An Economic Assessment of Banana Genetic Improvement and ...

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142 CHAPTER 10 Table 10.1 Ugandan cooking banana production, by production system, 2000 High productivity Medium productivity Low productivity Total Total Semicommercial Subsistence Total Semicommercial Subsistence Total Semicommercial Subsistence Production (million tons) 5.545 3.549 2.484 1.065 0.998 0.200 0.798 0.998 0.100 0.898 Production share (percent) 100 64 45 19 18 4 14 18 2 16 Yield (tons/ha) 12.31 21.91 25 17 10 18 9 5.29 11 5 Area harvested (thousand ha) 451 162 99 63 100 11 89 189 9 180 Area share (percent) 100 36 22 14 22 2 20 42 2 40 Sources: Compiled by the authors from the Uganda National Household Survey 1999/2000 (UBOS 2002) and Tushemereirwe et al. (2001). Notes: The Uganda National Household Survey data on banana production categorizes bananas by use: cooking, beer, and sweet (dessert). This table reports the allocation of the cooking banana production data into the six system categories.
ASSESSING THE IMPACT OF TECHNOLOGIES IN UGANDA 143 Table 10.2 Estimated yield losses caused by priority banana production constraints in each of the six production systems Constraint Typical losses Loss (percent) Year a High productivity Medium productivity Low productivity Semicommercial (percent) Subsistence (percent) Semicommercial (percent) Subsistence (percent) Semicommercial (percent) Subsistence (percent) Bananaweevil 50–70 Fourth 5 10 15 26 40 60 Nematodes 40–60 Fourth 10 15 14 28 23 51 Black Sigatoka 30–50 Third 6 15 18 28 30 50 Banana bacteria wilt 80–100 First 58 58 58 58 58 90 Low soil fertility 10–70 >Third 37 58 67 83 80 91 Sources: Compiled by the authors from unpublished sources (such as the National Agricultural Research Organization’s banana program surveys and reports) and from Rukazambuga, Gold, and Gowen (1998) for weevils, Speijer and Kajumba (1996) for nematodes, and Okaasai and Boa (2004) for bacteria wilt. Notes: A two-stage process was used to obtain the data. Following an initial review and compilation of available published and unpublished sources, an expert consultation was undertaken to extrapolate available evidence and estimate losses within each production system category. a Year of plantation cycle in which losses typically become significant. additional 538 thousand tons and 46 thousand tons, respectively, of the total national banana output (UBOS 2002). Although the high-productivity areas account for almost 65 percent of the total production, they account for only about 36 percent of the area planted in bananas. Conversely, the lowproductivity areas occupy some 42 percent of the area in bananas, but they provide only some 18 percent of total output. This pattern reflects the disparity in yield levels attained across production systems, ranging from only 5 tons per ha for the low-productivity subsistence systems to around 25 tons per ha among semicommercial producers in high-productivity areas. Low-yield subsistence-oriented production represents 40 percent of the banana producing area, the largest area share among the six groups. Medium- and low-yield semicommercial production each represent only 2 percent of the banana area. here> 1near 10.
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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
Page 108 and 109: here> 1near 7. 92 CHAPTER 7 Table 7
Page 110 and 111: 94 CHAPTER 7 ity in the two technol
Page 112 and 113: 96 CHAPTER 7 Table 7.2 Definition o
Page 114 and 115: 98 CHAPTER 7 The direct link betwee
Page 116 and 117: 100 CHAPTER 7 Table 7.3 Factors inf
Page 118 and 119: 102 CHAPTER 7 tive sign but are onl
Page 120 and 121: 104 CHAPTER 7 fertility management
Page 122 and 123: 106 CHAPTER 7 participatory decisio
Page 124 and 125: 108 CHAPTER 7 Stevens, J. P. 2002.
Page 126 and 127: 110 CHAPTER 8 defined as a function
Page 128 and 129: 112 CHAPTER 8 inputs or implement c
Page 130 and 131: 114 CHAPTER 8 Crop output is determ
Page 132 and 133: 116 CHAPTER 8 Table 8.1 Variable de
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Page 136 and 137: 120 CHAPTER 8 Table 8.4 Production
Page 138 and 139: 122 CHAPTER 8 during the SOM decomp
Page 140 and 141: 124 CHAPTER 8 Supplementary Tables
Page 142 and 143: 126 CHAPTER 8 Table 8A.3 Production
Page 144 and 145: 128 CHAPTER 8 Thomas, G. W. 1982. E
Page 146 and 147: 130 CHAPTER 9 (Nkuba et al. 1999).
Page 148 and 149: 132 CHAPTER 9 be reduced through la
Page 150 and 151: 134 CHAPTER 9 Table 9.2 Summary sta
Page 152 and 153: 136 CHAPTER 9 Table 9.3 Coefficient
Page 154 and 155: 138 CHAPTER 9 Table 9.4 Mean compar
Page 156 and 157: 140 CHAPTER 9 References Anandajaya
Page 160 and 161: 144 CHAPTER 10 (Kangire and Rutherf
Page 162 and 163: 146 CHAPTER 10 Table 10.4 Predomina
Page 164 and 165: here> 7near 10. 148 CHAPTER 10 Tabl
Page 166 and 167: 150 CHAPTER 10 Table 10.8 Present v
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
Page 178 and 179: 162 CHAPTER 11 4. 5. for which the
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 and 200: VILLAGE SOCIAL STRUCTURE IN UGANDA
Page 201 and 202: List of Principal Authors and Contr
Page 203 and 204: ABOUT THE AUTHORS 187 rigation, and
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