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

  • Chris M. Theobald
  • Mike Talbot
  • Fabian Nabugoomu

DOI: 10.1198/108571102230

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


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 inference Decision theory Local-area estimation Markov chain Monte Carlo Mixed-effects model Residual maximum likelihood Variety by environment data 

Copyright information

© International Biometric Society 2002

Authors and Affiliations

  • Chris M. Theobald
    • 1
  • Mike Talbot
    • 2
  • Fabian Nabugoomu
    • 3
  1. 1.Department of Mathematics and StatisticsUniversity of EdinburghEdinburghU.K.
  2. 2.Biomathematics and Statistics ScotlandEdinburghU.K.
  3. 3.Department of MathematicsMakerere UniversityKampalaUganda

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