The Future of Smallholder Farming in Eastern Africa - Uganda ...

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Table 4. Ordered Probit Maximum Likelihood Estimates Independent Variable Wetland Diversity Index Food Security Index Wetland Diversity Index Dummy -0.166 Food Security Dummy 0.173 - District Dummies 22 Apac 0.618 0.342 Kamuli 1.013* -0.010 Kayunga 1.137* -0.041 Kumi 2.012*** 0.286 Lira 1.708*** -0.136 Masindi 1.230** 0.279 Nakasongola 0.533 -0.081 Pallisa 1.468*** -0.237 Soroti 1.546*** 0.445 Swamp access 0.452** - Value of hunted products 29.9* - Value of Fish products 1.683*** 0.522*** Value of Fuel products 7.742*** - Value of Fuel products squared -8.806*** - Value of building products 6.577*** - Value of building products squared -2.127*** - Value of Livestock 2.284*** 0.010 Value of Livestock squared -0.815*** - Availability of family labour (Number of workers in the family) - 0.002 Value of Crop products - 0.074 Irrigated Agriculture 1.560*** 0.555** Log of Farm size - 0.395*** Value of gifts - 0.563** Trading - 0.198 Log of non-farm Income - 0.252** Dependency ratio - -0.067 Sex of household head - -0.131 Vulnerability - -10.300*** Number of observations Log likelihood Wald Chi-square Pseudo R2 209 -185.894 242.63*** 0.3712 187 -177.962 2613.88*** 0.2025 NOTE: ***, **, * means the coefficient is statistically significant at 1%, 5%, and 10% levels respectively. As shown by the regression results, Wetland Diversity shows a significant positive association with access to swamp land, hunting and fishing activities, fuel collection, livestock income, and irrigation. The presence of positive levels of these activities increases the diversity of wetland 22 Note Kaberamaido is not included and hence forms the base of comparison for the other Districts.
utilization for the household. However, extraction of fuel products, building products, and livestock grazing were shown to have non-linear relationships with wetland diversity. Their respective squared variables all have significant negative coefficients, which show that at a certain higher level of involvement, extraction of fuel and building/craft products or livestock grazing becomes the major household activity to the exclusion of others, reducing the diversity of wetland uses by the household. For the Food Security Index, the analysis shows that value of fish products, log of farm size, and log of non-farm income were all positive and significant at the 1% level while presence of irrigation, and value of gifts were positive and significant at the 5% level. The strongest variable by far, however, proved to be that of “vulnerability” which displays a strongly negative coefficient. The vulnerability of a household was determined by the absence of adult family members as potential income earners. 5.2.3 Determinants of Productivity The detailed results of the generalized least squares analysis of the determinants of productivity per acre for each of the major crops in the study area is presented in the tables in Appendix 5. Eight equations were estimated – one for each of the major crops produced in the study area. The variables used were the same for all of the equations. The table below summarizes the significance of the various coefficients for each variable in each of these eight equations. Table 5. Determinants of Productivity by Crop Note: some variables dropped due to multicollinearity where there were very few observations. Cassava Sw. Potato Groundnuts Millet Sorghum Maize Beans Simsim sign signif sign signif sign signif sign signif sign signif sign Signif sign signif sign signif 1st season + NS - *** + *** - ** + *** Dropped + NS + *** 2nd season - *** - *** dropped - *** + *** - *** dropped dropped Dry season - NS - *** dropped - NS dropped dropped dropped dropped irrigated + *** + *** dropped dropped + NS + *** + NS dropped logacres - *** - *** - *** - *** - *** - NS - *** - NS Family labour + *** + ** - NS - *** - *** + *** - *** + *** Labour2 - *** - ** + ** + *** + *** - *** + *** - ** Investment + *** + *** + NS + *** + *** + ** + *** + *** Apac + *** - *** dropped dropped dropped - *** - * + *** Kamuli - *** - ** dropped - *** dropped - *** dropped dropped Kayunga - *** - *** dropped - *** dropped - *** dropped dropped Kumi + *** - *** + ** - *** + *** + *** + NS dropped Lira + NS - *** + NS - *** + *** - * - ** + NS Masindi + *** + NS + NS dropped + *** - *** - NS dropped Nakasongol a - NS + NS + *** - *** dropped - *** - *** dropped Pallisa - *** - *** - NS - NS - NS - *** - ** dropped Soroti + *** - ** + NS - *** + NS - *** - NS - NS _constant + *** + *** + *** + *** + dropped + *** + *** + *** Kaberamaido is the basis for comparison for the district dummies Perrennial or no specified season is the basis for comparison for the seasonal dummies The results of the productivity analysis show a considerable consistency. All of the equations were well defined. Location, seasonality, investment in agricultural production inputs and use of irrigation were all significant determinants of productivity. Interestingly, the larger the size of the plot, the lower the productivity when other factors like District, season and investment in
Page 1 and 2:
Part One - Agricultural Productivit
Page 3 and 4:
I. Introduction Agricultural Produc
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came about from expansion of cultiv
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study, therefore, limited itself to
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Land allocation and yields of 12 cr
Page 11 and 12: Table 6: Key economic variables for
Page 13 and 14: local seed in absolute terms and th
Page 15 and 16: Table 11: Crosscutting priority con
Page 17 and 18: Table 13: General constraints to ma
Page 19 and 20: These results clearly demonstrate t
Page 21 and 22: Table 18: Crosscutting constraints
Page 23 and 24: V. Policy Implications The findings
Page 25 and 26: References Appleton, Simon (2001).
Page 27 and 28: 1.0 BACKGROUND 1.1 Introduction Low
Page 29 and 30: egins with a summary of the literat
Page 31 and 32: Source: National Wetlands Conservat
Page 33 and 34: shillings (§37-38). Every landowne
Page 35 and 36: Similarly, Howard (1995) used socia
Page 37 and 38: Uganda may well have more than a qu
Page 39 and 40: factors that explain household leve
Page 41 and 42: of inputs 18 , pests and diseases,
Page 43 and 44: (PMA p. 27). It also requires impro
Page 45 and 46: agriculturalists, who still do not
Page 47 and 48: to have covered 30,000 ha. During t
Page 49 and 50: 4.0 RESEARCH METHODOLOGY The breadt
Page 51 and 52: • What is the current extent of w
Page 53 and 54: Figure 2. Changes in Sources of Com
Page 55 and 56: Figure 5. Proportion of Households
Page 57 and 58: estimates are remarkably consistent
Page 59 and 60: FSI - Percentage by District Distri
Page 61: was corrected using the White-Huber
Page 65 and 66: The nature of the structures to con
Page 67 and 68: impact of irrigation on horticultur
Page 69 and 70: growing population. Wetland protect
Page 71 and 72: APPENDICES Appendix 1. The Wetlands
Page 73 and 74: Appendix 2. THE 5V’s PRIORITY CLA
Page 75 and 76: Appendix 5. Determinants of Product
Page 77 and 78: Estimated covariances = 79 Number o
Page 79 and 80: International Food Policy Research
Page 81 and 82: Scherr, Sara J., 1999b. Poverty-env
Page 83 and 84: Land Tenure, Agricultural Productiv
Page 85 and 86: 1.3 Statement of the problem Land s
Page 87 and 88: In order to achieve these objective
Page 89 and 90: densely populated Nyeri and Kakameg
Page 91 and 92: Figure 3: Conceptual Model Type of
Page 93 and 94: Figure 4: Location of the Study Dis
Page 95 and 96: By 2001, land demarcation and regis
Page 97 and 98: 3.4 Survey Design The survey was un
Page 99 and 100: 3.5.3 Model of Environmental Degrad
Page 101 and 102: questionnaire (Figure 8). Prominent
Page 103 and 104: Figure 10: Availability of hired la
Page 105 and 106: Figure 13 shows the level of cultiv
Page 107 and 108: joint significance test on the 4 dr
Page 109 and 110: Table 8: Regression Results on Maiz
Page 111 and 112: was classified into three categorie
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Table 9: Tobit Analysis with Enviro
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5.0 CONCLUSIONS AND POLICY IMPLICAT
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5.5 Access to Market A number of fa
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FAO (2003b). “Women and Food Secu
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Omiti John and Paul Obunde (2002).
