Food Supply Research Articles

Developing IoT Sustainable Real-Time Monitoring Devices for Food Supply Chain Systems Based on Climate Change Using Circular Intuitionistic Fuzzy Set

Developing IoT Sustainable Real-Time Monitoring Devices for Food Supply Chain Systems Based on Climate Change Using Circular Intuitionistic Fuzzy Set

Deterioration of people with Parkinson's symptoms during COVID-19 lockdown: results of a web-based survey in Northwestern Italy.

COVID-19 lockdowns were introduced to control the pandemic, however, they resulted in a global disruption of daily life and of individual and global health. Reduced accessibility of health services, unavailability of food and drugs, and mental health challenges had a huge impact on older people and on people living with disabling conditions such as Parkinson's disease (PD). We assessed whether and to what extent the more disabled and vulnerable people with Parkinson's (PwP) were affected by lockdowns. We analysed responses collected through a web-based survey of PwP according to their self-sufficiency [self-sufficient (SS); nearly self-sufficient (nSS); non-self-sufficient, cared for by family (NSS/F); non-self-sufficient, needs professional care (NSS/PC)]. Fears due to COVID-19 and difficulties with food supply were highest in NSS/F PwP. Difficulties with the supply of Parkinson's medication or other drugs were apparently not an issue, while problems accessing primary care physicians and neurologists were similar across all patient groups. On the contrary, difficulties with daily and motor activities were higher in NSS/F and NSS/PC PwP. PwP symptoms worsened in all groups, with NSS/F and NSS/PC participants experiencing the worst deterioration. Notably, the deterioration of PwP symptoms was specifically related to changes in daily and motor activities, with participants who reported less engagement in daily and motor activities experiencing the worst deterioration. Findings strongly support the need for decision-makers and healthcare providers to carefully re-evaluate the risk-benefit ratio of limiting healthcare accessibility for PwP, since evidence shows that lockdown measures primarily impact the groups who are most fragile and vulnerable.

Citizens’ perspectives on healthy weight approaches in low SEP neighborhoods: a qualitative study from a systems perspective

BackgroundThe physical and the social environment are important predictors of healthy weight, especially in low socioeconomic position (SEP) neighborhoods. Many Dutch municipalities have implemented a healthy weight approach (HWA). Yet, there is room for improvement. This system science study examined what influences the utilization of HWA facilities and activities, and what aspects can help to achieve a desired systems change (also called leverage point themes (LPTs)) in the HWA system as perceived by citizens living in low SEP neighborhoods.MethodAll research phases were performed with four citizens co-researchers. Forty-seven citizens living in low SEP neighborhoods were semi-structurally interviewed about the neighborhood HWA facilities and municipal HWA activities. A rapid coding qualitative analysis approach was applied per topic. The topics were citizens’ healthy living description, personal circumstances, and satisfaction with foot and cycle paths, sports facilities, playgrounds, green spaces, museums and theaters, community centers, churches, healthcare, school, food supplies, contact with neighborhood, unfamiliar and/or unused activities, familiar and used activities, unavailable but desired (lacking) activities, and reaching citizens.ResultsThe utilization of HWA facilities and activities was influenced by the overarching themes of social cohesion, familiarity, reaching citizens, maintenance, safety, physical accessibility, financial accessibility, social accessibility, fit with personal context, and fit with the neighborhood’s specific needs. Different overarching themes stood out across different facilities and activities. LPTs indicated the overarching themes needed in combination with one another for a specific activity or facility to increase utilization. For example, the LPT regarding foot and cycle paths was “accessible, safe, and maintained foot and cycle paths”. The LPTs regarding familiar and used activities were “customized activities; information provision (e.g., about possibilities to join without paying); social contact, meeting others, and everyone feels included”.ConclusionConducting inclusive qualitative research from a systems perspective among citizens living in low SEP neighborhoods has contributed valuable insights into their needs. This enables practical implementation of HWAs by providing a deeper understanding of the LPTs within the HWA system. LPTs can help HWA stakeholders to further develop current HWAs toward systems approaches. Future research could study the leverage points that may contribute to LPT implementation.

A Health Threat from Farm to Fork: Shiga Toxin-Producing Escherichia coli Co-Harboring blaNDM-1 and mcr-1 in Various Sources of the Food Supply Chain

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.

