نشریه آب و خاک
سال سی و هفتم شماره 6 (پیاپی 92، بهمن و اسفند 1402)

Limited fresh water resources and access to these resources as well as providing food security for the growing world population have led researchers to make extensive efforts in the field of optimal management of water consumption and determining the cultivation pattern in different regions. Therefore, identifying cultivated crops in a region and determining their area can be very effective in land management and water allocation in these regions. With the growth and advancement of technology in the field of satellite and remote sensing in recent decades, the use of satellite images in order to identify types of land use and types of cultivated products has expanded greatly. Sentinel-1 (radar) and Sentinel-2 (multi-spectral) satellites have been very popular in agriculture due to their improved spatial resolution (10 meters) and appropriate time resolution (5 days for Sentinel 2 and 12 days for Sentinel 1).

The studied area is located downstream of the Fariman dam in an area of 22.51 square kilometers (5122 hectares) and the central coordinates are 35 degrees 41 minutes and 59 seconds north latitude and 59 degrees 50 minutes and 49 seconds east longitude. In order to classify satellite images and produce crop maps, ground observation data is needed to train the classification model and also evaluate the accuracy of the results. For this purpose, sample points were taken from different land uses in the region, using GPS. Since it was not possible to take enough samples for all land uses and crops in the determined border, a larger sampling area was selected. Then, all collected data were sorted and for each class, 70% of the data was randomly used to train the classification model and 30% was used to validate the obtained classification results. In the present study, Sentinel 2 satellite images for the first 6 months (crop season) of 2021 and 2022 and digital elevation image (DEM) of the study area were considered. According to the surveys conducted and the reports of the agricultural jihad of Fariman city, the main crops cultivated in the region include maize, tomato, sugar beet, wheat and barley. Therefore, according to the phenological stages of these products in the region, the appropriate time series of images was selected. The accuracy of the classified map was evaluated using the Kappa coefficient and overall accuracy.

In order to identify and separate the land use in the study area according to the major cultivated crops, first the agricultural calendar of the crops was determined. Then, satellite images were selected based on crop cultivation period. Based on the evaluation indexes of commission error, omission error, overall accuracy as well as the Kappa coefficient, it was observed that the identification of classes and land use was done well and with high accuracy, so that the overall accuracy for the classification map of 2022 is equal to 0.97 and the kappa coefficient value was 0.94. In order to compare land use changes during the two years 2022 and 2021, classification was also done for the images of the crop year 2021. Since the training samples of agricultural crops were not available separately and in sufficient numbers in the crop year of 2021, the classification map of this year was produced only based on the type of land use, and all crops in one class entered the classification model training process. The values of overall accuracy and kappa coefficient in 2021 were obtained as 0.97 and 0.95 respectively. According to the obtained results, the area of the orchard class has increased since 2021 compared to 2022. After repeated field visits to the study area and investigation of some land uses that had been changed and turned into orchard use, it was found that in some areas in 2022 there was the growth of villa gardens and in some areas the farmers have converted cropland to orchard (construction of an orchard). Even in some cases, the old orchard in the region was destroyed by the farmers and the land was fallow for 2 to 3 years (2021, fallow). In 2022, the farmer built a new orchard. It is also necessary to mention that fallow lands are included in the soil class depending on whether they are newly plowed or have no vegetation, and if weeds have grown on these lands, they are included in the rangeland class. 

The effective management of water resources from dams for agricultural purposes necessitates the identification of land use downstream of the dams, along with determining the types of crops and their respective areas. In this study, Sentinel 2 satellite images were employed to classify and delineate land use associated with agricultural cultivation downstream of the Fariman dam in Razavi Khorasan Province, spanning the crop years of 2021 and 2022. The results indicate that the Sentinel 2 satellite demonstrates a high capacity to differentiate between various types of land use and crops. The generated map depicting changes in land use and crop cultivation areas can be instrumental in water use planning and the allocation of water resources.

Flood is one of the most destructive natural disasters that has a negative impact on social, economic and environmental dimensions. Floods usually occur following a prolonged period of rain or snowmelt in combination with unfavorable conditions. In this regard, all over the world, the occurrence of floods has intensified by 40% in the last two decades. In Asia, almost 90% of all human casualties caused by natural disasters are due to floods. The increase in flooding is usually due to increased environmental degradation such as urbanization, increased population growth, and deforestation. Periodic and regular occurrences of floods over a certain timeframe significantly amplify the detrimental impacts on living organisms. Urban areas in close proximity to rivers bear the brunt of these damages, owing to high population density, economic infrastructure, and transportation networks. However, these consequences can be alleviated through meticulous vulnerability analysis. One of the primary objectives pursued by researchers and policymakers is the precise modeling and zoning of floods to mitigate associated risks. Consequently, a myriad of methods and approaches have been devised for flood risk modeling and zoning to address this pressing issue. Among them, hydrological methods such as rainfall-runoff modeling and data-based techniques, which are unable to comprehensively analyze rivers and flood zones due to their one-dimensional nature. This is despite the fact that the morphology of the river is not stable and due to its high erosion potential, it also has a dynamic characteristic. In addition, these methods require fieldwork and large budgets for data collection. Hence, comprehensive flood management is necessary to reduce these effects. Therefore, this study was conducted with the aim of identifying areas sensitive to the risk of flooding in Famnat watershed located in Gilan province. Fomanat watershed is located in Gilan province and is considered a part of the first grade watershed of the Central Plateau. This area is located in the range of 36.89 to 37.57 degrees north latitude and 48.77 to 49.69 degrees east longitude. This region has an area of 3595 square kilometers, the highest point of which is 3088 meters and the lowest point is -69 meters. 

