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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.

The present study was conducted to evaluate the effectiveness of combining proximal, and remote sensing with environmental variables for predicting USDA (United States Department of Agriculture) so...

Near-saturated hydraulic properties are the key parameters for water transport models in the unsaturated zone and essential for management practices. This study was conducted to compare efficacy of multiple linear regression (MLR) and hybrid method of genetic algorithm with artificial neural network (GA-ANN) for prediction of near-saturated soil hydraulic properties in Moghan plain, north-western Iran. The results of MLR analysis indicated that this method had low potential to predict near-saturated soil hydraulic properties in the study area, which only could explain 14–38% of variability in the studied properties. Otherwise, GA-ANN was much higher powerful that could explain about 35–80% of total variability in the mentioned properties in the study area. The results of sensitivity analysis suggest that soil particle size distribution, organic matter, electrical conductivity and relative bulk density were the most crucial with variety of priorities for explaining variability of the near-saturated soil hydraulic properties in the study area in the semiarid region. In overall, it was concluded that application of intelligent system using the easily available soil properties as predictors could provide reliable estimates of near-saturated soil hydraulic properties at the filed scale.

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 simultaneous effect of the environmental factors as determinants of soil invertebrate community structures is little understood. This study was carried out at two depths (0–10, 10–20 cm) in the forest ecosystem located in Golestan province, north of Iran. The abundance, diversity, and richness of twelve taxonomic groups of soil invertebrates were modeled using three groups of environmental attributes consisted of topographic attributes and vegetation indices, soil properties and tree diversity indices as predictors. Genetic algorithm was applied to identify and select the effective environmental attributes; the non-linear machine learning, random forest (RF), was employed to predict abundance, diversity, and richness of invertebrates and to determine the most important variables controlling the horizontal and vertical distribution of invertebrates in the forest ecosystem. Results showed that RF was an accurate model for predicting abundance (RMSE = 0.15), diversity (RMSE = 0.18) and richness (overall accuracy = 0.51, kappa = 0.34) of soil invertebrates at the first depth. Similarly, at the second depth, RF showed a good prediction for abundance (RMSE = 0.20), diversity (RMSE = 0.18) and richness (overall accuracy = 0.23, kappa = 0.12) of soil invertebrates. Horizontal distribution of invertebrates' abundance was affected by soil microbial respiration (Resp), land surface temperature (LST), Normalized Difference Vegetation Index (NDVI), Shannon index of trees and soil moisture, while vertical distribution of invertebrates' abundance was more related to soil organic carbon (SOC) and soil moisture. Variable importance analysis confirmed that horizontal and vertical types of distribution of Shannon index of soil invertebrates were affected by the same factors, including land temperature, NDVI, soil respiration and moisture. Also, soil moisture, soil respiration, NDVI and richness of trees had significant effects on horizontal distribution of soil invertebrates' richness, but silt, Normalized Ratio Vegetation Index (NRVI), soil respiration and moisture had positive effects on vertical distribution of richness. In conclusion, that digital soil mapping (DSM) model could be apparently considered as a powerful and economical method in biodiversity modeling. Other supplementary data such as invertebrates feeding web are also suggested as the input dataset for further investigations.

The present study was conducted to explore the relationships between magnetic measures and some heavy metals in some igneous rocks in north-western Iran. For this purpose, four major rocks including ultrabasic, basalt, granite and andesite were selected and totally 60 samples were collected. The collected samples were analyzed for magnetic measurements ( $$ \chi_{\text{lf}} $$ , $$ \chi_{\text{hf}} $$ , $$ \chi_{\text{fd}} $$ ) and heavy metals concentration (Fe, Cr, Cu, Zn, Co, Mn and Ni) by atomic absorption spectroscopy. Some samples were analyzed by XRD for iron mineral characterization. The results indicated that the highest and lowest $$ \chi_{\text{lf}} $$ and all measured heavy metals were found in ultrabasic (as a basic rock) and in granite (as an acidic rock), respectively. X-ray analysis confirmed the higher presence of magnetite/maghemite in basic rocks. Positive and significant correlations were found between magnetic susceptibility at low frequency and all heavy metals’ concentration except for Ni. Overall, magnetic susceptibility serves as a preliminary assessment of rock samples, providing rapid, non-destructive, economical and easy information about heavy metal concentration in igneous rocks in the study area.

In order to investigate the development of forest soils formed on loess, six representative modern soil pedons were selected along a precipitation gradient extending from eastern Golestan (mean annual precipitation, MAP = 500 mm) to eastern Mazandaran Provinces (MAP = 800 mm). Physiochemical, micromorphological and magnetic properties, as well as clay mineralogy of soils were studied using standard methods. Soils are mainly classified as Alfisols and Mollisols. Downward decalcification and the subsequent clay illuviation were the main criteria of soil development in all study areas. Pedogenic magnetic susceptibility of pedons studied varied systematically across the precipitation gradient in Northern Iran, increasing from 14.66 × 10−8 m3 kg−1 at the eastern part to 83.75 × 10−8 m3 kg−1 at the western margin of this transect. The frequencydependent magnetic susceptibility showed an increasing trend with rainfall as well. The micromorphological study of soils indicated that there is a positive relationship between climate gradient (increasing rainfall) and the micromorphological index of soil development (MISECA). The area and thickness of clay coatings showed an increasing trend with rainfall. Grain size analysis indicates that pedogenic processes are responsible for changing original grain size distribution of loess in our soils. The correlation achieved among modern soil properties and precipitation could be applied to the buried paleosols in the whole study area to refer degree of paleosol development and to reconstruct the paleoclimate.

We examine the potential of magnetic susceptibility measurements to discriminate different soil drainage classes in the Gandoman region, central Iran. Four soil drainage classes, comprising poorly drained (PD), somewhat poorly drained (SPD), moderately well drained (MWD) and well drained (WD), were identified, and a total number of 48 soil profiles were excavated and studied. The soil samples were collected from all studied profiles from the genetic horizons individually. Magnetic susceptibility was measured at both low (0.46 kHz) and high (4.6 kHz) frequencies. The crystallized and amorphous iron forms were also measured using citrate-bicarbonate-dithionite solution and oxalate-ammonium extracts, respectively. The highest magnetic susceptibility was observed in WD soils, whereas the lowest susceptibility was observed in PD soils. The results of the predictor models developed by discriminate analysis showed that the use of magnetic susceptibility and iron forms could correctly predict about 90.9, 78.6, 85.7 and 88.9% of all profiles in WD, MWD, SPD and PD classes, respectively. Overall, the results indicate that magnetic susceptibility could be applied as a marker for the discrimination of drainage classes in the study area. Magnetic susceptibility is thus a quickly accessible and low-cost indicator for soil drainage classes for landownerships and subsequent analyses.

