Udder Health Monitoring for Prevention of Bovine Mastitis and Improvement of Milk Quality

A Comprehensive Review on Genomic Insights and Advanced Technologies for Mastitis Prevention in Dairy Animals

Mastitis, is a multi-etiological disease that significantly impacts milk production and reproductive efficiency. This is highly prevalent in dairy populations subjected to intensive selection for higher milk yield and where inbreeding is common. The issue is amplified by climate change and poor hygiene management, making disease control challenging. Key obstacles include antibiotic resistance, maximum residue levels, horizontal gene transfer, and limited success in breeding for resistance. Predictive genomics offers a promising solution for mastitis prevention by identifying genetic traits linked with susceptibility to mastitis. This review compiles the research and findings on genomics and its allied approaches such as pan-genomics, epigenetics, proteomics, and transcriptomics for diagnosing, understanding, and treating mastitis. In dairy production, artificial intelligence (AI), particularly deep learning (DL) techniques like convolutional neural networks (CNNs), has demonstrated significant potential to enhance milk production and improve farm profitability. It highlights the integration of advanced technologies like machine learning (ML), CRISPR, and pan-genomics to enhance our knowledge of mastitis epidemiology, pathogen evolution, and the development of more effective diagnostic, preventive, and therapeutic strategies for dairy herds. Genomic advancements provide critical insights into the complexities of mastitis, offering new avenues for understanding its dynamics. Integrating these findings with key predisposing factors can drive targeted prevention and more effective disease management.

Beyond conventional antibiotics approaches: Global perspectives on alternative therapeutics including herbal prevention, and proactive management strategies in bovine mastitis

Mastitis, an intramammary inflammation resulting from microbial infectious agents, continues to pose a significant challenge within the dairy sector, adversely affecting animal well-being and leading to substantial economic losses. These losses are attributed to decreased milk production, heightened culling rates, and the expenses related to diagnostics, veterinary care, medication, and labor. Moreover, additional costs emerge due to reduced forthcoming milk yields, compromised reproductive health, and increased susceptibility to various illnesses. Identifying the responsible agents is crucial for disease management and the implementation of antimicrobial treatments. Despite the prevalent use of antibiotic treatment, the pressing need for new therapeutic alternatives to combat bovine mastitis arises from limitations, including low cure rates, rising resistance, and the presence of antibiotic residues in milk. This review explores the potential application of herbal extracts and essential oils known for their antimicrobial properties as alternative options for managing pathogens in mastitis treatment. It examines various treatment methods and management strategies, particularly emphasizing the progress of herbal remedies and natural therapeutics in addressing mastitis, a significant concern in bovine populations and dairy herds.

The Influence of Crossbreeding on the Composition of Protein and Fat Fractions in Milk: A Comparison Between Purebred Polish Holstein Friesian and Polish Holstein Friesian × Swedish Red Cows

BACKGROUND/OBJECTIVES

In this study, the differences in protein and fat bioactive components between the milk from purebred Polish Holstein Friesian (PHF) cows and PHF cows crossbred with Swedish Red (SRB) were investigated. The objective was to assess the impact of genetic variation on the nutritional quality of their milk.

METHODS

This study was conducted at the Warsaw University of Life Sciences' (WULS) experimental dairy farm in Warsaw, Poland, and involved 60 primiparous cows divided into two groups: 30 PHF×SRB crossbred cows and 30 purebred PHF cows. All cows were housed in a free-stall system with an average lactation yield exceeding 10,000 kg/lactation. The milk composition analyses included total protein, casein, whey protein, fatty acid profiles, and vitamin content.

RESULTS

Milk from the PHF×SRB hybrids showed a significantly greater total protein content (3.53%) compared to that from the purebred PHF cows (3.28%). The casein content was higher in the hybrids' milk (2.90%) than the purebreds' milk (2.78%), while the whey protein levels were lower in the purebred milk (0.50%) than in the hybrid milk (0.63%). The hybrids exhibited higher concentrations of certain saturated fatty acids in their milk, while the purebreds' milk contained greater amounts of beneficial unsaturated fatty acids and fat-soluble vitamins-E, D, and K.

