| Sumario: | This study addresses the pressing need for inclusive and scalable agricultural insurance solutions
for smallholder wheat farmers in Ethiopia, who face persistent yield risks across diverse
agroecologies and farming systems. Despite the proven benefits of sustainable intensification (SI),
adoption remains low due to risk exposure, financial constraints, and limited access to insurance.
Existing area-based index insurance models often fail to reflect localized realities, resulting in high
basis risk and poor uptake. To bridge this gap, this research will develop a dynamic farm-level and
area yield index insurance model integrating sustainable intensification (SI) practices and risk
based farm typologies. The model will combine remote sensing, geospatial, and ground-truth
agronomic data through machine learning and simulation to enable accurate yield prediction and
premium estimation. Once calibrated, it will function with minimal inputs like NDVI, weather
data, and location ensuring cost-effective, scalable, and timely payouts. The research will also
evaluate the risk-reducing effects of SI, estimate SI-sensitive premiums, and assess adoption
drivers and farmers’ willingness to pay to ensure alignment with smallholders’ needs. Beyond
compensating losses, the study envisions insurance as a driver of technology adoption and farm
investment. Developing such holistic tools can enhance resilience and environmental
sustainability.
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