 To carry out the current research, firstly, by reviewing the sources and history of the research, as well as knowing the region, a map and layers of information related to the factors affecting flood susceptibility zoning were prepared. These layers include land use map, slope degree, geology, distance from waterway, digital map of height, direction, shape of land curvature, land curvature profile, rainfall and topographic humidity index, which are created using the collected data and also various additions in the environment. Geographic information system (Arcgis 10.4) was prepared. In this regard, machine learning models such as generalized linear model (GLM), multivariate adaptive regression model (MARS) and classification and regression tree model (CART) were used to zone the sensitivity of the watershed to floods. Also, among 100 flood events, 70% (70) were considered for training and 30% (30) for validation. In the following, using field survey and review of previous studies, 10 factors influencing the occurrence of floods in the watershed area were identified and used. Finally, the area under the ROC curve and the TSS index were used to evaluate the models.

 The results of the evaluation of the most important factors affecting the sensitivity of the watershed to floods indicated that the distance from the river, the height and the curvature profile had the greatest impact on the sensitivity of the region, and on the other hand, the factors of slope, geology and topographic humidity index had the least impact. Based on the obtained results, the areas covered by very low, low, medium, high and very high classes in the CART model were 26.6, 17.6, 21.2, 0.1 and 34.0%, respectively. These results for the GLM model were 13.6, 12.7, 16.2, 25.1 and 32.4 percent, respectively. Based on the obtained results, the CART model performed better than other models, so that AUC for MARS model was equal to 0.76, CART model was equal to 0.9 and GLM model was equal to 0.84. Also, the better performance of CART model compared to other models was confirmed by other indicators. So, based on TSS, MARS model equal to 0.52, CART model equal to 0.77 and GLM model equal to 0.66 were obtained.

Implementing the findings of this study can facilitate the adoption of effective management strategies to mitigate losses and casualties. Moreover, in developing nations grappling with restricted access to hydrogeological and soil data, the utilization of geographic information systems (GIS) and data mining techniques assumes a pivotal role in conducting comprehensive studies. These technologies offer valuable insights and support decision-making processes, enabling proactive measures to address flood risks and enhance disaster resilience in vulnerable regions.

The study of surface water quality control in water resources and environment management programs is very important. Surface water is one of the most important water sources that have crucial impact on agricultural, industrial, drinking and electricity production activities. Due to insufficient water sources with good quality and the increase in population growth rate and as a result of the increase in demand, the study of water quality parameters is very important. The Water Quality Index (WQI) serves as a prominent indicator in classifying surface water quality. Moreover, in recent years, the TOPSIS method has gained traction for evaluating water quality. This approach, known for its simplicity, is increasingly utilized in prioritizing river water and assessing its quality. Through this index, various components of water quality are condensed into a single numerical value, effectively expressing overall water quality. To ascertain the weight index, Shannon's entropy method was employed. Furthermore, to assess water suitability for drinking, agriculture, and industrial purposes, Schuler, Wilcox, and Piper diagrams were utilized. These diagrams provide valuable insights into the quality of water, aiding in decision-making processes regarding its utilization across different sectors. Therefore, the results of this study also confirmed the effectiveness of the TOPSIS method in identifying contaminated stations.

This research focuses on evaluating the water quality of three stations within the Aji Chai river watershed on an annual basis. These stations are identified as Arzanag, Akhola, and Markid. The assessment spans the years 2003 to 2021 and aims to classify water quality for both drinking and agricultural purposes. Utilizing the standards set forth by the World Health Organization, the surface water quality index of the Aji Chai basin is investigated to ascertain its suitability for drinking purposes. Shannon's entropy theory was used to prevent expert judgments in determining the weight of each parameter. TOPSIS method was used to classify eleven qualities including TDS, EC, pH, HCO3-, Cl-, , Ca2+, Mg2+, Na+ , K+ and TH. In all the three stations water quality were ranked, based on TOPSIS numerical values. Also, in order to check the quality of drinking, agricultural and industrial water, Schuler, Wilcox and Piper diagrams were used. 

The initial findings from the %RE error analysis revealed that throughout the entire statistical period (2003-2021), the %RE values were consistently close to zero, with the majority being positive. This suggests that the total number of cations surpasses the total number. In terms of the Shannon water quality index, the results indicate that Markid station exhibited the highest index value at 945.92, while Arzanag station displayed the lowest value at 127.365 among the surveyed stations. The results of the water quality index showed that Arzanag and Akhola stations are in an average condition (100 < EWQI < 150) and Markid station is in a very poor condition (EWQI > 200). According to Schuler's diagram, it was found that the water of Arzanag station is in the average level in terms of water quality, which is in a good position in terms of quality compared to the other two stations, while the water of Akhola station is in a good position. In the range of poor quality, Markid water was undrinkable, which ranked worst among the three stations. According to the Wilcox diagram, it was found that the water quality of Markid is very poor, which is even outside the boundary of the Wilcox diagram, while the water of Arzanag station was ranked 1st in terms of quality. Arzanag water is in C4S2 class in terms of quality. Finally, the water class of Akhola station was placed in the C4S4 class (in the Wilcox chart), which shows very low water quality. According to the TOPSIS method, the first priority in terms of water quality pollution belonged to Markid station. Two other stations, including Akhola and Arzanag, were ranked second and third in this respect. Therefore, the most important station in this basin is Markid station. 

The results of Shannon water quality index showed that among the stations, the highest index value is related to Markid station with a value of 945.92 and the lowest one is related to Arzanag station with a value of 127.365. According to Schoeller diagram, it was found that the water quality of Arzanag station is average, compared to the other two stations, it was in the right place and the water of Akhola station was in the range of poor quality. The quality of Markid water was found to be undrinkable, which was the worst one among all the three stations. The range of TOPSIS values in different stations is between 0.054 and 0.894, which belonged to the Arzanag and Markid stations, respective ly. According to the results of the Arzanag station, the best water quality condition and the Markid station were assigned the worst water quality condition among all the three stations.

