Month: April 2019

Fingerprinting untargeted analysis as potential tool to classify wine grape biotypes based on their variety and sanitary condition

By Pasquale Crupi, Marica Gasparro, Angelo Raffaele Caputo

The level of secondary metabolites in grapes mainly depends on cultivar type, but also on climate and growing conditions, including the sanitary status (Crupi et al. 2012; Coletta et al. 2014; Popovic-Djordjevic et al. 2017; Lakićević et al. 2018). Indeed, Grapevine leafroll associated virus (GLRaV) and Grapevine virus A (GVA), for instance, may reduce the rate of photosynthesis and, consequently, affect the overall quality of grapes and musts (Endeshaw et al. 2014; Alabi et al. 2016); although, they have showed a controversy effect on the biosynthesis of secondary metabolites (i.e. polyphenols) (Guīă and Buciumeanu 2016; Mannini and Digiaro 2017).
The goal of this study was to employ a metabolomic fingerprinting approach (through flow injection MS QqQ analyses) in order to group 71 wine grapes biotypes, belonging to three Apulian autochthonous varieties (i.e. Negro amaro, Malvasia Nera di Brindisi/Lecce, and Uva di Troia) on the basis of cultivar and virus complexes (such as, GLRaV3, GVA, GFLV, and GFKV), by which they are, eventually, infected. Therefore, the skins of Negro amaro (22), Malvasia Nera di Brindisi/Lecce (32), and Uva di Troia (17), whose identity was confirmed by genomic DNA microsatellites profiles, were extracted through a solution of water/ethanol/hydrochloric acid (70:30:1, v/v/v) and analyzed by means of a G6430 QqQ MS device (Agilent Technologies) in positive full scan mode and in 100-700 m/z range. The obtained [M+H]+ abundances were processed by logarithmic transformation and autoscaling, thereby they were subjected to a statistic multivariate tool (PCA). As reported in Figure 1, PCA allowed to distinguish Malvasia Nera (M) biotypes from Negramaro (N) and Uva di Troia (U), especially thanks to ions at m/z 115, 143, 261, 409, 445, and 561 which have the highest loadings (> 0.95) on PC1. Conversely, N and U were only slightly separated by the less determinant PC2 and PC3.

 

Figure 1. Principal component score of the investigated grape varieties (M: Malvasia Nera di Brindisi/Lecce; N: Negramaro; U: Uva di Troia) according to PC1, PC2, and PC3 obtained by autoscaled [M+H]+ abundances, and accounting for 81.12% of the total variance.

 

 

The clusterization of the biotypes affected by different virus complexes was really more tough (Figure 2); indeed, a clear distinction among infected plants was not observed, even though, very interesting, healthy grapes were sharply separated in the case of N and U (Figure 2b and c). Finally, from gathered findings the proposed metabolomic approach could be used as a rapid method for screening varietal and, less evidently, sanitary difference between grape biotypes.

Figure 2. Principal component scores of the investigated virus complexes (GLRaV3, GFLV, ArMV, GVA, and GFKV) in a) M, b) N, and c) U, according to PC1, PC2, and PC3 obtained by autoscaled [M+H]+ abundances, and accounting for 73.63%, 69.85%, and 75.64%, respectively, of the total variance.

 

 

Pasquale Crupi is a Research Scientist at CREA-VE, Council for Agricultural Research and Economics - Viticulture and Enology, Italy. His research is mainly focused on chemical characterization of metabolites in food matrices. From 2007 to 2013 he was member of Italian delegation to the International Organisation of Vine and Wine (OIV). From 2006 he is a Lecturer for many post-graduate courses. He is the author of more than 100 publications.
Dr. Crupi was educated in Chemistry at University of Bari (Italy). He received a Ph.D. in Biotechnologies of Food Products in 2008 at University of Foggia (Italy).

Gasparro Marica is a Researcher at CREA-VE, Council for Agricultural Research and Economics - Viticulture and Enology, Italy. Her research is mainly focused on virological analysis for the diagnosis of the main grapevine viruses; analysis of gene expression in nutrigenomic studies for the evaluation of the effects of grape intake on human health; molecular characterization of grapevine germplasm for varietal identification and study of relationships.
Dr.ssa Gasparro is graduated in Biological Sciences in 2005, at University of Bari (Italy) and she attended a postgraduate course in Biology of nutrition.

