Coefficients for estimating SAR from soil pH and EC data and calculating pH from SAR and EC values in salinity models

Robbins, C. W.

doi:10.1080/15324989309381332

Cited by 6 publications

(2 citation statements)

References 6 publications

(14 reference statements)

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“…For saline or saline-sodic soils that are undergoing the amendment procedure, or when applying high sodium adsorption ratio (SAR) irrigation water or wastes, it is necessary to monitor the status of soil exchangeable sodium percentage (ESP) or SAR frequently. This, alongside pH and EC monitoring, is advisable for selecting and adjusting water and waste, estimating the amount of amendments and management practices (Robbins 1993). The SAR and ESP are two acknowledged indices for evaluating the degree of soil sodicity.…”

Section: Introductionmentioning

confidence: 99%

Modeling of soil exchangeable sodium percentage using easily obtained indices and artificial intelligence-based models

Keshavarzi

Bagherzadeh

Omran

et al. 2016

Model. Earth Syst. Environ.

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Salinization and alkalization of land resources are the major obstacles to their optimal usage in many arid and semi-arid regions of the world, including Iran, since potential evapotranspiration is more noteworthy than precipitation in these areas. The amount of water that enters the soil is low and this results in salt accumulation in soils, which makes the soil infertile. Moreover, existence of salts, for example, sodium, in soils causes dispersion of soil particles and soil degradation, and intensifies soil erosion too. Monitoring exchangeable sodium percentage (ESP) variability in soils is both time-consuming and costly. However, in order to estimate the amounts of amendments and land management, it is necessary to know ESP variation and values in sodic or saline and sodic soils. Thus, introducing a method, which utilizes easily obtained indices to estimate ESP indirectly is more optimized and economical. Input and output data, i.e., EC e (dS m-1), clay (%), pH and ESP (%) were collected and measured from 100 soil samples in light of a stratified random sampling from Mashhad Plain, Khorasan-e-Razavi Province, Northeast Iran. This study aims to propose some models to estimate ESP by easily obtained properties of soil. In this regard, the efficiency of artificial intelligence-based (AI) models (i.e., Artificial Neural Network, ANN, and Adaptive Neuro-Fuzzy Inference System, ANFIS) was investigated and compared. Accuracy results showed that owing to highest R 2 and the lowest mean square error (MSE), ANFIS model predictions were superior to the MLP model for indirect estimation of soil exchangeable sodium percentage.

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Section: Introductionmentioning

confidence: 99%

Modeling of soil exchangeable sodium percentage using easily obtained indices and artificial intelligence-based models

Keshavarzi

Bagherzadeh

Omran

et al. 2016

Model. Earth Syst. Environ.

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“…To compare results from studies using different short cuts or analysis approaches (Robbins, 1993;Rashidi and Seilsepour, 2011;Elbashier et al, 2016), a clear understanding of the methods and associated units are needed. The SAR is calculated with the equation, ( )…”

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confidence: 99%

Chemical Amendments of Dryland Saline–Sodic Soils Did Not Enhance Productivity and Soil Health in Fields without Effective Drainage

et al. 2019

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A common restoration treatment for saline-sodic soils involves improving soil drainage, applying soil amendments (e.g., CaSO 4 , CaCl 2 , or elemental S), and leaching with water that has a relatively low electrical conductivity. However, due to high subsoil bulk densities and low drainable porosities, these treatments many not be effective in glaciated dryland systems. A 3-yr field study conducted in three model systems determined the impact of chemical amendments (none, CaCl 2 , CaSO 4 , and elemental S) on plant growth, microbial composition, temporal changes in electrical conductivity (EC e ), and the relative sodium content (%Na). Chemical amendments (i) either reduced or did not increase maize (Zea mays), soybean (Glycine max), and sorghum (Sorghum bicolor) yields; (ii) did not increase water infiltration or microbial biomass as determined using the phospholipid-derived fatty acid (PLFA) technique; and (iii) did not reduce EC e or %Na. These results were attributed to high bulk densities and low drainable porosities that reducing the drainage effectiveness in the model backslope and footslope soils, the presence of subsurface marine sediments that provided a source for sodium and other salts that could be transported through capillary action to the surface soil, high sulfate and gypsum contents in the surface soil, and relatively low microbial biomass values. The results suggests that an alternative multistep saline sodic soil restoration approach that involves increasing exchangeable Ca +2 through enhanced microbial and root respiration and increasing transpiration and soil drainage by seeding full season deep rooted perennial vegetation should be tested.

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Assessment of Data-based Models (ANN, ANFIS and SVR) for Estimation of Exchangeable Sodium Percentage (ESP) of Bafra Plain Soils

Taşan

Demir

2021

Communications in Soil Science and Plant Analysis

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Coefficients for estimating SAR from soil pH and EC data and calculating pH from SAR and EC values in salinity models

Cited by 6 publications

References 6 publications

Modeling of soil exchangeable sodium percentage using easily obtained indices and artificial intelligence-based models

Modeling of soil exchangeable sodium percentage using easily obtained indices and artificial intelligence-based models

Chemical Amendments of Dryland Saline–Sodic Soils Did Not Enhance Productivity and Soil Health in Fields without Effective Drainage

Assessment of Data-based Models (ANN, ANFIS and SVR) for Estimation of Exchangeable Sodium Percentage (ESP) of Bafra Plain Soils

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