مجله تحقیقات آب و خاک ایران
سال پنجاه و چهارم شماره 4 (پیاپی 88، تیر 1402)

Reliable estimates of the seasonal applied water and irrigation performance assessment indicators under current irrigation and farm management are perquisites for improving water resources management. In this study, seasonal applied water and irrigation performance assessment indicators of winter barley were studied by monitoring 25 farms under actual conditions, in Ardabil Province (Ardabil, Namin, Nir and Kosar counties), Iran, during the growing season 2020-2021. The seasonal estimates of the net irrigation requirement during the growing season 2020-2021 and its 10-year average ranged from 476 to 652 mm and from 403 to 535 mm (with a mean of 530 and 449 mm), respectively, over the study farms. The total sum of seasonal applied water and effective precipitation (I + Pe) and the barley grain yield ranged from 266 to 716 mm and from 0.14 to 4.07 ton ha-1 (with a weighted average, WA, of 475 mm and 2.33 ton ha-1), respectively. As a result of limited irrigation water availability, 3-91% (with a WA of 52%) of the intended yield of irrigated barley in the study area (4.5 ton ha-1) was achieved. Physical and economic water productivity indicators were significantly (P < 0.05) affected by farmer's skill level, crop rotation, type of irrigation water source, and irrigation method. The results indicated that farms with surface water supply showed the highest vulnerability to drought periods. Improving the on-farm water management flexibility and mitigating the negative impacts of hot and windy days during the grain filling stage can improve water productivity indicators in the study area.

Estimating the natural flow regime of a river is crucial for water resource management. Understanding the temporal and spatial distribution of river flow can greatly assist decision-makers in water resource management. This study aims to analyze changes in the Water Year Classification Index (WYCI) at 31 hydrological stations in Ardabil province (1974 to 2018) at different time scales and its spatial changes using GIS. The WYCI index was calculated at all hydrological stations, and the map of the spatial changes of the index was prepared using the inverse distance weighting method. According to the results, the maximum and minimum median flow values were related to the spring season (3.7) and summer (0.3) cms, respectively. The median flow values in the winter and autumn seasons were 2.5 and 1.3 cms respectively. The results showed that the average values of the WYCI index in autumn, winter, spring, and summer are 11.9, 10.7, 13.8, and 15.2, respectively. The status of the index for dry and wet periods was highly irregular (21.6) and moderately irregular (14.3), respectively. Based on the research findings, the snow-fed rivers located in the southern of Sabalan (Lay, Nir, Atashgah, and Yamchi stations) have a regular regime, while rivers in the eastern part of the province become more irregular towards summer with an increase in WYCI values. Overall, the analysis of spatial changes in WYCI indicates the existence of a unique spatial pattern at different time scales, and it is necessary to consider it in water resource management planning in Ardabil province.

Simulation of water uptake amount during crop growth period, leads to optimal management of water consumption. This research was conducted on maize S.C 704 in lysimetric space (in Qazvin), in 2021. The research aim was simulation of salinity stress effect on crop water uptake amount. The experiment was performed as factorial and in a completely randomized design. The effect of soil salinity with levels of; 0.5(S1), 1.7(S2), 2.5(S3), 3.5(S4), 4.5(S5), 5.5(S6), 6.5(S7) and 7.5(S8) dS.m-1, were investigated in growth stages of initial(P1), development(P2), mid(P3) and late(P4). For simulating the amount of water uptake reduction in salinity stress conditions, the functions of Van Genuchten, Van Genuchten-Hoffman, Dirksen-Augustijn and Mass-Hoffman were used. Models calibration was done by real data in S1, S3, S5 and S7 treatments and models evaluation was done in S2, S4, S6 and S8 treatments. The models calibration was done once for whole growth period (by constant coefficients) and again by considering the crop growth stages sensitivity (by variable coefficients). The slope of water uptake reduction at P1 to P4 stages and whole growth period, was estimated to be 6.81%, 7.4%, 9.13%, 4.25% and 7.9%, respectively. The highest to lowest crop sensitivity to salinity stress was observed in P3, P2, P1 and P4 stages, respectively. The functions calibration for whole growth period, was less accurate for determining the water uptake amount. Evaluation statistics (CRM: -0.0005, EF: 0.993, R2:0.995, RMSE: 0.016 and ME:0.031) showed that the Mass-Hoffman function was optimal model for simulation the water uptake reduction. By considering the effect of crop growth stages sensitivity, were done a more accurate estimation of water uptake and actual crop water requirement amounts.

