زراعت و فناوری زعفران

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بررسی ردپای آب محصول زعفران در دشت بیرجند تحت شرایط تغییر اقلیم

نوع مقاله : مقاله علمی پژوهشی

نویسندگان

1 دانشجوی دکتری علوم و مهندسی آب- منابع آب ، گروه علوم و مهندسی آب، دانشکده کشاورزی بیرجند، بیرجند، ایران

2 استاد، گروه علوم و مهندسی آب، دانشکده کشاورزی بیرجند، بیرجند، ایران

3 دانشیار ، گروه علوم و مهندسی آب، دانشکده کشاورزی بیرجند، بیرجند، ایران،

4 دانشیار ، گروه علوم و مهندسی آب، دانشکده مهندسی آب، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، گرگان، ایران

چکیده

بررسی و شناسایی میزان آب واقعی استفاده شده برای محصولات مختلف کشاورزی از اهمیت ویژه­ای برخوردار است و با در نظر گرفتن چنین ارزیابی­هایی می­توان راهکارهای مناسبی جهت کاهش مصرف آب کشاورزی ارائه داد که دارای اهمیت بالایی است. شاخص ردپای آب به عنوان یک شاخص جهانی مقدار واقعی آب مصرفی محصولات را براساس شرایط و اقلیم هر منطقه نشان می­دهد. از طرفی دیگر پدیده تغییر اقلیم از مهم­ترین چالش­های زیست محیطی است که بر منابع آب تأثیر بسزایی دارد. بنابراین ارزیابی این پدیده نیز جهت پیش­بینی تأثیر آن بر مصرف آب در بخش کشاورزی امری مهم و ضروری است. در این تحقیق به شبیه­سازی پارامترهای اقلیمی با استفاده از مدل گزارش ششم گردش عمومی جوی اقیانوسی MIROCES2L تحت سه سناریوی SSP1-2.6، SSP2-4.5 و SSP5-8.5 در دشت بیرجند پرداخته شده است و سپس با استفاده از نتایج آن محاسبه پیش­بینی ردپای آب آبی و ردپای آب سبز محصول استراتژیک زعفران در منطقه دشت بیرجند انجام شد. نتایج بخش اول نشان داد که حداقل دما و حداکثر دما در هر سه سناریو در آینده نزدیک (2022-2050) به طور کلی افزایش یافته است و پارامتر بارش در پاییز و زمستان افزایش و در بهار و تابستان کاهش می‌یابد. در بخش دوم نیز پیش­بینی عملکرد محصول زعفران توسط مدل NIO نشان داد که تحت سه سناریوی SSP1-2.6، SSP2-4.5 و SSP5-8.5 در آینده نزدیک (2038-2022) به طور میانگین به ترتیب به میزان 13/0، 21/0 و 05/0 کیلوگرم بر هکتار نسبت به دوره مشاهداتی (2021-2005) کاهش یافته است و در ادامه نتایج نشان داد با افزایش نیاز آبی در دوره آینده، ردپای آب آبی، ردپای آب سبز و ردپای آب کل محصول زعفران تحت تأثیر تغییر اقلیم در دوره آینده تقریبا به میزان 2 برابر نسبت به دوره مشاهداتی افزایش داشته است. هم­چنین نسبت مصرف آب آبی به آب سبز در این محصول در آینده تحت هر سه سناریو نسبت به دوره مشاهداتی از 91/1 تا 04/2 افزایش یافته است. بنابراین با وجود پدیده تغییر اقلیم، افزایش دما، افزایش نیاز آبی و در نهایت افزایش ردپای مصرف آب در منابع آب سطحی و زیرزمینی در دشت بیرجند در طی سال­های آینده، ضروری است تا برای پیاده­سازی الگوی مناسب مصارف آبی در دشت و به کار گرفتن راهکارهای مناسب و مؤثر جهت کاهش ردپای آب در منطقه مطالعاتی نیز روش­هایی مبنی بر کاهش سطح زیرکشت، کم آبیاری، تغییر الگوی کشت و تغییر تقویم زراعی نیز مطرح و اجرایی شوند.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Investigating the water footprint of saffron production in Birjand Plain under climate change conditions

