The effect of first-lactation calving season, milk production, and morphology on the survival of Simmental cows.

Longevity in dairy and dual-purpose cattle is a complex trait which depends on many individual and managerial factors. The purpose of the present study was to investigate the survival (SURV) rate of Italian Simmental dual-purpose cows across different parities. Data of this study referred to 2 173 primiparous cows under official milk recording that calved between 2002 and 2020. Only cows linearly classified for type traits, including muscularity (MU) and body condition score (BCS) were kept. Survival analysis was carried out, through the Cox regression model, for different pairwise combinations of classes of milk productivity MU, BCS, and calving season. Herd-year of first calving was also considered in the model. SURV (0 = culled; 1 = survived) at each lactation up to the 6th were the dependent variables, so that, for example, SURV2 equal to 1 was attributed to cows that entered the 2nd lactation. Survival rates were 98, 71, 63, 56, and 53% for 2nd, 3rd, 4th, 5th, and 6th lactation, respectively. Results revealed that SURV2 was not dependent on milk yield, while in subsequent parities, low-producing cows were characterized by higher SURV compared to high-producing ones. Additionally, cows starting the lactation in autumn survived less (47.38%) than those starting in spring (53.49%), suggesting that facing the late gestation phase in summer could increase the culling risk. The present study indicates that SURV in Italian Simmental cows is influenced by various factors in addition to milk productivity. However, it is important to consider that in this study all first-calving cows culled before the linear evaluation - carried out between mid- and late lactation in this breed - were not accounted for. Finding can be transferred to other dual-purpose breeds, where the cows' body conformation and muscle development - i.e. meat-related features - are often considered as important as milk performance by farmers undertaking culling decisions.

Long-term outcomes of the Italian Mycobacterium avium subspecies paratuberculosis control programme for dairy cattle.

BACKGROUND: The considerable epidemiological and economic implications of paratuberculosis, caused by Mycobacterium avium subspecies paratuberculosis (MAP), have placed importance on control efforts aimed at preventing MAP transmission. In this context, Italy issued national guidelines for the control and status certification of MAP in dairy cattle in 2013. METHODS: We assessed the long-term outcomes of the Italian MAP control programme for 14 dairy farms located in northern Italy by retrospectively reviewing the results of yearly serological tests, presence of clinical cases, MAP faecal shedding in serologically positive animals, farm management and health ranking as indicators of herd health between 2014 and 2021. RESULTS: A significantly higher number of serologically positive animals were observed between 2014 and 2016 than between 2017 and 2021, as well as an improving trend in the paratuberculosis health ranking for nine of the 14 farms. No clinical cases were reported. MAP shedding was detected in 9.4% of serologically positive animals. Discarding colostrum and prioritised culling of seropositive animals assisted by adoption of standardised serological testing were presumed to have a key role in MAP control, despite the reluctance of some farmers to address hygienic issues and improve the separation of calves from adult animals. LIMITATIONS: The small number of farms included in this study and the fact that these were not randomly selected may limit the generalisability of the findings. CONCLUSIONS: The Italian paratuberculosis control plan has provided measures to limit the uncontrolled spread of MAP infection within and between herds by promoting animal trading between farms certified as negative or low risk.

Partial herd hoof trimming results in a higher economic net benefit than whole herd hoof trimming in dairy herds.

