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

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Market prices have significant effects on fertilizer use. The coefficient on the fertilizer price indicates that a 10 percent increase in the price of fertilizer would reduce the proportion of fertilized plots by 8 percent. A 10 percent increase in the maize price would raise the proportion of fertilized plots by a similar amount. The coefficient on the price of cotton is the wrong sign and statistically significant. Part of the explanation is that there is very little variability in cotton prices. The positive and significant effect of agricultural wages on fertilizer use may reflect offfarm employment opportunities which make it easier for households to relieve the cash constraint to purchase fertilizer. Other things equal, small farms are somewhat more likely to use fertilizer than large ones, though the statistical significance is weak. This pattern may reflect greater labor use per hectare on small farms and/or a long-term tendency for farms to become smaller in more favorable areas. The probability of fertilizer use is higher on large plots than small, though the reasons for this are not obvious; there may be some fixed cost to applying fertilizer to a plot, making it less worthwhile for small plots. Zou (the excluded department) and Ouémé (dept5) are more likely to use fertilizer than the other four departments. Zou is the most accessible cotton-growing department, while Ouémé is close to Nigeria, the source of some smuggled fertilizer. The likelihood of fertilizing an irrigated plot is 27 percentage points greater than that of fertilizing an unirrigated plot. Not only is water a complement to fertilizer, but irrigated plots are more likely to be planted with high-value crops that provide a good return to fertilization. Crop mix is one of the strongest determinants of whether or not fertilizer is used on a given plot. Growing cotton on a plot raises the likelihood of applying fertilizer by 86 percentage points, making fertilization an almost certainty. Growing maize, rice, or vegetables also significantly increase the likelihood of using fertilizer, though to a lesser degree. The 57
omitted crops include sorghum, millet, cassava, sweet potatoes, and legumes that are less often fertilized. The interaction dummy variable for cotton growers and maize plots (ctg_mzp) is statistically significant and positive. This means that, other things equal, a cotton grower is much more likely to fertilize maize than a non-cotton grower. More specifically, being a cotton grower raises the probability of fertilizing a maize plot by 23 percentage points. This effect is probably linked to the availability of duty-free fertilizer on credit for cotton growers. Farmers buying maize seed are more likely to use fertilizer. This is expected since purchased seed is often of a high-yielding variety that responds better to fertilizer than do traditional varieties. On the other hand, the probability of using fertilizer is not affected by household composition, the sex of the head of household, education or literacy, or various measures of proximity. We now turn our attention to the determinants of the quantity of fertilizer used, conditional on fertilizer use. As shown in Table 9, fewer variables have a statistically significant relationship with the amount of fertilizer used compared to whether or not fertilizer is used. None of the labor, human capital, and market price variables are statistically significant at the 5 percent level. The determinants of the quantity of fertilizer used are as follows: Total fertilizer use increases with plot size but at a decreasing rate. In other words, the per hectare application rate declines with plot size. At the means, each additional hectare is associated with an additional 170 kilograms of fertilizer. Growing cotton on the plot does not increase the application rate and maize and rice cultivation are associated with lower application rates. Although crops other than cotton, maize, rice, and vegetables are not often fertilized, when they are, the application rates tend to be high. The interaction dummy indicating a cotton farmer growing maize has a negative and significant coefficient, suggesting that among maize fields that are fertilized, those of cotton farmers have lower application rates. One hypothesis is that this is the result of 58
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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
Page 14 and 15: 2.3 Evolution of Economic Policy 2.
