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.