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Soil petroleum hydrocarbon contamination in the wetlands could cause ecological risk, especially through leakage into water reservoirs. So, the detection of the spatial variability of total petroleum hydrocarbons (TPH) in these soils is very crucial. The variability of TPH and its associations with magnetic susceptibility (χ

Background As one of the main components of land-use change, deforestation is considered the greatest threat to global environmental diversity with possible irreversible environmental consequences. Specifically, one example could be the impacts of land-use changes from oak forests into agricultural ecosystems, which may have detrimental impacts on soil mobilization across hillslopes. However, to date, scarce studies are assessing these impacts at different slope positions and soil depths, shedding light on key geomorphological processes. Methods In this research, the Caesium-137 (137Cs) technique was applied to evaluate soil redistribution and soil erosion rates due to the effects of these above-mentioned land-use changes. To achieve this goal, we select a representative area in the Lordegan district, central Iran. 137Cs depth distribution profiles were established in four different hillslope positions after converting natural oak forests to rainfed farming. In each hillslope, soil samples from three depths (0–10, 10–20, and 20–50 cm) and in four different slope positions (summit, shoulder, backslope, and footslope) were taken in three transects of about 20 m away from each other. The activity of 137Cs was determined in all the soil samples (72 soil samples) by a gamma spectrometer. In addition, some physicochemical properties and the magnetic susceptibility (MS) of soil samples were measured. Results Erosion rates reached 51.1 t·ha− 1·yr− 1 in rainfed farming, whereas in the natural forest, the erosion rate was 9.3 t·ha− 1·yr− 1. Magnetic susceptibility was considerably lower in the cultivated land (χhf = 43.5 × 10− 8 m3·kg− 1) than in the natural forest (χhf = 55.1 × 10− 8 m3·kg− 1). The lower soil erosion rate in the natural forest land indicated significantly higher MS in all landform positions except at the summit one, compared to that in the rainfed farming land. The shoulder and summit positions were the most erodible hillslope positions in the natural forest and rainfed farming, respectively. Conclusions We concluded that land-use change and hillslope positions played a key role in eroding the surface soils in this area. Moreover, land management can influence soil erosion intensity and may both mitigate and amplify soil loss.

A b s t r a c t. The potential use of optimized support vector machines with simulated annealing algorithm in developing prediction functions for estimating soil aggregate stability and soil shear strength was evaluated. The predictive capabilities of support vector machines in comparison with traditional regression prediction functions were also studied. In results, the support vector machines achieved greater accuracy in predicting both soil shear strength and soil aggregate stability properties comparing to traditional multiple-linear regression. The coefficient of correlation (R) between the measured and predicted soil shear strength values using the support vector machine model was 0.98 while it was 0.52 using the multiple-linear regression model. Furthermore, a lower mean square error value of 0.06 obtained using the support vector machine model in prediction of soil shear strength as compared to the multiple-linear regression model. The ERROR% value for soil aggregate stability prediction using the multiple-linear regression model was 14.59% while a lower ERROR% value of 4.29% was observed for the support vector machine model. The mean square error values for soil aggregate stability prediction using the multiplelinear regression and support vector machine models were 0.001 and 0.012, respectively. It appears that utilization of optimized support vector machine approach with simulated annealing algorithm in developing soil property prediction functions could be a suitable alternative to commonly used regression methods. K e y w o r d s: soft computing, support vector machines, simulated annealing algorithm, soil shear strength, aggregate stability

Accurate mapping of soil organic carbon (SOC) and inorganic carbon (SIC) contents at regional scales can be very important for sustainable agriculture and soil management. Low variation in terrain attributes (classically used for digital soil mapping) at low relief areas calls for additional spatial data to explain soil variability. The main objective of this study was to evaluate the predictive capability of Sentinel-1 (radar) and Sentinel-2 (optical) time series and statistics, summarized as multi-temporal features (MTF) to improve the spatial predictions of SOC and SIC in Ghorveh plain, located in Kurdistan Province, Western Iran. A systematic grid sampling was then employed to collect 150 soil surface samples (0–30 cm) for SOC and SIC measurements. We applied boosted regression trees (BRT) and random forest (RF) to predict SOC and SIC contents by using covariate sets compiled from radar and optical time series and topographic attributes. Model performance, evaluated by 10-fold cross-validation, showed that RF using the covariate set containing time series of Sentinel-1, Sentinel-2 and topographic attributes performed the best in predicting SOC (RMSE = 0.23, ME = 0.005, R2 = 0.29). On the other hand, for SIC, the covariate set containing MTF of Sentinel-1, Sentinel-2 and topographic attributes ranked the best with BRT (RMSE = 0.77, ME= −0.001, R2 = 0.48). The study indicates that using the time series and MTF from multiple dates of remote sensing data with topographic attributes results in improved predictions. However, model performance for SIC and SOC was moderate to poor, respectively. Therefore more substantial studies would be required to verify if the computational effort is likely justified by an increase in accuracy in general.

