Search Results (640)

This paper investigates whether water-related climate stress predicts tail movements in food system assets and whether these spillovers vary across market regimes and investment horizons. Using daily data from January 2012 to January 2026, we examine the relationships among a water-risk proxy, agricultural commodities, agribusiness, and food supply-chain equities, and a fertilizer-related proxy. The analysis combines the cross-quantilogram with quantile spillover analysis in the frequency domain, allowing us to capture directional dependence in the tails of the distribution and short- and long-run connectedness. To account for structural change, we employ data-driven break detection and identify three major regimes: a pre-disruption period, a COVID-related adjustment phase, and a broader food system stress regime from early 2022 onward. The findings indicate that water-related climate stress has its strongest predictive power in the tails, especially for agribusiness and fertilizer-related assets, while the broad agricultural commodity basket is comparatively less sensitive. Lower-tail dependence is predominantly negative and often significant, whereas upper-tail dependence is generally positive, indicating asymmetric transmission under extreme market conditions. The spillover results further show that connectedness in the water–food system is mainly short-run, with agribusiness and fertilizer channels acting as the primary conduits of transmission. From a practical perspective, these findings suggest that investors and risk managers can use water-related market signals as early warning indicators of stress in food system assets, while policymakers can strengthen food system resilience through integrated water management, input market monitoring, and supply chain adaptation measures. The findings suggest that water-related climate stress is not merely an environmental constraint but a systemic source of food system risk with implications for resilience, risk monitoring, and integrated water-agriculture governance. Full article

Understanding the long-term dynamics of cropland water use efficiency (WUE) and its underlying environmental drivers is essential for ensuring food and water security, particularly for regions facing intensified climate change. Here, we investigated the spatial patterns and long-term trends of gross primary productivity (GPP), evapotranspiration (ET), and WUE in cropland ecosystems across Northeast China during the past two decades as the nation’s primary commodity grain base using the time-series Breathing Earth System Simulator (BESS) products. Subsequently, the ridge regression method was used to quantitatively disentangle the relative contributions of key climatic variables to the observed WUE trends of cropland. Our results revealed a pronounced decreasing gradient in both GPP and ET along the southeast–northwest direction. A significant increase in GPP was observed over the 20-year period (p < 0.01), with 95.94% of the cropland area showing positive trends. ET showed a slight, non-significant increase (p > 0.05), though 82.77% of pixels exhibited positive trends, particularly in the northwest. Consequently, WUE showed a widespread and significant enhancement (p < 0.01), with approximately 98% of cropland pixels exhibiting increasing trends. Attribution analysis identified air temperature as the dominant environmental variable, accounting for 92.4% of the observed WUE increase, while solar radiation and precipitation contributed modestly (3.4% and 3.2%, respectively). Our findings underscore the predominant role of thermal conditions in shaping the carbon–water coupling efficiency of agroecosystems in semi-arid to semi-humid transition zones. This study provides quantitative evidence that warming climate, rather than changes in water availability or radiation, has been the primary climatic factor driving the improved cropland WUE over the past two decades. These insights have important implications for developing adaptive water management strategies to enhance agricultural climate resilience in Northeast China and similar regions worldwide. Full article

Species of Penicillium are among the most important fungal pathogens responsible for postharvest diseases of agricultural crops worldwide. This review provides an overview of five economically important Penicillium spp., namely P. expansum, P. digitatum, P. italicum, P. citrinum, and P. oxalicum. Emphasis is placed on P. expansum, P. digitatum, and P. italicum which are the main causal agents of blue mold and green mold rots in pome fruits and citrus, commodities that dominate global fresh produce trade and long-term storage. While studies on plant-pathogenic Penicillium are mainly focused on these hosts, this review highlights reports of infections in other crops across diverse geographic regions, highlighting the broader host range of these species. The main aspects highlighted include host specificity and diversity, production of mycotoxins and other secondary metabolites, current management and control strategies, and the potential influence of climate change on disease incidence and severity. Understanding the biology and epidemiology of plant-pathogenic Penicillium species is essential, as several species are both pathogens and producers of mycotoxins, leading to quality deterioration and nutrient depletion resulting in economic losses. Full article

