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برنامه ساختاری نظام کشت محصولات منتخب زراعی حوضه آبریز تجن مبتنی بر پایداری منابع آبی | ||
تحقیقات آب و خاک ایران | ||
دوره 56، شماره 2، اردیبهشت 1404، صفحه 463-481 اصل مقاله (1.91 M) | ||
نوع مقاله: مقاله پژوهشی | ||
شناسه دیجیتال (DOI): 10.22059/ijswr.2024.380371.669773 | ||
نویسندگان | ||
حسین فولادی1؛ حمید امیرنژاد2؛ سمیه شیرزادی لسکوکلایه* 3 | ||
1گروه اقتصاد کشاورزی، دانشکده مهندسی زراعی، دانشگاه علوم کشاورزی و منابعطبیعی ساری، مازندران، ایران. | ||
2گروه اقتصادکشاورزی، دانشکده مهندسی زراعی، دانشگاه علوم کشاورزی و منابع طبیعی ساری، مازندران، ایران. | ||
3استادیار، گروه اقتصاد کشاورزی، دانشکده مهندسی زراعی، دانشگاه علوم کشاورزی و منابعطبیعی ساری، مازندران، ایران | ||
چکیده | ||
هدف اصلی تحقیق حاضر تدوین برنامه کشت بهینه محصولات زراعی حوضه آبریز تجن بود. بدین منظور از دو مدل رایج برنامهریزی ریاضی قطعی شامل برنامهریزی خطی و آرمانی و مدل غیرقطعی برنامهریزی خاکستری استفاده شد. در این راستا اطلاعات سری زمانی محصولات زراعی منتخب، منابع آب حوضه و میزان مصارف آب در بخش کشاورزی طی سالهای 1400-1396 حاصل از گزارشات سالانه کارشناسان سازمان جهاد کشاورزی و شرکت سهامی آب منطقهای استان مازندران گردآوری شد. نتایج حاکی از آن است الگوی آرمانی به لحاظ سودآوری از الگوی خطی بدتر و به لحاظ محیطزیستی از این الگو بهتر است و میتوان نتیجه گرفت که الگوی آرمانی میانه بین الگوهای فعلی و خطی است. اگر ملاک تعیین الگوی بهینه، سودآوری و صرفهجویی در مصرف نهادههای مختلکننده پایداری باشد، الگوی خاکستری با میانگین کاهش 23، 22 و 50 درصدی به ترتیب در استفاده از آب، کودهای شیمیایی و سموم کشاورزی ( بر اساس دامنه صرفهجویی در مصرف نهادههای مذکور)، الگوی مناسبتری جهت توصیه میباشد. همچنین، این الگو امکان کسب 21818 میلیارد ریال سود ناخالص را تنها از کشت محصولات جو، ذرت دانهای و برنج تضمین مینماید. علاوه بر این، مدلهای برنامهریزی خطی و آرمانی نمیتوانند برنامه مناسبی در شرایط ترسالی/خشکسالی به کشاورزان ارائه نمایند. لذا، پیشنهاد میشود فعالیتهای ترویجی در راستای آگاهیبخشی در خصوص منافع حاصل از پیادهسازی مدل برنامهریزی خاکستری بر آب و خاک منطقه و درآمد کشاورزان انجام شود. | ||
کلیدواژهها | ||
برنامهریزی ریاضی؛ سازگاری؛ عدم قطعیت؛ مازندران؛ منابع آب | ||
مراجع | ||
Agricultural Research, Education and Extension Organization. (2022). Agricultural Crop Cultivation Pattern Program. Volume 1: Report on Crop Cultivation Pattern for the Year 2022-2023. 32 Provinces of the Country. Tehran. (In Persian) Ahmed Musa, A. )2020(. Goal programming model for optimal water allocation of limited resources under increasing demands. Environment Development and Sustainability. https://doi.org/10.1007/s10668-020-00856-1. Amadeh, H., Daneshvar Kakhki, M., & Kopahi, M. (2001). Investigating the effects of price changes on the cultivation pattern of important crops in Khorasan province. Agricultural Sciences of Iran, 32(1), 147-156. (In Persian) Asadi, M. A., & 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. https://doi.org/10.22059/ijaedr.2018.249900.668543. (In Persian) Balovi, F., Liaghat, A., & Ebrahimian, H. (2022). Estimation of Water Footprint in Current Cropping Patterns and its Reduction Capacity in Optimal Patterns under Multiple Goals Conditions (Case Study; Varamin Region). Iranian Journal of Agricultural Economics and Development Research, 53(4), 987-999. https://doi.org/10.22059/ijaedr.2022.338478.669130. (In Persian) Biswas, A. & Baran, P. B. )2004(. Application of fuzzy goal programing technique to land use planning in agricultural system. Omega, 33(5), 391-398. doi:10.1016/j.omega.2004.07.003. Dadkhah Samreen, S. (2022). Determining the cultivation pattern of crops in Tehran province under conditions of uncertainty. Master's thesis. Faculty of Agricultural Management, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan. (In Persian) Darvishi, D. )2018(. A new Approach for Solving Linear Programming with Grey Variables Problem. Iranian Journal of Operations Research. 