Calcium Carbonate Equivalent Research Articles

Spatial prediction of soil properties through hybridized random forest model and combination of reflectance spectroscopy and environmental covariates

Spatial information on land and soil resources are critical towards addressing land degradation for ensuring sustainable soil and crop management. To address these needs, digital soil mapping techniques have emerged as an efficient and low-cost solution. Although digital soil mapping has typically leveraged geospatial environmental variables (e.g. remote sensing), the application and integration of spectroscopic data with those environmental variables remain limited. Hence, this study combines visible and near-infrared (Vis-NIR) spectroscopy, remote sensing, and topographic data and applies random forests, hybridized with particle swarm optimization algorithm (RF + PSO), to predict the spatial variability of soil clay content, electrical conductivity (EC), and calcium carbonate equivalent (CCE) for 370 km2 of agricultural land in western Iran. Using a conditioned Latin hypercube approach, 220 soil samples at the 0–20 cm depth increment were acquired throughout the study area. Three sets of environmental covariates were tested: Scenario A (Vis-NIR spectroscopy data), Scenario B (environmental data), and Scenario C (Vis-NIR spectroscopy + environmental data). According to the 10-fold cross-validation procedure with 100 replications, the RF + PSO model showed an acceptable level accuracy for all scenarios, although the accuracy of the RF + PSO model using the Scenario C data was higher than all other scenarios: the Lin’s Concordance Correlation Coefficient values were 0.77, 0.83, and 0.74 for the clay contents, EC, and CCE, respectively. The results demonstrated that the combination of Vis-NIR spectroscopic data and commonly available environmental covariates provided the best input data for the hybridized model and enhanced its performance.

Developing spectrotransfer functions (STFs) to predict basic physical and chemical properties of calcareous soils

Spectroscopy in visible (Vis) and near-infrared (NIR) provides a rapid, timely, non-destructive, low-preparation, and less expensive analysis of soil samples in comparison with traditional laboratory analysis. Therefore, the objectives of this study were to predict basic physical and chemical properties of calcareous soils using Vis-NIR spectral reflectance data by applying partial least square regression (PLSR) and stepwise multiple linear regression (SMLR) approaches and finally to develop spectrotransfer functions (STFs). The target soil properties including sand, silt, clay, pH, electrical conductivity (EC), calcium carbonate equivalent (CCE), and water-soluble sodium (Na), potassium (K), calcium (Ca), and magnesium (Mg), in 234 soil samples, and DTPA-extractable iron (Fe), manganese (Mn), copper (Cu), and zinc (Zn), in 161 soil samples, were measured using standard laboratory procedures in calcareous soils of Fars province, Iran. The spectra of soil samples in Vis-NIR region (400–2500 nm) were collected using Rapid Content Analyzer spectrophotometer (NIRS XDS model) apparatus. Results revealed the better performance of developed PLSR models compared to STFs to predict most of the target soil properties; however, their capability differences were not significant. The STFs predicted sand, clay, and CCE with very good accuracy (0.78 ≤ determination coefficient of validation dataset, R2val ≤ 0.83); pH, K, Mg, Fe, Mn, Cu, and Zn with good accuracy (0.65 ≤ R2val ≤ 0.71); and silt, EC, Na, and Ca with acceptable accuracy (0.55 ≤ R2val ≤ 0.63). It is strongly recommended to use the related STFs for predicting sand, clay, CCE, pH, K, Mg, Fe, Mn, Cu, and Zn of calcareous soils in order to save time and costs, less use of chemicals, and mapping large areas.

Effects of form, fineness and placement of lime with and without soil tillage on barley and canola growth and development

No-tillage systems and slow movement of surface-applied limestone can lead to stratification of soil acidity. Incorporation of lime by tilling soil is not preferred by producers following conservation agriculture practices. There is limited research on ways to facilitate lime movement without soil disturbance. This study aimed to determine the effects of form, fineness and calcium carbonate equivalent (CCE) of calcitic lime on its movement in soil with or without different soil disturbance actions and consequent effects on productivity of barley (Hordeum vulgare L.) and canola (Brassica napus L.). A field trial was established in the Western Cape province, South Africa. Ten treatments included a control, lime with different forms and fineness, and tillage practices. Due to the slow movement and reactivity of lime in soil, it was unlikely that growth responses from barley or canola would be detected within the first two years following liming. A once-off strategic tillage action promoted crop growth. This was attributed to deeper redistribution of lime in the soil (15–30 cm depth), among secondary agronomic responses to tillage. Strategic once-off tillage may address the stratification of both soil acidity and nutrients, such as calcium (Ca) and magnesium (Mg), and could be considered as an option to incorporate into management of no-tillage production systems.

