Dataset file can be found in the metadata below under "Attachments". Feed the Future’s Africa Lead II project partnered with The Mediae Company, a Kenya-based media education company, to develop a pilot season of Africa’s first agriculture-focused reality TV program: Don’t Lose the Plot (DLTP). Targeting youth in Kenya and Tanzania, the show aired in Kenya and Tanzania between May and July 2017. The program’s objectives were to encourage youth to consider farming as a lucrative career choice, provide information on how to start agribusinesses, and share useful agronomic information. Africa Lead commissioned Kantar Public East Africa to evaluate the impact of DLTP on knowledge, attitudes, and behavior, or intention to change behavior, related to farming and agribusiness practices. This data asset includes quantitative data collected through a cross-sectional household survey in Kenya and Tanzania. Data collection took place between August and December 2017 and targeted both viewers and non-viewers of DLTP aged 18 to 35 years. A total sample of 3,737 target individuals were interviewed in Kenya, including 406 verified viewers. In Tanzania, 3,383 target individuals were interviewed, including 527 verified viewers.

Feed the Future’s Africa Lead II project partnered with The Mediae Company, a Kenya-based media education company, to develop a pilot season of Africa’s first agriculture-focused reality TV program: Don’t Lose the Plot (DLTP). Targeting youth in Kenya and Tanzania, the show aired in Kenya and Tanzania between May and July 2017. The program’s objectives were to encourage youth to consider farming as a lucrative career choice, provide information on how to start agribusinesses, and share useful agronomic information. Africa Lead commissioned Kantar Public East Africa to evaluate the impact of DLTP on knowledge, attitudes, and behavior, or intention to change behavior, related to farming and agribusiness practices. This data asset includes quantitative data collected through a cross-sectional household survey in Kenya and Tanzania. Data collection took place between August and December 2017 and targeted both viewers and non-viewers of DLTP aged 18 to 35 years. A total sample of 3,737 target individuals were interviewed in Kenya, including 406 verified viewers. In Tanzania, 3,383 target individuals were interviewed, including 527 verified viewers.

Feed the Future seeks to reduce poverty and undernutrition in 19 developing countries including Kenya by focusing on accelerating growth of the agricultural sector, addressing root causes of undernutrition, and reducing gender inequality. This dataset (n=27,804, vars=8) contains data from sub-Module F2 Repeat: Sources of Income. Each household with data has multiple records (for each source of income the household collects from).

Feed the Future seeks to reduce poverty and undernutrition in 19 developing countries including Kenya by focusing on accelerating growth of the agricultural sector, addressing root causes of undernutrition, and reducing gender inequality. This dataset (n=69,540, vars=23) contains data from sub-Module E7: Durable Goods Expenditures. Each household with data for durable goods expenditures has multiple records (for the 38 durable goods in sub-Module E7). (69,540 records divided by 38 non-food items = 1,830 Module E households with sub-Module E7 data.)

Feed the Future seeks to reduce poverty and undernutrition in 19 developing countries including Kenya by focusing on accelerating growth of the agricultural sector, addressing root causes of undernutrition, and reducing gender inequality. This dataset (n=243,789, vars=28) contains variables from Module E1, Food Consumption Over Past 7 Days. Each household with food consumption data has multiple records (for the 133 food items in sub-Module E1). (243,789 records divided by 133 food items =1,833 Module E households with sub-Module E1 data.).

Feed the Future seeks to reduce poverty and undernutrition in 19 developing countries including Kenya by focusing on accelerating growth of the agricultural sector, addressing root causes of undernutrition, and reducing gender inequality. This baseline survey seeks to capture data on women’s empowerment in agriculture, household food security, consumption, nutrition, and wellbeing of households in the geographic areas targeted by Feed the Future interventions, known as Feed the Future Zones of Influence (ZOI). The ZOI in northern Kenya comprises nine counties and approximately two-thirds of Kenya’s total land area. This dataset is the household roster file with one record per household member captured in Module C of the questionnaire (n=8,555, vars=35).

Feed the Future seeks to reduce poverty and undernutrition in 19 developing countries including Kenya by focusing on accelerating growth of the agricultural sector, addressing root causes of undernutrition, and reducing gender inequality. This dataset (n=18,300, vars=17) contains variables from sub-Module E2, Non-Food Expenditures over Past 7 Days. Each household with data for non-food expenditurs over the past week has multiple records (for the 10 non-food items in sub-Module E2) (18,300 records divided by 10 non-food items=1,830 Module E households with sub-Module E2 data.)

Feed the Future seeks to reduce poverty and undernutrition in 19 developing countries including Kenya by focusing on accelerating growth of the agricultural sector, addressing root causes of undernutrition, and reducing gender inequality. This baseline survey seeks to capture data on women’s empowerment in agriculture, household food security, consumption, nutrition, and wellbeing of households in the geographic areas targeted by Feed the Future interventions, known as Feed the Future Zones of Influence (ZOI). The ZOI in northern Kenya comprises nine counties and approximately two-thirds of Kenya’s total land area. This dataset is a household-level file with records for each sampled household with a completed interview (n=1837, variables=102).

Feed the Future seeks to reduce poverty and undernutrition in 19 developing countries including Kenya by focusing on accelerating growth of the agricultural sector, addressing root causes of undernutrition, and reducing gender inequality. This dataset (n=3,662, vars=15) contain data from sub-Module E5 regarding non-food items that may or may not have been purchased (e.g., construction items such as wood poles and thatching grass). Each household with data for these construction items over the past 12 months has multiple records (for the two construction items in sub-Module E5). (3,662 records divided by 2 construction items = 1,831 Module E households with sub-Module E5 construction data.)

In order to better understand how the FARM II project reached farmer-based organizations (FBOs) and farmers across the Greenbelt region and how the project may have influenced beneficiaries to use improved technologies or management practices, FARM II conducted two process evaluations in March 2016. The evaluations were conducted in 28 payams across eight counties of Central, Eastern, and Western Equatoria states. This first evaluation, the FBO survey, assessed the technical and managerial capacity of elected leaders of farmer-based organizations that received assistance from the FARM II project. The second evaluation, the farmer survey, measured knowledge, attitudes, and the application of improved technologies and management practices by targeted farmer beneficiaries.