Search Results (267)

Producer share indicators summarize how value is distributed along agrifood supply chains, yet their temporal dynamics remain difficult to compare across products and periods. This paper proposes a reproducible time-series analytics framework to characterize and group producer-share trajectories derived from paired origin–destination price series. We compute producer share time series for a set of agrifood products and quantify similarity using complementary measures capturing co-movement and shape, including Pearson-correlation-based proximity and Euclidean distance on standardized representations. To reduce dimensionality and mitigate noise, we apply principal component analysis and perform unsupervised clustering (k-means) to identify classes of products exhibiting comparable producer-share dynamics. The resulting clusters provide an interpretable typology of market behaviors, highlighting homogeneous groups that may share structural drivers (e.g., commercialization patterns or intermediation margins). We further discuss how cluster membership can support decision-making in crop substitution and market monitoring by revealing products with analogous temporal responses. The proposed pipeline is simple to implement, fully data-driven, and adaptable to other commodity-price settings. Full article

Accurate forecasting of agricultural commodity prices is essential for managing market volatility, improving supply chain coordination, and supporting food security-related decision-making. Recent advances in deep learning have demonstrated strong potential for capturing nonlinear and temporal dependencies in commodity price dynamics. In this study, we propose a hybrid long short-term memory–multi-head attention (LSTM–MHA) framework for agricultural commodity price forecasting using global futures market data. The model is trained and evaluated on multivariate global commodity futures prices, reflecting internationally traded benchmark markets rather than region-specific domestic prices. While the empirical analysis is based on global data, the study is motivated by the relevance of international price movements for import-dependent regions, particularly West Africa, where global price transmission plays a critical role in domestic market dynamics. The experimental results demonstrate that the proposed model effectively captures short-term temporal dependencies and provides interpretable attention-based insights into lag relevance. An ablation study further highlights the trade-offs between forecasting accuracy and interpretability across different model configurations. The hybrid architecture combines the time-based pattern identification and weighting capabilities of multi-head attention with the sequential learning capabilities of LSTM. Mean absolute error (MAE), root mean squared error (RMSE), and mean squared error (MSE) were used to evaluate the model’s performance. With an MSE of 0.0124, an RMSE of 0.1114, and an MAE of 0.1097, the model outperformed conventional models like ARIMA and standalone LSTM by three to four times in error reduction. The findings suggest that attention-enhanced deep learning models can serve as valuable analytical tools for understanding global price dynamics and informing policy analysis and risk management in West African agricultural markets. Full article

An emerging paradigm in public health focuses on enhancing nutrition in existing food staples to reduce chronic disease at the population scale, rather than relying on individuals to change their behavior. This paradigm leverages plant and animal breeding, production practices, and processing to enhance nutrition, whereby foods consumed by millions can be improved at low incremental cost. This article supports and operationalizes this paradigm, illustrating the potential to improve diets through a case study that increases the arabinoxylan fiber content of commodity wheat through classical plant breeding (a non-GMO technology). The approach described in this article proposes to link agricultural and food science with health system implementation to deliver equitable access, improved healthcare outcomes and cost savings, and improved community health. Based on published dose–response relationships, comparative risk modeling indicates that modest fiber increases achieved by the commodity wheat breeding led to reduced population-level risks of 1–3% for cardiovascular disease, 3–4.5% for type 2 diabetes, and 1–3.5% for colorectal cancer, translating into substantial healthcare cost savings when implemented at a national scale. This article outlines possible low-risk pathways for implementing these nutrition increases at the population scale through commodity supply chains and community-level nutrition improvement efforts and evaluates the ranges of potential population-level impacts. Full article

The Baltic Dry Index (BDI) measures the cost of transporting dry bulk commodities such as coal, iron ore, and grain. As a key indicator of global trade, supply chain dynamics, and overall economic activity, accurate short-term forecasting of the BDI is crucial. This paper compares six univariate methods to obtain a more precise short-term BDI prediction model, providing valuable insights for decision-makers. The six forecasting techniques include Grey Forecast, ARIMA, Support Vector Regression, LSTM, GRU and EMD-SVR-GWO. Model performance is evaluated using three common metrics: Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). Our findings reveal that the novel EMD-SVR-GWO model outperforms the other univariate methods, demonstrating superior accuracy in forecasting monthly BDI trends. This study contributes to improved BDI prediction, aiding managers in strategic planning and decision-making. Full article

