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 (Fe, Al) 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 Fe and Al 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 and ME from −6.8 to 6.8 mmol kg. Overall, map quality was better for topsoil than for subsoil and better for Al contents than for Fe 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 Al. Thus, prediction uncertainties were generally reliable, though for Al 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 and carbon sequestration in agriculture.

中文翻译：

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非晶态铁氧化物和铝氧化物（氢氧化物）的高分辨率数字土壤测绘，以指导可持续的磷和碳管理

无定形铁和铝（氢）氧化物是控制磷有效性和碳储存动态的关键土壤特性。这些氧化物影响土壤保留磷和碳的潜力，从而影响作物生产、水质和碳封存等生态系统服务。在这项研究中，我们以 25 m 分辨率对荷兰 0 至 200 cm 之间的六个土壤深度层的草酸盐可提取铁和铝 (Fe, Al) 含量进行了空间预测，并使用分位数回归森林量化了相关的预测不确定性。为了进行模型训练和验证，使用了 Fe 和 Al 含量的地理参考数据，包括 12,110 次湿化学观测和 102,393 次近红外光谱观测。选择了超过 150 个空间协变量，提供有关土壤类型、气候、土壤生物、土地利用、地形、母质和空间（采样深度和倾斜坐标）的信息。通过将预测与使用来自农田的 4841 个土壤样本的独立数据集的观测结果进行比较来评估地图质量。土壤样本位置是通过分层随机抽样选择的，使我们能够使用基于设计的统计推断来评估地图质量。使用模型效率系数 (MEC)、均方根误差 (RMSE) 和平均误差 (ME) 等指标来评估地图质量。地图质量有所不同，具体取决于目标变量和土壤深度，MEC 范围为 0.19 至 0.80，RMSE 范围为 13.5 至 56.9 mmol kg，ME 范围为 -6.8 至 6.8 mmol kg。总体而言，表土的地图质量优于底土，Al 含量的地图质量优于 Fe 含量。通过计算 90% 预测区间的预测区间覆盖概率来评估预测不确定性质量，在所有情况下该概率都接近 0.90，对于 Al 略低于 0.90。因此，预测的不确定性总体上是可靠的，尽管铝含量的不确定性被稍微低估了。这些地图是针对特定地点的粪便和肥料管理策略的宝贵工具，旨在平衡农业生产、水质和碳固存。