Leveraging environmental covariates to predict crop variety performance in targeted environments

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Multi-environment trials are routinely conducted in order to assess the value for cultivation and use of candidate crop varieties for registration. While the primary purpose of these trials is variety registration, the data may also be used to derive recommendations to farmers. Whereas decisions for variety registration are usually based on variety means across all trial environments, recommendations to farmers require more targeted estimates of variety performance. Such more targeted approaches can be based on a characterization of targeted environments using covariate information. In this project, we will use the newly developed "enviromics" concept in order to make use of environmental covariate information to predict variety performance in any environment picked from the target population of environments for which multi-environment trials were performed. This prediction framework allows projecting the valuable results of multi-environment trials to farmers' environments in the target population of environments.