انتخاب منطقۀ مناسب در تولید گندم با استفاده از مفهوم رد پای آب و روش های تصمیم گیری اجتماعی (مطالعۀ موردی: استان اصفهان)

نوع مقاله : پژوهشی

نویسندگان

1 دانشجوی دکتری، دانشکدۀ مهندسی علوم آب، دانشگاه شهید چمران اهواز

2 دانشیار، دانشکدۀ مهندسی علوم آب، دانشگاه شهید چمران اهواز

3 استاد، دانشکدۀ مهندسی علوم آب، دانشگاه شهید چمران اهواز

4 دانشیار، دانشکدۀ محیط زیست، پردیس دانشکده ‏های فنی، دانشگاه تهران

چکیده

با بررسی هرچه دقیق‏تر رد پای آب در محصولات کشاورزی و همچنین، تغییرات آن در دورۀ زمانی بلندمدت می‏توان بهره‌برداری از منابع آب را مدیریت کرد. مطالعۀ حاضر با هدف بررسی روند سالانۀ رد پای آب در تولید گندم در شهرهای استان اصفهان و انتخاب مناسب‏ترین شهر این استان برای کشت گندم طی دورۀ آماری 1369- 1395 انجام شد. به منظور بررسی امکان وجود روند در سری زمانی رد پای آب در تولید گندم از آزمون روندیابی من‏کندال و تخمین‏گر شیب سن استفاده شد. بعد از رتبه‏بندی شهر‏ها از منظر رد پای آب و روند رد پای آب، با استفاده از قوانین انتخاب اجتماعی (SCR)، بهترین شهر برای کشت گندم در استان اصفهان مشخص شد. نتایج پژوهشش حاضر نشان داد متوسط رد پای آب کل در تولید گندم در استان اصفهان برابر (m3/ton) 73/4122 بود و با توجه به آمارۀ من‏کندال مشاهده شد که روند کاهشی و افزایشی وجود دارد و این روند در حالت کاهشی و در حالت افزایشی برای هیچ‌یک از شهر‏های استان معنا‏دار نبود. با توجه به مقدار شیب سن، دامنۀ تغییرات این شیب نیز برای رد پای آب کل برابر (m3/ton) 93 بود. همچنین، نتایج بیان‌کنندۀ این موضوع بود که مقدار شاخص رد پای آب در تولید یک محصول در یک منطقه، معیار قابل قبولی برای انتخاب آن منطقه برای کشت آن محصول نیست و با بررسی روند این شاخص و استفاده از روش‏های تصمیم‏گیری همچون قوانین انتخاب اجتماعی، می‏توان منطقۀ مناسب برای کشت هر محصول را تشخیص داد و تغییر الگوی کشت را در سیاست کار خود به منظور حفظ منابع آبی و افزایش بهره‏وری محصولات قرار داد. در مطالعۀ حاضر از نظر رد پای آب، فریدون‏شهر به عنوان بهترین شهر برای کشت گندم در استان اصفهان انتخاب شد.

کلیدواژه‌ها


عنوان مقاله [English]

Choosing a Suitable Area for Wheat Production Through the Concept of Water Footprint and Social Decision-making Methods (Case study: Esfahan Province)

نویسندگان [English]

  • Mohammad Reza Golabi 1
  • Fereidon Radmanesh 2
  • Ali Mohammad Akhoond-Ali 3
  • Mohammad Hossein Niksokhan 4
1 Ph.D. Student, Faculty of Water Sciences, Shahid Chamran University of Ahvaz
2 Associate Professor, Faculty of Water Sciences, Shahid Chamran University of Ahvaz
3 Professor, Faculty of Water Sciences, Shahid Chamran University of Ahvaz
4 Associate Professor, School of Environment, College of Engineering, University of Tehran
چکیده [English]

