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    Understanding human-water feedbacks of interventions in agricultural systems with agent based models: A review

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    Authors
    Alam, Mohammad Faiz
    McClain, M.
    Sikka, Alok
    Pande, S.
    Date Issued
    2022-10
    Date Online
    2022-10
    Language
    en
    Type
    Journal Article
    Review status
    Peer Review
    ISI journal
    Accessibility
    Open Access
    Usage rights
    CC-BY-3.0
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    Citation
    Alam, Mohammad Faiz; McClain, M.; Sikka, Alok; Pande, S. 2022. Understanding human-water feedbacks of interventions in agricultural systems with agent based models: a review. Environmental Research Letters, 17(10):103003. [doi: https://doi.org/10.1088/1748-9326/ac91e1]
    Permanent link to cite or share this item: https://hdl.handle.net/10568/121997
    External link to download this item: https://iopscience.iop.org/article/10.1088/1748-9326/ac91e1/pdf
    DOI: https://doi.org/10.1088/1748-9326/ac91e1
    Abstract/Description
    Increased variability of the water cycle manifested by climate change is a growing global threat to agriculture with strong implications for food and livelihood security. Thus, there is an urgent need for adaptation in agriculture. Agricultural water management (AWM) interventions, interventions for managing water supply and demand, are extensively promoted and implemented as adaptation measures in multiple development programs globally. Studies assessing these adaptation measures overwhelmingly focus on positive impacts, however, there is a concern that these studies may be biased towards well-managed and successful projects and often miss out on reporting negative externalities. These externalities result from coevolutionary dynamics of human-water systems as AWM interventions impact hydrological flows and their use and adoption is shaped by the societal response. We review the documented externalities of AWM interventions and present a conceptual framework classifying negative externalities linked to water and human systems into negative hydrological externalities and unexpected societal feedbacks. We show that these externalities can lead to long term unsustainable and inequitable outcomes. Understanding how the externalities lead to undesirable outcomes demands rigorous modeling of the feedbacks between human and water systems, for which we discuss the key criteria that such models should meet. Based on these criteria, we showcase that differentiated and limited inclusion of key feedbacks in current water modeling approaches (e.g., hydrological models, hydro-economic, and water resource models) is a critical limitation and bottleneck to understanding and predicting negative externalities of AWM interventions. To account for the key feedback, we find Agent Based Modeling (ABM) as the method that has the potential to meet the key criteria. Yet there are gaps that need to be addressed in the context of ABM as a tool to unravel the negative externalities of AWM interventions. We carry out a systemic review of ABM application to agricultural systems, capturing how it is currently being applied and identifying the knowledge gaps that need to be bridged to unravel the negative externalities of AWM interventions. We find that ABM has been extensively used to model agricultural systems and, in many cases, the resulting externalities with unsustainable and inequitable outcomes. However, gaps remain in terms of limited use of integrated surface-groundwater hydrological models, inadequate representation of farmers' behavior with heavy reliance on rational choice or simple heuristics and ignoring heterogeneity of farmers' characteristics within a population.
    CGIAR Author ORCID iDs
    Mohammad Faiz Alamhttps://orcid.org/0000-0002-5600-6108
    Alok Sikkahttps://orcid.org/0000-0001-9843-9617
    AGROVOC Keywords
    agricultural systems; water management; water systems; agent-based models; hydrological modelling; groundwater; surface water; irrigation; sustainability; equity; farmers; socioeconomic aspects
    Collections
    • IWMI Journal Articles [2546]
    • Sustainable and Resilient Food Production Systems (SuRF) [117]

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