Mphil Thesis Finalversion

UNIVERSITY OF CAPE COAST FARMERS’ PERCEPTIONS OF THE EFFECTIVENESS OF THE SASAKAWA GLOBAL 2000 PROGRAMME APPROACH TO AGRICULTURAL TECHNOLOGY DELIVERY IN NORTHERN MALAWI BY FRANCIS WAKISA CHILENGA THESIS SUBMITTED TO THE DEPARTMENT OF AGRICULTURAL ECONOMICS AND EXTENSION OF THE SCHOOL OF AGRICULTURE, UNIVERSITY OF CAPE COAST IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE AWARD OF MASTER OF PHILOSOPHY DEGREE IN AGRICULTURAL EXTENSION AUGUST 2008

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: ……………………………………………………………………………… ii

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 iii

delivery. Improving smallholder farmers’ access to farm credit through appropriate government interventions will also help smallholder farmers ensure food security at household level. iv

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.” v

DEDICATION To my parents, Kingsley Wakisa Chilenga and Rozalia Nandeka vi

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 vii

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 viii

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 ix

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 x

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 xi

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 xii

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 xiii

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 xiv

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 xv

29: Regression coefficients ................................................................................. 112 xvi

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 xvii

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, 1

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. 2

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 3

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; 4

• 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: 5

• 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 6

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? 7

• 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, 8

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 9

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. 10

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. 11

• 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. 12

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 13

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) 14

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 15

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 16

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. 17

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. 18

Figure 1: Map of Malawi Showing Location of the Sampled Districts 19

Figure 2: Location of Focal Study Areas in the Districts Sampled. NYIKA NATIONAL PARK 20

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 21

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. 22

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 23

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 24

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 25

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 26

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, 27

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 28

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., 29

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- 30

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. 31

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 Users Extension Researchers agents Interaction Definition of users’ technology and other information needs Relevant outputs sought from researchers and other agencies Definition of objectives of extension Implementation of programmes with users Figure 3: The Sustainable Development Extension Model Source: Greer and Greer (1996) 32

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 33

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 34

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). 35

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 36

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. 37

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 38

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 39

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 40

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. 41

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 42

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 43

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 44

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 45

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 46

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, 47

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). 48

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. 49

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 50

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). 51

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. 52

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 53

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 54

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 55

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. 56

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 57

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 58

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 59

Figure 4: A Conceptual Framework of the Perceived effectiveness of SG2000 Programme Approach to agricultural technology delivery Effectiveness of SG2000 Programme Approach Level of participation • Planning • Implementation Farmer perception of • monitoring technology • evaluation Farmer characteristics: age, gender, education, income level, years of farming experience, farm size, farm labour Extension Level of source, access to communication satisfaction with extension, access to methods technology farm credit Level of technology adoption Source: Author’s construct (2007) 60

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 61

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. 62

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). 63

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% 64

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 65

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 66

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 67

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 7 0.704 0.726 participation Perceptions on 4 0.834 0.829 effectiveness of MTP Perceptions on level of 6 0.804 0.873 satisfaction with technologies Perceptions on level of 6 0.721 0.806 technology adoption 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 68

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. 69

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 70

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. 71

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. 72

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) 73

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 74

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 75

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. 76

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. 77

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 78

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 79

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). 80

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%). 81

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 82

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 Cash Both cash and consumption 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 83

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. 84

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) 85

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. 86

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 87

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 88

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) 89

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 90

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), 91

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. 92

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). 93

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) 94

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 95

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. 96

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) 97

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. 98

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 t- (2- Sig. Difference. tailed) Perception of level Rumphi 75 4.65 0.48 1.331 16.932 .000 of participation Chitipa 119 3.32 0.55 Perception of MTP Rumphi 75 4.78 0.38 0.145 2.124 .035 effectiveness Chitipa 119 4.64 0.51 Perception of level Rumphi 75 4.61 0.23 0.076 1.205 .230 of satisfaction with Chitipa 119 4.53 0.51 technologies Perception of Rumphi 75 4.59 0.23 0.538 9.687 .000 technology adoption Chitipa 119 4.05 0.44 level 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) 99

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 100

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 t- (2- Sig. Difference tailed) Perception on level Female 88 3.81 0.73 -0.036 -0.301 0.764 of participation Male 106 3.85 0.92 Perception on MTP Female 88 4.55 0.54 -0.251 -3.842 0.000 effectiveness Male 106 4.81 0.36 Perception on level Female 88 4.46 0.48 -0.188 -3.101 0.002 of satisfaction with Male 106 4.65 0.35 technologies Perception on Female 88 4.22 0.48 -0.073 -1.110 0.268 technology Male 106 4.29 0.43 adoption level 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) 101

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 102

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. 103

Table 26: Correlation matrix showing the relationship between overall effectiveness of the SG 2000 approach and related variables. Explanatory X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 variable 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 X4=Level of farmer satisfaction with X8=Education of SG 2000 approach technologies disseminated X9=Landholding size X2=Level of farmer participation X5=Level of technology adoption X10=Household size X3=Perception of effectiveness of the X6=Age X11=Farm labour type management training plot X7=Gender X12=Access to farm credit 104

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 105

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 106

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 107

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 108

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. Explanatory variable Perception of level of technology adoption 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 109

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 110

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 111

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 R2 Adj. R2 Std. F.Change Sig. (unstdzed) error Constant 1.555 0.157 .000 MTP effectiveness -.135 .109 .104 .031 23.517 .000 Access to farm .094 .145 .136 .033 7.914 .005 credit P<0.05, n=194 Source: Field Data (2007) 112

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 113

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, 114

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 115

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% 116

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 117

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 118

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). 119

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, 120

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 121

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 122

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. 123

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 124

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. 125

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 126

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. 127

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. 128

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. 129

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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) 145

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 Cash (Please Both consumption tick) (Please (Please tick) 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) ________________________________________ 146

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 147

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. 148

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 5 4 3 2 1 activities (management training plots) 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 5 4 3 2 1 activities 18.7 Participation in joint evaluation of project 5 4 3 2 1 activities 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 5 4 3 2 1 production 19.2 Able to obtain high yields compared to ordinary 5 4 3 2 1 farming practices 19.3 Enhance farmers interest in the demonstrated 5 4 3 2 1 technologies 19.4 Generate active farmer participation in the 5 4 3 2 1 technology transfer process 149

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) No. Type of Technology Level of Satisfaction 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) No. Type of Technology Level of Adoption 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 1 150

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)______________________________________ 151

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 152

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Mphil Thesis Finalversion Document Transcript