mona golabi

The Crop Water Stress Index (CWSI) is a practical method for monitoring plant water status, detecting the onset of moisture stress, predicting crop yield, and irrigation schedule for variouscrops. The CWSI values range from zero to one, where zero indicatesthat the plant is not uunderwater stress and has the most ideal conditions for transpiration, while a value of one signifies maximum water stress and complete cessation of transpiration. The present research was conducted to calculate the CWSI,determine the moisture stress threshold for safflower and identify optimal irrigation management practices by analyzing the relationship between the CWSI and various deficit irrigation managements  approaches.The research was carried out over two agricultural seasons, 2020-2021 and 2021-2022at Khuzestan University of Agriculture and Natural Resources. The experiment used a randomized complete blocks design with a conventional irrigation treatment (CI) and four deficit irrigation treatments including (RDI80, RDI60: deficit irrigation adjusted by supplying 80% and 60% of soil moisture deficit, respectively) and (PRD80, PRD60: deficit irrigation in the form of partial root zone drying methods with 80% and 60% of soil moisture deficit, respectively) and with four repetitions. Results showed that the CI treatment had the lowest value of 0.17 and the RDI60 treatment had the highest CWSI at0.67. Irrigation planning was done with the aim of achieving the maximum efficiency of water productivity, and based on this, the relationship between the permissible limit of the temperature difference of the Canopy cover of the plant and the air for the months of March ((Tc-Ta)a = 1.618 - 0.215 VPD), April ((Tc-Ta)a = 2.46-0.220 VPD) and May ((Tc-Ta)a = 4.636-0.164 VPD) were obtained. The moisture stress threshold for safflower was obtained from the regression relationship between the CWSI and the temperature difference of the Canopy cover of the plant and the temperature of the surrounding air (Tc-Ta) equal to 0.61, which was introduced for the study area and with the research conditions

Crop modeling is a widely used method for simulating the crop yield under different irrigation managements. Due that, in the study, a water-driven crop model, AquaCrop, was used to simulate and determine the optimal irrigation quantity and interval in Dehkhoda Sugarcane Cultivation and Industry Group (DH) farms. The data were collected from DH farms during 2019-2021. The total area under study was 230 hectares. The evaluation results showed that the AquaCrop model had an acceptable error for simulating yield (RMSE<5.6 ton ha-1) and water productivity (RMSE<2.2 kg m-3). In addition, the results of favorable efficiency (d>0.99), excellent accuracy (NRMSE<0.1) and high determination of coefficient (R2>0.94) showed that the simulation results of AquaCrop can be trusted. For this reason, 21 irrigation scenarios were simulated using the AquaCrop model in order to obtain the most appropriate irrigation amount and interval. Based on the results, close-end furrow irrigation with interval of seven days and the amount of 33000 m3 ha-1 was determined as a suitable option in terms of yield (132 ton ha-1) and water productivity (4 kg m-3). However, this scenario is for normal water conditions and if water stress conditions are created; a maximum of nine days for irrigation interval and at least 21000 m3 ha-1 is a better option for implementation in DH farms.

Currently, about 90 percent of the population of developing countries live in arid and semi-arid areas. Therefore, it is necessary and inevitable to identify the key and effective factors on the growth of agricultural products . The purpose of this research is to evaluate the accuracy and efficiency of the latest version of the AquaCrop model for simulating seed yield, biomass and water productivity efficiency of safflower in the form of a randomized complete block design with one control treatment (Conventional irrigation) and four deficit -irrigation treatments, including (RDI80, RDI60: reduced irrigation adjusted by supplying 80 and 60% of soil moisture deficiency) and (PRD80, PRD60: fixed partial root-zone drying irrigation with supply of 80 and 60% of soil moisture deficiency) and with four repetitions in Khuzestan University of Agriculture and Natural Resources was conducted. In order to evaluate the reliability of the model, some statistical indicator including the average normalized root mean Square error (NRMSE), the Coefficient of Residual Moss (CRM), Coefficient of Determination (CD), maximum error percentage (ME), Wilmot index (d) and the modeling efficiency (EF) was used. The statistical index (NRMSE) was obtained for seed yield (4.14), biological yield (10.16) and water productivity efficiency (4.9) and showed that the accuracy of the model in simulation is excellent. The results of the model validation show the accuracy and efficiency of the model in simulating seed yield, biomass and water productivity efficiency, and therefore, this model can be used to manage safflower irrigation in different states of deficit irrigation.

