Search Results (413)

Accurate measurement of sheep body dimensions is fundamental for growth monitoring and breeding management. To address the limited segmentation accuracy and the trade-off between lightweight design and precision in existing non-contact measurement methods, this study proposes an improved model, YOLOv12n-Seg-SSM, for the automatic measurement of body height, body length, and chest circumference from side-view images of sheep. The model employs a synergistic strategy that combines semantic segmentation with 3D point cloud geometric fitting. It incorporates the SegLinearSimAM feature enhancement module, the SEAttention channel optimization module, and the ENMPDIoU loss function to improve measurement robustness under complex backgrounds and occlusions. After segmentation, valid RGB-D point clouds are generated through depth completion and point cloud filtering, enabling 3D computation of key body measurements. Experimental results demonstrate that the improved model outperforms the baseline YOLOv12n-Seg: the mAP@0.5 for segmentation reaches 94.20%, the mAP@0.5 for detection reaches 95.00% (improvements of 0.5 and 1.3 percentage points, respectively), and the recall increases to 99.00%. In validation tests on 43 Hu sheep, the R² values for chest circumference, body height, and body length were 0.925, 0.888 and 0.819, respectively, with measurement errors within 5%. The model requires only 10.71 MB of memory and 9.9 GFLOPs of computation, enabling real-time operation on edge devices. This study demonstrates that the proposed method achieves non-contact automatic measurement of sheep body dimensions, providing a practical solution for on-site growth monitoring and intelligent management in livestock farms. Full article

Feed formulation is a typical multi-objective optimization problem that aims to minimize cost while satisfying multiple nutritional constraints. However, existing methods often suffer from limitations in handling nonlinear constraints, high-dimensional decision spaces, and solution feasibility. To address these challenges, this study proposes a multi-objective feed formulation method based on an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II). A hybrid Dirichlet–Latin Hypercube Sampling (Dirichlet-LHS) strategy is introduced to generate an initial population with high feasibility and diversity, together with an iterative normalization-based dynamic repair operator to efficiently handle ingredient proportion and nutritional constraints. In addition, an adaptive termination mechanism based on the hypervolume improvement rate (Hypervolume Termination, HVT) is designed to avoid redundant computation while ensuring effective convergence of the Pareto front. Experimental results demonstrate that the Dirichlet–LHS strategy outperforms random sampling, Dirichlet sampling, and Latin hypercube sampling in terms of hypervolume and solution diversity. Under identical nutritional constraints, the improved NSGA-II reduces formulation cost by 1.52% compared with multi-objective Bayesian optimization and by 2.17% relative to conventional feed formulation methods. In a practical application to meat sheep diet formulation, the optimized feed cost is reduced to 1162.23 CNY per ton, achieving a 4.83% cost reduction with only a 1.09 s increase in computation time. These results indicate that the proposed method effectively addresses strongly constrained multi-objective feed formulation problems and provides reliable technical support for precision feeding in intelligent livestock production. Full article

To identify and evaluate the failure issues in the livestock manure-to-biomethane supply chain, this study employs a Bayesian network approach with three inference analysis methods: diagnostic analysis, sensitivity analysis, and maximum causal chain inference. First, the main hazard categories affecting the failure of the supply chain are identified, establishing risk indicators for feedstock collection, pretreatment, anaerobic digestion, purification and upgrading, transportation, and biomethane end-use. Then, the half-interval method and possibility superiority comparison are used to calculate and rank the severity of related accidents, obtaining the severity ranking of secondary indicators as well as the severity ranking of work items and risk items. Finally, Bayesian forward inference is applied to investigate the failure probability of the supply chain, combined with backward inference to identify the risk factors most likely to cause supply chain failures and trace the formation of failure hazards. The Bayesian sensitivity analysis method is ultimately applied to determine the key hazards affecting supply chain failures and the correlations between accident hazards, followed by validation. The results show that the failure probability of the supply chain through causal inference is approximately 54.76%, indicating relatively high failure risk. The three factors with the highest posterior probabilities are mechanical stirring failure C₃ (88.11%), corrosion-induced ammonia leakage poisoning D6, and equipment explosion caused by excessive pressure due to overheating during dehumidification heating D₉, which are the hazards most likely to cause failures in the supply chain. Improper operations and the toxicity of related chemicals are key hazards leading to supply chain failures, with the correlation between accident hazards presented as a hazard chain by integrating severity and accident probability, and the key risk points in the supply chain are identified. Full article

