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451 to 455 of 455 Results
Dataset / Software
Jul 2, 2019 - CIMMYT Research Data
Keil, Alwin; Mitra, Archisman; Parida, Nabakishore; Iftikar, Wasim, 2019, "Livelihood Impacts of Commercial Maize Production in the Plateau Ecology of Odisha, India.", hdl:11529/10548161, CIMMYT Research Data & Software Repository Network, V1, UNF:6:kG1mqKbaBXvlPdtQreHjvg==
Large tracts of land in the upland plateau ecology of Odisha are not suitable for rice production and are largely left fallow. In Mayurbhanj district of the plateau area, nearly half of the total cultivable area (43%) falls into the upland category that is not very suitable for g...
Dataset / Software
Jul 4, 2019 - CIMMYT Research Data
Global Maize Program, 2019, "International Early Yellow Hybrid Trial - IEYH0612", hdl:11529/10563, CIMMYT Research Data & Software Repository Network, V1
Summary results and individual trial results from the International Early Yellow Hybrid - IEYH, (Tropical Early Yellow Hybrid Trial - CHTTEY) conducted in 2006.
Dataset / Software
Jul 4, 2019 - CIMMYT Research Data
Global Maize Program, 2019, "International Late Yellow Hybrid Trial - ILYH0704", hdl:11529/10540, CIMMYT Research Data & Software Repository Network, V1
Summary results and individual trial results from the International Late Yellow Hybrid - ILYH, (Elite Tropical Late Yellow Normal and QPM Hybrid Trial - CHTTY) conducted in 2007.
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
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...
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