ارزیابی جامع مدل DSSAT-Nwheat در طیف وسیعی از مناطق اقلیمی ایران

نوع مقاله : علمی - پژوهشی

نویسندگان

گروه اگروتکنولوژی دانشکده کشاورزی، دانشگاه فردوسی مشهد، ایران

چکیده

ارزیابی جامع مدل شبیه‌سازی محصول با مجموعه‌ای از داده‌های مناسب از محیط، مدیریت، ارقام و خاک‌های متفاوت برای استفاده مطمئن از مدل در تجزیه و تحلیل نظام‌های کشاورزی ضروری است. مدل Nwheat که اخیراً به DSSAT اضافه شده است با استفاده از مشاهدات آزمایش‌های مزرعه‌ای در دامنه وسیعی از مناطق اقلیمی با مدیریت مختلف برای چهار رقم گندم (Triticum aestivum L.) شامل شهریار، پیشتاز، تجن و چمران مورد واسنجی و ارزیابی قرار گرفت. این ارقام به‌ترتیب مناسب برای کشت در مناطق سردسیر، معتدل، مرطوب و گرمسیر می‌باشد. نتایج اعتبارسنجی نشان داد که مدل DSSAT-Nwheat مراحل فنولوژیکی کاشت تا گل‌دهی و کاشت تا رسیدگی را به‌خوبی با مقادیر جذر میانگین مربعات خطا (RMSE) کمتر از چهار روز، جذر میانگین مربعات خطای نرمال شده (nRMSE) کمتر از سه درصد و شاخص توافق ویلموت (d) نزدیک به یک شبیه‌سازی کرد. همچنین نتایج اعتبارسنجی عملکرد دانه ارقام گندم نشان داد که مقدار RMSE از 568 کیلوگرم در هکتار برای رقم تجن تا 933 کیلوگرم در هکتار برای رقم چمران متغیر بود. به‌طور کلی، nRMSE و d برای عملکرد دانه ارقام به‌ترتیب کمتر از 20 درصد و بالاتر از 8/0 بود که دقت واسنجی را به‌خوبی نشان می‌دهد. واکنش مدل به افزایش دما در مناطق مختلف و سطوح مختلف 2CO متفاوت بود. به‌طوری‌که در منطقه اهواز، گرگان و مشهد افزایش دما تا نه درجه سانتی‌گراد در تمام سطوح غلظت 2CO، باعث کاهش عملکرد دانه شد که البته افزایش 2CO مقداری از اثرات منفی افزایش دما را تخفیف داد. امّا در منطقه سردسیر تبریز افزایش سه درجه سانتی‌گراد دما در سطوح غلظت 360، 540 و 720 پی‌پی‌ام 2CO به‌ترتیب باعث افزایش 8، 12 و 14 درصدی عملکرد دانه شد، ولی با افزایش بیشتر دما تا نه درجه سانتی‌گراد، عملکرد دانه 23، 15 و 10 درصد نسبت به دمای پایه کاهش یافت. به‌طور کلی، نتایج این مطالعه نشان داد که مدل DSSAT-Nwheat پاسخ‌های عملکرد دانه به طیف گسترده‌ای از مدیریت و شرایط محیطی را به‌خوبی پیش‌بینی می‌کند و می‌توان از آن برای ارزیابی تأثیرات مختلف مدیریت زراعی و تصمیم‌گیری در نظام‌های کشت گندم در شرایط اقلیمی جاری و آینده استفاده کرد.

کلیدواژه‌ها


عنوان مقاله [English]

Evaluation of DSSAT-Nwheat Model across a Wide Range of Climate Conditions in Iran

نویسندگان [English]

  • Mohammad Hasan Fallah
  • Ahmad Nezami
  • Hamid Reza Khazaie
  • Mehdi Nassiri Mahallati
Department of Agrotechnology, Faculty of Agriculture, Ferdowsi University of Mashhad, Iran
چکیده [English]

