Search Results (2,974)

Mediterranean agroecosystems are highly vulnerable to climate change, soil degradation, and declining soil organic carbon (SOC), threatening long-term agricultural sustainability. Carbon farming and regenerative agriculture have emerged as complementary approaches to restore soil functionality while contributing to climate change mitigation. This review synthesizes peer-reviewed literature published between 2015 and 2025 to assess the agronomic effectiveness of key regenerative and carbon farming practices in Mediterranean systems. A structured bibliographic analysis using Scopus and Web of Science evaluated practices influencing SOC dynamics, erosion control, water regulation, and associated ecosystem services. Evidence indicates that the introduction of cover crops in the crop rotation and reduced or no-tillage are the most consistently effective practices for enhancing SOC stocks, particularly when combined with organic amendments and diversified rotations. Crop diversification, intercropping, and agroforestry further support SOC accumulation and erosion control, especially in perennial systems such as vineyards and olive orchards. Organic inputs stimulate microbial-mediated carbon stabilization, while regenerative grazing contributes to nutrient cycling under context-specific conditions. Across practices, integrated management consistently delivers greater and more stable benefits than single interventions. Regenerative agriculture thus provides a systems-based foundation for carbon farming in Mediterranean agroecosystems. Long-term field experiments and improved monitoring frameworks remain essential to quantify carbon persistence and support policy implementation. Full article

Piscirickettsia salmonis is one of the most significant pathogens affecting salmon farming. Besides liver, head kidney and spleen, skeletal muscle has shown transcriptional immune responses to these bacteria, but the contribution of non-coding RNAs remains poorly understood. This study investigates the role of long non-coding RNAs (lncRNAs) in the immune response of rainbow trout skeletal muscle and primary myotube cultures infected with P. salmonis. Using RNA-seq data from both in vivo and in vitro muscle under control and infected conditions, the analysis identified 4263 candidate lncRNAs through a stringent bioinformatics pipeline. These lncRNAs were mostly classified as exonic and intergenic, showing distinct genomic distributions and structural differences depending on the source. Expression analyses revealed that cell type had a stronger effect on lncRNA profiles than infection status. From 764 differentially expressed lncRNAs, 191 were uniquely associated with infected and 180 with control conditions, mainly unannotated. Functional predictions based on co-expression and proximity to coding genes suggest that lncRNAs are primarily involved in downregulation of structural-cellular maintenance under control conditions, whereas during infection, they are related to immunity, signaling, and apoptosis. Overall, the findings indicate that lncRNAs exhibit origin-specific regulatory roles and are modulated by P. salmonis infection, highlighting their potential importance in fish immune responses. Full article

OpenWorld Role-Playing Games (OWRPGs) differ from other video game genres in that they offer players vast digital landscapes and significant freedom: they can fight, use diplomacy, play stealthily, or pursue activities like farming or trading. This player autonomy creates a space rich in unintended consequences, incidental learning being just one. A study of higher education (HE) students explored this phenomenon. While existing research on incidental learning in games focuses mainly on language acquisition, the current study argues that commercial OWRPGs have the same educational power as purpose-built learning games, and that the scope of incidental learning is far broader than previously examined. Using a constructivist grounded theory (CGT) approach, 25 semi-structured interviews were conducted and written gaming journals collected from 11 additional participants. All 36 participants played OWRPGs in their leisure time. The analysis identified 34 distinct forms of incidental learning, with language acquisition being just one. These were grouped into three categories: self-improvement, academic, and employability skills, with considerable overlap between each. This study shows that OWRPGs can act as a setting for incidental learning in skills over and above lexical improvement. As a result, it is suggested that future research analyse in further depth the skills this research has discovered, looks for further skills that may be honed via incidental learning, and examine other video game genres to highlight any forms of incidental learning that occur. Full article

