Search Results (233)

Tick infestation represents a significant constraint on livestock productivity in Saudi Arabia; however, there remains a substantial gap in research addressing tick species diversity, distribution, and their direct effects on milk production. This study aimed to morphologically and molecularly identify tick species infesting dairy cattle, quantify the impact of tick infestation on milk yield and composition, and contribute to the limited understanding of tick ecology and its economic implications in the region. Ticks were collected from infested cows and identified morphologically using taxonomic keys. Molecular identification was performed via PCR amplification of the mitochondrial cytochrome c oxidase subunit I (COI) gene. Milk production and quality parameters were assessed in tick-infested and healthy cows in Hafar Al-Batin, Eastern Saudi Arabia. Morphological and genetic analyses confirmed Hyalomma anatolicum as the predominant tick species in the study area, with COI sequences showing high similarity to regional isolates. Tick-infested cows exhibited substantial reductions in milk yield, fat, calcium, and potassium levels, indicating significant metabolic disruptions. Blood biochemical analysis revealed elevated levels of liver enzymes [aspartate aminotransferase (AST) increased by 238.6%, gamma-glutamyl transferase (GGT) by 155.7%], renal markers [creatinine increased by 788.9%, urea by 130.0%], and electrolyte imbalances [serum calcium decreased by 39.5%, potassium by 45.2%]. Hematological findings included increased white blood cell (WBC) and red blood cell (RBC) counts by 44.9% and 124.7%, respectively, along with a 53.1% decrease in hemoglobin (HGB), suggesting a systemic inflammatory response and possible anemia. This study is among the first to genetically confirm the presence of H. anatolicum in Hafar Al-Batin using molecular tools, thereby enhancing the accuracy of species-level identification and highlighting the physiological impact of tick burden on dairy productivity. Full article

The European Circular Economy Action Plan outlines a forward-looking strategy that emphasizes waste reduction and the acquisition of high-quality secondary resources. Previous research has shown that cocoa processing by-products contain compounds of interest for various industrial areas, making them an attractive matrix for reuse. However, a gap remains in our understanding of the safety of these by-products intended for feed. In this study, theobromine and caffeine were quantified by High-Performance Liquid Chromatography (HPLC-UV) in cocoa hulls for safety considerations, evaluating theobromine compliance with toxicological and safety levels, and considering their potential application as an ingredient in animal feed. In addition, the identification of phenolic components and associated antioxidant activity was conducted through High-Performance Thin-Layer Chromatography (HPTLC). This preliminary study indicates that theobromine content is a limiting factor for the inclusion of cocoa hulls in animal diets, as it restricts inclusion levels to remain within current regulatory limits. Examples of general estimates of dietary theobromine exposure at inclusion levels based on regulatory limits for dairy cows and veal calves confirmed a low risk for animal health. Furthermore, the detection of antioxidant activity linked to the presence of polyphenols highlights the potential of cocoa hulls as a sustainable food by-product for feed formulation. Full article

Individual cow identification is a prerequisite for automatically monitoring behavior patterns, health status, and growth data of each cow, and can provide the assistance in selecting excellent cow individuals for breeding. Despite high recognition accuracy, traditional implantable electronic devices such as RFID (i.e., Radio Frequency Identification) can cause some degree of harm or stress reactions to cows. Image-based methods are widely used due to their non-invasive advantages, but these methods have poor adaptability to different environments and target size, and low detection accuracy in complex scenes. To solve these issues, this study designs a Dy_Conv (i.e., dynamic convolution) module and innovatively constructs a Dynamic_Bottleneck module based on the Dy_Conv and S2Attention (Sparse-shift Attention) mechanism. On this basis, we replaces the first and fourth bottleneck layers of Resnet50 with the Dynamic_Bottleneck to achieve accurate extraction of local features and global information of cows. Furthermore, the QAConv (i.e., query adaptive convolution) module is introduced into the front end of the backbone network, and can adjust the parameters and sizes of convolution kernels to adapt to the scale changes in cow targets and input images. At the same time, NAM (i.e., normalization-based attention module) attention is embedded into the backend of the network to achieve the feature fusion in the channels and spatial dimensions, which contributes to better distinguish visually similar individual cows. The experiments are conducted on the public datasets collected from different cowsheds. The experimental results showed that the Rank-1, Rank-5, and mAP metrics reached 96.8%, 98.9%, and 95.3%, respectively. Therefore, the proposed model can effectively capture and integrate multi-scale features of cow body appearance, enhancing the accuracy of individual cow identification in complex scenes. Full article

