EFFECT OF SALINITY, SAR, Ca

Paliwal, K. V.; Gandhi, A.P.

doi:10.1097/00010694-197608000-00004

Cited by 39 publications

(7 citation statements)

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“…Equation () was used for the prediction of ESP in the present study for soil samples (Paliwal and Gandhi, 1976).

ESP = \frac{100 * (0.1149 + 0.0109 * SAR)}{1 + (0.1149 + 0.0109 * SAR)},

…”

Section: Materials and Methodologymentioning

confidence: 99%

“…To assess the accuracy of the soil ESP–SAR model, soil samples were selected at random from the experimental locations and compared to the results of the laboratory testing.

Y = k_{0} + k_{1} X,

where Y = Dependent variable, for example, ESP of soil X = independent variable (SAR of soil) and k 0 , k 1 = regression coefficients to estimate soil ESP using soil SAR. Equation () was used for the prediction of ESP in the present study for soil samples (Paliwal and Gandhi, 1976).

ESP = \frac{100 * (0.1149 + 0.0109 * SAR)}{1 + (0.1149 + 0.0109 * SAR)},

…”

Section: Materials and Methodologymentioning

confidence: 99%

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Assessment of salt‐affected soil in Indo‐Gangetic Plain of Uttar Pradesh and prediction using regression model

Hiranmai,

Neeraj

2023

Land Degrad Dev

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In the present study, multiple sites in the Indo‐Gangetic Plain (IGP) were examined, and most of the sites were found to be non‐saline, with electrical conductivity (EC) values below 4.0 dSm−1. The highest value of EC 8.5 dSm−1 was reported for samples collected from JAN‐2, which, followed by site RBL‐3, also shows a high value of EC 6.5 dSm−1. The highest CEC (cmolkg−1) was reported in samples collected from site JAN‐1 (8.4), JAN‐2 (8.2), and JAN‐3 (8.21), and the lowest at RBL‐2 (3.99). The highest exchangeable sodium percentage (ESP) value of 34.07% was observed at RBL‐3, indicating a potential salinity risk associated with elevated sodium contents. The analysis showed that salt was present at all study sites, and there were strong correlations between the variables examined. The relationship between measured ESP and predicted ESP shows a strong correlation with an r2 value of 0.91, indicating the applicability of ESP–sodium adsorption ratio (SAR). Paired samples t‐test results p‐value = 0.27 indicated no significant difference (p > 0.05) between the soil ESP values predicted by the soil ESP–SAR model and those obtained through laboratory testing. The statistical results of the study suggested that the soil ESP–SAR model was reliable for predicting ESP values. The soil ESP differences between the two methods were normally distributed, and the 95% limits of agreement were calculated as −7.79 and 5.55. The mean difference between the ESP–SAR model and the “Measured” method was calculated as 2.24, representing the average deviation between the two. The study presents the ESP–SAR model as a cost‐effective and efficient method for estimating soil ESP and assessing soil salinity. By analyzing soil properties and validating the model, it provides valuable insights for policymakers and stakeholders in combating agricultural land degradation in the IGP. The novelty lies in its comprehensive evaluation of soil salinity indices, reinforcing the applicability of the ESP–SAR model for future research and agricultural management.

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“…Equation () was used for the prediction of ESP in the present study for soil samples (Paliwal and Gandhi, 1976).

ESP = \frac{100 * (0.1149 + 0.0109 * SAR)}{1 + (0.1149 + 0.0109 * SAR)},

…”

Section: Materials and Methodologymentioning

confidence: 99%

“…To assess the accuracy of the soil ESP–SAR model, soil samples were selected at random from the experimental locations and compared to the results of the laboratory testing.

Y = k_{0} + k_{1} X,

ESP = \frac{100 * (0.1149 + 0.0109 * SAR)}{1 + (0.1149 + 0.0109 * SAR)},

…”

Section: Materials and Methodologymentioning

confidence: 99%

Assessment of salt‐affected soil in Indo‐Gangetic Plain of Uttar Pradesh and prediction using regression model

Hiranmai,

Neeraj

2023

Land Degrad Dev

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“…A higher amount of magnesium in water will adversely affect crop yields as the soils become more saline (Joshi and Rao, 2009). A ratio, i.e., the index of magnesium hazard, was developed by Paliwal and Gandhi (1976) as shown as follows:…”

Section: Magnesium Hazard (Mh)mentioning

confidence: 99%

Geogenic and anthropogenic contamination in river water and groundwater of the lower Cauvery Basin, India

Keerthan

Ramesh

Elango

2023

Front. Environ. Sci.