This study analyses the economic performance, sustainability, and resilience of family farming systems in São Tomé and Príncipe, using an approach that combines quantitative and qualitative data. Primary data were collected through a survey of 50 rural families from the seven districts of the country, focus group discussions, and field observations. Quantitative analysis included descriptive statistics and exploratory comparative procedures, complemented by economic evaluation, while thematic analysis examined the qualitative data. The findings reveal diversified agroforestry systems, integrating up to 33 crops and small-scale livestock production. At the individual and aggregate levels, agroforestry shows viable economic performance, with a net profit margin of 57.4%, capable of generating income and marketable surpluses. This improves rural livelihoods, strengthens resilience to climate and market shocks, and supports both subsistence and market-oriented production. Despite these strengths, structural constraints persist, including fragile value chains, limitations in access to credit and markets, low technology adoption, and climate vulnerability. Human capital, particularly education, emerges as a key factor in improving productivity and value creation. Integrated policies on access to resources and education are needed to promote diversification, multi-activity, and market integration as central strategies for increasing sustainability, food security, and risk reduction in family farming. Full article

Biodiversity and landscape heterogeneity are key components of agroecosystem functioning because they support ecosystem services and strengthen the capacity of agricultural systems to undertake sustainable agroecological transitions. This study assesses the landscape structure of the municipality of Ruvo di Puglia, within the Bio-Distretto delle Lame, to evaluate its potential to support such a transition. Bio-districts are territories in which farmers, local authorities, citizens, and other stakeholders collaborate to manage natural and agricultural resources sustainably, often with a strong connection to organic farming. The research combines freely available Sentinel-2 imagery with UAV-based ground truthing to update land-use/land-cover information and to derive landscape indicators. A systematic sampling scheme was designed in QGIS, and UAV flights over 14 areas were used to generate training and validation vectors. Two classification strategies were tested on 2024 Sentinel-2 data: a supervised pixel-based approach and an unsupervised multi-temporal object-based approach (GEOBIA). The best-performing map was obtained from the supervised classification of July NDVI data, with an overall accuracy of 91.76%. In respect to the 2018 official land-cover dataset indicates a decrease in agricultural land (−490.91 ha), a reduction in arable crops (−1216.43 ha), and an increase in permanent crops (+725.52 ha), suggesting a shift toward specialization. At the same time, natural and semi-natural areas increased, improving the landscape potential for ecological functions. However, the high fragmentation detected by the landscape metrics (average patch size approximately 0.25 ha) may limit habitat continuity and species stability. The results should therefore be interpreted as an assessment of landscape structure and potential biodiversity support, rather than as a direct measurement of biological diversity. Strengthening ecotones, hedgerows and semi-natural linear elements with native species would further improve landscape resilience and support agroecological planning. Full article

Accurate beef cattle body measurement data are crucial for growth assessment, phenotypic analysis, breeding management, and precision livestock farming. Traditional manual measurements are labor-intensive, time-consuming, and likely to cause stress in animals, making it difficult to meet the demands of large-scale livestock farming. This paper employs a structured systematic literature review method, in accordance with the PRISMA 2020 guidelines, to summarize research progress in vision-based beef cattle body measurement. This paper focuses on reviewing technical approaches such as 2D image-based measurement, 3D measurement using RGB-D and LiDAR, and multi-view fusion. It analyzes key technologies including image segmentation, keypoint detection, point cloud processing, 3D reconstruction, and geometric calculations, and compares the advantages and disadvantages of different methods in terms of measurement accuracy, robustness, cost, and farm applicability. The results indicate that 2D image-based methods are low-cost and flexible to deploy but have limited expressiveness for 3D body measurement parameters; RGB-D and LiDAR methods can provide spatial information but are affected by point cloud noise, occlusion, equipment costs, and data processing complexity; multi-view fusion can improve the completeness of body surface information but places high demands on calibration, registration, and system integration. Current research still faces challenges such as a lack of public datasets, inconsistent annotation standards, uncertainty regarding ground truth, insufficient cross-ranch generalization validation, and limited practical applications. Future research should focus on developing standardized datasets, conducting cross-scenario validation, advancing multimodal perception, creating lightweight models, and applying edge computing to drive the evolution of visual body measurement toward continuous monitoring and intelligent decision-making. Full article

