شبیه‌سازی عملکرد گندم دوروم (Triticum turgidum L.) در شرایط تنش شوری بر اساس مدل‌های آماری و مدل‌های کلان

نوع مقاله : علمی - پژوهشی

نویسندگان

1 شهیدباهنر کرمان

2 دانشگاه گیلان

چکیده

در مناطق خشک و نیمه خشک کمبود آب به عنوان عامل اصلی و شوری خاک عامل ثانویه کاهش رشد گیاه و عملکرد دانه به شمار می‌رود. بنابراین برای استفاده از منابع آب‌های کم کیفیت و لب‌شور، باید تجزیه و تحلیل کمّی واکنش گیاهان این مناطق نسبت به تنش شوری، توسط مدل‌های شبیه‌ساز انجام شود. در این پژوهش دو رویکرد کلی شبیه‌سازی شامل مدل‌های فرآیندی-فیزیکی و مدل‌های آماری-تجربی مورد بررسی قرار گرفت. بدین ترتیب که برای کمّی کردن اثر شوری بر عملکرد نسبی بذر گندم دوروم (Triticum turgidum L.) (رقم بهرنگ) در مقادیر مختلف شوری خاک، از مدل‌های فرآیندی-فیزیکی شامل مدل ماس و هافمن، ون‌گنوختن و هافمن، دیرکسن و همکاران و همایی و همکاران و همچنین مدل‌های آماری-تجربی شامل تابع اصلاح شده گومپرتز، تابع نمایی دوگانه و تابع اصلاح شده ویبول استفاده گردید. گیاهانی که با آب غیر شور آبیاری شده بودند به عنوان تیمار بهینه در نظر گرفته شدند و عملکرد مطلق سایر بوته‌ها نسبت به عملکرد در این تیمار بهینه سنجیده شد. پس از برداشت بوته‌ها، وزن دانه‌های به‌دست آمده در هر سطح شوری ثبت گردید. مقایسه کارآیی نسبی مدل‌ها بر اساس شاخص‌های آماری ضریب کارآیی اصلاح شده و شاخص مطابقت اصلاح شده نشان داد که در بین مدل‌های آماری-تجربی، تابع اصلاح شده گومپرتز بیشترین دقت را داشته‌اند. بررسی تطبیقی تمام مدل‌ها بر اساس شاخص‌های آماری فوق نشان داد که مدل همایی و همکاران دقیق‌ترین مدل برای شبیه‌سازی عملکرد گندم دوروم بوده است. همچنین، پارامترهای معادله همایی و همکاران از لحاظ فیزیکی دارای مفهوم بوده و کاملاً تعریف شده و به راحتی قابل اندازه‌گیری می‌باشد، در حالی‌که در مدل-های آماری-تجربی مقادیر پارامترهای هر معادله فاقد مفهوم بیوفیزیکی بوده و مقادیر مطلق هر پارامتر هیچ‌گونه اطلاعاتی از وضعیت رشدی گیاه بیان نمی‌کند. بنابراین در این پژوهش مدل همایی و همکاران به عنوان مدل بهینه برگزیده شد.

کلیدواژه‌ها


عنوان مقاله [English]

Simulating Durum Wheat (Triticum turgidum L.) Response to Root Zone Salinity based on Statistics and Macroscopic Models

نویسندگان [English]

  • Vahid Reza Jalali 1
  • safoora asadi kapourchal 2
1 Shahid Bahonar University of Kerman
2 University of Guilan
چکیده [English]

Introduction
Salinity as an abiotic stress can cause excessive disturbance for seed germination and plant sustainable production. Salinity with three different mechanisms of osmotic potential reduction, ionic toxicity and disturbance of plant nutritional balance, can reduce performance of the final product. Planning for optimal use of available water and saline water with poor quality in agricultural activities is of great importance. Wheat is one of the eight main food sources including rice, corn, sugar beet, cattle, sorghum, millet and cassava which provide 70-90% of all calories and 66-90% of the protein consumed in developing countries. Durum wheat (Triticum turgidum L.) is an important crop grows in some arid and semi-arid areas of the world such as Middle East and North Africa. In these regions, in addition to soil salinity, sharp decline in rainfall and a sharp drop in groundwater levels in recent years has emphasized on the efficient use of limited soil and water resources. Consequently, in order to use brackish water for agricultural productions, it is required to analyze its quantitative response to salinity stress by simulation models in those regions. The objective of this study is to assess the capability of statistics and macro-simulation models of yield in saline conditions.

Materials and methods
In this study, two general approach of simulation includes process-physical models and statistical-experimental models were investigated. For this purpose, in order to quantify the salinity effect on seed relative yield of durum wheat (Behrang Variety) at different levels of soil salinity, process-physical models of Maas & Hoffman, van Genuchten & Hoffman, Dirksen et al. and Homaee et al. models were used. Also, statistical-experimental models of Modified Gompertz Function, Bi-Exponential Function and Modified Weibull Function were used too. In order to get closer to real conditions of growth circumstances in saline soils, a natural saline water was taken from Maharlu Lake, Fars province, Iran. This natural and highly saline water with electrical conductivity of 512 dS/m diluted with fresh water to obtain the designated saline waters required for the experimental treatments. The designed experimental treatments were consisted of a non-saline water and five salinity levels of 2, 4, 6, 8 and 10 dS/m with three replicates. Three statistics of modified coefficient efficiency (E'), modified index of agreement (d') and coefficient of residual mass (CRM) were used to compare the used models and to assess their performances.

