ارائه مدل بهینه‌سازی آبیاری و آبشویی در شرایط محدودیت آب به منظور دست‌یابی به حداکثر سود خالص و حداقل آب آبشویی

نویسندگان

1 دانش آموخته دکترای گروه مهندسی آب دانشکده کشاورزی، دانشگاه فردوسی مشهد

2 استاد گروه مهندسی آب، دانشکده کشاورزی، دانشگاه فردوسی مشهد

3 گروه علوم و مهندسی آب، مرکز آموزش عالی کاشمر

چکیده

          آبیاری نقش بسیار مهمی در افزایش تولید مواد غذایی دارد و از آنجایی که مناطق خشک و نیمه­خشک با کمبود آب مواجه هستند، بهینه­سازی عمق آبیاری و آبشویی از اهمیت زیادی برخوردار می­باشد. استفاده از مدل­های شبیه­سازی از جمله مدل AquaCrop، در بررسی و تحلیل سناریوهای مختلف آبیاری و انتخاب مدیریت مناسب آب به­خصوص در شرایط کمبود منابع آب می­تواند بسیار کمک­کننده باشد. در این تحقیق از مدل AquaCrop واسنجی و صحت­سنجی شده برای دو رقم گندم در منطقه بیرجند و یک رقم گندم در منطقه مشهد استفاده گردید. کدنویسی انجام شده در نرم­افزار MATLAB به منظور بهینه­سازی آبیاری و آبشویی در شرایط محدودیت آب، با مدل AquaCrop لینک گردید. نتایج بهینه­سازی نشان داد که سود خالص برای بهترین مدیریت آبیاری و آبشویی در تمام سطوح شوری و رقم­های مختلف گندم به جز سطوح شوری 6/۸ و ۱۰ دسی­زیمنس بر متر رقم مشهد و سطح شوری ۶/۹ دسی­زیمنس بر متر رقم روشن، بیشتر از مدیریت­های موجود در تحقیق شهیدی و حق­وردی بود. در شرایطی که هدف، دستیابی به حداکثر سود خالص و حداقل زه­آب تولیدی بود مدل با تغییر نوع مدیریت آبیاری و کاهش دادن میزان آبشویی بخصوص آبشویی فقط در دو آبیاری آخر، میزان آب مصرفی را کاهش داد و میزان زه­آب تولیدی را به صفر رساند. همچنین نتایج نشان داد میزان کاهش آب ناخالص آبیاری در شوری­های بالاتر از حد آستانه تحمل گندم نسبت به دیگر سطوح شوری، بسیار کم بود و اختلاف ناچیزی داشتند. به علت اینکه در سطوح شوری بالا نسبت به سطوح شوری پایین، افت محصول در کم­آبیاری­ها بیشتر بود و در نتیجه در سطوح بالاتر از حد آستانه تحمل گندم، کم­آبیاری اقتصادی و مقرون به صرفه نبود. به طور کلی نتایج بهینه­سازی نشان داد که در منطقه بیرجند و مشهد می­توان با استفاده از بهترین مدیریت آبیاری و آبشویی در سطوح مختلف شوری، سود حاصله از کشت گندم را افزایش داد.

کلیدواژه‌ها

عنوان مقاله [English]

Introducing Optimized Irrigation – Leaching Model under Water Deficiency Conditions to Gain Maximum Net Benefit and Minimum Leaching Water

نویسندگان [English]

  • Masoud Mohammadi 1
  • Kamran Davary 2
  • hadi dehghan 3
1 Department of Water Engineeringو, Faculty of Agricultural, Ferdowsi University of Mashhad
2 Professor, Water Engineering Department, Ferdowsi University of Mashhad
3 Water Engineering Department, Kashmar Higher Education Institute, Kashmar, Iran
چکیده [English]

