The multidimensionality and complexity of assuring food security in a sustainable and inclusive way requires us to think in systems. Yet, sector specific models or agricultural productivity models are not able by construction to represent the non-linearity and time-dependent nature of the relations underpinning the agri-food system. Two alternative modelling approaches, i.e., System Dynamics (SD) and Agent Based Models (ABM), gained increasing attention in particular after the food commodities prices spikes in 2007 thanks to the conceptual and structural advantages that they provide to the study of food system complexity. In this paper, we develop a first, rigorous bibliometric analysis based on pattern recognition analysis reviewing the peer review journal publications focused on agri-food systems. Using the ISIWeb of Science dataset provided by Thomson Reuters, we apply citation/co-citation semantic metrics to analyse publications from 1970 to 2016 in the field of agricultural models divided in two categories that we define as: (i) agricultural complex systems modelling (ACSM) that includes SD and ABM modelling exercised; and (ii) agricultural modelling (AM) that includes traditional approaches to agri-food systems modelling rooted on the neoclassical approach (e.g., Computable General Equilibrium Models and Partial Equilibrium Models). The publications are identified by applying a filter of specific keywords to the search. We then compare how both approaches appear in the literature looking at the number of publications and citations by scientific journals, identifying key authors and journals, their frequency, the impact factor and citations, and looking at their trend through time. Results show the prevalence of AM approaches for the analysis of the agri-food sector on one side, and the smaller but growing contribution of the ACSM community and literature on the other. We conclude by remarking the need for more systematic analyses on the contribution of the two approaches to the analysis of the complex dynamics and behaviour of agri-food systems to inform evidence-based policies for sustainable and inclusive agriculture.
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