Search Results (137)

Globally, beef customers are seeking verified information regarding the carbon footprint (CF) of the products they buy. As a major supplier of premium grass-finished and natural grain beef supplying markets world-wide, JBS Southern Australia developed a certified Farm Assured (FA) program, launched in 2013, to provide quality beef from independently audited suppliers. This study conducted a life cycle assessment (LCA) with ‘cradle to farm gate’ and ‘cradle to processor gate’ boundaries, using two reference flows—(i) one kilogram (kg) of liveweight (LW) at the farm gate, and (ii) one kg of boxed beef at the processor gate—to assess the greenhouse gas (GHG) CF for beef produced in southern Australia. This study is the first to integrate individual animal carcass characteristics with brand level CF analysis at scale. This was achieved by developing a uniquely comprehensive dataset, with primary data supplied by 200 farms and individual animal data provided for 514,922 heads of cattle. The mean farm gate CF was 11.7 (standard deviation 0.4) kg carbon dioxide equivalent (CO₂-e) kg⁻¹ LW, and the mean boxed beef CF was 24.1 kg CO₂-e kg⁻¹ boxed beef. The study’s novel approach to data collection allowed for the CF to be stratified by region, carcass characteristics, farm of origin and product brand. Analysis revealed that the lowest farm-average and individual animal CFs were 29% and 48% lower than the supply chain average, respectively. These findings indicate that the CFs of beef produced from grass and natural grain-finished production systems in southern Australia were comparable or lower than the CFs of beef entering similar markets. Full article

Fruit preharvest–postharvest quality assessment is essential for precision harvesting, intelligent grading, storage management, and supply-chain loss reduction. However, conventional approaches mainly rely on single-point postharvest inspection and cannot adequately capture the long-term effects of preharvest fruit phenotypes and environmental dynamics on quality formation. To address limited prediction accuracy under few-label conditions, insufficient multimodal fusion, and weak cross-orchard generalization, this study proposes FruitSSL-QNet, a self-supervised multimodal learning framework for jointly modeling preharvest fruit images, environmental sensor time series, and postharvest quality indicators. The framework employs visual masked reconstruction to learn fine-grained phenotype features, including color, texture, lenticel distribution, disease spots, and maturity patterns. Environmental temporal masked modeling is used to capture the cumulative effects of temperature, humidity, light intensity, soil moisture, and rainfall. Bidirectional cross-attention, gated fusion, and contrastive alignment are further integrated to learn complementary and semantically consistent image–environment representations. Experimental results demonstrate that FruitSSL-QNet outperforms SVM, Random Forest, XGBoost, LSTM, GRU, TCN, Transformer, and MM-Transformer across multiple quality assessment tasks. The proposed model achieves a maturity recognition accuracy of $89.6\%$, exceeding MM-Transformer by $4.4$ percentage points. Compared with the corresponding baseline results, the prediction errors for sugar content, firmness, and shelf life are reduced by $21.1\%$, $22.2\%$, and $22.5\%$, respectively. The decay-risk AUC reaches $0.921$, and the cross-site F1-score reaches $0.867$, indicating strong risk discrimination and stable generalization across orchard environments. Ablation experiments further confirm the contributions of visual self-supervision, environmental temporal self-supervision, and cross-modal alignment. Full article

Bioeconomy is an important concept of economic development, supported at the highest political levels. However, its successful implementation calls for action within local markets. This study, therefore, examined the market readiness to engage in bioeconomy growth and emerging value chains in Italy, Slovenia, Germany, Poland, Slovakia, and Austria. The objectives were to assess the market readiness for placing novel bioproducts based on by-products and waste from primary production and agri-food processing sectors, and to evaluate the economics of their production. Specific goals were to estimate the availability of by-products and waste used for making new products, evaluate the main directions and trends in the use of by-products and waste, analyse the main barriers and restrictions to by-product and waste supplies to new enterprises and innovative applications, and make an economic assessment of the market entry of innovative products and their development. The study showed that the oil industry, with a high residue potential, was most often chosen to market new products. Other sectors where value chains can be created or modified are the fruit, winery, grain and milling, wood, hemp, and vegetable industries. PESTEL analysis demonstrated that economic factors, at both national and global levels, are the most common barriers to supplying by-products and waste to new business entities. Technological factors also significantly impede the delivery of by-products and waste to such new enterprises and their processing into novel products. In contrast, social conditions are the main factors stimulating supply of by-products and waste to such new plants. The results provide a preliminary insight into the Central European market and its enormous development potential, which is already implicated in the context of growing bioeconomy. Full article

