Background Success in any genomic prediction platform is directly dependent on
establishing a representative training set. This is a complex task, even in single-trait single …

Key message Incorporating feature-engineered environmental data into machine learning-
based genomic prediction models is an efficient approach to indirectly model genotype-by …

Multi-environment trials are routinely conducted in plant breeding to select candidates for
the next selection cycle. In this study, we compare the prediction accuracy of four developed …

Genomic selection has been implemented in several plant and animal breeding programs
and it has proven to improve efficiency and maximize genetic gains. Phenotypic data of …

Hybrid breeding programs are driven by the potential to explore the heterosis phenomenon
in traits with non-additive inheritance. Traditionally, progress has been achieved by crossing …

Genomic selection has become a reality in plant breeding programs with the reduction in
genotyping costs. Especially in maize breeding programs, it emerges as a promising tool for …

The development of crop varieties with stable performance in future environmental
conditions represents a critical challenge in the context of climate change. Environmental …

Technology advances have made possible the collection of a wealth of genomic,
environmental, and phenotypic data for use in plant breeding. Incorporation of …

Modern whole-genome prediction (WGP) frameworks that focus on multi-environment trials
(MET) integrate large-scale genomics, phenomics, and envirotyping data. However, the …

In the realm of multi-environment prediction, when the goal is to predict a complete
environment using the others as a training set, the efficiency of genomic selection (GS) falls …