Search Results (1,577)

Fresh agricultural product live-streaming involves multi-objective operational trade-offs rather than a simple channel choice problem. Farmers, influencers, and platforms must balance demand expansion and freshness assurance against pricing pressure, selling effort cost, preservation cost, revenue sharing, and platform fees. We examine a fresh agricultural product live-streaming supply chain involving a farmer, an influencer, and a platform under three modes: farmer self-streaming (FS), influencer streaming (IS), and farmer–influencer hybrid streaming (FIS). We develop game-theoretic models to jointly analyze pricing, selling effort, preservation effort, traffic spillover, and payoff allocation, and derive equilibrium decisions through analytical optimization and backward induction. The results show that the FS and IS modes follow different value-conversion logics: the FS mode relies more on farmer-channel information value, while the IS mode relies more on influencer-channel traffic value. The FIS mode combines these two value sources, but its advantage is not merely due to channel addition. A no-spillover comparison shows that the FIS mode creates value through channel addition and traffic-spillover-based synergy. The results also reveal threshold-like amplification in freshness-value realization within the modeled parameter environment: preservation investment becomes more valuable when preservation efficiency, consumer freshness sensitivity, demand scale, and channel complementarity are relatively strong. These findings guide live-streaming mode selection, traffic collaboration, and preservation investment. Full article

Agricultural E-commerce alters farmers’ production and marketing decisions, thereby shaping land-use intensity and livelihood stability. In the context of rapid digital transformation, enhancing farmers’ livelihood resilience is critical to sustainable rural development and resilience to socio-economic and environmental shocks. Using survey data from 542 rural communities in China, this study examines the relationship between agricultural E-commerce participation and farmers’ livelihood resilience across its multidimensional components. The empirical results show that participation in agricultural E-commerce is positively associated with farmers’ overall livelihood resilience, with notable positive associations with buffering capacity, learning capacity, and self-organization capacity. Among these, learning capacity exhibits the strongest association, followed by buffering capacity and self-organization capacity. Farmers’ digital capability positively moderates the relationship between E-commerce participation and livelihood resilience by facilitating access to and effective use of digital information, thereby lowering information costs and contributing to returns from online market participation. Heterogeneity analysis further indicates that the positive association between agricultural E-commerce participation and livelihood resilience is stronger among cooperative members, households with adequate production capital, non-agricultural households, and families with sufficient labor resources. By identifying the role of agricultural E-commerce in building livelihood resilience, this study contributes to the empirical understanding of how digital engagement may support inclusive and sustainable rural development under similar institutional conditions. Full article

Under the dual pressures of water scarcity and rising food demand, dynamic irrigation identification is essential for optimizing water allocation and improving agricultural water-use efficiency. However, constrained by the spatiotemporal resolution trade-off of single-source remote sensing data, limited capability in characterizing irrigation dynamics, and the difficulty of generating dynamic samples, current approaches remain insufficient for continuous and automated detection of irrigation events at fine scales. To address these limitations, this study proposes a dynamic irrigation identification method integrating spatiotemporal fusion and dynamic sample generation. First, daily 10 m re-modified perpendicular drought index (RPDI) time series are reconstructed using the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), and combined with in situ soil moisture observations to characterize RPDI responses to irrigation events. Second, canal water release records and RPDI response features are integrated to generate dynamic irrigation samples with per-time-step labels. Finally, a multi-head attention-enhanced Bi-LSTM model is developed for automated identification of irrigation signals. Application in the Yongji Irrigation District showed that the proposed model outperformed LSTM, Bi-LSTM, and single-head attention-enhanced Bi-LSTM in both per-time-step irrigation response state identification and actual irrigation event identification, with average test-set OA and F1 values of 0.965 and 0.945. Independent validation showed no false-positive or false-negative actual irrigation events and accurate irrigation frequency retrieval. By contrast, although the baseline models achieved relatively high per-time-step accuracy, with OA values ranging from 0.887 to 0.960, they failed to accurately retrieve irrigation frequency, with OAfreq ranging from 0.211 to 0.868. These results demonstrate that the proposed method is capable of dynamically identifying irrigation events and effectively retrieving irrigation frequency information. Full article