Estimating crop evapotranspiration (ETc) in arid and semi-arid areas can be difficult due to the dynamic nature of this process across both time and space. In addition, obtaining on-site measurements for this variable can be very time-consuming and costly. This study aimed to develop a framework that accurately estimates the sugarcane crop evapotranspiration on a spatio-temporal scale. This was achieved using four machine learning (ML) algorithms (MLR, CART, SVR, and GBRT) combined with remote sensing (RS) data and meteorological variables. Also, to reduce the dependence on several meteorological parameters in conventional ETc equations, the performance of eight different experimental temperature-based methods and four modified Hargreaves & Samani equations was evaluated compared to the standard FAO-Penman-Monteith method. For this purpose, weather data were collected from Hakim Farabi Sugarcane Agro-Industrial meteorological station for three years (2018-2021). Nine combinations of input variables (RS data and meteorological variables) were designed based on the IGR method and then evaluated by the ML algorithms. The results showed that the highest accuracy of ML algorithms based on R2, RMSE, and MAE statistics was obtained in CART (0.99, 0.41, and 0.18) and GBRT algorithms (0.99, 0.65, and 0.26), respectively. Regarding temperature-based methods, Ivanov’s equation had the best performance with an R2 of 0.91, while Baier and Robertson’s equation had the weakest performance with an R2 of 0.78 when estimating ETc. Overall, the combination of RS and ML algorithms effectively produced more precise and reliable ETc values on both temporal and spatial scales.

In recent years, the use of SWAT model has been used as a common tool for simulating flow rate, sediment production and evaluating different scenarios to reduce sediment production and runoff. The current research also aims to evaluate the changes in runoff and sediment under the influence of irrigation water reduction, using the SWAT model in the irrigation and drainage network of Dez River.

Recalibration and validation of the model for simulating runoff (Shoshtar, Arab Asad, Shavor Bridge, Harmeleh and Bamdej stations) and sedimentation (Shoshtar, Shavor Bridge, Harmaleh and Bamdezh stations) using statistical data from 1995 to 2012 and 2013 It was done until 2017. The simulation results were also evaluated using R2 and NSE coefficients. Then the scenarios of 10, 20 and 30% reduction of irrigation water were introduced to the model to evaluate their effect on runoff and sedimentation of the study area.

The results of model evaluation using R2 and NSE coefficients indicate the appropriate performance of the model in simulating the mentioned parameters. The results of the evaluation of scenarios of 10, 20 and 30 percent reduction of irrigation water show that applying the 10 percent scenario had the least impact on the amount of sedimentation. On the other hand, applying the scenario of 30% reduction of irrigation water will increase the runoff (30-60%) due to the reduction of the collection of runoffs for irrigation purposes and consequently the increase of the amount of sediment (20-50%) in the irrigation and drainage network of Dez River. The obtained results indicate that the increase in the amount of runoff in the basin, especially in the rainy season, has caused an increase in erosion and consequently an increase in sediment in the basin. Therefore, it can be stated that the time of applying the scenario and the characteristics of the soil in the area are the most effective components on the amount of runoff and sediment in the basin.

According to the results obtained, according to the effect of runoff on erosion and the amount of sediment in a watershed, it is necessary to use methods to control and reduce runoff, such as watershed operations, structures to contain and store runoff, and use modern irrigation methods to prevent soil erosion. Because traditional irrigation methods, in addition to water losses, cause a decrease in soil fertility and an increase in sediment in the drains of agricultural lands. In lands that are irrigated by traditional methods, sometimes the amount of water used is more than the infiltration capacity of the soil. This causes erosion of the surface layer of the soil by the runoff from water accumulation. Therefore, agricultural management approaches in basins prone to soil erosion should be focused on modern irrigation methods with minimum runoff and drainage output.

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

 In order to simulate the yield and water productivity of cucumber plant (Cucumis sativus L.), an experiment was conducted in the form of a completely randomized block design with three irrigation levels of 100, 85 and 75% of the water requirement in two growing seasons during 2017 and 2018 and using perceptron neural networks (MLP) and support vector machine (SVM) methods were used and finally, to select the appropriate and optimal model, the indices of explanatory coefficient, mean squared error and normalized mean squared error were used. The amount of irrigation water, number of leaves on the plant, temperature, evaporation rate and relative humidity were selected as input data and 60%, 20% and 20% of the total data were allocated for training, validation and testing of the model, respectively. The results showed that the MLP neural network with the inputs of irrigation water and number of leaves was more accurate in simulating fruit yield and water productivity in cucumber plants with an explanation coefficient of 0.92 and 0.86, respectively. The results of the sensitivity analysis indicated that the irrigation water input parameters are the most important effective parameters on the water consumption efficiency model and cucumber fruit yield with sensitivity coefficients of 0.9 and 0.86, respectively.

