Impact Of Agricultural Market Reforms On Smallholder Farmers In ...

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Table 4.1.89-Change in credit availability since 1992 by expenditure category Expenditure category (quintile) Poorest 2 3 4 Richest Total Has increased 12% 23% 21% 13% 16% 17% No change 84% 66% 67% 77% 72% 73% Has decreased 4% 8% 10% 8% 6% 7% Did not farm in 1992 3% 3% 2% 6% 3% Total 100% 100% 100% 100% 100% 100% Source: IFPRI-LARES Small Farmer Survey. Table 4.1.90-Change in credit availability since 1992 by department Department Atacora Atlantique Borgou Mono Ouémé Zou Total Has increased 4% 1% 8% 25% 25% 31% 17% No change 91% 98% 84% 73% 33% 56% 73% Has decreased 1% 8% 3% 26% 11% 7% Did not farm in 1992 4% 0% 0% 16% 1% 3% Total 100% 100% 100% 100% 100% 100% 100% Source: IFPRI-LARES Small Farmer Survey. Table 4.1.91-Ownership of agricultural assets Nbr. owned Percent among owning owners Nbr. owned including non-owners Value among owners (FCFA) Value including non-owners (FCFA) Cart 3% 1.1 .0 169,659 3,691 Plow 9% 1.3 .1 102,525 8,859 Harrow 2% 1.0 .0 49,041 659 Tractor 0% 1.6 .0 816,625 2,049 Cattle 14% 12.3 1.7 791,614 109,357 Work cattle 8% 6.5 .5 577,806 48,670 Goats/sheep 62% 8.7 5.4 71,582 44,612 Donkeys/horses 0% 1.0 .0 75,839 314 Pigs 6% 5.0 .3 38,201 2,236 Poultry 84% 22.6 19.1 23,500 19,749 Other animals 11% 3.8 .4 21,812 2,319 Source: IFPRI-LARES Small Farmer Survey. 161
Table 4.1.92-Percent of households owning agricultural assets by department Department Atacora Atlantique Borgou Mono Ouémé Zou Total Cart 3% 0% 13% 0% 0% 1% 3% Plow 17% 0% 43% 0% 0% 0% 9% Harrow 2% 0% 10% 0% 0% 0% 2% Tractor 2% 0% 0% 0% 1% 0% 0% Cattle 33% 2% 46% 0% 0% 9% 14% Work cattle 18% 2% 32% 0% 5% 0% 8% Goats/sheep 65% 54% 70% 67% 57% 61% 62% Donkeys/horses 1% 1% 0% 2% 0% 0% 0% Pigs 30% 0% 0% 0% 4% 5% 6% Poultry 89% 87% 80% 97% 83% 74% 84% Other animals 23% 4% 0% 32% 5% 0% 11% Other assets 91% 4% 95% 41% 12% 100% 39% Source: IFPRI-LARES Small Farmer Survey. Table 4.1.93-Value of agricultural assets by department (FCFA/household) Department Atacora Atlantique Borgou Mono Ouémé Zou Cart 3,028 0 20,588 0 0 214 Plow 22,149 0 37,773 0 0 0 Harrow 1,053 0 3,302 0 0 0 Tractor 4,511 0 9,246 0 0 0 Cattle 150,785 2,703 531,158 0 4,771 20,715 Work cattle 97,502 4,244 214,208 0 8,026 394 Goats/sheep 46,759 36,721 70,360 47,341 27,759 40,273 Donkeys/horses 1,493 0 736 0 0 0 Pigs 6,128 0 310 0 1,596 4,971 Poultry 25,069 18,650 18,549 18,856 14,636 22,981 Other animals 10,443 1,090 20 1,058 2,719 0 Other assets 1,319 0 14,088 40,605 3,165 36,734 Total 370,238 63,407 920,340 107,860 62,672 126,282 Source: IFPRI-LARES Small Farmer Survey. 162
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IMPACT OF AGRICULTURAL MARKET REFOR
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TABLE OF CONTENTS CHAPTER 1 - INTRO
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5.1.10 Expenditure patterns .......