CONCLUSIONS

These results indicate that genetic selection through crossbreeding can enhance the nutritional quality of milk. The differences observed in protein, fatty-acid, and vitamin content underscore the role of the genotype in milk composition, suggesting that breeding strategies can optimize dairy products' health benefits.

The prevalence and factors associated with mastitis in dairy cows kept by small-scale farmers in Dodoma, Tanzania

Bovine mastitis is among the major diseases of economic importance in the dairy industry worldwide. Hygienic conditions during milking and housing are important determinants of mastitis infections. However, arid and semi-arid areas have a scarcity of water, which is an essential component of cleanliness. Thus, the study determined the prevalence of mastitis and associated factors in Dodoma, a semi-arid region in Tanzania. Three hundred and sixty-eight lactating cows were selected randomly from 114 farms in the Dodoma urban district for mastitis determination based on clinical signs and the California Mastitis Test. About 59.8 % of the cows had mastitis, and the subclinical type dominated. Factors associated with mastitis prevalence were cleanliness (hand and udder washing before milking), farmers' awareness of mastitis, the cow's parity and lactation stage, and herd size. Milk yield was not associated with the mastitis status of the cow but was related to the breed of the cow, whether warm or cold water was used to wash the cow's udder, parity, and lactation stage. Improvement in education on the importance of hygiene is necessary, as 38.6 % of the farmers were unaware of mastitis. Further detailed studies on microbiology, such as bacteriologic culture and polymerase chain reaction, are recommended to formulate interventions.

From Herd Health to Public Health: Digital Tools for Combating Antibiotic Resistance in Dairy Farms

The emergence of antimicrobial resistance (AMR) is a significant threat to global food security, human health, and the future of livestock production. Higher rates of antimicrobial use in dairy farming and the sheer lack of new antimicrobials available for use focused attention on the question of how the dairy production sector contributed to the development of AMR and paved the path toward taking action to curtail it on the targeted type of farms. This paper aims to provide an introduction to a phenomenon that has gained considerable attention in the recent past due to its ever-increasing impact, the use of antimicrobial drugs, the emergence of antimicrobial resistance (AMR) on dairy farms, and seeks to discuss the possibilities of approaches such as digital health monitoring and precision livestock farming. Using sensors, data, knowledge, automation, etc., digital health monitoring, as well as Precision Livestock Farming (PLF), is expected to enhance health control and minimize disease and antimicrobial usage. The work presents a literature review on the current status and trends of AMR in dairy farms, an understanding of the concept of digital health monitoring and PLF, and the presentation and usefulness of digital health monitoring and PLF in preventing AMR. The study also analyses the strengths and weaknesses of adopting and incorporating digital technologies and artificial intelligence for dairy farming and presents areas for further study and level of use.

Application of MALDI-TOF MS to Identify and Detect Antimicrobial-Resistant Streptococcus uberis Associated with Bovine Mastitis

Streptococcus uberis is a common bovine mastitis pathogen in dairy cattle. The rapid identification and characterization of antimicrobial resistance (AMR) in S. uberis plays an important role in its diagnosis, treatment, and prevention. In this study, matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) was used to identify S. uberis and screen for potential AMR biomarkers. Streptococcus uberis strains (n = 220) associated with bovine mastitis in northern Thailand were identified using the conventional microbiological methods and compared with the results obtained from MALDI-TOF MS. Streptococcus uberis isolates were also examined for antimicrobial susceptibility using a microdilution method. Principal component analysis (PCA) and the Mann-Whitney U test were used to analyze the MALDI-TOF mass spectrum of S. uberis and determine the difference between antimicrobial-resistant and -susceptible strains. Using MALDI-TOF MS, 73.18% (161/220) of the sampled isolates were identified as S. uberis, which conformed to the identifications obtained using conventional microbiological methods and PCR. Using PCR, antimicrobial-resistant strains could not be distinguished from antimicrobial-susceptible strains for all three antimicrobial agents, i.e., tetracycline, ceftiofur, and erythromycin. The detection of spectral peaks at 7531.20 m/z and 6804.74 m/z was statistically different between tetracycline- and erythromycin-resistant and susceptible strains, respectively. This study demonstrates a proteomic approach for the diagnosis of bovine mastitis and potentially for the surveillance of AMR among bovine mastitis pathogens.

