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Food supply chains hold significant embodied carbon emissions that need to be mitigated and neutralized. This study aimed to explore the historical Greenhouse Gas (GHG) emissions associated with household food consumption at a local scale i.e. across the eight English regions and the four nations that comprise the United Kingdom (UK). UK EatWell guidelines were used to explore the potential change in emissions and food costs in a scenario of transitions to healthier diets across the study areas. These emissions were calculated based on food consumption data before the advent of the Covid-pandemic i.e. between the years 2001 and 2018. Spatial data analysis was used to explore if the study areas had any significant correlations with respect to the emissions during the study period. The results displayed a potential reduction in GHG emissions for all study areas in the explored scenario. Further impacts include a reduction in household food costs across a majority of the areas during the study period. However, a consistent trend of significant correlations among the study areas was absent. This study concludes that local or regional policymaking should take precedence over national regulations to achieve healthier diets that are both carbon-neutral and affordable for the households. [ABSTRACT FROM AUTHOR]

Food waste (FW) is the decrease in the mass of edible food during retail, food service, and final consumption. In recent years, research related to this topic has spread. However, there are still few references about food waste in the service sector in developing countries, which is a concern for three reasons: first, due to the overgrew of food consumption out of the home before the COVID-19 pandemic; secondly, due to the interest in carrying out this research at the local level avoiding regional or global generalizations and, finally for the nutritional implications derived from this research. With this background, this paper fills this gap and aims to evaluate food waste in the service sector, concretely in higher education cafeterias and canteens (HECC). Our specific objectives include obtaining information on the causes of FW in HECC in Santiago (Chile) using approximation methods that are better suited to the purposes of this paper. The study employed the qualitative research paradigm to analyze primary data and comprises a structured questionnaire survey and a qualitative semi-structured interview of HECC managers. Non-probabilistic sampling was used as a convenience sampling technique to select the HECC to be surveyed (n = 17). The results show a total FW by year comprised 309 tons of FW generated in the higher education canteens surveyed and 115 tons generated in the higher education cafeterias surveyed, representing 425 tons of FW annually. Divided by the total FW by the working days of HECC, the average FW generated daily is 1381 kg, corresponding to 1005 kg per day in higher education canteens and 376 kg per day in higher education cafeterias. [ABSTRACT FROM AUTHOR]

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This systematic review critically examines the diverse applications of Blockchain technology in the food supply chain and identifies areas where its potential remains underutilized. By analysing 60 Blockchain-based frameworks, the study highlights the most frequently employed drivers such as transparency, traceability, and security within food supply chains. Additionally, underexplored applications such as food donation and redistribution, supply chain financing, animal welfare, food waste management, and data analysis are identified, revealing opportunities for further innovation. The research employed NVivo 14 to analyze the extent of Blockchain's implementation in various food supply chain drivers, and the findings informed the development of a more diverse framework for Blockchain integration. Key insights demonstrate Blockchain's transformative potential, particularly in enhancing data integrity, trust, and operational efficiency through its immutable ledger and smart contracts, which streamline transactions, cut administrative costs, and reduce fraud. In terms of sustainability and safety, Blockchain improves traceability, accelerates safety responses, promotes environmental sustainability by tracking resource usage, and enhances humanitarian efforts with transparent, efficient resource distribution. Additionally, Blockchain facilitates food waste reduction by optimizing inventory and distribution, while ensuring surplus food reaches those in need. The study concludes by offering a roadmap for future research, pointing toward untapped dimensions of Blockchain's application in food traceability, sustainable supply chain management, and environmental & social impact. While the review provides a comprehensive understanding of Blockchain's current usage in food supply chains, the scope is limited by the systematic review process and specific inclusion criteria. This study serves as a foundation for exploring Blockchain's broader potential in shaping the future of food supply chains. [ABSTRACT FROM AUTHOR]

AbstractFood fraud has serious consequences including reputational damage to businesses, health and safety risks and lack of consumer confidence. New technologies targeted at ensuring food authenticity has emerged and however, the penetration and diffusion of sophisticated analytical technologies are faced with challenges in the industry. This review is focused on investigating the emerging technologies and strategies for mitigating food fraud and exploring the key barriers to their application. The review discusses three key areas of focus for food fraud mitigation that include systematic approaches, analytical techniques and package-level anti-counterfeiting technologies. A notable gap exists in converting laboratory based sophisticated technologies and tools in high-paced, live industrial applications. New frontiers such as handheld laser-induced breakdown spectroscopy (LIBS) and smart-phone spectroscopy have emerged for rapid food authentication. Multifunctional devices with hyphenating sensing mechanisms together with deep learning strategies to compare food fingerprints can be a great leap forward in the industry. Combination of different technologies such as spectroscopy and separation techniques will also be superior where quantification of adulterants are preferred. With the advancement of automation these technologies will be able to be deployed as in-line scanning devices in industrial settings to detect food fraud across multiple points in food supply chains. [ABSTRACT FROM AUTHOR]

