An Economic Assessment of Banana Genetic Improvement and ...

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184 APPENDIX E Table E.2 Average number of associations per village, by type of association, Uganda Elevation Table E.3. Percent of households belonging to associations in Uganda Elevation Association type Low High All Association type Low High All Total per village 11.69*** 15.65*** 13.06 Burial societies 2.27*** 1.21*** 1.88 Economically oriented associations 8.16*** 9.72*** 8.70 Informal credit 2.04*** 6.23*** 3.56 Formal credit 1.87*** 0.43*** 1.44 Agricultural 4.00*** 2.23*** 3.46 Trade-based 0.38*** 0.83*** 0.52 Culture-based 0.34*** 0.60*** 0.45 Faith-based 2.5** 3.59** 2.69 Note: *** and ** indicate statistical significance at the 1 and 5 percent levels, respectively. At least one association 70.63*** 93.68*** 76.38 Burial societies 47.55*** 92.63*** 58.79 Economically oriented associations 41.26*** 60.00*** 45.9 Informal credit 12.94*** 48.42*** 21.78 Formal credit 16.44*** 4.21*** 13.36 Agricultural 3.15 4.21 3.41 Trade-based 20.63 13.68 18.90 Culture-based 2.80 4.21 3.15 Faith-based 6.64*** 23.16*** 10.76 Note: *** indicates statistical significance at the 1 percent level. overcoming market imperfections, only 46 percent of households surveyed were members of economic associations, raising important questions about the determinants of participation. Participation of individuals in most types of associations, except in formal credit associations, is significantly greater in the highlands than the lowlands. Reference Katungi, E. 2006. Social capital and technology adoption on small farms: A case of banana production technology in Uganda. Ph.D. thesis, University of Pretoria, Pretoria, Republic of South Africa. .
List of Principal Authors and Contributors Pamela Nahamya Abodi is a senior research assistant at the IFPRI Kampala, Uganda, office. She has worked with IFPRI since November 2000 on a number of projects that focused on policies for improved land management in Uganda and banana-specific studies in the field of biotechnology economic impact assessment in East Africa. She has been working with research teams involved in survey design, data collection and processing for analysis, and report writing. She holds an M.S. in crop biotechnology (2001) and a B.S. in agriculture (1998) from Makerere University, Kampala. Fredrick Bagamba has worked as a research scientist with NARO for the past ten years. He is currently a Ph.D. student with the Development Economics Group, Wageningen University, Wageningen, The Netherlands. His current research focuses on biophysical and economic aspects influencing productivity of banana production systems in Uganda, with a particular focus on natural resource endowments and market access in banana production. Fredrick obtained an M.S. in agriculture from Makerere University, Kampala, Uganda (1995). The subject of his M.S. thesis was resource allocation efficiency in a banana-based cropping system in Uganda. Mgenzi Said Ramadhan Byabachwezi is a research officer at Maruku Research and Development Institute, Bukoba, Tanzania. He earned his M.S. in ecological agriculture at Wageningen University, Wageningen, The Netherlands (2000). He also has a B.S. in agriculture, obtained at Sokoine University of Agriculture, Morogoro, Tanzania (1991). He has worked on collaborative research activities with INIBAP, the Association for Strengthening Agricultural Research in Eastern and Central Africa's FOODNET program, and IFPRI. His specialization is plant protection, mainly in bananas. He has also been working on banana taxonomic activities. He was an analyst for the research summarized in this report. Svetlana Edmeades has been an economist at the Agriculture and Rural Development Department at the World Bank, Washington, D.C., since September 2006. Before joining the World Bank she was a postdoctoral fellow at IFPRI. She earned her Ph.D. in economics from North Carolina State University (2003), specializing in the fields of development and natural resources/environmental economics. Her dissertation focused on the development of a conceptual framework for the analysis of variety demand in semisubsistence agriculture, where markets are imperfect and variety characteristics are important factors in farmers’ decisions about growing staple crops. Her dissertation work was part of the research summarized in this report. Svetlana also holds an M.S. in economics from the University of Waikato, New Zealand (1998) and a B.S. in international relations from the University of the Americas, Mexico City (1995). 185
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An Economic Assessment of Banana Ge
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Contents List of Tables List of Fig
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Tables 3.1 Net marketing margins pe
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TABLES vii 6.5 Characteristics of h
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Figures 2.1 Sample domain: Elevatio
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Acknowledgments The International F
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SUMMARY xiii en’s education--and
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Acronyms and Abbreviations AGT ARDI
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Part I. Research Methods
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4 CHAPTER 1 practices in Africa are
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6 CHAPTER 1 periment station. Not a
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8 CHAPTER 1 niques (based on seed p
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10 CHAPTER 1 Cohen, J. I., and R. P
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CHAPTER 2 Elements of the Conceptua
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14 CHAPTER 2 ples of adoption disco
