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Climate change is a continuous spatiotemporal reality, possibly endangering the viability of the grapevine (Vitis vinifera L.) in the future. Europe emerges as an especially responsive area where the grapevine is largely recognised as one of the most important crops, playing a key environmental and socio-economic role. The mounting evidence on significant impacts of climate change on viticulture urges the scientific community in investigating the potential evolution of these impacts in the upcoming decades. In this review work, a first attempt for the compilation of selected scientific research on this subject, during a relatively recent time frame (2010–2020), is implemented. For this purpose, a thorough investigation through multiple search queries was conducted and further screened by focusing exclusively on the predicted productivity parameters (phenology timing, product quality and yield) and cultivation area alteration. Main findings on the potential impacts of future climate change are described as changes in grapevine phenological timing, alterations in grape and wine composition, heterogeneous effects on grapevine yield, the expansion into areas that were previously unsuitable for grapevine cultivation and significant geographical displacements in traditional growing areas. These compiled findings may facilitate and delineate the implementation of effective adaptation and mitigation strategies, ultimately potentiating the future sustainability of European viticulture.

Accurately predicting grapevine yield and quality is critical for optimising vineyard management and ensuring economic viability. Numerous studies have reported the complexity in modelling grapevine yield and quality due to variability in the canopy structure, challenges in incorporating soil and microclimatic factors, and management practices throughout the growing season. The use of multimodal data and machine learning (ML) algorithms could overcome these challenges. Our study aimed to assess the potential of multimodal data (hyperspectral vegetation indices (VIs), thermal indices, and canopy state variables) and ML algorithms to predict grapevine yield components and berry composition parameters. The study was conducted during the 2019/20 and 2020/21 grapevine growing seasons in two South Australian vineyards. Hyperspectral and thermal data of the canopy were collected at several growth stages. Simultaneously, grapevine canopy state variables, including the fractional intercepted photosynthetically active radiation (fiPAR), stem water potential (Ψstem), leaf chlorophyll content (LCC), and leaf gas exchange, were collected. Yield components were recorded at harvest. Berry composition parameters, such as total soluble solids (TSSs), titratable acidity (TA), pH, and the maturation index (IMAD), were measured at harvest. A total of 24 hyperspectral VIs and 3 thermal indices were derived from the proximal hyperspectral and thermal data. These data, together with the canopy state variable data, were then used as inputs for the modelling. Both linear and non-linear regression models, such as ridge (RR), Bayesian ridge (BRR), random forest (RF), gradient boosting (GB), K-Nearest Neighbour (KNN), and decision trees (DTs), were employed to model grape yield components and berry composition parameters. The results indicated that the GB model consistently outperformed the other models. The GB model had the best performance for the total number of clusters per vine (R2 = 0.77; RMSE = 0.56), average cluster weight (R2 = 0.93; RMSE = 0.00), average berry weight (R2 = 0.95; RMSE = 0.00), cluster weight (R2 = 0.95; RMSE = 0.13), and average berries per bunch (R2 = 0.93; RMSE = 0.83). For the yield, the RF model performed the best (R2 = 0.97; RMSE = 0.55). The GB model performed the best for the TSSs (R2 = 0.83; RMSE = 0.34), pH (R2 = 0.93; RMSE = 0.02), and IMAD (R2 = 0.88; RMSE = 0.19). However, the RF model performed best for the TA (R2 = 0.83; RMSE = 0.33). Our results also revealed the top 10 predictor variables for grapevine yield components and quality parameters, namely, the canopy temperature depression, LCC, fiPAR, normalised difference infrared index, Ψstem, stomatal conductance (gs), net photosynthesis (Pn), modified triangular vegetation index, modified red-edge simple ratio, and ANTgitelson index. These predictors significantly influence the grapevine growth, berry quality, and yield. The identification of these predictors of the grapevine yield and fruit composition can assist growers in improving vineyard management decisions and ultimately increase profitability.

The study assessed the yield and quality as well as the energy potential of biomass from stalks and pomace of four grape varieties, Riesling, Chardonnay, Zweigelt, and Merlot Vitis vinifera L., grown in temperate climate conditions. The research is innovative because the evaluation of the energy potential of biomass originating from Vitis vinifera L. has not been carried out so far in the northern wine-growing regions. Field studies were conducted in 2023 in the Experimental Vineyard of the University of Life Sciences in Lublin, located in southeastern Poland. Biometric yield assessment showed that Chardonnay vines were characterized by the lowest mass of clusters and peduncles, number of berries in the cluster, berry diameter, and peduncle size, and at the same time the highest berry mass among the assessed biotypes. Merlot clusters were characterized by the highest mass of clusters and the largest peduncles. Riesling had the most berries in the cluster, the heaviest peduncles, and the highest share of peduncles in the cluster mass (8.99%). For grape pomace, the LHV values range from 15.98 MJ kg−1 for the Chardonnay variety to 16.91 MJ kg−1 for Riesling, while for peduncles, these values range from 15.11 MJ·kg−1 for Merlot and Riesling to 15.26 MJ kg−1 for Chardonnay. The differences in pollutant emissions are more pronounced between grapevine varieties than between types of biomass (pomace vs. peduncles). The greatest variation among varieties was observed for carbon dioxide (CO2) emissions in the pomace category, while the smallest differences were noted for sulfur dioxide (SO2) emissions. Total gas emissions were highest for Zweigelt pomace (7.72 Nm3 kg−1) and lowest for Merlot (6.99 Nm3 kg−1), while for stalks, Chardonnay had the highest values (6.77 Nm3 kg−1) and Merlot the lowest (7.32 Nm3 kg−1). The largest variation among varieties was observed in the pomace category. These results indicate differences in exhaust gas emissions for different plant parts and grape varieties, which are relevant for optimizing production processes and ensuring sustainable development.

