An Economic Assessment of Banana Genetic Improvement and ...

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108 CHAPTER 7 Stevens, J. P. 2002. Applied multivariate statistics for the social sciences. Fourth edition. Mahwah, N.J.: Lawrence Erlbaum Associates. Tushemereirwe, W. K., I. N. Kashaija, W. Tinzaara, C. Nankinga, and S. New, eds. 2003. A guide to successful banana production in Uganda. A banana production manual. Kampala, Uganda: National Banana Research Programme, Kawanda Agricultural Research Institute. Wooldridge, W. J. 2002. Econometric analysis of cross section and panel data. Cambridge, Mass.: MIT Press. Yli Renko, H., E. Autio, and V. Tontti. 2002. Social capital, knowledge, and the international growth of technology-based new firms. International Business Review 11 (3): 279–304.
CHAPTER 8 Determinants of Banana Productivity and Technical Efficiency in Uganda Fredrick Bagamba, Ruerd Ruben, and Mariana Rufino This chapter analyzes the technical efficiency of banana production among Ugandan smallholders by estimating a stochastic production frontier model with inefficiency effects. The empirical models are formulated within the overarching framework of the agricultural household specified in Chapter 2. The data design is also summarized in Chapter 2, although additional information about soil quality was collected for this analysis from three sites selected for this purpose. Hypotheses are tested about the regional effects on productivity relationships and returns to scale. Specific hypotheses are tested with respect to the impact of market access and household- and farm-specific factors on technical efficiency. This chapter examines banana productivity as it relates to two important constraints in banana production: soil fertility and labor; it therefore complements the analysis in Chapter 6, which treats biotic constraints and the potential adoption of resistant cultivars, and Chapter 7, which examines the adoption of soil fertility management practices by farmers. Stochastic Frontier Production Function A large body of theoretical and empirical literature has investigated the measurement of efficiency of farm enterprises, using various methods. Ali and Byerlee (1991) have emphasized that the focus in analyzing economic efficiency should be the performance of the whole production system, including farmers and institutional support systems. These results can be used to pinpoint the factors that impede the capacity of farmers to reach their productivity potential. Technical efficiency (TE) can be estimated using one- or two-step approaches. In the twostep procedure, the production frontier is estimated first, and the technical efficiency of each firm is derived subsequently. In the second step, the derived technical efficiency variable is regressed against a set of variables that are hypothesized to influence the firm’s efficiency (Kalirajan 1981; Pitt and Lee 1981). However, the two-stage procedure lacks consistency in assumptions about the distribution of the inefficiencies. In step one, it is assumed that inefficiencies are independently and identically distributed in order to estimate their values. In step two, estimated inefficiencies are assumed to be a function of a number of firm-specific factors, violating the assumption in step one (Coelli, Rao, and Battese 1998). To overcome this inconsistency, Kumbhakar, Gosh, and McGuckin (1991) suggest estimating all the parameters in one step. In a one-step procedure, which we adopt for this study, the inefficiency effects are 109
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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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Page 82 and 83: 66 CHAPTER 5 Table 5.23 Percentage
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Page 86 and 87: 70 CHAPTER 5 considerably higher in
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
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 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 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
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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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