An Explainable Deep Learning Classifier of Bovine Mastitis Based on Whole-Genome Sequence Data-Circumventing the p >> n Problem

The serious drawback underlying the biological annotation of whole-genome sequence data is the p >> n problem, which means that the number of polymorphic variants (p) is much larger than the number of available phenotypic records (n). We propose a way to circumvent the problem by combining a LASSO logistic regression with deep learning to classify cows as susceptible or resistant to mastitis, based on single nucleotide polymorphism (SNP) genotypes. Among several architectures, the one with 204,642 SNPs was selected as the best. This architecture was composed of two layers with, respectively, 7 and 46 units per layer implementing respective drop-out rates of 0.210 and 0.358. The classification of the test data resulted in AUC = 0.750, accuracy = 0.650, sensitivity = 0.600, and specificity = 0.700. Significant SNPs were selected based on the SHapley Additive exPlanation (SHAP). As a final result, one GO term related to the biological process and thirteen GO terms related to molecular function were significantly enriched in the gene set that corresponded to the significant SNPs. Our findings revealed that the optimal approach can correctly predict susceptibility or resistance status for approximately 65% of cows. Genes marked by the most significant SNPs are related to the immune response and protein synthesis.

A New Method to Detect Buffalo Mastitis Using Udder Ultrasonography Based on Deep Learning Network

Mastitis is one of the most predominant diseases with a negative impact on ranch products worldwide. It reduces milk production, damages milk quality, increases treatment costs, and even leads to the premature elimination of animals. In addition, failure to take effective measures in time will lead to widespread disease. The key to reducing the losses caused by mastitis lies in the early detection of the disease. The application of deep learning with powerful feature extraction capability in the medical field is receiving increasing attention. The main purpose of this study was to establish a deep learning network for buffalo quarter-level mastitis detection based on 3054 ultrasound images of udders from 271 buffaloes. Two data sets were generated with thresholds of somatic cell count (SCC) set as 2 × 105 cells/mL and 4 × 105 cells/mL, respectively. The udders with SCCs less than the threshold value were defined as healthy udders, and otherwise as mastitis-stricken udders. A total of 3054 udder ultrasound images were randomly divided into a training set (70%), a validation set (15%), and a test set (15%). We used the EfficientNet_b3 model with powerful learning capabilities in combination with the convolutional block attention module (CBAM) to train the mastitis detection model. To solve the problem of sample category imbalance, the PolyLoss module was used as the loss function. The training set and validation set were used to develop the mastitis detection model, and the test set was used to evaluate the network's performance. The results showed that, when the SCC threshold was 2 × 105 cells/mL, our established network exhibited an accuracy of 70.02%, a specificity of 77.93%, a sensitivity of 63.11%, and an area under the receiver operating characteristics curve (AUC) of 0.77 on the test set. The classification effect of the model was better when the SCC threshold was 4 × 105 cells/mL than when the SCC threshold was 2 × 105 cells/mL. Therefore, when SCC ≥ 4 × 105 cells/mL was defined as mastitis, our established deep neural network was determined as the most suitable model for farm on-site mastitis detection, and this network model exhibited an accuracy of 75.93%, a specificity of 80.23%, a sensitivity of 70.35%, and AUC 0.83 on the test set. This study established a 1/4 level mastitis detection model which provides a theoretical basis for mastitis detection in buffaloes mostly raised by small farmers lacking mastitis diagnostic conditions in developing countries.

