The implications of global change for the sustainability of global and regional agroecosystems and food security continue to be a priority research concern. Corresponding insights have considerable implications for land use and land cover change, the carbon cycle, feedbacks to the climate system, energy-water-land coupled system dynamics, and broader socioeconomics as reflected in integrated assessment models. Agroecosystems are inherently complex. In particular, few aspects of agroecosystems are unaffected by human agency — the capacity of actors to act, directly or indirectly, to affect change. Hence, attempts to conceptualize or model agroecosystems as purely biophysical systems may result in biased insights or mask important consequences. At the same time, agency is contingent on scale. Therefore, diagnosing and predicting socioeconomic and ecological influences on agroecosystems are facilitated by conceptualizing, observing, and modeling the system at scales that are relevant to the questions that are being asked.
Researchers at Oak Ridge National Laboratory have explored a broad range of modelling tools and frameworks that can be applied to agroecosystem predictions. The researchers discovered that the processes in these models, including human agency, are generally designed to address a relatively bounded problem, which leads to a number of modeling limitations. First, models are often limited with respect to the scales they explicitly represent, and therefore may neglect consideration for institutional behavior, jurisdictional issues, or different levels of management responses. Second, models encounter significant challenges not simply with scales, but also with scaling. For example, farm level models do not consider issues of procurement, supply chains, and markets, which are influenced by agency at higher spatial and institutional levels. Third, the capacity to represent complex systems and their behavior is contingent on the availability of input data for model variables and processes as well as data for model calibration and validation. Fourth, the normative decisions made by model developers and users regarding what management options should be included ultimately influence model behavior and the results that are generated.
The research team asserts that a range of research pathways can help alleviate these challenges. Explicitly identifying the scales and levels relevant to the development or application of agroecosystem models can assist in identifying their strengths and weaknesses. This information can be used to prioritize model development or data needs and to identify model limitations and knowledge gaps affecting interpretation and use of results. Greater emphasis on model integration or coupling can be an effective pathway for linking top-down and bottom-up models to incorporate agency across multiple scales and levels. Meanwhile, the use of socioeconomic scenarios to define alternative futures can help to create context around models for those aspects of human systems that are not explicitly modeled.
Reference: Preston, B. L., A. W. King, K. M. Ernst, S. M. Absar, S. S. Nair, and E. S. Parish. 2015. “Scale and the Representation of Human Agency in the Modeling of Agroecosystems,” Current Opinion in Environmental Sustainability, DOI: 10.1016/j.cosust.2015.05.010. (Reference link)
Contact: Bob Vallario, SC 23.1, (301) 903-5758
SC-23.1 Climate and Environmental Sciences Division, BER
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