In the context of the continued increased global uptake of fingerprinting procedures to explore fluvial sediment sources, far less attention has been paid to dust source tracing and especially using different particle size fractions and low-cost tracers such as colour and magnetic susceptibility. The objective of this study, therefore, was to apportion local dust storm source contributions for the < 63-μm and 63–125-μm fractions of dust samples in a case study in central Iran. Colour and magnetic susceptibility properties were measured on 62 source samples and six dust storm samples. Statistical methods were used to select four different composite fingerprints for discriminating the dust sediment sources. These statistical approaches comprised (1) the Kruskal–Wallis H test (KW-H), (2) a combination of KW-H and discriminant function analysis (DFA), (3) a combination of KW-H and principal components and classification analysis (PCCA), and (4) a combination of KW-H and a general classification and regression tree model (GCRTM). Local dust source contributions were ascribed using a Bayesian un-mixing model using the final composite fingerprints. For both the < 63- and 63–125-μm fractions, the different composite signatures consistently suggested that alluvial fan material was the dominant source of the dust samples. The root mean square differences between the apportionment results using the different fingerprints ranged from 0.5 to 1.6% for the < 63-μm fraction and from 1.8 to 5.8% for the 63–125-μm fraction. The Wald-Wolfowitz runs test was used to compare the posterior distributions of the predicted source proportions created using the alternative final composite fingerprints and the results indicated that most of the pairwise comparisons were significantly different (p ≤ 0.05). For the < 63-μm fraction, the RMSE and MAE estimates of divergence between the modelled and known virtual source mixtures using the different final composite signatures ranged between 1.5 and 23.4% (with a corresponding mean value of 9.4%). The equivalent estimates for the 63–125-μm fraction were 1.2–20.1% (8.3%). The findings clearly demonstrate that colour and magnetic susceptibility tracers offer low-cost options for apportioning dust sources.

The wheat production variability is not well-understood in hilly region, especially in loess-derived soils of Golestan province in Iran with a sub-humid climate. Topography can greatly influence the production of agricultural crops by affecting soil quality. A study area located in Golestan province was selected in order to assess the spatial variability of wheat production and to develop regression models between the crop, soil properties, and topography attributes. The samples of wheat and soil were randomly taken from 100 points at different hillslope positions (i.e., shoulder, back-, foot-, and toe-slope). The soil physicochemical analysis and the measurement of wheat yield components were conducted. The digital elevation model (DEM; 10 m resolution) was used, and the topographic attributes (i.e., elevation, slope, wetness index, stream power index, curvature, erosivity factor, and watershed specific area) were calculated. The results showed that the greatest total yield and the highest grain yield were estimated to be 14.53 and 4.41 ton ha−1, respectively, in areas with a slope of less than 10%, which were significantly higher than those in the steep areas (slope classes of 10–30% and > 30%). The highest and the lowest total yields, with average values of 15.82 and 5.68 ton ha−1, were observed in the toeslope and shoulder slope positions, respectively. The greatest grain yields were obtained from the foot- and toeslope positions with the average values of 4.61 and 4.66 ton ha−1, respectively. The topographic curvature and wetness index had a significant correlation with the yield of wheat. According to the regression equations, topographic indexes can well justify the spatial variability of wheat yield, indicating the importance of these factors by influencing the distribution of moisture during the process of wheat production in the study region. The enhancements of wheat yield components in the lower slope positions could be attributed to an increase in soil depth and plant available water as well as to the accumulation of further soil organic matter and nutrient elements, including nitrogen and potassium, in such positions as a result of soil redistribution. Moreover, the results illustrated that by using easy accessable, cheap, and none destructive data (DEM derivatives and soil properties); it is possible to predict the production yield of wheat with a reliable estimation. We concluded that for better farming management and productivity in hilly regions, topographic attributes should be considered for plantation. Therefore, this study introduces the most suitable slope positions and topographic attributes for crop production with the least soil degradation. Shoulder and backslope positions are the most unsuitable slopes possibly better for orchards while toeslopes and footslopes could be used for intensive crop production.

The purpose of this study was to determine the relative contribution of sub-basin resources to sediment production by using magnetic susceptibility data as the tracer in Chehelgazi catchment, Sanandaj. For this purpose, 20 samples of the output 5 sub-basins were measured by harvesting and magnetic susceptibility. Kruskal-Wallis test results showed that in all three trackings, frequency high, low and dependent, at least two sources had the ability to differentiate. In the second step, the three tracers were tested on the discriminant analysis by the sub-basin source and two tracers with different power splitters showed the high frequency of 88% and the frequency dependence of 12%; power splitters both tracers together in the sub-basin splitters was 90%, so they were selected as the optimal combination; therefore, they have the capability to determine the relative contribution model of sediment. The results of Bayesian uncertainty model also indicated Todarsamadi sub-basin with 44.4% of the largest contribution and Doveyseh, Chatan and Cherendo sub-basins with 35.5, 7.9 and 4.5, respectively, and Madian Dol sub-basin with 4/4 percent had the lowest contribution to sediment production. Based on the available results, Todarsamadi and Doveyseh sub-basins had the highest amount of sediment production; so these results could be used in soil conservation and management planning.

