شبیه‌سازی عملکرد و بهره‌وری مصرف آب ارقام مختلف برنج تحت شرایط مختلف کاشت با کاربرد مدل‌های AquaCrop ، CropSyst و WOFOST

نوع مقاله : علمی پژوهشی

نویسندگان

1 دانشجوی کارشناسی ارشد آبیاری و زهکشی، گروه علوم و مهندسی آب، واحد اهواز، دانشگاه آزاد اسلامی، اهواز، ایران

2 استادیار گروه علوم و مهندسی آب، واحد اهواز، دانشگاه آزاد اسلامی، اهواز، ایران

3 استادیار بخش تحقیقات اصلاح و تهیه نهال و بذر، مرکز تحقیقات کشاورزی و منابع طبیعی استان خوزستان، سازمان تحقیقات، آموزش و ترویج‌کشاورزی، اهواز، ایران

چکیده

شبیه‌سازی عملکرد و بهره‌وری مصرف آب برنج با کاربرد مدل­های AquaCrop، CropSyst و WOFOST، پژوهشی در ایستگاه شاوور، مرکز تحقیقات کشاورزی و منابع طبیعی استان خوزستان انجام شد. در این پژوهش، دو عامل روش کاشت (نشایی، مستقیم رایج در منطقه و خشکه‌کاری) و ارقام برنج (عنبوری قرمز، چمپا و دانیال) مورد مطالعه قرار گرفتند. نتایج نشان داد که دقت مدل AquaCrop برای تعیین عملکرد بر اساس آماره‌های میانگین خطای اریب (36/0 تن بر هکتار)، جذر میانگین مربعات خطا (07/1 تن بر هکتار) و جذر میانگین مربعات نرمال شده (14 درصد) قابل قبول بود. مقادیر آماره‌های میانگین خطای اریب، جذر میانگین مربعات خطا و جذر میانگین مربعات نرمال شده برای بهره‌وری مصرف آب در مدل AquaCrop به ترتیب برابر با 11/0- کیلوگرم بر مترمکعب، 40/0 کیلوگرم بر مترمکعب و 15/0 به­دست آمد. این مقادیر برای عملکرد دانه توسط مدل WOFOST به­ترتیب برابر با 06/0 تن بر هکتار، 14/1 تن بر هکتار و 1 درصد و برای بهره‌وری مصرف آب توسط این مدل به­ترتیب برابر با 15/0 کیلوگرم بر مترمکعب، 40/0 کیلوگرم بر مترمکعب و 13 درصد برآورد گردید. همین مقادیر برای مدل CropSyst به­ترتیب برابر با 11/0 تن بر هکتار، 80/0 تن بر هکتار و 24 درصد برای عملکرد دانه و 15/0 کیلوگرم بر مترمکعب، 40/0 کیلوگرم بر مترمکعب و 14 درصد برای بهره‌وری مصرف آب شبیه‌سازی شد. با توجه به این نتایج، دقت هر سه مدل برای شبیه‌سازی عملکرد و بهره‌وری مصرف آب مطلوب بودند. با این وجود، مدل WOFOST دقت بهتری نسبت به دو مدل دیگر در اکثر تیمارها داشت. بنابراین، این مدل را می­توان برای شبیه‌سازی عملکرد و بهره‌وری مصرف آب ارقام مختلف برنج در نظر گرفت.

کلیدواژه‌ها


عنوان مقاله [English]

Yield and Water Productivity Simulation of Different Rice Cultivars under Various Planting Methods using AquaCrop, CropSyst and WOFOST Models

نویسندگان [English]

  • Seyed Amir Hossein Mousavi 1
  • Aslan Egdernezhad 2
  • Abdolali Gilani 3
1 M.Sc. Student of Irrigation and drainage, Department of Water Sciences and Engineering, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran
2 Assistant Professor, Department of Water Sciences and Engineering, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran
3 Assistant Professor, Seed and Plant Improvement Research Department, Khuzestan Agricultural and Natural Resources Research Center, AREEO, Ahvaz, Iran
چکیده [English]

