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This article presents a framework for food security and sustainability research, developed by industry, academia, and public sector experts. Key priorities for collaborative research include reassessing food system contexts and drivers, adapting food system activities, transforming food system outcomes, developing and applying food system methodologies, and adopting an ethical and just lens. The framework emphasises the need for coordinated action across multiple scales and sectors, focusing on synergies and trade-offs as opposed to isolated food activities, to address complex challenges in food security and sustainability.

We report the results of a structured expert elicitation to identify the most likely types of potential food system disruption scenarios for the UK, focusing on routes to civil unrest. We take a backcasting approach by defining as an end-point a Societal Event in which 1 in 2,000 people have been injured in the UK, which 40% of experts rated as “Possible (20–50%)” or “More likely than not (50–80%)” or “Very likely (>80%)” over the coming decade. Over a longer timeframe of 50 years, nearly 80% of experts rated such an event as “Possible (20–50%)” or “More likely than not (50–80%)” or “Very likely (>80%)”. The experts considered two food system scenarios and ranked their plausibility of contributing to the given societal scenario. For a timescale of 10 years the majority identified a food distribution problem as the most likely. Over a timescale of 50 years the experts were more evenly split between the two scenarios, but over half thought the most likely route to civil unrest would be a lack of total food in the UK. Overall, in the next 50 years, 45% of participants said there is a greater than 20% chance of civil unrest due to insufficient food in the UK due to extreme weather or ecological collapse. However, the experts stressed that the various causes are interconnected, can create cascading risks and highlighted the importance of a systems approach. We encourage food system stakeholders to use these results in their risk planning, and recommend future work to support prevention, preparedness, response and recovery planning.

The task of keeping a food-waste diary is considerable (Langley et al, 2010), which implies not only difficulties in recruitment and high dropout rates, but also the potential risk of self-selection and poor data quality (Sharp et al, 2010). Because foodwasting behaviours result from multiple routinized behaviours, underreporting is likely for these measures (Hebrok & Boks, 2017). Wastecomposition analysis focuses on the waste put out for collection, which implies that the researcher is unable to observe food waste that was disposed of by other means (e.g., sink waste disposal units, home composting, animal feed) (Parizeau et al, 2015). [...]in-home observation holds potential for improving the accuracy of measurements while lowering the burden for respondents. [...]due to the relatively high correlations between methods, most of the methods are appropriate measurement tools for determining the relative amount of in-home food waste from different households, with the exception of the general survey questions, which appear less appropriate in comparison.

The generation of food waste at both the supplier and the consumer levels stems from a complex set of interacting behaviours. Computational and mathematical models provide various methods to simulate, diagnose and predict different aspects within the complex system of food waste generation and prevention. This chapter outlines four different modelling approaches that have been used previously to investigate food waste: discrete event simulation, which has been used to examine how the shelf life of milk and many actions taken around shopping and use of milk within a household influence food waste; machine learning and Bayesian networks, which have been used to provide insight into the determinants of household food waste; agent-based modelling, which has been used to provide insight into how innovation can reduce retail food waste; and mass balance estimation, which has been used to model and estimate food waste from data related to human metabolism and calories consumed.