptions of the
programme beneficiaries. This study was, therefore, an attempt to answer the
following questions:
• what was the extent of farmers’ participation in SG 2000 Programme
activities?
• how did participant-farmers perceive the effectiveness of the use of the
management training plots as a method for technology transfer under SG
2000 Programme?
• what are the reactions of farmers’ to the technological package
disseminated under SG 2000 Programme?
• what are farmers’ adoption levels of the technologies disseminated to-date
under SG 2000 Programme?
8
• what were the major challenges and constraints preventing farmers from
adopting the technological recommendations? and as a central question
• how effective was the SG 2000 Programme approach to agricultural
technology delivery?
Objectives of the Study
General Objective
The primary objective of this study was to assess farmers’ perceptions of the
effectiveness of Sasakawa Global 2000 Programme approach to agricultural
technology delivery in Northern Malawi.
Specific Objectives
In order to achieve the above primary objective, the following specific objectives
were formulated, to:
1) describe the demographic and socio-economic characteristics of
participating farmers in terms of sex, age, formal education, household
size, farm labour sources, land holding size, years of farming experience,
level of income, major crops grown in the area, access to farm credit,
sources of extension services and extension teaching methods.
2) examine farmers’ perceptions of their level of participation in the SG 2000
Programme activities,
3) examine farmers’ perceptions of the effectiveness of the management
training plot as a method for technology delivery in SG 2000 Programme,
9
4) examine the degree of farmers’ satisfaction with the technological package
disseminated under the SG 2000 Programme,
5) examine farmers’ adoption levels of the technologies disseminated under
SG 2000 Programme
6) identify the constraints to non-adoption of technological recommendations
under the SG 2000 Programme, and
7) examine the relationships between selected farmers’ demographic and
socio-economic characteristics and their perceptions of the effectiveness
of the SG 2000 Programme approach to agricultural technology delivery.
Research Hypotheses
The following are the hypotheses that were tested in the research.
Hypothesis 1
H0: There are no significant differences in farmers’ perceptions of level of
participation, effectiveness of MTP, level of satisfaction and level of adoption
between Rumphi and Chitipa districts
H1: There are significant differences in farmers’ perceptions of level of
participation, effectiveness of MTP, level of satisfaction and level of adoption
between Rumphi and Chitipa districts
10
Hypothesis 2
H0: There are no significant differences in perceptions of level of participation,
effectiveness of MTP, level of satisfaction and level of adoption between male
and female participants
H1: There are significant differences in perceptions of level of participation,
effectiveness of MTP, level of satisfaction and level of adoption between male
and female participants
Hypothesis 3
H0: There is no significant relationship between farmers’ level of participation
and their socio-demographic characteristics such as age, gender, level of income,
years of farming experience, level of formal education, and access to credit.
H1: Farmers’ level of participation is significantly related to their and socio-
demographic characteristics such as age, gender, level of income, years of
farming experience, level of formal education, and access to credit.
Hypothesis 4
H0: There is no relationship between level of technology adoption by farmers and
their demographic and socio-economic characteristics.
H1: Level of technology adoption is significantly related to farmers’ demographic
and socio-economic characteristics
Hypothesis 5
H0: There is no relationship between technology adoption and the level of
farmers’ participation in the SG 2000 Programme.
11
H1: Technology adoption is significantly related to the level of farmers’
participation in the SG 2000 Programme.
Hypothesis 6
H0: There is no relationship between farmers’ perception of the effectiveness of
SG2000 Programme approach to technology delivery and their level of
participation.
H1: Farmers’ perception of the effectiveness of SG2000 Programme approach to
technology delivery is significantly related to their extent of participation.
Hypothesis 7
H0: There is no significant relationship between farmers’ perceptions of the
effectiveness of management training plot method to technology transfer and their
level of participation in the SG 2000 Programme.
H1: Farmers’ perception of the effectiveness of the management training plot
method to technology delivery is significantly related to their level of
participation in the programme.
Variables in the Study
• Perceived effectiveness of SG 2000 Programme approach to agricultural
technology delivery.
• Farmers’ socio-economic and demographic characteristics namely age,
gender, level of formal education, household size, years of farming, level
of income, farm labour, land holding size, access to extension services and
access to credit.
12
• Level of farmers’ participation in the SG 2000 Programme activities
• Farmers’ perceptions of the effectiveness of the MTP as a method for
technology transfer under SG 2000 Programme.
• Farmers’ satisfaction with the technological package disseminated under
SG 2000 Programme.
• Farmers’ adoption levels of technologies disseminated under SG 2000
Programme.
• Constraints to adoption of technological recommendations disseminated
under SG 2000 Programme.
Rationale for the Study
Malawi faces the challenge of achieving self-sufficiency in food
production and ensuring that there is adequate national food balance (GoM,
2007). One of the challenges in achieving self-sufficiency in food production
hinges on raising the food productivity among smallholder farmers through the
dissemination and adoption of modern technologies.
This study has documented strengths and weaknesses of SG 2000
Programme Approach to agricultural technology delivery in Northern Malawi
over the past nine (9) years. By pointing out the strengths and weaknesses of the
SG 2000 Programme Approach the study findings could provide guidance to SG
2000 Programme or any other related programme implemented along SG 2000
lines for enhancing the effectiveness of agricultural technology delivery.
13
Another benefit from the study could be provision of the current state of
maize production technologies adoption levels by farmers. By assessing the level
of adoption of maize production technologies disseminated under SG 2000
Programme and the factors influencing adoption, the findings have provided
information that could be used by policy makers, researchers and extension agents
to design appropriate strategies for improving and increasing agricultural
production in the country.
Since provision of farm inputs on credit was part of SG 2000 Programme
approach, the findings could provide a basis for gauging how policy changes may
affect farmers. Policy issues that constrain or enhance the provision of inputs on
loan may have a direct effect on food productivity and technology adoption
among smallholder farmers.
The overall study rationale is to make a contribution to designing effective
approaches to agricultural technology transfer so as to develop agriculture as a
sector of crucial importance to the country’s over-arching goals of achieving
poverty reduction and sustainable food security.
Delimitations
Sasakawa Global 2000 Programme was involved in the dissemination and
promotion of post harvest technologies, maize and rice production technologies
and minimum tillage practices. The study was narrowed to maize production
technologies because this was the principal focus of SG 2000 Programme. In
addition the study covered only two districts, namely, Chitipa, and Rumphi in the
14
Northern part of Malawi. The region was chosen because previous programme
evaluations had covered the two other regions, namely, central and southern
regions (Plucknett, et.al, 2002). The districts were selected because they are the
major maize growing areas in the region; maize is a major staple in the districts;
and because compared to other districts in the region a large number of farmers
participated in the SG 2000 Programme.
Definition of Key Terms
The following terms have been defined to facilitate understanding of this work:
Adoption: refers to the degree of use of a new technology in long run equilibrium
when a farmer has full information about the new technology and its potential
(Feder, Just and Zilberman, 1985).
Approach: refers to the basic planning philosophy of agricultural extension
programmes-a style of action within a system. “Agricultural extension strategies
and functions can be initiated and /or organized on the basis of an instrumental
(top-down) or an interactive mindset, that is, in a context that allows or does not
allow for an interactive approach” (Leeuwis, 2003, p. 210).
Effectiveness: refers to the degree to which goals are attained. In this study
effectiveness will be operationalised in terms of extension approach, level of
farmers’ participation in programme activities, farmers’ opinions about extension
methods used (in this case the Management Training Plots), farmers’ reactions to
the technological package, level of farmers’ adoption of technological
recommendations promoted, (Misra, 1997)
15
Perception: as used in the study, refers to a mental set, attitude or a conceptual
direction of an individual or group of individuals about an issue (Van den Ban &
Hawkins, 1996).
Rate of Adoption: refers to the relative speed with which an innovation is
adopted by members of the social system. It is measured as the number of
individuals who adopt a new idea in a specified period (Feder, et al, 1985).
Level of adoption: refers to the intensity of adoption of a given technology. It is
usually measured as the number of technologies being adopted and the number of
producers adopting them (Feder et. al., 1985).
Literacy: a literate person is one who can, with understanding, both read and
write a short simple statement on his or her everyday life (UNESCO, 2004). In the
case of Malawi a person is literate if he or she can read and write in English or
any other language (GoM, 2005)
Technology transfer: refers to a process in which an innovation originating in
one institution or system is adapted for use in another institution or system
(Rogers, 1983).
Description of Study Area Country Profile
Malawi is a landlocked developing country in southeastern Africa,
bordered by Tanzania to the north and north-east, Mozambique on the south,
south-east; and Zambia on the west. The country is 900 km long and 80-161km
16
wide with a total land area of 118,484km2, twenty (20) per cent of which is
covered by water.
Maize is the major staple food crop for most of Malawian families, with
cassava being preferred in parts of central and northern areas. Plantains are the
main staple in a small area of the northern region and rice is important crop
cultivated in the lakeshore and wetland areas. Sorghum, and finger millet are
secondary staples, with sweet potatoes, Irish potatoes and cassava being
considered as ‘snacks’, although planted areas and production have been
increasing significantly over recent years (FAO, 2005). Main export crops include
tobacco, tea, coffee, sugarcane, cotton and macadamia nuts and high quality rice.
Imported crops include maize, wheat and rice.
Malawi’s climate is sub-tropical with a rainy season starting from
November to April and a dry season from May to October.
Sampled Districts
Malawi is divided into three geopolitical regions, namely, southern,
central and northern regions. The regions are further subdivided into
administrative districts. The northern region consists of six administrative
districts. In terms of agricultural administration, the region is divided into two
agricultural development divisions (ADDs), namely Mzuzu ADD and Karonga
ADD. Each ADD is comprised of District Agricultural Development Offices
which are further subdivided into Extension Planning Areas (EPAs). SG2000
Programme partnered with the ADDs in her agricultural development efforts. The
17
SG2000 Programme operated in four (4) of the six districts in the region. The
study covered Chitipa and Rumphi districts. Chitipa district falls under Karonga
ADD and Rumphi falls under Mzuzu ADD. The principal reason for the choice of
the two districts is that they are the major maize growing areas in the region, a
crop whose technologies were promoted by SG2000. Another reason is that the
districts have larger number of farmers that benefited from the project to allow
the researcher to draw an adequate sample in order to obtain credible results that
would allow drawing some generalisable conclusions.
Chitipa District: A Brief Profile
Chitipa district lies to the northern tip of Malawi and is bordered by
Tanzania to the north, Zambia to the west, and Karonga and Rumphi districts in
the east and south respectively. The district has a total population of 157 872. The
district has a literacy level of 77.1 per cent. About 21.7 per cent of the population
has attained at least secondary education, 59.6 per cent primary education and
18.8 per cent have never attained any formal education. Average annual income
per capita in the district is estimated at US$230. About 14.8 per cent of the
population has access to credit (GoM, 2005). Major food and cash crops are
maize and tobacco respectively. Other crops cultivated include millet, cassava,
sweet potatoes and coffee.
18
Rumphi District: A Brief Profile
Rumphi district is bordered by Zambia to the west, and Karonga, Chitipa
and Mzimba districts in the north-east, north-west and south respectively. The
district has a total population of 149 486. The district has a literacy level of 89.3
per cent. The district has the highest literacy rate in the country. About 31.4 per
cent of the population has attained secondary education and above, 60.7 per cent
primary education and 7.9 per cent have never attained any formal education.
Average annual income per capita in the district is estimated at US$330.
However, only 13.4 per cent of the population does have access to credit (GoM,
2005). Maize is major food crop grown in the district. In terms of cash crop
cultivation, a good percentage of farmers rely on tobacco. Other crops grown
include cassava, sweet potatoes, and coffee.
19
Figure 1: Map of Malawi Showing Location of the Sampled Districts
20
Figure 2: Location of Focal Study Areas in the Districts Sampled.
NYIKA
NATIONAL
PARK
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CHAPTER 2: LITERATURE REVIEW
Introduction
This chapter reviews existing literature on the meaning of agricultural
extension, and its significance. It discusses four agricultural extension models
used in agricultural development namely, technology transfer, farmer first,
participatory, and the sustainable development extension models. Literature
review also covers agricultural extension in Malawi, the SG 2000 Programme and
agricultural development efforts in Malawi, extension communication methods,
farmer participation in extension programmes, adoption and diffusion of
innovations, determinants of technology adoption and adoption of maize
production technologies in Sub-Saharan Africa.
Agricultural extension: Meaning and its significance
Many definitions of agricultural extension emphasise its educational
dimension. Extension as defined by Maunder (1973 p. 3) refers to “a service or
system which assists farm people, through educational procedures, in improving
farming methods and techniques, increasing production efficiency and income,
bettering their standards of living, and lifting social and educational standards.”
Swanson and Claar (1984 p. 1) described extension as “an on-going process of
22
getting useful information to people and then assisting those people to acquire the
necessary knowledge, skills and attitudes to utilize effectively this information
and technology.” These two preceding definitions are referred to as enlightenment
definitions of extension. During the 1980s it was recognized that extension could
not just be regarded as ‘help’ and ‘being’ in the interest of the recipient (Leeuwis,
2003). It was realized that extension is in many ways an intervention that is
undertaken and/or paid for by a party who wants to influence people in a
particular manner, in line with certain policy objectives. In line with such views
new definitions of extension emerged. Extension has thus been viewed as ‘helping
behaviour consisting of the transfer of information, with the explicit intention of
changing mentality and behaviour in a direction that has been formulated in a
wider policy context” (Leeuwis, 2003: p. 25).
Goals lead the actions of individuals, groups, and organizations. While
pointing towards a future state, they are influenced if not determined by past
experiences (Nagel, 1997). They reflect the interests of their stakeholders and
differ, therefore, according to specific life situations, power positions, and
development philosophies. According to Nagel (1997), the prominent features of
a system, such as its organizational structure, the choice of clientele, its
operational design, and the methods used, are directly influenced by its set of
goals.
Members of rural communities, extension and other development
personnel, researchers, and staff of commercial or public service and support
organizations constitute the main actors/stakeholders within an extension system.
23
Empirical evidence shows a variety of forms in which interaction among these
groups is institutionalized. The variety of forms suggests a similar variety of
goals, and either could be used to classify extension approaches. In practice,
however, one finds an almost inseparable mixture of goals inhibiting a clear-cut
classification. Nagel (1997 p. 13) further argues that “it seems more appropriate to
use a broader category in goal classification, namely, selectivity with regard to
clientele, and treat the respective goals as a continuum.” Thus, the two end points
of this continuum would be marked as technology transfer and human resource
development, suggesting either a rather narrow technical or a broader
socioeconomic view of development. Studies have revealed that effective
investment in agricultural extension contributes directly to national wealth
through increased agricultural production and enhanced national food security. It
is thus recommended that extension be placed in the wider system of rural
development to achieve a balance in both social and economic development in
rural areas (Swanson, Farner and Bajal, 1990 ). To ensure broad-based
agricultural development it is essential that extension addresses the needs of all
groups of farmers. To achieve this, as noted by Swanson et al, (1990 p. 24) “a
more balanced approach to extension is required that addresses the needs of
productive commercial and small subsistence farmers.”
Extension as one of the major inputs in agricultural development has two
goals namely, economic and social goals. The main focus of economic goals of
extension is on raising production and productivity (Garforth and Harford (1995).
On the other hand, Garforth and Harford (1995) prefer that social goals focus on
24
food security; improving equity in access to, and security of the means of
production (including information, advice and inputs); poverty alleviation, and
improved nutrition. However, a conflicting role for extension depends on whether
it is seen as a mechanism to target social goals or economic goals. From a social
policy perspective, it is recommended that extension addresses the needs of the
poorer segment of the rural population (Garforth and Harford, 1995). However,
for those emphasizing economic goals, they would prefer other policy tools
(Garforth and Harford, 1995).
Agricultural Extension in Malawi: An Overview
The importance of agricultural extension as a means for technology
transfer is widely acknowledged, particularly in developing countries where the
majority of the population lives and agriculture is the main source of livelihood.
Agricultural extension work in Malawi began in colonial times as a result of
estates requiring higher agricultural productivity (GoM, 2000).
The concept of Master Farmers was incorporated into the mainstream of
extension activities during the later years of colonial rule. These Master Farmers
who were better off and innovative, received government support in terms of
inputs and extension services. They followed recommended practices and
therefore acted as demonstrations to other farmers. The rationale for this approach
was that such ‘demonstrations’ farmers could induce spread effects or
externalities in having their neighbours emulating them. However, Mhone (1987,
p. 59) noted “that during the colonial period the approach was roundly criticized
25
by nationalists since it was inequitable, particularly in that such farmers were
actually subsidized through taxation of their poorer neighbours.” An agricultural
cooperative was instituted in 1948 in order to enhance increased agricultural
production. At that time the cooperatives were involved in input supply,
commercial crop production, dairy farming and marketing.
Throughout these stages, the predominant extension approach involved
individual contact and coercion (GoM, 2000). Up until 1962 this was considered
appropriate for the time. The importance of group approach was recognized in the
1970s as a faster way of spreading messages to a wider farming community
during a period when major integrated projects were being introduced. In trying to
enhance the group approach, the Block System, a modified Training and Visit
System, was adopted in 1981 with the aim of improving farmer contact. The
group approach then went beyond specialized groups and tried to contact a wider
range of farmers, including the resource-poor and women. However, it was
observed that the majority of resource-poor farmers were not reached with
extension messages because of the Block Extension System’s top-down approach
and consequently the adoption rate did not improve (GoM, 2000).
SG 2000 and Agriculture Development in Malawi
Rapid population growth in Malawi has put tremendous pressure on the
agricultural sector to increase food production for domestic consumption and to
be more competitive on the international commodity markets. One of the factors
needed to “attain more rapid broad-based agricultural growth and rural
26
development” is the “strengthening of the institutional base for smallholder
agriculture (Staatz and Eicher, 1990, p. 28). As a part of that base agricultural
extension has the potential to be an important factor in increasing agriculture and
livestock productivity and rural incomes, as well as reducing hunger in Malawi by
providing a wide variety of services to rural families.
In the Malawi Growth and Development Strategy policy document (GoM,
2006), developing agriculture and raising smallholder productivity have been
recognized as major drives for growth and improved food security in the country.
Therefore, as part of agricultural development, agri-business involves the
development, dissemination and use of modern agricultural technology packages.
The argument for extension, public or private, is that it provides information as
input to the production process like seed or fertilizer. As Toulmin (1985) states,
“even when a new technology has been developed, its successful adoption by
farmers is not assured, since this will depend critically on the structure of input
and output prices and on the adequacy of the extension system through which the
supply of essential inputs can reach the producer” (p. 2-3). Also it is assumed that
extension will hasten the benefits of adoption of new practices or technologies
which lead to improved production. In the same vein, Pretty (1995) observes that
even if technologies are productive and sustainable if they are imposed on
farmers, then they will not be adopted widely.
SG 2000 Programme Approach is predicated on the assumptions that a
pool of technology appropriate for the country is available that could have a
significant impact, that citizens are poor, that the country is food insecure, and
27
that the government is committed to agricultural development. On that basis the
SG 2000 insists on working through government agencies rather than setting up a
parallel organization outside government (Breth, 1998). SG 2000 exemplifies the
importance of NGO-government partnership in development discourse. It expects
its programme efforts to be mainstreamed into government programmes once it
phases out.
Agricultural Extension Models: A Comparative Overview
Four basic models of agricultural extension are widely discussed in
literature: technology transfer, farmer first and participatory models (Frank and
Chamala, 1992; Chambers, Pacey and Thrupp, 1989). Greer and Greer (1996)
propose a fourth model of agricultural extension namely, the sustainable
development extension model.
The first model considers top-down technology transfer from researchers
to farmers through the extension agents. The farmer first approach, considers the
importance of the role of farmers in research and extension from the bottom- up
(Chambers, et al., 1989). The third model is a participatory approach which in
some ways integrates and extends the first two models. The participatory
approach relies on the involvement of researchers and farmers, as well as other
stakeholders in the extension process. The fourth model is the sustainable
extension model which is designed to ensure that agricultural information and the
systems that support its generation and dissemination are responsive to the needs
of those involved in decision making (Allen, Kilvington, Nixon and Yeabsley,
28
2002). While these models are by definition idealized abstractions of reality, they
provide guidance on the development and use of more specific extension
techniques.