Production, Composition and Nutritional Properties of Organic Milk: A Critical Review

Milk is one of the most valuable products in the food industry with most milk production throughout the world being carried out using conventional management, which includes intensive and traditional systems. The intensive use of fertilizers, antibiotics, pesticides and concerns regarding animal health and the environment have given increasing importance to organic dairy and dairy products in the last two decades. This review aims to compare the production, nutritional, and compositional properties of milk produced by conventional and organic dairy management systems. We also shed light on the health benefits of milk and the worldwide scenario of the organic dairy production system. Most reports suggest milk has beneficial health effects with very few, if any, adverse effects reported. Organic milk is reported to confer additional benefits due to its lower omega-6-omega-3 ratio, which is due to the difference in feeding practices, with organic cows predominantly pasture fed. Despite the testified animal, host, and environmental benefits, organic milk production is difficult in several regions due to the cost-intensive process and geographical conditions. Finally, we offer perspectives for a better future and highlight knowledge gaps in the organic dairy management system.

Novel insights into the associations between immune cell population distribution in mammary glands and milk minerals in Holstein cows

Udder health has a crucial role in sustainable milk production, and various reports have pointed out that changes in udder condition seem to affect milk mineral content. The somatic cell count (SCC) is the most recognized indicator for the determination of udder health status. Recently, a new parameter, the differential somatic cell count (DSCC), has been proposed for a more detailed evaluation of intramammary infection patterns. Specifically, the DSCC is the combined proportions of polymorphonuclear neutrophils and lymphocytes (PMN-LYM) on the total SCC, with macrophages (MAC) representing the remainder proportion. In this study, we evaluated the association between DSCC in combination with SCC on a detailed milk mineral profile in 1,013 Holstein-Friesian cows reared in 5 herds. An inductively coupled plasma-optical emission spectrometry was used to quantify 32 milk mineral elements. Two different linear mixed models were fitted to explore the associations between the milk mineral elements and first, the DSCC combined with SCC, and second, DSCC expressed as the PMN-LYM and MAC counts, obtained by multiplying the proportion of PMN-LYM and MAC by SCC. We observed a significant positive association between SCC and milk Na, S, and Fe levels. Differential somatic cell count showed an opposite behavior to the one displayed by SCC, with a negative association with Na and positive association with K milk concentrations. When considering DSCC as count, Na and K showed contrasting behavior when associated with PMN-LYM or MAC counts, with decreasing of Na content and increasing K when associated with increasing PMN-LYM counts, and increasing Na and decreasing K when associated with increasing MAC count. These findings confirmed that an increase in SCC is associated with altered milk Na and K amounts. Moreover, MAC count seemed to mirror SCC patterns, with the worsening of inflammation. Differently, PMN-LYM count exhibited patterns of associations with milk Na and K contents attributable more to LYM than PMN, given the nonpathological condition of the majority of the investigated population. An interesting association was observed for milk S content, which increased with increasing of inflammatory conditions (i.e., increased SCC and MAC count) probably attributable to its relationship with milk proteins, especially whey proteins. Moreover, milk Fe content showed positive associations with the PMN-LYM population, highlighting its role in immune regulation during inflammation. Further studies including individuals with clinical condition are needed to achieve a comprehensive view of milk mineral behavior during udder health impairment.

Microbiota Characterization of the Cow Mammary Gland Microenvironment and Its Association with Somatic Cell Count

