Bayesian analysis of agricultural field experiments

The paper describes Bayesian analysis for agricultural field experiments, a topic that has received very little previous attention, despite a vast fre quentist literature. Adoption of the Bayesian paradigm simplifies the inter pretation of the results, especially in ranking and selection. Also, comple x formulations can be analysed with comparative ease. by using Markov chain Monte Carlo methods. A key ingredient in the approach is the need for spat ial representations of the unobserved fertility patterns. This is discussed in detail. Problems caused by outliers and by jumps in fertility are tackl ed via hierarchical-t formulations that may find use in other contexts. The paper includes three analyses of variety trials for yield and one example involving binary data; none is entirely straightforward. Some numerical com parisons with frequentist analyses are made.