Soil salinization is a severe danger to agricultural activity in arid and semi-arid areas, reducing crop production and contributing to land destruction. This investigation aimed to utilize machine learning algorithms to predict spatial soil salinity (dS m−1) by combining environmental covariates derived from remotely sensed (RS) data, a digital elevation model (DEM), and proximal sensing (PS). The study is located in an arid region, southern Iran (52°51′–53°02′E; 28°16′–28°29′N), in which we collected 300 surface soil samples and acquired the spectral data with RS (Sentinel-2) and PS (electromagnetic induction instrument (EMI) and portable X-ray fluorescence (pXRF)). Afterward, we analyzed the data using five machine learning methods as follows: random forest—RF, k-nearest neighbors—kNN, support vector machines—SVM, partial least squares regression—PLSR, artificial neural networks—ANN, and the ensemble of individual models. To estimate the electrical conductivity of the saturated paste extract (ECe), we built three scenarios, including Scenario (1): Synthetic Soil Image (SySI) bands and salinity indices derived from it; Scenario (2): RS data, PS data, topographic attributes, and geology and geomorphology maps; and Scenario (3): the combination of Scenarios (1) and (2). The best prediction accuracy was obtained for the RF model in Scenario (3) (R2 = 0.48 and RMSE = 2.49), followed by Scenario (2) (RF model, R2 = 0.47 and RMSE = 2.50) and Scenario (1) for the SVM model (R2 = 0.26 and RMSE = 2.97). According to ensemble modeling, a combined strategy with the five models exceeded the performance of all the single ones and predicted soil salinity in all scenarios. The results revealed that the ensemble modeling method had higher reliability and more accurate predictive soil salinity than the individual approach. Relative improvement (RI%) showed that the R2 index in the ensemble model improved compared to the most precise prediction for the Scenarios (1), (2), and (3) with 120.95%, 56.82%, and 66.71%, respectively. We applied the best model in each scenario for mapping the soil salinity in the selected area, which indicated that ECe tended to increase from the northwestern to south and southeastern regions. The area with high ECe was located in the regions that mainly had low elevations and playa. The areas with low ECe were located in the higher elevations with steeper slopes and alluvial fans, and thus, relief had great importance. This study provides a precise, cost-effective, and scientific base prediction for decision-making purposes to map soil salinity in arid regions.

Viscosity, density, pour point, flash point, and oxidation stability are some quality properties of engine oil. The entrance of any contamination into the engine oil can change its properties. In the present study the effect of calcite and silica sand dust particles on the quality of engine oil, namely, Sepahan Oil Production API, SC-CC, SAE 40, was investigated. The presence, amount, and particle size of silica and calcite in the engine oil were determined by X-ray diffraction (XRD), inductively coupled plasma spectroscopy (ICP), and energy dispersive X-ray spectroscopy (EDX), respectively. The concentrations of the silica and calcite particles (0, 1, 2, and 3 g/L) were used according to the dust conditions in the east of Isfahan, Iran. The quality of the oil was measured after 20 hours of the engine operation. The results showed viscosity, density, and the pour point of the engine oil were significantly (P

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.

This research was conducted to evaluate the utilization of magnetic susceptibility measurements in the assessment of metal concentrations in soils developed on a range of parent materials in northwestern Iran. Eighty surface soil samples were collected from eight parent rocks including ultrabasic rocks, basalt, andesite, granite, marl, limestone, Qom formation, and shale. The collected samples were assessed to determine magnetic susceptibility at low frequency (χlf) and concentrations of some metals comprising chromium (Cr), iron (Fe), copper (Cu), nickel (Ni), zinc (Zn), cobalt (Co), and manganese (Mn). The results showed that the highest levels of metals and χlf were observed in basic and ultrabasic soils. Strong positive correlations (P 0.01) detected between χlf and Fe (0.87), Mn (0.78), Zn (0.74), Ni (0.90), Co (0.78), and Cr (0.90) in all samples indicated a potential for using magnetic susceptibility in semi-quantitative estimation of metal concentrations in soils of natural ecosystems. Multiple linear regression between metal contents and χ

In the digital soil mapping (DSM) framework, machine learning models quantify the relationship between soil observations and environmental covariates. Generally, the most commonly used covariates (MCC; e.g., topographic attributes and single-time remote sensing data, and legacy maps) were employed in DSM studies. Additionally, remote sensing time-series (RST) data can provide useful information for soil mapping. Therefore, the main aims of the study are to compare the MCC, the monthly Sentinel-2 time-series of vegetation indices dataset, and the combination of datasets (MCC + RST) for soil organic carbon (SOC) prediction in an arid agroecosystem in Iran. We used different machine learning algorithms, including random forest (RF), Cubist, support vector machine (SVM), and partial least square regression (PLSR). A total of 237 soil samples at 0–20 cm depths were collected. The 5-fold cross-validation technique was used to evaluate the modeling performance, and 50 bootstrap models were applied to quantify the prediction uncertainty. The results showed that the Cubist model performed the best with the MCC dataset (R2 = 0.35, RMSE = 0.26%) and the combined dataset of MCC and RST (R2 = 0.33, RMSE = 0.27%), while the RF model showed better results for the RST dataset (R2 = 0.10, RMSE = 0.31%). Soil properties could explain the SOC variation in MCC and combined datasets (66.35% and 50.82%, respectively), while NDVI was the most controlling factor in the RST (50.22%). Accordingly, results showed that time-series vegetation indices did not have enough potential to increase SOC prediction accuracy. However, the combination of MCC and RST datasets produced SOC spatial maps with lower uncertainty. Therefore, future studies are required to explicitly explain the efficiency of time-series remotely-sensed data and their interrelationship with environmental covariates to predict SOC in arid regions with low SOC content.

