摘要
The strong societal demand to reduce pesticide use and adaptation to climate change challenges the capacities of phenotyping new varieties in the vineyard.High-throughput phenotyping is a way to obtain meaningful and reliable information on hundreds of genotypes in a limited period.We evaluated traits related to growth in 209 genotypes from an interspecific grapevine biparental cross,between IJ119,a local genitor,and Divona,both in summer and in winter,using several methods:fresh pruning wood weight,exposed leaf area calculated from digital images,leaf chlorophyll concentration,and LiDAR-derived apparent volumes.Using high-density genetic information obtained by the genotyping by sequencing technology(GBS),we detected 6 regions of the grapevine genome[quantitative trait loci(QTL)]associated with the variations of the traits in the progeny.The detection of statistically significant QTLs,as well as correlations(R^(2))with traditional methods above 0.46,shows that LiDAR technology is effective in characterizing the growth features of the grapevine.Heritabilities calculated with LiDAR-derived total canopy and pruning wood volumes were high,above 0.66,and stable between growing seasons.These variables provided genetic models explaining up to 47%of the phenotypic variance,which were better than models obtained with the exposed leaf area estimated from images and the destructive pruning weight measurements.Our results highlight the relevance of LiDAR-derived traits for characterizing genetically induced differences in grapevine growth and open new perspectives for high-throughput phenotyping of grapevines in the vineyard.
基金
the Grand Est region for funding the purchase of the high-throughput phenotyping system and the Ph.D.thesis of E.C.
the“Plant Biology and Breeding”INRAE department for its fnancial support.
