Abstract:
Crop farming in Sub-Saharan Africa is constantly confronted by extreme weather events.
Researchers have been striving to develop different tools that can be used to reduce the impacts of
adverse weather on agriculture. Index-based crop insurance (IBCI) has emerged to be one of the tools
that could potentially hedge farmers against weather-related risks. However, IBCI is still constrained
by poor product design and basis risk. This study complements the efforts to improve IBCI design
by evaluating the performances of the Tropical Applications of Meteorology using SATellite data
and ground-based observations (TAMSAT) and Climate Hazards Group InfraRed Precipitation with
Station data (CHIRPS) in estimating rainfall at different spatial scales over the maize-growing season
in a smallholder farming area in South Africa. Results show that CHIRPS outperforms TAMSAT
and produces better results at 20-day and monthly time steps. The study then uses CHIRPS and a
crop water requirements (CWR) model to derive IBCI thresholds and an IBCI payout model. Results
of CWR modeling show that this proposed IBCI system can cover the development, mid-season,
and late-season stages of maize growth in the study area. The study then uses this information to
calculate the weight, trigger, exit, and tick for each of these growth stages. Although this approach is
premised on the prevailing conditions in the study area, it can be applied in other areas with different
growing conditions to improve IBCI design.
