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.

Association between management practices and estimated mastitis incidence and milk losses on robotic dairy farms

This study aims to describe the relation between farm-level management factors and estimated farm-level mastitis incidence and milk loss traits (MIMLT) at dairy farms with automated milking systems. In this observational study, 43 commercial dairy farms in Belgium and the Netherlands were included and 148 'management and udder health related variables' were obtained during a farm visit through a farm audit and survey. The MIMLT were estimated from milk yield data. Quarter-level milk yield perturbations that were caused by presumable mastitis cases (PMC) were selected based on quarter-level milk yield and electrical conductivity. On average, 57.6 ± 5.4% of the identified milk yield perturbations complied with our criteria. From these PMC, 3 farm-level MIMLT were calculated over a one-year period around the farm visit date: (1) the 'average number of PMC per cow per year', (2) the 'absolute milk loss per cow per day', calculated as the farm-level sum of all milk losses during PMC in one year, divided by the average number of lactating cows and the number of days, and (3) the 'relative milk loss', calculated as the farm-level sum of milk losses during PMC in one year, divided by the estimated total production in the absence of PMC. The 'average number of PMC per cow per year' was on average 1.81 ± 0.47. The PMC caused an average milk loss of 0.77 ± 0.26 kg per lactating cow per day, which corresponded to an average production loss of 2.38 ± 0.82% of the expected production in the absence of PMC. We performed a principal component regression (PCR) analysis to link the 3 MIMLT to the 'management and udder health related variables', whilst reducing the multicollinearity and the number of dimensions. The first principal component was mainly related to 'milking system brand, maintenance and settings'. The second component mainly linked to average productivity and somatic cell counts, whereas the third component mainly contained variables linked with mastitis management, treatment, and biosecurity. The 3 PCR models had R² ranging from 0.46 (for absolute milk loss per cow per day) to 0.57 (for relative milk loss). For all models, the second PC had the largest effect size. This analysis raises awareness of the impact of management factors on a factual basis and provides handles to take management actions to improve udder health.

Predicting dairy herd resilience on farms with conventional milking systems

This research paper addresses the problem that, thus far, there is no method available to predict herd resilience for farms that do not use automated milking systems (AMS). Recently, a methodology was developed to estimate both individual cow as well as herd resilience using daily milk yield observations at individual cow level from farms with AMS. This AMS-based method, however, is not suitable on farms that use conventional milking systems (CMS) where such individual cow milk yield observations are lacking. Therefore, this research aimed at predicting herd resilience using herd performance data that is commonly available on CMS farms. To do so, data consisting of 585 Dutch AMS farms where herd resilience estimates using the AMS-based method were available was examined. To predict herd resilience with herd performance data, only those data that are also commonly available on CMS farms were used in a 5-fold cross validation Random Forest model. These herd resilience estimates were subsequently compared with the AMS-based herd resilience estimates. Results showed that it is possible to predict with a 69.9% probability whether a herd performs with above or below average herd resilience using only variables available on CMS farms. Especially, the proportion of cows with an indication of rumen acidosis, proportion of cows with an elevated somatic cell count and the fluctuation in herd size over the years are good predictors of herd resilience. Since herd management decisions appear to affect herd resilience, a lower predicted herd resilience could be taken as a general indication that tactical or strategic management changes could be taken to improve the herd resilience.

Comparison of Cattle Housing Systems Based on the Criterion of Damage to Barn Equipment and Construction Errors

Simple Summary

As a result of many years of use, dairy cattle barns are subject to gradual wear and degradation. Damage to technical equipment can be identified in many areas in the barn. These areas are used by dairy cattle, so it is important to recognize the problem of damage and the associated health risks for animals. The problem of damage to internal equipment (e.g., damage to the floor, partitions between lying stalls, feed ladders, drinking bowls) applies to both tie-stall and freestall barns, which are the most common in dairy farms. Such premises became an inspiration to compare barns with a tie-stall system, a freestall system and their individual areas (lying, feeding, milking and social) in terms of the amount of damage but also construction errors. Most damage per one barn was found in the feeding area of objects with a tie-stall housing system. More cow health problems (e.g., laminitis, hoof problems) were identified in the barns with the freestall housing system. Equipment failures and construction errors may disrupt efficient and animal-safe dairy production in the barn. The results of the research study may be an incentive for farmers to check the barns in terms of their technical wear.

