Diego Jarquín

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

PhenoStream automates crop trait extraction from aerial imagery, enabling high-throughput phenotyping with reduced latency and manual effort.

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

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.

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

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

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

This paper evaluates genomic prediction models for groundnut yield traits across environments, finding that incorporating genotype-by-environment interaction improves predictive ability in some cross-validation schemes.

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

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

The paper improves prediction of binary traits by combining genomic and phenotypic data using a two-step statistical method that prioritizes phenotypic variables.

This study shows genomic selection (GS) is as effective as phenotypic selection (PS) for increasing soybean seed yield, with similar gains and reduced breeding costs.

The paper develops methods that integrate day length data with genomic prediction tools to improve prediction of time-related traits like days to heading in unobserved environments.

This study applies variance heterogeneity genome-wide association mapping to identify novel genomic loci and epistatic interactions influencing grain cadmium concentration in bread wheat.