Page 16 and 17: Under pressure from the World Bank
Page 18 and 19: anking system has been restored to
Page 20 and 21: Fertilizer can be imported outside
Page 22 and 23: particularly urban wage-earners. On
Page 24 and 25: Soulé (1996) describes the evoluti
Page 26 and 27: led to a more sustainable agricultu
Page 28: Table 2.4 - Gross farm revenue from
Page 31 and 32: Malawi’s economy is heavily depen
Page 33 and 34: the reduction of its operations in
Page 35 and 36: crops is also difficult because tob
Page 37 and 38: Table 3.1 CROP 1982/83 1983/84 1984
Page 40 and 41: CHAPTER 4: RESULTS OF THE BÉNIN SM
Page 42 and 43: where W v is the weight for a house
Page 44 and 45: heads. This suggests that some of t
Page 46 and 47: And school attendance is much highe
Page 48 and 49: Crop production is the main activit
Page 50 and 51: (33 percent). Livestock income is m
Page 52 and 53: Somewhat surprisingly, the relation
Page 54 and 55: It is no more common among richer h
Page 56 and 57: production, and women actually spen
Page 58 and 59: Seed use by crop Although only 18 p
Page 60 and 61: Looking at the results by departmen
Page 62 and 63: Ouémé presents an unusual case: j
Page 66 and 67: policies of SONAPRA to discourage
Page 68 and 69: crops are responsible for this incr
Page 70 and 71: Respondents were asked whether the
Page 72 and 73: Female-headed households, not surpr
Page 74 and 75: households are not considered credi
Page 76 and 77: In almost every category of agricul
Page 78 and 79: This discussion implies that farms
Page 80 and 81: eturns per hectare and labor-intens
Page 82 and 83: farm household and tends to be smal
Page 84 and 85: Surprisingly, the value of sales as
Page 86 and 87: Finally, proximity to an all-season
Page 88 and 89: Changes in crop marketing In this s
Page 90 and 91: Poor households are more likely to
Page 92 and 93: it is clear that virtually all grow
Page 94 and 95: production to total expenditure is
Page 96 and 97: a significant number of household (
Page 98 and 99: non-food spending. These are follow
Page 100 and 101: Larger farms are associated with hi
Page 102 and 103: This would not be surprising in lig
Page 104 and 105: Female-headed households appear to
Page 106 and 107: poorest expenditure category to jus
Page 108 and 109: headed household were more likely t
Page 110 and 111: Households in every expenditure cat
Page 112 and 113: 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
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With regard to cotton marketing, al
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Village leaders were asked to ident
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Marketing patterns Village leaders
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4.3: Results of the Bénin Survey o
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4.3.2 Survey methods At the time of
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officers have CM2 certificates. Thi
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Suppliers The GV representatives we
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Cotton area per GV ranges from 145
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The largest cost items are schools
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members and 10 percent of the membe
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Table 4.1.1-Description of sample o
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Table 4.1.7-Age, education, and lit
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Table 4.1.13-Ethnic background of h
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Table 4.1.19-Importance of each sou
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Table 4.1.25-Number of plots and ar
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Table 4.1.31-Land tenure by expendi
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Table 4.1.37-Change in labor alloca
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Table 4.1.42-Seed use by sex of hea
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Table 4.1.47-Perceived change in av
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Table 4.1.54-Share of producers of
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Table 4.1.59-Fertilizer use by farm
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Table 4.1.64-Characteristics of fer
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Table 4.1.69-Change in fertilizer a
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Table 4.1.75-Allocation of labor by
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Table 4.1.82-Allocation of labor by
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Table 4.1.89-Change in credit avail
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Table 4.1.94-Percent of households
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Table 4.1.102-Area allocation by cr
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Table 4.1.106-Area allocation by cr
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Table 4.1.110-Marketed share of out
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Table 4.1.114-Marketed share of out
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Table 4.1.117-Regression for market
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Table 4.1.120-Change in the type of
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Table 4.1.128-Perceived change in r
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Table 4.1.135-Main reason for stori
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Table 4.1.140-Summary of food expen
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Table 4.1.144-Food consumption by e
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Table 4.1.149-Results of regression
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Table 4.1.152-Value of household as
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Table 4.1.156-Value of household as
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Table 4.1.160-Housing characteristi
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Table 4.1.162-Housing characteristi
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Table 4.1.165-Main source of water
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Table 4.1.170-Source of information
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Table 4.1.176-Source of information
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Table 4.1.181-Regression for percei
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Table 4.2.5-Change in number of veh
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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
Page 344 and 345:
Strengthen the extension service by
Page 346 and 347:
The most time-consuming agricultura
Page 348 and 349:
Most common means of transport to t
Page 350 and 351:
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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