Angelo Raffaele Caputo is a scientific Technologist at Council for Agricultural Research and Economics, Research Centre for Viticulture and Enology, Italy. His scientific activity is mainly focused on safeguarding and enhancing viticultural biodiversity for implementation sustainable viticultural practices. From 2005 to 2012 he was a CREA delegate official and member of the Operative Secretariat of the National Service Coordination of the Vine Certification. From 2016 he was appointed Expert in the field of research and innovation in the ‘table and wine grapes’ sector – ‘wine agri-food processing’ by the Ministry of Agricultural Food, Forestry, and Tourism Policies (Italy). He is the author of numerous publications (more than 100) and projects coordinator.
Dr. Caputo got his Degree cum laude in Agricultural Sciences at the University of Bari (Italy) in 1987. In 1988 he qualified as an agronomist.

Those interested in a longer length report can download the working paper at:

https://www.tandfonline.com/doi/abs/10.1080/14786419.2019.1586703

References

Alabi OJ, Casassa LF, Gutha LR, Larsen RC, Henick-Kling T, Harbertson JF, Naidu RA. 2016. Impacts of grapevine leafroll disease on fruit yield and grape and wine chemistry in a wine grape (Vitis vinifera L.) cultivar. Plos One. 11(2):e0149666.

Coletta A, Berto S, Crupi P, Cravero MC, Tamborra P, Antonacci D, Daniele PG, Prenesti E. 2014. Effect of viticulture practices on concentration of polyphenolic compounds and total antioxidant capacity of Southern Italy red wines. Food Chem. 152:467–874.

Crupi P, Coletta A, Milella RA, Perniola R, Gasparro M, Genghi R, Antonacci D. 2012. HPLC-DAD ESI-MS analysis of flavonoid compounds in 5 seedless table grapes grown in Apulian region. J Food Sci. 77(2):C174–C181.

Endeshaw ST, Sabbatini P, Romanazzi G, Schilder AC, Neri D. 2014. Effects of grapevine leafroll associated virus 3 infection on growth, leaf gas exchange, yield and basic fruit chemistry of Vitis vinifera L. cv. Cabernet Franc. Scientia Hortic. 170:228–236.

Guīă IC, Buciumeanu EC. 2016. Effect of virus infection on grape quality and quantity (Vitis vinifera L., cv. Fetească neagră). Afr J Agric Sci Technol. 4:806–811.
Lakićević SH, Popović-Djordjević J, Pejin B, Djordjević A, Matijăsevic S, Lazic ML. 2018. An insight into chemical composition and bioactivity of ‘Prokupac’ red wine. Nat Prod Res.

Mannini F, Digiaro M. 2017. The effects of viruses and viral diseases on grapes and wine. In: Meng B, Martelli G, Golino D, Fuchs M, editors. Grapevine viruses: molecular biology, diagnostics and management. Cham (Switzerland): Springer.

Popovic-Djordjevic J, Pejin B, Dramicanin A, Jovic S, Vujovic D, Zunic D, Ristic R. 2017. Wine chemical composition and radical scavenging activity of some cabernet franc clones. Curr Pharm Biotechnol. 18(4):343–350.

Posted by in Viticulture

Aiming at grapevines with increased resistance to pathogens, reaching structural genome modifications

By Vera Quecini, Iraci Sinski, Patrícia S. Ritschel, Thor V. M. Fajardo

Resistance to pathogens is an important goal in the development of novel grapevine cultivars throughout the world by conventional and biotechnology-assisted breeding programs. Fungal and viral diseases cause direct losses in berry production, but also affect the quality of the final products. Methods for chemical control are available for fungus, but they increase the production costs, decrease fruit quality, and negatively affect culture sustainability. For viral pathogens, the situation is also complex, since it has to rely on the control of vector insects, if that is the way of transmission, or, on the replacement of highly colonized plants by new, clean ones. Thus, the introduction of genetic resistance is economically and environmentally desirable. Resistance to fungi and viruses is often found in wild or non-commercial Vitis genotypes, which simultaneously carry many undesirable traits to the progeny when used in crosses with elite cultivars. Recently, precision breeding strategies, such as genetic engineering and genome editing, allow the precise introduction of resistance characters in elite cultivars (Figure 1). However, the overall performance of the plants submitted to precision breeding techniques is not always as expected.

Figure 1. Schematic representation of the techniques used to generate genetically engineered grapevines.

