Water retention and saturated hydraulic conductivity are soil properties that are key determinants in crop growth and hydrological modelling. They are commonly estimated from basic soil characteristics such as bulk density, organic carbon content and texture by means of pedotransfer functions (PTFs). In order to assess and compare the inherent performance and the functional applicability in the Zambezi River Basin (ZRB) of the widely used Saxton & Rawls PTFs and a set of newly developed PTFs, we compiled measurements of water retention at pF0.0, 1.0, 2.0, 2.8, 3.4 and 4.2 and of saturated hydraulic conductivity (Ksat) on 631 soil samples throughout the ZRB. A total of 329 of the samples were related to 55 soil profiles available in the Africa Soil Profile database, whereas our own field campaign carried out in a 2,426-km² subbasin of the ZRB provided the remaining 302 samples related to 119 soil profiles. Apart from evaluating the Saxton & Rawls PTFs, we developed multiple linear regression (MLR) PTFs, and PTFs derived by three machine learning (ML) models: artificial neural network (ANN), random forest (RF) and support vector machine (SVM). All PTFs were first evaluated based on a comparison of the estimated and measured property values by means of R², mean absolute error (MAE) and root mean squared error (RMSE). For the ensemble of MLR-PTF and ML-PTFs, the R² of the six water content variables and the Ksat ranged from 0.55 to 0.85, whereas for the Saxton & Rawls PTFs the range was between 0.10 and 0.50. Secondly, all PTFs were subjected to a functional evaluation using the Food and Agriculture Organization (FAO) AquaCrop crop growth model. Dry season irrigation requirements for maize as computed by AquaCrop with measured versus estimated soil hydraulic properties revealed that ANN-PTFs provide AquaCrop outputs that come closest to AquaCrop outputs generated with measured soil hydraulic properties. This study shows the importance of performing functional evaluation of pedotransfer functions before their widespread application.

中文翻译：

---

赞比西河流域土壤水力特性土壤传递函数的开发与功能评价

保水性和饱和导水率是土壤特性，是作物生长和水文建模的关键决定因素。它们通常是通过土壤传递函数 (PTF) 从基本土壤特征（例如容重、有机碳含量和质地）估算的。为了评估和比较广泛使用的 Saxton & Rawls PTF 和一组新开发的 PTF 在赞比西河流域 (ZRB) 中的固有性能和功能适用性，我们编制了 pF0.0、1.0、 2.0、2.8、3.4 和 4.2 以及整个 ZRB 中 631 个土壤样品的饱和导水率 (Ksat)。共有 329 个样本与非洲土壤剖面数据库中可用的 55 个土壤剖面有关，而我们自己的实地调查在 2,426 公里²ZRB 的子流域提供了与 119 个土壤剖面相关的其余 302 个样本。除了评估 Saxton & Rawls PTF 之外，我们还开发了多元线性回归 (MLR) PTF，以及由三种机器学习 (ML) 模型派生的 PTF：人工神经网络 (ANN)、随机森林 (RF) 和支持向量机 (SVM) . 首先基于通过 R ²、平均绝对误差 (MAE) 和均方根误差 (RMSE)比较估计和测量的特性值来评估所有 PTF 。对于 MLR-PTF 和 ML-PTF 的集合，R ²在六个含水量变量中，Ksat 的范围在 0.55 到 0.85 之间，而 Saxton & Rawls PTF 的范围在 0.10 到 0.50 之间。其次，使用粮食及农业组织 (FAO) AquaCrop 作物生长模型对所有 PTF 进行功能评估。AquaCrop 计算的玉米旱季灌溉要求与测量的土壤水力特性与估计的土壤水力特性表明，ANN-PTF 提供的 AquaCrop 输出最接近使用测量的土壤水力特性生成的 AquaCrop 输出。这项研究表明了在广泛应用之前对 pedotransfer 功能进行功能评估的重要性。