Enhancing sustainable crop production through integrated nutrient management: a focus on vermicompost, bio-enriched rock phosphate, and inorganic fertilisers – a systematic review

Securing a consistent food supply remains a pressing global challenge, particularly for small-scale farmers grappling with obstacles in enhancing agricultural yields, especially in tropical soils. Integrated Nutrient Management (INM) techniques, employing organic manures like vermicompost and bio-enriched rock phosphate, emerge as recommended solutions. Vermicompost is lauded for its nutrient richness and positive soil health impacts. At the same time, bio-enriched rock phosphate serves as a sustainable alternative to conventional phosphorus fertilisers, specifically tailored for tropical soil conditions. Despite individual studies assessing the effects of vermicompost, bio-enriched rock phosphate, and soluble fertilisers on plant growth, a comprehensive overview of their combined application is noticeably lacking. To fill this gap, this study employs the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) method to explore the synergies of combining these elements and their impacts on crop production and the environment. This review is among the first to comprehensively summarize the complexities of combining vermicompost, bio-enriched rock phosphate, and chemical fertilisers on various crops. It thoroughly examines potential advantages, disadvantages, effects on agricultural systems, socio-economic implications, and existing policies governing their usage. Our findings reveal that the combined application of vermicompost, bio-enriched rock phosphate, and soluble fertilisers leads to significant improvements in plant growth, yield, and soil properties. The optimal impact is observed when vermicompost constitutes 25% and soluble fertiliser comprises 75 or 100% of the recommended fertiliser dosage. Moreover, incorporating a mixture of phosphate-solubilizing bacteria (PSB) strains in rock phosphate further enhances its positive effects. Despite these positive findings, we identified gaps in comprehensive approaches addressing socio-cultural dimensions and the lack of literature on prevailing policies regarding vermicompost use in agricultural systems highlighting the need for a more holistic understanding of vermicompost incorporation and a better grasp of the institutional frameworks guiding these practices. However, to secure sustainable crop production, farmers need to integrate vermicompost and biofertilisers with chemical fertilisers. In fostering the adoption of sustainable and inclusive agricultural practices on small rural properties, it is advisable to incorporate agricultural education into farmer training programs.

Design of a precise ensemble expert system for crop yield prediction using machine learning analytics

AbstractAgriculture is facing significant challenges in the development of crop yield forecasts, which are important aspects of decision‐making at the international, regional, and local levels. The area of agriculture is attracting growing attention because of increasing the demand for food supplies. To ensure future food supplies, crop yield prediction (CYP) provides the best decision‐making to assist farmers in agricultural yield forecasting efficiently. Nevertheless, CYP is a difficult endeavor because of the intricacy of the underlying mechanisms and the effect of numerous factors, including weather patterns, soil characteristics, and crop management techniques. In today's era, ensemble learning (EL) approaches have recently demonstrated significant promise for enhancing the reliability and accuracy of CYP. The success of the EL techniques depends on several facts, including how the base learner models are trained and how these are combined. This study provides important insights into the EL techniques for CYP. This paper proposes an expert system model named precise ensemble expert system for crop yield prediction (PEESCYP) to predict the best crop for agricultural land. The proposed PEESCYP model employs multiple imputation by chained equation (MICE) data imputation technique to treat the missing values of the collected dataset, the isolation forest (IF) technique for outlier detection, the ant colony optimization (ACO) technique to perform feature selection, robust scaling (RS) technique to perform data normalization, and the extra tree (ET) is used for classification to overcome the variance and overfitting problem of the single classifiers. The measurements of the proposed PEESCYP model have been collected by means of accuracy, precision, recall, and F‐1 score using a prepared dataset, which is collected from International Crops Research Institute for the Semi‐Arid Tropics (ICRISAT), and the proposed model is compared with different single‐classifier based ML models, EL models, and various existing models available in the literature. The results of this experiment underline that the proposed PEESCYP model outperforms the others.

Vicarious vs experiential information in the consumer’s construal level on imported food safety risk perception

Consumers acquire information about the imported food safety risks through distal channels such as the Internet versus proximal channels such as direct experience, and these channels influence the consumer perception of safety. However, the empirical analysis of the sources of information (distal–proximal) channels is second to none. This article explores whether direct experience with imported food induces different levels of safety risks compared to distal information through social media. Based on the Construal Level Theory (CLT), the study examines consumer responses in a survey involving 500 respondents out of 1000 distributed in South Korea, using high-level construal (vicarious information) versus a low-level construal (direct experience) framework to assess food safety risk perception. We narrowed the imported food from China to South Korea to obtain a robust analysis. Our analysis shows that vicarious (distal) sources of information increase perceived food safety risks in the minds of the consumer, while direct experience (proximity) decreases food safety risks. In a supplementary analysis, those respondents who had visited China exhibited lower levels of food safety risk of the imported food. We infer that the lower-level construal (experience) mediates the effects of higher-level construal (indirect, social media) on the imported food safety risk. However, the correlation is nonlinear, explaining why some studies find proximity while others find distance in correlation with the food safety risk. In a contribution to the literature, this study answers a longstanding question regarding international business in food supplies, supports the CLT framework, and offers a policy recommendation for both Korea and China to enhance their information channels to counteract misinformation.

