Search Results (98)

This study proposes an integrated framework for sustainable tropical agriculture by combining biochemical waste valorization with spatial carbon footprint estimation in ‘Phulae’ pineapple production. Peel and eye residues from fresh-cut processing were enzymatically converted into rare sugar, achieving average conversion efficiencies of 35.28% for peel and 37.51% for eyes, with a benefit–cost ratio of 1.56 and an estimated unit cost of USD 0.17 per gram. A complementary zero-waste pathway produced functional gummy products using vinegar fermented from pineapple eye waste, with the preferred formulation scoring a mean of 4.32 out of 5 on a sensory scale with 158 untrained panelists. For spatial carbon modeling, the Bare Land Referenced Algorithm (BRAH) and Otsu thresholding were applied to multi-temporal Sentinel-2 and THEOS imagery to estimate plantation age, which strongly correlated with field-measured emissions (r = 0.996). This enabled scalable mapping of plot-level greenhouse gas emissions, yielding an average footprint of 0.2304 kg CO₂ eq. per kilogram of fresh pineapple at the plantation gate. Together, these innovations form a replicable model that aligns tropical fruit supply chains with circular economy goals and carbon-related trade standards. The framework supports waste traceability, resource efficiency, and climate accountability using accessible, data-driven tools suitable for smallholder contexts. By demonstrating practical value addition and spatially explicit carbon monitoring, this study shows how integrated circular and geospatial strategies can advance sustainability and market competitiveness for the ‘Phulae’ pineapple industry and similar perennial crop systems. Full article

This study addresses the problem of agricultural waste utilization and nutrition for older adults by developing a food product based on a circular design approach. Pineapple core was used to produce a clean-label dietary powder without chemical or enzymatic treatment, relying on repeated rinsing and hot-air drying. The development process followed a structured analysis of physical, chemical, and sensory properties. The powder contained 83.46 g/100 g dietary fiber, 0° Brix sugar, pH 4.72, low water activity (aw < 0.45), and no detectable heavy metals or microbial contamination. Sensory evaluation by expert panelists confirmed that the product was acceptable in appearance, aroma, and texture, particularly for older adults. These results demonstrate the feasibility and safety of valorizing agri-waste into functional ingredients. The process was guided by the Transformative Circular Product Blueprint, which integrates clean-label processing, IoT-enabled solar drying, and decentralized production. This model supports traceability, low energy use, and adaptation at the community scale. This study contributes to sustainable food innovation and aligns with Sustainable Development Goals (SDGs) 3 (Good Health and Well-being), 9 (Industry, Innovation and Infrastructure), and 12 (Responsible Consumption and Production). Full article

Under the context of increasing demand for transparency, efficiency, and trust in food systems, digital traceability is emerging as a key strategy for improving value creation across agri-food supply chains. This study investigates how different governance structures influence the design and effectiveness of digital traceability systems. We develop an analytical framework linking four guiding questions (why, where, how, and who) to traceability performance and apply it to five Italian supply chains (wine, olive oil, cheese, pasta, and dairy) through 28 semi-structured interviews with companies, cooperatives, and technology providers. The results show that governance models shape traceability adoption and function. In captive systems (e.g., wine), traceability ensures compliance but limits flexibility, while in modular or relational systems (e.g., pasta and cheese), it fosters product differentiation and decentralized coordination. Across cases, digital traceability improved certification processes, enhanced consumer communication (e.g., via QR codes), and supported premium positioning. However, upstream–downstream integration remains weak, especially in agricultural stages, due to technical fragmentation and limited interoperability. The diverse experience data from company interviews reveal that only 30% of firms had fully integrated systems, and fixed costs remained largely unaffected, though variable cost reductions and quality improvements were reported in the olive oil and cheese sectors. The study concludes that digital traceability is not only a technical solution but a governance innovation whose success depends on the alignment between technology, actor roles, and institutional arrangements. Future research should explore consumer-side impacts and the role of public policy in fostering inclusive and effective traceability adoption. Full article

