عبدعلی ناصری

Based on this topic, the present study was conducted in order to evaluate the HYDRUS model in estimating the salinity and water table level of three farms in Gamboa irrigation and drainage network. This area is located in the agricultural lands of Hamidieh city.experiments were conducted in 2023 during three stages of irrigation in wheat farms, with area about 8 ha, in three fields with three lateral spacing 50, 75 and 80m. The results showed that HYDRUS had an underestimation error (MBE<0) in the estimation of two parameters, water table level and drain salinity, but the calculation error of both parameters was acceptable. In addition, the accuracy of the HYDRUS in simulating the water table level in all three farms was in the excellent category (0.0<NRMSE<0.1). The changes in the lateral distance had no effect on the HYDRUS error or accuracy. Failure to consider soil salinity at the beginning of the simulation caused the small error of estimating drain salinity in the first three days in all three farms. But at the end of the simulation, the salinity calculation error in all three farms was less than 7%. Also, when approaching the drainage outlet, due to the failure to consider the role of the collector in the HYDRUS, its accuracy decreased slightly compared to the beginning of the farm. However, considering the results, we can trust the accuracy of HYDRUS model in estimating the water table level and soil salinity in sugarcane farms.

In Iran, where there are 40 million acres of desert land, faces many challenges in combating desertification, the most significant of which is wind erosion. Afforestation is the easiest solution to deal with this phenomenon. Given the quantitative and qualitative limitations of water resources, selecting the appropriate irrigation method for rangeland plants is of great importance.In order to determine the most appropriate irrigation method for various rangeland plants, this study was conducted for two years (2019-2021) at Shahid Chamran University of Ahvaz. The experiment was conducted in a factorial design within a randomized complete blocks design with three replications.The main levels included four types of rangeland plants (Prosopis cineraria (T1), Conocarpus (T2), Albezia lebbek (T3), and Ziziphus (T4)), and the sub-levels included four types of irrigation (surface (I1), waterboxx (I2), pitcher (I3) ) and Bubbler (I4)).The results of the analysis of variance showed that the types of rangeland plants had a significant difference at the 1%  level in terms of measured indices (plant height, stem diameter, stem dry and wet weight, dry and fresh leaf weight, leaf area index, and water consumption volume, and water productivity). There was also a significant difference between irrigation methods at the 5% level  for all indices except stem diameter and leaf area index. Among the plant species, mesquite had the highest coverage (including height and dry and fresh weight), and the lowest amount of water consumption (averaging 996 liters per plant over two years), and the highest water productivity (9.72 kg/m3). Among the irrigation methods, the bubbler irrigation method had better coverage than other methods. In contrast, the waterbox irrigation method was superior in terms of water consumption and water productivity, saving 87% of water compared to surface irrigation and  increasing water productivity by 298%. Therefore, the waterboxx irrigation method can replace other irrigation methods in afforestation and desertification projects

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

In arid and semi-arid regions, optimal use of water resources and management of soil resources is necessary to achieve sustainable agriculture. Accumulation of solutes in the surface layer of the soil is one of the challenges of subsurface drip irrigation, especially in arid and semi-arid regions. In the drip irrigation system, the distribution and accumulation of soluble soil salts in the surface layer of the soil is more than the deeper layers and it increases with increasing distance from the drippers. Soil salinity in the subsurface drip irrigation method should not be ignored, because in this irrigation method, even in good water quality conditions, some amount of water is transferred to the soil and the salt concentration on the soil surface gradually increases. Therefore, it is necessary to monitor and control the soil salinity in subsurface drip irrigation conditions. This study was carried out in order to investigate the soil salinity and sodicity status under subsurface drip irrigation and furrow irrigation during sugarcane growth period.

This study was carried out in two experimental fields of subsurface drip irrigation (with a drip irrigation depth of 20 cm) and furrow irrigation located in the Khuzestan Hakim-Farabi Agro-Industry. In this study, the changes in soil salinity at the depths of 0-30, 30-60, and 60-90 cm and sodium absorption ratio (SAR) in the surface layer (0-30 cm) were investigated during the growth period of sugarcane (2 (T1), 4 (T2), 6 (T3), 8 (T4), 10 (T5) and 12 (T6) months after the start of irrigation). The average data were compared using the t-test at the 5% probability level.