 

 

In the current work, we have generated genetically engineered grapevines that express proteins with antimicrobial properties against fungal pathogens or a virus-derived sequence, in a hairpin orientation, which triggers the specific degradation of an RNA essential to the formation of novel infective viral particles. As expected, the genetically engineered grapevines exhibited increased resistance to the pathogens, in comparison to the non-engineered plants, although the infection was still detected in the modified plants (Figure 2). Further investigation demonstrated that the plants carrying larger portions of sequences from viral origin exhibited higher levels of a structural genome modification called methylation, and lower levels of resistance (Figure 3). Methylation consists in the addition of methyl radicals to certain bases of the DNA and is considered an epigenetic modification, since it interferes with the activity of the DNA without modifying the sequence. Taken together, our results demonstrated that the introduction of sequences of viral origins into grapevine genome is associated to genome structural changes and reduced expression of resistance against pathogens, thus indicating that the effectiveness of resistance strategies employing sequences of viral origin is subject to epigenetic regulation in grapevine.

Those interested in a longer length report can download the working paper at:
https://link.springer.com/article/10.1007/s11248-018-0082-1

 

Figure 2. Evaluation of disease responses against fungus (a-g) and virus (h-j) for genetic engineered and wild type plants. a In vitro assay of chitinase activity. Letters represent Tukey’s HSD at p < 0.05 (one-way ANOVA). b RNA hybridization employing a gene-specific probe. c Disease progression evaluated by the percentage of damaged leaf area after infection. d Light microscopy analyses of fungal structures in infected leaf discs at day 9. e Disease progression evaluated by the percentage of damaged leaf area after infection. f RNA hybridization employing a gene-specific probe. g Light microscopy analyses of fungal structures in infected leaf discs at day 5. h Frequency and intensity of leafroll symptom in hpGLR3 lines. i RT-PCR amplification of a GLRaV-3 specific fragment in symptomatic engineered plants. j Heatmap representation of GLRaV-3 coat protein levels detected by ELISA in genetic engineered and wild-type control plants.

Figure 3. Multivariate analyses of genomic features and trait expression in grapevine genetically engineered lines. a sPLS-DA plot of the individuals using the origin of the gene of interest as discriminant. Ellipses represent 95% confidence levels. b Clustered image map of the similarity matrix obtained by sPLS-DA results. Similarity is represented as heatmap, ranging from − 2.2 (blue) to 2.2 (red), and dendrograms derived from hierarchical clustering of the similarity results are represented for the genetically engineered lines (vertical) and variables (horizontal). c Correlation circle plot of the variables used in sPLS-DA analysis. d Heatmap and pie graph representation of the Pearson correlation between the investigated variables for the genetically engineered grapevine CHIT, OLP and hp-GLR3 lines. Pearson’s r is given inside the squares along with its p value.

 

 

 

 

 

Reference:
Dal Bosco D. et al. 2018. Expression of disease resistance in genetically modified grapevines correlates with the contents of viral sequences in the T-DNA and global genome methylation. Transgenic Research, v. 27 (4): 379–396. https://doi.org/10.1007/s11248-018-0082-1

Vera Quecini
Graduated in Agricultural Engineering at the ESALQ, in the University of São Paulo, Brazil, where she also got her MSc. in Agricultural Microbiology and Doctorate in Genetics and Plant Breeding. Subsequently, she worked as post-doctoral researcher at the University of Wageningen, in the Netherlands, and at the University of Columbia, Missouri, in the United States. Currently, she is a researcher at the Grapevine and Wine Research Center of the Brazilian Agricultural Research Corporation (Embrapa).

Viraci Sinski
Graduated in Biology at the University of Caxias do Sul, Brazil.Currently, she is the responsible technician for the Tissue Culture and Biotechnology Laboratory at the Grapevine and Wine Research Center of the Brazilian Agricultural Research Corporation (Embrapa).

Patrícia S. Ritschel
Graduated in Agronomy at the University of Brasília and proceeded to get her MSc. in Genetics and Plant Breeding at Viçosa Federal University and Doctorate in Biological Sciences (Molecular Biology) at the University of Brasília. Currently, she is a researcher at the Grapevine and Wine Research Center of the Brazilian Agricultural Research Corporation (Embrapa). She is the head scientist for the grapevine breeding program at Embrapa and is responsible for the development of the novel cultivars of seedless (BRS Vitória, BRS Isis and BRS Melodia) and seeded (BRS Núbia) table grapes, juice- (BRS Carmem and BRS Magna) and wine-production (BRS Bibiana) varieties.