Colostrum is the milk harvested during the first few hours after calving, which contains high levels of immunoglobulins, antimicrobial peptides, and growth factors essential for the health of neonates. The primary objective of this study was to investigate the genetic background of colostrum quality traits (based on Brix percentage) in Holstein cows. Using phenotypic records of 58,338 Holstein cows from 37 dairy farms, we identified significant systematic effects influencing colostrum quality measured by digital Brix refractometer, estimated genetic parameters, and performed weighted single-step genome-wide association studies (WssGWAS) to identify genomic regions and candidate genes associated with these traits. The average (±SD) Brix percentage was 23.76 ± 3.25%. With heritability values ranging from 0.21 ± 0.03 (Brix in third parity) to 0.30 ± 0.02 (Brix in second parity), colostrum quality was determined to be moderately heritable. Genetic correlations between colostrum quality across parities ranged from 0.37 ± 0.14 to 0.81 ± 0.13. For colostrum quality from cows in the first, second, and third parities, WssGWAS enabled the identification of 30, 32, and 38 genomic regions explaining 4.18%, 4.42%, and 5.58% of the total additive genetic variance, respectively. Two immune-related genes (CNR1 and ZXDC) were identified as promising candidate genes for colostrum quality traits. In summary, colostrum quality measured in first parity cows should be evaluated as a different trait from measurements in later parities in breeding programs. These findings provide useful information for dairy breeders to genetically improve colostrum quality in dairy cattle populations. Full article

Subclinical mastitis in dairy cows poses a significant challenge to the dairy industry, leading to reduced milk yield, altered milk composition, compromised animal health, and substantial economic losses for dairy farmers. A model based on the XGBoost algorithm, optimized with an Improved GOOSE Optimization Algorithm (IGOOSE), is presented in this work as an innovative approach for predicting subclinical mastitis in order to overcome these problems. The Dairy Herd Improvement (DHI) records of 4154 cows served as the model’s original foundation. A total of 3232 samples with 21 characteristics made up the final dataset, following extensive data cleaning and preprocessing. To overcome the shortcomings of the original GOOSE algorithm in intricate, high-dimensional problem spaces, three significant enhancements were made. First, an elite inverse strategy was implemented to improve population initialization, enhancing the algorithm’s balance between global exploration and local exploitation. Second, an adaptive nonlinear control factor was added to increase the algorithm’s stability and convergence speed. Lastly, a golden sine strategy was adopted to reduce the risk of premature convergence to suboptimal solutions. According to experimental results, the IGOOSE-XGBoost model works better than other models in predicting subclinical mastitis, especially when it comes to recognizing somatic cell scores, which are important markers of the illness. This study provides a strong predictive framework for managing the health of dairy cows, allowing for the prompt identification and treatment of subclinical mastitis, which enhances the efficiency and quality of milk supply. Full article