Sole ulcers, a common cause of lameness is the costliest non-infectious foot lesion in dairy cows and one of the most prevalent non-infectious foot lesions in freestall housing systems. Costs associated with sole ulcers are treatment costs, plus increased labor and decreased productivity and fertility. Routine hoof trimming is part of a strategy to manage sole ulcers. However, hoof trimming strategies differ among farms. The two most frequently applied strategies are: 1) partial herd hoof trimming with a 2-month interval between trims; and 2) whole herd hoof trimming at 6-month intervals. A Markov model was developed to investigate whether every 2 months partial herd hoof trimming or whole herd hoof trimming every 6 months was the most cost-effective strategy to avoid costs associated with sole ulcers. In this model, the net benefits for a 100-cow herd and the average productive life span of a dairy cow in intensive dairy systems of 3 years were evaluated. Partial herd hoof trimming was the most cost-effective strategy 100% of the time compared to whole herd hoof trimming, with a difference in 3-year net benefits of US$4,337 (95% CI: US$2,713-US$5,830). Based on sensitivity analyses, variables that were the sources of the biggest uncertainty in the model were herd size, the probability of being trimmed in a partial herd trim, and the prevalence of sole ulcers. To further investigate the impacts of herd size and of probability of being trimmed, various scenario analyses were conducted. With increasing herd size, the difference in net benefits in favor of partial herd hoof trimming increased even more. Scenario analyses about the probability of getting trimmed all indicated that targeted intervention increased the difference in net benefits in favor of partial herd hoof trimming. However, if the selection of cows to be trimmed in a partial herd trim was random, the whole herd hoof trimming strategy became cost-effective. Therefore, targeted selection and early intervention are necessary to decrease costs associated with sole ulcers.

Worries about bird flu in U.S. cattle intensify.

Farm worker becomes infected as H5N1 appears to spread between dairy cows in five states.

DairyCoPilot-Automated data compilation and analysis tools for DairyComp data assets.

Modern dairy farm management requires meaningful data and careful analysis to maximize profitability, cow health, and welfare. Current data platforms, such as DairyComp, lack robust integrated data analysis tools. Producers and consultants need dedicated tools to turn collected data sets into assets for informed decision-making processes. The DairyCoPilot app allows users to rapidly extract health and production data from DairyComp, then compile and analyze the data using a menu-driven point-and-click approach. Prospects for training consultants in applied data analysis skills make DairyCoPilot a tool to identify farm management bottlenecks with less time spent for data analysis, improving cow health, and dairy production. The DairyCoPilot Dashboard R Shiny application is published using RStudio Connect: https://connect.doit.wisc.edu/dairy-copilot/.

A smartphone-based diagnostic analyzer for point-of-care milk somatic cell counting.

BACKGROUND: Mastitis, a pervasive and detrimental disease in dairy farming, poses a significant challenge to the global dairy industry. Monitoring the milk somatic cell count (SCC) is vital for assessing the incidence of mastitis and the quality of raw cow's milk. However, existing SCC detection methods typically require large-scale instruments and specialized operators, limiting their application in resource-constrained settings such as dairy farms and small-scale labs. To address these limitations, this study introduces a novel, smartphone-based, on-site SCC testing method that leverages smartphone capabilities for milk somatic cell identification and enumeration, offering a portable and user-friendly testing platform. RESULTS: The central findings of our study demonstrate the effectiveness of the proposed method for counting milk somatic cells. Its on-site applicability, facilitated by the microfluidic chip, optical system, and smartphone integration, heralds a paradigm shift in point-of-care testing (POCT) for dairy farms and smaller laboratories. This approach bypasses complex processing and presents a user-friendly solution for real-time SCC monitoring in resource-limited settings. This device boasts several unique features: small size, low cost (<$1,000 total manufacturing cost and <$1 per test), and high accuracy. Remarkably, it delivers test results within just 2 min. Actual-sample testing confirmed its consistency with results from the commercial Bentley FTS/FCM cytometer, affirming the reliability of the proposed method. Overall, these results underscore the potential for transformative change in dairy farm management and laboratory testing practices. SIGNIFICANCE: In summary, this study concludes that the proposed smartphone-based method significantly contributes to the accessibility and ease of SCC testing in resource-limited environments. By fostering the use of POCT technology in food safety control, particularly in the dairy industry, this innovative approach has the potential to revolutionize the monitoring and management of mastitis, ultimately benefiting the global dairy sector.

Innovative applications of whey protein for sustainable dairy industry: Environmental and technological perspectives-A comprehensive review.

