Abstract:
Ground-based data on crop production in the USA is provided
through surveys conducted by the National Agricultural Statistics
Service (NASS) and the Census of Agriculture (AgCensus). Statistics
from these surveys are widely used in economic analyses, policy
design, and for other purposes. However, missing data in the surveys
presents limitations for research that requires comprehensive data for
spatial analyses.We created comprehensive county-level databases for
nine major crops of the USA for a 16-yr period, by filling the gaps in
existing data reported by NASS and AgCensus. We used a combination
of regression analyses with data reported by NASS and the
AgCensus and linear mixed-effect models incorporating county-level
environmental, management, and economic variables pertaining to
different agroecozones. Predicted yield and crop area were very close
to the data reported by NASS, within 10% relative error. The linear
mixed-effect model approach gave the best results in filling 84% of the
total gaps in yields and 83% of the gaps in crop areas of all the crops.
Regression analyses with AgCensus data filled 16% of the gaps in
yields and crop areas of the major crops reported by NASS.
