نشریه دانش آب و خاک
سال سی و سوم شماره 1 (بهار 1402)

Phytoremediation is a method for decreasing lead contamination in soil and the use of chelates increases the efficiency of this method. In this study, phytoremediation efficiency of savory plant (Satureja hortensis L.) to remove lead from contaminated soil was investigated in the presence of chelates including ethylene-diamine tetra-acetic acid (EDTA) and diethylene diamine penta-acetic acid (DTPA) and organic acids including acetic acid (AA), citric acid (CA) and tartaric acid (TA) at concentrations of 0 and 10 mmol kg-1. The soil was amended with five levels of 0, 100, 200, 300 and 400 mg kg-1 lead from lead nitrate source. The experiment was conducted as factorial based on completely randomized design with three replications. The results showed that the shoot dry weight was higher in organic acids treatments than synthetic chelates, and increasing of lead concentration up to 300 and 400 mg kg-1 decreased shoot dry weight. Acetic acid significantly increased the available lead concentration in the soil and the highest amount was obtained at level of 400 mg kg-1 lead in soil. The highest plant extraction potential was observed with acetic acid at 100 mg kg-1. Organic acids showed more effective role in increasing the extraction of lead compared to the than chelates. The results of this study showed that Satureja hortensis had high ability to extract lead from soil at Pb levels of up to 200 mg kg-1. Due to the high translocation efficiency of lead from root to shoot, this plant can be used for soil remediation with application of organic acids.

Due to pollution in nature by lead (Pb), the canola stalk (CS) and water vapor-activated biochar (BC) adsorbents were utilized to remove lead from polluted solutions in the lab. The water vapor-activated biochar adsorbent was prepared in 500-degree Celsius carbonization temperature and 700-degree Celsius activation temperature. Lead adsorption isotherm experiments were carried out in 25 degrees Celsius. Experiments were performed to determine the best adsorbent pH level and the best adsorbent dosage level. Both CS and BC adsorbents were amended with magnetic iron oxide nanoparticles (Fe3O4) at 5% (w/w), and the lead adsorption isotherm experiments were carried out using the new adsorbents. The results indicated that the water vapor-activated biochar adsorbent modified with Fe3O4 (BC-5%) is the best adsorbent for the lead pollutants removal. In this adsorbent, the maximum of adsorbed quantity was found equal to 63.62 mg g-1, the Langmuir constant was obtained equal to 0.06790 L mg-1, coefficient of determination was computed equal to 0.9798 and standard error of the estimate acquired equal to 3.219.

Rainfall-runoff modeling and predicting play an essential role in water resource managing, urban planning, reservoir operating, etc. Support vector machine (SVM), as one of the new models of artificial intelligence, has high capability and flexibility in predicting hydrological data. In this research, the idea of rainfall-runoff process modeling using the hybrid model of Particle Swarm Optimization-Wavelet Transform-Support Vector Machine (PSO-WT-SVM) is proposed. There are constant parameters in the SVM algorithm that should be appropriately determined by the user, whereas a wrong choice of these parameters results in a significant reduction in the model performance. In order to solve this problem, the Particle Swarm Optimization (PSO) algorithm is employed to find the best values of SVM constant parameters introducing the PSO-SVM hybrid model. In the next step, applying the Wavelet Transform (WT) pre-processing method on the raw data, this research aims at proposing PSO-WT-SVM hybrid model. Finally, the daily rainfall-runoff time series of the Silakhor plain located in Lorastan province are modeled and forecasted using the SVM single model, PSO-SVM, and PSO-WT-SVM hybrid models. The models' accuracy is assessed using DC and RMSE criteria. The results indicate that PSO-SVM and PSO-WT-SVM hybrid models with DC of 0.72 and 0.89, respectively, supersede the SVM single model with DC of 0.57 in the verification step for Silakhor plain rainfall-runoff time series modeling.

