i AN ASSESSMENT OF TOMATO PRICE VARIABILITY IN LUSAKA ...

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that don’t serve the interests of farmers or consumers very well (Hichaambwa and Tschirley, 2006). Soweto market in Lusaka is the largest wholesale and retail center for fresh fruits and vegetables (FFV) in the country. Located in the center of the city, it is a commercial hub for FFV and a wide assortment of other food items such as dry cereals, pulses, and tubers, among others. Despite the huge amounts of FFV and other food items it handles, this market has for a long time been in a poor state. It has poor and limited sanitation, a poor waste management system and a poor drainage system. Even though the local council authority collects market stall levies from the operators in this market, there has been little investments made to improve it. Coupled to its physical inadequacy is the absence of market information, and the lack of formal grades and standards. (Hichaambwa and Tschirley, 2006, Typsa Consulting Engineers and Architects, 2004) In an attempt to address some of the concerns in Soweto market, the European Union, in collaboration with the Ministry of Local Government and Housing is currently investing over 16 million Euros into a program called the Urban Markets Development Program (UMDP). Among other things, the UMDP has focused on the construction of improved physical infrastructure in selected markets of Lusaka, Ndola and Kitwe cities. In Lusaka, Soweto market is one of the markets that has benefited from this program (Hichaambwa and Tschirley, 2006). This program is currently ongoing in all selected markets and works in Lusaka’s Soweto market still continue. Despite the investment made by UMDP in Soweto market, it is not clear how meaningful a contribution the program will make 6
towards lowering the costs, encouraging higher quality and better price predictability, among other things, for tomatoes and other FFV, and generally improving wholesaling and retailing of fresh produce in the market. This paper shall focus on the wholesaling and retailing of tomatoes in Lusaka’s Soweto market. Among all FFV, tomatoes have the second largest share in both production and consumption in Zambia, following rape, (FSRP UCS data, 2007). Tomatoes are therefore one the most widely consumed fresh fruits and vegetables. However, farmers, traders, and consumers of these tomatoes are faced with tremendous price variability from day to day and also within days. Given the high level of variability of tomato prices, two important questions arise. Firstly, what is the effect of this variability on the riskiness of returns to farmers? And secondly, can market information lead to improved decision making that raises and stabilizes returns? This paper shall address these two questions. 1.2 Objectives of the Study The overall objective of this study is to evaluate the price variability of tomato in Lusaka’s Soweto market and to assess the effects of different production and marketing strategies on farmers’ performance. Soweto market accounts for the lion’s share of wholesale fresh produce transactions in Lusaka. The specific objectives of the study are: 1. To identify the level of price variability for tomato in Soweto market and evaluate how this compares with other markets around the world. 7
Page 1 and 2: AN ASSESSMENT OF TOMATO PRICE VARIA
Page 3 and 4: DEDICATION This thesis is dedicated
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: chains and independent supermarkets
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
Page 61 and 62: In the period January 2007 to June
Page 63 and 64: CHAPTER 3 TOMATO PRICE VARIABILITY
Page 65 and 66: In the hot wet season, disease prev
Page 67 and 68: patterns differ across markets, red
Page 69 and 70:
case of a flood, its effects could
Page 71 and 72:
3.2 Hypothesis Testing The level of
Page 73 and 74:
Table 3.2: Description of Data Used
Page 75 and 76:
Coefficient of Variation= Where;
Page 77 and 78:
Where; P t - the observed tomato pr
Page 79 and 80:
A look at price variability in each
Page 81 and 82:
For further comparison of the mean
Page 83 and 84:
Figure 3.2: Comparison of the Ratio
Page 85 and 86:
Figure 3.4: Comparison of Condition
Page 87 and 88:
thereby making the farmers more con
Page 89 and 90:
d. The absence of cold chain system
Page 91 and 92:
depend on local suppliers for their
Page 93 and 94:
The provision of market information
Page 95 and 96:
upon arrival in the market. Lower q
Page 97 and 98:
- Assets used in tomato production,
Page 99 and 100:
Tomato price data used for analysis
Page 101 and 102:
4.4 The Monte Carlo Model The Basel
Page 103 and 104:
CF Fixed conversion factor of 37.
Page 105 and 106:
- Seedling costs - Seed costs - Fie
Page 107 and 108:
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
Page 113 and 114:
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
Page 123 and 124:
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
Page 129 and 130:
CHAPTER 5 CONCLUSION This study was
Page 131 and 132:
top twelve tomato supply areas acco
Page 133 and 134:
Freshpikt and Rivonia are the main
Page 135 and 136:
on prediction errors from a price p
Page 137 and 138:
absence of a cold chain system, the
Page 139 and 140:
price variability, the expectation
Page 141 and 142:
In the scenario analysis of increas
Page 143 and 144:
price variability affects the diffe
Page 145 and 146:
facilitate the provision of low cos
Page 147 and 148:
APPENDICES 135
Page 149 and 150:
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
Page 165 and 166:
SECTION 4 - PLANTING AND HARVEST PA
Page 167 and 168:
155 _______________ _______________
Page 169 and 170:
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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