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

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shows that 78 percent of farmers with a college degree applied fertilizer. The comparable figures for application of certified seed for college graduates were 83 percent. Table 7: Crosstab Analysis for Education and Input Use Count (%) Education Level of the Land User Fertilizer Usage None Primary Secondary College University No 53 (78.8%) 116 (71.6%) 21 (55.3%) Yes 16 46 17 (23.2%) (28.4%) (44.7%) Pearson Chi-square 29.29192 - significance level of 0% 4 (22.2%) 14 (77.8%) Total - 194 (66.9%) 3 (100%) 96 (33.1%) Certified Seed Usage None Primary Secondary College University No 57 (82.6%) Yes 12 (17.4%) 123 (75%) 41 (25%) 26 (68.4%) 12 (31.6%) Pearson Chi-square 39.48855 - significance level of 0% 3 (16.7%) 15 (83.3%) - 209 (71.6%) 3 (100%) 83 (28.4%) Two-variable analysis showed that there were statistical variations between the two study districts. This was further confirmed by the Chow test on seven key variables that influence maize yield. The test yielded F-statistic of 12.56, which was significant at 1 percent level meaning that there were significant variations in data between the two districts. It was therefore necessary to use dummy variables to capture these regional differences (Table 8). 4.2 Econometric Models for Agricultural Productivity The regression analysis model described in section 3.6.2, with maize yield as the dependent variable, was run in three versions. First model or Model 1 (Table 8) had a number of explanatory variables. These include land tenure security, parcel size, level of environmental degradation, education, sex, slope, use of certified seed and fertilizer application, soil type, drainage development and application of manure. These factors, in conjunction with the dummy variables representing various sub-locations explained up to 60 percent of the variations in the maize yields. In order to come up with a reduced form model, explanatory variables were eliminated one by one based on their levels of significance. Model 2 is similar to Model 1, except the exclusion of sex and drainage development. The most preferred model, Model 3, has two other variables missing: manure and environmental degradation. A number of other variables that could have caused simultaneity bias were also dropped at the initial stages. Except for soil type, all the other variables in the reduced form equation had significance level of at most, 10 percent. A
joint significance test on the 4 dropped variables yielded F-Statistic of 0.296, which is insignificant at 10% level meaning that the four variables are not jointly significant and as such, we lost nothing by dropping them. It is notable that the dummy variables for Laikipia (D1-D4) and those for Suba (D5-D7) differ substantially. It was observed that the regional dummies for Laikipia were positive while those for Suba were negative. This in essence indicates the wide variation of maize yields between the two districts with those for Suba being substantially lower, and hence the negative dummy. In the next sections, we are going to examine some of the relationships that were derived from this model. Figure 14 shows the relative strengths of the relationships between agricultural productivity and some of its determinants that were considered in this study. It shows that productivity was more strongly related to education level of the land user and the slope of land than all the other determinants. Both were significant at 5 percent level. The detailed outcome of the regression is as in Table 8. Figure 14: Key factors influencing agricultural productivity Tenure Security Slope of Land Education of the land user Soil Type Agricultural Productivity Fertilizer Application Parcel Size Certified Maize Seed Models 4 and 5 were district specific versions of Model 1, which have also been reduced to minimize possibility of misspecification. The results in both districts concur for variables such as: environmental degradation; education of the land user; slope of land; sex of land user and drainage development. However, some of the results exhibit
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Part One - Agricultural Productivit
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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
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Table 6: Key economic variables for
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local seed in absolute terms and th
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Table 11: Crosscutting priority con
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Table 13: General constraints to ma
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These results clearly demonstrate t
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Table 18: Crosscutting constraints
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V. Policy Implications The findings
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References Appleton, Simon (2001).
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1.0 BACKGROUND 1.1 Introduction Low
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egins with a summary of the literat
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Source: National Wetlands Conservat
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shillings (§37-38). Every landowne
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Similarly, Howard (1995) used socia
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Uganda may well have more than a qu
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factors that explain household leve
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of inputs 18 , pests and diseases,
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(PMA p. 27). It also requires impro
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agriculturalists, who still do not
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to have covered 30,000 ha. During t
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4.0 RESEARCH METHODOLOGY The breadt
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• What is the current extent of w
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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 and 62: was corrected using the White-Huber
Page 63 and 64: utilization for the household. Howe
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: Figure 13 shows the level of cultiv
Page 109 and 110: Table 8: Regression Results on Maiz
Page 111 and 112: was classified into three categorie
Page 113 and 114: Table 9: Tobit Analysis with Enviro
Page 115 and 116: 5.0 CONCLUSIONS AND POLICY IMPLICAT
Page 117 and 118: 5.5 Access to Market A number of fa
Page 119 and 120: FAO (2003b). “Women and Food Secu
Page 121 and 122: Omiti John and Paul Obunde (2002).
Page 123 and 124: Annex 1: Pearson Correlation Coeffi
Page 125 and 126: SEXHH SEXLUSER TSECUR VEGCP1 CHILDE
Page 127 and 128: Part two - Agricultural Markets
Page 129 and 130: Market Liberalisation and Agricultu
Page 131 and 132: farms show annual fluctuations, wit
Page 133 and 134: Figure 2: Recent trends in domestic
Page 135 and 136: 5. The Model The interest of this s
Page 137 and 138: sources for each sector. α IM ji a
Page 139 and 140: Savings Households’ savings are a
Page 141 and 142: The value-added price is defined th
Page 143 and 144: Specifically, the Government polici
Page 145 and 146: Table 5: Calculated Tariff Rates (%
Page 147 and 148: disincentive to farmers. Inability
Page 149 and 150: competitiveness. The second one is
Page 151 and 152: Table 7: Wage Rates and Labour Inco
Page 153 and 154: Similar simulation is undertaken in
Page 155 and 156: 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
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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
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APPENDIXES Appendix A: Districts of
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Questions and responses Government
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Questions and responses Lack of coo
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CHAPTER TEN Animal Health Service D
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• Individuals are selected for tr
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Makueni district was selected becau
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1.5 Justification of the study The
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more sustainable (Leonard 1993; Uma
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of defined roles for CBAHWs within
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Interviews with veterinary officers
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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
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Social capital and watershed develo
Page 484 and 485:
Obare, G. A., S. W. Omamo, and J. C
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Washington D.C.: The World Bank Tem
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Appendix: A list of Contributors Ab
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Appendix: Projects Supported Under
Page 492:
IFPRI Eastern Africa Food Policy Ne
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