Smoke taint has become a prominent issue for the global wine industry as climate change continues to impact the length and extremity of fire seasons around the world. Although the issue has prompted a surge in research on the subject in recent years, no singular solution has yet been identified that is capable of maintaining the quality of wine made from smoke-affected grapes. In this review, we summarize the main research on smoke taint, the key discoveries, as well as the prevailing uncertainties. We also examine methods for mitigating smoke taint in the vineyard, in the winery, and post production. We assess the effectiveness of remediation methods (proposed and actual) based on available research. Our findings are in agreement with previous studies, suggesting that the most viable remedies for smoke taint are still the commercially available activated carbon fining and reverse osmosis treatments, but that the quality of the final treated wines is fundamentally dependent on the initial severity of the taint. In this review, suggestions for future studies are introduced for improving our understanding of methods that have thus far only been preliminarily investigated. We select regions that have already been subjected to severe wildfires, and therefore subjected to smoke taint (particularly Australia and California) as a case study to inform other wine-producing countries that will likely be impacted in the future and suggest specific data collection and policy implementation actions that should be taken, even in countries that have not yet been impacted by smoke taint. Ultimately, we streamline the available information on the topic of smoke taint, apply it to a global perspective that considers the various stakeholders involved, and provide a launching point for further research on the topic.

The atmospheric conditions are a strong modulator of grape berry composition, but further research is required to better understand this relationship, which is particularly pertinent under the context of climate change. The present study assesses the relationship between interannual variability in atmospheric conditions (mean, maximum and minimum air temperatures and precipitation totals) on grape berry quality attributes in three main Portuguese wine regions—Douro, Dão and Alentejo—and targets two major varieties growing in Portugal (cv. Touriga Nacional and cv. Aragonez/Tempranillo). Berry weight, titratable acidity (TA), pH, potential alcohol (PA), anthocyanins and total phenols index (TPI) data, collected two to three weeks after the end of the veraison until technological maturity, since 1999 in Douro, 2004 in Alentejo and 2008 in Dão, were selected. Meteorological data were obtained from both automatic weather stations and a climatic database defined at a very-high-resolution grid (<1 km) (PTHRES). The influence of daily mean, maximum and minimum air temperatures (November–October) and precipitation totals (April to June and July to September) on the above-mentioned berry quality parameters were first explored to identify the months/periods more influential to grape berry composition. Different statistical approaches were subsequently carried out to explore in greater detail these relationships. At technological maturity, temperature was negatively correlated to berry weight, titratable acidity, anthocyanins and TPI, but was positively correlated to pH and potential alcohol. Moreover, lowest levels of berry weight and TA (and highest levels of pH) were more frequent in warmer regions, while the opposite was seen in the cooler regions. PA, TPI and anthocyanins at maturity did not show a clear trend across regions. In addition, the maturation parameters of each site were grouped into two clusters—years where the maturation parameter is higher (cluster 1) and years where it is lower (cluster 2)—and significant differences in monthly mean temperatures between clusters were found. Overall, temperatures at veraison and maturation periods (June–August) were more influential in determining grape berry composition at harvest. The influence of precipitation was dependent on location and variety. The results also suggested that berry composition in Alentejo is more sensitive to atmospheric variability, while Aragonez seems more resilient than Touriga Nacional. These outcomes are based on a systematized and unprecedentedly large grape berry quality database in Portugal and provided the grounds for the development of grape quality forecast models, either to be used operationally in each vintage or for assessing potential modifications in berry composition in response to changing climates.