This study provides a structured operational-to-financial roadmap for soil organic carbon (SOC) sequestration in mineral soils as a specific carbon-farming pathway. It integrates SOC management; Monitoring, Reporting, and Verification (MRV) execution; financial recognition; and farmer adoption barriers. A comparison of carbon farming pathways is first presented to investigate their strengths and limitations, highlighting the specific importance of SOC management in mineral soils. For high-integrity carbon accounting, SOC gains should be assessed not only for quantity, but also for additionality, permanence, uncertainty, leakage, lifecycle emissions, and transparent verification. Credible MRV frameworks operationalize this logic: monitoring quantifies SOC changes, reporting ensures transparency, and verification provides independent assurance for carbon credit issuance and financial recognition. However, MRV execution faces several challenges, including high spatial variability of SOC, slow accumulation rates, methodological uncertainty, and high costs that limit scalability and reduce trust among stakeholders. Financial incentives are available from both public and private sources, supporting long-term soil carbon stabilization, verified carbon removals, and corporate insetting projects. Yet, adoption remains constrained by uncertain payments, poor transparency, contract and permanence concerns, as well as learning and operational costs for farmers. Addressing these bottlenecks is essential for transforming mineral-soil SOC sequestration into a scalable, high-integrity climate and economic opportunity. Full article

The transition from antibiotic-based (AB) to antibiotic-free (ABF) broiler production represents a major shift in poultry management, with potential implications for flock health, welfare, and processing outcomes. This study evaluated its impact on condemnation percentage (%condemnation) using Bayesian structural time-series (BSTS) analysis. Data from a Thai integrator comprised 105,899 truckload-level records (2015–2020) across 260 contract farms. The AB period (2015–2017) served as the baseline, and the ABF period (2018–2020) was assessed using counterfactual projections. Time-series decomposition and change-point analysis revealed an increasing trend in %condemnation during the early phase of ABF implementation, followed by a decline in 2020, with five structural shifts detected. The BSTS model estimated an absolute effect of +1.10% (95% CI: −1.50 to 3.80; p = 0.207) and a relative effect of +95% (95% CI: −38% to 657%), indicating no statistically significant causal impact. The transient increase may reflect short-term adaptation challenges, whereas subsequent stabilization may be associated with adaptation to ABF production and other concurrent management changes. Overall, the transition from AB to ABF production did not significantly affect %condemnation. Adaptive management measures were implemented as a company-wide policy but were not directly evaluated within the BSTS framework. Full article

In modern farm management, accurate detection of estrus behavior in dairy cows is essential for improving reproductive efficiency and enabling intelligent decision-making. Although fisheye lenses offer a wider field of view, they often introduce image distortion. This leads to geometric and scale deformation of cow mounting behavior features, which reduces detection accuracy. To address this issue, a lightweight model called Boundary-Enhanced Frequency-Domain Feature Pyramid Network YOLO (BFFPN-YOLO) was developed. It is designed for detecting dairy cow mounting behavior under fisheye imaging, incorporating boundary enhancement and frequency-domain compensation. Initially, the backbone network was equipped with the multi-scale dilated fusion structure SPPELAN. This structure expands the receptive field and preserves detailed information, thereby enhancing boundary modeling for targets with scale variations. Subsequently, a boundary-enhanced frequency-domain feature pyramid network (BFFPN) module was designed for reconstructing the top-down transmission path in the Neck. The module is composed of the frequency-domain detail compensation FreqFusion and the spatial attention enhancement SEAM. By strengthening boundary responses, compensating for high-frequency details, and replacing the traditional upsampling and concatenation operations, it effectively mitigates blurred target boundaries in images of dairy cow mounting behavior. The improved algorithm demonstrates strong detection performance, achieving a Precision of 88%, a Recall of 84.5%, and an mAP@0.5 of 92.7%. Compared with the original YOLOv11, these metrics were increased by 3.8, 2.3, and 4.6 percentage points, respectively. The model parameter count was reduced by 1.10 × 10⁶. In complex scenarios, edge features and high-frequency details of dairy cow mounting behavior are more accurately captured by the improved model. These improvements provide a reliable technical basis for the intelligent detection of estrus behavior. Full article

