Detection Of Mastitis Research Articles

Digital Twin-Driven Teat Localization and Shape Identification for Dairy Cow (Student Abstract)

Dairy owners invest heavily to keep their animals healthy. There is good reason to hope that technologies such as computer vision and artificial intelligence (AI) could reduce costs, yet obstacles arise when adapting these advanced tools to farming environments. In this work, we applied AI tools to dairy cow teat localization and teat shape classification, obtaining a model that achieves a mean average precision of 0.783. This digital twin-driven approach is intended as a first step towards automating and accelerating the detection and treatment of hyperkeratosis, mastitis, and other medical conditions that significantly burden the dairy industry.

Comparison of bacteriological culture method and multiplex real-time PCR for detection of mastitis

This study includes the evaluation of multiplex real-time PCR (rPCR) kit, which was developed to provide rapid diagnosis of mastitis infections, by working with milk samples of 2 different sources of mastitis and comparing the results with the classical bacteriological culture method (BC). A total of 273 bacteria were isolated in 226 samples (47.88%) out of 472 samples by BC. These were 139 (50.91%) Staphylococcus spp., 61 (22.34%) Streptococcus spp., 15 (5.49%) E. coli, 8 (2.93%) Enterococcus spp., 50 (18.31%) other bacteria. When we look at the multiplex rPCR results; 1052 positive were obtained for the gene regions of 14 different bacteria, 1 yeast, and 1 β-lactamase gene examined in 472 samples. While no searched gene region was found by rPCR in 78 (16.5%) of the 472 samples studied, at least 1 gene was detected in 394 (83.5%) samples. These 1052 positive samples by rPCR were; 263 (28.43%) Staphylococcus spp., 51 (5.51%) S. aureus, 57 (6.16%) Enterococcus spp., 49 (5.29%) C. bovis, 16 (1.73%) S. dysgalactiae, 84 (9.08%) S. agalactiae, 71 (7.67%) S. uberis, 73 (7.89%) E. coli, 14 (1.51%) Prototheca spp., 39 (4.21%) T. pyogenes/P. indolicus, 5 (0.54%) S. marcescens, 15 (1.62%) K. oxytoca/pneumonia, 117 (12.64%) Mycoplasma spp., 31 (3.35%) M. bovis, 40 (4.32%) yeast, and 127 samples (26.90%) were β-lactamase positive. When the antibiotic resistance of the isolates was evaluated, 78 (31.96%) tetracycline, 72 (29.5%) penicillin, and 60 (24.59%) clindamycin resistance were observed predominantly in Gram-positive isolates, while 6 (23.07%) tigecycline, 6 (23.07%) netilmicin, 6 (23.07%) pipercillin resistance was found in gram-negative isolates.While a bacteria and/or yeast gene was found by rPCR in 187 of 246 (76.01%) samples with no bacterial growth, a bacterium was isolated with BC in only 20 (8.84%) samples whose gene region was not found by rPCR. As a result, the multiplex rPCR system used in the diagnosis of mastitis has been found to be quite reliable as it can detect a large number of bacteria in a very short time compared to classical methods. Therefore, we advise the use of rPCR and/or culture for confirmation of clinical signs in mastitis and at routine mastitis surveillance.

Infrared Thermography of Teat in French Dairy Alpine Goats: A Promising Tool to Study Animal-Machine Interaction during Milking but Not to Detect Mastitis.

There is a need to develop tools for mastitis management in goats and to measure the effects of milking machines on teats. Infrared thermography (IRT), as shown in cows, was a good candidate for early mastitis detection and focusing on milking equipment and settings implicated in potential problems. The aim of this study was to test IRT to detect udder inflammation and the effects of mechanical milking on teats in relation to inflammation status, udder balance, and teat shape in Alpine goats. IRT spectra were compared before and after milking in 551 goats from three commercial herds compared to their individual SCC (somatic cell count). We found no regression or trend between logSCC and IRT measurement or response to milking, even in highly inflamed goat udders. The effect of milking was significant (p < 0.05) with global temperature reduction after milking, but differences were seen between teat parts and unbalanced half udders. The highest reduction in skin temperature was observed at the teat orifice (-1.06 ± 0.05) and the lowest at the teat barrel (-0.37 ± 0.05). The teats with long barrels showed more IRT reactions, which clearly indicates poor adaptation to the liners used. In conclusion, the IRT was not able to detect mastitis, but it is a good tool to diagnose the effects of the milking machine in order to adapt milking equipment and settings to the goats and improve their welfare.

