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User's guide for spatial analysis of field variety trials using ASREML
This manual describes an approach to the spatial analysis of field experiments based on the software package AS residual maximum likelihood (ASREML; Gilmour et al. 1999). It describes common sources of spatial variation ...
SAS macro for analysing unreplicated designs
Augmented or unreplicated experiment designs have two type of treatments, the replicated checks and the unreplicated new entries. The latter are usually considered to be random effects while the checks treatments are ...
El análisis AMMI y la gráfica del biplot en SAS
En este documento se presentan tres ejemplos de casos en los que se emplearon programas creados en SAS para obtener Modelos AMMI, asi como la prueba de Gollob (Gollob, 1967) para determinar la significancia de cada termino ...
The AMMI analysis and graphing the biplot
Genomic-enabled prediction with classification algorithms
(Springer Nature, 2014)
Pearson’s correlation coefficient (ρ) is the most commonly reported metric of the success of prediction in genomic selection (GS). However, in real breeding ρ may not be very useful for assessing the quality of the regression ...
Sashaydiall: A SAS program for hayman’s diallel analysis
(Crop Science Society of America (CSSA), 2018)
Different methods of diallel crossing are commonly used in plant breeding. The diallel cross analysis method proposed by Hayman is particularly useful because it provides information, among others, on additive and dominance ...
Extending the marker x environment interaction model for genomic-enabled prediction and genome-wide association analysis in durum wheat
(Crop Science Society of America (CSSA), 2016)
The marker ´ environment interaction (M´E) genomic model can be used to generate predictions for untested individuals and identify genomic regions in which effects are stable across environments and others that show ...
Increased prediction accuracy in wheat breeding trials using a marker x environment interaction Genomic Selection model
(Genetics Society of America, 2015)
Genomic selection (GS) models use genome-wide genetic information to predict genetic values of candidates of selection. Originally, these models were developed without considering genotype · environment interaction( G·E). ...