Introduction
Crop models can integrate the complex interactions of soil properties, climatic conditions, crop management practices, and crop genetic characteristics. One of the main aspects of crop simulation models is the possibility to use them across various environmental and management conditions, provided that they have been evaluated under a wide range of growing conditions. The Decision Support System for Agrotechnology Transfer (DSSAT) modeling platform is leading crop modeling system that is widely applied in various environments. Testing crop models under various temperature environments are essential to apply models to climate impact studies. The objective of this study was the testing and evaluation of DSSAT-Nwheat model across a wide range of climate conditions in Iran.
 Materials and Methods
Nwheat model, which recently integrated into DSSAT, was evaluated for four wheat cultivars using observations from field experiments included a wide range of climate and management. Cultivars were Shahriyar, Pishtaz, Tajan, and Chamran cultivated in cold, temperate, humid and tropical regions in Iran, respectively. The locations represent four different wheat mega-environments, a concept used by wheat breeders for testing cultivars. The management information used at each site was obtained from the Seed and Plant Improvement Institute. Daily weather data, management events, and soil characteristics imported to DSSAT. The performance of the DSSAT-Nwheat during the calibration and evaluation was assessed using different statistics, Root Mean Square Error (RMSE), Normalized Root Mean Square Error (nRMSE), Willmott’s index (d), and coefficient of determination (R2) of a 1:1 regression line. A sensitivity analysis was conducted using 30 years of observed weather data from Tabriz, Mashhad, Gorgan, and Ahwaz. For the sensitivity analysis scenarios, the temperaturewas increased by 3, 6, and 9°C, and atmospheric CO2 concentration levels were set at 360, 540, and 720 ppm.
 Results and Discussion
Evaluation results showed that DSSAT-Nwheat model simulated planting to anthesis and planting to maturity accurately with RMSE values less than four days, nRMSE less than 3%, and d index close to one. Also, evaluation of grain yield showed that RMSE varied from 568 kg ha-1 for Tajan cultivar up to 933 kg ha-1 for Chamran cultivar. In general, nRMSE and d index for grain yield were less than 20% and higher than 0.8, respectively, which showed good calibration accuracy. In DSSAT-Nwheat model, the specific heat stress function explains heat stress effects during grain filling on grain yield in cultivars. Chamran cultivar is somewhat resistant to end season heat stress, so the DSSAT-Nwheat model underestimated in the warm regions. Because the cultivars differ regarding resistance to the end season heat stress, crop models need to consider cultivar-specific tolerance to heat stress to better simulate temperature effects on wheat cropping systems. The response of the model to the increase in temperature was different in regions and levels of CO2 concentrations. Elevated atmospheric CO2 concentrations lessened some of the adverse effects of high temperature. Therefore, the sensitivity analysis of DSSAT-Nwheat model to temperature variations and elevated atmospheric CO2 concentrations showed that the model could be used in studies of climate change impacts on wheat production. This model can be employed to explore the integrated effects of temperature, atmospheric CO2concentrations, water, nutrients, and agronomic management practices in a range of wheat growing environments.
 Conclusion
The results of this study showed that the DSSAT-Nwheat model had reliably good performance under a wide range of management and environmental conditions. This calibrated model can now be used for assessing impacts of various agronomic management strategies and decisions in wheat cropping systems under current and anticipated climate change. But more importantly is the calibration method and using a large number of climatological data to calibrate.

کلیدواژه‌ها [English]

