Investigation of body and udder skin surface temperature differentials as an early indicator of mastitis in Holstein Friesian crossbred cows using digital infrared thermography technique

Thermal Imaging and Dimensionality Reduction Techniques for Subclinical Mastitis Detection in Dairy Sheep

Subclinical mastitis is a common and economically significant disease that affects dairy sheep production. Thermal imaging presents a promising avenue for non-invasive detection, but existing methodologies often rely on simplistic temperature differentials, potentially leading to inaccurate assessments. This study proposes an advanced algorithmic approach integrating thermal imaging processing with statistical texture analysis and t-distributed stochastic neighbor embedding (t-SNE). Our method achieves a high classification accuracy of 84% using the support vector machines (SVM) algorithm. Furthermore, we introduce another commonly employed evaluation metric, correlating thermal images with commercial California mastitis test (CMT) results after establishing threshold conditions on statistical features, yielding a sensitivity (the true positive rate) of 80% and a specificity (the true negative rate) of 92.5%. The evaluation metrics underscore the efficacy of our approach in detecting subclinical mastitis in dairy sheep, offering a robust tool for improved management practices.

Basic concepts, recent advances, and future perspectives in the diagnosis of bovine mastitis

Mastitis is one of the most widespread infectious diseases that adversely affects the profitability of the dairy industry worldwide. Accurate diagnosis and identification of pathogens early to cull infected animals and minimize the spread of infection in herds is critical for improving treatment effects and dairy farm welfare. The major pathogens causing mastitis and pathogenesis are assessed first. The most recent and advanced strategies for detecting mastitis, including genomics and proteomics approaches, are then evaluated . Finally, the advantages and disadvantages of each technique, potential research directions, and future perspectives are reported. This review provides a theoretical basis to help veterinarians select the most sensitive, specific, and cost-effective approach for detecting bovine mastitis early.

Short milking tube thermogram analysis: an indicator of mastitis in Murrah buffaloes

In the present study, thermal images of the short milking tube of the milking machine representing four udder quarters independently attached to a milking animal, along with pre-milking and post-milking udder and teat thermograms, were taken using a hand-held digital infrared thermal camera (DarviDTL007) during morning milking of lactating Murrah buffaloes (n = 132) to assess the mastitis status. California mastitis test (CMT) and somatic cell count (SCC) of milk samples were carried out to screen the udder quarters as healthy, subclinical (SCM), and clinical mastitis (CM). The thermograms revealed an increase (p < 0.05) of 2.19 and 3.72ºC in the mean values of short milking tube (SMT) surface temperature among SCM and CM quarters compared to healthy quarters, respectively. The mean values of udder skin surface temperature (USST) for pre-milking, milking, and post-milking of SCM and CM compared to healthy quarters showed an increase (p < 0.05) of 2.17, 1.96, and 1.61ºC and 3.11, 2.88, and 2.73ºC, respectively. Similarly, compared to healthy quarters, the mean values of teat skin surface temperature (TSST) for pre-milking and post-milking of SCM and CM showed an increase (p < 0.05) of 2.12 and 1.66ºC and 3.07 and 2.45ºC, respectively. Also, CMT and SCC results showed a strong positive correlation (r = 0.68-0.91, p < 0.01) with all the thermographic parameters. Thus, thermograms of SMT alone can be used as an efficient detection tool in assessing SCM among Murrah buffaloes.

Short-milking-tube thermograms: An alternative to udder thermograms for mastitis detection in Sahiwal cows

Mastitis is a multi-etiological production disease that causes substantial financial loss to dairy farmers. In this context, early detection of mastitis using thermograms can aid the dairy sector in managing mastitis efficiently, and this technology could be a supportive tool in precision dairy farming. Infrared cameras can detect minor temperature changes on the udder surface by taking multiple images of the udder and teat. In the current study, a thermogram of the short milking tube (SMT) of the milking machine, as well as the udder and teat of lactating Sahiwal cow (n = 100 quarters of 25 Sahiwal cows), was captured using a hand-held digital infrared thermal camera (DarviDTL007) during morning milking to assess the mastitis status. CMT and SCC of milk samples were carried out for further confirmatory diagnosis of healthy, sub-clinical (SCM), and clinical mastitis (CM). Cut-offs for short milking tube temperature were developed using the receiver operating characteristics analysis. Results of thermal image analysis revealed that the pre-milking, milking, and post-milking parameters of the udder and the teat skin surface temperatures showed a significant difference in the healthy, SCM, and CM-affected quarters. The thermogram analysis showed a significant increase (p < 0.05) of 1.11 and 2.04°C in the mean values of SMT surface temperature among SCM and CM quarters compared to healthy quarters, respectively. In addition, the values of CMT and SCC revealed a significant increase (p < 0.05) in SCM and CM samples and a positive correlation to SMT surface temperatures. Short milking tube thermograms can be a useful assessment tool for detecting sub-clinical mastitis in dairy animals.