Maintaining optimal humidity in livestock buildings during winter is a major challenge in cold climate regions due to the conflict between moisture-removing ventilation and the need for heat preservation. To address this issue, a novel condensation dehumidification system is proposed that utilizes the natural low temperature of cold winters. An integrated energy consumption model, coupling moisture and thermal balances, was developed to evaluate room temperature drop, dehumidification rate (DR), and the internal circulation coefficient of performance (IC-COP). The model was calibrated and validated with experimental data comprising over 150 operational cycles under varied operation conditions, including initial temperature differences (ranging from −20 to −5 °C), air flow rates (0.6–1.5 m/s), refrigerant flow rates (3–7 L/min), and high-humidity conditions (>90% RH). Correlation analysis showed that higher indoor humidity improved both DR and IC-COP. Four machine learning models—Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Random Forest (RF), and Multilayer Perceptron (MLP)—were developed and compared with a stacking ensemble learning model. Results demonstrated that the stacking model achieved superior prediction accuracy, with the best R² reaching 0.908, significantly outperforming individual models. This work provides an energy-saving dehumidification solution for enclosed livestock housing and a case study on the application of machine learning for energy performance prediction and optimization in agricultural environmental control. Full article

Digital twin technology is widely promoted as a transformative step for precision livestock farming, yet no fully realized, engineering-grade digital twins are deployed in commercial dairy or poultry systems today. This work establishes the current state of knowledge on dairy and poultry digital twins by synthesizing evidence through systematic database searches, thematic evidence mapping and critical analysis of validation gaps, carbon accounting and adoption barriers. Existing platforms are better described as near-digital-twin systems with partial sensing and modelling, digital-twin-inspired prototypes, simulation frameworks or decision-support tools that are often labelled as twins despite lacking continuous synchronization and closed-loop control. This distinction matters because the empirical foundation supporting many claims remains limited. Three critical gaps emerge: life-cycle carbon impacts of digital infrastructures are rarely quantified even as sustainability benefits are frequently asserted; field-validated improvements in feed efficiency, particularly in poultry feed conversion ratios, are scarce and inconsistent; and systematic reporting of failure rates, downtime and technology abandonment is almost absent, leaving uncertainties about long-term reliability. Adoption barriers persist across technical, economic and social dimensions, including rural connectivity limitations, sensor durability challenges, capital and operating costs, and farmer concerns regarding data rights, transparency and trust. Progress for cows and chickens will require rigorous validation in commercial environments, integration of mechanistic and statistical modelling, open and modular architectures and governance structures that support biological, economic and environmental accountability whilst ensuring that system intelligence is worth its material and energy cost. Full article

Swine manure, a heterogeneous livestock waste composed of solid and liquid excreta, can be sustainably converted through Chemical Looping Gasification (CLG) to produce syngas and bioenergy. Integrated with CO₂ capture, the process enables high-purity hydrogen generation and offers a potential route toward net-negative carbon emissions. The experimental campaign was conducted at 900 °C in a continuously operated 0.5 kW_th CLG unit consisting of two interconnected fluidized bed reactors (fuel and air). Ilmenite was employed as the oxygen carrier to provide the oxygen required for gasification. This study focuses on the gasification of raw swine manure, comprising both solid and liquid fractions. The solid fraction was introduced via a screw feeder, while the liquid fraction was simulated by injecting an ammonia–water solution as gasifying agents (water or ammonia + water). The effect of the liquid fraction on syngas composition, carbon conversion, and nitrogen species (N₂, NH₃, N₂O, NO₂, and NO) was evaluated at ammonia concentrations typical of swine manure (800–5600 mg/L). Results showed an average syngas composition for solid and liquid fraction feeding of ~31% CO₂, 20% CO, 41% H₂, 7% CH₄, and 0.5% C₂ hydrocarbons, with 91–96% carbon conversion. Benzene and naphthalene dominated the tar compounds. CO₂ capture potential reached 60%, with nitrogen mainly converted to N₂. Full article