The objective of this study was to characterize the morphological and magnetic properties of Sari loess-paleosol section in northern Iran for paleopedologic and paleoenvironmental interpretation. The section consisted of a modern soil (MS) and three paleosols (PS1, PS2, PS3) separated by loess layers (LS1, LS2 and LS3). Based on particle size distribution, clay mineralogy, carbonates distribution and size of secondary carbonates, pedogenic development of the soils was in order of PS3>PS2>PS1=MS. Presence of redoximorphic features in PS3 was attributed to alternate stagnic saturation due to local water or high precipitation. Dominance of smectite and vermiculite as well as large carbonated dolls in PS3 indicated suitable environment and sufficient time for pedogenic development. Magnetic properties (χlf and χfd%) were distinctly higher in MS, PS1 and PS2 when compared to loess layers. The Lowest magnetic properties values were observed in PS3 which can be the result of ferrimagnetic minerals destruction under hydromorphic conditions. The highest Fed content occurred in PS3, however, low χlf/Fed ratio indicated that majority of the iron minerals in PS3 are not magnetic. In conclusion, the particle size distribution, clay mineralogy and carbonates features were indicative of pedogenesis intensity, whereas, magnetic properties were useful to characterize the pedogenic environment.

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.

Increasing demand for food production, global change, and growing population are the enormous challenges in recent decades. Accurate soil maps and adequate models are indispensable tools to assist managers, scientists, and decision-makers in addressing these challenges. Legacy soil polygon maps at national and regional scales are available widely, but lack detail, and therefore effective methods such as digital soil mapping (DSM) are needed to disaggregate these maps. The objective of this study was to disaggregate a legacy 1:1,000,000 soil map by three methods of disaggregation: a supervised classification method (DSMART algorithm) and two unsupervised classification methods including fuzzy c-means (FCM) and k-means (KM) clustering in Borujen region, Chaharmahal-Va-Bakhtiari Province, Central Iran for both great group and subgroup Taxonomic levels. Although field validation indicated that the accuracy of the disaggregated soil maps was lower than that of the conventional soil map at both levels of Soil Taxonomy, disaggregated approaches produced more detailed soil maps when compared with the first, second, and third most probable soil classes of the conventional soil map. The higher overall accuracy of the conventional soil map was due to soil association units which consist of more than one soil taxonomic class. FCM and DSMART methods produced more accurate and detailed disaggregated soil maps than KM clustering algorithm at the great group and subgroup levels, respectively. We concluded that the decision on what method to use depends on the map, the level of available information (map detail), available expert knowledge, and the availability of the soil unit composition percentages in the soil map legend.

Knowledge about distribution of soil properties over the landscape is required for a variety of land management applications and resources, modeling, and monitoring practices. The main aim of this research was to conduct a spatially prediction of the top soil properties such as soil organic carbon (SOC), calcium carbonate equivalent (CCE), and clay content using digital soil mapping (DSM) approaches in Borujen region, Chaharmahal-Va-Bakhtiari province, central Iran. To achieve this goal, a total of 334 soil samples were collected from 0 to 30 cm depth. Three non-linear models including Cubist (Cu), Random Forest (RF), Regression Tree (RT) and a Multiple Linear Regression (MLR) were used to link environmental covariates and the studied soil properties. The environmental covariates were obtained from a digital elevation model (DEM) and satellite imagery (Landsat Enhanced Thematic Mapper; ETM). The model was calibrated and validated by the 10-fold cross-validation approach. Root mean square error (RMSE) and coefficient of determination (R2) were used to determine the performance of the models, and relative RMSE (RMSE%) was used to define prediction accuracy. According to the RMSE and R2, Cu and RF resulted in the most accurate predictions for CCE (R2 = 0.30 and RMSE = 9.52) and clay contents (R2 = 0.15 and RMSE = 7.86), respectively, while both of RF and Cu models showed the highest performance to predict SOC content (R2 = 0.55). Results showed that remote sensing covariates (Ratio Vegetation Index and band 4) were the most important variables to explain the variability of SOC and CCE content, but only topographic attributes were responsible for clay content variation. According to RMSE% results, it could be concluded that the best model is not necessarily able to make the most accurate estimation. This study recommended that more observations and denser sampling should be carried out in the entire study area. Alternatively, stratified sampling by elevation in homogeneous sub-areas was recommended. The stratified sampling probably will increase the performance of models.

In this study, pedotransfer functions were developed using magnetic measures and soil properties to evaluate the concentrations of some heavy metals (Cr, Co, Fe, Cu, Ni, Mn, and Zn) in soils developed on some igneous rocks (gabbro, diorite-gabbro, gabbro-diorite, monzodiorite, spilite basalt, granite, and porphyritic-granite) in Kurdistan province, western Iran. A total number of 105 samples from both rocks and the associated surface soils were taken; magnetic susceptibility at two frequencies, concentrations of selected metals, and physcio-chemical properties of soils were then measured. The highest concentrations of metals were obtained for Co, Ni, Fe, Mn, and Cu in soils developed on gabbro and for Zn and Cr in soils developed on porphyritic-granite and spilite basalt, respectively. Magnetic measurements in soils and associated rocks could explain 78, 74, 77, 72, 75, 68, and 69% of the total variability of Zn, Cu, Ni, Fe, Mn, Co, and Cr concentrations in the studied soils. Inclusion of soil physcio-chemical properties did not significantly improve the prediction. Therefore, it seems that magnetic measurements, as fast, non-destructive, and cost-effective tools, could solely be used to successfully predict heavy metals in natural ecosystems, especially in reconnaissance scale studies.

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

Due to the heavy costs of wind erosion control measures, the correct selection of technical methods is indispensable for a sustainable land management in arid and semiarid regions. Thus, th...