Modern beef cattle reproductive management faces increasing challenges due to the growing global demand for beef. Reproductive efficiency is a critical factor determining the productivity and profitability of beef cattle operations. Optimal reproductive performance in a beef cattle herd is achieved when each cow produces one calf per year, maintaining a calving interval of 365 days. However, this goal is difficult to achieve, as the gestation period in beef cows lasts approximately 280 days, leaving only 80–85 days for successful conception. Traditional methods, such as visual estrus detection, are becoming increasingly unreliable due to expanding herd sizes and the subjectivity of visual observation. Additionally, silent estrus—where ovulation occurs without noticeable behavioral changes—further complicates the accurate estrous-based identification of the optimal insemination period. To enhance reproductive efficiency, advanced technologies are increasingly being integrated into cattle management. Sensor-based monitoring systems, including accelerometers, pedometers, and ruminoreticular boluses, enable the precise tracking of activity changes associated with the estrous cycle. Furthermore, infrared thermography offers a non-invasive method for detecting body temperature fluctuations, allowing for more accurate estrus identification and optimized timing of insemination. The use of these innovative technologies has the potential to significantly improve reproductive efficiency in beef cattle herds and contribute to overall farm productivity and sustainability. The objective of this review is to examine advancements in smart technologies applied to beef cattle reproductive management, presenting commercially available technologies and recent scientific studies on innovative systems. The focus is on sensor-based monitoring systems and infrared thermography for optimizing reproduction. Additionally, the challenges associated with these technologies and their potential to enhance reproductive efficiency and sustainability in the beef cattle industry are discussed. Despite the benefits of advanced technologies, their implementation in cattle farms is hindered by financial and technical challenges. High initial investment costs and the complexity of data analysis may limit their adoption, particularly in small and medium-sized farms. However, the continuous development of these technologies and their adaptation to farmers’ needs may significantly contribute to more efficient and sustainable reproductive management in beef cattle production. Full article

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

Sheep and goat milk authenticity is of great importance, especially for countries like Greece, where these products are connected to the country’s rural economy and cultural heritage. The aim of the study is to evaluate the effectiveness of Fourier Transform Infrared Attenuated Total Reflectance (ATR-FTIR) spectroscopy in combination with chemometric techniques for the classification of cow, sheep, and goat milk and consequently support fraud identification. A total of 178 cow, sheep and goat milk samples were collected from livestock farms in Thessaly, Greece. Sheep and goat milk samples were confirmed as authentic by applying a validated Enzyme Linked Immunosorbent Assay (ELISA), while all samples were analyzed using ATR-FTIR spectroscopy in both raw and freeze-dried form. Freeze-dried samples exhibited clearer spectral characteristics, particularly enhancing the signals from triglycerides, proteins, and carbohydrates. Partial Least Squares Discriminant Analysis (PLS-DA) delivered robust discrimination. By using the spectral range between 600 and 1800 cm⁻¹, 100% correct classification of all milk types was achieved. These findings highlight the potential of FTIR spectroscopy as a fast, non-destructive, and cost-effective tool for milk identification and species differentiation. This method is particularly suitable for industrial and regulatory applications, offering high efficiency. Full article

The objective of this work was to explore the effect of taxonomically and phytochemically rich swards, as opposed to ‘status quo’ monoculture of ryegrass and white clover swards, on animals and milk, by assessing the metabolomic profile of plant and milk samples. The results of this study suggest that metabolomic profiles and metabolism are altered by dietary diversity and grazing management. Several metabolites associated with enhanced consumer health were elevated in milk from cows that were grazed in functionally diverse swards as opposed to monoculture of ryegrass and white clover swards. The results show the value of metabolomics for the identification of metabolites associated with a ‘pasture-fed diet’, which is considered beneficial to human health compared with grain-fed milk products. Metabolomics also provide evidence that grazing management and the presence of key forages may be more beneficial for market differentiation of milk products that can enhance consumer health than maximisation of sward species diversification. Full article