The soil carbon pool which is the sum of soil organic carbon (SOC) and soil inorganic carbon (SIC) is the second largest active store of carbon after the oceans and it is an important component of the global carbon cycle. Hence, accurate estimation of SOC and SIC as important carbon reservoirs in terrestrial ecosystems using fast, inexpensive and non-destructive methods is crucial for planning different climate change policies. The aim of the current research was to examine the effectiveness of Vis-NIR (visible and near-infrared spectroscopy: 350–2500 nm) and MIR (mid-infrared spectroscopy: 4000–400 cm−1) to characterize and estimate soil organic matter (SOM) and carbonates as main components of soil carbon stocks in Juneqan, Charmahal va Bakhtiari, Iran. To do so, a total of 548 soil samples from this area were collected (October 2015) and analyzed in laboratory (August 2017). In order to develop models capable of predicting SOM and carbonates content, seven spectral preprocessing methods comprising Absorbance (Abs), De-trending (Det), Continuum removal (CR), Savitzky-Golay derivatives (SGD), standard normal variate transformation (SNV), multiplicative scatter correction (MSC) and Normalization by range (NBR) were conducted along with five multivariate methods including Random Forest (RF), Partial Least-Squares Regression (PLSR), Artificial Neural Network (ANN), Support Vector Machine (SVM) and Gaussian Process Regression (GPR). The content of carbonates caused spectral reflectance intensity to augment on several ranges of spectrum and strong absorption feature at 2338 nm in the Vis-NIR and 714, 850, 870, 1796, 2150 and 2510 cm−1 in the MIR spectra range. SOM absorbed energy in several ranges, but also showed specific peaks in MIR. Both facts are associated with the structure of carbonates and SOM and its interaction with energy. The best combination of preprocessing and calibration models for carbonates quantification in Vis-NIR spectra was Det/PLSR (R2= 0.74, RPD= 2.19, RMSE= 6.45). For SOM, it was Det/PLSR (R2= 0.82, RPD= 2.41, RMSE= 0.75). The Det/RF (R2= 0.87, RPD= 2.44, RMSE= 0.66) for the quantification of SOM and MSC/RF (R2= 0.84, RPD= 2.84, RMSE= 5.50) for carbonates in MIR spectra range showed the greatest results. The stronger occurrence of spectral bands in MIR as well as the specificity of the absorption features indicated that this range produced better predictions. The obtained results highlighted the significant role of soil spectroscopy technique in predicting SOC and soil carbonates as key components of soil carbon stocks in the study area. Therefore, this technique can be used as a more cost-effective, time saving and nondestructive alternative to traditional methods of soil analysis.

The objective of the present study was to evaluate the efficiency of soil magnetic parameters for assessment of soil drainage classes in Juneqan district, Charmahal and Bakhtiari province, western Iran. Four soil drainage classes including well drained (WD), moderately well drained (MWD), intermittent poor drained (IPD) and poorly drainage (PD) were selected. A total of 89 soil pedons were described and soil samples were collected within the moisture control section. Magnetic susceptibility (MS) at high (χhf) and low (χlf) frequencies and frequency-dependent MS (χfd) were evaluated in the laboratory. Poorly crystalline iron (Feo) and pedogenic iron (Fed) values of all soil samples were also measured. The results revealed that among the four drainage classes, PD class showed the lowest χlf and χhf values, greatest of Feo and Feo/Fed, lowest contents of Fed, as well as the highest average increase of χlf on heating (at 500 °C). However, all mentioned features showed an inverse trend in the WD class as compared to PD. The results of discriminant analysis demonstrated that magnetic measures could prosperously discriminate between the selected drainage classes in this study area (average accuracy = 83.1%). Therefore, it can be concluded that MS technique could be used as a powerful, nondestructive and fast technique for separation of soil drainage classes in the present case.

There is an urgent need for reliable and cost-effective sediment source tracing techniques for apportioning aeolian sediment (sand dune) sources for guiding the selection of best management practices for wind erosion control. Accordingly, the main aim of this study was to quantify the contributions of aeolian sources to sand dune target sediment samples collected in a case study in central Iran using a source fingerprinting procedure based on low-cost fingerprints comprising colour and magnetic tracers. Colour (RGB), magnetic susceptibility (χlf and χhf) and 13 colour and magnetic indices were measured on 54 aeolian sediment source samples and ten aeolian target sediment samples. Three different composite fingerprints for discriminating and apportioning the aeolian sediment sources were selected based on a combination of statistical tests comprising the Kruskal–Wallis H test (KW-H), discriminant function analysis (DFA), principal component & classification analysis (PCCA), and a general classification & regression tree (GC&RT) model. The Modified MixSIR Bayesian un-mixing model was used to apportion aeolian source contributions using the final composite fingerprints. The composite signatures all suggested that the salt flat plain was the dominant (average 63%, and standard deviation, SD, 5.9%) source of the aeolian target sediment samples, whilst agricultural land was second (average 63%, SD 5.6%,) most important. The root mean square difference between the apportionment results based on the three composite fingerprints ranged from 0.2% to 8.3%. Pairwise comparisons of the posterior distributions for the predicted source proportions generated using the three composite signatures showed that eight of 12 pairwise comparisons were not significantly different. Virtual mixture accuracy tests of the fingerprinting models using the three composite signatures suggested errors ranging between 2.2%−20.6% (with a mean of 9.9%), 1.4%−17.0% (mean value 8.3%), and 0.03%−1.0% (mean value 0.8%). The results support the use of low-cost colour and magnetic tracers by investigations into aeolian sediment provenance.

