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This paper presents lessons of a replicable innovative decision support tool to systematize traditional indigenous
knowledge base for local climate monitoring and weather forecasting. The methodological tool, herein called the
traditional indigenous and local knowledge tool (TILKIT), was conceptualized under two training-of-trainers
initiatives on Climate-Smart Agriculture (CSA) in East Africa from March 2016 to December 2021. The aim was to
build local momentum for consensus-based ethnographic weather monitoring, local weather forecasting and agroweather advisory development for adoption by local stakeholders to improve agro-climatic extension service
delivery. The objective was to strengthen local capacity of smallholder farmer leaders, agribusiness value chain
partners, and field extension agents on the practical applications of indigenous climatology. Most of this indigenous
traditional knowledge or local technical knowledge (ITK or LTK) is now getting lost due to climate change and loss
of institutional memory but little effort is being made to identify and systematize the use of emerging ITK or LTK. It
is against this background that these initiatives conceptualised and developed an innovative approach to bridge the
gaps in order to address the challenges of salience, access, legitimacy, equity and integration of climate information
to meet users’ felt needs. The study adopted a transdisciplinary, participatory learning and action research (PLAR)
model to identify and confirm emerging local weather indicators and what they mean for local rainfall forecasting,
and to drive self-organization processes to bring indigenous climate knowledge into practical use in each community.
The tool emphasizes a consensus-based co-production of local weather forecasts and agro-weather advisories to
improve climate information services and extension service delivery. Testing and validation were conducted with
1,127 participants among various communities across Kenya, Tanzania and Uganda. Results comprise identified
ethnographic weather prediction indicators per locality, and their implications for local weather forecasting, which
for the first time is presented in probabilistic terms in a way local communities can associate with, and which can
compare and contrast empirically with conventional weather forecast language. The tool also provides actionable
agro-climate/ agro-weather advisories with appropriate lead times for local response and a basis for strategic
seasonal planning and operational risk management decision-making. Evidence from this work can be packaged for
sensitization to influence policy reforms and decision-making at various levels among relevant stakeholders in the
region.
