An Economic Assessment of Banana Genetic Improvement and ...

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114 CHAPTER 8 Crop output is determined by labor input, area allocated to the crop, and nutrient availability (mainly N and K for bananas). Organic amendment (animal manure, grass mulch, and crop residues) contribute to soil nutrients but also to the physical and chemical properties of soil, enabling a given land area to produce higher output. Crop output can be modeled as: 1nY = b0 + b11n A + b21n L + b3M + b4C + b51n SOM + b61n K + b7D1 + b 8D2 (25) where ln Y is the natural log of crop output (kg/year), β 0–8 are parameters to be estimated, ln A is the natural log of the area allocated to crop (cooking bananas; in acres), and ln L is the natural log of labor input (hours/year). Equation (15) can be estimated using two-stage least squares, with sand as the instrument for SOM and pH as that for for K. To obtain efficient estimates, equations (12), (14), and (15) are estimated simultaneously using a three-stage least squares method, which is the most appropriate technique to use to estimate a system of equations with endogenous variables included on the right hand side. The three equations (12), (14), and (15) can be collapsed into a reduced-form equation: 1nY = b0 + b11n A + b21n L + b3M b4C + b5sand + b6pH b7D1 + b 8D2. (16) Endogeneity Equation (16) is estimated using ordinary least squares. A problem could arise if labor input were endogenously determined. We test for endogeneity by first estimating the labor equation with wage rate, output price, household characteristics, and opportunities included on the right hand side. The residual obtained from the estimated labor equation is then included on the right hand side in the production function estimation. If the effect of the residual turns out to be significant (5 percent), then labor input is confirmed as endogenously determined. An instrumental variable approach or two-stage least squares would be the approach to use to obtain efficient and consistent estimates if valid instruments are available. If the soil quality variables are included in equation (9), ordinary least squares is valid for obtaining consistent and efficient estimates of manure and other organic amendments. When soil quality variables (sand and pH) are missing in equation (16), the manure and crop residue variables can be treated as endogenous, because farmers would tend to apply these inputs where soils are poor, and no application is carried out where the soil is fertile. We lack sufficient and valid instruments for manure and crop residues. Therefore the estimates for manure and crop residue should be interpreted with care. In the absence of endogenous variables on the right hand side, equation (16) can be consistently estimated using a stochastic frontier approach. Data The data design is described in Chapter 2 and Appendix D. Of the total sample of 660 farmers surveyed in Uganda, data for 508 were usable in the analysis. The production function is estimated for cooking bananas, whereas the whole sample was selected for farmers that grow bananas. Some farmers, especially in the lower elevation areas, had banana plots that were less than 2 years old and harvested no output. Others had abandoned plots and did not allocate labor to them. These farms were not included in the estimation. Some households had missing cases in some of the variables, and therefore were excluded from the sample. The final sampling frame consisted of 27 subcounties, of which 3 were selected (Ntungamo, Bamunanika, and Kisekka) to complement soil analyses. Ntungamo subcounty represents the high-elevation region,
here> 1near 8. BANANA PRODUCTIVITY AND TECHNICAL EFFICIENCY IN UGANDA 115 where banana production levels are high and there are no evident signs of decline, whereas Kisekka and Bamunanika subcounties represent the low-elevation region, where there is serious decline in yield and production. The sample stratification enables us to capture elevation-related effects (differences in rainfall, temperature, pests, and disease pressure) on banana production (Chapter 2 and Appendix D). Definitions of variables and summary statistics are shown in Table 8.1. The average area under cooking bananas is 0.58 acres. More area is allocated to cooking bananas in the high-elevation compared with that of the low-elevation region. The trend for allocation of labor and other inputs (manure, grass mulch, and crop residues) is the same, with farmers at high elevations applying greater quantities of inputs compared to those applied by farmers at low elevations.
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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
Page 80 and 81: 64 CHAPTER 5 Table 5.20 Average num
Page 82 and 83: 66 CHAPTER 5 Table 5.23 Percentage
Page 84 and 85: 68 CHAPTER 5 Table 5.26 Average dis
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 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 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 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
Page 164 and 165: here> 7near 10. 148 CHAPTER 10 Tabl
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
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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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