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16 CHAPTER 2 transgenic bananas cur
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18 CHAPTER 2 Figure 2.2 Sites sampl
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20 CHAPTER 2 Figure 2.4 Time profil
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Part II. Research Context
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here> 1near 3. 26 CHAPTER 3 major f
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28 CHAPTER 3 disease, which affects
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here> 1near 3. 30 CHAPTER 3 Table 3
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32 CHAPTER 3 Table 3.2 Banana produ
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34 CHAPTER 3 would only be able to
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36 CHAPTER 3 Nkonya, E., J. Pender,
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here> 1near 4. 38 CHAPTER 4 cash ne
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40 CHAPTER 4 Figure 4.1 Introductio
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42 CHAPTER 4 Research Introductions
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44 CHAPTER 4 Table 4.2 Names of the
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46 CHAPTER 4 The most widely used m
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48 CHAPTER 4 Ortíz, R., and D. Vuy
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here> 5.3near here>
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52 CHAPTER 5 Table 5.4 Percentage o
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54 CHAPTER 5 Table 5.7 Percentage o
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here> 9near 5. here> 10near 5. 56 C
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here> 12near 5. 58 CHAPTER 5 Table
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60 CHAPTER 5 Table 5.14 Number and
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here> 18near 5. 62 CHAPTER 5 Table
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64 CHAPTER 5 Table 5.20 Average num
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66 CHAPTER 5 Table 5.23 Percentage
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68 CHAPTER 5 Table 5.26 Average dis
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70 CHAPTER 5 considerably higher in
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Part III. Economic Assessment of Te
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76 CHAPTER 6 The agricultural house
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78 CHAPTER 6 grow, but have grown i
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80 CHAPTER 6 Table 6.2 Summary stat
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82 CHAPTER 6 tion. More frequent vi
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84 CHAPTER 6 Table 6.4 Prototype ho
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86 CHAPTER 6 Table 6.5 Characterist
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88 CHAPTER 6 References Cameron, A.
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90 CHAPTER 7 by-products of other f
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here> 1near 7. 92 CHAPTER 7 Table 7
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94 CHAPTER 7 ity in the two technol
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96 CHAPTER 7 Table 7.2 Definition o
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98 CHAPTER 7 The direct link betwee
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100 CHAPTER 7 Table 7.3 Factors inf
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102 CHAPTER 7 tive sign but are onl
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104 CHAPTER 7 fertility management
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106 CHAPTER 7 participatory decisio
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108 CHAPTER 7 Stevens, J. P. 2002.
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110 CHAPTER 8 defined as a function
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112 CHAPTER 8 inputs or implement c
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114 CHAPTER 8 Crop output is determ
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116 CHAPTER 8 Table 8.1 Variable de
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here> 8.6near here> 8.2near 8.3nea
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120 CHAPTER 8 Table 8.4 Production
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122 CHAPTER 8 during the SOM decomp
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124 CHAPTER 8 Supplementary Tables
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126 CHAPTER 8 Table 8A.3 Production
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128 CHAPTER 8 Thomas, G. W. 1982. E
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130 CHAPTER 9 (Nkuba et al. 1999).
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132 CHAPTER 9 be reduced through la
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Page 168 and 169: 152 CHAPTER 10 In terms of specific
Page 171: Part IV. Conclusions
Page 174 and 175: 158 CHAPTER 11 in cooking, brewing
Page 176 and 177: 160 CHAPTER 11 sumption and income
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Page 181 and 182: APPENDIX A Banana Taxonomy for Ugan
Page 183 and 184: BANANA TAXONOMY FOR UGANDA 167 Tabl
Page 185 and 186: BANANA TAXONOMY FOR UGANDA 169 Tabl
Page 187 and 188: BANANA TAXONOMY FOR TANZANIA 171 Ta
Page 189 and 190: BANANA TAXONOMY FOR TANZANIA 173 Ta
Page 191 and 192: APPENDIX C Use of Improved Banana V
Page 193 and 194: DETAILS OF SAMPLE SURVEY DESIGN 177
Page 199: VILLAGE SOCIAL STRUCTURE IN UGANDA
Page 203 and 204: ABOUT THE AUTHORS 187 rigation, and
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