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بهبود تاب‌آوری کشاورزان در مدیریت مشارکتی منابع آب تحت شرایط کم‌آبی در حوضهء دریاچهء ارومیه با استفاده از مدل‌سازی عامل محور | ||
| تحقیقات مهندسی سازه های آبیاری و زهکشی | ||
| دوره 26، تابستان - شماره پیاپی 99، تیر 1404، صفحه 74-55 اصل مقاله (909.29 K) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/idser.2025.369484.1619 | ||
| نویسندگان | ||
| سمیه امامی* 1؛ حسین دهقانی سانیج2؛ حجت امامی3 | ||
| 1دکتری سازه های هیدرولیکی، گروه علوم و مهندسی آب، دانشگاه تبریز، تبریز ایران | ||
| 2استاد، مؤسسه تحقیقات فنی و مهندسی کشاورزی، سازمان تحقیقات، آموزش و ترویج کشاورزی، کرج، البرز، ایران | ||
| 3دانشیار، گروه مهندسی کامپیوتر، دانشکده فنی و مهندسی، دانشگاه بناب، بناب، ایران | ||
| چکیده | ||
| در مناطق خشک و نیمه‌خشک با توجه به افزایش فشارهای اقلیمی و نهادی بر منابع آب، گذار از مدیریت فنی به رویکردهای اجتماعی–نهادی ضروری است. در این راستا، در پژوهش حاضر با بهره‌گیری از رویکرد ترکیبی کیفی- کمی و مدل‌سازی عامل محور (ABM)، به بررسی رفتار کشاورزان در مدیریت مشارکتی منابع آب در دشت میاندوآب، یکی از کانون‌های بحرانی حوضهء دریاچهء ارومیه، پرداخته شده است. داده‌ها از طریق پرسشنامه، مصاحبه‌های نیمه‌ساختاریافته و منابع ثانویه جمع‌آوری شد. در مدل ABM طراحی‌شده، کشاورزان به‌عنوان عاملان اصلی و شبکه‌های محلی و بازار محصولات به‌عنوان بازیگران مکمل در نظر گرفته و تصمیمات آبیاری بر اساس چهار شاخص کلیدی اقتصادی (ERI)، فناورانه (FTI, IL)، اجتماعی (KNRI)، فردی (MPI) و زیست‌محیطی (EPI) شبیه‌سازی شد. نتایج نشان داد که تاب‌آوری اقتصادی، انعطاف‌پذیری فناوری، استحکام شبکه‌های اجتماعی و انگیزه نوآوری کشاورزان به‌طور هم‌افزا، سرعت پذیرش فناوری‌های نوین و بهره‌وری آب را افزایش می‌دهند. فشارهای محیطی شدید، در صورت همراهی با انگیزه نوآوری بالا، محرکی قوی برای تغییر رفتار آبیاری و پذیرش فناوری‌های نوین می‌باشند. در حالی که کشاورزان با تاب‌آوری اقتصادی پایین و شبکه‌های اجتماعی ضعیف، بیشترین آسیب‌پذیری را داشتند. یافته‌ها تأکید می‌کند که سیاست‌های مدیریت منابع آب باید فراتر از اقدامات فنی، شامل بسته‌های حمایتی چندبعدی اقتصادی، آموزشی و تقویت شبکه‌های اجتماعی طراحی شوند. هم‌چنین کاربرد مدل‌های عامل محور بومی شده امکان پیش‌بینی اثر سیاست‌ها، بهینه‌سازی تخصیص منابع و بهبود مشارکت کشاورزان را فراهم می‌کند. نتایج این پژوهش می‌تواند به‌عنوان راهنمای عملیاتی برای بالا بردن تاب‌آوری، بهره‌وری آب و مدیریت مشارکتی منابع آب کشاورزی در مناطق بحرانی مورد استفاده قرار گیرد. | ||
| کلیدواژه‌ها | ||
| مدیریت منابع آب؛ مدل‌سازی مشارکتی؛ شبکه اجتماعی؛ مدل‌سازی عامل محور | ||
| عنوان مقاله [English] | ||
| Enhancing Farmers’ Resilience in Participatory Water Resources Management under Water Scarcity Conditions in the Lake Urmia Basin Using Agent-Based Modeling | ||
| نویسندگان [English] | ||
| Somayeh Emami1؛ Hossein Dehghanisanij2؛ Hojjat Emami3 | ||
| 1PhD in Hydraulic Structures, Department of Water Science and Engineering, University of Tabriz, Tabriz, Iran | ||
| 2Professor, Agricultural Research, Education and Extension Organization, Agricultural Engineering Research Institute, Karaj, Alborz, Iran. | ||
| 3Associate Professor, Department of Computer Engineering, Faculty of Engineering and Technology, University of Bonab, Bonab, Iran | ||
| چکیده [English] | ||
| Introduction In arid and semi-arid regions, pressures from climate change, including increasing temperatures, decreasing precipitation, and severe seasonal fluctuations, along with institutional constraints and weaknesses in resource management, have created fundamental challenges for farmers and hindered the realization of sustainable agriculture. The Lake Urmia basin, one of the most sensitive aquatic ecosystems in the country, provides a prime example of such critical conditions due to the significant decrease in water levels, increased salinity, and decline in the quality of water resources, which have resulted in widespread economic, social, and environmental consequences for local communities. Water scarcity not only reflects physical resource limitations but also generates complex interactions among economic, social, institutional, and environmental factors, directly influencing farmers' decision-making, irrigation technology choices, and the effectiveness of water resource management policies. In this context, socio-institutional resilience is a multidimensional and decisive factor, encompassing household economic capacities, flexibility and adaptability of agricultural technologies, active and stable social networks, and organizational and institutional skills, all of which can enhance the sustainability of agricultural systems in the face of environmental pressures and economic instabilities. However, previous studies have mostly analyzed economic, individual, or social indicators separately and have not comprehensively examined the complex interactions among