مجله آبیاری و زهکشی ایران
سال هجدهم شماره 6 (بهمن و اسفند 1403)

Identifying genotypes tolerant to deficit irrigation and introducing them to farmers and breeder is a suitable solution to improve the productivity of irrigation water. This study aims to evaluate 50 cucumber genotypes from the cucumber germplasm bank of University of Guilan in the greenhouse of the Faculty of Agricultural Sciences at University of Guilan under two irrigation levels: full irrigation and deficit irrigation (50% of full irrigation). The goal is to identify the best genotypes based on yield, irrigation water productivity, and economic productivity of irrigation water. These genotypes were selected for seed production and use in breeding programs. A drip irrigation system was used, and irrigation management was based on the water requirement of cucumbers inside the greenhouse. The cucumbers were harvested at commercial maturity, and yield, some yield component, irrigation water productivity, and economic productivity of irrigation water were determined. The results showed that irrigation significantly affected the number of fruits, irrigation water productivity, and economic productivity of irrigation water. The effects of genotype and the interaction between irrigation and genotype on fruit fresh weight, irrigation water productivity, and economic productivity of irrigation water were also significant. Based on the number of fruits per plant, fruit fresh weight yield, irrigation water productivity, and economic productivity of irrigation water, genotypes 42, 43, and 45 were identified as the best choices, while genotypes 13, 15, 16, and 18 were deemed inappropriate under the conditions of this study. Using the selected genotypes for cucumber seed production can simultaneously improve yield and irrigation water productivity, thereby enhancing the economic productivity of irrigation water.

Drought is a continuous natural climate phenomenon that usually occurs in all parts of the world, but their characteristics, type and intensity are different from one region to another. ,In this study, the severity of drought in six cities of South Khorasan during the statistical period of 1990-2020 was calculated with SPI and PNPI indices, and their sudden changes in drought were calculated using non-parametric Mann-Kendall tests. and Petit was examined. The results of the SPI index showed that Tabas experienced the most severe drought in 2006. The results of the PNPI index showed that a very severe drought occurred in 2001 and 2016. The results of the Mann-Kendall test in the analysis of the annual rainfall trend and the PNPI drought index showed a decreasing trend in five cities in the 1990s, and the SPI drought index showed an increasing trend in two cities and an increasing trend in three cities. The trend of rainfall in the city has been decreasing. Also, the results of Petit's test in determining the sudden change point of annual rainfall and PNPI drought index showed that it was significant at the level of 5% in all cities except Tabas, and on the time series of SPI drought index, all cities except Qaen have had a sudden change that these changes occurred in the first years of the study and became significant at the 5% level. The results of Mann-Kendall and Pettitt non-parametric tests showed that climate changes have occurred in South Khorasan province.

In order to investigate the effect of irrigation regime and bio-fertilizers on crop yield and water productivity index (WP), the amount of saturated and unsaturated fatty acids and the yield of milk thistle oil, a split-plot experiment was conducted in the form of a randomized complete block design with 10 treatments and three replications. It was conducted in Miandoab city located in the south of West Azerbaijan province in 2017-2018. The main factor was the irrigation regime (full irrigation and no irrigation) and the secondary factor was five levels of biological fertilizer (control, nitrogen (N), phosphate (P), potash (K) and complete (NPK)). According to the obtained results, the comparison of the average of the interaction effect showed that the maximum seed yield and the maximum biological yield of Milk thistle plant were observed for P and NPK treatments, respectively, under full irrigation conditions. Among the bio-fertilizer treatments, the P treatment accounted for the maximum amount of 27.4% and the treatment for not using bio-fertilizers for the minimum oil amount of 25.5%. According to the results, the yield of milk thistle oil is influenced by environmental and growth conditions; So, in the condition of full irrigation, the yield of oil (5.2 grams per plant and 255.7 kg ha-1) was obtained more than the condition of no irrigation (3.5 grams per plant and 171.4 kg ha-1). According to the obtained results, the maximum WP index in the full irrigation treatment is related to P and NPK. Also, in the treatment without irrigation, the maximum WP index related to NPK was obtained with a value of 1.19 kg m-3.

