Erratum to: Topsoil Carbon Stock and Soil Physicochemical Properties in Riparian Forests and Agricultural Lands of Southwestern Iran

Eurasian Soil Science - An Erratum to this paper has been published: https://doi.org/10.1134/S1064229321300013     

Soil physico-chemical,mineralogical, and micromorphological changes due to desertification processes in Yazd region,Iran

Nine pedons and 30 surface samples were taken, described, and analyzed to investigate the effect of desertification on soil quality indices, mineralogical, and micromorphological properties of three regions (desert, semi-desert, non-desert) in central Iran. The results showed that moving from a non-desert to a desert area decreased soil quality indices such as organic matter (from 0.5% to 0.2 %) and microbial respiration (from 1.59 to 0.11 mgCO₂ g^?1 soil). By contrast, increases were recorded for bulk density (1.2 to 1.9 Mg m^?3) and soil salinity (5 to 19 dS m^?1). Soil in the non-desert area had a sandy texture that led to good drainage conditions, while soil in the desert area had a clayey texture that led to poor drainage conditions. The mineralogical assemblage of the soil was approximately the same for all the three areas, with illite and chlorite being the main minerals. Kaolinite and smectite were present in lower concentrations. The micromorphological evidence showed that the main microstructure of the non-desert region was granular, probably because of the higher organic matter content in this area. In the desert region, platy structure was abundant. Overall, a more thorough perception of the desertification process was gained by studying its impact on soil properties.     

Effect of interceptor drainage on phosphorus transport and soil chemical characteristics under different cultivation conditions

Paddy and Water Environment - Drainage is a profitable water management operation in waterlogged soils, particularly in hot, arid environments where waterlogging is caused by irrigation and salts...     

Prediction of soil surface salinity in arid region of central Iran using auxiliary variables and genetic programming

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 (EC_e) with a suite of auxiliary data including predicted maps of apparent electrical conductivity (vertical: EC_av and horizontal: EC_ah), 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 EC_ah had the strongest influence on the prediction of soil salinity followed by salinity index wetness index, Landsat Band 3, multi-resolution valley bottom flatness index, elevation, and normalized difference vegetation index. Furthermore, results indicated that the genetic programming model predicted EC_e over the study area accurately (R² = 0.87, ME = ?1.04 and RMSE = 16.36 dSm^?1). Overall, it is suggested that similar applications of this technique could be used for mapping soil salinity in other arid regions of Iran.     

Effects of drought stress and rewatering on antioxidant systems and relative water content in different growth stages of maize (Zea mays L.) hybrids

Drought is a problem of the expanding universe which seriously influences crop production and quality. Approximately one-third of the cultivated area of the world suffers from constantly inadequate supplies of water. The present study aimed to determine the effects of drought and rewatering on activities of antioxidant enzymes, chlorophyll, proline, and relative water contents (RWC). In this experiment, six maize hybrids (Sc260, Sc370, Sc500, Sc647, Sc700, and Sc704) were examined in a pot study during the maize-growing season of 2011. Results indicated that the growth of hybrids was retarded under drought stress conditions and regained growth speed during rewatering. RWC, chlorophyll, and carotenoid contents were markedly decreased by the water deficit and reached normal values during rewatering in Sc647, Sc704, and Sc700. Our findings also indicated significantly higher activity levels of peroxidase and catalase and proline content in water-stressed plants than in well-watered plants, which decreased when the plants were rewatered, showing an inverse relationship to gluthatione reductase activity. According to the results, the better upregulation of the protective mechanism in Sc704 and Sc647 probably induced higher drought resistance. We concluded that antioxidant enzyme activity could provide a useful tool for depicting drought tolerance in maize hybrids in arid and semiarid regions.     

Digital mapping of cation exchange capacity using genetic programming and soil depth functions in Baneh region,Iran

This study evaluates the performances of a combination of genetic programming and soil depth functions to map the three-dimensional distribution of cation exchange capacity (CEC) in a semiarid region located in Baneh region, Iran. Using the conditioned Latin hypercube sampling method, the locations of 188 soil profiles were selected, which were then sampled and analyzed. In general, results showed that equal-area quadratic splines had the highest R², 89%, in fitting the vertical CEC distribution compared to power and logarithmic functions with R² of 81% and 84%, respectively. Our findings indicated some auxiliary variables had more influence on the prediction of CEC. Normalized difference vegetation index (NDVI) had the highest correlation with CEC in the upper two layers. However, the most important auxiliary data for prediction of CEC in 30–60 cm and 60–100 cm were topographic wetness index and profile curvature, respectively. Validation of the predictive models at each depth interval resulted in R² values ranging from 66% (0–15 cm) to 19% (60–100 cm). Overall, results indicated the topsoil can be reasonably well predicted; however, the subsoil prediction needs to be improved. We can recommend the use of the developed methodology in mapping CEC in other parts in Iran.     

Assessing and monitoring the soil quality of forested and agricultural areas using soil-quality indices and digital soil-mapping in a semi-arid environment

In recent decades, the conversion of forest to agricultural land has been a major worldwide concern and a cause of environmental and soil-quality degradation. In this study, soil-quality indices (SQIs) were applied using several soil properties to determine the effects of land use on soil quality in a 206.50 km² area in Kurdistan Province, Iran. The Weighted Additive Soil Quality Index (SQI_w) was calculated using two scoring methods and two soil indicator selection approaches. Nine soil-quality indicators/variables were measured for 124 soil samples (0–30 cm depth). Calculated SQIs were digitally mapped with a random forest (RF) model using auxiliary data. The RF model was the best predictor of the SQI computed using the total dataset (TDS) and linear score function (SQI_w-TDS-linear). Soil quality was better estimated using non-linear scoring (r² = 0.82) than with linear scoring (r² = 0.73). The mean values of all SQIs were significantly greater in forestland than cropland. It is clear that soil quality is considerably reduced by deforestation, and that best management practices that maintain soil quality and reduce erosion must be developed for the soils of this region if they are to remain productive.     

Soil structure changes due to different land-use practices in the central Zagruos region,Iran

In this study, the effect of land-use treatments and the feasibility of fractal dimension to quantify soil aggregate stability were investigated in the central Zagrous, Iran. For this purpose, the non-linear fractal dimension (D_nl), linear fractal dimension (D_l) and the mean weight diameter (MWD) of aggregates were compared. Soil samples from three sites with four adjacent land-use types, namely: forest area (F), cultivated lands adjacent to forest (CAF), pasture (P) and cultivated lands adjacent to pasture (CAP) were collected. Results showed that soils under cultivated lands had higher bulk density (BD) (1.30–1.38 Mg m^?3) compared to the adjacent soils under forest (1.19 Mg m^?3) and pasture (1.21 Mg m^?3). In the 0–15 cm layer, soil organic matter (SOM) content in the cultivated plots were respectively 30% and 31% lower compared to the forest and pasture soils. The lowest CVs belonged to D_nl (5–8%) demonstrating that D_nl was more accurate than D_l (8–14%) and MWD (30–53%) methods. CAP had the largest value of D_nl, while P had the smallest value of D_nl. Difference of D_nl between forest and pasture was not significant, whereas both of them significantly differed from CAF and CAP. D_l did not differ significantly between forest and CAF. There were significant differences between forest and pasture for the measured MWD. Both fractal dimensions had negative correlation with MWD, SOM, hydraulic conductivity (HC) and macroaggregates (>0.25 mm) and positive correlation with BD and total porosity (TP).