Subclinical mastitis is a common disease that threatens the welfare and health of dairy cows and causes huge economic losses. Somatic cell count (SCC) is the most suitable indirect index used to evaluate the degree of mastitis. To explore the relationship between SCC, diversity in the microbiome, and subclinical mastitis, we performed next-generation sequencing of the 16S rRNA gene of cow's milk with different SCC ranges. The data obtained showed that the microbiota was rich and coordinated with SCC below 2 × 105. SCC above 2 × 105 showed a decrease in the diversity of microbial genera. When SCC was below 2 × 105, the phylum Actinobacteriota accounted for the most. When SCC was between 2 × 105 and 5 × 105, Firmicutes accounted for the most, and when SCC exceeded 5 × 105, Firmicutes and Proteobacteria accounted for the most. Pathogenic genera such as Streptococcus spp. were absent, while SCC above 2 × 105 showed a decrease in the diversity of microbial genera. SCC was positively correlated with the percentage of Romboutsia, Turicibacter, and Paeniclostridium and negatively correlated with the percentage of Staphylococcus, Psychrobacter, Aerococcus, and Streptococcus. Romboutsia decreased 6.19 times after the SCC exceeded 2 × 105; the SCC increased exponentially from 2 × 105 to 5 × 105 and above 1 × 106 in Psychrobacter. Analysis of the microbiota of the different SCC ranges suggests that the development of mastitis may not only be a primary infection but may also be the result of dysbiosis in the mammary gland.

Multi-criteria decision analysis for supporting the selection of subclinical mastitis screening tests to use in large- and small-scale dairy farms in Türkiye

The production of high-quality and safe milk is closely associated with the udder health of dairy cows. While there are many mastitis diagnostic tests/methods available, choosing the most appropriate diagnostic test for a sustainable udder health control program could be a challenge. This study was aimed at selecting tests for the screening of subclinical mastitis on small- and large-scale dairy farms in Türkiye, using multi-criteria decision-making methods. An integrated approach employing the analytical hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS) together was used to select subclinical mastitis screening tests for on-farm use. While the AHP determines the weights of the evaluation criteria, the TOPSIS provides a final ranking. Nine different subclinical mastitis screening (SCM) methods (DeLaval somatic cell counter, PortaSCC test, California mastitis test (CMT), rapid culture, portable/hand-held electrical conductivity meter, infrared thermography, leukocyte esterase strip test, milk pH, UdderCheck test) were analyzed on the basis of five selection criteria (the market availability of the test, the diagnostic accuracy of the test, the cost of the test, the cow-side use of the test, and the practicality of the test). The selection criteria were determined based on literature review and stakeholder input. The weighting of the criteria with the AHP was based on the pairwise comparison of the criteria by stakeholders. The criteria were weighted from 1 to 9 according to their relative importance as follows: "1: equally important," "3: moderately important," "5: strongly important," "7: very strongly important," "9: extremely important," and "2, 4, 6, 8: intermediate values." Final ranking of SCM tests with the TOPSIS was based on the stakeholder evaluations of fulfillment of the criteria by the alternatives. The most appropriate screening test for both large- and small-scale dairy farms was determined to be the CMT. The CMT is a very useful, easy to perform, and low-cost tool for detecting subclinical mastitis. Being a major element of udder health control programs, the CMT, if regularly used on dairy farms in Türkiye, would enable the culling of chronically infected animals and the reduction of mastitis-associated economic losses. Furthermore, regular CMTs would contribute to reducing milk SCC and improving milk quality. In conclusion, multi-criteria decision-making methods not only provide a systematic approach that may assist both veterinarians and farmers in deciding on the best choice among the different tests available for the screening of subclinical mastitis but also offer potential benefits to policymakers, researchers, and other industry stakeholders.

Heritability and genetic correlations of total and differential somatic cell count with milk yield and composition traits in Italian Simmental cows