Accurate prediction of pore water pressure in the body of earth dams during construction is one of the most important factors in managing the stability of earth dams. In this study, using three different evolutionary neural network models including multilayer perceptron neural network with genetic algorithm, particle swarm optimization and Imperialist Competitive algorithm for estimating the pore water pressure in the body of Kabudwal earth dam at the time of construction, has been studied. Five features including fill level, construction time, reservoir level, impounding rate and fill speed during the 4-year statistical period were selected as the input of the hybrid model in piezometer EP19.7. The composition of the inputs was obtained using the feature selection method and the hybrid water cycle algorithm -artificial neural network. By performing hybrid algorithm and sensitivity analysis and feature selection method, fill level, construction time, reservoir level and dewatering speed were selected as the top four inputs, because the combination of these four features with MSE value of 1.1587 had the least error. In this study, artificial neural network weights are optimized to increase efficiency using the above three meta-heuristic algorithms. In general, according to statistical indicators, the results indicate acceptable accuracy of all three hybrid models. In terms of priority, the ANN-GA hybrid model with the highest accuracy and minimum error and values of , RMSE and MAE are equal to 0.9773, 0.0457 and 0.0399, respectively, is first priority and ANN-PSO and ANN-ICA hybrid models were given the next priorities, respectively.

Heavy metal pollution is one of the most important environmental problems worldwide and measuring their concentration in the soil is the first step in providing a solution to reduce their risks. However, conventional methods for estimating the amount of heavy metals in the soil are time-consuming and costly. Imagery and remote sensing techniques have shown good potential to be an alternative to the routine approaches for monitoring heavy metal pollution in soil. The aim of this study was to evaluate the efficiency of multivariate analysis methods and remote sensing technique to quantify soil contamination with zinc and lead under different land uses. Previous studies revealed the high concentrations of zinc and lead in Zanjan’ soils cause a serious concern regarding the health of humans in this province.

The study was conducted between the latitude 36° 20' 48" N to 36° 47'45" N, and longitude 48° 15' 51" E to 49° 00' 55" E in Zanjan province. The average daily temperature in the study area is 15.7°C and the mean annual precipitation is 335 mm. Rangeland, agriculture, industrial and residential areas are the main land uses in Zanjan. A total of 230 soil samples were collected at depths of 0–10 cm under different land uses from an area of ​​3424 Km2 at two grids intervals of 1.5 kilometers in industrial and residential areas, and 3 kilometers in rangeland and agriculture. Before chemical analysis, soil samples were air-dried and sieved. Soil lead and zinc contents were measured using an atomic absorption spectrometry (Perkin- Elmer: AA 200). Radiometric and geomatic corrections were performed on satellite images and the “special resampling” procedure was used to resample the 20 m images to 10 m. Thirty spectral indices were determined using Sentinel 2 satellite images. These indices were reported as practical indices for remote sensing assessment of land condition. The indices included; NDVI, NDRE, MTVI, MCAR, MNLI, GNDVI, SAVI, LCI, MTCI, PSRI, CI-RedEdge, CI-Green, NLI, TVI, EVI, STAVI, GRI, LSWI, MSAVI, BI, BI2, RI, CI and seven spectral ratios. Spectral estimation models were developed and evaluated by three methods of principal component regression (PCR), partial least squares regression (PLSR) and support vector machine regression (SVMR). The samples were randomly divided into the validation set (30%) and the calibration set (70 %). Therefore, 161 samples were used for calibrating and 69 samples for validating models. Levene’s test was performed to test the variance homogeneity between calibration and validation sets. The accuracy of models was evaluated using the coefficient of determination (R2), root mean square error of prediction (RMSEP) and the ratio of predicted deviation (RPD). Martens’ uncertainty test was used to identify important wavelengths for zinc and lead estimations.

The range of lead in the studied soils was 40 to 364 mg kg-1 and the range of zinc was 96 to 824 mg kg-1. The accuracy of spectral models was categorised into excellent (RPD ≥ 2.5 and R2 ≥ 0.8), good (2 ≤ RPD < 2.5 and R2 ≥ 0.7), moderate (1.5 ≤ RPD < 2 and R2 ≥ 0.6) and poor accuracy (RPD < 1.5 and R2 < 0.6) based on the soil spectral accuracy classification, that is presented by Askari et al., (2015 and 2019). The highest concentration of metals was observed in industrial and residential land uses. The SVMR model (RPD≥2.6 and R2≥0.84, RMSE≤ 40), had a better spectral estimation for both lead and zinc than the PLSR model (RPD≥ 1.9 and R2≥  0.7, RMSE≤ 53) and the PCR model  (RPD≥1.3 and R≥ 0.45, RMSE≤ 75). Red-edge and infrared range were identified as the most effective wavelength ranges for monitoring the contamination of lead and zinc in soil. Brightness and modified triangular vegetation index were the most effective indicators for spectral estimation of lead and zinc in the studied soils. The SVMR model showed high accuracy and the PLSR model showed acceptable accuracy for evaluating and monitoring lead and zinc contamination using Sentinel 2 images. Comparing the predicted and measured values of heavy metals with a 1:1 line showed an overestimation for low values of lead and zinc, and an underestimation for high values of lead and zinc.

This study revealed that the method of multivariate analysis and remote sensing data could provide a practical approach for rapid and quantitative assessment of soil heavy metal pollution in Zanjan province and areas with similar soil conditions. An accurate prediction of heavy metal pollution can be acquired using freely available Sentinel-2 multispectral imagery system.

In arid and semiarid regions of Iran, most of soils are calcareous and low in organic matter, therefore are deficient in micronutrients. This research was aimed to evaluate the bioavailability of selected micronutrients as affected by biochar and compost of trees pruning in the presence of PGPR in wheat rhizosphere. In a rhizobox study, an experiment carried out in a completely randomized design under greenhouse conditions. The factors including the organic sourses (pruning waste biochar, pruning waste compost and control), microbial inoculation (with and without PGPR). At the end of the growth period, organic matter (OM) cotent and bioavailability of micronutrients including iron (Fe), zinc (Zn), copper (Cu) and manganese (Mn) in the rhizosphere soil and their uptake by wheat plant were determined. The results indicated that OM, Fe, Zn, Mn and Cu were significantly increased in rhizosphere soil under the influence of organic sources and PGPR inoculation. Addition of biochar under inoculation conditions resulted in 84.6% and 17.9% increase in rhizosphere Fe and Mn, respectively, compared to the non-inoculated treatment. The presence of PGPR increased the bioavailability of Zn and Cu by 52.2% and 34.9%, respectively, in compost treatment compared to the non-inoculated ones. Application of organic sources and bacterial inoculation increased the micronutrient uptake by plant.

Irrigation plays an important role in increasing crop yield especially in arid and semi-arid regions, where optimizing irrigation depth and leaching is crucial. Using simulation models such as AquaCrop help to analyze different irrigation scenarios and also help farmers to optimize water resources management in such conditions. In this study, calibrated and validated AquaCrop model was used for two wheat varieties in Birjand region and one wheat variety in Mashhad region. A Matlab program has been developed to link to the AquaCrop in order to achieve the optimized values of irrigation and leaching in the water constraint conditions. The optimization results showed that net profit for the best irrigation and leaching management at all salinity levels and different wheat varieties, except for salinity levels of 8.6 and 10 dS m-1 in the Mashhad variety and level of 9.6 dS m-1 in the Roshan variety, was more than the current management researches of Shahidi and Haghverdi. While the aim is to achieve maximum profit and minimal drainage water, the model by changing the type of irrigation management and reduce leaching, especially in the last two irrigations only, reduced the amount of water consumed and the amount of drainage water to zero. The results also showed the reduction of gross irrigation water in the salinity levels more than tolerance threshold of wheat was less than the other salinity levels and the difference was completely negligible. Because in high salinity levels, yield reduction in deficit irrigation was more in comparison with low salinity levels, therefore deficit irrigation in salinity levels of more than the wheat tolerance threshold is not economic. Generally, the optimization results showed that in the area of Birjand and Mashhad, using the best irrigation and leaching management at different salinity levels can increase the benefit of wheat cultivation.

