Abstract
To effectively address food security, we need tools that assess governance measures (for example, strategic storage reserves, cash transfers or trade regulations) ex ante. Simulation models can estimate the impact of such measures via scenarios with differently governed food systems. On the basis of a systematic review of 110 simulation studies published over 2000–2021, we examined how food security governance has been represented, and identified needs for future simulation model development. We found that studies commonly used agent-based, system dynamics, and computable general equilibrium models; tended to be production, trade or consumption centric; assessed the impact of a wide variety of mostly treasure- or authority-based measures; and applied diverse food security indicators, mostly of access or availability. We also identified blind spots (for example, simulation of nodal measures) and proposed how to address these blind spots (for example, telecoupling) and to make food security governance simulation studies fit for meta-analyses (for example, harmonizing food security indicators for comparison).
Main
Global food security has been declining since 2015, with the COVID-19 pandemic exacerbating the situation. To address food security challenges, market mechanisms alone are insufficient as illustrated by past food price crises. Intervention in the form of governance is needed: public, private or communal entities implementing measures to improve food security. Effective governance requires measurement of the impact of implementation ex post or simulation ex ante. The literature on ex-post assessments is limited, but ex-ante assessments using simulation models are vast and growing. These studies use various simulation models to assess the impact of governance measures on food security within a food value chain context.
Modelling approaches
Food security governance was simulated using various model types like cellular automata (CA), agent-based models (ABM), system dynamics models (SDM), optimization models, partial equilibrium (PE), and computable general equilibrium (CGE) models. These models were thematically diverse, often covering bio-physical, social, and economic domains. Some studies used coupled models to simulate specific aspects like crop growth or hydrology.
Value chain coverage
No study covered all food value chain echelons, with most focusing on production only, consumption only, or a combination of production, consumption and trade. Distribution, processing or storage, and retail were rarely simulated.
Governance measures
The governance measures assessed were mostly treasure or authority based. Social protection policies were targeted towards vulnerable citizens while spatially targeted measures aimed at geographically vulnerable groups were less common.
Governance impacts
Governance impacts on food security were assessed using indicators for access (44%) or availability (27%). Few studies assessed utilization, nutrition quality of crops and diets, and social inequality.
Results
A total of 1,953 potentially relevant studies were identified through database search in Scopus and Web of Science. Among these, 110 remained after title and abstract screening, full-text reading and qualitative content analysis.
Discussion
The review connected fragmented landscapes of studies simulating food security governance implementation. It highlighted dominant modelling approaches (ABMs, SDMs, CGE models), governance measures (treasure- and authority-based), and value chain echelons (production, trade, consumption). Future research could benefit from harmonization of food security indicators and development of ABMs simulating nodal governance measures.
Data availability
The datasets generated during the current study are available in the FoodSecGovSim2 Code Repository on github and on the Data Repository on Dataverse.
Code availability
The code used to generate the results described in this review can be found in the FoodSecGovSim2 Code Repository on github.
SDGs addressed in the article
- SDG 2: Zero Hunger
Specific targets under SDG 2
- Target 2.1: End hunger and ensure access by all people, in particular the poor and people in vulnerable situations, to safe, nutritious and sufficient food all year round
- Target 2.3: Double the agricultural productivity and incomes of small-scale food producers, particularly women, indigenous peoples, family farmers, pastoralists and fishers, including through secure and equal access to land, other productive resources and inputs, knowledge, financial services, markets and opportunities for value addition and non-farm employment
- Target 2.4: Ensure sustainable food production systems and implement resilient agricultural practices that increase productivity and production, that help maintain ecosystems, that strengthen capacity for adaptation to climate change, extreme weather, drought, flooding and other disasters and that progressively improve land and soil quality
Indicators mentioned or implied in the article
- Indicator of food availability: Production of food and area used to grow crops or keep livestock
- Indicator of food access: Price of food commodities and income of citizens
- Indicator of food utilization: Consumption or purchase of food
- Indicator of stability: Self-sufficiency and stocks of food crops
Table of SDGs, Targets and Indicators
| SDGs | Targets | Indicators |
|---|---|---|
| SDG 2: Zero Hunger | Target 2.1: End hunger and ensure access to safe, nutritious and sufficient food | Price of food commodities, income of citizens |
| SDG 2: Zero Hunger | Target 2.3: Double the productivity and incomes of small-scale food producers | Production of food, area used for agriculture |
| SDG 2: Zero Hunger | Target 2.4: Ensure sustainable food production systems and resilient agricultural practices | Consumption or purchase of food, self-sufficiency, stocks of food crops |
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Fuente: nature.com
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