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CHAPTER 1 - INTRODUCTION 1.1 Backgr
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villageois, (GV)) was needed as the
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CHAPTER 2: POLICY BACKGROUND FOR BE
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2.3 Evolution of Economic Policy 2.
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Under pressure from the World Bank
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anking system has been restored to
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Fertilizer can be imported outside
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particularly urban wage-earners. On
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Soulé (1996) describes the evoluti
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led to a more sustainable agricultu
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Table 2.4 - Gross farm revenue from
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Malawi’s economy is heavily depen
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the reduction of its operations in
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crops is also difficult because tob
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Table 3.1 CROP 1982/83 1983/84 1984
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CHAPTER 4: RESULTS OF THE BÉNIN SM
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where W v is the weight for a house
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heads. This suggests that some of t
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And school attendance is much highe
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Crop production is the main activit
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(33 percent). Livestock income is m
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Somewhat surprisingly, the relation
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It is no more common among richer h
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production, and women actually spen
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Seed use by crop Although only 18 p
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Looking at the results by departmen
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Ouémé presents an unusual case: j
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Market prices have significant effe
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policies of SONAPRA to discourage
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crops are responsible for this incr
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Respondents were asked whether the
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Female-headed households, not surpr
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households are not considered credi
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In almost every category of agricul
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This discussion implies that farms
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eturns per hectare and labor-intens
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farm household and tends to be smal
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Surprisingly, the value of sales as
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Finally, proximity to an all-season
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Changes in crop marketing In this s
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Poor households are more likely to
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it is clear that virtually all grow
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production to total expenditure is
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a significant number of household (
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non-food spending. These are follow
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Larger farms are associated with hi
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This would not be surprising in lig
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Female-headed households appear to
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poorest expenditure category to jus
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headed household were more likely t
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Households in every expenditure cat
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An important result of this analysi
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oad network, although those citing
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questions posed by the IFPRI-LARES
Page 118 and 119: With regard to cotton marketing, al
Page 120 and 121: Village leaders were asked to ident
Page 122 and 123: Marketing patterns Village leaders
Page 124 and 125: 4.3: Results of the Bénin Survey o
Page 126 and 127: 4.3.2 Survey methods At the time of
Page 128 and 129: officers have CM2 certificates. Thi
Page 130 and 131: Suppliers The GV representatives we
Page 132 and 133: Cotton area per GV ranges from 145
Page 134 and 135: The largest cost items are schools
Page 136 and 137: members and 10 percent of the membe
Page 138 and 139: Table 4.1.1-Description of sample o
Page 140 and 141: Table 4.1.7-Age, education, and lit
Page 142 and 143: Table 4.1.13-Ethnic background of h
Page 144 and 145: Table 4.1.19-Importance of each sou
Page 146 and 147: Table 4.1.25-Number of plots and ar
Page 148 and 149: Table 4.1.31-Land tenure by expendi
Page 150 and 151: Table 4.1.37-Change in labor alloca
Page 152 and 153: Table 4.1.42-Seed use by sex of hea
Page 154 and 155: Table 4.1.47-Perceived change in av
Page 156 and 157: Table 4.1.54-Share of producers of
Page 158 and 159: Table 4.1.59-Fertilizer use by farm
Page 160 and 161: Table 4.1.64-Characteristics of fer
Page 162 and 163: Table 4.1.69-Change in fertilizer a
Page 164 and 165: Table 4.1.75-Allocation of labor by
Page 166 and 167: Table 4.1.82-Allocation of labor by
Page 170 and 171: Table 4.1.94-Percent of households
Page 174 and 175: Table 4.1.102-Area allocation by cr
Page 176 and 177: Table 4.1.106-Area allocation by cr
Page 178 and 179: Table 4.1.110-Marketed share of out
Page 180 and 181: Table 4.1.114-Marketed share of out
Page 182 and 183: Table 4.1.117-Regression for market
Page 184 and 185: Table 4.1.120-Change in the type of
Page 186 and 187: Table 4.1.128-Perceived change in r
Page 188 and 189: Table 4.1.135-Main reason for stori
Page 190 and 191: Table 4.1.140-Summary of food expen