The results showed that at all sampling times, electrical conductivity (EC), concentration of soluble cations (Na, Ca and Mg) and SAR of the soil in the subsurface drip irrigation field were more than the furrow irrigation field. The results indicated that the soil electrical conductivity (EC) at both the depths of 0-30 and 30-60 cm, in all investigated times except 2 and 10 months after the start of irrigation (surface irrigation in subsurface drip field), was significantly affected by the irrigation method. The results of this research showed that in all sampling times of salinity, the concentration of soluble cations (Na, Ca and Mg) and SAR of the soil in the subsurface drip irrigation field was higher than that in the furrow irrigation field. The greatest difference in the soluble concentration of Na, Ca and Mg in the soil in the two studied fields was related to the T4 (8 months after cultivation). At this sampling time, the average soil soluble concentration of Na, Ca and Mg in the subsurface drip irrigation field was 2.1, 1.95 and 1.93 times their values in the furrow irrigation field, respectively. In all sampling times except T5, the soil SAR at the 0-30 cm depth, in the subsurface drip irrigation field, was significantly higher than in the furrow irrigation field, and in T5 sampling time, it was significantly lower than farrow irrigation. At the T5 sampling time, surface irrigation in the sub-surface drip irrigation field led to leaching of accumulated salts especially Na in the surface layer of the soil and reduced the soil SAR.

According to the results of this study, it seems that besides using a subsurface drip irrigation systems in arid and semi-arid regions (like Khuzestan Province), it is necessary to provide the possibility of surface irrigation in order to manage soil salinity and sodicity. Moreover, considering that sugarcane is a perennial plant, it is necessary to pay attention to soil solute leaching in order to control salinity in the management of sugarcane fields.

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.

Soil moisture is one of the most important environmental parameters for water resources management and irrigation planning systems in agricultural areas. In agricultural areas, most soil moisture retrieval models are unstable in terms of their accuracy and performance during crop growth season. As a result, there is no consensus on which model performs optimally during the agricultural season. This is because of the uncertainties associated with model physics, input data, vegetation attenuation and soil characteristics. To deal with these practical concerns, in this research, a simple but effective soil moisture retrieval method for using combination of multiple models based on machine learning has been introduced. Firstly, the WCM with different vegetation descriptors were calibrated and validated in sugarcane fields for Sentinel-1 backscattering coefficients . For this purpose, soil moisture measurements of sugarcane fields (400 samples in total) during the plant growing season in 2020 were used. Then optimization of calculations was done using the GRNN. The results showed that WCMNDWI retrieves soil moisture more accurately than other models in the early stages of sugarcane growth, while WCMVWC and WCMLAI were more accurate in the late sugarcane growth. Time-series soil moisture retrieval accuracy using the combined method based on GRNN was higher than that of single WCM models. According to the results of the in situ validation for sugarcane fields, with the optimal combination of models, the minimum MAE) is less than 0.02 m3m-3, the RMSE is approximately 0.085 and the R it was equal to 0.7 for the growing season.

Agricultural water management studies require accurate information on actual evapotranspiration. This information must have sufficient spatial detail to allow analysis on the farm or basin level (Sanchez et al., 2008). The methods used to estimate evapotranspiration are grouped into two main groups, which include direct methods and indirect or computational methods (Alizade and Kamali, 2007). Basics of the indirect methods are based on the relationship between meteorological parameters, which impedes the use of these data with a lack or impairment. On the other hand, this information is a point specific to meteorological stations, and their regional estimates are another problem of uncertainty of their own. To this end, the use of remote sensing technology can be a suitable approach to address these constraints. Real evapotranspiration can be estimated by satellite imagery that has short and long wavelengths and is estimated using surface energy equations (Chihda et al., 2010). Examples of such algorithms include SEBAL (Bastiaanssen et al., 1998 Bastiaanssen, 2000;), METRIC (Allen et al., 2007), SEBS (Su, 2002). Among the above mentioned algorithms, energy billing algorithms have been used (Bagheriharooni et al., 2013; Teixeira et al., 2009). Among the factors of superiority of the SEBAL algorithm, in comparison with other remote sensing algorithms, is a satellite imagery analysis algorithm based on physical principles and uses satellite simulation and requires minimum meteorological information from ground measurements or air models (Bastiaanssen et al., 2002).