Climate change threatens crop yields and farming profitability, especially in drought-prone regions, requiring a transition to climate-resilient farming systems. Concurrently, growing demand for health-promoting and bio-based materials is creating new market opportunities for farmers. Sulla (Sulla coronaria Medik; syn. Hedysarum coronarium L.), a Mediterranean forage crop, may represent a strategic resource for sustainable intensification by simultaneously providing high-value commodities and a wide range of ecosystem services. This review explores the multifunctional potential of sulla following a holistic approach and is structured in thematic chapters, exploring: i. agronomy, ii. ecosystem services and agroecological value, iii. plant biochemical profile, iv. emerging applications for the bio-based industry, v. genetic diversity (including rhizobia diversity) and breeding perspectives for target environments and end-use. A SWOT analysis synthesizes strengths, research gaps and bottlenecks hindering large-scale adoption and valorization. The review proposes a strategic framework matching research priority with specific, actionable goals. The review aims to increase awareness of the multifaceted value of sulla as a promising model legume to increase sustainability in agriculture, promote product diversification and farming profitability, while assuring important ecosystem benefits. Full article

Pesticide residues represent an important group of chemical contaminants in agricultural commodities and require reliable analytical strategies for accurate monitoring. In this study, a high-throughput analytical workflow was applied for the determination of 210 pesticide residues in gherkins. Sample preparation was performed using the quick, easy, cheap, effective, rugged, and safe (QuEChERS) method, including extraction followed by dispersive solid-phase extraction clean-up. Residue determination was carried out using liquid chromatography–tandem mass spectrometry (LC-MS/MS) and gas chromatography–tandem mass spectrometry (GC-MS/MS). The analytical methods were comprehensively validated in the gherkin matrix in accordance with the SANTE 11312/2021 v2 guidelines. Limits of quantification were ≤0.01 mg kg⁻¹ for all compounds. Recovery values ranged from 75.7% to 113.7%, while precision values remained below 20%, demonstrating satisfactory method accuracy and precision. Expanded measurement uncertainty values ranged between 7.6% and 41.3%, confirming the robustness of the validated analytical workflow. The validated methods were subsequently applied to a large-scale monitoring dataset comprising 905 gherkin samples collected from five major production regions in Türkiye. Pesticide residues were detected in 67.6% of the analysed samples, and 37 different compounds were identified. The most frequently detected pesticides were flonicamid (36.2%) and propamocarb (27.5%). Multi-residue contamination was frequently observed, reflecting complex pesticide application patterns in gherkin cultivation systems. Although chronic exposure estimates remained well below toxicological thresholds for both adults and children, certain exposure scenarios indicated that acute exposure for children may warrant further attention. Full article

Cotton represents one of the world’s most significant agricultural commodities. However, severe weed proliferation in cotton fields seriously hampers the development of the cotton industry, making precise weed control essential for ensuring healthy cotton growth. Traditional object detection methods often suffer from computational complexity, rendering them difficult to deploy on resource-constrained edge devices. To address this challenge, this paper proposes AVGS-YOLO, a lightweight and enhanced model employing a Quadruple Synergistic Lightweight Perception Mechanism (QSLPM) for precise weed detection in complex cotton field environments. The QSLPM emphasizes synergistic interactions between modules. It integrates lightweight neck architecture (Slimneck) to optimize feature extraction pathways for cotton weeds; the ADown module (Adaptive Downsampling) replaces Conv modules to address model parameter redundancy; the small object attention modulation module (SEAM) enhances the recognition of small-scale cotton weed features; and angle-sensitive geometric regression (SIoU) improves bounding box localization accuracy. Experimental results demonstrate that the AVGS-YOLO model achieves 95.9% precision, 94.2% recall, 98.2% mAP50, and 93.3% mAP50-95. While maintaining high detection accuracy, the model achieves a lightweight design with reductions of 17.4% in parameters, 27% in GFLOPs, and 14.5% in model size. Demonstrating strong performance in identifying cotton weeds within complex cotton field environments, this model provides technical support for deployment on resource-constrained edge devices, thereby advancing intelligent agricultural development and safeguarding the healthy growth of cotton crops. Full article

Public opinion on online platforms now plays an important role in agricultural markets, which have always been unpredictable. Although sentiment analysis has been widely applied to agricultural texts, most existing studies typically focus only on classification accuracy without connecting results to actual market intelligence systems, especially in multilingual contexts. This paper introduces a reliability-aware transformer-based framework for analyzing sentiment in agricultural market intelligence across multiple languages. The framework leverages weakly supervised multilingual transformers to extract sentiment signals from large-scale unlabeled Thai and English texts about major agricultural commodities found online. To enhance robustness under weak supervision, the framework incorporates reliability-aware mechanisms, including confidence-based pseudo-label filtering, cross-source consistency refinement, and expert-guided calibration to reduce noise and account for bias between different data sources. Sentiment predictions are further aligned with market intelligence objectives through reliability-weighted aggregation, yielding interpretable sentiment indices that enable cross-lingual and cross-source comparability. We tested the framework extensively using a multilingual agricultural corpus derived from social media and news coverage of agriculture. The results show consistent improvements over both classical machine learning approaches and standard multilingual transformer baselines. Additional ablation studies and sensitivity analyses confirmed that reliability-aware mechanisms, particularly confidence thresholding, play a crucial role in getting the right balance between label quality and data coverage. Overall, the results indicate that reliability-aware multilingual sentiment analytics provide robust and actionable insights for agricultural market monitoring and policy analysis. Full article

Highly sensitive methods for trace-level aflatoxin determination are indispensable for cereal food safety and public health protection. This study developed a ZrO₂-assisted QuEChERS-UHPLC-MS/MS method for the simultaneous determination of AFB₁, AFB₂, AFG₁, and AFG₂ in maize, wheat, rice, and soybean. Systematic optimization identified acetonitrile as the optimal extraction solvent and 10 mg ZrO₂ in combination with PSA, C₁₈, and GCB as the optimal cleanup formulation, providing recoveries of 107.33–111.60%. Chromatographic baseline separation was achieved within 8.0 min using a moderate gradient program. The method exhibited excellent linearity (R² > 0.999) with LODs of 0.15–0.25 µg/kg and LOQs of 0.50–0.75 µg/kg. Negligible matrix effects (0.85–1.02) validated the efficacy of ZrO₂-assisted cleanup in eliminating co-extractive interferences in maize. Satisfactory accuracy (recoveries of 86.66–111.04%) and precision (RSDs < 14%) were obtained across all matrices. The method demonstrated consistent performance across diverse cereal and soybean matrices, fulfilling international regulatory requirements for routine aflatoxin monitoring in agricultural commodities. Full article

Coffee is among the primary agricultural commodities in international trade; however, mapping coffee crops in mountainous regions faces limitations due to high spectral variability and complex canopy structures. This study hypothesized that optimized spectral band combinations focused on the visible spectrum may outperform configurations including near-infrared (NIR) for coffee crop segmentation. This work aimed to evaluate how different spectral band combinations affect the performance of the U-Net for segmenting coffee crops in mountainous regions. Seven PlanetScope images (4 m resolution) from Matas de Minas, Brazil, covering different phenological stages in 2023–2024, were divided into 316 training patches and 25 test patches of 256 × 256 pixels and used to train U-Net models across five spectral band combinations: (B, G, R), (B, G, NIR), (B, R, NIR), (G, R, NIR), and (B, G, R, NIR). The visible spectrum combination (B, G, R) demonstrated superior performance with an overall Accuracy of 0.8669 and, for the Coffee Crops class, an F1-score of 0.8682 and an IoU of 0.7671, outperforming all NIR-inclusive configurations. Visible bands’ sensitivity to pigmentation variations proved more effective in heterogeneous environments, while NIR increased spectral confusion near native vegetation and crop edges. The model overestimated cultivated area by 18.3% due to mixed pixels from 4 m resolution and mountainous terrain. These findings confirm that visible-spectrum bands offer a cost-effective alternative for coffee segmentation, though higher spatial resolution is needed for improved boundary delineation. Full article

Commodity and carbon markets are central to natural resource allocation, energy security, and the effectiveness of carbon-pricing policies, yet their risk linkages can intensify sharply during crises. This study examines nonlinear, tail-dependent volatility spillovers across strategically important resource markets using a Quantile-on-Quantile connectedness framework. We employ weekly observed data from 3 January 2010 to 27 April 2025 for eleven futures markets spanning metals (copper, silver, gold), energy (WTI crude oil, heating oil, natural gas, gasoline), agricultural commodities (sugar, coffee, corn), and carbon emissions. Volatility is measured using GARCH-based estimates and embedded in quantile VAR dynamics to map state-contingent shock transmission across the distribution. The results indicate strong asymmetries: connectedness rises markedly in tail regimes and attains its highest levels during the COVID-19 pandemic and the Russia–Ukraine war, relative to the 2015–2016 energy market adjustment. Heating oil, gold, and natural gas frequently act as key volatility transmitters, while the carbon market shifts from a peripheral receiver to a more integrated and sometimes systemic node within the broader commodity risk network. The findings indicate that carbon-price risk propagates through resource markets in a regime-dependent manner, with implications for stress testing, tail-sensitive hedging, and the coordination of resource and climate policy under turbulent market states. Full article