Discrete-event simulation to analyse the impacts of a reduced minimum order quantity in the food industry: proposing a shared-savings contract

ABSTRACT Retail partners of the food processing industry benefit from lower minimum order quantities, as they are more responsive to changing market conditions and reduce inventory costs. However, production systems in the food processing industry focus on economies of scale by producing large volumes. A shared-savings contract helps to find a trade-off between the two parties, where the retailer shares a part of the cost savings with the manufacturer to cope with higher production costs. We propose a generic data-driven discrete-event simulation model to guide decision-making in the development of a shared-savings contract. Thereby, we examined breakdown and rework behaviours after setups with a Big Data analysis and a correlation analysis. We aim to evaluate the cost development at a cheese processing company, resulting from a reduced minimum order quantity. Our findings revealed that production costs significantly increase with higher breakdowns, setups, and rework. A 20% increase in orders, resulting from a reduced minimum order quantity, requires an additional production capacity of 3%. To conclude, transparent information sharing is key to developing a shared-savings contract in the food supply chain.

Современные проблемы обеспечения продовольственной безопасности

The article examines the problems of food security and the sequence of their identification, by reviewing the main indicators of food security, analyzing some of them, forecasting for the period 2023-2025. Such an indicator as the consumer price index for basic food products, and also suggests ways to solve the problems of food supply. During the implementation of theoretical studies, the assumption was confirmed that the main solution to the problem of food security for the country's population is to stimulate the agro-industrial sector to increase production volumes with the active use of import substitution.

Food waste in high income countries: Spanish fish value chain as a case study

High-income countries are expected to be more efficient in preventing food waste (FW) along the value chain compared to low- and middle-income countries, with the exception of the consumer stage. However, it is still a problem to be solved, especially in fresh food due to its perishability and in livestock due to its nutritional and economic impact. Following this prioritization, wild-caught fish was defined as a weak point for FW in the food system of high-income countries. Spain was used as a representative country for high- countries' fish sector due to its international trading and production/consumption ratios. The length of food supply chains (FSCs) was identified as a factor influencing FW incidence, highlighting short FSCs as a model for reducing FW. Regulations have been targeted as drivers but also as prevention and mitigation factors for FW. Technological tools and innovations already available in the market can be used to reduce FW, but there are still barriers affecting their adoption.

Investigating biological mechanisms of colour changes in sustainable food systems: The role of Starmerella bacillaris in white wine colouration using a combination of genomic and biostatistics strategies

This study explores the biological mechanisms behind colour changes in white wine fermentation using different strains of Starmerella bacillaris. We combined food engineering, genomics, machine learning, and physicochemical analyses to examine interactions between S. bacillaris and Saccharomyces cerevisiae. Significant differences in total polyphenol content were observed, with S. bacillaris fermentation yielding 6 % higher polyphenol content compared to S. cerevisiae EC1118. Genomic analysis identified 12 genes in S. bacillaris with high variant counts that could impact phenotypic properties related to wine color. Notably, SNP analysis revealed numerous missense and synonymous variants, as well as stop-gained and start-lost variants between PAS13 and FRI751, suggesting changes in metabolic pathways affecting pigment production. Besides that, high upstream gene variants in SSK1 and HIP1R indicated potential regulatory changes influencing gene expression. Fermentation trials revealed FRI751 consistently showed high antioxidant activity and polyphenol content (Total Polyphenol: 299.33 ± 3.51 mg GAE/L, DPPH: 1.09 ± 0.01 mmol TE/L, FRAP: 0.95 ± 0.02 mmol TE/L). PAS13 exhibited a balanced profile, while EC1118 had lower values, indicating moderate antioxidant activity. The Weibull model effectively captured nitrogen consumption dynamics, with EC1118 serving as a reliable benchmark. The scale parameter delta for EC1118 was 23.04 ± 2.63, indicating moderate variability in event times. These findings highlight S. bacillaris as a valuable component in sustainable winemaking, offering an alternative to chemical additives for maintaining wine quality and enhancing colours profiles. This study provides insights into the biotechnological and fermented food systems applications of yeast strains in improving food sustainability and supply chain, opening new avenues in food engineering and microbiology.

Decision on the introduction of blockchain technology in green supply chains considering altruistic preferences

Purpose This paper aims to examine the blockchain introduction and altruistic preference decisions of the supplier in agricultural food supply chains and discuss how the supplier decisions are influenced by blockchain technology and altruistic preference levels. Design/methodology/approach The author considers a single period two-level supply chain model to describe the supplier’s decisions. The supplier, as the leader of the game, decides whether to introduce blockchain technology and his own level of altruistic preferences. Consumers have environmental awareness and heterogeneity in green trust. Supply chain members determine their own product pricing and green effort level under Stackelberg game. Findings The results reveal the negative impact of unit verification cost in the technology introduction process on the supply chain. In addition, the supplier can adjust their profits by adjusting their altruistic preferences after introducing blockchain to offset the impact of blockchain through the influence of altruistic preferences as discussed by the author. Originality/value This paper investigates how the profits and green efforts of supply chain members are influenced by blockchain technology and altruistic preferences.

Realtime Optimization and Management System (ROAM): A Decision Support System for Digital Agriculture Systems

The growing disparity between food supply and demand requires innovative Digital Agriculture (DA) systems to increase farm sustainability and profitability. However, current systems suffer from problems of complexity stemming from the challenge of integrating diverse, often non-interoperable hardware and software components. In order to tackle these complexities to increase farm efficiency and understand the tradeoffs of these new DA innovations we developed Realtime Optimization and Management System (ROAM), which is a decision-support system developed to find a Pareto optimal architectural design to build DA systems. To find the Pareto optimal solution, we employed the Rhodium Multi-Objective Evolutionary Algorithm (MOEA), which systematically evaluates the trade-offs in DA system designs. Based on data from five live deployments at Cornell University, each DA design can be analyzed based on user defined objectives and evaluated under uncertain farming environments with ROAM. Paired with this, we develop a web interface that allows users to define personalized decision spaces and visualize decision tradeoffs. To help validate ROAM, it was deployed to a commercial farm where the user was recommended a DA architecture design method to increase farm efficiency. ROAM allows users to quickly make key decisions in designing their DA systems to increase farm profitability.

Group Decision Making Model for Selection of Performance Indicators for Sustainable Supplier Evaluation in Agro-Food Supply Chain

Sustainable food production is of vital importance to ensure food security, enhanced stability, environmental protection, and economic development for any developing nation. In an Agro- Food Supply Chain (AFSC), joint efforts of all supply chain actors inclined towards sustainability can contribute significantly towards sustainable production. Suppliers, who are intermediaries between Food Processing Firm (FPF) and sub-suppliers or farmers, must follow the FPF’s sustainability practices at their own level, and they must monitor the enactment of these activities at their sub-supplier level. In this regard, the study focuses on developing a group consensus mathematical model to derive a broader set of performance indicators to evaluate a supplier’s competency in maintaining, monitoring, and tracing sustainable performance in his sub-supplier network. A two-phased group consensus model is presented; it selects performance indicators using the Delphi method and it utilizes a feedback mechanism rooted in Group Analytical Hierarchy Process. This system is based on an iterative algorithm to generate weights that all decision makers accept. The proposed model is illustrated with an analysis of an Indian flour milling company. Kendall’s concordance coefficient is employed to understand the degree of agreement among decision makers before and after the application of the proposed model. Increase in its value post-application validates the efficacy of the model. This approach for performance indicator selection for sustainable supplier evaluation research is unique in the field of AFSC. Results of the study indicate that the top ranking indicator is ‘Financial Strength’ followed by ‘Social Image’. Key policy implications are drawn from the result findings which can be further integrated to support sustainable growth and development of suppliers and sub-suppliers in any AFSC.

Assessing blockchain and IoT technologies for agricultural food supply chains in Africa: A feasibility analysis

Assessing blockchain and IoT technologies for agricultural food supply chains in Africa: A feasibility analysis

Strategic and Tactical Planning Model for the Design of Perishable Product Supply Chain Network in Ethiopia

This article discusses the challenges associated with ensuring food security for a growing global population and emphasizes the critical role of the Agri Fresh Food Supply Chain in addressing these challenges such as mitigating food insecurity and reducing wastes. The economic repercussions of inefficiencies in perishable supply chains impact businesses' profit margins and consumers' access to essential goods. The focus is on the effective management of the complex perishable product supply chains, considering the economic significance of this sector and the need for advanced strategies to optimize the flow of time-sensitive products. The study discusses the limited consideration of product intrinsic characteristics in strategic and tactical planning decisions. The research aims to contribute to supply chain management by proposing a mathematical model (multi echelon, multi product, and multi period) for designing a perishable product supply chain network, considering factors like perishability, quantity discount policy, souring strategies and business continuity. The study explores the impact of these factors on the Agri Fresh Food Supply Chain design and its overall performances. Although the proposed model is designed to be applicable to any country or region, but its capabilities are shown through a real case study in Ethiopia. Results show that a agri-food supply chain design model that considers the intrinsic characteristics of the product, processing capacity level, and quantity discount leads to improved configurations of the food supply chain regardless of adding the associated cost.

