Trying to meet the Sustainable Development Goals is challenging. Food supply chains may have to become more efficient to meet the increasing food requirement of 10 Billion people by 2050. At the same time, food and nutrition security are at risk from increasingly likely shocks like extreme climate events, market shocks, pandemics, changing consumer preferences, and price volatility. Here we consider some possibilities and limitations regarding the improvement of resilience (the capacity to deal with shocks) and efficiency (here interpreted as the share of produced food delivered to consumers) of food supply chains. We employ an Agent Based Model of a generic food chain network consisting of stylized individuals representing producers, traders, and consumers. We do this: 1/ to describe the dynamically changing disaggregated flows of crop items between these agents, and 2/ to be able to explicitly consider agent behaviour. The agents have implicit personal objectives for trading. We quantify resilience and efficiency by linking these to the fraction of fulfilment of the overall explicit objective to have all consumers meet their food requirement. We consider different types of network structures in combination with different agent interaction types under different types of stylized shocks. We find that generally the network structures with higher efficiency are also more sensitive to shocks, while less efficient network types display more resilience. At first glance these results seem to confirm the existence of a system-level trade-off between resilience and efficiency similar to what is reported in business management and ecology literature. However, the results are modified by the trading interactions and the type of shock. In our simulations resilience and efficiency are affected by ‘soft’ boundaries caused by the preference and trust of agents (i.e., social aspects) in trading. The ability of agents to switch between trading partners represents an important aspect of resilience, namely a capacity to reorganize. These insights may be relevant when considering the reorganization of real-life food chains to increase their resilience to meet future food and nutrition security goals.

Evaluations of healthcare interventions, e.g. new drugs or other new treatment strategies, commonly include a cost-effectiveness analysis (CEA) that is based on the application of health economic (HE) models. As end users, patients are important stakeholders regarding the outcomes of CEAs, yet their knowledge of HE model development and application, or their involvement therein, is absent. This paper considers possible benefits and risks of patient involvement in HE model development and application for modellers and patients. An exploratory review of the literature has been performed on stakeholder-involved modelling in various disciplines. In addition, Dutch patient experts have been interviewed about their experience in, and opinion about, the application of HE models. Patients have little to no knowledge of HE models and are seldom involved in HE model development and application. Benefits of becoming involved would include a greater understanding and possible acceptance by patients of HE model application, improved model validation, and a more direct infusion of patient expertise. Risks would include patient bias and increased costs of modelling. Patient involvement in HE modelling seems to carry several benefits as well as risks. We claim that the benefits may outweigh the risks and that patients should become involved.

Introduction: Dynamic crop growth models are an important tool to predict complex traits, like crop yield, for modern and future genotypes in their current and evolving environments, as those occurring under climate change. Phenotypic traits are the result of interactions between genetic, environmental, and management factors, and dynamic models are designed to generate the interactions producing phenotypic changes over the growing season. Crop phenotype data are becoming increasingly available at various levels of granularity, both spatially (landscape) and temporally (longitudinal, time-series) from proximal and remote sensing technologies.Methods: Here we propose four phenomenological process models of limited complexity based on differential equations for a coarse description of focal crop traits and environmental conditions during the growing season. Each of these models defines interactions between environmental drivers and crop growth (logistic growth, with implicit growth restriction, or explicit restriction by irradiance, temperature, or water availability) as a minimal set of constraints without resorting to strongly mechanistic interpretations of the parameters. Differences between individual genotypes are conceptualized as differences in crop growth parameter values.Results: We demonstrate the utility of such low-complexity models with few parameters by fitting them to longitudinal datasets from the simulation platform APSIM-Wheat involving in silico biomass development of 199 genotypes and data of environmental variables over the course of the growing season at four Australian locations over 31 years. While each of the four models fits well to particular combinations of genotype and trial, none of them provides the best fit across the full set of genotypes by trials because different environmental drivers will limit crop growth in different trials and genotypes in any specific trial will not necessarily experience the same environmental limitation.Discussion: A combination of low-complexity phenomenological models covering a small set of major limiting environmental factors may be a useful forecasting tool for crop growth under genotypic and environmental variation.

At the Mid-Pleistocene Transition about 1 Ma, the dominant periodicity of the glacial-interglacial cycles shifted from ~40 to ~100 kyr. Here, we use a previously developed mathematical model to investigate the possible dynamical origin of these different periodicities. The model has two variables, one of which exhibits sawtooth oscillations, resembling the glacial-interglacial cycles, whereas the other variable exhibits spikes at the rapid transitions. When applying a sinusoidal forcing with a fixed period, there emerges a rich variety of cycles with different periodicities, each being a multiple of the forcing period. Furthermore, the dominant periodicity of the system can change, while the forcing periodicity remains fixed, due to either random variations or different frequency components of the orbital forcing. Two key relationships stand out as predictions to be tested against observations: (1) the amplitude and the periodicity of the cycles are approximately linearly proportional to each other, a relationship that is also found in the \(\delta ^{18}\hbox {O}\) temperature proxy. (2) The magnitude of the spikes increases with increasing periodicity and amplitude of the sawtooth. This prediction could be used to identify one or more currently hidden spiking variables driving the glacial-interglacial transitions. Essentially, the quest would be for any proxy record, concurrent with a dynamical model prediction, that exhibits deglacial spikes which increase at times when the amplitude/periodicity of the glacial cycles increases. In the specific context of our calcifier-alkalinity mechanism, the records of interest would be calcifier productivity and calcite accumulation. We believe that such a falsifiable hypothesis should provide a strong motivation for the collection of further records.

Plato’s well-known allegory of the cave describes an observer chained in a cave facing a blank wall on which shadows are projected of objects that are outside the cave. Only by breaking free from the chains can the observer submerge from the cave to see what the objects really look like. Ecological model features compare to the objects outside the cave in this allegory. By performing model analysis light is shed on these features, creating projections that researchers can see. Model analysis methodologies like bifurcation analysis and sensitivity analysis each focus on particular model features and thus allow researchers to uncover only part of the model behaviour. By combining methodologies for model analysis possibilities arise for unravelling more of the model’s behaviour, allowing researchers to ‘break free’. In this paper benefits and issues of combining model analysis methodologies are discussed using a case study. The case study involves three representations of the well-known Rosenzweig-MacArthur predator-prey model, namely the usual one where state variables and parameters have dimensions, a dimensionless representation, and a generalized representation. Based on the results we argue that researchers should combine bifurcation and sensitivity analysis methodologies when analyzing ecological models.