فهرست مطالب m. r. emdad

   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.

SALTMED model is one of the most practical tools for simulating soil salinity and crop production yield. Growth models are important and efficient tools for studying and evaluating the impact of different management conditions and scenarios on water, soil and plant relationships and can be used to make or predict appropriate management scenarios according to the region's conditions and to predict plant performance in the field. Since the performance of irrigation scenarios in field conditions are costly and time consuming, and due to the limited water resources in the country and the necessity of optimal water use in agriculture, using the efficient and generic models can be useful tool for simulating crop production and soil salinity variations. This research has been conducted in order to simulate soil salinity and yield production using SALTMED model in Azadegan Plain of Khuzestan province.

This study was carried out in wheat fields of Azadegan plain in Khuzestan province during 2014-2015 in three regions including Ramseh (as saline soil), Atabieh (as very saline soil) and Hamidieh (as control, non-saline soil). Three 10-hectare plots were selected in each area and a pilot with area of 2000 m2 was used for evaluation and measurement in each plot. First year data were used to calibrate the SALTMED model and second year field data were used to validate the model and to achieve the results in three conditions. The dominant soil texture in the area was clay loam. The quality of used irrigation water with average salinity of 2 dSm-1 was classified as C3-S1(high salinity with low sodium absorption ratio) and had no effect on wheat yield loss. In this study, version 3-04-25(2018) of SALTMED model was used and after calibrating in the first year, the results of simulated wheat grain yield and soil salinity variation values were used for model validation in different regions and in soils with different degrees of salinity, in the second year.

The average measured and simulated biomass yield in the first year were 6.6 and 6.1 t/ha, respectively. Furthermore, the average of measured and simulated of wheat grain yield was 2.9 and 2.6 t/ha, respectively. Some statistical indices including mean bias error, normalized root mean square error, and root mean square error for grain yield were 0.11, 0.04, and 0.12 t/ha, respectively. The values of the same statistical parameters for biomass were -0.49, 0.1, and 0.61t/ha, respectively. These results showed that the measured values of grain yield and wheat biomass were in good agreement with the simulated values using SALTMED model. The simulated and measured variations of soil salinity at three soil depths of 0-30, 30-60, and 60-90 cm, showed close agreement with each other in three layers. Root mean square error, normalized root mean square error, and mean bias error for soil salinity values were 1.3, 0.20, and -0.06, respectively. After calibrating the model in the first year, to validate this model in the second year, the results of three pilots locations in three regions of Ramseh (saline), Atabieh(very saline) and Hamidieh(non-saline) were used. Comparison of simulated and measured wheat grain yield and biomass values showed that there was no significant difference between simulated and measured values. The simulated values of grain yield and wheat biomass in the three non-saline, saline and very saline soils had high correlation with the measured values, indicating high accuracy and efficiency of this model in simulating grain and biomass yield in different degrees of soil salinity. Moreover, the trend of soil salinity changes simulated by the SALTMED model in three highly saline, saline and non-saline soils (for three soil layers) was close to the measured values. The SALTMED model with normalized root mean square error and mean bias error of 0.18 and -0.13, respectively, showed good accuracy in different salinity conditions. There was no significant difference (5% level) between the measured and simulated salinity values of the different soil layers. The mean standard error at the 0-30, 30-60, and 60-90 cm layers was 1.1, 1.05, and 0.81 dSm-1, respectively. Therefore, based on the results and statistical indices, it was found that SALTMED model had good accuracy and efficiency in simulating yield, biomass and soil salinity under different salinity conditions.

According to the results and statistical indices, SALTMED model had good performance and accuracy in simulating grain yield, biomass and soil salinity variations in different soil salinity conditions and so it can be used to predict wheat yield, yield components and soil salinity in different soil condition with different degrees of soil salinity to sustain soil and water and improve water productivity in similar areas.

A new probabilistic approach was adapted to the negative impact of climate on stream flow. To generate climate change scenarios in the future under the A2 emission scenario، the HadCM3 model was employed. By introducing climatic variable time series in future periods to the IHACRES hydrologic model، long-term stream flow simulation scenarios were produced. By fitting statistically different distributions on the runoff produced by using a goodness-of-fit test، the most appropriate statistical distribution for each month was chosen and relevant statistical parameters were extracted and compared with statistical parameters of the runoff in the base period. Results show that the long-term annual runoff average in the three future periods compared to the base period. Despite the reduction in the total runoff volume in the future periods will decrease compared to the baseline period، the decrease is related to the medium and high flows. In low flows، the total runoff volume for future periods compared to the baseline period will increase 47، 41، and 14%، respectively. To further assess the impact of annual average runoff on flows، it is necessary to examine correlation of time series using the stream flow transition probability. To compare the stream flow transmission probability in each of the future periods with base period stream flow in each month، stream flow was discretized and performance criteria were used. This approach was adapted for the Aidoghmoush River، East Azerbaijan. Results indicated a low coefficient of correlation and a high error indicator.