MUCH OF FARM POLICY IS DESIGNED TO MITIGATE CROP REVENUE RISK. THIS INCLUDES THE AGRICULTURAL RISK COVERAGE PROGRAM TRIGGERED ON COUNTY REVENUE AND THE MULTI-PERIL CROP INSURANCE PROGRAM WHICH USES LOSS EXPERIENCE IN THE COUNTY AS THE BASIS FOR RATING. THE PREMISE OF THIS PROPOSAL IS TO USE MULTIPLE SOURCES OF DIGITAL DATA TO ESTIMATE CORN AND SOYBEAN YIELD VARIABILITY THAT IS BOTH MORE GRANULAR AND MORE ACCURATE THAN CURRENTLY AVAILABLE. THIS PROJECT ADDRESSES THE ECONOMICS OF AGRICULTURE AND FOOD POLICY, CROP INSURANCE, PRICE STABILIZATION, AND INCOME SUPPORT. IT ALSO ADDRESSES THE SOCIAL AND ECONOMIC IMPLICATIONS OF 'BIG DATA' IN AGRICULTURE.WE HAVE THREE MAJOR OBJECTIVES. FIRST, WE PROPOSE MODELS OF U.S. CROP YIELD RISK SURFACE CONDITIONED ON HIGH-RESOLUTION SOIL, WEATHER, SATELLITE IMAGERY AND OTHER PRECISION AGRICULTURAL DATA. SECOND, WE PROPOSE TO DEVELOP MORE ACCURATE CROP INSURANCE RATES - REDUCING ADVERSE SELECTION BY ACCOUNTING FOR SOILS AND MORE EXACT FIELD LOCATIONS. WE WILL APPROACH THIS PROBLEM IN A WAY THAT SOILS AND OTHER SPATIAL INFORMATION CAN BE INTEGRATED INTO THE RMA ACTUARIAL SYSTEM. FINALLY, WE WILL DEFINE HOMOGENOUS AREAS OF AGGREGATION OTHER THAN CURRENT POLITICALLY-DEFINED COUNTY BOUNDARY AS THE RATING AREA FOR CROP INSURANCE AND THE YIELD MEASURE FOR ARC.THE OUTCOMES OF THIS WORK WILL BE THREEFOLD. FIRST, CROP INSURANCE AND ARC PROGRAMS CAN BE REFINED AND BECOME MORE EFFECTIVE. SECOND, SUBSIDIES CAN BE REDUCED GIVEN MORE ACCURATE RATES. FINALLY, INTEGRATING THE DATA AND ANALYTICS WILL ALLOW FOR CONSERVATION INCENTIVES AND INTEGRATION WITH INSURANCE PROGRAMS BY IDENTIFYING HIGH RISK REGIONS.