Dairy Cow Mastitis Detection by Thermal Infrared Images Based on CLE-UNet

Thermal infrared technology is utilized for detecting mastitis in cows owing to its non-invasive and efficient characteristics. However, the presence of surrounding regions and obstacles can impede accurate temperature measurement, thereby compromising the effectiveness of dairy mastitis detection. To address these problems, we proposed the CLE-UNet (Centroid Loss Ellipticization UNet) semantic segmentation algorithm. The algorithm consists of three main parts. Firstly, we introduced the efficient channel attention (ECA) mechanism in the feature extraction layer of UNet to improve the segmentation accuracy by focusing on more useful channel features. Secondly, we proposed a new centroid loss function to facilitate the network's output to be closer to the position of the real label during the training process. Finally, we used a cow's eye ellipse fitting operation based on the similarity between the shape of the cow's eye and the ellipse. The results indicated that the CLE-UNet model obtained a mean intersection over union (MIoU) of 89.32% and an average segmentation speed of 0.049 s per frame. Compared to somatic cell count (SCC), this method achieved an accuracy, sensitivity, and F1 value of 86.67%, 82.35%, and 87.5%, respectively, for detecting mastitis in dairy cows. In conclusion, the innovative use of the CLE-UNet algorithm has significantly improved the segmentation accuracy and has proven to be an effective tool for accurately detecting cow mastitis.

Evaluation of reticuloruminal temperature for the prediction of clinical mastitis in dairy cows challenged with Streptococcus uberis

Automated monitoring devices have become increasingly utilized in the dairy industry, especially for monitoring or predicting disease status. While multiple automated monitoring devices have been developed for the prediction of clinical mastitis (CM), limitations in performance or applicability remain. The aims of this study were to (1) detect variations in reticuloruminal temperature (RRT) relative to an experimental intramammary challenge with Streptococcus uberis and (2) evaluate alerts generated automatically based on variation in RRT to predict initial signs of CM in the challenged cows based on severity of clinical signs and the concentration of bacteria (cfu/mL) in the infected quarter separately. Clinically healthy Holstein cows without a history of CM in the 60 d before the experiment (n = 37, parity 1 to 5, ≥120 d in milk) were included if they were microbiologically negative and had a somatic cell count under 200,000 cells/mL based on screening of quarter milk samples 1 wk before challenge. Each cow received an intra-reticuloruminal automated monitoring device before the trial and was challenged with 2,000 cfu of Strep. uberis 0140J in 1 rear quarter. Based on interrupted time series analysis, intramammary challenge with Strep. uberis increased RRT by 0.54°C [95% confidence interval (CI): 0.41, 0.66] at 24 h after the challenge, which remained elevated until the end of the study. Alerts based on RRT correctly classified 78.3% (95% CI: 65.8, 87.9) of first occurrences of CM at least 24 h in advance, with a sensitivity of 70.0% (95% CI: 50.6, 85.3) and a specificity of 86.7% (95% CI: 69.3, 96.2). The accuracy of CM for a given severity score was 90.9% (95% CI: 70.8, 98.9) for mild cases, 85.2% (95% CI: 72.9, 93.4) for moderate cases, and 92.9% (95% CI: 66.1, 99.8) for severe cases. Test characteristics of the RRT alerts to predict initial signs of CM improved substantially after bacterial count in the challenged quarter reached 5.0 log₁₀ cfu/mL, reaching a sensitivity of 73.5% (95% CI: 55.6, 87.1) and a specificity of 87.5% (95% CI: 71.0, 96.5). Overall, the results of this study indicated that RRT was affected by the intramammary challenge with Strep. uberis and the RRT-generated alerts had similar accuracy as reported for other sensors and algorithms. Further research that includes natural infections with other pathogens as well as different variations in RRT to determine CM status is warranted.

