Climate smart agriculture rapid appraisal (CSA-RA): A tool for prioritizing context-specific climate smart agriculture technologies

Approaches that aim to identify and prioritize locally appropriate climate smart agriculture (CSA) technologies will need to address the context-specific multi-dimensional complexity in agricultural systems. The climate smart agriculture rapid appraisal (CSA-RA) is a mixed method approach that draws on participatory bottomup, qualitative, and quantitative tools to assess the heterogeneity of local contexts, and prioritize context-specific CSA options. This is an imperative if countries are to respond to the COP21 agreement and meet their intended nationally determined contributions (INDCs). The CSA-RA is designed to assess biophysical including climatic, socio-cultural, economic and technological characteristics at the household, farm and community/regional level. The CSA-RA employs gender-disaggregated methods, including gender differences in perceptions of climate change and its impacts. The CSA-RA combines common participatory rural appraisal (PRA) and rapid rural appraisal (RRA) tools into one methodology, that disaggregates the gender dimension, and includes resource mapping; climate calendars; historical calendars; cropping calendars; organization mapping; transect walks; key informant interviews; farmer interviews; and pairwise ranking matrix. The tool collects qualitative and quantitative data from various stakeholders (farmers, local leaders, researchers, local-level agricultural experts, private sector actors, donor organizations, and policy implementers), allowing expansive analysis, triangulation and validation. Application of the CSA-RA in Tanzania and Uganda reveals heterogeneity across the sites in terms of vulnerability, constraints and CSA priorities among different social groups (gender) and agro-ecological zones. Thus, the CSA-RA allows stakeholders to simultaneously take into account biophysical and socio-economic aspects to target and implement CSA.

Bibliographic Details

Main Authors:	Mwongera, Caroline, Shikuku, Kelvin Mashisia, Twyman, Jennifer, Läderach, Peter, Ampaire, Edidah L., Asten, Piet J.A. van, Twomlow, Stephen, Winowiecki, Leigh A.
Format:	Journal Article biblioteca
Language:	English
Published:	Elsevier 2017-02
Subjects:	climate-smart agriculture, agriculture, data analysis, agricultura climáticamente inteligente, agricultura, análisis de datos,
Online Access:	https://hdl.handle.net/10568/75715 https://doi.org/10.1016/j.agsy.2016.05.009 http://dx.doi.org/10.7910/DVN/HCFDU8
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