An Economic Assessment of Banana Genetic Improvement and ...

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106 CHAPTER 7 participatory decisionmaking. This conclusion has implication for the extension methodology. Extension sevices should not only emphasize the benefits of the technology but also include messages that prepare the community for meaningful and effective interactions. Although bilateral transfers are statistically significant in decisions about soil fertility management practices, the magnitude of coefficients is too small to matter much. The most important conclusion from this result is that although private social networks may be used to compensate for costs of market transactions, these do not overcome market constraints with respect to soil conservation investments. In addition to social capital, the research has identified other factors that need the attention of policymakers. These include market infrastructure, educational programs that influence farmers’ perceptions of the soil fertility problem, and knowledge about management practices and poverty in general. Improving access to markets for banana producers, raising the output price relative to the cost of labor, is likely to have the greatest impact of any single factor. Results provide strong evidence that market incentives increase both the probability of use and extent of use. A major implication of these findings is that banana growers in Uganda respond to market incentives. Household endowments of family labor and other production assets (such as livestock and per capita land availability) are critical for good management of bananas, reflecting market imperfections for these factors. Removing market imperfections completely is a long-term undertaking. In the meantime, there is need to identify technologies that demand less of farmers’ resources. Such technologies should also address the problem of increasing population pressure on the land. For example, because there is no market for mulching materials, the extensive use of this practice depends on the availability of land. Hence, use of this practice is negatively related to increased population pressure. The issue of soil fertility depletion in areas with greater access to road infrastructure also needs more attention for long-term banana productivity improvement. Although general improvement in access to markets increases the probability of using soil fertility management practices, it does not seem to motivate the extent of the use, due to higher opportunity costs of labor. This finding is of importance, given that improving banana productivity through soil fertility management will not be achieved based on high rates of use only but on the extent of their use. Moreover, the problem of soil fertility depletion is likely to be intensive in these areas, given the high levels of banana commercialization. High erosion potential encourages use of mulching practices but discourages use of manure, whose production function shifts inward when the erosion potential is perceived to be high. This result suggests that other techniques that reduce erosion potential need to be promoted along with manure, while the positive role of mulching on curbing erosion potential should be emphasized in extension messages. Finally, results support intensification of educational programs as a means of promoting the use of soil fertility management practices. Hence, more support to extension programs would increase use of these techniques. In the context of the research presented in this report, it should be emphasized that long-term improvement in banana production in Uganda will be achieved by relieving not only the constraints related to both pests and disease (addressed in Chapter 6), but those related to soil fertility. This chapter is an initial attempt to better understand the factors that influence whether banana farmers invest in soil fertility management. Chapter 9 directly examines the relationships of soil fertility and labor to banana productivity.
SOCIAL CAPITAL AND SOIL FERTILITY MANAGEMENT IN UGANDA 107 References Barr, A. 2000. Social capital and technical information flows in the Ghanian manufacturing sector. Oxford Papers 52 (3): 539–559. Boserup, E. 1965. The conditions of agricultural growth. In The economics of agrarian change under population pressure. New York: Aldine. Broeck, V. 2004. Social effects in growing bananas: Evidence from a Tanzanian village. Katholieke Universiteit Leuven, Leuven, Belgium. Manuscript. Ervin, C. A., and E. D. Ervin. 1982. Factors affecting the use of soil conservation practices: Hypothesis, evidence and policy implications. Land Economics 58 (3): 277–292. Fafchamps, M., and S. Lund. 2003. Risk-sharing networks in rural Philippines. Journal of Development Economics 71 (2): 261–287. Feder, G., and G. T. O’Mara. 1981. Farm size and the diffusion of green revolution technology. Economic Development and Cultural Change 30 (1): 59–76. Feder, G., and R. Slade. 1984. The acquisition of information and the adoption of new technology. American Journal of Agricultural Economics 66 (3): 312–320. Feder, G., and D. Umali. 1993. The adoption of agricultural innovations: A review. Technology Forecasting and Social Change 43: 215–239. Feder, G., R. E. Just, and D. Zilberman. 1985. Adoption of agricultural innovations in developing countries: A survey. Economic Development and Cultural Change 33 (2): 255–298. Granovetter, M. S. 1973. The strength of weak ties. American Journal of Sociology 78 (6): 1360–1380. Greene, W. H. 2000. Econometric analysis. Upper Saddle River, N.J.: Prentice-Hall. Hogset, H. 2005. Social networks and technology adoption. Selected paper, American Agricultural Economics Association Annual Meeting, July 1–3, Providence, R.I., U.S.A. Isham, J. 2000. A model of technology adoption with social capital. Ph.D. dissertation, University of Maryland, College Park, U.S.A. Kennedy, P. 1985. A guide to econometrics. Cambridge, Mass.: MIT Press. MAAIF and MFEPD (Ministry of Agriculture, Animal Industry and Fisheries and Ministry of Finance, Economic Planning and Development). 2000. Plan for the modernization of agriculture: Eradicating poverty in Uganda. Kampala, Uganda: MAAIF and MFEPD. Maddala, G. S. 1983. Limited dependent and qualitative variables in econometrics. London: Cambridge University Press. Mwakubo, S. M., G. A. Obare, J. Omiti, and L. Mohammed. 2004. The influence of social capital on sustainable agriculture in marginal areas in Kenya. Eastern Africa Food Policy Network Report 13. Kampala, Uganda: International Food Policy Research Institute. Nkonya, E., J. Pender, P. Jagger, D. Sserunkuma, C. Kaizzi, and H. Ssali. 2004. Strategies for sustainable land management and poverty reduction in Uganda. Research Report 133. Washington, D.C.: International Food Policy Research Institute. Pender, J., and J. Kerr, 1996. Determinants of farmer’s indigenous soil and water conservation investments in India’s semi-arid tropics. Environment and Production Technology Division Discussion Paper 17. Washington, D.C.: International Food Policy Research Institute. Rogers, E. M. 1995. Diffusion of innovations. New York: Free Press. Shiferaw, B., and S. Holden. 1998. Resource degradation and adoption of land conservation technologies in the Ethiopian Highlands: A case study in Andit Tid, North Shewa. Agricultural Economics 18 (3): 233–247.
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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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Page 74 and 75: here> 12near 5. 58 CHAPTER 5 Table
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Page 89: Part III. Economic Assessment of Te
Page 92 and 93: 76 CHAPTER 6 The agricultural house
Page 94 and 95: 78 CHAPTER 6 grow, but have grown i
Page 96 and 97: 80 CHAPTER 6 Table 6.2 Summary stat
Page 98 and 99: 82 CHAPTER 6 tion. More frequent vi
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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.
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
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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 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 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 158 and 159: 142 CHAPTER 10 Table 10.1 Ugandan c
Page 160 and 161: 144 CHAPTER 10 (Kangire and Rutherf
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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
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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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