تعیین ترکیب مناسب کشت محصولات زراعی در مقیاس یک مزرعه با استفاده از الگوی برنامه ریزی آرمانی فازی

نوع مقاله : مقالات پژوهشی

نویسندگان

دانشگاه فردوسی مشهد

چکیده

از موضوعات مهم در حوزه برنامه‌ریزی کشاورزی، دستیابی به الگو یا ترکیب مناسبی از محصولات مدنظر جهت کشت می باشد. در این راستا، با توجه به محدودیت های موجود، میزان منابع در دسترس و مدنظر قرار گرفتن اهداف یا آرمان‌های مختلف، برنامه ریزی و تصمیم گیری به منظوراستفاده بهینه از منابع و عوامل تولید با پیچیدگی‌های خاصی همراه می‌باشد.در نتیجه بکارگیری مدل‌های ریاضی تا حد زیادی می‌تواند به برنامه‌ریزی در این حوزه کمک نماید. هدف این مطالعه، تعیین الگوهای مناسب کشت محصولات زراعی یک مزرعه کشاورزی در استان خراسان شمالی می‌باشد که به این منظوراز مدل برنامه ریزی آرمانی فازی و حل آن بر اساس سناریوهای مختلف بهره گرفته شده است.بر اساس نتایج به دست آمده از فرایند ارائه شده، با توجه به محدودیت ها و آرمان های مسأله مربوطه، چهار الگوی کشت بر مبنای هشت سناریوی مطرح شده برای محصولات زراعی مزرعه مورد مطالعه پیشنهاد گردیدند. الگوهای کشت پیشنهادی به گونه‌ای ارائه شده‌اند که زمینه را برای تصمیم‌گیری بهتر پیرامون ترکیب مناسب کشت محصولات زراعی در شرایط مختلف بر اساس آرمان های مدنظر توسط تصمیم‌گیرندگان فراهم می‌آورند. در نهایت، الگوهای ارائه شده با توجه به بیشترین میزان دستیابی به سطح مطلوب تمامی آرمان‌ها اولویت‌بندی شدند که بر این اساس، تصمیم گیرندگان می‌توانند با توجه به اولویت مدنظر خود پیرامون هر یک از آرمان‌ها، الگوی مناسب کشت محصولات زراعی را انتخاب نمایند

کلیدواژه‌ها


عنوان مقاله [English]

Determining the Appropriate Crop Rotation Plan in a Farm Scale Using Fuzzy Goal Programming Model

نویسندگان [English]

  • A. Alizadeh Zoeram
  • Z. Naji Azimi
Ferdowsi University of Mashhad
چکیده [English]

Introduction One of the important subject in the field of agricultural programming is reaching to a pattern or appropriate crop rotation to plant. Existing constraints, including the amount of available resources, and different goals, makes the decision to optimize the use of resources and production factors a complicated task. Therefore, applying mathematical models can be a grate help in this field. The goal of this study is to determine the appropriate patterns of crop cultivation in a farm in the North Khorasan province.
Materials and Methods Implem enting fuzzy goal programming (FGP) model based on different scenarios was employed to achieve our goals. According to results ,represented process , constraints and problem goals, four plant patterns are offered based on eight proposed scenarios for crop products in this farm or this study. These proposed cultivation pattern can help to make better decision for determination the appropriate rotation of crops in different conditions and different goals by decision makers.
Results Discussion Finally, proposed cultivation patterns were prioritized according to maximum amount of reaching the desired level of total goals. Based on maximum level of reaching goals, different scenarios consisted of income, cost, production resources, income-cost, income-production resources, cost-production resources, income-cost-production resources with equal weights, and income-cost-production resources with different weights have been prioritized and four cropping pattern have been detected. In first pattern, three scenario consisted of scenario 1 (income), scenario 4 (income-cost) and scenario 5 (income-production resources) have combined. The second pattern have made scenario 2 (cost). In third pattern, scenario 3 (production resources), scenario 6 (cost-production resources) and scenario 7 (income-cost-production resources with equal weights) have combined. The scenario 8 (income-cost-production resources with different weights) have considered as fourth pattern, too. For each pattern, the level of reaching goals have been differentiated. In order to determine the appropriate pattern of cropping Euclidean distance have been used. The main difference between outputs of these patterns in pursuit of favorable culture could be due to labor, urea, and income, so the highest aspiration to achieve the desired level of labor have been to cultivation patterns 2 and 3. The desired level of urea fertilizer have been 3, and the highest aspirations and achieve the desired level of income of cropping pattern have been 1. Overall, the appropriate pattern of crop have selected based on the minimum Euclidean distance among of four patterns. In conclusion, Pattern 4 based on scenario 8 (income-cost-production resources with different weights) with minimum swing of desired level of goals have selected as appropriate pattern. Patterns 2, 3 and 1 situated in next priorities.
Conclusion In agriculture planning, sometimes, conflict between objectives occurs. Goal programming is a technique to achieve proper patterns in agricultural planning, by considering different objectives. Due to high uncertainty about the number of desired level of objectives, goal programming model results may be desirable to have or not to conform actual conditions. To resolve this problem, fuzzy goal programming can be utilized where in addition to consider the appropriate level of ideals, fluctuations can be defined for each of them. In this study, fuzzy goal programming models were applied. The proposed method of this study can help farmers to make decision to detect crop patterns. Therefore they can approach to the right decisions based on limited, available resources and importance of goals. Therefore, decision makers can select the appropriate pattern for cropping according to their priority for each goal.