Temperature is a key factor that can affect soil properties and processes. Surface tension-viscous flow (STVF) theory is widely used to explain the temperature effects on soil hydraulic properties. We hypothesized that one of the reasons for deviation of measured soil water retention data from the STVF theory predictions is due to the temperature effects on soil structural stability (i.e., near-saturated pore size distribution [PSD]). Therefore, the objective of this study was to evaluate the effect of temperature on soil structural stability as characterized using the high energy moisture characteristic (HEMC) method in a wide range of arid and semi-arid soils of Iran. Aggregates of 28 soil samples were fast-wetted to mimic the natural conditions of soils during rainfall and irrigation. Four ambient temperatures (5, 10, 15 and 30 °C) were applied using an incubator during the wetting of aggregates, and after that HEMCs in the matric suction (h) range of 2 to 50 hPa were measured. Both van Genuchten (VG) and modified van Genuchten (MVG) models were fitted to the HEMC data and structural stability indices were calculated. The results generally showed that ambient temperature significantly altered soil PSD and structural stability, and as temperature increased: (i) an expansion of entrapped air initiated by the formation of microbubbles, was enhanced, (ii) water content at near full saturation h = 2 hPa (θ2hPa) decreased, and at h = 50 hPa (θ50hPa) it increased, leading to a reduction in the volume of drainable pores [VDP], and thus (iii) the soil structural index [SI] and slope at the inflection point of HEMC [Si] decreased, and (iv) amount of macropores (h, 2–12 hPa) was markedly decreased, however, micropores (h, 12–50 hPa) were not notably affected or were increased at high temperature due to intensive shifting of macropores into micropores. These findings imply that soil structure was damaged due to loosening effects of high temperatures on inter-particles bonds and increased repulsive forces between clay particles. Cluster analysis showed that the effect of ambient temperature on the structural stability was greatest in carbonate-rich soils with low clay and organic carbon (OC) contents and was least in the soils rich in OC. The results of this study could be important for projecting the effect of global warming and climate change on soil structure and erosion.

Post-fire erosion is a main concern to society because it has inflicted serious damages in managed ecosystems. In this study, the impacts of fire and time after fire events on soil erosion (as predicted using 137Cs technique) and on some soil chemical and physical properties were investigated in steep rangelands of western Iran. Three sites in rangelands with similar slope gradients and parent materials were selected, and within each site, the burnt (1, 5, and 10 years after the fire events) and the unburnt treatments were studied and soil samples were collected from five depths (0–2.5, 2.5–5, 5–10, 10–20, and 20–40 cm) with three replicates. The results indicated that soil organic matter (SOM), total nitrogen (TN), available phosphorus (Pava), available potassium (Kava), electrical conductivity (EC), and bulk density (BD) were significantly different between burnt and unburnt treatments for two times (1 and 5 years) after fire events. No significant difference by Duncan’s test was obtained for these properties between the 10 and 5 years after fire events. In addition, clay and sand contents and magnetic measures (χlf, χhf) were significantly different between burnt and unburnt treatments for all the three times after fire. The results of soil erosion by the 137Cs technique showed that profile distribution model (PDM) estimated the mean soil erosion rate of 38.9 and 23.02 Mg ha−1 year−1 in the three studied years in the burnt and the unburnt rangelands, respectively. Fire events increased soil erosion rate and altered soil physical, chemical, and magnetic properties in the studied steep rangelands. Overall, the results confirmed that the 137Cs technique could be used as a rapid and efficient model to determine soil degradation in the rangelands. The fire diminished soil organic matter and, subsequently, reduced aggregate stability, and increased soil erosion and degradation in the burnt rangelands. Hence, understanding historical contexts of fire occurrences is paramount to increase our capacity for ecological transformations and management in the face of the critical situation.

Clear-cutting of Oak forests and intensive cultivation on the hilly regions of the Zagros Mountains has led to plausible threats on the natural ecosystem and catastrophic floods in western Iran. This study was conducted to explore the effects of clear-cutting of natural forests in different slope positions on some soil chemical and physical properties as well as quantification of soil redistribution using magnetic susceptibility. Two adjacent sites, including natural forest and cultivated lands, were selected and a total of six transects at three soil depths (0–10, 10–20, and 20–30 cm) and a total of seventy-two soil samples were examined. Soil properties such as particle size distributions, bulk density (ρb), calcium carbonate equivalent (CCE), soil organic matter (SOM), and magnetic susceptibility at low and high frequencies were measured. Results indicated that clear-cutting and cultivation for 50 years significantly (p < 0.05) increased ρb and CCE and reduced SOM and TN. Moreover, magnetic susceptibility at four slope positions was significantly (p < 0.05) lower compared with the natural forest. Using a simple proportional model to estimate the soil loss or gain and comparing the mean χlf in any given point confirmed a high rate of soil loss in cultivated sites. In the forest soils, low soil loss in shoulder position and high rate of deposition in footslope and backslope positions were obtained. In general, it is revealed that magnetic measures could provide valuable estimates of mid-term soil erosion and sedimentation in the hilly region following land use changes.

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.

Land-use type under different topographic conditions and human activities affects soil development. We investigated the effects of land-use, topography and human activity on soil classification changes in the Toshan watershed in northern Iran. Seven representative pedons derived from loess parent materials were studied on different land-uses and topographic positions. The studied pedons in forest (FO) on backslopes and footslope were classified as Calcic Haploxeralfs and Typic Haploxeralfs, respectively. The soils in abandoned lands (AB) and orchards (OR), where formerly under natural forests, located on the shoulder and backslopes positions were classified as Calcic Haploxeralfs and Vertic Haploxeralfs, respectively. Well-developed argillic horizons as indicators for higher degrees of soil evolution were observed in more-stable areas under the natural forest or less disturbed areas. Clay lessivage through these soil profiles have led to formation of Typic or Calcic Haploxeralfs, while under croplands (CP) were classified as Typic Calcixerepts. Conversion of sloping deforested areas to CP along with inappropriate management have accelerated soil erosion, resulting in unstable conditions in which decalcification and formation of developed soils cannot occur. Paddy cultivation in flat areas has caused to reduced conditions and formation of Typic Haplaquepts. Because of unfavorable conditions for chemical weathering (e.g. lower water retention compared to more-stable areas) no vermiculite was detected in the CP. The results showed that evolution and classification of the studied soils were strongly affected by land-use type, topography and management.

