توسعه چارچوبی برای ارزیابی ریسک خشکسالی کشاورزی بر گندم دیم

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانش آموخته / دکتری مهندسی آبیاری و زهکشی، گروه مهندسی آب، دانشکده کشاورزی، دانشگاه فردوسی مشهد.

2 مربی/ گروه مهندسی عمران، دانشکده مهندسی، دانشگاه آزاد اسلامی، واحد مشهد.

3 دانش آموخته / کارشناسی ارشد مهندسی آبیاری و زهکشی، گروه مهندسی آب، دانشکده کشاورزی، دانشگاه فردوسی مشهد.

4 استاد/ گروه مهندسی آب، دانشکده کشاورزی، دانشگاه فردوسی مشهد.

چکیده

کشاورزی دیم اولین بخشی است که از لحاظ اقتصادی مورد آسیب خشکسالی قرار می‌گیرد، از اینرو ارزیابی ریسک خشکسالی کشاورزی از اهمیت ویژه‌ای برخوردار است. هدف از انجام این تحقیق، توسعه یک چارچوب و مدل اجرایی برای کمی‌سازی ریسک خشکسالی کشاورزی با تمرکز بر محصول گندم دیم بوده است. در این تحقیق، ریسک خشکسالی کشاورزی بر اساس مخاطره خشکسالی و آسیب‌پذیری نسبت به خشکسالی در مراحل مختلف رشد محصول کمی می‌شود. احتمالات وقوع خشکسالی‌های با شدت مختلف در مراحل مختلف رشد محصول در نظر گرفته شده است. برای کمی‌سازی شدت‌ خشکسالی، از شاخص بارندگی و تبخیر-تعرق استاندارد شده (SEPI)، در مقیاس زمانی هفتگی استفاده می‌شود‌. از طرف دیگر، برای تعیین اثر خشکسالی بر محصول، مدل گیاهی آکواکراپ برای مدل‌سازی رشد محصول تحت شرایط اقلیمی مورد نظر واسنجی و اعتبارسنجی شده و افت محصول در اثر خشکسالی بدست می‌آید. برای تعیین مؤلفه آسیب‌پذیری، از منطق فازی استفاده می‌گردد. برای اجرای چارچوب توسعه داده شده، از داده‌های ایستگاه تحقیقات دیم سیساب واقع در خراسان شمالی استفاده شد. مقدار آسیب‌پذیری در منطقه مورد مطالعه از روش فازی، برابر با 6163/0 (بدون بعد) بدست آمد و در نهایت، مقدار ریسک خشکسالی گندم دیم در ایستگاه مورد مطالعه برابر با 3684/0 تن بر هکتار حاصل شد. نتایج حاصل از این مطالعه می‌تواند در فرآیند مدیریت ریسک و برنامه‌ریزی برای کاهش اثرات خشکسالی بر روی گندم دیم در مناطق مورد مطالعه و نیز تخمین نرخ بیمه کشاورزی در شرایط خشکسالی برای حداقل کردن ریسک آسیب خشکسالی بکار رود.

کلیدواژه‌ها


عنوان مقاله [English]

Developing a Framework for Agricultural Drought Risk Assessment for Rainfed Wheat

نویسندگان [English]

  • N. Khalili 1
  • H. Rezaee Pazhand 2
  • H. Derakhshan 3
  • k. Davary 4
1 PhD. Graduate of Irrigation and Drainage Engineering, Department of Water Engineering, College of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran.
2 Lecturer, Department of Civil, College of Engineering, Azad University of Mashhad, Mashhad, Iran.
3 M.Sc. Graduate of Irrigation and Drainage Engineering, Department of Water Engineering, College of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran.
4 Professor, Department of Water Engineering, College of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran
چکیده [English]

Agriculture, particularly rainfed agriculture, is the first sector being affected by drought; hence, evaluation of the agricultural drought risk is critically important for drought risk management. The objective of this paper is, therefore, to develop a systematic framework and realistic model for accurately quantifying agricultural drought risk with the focus on rainfed wheat. The proposed framework quantifies the agricultural risk based on the hazard and vulnerability levels for different stages of crop growth. To quantify the drought severity, we have employed Standardized Evapotranspiration and Precipitation Index (SEPI) as a drought index. On the other hand, to determine the drought effect on yield performance, Aquacrop model is adopted for different stages of crop growth to evaluate the yield lost due to the drought. For the vulnerability, fuzzy logic techniques are employed. The proposed framework is evaluated for the Sisab Rainfed Research Station in Northern Khorasan, Iran, using the 30-years (1980 to 2011) meteorological data. For this case, vulnerability, as a dimension less quantity, was calculated as 0.6163 and the drought risk level for rainfed wheat in Sisab Station was calculated as 0.3684 ton/acres. The developed framework can be used for systematic risk management to reduce the impact of drought effects as well as calculating agricultural insurance rates for droughty situations.

