Journal of Agricultural, Biological, and Environmental Statistics

, Volume 7, Issue 3, pp 403–419

A bayesian approach to regional and local-area prediction from crop variety trials

Authors

    • Department of Mathematics and StatisticsUniversity of Edinburgh
  • Mike Talbot
    • Biomathematics and Statistics Scotland
  • Fabian Nabugoomu
    • Department of MathematicsMakerere University
Article

DOI: 10.1198/108571102230

Cite this article as:
Theobald, C.M., Talbot, M. & Nabugoomu, F. JABES (2002) 7: 403. doi:10.1198/108571102230

Abstract

The inclusion of covariates in models for analyzing variety × environmental data sets allows the estimation of variety yields for specific locations within a region as well as for the region as a whole. Here we explore a Bayesian approach to the estimation of such effects and to the choice of variety using a possibly incomplete variety × location × year data set that includes location × year covariates. This approach allows expert knowledge of the crop and uncertainty about local circumstances to be incorporated in the analysis. It is implemented using Markov chain Monte Carlo simulation. An example is used to illustrate the approach and investigate its robustness.

Key Words

Bayesian inferenceDecision theoryLocal-area estimationMarkov chain Monte CarloMixed-effects modelResidual maximum likelihoodVariety by environment data
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Copyright information

© International Biometric Society 2002