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Exploring Dynamics of Water, Energy, and Food Systems in Agricultural Landscapes Using Mental Modeling: A Case of Varamin Plain, Iran

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Abstract

This study applies the mental model and cognitive mapping method to involve stakeholders in delineating the mutual relations between sources of water, energy, and food (WEF) production in the Varamin Plain (VP). Through involving farmers and managerial experts, the approach facilitates the deployment of community communication patterns to recognize and comprehend problems and move from single-loop learning to double-loop learning. The dynamic model was driven from the final mental model of the participants to reflect changes in the systems over time. The system dynamic (SD) model incorporates three scenarios for enhancing irrigation efficiency, managing groundwater extraction, and satisfying environmental needs. The results uncovered that the surface and underground water resources of the VP will gradually decrease within the next two decades in the range of 158 and 2700 million cubic meters (MCM) per year. Also, the plain suffers from water insecurity and a 162 MCM shortage. Consequently, focusing on understanding the nexus and nexus governance can enhance resource management and achieve sustainable development goals. Essentially, promoting collaborative governance, such as creating cooperative organizations and implementing double-loop learning, and instituting a water market, regulatory governance, and monitoring laws can improve the state of Varamin Plain’s resources. These results carry important policy implications for using mental models to consider dynamics for discussions on participatory management of the WEF system nexus and environmental management.

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Data availability

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

We would like to express our sincere gratitude to the professors from the Shahid Beheshti University and Tehran University for their contributions to fuzzy-logic cognitive mapping and mental modeling. Their efforts and dedication have significantly advanced our understanding of this complex subject and laid the foundation for future research in this field. we would also like to extend my appreciation to the active farmers in Varamin, as well as the managers of the agriculture sector, ABFA, and the Varamin Agricultural Research Center. Their hard work and commitment to improving the agricultural sector have been invaluable and played a crucial role in the success of this research.

Author contributions

This paper is based on research conducted by AS for their PhD thesis on ecological agriculture, under the supervision of KK, MRZ, and HV. All authors contributed to the concept and design of the study. AS and KK performed the literature review, data collection, data analysis, and writing of the first draft of the article. MRZ and HV provided feedback and made revisions to the first draft. All authors contributed to editing and revising subsequent drafts.

Funding

This study has not been funded by any organization or institute in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. Department of Agroecology, Environmental Sciences Research Institute, Shahid Beheshti University, G.C., P.O. Box 19835-196, Tehran, Iran

    Alireza Shahmohammadi & Korous Khoshbakht

  2. School of Engineering Design and Innovation, SEDI, Penn State, 213 Hammond Building, University Park, State College, PA, 16802, USA

    Hadi Veisi

  3. Environment and Natural Resources Economics Department, Environmental Sciences Research Institute, Shahid Beheshti University, G.C., P.O. Box 19835-196, Tehran, Iran

    Mohammad Reza Nazari

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  1. Alireza Shahmohammadi
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Correspondence to Korous Khoshbakht.

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Shahmohammadi, A., Khoshbakht, K., Veisi, H. et al. Exploring Dynamics of Water, Energy, and Food Systems in Agricultural Landscapes Using Mental Modeling: A Case of Varamin Plain, Iran. Environmental Management 73, 34–50 (2024). https://doi.org/10.1007/s00267-023-01875-0

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