WITHDRAWN Modeling Non-Response in the National Agricultural Classification Survey

Music City Center 

The USDA's National Agricultural Statistics Service (NASS) is committed to ensuring comprehensive coverage and representation of farms across the United States by maintaining a list frame of all known and potential U.S. farms. This comprehensive database serves as the foundation for data collection for agricultural surveys and censuses. A key tool in updating and refining the list frame is the National Agricultural Classification Survey (NACS). NACS is conducted in four phases leading up to the quinquennial Census of Agriculture (COA) (conducted in years ending in 2 and 7). NACS evaluates whether operations have agricultural activity and, if eligible, NASS adds them to the Census Mailing List (CML). However, budget constraints and rising nonresponse rates challenge the accuracy and representativeness of the NASS list frame. This study addresses these challenges by analyzing the integration of data from the most recent phase of NACS with both administrative and auxiliary data sources from the American Community Survey (ACS). The findings aim to inform strategies to enhance the list frame, improve sampling efficiency, and optimize resource allocation.

Machine Learning

Non-response

USDA 

Survey Research Methods Section