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

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Table 5.3 Determinants of terracing intensity using a Tobit model Machakos Taita-Taveta Combined Variables coefficient t-statistic Coefficient t-statistic coefficient t-statistic SLOPE 113.39 1.04 -0.11 -0.05 -0.29 -0.24 TENURE -2.45 -1.67* -189.49 -0.60 -1.53 -0.67 DISTH 0.18 1.29 0.195E-01 0.20 0.631E-01 1.15 LCROPAC 0.115E-01 2.91*** -154E-01 1.09 0.125E-01 2.36** EDUC 0.328 0.22 1.34 1.61 0.52 1.04 EXT -0.33 -0.36 -1.69 -0.52 -1.04 -0.90 WEALTH 55.26 0.72 -261.54 -1.22 -37.33 -0.42 SEX -604.13 -1.37 -444.82 -0.57 -391.40 -0.94 AGE 0.46 0.74 74.48 2.97*** 0.92 1.00 FAROR -41.42 -0.14 9.47 0.42 6.44 0.47 HHS 104.98 2.24** -76.76 -0.51 90.57 1.54 FAMCA -110.28 -1.01 176.74 0.32 2.69 0.93 INC -0.608E-03 -0.97 -0.381E-02 -0.92 -0.113E-02 -1.17 ACCESCOS 0.53 0.95 -4.62 -0.54 0.35 0.39 ERODE -0.475E-01 -0.04 10.97 0.15 1.43 0.80 CWORK 0.36 0.36 1848.07 2.82*** 0.40 0.27 MEDENS 1606.28 1.60 4936.16 1.35 3211.43 2.31** MEDIV -234.91 -2.38** -461.43 -1.25 -299.78 -2.15** PARTIC 83.46 0.97 -70.16 -0.33 -76.78 -0.82 COGNIT 12.45 0.60 -139.46 -1.75* -45.49 -1.85* LOC - - - - 1510.20 4.19*** PERCEP 88.16 0.87 84.59 0.33 105.39 0.90 SIGMA 1290 15.90*** 3107 10.83*** 2172.37 19.14*** Log -1140.66 -651.65 -1842.94 likelihood N 152 144 296 *Significant at P
productivity through soil conservation investments in order to meet subsistence requirements. Machakos appears to have other sources of income (off-farm) due to its proximity to Nairobi. Density of membership in groups by households is positive and significant for the combined data set, but looked at separately, the variable is positive but not significant for both districts. This suggests that some characteristics require a large data set for their effects to be realized. However, the sign is as expected. The significant result from the combined data implies that as the density of memberships increase, the likelihood of deciding to terrace and also the intensity of terracing will increase. This suggests that it is the networking that is important. The higher the share of households who participate in groups, the more networking takes place, which is likely to induce a household to decide to terrace or to do more terracing. This suggests that preferences of a community or group play an important role in shaping individual preferences, in particular, if there are social norms as to what constitutes a “good farmer.” In other words, membership density appears to work through peer pressure and information diffusion. The variable reflects the capacity to share information and facilitate the transformation of information into knowledge and action. As membership density increases, a critical level is reached that results in households investing in terraces. If deviation from this norm entails private costs to the farmer—for instance, in the form of social sanctions, guilt feelings, low self-esteem, or loss of prestige—higher investment in soil conservation is plausible. Furthermore, group membership connects a farmer to a variety of people and, thereby, to a wide information base, which may lead to a higher terracing intensity. The converse is true. Moreover, additional group membership from the same household reduces significantly moral hazard and adverse selection problems, because monitoring one another’s activities is not costly. We also note that soil conservation (terracing) has positive externalities, which improve soil productivity in the specific area in which such measures are employed but also via improved erosion control over a much wider area. That is, there are spillover benefits to the community as a whole in terms of reduced erosion. Membership diversity is found to be negative and significant. This is an interesting result, since it implies that the assumption is wrong that an organization with different characteristics—such as age, religion, political affiliation, and occupation—presents greater opportunities for information –sharing. Maybe it is not so much that it is wrong as that greater membership variability creates conflicts and makes it difficult for the group to mesh. As Balland and Platteau (1996) have argued, collective action is successful with homogeneous groups. Participation, the other element of social capital, is not significant. This implies that the extent of participation in decisionmaking does not influence either the decision to terrace or terracing intensity. Cognitive social capital has a negative and significant effect on terracing intensity for the combined data set and separately for Machakos. This element of social capital increases the perception of a soil erosion problem, yet reduces both the likelihood of undertaking a decision to terrace and the terracing intensity. This suggests that cognitive elements (relating to norms, values, attitudes, and beliefs) are acting contrary to expectation. The results also suggest that different elements of social capital do not act in the same direction. We find that group membership or membership density is positive and significant in relation to soil conservation investments. This implies that certain types of social capital suffer from negative productivity, while others enjoy positive productivity (Dasgupta forthcoming). Location is negative and significant. The results also show that Machakos has a significantly higher level of soil conservation investments than Taita-Taveta. This reflects a greater propensity for Machakos farmers to undertake investments to prevent soil erosion and conserve moisture. This may have to do with learning and copying from neighbours. In Machakos, some of this learning was in the form of an exogenous shock (Tiffen, Mortimore,
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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
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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
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 448 and 449: then summed up according to increas
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 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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