کلیدواژه‌ها [English]

  • Agricultural Drought Risk
  • Drought Hazard
  • Rainfed Wheat
  • vulnerability
Acosta-Michlik L, Kavi Kumar KS, Klein RJT and Campe S (2008) Application of fuzzy models to assess susceptibility to droughts from a socio-economic perspective. Regional Environmental Change 8:151–160
Aghayan S A, Kamali Gh and Hejam S (2015) Agricultural drought risk assessment for different climate region. Journal of Climate Research 21:61-72 (In Persian)
Allen RJ, DeGaetano AT (2001) Estimation missing daily temperature extremes using an optimized regression approach. International Journal of climatology 21:1305-1319
Ansari H, Davary K and Sanaienejad H (2010) Drought monitoring with new precipitation and Evapotranspiration index based on fuzzy logic. Journal of Water and Soil 24(1):38-52 (In Persian)
Arshad S, Morid S, Mobasheri MR and Agha Alikhani M (2008) Development of agricultural drought risk assessment model for Kermanshah province (Iran), using satellite data and intelligent methods. In: Proc. of First International Conference Drought Management: scientific and technological Innovations (Options Mediterranean’s, Series A), 12-14 June, Zaragoza, Spain, (80):303- 310
Bazrafshan J,  Khalili A,  Hoorfar A,  Torabi S and  Hejam (2009) Comparison of the performance of ClimGen and LARS-WG models in simulating the weather factors for diverse climates of Iran. Iran-Water Resources Research 5(1):44-57 (In Persian)
Bazrafshan J, Torabi S, Khalili A and Hoorfar A (2009) Drought risk analysis based on time dependent and independent data for rainfall, Case study: Sararood station in Kermanshah. In: Proc. of National Symposium of Ware Crisis in Agriculture and Natural Resources, 5 November, Rey, Iran (In Persian)
Behbahani MR, Rahimi khoob A, Nazari Far MH and Momeni R (2008) Agricultural drought risk Management for rainfed wheat, A case study: Hamedan Province. In: proc. of 3rd Iranian Conference of Water Resources Management, 14-16 October, Tabriz, Iran (In Persian)
Bodagh Jamali J, Asiaei M, Samadi Negab S and Javanmard S (2005) Drought risk management. 1st Ed, Mashhad, Sokhan Gostar publisher, 311 p (In Persian)
Chen J, Yang Y (2011) A fuzzy ANP-based approach to evaluate region agricultural drought risk. Procedia Engineering, 23:822 – 827
Chopra P (2006) Drought risk assessment using remote sensing and GIS: a case study of Gujarat. M.Sc. Thesis, International Institute for Geo-Information Science and Earth Observation, Enschede, Netherlands
Dalezios NR, Blanta A, Spyropoulos NV and Tarquis AM (2014) Risk identification of agricultural drought for sustainable Agroecosystems. Natural Hazards Earth System. Science 14:2435–2448
Foster T, Brozović N and Butler AP (2015) Why well yield matters for managing agricultural drought risk. Weather and Climate Extremes 10:11-19
Garviani GM (1988) Investigation of different phenological stages of rainfed wheat cultivars in Northern Khorasan. Jihad of ConstructionSymposium, Mashhad, Iran (In Persian)
Ghaseminejad S, Soltani S and Soffianian A (2014) Drought risk assessment in Isfahan Province. Journal of Water and Soil Science (JWSS): Journal of Science and Technology of Agriculture and Natural Resources 18(68):213-225
Hargreaves G H, Samani Z A (1985) Reference crop evapotranspiration from temperature. Transaction of ASAE 1(2):96-99
International Strategy for Disaster Reduction, United Nations (2007) Drought risk reduction framework and practices: Contributing to the   implementation of the Hyogof for action. UN, Switzerland, 98p
Kaewpruksapimon C (2006) Fuzzy logic technique for drought risk identification of Buriram province. M.S Thesis of Mahidol university, 170p
Khalili N, Alizadeh A, Rezaee Pazhand H, Ansari H, Ghahraman B, Kafi M and Davary K (2017) Interpolation of rainfall data using classical and geostatistical methods, Case study: Sisab station, North Khorasan. Iranian Journal of Irrigation and Drainage 1(11):93-103 (In Persian)