Energy input is one of the main factors of extracting water from groundwater resources. Therefore, pricing policies related to energy carriers affect the cost of water extraction and it also affects the amount of water extraction. In the past years, one of the reasons that has put more pressure on groundwater resources is the inappropriate subsidy policies for energy carrier prices. Given the close relationship between energy carriers prices and the costs of groundwater extraction, in the present study, it was tried to investigate the effect of energy carriers' subsidies decreasing on the amount of groundwater consumption, cropping patterns and agricultural income using the dynamic programming model in Shabestar plain. This plain is one of the critical areas in terms of groundwater resources in Iran. The results showed that decreasing energy carriers' subsidies caused extraction reduction of water from groundwater resources. For example, a 25 and 50 percent reduction in energy carrier subsidies would reduce groundwater extraction by about 4.5 and 6.5 million cubic meters per year, respectively. Also, areas under cultivation of some high water using and low-income products decrease and cropping patterns change toward more high-income products such as onions, apples and walnuts. The results indicated that adopting appropriate energy carrier subsidy policies could be useful for improving groundwater extraction conditions.

In this study, a comprehensive evaluation of the effect of cross-correlation coefficient between precipitation and evapotranspiration on the performance of SPEI in forecasting the long-term behavior of agricultural drought characteristics in 39 synoptic stations located in different climates and altitudes throughout Iran has been performed, and its performance has been compared with SPI. For this purpose, stochastic precipitation and evapotranspiration time series (10,000 pairs) were generated using the Lag-one Autoregressive Multiple-Site Model (Multi-AR(1)) for a wide range of correlation coefficients of the above two variables and then used to monitor and determine different drought characteristics. The results of this study showed the systematic and dependent behavior of different characteristics of drought with cross-correlation between precipitation and evapotranspiration. So that, the performance of the two SPEI and SPI indices was nonlinearly close to each other (R=0.85) by increasing the cross-correlation between the two variables (|R|⇒1), and for cross-correlation of zero, the maximum difference between the drought characteristics was obtained for the two indices. Finally, the results of this study have been used as a comprehensive guide in the accurate and realistic inference of drought events and characteristics and can be an effective aid in interpreting drought outcomes with the SPEI index.

Water crisis in the Middle East has made water security the main issue in national security. Water security is a vital element in supply of energy security and food security. Water security is one of the concepts that examines the situation of countries and watersheds in terms of access to water resources, and has emerged in response to the challenging nature of the global water crisis. Various hydrological, structural, institutional, political, social, economic and environmental factors are involved in water security. The present study is an attempt in investigation on the hydrological factors affecting the water security of the western sub-basin Lake Urmia. For this purpose, water availability, water use intensity, monthly runoff variability and portion of irrigated land to cultivated land as the selected indicators in assessing the water resource and consumption security in the western sub-basin of Lake Urmia by study areas Salmas, Urmia, Naqadeh, Oshnavieh, Mahabad and Bukan in the 5-year time period including 2006 and 2011. The results showed that the water resource and consumption security in the western parts of the Urmia Lake basin have relatively similar conditions and included in the weak and middle class. The score of water resource and consumption security for the whole western sub-basin of Lake Urmia for the years 2006 and 2011 is equal to 0.4 (medium) and 0.32 (weak), respectively. This is due to the reduction of total renewable water resources per capita and increasing the water use intensity in the western sub-basin of Lake Urmia, which requires a review in managing water consumption in different sectors.

Rapid population growth and the expansion of urbanization in large parts of the world have increased the need for various facilities and resources, of which water is undoubtedly the most important or one of the most important. This need, which is associated with the heterogeneous distribution of water resources and rainfall in the world, has caused numerous tensions in the field of water supply. It has also made the issue of "water security" an emerging challenge in the field of national and international security. Water security is one of the concepts that examines the situation of countries and watersheds in terms of access to water resources, and has emerged in response to the challenging nature of the global water crisis. Various hydrological, structural, institutional, political, social, economic and environmental factors are involved in water security. The present study wants to investigate the hydrological factors affecting water security in the western sub-basin of Lake Urmia and to provide an overview of the state of water resources and consumption security in the study areas.

