پارامتریابی و ارزیابی مدل SSM-iCrop2 برای پیش‌بینی رشد و عملکرد یونجه (Medicago sativa L.) در ایران

نوع مقاله : مقاله پژوهشی

نویسندگان

• شبنم پورشیرازی
• افشین سلطانی
• ابراهیم زینلی
• بنیامین ترابی

گروه زراعت،دانشکده تولید گیاهی، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، گرگان، ایران

10.22067/agry.2021.70433.1044

چکیده

برای اطمینان از مدل­های شبیه­سازی زراعی می­توان پارامتریابی و ارزیابی محصول را با مجموعه‌ای از داده‌های مناسب از محیط، مدیریت، ارقام و خاک‌های متفاوت انجام داد. بنابراین، هدف از این مطالعه، تعیین پارامترهای گیاهی و ارزیابی عملکرد علوفه یونجه (Medicago sativa L.) با استفاده از مدل SSM-iCrop2 در مناطق عمده تولید آن در ایران بود. این مطالعه عملکرد مدل را از نظر پیش‌بینی عملکرد تک چین و مجموع سالانه، مرحله فنولوژیک و نیاز آبی یونجه بررسی می‌کند. ارزیابی مدل بر اساس داده‌های آزمایشی مستقل از مرحله پارامتریابی انجام شد. مجموع عملکرد علوفه سالانه مشاهده شده بین 646 تا 4042 با میانگین 1717 گرم در مترمربع و نیاز آبی یونجه حاصل از برنامه NETWAT بین 5140 تا 12690 با میانگین 8746 مترمکعب در هکتار بود. عملکرد شبیه­سازی شده و نیاز آبی یونجه به‌ترتیب بین 693 تا 3296 با میانگین 1654 گرم در مترمربع و 4093 تا 16874 با میانگین 10940 مترمکعب در هکتار به‌دست آمد. همچنین، نتایج ارزیابی نشان داد که ضریب همبستگی (r)، جذر میانگین مربعات خطا (RMSE) و ضریب تغییرات (CV) برای عملکرد تک چین شبیه­سازی شده در مقایسه با مشاهده شده به‌ترتیب 79/0، 3/88 گرم در مترمربع و 78/26 درصد، برای عملکرد علوفه سالانه به‌ترتیب 90/0، 4/344 گرم در مترمربع، 05/20 درصد و برای نیاز آبی یونجه به‌ترتیب 43/0، 3503 مترمکعب در هکتار و 40 درصد به‌دست آمد. نتایج نشان داد که برآوردها برای متغیرهای مختلف قابل قبول است؛ بنابراین، می‌توان از این مدل برای تخمین عملکرد پتانسیل، خلأ عملکرد و اثرات تغییرات اقلیمی محصول یونجه استفاده کرد.

کلیدواژه‌ها

• مدل‌های گیاهان زراعی
• علوفه
• نیاز آبی
• فنولوژی

موضوعات

• بوم شناسی کشاورزی و دانش بومی

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عنوان مقاله [English]

Parameterization and Evaluation of a Simple Simulation Model (SSM-iCrop2) for Alfalfa (Medicago sativa L.) Growth and Yield in Iran

نویسندگان [English]

• Shabnam Pourshirazi
• Afshin Soltani
• Ebrahim Zeinali
• Benjamin Torabi

Department of Agronomy, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran.

چکیده [English]

Introduction
Crop simulation models are very useful tools for the evaluation of plant growth and development processes. Crop-simulating models may be used to estimate yield and evaluate climatic, plant, and management parameters on yield. Also, it may be used to predict crop water requirements under different conditions. Crop models should be evaluated and parameterized to simulate crop growth and development. Parameterization is used for precise simulation of crop growth and development and can estimate the best and most appropriate values for model parameters obtained via observed data or calibration. The objectives of this study were to describe the SSM-iCrop2 model, determine plant parameters, and evaluate alfalfa (Medicago sativa L.) in its major production regions using the SSM-iCrop2 model in Iran.
Materials and Methods
SSM-iCrop2 crop simulation model is a simplified form of SSM crop models which is suitable for the simulation of growth, development, and yield of different crops under different environmental conditions and large-scale estimation of crop production, especially in the evaluation of nutrient availability and climatic effects. This model includes sub-models of phenology, leaf expansion and senescence, dry matter production and distribution, and soil water balance. Daily weather data, agronomic management, soil properties, and plant parameters are required for simulation in this model. The present study investigates the performance of the SSM-iCrop2 model regarding the prediction of single cuts and overall cuts, phonologic stages, and water requirement of alfalfa under changing climatic conditions in Iran. To simulate the growth, development and yield of alfalfa using SSM-iCrop2 model in Iran, the major irrigated alfalfa production provinces, including East Azarbaijan, Hamedan, West Azarbaijan, Sistan and Baluchestan, Khorasan Razavi, Esfahan, Kordestan, Ghazvin, Ardabil, Markazi, Fars, Zanjan, Chaharmahal and Bakhtiyari and Tehran were identified based on the data available in Ministry of Agriculture statistics. Then, field experiment data required for model parameterization and estimation were collected from these provinces.
Results and Discussion
According to the results of the SSM-iCrop2 model parameterization, two cultivars with different leaf area indices (high-yielding and low-yielding) were identified in major alfalfa production provinces. The model was evaluated using independent experimental data that had not been used for parameterization. The evaluation results for alfalfa yield showed that the observed single-cut forage yield ranged from 112 to 640 g.m-2 with an average of 330 g.m-2; the observed total annual forage yield ranged from 646 to 4042 g.m-2 with an average of 1717 g.m-2; and the water requirement of alfalfa obtained from the NETWAT software was between 5140 to 12690 m3 ha-1 with an average of 8746 m3 ha-1. The predicted single-cut forage yield, predicted total annual forage yield, and alfalfa water requirement ranged from 189 to 457 g.m-2 with an average of 351 g.m-2, 693 to 3296 g.m-2 with an average of 1654 g.m-2, and 4093 to 16874 m3 ha-1 with an average of 10940 m3.ha-1, respectively. Overall, in the evaluation of observed versus simulated alfalfa forage yield, 31 points were obtained for single-cut yield with a correlation coefficient (r) of 0.79, root mean square error (RMSE) of 88.3 g.m-2, and coefficient of variation (CV) of 26.78%; and 21 points were obtained for annual yield with an r of 0.90, RMSE of 344.4 g.m-2, and CV of 20.05%. The evaluation results also showed that the observed versus simulated alfalfa water requirement had an r of 0.43, RMSE of 3503 m3 ha-1, and CV of 40%.
Conclusion
The results obtained from parameterization and evaluation of the SSM-iCrop2 model show that the mentioned model presents a logical prediction and accurate estimation of model parameters for yield and water requirement of alfalfa crops in Iran. Thus, this model may be used for the prediction of alfalfa yield under different climates and management conditions.

کلیدواژه‌ها [English]

• Crop models
• Forage
• Phenology
• Water requirement

مراجع

Agricultural Statistics. (2020). Tehran: Ministry of Agricultural Jihad, Deputy Minister of Planning and Economics, Information and Communication Technology Center. Available at web site https://www.maj.ir/. (verified 10 Oct 2020). (In Persian)

Aliahmadi, N., Hashmitabar, M., & Hoseini, S.M. (2021). Assessment of the effect of climate change on the production of horticultural products with a randomized production approach, Case study: Date product. Agricultural Economics Research, 12, 56-82. (In Persian with English Summary)

Dadrasi, A., & Torabi, B. (2017). Predict the growth and yield of corn in Hamedan. Iranian Journal of Field Crop Science, 4, 595-610. (In Persian with English Summary). https://doi.org/10.22059/IJFCS.2017.132723.653951

Dadrasi, A., Torabi, B., Rahimi, A., Soltani, A., & Zeinali, E. (2020). Parameterization and evaluation of a Simple Simulation Model (SSM-iCrop2) for potato (Solanum tuberosum L.) growth and yield in Iran. Potato Research, 63, 545-563. https://doi.org/10.1007/s11540-020-09456-y