The introduction of irrigation in vineyards of the Mediterranean basin is a matter of debate, in particular in those of the Douro Demarcated Region (DDR), due to the limited number of available studies. Here, we aimed to perform a robust analysis in three consecutive vintages (2018, 2019, and 2020) on the impact of deficit irrigation on the yield, berry quality traits, and metabolome of cv. ‘Touriga Nacional’. Results showed that in the peaks of extreme drought, irrigation at 30% crop evapotranspiration (ETc) (R30) was able to prevent a decay of up to 0.4 MPa of leaf predawn water potential (ΨPd), but irrigation at 70% ETc (R70) did not translate into additional protection against drought stress. Following three seasons of irrigation, the yield was significantly improved in vines irrigated at R30, whereas irrigation at R70 positively affected the yield only in the 2020 season. Berry quality traits at harvest were not significantly changed by irrigation, except for Total Soluble Solids (TSS) in 2018. A UPLC–MS-based targeted metabolomic analysis identified eight classes of compounds, amino acids, phenolic acids, stilbenoid DP1, stilbenoid DP2, flavonols, flavan-3-ols, di-OH- and tri-OH anthocyanins, and showed that anthocyanins and phenolic acids did not change significantly with irrigation. The present study showed that deficit irrigation partially mitigated the severe summer water deficit conditions in the DDR but did not significantly change key metabolites.

Manual vineyard yield estimation approaches are easy to use and can provide relevant information at early stages of plant development. However, such methods are subject to spatial and temporal variability as they are sample-based and dependent on historical data. The present work aims at comparing the accuracy of a new non-invasive and multicultivar, image-based yield estimation approach with a manual method. Non-disturbed grapevine images were collected from six cultivars, at three vineyard plots in Portugal, at the very beginning of veraison, in a total of 213 images. A stepwise regression model was used to select the most appropriate set of variables to predict the yield. A combination of derived variables was obtained that included visible bunch area, estimated total bunch area, perimeter, visible berry number and bunch compactness. The model achieved an R2 = 0.86 on the validation set. The image-based yield estimates outperformed manual ones on five out of six cultivar data sets, with most estimates achieving absolute errors below 10%. Higher errors were observed on vines with denser canopies. The studied approach has the potential to be fully automated and used across whole vineyards while being able to surpass most bunch occlusions by leaves.

The effects of intercropping grapevine with aromatic plants are investigated using a multi-disciplinary approach. Selected results are presented that address the extent to which crop diversification by intercropping impacts grapevine yield and must quality, as well as soil water and mineral nutrients (NO3-N, NH4-N, plant-available K and P). The experimental field was a commercial steep-slope vineyard with shallow soils characterized by a high presence of coarse rock fragments in the Mosel area of Germany. The field experiment was set up as randomized block design. Rows were either cultivated with Riesling (Vitis vinifera L.) as a monocrop or intercropped with Origanum vulgare or Thymus vulgaris. Regarding soil moisture and nutrient levels, the topsoil (0–0.1 m) was more affected by intercropping than the subsoil (0.1–0.3 m). Gravimetric moisture was consistently lower in the intercropped topsoil. While NO3-N was almost unaffected by crop diversification, NH4-N, K, and P were uniformly reduced in topsoil. Significant differences in grapevine yield and must quality were dominantly attributable to climate variables, rather than to the treatments. Yield stabilization due to intercropping with thyme and oregano seems possible with sufficient rainfall or by irrigation. The long-term effects of intercropping on grapevine growth need further monitoring.

The impact of projected climate change on wine production was analysed for the Demarcated Region of Douro, Portugal. A statistical grapevine yield model (GYM) was developed using climate parameters as predictors. Statistically significant correlations were identified between annual yield and monthly mean temperatures and monthly precipitation totals during the growing cycle. These atmospheric factors control grapevine yield in the region, with the GYM explaining 50.4% of the total variance in the yield time series in recent decades. Anomalously high March rainfall (during budburst, shoot and inflorescence development) favours yield, as well as anomalously high temperatures and low precipitation amounts in May and June (May: flowering and June: berry development). The GYM was applied to a regional climate model output, which was shown to realistically reproduce the GYM predictors. Finally, using ensemble simulations under the A1B emission scenario, projections for GYM-derived yield in the Douro Region, and for the whole of the twenty-first century, were analysed. A slight upward trend in yield is projected to occur until about 2050, followed by a steep and continuous increase until the end of the twenty-first century, when yield is projected to be about 800 kg/ha above current values. While this estimate is based on meteorological parameters alone, changes due to elevated CO₂ may further enhance this effect. In spite of the associated uncertainties, it can be stated that projected climate change may significantly benefit wine yield in the Douro Valley.