A study on the development and the application strategy of FT-MIRS-based models for the diagnosis of subclinical mastitis and milk quality classification in buffaloes

Buffalo mastitis detection and buffalo milk quality are of great importance to the world dairy industry. Somatic cell count (SCC) can be employed to assess mammary gland health and milk quality in dairy cows, but SCC detection is costly, and the detection instrument is expensive. The purpose of this study was to develop high and low SCC identification models using Fourier-transform mid-infrared spectrum (FT-MIRS), which could be used to diagnose subclinical mastitis (SCM) in buffalo and to determine the milk quality grade at a low cost. The dataset contained 899 buffalo milk samples collected from two regions in China. Firstly, the samples were divided into positive group above the threshold (SCM or unqualified milk) and negative group below the threshold (healthy or qualified milk) with SCC = 200 × 103 cells/mL (SCM in buffalo), 400 × 103 cells/mL (EU standard for raw cow milk, ES), 500 × 103 cells/mL (Indian standard for raw buffalo milk, IS), and 750 × 103 cells/mL (US standard for raw cow milk, US) as thresholds, respectively. Then, with FT-MIRS as predictive variables, predictive models were developed using Partial Least Squares Discriminant Analysis (PLSDA), Random Forest (RF), and Gradient Boosting Machine (GBM). The main results were as follows: the AUCval of the diagnostic model of SCM in buffalo using SCM criteria as a threshold was 0.84 (PLSDA). The AUCval values of the three buffalo milk quality classification models with ES, IS, and US as thresholds were 0.76 (PLSDA), 0.78 (GBM), and 0.84 (PLSDA), respectively. The predictive models established in this paper had a weak predictive ability for positive samples, and the “stepwise discriminant analysis” was recommended to improve the model application effect: The models were applied to classify samples as positive and negative, and then the samples with higher predictive probability were selected. Finally, the remaining samples were further differentiated using the reference methods of SCC detection. To conclude, FT-MIRS has the potential to predict buffalo mammary gland health status and buffalo milk quality grade, reduce the cost of SCC detection, and improve work efficiency, which will lay the foundation for rapid buffalo milk quality classification by raw milk regulatory authorities and rapid diagnosis of SCM in buffalo by farms.

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.

Understanding barriers to reducing antimicrobials on Australian dairy farms: A qualitative analysis.

Reducing antibiotic use in production animal systems is one strategy which may help to limit the development of antimicrobial resistance. To reduce antimicrobial use in food-producing animals, it is important to first understand how antibiotics are used on farm and what barriers exist to decreasing their use. In dairy production systems, mastitis is one of the most common reasons for administering antimicrobials. Therefore, it is important to understand the motivations and behaviours of dairy farmers in relation to the diagnosis, treatment and prevention of mastitis. In this study, we interviewed a sample of dairy farmers and dairy industry professionals from the major dairying regions of eastern Australia regarding their current practices used to diagnose, treat, and control subclinical and clinical mastitis. Inductive thematic analysis was used to code interview transcripts and identify the recurrent themes. Four overarching themes were identified: (1) the challenges associated with the detection and diagnosis of clinical mastitis, including with laboratory culture, (2) the motivations behind treatment decisions for different cases, (3) decisions around dry cow therapy and the role of herd recording, and (4) concerns regarding the development of antimicrobial resistance. This study identifies several challenges which may limit the ability of Australian dairy farmers to reduce antimicrobial use on farm, such as the need for rapid and reliable diagnostic tests capable of identifying the pathogenic causes of mastitis and the difficulties associated with conducting herd recording for the implementation of selective dry cow therapy. Industry professionals were concerned that farmers were not using individual cow records to aid in treatment decisions, which could result in unnecessary antimicrobial use. The results of this study can act as the basis for future research aimed at assessing these issues across the broader Australian dairy industry.

