A physically oriented approach to analysis and mapping of terroirs

作者：Bonfante A^*; Basile A; Langella G; Manna P; Terribile F

来源：Geoderma, 2011, 167-68: 103-117.

DOI：10.1016/j.geoderma.2011.08.004

• Terroir
• SWAP model
• CWSI
• Viticulture zoning
• Italy
• Terroir mapping

The identification of viticulture terroir is an important issue, strongly rooted on soil science, that has major impact on vineyard management and, through marketing, on farmer income. The concept of terroir is well established but still ambiguous. Nowadays, a unique methodology to the analysis and implementation of terroirs does not exist but most of the approaches are based on a landscape classification employing multicriteria qualitative parametric models. This work departs from these empirical models and it attempts to prove that a mechanistic approach (strongly based on hydropedology) to the analysis of terroir is feasible and it enables a better use of environmental features with respect to plant requirement and wine production. More specifically, the proposed approach aims to integrate the standard terroir analysis with soil-plant-atmosphere water balance simulation modelling and GIS databases in order to ameliorate the spatial analysis of terroirs. The study has been conducted in a test area located in the central part of the Campania region: the Valle Telesina (BN) (about 20,000 ha). This area has a long tradition in the production of high quality wines (DOC and DOCG) and it is characterised by a complex geomorphology with a large soil and climate variabilities. The approach have clearly showed the high potential in combining (i) crop water stress index (CWSI) after simulation modelling (SWAP model), (ii) spatially distributed "Amerine & Winkler" and "potential radiation" indexes and (iii) other (continuous and discontinuous) soil and climate data to ameliorate the spatial analysis of terroirs.
More specifically, important correlations have been obtained between a one year independent dataset of vineyard records (vegetative crop and must measurements) and CWSI estimates by the model (plant production and total water stress, r: -0.65*; fertile buds and water stress during the berry formation, r: -0.71). It is also important to emphasise that the undertaken approach is data demanding and it requires good quality of environmental (high resolution DEM), climate (daily climatic data) and soil data (soil hydraulic functions).

• 出版日期2011-11