Hailstorms induce large crop damages every year. In June last year, a hailstorm in France prompted local authorities to declare a ânatural emergencyâ to protect local farmers. While many viewed this as a freak storm, extreme weather conditions are becoming more frequent due to climate change.
Farmers can get hail insurance to cover against this risk, but the adoption of this risk management tool remains relatively low due to the cost. Technology can improve the efficiency of the operational management of hail insurance products to help reduce costs and make the coverage more accessible for farmers.
Weather data is needed to locate hailstorms and assess their intensity. However, the occurrence of an event doesnât mean yields are impacted. Conversely, remote-sensing enabled crop monitoring services, especially using satellites, allow the detection of changes and measurement of vegetation anomalies.
The purpose of our project is to combine these two data sources â weather models and satellite measurements â and leverage machine-learning and data science to create hail impact indices and evaluate their value for insurance companies and for farmers. Through the use of data analytics and tools such as Agriquest, we have already empowered customers with monitoring tools to evaluate the impact of weather events such as hailstorms on fields but a hail risk index would simplify that process while creating an industry standard to help manage costs for farmers.