A Bayesian multiple-trait multiple-environment genomic-enabled prediction poisson-lognormal model for count data

Osval Antonio Montesinos-Lopez Jose Crossa Philomin Juliana JOSAFHAT SALINAS RUIZ (2017)

^^

Dataset

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

A variational Bayes genomic-enabled prediction model with genotype × environment interaction

Osval Antonio Montesinos-Lopez Jose Crossa Francisco Javier Luna Vázquez JOSAFHAT SALINAS RUIZ (2017)

^^

Dataset

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

Replication Data for: Sparse multi-trait genomic prediction under incomplete block designs

Osval Antonio Montesinos-Lopez Brandon Alejandro Mosqueda González JOSAFHAT SALINAS RUIZ Abelardo Montesinos Jose Crossa (2022)

The efficiency of genomic selection methodologies can be increased by sparse testing where a subset of materials are evaluated in different environments. Seven different multi-environment plant breeding datasets were used to evaluate four different methods for allocating lines to environments in a multi-trait genomic prediction problem. The results of the analysis are presented in the accompanying article.

Dataset

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

A variational Bayes genomic-enabled prediction model with genotype × environment interaction

Osval Antonio Montesinos-Lopez Jose Crossa Francisco Javier Luna Vázquez JOSAFHAT SALINAS RUIZ (2017)

^^

Dataset

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA