آبیاری

Considering the lack of renewable water resources in the world and the high share of the agricultural sector in water consumption, makes increasing water productivity very important. Corn is an important type of fodder that is harvested for consumption as silage of the whole plant (Coors et al., 1997). Despite having one harvest stage, this plant has a high dry matter yield. Its silage is easily prepared and it is a tasty fodder with stable quality for livestock and has higher energy than other fodder (Curran and Posch, 2000). Research results in Hamedan province in two traditional and modern irrigation systems showed that the average physical water productivity for fodder corn in traditional and modern irrigation systems was 5.11 and 6.67 kg per cubic meter of water, respectively. The average economic water productivity for fodder corn in traditional and modern irrigation system was 7678 and 10068 rials per cubic meter of water, respectively (Seyedan and Mottaghi, 2019). The main purpose of this article was to measure the water received in the field of fodder corn fields in Behbahan city and to investigate their physical and economic water productivity. To do this, the applied irrigation water, physical and economic water productivity under different agricultural management and in irrigation networks and systems can be compared and evaluated.

This research was carried out in the field and in order to investigate the effects of independent variables on applied irrigation water, physical and economic water productivity in the cultivation of fodder corn in irrigation methods and networks under the management of farmers in the cropping season (2016-2017). It was implemented in Behbahan city. Thus, in 12 farms of the farmers, the applied fodder corn irrigation water was measured (without interfering in their irrigation program). The effective rainfall of the 2016-2017 crop year in Behbahan city was calculated using the United States Department of Agriculture (USDA) method. To determine the applied irrigation water, first, the inflow rate from the selected water source was measured with a suitable device (WSC flume, water meter and ultrasonic flow meter). The SPSS16 software was used for step-by-step linear multivariate regression analysis. Linear multivariate regression analysis was used to investigate the effects of independent variables on dependent parameters. One-way analysis of variance was performed with SPSS16 software. The purpose of using one-way analysis of variance was to statistically show a significant difference between the average of two or more "independent" groups. In order to compare the indicators of physical and economic water productivity , there was a need to resolve the difference. Therefore, to solve the scale difference, the common method of Z-Score standardization was used.

The results of the analysis of variance in the regression model showed that the growth period of the plant had the most significant positive effect on the amount of irrigation water with the t-statistic of 5.288. Performance with a t-statistic of 5.919 had the most significant positive effect on economic water productivity. Among the independent variables, yield and applied irrigation water had the most significant positive and negative effect on the physical productivity of water with the values of 6.419 and -6.381, respectively. The applied irrigation water varied from 4826 to 14733 cubic meters per hectare. The results of the analysis of variance and the descriptive results of the mean of the traits showed that the highest value of physical water productivity was equal to 10.128 kg/m3 in rain irrigation and the lowest value was 4.818 kg/m3 in surface irrigation in the traditional network. They had a significant difference. The calculated Z-Score values showed that 50 and 33% of the sprinkler and surface irrigation farms (modern network) had acceptable economic productivity, but 100% of the surface irrigation farms in the traditional network did not provide acceptable economic productivity.

The amount of irrigation water used in the fields with sprinkler irrigation method was 46 and 33% less than traditional and modern network fields that were irrigated by surface irrigation method. Also, the consumption of irrigation water in the surface irrigation method in the traditional network was 20% more than the surface irrigation in the modern irrigation network. This increase in water consumption was effective in reducing the irrigation efficiency of traditional networks compared to modern networks that irrigated with surface irrigation. The reason for the higher consumption of irrigation water in the surface irrigation method in the modern network compared to sprinkler irrigation can be attributed to the following factors: 1- The lack of irrigation management during the change of the irrigation shift of the fields, which sometimes happens due to the absence of the irrigation worker during the shift change. 2- lack of control of the incoming water of the fields due to the lack of meters or devices to measure the incoming flow. 3- Improper leveling of lands under the modern irrigation network, which increases the irrigation time of these fields. By covering the traditional irrigation canals with concrete or other coverings suitable for the climate of the region, water losses can be reduced. Also, the necessary training for the correct use and management of irrigation water according to the different stages of fodder corn growth will be effective in reducing water consumption. Water according to the stages of growth and development of the plant prevented the occurrence of under-irrigation stress in the stages of flowering and seed filling, which is the most important stage sensitive to water stress.

Cover plants, among the most commonly used plants in the realm of green spaces, are typically characterized by shallow roots. In this research, a simple and flexible algorithm has been introduced for calculating the basal crop coefficient and evaporation coefficient of shallow-rooted plants without the use of (micro-) lysimeters. The presented algorithm requires measurements of moisture at three depths for short-term calculations and only surface layer moisture monitoring for long-term calculations. Using this algorithm, it is possible to calculate the potential evaporation and transpiration of the plant. Furthermore, the presented algorithm is independent of time steps. To evaluate this algorithm, nine experimental plots were utilized, including six Frankenia plots with full coverage and three bare soil plots during the peak water demand period. All experiments were conducted at the educational site of irrigation systems in the vicinity of the meteorological site of Ferdowsi University of Mashhad (FUM). For this purpose, the water content and irrigation of these plots was fulfilled (at most) every 48 hr. The results indicated that the presented algorithm has good capabilities for estimating the basal crop and evaporation coefficients. Additionally, the basal crop coefficient for Frankenia plant was found to be 1, and the evaporation coefficient was 0.58. Therefore, this method can be employed for estimating the water requirements of different plants without using (micro-) lysimeters.

Due to the scarcity of water resources and ever-increasing demand for it, treated wastewater, if meeting agricultural standards, can serve as an alternative or supplementary water source for achieving sustainable agriculture. In this study, considering the water shortage problem and the presence of arable lands in the province of Qazvin, especially in the Buin Zahra region, the potential of using the effluent of Qazvin’s sewage treatment plant in Buin Zahra’s irrigation and drainage network was investigated. In the first phase, the environmental status of the Buin Zahra region was studied, and subsequently, for one year (2019-2020), monthly samples were taken from the raw wastewater inflow and the treated effluent. Physical, chemical, and microbial analyses of these samples were conducted to evaluate the efficiency of the Qazvin sewage treatment plant. Based on the area's cropping pattern and the quality requirements of agricultural products, a plan for the development of the irrigation and drainage network was devised, considering the quantity and quality of the produced effluent. The evaluations included gravitational transfer, significant aquifer depletion, soil characteristics, and the area's cropping pattern. Taking these factors and the social acceptance of the plan into account, the network development was proposed using 20.8 million m3 of effluent annually, covering 2000 ha of Buin Zahra's lands, and the appropriate cropping pattern and irrigation system were determined. Finally, given the importance of health and environmental issues, a comprehensive program for monitoring the quality of various components of the plan, including effluent, soil, agricultural products, and project workers, was prepared and presented to ensure the sustainable and safe use of this resource.

Determining and analyzing the indicators of water management for the crops of the country plays an important role in the planning of the agricultural sector.This study was accomplished to investigate water management indicators of irrigation systems in Wheat production in the upstream region of Karkheh basin and specifically Honam plain in Lorestan province. 14 wheat farms were selected in the mentioned area and were subjected to agronomic and water monitoring during the cropping season. Wheat applied irrigation water was determined by recording the irrigation schedule and measuring the flow rates. The water requirement of wheat was estimated by Penman-Monteith method and by applying plant coefficients. The yield and water productivity for wheat farms were obtained at the maturity stage. The results of agronomic and water monitoring of selected wheat fields showed that the type of agricultural, irrigation and fertilizer management of farmers played an important role in the amount of grain produced. Irrigation applied water, yield and WP was statistically different during the growth season for the farms. The average yield, the volume of irrigation water, the productivity of the irrigation water and the productivity of the applied water of wheat in the sprinkler irrigation system were equal to 6845 kg/ ha, 2325 m3/ha, 3 and 1.52 kg/ m3, respectively, and in the surface system equal to 5000 kg/ ha, 3237 m3/ha, 1.3 and 0.94 kg/ m3, respectively. The monitoring of the irrigation program in the selected fields showed that the farmers irrigated the mentioned fields on average 41% less than the full irrigation requirement of the plant. One of the important reasons for this is the special attention of farmers in the region to plant crops with high profitability, such as alfalfa and clover, despite the need for more water than wheat.

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).

Landfill leachate, a liquid resulting from waste decomposition, contains nutrients like ammoniacal-N, Na, K, and organic matter. Biological treatments effectively remove degradable organics from young landfill leachate, but aged leachate with recalcitrant organics requires combined physical-chemical and biological methods or advanced technologies, leading to higher treatment costs. Even after treatment, leachate may not meet environmental standards for release. In arid and semi-arid regions with water scarcity and low soil organic matter, leachate application to soil presents a potential solution. Soil’s properties enable it to retain and degrade pollutants while utilizing leachate’s nutrients to enhance fertility and crop growth. However, leachate composition and application rates are critical factors due to potential negative impacts from total nitrogen, salinity, and heavy metals. Alkaline pH in aged leachate reduces heavy metal contamination risk. Detailed leachate characterization before soil application is crucial to prevent environmental and functional problems. This review examines existing research on leachate irrigation’s effects on soil properties and plant nutrition, contributing to sustainable leachate management and agricultural practices in water-limited regions. Additionally, the review explores potential risks associated with leachate irrigation, including soil salinization, heavy metal accumulation, and groundwater contamination. By understanding both the benefits and drawbacks, informed decisions can be made regarding the suitability and implementation of leachate irrigation in specific contexts.

To carry out this study, keywords such as "Landfill leachate", "Composition of landfill leachate" and "Landfill leachate irrigation" were searched in the Web of Science, Google Scholar, ScienceDirect, and SID databases. For these keywords, 205 articles were found from 1989 to 2023. After the screening, quality review, and removal of repetitive and unrelated articles, 110 relevant articles were used. The main criterion for selecting articles was the effects of landfill leachate irrigation on the various properties of soil, and the nutrition of different plant species. The quality of the articles was evaluated through the Scimago Journal Rank (SJR) index, the citation, the Impact Factor, and the source normalized impact per paper (SNIP) index.

Landfill leachate presents a complex environmental challenge due to its potential for both soil contamination and enrichment. Leachate's xenobiotic and heavy metal components can induce soil contamination, altering the natural environment. Studies have documented reduced hydraulic conductivity, increased gas production, and altered microbial communities, ultimately impacting soil productivity.  Leachate percolation can also modify physicochemical characteristics, including reduced microbial biomass, phosphorus-fixing capacity, and pH shifts, depending on waste composition. Conversely, research highlights the potential benefits of leachate application in arid and semi-arid regions facing water scarcity and low soil organic matter. Leachate can contribute to the increased organic content, improved soil structure, and regulated pH, enhancing soil fertility and crop productivity.  The presence of macro and micro-nutrients such as Fe, Mn, N, P, and Zn further supports leachate's potential as a fertilizer. However, concerns remain regarding inhibitory chemicals in leachate and their potential detrimental effects on plant growth and yield. Studies report instances of leaf injury, reduced yield, and poor survival rates in certain plant species.  In contrast, research demonstrates the positive effects of diluted or low-strength leachate application, stimulating plant growth and enhancing yield, particularly for Brassica species and tree species like Acacia confusa, Leucaena leptocephali, and Eucalyptus tortellini. These contradictory findings underscore the intricate interplay of factors influencing leachate irrigation outcomes. Soil characteristics, plant species, leachate source and composition, application methods, and their interactions all play significant roles in determining the success or failure of leachate irrigation.

Landfill leachate, characterized by its elevated nitrogen and nutrient levels, presents a potential alternative water and fertilizer source for agricultural practices, particularly in arid and semi-arid regions facing water scarcity. However, responsible leachate utilization necessitates a comprehensive approach that balances maximizing benefits with minimizing environmental risks. Prior to agricultural application, detailed leachate characterization is crucial to determine its precise composition and suitability for irrigation. This includes quantifying heavy metal concentrations, salinity levels, and the presence of potentially toxic organic compounds.  Concurrent plant selection is equally important, prioritizing species with demonstrated tolerance to leachate constituents. Given the potential for salinity and heavy metal accumulation, continuous application of raw leachate, especially for sensitive crops, should be avoided. Implementing alternating irrigation regimes with conventional water sources can mitigate these risks while providing essential nutrients for plant growth.  Monitoring soil health indicators, including pH, organic matter content, and microbial activity, is vital to assess long-term impacts and implement necessary soil amendments. Determining optimal leachate application rates requires a multifaceted approach that considers plant-specific nitrogen requirements, leachate toxicity levels, and soil infiltration capacity.  This ensures adequate nutrient supply without exceeding the assimilative capacity of plants and soil, preventing environmental contamination. Further research is needed to investigate the long-term impacts of leachate irrigation on soil health, crop quality, and potential groundwater contamination. Developing standardized guidelines for leachate treatment and application, tailored to specific regional contexts and crop types, is crucial for promoting sustainable and responsible leachate utilization in agriculture.

The agriculture sector, as the biggest consumer of water to produce more food, has faced the challenge of water shortage. One of the problems ahead in the agricultural industry is the sustainable use of available resources such as land, water, and labor to increase agricultural production and development, which requires proper planning and management policies. Plant models can be used to investigate the long-term effects of quantitative and qualitative changes in irrigation water on crops, soil salinity, evaporation and transpiration, deep infiltration, and surface runoff. One of the widely used plant models is the AquaCrop model, which was presented and developed by the World Food and Agriculture Organization. The Aquacrop model is one of the crop yield estimation models that can be used for a wide range of crops including fodder crops, vegetables, grains, fruits, oil crops, and tubers. In this model, the state of various stresses including water and soil salinity, simulation of lack of irrigation, and crop yield are considered. Various studies have been conducted regarding the calibration and validation of crop forecasting models in our country, and much research has been conducted on wheat at the global level. In this research, the AquaCrop model was used to predict the biomass and grain yield of wheat in Qazvin. This model can be a good substitute for field measurements and can be used in areas where there is a lack of ground information.

In the present research, the data of water wheat cultivation in a lysimeter in Ismailabad, Qazvin were used. The input information of the AquaCrop model includes information on climate, soil, management, and plant characteristics. To calibrate and verify the model, some farm information was needed to be compared with the output of the AquaCrop model. The biomass of the wheat plant was determined by taking random samples of the 0.5×0.5 m2 with two replications per sampling hectare. To measure grain yield in the fields, four samples were taken at the end of the growing season at the end stage. The validated AquaCrop model was used to estimate the effect of three planting dates and three low irrigation conditions on wheat grain yield. In this step, the average regional information around the farms was used so that the implementation of the model is not unique to the conditions of a particular farm.

In terms of the investigated meteorological factors, the model has moderate sensitivity to maximum and minimum temperature and low sensitivity to rainfall. The change in the maximum temperature in this region increases the error of the simulation on average. Regarding the soil parameters, the sensitivity of the model to the crop capacity moisture, wilting point, saturated moisture, and saturated hydraulic conductivity, especially in saturated conditions, is low to medium. The most sensitive of the AquaCrop model was the change in the reference harvest index. The model simulated biomass values with higher accuracy than yield. In the calibration stage, the values CRM, NRMSE, and d for biomass were -0.15, 0.17, and 92% respectively. These values were obtained in the validation stage for biomass -0.1, 0.24, and 92 % respectively, and for yield -0.03, 0.06, and 80 % respectively. By running the model in different climatic scenarios, it was determined that the maximum delay in the planting date is on November 15. A 25% reduction in irrigation water reduced grain yield in wet, normal, and dry years by 15%, 20%, and 28 %, respectively, and a 50 % reduction in irrigation water reduced its amount by 20, 25, and 45 %, respectively.

Evaluation of the AquaCrop model for common plants in a region plays an important role in comparing crop performance in different conditions. In this research, the ability of AquaCrop 6 model to estimate the yield and biomass of wheat in Ismailabad Qazvin was investigated. The results showed that the model is capable of simulating these factors with high accuracy. The accuracy of the model in biomass simulation was higher than the grain yield. By implementing the calibrated model in different climatic scenarios, planting dates, and irrigation deficits in two regions, it was determined that to achieve optimal performance, the wheat planting date should not exceed 15 November. It was the use of calibration coefficients by spending a long time in the AquaCrop model so that a calibrated model can be used in many areas with proper accuracy. More accuracy in the simulated results can be achieved by using more calibration factors, but it is clear that the use of more calibration factors requires spending more time and money. Finding a general recalibrated model that can be used in large areas is a good solution in crop management at the farm-to-regional scale. Comparing the statistical parameters obtained in this study with previous studies on wheat yield modeling by the AquaCrop model shows that the results of this study are within an acceptable range.

This study was accomplished to investigate water management indicators of irrigation systems in forage corn production in Tehran and Alborz Provinces. Forage corn farms were monitored during the growing season in the cities of Eslamshahr, Pakdasht, Shahr-rey and Savojbolagh. Corn applied irrigation water was determined by recording the irrigation schedule and measuring the flow rates. The water requirement of forage corn was estimated by Penman-Monteith method and by applying plant coefficients. The yield and water productivity for forage corn farms were obtained at the maturity stage. Irrigation applied water was statistically different during the growth season for the farms. Irrigation water averaged 3799, 6659 and 7240 m3 ha-1 for drip, sprinkler and surface irrigation systems, respectively. Results indicated that the farms with surface, sprinkler and drip irrigation consumed 18%, 13% and 32% less water than the gross irrigation requirement, respectively. The average volume of corn irrigation water in the four regions Islamshahr, Pakdasht, Shahrari and Savojbolagh was 6826, 5923, 6134 and 8126 m3/ha, respectively. Corn yield for sprinkler and surface irrigation systems averaged 51.3 and 52.8 t ha-1 for Eslamshahr and 49 and 55.5 t ha-1 for Pakdasht, respectively. The highest water productivity (20.83 kg m-3) in Pakdasht obtained from drip irrigation.

The main activity of people in the Sistan region is agriculture and animal husbandry, which due to the drought and the unavailability of surface water and the intermittent flow of water in the Sistan River, the digging of irrigation wells has expanded in this area. The Sistan River continues of Afghanistan's Helmand River, which passes through agricultural and urban areas. Therefore, it is possible that some kinds of pollutants, especially heavy metals, can come from different sources. On the other hand, after entering the Sistan Plain, the river affects the water level of wells along the river. Therefore, in case of contamination, it may increase the concentration of heavy metals in wells. However, until now, the concentration of heavy metals in the water wells has not been investigated and only the salinity factor has been considered from the point of view of water quality. The quality of drinking water is associated with the concentration of physicochemical compounds such as nitrate, phosphate, various anions and cations, and organic and inorganic pollutants such as heavy metals. Drinking water contaminated with metals is turning into a primary health concern for human health care. Therefore, surveying the quality of water can be helpful in management. The aim of this study is to investigate the concentration of heavy metals in the wells along the Sistan River, in order to ensure the quality of water for agriculture, husbandry, and human consumption.

In this research, in order to ensure the appropriate quality of water in terms of the concentration of heavy metals and also the possible effect of EC and pH factors on their changes, the concentration of some elements and factors were mentioned in the water samples of 26 active wells. The samples were collected along the Sistan River (with a maximum distance of 1000 meters buffer zone) from the border point to the entrance to Hamoun Hirmand (Afzal-Abad branch, Lorg Bagh and Khwaje mountain) from January to March 2022. According to the investigation of the Afzal-Abad branch and the absence of wells, no samples were obtained from this section. At the same time as the sampling, the characteristics of the well, such as the year of establishment and the type of water usage were recorded. Sampling was done in three repetitions and during sampling pH, EC, TDS, and salinity variables were measured by a portable calibrated device and recorded three times. The collected samples were transferred to the laboratory in order to measure heavy metals in sterilized frosted glass containers. The standard method of the American Public Health Association (APHA) was used to measure each of the considered factors. After the preparation of samples, the concentration of heavy metals was measured with ICP. The obtained results were zoned using the normal kriging and co-kriging methods based on the selected model resulting from the prediction standard error. Geostatistical kriging methods (such as simple kriging, normal kriging, and co-kriging) were used for interpolation of heavy metals distribution. In the semi-variable analysis, the variability of the factors with respect to the spatial distance was organized by different linear, spherical, etc. functions using ArcGIS software.

The average amount of pH was measured at 8.31. The concentrations of salinity and TDS were 3.74 and 4.62 g l-1, respectively. EC value was also measured as 6322 µS/cm. The average concentration of Cr, Fe, Ni, Cu, Zn, and Pb was also obtained at 2.64, 124, 10, 5.9, 33, 17, 1.43, and 2.79 µg l-1 respectively. The trend of elements obtained Fe>Zn>Ni>Cu>Pb>Cr>Cd. The results indicated a low concentration of Cd, Ni, Cr, Cu, Fe, and Zn While Pb concentration was higher than the standard. The low concentration of the mentioned elements is due to the alkaline pH of water, which acts as a buffer and causes the elements to become insoluble and precipitate. Some dangerous elements such as mercury could not be measured due to their low concentration. The amount of salinity and EC factors also showed that the well water is not suitable for agriculture and livestock. The result of element zoning also showed that the concentration of metals increases from the border towards the lake. According to the age of the wells, it can be said that the reason for the decrease in the concentration of metals is due to the longer life of the wells and the chance of water mixing during the Sistan River water harvesting.

The result showed that the concentration of metals, except lead, is lower than the standard value. The low concentration of elements may be due to the alkaline condition of water, which acts as a buffer and causes the insoluble and finally precipitate. Or it can be due to the lower concentration in the bedrock, which can be obtained by surveying the geology of the bed of the wells. The variation in metal concentration among the sampling sites may be due to the age of the well. So up to the Sistan dam, where the wells are older, the concentration of elements is lower. It may be due to river flow, which can cause dilution of metals in wells during high water season. According to the amount of salinity and EC, irrigation with water can cause a quality decrease of soil that leads to loss of cultivated area. Moreover, the fact that water is not suitable for livestock and alternative sources should be introduced.

Water is a critical factor for the growth and fruiting of the grapevines. Considering the water scarcity crisis in Iran and most parts of the world in recent years, it is necessary to apply methods such as deficit irrigation for the optimal management of water use in agriculture. It has been determined that by deliberately reducing water consumption in vineyards, it is possible to preserve the existing water resources and improve the water use efficiency.

A research was carried out in summer 2023 in a randomized complete block design with three replications on 8-year-old vines of the Turkmen-4 variety, to investigate the effect of deficit irrigation levels on the quantitative and qualitative traits and water use efficiency of grapevines. The vines were planted with 2 x 4 meter intervals, were trained as a vertical trellis on a bilateral cordon system, and the vineyard was irrigated by drip irrigation. The experimental treatments included full irrigation (providing 100% of vine water requirement; as control), 25% deficit irrigation (providing 75% of vine water requirement) and 50% deficit irrigation (providing 50% of vine water requirement). Irrigation of the vineyard started from May 22 and continued until November 6 at 7-day intervals, according to the conventional procedure. The water requirement of each vine in non-stressed condition was calculated by a class A evaporation pan based on reference crop evapotranspiration (ETo) and crop coefficient (Kc) throughout the season. Then, the amount of water for each treatment was determined according to the irrigation levels in the treatments and applied in volume form.

The amounts of water consumption of control, 25% and 50% deficit irrigation treatments were 5140, 3855 and 2570 m3 per hectare, respectively. The results showed that irrigation levels had a significant effect on the berries length, berries diameter, cluster length, cluster width, berries weight, cluster weight, sugar percentage, chlorophyll index, relative water content, midday leaf water potential, vegetative growth, vine yield, yield index and water use efficiency. The 25% and 50% deficit irrigation treatments caused a decrease of 7.2% and 14.2% of the berry length compared to full irrigation, respectively. Also, these treatments caused a reduction of 8.3% and 13.9% of the berry diameter, respectively. While the 25% deficit irrigation treatment had no significant effect on the berries sugar content (°Brix), the 50% deficit irrigation treatment caused a significant decrease (5%) in sugar content compared to the control. Both relative water content and midday water potential of the leaves decreased significantly with the reduction of irrigation levels. Reducing the level of irrigation led to a significant decrease in the SPAD index and vine vegetative growth. Increasing the intensity of deficit irrigation had a significant negative effect on yield components including berry weight, cluster weight, vine yield and yield index. The highest and lowest yields were obtained from full irrigation and 50% deficit irrigation, respectively but the effect of 25% deficit irrigation on yield reduction was not significant. Although the 25% and 50% deficit irrigation treatments caused a 5.8% and 27.5% decrease in vine yield, respectively but these treatments increased water use efficiency by 34% and 44.5%, respectively compared to the control. The lowest water use efficiency was related to the control (3.53 kg of fresh fruit per cubic meter of water used), while the water use efficiency of vines under 25% and 50% deficit irrigation was 4.73 and 5.10 kg of fruit per cubic meter of water, respectively. The 25% and 50% deficit irrigation treatments had a statistically significant difference with the control in terms of water use efficiency, but the difference between the two was not significant.

In the present study, reducing the volume of irrigation water led to a decrease in vine yield, but what is important is the low yield reduction rate compared to the amount of water consumption. The decrease in vine yield was 5.8% and 27.5%, respectively with a 25% and 50% decrease in water consumption. Also, with 25% and 50% reduction in water consumption, the yield index decreased by 6.1% and 27.3%, respectively. Meanwhile, the water use efficiency of vines increased by 34% and 44.5% in response to 25% and 50% deficit irrigation treatments, respectively. It is recommended to apply 25% deficit irrigation to increase the water use efficiency of Turkmen-4 grapes in climatic conditions of Malayer, but 50% deficit irrigation leads to a decrease in quality of grapes.

The basic strategy to mitigate water crisis is to save agricultural water consumption by increasing productivity, which will result in more income for farmers and sustainable production. Due to the economic importance of barley production in the country, it is necessary to study the volume of irrigation water and water productivity to produce this strategic product. Based on extensive field research on irrigation water management and application of different irrigation methods in barley farms, the innovations of this research were: a) measuring water consumed and determining water use efficiency in barley production, b) the up-to-date of the measurements and research findings, c) findings applicability for application in agricultural planning at the national and regional levels, d) the ability to development the findings in barley farms at the national level to improve water use efficiency. The hypotheses of this research are: a) barley irrigation water is various in different regions, b) water applied in barley farms is more than the required one, c) the water use efficiency of barley is different in the main production areas, and d) The applied water of barley is not the same in different irrigation methods. Therefore, the main objective of this study is to determine the water consumed and water use efficiency in barley production; to measure the water applied to barley farms in the main production areas; to compare the water measured in the production areas with the net irrigation requirement; and finally to determine water use efficiency of the barley in the main production areas in the Iran.

 For this purpose, the volume of irrigation water and barley yield in 296 selected farms in 12 provinces (about 75% of the area under cultivation and production of barley in Iran) including Khuzestan, East Azerbaijan, Ardabil, North Khorasan, Fars, Khorasan Razavi, Tehran, Semnan, Markazi, Isfahan, Hamedan and Qazvin were measured directly. Farms in the mentioned provinces were selected to cover various factors such as irrigation method, level of ownership, proper distribution and quality of irrigation water. By carefully monitoring the irrigation program of selected farms during the growing season, the amount of irrigation water for barley during one year was measured. At the end of the season and after determining the average yield of barley during the 2020-2021 year, the values of irrigation water productivity and total water productivity (irrigation+effective rainfall) were determined in selected barley farms in each region. The volume of water supplied was compared with the gross irrigation requirements estimated by the Penman-Monteith method using meteorological data from the last ten years, and compared with the values of the National Water Document. Analysis of variance was used to investigate the possible differences in yield, irrigation water and water productivity in barley production.

To assess the reliability of statistical analysis, we evaluated the sufficiency of the number of measurements needed for both the quantity of irrigation water and the ley yield on the farms. Subsequently, we computed statistical indices, such as the mean and standard deviation. The results showed that the number of measurements of irrigation water and barley yield was to be 296 and 283, respectively, which was more than the number of measurements required for irrigation water (41 dataset) and yield (50 dataset). Therefore, the sufficiency of the data for the statistical analysis was reliable. The results showed that the difference in yield, volume of irrigation water and water productivity indices were significant in the mentioned provinces. The volume of barley irrigation water in the studied areas varied from 1900 to 9300 cubic meters per hectare and its average weight was 4875 cubic meters per hectare. The average barley yield in selected farms varied from 1630 to 7050 kg ha-1 and the average was 3985 kg ha-1. Irrigation water productivity in selected provinces ranged from 0.22 to 1.53 and its weight average was 0.90 kg m-3. Average gross irrigation water requirement in the study areas by the Penman-Monteith method using meteorological data of the last ten years and the national water document were 4710 and 4950 cubic meters per hectare, respectively. Irrigation efficiency of barley fields in the country is estimated at 62-65% without deficit irrigation.