To facilitate the adoption of a food safety standard by producers, it is essential to understand their perception of it. However, few empirical studies have examined how livestock farmers perceive food safety standards in Vietnam. This research examines sheep farmers’ attitudes towards Vietnamese Good Agricultural Practices (VietGAP), a type of a food safety standard in Vietnam. A sample size of 109 farmers was selected for interviews and a structured questionnaire was generated to collect data. Descriptive and bivariate analyses were employed. The study results show that sheep farmers were well aware of most VietGAP requirements. They perceived that adopting VietGAP requires practical changes in sheep farming systems, including: selecting breeding stock from clear sources to ensure sheep product traceability, collecting and treating wastes daily to protect the environment, and frequent sterilization of sheep cages. The farmers were changing several practices to comply with VietGAP. Key changed practices identified included: bought breeding stock from clear and reliable sources, frequent collecting and treating of sheep wastes, and used veterinary medicine according to instructions of veterinary medicine producers. Statistically significant relationships existing between the sheep farmers’ perceptions and their education level (Pearson = 0.229, p = 0.017), farm size (Pearson = −0.193; p = 0.049), gender (Eta = 0.173, p = 0.060), practice of using labours (Eta = 0.202, p = 0.028), training participation (Eta = 0.211, p = 0.022), credit participation (Eta = 0.177, p = 0.050), community-based organisations (Eta = 0.153, p = 0.087), and veterinary/extension contacts (Eta = 0.217, p = 0.019) were found. This means that a male sheep farmer who had a higher education level, possessed a smaller farm, practiced hired labours, participated in training/credit programs, was a member of community-based organisation, and had contacts with veterinary/extension workers likely perceived VietGAP better than their counterparts. Based on the findings of this study, it is recommended that the promotion of VietGAP for livestock farmers should be developed and carried out as joint attempts along the value chain actors. New food marketing practices and legal framework and policy for using safe food certifications are required to address to promote farmers’ adoption of VietGAP and facilitate transition towards a sustainable agri-food system in Vietnam. This study provides significant insights into safety food standard adoption by livestock farmers and highlights aspects that require to be considered when developing policies to improve the adoption of safety food standards in developing countries. Full article

Agricultural supply chains face growing scrutiny due to rising concerns over food authenticity, safety, and sustainability. These challenges stem from issues such as contamination risks, fraudulent labelling, and the absence of reliable, real-time tracking systems. Existing systems often rely on centralised databases and fragmented data flows, limiting traceability, data integrity, and end-to-end visibility. While blockchain technology offers potential, most research focuses narrowly on traceability, overlooking its role in establishing secure product identity and its integration with emerging tools. This review investigates how Decentralised Identifiers (DIDs), digital twins, and smart contracts—in conjunction with blockchain—can create verifiable digital representations of agricultural products and automate trust mechanisms. Through an analysis of over sixty recent sources and leading standards (e.g., W3C DIDs, Hyperledger Aries), the study identifies key gaps in interoperability, governance, and system maturity. A layered system architecture is proposed, and its application is demonstrated in a cold-chain case scenario. The paper concludes with a roadmap for empirical validation and policy alignment, contributing a practical and scalable framework for researchers, practitioners, and regulators advancing blockchain-enabled traceability systems. Full article

Procymidone (PCM) is an effective, low-toxicity fungicide commonly used to control plant diseases in grains, vegetables, and fruits. Its usage has significantly increased in recent years, resulting in higher residues in vegetables. This study developed a sensitive and rapid immunoassay method utilizing a gold- and fluorescence-labeled monoclonal antibody (mAb) for detecting PCM residues in vegetable samples. Under optimal conditions, the fluorescent microsphere-labeled monoclonal antibody immunochromatographic strips achieved a limit of detection (LOD) of 1.67 ng/mL, with a visual LOD of 50 ng/mL. Intra-batch accuracy ranged from 94.98% to 103.82%, with a coefficient of variation (CV) of 1.97% to 8.26%. Inter-batch accuracy ranged from 96.16% to 102.51%, with a CV of 4.62% to 8.91%. The visual detection range of the gold nanoparticle-labeled monoclonal antibody immunochromatographic strips was 50 to 200 ng/g. The method demonstrated excellent performance in actual vegetable samples, confirming its applicability across various matrices. This dual-method approach enables rapid screening of negative samples with gold test strips, followed by accurate quantitative analysis of positive samples using fluorescent test strips, thereby enhancing efficiency and addressing diverse detection needs. Consequently, this method holds significant market potential for practical applications. Full article