Edible coatings (ECs) derived from natural biopolymers represent an effective preservation strategy for fruits and vegetables and a promising postharvest approach aligned with the increasing demand for sustainable agricultural practices. These Generally Recognized As Safe (GRAS)-based coatings, which are mainly polysaccharide-, protein-, and lipid-based, can extend shelf-life with minimal impact on texture, flavor, and nutritional value, reducing reliance on synthetic packaging and helping mitigate food loss and waste. Beyond acting as a physical barrier, ECs can significantly influence fruit and vegetable metabolism by modulating biochemical and molecular processes. This review focuses on these effects by summarizing evidence from conventional analytical methods, including targeted metabolite analyses, as well as omics-based approaches, primarily transcriptomics and metabolomics, which remain poorly explored in the current EC research literature. Furthermore, integrated metabolomic and transcriptomic analyses are examined, as they offer a more comprehensive understanding of the molecular mechanisms underlying quality attributes, stress responses, and preservation outcomes. Collectively, this work offers detailed insights into coating-induced changes in metabolite profiles and gene expression in coated fruits and vegetables, including formulations derived from agri-food by-products and coatings enriched with bioactive compounds with antioxidant, antimicrobial, and antifungal properties. Overall, by addressing a current gap in the literature, it provides an integrative and innovative framework for interpreting coating performance at both applied and molecular levels, with potential relevance for the agri-food industry and for future research aimed at developing more sustainable, effective, and commodity-tailored postharvest technologies. Full article

Coffee, as a perennial commodity crop, plays a crucial role in global agricultural markets, regional livelihoods, and poverty alleviation. Yunnan Province of China (21°8′–29°15′N) represents the northernmost coffee-growing region worldwide, and its production has gained increasing attention in international markets. However, the absence of a spatially explicit and high-resolution coffee distribution dataset has constrained environmental assessment, land-use analysis, and policy-making in this subtropical and marginal growing region. In this study, we developed the first 10 m resolution Arabica coffee distribution dataset for Yunnan Province for the year 2023 using Sentinel-2 optical imagery and Shuttle Radar Topographic Mission (SRTM) terrain data within the Google Earth Engine (GEE) platform. An object-based workflow was implemented to generate spatially coherent mapping units, followed by supervised classification to identify coffee plantations. The resulting map achieved an overall accuracy (OA) of 0.87, with user accuracy (UA), producer accuracy (PA), and F1 score of 0.90, 0.96, and 0.93 for the coffee class, demonstrating its reliability for regional-scale applications. Feature contribution analysis indicates that shortwave infrared (SWIR) and red-edge information, particularly during the dry season, plays an important role in coffee discrimination. These results enhance confidence in the ecological relevance and stability of the mapping framework. The proposed workflow provides a practical and transferable approach for perennial crop mapping in complex mountainous environments. More importantly, the generated high-resolution coffee distribution dataset establishes a spatial baseline for monitoring land-use dynamics, assessing ecological impacts, and supporting sustainable coffee development in southwestern China. Full article

Agricultural seeds are sold as commodities yet seed quality can be non-uniform. Despite the extensive literature showing that plasma treatments of seeds provides advantages for many crops, lettuce studies, particularly in indoor farming systems, are limited. This study provides a systematic investigation of the impacts of non-thermal plasma treatments with various feed gases (N₂, O₂, dry air, and wet air) on the germination and growth characteristics of four lettuce cultivars (Red Oakleaf (RO), Black Simpson (BS), Valley Heart Romaine (VHR), and Paris Romaine (PR)) under controlled cultivation conditions in an agrivoltaic agrotunnel. Although the germination time was not conclusively affected by the treatments, the results show a complex interaction between germination rate and yield across the different cultivars and plasma treatments. Except for PR seeds (77.8% vs. 65.8% control), wet air plasma treatments increased germination rates by 18.7–100% over controls for all other cultivars. In yield analysis, wet air treatment had the strongest effect, especially for VHR (51.7 vs. 42.5 g/pot). Treatments did not notably affect RO. For BS, N₂ treatment gave the highest increase (54.2 vs. 48.1 g/pot), while PR responded best to O₂ treatment (58.4 vs. 51.8 g/pot). The energy consumption of plasma treatments was negligible for all treatments, while labor costs for small batches of seeds accounted for the largest share of secondary operating costs (839, 622, and 659 h/year, respectively for BS, VHR, and PR). Despite additional expenses, including labor, O&M, and degradation costs, the reduced seed requirements from higher germination rates and higher yield increased net profit by 12.0% compared to untreated cultivation in the most impacted (Valley Heart Romaine) lettuce. There is an opportunity for further cost optimization of the non-thermal plasma treatment for each type of lettuce seed. Full article

