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

Author

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  • Mwongera, Caroline
  • Shikuku, Kelvin M.
  • Twyman, Jennifer
  • Läderach, Peter
  • Ampaire, Edidah
  • Van Asten, Piet
  • Twomlow, Steve
  • Winowiecki, Leigh A.

Abstract

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 bottom-up, 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.

Suggested Citation

  • Mwongera, Caroline & Shikuku, Kelvin M. & Twyman, Jennifer & Läderach, Peter & Ampaire, Edidah & Van Asten, Piet & Twomlow, Steve & Winowiecki, Leigh A., 2017. "Climate smart agriculture rapid appraisal (CSA-RA): A tool for prioritizing context-specific climate smart agriculture technologies," Agricultural Systems, Elsevier, vol. 151(C), pages 192-203.
    • Handle: RePEc:eee:agisys:v:151:y:2017:i:c:p:192-203
    DOI: 10.1016/j.agsy.2016.05.009
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    9. World Bank Group, 2019. "Cote d’Ivoire Climate-Smart Agriculture Investment Plan [Plan d’Investissement d’une Agriculture Intelligente face au Climat en Côte d’Ivoire]," World Bank Publications - Reports 32745, The World Bank Group.
    10. Dunnett, A. & Shirsath, P.B. & Aggarwal, P.K. & Thornton, P. & Joshi, P.K. & Pal, B.D. & Khatri-Chhetri, A. & Ghosh, J., 2018. "Multi-objective land use allocation modelling for prioritizing climate-smart agricultural interventions," Ecological Modelling, Elsevier, vol. 381(C), pages 23-35.
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    12. Arenas-Calle, Laura N. & Ramirez-Villegas, Julian & Whitfield, Stephen & Challinor, Andrew J., 2021. "Design of a Soil-based Climate-Smartness Index (SCSI) using the trend and variability of yields and soil organic carbon," Agricultural Systems, Elsevier, vol. 190(C).
    13. Rodríguez-Barillas, María & Klerkx, Laurens & Poortvliet, P. Marijn, 2024. "What determines the acceptance of Climate Smart Technologies? The influence of farmers' behavioral drivers in connection with the policy environment," Agricultural Systems, Elsevier, vol. 213(C).
    14. Kabirigi, Michel & Abbasiharofteh, Milad & Sun, Zhanli & Hermans, Frans, 2022. "The importance of proximity dimensions in agricultural knowledge and innovation systems: The case of banana disease management in Rwanda," Agricultural Systems, Elsevier, vol. 202(C).
    15. Enock Warinda & Dickson M Nyariki & Stephen Wambua & Reuben M Muasya & Munir A Hanjra, 2020. "Sustainable development in East Africa: impact evaluation of regional agricultural development projects in Burundi, Kenya, Rwanda, Tanzania, and Uganda," Natural Resources Forum, Blackwell Publishing, vol. 44(1), pages 3-39, February.
    16. Kumar, Shalander & Craufurd, Peter & Haileslassie, Amare & Ramilan, Thiagarajah & Rathore, Abhishek & Whitbread, Anthony, 2019. "Farm typology analysis and technology assessment: An application in an arid region of South Asia," Land Use Policy, Elsevier, vol. 88(C).
    17. Acosta-Alba, Ivonne & Chia, Eduardo & Andrieu, Nadine, 2019. "The LCA4CSA framework: Using life cycle assessment to strengthen environmental sustainability analysis of climate smart agriculture options at farm and crop system levels," Agricultural Systems, Elsevier, vol. 171(C), pages 155-170.
    18. Thornton, Philip K. & Whitbread, Anthony & Baedeker, Tobias & Cairns, Jill & Claessens, Lieven & Baethgen, Walter & Bunn, Christian & Friedmann, Michael & Giller, Ken E. & Herrero, Mario & Howden, Mar, 2018. "A framework for priority-setting in climate smart agriculture research," Agricultural Systems, Elsevier, vol. 167(C), pages 161-175.
    19. Abrham Belay & Alisher Mirzabaev & John W. Recha & Christopher Oludhe & Philip M. Osano & Zerihun Berhane & Lydia A. Olaka & Yitagesu T. Tegegne & Teferi Demissie & Chrispinus Mutsami & Dawit Solomon, 2024. "Does climate-smart agriculture improve household income and food security? Evidence from Southern Ethiopia," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(7), pages 16711-16738, July.
    20. Purna Chandra Tanti & Pradyot Ranjan Jena & Raja Rajendra Timilsina & Dil Bahadur Rahut, 2024. "Enhancing crop yields and farm income through climate-smart agricultural practices in Eastern India," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 29(5), pages 1-28, June.

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