Evaluation of a Novel Infrared Thermography Projection to Assess Udder Health in Primigravid Dairy Heifers

Heifer mastitis in early lactation impacts negatively on animal welfare, milk production and longevity. A major challenge for the prevention and control of mastitis in dairy heifers is to establish when intramammary infection occurs because pre-partum secretum sampling is risky. We evaluated a ventrodorsal projection to capture thermal images of the entire udder of primigravid and compared results against caudocranial projection, which is used in lactating cattle. Based on the analysis of 119 heifers and images taken at 2 months and 2 weeks pre-partum, a very strong positive correlation (r = 0.91 and r = 0.96, respectively) was shown between caudocranial and ventrodorsal projections of hind quarters. Quarter maximum gradient temperatures were consistently greater on ventrodorsal projection than on caudocranial projection, and less variable than minimum gradient temperatures. The collection of ventrodorsal images is a simple one-step method involving the imaging of the entire udder in a manner safe for both the cattle and handlers. Together, these results demonstrate that a single projection can be used to scan the entire udder of primigravid dairy heifers in commercial farm conditions, with the potential to implement this as a routine method for the early detection of intramammary infection based on udder surface temperature.

Prediction of rectal temperature in Holstein heifers using infrared thermography, respiration frequency, and climatic variables

The objective of this study was to develop an equation to predict rectal temperature (RT) using body surface temperatures (BSTs), physiological and climatic variables in pubertal Holstein heifers in an arid region. Two hundred Holstein heifers were used from July to September during two consecutive summers (2019 and 2020). Respiratory frequency (RF) was used as a physiological variable and ambient temperature, relative humidity and temperature-humidity index as climatic variables. For the body surface temperatures, infrared thermography was used considering the following anatomical regions: shoulder, belly, rump, leg, neck, head, forehead, nose, loin, leg, vulva, eye, flank, and lateral area (right side). Initially, a Pearson correlation analysis examined the relationship among variables, and then multiple linear regression analysis was used to develop the prediction equation. Physiological parameters RT and RF were highly correlated with each other (r = 0.73; P˂0.0001), while all BST presented from low to moderate correlations with RT and RF. BST forehead temperature (FH) showed the highest (r = 0.58) correlation with RT. The equation RT = 35.55 + 0.033 (RF) + 0.030 (FH) + e_i is considered the best regression equation model to predict RT in Holstein heifers in arid zones. This decision was made on the indicators R² = 60%, RMSE = 0.25, and AIC = 0.25, which were considered adequate variability indicators.

Infrared thermography as a technique for detecting subclinical bovine mastitis

ABSTRACT Infrared thermography is a non-invasive diagnostic tool for early detection of subclinical mastitis in pasture-raised dairy cows. The study was conducted on eight dairy farms, in Sena Madureira, Acre, in the Western Amazon, during the rainy season. One hundred and thirteen lactating crossbred cows were monitored, with measurements performed once a day before milking, from 02:00 to 06:00 in the morning. The California Mastitis Test (CMT) was subsequently applied. Data processing was performed in the R programming language and evaluated by linear models. Our results show that cow udder surface temperature by infrared thermography showed significant differences (p<0.05) between the months of the year. Cow udder temperatures of thermographic images were significantly different (p<0.05) from CMT results for animals with subclinical mastitis. CMT results showed that 45 cows (39.8%) in January, 52 cows (46%) in February and 57 cows (50.4%) in March had subclinical mastitis. Infrared thermography is a technique that can be used for the early detection of subclinical mastitis in dairy herds in a grazing system, as it detected an increase in the udder surface temperature of the same cows that were positive for CMT.

