Applying Non-Survey Data and Machine Learning Techniques to Address Nonresponse in an Agricultural Area Frame Survey

Oregon Convention Center 

The June Area Survey (JAS) is an annual survey conducted by the United States (U.S.) Department of Agriculture's National Agricultural Statistics Service (NASS) to estimate crop acreages and to measure the coverage of the NASS list frame. The JAS is based on an area frame that offers complete coverage of the contiguous U.S. The design of the survey requires complete reports for all sampled tracts. Thus, the inevitable nonresponse in the survey must be addressed through observation of sampled areas or imputation. Time spent on these efforts is costly and the resulting data are less reliable than data obtained from full responses. Researchers at NASS have developed a new approach for integrating administrative data, geospatial data, and machine learning forecasting techniques to begin addressing nonresponse in the JAS with an automated imputation process. In this paper, the new process for automated imputation will be described, and the predicted impact on survey data quality is explored. Study results indicate that the automated imputation process produces estimates that are comparable to those produced using traditional methods.