مجله تحقیقات آب و خاک ایران
سال پنجاه و سوم شماره 9 (پیاپی 81، آذر 1401)

Land suitability classification is a useful management method to ensure using the planned and sustainable agricultural land according to their potential. Therefore, the suitability of irrigated plains in Iran was evaluated for wheat, barley, grain maize, rice and triticale in the years 2017 to 2022. The results showed that 1.4 million ha of the studied lands for wheat are in highly suitable class (S1), 2.8 million ha in moderately suitable class (S2), 1.39 million ha in marginally suitable class (S3), lands with an area of 524,531 ha in currently unsuitable class (N1) and 199,503 ha in permanently unsuitable class (N2). For barley, about 1.3 million ha are in S1 class, 2.2 million ha in S2 class, S3 class lands with an area of 1.3 million ha, about 501 thousand ha in N1 class and 489 thousand ha in N2 class. About 39 thousand ha for grain maize are in S1 class, 1.1 million ha in S2 class, S3 class lands with an area of 1.6 million ha, 853 thousand ha in N1 class and 999 thousand ha in N2 class. For rice cultivation, there are 310 thousand ha of land in S2 class, S3 class lands with 1.5 million ha, 877 thousand ha in N1 class and 949 thousand ha in unsuitable N2 class. There are 127,000 ha of land for Triticale in S1 class, about 549,000 ha in S2 class, 323,000 ha of land in S3 class, about 97,000 ha in N1 class and 83,000 ha in N2 class. Statistical analysis of the data showed that the separation of land suitability classes for all crops has acceptable accuracy. The main limiting characteristics were; texture, salinity/alkalinity, slope, drainage and in some areas climatic characteristics. With regard to the distinction of suitable and unsuitable lands, the application of the obtained results can be effective into correct programming for sustainable exploitation, production and cultivation pattern development.

A greenhouse study was conducted to investigate the effect of biochar on vegetative and reproductive stages of maize (Zea mays L.) under water deficit stress. A biochar produced from the pyrolysis of grapevine wood chips (at 350℃), was mixed with a sandy loam soil (from Alborz province) at the rates of 0, 0.5, 1, 2, 3, 4 and 5 % w/w, which were used for planting maize. The experiment consisted of two deficit irrigation treatments in vegetative (I1) and reproductive (I2) stages and one sufficient daily irrigation treatment throughout the growing season (I3). Plants in the period of deficit irrigation were subjected to successive wetting (via watering up to FC) and drying (via plant transpiration) soil cycles with water content varying between 0.30 and 0.18 cm3cm-3, respectively. Some plant physiological characteristics including transpiration rate, stomatal conductance and photosynthesis rate were determined during the drying cycles and biomass at the end of experiment. Soil water retention curve and unsaturated hydraulic conductivity (Kh) were measured by a combination of the Sandbox, Pressure plate, and Hyprop apparatus. The results showed that the addition of four percent biochar to the soil increased transpiration rate by 81, 100 and 49% in I1 and 228, 198 and 61% in I2 at soil water contents of 0.18, 0.20 and 0.23 cm3 cm-3, respectively. The effect of biochar on improving the physiological parameters of plants under drought stress in the reproductive stage was greater than the ones in the vegetative stage. Due to this improvement, the harvest index of plants in soils containing biochar increased by 35 and 50% in I1 and I2, respectively. Under the biochar impacts, grain water use efficiency also increased by 42% and 49% in I1 and I2, respectively. Given the small effect of biochar on increasing soil water retention especially in the dry range, it seems that the performance of biochar to keep higher Kh and matric flux potential were the most important factors to improve transpiration rate in the soil drying cycles.

In order to investigate the effect of different salinity levels and deficit irrigation on the growth and yield and water productivity of maize, an experiment was conducted in the research farm of the College of Agriculture, Shiraz University, Iran in 2018. The factorial experiment was performed as a randomized complete block design with three replications. Four salinity levels of irrigation water including control (0.6), 2, 3.5 and 5 dS m-1 and three levels of irrigation water depths including full irrigation, 75% and 50% of full irrigation were applied. Maximum seed yield, number of seeds per ear and total dry matter were observed in full irrigation treatment with salinity level of 0.6 dS m-1. Increased salinity from 0.6 to 3.5 dS m-1  had no significant effect on harvest index. Increasing the stress to 50% of full irrigation caused an average of 23% increase in seed maize water productivity. Maximum dry matter water productivity of 3.06 kg m-3was observed in the treatment of 50% full irrigation with salinity of 2 dS m-1. Therefore, in case of water shortage from the point of view of water resources management and soil salinity management, considering the salt leaching to prevent salt accumulation in the soil, treatment of 50% full irrigation with irrigation water salinity of 2 dS m-1 is recommended due to higher water productivity and lower yield slope per unit increase in salinity.

