Modeling Genotype X Environment Correlation Structures in Long-term Multilocation Forage Yield Trials

作者：Sripathi Raghuveer^*; Conaghan Patrick; Grogan Dermot; Casler Michael D

来源：Crop Science, 2018, 58(4): 1447-1457.

DOI：10.2135/cropsci2017.05.0292

Genotype x environment interactions are a critical aspect of field experiments to evaluate yield of forage cultivars. The objectives of this study were (i) to model genotypic effects across establishment years, locations, and harvest years of forage yield trials using variance-covariance structures, (ii) to predict cultivar performance across different environments, and (iii) to compare the relative efficiency of different cost reduction scenarios according to locations, harvest years, and replicates per sowing year. This study is focused on long-term ryegrass (Lolium spp.) yield trials conducted by the Department of Agriculture, Food and Marine (DAFM) in Ireland. Log likelihood responses of different models indicated that unstructured variance-covariance models were superior in fit to heterogeneous variance structure models. The unstructured variance-covariance models also improved predictability compared with heterogeneous variance structure models to estimate genetic effects. Among locations, Athenry (representing western Ireland) was consistently clustered with other locations in both first and second harvest years of late-and intermediate-maturity trials. For resource allocation, cost reduction by reducing the number of harvest years was not effective for the DAFM trials. A 20% reduction in cost by excluding one location or four replicates per sowing year resulted in only an 8% reduction in efficiency. Modeling genetic effects using unstructured variance-covariance models allowed us to group locations on the basis of genetic correlations to improve prediction accuracy of cultivar effects across different locations and to reduce costs associated with future cultivar trials.

• 出版日期2018-8