Evaluating vineyard suitability in Southwestern Poland using a knowledge-based indexing model

Abstract

Data from a selected geographic areas in the Silesian region of  south-western Poland is used to evaluate vineyard and wine grape growing suitability using a knowledge-based suitability model (TSVS) developed by Terra Spase and tested in vineyards in northern California. The model is a raster-based, weighted, linear indexing model, which combines the most relevant soil, topographic and climatic characteristics of a landscape together in order to determine the most suitable locations for vineyard establishment and sustainable development. The TSVS model utilizes environmental variables that are considered of particular importance to premium wine grape growing.  Topographic variables are derived from available Digital Elevation Models using ArcGIS and Spatial Analyst. Land-use derived raster for the areas of interest can be utilized as an additional factor to the model. Climatic parameters such as Growing Degree Days, multi-year monthly average and extreme temperatures and Frost Free Days are also utilized. Soil properties such as soil texture, pH, organic matter content and others are incorporated in the model along with the topographic characteristics. Interpolation techniques are applied to point-value variables to generate continuous raster surfaces of the same spatial resolution. All variables are expertly weighted in accordance to their relative influence in determining optimum conditions for wine grape growing. The results for the selected region in Poland will be presented. The final raster resulting from the combination of the reclassified data is rated and classified according to a knowledge based index. The model results in a classified raster showing values from 0 to 10 that reflects the vineyard suitability distribution for the studied region. The results show where potential new vineyard sites could be developed in southern Poland based on the combined influence of the soil, topographic, and climatic factors.