An Economic Assessment of Banana Genetic Improvement and ...

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84 CHAPTER 6 Table 6.4 Prototype households with high and low predicted demands for the potential host variety Nakitembe High demand (N = 72) Low demand (N = 72) Proportion of male decisionmakers (percent) 62.39*** 46.06*** Experience of decisionmaker (years) 9.94** 7.37** Household size 7.77*** 4.10*** Proportion of dependents in household (percent) 67.31*** 40.60*** Proportion of women in household 51.61 46.54 Average education of females in household (years) 3.28* 3.76* Average education of males in household (years) 4.12*** 5.42*** Assets (livestock value) (thousand Ush) 33.28*** 85.69*** Exogenous income (thousand Ush) 52.25*** 364.33*** Proportion of households living in brick house (percent) 57.99* 59.01* Farm area (acres) 5.75*** 3.46*** Banana area (acres) 1.04** 0.73** Banana share 0.34*** 0.41*** Time to market (hours) 1.37*** 0.87*** Distance to market (kilometers) 1.87 1.62 Proportion of net selling households (percent) 63.00 45.47 Proportion of net buying households (percent) 35.26 53.83 Proportion of households in Southwest Region (percent) 3.25*** 18.47*** Proportion of households in Central Region (percent) 81.34*** 70.57*** Proportion of households in Eastern Region (percent) 15.42*** 10.96*** Note: ***, **, and * indicate statistically significant differences between means and/or distributions at the 1, 5, and 10 percent levels, respectively. Figure 6.1 Change in predicted total industry demand for planting material of Nakitembe from effective trait insertion and other supporting investments, for different levels of resistance, by scenario Total number of mats (thousands) 550 500 450 400 350 300 250 BS BS + WE BS + WE + other 200 0 20 40 60 80 100 Effective resistance to biotic pressures (percent) Notes: BS is black Sigatoka and WE is weevils. The intercepts (in thousands) for the three lines are 259 (BS), 233 (BS + WE), and 267 (BS + WE + other). Their slopes (in thousands) are 36.01 (BS), 42.38 (BS + WE), and 48.55 (BS + WE + other).
MODEL OF POTENTIAL DEMAND FOR CULTIVARS IN UGANDA 85 Sensitivity of Farmer Demand The fitted model is used to analyze the sensitivity of total demand for host cultivars to genetic transformation by varying the perceived yield losses due to weevils and black Sigatoka fungal disease. Biotic constraints can be considered individually and jointly, demonstrating the gains that might be achieved in use levels through multiple compared to single gene insertion. Next, the effects of removing impediments to use through other public investments in education, market infrastructure, and extension are depicted, given the research investment made in genetic transformation. An increase in the supply (or farm-gate) price from a mean value of Ush 2,000 to Ush 4,000 is assumed as a result of improvement in bunch size and quality or market efficiency. The time taken to get to a nearby banana market is halved from a mean value of 1 hour, reflecting improvements in roads or better means of transportation. The education of the banana production decisionmaker is increased through completion of primary school (7 years) from a mean value of 5 years. Finally, more visits by extension agents are made to improve dissemination of information and knowledge about banana production, including understanding of pests and diseases. Nakitembe is again used as a representative example. Figure 6.1 depicts the sensitivity of farmer demand for planting material to changes in the effective resistance to biotic pressures when a single resistance gene (for black Sigatoka) or multiple resistance genes (for black Sigatoka and weevils) are successfully inserted into suitable banana genotypes, including the effect of supporting public investments. The demand increases twofold when full resistance is achieved, and diffusion of improved planting material is reinforced through investments in education, market infrastructure, and extension. Sensitivity of cultivar demand to insertion of multiple resistance genes is greater than the more gradual effect of reduction in loss from only one biotic constraint. For a crop affected by a complex of biotic pressures, the marginal effect on expected demand will be much greater (close to a 50 percent increase in the total number of plants) when insertion and expression of more than one resistance trait are jointly achieved. Note that two fundamental conditions are necessary for outcomes such as these to be borne out in the banana groves of Uganda, and neither of them is sufficient. The first condition, which is technological, is that genes must be successfully inserted and expressed. The second concerns farmers’ perceptions. Even if the gene is inserted and expressed, farmers may not perceive its effects, because disease or pest incidence is variable across a banana plantation, and the mechanisms generating some effects (such as weevils) are better understood than others (such as black Sigatoka, a more recently introduced disease). Where necessary, perceptions and understanding of losses from pests and plant diseases can be enhanced through educating farmers. As expected, overcoming impediments to adoption through public investments in education, extension, and market infrastructure augments the magnitude of the effect on demand induced through scientific (genetic) innovations. The combined changes in these variables act more as a shift than as a pivotal adjustment of the total aggregate demand for the planting material. About 50 percent of the shift is attributed to farm-gate price response, with education and extension having a smaller, though important, influence on its magnitude. Shortening the time taken to reach a nearby market has an offsetting effect (though only by 2 percent) by enabling some substitution of market purchase for on-farm production of food. Improvements in market signals, such that quality differentials across banana bunches are captured, could stimulate price responsiveness, with important implications for
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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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Page 89: Part III. Economic Assessment of Te
Page 92 and 93: 76 CHAPTER 6 The agricultural house
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Page 96 and 97: 80 CHAPTER 6 Table 6.2 Summary stat
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Page 102 and 103: 86 CHAPTER 6 Table 6.5 Characterist
Page 104 and 105: 88 CHAPTER 6 References Cameron, A.
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Page 120 and 121: 104 CHAPTER 7 fertility management
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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
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Page 142 and 143: 126 CHAPTER 8 Table 8A.3 Production
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Page 146 and 147: 130 CHAPTER 9 (Nkuba et al. 1999).
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134 CHAPTER 9 Table 9.2 Summary sta
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136 CHAPTER 9 Table 9.3 Coefficient
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138 CHAPTER 9 Table 9.4 Mean compar
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140 CHAPTER 9 References Anandajaya
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142 CHAPTER 10 Table 10.1 Ugandan c
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144 CHAPTER 10 (Kangire and Rutherf
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146 CHAPTER 10 Table 10.4 Predomina
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here> 7near 10. 148 CHAPTER 10 Tabl
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150 CHAPTER 10 Table 10.8 Present v
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152 CHAPTER 10 In terms of specific
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Part IV. Conclusions
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158 CHAPTER 11 in cooking, brewing
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160 CHAPTER 11 sumption and income
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162 CHAPTER 11 4. 5. for which the
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APPENDIX A Banana Taxonomy for Ugan
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BANANA TAXONOMY FOR UGANDA 167 Tabl
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BANANA TAXONOMY FOR UGANDA 169 Tabl
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BANANA TAXONOMY FOR TANZANIA 171 Ta
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BANANA TAXONOMY FOR TANZANIA 173 Ta
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APPENDIX C Use of Improved Banana V
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DETAILS OF SAMPLE SURVEY DESIGN 177
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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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