تخصیص بهینه آب و اراضی تحت شرایط عدم حتمیت با استفاده از مدل WFGP (مطالعه موردی: شهرستان همدان)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکترا اقتصاد کشاورزی، دانشگاه سیستان و بلوچستان

2 عضو هیات علمی مجتمع آموزش عالی سراوان

3 دانشجوی دکتری اقتصاد کشاورزی دانشگاه پیام نور تهران

4 عضو هیات علمی پژوعضو هیات علمی پژوهشکده کشاورزی دانشگاه زابل .هشکده کشاورزی دانشگاه زابل

چکیده

امروزه استفاده از مدل‌های برنامه‌‌‌ریزی ریاضی برای تعیین الگوهای زراعی مناسب از جایگاه ویژه‌‌‌ای برخوردار هستند. به همین منظور، در این مطالعه برای تعیین الگوی بهینه کشت و تخصیص آب و اراضی تحت شرایط عدم حتمیت در شهرستان همدان از مدل برنامه‌‌‌ریزی آرمانی فازی وزنی (WFGP) استفاده شد. این مدل با سه الگوی وزن‌های مساوی، وزن‌های متفاوت کاهنده، وزن‌های متفاوت افزاینده برای آرمان‌ها و محدودیت منابع آبی و با در نظر گرفتن اهداف زیست‌محیطی و اقتصادی طراحی شده است. داده‌‌‌های موردنیاز مربوط به سال 1393-1392 هستند که از پایگاه اطلاعاتی سازمان جهاد کشاورزی استان همدان جمع‌آوری شدند. اهداف به­کار‌‌‌گرفته شده در مدل فوق شامل حداکثر کردن سود ناخالص، حداکثر کردن نیروی کار مورد‌‌‌نیاز و استفاده مطلوب از کود شیمیایی هستند. بدین منظور، مجموع درجه عضویت همه آرمان‌های فازی در مدل حداکثر گردید. حل مدل پیشنهادی در محیط نرم‌‌‌افزاری GAMS 24.1 صورت گرفت. نتایج نشان داد که با ایجاد انعطاف در ضرایب فنی و به­کارگیری وزن‌‌‌های مساوی برای آرمان‌‌‌های فازی، منابع موجود به­صورت بهینه تخصیص‌یافته و مجموع سطح زیر کشت محصولات زراعی نسبت به شرایط فعلی 22 هکتار کاهش می‌‌‌یابد. اما با به­کارگیری وزن‌‌‌های متفاوت فزاینده و کاهنده برای آرمان‌‌‌ها، مجموع سطح زیر کشت محصولات زراعی به ترتیب 25 و 875 هکتار افزایش می‌‌‌یابد. با توجه به نتایج به­دست آمده، جهت توسعه بخش کشاورزی استان همدان، برنامه‌ریزی و مدل‌سازی از پایین به بالا پیشنهاد شد. برای تحقق این امر، نیاز است که تصمیمات لازم از سطح شهرستان شروع شده و تا سطح ملی ادامه یابند.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Optimal Allocation of Water and Land under Conditions of Uncertainty Using WFGP Model (Case Study: the City of Hamadan)

نویسندگان [English]

  • Hossein Badih Bbarzin 1
  • Hossain Jahantigh 2
  • Aَbozar Parhizkari 3
  • Zahra Ghafari Moghadam 4
1 Phd student of Agricultural Economics, University of Sistan and Baluchestan, Zahedan, Iran.
2 Assistant Professor, Saravan Center for Higher Education, Saravan, Iran.
3 Ph.D. Student of Agricultural Economics, Payame Noor University, Tehran, Iran.
4 Agricultural Resreach Center at University of Zabol, Zabol, Iran.
چکیده [English]

using mathematical programming models for determining appropriate cropping patterns has recently attracted a lot of attention. The agricultural sector is one of the most important and powerful economic sectors of the country. In the last ten years, its contribution to gross domestic product has been around 18% on average. The method of linear programming has been widely used in the fields of land allocation and determination of optimal cultivars since the 1960s. The purpose of linear programming is to maximize or minimize the objective function by considering a number of constraints and decision variables simultaneously. Fuzzy scheduling allows decision-makers to interfere with non-explicit data and data parameters in models. Compared to other models of math planning, it is more applicable and more flexible to be used in optimization problems and in determining the optimum crop cultivation patterns. Moreover, the results are more reliable (Rastegaripour and Sabouhi, 2009).
Mir Karimi et al. (2016) investigated the optimal cultivar pattern in the city of Amol using the ideal planning and taking into account the goals of reducing fertilizer use by seven percent. Their results showed a one-percent reduction in pesticides to protect the environment and a reduction of 93% of water use for the conservation of scarce water resources and sustainable agricultural development.
In this study, a fuzzy utopian planning model with three equal weight patterns, different weights, a decreasing weight and an incremental weight for ideals and water resource constraints are designed, taking into account environmental and economic objectives.
The main objective of this research is to optimize water and land resources in Hamadan province. First, we introduced a weighted fuzzy goal programming model (WFGP). Using this model, optimum cultivating model for farmers in Hamadan was determined, considering their income goals, environmental goals and sustainability of water resources of the region. Subsequently, the allocation between irrigation water inputs and land surface was calculated considering equal weights, different weights and different decreasing weights for the desired goals.

