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

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5.2. Determinants of grain prices In the previous discussion, an attempt was made to indicate the nature of grain markets and the behavior of grain prices. Understanding the behavior of daily market grain prices requires a critical investigation of the determinants of these prices. First, it is important to know how brokers set their selling price at the central market. The survey results show that about 69 percent of brokers used the price of the previous day in setting the current price for grain (Annex Table 2.12). Most brokers (78 percent) also take into account the prices of other brokers in setting their current sales prices (Annex Table 2.21). Another important factor is related to supply and demand conditions. Brokers undertake personal evaluation of total arrivals before the market opens at 6 a.m. Reports of arrivals from clients and records of trucks at the entry gate are also used in assessing the magnitude of supply to the central market (Annex Table 2.15). On the demand side, brokers use, among other things, reports of regular buyers, personal observations of total buyers in the market, specific dates of the month (e.g., pay period), holiday seasons, and entry or presence of large buyers in the market (Annex Table 2.17). Most brokers do not incur financial costs in getting information about demand, supply, and price levels in the market. The only investment they need is a bit of their time and personal effort, although the quality of information they collect is likely to be low. The ability of any market to function efficiently with respect to pricing depends in large part on the information available to market agents. Price determination requires information on daily demand, supply, quality of grain, previous stocks, and policy and related variables (such as time of government intervention). Government intervention in the grain market drives up grain prices. It has been documented that changes in daily price variability are crucially related to daily supply (which consists of new daily supply and the previous day’s stocks), daily demand, extent of competition, and other factors. In order to examine the competitive behavior of a market in a differentiated market structure, we used a hedonic model, which is a popular framework in grain marketing. The model specifies that price is a function of both supply and demand. The marginal implicit price of a characteristic is determined by differentiating the hedonic with respect to that characteristic, implying an empirical specification of the hedonic model of the form: P t = f ( p 1, p , Dp, Dh) t − d …………………………………………………………….. (5.6) where Pt is the wholesale price of a given crop type on any given market day, Pt-1 is the closing price of the previous day, Pd is the average opening price (price registered at 6:00 a.m.), Dp is the dummy for the date of the month (1 if the date coincides with pay period and 0 otherwise), and Dh is the dummy for a holiday (1 if a major holiday is approaching and 0 otherwise). The dummies are interpreted as differences from the price of the pay period and
holiday sales. The results of the regression analysis for wheat and maize crops are presented in Tables 5.11 and 5.12, respectively. 9 As shown in Table 5.11, all the explanatory variables affect the wholesale price of wheat positively, and they are all significant except for holiday (Table 5.11). Unlike wheat, pay period and holiday are negatively correlated with the wholesale price of maize and are statistically insignificant. This is perhaps because maize is not the main food item for the majority of the urban population. The common feature of the two regressions is that the opening price appears to be the main determinant of the spot price of each crop. Table 5.11: Results of the determinants of wheat price Dependent variable logarithm of wholesale price of wheat Coef. Std. error t P > |t| 95% conf. interval Log of the average opening 0.487 0.164 4.389 0.004 0.267 0.707 price Log of the closing price of 0.510 0.174 4.537 0.004 0.288 0.733 the previous day Dummy for pay period 0.010 0.005 1.500 0.051 -0.003 0.024 Dummy for holiday 0.002 0.005 0.211 0.729 -0.014 0.018 Constant 0.010 0.081 0.127 0.905 -0.142 0.161 Heteroskedastic-consistent standard errors Number of observations = 123 Observation-adjusted R2 = 0.97 Prob > F = 0.000 9 A regression analysis for teff was not conducted, as it is highly heterogeneous compared with other crops.