نویسندگان [English]

  • Fariba Niroomandfad 1
  • Abbas Khashei Siuki 2
  • Seyed Reza Hashemi 3
  • Khalil Ghorbani 4

1 PhD Candidatei in Water Science and Engineering- Water Resources, Department of Water Science and Engineering, Faculty of Agriculture, Birjand University, Birjand, Iran

2 Professor, Department of Water Science and Engineering, Faculty of Agriculture, University of Birjand, Birjand, Iran

3 Associate Professor (Department of Water Science and Engineering, Faculty of Agriculture, University of Birjand, Birjand, Iran)

4 Associate Professor (Department of Water Science and Engineering, Faculty of Water Engineering, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran)

چکیده [English]

Investigating and identifying the actual amount of water used for different agricultural products is of particular importance, and considering such evaluations, appropriate solutions can be provided to reduce agricultural water consumption, which is of great importance. The water footprint index as a global index shows the actual amount of water consumed by products based on the conditions and climate of each region. On the other hand, the phenomenon of climate change is one of the most important environmental challenges that has a significant impact on water resources. Therefore, it is important and necessary to evaluate this phenomenon in order to predict its impact on water consumption in the agricultural sector. In this research, the simulation of climate parameters using the MIROCES2L model of the 6th Report of the General Oceanic Atmospheric Circulation under three scenarios SSP1-2.6, SSP2-4.5, and SSP5-8.5 in Birjand plain has been done and then using those results to calculate the footprint prediction The blue water and green water traces of the strategic product of saffron were carried out in the Birjand Plain region. The results of the first part showed that the minimum temperature and maximum temperature in all three scenarios in the future (2050-2022) generally increased and the precipitation parameter increased in autumn and winter and decreased in spring and summer. In the second part, the prediction of saffron crop performance by NIO model showed that under three scenarios SSP1-2.6, SSP2-4.5 and SSP5-8.5 in the future (2022-2038) on average 0.13, 0.21 respectively and 0.05 kg/ha has decreased compared to the observation period (2005-2021) and the results showed that with the increase in water demand in the future period, the water footprint, the green water footprint and the total water footprint of the saffron crop under the influence of climate change In the future period, it has increased by almost 2 times compared to the observation period. Also, the ratio of blue water consumption to green water in this product has increased in the future under all three scenarios compared to the observation period from 1.91 to 2.04. Therefore, despite the phenomenon of climate change, increase in temperature, increase in water demand, and finally increase in the footprint of water consumption in surface and underground water sources in the Birjand plain in the coming years, it is necessary to implement a suitable model of water consumption in the plain and To use appropriate and effective solutions to reduce the water footprint in the study area, the methods of reducing the area under cultivation, less irrigation, changing the cultivation pattern and changing the agricultural calendar should also be proposed and implemented.

کلیدواژه‌ها [English]