Use of milk’s electrical conductivity in the detection of mastitis

Mastitis is a costly udder disease affecting dairy cattle. Decreased milk yield, treatment and labor costs, decreased milk quality and cow death etc. are some of the factors that make up costs. These losses are caused by subclinical mastitis without visible symptoms. Mastitis can be detected by performing one of the simple screening tests such as electrical conductivity (EC) measurement of milk. Our study evaluated the use of this method for the detection of subclinical and clinical mastitis.

Individual genomic loci, transcript level, and biochemical profile of immune and antioxidant markers associated with genetically identified bacterial mastitis in Shami goats in Egypt.

Mastitis in goats is unquestionably a grave concern, with far-reaching implications for both animal well-being and productivity, while also presenting a potential threat to public health. The study aimed to compare culture methods and multiplex PCR (m-PCR) in the detection of the most three common mastitis-causing pathogens (Staphylococcus aureus, Escherichia coli, and Streptococcus spp.) and investigate the gene expression, single nucleotide polymorphisms (SNPs), serum concentrations of immunological and antioxidant indicators linked to mastitis in Shami goats. One hundred Shami do (50 Shami goats with clinical mastitis and 50 normal goats taken as control group). The culture methods and m-PCR analysis were used to find the bacteria in the milk samples. Blood samples were obtained to assess some hemato-biochemical parameters, detect SNPs, and determine the expression of certain immunological and antioxidant indicators in the genes. The culture method detected the pathogens causing mastitis in 90% of the milk samples, but m-PCR detected them in 100% of the milk samples. SNPs linked to mastitis resistance/susceptibility in examined does were detected through DNA sequencing of immunological and antioxidant indicators. The magnitude of gene expression varied significantly between the resistant and mastitis-affected groups. Significant (P ˂ 0.05) elevations were noticed in WBCs count, mainly neutsrophils count, serum levels of BHB, NEFA, triglycerides, LDL-C, AST, ALT, ALP, creatinine, total protein, globulin, Ca, K, GPx, MDA, acute phase proteins, and cytokines in mastitis affected does as compared to control. While RBCs count, PCV%, lymphocytes count, serum concentration of glucose, cholesterol, HDL-C, albumin, Na, Cl, P, GSH, SOD, and catalase significantly (P ˂ 0.05) diminished in mastitis affected does compared to healthy ones. APPs and pro-inflammatory cytokines scored high sensitivities and NPVs but TNF-α and serum amyloid A (SAA) had the highest percentages of increase. The study confirmed that m-PCR is the most sensitive method for bacteria identification (S. aureus, E. coli, and Strept. spp.) while SNPs in antioxidant and immunological genes may be important genetic indicators for mastitis risk or resistance in Shami does. To establish an effective management plan and forecast the most sensitive risk time for illness onset, gene expression profiles of the tested genes may also be employed as proxy biomarkers. TNF-α and SAA may be precious indicators for the detection of caprine mastitis.

Evaluation of ARFI elastography for detecting active mastitis in sheep with previous fibrous lesions: a study of mammary parenchyma and supramammary lymph nodes.

