نشریه آب و خاک
سال سی و ششم شماره 3 (پیاپی 83، امرداد و شهریور 1401)

   Quinoa (Chenopodium quinoa) is native plant in Bolivia, Chile and Peru, which is widely adapted to different climatic conditions and can grow in all soils. This plant has shown adequate adaptation to arid and semi-arid areas conditions and is planted from areas with low elevation (sea level) to areas with an altitude of 4000 meters above sea level. Quinoa is often cultivated in areas with limited water resources, and it is rare to find quinoa cultivation under full irrigation conditions. Some studies have shown that quinoa yields slightly better under full irrigation (without water restriction) than quinoa under deficit irrigation. Crop growth models are very important tools in the study of agricultural systems and they can be used to simulate the yield of crop in different conditions. Given that the study of performance limiting factors requires numerous and costly research and experiments in different areas, so finding a way to reduce the number, time and cost of these experiments is worthwhile. Aquacrop model is one of the applied models that are used to simulate yield variations in different water and soil management.
   This investigation was carried out in two growing seasons of 2019 and 2020 to determine the efficiency of Aquacrop model for simulating Quinoa grain yield and biomass under imposing three stress treatments of 30, 50 and 70% of water consumption in development and mid-growth stages. Plant spacing was 40 cm between rows and 7 cm between plants within rows. Seeds of quinoa (Titicaca cultivar) were cultivated in the first decade of August 2019 and in the third decade of July 2020. The experiment was a randomized complete block design with three replications. Three deficit irrigation treatments including 30, 50 and 70% of available water were considered in two growth stages (development and mid-growth) in 18 experimental plots (3 × 4 m). Soil moisture in rooting depth (about 40 cm) was measured by TDR and after the soil moisture of the treatments reached the desired values, plots were irrigated until the soil moisture reached the field capacity. The results of grain and biomass yield in the first year were used to calibrate the Aquacrop model and the results of the second year were used to validate the model. Root mean square error (RMSE), normalized root mean square error (NRMSE), Willmott index (D), model efficiency (EF) and mean error deviation (MBE) were used to compare the simulated and observed values.
   The results of the first and second year were used to calibrate and validate the model, respectively. The results of the first year showed that irrigation with 50 and 70% of available water in the development stage reduced quinoa grain yield by 17 and 33%, respectively, compared to the control treatment. The application of these two deficit irrigation treatments in the middle stage reduced the yield by about 12 and 28%, respectively. The results of comparing the statistical indices of grain yield, biomass and water use efficiency showed that the NRMSE for grain, biomass and water use efficiency were 9, 8 and 14% in the first year and 9, 6 and 9% in the second years. Furthermore, the EF for these traits were 0.81, 0.77 and 0.64 in the first year and 0.68, 0.71 and 0.62, in the second year, respectively.
The results of calibration and validation of the model showed the accuracy and efficiency of the Aquacrop model in simulating grain yield, biomass and water use efficiency of quinoa. This model can be used to provide the most appropriate scenario and irrigation management for different levels of deficit irrigation managements.

Drought stress is the most important environmental factor limiting growth and development of plants worldwide. Growth reduction due to drought stress has been reported more than other environmental stresses. So far, many studies have been conducted on the relationship and correlation between important agronomic traits in rapeseed, which have introduced 1000-grain weight, number of seeds per pod and number of pods per plant as the most important traits with high correlation in yield. The results showed that the application of drought stress had an effect on the yield components of sesame and the cultivars that were more sensitive to drought stress had a greater decrease in their yield. The aims of this study were to investigate (1) the effect of consumed water volume as the independent variable on other variables of the study, and (2) the effect of total independent variables (yield components and other independent factors) on yield and water productivity (dependent variables). Finally, the most important independent variables affecting water productivity and the most sensitive variables to the amount of consumed water were determined.

In order to achieve aforementioned objectives of this study, an experiment was conducted during two growing season of 2011-2011 and 2010-2011 in Behbahan Agricultural Research Station. The experiment was conducted as randomized complete block design with 4 replications. The applied amount of water in drip irrigation was composed of four levels of 50, 75, 100 and 125% water requirement in main plots and two canola varieties Hyola 401 and RGS003 in sub plots were placed.

