Modelling food security : Bridging the gap between the micro and the macro scale
Müller, Birgit ; Hoffmann, Falk ; Heckelei, Thomas ; Müller, Christoph ; Hertel, Thomas W. ; Polhill, J.G. ; Wijk, Mark van; Achterbosch, Thom ; Alexander, Peter ; Brown, Calum ; Kreuer, David ; Ewert, Frank ; Ge, Jiaqi ; Millington, James D.A. ; Seppelt, Ralf ; Verburg, Peter H. ; Webber, Heidi - \ 2020
Global environmental change : human and policy dimensions 63 (2020). - ISSN 0959-3780
Agent-based models - Crop models - Economic equilibrium models - Food security - Land use - Model integration - Multi-scale interactions - Social-ecological feedbacks
Achieving food and nutrition security for all in a changing and globalized world remains a critical challenge of utmost importance. The development of solutions benefits from insights derived from modelling and simulating the complex interactions of the agri-food system, which range from global to household scales and transcend disciplinary boundaries. A wide range of models based on various methodologies (from food trade equilibrium to agent-based) seek to integrate direct and indirect drivers of change in land use, environment and socio-economic conditions at different scales. However, modelling such interaction poses fundamental challenges, especially for representing non-linear dynamics and adaptive behaviours. We identify key pieces of the fragmented landscape of food security modelling, and organize achievements and gaps into different contextual domains of food security (production, trade, and consumption) at different spatial scales. Building on in-depth reflection on three core issues of food security – volatility, technology, and transformation – we identify methodological challenges and promising strategies for advancement. We emphasize particular requirements related to the multifaceted and multiscale nature of food security. They include the explicit representation of transient dynamics to allow for path dependency and irreversible consequences, and of household heterogeneity to incorporate inequality issues. To illustrate ways forward we provide good practice examples using meta-modelling techniques, non-equilibrium approaches and behavioural-based modelling endeavours. We argue that further integration of different model types is required to better account for both multi-level agency and cross-scale feedbacks within the food system.
Representation of decision-making in European agricultural agent-based models
Huber, Robert ; Bakker, Martha ; Balmann, Alfons ; Berger, Thomas ; Bithell, Mike ; Brown, Calum ; Grêt-Regamey, Adrienne ; Xiong, Hang ; Le, Quang Bao ; Mack, Gabriele ; Meyfroidt, Patrick ; Millington, James ; Müller, Birgit ; Polhill, J.G. ; Sun, Zhanli ; Seidl, Roman ; Troost, Christian ; Finger, Robert - \ 2018
Agricultural Systems 167 (2018). - ISSN 0308-521X - p. 143 - 160.
The use of agent-based modelling approaches in ex-post and ex-ante evaluations of agricultural policies has been progressively increasing over the last few years. There are now a sufficient number of models that it is worth taking stock of the way these models have been developed. Here, we review 20 agricultural agent-based models (ABM) addressing heterogeneous decision-making processes in the context of European agriculture. The goals of this review were to i) develop a framework describing aspects of farmers’ decision-making that are relevant from a farm-systems perspective, ii) reveal the current state-of-the-art in representing farmers’ decision-making in the European agricultural sector, and iii) provide a critical reflection of underdeveloped research areas and on future opportunities in modelling decision-making. To compare different approaches in modelling farmers’ behaviour, we focused on the European agricultural sector, which presents a specific character with its family farms, its single market and the common agricultural policy (CAP). We identified several key properties of farmers’ decision-making: the multi-output nature of production; the importance of non-agricultural activities; heterogeneous household and family characteristics; and the need for concurrent short- and long-term decision-making. These properties were then used to define levels and types of decision-making mechanisms to structure a literature review. We find most models are sophisticated in the representation of farm exit and entry decisions, as well as the representation of long-term decisions and the consideration of farming styles or types using farm typologies. Considerably fewer attempts to model farmers’ emotions, values, learning, risk and uncertainty or social interactions occur in the different case studies. We conclude that there is considerable scope to improve diversity in representation of decision-making and the integration of social interactions in agricultural agent-based modelling approaches by combining existing modelling approaches and promoting model inter-comparisons. Thus, this review provides a valuable entry point for agent-based modellers, agricultural systems modellers and data driven social scientists for the re-use and sharing of model components, code and data. An intensified dialogue could fertilize more coordinated and purposeful combinations and comparisons of ABM and other modelling approaches as well as better reconciliation of empirical data and theoretical foundations, which ultimately are key to developing improved models of agricultural systems.