2021- CSA Monitoring/Midline: Santa Rita Climate-Smart Village (Honduras)
Harvard Dataverse (Africa Rice Center, Bioversity International, CCAFS, CIAT, IFPRI, IRRI and WorldFish)
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Title |
2021- CSA Monitoring/Midline: Santa Rita Climate-Smart Village (Honduras)
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Identifier |
https://doi.org/10.7910/DVN/A9SMP1
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Creator |
Bonilla-Findji, Osana
Eitzinger, Anton Martínez-Barón, Deissy Martínez-Salgado, Jesus David Alvarez-Espinosa, Osman |
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Publisher |
Harvard Dataverse
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Description |
The CSA framework allows to address three key research questions:
(Note that this 3d. question was not addressed in this specific 2021 monitoring, as farm level data were not collected) The 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): 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 labour, Decision making and control on CSA generated income). 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. For the Latin America implementation, some slight changes were made to specific modules of the questionnaire, related to site-specific data collection needs: |
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Subject |
Agricultural Sciences
Earth and Environmental Sciences Monitoring Climate Smart Agriculture Households Livelihoods Farmers Adaptation Food Security Climate Shocks |
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Language |
English
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Date |
2022-01-12
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Contributor |
Moreno Bustamante, Manuel Francisco
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Relation |
CCAFS Household Baseline Study, Latin America & South East Asia (2014-2015): https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/PWVLTU
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Type |
Survey data
Socio-economic Data Geographic Data Environmental Data Capacity Building |
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