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331 to 340 of 353 Results
Dataset / Software
Feb 15, 2016
Montesinos-López, Abelardo; Montesinos-López, Osval A.; Crossa, Jose; Burgueño, Juan; Eskridge, Kent M.; Falconi, Esteban; Singh, Pawan; He, Xinyao, 2015, "Genomic Bayesian Prediction Model for Count Data with Genotype × Environment Interaction", hdl:11529/10575, CIMMYT Research Data & Software Repository Network, V2
Genomic tools allow the study of the whole genome and are facilitating the study of genotype-environment combinations and their relationship with the phenotype. However, most genomic prediction models developed so far are appropriate for Gaussian phenotypes. For this reason, appr...
Dataset / Software
Jan 25, 2016
Jarquín, Diego; Pérez-Elizalde, Sergio; Burgueño, Juan; Crossa, José, 2015, "A Hierarchical Bayesian Estimation Model for Multi-Environment Plant Breeding Trials in Successive Years", hdl:11529/10463, CIMMYT Research Data & Software Repository Network, V2
In agriculture and plant breeding, multi-environment trials over multiple years are conducted to evaluate and predict genotypic performance under different environmental conditions, and to analyze, study, and interpret genotype × environment interaction (G×E). In this study, we p...
Dataset / Software
Dec 18, 2015
Cuevas, Jaime; Crossa, José; Soberanis, Víctor; Pérez-Elizalde, Sergio; Pérez-Rodríguez, Paulino; Campos, Gustavo de los, 2015, "Genomic Prediction of Marker × Environment Interaction Kernel Regression Models", hdl:11529/10580, CIMMYT Research Data & Software Repository Network, V1
The marker × environment interaction (M×E) decomposes the marker effects into main effects and interaction environmental-specific effects. The M×E interaction may be modeled through a linear kernel (Genomic Best Linear Unbiased Predictor, GBLUP) or with non-linear Gaussian kernel...
Dataset / Software
Dec 14, 2015
González-Camacho, Juan Manuel; Crossa, José; Pérez-Rodríguez, Paulino; Ornella, Leonardo; Gianola, Daniel, 2015, "Genome-enabled prediction using probabilistic neural network classifiers", hdl:11529/10576, CIMMYT Research Data & Software Repository Network, V1
Non-parametric methods have been shown to be effective in genome-enabled prediction, in particular, the multi-layer perceptron (MLP) and the radial basis function neural network (RBFNN). In this study, we evaluated and compared the performance of MLP classifier versus the probabi...
Dataset / Software
Oct 5, 2015
Dreisigacker, Susanne; Bonnett, David; Crossa, Jose; Poland, Jesse, 2015, "14th Semi-Arid Wheat Yield Trial Genotyping-by-sequencing Data", hdl:11529/10367, CIMMYT Research Data & Software Repository Network, V1
^^
Dataset / Software
Oct 5, 2015
Dreisigacker, Susanne; Bonnett, David; Crossa, Jose; Poland, Jesse, 2015, "16th Semi-Arid Wheat Yield Trial Genotyping-by-sequencing Data", hdl:11529/10374, CIMMYT Research Data & Software Repository Network, V1
^^
Dataset / Software
Oct 5, 2015
Dreisigacker, Susanne; Bonnett, David; Crossa, Jose; Poland, Jesse, 2015, "15th Semi-Arid Wheat Yield Trial Genotyping-by-sequencing Data", hdl:11529/10364, CIMMYT Research Data & Software Repository Network, V1
^^
Dataset / Software
Oct 2, 2015
Dreisigacker, Susanne; Bonnett, David; Crossa, Jose; Poland, Jesse, 2015, "17th Semi-Arid Wheat Yield Trial Genotyping-by-sequencing Data", hdl:11529/10376, CIMMYT Research Data & Software Repository Network, V3
^^
Dataset / Software
Oct 2, 2015
Dreisigacker, Susanne; Bonnett, David; Crossa, Jose; Poland, Jesse, 2015, "13th Semi-Arid Wheat Yield Trial Genotyping-by-sequencing Data", hdl:11529/10373, CIMMYT Research Data & Software Repository Network, V3
^^
Dataset / Software
Sep 23, 2015
Hodson, David, 2014, "Stem Rust Survey 2008", hdl:11529/10007, CIMMYT Research Data & Software Repository Network, V3
Rust survey collected in 2008.
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