The Technology Transfer Model
This model is a top-down approach to technology transfer. The starting
point is from the scientific institutions, where scientific experiments are done by
the scientists. The research priorities are also determined by the scientists
according to this approach. Scientists generate new innovations which they
believe are good for farmers and then pass them to extension agents. The
extension agents then transmit information about the innovation to the individual
farmers and explain the likely benefits in order to encourage them to adopt the
innovation (Chambers, et al., 1989). In many cases farmers do not adopt the new
innovations as rapidly as anticipated and for many reasons. The scientists often
concentrate on a product or a process which may not fulfill a genuine need for the
farmers. For example some innovations which are not suitable to the farmers in
the field seem to be suitable in the laboratories. Poor infrastructure and lack of
capital for promotion of the innovation also represent constraint to widespread
adoption (Frank and Chamala, 1992). In other cases there is a successful transfer
of technology, but subsequent problems with the use of the technology might
emerge. To date there has been a necessary and dramatic change in extension
thinking; from “technology transfer” to demand-driven approaches that empower
farmers through building on their knowledge. The technology transfer model is
29
associated with governments’ objectives of immediate food production, where
according to Swanson et al. (1990), pursuing an extension system that is narrowly
focused on technology transfer risks promoting growth without equity. In the
long-term, through failing to recognize the needs of all farmers, the consequences
may be a small proportion of very productive commercial farmers, whilst the vast
majority of rural people are left behind at the subsistence level.
Farmer First Model
The farmer first model contrasts strongly with the technology transfer
model. It acknowledges that farmers often have sound local knowledge and good
reasons for their behaviour, which may not be understood by scientists
(Chambers, et al, 1989; Frank and Chamala, 1992). Farmer experience with
experimentation and evaluation provides a basis on which scientists can learn
from and with farmers to set research priorities.
The main objective of the farmer first approach is to empower farmers to
learn and create better situations for themselves rather than being passive
recipients of new technology. Researchers do not drive the research, development
and extension process; they interact with and assist farmers. The process is
“bottom-up” with emphasis on bringing about changes that farmers want. All the
field work related to research is done in the farmers’ fields. The outcome of the
research process is usually a basket of choices from which to select, rather than a
package of practices to be adopted. In this way farmers are encouraged to make
wise and informed decisions based on their own situation (Chambers et. al.,
30
1989). The outcomes of this approach are that the decisions farmers will take may
not be associated with government policy. The farmers’ selection of the new
technology may also limit the marketing of other technologies.
An important limitation of the farmer first approach is that significant off-
farm, structural forces, which inevitably shape farmer priorities and decision-
making, can be overlooked. For instance, private sector infrastructure for the
marketing of a new technology can have a significant influence on on-farm IPM,
as can changes in relevant government regulations or consumer demand.
Participatory Model
Recently many researchers, extension officers and farmers have
recognized the need for a cooperative, participatory approach to examine
interacting sets of issues. Using this approach, an ill-defined agricultural problem
situation is viewed as a complex human activity system (Wilson, 1992). The
participatory approach views research, development and the extension process as
cyclic and interactive, and involving a wide range of key stakeholders. It
emphasises the involvement of key stakeholders in a cooperative and flexible
process to facilitate the implementation of specific innovations by primary
producers. Several types of workshop/ appraisal techniques could be used,
ranging from rapid rural appraisal, participatory rural appraisal, focus groups, and
structured workshops (Chamala and Mortiss, 1990). The common features of
these approaches are qualitative data gathering, active participation of those
having an interest in the research outcomes, and responsiveness to decision-
31
makers both on and off the farm. Fliegel (1993) points out that the participatory
approach applies particularly to packages of technologies rather than single
innovations.
Sustainable development extension model
Sustainable development extension is about engaging all stakeholders in
the process of learning and adaptive management and about negotiating how to
move forward in a complex world (Allen, et al., 2002). Within the sustainable
development extension model (Figure 3) there are tools and processes that
develop the capacity of players in the information system, and the users of
information, to make meaning of it, constructive debate is of great value and
contributes to the process development (Allen, et al., 2002). These two
complementary parts are very important for sustainable development extension
model; the process is shown by Greer and Greer (1996) who propose an
interdependency approach to extension. They argue that this model provides for
involving stakeholders in defining their needs and setting the goals of the
extension programme. The outcomes of this collaborative stakeholder process,
provides direction for the development of outputs in the form of research,
management strategies and other forms of technology. Once the outputs have
been achieved, the objectives of extension programmers are defined and these are
then put into the wider community, often through the more traditional processes
of extension such as talks, field days etc., which then eventually lead to some
level of implementation.
32
Extension Communication Methods
According to Venkatesan and Kampen (1998), an extension method is a means of
motivating farmers to adopt a recommended technology. Tools and techniques are
Figure 3: The Sustainable Development Extension Model Source: Greer and Greer (1996)
Interaction
Extension
agents
Researchers
Definition of users’ technology and other information needs
Definition of objectives of extension
Relevant outputs sought from researchers and other agencies
Implementation of programmes with users
Users
33
particular ways of operating a method (Leeuwis, 2003). The purpose of extension
work is to awaken the desire for technical, economic and social change and teach
practical and managerial skills.
All extension is based on group discussion, practical demonstration and
participation. Extension methods are often classified in terms of the target
audience (Adams,1982) namely:
• group methods: these are aimed at particular reference groups and
involve face to face contact between extension workers and farmers, for
instance, result and method demonstrations;
• individual methods: these are aimed at individual farmers who receive
the undivided attention of the extension worker, for example, farm visits
and farm surveys; and
• mass methods: these are aimed at the general farming community with no
personal contact between the extension worker and the audience, for
example, pamphlets, exhibits or radio broadcast.
A Comparison of Individual and Group Methods
Studies of agricultural development are increasingly showing that when
people who are already well organized or are encouraged to form groups, and
whose knowledge is sought and incorporated during planning and
implementation, are more likely to continue activities after project completion
(Cernea, Coulter, Russel, 1983). If people have responsibility, feel ownership and
are committed, then there is likely to be sustained change. A study 4-10 years
34
after the completion of twenty-five (25) World Bank financed agricultural
projects found that continued success associated clearly with local institution
building (Cernea, et al., 1983). Twelve (48%) of the projects achieved long-term
sustainability and it was these that local institutions were strong. In the others, the
rates of return had all declined markedly, contrary to expectations at the time of
project completion. This clearly indicated that projects were not sustainable where
there had been no attention to institutional development.
Adams (1982) noted that the choice of method should be commensurate
with involvement of farmers in the learning process. He further recommended that
whenever possible “training should be by discussion, practical demonstration and
participation, not by teaching methods borrowed from the classrooms of the
formal system” (Adams, 1982 p. 29). Therefore, the extension worker should aim
to obtain the maximum involvement of the farmers. The impact of the
demonstration is greater when it is conducted by farmers themselves. According
to Venkatesan and Kampen (1998), subsidized demonstration as a tool for
disseminating technologies has been practiced widely by governments both in
Asia and Africa. However, they have doubted the efficacy of such demonstrations
arguing that farmers often know that the farmers selected for such demonstrations
are generally the better-off farmers and are not therefore convinced that the
recommendations are appropriate for them. In addition, Venkatesan and Kampen
(1998) have argued that even if the demonstrations are held on the farms of
resource poor farmers, those factors which are the primary causes of their not
adopting the recommended technology namely, the cost of inputs and their
35
accessibility, are neutralized by the free or subsidized provision of inputs.
Without the subsidy on inputs the resource poor farmers are not likely to adopt the
demonstrated technologies and practices (Venkatesan, and Kampen, 1998).
On the contrary the SG 2000 Programme felt that the size of miniplots
adopted under the Training and Visit system were too small to have a
demonstrative effect on farmers. As a result they would prefer a much larger plot
and would neutralize the risk which farmers take in trying out a new technology
by subsidizing the cost of inputs (Venkatesan, and Kampen, 1998).
Farmer Participation in Extension Programmes
Definition of Participation
As defined by the World Bank (1996), participation is a process through
which stakeholders influence and share control over development initiatives and
the decisions and resources which affect them. Stakeholders may include farmers
themselves, project staff, donors and others.
Types and Levels of Participation
There are no commonly agreed upon indicators of participation for
measuring successful participation, because of the difficulty in assigning
indicators to processes and impacts (Vedeld, 2001). A more realistic approach, for
instance in an Indian context, is the instrumental view of participation which
perceives participation as a means of achieving certain goals, such as improving
the quality, effectiveness and sustainability of projects (Vedeld, 2001).
36
Widely used typologies and classifications of forms and levels of
participation according to Pretty (1994) are based on three dimensions : the
distribution of (a) information input and (b) decision making authority between
participants and interventionists in relation to (c) different key functions in
development planning, such as situation analysis, problem identification, goal
setting and implementation. Other authors (Paul, 1986; Biggs, 1989) also use the
level of involvement in decision-making as a basis for classifying different types
and degrees of participation. With regard to information input and decision-
making authority, the levels typically include, in ascending order:
a) Receiving information: participants are informed/told what a project will
do after it has been decided by others.
b) Passive information giving: participants can respond to questions and
issues that interventionists deem relevant for making decisions about
projects.
c) Consultation: participants are asked about their views and opinions openly
and without restrictions, but the interventionists unilaterally decide what
they will do with the information.
d) Collaboration: participants are partners in a project and jointly decide
about issues with project staff.
e) Self-mobilisation: participants initiate, work on and decide on projects
independently, with interventionists in a supportive role.
In its true meaning genuine participation of people is non-directive and does not
impose ideas on them; it is based on a dialogical process, it is educational and
37
empowering; starts from what people know and from where they are; is based on
resources mobilized by them; relies on their collective effort; promotes self
reliance but acknowledges the partnership among individuals and their change
agent as co-learners (Burkey, 1993; Oakley and Marsden, 1985). Therefore,
contrary to the general practice in rural development, people’s participation is not
limited to farmers attending meetings or contributing their labour to the
implementation of projects designed by officials.
Genuine participation also entails the active involvement of people in the
planning process and is enhanced by their interaction with experts through
educational methods that increase the influence farmers can exert upon the
programme planning process.
Benefits of Participation
An evaluation by World Bank (1996) found that putting responsibility in
the hands of farmers to determine agricultural extension programmes can make
services more responsive to local conditions, more accountable, more effective
and more sustainable. For example, farmer participation is essential in introducing
Integrated Pest Management (IPM) which requires farmers to invest effort and
resources in techniques that are knowledge intensive. According to World Bank
(1996) report, in Indonesia on-farm trials with substantial farmer involvement
have proved the best means to ascertain and demonstrate the potential benefits of
IPM.
38
The opportunities for improving technologies to improve farmer incomes
are expanded through participation, farmer-centred approaches to extension,
which encourage a holistic perspective shifting focus of attention from simple
production to the whole farming system. When farmers are made influential and
responsible clients rather than passive beneficiaries of the extension services,
sustainability both of the benefits of investment in the technology and of the
service itself may substantially be improved (World Bank, 1996). Participatory
methods have the capacity to increase farmer ownership of the technologies
promoted by extension management, especially when the methods are developed,
at least in part by the clients themselves and are based on technologies that they
have seen to be effective. At the same time when the value of the service is clear
to them, farmers are willing to contribute to its support, reducing dependence on
project funds for meeting recurrent costs (World Bank, 1996).
Costs of Participation
A higher level of training and skills is needed if extension staff are to
collaborate effectively with farmers, applying technical knowledge to site-specific
socio-economic and agronomic conditions, rather than delivering pre-packaged
messages. Extension agents also need training in participatory methods of
working with farmers (World Bank, 1996). Some of these additional costs can be
offset by reductions in the number of staff needed, as farmers themselves take on
more responsibilities, and the economies of “distance” methods are more fully
exploited. Additional time and resources are also needed to redefine and establish
39
the institutional framework for participation- for example, to decentralize fiscal
and administrative functions, to build collaborative partnerships, and to strengthen
the capacity of NGOs and farmer organizations. The costs of participation to
farmers can be substantial, particularly in terms of their time. Where participatory
programmes depend on significant contributions of cash and/or labour from
farmers, steps have to be taken to ensure that this does not exclude the poor from
sharing in the benefits.
Key Elements in Promoting Participation
The World Bank (1996) has identified three key elements in promoting
participation in agricultural extension programmes namely, stakeholder
commitment, institutional framework, and a two-way communication.
Stakeholder commitment: broad consultation from the outset is needed to ensure
sufficient commitment to change on the part of all stakeholders. Farmers
themselves may be skeptical of calls to contribute time, effort, or cash if their
experience of extension in the past has been negative.
The institutional framework: there is no one institutional model for delivering
participatory extension services. Some countries, such as Chile and Costa Rica are
using the private sector to carry out what was traditionally a public sector activity;
some are decentralizing and reorienting public sector agencies; and some are
working through NGOs and farmer organizations (World Bank, 1996). A multi-
institutional approach is common, recognizing that farmers get information from
several different sources, and that some organizations are more effective in
40
reaching certain categories of farmers. Defining and facilitating operational
linkages at an early stage is crucial. This can be approached through stakeholder
workshops during project preparation, to discuss possible forms of partnerships
and the allocation of responsibilities for implementation and support. Other key
issues include: instituting incentives and mechanisms for accountability to
farmers on the part of extensionists; identifying where legal and regulatory
changes are needed; training staff in participatory methods; building the capacity
of local farmers groups; and ensuring that local level institutions do not exclude
some groups of farmers from participation.
Two-way communication: In adopting a learning process approach, the function
of extension is not merely one of technology transfer but of ensuring effective
two-way flows of information with the aim of empowering farmers through
knowledge rather than issuing technical prescriptions.
Adoption and Diffusion of Innovations
Stages in the Adoption Process
Adoption studies indicate that adoption of innovations is not something
that happens overnight, but rather it is the final step in the sequence of stages.
Ideas vary about the precise number, nature and sequence of the stages through
which farmers progresses. However, the most widely used characterization of
stages in connection with the adoption of innovations derives from Rogers (1983).
The model builds heavily on normative theories about decision-making models
and consists of the following stages: awareness of the existence of a new
41
innovation, developing interest in the innovation, evaluation of the innovation’s
advantages and disadvantages, trial (testing innovations/ behaviour changes on
small scale), and adoption/ acceptance of the innovations.
An important practical conclusion relating to the stimulation of adoption is
that people require and search for different kinds of information during each
stage. The information requirements evolve from: “information clarifying the
existence of tensions and problems addressed by the innovation or policy
measure, information about the availability of promising solutions, information
about relative advantages and disadvantages of alternative solutions, feedback
information from one’s own or other people’s practical experiences, and
information reinforcing the adoption decision made” (Leeuwis, 2003 p. 130).
In addition, people use different sources of information in connection with
different stages of adoption. In countries with a well developed mass media
system, farmers usually become aware of innovations through such media. In later
stages they tend to prefer interpersonal contact with somebody in whose
competence and motivation they have confidence. This person may be a change
agent, but for most farmers exchanges of experiences with fellow farmers are
more important. In regions where there are few agricultural extension media,
demonstrations often play an important role in the early stages. Dasgupta’s
overview of 300 studies in India (Dasgupta, 1989) shows that change agents are
mainly influential during the early stages of the adoption process.
42
Adopter Categories and their Characteristics
An important finding from adoption research was that innovations are not
adopted by everyone at the same time. Particular innovations are used quickly by
some and only taken up later by others, while some never adopt them. More
importantly, adoption research suggests that there is a pattern in the rate at which
people adopt innovations, meaning that some usually adopt early, while others
adopt late. Such conclusions were arrived at through the analysis of adoption
indices which were used as a measure for innovativeness, defined as ‘the degree
to which an individual is relatively earlier than comparable others in adopting
innovations’ (Rogers, 1983, p. 22). An adoption index was usually calculated by
asking people whether, at a given time, they had adopted any of 10 to 15
innovations recommended by the local extension service. Individuals would
receive a point for each one adopted. On the basis of their score, adoption
researchers have typically classified people into five differently categories
namely; innovators (2.5%), early adopters (13.5%), early majority (34.0%), late
majority (34.0%), and laggards (16%).
Determinants of Adoption
A variety of studies are aimed at establishing factors underlying adoption
of various technologies. As such, there is an extensive body of literature on the
economic theory of technology adoption.
Several factors have been found to affect adoption. These include
government policies, technological change, market forces, environmental
43
concerns, demographic factors, institutional factors and delivery mechanisms.
Some studies classify the above factors into broad categories: farmer
characteristics, farm structure, institutional characteristics and managerial
structure (McNamara, Wetzstein and Douce, 1991) while others classify them
under social, economic and physical categories (Kebede, Gunjal and Coffin
1990). Others group the factors into human capital, production, policy and natural
resource characteristics (Wu and Babcock, 1998) or simply whether they are
continuous or discrete (Shakya and Flinn, 1985). By stating that agricultural
practices are not adopted in a social and economic vacuum, Nowak (1987)
brought in yet another category of classification. He categorizes factors
influencing adoption as informational, economic and ecological.
There is no clear distinction between elements within each category.
Actually, some factors can be correctly placed in either category. For instance,
experience as a factor in adoption is categorized under ‘farmer characteristics’
(McNamara, Wetzstein and Douce, 1991; Tjornhom, 1995) or under ‘social
factors’ (Kebede, Gunjal and Coffin 1990; Abadi-Ghadim and Pannell, 1999) or
under ‘human capital characteristics’. Perhaps it is not necessary to try and make
clear-cut distinctions between different categories of adoption factors. Besides,
categorization usually is done to suit the current technology being investigated,
the location, and the researcher’s preference, or even to suit client needs.
However, as some might argue, categorization may be necessary in regard to
policy implementation. Extensive work on agricultural adoption in developing
countries was pioneered by Feder, Just and Zilberman, (1985). Since then the
44
amount of literature on this subject has expanded tremendously. Because of this
extensive literature, the following section provides a review of selected factors as
they relate to agricultural technology adoption.
Economic Factors
Farm Size
Much empirical adoption literature focuses on farm size as the first and
probably the most important determinant. Farm size is frequently analyzed in
many adoption studies (Shakya and Flinn, 1985; Green and Ng'ong'ola, 1993;
Adesina and Baidu-Forson, 1995; Nkonya, Schroeder and Norman 1997;
Fernandez-Cornejo, 1998; Boahene, Snijders and Folmer, 1999; Doss and Morris,
2001; and Daku, 2002). This is perhaps because farm size can affect and in turn
be affected by the other factors influencing adoption. In fact, some technologies
are termed ‘scale-dependant’ because of the great importance of farm size in their
adoption.
The effect of farm size has been variously found to be positive
(McNamara, Wetzstein, and Douce, 1991; Abara and Singh, 1993; Feder, Just and
Zilberman, 1985; Fernandez- Cornejo, 1996, Kasenge, 1998), negative (Yaron,
Dinar and Voet, 1992) or even neutral to adoption (Mugisa-Mutetikka, Opio,
Ugen, Tukamuhabwa, Kayiwa, Niringiye and E. Kikoba, 2000). Farm size affects
adoption costs, risk perceptions, human capital, credit constraints, labor
requirements, tenure arrangements and more. With small farms, it has been
argued that large fixed costs become a constraint to technology adoption (Abara
45
and Singh, 1993) especially if the technology requires a substantial amount of
initial set-up cost, so-called “lumpy technology.” In relation to lumpy technology,
Feder, Just and Zilberman, (1985) further noted that only larger farms will adopt
these innovations. With some technologies, the speed of adoption is different for
small- and large- scale farmers. In Kenya, for example, a recent study (Gabre-
Madhin and Haggblade, 2001) found that large commercial farmers adopted new
high-yielding maize varieties more rapidly than smallholders.
Furthermore, access to funds (say, through a bank loan) is expected to
increase the probability of adoption. Yet to be eligible for a loan, the size of
operation of the borrower is crucial. Farmers operating larger farms tend to have
greater financial resources and chances of receiving credit are higher than those of
smaller farms.
A counter argument on the effect of farm size can be found in Yaron,
Dinar and Voet, (1992) who demonstrate that a small land area may provide an
incentive to adopt a technology especially in the case of an input-intensive
innovation such as a labor-intensive or land-saving technology. In that study, the
availability of land for agricultural production was low, consequently most
agricultural farms were small. Hence, adoption of land-saving technologies
seemed to be the only alternative to increased agricultural production.
Further, in the study by Fernandez-Cornejo (1996), farm size did not
positively influence adoption. The majority of the studies mentioned above
consider total farm size and not crop acreage on which the new technology is
practiced. While total farm size has an effect on overall adoption, considering the
46
crop acreage with the new technology may be a superior measure to predict the
rate and extent of adoption of technology (Lowenberg-DeBoer, 2000). Therefore
in regard to farm size, technology adoption may best be explained by measuring
the proportion of total land area suitable to the new technology.
Cost of Technology
The decision to adopt is often an investment decision. And as Caswell,
Fuglie, Ingram, Jans and Kascak. (2001) note, this decision presents a shift in
farmers’ investment options. Therefore adoption can be expected to be dependent
on cost of a technology and on whether farmers possess the required resources.