Large amounts of agricultural waste such as straw, leaves and pulps, with high nutritional value are produced every year. Grape pomace (GP) is rich in macro- and micro-nutrients and can be used as a soil amendment. However, due to its slow decomposition rate and the spread of diseases and pests, it should not be applied directly to the soil. Therefore, GP is composted in combination with other wastes. There is not enough information about the composting of GP and the effect of the produced composts on soil fertility in Iran. Hence, the aims of this study were twofold: to explore the impact of various GP composts on both soil fertility and spinach yield, relative to two levels of urea fertilizer, through a pot experiment conducted over two consecutive cultivation seasons; to categorize soil treatments based on fertilization regimes and timing (season), thus elucidating any patterns or trends in the observed effects. 

To investigate the effects of GP composts on soil fertility and spinach (Persius hybrid) yield, was conducted as a randomized complete block design with eight compost treatments, two levels of urea fertilizer (46%), and a control treatment (C0), in three replications and two continuous cropping seasons (spring and fall). Compost treatments included: high grape pomace (HG) (60-63%) with chickpea straw and alfalfa (HG-Ch-A), high GP with chickpea straw and sugar beet pulp (HG-Ch-B), high GP with alfalfa and sugar beet pulp (HG-A-B), high GP combined with chickpea straw, alfalfa, and sugar beet pulp (HG-All); four other compost treatments included low level of grape pomace (LG) (37-42%) combined with other residues/wastes similar to the first four treatments (LG-Ch-A, LG-Ch-B, LG-A-B, and LG-All). Urea treatments included: 150 kg per hectare (C150) (two-step top dressing) and 500 kg per hectare (C500) (three-step top dressing). A sandy loam soil was used for this experiment. The composts were separately mixed into the soil at a rate of 2% (by weight(. The first crop was grown for 50 days in May 2018 and the second crop was grown for 45 days in September 2018. In both seasons, the fresh and oven-dried weigh of spinach shoot and root were determined. Also, total concentration of K, Na, Ca, Mg, P, Fe, Zn, Cu, and NO3- were measured in spinach to determine the amount of soil elements taken up by the crop. In both seasons, soil pH and EC, and contents of soil organic carbon (OC), active carbon (AC), total nitrogen (TN), NO3-, NH4+, and exchangeable K, Ca, Mg, and Na, as well as available forms of P, Fe, Cu, and Zn were determined. One-way ANOVAs were applied separately to spring and fall data, and mean comparisons were made using Duncan's test at 0.05% level. To determine the similarities and dissimilarities of the different treatments based on their effect on soil characteristics, cluster analysis was performed on all soil characteristics that showed significant differences between treatments. 

In both cultivation periods, TN levels exhibited no significant variance across treatments. Notably, the highest potassium (K) levels were consistently observed in the HG-All and LG-All treatments, while the lowest K levels were consistently recorded in the C0, C150, and C500 treatments. In the initial cultivation period, no notable differences were observed between the C0, C150, and C500 treatments, except for potassium (K) and ammonium (NH4+), with significantly higher levels detected in the C0 treatment. Conversely, during the second cultivation period, significant disparities were observed among the C0, C150, and C500 treatments solely in terms of nitrate (NO3-) content, with notably higher nitrate levels detected in the C150 and C500 treatments. Through cluster analysis, all treatments from both cultivation periods were categorized into five distinct groups. Specifically, the C0, C150, and C500 treatments for each season were consistently grouped together, respectively, into groups one and two. All compost treatments of each season, except the HG-All treatment in the spring cultivation, were grouped into one class. In the second cultivation, the HG-Ch-A showed significantly higher EC than all treatments, except the HG-Ch-B. The LG-A-B treatment showed the highest amount of OC and C/N (in both cultivations), and NH4+ and Cu (in the second cultivation). The HG-Ch-A and HG-Ch-B treatments increased TN, P, K, Mg, OC, and AC in the second cultivation compared to the first. The amounts of all macronutrients and micronutrients, except Fe and Ca, increased in the compost treatments compared to the control and chemical treatments. In addition, an increase in EC was observed in the compost treatments compared to the control and chemical treatments, and an increase in pH compared to the C500 treatment. In the first cultivation, the LG-Ch-A and C500 treatments had significantly higher yields than the control. In the second cultivation, the LG-All, HG-All, HG-Ch-A, and LG-A-B treatments were the best compost treatments, while the LG-Ch-B and HG-Ch-B treatments were the weakest treatments in terms of soil fertility and plant yield. In both seasons, the absorption of elements by spinach depended on multiple factors, including the element type, its available content in the soil, its initial content in the composts (or fertilizer), soil pH, and yield. 

The application of GP composts over two consecutive growing seasons increased the levels of nitrogen, phosphorus, potassium, magnesium, zinc, copper, active carbon and organic carbon in the soils. These results are very important as magnesium, copper and zinc are rarely applied by farmers. In contrast, depletion of all elements, except organic carbon, occurred in the control and chemical fertilizer treatments due to plant uptake of elements. The combination of chickpea straw with sugar beet pulp is not recommended for the production of GP compost, especially at low GP levels, due to its minimal effect on soil fertility and plant yield. Despite the positive effect of the GP composts in increasing soil fertility, the continuous application of large amounts of these composts is not recommended in the arid regions due to the increase in soil EC and pH. The difference between the compost treatments after two applications of GP composts was less than after one application; these results were confirmed by cluster analysis, in the sense that all compost treatments in the second season were placed in one cluster.