  • Climate change
  • Developmental stages
  • Genetic coefficients
  • Modeling
  • Sensitivity analysis
Andarzian, B., Bakhshande, A.M., Bannayan, M., and Emam, Y., 2008. Evaluation of the CERES-wheat model in Ahvaz condition. Journal of Agronomy Research of Iran 6: 11-22. (In Persian with English Summary)
Angulo, C., Rotter, R., Lock, R., Enders, A., Fronzek, S., and Ewert, F., 2013. Implication of crop model calibration strategies for assessing regional impacts of climate change in Europe. Agricultural and Forest Meteorology 170: 32–46.
Asseng, S., Jamieson, P.D., Kimball, B., Pinter, P., Sayre, K., Bowden, J.W., and Howden, S.M., 2004. Simulated wheat growth affected by rising temperature: increased water deficit and elevated atmospheric CO2. Field Crops Research 85(2-3): 85-102.
Asseng, S., Keating, B.A., Fillery, I.R.P., Gregory, P.J., Bowden, J.W., Turner, N.C., Palta, J.A., and Abrecht, D.G., 1998. Performance of the APSIM-wheat model in Western Australia. Field Crops Research 57(2): 16-179.
Asseng, S., Milroy, S.P., and Poole, M.L., 2008. Systems analysis of wheat production on low water-holding soils in a Mediterranean-type environment I. Yield potential and quality. Field Crops Research 105(1-2): 97-106.
Bannayan, M., Crout, N.M.J., and Hoogenboom, G., 2003. Application of the CERES-wheat model for within-season prediction of winter wheat yields in the United Kingdom. Agronomy Journal 95: 114-125.
Bassu, S., Asseng, S., and Richards, R., 2011. Yield benefits of triticale traits for wheat under current and future climates. Field Crops Research 124(1): 14-24.
Esmaeilzadeh Moghaddam, M., Lotfali Aineh, G., Akbarimghadam, H., Abedini, M., Tahmasbi, S., Farhadnato, M., Seyahfer, M., Maghsoudinezhad, K., Poodineh, A., Shirvani, A., Sanei Nejad, A., Shahbazpour, A., Tahmasbi, S., Amir Bakhtiar, N., Nikzad, A.R., Tabibbaghfari, M., Abdollahi, A., Mardani, M., and Jokar, K., 2011. Final report: Evaluation of adaptability of wheat genotypes in elite regional wheat yield trials (ERWYT79-89) of Warm Southern Zone (Zone-II). Cereal Research Department, Seed and Plant Improvement Institute-SPII, Karaj, Iran. (In Persian)
Fath, B., and Jorgensen, S.E., 2011. Fundamentals of Ecological Modelling: Applications in Environmental Management and Research, (4th Ed.). Elsevier, Amsterdam.
Heng, L.K., Asseng, S., Mejahed, K., and Rusan, M., 2007. Optimizing wheat productivity in two rainfed environments of the West Asia-North Africa region using a simulation model. European Journal of Agronomy 26(2): 121-129.
Holzworth, D.P., Huth, N.I., Devoil, P.G., Zurcher, E.J., Herrmann, N.I., McLean, G., Chenu, K., Van Oosterom, E.J., Snow, V., Murphy, C., Moore, A.D., Brown, H., Whish, J.P.M., Verrall, S., Fainges, J., Bell, L.W., Peake, A.S., Poulton, P.L., Hochman, Z., Thorburn, P.J., Gaydon, D.S., Dalgliesh, N.P., Rodriguez, D., Cox, H., Chapman, S., Doherty, A., Teixeira, E., Sharp, J., Cichota, R., Vogeler, I., Li, F.Y., Wang, E.L., Hammer, G.L., Robertson, M.J., Dimes, J.P., Whitbread, A.M., Hunt, J., van Rees, H., McClelland, T., Carberry, P.S., Hargreaves, J.N.G., MacLeod, N., McDonald, C., Harsdorf, J., Wedgwood, S., and Keating, B.A., 2014. APSIM–evolution towards a new generation of agricultural systems simulation. Environmental Modelling and Software 62: 327-350.
Hoogenboom, G., Porter, C.H., Shelia, V., Boote, K.J., Singh, U., White, J.W., Hunt, L.A., Ogoshi, R., Lizaso, J.I., Koo, J., Asseng, S., Singels, A., Moreno, L.P., and Jones, J.W., 2017. Decision Support System for Agrotechnology Transfer (DSSAT) Version 4.7 (https://DSSAT.net). DSSAT Foundation, Gainesville, Florida, USA.
