High-resolution digital soil mapping of amorphous iron- and aluminium-(hydr)oxides to guide sustainable phosphorus and carbon management

•Digital Soil Mapping of oxalate-extractable iron and aluminium contents.•Predictions at 25 m spatial resolution across six soil depth intervals.•Prediction uncertainty using a quantile regression forest algorithm.•Map quality assessed with design-based statistical inference.•Maps aid in balancing crop production, water quality and carbon sequestration. Amorphous iron- and aluminium-(hydr)oxides are key soil properties in controlling the dynamics of phosphorus availability and carbon storage. These oxides affect the potential of soils to retain phosphorus and carbon, thus affecting ecosystem services such as crop production, water quality and carbon sequestration. In this study, we spatially predicted oxalate-extractable Fe and Al (FeOX, AlOX) contents in the Netherlands at 25 m resolution across six soil depth layers between 0 and 200 cm and quantified the associated prediction uncertainty using quantile regression forest. For model training and validation, geo-referenced data of FeOX and AlOX contents were used including 12,110 wet-chemical observations and 102,393 NIR spectroscopy observations. Over 150 spatial covariates were selected that provide information about soil typology, climate, soil organisms, land use, relief, parent material and space (sampling depth and oblique coordinates). Map quality was assessed by comparing predictions with observations using an independent data set of 4841 soil samples from agricultural fields. Soil sample locations were selected by stratified random sampling, allowing us to assess map quality using design-based statistical inference. Map quality was evaluated using the metrics Model Efficiency Coefficient (MEC), Root Mean Square Error (RMSE) and Mean Error (ME). Map quality differed, depending on the target variable and soil depth, with MEC ranging from 0.19 to 0.80, RMSE from 13.5 to 56.9 mmol kg−1 and ME from −6.8 to 6.8 mmol kg−1. Overall, map quality was better for topsoil than for subsoil and better for AlOX contents than for FeOX contents. Prediction uncertainty quality was evaluated by calculating the Prediction Interval Coverage Probability of the 90 per cent Prediction Interval, which were close to 0.90 in all cases and slightly below 0.90 for AlOX. Thus, prediction uncertainties were generally reliable, though for AlOX contents uncertainty was slightly underpredicted. The maps are a valuable tool for site-specific manure and fertiliser management strategies aiming to balance crop production, water quality