Application of natural organic matter derived components, i.e. humic acid, as fertilizer is a suitable way to improve soil fertility and increase yield and quality of agricultural products. Many researchers reported positive effects of humic acid on water holding capacity, soil aeration, root formation and development, microorganism activities, and availability of mineral nutrients in soil. Antagonistic interaction between soil phosphorus and some micronutrients, especially in calcareous soils, can cause micronutrients deficiency in plants. With regard to positive effects of organic compounds on bioavailability of mineral nutrients, it seems that humic acid can positively affect the phosphorus interaction with micronutrients. Therefore, investigation of the effects of humic acid incorporated into irrigation water, phosphate and iron fertilizers application, on nutrients concentration in plants and their interactions is considerable.

This study was carried out to investigate the effects of application of humic acid in irrigation water, and phosphate and iron fertilizers in soil, on corn growth and concentration of P, Fe, Mn, Zn, and Cu in corn tissues. To this aim, a factorial experiment was conducted based on completely randomized design, with three replications in greenhouse. The factors included humic acid in 0, 70, and 140 mg kg-1 levels, (7 times as fertigation during growth season; total use equal to 0, 490, and 980 mg kg-1 of soil, respectively), phosphorus (P, as monocalcium phosphate monohydrate) in 0 and 50 mg kg-1 levels, and Fe (as ferrous sulfate heptahydrate) in 0, 10, and 20 mg kg-1 levels. P and Fe treatments were mixed with 4 kg of air-dried soil (<2 mm in diameter) and filled to the pots. Six seeds of maize (Zea maye L. cv. Single cross 704) were seeded per pot, and three seedlings were finally kept and grown for two months. After harvest, fresh and dried weight of shoots were measured. The roots were accurately extracted from the soil, washed, dried at 65◦C, and weighed. Sample digestion and measuring concentration of P, Fe, Mn, Zn, and Cu were done according to conventional methods (P by a UV-Visible Spectrophotometer and metal elements by the GBS Savant Atomic Absorption Spectrometer). Statistical analyses were done by the IBM SPSS Statistics version 26 software.

According to this study results, the main effect of humic acid, on P concentration and dry matter of shoots and roots, was statistically significant. In presence of P (2nd P level), 490 and 980 mg kg-1 humic acid levels significantly increased the mean of dry matter compared to blank while humic acid had no significant effect on means of shoots and roots dry matter in 1st level of P (no P application). Increasing humic acid level from 490 to 980 mg kg-1, significantly decreased mean of shoots dry matter. The interaction effect between humic acid and the other two factors exhibited statistical significance concerning root dry matter. The treatment combination of 50 mg kg-1 of P, 490 mg kg-1 of humic acid, and 20 mg kg-1 of Fe yielded the highest mean root dry matter, which was 97% greater than that of the control. The 2nd level of P significantly increased the means of shoots P concentration in all levels of humic acid and Fe factors, compared to those of the 1st P factor level. There was no significant difference between means of shoots P concentration in different levels of humic acid and Fe factors, at the 1st level of P factor, separately. On the other hand, at the 2nd level of P factor, significant differences were observed between the means of P concentration for both other factors (significant interaction between P and humic acid, and between P and Fe Factors). Applying humic acid could significantly increase the means of shoots P concentration at the 2nd level of P factor, but there was no significant difference between those of 490 and 980 mg kg-1 levels. About the effect of Fe factor on shoots P concentration, only 10 mg kg-1 level of Fe significantly increased it. The main effect of the P and humic acid factors and interaction of the P and Fe factor on roots P concentration, were statistically significant. Roots P concentration increased significantly by 490 and 980 mg kg-1 humic acid levels. A significant increase of roots P concentration was observed in the 1st P factor level and 10 mg kg-1 level of Fe compared to the blank, and in 50 mg kg-1 level of P, Fe factor had no significant effect on it. The results showed that humic acid could not improve P uptake by corn from the soil with low available phosphorus (Olsen extractable P lower than 4 mg kg-1). The humic acid factor had no significant effect on Fe concentration of corn shoots, but its main effect and its triple interaction, with two other factors, on Fe concentration of the roots were statistically significant. There was no significant difference between the means of roots Fe concentration at the 1st level of P factor (9 treatments, various levels of humic acid and Fe factors). The highest mean of root's Fe concentration was found in treatment of the highest level of each factor, significantly more than those of the most of other treatments. About the Mn concentration in corn tissues, the Mn concentration in shoots was significantly increased by P fertilizer application, and Mn concentration in roots was significantly affected and increased by 490 and 980 mg kg-1 humic acid levels. The means of Mn concentration of roots in 490 and 980 mg kg-1 humic acid were not significantly different. The Zn concentration of corn shoots was significantly affected by interaction of the P and humic acid factors as the highest mean of it was in 0 mg kg-1 of P and 980 mg kg-1 humic acid levels, and there was no significant difference between those of other levels. The Zn concentration of corn roots was significantly increased by P applying and affected by the interaction of humic acid and Fe factors. When humic acid was at zero concentration level, Fe application of 20 mg kg-1 significantly decreased the Zn concentration of corn shoots while with humic acid application (490 and 980 mg kg-1) no significant difference was observed between the means. This result showed that humic acid can decrease the antagonistic effects of Fe and Zn in soil. The Cu concentration in shoots was significantly affected by the P and Fe factors. Usage of P fertilizer significantly increased the Cu concentration of corn shoots; on the contrary, the 2nd and 3rd levels of Fe factor (Fe applications) significantly decreased Cu concentration in shoots of corn. Moreover, using humic acid could significantly increase Cu concentration of corn roots without any significant interaction with the other two factors.

The findings suggest that in soils with very low available P, humic acid alone does not enhance the growth and dry matter yield of corn. However, the efficiency of phosphate fertilizer can be enhanced by applying humic acid fertilizer through irrigation water. Additionally, humic acid has been observed to mitigate antagonistic effects between P and certain micronutrients, as well as reduce antagonistic interactions among metal micronutrients. For the positive effect of humc acid on growth and adequate chemical composition of corn, concentration of 490 mg kg-1 humic acid is recommended.

