County level estimates of various agricultural commodities published by USDA’s
National Agricultural Statistics Service (NASS) are in heavy demand by users in
government, the private sector and the academic community. In particular,
accurate small area estimation of crop yields has become increasingly important
over recent years. While NASS has traditionally used ratio estimation to derive
yield numbers, model-based methods that make efficient use of available data
sources hold the promise of significant improvement over the standard approach.
Stasny, Goel and other researchers at the Ohio State University developed a
Bayesian mixed-effects county yield estimation algorithm with a spatial
component involving correlations among neighboring counties. Griffith (at
Syracuse University) proposed an alternative method involving Box-Cox and
Box-Tidwell transformations in conjunction with an autoregressive model. This
report documents a simulation study where the Stasny-Goel method, Griffith
method and standard ratio estimation were compared for twelve crops in ten
geographically dispersed states.
The Stasny-Goel method was found to be more efficient overall than either the
ratio or Griffith method. The two model-based approaches and the simulation
techniques used to compare them are described in some detail, followed by a
discussion of results of the study. Convergence issues associated with the Stasny-
Goel algorithm are also addressed, in particular the question of whether
acceptable estimates can be produced in cases where the algorithm fails to
converge within a preset upper limit on number of iterations