Comparing visible-near-infrared spectroscopy with classical regression pedotransfer functions for predicting near-saturated and saturated hydraulic conductivity of calcareous soils

Measurement of hydraulic parameters, which are very important in soil–water and environmental studies, is difficult, time-consuming, and expensive. Therefore, the present study aimed to predict near-saturated and saturated hydraulic conductivity (Kψ) of calcareous soils by: i) developing stepwise multiple linear regression (SMLR)-based pedotransfer functions (PTFs) using basic soil properties, ii) using partial least square regression (PLSR)-based method and spectral reflectance data in the visible and near infrared (Vis-NIR) range (PLSR-spectra), and iii) developing SMLR-based functions that combine (or not) basic soil properties and effective Vis-NIR spectral reflectance bands (spectro-pedotransfer functions, SPTFs, and spectrotransfer functions, STFs). Hydraulic conductivity was determined in the field from water infiltration measurements with a tension disk infiltrometer at 15, 10, 5, and 0 cm tensions at 102 locations across calcareous soils of the Fars province, Iran. The spectral reflectance of soil samples taken at the same location was in the range of Vis-NIR (400–2500 nm), while basic soil properties included particle-size distribution and its related parameters, pH, electrical conductivity, soluble sodium, potassium, calcium, and magnesium, sodium adsorption ratio, soil organic matter content (SOM), calcium carbonate equivalent (CCE), aggregates mean weight diameter, and bulk density. The developed PTFs showed relatively acceptable performance (validation determination coefficient, R2, values of 0.55, 0.52, 0.56, and 0.63 for K15, K10, K5, and K0 predictions, respectively). The PLSR-spectra performed poorer showing validation R2 values of 0.39, 0.47, 0.40, and 0.48 for predicting K15, K10, K5, and K0, respectively. Yet, the PLSR-spectra predicted CCE and SOM contents excellently (R2 ≥ 0.80 and residual prediction deviation, RPD ≥ 2.5 for both calibration and validation datasets), and clay relatively well but not pH. The effective spectral reflectance bands (mostly substitute with SOM and CCE) were very useful as input variables to predict K5 and K0 when coupled with basic soil properties resulting in SPTFs. On the other hand, to predict K15 and K10, once any soil properties were added to SMLR models, the effective spectral reflectance bands were disappeared and consequently STFs were developed. Overall, we concluded that the accuracy of the studied approaches to predict Kψ were ranked as PTF > PLSR-spectra ≈ STF for predicting K15 and K10 and PTF > SPTF > PLSR-spectra for predicting K5 and K0.

Nitrogen-enriched biochar co-compost for the amelioration of degraded tropical soil

ABSTRACT Tropical soils are often deeply weathered and vulnerable to degradation having low pH and unfavorable Al/Fe levels, which can constrain crop production. This study aims to examine nitrogen-enriched novel biochar co-composts prepared from rice straw, maize stover, and gram residue in various mixing ratios of the biochar and their feedstock materials for the amelioration of acidic tropical soil. Three pristine biochar and six co-composts were prepared, characterized, and evaluated for improving the chemical and biological quality of the soil against a conventional lime treatment. The pH, cation exchange capacity (CEC), calcium carbonate equivalence (CCE) and nitrogen content of co-composts varied between 7.78–8.86, 25.3–30.5 cmol (p+) kg−1, 25.5–30.5%, and 0.81–1.05%, respectively. The co-compost prepared from gram residue biochar mixed with maize stover at a 1:7 dry-weight ratio showed the highest rise in soil pH and CEC, giving an identical performance with the lime treatment and significantly better effect (p < .05) than the unamended control. Agglomerates of calcite and dolomite in biochar co-composts, and surface functional groups contributed to pH neutralization and increased CEC of the amended soil. The co-composts also significantly (p < .05) increased the dehydrogenase (1.87 µg TPF g−1 soil h−1), β-glucosidase (90 µg PNP g−1 soil h−1), and leucine amino peptidase (3.22 µmol MUC g−1 soil h−1) enzyme activities in the soil, thereby improving the soil’s biological quality. The results of this study are encouraging for small-scale farmers in tropical developing countries to sustainably reutilize crop residues via biochar-based co-composting technology.

