Spatial evaluation and trade‐off analysis of soil functions through Bayesian networks
MetadataShow full item record
StatisticsDisplay Item Statistics
CitationVrebos, D, Jones, A, Lugato, E, et al. Spatial evaluation and trade-off analysis of soil functions through Bayesian networks. Eur J Soil Sci. 2021; 72: 1575– 1589. https://doi.org/10.1111/ejss.13039
AbstractThere is increasing recognition that soils fulfil many functions for society. Each soil can deliver a range of functions, but some soils are more effective at some functions than others due to their intrinsic properties. In this study we mapped four different soil functions on agricultural lands across the European Union. For each soil function, indicators were developed to evaluate their performance. To calculate the indicators and assess the interdependencies between the soil functions, data from continental long‐term simulation with the DayCent model were used to build crop‐specific Bayesian networks. These Bayesian Networks were then used to calculate the soil functions' performance and trade‐offs between the soil functions under current conditions. For each soil function the maximum potential was estimated across the European Union and changes in trade‐offs were assessed. By deriving current and potential soil function delivery from Bayesian networks a better understanding is gained of how different soil functions and their interdependencies can differ depending on soil, climate and management. Highlights When increasing a soil function, how do trade‐offs affect the other functions under different conditions? Bayesian networks evaluate trade‐offs between soil functions and estimate their maximal delivery. Maximizing a soil function has varied effects on other functions depending on soil, climate and management. Differences in trade‐offs make some locations more suitable for increasing a soil function then others.
The following license files are associated with this item:
- Creative Commons
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-ShareAlike 4.0 International