Costs of production have deeply increased each year in the last decades, breeders are continuously looking for more cost effective and more efficient ways to produce milk. Despite the major signs of progress in productivity, it is fundamental to optimize rather than maximize the performances of the dairy cows. Mastitis is still a highly prevalent disease in the dairy sector which causes several economic losses and environmental effect. Its accurate and early diagnosis is crucial to improve profitability of dairy cows and contribute to a more sustainable dairy industry. Among mastitis reduction strategies, there is the urgent need to implement breeding objectives to select cows displaying mastitis resistance by investigating the genetic mechanisms at the base of the inflammatory response. Therefore, in this study we aimed to further understand the genetic background of the differential somatic cell count (DSCC), which provides thorough insights on the actual inflammatory status of the mammary glands. The objectives of this study were to estimate on a cohort of 20,215 Italian Simmental cows over a 3-yr period: (1) the heritability and repeatability values of somatic cell score (SCS) and DSCC, (2) the genetic and phenotypic correlations between these 2 traits and milk production and milk composition traits, (3) the heritability and repeatability values of SCS and DSCC within class of udder health status. Heritability was low both for SCS (0.06) and DSCC (0.08), whereas the repeatability values for these traits were 0.43 and 0.36, suggesting that the magnitude of cow permanent environmental effect for these traits is remarkable. The genetic and phenotypic correlation of SCS with DSCC was 0.612 and 0.605, respectively. Because both significantly differed from the unit, we must consider those traits as different ones. This latter aspect corroborates the need to consider the DSCC as a further indicator of inflammatory status which might be implemented in the Simmental breed genetic evaluation. It is worthy to mention that heritability estimates for SCS and DSCC were the highest in healthy cows compared with the other udder health classes. This implies that when the udder health status changes, it is most likely due to environmental factors rather than aspects related to the animal's genetics. In contrast, the highest additive genetic variance and heritability found for SCS and DSCC in the healthy group might reveal the potential to further implement breeding strategies to select for healthier animals.

Seasonal autoregressive integrated moving average (SARIMA) time-series model for milk production forecasting in pasture-based dairy cows in the Andean highlands

Milk production in the Andean highlands is variable over space and time. This variability is related to fluctuating environmental factors such as rainfall season which directly influence the availability of livestock feeding resources. The main aim of this study was to develop a time-series model to forecast milk production in a mountainous geographical area by analysing the dynamics of milk records thorough the year. The study was carried out in the Andean highlands, using time-series models of monthly milk records collected routinely from dairy cows maintained in a controlled experimental farm over a 9-year period (2008-2016). Several statistical forecasting models were compared. The Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percent Error (MAPE) were used as selection criteria to compare models. A relation between monthly milk records and the season of the year was modelled using seasonal autoregressive integrated moving average (SARIMA) methods to explore temporal redundancy (trends and periodicity). According to white noise residual test (Q = 13.951 and p = 0.052), Akaike Information Criterion and MAE, MAPE, and RMSE values, the SARIMA (1, 0, 0) x (2, 0, 0)12 time-series model resulted slightly better forecasting model compared to others. In conclusion, time-series models were promising, simple and useful tools for producing reasonably reliable forecasts of milk production thorough the year in the Andean highlands. The forecasting potential of the different models were similar and they could be used indistinctly to forecast the milk production seasonal fluctuations. However, the SARIMA model performed the best good predictive capacity minimizing the prediction interval error. Thus, a useful effective strategy has been developed by using time-series models to monitor milk production and alleviate production drops due to seasonal factors in the Andean highlands.

Antibacterial activity of medicinal plants on the management of mastitis in dairy cows: A systematic review

BACKGROUND

Mastitis is a disease of economic importance in dairy production systems. The common management regime for mastitis is the use of synthetic antibiotics, giving a new problem of antibiotic resistance. There is, therefore, a need to prospect for alternatives to conventional antibiotics from herbal plants.

OBJECTIVES

This systematic review evaluates the use of plants as alternatives for the control of mastitis in dairy cattle, focussing on the effectiveness of studied plants and plant-based products and possible implications on the use of these products in livestock health.

METHODOLOGY

The PRISMA model was implemented with searches done in five electronic databases: Scopus, ScienceDirect, PubMed, Ovid and Research4Life. Data were extracted from 45 studies with 112 plant species from plant species belonging to 42 different families. The specific keywords were 'mastitis', 'dairy cows' and 'medicinal plants'.

RESULTS

The most cited plant species included Allium sativum L., Azadirachta indica and Eucalyptus globulus Labill with the latter further exploring its components. Microbial species causing mastitis mainly were Staphylococcus aureus and Escherichia coli. The extraction methods used included maceration approach using ethanol, methanol and water as solvents for phytochemicals and chromatographic techniques for essential oils. A few studies explored the mode of action, and toxicities of the herbal extracts as well as evaluating their efficacy in clinical trials using animal models.

