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Search Results (550)

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Keywords = dairy cattle farming

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14 pages, 1316 KiB  
Article
Development of Mid-Infrared Spectroscopy (MIR) Diagnostic Model for Udder Health Status of Dairy Cattle
by Xiaoli Ren, Chu Chu, Xiangnan Bao, Lei Yan, Xueli Bai, Haibo Lu, Changlei Liu, Zhen Zhang and Shujun Zhang
Animals 2025, 15(15), 2242; https://doi.org/10.3390/ani15152242 - 30 Jul 2025
Viewed by 171
Abstract
The somatic cell count (SCC) and differential somatic cell count (DSCC) are proxies for the udder health of dairy cattle, regarded as the criterion of mastitis identification with healthy, suspicious mastitis, mastitis, and chronic/persistent mastitis. However, SCC and DSCC are tested using flow [...] Read more.
The somatic cell count (SCC) and differential somatic cell count (DSCC) are proxies for the udder health of dairy cattle, regarded as the criterion of mastitis identification with healthy, suspicious mastitis, mastitis, and chronic/persistent mastitis. However, SCC and DSCC are tested using flow cytometry, which is expensive and time-consuming, particularly for DSCC analysis. Mid-infrared spectroscopy (MIR) enables qualitative and quantitative analysis of milk constituents with great advantages, being cheap, non-destructive, fast, and high-throughput. The objective of this study is to develop a dairy cattle udder health status diagnostic model of MIR. Data on milk composition, SCC, DSCC, and MIR from 2288 milk samples collected in dairy farms were analyzed using the CombiFoss 7 DC instrument (FOSS, Hilleroed, Denmark). Three MIR spectral preprocessing methods, six modeling algorithms, and three different sets of MIR spectral data were employed in various combinations to develop several diagnostic models for mastitis of dairy cattle. The MIR diagnostic model of effectively identifying the healthy and mastitis cattle was developed using a spectral preprocessing method of difference (DIFF), a modeling algorithm of Random Forest (RF), and 1060 wavenumbers, abbreviated as “DIFF-RF-1060 wavenumbers”, and the AUC reached 1.00 in the training set and 0.80 in the test set. The other MIR diagnostic model of effectively distinguishing mastitis and chronic/persistent mastitis cows was “DIFF-SVM-274 wavenumbers”, with an AUC of 0.87 in the training set and 0.85 in the test set. For more effective use of the model on dairy farms, it is necessary and worthwhile to gather more representative and diverse samples to improve the diagnostic precision and versatility of these models. Full article
(This article belongs to the Section Animal Welfare)
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17 pages, 1486 KiB  
Article
Occurrence and Reasons for On-Farm Emergency Slaughter (OFES) in Northern Italian Cattle
by Francesca Fusi, Camilla Allegri, Alessandra Gregori, Claudio Monaci, Sara Gabriele, Tiziano Bernardo, Valentina Lorenzi, Claudia Romeo, Federico Scali, Lucia Scuri, Giorgio Bontempi, Maria Nobile, Luigi Bertocchi, Giovanni Loris Alborali, Adriana Ianieri and Sergio Ghidini
Animals 2025, 15(15), 2239; https://doi.org/10.3390/ani15152239 - 30 Jul 2025
Viewed by 118
Abstract
On-farm emergency slaughter (OFES) is employed when cattle are unfit for transport but still suitable for human consumption, thereby ensuring animal welfare and reducing food waste. This study analysed OFES patterns in Northern Italy, where a large cattle population is housed but information [...] Read more.
