نشریه آب و خاک
سال سی و هفتم شماره 5 (پیاپی 91، آذر و دی 1402)

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.

Climatic changes and human activities are among the important factors that affect the flow of rivers and it is very important to determine the contribution of these factors in order to better manage water resources. In recent years, there have been major changes in the watersheds, and the amount of runoff and river flow has decreased, or in some cases, the flow has increased due to the occurrence of floods. The issue of reducing the amount of runoff, especially in the arid and semi-arid regions of Iran, is one of the basic challenges related to the management of water resources. Hydrological changes primarily result from a combination of natural or climatic factors, including precipitation levels, air temperature, and overall warming of the Earth. Additionally, human activities, such as the construction of dams, creation of reservoirs, urbanization expansion, and indiscriminate harvesting, play a significant role. It is important to note that these factors are interconnected, and alterations in one can impact the others. The increase of greenhouse gases and climate change has caused a change in the hydrological cycle and the amount of runoff in the watersheds and has increased the number of climatic extreme events. The main purpose of this study is to determine the contribution of each of these factors on the discharge changes of the Gharehsoo River, one of the most important rivers of Ardabil province, using elasticity-based methods (non-parametric and Bodiko-based methods).

In this research, firstly, in order to determine the point of change in the amount of river runoff and to divide the base and change period, Petit's test was used during the statistical period of 1984-2019. This test was done using Xlstat software. According to the results of this test, there was a change in the annual flow time series in 1997, which was considered as the base period from 1984 to 1997 and from 1998 to 2019 as the period of changes. Then, the contribution of each of these factors was determined using elasticity-based methods.

In the elasticity-oriented method, the non-parametric method and the methods based on Bodiko's assumptions were used to calculate the elasticity coefficient.The results showed that in Samyan station, in the non-parametric method, the contribution of human activities is 88.26% and the contribution of climate change is 11.74%. The contribution of human activities and the contribution of climate change for the methods of Schreiber, Aldekap, Bodiko, Peek and Zhang, respectively 91.98 and 8.02, 90.02 and 9.97, 91.98 and 8.02, 90.80 and 9.20, 92.37 and 7.62 are estimated. In general, in the elasticity method, the contribution of human activities is 88.26 to 92.37 percent and the contribution of climate change is from 7.63 to 11.74 percent, depending on the non-parametric and Bodiko method. At the Dost-Beiglo station, employing the non-parametric method reveals that human activities account for 96.13% of the observed changes, while the remaining 3.87% is attributed to climate change. The contribution of human activities and the contribution of climate change for the methods of Schreiber, Eldekap, Bodiko, Pick and Zhang are 97.71 and 2.29, 97.42 and 2.58, 97.56 and 2.44, 97.48 and 2.52, 97.71 and 2.29 are estimated. In general, in the elasticity-oriented method, the contribution of human activities between 96.13 and 97.71 percent and the contribution of climate change from 2.29 to 3.87 percent, depending on the non-parametric and Boudico-oriented method, have been met.

In this research, different hydrometeorological data such as precipitation, evaporation and transpiration and monthly discharge from the Samyan and Dost Beiglo stations were used for the statistical period of 1982-2019. First, by using Pettitt's test, it was determined that the river flow rate has changed abruptly since 2016. Therefore, the entire statistical period was divided into two natural and change periods, and then, using elasticity-based methods, the contribution of human activities and the contribution of climate change were determined. According to the results obtained in both stations, the impact of human activities (more than 88%) on the basin's runoff is far more than climate change (less than 11%). Therefore, it seems necessary to prevent the effective human activities on reducing the river flow in solving and managing water problems in the basin.

Oil contamination affects the biological, physical, and chemical properties of soil. The abundance and diversity of soil microbial communities can significantly be influenced by petroleum hydrocarbons. Soil biological indicators including microbial population and enzyme activity, are highly sensitive to environmental stresses and respond to them quickly. Measuring the microbial population is one of the most common biological indicators which is used to study the quality and health of the soil. Also, measuring the activity of enzymes such as urease is one of the most sensitive indicators of oil-contaminated soils. There are some studies on the effects of oil contamination on microbial population and soil enzyme activity. Most of the studies have tested non-natural and short-term oil pollution and reported the adverse effects of oil hydrocarbons on microbial activities in soil. While the soil sample used in this research had natural and long-term contamination and the microorganisms are compatible with polluted conditions. The aim of this study was to investigate changes in the microbial population and urease activity in the presence of different levels of oil contamination, and how petroleum hydrocarbons can affect them. Petroleum hydrocarbons are toxic and persistent in soil, so it is necessary to study the pattern of changes in soil biological characteristics in effective soil management.

