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2017- CSA Monitoring: Lawra-Jirapa Climate-Smart Village (Ghana)

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

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Title 2017- CSA Monitoring: Lawra-Jirapa Climate-Smart Village (Ghana)
 
Identifier https://doi.org/10.7910/DVN/J31LJT
 
Creator Bonilla-Findji, Osana
Eitzinger, Anton
Andrieu, Nadine
Jarvis, Andy
Ouedraogo, Mathieu
Zougmoré, Robert
Nyuor, Anslem B.
Saaka Buah, Samuel
 
Publisher Harvard Dataverse
 
Description This dataset contains the files produced in the pilot implementation of the “Integrated Monitoring Framework for Climate-Smart Agriculture” in the Lawra-Jirapa Climate Smart Village (Ghana) in October 2017.



This monitoring framework developed by CCAFS is meant to be deployed annually across the global network of Climate-Smart Villages to gather field-based evidence by tracking the progress on:



  • Adoption of CSA practices and technologies, as well as access to climate information services and
  • Their related impacts at household level and farm level





      The CSA framework allows to address three key research questions:
    1. Who within each CSV community adopts which CSA technologies and practices and which are their motivations, enabling factors? To which extent farmers access and use climate information services?
    2. Which are the gender-disaggregated perceived effects of CSA options on farmers’ livelihood, agricultural, food security and adaptive capacity, and on key gender dimensions (participation in decision making, participation in CSA implementation and dis-adoption, control and access over resources and labour).

    3. Which are the CSA performance, synergies and trade-offs found at farm level?




    This CSA framework proposes a small set of standard Core Indicators linked to the research questions, and Extended indicators covering aspects related to the enabling environment.




      At household level (17 Core indicators):
    • 7 Core Uptake indicators (they track CSA Implementation and adoption drivers; CSA dis-adoption and drivers; Access to climate information services and agro-advisories, Capacity to use them and constraining factors).


    • 10 Core Outcome indicators (they track farmers perceptions on the effects of CSA practices on their Livelihoods, Food Security and Adaptive Capacity and on Gender dimensions.




    • Those include namely: CSA effect on yield/production, on Income, on Improved Food Access and Food Diversity, on Vulnerability to weather related shocks and on Changes in agricultural activities induced by access to climate information.
      Four are Gender related Outcome indicators (Decision-making on CSA implementation or dis-adoption, Participation in CSA implementation, CSA effect on labor, Decision making and control on CSA generated income).



    • An additional set of complementary Extended indicators allows to determine and track changes in enabling conditions and farmers characteristics such as: Livelihood security, Financial enablers, Food security, Frecuency of climate events, Coping strategies, Risk Mitigation Actions, Access to financial services and Training, CSA Knowledge and Learning.




    • At farm level, 7 CORE indicators:


    • 7 Core indicators are used to determine the CSA performance of the farms as well as synergies and trade-offs among the three pillars (productivity, adaptation and mitigation, via farm model analysis).




    • This integrated framework is associated with a cost-effective data collection App (Geofarmer) that allowed capturing information in almost real time.


      The survey questionnaire is structured around different thematic modules (Demographic, Livelihoods, Food Security, Climate events, Climate Services, CSA practices, Financial Services) connected to standard CSA metrics and the specific indicators.
 
Subject Agricultural Sciences
Earth and Environmental Sciences
Monitoring
Climate Smart Agriculture
Households
Livelihoods
Farm
Adaptation
Food Security
Climate Shocks
West Africa
Decision and Policy Analysis - DAPA
 
Language English
 
Contributor Ortega, Angelly
 
Type Survey data
Socio-economic Data
Geographic Data
Environmental Data
Capacity Building