Timely and accurate assessment of rice productivity, encompassing grain yield, aboveground biomass (AGB), and harvest index (HI), is essential for harvest planning, supply chain coordination, and food security. This study evaluates the feasibility of predicting all three productivity components using satellite and weather time series data while examining trade-offs between forecast accuracy and operational lead time. Five machine learning models (CatBoost, Gaussian Process Regression (GPR), Random Forest, Ridge regression, and TabPFN) were compared across six in-season prediction windows (December to May) using Sentinel-2 vegetation indices (Normalized Difference Vegetation Index (NDVI), Chlorophyll Index Red Edge 2 (CIRE2), Land Surface Water Index (LSWI)), weather variables (minimum and maximum temperature and radiation), and agronomic records from 256 commercial and experimental rice fields in southern New South Wales, Australia, over four growing seasons (2022–2025) using leave-one-year-out cross-validation. Rolling in-season forecasts were evaluated across December–May; March was selected for further analysis as a practical window that balances accuracy and timeliness for decision-making, with minimal additional error reduction in later months closer to harvest. TabPFN had the lowest RMSE for yield prediction (RMSE = 1.85 t ha⁻¹, $r=0.72$), Ridge had the lowest RMSE for AGB (RMSE = 3.05 t ha⁻¹, $r=0.77$), while tree-based models yielded the lowest RMSE for derived HI (RMSE ≈ 0.07). HI prediction showed weak regional relationships, with direct prediction yielding $\left|r\right|\approx 0.24$ and derived HI (predicted yield divided by predicted AGB) showing $r\approx 0$. Although strong correlations ($r>0.9$) between HI and vegetation indices were observed within individual site-seasons, consistent with other studies, these relationships were highly variable across site-seasons, reflecting the difficulty of inferring HI from canopy reflectance when biotic and/or abiotic stresses decouple AGB accumulation from grain filling. Both direct and derived HI approaches yielded comparable errors, indicating that satellite and weather data lack information content for regional-scale HI prediction. These findings support satellite-based yield and AGB forecasting for operational use. Full article

Geopolitical conflicts continue to disrupt global energy supply chains, causing sharp changes in energy prices. These changes reshape the cost structure and efficiency levels of grain circulation. Based on panel data from 30 Chinese provinces covering 2011–2022, this study constructs a proxy for grain circulation efficiency using a resource misallocation framework and counterfactual decomposition. We employ panel threshold regression and double machine learning methods to systematically examine the nonlinear impact of energy price levels on grain circulation efficiency and to reveal regional differences. The findings are as follows: (1) Energy price levels exhibit a significant single-threshold effect. When energy prices remain within a low range, rising prices exert a positive technology push effect; beyond the threshold, a cost-squeeze suppression dominates. (2) The eastern region shows the highest tolerance for price increases, whereas the western region has the lowest tolerance, with the central region falling in between. (3) Double machine learning feature importance analysis reveals that research and experimental development investment and economic development are the dominant factors affecting agricultural product circulation efficiency in the eastern region; water consumption and capital stock are the key factors in the central region; and energy prices, foreign trade dependence, and infrastructure are the most sensitive factors in the western region. This study provides empirical evidence for designing differentiated regional circulation policies and enhancing the resilience of the grain circulation system. Full article

This paper presents a comprehensive review on near-infrared (NIR) spectroscopy applied in food analysis, systematically elaborating its core principles, widespread industrial applications, advanced chemometric strategies, and cutting-edge technological progress. NIR spectroscopy (760–2500 nm), characterized by rapid, non-destructive detection and minimal sample preparation, has been widely implemented in quality evaluation and safety monitoring of grains, meat, fruits and vegetables, dairy, fermented products, tea, coffee, and other processed foods, realizing quantitative analysis of nutrients, freshness assessment, texture prediction, adulteration identification, origin tracing, and rapid preliminary screening of toxin/pesticide residues. A series of chemometric methods, including spectral preprocessing (SNV, MSC, S-G smoothing), feature extraction, and variable selection (CARS, PSO-CMW, ICPA), as well as linear/nonlinear modeling algorithms (PLS, SVM, BP-ANN, fuzzy clustering) significantly boost the accuracy and robustness of spectral analysis. Meanwhile, portable NIR devices and online monitoring systems promote on-site and real-time detection in food supply chains. Despite existing challenges such as calibration transfer, matrix interference, and model generalization, innovations like multimodal data fusion, deep learning integration, and intelligent algorithm optimization offer effective solutions. This review not only summarizes the latest research advances of NIR technology in the food field but also emphasizes its significant advantages as a rapid, non-destructive complementary tool to traditional destructive detection methods, providing theoretical support and technical reference for accelerating the industrial translation and standardized application of NIR spectroscopy, and ultimately safeguarding global food quality and safety. Full article