Against deglobalization and intensifying geopolitical conflicts, maritime bulk commodity supply chain vulnerability and resilience governance are strategic priorities for 75% of countries. To tackle rising global uncertainty, this study proposes the country-level risk identification, monitoring, and extrapolation (RIME) framework for such supply chains, which aligns with the theoretical demand for macro, end-to-end risk integration beyond the traditional firm-level focus. Based on the “supplier country–shipping route–importing country” spatiotemporal linkage, we construct the first standardized country-level vulnerability index. It overcomes the limitations of existing static and localized assessments by integrating spatiotemporal, multi-source risks across the full physical chain, thereby enabling dynamic, macro-level monitoring and supporting systematic diagnostics and trend tracking of national supply chain security. We also develop an emergent risk simulation technique to quantify the direction and intensity of compound disturbances as well as the system’s dynamic responses. Empirical validation with China’s iron ore imports shows that the index effectively captures risk evolution, while the simulations confirm that sudden disruptions amplify systemic risk. This framework fills national strategic security theoretical gaps and provides governments with dynamic monitoring, quantitative assessment, and policy forecasting tools. Full article

This study analyzes the competitive asymmetries and trade effects of the proposed EU–Mercosur Trade Agreement on the European Union’s (EU) and Polish agri-food sectors. The comparative analysis reveals that Mercosur holds a significant structural advantage driven by substantially lower labor costs, cheaper agricultural land, and a climate permitting multiple harvests. This cost advantage is further compounded by weaker regulatory standards (e.g., on pesticides and antibiotics). This structural edge is most pronounced in high-volume commodities, leading to Mercosur trade surpluses in products such as soybeans, sugar cane, and wheat, which pose the primary competitive threats to the EU market. Conversely, the EU maintains an intensive advantage through superior yields in intensive farming (e.g., maize) and specialization in high-value, processed products. This creates quantifiable export opportunities for EU/Polish producers in sectors where Mercosur is a consistent net importer, notably other frozen vegetables, preserved tomatoes, and apples. The findings confirm an asymmetric effect of liberalization, which necessitates a dual strategy of internal structural reform (e.g., the EU Protein Strategy) and the implementation of external protective mechanisms, including strategic Common Agricultural Policy (CAP) adaptations and safeguard clauses, to maintain the long-term competitiveness and Supply Chain Resilience of European agriculture. Full article

Global critical mineral production patterns differ markedly across the metals needed for advanced energy technologies. This study examines the extraction and processing landscape, in the year 2024, of six key commodities—lithium, cobalt, aluminum, nickel, manganese, and copper—to identify who the major players (countries and corporations) are in the critical mineral space and to understand what they are mining, where they are mining, and where are they sending their ore to be processed. This study aims to provide a snapshot of the critical mineral supply chain that serves as a useful resource for researchers and policymakers seeking to understand and improve the critical mineral supply chain. We analyze company financial filings, government datasets, and other public and proprietary sources for the year 2024. Then, we calculate production volumes and identify geographic and corporate concentration. The results show that copper and aluminum production and processing are relatively diverse, while lithium and cobalt extraction and processing are highly concentrated among a few countries and dominant firms. Nickel and manganese occupy an intermediate position, displaying moderate diversity with emerging signs of consolidation. Full article

The study conducted a review of Listeria prevalence, virulence, and adaptations associated with leafy vegetables from small-scale farms and their journey to markets. PubMed, Taylor and Francis, Oxford, and Google Scholar databases were utilised to search for English-language journal articles published between January 1992 and 2025. Studies utilised multi-locus sequence typing (MLST), polymerase chain reaction–restriction fragment length polymorphism (PCR-RFLP), multiplex PCR, pulsed-field gel electrophoresis (PFGE), and whole genome sequencing WGS, confocal scanning laser microscopy technique for the detection of Listeria species, followed by transcriptomic, phenotypic analyses, strand-specific RNA-sequencing, and membrane lipid profiling. ST₅, ST₁₂₁, and ST₃₂₁ are considered predominant and virulent and have been identified in two ready-to-eat commodities, while ST₁, ST₂, and ST₂₀₄ are considered hypervirulent strains in food processing environments. Immunocompromised groups can experience severe life-threatening infections, even death. Significant economic losses due to shutdowns for sanitary procedures can occur, impacting food security. Full article

