نوع مقاله : مقاله پژوهشی
نویسندگان
1 گروه جغرافیا، دانشکده علوم انسانی، دانشگاه هرمزگان،بندرعباس،ایران.
2 دانشیار گروه جغرافیا، دانشکده علوم انسانی، دانشگاه هرمزگان، بندرعباس،ایران.
3 استادیار علوم باغبانی، گروه کشاورزی، دانشکده کشاورزی و منابع طبیعی، دانشگاه هرمزگان، بندرعباس، ایران.
چکیده
کشور ایران جزء مناطق خشک و نیمه‌خشک دنیا محسوب می‌شود که حدود 65 تا 70 درصد اراضی زیر کشت محصولات عمده زراعی به گندم تعلق دارد. در این پژوهش به‌منظور پیش‌یابی اثر تغییر اقلیم بر عملکرد گندم پاییزه (آبی) جیرفت ابتدا به منظور شبیه‌سازی عملکرد گندم پاییزه از مدل گیاهی آکواکراپ استفاده شده است. سپس برای آشکار سازی تغییرات متغیرهای اقلیمی طی سال‌های 2020 تا 2098 از 17 مدل گردش عمومی جوّ از سری مدلهای CMIP5 تحت سناریوهای واداشت تابشی RCP4.5 و RCP8.5 استفاده شده است. خروجی این مدلها بر اساس ابزار مولد اقلیم MarkSimGCM ریزگردانی شده است. ارزیابی دقت مدل AquaCrop در شبیه‌سازی عملکرد گندم طی دوره پایه (1990-2016) نسبت به عملکرد مشاهده‌شده نشان دهنده دقت مطلوب این مدل با میزان ضرایب R2، MAE، RMSE و MAPE 7/0، 203، 237 و 43/11 درصد بوده است. نتایج پیش‌بینی عملکرد گندم طی دوره آتی نشان داد که تحت هر دو سناریو افزایش خواهد یافت که میانگین افزایش عملکرد طی دوره آتی نسبت به دوره پایه برای RCP4.5 75 و برای RCP8.5 91 کیلوگرم در هکتار خواهد بود. دلیل افزایش عملکرد این محصول کاهش دمای حداقل و حداکثر طی دوره آتی بخصوص در زمان انتهایی رشد گندم و کاهش تنش گرمایی است.
کلیدواژه‌ها
موضوعات
عنوان مقاله [English]
The Impact of Climate Change on Jiroft Spring Wheat Yield Using Radiative Forcing Scenarios.
نویسندگان [English]
- Yadollah Pourshanbeh 1
- Asadollah Khoorani 2
- Leila Jafari 3
1 Department of Geography, Faculty of Humanities, Hormozgan University,BandarAbbas, Iran.
2 Associate professor of Physical Geography, University of Hormozgan, BandarAbbas, Iran.
3 Assistant Professor of Horticultural Sciences, Department of Agriculture, Faculty of Agriculture and Natural Resources, Hormozgan University, Bandar Abbas, Iran.
چکیده [English]
Iran is considered one of the most arid and semi-arid regions in the world, where wheat accounts for about 65-70% of the cultivated land for the main crops. To predict the impact of climate change on the yield of fall wheat (blue) in Jiroft, the Aquacrop crop model was used to simulate fall wheat yield in this study. Then, 17 general atmospheric circulation models from the CMIP5 model suite were used under the RCP4.5 and RCP8.5 radiative forcing scenarios to show changes in climate variables from 2020 to 2098. The results of these models were fine-tuned using the MarkSimGCM climate generator. Evaluation of the accuracy of the AquaCrop model in simulating wheat yield during the baseline period (1990-2016) compared to the observed yield shows the optimal accuracy of this model with R2, MAE, RMSE, and MAPE coefficients of 0.7, 203, 237, and 11.43 percent.The results of predicting wheat yield in the future period show that it will increase under both scenarios, that the average yield increase in the future period compared to the base period is 75 kg/ha for RCP4.5 and 91 kg/ha for RCP8.5. The reason for the yield increase in this product is the reduction of minimum and maximum temperatures in the future period, especially at the end of wheat growth, and the reduction of heat stress.
کلیدواژه‌ها [English]
- Climate change
- Autumn wheat
- Jiroft
- AquaCrop
- Radiative forcing scenarios
- Ali, S., Liu, Y., Ishaq, M., Shah, T., Ilyas, A., & Din, I, 2017, Climate change and its impact on the yield of major food crops: Evidence from Pakistan. Foods, No. 6,pp. 39.
- Braun, H. J., Atlin, G. N., & Payne, T. S,2010, Multi-location testing as a tool to identify plant response to global climate change. Climate change and crop production, No. CIS-6053.
- Daryoush, R., Javad, K., Teimour, A, 2011, The Statistical-synoptic Analysis of Heavy Showers in Arid Regions of Iran: Kerman Province. Journal of Geography and Regional Development,NO. 8, pp. 203-224.
- Deihimfard, R., Eyni-Nargeseh, H., & Mokhtassi-Bidgoli, A, 2018, Effect of future climate change on wheat yield and water use efficiency under semi-arid conditions as predicted by APSIM-wheat model. International Journal of Plant Production, No. 12,pp. 115-125.
- Ewert, F., Rounsevell, M. D. A., Reginster, I., Metzger, M. J., & Leemans, R, 2005, Future scenarios of European agricultural land use: I. Estimating changes in crop productivity. Agriculture, Ecosystems & Environment, No. 107.pp. 101-116.
- Eyni Nargsha, H., Dehim Fard, R., Sufizadeh, S., Haghighat, M., Nouri, O, 2015, Predicting the impacts of climate change on irrigated wheat yield in Fars province using APSIM model. Journal of Crop Production, NO. 8, pp. 203-224.
- Hajjam, S., Khoshkho, Y., Shamsudini, R, 2008, Annual And Seasonal Precipitation Trend Analysis of Some Selective Meteorological Stations In Central Region of Iran Using Non-Poarametric Methods. Physical Geography Research Quarterly, NO. 1. pp. 0(0).
- Heidari Nia, M., ANasseri, A., Boroumand Nesb, S, 2012, Exploring the possibility of use in irrigation scheduling model AquaCrop sunflower in Ahvaz. Journal of Water Resources, NO. 5, pp. 37-50.
- Hosseini, T., Khoshravesh, M., Zayatbar Ahmadi, M, 2016, Effect of Climate Change and Evaluation of Planting Date on Soybean Yield. Journal of Water Research in Agriculture,NO. 29, pp. 559-575.
- IPCC, 2007. Summary for Policy Makers. Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report. Cambridge University Press Cambridge
- Jones, P. G., & Thornton, P. K, 2003, The potential impacts of climate change on maize production in Africa and Latin America in 2055. Global environmental change, No. 13, pp. 51-59.
- Karimzadeh Soureshjani, H., Emam, Y., Moori, S, 2012, Effect of post-anthesis drought stress on yield, yield components and canopy temperature of bread wheat cultivars. Journal of plant, NO. 1, pp. 38-56.
- Khorsand, A., Verdinejad, V., Shahidi, A, 2014, Performance evaluation of AquaCrop model to predict yield production of wheat, soil water and solute transport under water and salinity stresses. Journal of Water and Irrigation Management, NO. 4. Pp. 89-104.
- Pohlert, T, 2016, Non-parametric trend tests and change-point detection. CC BY-ND, 4.
- Sivakumar, M. V. K., Das, H. P., & Brunini, O, 2005, Impacts of present and future climate variability and change on agriculture and forestry in the arid and semi-arid tropics. In increasing climate variability and change pp. 31-72.
- Soleimani, F., Brumand, N., azareh, A, 2017, Evaluating the Trend of Spatial and Temporal Changes in Groundwater Quality in Jiroft Plain. Journal of Range and Watershed Managment, NO. 69, pp. 921-932.
- Valizadeh, J., Ziaei, S. M., & Mazloumzadeh, S. M, 2014, Assessing climate change impacts on wheat production (a case study). Journal of the Saudi Society of Agricultural Sciences, No. 13, pp. 107-115.
- Valizadeh, J., Ziaei, S. M., & Mazloumzadeh, S. M. (2014). Assessing climate change impacts on wheat production (a case study). Journal of the Saudi Society of Agricultural Sciences, No. 13,pp. 107-115.
- Wolf, J, 2002, Comparison of two potato simulation models under climate change. II. Application of climate change scenarios. Climate Research, No. 21,pp. 187-198.
- Xiao, D., Bai, H., & Liu, D, 2018, Impact of Future Climate Change on Wheat Production: A Simulated Case for China’s Wheat System. Sustainability, No. 10, pp.1277.
- Zeinali Mobarakeh, Z., Deihimfard, R., Kambouzia, J, 2018, Modelling the Impacts of Climate Change on Irrigated Wheat Yield Under Water Limited Conditions in Khorasan Razavi Province. Journal of Agricultural science and sustainable production, NO. 28, pp. 155-169.
)