Today, changing temperature and precipitation parameters are the major challenges of water resources. In this study, meteorological data of Birjand station have been used. Data on minimum daily temperature and maximum daily temperature and daily precipitation values of Birjand station during the years 1990 to 2014 were prepared as baseline data from Birjand regional water and then arranged. three models of IPSL-CM6A-LR, MIROC-ES2L, and MRI-ESM2-0 from the set of sixth report models (CMIP6) were used to evaluate the Projection of the effect of climate change on the maximum and minimum temperature and precipitation parameters. Using correlation coefficient (R), root mean square error (RMSE),and Kling-Gupta (KGE) evaluation tests, the MIROC-ES2L model was selected as the most suitable model, and using the latest diffusion scenarios that have been introduced as Socio-Economic Trajectory (SSP), the Projection of the effect of climate change on temperature and precipitation parameters of Birjand station was studied. To reduce the scale of the data, the CMhyd model was used and three scenarios, SSP1-2.6, SSP2-4.5, and SSP5-8.5 (2022-2050), were used for the next period. Then, using Mann-Kendall test and Sen’s Slope, the trend of observational data parameters was determined. In this testm each of the parameters of maximum temperature, minimum temperature and precipitation in the observation period and the future, there is a significant trend in some months and some months do not have a significant trend. The results of this study showed that the maximum and minimum temperature changes in the next period (2022-2050) compared to the observational data have an increasing trend and the precipitation parameter has a sinusoidal trend and is increasing in some months and decreasing in some months, but In general, It can be said that the average total monthly precipitation is increasing in the future under all three scenarios for the MIROC-ES2L model.

Water erosion is one of the most important challenges of agriculture and watershed management in the world and it has been considered by many researchers. To estimate water erosion, many experimental models have been proposed, of which the Revised Universal Soil Loss Equation (RUSLE) is one of the most widely used models for estimation of soil erosion. Rainfall erosivity (R) is one of the factors in this model. Direct calculation of R required meteorological gauge stations which are available at a limited number of synoptic stations. In this study, the attempt was to estimate rainfall erosivity using available data such as annual rainfall. Semnan province, with an area of 96816 km2, has a limited number of synoptic and rain gauge stations, makes it difficult to estimate rain erosivity in this province. In this study the auxiliary variables including digital elevation model (DEM), normalized vegetation index (NDVI), land surface temperature (LST) and global precipitation network data "Open Land Map Precipitation" (LMP) were used for spatial prediction of annual rainfall. The rainfall map of the study area was prepared using auxiliary data and using random forest (RF) model. Also in synoptic stations, the amount of erosivity was determined based on the EI30 index and average annual rainfall. Finally, the relation between rainfall and erosivity and annual rainfall was determined using nonlinear regression. Root mean square error (RMSE) and correlation coefficient (r) of RF model for prediction of annual rainfall were 16.9 mm and 0.98, respectively. The results of the rainfall map in the study area showed that the rainfall varied between 70-420 mm year-1.  Rainfall classification maps showed that near the half of the study area has annual rainfall less than 100 mm, 30% of the province has annual rainfall of between 100 and 150 mm and only about 17% of the province has annual rainfall more than 150 mm year-1. The maximum and minimum values of erosivity were 380 and 39 MJha-1mm h-1year-1 in the northern and southern part of the study area, respectively. Our results indicated using new method of data mining, it is possible to spatial prediction of rainfall and erosivity, especially in areas with small number of synoptic stations.

Wheat is a strategic crop for food security, so accurate forecasting of its performance is important for planning and adopting appropriate policies for import or export. Crop models use climatic, soil, cultivar type and crop management data to predict crop growth, development and yield. The purpose of this study was to improve the accuracy of estimating wheat yield using a combination of recommended crop models in Golestan province using Bayesian model averaging (BMA). In this study, first, the ability of DSSAT, CropSyst and SSM-Wheat models was evaluated to estimate wheat yield in Golestan province. According to the results, the DSSAT model with root mean square error (RMSE) equal to 290 kg ha-1, coefficient of determination (R2) equal to 96%, mean square root of normalized error (NRMSE) equal to 6.34 and the efficiency of the model (EF) equal to 0.9 has the most accurate estimation as compared to two other models. In the next step, using the BMA approach, binary and ternary combinations were taken from three crop models, which led to the production of four new models. Comparing the performance of BMA models with individual models, showed that the combination of models improves the accuracy of estimation. Thus, the amount of RMSE in the C23 model (which is the result of combining DSSAT and SSM-Wheat models (was reduced by 30% compared to the amount of yield calculated by the best single model (DSSAT) and reached 202 kg ha-1. Also, the value of R2 in the C23 model reached 97%. Therefore, the combination of models improves the accuracy, estimating wheat yield.