Implications of Sediment Properties on Phosphorus Availability to the Selenastrum capricornutum in Urmia Lake Rivers

Increasing anthropogenic loading of phosphorus (P) threatens aquatic ecosystems. The bioavailability of P in sediments for algal growth depends on several physiochemical properties such as silt and clay content, mineralogy, pH, electrical conductivity (EC), and carbonate content. This study was aimed at selecting the best chemical extraction method to characterize P-availability for the alga Selenastrum capricornutum. Principal component analysis of the data identified two components that cover 79.3% of the total variation, and these components were dominated by particle size distribution, active calcium carbonate equivalence, and EC. Many of the considered extractions were positively correlated with each other, with the exception being Bray-II. The sediments of some rivers had an Olsen-extractable P higher than 20 mg kg−-1, that is considered a threshold value above which the aquatic environment may become negatively affected. The average rank order of P extraction by single extractants was: Colwell > Mehlich III > 0.1 M NaOH > Olsen > Morgan > Soltanpour and Schwab (AB-DTPA) > Bray II. The Colwell-extractable P concentrations of sediments varied from 1.44 to 88.0 mg kg−-1. The Cowell extractant significantly correlated with algal growth (r2 = 0.92, P < 0.001) and gave a rough estimate of the amount of bioavailable P in sediments. The bioavailable content of P in the soils (agronomic requirement = 15 mg P kg−-1) around the Urmia Lake basin was shown to be high in comparison with river sediments. This article is protected by copyright. All rights reserved

A Proposed New Approach to Identify Limiting Factors in Assessing Land Suitability for Sustainable Land Management

ABSTRACT Making optimal use of scarce resources in developing countries is a major challenge for sustainable crop production. Characterization of factors limiting crop production is an important step in meeting this challenge. We outline and test a new method to identify characteristics of land limiting maize production in 61000 ha of the Qazvin province, northern Iran. Several soil profiles and augers were then investigated on soil samples collected from horizons of 21 representative profiles and analyzed for soil properties, such as particle size distribution, exchangeable sodium percentage, electrical conductivity, soil pH, organic carbon content, calcium carbonate equivalents, gypsum, and cation exchange capacity. Soil mapping units were surveyed and separated in the study area. Climatic, soil, and landscape characteristics were rated according to their likely influence on maize growth. Limiting characteristics included drainage, salinity/alkalinity, pH, coarse fragments, calcium carbonate equivalents, and slope. Our method shows the type and magnitude of limitation and as such is an improvement over existing approaches that only show where limitation is occurring. Our method lends itself to making transparent and quicker decision for the sustainable production of crops.

Effect of lime source, fineness and granulation on neutralisation of soil pH

Little information is available regarding the liming efficiency of various local lime sources and products currently available in South Africa. Recently, granulated micro-fine limes are being widely marketed as being superior to class A limes. Therefore, the aim of this study was to compare the effectiveness of various local, commercially available hydrated, calcitic and dolomitic lime sources (i.e., from different quarries) and forms (class A, micro-fine and granulated micro-fine) at increasing soil pH over time (365 days) under controlled conditions in two contrasting acid soils (sand and loam). Liming materials were applied adjusted for calcium carbonate equivalence (CCE) to achieve a target pHKCl of 5.5 and incubated at 85% field capacity. Micro-fine calcitic limes and hydrated limes increased pH the fastest (first 30–60 days), but the difference between these materials and class A limes attenuated over time. The difference in performance between class A limes was relatively minor (0.5 pH units), if applied based on CCE. Chemical purity (Ca%, CCE) was the main factor controlling liming effectiveness and particle fineness only related to the short-term reaction (4–7 days). Granulated limes showed the poorest performance in terms of pH correction attributed to poor solubility and spatial zonation of liming effects.

