Editorial: Advancing sustainability and resilience in agri-food supply chains through multi-criteria decision-making methods

Introduction

Agri-food supply chains (AFSCs) are found at the heart of many of the major challenges faced today, such as climate variability, geopolitical instability, digital disruption, and increased customer expectations for healthy, sustainably produced, yet affordable foods. In particular, small- and medium-sized enterprises (SMEs), which represent the vast majority of all AFSC stakeholders, face these challenges under conditions of limitations in their financial, technical, and human resource capabilities. This Research Topic was launched to examine how structured decision-support approaches can help agri-food stakeholders manage these pressures, with particular interest in multi-criteria decision-making (MCDM) techniques such as the Analytic Hierarchy Process (AHP), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Decision Making Trial and Evaluation Laboratory (DEMATEL), and Delphi. The 14 papers gathered in this Research Topic respond to that call, and together they show that agri-food decision problems are addressed through a broader methodological selection than MCDM alone. Alongside classical and hybrid MCDM frameworks, contributors draw on network analysis, econometric and empirical modeling, system dynamics, machine learning, and historical-institutional analysis. Overall, the Research Topic maps a range of decision-support logics, including structured multi-criteria methods for weighing competing objectives under uncertainty, data-driven and predictive methods for explaining observed outcomes, and qualitative or historical methods for outlining how institutions and path dependencies shape today's food systems.

In this context, the papers that have contributed to this Research Topic range across the entire AFSC, from field to plate. In order to demonstrate the link between these contributions, Figure 1 shows the conceptual framework that unites this Research Topic, with disruptions and sustainability drivers mapped onto the decision-support approaches used across the Research Topic. Also, in order to provide a brief and cross-cutting summary of how these studies push the frontiers of decision science, Table 1 organizes the contributions based on the agri-food context and decision-making approaches used.

Table 1. Methodological mapping and focus areas of the contributing articles.

Multi-criteria frameworks for sustainability and resilience assessment

The first cluster comprises the Research Topic's core MCDM contributions, applying multi-criteria frameworks to the assessment and prioritization problems that motivated this Research Topic. Akif and Büyüksaatçi-Kiriş develop a multi-criteria framework oriented toward sustainability and resilience in comparative analysis of agri-food systems in six European countries, proving that comprehensive assessment allows recognizing trade-offs that cannot be found out from independent analysis of sustainability and resilience criteria. On the farm scale, Vargas Mesa et al. apply a CRITIC-TOPSIS mixed method in prioritizing and allocating crops for family farming units in Colombia and prove that multi-criteria approaches may help small farmers operating on the principles of minifundia and microfundia in limited access to finance and market information. Ounalli et al. use a combination of Delphi technique and AHP in developing and weighting 65 sustainability criteria in the dairy value chain in Tunisia, providing a participatory and context-sensitive template for similar data- and governance-poor agri-food systems. Finally, Yao and Han apply MCDM techniques to marketing issues through the development of an AHP-consumer decision model for evaluating agricultural product brand design among four regional Chinese brands.

Resilience to shocks and disruption

A second cluster moves beyond classical MCDM to examine how AFSCs respond to simultaneous shocks, trade conflicts, pandemics, climate events, and warfare, using network analysis and empirical or econometric methods better suited to tracing how disruptions propagate through supply chains. Zhang R. et al. use a weighted node-degree analysis on United States Freight Analysis Framework data to determine how the 2018–2022 US–China trade dispute, Midwest floods, COVID-19, and droughts reorganized domestic and international flows of agri-food products and found that the production nodes in rural areas recovered quicker compared to logistics nodes in urban areas. Alnafissa and Ghanem examine the impact of Russia-Ukraine conflict on Egypt's food security by comparing food prices before and during the war and predicting future directions for a country whose grain supply is very dependent on imports. Hou and Wang explore whether agricultural insurance can improve China's overall ability to produce grain through risk protection and production encouragement channels. Wang et al. study the pricing strategies in online to offline food delivery under anti-food waste policy regulations to demonstrate that the efficiency of differentiated pricing strategy depends on the severity of regulations and the characteristics of consumers.

