Now showing items 1-10 of 41
Maize production environments revisited: A GIS-based approach
This publication presents a GIS-based approach for revising the descriptions of global maize production environments, called "mega-environments" (MEs), used by CIMMYT and its partners. A cluster analysis was performed on ...
Genomic-enabled prediction with classification algorithms
(Springer Nature, 2014)
Pearson’s correlation coefficient (ρ) is the most commonly reported metric of the success of prediction in genomic selection (GS). However, in real breeding ρ may not be very useful for assessing the quality of the regression ...
Data maize and wheat
Genomic prediction enhanced sparse testing for multi-environment trials
(Genetics Society of America, 2020)
QTL mapping in three tropical maize populations reveals a set of constitutive and adaptive genomic regions for drought tolerance
Despite numerous published reports of quantitative trait loci (QTL) for drought-related traits, practical applications of such QTL in maize improvement are scarce. Identifying QTL of sizeable effects that express more or ...
Genomic prediction in biparental tropical maize populations in water-stressed and well-watered environments using low-density and GBS SNPs
(Springer Nature, 2015)
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 validation sets are derived from the same ...
Effectiveness of Genomic Prediction of Maize Hybrid Performance in Different Breeding Populations and Environments
(Genetics Society of America, 2012)
Genomic prediction is expected to considerably increase genetic gains by increasing selection intensity and accelerating the breeding cycle. In this study, marker effects estimated in 255 diverse maize (Zea mays L.) hybrids ...