Thor V. M. Fajardo
Graduated in Agronomy at Viçosa Federal University, where he proceeded to get his MSc. in Phytopathology. Subsequently, he got his Doctorate at the University of Brasília and worked as post-doctoral researcher at CSIC-UPV, in Spain. Currently, he is a researcher at the Grapevine and Wine Research Center of the Brazilian Agricultural Research Corporation (Embrapa), working with plant virology, mainly focusing the detection and molecular characterization of viruses from grapevine and temperate fruit crops.

Posted by in Viticulture

Identifying chemical parameters and discriminant phenolic compounds from metabolomics to gain insight into the oxidation status of bottled white wines

By Elia Romanini and Donato Colangelo

Oxidation of white wine is a complex phenomenon that may occur both in winemaking and during bottle storage. It results in changes in the sensory, color, and aroma attributes (Ferreira, Hogg, & de Pinho, 2003). Oxygen is the limiting factor of this spoilage. Significant amounts of oxygen are stored in the headspace of the bottle, but, in addition, there are two other sources: there are variable amounts of oxygen already dissolved in the wine and some oxygen permeates through the closure. In presence of oxygen, the propensity of the wine to be altered is highly affected by temperature (Godden et al., 2001).
The primary substrates for oxidation in wines are phenolic compounds, especially flavonoids such as flavan-3-ols and their condensed products, proanthocyanidin (Fernandez-Zurbano et al., 1995). In the enological practice, compounds such as sulfur dioxide, ascorbic acid, and glutathione are considered key factors towards wine resistance to oxidation and the preservation of aroma stability (Ugliano et al., 2011).
This work analyzed white wines that were randomly oxidized based on evidence from sensory evaluation and sought to obtain a deeper insight of the oxidative phenomenon through sensory analysis, chemical parameters, and metabolomics. Five different white wines were utilized in this study: Falerno del Massico 2007 DOCG (FAL), Fiano di Avellino 2010 DOCG (FIA), Greco di Tufo 2010 DOCG (GRE), Picolit Colli Orientali del Friuli 2009 DOCG (PIC) and Verduzzo Friulano 2013 (VER). To identify and quantify differences among samples in relation to their oxidation degree, a triangle test (ISO 4120:2004) was carried out on all the wine samples that were tasted the same day.
An untargeted metabolomic screening was performed through high-resolution MS using a QTOF hybrid quadrupole time-flight mass spectrophotometer (Agilent Technologies Santa Clara, CA, USA) coupled with an UHPLC (UHPLC/QTOF-MS) chromatographic system. PCA was used as a descriptive method to examine the relationships among the variables and the grouping among samples (Lewi, 1992). Cross-validation of the OPLS-DA model was carried out to validate the model using CV-ANOVA (p < 0.01), and permutation testing was performed to avoid overfitting. Variable importance in projection (VIP analysis) was used to identify those metabolites with the highest discrimination potential (VIP score > 1.0).

 

 

The results from the chemical analyses are shown in Table 1. The wines were categorized into two groups according to their oxidation level as provided by sensory analysis: the score (high or low) was reported as the oxidation status of each sample. In detail, for GRE, FAL, PIC and VER wines, the advanced oxidative status could be deduced by the lower concentrations of total SO2, while significant differences in free SO2 levels between oxidation statuses were observed only for FIA and PIC wines. For both PIC and VER wines, the level of browning (A420nm) and a* were significantly higher in the “high ox” group. Statistics showed Pearson’s negative correlation between free SO2 and A420nm (Figure 1). Consequently, free SO2 levels were higher in samples with a low level of oxidation than in samples with a high level of oxidation.

