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Aim: To evaluate the relationship between carbon isotope ratio (δ13C) and wine grape viticultural and oenological performance in organic farming. Methods and results: The study was carried out for four years in the Chianti Classico wine production district (Central Italy), on five non irrigated vineyards conducted in organic farming. The reference variety was Sangiovese. Eleven sites were chosen for vine monitoring and grape sampling. The performance parameters were alcohol and must sugar content, sugar accumulation rate, mean berry weight, and extractable polyphenols. δ13C, stem water potential, and soil water availability were also monitored. Finally, soil nitrogen as well as yeast available nitrogen in the must were measured. δ13C was directly related to stem water potential and soil water deficit, and indicated a range of water stress conditions from none and moderate to strong. However, its relationship with viticultural and oenological results was contrary to expectation, that is, performance linearly increased along with soil moisture. On the other hand, the worst performance was obtained where both water and nitrogen were more limiting. Conclusions: The unexpected relationship between δ13C and Sangiovese performance was caused by low nitrogen availability. The studied sites all had low-fertility soils with poor or very poor nitrogen content. Therefore, in the plots where soil humidity was relatively higher, nitrogen plant uptake was favoured, and Sangiovese performance improved. Macronutrient being the main limiting factor, the performance was not lower in the plots where soil water availability was relatively larger. Therefore, the best viticultural result was obtained with no water stress conditions, at low rather than at intermediate δ13C values. Significance and impact of the study: Water nutrition is crucial for wine grape performance. δ13C is a method used to assess vine water status during the growing season and to estimate vine performance. A good performance is expected at moderate stress and intermediate δ13C values. A better knowledge of the interaction between water and nutrient scarcity is needed, as it can affect the use of δ13C to predict vine performance.

Aims: 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. Methods and results: 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 (P < 0.05). Conclusion: 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). Significance and impact of the study: 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.