International agricultural trade is key to improving global food security. It ensures access to more diversified foods (e.g. Krivonos and Kuhn 2019 ), acts as a safety net against local production shortfalls (Glauben et al. 2022) and helps make use of regional climatic or resource-related production advantages. While local production and short supply chains can reduce transport costs, they do not necessarily equate to resilient food systems or lower carbon footprints (Stein and Santini 2022). Currently, though, international agricultural trade is facing supply chain disruptions and rising world market prices resulting from the ongoing Covid-19 pandemic, increasing global food demand and extreme weather events. Both are threatening already strained food security, in particular in import-dependent, low-income regions. Geopolitical risks, such as the China- US trade war and Russia's invasion of Ukraine, are further rattling the food market. As the world's largest consumer of agricultural goods, China's trade strategies influence world markets, with ripple-down effects for consumers around the world, particularly in the Global South. This policy brief aims at shedding light on China's current market actions, and the likely short- and mid-term developments and their impacts. We argue for moderation in response to short-term shocks. Excessive mobility and trade restrictions as well as extreme stockpiling should be avoided. These harm the trade system's overall capacity to resist further and more serious global challenges related to population growth and climate change.

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.

Der internationale Agrarhandel ist ein Schlüsselfaktor für die globale Ernährungssicherheit. Er schafft ein vielfältigeres Nahrungsangebot (e.g. Krivonos und Kuhn 2019), sichert gegen lokale Produktionsausfälle ab (Glauben et al. 2022) und hilft, sich regionale Produktions- und Handelsvorteile zu Nutze zu machen. Auch wenn eine regionale Produktion und kurze Lieferketten Transportkosten reduzieren können, begünstigen sie aber nicht zwangsläufig resilientere oder gar klimaneutralere Ernährungssysteme (Stein und Santini 2022). In jüngerer Zeit sehen sich die Agrarmärkte weltweit mit zusätzlichen Herausforderungen und Unsicherheiten konfrontiert. Lieferkettenengpässe und Preissteigerungen in Folge der andauernden COVID - 19 Pandemie, steigende Nahrungsmittelnachfrage sowie zunehmende Extremwetterereignisse in Folge des Klimawandels belasten insbesondere in importbedürftigen Regionen mit niedrigen Pro-Kopf-Einkommen die ohnehin kritische Ernährungssituation zusätzlich. Zudem stellen jüngere geopolitische Risiken wie etwa der Handelskonflikt zwischen den USA und China oder der russische Einmarsch in die Ukraine den internationalen Agrarhandel auf den Prüfstand. Vor diesem Hintergrund nimmt China als weltgrößter Konsument und Importeur von Nahrungsmitteln eine zentrale Position im globalen Handelsgeschehen ein. Einfluss hat das Land somit auch auf Preisentwicklungen an internationalen Märkten und für globale Versorgungslagen, insbesondere im globalen Süden. Aus globaler Sicht kann insofern nur von ausgeprägten Mobilitäts- und Handelsrestriktionen sowie übermäßiger Lagerhaltung abgeraten werden. Alles dies schwächt das Sicherheitsnetz des globalen Agrarhandels und damit die Reaktionsfähigkeit des Handelssystems auf globale Herausforderungen im Zusammenhang mit dem Bevölkerungswachstum und Klimawandel.