Industrial waste management is critical to maintaining environmental sustainability. The dairy industry (DI), as one of the major consumers of freshwater, generates substantial whey dairy effluent, which is notably rich in organic matter and thus a significant pollutant. The effluent represents environmental risks due to its high biological and chemical oxygen demands. Today, stringent government regulations, environmental laws, and heightened consumer health awareness are compelling industries to responsibly manage and reuse whey waste. Therefore, this study investigates sustainable solutions for efficiently utilizing DI waste. Employing a systematic review approach, the research reveals that innovative technologies enable the creation of renewable, high-quality, value-added food products from dairy byproducts. These innovations offer promising sustainable waste management strategies for the dairy sector, aligning with economic interests. The main objectives of the study deal with, (a) assessing the environmental impact of dairy sector waste, (b) exploring the multifaceted nutritional and health benefits inherent in cheese whey, and (c) investigating diverse biotechnological approaches to fashion value-added, eco-friendly dairy whey-based products for potential integration into various food products, and thus fostering economic sustainability. Finally, the implications of this work span theoretical considerations, practical applications, and outline future research pathways crucial for advancing the sustainable management of dairy waste.

Cohort profile: The FarmMERGE project-Merging human and animal databases to investigate the relationship between farmer and livestock health and welfare. The HUNT Study.

Stockmanship is an important determinant for good animal welfare and health. The goal of the FarmMERGE project is to investigate the associations between farmer health and work environment, and the health, productivity and welfare of their livestock. We merged several livestock industry databases with a major total population-based health study in Norway (The Trøndelag Health Study 2017-2019 (HUNT4)). This paper describes the project's collection and merging of data, and the cohort of farmers and farms that were identified as a result of our registry merge. There were 56,042 participants of HUNT4 (Nord-Trøndelag County participants only, participation rate: 54.0%). We merged a list of HUNT4 participants whose self-reported main occupation was "farmer" (n = 2,407) with agricultural databases containing production and health data from sheep, swine, dairy and beef cattle from 2017-2020. The Central Coordinating Register for Legal Entities was used as an intermediary step to achieve a link between the farmer and farming enterprise data. We identified 816 farmers (89.5% male, mean age 51.3 years) who had roles in 771 farming enterprises with documented animal production. The cohort included 675 unique farmer-farm combinations in cattle production, 139 in sheep, and 125 in swine. We linked at least one HUNT4 participant to approximately 63% of the dairy farms, 53% of the beef cattle farms, 30% of the sheep farms, and 38% of the swine farms in Nord-Trøndelag County in the 2017-2019 period. Using existing databases may be an efficient way of collecting large amounts of data for research, and using total population-based human health surveys may decrease response bias. However, the quality of the resulting research data will depend on the quality of the databases used, and thorough knowledge of the databases is required.

The prevalence of damaged tails in New Zealand dairy cattle.

AIMS: To undertake a survey of the prevalence of tail deviations, trauma and shortening on a representative selection of New Zealand dairy farms, and to assess whether sampling based on milking order could be used instead of random sampling across the herd to estimate prevalence. METHODS: This was a cross-sectional observational study, with 200 randomly selected farms enrolled across nine regions of New Zealand via selected veterinary practices (one/region). Veterinary clinics enrolled 20-25 farms each depending on region, with 1-2 trained technicians scoring per region. All cows (n = 92,348) present at a milking or pregnancy testing event were tail scored using a modified version of the New Zealand Veterinary Association Industry Scoring System. Palpated lesions were recorded as deviated (i.e. non-linear deformity), shortened (tail shorter than normal) or traumatic (all other lesions). The location of lesions was defined by dividing the tail into three equal zones: upper, middle and lower. A cow could have more than one lesion type and location, and/or multiple lesions of the same type, but for the prevalence calculation, only the presence or absence of a particular lesion was assessed. Prevalence of tail damage calculated using whole herd scoring was compared to random sampling across the herd and sampling from the front and back of the milking order. Bootstrap sampling with replacement was used to generate the sampling distributions across seven sample sizes ranging from 40-435 cows. RESULTS: When scoring all cows, the median prevalence for deviation was 9.5 (min 0.9, max 40.3)%; trauma 0.9 (min 0, max 10.7)%, and shortening was 4.5 (min 1.3, max 10.8)%. Deviation and trauma prevalence varied between regions; the median prevalence of deviations ranged from 6% in the West Coast to 13% in Waikato, and the median prevalence of all tail damage from 7% in the West Coast to 29% in Southland. Sampling based on milking order was less precise than random sampling across the herd. With the latter and using 157 cows, 95% of prevalence estimates were within 5% of the whole herd estimate, but sampling based on milking order needed > 300 cows to achieve the same precision. CONCLUSIONS AND CLINICAL RELEVANCE: The proportion of cows identified as having damaged tails was consistent with recent reports from New Zealand and Ireland, but at 11.5%, the proportion of cows with trauma or deviation is below acceptable standards. An industry-wide programme is needed to reduce the proportion of affected cows.

