Barolo 2016: a preview of our vintage analysis

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The Bordeaux En Primeur is over and, as the wine industry moves on, we’d like to switch our focus to another region: Piedmont. As reported by Liv-ex, during the pandemic (https://www.liv-ex.com/2020/05/italy-star-fine-wine-market-continues-broaden-covid-19/), Italian wines faced a major growth in the exchange. With the 2016 vintage release of this year, we would like to take this opportunity to show a few measurements produced by Saturnalia algorithms and to discuss the vintage by putting the spotlight on two of the most important MGAs (MGA stands for Menzione Geografica Aggiuntiva, somehow similar to the concept of cru): Brunate and Cannubi. In particular, we carried out an analysis both of the weather and satellite patterns, and of the price variations of the vintage when compared to the previous ones. The following report is a subset of a more in-depth report to be published in September featuring all the major MGAs as well as more insights derived in collaboration with VignaVeritas. Enjoy!

An overview on 2016 vintage

The 2016 vintage registered scarce precipitations already since the beginning of the rest phase (whose starting point for our measures is November 1st), a trend that continued in the following phenology stages. Speaking of temperatures, 2016 was hot - but not as extreme as 2015 - with maximum temperatures exceeding the 35°C mark only a few times. Warm and dry conditions from June until August favoured an optimal development. The precipitations at the beginning and end of August did not compromise the production, which was rather sustained by the following warm September. [1]

Figure 1. Weather data collected in Castiglione Falletto in 2015 (end) and 2016

Overall comparison between vintages

A better understanding of 2016 can be acquired through a comparison with the data recorded for the previous vintages.

Figure 2. Comparison of precipitation in the rest phase

Figure 3. Comparison of precipitation between budburst and flowering

Figure 4. Comparison of precipitation between flowering and veraison

Figure 5. Comparison of precipitation from veraison to harvest

2016 vintage appears to be below average during winter, and also dry during the April-mid June period. From mid-June to August, rain was similar to vintage 2006, while a value similar to vintage 2010 has been recorded towards the end of the season.

A similar analysis has also been carried out by considering the Growing Degree Days, meaning the heat sum of days with a mean temperature higher than 10°C.

Figure 6. Comparison of GDD in the rest phase

Figure 7. Comparison of GDD between budburst and flowering

Figure 8. Comparison of GDD between flowering and veraison

Figure 9. Comparison of GDD between veraison and harvest

The analysis shows that spring was similar in terms of temperatures to previous seasons (but with lower precipitations) and, as mentioned earlier, summer was warm but not as extreme as 2003 and 2015 (confirmed by Figure 8). However, 2016 recorded hot temperatures until the harvest.

Another important point-of-view is given by surface soil moisture, collected by active and passive radar satellites, and part of the ESA CCI initiative [2-4]. New products, with higher spatial resolution, will provide even more precious insights in the near future.

Figure 10. Comparison of surface soil moisture measured from satellite

Figure 10 shows how 2014 vintage - considered only “good” by many - recorded higher soil moisture values in respect of other vintages from June until late August. August 2016 was very dry in terms of soil moisture, second only to 2011 and 2012.

Focus on two symbolic MGAs: Brunate and Cannubi

After a general introduction to the 2016 vintage, we’d like to zoom in and focus a bit more on two important MGAs, Brunate and Cannubi.

The Menzioni Geografiche Aggiuntive (MGA) are officially delimited areas of production within the Barolo DOCG appellation. If we were in France, they would probably be mentioned as Crus.

Brunate stretches across the towns of Barolo and La Morra, with an elevation ranging from 230 to 380 meters and exposed mostly towards south. Cannubi has instead an exposure in all directions as it encloses a hill and an elevation from 230 to 300 meters.

Figure 11. 3D view of Brunate and Cannubi, direction south-east.

Saturnalia algorithms take advantage of the continuous monitoring offered by satellites [5]. In particular, our system can produce different analysis. A brief review follows.

Figure 12. Saturnalia Evolution Index sampled on Brunate

Figure 13. Saturnalia Evolution Index sampled on Cannubi

The Saturnalia Evolution Index is a custom vegetation index, specifically developed to monitor vineyards. The charts in figures 12 and 13 describe the temporal evolution of the index across vintages. It is useful to describe the different patterns recorded within the vineyards and over different vintages, shining a light on potential differences. However, this kind of chart is more useful to technicians willing to understand the behaviour of their parcels. In order to simplify the analysis, Saturnalia leverages an algorithm capable of summarizing the season in a single value – called Saturnalia Vegetation Index –, displayed as a pixel. Furthermore, these values can be visualized in form of distribution of raw values (as in figures 14 and 15). An interpretation can take into account where the distribution is centred and how extended it is. In general, a broader distribution means more heterogeneity, while a narrower one is a proxy for homogeneity. According to our analyses on other areas, we can also say that lower values are linked to a more stressed vineyard. An optimum value of stress is an indicator of expected higher quality.

