Arbuscular mycorrhizal fungi (AMF) are widespread endosymbionts in terrestrial ecosystems and their exudates have important effects on soil properties. A greenhouse experiment was conducted with six AMF treatments including four exotic species inoculums (Funneliformis mosseae ,Claroideoglomus claroideum  and Rhizophagus irregularis and a mixed isolate of three species), one mixed native AMF species treatment and a sterilized soil (control) with four salinity levels (1, 5, 10 and 15 dS m^-1). AMF increased the soil (EEG) and total (TG) extractable glomalin, and also the hot water (HWC) and diluted acid (DAC) extractable carbohydrates compared to control treatment in all salinity levels. The native AMF species had the greatest effects on EEG, TG, HWC and DAC at 10 and 15 dS m^-1. Soil EEG and TG concentrations were higher in the mixed exotic AMF treatment than in each AMF species. The greatest glomalin concentration was related to F. mosseae at 1, 5 and 15 dS m^-1 but at 10 dS m^-1 the greatest glomalin concentration was related to C. claroideum. The greatest carbohydrate concentration was related to F. mosseae at 1 and 5 dS m^-1 but at 15 dS m^-1 significant differences were observed among the three AMF species. Our results showed that there is an interaction between salinity and different AMF species, and a combination of them determines the function of AMF.

Salinity and nutrient deficiency particularly nitrogen are two important limiting factors for yield production in arid and semi-arid regions. The objective of this study was to model basil response to combined salinity and nitrogen deficiency. To that end, modified Leibig-Sprengel (LS) and modified Mitcherlich-Baule (MB) and also some newly derived models based on combination of MB with salinity models of Maas and Hoffman (31), van Genuchten and Hoffman (36), Dirksen and Augustijn (17) and Homaee et al., (23) were evaluated. The experiment was conducted under four salinities including 1.175, 3, 5, and 8 dSm^-1 and four nitrogen levels including 100, 75, 50, and 0 percent of fertilizer requirements each with three replicates. Results indicated that from among the evaluated models, the derived models of MB and Maas and Hoffman (MB-MH) (nRMSE=4.9), MB and van Genuchten and Hoffman (MB-VG) (nRMSE=5.4), and also MB and Homaee et al., (MB-H) (nRMSE=7.0) provide best fits to the measured data. Also, the comparison of two modified LS and MB models indicated that the estimated relative yield for irrigation water salinity levels by modified LS model (nRMSE=4.6) provides better results (nRMSE=5.9). However, for soil nitrogen levels and interactive effects of salinity and nitrogen, the modified MB model (nRMSE=10.3) provided better outputs (nRMSE=14.4). Consequently, instead of the modified LS and MB models the proposed models in this research can be recommended for use.

The main objective of this study was evaluation of integrated management and mixing saline/fresh water on soil salinity distribution. For this purpose, a field was selected and 32 plots were made in it with a 6 m×2.5 m size. A split plot experiment was employed with two sunflower varieties (Alstar and Hisan33), four irrigation schemes (CIS) and four replications. Irrigation schemes being applied as treatments are: T1: every other irrigation with saline water (11 dS m^-1) and fresh water (2 dS m^-1) (every other irrigation), T2: fresh water - saline water, T3: mixed irrigation and T4: saline water - fresh water. Soil samples were collected from depth of 0-20, 20-40 and 40-60 cm in the early, mid and end of the irrigation season. The samples were analyzed for EC, Ca, Mg, Na and Cl. The result showed that soil salinity in depth of 40 cm is greater than salinity in depth of 20 and 60 cm in all treatments and for both sunflower varieties, in all growing stages. The maximum salinity concentration was observed in T2 among all treatments. Increasing irrigation depth has increased the soil extract’s Cl and Na in all treatments during growing season to 50 and 75 meq/L, respectively. The effects of CIS treatments are statistically significant on Ca and Mg in Alstar, and in all regimes affect on different depths. The minimum value of EC and maximum yield was observed in T4, T3, T1 and T2, respectively.

