Genomic prediction in multi-environment trials in maize using statistical and machine learning methods.

ABSTRACT: In the context of multi-environment trials (MET), genomic prediction is proposed as a tool that allows the prediction of the phenotype of single cross hybrids that were not tested in field trials. This approach saves time and costs compared to traditional breeding methods. Thus, this study aimed to evaluate the genomic prediction of single cross maize hybrids not tested in MET, grain yield and female flowering time. We also aimed to propose an application of machine learning methodologies in MET in the prediction of hybrids and compare their performance with Genomic best linear unbiased prediction (GBLUP) with non-additive effects. Our results highlight that both methodologies are efficient and can be used in maize breeding programs to accurately predict the performance of hybrids in speci

SUBMITTER: Barreto CAV 

PROVIDER: S-EPMC10784464 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature