In this study, the performance of 63 existing pedotransfer functions (PTFs) is evaluated to estimate oven-dry soil bulk density (BD) by using a dataset of 3316 soil cores taken in the farmlands of Campania (southern Italy). As expected, the lack of direct calibration yields prediction accuracy from unsatisfactory to rather weak. Therefore, we advance the working hypothesis that the use of hierarchical soil mapping information can make the application of existing PTFs more reliable. We show that grouping data according to land-systems classes or soil groups considerably improves the prediction ability quantified through the root mean squared error (RMSE) and the coefficient of determination (R²). An independent data set of 105 soil cores taken from two hillslopes in the Upper Alento River Catchment in southern Campania was used to verify our assumption. The validation step shows that the knowledge of a soil-landscape map is an efficient tool for improving the prediction of BD. This approach will be employed in a subsequent study to develop site-specific PTFs for the study region.

在这项研究中，通过使用来自坎帕尼亚（意大利南部）农田的3316个土壤核的数据集，评估了63种现有的pedotransfer函数（PTF）的性能，以估计干燥土壤的干重（BD）。不出所料，直接校准的缺乏会导致预测精度从不满意到较弱。因此，我们提出了工作假设，即使用分层土壤映射信息可以使现有PTF的应用更加可靠。我们表明，根据土地系统分类或土壤组对数据进行分组可以显着提高通过均方根误差（RMSE）和确定系数（R ²）量化的预测能力）。来自坎帕尼亚南部阿伦托河上游流域两个山坡的105个土芯的独立数据集用于验证我们的假设。验证步骤表明，土壤-景观图的知识是改善BD预测的有效工具。在随后的研究中将采用这种方法来为研究区域开发特定于站点的PTF。