Locally led adaptation metrics for Africa: a framework for building resilience in smallholder farming sectors

Kenya is one of several Sub-Saharan African countries vulnerable to climate change, which severely impacts their small-holder farming (SHF) sectors. To build resilience and reduce SHFs’ vulnerability to the impact of climate change, there has been ongoing advocacy for an increase in adaptation funds disbursed to these African countries. However, the effectiveness of adaptation funds relies heavily on the quality of metrics used for tracking and assessing adaptation needs and actions developed by SHFs. This study, which set out to evaluate the impact of existing locally led adaptation (LLA) metrics relevant to Kenya’s SHFs, systematically searched grey and journal articles published between 2007 and 2023 and found that these sources did not reveal the impact of LLA metrics on resilience of SHFs, nor did they provide a framework for developing adaptation metrics relevant to SHFs. Kenya’s SHF sector is strategically vital for both rural and national economies and is the lifeblood of vulnerable communities. To mitigate the impact of climate change on this sector, the present study developed the first framework for locally led adaptation metrics for SHFs by drawing on the context knowledge of Kenya’s SHFs and lessons from the resilience and adaptation policy literature. This framework requires five steps: (1) to carry out gender intersectionality analysis to unravel the diverse typologies of SHFs in Kenya in order to identify their adaptation needs; (2) to co-develop metrics with stakeholders, including SHFs, periodically reviewing their relevance; (3) to complement metrics with contextual data; (4) to develop a knowledge brokering platform for cross-community and cross-country learning; and (5) to connect with government and decision makers. While this study has provided guidance on implementing the locally led adaptation metrics for Africa (LAMA) framework in real-world settings, there is a need to explore further how quantitative metrics can be complemented with contextual data.