After a Data Error Shook Trust, USDA Turns to AI to Guard Against the Next One

The United States Department of Agriculture will run a new AI tool against its November 10 crop production report, after an error in its September corn data. The tool checks for anomalies before the report goes out. It adds a step in front of the department's existing data review rather than replacing it.

The error itself, reported by Reuters, was simple to describe and expensive to live with. A computer file combined data on irrigated and non-irrigated corn acres, which field surveyors had collected separately. Kansas and Nebraska carry more irrigated acreage than most corn states, and irrigated fields tend to yield more. The mistake pushed USDA's October correction up about 3% for Nebraska's yield estimate and down about 2% for Kansas's.

USDA Under Secretary Scott Hutchins said the new tool would have caught the September problem. He described its job plainly: it checks "that there are no anomalies" before a report goes out. Lance Honig, who chairs the agricultural statistics board at USDA's National Agricultural Statistics Service, called the error "literally a very isolated occurrence" and said the data collected at the field level was never in question. Hutchins also said the error was not related to the staff the department has lost. The department has lost staff since the government downsized last year.

This was not USDA's only recent data problem. The agency sharply revised last year's corn acreage estimate upward, and this summer it reported inaccurate beef export sales figures. Jim Gerlach, president of the brokerage A/C Trading, told Reuters: "Their reports have become so erratic." The National Corn Growers Association, which represents 35,000 farmers, called the September error unacceptable. Traders and farmers use these reports to set prices and decide when to plant, store, and sell, so a yield estimate off by a few percentage points moves real money.

USDA put a machine check in front of its existing human review, rather than cutting that review to move faster. The check targets the exact kind of mistake a careful eye can miss and a systematic check is designed to catch: a file that silently mixed two categories of data.

That is the same problem Foragentis created ForIntel to solve for market research and competitive intelligence. A number that sounds right and a number someone actually traced back to a real, cited source look identical on the page. The only way to tell them apart is to check, every time, before the number reaches a reader who will act on it. ForIntel checks every claim in a report against the source it cites. How plausible the claim sounds does not matter. USDA just reached the same conclusion about its own crop data, after farmers and traders told the agency, in public, that they no longer trusted it on faith.

Learn more about ForIntel's approach to verified research at https://foragentis.com.