کلیدواژه‌ها [English]

  • Irrigation Water
  • Cropping Pattern
  • Weighted Fuzzy Goal Programming
  • Lands Management

مراجع

1-Anonymous, a, 2012. Hamadan Agricultural Jihad. 2012. Database, Statistical Yearbook. Website (In Persian).
 2- Anonymous, b, 2012. Hamedan Meteorological Administration website. (In Persian).
 3- Asadpour, H., Khalilian, H., P. and Soltani, Gh. 2005. The Theory and Application of Fuzzy Ideal Linear Programming Model in Optimizing Crop Pattern. The Journal of Agricultural Economics and Development (13), pp. 328- 317. (In Persian).
 4-    Asadpoor, H., Pazhuhandeh, A., Moghadasi, R. and Yazdani, S. 2011. Determine theoptimum model in Dasht-e-Naz Sari with multiple objectives, Journal of Agricultural Extension and Education, 4 (1), pp. 83-96. (In Persian).
 5-    Berim Nezhad, A., and Yazdani S., 2004. Sustainability Analysis in Water Resources Management in the Agricultural Sector by Using Fraction Planning. A Case Study of Kerman Province, Journal of Research and Development in Agriculture and Horticulture, (2), pp. 16-63(In Persian).
 6-    Charnes, A. W. Cooper, W. and Ferguson, R. 1955. Optimal estimation of executive compensation by linear programming, Journal of the Institute of Management Science, vol. 1, pp. 138–151,
 7-    Chen, H.K. 1994. A note on a Fuzzy Goal Programming algorithm by Tiwari, Dharmar and Rao, Fuzzy Sets and Systems, (62), pp. 287-290.
 8-    Da- Silva, A.F. and Silva Marins, F.A. 2014. A fuzzy goal programming model for solving aggregate production-planning problems under uncertainty: A case study in a Brazilian sugar mill. Energy Economics,
 9-    Daneshvar M, Sahnoushi N. and Salehi Reza Abadi F, 2009. The Determination of Optimal Crop Pattern with Aim of Reduction in Hazards of Environmental, American Journal of Agricultural and Biological Sciences 4 (4), pp. 305- 310.
 10-   Goodman, D.A. 1974. A goal programming approach to aggregate planning of production and work force, Management Science, (12), pp. 1569- 1575.
 11- Hashemi's Agent, S. 2001. Iranian Agricultural Development Capabilities. Publications Institute for Agricultural Research Planning and Economics, pp.49 (In Persian).
 12- Hu, C. Zhang, S. and Wang, N. 2014. Enhanced interactive satisficing method via alternative tolerance for fuzzy goal programming with progressive preference. Applied Mathematical Modelling, Available online, http:// www. Sciencedirect .com/ science/ article/ pii/ S0307904X14001334.
 13- Ignizio, J.P. 1982. On the rediscovery of fuzzy goal programming, decision Sciences, 13(2): 331-336.
 14- Kehnsal, M. and Mohammadian, F. 2007. Application of Fuzzy Ideal Planning in Determining the Optimal Model of Cultivation of Crops, Selected Articles of the Sixth Conference on Agricultural Economics, Scientific Society of Agricultural Economics of Iran. (In Persian).
 15- Khalili- Damghani, K. Sadi- Nezhad, S. and Tavana, M. 2013. Solving multiperiod project selection problems with fuzzy goal programming based on TOPSIS and a fuzzy preference relation. Information Sciences, 253(10): 42-61
 16-  Kim, J. and Whang, K. 1998. A tolerance approach to the Fuzzy goal programming problems with unbalanced triangular membership function, European Journal of Operational Research, 107(2): 614–624.
 17- Lee, S.M. 1972. Goal Programming for Decision Analysis. Philadelphia, Auer Bach, Publishers.
 18- Liang, T.F. 2010. Applying fuzzy goal programming to project management decisions with multiple goals in uncertain environments. Expert Systems with Applications. 37(12), pp. 8499- 8507.
 19- Liao, C.N. and Kao, H.P. 2014. An evaluation approach to logistics service using fuzzy theory, quality function development and goal programming. Computers and Industrial Engineering. 68, pp. 54-64.