9(1), 85-96. 10.29252/iors.9.1.85. Ebadi, F. (2023). Food security and nutritional literacy. First edition. Institute of Planning Research, Agricultural Economics and Rural Development. https://doi.org/Available at: https://agri-peri.ac.ir. (In Persian) Esfandiari, V., Zarifian, Sh., Isanajad, A. & Rahli, H. (2024). The effect of optimizing the cultivation pattern of agricultural products on water consumption management with virtual water and water footprint approach. Environment and water engineering district, 10(2), 243-261. doi.org/10.22034/ewe.2023.385968.1848. (In Persian) Ganesan, K. )2007(. On some properties of interval Matrices. International Journal of Mathematical and Computational Sciences. 1(1):92-99. publications.waset.org/8101. Gao, J., Xu, X., Cao, G., Ermoliev, Y., Ermolieva, T. & Rovenskaya, E. (2021). Strategic decision-support modeling for robust management of the food–energy–water nexus under uncertainty. Journal of Cleaner Production. Volume 292. doi.org/10.1016/j.jclepro.2021.125995. Hacısuleyman, V. & Ozger, M. (2024). Optimal cropping patterns using linear programming and evaluation based on food-energy-water nexus. Global Journal of Environmental Science and Management, 10(51), 1-18. Doi: 10.22034/gjesm.2024.10.SI.01. Hazell, P., & Norton, R. (1987). Mathematical Programming for Economic Analysis in Agriculture. Biometrics, 43(4), 1032. Huang, G. H. (1996). IPWM: an interval parameter water quality management model. Engineering Optimization+ A35, 26(2), 79-103. https://doi.org/10.1080/03052159608941111. Huang, GH. and Moor, R.D. )1993(. Grey linear programing, its solving approach, and its application. International Journal of System Science, 24, 172-159. https://doi.org/10.1080/0020772930894 9477. Ignizio, J. P. (1976). Goal programming and extensions. Lexington Books Publisher. University of Michigan. Javanmardi, E., & Liu, S. (2019). Exploring grey systems theory-based methods and applications in analyzing socio-economic systems. Sustainability, 11(15), 4192. https://doi.org/10.3390/su11 154192. Jones, D., & Tamiz, M. (2010). Practical goal programming. (Vol. 141). Springer. Joolaie, R., Mirkarimi, S., Hasanvand, M., & Shirani Bidabadi, F. (2016). Management of Optimum Cropping Pattern of Crops in Mazandaran Province Using Goal Programming. Agricultural Economics and Development, 24(2), 71-94. https://doi.org/10.30490/aead.2016.59033. (In Persian) Kayacan, E., Ulutas, B., & Kaynak, O. (2010). Grey system theory-based models in time series prediction. Expert systems with applications, 37(2), 1784-1789. doi.org/10.1016/j.eswa.2009.07.064. Khedmatgozar, F., Amirinejad, S., & Zare Mehrjardi, M. (2014, April). Determining the optimal cropping pattern using gray planning in Jiroft city. National Conference on Sustainable Agriculture, Environment and Rural Development, Kuhdasht - Lorestan. (In Persian) Khodadadi, A. (2018). Investigating the effects of climate change