Early and non-destructive identification of fungal contamination in cereals is essential to support post-harvest management, reduce economic losses, and mitigate food safety risks along the wheat supply chain. Among filamentous fungi, Alternaria spp. are widespread contaminants of durum wheat and producers of toxic secondary metabolites such as alternariol (AOH), whose early detection remains analytically challenging. The aim of this study was to evaluate the potential of near-infrared transmittance (NIT) spectroscopy as a rapid, non-destructive pre-screening tool for the early identification of Alternaria-contaminated durum wheat lots and associated AOH risk. Samples from three durum wheat cultivars were artificially inoculated with Alternaria spp. and monitored over time. NIT spectra (570–1100 nm) were acquired in transmittance mode and analyzed using partial least squares (PLS) regression, focusing on the 870–1100 nm spectral region. Clear and time-dependent spectral differences were observed between inoculated and control samples, with the strongest discriminative features at 834 and 966 nm. Classification performance was high, with area under the curve (AUC) values between 0.96 and 0.97. ELISA analysis confirmed progressive AOH accumulation in inoculated kernels, consistent with the observed spectral changes, while control experiments excluded autoclaving and visual grain damage as confounding factors. From an applied perspective, the results indicate that NIT spectroscopy can support post-harvest decision-making as a rapid pre-screening approach, enabling the prioritization of suspect wheat lots for confirmatory analytical testing. Multivariate analysis further confirmed the consistency of spectral differences across datasets. Full article

Global climate warming has led to the frequent occurrence of natural disasters, threatening the stability of agricultural production and the survival of agricultural enterprises. The existing literature presents mixed evidence regarding the impact of natural disasters on corporate ESG performance. Some studies argue that natural disasters promote ESG performance; however, such conclusions only hold for non-agricultural enterprises. Agricultural enterprises are highly dependent on natural conditions, and their core production factors are vulnerable to direct damage from natural disasters. Meanwhile, they are characterized by long production cycles and high asset specificity. After disaster shocks, agricultural enterprises have to prioritize production recovery, so natural disasters exert a dominant negative effect on their ESG performance. Based on the above context, here we take the performance of Chinese A-share listed agricultural companies between 2010 and 2023 as the research sample to explore the impact of natural disasters on the ESG performance of agricultural enterprises. The empirical results show that natural disasters significantly inhibit the ESG performance of agricultural enterprises. Mechanism tests indicate that natural disasters weaken ESG performance by damaging supply chain resilience, hindering green innovation, and disrupting internal control. A cross-sectional heterogeneity analysis reveals that the inhibitory effect is more pronounced for large-scale enterprises, enterprises with lower executive green cognition, and enterprises located in areas that are not major grain-selling areas. This study enriches the research on the economic consequences of natural disasters and the factors influencing corporate ESG performance. It also provides important practical implications for strengthening the ESG fulfillment of agricultural enterprises and accelerating the cultivation of new productive forces in agriculture. Full article

Ensuring sustainable food production for a growing population requires robust tools like the Life Cycle Assessment (LCA), despite the fundamental complexities characterising the agri-food sector. This study evaluates the environmental impacts of beer and bread production, two important sectors, within a circular economy framework using the LCA. The analysis focuses on innovative products: bread incorporating brewery-spent grain and beer brewed from unsold bread. The study follows a cradle-to-gate approach, covering the entire upstream supply chain, including cultivation, milling, malting, and ingredient production. Cultivation emerges as the primary environmental hotspot in both systems. In bread production, the bakery and proofing phases also show high impacts, while in brewing, packaging is the dominant contributor, followed by boiling and hopping. For co-product processing, drying and transport are critical hotspots. Compared with conventional products, innovative circular products generally show lower environmental impacts, with exceptions related to organic cultivation and allocation constraints. Circular strategies notably reduce land use and marine eutrophication in most organic cases. Overall, the fully circular scenario outperforms the Conventional System in 13 impact categories, supporting the environmental potential of circular approaches in both sectors. Full article

