نوع مقاله : علمی - پژوهشی

نویسندگان

1 دانشجوی دکتری مهندسی آبیاری و زهکشی، گروه مهندسی آب، دانشگاه بین‌المللی امام خمینی ‌(ره)، قزوین

2 دانشیار گروه مهندسی آب، دانشگاه بین‌المللی امام خمینی ‌(ره)، قزوین

چکیده

برآورد پتانسیل تولید هر گیاه تابعی از شرایط اقلیمی، پتانسیل‌های ژنتیکی گیاه و دیگر عوامل گوناگون محیطی و مدیریتی است. ارزیابی توانمندی مناطق، در به‌فعلیت‌رساندن پتانسیل‌های ژنتیکی گیاهان، از نکات مهم برنامه‌ریزی‌های کلان در کشاورزی به‌حساب می‌آید. با توجه به جایگاه استان قزوین در تولید محصول ذرت و اهمیت کشت این محصول در استان، برآورد هرچه دقیق‌تر میزان عملکرد این محصول استراتژیک بسیار ضرورت دارد. در همین زمینه، با مطالعة یک دوره داده‌برداری یازده‌ساله، عملکرد محصول ذرت با مدل گیاهی جدید AquaCrop-GIS برآورد شد. پهنه‌بندی شاخص‌های کلیدی محصول از طریق مدل در سطح استان شبیه‌سازی شد. با بررسی نتایج این پارامترهای کلیدی، مشخص شد ایستگاه‌های مطالعاتی قزوین و معلم‌کلایه با میزان تبخیر‌و‌تعرق مرجع کمتر دارای بهره‌وری آب بیشتری است. در ادامه با استفاده از عملکرد محاسباتی، اجزای ردپای آب و ردپای آب کل محصول در محدودة ایستگاه‌های مطالعاتی تخمین زده شد. با بررسی معادلات رگرسیونی میان اجزای ردپای آب و ردپای آب کل محصول با عملکرد محصول در هر ایستگاه، مشخص شد روابط ردپای آب آبی با عملکرد به‌نسبت دیگر اجزای ردپای آب درمورد تمامی ایستگاه‌ها، از میزان ضریب تعیین (43/0= R2، 51/0= R2، 43/0= R2، 77/0= R2 و 79/0 = R2 به‌ترتیب درمورد ایستگاه‌های قزوین، آوج، معلم‌کلایه، تاکستان و بویین‌زهرا) و سطح معنی‌داری برخوردار است. به‌طورکلی ضریب تعیین این روابط در ایستگاه بویین‌زهرا با 88/0= R2، 79/0= R2، 56/0= R2 و 53/0= R2 به‌ترتیب برای ردپای آب سبز، آبی، خاکستری و ردپای آب نسبت به سایر ایستگاه‌ها، بیشتر برآورد شد؛ به این معنی که کاهش عملکرد در این ایستگاه تأثیر بسزایی بر افزایش ردپای آب کل محصول داشت.

کلیدواژه‌ها

عنوان مقاله [English]

Evaluation of Maize yield and Water Footprint Variability by AquaCrop-GIS Model

نویسندگان [English]

  • Rasta Nazari 1
  • Hadi Ramezani Etedali 2
  • Peyman Daneshkar Arasteh 2

1 Ph.D. Candidate of Irrigation and Drainage Engineering, Dep. of Water Sciences and Engineering, Imam Khomeini International University, Qazvin

2 Associate Prof., Dep. of Water Sciences and Engineering, Imam Khomeini International University, Qazvin

چکیده [English]

Estimation of the production potential of a crop is a function of climatic conditions, crop genetic potentials and various other environmental and managerial factors. Assessing the ability of regions to realize the genetic potential of crops is one of the important points of macro-planning in agriculture. Considering the position of Qazvin province in the production of Maize and the importance of cultivating this crop, estimating the yield of this strategic product as accurately as possible is very necessary. In this regard, by studying an 11-year statistical period, Maize yield was estimated with the new crop model AquaCrop-GIS. The zoning of key product indicators was simulated through the model in the province. By examining the results of these parameters, it finds that Qazvin and Moallem Kelayeh study stations with higher reference evapotranspiration rates have higher water productivity. Then, with the help of the computational yield, components of water footprints, and total water footprint of the crop was estimated within the study stations. By examining the regression equations in each station, it was found that the relationship between blue water footprint and crop yield compared to other water footprint components for all stations has a higher coefficient of determination (R2 = 0.43, R2 = 0.51, R2 = 0.43, R2 = 0.77 and R2 = 0.79 for Qazvin, Avaj, Moallem Kelayeh, Takestan and Buin Zahra stations, respectively) and level of significance. In general, the coefficient of determination of these relationships in Buin Zahra station with R2 = 0.88, R2 = 0.79, R2 = 0.56 and, R2 = 0.53, respectively, for green, blue, gray, and total water footprints compared to other stations were rated higher. This reduction in yield at the station had a significant effect on increasing the total water footprint of the crop.