In order to reduce water consumption and improve water productivity, it is suggested to manage water delivery to farms during the season and deliver water rights to them according to crops water requirements. To reduce water losses and enhance productivity in the barley farms, it is suggested the application of modern irrigation systems according to the farms conditions with the suitable operation; and modification and improvement of surface and traditional irrigation methods. Note that, water is only one of several necessary and effective inputs in the optimal and economic production of barley. On the other hand, attention should be paid to the optimal application of other inputs including: seeds, fertilizers, equipment and tools etc.

Lack of water resources is one of the important issues in the world. seepage losses from the canals are the most important part of losses during agricultural water conveyance. Considering the share of agriculture in the consumption of water resources, the estimation of water losses from the body of earthen canals is one of the major issues in the design and management of irrigation and drainage networks. According to the researches, unlined canals lose 50% of their transfer water through leakage. In addition to water losses due to seepage, one of the main reasons for the importance of checking the amount of leakage is the reduction of the quality of the land and soil around the water conveyance and distribution canals and the threat to the environment of the region. Average losses of water from irrigation canals in Iran, are on around 60%. The methods of estimating seepage losses from the earthen canals include the field method, empirical relationships, and theoretical methods. There are many empirical relationships in this field, but experience has shown that the coefficients of these relationships are different according to the conditions of each region and should be calibrated for local conditions (Rostamian and Abedi Kopaee, 2014). Mutema and Dhavu, (2021) stated that the characteristics of the canal such as wetted perimeter and wetted area, have a significant effect on water losses. The purpose of this research is to investigate the regression relationship between the leakage flow from earthen canals with hydraulic characteristics and flow.

In this study, using the information of the study conducted in the east of the Zayandeh Rood basin, the non-linear regression relationship between the seepage (s) and the inflow rate (Q), wetted perimeter, hydraulic radius (R), and hydraulic depth (D) was investigated. In order to derive regression relationships, 70% of the data related to two series of measurements in 9 canals were used. Validation of regression relationships was done using 30% of the data, which is related to the measurement in the third time and related to the same canals. The data were obtained from the direct measurement of the inlet and outlet flow rates and the geometric and hydraulic characteristics of the earthen canals, such as the shape and dimensions of the canal section and the water depth. In this research, the relative value of root means square error, RRMSE, mean absolute error, MAE, and mean error, MBE, were used to evaluate the accuracy of the relations extracted from the indices. The difference between the value calculated in the validation step obtained with each of the regression relations, with the measured values, is shown using RRMSE and MAE statistical indices. The smaller the values of these two statistics are, the more accurate the regression relationship will be in estimating the seepage from the canals under study. Also, the MBE index will be a positive or negative value, which will be an indicator of underestimation or overestimation of seepage losses, respectively.

The results showed that all three regression relationships the seepage (s) and the inflow rate (Q), wetted perimeter, hydraulic radius (R), had better accuracy than the best empirical relationship used in the study area (the Ingham equation). It should be noted that the above relations are probably valid for the flow ranges between 30 and 390 liters per second (values measured in the research of Salemi and Sepaskhah (2015) and in using these relations in this area for flow rates outside of this range, should be cautious. The values of the mentioned indices, RRMSE, MAE and MBE, related to the regression relationship between the amount of seepage and the inflow rate, were 0.1954, 0.0082, and 0.0058, respectively and in the regression relationship between the amount of seepage and the wetted perimeter, respectively, it is equal to 0.2113. 0.0091, 0.0058 were obtained. Also, according to the positive values of the MBE, it can be concluded that in all the extracted relationships, the average of the estimated data showed a lower estimate than the observed values. Comparing the results of this research with Heidarizadeh and Salemi's (2013) shows that except for the regression relation related to hydraulic depth regression relations have better accuracy than the best experimental equations that studied by them, that is, the Ingham relation. Also, compared to Vedernikov's theoretical method, the accuracy of the estimation of the mentioned regression relationships is lower, but the difference in the statistical indicators is not significant. Therefore, according to the previous studies and comparing it with the regression relationships obtained in this research, it is recommended that in the case of using empirical relationships to estimate the amount of leakage losses from earthen canals in this region, these relationships should be used instead of the empirical relationships. In this regard, Salmasi and Abraham (2020) in a research also dealt with the prediction of leakage from earthen canals using the finite element method as well as multivariable nonlinear regression, and their results also showed the high accuracy of the resulting regression relationships. Water crisis and its management and planning require detailed design of all components of water distribution and consumption systems. Assessing and quantifying leakage loss from irrigation canals is high important for the protection and management of water resources, and specifying the intensity of leakage loss, and for the evaluation of the potential benefits of leakage reduction techniques and technologies. In this research, the non-linear regression relationship between the seepage and some hydraulic characteristics of flow in irrigation canals, by using the information obtained from the studies of the seepage losses of earthen canals in the eastern region of Zayandeh Rood basin, were extracted to estimate these losses. The results of this research have shown the appropriate accuracy of regression relationships compared to other experimental equations used in the study area. Therefore, it is recommended to use the obtained regression relationships to estimate the loss of water leakage in this area. Of course, in using these relationships, it is better to control the inflow rate range and then use these relationships. In addition, in order to complete the studies, it is recommended to carry out similar studies on the channels of this region with a larger inflow rate.