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

To maximize milk production, efficiency, and profits, modern dairy cows are genetically selected and bred to produce more and more milk and are fed copious quantities of high-energy feed to support ever-increasing milk volumes. As demands for increased milk yield and milking efficiency continue to rise to provide for the growing world population, more significant stress is placed on the dairy cow's productive capacity. In this climate, which is becoming increasingly hotter, millions of people depend on the capacity of cattle to respond to new environments and to cope with temperature shocks as well as additional stress factors such as solar radiation, animal crowding, insect pests, and poor ventilation, which are often associated with an increased risk of mastitis, resulting in lower milk quality and reduced production. This article reviews the impact of heat stress on milk production and quality and emphasizes the importance of udder health monitoring, with a focus on the use of emergent methods for monitoring udder health, such as infrared thermography, biosensors, and lab-on-chip devices, which may promote animal health and welfare, as well as the quality and safety of dairy products, without hindering the technological flow, while providing significant benefits to farmers, manufacturers, and consumers.

Characteristics of thermal images of the mammary gland and of performance in sows differing in health status and parity

Precision livestock farming can combine sensors and complex data to provide a simple score of meaningful productivity, pig welfare, and farm sustainability, which are the main drivers of modern pig production. Examples include using infrared thermography to monitor the temperature of sows to detect the early stages of the disease. To take account of these drivers, we assigned 697 hybrid (BHZP db. Viktoria) sows to four parity groups. In addition, by pooling clinical findings from every sow and their piglets, sows were classified into three groups for the annotation: healthy, clinically suspicious, and diseased. Besides, the udder was thermographed, and performance data were documented. Results showed that the piglets of diseased sows with eighth or higher parity had the lowest daily weight gain [healthy; 192 g ± 31.2, clinically suspicious; 191 g ± 31.3, diseased; 148 g ± 50.3 (p < 0.05)] and the highest number of stillborn piglets (healthy; 2.2 ± 2.39, clinically suspicious; 2.0 ± 1.62, diseased; 3.91 ± 4.93). Moreover, all diseased sows showed higher maximal skin temperatures by infrared thermography of the udder (p < 0.05). Thus, thermography coupled with Artificial Intelligence (AI) systems can help identify and orient the diagnosis of symptomatic animals to prompt adequate reaction at the earliest time.

Thermography for disease detection in livestock: A scoping review

Infra-red thermography (IRT) offers potential opportunities as a tool for disease detection in livestock. Despite considerable research in this area, there are no common standards or protocols for managing IRT parameters in animal disease detection research. In this review, we investigate parameters that are essential to the progression of this tool and make recommendations for their use based on the literature found and the veterinary thermography guidelines from the American Academy of Thermology. We analyzed a defined set of 109 articles concerned with the use of IRT in livestock related to disease and from these articles, parameters for accurate IRT were identified and sorted into the fields of camera-, animal- or environment-related categories to assess the practices of each article in reporting parameters. This review demonstrates the inconsistencies in practice across peer-reviewed articles and reveals that some important parameters are completely unreported while others are incorrectly captured and/or under-represented in the literature. Further to this, our review highlights the lack of measured emissivity values for live animals in multiple species. We present guidelines for the standards of parameters that should be used and reported in future experiments and discuss potential opportunities and challenges associated with using IRT for disease detection in livestock.

Local Heat Treatment of Goat Udders Influences Innate Immune Functions in Mammary Glands

Heat stress and mastitis adversely affect milk production in dairy ruminants. Although the udder temperature is elevated in both conditions, the influence of this local temperature rise on milk production and immune function of ruminant mammary glands remains unclear. To address this question, we heated the mammary glands of goats by covering one half of the udder with a disposable heating pad for 24 h, the other uncovered half served as a control. Sixteen Tokara goats (1-5 parity) and three Shiba goats (1-2 parity) at the mid-lactation stage were individually housed, fed 0.6 kg of hay cubes and 0.2 kg of barley per day, and had free access to water and trace-mineralized salt blocks. Milk samples were collected every 6 h for 24 h after covering (n = 16), and deep mammary gland tissue areas were collected after 24 h (n = 5). The concentrations of antimicrobial components [lactoferrin, β-defensin-1, cathelicidin-2, cathelicidin-7, and immunoglobulin A (IgA)] in milk were measured by the enzyme-linked immunosorbent assay (ELISA). The localization of IgA was examined by immunohistochemistry. The mRNA expression and protein concentrations of C-C motif chemokine ligand-28 (CCL-28) and interleukin (IL)-8 in the mammary gland tissue were measured using quantitative polymerase chain reaction and ELISA, respectively. The somatic cell count in milk was significantly higher in the local heat-treatment group than in the control group after 12 h of treatment. The treatment group had significantly higher concentrations of cathelicidin-2 and IgA than the control group after 24 h of treatment. In addition, the number of IgA-positive cells in the mammary stromal region and the concentration of CCL-28 in the mammary glands were increased by local heat treatment. In conclusion, a local rise in udder temperature enhanced the innate immune function in mammary glands by increasing antimicrobial components.

