A typical household in Eastern Province
The baseline data describes the average household (see table A, annexe p.185, row 1 and 3) with the following characteristics: The household has 5.76 members, is headed by a male, and adults are in a monogamous relationship (75 %). Nearly all households (99 %) have access to land (data on the size of the land was not collected). 1 out of 3 households practises horticultural cultivation. Income generation is based on agriculture. The predominant activity is the marketing of crops (92 %), other activities include occasional labour (37 %) and small business activities (37 %). The sale of animal products (15 %) is rare. Remittances do not play a role (1 %). Data shows that the average age of the mother is 27 years and that she went to school for 3.5 years. Every second mother receives support for childcare (47
%) and a vast majority received nutrition counselling through Health Surveillance Assistance (HAS) and volunteer groups (63 %).
In contrast, the regression analysis shows that a typical undernourished household has more members and that the mother received fewer years of schooling.
Interestingly, the age of the mother plays an ambivalent role: with advancing age of the mother, the likelihood of a diverse diet decreases whereas the food security of her household increases. In terms of income sources, households that do not meet WMDD (Minimum dietary diversity women) and households that are moderately or severely food insecure (HFIES) earn less income from small businesses and depend more on irregular (temporarily) income. Statistically, the typical undernourished household has limited access to nutrition counselling.
Surprisingly, the quantitative results do not show statistically significant differences in the production quantity of crops and vegetables of typical well-nourished and typical underwell-nourished households. This indicates that well- nourished households complement their diet by purchasing additional food items, suggesting that financial resources and nutrition counselling influence food consumption choices.
Interrelations between food and nutrition security indicators
The indicators of undernourishment are statistically interrelated (see Box 5)25. Unsurprisingly, the proportion of children who receive the Minimum Acceptable Diet (MAD) is more than double in the group of women with sufficiently diverse diets than in the group of women with insufficiently diverse diets. The same relation holds true for the frequency of meals a child receives. However, these indicators are not sufficient to describe a typical undernourished household and their interrelation should not be mistaken for assigning causality. So, to combine all those explaining factors and to come closer to assigning causality, regression methods are used to analyse which factors contribute to undernourishment.
Box 5: Indicators used in the baseline studies
Relevant indicators: The baseline studies measured Food insecurity and dietary diversity by three indicators: Dietary Diversity Score (IDDS) consists of seven food groups for children (6-23 months; IDDS-CH) and ten food groups for women (IDDS-W). Minimum Dietary Diversity (MDD) is defined as the minimum intake of four food groups by children (MDD) and five food groups by women (MDD-W)(Kennedy et al., 2017). Minimum Meal Frequency (MMF) refers to meal frequency of children depending on age (WHO 2008).The FIES-H is a statistical measurement scale to measure the observed severity of food insecurity at the household level (FAO 2015).
Source: FAO, 2015; WHO, 2008.
Factors associated with food and nutrition security
The multiple regression analysis (annexe p. 188) uses the explanatory variables to analyse the determinants of IDDS-CH (row 1 and 2), MMF, MAD (row 3 and 4) and IDDS-W (row 5 and 6). Table 14 summarizes associated factors for individual indicators regarding food and nutrition security of women and children.
25 Referring to MAD, WMDD, HFIES. This means those who meet the criteria of undernourishment in one definition usually meet the criteria of the others.
Table 14: Associated factors regarding FNS of women and children
Dependent variables Statistically significant independent variables IDDS-CH (regression
includes HFIES)
Breastfeeding of children (--), children’s age (++), nutrition counselling (HSA and volunteer group ++), number of clinic visits for children under 5 (++), HFIES (-)
MMF Breastfeeding of children (++), income of business / petty trade (+), nutrition counselling from volunteer group (+)
MAD Breastfeeding of children (++), income of business / petty trade (+), nutrition counselling (HSA and volunteer group; ++), children´s age (++), care support by older sibling (--) IDDS-W (regression
includes HFIES)
Katete district (-), income from the sale of animal products (+), income of business / petty trade (+), number of household members (-), crop diversity (+), access to HSA (++), level of education (++), HFIES (--)
Source: Results based on statistical analysis.
Positive and large positive associations are indicated by (“+” for 90 % - or more - level of significance) and (“++” for 95 % - or more - level of significance), respectively. Negative and large negative associations are indicated by (-) and (- -), respectively.
Dietary diversity of children
The regression analysis indicates that nutrition counselling has a positive effect on dietary diversity of women and children in typical undernourished households:
nutrition counselling received through Health Surveillance Assistance (HAS, 53,25 %) and through volunteer groups (11,5 %) are statistically effective instruments for increasing the consumption of diverse foods.
In addition, the statistics show that clinic visits for children under 5 increase the likelihood of children eating a diverse diet. This highlights the importance of governmental nutrition counselling, particularly for undernourished households whose children are susceptible to stunting. Earning income (i.e. petty trade, small business activity) also increases the dietary diversity in typical undernourished households, and is positively associated with MAD, MMF, and IDDS-W.
The positive association of children’s dietary diversity (IDDS-CH) and children’s age, however, illustrates the poor quality of infants’ diet (6–11 months). Despite their need for diverse food intake, infants received a lower diversity of foods than
older children. This confirms trends observed in other African countries (Issaka et al., 2015b; Melkam et al., 2013; Mitchodigni et al., 2017). The higher dietary diversity among older children could be attributed to the fact that children are the main collectors of wild food items in Eastern Province (Mofya-Mukuka, 2015).
Socio-demographic determinants such as sex of household head, marital status, age of the respondent and production diversity show no correlation with the dietary diversity of children.
Minimum Acceptable Diet and Minimum Meal Frequency
Business income, nutrition counselling and care support are positively associated with complementary feeding practices. Caregiving by older siblings negatively affects the minimum meal frequency needed to meet a child’s nutritional requirements. This might be related to the high work burden of their primary caretaker. Mitchodigni et al. (2017) found that caregivers’ occupations are one of the main factors affecting complementary feeding practices, especially when it comes to children’s minimum meal frequency. Furthermore, secondary care takers such as siblings may have limited knowledge on feeding practices and the dietary needs of infants (Mitchodigni et al., 2017).
Dietary diversity of women associated with income generation
The regression analysis (see p. 185, table Z2) shows differences in income between undernourished and non-undernourished households, indicating that dietary decisions are influenced by a household’s annual income. The sale of animal products and petty trade are associated with higher dietary diversity among children and women, highlighting the importance of additional income sources.
This equally underscores the importance of encouraging women to diversify their income sources (i.e. by diversifying agricultural production, food processing, off-farm income activities) to diversify the diet of the household. However, time poverty might constrain women’s earning opportunities.
The regression also shows that more years of schooling have a positive effect on women´s dietary outcomes. Furthermore, nutrition counselling from Health Surveillance Assistance (HSA) significantly influences the dietary diversity of both women and children. While other studies have found a link between agricultural production and dietary diversity (Mitchodigni et al., 2017), the regressions only show a robust correlation between crop variety and dietary outcomes of women but not of children. In addition, the statistics show that horticultural production does not increase the likelihood of higher dietary diversity among women and
children. This indicates that households that engage in gardening do not consume their produce and might sell it instead.
In the following, the qualitative research results will shed light on the influence of the diversification of agricultural production on the dietary practices of women and small children.