One of the most common methods used to assess water security is the use of indicators so that they can express large amounts of complex information in a simple and understandable way. The method of assessing the status of water security using indicators is a new method that is proposed to countries to assess the current state of their water security in this way. In the present study, the state of water resources and consumption security in the western sub-basin of Lake Urmia (by study areas of Salmas, Urmia, Naqadeh, Oshnavieh, Mahabad and Bukan) in 5-year periods including 2006 and 2011 have been investigated. For this purpose, water availability, water use intensity, monthly runoff variability and Portion of irrigated land to cultivated land are considered as selected indicators in evaluating water security in terms of water resources and consumption. Due to the unit differences of the indicators, in order to facilitate the interpretation of the water security situation, the indicators are normalized in the range of 0 to 1 and based on the threshold / reference values. Then, using the weighted average method, the score of water resources and consumption security is calculated for each of the studied areas. At the end, based on the water resources and consumption security score, all areas are classified as "very poor", "poor", "average", "good" and "very good".

The results of the study showed that the water resource and consumption security situation in the western parts of the Urmia Lake basin has relatively similar conditions and is in the middle and poor class. None of the study areas in the western sub-basin of Lake Urmia have very high or even high water security. The score of water resource and consumption security for the whole western sub-basin of Lake Urmia for the years 2006 and 2011 is equal to 0.4 (medium) and 0.32 (weak), respectively. The best water security situation is in Bukan area and the worst situation is in Naqadeh area. The decreased water resource and consumption security over time periods has been due to the reduction access to water and increasing the Water use intensity in the western sub-basin of Lake Urmia, which requires a review in managing water consumption management in different sectors.

According to the results of the study, it can be stated, the trend of water security changes in the western sub-basin of Lake Urmia is decreasing and the root of water insecurity is different in each of the studied areas. This indicates the high need of the basin to make economic and infrastructure investments and increase the institutional capacity of the basin to manage and plan water resources, which shows the need for attention of planners and officials in the water sector.

 order to study the spatial patterns and prepare spatial distribution maps, point data are generalized to the surface by moving away from the site in the interpolation process. The values ​​of indefinite points are estimated by using the measured values at definite points. There are different methods for spatial data interpolation and any major defect in the spatial distribution of climatic factors and slight inattention to the appropriate method of interpolation method can lead to errors in the design estimates. So far, many researches have been done in the field of climate data interpolation in the world. The aim of this paper is determining the most appropriate method of rainfall extension pattern and fitting the best distribution function to the annual average rainfall data. In addition to the performances of different interpolation methods, the choice of the optimal model for zoning rainfall amounts in different time periods is the goal of paper.

Iran is located in West Asia and borders the Caspian Sea, Persian Gulf, and Oman. With an area of 1,648,000 square kilometers (636,000 sq. mi), Iran ranks seventeenth in size among the countries of the world. The study area is located between 32° 25' 14.67" N and 53° 40' 58.86" E. Iran has a variable and continental climate in which most of the relatively scant annual precipitation falls from October through April. The average elevation of this plateau is about 900 meters (2,953 ft.), but several of the mountains that tower over the plateau exceed 3,000 meters (9,843 ft.). Volcanic Mount Damavand, 5,610 meters (18,406 ft.), located in the center of the Alborz, is not only the country's highest peak but also the highest mountain on the Eurasian landmass west of the Hindu Kush .The monthly precipitation data from 79 synoptic stations with at least 30 years during the period from 1988 to 2017 were used. Very severe drought years and SPI values identified in the intervals of 3, 5 and 8 months until the end of May were extracted from monthly rainfall data in each year and in three ten-year steps. In order to determine the distribution of rainfall in 10-year time periods in the lack of data points, the indices of mean absolute value of relative error (MARE), mean bias error (MBE), mean absolute percent error (MAPE) and the root mean squared error (RMSE) has been used.

