Opportunities to improve the recommendation of plant varieties under the Recommended List (RL) system

Abstract

Recommended List (RL) is the UK plant variety recommendation system introduced in 1944 for supporting growers in making decisions on variety choices. The current analysis for RL is largely based on published works on trial designs and statistical analyses in the 1980s. Given the statistical advances that have been developed and adopted elsewhere, it is timely to review and update the methods for data analysis in RL. In addition, threats from climate change challenge the prediction of variety performance in future environments. Better variety recommendations, particularly for matching varieties to specific environments can be achieved through improved modeling of effects from genetics, environments and genetic by environment interactions. Here, we evaluated grain yield data from 153 spring barley varieties that were trialed for RL from 2002 to 2019. Our results show that the available methods for predicting variety performance are poor and inconsistent across environments without any clearly superior method. However, these shortcomings can be easily overcome by switching the statistical model for analyzing RL data to one that fits variety effects as random and accounts for genetic relationships among varieties. Lastly, we also discuss other possible approaches for analyzing RL data and highlight the relevance of genomics in both variety registration and recommendation.