 

 

The UHPLC/QTOF-MS metabolomic analysis was elaborated, including the classification of the oxidation status of wines. An unsupervised approach was initially carried out to better highlight the relatedness between the samples. From the fold-change-based cluster analysis (Figure 2), a significant differentiation between the samples could be observed, with the type of wine having a hierarchical priority. Nonetheless, within each type of wine, a separation of the samples based on the level of oxidation was observed. This separation was marked for FAL, PIC, and VER samples, even if FAL samples were not highly discriminated between low and high oxidation status from chemical characterization (Table 1). For GRE and FIA wines, a similar overlap between classes of oxidation has also been outlined for the chemical parameters (Table 1).
The following supervised OPLS-DA model (Figure 3) provided strong clustering according to the oxidation status. In fact, the goodness-of-fit R2Y was 0.92, and the prediction ability Q2Y was 0.71, both of which were well above the acceptability threshold of 0.5 (Rombouts et al., 2017). In the within factor of the discriminant compounds identified by VIP analysis, molecules included in the classes of anthocyanins, flavanols, flavonols, and phenolic acids were confirmed to be significantly affected when oxidation phenomena occurred in white wine, as already found (Oliveira et al., 2011). In fact, in white wines exposed to oxygen, a significant decrease in total phenols occurs, in which the flavonoid fraction remains stable and only the nonflavonoid fraction decreases (Singleton, 1989).

 

 

In our study, lower amounts of phenolic compounds, such as cyanidin 3-O-6′′-p-coumaroyl-glucoside, delphinidin 3-O-glucoside, quercetin 3-O-glucosyl-xyloside, dihydroquercetin, and quercetin 3-O-glucuronide, were observed in the highly oxidized samples. Evidence in the samples with low oxidation status outlined that hydroxycinnamates remain very important for oxidation-related issues in wine browning (Table 1) because they represent the major group of white wine phenols and possess remarkable antioxidant activity and antioxidant capacity (Oliveira et al., 2011).
Flavonols, such as quercetin, myricetin, isorhamnetin, kaempferol and the corresponding flavones, apigenin and luteolin, were highlighted as discriminant compounds of the oxidative status of white wine. 3-Methylcatechol was the only flavanol compound identified to be discriminant in the detection of the oxidative status of the white wines under study. In addition to the VIP scores, a significant difference (p < 0.05) could be observed between the two classes of oxidation for some all the compounds reported, namely delphinidin 3-O-glucoside, cyanidin, poncirin, apigenin 6-C-glucoside, kaempferol, 2-hydroxybenzoic acid.

 

 

 

This study indicated that the content of most of the phenolic compounds identified in the wines was diminished with oxidative aging, with the exception of 6-hydroxyluteolin, 6,8-dihydroxykaempferol, kaempferol 3-O-xylosyl-rutinoside, avenanthramide K, 7-hydroxysecoisolariciresinol caffeic acid, feruloyl tartaric acid, isoferulic acid, and p-coumaric acid ethyl ester.
These discriminant molecules detected as either significantly lower or higher in wines with low or high oxidation levels, respectively, can represent a first step for gaining insights into the complex mechanisms of random oxidation observed in bottled wines.

Those interested in a longer length report can download the working paper at:
https://www.sciencedirect.com/science/article/pii/S0308814619303966

 

 

Elia Romanini actually is a PhD student of Agrisystem - Doctoral School on the Agro-Food System. He received his master’s degree in Food Technology from the Università Cattolica del Sacro Cuore (Piacenza) and he worked for 2 years in a craft brewery. He is currently working on sustainability of fermented beverages, in particularly on wine haze forming protein and bentonite alternatives. He spent the last 6 months as a visiting PhD student at Australian Wine Research Institute.

Donato Colangelo is currently a Ph.D. candidate at Università Cattolica del Sacro Cuore in Piacenza, Italy. He received the Master Degree in food technology in 2013. His Ph.D. project has concerned the search for alternative approaches to bentonite fining in white winemaking with a special eye on the drawbacks of each alternative and considerations about the environmental impacts. As a visiting student, in 2017 he collaborated with the University of California, Davis, working on a project about the use and regeneration of cation exchange resins for wine fining aimed at finding winery and environmentally-friendly solutions. He has been working on bentonite effects on wine colloidal stability since 2014. Parallel area of study include the tartaric stability of wines, the effects of the wine matrix factors and of enological adjuvants on the kinetics of tartrate precipitation.

References:

Fernandez-Zurbano, P.; Ferreira V.; Pena, C.; Escudero, A.; Serrano, F.; Cacho, J., 1995. Prediction of oxidative browning in white wines as a function of their chemical composition. J. Agric. Food Chem. 43 (11), 2813-2817

Ferreira, A.C.S.; Hogg, T.; de Pinho, P.G., 2003. Identification of key odorants related to the typical aroma of oxidation-spoiled white wines. J. Agric. Food. Chem., 51, 1377-1381.