In this investigation, the Meshless Local Petrov-Galerkin (MLPG) method is used to model flow in the porous media of Rafsanjan plain. The discretization of the governing equations was done using the partial residual method and the radial basis function, and the model was implemented in two dimensions. A circular support domain was used for shape functions. The research focuses on the Rafsanjan Plain in southeastern Iran as a real case study, where no studies of meshless methods have been done. Computational models were implemented using MATLAB, integrating Radial Basis Functions (RBF) as the interpolation technique. The comparative analysis between observed experimental data and modeled values in flow dynamics showed a significant alignment. The sensitivity analysis of the parameters showed that the value of the shape parameter (αc) can have the greatest effect in achieving a more accurate prediction value. The accuracy of the calibrated parameters was evaluated by the coefficient of determination (R2=0.97). The high coefficient of determination shows a strong correlation between the observed and simulated data, which indicates that the model parameters are within an acceptable range.

For this reason, this study was conducted to investigate the effect of biochar and treated effluent on yield and water productivity based on maize's fresh, dry, and grain weight. Therefore, a factorial experiment based on randomized complete blocks was conducted in cylindrical pots with a height of 60 cm and a diameter of 30 cm and in the open air in Qom City in the 2022-2023 crop year. Experimental treatments were as follows: Three types of water with suitable quality (well) (I1), treated effluent (I2) and water and treated effluent mixing with the same ratio (I3) and three levels of biochar (made from cellulose materials from forest woods) including 10% of the volume of pots (B1), 5% volume percentage of pots was (B2) and no use of biochar (B3). The source of water supply was from a well around the desired land of suitable quality, which after storing in the pool with a drip irrigation system, the pots were watered. Also, the desired effluent was prepared from the Qom City wastewater treatment plant. The results showed that the interaction effect of water quality and the application of different levels of biochar on the harvest index, and water productivity based on wet, dry, and maize grain weight was significant at the 1% probability level, and grain yield was significant at the 5% probability level. The highest amounts of the harvest index and maize water productivity based on fresh, dry and grain weight traits were respectively 0.53, 2.04 kg/m3, 0.49 kg/m3, and 0.91 kg/m3, as well as the grain yield of 371.81 gr from B1I1 treatment. These traits increased by 43.2, 28.3, 44.1 and 35.8%, and grain yield by 36.1%, respectively, compared to the minimal treatments.

The types of environmental pollution that are created from different sources cause the reduction of soil quality, the expansion of water resources and the emergence of other environmental problems. Advance research, study and evaluation of the actual and potential sources of pollutants in the river catchment with an emphasis on monitoring heavy metals and surface water quality during the years 1398 to 1402 and aims to investigate the amount of pollutants in the said river using data analysis in the laboratory and Interpretation of satellite images in the ENVI software and simulation using the Qual2kw model to determine the degree of conformity with the standards, the analysis of the changes in the quality parameters of the river has been done. The results of the investigations show that the copper project (including three major activities of exploration and mining, concentrate production and concentrate processing tailings dam) along with agricultural activities, gardens and animal husbandry and residential centers, along with the geomorphology of the region, has caused changes in the water quality of River.During this study period, the amount of turbidity decreased by 3.54 NTU; BOD level, 3 mg/lit decrease; PH level, 8 units increase; The amount of TSS increased by 4.86 mg/lit and the amount of TDS increased by 4.9 mg/lit. In monitoring the quantitative changes of heavy metals using Landsat 8 satellite images, the highest and lowest square error is related to stations 4 and 2 in 1402 and 1, 4 and 2 in 2018;In order to improve the quality of the river, it is recommended to implement the approved and upstream laws in the implementation of the waste management laws, increasing tree planting along the riverbanks and ways to reduce the impact on surface water sources such as the use of potash soil stabilizers, etc. to be placed.