Ghaderpour, O., Rafiee, S., Sharifi, M., & Mousavi-Avval, S.H. (2018). Quantifying the environmental impacts of alfalfa production in different farming systems. Sustainable Energy Technologies and Assessments, 27, 109-118. https://doi.org/10.1016/j.seta.2018.04.002

Hammer, G. L., Sinclair, T. R., Boote, K. J., Wright, G. C., Meinke, H., & Bell, M.J. (1995). A peanut simulation model: I. Model development and testing. Agronomy Journal, 87, 1085-1093. https://doi.org/10.2134/agronj1995.00021962008700060009x

Hammer, G.L., Van Oosterom, E., McLean, G., Chapman, S.C., Broad, I., Harland, P., & Muchow, R.C. (2010). Adapting APSIM to model the physiology and genetics of complex adaptive traits in field crops. Journal of Experimental Botany, 61, 2185-2202. https://doi.org/10.1093/jxb/erq095

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., & Jones, J.W. (2019). Decision Support System for Agrotechnology Transfer (DSSAT) Version 4.7.5 Available at Web site https://DSSAT.net. DSSAT Foundation, Gainesville, Florida, USA.

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., & Ritchie, J.T. (2003). The DSSAT cropping system model. European Journal of Agronomy, 18, 235-265. https://doi.org/10.1016/S1161-0301(02)00107-7

Koo, J., & Dimes, J. (2013). HC27 Generic Soil Profile Database. In: Harvard Dataverse, V5. International Food Policy Research Institute, Washington, DC. https://doi.org/10.7910/DVN/90WJ9W

McCown, R.L., Hammer, G.L., Hargreaves, J.N.G., Holzworth, D.P., & Freebairn, D.M. (1996). APSIM: A novel software system for model development, model testing, and simulation in agricultural systems research. Agricultural Systems, 50, 255-271. https://doi.org/10.1016/0308-521X(94)00055-V

Moniri Far, H., Moradian, P., Ahmadi, R., & Moghaddam, A. (2020). Identification of suitable alfalfa cultivars for deficit irrigation conditions in Tabriz Plain. Journal of Agricultural Science and Sustainable Production, 30, 249-264. (In Persian with English Summary)

Monirifar, H., Roudsari, A.M., Ghassemi, S., & Tavasolee, A. (2020). Harvest time and cultivar effects on growth, physiological traits, yield and quality of alfalfa in saline condition. International Journal of Plant Production, 14, 453-462. https://doi.org/10.1007/s42106-020-00096-3

Nehbandani, A.R., Soltani, A., Zeinali, E., Raeisi, S., & Rajabi, R. (2015). Parameterization and evaluation of SSM-soybean model for prediction of growth and yield of soybean in Gorgan. Journal of Plant Production Research, 22, 1-26. (In Persian with English Summary)

Priestley, C.H.B., & TAYLOR, R.J. (1972). On the assessment of surface heat flux and evaporation using large-scale parameters. Monthly Weather Review, 100, 81-92. https://doi.org/10.1175/1520-0493(1972)100<0081:OTAOSH>2.3.CO;2

Ritchie, J. T., Singh, U., Godwin, D. C. & Bowen, W. T. (1998). In: G.Y. Tsuji, G. Hoogenboom and P.K. Thornton (Eds.) Cereal growth, development and yield. Understanding options for agricultural production. Springer, Dordrecht. p. 79-98. https://doi.org/10.1007/978-94-017-3624-4_5

Sinclair, T.R. (1986). Water and nitrogen limitations in soybean grain production I. Model development. Field Crops Research, 15, 125-141. https://doi.org/10.1016/0378-4290(86)90082-1

Sinclair, T.R., & Amir, J. (1992). A model to assess nitrogen limitations on the growth and yield of spring wheat. Field Crops Research, 30, 63-78. https://doi.org/10.1016/0378-4290(92)90057-G

Sinclair, T.R., & Muchow, R.C. (1995). Effect of nitrogen supply on maize yield: I. Modeling physiological responses. Agronomy Journal, 87, 632-641. https://doi.org/10.2134/agronj1995.00021962008700040005x

Sinclair, T. R., Muchow, R. C., & Monteith, J. L. (1997). Model analysis of sorghum response to nitrogen in subtropical and tropical environments. Agronomy Journal, 89, 201-207. https://doi.org/10.2134/agronj1997.00021962008900020009x

