L’étude “terroirs d’Anjou”: un exemple de caractérisation intégrée des terroirs viticoles, utilisable à l’échelle parcellaire

Grapevine yield has been historically overlooked, assuming a strong trade-off between grape yield and wine quality. At present, menaced by climate change, many vineyards in Southern France are far from the quality label threshold, becoming grapevine yield-gaps a major subject of concern. Although yield-gaps are well studied in arable crops, we know very little about grapevine yield-gaps. In the present study, we analysed the environmental component of grapevine yield-gaps linked to climate and soil resources in the Languedoc Roussillon. We used SAFRAN data and IGP Pays d’Oc wine yields from 2010 to 2018. We selected climate and soil indicators proving to have a significant effect on average wine yield-gaps at the municipality scale. The most significant factors of grapevine yield were the Soil Available Water Capacity; followed by the Huglin Index and the Climatic Dryness Index. The Days of Frost; the Soil pH; and the Very Hot Days were also significant. Then, we clustered geographical zones presenting similar indicators, facilitating the identification of resources yield-gaps. We discussed the number of zones with the experts of IGP Pays d’Oc label, obtaining 7 zones with similar limitations for grapevine yield. Finally, we analysed the main resources causing yield-gaps and the grapevine varieties planted on each zone. Mapping grapevine resource yield-gaps are the first stage for understanding grapevine yield-gaps at the regional scale.

Because terroir and cultivar are drivers of wine quality, is essential to investigate theirs effects on polyphenolic profile before promoting the implantation of a red minority variety in a specific area. This work, included in MINORVIN project, focuses in the polyphenolic profile of 7 red grapevines minority varieties of Vitis vinifera L. (Morate, Sanguina, Santafe, Terriza Tinta Jeromo Tortozona Tinta) and Tempranillo) from six typical viticulture Spanish areas: Aragón (A1), Cataluña (A2), Castilla la Mancha (A3), Castilla –León (A4), Madrid (A5) and Navarra (A6) of 2020 season. Polyphenolic substances were extracted from grapes. 35 compounds were identified and quantified (mg subtance/kg fresh berry) by HPLC and grouped in anthocyanins (ANT) flavanols (FLAVA), flavonols (FLAVO), hydroxycinnamic (AH), benzoic (BA) acids and stilbenes (ST). Antioxidant activity (AA, mmol TE /g fresh berry) was determined by DPPH method. The results were submitted to a two-way ANOVA to investigate the influence of variety, area and their interaction for each polyphenolic family and cluster analysis was used to construct hierarchical dendrograms, searching the natural groupings among the samples. Sanguina (A3) had the most of total polyphenols while Tempranillo (A5) those of ANT. Sanguina (A2) and (A3) reached the highest values of FLAVO, FLAVA and AA. These two last samples had also the maximum of AA. The effect cultivar and area were significant for all polyphenolic families analyzed. A high variability due to variety (>50%) was observed in FLAVA and the maximum value of variability due to growing area was detected in AA (86.41%), ANT and FLAVO (51%); the interaction variety*zone was significant only for ANT, FLAVO, EST and AA. Finally, dendrograms presented five cluster: i) Sanguina (A2); ii) Sanguina (A3); iii) Tempranillo (A5); iv) Tempranillo (A3); Terriza (A3,A5), Morate (A5,A6); v) Santafé (A1,A6); Tortozona tinta (A1,A3,A6); Tinta Jeromo (A3,A4).

Using viticultural and enological techniques to increase aromatics in white wine is a prized yet challenging technique for commercial wine producers. Equally difficult are challenges encountered in hastening phenolic maturity and thereby increasing color intensity in red wines. The ability to alter organoleptic and visual properties of wines plays a decisive role in vintages in which grapes are not able to reach full maturity, which is seen increasingly more often as a result of climate change. A new, yeast-based product on the viticultural market may give the opportunity to increase sensory properties of finished wines. Manufacturer packaging claims these yeast derivatives intensify wine aromas of white grape varieties, as well as improve phenolic ripeness of red varieties, but the effects of this application have been little researched until now. The current study applied the yeast derivative, according to the manufacture’s instructions, to the leaves of both neutral and aromatic white wine varieties, as well as on structured red wine varieties. Chemical parameters and volatile aromatics were analyzed in grape musts and finished wines, and all wines were subjected to sensory analysis by a tasting panel. Collective results of all analyses showed that the application of the yeast derivative in the vineyard showed no effect across all varieties examined, and did not intensify white wine aromatics, nor improve phenolic ripeness and color intensity in red wine.

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.

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.