The aim of the study was to evaluate the use of Acustic Radiation Force Impulse (ARFI) elastography in mammary parenchyma and supramammary lymph nodes, for detection of active mastitis in sheep with naturally infected chronic fibrous lesions. 27 female sheep were included and B-mode ultrasound and ARFI elastography images were obtained, acquiring qualitative (echogenicity and echotexture) and quantitative (shear rate, depth and short/long axis ratio) variables of 48 mammary glands. The glands were divided into three experimental groups: control group (CG) - healthy animals; LSCC- animals that presented fibrous lesions and SCC (somatic cell count) less than 500 x 103 cls/mL; HSCC: animals that presented fibrous lesions and SCC (somatic cell count) more than 500 x 103 cls/mL; The qualitative variables using B-mode ultrasonography, including echotexture and echogenicity, showed no significant differences between the evaluated groups and tissues (p = 0.9336 and p = 0.233, respectively) .In healthy areas of the gland, it was an increasing in shear wave velocity (SWV) in LSCC than in HSCC (p=0.04). When comparing the fibrosis in the LSCC and HSCC groups with their respective normal areas, the velocity increased in both groups: LSCC (p= 0,0007) and HSCC (p= 0,0001). When comparing the areas of fibrosis in LSCC and HSCC with the CG parenchyma, there was an increase in LSCC (p=0.001) and HSCC (p=0.0001). B-mode ultrasound indicate predominance of hypoechoic echogenicity in lymph nodes and reduced short/long axis ratio in cases of active subclinical mastitis. The supramammary lymph node showed increased SWV when comparing the CG with HSCC groups (p=0.02) and GC with LSCC (p=0.04). B-mode ultrasonography is useful for evaluating the mammary parenchyma, however, its application as a standalone diagnostic technique is not recommended. ARFI elastography indicates potential cutoff points for differentiating subclinical mastitis from healed mastitis, highlighting its importance as a tool for distinguishing normal areas from fibrous parenchymal areas. While this study did not establish specific cutoff points due to sample size limitations, further research with larger sample sizes could explore and define these critical thresholds.

Optimization of the different prediction techniques for mastitis disease detection in cows

The pervasive and economically impactful disease in dairy animals, mastitis, presents a significant challenge to the global dairy industry. Accurate and prompt diagnosis is paramount for effective treatment and to curb the spread within the herd. Leveraging advancements in machine learning for disease identification, this article reviews the application of ensemble models in detecting mastitis in cows. The document outlines mastitis, covering its origins, symptoms, and implications for milk supply and animal well-being, emphasizing the limitations of traditional diagnostic methods. It underscores the need for automated, reliable detection strategies and offers a thorough assessment of ensemble machine learning models consists of random forest, gradient boosting, bagging, and stacking. The evaluation scrutinizes model performance, considering datasets, feature selection methods, model designs, and assessment metrics. This review serves as a valuable resource for researchers, veterinarians, and dairy industry professionals seeking to implement machine learning algorithms for mastitis identification. Providing insights into the current landscape pertaining to cow mastitis worldwide , identifying knowledge gaps, and proposing solutions for enhanced accuracy, the review contributes to advancing mastitis detection, ultimately improving cow health and productivity.

Battery-Less NFC Conductivity Sensor for Bovine Mastitis Detection in Farming 4.0

Battery-Less NFC Conductivity Sensor for Bovine Mastitis Detection in Farming 4.0

SNP Analysis of TLR4 Promoter and Its Transcriptional Factor Binding Profile in Relevance to Bovine Subclinical Mastitis.

Bovine mastitis is a complex infectious disease that develops in the mammary gland, predominantly caused by a bacterial infection of mammary tissue. Genetic variability of mastitis is well established and depends upon different quantitative trait loci (QTL) related to mastitis resistance or susceptibility. The susceptibility is often attributed to single-nucleotide polymorphisms (SNPs) in the variable cow breed genomes. Several global investigative attempts have resulted in studies mapping mastitis to the variations in the relevant genes. Reports have been attributed to dramatic genetic expression changes in Toll-Like Receptor 4 (TLR4) genes in mastitis-positive cows. However, the mechanism behind this variable genetic expression of TLR4 genes has been studied poorly. The present study aims to investigate SCM through various screening tests like somatic cell count (SCC), electric conductivity (EC), pH, and California mastitis test (CMT) in milk samples. This study also aims to investigate possible mechanisms behind this variable expression of TLR4 by comparative SNP evaluation and transcriptional factor profile mining. So that the important genetic mutations and effects thereof can be exploited in selecting specific breeds with higher mastitis resistance and milk yield. Seventy Holstein Frisian (HF) crossbred dairy cows were selected in the present study. The animals were screened based on various diagnostic tests (SCC, pH, EC, and CMT). Blood samples (5mL) were collected for extraction of DNA followed by amplification of PPR1 and PPR2 of the promoter region and 5'UTR of the bovine TLR4 gene using specific primers. Sanger's enzymatic DNA sequencing technique sequenced the amplified PCR products. Further, the identification of SNPs was done through various bioinformatic tools used in this study. The findings of the present study revealed that CMT, EC, pH, and SCC could be used for the early detection of subclinical mastitis. In the present study, a significant increase in the EC, pH, and SCC in milk samples of animals affected with SCM was found in comparison to the healthy animals. The present study also revealed 16 SNPs falling in TLR4 promoter and 5' untranslated region (5'UTR) sequences in mastitis-positive genotypes compared to reference genomes. The study also investigates the potential transcriptional factor program deployed in response to variable mastitis development resistance. In the present study, the allelic and genotype frequencies of all SNP variants in the three regions viz., PPR1, PPR2, and 5'UTR, were the same indicating the absence of heterozygous condition at the respective loci. The present study has wide applicability for researchers developing mastitis-resistant breeding programs and the data generated may aid in the selection of better genetic breeds. The transcription factor binding profiles can serve as concrete leads about the studies on bovine mastitis at the molecular level and may also aid global research groups working on transcription factor (TF)-based molecular pathology of mastitis.

