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ABSTRACT AN ASSESSMENT OF TOMATO PRICE VARIABILITY IN LUSAKA AND ITS EFFECTS ON SMALLHOLDER FARMERS By Mukwiti Nchooli Mwiinga This paper discusses the structure and operation of the tomato subsector in Lusaka (Zambia), establishes the level of price variability for tomatoes in Lusaka’s Soweto market, and assesses the impact of tomato price variability on returns to tomato production. Price variability determination involved analysis of the coefficient of variation, conditional variance and the ratio of the mean absolute positive to negative price prediction errors. These results were compared with four other wholesale markets in Costa Rica, Taiwan, Sri Lanka, and the United States of America (Chicago). These other countries were chosen to capture a wide range of supply chain development, as proxied by purchasing power parity Gross Domestic Product (PPP GDP). The study revealed that (a) PPP GDP is strongly negatively (positively) associated with price variability (predictability), and (b) Zambia has the lowest PPP GDP, highest price variability, and least tomato price predictability. Monte Carlo simulation analysis was then conducted to establish the effect that three different scenarios would have on the tomato farmers’ net returns. Increased sales frequency reduces the variability of expected price but has no recognizable impact on the variability of profits. Supply chain improvements also reduced the variability of prices. The production of high quality tomatoes has very significant effects on returns to farmers. Some policy implications drawn include, the need to establish formal grades and standards, investment in cold chain systems and general improvement in the traditional wholesale and retail market infrastructure. ii
DEDICATION This thesis is dedicated to my family: my late father, Elijah Musembwa Mwiinga, my mother, Margaret Duntuula Mwiinga, my siblings Malita, Maelo, Mugwagwa and Mutinta, and my nieces Chiedza and Duntuula. Special dedication goes to my dearest father who always insisted on hard work and discipline in school, and to my mother for all her love and support during my studies, but most importantly, for being the ‘superwoman’ in my life. iii
Page 1: AN ASSESSMENT OF TOMATO PRICE VARIA
Page 5 and 6: to the Dean, School of Agricultural
Page 7 and 8: 3.4.1 Variability and Predictabilit
Page 9 and 10: LIST OF TABLES Table 2.1: Budget Sh
Page 11 and 12: LIST OF FIGURES Figure 1.1: Geograp
Page 13 and 14: 1.1 Background CHAPTER 1 INTRODUCTI
Page 15 and 16: (Ksh 1 )7.5 billion) of total veget
Page 17 and 18: chains and independent supermarkets
Page 19 and 20: towards lowering the costs, encoura
Page 21 and 22: Chapter 5 concludes the thesis with
Page 23 and 24: andomized cluster sample design. Th
Page 25 and 26: 2.2 Methods With these three sets o
Page 27 and 28: perceptions of farmers and traders
Page 29 and 30: Table 2.2: Budget Share of Differen
Page 31 and 32: Table 2.3: Budget Share of Differen
Page 33 and 34: Figure 2.1: Channel Map for Tomato
Page 35 and 36: Table 2.4: Key Characteristics of T
Page 37 and 38: Table 2.6 cont’d Area Province Ma
Page 39 and 40: Figure 2.2: Monthly Soweto Wholesal
Page 41 and 42: from all 150 supply areas while cha
Page 43 and 44: Table 2.7: Retail Outlet Market Sha
Page 45 and 46: Table 2.9: Retail Outlet Market Sha
Page 47 and 48: Freshmark mostly prefers to have la
Page 49 and 50: With the dual procurement system th
Page 51 and 52: Compared to Soweto market which had
Page 53 and 54:
During this period, tomato prices w
Page 55 and 56:
Figure 2.4: Tomato Pricing at Whole
Page 57 and 58:
Table 2.11: Mean Tomato Prices for
Page 59 and 60:
2.5 Summary and Conclusions 2.5.1 I
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In the period January 2007 to June
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CHAPTER 3 TOMATO PRICE VARIABILITY
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In the hot wet season, disease prev
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patterns differ across markets, red
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case of a flood, its effects could
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3.2 Hypothesis Testing The level of
Page 73 and 74:
Table 3.2: Description of Data Used
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Coefficient of Variation= Where;
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Where; P t - the observed tomato pr
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A look at price variability in each
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For further comparison of the mean
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Figure 3.2: Comparison of the Ratio
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Figure 3.4: Comparison of Condition
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thereby making the farmers more con
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d. The absence of cold chain system
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depend on local suppliers for their
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The provision of market information
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upon arrival in the market. Lower q
Page 97 and 98:
- Assets used in tomato production,
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Tomato price data used for analysis
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4.4 The Monte Carlo Model The Basel
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CF Fixed conversion factor of 37.
