Estimation of liquidity costs in agricultural futures markets is challenging because bid-ask spreads are usually not observed. Spread estimators that use transaction data are available, but little agreement exists on their relative accuracy and performance. We evaluate four conventional and a recently proposed Bayesian estimators using simulated data based on Roll’s standard liquidity cost model. The Bayesian estimator tracks Roll’s model relatively well except when the level of noise in the market is large. We derive an improved estimator that seems to have a higher performance even under high levels of noise which is common in agricultural futures markets. We also compute liquidity costs using data for hogs and cattle futures contracts trading on the Chicago Mercantile Exchange. The results obtained for market data are in line with the findings using simulated data.