Loïc Le Cunff

This paper presents a 3D + t MRI pipeline for non-destructive spatio-temporal phenotyping of fungal spread in grapevine wood, revealing early degradation signals and cultivar-dependent lesion differences.

The paper uses near-infrared spectroscopy to monitor sugar and acid changes in individual berries, finding that single berries ripen faster than mixed samples.

This study uses genomic prediction methods to improve grapevine breeding efficiency by identifying superior individuals for specific traits across two breeding programs.

This study applies phenomic prediction to grapevine using spectra to predict 15 traits, finding comparable predictive ability to genomic prediction across traits and populations.

This study identifies new QTLs and improves prediction accuracy for multiple grapevine traits using a diverse panel and multi-SNP models.

This study evaluates across-population genomic prediction in grapevine for 15 traits, finding predictive abilities of 0.6 for cross means and 0.26 for individual values, suggesting potential for genomic selection in breeding.

The paper compares genomic prediction and QTL detection methods in grapevine under climate change, finding penalized regression methods like Elastic Net to be effective for prediction and QTL discovery.