Climate-smart agriculture involves practices and crop modelling techniques aiming to provide practical answers to meet growers’ demands. For viticulturists, early prediction of harvest dates is critical for the success of cultural practices, which should be based on accurate planning of the annual growing cycle. We developed a modelling tool to assess the sugar concentration levels in the Douro Superior sub-region of the Douro wine region, Portugal. Two main cultivars (cv. Touriga-Nacional and Touriga-Francesa) grown in five locations across this sub-region were studied. Grape berry sugar data, with concentrations between 170 and 230 g L⁻¹, were analyzed for the growing season campaigns, from 2014–2020, as an indicator of grape ripeness conditioned by temperature factors. Field data were collected by ADVID (“Associação Desenvolvimento Da Viticultura Duriense”), a regional winemaker association, and by Sogrape, the leading wine company from Portugal. The “Phenology Modeling Platform” was used for calibrating the model with sigmoid functions. Subsequently, model optimizations were performed to achieve a harmonized model, suitable for all estates. Model performance was assessed through two metrics: root mean square error (RMSE) and the Nash–Sutcliffe coefficient of efficiency (EFF). Both a leave-one-out cross-validation and a validation with an independent dataset (for 1991–2013) were carried out. Overall, our findings demonstrate that the model calibration achieved an average EFF of 0.7 for all estates and sugar levels, with an average RMSE < 6 days. Model validation, at one estate for 15 years, achieved an R² of 0.93 and an RMSE < 5. These models demonstrate that air temperature has a high predictive potential of sugar ripeness, and ultimately of the harvest dates. These models were then used to build a standalone easy-to-use computer application (GSCM—Grapevine Sugar Concentration Model), which will allow growers to better plan and manage their seasonal activities, thus being a potentially valuable decision support tool in viticulture and oenology.

Tangible variation of sensory characteristics is often observed in wine aged in similar barrels. Barrel-to-barrel variation in barrel-aged wines was investigated in respect of the most important phenolic compounds of oenological interest. A red wine was aged in 49 medium-toasted oak (Quercus petraea) barrels, from four cooperages, for 12 months. The resulting wines were evaluated for chromatic characteristics, anthocyanin-related parameters, total phenols, flavonoids and non-flavonoids phenols, flavanol monomers, and oligomeric and polymeric proanthocyanidins. PCA and ANOVA were applied to investigate the relationships between barrels and to assess cooperage and individual barrel effect. Three cooperages influenced the wine similarly during aging. Anthocyanin-related parameters showed the highest variation, 25–37%, other phenolics varied 3–8.5%, and with two exceptions, chromatic characteristics changed 1.7–3%. The relationship between the number of barrels and the expected variation for each analytical parameter was calculated, as reference for future measurements involving barrel lots, either in wine production or experimental design.

Soil properties influence greatly the status of vine plants which consequently influences the quality of wine. Therefore, in the context of viticulture management, it is extremely important to assess the physical and chemical parameters of vineyards soils. In this study, the soils of two vineyards were analysed by near-infrared (NIR) spectroscopy and established analytical reference procedures. The main objective of this study was to verify if NIR spectroscopy is a potential tool to discriminate the soils of both vineyards as well as to quantify differences of soil's parameters. For that, a total of eight sampling spots were selected at each vineyard taking into consideration the soil type and sampled at different depths. The data analysis was performed using analysis of variance (ANOVA), principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) and partial least squares (PLS) regression. The ANOVA results revealed that 12 out of the 18 parameters analysed through the reference procedures can be considered statistically different (p < 0.05). Regarding PCA, the obtained results revealed a clear separation between the scores of both vineyards either considering NIR spectra or the chemical parameters. The PLS-DA model was able to obtain 100 % of correct predictions for the discrimination of both vineyards. PLS regression analysis using NIR spectra revealed R²_P and RER values higher than 0.85 and 10, respectively, for 8 (pH (H₂O), N, Ca²⁺, Mg²⁺, SB, CEC, ECEC and GSB) of the 18 chemical parameters evaluated. Concluding, these results demonstrate that it is possible to discriminate the soils of the different vineyards through NIR spectroscopy as well as to quantify several chemical parameters through soils NIR spectra in a rapid, accurate, cost-effective, simple and environmentally friendly way when compared to the reference procedures.