Mycotoxins are toxic secondary metabolites produced by filamentous fungi that contaminate agricultural commodities, posing risks to food safety, animal productivity, and human health. The gastrointestinal tract is the first and most critical site of exposure, where the intestinal epithelium functions as both a physical and immunological barrier against luminal toxins and pathogens. While extensive research has demonstrated that mycotoxins disrupt epithelial integrity through tight junction impairment, oxidative stress, apoptosis, and inflammation, their effects on the intestinal stem cell (ISC) compartment and epithelial regeneration remain insufficiently understood. This review integrates recent findings from in vivo, cell culture, and advanced 3D intestinal organoid and gut-on-chip models to elucidate how mycotoxins such as deoxynivalenol and zearalenone impair ISC proliferation, alter Wnt/Notch signaling, and compromise mucosal repair. We also discuss dose relevance, species differences, and the modulatory roles of the microbiome and short-chain fatty acids, as well as emerging evidence of additive or synergistic toxicity under co-exposure conditions. By bridging well-established mechanisms of barrier disruption with the emerging concept of ISC-driven regenerative failure, this review identifies a critical knowledge gap in mycotoxin toxicology and highlights the need for integrative models that link epithelial damage to impaired regeneration. Collectively, these insights advance understanding of mycotoxin-induced intestinal dysfunction and provide a foundation for developing nutritional, microbial, and pharmacological strategies to preserve gut integrity and repair. Full article

In an era defined by the imperative for sustainable, high-performance materials, this review examines the development and utility of key ester and ether derivatives from both cellulose and hemicellulose sourced from lignocellulosic biomass, with a special emphasis on waste feedstocks. Our findings indicate that these derivatives exhibit tunable physicochemical properties, enabling their broad use in established industrial sectors while also fueling the emergence of novel technological applications in nanotechnology, controlled delivery, tissue engineering, environmental remediation, electronics, and energy fields. This dual-polysaccharide platform demonstrates that underutilized biomass streams can be repurposed as valuable feedstocks, promoting a circular supply chain and supporting more sustainable solutions, thereby aligning with the goals of eco-friendly innovation in materials science. Future progress will likely depend on integrating green chemistry synthesis routes, optimizing waste-to-product conversion efficiency and scalability, and engineering derivatives for multifunctional performance, thus bridging the gap between commodity-scale use and high-tech material innovation. Full article

Potatoes are the most extensively cultivated vegetable crop in Canada and rank as the fifth largest primary agricultural commodity. Given their diverse end uses and significant market value, particularly in processed forms, ensuring consistent quality from harvest to consumption is of critical importance. Total glycoalkaloids (TGA) are nitrogen-containing secondary metabolites that are known to accumulate in the tuber as an effect of greening in-field or elsewhere in the supply chain. In this study, 210 Yukon Gold (YG) potatoes were exposed to a constant light source to green over a period of 14 days and sampled in 7-day intervals. The samples were scanned using a short-wave infrared (SWIR) hyperspectral imaging camera in the 900–2500 nm wavelength range. Once individually scanned, pixel-wise spectral data was extracted and averaged for each tuber and matched with its respective ground truth TGA values which were obtained using a High-Performance Liquid Chromatography (HPLC) system. Prediction models using the partial least squares regression technique were developed from the extracted hyperspectral data and reference TGA values. Wavelength selection techniques such as competitive adaptive re-weighted sampling (CARS) and backward elimination (BE) were deployed to reduce the number of contributing wavelengths for practical applications. The best model resulted in a correlation coefficient of cross-validation (R²_cv) of 0.72 with a root mean square error of cross-validation (RMSE_cv) of 51.50 ppm. Full article

The global push for electrification and decarbonization has sharply increased demand for critical raw materials—especially lithium and copper—heightening financial and strategic pressures on firms that lead these supply chains. Yet, the systemic financial risks arising from inter-firm interdependencies in this sector remain largely unexplored. This article presents a novel distance-based network framework to analyze systemic risk among the world’s top 15 lithium and copper producers (2020–2024). Firms are represented through standardized vectors of profitability and risk indicators (liquidity–solvency), from which we construct a two-layer similarity network using Euclidean distances. Graph-theoretic tools—including Minimum Spanning Tree, eigenvector centrality, modularity detection, and contagion simulations—reveal the structural properties and transmission pathways of financial shocks. The results show a robust-yet-fragile topology: while stable under minor perturbations, the network is highly vulnerable to failures of central firms. These findings highlight the utility of distance-based network models in uncovering hidden fragilities in critical commodity sectors, offering actionable insights for macroprudential regulators, investors, and corporate risk managers amid growing geopolitical and financial entanglement. Full article