In this study, 120 samples of oil-contaminated soils were collected from the oil-rich area of Naft-Shahr (located in the west of Kermanshah province) which had natural and long-term oil pollution. A nested design was used to analysis data in this research. The test factors included locations (4 locations) and 3 different levels of oil pollution: low (L), moderate (M), and high (H). Also, 10 replications were considered in the three levels of oil contamination. The collected soils were analyzed for physico-chemical (pH, EC, Ɵm, CCE, OC, soil texture) and biological properties (including urease activity, BR and SIR) using standard methods, and the concentration of oil pollutants was determined by the Soxhlet extractor. To determine the abundance of the culturable microbial population, bacterial counting was performed using nutrient agar (NA) and carbon-free minimal medium (CFMM) supplemented with crude oil as the media. Urease activity was measured by the indophenol blue method and finally, the results of measuring chemical, physical and biological properties were analyzed using principal component analysis (PCA).

 The average percentage of oil measured by Soxhlet method was 4.03%, 9.95% and 22.50% respectively for L, M and H levels. The results showed that the microbial population increased with the increase of contamination intensity. The highest microbial population counted in NA culture medium was 9.54 ×105 CFU/g in H soils and the lowest population was 3.25 × 105 CFU/g in L soils. In the CFMM culture medium, the highest population in H soils was 11.3 × 105 CFU/g and the lowest population in L soils was 11.8 × 104 CFU/g. For both NA and CFMM mediums, location 1 had the highest population and location 4 had the lowest microbial population. Oil contamination of soil samples led to a decrease in urease activity in such a way that the highest enzyme activity in soils was obtained with low contamination (594.90 µgNH4/g.h) and the lowest activity in heavily contaminated soils (176.11 µgNH4/g.h). Also, the lowest urease activity was observed in location 1 and the highest in location 4. Principal components analysis (PCA) was also performed and 71% of the variance of the samples could be explained by the first two components (biochemical component and physical component). The results of this research indicated an increase in the microbial population with an increasing of the intensity of oil pollution. It seems that the results obtained from the studies conducted on man-made pollution and natural pollution have differences in terms of the type of biological responses. Aged, long-term and natural oil pollution has caused the selection of oil-resistant microbial community, and therefore we see their positive response to the presence of oil compounds. Conversely, urease enzyme activity was found to be higher in soils with low pollution. This suggests that microbial activity, while influential, is not the sole determinant of urease activity, and various factors contribute to Soil Enzyme Activity (SEA). The type of petroleum pollutant, the direct effect of petroleum compounds on urease-producing microorganisms, as well as the non-microbial origin of urease in soil can be possible reasons for reducing urease activity in contaminated soils.

In areas where petroleum pollutants are naturally and long-term present in the soil, some oil-decomposing microbial groups use petroleum hydrocarbons as a source of carbon for their nutrition, so the abundance of oil-decomposing communities increases. The results showed an increase in the microbial population with an increase in the intensity of oil pollution. On the other hand, the activity of urease enzyme measured in soils with low pollution was higher because non-microbial factors may affect the activity of this enzyme and the increase in the microbial population is not related to the increase in the population of urease-producing microbes.

Soil, as habitat substrate, helps to regulate important ecosystem processes, including nutrient absorption, organic matter decomposition. Water availability and the well-being of humanity are directly linked to soil functions. On the other hand, vegetation with different species and ages have significant effects on the status of the surface soil layer through the creation of diverse environmental conditions and the production of different organic substances. However, few studies have been conducted in relation to the effect of the age of afforestation and the type of vegetation on the soil status. Considering that a practical, complete and effective assessment of soil condition should be the result of simultaneous measurement of physical, chemical and biological indicators, hereupon, the present study aimed to investigate the effect of 20-year old poplar stand, 20-year old maple stand, 10-year old poplar stand, 10-year old maple stand and rangeland cover, in plot 3 of Delak-Khil series of wood and paper forests in Mazandaran province, on the organic layer properties and physical, chemical and biological (including microbial activities, enzyme activity, earthworm population and biomass, the number of soil nematodes and root biomass) properties of the surface soil layer.  