On-farm emergency slaughter (OFES) is employed when cattle are unfit for transport but still suitable for human consumption, thereby ensuring animal welfare and reducing food waste. This study analysed OFES patterns in Northern Italy, where a large cattle population is housed but information on the practice is rarely analysed. A total of 12,052 OFES cases from 2021 to 2023 were analysed. Most involved female cattle (94%) from dairy farms (79%). Locomotor disorders were the leading reason (70%), particularly trauma and fractures, followed by recumbency (13%) and calving-related issues (10%). Post-mortem findings showed limbs and joints as the most frequent condemnation sites (36%), often linked to trauma. A significant reduction in OFES cases occurred over time, mainly due to fewer recumbency and calving issues, likely reflecting stricter eligibility criteria introduced in 2022. Weekly variations, with peaks on Mondays and lows on Saturdays, suggest that logistical constraints may sometimes influence OFES promptness. These findings suggest that on-farm management and animal handling could be improved further to reduce welfare risks and carcass waste. Due to the lack of standardised data collection and regulatory harmonisation, a multi-country investigation could improve our understanding of this topic and inform best practice. Full article
(This article belongs to the Special Issue Ruminant Welfare Assessment—Second Edition)
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25 pages, 3891 KiB  
Review
The Carbon Footprint of Milk Production on a Farm
by Mariusz Jerzy Stolarski, Kazimierz Warmiński, Michał Krzyżaniak, Ewelina Olba-Zięty and Paweł Dudziec
Appl. Sci. 2025, 15(15), 8446; https://doi.org/10.3390/app15158446 - 30 Jul 2025
Viewed by 289
Abstract
The environmental impact of milk production, particularly its share of greenhouse gas (GHG) emissions, is a topic under investigation in various parts of the world. This paper presents an overview of current knowledge on the carbon footprint (CF) of milk production at the [...] Read more.
The environmental impact of milk production, particularly its share of greenhouse gas (GHG) emissions, is a topic under investigation in various parts of the world. This paper presents an overview of current knowledge on the carbon footprint (CF) of milk production at the farm level, with a particular focus on technological, environmental and organisational factors affecting emission levels. The analysis is based on a review of, inter alia, 46 peer-reviewed publications and 11 environmental reports, legal acts and databases concerning the CF in different regions and under various production systems. This study identifies the main sources of emissions, including enteric fermentation, manure management, and the production and use of feed and fertiliser. It also demonstrates the significant variability of the CF values, which range, on average, from 0.78 to 3.20 kg CO2 eq kg−1 of milk, determined by the farm scale, nutritional strategies, local environmental and economic determinants, and the methodology applied. Moreover, this study stresses that higher production efficiency and integrated farm management could reduce the CF per milk unit, with further intensification having, however, diminishing effects. The application of life cycle assessment (LCA) methods is essential for a reliable assessment and comparison of the CF between systems. Ultimately, an effective CF reduction requires a comprehensive approach that combines improved nutritional practices, efficient use of resources, and implementation of technological innovations adjusted to regional and farm-specific determinants. The solutions presented in this paper may serve as guidelines for practitioners and decision-makers with regard to reducing GHG emissions. Full article
(This article belongs to the Special Issue Environmental Management in Milk Production and Processing)
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38 pages, 2956 KiB  
Review
The Use of Selected Machine Learning Methods in Dairy Cattle Farming: A Review
by Wilhelm Grzesiak, Daniel Zaborski, Marcin Pluciński, Magdalena Jędrzejczak-Silicka, Renata Pilarczyk and Piotr Sablik
Animals 2025, 15(14), 2033; https://doi.org/10.3390/ani15142033 - 10 Jul 2025
Viewed by 471
Abstract
The aim of this review was to present selected machine learning (ML) algorithms used in dairy cattle farming in recent years (2020–2024). A description of ML methods (linear and logistic regression, classification and regression trees, chi-squared automatic interaction detection, random forest, AdaBoost, support [...] Read more.
The aim of this review was to present selected machine learning (ML) algorithms used in dairy cattle farming in recent years (2020–2024). A description of ML methods (linear and logistic regression, classification and regression trees, chi-squared automatic interaction detection, random forest, AdaBoost, support vector machines, k-nearest neighbors, naive Bayes classifier, multivariate adaptive regression splines, artificial neural networks, including deep neural networks and convolutional neural networks, as well as Gaussian mixture models and cluster analysis), with some examples of their application in various aspects of dairy cattle breeding and husbandry, is provided. In addition, the stages of model construction and implementation, as well as the performance indicators for regression and classification models, are described. Finally, time trends in the popularity of ML methods in dairy cattle farming are briefly discussed. Full article
(This article belongs to the Special Issue Machine Learning Methods and Statistics in Ruminant Farming)
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21 pages, 681 KiB  
Article
Qualitative Risk Assessment of Foot-and-Mouth Disease Virus Introduction and Transmission to Dairy Farms via Raw Milk Transportation in Thailand: A Scenario-Based Approach
by Patidpong Chumsang, Tawatchai Singhla and Warangkhana Chaisowwong
Vet. Sci. 2025, 12(7), 623; https://doi.org/10.3390/vetsci12070623 - 27 Jun 2025
Viewed by 494
Abstract
Foot-and-mouth disease (FMD) significantly impacts global livestock industries, with raw milk transportation posing a recognized pathway for viral dissemination, particularly in endemic regions. This study aimed to evaluate the risk of FMD virus (FMDV) introduction and transmission to dairy farms via raw milk [...] Read more.
