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Linking farm diversification to household diet diversification: evidence from a sample of Kenyan ultra-poor farmers

Food Security volume 8, pages 1069–1085 (2016)Cite this article

Abstract

Starting with a conceptual framework adapted from Herforth and Harris (2013), we analyzed the nexus between farm production diversification and household diet diversity using data collected in 2011 for evaluation of the welfare and economic impacts of Kenya’s Cash Transfer for Orphans and Vulnerable Children (CT-OVC). We used a sample of 1,353 households drawn from six districts of western Kenya to test the hypotheses that on-farm production diversification correlates with household diet diversification and some production activities have stronger association with diet diversification than others in the context of ultra-poor, labor constrained families living in rural Kenya. Approximately 67 % of the sample households received cash transfers through the CT-OVC programme. Production diversification was positively and significantly associated with household diet diversification, with livestock ownership more strongly correlated than crop production. Poultry production had the most compelling correlation, followed by pulses. In both cases, the association was most plausibly attributed to an income effect rather than production-for-own consumption. These findings suggest that supporting investments in diversified livelihood systems in general and in small livestock assets such as poultry, sheep and goats in particular are viable interventions for the improvement of household food security and nutrition for very poor, marginalized smallholders. Under semi-autarkic smallholder agriculture, a diversification strategy, which integrates crop and livestock production, not only adds value directly via increasing diet diversity and quality and indirectly via income effects but also serves as a risk management instrument, protecting against weather and market shocks, and contributing to biodiversity and sustainable land management.

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Fig. 1
Map 1

Notes

  1. Albeit without unpacking how those options may be exercised (i.e. food allocation issues were not addressed during data collection).

  2. Stunting in children aged 0 to 5 years is currently estimated to be 35.9 % in Kenya’s lowest income quintile, relative to 13.8 % in its highest. Wasting prevalence is estimated to be 7.3 % in the lowest income quintile, relative to 2.5 % in the highest (KDHS 2014).

  3. In this paper we refer to ultra-poor households as those which i) were in the bottom 20 % of the consumption expenditure distribution, and ii) which fit CT-OVC targeting criteria, namely: presence of orphans and vulnerable children, low education level, poor dwelling quality, limited access to safe water, limited sources of income, and low asset ownership. The resulting demographic profile of beneficiaries proxies for families affected by HIV/AIDS. For more details on the CT-OVC programme’s targeting criteria and definition of “ultra-poor households”, see Handa et al. (2012).

  4. For more details on CT-OVC sample attrition see Handa et al. (2014) and Handa et al. (2015).

  5. Food was predominantly purchased or obtained from home production while the incidence of food received as gifts or eaten out was negligible standing below 5 %.

  6. Constructing food expenditure shares using different sources is a well-grounded technique in economic analysis for estimating consumption aggregates and evaluating, for example, poverty incidence at country level (see among others Deaton and Zaidi 2002). All food expenditure estimates reported below are in per capita terms, constructed based on number of adults and children residing in the household. For this analysis, a regional price deflator was also constructed to allow for comparison in consumption expenditure across different districts (Deaton and Zaidi 2002). We took this precaution as food prices in Kenya are markedly different across regions due to variation in market development and distance to port or surplus producing areas.

  7. To give a simple numeric example, if household “A” and household “B″ display a consumption expenditure made only by meat and cereals and if the first household consumed 20 % of meat and 80 % of cereals whereas the second consumed 40 % of meat and 60 % of cereals, the Simpson score will be greater for household “B″ relative to household “A” since the expenditure shares are more equally distributed with respect to the total food expenditure. The same holds for the Shannon index. The Shannon index works in a similar manner, yet, the difference in the two indices stem from the fact that the Simpson index squares the food shares, thus the weight of foods with smaller expenditure shares will be reduced relatively more than that with greater food shares. On the contrary, the Shannon index includes a “log” to the expenditure shares of foods so that weight of foods with greater shares will be reduced slightly relative to foods with smaller food shares.

  8. To avoid losing explanatory power in the multivariate regression analysis, we included only those crops harvested and livestock species reared which comprised 5 % or more of total farm output.

  9. While counting the number of income sources can capture income diversification; it does not automatically imply that households with more off-farm income sources have higher income levels relative to families engaged in fewer off-farm activities.

  10. This variable did not include participation in CT-OVC as this was controlled for separately.

  11. Note that quintiles of consumption expenditure were calculated based on the consumption data we extracted from the CT-OVC sample rather than ranking households based on quintiles of consumption expenditure obtained from a nationally representative household survey.

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Acknowledgments

Thanks go to FAO’s Protection to Production (PtoP) team for sharing data from the 2011 CT-OVC survey, conducted by the Carolina Population Center, UNC, and funded by the US National Institute of Mental Health (Grant Number 1R01MH093241), and by Eunice Kennedy Shriver National Institute of Child Health and Development (Grant Number R24 HD050924). The views expressed in this information product are those of the authors and do not necessarily reflect the views or policies of FAO while errors are solely responsibility of the authors.

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Authors and Affiliations

  1. Agricultural Development Economics Division, Food and Agriculture Organization of the United Nations, Viale delle Terme di Caracalla, 00153, Rome, Italy

    Alessandro Romeo, Janice Meerman, Mulat Demeke, Antonio Scognamillo & Solomon Asfaw

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  1. Alessandro Romeo
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  2. Janice Meerman
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  5. Solomon Asfaw
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Correspondence to Alessandro Romeo.

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Romeo, A., Meerman, J., Demeke, M. et al. Linking farm diversification to household diet diversification: evidence from a sample of Kenyan ultra-poor farmers. Food Sec. 8, 1069–1085 (2016). https://doi.org/10.1007/s12571-016-0617-3

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Keywords

  • Food security
  • Household diet diversification
  • Farm diversification
  • Social protection

JEL Classification

  • F63
  • D13
  • C50

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