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Viable agri-food supply chains: survival through systemic adaptations

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Abstract

Ensuring the continuous supply and availability of food during long-term disruptions remains a critical challenge for agri-food supply chains (ASCs). Traditional resilience strategies, which focus on short-term recovery, often fall short during prolonged or systemic crises. This study examines how ASCs adapted during the COVID-19 pandemic, demonstrating that sustained food access was achieved through systemic adaptations that moved beyond resilience to a more enduring state of viability. Using qualitative data from interviews and focus group discussions across urban, semi-urban, and rural regions (spatial ecologies), the study explores event-level adaptations made by stakeholders within production, logistics, and consumption segments of the agri-food channels. To explain consumer decision-making when switching between ASC channels, a four-mode ASC classification (M1–M4) and a Cost–Availability–Quality (CAQ) framework were developed. Here, a channel represents a distinct route through which fresh agri-food products flow from producers to consumers. Behaviour Over Time (BOT) graphs illustrate how channel usage evolved before, during, and after disruption. Findings reveal that viability was achieved through interconnected adaptations shaped by spatial context and enabled by digital tools, community networks, and policy support. The study provides a structured foundation for understanding ASC viability through real-world adaptation and offers a basis for future systems-modelling research.

Original languageEnglish
Article number1056
Number of pages38
JournalSystems
Volume13
Issue number12
DOIs
Publication statusPublished - Dec 2025

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 2 - Zero Hunger
    SDG 2 Zero Hunger

Keywords

  • adaptation(s)
  • agri-food supply chains (ASCs)
  • COVID-19 pandemic
  • disruption(s)
  • enabling factors
  • food security
  • resilience
  • spatial ecologies
  • systems modelling
  • viability

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