Technologies that are capital-intensive are only affordable by wealthier farmers
and hence the adoption of such technologies is limited to larger farmers who have
the wealth (Khanna, 2001). In addition, changes that cost little are adopted more
quickly than those requiring large expenditures, hence both extent and rate of
adoption may be dependent on the cost of a technology. Economic theory
suggests that a reduction in price of a good or service can result in more of it
being demanded.
Level of Expected benefits
Programs that produce significant gains can motivate people to participate
more fully in them. In fact, people do not participate unless they believe it is in
their best interest to do so. Farmers must see an advantage or expect to obtain
greater utility in adopting a technology. In addition, farmers must perceive that
47
there is a problem that warrants an alternative action to be taken. Without a
significant difference in outcomes between two options, and in the returns from
alternative and conventional practices, it is less likely that farmers, especially
small-scale farmers will adopt the new practice (Abara and Singh, 1993). A
higher percentage of total household income coming from the farm through
increased yield tends to correlate positively with adoption of new technologies
(McNamara, Wetzstein, and Douce, 1991; Fernandez-Cornejo, 1996).
Off-farm hours
The availability of time is an important factor affecting technology
adoption. It can influence adoption in either a negative or positive manner.
Practices that heavily draw on farmer’s leisure time may inhibit adoption
(Mugisa-Mutetikka et al., 2000). However, practices that leave time for other
sources of income accumulation may promote adoption. In such cases, as well as
in general, income from off-farm labor may provide financial resources required
to adopt the new technology.
Social Factors
Age of Adopter
Age is another factor thought to affect adoption. Age is said to be a
primary latent characteristic in adoption decisions. However there is contention
on the direction of the effect of age on adoption. Age was found to positively
influence adoption of sorghum in Burkina Faso (Adesina and Baidu-Forson,
48
1995), and IPM on peanuts in Georgia (McNamara, Wetzstein, and Douce, 1991).
The effect is thought to stem from accumulated knowledge and experience of
farming systems obtained from years of observation and experimenting with
various technologies. In addition, since adoption pay-offs occur over a long
period of time, while costs occur in the earlier phases, age (time) of the farmer
can have a profound effect on technology adoption.
However age has also been found to be either negatively correlated with
adoption, or not significant in farmers’ adoption decisions. In studies on adoption
of land conservation practices in Niger (Baidu-Forson, 1999), rice in Guinea
(Adesina and Baidu-Forson, 1995), fertilizer in Malawi (Green and Ng'ong'ola,
1993), Hybrid Cocoa in Ghana (Boahene, Snijders and Folmer, 1999), age was
either not significant or was negatively related to adoption.
Older farmers, perhaps because of investing several years in a particular
practice, may not want to jeopardize it by trying out a completely new method. In
addition, farmers’ perception that technology development and the subsequent
benefits, require a lot of time to realize, can reduce their interest in the new
technology because of farmers’ advanced age, and the possibility of not living
long enough to enjoy it (Caswell et al., 2001; Khanna, 2001). Furthermore,
elderly farmers often have different goals other than income maximization, in
which case, they will not be expected to adopt an income –enhancing technology.
As a matter of fact, it is expected that the old that do adopt a technology do so at a
slow pace because of their tendency to adapt less swiftly to a new phenomenon
(Tjornhom, 1995).
49
Education
Studies that have sought to establish the effect of education on adoption in
most cases relate it to years of formal schooling (Tjornhom, 1995; Feder, Just and
Zilberman, 1985). Generally education is thought to create a favorable mental
attitude for the acceptance of new practices especially of information-intensive
and management-intensive practices (Caswell et al., 2001) on adoption. However,
education is thought to reduce the amount of complexity perceived in a
technology thereby increasing a technology’s adoption.
Gender Issues and Concerns
Gender issues in agricultural production and technology adoption have
been investigated for a long time. Most show mixed evidence regarding the
different roles men and women play in technology adoption. In the most recent
studies, Doss and Morris (2001) in their study on factors influencing improved
maize technology adoption in Ghana, and Overfield and Fleming (2001) studying
coffee production in Papua New Guinea show insignificant effects of gender on
adoption. The latter study notes “effort in improving women’s working skills does
not appear warranted as their technical efficiency is estimated to be equivalent to
that of males” (p.155). Since adoption of a practice is guided by the utility
expected from it, the effort put into adopting it is reflective of this anticipated
utility. It might then be expected that the relative roles women and men play in
both ‘effort’ and ‘adoption’ are similar, hence suggesting that males and females
adopt practices equally.
50
Institutional Factors
Information
Acquisition of information about a new technology demystifies it and
makes it more available to farmers. Information reduces the uncertainty about a
technology’s performance hence may change individual’s assessment from purely
subjective to objective over time (Caswell et al., 2001). Exposure to information
about new technologies as such significantly affects farmers’ choices about it.
Feder and Slade (1984) indicate how, provided a technology is profitable,
increased information induces its adoption. However in the case where experience
within the general population about a specific technology is limited, more
information induces negative attitudes towards its adoption, probably because
more information exposes an even bigger information vacuum hence increasing
the risk associated with it. A good example is the adoption of recombinant bovine
Somatotropin Technology (rbST) in dairy production (McGuirk, Preston and
Jones, 1992; Klotz, Saha and Butler, 1995). Information is acquired through
informal sources like the media, extension personnel, visits, meetings, and farm
organizations and through formal education. It is important that this information
be reliable, consistent and accurate. Thus, the right mix of information properties
for a particular technology is needed for effectiveness in its impact on adoption.
Extension Contacts
Good extension programs and contacts with producers are a key aspect in
technology dissemination and adoption. A recent publication stated that “a new
51
technology is only as good as the mechanism of its dissemination” to farmers
(IFPRI, 1995 p. 168). Most studies analyzing this variable in the context of
agricultural technology show its strong positive influence on adoption. In fact
Yaron, Dinar and Voet (1992) show that its influence can counter balance the
negative effect of lack of years of formal education in the overall decision to
adopt some technologies.
The Combined Effect
Although most adoption literature concentrates on single technology
adoption – for example adoption of fertilizer (Green and Ng'ong'ola, 1993),
improved varieties like beans (Kato, 2000), hybrid cocoa (Boahene, Snijders and
Folmer, 1999) and many more, other studies investigate adoption of a
combination of technologies such as improved varieties and fertilizer (Nkonya,
Schroeder and Norman 1997; Shakya and Flinn, 1985). As such, some literature
(Feder, Just and Zilberman, 1985; Rogers, 1995) suggests that adoption of
technologies may in effect be enhanced because of complementarities that exist
between the technologies. Complementarities occur at two levels: at the factor
level and at the technology level. At the factor level, complementarities occur
from the manner in which combinations of factors act together to influence
adoption (Lionberger, 1960). Additionally, complementarities between factors
occur where all inputs considered together have a significant effect on adoption
but when the influence of one is held constant, the correlation between the other
remaining inputs and technology adoption is greatly lowered (Lionberger, 1960).
52
As such where inputs that are critical for adoption are in short supply – for
instance water supply that is critical for irrigation technology adoption, the
unavailability may hinder adoption.
Thus, crucial inputs must be readily available in order to encourage
adoption. At the technology level, complementarities occur because one
technology enhances the positive impacts of another. For example in some cases,
the high yield potential of seed can be realized only if fertilizer is applied. In fact,
in most studies addressing the use of improved seeds and fertilizer, a
complementary relationship is found between them. For example, in Northern
Tanzania, farmers tend to adopt improved maize seed in combination with
fertilizers (Nkonya, Schroeder and Norman (1997). The site-specificity of
agricultural practices leads to some authors asserting that adoption studies in
every region experiencing a technological change are warranted. This might be
because populations are heterogeneous and individual behavior is dynamic
(Feder, Just and Zilberman, 1985). Furthermore, there are numerous differences in
factor endowments and farmer characteristics among regions. Thus an adoption
study on a technology in a geographical setting does not imply that a similar study
of the same technology is unwarranted in another geographical setting. Moreover,
even within a geographical setting, different regions have varying adoption
patterns for the same type of technology. Yaron, Dinar and Voet (1992) assert that
extrapolations of adoption results should be avoided and that where possible
region specific studies should be encouraged.
53
Adoption of Maize Production Technologies in Sub-Saharan Africa
Use of Inorganic fertilizer and Improved Varieties
In Sub-Saharan Africa, low fertilizer consumption continues to raise
concerns about the continent’s ability to overcome its food production problems
exacerbated by high population growth rates across the continent (Townsend,
1999). This has been so because most farmers are not adequately compensating
for the soil nutrient loss caused by intensive cultivation practices.
Several price and non-price factors have been used to explain fertilizer use
in Africa. These include profitability of fertilizer use, labour availability, financial
liquidity, household assets, market access, and extension services (Townsend,
1999). The non-price explanatory variable which implicitly impact on price
variables is the distance from the fertilizer market. Lack of financial liquidity is
key to fertilizer adoption and the intensity of fertilizer use. Farmers, lacking
resources and assets, with differing attitude towards risk, are considered to be less
likely to adopt fertilizer. Townsend (1999) has noted that labour and extension
services are positively correlated to fertilizer adoption. Increased knowledge of
improved farming techniques along with availability of resources to apply this
knowledge is likely to increase fertilizer use. In addition, farmers will not use
fertilizer if it is not profitable-profitability in terms of agricultural output realized
from fertilizer usage.
A major problem facing African smallholder farmers as observed by
Holmen (2005) is not how to use inorganic fertilizers or high yielding varieties
but, rather, how to afford them. This has resulted in low adoption levels in many
54
African farms. In addition, there has been de-adoption of hybrids and fertilizers in
recent years. For instance in Malawi fertilizer use has either stagnated or declined.
However, Larson (2005) disagrees with Holmen (2005) and argues that adoption
rates of high yielding varieties are higher in Africa today than was the situation in
South Asia in the 1970s, suggesting that this aspect of technology is not as
constraining as may be popularly assumed. Larson (2005) has observed that the
relatively high percentage of farmers using maize hybrids and open pollinated
varieties is probably due to the long history of maize breeding in Sub-Sahara
Africa especially in southern and eastern Africa. However, it should be noted that
although farmers may report use of hybrids such statements somewhat refer to
recirculated hybrid seeds with poor production potential than hybrids proper’
(Holmen, 2005 p. 117).
Adoption of Other Crop Management Practices
As reported by Byerlee and Jewell (1997), the more common experience
in Africa has been that farmers fail to adopt the additional production practices
needed for sustained improvements in maize yields. According to them small-
scale farmers often reject recommendations for labour-intensive practices such as
plant spacing, frequent weeding and separate operations to applying fertilizer. In
their study on maize productivity in Malawi, Smale and Heisey (1997) noted that
differences in cultural practices appear to be associated with variety, fertilizer use
or both. When small-scale farmers intensify their maize production through use of
high yielding hybrid seeds or inorganic fertilizers, they tend to increase their
55
management levels through timely planting and weeding, higher plant densities or
planting after a rotation crop (Smale and Heisey, 1997 p. 76).
Conservation Tillage
Definition of Conservation Tillage
Conservation tillage is defined as a system or sequence of operations that
reduces the loss of soil or water in comparison to losses incurred under
conventional tillage systems, and it includes systems ranging from zero tillage and
reduced tillage to different forms of crop residue management (Pereira de Herrera
and Sain, 1999). The term conventional tillage refers to land preparation in which
there is maximum disturbance of the soil structure. There are two forms of
conservation tillage, namely, minimum tillage and zero tillage. Minimum tillage
refers to land preparation with minimum disturbance of the soil and application of
an herbicide, whereas zero tillage refers to land preparation done mechanically or
manually cutting the vegetation cover of the field and applying herbicide.
Impact of Conservation Tillage on Yield
Impact studies have revealed increased yields of maize under conservation
tillage compared to that cultivated under conventional tillage system. For
instance, Pereira de Herrera and Sain (1999) observed significant differences
between the mean maize yields of farmers who adopted conservation tillage and
those who did not. The mean maize yields were 3.3 tonnes/hectare for those who
adopted conservation tillage compared with 2.8 tonnes/hectare for those who did
56
not. However, they argued that the increase in yield was not necessarily
associated with the use of conservation tillage but could be attributed to other
factors (Pereira de Herrera and Sain, 1999).
Adoption of Conservation Tillage
Several circumstances, internal and external to the farm, have been
identified as important in farmers’ decisions to adopt soil conservation
technologies (Anderson and Thampapillai, 1990; Napier, 1991). The factors
mentioned in the literature are associated with their impact on the net-present
value of the differential flow of the expected benefits between conservation and
conventional tillage, for instance, factors such as topography, soil type, rainfall,
and cultivation system affect the flow of differences in yields between both
technologies. At the same time factors such as incentives, access to credit, input
subsidies, and product prices are associated with the value of the differences in
net benefits. The planning period and the farmer’s discount rate are two important
variables in the farmer’s perceptions of the costs and benefits of this type of
technology. The form of land tenure, farm size, age, the farmer’s degree of
knowledge about the problem of soil erosion, and the farmer’s level of education
are some of the factors associated with these two variables.
57
Conceptual framework
Introduction
In this study perceived effectiveness of SG 2000 Programme Approach to
agricultural technology delivery has been conceptualized in terms of four
parameters, namely
1) level of farmer participation in the programme,
2) extension communication methods used in the delivery of agricultural
services,
3) level of farmer satisfaction with technology disseminated and
4) level of technology adoption.
All four are based on farmers’ perceptions only. Farmers’ socio-demographic
characteristics are the main determining factors of differences in perceptions. The
conceptual variable used ‘effectiveness’ refers to the extension system’s ability to
achieve the specific goals set for it.
Apart from the importance of farmers and agriculture in the society and
economy concerned, several conditions appear to be necessary for the initiation
and organized development of agricultural extension work (Jones and Garforth,
1997). The prime condition is that information has been assembled, systematized,
and made available on good or progressive or new agricultural practices suited to
a particular environment, and is based on either (or both) the accumulation of
experience or findings from research (however rudimentary). Second, this
information is used, among other things, to educate professional agriculturists
who may further enlarge or refine this body of knowledge or become active
58
promoters and disseminators of it. Third, an appropriate administrative or
organizational structure exists by and within which the dissemination activities
may be established and conducted. Fourth, there is a legislative or some other
official mandate or influential proponent which prescribes or enables that
agricultural extension work is desirable and must occur. And fifth, there are
invariably a variety of antecedents which have attempted protoforms of
agricultural information and advice dissemination.
A farmer may be regarded as both a producer and a consumer. This
implies that a farmer may take into consideration “current consumption and
production ends” and also policy and physical effects. The consumption needs are
satisfied through own production though at times they are met through food
purchases. A farmer may react in a number of ways towards declining production
or/and variability in production that undermine consumption needs. Existing
practices may be modified or new ones may altogether be adopted.
Adoption studies in agriculture generally attempt to establish factors that
influence the adoption of a technology in a specific locality. It is nonetheless
recognized that attributes influencing the adoption of agricultural technologies are
inherent in the farmer and farm, in the technology itself, and the farmer’s
objectives (Adesina and Zinnah 1992). Farmer and farm attributes that influence
adoption include, but are not limited to, farm size, agro ecological zone, and
education level. The technology’s attributes are commonly considered in terms of
whether they are embodied or disembodied (e.g. seed or knowledge). Some of the
59
farmer characteristics that are postulated to have some influence on adoption
(Adesina and Zinnah, 1992) are:
• Household size: It is hypothesized that a larger household is more likely to
adopt technologies that are more labour intensive.
• Farm size: Because farmers who have more land are in a better position to
multiply seed, it is hypothesized that farm size (ha) has a positive impact
on probability of adoption.
• Farming experience: It is hypothesized that longer farming experience (yr)
contributes to better decision making and has a positive effect on adoption.
• Education level: Education contributes to general awareness and favors
adoption of new varieties.
• Age of household head: It is not certain whether this variable influences
adoption positively or negatively, owing to the erratic influence of age on
perceptions regarding change.
The success of an extension outreach in terms of adoption of technologies
depends largely upon the technology transfer mechanism. Awareness creation
is very important in any adoption process. The effectiveness of an extension
approach as perceived by farmers would determine to a great extent the
adoption of production recommendations. Figure 4 illustrates a conceptual
framework of the SG 2000 Programme Approach effectiveness
60
Figure 4: A Conceptual Framework of the Perceived effectiveness of SG2000 Programme Approach to agricultural technology delivery
Source: Author’s construct (2007)
Farmer perception of technology
Effectiveness of SG2000 Programme Approach
Level of satisfaction with
technology
Extension communication methods
Farmer characteristics: age, gender, education, income level, years of farming experience, farm size, farm labour source, access to extension, access to farm credit
Level of participation • Planning • Implementation • monitoring • evaluation
Level of technology adoption
61
Commonly used methods in agricultural extension (Van den Ban and
Hawkins, 1996) include;
• Individual methods such as visit and individual consultancy, office contact
and letter and telephone
• Group methods such as field demonstrations, field visits and tours, rapid
rural appraisal, participatory assessment, group meetings and training
(Participatory Training)
• Mass media methods such as newspapers, booklets, posters and radio
program.
It has been widely advocated that extension methods should regard a farmer as
an important decision maker in the adoption process because he/she is the primary
user of technologies being disseminated if sustainable adoption is to be achieved
(Pretty and Chambers, 1994). Thus, a lot of emphasis has been placed on
participatory approaches to programme/project planning, implementation,
monitoring and evaluation. The overriding objective in participatory approaches is
to enlist maximum participation from the primary stakeholders-the beneficiaries.
Maximum participation connects to a notion that there are different levels of
participation. Widely used typologies and classifications of forms and levels of
participation (Pretty, 1995) are based on three dimensions: the distribution of
information input; decision-making authority between participants and
interventionists in relation to different key functions in development planning,
such as situation analysis, problem identification, goal setting, implementation,
monitoring and evaluation. While some authors indicate that there is no best level
62
of participation, others emphasise that only higher levels of participation can lead
to sustainable results (Pretty, 1995). Participation may lead to the empowerment
of the participants. It has been observed that once farmers become owners of the
programme/project there is a greater likelihood that such a programme will be
effective and sustainable.
In conclusion, the encouragement of high farmer participation at all levels in
the technology transfer process through use of multiple extension methods may
lead to sustainable adoption of technologies if the technological attributes
conform to farmer characteristics. This in itself may constitute an effective
agricultural extension approach from the view-point of farmers in the long run.
63
CHAPTER 3: RESEARCH METHODOLOGY
Introduction
This chapter describes the research design used in the study, the
population of study, sampling techniques and sample size, instrumentation, data
collection and analysis procedures and data presentation.
Research Design
A descriptive-correlational survey research design was used for this study.
The reason for the choice of this method was to describe the nature of the
situation as it existed at the time of the survey. The correlational procedure was
preferred to enable the researcher to determine the extent of relationship existing
between variables. It also enabled the researcher to test the hypothesis about the
relationship between variables as well as to assess the magnitude and direction of
the relationship. Furthermore, the correlational procedure is commonly used
because it is relatively easy to design and conduct (Ary, Jacobs and Razavieh,
1979).
64
Population of Study
The population studied consisted of all farmers that benefited from
Sasakawa Global 2000 programme activities in Chitipa and Rumphi Districts in
Northern Malawi between 1998 and 2006.
Sampling and Sample Size
In this study a sampling frame was made available to the researcher by the
SG 2000 Programme Coordinators for the two districts. A list of farmers who
participated in the SG 2000 Programme was obtained from the respective District
Agriculture Offices, 155 farmers for Rumphi and 245 farmers for Chitipa district
giving a total of 400 farmers. A proportionate stratified random sampling was
used to select a sample of 75 farmers from Rumphi and 119 from Chitipa yielding
a sample size of 194 farmers. Each district represented a stratum. A potential and
easy method for selecting respondents would have been simple random sampling.
However, the following two reasons justified the preference of proportionate
stratified random sampling over simple random sampling (Ary, Jacobs and
Razavieh, 1979). First, proportionate stratified random sampling assures that you
will be able to represent not only the overall population, but also key subgroups of
the population. Secondly, proportionate stratified random sampling generally has
more statistical precision than simple random sampling. This is true since the
strata were homogeneous. Hence, it was expected that the variability within
stratum was lower than the variability for the population as a whole. At 95%
65
confidence level, the sample was considered adequate (Krejcie and Morgan,
1970).
Instrumentation
A validated researcher-designed interview schedule was used to collect
data from farmers. In order to measure the individual variables more accurately, a
Likert-type scale was used. The choice of the scale was based on the
consideration that this study was aimed at capturing farmers’ perceptions and the
Likert-type scale was considered very appropriate for this kind of study (Sirkin,
1999). The interview schedule consisted of the following sections;
The first section captured data on demographic and socio-economic
characteristics of respondents in terms of age, gender, household size, years of
farming, level of formal education, land holding size, farm labour type, and level
of income, access to extension services and access to credit.