Abstract

Dairy cattle housing systems are the subject of numerous studies, in which a strong emphasis is placed on the comparison of animal welfare, animal behavior, production indicators and labor inputs. Dairy cattle housing systems are linked to specific livestock buildings, which is a prerequisite for undertaking studies comparing barns and their technical equipment. The aim of the study was to compare barns with two types of housing systems, i.e., tie-stall and freestall, including the identification of technical wear in various areas used by animals. This objective was linked to the assessment of animal health problems in livestock facilities. The research covered 38 dairy farms, 19 of which kept cows in the tie-stall system and 19 in the freestall system. The barns in these farms were examined for technical damage and construction errors, assessed in four areas: lying, feeding, milking and social. The research results confirmed significant differences in the degree of damage to technical equipment in individual areas of barns and between barns with tie-stall and freestall housing systems. The conclusions indicate the need to link the degradation of barns and their technical equipment, as well as design errors with the evaluation of dairy cattle welfare in future studies.

Novel ways to use sensor data to improve mastitis management

Current sensor systems are used to detect cows with clinical mastitis. Although, the systems perform well enough to not negatively affect the adoption of automatic milking systems, the performance is far from perfect. An important advantage of sensor systems is the availability of multiple measurements per day. By clearly defining the need for detection of subclinical mastitis (SCM) and clinical mastitis (CM) from the farmers' management perspective, detection and management of SCM and CM may be improved. Sensor systems may also be used for other aspects of mastitis management. In this paper we have defined 4 mastitis situations that could be managed with the support of sensor systems. Because of differences in the associated management and the epidemiology of these specific mastitis situations, the required demands for performance of the sensor systems do differ. The 4 defined mastitis situations with the requirements of performance are the following: (1) Cows with severe CM needing immediate attention. Sensor systems should have a very high sensitivity (>95% and preferably close to 100%) and specificity (>99%) within a narrow time window (maximum 12 h) to ensure that close to all cows with true cases of severe CM are detected quickly. Although never studied, it is expected that because of the effects of severe CM, such a high detection performance is feasible. (2) Cows with mastitis that do not need immediate attention. Although these cows have a risk of progressing into severe CM or chronic mastitis, they should get the chance to cure spontaneously under close monitoring. Sensor alerts should have a reasonable sensitivity (>80%) and a high specificity (>99.5%). The time window may be around 7 d. (3) Cows needing attention at drying off. For selective dry cow treatment, the absence or presence of an intramammary infection at dry-off needs to be known. To avoid both false-positive and false-negative alerts, sensitivity and specificity can be equally high (>95%). (4) Herd-level udder health. By combining sensor readings from all cows in the herd, novel herd-level key performance indicators can be developed to monitor udder health status and development over time and raise alerts at significant deviances from predefined thresholds; sensitivity should be reasonably high, >80%, and because of the costs for further analysis of false-positive alerts, the specificity should be >99%. The development and validation of sensor-based algorithms specifically for these 4 mastitis situations will encourage situation-specific farmer interventions and operational udder health management.

Animal welfare outcomes and associated risk indicators on Austrian dairy farms: A cross-sectional study