For this purpose, some parts of the study area were selected which are continuous with each other and have minimum height difference from the sea level, minimum change in percentage and direction of slope. Then, in order to take samples from the organic and surface layer of the soil, three one-hectare plots with distances of at least 600 meters were selected in each study habitats. From each of the one-hectare plots, 5 leaf litter samples and 5 soil samples (30 cm × 30 cm by 10 cm depth) were taken to the laboratory for analysis . In total, 15 litter samples and 15 soil samples were collected from each of the habitats under study. One part of the soil samples was passed through a 2 mm sieve after air-drying to perform physical and chemical tests, and the second part of the samples was kept at 4 °C for biological tests. One-way analysis of variance tests was used to compare the characteristics of organic layer and soil between the studied habitats. In the following, Duncan's test (P>0.05) was used to compare the average parameters that had significant differences among different habitats.

The results of this research showed that afforested stands with different ages and pasture cover had a significant effect on the characteristics of the organic and surface soil layers. The results indicated the improvement of most of the characteristics of the organic and surface soil layer in the afforested stands, especially the 20-year old afforestation compared to the rangeland cover. The organic matter produced in 20-year old afforestation, especially with poplar species, had a higher quality (high nitrogen and carbon content and low carbon-to-nitrogen ratio) compared to organic matter produced in 10-year old afforestation and pasture cover. Most of the physicochemical characteristics of the soil under 20-year old afforestation were in a better condition than the other studied habitats. Also, according to the results of this research, the highest values of biological characteristics such as microbial activity, enzyme activity, and the population of earthworms and nematodes were observed in the subsoil of 20-year old afforestation especially with poplar species. Based on the results obtained from the principal component analysis, the higher values of nitrogen, phosphorus, calcium, magnesium and potassium content of the organic layer led to the improvement of soil fertility characteristics, microbial activities, enzyme activity, earthworm population, the number of soil nematodes and root biomass, respectively, under poplar and maple plantation for 20 years, meanwhile, 10-year old plantation, especially with maple species, and rangeland with the production of organic materials with high carbon content and carbon to nitrogen ratio, resulted in the reduction of organic matter decomposition (greater thickness of organic layer), and consequently the reduction of the mentioned properties of the surface soil layer. 

According to the findings of this research, it can be concluded that plantation with poplar species, especially after 20 years, had a higher ability to improve the soil condition compared to maple, which can be considered by managers in future afforestation. Also, with the passage of time, the presence of tree covers (poplar and maple) had a higher priority than rangeland cover in improving the fertility status and suitable edaphological conditions of the soil.

Soil pore size distribution curve and using the optimal ranges of the location and shape parameters of this curve can be used to evaluate the soil physical quality. This research was carried out in an area of about 220 hectares of Torogh Agricultural and Natural Resources Research and Education Station, to determine the optimal ranges for soil pore size distribution curve parameters using the soil physical quality index. Different soil textures and the diversity in soil properties are the distinct features of this research station.

Torogh Agricultural and Natural Resources Research and Education Station of Khorasan-Razavi province, with a semiarid climate, is located in south-east of Mashhad city. For the field measurements and laboratory analysis to determine the soil physical properties and indices, 30 points with different soil textures and structures were selected. Intact soil cores (5 cm diameter by 5.3 cm length) and disturbed soil samples were collected from 0-30 cm depth of each point. After the laboratory analysis and field measurements, 35 soil physical properties were measured and calculated. Soil particle size distribution and five size classes of sand particles, soil bulk, and particle density, dry aggregates mean weight diameter (MWD) and stability index (SI), soil moisture release curve (SMRC) parameters, S-index, soil porosity (POR) and air capacity (AC), soil pore size distribution (SPSD) curves, relative field capacity (RFC), plant available water measured in matric pressure heads of 100 and 330 hPa for the field capacity (PAW100 and PAW330), least limiting water range measured in matric pressure heads of 100 and 330 hPa for the field capacity (LLWR100 and LLWR330), integral water capacity (IWC) and integral energy (EI) of different soil water ranges, were the soil physical properties and indices which were determined in this study. Three parameters of modal, median, and mean pore sizes of the SPSD curves were considered as the location (central tendency), and three parameters of standard deviation, skewness, and kurtosis of the SPSD curves were considered as the shape parameters. Selection of the most important soil physical characteristics using principal component analysis (PCA) method by JMP software (ver. 9.02), weighting and scoring of the selected characteristics using PCA and scoring functions, respectively, and the summation of multiplied characteristics weights by their scores for each soil sample, were the four steps of calculation of the 0-1 value of soil physical quality index (SPQI). Soil samples were classified into four soil physical quality classes by SPQI values. The soils of the first class with the highest SPQIs (> 0.78) were considered to determine the optimal ranges of SPSD curves location and shape parameters.