Digital transformation and data-driven decision-making

A third cluster turns to digital transformation and data-driven decision-making, drawing on index construction, empirical analysis, system dynamics, and machine learning rather than multi-criteria weighting, with special emphasis on China's rural economy. First, Huang et al. prove that data factor-oriented industries contribute to the rural revitalization indirectly through rural entrepreneurship using the entropy-weighted TOPSIS approach in creating the index for rural revitalization. Second, Xi finds out that the level of farmers' digital connectivity, information-seeking abilities, and application skills positively influence the revenue from the sale of agricultural products, thus stressing the importance of productivity in bridging the rural digital divide. Third, Zhao builds a system dynamics model of the agricultural live streaming e-commerce system, shedding light on its feedback loops, which either support or destroy this fast-growing sales channel. Fourth, Zhang X. et al. develop the decision-making support system for small-scale beef cattle farming based on deep reinforcement learning replacing experience-based judgment with a data-driven model incorporating market, health, and resource variables.

Consumer behavior and the historical roots of food systems

The remaining two contributions expand the Topic's scope in both method and time horizon, pairing an MCDM-adjacent consumer study with a purely qualitative historical analysis. In their research, Cinar and Bozkiran Yilmaz conduct a consumer survey among Turkish people to estimate the willingness to pay for cultured beef, using CRITIC weighting and fuzzy paired comparison in order to identify the barriers to adoption of this novel source of protein and thus highlight that the shift toward sustainability also involves acceptance by the consumer. Looking into the past, Nie et al. analyze the transformation of food cultivation in Taiwan due to the Japanese colonial introduction of new food crops and prove that resource inequalities of colonial agricultural policy still affect food systems today.

Conclusion and outlook

Together, these fourteen articles reveal that the tools of agri-food decision-support research are not limited to a particular toolkit. Around half of the contributions use MCDM methods in the proper sense, namely AHP, TOPSIS, DEMATEL, Delphi, CRITIC, and their combinations, to tackle the problems where competing objectives have to be balanced: crop choice for small farmers, sustainability-resilience dilemmas in various countries, brands for cooperatives, and indicators for value chains. The other contributions broaden the scope of the Topic's decision-support framework using alternative tools better suited for addressing their respective issues: network modeling and econometric analysis of how shocks like trade disputes, epidemics, and war affect the supply chain and its impact on food security; system dynamics and deep reinforcement learning for capturing feedback in data-abundant and non-linear settings; and historical-institutional analysis for understanding how current food systems bear the mark of policy during colonial times.

In this context, the Research Topic's contribution is less a single method than a demonstration that the choice of decision-support tool tracks the structure of the fundamental problem; namely, multi-criteria evaluation in the case of problems where there are clear trade-offs which need to be considered, modeling based on data in the case of explaining or predicting from observable outcomes, and qualitative or historical methods when path dependency or institutional context is important even more than the specific decision. Therefore, three key cross-cutting issues can be marked. The first one concerns data and information asymmetry, which is a structural issue in all kinds of situations, from family farmers in Colombia to dairy producers in Tunisia and cattle farmers in China, and it is here that structured elicitation methods such as Delphi and AHP are really useful. Secondly, resilience is involved as a portfolio issue, not a shock-response issue, for the reason that different issues such as trade policy changes, epidemics, adverse weather, and war impact AFSCs differently; thus, the papers which integrate the method of MCDM with either network or econometric analysis suggest that hybrid methodologies constitute an exciting yet unexploited area of research. Lastly, digitalization works both ways in that digital marketing and digital agriculture create opportunities for income diversification and growth, but at the same time give rise to issues that cannot be solved solely by any of the methodologies discussed here, including MCDM.

Despite this methodological diversity, the Research Topic is unable to fully cover all the areas highlighted in the Topic's call for papers. First, few studies have incorporated Environmental, Social, and Governance (ESG) criteria within a single decision-making framework. Most studies that address these dimensions consider them separately rather than examining their combined effects. Hybrid MCDM approaches using IoT or blockchain data streams explicitly proposed in the call, though directly applicable to AFSC digitalization, still seem to be underrepresented. These limitations point to two clear directions for future research: (I) developing MCDM frameworks that combine multi-criteria weighting with real-time data from IoT or blockchain systems, and (II) conducting comparative studies to assess whether these approaches can be applied across different regions and types of shocks. We hope that the variety of methodological approaches and countries presented in this Topic will inspire additional research on these topics and would like to express gratitude to all the authors, reviewers and the readership of Frontiers in Sustainable Food Systems for their contribution into this Research Topic.

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Footnotes

Edited and reviewed by: Qingli Dong, University of Shanghai for Science and Technology, China