In 2017, an Austrian dairy company implemented a third-party animal-based assessment of health and welfare to stimulate welfare improvements on farms. Using this cross-sectional data set, we aimed at identifying prevailing welfare problems and associations thereof with main farm and management characteristics. Welfare outcome measures regarding body condition, cleanliness, diarrhea, integument alterations, claw condition, lameness, rising behavior, and avoidance distance toward humans were assessed by 13 trained observers. Data from health recordings and farm characteristics, such as housing system, feeding regimen, and pasture access, were collected via a questionnaire. Analyses included outcome measures from 23,749 individual cows on 1,221 farms [median (M) herd size = 19, interquartile range (IQR) = 16]. Herd-level prevalence of the outcome measures showed a high between-farm variability with highest median values for dirty lower hind leg (M = 46%, IQR = 47), signs of diarrhea (M = 28%, IQR = 39), and hairless patches on the tarsal joint (M = 21%, IQR = 36). Median prevalence of severe welfare problems, such as very lean cows, lesions, lameness, or mastitis treatments, were low compared with previously reported findings (very lean: 0%, IQR = 0; lesion tarsus: 0%, IQR = 4; moderately lame loose-housed: 7%, IQR = 16; mastitis treatments: 10%, IQR = 16). On half of the farms, at least 83% (IQR = 25) of the assessed cows could be touched in a standardized approach test, indicating a good human-animal relationship. Using generalized linear models, we found frequent associations with welfare outcome measures for the amount of milk delivered per cow (e.g., lower risk of very lean cows or dirty hind legs but higher risk of mastitis treatments or antibiotic dry-off with increasing milk delivery), housing system (e.g., loose-housed animals were at lower risk of lesions on the tarsal joint than animals kept in tiestalls, but at higher risk of being classified as very fat), and assessment period (winter vs. summer period). Beneficial associations were consistently found for an increasing number of days with access to pasture (e.g., body condition, integument alterations, lameness) as well as organic compared with conventional farming (e.g., integument alterations, claw health, lameness). Although the latter associations may be especially important for advisory services, in policy making, or when engaging with the public, other farm or management characteristics require careful attention, as they may have both beneficial as well as adverse impacts on welfare, calling for good management skills to avoid undesired effects.

Nanomaterials and Essential Oils as Candidates for Developing Novel Treatment Options for Bovine Mastitis

Nanomaterials have been used for diagnosis and therapy in the human medical field, while their application in veterinary medicine and animal production is still relatively new. Nanotechnology, however, is a rapidly growing field, offering the possibility of manufacturing new materials at the nanoscale level, with the formidable potential to revolutionize the agri-food sector by offering novel treatment options for prevalent and expensive illnesses such as bovine mastitis. Since current treatments are becoming progressively more ineffective in resistant bacteria, the development of innovative products based on both nanotechnology and phytotherapy may directly address a major global problem, antimicrobial resistance, while providing a sustainable animal health solution that supports the production of safe and high-quality food products. This review summarizes the challenges encountered presently in the treatment of bovine mastitis, emphasizing the possibility of using new-generation nanomaterials (e.g., biological synthesized nanoparticles and graphene) and essential oils, as candidates for developing novel treatment options for bovine mastitis.

Transmission dynamics of Staphylococcus aureus and Streptococcus agalactiae in a Dutch dairy herd using an automatic milking system

Staphylococcus aureus and Streptococcus agalactiae are important contagious mastitis pathogens and are considered to mainly transmit between cows through the milking machine. Controlling contagious mastitis on dairy farms requires a reduction of the transmission rate or the duration of intramammary infections (IMI), or both. These parameters may differ in dairy herds milked with an automatic milking system (AMS) as compared to those milked with a conventional milking system (CMS). The aims of this prospective longitudinal study were to estimate the transmission rate, the median duration of IMI and the basic reproduction number (R₀) of Staph. aureus and Strep. agalactiae in a Dutch AMS herd. Bacteriological cultures of quarter milk samples were collected every 2 wks. Using 3 different definitions of IMI, we estimated the transmission rate for Staph. aureus to be within the range of 0.002 (95 % CI: 0-0.005) quarter-day^-1 to 0.019 (95 % CI: 0.010-0.032) quarter-day^-1, and for Strep. agalactiae of 0.007 (95 % CI: 0.005-0.010) quarter-day^-1 to 0.019 (95 % CI: 0.011-0.032) quarter-day^-1, the median duration of chronic IMI at 95 (95 % CI: 72-125) days for Staph. aureus and at 86 (95 % CI: 67-111) days for Strep. agalactiae, and the R₀ between 0.16 (95 % CI: 0.05-0.27) and 0.34 (95 % CI: 0.20-0.48) for Staph. aureus, and between 0.64 (95 % CI: 0.41-0.87) and 0.68 (95 % CI: 0.48-0.88) for Strep. agalactiae. Transmission of these two contagious pathogens in this herd was limited and theoretically the IMI would not sustain, given that R₀ of both pathogens was lower than 1. The estimated transmission rate of Staph. aureus in this AMS herd was found to be comparable to those described for CMS herds, while for Strep. agalactiae, it was slightly higher than in CMS herds. The duration of Staph. aureus IMI was in line with results from CMS farms, while the duration of Strep. agalactiae was lower than what has been described in CMS herds. The R₀ of these contagious pathogens was found to be lower than the estimates in CMS herds. Our study suggests that the transmission rate of these two contagious pathogens in this AMS herd were comparable to what has been reported about well-performing CMS herds that have a low rate of transmission.