The texture of soil samples were loam (40 %), silt loam (23 %), silty clay loam (17 %), clay loam (13 %), and sandy loam (7 %). Soil organic carbon was between 0.26-1.05 (%), and the average soil bulk density was 1.45 (gr.cm-3). The MWD values of studied soil samples were between 0.94-2.88 (mm), an average of 1.93 (mm). The average modal, median, and mean pore sizes as the location parameters of the SPSD curves were 60.3 (μm), 12.4 (μm), and 6.5 (μm), respectively. The average of standard deviation, skewness, and kurtosis as the shape parameters of the SPSD curves were 71.56 (μm), -0.36 and 1.15, respectively. The average modal pore sizes showed that the pores with a size of 60 (μm) had the highest frequency in soil samples. The range of calculated standard deviation of SPSD curves, along with the difference between the minimum and maximum mean pore sizes (24.6 μm), implied the diversity of pore sizes in the studied soils. The results of PCA showed that the four soil physical properties of PAW330 (0.1-0.2 cm3.cm-3), PORt (0.40-0.51 cm3.cm-3), LLWR100 (0.12-0.22 cm3.cm-3) and SI (0.76-2.61 %) accounted for about 88% of the variance between soil samples and were selected to calculate the SPQIs. The PAW330, PORt, LLWR100, and SI were entered into the calculation of SPQIs with weights of 0.46, 0.31, 0.15, and 0.08, respectively. All the selected physical properties were scored using the scoring function of more is better. The maximum and minimum values of SPQIs for the studied soils were 0.84 and 0.14, respectively. Five soil samples with SPQIs greater than 0.78 were classified as class 1 with the highest physical quality. The ranges between the minimum and maximum values of the SPSD curves, location, and shape parameters of these five soils were proposed as the optimal ranges. In this regard, the ranges of 29-92 (μm), 5-16 (μm), and 2-7 (μm) were suggested for optimal ranges of modal, median, and mean pore sizes, respectively. The optimal ranges of standard deviation, skewness, and kurtosis of the SPSD curves were proposed as 22-81 (μm), (-0.38)-(-0.33), and 1.14-1.15, respectively.

The optimal ranges of SPSD curves location and shape parameters suggested in the literature may probably not apply to a wide range of agricultural soils. They must be evaluated in a more extensive range of land uses, soil management, and soil textures. In this research, the soils with the relatively higher physical quality had larger mean pore size and less SPSD curves standard deviation (less diversity of pore size) than the optimal ranges suggested in the literature. The optimal ranges of SPSD curves location and shape parameters proposed in this research are appropriate for medium to coarse-textured soils of regions with the semiarid climate in Iran.

Soils and landforms have a strong relationship and archive evidence of climatic and environmental changes. Alluvial fans are one of the most important landforms in arid and semi-arid regions of Iran. Climate changes in the Quaternary, especially in the late Pleistocene, had a significant effect on the evolutions of alluvial fans in arid and semi-arid regions. Alternate of sedimentation and soil formation in alluvial are the consequences of periodic climate change. Organisms are one of the main factors of soil formation. Biological crusts are part of organisms that are abundant in dry lands and especially in alluvial fans; however, their role in soil formation has been less studied. Biological soil crusts by providing the suitable biological activity, effect on trapping of aeoilian materials and hydrological processes affect the soil formation processes. The chemical properties of the soil affect the catabolic capacity of the soil and it is very different among the different layers of the soil. However, few studies have addressed the effect of processes on soil microbial respiration during change and evolution and pedogenic state. The objectives of this research were to 1) investigate the evolution of soils along the gradient from upstream to downstream of the alluvial fan and 2) investigate the changes in microbial respiration in different layers of soil and the factors affecting it.

The studied area is an alluvial fan in Razavi Khorasan province, in the southern slopes of the Binaloud mountain range. The climate of the region is semi-arid and the soil moisture and temperature regimes are Aridic border on Xeric and mesic, respectively. Three soil profile in the upper, middle, and base part of the alluvial fan were described. Bulk and undisturbed soil samples were collected from various soil horizons for subsequent physical, chemical, and micromorphological analyses. In addition, the microbial soil respiration was measured in all horizons. The soils were classified according to Soil Taxonomy and World Reference Base ‎methods. ‎

