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Socio-economic baseline survey data of 20 villages in Kitui district, Kenya in 2009

Harvard Dataverse (Africa Rice Center, Bioversity International, CCAFS, CIAT, IFPRI, IRRI and WorldFish)

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Title Socio-economic baseline survey data of 20 villages in Kitui district, Kenya in 2009
 
Identifier https://doi.org/10.7910/DVN/94JTZY
 
Creator Morimoto, Yasuyuki
Maundu, Patrick
Imbumi, Maryam
Kariuki, Lucy
Tumbo, Dominic
 
Publisher Harvard Dataverse
 
Description This is a baseline survey data that was collected within the project ‘Managing agricultural biodiversity for better nutrition and health, improved livelihoods and more sustainable production systems in sub-Saharan Africa’ implemented by Bioversity International. The survey was conducted in Kitui District in Eastern Province of Kenya between 14 July and 5 August 2009.
The area was chosen for best testing the research hypothesis: In rural farming communities there exists a relationship between access to local agrobiodiversity (domesticated, weedy and immediate wild) and dietary diversity which in turn has some link to nutritional and health status. Factors considered while choosing the area included: i) differential access to agrobiodiverisity among different communities living in close proximity, ii) easy access to the site, iii) high diversity of traditional foods and iv) richness in local knowledge.
A structured questionnaire was formulated with following 13 sections , A) Demographic, B) Farm charactaeristics, C) Crop/Fruits diversity, D) Farming systems and activities, E) Personal preferences, nutrition knowledge, F) Labour profile, G) Livestock production, H) Asset holding, I) Cost of hired implements/machines, J) Housing, K) Income, L) Marketing constraints, and M) Institutional variables. Through individual interview at the household visit, the questionnaire was administered to 295 households at 14-15 homesteads per village. The average survey time per household was 1 hour and 15 munitus. Five (5) local enumerators were recruited from the 20 villages with the help of local leaders and provided for the training.
These 295 households representing in total of 20 villages, 148 households in 10 villages in intervention (treatment) area, 147 households in the other 10 villages in none-intervention (control) area, were selected from a group of 60 villages which had been subjected to a clustering process based on soils, vegetation, agroecology, altitude and number of crop growing days per year, forming 6 clusters. From each of the 6 cluster groups (A-F), 1 or 2 target villages were selected randomly such that for each treatment village, there was a corresponding control village with similar characteristics (File 13: Project Area). Each village had 150-200 households. The households are defined as members of a family served from the same pot. The selected households have children under the age of 5 years and above 6 months which were randomly selected for study in each village. In order to establish a cause and effect relationship between the Agrobiodiversity, dietary diversity and nutrition/health, the research design combines two methods: 1) A“cross-sectional research design’ that surveys status only once and 2) a control group design where there is a control and treatment group (File 14: Research design).
 
Subject Agricultural Sciences
Social Sciences
AGRICULTURAL DEVELOPMENT
AGRICULTURAL EXTENSION
AGRICULTURAL LANDSCAPE
AGRICULTURAL RESEARCH
AGROBIODIVERSITY
BIODIVERSITY
BIODIVERSITY CONSERVATION
BODY MEASUREMENTS
CITIZEN PARTICIPATION
COMMUNITY DEVELOPMENT
DATA
DATABASES
DEVELOPMENT PROJECTS
DIET
EAST AFRICA
EVALUATION
FARM EQUIPMENT
FARM SIZE
FOOD SECURITY
HUMAN NUTRITION
KENYA
NUTRITION
RESEARCH
RESEARCH METHODS
SOCIAL INDICATORS
SOCIAL SCIENCES
SOCIOCULTURAL SYSTEMS
Africa
Food Environment and Consumer Behavior
CGIAR Research Program on Agriculture for Nutrition and Health
 
Date 2020-09-08
 
Contributor Garcia, Carolina
Morimoto, Yasuyuki
Kasoso, Alexander T.
Musyoka, Benedict
Mutembei, Damaris M.
Nyamu, Elizabeth
Benjamin, Erastus M.
 
Type Survey Data
Socio-economic Data
Household Survey Data