Search Results (194)

Methane emissions from dairy production constitute a significant share of agricultural greenhouse gas emissions in Poland and represent a key challenge under EU climate policy and the Common Agricultural Policy (CAP). This study evaluates dairy farmers’ acceptance of alternative methane mitigation measures (MMMs) and examines the cost-efficient design of agri-environmental contracts from a public-budget perspective. A Discrete Choice Experiment (DCE) conducted among 302 dairy farmers was used to estimate participation probabilities for different mitigation measures and contract attributes, including result-based (RB) and input-based (IB) payment schemes. These preference-based probabilities were subsequently embedded into a cost-minimisation optimisation framework that identifies the least-cost portfolios of MMMs capable of achieving increasing methane-reduction targets while remaining behaviourally feasible. The DCE results show significantly higher acceptance of RB contracts compared with IB schemes, strong resistance to vaccination-based measures, and relatively favourable preferences for biofiltration. Payment levels and environmental attitudes significantly influence participation decisions. When behavioural constraints are incorporated into the optimisation model, RB contracts allow for higher achievable methane reductions under the adopted assumptions, primarily due to higher participation rates of farmers in result-based contracts. The model indicates that, beyond moderate mitigation targets, IB schemes face participation limits that constrain scalability. Biofiltration consistently forms the backbone of cost-efficient portfolios, while less accepted measures enter optimal solutions only when ambition levels exceed the feasible potential of high-acceptance options, revealing a potential ambition–acceptance gap. Methodologically, the study integrates stated-preference data into a policy optimisation model, demonstrating how farmers’ quantified perceptions can be treated as structural inputs to environmental policy design rather than assuming full adoption of technically efficient measures. Conceptually, the framework links farmer participation, environmental effectiveness, and budget efficiency within a unified decision-support structure. The proposed framework contributes to sustainability-oriented policy design by linking environmental effectiveness, behavioural feasibility, and public-budget efficiency in methane mitigation strategies for the dairy sector. Although the results are scenario-based and conditional on assumed mitigation and cost parameters, they underline the importance of aligning environmental ambition with empirically grounded participation patterns when designing methane mitigation policies for the dairy sector. Full article

The poultry red mite Dermanyssus gallinae (PRM) is a hematophagous ectoparasite of major veterinary and public health concern, recognized as a potential vector of zoonotic pathogens. Despite recent advances in characterizing its microbiota, the temporal dynamics of the microbial community remain poorly understood. Here, we conducted a longitudinal metabarcoding survey of engorged PRM collected from a commercial cage-free laying hen farm over the laying hen’s productive cycle (30–105 weeks). High-throughput sequencing of the 16S rRNA V3–V4 region generated 412,078 sequences, identifying 186 bacterial species across all samples. Microbial richness peaked at 30 weeks (164 species), but sharply declined thereafter, with only 28, 55, and 43 species detected at 60, 90, and 105 weeks, respectively. Ordination (NMDS) and PERMANOVA analyses revealed significant temporal restructuring of microbial communities (R² = 0.76, p < 0.01), with distinct clustering across sampling points. A small subset of taxa persisted throughout time, including the genera Bartonella and Rickettsiella, while many species exhibited transient or stage-specific occurrence. Notably, zoonotic pathogens such as Staphylococcus aureus, Kocuria massiliensis, and Bartonella vinsonii were detected, suggesting that PRM may harbor microorganisms of potential medical and veterinary relevance. Overall, our findings demonstrate that PRM harbors a highly diverse but temporally dynamic microbiota, which progressively contracts into a community dominated by stable symbionts. These results highlight critical windows for microbial succession and reinforce the relevance of microbiome-based surveillance and integrated control strategies within a One Health framework. Full article