Characterization of dairy innovations in selected milksheds in Kenya using a categorical principal component analysis

To enhance milk quantity and quality which have continued to decrease in Kenya, various stakeholders have intervened through promotion of technical dairy innovations at the farm level including improved cow feeding, health management, promotion of exotic breeds, and milking hygiene. At the milkshed level, stakeholders’ focus has been on organizational innovations, specifically milk sale by farmers through groups. This study sought to characterize dairy innovations that have been adopted by farmers in the milkshed of three milk processors including New Kenya Co-operative Creameries Sotik (NKCC Sotik), Happy Cow Limited (HCL), and Mukurweini Wakulima Dairy Limited (MWDL), representing one state, private, and farmer-owned processor, respectively. Data were collected using a structured questionnaire from a sample of 1146 farmers (410, 382, and 354 in MWDL, HCL, and NKCC Sotik, respectively). A categorical principal components analysis was used to reduce 32 variables into four sets of uncorrelated components. Four categories were identified including principal component (PC) 1 (technical capacity), PC 2 (animal health management), PC 3 (organizational capacity), and PC 4 (milk hygiene). More farmers in the milkshed of MWDL adopted technical and organizational dairy innovations such as use of artificial insemination and milk sale through groups, respectively, than farmers in milkshed of NKCC and HCL. The county governments in the milkshed of HCL and NKCC Sotik need to strengthen cooperative societies to boost adoption of artificial insemination through arrangement in which milk is sold and payment of services offered on credit is settled from milk sale and ensure milk market availability throughout the year.

Between-herd variation in resilience and relations to herd performance

Resilient cows are minimally affected in their functioning by infections and other disturbances, and recover quickly. Herd management is expected to have an effect on disturbances and the resilience of cows, and this effect was investigated in this study. Two resilience indicators were first recorded on individual cows. The effect of herd-year on these resilience indicators was then estimated and corrected for genetic and year-season effects. The 2 resilience indicators were the variance and the lag-1 autocorrelation of daily milk yield deviations from an expected lactation curve. Low variance and autocorrelation indicate that a cow does not fluctuate much around her expected milk yield and is, thus, subject to few disturbances, or little affected by disturbances (resilient). The herd-year estimates of the resilience indicators were estimated for 9,917 herd-year classes based on records of 227,655 primiparous cows from 2,644 herds. The herd-year estimates of the resilience indicators were then related to herd performance variables. Large differences in the herd-year estimates of the 2 resilience indicators (variance and autocorrelation) were observed between herd-years, indicating an effect of management on these traits. Furthermore, herd-year classes with a high variance tended to have a high proportion of cows with a rumen acidosis indication (r = 0.31), high SCS (r = 0.19), low fat content (r = -0.18), long calving interval (r = 0.14), low survival to second lactation (r = -0.13), large herd size (r = 0.12), low lactose content (r = -0.12), and high production (r = 0.10). These correlations support that herds with high variance are not resilient. The correlation between the variance and the proportion of cows with a rumen acidosis indication suggests that feed management may have an important effect on the variance. Herd-year classes with a high autocorrelation tended to have a high proportion of cows with a ketosis indication (r = 0.14) and a high production (r = 0.13), but a low somatic cell score (r = -0.17) and a low proportion of cows with a rumen acidosis indication (r = -0.12). These correlations suggest that high autocorrelation at herd level indicates either good or poor resilience, and is thus a poor resilience indicator. However, the combination of a high variance and a high autocorrelation is expected to indicate many fluctuations with slow recovery. In conclusion, herd management, in particular feed management, seems to affect herd resilience.