Recent contributions to the theory of optimizing fertilizer dose in agricultural crop production have introduced Bayesian ideas in order to incorporate information on crop yield from several environments and on soil nutrients from a soil test, but have not used a fully Bayesian formulation. We present such a formulation, and demonstrate how the resulting Bayes decision procedure can be evaluated in practice using Markov-Chain Monte Carlo methods. The approach incorporates expert knowledge of the crop and of regional and local soil conditions, and allows a choice of crop variety as well as of fertilizer level. Alternative dose-response functions are expressed in terms of a common, interpretable set of parameters to facilitate model comparison and the specification of prior distributions. The approach is illustrated with a set of yield data from spring barley nitrogen-response trials, and is found to be robust to changes in the dose-response function and the prior distribution for indigenous soil nitrogen.