Since flooded rice fields are considered one of the major sources of methane production in agriculture, optimizing water and planting practices can help to reduce the emission of this gas. This study aimed to investigate the interaction effects of different rice planting methods and irrigation systems on yield and methane emissions in paddy fields. The experiment was conducted as a split-plot design within a randomized complete block design with three replications during the years 2019 and 2020 in Gorgan County. Four irrigation treatments, including sprinkler irrigation, continuous flooding, intermittent flooding, and tape irrigation, were applied as the main factor, while three planting methods, including direct planting, transplanting in non-puddled soil, and transplanting in puddled soil, were considered as sub-factors. The effects of planting year, irrigation system, and planting method on grain yield were significant at the 1% probability level. The highest grain yield was observed in the transplanting method on puddled soil, with a yield of 7,506 kg/ha. Grain yields under intermittent flooding, tape, and sprinkler irrigation systems decreased by 6.46%, 15.6%, and 39.03%, respectively, compared to continuous flooding. Among the different planting methods, the highest methane emission was recorded in the transplanting method on puddled soil, with 2.73 mg/m². Methane emissions in the transplanting method on non-puddled soil and direct seeding were reduced by 10.08% and 24.65%, respectively, compared to the transplanting on puddled soil. The lowest methane emission was observed in the tape irrigation system, with 0.42 mg/m². Methane emissions under intermittent flooding were 39% lower than in continuous flooding. Overall, optimizing irrigation management and planting methods in rice fields can significantly reduce methane emissions, thereby contributing to mitigating the effects of climate change. Based on the present study, the most effective approach is drip tape irrigation combined with non-puddled transplanting.

Wheat is one of the most important strategic agricultural products. One of the most important policies of the country is to achieve self-sufficiency in wheat production. The relatively large area under cultivation of this crop has caused a large volume of water resources to be consumed for wheat production. One of the solutions to further increase water productivity in wheat production is to use the drip irrigation method (Tape). This study aimed to investigate the effect of planting arrangement and lateral spacing on wheat water productivity at the Jolghe-Rukh Agricultural and Natural Resources Research Station in Khorasan Razavi Province during the years 2016 to 2018. This study was conducted using a T-test design and the samples were compared in pairs. Six treatments including a combination of planting arrangement (15, 20 and 30 cm) and lateral spacing (60 and 75 cm) and a furrow method were compared. The results of statistical analysis showed that the drip-Tape irrigation method with a spacing of 60 cm along with a regular planting arrangement of wheat rows with a spacing of 15 cm was superior to the other treatments implemented, and the difference in the studied traits was clearly evident compared to the furrow irrigation treatment. The highest yield and water productivity were obtained from this treatment (8500 kg/ha and 1.63 kg/m3), while the irrigation water consumption in this treatment was 21% less than the furrow irrigation method in the first year and 35% less in the second year.

Accurate estimation of water resources potential and their optimal use is a crucial issue in the water industry. Therefore, optimal reservoir operation is considered one of the most important management factors during dam operation periods. In real reservoir operations, decisions on water release are made, considering reservoir volume, demand quantities, and inflow forecasts. Given the uncertainty in dam reservoir operation data, one approach to addressing this uncertainty is through the use of fuzzy logic sets, as traditional fuzzy theories alone cannot fully account for the existing uncertainty in calculations. In this case, Z-numbers, as a new generation of fuzzy logic, have great potential in describing the uncertainty of human knowledge, as they consider both limitation and reliability.This research aims to determine optimal dam operation parameters for multi-step ahead considering data uncertainty and information reliability in optimization through Z-numbers, as a case study on the Alavian Dam. Efforts have been made to compare Z-number results with classical fuzzy logic, and genetic algorithm results. To evaluate these methods, performance indicators such as reliability, vulnerability, and stability were used, and the results showed that 77% of the genetic algorithm, 85% of classic fuzzy logic, and 92% of Z numbers of the needs related to the sustainable agricultural demand of Alavian Dam were met. The rule curve of optimal operating parameters obtained from Z-number modeling based on inflow forecasting through the method of Lower and Upper Bound Estimates (LUBE) showed that the observed release volume for Alavian Dam during the current flow year remained within the predicted range.