Soil information is essential for sustainable management of ecosystems. In many parts of Iran, soil information is either not available or difficult to obtain. Therefore, when no detailed map or soil information is accessible in a region of interest, one way to obtain information is to extrapolate information from other parts having soil information. This study was conducted to determine whether - machine-learning extrapolation method extracted from the reference region, i.e. Zarand can estimate soil classes in the interest region, i.e. Bam and reduce the costs of soil mapping. To identify similarities between reference and interest regions, homology of soil forming factors was determined using Gower's similarity index. Then, the multinomial logistic regression was extracted from the reference region and applied into the interest region to estimate soil classes. Moreover, soil classes were predicted in the interest region using direct soil observations; finally, the accuracy of the soil maps obtained from both methods was assessed. Based on Gower index, the study regions, namely Bam and Zarand, were to some extent similar in term of soil forming factors. The results showed that although the soil map derived from the extrapolation process indicated appropriate spatial coverage of soil classes in the interest area, the resultant predictive map of calibrated-in process was slightly more accurate, i.e. higher κ, lower Brier scores. Acceptable levels of predictive accuracy (60%) were achieved using extrapolated model while costs simultaneously significantly lowered. This study put forward the view that the extrapolation method was quite useful in predicting soil classes within areas where soil mapping by calibrated-in method might be too costly or time consuming or where soil observations may not be sufficient. Nevertheless, this research encouraged us to use extrapolated method to fill the gaps in the present soil map of Iran and to apply it as the base map to increase and improve the efficiency of digital soil mapping.

We explore changes in magnetic susceptibility (χlf), and various forms of Fe and ferrimagnetic minerals after incubation by crude oil in four different textural soils. Four soil classes: clay, sandy, silt loam and sandy loam, were collected in central Iran and incubated with 0, 2.5, 5.0, 7.5 and 10% w/w of crude oil at 80% moisture of field capacity for 1, 3 and 6 months. After each period of incubation, various forms of Fe using extraction techniques, χlf and total petroleum hydrocarbon (TPH) were measured. In addition selected samples from the four soil types were analyzed by XRD for characterization of ferrimagnetic minerals. The results showed that all forms of iron and magnetic susceptibility increased significantly with increasing concentration of crude oil and incubation time. The highest dithionite extractable iron (Fed), χlf, as well as the ratio of Fed to oxalate extractable iron (Feo) were obtained for 10% w/w crude oil after 6 months incubation due to formation of ferrimagnetic minerals. The increase in the concertation of ferrimagnetic minerals (magnetite/maghemite) was verified by XRD. Significant positive correlations were obtained among χlf, Fed and TPH after 6 months of incubation. The results from this study confirm that magnetic methods can be used as a quick and cost-effective means for evaluation of TPH in polluted soils during the remediation, as compared to the chemical analyses.

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.

The effects of parent rock types on soil evolution in arid areas were studied in a sequence of soils, derived from different lithologies in the Kopet Dagh basin (Northeastern Iran) using micromorphology, clay mineralogy, magnetic susceptibility and physico-chemical properties. The selected parent rocks and associated soils were shale (Haplocalcids), claystone (Haplotorrerts), gypsiferous marl (Haplogypsids), limestones (Haplocalcids and Torriorthents), siltstone and sandstones (Torriorthents). The results showed that the properties and development of soils were mainly affected by grain size and mineralogy of parent materials. Soil magnetic susceptibility (χlf) variations were attributed to the types of parent material and pedogenic processes. Redistribution of calcite and gypsum in soil profiles and natural and pedogenic formation of ferrimagnetic minerals were responsible for χlf variations. The soils clay mineral origins were found to be mainly of inheritance from parent materials. Smectite was the dominant clay mineral of the most soils. Based on the micromorphological index of soil evolution (MISECA), the soils studied were categorized into weakly developed Orthents, weakly to moderately developed Aridisols (Gypsids and Calcids) and moderately developed soils including Calcids and Torrerts. The degree of microstructure development, alteration of weatherable minerals and calcitic features were the most important criteria influencing assessment of soil development degree by MISECA index. The vertic features were only observed in soils of claystone in which there were considerable amounts of clay and smectite. High amounts of gypsum and low smectite content were mainly responsible for the lack of vertic behavior in other fine grained soils derived from shale and marl.

Magnetic susceptibility is a fast, inexpensive and reliable technique for estimating and monitoring the anthropogenic contamination of soil with heavy metals. However, it is essential to determine the factors affecting magnetic susceptibility before applying this technique to environmental studies. The objectives of this study were to investigate i) the effect of parent materials and land use on the magnetic susceptibility and concentrations of Fe, Ni, Pb and Zn, and ii) capability of magnetic susceptibility as an indicator of anthropogenic heavy metals contamination of soil in Mashhad plain, northeastern Iran. One hundred seventy-eight composite surface soil samples (0–10 cm) were taken. The aqua-regia extractable concentrations of Fe, Ni, Zn and Pb were determined by atomic absorption spectroscopy. Magnetic susceptibility at low and high frequency (χ lf and χ hf ) were measured and frequency dependent susceptibility (χ fd ) was calculated. The average concentrations of Fe, Ni, Pb and Zn were 22,812, 61.4, 74.1 and 31.6 mg kg − 1 , respectively. The highest contents of Pb (69.1 mg kg − 1 ) and Zn (149 mg kg − 1 ) were observed in urban area. The highest concentration of Ni was 41,538 mg kg − 1 observed in the soils developed from ultramafic rocks. Magnetic susceptibility varied from 20.3 on marly sediments to 311.8 × 10 − 8 m 3 kg − 1 on ultramafic rocks. A positive strong correlation (P value r = 0.88) was obtained between Ni and χ lf . There were no significant relationships between Zn and Pb with χ lf, therefore it seems that magnetic susceptibility has not been affected significantly by anthropogenic activities which enhanced Pb and Zn concentrations in urban soils. The results indicated that magnetic susceptibility was mainly controlled by Ni containing minerals with lithogenic origin. Therefore, in the soils studied, magnetic susceptibility could not be employed as indicator of anthropogenic contamination of soil with heavy metals.