The use of water resources can be managed by examining the water footprint of agricultural products more precisely, as well as its long-term variations. The present study aims to determine the annual water footprint trend of wheat production in Esfahan province from 1982 to 2016. To investigate the trend of water footprint time series of wheat production, the Mann-Kendall Trend test, and Sen's slope estimator were applied. After ranking the cities in terms of the water footprint and water footprint trend using the Social Choice Rules (SCR), Esfahan province was identified as the best city for wheat cultivation. The results showed that the average total water footprint of wheat production in Esfahan province was 4122.73 m3/ton and according to Mann-Kendall statistics, there was an insignificant decreasing and increasing trend. According to the value of Sen's slope estimator, the range of this slope was 93 (m3 / ton) for the total water footprint. The results also indicated that the amount of water footprint indicator in producing a product was not an acceptable criterion to choose that area for cultivating that product. By using the trend of this indicator and decision-making methods, e.g., social choice rules, it is possible to identify the best region for cultivating each product, change the pattern of cultivation in its policy of work, preserve water resources, and increase productivity. In this study, Fereydun Shahr, a city in Esfahan province, was selected as the best place for wheat cultivation in terms of the water footprint.

کلیدواژه‌ها [English]

  • water footprint
  • Trend analysis
  • Mann-Kendall
  • Sen's slope estimator
  • Social Choice Rules
  • Esfahan
[1]. Sohrabi R. Preparation of a mathematical model for calculating the optimal use of virtual water for supplying the required water to the region in terms of economic, social and food security. Master's Thesis. Sharif University of Technology. 2007. [Persion]
[2]. Aligholinia T, Rezaie H, Behmanesh J, Montaseri M. Presentation of water footprint concept and its evaluationin Urmia lake watershed agricultural crops. Journal of Water and Soil Conservation. 2016; 23 (3): 337-344. [Persion]
[3]. Ababaei B, Ramezani Etedali H. Estimation of Water Footprint parsts in National Wheat Production. Journal of Water and Soil. 2016; 29 (6): 1458-1468. [Persion]
[4]. Chouchane H, Hoekstra AY, Krol MS, Mekonnen MM. Water footprint of Tunisia from an economic perspective. Ecological Indicators. 2015 May 1; 52: 311-319.
[5]. Gholamhossien pour jafari nejad A, Alizadeh A, Neshat A. Study on Ecological Water Footprint and indicators of virtual water in Agricultural Section of Kerman Province. Irrigation and Water Engineering Scientific Research Journal. 2013; 4(3), 80-89. [Persion]
[6]. HuiSu M, HuiHuang C, YangLi W, ToTso C, ShengLur, H. Water footprint analysis of bioethanol energy crops in Taiwan. Journal of Cleaner Production. 2015 Feb 1; 88: 132-138.
[7]. Rodriguez CI, de Galarreta VR, Kruse EE. Analysis of water footprint of potato production in the pampean region of Argentina. Journal of Cleaner Production. 2015 Mar 1; 90: 91–96.
[8]. Mohammadi A, Yousefi H, Noorollahi Y, Sadatinejad J. Choosing the best province in potato production using water footprint assessment. Ecochydrology. 2017; 4(2): 523-532. [Persion]
[9]. Yousefi H, Mohammadi A, Noorollahi Y, Sadatinejad SJ. Water footprint evaluation of Tehran’s crops and garden crops. Journal of Water and Soil Conservation. 2018; 24(6): 67-85. [Persion]
[10]. Khaliq MN, Ouarda T, Gachon P, Sushama L, St-Hilaire, A. Identification of hydrological trends in the presence of serial and cross correlations: A review of selected methods and their application to annual flow regimes of Canadian rivers. Journal of Hydrology. 2009 Apr 30; 368(1-4): 117-130.
[11]. Fang Sang Y, Wang Z, Liu CH. Comparison of the MK test and EMD method for trend identification in hydrological time Series. Journal of Hydrology. 2014 Mar 14; 510: 293-298.
[12]. Chen H, Guo S, Xu CY, Singh VP. Historical temporal trends of hydro-climatic variables and runoff response to climate variability and their relevance in water resource management in the Hanjiang basin. Journal of Hydrology. 2007 Oct 15; 344(3-4): 171-184.
 