Invited review: Toward a common language in data-driven mastitis detection research

Sensor technologies for mastitis detection have resulted in the collection and availability of a large amount of data. As a result, scientific publications reporting mastitis detection research have become less driven by approaches based on biological assumptions and more by data-driven modeling. Most of these approaches try to predict mastitis events from (combinations of) raw sensor data to which a wide variety of methods are applied originating from machine learning and classical statistical approaches. However, an even wider variety in terminologies is used by researchers for methods that are similar in nature. This makes it difficult for readers from other disciplines to understand the specific methods that are used and how these differ from each other. The aim of this paper was to provide a framework (filtering, transformation, and classification) for describing the different methods applied in sensor data-based clinical mastitis detection research and use this framework to review and categorize the approaches and underlying methods described in the scientific literature on mastitis detection. We identified 40 scientific publications between 1992 and 2020 that applied methods to detect clinical mastitis from sensor data. Based on these publications, we developed and used the framework and categorized these scientific publications into the 2 data processing techniques of filtering and transformation. These data processing techniques make raw data more amendable to be used for the third step in our framework, that of classification, which is used to distinguish between healthy and nonhealthy (mastitis) cows. Most publications (n = 34) used filtering or transformation, or a combination of these 2, for data processing before classification, whereas the remaining publications (n = 6) classified the observations directly from raw data. Concerning classification, applying a simple threshold was the most used method (n = 19 publications). Our work identified that within approaches several different methods and terminologies for similar methods were used. Not all publications provided a clear description of the method used, and therefore it seemed that different methods were used between publications, whereas in fact just a different terminology was used, or the other way around. This paper is intended to serve as a reference for people from various research disciplines who need to collaborate and communicate efficiently about the topic of sensor-based mastitis detection and the methods used in this context. The framework used in this paper can support future research to correctly classify approaches and methods, which can improve the understanding of scientific publication. We encourage future research on sensor-based animal disease detection, including that of mastitis detection, to use a more coherent terminology for methods, and clearly state which technique (e.g., filtering) and approach (e.g., moving average) are used. This paper, therefore, can serve as a starting point and further stimulates the interdisciplinary cooperation in sensor-based mastitis research.

Comparative genomic analysis of Escherichia coli isolates from cases of bovine clinical mastitis identifies nine specific pathotype marker genes

Escherichia coli is a major causative agent of environmental bovine mastitis and this disease causes significant economic losses for the dairy industry. There is still debate in the literature as to whether mammary pathogenic E. coli (MPEC) is indeed a unique E. coli pathotype, or whether this infection is merely an opportunistic infection caused by any E. coli isolate being displaced from the bovine gastrointestinal tract to the environment and, then, into the udder. In this study, we conducted a thorough genomic analysis of 113 novel MPEC isolates from clinical mastitis cases and 100 bovine commensal E. coli isolates. A phylogenomic analysis indicated that MPEC and commensal E. coli isolates formed clades based on common sequence types and O antigens, but did not cluster based on mammary pathogenicity. A comparative genomic analysis of MPEC and commensal isolates led to the identification of nine genes that were part of either the core or the soft-core MPEC genome, but were not found in any bovine commensal isolates. These apparent MPEC marker genes were genes involved with nutrient intake and metabolism [adeQ, adenine permease; nifJ, pyruvate-flavodoxin oxidoreductase; and yhjX, putative major facilitator superfamily (MFS)-type transporter], included fitness and virulence factors commonly seen in uropathogenic E. coli (pqqL, zinc metallopeptidase, and fdeC, intimin-like adhesin, respectively), and putative proteins [yfiE, uncharacterized helix-turn-helix-type transcriptional activator; ygjI, putative inner membrane transporter; and ygjJ, putative periplasmic protein]. Further characterization of these highly conserved MPEC genes may be critical to understanding the pathobiology of MPEC.