The widespread presence of Fusarium mycotoxins in animal feed is a global issue, not only for the health of livestock but also for ensure the safety of food as an end product. High concentrations of zearalenone (ZEN) and deoxynivalenol (DON) have been detected in the diets of Japanese Black (JB) and Holstein Friesian (HF) breeding herds. Consequently, we monitored serum biochemical parameters over a long time in both herds, focusing on anti-Müllerian hormone (AMH) levels and acute-phase inflammation. Additionally, urinary 8-hydroxy-2′-deoxyguanosine (8-OHdG) and progesterone levels were measured in the HF herd. The JB herd, a ZEN-dominant model with low DON contamination, demonstrated ZEN levels that exceeded the Japanese limit in the purchased total mixed rations (TMR). Conversely, the HF herd, which primary consumes DON-dominant feed with low ZEN contamination, had high DON levels in the dent corn silage. Specifically, the JB herd's TMR contained 1.79 mg/kg ZEN and 0.58 mg/kg DON, whereas the HF herd's silage had 15.3 mg/kg DON (dried sample) and 0.1 mg/kg ZEN. Enzyme-linked immunoassay were used to measure urinary ZEN-DON levels following confirmation through liquid chromatography-tandem mass spectrometry. Urinary ZEN-DON levels measured were significantly correlated (p < 0.05, r > 0.6) in both herds. In the HF herd, AMH levels increased (p = 0.01) and serum amyloid A (SAA) levels decreased (p = 0.02) when contaminated and at the end of the monitoring period. Additionally, urinary ZEN and DON levels were significantly correlated with SAA levels (ZEN: p = 0.00, r = 0.46; DON: p = 0.03, r = 0.33), with an increase in ZEN and DON levels resulting in higher SAA levels. The JB herd showed no significant differences. Additionally, in the HF herd, 8-OHdG/Cre levels increased significantly during major contamination periods (p < 0.05). Clinical data from the HF herd indicated an increase in mastitis cases and treatment rates during periods of major contamination. Abortion rates in the HF herd decreased from 22.9% (before monitoring) to 8.9% (during the high contamination period) and finally to 1% (at the end of the monitoring period), with corresponding increases in progesterone levels. ZEN-DON contamination adversely affects breeding cattle's productivity, reproductive performance, and health. Therefore, monitoring urinary ZEN-DON is valuable for detecting contaminants and ensuring the safety of food products. [ABSTRACT FROM AUTHOR]

Background: This study presents an integrated multi-product, multi-period queuing location-allocation model for a sustainable, three-level food supply chain involving farmlands, facilities, and markets. The model employs M/M/C/K queuing systems to optimize the transportation of goods, enhancing efficiency and sustainability. A mixed-integer nonlinear programming (MINLP) approach is used to identify optimal facility locations while maximizing profitability, minimizing driver waiting times, and reducing environmental impact. Methods: The grasshopper optimization algorithm (GOA), a meta-heuristic algorithm inspired by the behavior of grasshopper swarms, is utilized to solve the model on a large scale. Numerical experiments demonstrate the effectiveness of the proposed model, particularly in solving large-scale problems where traditional methods like GAMS fall short. Results: The results indicate that the proposed model, utilizing the grasshopper optimization algorithm (GOA), effectively addresses complex and large-scale food supply chain problems. Compared to GAMS, GOA achieved similar outcomes with minimal differences in key metrics such as profitability (with a gap ranging from 0.097% to 1.11%), environmental impact (0.172% to 1.83%), and waiting time (less than 0.027%). In large-scale scenarios, GOA significantly reduced processing times, ranging from 20.45 to 64.78 s. The optimization of processing facility locations within the supply chain, based on this model, led to improved balance between cost (up to $74.2 million), environmental impact (122,112 hazardous units), and waiting time (down to 11.75 h). Sensitivity analysis further demonstrated that increases in truck arrival rates and product value had a significant impact on improving supply chain performance. [ABSTRACT FROM AUTHOR]

Background: Sustainability in food supply chains is desired for production, logistics, and waste management. However, food supply chains (SCs) have complex systems that differ from other SCs. Managing such complexities is challenging for small and medium-sized enterprises (SMEs) due to their heightened constraints, such as limited resources and inadequate awareness. Despite the challenges, there is fragmented research, understanding, and approaches to assist SMEs. SC designs need to be adapted so that SMEs can navigate the challenges and avert high wastage. The main objective of this research was to identify the practices influencing sustainable SC designs in a developing country and create a simulation model to illustrate the potential benefits and challenges of promoting sustainability in the banana SC in Malawi. Methods: Mixed-methods research was utilised, employing a literature review, participant interviews, observations, and survey data collected from 353 participants from three districts in Malawi to gain insight into the banana SC problem, establish objectives, and develop a simulation model complemented by design science research. Results: The research identified awareness, collaboration, efficiency, governance, knowledge sharing, and resilience as sustainability practices in the case study banana SC, thus forming a sustainability model. Simulation results showed improvements in key performance indicators like shelf-life, lead-time, quality, throughput, and waste through SC operation reorganisation. Conclusions: Sustainable models must be tailored to the specific challenges inherent in developing food supply chains in developing nations. The development of the models has significant managerial implications, notably enhancing strategic planning, operational efficiency, risk management, alignment of sustainability goals, performance monitoring, stakeholder engagement, and resource optimisation. [ABSTRACT FROM AUTHOR]

Food safety has emerged as the topmost priority in the current fast‐paced food industry era. According to the World Health Organization, around 600 million people, approximately 1 in 10 individuals worldwide, experience illness due to contaminated food consumption, resulting in nearly 0.42 million fatalities annually. The recent development in software and hardware sectors has created opportunities to improve the safety concerns in the food supply chain. The objective of this review is to explain the fundamentals of blockchain and its integration into the supply chain of various food commodities to enhance food safety. This paper presents the analysis of 31 conceptual works, 10 implementation works, 39 case studies, and other investigations in blockchain‐based food supply chain from a total of 80 published papers. In this paper, the significance of adapting conceptual ideas into practical applications for effectively tracing food commodities throughout the supply chain has been discussed. This paper also describes the transformative role of blockchain platforms in the food industry, providing a decentralized and transparent ledger to access real‐time and immutable records of a product's journey. In addition, both the positive impacts and challenges associated with implementing blockchain technology in the food supply chain have been evaluated. In summary, the blockchain‐based food supply chains offer greater transparency, traceability, and trust, ultimately resulting in higher standards of food safety and quality. [ABSTRACT FROM AUTHOR]

