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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.

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.

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.

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.

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.

Soil surface shear strength is an important input parameter in the process-based soil erosion models, but its direct measurement at the watershed scale is difficult, time-consuming and costly. This study was conducted to predict in situ shear strength of the soil surface using multiple-linear regression (MLR). The land use impact on the surface shear strength was examined as well. A direct shear box was constructed to measure in situ shear strength (cohesion, c and angle of internal friction, φ) of the soil surface in the Semirom region located in Isfahan province, central Iran. The shear device consisted of a circular shear box (with 10 cm internal diameter and height of 1 cm), an S-shaped load cell for measuring horizontal (shear) stress and an electric motor for applying the shear stress. The normal stress acting on the failure surface was adjusted by adding weights on the shear box. Soil surface shear strength was determined using the shear box at 100 locations under three land use systems of grassland, irrigated farming and dryland farming. Soil particle size distribution (clay, silt, sand and fine clay), organic matter content (OM), carbonate content, bulk density and gravel content were determined as predictors of the surface shear strength. Normalized difference vegetation index (NDVI) was also calculated using satellite images. The MLR was employed to predict the shear strength (i.e. c and φ) using two groups of input variables: i) easily-available soil properties (pedotransfer functions, PTFs) and ii) easily-available soil properties and NDVI (soil spatial prediction functions, SSPFs). A strong negative correlation (r = − 0.72, p

We evaluated the suitability and performance of boosted regression trees (BRT) as a potential technique for soil mapping using a limited point dataset in an arid region of Iran. The model was applied using two approaches: logistic-BRT as an indirect approach and multiclass-BRT as a direct approach to produce soil class maps. In indirect prediction, the occurrence of relevant diagnostic horizons was first mapped and various maps were then combined for a pixel-wise classification by combining the presence or absence of diagnostic horizons. To allow combination of the indicator maps for classification into great groups, a decision tree was defined for linking the occurrence of diagnostic horizons to a soil great group. In direct prediction, the dependent variable was the great group itself and; therefore, the probability distribution of the soil great groups was directly predicted. Auxiliary data used in this study to represent predictive soil forming factors were terrain attributes and Landsat data as quantitative variables and a geomorphology map as categorical variable. To assess the added value of the geomorphology map, the most laborious auxiliary variable to obtain, the BRT-performance of 2 data situations was compared: (i) using only the DEM and remote sensing covariates and (ii) additionally using the geomorphology map. Results showed that the geomorphology map contributed importantly to the prediction accuracy. When it was removed from the predictors, the prediction accuracy strongly decreased. The maximum reduction in predictive performance of both indirect and direct models occurred in the absence of geomorphology map, particularly for soil classes whose presence has direct relationship with geomorphic surfaces. Generally, poor predictions seemed to be mainly due to low sample size, high variability of ancillary predictors and the inability of the geomorphology map to differentiate the strata in detail. In most predictions, the purity was better for the direct model. The indirect method predicted a high probability of salic horizon in playa landform, gypsic horizon in gypsiferous hills and calcic horizon in alluvial fans. The indirect method could provide an insight into the causes of errors in prediction at the level of diagnostic horizons, which could help in selecting better covariates.

Soil management and land use via the effects on soil characteristics can indirectly change soil hydraulic properties. This study was conducted to investigate the impacts of different management and land uses on the near-saturated soil hydraulic properties in Koohrang region of central Zagros, Chaharmahal-va-Bakhtiari province, Iran. Major land uses in the area were pasture, dryland farming, irrigated farming and fallow. Unsaturated water infiltration was measured at the consecutive inlet matric suctions (h) of 2, 5, 10 and 15 cm using a tension infiltrometer at 100 locations (40 in pasture, 33 in dryland farming, 15 in irrigated farming and 12 in fallow). 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 calculated. A completely random design was used in which soil texture and land use system were analyzed separately. The averages were compared by the least significant difference test at 5% of probability. The λc and unsaturated/saturated hydraulic conductivity [K(h)] values were not significantly affected by soil textural classes. However, the land use systems significantly affected soil hydraulic parameters (K(h), steady-state flux, q(h), and sorptivity, S(h)), and the differences became greater with decreasing h (towards saturation). The averaged K(h), q(h) and S(h) values were lower in pasture soils when compared with the cultivated lands which were associated with lower organic matter and higher degree of compactness of pasture soils due to overgrazing. The λc was significantly greater in the fallow and pasture land uses than in dryland farming, and intermediate value belonged to the irrigated farming. For all of the land use systems, minimum values of S(h) were observed at h = 10 cm. The S(h) decrease with h decrease in the range 15–10 cm might be partially associated with swelling of smectite clays, reducing the size of soil pores. The dryland farming increased water infiltration and unsaturated hydraulic conductivity when compared to the other land uses. In this region, averages of soil hydraulic properties are mainly influenced by soil structure and management practices rather than by intrinsic soil properties like texture. A small change in degree of compactness in the swelling soils would significantly influence water infiltration and hydraulic properties, indicating structural susceptibility of the soils to management practices. Therefore, the degree of compactness (e.g. relative bulk density) could be considered as an important index of land use management.

In order to contribute to the understanding of carbonate enrichment and clay illuviation in loess-derived soils of subhumid to humid regions, the development of soils was studied along a climate gradient with xeric and udic soil moisture regimes (SMR) and thermic and mesic soil temperature regimes (STR), respectively, in the Golestan Province, Northeastern Iran. Six representative pedons along a climate gradient were investigated. Soils were classified mainly as Hapludalfs and Haploxeralfs. Stability of the geomorphic surface under forest vegetation associated with high leaching conditions has provided appropriate conditions for decalcification followed by clay migration through the profile and formation of argillic horizons in all the studied soils. Clay content of the Bt horizons, soil organic carbon concentration of the A horizons, and depth of the Bk horizons increased significantly with increasing precipitation and decreasing temperature. There was a considerable decrease in silt content with soil development. The main pedofeatures observed in the Bt horizons were clay coatings and decalcified zones. Nodules, coatings and hypocoatings were the main calcitic pedofeatures observed in the Bk horizons. Occurrence and preservation of clay coatings were more pronounced in the udic regions with illite and vermiculite as the dominant clay minerals. Type of clay minerals, shrink/swell properties, and precipitation rate are factors affecting the abundance and preservation of clay coatings. In the strongly developed horizons of the udic SMR, the occurrence of vermiculite clay minerals could reduce the shrink/swell potential and increase the amount of clay coatings. The presence of crystallitic b-fabrics and the high carbonate contents (CaCO 3 ) in the lower horizons (Bk) were mainly related to decalcification processes under descending water flow in the overlying horizons.