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.

Soil organic carbon (SOC) storage is a basic measure used to study soil productivity, hydrology and the balance among greenhouse gases. Variation of SOC is controlled by environmental factors such as land use and topography. Toshan watershed located in northern Iran was selected to study the effects of different land uses i.e. forest (FO), cropland (CP), orchard (OR) and abandoned land (AB) on different slope gradients and aspects on SOC both in surface (0–30 cm) and subsurface (30–100 cm) layers. A total of 364 soil samples plus 1638 undisturbed ones were collected from two soil layers in 182 sampling sites. Results showed that the surface 30 cm soil layer was solely responsible for 54.8% of SOC density. On average, FO with 22.84 kg m− 2 had the highest SOC density in 0–100 cm layer. Deforestation and agricultural activities have resulted in a significant 48.2% decrease of SOC density in 0–30 cm soil layer. North facing slope (N) aspect and also flat area of all land uses had the higher SOC density compared to east (E) and west facing slope (W) aspects in this subhumid region in 0–100 cm layer. Generally, in the upper 100 cm soil layer of deforested lands, gentle and moderate slopes had higher SOC density than steeper slopes. There was a positive significant correlation between SOC density and clay content. The largest amount of SOC storage was observed in the surface 30 cm layer accounting for 74,907.94 Mg (54.4% of total SOC storage), indicating the important key role of topsoil in conserving SOC. FO with one-third proportion of the total area stored the largest amount of SOC (39,325.55 Mg; 52.5%) in surface layer. In conclusion, protection of forest lands is higher important to increase SOC storage. Agricultural activities on steep E and W aspects in deforested lands must be reduced or prohibited. The subsoil has almost the same contribution to SOC storage and therefore should be carefully considered for management measures. Generally, interaction between environmental factors on storing SOC and also rate of carbon loss to the atmosphere was significant.

Petrosalic horizon (cemented by soluble salts) is identified as a root-restricting layer in the arid and semi-arid regions. The knowledge of the variability of cemented layers within the soil profiles provides valuable information to decision-makers for agricultural and/or engineering activities. This study was conducted to evaluate the potential of ground-penetrating radar in detecting petrosalic horizon within the soil pedon in an arid area (Kerman Province, southeastern Iran). The measurements were performed using an impulse ground-penetrating radar system with a centre frequency of 250 MHz along ten parallel transects of 100-m length and 10-m distance between two consecutive transects at two sites. The results of the soil stratigraphy indicated the presence of petrosalic horizons at 12- to 36-cm depth. The ground-penetrating radar images showed patterns corresponding to these soil horizons. The interpreted radargrams were quite consistent with the findings of the soil pedons. It is, therefore, suggested that ground-penetrating radar could be employed to detect the boundaries between different soil horizons in the arid areas with various degrees of conductivity. This, in turn, helps differentiate soilmapping units and improve the reliability and accuracy of digital soil maps. The results confirmed that ground-penetrating radar could be used as a rapid, efficient and non-destructive tool providing highresolution and continuous visualisation of the soil horizons to detect the presence and depth of petrosalic horizons. Such information could be crucial in making decisions in agricultural practices (e.g., creating gardens) and engineering activities (e.g., constructing roads and buildings).

A comprehensive understanding of factors that influence soil properties and soil biota, such as tree composition, is essential for the management and conservation of biodiversity. This study was conducted to explore the effects of tree composition on a number of properties and invertebrates of soil in the forest ecosystem of northern Iran. Eleven plots were selected differing in the composition of the following tree species: Persian ironwood (Parrotia persica V.), maple (Acer velutinium B.), and hornbeam (Carpinus betulus L.). In each plot, we measured pH, particle size distribution, electrical conductivity (EC), calcium carbonate equivalent (CCE), organic carbon (OC), total nitrogen (TN), and soil microbial respiration rate (Resp), at depths of 0–10 cm and 10–20 cm. In addition, abundance, Shannon index, richness, and evenness of the soil invertebrates, at both depths, were determined. Accordingly, a total number of 636 and 312 invertebrates were recorded from the topsoil and subsoil, respectively. EC, pH, CCE, C/N, silt, sand, abundance, and richness of the soil invertebrates had a significant correlation with the tree abundance. The soil properties significantly differed among the plots having different tree species composition (P ≤ 0.05). The depths of the soil also had significant effects on the soil properties. The study revealed that the plot including single tree species (maple) had the highest invertebrate abundance (24.67), while the highest OC (6.06%), the greatest TN (0.40%), and the uppermost soil resp. (434 mg CO2 kg−1 day−1) were observed in the Persian ironwood-maple, which has two tree species. In addition, hornbeam-maple-Persian ironwood plot had the highest Shannon index (1.48) and the greatest richness of invertebrates (6.17), while the Persian ironwood-hornbeam-maple plot had the highest invertebrate evenness. However, the hornbeam plot had the lowest abundance, Shannon index, and richness of invertebrates (0.93). Overall, the results revealed that soil properties and soil invertebrate communities were affected by the dominant tree species, and their effects could be lowered or intensified by varying the proportion of different tree species.

This study was performed to determine the variability of magnetic susceptibility to discriminant soil great groups in an arid region including six soil great groups in the Borujen Region, Chaharmahal-Va-Bakhtiari Province, central Iran. Seventy-three soil profiles were described and classified in six soil great groups (i.e., Calcixerepts, Haploxerepts, Haploxeralfs, Xerorthents, Calcixerolls, and Haploxerolls) according to the Soil Survey Staff. Soil organic carbon (OC), calcium carbonate equivalent (CaCO3), pH, electrical conductivity (EC), dithionite extractable iron (Fed), oxalate extractable iron (Feo), iron pyrophosphate (Fep), magnetic susceptibility at low and high frequencies (χlf and χhf), and frequency-dependent (χfd) were measured in the laboratory for the soils selected from control sections. Among the six soil great groups, Haploxerolls showed the highest χlf and χhf values, lowest Feo, highest Fed and Fed-Feo, as well as the highest Fep. However, all stated features showed an inverse trend for Haploxeralfs great group. The results of discriminant analysis indicated that the accuracy of 54.11% was achieved to discriminant soil great group for the combination of magnetic measures, different form of Fe, and soil physicochemical properties in the control section. Therefore, it can be concluded that magnetic susceptibility could be used as a fast and nondestructive technique for discrimination of soil great groups in the present case.