Despite the negative impacts caused by the drawn-out COVID-19 pandemic on the usual way of life and health of billions around the world, the pandemic's particular disruption of Hong Kong's supply chain was revealed to have impacted food purchasing habits during the pandemic. It brought about increased health and environmental awareness through the increased purchasing of healthier food choices, home food preparation and the increased usage of reusable bags. However, despite these shifts, the residents' behavioural attitudes towards food wastage or the use of plastic bags and containers did not change. The original finding in this study seems to suggest that food wastage behaviour in Hong Kong is not influenced by external drivers, such as strict COVID-19 containment measures, nor is it affected by increased environmental awareness. Food-waste management strategies aimed at reducing food wastage at the consumer level of the food supply chain should, therefore, focus on targeting engrained behavioural habits and not simply raising awareness on the matter. Our aim is to investigate whether pandemic restrictions have led to a shift towards less-frequent food purchases in larger quantities and how such changes may affect food wastage generation. Survey data were collected from 253 Hong Kong residents. The study mainly adopted various quantitative research methods, including descriptive statistics, reliability tests, t-tests, ANOVA, and non-response bias. The Statistical Package for Social Science (SPSS) version 28 was used to investigate the useable data gathered from the respondents of this study. Results suggested that Hong Kong residents preferred supermarket and online food purchases during the COVID-19 pandemic to their usual offline/wet market purchasing habits before the pandemic. Also, the food purchasing rate was principally self-reported as infrequent and at high volumes during the COVID-19 pandemic. While an increased preference for home-prepared meals took precedence over consuming meals at restaurants, no significant change in food wastage habits was observed due to pandemic measures. Interestingly, although an increasing usage of reusable bags was observed, resident behaviour toward plastic bag usage was not affected by the COVID-19 pandemic containment measures. [ABSTRACT FROM AUTHOR]

Food supply chains face many challenges, including those associated with supply chain quality management practices. Supply chain quality management practices are activities and processes adopted to achieve quality goals. The practices can be quite challenging, especially for food supply chains that are sustainability-oriented, such as those found in alternative food networks. This paper introduces a notion of supply chain quality management practice hotspots to capture incidences of quality management practice challenges that manifest in food supply chains. An approach for identifying supply chain quality management practice hotspots is developed in the paper and illustrated with multiple case studies. Findings show that incidences of supply chain quality management practice hotspots exist in practice, and practitioners perceive the notion of supply chain quality management practice hotspots to be valuable, especially in providing insights into supply chain processes and use as a basis for performance improvement. The paper indicates that the severity of supply chain quality management practice hotspots is influenced by the importance placed on supply chain processes, supply chain practices, and the severity of the quality management practice challenges the supply chain faces. Implications of findings and recommended areas of future work are presented. [ABSTRACT FROM AUTHOR]

Waste in the perishable food supply chain is a challenge that data-driven forecasting methods can tackle. However, integrating such methods in supply chain planning requires development efforts. In this regard, understanding user expectations is the first development step. This study scrutinizes the expectations of a data-driven forecasting method for perishable food. The intended development is a joint initiative of a consortium containing three perishable grocery handling firms. Besides planning expectations, the study identifies and ranks demand-sensing factors that can enable data-driven forecasting for food waste prevention. As the participating firms compete in the same region, horizontal collaboration implications are additionally explored in this context. Accordingly, the study extracts relevant performance measures parallelized to food waste. A two-round Delphi study is used to collect the expectations from a data-driven forecasting method. Individual semi-structured interviews with experts from the initiative firms are conducted in the first Delphi round. Based on the extracted propositions in each interview, industrial experts jointly readdressed and ranked the extracted propositions in the second Delphi round, i.e., focus group workshop. The results reveal that the perishability characteristic emerges as a common expectation in linking supply chain planning with data-driven forecasting. This empirical study contributes to the research on supply chain forecasting and addresses the pertinent aspects of developing data-driven approaches to prevent food waste. [ABSTRACT FROM AUTHOR]

In the face of the impending challenge of feeding a growing global population, one-third of all food produced ends up as waste. A notable contributor to this problem is the wastage of a third of perfectly edible and nutritious fresh produce because they need to meet the high cosmetic standards expected by consumers. Eliminating this wastage of imperfect produce is, therefore, a crucial and sustainable means to increase the food supply for a growing global population. This can be achieved through automated sorting of good, bad and imperfect produce using automation, robotics and machine vision. A prerequisite for such automated sorting is fast and accurate machine vision algorithms for successful differentiation between good, bad and imperfect produce. Training such algorithms requires large image datasets. While much work has gone into collecting images of good and bad produce, to the best of our knowledge, no such dataset exists for imperfect produce items. In this paper, we attempt to fill this gap by developing the first publicly available dataset of good, bad and imperfect produce items. The dataset has been made publicly available on the Harvard Dataverse for use in training machine vision algorithms for sorting good, bad and imperfect produce. It is our hope that this open dataset will contribute to improving research and practice for sorting and saving imperfect produce in the food supply chain. [ABSTRACT FROM AUTHOR]

This paper presents a conceptual framework aimed at exploring the connections between integration within the food supply chain and the three pillars of sustainability: economic, environmental, and social. The analysis combines theoretical insights with empirical research, specifically focusing on the economic aspect of sustainability, measured through efficiency. The study emphasizes the critical role of integrating participants across the food supply chain to achieve sustainability goals. It discusses methodologies for assessing integration levels within the chain, providing a theoretical and analytical basis for modeling integration and efficiency within the supply chain. To empirically validate these relationships, an extended stochastic frontier analysis (SFA) method was applied to selected sectors within the food processing industry. The results demonstrate that enhancing integration with suppliers and buyers can significantly improve efficiency within the food chain. In conclusion, through deductive reasoning, this study asserts that integration plays a pivotal role in advancing economic sustainability objectives within the food sector. This framework contributes to a deeper understanding of how integration influences sustainability outcomes, offering valuable insights for stakeholders aiming to enhance sustainability in food systems. [ABSTRACT FROM AUTHOR]