Sequences of sedimentation and soil formation were observed in the soil profiles. Vesicular (V), argillic (Bt), argillic-calcic (Btk), calcic (BCk) and cambic (Bw) horizons were the diagnostic soil horizons of the studied soils. Soil profiles of the middle and base were Xeric Calciargids in the subgroup category of Soil Taxonomy; while soil profile of the apex soil was Xeric Haplocambids. In the profiles, a thin vesicular horizon (V) was formed under the desert pavement. Below the vesicular horizon, evidence of clay illuviation, pedogenic carbonate nodules, and calcium oxalates in roots were observed in thin sections. This evidence shows the role of biological crusts in the formation of these features. In the lower horizons of the profiles, pedogenic carbonate nodules, carbonates pendants and clay coatings were observed. It seems that the upper soil (vesicular and underlying Bt horizons) were developed in the more humid periods of the Holocene, and biological crusts also played a key role in the processes of calcification and clay illuviation. The argillic horizons in the lower layers were formed during the stable periods of the late Pleistocene. The irregular microbial respiration mainly indicated difference in microbial activities labile organic matter content. The argillic horizons had the lowest microbial respiration, due to decomposition of organic materials during soil formation. In contrast, soil respiration was the highest in surface and calcic horizons. It seems that preservation of organic materials by carbonate complication. However, it is suggested to investigate the carbon fractions in relation to microbial biomass in the studied horizons.

In this area, biological crusts and vegetation affected the formation of soil in the aeolian sediments of the Vk and AVk horizons and played a significant role in creating the Bt horizon in profiles 2 and 3. The study of landform profiles showed the formation of calcic and argillic horizons in the past climate, while the Bt horizon of the upper layers was formed in the current Holocene period. This form of the argillic horizon is slightly different from the soils of the Iranian region because these horizons have not been reported so far. It has been proven that there were humid periods in the Holocene, and it needs more studies at present. The study of soil microbial respiration in landform horizons showed that argillic horizons decreased the amount of microbial respiration, while it increased in classical horizons.

The gradual decrease in the fertile soils surface due to environmental pollution and urbanization phenomena has reduced the possibility of sufficient fodder production. In addition, the strict dependency of the agricultural sector on water resources in an age of drastic climate change necessitates providing novel solutions for agricultural production. One of the methods that has gained attention for providing fodder is its production through soilless culture techniques.  Maize can be a suitable option for fodder production in soilless culture due to high starch and sugar content, low seed cost, high biomass production, and rapid growth. Proper nutritional management of maize in soilless culture is highly important for increasing the quantity and quality of forage greenery. Little information is available regarding the impact of nitrogen form on the growth, yield and chemical composition of forage plants including maize in soilless culture. This experiment was conducted to investigate the effect of nitrogen form on the chemical composition, leaf photosynthetic pigments concentration and yield of two fodder maize (Zea mays L.) cultivars in soilless culture.

A factorial experiment based on randomized complete block design was conducted with the two factors of ammonium to nitrate ratio in the nutrient solution (0:100, 12.5:87.5, 25:75, 37.5:62.5 and 50:50) and maize cultivars (i.e., single cross hybrid 704 and single cross 410) and four replications in hydroponic culture at the greenhouse of Shahrekord University. After seed germination and emergence of the first two leaves, the maize seedlings were transferred to 10-liter plastic pots containing perlite (0.5-5 mm) and were manually fertigated with different ammonium to nitrate ratios on a daily basis. Before harvesting, chlorophyll a, b and (a+b), and carotenoids were quantified in leaves of plants. At the end of the tasseling stage, the plants were harvested. After harvesting, the root, stem, and leaf parts were separated, and the fresh weights of the samples were measured. Plant samples were dried in an oven at 60 °C. Then, dry weights of samples were measured and samples (root and leaf + stem) were ground for nutrient analysis including of N, P and K. Analysis of variance was performed using SAS software version 9.4. Means comparison was conducted using Duncan's multi-range test at p <0.05.