Our work develops the financial engineering module of the REGENerative Agriculture (REGENA) Production Function, with Soil Organic Carbon (SOC) as ecosystem service and contract underlying index, contributing to the global literature and business practices. Specifically, we design and engineer a 30-year Net Present Value (NPV) intergenerational ecological bond instrument tailored for carbon farming (CF) as a part of regenerative practices. With SOC constituting a fundamental soil health indicator for the European Union Soil Observatory (EUSO), we model the flow of value from atmospheric CO₂ removal and its metabolism into SOC within a stochastic SOC Value at Risk (VaR) framework. We assess the SOC VaR in five experimental plots in five Mediterranean countries in South Europe and North Africa for three different treatments in each plot. In turn, the SOC VaR is incorporated into an adjusted Shannon entropy index (H(X)_ADJ) to estimate the coefficient of a positive, net-zero, or negative carbon balance and further assess the risk-adjusted discount rate. The monetary value per gram of carbon per kilogram of soil (g C/kg Soil) signifies a clear advantage of combined regenerative treatments. Finally, three selected extensions of our work are discussed, such as the application of the framework to other nutrients, the establishment full cost–benefit accounting methods for monetizing the environmental benefits of CF to upscale investments and the lifecycle accounting of ecosystem services. Full article

Blockchain technology can enhance traceability and trust in contract farming supply chains, yet high implementation costs deter adoption by supply chain participants. This study examines the synergistic mechanisms between blockchain adoption strategies and government subsidy policies. We develop a multi-stage Stackelberg game model involving an agricultural enterprise, an e-commerce platform, and a government, and comparatively analyze six decision-making scenarios across non-subsidy, unilateral subsidy, and full-chain subsidy settings. Three key findings emerge. First, blockchain investment has a cost–effect threshold below which consumer traceability preferences do not translate into profit gains. Second, well-designed subsidies overcome investment inertia and yield Pareto improvements in both profits and social welfare, with the full-chain subsidy model (Model BG) maximizing social welfare; however, subsidies exhibit distinct efficiency boundaries, and over-subsidization causes resource misallocation. Third, both supply chain parties tend to free-ride on the other’s investment, creating strategic conflicts that necessitate differentiated subsidy mechanisms tailored to specific dominance structures. These findings provide policy guidance for facilitating agricultural digital transformation and enhancing supply chain coordination. Full article

Agri-environmental subsidies had been implemented to promote sustainable agriculture in regions such as the EU and the U.S. prior to the year 2000. Contract-Based Agri-Environmental Schemes (AESs) are designed to promote green, sustainable agriculture by employing environmentally friendly farming practices (EFFPs) to reduce pollution and meet other environmental goals. A central challenge, however, is the limited inclusion of small farmers, who are key to agricultural sustainability and form the backbone of production, particularly in developing countries. This study aims to investigate the preferences and participation of small farmers in AESs to enable effective policy design. Using discrete choice experiments (DCEs) and a latent class model (LCM) on survey data collected in 2017 from three key rice-producing counties in China—Fangzheng (Heilongjiang), Qingtongxia (Ningxia), and Yixing (Jiangsu)—allowed us to identify two distinct preference classes: “experienced adopters” and “potential adopters”. The results confirmed (1) a high participation rate of small farmers in AESs. Compensation can further motivate them to sign a contract. (2) There is significant heterogeneity among small farmers’ preferences on various EFFPs, so flexible and modulated schemes are needed; (3) those with experience in EFFPs are more likely to participate in AESs; and (4) the modular AES contract with progressive subsidy ties makes payments directly based on EFFP adoption, addressing the shortcomings of China’s current area-based subsidy system. The results of this paper can help policymakers fine-tune farming policies that effectively engage smallholders, thereby alleviating tensions over production–pollution cycles and fostering a more targeted and sustainable agricultural policy system. Full article

