Data utilized in African Green Climate Fund Agricultural Adaptation Projects and its availability in selected Decision Support Tools (DSTs)
Domain:
agricultureclimateRecord type:
dataset
Creator:
Ros
SteJon
SteJonEditor:
BioBioAll
Publisher:
Har
Host:
The dataset aims to provide insight into the data needed for developing successful projects for the Green Climate Fund (GCF). It includes information on climate hazards, vulnerability, exposure, and response in 20 accepted agricultural adaptation projects in Africa.
Metodology:An initial dataset was collated from 20 representative agricultural adaptation projects out of an approximately 35 approved at the time. These projects were strategically selected to reflect a range of interventions and geographical alignment with the countries involved in the Accelerating Impact of CGIAR Climate Research (AICCRA) project. The projects spanned multiple African nations, including (project numbers): Burkina Faso (FP074), Ethiopia (FP058), Gambia (FP011), Ghana (FP114), Kenya (FP113, FP175), Liberia (FP160), Malawi (FP002), Mali (FP012), Namibia (FP023, FP024, SAP001), Niger (SAP012), Senegal (FP003, FP021, FP049), Tanzania (FP041, FP179), Uganda (FP034), and Zambia (FP072). The data extraction process involved going through project proposals to categorize information according to the Intergovernmental Panel on Climate Change (IPCC) Risk Framework. This framework encompasses climate hazards, vulnerability, exposure, and response strategies. Consequently, the compiled dataset comprised diverse data, ranging from meteorological and climatological factors influencing climate variability and extremes to socio-economic, environmental, and demographic variables affecting climate risk sensitivity. It also included data on the exposure of the regions to climate risks and detailed proposed adaptive responses and strategies. The extraction and classification were conducted by a project team with expertise in agriculture and climate change, resulting in a database describing information in successful GCF proposals. Following the data compilation, a selection of well-known Decision Support Tools (DST) was made, drawing from the authors' experience. The team then mapped each data element from the GCF database to the corresponding DST that could furnish the required information, ensuring alignment by country and the specificity of the data type.
Metodology:An initial dataset was collated from 20 representative agricultural adaptation projects out of an approximately 35 approved at the time. These projects were strategically selected to reflect a range of interventions and geographical alignment with the countries involved in the Accelerating Impact of CGIAR Climate Research (AICCRA) project. The projects spanned multiple African nations, including (project numbers): Burkina Faso (FP074), Ethiopia (FP058), Gambia (FP011), Ghana (FP114), Kenya (FP113, FP175), Liberia (FP160), Malawi (FP002), Mali (FP012), Namibia (FP023, FP024, SAP001), Niger (SAP012), Senegal (FP003, FP021, FP049), Tanzania (FP041, FP179), Uganda (FP034), and Zambia (FP072). The data extraction process involved going through project proposals to categorize information according to the Intergovernmental Panel on Climate Change (IPCC) Risk Framework. This framework encompasses climate hazards, vulnerability, exposure, and response strategies. Consequently, the compiled dataset comprised diverse data, ranging from meteorological and climatological factors influencing climate variability and extremes to socio-economic, environmental, and demographic variables affecting climate risk sensitivity. It also included data on the exposure of the regions to climate risks and detailed proposed adaptive responses and strategies. The extraction and classification were conducted by a project team with expertise in agriculture and climate change, resulting in a database describing information in successful GCF proposals. Following the data compilation, a selection of well-known Decision Support Tools (DST) was made, drawing from the authors' experience. The team then mapped each data element from the GCF database to the corresponding DST that could furnish the required information, ensuring alignment by country and the specificity of the data type.
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