on cropping patterns in conditions of uncertainty in Khuzestan province (Case study: Dasht-Azadegan). Master's thesis. Department of Agricultural Economics. Faculty of Agriculture. Ferdowsi University of Mashhad, Iran. (In Persian) Kolahi, M., Hosseinali, F., & Karimaei Tabarestani, M. (2023). Determining the optimal cultivation pattern by considering the concept of virtual water and economic benefits (Case Study: Omrani Plain in Khorasan Razavi). Iranian Journal of Irrigation & Drainage, 16(6), 1221-1232. https://idj.iaid.ir/article_163507_8ea33ba349259d99f8ac0493901cf1ce.pdf. (In Persian) Kumari, M., Singh, O., & Meena, D. C. (2017). Optimising Cropping Pattern in Eastern Uttar Pradesh Using Sen's Multi Objective Programming Approach. Agricultural Economics Research Review, 30(2), 285-291. DOI: 10.22004/ag.econ.273047. Leeuwis, C., Leeuwis, C., & Ban, A. (2004). Communication for rural innovation, Vol. 231, Wiley Online Library. Li, M. & Guo, P. (2014). A multi- objective optimal allocation model for irrigation water resources under multiple uncertainties. Applied Mathematical Modelling, 19: 4897-4911. doi.org/10.1016/j.apm.2014.03.043. Li, Y., Yin, Y., & Zhang, W. (2023). Water Footprint Assessment of Major Crops in Henan Province and Reduction Suggestions. Water, 15(6), 1135. doi.org/10.3390/w15061135. Li, Y.P., Huang, GH. & Nie, S.L. )2006(. An interval-parameter multistage stochastic programming model for water resource management under uncertainty. Advance Water Resource, 29, 776-789. doi.org/10.1016/j.advwatres.2005.07.008. Liu, S., & Lin, Y. (2006). Grey Information: Theory and Practical Applications. Springer-Verlag, Londun Ltd, 191-243. DOI:10.1007/1-84628-342-6 Maqsood, I., uang, G.H. & Yeomans, J.S. )2005(. An interval-parameter fuzzy two-stage stochastic programming for water resources management under uncertainty. European Journal of Operational Research, 167, 208–225. DOI:10.1016/j.ejor.2003.08.068. Mardani Najafabadi, M., Abdshahi, A., & Shirzadi Leskokalaye, S. (2020). Determining the optimal pattern of crop cultivation with emphasis on the proper use of inputs that disturb sustainable agriculture: application of stable multi-objective deficit linear programming method. Journal of agricultural knowledge and sustainable production, 30(1), 241-256.(In Persian) Ministry of Agricultural Jahad (2022). Deputy of Statistics, Information and Communication Technology Center. Agricultural Statistics, Crops Volume 1 and Horticultural Products Volume 3, (2020-2021). www.maj.ir. (In Persian) Mirkarimi, S. (2019). Optimal allocation and distribution of water resources in the Gorganrud-Qarasu Basin and investigating the externality effects of water: a stochastic bankruptcy approach. Sari Agricultural Sciences and Natural Resources University. Sari, Mazandaran Province. (In Persian) Mohammadi, H., Dehbashi, V., & Mehdizadeh Rayeni, M. J, Bagheri, A.,. (2023). Determining the optimal cultivation pattern of agricultural products in Jiroft city with the approach of reducing pollution and water consumption. Journal of Environmental Science and Technology, 25(1), 1-21. https://sanad.iau.ir/fa/Journal/jest/Article/838664. (In Persian) Mohseni, S., & Shahraki, J. (2015). The application of gray fuzzy planning in the allocation of water resources in Yazd city. Agricultural Economics Research, 7(27), 