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1 to 10 of 91 Results
Dataset / Software
Aug 28, 2015 - CIMMYT Seeds of Discovery
Saint Pierre, Carolina; Solís Moya, Ernesto; Hernández Muela, Víctor Manuel; Ireta Moreno, Javier; Fuentes Dávila, Guillermo; Figuero López, Pedro; Zamora Villa, Víctor Manuel; Burgueño, Juan; Mathews, Ky; Barrios, Jose Luis; Castel Vega, Victor Ivan; Singh, Sukwinder, 2015, "Wheat Mexican Adaptation Trial Winter 2011-2012", hdl:11529/10016, CIMMYT Research Data & Software Repository Network, V1
A diverse panel of 1,320 spring wheat lines (Triticum aestivum L. and Triticum durum L.) was sown under optimal conditions at five contrasting environments (during the winter cycle) in Mexico. These data will contribute to the efforts for identifying genetic regions related to gr...
Dataset / Software
Feb 27, 2016 - CIMMYT Seeds of Discovery
Bonnett, David; Saint Pierre, Carolina; Jafarzadeh, Jafar; Jiménez Tapia, José Luis, 2015, "Wheat Derived Synthetics Yield Potential Evaluation 2011-2012", hdl:11529/10393, CIMMYT Research Data & Software Repository Network, V2
Derived synthetic wheat lines were tested for yield potential under normal irrigation in Ciudad Obregon in the 2011-2012 growing season. These materials were derived from bi-parental crosses, backcrosses, or topcrosses between bread wheat lines and synthetic hexaploid wheat lines...
Dataset / Software
Sep 12, 2016 - CIMMYT Seeds of Discovery
Bonnett, David; Saint Pierre, Carolina; Jafarzadeh, Jafar; Dreisigacker, Susanne; Sansaloni, Carolina; Jiménez Tapia, José Luis; Delgado, Roman, 2016, "Wheat Derived Synthetics Heat Tolerance, Drought Tolerance, and Yield Potential Evaluation 2011-2014", hdl:11529/10656, CIMMYT Research Data & Software Repository Network, V2
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Dataset / Software
Feb 27, 2016 - CIMMYT Seeds of Discovery
Bonnett, David; Saint Pierre, Carolina; Jafarzadeh, Jafar; Jiménez Tapia, José Luis, 2015, "Wheat Derived Synthetics Heat Tolerance Evaluation 2011-2012", hdl:11529/10394, CIMMYT Research Data & Software Repository Network, V2
The materials in this trial were subjected to very severe heat stress that significantly impacted yield so all results should be analy zed with caution. In this trial derived synthetic wheat lines were tested for heat tolerance via late sowing in Ciudad Obregon in the 2011-2012 g...
Dataset / Software
Feb 27, 2016 - CIMMYT Seeds of Discovery
Bonnett, David; Saint Pierre, Carolina; Jafarzadeh, Jafar; Jiménez Tapia, José Luis, 2015, "Wheat Derived Synthetics Drought Tolerance Evaluation 2011-2012", hdl:11529/10349, CIMMYT Research Data & Software Repository Network, V2
Derived synthetic wheat lines were tested for drought tolerance via late sowing and reduced irrigation in Ciudad Obregon in the 2011-20 12 growing season. These materials were derived from bi-parental crosses, backcrosses, or topcrosses between bread wheat lines and synthetic hex...
Dataset / Software
Sep 28, 2018 - CIMMYT Research Data
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.
Dataset / Software
Sep 21, 2018 - CIMMYT Research Data
Basnet, Bhoja Raj; Crossa, José; 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
Jul 12, 2019 - CIMMYT Research Data
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
May 16, 2019 - CIMMYT Research Data
Singh, Ravi; Mondal, Suchismita; Crespo, Leonardo; Kummar, Uttam; Imtiaz, Muhammad; Lan, Caixia; Randhawa, Mandeep; Bhavani, Sridhar; Singh, Pawan K.; Huerta, Julio; He, Xinyao; Rahman, Mokhles; Pinto, Francisco; Perez Gonzalez, Lorena; Juliana, Philomin; Singh, Daljit; Lucas, Mark; Poland, Jesse, 2016, "Phenotypic data from trials conducted by the CIMMYT Bread Wheat Breeding Program", hdl:11529/10696, CIMMYT Research Data & Software Repository Network, V6
Phenotypic data were collected in on-station field trials for advanced breeding lines from the CIMMYT Bread Wheat breeding program over several years.
Dataset / Software
Aug 12, 2019 - CIMMYT Research Data
Montesinos-López, Osval A ; Montesinos-López, Abelardo; Tuberosa, Roberto; Maccaferri, Marco; Sciara, Giuseppe; Ammar, Karim; Crossa, Jose, 2019, "Multi-trait multi-environment genomic prediction of durum wheat", hdl:11529/10548262, CIMMYT Research Data & Software Repository Network, V1
In this paper we cover multi-trait prediction of grain yield (GY), days to heading (DH) and plant height (PH) of 270 durum wheat lines that were evaluated in 43 environments (location-year combinations) in Bologna, Italy. The results of the multi-trait deep learning method also w...
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