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.

The crop water stress index based on canopy–air temperature differences (CWSIdT) is an appropriate index for monitoring the water status of croplands but is rarely used to evaluate the water status of forest ecosystems. However, CWSIdT is rarely used in forest ecosystems. Investigating the possibility of using the crop water stress index (CWSI) for planning the irrigation of four species of pasture trees, Prosopis cineraria (T1), Conocarpus (T2), Albezia lebbek (T3) and Ziziphus (T4), under four methods of surface irrigation, bubbler, pitcher, and waterboxx system, was selected. This research was conducted in the year 2021 in the experimental farm of the Faculty of Water and Environmental Engineering of Shahid Chamran University of Ahvaz and lasted for 9 months. The experiment was conducted as a factorial in the form of a randomized complete block design with 3 replications. The crown temperature, actual evaporation and transpiration and meteorological factors of the field were continuously and simultaneously measured during the period from the beginning of April to the end of December. Infrared thermometers was used to measure crown temperature and vapor pressure deficiency values for different irrigation treatments. The results showed that under well-watered conditions, canopy-air temperature difference and canopy surface transpiration rate are both controlled by the net radiation received by the canopy surface The highest and lowest calculated CWSI values were related to waterboxx and Pitcher irrigation with values of 1.01 and 0.81, respectively, which were related to Ziziphus and Konocarpus trees. Statistical analysis showed that the effect of irrigation method on plant water stress index values in spring and autumn seasons was significant at the level of 5% and the average values of plant water stress index increased with water consumption reductions of 87 and 59% in waterbox and pitcher irrigation systems, respectively. However, there was no significant difference observed in the summer season. Also, the effect of tree type on the CWSIdT index during the period was important at a 1% level. In addition, the difference in vegetation and air temperature were used as a determining factor to determine irrigation time, which can be used to estimate pasture tree water stress.

In the south of Iran, due to being located in the arid and semi-arid regions, the agricultural sector faces soil salinity resulting from the capillary rise of salt from the shallow saline water table to the soil surface and reducing crop yield. The objective of this research was to investigate the effect of surface drip irrigation (DI) and subsurface irrigation (SDI) methods on soil profile moisture and salinity distribution, maize (SC 703) yield, and irrigation water use efficiency in the presence of shallow saline water table (60 and 100 cm), during summer and spring seasons of 2021. A factorial experiment was conducted based on randomized complete block design with three replications in drained lysimeters filled with loamy soil (specific gravity 1.45 g cm-3) in field conditions. The results showed that the accumulation of salt above the dripper in subsurface irrigation method at both SDI100 and SDI60 treatments, was significantly more than the surface irrigation method (p<0.01). The results also revealed that summer maize yield was more than spring maize yield. Furthermore, in the summer season, irrigation in the root development zone in the SDI100 and SDI60 treatments caused an increase in the maize yield by 25 and 61%, respectively, and Water Productivity Index (WP) by 25 and 58%, respectively, in comparison to DI100 and DI60. Evaporation in SDI60 and SDI100 treatments was 26-30% and 43-51% lower than DI60 and DI100 treatments, respectively. The results of this research showed that SDI is more effective than DI in controlling soil profile salinity and plant yield.

