Modeling Genotype × Environment Correlation Structures in Long‐term Multilocation Forage Yield Trials

Abstract

Genotype × 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.

Selection of maize hybrids: an approach with multi-trait, multi-environment, and ideotype-design

“…In plant breeding, multi-environment trials (MET) are useful for evaluating genotypes, testing their performance in a range of environments, and selecting the most superior (Alves et al 2020). In MET, the genotype-by-environment (G×E) interaction is a factor influencing the performance of genotypes under environmental variation (Resende 2015), resulting in a change in genotyping ranking over different environments, which makes genetic selection difficult (Sripathi et al 2018). In general, various traits have been evaluated in maize breeding, with the aim of supporting the selection and recommendation of the ideotype (i.e., genotypes with simultaneous superior performance in many traits).…”

Análise de fatores e modelagem de estruturas de (co)variâncias para ensaios multiambientes na seleção de linhagens e progênies de feijoeiro

“…Modelar os efeitos genéticos e residuais na presença da interação G×A permite obter um modelo mais realista para dados provenientes de experimentos multiambientes, uma vez que o efeito da interação é devido à heterogeneidade de variância genética e à falta de correlação de genótipos entre pares de ambientes . A eficiência da modelagem de diferentes estruturas de (co)variâncias para efeitos genéticos e não genéticos foi relatada como eficaz no milho (Dodig et al, 2021;Pereira et al, 2021); forragem (Sripathi et al, 2018), cana-de-açúcar (Balsalobre et al, 2016, trigo e feijão (Melo et al, 2020). Além disso, por meio desta abordagem, é possível aumentar a acurácia dos valores genéticos e, consequentemente, a eficiência da seleção (So e Edwards, 2011).…”