Smartphone-based plant identification increasingly serves as the edge tier of agricultural Internet of Things (IoT) systems, where models must adapt to crowdsourced data under bandwidth, memory, and energy constraints. No prior work, to our knowledge, has systematically investigated continual learning at the scale of thousands of fine-grained medicinal plant species from crowdsourced images, nor how retraining frequency affects the cost–performance trade-off in an IoT model-lifecycle setting. We evaluate three continual learning strategies, naïve fine-tuning, experience replay, and Learning without Forgetting, under periodic retraining schedules (updating every K increments), tested on 2719 species (≥25 images each) from the Viet Medi Species 2026 dataset (310,647 images; 4799 species total). All three strategies exhibit negative forgetting (performance improvement rather than degradation) in the instance-incremental setting, with naïve fine-tuning and LwF showing the strongest gains. Periodic retraining with $K=2$ halves retraining operations while maintaining comparable performance. A baseline MobileNetV2 model achieves 54.07% top-10 accuracy across 2719 species and has been deployed via TensorFlow Lite (FP16, ∼11.5 MB) in the Med Herb Lens Android application. In this regime, naïve fine-tuning offers a favourable cost–performance trade-off and is a reasonable default for instance-incremental agricultural IoT deployments. Full article

The paper suggests a distributed cross-layer IoT architecture that combines LoRaWAN (Long Range Wide Area Network) with federated learning (FL) to improve reliability, scalability, and fault tolerance in multi-layer vertical farming systems in dense and dynamic environments. Unlike the traditional frameworks that rely on independent measures of QoS (Quality of Service), the proposed framework directly represents the inter-layer relationships, such as heterogeneity of latencies, robustness of connectivity, and propagation of faults. One of the contributions is the development of a cohesive cross-layer evaluation framework with six strictly defined metrics: MLDC (Multi-Layer Deployment Capacity), C-LCRI (Cross-Layer Connectivity Robustness Index), C-LFCI (Cross-Layer Fault Containment Index), SART (Smart Adaptive Recovery Time), and AIRSM (AI Resilience Score Metric), which allows for quantitatively characterizing latency differences, network resilience, fault containment, recovery efficiency, AI robustness, and energy-performance trade-offs. The experimental results show that the proposed Smart Distributed LoRaWAN–Federated Learning architecture operates reliably in high-density and multi-layer vertical farming environments, and is scalable to handle larger amounts of data. The proposed system guarantees a packet delivery ratio (PDR) of around 95% under a large-scale deployment with up to 1050 IoT nodes spread across seven cultivation layers, with a latency reduction of nearly 60%, less than 1.6 J/msg on average energy consumption, and a fault recovery time of less than 0.3 s in case of network disruptions. The proposed framework was validated using large-scale simulation scenarios developed based on experimentally reported LoRaWAN communication characteristics and agricultural IoT deployments, and operational conditions at the edge intelligence. This evaluation included up to 1050 sensing nodes in 7 vertical farming layers to approximate a realistic deployment of smart farming in a large-scale environment while keeping consistency with the recorded communication and reliability profile. Full article

Fungicide residues in complex food matrices represent an increasingly important challenge in food safety monitoring because intensive agricultural practices, diverse fungicide chemistries, and tropical production conditions can generate multiclass contamination patterns, particularly in Southeast Asian food systems. This review critically evaluates literature published between 2019 and 2026 on microchemical analytical strategies for multiclass fungicide residue determination in fruits, vegetables, rice, spices, and processed foods. The review focuses on the integration of miniaturized and green sample preparation techniques, including modified QuEChERS, dispersive liquid–liquid microextraction, solid-phase microextraction, hollow-fiber liquid-phase microextraction, magnetic solid-phase extraction, and deep eutectic solvent-based extraction, with advanced chromatographic and mass spectrometric platforms. Current evidence shows that these methods can reduce solvent consumption, improve analytical efficiency, and support sensitive residue determination when coupled with UHPLC–MS/MS, GC–MS/MS, and high-resolution mass spectrometry. However, method performance remains strongly matrix-dependent and is constrained by matrix effects, limited standardization of emerging extraction materials, inconsistent validation practices, and trade-offs among selectivity, throughput, cost, and sustainability. No single extraction strategy is universally optimal for all food matrices or fungicide classes. Future research should therefore prioritize matrix-adapted hybrid workflows, harmonized validation protocols, improved detection of transformation products, and broader use of high-resolution screening strategies to support reliable, sustainable, and regulatory-compliant fungicide residue monitoring. Full article