Evapotranspiration is one of the main components of water balance in agriculture and is one of the effective and efficient factors for accurate irrigation planning and management. Direct measurement of evapotranspiration values is time consuming and costly. On the other hand, modeling such a complex process in which many parameters interact with each other is so difficult that it is not possible to simplify the issue without multiple assumptions. Therefore, accurate estimation of this parameter has always been considered by the researchers. In the other point of view, the FAO-56 method was used as the accurate and accepted method for calculating reference evapotranspiration. One of the weaknesses of this model is its dependence on various meteorological variables. Therefore, it is necessary to use methods which need low number of meteorological variables and estimate the reference evapotranspiration with high accuracy. Additionally, due to the use of many meteorological variables and the complexity of the calculations, it is difficult to use FAO-56 method in all regions. Therefore, in the recent years, many researchers implemented machine learning methods to estimate reference evapotranspiration. Most studies in the field of reference evapotranspiration estimation use experimental models that require all the effective reference evapotranspiration parameters to provide an acceptable estimate. Hence, the aim of the current study was to present a superior model from three machine learning models, including random forest (RF), gradient boosted tree (GBT) and generalized linear model (GLM) for estimating reference evapotranspiration in three synoptic stations located at arid, semi-arid and wet climates of Iran. To the best of our knowledge, the proposed GBT and GLM methods have not been used for estimating reference evapotranspiration in the mentioned stations.

In this research, the FAO-56 method was used to estimate the reference evapotranspiration. Also, three machine learning methods including GBT, GLM and RF were implemented to estimate the amount of reference evapotranspiration. Daily parameters of some fundamental and effective meteorological variables on evapotranspiration during 21-years statistical period (2000-2020) were collected in three stations located at different climates including Yazd station (arid), Birjand station (semi-arid) and Sari station (wet). In order to investigate the possibility of using different combinations of meteorological parameters to estimate the reference evapotranspiration as accurately as possible, seven different combinations of meteorological parameters were defined. The accuracy of the utilized methods was evaluated using three criteria such as correlation coefficient, scattering index and Nash-Sutcliffe coefficient. Additionally, Taylor diagrams were implemented for evaluating the accuracy of the used methods. It should be noted that the Taylor diagram shows the three parameters of root mean square error, correlation coefficient and standard deviation simultaneously in one figure. Also, the most suitable combination of meteorological parameters that had good accuracy for estimating reference evapotranspiration, was suggested.

The results showed that in the best model at Birjand Station, and Yazd stations scenario number three by two meteorological variables of temperature and wind speed and in Sari station the scenario number two with temperature and relative humidity, the gradient boosted tree model was reinforced with Nash-Sutcliffe coefficient of 0.804, 0.826 and 0.733, with correlation coefficient of 0.997, 0.997 and 919 and scatter index of 0.249, 0.218 and 0.361 and the generalized linear model with Nash-Sutcliffe coefficient of 0.892, 0.931 and 0.869 correlation coefficient of 0.952, 0.966 and 0.933 and scatter index of 0.185, 0.137 and 0.252, respectively. Finally, the RF method with Nash-Sutcliffe coefficient of 0.954, 0.956 and 0.929, correlation coefficient of 0.978, 0.978 and 0.965 and scatter index of 0.121, 0.110 and 0.186 had good performance for estimating the reference evapotranspiration. On the other hand, in all methods, the scenario number seven using the meteorological parameters of temperature, relative humidity of sunny hours and wind speed in all three stations, presented the most accurate performance. Therefore, all three methods may be proposed as models with high degree of accuracy for estimating reference evapotranspiration.

Reference evapotranspiration is one of the main components of water balance in agriculture and is one of the effective and influential factors for accurate irrigation planning. Therefore, accurate estimation of this parameter has a significant role on reducing excessive water consumption. In this study, three data-driven models of RF, GBT and GLM were used in three stations of Yazd, Birjand and Sari stations. The obtained results indicated that the seventh scenario using all four meteorological parameters in all stations with the highest correlation coefficient, the lowest scatter index and the highest Nash-Sutcliffe coefficient provided most accurate estimates of the reference evapotranspiration and may be recommended for proper estimation of reference evapotranspiration.

Many countries around the world are currently facing a major challenge of producing food from limited water resources. Water scarcity, coupled with the misuse of valuable water resources, does not meet the current growing demand for food by the population. Therefore, in the current situation and in order to alleviate poverty and hunger, the most important challenge for the agricultural sector is the strategy of using water more efficiently. In the long run, increasing water productivity to expand irrigated lands and increasing rainfall productivity to produce more food is a key factor in combating hunger and reducing poverty (Kiani and Sedaghat Doost, 2016). Water scarcity in Iran is also an undeniable climatic reality. The total volume of water resources by rainfall is 403 billion cubic meters in the country, of which a significant amount becomes unavailable in the form of evaporation. The volume of renewable water resources is about 100 billion cubic meters, which is a water consumptionin the agricultural sector of about 80% (Naseri et al., 2017). Moreover, the rapid increase in population along with the expansion of urbanization has increased water consumption; whaich has led to the consumption of about 69% of the country's total renewable water that is very high compared to other countries. Meanwhile, agriculture in Iran is in dire need of water. According to the studies, identifying the factors affecting water productivity is of great importance due to the existing conditions (water scarcity) and factor analysis can be used in this regard. Therefore, the present study was planned and conducted to investigate the factors acting water productivity by means of factor analysis.

The study area is located in the central latitude and longitude of Qazvin province between 48 degrees and 44 minutes to 50 degrees and 51 minutes in the east of Greenwich meridian and 35 degrees and 24 minutes to 36 degrees and 48 minutes in the north latitude relative to the equator. The total area under cultivation of crops and horticulture in Qazvin province is 334.7 thousand hectares on average, of which about 2323.4 thousand hectares (equivalent to 66.5%) are irrigated and about 1112.3 thousand hectares are (equivalent to 33.5%) rainfed (Anonymous 2019a, Anonymous 2019b). In this study, three methods including documentary and library study, searching through electronic sources, and field study have been used to collect the required information. The data collection tool was a questionnaire that consists of two parts: personal profile and variables affecting water productivity. The sampling method in this study was completely random. In this study 317 questionnaires were completed. The percentage of variance and eigenvalues of different factors were used to determine the maximum effect of factors in explaining the variance of the data.