The second section captured data on level of farmer participation in
SG2000 Programme activities and effectiveness of methods of delivery. The third
section examined the agricultural technologies disseminated, farmers’ satisfaction
with the technologies, their level of adoption and the constraints to adoption. The
last section examined farmers’ perception of the effectiveness of the SG 2000
Programme approach to technology delivery.
Under level of farmers’ participation in the SG 2000 Programme
activities, data collected were participation in planning, implementation and
66
evaluation of activities. A five point Likert-type scale was constructed ranging
from 5 to 1 in this case 5=very high, 4=high, 3=moderate, 2=low, 1=very low.
Farmers’ perceptions of the effectiveness of the management training plot
(MTP) as a method for technology transfer was measured in terms of ability to
provide technical information on best-bet maize management practices, ability to
provide technical information on conservation tillage, ability to create interest to
other members of the community, ability to raise awareness to other members of
the community, ability to attract active farmer participation. The data were
collected on five point Likert-type scale where 5=very effective, 4=effective,
3=somewhat effective, 2=not effective, 1=very ineffective
In order to measure the level of farmers’ satisfaction with the
technological package disseminated, data were collected on a five point Likert-
type scale where 5=very high, 4=high, 3=moderate 2=low, 1=very low.
Farmers’ adoption level of the technologies disseminated was measured in
terms of the extent to which farmers have put to use the technological
recommendations. A five point Likert-type scale was developed to collect data
where 5=very high, 4=high, 3=moderate, 2=low, 1=very low.
To determine overall farmers’ perception of the effectiveness of SG2000
approach to technology delivery, farmer opinions were collected on five point
Likert-type scale where 5=very effective, 4=effective, 3=somewhat effective,
2=not effective, 1=very ineffective
The instrument consisted of both close-ended and open-ended questions.
Open-ended questions allow the respondents to make comments or suggest a
67
range of other possibilities. This allows researcher to gather data to explain
responses to close-ended questions. The researcher also held two group
discussions to cross-check data gathered using the interview schedule.
Validation of Instrument
In order to ascertain that the instrument measures what it purports to
measure, it must go through some judgement by both the researcher and experts in
the field of study. Face validity was determined by the researcher. It was equally
important that the items and questions covered the full range of the issue or
attitude measured. An assessment of the instrument in this respect, that is, its
content validity was judged by the researcher’s supervisors.
Pilot-testing the Instrument
The researcher pilot-tested the instrument in July 2007 in Chitipa District
in order to ascertain that it was reliable in terms of clarity of the questions and
ease of understanding. This enabled the researcher to detect any possible errors
and revise the instrument accordingly to ensure internal consistency among the
items. According to Kumar (1996), the field test should not be carried out on the
sample of your study but on a similar population from which the sample is drawn.
Therefore, in this study the pilot testing was conducted by interviewing selected
farmers who also participated in the SG 2000 Programme. A total of 20 farmers
were interviewed. Twenty (20) is considered an optimal size for reliability
analysis. A Cronbach-alpha coefficient was calculated on all interval data to
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determine instrument reliability. The alpha level was set at 0.7 which is an
indicator that there is a strong association among the items. Cronbach’s alpha test
was used to assess the reliability of the attitude measures. Results indicate the
scale used was reliable with a cronbach alpha ranging from 0.704-0.834 implying
consistency in the responses among farmers interviewed. Table 1 gives a
summary of reliability statistics.
Table 1: Reliability Coefficients Score n of items Cronbach alpha Std Cronbach alpha
Perceptions on level of
participation
7 0.704 0.726
Perceptions on
effectiveness of MTP
4 0.834 0.829
Perceptions on level of
satisfaction with
technologies
6 0.804 0.873
Perceptions on level of
technology adoption
6 0.721 0.806
n=20
Source: Field Data (2007)
Training of Interviewers
The researcher was assisted by four (4) Agricultural Extension
Development Officers (AEDOs) who were trained for two (2) days in July 2007
on the administration of the structured interview schedule. The purpose of this
training was to enable the AEDOs understand the objectives of the study and also
69
to get acquainted with the content of the interview schedule. This helped ensure
that quality and reliable data were obtained.
Data Collection
An interview schedule was used to collect data from sampled farmers. The
research assistants and the researcher used the local vernacular language
(Tumbuka) to facilitate understanding of the questions by respondents. Data
collection exercise lasted for two months (late July to early September 2007).
Data Management and Analysis
After completion of the data collection exercise, data cleaning was done
by scrutinizing the completed schedules to identify and minimize as far as
possible errors, incompleteness, misclassification and gaps in the information
obtained from the respondents. Data were then coded and analysed using
Statistical Package for Social Scientists (SPSS) software package. In most of the
analysis descriptive statistics were computed for variables for each objective as
outlined below.
Objective 1: To describe the demographic and socio-economic characteristics of
participating farmers, descriptive statistics such as frequency distributions,
percentages, means and standard deviations were computed for the variables.
Objective 2: Descriptive statistics were used to describe the extent of farmers’
participation in the programme activities. Frequency distributions, percentages,
means, and standard deviations were computed.
70
Objective 3: Descriptive statistics were used to describe the pattern of farmers’
perceptions of the effectiveness of the Management Training Plot as a method for
technology transfer. Frequency distributions, percentages, means and standard
deviations were computed.
Objective 4: Descriptive statistics were used to describe the pattern of the extent
of farmers’ satisfaction with the technological package disseminated. Frequency
distributions, percentages, means and standard deviations were computed.
Objectives 5 and 6: Descriptive statistics were used to analyse farmers’ adoption
levels of the technologies disseminated and the constraints to non-adoption of
technological recommendations. Frequency distributions, percentages, means, and
standard deviations were computed to describe the data.
Hypotheses Testing
Researchers generally specify the probability of committing a Type 1
Error that they are willing to accept, that is, a priori (Trochim, 2000). In the social
sciences most researchers select an alpha= 0.05. This means that the researcher is
willing to accept a probability of 5% of making a Type 1 error, of assuming a
relationship between variables exists when it really does not. Therefore, in this
study an alpha of 0.05 was set as a priori to examine any statistical significance
between and among selected variables. An independent sample t-test was
computed to compare any significant differences between two (2) means across
selected groups, that is, between the two districts, males and females in terms of
71
level of participation, perceived effectiveness of method of delivery, perceived
effectiveness of SG 2000 Programme approach and level of technology adoption.
The following variables were correlated; level of farmer participation,
perceived effectiveness of method of delivery, perceived effectiveness of SG2000
approach, level of technology adoption, level of formal education, age, gender,
level of income, type of farm labour, farm size, years of farming, access to credit,
access to market. All these relationships were examined using Pearson Product
Moment Correlation Coefficient (r) which is the most widely used and sensitive
correlation coefficient in data analysis. The Davis Conversion (Davis, 1971)
Scheme was used to interpret the relationships between variables as indicated
Table 2 below.
Table 2: Davis Conversion for correlations Magnitude Interpretation
1.0 Perfect
0.70 to 0.99 Very strong association
0.50 to 0.69 Substantial association
0.30 to 0.49 Moderate association
0.10 to 0.29 Weak association
0.01 to 0.09 Very weak association
Source: Davis (1974)
A stepwise regression analysis for variables exhibiting significant relationships
was run to identify the best predictors of the dependent variable understudy, that
is, effectiveness of the SG 2000 Programme approach to agricultural technology
delivery.
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CHAPTER 4: RESULTS AND DISCUSSION
Introduction
This chapter reports on the major findings of the study carried out in
Northern Malawi on the perceived effectiveness of the Sasakawa Global 2000
Programme Approach to agricultural technology delivery.
Demographic and Socio economic Characteristics of Farmers -
This section of the chapter gives a broad view of the demographic and socio-
economic characteristics of farmers. These are sex, age, education background,
income level, farm labour type employed, and years of farming experience. Other
characteristics are types of crops grown, access to extension services and access
to credit.
Sex
A total of 194 farmers participated in this study. Results show that a
majority of farmers (54.6%) were males (Table 3) with 45.4% being female.
Although it is clear that women are responsible for at least 70 percent of the
farming activities in almost all communities in Malawi (Doss & MacDonald,
1999), their relative proportion in formal agricultural activities, such as extension,
is low.
73
Table 3: Sex distribution of respondent-farmers in the study area Sex of farmer Frequency
Percent (%)
Female 88 45.4 Male 106 54.6 Total 194 100.0
n=194
Source: Field Data (2007)
Age
In general, farmers aged 30-39 years and 40-49 years ranges constituted
the bulk of respondents representing 28.4% and 27.8% respectively. The mean
age of farmers was 44 years with a standard deviation of 12.7 years. This implies
that there were greater differences among the sampled farmers in terms of age.
The mean age implies that most of the farmers are still young and have the ability
to carry out farming activities. However, farmers aged 20-29, which can be
considered as a very youthful age bracket, was very low (11.9%). Results are
presented in Table 4 below.
Table 4: Age distribution of respondent-farmers in the study area Age group Frequency Percent (%) Cumulative %
20-29 23 11.9 11.9 30-39 55 28.4 40.2 40-49 54 27.8 68.0 50-59 35 18.0 86.1 60-69 19 9.8 95.9 70+ 8 4.1 100.0
Total 194 100.0 - n=194, Mean=44, SD=12.7, Range=57, Minimum=20, Maximum=77
Source: Field Data (2007)
74
On the relationship between age and adoption, Caswel et. al (2001), has
noted that increasing age reduces the probability of adopting technologies. Older
farmers, perhaps because of investing several years in a particular practice, may
not want to jeopardize it by trying out a completely new method. In addition,
farmers’ perception that technology development and the subsequent benefits,
require a lot of time to realize, can reduce their interest in the new technology
because of farmers’ advanced age, and the possibility of not living long enough to
enjoy it (Caswell et al., 2001; Khanna, 2001). Furthermore, elderly farmers often
have different goals other than income maximization, in which case, they will not
be expected to adopt an income –enhancing technology. As a matter of fact, it is
expected that the old that do adopt a technology do so at a slow pace because of
their tendency to adapt less swiftly to a new phenomenon (Tjornhom, 1995). On
the other hand, young farmers tend to have more education and are often
hypothesized to be more willing to innovate (Ejembi, Omoregbee & Ejembi,
2006).
Formal Education
Results in Table 5 indicate that a total of 5.2 percent (10) farmers had no
formal education compared to 66.5 percent (81), who had done primary
schooling. Farmers who did not attain any formal education indicated that they
had undergone adult literacy programmes such that they were able to read and
write. In general, the results show that a majority of farmers (94.8%) interviewed
had received some level of formal education to a larger extent. This is an
75
indication that literacy levels are high in the study area. These findings seem to
agree with previous findings from an Integrated Household Survey Report (GoM,
2005) in which the North registered higher literacy levels (90%) compared with
the Southern and Central regions which registered 71% and 75% respectively.
Table 5: Formal education level of respondent-farmers in the study area Level of Formal Education Frequency Percent Cumulative %
Some primary school 48 24.7 24.7
Completed primary school 81 41.8 66.5
Junior secondary education 29 14.9 81.4
Senior secondary education 22 11.3 92.8
Tertiary education 4 2.1 94.8
No formal education 10 5.2 100.0
Total 194 100.0
n=194
Source: Field data (2007)
That a majority of farmers are literate means that these farmers would be
more receptive to information pertaining to farming practices. Education has been
found (Caswel et. al., 2001) to create a favorable mental attitude for the
acceptance of new practices especially of information-intensive and management-
intensive practices on adoption. Similarly, Adesina and Zinnah (1992) have also
echoed that education contributes to general awareness and thus favours adoption.
If the amount of complexity perceived in a technology is reduced the likelihood of
a technology’s adoption may thus be increased. Therefore, one would expect
more farmers adopting the SG 2000 recommended agricultural technologies. It is
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thus not surprising that a majority of farmers reported adoption of the
technologies.
Household size
Majority of farmers (49.5%) had a family size of 6-8 persons followed by
30.4% whose family size ranged from 3 to 5 persons (Table 6). Mean household
size for the survey respondents was 6.6 persons with a standard deviation of 2.09.
The mean household size was found to be higher compared to previous findings
whereby the Northern Region of Malawi registered an average household size of
4.9 and a 4.5 national household size (GoM, 2005).
Table 6: Household size distribution of respondent-farmers in the study area Household size Frequency Percent (%) Cumulative %
2 3 1.5 1.5
3-5 59 30.4 32.0
6-8 96 49.5 81.4
9-1 35 18.0 99.5
12+ 1 0.5 100
Total 194 100
n=194, Mean=6.6, SD=2.09, Range=10, Minimum=2, Maximum=12
Source: Field Data (2007)
In general, large household sizes are typical of African societies.
However, the implication of these findings is that large families may result in land
pressure such that modern agricultural technologies that enhance agricultural
productivity should continually be promoted.
77
Farm Labour
Labour is one of the most important inputs in agricultural production.
Findings of the study indicated that a majority of the respondents (50%) employed
both family and casual labour on their farms followed by 34 percent, who used
own family labour. About 12.4% farmers used both family and regular labour. A
small percentage of farmers (1.5%) had the capacity to employ regular farm
labour. These were mainly commercial farmers who operated tobacco estates.
Results are presented in Table 7 below.
Table 7: Frequency distribution of farm labour sources as reported by
respondent-farmers
Source of labour Frequency Percent (%)
Family only 66 34.0
Casual only 4 2.1
Regular farm labour 3 1.5
Both family and casual 97 50.0
Both family and regular labour 24 12.4
Total 194 100.0
n=194
Source: Field Data (2007)
The study findings imply that there is heavy reliance on family and casual
labour in farm operations in the area. Farmers who hired casual and regular labour
did so probably to cope with peak periods in farming, but this only complemented
and did not substitute for the family labour on which a majority of families
depended.
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Land holding size
Majority of farmers (47.9 %) were found to own land holdings of sizes
ranging from 1 hectare to 2.99 hectares. Mean land holding size was 2.39 with a
standard deviation of 0.85. This shows that there was little variation in the
landholdings for a majority of farmers interviewed. The relatively high mean land
holding size could be due to cultivation of marginal and less productive land
because average land holding size per household in Malawi is 1.2 hectares while
the average land per capita is 0.33 hectares (GoM, 2007). In addition, per capita
land holdings are highly skewed with the poor holding only 0.23 hectares per
capita compared to the non-poor that hold 0.42 hectares per capita. Table 8 shows
a frequency distribution of land holding sizes for the farmers.
Table 8: Frequency distribution of landholding size as reported by
respondent- farmers in the study area
Land holding size(ha) Frequency Percent (%)
Less than 1ha 24 12.4
1-2.99ha 93 47.9
3.0-4.99ha 54 27.8
5ha or more 23 11.9
Total 194 100.0
n=194, Mean=2.39, SD=0.85
Source: Field data (2007)
Farm/landholding size is frequently analyzed in many adoption studies
(Shakya et. al 1985; Green and Ng’ong’ola, 1993; Adesina et. al. 1995; Nkonya
79
et. al. 1997; Fernandez-Cornejo, 1998; Boahene et. al. 1999; Doss et. al. 2001;
and Daku, 2002). This is perhaps because landholding size can affect and in turn
be affected by the other factors influencing adoption. In fact, some technologies
are termed ‘scale-dependant’ because of the great importance of farm size in their
adoption (Feder, Just and Zilberman, 1985). Disentangling farm size from other
factors hypothesized to influence technology adoption has been problematic.
Feder et al. (1985) thus, caution that farm size may be a surrogate for other
factors, such as wealth, risk preferences, and access to credit, scarce inputs, or
information. Moreover, access to credit is related to farm size and land tenure
because both factors determine the potential collateral available to obtain credit.
Years of Farming Experience
More than 37 percent had at least 15 years of farming experience (Table
9). That a considerable proportion of farmers in the sample had more than 15
years of farming experience seems to suggest that most farmers in the area must
have started farming in their youth and regard it as a way of life. The mean
farming experience was 20.39 years (SD= 11.32) implying that there was a great
variation in the years of farming experience. Nevertheless, the length of
experience in farming is probably an indicator of a person’s commitment to
agriculture. It may not necessarily predispose him/her to adoption of new
practices. However, it is more logical to expect veteran farmers to be less
receptive to extension messages. The observation is a strong case in favour of the
need for government at all levels and other organizations interested in agricultural
80
development to design more effective strategies to attract youth to agriculture and
help them to make a career of it.
Table 9: Frequency distribution of years of farming experience as reported
by respondent-farmers
Years of farming Frequency Percent (%) Cumulative %
Less than 5 11 5.7 5.7
5-14 51 26.3 32.0
15-24 72 37.1 69.1
25-34 34 17.5 86.6
35-44 19 9.8 96.4
45-54 7 3.6 100
Total 194 100.0 -
n=194, Mean=20.39, SD=11.32, Range=48, Minimum=1.0, Maximum= 49.0
Source: Field Data (2007)
Income level
Farmers were also asked to estimate how much income in Malawi Kwacha
(MK) they obtain from their farm produce per annum. A majority of the farmers
(30.4%) (Table 10) indicated that they got incomes of less than MK29999.00. At
the time of the survey, US$1.00 was equivalent to MK141.87. As income bracket
increased, the number of farmers decreased. This generally agrees with previous
findings that income levels of a majority of families in Malawi are very low such
that most people live on less than a dollar ($1) a day ( GoM, 2005).
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Table 10: Frequency distribution of income levels of respondent- farmers Income category Frequency Percent (%)
Less than MK29,999 59 30.4
MK30,000-MK49,999 44 22.7
MK50,000-MK69,999 42 21.6
MK70,000-MK89,999 24 12.4
MK90,000-MK109,999 12 6.2
More than MK110,000 13 6.7
Total 194 100.0
n=194
Note: US$1.00 = MK141.87 (Reserve Bank of Malawi, 2007)
Source: Field Data (2007)
Major crops grown
Farmers grew a wide range of crops. All sampled farmers indicated that
they grew maize on their piece of land. That all sampled farmers grew maize is
not a surprise because maize is a major staple in the two study districts. In
Malawi, national food security is mainly defined in terms of access to maize, the
main staple food. Thus, even if the total production is above the minimum food
requirement but maize supply is below the minimum food requirement the nation
is deemed to be food insecure. Table 11 shows statistics of crops grown. The
second widely cultivated crop was groundnuts (92.8%) followed by sweet
potatoes (85.6), cassava (72.7), beans (70.6%), tobacco (64.9%), soybeans
(49.0%), millet (13.9%), paprika (11.3%), and sunflower (9.3%).
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Table 11: Summary statistics of major crops grown as reported by respondent-farmers Crop Frequency Percent (%)
Maize 194 100.0
Groundnuts 180 92.8
Phaseolus beans 137 70.6
Tobacco 126 64.9
Sweet potatoes 166 85.6
Paprika 22 11.3
Cassava 141 72.7
Millet 27 13.9
Sunflower 18 9.3
Soybeans 95 49.0
n=194
Source: Field Data (2007)
Utilisation of cultivated crops
In the study districts respondent-farmers had only one major cash crop
namely tobacco. Another alternative cash crop was paprika. However, other major
crops such as maize, groundnuts and cassava were either grown for cash or home
consumption. About 90% of farmers reported that they cultivated maize for both
cash and home consumption. Similarly, 46.9 % farmers cultivated cassava for
both cash and home consumption. Farmers reported tobacco as their major cash
crop seconded by paprika. For details see (Table 12).
In Malawi, maize is mainly grown to meet the subsistence needs of many
farming households. However, the indication that most farming households grow
maize for both cash and home consumption may impact negatively on household
83
food security as most households may be tempted to sell beyond their surplus
grain to meet other basic household requirements. Food budgeting should thus be
incorporated in extension messages disseminated to farmers.
Table 12: Utilization of major crops grown as reported by respondent-farmers Crop Home
consumption
Cash Both cash and
home consumption
Maize 9.8 - 90.2
Groundnuts 35.1 - 58.2
Phaseolus beans 51.5 0.5 18.6
Tobacco - 64.9 -
Sweet potatoes 59.3 - 26.3
Paprika - 11.3 -
Cassava 24.7 1.0 46.9
Millet 2.6 0.5 10.3
Sunflower 2.1 - 6.7
Soybeans 5.7 2.6 42.3
Source: Field Data (2007)
It is not surprising that more than 64% of farmers (refer to Table 12)
reported tobacco as a major cash crop in the study area. Tobacco is major cash
earner for most smallholder farmers in Malawi. It accounts for about 60 % of
the country’s merchandise exports, 23 % of its total tax base and as much as
10 % of GDP (GoM, 2007). Malawi is more dependent on tobacco for export
and tax revenue than any other country in the world (GoM, 2007). Tobacco
income is (and has been for many years) the major source of wealth in
84
Malawi, and the performance of the sector is crucial to the economy and its
economic vulnerability (GoM, 2007). However, with recent declining tobacco
prices and threats paused by the anti-smoking lobby campaign (GoM, 2007)
farmers need to diversify away from tobacco production. Recently,
smallholder farmers have started to diversify successfully into paprika
production and export). However, the export volume of paprika remains low
(GoM, 2007).