  • : climate change
  • crop yield
  • water footprint
  • NIO
  • CMIP6
مراجع
Aligholinya, T., Sheibany, H., Mohamadi, O., & Hesam, M. (2019). Comparison and evaluation of blue, green and gray water footprint of wheat in different climates of Iran. Iran-Water Resources Research, 15(3), 234-245. https://doi.org/10.20.1001.1.17352347.1398.15.3.18.9.
Azari, M., Moradi, H.R., Saghafian, B., & Faramarzi, M. (2016). Climate change impacts on streamflow and sediment yield in the North of Iran. Hydrological Sciences, 61(1), 123–133. https://doi.org/10.1080/02626667.2014.967695.
Ayala, L.M., van Eupen, M., Zhang, G., Pérez-Soba, M., Martorano, L.G., Lisboa, L.S., & Beltrao, N.E. (2016). Impact of agricultural expansion on water footprint in the Amazon under climate change scenarios. Science of the Total Environment, 569, 1159-1173.  https://doi.org/10.1016/j.scitotenv.2016.06.191.
Babaee, M., Maroufpoor, S., Jalali, M., Zarei, M., & Elbeltagi, A. (2021). Artificial intelligence approach to estimating rice yield. Irrigation and Drainage, 70(4), 732-742. https://doi.org/ 0.1002/ird.2566.
Bahmanyar, M. )2002(. Birjand Negin Kavir. Tehran University Publications, Tehran, Iran. pp. 254. [In Persian].
Bazrafshan, O., Etedali, H.R., Moshizi, Z.G.N., & Shamili, M. (2019a). Virtual water trade and water footprint accounting of Saffron production in Iran. Agricultural Water Management, 213, 368-374. https://doi.org/10.1016/j.agwat.2018.10.034.
Bazrafshan, O., Zamani, H., Etedali, H.R., & Dehghanpir, S. (2019b). Assessment of citrus water footprint components and impact of climatic and non-climatic factors on them. Scientia Horticulturae, 250, 344-351. https://doi.org/doi.org/10.1016/j.scienta.2019.02.069.
Babazadeh, H., & Saraeetabrizi, M. (2013). Calibration of SWAP model for simulating crop yield, biological yield and soybean water use efficiency. Irrigation Sciences and Engineering, 35(4), 83-96. https://doi.org/20.1001.1.25885952.1391.35.4.9.9.
Baghalian, K., Sheshtamand, M.S., & Jamshidi, A.H. (2010). Genetic variation and heritability of agro-morphological and phytochemical traits in Iranian saffron (Crocus sativus L.) populations. Industrial Crops and Products, 31(2), 401-406. https://doi.org/10.1016/j.indcrop.2009.12.010.
Bazrafshan, O., & Moshizi, Z.G.N. (2019c). Assessment of water use efficiency and water footprint of saffron production in Iran. Saffron Agronomy and Technology, 7(4), 505-519. (In Persian with English Abstract). https://doi.org/10.22048/jsat.2019.141824.1311.
Bocchiola, D., Nana, E., & Soncini, A. (2013) Impact of climate change scenarios on crop yield and water footprint of maize in the Po valley of Italy. Agricultural Water Management, 116, 50-61. https://doi.org/10.1016/j.agwat.2012.10.009.
Chen, H., & Sun, J. (2015). Assessing model performance of climate extremes in China: an intercomparison between CMIP5 and CMIP3. Climatic Change, 129, 197-211. https://doi.org/10.1007/s10584-014-1319-5.
Chico, D., Aldaya, M.M., & Garrido, A. (2013). A water footprint assessment of a pair of jeans: the influence of agricultural policies on the sustainability of consumer products. Journal of Cleaner Production, 57, 238-248.  https://doi.org/10.1016/j.jclepro.2013.06.001.
Chapagain, A.K., Hoekstra, A.Y., Savenije, H.H., & Gautam, R. (2006). The water footprint of cotton consumption: An assessment of the impact of worldwide consumption of cotton products on the water resources in the cotton producing countries. Ecological Economics, 60(1), 186-203.  https://doi.org/10.1016/j.ecolecon.2005.11.027.