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Annex 1: Pearson Correlation Coeffi
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SEXHH SEXLUSER TSECUR VEGCP1 CHILDE
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Part two - Agricultural Markets
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Market Liberalisation and Agricultu
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farms show annual fluctuations, wit
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Figure 2: Recent trends in domestic
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5. The Model The interest of this s
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sources for each sector. α IM ji a
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Savings Households’ savings are a
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The value-added price is defined th
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Specifically, the Government polici
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Table 5: Calculated Tariff Rates (%
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disincentive to farmers. Inability
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competitiveness. The second one is
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Table 7: Wage Rates and Labour Inco
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Similar simulation is undertaken in
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References Block, S. and C.P. Timme
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Tyler, G. J. and Akinboade, O. 1992
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(A.10) ai ∑ P ϖ P i=1,...,3; j =
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(A.32) price (A.33) P Q = P ( 1+ td
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Table A.2: Variables of KEGEM Varia
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VA A i Efficiency parameter in valu
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CHAPTER FIVE Post-Liberalization Ma
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As alluded to earlier, initial agri
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directly to dairy farmers as animal
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to engage in this new role, the gov
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2.0 RESEARCH METHODOLOGY 2.1 Overal
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markets with minor gaps in data ser
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Price-series correlations are conve
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Migori Awendo Postliberalization 1,
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model depends on whether or not the
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Table 6b--Unit root tests for price
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Table 8a--Unit root tests for OLS r
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3.3 The Role of Infrastructure and
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Table 13--Reported distances from t
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Table 15--Reported maize storage pr
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Table 17--Traders’ perception of
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3.3.6.2 Awareness of the Liberaliza
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Retailer (open-air market) 21 13 7
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3.4 Maize Marketing Costs Traders w
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3.4.4 License and Market Fees Accor
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5.Develop farmers’ and traders’
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Coulter, J., and J. A. F. Compton.
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Meilink, A. H. 1987. Food consumpti
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Figure 1 Maize surplus and deficit
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Figure 3 Trends in Maize Prices 199
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Price per 90kg bag (Kshs) Mbeere Di
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Figure 4 Modes of transporting maiz
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Figure 6: Limitations to storage ca
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Figure 8: Problems experienced in a
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CHAPTER SIX Grain Marketing in Addi
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A third, related explanation argues
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educed marketing costs related to p
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1.4. Research questions This study
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1.6. Organization of the report Thi
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the cases, respectively (Table 2.4)
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Brokers believe that most regional
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There is also a close relationship
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Table 2.14: Buyers’ methods for i
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Source: Survey, 2002. Table 2.18: D
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Table 2.22: Method used to grade di
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Table 2.25: Access to credit broker
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It has been reported that grain pur
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3. GRAIN RETAILERS Retailers serve
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Like brokers, retailers use cash or
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Table 3.11: Buyers’ information a
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Table 3.14: Retailers’ views of g
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Getting reliable price information
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Table 4.4: How do you set a sales p
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Table 4.7: Means of settling disput
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a.m. The survey included wholesale
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Table 5.3: Instability index of dai
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Table 5.7: Seasonality index of who
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Figure 5.3: Ratio of wholesale to r
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Table 5.8: Results of stationary te
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Table 5.10: Summary of Granger caus
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holiday sales. The results of the r
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Table 6.1: Storage capacity of the
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Intermarket grain flow is coordinat
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• a legal, regulatory, and enforc
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REFERENCES Al-Hassen, R., A. Dorwar
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Seppala, P. 1998. Decisive liberali
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achieve some welfare objective. The
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Annex Table 2.10: Have you faced di
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Annex Table 2.21: Do you consider p
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Annex Table 3.3: Cost structure of
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Annex Table 4.2: Complementary acti
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CHAPTER SEVEN Post-Harvest Grain Ma
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only (see Abebe 2000: 80-83). That
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of processing as an important post-
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occur at all stages of post-harvest
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Endowment & access to Resources (e.
