A bayesian approach to regional and local-area prediction from crop variety trials
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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.
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- A bayesian approach to regional and local-area prediction from crop variety trials
Journal of Agricultural, Biological, and Environmental Statistics
Volume 7, Issue 3 , pp 403-419
- Cover Date
- Print ISSN
- Online ISSN
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- Bayesian inference
- Decision theory
- Local-area estimation
- Markov chain Monte Carlo
- Mixed-effects model
- Residual maximum likelihood
- Variety by environment data
- Author Affiliations
- 1. Department of Mathematics and Statistics, University of Edinburgh, The King’s Buildings, EH9 3JZ, Edinburgh, Scotland, U.K.
- 2. Biomathematics and Statistics Scotland, The King’s Buildings, EH9 3JZ, Edinburgh, Scotland, U.K.
- 3. Department of Mathematics, Makerere University, P.O. Box 7062, Kampala, Uganda