The results showed that in single-cross hybrid 704 and single-cross 410 cultivars, respectively, increasing the applied ammonium to 37.5% and 50% in the nutrient solution caused a significant increase in the shoot nitrogen concentration. Application of ammonium in the nutrient solution led to an increase in shoot and root phosphorus concentration in both maize cultivars compared to the nutrient solution without ammonium. The highest concentration of phosphorus in shoot (18.02 g.kg-1) was observed in the single-cross hybrid 704 cultivar when maize plants fed with a nutrient solution containing 50 percent ammonium, which was 3.2 times higher than the shoot phosphorus concentration in plants fed with nutrient solution without ammonium. Furthermore, at the 50:50 ammonium to nitrate ratio in the nutrient solution, the lowest root potassium concentration was recorded in both maize cultivars. In single-cross hybrid 704 cultivar, application of nutrient solution with ammonium to nitrate ratio of 50:50 resulted in a significant 31% decrease in leaf chlorophyll a concentration compared to plants fed with a nutrient solution containing 25% ammonium (with the highest chlorophyll content). The leaf chlorophyll a concentration in single-cross 410 cultivar showed an increasing trend with increasing ammonium in the nutrient solution up to 25 percent, and then a decreasing trend with further increase in the ammonium proportion. Moreover, a 31.4% significant decrease in chlorophyll b concentration was observed in plants fed with a 50:50 ammonium to nitrate ratio compared to plants fed with a 37.5: 62.5 ammonium to nitrate ratio. The highest leaf carotenoid concentration was recorded in single-cross hybrid 704 cultivar and at 25:75 ammonium to nitrate ratio, which was 1.4 times higher than the leaf carotenoid concentration compared to plants fed with nutrient solution without ammonium. The highest relative leaf moisture content was observed in the plants nourished with ammonium to nitrate ratio of 25:75, which showed a significant 20% increase compared to the ammonium-free nutrient solution. The results also indicated that the application of 50% of nitrogen in the form of ammonium in the nutrient solution led to a significant decrease in the leaf surface area of maize. The highest shoot and root fresh weights were obtained in the plants nourished with 25:75 ammonium to nitrate ratio and in the single-cross hybrid 704 cultivar. The results showed that the highest water (solution) use efficiency based on fresh weight was recorded in plants fed with 25:75 ammonium to nitrate ratio and in the single-cross hybrid 704 cultivar.

Based on the results of the present study, the highest shoot and root fresh weights of both maize cultivars were obtained in plants fed with 25:75 ammonium to nitrate ratio. Given the limitations of water resources and rainfall, optimal use of minimum water to produce maximum agricultural crops must be cnsidered. According to the results of this research, application of nutrient solution with ammonium to nitrate ratio of 50:50 led to ammonium toxicity and a reduction in forage yield in both maize cultivars. Therefore, replacing 25% nitrate in the nutrient solution with ammonium and selecting the single-cross hybrid 704 cultivar (due to higher yield compared to single cross 410 cultivar) is recommended to achieve maximum fodder yield in soilless culture under conditions similar to this study.

Climate change has led to changes in the frequency, intensity, duration, and spatial distribution of climate extremes. During the last decade (2011-2020), the average global temperature was 0.1 ± 1.1 oC higher than in the preindustrial era. Iran and especially the Urmia Lake basin is one of the most vulnerable areas to climate change. Urmia lake basin has received the special attention of policymakers and planners since it is the location of Lake Urmia, and it also holds nearly 7% of Iran's water resources. A huge program of dam construction and irrigation networks has been started in this basin in the northwest of Iran since the late 1960s. Despite the increasing attention to Lake Urmia since 1995, the water level of this lake has decreased. During the drought of 1990-2001, Lake Urmia experienced a decrease in its level without any recovery and is decreasing at an alarming rate. Therefore, it is necessary to project the future climate of the Urmia Lake basin and especially extreme precipitation based on the latest climate change models.

The CMIP6 models were used to investigate the future projection of extreme precipitation in the Lake Urmia basin. Considering the horizontal resolution, availability of daily data, and climate sensitivity, we selected five models including GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, and UKESM1-0-LL. The horizontal resolution of all five models is 0.5o. The 25-year historical period (1990-2014) and the 25-year projection period for the near future (2026-2050) were chosen to analyze the extreme precipitation in the Urmia Lake Basin. The future projection was considered under three shared socioeconomic pathways (SSPs) scenarios. These scenarios include SSP1-2.6, SSP3-7.0, and SSP5-8.5 scenarios. Mean bias error (MBE) and Normalized Root Mean Square Error (NRMSE) were computed to evaluate the individual models and the multi-model ensemble generated by Bayesian Model Average (BMA) method. To assess extreme precipitation, we used four indices including the Number of heavy precipitation days (R10mm), the number of very heavy precipitation days R20mm), the Maximum 1-day total precipitation (Rx1day), and the Simple Daily Intensity Index (SDII).