The main purpose of this study was to determine the return and physical and economical water productivity in the production of horticultural products using drip irrigation system under research conditions in Horticultural Sciences Research Institute (HSRI) in Alborz province during 2016-2019. In this study, the return criteria including; gross income, the percentage of sale return and the percentage of investment return, and also physical and economical productivity analysis were used to measure water productivity. According to the results, during 2017-2019, the average physical productivity of irrigation water in apple and pear genotypes was determined to be 2.75 and 1.7 kg/m3, respectively. The average economic productivity of water resulting from the gross profit of horticultural products in apple and pear genotypes was determined to be 39990.5 and 211486.7 Rials/m3 respectively, in Kamalshahr station. Results also showed that in 2016-2019, the average physical productivity of irrigation water in production of sour cherry, cherry, peach and apple genotypes was 1.08, 0.99, 1.69 and 1.61 kg/m3, respectively, and the average economic productivity of water based on gross profit of horticultural products in the genotypes of sour cherry, cherry, peach and apple were determined to be 106174.8, 95127.8, 25236.3 and 23011.2 Rials/m3, respectively, in Meshkindasht. The results of this study indicated the favorable state of the average economic productivity of water resulted from the profit of the production of apples and pears in Kamal Shahr station compared to the results of studies conducted in some parts of the country. However, in these stations the physical and economic productivity of water in the production of apple, pear and peach, have been generally much lower compared to the average ones resulted in some studied countries. So, investigation of their reasons using comparative studies and identifying strategies to increase water productivity in the production of these products at these research stations is recommended.

Accurate estimation of irrigation water quantity, water productivity and irrigation water application efficiency of cultivated wheat in Iran are important and key indicators in agricultural sector planning. The purpose of this study was to investigate the results of field and farm measurements of irrigation water and wheat yield under farmers' management and compare them with estimating the NIAZAB system in 37 Township during a crop season (2016-2017) as well as determining wheat water productivity and application efficiency. The results showed that the average amount of wheat irrigation water measured in the farms and systems were 5710 and 5894 cubic meters per hectare, respectively, and the average grain yield of wheat measured in the farms and systems were 5215 and 4949 kg, respectively. The average water productivity of wheat measured in the farms and NIAZAB system were 1 and 0.9 kg/m3. The results showed that the NIAZAB system with a normal root mean error of 0.19% and agreement coefficient of 0.93 estimates the amount of wheat irrigation water in the township scale and wheat grain yield with a normal root mean error of 0.17% and agreement coefficient of 0.95 and estimates the productivity of wheat water with a normal root mean error of 0.24%, the efficiency of irrigation water application with a normal root mean error of 0.19% and the coefficient of agreement of 0.93. The values of the efficiency coefficient of the model show that the model provides acceptable results in determining the amount of irrigation water and wheat water productivity in the country farms. Therefore, the NIAZAB system can be used to estimate the volume required for the irrigation of plants in Iran and also in the farm water management.

Arjan wetland is one of the most internationally important wetlands of Iran.  This wetland has been dried in the last two decades, except for short periods in other dry seasons. The purpose of this research was to study the long-term changes in meteorological and hydrological conditions using the data of stations located in the wetland watershed and adjacent watersheds. To this end the rainfall, evaporation, temperature and groundwater aquifers status and runoff of the wetland basin were analyzed. The data showed a decreased trend of precipitation and severe underground water harvesting in the studied area. Comparison of the amount of water stored in the reservoir of Arjan wetland in the current situation (95.49 mm3) to what reported in the Atlas of Water Resources of Iran (109.40 mm3) shows a significant decrease in recent years. Comparison of the current estimated runoff (13.90 mm3) with one reported in the Atlas of Water Resources (15.64 mm) shows a decrease in this factor. Moreover, the results showed that the groundwater level had a decreasing trend and the last estimated years had the lowest levels which clearly shows the negative impacts of numerous wells have been dug in the studied area. In conclusion, the results of this study show that the drying of Arjan wetland is affected by both climatic and anthropogenic factors. But the impact of anthropogenic factors is much more serious and it is strongly recommended to reduce the water harvest from aquifer to rehabilitate the wetland.