This experiment was carried out in a field near the village of Takhshi Mahalle, located 5 km northwest of Gorgan city with geographical coordinates (54° 17´ 56 ʺ N) (52° 51´ 36 ʺ E) in 2022. The physical and chemical properties of the soil were measured at a depth of 0-30 cm in different parts of the farm and the final composite soil was analyzed in the laboratory. Water was measured using conventional methods of sampling and testing water and wastewater. The experiment was conducted as a randomized complete block design with 3 replications. The treatments included control (with distilled water), foliar spraying of iron sulfate micronutrient elements [FeSO4.7H2O (20%Fe)], zinc sulfate [ZnSO4.7H2O (22% Zn)], and iron sulfate + zinc sulfate at a concentration of 5 per thousand at the 4-leaf stage, the 8-leaf stage and both stages (4-leaf and 8-leaf). Foliar spraying was done in the early morning and drip irrigation was used. Plants were harvested 120 days after planting, washed with distilled water and dried with tissue paper. The samples were air-dried and then oven dried at 70˚C to a constant weight in a forced air-driven oven. Iron and zinc concentrations were determined by an atomic absorption device. In order to determine the protein percentage and yield in different treatments, total nitrogen was measured by the Kjeldahl method. The protein percentage and yield were calculated using the following formula:  
Statistical data were analysed using SAS software (9.4) and the mean values were compared using LSD tests (at 5% level).

The obtained results showed that all treatments effects were significant (P<0.01) (fresh forage P<0.05). Among all the treatments and measured traits, the control treatment showed the lowest value. The highest iron concentration with an average of 175.14 mg kg-1 was obtained using iron foliar spraying in both 8 and 4 leaf stages, which increased 22.73 and 34.39% in comparison with only using iron foliar application in 4 and 8 leaf stages, respectively. Zinc foliar spraying at both the 4 and 8 leaf stages resulted in the highest zinc concentration of 71.02 mg kg-1 in forage corn, increasing zinc concentration by 89.86% over the control. In both 4 and 8 leaf stages, an iron and zinc foliar application had the highest chlorophyll index with an average of 57.63. The highest nitrogen content, averaging 2.80%, was observed following foliar spraying of iron and zinc during both the 4 and 8 leaf stages. This represents an increase of 5% and 23.92% compared to iron and zinc foliar application treatments during the respective stages. Consequently, the highest yield and protein percentage were also attained, averaging 310.75 grams per square meter and 17.50%, respectively, with simultaneous foliar application of iron and zinc during both the 4 and 8 leaf stages. 

The optimal outcomes for measured traits were observed when iron and zinc were concurrently applied at both the 4 and 8 leaf stages. Therefore, it is advisable to administer iron and zinc simultaneously during these growth stages to ensure the attainment of forage with desirable quantitative and qualitative characteristics.

Understanding the particle size distribution (PSD) is of great importance for plant growth and soil management. In recent years, the science of soil has witnessed a significant increase in digital soil mapping (DSM) activities. In this regard, machine learning models (ML) have emerged as an alternative and tool for DSM, which are mainly used for data mining and pattern recognition purposes, and are now widely used for regression and classification tasks in all fields of science. Hence, this study was undertaken to spatially model sand, silt, and clay particles utilizing machine learning models such as Random Forest (RF), Support Vector Regression (SVR), and the Co-Kriging geostatistical model. Additionally, auxiliary variables with high spatial resolution were incorporated into the analysis. This investigation was conducted in a section of the Marvdasht plain, located in Fars province.

The present study was conducted in a part of Marvdasht plain located between 35.82´41°52' to 1.07´57°52' east longitude and 35.02´48°29' to 14.72´2°30' north latitude, and 40 km north of Shiraz with an area of about 50,000 hectares. After determining the study area boundaries, the positions of 200 sampling points were determined using the R software and the conditioned Latin hypercube sampling method. In other words, for soil feature modeling, 200 samples were taken from two depths of zero to 30 and 30 to 60 centimeters in the study area. Then, the samples were transferred to the laboratory, dried, and passed through a 2 mm sieve. Finally, the soil texture components were measured by the hydrometer method. The environmental variables used in this study are a wide range of representatives of soil-forming factors that were prepared as much as possible from sources with minimum cost and high accessibility. In total, 75 environmental variables were prepared, and the raster format related to all environmental variables, including 39 elevation and altitude variables and 36 remote sensing measurement variables, was extracted. Finally, the factor-tuning inflation variance and Boruta algorithm were used to select the optimal variables.

The minimum amount of clay was measured at 10.21% and 10.45%, respectively, and the maximum amount was 32.65% and 36.35% at the surface and subsurface depths. The average amount of clay in all samples was 37.91% and 35.61%. The average amount of sand was measured at 25.65% and 26.02% at the surface and subsurface depths, respectively. The maximum amount of sand was observed in the northern and higher parts of the study area and was equal to 54.68% and the minimum amount was predicted in the low-lying areas of the study area. Low-lying areas and sedimentary plains in the central part of the study area contained high amounts of silt. Four depth variables valley depths (VD), texture (TE), topographic wetness index (TWI), and clay index (CI) related to geomorphometric parameters and the normalized difference vegetation index (NDVI) variable related to remote sensing indices were selected as optimal variables. The RF model with R2 of 54.0% and 36.0% for predicting sand, 48.0% and 64.0% for predicting silt, and 52.0% and 49.0% for predicting clay at both surface and subsurface depths performed better than the SVR and Co-Kriging models. The most effective variable in predicting the spatial distribution of soil particles was VD with relative importance of 60% and 65% for predicting sand at the surface and subsurface depths, 70% for predicting silt at the surface depth, and 70% and 65% for predicting clay at both surface and subsurface depths, respectively. Only TE and TWI variables were more important than VD for predicting silt at subsurface depth. These results show that topographic variables are effective in the spatial variation of soil particles. Unlike clay, the highest amount of sand in both depths was observed in the northern part and the highest part of the study area, and the lowest amount was predicted in the low-lying areas of the study area.