Foot-and-mouth disease (FMD) significantly impacts global livestock industries, with raw milk transportation posing a recognized pathway for viral dissemination, particularly in endemic regions. This study aimed to evaluate the risk of FMD virus (FMDV) introduction and transmission to dairy farms via raw milk transportation in Ban Thi District, Thailand. A qualitative risk assessment methodology, adhering to WOAH guidelines, was employed. Data were collected through structured farmer surveys (n = 109), expert interviews (n = 12), and reviews of national disease surveillance data and scientific literature. The risk assessment, utilizing a scenario tree approach for domestic dairy cattle, revealed a moderate overall risk of FMDV transmission. This finding is primarily attributed to critical gaps in on-farm biosecurity practices, potential contamination at milk collection centers, and significant challenges in detecting subclinical carrier animals. While the qualitative approach presented inherent limitations and uncertainties, the study successfully highlighted key vulnerabilities. The results underscore the urgent necessity for implementing targeted biosecurity protocols, developing more robust surveillance strategies for FMDV carriers, and establishing standardized risk assessment frameworks to mitigate potential outbreaks and protect the regional dairy industry. Full article
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13 pages, 255 KiB  
Communication
Aerobic Uterine Pathogens in Dairy Cattle: Surveillance and Antimicrobial Resistance Profiles in Postpartum Endometritis
by Ionica Iancu, Sebastian Alexandru Popa, Janos Degi, Alexandru Gligor, Ionela Popa, Vlad Iorgoni, Paula Nistor, Kálmán Imre, Ileana Nichita and Viorel Herman
Antibiotics 2025, 14(7), 650; https://doi.org/10.3390/antibiotics14070650 - 26 Jun 2025
Viewed by 577
Abstract
Bovine uterine infections remain a widespread challenge in dairy production systems, contributing to reduced fertility and overall herd performance. Background/Objectives: Postpartum uterine infections significantly affect dairy cattle fertility and productivity. This study aimed to identify aerobic bacterial pathogens associated with clinical endometritis [...] Read more.
Bovine uterine infections remain a widespread challenge in dairy production systems, contributing to reduced fertility and overall herd performance. Background/Objectives: Postpartum uterine infections significantly affect dairy cattle fertility and productivity. This study aimed to identify aerobic bacterial pathogens associated with clinical endometritis in Romanian dairy cows and evaluate their antimicrobial resistance profiles. Methods: Uterine swab samples (n = 348) were collected from clinically affected cows across multiple farms. Bacteria were isolated and identified using conventional culture methods and MALDI-TOF MS. Antimicrobial susceptibility testing was performed using the VITEK® 2 system with GN 96 and GP 79 cards. Statistical analysis was conducted using the chi-square (χ2) test. Results: A total of 387 bacterial isolates were recovered, with over half of the samples showing mixed bacterial contamination. Escherichia coli was the most frequently identified pathogen (44.9%), followed by Staphylococcus spp. (17.3%) and Klebsiella spp. (14.5%). Gram-negative isolates showed high resistance to tetracycline and ampicillin, while retaining susceptibility to imipenem and polymyxin B. Among Gram-positive isolates, Streptococcus spp. were highly susceptible to β-lactams, while Staphylococcus spp. showed moderate resistance to penicillin and macrolides. Conclusions: This study highlights the prevalence of key aerobic pathogens and their resistance profiles in Romanian dairy herds. These findings support the need for targeted diagnostics and rational antimicrobial use to improve uterine health and therapeutic outcomes in dairy cattle. Full article
(This article belongs to the Special Issue Detection of Bacteria and Antibiotics Surveillance in Livestock)
17 pages, 484 KiB  
Article
Annual and Seasonal Trends in Mastitis Pathogens Isolated from Milk Samples from Dairy Cows of California’s San Joaquin Valley Dairies Between January 2009 and December 2023
by Daniela R. Bruno, Karen H. Tonooka, Terry W. Lehenbauer, Sharif S. Aly and Wagdy R. ElAshmawy
Vet. Sci. 2025, 12(7), 609; https://doi.org/10.3390/vetsci12070609 - 21 Jun 2025
Viewed by 798
Abstract
Bovine mastitis is a significant disease affecting dairy cattle worldwide, impacting milk quality and farm profitability. Understanding pathogen distribution is crucial for effective disease management. This study analyzed 319,634 individual cow milk samples submitted to the UC Davis Milk Quality Laboratory between 2009 [...] Read more.