Prediction and variability mapping of some physicochemical characteristics of calcareous topsoil in an arid region using Vis–SWNIR and NIR spectroscopy

Site-specific management of soils needs continuous measurements of soil physicochemical characteristics. In this study, Vis–NIR spectroscopy with two spectroscopic instruments, including charge-coupled device (CCD) and indium-gallium-arsenide (InGaAs) spectrometers, was adopted to estimate some physicochemical characteristics of a calcareous topsoil in an arid climate. Partial least squares (PLS) as linear and artificial neural networks (ANN) as nonlinear multivariate techniques were utilized to enhance the accuracy of prediction. The best predictive models were then used to extract the variability maps of physicochemical characteristics. Diffuse reflectance spectra of 151 samples, collected from the calcareous topsoil, were acquired in the visible and short-wavelength near-infrared (Vis–SWNIR) (400–1100 nm) and near-infrared (NIR) (950–1650 nm) spectral ranges using CCD and InGaAs spectrometers, respectively. The results showed that NIR spectral data of the InGaAs spectrometer was necessary to reach the best predictions for all selected soil properties. The best predictive models based on the optimum spectral range could allow us the excellent predictions of sand (RPD = 2.63) and silt (RPD = 2.52), and very good estimations of clay (RPD = 2.35) and electrical conductivity (EC) (RPD = 2.224) by ANN and very good prediction of calcium carbonate equivalent (CCE) (RPD = 2.01) by PLS. The CCD device, however, resulted in acceptable predictions of sand (RPD = 2.13, very good) and clay (RPD = 1.66, fair) by ANN, and silt (RPD = 1.78, good), EC (RPD = 1.84, good) and CCE (RPD = 1.67, fair) by PLS. Similar variability was attained between pairs of predicted maps by best models and reference-measured maps for all studied soil properties. For clay, sand, silt, and CCE, the Vis/SWNIR-predicted and equivalent reference-measured maps had acceptable similarities, indicating the potential application of low-cost CCD spectrometers for prediction and the variability mapping of these parameters.

Application of artificial neural network and gene expression programming to estimate soil microbial metabolic quotient

The soil microbial metabolic quotient (qCO2) calculated on the basis of the relationship between the basal soil respiration and the carbon of the microbial biomass provides a method to convey the activity and size of soil microbial populations; therefore, it is extensively utilized as a factor of the true microbial activities in soil. In the current study, artificial neural network (ANN) and gene expression programming (GEP) algorithms were utilized to appraise qCO2. Data from 150 soil samples found in Gilan, Ardabil and East Azerbaijan provinces, Iran were utilized to test these methodologies. The surface soil samples were prepared from different land uses. To feed the applied models, several combinations of the input parameters containing pH, soil texture, organic carbon (OC), carbonate calcium equivalent (CCE), electrical conductivity (EC), number of bacteria, fungi and actinomycete, substrate induced microbial soil respiration (SIR) and climatic parameters including annual precipitation (AP) and mean annual temperature (MAT) were utilized. To assess the models' performance accuracy, the root mean square error (RMSE) and the coefficient of determination (R2) were utilized. The most elevated R2 and least RMSE were accomplished for the models that utilized all accessible input parameters. Among the different scenarios, in the best condition GEP was able to estimate 52% of qCO2 variability while 71% of its variability was estimated by ANN. Our results displayed that the most effective easily measured parameters in estimating qCO2 were soil texture, climatic data, pH, EC, OC and CCE; where approximately 52% and 44% of its inconsistency were predictable by the ANN and GEP, individually. Based on the obtained results, ANN was better than the GEP in assessing of microbial metabolic quotient.

Assessment of Soil Redistribution Following Land Rehabilitation with an Apple Orchard in Hilly Regions of Central Iran

This study was executed to explore soil redistribution and soil quality changes induced by land degradation and then rehabilitation by orchard plantation in different slope positions in a semi-arid region in central Iran. A total of 72 surface soil samples (0–30 cm) were collected from three land uses (natural rangelands, dryland farming, and apple orchards) in four slope positions (shoulder, backslope, footslope, and toeslope). The soil physicochemical properties and magnetic parameters were measured, and soil redistribution was determined in the selected soil samples using the 137Cs technique. The results showed that rangeland degradation and, subsequently, rainfed cultivation, led to a significant decline in the soil quality indicators, such as soil organic matter (SOM), total nitrogen (TN), available potassium (Kava), and available phosphorous (Pava), thus incurring further soil loss, as determined by the 137Cs technique. Conversely, the conversion and rehabilitation of drylands to apple orchards cultivated on the contour terraces improved soil quality significantly and decreased soil loss (p &lt; 0.05) and soil quality grade (p &lt; 0.01). Additionally, the findings indicated that slope positions relative to land use change had a reasonable impact on the variability of soil properties and soil loss and deposition. The results of 137Cs analysis showed that the drylands had the highest soil loss (185.3 t ha−1 yr−1) and maximum sedimentation (182. 5 t ha−1 yr−1) in the shoulder and footslope positions, respectively. The random forest model applied between 137Cs inventory and soil properties indicated that calcium carbonate equivalent (CCE), TN, Pava, Kava, and bulk density (ρb) could explain 75% of the total variability in 137Cs inventory with high R2 (0.94) and low RMSE (111.29). Magnetic measurements have shown great potential as a cost-effective and fast method for assessing soil redistribution in hilly regions, as confirmed by the findings of the 137Cs analysis, which agreed well with the magnetic susceptibility at low frequency (χlf). Overall, the results confirmed that restoring abandoned dryland by orchard cultivation may improve soil quality and diminish soil loss in the semi-arid region of Iran. However, further research is required to assess other aspects of the ecosystem affected by this restoration.