Lithium iron phosphate (LiFePO₄) (LFP) has emerged as the most popular cathode material in the current lithium battery market because of its stable charge–discharge cycle performance, low cost, and high safety. Moreover, this material does not require scarce resources such as nickel and cobalt, which alleviates supply chain conflicts and reduces the environmental and health impacts associated with Ni and Co. In this study, a cost-effective preparation method is implemented to synthesize a series of all-element integrated LiFePO₄ precursors using precursor solutions with varying concentrations of oxalic acid. The final LFP materials are subsequently obtained through a one-step heat treatment. To evaluate the advantages of this method, we compare the structural and electrochemical properties of the obtained LFP materials with those synthesized via the traditional solid-phase method. The experimental results reveal that the LFP material synthesized using an oxalic acid solution with a concentration of 0.125 mol L⁻¹ exhibits optimal performance. This material has a grain size in the range of 300–500 nm, which is smaller and more uniform than those of the other samples. This initial specific discharge capacity of the designed LFP is 150.3 mAh·g⁻¹, with an initial coulombic efficiency of 88%. Notably, the material maintains a high capacity of 98 mAh·g⁻¹ even at −20 °C and achieves a discharge capacity of 98.7 mAh·g⁻¹ at a high discharge rate of 5 C. The lithium-ion diffusion coefficient was determined to be 7.1 × 10⁻¹² cm² s⁻¹, which is approximately 2.5 times greater than that of the material synthesized via the solid-phase ball-milling method. These results highlight the significant improvements in both the structural and electrochemical properties of LFP materials synthesized through this novel liquid-phase method. Full article

Ensuring data security and privacy has emerged as a serious concern in the realm of blood supply chain. This is mainly because of sensitivity of donor information, the involvement of multiple stakeholders, and the need for transparent traceability. This paper proposes a novel privacy-preserving, permissioned blockchain framework for blood supply chain management that integrates Hyperledger Fabric, the InterPlanetary File System (IPFS), and a Zero-Knowledge Proof (ZKP)-based authentication protocol. The framework introduces a Pseudonymous Role-Bound Zero-Knowledge Authentication (PRZKA) mechanism that enables donors to authenticate and authorize access to their medical data without revealing their real identities. Context-specific pseudonyms derived through cryptographic hash-to-curve operations ensure unlinkability across different healthcare interactions, while Schnorr-style challenge–response proofs prevent replay attacks and credential misuse. Sensitive donor information is protected using Fabric Private Data Collections, whereas encrypted medical records are stored off-chain in IPFS, with only secure content identifiers recorded on the blockchain. Smart contracts enforce fine-grained, consent-aware access control policies and maintain immutable audit logs of all access events. The proposed system architecture combines an off-chain ZKP gateway with on-chain authorization logic to minimize blockchain overhead while preserving strong security guarantees. Furthermore, a performance evaluation framework is defined, including metrics, workload scenarios, and system configurations, to support future empirical validation. Security analysis indicates that the proposed framework enhances privacy, prevents identity linkage, and enables auditable, consent-driven data sharing compared with existing blockchain-based healthcare solutions. Full article

This study investigates the comminution behavior and beneficiation potential of lithium-bearing ores, zinnwaldite from Cínovec (Czech-Germany border) and lepidolite from Villasrubias (Spain) by integrating mineralogical analysis and mechanical characterization. The research is driven by Europe’s need for secure lithium supply chains. In particular, it focuses on the challenges associated with low-grade, fine-grained lithium micas found in hard-rock ores, which offer significant potential to supply in Europe but also pose substantial processing challenges. QMA (Quantitative Microstructural Analysis) revealed distinct differences in the textural and structural characteristics of the studied ores. Zinnwaldite-bearing rocks are coarser-grained with high interlocking and roughness, while lepidolite-bearing samples showed finer grains, lower roughness, and more disseminated mica distribution, indicated by their low clustering degree. In terms of mechanical characterization, zinnwaldite-rich ores have the lowest compressive strength, while lepidolite-rich samples showed the highest values, attributed to their finer grain size and more cohesive structure. This suggests that lepidolite may require higher energy input and finer crushing stages to achieve the target liberation size. These features influenced the breakage behavior observed during mechanical testing and comminution and are essential for enabling selective comminution, separating mica from gangue material. This study contributes to analyzing the potential of European hard-rock lithium resources from the perspective of upstream comminution, which is an essential step influencing downstream energy consumption, reagent use, and overall recovery efficiency. The results of this research emphasize that selective comminution should not rely solely on mineral hardness contrasts but must incorporate microstructural parameters such as clustering, grain size distribution, and orientation. Full article