In general, with the aim of this research, maps of the spatial distribution of soil texture components were prepared at both surface and subsurface depths using machine learning and geostatistical approaches along with environmental covariates in a part of Marvdasht plain. Among the selected environmental covariates, topographic attributes, especially the valley depth (VD), had the highest effect in justifying the spatial prediction of soil texture components. Also, the results of comparing the performance of machine learning models supported the higher efficiency of the RF model than other models. Therefore, the approach used in this study to prepare a map of soil texture components can be useful as a guide for mapping useful soil features in areas with similar climatic and topographic conditions.

Today, it is an inevitable necessity to make use of advanced and efficient technologies in order to increase productivity and gain a better economic status. Among different methods attracted the attention of researchers for enhancement in quantity and quality yield, cold plasma technique as a modern procedure has shown a promising prospects. Despite the importance of using cold plasma in agriculture, studies have focused more on the effect of this technique on reducing microbial load in agricultural products, less on absorption of nutrients in plants. Therefore, the objectives of this experiment were to evaluate the impacts of plasma treatment of corn seeds and plasma activated water (PAW) on growth and concentration of zinc and iron in the shoots of corn. 

This research was conducted as a factorial experiment based on completely randomized design (CRD) with 3 replications in a research greenhouse in agricultural and natural resources research and education center of Khorasan Razavi. The factors of experiment were three types of seed (control seeds, seeds treated with dry plasma and wet plasma), two kinds of irrigation water (distilled water and PAW) and two levels of foliar spray (without foliar spray and foliar spray with iron and zinc). Required mass of soil, was gathered, air-dried, sieved from 5 mm mesh and weighted in 6 packs. Based on the soil test values the required macro, micronutrients (except for iron and zinc) was calculated and added to the soil, and then the soil samples were moved to the pot. PLASMA BIOTEC Company located in Khorasan Razavi Park of Sciences and Technology, Mashhad, Iran performed plasma treatment of seeds and water. Plasma treated corn seeds were planted on May 18th with a density of 6 seeds in each pot. Plantlets were reduced to 2 plants after germination and establishment and irrigation was continued with desired treatments. Shoots of each pot was cut 8 weeks after sowing, 1 cm above the ground and delivered to the laboratory, where the samples were washed, dried, grounded  and the concentration of zinc and iron were measured using the atomic absorption device (Perkin Elmer, 2380) in dry ash digested in 2 N HCl acid. Data were statistically analyzed by SAS statistical software (version 9.4). Comparison of means for the main effects and interactions was performed by Tukey’s test at 5 percent confidence interval. 

Comparison of means for the interaction effects of water × seed × foliar spray showed that the minimum concentration of iron (147.67 mg/kg) was observed in plants grown from non-treated seeds, not foliar sprayed and irrigated with non-PAW (treatment 1 in Table 7). On the other hand, plants grown from wet plasma treated seeds and received foliar spray showed the highest concentration of iron regardless of irrigation water type (treatments 10 and 12 in Table 7). Comparison of means also shows that iron concentration in plants grown from dry plasma treated seeds had no significant difference with that of non-treated seeds (treatments 1 and 5 or 2 and 6). The mean comparison results for zinc concentrations showed that the minimum value was related to plants grown from non-treated seeds, not foliar sprayed and irrigated with non-PAW (treatment 1 in Table 8). The comparison of the simple effects of the type of seed on the concentration of zinc in shoots (Table 6) showed that wet plasma seeds caused a significant increase in the concentration of zinc. However, comparison of means for the interaction effects of water × seed × foliar spray showed that the effect of plasma treatment on zinc concentration was effective only in treatments that received foliar spray (comparison of treatment 2 with 10 in table 8). Based on these results the highest zinc concentration was observed in plants grown from wet plasma seeds and received foliar spray at the same time (treatment 12 in Table 8). In addition, the comparison of treatment 1 with treatment 4 and treatment 9 with treatment 2 indicates that in order to increase the concentration of zinc in plant, plasma treatment of seeds cannot replace the foliar spray method. Comparison of means for the interaction effects of water × seed × Foliar spray showed that the minimum yield was observed in plants grown from non- treated seeds, irrigated with non- activated water and not sprayed with iron and zinc solution (treatment 1 in Table 9). However, the similar treatment which grown from wet plasma treated seeds (treatment 9), showed significantly higher yield. Dry plasma, without foliar spray and without PAW (treatment 5) had no significant priority over the control. Plants grown from seeds treated with wet plasma and without foliar spray could not significantly show more iron and zinc content over the control, while their shoot yield was higher.

Based on the findings of this study, it can be inferred that irrigation with PAW and utilizing seeds treated with dry plasma exhibited no significant impact on augmenting zinc and iron content, as well as shoot yield. Conversely, wet plasma treatment, while not yielding significant enhancements in the concentration of iron and zinc within the plant, did result in increased yield. It is crucial to note that the extent of influence exerted by factors such as frequency and duration of seed exposure to plasma conditions on the observed outcomes may vary significantly. Therefore, optimizing methodology and conducting further research in this domain are imperative for a comprehensive understanding of these processes.