Automated dairy cattle lameness detection utilizing the power of artificial intelligence; current status quo and future research opportunities.

Lameness represents a major welfare and health problem for the dairy industry across all farming systems. Visual mobility scoring, although very useful, is labour-intensive and physically demanding, especially in large dairies, often leading to inconsistencies and inadequate uptake of the practice. Technological and computational advancements of artificial intelligence (AI) have led to the development of numerous automated solutions for livestock monitoring. The objective of this study was to review the automated systems using AI algorithms for lameness detection developed to-date. These systems rely on gait analysis using accelerometers, weighing platforms, acoustic analysis, radar sensors and computer vision technology. The lameness features of interest, the AI techniques used to process the data as well as the ground truth of lameness selected in each case are described. Measures of accuracy regarding correct classification of cows as lame or non-lame varied with most systems being able to classify cows with adequate reliability. Most studies used visual mobility scoring as the ground truth for comparison with only a few studies using the presence of specific foot pathologies. Given the capabilities of AI, and the benefits of early treatment of lameness, longitudinal studies to identify gait abnormalities using automated scores related to the early developmental stages of different foot pathologies are required. Farm-specific optimal thresholds for early intervention should then be identified to ameliorate cow health and welfare but also minimise unnecessary inspections.

A qualitative survey approach to investigating beef and dairy veterinarians' needs in relation to technologies on farms.

Globally, farmers are being increasingly encouraged to use technologies. Consequently, veterinarians often use farm data and technologies to provide farmers with advice. Yet very few studies have sought to understand veterinarians' perceptions of data and technologies on farms. The aim of this study was to understand veterinarians' experiences and opinions on data and technology on beef and dairy farms. An online qualitative survey was conducted with a convenience sample of 36 and 24 veterinarians from the United Kingdom and Ireland, respectively. The data were analysed using reflexive thematic analysis to generate four themes: (1) Improving veterinary advice through data; (2) Ensuring stock person skills are retained; (3) Longevity of technology; and (4) Solving social problems on farms. We show that technologies and data can make veterinarians feel more confident in the advice they give to farmers. However, the quality and quantity of data collected on cattle farms were highly variable. Furthermore, veterinarians were concerned that farmers can become over-reliant on technologies by not using their stockperson skills. As herd sizes increase, technologies can help to improve working conditions on farms with multiple employees of various skillsets. Veterinarians would like innovations that can help them to demonstrate their competence, influence farmers' behaviour, and ensure sustainability of the beef and dairy industries.

A new approach and insights on modelling the impact of production diseases on dairy cow welfare.