Technological interventions and advances in the diagnosis of intramammary infections in animals with emphasis on bovine population-a review

Mastitis, an inflammation of the udder, is a challenging problem in dairy animals accounting for high economic losses. Disease complexity, degree of economic losses and increasing importance of the dairy industries along with public health concerns envisages devising appropriate diagnostics of mastitis, which can offer rapid, accurate and confirmatory diagnosis. The various diagnostic tests of mastitis have been divided into general or phenotypic and specific or genotypic tests. General or phenotypic tests are those that identify general alterations, which are not specific to any pathogen. Genotypic tests are specific, hence confirmatory for diagnosis of mastitis and include specific culture, polymerase chain reaction (PCR) and its various versions (e.g. qRT-PCR), loop-mediated isothermal amplification, lateral flow assays, nucleotide sequencing, matrix-assisted laser desorption ionization time-of-flight mass spectrometry, and other molecular diagnostic methods. However, for highly specific and confirmatory diagnosis, pure cultures still provide raw materials for more sophisticated diagnostic technological interventions like PCR and nucleotide sequencing. Diagnostic ability of like infra-red thermography (IRT) has been shown to be similar to California mastitis test and also differentiates clinical mastitis from subclinical mastitis cases. As such, IRT can become a convenient and portable diagnostic tool. Of note, magnetic nanoparticles-based colorimetric biosensor assay was developed by using for instance proteolytic activity of plasmin or anti-S. aureus antibody. Last but not least, microRNAs have been suggested to be potential biomarkers for diagnosing bovine mastitis. This review summarizes the various diagnostic tests available for detection of mastitis including diagnosis through general and specific technological interventions and advances.

Short communication: Diagnosis and classification of clinical and subclinical mastitis utilizing a dynamometer and a handheld infrared thermometer

In times of ongoing automatization of dairy cow husbandry, objective and reliable tools for mastitis diagnostic are highly in demand. The objective of this study was to investigate the diagnostic value of a handheld dynamometer and an infrared thermometer to diagnose and score clinical and subclinical mastitis and to compare those values with results from palpation of the udder tissue. Overall, 218 cows with clinical mastitis (i.e., 46 mild, 106 moderate, and 66 severe cases), 142 with subclinical mastitis, and 68 healthy cows were enrolled. Our data provide evidence that the dynamometer is an accurate diagnostic tool to differentiate between healthy udder quarters, and those with subclinical and clinical mastitis. Furthermore, the severity score of clinical mastitis can be estimated by dynamometer. The firmness threshold for the detection of clinical mastitis was 1.002 kg. Using a threshold of 1.175 kg in clinical mastitis quarters, it was possible to differentiate between negative and positive bacteriological results. A differentiation between healthy and clinical mastitis quarters with the infrared thermometer was possible, albeit udder surface temperatures were highly influenced by ambient temperature. Udder surface temperature increased by 0.15 to 0.18°C for each degree of ambient temperature. In conclusion, the utility of an infrared thermometer to estimate the udder health status of dairy cows is limited, whereas the handheld dynamometer appeared to be an accurate and objective method.

Measurements of peripheral and deep body temperature in cattle - A review

Automation of the measurement of the physiological and behavioural parameters of livestock has become an important goal for both scientists and farmers. Accurate data and knowledge about farmed animals, especially in cattle breeding, are needed. Proper early diagnosis of a cow's health status in real time allows for preventing the development of infection, oestrus detection and leads to reduced environmental stress. Thus, it contributes to more effective herd management. Among the physiological parameters, body temperature and its fluctuations are key indicators of health and well-being in animals. Currently, along with the development of technical solutions and their implementation, increasingly more attention is being paid to the continuously measurement of body core and peripheral temperature in animals. Recently there has been an increased number of publications devoted to this subject. However, there is a need to systematise this knowledge as these studies have had different purposes, have been performed in various environmental conditions, and the measurements were taken using different methods and equipment. As such, the results obtained by the different authors often may not be comparable. For this reason, this paper has two main purposes: to present the most widely used continuous methods of peripheral and body core temperature measurement, and to show its references values which characterise the individual locations of the cattle body in thermoneutral ambient. An analysis of the professional publications regarding measurements of peripheral and deep body temperature led to the conclusion that these methods have high research and diagnostic potential. However, it is necessary to standardised research to enable better and more comparable results, including among others; different cattle groups, animal age, health and environmental conditions.

