تبیین مؤلفه‌های اثرگذار بر بهره‌وری آب کشاورزی در ایران با بهره‌گیری از روش دلفی فازی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مهندسی آبیاری و آبادانی، دانشکدگان کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، ایران.

2 گروه مهندسی آبیاری و آبادانی، دانشکدگان کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، ایران؛ و دانشگاه بین‌المللی امام خمینی، قزوین، ایران.

10.22059/jwim.2025.399914.1249

چکیده

پژوهش حاضر با هدف شناسایی و تحلیل مؤلفه‌های اثرگذار بر بهره‌وری مصرف آب در بخش کشاورزی با استفاده از تحلیل دلفی فازی طراحی شده است. در مرحله‌ی مرور نظام‌مند ادبیات، تعداد ۱۹۴ عامل استخراج شد، که در سه گروه تغییر اقلیم (۴۳ عامل، 1/22 درصد)، سیاست‌ها (۷۷ عامل، 6/39 درصد) و فناوری‌ها (۷۴ عامل، 2/38 درصد) دسته‌بندی شدند. این عوامل در قالب پرسشنامه نیمه‌ساختاریافته‌ بین ۱۸ خبره متخصص توزیع گردید و با اعمال آستانه توافق 8/0 در مدل دلفی فازی، داده‌ها فازی‌سازی، تجمیع و دیفازی شدند. نتایج نشان داد که ۳۲ عامل (معادل 5/16 درصد) به‌عنوان عوامل کلیدی واجد اجماع انتخاب شدند. از میان آن‌ها، ۱۸ عامل از گروه سیاست‌ها (4/23 درصد از این گروه)، ۱۰ عامل از گروه فناوری (5/13 درصد از این گروه) و ۴ عامل از گروه تغییرات اقلیمی (3/9 درصد از این گروه) بودند. بالاترین مقدار دیفازی مربوط به «تولید بذرهای هیبرید» بود (92/0)، که جایگاه فناوری‌های زیستی را به‌عنوان ابزار کارآمد ارتقاء بهره‌وری آب نشان می‌دهد. براساس نتایج پژوهش، دو محور قابل‌کنترل: زیرساخت‌های داده‌محور و نوآوری‌های فناورانه به‌عنوان مؤثرترین عوامل در ارتقای بهره‌وری آب کشاورزی شناخته می‌شوند.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Exploring the Determinants of Agricultural Water Productivity in Iran using the fuzzy Delphi method

نویسندگان [English]

  • Tahmine Dehghani 1
  • Abdolmajid Liaghat 1
  • Bijan Nazari 2
1 Department of Irrigation and Reclamation Engineering, Faculty of Agriculture and Natural Resources, University of Tehran, Karaj, Iran.
2 Department of Irrigation and Reclamation Engineering, Faculty of Agriculture and Natural Resources, University of Tehran, Karaj, Iran; and Imam Khomeini International University, Qazvin, Iran.
چکیده [English]

This study aims to identify and analyze the key factors influencing water productivity in agriculture through the application of the Fuzzy Delphi Method.Through a systematic literature review, 194 factors were extracted and categorized into three domains: climate change (43 factors, 22.1 Percent), policy (77 factors, 39.6 Percent), and technology (74 factors, 38.2 Percent). These factors were incorporated into a semi-structured questionnaire distributed to 18 experts. Applying a consensus threshold of 0.8 in the fuzzy Delphi model, the data were fuzzified, aggregated, and defuzzified. Findings revealed that 32 factors (16.5 Percent) were selected as consensus-based key determinants. Among them, 18 factors were from the policy domain (23.4 Percent), 10 from the technology domain (13.5 Percent), and 4 from the climate change domain (9.3 Percent). The highest defuzzified value corresponded to "hybrid seed production" (0.92), highlighting the significance of biotechnological innovations in enhancing water productivity. Other consensus-driven factors included "promotion of genetic improvement" (0.90), "establishment of national Based on the research findings, two controllable pillars—data-driven infrastructure and technological innovations—are recommended as the most effective factors for enhancing agricultural water productivity.