Access to credit
Access to credit facilities is an important component as far as agricultural
production is concerned. It is thus believed that a lack of adequate access to credit
may have significant negative consequences on various aggregate and household
level incomes, including technology adoption, agricultural productivity, food
security, nutrition, health and overall household welfare (Diagne, Zeller, and
Sharma, 2000). Research findings indicate that a majority of farmers (75.3%) had
ever accessed credit (Table 13). However, nearly all of the credit accessed was in
form of agricultural inputs mostly fertilizers and seed.
Use of credit
Study findings revealed that the most common reason why farmers
obtained credit was to use for the purchase of agricultural inputs. As presented in
Table 14, about 41.2 % farmers reported this as a reason for obtaining credit. The
second major reason reported (33%) is that the recipients wanted to use credit as
business start-up capital.
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Reasons for not accessing credit
Results obtained from usable data indicate that a majority of farmers cited
‘they did not have any need for credit’ (16.5%) as a reason for their not accessing
credit, followed by lack of collateral (5.2%). A small proportion (1.0) cited
rejection of loan application as another reason constraining them from accessing
credit. Detailed results are presented in Table 15.
Table 13: Distribution of respondent-farmers who have ever accessed credit
in the study area
Response Frequency Percent (%)
Yes 146 75.3
No 48 24.7
Total 194 100
n=194
Source: Field Data (2007)
Table 14: Use of credit as reported by respondent-farmers Use of credit Frequency Percent (%)
Business start-up capital 64 33.0
For farming (farm inputs) 80 41.2
For construction 2 1.0
Total 146 75.2
Source: Field Data (2007)
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Table 15: Frequency distribution of respondent-farmers’ reasons for not
accessing credit
Reason Frequency Percent (%)
Had no need for credit 32 16.5
Application was rejected 2 1.0
Did not have collateral 10 5.2
Not applicable 46 75.3
Total 190 97.9
Source: Field Data (2007)
Table 16: Sources of credit by respondent-farmers Credit Source Frequency Percent (%)
Formal banks 3 1.5
Money lenders 2 1.0
Non-government organization 145 74.7
Source: Field data (2007)
Sources of credit
A majority of farmers (74.7%) reported that they accessed credit from
non-governmental organizations including Sasakawa Global 2000 (Refer to Table
16 above). Sasakawa Global 2000 programme’s farm credit package included
fertilizers, seed and herbicides which were issued to participating farmers unable
to access such farm inputs.
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Sources of agricultural extension services
As revealed in the Table 17 presented below, all interviewed farmers
reported government extension workers as main providers of extension services
followed by non-governmental extension staff (43.3%). Fellow farmers (32.5%)
and farmer-based organizations (3.6%) were also cited as sources of extension
services. The small share for farmer-based organisations in extension services
delivery could probably be attributed to fewer existing functional farmer-based
organisations in the country capable of being actively involved in extension work
(GoM, 2005).
Table 17: Respondent-farmers’ sources of agricultural extension services in
the study area
Source of extension services Frequency Percent (%)
Government extension staff 194 100
Fellow farmers 63 32.5
Non-governmental extension staff 84 43.3
Farmer-based organizations 7 3.6
n=194
Source: Field Data (2007)
These results seem to underscore the important role that government
extension workers play in the dissemination of agricultural technologies and
hence the need for government to build more capacity for them to effectively
carry out extension work. According to Halim and Ali (1997), deficiencies in
knowledge and skills are common among extension personnel in Africa, Asia and
88
Latin America due to poor education background. Consequently, they recommend
the provision of regular in-service training to frontline extension personnel.
That more than 32 percent farmers cited fellow farmers as a source of
extension services is something that should be encouraged especially in the wake
of farmer to farmer extension currently being advocated (Scarborough, Killough,
Johnson & Farrington (1997). In addition, the fact that farmers learn extensively
from each other provides an argument against conventional technology
dissemination strategies that view farmers as passive recipients of knowledge and
skills.
Extension teaching methods experienced by farmers
In this study, farmers were also asked to identify the extension teaching
methods used by extension workers in the dissemination of agricultural
production technologies. The findings in Table 18 show the distribution of
extension teaching methods identified by farmers. Majority of farmers (94.8%)
identified method demonstration as an extension teaching method used by
extension workers followed by field days (88.7%) as another common extension
teaching method used in the area. Other extension teaching methods were result
demonstration (50%), group discussions (42.3%), radio (24.7%), leaflets (23.2%),
posters (20.6%), and farm exhibits (19.1%). Farm magazine was the least
mentioned extension teaching method constituting 2.6% probably because of the
language used. The farm magazine circulated by MoAFS’s Department of
89
Extension in mainly written in Chichewa which is not a vernacular language for
the farmers in the study area.
The implication of these findings is that group contact methods (result and
method demonstrations and field days) ranked highest in the order of acquiring
knowledge and skills. This may be as a result of the characteristic nature of the
method of giving information and deeper understanding of the innovation of
interest. The group contact method enhances interaction which may focus much
emphasis on the technology thereby enhancing better understanding. Skills are
better acquired through group contact methods. These methods have the nature of
practical demonstration which will help the farmer from desire stage through
conviction and probably into taking action (Rogers, 1983).
Table 18: Extension teaching methods as experienced by respondent-farmers
in the study area
Extension method Frequency Percent
Result demonstration 97 50.0
Method demonstration 184 94.8
Farm exhibits 37 19.1
Radio 48 24.7
Leaflets 45 23.2
Posters 40 20.6
Farm magazines 5 2.6
Group discussions 83 42.3
Field days 172 88.7
Source: Field Data (2007)
90
Adams (1982, p.29) noted that “just as important as the choice of method
is the involvement of farmers in the teaching process”. He further argued that
whenever possible “training should be by discussion, practical demonstration and
participation, not by teaching methods borrowed from the classrooms of the
formal system” (p. 29). The impact of the demonstration is greater when it is
conducted by farmers themselves. All these will prompt the farmer to take action
which invariably leads to a change in attitude. It is thus very imperative that
appropriate extension teaching methods be used to pass across appropriate
technologies given the nature of the technology to disseminate.
Farmers’ Perceptions of the Level of Participation in SG 2000 Programme
The second objective of the study was to determine the extent to the
Sasakawa Global 2000 Programme Approach allowed for involvement of farmers
in programme activities. Data presented in Table 19 that follows show that the
level of farmers’ participation in such areas as attendance of meetings, planning,
monitoring and evaluation of project activities was high with mean rating ranging
from 4.0 to 4.5. Results also show that there was very little variation in their
perceptions of their level of participation in those same activities with SD varying
between 0.71 and 0.95. As regards farmers’ participation in the organization of
field days and meetings, and group discussions, there was moderate participation
in these areas (mean rating of 3.0-3.53). However, farmers’ opinions varied
substantially on these three domains with SD=2.59 for organization of field days,
organization of meetings (SD=3.07), and group discussions (SD=1.45). The
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overall mean rating for the level of participation can be considered to be high
(mean=3.83) with very minimal variation in farmers’ perceptions.
However, focus group discussions the researcher held with farmers
revealed that the type of planning in which farmers were involved was planning
for demonstrations and not necessary working out plans based on their demands.
Hence, it can be argued that the SG 2000 Programme Approach to some extent
aligned itself with the technology transfer model. According to Frank et. al
(1990), with respect to this model, there is a successful transfer of technology in
some cases, but subsequent problems with the use of the technology might
emerge.
Table 19: Respondent-farmers perceptions of level of participation in SG 2000 Programme Items Mean SD
Participation in planning of project activities 4.50 0.72
Attendance of meetings 4.06 0.95
Organizing field days 3.53 2.59
Group discussions 3.62 1.45
Organizing farmers’ meetings 3.07 1.44
Joint monitoring of project activities 4.12 0.71
Joint evaluation of project activities 4.09 0.91
Overall mean=3.83, SD=0.84, Range=1.43 Rating scale 1=very low, 2=low, 3=moderate, 4=high, 5=very high n=194 Source: Field data (2007)
The technology transfer model is associated with governments’ objectives
of immediate food production, where according to Swanson et al. (1990),
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pursuing an extension system that is narrowly focused on technology transfer
risks promoting growth without equity. In the long-term, through failing to
recognize the needs of all farmers, the consequences may be a small proportion of
very productive commercial farmers, whilst the vast majority of rural people are
left behind at the subsistence level.
Nonetheless, encouraging farmers to be actively involved in planning,
implementation, monitoring and evaluation of extension programmes may foster
respect and confidence in the farmers involved. It may also foster a process of
cultural awareness and change, as the planning and assessment could oblige the
participants to take account of their situation and responsibilities of different
people in the communities, for instance, the different needs of men and women
and different barriers they face in trying to change their situation.
Farmers’ Perceptions of the Effectiveness of the Management Training Plot
as used under SG 2000 Programme Approach
The management training plot (MTP) was probably the single most
important strategy that the SG 2000 Programme Approach used to disseminate the
agricultural technologies. Results from the survey indicate that the strategy was
perceived as very effective (mean rating =4.63-4.81) by most farmers with
minimal variation in their perceptions (SD=0.46-0.60). Overall mean rating for
the management training plot effectiveness was 4.69 with a standard deviation of
0.47. Table 20 below presents detailed results.
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Table 20: Respondent-farmers perceptions of effectiveness of management
training plot as used under SG 2000 Programme Approach
Items Mean SD
Provide technical information on maize production 4.64 0.60
Able to obtain high yields 4.81 0.46
Enhance farmers’ interest in the demonstrated
technologies 4.69 0.55
Generate active farmer participation 4.63 0.58
Overall mean=4.69, SD=0.47, Range=0.18
Rating scale: 1=very ineffective, 2=ineffective, 3=somewhat effective,
4=effective, 5=very effective
n=194 Source: Field data (2007)
The management training plot (MTP) method employs intensive crop
management practices on small piece of land (0.2 hectares). The plot is managed
by a farmer but under constant supervision by the extension worker for technical
assistance. As a result farmers were able to obtain high yields. Thus, effectiveness
of an extension method as perceived by farmers would determine to a great extent
the adoption of production recommendations (Bolorunduro, Iwuanyanwu,
Aribido, and Adesehinwa, 2004 ). From this study, the MTP which was rated as
being effective demands that it should be promoted by government extension
agencies to promote adoption of agricultural technologies. The management
training plot encouraged farmers to learn through experimentation building on
their own knowledge and practices and blending them with new ideas (Ito et. al.,
2006).
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Farmers’ Perceptions of the Level of Satisfaction with Technologies
Disseminated under SG 2000 Programme
As shown in Table 21 below, farmers expressed high degree of
satisfaction (mean=4.56, SD=0.43, Range=0.70) with the agricultural
technologies disseminated under SG 2000 Programme. However, in their
perceptions more farmers differed on use of herbicides in their fields (SD=1.00).
Conservation agriculture using herbicides is a new technology in Malawi (Ito et
al. 2006). Consequently, farmers doubted that weeds could be suppressed by the
mere application of herbicides (Ito et al. 2006). Farmers also expressed concern
on timely planting because they needed to apply a post-emergence herbicide
before planting. This is a genuine concern owing to the unpredictable rainfall
pattern in the country. Based on these findings, adoption of herbicides could be
enhanced if farmers were furnished with more information pertaining to
herbicides.
Table 21: Respondent-farmers’ perceptions of level of satisfaction with technologies disseminated under SG 2000 Programme Items Mean SD
Satisfaction with 25cm plant spacing 4.59 0.53
Satisfaction with 75cm row spacing 4.74 0.53
Satisfaction with use of improved varieties 4.74 0.51
Satisfaction with use of inorganic fertilizers 4.75 0.44
Satisfaction with fertilizer application method 4.51 0.59
Satisfaction with use of herbicides 4.05 1.00
Overall mean=4.56, SD=0.43, Range=0.70 Rating scale 1=very low, 2=low, 3=moderate, 4=high, 5=very high n=194 Source: Field data (2007)
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Table 22: Respondent-farmers’ perceptions of level of adoption of
technologies disseminated under SG 2000 Programme
Items Mean SD
Adoption of 25cm plant spacing 4.30 0.55
Adoption of 75cm row spacing 4.47 0.58
Adoption of improved varieties 4.47 0.58
Adoption of inorganic fertilizers 4.48 0.55
Adoption of fertilizer application method 4.23 0.58
Adoption of use of herbicides 3.61 0.95
Overall mean=4.26, SD=0.45, Range=0.87 Rating scale: 1=very low, 2=low, 3=moderate, 4=high, 5=very high n=194 Source: Field data (2007)
Farmers’ Perceptions of the Level of Adoption of Technologies Disseminated
under SG 2000 Programme
The recommended practices that registered high adoption rates are 25cm
plant spacing, 75cm row spacing, use of improved varieties, use of inorganic
fertilizers and fertilizer application method. The mean rating for these
technologies ranged from 4.23 to 4.47, SD=0.55-0.58 (Table 22). On the other
hand adoption of the use of herbicides was moderate (mean=3.61) with
considerable degree of variation in their perceptions (SD=0.95).
The observed adoption levels of the recommended agricultural practices in
this study reflected the adoption behaviour of small-scale farmers. Adoption of
preventive innovations, such as use of herbicides tends to be low due to fatalism
(Ejembi, et. al., 2006). The belief that a person’s destiny is predetermined and,
therefore, unchangeable, (Ejembi, et. al. 2006) seems to motivate most farmers
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not to adopt preventive technologies. Consequently, innovations, such as
fertilizer, plant or row spacing and improved crop varieties, which have
immediate demonstrable results, are more readily adopted compared to those that
are capital intensive, preventive, and requires a long gestation period before
observable changes can be noticed. In addition, the low adoption levels of
herbicide use could be due to risk averse on the side of farmers. Small holder
farmers are very much risk averse at trying out new technologies.
Constraints to adoption of agricultural technologies disseminated under SG
2000 Programme
Despite high adoption rates in the study area, farmers also indicated some
constraints that prevented them from a full-scale adoption of the technologies
disseminated under the SG 2000 Programme (Refer to Table 23). A majority of
farmers indicated that labour was a major constraint for 75cm row spacing
(46.9%), 25cm plant spacing technology (64.4%), and fertilizer application
method (69.9%). Farmers also indicated high costs of farm inputs-use as with
improved maize seed (43.8%), use of inorganic fertilizer (49.5%) and herbicides
(52.6%).
The results seem to reinforce previous findings. Byerlee and Jewell (1997)
reported that small-scale farmers often reject recommendations for labour-
intensive practices such as plant spacing, and separate operations to applying
fertilizer.
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Table 23: Frequency distribution of the constraints to adoption of technologies disseminated under SG 2000 Programme as reported by farmers Technology Constraint Frequency Percent
25 cm plant spacing High labour requirement 125 64.4
Limited potential for intercropping 2 1.0
75cm row spacing High labour requirement 91 46.9
Limited potential for intercropping 1 0.5
Use of improved varieties High costs of improved maize seed 85 43.8
Distance to input markets too long 1 0.5
Improved varieties not drought tolerant 3 1.5
Improved varieties not resistant to pests and diseases 5 2.6
Use of inorganic fertilizers High costs of fertilizer 96 49.5
Fertilizer application method High labour requirement 135 69.6
Use of herbicides High labour requirement 20 10.3
High costs of herbicides 102 52.6
High infestation of termites 1 0.5
High carry-over of pests and diseases 54 27.8
n=194
Source: Field Data (2007)
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In addition, Holmen (2005) also pointed out the affordability of inorganic
fertilizers and high yielding varieties as a major problem facing African
smallholder farmers. The implication of these findings is that in order to increase
levels of adoption of these technologies costs of farm inputs should be reduced to
affordable levels. Recent efforts by government for initiating a farm input subsidy
programme across the country should be commended. However, other sustainable
initiatives must be explored.
Independent sampled t-test –comparison of means of level of participation,
perception on management training plot effectiveness, level of satisfaction
with technologies and level of technology adoption by districts
An independent sampled t-test was computed to compare the farmers from
the two districts in terms of level of farmer participation, perceptions of the
effectiveness of the management training plot, level of satisfaction with
technologies disseminated and level of technology adoption. Results (refer to
Table 24) reveal that there were statistically significant differences (all at p<0.05)
between farmers from Chitipa and Rumphi districts on the four domains
compared: level of farmer participation in the programme, perceptions on the
effectiveness of the management training plot, and level of technology adoption.
The inter-district means were different. Rumphi was observed to have relatively
greater means compared to Chitipa. Similarly, standard deviations for Rumphi
were relatively small than those of Chitipa implying little variations in farmers’
perceptions of the four domains compared.
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The significant differences observed could be attributed to year of entry
into the programme. In Northern Malawi, Rumphi was chosen as the first SG
2000 Programme area under Mzuzu Agricultural Development Division (ADD).
The project commenced in 1998. Chitipa was incorporated in 2003 and falls under
Karonga Agricultural Development Division. Therefore it was expected that
farmers from Rumphi district would be much more experienced with the
technologies disseminated than their Chitipa counterparts.
Table 24: An independent sample t-test analysis by selected district
Sub-score District
n Mean
SD Mean Difference.
t- (2-tailed)
Sig.
1.331 16.932 .000 Perception of level of participation
Rumphi Chitipa
75 119
4.65 3.32
0.48 0.55
0.145 2.124 .035 Perception of MTP effectiveness
Rumphi Chitipa
75 119
4.78 4.64
0.38 0.51
0.076 1.205 .230 Perception of level of satisfaction with technologies
Rumphi Chitipa
75 119
4.61 4.53
0.23 0.51
0.538 9.687 .000 Perception of technology adoption level
Rumphi Chitipa
75 119
4.59 4.05
0.23 0.44
P< 0.05 Rating scales:
For MTP effectiveness: 1=very ineffective, 2=ineffective,
3=somewhat effective, 4=effective, 5=very effective
For level of participation, level of satisfaction & level of adoption:
1=very low, 2=low, 3=moderate, 4=high, 5=very high
Source: Field data (2007)
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Independent sampled t-test –comparison of means of perception on level of
participation, perception on management training plot effectiveness, level of
satisfaction with technologies and level of technology adoption by sex of
respondents
In this section the four domains were compared on sex of respondents
(refer to Table 25). These domains were level of farmer participation, perceptions
on the effectiveness of the management training plot, level of satisfaction with
technologies disseminated and level of technology adoption. Results from an
independent samples t-test reveal statistically significant differences of
perceptions between males and females on management training plot
effectiveness (p<0.05, and level of satisfaction with technologies (p<0.05). The
inter-sex means were different. Means for males were higher than those for
females. For standard deviations, except for level of participation, the standard
deviations for females were greater than those for males.
No statistical difference was observed between men and women on their
level of participation in the programme. The results show that SG 2000
participating farmers were committed to the project probably due to voluntary
selection into the project thus revealing equal participation in planning,
monitoring and evaluation of project activities.
An interesting observation is that statistically significant differences were
observed between men and women on perception of the MTP effectiveness and
level of satisfaction with technologies. Doss (1999) observes that in many places
in Africa, there has been a strict division of labour by gender in agriculture. This
division of labour may be based on crop or task. Doss (1999) reports that one
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frequently made distinction is that cash crops and export crops are ‘male crops,’
while subsistence crops are ‘female crops.’ Hence, despite the fact a majority of
the participating farmers were males, the actual crop management activities like
planting, weeding, ridging, harvesting, storage and food processing may have
been done by their wives. The implication is that females would thus be more
knowledgeable of the technologies and the management training plot and their
perceptions would thus differ significantly from their male counterparts
Table 25: An independent sampled t-test analysis by sex of respondent-farmers
Sub-score Sex
n Mean
SD Mean Difference
t- (2-tailed)
Sig.
-0.036 -0.301 0.764 Perception on level of participation
Female Male
88 106
3.81 3.85
0.73 0.92
-0.251 -3.842 0.000 Perception on MTP effectiveness
Female Male
88 106
4.55 4.81
0.54 0.36
-0.188 -3.101 0.002 Perception on level of satisfaction with technologies
Female Male
88 106
4.46 4.65
0.48 0.35
-0.073 -1.110 0.268 Perception on technology adoption level
Female Male
88 106
4.22 4.29
0.48 0.43
P< 0.05 Rating scales:
For MTP effectiveness: 1=very ineffective, 2=ineffective,
3=somewhat effective, 4=effective, 5=very effective
For level of participation, level of satisfaction & level of adoption:
1=very low, 2=low, 3=moderate, 4=high, 5=very high
Source: Field data (2007)
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As regards farmer perception on technology adoption level, the study
findings are in agreement with previous findings on the influence of gender on
technology adoption. In recent studies, Doss and Morris (2001) in their study on
factors influencing improved maize technology adoption in Ghana, and Overfield
and Fleming (2001) studying coffee production in Papua New Guinea reported
insignificant effects of gender on adoption. The latter of these studies noted
“effort in improving women’s working skills does not appear warranted as their
technical efficiency is estimated to be equivalent to that of males” (p. 155). Since
adoption of a practice is guided by the utility expected from it, the effort put into
adopting seems to reflect the anticipated utility. It might then be expected that the
relative roles women and men play in both ‘effort’ and ‘adoption’ are similar,
hence suggesting that males and females adopt practices equally.
