Ongoing changes in the global environmental conditions foster plant breeding research to
develop climate-smart cultivars as fast as possible. Data analytics are essential for achieving …

One of the most important applications of genomic selection in maize breeding is to predict
and identify the best untested lines from biparental populations, when the training and …

Genomic selection (GS) models use genome-wide genetic information to predict genetic
values of candidates of selection. Originally, these models were developed without …

The usefulness of genomic prediction in crop and livestock breeding programs has
prompted efforts to develop new and improved genomic prediction algorithms, such as …

Globally, over 2 billion people suffer from malnutrition due to inadequate intake of
micronutrients. Genomic-assisted breeding is identified as a valuable method to facilitate …

When information on multiple genotypes evaluated in multiple environments is recorded, a
multi-environment single trait model for assessing genotype× environment interaction (G× E) …

In this paper we propose a Bayesian multi-output regressor stacking (BMORS) model that is
a generalization of the multi-trait regressor stacking method. The proposed BMORS model …

Predicting the consequences of manipulating genotype (G) and agronomic management (M)
on agricultural ecosystem performances under future environmental (E) conditions remains …

Genomic selection (GS) is transforming plant and animal breeding, but its practical
implementation for complex traits and multi-environmental trials remains challenging. To …

Traditional breeding strategies for selecting superior genotypes depending on phenotypic
traits have proven to be of limited success, as this direct selection is hindered by low …