Purpose: The rising number of food recalls has raised concerns about complexity, globalization and weak governance in the food supply chain. This paper aims to investigate the recall of plant-based products with data from the US Food and Drug Administration. Design/methodology/approach: Introducing the structural topic modeling method allowed us to test theories on recall in the context of sustainable food consumption, enhancing the understanding of food recall processes. This approach helps identify latent topics of product recalls and their interwoven relationships with various stakeholders. Findings: The results answer a standing research call for empirical investigation in a nascent food industry to identify stakeholders' engagements for food safety crisis management for corporate social responsibility practices. This finding provides novel insights on managing threats to food safety at an industry level to extend existing antecedents and consequences of product recall at a micro level. Practical implications: For practitioners, this empirical finding may provide insights into stakeholder management and develop evidence-based strategies to prevent threats to food safety. For public policymakers, this analysis may help identify patterns of recalls and assist guidelines and alarm systems (e.g. EU's Rapid Alert System for Food and Feed) on threats in the food supply chain. Originality/value: Two detected clusters, such as opportunisms of market actors in the plant-based food system and food culture, from the analysis help understand corporate social responsibility and food safety in the plant-based food industry. [ABSTRACT FROM AUTHOR]

Sustainable development involves multi‐level, multi‐actor and multi‐faceted processes and interrelations for which, ideally, comprehensive theoretical frameworks are required to understand the nature, dynamics and heterogeneity of changes generating greater sustainability in rural areas. Studies have utilised the rural web framework to explain these processes and interactions, but its application is lacking in sub‐Saharan Africa. This study addresses the gap in the literature by using the framework to (1) explore sustainable development indicators and (2) evaluate their socioeconomic determinants based on a questionnaire survey with data collected from 600 rural households in Ghana. Additionally, formal interviews were conducted with 60 participants, comprising village leaders, not‐for‐profit organisations and officers from municipal assemblies. This research shows that endogeneity, novelty production, and sustainability indicators describe rural regions as agriculturally diversified economies. Several factors contribute significantly to agricultural diversification, including social relationships, social groups, market participation, and the presence of migrants. These findings have implications for achieving two of the United Nations' Sustainable Development Goals (SDGs), SDG 1 (zero poverty) and SDG 2 (zero hunger), as they highlight the benefits of social and economic linkages between rural and urban areas within the context of agricultural production, exchange of resources and the mitigation of accessibility barriers to resources. We suggest that policies seeking to increase agricultural diversification, food security and achieve sustainable rural development should strengthen social relationships and institutional structures, increase market opportunities, assist farmers with organic certifications and bridge the rural–urban development gap. [ABSTRACT FROM AUTHOR]

The dissemination of resistant pathogens through food supply chains poses a significant public health risk, spanning from farm to fork. This study analyzed the distribution of Shiga toxin-producing Escherichia coli (STEC) across various sources within the animal-based food supply chain. A total of 500 samples were collected from livestock, poultry, the environment, fisheries, and dairy. Standard microbiological procedures were employed to isolate and identify E. coli isolates, which were further confirmed using MALDI-TOF and virulence-associated genes (VAGs) such as stx1, stx2, ompT, hylF, iutA, fimH, and iss. The phenotypic resistance patterns of the isolates were determined using the disc diffusion method, followed by molecular identification of antibiotic resistance genes (ARGs) through PCR. STEC were subjected to PCR-based O typing using specific primers for different O types. Overall, 154 (30.5%) samples were confirmed as E. coli, of which 77 (50%) were multidrug-resistant (MDR) E. coli. Among these, 52 (67.53%) isolates exhibited an array of VAGs, and 21 (40.38%) were confirmed as STEC based on the presence of stx1 and stx2. Additionally, 12 out of 52 (23.07%) isolates were identified as non-O157 STEC co-harbouring mcr-1 and blaNDM-1. O26 STEC was found to be the most prevalent among the non-O157 types. The results suggest that the detection of STEC in food supply chains may lead to serious health consequences, particularly in developing countries with limited healthcare resources. [ABSTRACT FROM AUTHOR]

This study aims to determine the functionality and benefits of blockchain technology in food procurement. Within the scope of the study, Migros Trade Inc., a retail food supply company implementing blockchain technology in Türkiye, was examined as a case study. The study employed a qualitative research case study design, asking expert participants from Istanbul and Izmir to give their opinions on pre-designed themes, and conducting descriptive analyses of the data collected. The themes of the research are food supply chain, good agricultural practices, sustainability in food, blockchain applications in food supply and food safety. At the end of the study, it was found that the transparency and traceability principles of blockchain have a positive impact on consumers and supply chain stakeholders. However, challenges such as technical infrastructure and performance, as well as the reasons for not starting the process directly from the field, were the problems experienced. The data shows that blockchain technology is an efficient system that should be expanded in the food supply chain.

It is crucial to increase the resilience of the global food production and distribution systems against the growing concerns relating to factors that could cause global catastrophic infrastructure losses, such as nuclear war or a worldwide pandemic. Currently, such an event would result in the global loss of industry, including the ability to drill and refine crude oil. In such an event, the existing above-ground reserves of diesel and gasoline are likely to still be intact but would only be able to power the production and transportation of food between 158 days and 481 days with 80% confidence, where the mean is 195 days at current rates. This paper investigates a novel group of interventions in relation to the scenario of providing food under these conditions. It was found that by using a plausible combination of wood gasification, increasing vehicle utilisation rate, and reducing food consumption, the stockpile duration could increase to between 382 days and 1501 days with 80% confidence, where the mean is 757 days. This is an improvement in mean duration by a factor of 3.9. It was discovered that diesel is the limiting fuel in all scenarios due to wood gas only being a partial replacement for diesel fuel and also because of the prevalence of diesel engines in both the agricultural and trucking industries. A sensitivity analysis was completed identifying that reducing food consumption to minimum levels was the most effective method to prolong diesel reserves. The other factors that benefited from extending fuel reserves in terms of their effectiveness are reducing the lag time before gasification devices are installed, increasing the rate at which gasification devices are installed, and increasing the agricultural equipment utilisation rate.

