Autor: Leonardo Abdiel Crespo Herrera

Results from rapid-cycle recurrent genomic selection in spring bread wheat

Susanne Dreisigacker Leonardo Abdiel Crespo Herrera Alison Bentley Jose Crossa (2022)

Empirical studies of early generation genomic selection strategies for parental selection or population improvement are still lacking in wheat and other major crops. We show the potential of rapid-cycle recurrent GS to increase genetic gain for grain yield in wheat. We show a consistent realized genetic gain for grain yield after three cycles of recombination of bi-parental F1’s, when summarized across two years of phenotyping.

Dataset

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

Fifty years of semi-dwarf spring wheat breeding at CIMMYT: Grain yield progress in optimum, drought and heat stress environments

Suchismita Mondal Somak Dutta Leonardo Abdiel Crespo Herrera JULIO HUERTA_ESPINO Hans-Joachim Braun Ravi Singh (2019)

This dataset provides supplementary files related to fifty years of semi-dwarf spring wheat breeding at CIMMYT: Grain yield progress in optimum, drought and heat stress environments.

Dataset

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

Replication Data for: Efficient Arabinoxylan Assay for Wheat: Exploring Variability and Molecular Marker Associations in Wholemeal and Refined Flour

Susanne Dreisigacker Jose Crossa Leonardo Abdiel Crespo Herrera Maria Itria Ibba (2024)

This dataset is derived from a study focused on developing an efficient method for arabinoxylan quantification, called PentoQuant. It includes phenotypic and molecular characterization data from 606 bread wheat samples developed through the spring bread wheat breeding program. The dataset comprises total and water-extractable arabinoxylan content values measured using the PentoQuant protocol. Furthermore, it incorporates results obtained from analyzing the same 606 lines with four molecular markers associated with two major QTLs for arabinoxylan content variation in wheat, located on chromosomes 1B and 6B.

Dataset

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

Replication Data for: Sparse kernel models provide optimization of training set design for genome-based prediction in multi-year wheat breeding data

Marco Lopez-Cruz Susanne Dreisigacker Leonardo Abdiel Crespo Herrera Alison Bentley Ravi Singh Suchismita Mondal Paulino Pérez-Rodríguez Jose Crossa (2021)

When genomic selection (GS) is used in breeding schemes, data from multiple generations can provide opportunities to increase sample size and thus the likelihood of extracting useful information from the training data. The Sparse Selection Index (SSI), is is a method for optimizing training data selection. The data files provided with this study include a large multigeneration wheat dataset of grain yield for 68,836 lines generated across eight cycles (years) as well as genotypic data that were analyzed to test this method. The results of the analysis are published in the corresponding journal article.

Dataset

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

Replication Data for: Allocation of wheat lines in sparse testing for genome-based multi-environment prediction

Leonardo Abdiel Crespo Herrera Ravi Singh Suchismita Mondal Philomin Juliana DIEGO JARQUIN Jose Crossa (2021)

Sparse testing can be used in plant breeding and genome-based prediction. In sparse testing not all of the lines are sown in all environments. The phenotypic and genotypic data files provided in this dataset were used to execute an analysis of three general cases of the composition of the sparse testing allocation design for wheat breeding.

Dataset

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA