پارامتریابی و ارزیابی مدل SSM-iCrop2 برای پیش‌­بینی تولید علوفه مراتع (مطالعه موردی: مناطق کوهستانی البرز میانی)

نویسندگان
مهرنوش پارسا 1
حسین بارانی 2
افشین سلطانی 3
عبدالرضا بهره مند 4
حسن قلیچ نیا 5
1 دانشجوی دکتری مرتعداری، دانشکده‌ی مرتع و آبخیزداری، دانشگاه علوم کشاورزی و منابع طبیعی گرگان
2 دانشیار گروه منابع طبیعی، دانشکده‌ی مرتع و آبخیزداری، دانشگاه علوم کشاورزی و منابع طبیعی گرگان
3 استاد گروه زراعت، دانشکده‌ی مرتع و آبخیزداری، دانشگاه علوم کشاورزی و منابع طبیعی گرگان
4 استاد گروه منابع طبیعی، دانشکده‌ی مرتع و آبخیزداری، دانشگاه علوم کشاورزی و منابع طبیعی گرگان
5 دانشیار گروه علوم مرتع، مرکز تحقیقات کشاورزی و منابع طبیعی ساری
10.22034/wmji.2026.2091733.1141
چکیده
تغییر اقلیم، از بزرگ‌ترین چالش‌هایی است که بشر در قرن بیست‌ویک با آن مواجه بوده است چراکه می­تواند اثرات شدیدی بر منابع آب، کشاورزی، انرژی، گردشگری و حتی زیست اقلیم انسان داشته باشد یکی از ابزارهای مناسب جهت برآورد تولید مرتع با تکیه‌بر اطلاعات آب‌وهوایی مدل‌های شبیه‌سازی است. ازآنجاکه پوشش گیاهی به‌عنوان یکی از اجزای مهم بوم‌سازگان طبیعی، تأثیرپذیری بسیار زیادی از اقلیم دارد شاخص­ها و مدل­های زیادی در خصوص رابطه اقلیم و پوشش گیاهی ارائه‌شده است که می­توانند روابط موجود و اثرات متقابل را بهتر بیان کنند. این مطالعه، باهدف تعیین پارامترهای مؤثر بر تولید گیاهان مرتعی نواحی کوهستانی البرز میانی و ارزیابی اثر تغییر اقلیم با استفاده از مدل آب‌وهوایی LARS WG6 و مدل SSM-iCrop2 در مناطق (پلور، رینه، کنگر چال و پشتکوه) به انجام رسیده است. در این پژوهش داده‌های آماری ایستگاه هواشناسی سینوپتیک آبعلی از سال (۱۹۸۳) و کیاسر از سال (۲۰۰۲) استان مازندران برای دوره ۱۵ ساله جمع‌آوری و داده‌های (دمای کمینه، دمای بیشینه، بارندگی و تابش خورشیدی روزانه) برای برآورد تولید ماده خشک وارد مدل SSM-iCrop2 شد. در ادامه خروجی مدل تغییر اقلیم GCM در مناطق موردمطالعه با استفاده از مدل ریزمقیاس شده LARS-WG6 و با در نظر گرفتن سناریوهای تغییر اقلیم RCP4/5 و RCP8/5 مورد ارزیابی قرار گرفتند. در مدل تولید SSM-iCrop2 با اعمال سناریوهای تغییر اقلیم دو مدل CSIRO-MK36 و IPSL-CM5A-MR پارامترهای تولید و فنولوژی محاسبه و سپس برآورد شده است. طبق خروجی‌های مدل در هر دو سناریوی RCP4/5 و RCP8/5 در هر دو دوره ۲۰۴۰-۲۰۲۱ و ۲۰۶۰-۲۰۴۱ دمای بیشینه و کمینه و بارش افزایش خواهد داشت. میزان بارندگی، دمای بیشینه و دمای کمینه به ترتیب برای ایستگاه آبعلی ۹۷/۰-۹۱/۰ درصد، ۲/۲-1/7 و ۸۸/۱-1/56 درجه سانتی‌گراد و برای ایستگاه کیاسر مقادیر پارامترهای فوق به ترتیب 0/96-0/87 درصد، 2/18-1/72 و 1/89-1/62 درجه سانتی‌گراد نسبت به دوره پایه افزایش خواهد داشت. ارزیابی تولید نشان داد مقدار جذر میانگین مربعات خطا برای تولید ماده خشک ۲۲ گرم بر مترمربع که معادل ۲۱ درصد میانگین کل ماده خشک مشاهده‌شده است و ضریب تغییرات ۲۰ درصد بوده است که نشان داد شبیه‌سازی با استفاده از مدل SSM-iCrop2 رضایت‌بخش بوده و برآورد قابل‌قبول پارامترها و کارایی مدل در پیش‌بینی رشد و تولید گیاهان مرتعی است. با توجه ‌به سناریوهای تغییر اقلیم برای آینده نزدیک (۲۰۴۰-۲۰۲۱) و آینده دور (۲۰۶۰-۲۰۴۱) در هر چهار سایت موردمطالعه، طول فصل رشد کاهش و تولید مرتع افزایش خواهد یافت.
کلیدواژه‌ها
پیش­بینی تولید
تغییر دما
طول فصل رشد
مدل اقلیمی
مدل برآورد تولید گیاهی

عنوان مقاله English

Parameterization and Evaluation of the SSM-iCrop2 Model for Predicting Rangeland Forage Production (Case Study: Middle Alborz Mountainous Regions)

نویسندگان English

Mehrnoush parsa 1
Hossein Barani 2
Afshin Soltani 3
Abdolreza Bahremand 4
Hasan Ghelichnia 5
1 PhD in Rangeland Science, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran.
2 Department of Rangeland Management, Faculty of Rangeland and Watershed Management, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran.
3 Department of Watershed Management, Faculty of Rangeland and Watershed Management, University of Agricultural Sciences and Natural Resources, Gorgan, Iran
4 Department of Agriculture, Faculty of Agricultural Plant products, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran
5 Forest and Rangeland Research Department, Mazandaran Agricultural and Natural Resources Research and Education Center. Research Organization, Sari, Iran
چکیده English

Climate change is one of the biggest challenges that humanity has faced in the 21st century because it can have severe impacts on water resources, agriculture, energy, tourism, and even human bioclimate. One of the appropriate tools for estimating rangeland production based on climate data is simulation models. Since vegetation, as one of the important components of natural ecosystems, is highly influenced by climate, many indicators and models have been presented regarding the relationship between climate and vegetation that can better describe the existing relationships and mutual effects. This study aimed to determine the parameters affecting rangeland plant production in the mountainous areas of the Middle Alborz and to assess the impact of climate change using the LARS WG6 climate model and the SSM-iCrop2 model in the regions (Plor, Raineh, Kangar Chal, and Poshtkooh). In this study, statistical data from the Abali synoptic meteorological station since (1983) and Kiasar since (2002) of Mazandaran province were collected for a 15-year period and the data (minimum temperature, maximum temperature, rainfall and daily solar radiation) were entered into the SSM-iCrop2 model to estimate dry matter production. Next, the output of the GCM climate change model in the study areas was evaluated using the downscaled LARS-WG6 model and considering the RCP4/5 and RCP8/5 climate change scenarios. In the SSM-iCrop2 production model, production and phenology parameters were calculated and then estimated by applying the two CSIRO-MK36 and IPSL-CM5A-MR climate change scenarios. According to the model outputs, in both RCP4/5 and RCP8/5 scenarios, maximum and minimum temperatures and precipitation will increase in both the periods 2040-2021 and 2041-2060. The rainfall, maximum and minimum temperatures will increase by 0.91-0.97%, 1.7-2.2% and 1.56-1.88°C for Abali station respectively and by 0.87-0.96%, 1.72-2.18% and 1.62-1.89°C for Kiasar station respectively compared to the base period. The production assessment showed that the root mean square error for dry matter production was 22 g/m2, which is equivalent to 21% of the total observed dry matter average and the coefficient of variation was 20%, which indicated that the simulation using the SSM-iCrop2 model was satisfactory and the parameters and model efficiency in predicting the growth and production of rangeland plants were acceptable. Considering the climate change scenarios for the near future (2021-2040) and the distant future (2041-2060) in all four study sites, the length of the growing season will decrease and rangeland production will increase.

کلیدواژه‌ها English

Production forecast
temperature change
growing season length
climate model
crop production estimation model
1-Abkar, A. J., Habibnejad, M., Soleimani, K., and Naqvi, H. 2013. Investigating the efficiency of the SDSM model in simulating temperature indices in arid and semi-arid regions. Quarterly Scientific Research Journal of Irrigation and Water Engineering. 14. 17-1. (In Persian)
2-Amiri, E., and Rezaei, R. 2009. Testing the modelling capability of ORYZA2000 under water–nitrogen limit conditions in northern Iran. World Applied Sciences Journal, 6(8), 1113-1122. (In Persian)
3-Bond, W.J., and Midgley, G.F. 2000. A proposed CO2‐controlled mechanism of woody plant invasion in grasslands and savannas. Global Change Biology, 6(8), 865-869.
4-Dadrasi, A., Torabi, B., Rahimi, A., Soltani, A., and  Zeinali, E. 2020. Parameterization and evaluation of a simple simulation model (SSM-iCrop2) for potato (Solanum tuberosum L.) growth and yield in Iran. Potato Res. 1-19. (In Persian)
5-Dukes, J.S., N.R. Chiariello, E.E. Cleland, L.A. Moore, M.R. Shaw, et al. 2005. Responses of grassland production to single and multiple global environmental changes. PLoS Biology 3(10): e319.
6-Ganjurjav, H., Gao, Q., Zhang, W., Liang, Y., Li, Y., Cao, X., Wan, Y., Li, Y., and Danjiu, L. 2015. Effects of warming on CO2 fluxes in an alpine meadow ecosystem on the central Qinghai-Tibetan Plateau. PLoS ONE, 10(7), e0132044. 
7-Hopping, K. A., Knapp, A. K., Dorji, T., and Klein, J. A. 2018. Warming and land use change concurrently erode ecosystem services in Tibet. Global Change Biology, 24(11), 5534–5548. 
8- Hovenden, M. J., Newton, P. C., Wills, K. E., Janes, J. K., Williams, A. L., Vander Schoor, J. K., and Nolan, M. J. 2008. Influence of warming on soil water potential controls seedling mortality in perennial but not annual species in a temperate grassland. New Phytologist, 180(1), 143-152.‏
9-Hudson, L.N., Newbold, T., Contu, S., Hill, S.L., Lysenko, I., De Palma, A., Phillips, H.R., Senior, R.A.,  Bennett, D.J., Booth, H., and Choimes, A., 2014. The PREDICTS database: a global database of how local terrestrial biodiversity responds to human impacts. Ecology and evolution, 4(24), 4701-4735.
10-Jochum, G.M., Mudge, K.W., and Thomas, R.B., 2007. Elevated temperatures increase leaf senescence and root secondary metabolite concentrations in the understory herb Panax quinquefolius (Araliaceae). American Journal of Botany, 94(5), 819-826.
11-Kamari, H., Zeinali, E., Soltani, A,. and Ghaderifar, F. 2019. Parameterization and evaluation of the SSM-iCrop2 model for predicting cotton growth and yield in Iran. Simulation of cotton growth and yield using the SSM-iCrop2 model. Journal of Iranian Cotton Research. 8(2). 72-49. (In Persian)
12-Karandish, F. Mousavi, S. and Tabari, H. 2016. Temporal and spatial changes in precipitation in Iran under the influence of climate change until the year 2100. (In Persian)
13-Kay, A.L., Davies, H.N., Bell, V.A., and Jones, R.G., 2009. Comparison of uncertainty sources for climate change impacts: flood frequency in England. Climatic Change, Vol. 92, pp. 41-63.
14-Keramat, Saleh, Torabi, B, Soltani, F, and Zainali, A. 2023. Parameterization and evaluation of the SSM-iCrop2 model for simulating rice plant growth and yield in Iran. Journal of Plant Production Research, 30 (1), 47-21. (In Persian)
15-Khaliqi, M.M. Khaliqi, N., and Farahpour, M. 2006. Investigating the social and ecological sustainability of rangeland exploitation methods. Rangeland and Desert Research. 13, 82-93. (In Persian)
16-Khodamoradpour, M. Khani, F. and Mahmourvand, R. 2018. Forecasting climate change in air temperature using the RegCM model coupled with the lake model in the next decade (case study of northwest and west Iran). Second National Conference on Meteorology and Hydrology of Iran, 19. (In Persian)
17-Klein, J. A., Harte, J., and Zhao, X. Q. 2007. Experimental warming, not grazing, decreases rangeland quality on the Tibetan Plateau. Ecological Applications, 17(2), 541–557.
18-Kohli, M., Mijiddorj, T.N., Suryawanshi, K.R., Mishra, C., Boldgiv, B.,and Sankaran, M., 2021. Grazing and climate change have site‐dependent interactive effects on vegetation in Asian montane rangelands. Journal of Applied Ecology, 58(3), pp.539-549
19-Louhaichi, M., Ouled Belgacem, A., Petersen, S.L., and Hassan, S., 2019. Effects of climate change and grazing pressure on shrub communities of West Asian rangelands. International Journal of Climate Change Strategies and Management, 11(5), 660-671.
20-Mokgotsi, R.O., 2018. Effects of bush encroachment control in a communal managed area in the Taung region, North West Province, South Africa (Doctoral dissertation, North-West University).
21- Morgan, J. A., LeCain, D. R., Pendall, E., Blumenthal, D. M., Kimball, B. A., Carrillo, Y., … and West, M. C4 grasses prosper as carbon dioxide eliminates desiccation in warmed semi-arid grassland. Nature 476, 202–206.
22. Miller, J., Franklin, J., and Aspinall, R. 2007. Incorporating spatial dependence in predictive vegetation models. Ecology Model, 202: 225-242
23-Nehbehdani, A., Barani, H., Soltani, A., and., Torabi, B. 2019. Modeling production in the country's deserts under current and future climatic conditions. Agricultural Research, Education and Extension Organization. (In Persian)
24-Ouled Belgacem, A., and Louhaichi, M., 2013. The vulnerability of native rangeland plant species to global climate change in the West Asia and North African regions. Climatic Change, 119, 451-463.
25-Polley, H.W., Bailey, D.W., Nowak, R.S., and Stafford-Smith, M., 2017. Ecological consequences of climate change on rangelands. Rangeland systems: Processes, management and challenges, 229-260.
26-Pourshirazi, Sh. Soltani, H. Zainali, A. and Torabi, B. 2023. Parameterization and evaluation of the iCrop2-SSM model for predicting alfalfa growth and yield in Iran, Journal of Agricultural Ecology, 15(1), 169-189. (In Persian)
27-Rahban, S. Torabi, B. Soltani, L. and Zainali, A. 2021. Using the SSM-iCrop model to predict the phenology, yield and water efficiency of rapeseed under Iranian conditions. Journal of Agricultural Ecology. 13(1), 157-177. (In Persian)
 28-Sedaghatkerdar, A., and Fattahi, E. 2008. Warningof droughtindicesinIran, Journalof   Geographyand Development, University of Sistanand Baluchestan; 6 (11): 76-59.(In  Persian) 
29-Senda, T.S., Kiker, G.A., Masikati, P., Chirima, A., and van Niekerk, J., 2020. Modeling climate change impacts on rangeland productivity and livestock population dynamics in Nkayi District, Zimbabwe. Applied sciences, 10(7), 23-30.
30-Sherry, R.A., E. Weng, J.A. Arnone, D.W. Johnson, D.S. Schimel, P.S. Verburg, L.L. Wallace., and Luo, Y.2008. Lagged effects of experimental warming and doubled precipitation on annual and seasonal aboveground biomass production in a tallgrass prairie. Global Change Biology 14, 2923–2936.
31-Sinclair, T., Farias, J., Neumaier, N., and Nepomuceno, A. 2003. Modeling nitrogen accumulation and use by soybean. Field Crop Res. 81, 149-158.
32-Smith, S.D., and R.S. Nowak. 1990. Ecophysiology of plants in the Intermountain lowlands. In Ecological studies, eds. C.B. Osmond, L.F. Pitelka, and G.M. Hidy. Vol. 80. Plant biology of the basin and range. Heidelberg: Springer-Verlag.
33-Soltani, A., and T.R. Sinclair, 2012. Modeling physiology of crop development, growth and yield. CAB International, Wallingford, UK. (In Persian)
34-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. (In Persian)
35-Soltani, A., and Mirzaei, A.R. 2021. Sustainable Agriculture. (In Persian)
36-Soraya, M., Tavousi, T., and Amini, M. 2007. Study of the trend of changes in monthly temperature characteristics of Zahedan city during the last half century. Master's thesis, Faculty of Humanities, University of Sistan and Baluchestan. (In Persian)
37-Tebaldi, C., and Lobell. D. 2015. Estimated impacts of emission reductions on wheat and maize crops. Climatic Change. DOI 10.1007/s10584-015-1537-5.
38-Torabi, B., A., Soltani, S. Rahban, A. Habib-Pour-Kashfi, S., and Pourshirazi, M. Alimoqam. 2019. Assessment of the impact of future climate on the performance of important agricultural and horticultural crops in Iran, Appendix 7 in Soltani et al. 2019. Analysis of the country's food security until 2050 by modeling the interrelationship of water, land, food and the environment: Perspectives and necessary policies. Research project report. Agricultural Research, Education and Extension Organization. (In Persian)
39-Wang, Q-x., Fan, X-h., Qin, Z-d., and Wang, M.-b. 2012. Change Trends of Temperature and Precipitation in the Loess Plateau Region of China, 1961–2010. Global and Planetary Change, 138–147.