Abstract A large number of nodes, and significant system overhead are among the challenges. To tackle these problems, a new PBFT technique known as trace-PBFT (t-PBFT) is being contemplated for adoption in the food supply chain. The PBFT algorithm, short for Practical Byzantine Fault Tolerance, serves as the fundamental algorithm. The linkages throughout the supply chain may be categorized into three primary groups. As a response to the continuing discussion over the amount of data, the status of each node is continuously updated in real time. This metric is utilized to evaluate the dependability of the principal node and to analyze the overall performance of the nodes. To minimize the amount of data transmitted between nodes, we optimized the consensus process of the first algorithm. The distinct attributes of offering collaborative food technology were considered throughout this procedure. Experimental evidence indicates that the t-PBFT approach outperforms the PBFT algorithm in terms of throughput, request delay, and information overhead. An architectural paradigm is proposed by integrating the t-PBFT algorithm with a federated network. This model was developed in response to the distinct demands of the food supply chain. This model effectively captures data at each stage of the food supply chain to ensure information tracking while preserving the safety of the food technology flow process.

The issue of food security continues to be a prominent global concern, affecting a significant number of individuals who experience the adverse effects of hunger and malnutrition. The finding of a solution of this intricate issue necessitates the implementation of novel and paradigm-shifting methodologies in agriculture and food sector. In recent times, the domain of artificial intelligence (AI) has emerged as a potent tool capable of instigating a profound influence on the agriculture and food sectors. AI technologies provide significant advantages by optimizing crop cultivation practices, enabling the use of predictive modelling and precision agriculture techniques, and aiding efficient crop monitoring and disease identification. Additionally, AI has the potential to optimize supply chain operations, storage management, transportation systems, and quality assurance processes. It also tackles the problem of food loss and waste through post-harvest loss reduction, predictive analytics, and smart inventory management. This study highlights that how by utilizing the power of AI, we could transform the way we produce, distribute, and manage food, ultimately creating a more secure and sustainable future for all.

Introduction: The COVID-19 pandemic caused mainstream food supply chain disruptions, increased food security concerns, and impacted agrifood production and distribution systems. Short food supply chains provided consumers with an alternative method to acquire food outside conventional food supply chains. In this paper, innovation was seen as a solution to external challenges or problems in global food supply chains during a pandemic crisis. The solution was analyzed in the context of the United States and changes in the consumers' behavior and purchasing patterns in the direction of more accepting short supply chains, which led to the successful overcoming of crisis or adaptation of consumers to crisis circumstances. Methods: The Diffusion of Innovations was selected as the theoretical framework. Data were collected from 1,002 American adults from July 13 to August 18, 2021, through an online survey. Results and discussion: Results showed that consumers with higher educational attainment and financial status, from non-rural areas, and with more conservative viewpoints had higher innovation adoption levels, and they were more likely to embrace short food supply chains as an alternative during disruptions to conventional supply chains. Recommendations provide strategies to increase the use of short supply chains during the crisis to better meet the needs of consumers in the food system and improve marketing and communication efforts. Marketing and communication initiatives should engage these food innovators as opinion leaders to increase the adoption of short food supply chains to stabilize food supply in preparation for future crises. [ABSTRACT FROM AUTHOR]

By diminishing the risk factors associated with the food supply chain (FSC), we have recourse to strengthen the food supply chain's resilience, decrease food waste, and increase its sustainability. Prioritizing and identifying the risk factors impacting the sustainability of the food supply chain is essential for managing uncertainty and averting unfavorable consequences. This study attempts to identify and rank the most significant risks affecting the sustainability of the food supply chain under an uncertain environment. We use the α-Discounting multi-criteria decision-making (α-D MCDM) method for the main three risk factors: the risks of supply, the risks of demand, and the risks of operations. The primary causes of the challenges in assessing the food supply chain's risk elements include inaccurate assessment data, DMs' subjective preferences, and DMs' differing opinions and thoughts about the criteria. Unfortunately, earlier research fell short of fully resolving these issues. A hybrid three-phase neutrosophic MCDM method is proposed by integrating triangular neutrosophic numbers (TNNs), TNN-AHP, and TNN-CoCoSo to close this gap. In this manner, it may efficiently handle ambiguity. The application of the suggested framework is then explored using the top six food and beverage businesses in the world: Nestle (A1), PepsiCo (A2), Coca-Cola (A3), Danon (A4), Anheuser-Busch InBev SA (A5), and Mondelez International (A6). The results show the sustainability rankings from best to worst, which were established on the groups of decision-makers assessments based on the importance of the risk factors that have to be handled. To gain additional insight into the rationale and resilience of this framework, sensitivity evaluation and comparative analysis have been employed in this study. [ABSTRACT FROM AUTHOR]

In the Industry 4.0 era, many pioneering industries are leveraging emerging technologies such as the Internet of Things (IoT) as solutions in the digital age. One of the largest and most active industries in Iran is the food industry, which stands to benefit significantly from these advancements. Achieving a sustainable competitive advantage is often possible at the level of the supply chain, where companies use information and communication technologies, such as IoT, to coordinate information, finances, and materials among supply chain actors. This research aimed to identify the key success factors (KSFs) for implementing IoT in the food supply chain. Firstly, through a systematic literature review, the KSFs for IoT implementation in the food supply chain were identified. To develop a measurement model, confirmatory factor analysis using structural equation modeling was employed, making the research applied-descriptive. A questionnaire was designed and completed by 142 members of the "Amadeh Laziz" supply chain (a case study), who were selected using a stratified random sampling method. Confirmatory factor analysis and LISREL 8.83 were then used to validate the proposed model. Finally, the cause-and-effect relationship between KSFs in IoT implementation in the food supply chain was analyzed using Grey DEMATEL. Based on the confirmatory factor analysis findings, the KSFs in implementing IoT in the food supply chain were identified as technical, economic, legal, cultural and social, security, applicability of IoT throughout the supply chain, and implementation of IoT applications. Thus, the measurement model included eight factors and 27 measures. According to the cause-and-effect relationship findings, "Implementation of IoT applications" and "Economic" factors were found to be mostly influenced, while "Applicability of IoT throughout the supply chain" and "Technical" factors were recognized as the most influential. The results of this research can guide food producers and technology policymakers in their supply chains and help avoid trial and error in IoT implementation by leveraging global and national experiences. [ABSTRACT FROM AUTHOR]

