Performance of Soil Cation Exchange Capacity Pedotransfer Function as Affected by the Inputs and Database Size

Khodaverdiloo, Habib; Momtaz, H. R.; Liao, Kaihua

doi:10.1002/clen.201700670

Cited by 18 publications

(5 citation statements)

References 32 publications

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“…Widely accepted knowledge demonstrates that soils can buffer exogenous acid input by means of multiple mechanisms derived from various processes [4,13]. Soil exchangeable cations, which greatly depend on soil pH, SOM, and clay [32][33][34], can exchange with exogenously input H + to mitigate its effect on the soil acidity. Therefore, positive linear relationships between the soil CEC and acid-buffering capacity have been frequently reported across studies [15,18,35].…”

Section: Discussionmentioning

confidence: 99%

Soil pH Responses to Simulated Acid Rain Leaching in Three Agricultural Soils

et al. 2019

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Soil has the nature of acidity and alkalinity, mostly indicated by soil pH that could greatly affect soil ecological processes and functions. With exogenous inputs of acidic materials (such as acid rain), soils may more or less resist to maintain their pH levels within specific thresholds by various buffering processes. It has been well established that soil properties such as cation exchange capacity (CEC), soil organic matter (SOM), and clay content play important roles in mitigating the effects of acid inputs, but the factors varied across soils. This microcosm experiment was conducted to investigate changes in the soil pH and quantitatively estimate the critical pH threshold of simulated acid rain for three highly weathered soils (red soil, lateritic red soil, and latosol) that are typical soil types widely distributed across the world’s subtropical and tropical climatic zones, as well as important influential factors, after continuously adding different levels of simulated acid rain on the surface of soil cores. The results showed that the change in the soil pH was not significantly different among the three soils, although it was exponentially related to soil CEC and clay content. Resultantly, the latosol that had high soil CEC and clay content was more resistant to simulated acid rain, especially when relatively weak simulated acid rain treatments were applied. The lateritic red soil that contained the lowest soil CEC and clay content showed the greatest decline in the soil pH under the strongest simulated acid rain treatment of pH being 2.5. Furthermore, we estimated the critical pH threshold of simulated acid rain for the three soils and observed that it was considerably different among the soils. Surprisingly, the pH threshold of simulated acid rain was also positively related to the soil CEC and clay content, therefore making the highest pH threshold in the latosol. Our results imply that soil CEC and clay content may play critical roles in the soil acid-buffering processes from two aspects; it could not only contribute to the soil acid-buffering capacity, but also affect the threshold of acidity of acid rain below which abrupt soil acidification may occur.

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Section: Discussionmentioning

confidence: 99%

Soil pH Responses to Simulated Acid Rain Leaching in Three Agricultural Soils

et al. 2019

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“…These regions include arid/semi‐arid regions, and many developing nations, where resource constraints limit the number of new measurements that can be made. The accuracy (within‐sample performance) and reliability (agreement with other PTFs in out‐of‐sample estimations) of PTFs largely depend on the number of soil samples used in their construction (Khodaverdiloo et al, 2018; Pachepsky et al, 1998; Pachepsky & Rawls, 1999). A potential response to the need for both local soil information and sufficient sample size is to separate the functional form of the PTF equations obtained from large datasets from the specific value of the coefficients in those equations.…”

Section: Introductionmentioning

confidence: 99%

Recalibration of existing pedotransfer functions to estimate soil bulk density at a regional scale