In agriculture, manuring offers several benefits, which include improving soil fertility, structure, water retention, and aeration; all these factors favor plant health and productivity. However, improper handling and application of manure can pose risks, such as spread of pathogens and water pollution. Mitigation of such risks requires not only proper storage and composting practices, but also compliance with correct application periods and techniques. Spaceborne Earth observation can contribute to mapping manure applications and identifying possible critical situations, yet manure detection from satellite data is still a largely open question. The aim of this research is an automated, machine learning (ML)-based approach to detecting manure application on crop fields in time sequences of spaceborne, multi-source optical Earth Observation data. In the first stage of this research, multispectral data alone was considered; a pool of different spectral indexes were analyzed to identify the ones most impacted by manure application. Increments of the selected indexes from one satellite acquisition to the next were used as features to train and test various machine learning models. Two agricultural areas—one in Spain and one in Italy—were considered. Fair levels of accuracy were achieved when training and testing were carried out in the same geographical context, whereas ML models trained on one context and tested on the other reported significantly lower—albeit still acceptable—accuracy levels. In the stage that followed, thermal data was integrated and used alongside multispectral indexes. This addition led to significant improvements in accuracy levels, despite possible thermal-to-multispectral sampling mismatch in time series. Our results appear to indicate that ML-based approaches to manuring detection from space require training on the targeted geographical context, although transfer learning can probably be leveraged and only fine-tuning training will be needed. Spaceborne thermal data, where available, should be included in the input data pool to improve the quality of the final result. The proposed method is meant as a first step towards a suite of techniques that should enable large-scale, consistent monitoring of agricultural activities to check compliance with environmental regulations and provide enhanced traceability information for food products. Full article

To address the pressing challenges of quality and sustainability in agricultural product supply chains, this paper proposes a multi-stakeholder collaborative governance framework. Adopting the perspective of collaborative governance and sustainability, the paper develops an evolutionary game model of the Chinese agricultural product supply chain. This model involves four key stakeholders: agricultural enterprises, the government, NGOs, and consumers. It integrates sustainability principles to ensure that the decisions of each stakeholder contribute to the quality and safety of agricultural products while also promoting long-term environmental and social well-being. The simulation results demonstrate the critical importance of multi-stakeholder collaboration in strengthening governance and promoting sustainability. Based on these findings, the government is advised to implement technology-driven oversight mechanisms, such as AI-based inspections and blockchain traceability. Additionally, a tiered penalty system should be established, escalating penalties for repeat offenders. Regulatory cost-sharing mechanisms can also help ensure continuous enforcement without imposing excessive financial burdens on any single party. NGOs play a vital role in exposing corporate violations and can be more effective through targeted financial support and crowdsourced monitoring platforms. Consumers, as active participants in governance, should be incentivized with reward-based reporting systems and verified compliance feedback, which influence enterprise reputation. Public–private sustainability partnerships and real-time transparency platforms can further facilitate active participation from all stakeholders. By implementing these measures, the regulatory framework can evolve from traditional enforcement to a dynamic, data-driven governance model, fostering long-term sustainability and risk reduction in agricultural production. Full article