Safflower (Carthamus tinctorius L.) is a multipurpose crop that is widely grown in arid and semi-arid regions of the world, mainly to produce high-quality edible oil rich in unsaturated fatty acids. The objective of this study is to investigate the effects of regulated deficit irrigation (RDI) and partial root zone drying (PRD) methods on yield, yield components and water productivity efficiency of safflower. This study was conducted in the Experimental Research Station located in Agricultural and Natural Resources Sciences University of Khuzestan during the 2020 to 2021 and 2021 to 2022 growing seasons. The experiment was designed as a completely randomized block with one control treatment (Conventional irrigation) and 4 deficit irrigation treatments and in four replications. The results showed that the different irrigation methods had a significant and decreasing effect on plant height, number of seeds per plant, thousand seed weight, seed yield, biological yield and oil yield traits. Comparing RDI80 and PRD80 treatments, the highest seed yield (3.4 tons/ha) and oil yield (845.7 kg/ha) and the highest value of water productivity index was obtained from RDI80 treatment. Also, comparing RDI60 and PRD60 treatments, the highest seed yield (2.2 tons/ha) and oil yield (584 kg/ha) was obtained from PRD60 treatment. Therefore, according to the obtained results, it is recommended to use the RDI80 regulated deficit irrigation method to reduce 20% and the PRD60 partial root zone drying method to save 40% of water requirement for safflower cultivation in hot and dry areas with limited water resources.

In order to investigate the effect of irrigation deficit on cucumbers grown in greenhouse conditions, an experiment was conducted in a completely randomized block design with three irrigation levels of 100, 85 and 75% water requirement in two growing seasons during the years 2018 and 2019.Performance and quantitative and qualitative performance indicators were measured during the experiment. The results showed that in the first crop of treatments of 85 and 75 percent of water requirement, the amount of water consumption productivity decreased by 6.12 and 15.73 percent compared to the control treatment, respectively. Also in the second crop, the amount of water consumption in treatments of 85 and 75% of water requirement compared to the control treatment has decreased by 3.4 and 12.6%. Analysis of data related yield indices and plant growth indices showed that with decreasing water consumption, water productivity and performance are significantly reduced.

This study examined the feasibility of using zeolite clinoptilolite to filter nitrate, phosphate pollutants and salt from the agricultural drainage water. To that end, significant pollutant absorption parameters, such as adsorbent particle size, pollutant concentration, salinity, temperature, retention time, pH, and adsorbent concentration were optimized in the batch condition. Having optimized the parameters, the researchers conducted adsorption experiments on an experimental model, similar to the subsurface drainage systems applied in farms. Adsorption experiments were carried out at the optimized parameter levels on four models, namely a reference model (D0), a model with adsorbents around the drains (D1), a model with adsorbents around the plant roots (D2), and a model with adsorbents on the soil surface (D3). These models were fed with untreated drainage water from the farms in the south of Khuzestan during the fertilization season. The results showed 63 percent nitrate removal efficiency, 39 percent phosphate removal efficiency and 79 percent salt removal efficiency by using 30 g.L-1 of 1000 µm adsorbent particles for a pH of 5, initial pollutant concentration of 80 mg.L-1 nitrate and 10 mg.L-1 phosphate in 12 dS/m salinity during a 90-minute retention time period at 50 °C ambient temperature. These parameter levels led to nitrate, phosphate, and salt removal efficiencies of 59.72 percent, 29.28 percent, and 77.47 percent respectively, in the model with clinoptilolite adsorbents around the drains (D1).

This study was conducted to investigate the effect of silica nanoparticles on the yield and water productivity of cucumber plant and to determine the most appropriate method of application of silica nanoparticles (leaf and root nutrition)In order to investigate the effects of silica nanoparticles on cucumber yield, an experiment was conducted in a factorial design based on completely randomized block design with 4 replications in two growing seasons during 2018and2019 in the greenhouse of Shahid Chamran University of Ahvaz, Iran. Treatments included silica nanoparticles with concentrations of 0,25,50,75 and 100 ppm and two methods of foliar application on leaves and root feeding.The results showed that the highest and lowest values of fruit weight in the first and second crops were in the treatment of 50mg/l nanoparticles by foliar application (first crop:weight10484.3and second crop:weight8039g) and 0mg/l nanoparticles were obtained by root solution (first culture:weight7700g and second culture: weight6280g). Also, the highest amount of water productivity was related to the treatment of nanoparticles with a concentration of 50ppm under foliar application (first culture:71.32and second culture:64.83).This study showed that the use of silica nanoparticles can have a positive effect on cucumber yield and caused a 32.11and21.2% increase in yield in the first and second crops, respectively, compared to the control treatment. The use of nanoparticles increases water productivity and increases cucumber plant yield, the use of silica nanoparticles with a concentration of 50 ppm as foliar application on the leaves can be a good option to increase yield in cucumber.

