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Efficiency of Two Models for Prediction of Exchangeable Sodium Percentage from Sodium Adsorption Ratio on Saline and Non Saline Soil

Title: Efficiency of Two Models for Prediction of Exchangeable Sodium Percentage from Sodium Adsorption Ratio on Saline and Non Saline Soil
Authors: Mohammed M A Elbashier; Mohammed H A Ebrahim; Abdelrahaman A Musa; Albashir A S Ali; Moamer A A Mohammed
Contributors: The Pennsylvania State University CiteSeerX Archives
Source: http://www.hrpub.org/download/20160130/UJAR6-10405481.pdf.
Publication Year: 2016
Collection: CiteSeerX
Description: The relationships between soil physical and chemical properties play a key role in facilitating the measurement of soil properties, particularly Exchangeable Sodium percentage (ESP) measurement, which is often using laborious and time-consuming laboratory tests. The aim of this study is to investigate the efficiency of the United States Salinity Laboratory (USSL) model and the ESP-SAR model for prediction of exchangeable Sodium percentage (ESP) from Sodium Adsorption Ratio (SAR) on saline and non-saline soil samples. For this purpose, 23 soil samples were collected from the field of experiment, Jabal Awliya, south of Khartoum state, Sudan. Exchangeable Sodium Percentage (ESP) was estimated as a function of soil SAR in order to compare the predicted results with measured ESP using laboratory tests. The results show that on saline soil samples, the Standard Error of Mean (SEM) of predicted ESP obtained by USSL model and ESP-SAR model was (1.084) and (1.463) respectively. On non-saline soil samples, the Standard Error of Mean (SEM) of predicted ESP acquired by USSL model was (0.7034) and (0.6070) for ESP-SAR model. The statistical results indicated that USSL model has a good prediction on saline soil samples compared with ESP-SAR model. On non-saline soil samples, USSL model showed less prediction performance than ESP-SAR model. It can be concluded that the United States Salinity Laboratory model can be recommended on saline soil samples and ESP-SAR model is more reliable on non-saline soil samples.
Document Type: text
File Description: application/pdf
Language: English
Relation: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1052.3345
Availability: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1052.3345; http://www.hrpub.org/download/20160130/UJAR6-10405481.pdf
Rights: Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Accession Number: edsbas.576F2C5F
Database: BASE