Leaves Chlorophyll content can be used as an indicator to determine nitrogen status in plants. The soil–plant analyses development (SPAD) meter is one of the most widely used tools in crop nitrogen management studies. The current research was aimed to investigate crop nitrogen status, introduce a standardized index called the normalized chlorophyll meter index (NSPAD) and find its relationship with nitrogen nutrition in lettuce. For this purpose, lettuce was cultivated in the research greenhouse of the College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran during two periods. Different nitrogen fertilizer treatments were applied at six levels with three replications. The values of dry matter, nitrogen concentration and chlorophyll were measured seven times during the growth period. Nitrogen nutrition indices (NNI) and NSPAD were calculated for the first period and the NSPAD-NNI relationship was extracted. Then the obtained relationship was verified by the data of the second cultivation. The results showed that the chlorophyll meter readings and nitrogen concentration have a positive correlation with the increase in the level of fertilizer application. Also, the validation results of NSPAD-NNI relationship showed a good performance with R2, MBE and RRMSE values equal to 0.79, -0.01 and 11.41%, respectively. Therefore, the developed relationship can be used as a fast, non-destructive and reliable approach for sustainable nitrogen fertilizer management of lettuce grown under greenhouse conditions.

Saline drainage water are known as a problem, but by creating management approaches, drainage water can be made in line with sustainable development by compensating for the limitation of water resources. Biochar is a soil additive and a management approach in agriculture. In this research, the effect of different levels of irrigation water salinity (2.5, 5 and 10 dSm-1, S1, S2 and S3), biochar (zero, 1 and 2%, B0, B1 and B2) and water supply (60, 80, 100 and 120%, M1, M2, M3 and M4) in the form of factorial split plot experiment in time in the form of randomized complete block, in three replications during the years 2018 and 2019 in the research farm of Shahid Chamran University of Ahvaz, on yield and water efficiency Fodder sorghum was investigated in three harvests. The results showed that the effect of different levels and their interaction effects on the yield and efficiency of fresh and dry sorghum water consumption is significant at the level of 1%. The highest and lowest fresh yields were obtained in B2S1M4 and B2S3M1 treatment (20.52, 135.52 t ha-1). The lowest fresh and dry water productivity of fresh and dry fodder was obtained in B2S3M3 treatment with values of 2.5 and 0.91 kgm-3. Treatments B2S1M3 and B2S1M2 had the highest productivity of fresh and dry fodder water, 10.54 and 4.4 kgm-3, respectively. In irrigation conditions with saline water, the yield and productivity of fresh fodder is sensitive to the amount of biochar, in such a way that in supplying 100% of the water requirement with saline water (10 dSm-1), adding 1 and 2% causes a change of +2.6% and -4.7% in performance and +1.9% and -2.5% in the efficiency of fresh fodder water consumption.

In the analysis of climatic and hydrological events, precipitation is considered as a main parameter and therefore, measuring precipitation data with high Spatio-temporal resolution is very important in predicting weather patterns. Accurate measurement of precipitation on the land surface is very challenging due to the scattering of rain gauge networks, temporal and spatial diversity, wind effects, and topography. In recent decades, the use and development of satellite products and remote sensing techniques have become widespread which is used in precipitation estimation. The aim of this study was to evaluate the satellite precipitation data of TRMM, CHIRPS, Persiann-CDR and GPM-IMERG and compare them with rain gauge data in the north and northwestern region of the country (including Gilan, Ardabil, East Azerbaijan, and West Azerbaijan provinces). For this purpose, after receiving the satellite data series and pre-processing them, an evaluation was performed between the satellite data on a daily, monthly and seasonal time scale with the observational data. Evaluation of the results is performed using definite indicators including POD, CSI, FAR, Bias and statistical criteria including correlation coefficient (Corr) and Normalized Root Mean Square Error (nRMSE). The study period was selected from January 1, 2017 to December 31, 2021 on 56 synoptic stations. The results of most indicators and statistical criteria (Corr, nRMSE, POD, and CSI) showed that in all products the lowest error is related to the southwest of the study area which increases (south of West Azerbaijan province) by moving toward the east of the region and the Caspian coast.  In assessing the regional average precipitation, the results of IMERG, CHIRPS and Persiann-CDR were close to each other and with a slight difference (except in the nRMSE criterion) the IMERG product is superior. Also, in the study of seasonal estimates, the results of CHIRPS and Persiann-CDR were more reliable, but in order to use IMERG and TRMM, it is suggested that the estimates be accurized using different error correction methods. Finally, according to the results of this study, each product based on the type of topography and climate of the region provides a different result in estimating rainfall and there is a need for further studies according to the type of events in each region and a more detailed study of each product.

