If They Grow It, Will They Eat and Grow? Evidence from Zambia on Agricultural Diversity and Child Undernutrition

Abstract

In this article we address a gap in our understanding of how household agricultural production diversity affects the diets and nutrition of young children living in rural farming communities in sub-Saharan Africa. The specific objectives of this article are to assess: (1) the association between household agricultural production diversity and child dietary diversity; and (2) the association between household agricultural production diversity and child nutritional status. We use household survey data collected from 3,040 households as part of the Realigning Agriculture for Improved Nutrition (RAIN) intervention in Zambia. The data indicate low agricultural diversity, low dietary diversity and high levels of chronic malnutrition overall in this area. We find a strong positive association between production diversity and dietary diversity among younger children aged 6–23 months, and significant positive associations between production diversity and height for age Z-scores and stunting among older children aged 24–59 months.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Acknowledgements

This research was supported by the CGIAR Research Program on Agriculture for Nutrition and Health (A4NH) led by IFPRI, and Concern Worldwide through the Realigning Agriculture to Improve Nutrition (RAIN) project. The RAIN project receives funding from Irish Aid and the Kerry Group. We thank Marie Ruel, Derek Headey, John Hoddinott, Benjamin Davis and two anonymous referees for helpful comments. All errors and omissions are our own.

Notes

1. Gold-standard nutritional methods of measuring dietary diversity such as weighed food intakes or 24-hour food recalls are complex and time-consuming, and are not appropriate for all research and programming contexts. Nor are they always necessary: Simpler dietary diversity scores, using a count of foods or food groups consumed by an individual over a reference period (usually 24 hours), have been constructed and validated for these contexts. Individual dietary diversity scores (World Health Organisation, 2010) predict diet quality in terms of micronutrient content, having been shown to be strongly and consistently associated with the micronutrient density of the diet in children under 2, in different populations and contexts, and independent of socio-economic and demographic factors (Ruel et al., 2014).

2. Agricultural production diversity was not collected with the diversity score in mind; this was therefore constructed from recalled production data at plot level, aggregated to household level. Foods produced were assigned to a group based on their known physical and nutritional properties corresponding to the different food groups available. One crop may be assigned to more than one food group if it has multiple edible parts (for instance where both tubers and leaves are known to be consumed). It is possible that some vegetable crops were miscategorised due to the nutrient content of the exact variety grown; however, production diversity was so low in general within the study area, that this is unlikely to have affected results.

3. When log of agricultural income is included in the regression of dietary diversity on production diversity, the coefficients production diversity variables remain similar in magnitude and significance as reported here, and the coefficient on log of agricultural income is much smaller in magnitude as compared with the coefficient on production diversity. This shows that even when we include measures of production diversity and agricultural income in the same regression we find that the correlation of the production diversity measure with dietary diversity is much stronger, reflecting that it is not the greater income that may be associated with greater production diversity that is causing the greater dietary diversity, but the greater production diversity itself which is driving this.

4. Results from quantile regressions not reported here. They can be provided upon request by the authors.

5. Results from quantile regressions not reported here. They can be provided upon request by the authors.

6. The maximum production diversity at the cluster level could be correlated with the household’s production diversity due to agro-climatic reasons, as well as if neighbours imitate each other.

7. Results not reported here. They can be provided upon request by the authors.

8. We find that the point estimates are slightly smaller and standard error unchanged. As a result some estimates lose significance.