کلیدواژه‌ها [English]

  • Agricultural Water Management
  • Climate Resilience
  • Policies
  • Technologies

مراجع

  1. Ahmadpari, H., & Khaustov, V. (2025a). Agricultural drought monitoring using meteorological indices in Darreh Dozdan Basin, Iran. Advances in Civil Engineering and Environmental Science, 2(2), 72–84.
  2. Ahmadpari, H., & Khaustov, V. (2025b). Analyzing meteorological and hydrological droughts in the Darreh Dozdan River Basin through drought indices. Journal of Environmental Engineering and Water Resources. (Articles in press). (In Persian).
  3. Akhavan Gighlou, K., Kheiri, M., Ahmadpari, H., Abbasi, S., & Kalateh, F. (2023). Assessment of physical, economic, and virtual water productivity indices in agricultural products (Case study: Moghan irrigation and drainage network, Ardabil province). Water and Soil Resources Management and Modeling, 3(3), 277-295. (In Persian).
  4. Alcantud, J. C. R. (2023). Multi-attribute group decision-making based on intuitionistic fuzzy aggregation operators defined by weighted geometric means. Granular Computing, 8(6), 1857-1866.‏
  5. Alharbi, M. G., & Khalifa, H. A. E. W. (2021). Enhanced Fuzzy Delphi Method in Forecasting and Decision‐Making. Advances in fuzzy systems, 2021(1), 2459573.‏
  6. Ammann, J., Umstätter, C., & El Benni, N. (2022). The adoption of precision agriculture enabling technologies in Swiss outdoor vegetable production: a Delphi study. Precision Agriculture, 23(4), 1354–1374.
  7. Basereh, F., Ahmadpari, H., & Sharifi, M. (2023). Assessment of physical, economic, and virtual water productivity indices in agricultural products (Case study: Dehloran County, Ilam Province). Water and Soil Resources Management and Modeling, 4(3), 1–18. (In Persian)
  8. Bazrafshan, J., Khalili, A., Zand Parsa, Sh., Sepaskhah, A., Alizadeh, A., & Farhudi, J. (2022). Documentary analysis of emerging irrigation and agricultural water technologies worldwide and their localization potential in Iran. Strategic Research Journal in Agricultural Sciences and Natural Resources, 7(2), 139-158. (In Persian).
  9. Biasetton, N., Disegna, M., Barzizza, E., & Salmaso, L. (2023). A new adaptive membership function with CUB uncertainty with application to cluster analysis of Likert-type data. Expert Systems with Applications, 213, 118893.
  10. Chavula, P., Kayusi, F., Lungu, G., Mambwe, H., & Uwimbabazi, A. (2024). AI Application in Climate-Smart Agricultural Technologies: A Synthesis Study. LatIA, 2, 330-330.‏
  11. De Hierro, A., Sánchez, M., Puente-Fernández, D., Montoya-Juárez, R., & Roldán, C. (2021). A fuzzy Delphi consensus methodology based on a fuzzy ranking. Mathematics, 9(18), 2323.
  12. Dehghani, T., Ahmadpari, H., Aghelmirrezaei, F., & Godarzi, A. (2020a). Analysis of hydrological drought using streamflow drought index (Case study: Dez River Basin, Iran).  In: Proceeding of5th International Conference on Food Science, Organic Agriculture and Food Security, Tbilisi, Georgia.
  13. Dehghani, T., Ahmadpari, H., Aghelmirrezaei, F., & Godarzi, A. (2020b). Meteorological drought monitoring using several drought indices (Case study: Aligudarz county). In: Proceeding of 7th International Conference on Innovation in Science and Technology, 88-99, Amsterdam, Nederland.
  14. Dehghani, T., Liaghat, A., Rezaei Rad, H., & Ahmadpari, H. (2024). Estimating grain corn yield based on Landsat 8 satellite imagery (Case study: Shahid Beheshti agro-industry lands, Dezful). Iranian Journal of Irrigation and Drainage, 18(3), 433-447. (In Persian)
  15. Du, M., Lei, J., & Li, S. (2025). Navigating the path to food security in China: Challenges, policies, and future directions. Foods, 14(4), Article 644.
  16. Ebrahimi, A. (2025). The impact of water tariffs in the agricultural sector on productivity and resource sustainability. Iranian Water Resources Research, 21(1), 72-87. (In Persian).
  17. Food and Agriculture Organization of the United Nations. (2021). FAO empowers Iranian farmers to improve agriculture water productivity in Qazvin Province. Food and Agriculture Organization of the United Nations. https://www.fao.org/family-farming/detail/ar/c/1370591
  18. Food and Agriculture Organization of the United Nations. (2017). Water accounting and auditing–A sourcebook (FAO Water Reports No. 43). Retrieved from https://www.fao.org/3/i8868en/I8868EN.pdf