In order to investigate the effect of salicylic acid and bio fertilizers on the physiological and biochemical characteristics of chickpea cultivars in the dry conditions of Kermanshah province, an experiment was conducted in the form of split factorial design based on randomized complete blocks in three replications in 2018 in the farm of Homil Agricultural Jihad Center in Islam city. Abad-e gharb was implemented. The experimental treatments include three levels (without use, half mill molar and one mill molar) of salicylic acid foliar spraying before flowering and the application of biological fertilizer as a seed in four levels (control, fertile phosphate 2, Petabaror 2 and Petabaror 2 and fertile phosphate 2) and four chickpea varieties. (Adel, Mansour, Azkan and Gokso) and factorially placed in sub-plots. The results of the variance analysis of the data showed that all traits of chlorophyll a, chlorophyll b, total chlorophyll, proline, soluble protein, catalase, superoxide dismutase were affected by salicylic acid and bio fertilizers at the level of 1%, and these traits except the superoxide dismutase trait were also affected by the number. The interaction between salicylic acid and biofertilizers had a significant effect on chlorophyll a, chlorophyll b, total chlorophyll, proline and catalase. The highest percentage of proline and soluble proteins was obtained in the simultaneous combination of salicylic acid and biofertilizers for Adel variety. With the use of 1 millimolar salicylic acid, the amount of proline increased by 21.6% compared to its non-use. So that the highest amount of proline was 0.0884 mg/g fresh weight of the leaf related to the treatment of 1 millimolar salicylic acid. Also, the highest amount of catalase enzyme was in the treatment of not using biofertilizers (0.2937 enzyme units per milligram of protein) and the lowest value was related to the combined use of Fertilizer + Petaferyl phosphate (0.2290 enzyme units per milliliter of protein). So that the amount of catalase enzyme was 22% lower in the treatments using biofertilizers compared to the treatments using fertilizers. Therefore, it can be stated that the simultaneous use of salicylic acid and biofertilizers increase the mentioned physiological activities. What is evident from the results is that the combined use of salicylic acid and biofertilizers had the highest performance compared to the separate use of applied treatments.

Due to environmental considerations, application of rockfill in designing of hydraulic structures, such as rockfill dams is increased. According to hydraulic principles, by increasing water surface and bed slope as well as significant curvature of streamlines, the vertical pressure distribution is deviated from hydrostatic state. Therefore, because of severe energy loss and big difference between upstream and downstream water surface elevation in rockfill porous media, pressure distribution is expected to be non-hydrostatic. In this study, using the distribution of non-hydrostatic pressure, the energy equation and considering the quadratic form of friction loss for non-Darcy flows, the equation representing the longitudinal profile of the water surface inside the rockfill dam is presented. In order to check the accuracy of this equation, experiments on homogeneous rockfill dams consisting of round river materials with average diameters of 1.68, 2.27, 4.07 and 4.84 cm and in two lengths of dams (50 and 100 cm), were performed in the hydraulic laboratory of the department of Irrigation and Reclamation Engineering, University of Tehran. Numerical solution of the equation representing the free surface flow inside the rockfill dam in comparison with the laboratory observations show that the equation is well able to calculate the longitudinal profile of the water surface in rockfill dams at low and high discharges. Statistical analysis of the results shows that the average relative error of estimates is 3.96%.

Identification of sediment production areas, patterns of its delivery from sources to the transport network and the location of deposition sinks are necessary to manage soil erosion. The term connectivity describes the relationship between the sources of runoff and sediment production in the upstream of the watershed and the corresponding sedimentation areas in the downstream. Based on the approach used, there are two types of structural connectivity and functional connectivity. Connectivity index (IC) is one of the most widely used indices to quantify sediment connectivity. The selection of an appropriate weighting factor is one of the challenging issues in IC calculation and it is chosen according to the conditions of each region, the characteristics of the soil surface and available data as well. Therefore, this study was conducted with the aim of reviewing, analysis and collecting past studies so far in order to have a comprehensive look at the methods of quantification of IC and introducing more appropriate approaches for future research in this field. This study reviewed 90 studies related to different dimensions of IC. The review of 25 studies on IC weighting factor published between 2008 to 2022 showed that the two frequently used variables are the C factor of USLE and surface roughness (31% and 25% respectively).It is also suggested that sediment connectivity can be investigated at different time scales, taking into account both structural and functional connectivity. As a result, with better understanding of sediment connectivity, it can be used as a tool for sustainable management of watersheds.