Results and discussion
Comparing the relative performance of models based on statistical indices of Modified Coefficient Efficiency (E') and Modified Index of agreement (d') indicated that the nonlinear model of Homaee et al. is most accurate between process-physical models and Modified Gompertz Function is most accurate between statistical-experimental models. Comparison assessment of all models based on statistical index indicated that Homaee et al. model was the most accurate model for simulation of durum wheat yield. This is while the parameters of Homaee et al. equation is well-defined concept and is easily measurable, but in statistical-experimental models, parameters of each model have no biophysical concept and the absolute values of each parameter do not express any information about development status of the plant. So, the nonlinear model of Homaee et al. was chosen as the optimal model in this research.

Conclusion
Most of the plants such as wheat, are sensitive to salinity and by increasing the age, their sensitivity to salinity are reduced. Based on the obtained results of this study, by knowing and quantitative assessment of the dominant cultivars sensitivity of each region, as well as using appropriate simulation models, one can use brackish or saline waters to partly compensate fresh water shortage for scientific and extension Agricultural programs.

کلیدواژه‌ها [English]

  • environmental stress
  • Modified Gompertz Function
  • Saline water
  • simulation
Akbari Ghogdi, E., Izadi-Darbandi, A., Borzouei, A., and Majdabadi, A. 2011. Evaluation of morphological changes in some wheat genotypes under salt stress. Journal of Science and Technology of Greenhouse Culture 1(4): 71-83. (In Persian with English Summary)
Basso, B., Cammarano, D., and Carfagna, E. 2013. Review of Crop Yield Forecasting Methods and Early Warning Systems. FAO Publication, Rome, Italy.
Dirksen, C., Kool, J.B., Koorevaar, P., and Van Genuchetn, M.T. 1993. HYSWASOR- Simulation Model of Hysteretic Water and Solute Transport in the Root Zone. In: D. Russo and G. Dagan (Eds.).Water Flow and Solute Transport in Soils. Springer, Berlin, Heidelberg. p. 99-122.
Eskandari, M., Homaee, M., Asadi Kapourchal, S., and Mirnia, S.K. 2014. Barley seed germination in NaCl+CaCl2 solution, natural saline water and saline soil. Cereal Research 3(4): 335- 347. (In Persian with English Summary)
FAO. 2010. Extent and causes of salt-affected soils in participating countries. Available at Web site: URL: http://www.fao.org/ag/AGL/agll/spuch/topic4.htm.
Feddes, R.A., Kowalik, P., and Zarandy, H. 1978. Simulation of Field Water Use and Crop Yield. Pudoc. Wageningen. The Netherlands.
Gardner, W.R. 1960. Dynamic aspects of water availability to plants. Soil Science 89: 63-73.
Gompertz, B. 1825. On the nature of the function expressive of the law of human mortality and on a new mode of determining the value of life contingencies. Philosophical Transactions of the Royal Society of London 115: 513-583.
Hadi, M.R., Khosh Kholgh Sima, N.A., Khavarinejad, R., and KiyamNekoie, S.M. 2008. The effect of elements accumulation on salinity tolerance in seven genotype durum wheat (Triticum turgidum L.) Collected from the Middle East. Iranian Journal of Biology 21(1): 326-340. (In Persian with English Summary)
Homaee, M., and Feddes, R.A. 2002. Modeling the Sink Term under Variable Soil Water Osmotic Heads. In: Hassanizadeh et al. (Eds.), Developments in water resources 47 (1); Computational methods in water resources. Elsevier Science B.V., the Netherlands p. 17-24.
Homaee, M., Dirksen, C., and Feddes, R.A. 2002a. Simulation of root water uptake. I. Non-uniform transient salinity using different macroscopic reduction functions. Agricultural Water Management 57(2): 89-109.
Homaee, M., Feddes, R.A., and Dirksen, C. 2002b. Simulation of root water uptake. III. Non-uniform transient combined salinity and water stress. Agricultural Water Management 57(2): 127-144.
Homaee, M., Feddes, R.A., and Dirksen, C. 2002c. A macroscopic water extraction model for non-uniform transient salinity and water stress. Soil Science Society of America Journal 66(6): 1764-1772.
Homaee, M., Feddes, R.A., and Dirksen, C. 2002d. Simulation of root water uptake. II. Non-uniform transient water stress using different reduction functions. Agricultural Water Management 57(2): 111-126.
Iran Water Resources Management Company (IWRMC). 2015. Available at Web site: http://www.wrm.ir
Jalali, V.R., and Homaee, M. 2010. Modeling the effect of salinity application time of root zone on yield of canola (brassica napus l.). Agricultural Crop management (Journal of Agriculture) 12(1): 29-40. (In Persian with English Summary)
