A New Automatic Stratification Method for U.S. Agricultural Area Sampling Frame Construction Based on the Cropland Data Layer

This paper, for the first time, proposes to apply USDA National Agricultural Statistics Service (NASS) Cropland Data Layer (CDL) geospatial data for stratifying U.S. agricultural land. A new automated method is proposed to stratify the NASS state level area sampling frames (ASFs) by automatically calculating percent cultivation at the primary sampling unit (PSU) level based on the CDL data. The NASS CDLs are 30-56.0 m raster-formatted, georeferenced, cropland cover classifications derived from satellite data. The CDL stratification experiment was successfully conducted for Oklahoma, Ohio, Virginia, Georgia, and Arizona. The stratification accuracies of the traditional (visual interpretation) and new automated CDL stratification methods were compared based on 2010 June Area Survey data. Experimental results indicated that the CDL stratification method achieved higher accuracies in the intensively cropped areas, while the traditional method achieved higher accuracies in low or nonagricultural areas. The differences in the accuracies were statistically significant at a 95% confidence level. It was found that using multiyear composite, CDL-based cultivated layers did not improve stratification accuracies as compared to the results of single-year CDL data. Two applications of the CDL-automated stratification method in official USDA NASS operations are described. The novelty of the proposed method was using geospatial CDL data to objectively and automatically compute percent cultivation of the ASF PSUs as compared to the traditional method that subjectively determines percent cultivation using visual interpretation of satellite data. This proposed new CDL-based process improved efficiency, objectivity, and accuracy as compared to the traditional stratification method.