Bovine mastitis is a significant disease affecting dairy cattle worldwide, impacting milk quality and farm profitability. Understanding pathogen distribution is crucial for effective disease management. This study analyzed 319,634 individual cow milk samples submitted to the UC Davis Milk Quality Laboratory between 2009 and 2023 to assess pathogen prevalence, seasonal variations, and long-term trends. Routine microbiological cultures identified major and minor mastitis pathogens, with additional testing for Mycoplasma spp. Statistical analyses evaluated annual and seasonal trends in bacterial isolation rates. Results indicated that environmental pathogens, particularly non-aureus staphylococci and coliforms, were most frequently isolated, while contagious pathogens (Staphylococcus aureus, Streptococcus agalactiae, and Mycoplasma spp.) were less prevalent. Seasonal trends revealed higher contamination rates in Winter and increased no-growth samples in Summer. The study also observed a decline in sample submissions in recent years, possibly reflecting evolving dairy management practices. These findings provide a comprehensive perspective on mastitis pathogen dynamics in California’s Central Valley, supporting improved milk quality control measures and tailored mastitis prevention strategies. Full article
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48 pages, 9168 KiB  
Review
Socializing AI: Integrating Social Network Analysis and Deep Learning for Precision Dairy Cow Monitoring—A Critical Review
by Sibi Chakravathy Parivendan, Kashfia Sailunaz and Suresh Neethirajan
Animals 2025, 15(13), 1835; https://doi.org/10.3390/ani15131835 - 20 Jun 2025
Viewed by 1005
Abstract
This review critically analyzes recent advancements in dairy cow behavior recognition, highlighting novel methodological contributions through the integration of advanced artificial intelligence (AI) techniques such as transformer models and multi-view tracking with social network analysis (SNA). Such integration offers transformative opportunities for improving [...] Read more.
This review critically analyzes recent advancements in dairy cow behavior recognition, highlighting novel methodological contributions through the integration of advanced artificial intelligence (AI) techniques such as transformer models and multi-view tracking with social network analysis (SNA). Such integration offers transformative opportunities for improving dairy cattle welfare, but current applications remain limited. We describe the transition from manual, observer-based assessments to automated, scalable methods using convolutional neural networks (CNNs), spatio-temporal models, and attention mechanisms. Although object detection models, including You Only Look Once (YOLO), EfficientDet, and sequence models, such as Bidirectional Long Short-Term Memory (BiLSTM) and Convolutional Long Short-Term Memory (convLSTM), have improved detection and classification, significant challenges remain, including occlusions, annotation bottlenecks, dataset diversity, and limited generalizability. Existing interaction inference methods rely heavily on distance-based approximations (i.e., assuming that proximity implies social interaction), lacking the semantic depth essential for comprehensive SNA. To address this, we propose innovative methodological intersections such as pose-aware SNA frameworks and multi-camera fusion techniques. Moreover, we explicitly discuss ethical challenges and data governance issues, emphasizing data transparency and animal welfare concerns within precision livestock contexts. We clarify how these methodological innovations directly impact practical farming by enhancing monitoring precision, herd management, and welfare outcomes. Ultimately, this synthesis advocates for strategic, empathetic, and ethically responsible precision dairy farming practices, significantly advancing both dairy cow welfare and operational effectiveness. Full article
(This article belongs to the Section Animal Welfare)
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28 pages, 1191 KiB  
Review
piRNAs as Potential Regulators of Mammary Gland Development and Pathology in Livestock
by Wenjing Yu, Zixuan Zhang, Zhonghua Wang, Xusheng Dong and Qiuling Hou
Vet. Sci. 2025, 12(6), 594; https://doi.org/10.3390/vetsci12060594 - 17 Jun 2025
Viewed by 684
Abstract
PiRNAs are a subclass of non-coding RNAs, 26–31 nucleotides (nt) in length, that form regulatory complexes through their interaction with PIWI proteins. Studies in model organisms have demonstrated that piRNAs play crucial roles in tissue development and in predicting disease outcomes, positioning them [...] Read more.