Digital agriculture employs a wide range of sensing, actuation, and analytics technologies to optimize productivity, sustainability, and decision-making in farming operations. However, rural and remote regions face persistent barriers, including limited network coverage and insufficient support for both low- and high-throughput applications, which hinder the deployment of conventional and broadband-intensive Internet of Things solutions. A central challenge is the lack of adequate field-level network infrastructure, with connectivity often unavailable or unreliable. This article presents a comprehensive survey of Broadband-based IoT (B-IoT) as a solution for supporting both low- and high-data-rate digital agriculture applications, including UAVs, computer vision, and extended reality, even in settings without continuous internet connectivity. Using a structured narrative-review approach, this survey synthesizes relevant peer-reviewed and technical literature on B-IoT-enabled digital agriculture and organizes the evidence around communication key performance indicators (KPIs), deployment constraints, and four technology domains: sensing, connectivity, intelligence/compute, and control/application. It examines how technologies such as 5G/6G, dynamic spectrum access, non-terrestrial networks, and edge computing can help address connectivity and infrastructure gaps in underserved agricultural areas. Furthermore, we introduce and analyze the concept of Evolved-Variety Technologies, which combines modified state-of-the-art modules with next-generation networks to create flexible, modular, and scalable system designs adaptable to diverse topographical and operational conditions. Beyond technical evaluations, the article examines economic feasibility, environmental sustainability, and policy implications, emphasizing the need for coordinated roles among governments, telecom providers, and agribusiness stakeholders. Our findings advocate for hybrid telecom architectures that integrate terrestrial and non-terrestrial components, leveraging emerging technologies to reduce the rural–urban digital divide and enable scalable, data-driven agriculture in underserved regions. Full article

This systematic review paper examines how technical, operational, and structural factors influence the adoption of digital agriculture among small-scale farmers in Sub-Saharan Africa. Guided by PRISMA protocols, the study applies a hybrid thematic synthesis across six dimensions: technical, operational, policy and regulatory, governance, social and cultural, and environmental. The findings indicate that digital tools can generate substantial benefits, including yield increases of 10–30% (documented primarily for mobile-based advisory services and precision input management in East African horticulture and West African cocoa value chains) and price gains of 15–25%, with adoption rates of 70–80% in settings characterised by robust infrastructure, strong institutional support, and effective value chain integration. However, these benefits are unevenly distributed and tend to concentrate in “islands of adoption” characterized by robust infrastructure, strong institutional support, and effective value chain integration. While technical (94.9%) and operational (91.5%) factors dominate the literature, their impact is constrained by persistent structural barriers, including weak policy implementation (79.7%), fragmented governance systems (76.3%), and socio-cultural exclusion—such as gender disparities, age-related digital divides, and language misalignment (71.2%). The review identifies five minimum conditions for meaningful adoption: (i) affordable connectivity and access to digital devices; (ii) context-specific digital literacy; (iii) culturally relevant, user-centred design; (iv) robust institutional ecosystems; and (v) enabling policy and financial frameworks. Overall, the findings underscore that digital agriculture adoption is a socio-technical process shaped not only by technological innovation but also by institutional arrangements and user capabilities. Comparative cases, such as Kenya’s Farm.ink and the less successful EZ Farm initiative, further highlight the importance of integrated, context-responsive approaches to ensure that digital agriculture enhances, rather than marginalizes, small-scale farmers. Full article

Italy is among the world’s leading producers and exporters of chestnut. Over the past two decades, however, the sector has undergone significant structural changes driven by phytosanitary shocks and evolving market conditions. This study examines the structural and economic heterogeneity of Italian commercial chestnut farms over the period 2019–2023, aiming to identify recurrent production configurations and assess their economic performance and territorial distribution within the Farm Sustainability Data Network (FSDN) field of observation. The analysis is based on a balanced panel of 96 farms, from which a subsample of 77 inliers was identified through robust multivariate diagnostic tests. Farm-level indicators were aggregated over five years to capture medium-term positioning. Principal Component Analysis (PCA) was used to identify the main latent dimensions of variability, and fuzzy k-means clustering was subsequently performed on the resulting component scores. A five-cluster configuration was selected on the basis of internal validity indices, bootstrap stability, fuzzifier sensitivity and leave-one-variable-out robustness checks. The results reveal pronounced multidimensional differentiation within the observed sample. High economic intensity does not necessarily translate into greater margin stability, the effects of structural maturity vary according to cost exposure and labor organization. Territorial differentiation is statistically significant but not deterministic. Overall, the analysis provides an empirical characterization of structural profiles and their associated trade-offs within the observed commercial segment, offering insights into differentiated policy responses for perennial Mediterranean farming systems. Full article