Frequent major emergencies threaten the security of the feed grain import supply chain. Enhancing import resilience is essential for supporting a new development pattern. However, research on a dedicated system to evaluate the resilience of China’s feed grain imports remains limited. In addition, strategies to strengthen resilience based on country-specific distances are still underexplored. This study constructs a comprehensive indicator system for China’s feed grain import resilience, using data from 2000 to 2023. It empirically examines the impact of country distance on this resilience across four dimensions: geographic distance, economic distance, institutional distance, and cultural distance. The findings indicate that country distance has an inhibitory effect on China’s feed grain import resilience. This conclusion holds true even after testing various adjustments, such as changes to core explanatory and dependent variables, modifications in sample sizes, alterations in measurement methods, and the introduction of instrumental variables. Further analysis reveals that country distance undermines feed grain import resilience by significantly reducing trade efficiency. However, the Belt and Road Initiative (BRI) and Regional Trade Agreements (RTA) help mitigate the negative impact of country distance on resilience. To strengthen China’s feed grain import resilience, it is crucial to enhance cultural and institutional trust, improve trade efficiency, and optimize import distribution. This study provides empirical evidence to support the safety of China’s feed grain imports and promote efficient, mutually beneficial trade in feed grains with partner countries. Full article

Industrial hemp (Cannabis sativa L.) harvesting for grain represents a critical technological bottleneck in the modern supply chain, driven by a fundamental conflict between the plant’s resilient morphology and standard agricultural machinery. This review provides an analytical synthesis of harvesting methodologies, evaluating their performance against specific biological constraints such as extreme plant height (up to 4.5 m), high tensile fiber strength, and indeterminate ripening. Data synthesis reveals that hemp cutting is approximately 80 times more energy-intensive than for traditional forage crops, requiring an average maximum force of 243 N per stem. Comparative analysis demonstrates that while conventional whole-plant harvesting faces seed losses ranging from 26% to 46%, selective systems like specialized panicle mowers reduce these losses to nearly 2 kg·ha⁻¹ by targeting only the mature inflorescences. To ensure seed integrity and operational stability, the review identifies concrete technological priorities: the use of abrasion-resistant alloys for cutting edges, the implementation of non-binding shaft shielding (e.g., ABS piping), and a 40–50% reduction in threshing cylinder speeds compared to cereal settings. Future advancements must focus on specialized, high-clearance selective machinery and adaptive control systems to reconcile hemp’s unique physiology with industrial-scale efficiency. Full article

Under China’s rural revitalization and agricultural modernization strategies, digital village construction overcomes resource limits to drive transformation. Using 2013–2022 provincial panel data and a case study of Lin’an, Hangzhou, this study reveals how digital villages boost high-quality agriculture. The empirical results show they significantly enhance agricultural total factor productivity via three paths: IoT-driven precision production, blockchain-enabled green value addition, and e-commerce direct sales demonstrate more pronounced effectiveness in major grain-producing regions and those characterized by balanced production and sales. Simultaneously, this study employs the instrumental variable (TI) approach to address endogeneity from reverse causality and omitted variables. Mechanism testing reveals agricultural technological innovation exerts a significant 77.5% mediating effect. Finally, digital rural construction exhibits a non-linear threshold (0.3082); surpassing it triggers a gradual slowdown in growth with decreasing marginal returns. The Lin’an case validates the empirical results while revealing structural barriers, including industrial chain penetration gaps, data silos, and factor supply constraints, leading to the formulation of targeted optimization strategies. The practical contribution of this study is the proposal of a “data-value-technology” closed loop: public brands like “Tianmu Mountain Treasures” channel premiums into R&D funds, creating a self-sustaining mechanism. The findings indicate that digital villages drive high-quality agriculture primarily through direct effects, powered by full-chain tech coordination, institutional reform, and inclusive factor supply. Finally, this study proposes a coordinated governance framework encompassing “technical synergy, institutional innovation, and factor optimization,” providing theoretical support and strategic references for optimizing the pathways of regional agricultural digital transformation. Full article