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Degradation of water quality is a major challenge in both developed and developing countries since it poses a great risk to ecological health. The Cauvery river is one of the most important rivers of southern India, where the effect of seasonal variation on the quality of river water and its adjoining groundwater on the lower part of the basin has not yet been studied. Hence, the present study was carried out to evaluate the river water and adjacent groundwater for drinking and irrigational purposes, and also to identify the sources affecting the water quality. About 118 river water and 131 groundwater samples were collected over 4 months from May 2018 to August 2019. The water samples were analyzed for electrical conductivity (EC), pH, major and minor ions, and trace elements. The major, minor, and trace elements were then compared with the Bureau of Indian Standards and World Health Organization for drinking water quality assessment. The drinking water quality was assessed, indicating that both river water and groundwater were unsuitable in the coastal regions, especially during dry seasons. Irrigation water quality was also assessed, which indicates that most of the river water and groundwater samples were unsuitable near the coastal region. The quality of river water and groundwater in the lower Cauvery Basin was found to be better during wet periods compared to the dry season. It was observed that the rock–water interactions was the major factor controlling the water quality for groundwater and surface water, followed by anthropogenic activities such as disposal of domestic sewage and effluents into the river, as runoff from irrigated lands. The study emphasizes the development of a finer observational network for water quality, along with stringent monitoring of the disposal of contaminants in the rivers and groundwater.

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“…(3) Salt from these soils were washed out and the soils air dried. (4) Salts were mixed using MgCl 2 , CaCl 2 , Na 2 CO 3 , NaHCO 3 , and Na 2 SO 4 with molar concentration ratios of 11.74:8.54:1.00:15.39:20.83. These represent the average salt constitution in the Hetao Irrigation District.…”

Section: Soil Preparationmentioning

confidence: 99%

“…One robust SMC indicator is gravimetric water content (GWC) [1], but it is restricted to a small volume of soil. For SSC measurements, electrical conductivity (EC), total soluble salts (TSS) [2], sodium adsorption ratio (SAR) and exchangeable sodium percentage (ESP) [3] are typical indicators. Some more practical in situ measurements of SMC and SSC, such as time domain reflectometry (TDR), electromagnetic induction, e.g., EM38 [4], Hydra-probe [5] and SCT-10, employ probes or sensors.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Soil Moisture Content and Soil Salt Concentration from Hyperspectral Laboratory and Field Data

Zeng

Huang

et al. 2016

Remote Sensing

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This research examines the simultaneous retrieval of surface soil moisture and salt concentrations using hyperspectral reflectance data in an arid environment. We conducted laboratory and outdoor field experiments in which we examined three key soil variables: soil moisture, salt and texture (silty loam, clay and silty clay). The soil moisture content models for multiple textures (M_SMC models) were based on selected hyperspectral reflectance data located around 1460, 1900 and 2010 nm and resulted in R 2 values higher than 0.933. Meanwhile, the soil salt concentrations were also accurately (R 2 > 0.748) modeled (M_SSC models) based on wavebands located at 540, 1740, 2010 and 2350 nm. When the different texture samples were mixed (SL + C + SC models), soil moisture was still accurately retrieved (R 2 = 0.937) but the soil salt not as well (R 2 = 0.47). After stratifying the samples by retrieved soil moisture levels, the R 2 of calibrated M_SSC SMC models for soil salt concentrations improved to 0.951. This two-step method also showed applicability for analyzing soil-salt samples in the field. The M_SSC SMC models resulted in R 2 values equal to 0.912 when moisture is lower than 0.15, and R 2 values equal to 0.481 when soil moisture is between 0.15 and 0.2.

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EFFECT OF SALINITY, SAR, Ca

Cited by 39 publications

References 0 publications

Assessment of salt‐affected soil in Indo‐Gangetic Plain of Uttar Pradesh and prediction using regression model

Assessment of salt‐affected soil in Indo‐Gangetic Plain of Uttar Pradesh and prediction using regression model

Geogenic and anthropogenic contamination in river water and groundwater of the lower Cauvery Basin, India

Prediction of Soil Moisture Content and Soil Salt Concentration from Hyperspectral Laboratory and Field Data

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