An Economic Assessment of Banana Genetic Improvement and ...

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130 CHAPTER 9 (Nkuba et al. 1999). The results of on-farm testing showed that on average the new banana cultivars yielded a bunch weight of 18.9 kg, compared with 9.7 kg for local cultivars (Byabachwezi, Steenhuijesen, and Rwezaula 1997; Nkuba, Ndege, and Mkulila 2002). Initial assessment revealed that these cultivars are acceptable to farmers for their multiple uses (cooking, dessert, roasting, and brewing) and good marketability. From 1997 to 2002 about 2.5 million suckers (or planting material) from the new banana cultivars had already been distributed to farmers. Knowledge gaps remain, however. For example, although KCDP analyzes initial adoption rates and acceptability in project reports, more systematic information about the extent and determinants of adoption, as well as the economic impacts of adoption, is needed. In Tanzania, there is increasing need for investment in the improvement of crops and livestock, which is one of the government strategies on reduction of rural poverty (MAFS 2001). Similar to other developing economies, despite the large financial resources spent on development programs each year, there is limited awareness of the actual impact of programs on the poor (Baker 2000). This situation poses basic questions about whether targeted interventions have achieved the intended benefits and their effectiveness and efficiency. Although banana growers in Tanzania have participated in a number of extension and research programs, there is little documentation of program impact. This chapter focuses on identifying the determinants of use of the new banana cultivars and assessing the impacts of use on production vulnerability. The new banana cultivars are the hybrids (from FHIA) formally introduced by KCDP in the region. The analysis is used to better understand the characteristics and production potential of adopters compared to nonadopters in areas exposed to the program interventions. Other aspects of adoption and impact are analyzed in Nkuba (2007). The next section describes household income-earning strategies and the resource base of household farms in the bananabased production system of Kagera Region. The conceptual approach is the theoretical framework of the agricultural household, presented in Chapter 2. The econometric approach is a treatment effects model, described below. The model is applied to data collected through a stratified random sample of households (Chapter 2 and Appendix D). Stratifying variables include exposure to new cultivars through programs, to control for treatment effects, and elevation. Elevation is related to pest and disease pressures. We contend that pest and disease pressures have driven farmers to seek new, disease-free planting material. Production Systems and Income Sources The banana/coffee-based farming system is dominated by smallholder farmers, with an average farm size ranging from 0.5 to 1.5 ha. There are three types of land use, locally known as Kibanja, Kikamba, and Rweya. Kibanja is a permanent field for cultivation of bananas. Often, bananas are intercropped with coffee and, during the rainy season, with beans. Although households sell surplus banana production for cash generation, coffee is the main cash crop, with the region leading in coffee production in the country. Tea is also grown along the lake shores of the region. Other crops intercropped in Kibanja are maize, and root and tuber crops, fruits, and vegetables. Kibanja provides food and income for the household. Kikamba is an area found adjacent to Kibanja, and it is used for cultivating annual crops (beans, maize, and groundnuts). Rweya is grassland used for grazing, cutting mulch grass, and shifting cultivation of annual crops, such as bambara nuts (Lorkeers 1995). Among the banana cultivars grown in Kagera Region are the EAHBs, which have been endemic (or constantly present) in the
USE OF HYBRID CULTIVARS IN KAGERA REGION, TANZANIA 131 Table 9.1 Five-year average areas under crop production by district in Kagera Region, 1996/97–2000/01 (ha) Crop type Bukoba Muleba Biharamulo Ngara Karagwe Total Foodcrops Bananas 47,830 36,807 1,870 25,549 41,289 153,345 Maize 11,490 4,440 25,445 4,347 25,523 71,245 Sorghum — — 8,675 3,056 2,500 14,231 Sweet potatoes 15,970 6,060 13,647 151 6,961 42,669 Cassava 15,551 15,401 33,570 9,524 34,763 108,809 Paddy — — 1,826 — — 1,826 Beans 21,560 8,631 16,795 5,786 32,856 85,628 Potatoes — — — — 361 361 Subtotal 112,401 71,339 101,829 48,292 144,254 478,115 Cash crops Coffee 31,434 19,374 617 762 15,895 68,082 Tea 1,129 133 — — — 1,262 Cotton — — 7,191 — — 7,191 Subtotal 32,563 19,507 7,808 762 15,895 76,535 Total 144,964 90,846 109,637 49,054 160,149 554,650 Source: Regional Commissioner’s Office, Bukoba, Kagera Region, 2003. Note: — indicates none reported. region since 500–1000 A.D. (Byabachwezi and Mbwana 1999), and other exotic and hybrid bananas. Based on their uses, bananas produced in the region can be classified into four use types: cooking, beer, roasting, and sweet (dessert) (see Chapter 3). Bananas cover about 33 percent and 26 percent of the total land under crops in Bukoba and Karagwe districts, respective ly, of Kagera Region, where this study was implemented (Table 9.1). During 1996– 2001, annual average production of bananas was 919,995 tons (RCO 2003). Karagwe District contributed about 45 percent of the total production in the region, with the Bukoba contributing 23 percent, Muleba 20 percent, Ngara 11 percent, and Biharamulo 1 percent. Banana production is seasonal, with the peak season spanning between the months of June and August, and the lowest production levels attained during November. here> 1near 9.
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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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Page 98 and 99: 82 CHAPTER 6 tion. More frequent vi
Page 100 and 101: 84 CHAPTER 6 Table 6.4 Prototype ho
Page 102 and 103: 86 CHAPTER 6 Table 6.5 Characterist
Page 104 and 105: 88 CHAPTER 6 References Cameron, A.
Page 106 and 107: 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
Page 134 and 135: here> 8.6near here> 8.2near 8.3nea
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 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 158 and 159: 142 CHAPTER 10 Table 10.1 Ugandan c
Page 160 and 161: 144 CHAPTER 10 (Kangire and Rutherf
Page 162 and 163: 146 CHAPTER 10 Table 10.4 Predomina
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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 195 and 196: DETAILS OF SAMPLE SURVEY DESIGN 179
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DETAILS OF SAMPLE SURVEY DESIGN 181
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VILLAGE SOCIAL STRUCTURE IN UGANDA
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List of Principal Authors and Contr
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ABOUT THE AUTHORS 187 rigation, and
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