کلیدواژه‌ها [English]

  • Agriculture Planning
  • Fuzzy theory
  • Goal Programming
  • Plant Pattern
1- Asadpour H., Khalilian S., and PeykaniGh.R. 2005. Theory and application of linear goal programming in croppingpattern optimization. Journal of Agricultural Economics and Development, Efficiency and Productivity: 307-328. (in Persian with English abstract).
2- RaeiJadidi M., and Sabouhi M. 2010. Agricultural planning using by a fuzzy multi objective planning model. Journal of Sustainable Agriculture, 2(1): 11-22. (in Persian with English abstract)
3- Kohansal M.R., and Mohammadian F.2007. Application of fuzzy goal programming in determining the optimal pattern for cropping. Journal of Economics and Agriculture, 1(2): 169-183. (in Persian with English abstract)
4- Mardani M.R., Babaei M., and AsemaniA. 2013. Determining the optimal pattern for agriculture. Operations Research and Applications, 1(36): 67-77. (in Persian with English abstract).
5- Biswas, A., and Pal B.B. 2005. Application of fuzzy goal programming technique to land use planning in agricultural system. The International Journal of Management Science, 33(5): 391-398.
6- Charnes A., and Cooper W.W. 1961. Management models and industrial application of linear programming. John Wiley & Sons. Inc. New York.
7- Chen, H. K. 1994. A note on a fuzzy goal programming algorithm by Tiwari, Dharmar and Rao.Fuzzy Sets and Systems, 62 (2): 287-290.
8- Gomez J.A., and Risog L. 2004. Irrigation water pricing: differential impacts on irrigated farms. Agricultural Economic. 31(1): 47-66.
9- Hannan E.L. 1982. Contrasting fuzzy goal programming and fuzzy multi-criteria programming. Decision Sciences, 13(2): 337-339.
10- Ignizio J.P. 1982. On the rediscovery of fuzzy goal programming. Decision Sciences, 13(2): 331-336.
11- Itoh T., Ishii H., and Nanseki T. 2003. A model of crop planning under uncertainty in agricultural management. International Journal of Production Economics, 81-82: 555-558.
12- Kim J.S. and Whang K. 1998. A tolerance approach to the fuzzy goal programming problems with unbalanced triangular membership function. European Journal of Operational Research, 107(4): 614–624.
13- Kuhn H.W., and Tucker A.W. 1951. Nonlinear programming. Proceeding of the second Berkeley symposium on mathematical statistics and probability, J. Neyman, Ed. University of California press. Berkeley, U.S.A.
14- Narasimhan R. 1981. On fuzzy goal programming: Some comments. Decision Sciences, 12(3): 532-538.
15- Romero C. 1991. Handbook of critical issues in goal programming. Pergamon Press, Oxford.
16- Rubin P.A., and Narasimhan, R. 1984. Fuzzy goal programming with nested priorities. Fuzzy Sets and Systems, 14 (3): 115-129.
17- Sarker R.A., and Quaddus M.A. 2002. Modelling a nationwide crop planning problem using a multiple criteria decision making tool. Computers and Industrial Engineering, 42(2): 541-553.
18- Sharma D.K., Jana R.K., and Guar A. 2007. Fuzzy goal programming for agricultural land allocation problems. Yugoslav Journal of Operational Research, 17(1): 31-42.
19- Tiwari R.N., Dharmar S., and Rao J.R. 1986. Priority structure in fuzzy goal programming. FuzzySets and Systems, 19(3): 251-259.
20- Tzeng G.H. 2003. Multiple–objective decision–making in the past, present and future. Journal of Da-yeh University, 12(2): 1-8.
21- Wheeler B.M., and Russell J.R.M. 1977. Goal programming and agricultural planning. Operational Research Quarterly, 28(1): 21-32.
22- Yu P.L. 1973. A class of solution for group decision problems. Management Science, 19(8): 936-946.
23- Zadeh L.A. 1965. Fuzzy sets. Information and Control, 8: 338-353.
24- Zimmermann H.J. 1985. Application of fuzzy set theory to mathematical programming. Information Science, 34(1): 29-58.
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