An integrated approach for understanding the factors that facilitate or constrain the adoption of soil carbon enhancing practices in East Africa, Kenya and Ethiopia

The survey data on soil carbon enhancing practices in Ethiopia is systematically organized in Microsoft Excel tables. The data entails general household characteristics, plot characteristics, crops grown, yield, practices implemented, inputs, livestock ownership, social capital, access to credit, access to extension services

An integrated approach for understanding the factors that facilitate or constrain the adoption of soil carbon enhancing practices in East Africa, Kenya and Ethiopia.

The survey data on soil carbon enhancing practices in Ethiopia is systematically organized in Microsoft Excel tables. The data entails general household characteristics, plot characteristics, crops grown, yield, practices implemented, inputs, livestock ownership, social capital, access to credit, access to extension services

An integrated approach for understanding the factors that facilitate or constrain the adoption of soil carbon enhancing practices in East Africa, specifically Western Kenya

The survey data on soil carbon enhancing practices in western Kenya is systematically organized in Microsoft Excel tables. The data entails general household characteristics, plot characteristics, practices implemented, yield, inputs, livestock ownership, social capital, access to credit, access to extension services and sources of income

An integrated approach for understanding the factors that facilitate or constrain the adoption of soil carbon enhancing practices in East Africa, specifically Western Kenya

The survey data on soil carbon enhancing practices in western Kenya is systematically organized in Microsoft Excel tables. The data entails general household characteristics, plot characteristics, practices implemented, yield, inputs, livestock ownership, social capital, access to credit, access to extension services and sources of income

Survey data on the costs and benefits of livestock management practices in the lowlands of Ethiopia

The survey data on costs-benefit analysis of livestock management practices in the lowlands of Ethiopia is systematically organized in Microsoft Excel tables. Each of the three land management practices, that is, rangeland improved, restoration of degraded land, and fodder cropping and poultry expansion have data contained in excel tables. The data entails general household characteristics, the region where the practices are common and activities households indulged in before and after introduction of land management practices. The data also contains costs of inputs and labour required for the land management practices; mainly for implementation, maintenance and operation. Additionally, the tables include data on products generated from the land management practices as well as their market prices

Survey data on the costs and benefits of livestock management practices in the lowlands of Ethiopia

The survey data on costs-benefit analysis of livestock management practices in the lowlands of Ethiopia is systematically organized in Microsoft Excel tables. Each of the three land management practices, that is, rangeland improved, restoration of degraded land, and fodder cropping and poultry expansion have data contained in excel tables. The data entails general household characteristics, the region where the practices are common and activities households indulged in before and after introduction of land management practices. The data also contains costs of inputs and labour required for the land management practices; mainly for implementation, maintenance and operation. Additionally, the tables include data on products generated from the land management practices as well as their market prices

Kenya County climate risk profile data

The Kenya climate risk profile data contains climate, biophysical, socio economic and demographic characteristics, crops production, stakeholders, characterization of selected value chains and risks and adaptation components. All the dataset, except climate records, were collected in three phases between 2016 and 2021. The risk profiles covered the 45 rural counties of Kenya (excluding the 2 urban counties of Nairobi and Mombasa) and were developed in partnership with the Kenya Ministry of Agriculture, Livestock, Fisheries and Cooperatives (MoALFC). Methodology: The methodology combined literature review (peer-reviewed journals, grey literature), data collection from key statistical resources (national census, county development plan, etc.), climate modelling and qualitative data collection tools such as key informant interviews, participatory workshops, and focus group discussions. For each profile, a prioritization process took place in the county with the key relevant stakeholders. The process included a presentation of the ten main value chains (VCs) of the county and a selection of the four main value chains by assessing them against a set of criteria: contribution to food security, productivity, importance to the economy; resilience to current and future climate change; population engaged in the value chain; and engagement of poor and marginalized groups