Predicting the performance of new plant genotypes under new environmental conditions could accelerate the development of locally adapted and climate resilient cultivars. Enabling these predictions may rely on extending the genomewide prediction framework to include environmental covariates (EC), but such models have generally been tested under limited, less breeding-realistic contexts. Using a barley (Hordeum vulgare L.) multi-environment dataset, our objectives were to compare multi-environment prediction models and scenarios that target genotypes from different breeding generations, use different levels and timescales of ECs, and are applied to different agronomic and quality traits. When predicting the phenotypes of previously tested genotypes in untested environments, models that included the interaction of genomewide markers and pre-selected in-season ECs resulted in more accurate predictions (rMG or rMP) within (rMG = 0.56–0.94) and across (rMP = 0.63–0.87) environments; similar accuracy was achieved within (rMP= 0.46–0.89) and across (rMP= 0.87–0.95) locations when using only ECs from realistically available historical climate data. Shifting the prediction target to a distinct, untested offspring population slightly reduced model performance within environments or locations, but rMP across environments (rMP= 0.60–0.86) or locations (rMP= 0.87–0.94) remained very high. Though we achieved moderately high rMP for most traits in the challenging scenario of predicting the offspring population in holdout environments, the similarity between training and target environments, like that between populations, will be a limiting factor for enabling accurate predictions of new genotypes under new growing conditions.
Bibliographical noteFunding Information:
We thank the many collaborators that helped conduct field trials: D. Sweeney, M. Sorrells, C. Kapp, K. D. Kephart, L. Elmore, J. Sherman, E. J. Stockinger, S. Fisk, P. Hayes, S. Daba, M. Mohammadi, L. Trumble, N. Hughes, L. Lukens, P. González Barrios, L. Gutiérrez, A. Mills, C. Sellmer, R. Horsley, M. Hochhalter, J. Goudet, H. Darby, C. Duley, C. Evans, and G. Hu. We also thank E. Schiefelbein, G. Velasquez, and K. Beaubien for technical support and S. Chao for assistance with genotyping. Resources from the Minnesota Supercomputing Institute were used to complete this project. This research was supported by the U.S. Wheat and Barley Scab Initiative, the Minnesota Department of Agriculture, Rahr Malting Company, the Brewers Association, the American Malting Barley Association, USDA‐NIFA Hatch Project 149–447, and USDA‐NIFA Grants 2018‐67011‐28075 and 2018‐67013‐27620.
© 2022 The Authors. Crop Science published by Wiley Periodicals LLC on behalf of Crop Science Society of America. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.