Urban soil properties distinguished by parent material, land use, time since urbanization, and pre-urban geomorphology

The typically high lateral and vertical variation of urban soil properties challenges the interpretation and prediction of their spatial patterns. The aim of this study was to investigate physico-chemical properties of, and pedogenesis in urban soils with different parent materials, and to evaluate whether information on land use, time since urbanization, and pre-urban geomorphology can help to better understand and spatially predict urban soil properties in the historic city of Ghent, Belgium. Sixteen soils were collected from sites with various land uses in three quarters that were urbanized at different times. They were described and characterized by laboratory methods (soil pH, electrical conductivity, soil organic carbon content, black carbon content, equivalent calcium carbonate content, soil particle size distribution, coarse fragments content) and portable X-ray fluorescence (total Cr, Ni, Cu, Zn, and Pb content). Site history and pre-urban geomorphology were deducted from archeological reports and historical imagery. Our results revealed that human-transported materials (HTMs) are common in soils in Ghent. Decalcification, eluvation or illuvation in HTM were only evidenced in soils for which landfill occurred >100 years ago, which had not been disturbed recently. Hence, soil properties were strongly influenced by compositional differences (e.g., nature or type of artifacts) between (native or human-transported) parent material types. In contrast to land use, time since urbanization and pre-urban geomorphology were indicative of parent material type, soil properties, and human soil modifications. More specifically, soils in the old, medieval quarter Inner City consisted mainly of urbic or spolic parent material and had a lower sand content, and a higher content of silt, coarse fragments, and heavy metals (Ni, Cu, Zn, Pb) compared to the young quarter Wondelgem, where soils mainly developed in native parent material or lithic HTM. Furthermore, soils situated in raised, former floodplains had a higher soil pH, electrical conductivity, calcium carbonate content, silt content, and coarse fragments content, and a lower sand content compared to soils of the periglacial plain. Multidisciplinary data sources (e.g., historical maps) can be integrated to better comprehend the complex urban soilscape, where past and current human interventions, pre-urban geomorphology and hydrography are all at play.

Soil heterogeneity: A comparative assessment of soils from two different AESR, southern India

A comparative study was carried out to assess the nature and characteristics of soils from two agro-ecological sub regions (AESU and AESR) from two states of South India. Using sentinel-2 satellite data, google imagery and top sheet, a detailed soil survey was taken up in AESU and AESR. Major soils from upland as well as from lowlands were compared to assess the soils heterogeneity. Results showed that there was a high heterogeneity between properties of soils AESU and AESR. Variability existed in climatic conditions (tropical humid region in AESU and semiarid condition in AESR), physiographic (valley plain to gently, very gently, moderate, steep and very steep slopes in AESU and levelled to gently and very gently slopes in AESR ), geology (charnockite and granite gneiss in AESU whereas granite gneiss in AESR), land use (rubber, paddy, coconut in AESU and paddy, mango, pigeon pea, groundnut in AESR), soil classification (Ultisols and Alfisols in AESU whereas Alfisols and Inceptisols in AESR) and land capability (soil and water conservation measures must in AESU and slope was manageable with agronomic management practices in AESR). Soils varied in texture (clay, sandy clay and clay loam in AESU whereas sandy clay loam to sandy loam in AESR), consistency and depth of soils (shallow to very deep in AESU and shallow to deep in AESR). Clay content (32.9 to 63.5%) was dominant fraction in AESU whereas sand (52.1 to 72.1%) was in AESR. Soil acidity (strongly acid to very strongly acid) was a major problem (both hydrogen and aluminium saturation) in AESU whereas slightly acidic to neutral and slightly alkaline in AESR. Organic carbon content was high (up to 25.9 gkg-1) in AESU whereas it was up to 10.0 gkg-1 in AESR. Base saturation was &lt;35% in AESU and &gt;75% up to 100% in AESR. Calcium carbonate equivalent is &gt;5% in AESR and not a problem in AESU. CEC was low (&lt;12 cmol (p+) kg–1) in AESU and good (&gt;12 cmol (p +) kg–1) in AESR.

