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

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Nzoia District alone accounts for approximately 40 percent <strong>of</strong> the total marketed maize<br />

production <strong>in</strong> the country (Nyangito and Ndirangu 1997). <strong>The</strong> district has both large-scale<br />

and small-scale producers who grow maize for commercial purposes. <strong>The</strong> latter are typical <strong>of</strong><br />

most commercial small-scale producers <strong>in</strong> the country, who use low levels <strong>of</strong> commercial<br />

<strong>in</strong>puts and produce for both subsistence and commercial purposes. In view <strong>of</strong> the dom<strong>in</strong>ant<br />

role that Trans Nzoia plays as a maize producer <strong>in</strong> the country and its impact on the national<br />

maize market, we would expect producers and traders <strong>in</strong> the district to have developed a good<br />

<strong>in</strong>formation network on the function<strong>in</strong>g <strong>of</strong> the maize market. We therefore chose the district<br />

to represent the major surplus areas.<br />

In Migori District <strong>in</strong> Nyanza Prov<strong>in</strong>ce small-scale farmers grow maize both as a food and as a<br />

cash crop. In contrast to Trans Nzoia, farmers and traders <strong>in</strong> Migori would be expected to<br />

have a less-developed market <strong>in</strong>formation network s<strong>in</strong>ce the district accounts for a small<br />

proportion <strong>of</strong> the marketed surplus <strong>in</strong> the country. It was chosen to represent the m<strong>in</strong>or<br />

surplus areas.<br />

Mbeere District <strong>in</strong> the <strong>Eastern</strong> Prov<strong>in</strong>ce is a typical rural district <strong>in</strong> Kenya that is poorly<br />

served <strong>in</strong>ternally <strong>in</strong> terms <strong>of</strong> <strong>in</strong>frastructure. Consequently, market <strong>in</strong>formation flow <strong>in</strong> the<br />

district is expected to be <strong>in</strong>adequate. It produces relatively little maize, and, because <strong>of</strong> its<br />

relatively low population, it is not a major consumption area. <strong>The</strong> district was chosen to<br />

represent the m<strong>in</strong>or deficit regions.<br />

Nairobi is an extra prov<strong>in</strong>cial urban district and the major maize-consum<strong>in</strong>g area <strong>in</strong> the<br />

country. <strong>The</strong> district, with approximately 7.5 percent <strong>of</strong> the country’s population, consumes<br />

about 8 percent <strong>of</strong> the national maize <strong>in</strong>take (CBS 2000; M<strong>in</strong>istry <strong>of</strong> Agriculture 1999).<br />

Nairobi is the ma<strong>in</strong> commercial center and seat <strong>of</strong> government and the <strong>in</strong>frastructure (such as<br />

roads and telecommunications) is fairly well developed <strong>in</strong> contrast to the rural districts <strong>of</strong><br />

Kenya. As a result market <strong>in</strong>formation flow is expected to be quite good. Nairobi was chosen<br />

to represent the major deficit areas.<br />

A sampl<strong>in</strong>g design for respondents was developed on the basis <strong>of</strong> the number <strong>of</strong> market sites<br />

<strong>in</strong> each district, the number and k<strong>in</strong>d <strong>of</strong> traders <strong>in</strong> each market site, the volume <strong>of</strong> maize<br />

handled by <strong>in</strong>dividual traders, and other relevant characteristics <strong>of</strong> the markets. Information<br />

on these market characteristics was generated dur<strong>in</strong>g a reconnaissance survey. Sixty traders<br />

<strong>in</strong> each <strong>of</strong> the four selected districts were sampled for <strong>in</strong>terviews, giv<strong>in</strong>g a total sample <strong>of</strong> 240<br />

traders. Data from 234 respondents are used for the analysis. <strong>The</strong> other six questionnaires<br />

provided <strong>in</strong>complete <strong>in</strong>formation.<br />

2.2 Sources <strong>of</strong> Data<br />

Secondary data on maize production and consumption were obta<strong>in</strong>ed from the M<strong>in</strong>istry <strong>of</strong><br />

Agriculture. <strong>The</strong> m<strong>in</strong>istry’s method <strong>of</strong> calculat<strong>in</strong>g maize consumption was used to calculate<br />

consumption <strong>of</strong> maize <strong>in</strong> the various districts <strong>of</strong> Kenya (M<strong>in</strong>istry <strong>of</strong> Agriculture 1999). <strong>The</strong><br />

method assumes that each urban and rural consumer respectively consumes 98 and 117<br />

kilograms <strong>of</strong> maize annually. Multiply<strong>in</strong>g the estimated per capita consumption by the<br />

population <strong>in</strong> a district gives an estimate <strong>of</strong> total consumption.<br />

Maize price data were obta<strong>in</strong>ed from various sources <strong>in</strong>clud<strong>in</strong>g the Central Bureau <strong>of</strong><br />

Statistics, M<strong>in</strong>istry <strong>of</strong> Agriculture reports, the NCPB, and other published sources. Most <strong>of</strong><br />

the price data collected could not be used, as many <strong>of</strong> the series were <strong>in</strong>complete. Attempts to<br />

comb<strong>in</strong>e different sources were plagued by <strong>in</strong>consistencies <strong>in</strong> methods <strong>of</strong> data collection<br />

among the various agencies. No s<strong>in</strong>gle source yielded a usable time series. A small number <strong>of</strong>

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