Monitoring foot surface temperature using infrared thermal imaging for assessment of hoof health status in cattle: A review

Detection of lameness early in cows is important from the animal welfare point of view and for reducing economic losses. Currently, many studies are being conducted for assessment of hoof health status by measuring the surface temperature of skin in cattle and other animal species in different parts of the world. Infrared Thermography (IRT) is able to detect lesions of hooves associated with lameness by measuring the changes in coronary band and hoof skin surface temperature. The surface temperature of a lame limb will be increased when the hoof has lesion(s). IRT has been used as a non-invasive diagnostic tool for early detection of hoof lesions based on the temperature difference between affected and non-affected hoof and maximum foot temperature on the regions of interest. In spite of having many potential applications in cattle production, factors affecting the temperature readings in thermograms must also are considered while taking images. Standard operating procedures must be established before taking thermographs under different circumstances, by considering all the factors that affect its normal function. IRT may help in minimising the cost of veterinary services, low yield, compromised fertility and culling expenses, where lameness cannot be resolved in early stages.

Mechanical/thermal sensitivity and superficial temperature in the stump of long-term tail-docked dairy cows

Background

Tail docking of dairy cows is a painful procedure that affects animal welfare level. The aims of this study were first to evaluate the response to mechanical and thermal stimulation, and second to determine the superficial temperature of the stump of tail-docked dairy cows.

Methods

One hundred and sixty-four dairy cows were enrolled. From these, 133 cows were assigned to the tail-docked (TD) group and 31 cows were selected as control animals. The following sensory assessments to evaluate pain in tail-docked cows were performed. Sensitivity of the tail region in both groups of animals was evaluated using a portable algometer. Cold and heat sensitivity assessment was performed using a frozen pack (0 °C) and warm water (45 °C), respectively. Pinprick sensitivity was evaluated using a Wartenberg neurological pinwheel. Superficial temperature was evaluated using a thermographic camera. All sensory assessments and superficial temperature were evaluated in the ventral surface of the tail stump (TD) and tail (C).

Results

Pressure pain threshold was lower in TD cows (5.97 ± 0.19 kg) compared to control cows (11.75 ± 0.43 kg). Heat and cold sensitivity was higher in the TD cows compared to control cows with 29% and 23% of TD cows responding positively, respectively. Similarly, after pinprick sensitivity test was performed, 93% of TD cows elicited a positive response to stimulation. Tail-docked cows had lower superficial temperature (26.4 ± 0.27 °C) compared to control cows (29.9 ± 0.62 °C).

Discussion

Pressure pain threshold values in both groups of animals were higher than those previously reported for TD pigs, sows and cows. In contrast, pinprick stimulation evaluates the presence of punctate mechanical hyperalgesia/allodynia, usually related to traumatic nerve injury, and this association may reveal that it is possible that these animals developed a disorder associated to the development of a tail stump neuroma and concurrent neuropathic pain, previously reported in TD lambs, pigs and dogs. Thermal sensitivity showed that TD cows responded positively to heat and cold stimulation. These findings suggest that long-term TD cows could be suffering hyperalgesia/allodynia, which may be indicative of chronic pain. Lower superficial temperature in the stump may be associated to sympathetic fiber sprouting in the distal stump, which can lead to vasoconstriction and lower surface temperatures. Further studies are needed in order to confirm neuroma development and adrenergic sprouting.

Infrared thermography to monitor body and udder skin surface temperature differences in relation to subclinical and clinical mastitis condition in Karan Fries (Bos taurus x Bos indicus) crossbred cows