Soil heterogeneity: A comparative assessment of soils from two different AESR, southern India

A comparative study was carried out to assess the nature and characteristics of soils from two agro-ecological sub regions (AESU and AESR) from two states of South India. Using sentinel-2 satellite data, google imagery and top sheet, a detailed soil survey was taken up in AESU and AESR. Major soils from upland as well as from lowlands were compared to assess the soils heterogeneity. Results showed that there was a high heterogeneity between properties of soils AESU and AESR. Variability existed in climatic conditions (tropical humid region in AESU and semiarid condition in AESR), physiographic (valley plain to gently, very gently, moderate, steep and very steep slopes in AESU and levelled to gently and very gently slopes in AESR ), geology (charnockite and granite gneiss in AESU whereas granite gneiss in AESR), land use (rubber, paddy, coconut in AESU and paddy, mango, pigeon pea, groundnut in AESR), soil classification (Ultisols and Alfisols in AESU whereas Alfisols and Inceptisols in AESR) and land capability (soil and water conservation measures must in AESU and slope was manageable with agronomic management practices in AESR). Soils varied in texture (clay, sandy clay and clay loam in AESU whereas sandy clay loam to sandy loam in AESR), consistency and depth of soils (shallow to very deep in AESU and shallow to deep in AESR). Clay content (32.9 to 63.5%) was dominant fraction in AESU whereas sand (52.1 to 72.1%) was in AESR. Soil acidity (strongly acid to very strongly acid) was a major problem (both hydrogen and aluminium saturation) in AESU whereas slightly acidic to neutral and slightly alkaline in AESR. Organic carbon content was high (up to 25.9 gkg-1) in AESU whereas it was up to 10.0 gkg-1 in AESR. Base saturation was &lt;35% in AESU and &gt;75% up to 100% in AESR. Calcium carbonate equivalent is &gt;5% in AESR and not a problem in AESU. CEC was low (&lt;12 cmol (p+) kg–1) in AESU and good (&gt;12 cmol (p +) kg–1) in AESR.

Integration of Multiple Models with Hybrid Artificial Neural Network‐Genetic Algorithm for Soil Cation‐Exchange Capacity Prediction

The potential of the soil to hold plant nutrients is governed by the 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 this study, a multiple model integration scheme supervised with a hybrid genetic algorithm‐neural network (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 incorporation into 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. With 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 a Nash Sutcliffe Efficiency (NSE) = 0.87, Root Mean Square Error (RMSE) = 2.885, Mean Absolute Error (MAE) = 2.249, Mean Absolute Percentage Error (MAPE) = 12.072, and coefficient of determination (R2) = 0.884. The proposed MM‐GANN model is a reliable intelligence‐based approach for the assessment of soil quality parameters intended for sustainability and management prospects.

Soil Quality and Evaluation of Spatial Variability in a Semi-Arid Ecosystem in a Region of the Southeastern Iberian Peninsula (Spain)