The purpose of this study was to investigate the environmental impacts of sugarcane production in Hakim Farabi Agro-Industry Company through life cycle assessment method in the 2019 crop year. The production of one ton of sugarcane considered as the functional unit. The required data was collected through face-to-face interviews with managers and experts. The results indicated that diesel fuel, electricity, urea fertilizer and direct field emissions from sugarcane cuttings had the greatest effect on all sectors in the studied effect groups. The evaluation showed that Marine aquatic Ecotoxicity released 103447.86 kg 1, 4-DB per one ton of sugarcane which has the highest potential for environmental damage. Therefore, three scenarios were defined to evaluate the effects of sugarcane cultivation on the impact categories in order to reduce water consumption, reduce fertilizers and chemical pesticides consumption and reduce electricity and diesel fuel consumption. The emission rate of this impact category after applying the first, second and third scenarios is estimated to be 99714.6, 95628.02 and 91894.77 kg 1, 4-DB per one ton of sugarcane, respectively. N2O, CO2 and CH4 greenhouse gas emissions were estimated to be 0.915, 261 and 0.506 kg, respectively. After applying the scenarios, CO2, CH4 and N2O emissions were estimated as follows: N2O emissions were 0.871, 0.827 and 0.784 kg, respectively, CO2 emissions were 251, 240 and 230 kg, respectively and CH4 emissions were 0.491, 0.475 and 0.460 kg, respectively.

The salinity of water and soil resources and lack of appropriate quality water resources are major threats to agricultural development in arid and semiarid regions such as Iran, Khouzestan province. The implementation of haloculture projects causes the availability of saline water resources in these areas. Therefore, the study on the effects of salinity on seed generation was the essential aim of current research. In this study, because of the importance of nutrition, medical and industrial of Chia (Salvia hispanica L.), Guar (Cyamopsis tetragonoloba L.), Luffa (Luffa cylindrical L.), and Karela (Momordica charantia L.), the effect of saline water on seed germination indices were evaluated. The seed germination indices consisted of germination percentage (Gp%), the coefficient of the velocity of germination (Gi), seed vigor index (Vi), germination uniformity (GU), salinity tolerance index (STI), dry weight, fresh weight, and the percentage of moisture of the radical and plumule were determined under salinity stress. To achieve the aims of the current study, four salinity levels were used, including Karoon River water (as a control treatment) with an average electrical conductivity of 1.21 dS /m and diluted drain water with an electrical conductivity of 5, 10, 15, 20 dS/m (S1, S2, S3, S4, S5, respectively) in three replications (R1, R2, and R3). The experimental design was completely random. The analysis of variance of measured indices in the experiment showed that the effects of salinity on germination percentage of Guar and Luffa at 1% and Karela and Chia at 5% level of probability were significantly affected by salinity stress. The effect of salinity on the velocity of germination of the studied species was significant. Also, the salinity effect on the seed vigor index of Guar and Chia was significant at the 1% level. Increasing salinity significantly decreased the seed vigor index in the mentioned species. According to the results of this study, among four seeds, Chia and Guar were identified as the most tolerant plant to salinity stress in the seedling stage.

This study was carried out to investigate the feasibility of using Constructed Wetland (CW) systems in removing nitrate and phosphate pollutants and salts from agricultural runoff. For this purpose, two Subsurface-Flow Constructed Wetlands (SFCW) were used in series; one with horizontal flow and another with vertical flow.  These wetlands were first filled with gravel (2-3 mm in size) as a bed material with a height of  0.1 m (according to EPA recommendations). The drain pipes were then covered with a 0.2 m layer of 70:30 gravel–zeolite-clinoptilolite mixture. The systems were studied with and without planted phragmites australis (common reed), and their performance were investigated in terms of pollutant removal efficiency using agricultural runoff containing 12 dS.m-1 salinity, 80 mg.L-1 nitrate and 10 mg.L-1 phosphate. It was found that as pollutants continue to pass through the beds, the removal efficiency is reduced in wetlands with and without planted reed. The final efficiencies of hybrid HF–VF CW, VF–HF CW, VSFCW, and HSFCW models were calculated to be 69.39, 65.81, 73.57, and 94.75% in the wetland with planted reeds and 60.40, 57.16, 55.58, and 53.91% in the wetland without planted reed, respectively. Finally, it is concluded that the hybrid HF–VF CW with the presence of reed plant and with higher efficiency offers an acceptable level of nitrate, phosphate, and salt removal.

