ارزیابی و انتخاب بهینه معیارهای الگوی کشت مبتنی بر توسعه پایدار

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانش آموخته کارشناسی ارشد مهندسی منابع آب/ گروه مهندسی آب، دانشکده کشاورزی، دانشگاه باهنر کرمان.

2 استادیار / گروه مهندسی آب، دانشکده کشاورزی، دانشگاه باهنر کرمان.

3 استادیار/ گروه مهندسی آب، دانشکده کشاورزی، دانشگاه باهنر کرمان.

4 دانش آموخته کارشناسی ارشد علوم رایانه / گروه علوم رایانه، دانشکده ریاضی و کامپیوتر، دانشگاه باهنر کرمان.

چکیده

بحران کم آبی و سهم بالای بخش کشاورزی در استفاده از منابع آبی، منجر شده است که همواره مطالعات بسیاری جهت بهبود مصرف آب در بخش کشاورزی انجام شود که تعداد قابل ملاحظه‌ای از این مطالعات به اجرای الگوی کشت بهینه، جهت استفاده مناسب از منابع آبی تاکید کرده‌اند. اما دراین میان به تاثیر و نقش معیارهای انتخابی در تعیین الگوی کشت و تاثیر آن بر کارایی استفاده از منابع آبی کمتر توجه شده است. برای تعیین الگوی کشت در هر منطقه معیارهای بسیاری وجود دارد که انتخاب هر سری از معیارها، منجر به نتایج متفاوت می‌گردد. به همین دلیل بررسی و مطالعه جهت انتخاب مناسب معیارهای الگوی کشت که منجر به استفاده بهینه از منابع آبی در بخش کشاورزی شود، امری ضروری می‌باشد. در این راستا در مقاله حاضر با تکیه بر توسعه پایدار، معیارهای مختلف الگوی کشت مورد ارزیابی قرار گرفته و در نتیجه یازده معیار به عنوان معیارهای اصلی انتخاب شده‌ و برای دو محصول پسته و گل محمدی در حوضه "کویر درانجیر" اولویت‌بندی و بررسی می‌شوند. این اولویت‌بندی در قالب یک مساله بهینه‌سازی با تابع هدفی مبتنی بر توسعه پایدار پیشنهاد و جستجو می‌شود. حل این مساله از طریق روش‌های الگوریتم ژنتیک تصادفی و آشوبناک (تنت، هنون و لجستیک) مورد مطالعه قرار داده می‌شود. نتایج عددی حل این مساله نشان از قابلیت بهتر الگوریتم‌ ژنتیک آشوبی تنت نسبت به دیگر روش‌های مزبور دارد. همچنین تحلیل نتایج عددی حاصله از حل این مساله بهینه‌سازی، بیان‌گر دسته‌بندی معیارها به ترتیب دسته معیارهای زیست محیطی، اقتصادی، و اجتماعی می‌باشد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Assessment and optimum selection of crop pattern criteria relying on sustainable development

نویسندگان [English]

  • Elham Kazemi Korani 1
  • Marzieh Samareh Hashemi 2
  • Soodabe Golestani Kermani 3
  • Meysam Samare Ghasem Shabjere 4
1 M.Sc. Graduate of Water Resources Engineering, Department of Water Engineering, College of Agriculture, Shahid Bahonar University of Kerman.
2 Assistant Professor, Department of Water Engineering, Shahid Bahonar University of Kerman, Kerman, Iran. Email: samare@uk.ac.ir
3 Assistant Professor, Department of Water Engineering, Shahid Bahonar University of Kerman, Kerman, Iran. Email: s.golestani@uk.ac.ir
4 M.Sc. Graduate of Computer Science, Department of Computer Science, College of Mathematics and Computer Science, Shahid Bahonar University of Kerman.
چکیده [English]

Due to the water crisis and the large proportion of agriculture sector in water withdrawal, many studies have been done on water efficiency improvement in agriculture. One of the proposed solutions is optimal crop pattern implementation. However, less attention has been paid to the effect and role of crop pattern criteria in the determination of crop pattern and water resources use efficiency. There are many crop pattern criteria to determine crop pattern and crop efficiency in a selected area, selecting different criterion results in different outcomes. Therefore, proper selection of criteria is pivotal to the optimal use of water resources. In this paper crop pattern criteria has been evaluated relying on sustainable development, therefore eleven criteria have been selected as the main criteria and prioritized for Pistachio and Damask rose in Kavirdaranjir Basin. To prioritize the criteria an optimization problem, which its objective function is based on sustainable development, has been developed. The problem has been solved, using random and chaotic (Tent, Henon, and Logistic) genetic algorithms. The results show Tent chaotic genetic algorithms performance is the best comparing other selected algorithms. In addition, the analysis of optimization problem results shows the priority of the criteria is the environmental, economic and social category.

کلیدواژه‌ها [English]

  • sustainable development
  • criterion
  • Stochastic Genetic Algorithm
  • Chaotic Genetic Algorithm
  • optimization
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