It is possible to predict Sangiovese wine quality through a limited number of variables measured on the vines

Abstract

<p style="text-align: justify;"><strong>Aims</strong>: The research work aimed at creating and testing a method to evaluate vine performance of Sangiovese (VPS), in particular, a method able to predict the potential oenological result through a limited number of variables measured on the vines.</p><p style="text-align: justify;"><strong>Methods and results</strong>: A matching table was created on the basis of literature and the experience acquired over twenty years of research activity on Sangiovese vine and wine quality in Tuscany, which allowed the selection of eight viticultural parameters and three VPS classes. In order to validate the matching table, a specific experiment was conducted during the years 2002 and 2003 in 10 vineyards (selected from 7 farms) representative of the main soils and climates of the vine cultivation areas of the Province of Siena (Italy). The experimental results validated the proposed matching table through a non parametric statistical analysis. A multivariate regression analysis between wine sensory evaluation (score) and viticultural parameters significantly predicted wine quality even with only 4 grape parameters (<em>P</em> < 0.05).</p><p style="text-align: justify;"><strong>Conclusion</strong>: It was possible to predict VPS by means of a matching table based upon eight simple viticultural parameters. The reliability of the wine quality prediction increased proportionally according to the number of viticultural parameters, but remained rather high (R<sup>2</sup> = 0.606) when taking into account only sugar content, sugar accumulation rate, mean berry weight, and extractable polyphenol index (EPI).</p><p style="text-align: justify;"><strong>Significance and impact of the study</strong>: It is now possible to predict the quality of Sangiovese wines with a few selected grape parameters. Because of the wide variability in soil and climatic condition of the viticultural areas of the Province of Siena, where the method was developed, and the strong climatic contrast between the years when the method was validated, the use of both matching table and multiple regression is recommended for VPS prediction in Mediterranean environments.</p>

Microbial Functional Diversity in Vineyard Soils: Sulfur Metabolism and Links With Grapevine Plants and Wine Quality

“…Therefore, in addition to the well-established effects due to nitrogen limitation, we suggest that the reduced availability of nitrogen and sulfate in BRO12 soil might have reduced the GSH levels in berries and increased the amount of phenolic compared to the grapes deriving from BRO11 site. This hypothesis is also consistent with the overall VPS values related to wine quality (BRO11 = 62, BRO12 = 81) determined in our previous work (Costantini et al, 2013), as one of the main parameters used to calculate the VPS values is the phenolic compounds content (Bucelli et al, 2010; Figure 7).…”

“…On the other hand, the grapes of BRO12 showed a small increase of the average sugar content (23.2 • Brix, instead 22.2 • Brix of BRO11), extractable polyphenols (1,883 vs. 1,638 mg•l −1 ) and significantly (p < 0.05) higher total anthocyanins (1,500 vs. 1,316 mg •l −1 , data available only for 2008 and 2009). The overall wine quality was determined by means of the "Vine Performance of Sangiovese" (VPS) index (Bucelli et al, 2010), which showed a higher VPS value (VPS = 81) in BRO12 than in BRO11 (VPS = 62) over the three vintages.…”

Effect of Oenotannin Addition on the Composition of Sangiovese Wines from Grapes with Different Characteristics

“…The values for measured parameters were considered suitable for the current target enological model of Sangiovese wines (Bucelli et al 2010). The differences in composition between groups a and b (2003 harvest) were determined by the characteristics of the soil.…”

“…The effect of these environmental factors on grape composition has been reported (Cohen and Kennedy 2010). The composition of grapes harvested on 20 Sept and 5 Oct was considered slightly suitable for Sangiovese wines, whereas the composition of grapes harvested on 19 Oct was considered more suitable (Bucelli et al 2010). .…”

Unsupervised classification of very high remotely sensed images for grapevine rows detection

“…1). It displays the pedological and climatological characteristics best suited for the production of quality wines [Bucelli et al, 2010]. The average dimension of the vineyards is about 4 hectares, a surface representative of many other Tuscan estates located in hilly, often steeply sloping areas.…”