Salinity stress has significantly reduced world crop growth and production, especially in arid and semi-arid regions (Bahadorkhah and kazemeini, 2014). Drought and salinity reduce crop productivity, especially in arid and semi-arid regions. Therefore, finding a crop which produces yield under these adverse conditions is very important (Razzaghi et al, 2011). Quinoa is a new plant with a high nutritional value that has led the World Food Organization (FAO) to call it vegetarian caviar (Seifati et al, 2015). Quinoa is a species of passionate herb (Adolf et al, 2013), and most Quinoa cultivars have the ability to grow in salinity with a concentration of 40 dS / m and even more. This amount of salinity is too high for most crops (Jacobsen et al, 2003, Hariadi et al, 2010).

In the modern world, it is necessary to have a sustainable agricultural system in which natural resources are utilized and at the same time does make any harmful to the environment. Nitrogen leaching is one of the most important problems in intensive farming for high crop production. Application of organic matter lead to reduce chemical inputs and achieve sustainable agricultural goals. Application of modified materials such as biochar and hydrochar can play an important role in creating optimal conditions for growth, increasing plant yield and reducing water consumption, as well as reducing nitrate leaching. In the present study, the effect of biochar and hydrochar, derived from sugar cane bagasse, on crop yield, water productivity and nitrogen leaching in maize cultivation under two irrigation levels of full and dificit (up to 30%) and two fertilization levels of 200 and 160 kg/ha of nitrogen for each sources of urea, biochar and hydrochar were investigated. Adding biochar and hydrochar to the soil increased the yield and water productivity and reduced drainage outflow and nitrogen leaching. In contrast to hydrochar,  biochar has a more effective role for the above mentioned parameters due to its high porosity and specific surface area. In comparison with urea application, using biochar and hydrochar at the optimum moisture conditions reduced drainage outflow 9.2 and 3.1% and nitrogen leaching 2.6 and 3.4%, respectively. Therefore, the use of biochar and hydrochar could be considered as an effective solution to reduce the negative effects of agriculture.

Agriculture is one important sectors of the economic and impact on the environment. Assessing the environmental impacts of agricultural production is one of the effective ways to achieve sustainable agriculture and preserve natural resources. The present study evaluates environmental impacts of corn production in Khuzestan province as a strategic crop cultivated twice a year. Data were collected from the province's agricultural Jahad, library information and experimental data of farmers in the area. field experiments were carried out in order to determine the irrigation water and yield from March 96 to December 97. Environmental impacts of global warming potentials, acidification potential, Eutrophication potential, fossil fuel consumption, water consumption, phosphate consumption and of potash consumption were investigated. For one ton of corn grain as a functional unit, it was determined that drying Eutrophication and discharging of phosphate sources had the highest potential for damage to the environment, and each of them had a contribution of 18.73% and 34.08% in environmental costs of pollutant emissions. In the meantime, the share of water resources consumption in environmental damage impacts was 22.85%. Environmental index (EcoX) for spring and autumn cultivations was 0.92 and 0.70 respectively, and RDI for spring and autumn cultivars respectively was 1.75 and 1.29, respectively. So, in order to reduce the pollution of corn production in the region, it is necessary to manage the optimum use of chemical fertilizers, replace them with organic fertilizers and use of biological fertilizers, use of nitrogen supply sources with less pollution potential. It is also recommended to plant early varieties in spring crop with high potential and consistent with the region's hydrological conditions.

The problem of salinity of water resources and lack of quality water resources is one of the main threats to agricultural development in arid and semi-arid areas such as Khuzestan. Regarding existing problems, low irrigation method, using high salinity drainage, planting salinity resistant plants, and increasing water productivity are good management options. So, a study was conducted to determine the water sensitivity coefficient, salinity stress and Kochia water productivity in salinity and low water stress conditions in Ahvaz. The present study was carried out on Kochia plant at four salinity levels including Karoon river water (as the control with EC equal to 2.5 dS/m), 10, 15, 20 (dS/m) (S1, S2, S3 and S4, respectively) as the main factor and three levels of water deficit including 100, 75 and 50% water demand (I1, I2 and I3 respectively) as the sub plots in three replications (R1, R2 and R3) and as lysimetric in split plot and complete block designs. According to the results, the salinity stress coefficient in this study was between 0.06 and 0.89. The results showed that the coefficient of salinity decreased (Ks) by increasing water salinity. Also, the results showed that the Ks coefficients obtained in combination of salinity stress and 50% water deficit were higher than 75% water deficit stress. The average of Ky coefficient obtained in this study in under deficit irrigation conditions of 75% and 50% of water requirement was 0.19 and 0.60, respectively, indicating that Kochia has a low drought coefficient, which indicates high tolerance of this plant is not affected by severe drought stress conditions. The results also showed that the water productivity in the forage production of the Kochia plant is acceptable and high even under severe stress conditions, combined with salinity and water deficit. In this research, it was observed that salinity reduced water productivity and water utilization in deficit irrigation conditions was more than full irrigation. In general, the results of this study showed that kochia can be used as an appropriate forage plant in areas such as Ahvaz, where irrigation water and soil are not of desirable quality even without the need for leaching and soil remediation, which would cost a lot to producers.