Among the surface irrigation methods, furrow irrigation is the most common and adapted method to mechanization for agricultural system. Considering the lack of water resources and the problems that sugarcane cultivation and industries have faced in recent years, increasing the efficiency and productivity of the existing surface irrigation systems is of great importance. This study was conducted in order to investigate the effect of slope, furrow length and flow rate on sugarcane and sugar yield. For this purpose, an experiment was conducted in the form of split split plot in the form of randomized complete blocks with three replications in the Mirza kochak Khan Agricultural Company. This design was done with three treatments and each treatment at three levels including: 0.02, 0.04 and 0.06% slope, 150, 200 and 250 m length of the furrow and flow rate of 2, 2.5 and 3 lit/s. The yields of sugarcane and sugar in this study were 65.8 to 120.4 t/ha and 6.3 to 12.5 t/ha, respectively. The yield of sugarcane and sugar was significantly affected by the slope and length of furrow and flow rate. The average changes were significant due to the mutual effect of slope, furrow length and flow rate on sugarcane yield (P<0.01). 250 meters long, 2 lit/s flow rate and 0.04% slope with sugarcane yield of 120.4 t/ha and sugar yield of 12.6 t/ha, it is better for sugar production in the sugarcane fields of Mirza kochak Khan Agricultural Company. A combination of a 250 m long furrow with a slope of 0.04% and a flow rate of 2 lit/s can be recommended due to the reduction of deep infiltration losses and the reduction of water drainage and optimal water consumption, which has led to the production of sugarcane and sugar with appropriate performance.

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.

The purpose of this study was to investigate the effect of the depth of application and the discharge of drippers on the distribution of soil salinity in the sugarcane field under subsurface drip irrigation. This research was conducted as a split-plot experiment based on complete random blocks design in four replications. Experimental treatments including two dripper distances on the secondary pipe (including L1: 30 cm with a flow rate of 2.2 L h-1 and L2: 50 cm with a flow rate of 3.8 L h-1) and three dripper placement depths (Including D1: 15 cm, D2: 25 cm and D3: 35 cm). In order to investigate the effect of different treatments on salinity distribution in the soil, soil samples were collected four and six months after the start of subsurface drip irrigation, from the soil around the drippers at different intervals. The results of the salinity distribution pattern showed that under the conditions of subsurface drip irrigation, the highest salinity was observed in the bottom of the furrow and in the upper layer of the soil on the mound. Six months after the start of subsurface drip irrigation, the salinity of the soil profile in all treatments increased compared to the beggining, so that the salinity of the area around the drippers in most of the treatments reached above 3 dS m-1. The highest amount of salinity in the upper soil layers was observed in D3L2 treatment. Also, the most favorable salinity distribution pattern in the root development area was observed in D2L1 treatment. Therefore, it can be concluded that the use of drippers at a depth of 25 cm with intervals of 30 cm and a flow rate of 2.2 L h-1 is more suitable for managing soil salinity in the subsurface drip irrigation system.

Furrow irrigation is one of the most common and suitable methods of surface irrigation that is used in the fields of Mirzakochek Khan Sugarcane Company in the southwest of Khuzestan. Considering the problems that sugarcane companies have recently had due to the water resources scarcity, increasing the efficiency of surface irrigation systems with the aim of reducing water supply from Karun River is necessary. This study was conducted to investigate the effect of slope, furrow length and discharge on water application (Ea) and storage efficiencies (Es) as split-split plots and randomized complete blocks design with three replications from August 2018 to September 2019 in one of the farms of Mirza Kochakh Khan Company. This study was carried out with three treatments, each at three levels, including: slope of 0.02, 0.04 and 0.06%, length of 150, 200 and 250 meters and flow rate of 2, 2.5 and 3 l/s. Ea was obtained between 64.7% and 77.4% and Es between 65.2% and 100%. The highest Ea was corresponded to a slope of 0.04%, a flow rate of 2 l/s and a length of 250 meters. The highest Es was corresponded to the slopes of 0.04 and 0.06%, flow rate of 2 l/s and the length of 250 meters. Consequently, the slope of 0.04%, the flow rate of 2 l/s and the length of 250 meters are sujestted for furrow irrigation system of the sugarcane lands in the south of Ahvaz due to the optimal value of Ea and Es.