The performance of five CMIP6 individual models and the multi-model ensemble in the Lake Urmia basin during the period of 1990 to 2014 was evaluated against eight ground stations. The investigation of the annual precipitation showed that this variable is underestimated in CMIP6 models in the basin averaged. The maximum and minimum bias values model was seen in Saqez station by -9.64 mm for the MRI-ESM2-0 and -0.43 mm for the UKESM1-0-LL, respectively. The highest average MBE in the Urmia Lake basin was respectively obtained for GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, and UKESM1-0-LL models. Among the examined models, MPI-ESM1-2-HR has shown the highest efficiency among the examined individual models.Variations in the number of heavy precipitation days during the historical period (1990-2014) have distinguished three main areas for the Lake Urmia basin. The main hotspot of heavy precipitations in the Urmia Lake basin is located in the southwest of Kurdistan province with a long-term average of 25.4 days. The next hotspots are the northwest and the northeast of the basin. In the historical period (1990-2014), the precipitation intensity index Rx1day experienced considerable variability. Based on CMIP6-MME, the value of the Rx1day index in the Urmia Lake basin is estimated between a minimum of 16.3 mm and a maximum of 63.3 mm. The maximum variation of this index is seen in the southern areas of the basin, especially on the border with Iraq.

Evaluation of individual CMIP6 models showed that these models underestimated precipitation in the Lake Urmia basin during the historical period (1990-2014). The CMIP6-MME has significantly improved precipitation estimation. The results of the investigation of days with heavy and very heavy precipitation showed that the two indices R10mm and R20mm are increasing in most areas of the Lake Urmia basin by the middle of the 21st century. Trend analysis showed that the days with heavy and very heavy precipitation will increase under different SSP scenarios in most areas of the Lake Urmia basin, especially in the northern and western regions. Also, days with heavy and very heavy precipitation will have a greater contribution than normal precipitation days in the future. It is expected that the intensity of precipitation will increase in the coming decades in the Lake Urmia basin, and this increase is more for the western and northern regions than for other regions of the basin. This result may potentially increase the flood risk in Lake Urmia.

Drought is one of the greatest challenges of our time due to the dangers it poses to the world. In arid and semi-arid regions, it is necessary to continuously monitor agricultural systems that face water shortages and frequent droughts. Therefore, it is necessary to have large-scale information about agricultural systems and land use for managing and making decisions for the sustainability of food security. Continuous monitoring of drought requires a large amount of information to be processed with great speed and accuracy. Due to the complexity and impact of various factors on drought, in recent years, the methods of combining several factors to create a comprehensive drought index have received much attention. Machine learning and deep learning methods can provide a more accurate and efficient tool to predict droughts and be used in drought risk management. The review of sources shows that until now no studies have been conducted in the field of drought monitoring using deep learning approach and satellite images in the catchment area of Lake Urmia in Iran. A large part of its economic activities is dedicated to agriculture. The increase in temperature, the increase in evaporation-transpiration and the excessive use of water resources for agriculture have caused an upward trend in the frequency of droughts in this basin during consecutive years, one of the harmful effects of which is a significant decrease in the lake level. Therefore, for drought management in this basin, it is very important to identify drought behavior so It is very important to determine appropriate and reliable indicators to measure and predict the effects of droughts. According to the investigations, it was observed that most of the studies in the field of drought in this basin have been carried out from the meteorological point of view, or by individual plant indicators, so in this study, using the approach of principal component analysis, we tried to provide a composite drought index for drought modeling and forecasting.

In this research, satellite images and deep learning and machine learning methods have been used to predict the Combined Drought Index. For this purpose, satellite images were first obtained for the study area and pre-processing was done on the data. Then, all the data were converted to a scale with a spatial resolution of 500 meters, and the VCI index was calculated using NDVI data, the TCI index using the land surface temperature product, and the CWSI index using the Modis evapotranspiration product, and finally, CDI drought index was calculated using principal component analysis method. Then the correlation between CDI data and other meteorological variables including evapotranspiration, potential evapotranspiration, land surface temperature during the day, and land surface temperature at night was calculated. Finally, the CDI index is modeled using deep learning and machine learning methods.

This study modeled the Combined Drought Index based on a different combination of input variables and deep learning and machine learning methods. Examining the results showed that the variables of the normalized difference vegetation index, the land surface temperature during the day and at night, evapotranspiration, and potential evapotranspiration were the most influential parameters for modeling the CDI index, and all four methods with acceptable accuracy and error have been able to model the combined drought index. The CART model with a correlation coefficient of 0.96, RMSE equal to 0.029, and Nash Sutcliffe coefficient of 0.92 was chosen as the best model among the methods.

