A Proposed Satellite-Based Crop Insurance System for Smallholder Maize Farming

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 p...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2022-03, Vol.14 (6), p.1512
Main Authors: Masiza, Wonga, Chirima, Johannes George, Hamandawana, Hamisai, Kalumba, Ahmed Mukalazi, Magagula, Hezekiel Bheki
Format: Article
Language:English
Subjects:
Adverse selection
Agricultural production
Agriculture
Cereal crops
Corn
Crop insurance
crop water requirements
Crops
Datasets
Environmental hazards
Estimates
Extreme weather
Farmers
Farming
Farms
Ground-based observation
Growing season
Humidity
index insurance
Insurance
maize
Meteorology
Moral hazard
Precipitation
Product design
Rain
Rainfall
Remote sensing
Satellite observation
Small farms
smallholder
Water requirements
Weather
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Summary: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.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs14061512