This study reports the seasonal and spatial variations of atmospheric dust deposition rates (DDR) and dust-borne heavy metals concentrations in the city of Isfahan and its surrounding areas in central Iran. Dust samples were collected from 67 different sites on a monthly basis from June 2012 to May 2013 and topsoil samples were taken only once from the same sites. Fall and winter seasons exhibited the lowest DDR due to the higher precipitation while the highest rate was observed in the summer season. The northern and central parts of the desert land in the study area recorded the highest annual DDR with a mean value of 61.24 ton km−2 year−1. Seasonal distribution of dust-borne heavy metals concentrations showed that almost all the elements followed the trend winter > fall ≥ spring > summer. Spatial distributions of dust-borne Cd, Cu, Ni, Pb, and Zn almost followed the same pattern with highest concentrations in the western stretches of the study area and in the city of Isfahan. The highest concentration of Hg and As were observed in the urban and desert rural areas. Cr recorded its highest concentration in the urban area while dust-borne Co exhibited a fairly uniform distribution over the whole study area. Results of crustal enrichment factor (EFc) analysis showed that anthropogenic sources contribute a substantial amount of all studied elements in dust particles rather than crustal origin. Fossil fuel, vehicle traffic, and industrial activities seem to be the most important anthropogenic factors responsible for dust elemental pollution in the study area.

The efficiency of different digital and conventional soil mapping approaches to produce categorical maps of soil types is determined by cost, sample size, accuracy and the selected taxonomic level. The efficiency of digital and conventional soil mapping approaches was examined in the semi-arid region of Borujen, central Iran. This research aimed to (i) compare two digital soil mapping approaches including Multinomial logistic regression and random forest, with the conventional soil mapping approach at four soil taxonomic levels (order, suborder, great group and subgroup levels), (ii) validate the predicted soil maps by the same validation data set to determine the best method for producing the soil maps, and (iii) select the best soil taxonomic level by different approaches at three sample sizes (100, 80, and 60 point observations), in two scenarios with and without a geomorphology map as a spatial covariate. In most predicted maps, using both digital soil mapping approaches, the best results were obtained using the combination of terrain attributes and the geomorphology map, although differences between the scenarios with and without the geomorphology map were not significant. Employing the geomorphology map increased map purity and the Kappa index, and led to a decrease in the ‘noisiness’ of soil maps. Multinomial logistic regression had better performance at higher taxonomic levels (order and suborder levels); however, random forest showed better performance at lower taxonomic levels (great group and subgroup levels). Multinomial logistic regression was less sensitive than random forest to a decrease in the number of training observations. The conventional soil mapping method produced a map with larger minimum polygon size because of traditional cartographic criteria used to make the geological map 1:100,000 (on which the conventional soil mapping map was largely based). Likewise, conventional soil mapping map had also a larger average polygon size that resulted in a lower level of detail. Multinomial logistic regression at the order level (map purity of 0.80), random forest at the suborder (map purity of 0.72) and great group level (map purity of 0.60), and conventional soil mapping at the subgroup level (map purity of 0.48) produced the most accurate maps in the study area. The multinomial logistic regression method was identified as the most effective approach based on a combined index of map purity, map information content, and map production cost. The combined index also showed that smaller sample size led to a preference for the order level, while a larger sample size led to a preference for the great group level.

137 Cs technique has been widely used for the evaluation rates and patterns of soil erosion and deposition. This technique requires an accurate estimate of the values of 137 Cs inventory at the reference site. This study was conducted to evaluate the variability of the inventory of 137 Cs regarding to the sampling program including sample size, distance and sampling method at a reference site located in vicinity of Fereydan district in Isfahan province, west-central Iran. Two 3 × 8 grids were established comprising large grid (35 m length and 8 m width), and small grid (24 m length and 6 m width). At each grid intersection two soil samples were collected from 0 to 15 cm and 15–30 cm depths, totally 96 soil samples from 48 sampling points. Coefficients of variation for 137 Cs inventory in the soil samples was relatively low (CV = 15%), and the sampling distance and methods used did not significantly affect the 137 Cs inventories across the studied reference site. To obtain a satisfactory estimate of the mean 137 Cs activity in the reference sites, particularly those located in the semiarid regions, it is recommended to collect at least four samples along in a grid pattern 3 m apart.