In general, using the above errors in the quarterly, five-month and eight-month periods between definitive interpolation methods, radial basis functions (RBF) and local polynomial interpolation (LPI) compared to the other two methods (IDW, GPI) has provided proper results that is consistent with the results of Misaghi (2006) and Eivazy (2012). Regarding the method of radial functions, the quadratic interpolation method had the lowest correlation for all variables except rainfall with a period of 3 months. Kriging methods have given better results compared to IDW method, which is consistent with the results of Soleimani et al. (2004) and Zabihi et al. (2011). The methods of kriging, simple kriging method (second-order logic) in the interpolation parameters of mean rainfall, (3 and 5 months), simple kriging method (exponential) in the interpolation variables precipitation with a period of 8 months were the best methods. Therefore, kriging method has presented relatively more accurate results, which is consistent with the results of Mirmousavi et al. (2010).

The results showed that the most droughts occurred in the second decade of the third decade, the trend is still less severe drought is occurring and the lowest is in the first decade. In order to study the effect of SOI and NAO on drought and wet years in the country, the correlation matrix was determined to investigate the drought and wet periods. Then, geostatistical modelings were performed to describe these effects. Finally, based on the study of stations and zoning models, it can be stated that the Lanina phenomenon has a little effect on drought and annual rainfall and the El Nino phenomenon has a relatively greater effect. The results show that GPI and IDW methods are not considered as good performance. These methods are based on the fit of polynomial functions on spatial data. According to the results obtained in every area and for different parameters, a single method cannot be selected as the most appropriate method and the most appropriate method can be selected. Based on the results, the most appropriate choice depends on the characteristics of the regions and a single method cannot be selected for all regions. The results show that GPI and IDW methods are not suitable and modeling in these two methods is based on fitting polynomial functions to spatial data. RBF and LPI methods have provided better output rather than other methods because the results of the 3 and 5 month periods were above 62% and the 8 months were above 72%.

In recent decades, agricultural production has relied heavily on the use of chemical inputs, which has led to major environmental problems. The use of chemical fertilizers and pesticides has led to the destruction of water and soil resources, reducing the population and diversity of soil microbes, water and air pollution, increasing the resistance of pests and pathogenes to various chemical pesticides, etc. One of the solutions to this problem is to use the principles of sustainable agriculture. Sustainable agriculture is an integrated system based on ecological principles and in this system, plant residues, animal manures, organic and bio-fertilizers are used instead of chemical compounds such as fertilizers and pesticides. In addition to supplying nutrients in the soil, it leads to weed and pest controlling and increases the microbe’s biodiversity in soil. Useful soil microorganisms such as mycorrhizal and endophytic fungi play an important role in the nutrients cycling, especially phosphorus and some other trace elements in ecosystems and plant protection against environmental and agricultural stresses. The fungal hyphae often extend into the portion of the soil that is not penetrated by roots or root hairs, and led to increase the absorption of water and nutrients for the plant. Endophyte fungus Piriformospora indica also has a wide range of host plants by colonizing their roots and stimulates the growth of its host plants and increases their yield. The use of these fungi in nutrient-poor soils increases the growth and yield of agricultural products along with reducing the use of chemical fertilizers and pesticides. Coriander (Coriandrum sativum L.) is an annual herb of the Apiaceae or Umbelliferae families, native to the Mediterranean region that has been cultivated since human antiquity. In addition to its oral application, this plant also has healing properties and is also considered as a medicinal plant. Coriander consumption is recommended in the treatment of various infectious diseases such as typhoid fever and various diseases in general. The shoots of this plant contain essential oils and active ingredients, and decanol is the main components of its essential oil. Whereas global approaches to the production of medicinal plants are aimed at improving the quantity and quality of the active ingredient of medicinal plants, healthy nutrition of these plants through eco-friendly approaches such as the use of biological fertilizers, is the most consistent with the goals of sustainable agriculture and leads to improved quantitative and qualitative yield of these plants. Accordingly, the study of the effect of beneficial soil fungi (mycorrhizal and endophytic) on vegetables such as coriander due to their high consumption by humans seems necessary. Therefore, in this study the effect of inoculation of two species of arbuscular mycorrhizal fungi (Diversispora versiformis ‌ and Rhizophagus irregularis) and one species of endophytic fungus (Piriformospora indica) individually and/or in combinations on the chemical composition and production of coriander essential oil was investigated.