Hoogenboom, G., Wilkens, P.W., and Tsuji, G.Y., 1999. DSSAT v3, Vol. 4. University of Hawaii, Honolulu, HI.
Hussain, J., Khaliq, T., Ahmad, A., and Akhtar, J., 2018. Performance of four crop models for simulations of wheat phenology, leaf growth, biomass and yield across planting dates. PLoS One 13(6): 1-14.
Jones, J.W., Hoogenboom, G., Porter, C.H., Boote, K.J., Batchelor, W.D., Hunt, L.A., Wilkens, P.W., Singh, U., Gijsman, A.J., and Ritchie, J.T., 2003. DSSAT Cropping System Model. European Journal of Agronomy 18: 235-265.
Kassie, B.T., Asseng, S., Porter, C.H., and Royce, F.S., 2016. Performance of DSSAT-Nwheat across a wide range of current and future growing conditions. European Journal of Agronomy 81:27-36.
Keating, B.A., Carberry, P.S., Hammer, G.L., Probert, M.E., Robertson, M.J., Holzworth, D., Huth, N.I., Hargreaves, J.N.G., Meinke, H., Hochman, Z., McLean, G., Verburg, K., Snow, V., Dimes, J.P., Silburn, M., Wang, E., Brown, S., Bristow, K.L., Asseng, S., Chapman, S., McCown, R.L., Freebairn, D.M., and Smith, C.J., 2003. An overview of APSIM: a model designed for farming systems simulation. European Journal of Agronomy 18(3-4): 67-288.
Keating, B.A., Meinke, H., Probert, M.E., Huth, N.I., and Hills, I.G., 2001. NWheat: Documentation and Performance of a Wheat Module for APSIM. CSIRO Australia: Tropical Agriculture Technical Memorandum 9: 66pp.
Kiani, A., Koocheki, A.R., Nassiri Mahallati, M., and Banayan, M., 2004. CERES-Wheat model evaluation at two different climatic in Khorasan province, П Phenology and growth parameter simulation. Journal of Desert 9: 125-142. (In Persian with English Summary)
Liu, B., Asseng, S., Liu, L.L., Tang, L., Cao, W.X., and Zhu, Y., 2016. Testing the responses of four wheat crop models to heat stress at anthesis and grain filling. Global Change Biology 22: 1890-1903.
Mahru, A.H., Soltani, A., Galeshi, S., and Kalate-Arabi, M., 2010. Estimates of genetic coefficients and evaluation of model DSSAT for Golestan province. Electronic Journal of Crop Production 3(2): 229-253. (In Persian with English Summary)
Matthews, R.B., Rivington, M., Muhammed, S., Newton, A.C., and Hallett, P.D., 2013. Adapting crops and cropping systems to future climates to ensure food security: the role of crop modelling. Global Food Security 2(1):24-28.
Najafi Mirok, T., Rezaei, M., Chaychi, M., Babaei, T., Sanjari, A., Ghodsi, M., Razavi, A.R., Yazdansepas, A., Torabi, M., Hooshyar, R., Atahosseini, M., AliSafavi, S., Mahfouzi, S., Aminzadeh, G., Nazeri, M., Rezaie, A., Jasemi, S., Ghorbani, A., and Amini, A., 2011. Final Report: Evaluation of adaptability of wheat genotypes in elite regional wheat yield trials (ERWYT81-89) of Cold Zone (Zone-IV). Cereal Research Department, Seed and Plant Improvement Institute-SPII, Karaj, Iran. (In Persian)
Najafian, G., Ahmadi, G., Qandi, A., Shabanzadeh, B., Sarikhani, S., Jafarnejad, A., Hassanpour, J., Zare Faizabadi, A., HajAkhonde Meybodi, H., Mohammadkhani, A., Nategh, Z., Jokar, R., Ghavidel, N., Niazi, A., Sahraee, M., Minoo, J., Kalameh, A., Ammarloo, M., Nazari Beyranvand, H., Sadeghi, D., Dehghani, M.H., Mansouri, J., Tabeie., M., and Kohistani, B., 2010. Final Report: Evaluation of adaptability of wheat genotypes in elite regional wheat yield trials (ERWYT82-88) of Temperate Zone (Zone-III). Cereal Research Department, Seed and Plant Improvement Institute-SPII, Karaj, Iran. (In Persian)
Nouri, M., Homaee, M., Bannayan, M., and Hoogenboom, G., 2016. Towards modeling soil texture-specific sensitivity of wheat yield and water balance to climatic changes. Agricultural Water Management 177: 248-263.