Microbial communities play a seminal role in biogeochemical cycles and can be influenced by both biotic and abiotic factors. A few studies highlighting the importance of topographic factors influencing soil microbial community. This study was conducted to investigate linear and nonlinear relationships between soil properties, topographic attributes and vegetation indexes with soil microbial communities in Shast Kalate (Bahram Nia) forest at the north of Iran. To analyze relationships between soil microbial community and selected variables, correlation analysis, multiple linear regressions (MLR) and machine learning approaches including artificial neural network (ANN) and random forest (RF) were applied. To validate modeling of soil microorganisms using environmental variables, two input datasets were used including soil properties, topographic and vegetation indexes (STV dataset) and topographic and vegetation indexes (TV dataset). Phospholipid fatty acids (PLFA) were analyzed to measure the structure of soil microbial communities. Linear and nonlinear analyses indicated that the results of the models developed by STV and TV datasets perfectly accorded (r = 0.72; p

Nowadays through the world as well as in Iran, petroleum hydrocarbons have majority contribution in environmental ricks for human and other organisms. Therefore, this study evaluates hydrocarbon pollution effects on soil chemical properties, as well as soil magnetic susceptibility on a petroleum refinery compound in Isfahan province, Iran. It also examines the efficacy of using magnetic signatures to predict hydrocarbon pollution of soils. Two sites (polluted and unpolluted) with similar intrinsic soil properties and environmental attributes were selected. A total of 120 soil samples were collected at two depths of 0–10 and 10–30 cm. Laboratory analysis included measurement of electrical conductivity (EC), pH, aqua regial extractable Fe (FeA), dithionite extractable Fe (Fed), magnetic susceptibility at low frequency (χlf), total petroleum hydrocarbon (TPH), as well as powdery X-ray characterization. Polluted soils by petroleum hydrocarbons had significantly higher EC, χlf, FeA and Fed and lower pH values compared to the unpolluted soils. Positive significance (r = 0.88, p

The most important properties affecting the soil loss and runoff were investigated, and the effects of land use on the soil properties, together with the erodibility indices in a semiarid zone, central Iran, were evaluated. The locations of 100 positions were acquired by cLHS and 0–5-cm surface soil layer samples were used for laboratory analyses from the Borujen Region, Chaharmahal-Va-Bakhtiari Province, central Iran. To measure in situ runoff and soil erodibility of three different land uses comprising dryland, irrigated farming, and rangeland, a portable rainfall simulator was used. The results showed that the high variations (coefficient of variation, CV) were obtained for electrical conductivity (EC), mean weight diameter (MWD), soil organic carbon (SOC), and soil erodibility indices including runoff volume, soil loss, and sediment concentration (CV ~ 43.6–77.4%). Soil erodibility indices showed positive and significant correlations with bulk density and negative correlations with SOC, MWD, clay content, and soil shear strength in the area under investigation. The values of runoff in the dryland, irrigated farming, and rangeland were found 1.5, 28.9, and 58.7 cm3; soil loss in the dryland, irrigated farming, and rangeland were observed 0.25, 2.96, and 76.8 g; and the amount of sediment concentration in the dryland, irrigated farming, and rangeland were found 0.01, 0.11, and 0.15 g cm−3. It is suggested that further investigations should be carried out on soil erodibility and the potential of sediment yield in various land uses with varying topography and soil properties in semiarid regions of Iran facing the high risk of soil loss.

Aggregate stability is a useful soil physical dynamic index of soil resistivity to surface wind and water erosion in all ecosystems, especially, in arid and semi-arid regions. Two machine learning techniques including support vector machines (SVMs) and artificial neural networks (ANNs) were used to develop predictive models for the estimation of geometric mean diameter (GMD) of soil aggregates. An empirical multiple linear regression (MLR) model was also constructed as the benchmark to compare their performances. Furthermore, the influence of feature space dimension reduction using parallel genetic algorithm (PGA) on the prediction accuracy of all investigated techniques was evaluated. The ANN model achieved greater accuracy in GMD prediction as compared to the MLR and SVM models. The obtained ERROR% value in GMD prediction using the ANN model was 6.9%, while it was 15.7 and 10.6% for the MLR and SVM models, respectively. Feature selection using PGA improved the prediction accuracy of all investigated tec...

There is a growing demand for digital soil maps for environmental planning, modeling and management. If mapped soil classes are taken from a hierarchical taxonomic system, a question arises: which taxonomic level is most appropriate to be depicted on the map with a given sample size, available environmental covariates and the strength of predictive relations between covariates and the soil classes? Pedodiversity, the study and measurement of soil diversity, can be considered as a framework to analyze spatial patterns depicted on soil maps. This paper discusses the selection of the taxonomic level for soil mapping in an arid region in southeast Iran on the basis of (1) the purity of a digital soil class map derived from an artificial neural network (ANN) prediction method using environmental covariates and (2) pedodiversity indices of these soil maps. The prediction of soil classes and the calculation of diversity indices were carried out for taxonomic categories of order, suborder, great group, and subgroup. Using the feed forward back-propagation algorithm, three-layer ANNs with input, hidden and output layers were trained for soil class prediction at each category level. In most predictions, the combined use of terrain attributes and geomorphic surfaces provided the best results. When the taxonomic level changed from order to subgroup, the purity decreased, whereas the values of the diversity indices increased. The highest purity and lowest diversity are observed at the order level, indicating a good quality map in terms of its purity, but reflecting only little soil diversity, thus with a low usage potential. On the other hand, soil maps at the level of subgroup illustrate high diversity and low purity, so that the predicted map units are highly uncertain. This map is also inappropriate for users. We introduced an index combining the diversity and purity which indicated that the best taxonomic level for soil mapping in the study area is the great group, with both high diversity and purity.