Irrigation plays an important role in increasing crop yield especially in arid and semi-arid regions, where optimizing irrigation depth and leaching is crucial. Using simulation models such as AquaCrop help to analyze different irrigation scenarios and also help farmers to optimize water resources management in such conditions. In this study, calibrated and validated AquaCrop model was used for two wheat varieties in Birjand region and one wheat variety in Mashhad region. A Matlab program has been developed to link to the AquaCrop in order to achieve the optimized values of irrigation and leaching in the water constraint conditions. The optimization results showed that net profit for the best irrigation and leaching management at all salinity levels and different wheat varieties, except for salinity levels of 8.6 and 10 dS m-1 in the Mashhad variety and level of 9.6 dS m-1 in the Roshan variety, was more than the current management researches of Shahidi and Haghverdi. While the aim is to achieve maximum profit and minimal drainage water, the model by changing the type of irrigation management and reduce leaching, especially in the last two irrigations only, reduced the amount of water consumed and the amount of drainage water to zero. The results also showed the reduction of gross irrigation water in the salinity levels more than tolerance threshold of wheat was less than the other salinity levels and the difference was completely negligible. Because in high salinity levels, yield reduction in deficit irrigation was more in comparison with low salinity levels, therefore deficit irrigation in salinity levels of more than the wheat tolerance threshold is not economic. Generally, the optimization results showed that in the area of Birjand and Mashhad, using the best irrigation and leaching management at different salinity levels can increase the benefit of wheat cultivation.

کلیدواژه‌ها [English]

