JWSS - Journal of Water and Soil Science

Salinity and ions toxicity are one of the main problems of agricultural lands in arid and semi-arid regions, such as Iran. In addition to the salinity problem, some other marks like boron toxicity in crops have been seen in Hossein Abad area as one of the main agricultural regions of Yazd. Therefore, this study intends to evaluate and analyze spatial variability of soil salinity as an aspect of soil degradation, and prepares soil salinity and boron maps. A regular grid sampling scheme was done through a 150 m interval. Salinity and boron were measured at the depth of 0 to 30 cm. Totally 104 samples were measured. After statistical analysis of the data and studying their distribution, Kriging estimator was used for mapping the mentioned variables. Results showed that the region has a salinity problem and does not have any boron toxicity. According to the relationship of nugget effect and sill, there was a strong dependency among all the measured factors except for boron and pH factors. The least salinity was observed in cultivated areas due to the leaching process. The boron range was between 0.07 and 1.6 mg kg-1. Salinity and soil boron were significantly correlated at 99 % confidence level. Based on the Spearman and Pearson tests, there was a positive correlation between SAR and salinity at 99 % confidence level, which shows the region has more sodic salts than others. Also, pH of the region did not present any problem for growing crops.

Water Uptake by the root under salinity and water Stress in unsaturated soils was investigated through mathematical equations in three Groups of additive, multiplicative and non-consumptive. This study was an effort to assess six water uptake functions of van Genuchten (additive and multiplicative), Dirksen et al., Van Dam et al, Skaggs et al, and Homaee, for Rasht Hashemi rice under salinity and water stress conditions. Based on field observations of Hashemi Rasht rice in 1386 and 1389, crop growth simulation model of SWAP was calibrated and validated with a correlation coefficient of 0.97 and 0.95, respectively. Water Uptake Reduction Models' parameters were determined by the simulated data using SAS statistical software. Results showed that for the anticipated reduction of Water Uptake in rice water and salinity stress conditions for Rasht Hashemi rice, Homaee model is best.

This study aimed to evaluate the performance of TM satellite data acquired in June 2009 to map soil salinity in southeast of Isfahan province. Ground salinity data (EC) was collected within 9 pixels, covering an area of approximately 8100 m2 using stratified random sampling technique at 53 sample sites. Spectral indices including TM bands, BI, SI1, SI2 and SI3, PC1, PC2, PC3 and also multiple linear regression modeling and maximum likelihood classification techniques were applied to the geometrically corrected image. Results of regression analysis showed that the TM band 4 had the strongest relationship with EC data (R2=0.48) and also the relationship of the modeling image using TM 3, TM 4, TM5 and PC3 was significant at the 99% confidence level. The accuracy assessment of the stratified TM4 and modeling image into five classes including 0-4, 4-20, 20-60, 60-100 and EC>100 ds/m indicated that there was more than 86% agreement with the field measurements of EC data. Therefore, it can be concluded that the discretely classified salinity maps have higher accuracy than regression methods for identifying broad areas of saline soils, and can be used as appropriate tools to manage and combat soil salinization.

The present study was conducted in order to investigate the effects of silicon and salinity on growth, chemical composition and physiological properties of rice var. Hashemi, in the greenhouse of agricultural college of University of Tabriz during 2011. It was arranged as factorial based on a completely randomized design with three factors consisting of silicon at four levels (control, 100, 200 and 300 mg/kg soil), salinity at four levels (control, 2, 4 and 8 dS/m) and source of salinity at two levels (NaCl and combination of different salts) with three replications. The results showed that increasing soil salinity resulted in a decrease in shoot dry weight, catalase activity and concentrations of phosphorus, potassium and reducing sugars, and an increase in glycine betaine content in the plants. The comparison between two sources of salinity also revealed that plants treated with a combination of salts experienced less vigorous decrease in dry weight and potassium, and reducing sugars' concentrations. Meanwhile, the enhancement in glycine betaine content appeared to be less intense in these plants. Hence, it could be concluded that the salinity resulting from a combination of different salts caused less damage to plants than NaCl salinity. Silicon supplementation resulted in increasing of shoot dry weight, catalase activity and concentrations of reducing sugars, glycine betaine, phosphorus and potassium. Therefore, silicon nutrition alleviated suppression effects resulting from the presence of soil salts, which means, enhanced the salt tolerance of rice.

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.