[13]. Hamed KH, Rao AR. A modified Mann–Kendall trend test for autocorrelated data. Journal of Hydrology. 1998 Jan 30; 204(1-4): 182-196.
[14]. Yue S, Pilon P, Phinney B, Cavadias G. The influence of autocorrelation on the ability to detect trend in hydrological series. Hydrological Processes. 2002 Jun 19; 16(9):1807-1829.
[15]. Arrow KJ, Sen AK, Suzumara K. (Eds.). Handbook of Social Choice and Welfare. vol. II. Elsevier Science, Amsterdam. 2005.
[16]. Read L, Mokhtari S, Madani K, Maimoun M, Hanks C. A Multi-Participant, Multi-Criteria Analysis of Energy Supply Sources for Fairbanks, Alaska. World Environmental and Water Resources Congress. 2013: 1247-1257.
[17]. Barberà S, Jackson M, Neme A. Strategy-proof allotment rules. Games and Economic Behavior. 1997 Jan 1; 18(1): 1–21.
[18]. Easter W, Hearne R. Water markets and decentralized water resources management: international problems and opportunities. Water Resources Bulletin. 1995 Feb 1; 31(1): 9–20.
[19]. Allen RG, Pereira LS, Raes D, Smith M. Crop Evapotranspiration– Guidelines for Computing Crop Water Requirements. Drainage and Irrigation Paper 56. Food and Agriculture Organization, Rome, 1998.
[20]. Montaseri M, Rasouli Majd N, Behmanesh J, Rezaie H. Evaluation of Agricultural Crops Water Footprint with Application of Climate Change in Urmia Lake basin. Journal of Water and Soil. 2016; 30(4), 1075-1089. [Persion]
[21]. Hoekstra AY, Chapagain AK, Aldaya MM, Mekonnen MM. Water Footprint Manual. State of the art 2009. Enschede: Water Footprint Network. 2009.
[22]. Chukalla AD, Krol MS, Hoekstra AY. Green and blue water footprint reduction in irrigated
agriculture: effect of irrigation techniques, irrigation strategies and mulching. Hydrology and Earth System Science. 2015 Jun 16; 19: 4877-4891.
[23]. Zhuo L, Mekonnen MM, Hokestra AY, Wada Y. Inter- and intra-annual variation of water footprint of crops and blue water scarcity in the Yellow River basin (1961–2009). Advances in Water Resource. 2016 Jan 1; 87: 29–41.
[24]. Dota A, Theodossiou N. Estimation of green and blue water footprint. Application in the agricultural sector of Karditsa Prefecture. Proceedings of the 12th International Conference on Protection and Restoration of the Environment. 2014 Jul 1; 64-71.
[25]. Mann HB. Non-parametric tests against trend. Econometrica. 1945 Jul 1; 13(3): 245-259.
[26]. Kendall MG. Rank Correlation Methods. Oxford, England: Griffin. 1948.
[27]. Kendall MG. Rank Correlation Methods, 4nd Ed., Oxford, England: Griffin. 1970.
[28]. Partal T, Kahya E. Trend analysis in Turkish precipitation data. Hydrological Processes. 2005 Dec 23; 20(9): 2011–2026.
[29]. Sen PK. Estimates of the regression coefficients based on Kendall’s tau. Journal of the American Statistical Association. 1968 Dec 1; 63(324): 1379-1389.
[30]. Theil H. A rank-invariant method of linear and polynomial regression analysis, Part 3. Proc Koninalijke Nederlandse Akad Weinenschatpen A. 1950; 53:1397–1412.
[31]. Alizadeh MR, Nikoo MR, Rakhshandehroo GR. Developing a Multi-Objective Conflict-Resolution Model for Optimal Groundwater Management Based on Fallback Bargaining Models and Social Choice Rules: a Case Study. Water Resources Management. 2017 Mar 1; 31(5): 1457–1472.