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

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especially when considered on an ad hoc basis. If the livestock values were decomposed on an annual basis, it might provide evidence that crop values are still higher. The mean distance of various plots from the households (DISTH) measures 357 meters in Machakos, compared with 2,066 meters in Taita-Taveta, and these mean distances are significantly different. This could be because farm sizes are relatively bigger in Taita- Taveta (approximately 2.5 acres) than in Machakos (approximately 2.0 acres). The mean farm sizes are also significantly different. The average farm size of 2.0 to 2.5 acres compares unfavorably to that of a sample from several districts in the high-potential agricultural areas of Kenya (Jayne et al. 1998), and yet the study sites are located in marginal areas. Since the household sizes are more or less similar, while the mean farm sizes are significantly different at the two sites, it follows, therefore, that the mean per capita farm size (FARMCA) is significantly different. The mean values of FARMCA indicate that Machakos is more densely populated than Taita-Taveta. The mean terrace length (TERRACE) in Machakos is significantly higher than in Taita-Taveta. This could be for several reasons: First, soil conservation campaigns have been more sustained and intense in Machakos than in Taita-Taveta. Second, the higher income status could have provided an impetus to invest more in terracing. Conversely, it can also be argued that terracing, and hence soil conservation, has enabled farmers in Machakos to improve their incomes from agriculture. The mean household size (HHS) in both sites is six members. The use of manure and fertilizer to condition the soil and the intensity of use is varied, and the mean values are significantly different. For example, while farmers in Machakos use fertilizer (FERTUSE) at a mean rate of 45 kilograms per acre, those in Taita-Taveta use none. To find an area in Kenya where fertilizer use in crop production is zero or near zero is surprising. Even where livestock are present, the intensity of manure use in Taita-Taveta, about 480 kilograms per acre is only a third of that used in Machakos (1,160 kilograms per acre. The fertilizer and manure use intensity in Machakos is on average comparable to other areas in the highpotential agricultural regions of Kenya (Obare 2000) and in the rest of Kenya (KARI 1998; Ministry of Agriculture 1989). The mean value of wealth index (WEALTH), farm slope index (SLOPE), education level of the household head (EDUC), and the land tenure system (TENURE) are not significantly different between the two sites. Although these variables are likely to have an effect on soil conservation, it would be difficult to indicate the direction of this effect in the two sites. 5.1. Household perceptions of soil erosion problems in marginal areas For an individual to make a decision on whether or not to commit resources to mitigate the effects of a given problem, that person must first appreciate the existence of such a problem. And the same individual must then evaluate the severity of the problem and rank it accordingly. In the two study sites, we we presume that the residents recognize that there is a soil erosion problem. Table 5.2 shows the factors that influence the perception of a soil erosion problem in farmers’ fields. Five variables are significant for the combined data set: distance to the crop fields from the homestead, wealth, age of household head, household size, and cognitive social capital. The slope variable is only positive and significant for Machakos District. This may be linked to higher education and extension levels in Machakos, compared with Taita-Taveta. In any case, the higher the slope category in Machakos, the higher the probability that a farm household will recognize that they have a soil erosion problem. The combined data set even has an unexpected sign, which perhaps stems from the
large data set, which tends to balance off relative effects. Moreover, even the slope index is higher in Machakos, although the differences are not significant. Table 5.2: Ordered Probit results of perception of a soil erosion problem Machakos Taita-Taveta Combined Variables Coefficient t-statistic Coefficient t-statistic Coefficient t-statistic SLOPE 0.301 3.082*** 0.120E-03 0.156 -0.126E-03 -0.207 TENURE -0.155 -1.934* -0.512E-01 -0.551 -0.606E-01 -1.196 DISTH 0.288E-04 0.188 0.230E-04 0.628 0.523E-04 1.912* LCROPAC -0.412E-05 -1.173 -0.249E-06 -0.071 -0.224E-05 -1.025 EDUC -0.752E-01 -2.731*** 0.249E-04 0.108 -0.304E-04 -0.154 EXT -0.706E-04 -0.081 0.250E-03 0.001 0.492E-03 0.187 WEALTH -0.100 -1.294 -0.640E-01 -1.209 -0.718E-01 -2.028** SEX 0.197 0.406 0.161 0.546 0.113 0.538 AGE -0.578E-03 -1.275 -0.910E-03 -0.130 -0.826E-03 -1.950* FAROR -0.44 -1.437 -0.167E-02 -1.446 -0.114E-02 -1.397 HHS 0.403E-01 0.944 0.629E-01 1.619 0.601E-01 2.246** FAMCA -0.145 -1.544 0.218 1.125 0.619E-01 1.214 INC 0.864E-06 1.265 0.294E-06 0.189 0.445E-06 0.942 ACCESCOS -0.245E-03 -0.468 0.425E-02 1.671* -0.108E-03 -0.156 ERODE 0.110E-02 0.024 0.170E-03 0.005 0.289E-03 0.115 CWORK 0.462E-03 0.350 0.173E-02 1.703* 0.805E-03 0.997 MEDENS -0.386E-01 -0.045 1.630 0.899 0.459 0.520 MEDIV 0.154E-01 0.176 -0.134 -0.719 -0.266E-01 -0.301 PARTIC -0.287E-01 -0.389 -0.552E-01 -0.618 -0.401E-01 -0.859 COGNIT 0.842E-01 4.008*** 0.306E-01 1.284 0.574E-01 4.192*** LOC - - - - -0.236 -1.405 Log likelihood -207.174 -205.315 -435.295 Chi-squared 52.034 20.666 42.331 N 152 144 296 *Significant at P
Page 1 and 2:
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
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 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 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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