Characterization of bunch compactness and identification of associated genes in a diverse collection of cultivars of Vitis vinifera L.

Nitrogen (N) is applied in the vineyard or the winery in wine production systems. The influence of different routes of N application is not well understood.

Currently, the main goal of agriculture is to promote the resilience of agricultural systems in a sustainable way through the improvement of use efficiency of farm resources, increasing crop yield and quality under climate change conditions. This last is expected to drastically modify plant growth, with possible negative effects, especially in arid and semi-arid regions of Europe on the viticultural sector. In this context, the monitoring of spatial behavior of grapevine during the growing season represents an opportunity to improve the plant management, winegrowers’ incomes, and to preserve the environmental health, but it has additional costs for the farmer. Nowadays, UAS equipped with a VIS-NIR multispectral camera (blue, green, red, red-edge, and NIR) represents a good and relatively cheap solution to assess plant status spatial information (by means of a limited set of spectral vegetation indices), representing important support in precision agriculture management during the growing season. While differences between UAS-based multispectral imagery and point-based spectroscopy are well discussed in the literature, their impact on plant status estimation by vegetation indices is not completely investigated in depth. The aim of this study was to assess the performance level of UAS-based multispectral (5 bands across 450-800nm spectral region with a spatial resolution of 5cm) imagery, reconstructed high-resolution satellite (Sentinel-2A) multispectral imagery (13 bands across 400-2500 nm with spatial resolution of <2 m) through Convolutional Neural Network (CNN) approach, and point-based field spectroscopy (collecting 600 wavelengths across 400-1000 nm spectral region with a surface footprint of 1-2 cm) in a plant status estimation application, and then, using Bayesian regularization artificial neural network for leaf chlorophyll content (LCC) and plant water status (LWP) prediction. The test site is a Greco vineyard of southern Italy, where detailed and precise records on soil and atmosphere systems, in-vivo plant monitoring of eco-physiological parameters have been conducted.

The Pomerol vineyard, located 35 km east of Bordeaux, covers around 800 ha on the left bank of the Isle. There is a system of fluvial terraces with more or less coarse gravel and pebble spreading, resting on a Tertiary substratum ranging from the Middle to Upper Eocene to the Lower Oligocene (Dubreuilh, 1993). This interweaving of terraces of varying thickness results in a brutal superposition of differentiated materials which give rise to various types of soil. Several site studies in this sector of the Libounais show significant morphological and analytical differences from one point to another (Guilloux et al ., 1978; Duteau, 1982; Van Leeuwen et al.., 1989). The distribution of the soils of the Pomerol vineyard was studied and resulted in a cartography at 1/25000th (Merouge, 1995).

Dead or dying vines must be replaced regularly in order to ensure the sustainability of a vineyard. Successful plant replacement is crucial to maintain yield and quality by encouraging balanced root and leaf development in vines. However, young vines planted within an established vineyard encounter several problems, ranging from poor soil conditions to competition with older vines with well-established root systems.  

Grape composition is of high interest for producing quality wines. For that, grape analyses are necessary, and they still require sample preparation, whether with classical analyses or with NIR analyses.