Aloe vera L. is one of the medicinal and perennial plants, which is cultivated in arid and semi-arid regions. Although this plant is relatively resistant to lack of irrigation, but in the conditions of commercial cultivation, it is necessary to pay attention to the supply of irrigation water during the growing season. Due to that, the present study was conducted in order to investigate the effect of the amount of irrigation water (I1: 50, I2: 75 and I3: 100% of water requirement) and Trava 2020 superabsorbent (A1: zero, A2: 5 and A3: 10 grams per kilogram soil) on the yield and yield components of Aloe vera. These treatments were applied in three replications and the crop was harvested in two times include: autumn 2020 and spring 20201. The experiment was carried out in a research farm in Omidiye city with longitude 30°37’ and latitude 49°49’. Leaf length, leaf width, leaf weight, number of leaves and number of offset in the second harvest were 26.8%, 25.1%, 25.5%, 27.0%, 24.1% and 17.5% more than the first harvest. Increasing the consumption of superabsorbent caused an increase in leaf length. The difference between the leaf length in two treatments A3 and A2 was about 3.4% and between two treatments A3 and A1 was about 6.9%. With the increase of superabsorbent consumption, the leaf width increased so that its value reached from 79 mm in A1 treatment to 83 mm in A3 treatment.

From the economic and medicinal point of view, Aloe vera is a herbaceous and perennial plant that has attracted the attention of many farmers Iran. Climatic conditions throughout the year and the quantity and quality of irrigation water affect Aloe vera yield. For this reason, the research was conducted with the aim of investigating the effect of aforementioned factors on the Aloe vera yield and yield components. To achieve this goal, a strip-block design was carried with two factors, include: amount of irrigation water at three levels (I1: 50, I2: 75 and I3: 100% of irrigation water requirement) and irrigation water salinity at two levels (S1: 5 and S2: 10 dS.m-1) with three replications, in a research farm in Omidiye with longitude 30°37° and latitude 49°49°. The first harvest was done in the fall of 2019 and the second harvest in the spring of 2020. The results showed that in the first harvest, irrigation had a significant effects on leaf width, leaf fresh weight (P-value≤0.05), gel weight, the ratio of gel weight to leaf weight, the number of leaves and the water use efficiency (P- value≤0.01). Salinity had a significant effects on leaf thickness (P-value≤0.05), leaf width, leaf fresh weight, gel weight, the ratio of gel weight to leaf weight and water use efficiency (P-value≤ 0.01). In the second harvest, irrigation had a significant effects on the ratio of gel weight to leaf weight, number of leaves and water use efficiency and salinity had a significant effects on leaf width, leaf thickness, leaf fresh weight, gel weight , the ratio of gel weight to leaf weight and water use efficiency . In both the first and second harvests, the interaction effect of irrigation water and salinity only showed a significant effect on the water use efficiency (P-value ≤ 0.05).

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

The climate change, the intensity and continuity of drought periods, will affect the frequency of its occurrence in the coming decades. Iran is one of the countries that suffered the most damage from drought. Soil infiltration, which is very important in the design and management of furrow irrigation, changes over time. This issue will have a great impact on the irrigation efficiency. The purpose of this research is to investigate the temporal changes of the parameters of the Kostiakov-Lewis infiltration equation in furrow irrigation of sugarcane plant. The values of inflow, outflow and advance time were measured during 4 irrigation in 6 repetitions.The infiltration parameters were estimated using the advance time data and the hydrograph of the inflow and outflow using volume balance method. The results showed that there are significant temporal changes between the average values and the values of the coefficients of the Kostiakov-Lewis infiltration equation in each irrigation and the maximum change was related to the first irrigation, so that the range of parameter a changes in the range of 0.59 - 0.09 and the maximum value was related to the first irrigation, the range of changes in parameter k were observed in the range of 0.90 - 0.25, the highest of which was observed in the second irrigation. The average of f0 were equal to 7.333* 10-5 m^3/m.min with a CV of 16%. The results showed that the trend of time changes of infiltration parameters is not constant and more research is needed in this field.