Drought is an unavoidable abiotic stress that restricts the growth rate of many crops in arid regions. Potassium (K) is an essential nutrient and the most abundant cation in the plant, which is critical to decreasing the adverse effects of drought stress. K is supplied naturally by mica and other K-bearing primary minerals in the soil. This paper aimed to investigate the ability of barley roots (without interaction with soil organisms) to release K from phlogopite, to utilize the released K, and to transform this mineral to other minerals under drought stress conditions. A greenhouse pot experiment involving no soil organism was conducted in a completely randomized design with a factorial arrangement and three replications. The growth medium was a mixture of quartz sand (as filler) and three levels of phlogopite as a K-bearing mineral. During the five-month growth period, the test plant was supplied with either a complete nutrient solution or a K-free one. Three levels of drought stress were applied including no drought stress, moderate drought stress, and severe drought stress. At the end of the growth period, both shoot and root of the plant were separated and, their total K content was extracted and then determined by a flame photometer. Besides, the mineralogy of the clay fraction of the samples taken from the rhizosphere was studied using the XRD technique. The results indicated that drought stress reduced the K uptake of the plant. However, the application of K as either phlogopite mineral or K containing nutrient solution could somehow ameliorate the negative effects of drought stress. In K-free treatments, the amount of K released from phlogopite mineral was higher than in the treatments which received a complete nutrient solution. The higher level of added phlogopite did not necessarily supply more available K for plant, indicating that the application of a low amount of phlogopite was sufficient to supply the level of K needed. Results also stated that phlogopite was partly biotransformed to vermiculite due to the uptake of K by the plant roots. The rate of phlogopite transformation to vermiculite was found to be inversely correlated with the amount of phlogopite added. It was also negatively correlated with the level of drought stress applied. In general, it appears that water stress dramatically influences the K release rate from mineral and its biological weathering rate. Besides, the negative effects of drought stress could be managed by adding K-bearing minerals to growth media.

Soil presents a high vulnerability to the environmental degradation processes especially in arid and semiarid regions, requiring research that leads to its understanding. To date, there are no detailed soil maps covering a large extension of the Middle East region, especially for calcium carbonate content. Thus, we used topsoil data (0–20 cm) from more than 5000 sites for mapping near 3,338,000 square km of the Middle East. To do this, we used covariates obtained from remote sensing data and random forest (RF) algorithm. Around 65% of the soil information was acquired from Iranian datasets and the remaining from the World Soil Information Service dataset. By using 30 covariates layers—soil, climate, relief, parent material and age features— we then trained and tuned RF regression models—in R software— and used the optimal ones (according to the minimum root mean square error) for making spatial predictions—within Google Earth Engine— of topsoil attributes and associated uncertainties at 30 m resolution. All covariates were relatively important for mapping topsoil attributes, ranging from 4% to 98%. Annual precipitation, temperature annual range and elevation were the most important ones (>31%). Overall, the prediction models trained by RF explained around 40–66% of the variation present in topsoil attributes. The ratio of the performance to interquartile distance (RPIQ) ranged between 1.59 and 2.83, suggesting accurate models. Our predicted maps indicated that sandy and loamy soils with poor organic carbon levels, alkaline reaction and high calcium carbonate content were widespread in middle eastern topsoils. Our framework overcomes some limitations related to high computational requirements and enables accurate predictions of topsoil attributes. Our maps presented correct pedological correspondences and had realistic spatial representations and interesting levels of uncertainties.

This study was conducted to derive the relationships of soil shrinkage parameters and indices with soil and environmental variables in calcareous soils. Ninety nine undisturbed clods were collected from surface soils in hilly regions of Cherlgerd, western Iran. Soil shrinkage curve was measured based on Archimedes ' principle, by covering the clods with an acrylic resin. The shrinkage curve data were modeled using Peng and Horn (2005) model. The model's fitting parameters and several shrinkage indices (i.e. relative void ratio changes, mean slopes at various shrinkage zones, coefficient of linear extensibility, and total and relative shrinkage capacities) were predicted using multiple linear regression models by including soil properties (pedotransfer functions, PTFs) and by combination of soil properties and environmental variables (soil spatial prediction functions, SSPFs) as inputs. The results showed that, on average, the structural, proportional, residual and zero shrinkage zones comprised 17.2, 66.2, 15.2 and 1.4% of total shrinkage for the studied soils. The shrinkage capacity (ShC) and relative shrinkage capacity (Δ e total-rel ) varied, respectively, in the ranges 0.204–0.641 and 0.288–0.589 in the studied soils. While clay fraction increased the ShC and Δ e total-rel , organic matter had a diminishing effect on the Δ e total-rel . An extended structural zone was observed in fine-textured soils, presumably due to greater aggregation. Volume change in the structural shrinkage zone was greater in weakly-structured calcareous soils because carbonates would minimize resistance of aggregates against the shrinkage forces. PTFs could explain 12–48% of variability of the model's parameters, and the inclusion of topographic attributes (i.e. SSPFs) significantly increased R 2 values. Developed PTFs could explain 11–41% of variability of the shrinkage indices. The particle size fractions and relative bulk density were identified as most important soil properties for the prediction of shrinkage indices. Overall, the use of SSPFs by including topographic attributes such as dispersal area, elevation, surface curvature and plan curvature and normalized difference vegetation index (NDVI) could improve the performance of the prediction functions for soil shrinkage indices.

Loess accumulation and paleosol formation are important Quaternary geoarchives in northern Iran. Two loess-paleosol sequences at Mobarakabad and Aghband were examined using soil morphology and micromorphology, mineralogy, magnetic susceptibility (MS), free Fe oxides and calcium carbonate equivalent. The loess-paleosol sequences provide a record of changes in paleo-rainfall in the northern Iran. Micromorphological and MS differences between the loess and paleosols in the sections reflect changes in climate. The different behaviors of magnetic susceptibility between Aghband and Mobar-akabad loess are mainly produced by their different pedogenic environments, topography and climatic conditions. As a result, the MS at the Mobarakabad section is much higher than at Aghband. Paleoeco-logical reconstructions coupled with magnetic depletion in paleosols at Mobarakabad suggest a wetter climate dominating during the formation of the paleosols.