Simulation of rice yield and its water productivity studied using AquaCrop, WOFOST and CropSyst models, in an experiment at Khuzestan Agricultural Research Station. In this study, three types of planting methods (D1: transplanting, D2: direct seeding, and D3: dry bed seeding) and three rice cultivars (V1: Red-Anbori, V2: Champa, V3: Danial) were considered. Results of MBE (0.36 t.ha-1), RMSE (0.1.07 t.ha-1) and NRMSE (0.14 t.ha-1). MBE, RMSE and NRMSE values for water productivity calculated by using AquaCrop model were -0.11 kg.m-3, 0.40 kg.m-3 and 0.15, respectively. The values for yield simulation using WOFSOT model were 0.06 ton.ha-1, 1.14 t.ha-1 and -0.01, respectively, and aforementioned values for water productivity simulated by WOFOST were 0.15 kg.m-3, 0.40 kg.m-3 and -0.13, respectively. The mentioned values for CropSyst simulated as 0.11 t.ha-1, 0.80 t.ha-1 and -0.24 for yield and 0.15 kg.m-3, 0.40 kg.m-3 and -0.14 for water productivity, respectively. According to the results, accuracy for all models were accepted to simulate rice yield and water productivity. However, WOFOST accuracy was better than the other models in most treatments. Thus, it is recommended to use WOFOST for simulation of rice yield and water productivity at different rice cultivars.

کلیدواژه‌ها [English]