Figure 14. SVI distribution computed over Brunate

Figure 15. SVI distribution computed over Cannubi

Furthermore, the SVI raw values can be aggregated into five classes to facilitate visualisation on a map, with colours ranging from brown (very low) to dark green (very high). Figures 16 and 17 show how the SVI maps are overlapped. Figures from 18 to 25 include the top-view, the legend for the mentioned classes and a pie chart with the distribution of values for each class.

In general, Cannubi shows a lower SVI distribution compared to Brunate for the same vintages, and also a lower variability. In both cases, the 2016 distribution is the lowest mean value. Dry and warm weather certainly played a key role.

Figure 16. 3D view of Brunate with SVI map.

Figure 17. 3D view of Cannubi with SVI map.

Figure 18. SVI index map and distribution of classes, Brunate, vintage 2013.

Figure 19. SVI index map and distribution of classes, Brunate, vintage 2014.

Figure 20. SVI index map and distribution of classes, Brunate, vintage 2015.

Figure 21. SVI index map and distribution of classes, Brunate, vintage 2016.

Figure 22. SVI index map and distribution of classes, Cannubi, vintage 2013.

Figure 23. SVI index map and distribution of classes, Cannubi, vintage 2014.

Figure 24. SVI index map and distribution of classes, Cannubi, vintage 2015.

Figure 25. SVI index map and distribution of classes, Cannubi, vintage 2016.

What about prices?

Let’s now focus on prices, how wines from this region are performing and a comparison across recent vintages [6].

Figure 26. Comparison of average price per bottle on vintages from 2010 to 2016.

Figure 26 shows the average of a sample of 200 different wines (a combination of different producers, wines and vintages) from Brunate and Cannubi considering vintages from 2010 to 2016. Brunate wines tend to have higher market price on average than equivalent wines from Cannubi, particularly for the Riserva (B. € 150, C. € 80), but also for the standard type (normale B €100, normale C €80).

Figure 27. Average price per bottle on vintages from 2010 to 2016, Brunate

Figure 28. Average price per bottle on vintages from 2010 to 2016, Cannubi.

Figures 27 and 28 illustrate the average price per bottle grouped by producer. Brunate has 13 labels while Cannubi 17. Moreover, the difference between highest and lowest price is much higher for Brunate (€ 242) compared to Cannubi (€ 152), with Rinaldi’s wines over € 260 and Borgogno’s around €180. Figure 29 is a combination of the previous charts, ordered by price.

Figure 29. Average price per bottle, grouped by single label

Another interesting measure is the deviation from the average price, computed for each vintage (see figures 30 and 31). Brunate wines show significantly higher prices than average in 2016 (138%), particularly when compared to 2010 and 2015, which are the second and third most expensive vintages. Cannubi prices are similar between 2015 and 2016 and both are again significantly higher than average (134-135%). Brunate wines seem to show more resilience than Cannubi in 2014 (98% Brunate, 81% Cannubi), whilst the opposite is true for 2013 (Cannubi 95%, Brunate 92%).

Figure 30. Variation from average, Brunate

Figure 31. Variation from average, Cannubi

This analysis was conducted on a relatively small and heterogeneous sample, but it can be helpful to give an idea of how relative values move according to vintage variations within Barolo, indicating a tendency towards the creation of quality hierarchy within the different MGAs.

More data will be available in a comprehensive analysis to be published later.

References

[1] Weather data collected by VignaVeritas

[2] Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., Dorigo, W. (2019) Evolution of the CCI Soil Moisture Climate Data Records and their underlying merging methodology. Earth System Science Data 11, 717-739. https://doi.org/10.5194/essd-11-717-2019

[3] Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001.

[4] Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-13. 10.1109/TGRS.2017.2734070.

[5] Claverie, M., Ju, J., Masek, J. G., Dungan, J. L., Vermote, E. F., Roger, J.-C., Skakun, S. V., & Justice, C. (2018). The Harmonized Landsat and Sentinel-2 surface reflectance data set. Remote Sensing of Environment, 219, 145-161.

[6] Pricing data collected from Liv-ex.