The treatments were included single fungus inoculation (RI: R. irregularis, DV: D. versiformis, PI: P. indica), integrated fungal inoculations (PI+DV, PI+RI, RI+DV and PI+RI+DV), positive control treatment (with urea fertilizer and triple superphosphate, without fungus), and the negative control (no chemical fertilizer, no fungus). The experiment was conducted in a completely randomized design with four replications in greenhouse conditions. Fresh and dry weight of roots and shoots, percentage of root colonization, leaf phenol, antioxidant compounds, carotenoids, total chlorophyll and essential oil were measured in coriander plant.

The results showed that the effect of all three fungal species (mycorrhiza and endophyte) both separately and in combination on the measured parameters was significant (p <0.01). Combined treatment of three fungi (PI + RI + DV) showed the highest yield in fresh and dry weight of shoot part by 8.62% and 17.94%, respectively. In the case of fresh and dry weight of root part, DV treatment had the best performance by 22.66% and 48.04%, respectively. The root colonization increase (91.75%) in PI + RI, phenol (72.5%) in RI + DV, antioxidant componds (11.81%), total chlorophyll (7.81%) and carotenoids (5.87%) in PI, and essential oil yield (107.2%) in PI + DV were recorded. All increases reported above are in comparison with negative control treatment.

According to the results of this study, in order to achieve higher yield in coriander, it is better to use single treatments of mycorrhizal fungi. Also, based on the results of this experiment, to enhance the performance of mycorrhizal fungi to improve the growth and development of coriander, especially to improve the yield of essential oil, it is better to use a combination of mycorrhizal fungi and endophyte. It should be noted that the results of this experiment were obtained in greenhouse conditions and to finally confirm the effectiveness of the fungi used in this experiment, therefore further studies should be performed at the field conditions along with measuring more traits to provide clearer and applied results.

Due to decreasing water resources and increasing irrigation interval in gardens, implementation of modern irrigation systems such as under-pressure irrigation is necessary. But the main concern is the effects of changing traditional irrigation systems to under-pressure irrigation systems from the point of view of supplying water needs of old trees and temporal variations of soil characteristics. In this study, two methods of subsurface-flood irrigation and surface-drip irrigation were implemented in Yazd Agricultural and Natural Resources Research and Education Center. The soil water content was monitored during the experimental period (18 May 2019 to 10 January 2020) by installing TDR probes in three soil depths of 15, 30 and 60 cm for each irrigation method with three replications. The results showed that soil water content at the soil depths of 15 cm and 30 cm were higher for surface-drip irrigation as compared to the subsurface-flood irrigation at the end of the irrigation interval. Two days after irrigation, the effects of subsurface-flood irrigation on soil moisture distribution became apparent at the deep layers of the soil, such a way that soil water content at the soil depth of 60 cm was higher for subsurface-flood than surface-drip irrigation. The measuring of the soil water content at the soil depth of 60 cm in subsurface-flood irrigation showed that the irrigation interval could be increased about 9 days as compared to the surface- drip irrigation. However, because of applying water below the soil surface for the subsurface- flood irrigation, the salinity increment occurred at surface soil.

 Education is the main basis for the growth of the individual and society and plays an important role in the sustainable development of a country. Education in the agricultural sector plays an important role in human resources supply. Numerous studies have been conducted to achieve educational solutions around the world. In Nigeria, educational videos were used to educate farmers in the form of animation, and the results of this study showed that knowledge transfer through information and communication technologies has a positive and significant effect (Mirgohar and Movahed Mohammadi 2008). Inside the country, research conducted in Ramshir has shown that participation in training and extension courses has a significant effect on increasing the level of professional knowledge of farmers and farmers' professional knowledge is directly related to age and agricultural work experience (Zare and Zolali 2015). Review of studies confirms the effectiveness of farmers' educational services and shows that there is always a need for education to properly use land and water resources. In this study, farmers' learning and educational resources were first identified. Then, the utilization status of these resources has been evaluated and analyzed separately for irrigation systems (surface and presssurized) and its relationship with the variables of age, level of education and background of using the irrigation system, by statistical tests.