Beef meat quality and value are influenced by the breed, sex, and age of slaughtered animals. This study aimed to evaluate lipofuscin pigment autofluorescence as a method for age classification in beef meat samples and to determine the sex of market-obtained meat using PCR-based amelogenin gene amplification. Deboned beef meat samples from M. longissimus dorsi and M. biceps femoris were collected from 67 slaughtered cows with known age and sex. Additionally, 48 market samples were tested for sex identification and age classification using the same methods. Lipofuscin deposition was first observed at 1.5 years, and autofluorescence analysis effectively distinguished between meat from younger animals (1.5–2.2 years) and older ones (3–13 years), with a statistically significant difference (p < 0.001). Lipofuscin levels and excitation intensity increased with age, and no differences were found between the two muscles analyzed. The sex determination results were fully consistent with the records, and 55.2% of animals aged 3 years and older were identified as female. These findings demonstrate the reliability of lipofuscin autofluorescence for binary age determination in beef and support the potential of combining age and sex classification to identify meat derived from older dairy cows in the marketplace. Full article

Metabolic and inflammatory stress during early lactation poses significant risks to dairy cow health and productivity. This study aimed to assess the physiological, metabolic, and inflammatory differences between dairy cows producing low (LL; <4.5%) and high (HL; ≥4.5%) milk lactose, focusing on C-reactive protein (CRP), liver function markers, iron metabolism, and reticulorumen health. A total of 71 clinically healthy lactating multiparous cows (20–30 days postpartum) were monitored using real-time physiological sensors, milk composition analysis, blood biomarkers and continuous reticulorumen pH measurement (every 10 min). Cows in the LL group showed significantly higher aspartate transaminase (AST) activity (p = 0.042), lower serum iron (Fe) concentration (p = 0.013), and reduced reticulorumen pH (p = 0.03). Although CRP concentrations did not differ significantly between groups, correlation analysis revealed positive associations with non-esterified fatty acids (NEFA) (r = 0.335, p = 0.043), reticulorumen pH (r = 0.498, p = 0.002), and body temperature (r = 0.372, p = 0.023). Receiver operating characteristic (ROC) analysis identified gamma-glutamyl transferase (GGT) (AUC = 0.66), AST (AUC = 0.63), and NEFA (AUC = 0.58) as moderate predictors of low milk lactose levels. Conversely, Fe (AUC = 0.66) and reticulorumen pH (AUC = 0.64) showed moderate ability to predict higher lactose content. These results support the integration of milk lactose, liver enzymes, and inflammatory biomarkers into precision health monitoring protocols. The combined use of CRP and milk lactose as complementary biomarkers may enhance the early identification of metabolic stress and support more targeted dairy herd health management. Full article

Background: Accurate estrus identification in dairy cows is essential for enhancing reproductive efficiency and economic performance. The dispersed nature of estrus data and individual cow differences pose significant challenges for accurate identification. Methods: This study gathered cow estrus data from 812 literature sources using Python 3.9 crawler technology. The data were then preprocessed using CiteSpace 6.4. We constructed a knowledge graph depicting physiological, behavioral, and appearance changes during estrus through entity and relationship extraction. To uncover potential relationships within the graph, we applied and compared two association rule algorithms: FP-Growth and Apriori. We utilized Boolean functions derived from association rule learning to validate the ability of the rules to identify normal estrus. Additionally, we employed an enhanced Iforest-OCSVM anomaly detection model to assess the performance of the association rules in detecting abnormal estrus. Furthermore, we optimized the Incremental FP-Growth Algorithm for Dynamic Knowledge Expansion. Results: Based on the initial knowledge graph with 86 entities and 9 relationships, mining added 17 new strong association relationships marked by ‘with’, enhancing its completeness and providing deeper insights into estrus behaviors and physiological changes. Furthermore, these strong association rules exhibited notable effectiveness in both normal and abnormal estrus detection, validating their robustness in practical applications. The algorithm’s optimization bolstered its scalability, making it more adaptable to future data expansions and complex knowledge integrations. Conclusions: By constructing a knowledge graph that integrates physiological, behavioral, and appearance changes during estrus, we established a comprehensive framework for understanding cow estrus. Association rule mining, particularly with the FP-Growth algorithm, added 17 new strong association relationships to the graph, enriching its content and offering deeper insights into estrus behaviors and physiological changes. The strong association rules derived from FP-Growth demonstrated notable effectiveness in both normal and abnormal estrus detection, validating their robustness and practical utility in enhancing estrus identification accuracy, and providing a robust foundation for future multi-dimensional estrus research. Full article