Despite the rapid expansion of unmanned aerial vehicle (UAV) applications across precision agriculture, logistics, infrastructure inspection, disaster response, and aerial surveying, objective and quantitative hardware evaluation criteria for UAV components remain insufficiently developed. This paper proposes quantitative key performance indicators (KPIs) for thirteen core hardware subsystems, including airframe and propulsion, battery and power supply, flight control, wireless communication, imaging (camera), Global Positioning System (GPS)/Global Navigation Satellite System (GNSS) positioning, thermal management, acoustic and vibration characteristics, AI-based autonomous flight, electromagnetic compatibility (EMC), cybersecurity, and reliability and environmental qualification, together with LiDAR payload evaluation criteria. International standardization activities by 3GPP (Release 15/17), IEEE (1936–1958 series), American society for photogrammetry and remote sensing (ASPRS), and national regulatory frameworks are synthesized to define measurable performance metrics and recommended test methods for each subsystem. An integrated KPI matrix maps application-domain-specific performance targets—encompassing surveying (real-time kinematic (RTK) horizontal accuracy ≤ 2 cm root-mean-square error (RMSE), ground sample distance (GSD) ≤ 2 cm/px), infrastructure inspection (LiDAR payload up to 8 kg, beyond visual line-of-sight (BVLOS) latency ≤ 140 ms), and logistics delivery (payload ≥ 2 kg, precision landing ≤ 50 cm)—demonstrating that no universal platform can simultaneously satisfy all domain requirements. A fuzzy-AHP weighting procedure and inter-subsystem coupling analysis are introduced to address size, weight, and power (SWaP) trade-off relationships that purely additive scoring models cannot capture. The proposed evaluation framework is intended to contribute practically to UAV standardization, certification, and quality management across the full design–procurement–operation lifecycle. Full article

With the global green transition of trade and rising sustainable development demands in agri-food trade, non-tariff barriers (NTBs) have become a critical factor influencing the sustainable development of agricultural trade. As a major global producer and exporter of fruits, China’s fruit trade is inevitably affected by non-tariff barriers. This study therefore examines the impacts of NTBs on China’s fruit exports to APEC countries. We use panel data of 13 APEC countries from 2001 to 2023 and apply an extended gravity model. This study takes the total number of TBT and SPS notifications as the proxy variable for NTBs. It empirically tests the trade effect of NTBs on China’s fruit exports and conducts heterogeneity analysis via quantile regression and grouped regression. Baseline results show that NTBs significantly reduce China’s fruit exports. The negative effects of NTBs are more pronounced for small-scale export markets and processed fruit products. Furthermore, China’s economic development can effectively mitigate the adverse trade impacts of NTBs. These findings furnish actionable implications for balancing food safety governance and sustainable agricultural development. Full article

Continuous monitoring of climatic variables is essential for precision viticulture and data-driven decision support systems. However, agricultural sensor networks are frequently affected by missing data due to hardware failures, communication issues, or maintenance interruptions. In this work, we propose a spatio-temporal graph-based autoencoder for reconstructing missing temperature and relative humidity time series collected from a five-node vineyard sensor network over a two-year period. The model combines a GRU-based temporal encoder, augmented with a time-decay imputation mechanism applied to the input data, with a GraphSAGE spatial module, enabling the joint exploitation of temporal dynamics and inter-node spatial correlations. Experimental results on real-world data show that the proposed approach achieves accurate reconstruction under controlled missing-data scenarios generated through structured artificial masking. For moderate corruption levels ($p=0.3$), the model attains reconstruction losses of $0.003$ for temperature and $0.005$ for humidity using short temporal windows (L = 36~3 h), corresponding to MAE values below 0.03 °C and 0.1%, respectively. Even at higher corruption levels ($p=0.7$), performance remains stable, with losses below $0.008$ and $0.011$, and MAE values within 0.05 °C and 0.17%. The results highlight a trade-off between temporal context and reconstruction accuracy: shorter windows yield lower absolute errors under moderate corruption whereas, under extreme data loss ($p=0.9$), the longer windows (L = 144~12 h) reduce the composite temperature reconstruction loss from 0.027 to 0.021. Additionally, temperature is consistently reconstructed more accurately than humidity, reflecting its smoother dynamics and stronger spatial coherence. Full article

