Abstract
Cold-air pools can have several different impacts on viticulture, including final grape quality and yields. This study focuses on cold pools in the upper Douro Valley, which is one of the ...most important viticultural regions of northern Portugal. First, digital elevation model data were analyzed to identify pixels corresponding to the valley floors of the Douro and selected side valleys. Next, the topographic amplification factor was calculated for each of these pixels. Down-valley gradients in the topographic amplification factor were used to identify locations where cold air in the valley was likely to pool. High-time-resolution meteorological data recorded between January 2011 and December 2017 were analyzed to identify cold-pool events at one location in the main Douro Valley. The cold pools were assigned to seven different categories on the basis of their temporal behavior. There was a clear seasonal cycle in numbers of cold pools, with most observed during winter and the fewest in summer. The maximum strengths of the cold pools could occur at any time during the night, although the majority peaked around the middle of the night. This study is believed to be the first to examine cold pools in the upper Douro Valley.
A correct definition of the most adequate strategies and/or course of action to improve the sustainability of the wine industry must start with an evaluation, as objective and accurate as possible, ...of the sustainability performance of its products and processes. The main goal of this work is to perform a comparative sustainability evaluation of two Portuguese wines: a high market value “terroir” wine produced in small quantities, using grapes from a single vineyard, and a branded wine with lower market value, produced in large quantities using grapes from various regions. The evaluation follows a life cycle perspective and is based on seven sustainability indicators, selected taking into account the main issues pertinent to the wine industry. The functional unit is 0.75 L of wine produced that is the most common capacity of the wine bottles. The environmental and economic information used for the evaluation is mainly primary data obtained from the company, and complemented whenever necessary with secondary data from the literature or life cycle inventory databases. Results show that the main differences between the two wines are their water intensity and wastewater generated, being the values of the branded wine more than double those of the “terroir” wine, which is attributable to differences in the winemaking process, in particular the need to remove the SO2 added in the branded wine production. The calculated values for the carbon emissions are in good agreement with literature works. Some recommendations for improvement of the process sustainability are given.
•A comparative sustainability assessment of two Portuguese wines is performed.•Wines differ in terms of winemaking process, produced volume and market value.•System boundary includes winemaking, transportation of must and grapes, bottling and packaging.•Sustainability indicators use data mainly from real industrial practice.
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 R2P 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.
•Hyperspectral data and pigment’s quantification were used to predict Ψpd.•Statistical and Biostatistical modelling approaches were performed to predict Ψpd.•Several machine learning algorithms were ...applied in both modelling approaches.•The biostatistical approach is supported by the leaf’s pigments quantification.•Both modelling approaches present good results to predict Ψpd.
Predawn leaf water potential (Ψpd) is widely used to assess plant water status. Also, pigments concentration work as proxy of canopy’s water status. Spectral data methods have been applied to monitor and assess crop’s biophysical variables. This work developed two models to estimate Ψpd using a hand-held spectroradiometer (400−1010 nm) to obtain canopy and foliar reflectance in four dates of 2018 and a pressure chamber to measure Ψpd. Two modelling approaches, combining spectral data and several machine learning algorithms (MLA), were used to estimate Ψpd in a commercial vineyard in the Douro Wine Region. The first approach estimated Ψpd through vine’s canopy reflectance; several vegetation indices (VIs) were computed and selected, namely the SPVIopt1_950;596;521; SPVIopt2_896;880;901; PRI_CI2opt_539;560,573;716 and NPCIopt_983;972, as well as a time-dynamic variable based on Ψpd (Ψpd_0). The second modelling approach is based on pigments’ concentrations; several VIs were optimized for non-correlated pigments of vine’s leaves, assessed by its hyperspectral reflectance. The following variables for Ψpd estimation were selected through stepwise forward method: Ψpd_0; NRIgreen_LUT520;532; NRIgreen_LWC540;551. The B-MARS algorithm performed the best results for both modelling approaches, presenting a RRMSE in both validation modelling approaches between 13–14%.
Climate is arguably one of the most important factors determining the quality of wine from any given grapevine variety. This study focuses on three wine-growing regions in northern Portugal: Vinho ...Verde, Trás-os-Montes and Douro, the latter coinciding with Porto. High rainfall during late spring (April to June) can promote growth of the vines but increases the risk of fungal disease. High rainfall during harvest time (August to October) also bears the potential for severe operational disruption and heavy economic losses. The probability of unprecedented rainfall totals in spring and the harvest season over wine-growing regions of northern Portugal has been assessed. A large ensemble of initialised climate model simulations is analysed, and the probability of unprecedented rainfall in each season is quantified. Seasonal rainfall totals considerably higher than any observed are possible in the current climate. An unprecedented rainfall event in either season could occur with a probability between 0.01 and 0.05 in the present climate. Extreme value analysis was applied to rainfall totals from observations and the model ensemble, and the return periods of known extreme rainfall events are calculated. Similar probabilities for unprecedented rainfall totals were calculated. A year similar to 1993, when both seasons were exceptionally wet, would be expected to occur, on average, just once in the next 70–80 years in the current climate. These results could inform the requirements for improved vineyard management and resilience, such as design of drainage channels, access roads and terraces.