The results of the analysis of variance of the regression model showed that the higher absolute value of beta coefficients and t-statistic of each independent variable caused that variable to be introduced as the most sensitive independent variable affecting the dependent variable. Therefore, the independent variable of water volume, with beta coefficient of 0.860 and t-statistic of 13.246 had the greatest effect on plant height variable. In terms of yield, the studied variables (the number of pods per plant, the number of seeds per pod, and 1000-seed weight, consumed water volume, flowering period, growth period and plant height) showed 74.1% of variation (R2 = 0.741) of dependent variable (Yield of canola). The consumed water volume with the highest absolute value of beta coefficient of 0.563 and t-statistic with 2.967 had the most significant effect on yield at the level of 1%. Among the dependent variables, the consumed water volume with the highest absolute value of beta -1.013 and t-statistic at -12.415 had the most significant effect on water productivity at the level of 1%. consumed of water volume with the highest absolute value of beta coefficient of 0.563 and t-statistic with 2.967 had the most significant effect on performance at the level of 1%. The results of Pearson correlation coefficient showed that the highest correlation between the number of pods per plant and seed per pod with both plant height were calculated to be 0.763 and 0.849, respectively, indicating that increasing plant height was effective in increasing the number of pods per plant and seed per pod.

The results of analysis of variance of regression model showed the effect on volume of consumed water as an dependent variable through other variables (number of pods per plant, number of seeds per pod, yield, water productivity, 1000-seed weight, flowering period, growth period and plant height). Results showed a significant effect of all variables at the level of 1%, except for the variable of flowering period which had a significant effect but just at 5%. The volume of consumed water by r= 66.2% on grain yield variation in the pods, had the most significant effect on yield components. Therefore, seed number in the pods received the most negative effect from reducing water consumption due to drought stress. With increasing the growth period of canola, water productivity showed a significant decrease at 1%. The results of Pearson correlation coefficient showed that grain water productivity had a negative and significant correlation at the level of 1% with all variables. The highest correlation between water productivity (r = -0.939) was calculated with volume of consumed water, which indicates the importance of reducing water consumption in increasing canola water productivity.

 Arid and semi-arid climates prevail in Iran. The precipitation is also sparsely distributed in most areas of the country. Therefore, there is a need for management measures to overcome the water crisis. One of these measures is designing rainwater harvesting systems that can meet some of the non-potable needs and reduce runoff in urban areas. The main components of rainwater harvesting systems in residential regions include the catchment area, storage tank, and water transfer system from the catchment area to the tank. The storage tank is the biggest investment in a rainwater harvesting system, as most buildings are not equipped with a storage system. Therefore, tank capacity should be determined optimally to minimize project implementation costs. The stored water volume and the project profit increases with increasing the tank volume. However, in this case, the price of the tank increases. Therefore, the tank capacity should be optimally designed to justify economic exploitation.

 In order to evaluate the feasibility of using rainwater harvesting systems, the tanks’ volume was optimized. Due to the higher rainfall of Ardabil relative to the average rainfall of the country, it is expected that this area has a good potential for the implementation of rainwater harvesting systems. Therefore, this region was selected as the study area under the scenario of a residential house with ​​100 and 200 m2 catchment areas and four inhabitants. The amount of rainfall in the region is one of the primary parameters in determining the volume of rainwater collection tanks. Some of the precipitated water is always inaccessible due to evaporation from the surface. Nonetheless, since there is almost no sunlight during and immediately after rainfall, and also the received water enters the reservoirs shortly after precipitation, evaporation was assumed to be zero. Daily precipitation data for 42 years (from 1977 to 2019) were retrieved from the Ardabil Meteorological site. The daily water balance modeling method was used to analyze the rainwater harvesting systems due to the simplicity of interpretation, high accuracy and better general acceptance. Daily precipitation data were entered into this model and used as the primary source to meet the domestic demands. Simulation of rainwater harvesting systems was performed using daily precipitation data in MATLAB software, and the reliability of these systems was calculated for different tank volumes. Then, considering the price of drinking water and the current price of tanks in the market, the optimal volume of tanks was calculated using the Genetic Algorithm. Finally, the annual volume of water supply and the amount of water savings in case of using the optimal volumes of tanks were also estimated.

