摘要
Purpose Evaluation of soil functionality in Life Cycle Assessment (LCA) has progressively gained importance, although only a small cluster of studies deliver detailed guidelines on how to calculate quantified indicators. In addition, there is a lack of bibliography assessing impacts on the pedosphere due to spatially differentiated land use changes (LUC). In this study, an automated geospatial simulation of LUC based on crop rotation probabilities in Luxembourg was implemented in the programming environment R. Furthermore, this method based on coupling LCA and geographic information system (GIS) was used to calculate changes in soil functionality by implementing both the soil organic carbon (SOC) method and the Land Use Indicator Value Calculation Tool (LANCA?). The developed R script was then applied to a case study dealing with maize production for bioenergy purposes in Luxembourg. Methods On the one hand, geo-referenced crop information in Luxembourg for the period 2005-011 was used to calculate the estimated probability in which crop rotation occurs in combination with maize expansion to meet bioenergy production requirements by 2020. On the other hand, geo-referenced information for a wide range of parameters relevant in assessing soil functionality was stored in a geospatial database and mapped using R's geospatial data manipulation, analysis and visualisation capabilities. The geospatial data were used as input for the R LANCA? model, which calculates the environmental impacts associated with the five indicators considered in the model (erosion resistance, physicochemical filtration, mechanical filtration, biotic production and groundwater replenishment) for all the cultivated areas in Luxembourg. Results and discussion The application of the two models demonstrated the significant differences in soil functionality in Luxembourgish arable land, namely between the north and south of the country. Spatial differentiation was found to be important in all indicators, except biotic production and physicochemical filtration, in which the availability of more detailed datasets and more specific methods is a must. Finally, the coupling of GIS and LCI data proved to be an interesting tool for estimating transition probabilities in crop rotation and, therefore, useful in forecasting suitable areas to implement future agricultural policies. Conclusions GIS and LCI data coupling may constitute an interesting pathway to combine environmental impact assessment and spatial differentiation, provided that further improvements are performed in the method, including important soil parameters or farmer behaviour. In addition, the spatial mapping of environmental impacts can provide important support in terms of policy-making, conservation of natural resources, landscape, or agricultural planning.