JWSS - Journal of Water and Soil Science

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Life in central Iran depends on the Zayandehrood river, making the preservation of its quality important. Salinization and pollution of the flow as a result of different organic and inorganic pollutants create serious threats to the environment and to the agricultural activities in the region. Although the role of the main drains discharged into the Zayandehrood in changing its quality is well known, little information is available on the volume and quality of the discharged drains. The purpose of this research was to study the quantity and quality of three main drains (Zoab-Ahan, Rudasht and Segzi) discharging into the Zayandehrood, Monthly samples were taken from each drain and their chemical properties were measured in 1998. The discharge rates were also measured simultaneously at sections near the discharge points.

The results showed that all three drains were alkaline and, therefore, had no negative effects on the pH of the river flow. Mean annual EC of Zoab-Ahan, Rudasht and Segzi drains were 5.56, 27.18 and 42.41 dS/m, respectively, and the salt loads discharged into the river by these drains were 39258.4, 37672.9 and 259781.2 ton/year, respectively. Annual mean N concentrations were 4.49, 3.92 and 4.18 mg/L and annual mean P concentrations were 0.26, 0.16 and 0.12 mg/L in the drains, respectively. The most important effect of the drains on the Zayandehrood was salinization, especially in the lower parts of the river. The increase in soluble salt contents of the river after Segzi drain discharge was so high that it made the water thereafter unusable for any purpose.

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In Guilan province, Sefidrud River, as the main source of irrigating rice in Guilan province, has been subjected to increasing salinity and a decreasing discharge because of decreasing in the volume of sefidrud dam, diverting water upstream and entering different sewages into the river. This research tries to determine optimum irrigation depth and intermittent periods in proportion to salinity resistance at different growth stages using optimization- simulation model. After calibration, Agro-hydrological SWAP model was used to simulate different growth stages of rice. Optimization results were obtained for managing fresh and saline intermittent water, 8-day intermittent period, for salinity of 0.747 dS/m in sensitive maturity stage and salinity of 3.36 dS/m in resistant vegetative, tiller and harvest growth stages. It is suggested that the depth of irrigation water be 1, 3, 3 and 5 cm for vegetative, tiller, maturity and harvest stages, respectively. Comparing managements of irrigation and saline based on the resistance of different growth stages to salinity and exploitation of irrigating water with a constant salinity during growth periods of the plant showed that irrigation management based on resistance of different growth periods of the plant to salinity causes rice yield to be improved by 23percent.

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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.

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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.

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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.

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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.