Today, by increasing industrial and agricultural activities and the subsequent increase in pollutants, soil and water pollution has become a serious environmental problem. The increase of pollutants in the soil in the conditions of dry climate and drought stress have adverse effects on the biological indicators. In this study the combined effect of cadmium at six levels (0, 5, 10, 20, 40 and 80 mg kg-1 soil) in the form of cadmium sulfate (CdSO4) and three irrigation intervals (2, 4 and 7 days) as a factorial experiment in the form of a completely random basic design with three replications was investigated on spinach (Spinosa oleracea) and lettuce (Lactuca sativa) in calcareous soil with silty clay texture. The obtained results showed that increasing the concentration of cadmium in the soil caused a significant decrease in the average dry weight of leaves and stems of lettuce and spinach. The average dry weight reduction percentage compared to the control at the highest level of cadmium was observed in lettuce leaves and stems as 63 and 46% and in spinach leaves and stems as 54 and 38%, respectively. Increasing the irrigation intervals decreased the dry weight of lettuce and spinach plants significantly compared to the control. The percentage of mean dry weight reduction in 4 and 7 days irrigation intervals compared to 2 days irrigation interval in lettuce leaf and stem was 23, 41, 31 and 63% and in spinach was 19, 39, 24 and 49%, respectively. The increase in cadmium levels increased the concentration of this element in the stem and leaves of lettuce and spinach plants, and by increasing irrigation intervals, the concentration and uptake of cadmium in the leaves and stems of spinach and lettuce decreased. The results of this research showed that the spinach plant absorb and transport cadmium metal more than the lettuce. The stress sensitivity index showed that the moisture stress has reduced the crop tolerance to cadmium. Based on the decrease in dry weight at different levels of cadmium, it can be said that the stem of two plants is more tolerant to cadmium than the leaf.

Spatiotemporal estimation and monitoring of soil moisture based on remote sensing observations (optical and thermal) is challenging due to its physical nature in high vegetation conditions, necessitating improving and increasing the accuracy of soil moisture estimation in these areas. Therefore, this research aimed to develop a new approach to estimating surface soil moisture in agricultural fields with dense vegetation using machine learning algorithms by incorporating optical and thermal remote sensing data and soil physical properties. For this objective, 16 Landsat-8 satellite images and more than 430 control locations were used during the sugarcane crop’s growth period in 2018-2019 at the Hakim Farabi Sugarcane Agro-Industrial company in the Khuzestan province of Iran. A set of 10 scenarios of various unique combinations of the available input variables were developed and then evaluated by five machine learning algorithms, including multiple linear regression (MLR), decision tree-based algorithms (CART and M5P), and ensemble learning-based algorithms (gradient-boosted regression trees (GBRT) and random forest regression (RFR)). According to the results, the highest correlation between input variables and surface soil moisture was observed in Soil Wetness Index (SWI) and Normalized Soil Moisture Index (NSMI) with R values of 0.79 and 0.69, respectively. Also, the highest accuracy of machine learning algorithms based on R2, RMSE, and MAE results was obtained in GBRT (0.99, 0.011, and 0.006) and RFR (0.99, 0.014, and 0.007), respectively. In general, the findings of this research show the importance of using variables based on Landsat-8 remote sensing data in combination with ensemble learning algorithms that can be independent of any ground measurements.

