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Mostrando ítems 1-10 de 17
Presentation
Mejoramiento de la calidad de trigo
(CIMMYT, 2018)
Presentation
Calidad del grano de trigo
(CIMMYT, 2018)
Presentation
Mejoramiento de la calidad de trigo
(CIMMYT, 2017)
Article
Aerial high‐throughput phenotyping enables indirect selection for grain yield at the early generation, seed‐limited stages in breeding programs
(Crop Science Society of America (CSSA), 2020)
Article
Climate change has increased genotype-environment interactions in wheat breeding
(Research Square, 2020)
Article
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 ...
Article
Integrating genomic-enabled prediction and high-throughput phenotyping in breeding for climate-resilient bread wheat
(Springer, 2019)
Genomic selection and high-throughput phenotyping (HTP) are promising tools to accelerate breeding gains for high-yielding and climate-resilient wheat varieties. Hence, our objective was to evaluate them for predicting ...
Article
Genetic gains for grain yield in CIMMYT’s semi-arid wheat yield trials grown in suboptimal environments
(Crop Science Society of America (CSSA), 2018)
Wheat (Triticum aestivum L.) is a major staple food crop grown worldwide on >220 million ha. Climate change is regarded to have severe effect on wheat yields, and unpredictable drought stress is one of the most important ...
Article
Genomic and pedigree-based prediction for leaf, stem, and stripe rust resistance in wheat
(Springer, 2017)
The unceasing plant-pathogen arms race and ephemeral nature of some rust resistance genes have been challenging for wheat (Triticum aestivum L.) breeding programs and farmers. Hence, it is important to devise strategies ...
Article
Hybrid wheat prediction using genomic, pedigree, and environmental covariables interaction models
(Crop Science Society of America, 2019)
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 ...