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- Seedling costs - Seed costs - Fie
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differences were due to differences
Page 109 and 110:
Table 4.2: Farmer Characteristics B
Page 111 and 112:
Group 1 Table 4.4: Basic Informatio
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farmers can in practice suffer a to
Page 115 and 116:
4.5.2 Simulation Results for the Di
Page 117 and 118:
Scenario 1: Increased Sales Frequen
Page 119 and 120:
knowledge of what their yields and
Page 121 and 122:
Table 4.12: The Effect of Supply Ch
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Table 4.13: Production of Low and H
Page 125 and 126:
quality; when supplies are high in
Page 127 and 128:
etter farmers would attempt to have
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CHAPTER 5 CONCLUSION This study was
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top twelve tomato supply areas acco
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Freshpikt and Rivonia are the main
Page 135 and 136:
on prediction errors from a price p
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absence of a cold chain system, the
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price variability, the expectation
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In the scenario analysis of increas
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price variability affects the diffe
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facilitate the provision of low cos
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APPENDICES 135
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i. What type of farmers do they pre
Page 151 and 152:
Taipei, Taiwan Table A2.3. Model Su
Page 153 and 154:
Colombo, Sri Lanka A2.7. Model Summ
Page 155 and 156:
APPENDIX 3. Graphs of Price Predict
Page 157 and 158:
Figure A3.3. Price Prediction Resid
Page 159 and 160:
Figure A3.5. Price Prediction Resid
Page 161 and 162:
9. Name of Main Respondent (if diff
Page 163 and 164:
SECTION 3 - HARVEST AND SALES OF CR
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SECTION 4 - PLANTING AND HARVEST PA
Page 167 and 168:
155 _______________ _______________
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5.2 Planted Seedlings Phase Field #
Page 171 and 172:
PHASE 2 - FIELD PREPARATION 5.5 Enu
Page 173 and 174:
5.7 Irrigation prior to transplanti
Page 175 and 176:
4=Spraying 5=Weeding 6=Placement of
Page 177 and 178:
Field # Please list all the types o
Page 179 and 180:
that you used in your tomato produc
Page 181 and 182:
Trailer Van/light truck Borehole Bo
Page 183 and 184:
Field # Time from 1 st harvest Phas
Page 185 and 186:
Second Field Field # Time from 1 st
Page 187 and 188:
Field # Time from 1 st harvest Phas
Page 189 and 190:
Second field: 8.0 Harvesting and Ma
Page 191 and 192:
APPENDIX 5. Distribution of Sampled
Page 193 and 194:
Figure A6.2. Distributions for Cost
Page 195 and 196:
Figure A6.4. Distributions for Cost
Page 197 and 198:
APPENDIX 7. Histograms of Farmer Pr
Page 199 and 200:
Figure A7.3. Baseline Scenario: His
Page 201 and 202:
Figure A7.5. Increased Sales Freque
Page 203 and 204:
Figure A7.7. Increased Sales Freque
Page 205 and 206:
Figure A7.9. Supply Chain Improveme
Page 207 and 208:
Figure A7.11. Supply Chain Improvem
Page 209 and 210:
Figure A7.13. Scenario on Quality o
Page 211 and 212:
Figure A7.15. Scenario on Quality o
Page 213 and 214:
Figure A7.17. Scenario on Low Quali
Page 215 and 216:
Figure A7.19. Scenario on Low Quali
Page 217 and 218:
BIBLIOGRAPHY 205
Page 219:
Stevens, C. and J. Kennan (1999). W
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