This study examines the hydrological response of the Klambu Dam Catchment in Central Java, Indonesia, to climatic and land cover changes from 2000–2023, with simulations extending to 2040. Utilizing CHIRPS satellite data calibrated with six ground stations, monthly precipitation and temperature datasets were analyzed and projected via linear regression aligned with IPCC scenarios, revealing a marginal temperature decline of 0.21 °C (from 28.25 °C in 2005 to 28.04 °C in 2023) and a 17% increase in rainfall variability. Land cover assessments from Landsat imagery highlighted drastic changes: a 73.8% reduction in forest area and a 467.8% increase in mixed farming areas, alongside moderate fluctuations in paddy fields and settlements. The Thornthwaite-Mather water balance method simulated monthly discharge, validated against observed data with Pearson correlations ranging from 0.5729 (2020) to 0.9439 (2015). Future projections using Cellular Automata-Markov modeling indicated stable volumetric flow but a temporal shift, including a 28.1% decrease in April rainfall from 2000 to 2040, contracting the wet season and extending dry spells. These shifts pose significant threats to agricultural and aquaculture activities, potentially exacerbating water scarcity and economic losses. The findings emphasize integrating dynamic land cover data, climate projections, and empirical runoff corrections for climate-resilient watershed management. Full article

Salmonella is a Gram-negative enteric bacterium responsible for the foodborne and waterborne disease salmonellosis, which was the second most reported foodborne gastrointestinal infection in humans in the European Union in 2023. Animals represent the principal reservoir of this pathogen, with animal-derived food products serving as the main route of transmission to humans. In a household context, having numerous animals can be a crucial factor for contracting Salmonella spp. infection. In the present study, we report a case of a familiar outbreak of Salmonella Thompson that occurred in 2024 in central Italy, involving an infant and the companion animals (a dog, a cat and ten birds) of the family’s farm. To support the epidemiological investigations, antimicrobial susceptibility testing and whole-genome sequencing (WGS) were conducted on strains from the human case and from animals. Eleven strains were isolated in total, from fecal samples collected from the child and the animals at different times. WGS confirmed the genetic relatedness between human and animal isolates, supporting the hypothesis of a shared source of infection, but genes or plasmid involved in antibiotic resistance were not found. Moreover, AST revealed that isolates were fully susceptible to major antimicrobial classes tested. Despite being an uncommon serotype, the involved Salmonella Thompson serovar 6,7: k:1,5 O:7 (C1) demonstrated a high pathogenic potential, emphasizing the need for vigilance even toward serotypes not typically associated with major public health concerns. Moreover, these findings underscore the critical need for an integrated One Health approach to effectively monitor, prevent, and control zoonotic infections. Full article

Phalaenopsis orchids are one of Taiwan’s key floral export products, and maintaining consistent quality is crucial for international competitiveness. To improve production efficiency, many orchid farms outsource the early flask seedling stage to contract growers, who raise the plants to the 2.5-inch potted seedling stage before returning them for further greenhouse cultivation. Traditionally, the quality of these outsourced seedlings is evaluated manually by inspectors who visually detect defects and assign quality grades based on experience, a process that is time-consuming and subjective. This study introduces a smart image-based deep learning system for automatic quality grading of Phalaenopsis potted seedlings, combining computer vision, deep learning, and machine learning techniques to replace manual inspection. The system uses YOLOv8 and YOLOv10 models for defect and root detection, along with SVM and Random Forest classifiers for defect counting and grading. It employs a dual-view imaging approach, utilizing top-view RGB-D images to capture spatial leaf structures and multi-angle side-view RGB images to assess leaf and root conditions. Two grading strategies are developed: a three-stage hierarchical method that offers interpretable diagnostic results and a direct grading method for fast, end-to-end quality prediction. Performance comparisons and ablation studies show that using RGB-D top-view images and optimal viewing-angle combinations significantly improve grading accuracy. The system achieves F1-scores of 84.44% (three-stage) and 90.44% (direct), demonstrating high reliability and strong potential for automated quality assessment and export inspection in the orchid industry. Full article