Grassland-based livestock systems across Mexico’s arid and semi-arid belt are increasingly exposed to drought, degrading forage reliability, and soil function. This review synthesizes evidence on native C4 grasses and forage shrubs as complementary building blocks of drought-resilient swards. We searched Web of Science, Scopus, CAB Abstracts and key grey sources (USDA/NRCS Plant Guides, USFS FEIS, Tropical Forages, SNICS) for 1990–2025 studies in English/Spanish. Dominant native grasses (Bouteloua spp., Hilaria belangeri, Digitaria californica, Trichloris crinita, Sporobolus airoides, Panicum hallii) provide high warm-season digestibility and structural cover via C4 physiology, basal/intercalary meristems, and deep/fibrous roots. Forage shrubs (Atriplex canescens, Desmanthus bicornutus, Leucaena leucocephala, Flourensia cernua, Prosopis spp.) bridge the dry-season protein/energy gap and create “resource islands” that enhance infiltration, provided anti-nutritional risks (mimosine/DHP, tannins, salts/oxalates, terpenoids) are managed by dose and diet mixing. We integrate these findings into a Resistance–Recovery–Persistence framework and translate them into operations: (i) site-matching rules for species/layouts, (ii) PLS (pure live seed)-based seed specifications and establishment protocols, (iii) grazing TIDD (timing–intensity–distribution–duration) with a practical monitoring dashboard (CP targets, stubble/cover thresholds, NDVI/SPEI triggers). Remaining bottlenecks are seed quality/availability and uneven extension; policy alignment on PLS procurement and regional seed increase can accelerate adoption. Mixed native grass–shrub systems are a viable, scalable pathway to strengthening drought resilience in Mexican rangelands. Full article

Metal foreign objects left in fields pose a significant challenge during silage harvester operation, leading to reduced mechanical efficiency, compromised feed quality, and risks to livestock safety. However, due to the complex and demanding working environment of agricultural machinery, such as high levels of vibration, dust, and temperature/humidity fluctuations, and the minimal dimensions of critical metallic foreign objects, which often require detection down to a few millimeters, the application of traditional metal detection technology faces significant technical challenges in this field. As a result, metal detection devices have not yet become standard equipment on silage harvesters in China. By consulting the relevant literature, this paper systematically analyzes the basic principles of metal detection technology, compares the technical characteristics of metal detection devices in the field of agricultural machinery and equipment at home and abroad, and puts forward suggestions for the challenges of reliability, foreign object removal, and system response time of metal detection devices. The application of metal detection technology in the field of agricultural machinery and equipment provides information support. Full article

Air emissions from livestock farming, particularly ammonia (NH₃) and particulate matter (PM_2.5 and PM₁₀), constitute a major environmental and occupational health concern. The aim of this work was to assess the compliance with the Verification of Environmental Technologies for Agricultural Production (VERA) protocol in livestock emission monitoring studies and to propose the Adherence VERA Index (AVI) as a novel quantitative tool for standardizing methodological evaluation. A literature search was conducted in PubMed and Scopus, identifying 26 eligible studies published between January 2012 and June 2025. Data were extracted on farm characteristics, analytical methods, environmental variables, and emission outcomes, and evaluated across the five VERA protocol domains. The review revealed substantial methodological heterogeneity and overall suboptimal compliance with the VERA protocol, with frequent deficiencies in the reporting of key parameters such as ventilation rate, sampling strategy, and emission estimation methods. In this context, the AVI, by condensing core VERA requirements into a concise and operational metric, may facilitate protocol uptake and improve reporting compliance compared with the full VERA framework. Notably, several studies reported NH₃, PM_2.5 and PM₁₀ concentrations exceeding occupational and environmental exposure thresholds, particularly in swine and poultry farms, highlighting critical risks to workers’ health. These findings underscore the need for enhanced standardization, integration of occupational health metrics, and improved air quality monitoring to ensure reliable exposure assessment and to safeguard both environmental and worker health in the livestock sector. Full article

