Aude Coupel‐Ledru

The paper identifies genetic regions associated with leaf ecophysiological traits in grapevine under greenhouse and field conditions, showing that multi-environment analysis improves the detection of stable QTLs.

This paper evaluates high-throughput tools for predicting leaf traits in grapevines under different water conditions, finding spectroscopy and poro-fluorometry effective for trait prediction and heritability estimation.

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.