This review provides a comparative critical synthesis of biochar and enhanced rock weathering (ERW), identifies key trends and gaps in soil carbon research, and outlines pathways for improving carbon sequestration and monitoring in soil systems. From a global perspective, materials and agricultural studies were read to examine the properties of these amendments and their effects in cropland and forest soils. The main research question guiding this literature review was as follows: What are common trends in published biochar and ERW studies? Major themes were derived from the stated question and structure the Discussion. The Web of Science provided access to relevant literature for both biochar and ERW, and a total of 38 articles (biochar: 17; ERW: 21) were read and covered in this paper. The findings conveyed the growing number of Chinese studies on these amendments to resolve climate-related soil quality affecting crop yields and potential for carbon sequestration, namely carbon dioxide removal or CDR—which sequesters CO₂ that is already in the atmosphere. Studies commonly used application rates of <5% for biochar and 5 or 50 t/ha for ERW, with (wood) biochar commonly processed at temperatures of 500–550 °C. Finer powders were known to be more effective due to their increased surface area, although there were emissions trade-offs to consider for climate change mitigation. There were options for using glacial rock flour (GRF) as an alternative. For ERW, the type of minerals matters, with basaltic amendments being most investigated and minerals like zeolite, for example, having quick responses and potential to filter out heavy metals. Depth of analysis was an issue in the studies, especially affecting ERW work—which needs to adopt greater depths (>60 cm) and both soil organic carbon (SOC) and soil inorganic carbon (SIC) or total carbon need address, particularly for ERW since studies only provided selective coverage. Biochar studies tended to focus more on crop yields and were not as concerned as ERW studies in CDR. Many studies agreed that these are promising products that need to be economically compared before being applied at a large scale. More field studies are needed to test biochar, while limitations imposed by soil pH (acidification affecting dissolution and nutrient availability) and climate need consideration for ERW—especially since it works best in warm, humid climates. The application rate and duration are important variables to also consider for ERW, and both SOC and SIC dynamics are subsystem components requiring consideration. Ultimately, studies call for field trials executed in the long term at greater depth and in different climates and representing different soil types. Full article

In this article, we analyze the relationship between agricultural support measures and food import dependency for a 52-country sample from 1985 to 2017 using databases from the World Bank, the Center for Systemic Peace, and the Groningen Center for Growth and Development. Using an instrumental variable framework, we apply a continuous treatment effect and control for endogeneity to describe the extent of food import dependency in response to domestic support for agriculture. Our results suggest heterogeneous associations for aggregate food import dependency at different levels of political aid intensity, while our analysis further reveals nonlinear dose–response patterns, suggesting that moderate levels of agricultural support are associated with lower food import dependency, whereas very high support intensities are not systematically associated with additional reductions. Although estimates of dose–response functions confirm that countries providing moderate support to agriculture tend to exhibit lower levels of agri-food import dependency, these findings should nevertheless be interpreted cautiously given the potential limitations regarding instrument validity and data availability. The primary contribution of our study is the explicit modeling of heterogeneous treatment intensity effects and potential endogeneity. These findings should therefore be interpreted as conditional empirical associations rather than definitive causal effects. Full article

Fire ants are known for their aggressive behavior, omnivorous diet, and construction of mounds on the soil surface. Their dispersal is facilitated by trade and habitat fragmentation, which have led to negative impacts on biodiversity, public health, and agriculture in many countries. In Brazil, information about their impacts is scarce and mostly limited to reports from the North Region. In the Atlantic Forest, a biome where most of Brazil’s population resides, there are no records of impacts associated with fire ants. This study examined farmers’ perceptions of the impacts of fire ants in the Atlantic Forest. A questionnaire was administered to collect information on respondents’ profiles, property characteristics, perceived impacts of fire ants, management practices, and health-related issues. Most respondents reported the regular presence of fire ants on their properties, although the perceived impacts on agricultural productivity were generally low to moderate, and control costs were typically less than $17. Widespread use of pesticides for fire ant control is reported by most farmers. Regarding stings, 85.1% of farmers reported having been stung, but only 0.6% required hospitalization. The most common reaction was itching. This pioneering study revealed that, although fire ants are present on many properties within the Atlantic Forest, the reported economic and health impacts are lower than expected, with most farmers experiencing minimal losses. Overall, the results for our sample suggest that the presence of fire ants does not result in significant economic losses for farmers. Nevertheless, fire ants are not overlooked, as non-conservationist control methods are employed. Such practices may lead to colony fragmentation, increasing their abundance and potentially negatively affecting local biodiversity. Full article