De Oliveira, V.A., de Mello, C.R., Beskow, S., Viola, M.R., & Srinivasan, R. (2019). Modeling the effects of climate change on hydrology and sediment load in a headwater basin in the Brazilian Cerrado biome. Ecological Engineering, 133, 20–31.  https://doi.org/10.1016/j.ecoleng.2019.04.021.
Fulton, J., Norton, M., & Shilling, F. (2019). Water-indexed benefits and impacts of California almonds. Ecological Indicators, 96, 711-717.  https://doi.org/10.1016/j.ecolind.2017.12.063.
FAO. (2010). ‘CROPWAT 8.0 model’, FAO, Rome, www.fao.org/nr/water/infores_ databases_cropwat.html.
Eyring, V., Bony, S., Meehl, G.A., Senior, C.A., Stevens, B., Stouffer, R.J., & Taylor, K.E. (2016). Overview of the coupled model intercomparison project phase 6 (CMIP6) experimental design and organization. Geoscientific Model Development, 9(5), 1937-1958. https://doi.org/10.5194/gmd-9-1937-2016, 2016.
Gerkani Nezhad Moshizi, Z., Bazrafshan, O., Ramezani Etedali, H., Esmaeilpour, Y., & Collins, B. (2022). The effect of past climate change on the water footprint trend in saffron at homogeneous agroclimatic regions of Khorasan. Journal of Saffron Research, 10(2), 295-311. (In Persian with English Abstract).  https://doi.org/10.22077/jsr.2022.5742.1199.
Govere, S., Nyamangara, J., & Nyakatawa, E.Z. (2022). Beneficial effect of climate change on wheat yield and water footprints in the Middle-Manayame sub-catchment, Zimbabwe. Journal of Water and Climate Change, 13(8), 2895. https://doi.org/10.2166/wcc.2022.038.
Gupta, V., Singh, V., & Jain, M.K. (2020). Assessment of precipitation extremes in India during the 21st century under SSP1-1.9 mitigation scenarios of CMIP6 GCMs. Journal of Hydrology, 590(1), 125422.  https://doi.org/10.1016/j.jhydrol.2020.125422.
Gupta, H.V., Kling, H., Yilmaz, K.K., & Martinez, G.F. (2009). Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modeling. Journal of Hydrology, 377(1-2), 80-91.  https://doi.org/10.1016/j.jhydrol.2009.08.003.
Hoekstra, A.Y., Mekonnen, M.M., Chapagain, A.K., Mathews, R.E., & Richter, B.D. (2012). Global monthly water scarcity: blue water footprints versus blue water availability. PloS One, 7(2), e32688. https://doi.org/10.1371/journal.pone.0032688.
Hoekstra, A.Y., & Chapagain, A.K. (2007). Water footprints of nations: water use by people as a function of their consumption pattern. Integrated Assessment of Water Resources and Global Change: A north-south analysis, 35-48.  https://doi.org/10.1007/978-1-4020-5591-1_3.
IPCC. (2001). Climate change, Impacts Adaptation and Vulnerability. Contribution of Working Group II to the third assessment report of the Intergovernmental Panel on Climate Change. UK: Cambridge University Press. https://doi.org/doi.org/10.1002/joc.775.
Jafarzadeh, A., Khashei-Siuki, A., & Shahidi, A. (2015). Modeling of climate change effects on saffron water requirement in south Khorasan province by GIS. Journal of Saffron Research, 3(2), 163-174.  (In Persian with English Abstract). https://doi.org/10.22077/jsr.2015.292.
Khanali, M., Shahvarooghi Farahani, S., Shojaei, H., & Elhami, B. (2017). Life cycle environmental impacts of saffron production in Iran. Environmental Science and Pollution Research, 24, 4812-4821.  https://doi.org/10.1007/s11356-016-8228-2.
Mail Santosh, S., Shrsath Paresh, B., & Islam, A. (2021). A high-resolution assessment of climate change impact on water footprints of cereal production in India. Scientific Reports, 11, (8715).  https://doi.org/10.1038/s41598-021-88223-6.
Mekonnen, M.M., & Gerbens-Leenes, W. 2020. The water footprint of global food production. Water, 12(10), 2696. https://doi.org/10.3390/w12102696.
