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

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

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 …

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

We introduce the R-package learnMET, developed as a flexible framework to enable a
collection of analyses on multi-environment trial breeding data with machine learning-based …

Genotype-by-environment interaction (GEI) is among the greatest challenges for maize
breeding programs. Strong GEI limits both the prediction of genotype performance across …

Genomic selection (GS) has the potential to improve the selection gain for complex traits in
crop breeding programs from resource-poor countries. The GS model performance in multi …

Genomic prediction provides an efficient alternative to conventional phenotypic selection for
developing improved cultivars with desirable characteristics. New and improved methods to …

Machine learning techniques for crop genomic selections, especially for single-environment
plants, are well-developed. These machine learning models, which use dense genome …

Genotype-by-environment (G× E) interactions can significantly affect crop performance and
stability. Investigating G× E requires extensive data sets with diverse cultivars tested over …