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291 to 300 of 304 Results
Dataset / Software
Feb 17, 2019
Trachsel, Samuel; Dhliwayo, Thanda; Gonzalez Perez, Lorena; Mendoza Lugo, Jose Alberto; Trachsel, Mathias, 2019, "Replication Data for: Estimation of Physiological Genomic Estimated Breeding Values (PGEBV) Combining full Hyperspectral and Marker Data Across Environments for Grain Yield Under Combined Heat and Drought Stress in Tropical Maize (Zea mays L.)", hdl:11529/10548168, CIMMYT Research Data & Software Repository Network, V1
This file provides supporting material for the manuscript entitled ' Estimation of Physiological Genomic Estimated Breeding Values (PGEBV) Combining full Hyperspectral and Marker Data Across Environments for Grain Yield Under Combined Heat and Drought Stress in Tropical Maize (Ze...
Dataset / Software
May 11, 2020
Ibba, Maria Itria; Crossa, Jose; Montesinos-López, Osval A.; Montesinos-López, Abelardo; Juliana, Philomin; Guzman, Carlos; Dolorean, Emily; Dreisigacker, Susanne ; Poland, Jesse, 2020, "Replication Data for: Genome-based prediction of multiple wheat quality traits in multiple years", hdl:11529/10548423, CIMMYT Research Data & Software Repository Network, V1
The use of genomic prediction could greatly help to increase the efficiency of selecting for wheat quality traits by reducing the cost and time required for this analysis. This study contains data used to evaluate the prediction performances of 13 wheat quality traits under two m...
Dataset / Software
Dec 8, 2018
Yuan, Yibing; Cairns, Jill E.; Babu, Raman; Gowda, Manje; Makumbi, Dan; Magorokosho, Cosmos; Zhang, Ao; Liu, Yubo; Wang, Nan; Hao, Zhuanfang; San Vicente, Felix; Olsen, Michael S.; Prasanna, Boddupalli M. ; Lu, Yanli; Zhang, Xuecai, 2018, "Replication Data for: Genome-Wide Association Mapping And Genomic Prediction Analyses Reveal the Genetic Architecture of Grain Yield and Flowering Time Under Drought and Heat Stress Conditions in Maize", hdl:11529/10548156, CIMMYT Research Data & Software Repository Network, V1
Drought stress, heat stress, and combination of drought stress and heat stress have been recognized as the major abiotic constraints to maize yields in the main production regions. The phenotypic data used in the current study had been published by Jill E. Cairns et al in 2013 in...
Dataset / Software
Apr 1, 2020
Guo, Rui; Dhliwayo, Thanda; Mageto, Edna; Palacios-Rojas, Natalia; Lee, Michael; Yu, Diansi; Ruan, Yanye; Zhang, Ao; San Vicente, Felix; Olsen, Michael; Crossa, Jose; Prasanna, Boddupalli M.; Zhang, Lijun; Zhang, Xuecai, 2020, "Replication Data for: Genomic prediction of kernel zinc content in multiple maize populations using genotyping-by-sequencing and repeat amplification sequencing markers", hdl:11529/10548362, CIMMYT Research Data & Software Repository Network, V1
An association-mapping panel (DTMA) and two DH populations (DH1 and DH2) were used in the current study, which in total includes 487 materials. The dataset includes three types of files. One is the genotype of 487 lines sequenced by GbS, named DTMA_DH2_DH3-955690.hmp.txt; one is...
Dataset / Software
May 15, 2020
Mageto, Edna K.; Crossa, Jose; Perez-Rodriguez, Paulino; Dhliwayo, Thanda; Palacios-Rojas, Natalia; Lee, Michael; Guo, Rui; San Vicente, Félix; Zhang, Xuecai; Hindu, Vemuri, 2020, "Replication Data for: Genomic Prediction with Genotype by Environment Interaction Analysis for Kernel Zinc Concentration in Tropical Maize Germplasm", hdl:11529/10548331, CIMMYT Research Data & Software Repository Network, V1
The Zinc association mapping (ZAM) panel is a set of 923 elite inbred lines from the International Maize and Wheat Improvement Center (CIMMYT) biofortification breeding program. The panel represented wide genetic diversity for kernel Zn and is comprised of several lines with tole...
Dataset / Software
Jul 12, 2019
Howard, Reka; Gianola, Daniel; Montesinos-López, Osval; Juliana, Philomin; Singh, Ravi; Poland, Jesse; Shrestha, Sandesh; Perez-Rodriguez, Paulino; Crossa, Jose; Jarquin, Diego, 2019, "Replication Data for: Joint use of genome, pedigree and their interaction with environment for predicting the performance of wheat lines in new environments", hdl:11529/10548169, CIMMYT Research Data & Software Repository Network, V3
In this study, we evaluated genome-based prediction using 35,403 wheat lines from the Global Wheat Breeding Program of the International Maize and Wheat Improvement Center (CIMMYT). We implemented eight statistical models that included genome-wide molecular marker and pedigree in...
Dataset / Software
Jun 25, 2019
Lobell, David B.; Banziger, Marianne; Magorokosho, Cosmos; Vivek, Bindiganavile, 2019, "Replication Data for: Nonlinear heat effects on African maize as evidenced by historical yield trials", hdl:11529/10548190, CIMMYT Research Data & Software Repository Network, V1
This dataset provides supplementary files for the trials and sites described in the 2011 paper in https://www.nature.com/nclimate/ available at https://dx.doi.org/10.1038/NCLIMATE1043
Dataset / Software
Mar 24, 2020
Jarquin, Diego; Howard, Reka; Beyene, Yoseph; Gowda, Manje; Burgueño, Juan; Martini, Johannes; Pacheco, Angela; Covarrubias, Eduardo G.; Crossa, Jose, 2020, "Sparse designs for genomic selection using multi-environment data", hdl:11529/10548369, CIMMYT Research Data & Software Repository Network, V3
This research study the genomic-enabled prediction accuracy of the composition of the following sparse testing allocation design: (1) all non-overlapping (0 overlapping) lines in environments, (2) all overlapping (0 non-overlapping) lines tested in all the environments, and (3) c...
Dataset / Software
Sep 21, 2018
Basnet, Bhoja Raj; Crossa, Jose; Pérez-Rodríguez, Paulino; Manes, Yann; Singh, Ravi ; Rosyara, Umesh; Camarillo-Castillo, Fatima; Murua, Mercedes, 2018, "Supplemental data for hybrid wheat prediction using genomic, pedigree and environmental covariables interaction models", hdl:11529/10548129, CIMMYT Research Data & Software Repository Network, V1
Genomic prediction of hybrids unobserved in field evaluations is crucial. In this study, we used genomic G×E models for hybrid prediction, where similarity between lines was assessed by pedigree and molecular markers, and similarity between environments was accounted for by envir...
Dataset / Software
Sep 28, 2018
Montesinos-López, Osval A ; Montesinos-López, Abelardo; Crossa, Jose; Gianola, Daniel ; Hernández-Suárez, Carlos M.; Martín-Vallejo, Javier, 2018, "Supplemental data for multi-trait, multi-environment deep learning modeling for genomic-enabled prediction of plant traits", hdl:11529/10548134, CIMMYT Research Data & Software Repository Network, V1
This study provides supplemental data to support an investigation of the power of multi-trait deep learning (MTDL) models in terms of genomic-enabled prediction accuracy.
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