Participatory Assessment and Adaptation for Resilience to Climate Change

How to Cite

Choptiany, J. M. H., Graeub, B. E., Hatik, S., Conversa, D., & Ledermann, S. T. (2019). Participatory Assessment and Adaptation for Resilience to Climate Change. Consilience, (21), 17–31.


Traditional monitoring and evaluation tools are often costly and are not well placed to assess complex, multi-dimensional dynamic livelihood attributes, such as resilience. While these approaches may be accurate in measurement, they often fail to empower respondents to take action. Drawbacks of other participatory resilience approaches include a requirement for a large amount of information, significant time required to administer surveys, and data analysis burdens (Schipper and Langston 2015, 19; COP 2016). They also need a baseline and an end line to assess change. They typically fail to capture how farmers actively address specific shocks and stresses or what lessons can be learned. Given these limitations, an opportunity exists to review alternative approaches and develop different tools. Our approach and tool, farmbetter, attempts to build on lessons learned from developing and implementing these tools. Rather than characterizing the user as lacking agency in a challenging environment, farmbetter helps users make conscious decisions to adapt and withstand specific shocks and stresses such as floods, drought, or conflict, which can require contradictory coping strategies. We aim to then further connect farmers whose properties have similar agro-ecological conditions and challenges to better facilitate knowledge-sharing between food producers, ultimately improving learning and resilience. This allows for the connection of big data with (traditional) knowledge and competences already being used by farmers to improve resilience. This is enabled by advances in technology (e.g. mobile phone availability), which offer new opportunities to more easily and cheaply collect data as well as giving access to marginalized groups’ views and knowledge (e.g. Raghavan et al. 2016).
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