کلیدواژه‌ها [English]

  • Production potential
  • Water productivity
  • Study stations
  • Regression equations
  • Significance level
  • AquaCrop-GIS
Alizadeh, H., Nazari, B., Parsinezhad, M., Ramazani Etedali, H., Janbaz, H., 2010. Evaluation of AquaCrop Model on Wheat Deficit Irrigation in Karaj Area. Iranian Journal of Irrigation and Drainage, 4 (2), pp.273-283.
Allen, R.G., Pereira, L.S., Raes, D. & Smith, M., 1998, Crop Evapotranspiration: Guidelines for Computing Crop Water Require-mentsm, FAO Drainage and Irrigation Paper, 56, Food and Agriculture Organization, Rome.
Andarzian, B., Bannayan, M., Steduto, P., Mazraeh, H., Barati, ME., Barati, M.A. & Rahnama, A., 2011, Validation and Testing of the AquaCrop Model under Full and Deficit Irrigated Wheat Production in Iran, Agric. Water Manag, 100(1), PP. 1-8.
Babazadeh, H., & Sarai Tabrizi, M. 2012. Assessment Of Aqua Crop Model Under Soybean Deficit Irrigation Management Conditions. Journal of Water and Soil (Agricultural Sciences and Technology), 26(2), 329-339.
Farahani, H.J., Izzi, G. & Oweis, T.Y., 2009, Parameterization and Evaluation of the AquaCrop Model for Full and Deficit Irrigated Cotton, Agron. Agronomy Journal, 101(3), PP. 469-476.
García-Vilaa, M. & Fereresa, 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, PP. 21- 31.
Geerts, S., Raes, D., Garcia, M., Miranda, R., Cusicanqui, J.A., Taboada, C., Mendoza, J., Huanca, R., Mamani, A., Condori, O., Mamani, J., Morales, B., Osco, V., Steduto, P., 2009. Simulating Yield Response to Water of Quinoa (Chenopodium Quinoa Willd.) with FAO-AquaCrop, Agronomy Journal, 101, PP. 499-508.
Gutam, S., 2011, Dry Matter Partitioning, Grain Filling and Grain Yield in Wheat Genotypes, Communications in Biometry and Crop Science, 6(2), pp. 48-63.
Heng, L.K., Hsiao, T.C., Evett, S., Howell, T. & Steduto, P., 2009, Validating the FAO AquaCrop Model for Irrigated and Water Deficient Field Maize, American Society of Agronomy, 101, PP. 488-498.
 
Hoekstra, A.Y. & Chapagain, A.K., 2008, Globalization of Water: Sharing the Planet’s Freshwater Resources, Blackwell Publishing, Oxford, UK.
Hoekstra, A.Y. & Hung, P.Q., 2002, Virtual water trade: A Quantification of Virtual Water Flows between Nations in Relation to International Crop Trade, Value of Water Research, Report Series No. 11, UNESCO-IHE. Delft, the Netherlands.
Hoekstra, A.Y., Chapagain, A.K., Aldaya, M.M. & Mekonnen, M.M., 2009, Water Footprint Manual: State of the Art 2009, Water Footprint Network, Enschede, the Netherlands.
Hsiao, T.C., Heng, L.K., Steduto, P., Rojas-Lara, B., Raes, D. & Fereres, E., 2009, AquaCrop-the FAO Crop Model to Simulate Yield Response to Water, III: Parameterization and Testing for Maize, Agronomy Journal, 101, PP. 448-459.
Jiang, Y., Xu, X., Huang, Q.Z., Huo, Z.L. & Huang, G.H., 2015, Assessment of Irrigation Performance and Water Productivity in Irrigated Areas of the Middle Heihe River Basin Using a Distributed Agro-Hydrological Model, Agr. Water Manage, 147, PP. 67-81.
Langhorn, C., 2015, Simulation of climate change impacts on selected crop yields in southern Alberta, Doctoral dissertation, Lethbridge, Alta: University of Lethbridge, Dept. of Geography.
Lorite, I.J., García-Vila, M., Santos, C., Ruiz-Ramos, M. & Fereres, E., 2013, AquaData and AquaCrop-GIS: Two Computer Utilities for Temporal and Spatial Simulations of Water -Limited Yield with AquaCrop, Computers and Electronics in Agriculture, 96: PP. 227-237.
Nazari, R., Ramezani Etedali, H., Nazari, B. & Collins, B., 2020, The Impact of Climate Variability on Water Footprint Components of Rainfed Wheat and Barley in the Qazvin Province of Iran, Irrig. and Drain., 69: PP. 826-843. https://doi.org/10.1002/ird.2487.
Parvaz, G., Rostaminya, M., Alizadeh, H., 2018. Optimization of the Cropping Pattern Using AquaCrop-GIS (Case Study: Dehloran Plain, Ilam Province). IRANIAN JOURNAL of SOIL and WATER RESEARCH,  49(4), pp.865-877.
Raes, D., Steduto, P., Hsiao, T.C. & Fereres, E., 2009, AquaCrop-the FAO Crop Model to Simulate Yield Response to Water: Reference Manual Annexes.
Raes, D., Steduto, P., Hsiao, T.C. & Fereres, E., 2013, Refernce Manual: AquaCrop Plug-in Program Version (4.0), FAO, Land and Water Division, Rome, Italy.
Ramezani Etedali, H., Liaghat, A., Parsinejad, M., Tavakkoli, A., 2016. AquaCrop Model Calibration and Evaluation in Irrigation Management for Main Grains. Iranian Journal of Irrigation and Drainage, 10 (3), pp.389-397.
Sadras, V.O., 2004, Yield and Water-Use Efficiency of Water-and Nitrogen-Stressed Wheat Crops Increase with Degree of co-Limitation, European Journal of Agronomy, 21(4), PP. 455-464.
Salemi, H.R., Soom, M.A.M., Lee, T.S., Mousavi, S.F., Ganji, A. & Yusoff, M.K., 2011, Application of AquaCrop Model in Deficit Irrigation Management of Winter Wheat in Arid Region, African J. Agric. Res., 610, PP. 2204-2215.
Tavakoli, A.R., Oweis, T., Ashrafi, S., Asadi, H., Siadat, H. & Liaghat, A., 2010, Improving Rainwater Productivity with Supplemental Irrigation in Upper Karkheh River Basin of Iran, International Centre for Agricultural Research in the Dry Areas (ICARDA), Aleppo, Syria.