Methodological Abilities of Integrated Models to Support Agricultural Landscape Resilience: Current Status and Research Perspectives
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Quang Bao Le. (18/12/2016). Methodological Abilities of Integrated Models to Support Agricultural Landscape Resilience: Current Status and Research Perspectives. Toulouse, France: Sabine Sauvage (Curator), José Miguel Sanchez Perez, Andrea Emilio Rizzoli.
Abstract
It is important to increase the resilience of rural landscapes in the face of global changes. It
is widely recognized that integrated modeling is often a methodological choice to study landscape
resilience because the task is often beyond the ability of direct, empirical studies. However, so far
there has not yet been a systemized, critical review on methodological abilities current modeling
approaches can have for supporting management for agricultural landscape resilience. This review
study (i) highlighted the methodological abilities of integrated system modeling ideally needed for
agricultural landscape management for resilience, (ii) reviewed strengths and weakness of common
integrated modeling methods with respects to these required methodological abilities, and (iii)
discussed perspectives of modeling research toward meeting these abilities. Based on common
frameworks of socio-ecological systems, we identified nine methodological abilities that would be
ideally needed for integrated modeling for supporting agricultural landscape resilience: (1)
representing social-ecological complementariness, (2) anticipating multiple performances in a
distributed way, (3) explaining behavioral change of multiple human actors, (4) representing flexible,
multi-scale feedbacks, (5) capturing intra- and inter-farm heterogeneity, (6) explaining farm's structural
changes, (7) being sensitive to key drivers, (8) managing uncertainty, and (9) mediating effective
participation. Seven common integrated modeling approaches selected for our review are: (a) material
flow analysis, (b) system dynamics, (c) Bayesian network, (d) bio-economic optimization, (e) coupled
components, (f) cellular automata, and (g) multi-agent systems (agent-based model). The results are
the matrices of concise narrative assessments with references to published examples, rather than
abstract scores, of each modeling approach against the nine methodological criteria. The matrices can
serve as methodological maps that help citizen scientists, with own context, to position themselves
and wherefrom identify relevant modeling directions towards meeting the required methodological
criteria better. We demonstrated the potential usage of the reviewed matrices with different typical use
cases.
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Le, Quang Bao https://orcid.org/0000-0001-8514-1088