Bayesian analysis of mixed effect models and its applications in agriculture

The mixed effect models has been discussed and implemented from Bayesian viewpoint. In this paper we have made Bayesian analysis of mixed effect models and illustrated its application in agriculture. We focus on linear mixed models with a random intercept and fixed slope. The basic idea behind this approach is to model the phenomenon under study in stages and analyze that model in Bayesian framework. Advancement in the computational power of high speed computers has aided the application part. Suitable illustrations have been proposed on real data set generated on potato crop in year 2005-2006 at five different locations with twelve genotypes including both Yield and Growth attributing characters (tuber weight and Average tuber No.). The models used in this paper have been fitted by lme (fixed, data, random) of nlme library by pinheiro and Bates (2000) and it was observed on BIC(Bayesian information criteria) that we should treat locations as random and not as fixed