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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
Page 223 and 224: CHAPTER SIX Grain Marketing in Addi
Page 225 and 226: A third, related explanation argues
Page 227 and 228: educed marketing costs related to p
Page 229 and 230: 1.4. Research questions This study
Page 231 and 232: 1.6. Organization of the report Thi
Page 233 and 234: the cases, respectively (Table 2.4)
Page 235 and 236: Brokers believe that most regional
Page 237 and 238: There is also a close relationship
Page 239 and 240: Table 2.14: Buyers’ methods for i
Page 241 and 242: Source: Survey, 2002. Table 2.18: D
Page 243 and 244: Table 2.22: Method used to grade di
Page 245 and 246: Table 2.25: Access to credit broker
Page 247 and 248: It has been reported that grain pur
Page 249 and 250: 3. GRAIN RETAILERS Retailers serve
Page 251 and 252: Like brokers, retailers use cash or
Page 253 and 254: Table 3.11: Buyers’ information a
Page 255 and 256: Table 3.14: Retailers’ views of g
Page 257 and 258: 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: Table 5.10: Summary of Granger caus
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 and 310: (6) β i = β 0 λ i , 0
Page 311 and 312: Intercrop differences Liquidity con
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
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Table 13: Regression results (Tobit
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of grain production was reportedly
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instruments to mitigate price risks
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Coulter, J. and G. Onumah, (2002).
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Appendix: Supplementary Tables Appe
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CHAPTER EIGHT Collective Action in
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The objectives of DRS were to popul
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increasing participation. Thus, the
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Table 2. Perceived change in benefi
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unsatisfactory (Table 8). Flaws in
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Table 14. Measures of wealth status
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Results from Regression Analysis Ta
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However, to maintain these benefits
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Table 18. Determinants of participa
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_______ 1981. Common property and c
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Management Regulations and Enforcem
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In theory, under PFM, the governmen
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3.2 Desk study The desk study invol
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Table 1: Number and gender of indiv
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Poverty Increase in number of fishe
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Table 5: Responses given by various
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wages after the catch is sold (see
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70 60 No of respondents 50 40 30 20
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4.2.3 Lack of attention to social i
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5.3 Regulate the construction of re
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REFERENCES Allison, E. H., and P. M
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APPENDIXES Appendix A: Districts of
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Questions and responses Government
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Questions and responses Lack of coo
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CHAPTER TEN Animal Health Service D
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• Individuals are selected for tr
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Makueni district was selected becau
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1.5 Justification of the study The
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more sustainable (Leonard 1993; Uma
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of defined roles for CBAHWs within
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Interviews with veterinary officers
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The estimated model was of the form
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Gender remains a sensitive issue. P
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The most common ailments and diseas
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4.3 Factors influencing the level o
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Table 9 -- Factors influencing the
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Table 10 - Factors determining the
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Table 12 -- Current mean numbers of
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Figure 4: Percentage of household h
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CBAHWs were the most utilized chann
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Users of veterinarians’ services
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Table 18 -- Levels of incomes and l
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higher average calving per cow, whi
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Table 21 -- CBAHWs’ ranking of sp
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epresentative of livestock keepers
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Despite the restriction imposed by
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veterinarians. Such associations co
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References Akabwai, D., V. Shean, a
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Johnson, P. J., and C. Chahuares. 1
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Appendix 1: The Logistic Procedure
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Social Capital and Sustainable Agri
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1.1 Soil conservation programs and
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1.3 Research Objectives and Hypothe
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Institutional and Policy factors -
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land pressure is high (Holden 1991)
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include density of membership in gr
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then summed up according to increas
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Social capital, resource use, and a
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Table 4.4 Farmers’ perception of
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Table 4.5: Empowerment and politica
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Table 4.8: Indicators of collective
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Table 4.10: Comparison of means of
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5. The influence of social capital
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especially when considered on an ad
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households may pay less attention t
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Table 5.3 Determinants of terracing
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and Gichuki 1994). In the 1940s, th
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Table 5.4. Determinants of soil con
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5.3. Social capital, resource use,
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6. Implications for policy and prog
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essential for the formation of exte
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BIBLIOGRPAHY Admassie, Y., L. I. Mw
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Gambetta, D. 1988. Can we trust tru
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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