Digital soil mapping techniques which incorporate the digital auxiliary environmental data to field observation data using software are more reliable and efficient compared to conventional surveys. Therefore, this study has been conducted to use k- Nearest Neighbors (k-NN) and artificial neural network (ANN) to predict spatial variability of soil salinity in Ardekan district in an area of 700 km², in Yazd province. In this study, 180 soil samples were collected in a grid sampling manner and then soil chemical and physical properties were measured in laboratory. Environmental auxiliary variables were included topographic attributes, remote sensing data (ETM+) and apparent electrical conductivity (ECa). The result of the study showed that the K-mean nearest neighborhood had higher accuracy than ANN models for predicting soil electrical conductivity (ECe). Overall, k-NN models could provide significant relationships between soil salinity data and environmental auxiliary variables. The k-NN model had the root mean square and coefficient of determination of 12.10 and 0.92, respectively, between predicted and observed ECe data. Also, apparent EC, and remotely sensed indices and wetness index were identified as the most important factors for predicating the soil salinity in the studied area.

Salinity and alkalinity decreases physical, chemical and biological quality of soils and as a result reduces crop yield. This study aims to evaluate spatial variability of soil salinity in Ahvaz using geostatistical approaches. Accordingly, 69 surface soil samples (0-10 cm) were collected and their electrical conductivities (EC) were measured in 1:1 soil: water extracts. The data were then analyzed using ordinary kriging (OK), log-normal kriging (LOK) and indicator kriging (IK) interpolation techniques to produce soil salinity maps. Finally, the quality control of soil maps was performed by calculation of root mean square error (RMSE) and coefficient of determination (R²). The results indicated that due to the lowest RMSE and the highest R² values, the LOK interpolation method is the best approach in mapping soil salinity in Ahvaz. The results also illustrated that based on defined threshold values (4, 8, 16, and 32 dS m^-1) the indicator kriging methods have been able to show risk of soil salinity in the area. Based on this, most of the area is covered by soils with salinity higher than 4 dS m^-1. Evaluation of final soil maps showed that the highest concentrations of salts are related to the western and southwestern parts of Ahvaz city. In contrast, the lowest amounts of salinity were found in Eastern and Northern parts of the city.

Today, modern irrigation systems are constructed at a very high cost to operate for optimal use of water and soil. Lack of appropriate technical, social and economic studies, caused high maintenance costs of these facilities during operation. Water resources have been polluted due to industrial development, increasing human population and non-compliance with environmental standards. Most of hydraulic structures are built in areas with poor water quality. Furthermore, engineering properties of fine-grained soils, especially the clay soils, depend on factors such as salinity of solute in the pore water. So that any change in salinity of solute leads to change in the physical and mechanical properties of soils, and consequently make damage to hydraulic structure. This study investigated the effect of water salinity on engineering properties of fine-grained soils. For this purpose, NaCl, with 5 different levels (0, 0.1, 0.2, 0.41 and 0.72 mol/L) was added to the soil and the mechanical properties of soil including compaction, shear parameters, Atterberg limits and consolidation parameters were investigated. The results showed that the addition of NaCl had made no significant changes to the maximum dry unit weight and optimum moisture content of the soil, but it reduced cohesion of soil and increased the internal friction angle .Also, Limit Liquid (LL) are decreased, but it had little effect on the Plastic Limit(PL) of soil.
 

The current study aims to find the best methods of using remote sensing and supervised classification algorithms in long-term salinity monitoring of salinity changes in the Atabieh area with an area of 5000 hectares in the west of Khuzestan province. The procedure is based on the separation of different levels of saline soils utilizing information obtained from Landsat 7 and 8 satellite images (2001 to 2015) along with salinity data taken from the study area, and salinity indices including SI1, SI2, SI3, NDSI, IPVI, and VSSI. The results show the expansion of the saline zone trend in the soils of the study area, among which, soils with EC of more than 16 dS m-1 (very saline) have the highest frequency. The area of saline soils has increased significantly over the past 15 years, with a saline land area increasing by more than 90%. The percentage of salinity class is low (S1). According to this study, the only significant index in soil salinity at a 95% confidence level is the SI3 index, which has been able to have a good estimate of the increasing changes in soils in the region. The results of the supervised classification showed that the support vector machine (with an overall accuracy of 95.78 and a kappa coefficient of 0.89) is more accurate. After the vector machine method, the methods of minimum distance, maximum likelihood, and distance of Mahalanobis have the highest accuracy, respectively. Based on salinity maps obtained in years in 2001, 2005, 2010, and 2015, it can be said that the salinity rate in the whole of the study area was progressing and at the same time the salinity area in the middle and high classes increased decreased and on the other hand, the salinity area in the high class in 2001 gradually increased and distributed in 2015 throughout the region.