Agricultural Global Value Chains (GVCs) link input suppliers, primary production, processing, and consumption across borders but are increasingly exposed to upstream disruptions. This study develops a network-based Leontief framework to analyze international trade in agricultural GVCs, explicitly modeling fixed-proportions technologies, intermediate input dependence, trade costs, and capacity constraints. It traces how final demand and supply-side shocks propagate through multi-country input–output networks, affecting both quantities and prices. A stylized numerical illustration motivated by war-related disruptions in Ukraine demonstrates how export constraints, trade frictions, and fertilizer shortages can be represented within the proposed framework. The illustrative exercise shows how nonlinear downstream effects may arise mechanically within a fixed-coefficient production network when upstream constraints bind. Fertilizer availability is treated as a potential amplification channel rather than as an empirically estimated determinant of output losses. Full article

Bats (Chiroptera) account for approximately 25% of all known mammalian species and provide essential ecological services, including insect regulation, pollination, and seed dispersal. Despite their importance, they face significant conservation threats and persistently negative social perceptions. Owing to their innate immunity and tolerance, bats constitute a particularly efficient natural reservoir for a wide variety of potentially zoonotic viruses. Over the past two decades, bat-associated viruses have been central to multiple outbreaks of emerging infectious diseases. From severe acute respiratory syndromes to filoviral hemorrhagic fevers, bats have consistently acted as key reservoirs in pathogen emergence. This has further damaged the public perception of bats as dangerous animals and vectors of serious diseases, in some cases leading to increased persecution of their populations. However, spillover events should not be attributed to bats, but rather to human-driven environmental changes—including deforestation, land-use transformation, agricultural intensification, urban expansion, biodiversity loss, wildlife trade and research biosecurity—that amplify contact among humans, livestock, and wildlife or their potential zoonotic pathogens. Safeguarding bat populations, minimizing direct interactions with wildlife, and preserving intact ecosystems are critical not only for bat conservation but also for reducing zoonotic spillover risk. Furthermore, it is essential to strengthen social communication regarding the importance of bats, in order to counteract their negative reputation and promote greater public understanding of their ecological value. This article reviews health, sociological, and conservation dimensions of the issue, situating them within a broader context to provide an integrated, multidisciplinary understanding. Potential solutions and priority directions for future research are also discussed. Full article

Ecuador’s agricultural sector plays a strategic role in the national economy; however, agricultural data remains fragmented across heterogeneous and isolated sources, limiting integrated analysis and evidence-based decision-making. This study proposes and implements a Big Data analytics framework based on the Medallion architecture and interactive dashboards to integrate, process, and visualize agricultural indicators from INEC, ESPAC, Ecuador Open Data, and FAOSTAT for the 2010–2024 period. The proposed framework adopts the Team Data Science Process (TDSP) methodology and structures workflows into Bronze, Silver, and Gold layers using Databricks for scalable data ingestion, transformation, and dimensional modeling. Interactive dashboards were developed in Tableau Public to support dynamic analysis of agricultural production, trade, producer prices, losses, and producer profiles. A comparative performance evaluation between Databricks Free Edition and Azure Databricks was conducted using SQL analytical workloads and dashboard interaction tests. Results showed that Azure Databricks reduced query execution times by up to 57%, especially in aggregation and join operations. Usability validation with 31 agricultural stakeholders reported high acceptance levels, including a 100% recommendation rate and a data trust score of 4.45/5. The findings demonstrate that scalable and low-cost Big Data technologies can effectively support agricultural digital transformation. Full article