Understanding the effect of genetic factors controlling flowering time is essential to fine-tune crop development to each target environment and to maximize yield. A set of 35 durum wheat genotypes of spring growth-habit ...

Aims: This study aimed to quantify the changes in root and aerial biomass of durum wheat brought about by the introduction of the Rht-B1b dwarfing allele and their effects on yield formation. Methods A historical series ...

The single most important decision in plant breeding programs is the selection of appropriate crosses. The ideal cross would provide superior predicted progeny performance and enough diversity to maintain genetic gain. The ...

Cereal cyst nematodes (CCNs) are plant parasites that significantly limit global cereal production. The most frequently reported pathogenic species are Heterodera avenae, H. filipjevi, and H. latipons. One of the most ...

New methods and algorithms are being developed for predicting untested phenotypes in schemes commonly used in genomic selection (GS). The prediction of disease resistance in GS has its own peculiarities: a) there is consensus ...

This study describes the genetic diversity and population structure of 194 native maize populations from 23 countries of Latin America and the Caribbean. The germplasm, representing 131 distinct landraces, was genetically ...

Background: Single nucleotide polymorphisms (SNPs) are ideally suited for the construction of high-resolution genetic maps, studying population evolutionary history and performing genome-wide association mapping experiments. ...

One of the biggest challenges for genetic studies on natural or unstructured populations is the unbalanced datasets where individuals are measured at different times and environments. This problem is also common in crop ...

Genomic selection (GS) has been promising for increasing genetic gains in several species. Therefore, we evaluated the potential integration of GS for grain yield (GY) in bread wheat (Triticum aestivum L.) in CIMMYT's elite ...

In this study, we used genotype × environment interactions (G×E) models for hybrid prediction, where similarity between lines was assessed by pedigree and molecular markers, and similarity between environments was accounted ...