Modeling soil cation exchange capacity using soil parameters: Assessing the heuristic models

Shiri, Jalal; Keshavarzi, Ali; Kisi, Özgür; Iturrarán-Viveros, Ursula; Bagherzadeh, Ali; Mousavi, Rouhollah; Karimi, Shahram

doi:10.1016/j.compag.2017.02.016

Cited by 50 publications

(24 citation statements)

References 29 publications

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“…Specifically, in semi-arid landscapes, soil texture, organic carbon content and calcium carbonate content are able to reflect the spatial distributions of soil nutrients and vegetative cover (Shiri et al, 2017;Keshavarzi et al, 2018). Soil particle distribution is considered as a key parameter of soil quality because soil clay content controls soil cation-exchange capacity (Khaledian et al, 2017a;Sulieman et al, 2018), soil aggregate stability (Schjønning et al, 2007), and soil organic carbon preservation (Zeraatpishe and Khormali, 2012).…”

Section: Introductionmentioning

confidence: 99%

Determining the spatial distribution of soil properties using the environmental covariates and multivariate statistical analysis: a case study in semi-arid regions of Iran

et al. 2019

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Natural soil-forming factors such as landforms, parent materials or biota lead to high variability in soil properties. However, there is not enough research quantifying which environmental factor(s) can be the most relevant to predicting soil properties at the catchment scale in semi-arid areas. Thus, this research aims to investigate the ability of multivariate statistical analyses to distinguish which soil properties follow a clear spatial pattern conditioned by specific environmental characteristics in a semi-arid region of Iran. To achieve this goal, we digitized parent materials and landforms by recent orthophotography. Also, we extracted ten topographical attributes and five remote sensing variables from a digital elevation model (DEM) and the Landsat Enhanced Thematic Mapper (ETM), respectively. These factors were contrasted for 334 soil samples (depth of 0-30 cm). Cluster analysis and soil maps reveal that Cluster 1 comprises of limestones, massive limestones and mixed deposits of conglomerates with low soil organic carbon (SOC) and clay contents, and Cluster 2 is composed of soils that originated from quaternary and early quaternary parent materials such as terraces, alluvial fans, lake deposits, and marls or conglomerates that register the highest SOC content and the lowest sand and silt contents. Further, it is confirmed that soils with the highest SOC and clay contents are located in wetlands, lagoons, alluvial fans and piedmonts, while soils with the lowest SOC and clay contents are located in dissected alluvial fans, eroded hills, rock outcrops and steep hills. The results of principal component analysis using the remote sensing data and topographical attributes identify five main components, which explain 73.3% of the total variability of soil properties. Environmental factors such as hillslope morphology and all of the remote sensing variables can largely explain SOC variability, but no significant correlation is found for soil texture and calcium carbonate equivalent contents. Therefore, we conclude that SOC can be considered as the best-predicted soil property in semi-arid regions.

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Section: Introductionmentioning

confidence: 99%

Determining the spatial distribution of soil properties using the environmental covariates and multivariate statistical analysis: a case study in semi-arid regions of Iran

et al. 2019

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“…This parameter also indicates the soil potential to provide calcium, magnesium and potassium to plants [43]. In the present study, the samples showed a Journal of Agricultural Chemistry and Environment high cation exchange capacity, with an average value of 36.34 meq/100 g, and are hence considered as soil with great natural fertility [44].…”

Section: ) Electrical Conductivitymentioning

confidence: 51%

Robust Soil Quality Index for Tropical Soils Influenced by Agricultural Activities

Rangel‐Peraza¹,

Padilla-Gasca²,

López-Corrales³

et al. 2017

JACEN

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The knowledge of the soil quality plays a vital role in the agricultural sector. Despite its importance, there is scarce scientific information concerning this regard. The objective of this research is to develop a methodology to identify and select the most appropriate indicators of Soil Quality Index (SQI) in a region with high agricultural activity. For its conformation, a descriptive statistical analysis and a Pearson correlation matrix were performed and the indicators that showed greater variation were identified using a Principal Components Analysis (PCA). A sensitivity analysis was carried out and the most sensible soil indicators of SQI were identified. This statistical procedure was also used to specify the weights of the indicators in SQI. The variables resulting from the multiparametric statistical analysis were pH, organic matter, sodium, calcium, iron, zinc, cation exchange capacity and electrical conductivity. The robustness of the SQI obtained in this study was demonstrated through simulations carried out by the numerical optimization through simplex method. The Soil Quality Index range obtained (0.54 -0.75) locates Culiacan Valley soils as moderate/high quality.

