LiDAR Is Effective in Characterizing Vine Growth and Detecting Associated Genetic Loci.

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 (R2) 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.

Assessment of new low input vine systems: Dataset on environmental, soil, biodiversity, growth, yield, disease incidence, juice and wine quality, cost and social data.

This article describes the environmental, agronomical, economical and social indicators characterization of eleven new low input vine systems to explore their multicriteria performances. These data also describe the reproducibility of all the indicators over six harvest years (2013-2018) over eleven selected winegrower plots. The environmental characterizations are total treatment frequency index, fungicide treatment frequency index, I-Phy indicator from Indigo® method, copper rate, soil compaction, bacterial activity in soil, bacterial molecular biomass, potential nitrogen mineralization rate, amont of avalaible nitrogen in soil at veraison, total specific floristic richness, and relative pollination value. The agronomical characterizations are vine phenology, vine vigour, plot yield, powdery mildew intensity on bunches at harvest, grey rot intensity on bunches at harvest, juice sugar rate (total soluble solids) and juice total acidity rate (titrable acidity) at harvest, wine characteristics after microvinification. The economical characterizations are brut semi-margin at the plot and at the farm scale. The sociological characterizations are human capital, penibility, hardouness work and pesticide risk.