Khalili A, Bazrafshan J (2007) Assessment of return period and risk of drought duration using Annual Precipitation in Iran old stations. In: proc. of 3rd Iranian Conference of Water Resources Management, 23-24 January, Isafahn, Iran (In Persian)
Khalili N, Davary K, Ansari H, Alizadeh A and Rezayi Pazhand H (2013) Agricultural drought monitoring of the rainfed wheat in weekly time scale for the rainfall researches station of Sisab. Iranian Journal of Irrigation and Drainage 2(7):133-145 (In Persian)
Khalili N, Davary K, Alizadeh A, Ansari H, Rezaee Pazhand H, Kafi M and Ghahraman B (2016) Evaluation of the performance of ClimGen and LARS-WG models in generating rainfall and temperature time series in rainfed research station of Sisab, Northern Khorasan. Journal of Water and soil 30(1):322-333 (In Persian)
Mirzaei Nadooshan F, Morid S and Arshad S (2010) Agriculture drought risk in the cities of Kermanshah Province. Journal of Agricultural Engineering Research 11(3):1-14 (In Persian)
NazariFar MH, Banejad HV and Sabziparvar AA (2008) Drought risk monitoring and applying it in water resources management (Case Study: Hamedan Province). In: Proc. of 13th Iranian Geophysics Conference, 6-8 May, Tehran, Iran, 370-374 (In Persian)
Raes D, Steduto P, Hsiao TC and Fereres E (2009) AquaCrop-The FAO crop model for predicting yield response to water: II. Main algorithms and software description. Agronomy Journal 101:438–447
Richardson CW, Wright DA (1984) WGEN: a model for generating daily weather variables, Report. United States Department of Agriculture, Agriculture Research Service, ARS-8, 83p
Sahebjam AA, Abbaspoor Tabrizi A, Mahdavi M and Baghdadi M (2007) Detailed soil survey and land classification of Sisab agricultural and natural research station area. Soil and Water Research Institute, 70p (In Persian)
Semenov MA, Brooks RJ, Barrow EM and Richardson CW (1998) Comparison of the WGEN and LARS-WG stochastic weather generators for diverse climates. Climate Research 10:95-107
Shahid Sh, Behrawan H (2008) Drought risk assessment in the western part of Bangladesh. Natural Hazards 46:391–413
Shokohi M, Bazrafshan J and Ghahreman N (2014) Agricultural drought risk assessment for rainfed barley crop using limited data (Case study: East Azarbaijan, Iran). Iranian Journal of Soil and Water Research 44(2):123-133 (In Persian)
Skakun S, Kussul N, Shelestov A and Kussul O (2016) The use of satellite data for agriculture drought risk quantification in Ukraine, Geometrics. Natural Hazards and Risk 7(3):901-917
Steduto PT, Hsiao C,  Raes D and Fereres E (2009) AquaCrop-The FAO crop model to simulate yield response to water: I. Concepts and underlying principles. Agronomy Journal 101:426–437
Stöckle CO, Campbell GS and Nelson R (1999) ClimGen manual. Biological systems engineering department, Washington State University, Pullman, WA, 28p
Tsakiris G (2009) A paradigm for applying risk and hazard concepts in proactive planning. Chapter 7 in Iglesias, A. et al (Eds), Coping with drought risk in agriculture and water supply systems. Springer, 81-91
Tsakiris G, Tigkas D (2007) Drought risk in agriculture in Mediterranean regions, Case study:  Eastern Crete. Chapter 19 in: Rossi et al. (Eds), Methods and tools for drought analysis and management, Springer
Wang Z L, Wang J and Wang JS (2015) Risk assessment of agricultural drought disaster in Southern China. Discrete Dynamics in Nature and Society, 2015: ID 172919, 8p
Wu H, Wilhite DA (2004) An Operational agricultural drought risk assessment model for Nebraska. USA. Natural Hazards 33:1-21
Zadox JC, Chang TT and Konzak CF (1974) A decimal code for the growth of cereals. Weed Research 14:415-421