The agri-food sector is expanding, driven by growing global demand. At the same time, it faces the challenge of increasing its efficiency and adopting sustainable practices. This study aimed to map scientific production in this field, identifying trends, emerging themes, critical gaps, and future directions for research. A bibliometric analysis was conducted with 5141 papers published between 1977 and 2024, extracted from the Scopus and Web of Science databases. We applied keyword co-occurrence analysis, thematic analysis, thematic evolution, and three-field graphs using the metrics betweenness centrality, closeness centrality, and PageRank. The results revealed a significant growth in publications in the agri-food sector, especially after 2012, emphasizing the high centrality and relevance of themes such as sustainability, agri-food, and agriculture. Topics such as bioactive compounds, blockchain, and traceability were identified as areas of growing interest, and the circular economy stood out as an emerging topic. Italy, Spain, and France lead in scientific production and international collaboration. The most prominent journals were Sustainability, the Journal of Cleaner Production, and Agriculture and Human Values. Research in the sector is expanding, focusing on sustainability, the circular economy, and bioactive compounds. International collaborations and high-impact journals are pillars for advances in the sector. Full article

The quality and price of navel oranges vary depending on their geographical origin, thus providing a financial incentive for origin fraud. To prevent this phenomenon, it is necessary to explore a fast, non-destructive, and precise method for tracing the origin of navel oranges. In this study, a total of 490 Newhall navel oranges were selected from five major production regions in China, and the diffuse reflectance near-infrared spectrum in 4000–10,000 cm⁻¹ were non-invasively collected. We examined seven preprocessing techniques for the spectra, including Savitzky–Golay (SG) smoothing, first derivative (FD), multiplicative scattering correction (MSC), combinations of SG with MSC (SG+MSC), SG with FD (SG+FD), MSC with FD (MSC+FD), and three combined (SG+MSC+FD). A one-dimensional convolutional neural network (1DCNN) deep learning model for geographical origin tracing of navel orange was established, and five machine learning algorithms, i.e., partial least squares discriminant analysis (PLS-DA), linear discriminant analysis (LDA), support vector machine (SVM), random forest (RF), and back-propagation neural network (BPNN), were compared with 1DCNN. The results show that the 1DCNN model based on the SG+FD preprocessing method achieved the optimal performance for the testing set, with prediction accuracy, precision, recall, and F1-score of 97.92%, 98%, 97.95%, and 97.90%, respectively. Therefore, NIRS combined with deep learning has a significant research and application value in the rapid, nondestructive, and accurate geographical origin traceability of agricultural products. Full article

Efficient management of soil nutrients is essential for optimizing crop production, ensuring sustainable agricultural practices, and addressing the challenges posed by population growth and environmental degradation. Smart agriculture, using advanced technologies, plays an important role in achieving these goals by enabling real-time monitoring and precision management of nutrients. In open-field soil cultivation, spatial variability in soil properties demands site-specific nutrient management and integration with variable-rate technology (VRT) to optimize fertilizer application, reduce nutrient losses, and enhance crop yields. Hydroponic solution cultivation, on the other hand, requires precise monitoring and control of nutrient solutions to maintain optimal conditions for plant growth, ensuring efficient use of water and fertilizers. This review aims to explore recent trends in soil and solution nutrient sensing technologies for open-field soil and facilitated hydroponic cultivation, highlighting advancements that promote efficiency and sustainability. Key technologies include electrochemical and optical sensors, Internet of Things (IoT)-enabled monitoring, and the integration of machine learning (ML) and artificial intelligence (AI) for predictive modeling. Blockchain technology is also emerging as a tool to enhance transparency and traceability in nutrient management, promoting compliance with environmental standards and sustainable practices. In open-field soil cultivation, real-time sensing technologies support targeted nutrient application by accounting for spatial variability, minimizing environmental risks such as runoff and eutrophication. In hydroponic solution cultivation, precise solution sensing ensures nutrient balance, optimizing plant health and productivity. By advancing these technologies, smart agriculture can achieve sustainable crop production, improved resource efficiency, and environmental protection, fostering a resilient food system. Full article