Khodaverdiloo

Bahrami

Rahmati

et al. 2022

European J Soil Science

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Soil bulk density (ρ b ) is an important indicator of soil quality, productivity, compaction and porosity. Despite its importance, ρ b is often omitted from global datasets due to the costs of making many direct ρ b measurements and the difficulty of direct measurement on rocky, sandy, very dry, or very wet soils. Pedotransfer functions (PTFs) are deployed to address these limitations. Using readily available soil properties, PTFs employ estimator equations to fit existing datasets to estimate properties like ρ b . However, PTF performance often declines when applied to soils outside those in the training dataset.Potentially, recalibrating existing PTFs using new observations would leverage the power of large datasets used in the original PTF derivation, while updating information based on new soil observations. Here, we evaluate such a recalibration approach for ρ b estimation, benchmarking its performance against two alternatives: the original, uncalibrated PTFs, and novel, local PTFs derived solely from new soil observations. Using a ρ b dataset of N = 360 total observations obtained in West Azerbaijan, Iran, we varied the local dataset size (with N = 15, 30, 60, and 360) and recalibrated four existing PTFs with these data.Local PTFs were generated based on stepwise multiple linear regression for the same datasets. The same PTFs (original, recalibrated, and local) were also applied to the study area, and the resulting ρ b estimates were compared with the global SoilGrids dataset. Recalibration of PTFs reduced errors relative to the original uncalibrated PTFs; for instance, the NSE increased from À22.07 to 0.30 (uncalibrated) to 0.20-0.41 (recalibrated), and RMSE decreased from 0.12 to 0.60 Mg m À3 (uncalibrated) to 0.10-0.13 Mg m À3 (recalibrated). The recalibrated PTFs performance was comparable to or better than local PTFs applied to the same data. Recalibration of existing PTFs with local/regional uses provides a viable alternative to the use of global datasets or the development of local PTFs in data-scarce regions. Highlights• Existing global PTFs were calibrated and tested using a small dataset for local utilisation.• Several new local PTFs were also developed using the same datasets.

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“…Fooladmand (2008) derived PTF's using multiple linear regression between CEC and soil textural data including sand content, clay content, geometric mean particle-size diameter, the soil particle-size distribution, and soil organic matter content. Several PTF's relating soil CEC with soil's sand, silt or clay fractions, and soil organic carbon content evaluated by (Khodaverdiloo et al, 2018).Scholars took into account of calibration dataset size on the prediction accuracy of soil CEC. These classical pedotransfer function-based approaches often suffer from a high degree of inaccuracy due to spatial scale dependence, non-linear relationships among variables and incompetence to handle mixed data (Van Looy et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Modeling Soil Cation Exchange Capacity in Arid Region of Iran: Application of Novel Hybrid Intelligence Paradigm.

Shahabi

Naganna³

et al. 2021

Preprint

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The potential of the soil to hold plant nutrients is governed by cation exchange capacity (CEC) of any soil. Estimating soil CEC aids in conventional soil management practices to replenish the soil solution that supports plant growth. In the present study, a multiple model integration scheme driven by hybrid GANN (MM-GANN) was developed and employed to predict the accuracy of soil CEC in Tabriz plain, an arid region of Iran. The standalone models (i.e., artificial neural network (ANN) and extreme learning machine (ELM)) were implemented for incorporating in the MM-GANN. In addition, it was tested to enhance the prediction accuracy of the standalone models. The soil parameters such as clay, silt, pH, carbonate, calcium equivalent (CCE), and soil organic matter (OM) were used as model inputs to predict soil CEC. By the use of several evaluation criteria, the results showed that the MM-GANN model involving the predictions of ELM and ANN models calibrated by considering all the soil parameters (e.g., Clay, OM, pH, Silt, and CCE) as inputs provided superior soil CEC estimates with an NSE = 0.87. The proposed MM-GANN model is a reliable intelligence based approach for the assessment of soil quality parameters intended for sustainability and management prospects.

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Performance of Soil Cation Exchange Capacity Pedotransfer Function as Affected by the Inputs and Database Size

Cited by 18 publications

References 32 publications

Soil pH Responses to Simulated Acid Rain Leaching in Three Agricultural Soils

Soil pH Responses to Simulated Acid Rain Leaching in Three Agricultural Soils

Recalibration of existing pedotransfer functions to estimate soil bulk density at a regional scale

Modeling Soil Cation Exchange Capacity in Arid Region of Iran: Application of Novel Hybrid Intelligence Paradigm.

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