Julián García-Abadillo

Reaction Norm Modeling of High-Dimensional Genomic and Environmental Data Improves Prediction Accuracy in Winter Wheat

This paper improves genomic prediction accuracy in winter wheat by integrating genotype-by-environment interactions and environmental covariates across multiple environments.

Genotypic and multi-environment phenotypic evaluation of the lima bean USDA National Plant Germplasm System collection

The paper evaluates genotypic and phenotypic traits of lima bean accessions across environments to support breeding, identifying genomic associations and establishing a core collection.

Multi-trait Multi-environment Genomic Prediction Strategies for Miscanthus sacchariflorus Populations

This study evaluates MTME genomic prediction models for Miscanthus sacchariflorus, finding they improve prediction for some traits but not all, depending on the cross-validation scenario.

Optimizing resource allocation in Miscanthus breeding with sparse testing designs for genomic prediction

This study evaluates sparse testing designs for genomic prediction in Miscanthus breeding to reduce phenotyping costs while maintaining predictive accuracy.

Weather Characterization for Optimizing Genomic Prediction in Miscanthus sacchariflorus

This study optimizes genomic prediction in Miscanthus sacchariflorus by incorporating weather data to improve model performance across different locations.

ENHANCING GENOMIC PREDICTION MODELS IN MISCANTHUS POPULATIONS BY INCORPORATING THE GENOTYPE-BY-ENVIRONMENT INTERACTION

The paper evaluates cross-validation schemes for genomic selection in perennial Miscanthus species, showing improved predictive ability by incorporating genotype-by-environment interactions.

Bayesian AMMI-Based Simulation of Genotype x Environment Interactions

This paper introduces a Bayesian AMMI-based framework to simulate genotype-by-environment interactions, showing improved prediction accuracy and interpretable visualization of GEI effects.

Introducing CHiDO a No Code Genomic Prediction Software implementation for the Characterization & Integration of Driven Omics

This paper introduces CHiDO, a no-code platform for integrating multi-omics data to build predictive models for crop resilience under climate change.

Sparse Testing Designs for Optimizing Predictive Ability in Sugarcane Populations

This study uses sparse testing designs with genomic prediction models to optimize predictive accuracy for sugarcane traits across environments.