73-90. https://jae.marvdasht.iau.ir/article_989_b52b098e5fff28a6d1e73325ddbabbb4.pdf. (In Persian) Musa, A. A. (2021). Goal programming model for optimal water allocation of limited resources under increasing demands. Environment, Development and Sustainability, 23, 5956-5984. DOI: 10.1007/s10668-020-00856-1. Niu, G., Li, Y., Huang, G., Liu, J., & Chen, M. (2016). Interactive fuzzy-boundary interval programming for water resources management of the Hetao Basin, China. Journal of Irrigation and Drainage Engineering, 142(12), 04016056. doi.org/10.1061/(asce)ir.1943-4774.0001088 Papageorgiou, E. I., & Salmeron, J. L. (2012). Learning fuzzy grey cognitive maps using nonlinear Hebbian-based approach. International Journal of Approximate Reasoning, 53(1), 54-65. doi.org/10.1016/j.ijar.2011.09.006 Rasekh, Z. (2022). Determining the optimal pattern of sustainable cultivation using fuzzy goal programming, a case study of Nangarhar Province, Afghanistan. Master's thesis. Faculty of Agricultural Management, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan. (In Persian) Rastegaripour, F. & Sabohi Saboni, M. )2008(. Determining the cultivation pattern using gray fuzzy programming in Qochan city. Agricultural sciences and techniques and natural resources. 13 (48), 405-413. (In Persian) Regional Water Company of Mazandaran. (2022). Water Resources Assessment Report. Water Resources Balance Report for the Sari-Naka Study Area. (https://www.mzrw.ir). Ren, CH., Guo, P., Tan, Q. & Zhang, L. (2019). A multi-objective fuzzy programming model for optimal use of irrigation water and land resources under uncertainty in Gansu Province, China. Journal of Cleaner Productio, 164: 85-94. doi.org/10.1016/j.jclepro.2017.06.185 Sabuhi, M., & khosravi, M. (2009). Determination of Cropping Pattern by Grey Fuzzy Programming Approach: A Case Study of Quchan City [Research]. Journal of Crop Production and Processing, 13(48), 405-413. http://jcpp.iut.ac.ir/article-1-1015-fa.html. (In Persian) Sani, F. & Dashti, Q. (2021). Determining the optimal cultivation pattern compatible with water scarcity under conditions of uncertainty with a stable goal programming approach. Water and Soil Science Journal, 31(1), 15-30. 10.22034/ws.2021.11626. (In Persian) Sharifinia, M., Imanpour, J., & Bozorgi, A. (2012). Ecological assessment of the Tajan River using feeding groups of benthic macroinvertebrates and biotic indices [Research]. Iranian Journal of Applied Ecology, 1(1), 80-95. http://ijae.iut.ac.ir/article-1-32-fa.html. (In Persian) Soltani, H. A., & Khajehpour, E. (2020). Optimal cropping pattern in Afghanistan considering environmental sustainability. International Journal of Agricultural Management and Development (IJAMAD), 10(4), 333-346. 20.1001.1.21595852.2020.10.4.2.3. Sotoudeh, A. (2014). The application of fuzzy goal programming in the management of selected crop cultivation patterns in Golestan province (case study: Gorgan city). Master's thesis. PayamNoor University of Alborz Province. Faculty of Agriculture and Natural Resources. (In Persian) Statistical Center of Iran. (2020). Mazandaran Province Statistical Yearbook. Chapter One: Land and Climate and Chapter Four: Agriculture. (www.amar.org.ir). (In Persian) Wheeler, B., & Russell, J. (1977). Goal programming and agricultural planning. Journal of the Operational Research Society, 28(1), 21-32. | ||
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