Soil moisture is one of the most important parameters in water, soil and plant resources management. Therefore, the present study was conducted to evaluate the efficiency of thermal and optical remote sensing data in order to estimate soil moisture and irrigation planning in sugarcane fields of Khuzestan Province, Iran. For this purpose, soil moisture content for 9 passes of Landsat 8 and Sentinel 2 satellites was calculated using thermal and optical trapezoidal methods from April to October 2020 in Amirkabir Sugarcane Agro-industry fields. To validate the results, the measured soil moisture content data of 337 ground control points located in 18 sugarcane-growing fields measured by TDR350 dehumidifier were used simultaneously with the passage of the satellites. The results showed that TOTRAM model with a determination coefficient of 0.82 and error rate of RMSE and NRMSE as 4.45% and 12.9%, and OPTRAM model with an explanation coefficient of 0.93 and RMSE and NRMSE error of 3.14% and 12.1% were able to properly estimate soil surface moisture in sugarcane fields. Also, the results of evaluation of soil moisture maps for irrigation planning of sugarcane fields showed that these data could be used for irrigation planning with average NRMSE error of 16% and 9% in relation to ground irrigation time data for TOTRAM and OPTRAM models, respectively. In this regard, OPTRAM model data were more efficient compared to thermal data, due to better spatial resolution of optical data and less effect by environmental factors such as temperature and relative humidity of air and also the effect of adjacent pixels.

The simultaneity of sugarcane's dry season with summer heat and high evaporation makes it one of the most water-consuming plants, considering that sugarcane is cultivated in large areas in Khuzestan province Evaluation of water consumption water productivity in sugarcane cultivated lands is very important.the present study was conducted with the aim of evaluating the effect of subsurface drip irrigation on water productivity and quantitative and qualitative yield of sugarcane in the second Ratoon and comparing it with the conventional irrigation system (closed-end furrow irrigation). Experiment with a fixed working depth of 20-cm and two distances of droppers (L1=50, L2=60-cm) and two flow rates (Q1=2.3, Q2=3.6-lit/hr.). It was done with four repetitions and in the form of a block design and as a split plot. The results of variance analysis of quantitative traits showed that the highest stem height and sugarcane yield in the treatment (Q2.3, L50) were The order of 211-cm and 88-ton/ha was observed. The results of variance analysis of qualitative traits also showed that the highest yield of pure sugar with 11-ton/ha was related to the treatment (Q2.3 L50) and the lowest amount was 6.6-ton/ha. in conventional irrigation treatment (control). The highest water productivity per cane and sugar produced for the treatment (Q2.3, L50) was obtained, 4.64 and 0.58 kg/m3, respectively, which compared to the control treatment, caused There has been an increase of about 52% in water productivity. Finally, the treatment (Q2.3, L50) had the highest yield and water productivity.

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.

Hydraulic conductivity is one of the most important physical properties of soil, and knowing it plays a vital role in investigating the transport of solutes and pollutants in porous environments such as soil. This study aims to obtain saturated hydraulic conductivity pedotransfer functions (PTFs) using soil properties and satellite images. In this regard, the hydraulic conductivity of soil saturation was performed using the Inversed augerhole method in a part of the southwestern lands of Khuzestan province at 50 points. Then, at these points, surface samples of the soil were taken and soil properties such as soil texture, electrical conductivity, soil organic carbon, and saturated moisture were determined in the laboratory. In the next step, the indices of Sentinel-2 satellite images were calculated in three categories of soil, vegetation, and moisture indices and 11 PTFs, (PTF1-PTF11) for saturated hydraulic conductivity were obtained in four stages by combining soil properties and these indices. Finally, the spatial distribution of saturated soil hydraulic conductivity was obtained using the random forest model. The results of the modeling of PTFs of saturated hydraulic conductivity showed that among the 11 models with which PTFs of hydraulic conductivity were performed, the combination of three vegetation indices with soil-found early properties was the most effective for estimating the saturated hydraulic conductivity (PTF7). The values of R2, RMSE and MAE for this case were equal to 0.83, 0.40 and 0.166 respectively. Finally, the spatial distribution of saturated hydraulic conductivity using the Random Forest model showed that this model performs the spatial distribution of saturated soil hydraulic conductivity of soil well. Based on the obtained results, it can be found that the combination of soil properties with the indices obtained from the Sentinel-2 satellite images, creates PTFs of saturated hydraulic conductivity of the soil with very high accuracy.