Examining the age and education level of farmers showed that farmers are more in the age range of 40- 61 years and older and their education is mostly Middle School Diploma and lower. The first factor among the nine extraction factors, with a specific value of 4.55, explained 14.67% of the variance of the whole analyzed set. Then the second factor, with a specific value of 3.84, was able to explained 12.39% of the variance of the set. The third factor, with a specific value of 3.22, explained 10.39% of the variance of the whole set under analysis. The fourth, fifth, sixth, seventh, eighth, and ninth factors, with special values ​​of 2.56, 1.65, 1.60, 1.50, 1.40, and 1.16, explained about 8.25, 31. 5, 16.5, 4.84, 4.50, and 3.74 the total variances, respectively. Overall, the nine extracted factors were able to explain about 69.25% of the total variance, which indicates the appropriate variance explained by the extracted factors. Among the nine factors obtained in this study, the components of agricultural-managerial measures and technical-infrastructure measures were recognized as the most important ones. The former factor explained 14.67% of the variance of the whole set under analysis and the lattar factor was able to explain 12.39% of the variance of the set.

In this research, nine extracted factors were included in the order of "agricultural-managerial measures", "technical-infrastructural measures", "irrigation management", "extension factors", "fertilizer recommendations", "field time management", "mechanization", "farmer's education level" and "principled control of pests and weeds". The results of this study can be used by managers and researchers in planning to improve water productivity in the agricultural sector.

A large part of Iran is located in arid and semi-arid climates. Despite the existing shortcomings, Iranian agriculture is highly dependent on irrigation water. As water is considered as the most important and limiting input of agricultural production in Iran (Zibaie, 2007). Rapeseed (Brassica napus L.) is the third annual oil crop in the world after palm oil and soybeans, which is cultivated for its edible oil and is easily replaced by cereals (FAO, 2013). The main goal in improving agricultural water productivity in the world is to increase agricultural products with less water consumption, thus reducing the share of water in the agricultural sector and allocating more water to other uses and, most importantly, the water needs of the environment (Heydari, 2014). So far, no report has been presented on the evaluation of different irrigation systems in rapeseed cultivation in East Khuzestan. The main purpose of this article was to measure the irrigation water of rapeseed fields in Behbahan city and compare their physical and economic water productivity. In this way, the physical and economic productivity of water under different agricultural management and in irrigation systems can be compared and evaluated.

This project was implemented in the field to determine canola irrigation water under the management of farmers in Behbahan city. In 26 farms, the volume of canola irrigation water (without interfering with their irrigation program) was measured. To determine the volume of irrigation water, first the amount of inflow from the selected water source was measured with a suitable device (WSC flume, meter or ultrasonic flow meter). Linear multivariate regression analysis was used to investigate the effects of the independent variable on the above dependent parameters. Water requirement was calculated based on the FAO Penman-Monteith model using the daily statistics of Behbahan Synoptic Meteorological Station (minimum and maximum daily temperature, minimum and maximum daily humidity, wind speed and maximum sunny hours) and using ETCalculator software. One-way analysis of variance was used to show a statistically significant difference between the means of two or more independent groups. To compare physical and economic productivity indicators of irrigation water, there was a need to resolve the difference. Therefore, the standard Z-Score standardization method was used to resolve the scale difference. SPSS16 software was used for statistical analysis.

The average irrigation water productivity in 26 farms ranged from 0.351 to 1.545 kg per cubic meter and economic water productivity ranged from 6940 to 3719660 Rials per cubic meter. Irrigation water volume with t-statistic (-11.193) and equivalent beta coefficient (-0.056) had a significant negative effect at the level of 1% on the irrigation water productivity and a significant negative effect at level 5% had on the economic water productivity. The method of irrigation of selected farms (surface or sprinkler) had a significant effect at the level of 5% on the physical and economic irrigation water productivity. The calculated scores according to Z-Score showed that 31% of the farms that had sprinkler irrigation system were in good condition in terms of physical and economic productivity of irrigation water and none of the farms in the surface irrigation system were ranked well in terms of irrigation water productivity, but 8% of the irrigation farms had good economic productivity. The results of Pearson correlation coefficient showed that rapeseed yield had a positive and significant correlation at the level of 1% and 0.608 with irrigation water productivity, while yield had no significant correlation with economic water productivity.

Comparison of the results of average traits showed that the implementation of sprinkler irrigation system in the studied farms has increased the physical and economic productivity of irrigation water. The high cost of implementing the irrigation system in agricultural lands has not caused these farms to have lower physical and economic productivity than surface irrigation farms, but instead the cost of implementing the irrigation system during ten years of using this system has led to better management use of irrigation water, increase the physical and economic productivity of irrigation water. This management is mainly implemented by irrigation and drainage companies by shortening the irrigation period and increasing irrigation times. Prevents grain filling and by adjusting the irrigation hour at each irrigation time by moving the sprinklers by irrigation workers, prevents water wastage and deep percolation of water from the root zone and compared to surface irrigation from water losses prevents the end of irrigation furrows. This management, along with reducing labor costs while keeping the canola crop, has reduced costs and increased the physical and economic productivity of irrigation water.

Due to the rising climate change, chilling and frost have affected agricultural production in many areas. At the same time, frost also causes freezing in irrigation systems. In the last decade, frost usually occurred yearly in farming regions and affected the economy of farmers. A bibliometric analysis of these researches may provide the importance of this topic and future research trends. Bibliometric is a statistical method to analyze bibliometric publications data such as peer-reviewed journal articles, books, conference proceedings, periodicals, reviews, reports, and related documents. It has been widely used to present the relations of research domains with quantitative methods. “Frost” and “chilling” were used as the keywords to reach the relevant publications. In this article, we used VOSviewer software for bibliometric analysis. We found 344 documents about the late spring frost. More than 79% of the publications were in the form of scientific journals and conference and review articles. The publication of articles showed a steep increase after 2014. The most publication was in 2021. The United States was the largest producer of scientific papers in the world. The highest-ranking journal was Acta Horticulturae, with 45 publications on frost and chilling. “Climate change,” with a total link strength of 56, appeared as the most frequent .