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Evaluation and analysis of different regression models for estimation of ECe from EC1:5—With a case study from Buin‐Zahra, Iran*
Irrigation and Drainage ( IF 1.6 ) Pub Date : 2020-07-06 , DOI: 10.1002/ird.2488
Mehrdad Hassannia 1 , Bijan Nazari 1 , Abbas Kaviani 1 , Abbas Sotoodehnia 1
Affiliation  

Soil salinity is an important parameter in irrigation, drainage, and environmental studies. Determining the electrical conductivity of soil saturated paste extract (ECe) is a well‐known method, but its use is limited because its production process is time‐consuming and difficult. The 1:5 solution electrical conductivity (EC1:5) is an alternative simplified method. The aim of this study was to evaluate ECe‐from‐EC1:5 conversion models. A total of 123 samples from 3 soil layers were analysed. The research was planned in two phases: 1) model evaluation in soil layers, 2) model evaluation in ECe categorized data. Results show that the linear model was the best model in the second layer (R2 = .66, mean absolute percentage error [MAPE] = 0.058), and the exponential model was the best in the other layers (R2 = .65 to 0.67, MAPE = 0.085 to 0.060). Also, exponential and linear regressions were the best ECe estimation models in the ECe < 4 and ECe > 4 categories, respectively. In addition, the conversion factor (f) for converting ECe and EC1:5 was obtained as 3.7–13.9, 2.8–5.0, and 3.4–5.8 for three soil layers, respectively. Results show that the use of general recommendation tables for f will lead to considerable error, and the factor must be determined specifically for each region.

中文翻译:

EC1:5估算ECe的不同回归模型的评估和分析-以伊朗Buin-Zahra为例

土壤盐分是灌溉,排水和环境研究中的重要参数。确定土壤饱和糊状提取物(ECe)的电导率是一种众所周知的方法,但由于其生产过程既费时又困难,因此使用受到限制。1:5溶液电导率(EC 1:5)是另一种简化方法。这项研究的目的是评估ECe从EC 1:5转换模型。共分析了来自3个土壤层的123个样品。研究计划分两个阶段进行:1)在土壤层中进行模型评估,2)在ECe分类数据中进行模型评估。结果表明,线性模型是第二层的最佳模型(R 2= 0.66,平均绝对百分比误差[MAPE] = 0.058),而其他层中的指数模型最好(R 2 = 0.65至0.67,MAPE = 0.085至0.060)。此外,指数回归和线性回归分别是ECe <4和ECe> 4类别中最佳的ECe估计模型。另外,对于三个土壤层,转换ECe和EC 1:5的转换因子(f)分别为3.7-13.9、2.8-5.0和3.4-5.8。结果表明,对f使用通用推荐表将导致相当大的误差,并且必须针对每个区域专门确定该因素。
更新日期:2020-07-06
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