The usefulness of genomic prediction (GP) for many animal and plant breeding programs
has been highlighted for many studies in the last 20 years. In maize breeding programs …

Anthropogenic change exposes populations to environments that have been rare or entirely
absent from their evolutionary past. Such novel environments are hypothesized to release …

Maize production in sub-Saharan Africa (SSA) faces challenges due to the damage caused
by the parasitic weed, Striga hermonthica (Del.) Benths and low soil nitrogen. To address …

The effects of climate change create formidable challenges for breeders striving to produce
sufficient food quantities in rapidly changing environments. It is therefore critical to …

Sweet corn breeding programs, like field corn, focus on the development of elite inbred lines
to produce commercial hybrids. For this reason, genomic selection models can help the in …

Multi-environment trials (MET) are fundamental for assessing genotype-by-environment
interaction (GxE) effects, adaptability and stability of genotypes and provide valuable …

In hybrid sorghum breeding, basic agronomic traits, such as days to flowering and plant
height, of sorghum seed parents must be within a specific range for hybrid seed production …

Machine learning methods such as multilayer perceptrons (MLP) and Convolutional Neural
Networks (CNN) have emerged as promising methods for genomic prediction (GP). In this …

Abstract Tef [Eragrostis tef (Zucc.) Trotter L.] is a most important cereal crop in Ethiopia in
terms of production, consumption and cash. The study was carried out to investigate grain …

One of the most explored crop plants in genomics studies is maize (Zea mays L.). It has
served as a model for developing and incorporating biotechnology and genomics …