The supervisory control and data acquisition (SCADA) data are increasingly used for wind turbine anomaly detection, but purely data-driven methods may be limited by weak physical interpretability, class imbalance, and reduced generalization under changing wind-farm operating conditions. Although the Extreme Gradient Boosting (XGBoost) is effective for structured nonlinear classification, its use in SCADA-based anomaly detection remains affected by label quality, probability calibration, and cross-farm transferability. This paper validates a physics-informed XGBoost decision layer using residual-based indicators, including power-curve residuals, gearbox and generator thermal residuals, rotor-speed variance, active-power ratio, and wind-speed fluctuation. Comprehensive Anomaly Detection Benchmark for Wind Turbine SCADA Data (CARE) logbook labels are used as the reference labels, while 2σ, 3σ, and 4σ residual thresholds are evaluated as competing rule-based detectors. The decision layer is trained and internally tested using event-grouped chronological splits from Wind Farm A and externally evaluated on unseen Wind Farms B and C. The results show physically interpretable anomaly detection behavior, although performance varies across validation settings. Under external Farm A to Farm B/C transfer, XGBoost achieved row-level F1-scores of 0.6296 and 0.6551, respectively. Shapley additive explanations (SHAPs) link anomaly predictions mainly to thermal, power-conversion, and operating-context features. The findings support the proposed decision layer as an interpretable benchmark-validation framework, while showing that additional maintenance-log validation is required before definitive component-level fault-diagnosis claims can be made. Full article

This cross-sectional study assessed bulk-tank raw milk quality, subclinical mastitis burden, and farmers’ health management practices across 83 sheep and goat farms in the Epirus Region of northwestern Greece (October 2022–April 2023). Bulk-tank milk composition (fat 5.82%, protein 5.35%, lactose 4.82%) was consistent with Mediterranean small-ruminant norms. Mean somatic cell count (SCC) was 1123 ± 913 × 10³ cells/mL (median 883 × 10³); only 10.0% of farms met the healthy threshold (SCC ≤ 200 × 10³ cells/mL) and 26.2% exceeded the EU limit of 1500 × 10³ cells/mL (Regulation EC No 853/2004). SCC correlated positively with total bacterial count (Spearman ρ = 0.549, p < 0.001). Farmers were predominantly middle-aged (50.0 ± 11.8 years), exclusively male, and had low levels of formal education (9.0% university-educated); only 14.3% had regular veterinary support, and 34.9% practised post-milking teat dipping. Despite 51.3% self-reporting mastitis, no management or demographic variable was significantly associated with SCC after Bonferroni correction; education showed the strongest trend (Kruskal–Wallis H = 9.13, p = 0.058). The findings reveal widespread, largely undiagnosed subclinical mastitis driven by structural gaps in veterinary support, education, and hygiene practice, with direct implications for animal health and targeted advisory intervention. Full article

When digital technologies are disconnected from organizational routines, they fail to create and capture real value in organic agriculture. Anchored in socio-technical systems theory, a five-dimensional analytical framework covering technology, structure, people, tasks, and rules is proposed in this study. Using survey data from 753 organic farms in China, this study combines PLS-SEM, fsQCA, NCA, and NConfA to uncover the core factors and the configurational and necessary mechanisms that drive value creation in organic agriculture through digitalization. The findings reveal that task and structure are the most central drivers of value creation. High value creation results from three equifinal configurations, namely a dual-core people–task path, a deep socio-technical coupling path, and an institution–structure–task support path, whereas low value creation is directly associated with the absence of task or structure. Task, people, and structure are also identified as necessary conditions for high value creation. In contrast, the value creation process exhibits stage-dependent threshold effects, with the dominant logic shifting from individual capability-driven mechanisms to system collaborative mechanisms across development stages and organizational contexts. These results indicate that value creation in organic agriculture driven by digitalization is not a simple linear extension of technology into production, but a process of socio-technical reconfiguration embedded in specific task settings. Full article