CONCLUSION

Plants with defined levels of phytochemicals were essential sources of antibacterials. Standardisation of analytical methods is required.

Mastitis in Dairy Cattle: On-Farm Diagnostics and Future Perspectives

Mastitis is one of the most important diseases in dairy cattle farms, and it can affect the health status of the udder and the quantity and quality of milk yielded. The correct management of mastitis is based both on preventive and treatment action. With the increasing concern for antimicrobial resistance, it is strongly recommended to treat only the mammary quarters presenting intramammary infection. For this reason, a timely and accurate diagnosis is fundamental. The possibility to detect and characterize mastitis directly on farm would be very useful to choose the correct management protocol. Some on-field diagnostic tools are already routinely applied to detect mastitis, such as the California Mastitis Test and on-farm culture. Other instruments are emerging to perform a timely diagnosis and to characterize mastitis, such as Infra-Red Thermography, mammary ultrasound evaluation and blood gas analysis, even if their application still needs to be improved. The main purpose of this article is to present an overview of the methods currently used to control, detect, and characterize mastitis in dairy cows, in order to perform a timely diagnosis and to choose the most appropriate management protocol, with a specific focus on on-farm diagnostic tools.

Precision technologies for the management of reproduction in dairy cows

Precision livestock farming (PLF) utilizes information and communication technology (ICT) to continuously monitor, control, and enhance the productivity, reproduction, health, welfare, and environmental impact of livestock. Technological advancements have facilitated the seamless flow of information from animals to humans, enabling practical decision-making processes concerning health, reproduction management, and calving surveillance. With the increasing population of livestock per farm, it has become impractical for farmers to individually track every animal within these large groups. Historically, cattle management decisions heavily relied on human observation, judgment, and experience. However, it is impossible for a single individual to gather reliable audio-visual monitoring data round the clock. Presently, dairy cows exhibit subtler indicators of estrus, resulting in a substantial chance of missing an estrus cycle. Furthermore, calving complications sometimes go unnoticed on farms, resulting in a higher number of culled cattle. In addition, an increasing number of crossbred cows experience delayed return to estrus after calving due to low body condition scores (BCS). The decline in BCS during the dry period is associated with a reduced likelihood of pregnancy following the first and second postpartum inseminations. Precision technologies enable the monitoring and tracking of an individual cow's physiological behavior and reproductive parameters, thereby optimizing management practices and farm performance. Despite the exploration of various technologies, there are still some common challenges that need to be addressed, including battery lifespan, transmission range, specificity and sensitivity, storage capacity, and economic affordability. Nonetheless, the demand for these tools from farmers and researchers is growing, and the implementation of PLF in grazing systems can yield positive outcomes in terms of animal reproductive welfare and labor optimization. This review primarily focuses on the different aspects of reproduction management in dairy using sensors, automated cameras, and various computer software.

The use of a homeopathic preparation in the treatment of subclinical form of mastitis in cows

Background

Mastitis is a disease of productive cows, widespread throughout the world, and characterized by significant economic damage to the dairy industry. The subclinical form of this disease is aggravated by additional difficulties with its diagnosis and the lack of clear treatment protocols.

Aim

Therefore, the study of the effectiveness of diagnostic studies and the search for new methods of treatment of latent forms of mastitis is an important direction in scientific research in countries with developed dairy cattle breeding.

Methods

Studies conducted on the number of dairy cows of the production cooperative "Izhevsky" of the Akmola region of the Republic of Kazakhstan showed that when using rapid tests Kenotest, Somatest, Mastidine test, and Wideside test, the same results were obtained when the disease was detected in cows. The effectiveness of the tests was at the level of 60%-62% when using the settling sample as a control. Medical procedures were carried out using the Aquaton-2 microwave radiation apparatus and a homeopathic preparation. When using physiotherapy with microwave radiation, a decrease in the level of microbial contamination of milk from the treated part of the udder by 1.5-5 times was observed.

