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

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then summed up according to increasing levels of social capital. This type of aggregation obviously involves strong assumptions about underlying common scales. Yet in practice, this method is commonly used and the resulting indicators have proven useful, especially in the context of multivariate analysis (Grootaert and van Bastelaer 2002). Cognitive social capital raises the perception of sources of possible support and know-how and their potential reliability, which may find their way into increased soil conservation investments. 3.3 Data analysis methods Two types of data analysis, qualitative and econometric, were carried out in order to provide a proper balance. Although some issues emerge from both types of analysis, others are only captured well using one method of analyses or the other. Using both is likely to yield a deeper understanding of social capital and soil conservation and to inform policy design better. With qualitative analysis, the basic objective was to map social capital, its characteristics, and patterns over time. This was achieved through group discussions with village members, which makes it possible to infer which forms of collective action are successful. Particular attention was given to actions linked to soil and water conservation. In particular, the discussions focused on the history of terracing drives in the communities, the proportion of villages terraced under collective action, participation rates, collective action and gender, and farmers’ attitudes toward collective action. Finally, the group’s perceptions on strengthening the positive forms of social capital and reducing the negative ones were sought. 3.4 Specification of the empirical models Before any econometric analysis could be done, descriptive statistics were examined with a view to understanding the distribution of the data. Measures of central tendencies, box-plots, cross-tabulations and t-tests were conducted to explore the differences in types and amounts of soil conservation investments among various aspects of social capital. The econometric packages used were SPSS and LIMDEP. Perception of soil erosion problem The decision on whether to invest in soil conservation measures and the level of investments depends a great deal on whether a farmer perceives that there is a soil erosion problem or not. The conceptual framework is a model that predicts how a farmer on marginal lands with given attributes will perceive the problem. The Probit model is used to identify the important factors explaining the perception of soil erosion problems. The general function of the perception model is PROB ( PERCEP) = f ( SLOPE, TENURE, DISTH, LCROPAC, MEDENS, MEDIV, PARTIC, COGNIT, EDUC, WEALTH, SEX , FAROR, HHS, FAMCA, INC, AGE, ACCESCOS, ERODE, CWORK, LOC).................................................................(1)
Decision to terrace and terracing intensity A censored Tobit is used to model adoption of terracing technology and the intensity of terracing on the decision to terrace in the first place. This is because information on the dependent variable from population is limited in its range, especially for the nonadopters. Observations on the dependent variable, corresponding to known values of independent variables, cannot be observed or are missing. Exclusion of nonadopters results in sample selection bias and attendant biases in the estimated coefficients (Heckman 1979; Feder and Umali 1993). Estimation of Ordinary Least Squares (OLS) with a dichotomous dependent variable would be inappropriate because resulting parameters would be inefficient due to the heteroscedastic structure of the error term. This model makes estimates consistent and is specified as TERRACE = f ( SLOPE, TENURE, DISTH, LCROPAC, MEDENS, MEDIV, PARTIC, COGNIT, EDUC, WEALTH, SEX , FAROR, HHS, FAMCA, INC, AGE, ACCESCOS, ERODE, CWORK, PERCEP, LOC)..............................................(2) Social capital, terracing intensity, and crop productivity The impact of social capital on crop productivity and vice-versa may or may not be direct. Alternatively, some factors may simultaneously affect terracing intensity and crop productivity, in which case, we are dealing with a simultaneous equations model in that two or more endogenous variables are determined jointly within the model, as a function of exogenous variables, predetermined variables, and error terms. This simultaneity induces correlation between the regressors and error terms of each equation in the systems, thus causing OLS to be inconsistent in estimating parameters. As a result, the main estimating technique that is used is the Two-Stage Least Squares (2SLS). This is essentially a single-equation method, which does not use all the information contained in the detailed specification of the rest of the model. Although, in principle, information on the complete structure, if correct, will yield estimators with greater asymptotic efficiency than that attainable by limited-information methods (Green 2000), full information methods require extensive data, which in most cases is not available. In addition, they often suffer from misspecification problems. Since we are dealing with censored variables, the Heckman Two-Stage Estimation Technique is used (Maddala 1983). In this procedure, an inverse mills ratio (IMR) is computed and then included as a separate explanatory variable in the estimation process. The Two-Stage Least Squares equation is given below: TERRACE = f ( SLOPE, TENURE, DISTH, LCROPAC, MEDENS, MEDIV, PARTIC, COGNIT, EDUC, WEALTH, SEX , FAROR, HHS, FAMCA, INC, AGE, ACCESCOS, ERODE, CWORK, PERCEP, LOC, IMR)......................................(3)