Galician wheat (Triticum aestivum L.) serves as the foundational component of Galician bread, a traditional Spanish product granted the Protected Geographical Indication (PGI, EU quality scheme), which is primarily conserved at the Agricultural Research Center of Mabegondo (CIAM), Xunta de Galicia, A Coruña, Spain. This study evaluated 20 ecotypes and cultivars, in comparison to 4 Galician wheats and 14 commercial wheat varieties used as references. Seventeen simple sequence repeats (SSRs) were evaluated to elucidate their genetic structure, determine their origins, and differentiate them from commercial cultivars for traceability purposes. In total, 296 wheat plants were analyzed, revealing 156 unique genotypes, 13 of which were from commercial cultivars and 143 of which were from local cultivars and ecotypes. The SSR loci revealed 221 microsatellite alleles, with an average of 11 alleles per locus. Of these, 151 alleles were found in local cultivars and ecotypes, and 134 were present in commercial cultivars, with 65 and 50 alleles exclusive to each group, respectively. A Structure software analysis demonstrated substantial genetic differentiation (Fst = 0.26) between two primary clusters, RPP1 (comprising commercial cultivars, and two ecotypes, 41 and 43) and RPP2 (consisting of local cultivars, elite lines, and ecotypes). Moreover, neighbor-joining tree analysis and principal component analysis (PCA) confirmed the high differentiation between these clusters, highlighting the singularity of Galician wheat, which is useful for the traceability of Galician bread. Furthermore, the SSRs were effective in tracking the use of Galician wheat, which displayed specific Galician alleles, in flour, sourdough, and bread samples, corroborating previous findings even when a greater number of Galician ecotypes were included. Full article

Shandong and Henan provinces face significant pest and disease issues, creating a strong demand for green pest control technologies. This paper analyzes the impact of government subsidies and quality certification on farmers’ adoption of green pest control technologies, based on 419 survey responses collected through stratified sampling in Shandong and Henan provinces in 2024, using the Heckman two-stage model. The results show the following: (1) Government subsidies and quality certification significantly promote farmers’ adoption of green pest control technologies, with regression coefficients of 0.260 and 0.493, respectively. (2) An interaction effect exists between government subsidies and quality certification on farmers’ adoption of green pest control technologies, with a coefficient of 0.454. For a given government subsidy, higher quality certification levels increase the likelihood of farmers adopting green pest control technologies. (3) From the perspective of human capital quantity, there is obvious heterogeneity in the impact of government subsidies and quality certification on farmers’ adoption of green pest control technologies. (4) From the perspective of generational differences, quality certification has obvious heterogeneity on farmers’ adoption of green pest control technologies, while there is no obvious generational difference in government subsidies. Therefore, it is necessary to establish a stable and multi-channel government transfer payment system, improve the construction of the agricultural product quality traceability system, take a two-pronged approach, and complement each other’s strengths to build a targeted incentive mechanism based on different groups of farmers. Full article

This study presents a blockchain-based traceability system designed specifically for the olive oil supply chain, addressing key challenges in transparency, quality assurance, and fraud prevention. The system integrates Internet of Things (IoT) technology with a decentralized blockchain framework to provide real-time monitoring of critical quality metrics. A practical web application, linked to the Ethereum blockchain, enables stakeholders to track each stage of the supply chain via tamper-proof records. Key functionalities include smart contracts that automate quality checks, ensuring data integrity and providing immediate verification of product authenticity. Initial user feedback highlights the system’s potential to enhance transparency and reduce fraud risks in the olive oil market, supporting consumer trust and regulatory compliance. This approach offers a scalable solution adaptable to other high-value agricultural products, demonstrating the blockchain’s transformative potential for secure and transparent food traceability. Full article

To fight counterfeits, and to protect the consumer, the interest in certifying the origin of agricultural goods has been growing in recent years. In this context and to increase the accuracy of zoning models, multiple analytical techniques must be combined via a multivariate approach. During the sampling campaign, leaves and fruits (olives or drupes) were collected from multiple orchards and farms. By means of HPLC-DAD, metabolite levels were evaluated and combined with the trace and ultra-trace metal/metalloid levels evaluated by ICP-MS (QqQ). The combined dataset was then used to develop a model for geographical traceability. Furthermore, the mineral content of the soil, evaluated by means of ICP-MS, was correlated with both the mineral content in the leaves and drupes and the metabolomic profiles to further investigate the connection between the orchard’s location and characteristics of the final products. Full article