Ag_Insure

The current state of the problem hinges on the relative high costs of insurance policies due to the low quality and coverage of historical climate data. The latter is a prevalent problem across the developing world, where climate measurement infrastructure (CMI) is scarce and sparsely distributed. We seek to overcome this problem by introducing the use of data analytics, along with climate science modelling to undertake the risk assessment (RA) of climate perils. This way we will be able to increase the geographical granularity of the climatic RA, significantly lowering the costs associated to basis risk. Lower basis risks translate into lower costs of serving markets where current data availability renders it unfeasible. In the pilot stage, we will create and validate a model of RA for droughts and floods for soybean producers in Bolivia.

Last update: October 05, 2023