  • AquaCrop model
  • Iirrigation management
  • Birjand
  • Matlab
  • Wheat

مراجع

Abbate PE, Dardanelli JL, Cantarero MG, Maturano M, Melchiori RJM and Suero EE, 2004. Climatic and water availability effects on water-use efficiency in wheat. Crop Science 44(2): 474-483.‏
Aghakhani A, Feizi M, Solhi M and Ramezani Etedali M, 2013. Water desalination for agriculture, necessity, importance and limitations. Journal of Land Management 1 (1): 17-31 (In Persian with English abstract).
Agricultural organization of Khorasan Razavi, 2014 [On Line]. Available from: https://koaj.ir/Modules/showframework.aspx?RelFacilityId=1241&ObjectID=252&FrameworkPageType=SEC.
Akbari M, 2004. Combining of remote sensing, field data and SWAP simulation model for improving field irrigation management. PhD Dissertation, Tarbiat Modares University.
Dominguez A, Tarjuelo JM, De Juna JA, Lopez-Mata E, Breidy J and Karam F, 2011. Deficit irrigation under water stress and salinity conditions: The MOPECO-Salt model. Agricultural Water Management 98(9): 1451-1461.‏
Egli DB and Bruening W, 1992. Planting date and soybean yield: evaluation of environmental effects with a crop simulation model: SOYGRO. Agricultural and Forest Meteorology 62(1-2): 19-29.‏
García-Vila M and Fereres E, 2012. Combining the simulation crop model AquaCrop with an economic model for the optimization of irrigation management at farm level. European Journal of Agronomy 36(1): 21-31.‏
García-Vila M, Fereres E, Mateos L, Orgaz F and Steduto P, 2009. Deficit irrigation optimization of cotton with AquaCrop. Agronomy Journal 101(3): 477-487.‏
Geerts S, Raes D and Garcia M, 2010. Using AquaCrop to derive deficit irrigation schedules. Agricultural Water Management 98(1): 213-216.‏
Haghverdi A, 2011. Deriving crop-water-salinity production function for spring wheat using response surface methodology. PhD Dissertation, Ferdowsi University of Mashhad.
Jones CA, Kiniry JR and Dyke PT, 1986. CERES-Maize: a simulation model of maize growth and development, User's guide of CERES-Maize. Texas University Press College Station, USA.
Khajeh Roshanaei N, Daneshvar Kakhki M and Mohtashami GR, 2010. Estimating economic value of water in production function method, applying classic and entropy approaches (Case study: wheat in Mashhad). Agricultural Economics and Development 24(1): 113-119 (In Persian with English abstract).
Kroes JG and Van Dam JC, 2008. Reference Manual SWAP, version 3.2. Alterra Green World Research. Wagennigen. Report 1649.‏
Kumar P, Sarangi A, Singh DK and Parihar SS, 2014. Evaluation of AquaCrop model in predicting wheat yield and water productivity under irrigated saline regimes. Irrigation and Drainage 63(4): 474-487.‏
Majnooni-Heris A, Zand-Parsa S, Sepaskhah AR, Kamgar-Haghighi AA and Yasrebi J, 2011. Modification and validation of maize simulation model (MSM) at different applied water and nitrogen levels under furrow irrigation. Archives of Agronomy and Soil Science 57(4): 401-420.‏
Marinov D, Querner E and Roelsma J, 2005. Simulation of water flow and nitrogen transport for a Bulgarian experimental plot using SWAP and ANIMO models. Journal of Contaminant Hydrology 77(3): 145-164.‏
Memariani A, Amini A and Alinezhad A, 2009. Sensitivity analysis of simple additive weighting method (SAW): the results of change in the weight of one attribute on the final ranking of alternatives. Journal of Optimization in Industrial Engineering (4): 13-18.‏
Meyer GE, Curry RB, Streeter JG and Baker CH, 1981. Simulation of reproductive processes and senescence in indeterminate soybeans. Transactions of the ASAE 24(2): 421-0429.‏
Mohammadi M, Davary K, Ghahraman B, Ansari H and Haghverdi A, 2015. Calibration and validation of AquaCrop model for simulation of spring wheat yield under simultaneous salinity and water stress. Journal of Water Research in Agriculture 29 (3): 277- 295 (In Persian with English abstract).
Mohammadi M, Ghahraman B, Davary K, Ansari H and Shahidi A, 2015. Validation of AquaCrop model for simulation of winter wheat yield and water use efficiency under simultaneous salinity and water stress. Journal of Water and Soil 29(1): 67-84 (In Persian with English abstract).
Molden D, Murray-Rust H, Sakthivadivel R and Makin I, 2003. A water-productivity framework for understanding and action. Pp. 1-18. In: Kijne JW, Barker R and Molden D (eds). Water Productivity in Agriculture: Limits and opportunities for improvement. CAB international publishing. UK.
Podvezko V, 2011. Comparative analysis of MCDA methods SAW and COPRAS. Engineering Economics 22(2): 134-146.‏
Raes D, Geerts S, Kipkorir E, Wellens J and Sahli A, 2006. Simulation of yield decline as a result of water stress with a robust soil water balance model. Agricultural Water Management 81(3): 335-357.‏
Raes D, Steduto P, Hsiao TC and Fereres E, 2009. AquaCrop-The FAO crop model for predicting yield response to water: II. Main algorithms and software description. Agronomy Journal 101(3):438-447.‏
Salemi H, Soom MAM, Lee TS, Mousavi SF, Ganji A and Yusoff MK, 2011. Application of AquaCrop model in deficit irrigation management of winter wheat in arid region. African Journal of Agricultural Research 6(10): 2204-2215.‏
Shahidi A, 2008. Interaction of deficit irrigation and salinity on yield and yield components of wheat cultivars and determining water – salinity production function in the Birjand region. PhD Dissertation, Shahid Chamran University of Ahvaz.
Shamsnia SA and Pirmoradian N, 2013. Simulation of Rainfed wheat yield response to climatic fluctuations using model (Case study: Shiraz region in southern of Iran). International Journal of Engineering Science Invention 2(5): 51-56.‏
Singh A, Saha S and Mondal S, 2013. Modelling irrigated wheat production using the FAO AquaCrop model in West Bengal, India, for sustainable agriculture. Irrigation and Drainage 62(1): 50-56.‏
Singh R, 2004. Simulations on direct and cyclic use of saline waters for sustaining cotton–wheat in a semi-arid area of north-west India. Agricultural Water Management 66(2): 153-162.‏
Steduto P, Hsiao TC, Raes D and Fereres E, 2009. AquaCrop—The FAO crop model to simulate yield response to water: I. Concepts and underlying principles. Agronomy Journal 101(3): 426-437.‏
Todorovic M, Albrizio R, Zivotic L, Saab MTA, Stöckle C and Steduto P, 2009. Assessment of AquaCrop, CropSyst, and WOFOST models in the simulation of sunflower growth under different water regimes. Agronomy Journal 101(3): 509-521.‏
Vaez Madani MA, Fakheri Fard A and Majnooni- Heris A, 2019.  Using combined AquaCrop model and Thomas- Fering method in analyzing rainfed wheat yield. Water and Soil Science-University of Tabriz 29 (3): 95-108 (In Persian with English abstract).
Van Dam JC, Groenendijk P, Hendriks RF and Kroes JG, 2008. Advances of modeling water flow in variably saturated soils with SWAP. Vadose Zone Journal 7(2): 640-653.‏
Zand-Parsa S, Sepaskhah AR and Ronaghi A, 2006. Development and evaluation of integrated water and nitrogen model for maize. Agricultural Water Management 81(3): 227-256.‏
 
 
 
دانش آب و خاک
دوره 33، شماره 1
فروردین 1402
صفحه 93-107

فایل ها

هم رسانی

ارجاع به این مقاله

آمار