The results showed that the percentage of reliability is directly related to the volume of the tank, thus, the reliability percentage also increases with increasing the tank capacity. As the volume of the tank increases, the slope of the increasing reliability percentage decreases continuously, to the point that this slope becomes almost zero. Comparing the reliability percentage obtained for 100 and 200 m2 rooftops indicated that 200 m2 rooftop had a higher reliability percentage than 100 m2 rooftop due to receiving much more rainfall and meeting the water need for a longer duration. By comparing the results of overflow ratio for 100 and 200 m2 rooftops, it can also be concluded that using a fixed size tank, the overflow in 200 m2 rooftop is higher, which is due to receiving more water volume than 100 m2 rooftop. The results also showed that by using a 5 m3 tank, 44.5 and 24 m3 of water can be stored annually from the 200 and 100 m2 catchment areas, respectively, these are equal to 28 and 19 m3, respectively, if 1 m3 tank is used. The optimal tank volumes for 100 and 200 m3 rooftops are equal to 0.59 and 1.66 m3, respectively. Since the tanks are made in specific volumes, the obtained volumes were rounded to the closest volumes available in the market. Thus, a 1.5 m3 tank was used for a 200 m2 rooftop and a 0.5 m3 tank was applied for a 100 m2 rooftop.

Application of a tank of 0.5 m3 for the rooftop of 100 m2 was the most profitable for saving 17% of water consumption, annually. Moreover, the optimal tank volume for the 200 m2 rooftop was selected to be 1.5 m3, saving about 32 % of water consumption per year. Water-saving percentages indicate the high potential of our study area for the implementation of rainwater harvesting systems.

 Grape is one of the most important horticultural products in the world and Iran which has been noticed due to its cultivation area, high economic and nutritional values. Annually, about 68 million tons of grape are produced in the world. Iran, with 309,000 ha cultivation area and about 3.3 million tons share of production, is the 11th largest producer of this fruit in the world. Recent studies have shown that plant nutrition and soil fertility have significant effect in the reduced yield quality in the grape fields of our country. Plant nutrition as an influential factor is a function of the interaction of nutrients and environmental conditions. Assessing the nutritional status of plants is necessary to achieve the relationship between nutrients availability in the soil, the amount of elements in the plant and yield. Plant analysis method is used to optimize fertilizer application and diagnose plant nutrition disorders. The plant analysis method is useful for evaluating plant nutrition if an appropriate method to be used to diagnose and interpret the results. Tissue nutrient status can be diagnosed by the Critical Value Approach (CVA), the Diagnosis and Recommendation Integrated System (DRIS), and Compositional Nutrient Diagnosis (CND). Only DRIS and CND provide nutrient imbalance indexes, although no threshold value has been validated yet for diagnostic purposes. CND method expresses interactions by considering the ratio of one element to all elements. In this method, high and low functional groups are separated with great accuracy with the help of mathematical and statistical methods and the application of the cumulative function of variance ratio of nutrients and chi-square distribution function. A critical CND imbalance index was derived from the chi-square distribution function. Due to the importance of grape production in the country and the lack of required nutritional norms, this study was conducted to investigate the nutritional status of grape fields using the CND method.
In order to evaluate the nutritional status of grape fields in the Hamedan province, this study was conducted in the cropping years of 2017-2020. Every year, 40 different orchards were selected in each of the regions. The orchards were selected in such a way that they had different ranges of yield and soil properties. A database containing laboratory and field data was created for each grape field. The geographical location was recorded for the orchards. In each orchard, plant (leaf) samples were prepared and analyzed based on suitable laboratory methods. At the end of the season, the yield and its components were determined by visiting each orchard. Concentrations of nitrogen, phosphorus, potassium, calcium, magnesium, iron, zinc, manganese, and copper were measured in grape leaves. The project database was completed and CND indices were calculated for each nutrient element. The selected grape fields were divided into two groups with high and low yield based on yield. The CND norms and indexes were computed according to computation steps of Parent and Dafir. The Cate–Nelson ANOVA procedure was used to partition yield data between two groups by maximizing the between-groups sums of squares to determine the threshold values for CND indexes required to compute the critical CND r2 value. We used 83 observations for developing the nutrient norms.
 The results of the indices calculated by the method of CND showed that the grape fields were deficient in nitrogen and potassium among the macronutrients and iron and manganese among the micronutrient elements. There was a correlation (0.25) between nutritional balance index and yield that was significant at 1 percent probability level. Potassium index was negative in 83% of low yield orchards. After potassium, nitrogen had a negative index in 58% of medium and low yield orchards. Phosphorus had the most positive index among macronutrients and was positive in most orchards. Among the micronutrients, manganese, iron, and zinc indices were negative in 59%, 49% and 73% of the orchards, respectively. The presence of calcareous conditions in the soils of the region can be the reason for this deficiency. The boron index was positive in some orchards and negative in some other orchards. Furthermore, in total, the index of unknown factors was negative in 41% of grape fields in Hamadan province.
 The results indicated that management of evaluated orchards was not suitable and application of chemical fertilizers was unbalanced. The results of this study can be used in grape fields to increase yield and product quality. Therefore, it is recommended to use deficient elements in the fertilization program to improve yield.