The knowledge on the relationships of protein and micronutrient concentration in wheat grain with edaphic characteristics could provide valuable information for site-specific fertilization of crops for producing grains denser in micronutrients such as iron (Fe) and zinc (Zn) in rain-fed agriculture. In this study, we used soil properties and topographic parameters in the artificial neural network (ANN) methodology as a power tool for improving models for predicting wheat grain micronutrient and protein contents in the hilly regions of western Iran. Soil and grain samples were collected from 1 m2 plots using the stratified random method, whereas the slope positions were considered as the basis of soil sampling, at 100 selected points. The mean grain Zn, Fe, Cu (copper) and Mn (manganese) concentrations were 37.02, 65.86, 14.79 and 44.93 mg–1 kg–1, respectively, and mean grain protein was 13.76%. Application of the ANN models for predicting Zn, Fe, Cu, Mn and protein contents in grains improved prediction b...

A study was made to determine the influence of pasture degradation on soil quality indicators that included physical, chemical, biological and micromorphological attributes, along the hillslope positions in Chaharmahal and Bakhtiari province, western Iran. Soil samples from different slope positions were collected from 0 to 30 cm depth for physical and chemical properties and from 0 to 15 cm depth for biological properties at two adjacent sites in the two ecosystems: natural pasture and cultivated land. Soil quality indicators including bulk density, mean weight diameter, soil organic carbon (SOC), particulate organic material (POM) in aggregate fractions, total nitrogen, available potassium, available phosphorus, cation exchange capacity, soil microbial respiration (SMR) and microbial biomass C and N were determined. The results showed that SOC decreased cultivation from 1.09 to 0.77 % following pasture degradation. The POM decreased by about 19.35 % in cultivated soils when compared to natural pasture; also, SMR and microbial biomass C and N decreased significantly following pasture degradation. Furthermore, aggregate stability and pore spaces decreased, and bulk density increased in the cultivated soils. Overall, our results showed that long-term cultivation following pasture degradation led to a decline in soil quality in all selected slope positions at the site studied in the semiarid region.

Soil depth generally varies in mountainous regions in rather complex ways. Conventional soil survey methods for evaluating the soil depth in mountainous and hilly regions require a lot of time, effort and consequently relatively large budget to perform. This study was conducted to explore the relationships between soil depth and topographic attributes in a hilly region in western Iran. For this, one hundred sampling points were selected using randomly stratified methodology, and considering all geomorphic surfaces including summit, shoulder, backslope, footslope and toeslope; and soil depth was actually measured. Eleven primary and secondary topographic attributes were derived from the digital elevation model (DEM) at the study area. The result of multiple linear regression indicated that slope, wetness index, catchment area and sediment transport index, which were included in the model, could explain about 76 % of total variability in soil depth at the selected site. This proposed approach may be applicable to other hilly regions in the semi-arid areas at a larger scale.

Surface soil shear strength can be a useful dynamic index for soil erodibility and thus a measure of soil resistance to water erosion. In this study, we evaluated the predictive capabilities of artificial neural networks (ANNs) and an adaptive neuro-fuzzy inference system (ANFIS) in estimating soil shear strength from measured particle size distribution (clay and fine sand), calcium carbonate equivalent (CCE), soil organic matter (SOM), and normalized difference vegetation index (NDVI). The results showed that the ANN model was more feasible in predicting the soil shear strength than the ANFIS model. The root mean square error (RMSE), mean estimation error (MEE), and correlation coefficient (R) between the measured soil shear strength and the estimated values using the ANN model were 0.05, 0.01, and 0.86, respectively. In ANFIS analysis, the RMSE was 0.08 and a lower correlation coefficient of 0.60 was obtained in comparison with the ANN model. Furthermore, the ANN and ANFIS models were more accurate in predicting the soil shear strength than was the conventional regression model. Results indicate that the ANN model might be superior in determining the relationships between index properties and soil shear strength.

Sustainable land resource management depends on the good assessment and planning of current landscapes. This may be possible with application of multidisciplinary researches, as this study presented a multidisciplinary approach in a spatial database frame work using Geographic Information System. �Agroecological zones� concept is used to integrating and characterizing homogenous spatial units. This approach combine theme layers include of available water resources, climate, terrain and soil conditions, associated with land use and settlement patterns. Climatic indices layers, including of growing degree days, aridity index, length of growing period and freezing period were created using the correlation between climatic parameter and digital elevation model. Using this approach the Borujen watershed was divided in 28 �agroecological zones� which defined 3 landscapes or agricultural regions. The most important constraints for developing agriculture in landscape I has topographic, climatic and soil constrains, landscape II has topographic and soil constrains and landscape III has the some limitations of soil. Landscape I and II are much less attractive from an agricultural perspective. Landscape III is suitable for agriculture but, the potential for rainfed cropping system is limited by a lack of growing period during which neither temperature nor moisture is limiting to plant production. In general, the case study of the Borujen watershed indicated that this approach can be used for different scales and adaptive to the particular planning.

