Successful adoption of precision viticulture at the farm level depends on the appreciation of vineyard spatial variability. Knowing the spatial variability of soil properties is a challenge, often very expensive and labor intensive. An alternative approach could be the combined utilization of proximal and remote sensors. This study combined proximal (Geonics EM38‐MK2) and remote (normalized difference vegetation index, NDVI) sensing aimed at mapping homogeneous zones (HZs) of two 3.5‐ha vineyards in the Chianti wine district (Italy). Two HZs in each vineyard were obtained by a k‐means clustering of the first two factors of the principal component analysis performed on four maps: (i) apparent electrical conductivity, obtained by EM38‐MK2 at 0 to 75 cm (ECa1) and (ii) 0 to150 cm (ECa2); (iii) topographic wetness index (TWI), calculated from a digital elevation model; and (iv) NDVI extrapolated by multispectral airborne images. Only ECa1 and ECa2 were correlated with some physical (silt and gravel content) and hydrologic (available water capacity) features of the soils. These two variables could also better discriminate the two HZs with respect to NDVI and TWI. The grapes (Vitis vinifera L.) of the selected HZs were separately harvested and vinified to test the differences in the wine quality. Significant differences emerged between the wines produced from the two HZs, especially in terms of color intensity, dry extract, and anthocyanin content. A wine tasting after 6‐mo aging of the wines confirmed the differences between the wines produced in the two zones, especially in terms of color, structure, and total score.

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This work aimed at setting up a multivariate and geostatistical methodology to map viticultural terroirs at the province scale (1:125,000). The methodology is based upon the creation of a GIS storing all the viticultural and oenological legacy data of experimental vineyards , long-term climate data, digital elevation model, soilscapes (land systems) and legacy data of soil profiles.Environmental parameters related to viticulture, selected by an explorative PCA, were: elevation, mean annual temperature, mean soil temperature, annual precipitation, clay, sand and gravel content of soils, soil water availability, redoximorphic features and rooting depth.The variables interpolated using geostatistical methods, were used for a k-means clustering aimed to map the Natural Terroir Units (NTU). Vineyards of the province of Siena was subdivided into 9 NTU.Both the historical DOCG (Chianti Classico, Brunello di Montalcino and Nobile di Montepulciano) and other DOCs were characterized by three or four NTU, whereas the wider Chianti and Chianti Colli senesi DOCG was mainly constituted by seven NTU.

In this paper we report an experimental study to assess the process of Sr-isotope uptake from the soil and its transfer to the grapevine and then to the wine made through micro-vinification. The experimental work has been carried out with a deep control of the boundary conditions (i.e., type of soil, geologic substratum, ground water supply, etc.) on 11 selected vine-plant sites over a period of four harvest years. Sr-isotopes have been determined on grape-bunches, grapevine sap, on the bioavailable fraction of the soil, on bulk soil, and on the rocks of the substratum. No significant Sr-isotope variability has been observed among micro-vinifications from different harvest years. A slight but significant Sr-isotope variability occurred among wines from rows embedded on different soil type. The Sr-isotope data on micro-vinifications well match those of grapevine sap and bioavailable fraction of soils, all of them falling well within the whole geological range of the bedrock, despite an evident decoupling between bioavailable fraction, whole soils and bedrocks does exist. This decoupling has been ascribed to differential geochemical behaviour of minerals in response to pedogenetic processes. The findings of our experiments indicate that the biological activity of the vine is not able to change the original Sr/Sr composition up-taken from the bio-available fraction of the soil. Thus, the Sr/Sr of the wine is an unadulterated feature of the terroir.

<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> &lt; 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 (R² = 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>