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1 to 10 of 59 Results
Dataset / Software
Jan 30, 2019 - CIMMYT Research Data
Dreisigacker, Susanne; Sharma, Rajiv Kumar; Huttner, Eric; Karimov, Aziz; Singh, Pawan; Sansaloni, Carolina; Shrestha, Rosemary; Sonder, Kai; Braun, Hans-Joachim, 2019, "Sustainable Wheat & Maize Production in Afghanistan (ACIAR funded, Project number: CIM/2011/026)", hdl:11529/10548167, CIMMYT Research Data & Software Repository Network, V1
Since 2012, Australian support has resulted in the release of 23 wheat varieties by Afghanistan’s National Varietal Release Committee (NVRC). These varieties deliver both improved yield and disease tolerance. A 2015/16 farmer survey confirmed that farmers were indeed sharing seed...
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
Sep 20, 2019 - CIMMYT Seeds of Discovery
Ledesma Ramírez, Lourdes ; Solís Moya, Ernesto; Iturriaga, Gabriel; Sehgal, Deepmala; Humberto Reyes Valdes, M.; Montero Tavera, Víctor; Sansaloni, Carolina P.; Burgueño Ferrera, Juan A.; Ortiz, Cynthia; Aguirre Mancilla, César L.; Ramírez Pimentel, Juan G.; Huerta Espino, Julio; Vikram, Prashant; Sukhwinder, Singh, 2019, "Replication data for: GWAS to identify genes for resistance to yellow rust in wheat pre-breeding lines derived from diverse exotic crosses", hdl:11529/10548313, CIMMYT Research Data & Software Repository Network, V1
This study contains DArTseq genotypic data used for a genome-wide association study.
Dataset / Software
Aug 26, 2019 - CIMMYT Seeds of Discovery
Rosyara, Umesh; Kishii, Masahiro; Payne, Thomas; Sansaloni, Carolina Paola; Singh, Ravi; Braun, Hans-Joachim; Dreisigacker, Susanne, 2019, "Replication Data for: Genetic contribution of synthetic hexaploid wheat to CIMMYT’s spring bread wheat breeding germplasm", hdl:11529/10548269, CIMMYT Research Data & Software Repository Network, V1
A total of 359 genotypes used in this study included three Ae. tauschii lines, 30 durum wheat lines, eight synthetic hexaploid wheat, 253 synthetic derivative lines, and 63 bread wheat lines. All entries were genotyped with the DArTseq® technology at the Genetic Analysis Service...
Dataset / Software
Feb 27, 2019 - CIMMYT Research Data
Liu, Caiyun; Sukumaran, Sivakumar; Claverie, Etienne; Sansaloni, Carolina; Dreisigacker, Susanne; Reynolds, Matthew , 2019, "Genetic and phenotypic data of Syn/Weebil recombinant inbred lines under drought and heat stresses", hdl:11529/10548170, CIMMYT Research Data & Software Repository Network, V1
We studied a RIL population of 276 entries derived from a cross between SYN-D × Weebill 1. SYN-D (Croc 1/Aegilops Squarrosa (224)//Opata) is a synthetic derived hexaploid wheat with dark green broad leaves without wax. The RILs did not segregate for Rht-B1, Rht-D1, Ppd-A1, Ppd-D1...
Dataset / Software
Jul 18, 2019 - CIMMYT Research Data
Cuevas, Jaime; Montesinos-López, Osval A; Juliana, Philomin; Pérez-Rodríguez, Paulino; Burgueño, Juan; Guzman, Carlos; Montesinos-López, Abelardo; Crossa, Jose, 2019, "Deep kernel of genomic and near infrared predictions in multi-environment breeding trials", hdl:11529/10548180, CIMMYT Research Data & Software Repository Network, V4
In genomic prediction deep learning artificial neural network are part of machine learning methods that incorporate parametric, non-parametric and semi-parametric statistical models. Kernel methods are seeing more flexible, and easier to interpret than neural networks. Kernel met...
Dataset / Software
Aug 15, 2019 - CIMMYT Research Data
Crossa, Jose; Martini, Johannes; Gianola, Daniel; Pérez-Rodríguez, Paulino; Burgueño, Juan; Singh, Ravi; Juliana, Philomin; Montesinos-López, Osval; Cuevas, Jaime, 2019, "Deep kernel and deep learning for genomic-based prediction", hdl:11529/10548273, CIMMYT Research Data & Software Repository Network, V1
Deep learning (DL) is a promising method in the context of genomic prediction for selecting individuals early in time without measuring their phenotypes. iI this paper we compare the performance in terms of genome-based prediction of the DL method, deep kernel (arc-cosine kernel,...
Dataset / Software
Oct 26, 2018 - CIMMYT Research Data
Montesinos-López, Osval A ; Montesinos-López, Abelardo; Crossa, Jose; Cuevas, Jaime; Montesinos-López, José Cricelio; Gutiérrez, Zitlalli Salas; Lillemo, Morten; Juliana, Philomin; Singh, Ravi, 2018, "A Bayesian genomic multi-output regressor stacking model for predicting multi-trait multi-environment plant breeding data", hdl:11529/10548141, CIMMYT Research Data & Software Repository Network, V1
A new statistical model is presented for genomic prediction on maize and wheat data comprising multi-trait, multi-environment data.
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