Animal welfare is becoming an important consideration in animal health-related decision-making. Integrating considerations of animal welfare into the decision-making process of farmers involves recognising the significance of health disorder impacts in relation to animal welfare. Yet little research quantifies the impact, making it difficult to include animal welfare in the animal health decision-making process. Quantifying the impact of health disorders on animal welfare is incredibly challenging due to empirical animal-based data collection constraints. An approach to circumvent these constraints is to rely on expert knowledge whereby perceived welfare impairment weights are indicative of the negative welfare effect. In this research, we propose an expertise-based method to quantify the perceived impact of sub-optimal mobility (SOM) on the welfare of dairy cows, because of its welfare importance. We first quantified perceived welfare impairment weights of SOM by eliciting expert knowledge using adaptive conjoint analysis (ACA). Second, using the perceived welfare impairment weights, we derived the perceived welfare disutility (i.e., perceived negative welfare effect) of mobility scores 1-5 (1 = optimal mobility, 5 = severely impaired mobility). Third, using the perceived welfare disutility per mobility score, we quantified the perceived welfare impact at case- and herd-level of SOM for different SOM severity. Results showed that perceived welfare disutility increased with each increase in mobility score. However, the perceived welfare impact of SOM cases with lower mobility scores was higher compared to SOM cases with higher mobility scores. This was because of the longer-lasting duration of the SOM cases with lower mobility scores. Moreover, the perceived herd-level welfare impact was largely due to SOM cases with lower mobility scores because of the longer duration and more frequent incidence compared to the SOM cases with higher mobility scores. These results entail that better welfare of dairy cows with respect to SOM can be achieved if lower mobility scores are detected and treated sooner. Our research demonstrates a novel approach that quantifies the perceived impact of health disorders on animal welfare when empirical evidence is limited.

Dairy farmers' preferences for immediate versus late calf separation.

The practice of separating calves from their dams right after birth is one of the main dairy cattle welfare concerns among the public. Farmer perceptions about the practice and the available alternative contact systems in the US are however, not well understood. Using data from a sample of Wisconsin dairy farmers, the study examines farmer preferences for different prolonged (relative to immediate separation) contact systems and the potential role of farm structural and individual-level specific factors. Four alternative hypothetical systems - free dam, half day, foster cow, and restricted suckling systems - differing on the basis of calf access to dam-were considered. The analysis controls for farm size, production system (conventional, organic, grazing), farmer socio-demographic characteristics in addition to farm animal welfare (FAW) perceptions. The findings indicate that amongst the set of alternative practices considered, foster cow system is most preferred alternative to the conventional early separation practised by most farmers. This is followed by restricted suckling systems. The least ranked alternative system is free calf-dam contact. The analyses further indicate that calf contact duration is negatively associated with herd size. Relative to conventional farms, calf-dam pairs on grazing, organic and mixed farms are less likely to be separated immediately after birth. Prolonged calf-dam contact is also prevalent amongst older farmers. Overall, the findings imply that farming system and structural considerations may be the key drivers of transition to prolonged contact systems in dairy production. Foster cow systems may offer the most feasible pathway for achieving this objective.

Cattle production strategies to deliver protein with less land and lower environmental impact.

Global land resources are over-exploited and natural habitats are declining, often driven by expanding livestock production. In Ireland, pastureland for grazing cattle and sheep account for circa 60% of terrestrial land use. The agriculture, forestry and other land use sector (AFOLU) is responsible for 44% of national greenhouse gas (GHG) emissions. A new Grassland Animal response Model (GLAM) was developed to relate livestock-cohort grass and feed requirements to farm-grassland system areas, enhancing environmental assessment of prospective AFOLU configurations. Although land conversion targets are often well-defined, they tend to lack a clear definition of where land sparing can occur. Through analyses of 10 scenarios of milk and beef production and management strategies, we found that displacing beef cows with dairy cows can increase national protein output while sparing up to 0.75 million ha (18%) of grassland (albeit with a minor increase in overseas land requirement for additional concentrate feed). Reducing slaughter age, increasing exports of male dairy calves and increasing grassland use efficiency on beef farms each achieved between 0.19 and 0.32 million ha of land sparing. Sexed semen to achieve more favourable male-female birth ratios had a minor impact. GHG emissions, ammonia emissions and nutrient leaching were only reduced substantially when overall cattle numbers declined, confirming the need for cattle reductions to achieve environmental objectives. Nonetheless, application of GLAM shows potential for improved grass and cattle management to spare good quality land suitable for productive forestry and wetland restoration. This change is urgently needed to generate scalable carbon dioxide removals from the land sector in Ireland, and globally.

Valuation of livestock population and national feed security to enhance livestock productivity in Ethiopia.