PiRNAs are a subclass of non-coding RNAs, 26–31 nucleotides (nt) in length, that form regulatory complexes through their interaction with PIWI proteins. Studies in model organisms have demonstrated that piRNAs play crucial roles in tissue development and in predicting disease outcomes, positioning them as promising targets for developmental regulation and therapeutic intervention. In contrast, research on piRNAs in animal husbandry is still in its early stages and has not received sufficient attention. Despite this, the few studies available in livestock research have revealed that piRNAs serve as key regulators of reproductive development, underscoring their significant regulatory potential in farm animals and justifying further investigation. Accordingly, this review uses the bovine mammary gland as an exemplary case to summarize the progress in piRNA research related to mammary development and disease. The role of piRNAs in regulating breast cancer stem cell proliferation and modulating inflammatory progression is a highly active area of research. We hypothesize that piRNAs may play a potential role in regulating both mammary gland development and mastitis, making them promising targets for enhancing mammary development and overall health in dairy cattle and providing a theoretical foundation for further piRNA applications in animal husbandry. Full article
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16 pages, 2853 KiB  
Article
Detecting Lameness in Dairy Cows Based on Gait Feature Mapping and Attention Mechanisms
by Xi Kang, Junjie Liang, Qian Li and Gang Liu
Agriculture 2025, 15(12), 1276; https://doi.org/10.3390/agriculture15121276 - 13 Jun 2025
Viewed by 585
Abstract
Lameness significantly compromises dairy cattle welfare and productivity. Early detection enables prompt intervention, enhancing both animal health and farm efficiency. Current computer vision approaches often rely on isolated lameness feature quantification, disregarding critical interdependencies among gait parameters. This limitation is exacerbated by the [...] Read more.
Lameness significantly compromises dairy cattle welfare and productivity. Early detection enables prompt intervention, enhancing both animal health and farm efficiency. Current computer vision approaches often rely on isolated lameness feature quantification, disregarding critical interdependencies among gait parameters. This limitation is exacerbated by the distinct kinematic patterns exhibited across lameness severity grades, ultimately reducing detection accuracy. This study presents an integrated computer vision and deep-learning framework for dairy cattle lameness detection and severity classification. The proposed system comprises (1) a Cow Lameness Feature Map (CLFM) model extracting holistic gait kinematics (hoof trajectories and dorsal contour) from walking sequences, and (2) a DenseNet-Integrated Convolutional Attention Module (DCAM) that mitigates inter-individual variability through multi-feature fusion. Experimental validation utilized 3150 annotated lameness feature maps derived from 175 Holsteins under natural walking conditions, demonstrating robust classification performance. The classification accuracy of the method for varying degrees of lameness was 92.80%, the sensitivity was 89.21%, and the specificity was 94.60%. The detection of healthy and lameness dairy cows’ accuracy was 99.05%, the sensitivity was 100%, and the specificity was 98.57%. The experimental results demonstrate the advantage of implementing lameness severity-adaptive feature weighting through hierarchical network architecture. Full article
(This article belongs to the Special Issue Computer Vision Analysis Applied to Farm Animals)
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20 pages, 2672 KiB  
Article
Assessing the Impacts of Dairy Farm Antimicrobial Use on the Bovine Fecal Microbiome
by Andrew J. Steinberger, Juliana Leite de Campos, Ashley E. Kates, Tony L. Goldberg, Pamela L. Ruegg, Nasia Safdar, Ajay K. Sethi, John M. Shutske and Garret Suen
Animals 2025, 15(12), 1735; https://doi.org/10.3390/ani15121735 - 12 Jun 2025
Viewed by 1033
Abstract
Rising rates of antimicrobial-resistant infections have prompted increased scrutiny on antimicrobial use (AMU) in livestock agriculture. Dairy farms primarily use antimicrobials to maintain animal health and welfare by treating and preventing infectious diseases. However, the impact of dairy farm AMU practices on the [...] Read more.
