Baseline Study of Striga Control using IR Maize in Western Kenya

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Chapter 5Livelihood Strategies and Outcomes5.1 Livelihood strategies: Rural non-farm activitiesFarmers diversify their livelihoods by engaging in non-farm activities. This appears to be oneof the most common strategies to hedge risk and reduce the negative impacts of shocks,trends and seasonality in agricultural production. Respondents in western Kenya wereinvolved in about 15 non-farm income activities for both male and female headed households(Table 5.1). A high proportion of households were found engaged in petty trading, followedby agricultural wage employment, and non-agricultural wage employment. Other non-farmincome generating activities that were of importance are selling fuel wood and charcoal,handicraft, food for work, selling prepared food/drinks and professional work. All age groupswere involved in non-farm activities: adult males, adult females and youths. The number ofhouseholds involved in these activities was higher in Nyanza province compared to Westernprovince.Table 5.1. Involvement of household members in non-farm income sources (% ofrespondents)Male Headed HouseholdsFemale Headed HouseholdsAll(594)Nyanza(289)Western(305)All(208)Nyanza(113)Western(95)Honey production 1.0 1.7 0.3 0 0 0Agricultural wage employment 26.2 32.9 19.7 32.7 38.9 25.3Non-agricultural wage employment 27.4 28.7 26.2 18.8 21.2 15.8Food for work 10.4 13.5 7.5 17.8 17.7 17.9Petty trading 39.8 49.1 31.1 38.5 44.2 31.6Handicraft 10.4 18.7 2.6 11.5 21.2 0Transport service 5.0 4.2 5.9 1.0 0 2.1Grain mills 1.2 0.3 2.0 1.0 0.9 1.1Fishing 1.0 1.7 0.3 0 0 0Hunting and gathering of wild food 1.3 1.7 1.0 1.0 0.9 1.1Selling fuel wood and charcoal 14.1 20.8 7.9 17.8 26.5 7.4Selling prepared food/drinks 7.9 13.1 3.0 10.1 16.8 2.1Professional work 18.5 17.0 20.0 5.3 5.3 5.3Traditional medicine 1.7 1.7 1.6 2.4 4.4 0Rent income 2.9 2.8 3.0 0.5 0 1.1Figures in brackets indicate number of respondents5.2 Household income and povertyThe annual income was calculated at an average of Ksh 52,678 per household, with theincome in Western a bit higher than that in Nyanza (Table 5.2). Off-farm income was themajor source of income, followed by farm income. Maize income represented about 27% ofcash income and about 48% of value of gross income from crop production, confirming theimportance of maize not only as a food crop but also as a cash crop in western Kenya. Theproportion of maize income in the farm income was higher in Nyanza compared to Western.The per capita household income corresponded to about US$ 0.34/day for Nyanza and US$0.36/day for Western, which is far below the World Bank poverty line of US$ 1/day/personused in international poverty comparisons.32
Table 5.2. Household income in both seasonsAll Nyanza WesternN 802 402 400Farm income (Ksh) 17,389.8 9,337.7 25,482.3Remittances (Ksh) 1,968.9 2,492.9 1,442.3Off-farm income (Ksh) 33,319 36,056 30,569.5Total household income (Ksh) 52,678 47,887 57,494Per capita household income (Ksh) 9,082 8,706 9,273.2Per capita per day household income (US$) 0.355 0.340 0.358N = Number of respondentsFollowing World Bank’s approach to local poverty assessment (for example World Bank,1996), total household income, which is composed of farm income, off-farm income andtransfer incomes, was used to set a relative poverty line equivalent to two-thirds of the meanper capita income (Ksh 18/day/person). The results are presented in Figure 5.1 and they showthat about 58% of the sample farmers are poor. Poverty is slightly higher in Western (60%)than in Nyanza (57%) and is more pervasive among female-headed households (64%) thanmale-headed households (57%). The analysis of poverty by district is even more revealingthan that by province or by household headship. Poverty differentials across provinces mask alot of variation across districts, even within the same province. The level of poverty variesfrom 48% in Teso to 82% in Vihiga district, all in Western province. Bondo has the secondhighest (69%) prevalence of poverty, whereas Siaya has the second lowest (49%), all inNyanza province. The results confirm how inappropriate it would be to make generalisationsabout poverty based on broader geographical classifications. This is because poverty is aresult of complex socio-economic, agro-ecological, and institutional and policy variables.Figure 5.1. Poverty by province and by gender7060585760576450% Poor403020100AllNyanzaProvinceWesternMale headedGenderFemale headed5.3 Determinants and correlates of povertyA logistic regression model of determinants of poverty was estimated to examine thedeterminants and correlates of poverty. The logit maximum likelihood estimates and themarginal effects are presented in Table 5.3. The model fits the data well as indicated by thehighly significant chi-squared model test statistics (Hosmer-Lemeshow and Likelihood Ratio)and 74% correct prediction. All the coefficients of the variables have the expected signs, as it33
Page 5 and 6: Acronyms and AbbreviationsAATF Afri
Page 7: control technologies. Promoting bot
Page 10 and 11: Figure 1.1. GIS maps showing sample
Page 12 and 13: ln(maizeoutput ) = β + β ln(Land
Page 15 and 16: Chapter 2Socioeconomic Characterist
Page 17 and 18: Figure 2.2. Major forms of financia
Page 19 and 20: Another interesting parameter on hu
Page 21 and 22: Chapter 3Crop Production and Produc
Page 23 and 24: Table 3.3. Seed use in NyanzaLong r
Page 25 and 26: Table 3.5. Crop productivity (kg/ha
Page 27 and 28: Chapter 4Striga and Striga Control
Page 29 and 30: N 402 400Uprooting (%) 82.3 86.0Bur
Page 31: of seed (Table 4.9). For the sustai
Page 35 and 36: Table 5.3. Logit model estimates of
Page 37 and 38: household members were used for thi
Page 39 and 40: nutritional status of children are
Page 41 and 42: Chapter 6Empirical Analysis of Maiz
Page 43 and 44: Land × Labour β 12-0.011 -0.385La
Page 45 and 46: 50-75 45 53 54 5475-100 29 18 25 24
Page 47 and 48: Table 7.1. Types of development pro
Page 49 and 50: Chapter 8Conclusions and Recommenda
Page 51 and 52: promise for unlocking the maize pro
Page 53: Ray SC. 1985. Measurement and test

Baseline Study of Striga Control using IR Maize in Western Kenya

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