In the last two decades, as the importance of soil has been recognized as a key component of any ecosystem, there has been an increased global demand to establish criteria for determining soil quality and to develop quantitative indices that can be used to classify and compare that quality in different places. The preliminary estimation of the attributes involved in soil quality was made taking into account the opinion of the experts and our own experience in a semi-arid ecosystem. In this study, 16 soil properties have been selected as potential indicators of soil quality, in a region between Campo de Montiel and Sierra de Alcaraz (Spain): sand and clay percentage, pH, electrical conductivity (EC), soil organic carbon (OC), extractables bases of change (Na, K, Ca and Mg), cationic exchange capacity (CEC), carbonate calcium equivalent (CCE), bulk density (BD), water retention at 33 kPa field capacity and 1500 kPa permanent wither point (GWC33 kPa and GWC1500 kPa), coefficient of linear extensibility (COLE) and factor of soil erodibility (K). The main objective has been to develop an adequate index to characterize the quality of the soils in a semi-arid Mediterranean ecosystem. The preliminary estimation of the attributes involved in soil quality was made considering the opinion of the experts and our own experience in semi-arid ecosystems. Two indicator selection approaches have been used to develop the Soil Quality Index (SQI) (total data set -TDS- and minimum data set -MDS-), scoring functions (linear -L- and nonlinear -NL-) and methods (additive -A-, additive weighted -W- and Nemoro -N-. The quality indices have been calculated, considering the properties of the soil control section (between 0 and 100 cm depth), using 185 samples, belonging to horizons A, B and C of 51 soil profiles. The results have shown that the election of the soil properties, both of the topsoil and subsoil, is an important help in establishing a good relationship between quality, soil functions and agricultural management. The Kriging method has been used to determinate the spatial distribution of the soil quality grades. The indices that best reflect the state of soil quality are the TDS-L-W and TDS-L-A should go as sub-indices, as they are the most accurate indices and provide the most consistent results. These indices are especially indicated when carrying out detailed or semi-detailed studies. However, the MDS-L-W and MDS-L-A should go as sub-indices, which use only a limited number of indicators, are best for large-scale studies. The indicators with the greatest influence on soil quality for different land uses and those developed on different rocks, using linear scoring functions, are the following: (Clay), (GWC1500 kPa) and (Ca). These results can also be expressed as follows: the best soils in this region are deep soils, with a clay texture, with high water retention and a neutral or slightly basic pH. However, the indicators with the greatest influence on soil quality, using nonlinear scoring functions, are: (OC Stock), (Ca) and (CaCO3). In other words, the most important indicator is the organic carbon content, which is not logical in the case of a region in which the soils have an excessively low SOC content (0.86%).

Counterions, smectite, and palygorskite increase microstructural stability of saline-sodic soils

Saline-sodic soils are susceptible to wind and water erosion when the dispersive effect of sodium overcomes inter-particle bonds. Rheological parameters of viscoelasticity can help to quantify inter-particle attractive forces and account for the effect of salinity in these soils. The main objective of the present study was to investigate the viscoelasticity behavior of saline-sodic soils of the Sirjan playa in south-central Iran. Three representative pedons were excavated and described by horizon. Soil physicochemical properties and rheological properties were determined, namely the micromechanical parameters flow point (γf), loss factor tan δ, and integral z, with samples analyzed at three matric potentials (0, −6, and −15 kPa). Results showed that soil microstructural stiffness was mainly influenced by soil texture, clay minerals, gypsum, calcium carbonate equivalent (CCE), and matric potential. The dispersive effect of sodium, as indicated by low integral z and γf values, decreased with increasing gypsum content in − 6 and − 15 kPa matric potentials (0.6 < r < 0.8) and CCE percentage in the quasi-saturated (0 kPa) condition (r > 0.8). However, greater microstructural stability (i.e., higher integral z and γf) was observed for fine-textured soils with relatively high amounts of smectite and palygorskite and low pH. Furthermore, integral z and γf increased with lower matric potentials due to the stabilizing effect of menisci forces. Therefore, the viscoelastic behavior of the saline-sodic soils was negatively associated with water content and high sodium concentration, while the presence of smectite, palygorskite, gypsum, and CCE improved the soil physical conditions and thus the rigidity of the porous system. These results demonstrate that rheological measurements can identify saline-sodic soils that have strongly degraded microstructural stability and would most benefit from active management and amelioration.

Spatial prediction of soil surface properties in an arid region using synthetic soil image and machine learning