The potential of thin-section petrography for the analysis of ancient ceramic materials, such as pottery vessels, figurative objects and building materials made of fired clay, was already recognized during the 19th century, but its use has become more intensive during the past 80 years. Since pottery is the most common and typologically datable artifact in archaeological excavations from the pottery Neolithic period onwards (some 7000–8000 years ago), the analysis of pottery, including its composition, is a central component of archaeological research. As ceramic materials are made of fired clay, which in turn is procured from soils, weathered rocks and geological formations, the mineralogical composition of the ceramic artifacts represents the clay sources. The study of the mineralogical and rock fragment composition of thin sections of ancient ceramic artifacts can yield the characterization of the clay and soil type and thus the geographic location or area of the clay source. Since in antiquity we assume clay was not precured from a distance of more than one day’s walk from the production site (‘site catchment area’), the production location can be detected as well. Thus, petrographic analysis can identify the trade of artifacts and commodities (if the ceramics are containers) in antiquity, which can shed light on political and cultural links and trade between ancient societies and their economic and social structure. In addition, since clay was often treated by ancient potters to improve its quality (levigation, clay mixing, addition of temper), technological aspects of the production sequence (chaîne opératoire) can also be acquired by petrographic analysis. Today, petrographic analysis is part of many standard studies of ancient pottery. While it is an old and relatively ‘low tech’ method, the accessibility of the equipment needed and its high analytic potential maintains its important and common position in archaeological research. This article describes the method and its analytical potential from the archaeological point of view and briefly mentions several archaeological case studies exemplifying its wide and diversified potential in the study of ancient ceramics in past decades. Full article

Many Time-Sensitive Networking (TSN) workloads require deterministic service across heterogeneous links, yet commodity WLANs are contention-based. Although IEEE 802.11be introduced Restricted Target Wake Time (r-TWT) for prioritized access, its ability to robustly guarantee determinism in mixed deployments with legacy devices remains unverified. We propose a standards-aligned scheme that composes r-TWT, Quiet Time Period (QTP), and an optional Randomized Enqueue (RE) policy. These three mechanisms act in concert to protect the Scheduled Traffic (ST) service period (SP) while minimizing the impact on Non-Scheduled Traffic (NST). To analyze how the proposed scheme impacts existing WLANs, we focus the analysis on how the scheme reshapes the contention period (CP)—where opportunistic capacity is realized—by modeling SP/CP timing with renewal theory and embedding it into an EDCA Markov chain. Simulation results confirm that the proposed scheme protects ST determinism: ST throughput remains pinned to the ceiling with zero observed outage and bounded delay across a wide range of station counts. The proposed scheme minimizes NST throughput degradation in the system-peak throughput range (8–12 stations). Full article

Agricultural markets are increasingly exposed to global risks as climate change intensifies and macro-financial volatility becomes more prevalent. This study examines the dynamic interconnection between major agricultural commodities—soybeans, corn, wheat, rough rice, and sugar—and key uncertainty indicators, including climate policy uncertainty, global economic policy uncertainty, geopolitical risk, financial market volatility, oil price volatility, and the U.S. Dollar Index. Using a Time-Varying Parameter Vector Autoregressive (TVP-VAR) model with monthly data, we assess both internal spillovers within the commodity system and external spillovers from macro-level uncertainties. On average, the external shock from the VIX to corn reaches 12.4%, and the spillover from RGEPU to wheat exceeds 10%, while internal links like corn to wheat remain below 8%. The results show that external uncertainty consistently dominates the connectedness structure, particularly during periods of geopolitical or financial stress, while internal interactions remain relatively subdued. Unexpectedly, recent global disruptions such as the COVID-19 pandemic and the Russia–Ukraine conflict do not exhibit strong or persistent effects on the connectedness patterns, likely due to model smoothing, stockpiling policies, and supply chain adaptations. These findings highlight the importance of strengthening international macro-financial and climate policy coordination to mitigate the propagation of external shocks. By distinguishing between internal and external connectedness under climate stress, this study contributes new insights into how systemic risks affect agri-food systems and offers a methodological framework for future risk monitoring. Full article