Relationship between overall effectiveness of SG 2000 Programme Approach
to agricultural technology delivery and selected variables
Results of a bivariate correlation analysis of the effectiveness of SG 2000
Programme Approach and selected variables indicate statistically significant
relationships between some of the variables (Refer to Table 27). Farmers’ overall
perception of the effectiveness of the SG 2000 Programme Approach had
significant relationships with effectiveness of the management training plot (r=-
0.330), level of farmers’ satisfaction with the technologies disseminated (r=-
0.197) and access to farm credit (r=0.240). Positive but statistically insignificant
relationships were observed between perceived effectiveness of the SG 2000
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approach and age (r=0.013), land holding size (r=0.093), and farm labour sources
(r=0.068).
The implication of the findings is that putting responsibility in the hands
of farmers as is the case with the management training plot can make services
more effective. According to World Bank (1996) report, in Indonesia on-farm
trials with substantial farmer involvement have proved the best means to ascertain
and demonstrate the potential benefits of IPM. Making farmers influential and
responsible clients rather than passive beneficiaries of the extension services, can
improve farmers’ knowledge and hence may result in changes in the way farmers
perceive the potential benefits of extension services.
Research findings also revealed that there was a positive substantial and
significant relationship between technology adoption and level of farmers’
participation in the programme (r=0.639)). Based on these findings the null
hypothesis of no significant relationship between technology adoption and level
of farmers’ participation in the programme was rejected.
A statistically significant positive but moderate relationship between
technology adoption and level of farmers’ satisfaction with the technological
recommendations (r=0.296)) was also observed suggesting that unless farmers are
satisfied with technology they cannot adopt.
From the correlation analysis no any significant relationship was observed
between farmers’ perception of the effectiveness of SG 2000 Programme
Approach and their level of participation in the programme.
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Table 26: Correlation matrix showing the relationship between overall effectiveness of the SG 2000 approach and related variables. Explanatory variable
X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12
X1 1.00 X2 -.066 1.00 X3 -.330** .166* 1.00 X4 -.197** .102 .462** 1.00 X5 -.090 .639** .086 .296** 1.00 X6 .013 .053 -.115 -.117 .037 1.00 X7 -.138 .022 .267** .218** .080 .117 1.00 X8 -.107 .045 .106 .145* .024 -.003 .063 1.00 X9 .093 -.157* -.145* -.069 -.144* .375** .079 -.019 1.00 X10 -.038 -.034 -.083 .027 -.023 .211** .092 -.132 .162* 1.00 X11 .068 -.006 .209** .249** .089 .052 .216** .014 .132 .095 1.00 X12 .240** -.348** -.165* -.208** -.241** -.003 -.029 .009 .087 -.236** .045 1.00 ** p<0.01 (2-tailed), *p<0.05 (2-tailed)
Key
X1=Overall perception on effectiveness
of SG 2000 approach
X2=Level of farmer participation
X3=Perception of effectiveness of the
management training plot
X4=Level of farmer satisfaction with
technologies disseminated
X5=Level of technology adoption
X6=Age
X7=Gender
X8=Education X9=Landholding size X10=Household size X11=Farm labour type X12=Access to farm credit
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However the positive relationship between farmers’ perception on the
effectiveness of SG 2000 Programme Approach and their level of participation
suggest that the actual involvement of farmers in extension programmes has some
positive impact on farmers’ attitudes towards extension programmes. Thus wider
involvement of farmers in all phases of extension programmes should be
encouraged and promoted.
Relationship between level of participation and farmers’ demographic and
socio-economic characteristics
The results of a bivariate correlation analysis as presented in Table 26
showed that farmers’ perceptions on their level of participation in SG 2000
Programme had a statistically significantly moderate but negative relationship
with their access to credit (r=-0.384). Similarly, there was a statistically
significant weak but negative relationship between land holding size and level of
farmers’ participation (r=-0.157). Very weak associations were observed between
level of farmer participation and education level of farmer (r=0.045)), age
(r=0.053), farming experience (r=0.132) and income level (r=0.123).
The positive association of age, education, income and years of farming
experience of the farmer with farmers’ level of participation implies these
variables exerted some positive influence on farmers’ level of participation in
extension programmes. Thus knowledge of the factors that affect farmer
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participation may enable extension agents design effective extension programmes
to facilitate farmer participation and subsequently adoption of technologies.
Table 27: Relationship between respondent-farmers’ level of participation in
the programme and related selected demographic and socio-economic
characteristics
Farmers’ perception on level of participation
Variables r p-value
Age 0.053 0.460
Land holding size -0.157* 0.029
Education level 0.045 0.531
Farming experience 0.132 0.066
Income level 0.123 0.087
Access to credit -0.348** 0.000
*p<0.05 (2-tailed), ** p<0.01 (2-tailed)
Source: Field Data (2007)
Relationship between level of technology adoption and selected farmers’
demographic and socio-economic characteristics.
A correlation analysis was run to examine if there were any statistically
significant relationships between level of technology adoption and selected
demographics and socio-economic characteristics of farmers (Table 27). Results
from the bivariate correlation indicate that there was a significant relationship
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between adoption and land holding size of farmer. Household size and age of
farmer had a negative but insignificant relationship with technology adoption. The
rest of the variables showed positive relationships with technology adoption,
though not significant.
Age
Age is a factor thought to affect adoption. Age is said to be a primary
latent characteristic in adoption decisions. However, the study found that age was
negatively correlated (r=-0.037) with adoption and not significant in farmers’
adoption decisions. The results contradict findings from a study by Adesiina and
Baidu-Forson (1995) who reported a positive influence of age on adoption of
sorghum in Burkina Faso. However, the findings are in agreement with a previous
finding by Green and Ng’ong’ola (1993). In their study on adoption of fertilizer
technological package in Malawi, they found that age had a negative and
insignificant relationship with adoption. The aged persons may be less change
prone and reluctant to adopt new technologies on their farms. Older farmers,
perhaps because of investing several years in a particular practice, may not want
to jeopardize it by trying out a completely new method.
Education
Education was found to be positively (r=0.024) related with level of
technology adoption. However, the relationship was statistically insignificant.
Generally education is thought to create a favorable mental attitude for the
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acceptance of new practices especially of information-intensive and management-
intensive practices (Caswell et al., 2001) on adoption. In addition, education is
thought to reduce the amount of complexity perceived in a technology thereby
increasing a technology’s adoption.
Household size
The study also examined whether there was any significant relationship
between household size and technology adoption. A negative relationship was
found between the two variables, household size and technology adoption (r=-
0.023). However, the relationship did not have any statistical significance. The
findings are partly in agreement with work by Simtowe, Zeller and Phiri (2006)
researching on adoption of hybrid maize in Malawi. They reported a negative and
significant effect of household size on the level of adoption for hybrid maize.
They argued that the negative effect of household size on the extent of adoption
could be explained by the fact that once the decision to grow hybrid maize is
made based on abundant labor available, the extent of adoption would depend on
the ability of the household to finance the purchase of complementary inputs
required for the cultivation of hybrid maize. This is particularly true because
hybrid maize requires more capital for the purchase of fertilizer and seed than it
requires labor because it is not labor intensive.
Land holding size
The findings of this study are in agreement with studies by Yaron et. al.
(1992), Fernandez-Cornejo (1996), who found negative relationships between
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technology adoption and farm size. Other studies (Feder, Just and Zilberman,
1985; Fernandez- Cornejo, 1996, Kasenge, 1998; Chirwa, 2003) reported positive
relationship between land holding and technology adoption.
Table 28: Relationship between level of technology adoption and selected
respondent-farmers’ demographic and socio-economic characteristics.
Perception of level of technology adoption Explanatory variable
r p-value
Age -0.037 0.613
Education 0.024 0.745
Household size -0.023 0.746
Landholding size -0.144* 0.046
Income level 0.054 0.453
Farm labour type 0.089 0.219
Farming experience 0.032 0.655
Access to farm credit -0.241** 0.001
*p<0.05 (2-tailed) , **p<0.01 (2-tailed)
Source: Field Data (2007)
The effect of farm size can be found in Yaron et. al. (1992) who
demonstrate that a small land area may provide an incentive to adopt a technology
especially in the case of an input-intensive innovation. In that study, the
availability of land for agricultural production was low, consequently most
agricultural farms were small. Hence, adoption of land-saving technologies
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seemed to be the only alternative to increased agricultural production. Feder, Just
and Zilberman (1985) concluded that the wide variety of empirical results suggest
that size of farm is a surrogate for a number of potentially important factors such
as access to information and access to farm inputs. Since the influence of those
factors varies in different areas and over time, so does the relationship between
farm size and adoption behaviour.
Income level
A positive but insignificant relationship between level of technology
adoption and income level of farmers (r=0.054) was observed. However, the
relationship was not significant. A similar study by Doss (1999) found a positive
correlation between technology adoption and household income. He argued that
“although adopting new technology may increase household income, some
threshold of income and information may need to be achieved before a farmer is
willing to innovate and adopt new technologies” (p. 14). The implication is that
wealthier farmers have greater access to resources and may be more able to
assume risk.
Farm labour
A positive but statistically insignificant relationship was also found
between farm labour source and level of technology adoption (r=0.089). This
result appears to reinforce similar findings of other studies. In their study of
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factors affecting adoption in Malawi, (Green and Ng’ong’ola, 1993) found that
availability of regular labour positively influenced a practice’s adoption.
Farming experience
The findings revealed a positive relationship between a farmer’s years of
farming experience and level of technology adoption (r=0.032). However, the
relationship was not statistically significant. More years of farming experience is
hypothesized to increase the probability of technology adoption because
experience helps an individual to think in a better way and makes a person more
mature and able to take right decisions (Adesina and Zinnah, 1992).
Access to farm credit
In this study a farmer’s access to farm credit was found to be statistically
significant with but negatively related to level of technology adoption. Similarly,
in their study on access to credit and hybrid maize adoption in Malawi, Simtowe,
Zeller and Phiri (2006) observed that factors that influence the decision to adopt
hybrid maize are not necessarily the same factors that affect the extent of
adoption. They compared two categories of households, credit-constrained and
credit-unconstrained and reported that factors that affected adoption decisions
among credit-constrained households were different from those that affected
adoption in the unconstrained regime. For, example, while credit had a positive
effect on adoption in the constrained regime, it had a negative effect on
unconstrained households, though not significantly. Feder et. al (1984) also
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observed that the lack of credit does not inhibit adoption of innovations that are
scale neutral. For instance adopting technologies such as plant spacing, row
spacing, and fertilizer application method does not require a farmer to have any
heavy initial capital investment.
Predictors of the overall effectiveness of the SG 2000 Programme Approach
to agricultural technology delivery
The independent variables with significant relationships that were
correlated with perception on the overall effectiveness of the SG 2000 Programme
Approach were used in the multiple regression analysis which included farmers’
perception on the effectiveness of the management training plot, level of
satisfaction with the technology and access to farm credit. Utilizing a stepwise
regression method two (2) variables remained in the equation, namely, perception
of the effectiveness of the management training plot and farmer’s access to farm
credit. The other variables were eliminated. Table 29 gives a summary of the
regression analysis.
Table 29: Regression coefficients
Predictors Beta
(unstdzed)
R2 Adj. R2 Std.
error
F.Change Sig.
Constant 1.555 0.157 .000
MTP effectiveness -.135 .109 .104 .031 23.517 .000
Access to farm
credit
.094 .145 .136 .033 7.914 .005
P<0.05, n=194 Source: Field Data (2007)
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The result of the multivariate linear regression indicated that two (2)
factors explained 24% of the effectiveness of the SG 2000 Programme Approach.
The management training plot effectiveness explained 10.4 per cent (Adjusted
R2=0.104) while 13.6 per cent (Adjusted R2 =0.136) was explained by farmers’
access to farm credit. The implication is that there are other important factors that
may have contributed substantially to effectiveness of the SG 2000 Programme
Approach which were not investigated in this research. The regression analysis
provides variables which are statistically significant (p<0.05) so the following
equation was formulated to estimate farmers’ overall perceptions of the
effectiveness of the SG 2000 Programme Approach to agricultural technology
delivery.
Y=α+βΧ1+βX2 , which yields: Y= 1.555-0.135X1+0.094X2
Where: Y=Overall effectiveness of the SG 2000 Programme Approach,
α=Constant, β=Unstandardised beta,
X1=Effectiveness of the management training plot
X2=Farmer’s access to farm credit
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CHAPTER 5: SUMMARY, CONCLUSIONS AND RECOMMENDATIONS
Introduction
This chapter summarizes the major findings and then presents the main
conclusions and recommendations based on the findings. Furthermore,
suggestions are made for future research direction.
Summary of Thesis
The study examined farmers’ perceptions of the effectiveness of the SG
2000 Programme Approach to agricultural technology delivery in Northern
Malawi. Specifically the study sought to:
1) describe the demographic and socio-economic characteristics of
participating farmers in terms of sex, age, formal education, household
size, farm labour sources, land holding size, years of farming experience,
level of income, major crops grown in the area, access to farm credit,
sources of extension services and extension teaching methods.
2) examine farmers’ perceptions of their level of participation in the SG 2000
Programme activities,
3) examine farmers’ perceptions of the effectiveness of the management
training plot as a method for technology delivery in SG 2000 Programme,
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4) examine the degree of farmers’ satisfaction with the technological package
disseminated under the SG 2000 Programme,
5) examine farmers’ adoption levels of the technologies disseminated under
SG 2000 Programme
6) identify the constraints to non-adoption of technological recommendations
under the SG 2000 Programme, and
7) examine the relationships between selected farmers’ demographic and
socio-economic characteristics and their perceptions of the effectiveness
of the SG 2000 Programme approach to agricultural technology delivery,
In Malawi where agricultural extension plays an important role in the
dissemination and adoption of agricultural technologies, this study is of
significance importance in that any positive findings of the SG 2000 model will
help both government and non-governmental organizations involved in
agricultural extension services provision to address some of the many shortfalls
facing the dissemination and adoption of agricultural technologies.
The study was carried out in two districts of Northern Malawi, namely
Rumphi and Chitipa. The districts were purposively selected because they were
major maize growing areas in the region and that previous SG 2000 evaluations
were concentrated in the other two regions, that is, southern and central regions.
This study used a descriptive-correlational survey design. A random
sample of 194 participating farmers was selected for the study. A validated
researcher-designed interview schedule was used to collect the required
information from farmers. To measure the individual variables more accurately a
116
Likert-type scale was used to gather farmers’ attitudes. Data was then coded and
analysed using Statistical Product for Services Solutions (SPSS). Frequencies,
percentages, means, and standard deviations were computed to describe the nature
of the data.
An independent samples t-test was computed to compare any significant
differences between means across selected groups that is, between the two
districts, and males and females in terms of level of participation, perceived
effectiveness of method of delivery, perceived effectiveness of SG 2000
Programme Approach and level of technology adoption. A correlation analysis
was done for the following variables; level of farmer participation, perceived
effectiveness of method of delivery, perceived effectiveness of SG 2000
Programme Approach, level of technology adoption, level of formal education,
age, gender, level of income, type of farm labour, farm size, years of farming,
access to credit, access to market. All these relationships were examined using
Pearson Product Moment Correlation Coefficient (r) which is the most widely
used correlation coefficient in data analysis. Research findings are summarized as
follows.
Farmers’ demographic and socio-economic characteristics
Majority of the sampled farmers (54.6%) were males. Female farmers
constituted 45.4% of the total sampled farmers. In general, farmers falling in age
groups of 30-39 and 40-49 constituted the bulk of respondents representing 55%
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and 54% respectively. The mean age of farmers was 44 with a standard deviation
of 12.7.
Results also revealed that a total of 94.8 percent farmers reported to have
had formal education. The small percentage of farmers (5.2%) who reported no
formal education indicated they had undergone adult literacy programmes such
that they were able to read and write. So in general a majority of farmers
interviewed were literate. Majority of farmers (49.5%) had a family size of 6-8
followed by 30.4% whose family size ranged from 3 to 5 persons. Mean
household size for the respondents was 6.6 with a standard deviation of 2.09. A
majority of the respondents (50%) employed both family and casual labour on
their farms followed by 34 percent who used own family labour. A small
percentage of farmers (1.5%) had the capacity to employ regular farm labour.
A majority of farmers (47.9 %) were reported to own landholdings of sizes
ranging from 1 to 2.99 hectares. Mean land holding size was 2.39 hectares with a
standard deviation of 0.85. With increasing population pressure mean landholding
size can be considered to be relatively large.
More than 37.1% had at least 15 years of farming experience. The mean
farming experience was 20.39 with a standard deviation of 11.32 implying that
there was a great variation in the years of farming experience among farmers. A
majority of the farmers (30.4%) indicated that they got incomes of less than
MK29999.00. As income bracket increased, the number of farmers decreased.
This generally agrees with previous findings that income levels of a majority of
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families in Malawi are very low such that most people live on less than a dollar
($1) a day (Integrated Household Survey Report, 2005).
Farmers grew a wide range of crops. All sampled farmers indicated that
they grew maize on their piece of land. The second widely cultivated crop was
groundnuts (92.8%) followed by sweet potatoes (85.6), cassava (72.7), beans
(70.6%), tobacco (64.9%), soybeans (49.0%), millet (13.9%), paprika (11.3%),
and sunflower (9.3%). Tobacco and paprika were solely cultivated for cash while
the rest of the crops were grown for both cash and food. Other crops grown were
ground beans, vegetables, cowpeas and pigeon peas.
Farmers’ access to credit facilities was also examined. Research findings
indicate that a majority of farmers (75.3%) had ever accessed credit. However,
nearly all of the credit accessed was in form of agricultural inputs mostly
fertilizers and seed. Results obtained from usable data indicate that a majority of
farmers cited ‘they did not have any need for credit’ (16.5%) as a reason for their
not accessing credit, followed by lack of collateral (5.2%). A small proportion
(1.0) cited rejection of loan application as another reason constraining them from
accessing credit. A majority of farmers (74.7%) reported that they accessed credit
from non-governmental organizations including SG 2000 Programme. Sasakawa
Global 2000 Programme’s farm credit package included fertilizers, seed and
herbicides which were issued to participating farmers unable to access such farm
inputs.
As regards sources of extension services, all interviewed farmers reported
government extension workers as main providers of extension services followed
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by non-governmental extension staff (43.3%). Farmer-based organizations scored
a low percentage (3.6%) in terms of extension services provision
The study also sought to identify the extension teaching methods used by
extension workers in the dissemination of agricultural production technologies.
Majority of farmers (94.8%) identified method demonstration as an extension
teaching method used by extension workers followed by 88.7% of farmers who
identified field days as another common extension teaching method used in the
area. Other extension teaching methods were result demonstration (50%), group
discussions (42.3%), radio (24.7%), leaflets (23.2%), posters (20.6%), and farm
exhibits (19.1%). Farm magazine was the least mentioned extension teaching
method constituting 2.6%.
Farmers’ perception of level of participation in the SG 2000 programme
Findings from the study show that the level of farmers’ participation in
such areas as attendance of meetings, planning, monitoring and evaluation of
project activities was high with mean rating ranging from 4.0 to 4.5. Results also
show that there was very little variation in their perceptions of their level of
participation in the indicated activities with standard deviations ranging from 0.71
to 0.95. With respect to farmers’ participation in the organization of field days
and meetings, and group discussions, there was moderate participation in these
areas (mean rating of 3.0-3.53). However, farmers’ opinions varied substantially
on these three domains with SD=2.59 for organization of field days, organization
of meetings (SD=3.07), and group discussions (SD=1.45).
120
Farmers’ Perceptions of the Effectiveness of the MTP as used by SG 2000
Programme
Results from the survey indicate that the management training plot
strategy was perceived as being very effective (mean rating =4.63-4.81) by most
farmers with minimal variation in their perceptions (SD=0.46-0.60). Overall mean
rating for the management training plot effectiveness was 4.69 with a standard
deviation of 0.47.
Farmers’ perceptions of level of satisfaction with technologies disseminated
under SG 2000 Programme
Farmers expressed high degree of satisfaction (mean rating of 4.05 to
4.75) with the agricultural technologies disseminated under SG 2000 programme.