Godden, P., Francis, L., Field, J., Gishen, M., Coulter, A., Valente, P., Høj, P. & Robinson, E. 2001. Wine bottle closures: physical characteristics and effect on composition and sensory properties of a Semillon Wine 1. Performance up to 20 months post-bottling. Australian Journal of Grape and Wine Research, 7, 62–105.

ISO 4120:2004. Sensory analysis – Methodology – Triangle test.

Lewi P.J., 1992. Multivariate data display. In: “multivariate Pattern recognition in chemometrics”. brereton (Ed.). p.43. Elsevier, London.

Oliveira, C. M., Ferreira, A. C. S., De Freitas, V., & Silva, A. M. (2011). Oxidation mechanisms occurring in wines. Food Research International, 44(5), 1115–1126.

Rombouts, C., Hemeryck, L. Y., Van Hecke, T., De Smet, S., De Vos, W. H., & Vanhaecke, L., 2017. Untargeted metabolomics of colonic digests reveals kynurenine pathway metabolites, dityrosine and 3-dehydroxycarnitine as red versus white meat discriminating metabolites. Scientific Reports 7, Article number: 42514.

Singleton, V.L. 1989. Browning and oxidation of must and wines. In Proceedings 4th Annual Midwest Regional Grape and Wine Conference. D.V. Peterson et al. (Eds.), pp. 87-93. State Fruit Experiment Station, Southwest Missouri State University, Mountain Grove.

Ugliano, M.; Kwiatkowski, M.; Vidal S. P.; Capone, D.; Siebert, T.; Dieval, J-B.; Aagaard, O, Waters, E.J., 2011. Evolution of 3-mercaptohexanol, hydrogen sulfide and methyl mercaptan during bottle storage of Sauvignon blanc wines. Effect of glutathione, copper, oxygen exposure and closure-derived oxygen, Journal of Agricultural and Food Chemistry, 59(6), 2564-2572.

Posted by in Enology

The impact of climate change on grapevine phenology and the influence of altitude: a regional study

By Azra Alikadić

In the near future, climate change is expected to significantly influence grape and wine quality (Jones et al., 2005). In fact, temperature is the main driver of grapevine phenology (Gladstones, 2011; Bock et al., 2011). In terms of climate change, the Mediterranean region is a climate “hot spot”, where temperature is expected to increase even more than in other world regions (Jones, 2007; Moriondo et al., 2013), leading to too warm conditions for the production of specific wine types (Jones et al., 2005). Simulations of the effect of climate change on the phenology of grapevines indicate shorter growing seasons, earlier occurrences of phases and shorter phase duration in the future.
In Alikadic et al., 2019 we took into consideration a geographically complex region, with a long history of wine production, the province of Trento in the Italian Alps. Wine industry is highly specialized in the region and several grapevine varieties are traditionally grown, both in the valley bottom and in several mountainous areas. Our aim was to assess the influence of climate change on grapevine phenology, considering the different features of five varieties (Chardonnay, Pinot Noir, Sauvignon Blanc, Merlot, Pinot Gris) and five phenological phases (Bud burst, Flowering, Fruit set, Veraison, Harvest) at different altitudes (from 67 m to 950 m a.s.l.), carried out in two future periods of time 2021-2050 and 2071-2099 (Figure 1).

 

 

Figure 1. Location of the vineyards in the study area (central section enlarged for details), with a total of 25 836 vineyards for five different varieties.

 

The difference between the baseline period (the mean of 2002-2014) and the predicted scenarios increases significantly after budburst (Figure 2), following the trend of increased temperatures in the different scenarios and determining a progressive advancement of the phases as they approach harvest. Budburst shows a diverse correlation with temperature scenarios due to chilling requirements. In fact, higher temperatures can lead to a delayed attainment of this phase.
The following three grapevine phenophases (flowering, fruit set, and veraison) show the same trend in response to temperature increase. The difference between the baseline period and the predicted scenarios increases for each following phase, hence the difference is smallest for flowering and highest for veraison. All varieties have an earlier harvest date. The two varieties grown at the lowest elevations, Pinot Gris and Merlot, have the smallest difference between baseline period and F1, from five to ten days. The highest differences are for the varieties Pinot Noir, Chardonnay and Sauvignon Blanc and range from 10 up to 18 days. In the second time period, harvest is also advanced, reaching 30 days in the warm scenario (Pinot Noir).

