Data integration model to assess soil organic carbon availability

Soil data acquisition and assessment are crucial phases in the evaluation of soil degradation scenarios. To overcome the lack of field data, flexible sampling approaches can be used to complement conventional soil sampling. For the assessment of soil quality, it is necessary to integrate different soil support data and to provide a coherent spatial characterization of soil properties. This study proposes a new model to combine soil data from two different supports: “point” data, which refers to the concentration measured in the topsoil layer, and “bulk” data, which refers to the concentration measured for the whole soil depth sampled. The method developed uses a geostatistical co-simulation algorithm based on the experimental bi-distribution between both types of soil supports to compute co-simulated values. This new approach was applied to assess Soil Organic Carbon (SOC) availability in the topsoil. The results were used to identify critical areas in the Left Margin of the Guadiana River; an area in the South of Portugal with a high susceptibility to desertification.

Research Highlights
► Integration model for soil data from different sampling sources.
► New stochastic co-simulation computes the spatial variability of soil organic carbon in the topsoil layer.
► New approach for soil sampling strategies.