Kochia is one of the most salt and drought tolerance plants that can be used in forage production for livestock in the absence of suitable water and soil resources. This study was conducted to evaluate the yield and yield components of Kochia under salinity and water stress condition. The main plots included salinity levels of Karoon water (2.5), 10, 15 and 20 dS/m, and subplots included three irrigation levels; 100, 75 and 50% of the crop water requirement. The studied traits were weight of the aerial part of the plant, fresh and dry weight of leaf and stem, fresh and dry weight of a bush and plant height. Results showed that the salinity stress had a significant effect on the proposed traits at 1% level. Also, different irrigation treatments had a significant effect on the studied traits. Individual effects of salinity and deficit irrigation stresses showed that the control treatments including salinity (Karoon water) and full irrigation (100% water requirement) produced 52 and 49 tons of fresh forage per hectare, respectively, which were superior among stress treatments in terms of all traits measured in the research. The interaction effect of salinity and drought stresses on fresh weight of one plant, fresh weight of leaf and leaf dry weight were significant at 5% level, on plant height were significant at 1% level, and on the other traits were not significant. Kochia plant under favorable conditions and under severe salinity and drought stresses could produce 57 and 34 tons per hectare fresh fodder respectively. Finally, the correlation among all traits were evaluated. The results showed that the all proposed traits including; total weight of the fresh plant, fresh and dry weight of one plant, dry and fresh weight of leaf and stem, plant height, stem diameter and the numbers of branches showed a positive and significant correlation with the salinity and drought stresses.

Water and nitrogen are the main factors limiting the maximum yield Achievement. Application of nitrogen fertilizer has great impact to increasing agricultural production. On the other hand, the high use of these fertilizers is due to the low efficiency of these fertilizers. This research was conducted in response to the challenges of water scarcity and inappropriate management of nitrogen utilization. To quantitate the reaction of the plant and calculate the water and fertilizer efficiency, two irrigation levels, full irrigation (I1) and deficit irrigation equal 30% (I2) as the first factor and two nitrogen levels of 200 (N1) and 160 (N2) kg per hectare, it was considered as a second factor. Experiments were carried out as field treatments and in lysimeters as a factorial in a completely randomized block design with three replications. The cultivars used were maize hybrid 611 and 704 mediator SC, which were cultivated in two seasons of spring and autumn, respectively. Based on the results of this study, leaf area index with higher nitrogen application in the same moisture conditions is increased. Nitrogen increase, while that enough water is available to the plant, increases the weight of 1000 seeds, grain yield, biological yield and harvest index. Optimum use of water and nitrogen fertilizer increases grain yield and therefore increases water use efficiency. With increasing nitrogen content, nitrogen use efficiency decreases in corn cultivation. Under stress conditions, with increasing nitrogen consumption, fertilizer N-recovery efficiency is also reduced by decreasing the absorption of this element by the plant. The economic and biological efficiency of water use in autumn cultivation was more than spring cultivation. Low spring yield and high rainfall during autumn crop period were due to the efficiency and more productivity of water use in autumn cropping. In Khuzestan weather conditions, the use of early cultivars is recommended instead of the mediator for spring crop due to coincide the pollination time with intense heat.

In recent years, concern over the long-term effects of heavy metals has been increased as environmental pollutants. Environmental pollution of heavy metals is one of the major environmental issues. Unfortunately, due to the uncontrolled entry of various types of industrial waste, their input is increasing into air, water and soil sharply. Heavy metals are irresolvable and tend to accumulate in the organs and tissues of living organisms that cause a variety of diseases and disorders for humans and other living organisms. Recently, biological methods and technologies such as biosorbent and bio-accumulation have been used to help researchers to confront the problem of removing heavy metals from sewage. In bio-accumulation technology is used living biota to remove metals. However, in the second method, or biological absorption is used of dead or inactive biologically is this purpose. The main objective of this research is to determine the capability of the storage of cadmium as heavy metal by modified Ceratophyllum demersum biomass. In addition, the effect of pH on adsorption rate, contact time on adsorption rate, adsorbent adsorption, initial concentration of adsorbant (cadmium) were evaluated on adsorption. Also, kinetic and isotherm models of absorption were determined.