  19. Food and Agriculture Organization of the United Nations. (2019). Policy guide to improve water use efficiency in small-scale agriculture: The case of Burkina Faso, Morocco and Uganda. Retrieved from http://www.fao.org/3/ca7144en/CA7144EN.pdf
  20. Gierczyk, M., Gromkowska-Melosik, A., Scott, S., & Parker, C. (2024). The snowball sampling strategy in the field of social sciences: Contexts and considerations. Przegląd Badań Edukacyjnych, 43(2), 87-104.
  21. Groher, T., Heitkämper, K., Walter, A., Liebisch, F., & Umstätter, C. (2020). Status quo of adoption of precision agriculture enabling technologies in Swiss plant production. Precision Agriculture, 21(6), 1327-1350.
  22. Gruère, G., & Le Boëdec, H. (2025). Navigating pathways to reform water policies in agriculture. OECD Publishing.
  23. Habibi, A., Jahantigh, F., & Sarafrazi, A. (2015). Fuzzy Delphi technique for forecasting and screening items. Asian Journal of Research in Business Economics and Management, 5(2), 130-143.
  24. Hashemi, S. Z., Darzi-Naftchali, A., Karandish, F., Ritzema, H., & Solaimani, K. (2024). Enhancing agricultural sustainability with water and crop management strategies in modern irrigation and drainage networks. Agricultural Water Management, 305, Article 109110.
  25. Hsu, Y. L., Lee, C. H., & Kreng, V. B. (2010). The application of fuzzy Delphi method and fuzzy AHP in lubricant regenerative technology selection. Expert Systems with Applications, 37(1), 419-425.
  26. Inanloo Tayfe Yaghmurlu, M., Nazari, B., & Sotoodehnia, A. (2023). Investigating the affecting components of water productivity by factor analysis approach (Case study: Qazvin Province). Journal of Water and Soil Knowledge, University of Tabriz, 33(3), 69-81. (In Persian)
  27. Islami, A., Shirvanian, A., & Zareian, R. (2024). The impact of crop pattern reform on physical and economic irrigation water productivity of field crops in Fars Province: A case study of Qaderabad–Madarsoliman Plain. Journal of Irrigation and Drainage Structures Engineering Research, 25(Spring), 17-37. (In Persian)
  28. Islamic Consultative Assembly. (2023). The Seventh Five-Year Development Plan of the Islamic Republic of Iran (2024-2028). Official Gazette of the Islamic Republic of Iran, No. 23093. (In Persian)
  29. Islamic Republic of Iran. (2010). Law on Increasing Productivity in Agriculture and Natural Resources. Islamic Consultative Assembly. Retrieved from https://rc.majlis.ir/fa/law/show/782387
  30. Jafari, A. M., Nikooei, A., Ghadami Firouzabadi, A., Shanazi, K., & Bakhtiari, M. R. (2023). The impact of crop pattern reform policies on water resource conservation and wheat self-sufficiency: A case study of Kaboudarahang Plain. New Approaches in Water and Environmental Engineering, 2(2), 170-183. (In Persian).
  31. Jiang, L., Tian, Y., Chen, N., & Luo, Y. (2023). An empirical exploration into the determinants of rice farmers’ decisions to adopt low-carbon agricultural technologies in Hubei Province, China. Mitigation and Adaptation Strategies for Global Change, 28(4), 15.‏
  32. Latifi, S., Rahali, H., Yadavar, H., Sa’di, H., & Shahrastani, S. A. (2018). Identification and explanation of implementation stages of conservation agriculture development in Iran using fuzzy Delphi approach. Iranian Journal of Biosystems Engineering, 49(1), 107-120. (In Persian)
  33. Li, M., Zhou, S., Shen, S., Wang, J., Yang, Y., Wu, Y., ... & Lei, Y. (2024). Climate-smart irrigation strategy can mitigate agricultural water consumption while ensuring food security under a changing climate. Agricultural Water Management, 292, Article 108663.
  34. Mabhaudhi, T., Chimonyo, V. G., Senzanje, A., & Chivenge, P. P. (2025). Enhancing Water and Food Security Through Improved Agricultural Water Productivity: New Knowledge, Innovations and Applications (p. 391). Springer Nature.‏
  35. Mahadevan, M., Noel, J. K., Umesh, M., Santhosh, A. S., & Suresh, S. (2024). Climate change impact on water resources, food production and agricultural practices. In The Climate-Health-Sustainability Nexus: Understanding the Interconnected Impact on Populations and the Environment (pp. 207-229).