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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
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Figure 5. Proportion of Households
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estimates are remarkably consistent
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FSI - Percentage by District Distri
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was corrected using the White-Huber
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utilization for the household. Howe
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The nature of the structures to con
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impact of irrigation on horticultur
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growing population. Wetland protect
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APPENDICES Appendix 1. The Wetlands
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Appendix 2. THE 5V’s PRIORITY CLA
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Appendix 5. Determinants of Product
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Estimated covariances = 79 Number o
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International Food Policy Research
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Scherr, Sara J., 1999b. Poverty-env
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Land Tenure, Agricultural Productiv
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1.3 Statement of the problem Land s
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In order to achieve these objective
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densely populated Nyeri and Kakameg
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Figure 3: Conceptual Model Type of
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Figure 4: Location of the Study Dis
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By 2001, land demarcation and regis
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3.4 Survey Design The survey was un
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3.5.3 Model of Environmental Degrad
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questionnaire (Figure 8). Prominent
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Figure 10: Availability of hired la
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Figure 13 shows the level of cultiv
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joint significance test on the 4 dr
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Table 8: Regression Results on Maiz
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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
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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
Page 398 and 399: The estimated model was of the form
Page 400 and 401: Gender remains a sensitive issue. P
Page 402 and 403: The most common ailments and diseas
Page 404 and 405: 4.3 Factors influencing the level o
Page 406 and 407: Table 9 -- Factors influencing the
Page 408 and 409: Table 10 - Factors determining the
Page 410 and 411: Table 12 -- Current mean numbers of
Page 412 and 413: Figure 4: Percentage of household h
Page 414 and 415: CBAHWs were the most utilized chann
Page 416 and 417: Users of veterinarians’ services
Page 418 and 419: Table 18 -- Levels of incomes and l
Page 420 and 421: higher average calving per cow, whi
Page 422 and 423: Table 21 -- CBAHWs’ ranking of sp
Page 424 and 425: epresentative of livestock keepers
Page 426 and 427: Despite the restriction imposed by
Page 428 and 429: veterinarians. Such associations co
Page 430 and 431: References Akabwai, D., V. Shean, a
Page 432 and 433: Johnson, P. J., and C. Chahuares. 1
Page 434 and 435: Appendix 1: The Logistic Procedure
Page 436 and 437: Social Capital and Sustainable Agri
Page 438 and 439: 1.1 Soil conservation programs and
Page 440 and 441: 1.3 Research Objectives and Hypothe
Page 442 and 443: Institutional and Policy factors -
Page 444 and 445: land pressure is high (Holden 1991)
Page 446 and 447: include density of membership in gr
Page 450 and 451: Social capital, resource use, and a
Page 452 and 453: Table 4.4 Farmers’ perception of
Page 454 and 455: Table 4.5: Empowerment and politica
Page 456 and 457: Table 4.8: Indicators of collective
Page 458 and 459: Table 4.10: Comparison of means of
Page 460 and 461: 5. The influence of social capital
Page 462 and 463: especially when considered on an ad
Page 464 and 465: households may pay less attention t
Page 466 and 467: Table 5.3 Determinants of terracing
Page 468 and 469: and Gichuki 1994). In the 1940s, th
Page 470 and 471: Table 5.4. Determinants of soil con
Page 472 and 473: 5.3. Social capital, resource use,
Page 474 and 475: 6. Implications for policy and prog
Page 476 and 477: essential for the formation of exte
Page 478 and 479: BIBLIOGRPAHY Admassie, Y., L. I. Mw
Page 480 and 481: 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
Page 488 and 489: 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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