Spatial aggregation of soil property predictions in support of local land management

Over the past decades, numerous practical applications of Digital Soil Mapping have emerged to respond to the need of land managers. One important contribution to this effort is the release of regional‐scale soil maps from the GlobalSoilMap (GSM) project. While the GSM project aims at producing soil property predictions on a fine 90 × 90 m grid at the global scale, land managers often require aggregated information over larger areas of interest (e.g. farms, watersheds, municipalities). This study evaluated a geostatistical procedure aiming at aggregating GSM grids to a land management scale, thereby providing land suitability maps with associated uncertainty for the French region ‘Languedoc‐Roussillon’ (27 236 km²). Specifically, maps were derived from three GSM prediction grids (pH, organic carbon and clay content) by calculating the proportion of ‘suitable’ agricultural land within a municipality, where suitability was defined as having soil property values below or above a predefined threshold (pH < 5.5, OC < 10 g/kg, clay > 375 g/kg). Calculation of these nonlinear spatial aggregates and the associated uncertainty involved a three‐step approach: (i) sampling from the conditional probability distributions of the soil properties at all grid cells by means of sequential Gaussian simulation applied to a regression kriging model, (ii) transformation of soil properties to suitability indicators for all grid cell samples generated in the first step and (iii) spatial aggregation of the suitability indicators from grid cells to municipalities. The maps produced show large differences between municipality areas for all three land suitability indicators. The uncertainties associated with the aggregated suitability indicators were moderate. This approach demonstrated that fine‐scale GSM products may also fulfil user demands at coarser land management scales, without jeopardizing uncertainty quantification requirements.