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Water quality and quantity in agricultural systems of arid and semi- arid regions of the world are of great importance. In this regard the trend to pressurized irrigation systems compared to surface irrigation, elevating water use efficiency, has drastically increased in the agriculture sector. The present study aimed to assess land capability for different types of irrigation systems including surface, drip, and sprinkler practices by parametric and fuzzy approaches to evaluate the capability of cultivated lands on 6131 km2 of the Mashhad Plain, Khorasan Razavi Province, northeast Iran. In this regard land qualities (drainage and slope), soil physical and chemical properties (texture, depth, salinity, drainage, calcium carbonate and gypsum percentage) and climate conditions (wind velocity) were evaluated by using the Geographic Information System (GIS). Based on parametric approach, some 1116.5 ha of the study area were classified as highly suitable (S1 class) for surface irrigation, while the corresponding values by fuzzy approach accounted for 6099.7 ha of the region. The moderately suitable class of S2, assessed by parametric and fuzzy approaches, included 5014.5 and 31.3 ha of the plain, respectively. It was revealed that the land capability indices were in higher classes (S1 to S2) by drip and sprinkler irrigation compared to the surface irrigation system and the soil texture was detected as the most limiting factor for using the surface irrigation system. With respect to current soil and climate conditions in the study area, the most efficient irrigation systems are drip and sprinkler practices.

The frequency and severity of high temperature and drought extremes are expected to increase under future climate change (CC) and considerably affect the agricultural sector particularly in water-limited ecosystems. This study was conducted to assess future CC impacts on rainfed wheat yield, water requirement (CWR), water use efficiency (WUE), precipitation use efficiency (PUE), reference crop evapotranspiration (ET0), and agricultural rainfall index (ARI) in northeast of Iran. The outputs of five global climate models (GCMs) under RCP-4.5 and RCP-8.5 during three time periods (i.e., the 2025s, 2055s, and 2085s) downscaled by MarkSimGCM model were applied. CWR was estimated using the CROPWAT 8.0 model. Further, the CSM-CERES-Wheat model was employed to simulate rainfed wheat yield, WUE, PUE, and ET0 responses to CC. The results showed that the mean monthly ET0 and CWR would likely increase under both emission pathways over the studied sites. The mean monthly ARI is also anticipated to decline in the future indicating a drier climatic condition over northeastern Iran by 2100. Furthermore, CC is highly likely to decrease rainfed grain yield, WUE, and PUE during the current century. The largest changes in ET0, ARI, CWR, yield, WUE, and PUE were projected in the late twenty-first century (the 2085s) under RCP-8.5. The CC-induced wheat yield loss will likely endanger food security in the country. Yield reduction can be partially offset by adopting appropriate adaptation measures.

Climate change adversely impacts crop production and imposes a wide range of constraints on agricultural systems especially in water-limited environments. Management strategies to enhance adaptation capacity are needed to mitigate climate change effects. The objective of this study was to investigate the potential of changing planting dates and planting densities as adaptation strategies to climate change for irrigated and rainfed wheat for possible enhancement of crop yield, harvest index and water use efficiency at three locations in northeast of Iran (Mashhad, Sabzevar and Torbat-h). For this purpose, the outputs of five global climate models under RCP-4.5 and RCP-8.5 emission scenarios during three time periods (i.e., the 2020, 2050 and 2080) downscaled by MarkSimGCM were used to run the CSM-CERES-Wheat (v4.6) model. The results indicated that crop production will be reduced as affected by climate change based on prevailing and two other planting dates and planting densities in the future climate change, under all scenarios and years. In general, later planting dates with planting density of 400 plants m−2 caused higher production which leads to less yield reduction by about 8, 11 and 10% for irrigated wheat and 27, 21 and 26% for rainfed wheat on average across all periods and scenarios compared to current management practices in Mashhad, Sabzevar and Torbat-h, respectively. Based on this study, it seems that changing planting dates and densities can be beneficial for adaptation of wheat to climate change.

Precipitation forecasts are of high significance for different disciplines. In this study, precipitation was forecasted using a wide range of teleconnection signals across different precipitation regimes. For this purpose, four sophisticated machine learning algorithms, i.e., the Generalized Regression Neural Network (GRNN), the Multi-Layer Perceptron (MLP), the Multi-Linear Regression (MLR), and the Least Squares Support Vector Machine (LSSVM), were applied to forecast seasonal and annual precipitation in 1- to 6-months lead times. To classify precipitation regimes, precipitation was clustered using percentiles. The indices quantifying El Niño-Southern Oscillation (ENSO) phasing showed the highest association with autumn, spring, and annual precipitation over the studied areas. The MLP and LSSVM algorithms provided satisfactory forecasts for almost all cases. However, our results indicated that the performance of LSSVM decreased in testing data, implying the tendency of this algorithm towards overfitting. The MLP showed a more balanced performance for the training and testing sets. Consequently, MLP seems best suited to be used for forecasting precipitation in our study area. The modeling algorithms provided less reliable forecasts for the regions corresponding to the 10–40th percentiles, mostly located in hyper-arid and arid environments. This underscores the inherent difficulty of precipitation forecasting in the hyper-arid and arid areas, wherein precipitation is very erratic and sparsely distributed. Our findings illustrate that clustering precipitation regimes to consider microclimate seems vital for reliable precipitation forecasting. Moreover, the results seem useful to design preventive drought/flood risk management strategies and to improve food-water security in Iran.

There is little consensus about the type of maintenance fluid therapy and it’s the effect on serum sodium in adults. Thus, this study was conducted to assess the effect of maintenance fluid therapy on serum sodium of hospitalized patients in the intensive care unit. This randomized clinical trial was carried out on 64 patients aged 18-90 years hospitalized in the intensive care unit (ICU) of Imam Sadjad and Shahid Beheshti hospitals, Yasuj, Iran, in 2017. These patients were randomly allocated to take 2500-3000 milliliters of intravenous maintenance isotonic (0.9% saline) or hypotonic (0.45% saline) fluids daily. Blood and urine samples were taken to measure biochemical parameters before and 48 hours after the intervention. Data analyses were done by using SPSS 16 software via descriptive and analytic statistics. Twenty-eight patients in the 0.9% saline group (19 male and 9 female) and 32 patients in 0.45% saline (20 male and 12 female) completed the study. There was no significant difference between two groups in sodium (P=0.94), potassium (P=0.21), sugar (P=0.91), creatinine (P=0.21), Blood Urea Nitrogen (P=0.99), systolic (P=0.81) and diastolic (P=0.73) blood pressure, PH (P=0.27), bicarbonate (P=0.8), and urine specific gravity (P=0.73). Based on the results of this study, it was shown that the administration of maintenance hypotonic fluids has been appropriate for the patients and will not face them with the risk of hyponatremia.