 Given the energy crisis in the world, increasing environmental pollution, clean, renewable energy and the reduction of environmental pollution are needed. Soil is the main source of agricultural production. Therefore, maintaining soil health and fertility is very important for sustainable food production. Nanotechnology is a good way to reduce soil issues in agriculture, a promising method to improve soil properties and significant capacity to increase yield. Nanotechnology is one of the newest technologies that is used in all fields of science and research due to its high potential and unique features, including natural resources and soil protection. Nanoparticles have the ability to change some physical, mechanical and chemical properties of soil due to their very high specific surface area and activity. Nanoparticles increase the cation exchange capacity of soil and soil porosity. Among all nanoparticles, zinc oxide (ZnO) is one of the most widely used nanoparticles. Zinc oxide nanoparticles due to their high specific surface area can act as a bonding agent between particles and stabilize the soil structure by flocculating soil particles. Although many studies have used zinc oxide nanoparticles (ZnO) in the field of heavy metal contamination in soil, aqueous solutions and plants, the effect of one nanoparticle on soils with different textures has been less reported. Therefore, objective of this study was to investigate the effect of zinc oxide nanoparticles on some physical and chemical properties of soils with different textures.
 In this study, three soil samples with different textures, including sandy loam, loam and clay were collected from three locations as Malayer, Abbasabad and Nahavand, in Hamedan province, respectively. Samples were taken from soil surface (0-20 cm depth). The soil samples were transferred to the Soil Physics Laboratory. After  air drying, they were passed through a 4 mm sieve and mixed with specific weight percentages of zinc oxide (ZnO) nanoparticles (zero, 0.5, 1 and 3 % W/W) in three replications. After preparing the treated samples, the soils were homogeneously poured into plastic containers measuring 18 × 5.5 × 18 cm with a specific bulk density related to the field. The treated soils in plastic containers, were wetted and dried with municipal water for 120 consecutive incubation period. After 120 days from the start of incubation, the samples were taken from the containers. Some physical and chemical properties including pH, cation exchange capacity, organic matter, calcium carbonate and electrical conductivity were measured.
 The results showed that the use of nanoparticles increased the cation exchange capacity in two textures of loamy and clay soils. The increment was significant compared to the control in loamy soil at two levels of 1 and 3% and in clay soil in all three levels of 0.5, 1 and 3%. Electrical conductivity increased and decreased (P <0.05) at 3% level for loamy soil and at 3% for sandy loam and clay soils, respectively. In contrast, the application of nanoparticles led to a decrease in pH and organic matter content (P <0.05) in sandy loam and clay soils, respectively. At the level of zero and 0.5%, the order of pH was: sandy loam> clay> loamy soil, with significant differences. But at the level of 1%, the order of pH was: sandy loamy> loamy> clay, with significant differences. At 3% level, the order of pH was: loamy> sandy loam> clay, with significant differences. At all levels of zero, 0.5, 1 and 3% of zinc oxide nanoparticles, the amount of organic matter was significantly in loamy> clay> sandy loam. Application of different levels of zinc oxide nanoparticles in clay soil reduced the percentage of calcium carbonate (P <0.05) (at the 3% by weight level), but had no effect on the amount of this variable in sandy loam and loamy soils. At all levels of zero, 0.5, 1 and 3%, the amount of soil calcium carbonate was significantly in the following order: clay> sandy loam> loam.
 According to the results obtained in this study, it can be concluded that the use of nanoparticles can be a good solution to reduce the harmful environmental effects of chemical fertilizers. In addition to the positive effect of zinc oxide nanoparticles on physical and chemical parameters in different textures, the selection of the most optimal level of zinc oxide nanoparticles should be economically applicable. This requires further studies to determine the significant effects of nanoparticles on the physicochemical properties of the soils in different conditions to determine the optimal amount of nanoparticles, in order to save costs.