The study area is located in the central latitude and longitude of Qazvin province between 48 degrees and 44 minutes to 50 degrees and 51 minutes east of the Greenwich meridian and 35 degrees and 24 minutes to 36 degrees and 48 minutes north latitude relative to the equator. The total area under cultivation of crops and horticulture in Qazvin province was on average 334.764 thousand hectares, of which about 2323.439 thousand hectares (equivalent to 66.5%) irrigated and about 1112.325 thousand hectares (equivalent to 33.5%) has been rainfed (Anonymous 2019a, Anonymous 2019b). The research method was the Delphi technique and the survey tool was a questionnaire. The score of learning status of learning and educational resources was calculated using the mean index. Kolmogorov and Smirnov tests were used to check the normality of the data. To compare the score of each item with the average Likert scale (3), a one-sample t-test (parametric test) and one-sample sign test (non-parametric) were used. To compare the three perspectives of farmers, experts, and trainers, Kruskal-Wallis (non-parametric) and Mann-Whitney (non-parametric) tests were used. Moreover, Pearson (parametric) and Spearman (non-parametric) tests were used to assess the correlation between the use of learning and educational resources with the variables of age, level of education and background of using the irrigation system.

Evaluations showed that the utilization status of learning and educational resources from three perspectives of "farmers", "experts" and "trainers" has a score of 1.73±1.08, 2.47±0.94, and 1.99±0.79, respectively (out of a total score of 5) for crop farmers with surface irrigation system, 1.98±1.05, 2.52±0.84 and 2.39±0.95 (out of a total score of 5) for crop farmers with pressurized irrigation system, 2.19±1.08, 2.28±0.87 and 2.12±0.96 (out of a total score of 5) for gardeners with surface irrigation system and 2.55±0.92, 2.38±0.88 and 2.37±1.21 (out of a total score of 5) for gardeners with pressurized irrigation system. The results of correlation tests also showed that there was no significant effect on the use of the learning and educational resources of farmers in any of the groups. Background of using the irrigation system only in the group of farmers with a pressurized irrigation system had a positive and significant relationship and the level of education in all groups had a positive and significant effect. The highest use of learning and educational resources based on the three views expressed in the items "experience gained over time" and "benefiting from the experience of other farmers (leading farmers)" versus the lowest use of items related to "computer software and internet by computer", "mobile programs and mobile internet", "brochures, magazines and educational videos", "radio programs" and "attending classes and workshops".

The results of this research showed that farmers have basic knowledge about information and communication technology and are not fully aware of it and participate less in training classes. Also, in general, the utilization status of learning and educational resources from the three perspectives expressed is in the middle to low state. These results showed capacity building and change in content and how to use learning resources and education is necessary according to the conditions, interests, and motivational contexts of farmers. It is also suggested that to provide sustainable education, the provision of support and monitoring services should be considered on an ongoing basis alongside training programs.

Accurate determination of the amount of incoming solar radiation at Earth’s surface is important for both climate studies and solar power applications. Satellite-based datasets of solar radiation offer wide spatial and temporal coverage, but careful validation of their quality is a necessary prerequisite for reliable utilization. In this study, daily SARAH satellite data was used to estimate daily solar radiation at weather stations in northwest Iran in 2009 and 2010. According to the results, SARAH radiation data had a high accuracy in estimating solar radiation at all five stations. At the Tabriz station, the error of estimation of radiation was less than that of other stations. According to GSD evaluation criteria, in each station, the estimation error in summer and winter was the lowest (GSD = 6.2 - 12.5) and the highest (GSD = 18.2-25.2) respectively. The results showed that factors such as topography and snow cover affect the accuracy of SARAH radiation data estimation.