BACKGROUND: Feed is a major input in the livestock industry and covers about 60%-70% of the total cost of producing meat, milk and eggs. Inadequate feed supply in terms of quality and quantity leads to lower production performance in livestock. However, the development of an appropriate livestock production strategy through efficient utilization of existing feed resources could raise the production and per capita consumption of livestock products. Efficiency of feed resource utilization can be measured as the ratio between input to production activities and output (e.g. kg of protein used per unit of meat, milk and eggs produced or hectare of land used per unit of milk produced). METHODOLOGY: This study was designed with the objective of evaluating the livestock population and national feed security to enhance livestock productivity in Ethiopia. To achieve this objective, data were collected from the websites of the Ethiopian Central Statistical Agency from 2007 to 2021, FAO publications and websites, books and journals. The data obtained on different feed resources, livestock population and livestock feed requirement and balance were entered into an MS Excel spread sheet (Excel, 2010) and analysed using the general linear model (PRO GLM) procedure of SAS (2014) and multivariate analysis of covariance. RESULTS: The study results revealed that the livestock population had increased from 58.31 million tropical livestock units (TLU) to 81.10 million tropical livestock units (TLU), and the emission of entericCH4 had increased from 2511.08 Gg/year to 3661.74 Gg/year from 2008 to 2021. The study results also showed that the major available feed resources for ruminants are natural pasture and crop residues, which account for 56.83% (87.56 × 106 ) and 37.37% (57.57 × 106 ) of total feed production in the country, respectively. The contribution of concentrate and improved cultivated pasture and feed from permanent crops used as feed sources is very insignificant (3.05% and 1.96%, respectively). The estimated quantity of these feed resources was sufficient to meet the livestock feed requirement in the country in terms of dry matter (DM), digestible crude protein (DCP) and MEJ, which estimated about 153.31 × 106  t, 4.56 × 106  t and 1203.97 × 109  MJ DM, DCP and MEJ, respectively. The estimated livestock feed requirements were 134.62 × 106 , 4.52 × 106 , and 918.83 × 109 in DM, DCP and MEJ, respectively. The supply covered about 114.33, 100.04 and 131.33% of the DM, DCP and MEJ total annual feed requirements of livestock in the country. Hence, the current feed surplus obtained on feed requirements of ruminants and equines can support the nutrient requirements of 500 × 106 broilers, about 5 × 106 bulls, about 50 × 106 small ruminants or 3 × 106 crossbred lactating dairy cows, yielding 10 L of milk per day. CONCLUSIONS: The findings of study indicated that natural pasture and crop residues cover a major proportion of the annual feed supply in the country. Therefore, proper grazing management, feed conservation practices, improving grazing land vegetation through clearing invasive species, replacing the grazing land with an improved grass and legume mixture, effective collection, conservation and proper utilization of crop residues, and other alternative options such as the use of chemical, physical and biological treatments to improve the nutritive value of fibrous feed should be practiced. More effective extension services and farmer training are also required to increase feed productivity and, hence, human development.

Impacts of preweaning colostrum feeding practices and health measures on dairy cow production, while accounting for genetic potential.

Calf management and health are essential for setting up the foundation of a productive cow. The objectives of this study were to estimate the impact of preweaning practices on milk production parameters while accounting for an animal's genetic potential in New Brunswick, Canada. A retrospective cohort study was performed on 220 heifer calves from eight herds born in 2014-2015. Preweaning practices and health data were recorded by producers and reviewed by the herd veterinarian for each calf. The herd veterinarian also visited the farms to collect serum samples from calves and frozen colostrum samples. The production outcomes assessed were milk, protein and fat yields, standardized to 305 d for the first lactation (L1) and a combined group of lactations two and three (L2 + 3). The genomic potential was determined as genomic parent averages (GPA) for the associated production parameters. Analysis was performed with multivariable linear (L1) and linear mixed (L2 + 3) regression models. In L1, for every 1.0 kg increase in weaning weight, milk, protein, and fat yield increased by 25.5, 0.82, and 1.01 kg, respectively (P < 0.006). Colostrum feeding time (CFT) positively impacted L1 milk and protein production, with feeding between 1-2 h of life producing the greatest estimates of 626 kg of milk and 18.2 kg of protein yield (P < 0.007), compared to earlier or later CFT. Fat yield production was decreased by 80.5 kg (P < 0.006) in L1 when evaluating animals that developed a preweaning disease and were not treated with antibiotics compared to healthy untreated animals. Impacts on L2 + 3 were similar across all production outcomes, with a positive interaction effect of CFT and weaning weight. Compared to CFT < 1 h, the later CFT groups of 1-2 h and > 2 h produced greater yield outcomes of 68.2 to 72.6 kg for milk (P < 0.006), 2.06 to 2.15 kg for protein (P < 0.005), and 1.8 to 1.9 kg for fat (P < 0.045) for every 1 kg increase of weaning weight, respectively. The fit of all models was significantly improved with the inclusion of GPA. These results indicate that colostrum management and preweaning health measures impacted production parameters as adults. The inclusion of GPA significantly improved the accuracy of the models, indicating that this can be an important parameter to include in future studies.