Based on the “three-dimensional” perspective of modern circulation theory, this study constructs an index system for evaluating the circulation efficiency of agricultural products. The circulation efficiency index values are computed from panel data from 2015 to 2022 in China, and regression estimation is applied to estimate their impact on contract farming coverage. The findings reveal that the circulation efficiency of agricultural products has a significant driving effect on the development of contract farming, and the key mechanism lies in logistics efficiency. Moreover, its impact exhibits regional heterogeneity. Accordingly, we propose policy recommendations to improve contract farming coverage. Full article

Using panel data from 182 county-level cotton-growing regions in the Middle and Lower Reaches of the Yangtze River (2000–2022), this study investigates the drivers of cotton planting area contraction, focusing on the synergistic impacts of non-farm employment, agricultural policies, and their synergies, while verifying mechanisms via rural labor outflow and cotton economic returns. From a sustainability perspective, cotton planting area and output were relatively stable with fluctuations in 2000–2010, but plummeted by 80.6% and 82.8%, respectively, by 2022 (a “cliff-like” decline). Empirical results from the Spatial Durbin Model (SDM) show: (1) Non-farm employment significantly reduces local cotton cultivation and exhibits spatial spillover effects—counties neighboring or economically similar to regions with higher non-farm employment experience greater pressure for contraction; (2) This contraction is more pronounced in counties with smaller rural populations and lower cotton returns, confirming that labor scarcity and low profitability are key channels; (3) Agricultural policies exacerbate the decline: the 2005 Reward Policy for Major Grain-Producing Counties triggers cotton-to-grain substitution, while the 2014 shift from cotton temporary stockpiling to target price subsidies further accelerated the contraction of cotton cultivation in inland regions. This study contributes to understanding agricultural system transitions in the Yangtze River Basin, offering insights for optimizing sustainable planting structure adjustment and balancing food security with cash crop development under rural economic transformation. Full article

Farmland abandonment poses a significant threat to China’s food security by contributing to inefficient land use. Utilizing remote sensing data and the multiple cropping index extraction method, this study extracts abandonment cropland information and analyzes its spatiotemporal patterns across China, with its findings validated against the “China Rural Revitalization Survey” (CRRS) data. The results indicate that since the 21st century, China’s cropland abandonment rate has fluctuated around 5.86%, affecting an average of 7.6 million hectares annually. Spatially, cropland abandonment is more severe in southern China, with hotspots clustered around 25° N and 30° N latitudes. This southward shift exacerbates the spatial mismatch between water resources and cropland. Furthermore, abandonment is particularly pronounced in grain production—marketing balance areas and main marketing areas, intensifying pressure on national food self-sufficiency. Slope and fragmentation also drive abandonment, with steeper (>15°) and more fragmented plots showing higher susceptibility. These complex patterns are uncovered through the study’s systematic innovations—a dual-indicator quantification method, a multi-source validation framework, a dynamic spatiotemporal atlas, and a novel interpretation of abandonment multifunctionality, which also positions farmland reuse as a buffer against unemployment risks. We thus recommend addressing land fragmentation as a core strategy, through high-standard farmland construction, innovative contract rights certification, and expanded agricultural socialized services to promote moderate-scale farming. Finally, we urge the adoption of region-specific and category-based recultivation approaches, supported by clear governance priorities. Full article

This paper introduces a novel scheduling model that integrates weather-based productivity coefficients into multi-unit construction projects, aiming to enhance profit and reduce delays. The method is suitable especially for renewable energy, open-area projects. The authors propose a flow-shop optimization framework that considers key aspects of construction contracts, e.g., contractual penalties, downtime losses, and cash flow constraints. A proprietary Tabu Search (TS) metaheuristic algorithm variant is used to solve the resulting NP-hard problem. Numerical experiments on multiple test sets indicate that the TS algorithm consistently outperforms other methods in finding higher-profit schedules. A real-world wind farm case study further demonstrates substantial improvements, transforming an initially loss-making operation into a profitable venture. By explicitly accounting for weather disruptions within a formalized scheduling model, this work advances the understanding of reliable project planning under uncertain environmental conditions. The solution framework offers contractors an effective tool for mitigating scheduling risks and optimizing resource usage. The integration of weather data and cash flow management increases the likelihood of on-time and on-budget project delivery. Full article