A First Investigation into the Use of Differential Somatic Cell Count as a Predictor of Udder Health in Sheep.

Differential somatic cell count (DSCC), the percentage of somatic cell count (SCC) due to polymorphonuclear leukocytes (PMNs) and lymphocytes (LYMs), is a promising effective diagnostic marker for dairy animals with infected mammary glands. Well-explored in dairy cows, DSCC is also potentially valid in sheep, where clinical and subclinical mastitis outbreaks are among the principal causes of culling. We pioneered the application of DSCC in dairy ewes by applying receiver-operating characteristic (ROC) curve analysis to define the most accurate thresholds to facilitate early discrimination of sheep with potential intramammary infection (IMI) from healthy animals. We tested four predefined SCC cut-offs established in previous research. Specifically, we applied SCC cut-offs of 265 × 103 cells/mL, 500 × 103 cells/mL, 645 × 103 cells/mL, and 1000 × 103 cells/mL. The performance of DSCC as a diagnostic test was assessed by examining sensitivity (Se), specificity (Sp), positive predictive value (PPV), negative predictive value (NPV), and area under curve (AUC) analyses. The designated threshold value for DSCC in the detection of subclinical mastitis is established at 79.8%. This threshold exhibits Se and Sp of 0.84 and 0.81, accompanied by an AUC of 0.88. This study represents the inaugural exploration of the potential use of DSCC in sheep's milk as an early indicator of udder inflammation.

A deep learning approach combining DeepLabV3+ and improved YOLOv5 to detect dairy cow mastitis

Dairy cow mastitis has a great impact on the productivity of dairy cows and the profits of livestock farms. Early detection is of great significance to improve the efficiency of mastitis treatment. However, due to the low resolution of thermal infrared images and the complexity of the living environment of dairy cows, it is difficult to detect the eyes and udders of cows, which reduces the detection accuracy of mastitis. To solve this problem, this paper proposes a two-stage model (DCYOLO) integrating the DeepLabV3 + semantic segmentation network and an improved YOLOv5 target recognition network, which is used to detect the eyes and udders of dairy cows under complex background and applied to the severity classification of dairy cow mastitis. In the first stage, the DeepLabV3 + model was used to segment the eyes and udders of dairy cows from thermal infrared images. In the second stage, the segmented image was input into the target recognition YOLOv5 network for key parts recognition. Finally, to further improve the detection accuracy of the model, a convolutional block attention module (CBAM) was added at the end of the main part of the YOLOv5 model. After comparing different semantic segmentation and target recognition networks, the DeepLabV3 + network and YOLOv5 network performed best. The mIoU and mean pixel accuracy (mPA) of the DeepLabV3 + network reached 86.98 % and 92.92 %, respectively. The mean average precision (mAP) and F1 scores of the YOLOv5 network for unsegmented thermal infrared images reached 93.4 % and 90.9 %, respectively. The CBAM-added YOLOv5 (CAYOLOv5) model was combined with the DeepLabV3 + model. Compared with the single YOLOv5 model, the mAP and F1 scores of DCYOLO increased by 5.4 % and 5.3 %, respectively. Therefore, the proposed model can achieve more accurate positioning of key parts of dairy cows. Based on this model, the eye and udder temperature differences of 50 dairy cows were extracted for mastitis detection, and the detection results were compared with the results of the somatic cell count (SCC) approach. The results showed that the classification accuracy of mastitis was 86 %, and the average sensitivity and specificity were 79.41 % and 92.49 %, respectively. The dairy cow mastitis detection method based on the two-stage model can accurately locate the key parts of dairy cows and realize the automatic detection and classification of dairy cow mastitis, and the accuracy is high.

