تعیین الگوی کشت بهینه سازگار با کم‌آبی تحت شرایط عدم قطعیت با رویکرد برنامه‌ریزی آرمانی استوار

نویسندگان

1 دانشجوی دکتری اقتصاد کشاورزی، گروه اقتصاد کشاورزی، دانشکده کشاورزی، دانشگاه تبریز

2 استاد اقتصاد کشاورزی، گروه اقتصاد کشاورزی، دانشکده کشاورزی، دانشگاه تبریز

چکیده

تغییر الگوی کشت با توجه به تغییر اقلیم و تداوم خشکسالی­ها از رویکردهای اساسی سیاست­گذاران بخش کشاورزی جهت سازگاری با کم­آبی است و اجرای طرح تغییر الگوی کشت در شهرستان سراب به عنوان یکی از نقاط عمده تولید محصولات کشاورزی در استان آذربایجان شرقی احساس می شود. از آنجایی که عدم قطعیت از جمله جوانب اجتناب­ناپذیر در برنامه­ریزی­ها و تصمیم­گیری­های زراعی است، لذا مطالعه حاضر به معرفی و کاربرد مدل برنامه­ریزی آرمانی استوار (RGP) در بهینه­سازی الگوی کشت تحت شرایط عدم قطعیت در شهرستان سراب می­پردازد. آمار و اطلاعات مطالعه از سازمان جهاد کشاورزی استان آذربایجان شرقی و تکمیل پرسشنامه طی سال زراعی 97-1396 بدست آمد. علاوه بر رهیافت RGP، مدل­های LP وGP برای تعیین الگوی بهینه کشت بکار گرفته شدند. نتایج بیانگر برتری مدل RGP بر دیگر مدل­ها به لحاظ دستیابی همزمان به اهداف حداکثر سود و کاهش مصرف آب می­باشد که نشان­دهنده بهینه نبودن وضعیت موجود بهره­برداری از آب در منطقه مورد مطالعه می­باشد. اجرای الگوی بهینه کشت براساس مدل RGP باعث افزایش سود و اشتغال به ترتیب به میزان 7/1 و 32/1 درصد و همچنین کاهش مصرف آب، کود و سموم شیمیایی به ترتیب به مقدار 7/7، 3/12 و 12 درصد نسبت به الگوی کشت فعلی می­گردد. در میان محصولات مورد مطالعه، گندم بیشترین افزایش سطح زیر کشت را داشته است. از این­رو یک محصول حائز اهمیت در منطقه است و بایستی نسبت به کشت آن به عنوان یک محصول استراتژیک در مساحت بیشتر اقدام شود.

کلیدواژه‌ها


عنوان مقاله [English]

Determining Optimal Cropping Pattern for Adaptation of Water Scarcity under Uncertainty using Robust Goal Programming Approach

نویسندگان [English]

  • fatemeh sani 1
  • Ghader Dashti 2
1 PhD Student of Agricultural Economics, Faculty of Agriculture, University of Tabriz, Iran
2 Prof., Dept. of Agricultural Economics, Faculty of Agriculture, University of Tabriz, Iran
چکیده [English]

Changing the cropping pattern due to the climate change and drought persistence is one of the key approaches for agricultural policy makers for adaptation of water scarcity. Also, implementation of changing the cropping pattern in Sarab County is felt as one of the major points of agricultural production in East Azerbaijan province. Since the uncertainty is one of the unavoidable aspects in agricultural planning and decision making, this study introduces and applies the robust goal programming (RGP) model as a tool for optimization of cropping pattern under uncertainty. The required data and information for this study were collected through Sarab County Agriculture Organization and completing the questionnaire in the period of 2017-2018. In addition to the RGP approach, the LP and GP models were solved to determine the optimal cropping pattern. The results showed RGP model in terms of achieving the goals of maximize profit and minimize water consumption was superior to other models, which indicated that the current status of the water operation in the studied area was not optimal. Implementation of optimal cropping pattern based on RGP model would increase profited and employment by 1.7 and 1.32 percent, respectively and it also could reduce water consumption, fertilizer and chemical pesticides consumptions by 7.7, 12.3 and 12 percent, respectively relative to the current cropping pattern. Among the studied crops, wheat had the highest increase regarding cultivated area. Therefore, wheat is an important crop in the region and it should be cultivated as a strategic crop in greater volume.

کلیدواژه‌ها [English]