  • Carbon- driven Model
  • Crop Modeling
  • Dry Bed Seeding
  • Radiation-driven Model
  • Water-driven Model
· Aalaee Bazkiaei, P., B. Kamkar, E. Amiri, M. Rezaei, H. Kazemi, and S. Akbarzadeh. 2020. Simulation of growth and yield and evaluation of rice production productivity under irrigation management and planting date using AquaCrop model. Water and Soil Resources Conservation. 9(2): 17-34. (In Persian).
· Ahmadee, M., A. Khashei Siuki, and M.H. Sayyari. 2015. Comparison of efficiency of different equations to estimate the water requirement of saffron (Crocus sativus L.) (case study: Birjand plain, Iran). Journal of Agronomy. 8(4): 505-520. (In Persian).
· Anonymous. 2008. IRRI Background paper: The rice crisis: What needs to be done? IRRI, Los Baños, Philippines, www.irri.org/12pp.
· Anonymous. 2017. FAOSTAT. Statistical Databases. Food and Agriculture Organization of the United Nations. http:/ www.fao.org.
· Ansari, M.A., A. Egdernezhad, and N.A. Ebrahimipak. 2019. Simulating of potato (Solanum tuberosum L.) yield under different irrigation conditions using AquaCrop and CropSyst models. Crop Ecophysiology. 13(50-2): 287-304. (In Persian).
· Boogaard, H.L., C.A. van Diepen, R.P. Rotter, J.M.C.A. Cabrera, and H.H. van Laar. 1998. WOFOST 7.1; user's guide for the WOFOST 7.1 crop growth simulation model and WOFOST Control Center 1.5 (No. 52). SC-DLO.
· Bouman, B.A.M., H. van Keulen, H.H. van Laar, and R. Rabbinge. 1996. The School of de Wit, crop growth simulation models: pedigree and historical overview. Agricultural Systems. 52: 171-198.
· Canfalonieri, R., and S. Bocchi. 2005. Evaluation of CropSyst for simulation the yield of flooded rice in Northern Italy. European Journal of Agronomy. 23:315-326.
· Eitzinger, J., M. Trnka, J. Hosch, Z. Zalud, and M. Dubrovsk. 2004. Comparison of CERES, WOFOST and SWAP models in simulating soil water content during growing season under different soil conditions. Ecological Modelling. 171: 223-246.
· Esmaeilian, Y., and M. Ramroudi. 2018. Evaluation of AquaCrop model in simulating yield and water productivity of three corn hybrid under hot-dry climatic conditions. Crop Ecophysiology. 12(47-3): 355-376. (In Persian).
· Farahani, H.J., G. Izzi, P. Steduto, and T.Y. Oweis. 2009. Parameterization and evaluation of AquaCrop for full and deficit irrigated cotton. Agronomy. 101: 469-476.
· Garcia-Vila, M., E. Fereres, L. Mateos, F. Orgaz, and P. Steduto. 2009. Deficit irrigation optimization of cotton with AquaCrop. Agronomy. 101: 477-487.
· Geerts, S., and D. Raes. 2009. Defecit irrigation as on-farm strategy to maximize crop water productivity in dry areas. Agricultural Water Management. 96: 1275-1284.
· Geerts, S., D. Raes, M. Garcia, R. Miranda, and J.A. Cusicanqui. 2009. Simulating yield response to water of quinoa (Chenopodium quinoa Willd.) with FAO-AquaCrop. Agronomy. 101: 499-508.
· Gilani, A., S. Ataallah, S. Jalali, and K. Limouchi. 2017. Evaluation the effects of sowing dates on the peduncle anatomy and grain yield of the rice cultivars in the climatic condition of Khuzestan province. Crop Ecophysiology. 10(40-4): 975-990. (In Persian).
· Gilani, A.A., Sh. Absalan, and S, Jalali. 2010. Comparison of dry bed seeding to current cultivation methods of rice based on water consumption. Research Project. Rice Research Institute of Iran. 22 p. (In Persian).
· Heng, L.k., T.C. Hsiao, S. Evett, T. Howell, and P. Steduto. 2009. Validating the FAO AquaCrop model for irrigated and water deficient field maize. Agronomy Journal. 101(3):488-498.
· Hsiao, T.C., L. Heng, P. Steduto, B. Rojas-Lara, D. Raes, and E. Fereres. 2009. AquaCrop-The FAO crop model to simulate yield response to water: III. parameterization and testing for maize. Agronomy Journal. 101(3): 448-459.
· Mohseni, M., A.A. Montazar, and A. Rahimi Khoub. 2009. Evaluation of CropSyst model for water-nitrogen interactions in wheat yield and water productivity. Iranian Journal of Irrigation and Drainage. 3(1): 113-125. (In Persian).
· Moriondo, M., F. Maselli, and M. Bindi. 2007. A simple model of regional wheat yield based on NDVI data. Europian Journal of Agronomy. 26: 266-274.
· Moumeni, R., S.M.R. Behbahani, M.H. Nazarifar, and B. Azadegan. 2008. Zoning of water productivity of water by CropSyst model in different water periods (Case studt: Karkheh watershed). Iranian Journal of Irrigation and Drainage. 2(1): 63-76. (In Persian).
· Pala, M., C.O. Stockle, and H.C. Harris. 1996. Simulation of durum wheat (Triticum turgidum ssp. durum) growth under different water and nitrogen regimes in a Mediterranean environment using CropSyst. Agricultural Systems. 51(2): 147-163.
· Raes, D., P. Steduto, T.C. Hsiao, and E. Fereres. 2009. AquaCrop- the FAO crop model to simulate yield response to water II. Main algorithms and software description. Agronomy Journal. 101: 438–447.
· Saadati, Z., N. Pirmoradian, and M. Rezaei. 2013. Yield response simulation of two local rice varieties to irrigation management using CropSyst model. Water and Soil Science. 17(64): 69-81. (In Persian).
· Saadati, Z., N. Pirmoradianand, and M. Rezaei. 2011. Calibration and evaluation of AquaCrop model in rice growth simulation under different irrigation managements. 21th International Congress on Irrigation and Drainage. October19-23, Tehran, Iran, 589-600.
· Sanjani, S. 2012. Agroecological zoning and study of yield gap of wheat, sugar beet and corn in Khorasan province. Ph.D. Thesis. Ferdowsi University of Mashhad. (In Persian).
· Singh, A.K., R. Tripathy, and U.K. Chopra. 2008. Evaluation of CERES- wheat and CropSyst models for water-nitrogen interactions in wheat crop. Agricultural Water Management. 95(7): 776-786.
· Song, Y.I., D.L. Chen, and W.J. Dong. 2006. Influence of climate on winter wheat productivity in different climate regions of China, 1961–2000. Climate Research. 32: 219–227.
· Stockle, C.O. and R.L. Nelson. 1996. Cropsyst User’s manual (Version 2.0). Biological Systems Engineering Dept., Washington State University, Pullman, WA, USA.
· Todorovic, M., R. Albrizio, L. Zivotic, M. Abisaab, and C. Stwckle. 2009. Assessment of AquaCrop, CropSyst and WOFOST models in the simulation of sunflower growth under different water regimes. Agronomy. 101: 509-521.
Yang, H.S., A. Dobermann, J.L. Lindquist, D.T. Wolters, T.J. Arkebauer, and K.G. Cassman. 2004. Hybrid-maize—A maize simulation model that combines two crop modeling approaches. Field Crops Research. 87: 131–154.