Rising rates of antimicrobial-resistant infections have prompted increased scrutiny on antimicrobial use (AMU) in livestock agriculture. Dairy farms primarily use antimicrobials to maintain animal health and welfare by treating and preventing infectious diseases. However, the impact of dairy farm AMU practices on the cattle fecal microbiome remains largely unclear, partly due to difficulties in quantifying AMU. This study leveraged quantitative AMU data from 40 large commercial dairy farms to identify farms with low (n = 4) and high (n = 4) AMU. Using 16S rRNA gene amplicon sequencing, we compared the fecal bacterial communities of dairy calves and cows (healthy, cull, sick) by both AMU designation (high/low) and by individual farm AMU, summarized by animal defined daily dose (DDD) and mg/kg. We found significant differences in beta-diversity between cattle from high- and low-AMU groups using either method and found that Corynebacterium and Clostridium abundances increased with farm AMU. Additionally, we found fecal bacterial communities differed across farms within high- and low-AMU groupings, highlighting the need to account for farm-to-farm variation when assessing AMU impacts. These findings suggest that dairy farm AMU influences the fecal microbiome and identifies specific taxa that warrant further investigation as potential reservoirs for antimicrobial resistance genes. Full article
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5 pages, 176 KiB  
Commentary
Highly Pathogenic Avian Influenza A(H5N1) Virus: How Far Are We from a New Pandemic?
by Giovanni Di Guardo
Vet. Sci. 2025, 12(6), 566; https://doi.org/10.3390/vetsci12060566 - 9 Jun 2025
Cited by 2 | Viewed by 1112
Abstract
The focus of this commentary is represented by the pandemic risk associated with the highly pathogenic avian influenza (HPAI) A(H5N1) virus, clade 2.3.4.4b. More in detail, the herein dealt pandemic alarm appears to be primarily justified by the huge and progressively growing number [...] Read more.
The focus of this commentary is represented by the pandemic risk associated with the highly pathogenic avian influenza (HPAI) A(H5N1) virus, clade 2.3.4.4b. More in detail, the herein dealt pandemic alarm appears to be primarily justified by the huge and progressively growing number of virus-susceptible domestic and wild birds and mammals, including threatened marine mammal species like South American sea lions and elephant seals as well as harbour porpoises, bottlenose dolphins and polar bears. Of major concern is the susceptibility of dairy cattle to HPAI A(H5N1) virus, particularly the documented and unprecedented colonization of host’s mammary gland tissue, resulting in viral shedding through the milk alongside a large series of cases of infection in dairy farm workers in several USA locations. Despite well-documented zoonotic capability, no evidences of a sustained and efficient HPAI A(H5N1) viral transmission between people have been hitherto reported. If this were to happen sooner or later, a new pandemic might consequently arise. Therefore, keeping all this in mind and based upon the lessons taught by the COVID-19 pandemic, a “One Health, One Earth, One Ocean”-centered approach would be absolutely needed in order to deal in the most appropriate way with the HPAI A(H5N1) virus-associated zoonotic and pandemic risk. Full article
(This article belongs to the Section Veterinary Microbiology, Parasitology and Immunology)
13 pages, 495 KiB  
Article
Distribution of Treponema Species in Active Digital Dermatitis Lesions and Non-Lesional Skin of Dairy Cattle
by Simona Mekková, Miriam Sondorová, Natália Šurín Hudáková, Viera Karaffová, Marián Maďar, Pavel Gomulec and Pavol Mudroň
Microbiol. Res. 2025, 16(6), 119; https://doi.org/10.3390/microbiolres16060119 - 5 Jun 2025
Viewed by 779
Abstract
This study examined the prevalence, distribution, and detection methods linked to Treponema species associated with active bovine digital dermatitis (BDD) in dairy cattle. Tissue, surface swabs, interdigital space swabs, and faecal samples were collected from 20 Holstein-Friesian cows from a farm in Eastern [...] Read more.