 In addition to the minerals, weathering in soil which depends on soil forming factors and processes, plants rhizosphere release components which affect soil minerals and finally their weathering. If the soil is polluted by heavy metals, root exudates will be influenced resulting in decreasing microbial activity. Many studies showed minerals weathering in rhizospheric medium for both natural soils and pure clay minerals but information about the effect of pollution of rhizosphere on clay minerals weathering is limited. This study was conducted to investigate the effect of cadmium pollution on the transformation of clay minerals in wheat rhizosphere in a dominant soil of Shahrekord plain (Chaharmahal soil series).

 Soil samples were collected from 0-20 cm depth of Chaharmahal soil series based on the 1:50,000 scale soil map. A factorial experiment as completely randomized design with three replications and three cadmium levels (0, 5, and 10 mg kg-1 from cadmium) was performed in two environments including bulk soil and rhizospheric soil (18 samples in total) in greenhouse conditions for 16 weeks. Necessary care was taken during the growth period and the soil moisture was kept constant at the field capacity. At harvest time, the rhizosphere soil was separated from bulk soil. Then, the soil samples were air dried and passed through a 2 mm sieve. The mineralogy was examined by X-ray diffraction (XRD) in the studied soil after plant harvest (including rhizospheric soil and bulk soil) in unpolluted samples. Then, results were compared with minerals in polluted rhizosphere media. Dissolved organic carbon (DOC) and pH in the rhizosphere and bulk soils were also determined.

The results showed that the effect of contamination on soil pH was not significant but the pH value in rhizosphere soil was significantly lower than the bulk soil. The average pH in the soil was 7.8 and in the rhizosphere reduced to 7.5. The interaction of medium (rhizosphere and bulk soil) and contamination on the amount of dissolved organic carbon was significant (p < 0.01). The amount of dissolved organic carbon in the rhizosphere at 170.6 mg Kg-1 was significantly higher than the bulk soil (104.6 mg kg-1), which could be due to root secretions. In the rhizosphere, increasing the contamination level to 5 mg kg-1 decreased by 19% and contamination of 10 mg kg-1 caused a 21% decrease in dissolved organic carbon. The amount of dissolved organic carbon in the rhizosphere was 39% higher than the bulk soil. The average of dissolved organic carbon in the rhizosphere and bulk soil was 170.6 and 104.6 mg kg-1, respectively. Based on mineralogical results, mica, smectite, chlorite, kaolinite and palygorskite minerals were detected in the bulk soil. Comparison of clay minerals samples in the bulk soil and rhizosphere showed that the trioctahedral chlorite transformed to hydroxy-interlayer vermiculite (HIV) in the rhizosphere soil. The presence of HIV was identified by an increase in the intensity ratio of the 10 and 14 angstrom peaks after K-saturation. In rhizospheric soils, the intensity of the 14 angstrom peak decreases in K-550ºC treatment. Furthermore, in the rhizospheric soils, a clear increase in the intensity of the 10 angstrom peak was observed from K-air dried to K-550ºC treatments which can be related to the presence of HIV which can be attributed to the changing conditions of the rhizosphere, including reducing pH and increasing the dissolved organic carbon and the activity of microorganisms. Comparison of diffractograms for clay fraction of rhizospheric soil with different contamination levels after cultivation showed that the type of minerals in contaminated levels was similar to non-contaminated conditions, but the amount of trioctahedral chlorite was the highest in higher contaminated soil. The peak intensity of 14 angstrom in potassium saturated sample heated at 550°C was lower in non-contaminated soil. Also, at the level of 10 mg kg-1 cadmium contamination, the chlorite peak had the highest intensity which indicates less chlorite was transformed to HIV in the contaminated soils.