Nutritional Composition of Post-Catastrophic Foods.

In addition to current challenges in food production arising from climate change, soil salinization, drought, flooding, and human-caused disruption, abrupt sunlight reduction scenarios (ASRS), e.g., a nuclear winter, supervolcano eruption, or large asteroid or comet strike, are catastrophes that would severely disrupt the global food supply and decimate normal agricultural practices. In such global catastrophes, teragrams of particulate matter, such as aerosols of soot, dust, and sulfates, would be injected into the stratosphere and block sunlight for multiple years. The reduction of incident sunlight would cause a decrease in temperature and precipitation and major shifts to climate patterns leading to devastating reductions in agricultural production of traditional food crops. To survive a catastrophic ASRS or endure current and future disasters and famines, humans might need to rely on post-catastrophic foods, or those that could be foraged, grown, or produced under the new climate conditions to supplement reduced availability of traditional foods. These foods have sometimes been referred to as emergency, alternate, or resilient foods in the literature. While there is a growing body of work that summarizes potential post-catastrophic foods and their nutritional profiles based on existing data in the literature, this article documents a list of protocols to experimentally determine fundamental nutritional properties of post-catastrophic foods that can be used to assess the relative contributions of those foods to a balanced human diet that meets established nutritional requirements while avoiding toxic levels of nutrients. © 2024 The Author(s). Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Total digestible glucans Basic Protocol 2: Apparent protein digestibility Basic Protocol 3: Vitamins B1, B3, B9, C, and D2 by HPLC Basic Protocol 4: Total antioxidant activity (DPPH-scavenging activity) Basic Protocol 5: Total phenolic compounds (Folin-Ciocalteu reagent method) Basic Protocol 6: Mineral content by ICP-OES.

An application of machine learning to classify food waste interventions from a food supply chain perspective

PurposeThe purpose of this paper is to demonstrate the efficacy of machine learning (ML) in managing natural language processing tasks, specifically by developing two ML models to systematically classify a substantial number of food waste interventions.Design/methodology/approachA literature review was undertaken to gather global food waste interventions. Subsequently, two ML models were designed and trained to classify these interventions into predefined supply chain-related groups and intervention types. To demonstrate the use of the models, a meta-analysis was performed to uncover patterns amongst the interventions.FindingsThe performance of the two classification models underscores the capabilities of ML in natural language processing, significantly enhancing the efficiency of text classification. This facilitated the rapid and effective classification of a large dataset consisting of 2,469 food waste interventions into six distinct types and assigning them to seven involved supply chain stakeholder groups. The meta-analysis reveals the most dominant intervention types and the strategies most widely adopted: 672 interventions are related to “Process and Operations Optimisation”, 457 to “Awareness and Behaviour Interventions” and 403 to “Technological and Engineering Solutions”. Prominent stakeholder groups, including “Processing and Manufacturing”, “Retail” “Government and Local Authorities” and “NGOs, Charitable Organisations and Research and Advocacy Groups”, are actively involved in over a thousand interventions each.Originality/valueThis study bridges a notable gap in food waste intervention research, a domain previously characterised by fragmentation and incomprehensive classification of the full range of interventions along the whole food supply chain. To the best of the authors’ knowledge, this is the first study to systematically classify a broad spectrum of food waste interventions while demonstrating ML capabilities. The study provides a clear, systematic framework for interventions to reduce food waste, offering valuable insight for practitioners in the food system, policymakers and consumers. Additionally, it lays the foundation for future in-depth research in the food waste reduction domain.

Improving the efficiency of food supplies for a trading company based on an artificial neural network

This article presents the proper organization of the supply chain to meet consumer demand, which is crucial for modern commercial enterprises involved in the sale of various products. Studies indicate that a company's success is linked to the satisfaction of its customers. To optimize the supply chain, this study will consider the use of artificial neural network models. The results of this model will seek a balance between demand and supply, helping determine the necessary quantity of goods to satisfy demand and prevent overproduction. By using this model, the company can fully meet the needs of its customers. Additionally, the company saves its resources and labor costs and reallocates them to other tasks. The model demonstrates the optimization of production and supply business processes, as well as an increase in efficiency.

Assessing the Impact of Catastrophic Electricity Loss on the Food Supply Chain

Assessing the Impact of Catastrophic Electricity Loss on the Food Supply Chain