  • Optimal Cropping Pattern
  • robust optimization
  • Sarab
  • uncertainty
  • Water
Abdeshahi A, Mardani Najafabadi M and Zeinali M, 2019. Application of multi-objective fuzzy nonlinear programming model to determining the optimal cropping pattern of crop production in Molathani County. Research report, Agriculture Sciences and Natural Resources, University of Khuzestan.  No. 112.411.1. (In Persian with English abstract)
Abdi RokniKh, AbediS and Kashiri Kolaei F, 2019. Effect of optimization of chemical fertilizers consumption on optimal cropping pattern in the framework of positive mathematical programming (case study of Sari Goharbaran). Journal of Agricultural Economics Research 11(42): 263-276. (In Persian with English abstract)
Alabdulkader AM, Al-Amoud AI and Awad FS, 2012. Optimization of the cropping pattern in Saudi Arabia using a mathematical programming sector model. Agricultural Economics 58(2): 56-60.
Anagnostopoulos KP and Petalas C, 2011. A fuzzy multi-criteria benefit–cost approach for irrigation projects evaluation. Agricultural Water Management 98(9): 1409-1416.
Asadi MA and Najafi Alamdarlo H, 2019. Economic evaluation of optimum cultivating pattern for reducing the use of groundwater in Dehgolan plain. Iranian Journal of Agricultural Economics and Development Research 50(1): 29-43. (In Persian with English abstract)
Asghari Moghaddam A and Vadiati M, 2016. Groundwater quality ranking of Sarab plain for drinking purpose using entropy method. Water and Soil Science- University of Tabriz 26(3): 1-13. (In Persian with English abstract)
Avazyar M, Ahmadpour Borazjani  M and Zyaei S, 2018.  Determine optimal crop pattern with an emphasis on increasing the irrigation efficiency in lands of Mollasadra Dam in Fars province. Journal Management System 11(36 ): 21-32.  (In Persian with English abstract)
Bahraminasab M, Dorandish A and Kohansal M, 2014. Application of fuzzy programming with interval programming approach to determine the optimal cropping pattern of Esfarayen County. Agricultural Economics & Development 28(1): 83-91. (In Persian with English abstract)
Bakhshoudeh M and Baghestani M, 2008. A study on the optimal cropping pattern in Iran using nonlinear-fractional programming. Journal of Financial Economics 1(4): 57-70. (In Persian with English abstract)
Belaid A and Torre DL, 2010. A generalized stochastic goal programming model. Applied Mathematics and Computation 215(12): 4347-4357.
Ben-Tal A and Nemirovski A, 1999. Robust solutions of uncertain linear programs. Operations Research Letters 25(1): 1-13.
Bertsimas D and Sim M, 2004. The Price of Robustness. Operations Research 52(1): 35-53.
Fallahi E, Khaliliyan S and Ahmadiyan M, 2013. Optimizing cropping pattern with emphasis on water resource restrictions: a case study of Seidan-Farough plain, Marvdasht Township. Journal of Agricultural Economics Research 5(2): 91-115. (In Persian with English abstract)
Filippi C, Mansini R and Stevanato E, 2017. Mixed integer linear programming models for optimal crop selection. Computers and Operations Research 81: 26-39.
Hosseinzad J, Javadi A, Hayati B, Pishbahar E and Dashti GH, 2011. Application of optimal control model in groundwater extraction (case study: Ajabshir Plain). Agricultural Economics & Development 25(2): 212-218. (In Persian with English abstract)
Li YP, Huang GH, Yang ZF and Nie SL 2008. Interval-fuzzy multistage programming for water resources management under uncertainty. Resources, Conservation and Recycling 52: 800-812.
Majnooni Heris A and Asadi E, 2013. Principles and Concepts of Irrigation. Amidi Publications, Tabriz. (In Persian with English abstract)
Manos B, Papathanasiou J, Bournaris T and Voudouris K, 2010. A multi-criteria model for planning agricultural regions within a context of groundwater rational management. Journal of Environmental Management 91: 1593-1600.
Mardani M, Ziaei S and Nikouei A, 2018. Optimal cropping pattern modifications with the aim of environmental-economic decision making under uncertainty. International Journal of Agricultural Management and Development 8(3): 365-375.
Mardani Najafabadi M, Ziaee S, Ahmadpour Borazjani M and Nikouei A, 2019. Mathematical programming model (MMP) for optimization of regional cropping patterns decisions: A case study. Agricultural Systems 173: 218-232. (In Persian with English abstract)
Mardani Najafabadi M, AbdeshahiA  and  Shirzadi Laskookalayeh S, 2020. Determining the optimal cropping pattern with emphasis on proper use of sustainable agricultural disruptive inputs: application of robust multi-objective linear fractional programming. Journal of Agricultural Science and Sustainable Production 30(1): 241-256. (In Persian with English abstract)
Rath A, Samantaray S and Swain PC, 2019. Optimization of the cropping pattern using Cuckoo Search Technique. In Smart Techniques for a Smarter Planet. Pp. 19-35. In: Mishra MK, Mishra BSP, Patel YS and Misra R (eds). Smart Techniques for a Smarter Planet. Springer- Switzerland.
Sabohi M, Soltani GH and Zibaie M, 2007. Evaluation of the strategies for groundwater resources management: a case study in Narimani Plain, Khorasan Province. Journal of Science and Technology of Agriculture and Natural Resources 11(1): 475-484. (In Persian with English abstract)
Seamus M and Surendra M, 2008. Lexicographic goal programming and assessment tools for a combinatorial production problem. Pp. 148-184. In: Lam Thu Bui and Sameer Alam (eds). Multi-Objective Optimization in Computational Intelligence: Theory and Practice. Australia.
Stephen C, Leung H and Shirley SW, 2009. A goal programming model for aggregate production planning with resource utilization constraint. Computers and Industrial Engineering 56: 1053-1065.
Taleschi Amirkhizi M, Delirhasannia R, Haghighatjou P and Majnooni Heris A, 2019. Determining water quality of agricultural wells for use in pressurized irrigation systems of Sarab plain, Iran. Water and Soil Science- University of Tabriz 29(2): 185-198. (In Persian with English abstract)
Tan Q and Zhang T. 2018. Robust fractional programming approach for improving agricultural water-use efficiency under uncertainty. Journal of Hydrology 564: 1110-1119.