An Economic Assessment of Banana Genetic Improvement and ...

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APPENDIX D Details of Sample Survey Design Melinda Smale, Pamela Nahamya Abodi, Ingrid Rhinehart, Svetlana Edmeades, Mgenzi Said Ramadhan Byabachwezi, Charles A. Eledu, Kephas Nowakunda, and Stanley Wood As explained in Part II, Research Context, the introduction of improved varieties to farmers in selected banana-growing areas is one of the approaches among those currently recommended by national and international organizations and NGOs to counter banana pests, diseases, and declining soil fertility. This appendix details the sampling frame employed to document an adoption baseline for improved varieties and management practices, and to diagnose impediments to present and future use. Population Domain The domain is purposely selected to cover areas specializing in banana production, including those with currently declining, increasing, and intermediate levels of production. These correspond roughly to the central and southwest geographical zones in Uganda, and Kagera Region of Tanzania. Municipalities (urban areas) were excluded from the domain as unrepresentative. Southern districts of Kagera Region were also omitted, because they presented costly logistical challenges. Districts in Kagera include Bukoba and Karagwe. A geo-referenced map of East Africa indicating principal banana growing areas was provided by INIBAP to delineate the domain. The EAHB (Musa AAA-EA) is the dominant genomic group in the region, featuring mainly in the highland landscapes of Rwanda, Burundi, eastern Democratic Republic of Congo (DRC), Uganda, western Tanzania, and Kenya (Chandler 1995). The group includes two major use classes (cooking banana, or matooke, and beer banana, or mbidde), a number of clone sets and a large number of cultivars (Karamura and Karamura 1994; Appendixes A and B). Stratum Definition Stratification improves sampling efficiency to the extent that it captures major differences in parameters influencing the variability of the characteristics to be measured, and if it results in groups that are relatively homogeneous with respect to those characteristics. The key characteristics estimated in this research are adoption of current materials and predicted adoption of pest- and disease-resistance materials. Because there are trade-offs in precision as the number of stratifying variables increases with a fixed sample size, we confined the variables to two. The first is environmental. A critical parameter for the adoption of varieties with improved resistance to pests and disease is the yield advantage attained relative to other banana varieties. 176
DETAILS OF SAMPLE SURVEY DESIGN 177 In addition to farmers’ management practices, which are part of farmer decisionmaking and thus cannot serve as a control variable, relative yield advantages depend on the disease pressure and productivity potential of the growing environment. In consultation with INIBAP, IITA, NARO, and ARDI scientists, elevation was selected to represent the numerous correlated factors that affect the incidence and severity of most pests and diseases of bananas in the Lake Victoria region (Speijer et al. 1994). Elevation is also related to soil quality, climate, and the surrounding vegetation in these environments (Tushemereirwe et al. 2001). Low elevation was defined as below 1,200 m.a.s.l. and high elevation was defined as above this level. The second stratifying variable is institutional: previous exposure to new banana varieties (exposed or not exposed). Areas of exposure were defined as LC3s or wards where researchers or extension or other program agents had introduced improved planting material (banana suckers) in at least one community. Areas with no exposure are those where no organized program designed to diffuse improved planting material has been conducted, according to personal consultations with NARO and ARDI, and KCDP records. Areas included in exposed strata represent the factual and those included in the nonexposed strata represent the counterfactual in predicting impacts of improved banana varieties. Four strata were delineated (i = elevation, j = exposure): (1) low elevation, with exposure (i = 1, j = 1); (2) low elevation, without exposure (i = 1, j = 0); (3) high elevation, with exposure (i = 2, j = 1); and (4) high elevation, without exposure (i = 2, j = 0). Other factors known to contribute to variation in adoption probabilities (such as market access and agroecological zone) were not used as criteria for stratification, because large differences in variation were not readily observable at the level of the administrative units sampled and there were no a priori grounds for selection of a cutoff point. Instead, market access was measured continuously in the household survey instruments. Geo-referenced data about banana production systems, a digital elevation model, maps of administrative units, and information concerning previous diffusion of banana planting material were used to disaggregate the domain into the four strata. The domain and four strata were then mapped onto the administrative level of ward in Tanzania and LC3 in Uganda. Wards and LC3s were designated as high or low elevation, based on a simple majority proportion of the unit being above or below 1,200 m.a.s.l. ARDI knowledge of any exposure in the area (even if only one instance) classified the ward or LC3 as exposed, and complete lack of exposure classified the ward or LC3 as nonexposed. It is important to note that the administrative level of ward in Tanzania is a relatively large area. Although the size of these units is not necessarily optimal for our sampling purposes, it is the lowest administrative level for which we were able to obtain accurate digitized geographic information. This constraint results in a much larger area to be aggregated for purposes of stratification. Therefore, the possibility of a selected village in a ward to be of high elevation or exposed when the ward is classified as low elevation or nonexposed (or vice versa) is higher in the Tanzania portion of the survey area than in the Uganda portion. Allocation of Primary Sampling Units to Strata An efficient allocation of sample to strata in formal sampling schemes is one that minimizes variation within the stratum and maximizes variation between the strata, in turn minimizing overall sampling error (Hansen, Hurwitz, and Madow 1953). When the variances in population parameters are known, the sample can be allocated opti-
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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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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
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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
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: APPENDIX C Use of Improved Banana V
Page 195 and 196: DETAILS OF SAMPLE SURVEY DESIGN 179
Page 197 and 198: DETAILS OF SAMPLE SURVEY DESIGN 181
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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