A Bayesian Approach to Regional and Local-Area Prediction From Crop Variety Trials
| dc.contributor.author | Theobald, Chris M. | |
| dc.contributor.author | Talbot, Mike | |
| dc.contributor.author | Nabugoomu, Fabian | |
| dc.date.accessioned | 2022-12-05T07:20:27Z | |
| dc.date.available | 2022-12-05T07:20:27Z | |
| dc.date.issued | 2002 | |
| dc.description.abstract | The inclusion of covariates in models for analyzing variety£ environmentaldata sets allows the estimation of variety yields for speci c 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 approachallows expert knowledge of the crop and uncertainty about local circumstances to be incorporated in the analysis. It is implemented usingMarkov chain Monte Carlo simulation.An example is used to illustrate the approach and investigate its robustness. | en_US |
| dc.identifier.citation | Theobald, C. M., Talbot, M., & Nabugoomu, F. (2002). A Bayesian approach to regional and local-area prediction from crop variety trials. Journal of Agricultural, Biological, and Environmental Statistics, 7(3), 403-419. | en_US |
| dc.identifier.uri | https://link.springer.com/article/10.1198/108571102230 | |
| dc.identifier.uri | https://nru.uncst.go.ug/handle/123456789/5843 | |
| dc.language.iso | en | en_US |
| dc.publisher | Journal of Agricultural, Biological, and Environmental Statistics | en_US |
| dc.subject | Bayesian inference | en_US |
| dc.subject | Decision theory | en_US |
| dc.subject | Local-area estimation | en_US |
| dc.title | A Bayesian Approach to Regional and Local-Area Prediction From Crop Variety Trials | en_US |
| dc.type | Article | en_US |