Results

Biologically active substances of plant origin in the homeopathic preparation, due to the immunostimulating effect, made it possible to increase the level of γ-globulins in the blood serum of sick animals during the application.

Conclusion

The complex use of both methods in the treatment of animals with a subclinical form of mastitis made it possible to reduce the level of somatic cells in the milk of the affected udder lobe to a level that cannot be determined using Kenotest in 4-6 days, which is 2-4 days faster than using these methods separately.

Analysis of the effect of polymorphisms within the CATHL7 gene on dairy performance parameters

Introduction

Antimicrobial peptides, including cathelicidins, play a significant role in farm animals, influencing animal welfare, immunity, and thus the quality of animal products.

Material and Methods

The study used amplification-created restriction site and PCR-restriction fragment length polymorphism to analyse single nucleotide polymorphisms of the CATHL7 gene encoding the BMAP-34 protein in cattle, at positions 2,383 G > C and 2,468 G > C. The material was collected from 279 Polish Black-and-White Holstein-Friesian dairy cows.

Results

There were statistically significant differences between milk performance parameters in cows with the CATHL7/HhaI and CATHL7/HinfI genotypes. In the case of the CATHL7/HhaI polymorphism, the highest milk yield and protein and lactose content and the lowest somatic cell count in milk were observed for the CC genotype, while the fat content was the highest in milk from cows with the GG genotype. In the case of the CATHL7/HinfI polymorphism, the highest protein and lactose content in milk was observed for the CC genotype.

Conclusion

The results were statistically significant, which suggests that the search for relationships can be continued, and that the results can be used to improve selection programmes supporting dairy farming.

“Got Milk Alternatives?” Understanding Key Factors Determining U.S. Consumers’ Willingness to Pay for Plant-Based Milk Alternatives

Milk is an important dairy product in U.S. food retail. Lifestyle changes toward climate-conscious consumption, animal welfare, and food safety concerns have increased the popularity of plant-based milk alternatives. This study is focused on such beverages and provides insights and best practice recommendations for marketing managers in the U.S. food retail sector. An online survey was distributed to explore factors explaining the intentions of U.S. consumers to purchase and pay a premium for plant-based milk alternatives. Food curiosity and food price inflation were identified as relevant for both willingness to buy and willingness to pay a price premium. In addition, animal welfare concerns and the green and clean product image of plant-based alternatives were relevant to the willingness to pay a premium for plant-based milk.

Innovations in Cattle Farming: Application of Innovative Technologies and Sensors in the Diagnosis of Diseases

Precision livestock farming has a crucial function as farming grows in significance. It will help farmers make better decisions, alter their roles and perspectives as farmers and managers, and allow for the tracking and monitoring of product quality and animal welfare as mandated by the government and industry. Farmers can improve productivity, sustainability, and animal care by gaining a deeper understanding of their farm systems as a result of the increased use of data generated by smart farming equipment. Automation and robots in agriculture have the potential to play a significant role in helping society fulfill its future demands for food supply. These technologies have already enabled significant cost reductions in production, as well as reductions in the amount of intensive manual labor, improvements in product quality, and enhancements in environmental management. Wearable sensors can monitor eating, rumination, rumen pH, rumen temperature, body temperature, laying behavior, animal activity, and animal position or placement. Detachable or imprinted biosensors that are adaptable and enable remote data transfer might be highly important in this quickly growing industry. There are already multiple gadgets to evaluate illnesses such as ketosis or mastitis in cattle. The objective evaluation of sensor methods and systems employed on the farm is one of the difficulties presented by the implementation of modern technologies on dairy farms. The availability of sensors and high-precision technology for real-time monitoring of cattle raises the question of how to objectively evaluate the contribution of these technologies to the long-term viability of farms (productivity, health monitoring, welfare evaluation, and environmental effects). This review focuses on biosensing technologies that have the potential to change early illness diagnosis, management, and operations for livestock.