Socio-ecological System Models for Supporting Farm Resilience: Research Needs, Gaps and Promising Approaches
It is important to increase the resilience of food production systems in the face of a changing climate, land scarcity, and changing demographics and market conditions. As farm resilience is a high-level system property emerged from social-ecological interactions, its direct measurement is difficult because it requires measuring the thresholds or boundaries that separate alternate stability regimes of the farm system. However, systems' modeling for supporting agricultural resilience is still in an early stage. Through critical review of state-of-the art literature, we highlighted the new requirements of agricultural system modeling as they apply to management for farm resilience, limitations of contemporary agricultural systems modeling approaches, and promising directions for future research on the field. We conceptualized criteria for evaluating models' suitability for farm resilience studies. Multi-agent systems (MAS) modeling has appeared as a promising approach for understanding farming resilience that results from rich interactions and feedback among adaptive decision-making and natural processes (e.g. energy, mineral nutrient and water flows). Using the above-mentioned criteria we also analyzed the current limitations of this model family and elaborate possible future developments as subjects of follow-up studies. I will show progresses of our on-going projects on agrarian landscape transitions using hybrid MAS modeling. At the end, I introduce a CGIAR working group on Integrated Systems Analysis and Modeling (iSAMG), in which our work embedded, to support building agricultural livelihood security in dryland at scale.
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Author: Devare, MedhaDate: 2017-03-02Type: Internal DocumentStatus: Open accessThis document is designed to present and offer guidance for using CG Core, the set of metadata elements used by CGIAR Research Center and CRP repositories, in order to facilitate cross-repository searching and enhance ...
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