Background Red blotch disease, caused by Grapevine red blotch virus (GRBV, genus Grablovirus , family Geminiviridae ), negatively impacts vine health, fruit yield, and quality, leading to substantial economic losses to growers. While recent studies have enhanced our understanding of the epidemiology of GRBV and its effects, little is known about the molecular basis of the host-virus interactions. Since small RNAs (sRNAs) are known to play a central role in host-virus interactions, this study was undertaken to investigate sRNA dynamics in leaves and berries at two phenological stages (asymptomatic pre- and symptomatic post-véraison) of GRBV-infected grapevines ( Vitis vinifera cv. Merlot). Results Among the 140 microRNAs (miRNAs) detected, 41 isoforms belonging to 18 miRNA families exhibited significant differential expression in response to GRBV infection. Furthermore, 50 miRNAs showed differential expression in samples from pre- and post-véraison stages. A total of 58 conserved and 41 novel targets for known V. vinifera miRNAs were validated using degradome sequencing data from leaf samples of pre- and post-véraison stages. Additionally, virus-derived siRNAs (vsiRNAs) specific to GRBV were present only in GRBV-positive samples. The vsiRNAs predominantly ranged from 19 to 24 nucleotides (nt), with the 21nt size being the most abundant. Mapping vsiRNAs across the GRBV genome revealed an uneven distribution, with vsiRNA-generating hotspots predominantly located in the V3 ORF. Of the 83 most abundant vsiRNAs, grapevine target transcripts were identified for eight of them. Conclusions Identification of differentially expressed miRNAs and vsiRNAs, as well as their targets, offered important insights into various pathways and mechanisms that were affected in grapevine infected with GRBV and in modulating different host responses in leaves and berries. This research serves as a foundation for a better understanding of the molecular interactions in this plant-geminivirus pathosystem.

Summary Since their discovery, single-domain antigen-binding fragments of camelid-derived heavy-chain-only antibodies, also known as nanobodies (Nbs), have proven to be of outstanding interest as therapeutics against human diseases and pathogens including viruses, but their use against phytopathogens remains limited. Many plant viruses including Grapevine fanleaf virus (GFLV), a nematode-transmitted icosahedral virus and causal agent of fanleaf degenerative disease, have worldwide distribution and huge burden on crop yields representing billions of US dollars of losses annually, yet solutions to combat these viruses are often limited or inefficient. Here, we identified a Nb specific to GFLV that confers strong resistance to GFLV upon stable expression in the model plant Nicotiana benthamiana and also in grapevine rootstock, the natural host of the virus. We showed that resistance was effective against a broad range of GFLV isolates independently of the inoculation method including upon nematode transmission but not against its close relative, Arabis mosaic virus. We also demonstrated that virus neutralization occurs at an early step of the virus life cycle, prior to cell-to-cell movement. Our findings will not only be instrumental to confer resistance to GFLV in grapevine, but more generally they pave the way for the generation of novel antiviral strategies in plants based on Nbs.

Climate change poses significant challenges to agriculture, a sector with a long-standing tradition in the Mediterranean basin. The region faces altered rainfall patterns, extreme temperatures, aridification, loss of biodiversity, and changes in crop yield and quality. These impacts, combined with intensive farming practices, threaten long-term agricultural sustainability. This study investigates agrivoltaics (AVs), a dual-use technology that integrates solar energy production (photovoltaic panels) with agriculture, as a potential solution to enhance resilience and adaptation of crops. Research at an AV system in Puglia (Southeastern Italy), combined with grapevine (Vitis vinifera L.), assessed soil moisture, temperature, and microclimate conditions together with vine yield and fruitfulness. Results showed that shading from photovoltaic panels increased soil moisture and moderated soil temperature, thus benefiting crops. Vines beneath the panels yielded more grapes (+277%) than in the full sun, confirmed by even the better bud fruitfulness of the shaded canes. While panels had minimal impact on air temperature, they reduced wind speed and vapor pressure deficit, creating a better microenvironment for vines. Spectral analysis revealed an increase in UV and blue light under the panels, potentially affecting photosynthesis. The AV system also produced substantial electricity, more than 90% compared to a ground-mounted system, demonstrating its dual-use application. The higher land equivalent ratio (LER) achieved by the AV system (3.54) confirmed that such systems can be advantageous in areas with a Mediterranean climate, allowing crop and energy production on the same land.