The best management practices are solutions to reduce non-point pollution in catchments. In many cases, the use of these solutions requires knowing the features of the watershed and investing in this sector. Accordingly, the use of computer models to simulate real catchment conditions can be an effective way to reduce time and cost. This research aimed to investigate the effect of different management scenarios on non-point source pollution losses in Dashte Bezorg catchment in Iran using the ArcSWAT model. To collect observational data, river water was sampled from September 2020 to June 2021. Calibration data were selected from September to March and validation data from April to June. After identifying critical areas, three non-structural scenarios and five structural scenarios were simulated using the model. The results revealed that the ArcSWAT model provides a good prediction in estimating non-point source pollution loads (nitrate, total nitrogen, and total phosphorus). The "wheat-potato-tomato" and "wheat, rice-wheat, mung bean-wheat" rotation scenarios showed the highest total nitrate and nitrogen loss, while the lowest total phosphorus loss was observed in the "wheat-potato-tomato" rotation. The terracing and buffer strip methods were recognized as the best methods of reducing the load of non-point pollution. The findings showed that the application of management practices in dominant land use and reduc the degree of slope can significantly reduce the non-point source pollution loads.

The purpose of this study is to investigate the characteristics of plant growth promoting rhizobacteria that stimulate plant growth, such as biological nitrogen fixation, phosphate solubilization, and produce hydrogen cyanide and exopolysaccharides, to prepare suitable and efficient biofertilizers. In this research, 135 Rhizobacteria strains were isolated, screened and identified from saline and arid soils in Kerman Province, and some of their growth-promoting characteristics were studied. The Rhizobacteria strains were studied with respect to their ability to fix nitrogen, solubilize insoluble organic and inorganic phosphates in solid and liquid culture media, and produce hydrogen cyanide and exopolysaccharides. Their growth at various sodium chloride concentrations (0, 1, 3, and 5%) and at different polyethylene glycol 6000 concentrations (water potential 0, -1, -2, and -3.5 MPa) was also investigated. According to the results, 72% and 76% of the Rhizobacteria strains exhibited almost no growth at 5% sodium chloride concentration and at water potential -3.5 MPa, respectively. In addition, 50% of them were able to produce hydrogen cyanide under saline conditions. Three of the strains (SM16, SM65, and SM89) were the most efficient producers of exopolysaccharides and had high phosphate solubilization ability (102, 98.5 and 121.7 mL/L, respectively) and were also among Rhizobacteria strains resistant to abiotic stresses. Three strain safe and efficient strains can have great potential to be used as a high quality biofertilizer for improving growth of crop plants in the region.

The use of coupled simulation-optimization models to extract the optimal role curve of dams is one of the effective strategies for optimal management of reservoirs. In certain optimization techniques, historical data of the inflow to the reservoir is usually used to extract the optimal role curve of the dam. It is possible that in the coming years, with the change of the inflow to dams, the parameters based on which the optimal role curve was extracted may no longer work and the results may be unexpected. The objective of this research is to provide a solution for extracting the optimal role curve in real time so that by changing the inflow to the dam in the future without re-executing the optimization algorithm and using artificial intelligence techniques in the shortest time, the optimal role curve compatible with the new conditions can be obtained. In this research, the integration of the NSGA-II multi-objective algorithm and the WEAP simulation model is used to derive optimal policies based on historical data. Then, using the support vector machine method and the results obtained from the output of the optimization algorithm, a new structure is developed so that the optimal role curve can be obtained in real time and based on new inputs. The results indicate that the average error of the optimal role curve extracted from support vector machines is less than 2.5% compared to the role curve obtained from the NSGA-II algorithm in the calibration and validation stages. Therefore, the developed support vector machine model has the ability to quickly provide optimal operation policies in such a way that provides the possibility of optimal management of the system in real time, according to the new data of the inflow to the dam.