individual, social, technological, and environmental factors. As a result, the ability to predict the agricultural system's response to various policies and climate shocks has been limited. Moreover, an integrated framework for optimal water resource management and for enhancing farmers' resilience has not yet been provided. Methodology This study was conducted using a mixed qualitative and quantitative approach combined with agent-based modeling (ABM) to accurately represent the complex interactions between farmers, social-institutional networks, and environmental pressures in the Miandoab Plain, south of Lake Urmia. The study population included 50 farmers, 10 agricultural Jihad experts, and 10 representatives of the regional water organization. Data were collected through structured questionnaires, semi-structured interviews, and secondary sources. Six key indicators were defined in the model: economic resilience (ERI), technological flexibility (FTI), knowledge network strength (KNRI), innovation motivation in practice (MPI), environmental pressure (EPI), and irrigation technology level (IL). Farmers, as the main agents, interacted continuously with local knowledge networks and product markets, and their decision-making rules were shaped by individual motivations, environmental pressures, and the observed behavior of others. To examine the system's response under different conditions, four simulation scenarios were designed: fluctuations in agricultural product prices, introduction of new irrigation technologies at varying adoption rates, changes in the density of knowledge networks, and the occurrence of climate shocks and successive droughts. Quantitative data were analyzed using SPSS, and qualitative data were processed using open and axial coding in MAXQDA. Model validation was conducted by comparing outputs with historical data, consulting local experts, and performing sensitivity analyses to ensure the model accurately represents farmers' behavior and socio-institutional dynamics under various environmental pressures. Results and Discussion Simulation results showed that farmers' adoption of new technologies and changes in irrigation patterns are influenced by a combination of economic, individual, technological, social, and environmental factors. Farmers with higher economic resilience and denser social networks adopted new technologies faster, resulting in increased water productivity. Environmental pressures, particularly drought and water scarcity, were strong drivers for adoption, but their effects were sustainable only when combined with strong individual motivation and active knowledge networks. The technological flexibility index and innovation motivation were decisive in determining the speed and extent of technology adoption. Social networks and knowledge network strength enhanced collective learning and experience sharing, thereby creating synergistic effects with other indicators. The combination of low economic capacity and high environmental pressure highlighted the need for targeted support and guidance to help farmers adapt to new technologies. Analysis also showed that one-dimensional policies, such as simply providing subsidies or unsupervised training, have limited effectiveness and may even be counterproductive. In contrast, multidimensional policy packages, including targeted and continuous education, performance-based subsidies, strengthening local networks, and transparency in water allocation, create synergistic and sustainable impacts. International experiences indicate that farm schools, participatory local networks, and subsidy reforms can significantly increase farmer engagement and water productivity. Agent-based modeling also allows predicting farmers' responses to policies, testing different scenarios, and optimizing resource allocation before implementation. Conclusions The adoption of new irrigation technologies and sustainable water management results from complex interactions among economic, social, institutional, and environmental factors. Successful participatory policies require maintaining a minimum level of water resource sustainability, implementing multidimensional policy packages, strengthening social and institutional networks, and using agent-based modeling tools to assess policy effectiveness. This study demonstrated that the combination of economic indicators, innovation motivation, social networks, and environmental pressures can significantly determine the success or failure of policies. The proposed framework provides a practical and scientific roadmap for sustainable water management in areas affected by water crises. | ||
| کلیدواژه‌ها [English] | ||