Limitation of water resources, lack of irrigation planning and undesirable use of water are the main factors limiting agricultural development and increasing food production in Iran. Plant and mathematical models are suitable tools for improving irrigation management, crop increase and irrigation efficiency to simulate soil moisture estimation and plant irrigation planning. This study was conducted in 2017 with the aim of investigating the ability of AquaCrop model to irrigate and estimate soil moisture in sugarcane cultivation lands located in the south of Khuzestan province. The results of soil moisture simulation and irrigation planning were compared with the values measured in the field by AquaCrop model. Overall, the results showed that the accuracy of the model in estimating irrigation planning is more than the soil moisture estimation. The accuracy of the model was somewhat higher in estimating soil moisture with the measured values at the farm level. So that the mean square tide of error (RMSE), tide mean squares of normalized error (NRMSE), coefficient of determination (R²), compatibility index (d) and residual coefficient (CRM) were 2.12%, 0.08, 0.71, 0.88 and -0.035, respectively. Also, the results of simulation of sugarcane irrigation round in the field showed that four irrigation rounds, which on average were 4800 cubic meters per hectare, were saved compared to the control farms. The yield of this field was 128 tons per hectare and the average yield of control farms was 103.60 tons per hectare. The results showed that AquaCrop model can be used as a suitable method for planning and irrigation management of sugarcane cultivation lands.

Soil moisture is one of the key parameters in water resources studies and irrigation remote planning. Measuring soil moisture on a large scale is costly and very time consuming. Traditional methods of measuring soil moisture at farm level cannot show the spatial changes of moisture in the best way. Various new methods have been developed to use satellite data to model soil moisture based on thermal images. This study was conducted in 2020 with the aim of investigating the ability of thermal satellite imagery to estimate soil moisture and to plan irrigation rounds of lands in sugarcane industry of Amirkabir located in the south of Khuzestan province. For this purpose, during growing season of sugarcane, soil moisture content was calculated for 9 crossings Landsat 8 satellite and evaluated using 180 ground control points, and also daily irrigation data of 32 farms (25-hectare) were recorded during the study period. The results showed that the accuracy of the model is suitable for estimating soil moisture with the measured values at the farm level. The mean square root of normalized error (NRMSE) was 12.9% and the coefficient of determination (R²) was 0.82. Also, the results of soil moisture in irrigation management of sugarcane fields showed that thermal trapezoidal model is effective due to using thermal bands to environmental factors such as relative humidity percentage, average air temperature, pest (leaf dryness) and plant temperature, and somewhat in June and July causes errors in irrigation planning of sugarcane fields. The mean square root of normalized error (NRMSE) during soil water stress was 24.32%, during irrigation time was 22.20%, at average humidity was 11.7%, during high humidity was 13.20% and during irrigation was 8.86%. Consequently, the accuracy of thermal trapezoidal model for planning irrigation of farms in estimating soil water stress and field irrigation time in some periods of growing season is moderate and for fields having sufficient soil moisture is well.

Using synthetic filters in any location needs familiarity about benefits and challenges of its usage .Currently synthetic filter is widely used in many places in the world .But it hasn't been accepted in Iran because there isn’t enough   knowledge and information about benefits and disadvantages of synthetic filters (Ritzema et al., 2006).In the field of synthetic coatings, research and studies have been conducted around the world. Arvahi and Naseri (2007) investigated the application of synthetic and mineral filters in underground drainage systems. The results of this study showed that the performance of the PP450 synthetic filter is similar to the sand filter and can provide drainage requirements and is technically and economically feasible in Abadan's subsoil drainage system. Faure (2006) presented an experimental model for predicting geotextile clogging. With the help of this model, the mass of the particles of soil particles enclosed within the geotextile could be measured. The results of the Faure experimental model show that the eclipse of geotextiles depends on the mass of the particles entrapped inside the geotextile.Water and soil of Ramhormoz Fajr region have high calcium content and in this case, the probability of clogging of filters increases due to chemical activity. In order to examine this issue, it was first necessary to simulate the conditions in a physical model to determine the effect of synthetic filtration on clogging. In general, the objectives of this study were:1- Evaluation of synthetic filter performance in the conditions of Ramhormoz under continuous flow operation.2- Investigation of Eclipse of synthetic filter in wet conditions and drying of soil (discontinuous).3- Studying filtration of synthetic filter due to temperature changing conditions (discontinuous).