Probabilistic soil mapping by Bayesian inference to assess suitability for derocking in northwest Syria

Abstract

Land reclamation by rock removal has wide potential in areas with shallow or rocky soils. In northwest Syria, this practice is hindered in its implementation by a lack of physical soil suitability data, principally soil rockiness and soil depth to hard rock. These soil properties were surveyed in a limited study area, resulting in a hard-boundary thematic soil map (64-94% accuracy per property). Bayesian inference is proposed as a low-cost upscaling method that yields a set of pixel-based probability maps, providing improved input for spatial decision support models. Whereas the achieved spatial upscaling (from 2510 to 19100ha) outweighed the decrease in overall accuracy (down to 26-57%), probability maps require dedicated validation and manipulation procedures. This research contributes to methods for the creation, validation and interpretation of probabilistic soil property maps for quantitative land evaluation. First, we evaluated three postprocessing and validation methods for probabilistic soil property maps, identifying the use of prediction rates' as the best approach in a spatial planning context. Next, we demonstrated how the maps can support decision-making for land management activities by simulating the expected losses and gains from interventions, in a decision-theoretic approach. Based on the simulations, the investments in large-scale derocking projects in northwest Syria will pay off in terms of increased agricultural productivity in less than 10years.