Although hybrid crop varieties are among the most popular agricultural innovations, the
rationale for hybrid crop breeding is sometimes misunderstood. Hybrid breeding is slower …

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

High-dimensional and high-throughput genomic, field performance, and environmental data
are becoming increasingly available to crop breeding programs, and their integration can …

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

Genomic breeding technologies offer new opportunities for grain yield (GY) improvement in
common wheat. In this study, we have evaluated the potential of genomic selection (GS) in …

Fusarium head blight (FHB) incited by Fusarium graminearum Schwabe is a devastating
disease of barley and other cereal crops worldwide. Fusarium head blight is associated with …

Genomic prediction models are often calibrated using multi-generation data. Over time, as
data accumulates, training data sets become increasingly heterogeneous. Differences in …

Prediction models based on pedigree and/or molecular marker information are now an
inextricable part of the crop breeding programs and have led to increased genetic gains in …

Abstract Soybean [Glycine max (L.) Merr.] is a significant source of protein and oil and is also
widely used as animal feed. Thus, developing lines that are superior in terms of yield …

The success of genomic selection (GS) in breeding schemes relies on its ability to provide
accurate predictions of unobserved lines at early stages. Multigeneration data provides …