In-Line Detection of Clinical Mastitis by Identifying Clots in Milk Using Images and a Neural Network Approach.

Automated milking systems (AMSs) already incorporate a variety of milk monitoring and sensing equipment, but the sensitivity, specificity, and positive predictive value of clinical mastitis (CM) detection remain low. A typical symptom of CM is the presence of clots in the milk during fore-stripping. The objective of this study was the development and evaluation of a deep learning model with image recognition capabilities, specifically a convolutional neural network (NN), capable of detecting such clots on pictures of the milk filter socks of the milking system, after the phase in which the first streams of milk have been discarded. In total, 696 pictures were taken with clots and 586 pictures without. These were randomly divided into 60/20/20 training, validation, and testing datasets, respectively, for the training and validation of the NN. A convolutional NN with residual connections was trained, and the hyperparameters were optimized based on the validation dataset using a genetic algorithm. The integrated gradients were calculated to explain the interpretation of the NN. The accuracy of the NN on the testing dataset was 100%. The integrated gradients showed that the NN identified the clots. Further field validation through integration into AMS is necessary, but the proposed deep learning method is very promising for the inline detection of CM on AMS farms.

Status of subclinical mastitis in lactating cows of selected dairy farms in Bangladesh and farmers’ understanding of mastitis

Background: Subclinical mastitis, a disease that is economically important to dairy cows, affects milk production. Management of udder health is an essential element in the process of safe milk production. Consumption of milk with a high somatic cell count (SCC) may also pose a health risk to humans. Objectives. We aimed to evaluate the SCC method for the detection of SCM in dairy cows and to assess farmers’ knowledge, attitudes, and practices towards mastitis detection and control. Methods. A cross-sectional study was conducted to evaluate the current status of subclinical mastitis (SCM) in lactating dairy cows. The research work was also designed to assess farmers’ knowledge, attitudes, and practices about bovine mastitis. A total of 320 milk samples from 80 lactating cows were collected for physical examination and tested for subclinical mastitis using SCC. Results. Out of 80 lactating cows examined, 53 (66%, 95% CI: 55-76%) cows were diagnosed with subclinical mastitis. The average cow level SCC per ml of milk was 503101. According to the farmers’ self-report, 100% of farms practiced hand milking and 100% of milkmen washed their hands before milking. The majority of farms (90%) had brick floors. Most of the farmers (70%) knew about screening for subclinical mastitis. However, they never performed screening to detect subclinical mastitis. Conclusion. The high prevalence of SCM in dairy raised significant concerns about farm management, personal hygiene and biosecurity practices. Further study is needed to identify the etiologies of SCM and its associated risk factors.

Epidemiological Studies of Subclinical Mastitis in Dairy Goats in Lumajang Regency, East Java, Indonesia

Subclinical mastitis is an economically important disease in dairy goat farming. Lumajang is a center for dairy goats in East Java. However, routine examination of subclinical mastitis for screening tests has no data. This study aimed to provide prevalence data and identification factors that influenced subclinical mastitis. Therefore, this study was carried out as part of the national surveillance program. The research design is a cross-sectional study with simple random sampling. Samples came from 19 farms with a total individual sample of 224 dairy goats. Detection of subclinical mastitis using an undirect test with the California Mastitis Test (CMT). Identification of management factors and characteristics that can influence subclinical mastitis in dairy goat farms using a structured questionnaire. The results showed that the prevalence of subclinical mastitis in Lumajang District was 36.2%. There was a significant difference in subclinical mastitis in each village (p&lt;0.05). The highest prevalence of subclinical mastitis was in Kandangtepus village. A farmer's characteristic factor that significantly influences subclinical mastitis is the farmer's experience. The aspect of livestock management that significantly affects subclinical mastitis is structured work management. Subclinical mastitis was prevalent in dairy goats in Lumajang District and requires prevention efforts. Prevention approach through education for farmers to increase knowledge, attitude, and socialization to improve livestock management.

