Antioxidant activity of grape seed and skin extract during ripening

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.

In viticulture, the challenges of local climate modelling are multiple: taking into account the local environment, fine temporal and spatial scales, reliable time series of climate data, ease of implementation and reproducibility of the method. At the local scale, recent studies have demonstrated the contribution of spatialization methods for ground-based climate observation data considering topographic factors such as altitude, slope, aspect, and geographic coordinates (Le Roux et al, 2017; De Rességuier et al, 2020). However, these studies have shown questions in terms of the reproducibility and sustainability of this type of climate study. In this context, we evaluated the potential of MODIS thermal satellite images validated with ground-based climate data (Morin et al, 2020). Previous studies have been encouraging, but questions remain to be explored at the regional scale, particularly in the dynamics of the massive use of bioclimatic indices to classify the climate of wine regions. The results at the local scale were encouraging, but this approach was tested in the current study at the regional scale. Several objectives were set: 1) to evaluate the downscaling method for land surface temperature time series, 2) to identify regional thermal structure variations. We used weekly minimum and maximum surface temperature time series acquired by MODIS satellites at a spatial resolution of 1000 m and downscaled at 500 m using topographical variables. Two types of analyses were performed:

Vitis champinii cultivars Ramsey and Dog-ridge are main choices for rootstocks to adapt viticulture in semi-arid and arid regions thanks to their distinctive tolerance to drought and salinity. However, genetic studies on non-vinifera rootstocks have heavily relied on the grapevine (Vitis vinifera) reference genome, which difficulted the assessment of the genetic variation between rootstock species and grapevines. In the present study, this limitation is addressed by introducing a novo phased genome assembly and annotation of Vitis champinii. This new Vitis champinii genome was employed as reference for mapping RNA-seq reads from the same species under drought and salt stresses, and for comparison the same reads were also mapped to the Vitis vinifera PN40024.V4 reference genome. A significant increase in alignment rate was gained when mapping Vitis champinii RNA-seq reads to its own genome, compared to the Vitis vinifera PN40024.V4 reference genome, thus revealing the expression levels of genes specific to Vitis champinii. Moreover, differences in coding sequences were observed in ortholog genes between Vitis champinii and Vitis vinifera, which therefore challenges previous differential expression analyses performed between contrasting Vitis genotypes on the same gene from the Vitis vinifera genome. Genes with possible implications in drought and salt tolerance have been identified across the genome of Vitis champinii, and the same genomic data can potentially guide the discovery of candidate genes specific from Vitis champinii for other traits of interest, therefore becoming a valuable resource for rootstock breeding designs, specially towards increased drought and salinity due to climate change.

Optimizing the use of water and improving irrigation strategies has become increasingly important in most winegrowing countries due to the consequences of climate change, which are leading to more frequent droughts, heat waves, or alteration of precipitation patterns. Optimized irrigation scheduling can only be based on a reliable knowledge of the vineyard water status.

In this context, this work aims at the development of a novel methodology, using a contactless, miniaturized, low-cost NIR spectral tool to monitor (on-the-go) the vineyard water status variability. On-the-go spectral measurements were acquired in the vineyard using a NIR micro spectrometer, operating in the 900–1900 nm spectral range, from a ground vehicle moving at 3 km/h. Spectral measurements were collected on the northeast side of the canopy across four different dates (July 8th, 14th, 21st and August 12th) during 2021 season in a commercial vineyard (3 ha). Grapevines of Vitis vinifera L. Graciano planted on a VSP trellis were monitored at solar noon using stem water potential (Ψs) as reference indicators of plant water status. In total, 108 measurements of Ψs were taken (27 vines per date).

Calibration and prediction models were performed using Partial Least Squares (PLS) regression. The best prediction models for grapevine water status yielded a determination coefficient of cross-validation (r2cv) of 0.67 and a root mean square error of cross-validation (RMSEcv) of 0.131 MPa. This predictive model was employed to map the spatial variability of the vineyard water status and provided useful, practical information towards the implementation of appropriate irrigation strategies. The outcomes presented in this work show the great potential of this low-cost methodology to assess the vineyard stem water potential and its spatial variability in a commercial vineyard.

Global climate change affects regional climates and hold implications for wine growing regions worldwide. Although winegrowers are constantly adapting to internal and external factors, it seems relevant to develop tools, which will allow them to better define actual and future agro-climatic potentials. Within this context, we develop a modelling approach, able to simulate the impact of environmental conditions and constraints on vine behaviour and to highlight potential adaptation strategies according to different climate change scenarios. Our modeling approach, named SEVE (Simulating Environmental impacts on Viticultural Ecosystems), provides a generic modeling framework for simulating grapevine growth and berry ripening under different conditions and constraints (slope, aspect, soil type, climate variability…) as well as production strategies and adaptation rules according to climate change scenarios. Each activity is represented by an autonomous agent able to react and adapt its reaction to the variability of environmental constraints. Using this model, we have recently analyzed the evolution of vineyards’ exposure to climatic risks (frost, pathogen risk, heat wave) and the adaptation strategies potentially implemented by the winegrowers. This approach, implemented for two climate change scenarios, has been initiated in France on traditional (Loire Valley) and emerging (Brittany) vineyards. The objective is to identify the time horizons of adaptations and new opportunities in these two regions. Carried out in collaboration with wine growers, this approach aims to better understand the variability of climate change impacts at local scale in the medium and long term.