Overall mean rating for degree of satisfaction with technologies was 4.56 with
little variation in their perceptions, SD=0.43. However, in their perceptions more
farmers differed on use of herbicides in their fields (SD=1.00
Farmers’ perceptions of level of adoption of technologies disseminated under
SG 2000 Programme
The recommended practices that registered high adoption rates are 25cm
plant spacing, 75cm row spacing, use of improved varieties, use of inorganic
fertilizers and fertilizer application method. The mean rating for these
technologies ranged from 4.23 to 4.47, SD=0.55-0.58. On the other hand,
121
adoption of the use of herbicides was moderate (mean=3.61) with considerable
degree of variation in adoption levels among farmers (SD=0.95).
Results from an independent samples t-test analysis revealed that there
were statistically significant differences between farmers from Chitipa and
Rumphi districts on three domains compared: level of farmer participation in the
programme (p<0.05), perceptions on the effectiveness of the management training
plot (p<0.05), and level of technology adoption (p<0.05).
Similarly, findings from an independent samples t-test by sex of
respondents revealed statistically significant differences on perceptions between
males and females on management training plot effectiveness (p<0.050, and level
of satisfaction with technologies (p<0.05). No any statistical difference was
observed between men and women on their level of participation in the
programme implying that gender had no effect on level of farmer participation in
the programme.
Despite a considerable number of farmers adopting maize production
technologies, farmers also indicated some constraints that prevented them for a
full-scale adoption of the technologies disseminated under SG 2000 programme.
Labour was cited as a big impediment to adoption for 25cm plant spacing
technology (64.4%), 75cm row spacing (46.9%), and fertilizer application method
(69.9%). Other farmers indicated high costs of farm inputs-use of improved maize
seed (43.8%), use of inorganic fertilizer (49.5%) and herbicides (52.6%).
The result of a bivariate correlation analysis showed that farmers’
perceptions of their level of participation in SG 2000 programme had a
122
statistically significant moderate but negative relationship with their access to
credit (r=-0.384). Similarly, there was a statistically significant weak but negative
relationship between land holding size and level of farmers’ participation (r=-
0.157). Level of participation correlated positively with gender of farmer,
education level, age, farming experience and income level.
Level of technology adoption correlated negatively with land holding size
of farmer; however, the relationship was significant. Household size and age of
farmer had also a negative and statistically insignificant relationship with
technology adoption. Level of technology adoption correlated positively with
education, income level, gender, and farming experience.
The result of a bivariate correlation test showed that farmers’ perceptions
on their level of participation in SG 2000 Programme had a statistically
significant moderate but negative relationship with their access to credit (r=-
0.384). Similarly, there was a statistically significant weak but negative
relationship between land holding size and level of farmers’ participation (r=-
0.157). Level of participation correlated positively with education level
(r=0.045)), age (r=0.053), farming experience (r=0.132) and income level
(r=0.123).
A bivariate correlation analysis of the effectiveness of SG 2000 and its
related variables revealed statistically significant relationships between farmers’
overall perception of the effectiveness of the approach with effectiveness of the
management training plot (r=-0.330), level of farmers’ satisfaction with the
technologies disseminated (r=-0.197) and access to farm credit (r=0.240). Positive
123
but statistically insignificant relationships were observed between perceived
effectiveness of the approach and age (r=0.013), land holding size (r=0.093), and
farm labour used (r=0.068).
The result of the multivariate linear regression indicated that the
management training plot effectiveness explained 10.4 per cent (Adjusted
R2=0.104) of the effectiveness of the SG 2000 Programme Approach while 13.6
per cent (Adjusted R2 =0.136) was explained by access to farm credit. The
implication is that there are other important factors that may have contributed
substantially to effectiveness of the SG 2000 Programme Approach which were
not investigated in this research.
Conclusions
Based on the findings of this study, the following conclusions were drawn:
1. A majority of the respondent-farmers are still young (mean =44 years) and
by implication have the ability to carry out farming activities.
2. A highly significant proportion of farmers nearly 95% had formal
schooling thus implying higher literacy level in the area.
3. A majority of farmers (50%) utilize both family and casual labour on their
farms. A miniscule proportion of farmers (1.5%) had the capacity to
employ regular labour implying that regular labour may be very expensive
in the area.
124
4. At the time of the survey, a majority of farmers were found to cultivate a
small amount of their own land (landholdings of 1-2.99 hectares) with
mean land holding size of 2.39 hectares.
5. A substantial proportion of farmers (37.1%) had been farming for at least
15 years. Farmers can thus be considered to have acquired a lot of farming
experience over the years.
6. Generally a majority of respondent-farmers reported low annual income
levels. At the time of the survey farmers earned less than US$200 per
year.
7. In general all farmers grew a wide range of crops. All sampled farmers
indicated that they grew maize on their piece of land. Maize is the major
staple crop in Northern Malawi and indeed the nation as a whole. A
majority of farmers reported tobacco as their major cash crop.
8. A significant majority of farmers (75.3%) had accessed credit. However,
nearly all of the credit accessed was in form of agricultural inputs mostly
fertilizers and seed. Those who had never accessed credit cited lack of
interest in credit borrowing indicating the harsh methods of credit
recovery employed by lenders.
9. Government extension staff remain major source of extension services
followed by non-governmental organizations and fellow farmers.
10. A majority of farmers identified group contact extension methods as the
most popular extension teaching methods used by extension workers in
125
their area. The group contact methods were method demonstrations, field
days, and result demonstration.
11. The SG 2000 Programme Approach attracted a higher level of farmer
participation particularly in such areas as planning, monitoring and
evaluation of project activities. On the other hand, farmers’ participation
in organization of field days, meetings and participation in group
discussions was moderate.
12. The management training plot which was probably the principal extension
teaching method was rated as being very effective in provision of maize
production knowledge, yield improvements, stimulating farmer interest in
the disseminated technologies and eliciting active farmer participation.
13. A majority of farmers were highly satisfied with the technologies
disseminated. However farmers expressed moderate satisfaction with use
of herbicides. This being the case, more farmers registered high adoption
rates of plant spacing, row spacing, use of inorganic fertilizer and fertilizer
application method. Few farmers adopted use of herbicides.
14. Generally a majority of farmers from the study area were very satisfied
with the technologies disseminated under SG 2000 Programme. However,
farmers differed on their level of participation in the programme and level
of adoption of the technologies.
15. Gender was found to have a significant influence on farmers’ perception
of the management training plot and their level of satisfaction with
technologies.
126
16. In general labour was found to be a constraint associated with adoption of
row spacing, fertilizer application method and plant spacing while
exorbitant farm input prices were found to be a major factor affecting
adoption of improved maize seed, inorganic fertilizers and herbicides.
17. Farm size was found to have an inverse relationship with level of adoption
of the technologies disseminated under SG 2000 Programme in the study
area suggesting that the technologies disseminated were not scale
dependent.
18. Level of farmer participation in the SG 2000 Programme was found to
have a strong and significant relationship with level of adoption of
technologies disseminated.
19. The management training plot and access to farm credit were the only
factors found to explain the effectiveness of the SG 2000 Programme
Approach.
Recommendations
The following recommendations are made based on the study findings;
1. To address the problem of shrinking land holdings among smallholder
farmers in the longer-term, the Government of Malawi through the
Ministry of Lands and Natural Resources should carefully implement the
newly formulated national land policy to ensure security of tenure and that
the landless or near landless have access to land. Ensuring security of
tenure will help in developing the land market by facilitating access to
127
financial or physical capital which may have implications of increased
agricultural productivity.
2. Extension staff of both MoAFS and NGOs should promote farmer-to-
farmer extension approaches in order to reach out to more farmers in the
face of resource constraints.
3. In order to enhance farmers’ acquisition of knowledge and skills in new
technologies, the Department of Agricultural Extension Services of
MoAFS should promote and mainstream the management training plot
(MTP) as a method of agricultural technology delivery into public
extension programmes.
4. The Government of Malawi in collaboration with NGOs should design
appropriate interventions for improving farmers’ access to farm credit in
order to increase agricultural production to meet the challenge of
achieving self-sufficiency in food production both at household and
national levels.
5. The strong positive and significant relationship between level of farmer
participation and technology adoption may be an indication of the benefits
of involving farmers in different phases of the project/programme cycle. It
is thus strongly recommended that the MoAFS should promote farmer
participation in planning, implementation, monitoring and evaluation of
different agricultural extension programme activities for sustained
adoption of technologies. To achieve this, MoAFS should institutionalize
participatory extension approaches for increased farmer participation.
128
6. The significant differences between men and women in their perceptions
of the management training plot and level of satisfaction with the
technologies is an indication that there are gender differences in farming
systems. To address the gender issue, project planners for both MoAFS
and NGOs should investigate the intrahousehold decision-making process.
For each situation and condition, planners should identify goals, decision
criteria, and the context of the decisions for women before project
implementation.
Future Research Direction
1. This study is not exhaustive. It was limited to farmers’ opinions due to
constraints of time and financial resources. However, a clear
understanding of the effectiveness of agricultural technology transfer
would be more exhaustive if diverse views from all key stakeholders were
solicited. Thus, a similar study comparing views from all key stakeholders
namely, SG 2000 Programme officials, Agricultural Extension staff of the
Ministry of Agriculture and Food Security, farm input dealers and farmers
would greatly contribute to the available literature on effectiveness of
extension approaches.
2. Since SG 2000 Programme implemented its activities in partnership with
government’ s public extension system, a study on the effectiveness of
government/non-governmental organization collaboration in the delivery
of extension services would be of great significance.
129
3. This study has not provided the economic impact of the SG 2000
Programme. The quantifiable production impact of agricultural extension
programmes may be an area of great importance to policy-makers both at
national and international levels. Policy makers might want to have an
indication of the returns from major programme investments including
agricultural extension. Therefore, it is essential that expenditures in
extension should be followed by rigorous efforts to measure the impact on
farmers. A comprehensive study of this kind would serve that purpose but
specific to Malawi.
130
REFERENCES
Abadi-Ghadim, K. A., & Pannell, D.J. (1999). “A Conceptual Framework of
Adoption of an Agricultural Technology.” Agricultural Economics 21:
145-154.
Abara, I. O. C. & Singh, S. (1993). “Ethics and Biases in Technology Adoption:
The Small Farm Argument.” Technological Forecasting and Social
Change. 43: 289-300.
Adams, M.E. (1982). Agricultural Extension in Developing Countries. Essex
CM20 2JE: Longman Group Ltd.
Adesina, A. & Baidu-Forson, J. (1995). “Farmers’ Perceptions and Adoption of
New Agricultural Technology: Evidence from Analysis in Burkina Faso
and Guinea, West Africa.” Journal of Agricultural Economics.
Adesina, A. & Zinnah, M. (1992). Adoption, Diffusion and Economic Impacts of
Modern Mangrove Rice Varieties in Western Africa: Further results from
Guinea and Sierra Leone. In: Towards Paradigm for Farming System
Research/Extension. Working Paper for the 12th Annual Farming System
Symposium. East Lansing: Michigan State University.
Allen, W., Kilvington, M., Nixon, C. & Yeabsley, J. (2002). Sustainable
Development Extension Ministry of Agriculture and Forestry Technical
Paper No: 2002. Website:http://www.maf.govt.nz/mafnet/rural-nz/people-
andtheir-issues/education/sustainable-development-extension/index.htm.
(Accessed 10th May 2007)
131
Anderson, J., and J. Thampapillai. (1990). “Soil Conservation in Developing
Countries: Project and Policy Intervention.” Policy and Research Series 8.
Washington, D.C.: World Bank.
Ary D., Jacobs, L.C. & Razavieh, A. (1979). Introduction to Research in
Education (2nd ed). New York: Holt, Rinehart and Winston.
Baidu-Forson, J. (1999). “Factors Influencing Adoption of Land-enhancing
Technology in the Sahel: Lessons from a Case Study in Niger.” Journal of
Agricultural Economics. 20:231-239.
Biggs, S.D. (1989). Resource Poor Farmer Participation in Research: A Synthesis
of Experiences from Nine Agricultural Research Systems. On-Farm
Client-Oriented Research (OFCOR) Comparative Study Paper No. 3. The
Hague: International Service for National Agricultural Research (ISNAR).
Boahene, K., Snijders, T.A.B., & Folmer, H. (1999). “An Integrated Socio-
Economic Analysis of Innovation Adoption: The case of Hybrid Cocoa in
Ghana.” Journal of Policy Modeling. 21(2):167-184.
Bolorunduro, P.I., Iwuanyanwu, I.E.J., Aribido, S.O., and Adesehinwa, A.O.K.
(2004). “Effectiveness of Extension Dissemination Approaches and
Adoption of Livestock and Fisheries Technologies in Nigeria.” Food,
Agriculture and Environment 2: 298-302
Breth, S.A. (ed)(1998). Agricultural Intensification in Sub-Saharan Africa.
Geneva: Centre for Applied Studies in International Negotiations.
Burkey, S. (1993). People first: A guide to self-reliant participatory rural
development. New Jersey, Trenton: Zed Books Ltd.
132
Byerlee, D. & Jewell, D. (1997). “The Technological Foundation of the
Revolution.” In: Byerlee, D. & Eicher, C.K. (eds.), Africa’s Emerging
Maize Production. Colorado, Denver: Lynne Renner Publishers.
Caswell, M., Fuglie., K., Ingram,C., Jans C., & Kascak, C. (2001). Adoption of
Agricultural production practices: Lessons learned from the US.
Department of Agriculture area studies project. Washington DC. US
Department of Agriculture.
Cernea, M.M., Coulter, J.K. & Russel, F.A (eds). Agricultural Extension by
Training and Visit: The Asia Experience. Washington: The World Bank
Chamala, S. & Mortiss, P. (1990). Working Together for Land Care. Brisbane:
Australian Academic Press.
Chambers, R., Pacey, A. & Thrupp, L. A. (1989). Farmer first: Farmer Innovation
and Agricultural Research. London: Intermediate Technology
Publications.
Chirwa, E. W. (2003). Fertilizer and Hybrid Seeds Adopting among Smallholder
Maize Farmers in Southern Malawi. Zomba: Department of Economics,
University of Malawi.
Dasgupta, S. (1989). Diffusion of Agricultural Innovations in Village India. New
Delhi: Wiley Eastern
Davis, J.A. (1974). Elementary Survey Analysis. Englewood, N.J : Prentice-Hall.
Diagne, A, Zeller, M & Sharma, M. (2000). Empirical measurements of
households’ access to credit and credit constraints in developing countries:
Methodological issues and evidence. Washington, D.C: International Food
Policy Research Institute (IFPRI).
133
Doss, C.R. (1999). Twenty-Five Years of Research on Women Farmers in
Africa: Lessons and Implications for Agricultural Research Institutions;
with an Annotated Bibliography. CIMMYT Economics Program Paper
No. 99-02. Mexico D.F.: CIMMYT.
Doss, C. & McDonald, A. (1999). Gender Issues and the Adoption of Maize
Technology in Africa: An Annotated Bibliography. The Annotated
Bibliography of Twenty-Five Years of Research on Women Farmers in
Africa: Lessons and Implications for Agricultural Research Institutions;
with an Annotated Bibliography. CIMMYT Economics Program Paper
No. 99-02. Mexico D.F.: CIMMYT.
Doss, C.R., & Morris, M.L., (2001). “How Does Gender Affect the Adoption of
Agricultural Innovation? The Case of Improved Maize Technologies in
Ghana.” Journal of Agricultural Economics. 25:27-39.
Dowswell, C. & Russel, N.C. (1991). Workshop Summary. In: Africa’s
Agricultural Development in the 1990s: Can it be Sustained?
CASIN/SAA/Global 2000. Tokyo: Sasakawa Africa Association
Daku, L. (2002). “Assessing farm-level and aggregate economic impacts of olive
integrated pest management programs in Albania.” PhD. Dissertation.
Virginia Polytechnic Institute and State University.
Ejembi, E.P., Omoregbee, F.E. & Ejembi, S.A. (2006). “Farmers’ Assessment of
the Training and Visit Exstension System in Central Nigeria: Evidence
from Barkin Ladi, Plateau State.” Journal of Social Sciences 12 (3):207-
212
134
Esser, K., R. Øygard, C. Chibwana and M. Blakie. (2005). Opportunities for
Norwegian Support to Agricultural Development in Malawi. Noragric
Report 27. Ås: Noragric, Norwegian University of Life Sciences.
FAO (2005). Special Report Malawi. Website: http://www.fao.org/giews/ .
(Accessed 11th February 2007).
Farrington, J. (1997). “The Role of Non-Governmental Organisations in
Extension.” In: Swanson, B.E., Bentz, R.P & Sofranko, A.J. (eds) (1997).
Improving Agricultural Extension: A Reference Manual. Rome: FAO
Feder, G., Just, E.R. & Zilberman, D. (1985). “Adoption of Agricultural
Innovations in Developing Countries: A Survey.” Economic Development
and Cultural Change. 33:255-298.
Feder, G. and Slade, R. (1984). “The acquisition of information and the adoption
of new technology.” American Journal of Agricultural Economics.
American Agricultural Economics Association. 66:312-320.
Fernandez-Cornejo, J. (1998). “Environmental and Economic Consequences of
Technology Adoption: IPM in Viticulture.” Agricultural Economics,
18:145-155.
Fernandez-Cornejo, J. (1996). “The Microeconomic Impact of IPM Adoption:
Theory and Application.”Agricultural and Resource Economic Review.
25:149-160.
Fliegel, F. C. (1993). Diffusion Research in Rural Sociology. Westport, CT:
Greenwood Press.
135
Frank, B. & Chamala, S. (1992). Effectiveness of Extension Strategies. In
G.Lawrence., F. Vanclay, & B. Furze (eds.). Agriculture, Environment
and Society pp.122-140. Melbourne: Macmillan.
Gabre-Madhin, E.Z. & Haggblade, S. (2001). Success in African Agriculture:
Results of an Expert Survey. Washington DC: International Food Policy
Research Institute.
Garforth, C. & Harford, N. (1995). Issues in Agricultural Extension: Experiences
of Agriculture and Natural Resource Management Programmes through
the 1980s and 1990s. AERDD Working Paper 95/9.Reading AERDD: The
University of Reading.
Garforth, C. & Lawrence, A. (1997). Supporting Sustainable Agriculture through
Extension in Asia. ODI Natural Resource Perspectives No 21. London:
ODI
Government of Malawi (2007). Agricultural Development Programme. Lilongwe:
Ministry of Agriculture and Food Security.
Government of Malawi (2006). Malawi Growth and Development Strategy 2006-
2011. Website: http:// www.malawi.gov.mw/. (Accessed 10th May 2007).
Government of Malawi (2005). Integrated Household Survey II. National
Statistical Office. Zomba. Website: http://www.nso.malawi.net/ (Accessed
10th May 2007).
Government of Malawi (2005). Irrigation, Rural Livelihoods and Agricultural
Development Project. Lilongwe: Ministry of Agriculture
136
Government of Malawi (2000). Agricultural Extension Policy in the New
Millennium. Lilongwe: Ministry of Agriculture and Food Security.
Green, D.A.G. & Ng’ong’ola D.H., (1993). “Factors Affecting Fertilizer
Adoption in Less Developed Countries: An Application of Multivariate
Logistic Analysis in Malawi.” Journal of Agricultural Economics. 44
(1):99-109.
Greer, J. & Greer G. (1996). Experiences of agricultural extension. MAF Policy
Information Paper No. 12. Website:
http://www.maf.govt.nz/MAFnet/publications(Accessed 15th May 2007)
Halim, A. & Ali, M.M. (1997). “Training and Professional Development.” In:
Swanson, B.E., Bentz, R.P & Sofranko, A.J. (eds) (1997). Improving
Agricultural Extension: A Reference Manual. Rome: FAO
Harper, J. K., Rister M. E., Mjelde J. W., Drees B. M., & Way M. O.(1990).
“Factors influencing the adoption of insect management technology.”
American Journal of Agricultural Economics. 72(4):997-1005.
Henerson, M.E., Morris, L.L., & Fitz-Gibon, C.T. (1987). How to Measure
Attitudes. Newbury Park, CA: SAGE.
Holmen, H. (2005). “The State of Agricultural Intensification in Sub-Saharan
Africa.” In: Djurfeldt, G., Holmen, H., Jirstrom, M. & Larson, R. (eds).
The African Food Crisis. Lessons from the Asian Green Revolution.
Wallingford: CABI Publishing.
IFPRI (1998). Pest Management and Food Production: Looking into the future.
20 (52). Washington DC. IFPRI.
137
Ito, M., Mastumoto, T. & Quinones, M.A. (2007). “Conservation Practice in sub-
Saharan Africa: The Experience of Sasakawa Global 2000.” Crop
Protection Journal. 26:417-423
Jones, G.E. & Garforth, C. (1997). “ The History, Development and Future of
Agricultural Extension.” In: Swanson, B.E., Bentz, R.P & Sofranko, A.J.