Problem definition: Commodity prices have exhibited significant volatility in recent times, which poses an exogenous risk factor for commodity-processing and commodity-trading firms. Accurate commodity price forecasts can help firms leverage data-driven procurement policies that incorporate the underlying price volatility for financial and operational hedging decisions. However, historical prices alone are insufficient to obtain reasonable forecasts because of the extreme volatility. Methodology/results: Building on the hypothesis that commodity prices are driven by real-world events, we propose a method that automatically extracts events from news articles and combines them with price data using a neural network-based predictive model to forecast prices. In addition to achieving a high prediction accuracy that outperforms several benchmarks (by up to 13%), our proposed model is also interpretable, which allows us to identify meaningful events driving the price fluctuations. We found that the events frequently associated with major fluctuations in the price include "natural," "hike," "policy," and "elections," all of which are known drivers of price change. We used a corpus containing about 1.6 million news articles of a major Indian newspaper spanning 15 years and daily prices of four crops (onion, potato, rice, and wheat) in India to perform this study. Our proposed approach is flexible and can be used to predict other time series data, such as disease incidence levels or macroeconomic indicators, that are also influenced by real-world events. Managerial implications: Firms can leverage price forecasts from our system to design inventory and procurement policies in the face of uncertain commodity prices. Commodity merchants can also use the forecasts to design optimal storage policies for physical trading of commodities when prices are volatile. Our findings can also significantly impact policymakers, who can leverage the information of impending price changes and associated events to mitigate the negative effects of price shocks. History: This paper has been accepted in the Manufacturing & Service Operations Management Frontiers in Operations Initiative. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0641. [ABSTRACT FROM AUTHOR]

The Food and Agriculture Organization (FAO) reports that Latin America and the Caribbean experienced the most rapid rise in food insecurity, with approximately 47.7 million individuals in the region affected by hunger in 2022. In Chile, almost three million people, 15.6% of the country's population, do not have regular access to sufficient nutritious food and suffer from some form of food insecurity. Moreover, Chile is particularly susceptible to the impacts of climate change and contends with the depletion of several critical natural resources, notably water, stemming from severe and prolonged drought conditions. This article aims to comprehensively evaluate food loss and waste (FLW) in Chile and analyzes the implications of FLW on the sustainable development goal (SDG). This will be achieved by utilizing a top-down mass balance methodology that integrates various data sources and an in-depth analysis of the main food categories at different stages of the food supply chain (FSC). In 2021, Chile generated 5.18 million tons of FLW, with fruit being the largest contributor at 2.5 million tons (48% of total FLW). Vegetables accounted for 0.8 million tons (16%). Other food groups each contributed 1% to 10% of the total FLW. Per capita FLW was 295 kg. FLW varied by food group and FSC stage. Fruits, vegetables, starchy roots, and pulses had the most FLW early in the FSC, while cereals had it later. Comparing FLW with the domestic supply quantity of food, it is observed that 68% of the fruit available for the population was discarded. Vegetables and starchy roots also showed significant volumes of discarded food, with 48% and 29% of the availability of these products in Chile. Furthermore, we explore the implications of FLW on realizing SDG 2—zero hunger, particularly emphasizing its correlation with target 12.3. The research underscores the potential of its findings to significantly shape public policies and strategies concerning FLW and their alignment with the associated SDGs, making a tangible impact on the lives of millions. [ABSTRACT FROM AUTHOR]

Considering sustainability and resilience together is crucial in food supply chain (FSC) management, as it ensures a balanced approach that meets environmental, economic and social needs while maintaining the system's capacity to withstand disruptions. Towards this, a multi-objective optimisation model is proposed in this study to create an integrated sustainable and resilient FSC. The proposed model employs four objective functions -- each representing a dimension of sustainability and one for resilience and utilises an augmented e-constraint method for solving. The findings highlight the interplay between sustainability aspects and resilience, illustrating that overemphasis on any single dimension can adversely affect others. Further, the proposed model is applied to the case of Indian mango pulp supply chain and several inferences are derived. The proposed model would assist decision-makers in making a well-balanced choice based on sustainability and resilience considerations. [ABSTRACT FROM AUTHOR]

A large number of nodes, and significant system overhead are among the challenges. To tackle these problems, a new PBFT technique known as trace-PBFT (t-PBFT) is being contemplated for adoption in the food supply chain. The PBFT algorithm, short for Practical Byzantine Fault Tolerance, serves as the fundamental algorithm. The linkages throughout the supply chain may be categorized into three primary groups. As a response to the continuing discussion over the amount of data, the status of each node is continuously updated in real time. This metric is utilized to evaluate the dependability of the principal node and to analyze the overall performance of the nodes. To minimize the amount of data transmitted between nodes, we optimized the consensus process of the first algorithm. The distinct attributes of offering collaborative food technology were considered throughout this procedure. Experimental evidence indicates that the t-PBFT approach outperforms the PBFT algorithm in terms of throughput, request delay, and information overhead. An architectural paradigm is proposed by integrating the t-PBFT algorithm with a federated network. This model was developed in response to the distinct demands of the food supply chain. This model effectively captures data at each stage of the food supply chain to ensure information tracking while preserving the safety of the food technology flow process. [ABSTRACT FROM AUTHOR]