| Water Resources Management, Participatory Modeling, Supportive Policies, Social Simulation | ||
| مراجع | ||
|
Ahmadi, A., Keshavarz, M., & Ejlali, F. (2025). Resilience to climate change in agricultural water-scarce areas: The major obstacles and adaptive strategies. Water Resources Management, 39(3), 1195-1214. Alawode, A. (2025). Evaluating agricultural subsidy reforms and their effects on smallholder farmer income and efficiency. International Journal of Advance Research Publication and Reviews, 2(5), 180-201. Cai, Y., & Xiong, W. (2017). An agent-based simulation of cooperation in the use of irrigation systems. Complex Adaptive Systems Modeling, 5(9). Chai, Y., & Schoon, M. (2016). Institutions and government efficiency: decentralized irrigation management in China. International Journal of the Commons, 10(1). Chang, J., Jiang, H., Liu, J., & Li, M. (2024). How the digital economy enhances the grain supply chain resilience in China: exploring the moderating effects of government innovation-driven. Frontiers in Sustainable Food Systems, 8, 1439593. Chen, T., Yang, F., Li, Y., & Zhang, Z. (2024). Two-Way FDI assists agricultural sustainable development: Based on digitalization and greening perspectives. Plos One, 19(2), e0296896. Cremers, L., Ooijevaar, M., & Boelens, R. (2005). Institutional reform in the Andean irrigation sector: Enabling policies for strengthening local rights and water management. Natural Resources Forum, 29(1), 37-50. Das, U., Ansari, M. A., & Ghosh, S. (2022). Effectiveness and upscaling potential of climate smart agriculture interventions: Farmers' participatory prioritization and livelihood indicators as its determinants. Agricultural Systems, 203, 103515. Dahwa, E., Mudzengi, C. P., Mubvuma, M., Maravanyika, T., Chapungu, L., & Chikodza, E. (2022). Optimizing productivity in semi-arid dryland agriculture for developing countries: Insights from Zimbabwe. In Climate change adaptations in dryland agriculture in semi-arid areas (pp. 233-249). Singapore: Springer Nature Singapore. Emami, S., & Dehghanisanij, H. (2024). Fault Tree Analysis of Trade-Offs between Environmental Flows and Agricultural Water Productivity in the Lake Urmia Sub-Basin Using Agent-Based Modeling. Water, 16(6), 844. Fang, H. (2023). Actionable science for irrigation. In Actionable science of global environment change: From big data to practical research (pp. 203-228). Cham: Springer International Publishing. Franzén, F., Hammer, M., & Balfors, B. (2015). Institutional development for stakeholder participation in local water management—An analysis of two Swedish catchments. Land Use Policy, 43, 217-227. Gany, A. H. A., Sharma, P., & Singh, S. (2019). Global review of institutional reforms in the irrigation sector for sustainable agricultural water management, including water users’ associations. Irrigation and Drainage, 68(1), 84-97. Hariram, N. P., Mekha, K. B., Suganthan, V., & Sudhakar, K. (2023). Sustainalism: An integrated socio-economic-environmental model to address sustainable development and sustainability. Sustainability, 15(13), 10682. Hassaniyan, A. (2024). Iran’s water policy: environmental injustice and peripheral marginalisation. Progress in Physical Geography: Earth and Environment, 48(3), 420-437. Iran Water Resources Management Company. (2023). Water balance report of groundwater resources in the Miandoab plain. Tehran: Office for Protection and Utilization of Water Resources. Kiptot, E., & Franzel, S. (2014). Voluntarism as an investment in human, social and financial capital: evidence from a farmer-to-farmer extension program in Kenya. Agriculture and Human Values, 31(2), 231-243. Kumar, A., Devi, J. P., Bhaskar, D., Shekhar, S., Chatterjee, U., & Mohanasundari, T. (2025). Perceptions and impacts of climate change in Central India: A study of tribal communities. Theoretical and Applied Climatology, 156(8), 1-21. Kumar, R., & Singh, P. (2021). Energy subsidies and sustainable irrigation in India: Policy reform for resource efficiency. Energy Policy, 150, 112014. Lee, H., Calvin, K., Dasgupta, D., Krinner, G., Mukherji, A., Thorne, P., & Zommers, Z. (2023). Climate change 2023: synthesis report. Contribution of working groups I, II and III to the sixth assessment report of the intergovernmental panel on climate change. Li, C., Li, X., & Jia, W. (2022). Non-farm employment experience, risk preferences, and low-carbon agricultural technology adoption: Evidence from 1843 grain farmers in 14 provinces in China. Agriculture, 13(1), 24. Ministry of Energy of Iran. (2022). Water Resources Statistical Yearbook – Water Year 2021–2022. Tehran: Office of Basic Water Resources Studies. Mousavian, H. M., Shakouri, G. H., Mashayekhi, A. N., & Kazemi, A. (2020). Does the short-term