The availability of phosphorus (P) is a limiting factor for the production of crops due to its reactions with soil components. Furthermore, there are concerns about the depletion of non-renewable global rock phosphate (the main source of P) reserves because of the high demand for P fertilizers. Therefore, it is essential to revisit existing agricultural practices to determine new resource management practices that utilize renewable resources. The application of sewage sludge could be an alternative P source; contrary to inorganic fertilizers, sewage sludge is cheap, contains nutrients, and improves soil quality due to contained organic matter. The total P content of sewage sludge may vary from less than 0.1% to over 14% on a dry solid basis, depending on the nature of the raw sewage being treated and the treatment process under consideration. However, the use of organic P resources can affect the soil chemistry, leading to changes to the P fractions and their quantities. Hence, the objective of this study was to compare the effect of the application of municipal sewage sludge and triple superphosphate on the distribution of soil-P fractions under saline and non-saline conditions.

To investigate the effect of municipal sewage sludge and triple superphosphate on changes in P fractions an incubation experiment was conducted in a completely randomized factorial design with three levels of triple superphosphate (0, 75, and 100 Kg ha-1 which were named T0, T1, and T3, respectively), three levels of municipal sewage sludge (0, 0.25 and 0.5% w/w which were named M0, M1 and M3, respectively), two levels of salinity of irrigation water (2 and 12 dS m−1, which were named saline and non-saline, respectively) and three replicates. The total number of samples was 54. The treated soils were incubated for three months and maintained at field capacity by adding the appropriate amount of saline and non-saline waters. P fractionated to KCl-P (soluble and exchangeable P), NaOH-P (Fe- and Al bound P), HCl-P (Ca-bound P), Res-P (residual P), and organic-P by sequential extraction method. Moreover, P percentage recovery for Olsen-P at each treatment was calculated. P concentration in samples was determined by the molybdate method. Data analysis was performed by MSTAT-C software, and the means were compared at α꞊5% by Duncan test. 

The results showed that although the relative distribution of fractions followed the order of HCl-P < Organic-P < KCl-P < NaOH-P <Res-P, the changes in each fraction were dependent on the type of treatment and fraction. The amounts of KCl-P for application of municipal sewage sludge and fertilizer TSP combined, especially, T2M2 were 3.1 and 2.3 times higher than T0M0 in non-saline and saline conditions, respectively. The same result was obtained for NaOH-P. The combined and separate application of municipal sewage sludge diminished the relative distribution of HCl-P compared with triple superphosphate and control treatments in both salinities. However, the HCl-P in all treatments was more than 57% of the total P, suggesting that most of the soil P was in the carbonate phase. The treatments did not have a considerable impact on Res-P. The relative distribution of Organic-P increased by increasing levels of salinity and municipal sewage sludge. Therefore, it seems that municipal sewage sludge addition along with fertilizer P can reduce the negative effects of salinity and increase soil P availability compared with alone use of P fertilizer through growing the contents of KCl-P, NaOH-P, and organic-P fractions and, consequently, decreasing P entry into HCl-P fraction. Moreover, the application of municipal sewage sludge plus triple superphosphate increased P recovery as Olsen-P compared to a separate application of triple superphosphate which confirmed the advantage of the combined use of these sources.

The findings of this study indicate that the simultaneous application of municipal sewage sludge and triple superphosphate can effectively improve phosphorus (P) availability in saline conditions. This enhancement is attributed to the alteration of the relative distribution of non-stable P fractions, such as KCl-P and NaOH-P, which increase, while stable P fractions like HCl-P decrease. Moreover, the addition of municipal sewage sludge into soils led to a significant increase in organic C as well as the relative distribution of organic-P. Therefore, application of municipal sewage sludge can improve the physico-chemical properties of saline soil along with increase of P availability. Hence, further research on the growth response of halophyte plants as affected by these treatments is recommended.

Water plays a crucial role in ensuring the sustainable development of any region. Given that our country consists primarily of arid and semi-arid regions, where the majority of rivers are also found, along with the critical state of groundwater extraction and the growing importance of surface water, It is crucial to have a deep understanding of the future condition of water resources within the country's watersheds (Fathollahi et al., 2015). By utilizing intelligent models, it becomes feasible to represent the inherent relationships between data that cannot be solved by conventional mathematical methods. Support vector machine (SVM) and Random Forest algorithms are two types of machine learning methods that utilize essential algorithms for making repeated and accurate predictions (Kisi & Parmarm, 2016). The most recent study conducted by Zarei et al. (2022) evaluated the risk of flooding using data mining models of SVM and RF (case study: Frizi watershed). By analyzing the results, it was found that both the SVM algorithm and the new random forest algorithm showed higher accuracy in predicting flooding risks, both in terms of the educational data and algorithmic performance. The purpose of this study is to simulate the precipitation-runoff process in the hydrometric stations at the end of the Maragheh plain (Khormazard station on the Mahpari chai river and Bonab station on the Sufichai river) in East Azerbaijan province using support vector machine and random forest modeling algorithms. This study has been conducted over a period of 43 years, making it one of the few research cases in this area.