Geochemical, mineralogical, and micromorphological characteristics of soils and their relevant parent rocks including loess, ignimbrite, sandstone and limestone were investigated to identify the soil-parent material uniformity and the weathering degree of soils in Golestan Province, northern Iran. Highly developed Calcixerolls and moderately developed Haploxerepts were formed on loess and limestone, respectively. In contrast, the soils formed on ignimbrite and sandstone were non-developed Entisols. Illite was the dominant clay mineral found in ignimbrite and sandstone in both the A horizon and parent material. In loess derived soils however, smectite was dominant especially in the Bt horizon compared to its parent material indicating partly to its pedogenic formation. In limestone, illite and vermiculite were dominant both in the A and C horizons. Ti/Zr ratio proved that the studied soils were closely related to their underlying parent materials geochemically.Chemical index of alteration (CIA), micromorphological index of soil development (MISECA), smectite/illite+chlorite ratio and magnetic susceptibility were applied to investigate the degree of soil development. Results showed that the most and the least developed soils were those formed on loess deposits and limestone, respectively. Application of the different geochemical and pedogenetic approaches was proved to be useful in identifying the relevance of soils to their underlying parent materials and also their degree of development.

This study used discriminant analysis to determine the efficacy of magnetic measures for discriminating between four soil moisture regimes in northern Iran. The study area was located on loess deposits and loess-like soils containing similar parent material. Four soil moisture regimes including aridic, xeric, udic, and aquic were selected. A total of 25 soil profiles were drug from each regime and composite soil samples were collected within the moisture control section. A set of magnetic measures including magnetic susceptibility at low (χ lf ) and high (χ hf ) frequencies, frequency-dependent magnetic susceptibility (χ fd ), saturation isothermal remnant magnetization (SIRM), and isothermal remnant magnetization (IRM 100 mT, IRM 20 mT ) were measured in the laboratory. Dithionite citrate bicarbonate (Fe d ) and acid oxalate (Fe o ) contents of all soil samples were also determined. The lowest and highest χ lf and χ hf were observed in aquic and udic moisture regimes, respectively. A similar trend was obtained for Fe d and Fe d –Fe o . The significant positive correlation between Fe d and SIRM (r = 0.60; P

This study was conducted to estimate soil clay content in two depths using geophysical techniques (Ground Penetration Radar—GPR and Electromagnetic Induction—EMI) and ancillary variables (remote sensing and topographic data) in an arid region of the southeastern Iran. GPR measurements were performed throughout ten transects of 100 m length with the line spacing of 10 m, and the EMI measurements were done every 10 m on the same transect in six sites. Ten soil cores were sampled randomly in each site and soil samples were taken from the depth of 0–20 and 20–40 cm, and then the clay fraction of each of sixty soil samples was measured in the laboratory. Clay content was predicted using three different sets of properties including geophysical data, ancillary data, and a combination of both as inputs to multiple linear regressions (MLR) and decision tree-based algorithm of Chi-Squared Automatic Interaction Detection (CHAID) models. The results of the CHAID and MLR models with all combined data showed that geophysical data were the most important variables for the prediction of clay content in two depths in the study area. The proposed MLR model, using the combined data, could explain only 0.44 and 0.31% of the total variability of clay content in 0–20 and 20–40 cm depths, respectively. Also, the coefficient of determination (R 2 ) values for the clay content prediction, using the constructed CHAID model with the combined data, was 0.82 and 0.76 in 0–20 and 20–40 cm depths, respectively. CHAID models, therefore, showed a greater potential in predicting soil clay content from geophysical and ancillary data, while traditional regression methods (i.e. the MLR models) did not perform as well. Overall, the results may encourage researchers in using georeferenced GPR and EMI data as ancillary variables and CHAID algorithm to improve the estimation of soil clay content.

Spatial information on soil salinity is increasingly needed for decision making and management practices in arid environments. In this article, we attempted to investigate soil salinity variation via a digital soil mapping approach and genetic programming in an arid region, Chah-Afzal, located in central Iran. A grid sampling strategy with 2-km distance was used. In total, 180 soil surface samples were collected and then analyzed. A symbolic regression was then adopted to correlate electrical conductivity (ECe) with a suite of auxiliary data including predicted maps of apparent electrical conductivity (vertical: ECav and horizontal: ECah), Landsat spectral data and terrain attributes derived from a digital elevation model. The accuracy of the genetic programming model was evaluated using root mean square error (RMSE), mean error (ME), and coefficient of determination (R2) based on an independent validation data set (20% of database or thirty soil samples). In general, results showed that ECah had ...

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

Demand for accurate soil information is increasing for various applications. This paper investigates the history of soil survey in Iran, particularly more recent developments in the use of digital soil mapping (DSM) approaches rather than conventional soil mapping (CSM) methods. A 2000–2019 literature search of articles on DSM of areas of Iran in international journals found 40 studies. These showed an increase in frequency over time, and most were completed in the arid and semi-arid regions of central Iran. Artificial Neural Networks (ANN), Random Forests (RF), and Multinomial Logistic Regression (MnLR) were the most commonly applied models for predicting soil classes and properties and ANN performed best in most comparative studies. Given the scale of inquiry of most studies (local or regional), quantitative environmental variables such as terrain attributes and remote sensing data were frequently used whereas qualitative variables such as geomorphology, geology, land use, and legacy soil maps were rarely used. The literature review of CSM showed that this method is incapable of defining the spatial distribution of soils and also provides a lower accuracy than DSM method. This review has identified research gaps that need filling. In Iran, coherent national scale DSM with consistent methodology is needed to update legacy soil maps, and to apply DSM in forestlands, hillslopes, deserts, and mountainous regions which have largely been ignored in recent DSM studies. This review should also be useful for producing more local and regional digital soil maps more rapidly as it helps show which covariates and mathematical methods have been best suited to this scale of DSM in Iran.