Evaluation of spatial variability and mapping of soil properties is critical for sustainable agricultural production in arid lands. The main objectives of the present study were to spatialize soil organic carbon (SOC), soil particle size distribution(clay, sand, and silt contents), and calcium carbonate equivalent (CCE) by integrating multisource environmental covariates, including digital elevation model (DEM) and remote sensing data by machine learning (Cubist, Cu and random forest, RF) in an arid region of Iran. Additionally, Synthetic Soil Images (SySI) were achieved from multi-temporal images of bare soil pixels based on Landsats 4, 5, 7, 8, and a DEM. Three hundred topsoil samples (0–30 cm depth) were collected based on the conditioned Latin hypercube sampling (cLHS) approach in Afzar district, Fars province, southern Iran. The models were calibrated and validated by the 10-fold cross-validation approach, and the performance was evaluated using root mean square error (RMSE), the ratio of the performance to interquartile distance (RPIQ), and coefficient of determination (R 2). Also, the prediction accuracy was assessed by the relative RMSE (rRMSE). The performance of the best models based on the RPIQ index showed that the model for predicting clay (1.89) had a good prediction, for sand (1.64), SOC (1.55), and CCE (1.59) had a fair prediction, while the model for silt (1.13) performed poorly. We found that the Cu and RF models had the highest and lowest prediction accuracies for CCE (rRMSE = 14.31%) and SOC (rRMSE = 43.93%), respectively. We discovered that a combination of high-quality RS data (SySI) and variables derived from DEM were reasonably able to predict soil properties. We revealed a strong promise to enhance the accuracy of digital soil mapping, especially in regions with limited soil data. Moreover, the application of RS data can reduce the soil sampling cost and, accordingly, soil mapping.

Long-Term Effects of Calcium-Based Liming Materials on Soil Fertility Sustainability and Rye Production as Soil Quality Indicators on a Typic Palexerult

Liming is a common practice used to improve acidic soil properties, as is essential for agricultural quality. A long-term field experiment with one lime rate (6000 kg/ha of carbonate calcium equivalent) and three calcium-based liming amendments (gypsum, limestone and sugar foam) was maintained on a Typic Palexerult for 10 years in order to determine changes in soil acidity and to assess the effects on crop (rye) yields. The soil acidity conditions decreased with all the amendments tested, but the sugar foam and limestone was more effective than gypsum over a long-term period. No significant changes in organic soil matter levels between the treatments tested were found. Interestingly, an increase in the leaching of organic soil matter was observed in limed soils. Lime application significantly increased the total rye biomass compared to the control soils during the whole experiment (2002–2011). Yield trends observed in spike and stem biomass were similar to those reported for total rye biomass. In this respect, at the end of the research, gypsum, limestone and sugar foam increased in relation to the total production of rye biomass by 16%, 32% and 38%, respectively, as compared to the control soils. Additionally, a significant and prolonged difference in calcium concentrations in rye stems between unlimed and limed subplots was observed. However, in spite of the results presented here, further investigations are needed to gain a better understanding of the long-term effects of liming on the chemical properties of soil.

Profiles and potential health risks of heavy metals in polluted soils in NE-Iran

Metal contamination of soil is primarily attributed to human activities and there is an increasing need to conduct risk assessments to characterize the nature and magnitude of risks to humans and ecological receptors from these contaminants. Trace metals (As, Sb, Cd, Cr, Pb, Co, Ni, V, Se, Zn, Fe) in soils from the Chelpu region of North East Iran were analyzed by ICP-OES to assess pollution using the geo-accumulation index (Igeo) and the enrichment factor (EF). The United States Environmental Protection Agency (USEPA) model was applied to calculate human health risk (HHR). Physicochemical and textural properties of the soil including pH, electrical conductivity (EC), organic matter (OM), calcium carbonate equivalent (CCE) were examined in 26 surface and subsurface soils and related to metal levels. Metal concentrations varied considerably [As (7.1–1448.8 mg/kg), Sb (7.7–74.4 mg/kg), Pb (0.6–26.5 mg/kg), Cd (1–2.6 mg/kg), Cr (67.5–527 mg/kg), Co (18.4–42.6 mg/kg), Ni (85.9–548.6 mg/kg), Sr (180.3–670.6 mg/kg), V (85.3–158.4 mg/kg), Se (0.4–3.10 mg/kg), Zn (44.6–104.9 mg/kg), and Fe (29,400–51,800 mg/kg)]. The Igeo indicated that soils were moderately polluted with As, Se, Ni, and Co but heavily to extremely contaminated with Sb and Cd compared with natural soils (USEPA). The enrichment factor (EF) values demonstrated that levels of As, Ni, and Co were moderately enriched but levels of Se, Cd, and Sb were substantially enriched. In contrast, Pb, Cr, Sr, V, and Zn levels were low. Using a human health risk assessment approach, non-carcinogen effects (HI >1) through ingestion of 13.04% of soil samples were determined. Carcinogenic effects through the ingestion pathway were observed for As and Cr in 13% and 3.9% of the soil samples, respectively. Organic matter (OM) content was significantly correlated with As (0.461, α = 0.05), Sb (0.605, α = 0.01), and Se (0.552, α = 0.01).