This study examined the prevalence, distribution, and detection methods linked to Treponema species associated with active bovine digital dermatitis (BDD) in dairy cattle. Tissue, surface swabs, interdigital space swabs, and faecal samples were collected from 20 Holstein-Friesian cows from a farm in Eastern Slovakia. Molecular analysis revealed that all cows tested positive for at least one Treponema species. The most prevalent species were Treponema medium (100%), Treponema pedis (95%), and Treponema brennaborense (75%). Distribution analysis demonstrated significant differences in the occurrence of these species across sampling methods, with T. pedis being more prevalent in tissue biopsies and surface swabs (p < 0.001), and T. brennaborense in surface swabs (p < 0.001). A comparison of qualitative real-time PCR and standard PCR revealed that real-time PCR detected T. pedis and T. brennaborense in 70% and 75% of tissue samples, respectively, while standard PCR failed to detect T. brennaborense. Furthermore, real-time PCR showed a significantly higher prevalence of T. brennaborense (p < 0.001). These findings underscore the enhanced sensitivity of real-time PCR in detecting T. brennaborense and highlight the complex distribution of Treponema species in BDD lesions, which may inform the development of more effective control strategies for BDD. Full article
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10 pages, 2975 KiB  
Article
Differential Distribution of Trypanosoma vivax and Trypanosoma theileri in Cattle from Distinct Agroecological Regions of Central Argentina
by Maria Celeste Facelli Fernández, Johann Barolin, Martin Allassia, Javier Hernan Gonzalez, Pablo Martin Beldomenico and Lucas Daniel Monje
Parasitologia 2025, 5(2), 27; https://doi.org/10.3390/parasitologia5020027 - 5 Jun 2025
Viewed by 538
Abstract
Bovine trypanosomiasis, caused by Trypanosoma vivax, affects livestock productivity and is increasingly being reported in South America. This study aimed to detect and characterize Trypanosoma spp. infections, with a focus on T. vivax, in cattle from two distinct agroecological regions of [...] Read more.
Bovine trypanosomiasis, caused by Trypanosoma vivax, affects livestock productivity and is increasingly being reported in South America. This study aimed to detect and characterize Trypanosoma spp. infections, with a focus on T. vivax, in cattle from two distinct agroecological regions of central Argentina: a dairy-producing plain, located in the Espinal ecoregion, and a riparian zone, dedicated to beef production, located in the Delta and Islands of Paraná ecoregion. A total of 220 blood samples were collected from nine cattle farms and analyzed using real-time PCR, melting curve analysis, and the sequencing of 18S rRNA gene fragments. Trypanosoma vivax was detected at low prevalence (2.73%), exclusively in dairy cattle. In contrast, the prevalence of Trypanosoma theileri was much higher (10.91%), and it was found mainly in beef cattle from the riparian region. Phylogenetic analyses confirmed the species identity in all sequenced samples. No trypanosomes were observed by microscopy, and none of the animals showed clinical signs. The results indicate a differential distribution of T. vivax and T. theileri between regions and production systems. Although the study initially focused on T. vivax, the detection of T. theileri highlights the need to consider multiple Trypanosoma species in epidemiological surveys. This study contributes new data on the occurrence of bovine trypanosomes in central Argentina under extensive and semi-intensive management systems. Full article
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20 pages, 520 KiB  
Review
Towards an Application of the Life Cycle Assessment Framework for GHG Emissions of the Dairy System: A Literature Review
by Fern T. Baker and Stephen Axon
Land 2025, 14(6), 1207; https://doi.org/10.3390/land14061207 - 4 Jun 2025
Cited by 1 | Viewed by 730
Abstract
Farm simulation models are a popular form of measuring greenhouse gas emissions (GHGe) from the agricultural industry as they are holistic and cost effective. The simulation models often follow the well-accepted life cycle assessment (LCA) framework to estimate the GHGe from the complete [...] Read more.
Farm simulation models are a popular form of measuring greenhouse gas emissions (GHGe) from the agricultural industry as they are holistic and cost effective. The simulation models often follow the well-accepted life cycle assessment (LCA) framework to estimate the GHGe from the complete system from cradle to farm-gate. However, several studies have highlighted flaws in the methodology and accuracy of the application of the LCA tool, underestimating emissions based on the scope of the study. GHGe vary considerably across livestock species, with cattle contributing to the highest proportion, from dairy and beef production. An extensive literature review evaluating the application of the LCA tool for measuring and comparing dairy farm GHGe has not been conducted. The current review evaluates the literature on LCAs of the dairy system across the globe, to highlight the flaws in poor scope design, the potential to underestimate emissions, and significant trade-offs disregarding vital variables. Full article
(This article belongs to the Section Land Use, Impact Assessment and Sustainability)
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