In this study artificial neural network (ANN) models were designed to predict the biomass and grain yield of barley from soil properties; and the performance of ANN models was compared with earlier tested statistical models based on multivariate regression. Barley yield data and surface soil samples (0–30 cm depth) were collected from 1 m2 plots at 112 selected points in the arid region of northern Iran. ANN yield models gave higher coefficient of determination and lower root mean square error compared to the multivariate regression, indicating that ANN is a more powerful tool than multivariate regression. Sensitivity analysis showed that soil electrical conductivity, sodium absorption ratio, pH, total nitrogen, available phosphorus, and organic matter consistently influenced barley biomass and grain yield. A comparison of the two methods to identify the most important factors indicated that while in the ANN analysis, soil organic matter (SOM) was included among the most important factors; SOM was exclude...

We show with the results of a study conducted in the Hamadan Province, Iran as to how the use of composite sampling for estimating mean zinc concentration in the soil can help reduce analytical costs by reducing the number of analysis required. We also introduce post-stratification methodology in the composition step to take advantage of possible spatial dispersion. We speculate that the zinc concentration value depends on the sample location, we first stratified the sample set and then composite units from different strata randomly. The results of a simulation study show that the use of this approach not only reduces the total costs but also increases the precision of the estimator.

The spatial pattern of soil redistribution rate was investigated using cesium-137 ((137)Cs) within a cultivated complex hillslope in western Iran. The relationship between soil redistribution rate and soil organic carbon and total nitrogen pattern were studied using co-regionalization analysis. Ninety-one soil cores were sampled for (137)Cs, total nitrogen, and soil organic carbon measurements. The simplified mass balance model estimated a gross erosion rate of 29.8 t ha(-1) yr(-1) and a net soil deposition rate of 21.8 t ha(-1) yr(-1); hence, a net soil loss rate of 8 t ha(-1) yr(-1). This magnitude of soil erosion rate is higher than the acceptable rate in semiarid regions. Co-regionalization analysis and co-dispersive coefficients among the selected variables showed that only a small fraction of the variability in total nitrogen and soil organic carbon could be explained by soil redistribution and that the remaining might be the result of different management practices by local farmers.

Terrain attributes and soil properties are the major parameters to be considered for sustainable agricultural productivity and landscape management in dry farming. The objectives of this study were to predict the biomass, grain yield, and grain protein in wheat grown in hilly regions using an artificial neural network model and to identify the most important topographic attributes and soil properties affecting the variability of the wheat-yield components under rainfed conditions in the semiarid and hilly regions of western Iran. Wheat-yield data and surface-soil samples (0–30 cm depth) were collected from 1-m2 plots at 100 selected points. The sampling points were chosen in a stratified random manner on given geomorphic surfaces including summit, shoulder, backslope, footslope, and toeslope at the site. Primary and secondary terrain attributes were calculated using the digital elevation model. Selected soil physical and chemical properties were measured for the collected soil samples. Prediction...

Conversion of natural land resources into croplands, which is triggered by the rapid population growth, causes serious soil degradation. A loess hillslope located in eastern Golestan province of Iran was selected to study the role of deforestation and slope position on soil quality attributes. Surface (0–30 cm) and subsurface (30–60 cm) soil samples were taken from five slope positions (summit, SU, shoulder, SH, backslope, BS, footslope, FS and toeslope, TS) of forest (FO) and adjacent deforested cultivated land (DEF) in a factorial trial with completely randomized design. Ten pedons were also investigated and undisturbed soil samples were taken from different horizons for micromorphological studies. The texture of the original loess is silt loam. The soil textural class varies from silty clay loam in FO to silt loam in DEF, mainly due to the loss of finer particles as a result of soil erosion followed by deforestation and long-term cultivation. Mean weight diameter (MWD) of aggregates decreased following deforestation (0.88 mm compared to 1.49 mm in FO), as a result of considerable losses of organic carbon (OC) and breakdown of aggregates. Bulk density (BD) increased and soil infiltration rate decreased by about 50% in DEF. Reduction of annual organic matter input to soil as a result of deforestation and also rapid oxidation of organic matter in DEF were responsible for a significant decrease (>70%) in OC and total nitrogen (TN). Soil microbial respiration (SMR) also decreased significantly, following deforestation. Carbon, and N contents and population of fungi were significantly higher in all hillslope positions of the FO than the DEF. Changes in soil quality attributes were not significant in different slope positions of FO, which might be related to the stability of forest landscape. Effect of different slope positions on soil quality attributes was more pronounced in the DEF. The SH and BS were found as the most susceptible positions to erosion in DEF. Soils of the FO were mainly classified as Alfisols and Mollisols with evidences for clay illuviation compared to the weakly developed Inceptisols formed in the DEF. Micromorphological investigations revealed that the FO soils had strong granular and crumb microstructure with a high porosity indicating the presence of high amount of organic matter. The high microbial and faunal activity was confirmed through the presence of excremental pedofeatures in the topsoil of the FO. Lower organic matter and consequently microbial activity in the topsoil of the DEF have resulted in the massive microstructure with little porosity.