In crop experiments measurements are often made by a judge evaluating the crops' conditions after treatment. In the present paper an analysis is proposed for experiments where plots of crops treated differently are mutually ranked. In the experimental layout the crops are treated on consecutive plots usually placed side by side in one or more rows. In the proposed method a judge ranks several neighbouring plots, say three, by ranking them from best to worst. For the next observation the judge moves on by no more than two plots, such that up to two plots will be re-evaluated again in a comparison with the new plot(s). Data from studies using this set-up were analysed by a Thurstonian random utility model, which assumed that the judge's rankings were obtained by comparing latent continuous utilities or treatment effects. For the latent utilities a variance component model was considered to account for the repeated measurements. The estimation was based on a Bayesian approach, which was analysed via Markov Chain Monte Carlo sampling. A simulation study showed that the approach was able to estimate the relative ordering of the treatments. The efficiency of the estimation increased with the overlap of the ranked observations. The approach was compared with a more traditional analysis of variance analysis applied to a proportional score for each plot by applying both methods to a real experiment. The relative ranking of the treatment effects were almost the same for both approaches. This method seems to be less sensitive to the judge and also eliminates any possible drift over the judging process in the more traditional method.