In metabolomics, data generated by untargeted approaches can be very complex due to the typically extensive number of features in raw data (with and without chemical relevance), dependence on raw ...data preprocessing methods, and lack of selective data mining tools to appropriately interpret these data. Extraction of meaningful information from these data is still a significant challenge in metabolomics. Moreover, currently available tools may overprocess the data, eliminating useful information. This work aims at proposing a data mining tool capable of dealing with metabolomics data, specifically liquid chromatography-mass spectrometry (LC-MS) to enhance the extraction of meaningful chemical information. The algorithm construction intended to be as general as possible in highlighting chemically relevant features, discarding non-informative signals specially background features.
The proposed algorithm was applied to an LC-MS data set generated from the analysis of grapes collected over a developmental period encompassing a 4-month period. The algorithm outcome is a short list of features from metabolites that are worth to be further investigated, for example by HRMS fragmentation for subsequent identification. The performance of the algorithm in estimating potentially interesting features was compared with the commercial MZmine software. For this case study, the MZmine output yielded a final set of 37 features (out of 1543 initially identified) with noise features while the proposed algorithm identified 99 systematic features without noise. Also, the algorithm required 2 times less user-defined parameters when compared to MZmine. Globally, the proposed algorithm demonstrated a higher ability to pin-point features that may be associated with grapes developmental and maturation processes requiring minimal parameters definition, thus preventing user uncertainty and the compromise of experimental information.
•An algorithm for complex untargeted LC-MS data analysis was developed.•The algorithm highlights features that are worth to be further investigated.•Application to grape metabolomic profile originated 99 features.•The algorithm provided almost 3 times more features than MZmine using fewer inputs.
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 change impacts on grapes and olives in Europe are assessed.•Heat stress and dry conditions are expected to become major issues in the Mediterranean.•Emerging compound events will become more ...frequent.•Production patterns will need to adapt to increasing levels of climate variability.
Co-design processes involving the scientific community, practitioners, end users and stakeholders can efficiently characterize harmful weather events during the growing season that potentially result in losses of crop yield and quality. This study builds on the experience of the EU Horizon 2020 project MED-GOLD for grape and olive. The identified agro-climate indicators are extended from the MED-GOLD regions to the entire ones where grape and olive are currently grown in Europe and Turkey, and used to assess climate change impacts with intrinsic adaptation relevance stemming from the co-design process. Before 2000, only a low fraction of the European grape and olive growing areas was exposed to extreme weather events as revealed by the agro-climate indicators, but this has changed rapidly afterward. Projections show increasingly widespread extreme high temperature events from 2020 to 2080. Approximately one-third of grapevine regions and over half of olive cultivation areas are expected to experience extreme drought conditions. Additionally, the frequency of compound extreme events will increase in the future, especially in the Mediterranean region and under the high-end emission scenario RCP8.5. This outcome calls for a new decision-making mindset that embeds expected levels of climate variability and extremes as the “new normal” for grape and olive in Europe. This will facilitate deployment of the required biophysical, economic and policy adaptation tools.
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•An IoT spectral sensing system for monitoring the grape berry ripening process through reflectance signals was developed.•The system was evaluated in both lab and field environments ...for red and white grapes.•The PCA analysis of the reflectance data collected showed a trend related to the grape berry ripening process.•A Partial Least Squares model for predicting the Total Soluble Solids content in red and white grapes was applied.
The present work proposes a novel autonomous Internet of Things (IoT) spectral sensing system for in-situ optical monitoring of grape ripening through reflectance signals. To this end, tailor-made hardware for this IoT end node was developed, characterized, and operated in both lab and field conditions. It included three complementary modules: the optical module, the host module, and the controller module. The optical module included four photodetectors and four LEDs with maximum emission wavelength centered at 530, 630, 690, and 730 nm that was placed in direct contact with the grape berry. The host module included the LED driver and the analog front-end for signal acquisition. Finally, the controller module provided full control of the system and ensured data storage, power management, and connectivity. The system was capable of measuring reflectance in the range of 4 – 100 % with a linear response (r2 > 998) and with a high reproducibility among different optical units. This design made it possible to collect reflectance signals from red (cv. Touriga Nacional) and white (cv. Loureiro) grape varieties in both lab and field environments. The relationship between this optical fingerprint (comprised of the different reflectance intensities recorded) and the evolution of grape berry quality parameters throughout the ripening period (for approximately two months), was analyzed and discussed. Lab data was used to establish a multivariate model based on Partial Least Squares for the prediction of the Total Soluble Solids (TSS) content in both varieties. The model error (Root Mean Square Error in Cross Validation) was 2.31 and 0.73 °Brix for Touriga Nacional and Loureiro, respectively. This model was applied to data acquired in the field in an illustrative example of the potential of the system to predict TSS in real time. The field observations collected during the monitoring period also provided relevant information about the potential issues that may occur during the unattended operation of the optical sensors. Additionally, the modular architecture of the optical module proposed makes it possible to use different LEDs and photodetectors, as well as the assembly of optical filters. This creates the possibility of using the same principles for measuring reflectance in different spectral ranges (e.g. IR) or even fluorescence. The results herein described paved the work for future developments of this technology that will include the development of prediction models for the most relevant grape ripening parameters based on reflectance data, as well as its operation as part of a Wireless Sensor Network.
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