Thinner topsoil improves vine growth and fruit composition in Mid-Atlantic United States vineyards

The interaction between cultivar and growing environment is the base of wine quality and typicality. In recent time the behaviour of different clones within the same cultivar became another fundamental factor influencing the enological result. In order to clarify cultivar/clone/environment relations, a trial was carried out in 2008 studying the performances of three clones of ‘Nebbiolo’, grown in different environments: south-east Piedmont (hilly and characterized by a loamy and alkaline soil) and north-east Piedmont (a plain area characterized by a sandy and acidic soil).

Context and Purpose of the study. Arbuscular mycorrhizal fungi (AMF) symbiosis with grapevines is a key component of vineyard ecosystems.

Grapevine is grown as a graft since the end of the 19th century. Rootstocks not only provide tolerance to Phylloxera but also ensure the supply of water and mineral nutrients to the scion. Rootstocks are an important mean of adaptation to environmental conditions, because the scion controls the typical features of the grapes and wine. However, among the large diversity of rootstocks worldwide, few of them are commercially used in the vineyard. The aim of this study was to investigate the extent to which rootstocks modify the mineral composition of the petioles of the scion. Vitis vinifera cvs. Cabernet-Sauvignon, Pinot noir, Syrah and Ugni blanc were grafted onto 55 different rootstock genotypes and planted in a vineyard as three replicates of 5 vines. Petioles were collected in the cluster zone with 6 replicates per combination. Petiolar concentrations of 13 mineral elements (N, P, K, S, Mg, Ca, Na, B, Zn, Mn, Fe, Cu, Al) at veraison were determined. Scion, rootstock and the interaction explained the same proportion of the phenotypic variance for most mineral elements. Rootstock genotype showed a significant influence on the petiole mineral element composition. Rootstock effect explained from 7 % for Cu to 25 % for S of the variance. The difference of rootstock conferred mineral status is discussed in relation to vigor and fertility. Rootstocks were also genotyped with 23 microsatellite markers. Data were analysed according to genetic groups in order to determine whether the petiole mineral composition could be related to the genetic parentage of the rootstock. Thanks to a highly powerful design, it is the first time that such a large panel of rootstocks grafted with 4 scions has been studied. These results give the opportunity to better characterize the rootstocks and to enlarge the diversity used in the vineyard.

a) wine, a qualitative and user-friendly product, favors a visual support, even for a scientific study because it refers to the image of the terroir, in particular by its visible landscape. b) the vineyard landscape, which is fairly open by definition, favors this type of approach. c) the framework of the Terroir Test conducted by the URVV (INRA – Angers) comprises 15 micro-plots of 100 strains, and requires at this scale precise surveys of the environment, hence systematic shots, of the center of the plot, over 360°, at 50 mm intervals, at 1.70 m from the ground and horizontally.

The need to rationalize agricultural inputs has recently increased interest in assessing vineyard variability in order to implement variable rate input applications, so-called ‘precision viticulture’. In many viticultural areas globally, precision viticulture is already widely used such as for selective harvesting and variable rate application (VRA) of inputs such as irrigation and/or fertilizer. Robust VRA relies on having a geostatistically accurate map (of one or more vineyard attributes) requiring high sampling densities, which can be cost- and time-prohibitive to obtain. Previous work on spatial interpolation using kriging have upscaled ground-based measurements, but such upscaling strategies are applicable only when vineyard conditions are spatially continuous and satisfies the assumption of second-order stationary processes. Alternatively, mixed models that combine kriging and auxiliary information, such as the regression kriging (RK) method, are more instructive for spatial predictions. In order to improve prediction accuracies, it is therefore necessary to incorporate additional information to achieve accurate spatial patterns with low error.