boost of renewable energies guarantee their stable long-term growth? Assessment of the dynamics of feed-in tariff policy. Renewable Energy, 159, 1252-1268. Nagrah, A., Chaudhry, A. M., & Giordano, M. (2016). Collective action in decentralized irrigation systems: Evidence from Pakistan. World Development, 84, 282-298. Nhemachena, C., & Hassan, R. (2007). Micro-level analysis of farmer's adaption to climate change in Southern Africa. Intl Food Policy Res Inst. Nkonya, E., Kato, E., Msimanga, M., & Nyathi, N. (2023). Climate shock response and resilience of smallholder farmers in the drylands of south-eastern Zimbabwe. Frontiers in Climate, 5, 890465. Nordmeyer, E. F., & Mußhoff, O. (2023). Understanding German farmers’ intention to adopt drought insurance. Journal of Environmental Management, 345, 118866. Oiganji, E., Igbadun, H., Amaza, P. S., & Lenka, R. Z. (2025). Innovative technologies for improved water productivity and climate change mitigation, adaptation, and resilience: a review. Journal of Applied Sciences and Environmental Management, 29(1), 123-136. Pahl-Wostl, C., Craps, M., Dewulf, A., Mostert, E., Tabara, D., & Taillieu, T. (2007). Social learning and water resources management. Ecology and Society, 12(2). Prajapati, C. S., Priya, N. K., Bishnoi, S., Vishwakarma, S. K., Buvaneswari, K., Shastri, S., ... & Jadhav, A. (2025). The role of participatory approaches in modern agricultural extension: bridging knowledge gaps for sustainable farming practices. Journal of Experimental Agriculture International, 47(2), 204-222. Reichelt, N., & Nettle, R. (2023). Practice insights for the responsible adoption of smart farming technologies using a participatory technology assessment approach: The case of virtual herding technology in Australia. Agricultural Systems, 206, 103592. Reidsma, P., Bakker, M. M., Kanellopoulos, A., Alam, S. J., Paas, W., Kros, J., & de Vries, W. (2015). Sustainable agricultural development in a rural area in the Netherlands? Assessing impacts of climate and socio-economic change at farm and landscape level. Agricultural Systems, 141, 160-173. Ren, Z., Fu, Z., & Zhong, K. (2022). The influence of social capital on farmers’ green control technology adoption behavior. Frontiers in Psychology, 13, 1001442. Rusike, E. L. I. J. A. H. (2005). Exploring food and farming futures in Zimbabwe: A citizens’ jury and scenario workshop experiment (pp. 249-255). London: Zed Books. Salajegheh, S., Jafari, H. R., & Pourebrahim, S. (2020). Modeling the impact of social network measures on institutional adaptive capacity needed for sustainable governance of water resources. Natural Resource Modeling, 33(4), e12277. Shiferaw, B. A., Okello, J., & Reddy, R. V. (2009). Adoption and adaptation of natural resource management innovations in smallholder agriculture: reflections on key lessons and best practices. Environment, Development and Sustainability, 11(3), 601-619. Shi, H., Zhang, Y., Bian, M., & Zhang, J. (2024). Influence of energy poverty on agricultural water efficiency using a panel data study in China. Scientific Reports, 14(1), 2064. Silva, T. A., Ferreira, J., Calijuri, M. L., dos Santos, V. J., do Carmo Alves, S., & de Siqueira Castro, J. (2021). Efficiency of technologies to live with drought in agricultural development in Brazil's semi-arid regions. Journal of Arid Environments, 192, 104538. Slavova, M., & Karanasios, S. (2018). When institutional logics meet information and communication technologies: examining hybrid information practices in Ghana’s agriculture. Journal of the Association for Information Systems, 19(9), 4. Tingey-Holyoak, J. L., & Pisaniello, J. D. (2017). Strategic responses to resource management pressures in agriculture: Institutional, gender and location effects. Journal of Business Ethics, 144(2), 381-400. Thakur, A. K., & Uphoff, N. T. (2017). How the system of rice intensification can contribute to climate‐smart agriculture? Agronomy Journal, 109(4), 1163-1182. Thissen, W., Kwakkel, J., Mens, M., van der Sluijs, J., Stemberger, S., Wardekker, A., & Wildschut, D. (2017). Dealing with uncertainties in fresh water supply: Experiences in the Netherlands. Water Resources Management, 31(2), 703-725. Wada, Y., de Graaf, I. E., & van Beek, L. P. (2016). High‐resolution modeling of human and climate impacts on global water resources. Journal of Advances in Modeling Earth Systems, 8(2), 735-763. West Azerbaijan Regional Water Company. (2022). Descriptive report of the Miandoab plain. Urmia: Deputy for Basic Water Resources Studies. | ||
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آمار تعداد مشاهده مقاله: 189 تعداد دریافت فایل اصل مقاله: 49 |
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