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1 to 10 of 12 Results
Dataset / Software
Jul 11, 2018 - CIMMYT Research Software
Pacheco, Ángela; Vargas, Mateo; Alvarado, Gregorio; Rodríguez, Francisco; Crossa, José; Burgueño, Juan, 2015, "GEA-R (Genotype x Environment Analysis with R for Windows) Version 4.1", hdl:11529/10203, CIMMYT Research Data & Software Repository Network, V16
In agricultural experimentation, a large number of genotypes are normally tested over a wide range of environments. The occurrence of the genotype (G) X environment (E) interaction (GEI) effect further complicates the selection of superior genoty pes for a target population of en...
Dataset / Software
Jul 11, 2018 - CIMMYT Research Software
Rodríguez, Francisco; Alvarado, Gregorio; Pacheco, Ángela; Crossa, José; Burgueño, Juan, 2015, "AGD-R (Analysis of Genetic Designs with R for Windows) Version 5.0", hdl:11529/10202, CIMMYT Research Data & Software Repository Network, V13
A major objective of biometrical genetics is to explore the nature of gene action in determining quantitative traits. This also includes determination of the number of major genetic factors or genes responsible for the traits. Diallel Mating Designs have been designed to deal wit...
Dataset / Software
Jul 11, 2018 - CIMMYT Research Software
Alvarado, Gregorio; López, Marco; Vargas, Mateo; Pacheco, Ángela; Rodríguez, Francisco; Burgueño, Juan; Crossa, José, 2015, "META-R (Multi Environment Trail Analysis with R for Windows) Version 6.03", hdl:11529/10201, CIMMYT Research Data & Software Repository Network, V21
META-R is a set of R programs that performs statistical analyses to calculate BLUEs, BLUPs, genetic correlations among locations and genetic correlations between variables, broad-sense heritability, and other statistics for breeding trials are given too, in order to make boxplots...
Dataset / Software
Mar 8, 2016 - CIMMYT Research Software
Alvarado, Gregorio; Cerón, Jesús; Crossa, José; Burgueño, Juan, 2015, "SI-SAS. A SAS Code to Calculate Several Selection Indexes", hdl:11529/10242, CIMMYT Research Data & Software Repository Network, V4
SI-SAS is a code written in SAS to produce several selection indexes. To run the code you have to have SAS 9.2 or higher installed in your computer. The first task is to change the folder where the data file to analyze is located and the name of the file. The data file must be in...
Dataset / Software
Mar 8, 2016 - CIMMYT Research Software
Vargas, Mateo; Combs, Emily; Alvarado, Gregorio; Atlin, Gary; Crossa, José, 2015, "META-SAS: A Suite of SAS Programs to Analyze Multienvironment", hdl:11529/10217, CIMMYT Research Data & Software Repository Network, V4
Multienvironment trials (METs) enable the evaluation of the same genotypes under a v ariety of environments and management conditions. We present META (Multi Environment Trial Analysis), a suite of 31 SAS programs that analyze METs with complete or incomplete block designs, with...
Dataset / Software
Jan 25, 2016 - CIMMYT Research Data
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 - CIMMYT Research Data
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 - CIMMYT Research Data
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
Sep 21, 2015 - CIMMYT Research Data
Cerón-Rojas, J. Jesús; Crossa, José; Arief, Vivi N.; Basford, Kaye; Rutkoski, Jessica; Jarquín, Diego; Alvarado, Gregorio; Beyene, Yoseph; Semagn, Kassa; DeLacy, Ian, 2015, "Application of a Genomics Selection Index to Real and Simulated Data", hdl:11529/10199, CIMMYT Research Data & Software Repository Network, V10
We apply a Genomic Selection Index (GSI) to simulated and real data sets with four traits and numerically we compared its efficiency with that of the phenotypic selection index (PSI) using the ratio of the GSI response over the PSI response. In addition, we used two additional cr...
Dataset / Software
Sep 21, 2015 - CIMMYT Research Data
Montesinos-López, Osval Antonio; Montesinos-López, Abelardo; Crossa, José; Burgueño, Juan; Eskridge, Kent, 2015, "Genomic-Enabled Prediction of Ordinal Data with Bayesian Logistic Ordinal Regression", hdl:11529/10254, CIMMYT Research Data & Software Repository Network, V5
Most genomic-enabled prediction models developed so far assume that the response variable is continuous and normally distributed. The exception is the probit model developed for ordered categorical phenotypes. In statistical applications, due to the easy implementation of the Bay...
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