Jalali, V.R., Homaee, M., and Mirnia, K. 2008. Modeling canola response to salinity on vegetative growth stages. Journal of Agricultural Engineering Research 8: 95-112. (In Persian with English Summary)
Koocheki, A., Fallahpour, F., Khorramdel, S and Jafari, L. 2014. Intercropping wheat (Triticum aestivum L.) with canola (Brassica napus L.) and their effects on yield, yield components, weed density and diversity. Journal of Agroecology 6(1): 11-20. (In Persian with English Summary)
Lapp, M.S., and Skoropad, W.P. 1976. A mathematical model of conidial germination and appressorial formation for Colletotrichum graminiocola. Canadian Journal of Botany 54(19): 2239–2242.
Maas, E.V., and Grattan, S.R. 1999. Crop Yields as Affected by Salinity. In: R.W. Skaggs and J. van Schilfgaarde (Eds.). Agricultural Drainage. Madison. WI: ASA, CSSA, SSA. Agron. Monograph 38. p. 55-108.
Maas, E.V., and Hoffman, G.J. 1977. Crop salt tolerance-current assessment. Journal of the Irrigation and Drainage Division 103: 115-134.
Nekahi, M.Z., Soltani, A., Siahmarguee, A., and Bagherani, N. 2014. Yield gap associated crop management in wheat (Case study: Golestan province-Bandar-gaz). Electronic Journal of Crop Production 7(2): 135-156. (In Persian with English Summary)
Oleson, B.T. 1996. World Wheat Production Utilization and Trade. In: W. Bushuk and V.F. Rasper (Eds.). Wheat production, properties and quality. Chapman and Hall p. 1-11.
Omidi, M., Siahpoosh, M.R., Mamghani, R., and Modarresi, M. 2013. The effects of terminal heat stress on yield, yield components and some morpho-phenological traits of wheat genotypes in Ahwaz weather conditions. Electronic Journal of Crop Production 6(4): 33-53. (In Persian with English Summary)
Pansu, M., and Gautheyrou, J. 2006. Handbook of Soil Analysis, Mineralogical, Organic and Inorganic Methods. Springer.
Razeghi Jahromi, F., Shahsavand Hassani, H., and Rezaeithe, A.H. 2012. Study of salt stress effects on yield and its components of new cereal (primary tritipyrum lines: AABBEbEb) in comparison with wheat and triticale. Electronic Journal of Crop Production 4(1): 1-16. (In Persian with English Summary)
Rezvani, H., Asghari, J., Ehteshami, S.M.R., and Kamkar, B. 2013. Study the response of yield and component yield of wheat cultivars in competition with wild mustard in Gorgan. Electronic Journal of Crop Production 6(4): 187-214. (In Persian with English Summary)
Richards, L.A. 1931. Capillary conduction of liquids in porous mediums. Physics 1:318-333.
Saadat, S., Homaee, M., and Liaghat, A.M. 2005. Effect of soil solution salinity on the germination and seedling growth of sorghum plant. Journal of Soil and Water Science 19: 243-254. (In Persian with English Summary)
Saadat, S., and Homaee, M. 2015. Modeling sorghum response to irrigation water salinity at early growth stage. Agricultural Water Management 152: 119-124.
Saraee Tabrizi, M., Babazadeh, H., Homaee, M., Kaveh, F., and Parsinejad, M. 2015. Simulating basil response to irrigation water salinity. Journal of Water Research in Agriculture 28(4): 691-701. (In Persian with English Summary)
Sayar, R., Bchini, H., Mosbahi, M., and Khemira, H. 2010. Response of durum wheat (Triticum durum Desf.) growth to salt and drought stresses. Czech Journal of Genetics and Plant Breeding 46(2): 54–63.
Seed and Plant Improvement Institute. 2013. Available at Web site: www.spii.ir/spSPII/default.aspx? Page= Document & app = Documents & docId=11952&docParId=11470
Steppuhn, H., Van Genuchten, M.T., and Grieve, C.M. 2005. Crop ecology, management and quality: Root-zone Salinity: I. Selecting a product-yield index and response function for crop tolerance. Crop Science 45(1): 209-220.
Van Genuchten, M.T. 1983. Analyzing crop salt tolerance data: Model description and user’s manual. UDSA, ARS, U.S. Salinity Lab. Research Report No. 120. U.S. Government Printing Office, Washington, DC.
Van Genuchten, M.T., and Hoffman, G.J. 1984. Analysis of crop salt tolerance data. In: I. Shainberg and J. Shalhevet (Eds.). Soil Salinity under Irrigation Process and Management. Ecological Studies 51. Springer-Verlag. New York p. 258-271.
Wang, D., Poss, J.A., Donovan, T.J., Shannon, M.C., and Lesch, S.M. 2002. Biophysical properties and biomass production of elephant grass under saline conditions. Journal of Arid Environments 52(4): 447-456.
Weibull, W. 1951. A statistical distribution function of wide application. Journal of Applied Mechanics 18: 293-297
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