Soil bulk density (ρ_b) is an important indicator of soil quality, productivity, compaction and porosity. Despite its importance, ρ_b is often omitted from global datasets due to the costs of making many direct ρ_b measurements and the difficulty of direct measurement on rocky, sandy, very dry, or very wet soils. Pedotransfer functions (PTFs) are deployed to address these limitations. Using readily available soil properties, PTFs employ estimator equations to fit existing datasets to estimate properties like ρ_b. However, PTF performance often declines when applied to soils outside those in the training dataset. Potentially, recalibrating existing PTFs using new observations would leverage the power of large datasets used in the original PTF derivation, while updating information based on new soil observations. Here, we evaluate such a recalibration approach for ρ_b estimation, benchmarking its performance against two alternatives: the original, uncalibrated PTFs, and novel, local PTFs derived solely from new soil observations. Using a ρ_b dataset of N = 360 total observations obtained in West Azerbaijan, Iran, we varied the local dataset size (with N = 15, 30, 60, and 360) and recalibrated four existing PTFs with these data. Local PTFs were generated based on stepwise multiple linear regression for the same datasets. The same PTFs (original, recalibrated, and local) were also applied to the study area, and the resulting ρ_b estimates were compared with the global SoilGrids dataset. Recalibration of PTFs reduced errors relative to the original uncalibrated PTFs; for instance, the NSE increased from −22.07 to 0.30 (uncalibrated) to 0.20–0.41 (recalibrated), and RMSE decreased from 0.12 to 0.60 Mg m⁻³ (uncalibrated) to 0.10–0.13 Mg m⁻³ (recalibrated). The recalibrated PTFs performance was comparable to or better than local PTFs applied to the same data. Recalibration of existing PTFs with local/regional uses provides a viable alternative to the use of global datasets or the development of local PTFs in data-scarce regions.

中文翻译：

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重新校准现有的 pedotransfer 函数以估计区域范围内的土壤容重

土壤容重 ( ρ _b ) 是衡量土壤质量、生产力、压实度和孔隙度的重要指标。尽管 ρ b 很重要，但由于进行许多直接ρ _b测量的成本以及在岩石、沙质、非常干燥或非常潮湿的土壤上直接测量的难度， ρ _{b经常从全球数据集中被忽略。}部署了 Pedotransfer 功能 (PTF) 来解决这些限制。使用现成的土壤特性，PTF 使用估计方程来拟合现有数据集来估计诸如ρ _{b之类的特性}. 然而，当应用于训练数据集中以外的土壤时，PTF 性能通常会下降。潜在地，使用新的观测重新校准现有的 PTF 将利用原始 PTF 推导中使用的大型数据集的力量，同时根据新的土壤观测更新信息。在这里，我们评估了这种用于ρ _b估计的重新校准方法，将其性能与两种替代方案进行了基准比较：原始的未校准 PTF 和仅来自新土壤观测的新型局部 PTF。使用 在伊朗西阿塞拜疆获得的N = 360 个总观测值的ρ _b数据集，我们改变了本地数据集的大小（ N = 15、30、60 和 360）并使用这些数据重新校准了四个现有的 PTF。基于相同数据集的逐步多元线性回归生成局部 PTF。相同的 PTF（原始的、重新校准的和本地的）也应用于研究区域，并将得到的ρ _b估计值与全球 SoilGrids 数据集进行比较。相对于原始未校准的 PTF，PTF 的重新校准减少了误差；例如，NSE 从 -22.07 增加到 0.30（未校准）到 0.20-0.41（重新校准），RMSE 从 0.12 到 0.60 Mg m ^-3（未校准）降低到 0.10-0.13 Mg m ^-3（重新校准）。重新校准的 PTF 性能与应用于相同数据的本地 PTF 相当或更好。使用本地/区域用途重新校准现有 PTF 为使用全球数据集或在数据稀缺地区开发本地 PTF 提供了可行的替代方案。