Household Survey Data on Cost Benefit Analysis of Climate-Smart Soil Practices in Western Kenya
Harvard Dataverse (Africa Rice Center, Bioversity International, CCAFS, CIAT, IFPRI, IRRI and WorldFish)
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Title |
Household Survey Data on Cost Benefit Analysis of Climate-Smart Soil Practices in Western Kenya
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Identifier |
https://doi.org/10.7910/DVN/K6JQXC
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Creator |
Ng’ang’a, Stanley Karanja
Mwungu, Chris M Mwongera, Caroline Kinyua, Ivy Notenbaert, An Girvetz, Evan |
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Publisher |
Harvard Dataverse
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Description |
This household survey was conducted among 88 respondents by CIAT in three counties of western Kenya (Bungoma, Kakamega, and Siaya) in 2016. The main aim of the project was to conduct a cost benefit analysis of eight climate-smart soil (CSS) practices, as a step toward understanding whether they were beneficial or not both from a private and social point of view. This knowledge could then be potentially used to enlighten farmers, policy makers and development practitioners about soil protection and rehabilitation practices that are most cost-effective when implemented on farms. Such knowledge also provides a rationale that can be used as a basis for promoting selected CSS practices. Farm practices were considered as “climate smart” if they could improve the soil-nitrogen cycle, enhance soil fertility, improve crop productivity, improve soil biodiversity, promote soil conservation, increase soil biomass, reduce soil erosion, reduce volatility in crop and livestock production, and reduce water pollution. These practices could, in turn, boost food production, income, and households’ ability to adapt to climate change. Variables collected include: 1) general information about each site, 2) household age, gender, education level, and farming experience, 3) farm activities (without intervention), 4) implemented CSS practices such as the use of improved seeds, agroforestry, inorganic fertilisers, liming, and organic manure socio economic characteristics, and farm output, 5) crop and livestock yields, prices for farm inputs and outputs, the cost of implementing farm activities (both before and after intervention), 7) household financial information and, 8) environmental effects. Identifying variables such household head information, contact details and geographical locations of the households have not been provided in the data but they can be availed upon request.
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Subject |
Agricultural Sciences
Social Sciences Soil Farm production Cost benefit analysis Climate-smart soil practices Kenya Soils Africa Decision and Policy Analysis - DAPA Agrobiodiversity - AGBIO |
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Language |
English
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Date |
2016
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Contributor |
Mwanzia, Leroy
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Type |
Survey Data
Socio-economic Data Soil Data Primary Data |
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