  36. Marques, J., Guilló, M., Bas, E., Ramazanova, M., & Albuquerque, H. (2023). Setting research priorities for effective climate change management and policymaking: A Delphi study in Bolivia and Paraguay. Sustainability, 15(20), Article 14993.
  37. Memmedova, K., & Ertuna, B. (2023). Development of a fuzzy Likert scale to measure variables in social sciences. Information Sciences, 654, Article 119792.
  38. Mirzaei, A., Ebrahimian, H., Nazi Qomshlo, A., & Raja, A. (2020). Evaluation of performance and water productivity in various corn hybrids under drip-tape irrigation system. Iranian Journal of Water and Soil Research, 52(1), 161–173. (In Persian)
  39. Najm Abed Al‑Nuaimi, S., Khairy Abdullah, S., Fadhel Eisa Muhsin, A., Salman Husein, B., Abdul Kareem Khazal, F., Amini, A., & Ahmadpari, H. (2025). International climate change law: Strategies for mitigation, adaptation, and accountability. Water and Soil Management and Modelling, 5(Special Issue: Climate Change and Effects on Water and Soil), 18-31.
  40. Nouri, M., Homaee, M., Pereira, L. S., & Bybordi, M. (2023). Water management dilemma in the agricultural sector of Iran: A review focusing on water governance. Agricultural Water Management, 288, 108480.
  41. Patle, G. T., Kumar, M., & Khanna, M. (2020). Climate-smart water technologies for sustainable agriculture: A review. Journal of Water and Climate Change, 11(4), 1455-1466.
  42. Pourgholam Amiji, M., Haji Rad, A., Ahmadali, Kh., & Liaghat, A. (2024). Smart irrigation based on the Internet of Things (IoT). Iranian Journal of Water and Soil Research, 55(9), 1647-1678. (In Persian).
  43. Prutzer, E., Patrick, A., Ishtiaque, A., Vij, S., Stock, R., & Gardezi, M. (2023). Climate-smart irrigation and responsible innovation in South Asia: A systematic mapping. Ambio, 52(12), 2009-2022.
  44. Sablatzky, T. (2022). The Delphi method. Hypothesis: Research Journal for Health Information Professionals, 34(1).
  45. Shah, W. U. H., Hao, G., Yasmeen, R., Yan, H., Shen, J., & Lu, Y. (2023). Role of China's agricultural water policy reforms and production technology heterogeneity on agriculture water usage efficiency and total factor productivity change. Agricultural Water Management, 287, 108429.‏
  46. Smith, M. D., Sikka, A., Dirwai, T. L., & Mabhaudhi, T. (2023). Research and innovation in agricultural water management for a water‐secure world. Irrigation and Drainage, 72(5), 1245-1259.
  47. Srivastav, A. L., Dhyani, R., Ranjan, M., Madhav, S., & Sillanpää, M. (2021). Climate-resilient strategies for sustainable management of water resources and agriculture. Environmental Science and Pollution Research, 28(31), 41576-41595.‏
  48. Supreme Council of Cultural Revolution (2023) Knowledge-Based Food Security Document. (In Persian).
  49. High Council of Water. (2023). National Water Roadmap. (In Persian).
  50. Wang, M., & Zhang, K. (2022). Improving agricultural green supply chain management by a novel integrated fuzzy-delphi and grey-WINGS model. Agriculture, 12(10), 1512.‏
  51. Xu, Q., Boelens, R., & Veldwisch, G. J. (2024). High-Efficiency Irrigation. International Journal of the Commons, 18(1), 148-163.
  52. Yin, T., & Hanif, H. (2024). Fuzzy Delphi method: A step-by-step guide to obtain expert consensus on MUETBot functionalities. International Journal of Academic Research in Business and Social Sciences, 14(4), 1097-1104.
  53. Yin, W., Yang, X., & Liu, W. (2025). Sustainable management and regulation of agricultural water resources in the context of global climate change. Sustainability, 17(6), Article 2760.
  54. Yuan, X., Li, S., Chen, J., Yu, H., Yang, T., Wang, C., ... & Ao, X. (2024). Impacts of global climate change on agricultural production: A comprehensive review. Agronomy, 14(7), Article 1360.
  55. Zamani, O., Azadi, H., Mortazavi, S., Balali, H., Moghaddam, S., & Jurík, Ľ. (2020). The impact of water-pricing policies on water productivity: Evidence of agriculture sector in Iran. Agricultural Water Management, 245, Article 106548.
  56. Zhou, K., & Li, J. (2024). Impact of the comprehensive agricultural water use reform policy on food production: Quasinatural experimental evidence from China. Agricultural Water Management, 302, 108981.‏
مدیریت آب و آبیاری
دوره 15، شماره 4
اسفند 1404
صفحه 805-826

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