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“…The following plan for GEP-based simulation of soil P (target variable) using the mentioned input variables was followed in this study [67]. The first step was to select a fitness function, although various absolute-and relative-error-based fitness functions might be used for modeling soil P. Then, the root relative squared error (RRSE) was applied as advised by the literature (e.g., [25]). Second, it is essential to choose the predictor parameters and sets of function.…”

Section: Gene Expression Programming (Gep)mentioning

confidence: 99%

“…The use of the wavelet transform approach might be applied in this context for improving the models' overall performance accuracy. It is interesting to note that the use of AI techniques in this regard is very limited, at least, in semiarid areas and larger scales (e.g., [23,24]) despite their wider applications in various soil and water analysis issues, such as modeling soil cation exchange capacity [8,25], modeling soil bulk density [26], predicting groundwater level fluctuations [27], estimating reference evapotranspiration [28], simulating watershed sediment amount [29], and estimating terrestrial parameters such as solar radiation [30], as well as groundwater pollution studies [31].…”

Section: Introductionmentioning

confidence: 99%

Estimating Soil Available Phosphorus Content through Coupled Wavelet–Data-Driven Models

et al. 2020

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Soil phosphorus (P) is a vital but limited element which is usually leached from the soil via the drainage process. Soil phosphorus as a soluble substance can be delivered through agricultural fields by runoff or soil loss. It is one of the most essential nutrients that affect the sustainability of crops as well as the energy transfer for living organisms. Therefore, an accurate simulation of soil phosphorus, which is considered as a point source pollutant in elevated contents, must be performed. Considering a crucial issue for a sustainable soil and water management, an effective soil phosphorus assessment in the current research was conducted with the aim of examining the capability of five different wavelet-based data-driven models: gene expression programming (GEP), neural networks (NN), random forest (RF), multivariate adaptive regression spline (MARS), and support vector machine (SVM) in modeling soil phosphorus (P). In order to achieve this goal, several parameters, including soil pH, organic carbon (OC), clay content, and soil P data, were collected from different regions of the Neyshabur plain, Khorasan-e-Razavi Province (Northeast Iran). First, a discrete wavelet transform (DWT) was applied to the pH, OC, and clay as the inputs and their subcomponents were utilized in the applied data-driven techniques. Statistical Gamma test was also used for identifying which effective soil parameter is able to influence soil P. The applied methods were assessed through 10-fold cross-validation scenarios. Our results demonstrated that the wavelet–GEP (WGEP) model outperformed the other models with respect to various validations, such as correlation coefficient (R), scatter index (SI), and Nash–Sutcliffe coefficient (NS) criteria. The GEP model improved the accuracy of the MARS, RF, SVM, and NN models with respect to SI-NS (By comparing the SI values of the GEP model with other models namely MARS, RF, SVM, and NN, the outputs of GEP showed more accuracy by 35%, 30%, 40%, 50%, respectively. Similarly, the results of the GEP outperformed the other models by 3.1%, 2.3%, 4.3%, and 7.6%, comparing their NS values.) by 35%-3.1%, 30%-2.3%, 40%-4.3%, and 50%-7.6%, respectively.

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Modeling soil cation exchange capacity using soil parameters: Assessing the heuristic models

Cited by 50 publications

References 29 publications

Determining the spatial distribution of soil properties using the environmental covariates and multivariate statistical analysis: a case study in semi-arid regions of Iran

Determining the spatial distribution of soil properties using the environmental covariates and multivariate statistical analysis: a case study in semi-arid regions of Iran

Robust Soil Quality Index for Tropical Soils Influenced by Agricultural Activities

Estimating Soil Available Phosphorus Content through Coupled Wavelet–Data-Driven Models

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