Summary: This article explores the impact of the 'Quadruple Whammy' consisting of Brexit, COVID‐19, Conflicts (Russia‐Ukraine and Israel‐Palestine) and Natural disasters on the food supply chain in Europe. This research adopted a two‐phase methodology comprised of the e‐Delphi technique followed by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta‐Analyses) approach within the context of these four identified challenges. The objective of this article is to analyse the challenges faced by the European food supply chain due to these four factors. The article examines the impact of political isolationism such as Brexit on trade, cost and border controls, while also discussing the effects of COVID‐19 on labour, supply chains and the rise of e‐commerce. In addition, the article examines the impact of conflicts on food access and availability and the role of international aid and assistance. The effects of natural disasters, such as the Turkish and Moroccan earthquakes, floods in Spain and Portugal and the Moroccan drought, on food security are also analysed. The article offers several strategies for taming the quadruple whammy, such as investing in local food production and supply chains, diversifying supply chains and trade partnerships and strengthening food safety regulations and standards. The importance of building resilience and preparedness in the face of these challenges is emphasised and the article concludes with final thoughts and recommendations. [ABSTRACT FROM AUTHOR]

It is crucial to increase the resilience of the global food production and distribution systems against the growing concerns relating to factors that could cause global catastrophic infrastructure losses, such as nuclear war or a worldwide pandemic. Currently, such an event would result in the global loss of industry, including the ability to drill and refine crude oil. In such an event, the existing above-ground reserves of diesel and gasoline are likely to still be intact but would only be able to power the production and transportation of food between 158 days and 481 days with 80% confidence, where the mean is 195 days at current rates. This paper investigates a novel group of interventions in relation to the scenario of providing food under these conditions. It was found that by using a plausible combination of wood gasification, increasing vehicle utilisation rate, and reducing food consumption, the stockpile duration could increase to between 382 days and 1501 days with 80% confidence, where the mean is 757 days. This is an improvement in mean duration by a factor of 3.9. It was discovered that diesel is the limiting fuel in all scenarios due to wood gas only being a partial replacement for diesel fuel and also because of the prevalence of diesel engines in both the agricultural and trucking industries. A sensitivity analysis was completed identifying that reducing food consumption to minimum levels was the most effective method to prolong diesel reserves. The other factors that benefited from extending fuel reserves in terms of their effectiveness are reducing the lag time before gasification devices are installed, increasing the rate at which gasification devices are installed, and increasing the agricultural equipment utilisation rate. [ABSTRACT FROM AUTHOR]

The issue of food security continues to be a prominent global concern, affecting a significant number of individuals who experience the adverse effects of hunger and malnutrition. The finding of a solution of this intricate issue necessitates the implementation of novel and paradigm-shifting methodologies in agriculture and food sector. In recent times, the domain of artificial intelligence (AI) has emerged as a potent tool capable of instigating a profound influence on the agriculture and food sectors. AI technologies provide significant advantages by optimizing crop cultivation practices, enabling the use of predictive modelling and precision agriculture techniques, and aiding efficient crop monitoring and disease identification. Additionally, AI has the potential to optimize supply chain operations, storage management, transportation systems, and quality assurance processes. It also tackles the problem of food loss and waste through post-harvest loss reduction, predictive analytics, and smart inventory management. This study highlights that how by utilizing the power of AI, we could transform the way we produce, distribute, and manage food, ultimately creating a more secure and sustainable future for all. [ABSTRACT FROM AUTHOR]

Adaptive strategies that build resilience within food systems are urgently needed to address food supply chain disturbances. Increasingly severe and compounding natural disasters, conflict and the Covid-19 pandemic have led to recurrent food shortages, price spikes and increased food inequities in recent years, threatening food security. 'Regional food systems' (RFSs), that aim to scale up local food production and increase consumer access to food, present many opportunities for adaptive social and ecological developments. However, appreciation of food system resilience at the regional scale remains fragmented, therefore this research aimed to develop a cohesive synopsis of how resilience is conceptualised and enabled within RFSs. A systematic literature review of 688 articles identified 53 of relevance. Analysis revealed 3 'core constructs' with 15 associated 'key themes'. A total of 130 'regional food system resilience' strategies were synthesised from the literature, along with their documented challenges, opportunities and recommendations. Findings revealed that RFS resilience strategies rely on: 1) robust supply chain networks and relationships that feature implicit and explicit values; and 2) access to infrastructure that supports scaling up. Strategic planning and policy-enabled infrastructure and network development will strengthen RFS resilience, and food policy councils are key facilitators of these developments. Greater policy integration related to land use is needed to ensure farmers can access productive agricultural land now and into the future. This research contributes to a greater understanding of how resilience can be optimised at the regional scale and support decision-making to address food security for acute disasters and long-term climatic changes. [ABSTRACT FROM AUTHOR]

With the ever-growing popularity of food deliver, more and more consumers are embracing this convenience as part of their lifestyle. However, the issue of food waste created by the food deliver industry has become a pressing concern in society. This paper aims to examine the link between food waste and food delivery services, and investigate the effects of anti-food waste regulations on the generation of food waste and the choice of logistics strategies in an Onlineto- Offline (O2O) supply chain. Using game-theoretical approach, we focus on two prominent logistics strategies--the restaurant-free self-logistics strategy (RF strategy) and the platform-charge logistics strategy (PC strategy). Our research results show that anti-food waste regulation can effectively reduce food waste in food delivery service under the PC logistics strategy. The choice of logistics strategy is constrained by the online market potential, the relative logistics costs of platform logistics, and anti-food waste regulations. If the anti-food waste regulation is strict, as long as the size of the food delivery market and the relative logistics costs of platform logistics are not simultaneously small, the RF strategy will be the equilibrium strategy, whereas the supply chain members should choose the PC strategy. The study thus offers useful inferences for theory and practice. [ABSTRACT FROM AUTHOR]

Summary: Nowadays, the adoption of digital technologies getting a significant pace. It has the potential to improve the existing dynamics of the food supply chains and improve its transparency. Many times, uncertainties prevail in the food supply chain performance systems, which cause amplification of risk. These uncertainties are due to a lack of product traceability mechanisms in the food supply chains. Hence, it becomes vital to harness the potential of digital technologies like the Internet of Things, Blockchain, and common digital platforms to improve the flow of information and products in the food supply chain. The presented work is aimed at assessing the impact of digital technologies on risk identification and its mitigation in the food supply chain dynamics. For the same, risks are assessed relative to its mitigation strategy by the two‐stage methodology of preference rating approach. Here, the impacts of risk under consideration are quantified relative to every risk mitigation strategy for a better depiction of the potential of digital transformation of the food supply chain. It yields the broadening of its implication perspectives at operational, strategic and tactical levels of the food supply chain and makes them future‐ready. [ABSTRACT FROM AUTHOR]

This research aims to examine how consumers' consumption behaviors change in response to food supply chain disruptions and food security crisis during COVID-19. A new conceptual framework of consumer food stress and food consumption paradigms for crises is proposed based on the pillars of food security. This study also investigates the mediating effects of food prices and food stress on the proposed framework between pillars of food security and consumption behavior. A range of quantitative techniques was adopted for collecting the data during the COVID-19, with the sample of Bangladeshi consumers (n = 1150) generating a precise measurement. The study applied PLS-SEM (partial least squares-structural equation modeling) to predict food consumption behavior model and expose relevant relationships between all the constructs. Findings reveal that food access, food quality and safety, and food pricing directly impact consumer food stress and consumption behavior. Also, consumers' consumption behavior is ultimately influenced by food stress. However, food availability has no significant influence on food stress. This research is one of the few empirical studies exploring a range of food security variables that helps explain why consumers are so anxious about food during COVID-19. This study discussed theoretical and practical implications that help food producers, suppliers, marketers, and policymakers maintain a stable food supply chain management and manage food crises during national emergencies. [ABSTRACT FROM AUTHOR]

Modern agriculture is a complex system that demands real‐time and large‐scale quantification of trait values for evidence‐based decisions. However, high‐profile traits determining market values often lack high‐throughput phenotyping technologies to achieve this objective; therefore, risks of undermining crop values through arbitrary decisions are high. Because environmental conditions are major contributors to performance fluctuation, with the contemporary informatics infrastructures, we proposed enviromic prediction as a potential strategy to assess traits for informed decisions. We demonstrated this concept with wheat falling number (FN), a critical end‐use quality trait that significantly impacts wheat market values but is measured using a low‐throughput technology. Using 8 years of FN records from elite variety testing trials, we developed a predictive model capturing the general trend of FN based on biologically meaningful environmental conditions. An explicit environmental index that was highly correlated (r = 0.646) with the FN trend observed from variety testing trials was identified. An independent validation experiment verified the biological relevance of this index. An enviromic prediction model based on this index achieved accurate and on‐target predictions for the FN trend in new growing seasons. Two applications designed for production fields illustrated how such enviromic prediction models could assist informed decision along the food supply chain. We envision that enviromic prediction would have a vital role in sustaining food security amidst rapidly changing climate. As conducting variety testing trials is a standard component in modern agricultural industry, the strategy of leveraging historical trial data is widely applicable for other high‐profile traits in various crops. [ABSTRACT FROM AUTHOR]

Purpose: This paper proposes an Information and Communication Technology (ICT) platform to increase the sustainability and resilience of smallholders to face supply chain disruptions in the event of COVID-like crises. The platform facilitates interactions between smallholders and buyers, workers and freight transport companies in agri-food ecosystems. Furthermore, this research work presents the implementation of the freight transport companies' platform module. Design/methodology/approach: The research work begins with a literature review aiming at analyzing current available ICT solutions supporting smallholders and other actors in the agri-food supply chain. This analysis identifies the research gaps which have to be filled by the platform. Then, the authors proceed with the analysis of the operational scenarios of each platform actor by interacting with experts and operators working in the agri-food sector. The results of such analysis resulted in a comprehensive, unambiguous and consistent set of specification being used to define the platform structure and modules architecture. The platform modules have been developed by using the web-application framework Laravel. Findings: Preliminary tests show that the proposed platform is usable and promises to improve the resilience and economic, social and environmental sustainability of agri-food supply chains, with a focus on smallholders. Originality/value: The research work allows players in the agri-food supply chain and in particular small local producers to react and mitigate the impact of COVID-like crises through development of a platform in which smallholders, citizens (buyers and workers) and freight transport companies are simultaneously present. [ABSTRACT FROM AUTHOR]

The COVID-19 outbreak has revealed weaknesses in the supply chains (SCs) and how easily it can be influenced by these disruptions. Food supply chains (FSCs) is one of the most affected SCs, and it needs to be more resilient against SC disruptions because their vulnerable structure such as having perishable products. Therefore, this article aims to uncover the need for resilience in FSCs during the COVID-19 outbreak. For this purpose, the enablers of resilience on FSCs are determined after a detailed examination of the current literature. Then, the graph theory matrix approach has been used to reveal the relationships between these enablers and investigate importance of enablers of resilience in FSCs during COVID-19 outbreak. It is significant to determine preference of enablers and rank of importance to take actions effectively. Depending on the results, the rank orders of the enablers are classified as readiness, collaboration with stakeholders, IT alignment, risk aware, responsiveness, flexibility, appearance and sustainability, respectively. Suggested implications can be provided benefits for policymakers and managers in FSCs. [ABSTRACT FROM AUTHOR]