This study was conducted to evaluate the ability of infrared thermography (IRT) for the early detection of mastitis. A total of 200 quarters of Karan Fries cows (50) were monitored for body temperature and udder skin surface temperature (USST) prior to milking using FLIR (Forward Looking Infrared) i5 camera. Milk samples were collected from each quarter and screened for mastitis using somatic cell count (SCC), electrical conductivity (EC) and California mastitis test (CMT). The mean (±SD) body temperature of an individual cow during the study period was 37.17±0.07°C. The mean (±SD) body and USST (37.16±0.06°C) of non mastitis cow did not differ significantly; however, the mean USST of the mastitis affected quarters were significantly higher than the body and non-mastitis quarter temperature. The mean (±SD) USST of the subclinical and clinical mastitis affected quarters were 37.9±0.09°C and 38.2±0.10°C, respectively which is 0.8 and 1.1°C higher than the body and non-mastitis quarter temperature. The increase in USST of subclinical mastitis quarters showed a positive correlation with the SCC. It is concluded that IRT technique could be used as a potential non-invasive, quick cow side diagnostic tool for screening and early detection of mastitis in crossbred cows.

The use of thermal imaging measurements in dairy cow herds

temperature distribution on the surface of an animal’s body. The surface temperature values obtained in IRT depend on the quantitative impact of the conditions of the surrounding environment and the thermoregulatory response of the animal. Besides the blood perfusion volume, the skin temperature depends on the metabolic rate of tissues, the type and colour of the hair coat, and the thickness of the adipose tissue. In a healthy organism, the temperature distribution between individual parts of the body shows a high degree of symmetry. Analysis of the surface temperature distribution of a given area of the body of an animal which is in homeostasis with the external environment provides a great deal of valuable information. By comparing the same parts of the body, we can easily identify hot spots, and the additional knowledge gained during more extensive veterinary diagnostics increases the chance of establishing the cause of this condition. The reliability of IRT depends on the technical parameters of the cameras, environmental conditions, the operator’s experience, the animal’s individual characteristics, and the testing methodology. As many factors can affect the surface temperature distribution of an animal’s body, and thus the result of the thermographic measurement, the effect of any stimuli interfering with the measurements should be minimized during thermal imaging. Additionally, in order to reduce the risk of misinterpretation of the image, normalization protocols for imaging parameters, i.e. standards ensuring reliable results, should be applied. The main limitation in the implementation of these standards in thermography of livestock animals is that it is not possible to compare thermograms made under different environmental conditions. Research has been carried out to assess the suitability of thermal imaging cameras in diagnosing inflammatory changes in the skin of cattle. The technique was found to have great potential in predicting local inflammation (hoof, udder or skin disease). Normalization protocols must be developed for imaging parameters, i.e. standards that will ensure reliable results in a variety of environmental conditions.

Symposium review: Novel strategies to genetically improve mastitis resistance in dairy cattle

Mastitis is a disease of major economic importance to the dairy cattle sector because of the high incidence of clinical mastitis and prevalence of subclinical mastitis and, consequently, the costs associated with treatment, production losses, and reduced animal welfare. Disease-recording systems compiling data from a large number of farms are still not widely implemented around the world; thus, selection for mastitis resistance is often based on genetically correlated indicator traits such as somatic cell count (SCC), udder depth, and fore udder attachment. However, in the past years, several countries have initiated collection systems of clinical mastitis, based on producers recording data in most cases. The large data sets generated have enabled researchers to assess incidence of this disease and to investigate the genetic background of clinical mastitis itself, as well as its relationships with other traits of interest to the dairy industry. The genetic correlations between clinical mastitis and its previous proxies were estimated more accurately and confirmed the strong relationship of clinical mastitis with SCC and udder depth. New traits deriving from SCC were also studied, with the most relevant findings being associated with mean somatic cell score (SCS) in early lactation, standard deviation of SCS, and excessive test-day SCC pattern. Genetic correlations between clinical mastitis and other economically important traits indicated that selection for mastitis resistance would also improve resistance against other diseases and enhance both fertility and longevity. However, milk yield remains negatively correlated with clinical mastitis, emphasizing the importance of including health traits in the breeding objectives to achieve genetic progress for all important traits. These studies enabled the establishment of new genetic and genomic evaluation models, which are more efficient for selection to mastitis resistance. Further studies that are potential keys for future improvement of mastitis resistance are deep investigation of the bacteriology of mastitis, identification of novel indicator traits and tools for selection, and development of a larger female reference population to improve reliability of genomic evaluations. These cutting-edge studies will result in a better understanding of the genetic background of mastitis resistance and enable a more accurate phenotyping and genetic selection to improve mastitis resistance, and consequently, animal welfare and industry profitability.