This study was conducted to evaluate the efficacy of the ensemble machine learning model to predict the spatial variation of soil organic carbon (SOC) concentration in a deciduous forest ecosystem in northern Iran. To do this, a total of 153 soil samples by applying regular systematic sampling grid at two depths (0–10 and 10–20 cm) were collected. Two scenarios through digital soil mapping (DSM) were considered to establish the predictive models for estimating SOC including (i) combination of selected topographic attributers (T), remotely sensed data (R), and soil properties (S) (TRS) and (ii) combination of topographic attributes and remotely sensed data (TR). The ensemble model for predicting of SOC was consisted of six machine learning algorithms: partial least squares regression (PLSR), generalized linear model (GLM), recursive partitioning and regression trees (rpart), support vector machines (SVM), random forest (RF) and k-nearest neighbors (kNN). The 10-fold cross-validation with three replications was employed to evaluate the performance of models by root mean square error (RMSE) and the coefficient of determination (R2). The results showed that SOC was varied from 2.05 to 13.16% with a mean of 5.67% in first depth (0--10 cm) and from 1.56 to 9.56% with a mean of 3.99% in second depth (10–20 cm). According to the RMSE and R2 results ensemble machine learning model, first scenario (TRS) showed higher performance (higher R2 and lower RMSE) than second scenario (TR) for prediction of SOC in first depth (R2 = 0.74, RMSE = 0.78%) and second depth (R2 = 0.65, RMSE = 0.90%). Also, in the best scenario (TRS) for individual models, results of validation revealed that GLM, SVM and RF for the first depth and PLSR, GLM, SVM, and RF for the second depth were the most accurate machine learning algorithms in ensemble modeling based on RMSE. Our finding indicated that soil properties had an important contribution to the spatial variability of SOC in the studied forest soil. Moreover, topographic attributes and vegetation indexes were found to be auxiliary attributes in the modeling of SOC and could use for quick and cost-effective assessment of SOC concentration in order to management practices in forest soils.

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 spatial variability of the hydraulic properties of near-saturated soil was investigated in Moghan plain, northwestern Iran. To include all types of cultivated crops and examine the effects of the distance, through nested grid design, 212 sites were selected, with a distance interval of 200, 400, and 800 m. Soil samples were collected from 0- to 20-cm depth for determination of selected soil chemical and physical properties in the laboratory. A tension infiltrometer was employed to consecutively measure the unsaturated infiltration at matric suctions (hm) of 2, 5, 10, and 15 cm. The infiltration data was modeled using Wooding’s analytical method, and best-fit values of Gardner’s parameters of saturated hydraulic conductivity (Ks) and macroscopic capillary length (λc) were derived. The data was also modeled using numerical method in DISC software, and the van Genuchten parameters (θs, α, n, and Ks(DISC)) were optimized. The results of the study revealed that Ks(DISC) had the highest coefficient of variation (CV), i.e., 212%, among the hydraulic parameters of the soil; shape parameter n, conversely, had the lowest CV. Once the means of the hydraulic parameters were compared, no significant differences were found in the hydraulic parameters among the cultivated crop types. To map the spatial variability of soil parameters by means of ordinary kriging, a spherical model, chosen based on mean error (ME) values and root-mean-square error (RMSE), was used. Based on the semi-variogram parameters, i.e., range, slope, and nugget to sill ratio, the spatial distribution of soil properties was not consistent in the studied area. The lowest and largest ranges of spatial dependency were 1021 and 4500 m for unsaturated hydraulic conductivities at matric suctions of 15 and 2 cm (K15 and K2), respectively. The spatial dependencies of most variables under investigation were moderate to strong. Overall, the findings of this study put forward the view that the variability of soil hydraulic parameters might be controlled conjointly by variability in intrinsic soil properties, namely, particle size distribution, and bulk density, and several management practices in the plain have paramount importance. Policy makers and farm managers can effectively make use of the maps made in this study to manage their in site-specific irrigation practices.

This study was conducted to explore the relationships between magnetic susceptibility and some soil heavy metals concentrations in various particle sizes in an industrial site, central Iran. Soils were partitioned into five fractions (

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.

In the present study, artificial neural networks (ANNs) were employed to develop models to predict soil organic carbon density (SOCD) at different depths of soil layers. Selected environmental variables such as vegetation indices, soil particle size distribution, land use type, besides primary and secondary terrain attributes were considered as the input variables. According to the results, the ANN models explained 77% and 72% of the variability in SOCD at soil layer depths of 0–20 cm and 20–40 cm, respectively, at the site studied. Sensitivity analyses showed that the most considerable positive contribution of variables for predicting SOCD included the land use type, normalized difference vegetation index (NDVI) > normalized difference water index (NDWI) > silt > clay > elevation in the 0–20 cm soil layer. On the other hand, for the 20–40 cm soil layer, the land use type following NDVI > NDWI > clay > silt were identified as the most powerful predictive factors. In the Deylaman region, in both soil layer...

This study was performed to explore the variability of magnetic susceptibility between soil great groups in an arid region including seven soil great groups of south-eastern Iran. In the studied area 126 soil profiles were described and classified in seven soil great groups according Soil Survey Staff. Soil samples were collected from control sections and some soil properties such as organic matter (OM), calcium carbonate equivalent (CCE), gypsum, pH, electrical conductivity (EC), dithionite extractable iron (Fed), oxalate extractable iron (Feo), magnetic susceptibility at low and high frequencies (χlf and χhf) and frequency-dependent magnetic susceptibility (χfd) were measured in the laboratory. The lowest χlf was obtained for Torriorthents and Torripsamments soil groups with lowest soil development degree. The highest magnetic susceptibility was observed for Calcigypsids and Haplocalcids soil great groups. Similar trend to χlf was observed for seven studied soil great groups in respect to Fed and inversely trend to χfd. Correlation analyses revealed that Fed was significantly related with χlf and low value of χfd in all soil profiles revealed the formation of stable single domains (SSD) due to pedogenic processes. Achieved negative correlations between χlf and carbonates, organic matter and gypsum confirmed diamagnetic behaviors of these soil components. The results of discriminant analysis indicated that combining magnetic measures such as magnetic susceptibility and dependent susceptibility and iron forms could not successfully predict all soil great groups in the studied area and average 57% accuracy was achieved.