The impact of calf management and health events have been predominately investigated during the preweaning period. However, calfhood events could also impact the animal's health and productivity as an adult. Results from this study indicate that colostrum feeding time and weaning weight were associated with production outcomes (milk, protein, and fat yields) across the first three lactations, and disease and antibiotic treatment can be detrimental to fat yield in the first lactation. By including genetic potential in the assessment of preweaning colostrum practices and health measures on production outcomes, we can more precisely identify areas to optimize calf management.

Comparison of Sensor-Based and Audible Detection of Milking Liner Slips during Machine Milking of Dairy Cows.

On-farm milk flow meter technology facilitates real-time assessment of individual cow milking observations and could be used to detect milking liner slips during machine milking of dairy cows. Here, we compared the accuracy of on-farm milk flow meters for detecting milking liner slips with that of audible detection and that of a portable vacuum recording system. Compared to audible detection methods, the on-farm milk flow meter facilitated the detection of milking liner slips with moderate accuracy. Using the vacuum recording system as the gold standard, the milk flow meter system failed to detect most of the liner slips, leading to poor agreement between the two devices. We conclude that the on-farm milk flow meter system tested here compared well with audible detection; however, when vacuum recordings were considered, we found significant levels of under-detection. Taken together, dairy operators may use the on-farm milk flow meter system to inform adjustments of the milking machine settings and monitor milking routine performance. However, the system is not suitable for monitoring short-duration vacuum fluctuations. Future research is warranted to optimize the sensor-based detection of milking liner slips.

Role of infrastructure and operation in disease prevalence in dairy farms: groundwork for disease prevention-based antibiotic stewardship.

Attempts at regulating misuse of antibiotics in the dairy industry have been ineffective, especially in low- and middle-income countries, who also typically have high burden of preventable infectious disease, we propose a disease prevention-based approach to minimize the need and in turn consumption of antibiotics in dairy farms. Since the immediate environment of the animals is key to disease prevalence, we targeted the infrastructure- and operation-related factors in dairy farms and their link with prevalence of most common diseases and symptoms. We conducted four focused group discussions and a cross-sectional survey in 378 dairy farms to investigate disease prevalence and associated infrastructural (housing system, and manger shape), and operational (waste management, feed management, and type of cleaning agent) parameters. The most common diseases (Mastitis and secondary infections related to Foot-and-mouth disease) and symptoms (fever and diarrhoea) in the focus area were linked with the infrastructural and operational factors on the dairy farm with higher disease prevalence reported in dairy farms, where the animals were exposed to variations in diurnal temperatures or were hard to clean. We further used ML classifiers - Neural Network (NN), k-Nearest Neighbour (kNN), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF) - to corroborate the relationship between infrastructure and operations of the dairy farms and disease prevalence- The DT classifier on randomly sampled data could predict the prevalence of the two most common diseases (accuracy = 92%, F1-score = 0.919) Our results open new avenues for cost-effective interventions such as use of curve-edged mangers, use of rubber mats on floors, not reusing leftover feed etc. in dairy farms to prevent the most common diseases and symptoms in dairy farms and reduce the need and consumption of antibiotics.