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 …

In this study, we compared the prediction accuracy of the main genotypic effect model (MM)
without G× E interactions, the multi-environment single variance G× E deviation model …

The phenomenon of genotype× environment (G× E) interaction in plant breeding decreases
selection accuracy, thereby negatively affecting genetic gains. Several genomic prediction …

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

In genomic selection (GS), genotype× environment interaction (G× E) can be modeled by a
marker× environment interaction (M× E). The G× E may be modeled through a linear kernel …

One of the most widely used kernel functions in genomic-enabled prediction is the Gaussian
kernel. Selection of the bandwidth parameter for kernel regression has generally been …

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 …

Zinc (Zn) deficiency is a major risk factor for human health, affecting about 30% of the world's
population. To study the potential of genomic selection (GS) for maize with increased Zn …

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 …

High throughput genotyping, phenotyping, and envirotyping applied within plant breeding
multienvironment trials (METs) provide the data foundations for selection and tackling …