Publications
3 results found
Gibson M, Pereira JP, Slade R, et al., 2022, Agent-based modelling of future dairy and plant-based milk consumption for UK climate targets, JASSS-THE JOURNAL OF ARTIFICIAL SOCIETIES AND SOCIAL SIMULATION, Vol: 25, ISSN: 1460-7425
A reduction in the production and consumption of meat and dairy across much of the world is critical for climate change mitigation, the alleviation of ecological stress, and improved health. We update an agent-based model (ABM) of historic UK milk consumption and apply it to scenarios of dairy reduction and adoption of plant-based milk (PBM) out to 2050. The updated model is comprised of a cognitive function, where agents perceive the physical, health and environmental characteristics of milk choice, which is modified by habit and social influence. We use European Social Survey 2018 and British Social Attitudes 2008 survey data to empirically inform the model. Taking a backcasting approach, we calibrate parameters against published UK dairy reduction targets (2030 and 2050), and test how different price relationships, and characterisations of environmental concern, may affect simulated milk consumption from 2020 to 2050. Scenarios for core targets (20% less dairy by 2030 and 35% by 2050) largely produced plausible consumption trajectories. However, at current pricing of dairy and PBM, simulated consumption was mostly unable to deliver on desired core targets, but this improved markedly with dairy prices set to organic levels. The influence of changing environmental concern on milk choice resulted in higher levels of dairy milk reduction. When modelled as transient, intense shocks to public concern, consumption patterns did not fundamentally change. However, small, incremental but permanent changes to concern did produce structural changes to consumption patterns, with dairy falling below plant-based alternatives at around 2030. This study is the first to apply an ABM in the context of scenarios for dairy reduction and PBM adoption in service to UK climate-related consumption targets. It can serve as valuable bottom-up, alternative, evidence on the feasibility of dietary shift targets, and poses policy implications for how to address impediments to behavioural change
Gibson M, Slade R, Pereira JP, et al., 2021, Comparing mechanisms of food choice in an agent-based model of milk consumption and substitution in the UK, JASSS-THE JOURNAL OF ARTIFICIAL SOCIETIES AND SOCIAL SIMULATION, Vol: 24, ISSN: 1460-7425
Substitution of food products will be key to realising widespread adoption of sustainable diets. We present an agent-based model of decision-making and influences on food choice, and apply it to historically observed trends of British whole and skimmed (including semi) milk consumption from 1974 to 2005. We aim to give a plausible representation of milk choice substitution, and test different mechanisms of choice consideration. Agents are consumers that perceive information regarding the two milk choices, and hold values that inform their position on the health and environmental impact of those choices. Habit, social influence and post-decision evaluation are modelled. Representative survey data on human values and long-running public concerns empirically inform the model. An experiment was run to compare two model variants by how they perform in reproducing these trends. This was measured by recording mean weekly milk consumption per person. The variants differed in how agents became disposed to consider alternative milk choices. One followed a threshold approach, the other was probability based. All other model aspects remained unchanged. An optimisation exercise via an evolutionary algorithm was used to calibrate the model variants independently to observed data. Following calibration, uncertainty and global variance-based temporal sensitivity analysis were conducted. Both model variants were able to reproduce the general pattern of historical milk consumption, however, the probability-based approach gave a closer fit to the observed data, but over a wider range of uncertainty. This responds to, and further highlights, the need for research that looks at, and compares, different models of human decision-making in agent-based and simulation models. This study is the first to present an agent-based modelling of food choice substitution in the context of British milk consumption. It can serve as a valuable pre-curser to the modelling of dietary shift and sustainable
Gibson MF, Rao ND, Slade RB, et al., 2020, The role of energy in mitigating grain storage losses in India and the impact for nutrition, Resources, Conservation and Recycling, Vol: 163, ISSN: 0921-3449
Globally, India's population is amongst the most severely impacted by nutrient deficiency, yet millions of tonnes of food are lost along the supply chain before reaching consumers. Across food groups, grains represent the largest share of daily calories and overall losses by mass in India. This study quantifies energy input to minimise storage losses across India, responsible for up to a quarter of grain losses. In doing so, we explore links between three Sustainable Development Goals-SDG2, SDG7, and SDG12-, and provide insight for development of joined up agriculture and health policy in the country. Focusing on rice, wheat, maize, bajra, and sorghum, we quantify one route to reduce losses in supply chains, by modelling the energy input to maintain favourable climatic conditions in modern silo storage. We quantify key nutrients (calories, protein, zinc, iron, vitamin A) contained within these losses, and calculate roughly how much deficiency in these dietary components could be reduced if grain losses were eliminated. Our modelling indicates that maize has the highest energy input intensity for storage, at 110 (18) kWh per tonne of grain (kWh/t), and wheat the lowest, at 72 (14) kWh/t. This energy cost represents 8%-16% of the energy input required in grain production. We estimate if grain losses across the supply chain were saved and targeted to India's nutritionally deficient population, average protein deficiency could reduce by 46±4%, calorie by 27±2%, zinc by 26±2% and iron by 11±1%.
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