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Figure 2: The Study Areas (shaded a
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(6) β i = β 0 λ i , 0
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Intercrop differences Liquidity con
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eplace old animals (19 percent of h
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Table 5. Number and capacity of gra
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Table 7b.Estimated grain loss durin
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7.1.3. Grain Marketing Practices an
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1.8 Producers' price 1.6 1.4 1.2 1
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next season, grain prices tend to b
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Table 13: Regression results (Tobit
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of grain production was reportedly
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instruments to mitigate price risks
Page 331 and 332:
Coulter, J. and G. Onumah, (2002).
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Appendix: Supplementary Tables Appe
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CHAPTER EIGHT Collective Action in
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The objectives of DRS were to popul
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increasing participation. Thus, the
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Table 2. Perceived change in benefi
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unsatisfactory (Table 8). Flaws in
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Table 14. Measures of wealth status
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Results from Regression Analysis Ta
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However, to maintain these benefits
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Table 18. Determinants of participa
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_______ 1981. Common property and c
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Management Regulations and Enforcem
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In theory, under PFM, the governmen
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3.2 Desk study The desk study invol
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Table 1: Number and gender of indiv
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Poverty Increase in number of fishe
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Table 5: Responses given by various
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wages after the catch is sold (see
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70 60 No of respondents 50 40 30 20
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4.2.3 Lack of attention to social i
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5.3 Regulate the construction of re
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REFERENCES Allison, E. H., and P. M
Page 377 and 378:
APPENDIXES Appendix A: Districts of
Page 379 and 380:
Questions and responses Government
Page 381 and 382:
Questions and responses Lack of coo
Page 383 and 384:
CHAPTER TEN Animal Health Service D
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• Individuals are selected for tr
Page 388 and 389:
Makueni district was selected becau
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1.5 Justification of the study The
Page 392 and 393:
more sustainable (Leonard 1993; Uma
Page 394 and 395:
of defined roles for CBAHWs within
Page 396 and 397:
Interviews with veterinary officers
Page 398 and 399:
The estimated model was of the form
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Gender remains a sensitive issue. P
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The most common ailments and diseas
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4.3 Factors influencing the level o
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Table 9 -- Factors influencing the
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Table 10 - Factors determining the
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Table 12 -- Current mean numbers of
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Figure 4: Percentage of household h
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CBAHWs were the most utilized chann
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Users of veterinarians’ services
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Table 18 -- Levels of incomes and l
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higher average calving per cow, whi
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Table 21 -- CBAHWs’ ranking of sp
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epresentative of livestock keepers
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Despite the restriction imposed by
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veterinarians. Such associations co
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References Akabwai, D., V. Shean, a
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Johnson, P. J., and C. Chahuares. 1
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Appendix 1: The Logistic Procedure
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Social Capital and Sustainable Agri
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1.1 Soil conservation programs and
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1.3 Research Objectives and Hypothe
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Institutional and Policy factors -
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land pressure is high (Holden 1991)
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include density of membership in gr
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then summed up according to increas
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Social capital, resource use, and a
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Table 4.4 Farmers’ perception of
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Table 4.5: Empowerment and politica
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Table 4.8: Indicators of collective
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Table 4.10: Comparison of means of
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5. The influence of social capital
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especially when considered on an ad
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households may pay less attention t
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Table 5.3 Determinants of terracing
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and Gichuki 1994). In the 1940s, th
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Table 5.4. Determinants of soil con
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5.3. Social capital, resource use,
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6. Implications for policy and prog
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essential for the formation of exte
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BIBLIOGRPAHY Admassie, Y., L. I. Mw
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Gambetta, D. 1988. Can we trust tru
Page 482 and 483:
Social capital and watershed develo
Page 484 and 485:
Obare, G. A., S. W. Omamo, and J. C
Page 486 and 487:
Washington D.C.: The World Bank Tem
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Appendix: A list of Contributors Ab
Page 490 and 491:
Appendix: Projects Supported Under
Page 492:
IFPRI Eastern Africa Food Policy Ne
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