This study investigated the bacterial community composition and diversity in air and exercise yard bedding samples from large-scale donkey farms in Liaocheng, China, during summer using 16S rRNA high-throughput sequencing. Air samples were collected from five functional areas of donkey barns, while bedding samples were obtained from eight farms housing Dezhou donkeys. Sequencing analysis revealed 894 operational taxonomic units (OTUs) in air samples and 3127 OTUs in bedding samples. Alpha diversity indices indicated that the mare barn exhibited the highest microbial diversity in air, while the foal barn showed the lowest. Actinobacteriota, Proteobacteria, and Firmicutes were the dominant phyla across different functional areas. Rhodococcus was identified as the predominant airborne genus, representing a potential pneumonia risk in foals. In bedding materials, Firmicutes, Actinobacteriota, and Proteobacteria predominated, with Corynebacterium, Salinicoccus, and Solibacillus as dominant genera. Several potentially pathogenic bacteria were detected, including Rhodococcus, Corynebacterium, Clostridium, Streptococcus, and Escherichia-Shigella. These findings provide critical insights into the microbial ecology of intensive donkey farming environments and offer scientific evidence for developing targeted biosecurity strategies to safeguard animal health and promote sustainable livestock production. Full article

Rural and agricultural runoff continues to pose a threat to water quality and human health despite a plethora of research identifying likely causes. Large livestock operations and leaking septic systems have proven to be significant sources of both nutrients and bacteria in the form of algal blooms and antibiotic-resistant Escherichia coli. These impacts are often witnessed on a watershed scale. Implementing remedies is complicated, as livestock operations are defined as point-source facilities under the USA Clean Water Act (CWA) but regulated as non-point-source entities under a NPDES CAFO general permit. Non-point-source pollutant assessment of watersheds involves a wide array of sampling parameters that focus primarily on impacts after-the-fact and lack regulatory teeth. This watershed management approach is not sustainable, as evidenced by the continual degradation of our rural watersheds. This study lays out streamlined methods and techniques incorporating focused parameters that can infer point-source pollutant pathways even in already impaired waterways. We applied this methodology to the Pine River Watershed in central Lower Michigan after the appearance of an algal bloom downstream from several potential nutrient inputs. Findings show that the application of these unique methods and techniques results in the successful identification of point-source inputs. These methods are inexpensive and demand few resources, and hence they are easily reproduced and replicated. Therefore, by regulating large livestock operations as point-source discharge entities, it is possible for local communities, educational institutions, and regulatory agencies to identify likely pollutant sources in a way that promotes higher water quality and long-term sustainability. Full article

(1) Background: Ecuador is the fourth largest milk producer in Latin America, where ap-proximately 80% of production originates from small family farms located in the Andean region. Despite their socioeconomic importance, these farms face challenges related to low technical efficiency. While there are specific studies on efficiency in dairy systems from other regions, a knowledge gap persists regarding the temporal evolution of technical efficiency (TE) in Ecuadorian Andean dairy farms, especially during crisis periods such as the COVID-19 pandemic. The objective of this study was to evaluate the evolution of TE of family dairy farms in the Ecuadorian Andean region during the period 2018–2024 and to analyze the impact of the pandemic on said efficiency. (2) Methods: Data Envelopment Analysis (DEA) with input orientation and bootstrap simulation was employed to estimate TE, using data from a representative sample that included between 2370 and 2987 farms per year (approximately 25% of the national database of the Ministry of Agriculture and Livestock). Farms were selected based on the availability of complete information on key variables: number of milking cows, area dedicated to forage, family and hired labor (annual hours), and total annual milk production. Statistical analysis included ANOVA to compare mean TE values between years, post-hoc tests to identify specific differences between periods, and the identification of factors related to the TE. (3) Results: The mean TE of Andean dairy farms increased significantly from 0.37 in 2018 to 0.44 in 2024 (p < 0.10), evidencing sustained improvement, although the mean is still distant from the efficiency frontier. The analysis revealed a notable decrease in TE during 2020–2021, coinciding with the period of greatest impact of the COVID-19 pandemic, followed by progressive recovery in subsequent years. The TE distribution showed that between 70% and 75% of farms remained below 0.50 throughout the analyzed period, while only 8–12% achieved levels above 0.70. The main sources of technical inefficiency identified were relative excesses of labor and forage area in relation to milk production obtained. When compared with international studies, Ecuadorian farms present TE levels substantially lower than those reported in the European Union (>0.80) and similar to or slightly lower than those found in Turkey (0.61–0.71). (4) Conclusions: Family dairy farms in the Ecuadorian Andean region operate with technical efficiency levels considerably below their potential and international standards, suggesting substantial scope for improvement through the optimization of productive resource use, particularly labor and land. The COVID-19 pandemic impacted the sector’s efficiency negatively but temporarily, demonstrating resilience and recovery capacity. These findings are relevant to the design of public policies and technical assistance programs aimed at sustainable intensification of family dairy production in the Andes, with an emphasis on improving labor productivity and the efficient use of forage area. Full article