In the present study, the effect of modification of Ceratophyllum demersum on the removal of Cadmium from aqueous solution was investigated. Ceratophyllum demersum or blue fork is a submerged plant that is commonly found in aqueous humorous streams containing moderate to high levels of nutrients. One of the suitable environments for growth of Ceratophyllum demersum is low depth and laminar flow of water channels. In this regard, a search was conducted on irrigation channels inside Shahid Chamran University of Ahvaz and large quantities of this plant were observed in many parts of these channels. The plant was collected from the entrance channel of the Karoon River to the university. After collecting the plant and washing it with urban water and distilled water and drying it in free air was dried at 70 ° C. After that the dry matter was milled and it passed through the standard No. 50 sieve. In this study, alkaline solutions (0.5M NaOH solution) were used to modify biomass. This method has been shown to be effective in similar studies, and has greatly increased the absorption capacity of adsorbents. Preparation of cadmium storage solution was performed based on the methods presented in the standard reference for water and wastewater testing.

The morphology characteristics of biosorbent surface by Scanning Electron Microscope (SEM) were studied and desirable effects of modification on characteristics of biosorbent surface were proved. The result of study showed that by increasing pH from 3 to 8, the removal efficiency increased from 93% to 97% at pH 7, and then decreased to 85% at pH 8. In addition, adsorption capacity, in similar way, increased from 7.04 to 7.35 and then decreased to 6.44 mg/g. Therefore, pH 7 was determined as optimum pH. Increasing contact time, from 5 to 240 minute, caused changes in removal efficiency from 67% to 98% after 180 minute, and then decreased slightly. Adsorption capacity, in similar way, increased from 6.25 to 9.13 mg/g and then decreased slightly and contact time of 60 minute was determined as equilibrium time. Increasing dose of biosorbent from 0.02 to 4 g/L, causing increase of removal efficiency from 37% to 99% and decrease of adsorption capacity from 169.5 to 2.35 mg/g and finally dose of 10 mg/L was determined as proper dose of biosobent. Increasing of initial concentration of Cadmium solution from 10 to 200 mg/L led to decrease in removal efficiency from 96% to 31%, and increase in adsorption capacity from 9.18 to 59.7 mg/g, and concentration of 10 mg/L was determined as optimum initial concentration of Cadmium. Finally, kinetic and isotherm adsorption models were studied. In kinetic models, pseudo-second order kinetic model, with correlation coefficient of 0.99 described biosorption better than pseudo-first order. In isotherm models, the Langmuir isotherm with correlation coefficient of 0.99 described biosorption process such better than other models. Based on results obtained in this study, the high capability of modified Ceratophyllum demersum, as a favorable biosorbent for cadmium removal from aqueous solution was proved.

The images from the SEM device showed that adsorption modification increased the absorption capacity to absorb cadmium ions. The highest efficiency was achieved in pH equal to seven. According to the economic considerations and optimum consumption of the energy 60 minutes was determined as the time of equilibrium. Kinetic modeling shows that Pseudo second order has the best matching with experimental data.

According to importance of virtual water in water management, the amount of virtual water were determined in Rajae, Ramshir and Hendijan irrigation and drainage networks. First, the crop water requirement was calculated by using meteorological data of 10 years by CROPWAT. Then, the virtual water of the networks were determined by using crop yield. Also, the networks according to virtual water were evaluated by questionnaire and analytical hierarchy process. Results showed that the virtual water of wheat in Rajae, Ramshir and Hendijan irrigation and drainage networks was respectively 7183.33, 7148.88 and 7501.11 m3/year. The amount of barley virtual water in the mentioned networks were obtained respectively 8033.33, 8252.78 and 8705.56 m3/years and for sesame in Rajae irrigation and drainage network 9836.28 and for Ramshir was 10252.31 m3/year. Comparison between the amounts of calculating data and the delivered water indicated that in Rajae, Ramshir and Hendijan irrigation and drainage networks had given the water respectively 31, 23 and 28 percent more than actual water requirement. The results of AHP and ranking of mentioned networks by using Super Decisions with virtual water approach, showed that field factors in Rajae and Hendijan networks and climate factors had the greatest weight in Ramshir network. The least influential factor was economics factors. Comparing the result of AHP and computational of virtual water showed that Hendijan network is in the first rank. Qualitative assessment of operation of network utilization according to virtual water and water productivity showed that mention networks are in the undesirable condition.

