System Dynamics Modeling for Agricultural and Natural Resource Management Issues: Review of Some Past Cases and Forecasting Future Roles

Abstract

Contemporary issues in agriculture and natural resource management (AGNR) span a wide spectrum of challenges and scales-from global climate change to resiliency in national and regional food systems to the sustainability of livelihoods of small-holder farmers-all of which may be characterized as complex problems. With rapid development of tools and technologies over the previous half century (e.g., computer simulation), a plethora of disciplines have developed methods to address individual components of these multifaceted, complex problems, oftentimes neglecting unintended consequences to other systems. A systems thinking approach is needed to (1) address these contemporary AGNR issues given their multi-and interdisciplinary aspects; (2) utilize a holistic perspective to accommodate all of the elements of the problem; and (3) include qualitative and quantitative techniques to incorporate "soft" and "hard" elements into the analyses. System dynamics (SD) methodology is uniquely suited to investigate AGNR given their inherently complex behaviors. In this paper, we review applications of SD to AGNR and discuss the potential contributions and roles of SD in addressing emergent problems of the 21st century. We identified numerous SD cases applied to water, soil, food systems, and smallholder issues. More importantly, several case studies are shown illustrating the tradeoffs between short-term and long-term strategies and the pitfalls of relying on quick fixes to AGNR problems (known as "fixes that backfire" and "shifting the burden", well-known, commonly occurring, systemic structures-or archetypes-observed across numerous management situations [Senge, P.M. The Fifth Discipline, 1st ed.; Doubleday: New York, NY, USA, 1990.]). We conclude that common attempts to alleviate AGNR problems, across continents and regardless of the type of resources involved, have suffered from reliance on short-term management strategies. To effectively address AGNR problems, longer-term thinking and strategies aimed at fundamental solutions will be needed to better identify and minimize the often delayed, and unintended, consequences arising from feedback between management interventions and AGNR systems.

The Potential for New Donkey Farming Systems to Supply the Growing Demand for Hides

“…System dynamics (SD) modelling with its focus on conditional projections of dynamic behaviour rather than on precise predictions is therefore particularly well suited to the study of a donkey farming system to meet the demand for donkey hides for the ejiao industry. See Turner et al for an overview of SD modelling applied to agricultural and natural resource issues [46].…”

Development of Organic Farming in Europe at the Crossroads: Looking for the Way Forward through System Archetypes Lenses

“…System dynamics is methodology for analyzing and managing dynamic complex problems, e.g. in the context of food systems [18,31,32,[35][36][37]. As a methodology, system dynamics provides a set of qualitative (e.g., causal loop diagrams and system archetypes) as well as quantitative (e.g., computer-based simulation models) analytical tools.…”

“…As a methodology, system dynamics provides a set of qualitative (e.g., causal loop diagrams and system archetypes) as well as quantitative (e.g., computer-based simulation models) analytical tools. These tools enable us to represent feedback loops, handle non-linear relationships, consider delays and accumulations, explain the behavior of a system over time and formulate policies to improve it [18,31,32,[35][36][37]. The system dynamics approach is typically used to analyze long-term developments and assumes a high level of aggregation [18,37].…”

“…The modeling process itself consists of several iterative stages, starting from defining problems dynamically, through mapping and modeling, to building confidence in the model and devising policies [33]. Often the modeling involves stakeholders (e.g., to define mental models within a particular system) and motivates academics themselves to adopt a nonlinear mental model (e.g., to look for and develop the feedback processes of a dynamic problem) [35]. More information about the methodological stance of system dynamics can be found in [33] and [38].…”

“…Qualitative system dynamics tools have proven to be useful in numerous analyses of diverse food systems problems [18,[35][36][37]. This paper explores the application of system archetypes for: (1) analyzing and anticipating difficulties in the development of organic farming in the EU; and (2) finding effective ways to address these difficulties.…”