An Economic Assessment of Banana Genetic Improvement and ...

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94 CHAPTER 7 ity in the two technology demand equations is also correlated. Estimating them jointly was not considered to be worthwhile, because the set of explanatory variables is identical in both equations (Greene 2000, 616), implying no gains in statistical efficiency. Thus, each technology was treated separately. Dependent Variables We considered eight practices recommended to farmers for soil fertility management in banana groves: (1) mulch with grass; (2) mulch with crop residues; (3) mulch with kitchen residues; (4) addition of cattle manure, (5) goat manure, (6) pig manure, or (7) poultry manure; and (8) composting of homestead refuse to make manure (compost manure). The eight practices were subjected to a factor analysis procedure using principal components to analyze correlation between them and determine whether they could be represented by a smaller number of components. 3 Based on the criterion of an eigenvalue greater than unity, the eight soil management practices were grouped into four independent packages according to four unobserved factors. 4 The latent factors were interpreted as mulching, animal manure, composting, and traditional technologies. Based on the results from the factor analysis, the eight types of organic fertilizers were collapsed into two dependent variables: mulching practices and manure application. Use of mulching technology is defined as the practice of applying crop residues (nonbanana crop residues) or grass to mulch banana plantations. Use of this type of mulch materials in banana production involves costs for gathering, transportation, and application, which may not be affordable by some households. Use of manure technology is defined as the practice of applying animal waste (from cattle and pigs) or compost manure in the banana plantations. These organic fertilizers also involve a cost of access, transportation, and application that may limit their use. The omitted category (1 – δ) is the traditional method of recycling banana residues and applying household refuse, used by virtually every farmer on at least some mats. Organic household refuse (kitchen residues, goat and poultry waste) is collected in mixtures from the homestead as part of the cleaning activity and spread between mats to control weeds, though some farmers reported that use was not deliberate. Farmers are advised to compost the household refuse and other organic materials before applying it in banana plantations to facilitate quick decomposition (Tushemereirwe et al. 2003). Therefore, this method may not be adequate for soil fertility management in banana plantations. In addition, little additional information would be gained by investigating 3 A principal components analysis with Varimax rotation was applied (STATA 8.0). Principal components analysis partitions the sum of the variances for a set of variables by taking uncorrelated, linear combinations of those that account for the latest eigenvalues of the covariance matrix. The components are not easily interpreted, however. The Varimax rotation procedure, one of several types of rotation, enables a more straightforward interpretation of components in terms of factors “loading” high on some variables and low on others (Stevens 2002). 4 The four factors explain 73 percent of total variance in the adoption components. Only variables with factor loading equal to or greater than 0.5 were considered significantly correlated with the underlying latent variable and hence grouped together. Factor 1 explains about 28 percent of the variance in the eight management practices. Organic household refuse (kitchen residues, goat manure, and poultry manure) were highly correlated with the first latent factor. Mulching techniques using the organic materials that are gathered from sources other than the banana crop (crop residues and grass) loaded on factor 2 and explain 18 percent of the total variance in the use of soil fertility management practices. Organic manure from animals that are rarely kept in the homestead (cattle and pigs) also clustered together, while the technique of composting household refuse before applying it to banana plants instead of applying it directly seems to be used independently of other soil fertility management practices.
SOCIAL CAPITAL AND SOIL FERTILITY MANAGEMENT IN UGANDA 95 the determinants of use of this technology, because almost every farmer uses it. Explanatory Variables and Hypotheses Table 7.2 shows the definitions of explanatory variables, summary statistics, and hypothesized effects. Conceptual variables included in the analysis of the soil fertility management decisions are given by reducedform equation, and their operational definition was guided by the literature. Banana production is semisubsistence across much of the survey domain, with uneven access to markets and market participation, consistent with the nonseparable case of the household model. When the consumption and production decisions are nonseparable, comparative statics effects are ambiguous. Thus, hypothesized effects are motivated by related adoption literature and previous information concerning banana production in Uganda. here> 7.2near
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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
Page 60 and 61: 44 CHAPTER 4 Table 4.2 Names of the
Page 62 and 63: 46 CHAPTER 4 The most widely used m
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Page 68 and 69: 52 CHAPTER 5 Table 5.4 Percentage o
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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
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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 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
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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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