The results showed that DOC in the rhizosphere soil was significantly higher than the bulk soil, whereas pH significantly decreased in the rhizosphere soil compared to the bulk soil. In both the rhizosphere and the bulk soils, increasing the contamination caused a decreasing trend in dissolved organic carbon. Mineralogical results of the rhizospheric and the bulk soils showed that trioctahedral chlorite was transformed to hydroxy-interlayer vermiculite (HIV). In addition, rhizosphere contamination reduced the chlorite transformation. The results suggest that soil contamination with a negative impact on plant activity and soil could even prevent the availability of nutrients from the clay minerals structure.

Precipitation is one of the most important components of water cycle. Accurate precipitation measurement is essential for flood forecasting and control, drought analysis, runoff modeling, sediment control and management, watershed management, agricultural irrigation planning, and water quality studies. Determining the correct amount of precipitation in cities and rural areas is also important for managing floods. The precipitation process is completely non-linear and involves randomness in terms of time and space. Therefore, it is not easy to explain that with simple linear models due to various climatic factors and may contain major errors. Therefore, various methods and models have been proposed to evaluate, and predict precipitation. This study aimed to estimate the daily precipitation of Tabriz based on hybridized tree-based and Bagging methods by using neighboring stations.

In the present study, the rainfall data of adjacent stations in Urmia lake basin (Sahand, Sarab, Urmia, Maragheh and Mahabad) were employed in 1986-2021 to estimate the daily rainfall in Tabriz. About 70% of data were considered for calibration and 30% of data were applied for validation. Using the correlation matrix and Relief algorithm, various input components were identified. Modeling was performed using tree-based data mining methods including M5P, RT and REPT and Bagging method. The daily precipitations of Tabriz was decomposed into their components by seasonal-trend analysis method. Its components, including trend, seasonal and residual, were used in different input scenarios to investigate the effect of these components on improving the modeling results. To evaluate the modeling performance, the indices of correlation coefficient, Root Mean Square Error, Nash-Sutcliffe Efficiency and modified Wilmot coefficient were applied.

RT and REPT methods increased the accuracy of the model and decreased its error when they were used as the basic algorithm of the Bagging method. This was not the case with the M5P method, as the results were slightly weaker. It was also observed that Tabriz rainfall is largely influenced by Sahand rainfall, as the most models gave reliable estimates by using the rainfall data for Sahand station. This can be explained by the high correlation between Tabriz rainfall and Sahand. The results showed that the first scenario (Sahand) for M5P, RT, REPT and B-M5P method, the fifth scenario (Sahand, Sarab, Urmia, Maragheh and Mahabad) for the B-RT method, and the fourth scenario (Sahand, Sarab, Urmia and Mahabad) for the B-REPT method were the best scenarios. The best performance was found for the scenario 1 of the M5P decision tree model, followed by the Bagging method with the M5P base algorithm. In general, it was concluded that application of the Bagging method produced reliable results. Modeling without considering the decomposition components was compared with modeling with decomposition components. Adding seasonal, trend and residual components to the modeling input combinations significantly improved the accuracy of the results. Application of Bagging method in most cases also increased the modeling accuracy. The first scenario (Sahand and residual) for M5P and B-M5P methods, the tenth scenario (residual, trend, seasonal, Sahand and Sarab) for RT, REPT and B-REPT methods, and the eighth scenario (residual, trend and Sahand) for B-RT method were selected as the best scenarios. As a result, among the stations, Sahand, due to proximity and high correlation, and Sarab, due to greater correlation, had a great impact on precipitation in Tabriz. In general, the Bagging method with the basic M5P algorithm (B-M5P) was best suited in the first scenario. Thus, adding precipitation analysis components and using the Bagging method improve the modeling results with tree-based data mining methods.

Our results showed that Bagging method provided acceptable results in most cases. In the first case, the first scenario of M5P method including Sahand precipitation data was selected as the superior method and scenario. As a result, Sahand was the most effective station in estimating Tabriz rainfall with the highest correlation and the shortest distance from Tabriz. In the second case, with the decomposition components, the accuracy of the results increased significantly. The Bagging method with the basic M5P algorithm, the parameters of Sahand precipitation and the residual of Tabriz precipitation was considered as the best modeling algorithm. It can be concluded that using Bagging method and decomposition components with the closest station to the studied station results in the highest accuracy. Therefore, Bagging models with tree-based algorithm can be considered as simple and widely used methods.