 20- Mir Karimi, Sh., Julia, R., Eshraghi, F. and Shirani Beid Abadi, F. 2016. Managing Crop Crop Pattern with Emphasis on Environmental Considerations (Case Study: Amol County), Environmental Science and Technology, 18 (2), pp. 253-263. (In Persian).
 21- Mohammadi, H., Bustani, F. and Babolzadeh, F. 2011. Determination of Optimal Cropping Pattern Using Nonlinear Multi-objective Optimization Algorithm, Journal of Water and Wastewater, 4, pp. 43-55. (In Persian).
 22- Mohammadian F, Shahnoshini N., Ghorbani M. and Aqel H, 2010. Formulation of a Sustainable Crop Pattern in Freiman Plain of Torbat Jam. Agricultural Economics, (2), pp. 1-42. (In Persian).
 23- Narinyani, A.  1980. Indefinite sets - a new type of data for knowledge representation, Preprint 232, Computer Center of the USSR Academy of Sciences, Novosibirsk,
 24- Parhzikari, A., Mozafari, M., Khaki, M. And Taghizzadeh Ranjbari, h. 2015. Optimal allocation of water and land resources in Rudbaralmoot area using FGFP model. Journal of Water and Soil Conservation, 4 (4), pp. 24-11. (In Persian).
 25- Parhzikari, A., Sabouhi, M. 2012. Operational management and optimal allocation of water resources for determining the optimal cultivating model (Approach to Ideal Fractionation Model). Third Conference on Integrated Water Resources Management, Sari University of Natural Resources, Faculty of Agriculture, August 2012. (In Persian).
 26- Rastegaripour, F., Sabouhi, M. 2009. Determination of Crop Pattern Using Gray Fuzzy Programming Case Study of Ghoochan City, Journal of Agricultural Science and Technology, 13 (48), pp. 413-405. (In Persian).
 27- Rubin, P.A., Narasimhan, R. 1984. Fuzzy Goal Programming with nested priorities, fuzzy sets and systems, 14, pp. 115- 129.
 28- Sabouhi M. and Ziaei, S. 2009. Optimization of Crop Pattern Using Fuzzy Ideal Planning with Appropriate Change Approach: A Case Study of Neyshabour City, Journal of Agricultural Economics, 3 (1), pp. 219-229. (In Persian).
 29- Sen, N. and Nandi, M. 2012. A goalprogramming approach to rubber- tea intercropping management in Tripura, Asian Journal of Management Research, 3(1), pp.178- 183.
 30- Sharma, D.K., Ghosh, D.  and Alade, J.A. 2003. Management Decision making for sugar can fertilizer mix problems through goal programming? Journal of Applied Mathematics and Computing, 13(1- 2), pp. 323- 334.
 31- Sharma, D.K., Jana, R.K., and Gaur, A. 2007. Management decision-making for sugar cane fertilizer mix problems through goal programming, Yugoslav Journal of Operations Research, 17(3), pp. 31- 42.
 32- Shirzadi, S., Kiikha, A. and Sabouhi, M. 2009 Optimal allocation of water in water supply networks of Zabul agricultural sector under risk and non-risk conditions. Seventh Agricultural Economics Conference, Tehran. (In Persian).
 33- Tiwari, R.N., Dharmar, S. and Rao, J.R. 1987. Fuzzy Goal Programming an additive model, fuzzy sets and systems, 24(1): 27– 34.
 34- Zeng, X., Shaozhong, K., Fusheng, L., Lu, Z, and Ping G, 2010. Fuzzy multi-objective linear programming applying to crop area planning,Agricultural Water Management,Vol 98(), 1, Pages 134-142,
 35- Zimmerman, H.J. 1978. Fuzzy programming and linear programming with several objective function, fuzzy set and systems1, P: 45- 55.
 36- Zimmerman, H.J 1985. Fuzzy set theory and its applications. Kluwer Academic, Dordrecht.
علوم و مهندسی آبیاری
دوره 42، شماره 2
تیر 1398
صفحه 167-180

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سابقه مقاله

  • تاریخ دریافت: 03 آذر 1395
  • تاریخ بازنگری: 11 مهر 1396
  • تاریخ پذیرش: 15 مهر 1396
  • تاریخ انتشار: 01 تیر 1398

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