 

Figure 2. Occurrence of five different phenological phases for five varieties. Box limits are first and third quartile.

 

The temperature is projected to increase more at higher altitudes, thus decreasing altitudinal gradient. As a consequence, the time span between different phenological phases at different altitudes is expected to shorten. In practice, a comparatively shorter “technical growing season” (TGS: budburst to harvest) is predicted for vineyards located at higher altitudes. The harvest time is significantly more influenced by altitude than the earliest phenological phase (budburst). It is observed that in the period 2021-2099 harvest timing is expected to decrease by approximately 3 days (100 m)-1, leading to a more concentrated harvest window for growers and especially for wineries, which are supposed to separately deal with each variety.

 

Figure 3. Linear regression of the difference between baseline period and future scenarios vs. altitude for the variety Chardonnay: A) CGS temperature B) TGS duration C) Budburst date change D) Harvest date change.

 

The expected effects of climate change are not homogeneous in a mountainous province like Trentino. In this region, a significant higher change in grapevine phenology is expected, following the higher increase in phenological forcing temperature at higher altitudes, where base values are lower. Harvest time advance will likely bring a shortening of the harvest time gap between mountain and valley-bottom sites, due to the faster phenological development at higher elevations. Hence, this change will require management adjustments in work scheduling for both grapevine growers and wine producers.
A proactive approach of grapevine growers to find new suitable areas for traditional varieties would bring benefits to quality maintenance of some wines and, at the same time, the introduction of new varieties could open a new market. In such case, a further assessment of the effects of the local environmental conditions on wine quality as well as a market assessment for new grape varieties is recommended. As a future perspective, we recommend development of a methodology for high-resolution crop suitability analysis that takes into consideration historical and predicted growing patterns from this study as well as the correlated quality parameters per variety. Furthermore, the model could be used to evaluate the impact of extreme weather events (e.g. heat waves), which have increased in the past decades as a consequence of climate change. Such exposures are expected to have an additional impact on phenology of crops and the final quality of wine. We thus wish for an inclusion of the assessment of short-term temperature extremes in future suitability and climate change adaptation.

Those interested in a longer length report can download the working paper at:
https://www.sciencedirect.com/science/article/pii/S0168192319300875

 

 

Azra Alikadić
Specializes in the study of climate structure and suitability for agriculture, and how climate variability and change influence plant growth, food production and quality. Holds a B.Sc. and M.Sc. from Swedish University of Agricultural Sciences in Horticultural Sciences, and a Ph.D. from the University of Bologna in Agricultural Economy and Policy with a concentration in climate change mitigation and adaptation for the agricultural sector. Research interests include, agriculture; phenology, food supply chain, rural development, sustainability, climate change; quantitative methods in spatial and temporal analysis, policy; adaptation and mitigation of climate change.
Now the research focuses on adaptation strategies of climate change in the sector by studying the phenological response to climate change, regional studies, big data handling. Models climate impact and phenological changes resulting from global warming for regional studies. Processing and analyzing the data, environmental change and the causes of change are then identified by using of geographical information systems (GIS), statistical programming (R), and database management (SQL).

alikadic@fbk.eu
https://it.linkedin.com/in/azraalikadic

References:

  1. Alikadic, Azra; Pertot, Ilaria; Eccel, Emanuele; Dolci, Claudia; Zarbo, Calogero; Caffarra, Amelia; De Filippi, Riccardo; Furlanello, Cesare, 2019. The impact of climate change on grapevine phenology and the influence of altitude: A regional study. Agricultural and forest meteorology. 271, 73 – 82. 
  2. Jones, G. V., White, M.A., Cooper, O.R., Storchmann, K., 2005. Climate change and global wine quality. Change 73, 319–343.
  3. Gladstones, J.S., 2011. Wine, terroir and climate change. Wakefield Press, Kent Town, South Australia, 2011.
  4. Bock, A., Sparks, T., Estrella, N., Menzel, A., 2011. Changes in the phenology and composition of wine from Franconia, Germany. Res. 50, 69–81.
  5. Jones, G. V., 2007. Climate Change: observations, projections and general implications for viticulture and wine production, Working paper No. 7.
  6. Moriondo, M., Jones, G. V., Bois, B., Dibari, C., Ferrise, R., Trombi, G., Bindi, M., 2013. Projected shifts of wine regions in response to climate change. Climate Change 119, 825–839.
Posted by in Viticulture