بررسی کارایی مدل سالت‌مد در شبیه‌سازی عملکرد گندم و شوری در شرایط خاک‌شور و غیر‌شور

مدل سالت مد یکی از مدل‌های کاربردی بوده که به منظور شبیه‌سازی تغییرات عملکرد و شوری خاک در شرایط مختلف بکار می‌رود. این پژوهش در سال زراعی 94-93 و 95-94 در اراضی گندمکاری دشت آزادگان استان خوزستان و در سه منطقه رامسه (خاک شور)، عتابیه (خاک خیلی شور) و حمیدیه (خاک غیر شور) با هدف ارزیابی این مدل در شرایط شور اجرا شد. در این راستا سه قطعه 10 هکتاری در هر منطقه انتخاب و در هر یک از این قطعات یک پایلوت به مساحت 2000 متر مربع در نظر گرفته شد. در سال اول این مدل مورد واسنجی و در سال دوم برای سه شرایط خاک از نظر شوری در سه منطقه گندم‌کاری عتابیه، حمیدیه و رامسه (استان خوزستان) بمنظور بررسی تغییرات عملکرد دانه و کل و نیز تغییرات شوری خاک مورد استفاده واقع گردید. نتایج نشان داد که تفاوت معنی‌داری بین عملکرد دانه و بیوماس گندم اندازه‌گیری و شبیه‌سازی شده با مدل سالت مد در مناطق مورد نظر (با شوری‌های کم، متوسط و زیاد) وجود ندارد. خطای استاندارد عملکرد دانه و بیوماس گندم به ترتیب 0.16 و 0.63 تن در هکتار و ریشه میانگین مربعات خطای نرمال شده برای عملکرد دانه و بیوماس گندم 0.08 تعیین گردید. از طرف دیگر مقادیر شوری خاک اندازه‌گیری شده در سه لایه (30-0، 60-30 و 90-60 سانتی‌متر) با مقادیر شبیه‌سازی شده توسط مدل سالت مد تفاوت معنی‌داری نداشته و محدوده تغییرات خطای استاندارد شوری خاک بین 0.81 تا 1.1، ریشه میانگین مربعات خطای نرمال شده 0.18 و میانگین انحراف خطای شوری خاک 0.13- بدست آمد. بنابراین این مدل از قابلیت، کارایی و دقت بالایی در شبیه‌سازی عملکرد و شوری خاک برخوردار است.

Evaluation of SALTMED Model in Simulation of Wheat Yield and Soil Salinity Variations at Saline and Nonsaline Conditions

Introduction: 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. Materials and Methods: This study was carried out in wheat fields of Azadegan plain in Khuzestan province during 20142015 in three regions including Ramseh (as saline soil), Atabieh (as very saline soil) and Hamidieh (as control, nonsaline soil). Three 10hectare 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 dSm1 was classified as C3S1(high salinity with low sodium absorption ratio) and had no effect on wheat yield loss. In this study, version 30425(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. Results and Discussion: 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  030, 3060, and 6090 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(nonsaline) 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 nonsaline, 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 nonsaline 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 030, 3060, and 6090 cm layers was 1.1, 1.05, and 0.81 dSm1, 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. Conclusion: 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.