O’Leary, G., Christy, B., Nuttall, J., Huth, N., Cammarano, D., Stockle, C., Basso, B., Shcherbak, I., Fitzgerald, G., Luo, Q., Farre-Codina, I., Palta, J., and Asseng, S., 2015. Response of wheat growth, grain yield and water use to elevated CO2 under a free-air CO2 enrichment (FACE) experiment and modelling in a semi-arid environment. Global Change Biology 21(7): 2670-2686.
Oteng-Darko, P., Yeboah, S., Addy, S.N.T., Amponsah, S. and Owusu Danquah, E., 2013. Crop modeling: A tool for agricultural research – A review. E3 Journal of Agricultural Research and Development (EJARD) 2(1): 1-6.
Reyenga, P.J., Howden, S.M., Meinke, H., and McKeon, G.M., 1999. Modelling global change impacts on wheat cropping in south-east Queensland, Australia. Environmental Modelling and Software 14(4): 297-306.
Ritchie, J.T., Singh, U., Godwin, D., and Bowen, W.T., 1998. Cereal growth, development, and yield. In: G.Y. Tsuji, G. Hoogenboom, and P.K. Thornton (Eds.). Understanding Options for Agricultural Production. The Netherlands: Kluwer Academic, Dordrecht, p. 79-98.
Saadati, Z., Delbari, M., Amiri, E., Panahi, M., Rahimian, M.H., and Ghodsi, M., 2016. Assessment of CERES-Wheat model in simulation of varieties of wheat yield under different irrigation treatments. Journal of Water and Soil Resources Conservation 5(3): 73-85. (In Persian with English Summary)
Sinclair, T.R., and Seligman, N., 2000. Criteria for publishing papers on crop modeling. Field Crops Research 68: 165-172.
Singh, A.K., Tripathy, R., and Chopra, U.K., 2008. Evaluation of CERES-Wheat and CropSyst models for water-nitrogen interactions in wheat crop. Agricultural Water Management 95(7): 776-786.
Soltani, A., Hammer, G.L., Torabi, B., Robertson, M.J., and Zeinali, E., 2006. Modeling chickpea growth and development: phonological development. Field Crops Research 99: 1-13.
SPII (Seed and Plant Improvement Institute)., 2015. Introduce of crops cultivars (Food Security and Health, Vol. 1) Agricultural Research, Education and Promotion Organization, Karaj, Iran. (In Persian)
Vahabzadeh, M., Kalateh, M., Jafarbayeh, J., Khavareinejad, M.S., Ghasemi, M., Souqi, H., Sheikh, F., Afshari, F., Ebrahimnejad, S., Dehghan, M.A., Naseri, A., Babaei Gol, A., Morteza Gholi, M., Khalilzadeh, G., Nouri, T., Abrodi, A.M. Saeedi, A., Fallah, H., Aminzadeh, G., and Lotfinejad, L., 2008. Final Report: Evaluation of adaptability of wheat genotypes in elite regional wheat yield trials (ERWYT79-86) of North Warm and Humid Zone (Zone-I). Cereal Research Department, Seed and Plant Improvement Institute-SPII, Karaj, Iran. (In Persian)
Wang, X., Kemanian, A., and Williams, J., 2011. Special features of the EPIC and APEX modeling package and procedures for parameterization, calibration, validation, and applications. In: L.R. Ahuja and L. Ma (Eds.). Methods of Introducing System Models into Agricultural Research. American Society of Agronomy, Madison. p. 177-208.
White, J.W., Hoogenboom, G., Kimball, B.A., and Wall, G.W., 2011. Methodologies for simulating impacts of climate change on crop production. Field Crops Research 124(3): 357-368.
Willmott, C.J., 1982. Some comments on the evaluation of model performance. Bulletin of American Meteorology Society 63: 1309-1313.
Yang, J.M., Yang, J.Y., Liu, S., and Hoogenboom, G., 2014. An evaluation of the statistical methods for testing the performance of crop models with observed data. Agricultural Systems 127: 81–89.
Zacharias, M., Kumar, S.N., Singh, S.D., Swaroopa, D.N., and Aggarwal, P.K., 2015. Evaluation of a regional climate model for impact assessment of climate change on crop productivity in the tropics. Current Science 108(6): 1119-1126.
CAPTCHA Image