Foresight of Agricultural Water Productivity in Iran by 2040 Using Cross-Impact Matrix Analysis

Document Type : Research Paper

Authors

1 PhD Candidate in Irrigation and Drainage, Department of Irrigation and Reclamation Engineering, Faculty of Agriculture and Natural Resources, University of Tehran, Karaj, Iran.

2 Ass2- Associate Professor of the Department of Irrigation and Development Engineering, Faculty of Agriculture and Natural Resources, University of Tehran & Faculty member of Imam Khomeini International University, Qazvin, Iran,

3 Professor, Department of Irrigation and Reclamation Engineering, Faculty of Agriculture and Natural Resources, University of Tehran, Karaj, Iran.

Abstract

Given the growing challenges in water resource utilization, foresight in agricultural water productivity is considered a strategic necessity for enhancing resilience and food security in Iran. This study aimed to design forward-looking scenarios for improving agricultural water productivity by 2040, using the Cross-Impact Balance (CIB) method and ScenarioWizard 5.2 software. The initial matrix included 13 driving variables, each defined in three states, resulting in the extraction of nine coherent scenarios. Scenarios 1, 4, and 7 (pessimistic) contained the highest number of strongly consistent assumptions, indicating their logical coherence. In contrast, scenarios 3, 6, and 9 (optimistic), with total impact scores of 405, 408, and 381 respectively, demonstrated the greatest potential for strategic intervention and are thus considered more promising options for desirable foresight planning. Evaluation of the scenarios revealed that moderate options (second state) received the highest impact scores. For example, policy variable B2, titled “Modernization of 30–60% of farmland equipment with government support,” scored 69.4%, reflecting the system’s preference for realistic pathways and avoidance of extreme or minimal choices. Nevertheless, some optimistic states (third state) also showed significant presence in technological domains. For instance, variables D3 (“Empowerment of 50–80% of agricultural cooperatives and Water Users' Association (WUA)”), F3 (“Water rationing with periodic control”), and G3 (“Smart meter coverage for 60–90% of wells”) accounted for approximately 38% of the total impact score. Therefore, in areas related to technology, data-driven management, and farmer participation, the system tends to favor innovative and transformative actions. Based on the findings of this study, it is recommended that agricultural water policy focus on a combination of realistic and innovative measures, such as empowering local communities through the adoption of transformative technologies.

Keywords

References

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Journal of Water Research in Agriculture
Volume 39, Issue 3
December 2025
Pages 279-301

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