Soltani, A., Ghassemi-Golezani, K., Khooie, F.R., & Moghaddam, M. (1999). A simple model for chickpea growth and yield. Field Crops Research, 62, 213-224. https://doi.org/10.1016/S0378-4290(99)00017-9

Soltani, A., Robertson, M. J., Torabi, B., Yousefi-Daz, M., & Sarparast, R. (2006). Modelling seedling emergence in chickpea as influenced by temperature and sowing depth. Agricultural and Forest Meteorology, 138, 156-167. https://doi.org/10.1016/j.fcr.2011.06.021

Soltani, A., & Hoogenboom, G. (2007). Assessing crop management options with crop simulation models based on generated weather data. Field Crops Research, 10, 198-207. https://doi.org/10.1016/j.fcr.2007.06.003

Soltani, A. (2009). Mathematical modeling in field crops. JDM Press, Mashhad, Iran. (In Persian)

Soltani, A., & Sinclair, T.R. (2012). Modeling physiology of crop development, growth and yield. CABI Publisher, 312 p. https://doi.org/10.1079/9781845939700.0000

Soltani, A., Maddah, V., & Sinclair, T.R. (2013). SSM-wheat: A simulation model for wheat development, growth and yield. International Journal of Plant Production, 7, 711-740. https://doi.org/10.22069/IJPP.2013.1266

Soltani, A., and Sinclair, T.R. (2015). A comparison of four wheat models with respect to robustness and transparency: Simulation in a temperate, sub-humid environment. Field Crops Research, 175, 37-46. https://doi.org/10.1016/j.fcr.2014.10.019

Soltani, A., Alimagham, S.M., Nehbandani, A., Torabi, B., Zeinali, E., Dadrasi, A., Zand, E., Ghassemi, S., Pourshirazi, S., Alasti, O. Hosseini, R.S., Zahed, M., Arabameri, R., Mohammadzadeh, Z., Rahban, S., Kamari, H., Fayazi, H., Mohammadi, S., Keramat, S., Vadez, V., Van Ittersum, M.K., & Sinclair, T.R. (2020). SSM-iCrop2: A simple model for diverse crop species over large areas. Agricultural Systems, 182, 102855. https://doi.org/10.1016/j.agsy.2020.102855

Stöckle, C.O., Donatelli, M., & Nelson, R. (2003). CropSyst, a cropping systems simulation model. European Journal of Agronomy, 18, 289-307. https://doi.org/10.1016/S1161-0301(02)00109-0

Tanner, C.B., & Sinclair, T.R. (1983). Efficient water use in crop production: Research or re-search? In: H.M. Taylor, W.R. Jordan and T.R. Sinclair (Eds). Limitations to Efficient Water Use in Crop Production. American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, Madison, Wisconsin. p. 1–27. https://doi.org/10.2134/1983.limitationstoefficientwateruse.c1

Torabi, B., Ebrahimi, N., Soltani, A., & Zeinali, E. (2020). Parameterization and evaluation of SSM iCrop Model for prediction of growth and development of faba bean in climatic conditions of Gorgan. Journal of Crops Improvement, 22, 531-542. (In Persian with English Summary). https://doi.org/10.22059/jci.2020.273742.2148

Vatankhah, T., Moosavi, S. N., & Tabatabaei, S.M. (2020). The economic impacts of climate change on agriculture in Iran: ‎A CGE model analysis. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 42, 1935-1949. https://doi.org/10.1080/15567036.2019.1604903

Wahbi, A., & Sinclair, T. R. (2005). Simulation analysis of relative yield advantage of barley and wheat in an eastern Mediterranean climate. Field Crops Research, 91, 287-296. https://doi.org/10.1016/j.fcr.2004.07.020

Zhao, C., Liu, B., Xiao, L., Hoogenboom, G., Boote, K.J., Kassie, B.T., Pavan, W., Shelia, V., Kim, K.S., Hernandez-Ochoa, I.M., & Wallach, D. (2019). A SIMPLE crop model. European Journal of Agronomy, 104, 97-106. https://doi.org/10.1016/j.eja.2019.01.009