The vaginal microbiota plays a crucial role in bovine reproductive health in the periparturient period. This study aimed to characterize the naturally occurring bacterial species in the vaginal microbiome of healthy Holstein Frisian cows and evaluate their antibiotic sensitivity. Vaginal samples were collected from 44 healthy cows on three dairy farms. A total of 54 bacterial species were detected, with Gram-positive bacteria comprising 87% of the isolates. The most prevalent genera were Bacillus, Streptococcus, and Staphylococcus. Antibiotic susceptibility tests indicated that some isolates carried resistance genes, but most remained sensitive to commonly used antibiotics. The average vaginal mucosa pH was 7.2. These findings provide valuable insights into the diversity of vaginal microbiota of healthy dairy cows. Understanding the bacterial composition and antibiotic susceptibility can support reproductive health management and prudent use of antibiotics in dairy herds. Full article

Background/Objectives: The resistance to occult mastitis in dairy cows is a multifaceted trait influenced by a variety of genetic and environmental factors, posing significant challenges to its prevention and treatment. Methods: In this study, a cohort of 389 Holstein dairy cows was selected for investigation. The genes NOD2, CXCR1, SPP1 and LF, which are implicated in resistance to occult mastitis, were genotyped utilizing the efficient and cost-effective Kompetitive Allele-Specific PCR (KASP) technology. Additionally, the study analyzed the association between various single nucleotide polymorphisms (SNPs) and the somatic cell score in Holstein dairy cows. Multi-locus penetrance variance analysis (MPVA) analysis was also conducted to assess the resistance of different genotypic combinations to recessive mastitis in dairy cows. A genotyping kit for occult mastitis resistance was developed. Subsequently, 300 Holstein cows were randomly selected to evaluate the accuracy of the kit’s classification and resistance detection. Results: The findings revealed that the most effective genotype combination was SPP1(AA)-CXCR1(CC)-NOD2(CA)-LF(GA). Upon verification, the genotyping kit for recessive mastitis resistance in dairy cows exhibited an accuracy rate of 100% for individual genotyping and 95.90% for resistance detection. Conclusions: From the perspective of disease resistance genetics, this study lays a foundation for the precise management of dairy cow herds. It enables the early identification and removal of individuals susceptible to subclinical mastitis, thereby improving the overall quality of the cattle population. Full article

Background: The presence and concentration of lipids in serum of dairy cows have significant implications for both animal health and productivity and are potential biomarkers for several common diseases. However, information on serum lipid composition is rather fragmented, and lipid remodelling during the transition period is only partially understood. Methods: Using a combination of reversed-phase liquid chromatography-mass spectrometry (RP-LC-MS), hydrophilic interaction-mass spectrometry (HILIC-MS), and lipid annotation software, we performed a comprehensive identification and quantification of serum of dairy cows in pasture-based Holstein-Friesian cows. The lipid remodelling induced by negative energy balance was investigated by comparing the levels of all identified lipids between the fresh lactation (5–14 days in milk, DIM) and full lactation (65–80 DIM) stages. Results: We identified 535 lipid molecular species belonging to 19 classes. The most abundant lipid class was cholesteryl ester (CE), followed by phosphatidylcholine (PC), sphingomyelin (SM), and free fatty acid (FFA), whereas the least abundant lipids included phosphatidylserine (PS), phosphatidic acid (PA), phosphatidylglycerol (PG), acylcarnitine (AcylCar), ceramide (Cer), glucosylceramide (GluCer), and lactosylceramide (LacCer). Conclusions: A remarkable increase in most lipids and a dramatic decrease in FFAs, AcylCar, and DHA-containing species were observed at the full lactation compared to fresh lactation stage. Several serum lipid biomarkers for detecting negative energy balance in cows were also identified. Full article