To enhance wind farm frequency regulation in renewable-dominant power systems, this paper proposes a bi-level hybrid framework integrating deep learning and model predictive control (MPC) by retaining the critical wake propagation delay while neglecting higher-order turbulence effects. The upper layer employs a synthetic inertial intelligent control strategy based on contractive autoencoder (CAE) and deep neural network (DNN). Particle swarm optimization (PSO) obtains optimal synthetic inertial parameters for dataset construction, CAE extracts features from multi-dimensional inputs, and DNN outputs optimal coefficients to determine the total power deficit the wind farm needs to supply. The lower layer uses a nonlinear model predictive control (NMPC) strategy with the discretized rotor motion equation as the prediction model and optimization under constraints to allocate the total power deficit to each turbine. MATLAB/Simulink case studies show that, compared with fixed-coefficient synthetic inertial control, the proposed framework raises the frequency nadir by 0.01–0.02 Hz, shortens the settling time by over 200 s under 2–4% load disturbances, and maintains rotor speed within the safe range. This work significantly enhances the wind farm’s frequency regulation performance, contributing to power system and energy sustainability. Full article

This study evaluates the economic feasibility of offshore wind farm development on idle coastal areas in Korea, focusing on the Wando Geumil Offshore Wind Farm (GOWF) as a representative case. Offshore wind farms are increasingly recognized as key contributors to achieving carbon neutrality, and Korea’s coastal idle zones offer strategic potential for large-scale deployment with minimal land-use conflict. To address market uncertainty—particularly the sensitivity of revenues to the Renewable Portfolio Standard (RPS) and Renewable Energy Certificate (REC) weight—this research applies both the Discounted Cash Flow (DCF) method and Real Options Analysis (ROA), incorporating expansion and contraction scenarios. Using eleven years of historical System Marginal Price (SMP) and REC data, we estimate price volatility via a Geometric Brownian Motion (GBM) model (σ = 23.04%). The DCF results indicate a negative Net Present Value (NPV) of −313.7 million USD, suggesting baseline infeasibility. In contrast, ROA adds strategic value, with the expansion option yielding 69.6 million USD and the contraction option 2.1 million USD in additional project value. These findings demonstrate that integrating policy-driven revenue uncertainty into ROA substantially alters investment recommendations, offering practical guidance for optimizing offshore wind farm deployment on Korea’s idle coastal sites. Full article

With the gradual shift towards the use of electric vehicles (EV), electricity demand is expected to increase especially in energy communities. Therefore, it is important to investigate how energy is generated as the provenance of electricity supply is directly linked to climate change. There are only a few studies that investigated the internet of energy and energy provenance, but this area of research is important to prevent the rebound effect of CO₂ emission due to the lack of a transparent approach that verifies the source of electricity consumed for charging EVs. The energy system is a complex network, which results in difficulty verifying the source of electricity as related to the generation of energy. Identifying the provenance of electricity is challenging since electricity is a non-physical element. Moreover, the volatility of a Renewable Energy Source (RES), such as solar and wind power farms, in relation to the complex electricity distribution system makes tracking and tracing challenging. Disruptive technologies, such as Distributed Ledger Technologies (DLT), have been previously adopted to trace the end-to-end stages of products. Likewise, artificial intelligence (AI) can be adopted for the optimization, control, dispatching, and management of energy systems. Therefore, this study develops a decentralized intelligent framework enabled by AI-based DLT and smart contracts deployed to accelerate the development of the internet of energy towards energy provenance in energy communities. The framework supports the tracing and tracking of RES type and source consumed for charging EVs. Findings from this study will help to accelerate the production, trading, distribution, sharing, and consumption of RES in energy communities. Full article