To prevent swine fever transmission, swine farms in China adopt enclosed management, making strict farm personnel biosecurity essential for minimizing the risk of pathogen introduction. However, current shower-in procedures and personnel movement records on many farms still rely on manual logging, which is prone to omissions and cannot support enterprise-level supervision. To address these limitations, this study develops a digital personnel management system designed specifically for the changing-room environment that forms the core biosecurity barrier. The proposed three-tier architecture integrates distributed identification terminals, local central controllers, and a cloud-based data platform. The system ensures reliable identity verification, synchronizes templates across terminals, and maintains continuous data availability, even in unstable network conditions. Fingerprint-based identity validation and a lightweight CAN-based communication mechanism were implemented to ensure robust operation in electrically noisy livestock facilities. System performance was evaluated through recognition tests, multi-frame template transmission experiments, and high-load CAN/MQTT communication tests. The system achieved a 91.4% overall verification success rate, lossless transmission of multi-frame fingerprint templates, and stable end-to-end communication, with mean CAN-bus processing delays of 99.96 ms and cloud-processing delays below 70.7 ms. These results demonstrate that the proposed system provides a reliable digital alternative to manual personnel movement records and shower duration, offering a scalable foundation for biosecurity supervision. While the present implementation focuses on identity verification, data synchronization, and calculating shower duration based on the interval between check-ins, the system architecture can be extended to support movement path enforcement and integration with wider biosecurity infrastructures. Full article

Agricultural and pastoral parks in China possess abundant biomass resources, such as crop straw and livestock manure. However, insufficient distribution generation capacity and a lack of effective coordination strategies lead to low energy utilization efficiency and high carbon emissions. To address these issues, in this study, a coordinated microgrid optimization strategy is proposed based on multi-energy complementarity. A source–load multi-energy coupling model is established by analyzing the dynamic characteristics of biomass energy flow and incorporating a flexible load demand response mechanism. An optimization model aimed at minimizing operational costs is then developed to coordinate heterogeneous energy sources. Simulations under typical wind–solar–load scenarios demonstrate that the proposed strategy improves operational economy by 12.6% and reduces carbon emissions by 23.3% compared to conventional methods through optimized allocation of demand response resources. Full article

Dairy and livestock farms produce considerable amounts of wastewater, which could pose an environmental risk if not properly treated and discharged. Conventional treatment plants can represent an inadequate and costly solution in terms of operation and maintenance, especially for small and medium-sized farms. Thus, a valid and sustainable alternative can be provided by constructed wetland (CW). This paper analyzed the use of CW systems at different scales to treat dairy wastewater (DWW) and livestock wastewater (LWW) all around the world over the last thirty years. This systematic review identified 50 case studies reported in 50 publications from 22 countries: 20 CW for LWW and 30 for DWW. Per each type of WW, the analysis reported and compared the following: CW layout, type of substrates, vegetations planted, design parameters, removal efficiencies and management aspects. Gravel and sand are the most common substrates used in CW to treat both types of WW. Regarding vegetation, Phragmites australis is the most commonly used species in CWs treating LWW, whereas Typha spp. are the most frequently used in CWs treating DWW. Hybrid CW showed the highest removal performance for all parameters reported. This review can improve knowledge on CW, offering a technical and practical overview of the status of CW for treating LWW and DWW. Full article