The Maragheh Sufi chai basin is situated in the eastern region of Lake Urmia, within the East Azarbaijan province. It covers an area of 611.89 square kilometers and is located between longitudes 45° and 40´ to 46° and 25´and latitudes from 37° and 15´ to 37° and 55´ north. The average height of the basin is 1767 meters above sea level (Sharmod et al., 2015). Based on the substantial changes observed in the runoff trend in the data since 1994 (without any noticeable change in the precipitation trend), the available data was divided into two distinct periods. The first period spans from 1976 to 1994, and the second period covers the years 1995 to 2019. To simulate rainfall-runoff, first the average rainfall of Maragheh plain was calculated by polygonal method. Subsequently, this data was combined with the discharge output from Bonab and Khormazard stations, with a one-day time lag. These inputs were then utilized in two models, SVM (kernel function) and RF. For this purpose, 70% of the data was used for the training stage and 30% of the data was used for the validation stage. Then, the rainfall and runoff training sets from one day before were chosen as the predictor variables, while the runoff training set was designated as the target variable. Several combinations of runoff and rainfall inputs were evaluated for the purpose of modeling. The inputs consist of the monthly Q and P values that were recorded previously (Pt, Qt-1), while the output represents the current runoff data (Qt), with the subscript t indicating the time step. As a result, two input combinations were constructed from Q and P data (as seen in Table 3) and SVM and RF models were used for rainfall-runoff modeling to determine the optimal input combination.
Calculating average rainfall through the Thiessen Polygons method Thiessen polygons, which are Voronoi cells, are used to define rainfall polygons that correspond to the surface area (Ai). These polygons are used to weight the rainfall measured by each rain gauge (ri). Consequently, the area-weighted rainfall is equivalent to:
(1)
Random Forest Algorithm
Random forest is a modern type of tree-based methods that includes a multitude of classification and regression trees. This algorithm is one of the most widely used machine learning algorithms due to its simplicity and usability for both classification and regression tasks.
Support Vector Machine (SVM) algorithm
Support vector machines works like other artificial intelligence methods based on data mining algorithm. The most important functions of the support vector machine model are classification and linearization or data regression.
       Evaluation Criteria
To evaluate the models and compare their effectiveness, this research employs metrics such as the root mean square error (RMSE), correlation coefficient (r), explanation coefficient (R2) and Nash-Sutcliffe efficiency coefficient (NS) are used. Below are the relationships among these criteria:
(2)
(3)                                                     
(4)
(5)

 Figure 6 displays the time series data for rainfall and runoff during the two study periods, before and after 1994.The analysis of the figures showed that for Bonab station, during the two study periods, the value of Kendall's statistic for precipitation variable was 0.044 and 0.028, respectively. For Khormazard station, this statistic value for the first and second period was 0.030, and 0.028, respectively. However, these values are not significant at the 95% level. This indicates that the annual rainfall for the two studied stations during these years is not statistically significant. Therefore, it is concluded that the annual rainfall in these stations between the years 1976 to 2019 did not show any significant trend. The variations observed during this period were deemed normal, suggesting that the time series of rainfall displayed fluctuating patterns. However, it should be noted that there were instances of both increasing and decreasing trends in certain years Examining the time series reveals varying trends Initially, the outflow from Bonab station (both a and b) displayed fluctuating patterns, followed by periods of both decreasing and increasing trends. However, in recent years, there has an increase in outflow from this station. The Mann-Kendall test statistic for the two study periods for this station is 0.325 and 0.512, respectively. These values are significantly different at the 95% level, indicating that the increasing trend of discharge for both time periods was statistically significant. The reason for this trend at the Bonab station, compared to other entrance stations to Lake Urmia, is the lower demand for water in the Sofichai basin for agricultural and industrial purposes, in contrast to other rivers. To explore the root cause of this issue, studies should be conducted to examine both underground and surface water sources, as well as the utilization of water in the agricultural and industrial sectors of this region. On the contrary, the trend observed at Khormazard station (c and d) is different. Unlike Bonab station, the discharge from Khormazard station exhibited a complete downward trend. The Mann-Kendall test statistic for the discharge variable during our two research periods were -0.269 and -0.412, respectively. At the 95% level, the decreasing trend of discharge in this station was found to be significant. On the other hand, it is apparent that the volume of discharge in this hydrometric station has decreased drastically since 1976 (d). Apart from 2007, when there was a sudden increase in discharge volume, the water inflow into lake Urmia has remained at its lowest level throughout the years. To analyze the Bonab and Khormazard stations during two distinct periods, rainfall and runoff statistics (average, minimum, maximum) for the first period (1976-1994) and the second period (1995-2019) are presented in Tables 4 and 5. Based on the data presented in both tables, the Bonab station displays the highest average rainfall and runoff values in the total data column, while the Khormazard station has the lowest average rainfall and runoff values.
As mentioned, in order to model rainfall-runoff data using SVM and RF models, a portion of the data was used for training purposes, while another portion was used for validation. Tables 5 and 6 present the values of the calculated statistical indicators associated with the results obtained from the training and validation sections for both SVM and RF models. According to the results of Tables 6 and 7, it is clear that in both study periods, the SVM model outperformed the RF model at the Bonab station. The SVM model demonstrated superior accuracy in simulating both flow rate and monthly rainfall. Conversely, at the Kharmazard station during these periods, the RF model displayed better performance compared to the SVM model. The modeling results in the test set for both stations revealed that the mutual correlation values for the first and second study periods at the Bonab station were 0.85 and 0.84, respectively. For the Kharmazard station, these values were 0.79 and 0.75, respectively.

The results indicate that for both periods at the Bonab station, the SVM model exhibited higher efficiency compared to the RF model. Conversely, at the Khormazard station, the RF model outperformed the SVM model for both periods. Mutual correlation values for the test sets were 0.85 and 0.84 for the first and second study periods at the Bonab station, respectively, for the SVM model test set. For the Khormazard station, these values were 0.79 and 0.75, respectively, for the RF model test set. Other notable findings of this research include the analysis of the time series data for rainfall and runoff over 43 years. Graphs obtained for both stations, along with the Mann-Kendall statistic for precipitation and flow parameters, revealed no discernible trend in precipitation during the two study periods. Instead, precipitation in these areas displayed fluctuating patterns However, the analysis of the time series and statistical values for the discharge of Sofichai and Mahpari chai rivers at the Bonab and Khormazard stations showed different results. In the Bonab station, the discharge exhibited fluctuations, with an increase observed in the second period. Conversely, at the Khormazard station, the discharge trend was downward in both study periods. The volume of Mahpari chai River outflow notably decreased in recent years, as evidenced by the Mann-Kendall statistic showing a decreasing trend.