Abstract
The food system of the future must be resilient to unprecedented shocks. The complex food system will respond in many ways – brittle parts may break, others may adapt or even benefit. Multiple and compounding shocks may irreversibly tip the system into new, stable but degraded states. Food policy, and the emergent configurations of markets and supply chains that it drives, must at minimum avoid making food systems more fragile, but ideally should account for and even enhance resilience. ‘Resilience’ is an inherently complex property of any system, emerging from interconnectivity, covering the capacity of a system to withstand (robustness), recover from (recovery), and respond to (adaptation) shocks. To appropriately capture the distributional and dynamic processes fundamental to food system resilience requires a complex systems approach. Here we show that the different shocks expected to impact food systems produce different patterns of resilience (e.g., strong recovery but weak robustness) across SDG relevant outcomes and farming populations. We shed new light on how resilience (fails to) emerges in current food systems in the face of repeated shocks, with analysis revealing different resilience strengths and weaknesses, depending on shock type (e.g., adaptation to, but poor recovery from, general system shocks). Moreover, our results provide novel insights into the impact of policies as shocks, wherein we demonstrate not only the dangers of lacking commitment mechanisms to ambitious policies, but also the dangers of insufficient ambition. Our findings emphasise the importance of tailoring food system policies to the realities of local expected shock patterns. Without approaches that can capture the emergent properties of resilience, policy design is likely to inadequately address where current food systems are vulnerable (e.g., who will be worst affected, and in what manner?), undermining long-term success and risking harmful compounding cycles of policy reversals and further uncertainty.
Generated Summary
This document explores the resilience of food systems under various shock scenarios and policy interventions. The study employs the TELLUS model, an agent-based simulation, to analyze the interactions of farmers, markets, and policies over a 30-year period. The research focuses on understanding how different shock patterns, including direct environmental shocks and general food system shocks, affect food production, farmer livelihoods, and environmental outcomes. It also investigates the impact of policy reversals and debt forgiveness on resilience. The analysis considers various system constraints, policies (including regenerative subsidies and priming policies), and the dynamic responses of the food system to these factors. The goal is to identify how policies can enhance or diminish resilience in the face of expected shocks, providing insights for more effective food system policy design.
Key Findings & Statistics
- Direct Environmental Shocks: The document notes that these shocks affect total production, but have minimal impact on livelihoods and environmental outcomes. This is attributed to the direct yield-impact nature of the shocks and market mechanisms that offer higher prices for scarce products, reducing the risk to commercial livelihoods (Fig. 3).
- General System Shocks: These shocks, affecting labor, product demand, and input costs, impact production in more long-lasting ways. They decrease production depth and recovery as the system shifts (Fig. 4). The effect of general system shocks decreases over time, due to reduction in average farm size.
- Debt Forgiveness: Year 1 debt forgiveness aligns constrained systems with unconstrained ones, preventing farmer exits and production losses. It also reduces the risk of policy reversals and enables responsiveness to regenerative policies (Fig. B1).
- Impact of Reversals: Reversals of policies (intended to increase production) often fail. The findings indicate that reversals intended to improve production never achieve their aims. The effect is particularly notable in recovery post direct environmental shocks (Fig. 2).
- Shock Severity & Duration: Direct environmental shocks were modeled at 90% yield reduction for two years, and general food system shocks were modeled with a 90% reduction in demand, 50% reduction in available labor, and a 90% increase in supply costs. These shocks were designed to occur at increasing frequency (years 6, 13, 19, and 23).
- Policy: Priming Policy Bundle: The priming policy bundle starts at $500 per hectare for regenerative practices, tapering to $100 over 12 years, followed by a carbon tax on non-regenerative practices. This is designed to incentivize regenerative agriculture and potentially lead to a “critical mass” of adoption. It also includes incentives for co-ops, a ban on pesticides, and support for new practices.
Other Important Findings
- Direct Environmental Shocks Distributional Outcomes: Commercial large-holders are generally robust to direct environmental shocks, commercial small-holders are more readily able to enter the system post-shocks to take advantage of any newly available land and demand-supply gaps (Fig. 3). However, this can produce some turnover in commercial small-holders, as the next shock can force the most vulnerable/overleveraged out.
- Market Impact of Direct Environmental Shocks: Higher numbers of production contracts undertaken (see Fig. A1), suggesting responsiveness from both producers and buyers to seek security.
- Policy Reversals: Reversals are highly likely to occur, irrespective of shock type. The impact on production means reversals are likely regardless of constraints or active policies. The reversal is triggered by a loss in production of >= 10% across two consecutive years.
- Policy Ambition and Reversal Likelihood: The priming policy bundle is most likely to cause a reversal within the first 5 years of implementation, but is then no longer likely to tempt a reversal, in this study.
- Impact of Labor Market Shocks: Commercial large holders are the most impacted when labor markets are affected, and may struggle the most to bounce back from shocks. Conversely, small-holders are not impacted in the same degree (Fig. 5).
- Environmental Outcomes: Environmental outcomes, unlike production and livelihoods, are not directly affected by shocks, but are more affected by constraints and policy conditions.
- Importance of Diagnostics: It highlights the importance of diagnostic accuracy in both determining sufficient policy ambition, but also in determining the causal mechanisms behind system changes.
Limitations Noted in the Document
- Model Simplifications: The document acknowledges that the model uses simplified representations of the system response.
- Burn-In Period: The study does not cut off the initial period of adjustment at the beginning of the simulation period, which may mean the results of the constraints are over or underestimated.
- Focus on Large-Holder Systems: Most findings reflect the large-holder system and findings may not be reflective of all types of farmers.
- Generalizations of Shocks: Expected direct environmental shock patterns were generalized, despite varying regional impacts, and may not capture the complexity of real-world scenarios.
Conclusion
The study underscores the complex interplay of shocks, policies, and farmer characteristics in determining the resilience of food systems. The research provides insights into how different shocks affect food production, farmer livelihoods, and environmental outcomes, and how policies can be designed to enhance or diminish resilience. The findings highlight the importance of considering the distributional effects of policies, particularly concerning commercial large-holders and small-holders. The research suggests that strong, early incentives for engaging in regenerative practices, along with policy commitment mechanisms, are crucial for building long-term resilience. The study emphasizes that policy design must not only acquire sufficient information about farmers and their constraints but also understand and anticipate potential shock patterns. Coherent, comprehensive design can target key difference-makers within a system and assist policy-makers in resisting policy reversals that may lead to worse outcomes. Furthermore, the study stresses that policy reversals intended to increase production levels following a perceived decrease often fail to achieve this goal and can harm long-term production. It is recommended to design policies with the appropriate level of ambition, accompanied by sufficient commitment mechanisms to prevent deleterious policy reversals. Finally, the research calls for a more nuanced understanding of system constraints, farmer archetypes, and the potential impacts of various shock types to create more effective and sustainable food systems.