Page 194 and 195: Table 4.1.144-Food consumption by e
Page 196 and 197: Table 4.1.149-Results of regression
Page 198 and 199: Table 4.1.152-Value of household as
Page 200 and 201: Table 4.1.156-Value of household as
Page 202 and 203: Table 4.1.160-Housing characteristi
Page 204 and 205: Table 4.1.162-Housing characteristi
Page 206 and 207: Table 4.1.165-Main source of water
Page 208 and 209: Table 4.1.170-Source of information
Page 210 and 211: Table 4.1.176-Source of information
Page 212 and 213: Table 4.1.181-Regression for percei
Page 214 and 215: Table 4.2.5-Change in number of veh
Page 216 and 217: Table 4.2.12-Availability and sourc
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Table 4.2.21-Number of GFs per vill
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Table 4.2.30-Percentage of villages
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Table 4.2.34-Distribution of villag
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Table 4.3.1-Distribution of GVs by
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Table 4.3.9-Number of each type of
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Table 4.3.12-Percentage of secretar
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Table 4.3.19-Distribution of GVs by
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Table 4.3.24-Percentage of inputs s
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Table 4.3.32-Average value of curre
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Table 4.3.40-Percentage of GVs in w
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CHAPTER 5 - RESULTS FROM THE MALAWI
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The weights are used to calculate a
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On average, around 20 percent of th
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agricultural work. Other less commo
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5.1.4 Time allocation In Malawi, as
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attributed it to the fact that fert
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insignificant. The coefficient on t
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As in the results from the previous
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most important sources of input on
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applied for credit compared to 20 p
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less than 1 mt per ha which are fai
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5.1.9 Agricultural marketing Market
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Households that belong to a club al
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most important contractors are the
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indicates that the extent of povert
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wheat, and fruits and nuts, a highe
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Surprisingly, the education of the
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Housing characteristics The overwhe
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It was also important to find out w
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Surprisingly, tobacco growers are m
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Over three-quarter of the EPAs repo
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The changes in the number and types
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5.2.11 Storage Facilities On averag
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Table 5.2 - Household characteristi
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Table 5.7 - Percentage of household
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Table 5.12 - Farm labor use and all
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Table 5.15 - Determinants of the de
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Table 5.17 - Sources of fertilizer
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Table 5.22 - Access and use of cred
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Table 5.25 - Percent of households
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Table 5.34 - Crop disposal by type
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Table 5.38 - Determinants of Market
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Table 5.42 - Summary of expenditure
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Table 5.45 - Food consumption by so
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Table 5.51 - Food consumption by ex
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Table 5.56 - Determinants of per ca
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Table 5.59 Percent of households ow
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Table 5.62 - Characteristics of the
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Table 5.68 - Determinants of percei
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Table 5.71 - Average yields per cro
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Table 5.77 - Changes in the availab
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Figure 5.1 Distribution of househol
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Figure 5.7 - Percentage of Househol
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CHAPTER 6 - SUMMARY, CONCLUSIONS AN
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Fertilizer Half of the far
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Regression analysis indicates that
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Among those seeing improvement, the
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Agricultural markets Three quart
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The economic reforms have boosted p
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department. The farms with very lit
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Strengthen the extension service by
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The most time-consuming agricultura
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Most common means of transport to t
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Small farmers in Malawi are mainly
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households have less assets and les
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Place more serious efforts to impro
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REFERENCES Ahoyo Adjovi, N.R. and F
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