Forage yield and composition of Avena strigosa Schreb at different harvesting frecuencies and intensities

Edmundo García-Moya Juan Burgueño Rodolfo Ramírez-Valverde Luis Alberto Miranda-Romero (2022)

Artículo

Saia Oats Sward Height Residual Foliage CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA HARVESTING FREQUENCY FOLIAGE AVENA NUDA YIELDS

BGGE: A new package for genomic prediction incorporating genotype by environments models

Italo Granato Jaime Cuevas Francisco Javier Luna Vázquez Jose Crossa Juan Burgueño Roberto Fritsche-Neto (2018)

One of the major issues in plant breeding is the occurrence of genotype by environment (GE) interaction. Several models have been created to understand this phenomenon and explore it by selecting the most stable genotypes. In the genomic era, several models were employed to simultaneously improve selection by using markers and account for GE interaction. Some of these models use special genetic covariance matrices. In addition, multi-environment trials scales are getting larger, and this increases the computational challenges. In this context, we propose an R package that, in general, allows building GE genomic covariance matrices and fitting linear mixed models, in particular, to a few genome GE models. Here we propose a function to create the genomic kernels needed to fit these models. This function makes genome predictions through a Bayesian linear mixed model approach. A particular treatment is given for structured dispersed covariance matrices; in particular, those structured as a block diagonal that are present in some GE models in order to decrease the computational demand. In empirical comparisons with Bayesian Genomic Linear Regression (BGLR), accuracies and the mean squared error were similar; however, the computational time was up to five times lower than when using the classic approach. Bayesian Genomic Genotype × Environment Interaction (BGGE) is a fast, efficient option to create genome GE kernels and make genomic predictions.

Dataset

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

Metrics for optimum allocation of resources on the composition and characterization of crop collections: The CIMMYT wheat collection as a proof of concept

M. Humberto Reyes-Valdés Juan Burgueño Carolina Sansaloni Thomas Payne Rosa Angela Pacheco Gil (2022)

Artículo

Crop Genebanks Optimization Relative Balance CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA CROPS GENE BANKS WHEAT

Evaluation of maize landraces and pre-breeding materials under the Seeds of Discovery initiative in 2016

Terence Molnar Marcela Carvalho Juan Burgueño Jose Crossa Samuel Trachsel Monica Mezzalama Denise Costich Sarah Hearne (2018)

These data describe the evaluation of landraces and landrace-derived pre-breeding materials for biotic and abiotic stress resistance as well as for general yield potential in 2016. Populations and accessions of interest for terminal drought and Tar Spot tolerance were evaluated for yield potential and response to both stresses under the MasAgro Biodiversidad project. Populations and accessions of interest for terminal heat and MCMV tolerance were evaluated for response to both stresses under the MAIZE CRP project.

Dataset

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

Algorithmic differentiation of linear mixed models with variance-covariance structures

Fernando Henrique Toledo Jose Crossa Juan Burgueño Keith Gardner Rosa Angela Pacheco Gil (2023)

Objeto de congreso

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MATHEMATICAL MODELS ALGORITHMS LINEAR MODELS

Wheat Linked Topcross Population Information from the Seeds of Discovery - MasAgro Biodiversidad Project

Prashant Vikram Carolina Saint Pierre Thomas Payne Juan Burgueño Carolina Sansaloni (2017)

The Linked Topcross Population (LTP) was generated to introgress useful traits from wheat germplasm bank accessions, including synthetic hexaploids and landraces, into elite wheat varieties. In addition to generating pre-breeding materials selected for important traits such as heat and drought tolerance, this population has been used to generate data that can be useful for several applications, including genome-wide association studies.

Dataset

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

Wheat Linked Topcross Population Information from the Seeds of Discovery - MasAgro Biodiversidad Project

Prashant Vikram Carolina Saint Pierre Thomas Payne Juan Burgueño Carolina Sansaloni (2017)

The Linked Topcross Population (LTP) was generated to introgress useful traits from wheat germplasm bank accessions, including synthetic hexaploids and landraces, into elite wheat varieties. In addition to generating pre-breeding materials selected for important traits such as heat and drought tolerance, this population has been used to generate data that can be useful for several applications, including genome-wide association studies.

Dataset

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

Forage yield and composition of black oat in monoculture and in association with winter vetch

Edmundo García-Moya Juan Burgueño Rodolfo Ramírez-Valverde Luis Alberto Miranda-Romero (2022)

Artículo

Avena strigosa Forage Height Harvest Intensity CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA AVENA NUDA HARVESTING VICIA VILLOSA MONOCULTURE

Genomic-enabled prediction in maize using kernel models with genotype × environment interaction

Massaine e Sousa Jaime Cuevas Evellyn Couto Paulino Pérez-Rodríguez DIEGO JARQUIN Roberto Fritsche-Neto Juan Burgueño Jose Crossa (2017)

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Dataset

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

Worldwide selection footprints for drought and heat in bread wheat (Triticum aestivum L.)

Ana Luisa Gómez Espejo Carolina Sansaloni Juan Burgueño Fernando Henrique Toledo Adalberto Benavides-Mendoza M. Humberto Reyes-Valdés (2022)

Artículo

Genome–Environment Associations Climatic Variables Hormonal Elicitors CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA ADAPTATION DROUGHT STRESS HEAT STRESS LANDRACES TRITICUM AESTIVUM