The Brazilian Amazon has been endangered by agro-extractivism, a development model characterized by the expansion of the agricultural frontier to produce raw commodities embedded in power-asymmetrical commodity chains. Recently, the socio-biodiversity economy has emerged as an alternative development model, aimed at reconciling local development with nature conservation. While the environmental and social contrasts between the two models are well documented, the commercial dimension of the socio-biodiversity economy remains underexplored. These two models are typically approached as separate systems, yet their coexistence and interaction within the same actors and across interconnected value chains has not been empirically examined. In this paper, we provide a qualitative analysis of dynamics and upgrading mechanisms in two cocoa value chains in the Brazilian Amazon: raw (bulk) and fine-flavor (fino) cocoa. Through this comparison, we examine how each chain differs in terms of commercial relations and how socio-biodiversity economy and agro-extractivism interact within the commercial sphere. The research took place in three municipalities along the Transamazon highway between March and September 2024. Data were gathered through semi-structured interviews with cocoa producers, buyers, and supporting actors such as NGOs, companies, and public agencies, complemented by participant observation and participation in cocoa-related events. Findings suggest that the bulk and fino cocoa chains present distinct commercial configurations, the former displaying agro-extractivist patterns, the latter consistent with the socio-biodiversity economy. Cocoa production in the region is part of an emergent socio-biodiversity economy that remains commercially embedded in agro-extractivism. Notably, farmers engage in both chains as part of their livelihood strategies, while relying predominantly on the bulk trade. We argue that the fino cocoa chain may represent a pathway for transforming commercial relations in the region, provided that the structural conditions sustaining agro-extractivist patterns in the bulk chain are addressed. More broadly, we show that production-level transitions toward sustainable farming do not automatically translate into the transformation of commercial relations, and call for greater analytical attention to the commercial dimension of socio-biodiversity economies. Full article

Sweeteners occupy a pivotal role in the global transition toward sustainable, health-aligned, and resource-efficient food systems. Conventional sucrose production carries significant environmental burdens, while escalating metabolic health concerns intensify demand for viable alternatives. This paper reframes sweeteners not as commodity ingredients, but as digitally engineered, biologically manufactured, and circularity-optimized materials within the emerging bioeconomy. Advances in artificial intelligence (AI), metabolic engineering, precision fermentation, and lignocellulosic valorization are fundamentally reshaping sweetener innovation. We introduce the Sweetener Innovation 4.0 framework, in which AI functions as the integrative engine linking molecular design, bioprocess optimization, and system-level sustainability. Across diverse sweetener classes, including steviol glycosides, mogrosides, rare sugars, sweet proteins, and forestry-derived polyols, AI accelerates discovery, improves metabolic flux control, optimizes downstream processing and enables more adaptive manufacturing systems. This digital–biological convergence is progressively decoupling sweetness production from land-intensive agriculture, reducing dependence on geographically constrained crops, and enabling resilient, low-carbon manufacturing pathways. Comparative life-cycle assessments highlight substantial sustainability gains, but also reveal persistent methodological gaps, particularly in accounting for downstream-processing energy and digital infrastructure emissions. Socioeconomic analysis further underscores the importance of equitable transitions, transparent labeling, and effective consumer communication as fermentation-derived sweeteners enter global markets. Looking forward, we identify key frontiers for Sweetener Innovation 4.0, including de novo AI-designed sweeteners, autonomous fermentation systems, carbon-negative feedstocks, personalized sweetness modulation, and integrated circular biorefineries. Together, these developments position sweeteners as a top domain for demonstrating how AI, biotechnology, and sustainability principles can jointly reshape ingredient development and industrial systems within the 21st-century circular-economy. Full article