Salinity stress is one of the most important factors limiting the yield of plants in arid and semi-arid regions. Seaweed extract fertilizer, as one of the biofertilizers, increases the strength and resistance of plants against abiotic stresses through the acceleration of germination, further expansion of roots and absorption of nutrients.This research was conducted in order to investigate the effect of seaweed extract on the yield and water productivity of sweet peppers under the conditions of irrigation water salinity. The treatments include 3 levels of irrigation water salinity (S1, S2, S3, respectively equivalent to 1, 3 and 5 dS/m) and spraying different concentrations of seaweed extract (SE1, SE2, SE3 and SE4, respectively equivalent to 0 , 0.5, 1.5 and 3 gr/lit). Foliar spraying of zero treatment (control treatment without seaweed) was done with distilled water. The first fruit harvest was done in the middle of May 2024 and a total of 8 harvests were done. In each harvest, the parameters of plant height, weight, number and diameter of fruit, yield, plant leaf area index, leaf chlorophyll a and b and fruit vitamin C values were measured and water productivity was calculated. The results showed that the effect of seaweed extract and irrigation water salinity on the measured parameters was significant at the probability level of one and five percent. The highest value of height (98.5 cm), leaf area index (57.8 cm2), yield (32.6 ton/ha), water productivity (4.07 kg/m3) and chlorophyll a (15.6 mg/g) and chlorophyll b (9.6 mg/g) from the S1SE4 treatment (water salinity 1 dS/m and foliar application of 3 gr/l seaweed extract) It was obtained. In general, the results of this study showed that salinity stress has negative effects on the growth and yield of sweet pepper plants, and these negative effects can be reduced by spraying seaweed extract.

Agriculture is the main economic pillar of the Helmand Basin; however, it faces severe water scarcity due to unsustainable consumption. Previous studies have optimized cropping patterns based on economic and water factors in arid regions. However, the unique conditions of Helmand require a multi-objective optimization approach that integrates economic, ecological, and environmental factors. This study develops a multi-objective mathematical model to determine the optimal cropping pattern for Helmand to maximize farmer profits while minimizing water consumption and environmental impacts. The model simulates cropping areas for major regional crops using multi-objective linear programming. In this regard, to obtain the optimal cultivation pattern with the desired objectives, the genetic algorithm was used as a random optimization method. The desired information was obtained from the Agricultural and Jihad Organization and Regional Water of Sistan and Baluchestan Province, and the relevant calculations were performed in MATLAB and CROPWAT software. The crop year determined in this study is 2018-2019 and the selected crops include wheat, corn, barley, black grapes, cucumber, eggplant, onion, legumes, and medicinal plants. Results indicate that the optimal cropping pattern reduces annual water consumption by 29%, nitrogen leaching by up to 14%, and increases economic yield by 11% compared to current practices. Virtual water imports of wheat and corn reduce local water consumption by 18% and improve economic yield by up to 5%. This integrated modeling approach provides data-driven scenario-based decision support for guiding policies on optimal cropping strategies. The findings demonstrate the potential of multi-objective optimization combined with virtual water imports to sustainably enhance agriculture despite water scarcity. This study can be used as a road map for policy makers and farmers to improve and sustain agriculture in the Hirmand basin.

Estimating the amount of sugarcane crop has a key role for a wide range of applications such as sugarcane production management. In this research, the prediction of sugarcane crop yield with the methods of Normalized Differential Greenness Index (NDVI), Enhanced Greenness Index (EVI) and Soil Amended Vegetation Spectral Index (SAVI) extracted from satellite images is examined. was placed for this, Landsat 8 and 9 and Sentinel 2 satellite images from March 1401 to February 1402 were used. First, three fields with different soil texture were selected, then satellite images were taken in three stages for each field according to the simultaneous passage of two satellites from the study area, and at specific points the values of NDVI, EVI and SAVI indices after correction and processing It was extracted from satellite images and their correlation with observed vield data was obtained. The results showed that SAVI of Sentinel 2 satellite in loamy soil texture and EVI of Sentinel 2 satellite in loamy-clay-silty and clayey soil texture were more related with explanation coefficient of 0.76. Then, based on the superior index, the fitted linear regression relationship between the index and yield was obtained for each farm with different soil texture. The results of the relationship analysis showed a strong correlation between the observed and calculated values with the linear regression relationship of sugarcane yield in all three fields. The highest explanatory coefficient and predictive correlation of the regression relationship with observed values of 0.83 and 0.91 were obtained in the field with loamy soil texture. In general, the results of this research showed that the more the heavy texture of the soil is reduced, the more reliable the results of the fitted regression relationship can be.