Current science and technology has been an enabling factor in the ability to model production shocks from climatic disasters such as droughts, floods and heat waves, as manifested in the development of various international, regional and national EWS of food security. Production shock models have been used for a number of years in developing crop insurance programs at the micro level (small scale farmers) and at the macro level (government assets), ( e.g. Syroka and Nucifora 2010, Gonzalez 2009, Mortgatt et al. 2012). This effort has been facilitated by the availability of spatially dense gridded weather data derived from weather station data at the national level and regional to global remote sensing data. The availability of large spatial coverage of weather data in most parts of the world goes back 30 years and the availability of relatively simple, general and soil‐ plant‐atmosphere based crop growth models for most of the major crop s has made it possible to design insurance programs at the micro and macro levels. A framework similar to that used in natural catastrophe property models is used to simulate weather events that are likely in the future (that go beyond the 30 years of historical data) and crop yield models relate the simulated weather events to crop production shortfalls to generate a probabilistic distribution of yield reductions at various return periods.

There are a number of issues that arise in the use of gridded weather data for crop modeling (Gommes, R. and Kayitakire, F. 2012). Remote sensing (RS) data on weather are first of all not as accurate as data from reliable and accurate ground weather stations and are only available for about 30 years. However, most developing countries have limited ground weather station coverage and the number of consistently reliable and accurate weather stations is even smaller. Therefore, there is an over‐reliance on RS data, most of which may not have been groundtruthed. This could lead to considerable uncertainties in spatial and temporal distribution of the intensity of the weather events. In addition, the use of crop models at a grid level would require an averaging of culitvars (i.e. cultivated varieties of crops), environment (i.e. soil and weather) and management (i.e. growing season, use of rainwater and irrigation, use of fertilizers, etc.) which could lead to even higher uncertainties. Therefore, there is a real risk that the use of crop models based on gridded RS data may have imperfect correlation with actual crop production. The imperfect correlation between the estimated and actual crop production is referred to as the basis risk. The goal in crop modeling is to reduce basis risk as much as possible so that the model output and ground measurements are highly correlated (Clarke and Hill 2013). At the national/ regional scale which is of interest in this paper the consensus among researchers is that basis risk is lower as a result of aggregation at the grid level relative to the individual farmer level (Gommes and Kayitakire 2012).

# <u>Rationale for Insuring Food Security</u>

The rationale for insurance to small‐scale farmers in developing countries is that insurance gives small farmers the capacity to take risks to purchase higher quality inputs (seeds, fertilizers, pesticides/herbicides) and get access to irrigation backed by the safety net which insurance offers in case of catastrophe type of losses. Insurance effectively works as collateral to have access to credit from banks because with the insurance there is a guaranteed payout even in years of low yields. With access to improved input supplies, the farmer is gradually able to build a surplus in good years to survive catastrophe type of years. This rationale of insurance to small scale farmers is very much 6