From plant water status to wine flavonoid composition: a precision viticulture approach in a Sonoma county vineyard

The overall quality of aged wines is in part due to the development of complex aromas over a long period (1.) The apparition of this aromatic complexity depends on multiple chemical reactions that include the liberation of odorous compounds from non-odorous precursors. One example of this phenomenon is found in dimethyl sulphide (DMS) which, with its characteristic odor truffle, is a known contributor to the bouquet of premium aged wine bouquet (1). DMS supposedly accumulates during the ten first years of ageing thanks to the hydrolysis of its precursor dimethylsulfoniopropionate (DMSp.) DMSp is a possible secondary by-product from the degradation of S-methylmethionine (SMM), an amino acid iden- tified in grapes (2), which can be metabolized by yeast during alcoholic fermentation.

REVINE is a 3 year European projected funded by PRIMA programme which proposes the adoption of regenerative agriculture practices with an innovative and original perspective, in order to improve the resilience of vineyards to climate change in the Mediterranean area.
Regenerative agriculture ameliorates soil structure and microbial biodiversity that, in turn, leads to crop resilience against biotic and abiotic stressful factors. Moreover, enrichment of beneficial microbes in the rhizosphere, such as PGPR and PGPF, are known to trigger the plant immunity inducing the priming state.

In a previous zoning program (1998-2002), climatic and pedological factors were able to distinguish 14 terroir within the Soave DOC area where wine characteristics are well recognizable. Nevertheless, in the past vinegrowers identified several vineyards where a better quality of the grapes and wines could be obtained. So, « beyond the zonation » will aim to suggest a new methodology to characterise the Cru, starting with 15 vineyards that were selected in the Soave Classico DOC area. In the year 2005, a meteorological station was positioned in each vineyard and temperature data were collected; because of the limited area of investigation, only 3 rain sensors were set up.

The work shows the results of a year of experimentation (2020) in a Syrah variety vineyard in La Roda (Castilla-La Mancha, Spain). The trial approach was on a randomized block design with two factors: Irrigation (I) and Pruning (P).
Irrigation schedules were adjusted to apply amounts close to 1,500 m3/ha. With this provision, 2 different irrigation treatments were proposed: I1) Start of irrigation from pea-sized grape to post-harvest (providing at least 20 % of the total amount of irrigation water to be provided post-harvest); I2) Start of irrigation from pea-sized grape to harvest (usual irrigation practice in the study area). Pruning was proposed with two treatments, one at the end of January (P1), which is pruning on a conventional date; and P2) pruning carried out at the beginning of budding. In total, 4 repetitions were designed with 4 elementary plots, each one of them representing one of the proposed treatments (I1P1; I1P2; I2P1; I2P2). In total, 16 plots were worked on and each elementary plot consisted of 30 strains, distributed in 3 lines.
The productive response was evaluated with the yield results of the harvest harvested at 23 ºBrix. The qualitative response was measured in the musts through the indices of technological (acidity, pH and potassium) and phenolic maturity and aromatic compounds in free and glycosylated fractions. The treatments tested had, in general, an effect on the different variables analyzed.

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.