Electrical conductivity and total dissolved solid of raw milk for the detection of bovine subclinical mastitis.

Bovine subclinical mastitis (SCM) is highly prevalent among dairy cattle. A cross-sectional study was conducted in Bangladesh to evaluate the performance of electric conductivity (EC) and total dissolved solids (TDS) tests for the detection of SCM. We randomly selected 108 milk samples from cows of different breeds in the primary milk-producing region of Pabna and Sirajgonj districts of Bangladesh. Samples were subjected to the California mastitis test (CMT), white side test (WST), electric conductivity (EC), TDS, and culture. A cow was considered positive for SCM if it tested positive in CMT, WST, and culture, whereas a cow was considered negative for SCM if it tested negative in all three methods. These gold standards have been used to evaluate the performance of the EC and TDS tests. The optimal EC and TDS cutoff values for the detection of SCM were determined using the "optimal cutoff" function in R version 4.3.1. The optimal EC cutoff value for SCM detection was found to be 6159 μS/cm or 6.16 mS/cm. A positive likelihood ratio (LR+) of 31.2 and an area under the curve (AUC) of 0.905 were obtained for this cutoff value. The optimal cutoff value for TDS was 3100 mg/L of milk, which resulted in a positive LR+ of 45.5 and an AUC of 0.924. To the best of our knowledge, this is the first study to evaluate the performance of EC and TDS tests in detecting SCM in Bangladesh. These results suggest that EC and TDS tests, which are inexpensive, rapid, and easy to conduct, can effectively detect SCM at the farm level.

Effectiveness of the California Mastitis Test (CMT), Reductase Test, and Alcohol Test for Dairy Cows Subclinical Mastitis Detection

Mastitis is an inflammation of the mammary glands caused by pathogenic bacteria Mastitis divided into 2 types, clinical mastitis which has symptoms of swelling and changes in the shape of the mammary gland, while subclinical mastitis does not indicate clear symptoms. Subclinical mastitis is a very high case in Indonesia, around 97-98%, while clinical mastitis is 2-3%. Effectiveness of the california mastitis test (CMT), reductase test and alcohol test to detect subclinical mastitis in dairy cattle. The number of samples taken was 60 samples of fresh dairy milk from 15 cattles in Medowo Village as much as 50 ml for each sample. The results showed that the test was very effective in identifying subclinical mastitis in dairy cattle. 15% positive results were obtained for subclinical mastitis using CMT, 10% of the milk quality was not good during the reductase test and 18.33% positive with the alcohol test from a total of 60 samples obtained.

Indirect Sensing of Subclinical Intramammary Infections in Dairy Herds with a Milking Robot.

This study determined the impact of subclinical intramammary infections (IMIs), such as the major and minor udder pathogens (MaPs and MiPs), on the somatic cell count (SCC) in cow milk and investigated the possibilities of indirect sensing of the udder pathogens using the mastitis detection index (MDi) (DeLaval, Tumba, Sweden). The MDi incorporates quarter-level milk electrical conductivity, blood in milk, and milking interval. The case group (n = 21; MDi ≥ 1.4) was compared with the control group (n = 24; MDi < 1.4) for the presence of IMIs. The microbiological investigation of udder quarter foremilk samples was performed two times with an interval of 10 to 14 days. The case and control groups differed in terms of the occurrence of MaPs and MiPs in milk. During the continuous subclinical IMI and the episodic MaP infection, a higher SCC was detected compared with the episodic MiP infection or quarters without IMI. The novel finding of this study was that by using the milk quality sensor for the sensing of subclinical IMIs, there was an indication for the successful detection of episodic MaPs. However, the sensing of the continuous subclinical IMIs was not possible in the current study and still needs to be investigated.