Soil salinity is best estimated by saturated paste extract (ECe), the most reliable monitoring method to assess plant growth that is directly related to the field water content. However, this procedure is laborious and time-consuming, therefore, more convenient methods such as 1:5 soil: water extract is commonly used to estimate the ECe. Traditionally, a conversion factor (CF) based on a linear correlation between the diluted extract and ECe is employed for the estimation purposes. However, CF is affected by site-specific conditions. The objective of this research is to demonstrate a novel modelling approach that allows incorporating site-specific soil and irrigation water parameters to improve the accuracy of the ECe estimation. A total of 177 soil samples were collected from agricultural soils in the Jordan Valley representing different soil textures, crops and water qualities. ECe, EC_1:5, clay and sand content, soil texture and saturation percentage (θ_SP) were determined. The collected data were used to construct models using three distinct approaches: traditional CF; incorporating θ_SP as a surrogate of soil texture to cater the CF to the site-conditions, and Artificial Neural Networks to incorporate site-specific parameters. The neural network model gave the most accurate estimates (R² = 0.987, MSE = 2.39) and was able to handle the heteroscedasticity of the data. Meanwhile, the incorporation of θ_SP to estimate the CF that best represent the site has shown improved prediction quality over the traditional CF approach as it was more capable of handling the heteroscedasticity of the data. The neural network model allows for the incorporation of location-specific parameters and therefore offers a flexible tool for better management of agricultural soils. Although, this work used a case study location to demonstrate the concepts discussed, the approach is generalizable and can be easily adapted to other locations.

最好通过饱和糊状提取物（ECe）估算土壤盐分，饱和糊状提取物（ECe）是评估植物生长的最可靠监测方法，与田间含水量直接相关。但是，此过程既费力又费时，因此，更方便的方法（例如1：5土壤：水提取物）通常用于估算ECe。传统上，基于稀释提取物和ECe之间线性关系的转换因子（CF）用于估算目的。但是，CF受站点特定条件的影响。这项研究的目的是证明一种新颖的建模方法，该方法可以结合特定地点的土壤和灌溉用水参数来提高ECe估算的准确性。从约旦河谷的农业土壤中收集了总共177个土壤样品，这些土壤样品代表了不同的土壤质地，农作物和水质。欧洲共同体_1：5，粘土和砂含量，土壤质地和饱和度百分比（θ _SP）进行了测定。收集的数据用于通过三种不同的方法来构建模型：传统CF；掺入θ _SP土壤质地的替代，以迎合所述CF到现场条件，和人工神经网络掺入位点特异的参数。神经网络模型给出了最准确的估计值（R ²  = 0.987，MSE = 2.39），并且能够处理数据的异方差性。同时，θ的掺入_SP估计最能代表该站点的CF显示出比传统CF方法更好的预测质量，因为它能够处理数据的异方差性。神经网络模型允许结合特定位置的参数，因此提供了一种灵活的工具，可以更好地管理农业土壤。尽管此工作使用案例研究场所来演示所讨论的概念，但是该方法具有通用性，可以轻松地应用于其他场所。