Subsistence production, markets, and dietary diversity in the Kenyan small farm sector

Muthini, Davis; Nzuma, Jonathan M.; Qaim, Matin

doi:10.1016/j.foodpol.2020.101956

Cited by 40 publications

(38 citation statements)

References 33 publications

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“…Crop diversification was associated with a higher proportion of food consumed from own harvest (Fig. 2 (Panels B and D, 'a' path)), a finding aligned with other studies (35,65) . One study in Kenya also found that production of an additional crop was associated with a lower diversity of purchased foods while still associated with a higher diversity of own-produced foods (65) .…”

Section: Mechanisms Underlying the Association Of Crop Diversity With Child Diet Diversitysupporting

confidence: 87%

Crop diversity is associated with higher child diet diversity in Ethiopia, particularly among low-income households, but not in Vietnam

Esaryk

Reynolds

Fernald

et al. 2021

Public Health Nutr.

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Objectives: To examine associations of household crop diversity with school-aged child dietary diversity in Vietnam and Ethiopia and mechanisms underlying these associations. Design: We created a child diet diversity score (DDS) using data on seven food groups consumed in the last 24 h. Generalised estimating equations were used to model associations of household-level crop diversity, measured as a count of crop species richness (CSR) and of plant crop nutritional functional richness (CNFR), with DDS. We examined effect modification by household wealth and subsistence orientation, and mediation by the farm’s market orientation. Setting: Two survey years of longitudinal data from the Young Lives cohort. Participants: Children (aged 5 years in 2006 and 8 years in 2009) from rural farming households in Ethiopia (n 1012) and Vietnam (n 1083). Results: There was a small, positive association between household CNFR and DDS in Ethiopia (CNFR–DDS, β = 0·13; (95 % CI 0·07, 0·19)), but not in Vietnam. Associations of crop diversity and child diet diversity were strongest among poor households in Ethiopia and among subsistence-oriented households in Vietnam. Agricultural earnings positively mediated the crop diversity–diet diversity association in Ethiopia. Discussion: Children from households that are poorer and those that rely more on their own agricultural production for food may benefit most from increased crop diversity.

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Section: Mechanisms Underlying the Association Of Crop Diversity With Child Diet Diversitysupporting

confidence: 87%

Crop diversity is associated with higher child diet diversity in Ethiopia, particularly among low-income households, but not in Vietnam

Esaryk

Reynolds

Fernald

et al. 2021

Public Health Nutr.

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“…The area of land to be cultivated is an important factor for dietary diversity, however, and households that own more agricultural land tend to enjoy higher dietary diversity [ 5 , 56 ]. Access to different foods is a good technique to estimate the diversity of the diets of rural households, taking into account that each food has its own importance among all food groups, including staple foods [ 56 , 57 ]. It is crucial for households to not only consume adequate amounts of food but also safe and diversified foods.…”

Section: Discussionmentioning

confidence: 99%

Factors That Determine the Dietary Diversity Score in Rural Households: The Case of the Paute River Basin of Azuay Province, Ecuador

Cordero-Ahiman

Vanegas

Franco-Crespo

et al. 2021

IJERPH

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Inadequate food and nutrition affect human well-being, particularly for many poor subpopulations living in rural areas. The purpose of this research was to analyze the factors that determine the Household Dietary Diversity Score (HDDS) in the rural area of the Paute River Basin, Azuay Province, Ecuador. The sample size of 383 surveys was determined by a stratified random sampling method with proportional affixation. Dietary diversity was measured through the HDDS, with 12 food groups (cereals; roots and tubers; fruits; sugar/honey; meat and eggs; legumes or grains; vegetables; oils/fats; milk and dairy products; meats; miscellaneous; fish and shellfish) over a recall period of 7 days. A Poisson regression model was used to determine the relationship between the HDDS and sociodemographic variables. The results show that the average HDDS of food consumption is 10.89 foods. Of the analyzed food groups, the most consumed are cereals; roots and tubers; fruits; sugar/honey. In addition, the determinants that best explain the HDDS in the predictive model were housing size, household size, per capita food expenditure, area of cultivated land, level of education, and marital status of the head of household. The tools used in this research can be used to analyze food and nutrition security interventions. Furthermore, the results allow policymakers to identify applicable public policies in the fight against hunger.

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“…These are the challenges to quantify the impact of agricultural policies with more completeness to the FS&N analysis (Qureshi et al, 2015). Even when assessments of agricultural interventions on nutrition outcomes highlight a scarce evidence to advise on the prioritisation among competing nutrition-sensitive agricultural policies (Masset et al, 2012), literature results point out that improving commercialisation channels (e.g., transport infra-structureAtack et al, 2009;Donaldson & Hornbeck, 2016;Zeller et al, 1998) could lead to positive impact on farmers' income and food access (Chege et al, 2015), on nutrition (Carletto et al, 2017) and on diet diversity (Koppmair et al, 2017), enhancing access to higher-value nutritious foods, such as fruits, vegetables, and animal-source products, which are more perishable than staple foods (Muthini et al, 2020). Improving food access with region-specific public policies could tackle nutritional deficits locally avoiding intensifying elsewhere (Desiere et al, 2018;Ecker et al, 2010).…”

Section: Modelling For Fsandn-sensitive Policy Evaluationmentioning

confidence: 99%

Do agri-food market incentives improve food security and nutrition indicators? a microsimulation evaluation for Kenya

et al. 2021

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The sustainable development goal #2 aims at ending hunger and malnutrition by 2030. Given the numbers of food insecure and malnourished people on the rise, the heterogeneity of nutritional statuses and needs, and the even worse context of COVID-19 pandemic, this has become an urgent challenge for food-related policies. This paper provides a comprehensive microsimulation approach to evaluate economic policies on food access, sufficiency (energy) and adequacy (protein, fat, carbohydrate) at household level. The improvement in market access conditions in Kenya is simulated as an application case of this method, using original insights from households’ surveys and biochemical and nutritional information by food item. Simulation’s results suggest that improving market access increases food purchasing power overall the country, with a pro-poor impact in rural areas. The daily energy consumption per capita and macronutrients intakes per capita increase at the national level, being the households with at least one stunted child under 5 years old, and poor households living areas outside Mombasa and Nairobi, those which benefit the most. The developed method and its Kenya's application contribute to the discussion on how to evaluate nutrition-sensitive policies, and how to cover most households suffering food insecurity and nutrition deficiencies in any given country.

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Subsistence production, markets, and dietary diversity in the Kenyan small farm sector

Cited by 40 publications

References 33 publications

Crop diversity is associated with higher child diet diversity in Ethiopia, particularly among low-income households, but not in Vietnam

Crop diversity is associated with higher child diet diversity in Ethiopia, particularly among low-income households, but not in Vietnam

Factors That Determine the Dietary Diversity Score in Rural Households: The Case of the Paute River Basin of Azuay Province, Ecuador

Do agri-food market incentives improve food security and nutrition indicators? a microsimulation evaluation for Kenya

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