174 USDA RMA provides a dashboard that shows farms experiencing drought and the trend of crop damage. “CLUs started a really big geospatial shift, but it also meant that we had to have a computer platform that could handle it.” — Jim Hipple, physical scientist, Business Analytics Division, USDA RMA A Data Science Partner To handle big data processing at scale, the USDA works with the Center for Agribusiness Excellence at Tarleton State University in Texas. “We leverage the advanced analytics from the university effort to better understand the integrity of a policy, and to seek out waste, fraud, and abuse,” Hipple said. By adding tabular data to the map, crop insurance compliance investigators can spot patterns and irregularities that indicate potential insurance problems. The key, according to Troy Thorne, director of the Center for Agribusiness Excellence at Tarleton State University, is in identifying inefficiencies—places where the connection between the land and what it produces seems odd or unusual. Thorne cited the practice of yield switching as an example. Crop insurance operates on the principle of a yield history. If a field has produced the same crop with the same farming practices for three years, insurers average the output to The Rise of Field-Level Awareness “Crop insurance policies have gotten more specific about the field location,” Hipple said. “That helped us better understand conditions on each farm.” The USDA’s Farm Service Agency, a sister agency to RMA, mapped the location of every field down to what’s known as a common land unit (CLU). To accomplish this digital transformation, more than 2,500 field service centers across the country were equipped with GIS. When farmers report their planting intentions through their acreage report at the start of a growing season, the field boundaries are compiled into a database. Over nearly a decade, more than 36 million CLU boundaries were recorded along with land ownership, soil, and crop type. These digital records can be easily updated and analyzed to visualize agricultural trends, replacing paper maps. Investigators use this field awareness to ask location questions related to claims and speed the processing of insurance payments after disaster strikes. A farmer inspects a head of wheat while standing in a fully ripened grain field during the fall harvest. A hot and dry June produced yields far below the field's average, a loss that crop insurance was designed to cover.
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