Maleki, F., Kazemi, H., Siahmargue, A., & Kamkar, B. (2019). Investigation of climatic factors of Azadshahr township (Golestan province) in order to development of saffron cropping. Journal of Saffron Research, 7(1), 123-143. (In Persian with English Abstract). https://doi.org/10.22077/jsr.2018.1420.1056.
Montaseri, M., Rasouli Majd, N., Behmanesh, J., & Rezaie, H. (2015). Evaluation of agricultural crops water footprint with application of climate change in Urmia lake basin. Journal of Water and Soil, 30(4), 1075-1089. (In Persian).  https://doi.org/10.22067/jsw.v30i4.45420.
Mekonnen, M.M., & Hoekstra, A.Y. (2011). The green, blue and grey water footprint of crops and derived crop products. Hydrology and Earth System Sciences, 15(5), 1577-1600.  https://doi.org/10.5194/hessd-8-763-2011.
Op de Hipt, F., Diekkruger, B., Steup, G., Yira, Y., Hoffmann, T., & Rode, M. (2018). Modeling the impact of climate change on water resources and soil erosion in a tropical catchment in Burkina Faso, West Africa, CATENA, 163, 63–77. https://doi.org/10.1016/j.catena.2017.11.023.
Op de Hipt, F., Diekkruger, B., Steup, G., Yira, Y., Hoffmann, T., Rode, M., & Naschen, K. (2019). Modeling the effect of land use and climate change on water resources and soil erosion in a tropical West African catch-ment (Dano, Burkina Faso) using SHETRAN, Science of Total Environment, 653, 431–445.  https://doi.org/10.1016/j.scitotenv.2018.10.351.
Omolaye, O.P., & Badmos, T.A. (2017). Predictive Comparative Analysis of NARX and NIO Time Series Prediction. American Journal of Engineering Research (AJER), 6(9), 155-165.
O’Neill, B.C., Tebaldi, C., & van Vuuren, D.P. (2017). The scenario model intercomparison project (ScenarioMIP) for CMIP6. Geoscientific Model Development, 9, 3461–82. https://doi.org/10.5194/gmd-9-3461-2016.
Rahimipour Anaraki, M.R., Mohammadi, A., Rafieian, M., Arjmandi, R., & Karimi, S. (2020). Evaluation of virtual water and water footprint of crop production (Case study: Qaleganj County). Journal of Arid Regions Geographics Studies, 11(41), 77-92.
Rajaee, T., Ebrahimi, H., & Nourani, V. (2019). A review of the artificial intelligence methods in groundwater level modeling. Journal of Hydrology, 572, 336-351.  https://doi.org/10.1016/j.jhydrol.2018.12.037.
Raziei, T., & Sotoudeh, F. (2017). Investigation of the accuracy of the European Center for Medium Range Weather Forecasts (ECMWF) in forecasting observed precipitation in different climates of Iran. Journal of the Earth and Space Physics, 43(1), 133-147.   https://doi.org/10.22059/JESPHYS.2017.57958.
Sadeghiyan, A., Vagheiy, Y., & Mohammadzadeh, M. (2013). Spatial-temporal prediction of groundwater level in Birjand region using Kriging method. Journal of Water and Wastewater; Ab va Fazilab, 24(1), 94-100. [In Persian]. https://www.wwjournal.ir/article_2343.html?lang=en.
Su, B., Huang, J., Mondal, S.K., Zhai, J., Wang, Y., Wen, S., & Li, A. (2021). Insight from CMIP6 SSP-RCP scenarios for future drought characteristics in China. Atmospheric Research, 250, 105375. https://doi.org/10.1016/j.atmosres.2020.105375.
Wang, X.Y. (2010). Irrigation water use efficiency of farmers and its determinants: Evidence from a survey in northwestern China. Agricultural Sciences in China, 9(9), 1326-1337. https://doi.org/10.1016/S1671-2927(09)60223-6.
Zia, H., Lashkaripour, G.H., & Rostami Barani, H.R. (2006). Geoelectric Study Artificial recharge Flood Distribution in the Birjand for Detection of hydrogeological conditions. Tenth Symposium of Geological Society of Iran, Tarbiat Modarres University. https://cds.climate.copernicus.eu/cdsapp#!/dataset/projections-cmip6?tab=form.
[In Persian].
زراعت و فناوری زعفران
دوره 11، شماره 3 - شماره پیاپی 40
مهر 1402
صفحه 301-320
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