Maize cultivation is considered as one of the main crops in the Khuzestan province. Mostly, in planning water requirement is calculated as a point, while irrigation is preformed locally. Therefore, this data must be converted from the spot to the region. In this research, in order to determination irrigation scheduling in Khuzestan province were planted spring Maize in lysimeter at the Faculty of Water Sciences Engineering of Shahid Chamran University of Ahvaz. The aim of this cultivation were compared between the current methods of determination of water requirement and lysimeteric method and determine the appropriate equation. The results showed that the Blaney-Criddle equation is the best method to estimation evapotranspiration in case study area. After determining the appropriate equation of water requirement, mention equation and meteorological data were used for zoning Maize water requirement with geostatistical methods.The zoning was performed using ARC GIS 10.3. First, the data were normalized. Then, according to the lowest RMSE were selected the best semi variogram and zoning model. The results show that simple kriging with circular semi circular model in all months is considered optimal model. RMSE value of this model was calculated, the amount of it was 0.08, 0.17, 0.46 and 0.48 for March, April, May and June. The result of water requirement zoning showed that this parameter in south is more than north of Khuzestan province. Also, the amount of water requirement in last months is more than first months. During the study period (from March to June), water requirement was 1.98, 5.52, 11.78 and 11.97 (mm/day) monthly. Small amounts of RMSE was indicated the optimum of water requirement zoning.

Surface irrigation methods are the most common methods for irrigation of agricultural land. These methods are superior to sprinkler, drip and underground irrigation, because they have lower costs of funding and implementation, is inexpensive, needed maintenance of equipment is simple and does not require skilled labor. New requirements for the use of municipal water, energy, industrial, and military intends to further improve the performance of surface irrigation systems. In other words, the low efficiency of surface irrigation is not related to the method of it, but the weakness is because of the design, implementation and management. Due to the special place of Khuzestan province in southwest of Iran in agriculture and applied surface irrigation for most of the farms in this province, in the present study was simulated water flow in furrow irrigation by using WinSRFR4.1 and the optimum length of furrow was determined in the experimental farm of the Water Sciences Engineering Shoshtar University. For this purpose, advance and recession of flow were simulated by Zero inertia and Kinematic wave model and result were compared with observed data.
In order to calculate and predicte advance and recession curves field measurement is necessary, but it takes a lot of time and cost. Therefore, the use of mathematical models and software for access information is important. In this research the amount of advance in furrow irrigation was measured and the results were compared with simulation of WinSRFR4.1.Field experiments was conducted in the research field of Water Sciences Engineering Shoshtar University. Data were collected from three furrows. The lengths of them were 60, 80 and 100 meters. Irrigation was performed under three discharges (1, 1.25 and 1.5 L/s), with three iterations. Three experiments furrows were provided which central furrow was for measurement data and side furrows were as buffer furrow. Before experiment in order to determine soil texture undisturbed soil samples were prepared from four depths 25-0, 50-25, 75-50 and 100-75 cm. Soil texture was determined in laboratory using the hydrometer method. The water supply was transported by pumping the water to the farm. This research was conducted in the winter of 2012 and spring of 2013. The end of furrow was opened. Input and output flows were controlled with W.S.C flume. Infiltration data were also measured according to two point method. Model using 27 set of field data was run and the results were compared with WinSRFR4.1 software. For evaluating the results of the model were used Esfandiari and Maheshwari’s statistical method.
For comparison between measurement and simulation data , , and indices were applied. The results of this study indicated that the phase advance of predicted values for all models is greater than the observed values. The average relative error rate of zero inertia models was 9.588 percent which indicated that Zero inertia models is the best model to predict the advance phase. The worst predictions were for Kinematic wave models with an average relative error equal to 33.21%. According to the results, the value of λ and for the Zero inertia model was 1.0912 and 98.76 respectively. The amount of error of field parameters such as; infiltration characteristic and hydraulic resistance are important for selection of model. So, for adjusting the amount of error in all models to predict an acceptable threshold tolerance was defined.
According to the results, Zero inertia and Kinematic wave models estimated advance time more than real condition. The results of the Zero inertia model were closer to measurement data than a Kinematic wave model. The maximum relative error was in discharge equal to 1.25 (l/s) in all models. With increasing the length of the furrow and input discharge the relative error of zero inertia models will be decreased. The results represented that in low discharge the infiltration depth was increased. Finally, it can be concluded thatthe zero inertia models are suitable for the study area and in study area, suitable length of the furrow is 100 (m).

In recent years, several models have been presented for water, soil and plant relationships that one of them is AquaCrop model, which in this study were used to predict sugarcane yield and soil salinity profiles under salinity stress in the southern Khuzestan. In order to achieve the required number of input factors, model was calibrated and the amount of soil surface covered by an individual seedling at 90% emergence, maximum canopy cover and harvest index were determined. These coefficients were determined 7.2 (cm2), 90% and 40% respectively. Then the model was performed and simulated and measured values were compared using statistical indicators. The coefficient of determination between measured and simulated yield data was achieved 0.97, the normal root mean square error 8%, Nash-Sutcliffe efficiency 0.83 and coefficient of residual mass -0.09. The results showed that the model has acceptable performance to estimated sugarcane yield and the model in condition of current study was over estimated. Also, coefficient of determination of soil salinity was more than 80% and the model estimated the amount of soil salinity more than measurement.