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

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lowest in Lira mainly due, perhaps, to pests and diseases that was reported in the previous season and during the past years (refers to Tables 12 and 13). Certainly, the differences were not due to maize technology because 77% and 79% of farmers planted improved maize in Apac and Lira, respectively. Profitability of Maize Maize profit was computed by subtracting total maize production costs from maize revenue. Total costs include variable costs (labor, seed, herbicides and fertilizers) and fixed costs (mainly farm implements such as hoes, ox-ploughs, axes, etc). Maize revenue is the product of the quantity of maize produced and the price. The difficulty faced in computing maize profits was apportioning fixed costs. For example, most households grow a multiple of crops using the same farm implements. It is difficult to share out the fixed costs across various crops. In this study, the fixed costs associated with crop production were simply divided by the number of major crops grown. This has the possibility of under- or over-estimating fixed costs for a particular crop, thereby over-or under-stating its profitability. Table 5 presents overall maize profits per hectare by district, without differentiating between the types of maize planted. The results show great variability in maize profitability, with Lira having the lowest level of profitability and Hoima the highest. Even within the same agro-ecological zone, there are wide variations. Apac and Lira are in the same agro-ecological zone; however, it is about 50% more profitable to grow maize in Apac than in Lira. Hoima and Iganga are also in the same agro-ecological zone, but maize profitability in the former is about one-and-a-half times more than in the latter. However, the differences in maize profitability per hectare (without differentiation by maize variety) are not statistically significant, except between Lira and Hoima. Table 5: Maize profits per hectare Farming System District Sh/Ha Northern Coffee Banana Source: Survey 2001 Apac 173,563 Lira 108,336 Iganga 162,766 Hoima 216,715 The key objective in assessing maize profitability was to determine whether or not it was profitable to grow improved maize seed compared to local or indigenous maize seed. Table 6 presents a comparison of maize profit per hectare, maize price, and maize yield for the entire sample, controlling for the type of maize seed planted. A test of the difference in averages shows that farmers who planted improved maize had an average profit that was statistically significantly higher than the average profit for farmers who planted the local maize variety. Two more variables are compared: price of maize and yield. Results show that the difference in price between local and improved maize was not significant. Maize yield was significantly higher for improved maize than for local maize. These results clearly indicate that differences in maize profitability are due to the type of maize seed grown. Growing improved maize seed is more profitable than growing local maize. Table 6 also presents the three variables (profit, price and yield) compared by farming system for the entire sample. Except for yield, the results clearly indicate that maize profitability is not determined by where it is grown, and that there are no significant differences in the price received by farmers in the two farming systems (Northern and Coffee-Banana).
Table 6: Key economic variables for maize by variety and agro-ecological zone Improved maize (n=179) Local maize (n=109) T-test 5 (mean difference) P-value Maize Profit (Sh/Ha) 190,531 111,872 0.0555 Price (Sh/Mt) 176,139 173,009 0.5663 Yield (Mt/Ha) 1.37 1.00 0.0414 Coffee-banana system (n=130) Northern system (n=160) Maize Profit (Sh/Ha) 186,005 140,135 0.2499 Price (Sh/Mt) 170,625 178,625 0.1278 Yield (Mt/Ha) 1.47 1.04 0.0165 Maize profitability of local and improved varieties was also compared across the four districts. Improved maize was more profitable than local maize in all four districts in absolute terms, but statistically significant (5% level of significance) in only two districts (Hoima and Lira) as shown in Table 7. Table 7: Maize profit per hectare by variety and district District Improved maize Local maize T-test (mean difference) P-value Apac 201,083 (60) 81,829 (18) 0.383 Hoima 378,427 (22) 108,927 (33) 0.020 Iganga 184,427 (32) 147,703 (41) 0.395 Lira 120,200 (65) 62,980 (17) 0.024 Source: Survey 2001. Numbers in parentheses are the number of observations Maize profitability of local and improved varieties was also compared, controlling for farm size. Again, improved maize was more profitable in absolute terms across the four farm size quartiles, and the differences in averages were statistically significant in the first, third and fourth quartiles (Table 8). These findings show planting improved maize is profitable for all farm sizes, implying that scale may not be important in explaining profitability of maize. Table 8: Maize profit per hectare, by farm size Quartile Improved maize Local Maize T-test (mean difference) P-value 1 347,051 (46) 153,845 (62) 0.053 2 186,749 (42) 153,254 (24) 0.436 3 182,347 (43) 60,030 (9) 0.056 4 126,049 (48) 61,136 (14) 0.052 Source: Survey 2001. Numbers in parentheses are the number of observations Regression results Table 9 presents the results of regression analysis for maize profit as the dependent variable. All variables are in logarithms except number of extension visits and the dummy for type of maize 5 This it a student t-test for the difference in means of improved maize seed and local maize seed that were grown by the household, but here only the p-values are presented.
Page 1 and 2: Part One - Agricultural Productivit
Page 3 and 4: I. Introduction Agricultural Produc
Page 5 and 6: came about from expansion of cultiv
Page 7 and 8: study, therefore, limited itself to
Page 9: Land allocation and yields of 12 cr
Page 13 and 14: local seed in absolute terms and th
Page 15 and 16: Table 11: Crosscutting priority con
Page 17 and 18: Table 13: General constraints to ma
Page 19 and 20: These results clearly demonstrate t
Page 21 and 22: Table 18: Crosscutting constraints
Page 23 and 24: V. Policy Implications The findings
Page 25 and 26: References Appleton, Simon (2001).
Page 27 and 28: 1.0 BACKGROUND 1.1 Introduction Low
Page 29 and 30: egins with a summary of the literat
Page 31 and 32: Source: National Wetlands Conservat
Page 33 and 34: shillings (§37-38). Every landowne
Page 35 and 36: Similarly, Howard (1995) used socia
Page 37 and 38: Uganda may well have more than a qu
Page 39 and 40: factors that explain household leve
Page 41 and 42: of inputs 18 , pests and diseases,
Page 43 and 44: (PMA p. 27). It also requires impro
Page 45 and 46: agriculturalists, who still do not
Page 47 and 48: to have covered 30,000 ha. During t
Page 49 and 50: 4.0 RESEARCH METHODOLOGY The breadt
Page 51 and 52: • What is the current extent of w
Page 53 and 54: Figure 2. Changes in Sources of Com
Page 55 and 56: Figure 5. Proportion of Households
Page 57 and 58: estimates are remarkably consistent
Page 59 and 60: FSI - Percentage by District Distri
Page 61 and 62:
was corrected using the White-Huber
Page 63 and 64:
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
Page 77 and 78:
Estimated covariances = 79 Number o
Page 79 and 80:
International Food Policy Research
Page 81 and 82:
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
Page 87 and 88:
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
Page 93 and 94:
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
Page 103 and 104:
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
Page 133 and 134:
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 =
Page 161 and 162:
(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
Page 283 and 284:
REFERENCES Al-Hassen, R., A. Dorwar
Page 285 and 286:
Seppala, P. 1998. Decisive liberali
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achieve some welfare objective. The
Page 289 and 290:
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
Page 347 and 348:
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
Page 381 and 382:
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
Page 388 and 389:
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
Page 396 and 397:
Interviews with veterinary officers
Page 398 and 399:
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
Page 466 and 467:
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
Page 478 and 479:
BIBLIOGRPAHY Admassie, Y., L. I. Mw
Page 480 and 481:
Gambetta, D. 1988. Can we trust tru
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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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