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

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Table 2: Description of Variables Variable Code Definition/expectation Expected sign Household size FSIZE High demand for cash to meet nongrain or Positive nonagricultural consumption needs of the household members triggers larger sales during the first quarter. Gender SEX A dummy variable, which takes a 1 if the Positive household head is male and 0 otherwise. It is expected that male-headed households tend to market their crops earlier than female-headed households. Education level EDUC The highest level of formal education Negative achieved by a member of the household. Education is expected to influence the decision to sell grain in such a way that those households with higher education levels are hypothesized to demonstrate better- informed sales decisions and vice versa. Proportion of grain sales during the first quarter is expected to be lower for those households with better education. Chemical application. CHEM A dummy variable that takes on a value of 1 if a household used chemicals to reduce post-harvest grain loss and 0 otherwise. Application of pesticides is expected to reduce the risk of post-harvest grain loss, Negative Storage capacity Livestock ownership Expectation of losses STCAP TLU EXPLOSS thus reducing instant grain sales. Estimated total nonbag storage capacity owned by households. With larger capacities of granaries, the proportion of grains to be sold during the first quarter is expected to be less. Total livestock owned by households expressed in terms of Tropical Livestock Units. Standard conversion factors have been used for different categories of livestock (see Appendix Table 2). Livestock ownership is expected to reduce instant crop sales since households may resort to livestock (and/or livestock products) sales to meet cash needs. The proportion of output that farmers expect to lose due to storage pests (or other factors) if grain crops were to be stored until prices rise substantially (in this case until the third quarter). It is hypothesized that fear of risk of loss might cause farmers to sell their crops Negative Negative Positive
Intercrop differences Liquidity constraints PRICDIF TAXLOAN sooner rather than later. The difference in average prices of grains that prevailed between the third quarter (July to September, when highest) and the first quarter (January to March, when lowest) measured in birr per kilogram 24 . On the one hand, this variable captures the market risk element; i.e., as the difference increases farmers postpone their sales until later, when prices actually get higher; and conversely, as the price difference narrows, farmers prefer to sell their crops sooner, since waiting is associated with a higher risk premium, especially since post-harvest grain loss could be larger. However, since the price differences are the same for each location (not household-specific), each household’s risk perception is not captured; therefore, this interpretation might be misleading. On the other hand, since price differences are largest for teff and smallest for maize, this variable might capture the intercrop differences (could be considered crop dummies). In this case, the variable takes on a negative coefficient, suggesting a tendency toward instant sales for Bako and delayed sales for Ada (capturing the intercrop differences in susceptibility to postharvest grain losses). The total monetary value of grains sold to cover dues such as repayment of input loans and/or land taxes. It is expected that farmers’ instant crop sales are largely involuntary but triggered primarily by the need to pay the government for input loans, land taxes, or the like. Positive Positive 24 Since we could not generate grain price data at the household level, we used the average of grain wholesale prices for each location. Prices of the major crop in each location are considered since farmers use these crops as “cash” crops ( teff for Ada, wheat for Hetosa, and maize for Bako.
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
Page 259 and 260: Table 4.4: How do you set a sales p
Page 261 and 262: Table 4.7: Means of settling disput
Page 263 and 264: a.m. The survey included wholesale
Page 265 and 266: Table 5.3: Instability index of dai
Page 267 and 268: Table 5.7: Seasonality index of who
Page 269 and 270: Figure 5.3: Ratio of wholesale to r
Page 271 and 272: Table 5.8: Results of stationary te
Page 273 and 274: Table 5.10: Summary of Granger caus
Page 275 and 276: holiday sales. The results of the r
Page 277 and 278: Table 6.1: Storage capacity of the
Page 279 and 280: Intermarket grain flow is coordinat
Page 281 and 282: • a legal, regulatory, and enforc
Page 283 and 284: REFERENCES Al-Hassen, R., A. Dorwar
Page 285 and 286: Seppala, P. 1998. Decisive liberali
Page 287 and 288: achieve some welfare objective. The
Page 289 and 290: Annex Table 2.10: Have you faced di
Page 291 and 292: Annex Table 2.21: Do you consider p
Page 293 and 294: Annex Table 3.3: Cost structure of
Page 295 and 296: Annex Table 4.2: Complementary acti
Page 297 and 298: CHAPTER SEVEN Post-Harvest Grain Ma
Page 299 and 300: only (see Abebe 2000: 80-83). That
Page 301 and 302: of processing as an important post-
Page 303 and 304: occur at all stages of post-harvest
Page 305 and 306: Endowment & access to Resources (e.
Page 307 and 308: Figure 2: The Study Areas (shaded a
Page 309: (6) β i = β 0 λ i , 0
Page 313 and 314: eplace old animals (19 percent of h
Page 315 and 316: Table 5. Number and capacity of gra
Page 317 and 318: Table 7b.Estimated grain loss durin
Page 319 and 320: 7.1.3. Grain Marketing Practices an
Page 321 and 322: 1.8 Producers' price 1.6 1.4 1.2 1
Page 323 and 324: next season, grain prices tend to b
Page 325 and 326: Table 13: Regression results (Tobit
Page 327 and 328: of grain production was reportedly
Page 329 and 330: instruments to mitigate price risks
Page 331 and 332: Coulter, J. and G. Onumah, (2002).
Page 333 and 334: Appendix: Supplementary Tables Appe
Page 335 and 336: CHAPTER EIGHT Collective Action in
Page 337 and 338: The objectives of DRS were to popul
Page 339 and 340: increasing participation. Thus, the
Page 341 and 342: Table 2. Perceived change in benefi
Page 343 and 344: unsatisfactory (Table 8). Flaws in
Page 345 and 346: Table 14. Measures of wealth status
Page 347 and 348: Results from Regression Analysis Ta
Page 349 and 350: However, to maintain these benefits
Page 351 and 352: Table 18. Determinants of participa
Page 353 and 354: _______ 1981. Common property and c
Page 355 and 356: Management Regulations and Enforcem
Page 357 and 358: In theory, under PFM, the governmen
Page 359 and 360: 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
Page 482 and 483:
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
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IFPRI Eastern Africa Food Policy Ne
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