In this research, different combinations of input variables extracted from satellite image products were evaluated in the form of 6 independent scenarios to predict the Combined Drought Index. By examining the evaluation parameters including correlation coefficient, Nash Sutcliffe coefficient, and root mean square error, it was found that all four methods can estimate the combined drought index with acceptable accuracy and error. Among all the methods, the CART method performed better (R=0.96 and RMSE=0.029) than the other methods for predicting the time series of the Combined Drought Index. On the other hand, the SVM method has been able to model the combined drought index with acceptable accuracy (R=0.94 and RMSE=0.034). However, contrary to expectations, two deep learning methods were able to model the combined drought index with less accuracy than machine learning methods. In general, by examining the results, it was found that with the method presented in this research, it is possible to accurately predict the CDI combined drought index time series and predict drought in different periods of plant growth, and use its results for regional drought management and policies, especially in Basins without statistics.

Due to global warming and climate change, droughts and extreme precipitation events are increasing. Therefore, it is of special importance to know the characteristics of precipitation in the region in order to manage water resources effectively especially during torrential rainfall events. This can help to reduce the risk of these events and increase water reserves with proper management. These precipitation characteristics which are the objectives of the present study, include the temporal-spatial distribution of precipitation in different parts of the study area, as well as the number of days with and without precipitation and the maximum precipitation occurring in the region. Also, these precipitation characteristics should give us information about extreme precipitation events.

This research analyzed the characteristics of precipitation in Markazi province over a 30-year period (from the crop year 1991-1992 to 2020-2021) using statistical methods and the spatial distribution was drawn and analyzed with ArcGIS software. This province includes the 12 meteorological stations of Arak, Mahalat, Saveh, Tafresh, Ashtiyan, Komeijan, Khondab, Shazand, Khomein, Delijan, Farmahin and Gharqabad, which the precipitation data of these stations were investigated. The trend of precipitation changes in monthly, seasonal, and annual time scales were also examined using the Mann-Kendall test. Moreover, extreme precipitation was assessed using four indices: total extreme precipitation (R95p), number of days with precipitation above the station’s extreme precipitation threshold (R95d), absolute intensity of extreme precipitation (AEPI) and the fraction of total rainfall from events exceeding the extreme threshold (R95pT). The latter index represents the ratio of extreme precipitation to annual precipitation in rainy days (daily rainfall above 1 mm).

This study reveals that, on average, 53% of the annual precipitation is accounted for by the maximum index of R95pT, which indicates the percentage of extreme precipitation that occurred at each station relative to its the precipitation of the corresponding year. Knowing the timing of these extreme events can help to manage floods and optimize water resources. More than 20% of these precipitations occurred in March. The spatial distribution of rainfall in Markazi province shows that the south-west regions have the highest average annual and seasonal rainfall, except for the summer season, while the eastern regions have the lowest. The winter season has the highest rainfall on average, followed by spring and autumn. March is the rainiest month with a coefficient of variation of 0.8 and an average monthly rainfall of 55.6 mm during the studied period. Due to most extreme precipitation events occurring in this month, it has the highest importance for water storage and management throughout the year. The average precipitation in March ranges from 32.6 mm (Saveh station) to 91.6 mm (Shazand station) across the stations of the province. The maximum rainfall in this month varies from 124.4 to 254.6 mm among the stations of the Markazi province, which is a considerable amount compared to the provincial average crop year. The standard deviation of precipitation in this month is between 28.7 and 61.3 mm, and the coefficient of variation at the stations of the province is between 0.6 and 0.9. Moreover, in terms of average monthly rainfall 22Nov-21Dec, 20Feb-19Mar, and 23Oct-21Nov are the next priority months for water storage management after 20Mar-19Apr, with average monthly rainfall of 39.3, 38.2, and 36.3 mm, respectively. The Mann-Kendall non-parametric test results did not reveal a consistent trend, but it showed that most of the meteorology stations in Markazi province had a significant decreasing trend in the rainfall in 21Jan-19Feb at a 90% confidence level. The analysis of extreme precipitation indices indicated that Shazand station had the highest extreme precipitation threshold value (28 mm), while Saveh and Delijan stations had the lowest (15 mm). The extreme precipitation threshold average of 30 years in other meteorological stations of Markazi province are 21mm in Arak, 17mm in Tafresh, 21mm in Khomeyn, 19mm in Mahallat, 17mm in Komeijan, 16mm in Farmahin, 21mm in Khondab, 17mm Gharqabad and 18mm in Ashtiyan.

The spatial distribution of rainfall in Markazi Province shows that the southwest regions have the highest average annual and seasonal precipitation, except for summer, while the east regions have the lowest. The average monthly rainfall also indicates that March has the highest rainfall among all months of the year, and that about 20% of the annual extreme precipitation occurs in this month.