(eds) (1997). Improving Agricultural Extension: A Reference Manual.
Rome: FAO
Kasenge, V. (1998). “Socio-economic factors influencing the level of Soil
Management Practices on Fragile Land.” In: Shayo-Ngowi, A.J., G. Ley
and F.B.R Rwehumbiza (eds). Proceedings of the 16th Conference of Soil
Science Society of East Africa 13th-19th, December 1998, Tanga,
Tanzania pp.102-112.
Kato, E. (2000). “An analysis of factors affecting adoption of K131 bean variety
by women groups in Luuka County, Iganga district.” MSc thesis,
Makerere University.
Kebede, Y., Gunjal K. & Coffin G.(1990). “Adoption of New Technologies in
Ethiopian Agriculture: The case of Tegulet-Bulga District, Shoa
Province.” Journal of Agricultural Economics. 4:27-43.
Khanna, M.(2001). “Sequential Adoption of Site-Specific Technologies and its
Implications for Nitrogen Productivity: A Double Selectivity Model.”
American Journal of Agricultural Economics. 83(1):35-51
138
Klotz, C., Saha, A., & Butler, L.J. (1995). “The Role of Information in
Technology Adoption: The Case of rbST in the California Dairy
Industry.” Review of Agricultural Economics, 17: 287-298.
Kumar, R. (1996). Research Methodology. New Delhi: SAGE Publications Ltd.
Larson, R. (1985). ‘Crisis and Potential in Smallholder Food Production:
Evidence from Micro-level.’ In: Durfeldt, G., Holmen, H., Jirstrom &
Larson R (eds). The African Food Crisis: Lessons from the Asian Green
Revolution. Wellington: CABI Publishing.
Langyintuo, A. (2004). Malawi Maize Sector Stakeholders Workshop Report.
Lilongwe: CYMMT
Leeuwis, C.(2003). Communication for Rural Innovation: Rethinking Agricultural
Extension (3rd ed). Oxford: Blackwell Publishing.
Lionberger, H.F. (1960). Adoption of New Ideas and Practices. Iowa State:
University Press.
Lowenberg-DeBoer, J.(2000). “Comments on Site-Specific Crop Management:
Adoption Patterns and Incentives.” Review of Agricultural Economics.
22(1):245 247.
Maunder, H. (1973). Agricultural Extension. A Reference Manual. Rome: FAO.
McGuirk, A. M., Preston, W.P., & Jones, G.M. (1992). “Introducing Foods
Produced using Biotechnology: The Case of Bovine Somatotropin.”
Southern Journal of Agricultural Economics. pp 209-223.
139
McNamara, K. T., Wetzstein M.E., & Douce G.K.(1991). “Factors Affecting
Peanut Producer Adoption of Integrated Pest Management.” Review of
Agricultural Economics. 13:129-139.
Mhone, G.C.Z. (1987). “Agriculture and Food Policy in Malawi: A Review,” In:
Mkandawire, T. & Bourename, N. (eds.). The State and Agriculture in
Africa. London: Codesria Book Series.
Misra, D.C. (1997).” Monitoring Agricultural Extension and Resources.” In:
Swanson, B.E., Bentz, R.P & Sofranko, A.J. (eds) (1997). Improving
Agricultural Extension: A Reference Manual. Rome: FAO
Mubiru, J., Ojacor, F., Yiga L. & Foster A.M. (1999). Developing a Technology
Demonstration Programme based on Cost Sharing with Farmers in
Uganda. In: Breth, S. A. (ed). Partnership for Rural Development in Sub-
Saharan Africa. Geneva: Centre for Applied Studies in International
Negotiations.
Mugisa-Mutetikka, M., Opio A.F.., Ugen M.A.., Tukamuhabwa., P. Kayiwa B.S.,
Niringiye C. & Kikoba E.(2000). “Logistic Regression Analysis of
Adoption of New Bean Varieties in Uganda.” Unpublished.
Nagel, (1997). “Alternative Approaches to Organising Extension.” In: Swanson,
B.E., Bentz, R.P & Sofranko, A.J. (eds) (1997). Improving Agricultural
Extension: A Reference Manual. Rome: FAO
Napier, T.L. (1991). “Factors affecting acceptance and continued use of soil
conservation practices in developing societies: A diffusion perspective.”
Agriculture, Ecosystems, and Environment 36: 127-140.
140
Nkonya, E., Schroeder T., & Norman D. (1997). “Factors Affecting Adoption of
Improved Maize Seed and Fertilizer in Northern Tanzania.” Journal of
Agricultural Economics. 48 (1):1-12
Norton, G. & Elaine B. (1995). Journal of Extension. Cooperative Research
Centre for Tropical Pest Management, Brisbane:University of Queensland,
Website: http://www.joe.org/joe/1995august/a1.html.( Accessed 15th May
2007)
Nowak, P. (1987). “The Adoption of Agricultural Conservation Technologies:
Economic and Diffusion Explanations.” Rural Sociology. 52(2): 208-220.
Nubkupo K. & Galiba M. (1999). Agricultural Intensification in West Africa:
Insights from SG2000’s Experience. Paper Presented at the Workshop on
Agricultural Transformation, Nairobi, Kenya. Website: www.saa-
tokyo.org.( Accessed 10th June 2007).
Oakley, P., & Marsden, D. (1985). Approaches to Participation in Rural
Development.Geneva: International Labor Organization.
Overfield, D. & Fleming, E. (2001). “A Note on the Influence of Gender
Relations on the Technical Efficiency of Smallholder Coffee Production in
Papua New Guinea.” Manuscript. Journal of Agricultural Economics
pp.153-156.
Paul, S. (1986). Community Participation in Development Projects. The World
Bank Experience. Paper Presented at the Economic Development Institute
workshop on Community Participation, Washington.
141
Pereira de Herrera, A., & G. Sain. (1999). “Adoption of Maize Conservation
Tillage in Azuero, Panama.” CIMMYT Economics Working Paper No. 99
(01). Mexico, D.F.: CIMMYT.
Plucknett, D.L., Matsumoto, T. & Takase, K. (2002). A Review of the Sasakawa
Global 2000 Programme in Malawi. Tokyo: The Nippon Foundation.
Pretty, J.N. & Chambers R. (1994). Towards a Learning Paradigm: New
Professionalism and Institutions for Agriculture. In: Scoones, I. &
Thompson J. (eds). Beyond Farmer First. Rural People’s Knowledge,
Agricultural Research and Extension Practice, pp 182-202. London :
Intermediate Technology Publications.
Pretty, J.N. (1995). Regenerating Agriculture: Policies and Practice for
Sustainability and Self-reliance. London: Earthscan
Rogers, E.M. (1983). Diffusion of Innovations. (3rd ed). New York: The Free
Press.
Rogers, E.M. (1995). Diffusion of Innovations. (4th ed). New York: The Free
Press.
Sasakawa Africa Association Report (2001-2002). Website: www.saa-tokyo.rg
.(Accessed 10th June 2007)
Scarborough, V., Killough, S., Johnson, D.A. & Farrington, J. (1997). Farmer-Led
Extension: Concepts and Practices. London: Intermediate Technology
Publications.
Schwartz, L.A. (1991). Extension in Africa: An Institutional Analysis. Michigan,
MI: Michigan State University.
142
Shakya, P. B. & Flinn J. C. (1985). “Adoption of Modern Varieties and Fertilizer
Use on Rice in the Eastern Tarai of Nepal.” Journal of Agricultural
Economics. 36(3):409-419.
Simtowe, F., Zeller, M. & Phiri, A. (2006). The Impact of Access to Credit on the
adoption of hybrid maize in Malawi. An Empirical test of an Agricultural
Household model under credit market failure. Washington DC:
International Food Policy Research Institute.
Sirkin, R.M. (1999). Statistics for the Social Sciences (2nd ed.). London: SAGE
Publications.
Smale M. & Heisey, P. (1997). ‘Maize Productivity in Malawi’, in Byerlee, D. &
Eicher, C.K. (eds.) Africa’s Emerging Maize Production. Colorado,
Denver: Lynne Renner Publishers, P63-80.
Staatz, J. M. & Eicher, C.K. (1990). “Agricultural Development Ideas in Historic
Perspective.” In: Staatz, J.M. & Eicher, C.K. Baltmore (eds). Agricultural
Development in the Third World Baltimore, Maryland: Johns Hopkins
University Press.
Swanson, B.E. & Clair, J.B. (1984). The History and Development of Agricultural
Extension. In: Swanson B.E (ed), Agricultural Extension. A Reference
Manual, pp 1-19. Rome: FAO.
Swanson, B. E., Farner, B.J. & Bajal, R. (1990). The Current State of
Agricultural Extension Worldwide. In: Report of the Global Consultation
on Agricultural Extension. Rome: FAO
143
Tjornhom, J.D. (1995).“ Assessment of Policies and Socio-Economic Factors
Affecting Pesticide Use in the Philippines.” MS. thesis, Virginia
Polytechnic Institute and State University.
Toulmin, C. (1985). “The Allocation of Resources to Livestock Research in
Africa.” In: African Livestock Policy Analysis Network Paper, no. 4
Addis Ababa: International Livestock Centre for Africa.
Townsend, R.F. (1999). Agricultural Incentives in Sub-Saharan Africa: Policy
Challenges. Washington DC: The World Bank.
Trochim, W. (2000). The Research Methods Knowledge Base (2nd ed).
Cincinnati, OH: Atomic Dog Publishing,
UNESCO (2004). The Plurality of Literacy and its Implications for Policies and
Programmes. Position Paper. Paris: UNESCO
Van den Ban, A.W. & Hawkins, H.S. (1996). Agricultural Extension (2nd ed).
Oxford: Blackwell Science.
Vedeld, T. (2001). Participation in Project Preparation: Lessons from World Bank
( Projects in India. Washington DC: The World Bank.
Venkatesan, V. & Kampen, J. (1998). Evolution of Agricultural Services in Sub-
Saharan Africa: Trends and Prospects. Washington DC: The World Bank.
Wilson, J. (1992). Changing agriculture: An Introduction to Systems Thinking.
Kenthurst, Australia: Kangaroo Press.
World Bank (1996). The World Bank Participation Source Book. Washington
DC: The World Bank.
144
Wu, J. & Babcock B.A. (1998). “The Choice of Tillage, Rotation and Soil
Testing Practices: Economic and Environmental Implications.” American
Journal of Agricultural Economics. 80 :494-511.
Yaron, D., Dinar A. & Voet H. (1992). “Innovations on Family farms: The
Nazareth Region in Israel.” American Journal of Agricultural Economics.
American Agricultural Economics Association pp. 361-370.
145
APPENDIX I: FARMERS’ INTERVIEW SCHEDULE
Farmers’ Perceptions of the Effectiveness of SG2000 Programme Approach to
Agricultural Technology Delivery in Northern Malawi
INSTRUCTIONS
1. All respondent code numbers should start with zero (0). For instance, 001
as respondent one (1), 024 for respondent number 24 and so forth.
2. For each of the questions put a mark [ ] in the box against the
appropriate response.
3. Do not circle responses.
4. Thank the respondent after completion of the interview schedule.
Code: MPhil/SG2000/2007/_________________
SECTION I: SOCIO-ECONOMIC CHARACTERISTCS
1. What is your highest level of formal education attained?
1.1. [ ] Some primary school 1.2. [ ] Completed primary school
1.3. [ ] Junior secondary education 1.4. [ ] Senior secondary education
1.5. [ ] Tertiary education
1.6. [ ] Other (Specify)___________________________________
2. For how long have you been farming on your own?______________years
3. What is the size of land (in hectares) that you cultivate?
3.1. [ ] Less than 1ha 3.2. [ ] 1.0-2.99 ha
3.3. [ ] 3.0-4.99 ha 3.4. [ ] More than 5.0 ha
4. What type of labour do you use on your farm?
4.1. [ ] Family labour
4.2. [ ] Casual labour
4.3. [ ] Regular farm labour
4.4. [ ] Mixed (family and casual labour)
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4.5. [ ] Mixed (family and regular labour)
4.6. [ ] Other (specify)_________________________________
5. What is your total annual income category in Malawi Kwacha (MK)?
5.1. [ ] Less than MK29 999 5.2. [ ] MK30 000-MK49 999
5.3. [ ] MK50 000-MK69 999 5.4. [ ] MK70 000-MK89 999
5.5. [ ] MK90 000-MK109 999 5.6. [ ] More than MK110 000
6. Rank the crops you grow according to the order of importance and indicate its
use.
Crop Rank Home
consumption
(Please tick)
Cash (Please
tick)
Both
(Please
tick)
Maize
Groundnuts
Phaseolus beans
Tobacco
Sweet potatoes
Paprika
Cassava
Other
Access to Agricultural Production Facilities
7. Have you ever obtained credit/loan?
7.1. [ ] Yes 7.2. [ ] No
8. If “yes” to question 7, from where did you actually obtain the credit?
8.1. [ ] Bank 8.2. [ ] Money lenders
8.3. [ ] Cooperatives 8.4. [ ] Friends and relatives
8.5. [ ] Non-Governmental Organisations
8.6. [ ] Other source(s) ________________________________________
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9. For what did you use the credit? (tick all that apply)
9.1. [ ] General household consumption 9.2. [ ] To start farming business
9.3. [ ] To expand farming business 9.4. [ ] For construction
9.5. [ ] For school
9.6. [ ] For social activity (funeral, wedding etc.).
9.7. [ ] Other (specify)____________________________________
10. If “No” to Question 7, why not?
10.1. [ ] Never had the need for a loan
10.2. [ ] Application was rejected
10.3. [ ] Did not have collateral
10.4. [ ] Other (specify) __________________________________
11. To whom do you sell your surplus maize produce? (tick all that apply)
11.1. [ ] local traders 11.2. [ ] Private markets
11.3. [ ] Government markets
11.4. [ ] Others (Specify)__________________________________
12. Are you satisfied with the price they pay you?
12.1. [ ] Yes 12.2. [ ] No
SECTION II: AGRICULTURAL EXTENSION SERVICES
13. Do you have access to agricultural extension services?
13.1 [ ] Yes 13.2 [ ] No
14. What are the sources of agricultural extension services in your area? (tick all
that apply)
14.1 [ ] Government Agricultural Extension agents
14.2 [ ] Fellow farmers
14.3 [ ] Non-Governmental Organisations (NGOs)
14.4 [ ] Farmer-Based Organizations
14.5 [ ] Other (specify)________________________________________
15. What are the methods used by extension workers in the dissemination of
agricultural technologies in your area? (tick all that apply).
15.1. [ ] Result demonstrations 15.2. [ ] Method demonstration
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15.3. [ ] Farm exhibits 15.4. [ ] Radio broadcast
15.5. [ ] Leaflets 15.6. [ ] Posters
15.7. [ ] Mobile van 15.8. [ ] Farm magazines
15.9. [ ] Group discussions 15.10. [ ] Field days
15.12. [ ] Other (specify)________________________________
SECTION III: PARTICIPATION IN SG 2000 PROGRAMME
16. What period did you participate in the SG2000 project?
16.1. [ ] 1998-2006 16.2. [ ] 1999-2006
16.3. [ ] 2000-2006 16.4. [ ] 2001-2006
16.5. [ ] 2002-2006 16.6. [ ] 2003-2006
16.7. [ ] 2004-2006 16.8. [ ] 2005-2006
17. How did you become the beneficiary of the SG2000 project?
17.1. [ ] Selected by government Agricultural Extension Worker
17.2. [ ] Selected by local leaders
17.3. [ ] Volunteered myself
17.4. [ ] Selected by SG2000 project officials
17.5. [ ] Other (specify)_______________________________________
18. Perceptions on level of participation in SG2000 project activities
Activities that were implemented by SG2000 Programme are listed below. For
each activity, indicate the level of your participation.
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Use the following five-point scale for the responses:
5=Very high (VH) 4=High (H) 3=Moderate (M) 2=Low (L) 1=Very low (VL)
No. ACTIVITY VH H M L VL
18.1 Participation in the planning of SG2000 project
activities (management training plots)
5 4 3 2 1
18.2 Attendance of farmers’ meetings 5 4 3 2 1
18.3 Participation in organizing field days 5 4 3 2 1
18.4 Participation in group discussions 5 4 3 2 1
18.5 Participation in organizing farmers’ trainings 5 4 3 2 1
18.6 Participation in joint monitoring of project
activities
5 4 3 2 1
18.7 Participation in joint evaluation of project
activities
5 4 3 2 1
19. Perceptions on the effectiveness of the minimum tillage plot (MTP)
In the table that follows several statements have been listed in relation to the
effectiveness of the management plot as a method for technology transfer. Use the
following five-point scale for the responses:
5=Very effective (VE) 4=Effective (E) 3=Somewhat effective (SE) 2=Ineffective
(I)1=Very ineffective (VI)
No. ITEM VE E SE I VI
19.1 Provide technical information on maize
production
5 4 3 2 1
19.2 Able to obtain high yields compared to ordinary
farming practices
5 4 3 2 1
19.3 Enhance farmers interest in the demonstrated
technologies
5 4 3 2 1
19.4 Generate active farmer participation in the
technology transfer process
5 4 3 2 1
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20. Farmers’ level of satisfaction with technological package
Below are agricultural technologies that were provided by the SG2000 Project.
For each of the technologies, indicate the level of your satisfaction. Use the
following five-point scale for level of satisfaction:
5=Very high (VH) 4=High (H) 3=Moderate (M) 2=Low (L) 1=Very low (VL)
Level of Satisfaction No. Type of Technology
VH H M L VL
20.1 25 cm plant spacing 5 4 3 2 1
20.2 75 cm row Spacing 5 4 3 2 1
20.3 Use of improved varieties 5 4 3 2 1
20.4 Use of inorganic fertilizers (fertilizer) 5 4 3 2 1
20.5 Fertilizer application method 5 4 3 2 1
20.6 Use of herbicides (pre-and post-emergence) 5 4 3 2 1
21. Perceptions on level of technology adoption.
Below is a list of the technologies disseminated. For each indicate your level of
adoption up through 2006. Use the following five-point scale for level of
adoption:
5=Very high (VH) 4=High (H) 3=Moderate (M) 2=Low (L) 1=Very low (VL)
Level of Adoption No. Type of Technology
VH H M L VL
21.1 Plant spacing 5 4 3 2 1
21.2 Row Spacing 5 4 3 2 1
21.3 Use of improved varieties 5 4 3 2 1
21.4 Use of inorganic fertilizers 5 4 3 2 1
21.5 Fertilizer application method 5 4 3 2 1
21.6 Use of herbicides (conservation farming) 5 4 3 2 1
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22. Constraints to agricultural technology adoption
For each of the corresponding response about the level of adoption of the
indicated technologies, can you please explain why?
a) Plant spacing
1. [ ] High labour requirement
2. [ ] Limited potential for intercropping
3. [ ] Other (specify) ____________________________________
b) Row spacing
1. [ ] High labour requirement
2. [ ] Limited potential for intercropping
3. [ ] Other (specify) _____________________________________
c) Use of improved varieties
1. [ ] Costs of improved maize seed too high
2. [ ] Distance to market (where to obtain improved maize seed) too long
3. [ ] Improved varieties not drought resistant
4. [ ] Improved varieties not resistant to pests and diseases
5. [ ] Other (specify) ____________________________________
d) Use of inorganic fertilizers
1. [ ] Fertiliser costs too high
2. [ ] Prefers use of organic manure
3. [ ] Distance to fertiliser market too long
4. [ ] Other (specify) ____________________________________
e) Fertilizer application method
1. [ ] High labour requirement
2. [ ] Other (specify) ____________________________________
f) Use of herbicides (conservation farming)
1. [ ] High labour requirement
2. [ ] Costs of herbicides are too high
3. [ ] Termites infestation high
4. [ ] High carry over of pests and diseases from one season to next
5. [ ] Other (specify)______________________________________
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23. How do you rate the overall effectiveness of SG2000 Approach to agricultural
technology delivery?
23.1. [ ] Very effective 23.2. [ ] Effective
23.3. [ ] Somewhat effective 23.4. [ ] Ineffective
23.5. [ ] Very ineffective
SECTION IV: DEMOGRAPHIC CHARACTERISTCS
24. Gender of Respondent
24.1. [ ] Female 24.2. [ ] Male
25. What was your age on your last birthday? _____________________years.
26. What is the total number of people resident in your household? _________
27. Please indicate the total numbers of household members corresponding to
each category.
Category Number
Children less than 10 years
Children 10-14 yrs
Children 15-18 yrs
Adults more than 18 yrs
SECTION V: CONTROL DATA
Name of Interviewer_______________________________________________
District: _________________________________________________________
Village: _________________________________________________________
Date of interview _________________________________________________
End of Schedule
Thank you for your cooperation
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